From 8ed46267440d5f14d40617b67dfba83ad3f85fe8 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@rug.nl>
Date: Wed, 28 Jan 2026 16:49:26 +0100
Subject: [PATCH 01/81] Updated Syllabus

---
 Syllabus.md | 116 ++++++++++++++++++++++++++++++----------------------
 1 file changed, 68 insertions(+), 48 deletions(-)

diff --git a/Syllabus.md b/Syllabus.md
index faa1fb5..2051a29 100644
--- a/Syllabus.md
+++ b/Syllabus.md
@@ -136,87 +136,107 @@
 -   Kernel Trick
 
 
-# Session 4: Ensemble Methods
+# Session 4: Unsupervised Learning - Clustering and Dimensionality Reduction
 
 
-## Introduction to Ensemble Methods
+## Introduction to Unsupervised Learning
 
--   What are Ensemble Methods?
--   Why Use Ensemble Methods?
--   Types of Ensemble Methods
+-   Supervised vs Unsupervised
+-   Dimensionality Reduction
+-   Clustering
+-   Anomaly Detection
 
 
-## Bagging and Random Forests
+## Dimensionality Reduction
 
--   Bagging (Bootstrap Aggregating) Overview
--   Random Forests
--   Feature Importance in Random Forests
+-   Curse of Dimensionality
+-   Principal Component Analysis (PCA)
+-   Choosing the number of components
+-   Linear Discriminant Analysis (LDA)
+-   t-SNE and UMAP (Visualization techniques)
 
 
-## Boosting
+## Clustering
 
--   Boosting Overview
--   AdaBoost
--   Gradient Boosting
--   XGBoost
+-   k-Means Clustering
+-   Elbow Method and Silhouette Score
+-   Hierarchical Clustering (Dendrograms)
+-   DBSCAN (Density-based clustering)
 
 
-## Ensembles of Ensembles
+## Anomaly Detection
 
--   Stacking
--   Multi-level EoE
+-   Isolation Forests
 
 
-# Session 5: Unsupervised Learning - Clustering and Dimensionality Reduction
+# Session 5: Neural Networks I - Foundations and Optimization
 
 
-## Introduction to Unsupervised Learning
+## Introduction to Artificial Neural Networks
 
--   Dimensionality Reduction
--   Clustering
--   Anomaly Detection
+-   Biological Inspiration
+-   The Perceptron Model
+-   Limitations of the Perceptron (XOR problem)
 
 
-## Dimensionality Reduction
+## The Multilayer Perceptron (MLP)
 
--   Principal Component Analysis (PCA)
--   Autoencoders
--   Linear Discriminant Analysis (LDA)
+-   Input, Hidden, and Output Layers
+-   Activation Functions (Sigmoid, Tanh, ReLU, Softmax)
+-   Forward Propagation (Matrix notation)
 
 
-## Clustering
+## Training Neural Networks
 
--   k-Means Clustering
--   Hierarchical Clustering
+-   Loss Functions (MSE vs. Cross-Entropy)
+-   Gradient Descent and Stochastic Gradient Descent (SGD)
+-   Backpropagation intuition
+-   The Vanishing Gradient Problem
 
 
-## Anomaly Detection
+## Optimizing Neural Networks
 
+-   Weight Initialization strategies
+-   Optimizers (Momentum, RMSProp, Adam)
+-   Regularization (Dropout, Batch Normalization, Early Stopping)
 
-# Session 6: Artificial Neural Networks and Deep Learning
 
+## Building a Network in PyTorch/TensorFlow
 
-## Introduction to Neural Networks
+-   Tensors and Operations
+-   Defining a simple architecture
+-   The Training Loop
 
--   What are Neural Networks?
--   Biological Inspiration
--   Structure of a Neural Network
--   Activation Functions
 
+# Session 6: Neural Networks II - Deep Learning, Transformers, and GenAI
 
-## Training Neural Networks
 
--   Forward Propagation
--   Backpropagation
--   Loss Functions
--   Gradient Descent
+## Convolutional Neural Networks (CNNs)
+
+-   Convolution and Pooling Layers
+-   Padding and Stride
+-   Common Architectures (VGG, ResNet)
+
+
+## From RNNs to Transformers
+
+-   Recurrent Neural Networks (RNNs) and LSTMs
+-   The bottleneck of sequential processing
+-   The Attention Mechanism
+-   The Transformer Architecture (Encoder-Decoder)
+
+
+## Large Language Models (LLMs)
+
+-   Evolution of LLMs (BERT, GPT, Llama)
+-   Pre-training, Fine-tuning, and Inference
+-   Tokenization and Context Windows
+-   Prompt Engineering basics
 
 
-## Deep Learning
+## Generative AI and Modern Applications
 
--   What is Deep Learning?
--   Deep Neural Networks
--   Convolutional Neural Networks (CNNs)
--   Recurrent Neural Networks (RNNs)
--   Autoencoders
--   Generative Adversarial Networks (GANs)
+-   Text Generation (Next-token prediction)
+-   Retrieval-Augmented Generation (RAG)
+-   Introduction to Diffusion Models (Image Generation)
+-   Ethical considerations and Bias in GenAI

From 62764b945f818766ad2af857c79a747e8439f722 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@rug.nl>
Date: Wed, 28 Jan 2026 16:57:03 +0100
Subject: [PATCH 02/81] Switched to new version

---
 .../S01 Introduction to Machine Learning.pdf  |  Bin 264063 -> 0 bytes
 .../S01 Introduction to Machine Learning.tex  |  349 -
 ...01C00 Working with Jupyter on Habrok.ipynb |  144 -
 .../S01C01 What is Machine Learning.ipynb     |  755 ---
 ...S01C02 The Machine Learning Workflow.ipynb | 2189 -------
 ...1C03 Exploratory Data Analysis (EDA).ipynb |  633 --
 ...ain-Test Splits and Cross-Validation.ipynb |  615 --
 .../S01C05 Data Preprocessing.ipynb           | 1929 ------
 .../S01C06 Feature Engineering.ipynb          |  208 -
 ...d Learning - Regression and Evaluation.pdf |  Bin 460078 -> 0 bytes
 ...d Learning - Regression and Evaluation.tex |  625 --
 .../S02C01 Simple Linear Regression.ipynb     |  780 ---
 .../S02C02 Robust Regression.ipynb            |  475 --
 ...03 Evaluation Metrics for Regression.ipynb | 1902 ------
 .../S02C04 Multiple Linear Regression.ipynb   | 1301 ----
 .../S02C05 Regularized Regression.ipynb       |  697 --
 .../S02C06 Nonlinearities in Regression.ipynb |  561 --
 ...erfitting, and Hyperparameter Tuning.ipynb |  524 --
 ...arning - Classification and Evaluation.pdf |  Bin 375468 -> 0 bytes
 ...arning - Classification and Evaluation.tex |  464 --
 ...03C01 Introduction to Classification.ipynb | 2706 --------
 .../S03C02 Classification Basics.ipynb        | 1185 ----
 .../S03C03 Evaluation Metrics.ipynb           | 1408 ----
 .../S03C04 k-NNs and Decision Trees.ipynb     |  861 ---
 .../S03C05 Support Vector Machines.ipynb      | 3144 ---------
 .../S04 Ensemble Methods.pdf                  |  Bin 206393 -> 0 bytes
 .../S04 Ensemble Methods.tex                  |  330 -
 ...C01 Introduction to Ensemble Methods.ipynb | 3007 ---------
 src/S04 Ensemble Methods/S04C02 Bagging.ipynb | 5204 ---------------
 .../S04C03 Boosting.ipynb                     | 5795 -----------------
 .../S04C04 Ensemble of Ensembles.ipynb        | 2996 ---------
 ...lustering and Dimensionality Reduction.pdf |  Bin 429160 -> 0 bytes
 ...lustering and Dimensionality Reduction.tex |  502 --
 ...01 Overview of Unsupervised Learning.ipynb |  745 ---
 ... Dimensionality Reduction Techniques.ipynb |  636 --
 .../S05C03 Clustering Algorithms.ipynb        |  776 ---
 .../S05C04 Anomaly Detection.ipynb            | 1175 ----
 ...cial Neural Networks and Deep Learning.pdf |  Bin 139366 -> 0 bytes
 ...cial Neural Networks and Deep Learning.tex |  405 --
 ...6C01 Introduction to Neural Networks.ipynb |  526 --
 ...Convolutional Neural Networks (CNNs).ipynb |  520 --
 .../S06C03 Autoencoders.ipynb                 |  420 --
 ...6C04 Recurrent Neural Networs (RNNs).ipynb |  119 -
 ...nerative Adversarial Networks (GANs).ipynb |  166 -
 44 files changed, 46777 deletions(-)
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diff --git a/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.tex b/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.tex
deleted file mode 100644
index a83f499..0000000
--- a/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.tex	
+++ /dev/null
@@ -1,349 +0,0 @@
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-\title{Machine Learning in Python}
-\subtitle{Introduction to Machine Learning}
-\author[Marocico, Tatar]{Cristian A. Marocico, A. Emin Tatar}
-\institute[CIT]{Center for Information Technology\\University of Groningen}
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- pdfauthor={},
- pdftitle={Machine Learning in Python},
- pdfkeywords={},
- pdfsubject={},
- pdfcreator={Emacs 30.1 (Org mode 9.7.11)}, 
- pdflang={English}}
-\begin{document}
-
-\maketitle
-\begin{frame}{Outline}
-\setcounter{tocdepth}{1}
-\tableofcontents
-\end{frame}
-
-\section{Introduction to Machine Learning}
-\label{sec:org2d04d73}
-\begin{frame}[label={sec:org0aaa2e5}]{What is Machine Learning?}
-\begin{definition}[Machine Learning]\label{sec:orgec8d6d1}
-\pause
-\alert{\alert{Machine Learning}} is the study of algorithms that learn patterns from data to make predictions or decisions without being explicitly programmed.
-\end{definition}
-\begin{alertblock}{ML vs Traditional Programming:}
-\pause
-\begin{itemize}[<+->]
-\item Traditional: input + rules → output
-\item ML: input + output → rules (the model)
-\end{itemize}
-\end{alertblock}
-\end{frame}
-\section{The Machine Learning Workflow}
-\label{sec:org79863f5}
-\begin{frame}[label={sec:org3c14a33}]{The Machine Learning Workflow}
-\pause
-A typical \alert{Machine Learning} workflow has the following structure:
-\pause
-\begin{itemize}[<+->]
-\item Problem Definition
-\item Data Collection
-\item \alert{\alert{Train-Test Split}}
-\item \alert{\alert{Data Exploration and Preprocessing}}
-\item \alert{\alert{Model Selection}}
-\item \alert{\alert{Training and Validation}}
-\item \alert{\alert{Evaluation}}
-\item Deployment and Monitoring
-\end{itemize}
-\end{frame}
-\section{Train-Test Splits and Cross-Validation}
-\label{sec:orgb850f45}
-\begin{frame}[label={sec:org3f24e79}]{Train and Test Datasets}
-\begin{definition}[Splitting the dataset]\label{sec:orga939d42}
-\pause
-In machine learning, we split the dataset into two parts:
-\pause
-\begin{itemize}[<+->]
-\item \alert{\alert{Training Set}}: Used to train the model.
-\item \alert{\alert{Testing Set}}: Used to evaluate the model's performance.
-\end{itemize}
-\end{definition}
-\begin{alertblock}{Why Split?}
-\begin{itemize}[<+->]
-\item To ensure the model generalizes well to unseen data.
-\item To prevent overfitting, where the model learns noise in the training data instead of the underlying pattern.
-\item The \alert{\alert{test}} dataset is put aside and not used until the end, when the model is evaluated.
-\end{itemize}
-\end{alertblock}
-\end{frame}
-\begin{frame}[label={sec:org073120d}]{Cross-Validation}
-The \alert{\alert{train}} dataset can be further split into \alert{\alert{training}} and \alert{\alert{validation}} sets to tune the
-model's hyperparameters and prevent overfitting.
-\end{frame}
-\begin{frame}[label={sec:orgf0c6674}]{Cross-Validation}
-\begin{definition}[Cross-Validation]\label{sec:org96fb9e4}
-\pause
-Cross-validation is a technique to assess how the results of a statistical analysis will generalize to an independent dataset.
-\pause
-\begin{itemize}[<+->]
-\item \alert{\alert{K-Fold Cross-Validation}}: The dataset is divided into K subsets. The model is trained on K-1 subsets and tested on the remaining subset. This process is repeated K times, with each subset used as the test set once.
-\item \alert{\alert{Stratified K-Fold}}: Ensures that each fold has the same proportion of classes as the entire dataset, which is particularly useful for imbalanced datasets.
-\end{itemize}
-\end{definition}
-\end{frame}
-\section{Exploratory Data Analysis (EDA)}
-\label{sec:org47f21aa}
-\begin{frame}[label={sec:org07f3774}]{Exploratory Data Analysis (EDA)}
-\begin{definition}[Exploratory Data Analysis (EDA)]\label{sec:org89b13f7}
-\pause
-\alert{\alert{Exploratory Data Analysis}} (EDA) is the process of analyzing datasets to summarize their main characteristics, often using visual methods.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org9247409}]{Exploratory Data Analysis (EDA)}
-\begin{example}[Purpose of EDA]\label{sec:org741005b}
-\pause
-\begin{itemize}[<+->]
-\item To understand the data distribution and relationships between variables.
-\item To identify patterns, trends, and anomalies.
-\item To generate hypotheses and inform feature engineering.
-\end{itemize}
-\end{example}
-\begin{example}[EDA Techniques]\label{sec:org99fb678}
-\pause
-\begin{itemize}[<+->]
-\item \alert{\alert{Descriptive Statistics}}: Mean, median, mode, standard deviation, etc.
-\item \alert{\alert{Data Visualization}}: Histograms, scatter plots, box plots, etc.
-\item \alert{\alert{Correlation Analysis}}: Identifying relationships between variables.
-\item \alert{\alert{Missing Value Analysis}}: Identifying and handling missing data.
-\item \alert{\alert{Outlier Detection}}: Identifying and handling outliers in the data.
-\end{itemize}
-\end{example}
-\end{frame}
-\section{Data Preprocessing}
-\label{sec:orgaf9aa64}
-\begin{frame}[label={sec:org733ef7e}]{Data Preprocessing}
-\begin{definition}[Data Preprocessing]\label{sec:org507807c}
-\pause
-\alert{\alert{Data Preprocessing}} is the process of transforming raw data into a format suitable for analysis and modeling.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orgfe2bb4d}]{Data Preprocessing}
-\begin{example}[Purpose of Data Preprocessing]\label{sec:org1fdb553}
-\pause
-\begin{itemize}[<+->]
-\item To clean the data by handling missing values, outliers, and inconsistencies
-\item To normalize or scale numerical features
-\item To encode categorical variables into numerical formats
-\end{itemize}
-\end{example}
-\begin{example}[Common Data Preprocessing Techniques]\label{sec:org79eb6f1}
-\begin{itemize}[<+->]
-\item \alert{\alert{Handling Missing Values}}: Techniques include imputation (mean, median, mode), deletion, or using algorithms that can handle missing data.
-\item \alert{\alert{Outlier Detection and Treatment}}: Identifying outliers using statistical methods (e.g., Z-score, IQR) and deciding whether to remove or transform them.
-\item \alert{\alert{Feature Scaling}}: Normalization or standardization.
-\item \alert{\alert{Encoding Categorical Variables}}: Techniques include one-hot encoding, label encoding, or using embeddings for high-cardinality categorical variables.
-\end{itemize}
-\end{example}
-\end{frame}
-\section{Feature Engineering}
-\label{sec:orga965fd7}
-\begin{frame}[label={sec:orgc951175}]{Feature Engineering}
-\begin{definition}[Feature Engineering]\label{sec:org6cd6398}
-\pause
-\alert{\alert{Feature Engineering}} is the process of using domain knowledge to select, modify, or create features (variables) that improve the performance of machine learning models.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org31dfe16}]{Feature Engineering}
-\begin{example}[Purpose of Feature Engineering]\label{sec:org1010237}
-\pause
-\begin{itemize}[<+->]
-\item To improve model performance by providing more relevant information.
-\item To reduce dimensionality by selecting the most important features.
-\item To create new features that capture important patterns in the data.
-\end{itemize}
-\end{example}
-\begin{example}[Common Feature Engineering Techniques]\label{sec:org5de6103}
-\begin{itemize}[<+->]
-\item \alert{\alert{Feature Selection}}: Techniques include filter methods (e.g., correlation), wrapper methods (e.g., recursive feature elimination), and embedded methods (e.g., LASSO).
-\item \alert{\alert{Feature Transformation}}: Techniques include logarithmic transformation, polynomial features, and interaction terms.
-\item \alert{\alert{Feature Creation}}: Creating new features based on existing ones, such as aggregating time series data, extracting date/time components, or creating domain-specific features.
-\end{itemize}
-\end{example}
-\end{frame}
-\end{document}
diff --git a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter on Habrok.ipynb b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter on Habrok.ipynb
deleted file mode 100644
index 5eb924e..0000000
--- a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter on Habrok.ipynb	
+++ /dev/null
@@ -1,144 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "67b5b1a3",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Working with Jupyter Notebooks\n",
-    "\n",
-    "Throughout this workshop, we will be working in Jupyter Notebooks / JupyterLab. Jupyter Notebooks are a popular tool for data science and machine learning, allowing you to write and execute code in an interactive environment.\n",
-    "\n",
-    "In order to have a uniform environment, we will be using the Habrok High Performance Computing (HPC) cluster, through the Jupyter Python Interactive App on the Habrok web portal, located at [https://portal.hb.hpc.rug.nl](https://portal.hb.hpc.rug.nl). You will need to log in with your university account, and then you can start the Jupyter Python Interactive App.\n",
-    "\n",
-    "The app is already setup with the necessary packages for this workshop, but you will also build your own environment and Jupyter kernel."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eda28a27",
-   "metadata": {},
-   "source": [
-    "## Building your own Jupyter kernel on Habrok\n",
-    "\n",
-    "In order to build your own Jupyter kernel, you will need to create a new Python environment. This can be done on Habrok by following these steps:\n",
-    "\n",
-    "-   From the Habrok web portal, navigate to the \"Clusters\" tab and select one of the available Shell sessions (e.g., \"Habrok Login1 Shell\").\n",
-    "-   In the Shell session, first load the Python module by running:\n",
-    "\n",
-    "```shell\n",
-    "module load Python/3.13.1-GCCcore-14.2.0\n",
-    "```\n",
-    "\n",
-    "-   Next, create a new Python environment using `venv`:\n",
-    "\n",
-    "```shell\n",
-    "python3 -m venv $HOME/venvs/ml_workshop\n",
-    "```\n",
-    "\n",
-    "-   Activate the new environment:\n",
-    "\n",
-    "```shell\n",
-    "source $HOME/venvs/ml_workshop/bin/activate\n",
-    "```\n",
-    "\n",
-    "-   Install the necessary packages for the workshop:\n",
-    "\n",
-    "```shell\n",
-    "pip install -r requirements.txt\n",
-    "```\n",
-    "\n",
-    "-   Finally, install the Jupyter kernel:\n",
-    "\n",
-    "```shell\n",
-    "python -m ipykernel install --user --name=ml_workshop --display-name=\"ML Workshop\"\n",
-    "```\n",
-    "\n",
-    "Now that the kernel has been created, you can start the Jupyter Python Interactive App again from the Habrok web portal. In the app form, select the correct Python Version (3.13.1), the \"Custom virtual environment\" option for the Python Environment, and then select the path to your newly created environment, which should be `$HOME/venvs/ml_workshop`. When you start the app, the Jupyter kernel will be available, and you can select it from the \"Kernel\" menu in the Jupyter Lab interface (\"ML Workshop\")."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7b80feaa",
-   "metadata": {},
-   "source": [
-    "## Building your own Jupyter kernel on your local machine\n",
-    "\n",
-    "Similarly, you can build your own Jupyter kernel on your local machine. The steps are similar to those on Habrok, but you will need to install Python on your local machine first. You can download Python from the official website: [https://www.python.org/downloads/](https://www.python.org/downloads/).\n",
-    "\n",
-    "After following the steps above, you can start Jupyter Lab on your local machine by running:\n",
-    "\n",
-    "```shell\n",
-    "jupyter-lab\n",
-    "```\n",
-    "\n",
-    "which will open a new tab in your web browser with the Jupyter Lab interface. You can then create a new notebook and select the kernel you created from the \"Kernel\" menu."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d9ac32f2",
-   "metadata": {},
-   "source": [
-    "## Building your own Jupyter kernel on Google Colab\n",
-    "\n",
-    "If you prefer to use Google Colab, you can also build your own Jupyter kernel there. Google Colab is a free Jupyter notebook environment that runs in the cloud and is particularly useful for machine learning and data science tasks.\n",
-    "\n",
-    "To use Google Colab, you will need a Google account. Once you have an account, you can go to [Google Colab](https://colab.research.google.com/) and create a new notebook. You can then install the necessary packages by running the following code in a code cell:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "01559c58",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install -r requirements.txt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "83a78148",
-   "metadata": {},
-   "source": [
-    "We do not recommend using Google Colab for your research, as it is not a suitable environment for running large-scale machine learning models. However, it can be useful for quick prototyping and testing of small models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "47df9255",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip list"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb b/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb
deleted file mode 100644
index 9b990a6..0000000
--- a/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb	
+++ /dev/null
@@ -1,755 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "258dca2b",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# What is Machine Learning?\n",
-    "\n",
-    "**Machine Learning** (ML) is a subfield of **artificial intelligence** (AI) that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit instructions. Instead, ML systems learn from data, identifying patterns and making decisions based on that data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "16ee15a1",
-   "metadata": {},
-   "source": [
-    "## ML vs. Traditional Programming\n",
-    "\n",
-    "In traditional programming, a programmer writes explicit rules and instructions for the computer to follow. In contrast, machine learning allows the computer to learn from data and improve its performance over time without being explicitly programmed for every task.\n",
-    "\n",
-    "One way to think about the difference is:\n",
-    "\n",
-    "-   **Traditional Programming**: input + model $\\Rightarrow$ output\n",
-    "-   **Machine Learning**: input + output $\\Rightarrow$ model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5c3732f0",
-   "metadata": {},
-   "source": [
-    "## Key Concepts in Machine Learning\n",
-    "\n",
-    "Some key concepts in machine learning which will be useful to introduce early on in the workshop include:\n",
-    "\n",
-    "-   **Data**: The foundation of machine learning. Data can be structured (e.g., tables, databases) or unstructured (e.g., text, images).\n",
-    "-   **Observations**: Individual data points or instances in the dataset. In a tabular dataset, observations are the rows that represent different examples.\n",
-    "-   **Features**: Individual measurable properties or characteristics of the data. In a tabular dataset, features are the columns that represent different attributes.\n",
-    "-   **Labels**: The target variable or outcome that the model is trying to predict. This is usually one of the columns in a tabular dataset.\n",
-    "-   **Model**: A mathematical representation of the relationship between features and labels. The model is trained on the data to learn these relationships.\n",
-    "-   **Training**: The process of teaching a machine learning model using a dataset. During training, the model learns to identify patterns in the data.\n",
-    "-   **Testing**: Evaluating the performance of a trained model on a separate dataset to assess its accuracy and generalization capabilities."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "28256afd",
-   "metadata": {},
-   "source": [
-    "## Types of Machine Learning\n",
-    "\n",
-    "Machine learning can be broadly categorized into three main types based on the nature of the learning task:\n",
-    "\n",
-    "-   **Supervised Learning**: The model is trained on a labeled dataset, where each observation has a corresponding label. The goal is to learn a mapping from features to labels. Examples include **classification** and **regression** tasks.\n",
-    "-   **Unsupervised Learning**: The model is trained on an unlabeled dataset, where the goal is to find patterns or groupings in the data. Examples include **clustering** and **dimensionality reduction** tasks.\n",
-    "-   **Reinforcement Learning**: The model learns by interacting with an environment and receiving feedback in the form of rewards or penalties. The goal is to learn a policy that maximizes cumulative rewards over time.\n",
-    "\n",
-    "We will explore these concepts in more detail in a bit, but for now, it is important to understand that machine learning relies heavily on data and the relationships between features and labels.\n",
-    "\n",
-    "We will also introduce more advanced concepts in later sessions, as needed."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "50bf583b",
-   "metadata": {},
-   "source": [
-    "## Practical Demonstration\n",
-    "\n",
-    "The `scikit-learn` library, which is the most populuar machine learning library in Python, provides a wide range of tools for building and evaluating machine learning models. It includes datasets, preprocessing utilities, and various algorithms for classification, regression, clustering, and more.\n",
-    "\n",
-    "We will start by exploring some of the built-in datasets in `scikit-learn`, which are useful for learning and practicing machine learning concepts. These datasets are often used as benchmarks for testing algorithms and understanding their behavior.\n",
-    "\n",
-    "The first dataset we will explore is the **Iris dataset**, which is a classic dataset in machine learning. It contains measurements of iris flowers and their corresponding species labels. The dataset has four features (sepal length, sepal width, petal length, and petal width) and three classes (species of iris).\n",
-    "\n",
-    "Import the necessary libraries and load the Iris dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b4f026b3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.datasets import load_iris\n",
-    "data = load_iris()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f267a0d8",
-   "metadata": {},
-   "source": [
-    "The `iris` dataset is loaded as, essentially, a dictionary:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "c264fe27",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'sklearn.utils._bunch.Bunch'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(type(data))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "70f33251",
-   "metadata": {},
-   "source": [
-    "Let's print the keys of this dictionary:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "2b65547f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(data.keys())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "57459dc7",
-   "metadata": {},
-   "source": [
-    "Print the description of the dataset to understand its contents."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "e52b12e4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      ".. _iris_dataset:\n",
-      "\n",
-      "Iris plants dataset\n",
-      "--------------------\n",
-      "\n",
-      "**Data Set Characteristics:**\n",
-      "\n",
-      ":Number of Instances: 150 (50 in each of three classes)\n",
-      ":Number of Attributes: 4 numeric, predictive attributes and the class\n",
-      ":Attribute Information:\n",
-      "    - sepal length in cm\n",
-      "    - sepal width in cm\n",
-      "    - petal length in cm\n",
-      "    - petal width in cm\n",
-      "    - class:\n",
-      "            - Iris-Setosa\n",
-      "            - Iris-Versicolour\n",
-      "            - Iris-Virginica\n",
-      "\n",
-      ":Summary Statistics:\n",
-      "\n",
-      "============== ==== ==== ======= ===== ====================\n",
-      "                Min  Max   Mean    SD   Class Correlation\n",
-      "============== ==== ==== ======= ===== ====================\n",
-      "sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
-      "sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
-      "petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
-      "petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
-      "============== ==== ==== ======= ===== ====================\n",
-      "\n",
-      ":Missing Attribute Values: None\n",
-      ":Class Distribution: 33.3% for each of 3 classes.\n",
-      ":Creator: R.A. Fisher\n",
-      ":Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
-      ":Date: July, 1988\n",
-      "\n",
-      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
-      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
-      "Machine Learning Repository, which has two wrong data points.\n",
-      "\n",
-      "This is perhaps the best known database to be found in the\n",
-      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
-      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
-      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
-      "type of iris plant.  One class is linearly separable from the other 2; the\n",
-      "latter are NOT linearly separable from each other.\n",
-      "\n",
-      ".. dropdown:: References\n",
-      "\n",
-      "  - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
-      "    Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
-      "    Mathematical Statistics\" (John Wiley, NY, 1950).\n",
-      "  - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
-      "    (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
-      "  - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
-      "    Structure and Classification Rule for Recognition in Partially Exposed\n",
-      "    Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
-      "    Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
-      "  - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
-      "    on Information Theory, May 1972, 431-433.\n",
-      "  - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
-      "    conceptual clustering system finds 3 classes in the data.\n",
-      "  - Many, many more ...\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(data['DESCR'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ce4601da",
-   "metadata": {},
-   "source": [
-    "Load the data and target variables into a Pandas DataFrame for easier manipulation and visualization."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "c2d48f01",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal length (cm)</th>\n",
-       "      <th>sepal width (cm)</th>\n",
-       "      <th>petal length (cm)</th>\n",
-       "      <th>petal width (cm)</th>\n",
-       "      <th>species</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4.7</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4.6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>145</th>\n",
-       "      <td>6.7</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.2</td>\n",
-       "      <td>2.3</td>\n",
-       "      <td>virginica</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>146</th>\n",
-       "      <td>6.3</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1.9</td>\n",
-       "      <td>virginica</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>147</th>\n",
-       "      <td>6.5</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>virginica</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>148</th>\n",
-       "      <td>6.2</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>5.4</td>\n",
-       "      <td>2.3</td>\n",
-       "      <td>virginica</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>149</th>\n",
-       "      <td>5.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>virginica</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>150 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
-       "0                  5.1               3.5                1.4               0.2   \n",
-       "1                  4.9               3.0                1.4               0.2   \n",
-       "2                  4.7               3.2                1.3               0.2   \n",
-       "3                  4.6               3.1                1.5               0.2   \n",
-       "4                  5.0               3.6                1.4               0.2   \n",
-       "..                 ...               ...                ...               ...   \n",
-       "145                6.7               3.0                5.2               2.3   \n",
-       "146                6.3               2.5                5.0               1.9   \n",
-       "147                6.5               3.0                5.2               2.0   \n",
-       "148                6.2               3.4                5.4               2.3   \n",
-       "149                5.9               3.0                5.1               1.8   \n",
-       "\n",
-       "       species  \n",
-       "0       setosa  \n",
-       "1       setosa  \n",
-       "2       setosa  \n",
-       "3       setosa  \n",
-       "4       setosa  \n",
-       "..         ...  \n",
-       "145  virginica  \n",
-       "146  virginica  \n",
-       "147  virginica  \n",
-       "148  virginica  \n",
-       "149  virginica  \n",
-       "\n",
-       "[150 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "df = pd.DataFrame(data=data['data'], \n",
-    "                  columns=data['feature_names'])\n",
-    "df['species'] = data['target_names'][data['target']]\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8aae2343",
-   "metadata": {},
-   "source": [
-    "Finally, let's save the DataFrame to a CSV file for later use."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e1581e04",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iris dataset saved to 'iris.csv'\n"
-     ]
-    }
-   ],
-   "source": [
-    "df.to_csv('../../data/iris.csv', index=False)\n",
-    "print(\"Iris dataset saved to 'iris.csv'\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9215f289",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "Explore the California housing dataset:\n",
-    "\n",
-    "-   Load the California housing dataset from `scikit-learn` and transform it into a `pandas.DataFrame`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "7a8eb4a6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>MedInc</th>\n",
-       "      <th>HouseAge</th>\n",
-       "      <th>AveRooms</th>\n",
-       "      <th>AveBedrms</th>\n",
-       "      <th>Population</th>\n",
-       "      <th>AveOccup</th>\n",
-       "      <th>Latitude</th>\n",
-       "      <th>Longitude</th>\n",
-       "      <th>MedHouseVal</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>8.3252</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>6.984127</td>\n",
-       "      <td>1.023810</td>\n",
-       "      <td>322.0</td>\n",
-       "      <td>2.555556</td>\n",
-       "      <td>37.88</td>\n",
-       "      <td>-122.23</td>\n",
-       "      <td>4.526</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>8.3014</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>6.238137</td>\n",
-       "      <td>0.971880</td>\n",
-       "      <td>2401.0</td>\n",
-       "      <td>2.109842</td>\n",
-       "      <td>37.86</td>\n",
-       "      <td>-122.22</td>\n",
-       "      <td>3.585</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>7.2574</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>8.288136</td>\n",
-       "      <td>1.073446</td>\n",
-       "      <td>496.0</td>\n",
-       "      <td>2.802260</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>-122.24</td>\n",
-       "      <td>3.521</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>5.6431</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>5.817352</td>\n",
-       "      <td>1.073059</td>\n",
-       "      <td>558.0</td>\n",
-       "      <td>2.547945</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>-122.25</td>\n",
-       "      <td>3.413</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3.8462</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>6.281853</td>\n",
-       "      <td>1.081081</td>\n",
-       "      <td>565.0</td>\n",
-       "      <td>2.181467</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>-122.25</td>\n",
-       "      <td>3.422</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20635</th>\n",
-       "      <td>1.5603</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>5.045455</td>\n",
-       "      <td>1.133333</td>\n",
-       "      <td>845.0</td>\n",
-       "      <td>2.560606</td>\n",
-       "      <td>39.48</td>\n",
-       "      <td>-121.09</td>\n",
-       "      <td>0.781</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20636</th>\n",
-       "      <td>2.5568</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>6.114035</td>\n",
-       "      <td>1.315789</td>\n",
-       "      <td>356.0</td>\n",
-       "      <td>3.122807</td>\n",
-       "      <td>39.49</td>\n",
-       "      <td>-121.21</td>\n",
-       "      <td>0.771</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20637</th>\n",
-       "      <td>1.7000</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>5.205543</td>\n",
-       "      <td>1.120092</td>\n",
-       "      <td>1007.0</td>\n",
-       "      <td>2.325635</td>\n",
-       "      <td>39.43</td>\n",
-       "      <td>-121.22</td>\n",
-       "      <td>0.923</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20638</th>\n",
-       "      <td>1.8672</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>5.329513</td>\n",
-       "      <td>1.171920</td>\n",
-       "      <td>741.0</td>\n",
-       "      <td>2.123209</td>\n",
-       "      <td>39.43</td>\n",
-       "      <td>-121.32</td>\n",
-       "      <td>0.847</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20639</th>\n",
-       "      <td>2.3886</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>5.254717</td>\n",
-       "      <td>1.162264</td>\n",
-       "      <td>1387.0</td>\n",
-       "      <td>2.616981</td>\n",
-       "      <td>39.37</td>\n",
-       "      <td>-121.24</td>\n",
-       "      <td>0.894</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>20640 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
-       "0      8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
-       "1      8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
-       "2      7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
-       "3      5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
-       "4      3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
-       "...       ...       ...       ...        ...         ...       ...       ...   \n",
-       "20635  1.5603      25.0  5.045455   1.133333       845.0  2.560606     39.48   \n",
-       "20636  2.5568      18.0  6.114035   1.315789       356.0  3.122807     39.49   \n",
-       "20637  1.7000      17.0  5.205543   1.120092      1007.0  2.325635     39.43   \n",
-       "20638  1.8672      18.0  5.329513   1.171920       741.0  2.123209     39.43   \n",
-       "20639  2.3886      16.0  5.254717   1.162264      1387.0  2.616981     39.37   \n",
-       "\n",
-       "       Longitude  MedHouseVal  \n",
-       "0        -122.23        4.526  \n",
-       "1        -122.22        3.585  \n",
-       "2        -122.24        3.521  \n",
-       "3        -122.25        3.413  \n",
-       "4        -122.25        3.422  \n",
-       "...          ...          ...  \n",
-       "20635    -121.09        0.781  \n",
-       "20636    -121.21        0.771  \n",
-       "20637    -121.22        0.923  \n",
-       "20638    -121.32        0.847  \n",
-       "20639    -121.24        0.894  \n",
-       "\n",
-       "[20640 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.datasets import fetch_california_housing\n",
-    "data = fetch_california_housing(as_frame=True)\n",
-    "df = data.frame\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d3323aab",
-   "metadata": {},
-   "source": [
-    "-   Print the first few rows of the dataset, the names of the features and the target variable, and the number of observations and features in the dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "42ab1062",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
-      "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
-      "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
-      "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
-      "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
-      "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
-      "\n",
-      "   Longitude  MedHouseVal  \n",
-      "0    -122.23        4.526  \n",
-      "1    -122.22        3.585  \n",
-      "2    -122.24        3.521  \n",
-      "3    -122.25        3.413  \n",
-      "4    -122.25        3.422  \n",
-      "Features: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude']\n",
-      "Target variable: MedHouseVal\n",
-      "Number of observations: 20640\n",
-      "Number of features: 8\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Print the first few rows of the dataset\n",
-    "print(df.head())\n",
-    "\n",
-    "# Print the names of the features and the target variable\n",
-    "print(\"Features:\", df.columns[:-1].to_list())\n",
-    "print(\"Target variable:\", df.columns[-1])\n",
-    "\n",
-    "# Print the number of observations and features (excluding the target variable) in the dataset\n",
-    "print(\"Number of observations:\", df.shape[0])\n",
-    "print(\"Number of features:\", df.shape[1] - 1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "56859ced",
-   "metadata": {},
-   "source": [
-    "-   Save the DataFrame to a CSV file for later use."
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb b/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb
deleted file mode 100644
index 7913569..0000000
--- a/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb	
+++ /dev/null
@@ -1,2189 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "0eecd07c",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# The Machine Learning Workflow\n",
-    "\n",
-    "A typical machine learning workflow is a structured process that guides the development of machine learning models. It consists of several key steps, each contributing to the successful deployment of a model. The workflow can be summarized as follows:\n",
-    "\n",
-    "1.  **Problem Definition**: Clearly define the problem you want to solve with machine learning. This includes understanding the context and the specific goals of the project.\n",
-    "2.  **Data Collection**: Gather the necessary data that will be used to train and evaluate the model. This data can come from various sources, such as databases, APIs, or web scraping.\n",
-    "3.  **Data Exploration and Preprocessing**: Analyze the collected data to understand its structure, quality, and characteristics. This step often involves cleaning the data, handling missing values, and transforming features to make them suitable for modeling.\n",
-    "4.  **Model Selection**: Choose the appropriate machine learning algorithms based on the problem type (e.g., classification, regression) and the nature of the data. This may involve experimenting with different models to find the best fit.\n",
-    "5.  **Model Training and Validation**: Train the selected model on the training dataset and validate its performance using a separate validation set. This step includes tuning hyperparameters to optimize the model's performance.\n",
-    "6.  **Model Evaluation**: Assess the model's performance using metrics relevant to the problem, such as accuracy, precision, recall, or F1 score. This evaluation helps determine if the model meets the desired criteria.\n",
-    "7.  **Model Deployment and Monitoring**: Once the model is deemed satisfactory, deploy it to a production environment where it can make predictions on new data. Continuous monitoring is essential to ensure the model remains effective over time, especially as new data becomes available."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7c417059",
-   "metadata": {},
-   "source": [
-    "## Practical Demonstration\n",
-    "\n",
-    "We will demonstrate a simple machine learning workflow using the California housing dataset. This dataset is commonly used for regression tasks and contains information about various features of houses in California, along with their median house values.\n",
-    "\n",
-    "We begin by loading the dataset, exploring its structure, and then proceeding through the steps of the machine learning workflow, including data preprocessing, model training, and evaluation (we will not demonstrate deployment in this notebook).\n",
-    "\n",
-    "-   **Load the California housing dataset**: This dataset is available in the `sklearn.datasets` module and can be easily loaded into a pandas `DataFrame`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "71da8cfb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "dict_keys(['data', 'target', 'frame', 'target_names', 'feature_names', 'DESCR'])\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.datasets import fetch_california_housing\n",
-    "data = fetch_california_housing(as_frame=True)\n",
-    "print(data.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "ec67de3e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>MedInc</th>\n",
-       "      <th>HouseAge</th>\n",
-       "      <th>AveRooms</th>\n",
-       "      <th>AveBedrms</th>\n",
-       "      <th>Population</th>\n",
-       "      <th>AveOccup</th>\n",
-       "      <th>Latitude</th>\n",
-       "      <th>Longitude</th>\n",
-       "      <th>MedHouseVal</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>8.3252</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>6.984127</td>\n",
-       "      <td>1.023810</td>\n",
-       "      <td>322.0</td>\n",
-       "      <td>2.555556</td>\n",
-       "      <td>37.88</td>\n",
-       "      <td>-122.23</td>\n",
-       "      <td>4.526</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>8.3014</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>6.238137</td>\n",
-       "      <td>0.971880</td>\n",
-       "      <td>2401.0</td>\n",
-       "      <td>2.109842</td>\n",
-       "      <td>37.86</td>\n",
-       "      <td>-122.22</td>\n",
-       "      <td>3.585</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>7.2574</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>8.288136</td>\n",
-       "      <td>1.073446</td>\n",
-       "      <td>496.0</td>\n",
-       "      <td>2.802260</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>-122.24</td>\n",
-       "      <td>3.521</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>5.6431</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>5.817352</td>\n",
-       "      <td>1.073059</td>\n",
-       "      <td>558.0</td>\n",
-       "      <td>2.547945</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>-122.25</td>\n",
-       "      <td>3.413</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3.8462</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>6.281853</td>\n",
-       "      <td>1.081081</td>\n",
-       "      <td>565.0</td>\n",
-       "      <td>2.181467</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>-122.25</td>\n",
-       "      <td>3.422</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
-       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
-       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
-       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
-       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
-       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
-       "\n",
-       "   Longitude  MedHouseVal  \n",
-       "0    -122.23        4.526  \n",
-       "1    -122.22        3.585  \n",
-       "2    -122.24        3.521  \n",
-       "3    -122.25        3.413  \n",
-       "4    -122.25        3.422  "
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = data.frame\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "b5005e59",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "California Housing Dataset\n",
-      ".. _california_housing_dataset:\n",
-      "\n",
-      "California Housing dataset\n",
-      "--------------------------\n",
-      "\n",
-      "**Data Set Characteristics:**\n",
-      "\n",
-      ":Number of Instances: 20640\n",
-      "\n",
-      ":Number of Attributes: 8 numeric, predictive attributes and the target\n",
-      "\n",
-      ":Attribute Information:\n",
-      "    - MedInc        median income in block group\n",
-      "    - HouseAge      median house age in block group\n",
-      "    - AveRooms      average number of rooms per household\n",
-      "    - AveBedrms     average number of bedrooms per household\n",
-      "    - Population    block group population\n",
-      "    - AveOccup      average number of household members\n",
-      "    - Latitude      block group latitude\n",
-      "    - Longitude     block group longitude\n",
-      "\n",
-      ":Missing Attribute Values: None\n",
-      "\n",
-      "This dataset was obtained from the StatLib repository.\n",
-      "https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\n",
-      "\n",
-      "The target variable is the median house value for California districts,\n",
-      "expressed in hundreds of thousands of dollars ($100,000).\n",
-      "\n",
-      "This dataset was derived from the 1990 U.S. census, using one row per census\n",
-      "block group. A block group is the smallest geographical unit for which the U.S.\n",
-      "Census Bureau publishes sample data (a block group typically has a population\n",
-      "of 600 to 3,000 people).\n",
-      "\n",
-      "A household is a group of people residing within a home. Since the average\n",
-      "number of rooms and bedrooms in this dataset are provided per household, these\n",
-      "columns may take surprisingly large values for block groups with few households\n",
-      "and many empty houses, such as vacation resorts.\n",
-      "\n",
-      "It can be downloaded/loaded using the\n",
-      ":func:`sklearn.datasets.fetch_california_housing` function.\n",
-      "\n",
-      ".. rubric:: References\n",
-      "\n",
-      "- Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n",
-      "  Statistics and Probability Letters, 33:291-297, 1997.\n",
-      "\n",
-      "Feature names: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude']\n",
-      "Target variable: MedHouseVal\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Display some key information about the dataset\n",
-    "print(\"California Housing Dataset\")\n",
-    "print(data.DESCR)  # Description of the dataset\n",
-    "print(\"Feature names:\", data.feature_names)\n",
-    "print(\"Target variable:\", data.target.name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d14c0100",
-   "metadata": {},
-   "source": [
-    "-   **Quick exploration**: We will check the shape of the dataset, the names of the columns, and identify the target variable; we will also visualize the correlation matrix to understand the relationships between features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "1e91f7ec",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Shape: (20640, 9)\n",
-      "Columns: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude', 'MedHouseVal']\n",
-      "Target: MedHouseVal\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Shape:\", df.shape)\n",
-    "print(\"Columns:\", df.columns.tolist())\n",
-    "print(\"Target:\", data.target.name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "940fc859",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x1200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualize the correlation matrix\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "plt.figure(figsize=(15, 12))\n",
-    "sns.heatmap(df.corr(), annot=True, fmt=\".2f\", cmap='coolwarm', square=True)\n",
-    "plt.title('Correlation Matrix of California Housing Features')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a48cb8a0",
-   "metadata": {},
-   "source": [
-    "-   **Split features and target**: We will separate the features (independent variables) from the target variable (dependent variable)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "91afe480",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X = df.drop(columns=[data.target.name])\n",
-    "y = df[data.target.name]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1dbd4290",
-   "metadata": {},
-   "source": [
-    "-   **Train-test split**: We will split the dataset into training and testing sets to evaluate the model's performance on unseen data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "8eeb0674",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8ed604b0",
-   "metadata": {},
-   "source": [
-    "-   **Model training**: We will use a simple linear regression model to fit the training data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "f2b97f13",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
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-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('copy_X',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">copy_X&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">1e-06</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('positive',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">positive&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
-      ],
-      "text/plain": [
-       "LinearRegression()"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "model = LinearRegression()\n",
-    "model.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1ae83132",
-   "metadata": {},
-   "source": [
-    "-   **Prediction and evaluation**: Finally, we will make predictions on the test set and evaluate the model's performance using mean squared error (MSE) as the metric."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "df10f0df",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MSE on test set: 0.56\n",
-      "R-squared on test set: 0.58\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import mean_squared_error\n",
-    "y_pred = model.predict(X_test)\n",
-    "\n",
-    "mse = mean_squared_error(y_test, y_pred)\n",
-    "print(f\"MSE on test set: {mse:.2f}\")\n",
-    "\n",
-    "r2 = model.score(X_test, y_test)\n",
-    "print(f\"R-squared on test set: {r2:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8bd82f35",
-   "metadata": {},
-   "source": [
-    "-   **Visualizing the predictions**: We can also visualize the predictions against the actual values to get a better understanding of the model's performance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "06817324",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from matplotlib import pyplot as plt\n",
-    "import seaborn as sns\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.regplot(x=y_test, y=y_pred, scatter_kws={'alpha':0.5}, line_kws={'color':'red'})\n",
-    "plt.xlabel(\"Actual Values\")\n",
-    "plt.ylabel(\"Predicted Values\")\n",
-    "plt.title(\"Actual vs Predicted Values\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8f8ddef8",
-   "metadata": {},
-   "source": [
-    "Finally, let's save the California housing dataset to a CSV file for later use."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "efbb0896",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "California housing dataset saved to 'california_housing.csv'\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Save the DataFrame to a CSV file\n",
-    "df.to_csv('../../data/california_housing.csv', index=False)\n",
-    "print(\"California housing dataset saved to 'california_housing.csv'\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0a93e4e",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "Use the Ames housing dataset from OpenML to practice the machine learning workflow. The Ames housing dataset is a well-known dataset for regression tasks, containing various features of houses in Ames, Iowa, along with their sale prices. The dataset is available on OpenML and can be loaded using the `fetch_openml` function from `sklearn.datasets`.\n",
-    "\n",
-    "-   Load the Ames housing dataset from OpenML (`house_prices`);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "df7cdf22",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.datasets import fetch_openml\n",
-    "import pandas as pd\n",
-    "\n",
-    "data = fetch_openml('house_prices', as_frame=True, parser=\"auto\")\n",
-    "df = data.frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "81bd3488",
-   "metadata": {},
-   "source": [
-    "-   Explore its structure"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "a6d26540",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Ames Housing Dataset\n",
-      "Number of samples: 1460\n",
-      "Number of features: 80\n",
-      "Target variable: SalePrice\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Ames Housing Dataset\")\n",
-    "print(\"Number of samples:\", df.shape[0])\n",
-    "print(\"Number of features:\", df.shape[1] - 1)\n",
-    "print(\"Target variable:\", data.target.name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "87472ab7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 1460 entries, 0 to 1459\n",
-      "Data columns (total 81 columns):\n",
-      " #   Column         Non-Null Count  Dtype  \n",
-      "---  ------         --------------  -----  \n",
-      " 0   Id             1460 non-null   int64  \n",
-      " 1   MSSubClass     1460 non-null   int64  \n",
-      " 2   MSZoning       1460 non-null   object \n",
-      " 3   LotFrontage    1201 non-null   float64\n",
-      " 4   LotArea        1460 non-null   int64  \n",
-      " 5   Street         1460 non-null   object \n",
-      " 6   Alley          91 non-null     object \n",
-      " 7   LotShape       1460 non-null   object \n",
-      " 8   LandContour    1460 non-null   object \n",
-      " 9   Utilities      1460 non-null   object \n",
-      " 10  LotConfig      1460 non-null   object \n",
-      " 11  LandSlope      1460 non-null   object \n",
-      " 12  Neighborhood   1460 non-null   object \n",
-      " 13  Condition1     1460 non-null   object \n",
-      " 14  Condition2     1460 non-null   object \n",
-      " 15  BldgType       1460 non-null   object \n",
-      " 16  HouseStyle     1460 non-null   object \n",
-      " 17  OverallQual    1460 non-null   int64  \n",
-      " 18  OverallCond    1460 non-null   int64  \n",
-      " 19  YearBuilt      1460 non-null   int64  \n",
-      " 20  YearRemodAdd   1460 non-null   int64  \n",
-      " 21  RoofStyle      1460 non-null   object \n",
-      " 22  RoofMatl       1460 non-null   object \n",
-      " 23  Exterior1st    1460 non-null   object \n",
-      " 24  Exterior2nd    1460 non-null   object \n",
-      " 25  MasVnrType     1452 non-null   object \n",
-      " 26  MasVnrArea     1452 non-null   float64\n",
-      " 27  ExterQual      1460 non-null   object \n",
-      " 28  ExterCond      1460 non-null   object \n",
-      " 29  Foundation     1460 non-null   object \n",
-      " 30  BsmtQual       1423 non-null   object \n",
-      " 31  BsmtCond       1423 non-null   object \n",
-      " 32  BsmtExposure   1422 non-null   object \n",
-      " 33  BsmtFinType1   1423 non-null   object \n",
-      " 34  BsmtFinSF1     1460 non-null   int64  \n",
-      " 35  BsmtFinType2   1422 non-null   object \n",
-      " 36  BsmtFinSF2     1460 non-null   int64  \n",
-      " 37  BsmtUnfSF      1460 non-null   int64  \n",
-      " 38  TotalBsmtSF    1460 non-null   int64  \n",
-      " 39  Heating        1460 non-null   object \n",
-      " 40  HeatingQC      1460 non-null   object \n",
-      " 41  CentralAir     1460 non-null   object \n",
-      " 42  Electrical     1459 non-null   object \n",
-      " 43  1stFlrSF       1460 non-null   int64  \n",
-      " 44  2ndFlrSF       1460 non-null   int64  \n",
-      " 45  LowQualFinSF   1460 non-null   int64  \n",
-      " 46  GrLivArea      1460 non-null   int64  \n",
-      " 47  BsmtFullBath   1460 non-null   int64  \n",
-      " 48  BsmtHalfBath   1460 non-null   int64  \n",
-      " 49  FullBath       1460 non-null   int64  \n",
-      " 50  HalfBath       1460 non-null   int64  \n",
-      " 51  BedroomAbvGr   1460 non-null   int64  \n",
-      " 52  KitchenAbvGr   1460 non-null   int64  \n",
-      " 53  KitchenQual    1460 non-null   object \n",
-      " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
-      " 55  Functional     1460 non-null   object \n",
-      " 56  Fireplaces     1460 non-null   int64  \n",
-      " 57  FireplaceQu    770 non-null    object \n",
-      " 58  GarageType     1379 non-null   object \n",
-      " 59  GarageYrBlt    1379 non-null   float64\n",
-      " 60  GarageFinish   1379 non-null   object \n",
-      " 61  GarageCars     1460 non-null   int64  \n",
-      " 62  GarageArea     1460 non-null   int64  \n",
-      " 63  GarageQual     1379 non-null   object \n",
-      " 64  GarageCond     1379 non-null   object \n",
-      " 65  PavedDrive     1460 non-null   object \n",
-      " 66  WoodDeckSF     1460 non-null   int64  \n",
-      " 67  OpenPorchSF    1460 non-null   int64  \n",
-      " 68  EnclosedPorch  1460 non-null   int64  \n",
-      " 69  3SsnPorch      1460 non-null   int64  \n",
-      " 70  ScreenPorch    1460 non-null   int64  \n",
-      " 71  PoolArea       1460 non-null   int64  \n",
-      " 72  PoolQC         7 non-null      object \n",
-      " 73  Fence          281 non-null    object \n",
-      " 74  MiscFeature    54 non-null     object \n",
-      " 75  MiscVal        1460 non-null   int64  \n",
-      " 76  MoSold         1460 non-null   int64  \n",
-      " 77  YrSold         1460 non-null   int64  \n",
-      " 78  SaleType       1460 non-null   object \n",
-      " 79  SaleCondition  1460 non-null   object \n",
-      " 80  SalePrice      1460 non-null   int64  \n",
-      "dtypes: float64(3), int64(35), object(43)\n",
-      "memory usage: 924.0+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "0e83bacc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 3000x2400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualize the correlation matrix\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "# Select the numeric columns for correlation analysis\n",
-    "numeric_cols = df.select_dtypes(include=['float64', 'int64']).columns\n",
-    "plt.figure(figsize=(30, 24))\n",
-    "sns.heatmap(df[numeric_cols].corr(), annot=True, fmt=\".2f\", cmap='coolwarm', square=True)\n",
-    "plt.title('Correlation Matrix of Ames Housing Features')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f38fd464",
-   "metadata": {},
-   "source": [
-    "-   Retain only the feature with the highest correlation to the target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "c19b19f2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Retain only a single feature for simplicity\n",
-    "df = df[['OverallQual', 'SalePrice']]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b7a0990",
-   "metadata": {},
-   "source": [
-    "-   Split the features and target variable"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "089fc52a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Separate features and target\n",
-    "X = df.drop(columns=[data.target.name])\n",
-    "y = df[data.target.name]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e0aa557a",
-   "metadata": {},
-   "source": [
-    "-   Perform the train-test split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "f544868c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a9d15a9f",
-   "metadata": {},
-   "source": [
-    "-   Choose a different regression model, such as Decision Tree Regression or Random Forest Regression (you should search on the web for how to use these models in `scikit-learn`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "c57ad7f9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Use Random Forest Regression\n",
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "model = RandomForestRegressor()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b954ce05",
-   "metadata": {},
-   "source": [
-    "-   Train the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "c662a92d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-2 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-2 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-2 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-2 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('criterion',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">criterion&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;squared_error&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_depth',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_split',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
-       "            <td class=\"value\">2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_features&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
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-       "                 onclick=\"copyToClipboard('bootstrap',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">bootstrap&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('oob_score',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">oob_score&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
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-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_samples',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_samples&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
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-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
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-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
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-       "    navigator.clipboard.writeText(fullParamName)\n",
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-       "\n",
-       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
-      ],
-      "text/plain": [
-       "RandomForestRegressor()"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8817779f",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "cb5408cb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MSE on test set: 2143455107.49\n",
-      "R-squared on test set: 0.72\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import mean_squared_error\n",
-    "\n",
-    "y_pred = model.predict(X_test)\n",
-    "mse = mean_squared_error(y_test, y_pred)\n",
-    "print(f\"MSE on test set: {mse:.2f}\")\n",
-    "\n",
-    "# Calculate R-squared\n",
-    "from sklearn.metrics import r2_score\n",
-    "r2 = r2_score(y_test, y_pred)\n",
-    "print(f\"R-squared on test set: {r2:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "850c6a2f",
-   "metadata": {},
-   "source": [
-    "-   Visualize the predictions against the actual values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "4b555548",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from matplotlib import pyplot as plt\n",
-    "import seaborn as sns\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.regplot(x=y_test, y=y_pred,\n",
-    "            scatter_kws={'alpha':0.5},\n",
-    "            line_kws={'color':'red'})\n",
-    "plt.xlabel(\"Actual Values\")\n",
-    "plt.ylabel(\"Predicted Values\")\n",
-    "plt.title(\"Actual vs Predicted Values\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5efa32b4",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "In this section, we have covered the machine learning workflow, including problem definition, data collection, exploration, preprocessing, model selection, training, evaluation, and deployment. We demonstrated a simple workflow using the California housing dataset, and we practiced the workflow using the Ames housing dataset."
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S01 Introduction to Machine Learning/S01C03 Exploratory Data Analysis (EDA).ipynb b/src/S01 Introduction to Machine Learning/S01C03 Exploratory Data Analysis (EDA).ipynb
deleted file mode 100644
index 1700155..0000000
--- a/src/S01 Introduction to Machine Learning/S01C03 Exploratory Data Analysis (EDA).ipynb	
+++ /dev/null
@@ -1,633 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "04a5d3b0",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Exploratory Data Analysis (EDA)\n",
-    "\n",
-    "**Exploratory Data Analysis** (EDA) is a crucial step in the data science process, allowing us to understand our data before diving into modeling. It involves summarizing the main characteristics of a dataset, often using visual and statistical methods. The goal is to gain insights into the data's structure, detect anomalies, and identify relationships between variables."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8aa69739",
-   "metadata": {},
-   "source": [
-    "## Goals of EDA\n",
-    "\n",
-    "-   Understanding the structure and shape of the data\n",
-    "-   Detecting outliers, missing values, and noise\n",
-    "-   Identifying relationships between variables\n",
-    "-   Generating hypotheses or informing modeling decisions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f9199361",
-   "metadata": {},
-   "source": [
-    "## Typical Techniques\n",
-    "\n",
-    "-   Summary statistics: mean, std, min, max, missing values\n",
-    "-   Visualizations:\n",
-    "    -   Histograms, boxplots $\\Rightarrow$ distribution of features\n",
-    "    -   Scatter plots $\\Rightarrow$ relationships between variables\n",
-    "    -   Correlation heatmaps\n",
-    "\n",
-    "It is important to perform Exploratory Data Analysis (EDA) before building machine learning models, since it helps to avoid common pitfalls such as garbage in, garbage out."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b734c0fb",
-   "metadata": {},
-   "source": [
-    "## Practical Demonstration\n",
-    "\n",
-    "In this section, we will perform EDA on the California Housing dataset using Python libraries such as Pandas, Matplotlib, and Seaborn. We will cover the following steps:\n",
-    "\n",
-    "-   Load the dataset using Scikit-learn's `fetch_california_housing`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "a80f895a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 20640 entries, 0 to 20639\n",
-      "Data columns (total 9 columns):\n",
-      " #   Column       Non-Null Count  Dtype  \n",
-      "---  ------       --------------  -----  \n",
-      " 0   MedInc       20640 non-null  float64\n",
-      " 1   HouseAge     20640 non-null  float64\n",
-      " 2   AveRooms     20640 non-null  float64\n",
-      " 3   AveBedrms    20640 non-null  float64\n",
-      " 4   Population   20640 non-null  float64\n",
-      " 5   AveOccup     20640 non-null  float64\n",
-      " 6   Latitude     20640 non-null  float64\n",
-      " 7   Longitude    20640 non-null  float64\n",
-      " 8   MedHouseVal  20640 non-null  float64\n",
-      "dtypes: float64(9)\n",
-      "memory usage: 1.4 MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.datasets import fetch_california_housing\n",
-    "\n",
-    "# Load the California Housing dataset\n",
-    "data = fetch_california_housing(as_frame=True)\n",
-    "\n",
-    "# Convert to DataFrame\n",
-    "df = data.frame\n",
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2252c896",
-   "metadata": {},
-   "source": [
-    "-   Show descriptive statistics (mean, std, min, max) with Pandas."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "e4fc59df",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Descriptive Statistics:\n",
-      "             MedInc      HouseAge      AveRooms     AveBedrms    Population  \\\n",
-      "count  20640.000000  20640.000000  20640.000000  20640.000000  20640.000000   \n",
-      "mean       3.870671     28.639486      5.429000      1.096675   1425.476744   \n",
-      "std        1.899822     12.585558      2.474173      0.473911   1132.462122   \n",
-      "min        0.499900      1.000000      0.846154      0.333333      3.000000   \n",
-      "25%        2.563400     18.000000      4.440716      1.006079    787.000000   \n",
-      "50%        3.534800     29.000000      5.229129      1.048780   1166.000000   \n",
-      "75%        4.743250     37.000000      6.052381      1.099526   1725.000000   \n",
-      "max       15.000100     52.000000    141.909091     34.066667  35682.000000   \n",
-      "\n",
-      "           AveOccup      Latitude     Longitude   MedHouseVal  \n",
-      "count  20640.000000  20640.000000  20640.000000  20640.000000  \n",
-      "mean       3.070655     35.631861   -119.569704      2.068558  \n",
-      "std       10.386050      2.135952      2.003532      1.153956  \n",
-      "min        0.692308     32.540000   -124.350000      0.149990  \n",
-      "25%        2.429741     33.930000   -121.800000      1.196000  \n",
-      "50%        2.818116     34.260000   -118.490000      1.797000  \n",
-      "75%        3.282261     37.710000   -118.010000      2.647250  \n",
-      "max     1243.333333     41.950000   -114.310000      5.000010  \n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Descriptive Statistics:\")\n",
-    "print(df.describe())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "44b603ca",
-   "metadata": {},
-   "source": [
-    "-   Count missing values (if any)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "1e20d98a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Missing Values:\n",
-      "MedInc         0\n",
-      "HouseAge       0\n",
-      "AveRooms       0\n",
-      "AveBedrms      0\n",
-      "Population     0\n",
-      "AveOccup       0\n",
-      "Latitude       0\n",
-      "Longitude      0\n",
-      "MedHouseVal    0\n",
-      "dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Missing Values:\")\n",
-    "print(df.isnull().sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cfe6ca10",
-   "metadata": {},
-   "source": [
-    "-   Explore pairwise relationships with `pairplot()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "c648a50b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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ZH+qwDllbXa/rz54kjz+ka57ZqEVVpcpzWFN20xOtN5p9wYR5RN9mP2f66JRvynaW8FbQ1hWZ3WJWZVlxynJWlhXLbjF3uM6dJYzE128VYwtTlkHqJJGni+3tC4ZTDu+NByu90XXRYFfX7Jc/FFZja6DD7qUqy4o179jDVJQbOdZ46AX0n2Z/SM2+YKfnmMYWvxacOC5lAiOAvtfiD8WuoS6ZOb7T8+35R49Wtt0ijz+kNdX1uqyyVIFQ8rVSfDJLIGzos33Ng6YbQqA/NHs7P7c2e4MqJL4AHIRP9zZLkuYcNVxrquv00R6Xjh8/rJ9LBQAAgIFgyCaj/P3vf4/9fsopp8gwOk7UGMjynTZV72vWUSNytdftS/o8/gae2WzSqstO0Ps7G/TsOzu14MRxsbdhRuY79f6OBm1o101G+xYDUj1QnVNeopJcblgAA0l+Fwli8QlkLo9fdc1+uVsDsTpi1drtSQ/4mr2Rvz3+kFaurtYZU0bqv1K0IBL9+/qzJiUtN/rAIZV11fVaXHVk0vDZ5SUakRfpB72x1R9rZST+jdtZRxbLYjLJEwh22M95dxNGOkociUqVpNDV9nZYUyfJ9EYi36F2XTQUuL0B+YNhZdssWrGwQtl2i354xkTdcLZZDS1+hcKGNuxs0F1/2qobzp4siSRLoD+5WwM6rNCpj3a7Y+cSjz8km8WsNdv26en1O/TjcybruHFFsWRFAP2rMMumuubI99Ho+dZps8Ra3Nz8ZaMMQ5p+WIG+aGjV/y6eqb99UhtpjaGt68Y55SV6oy3BtqNkFrohBA5eYbZd/732cy2qLNUNZ09SszekPKdVe91e/X7DLv3wjEnc2wFwULbtbdKwbJsmjc6TzWLSpi9IRgEAAEDEkE1GyUSxB8begPKzbCrJsask1669bqv+tGWPTp04MmH8jm7gnTZpuJ6+4iTd/tKWhOGz27rQWPp0YjcZ8S0GFLa74T+nvER3zz+at9OAAaYk155wQz9efALZ7sbWpC5e4ltEiq8Lcp2JTaz6g4mtMbVvit1qNmnpaWVJiS2dJXs42yVttK9jch02LXx0vRZVleqKqiNjLTq1f0hx9/yjlW23JNSZw7LtsbdwU4kmjHSUOBKVKkmhs+1dVVasjbsak4b3ViJfTxKPhqp8p02BcFgOm0UrX/9UF580Xg/+rVpr47uKKivW5VWlCgRDsX2T6rzL+Q5Iv4Jsm+wWs8YX5+ieVz9OjNXyEi2uirSi4LSaY8mKUcQt0D/ys2waPyxHv/zacRpZ4NQHOxu07I9b5fGHlG236PHLTtDK1dsSuo2Nfv80y6R8p013zT9aNzy3SW9sq9OiqlI9vm57j7pNBNC5kML6/hkT9d72/ZIi38taAyHVuiPdPocUJq4AHJRP9zbpsKJsWc1mjchz6ouG1v4uEgAAAAYIklEyRKoHxtG3wkqLs7XH1SqTSfrNlScp226RZFJ9s0//8/a/km7gHX14oZa9tCXhxr4kbdjZqHNcPv3v4hO11+1L7Ds4GNac8hJNGJGr17//FTV5A8pz2lSSyw1+YCAqyLYn3NCPik/ucHn8SfWKlNwikhRJqKht1/rSvmZfLAHluHFFyrZZFJahNz+rjyWgpEps6SzZY1iOvdM6piTXrhnji7RydbWWnlamjWsbkuq493Y0aOd+j1aurtaa6gPrNru8RI9fdoIuf+LdpISUyriEkdomn2aXl6Ts+qajBJLOtvfyedO07OWPkubTPpHvYB+gdjfxaCgrybXL1RrQA3/9VN+de5SafUFdOWeCbjh7sgKhsNytQb27Y7+efnunfnTWRN0z/2i1+EMdnnd5GxtIrxybRe/+a79CYUPXzj1KS081lOOIXN+u/mSvfv32Dv3wjIkqaldPdna9TNwC6ZMq9maXlej5qyoVDIVlSHK1BnT92ZP1/bjz7qq126W1/9LVp5dpZL5DBdl2rVhYobpmv3zBUI+7TQTQObvZrNomn17ZvCehe+bZZSWaMCJPI/KIKQAH57PaFh05PEeSVJht0163t59LBAAAgIGCZJQM0NED4ze21en65zbp9gumKhw29OqHNZp+WIHqW/wqzLZpZL5Tl80q1duf70948FpVVpLwRpqU2IrKuucP3JSIPkjOtVt09/yjNTLfqZH56V1fAL1jTGFW7IZ+quSOumZ/yoQLKbFFpKqyYt16wTQteOSthHGyrKlbX4pPQGmf2DK7vET1zT4tPa0s1oJKNPHto90uWcwmTRiR2+E6FWTbdff8o/X3T/dp2mH5mjI6X4urjkzoWujKOUdq5eptCTdYJcXW9bYLpupHv98UGz67vETLLpwmd6tfFx17mEpy7frKUcM7TeTp6fb++b8f0+F+kA7tAWp3Eo+GuoJsu/Y1e3Xtv03UP3c2aOLoyIms2RdUls2iYTl2fVbbpAUzx0kmKctu0dJnNqY87/I2NpB+vlBYx4wr0n1//ljDch0ake9Qszek/Cyrzp8+RsePLZLJJJnipunsepm4BdKno9hbU12n5X/8SLedP1Xb9jXLZjHr1S01+mi3SxfPHK+Pdrti14sFWbZYfBZkR66RNu5s6HS5dEMI9JwhacXrKb4nVddJJun2C6f2T8EAZLzaJq9mHFEkKdKqNskoAAAAiCIZJQN09sB4zbY6ba/36PBh2Xrxn7sTkkyqyor143Mm61tfOVK/eO3A8GDIkJTYpYbNYpbVYlLFuCJt3NkYS15ZV10vk6R7/t8xGs0bpUDGid7QT8Wd4iZ+fL2Q67DqT9+drVy7RaGwocmj8xPqIqfdohWrtyW1TNI+ASWa2FJZVqyfnj9FYUP6wz93JySwVJUV6+bzpspqNqkrhqRXNu3Rjf+3OTYsPgEmVcJd1JptdbrhrEl67NIZ8gXDclgjbwc6rGYdPbYobrup00SejnS0vTvbD73xALWrxCNIVrNZe91ejSvO1odfujQy3xlLhtrr8uqqU8v0i9c+1XVnTer0vMvb2ED6hQ3pv/7ysb77bxP13r8SuxLY62rVrCOLFTDCCrVd00qdXy8Tt0D6dPVddY/bK4vZpKVPv6+KcYX6ZtWR2tfk1VWnlMnVGtD/Lp6Zclq6IQR6ny8YTkpEiVqzra7T7lQBoCO+YEhub1CF2ZFzc1GOXZu+cPVzqQAAADBQkIySAVI9MI6XbbfokTc+S7qp8P7ORm350q3zp4/RqRNHyOMPKddhVbbdov++dIbGDcvW7S9t6bBFg2hCytrqerW269ICQGZzefzKsln00CXHxVomefadnbpr/tEpWzpZXFWqGUcU6TtfmSCHzazCLLvChpGUiBIV37KKJOU6rKoYV6Rd+z16bO32pOnWVtdr2ctb9J//cWyX5b7+uU2x7nfik2ck6X8Xz1QobHQyB6nJF9TiJ99LGDanvCQp6aOzBJLeVNfs14YdDSlbi1m1dnu3H6B2VN6D7f5nsAkb0pcNrZpyWL5SpTz5AmEdM7ZQYUNytfo7nRdvYwPpFQ4bWnDCeIWNcIp4NamuxacReU6ZLAeGdnW9TNwC6dFV7FnMpsj3z28cr5H5WQqEwwobhtzeoDbuatSqtdt1/Pgi3d2uNTi6IQR6X7M3mPD9qf33jmZvsL+LCCAD1TdHvj8XZEWSUQqz7NrX5JNhGDKZun7hCAAAAIMbySgZoKu3wsJhQxfPHJ/QHU+23aIHLz5OTptZN7/4YcKD38qyYv3ozEm6609btbaTFg1Wrd0eu0lR3+KX9jUP2YeYwGCSqkuYyrJirbrsBN3/109VMa5IiypLE25O/vrtHZoypkAX//f6WOLG53UtnS4n/s26bIdFG3c26LhxRUn1TtTa6no1e4OddgUW//ZtfPdi8ckzT38z9Ru2sbLYLQm/R+u5T2ubNSzH3uf1XLMv0Gl3Ry2+g3+Aeijd/ww2vmBIx48v0h6XVy9v3pNwXpxdVqIbx07SmVNGyu0NKt/Z+eURb2MD6eUNBHVESbZ2u7z64+Y9CeeN2WXFWjq8XGHDUK3bq/y27j1oRQHoH13FntNqkas1oMOKsnTrix8mvEAR/yJE+9bg6IYQ6H15WdZOv3fkZXGLEEDP7WvySZIKsiLn5qIcm1oDITX7glyDAwAAgGSUTJDrtKqqrDjlA9zKsmK1BkLKsln08NeOVzBs6P2dDcq2m5VlN+vBti4y4q2rrpdZn+iYcYVa/fG+pHmuq67XFVVH6tiFhUk3KYbqQ0xgsOioS5h11fVq8QV18czxKW9OXl5ZKmvbGy3R7g66evjgsJpj07++tVbnTh+tXEfnp534N9dTtejRHJeYsaiqVI+vS25l5c3P6zutM6M6SmY5lHruYFohKcyy654/f9Jhd0c/mze9x+WIluVQu/8ZTEwyyR8ytOJvyefFNdV10p8+1s/mTZM/GJbJJN7GBvpRUbZD3mBYK1dvS6rL11TXSyaT7pg3TYU5du1r9qkg204rCkA/6Sz2ZpeXyGyWhuXYdeuLH6Z8EcIs6co5R+r+v25Lag2ObgiB3pVltaT8/hTtnvlnFx3c9w4AQ1tdczQZpa2bnrbzdG2Tj2QUAAAAyNzfBUDXWnxBXVZZqtllJQnDK8uK9dPzp+qp9Tt0yX+v1zdWvaNFT7yrjTsbdPa00ZJh6rg/4Oo6HTeuqMNlFmTbUt6kiD7EdHk678IAwMAU37JIe05bxzcnH1+3XQXZB24iNHkDsYcPqVSWFWvjrkbNLi/R7RdM07GHF2q3y6tmX+dNP0dvVOxubNXSZzbq9Pv+oYseelOn/+c/dPUzG1WQZY+1bFIxtjBlN0Gr1m7vsM5cemq5/vZJraSOk1kOtp7rqMy7G1s7nc4fCnfa3ZE/dHB9t3e2r6MJRUOJoc639Zptdfqy0atcp02GDN01/+ik45u3sYG+4Q+FFQwbHV/HbqvT7kav9jcHFDYiXbNFW1EgboG+VZBt1/J501QVl/ArSVVlxbr05CO04JG3Vdvk7bBlvDXV9Tp98ghl2y0pu9MqyLZrwohcHTuuSBNG5BLLwCHwBEIdXguvra6XJ0D3zAB6bl+TTyZJ+W2tKxW23Tva6/b2Y6kAAAAwUNAySgZwtQZ0zTMb9dQ3Z+qyyiPkC4ZjLQ7c9aetSa2brKuu180vfKilp5Z3Ol+rpeN+O60WU+wmRao+hRs9AW4EAhnIneImf1R83Le3rrpeN5x9oM7Ic9o6bD59dnmJbrtgqiTpiqpSbatt1lVPv69FVaUqybVrdllxygeM0TfXO2vR45Y/fKibz5uiG/9vc0I3QPE8/pCueWajnr3yJF3WdISCYUOjC5wKhyNdtRx9WKGWnlamGeOKElpEab+s9m/nduZQWiHpKkGnpYvPOyxTa+fJJq7Wg+/+JxN1th2j57k8p1Xb61p0eFGWcuwW3sYG+kmzL6hQ2Ojw82y7RQXZNu1v8cvVGtRnbV1J0ooC0PdcHr9uf/kjnVA6TNedNUlmk0ktvqCsFrPWbIt8T+3omi2q1u1rOw/z9jSQTs3eYMr7O+/vbNCqtdvV7D247x0Ahra6Zp/ynFZZzZF71dGWUaLd9wAAAGBoIxklA+Q7bfL4Q9rf4tfiJ9+LDX/s0hkpu9mRIm+1/PjcKZ3Ot7iDG/Ozy0vkDURuGHbUjcXstrdM6a4HyCydda3T0NJ5ckKDJ/J5fHcHJklnTxutS08+kChX2+RTls2i0W31Q3zf5KvWbtcDCysUlhISX+LfXP+strnTFj1+cu5kzSkviSXlpeLxh2Q2SRaTSYcXZ2n5yx8lJMBUlhXr/KNHK9tukcef+g3AVG/ndqQ7rZB09DC0q+6ODvbBTLa981N8tIWZoaIgy6ZgiofbXXXXNGFEbl8WE4Ai9WJHrUJFY/Znf0ys1+O7WCP5BOg7dc1+vflZvRacOE53v/pxwvVdZVmxHlhYEevqsTMnH1lMd1pAmuU6rSmve6OxmuvkFiGAntvX5FNh3PW302ZRls2iWjfJKAAAAKCbnowQ7Qpj465GVcY1f9zVG2YmQwnjx6ssK5bZbEr6vLKsWEtOLVN+202IjrqxWEN3PUBG6qxrHVsnrSVJUjBkJCSNuDx+Xff7Tbrx+c1a/OR7uuqp97X4yfd04/9t1nVx9UOu3RqrR6KtllSMK9Jjl87QY5fO0J+vna075k1TXbNXn+1r7rJFj1Z/SCsWVmjC8JykrniiqsqKVdvk03s7G3R7u0QUKZIIs/yPW7WoqrTD5fQkCaSzFmekzhNbOtsn8Yk/PZWqjo+qLCuWxdz1g6HBZESeQyYlnxd7u7smAIeuJNeeMl6lAzHbvl4nZoH+4fYGOjyXRrt6DBmGZndyTbJxV6McNjOJZECaOSzmDmP1iXXb5bBwixBAz9U1+2Nd9ETlOa1q7OLeDgAAAIYGXnvIANGuMH72x490/VmT1OQNytUa0Lhh2Vp6WplWrd2e8s3+sMK6vDLyoLX9G2qXV5aq1u3TudNH64azJ2lfk0/D8xx6fWutFj3xrp698iRVlRWrYmxhr3VjAaD/ddS1zpzyEo0blq055SUJw6Nml5foyJJsLbswkjTiD4XVGgjp4pPG6fKq0ljTztG6aM22OtU2+VSQbVeTL9hh9z++YFiBkKE1O+o0uiBLSx5Zr2euOKnTdXDaIi16GIZ0aeURCstIquMuqyxVts2i4zrpimfNtjp9+ysTUn7e0ySQQ2ndpLN9Ek38ORhWs6nTc8BQS0ZxtwYkk3R5ZakcVrOmjClQxdhC5ditOm5ckSrGFSWdTznPAf2jINuuPW5vyjps1pHFsXqbriSB/pfvtGnGuCJJ0qLK0qRuP9ZV12tx1ZG6+bypuu3lLSmvSa55ZqMuOvaw/loFYMjwBsPauLNRS08rS9lNj7eLF54AIJXaJq8KshKvv3MdVjV6hlbXwAAAAEiNZJQMMaYwSzecPVk3/t+mhDdBq9qaU73mmY0JD9CqyopV4/Lp6fU7VDGuKHZj0GE1a+OuRj2/YZeuPWOichwWfdHQKofVos1fuGQyRbq3CATDWj5vuj7b19xpuXrSjYXL41dds19ub0D5WTaV5Nh5WAD0gzGFWVqxsEJ1zX41eQPKtltls5i01+3VbRdO1S1/2KI12+piD/lOPrJYdqtZHn9IdqtF3kBYNmtIdrNZxdl25Tqt+uqxh+mUo0pkNplltZhU4/YqFDa0o64lkgTQpqMuUSrLinXNaeV66OLjlOuwqrKsOGUCS2VZsV7evEebdjXqlvOn6obnNmnBieOS6rhrntmoXy+eKWsXb/dZ2loP6ajLoO6Ktm6SKpGnO4kt7fdJntOmktxDqyOLc+y685WtKc8Bv3lnp37+78cc9LwzzV63Vzc8v1k3nztF/7dhl645/Sj9558/TjoGV15coc1fujRtTEHs5nzYSO7aB0B67XV7lW2z6JkU17Ghtu62SnLt+u9LT0iKZbqSBPpWSa5dobChjWs/T4zFshI9f9XJqnH5lJ9l1V8/rtF500envGabMb6ILnqAPtDqD2rlxRVatbZdN8xt18Gt/mA/lg5Aptrf4tdRI/MShuU4rGpsJRkFAAAAJKNkDJfHrxuf35zUJPnatr8XVZXGbibMLi/Rsgun6d5Xt+rimeOTHvqeNmm4rj9rsm59MfnNtJ+eN1W/+sfnamwN6Jd//0w/PGtip+XqbjcWuxtbdf1zm7Sm3Vv/d/GwAOgz8QlhuQ6rnBazCrPt+skLm2N1QbbdopvPnaybz5ssi9msW//wYdKDhZvOmyyPP6SwNfJAcK/bpxpXq44/YpgufvRtTRyVp8srS/XVX76pinGFuuHsSbHpO2vG3SyTfnzOJHn8Qf30/Km6/aUtsTpOiiTZ3XL+VF386Nuqa/brthe3aMGJ4zps+aQgy6ZAqPO3+zz+UOxBZ77TpmE59oNKAumN1k0Ksns3Qa8g267bLpymG57blLCNDrXFlUzU0OJXIBiSxSL96MxJuukPH3Z4DJ49fZQWP/lebDgPtoG+1+zzKxiWrj97sm5/aUtCHfbUN2cq227RqstO0N2vftxhV5IrFlYMqXoO6E/L2rV4Iklrqut0+8sfqWJckf7Z1hLDqHynbnoh8Tvt7CF4XQL0l6Jsu37x2qcJ37EktcWkSbdfOLV/CgYgozW2BpTjSHzEkG23qJHuMwEAACCSUQa86MNjXzCUkMgRb211vX587hRVjC2UxWRSyDD0r/oWXXLSERqWa9cd86apNRiW2xNpkSQQCuvuV7emfBC37OUtuvncKRqR59SkMfnatKtRs8uKk5JgpMiNw+68weby+JMSUaRI9wc8LAD6xu7GVl3/+01aU30gDp+9cqZWrK5Oqgu+dHl1TMjQspc+TIr9NdV1uvvVj3XJzPFy2iyymCJdvRiSmlp9+s2Vs+QJBNXiDen3356lPS6vttU0xeqRzrr+WlNdpz1unx5ft11LT52gGUcM0+Xt3p69609bYwkoa6rr9O1TUnezM3fyCDlskVZROqrDKsuK9f7Ohtj0v/3WSbJaTKpt8unzupYet+DUvnWTHIdVdotZtU1eeQKhlPPqbotR3Rkv1TjpaHElE7m9Af38349VjdsrXyAcS0Dyh8IakeeUzWLSHpdXNotZJbl2ZdstCV1Oca4C+pbDbFFDi0/f/vUG/fz/HaNbL5gmXzAkjz+kPIdVv//2LJkkVYwr0sadjUndVdLFFtB36pr9CddZ0Zb1jhtXJKvZpOF5Dp0+aYT8wZC217doRukwXXf2JLX6Q8q2WzXEeg0E+pUvFNbWmiY9dukMjch3qNkbUp7Tqr1ur65/bpN8XSTyA0B7hmHI3RpQjj3xEUOuw6oat7efSgUAAICBhGSUASy+NZGHLjmu03H3NLbq6mc2auXFke4v4t90mV1WrMurSrX06UhXPrPLSnRp5RF6+/P9STfv11TX67qzJ+n/+9VbqhhXqOPHFem2C6fplj98mNRCwbILp2nn/hZlt/g7fWBb1+zvMJGGhwVA+rk8/qREFCnSbOq6dg8PHrz4OO1xtSpkGFo4c7wurzoy1oe4xx9Stt2ii2eO1xPrtic8eKgsK9bJE6bp9pc/0uqP98WGR1szmT62ULe+uEW+Lvoht1pMWrOtTmHDUMW4ooQWKqIumTk+9nuqbnZOmzQ80q3Zc5u0YWejHlhYobCU1BLU5ZWluuaZjbFhuU6bfvz85qQue+6af7Ry7JaEVmXsFrMaW/3Kth/4Pc9pU47DKpNJysuy6dY/JCbzxM+rvsUvQ+p0nLpmv5p9ARVk2XXzCx8m7L/2LUulSjaKtoASMgz5Q+FItzOhsJp8wSFV5+6sb1Guw6YWf0gF2TbJMOmDuCQkKf48+b6OH1eY1P0d5yqgbwXDhvKcVnn8IQUNQz99MbE1o9llJfrBmRP1WW1Tyu4qpZ51JQng4LnbYi3bbtG3vnKkzp46Wste3pLUFd7llaV6dv1OLZg5TlaTSf+qa9GMI4apel+z7vzTx1pJ0ieQdq2+oJ6+4iQte2lLYgtFZcV6+oqT1MK5E0APeQNhBUKGcp3tklGcVrn2UqcAAACAZJQBq31rIg6rucNxs+0WDc9z6DdXztI9r27tsMnVK+ccqfv/ui3ysNIkPXvlSVr0xLuqa05sNnGPy6tFVaWqGFuosGHIFwzr8spSfbPqSHkCoVgLBctf/kiTxuRr5erqTrvccbV23iwjDwuA9Kpt8iUlokhSszcUe3u1YmyhHFazRhU49djaz/Xj5z+MjVfZ1of45i9dOnXiCN376sdJLY2sq67XTS98qGPHFcHjWe8AAQAASURBVCUko6ytrtftL23RT8+fqopxRSotyemwnNl2i8YUOPXYpTPkC4Y1bli2JMUSYaLiE1p8gbAWVZbqhrMnqdkbUmF2pPWnn73yUayM1zyzUYuqSvW9uUfJ1dZn8cZdjQkPL6vKiuVu9Se1EvPGtjpd/9wm/fjsSfrjhzWxshxIZlmvinGFWlxVqv0tAf332s/b3tRv6HBeS04tU7bdknI7Rsc5Z9po3fj8Zi09razTea1cWCFJKZON3tvRoB37PXpw9baE5VSVFeuOi6ZrfHHH+2Kw2Ov26ifPb9btF05TXbNPI/Od+snzm1OfJ00mLTl1gu7986cKK7H7O4lzFdCXTCaTbBaznrj8BK14fVvK7j8k6ZYLpui+v3ySFK9S97uSBHBo8p22WEJzlt2sZS9vSXmdKEVaM3ryzX/p1vOnatphBVr/+X4df0Sh1myrU22Tj2QUIM2G5Tp0U4run9dU12vZSx9p+UXT5PKQgA2g+6L3WHLsloThuQ6rXB6+QwMAAEDqOMMB/ap9ayIbdzWqsqw4abxsu0WrLp2he179WLVN3pRdUUiRm/ZVZSUH/t5WJ4vJpN99a5bGFh1IIMm2W3TEsBxt3NmgxU++p8VPvqez/2uNVq3brqBh6Ie/+6cWP/meVq6u1l8/rtW500fr2StP0nfnHqV11XVytesPdHdjq7yBzltC4GEBkF6NralvAORnWfTAwopYvL+9fb9++uKWpAf166rr9fjaf2l4rkP7mnwd1jNr27rhSTU8GDZUMbZQJqnDuuyxS2fothe3aPGT7+mqp97XeSvW6p87G/X8VSfryctP1NLTIkkc0eS8uZNG6PBhWXpi3Xadv2KdFj76ts7+rzW699VPdPHM8cpuuxni8Ye0cnW1vrHqHRmSnlj3L61cXZ2QiLJ83nQtfXpjUrmkSH252+XVxp0NemBhhbLtlsg2Wbddi6pK27bPdu1xtWpddb2OG1eU9OA0fl5hw+h0O67ZVqcR+Q5JUsXYwk7nVdvk6zDZaFFVqVa0S0SRIvvjJ89v1hcNnpTzHUwaWiJdBzS0+DQ81yFPINTpdj914sjY/j1uXFHC55yrgL7h8vjVGgwpbEihsNHptW2t26dvfaVMM9rFa3e7kgRw6Epy7brtgqly2swKhdVhzK5ru05cs61ONW6vdru8em1rjWxms773b+Xyh8LauLNBn+1rTvpOCaB3tHZ2LVxdp9ZAKOllJQDoTGPbC4g5jsT3XXMcVjX5ggrS/RcAAMCQR8soA5S73RvYq9Zu1wNtb8DHP5i86dzJevBv1VpbXa+L47quSCUYNhL+3rHfo6fX79Bjl56geQ+tk8cf0k3nTtbtL29Jevi5rrpeZpn01DdnqsETiHXb8a/6Fm3Z7VbF2EINy7HH+gMtyLbHWnc5ZmxhUjcaUXN4WAD0OpfHH+tSJj/LpuJsu7LtlqQuDEJh6Yl12w+8rTq2MOnN8qg11XX67txyBUNGys+jOuqGp9kX1F63V1azWZdXlkpKrsse+lt1irf06nT7yx/FWht57NIZen9ng+68aJr+f/bOPD6q+tz/79mXrJMZdhMITCQkAQyyJ1FBW0E2Le3vCtxeILi0CPbXxaIWUIFWaXu7KHrbKii9P8F7W+uGS1vFVoKCCKlAACGCBGVNyDb7zJnz+2NyDjOZJUERIfm+Xy9fkpkzZ86cOc/3e+b7fJ7PMyo/J67NjfKaMHJcpbwnIHH3xmr+8t3xSLJMqy9EhllPqy9IMCzFnB9HupHVM4epvdSzrQYqy/L5885j6n631jZQ2fZZttQ2MK/t33qtJuU5CoXlDtsVKc93tF2zN0iybyTV91lV20CrL5Ry310BZS7NsBr58NMmemfFu3dFc6LZq36/et2571HMVQLBxaPeFQAZHnltH7dVDEq5bbM3yO/+8TE/+PqV6mPlTjsP3zJUVHULBBeJLKuRkf1tLH1pb0wrxUQo9zUtviD/b9tRSvNsLH+5hrsmDOJbv3tPvRdL5bopEAg+Px3d/7t8oQ5/ywgEAkE0ivtJezFKujHyd4svRE6auC8XCAQCgUAg6M4IMcolSma7CmwliVpZns+C8oFkmPS0+IJkWQxqOw2rQceiiU5Kc7Pxh8KYDTpVNOIJSKpLgIJJr6WqtoGVr+6jsjyf6rpGhl6RFdOeI5ottfXMKxvAgvUfcMOQnrx0VxmhsIxRp0Wj0bCrrpHFG6sZ2d/GIzOH4QtKbDlUz86jjQmFNBUFDlbPHCaSBQLB56C94CTdpMfjDyEDD7QJNJQWPOMH2llfOZp0k56gFOZH//shXy/pjVajiXFBSSV8sBp1GPVaTAZ4Ys6IuPFFIVlLsXSTnlf3nGDJpEJ+8+ZBSvNsVJbl4w+FMem1MWNZexTRx5rNtWg1GlbMKGbbxw3UnfWmrL5VhCLReAISnzV5eebdT1g6ZQhSWKaqtoEDx1u489qBBCWZUf1z6Gez8NDLe+Pa2yybWszpZh9rEpwz5d+2tOQOGkorIq9V6tR5TNWiTdlfMjFKR0KW7tB2RnEzCUkyg/tkEmg7J9HtqaLnS51Go7r72KyR117TwVzVPhYdaUYxrwkEX4AWXxCzQUdh3ywk+dwIlyhuHelGdtY1YjLoeGLOCLItBvrbrfSzWTt8HxG7AsGFwxOUqK5rYsmkQrXdYqJ7HOW+pke6Keb+7rvXDuKJOSNY+OwuPAGJdw7Vc+/zu3lsVqmIS4HgApJu0ie9D15XdYQ0kx6jThgoCwSCzqO06Ulv74xijvzd5AkIMYpAIBAIBAJBN0eIUS5RHOlGrilw8M6h2NYLeq2GXpkmWr0hNBoNOq0Gq1GH1aijn82CQa+l2RtUFxT2HW/m0VmlbNh+NGY/ZU471ceagEhrgnsnFQLwaaM35XH5Q2Ec6UYWTSxgxSux/cArChxsvH0sC9bv4N7nd7N0ahFwTkhz57UDWdL2Ph6/RHaaIU4gIxB0ZTqb+DrV4qPRHaDFFyLTosdmNdIr08ypFh9NngA6rTahUOKeGwt57K2DqhDl0VmlPL31SIw7RrnTzh/+YySr39hPY35OzPsmEz4o+/rFGwdiRC7jBtopH+TAG5TYWdfI/uPN6rgSTZnTjoaIQOQ3bx5k2ZQith0+d+wajQajXpvQvUVBEVZsOVTP4TNuNu05weIJBUnPdfRr2h9L9bEmthyKOK6MGpBDvywzk4p74w9JWAw6dFpN3PmFiJvIyk01rLy5hLVzRxKUZAY4rLz5g2tocAXIshp4eVEZOo2GtXNHqiK9aEHg+vkjqXf5STNFvtcsi55vXNWP6woctPoj53Ff1HlUWrQlcpYqc9pJM+qRwnLCbUxt5zTZYnN3aDuTbtRxQ2FPTHotDa4AFqOWX31rGMNys/EGJdx+iWyrli2HzrD/eDM3DOnFqRYfFU47eo2WtXNHcrrVn3T/x5u8LHl+d0xbPVHNLRB8MTLNBho9AUpzs/ng6FkevmUofbLNWI160ow6AlKYFm+IHUfPcqDtPtfjD52XEEXErkBwYfEGJB6fPYKaz5rpk2WhT5YZvU7LuIF2JhX35s39p9j9aZN6X2Nsu+dU7tWafUE2bK/jL98dTzAsgwybPzpFgzsgxCgCwQVEq4H180ciyREXA5dPIsOsp9xp59oCO1otwg1QIBCcF4oYJc0Uu76riFOStY0WCAQCgUAgEHQfhBjlEiXLauSRmcO49/ndvHOoHqtRx5rZpTxddYTfvHlI3a7C6eDx2SPISTfy4Cs1fHSyldUzh9Ejw0j5IAdThvah1RdkyaRC3qg5CUQSmPPL8rl7Y7W6n1Z/iOq6RrUiPBl5OVY23jGWh1/bH98a41A9sizz3B1jqTvrpdkTZN28Ueyqa+S59+so7pvF6jcOxLmjPHzLUK7I6ThxIBBcznQ28VXX4Oa+F/bExEm5085PbxnKildqGNI3i+q6xjjhQVVtAxrNRwzPzebNA2eoLM/n6agWPNGihCP1bv7vDVdi0MUKQJIJH6L3lUrksvLmEn7+xoGY1yrjTbitun3b4bOEE3h51Bxv4fHZI7hrw644QYrVqOMKm0WttO2ZYaI0z4ac1BMkQpYlVmzRfuzbWtvAvZMLeeT1A6x8dT+PzirlV1sPUlmWn9Rxpaq2gRNNPhas/0B9rMJpZ0HFQDxNEk9VHY45f2VOO4/OKuXujdWsmlGMLc3EAy/XxH2/y6YW851nt1HYO4MHp5dw6x/eA5K3aCtz2lk8sYBsq4FmT5BFE5xx25x1Bfjj/FGEZDnhYnOmuevfAmRaDCydWkSLL0iGRY8UDlPUN+KQEmW4QL8sC6W52Tz61kF+9PXBLJ9WTKsvqH7P1xQ44qqzlVZ0W9qJRkU1t0DwxXCkGwmGwzR5g1xf2IuQJOMLSlgMWsIyHDrVSo8MM/uONzN7TH82bD/K928YzDd/917CWG2PiF2B4MLS7AngSDMSlML8ff8pvj12AIdOtTKwRzo6jQZZlrmxqBc3D+/Lb946yPyyfE40R1q7RjulbDlUz/FmH5XP7KDMaVfvbT7P8QjXI4EgMWadFkO6KaHY2p5uRgciXgQCwXkRKYjUotfGFjcpYpRmIUYRCAQCgUAg6PZ0/UzUZUzfbAuPzSrlZIuPQCjMz9tcCSA2sWzUa/H4Q4wfZOeBacU89Ep8onPVzUMZ1i+btXNHUn2sibs3VsckfLMsBpZOKcKo01LhdLCltj7ueMqcdt6oOcmHdU3MLRvAtsNn45LGVbUNnG7xU/nMDvWxCqedtfNG8ds3D8Ylubccqufev+xm9cxhnapkFQguRzqb+DrV4osTokAkrn7ywh6uyrNRmpsdIwKJZsuheuaNHwAQs10yAUmF087auSNZsP4DPAEpTvigjDOTi3tT1CeTBeUDkWWZZ7cfTXiMy17cy/yyfL55da7afqf6WBMbtx/lvpuKWDTRicWg5UyLn017TsSJKxZNcHLntQP59d/PCe6sRh1r547k528ciGkpVOa0c8OQXtxQ2JM3D5yOOxcVTge9Ms28dnc5R896MOq0Cce+0y1+ttY2sGiiUxXczBnTP+H5VWhf2bOltgE0GiaX9I47L1trG9ACLy0qw6jVcv+Lib/flZtqWD1zGAvWf8BDr9Twg69dyf0v7FWdpZZOGcL9k4dw1hMgEApzutXPgByrulh8stXHlKF9YlofOdL0pJmNCcUvD04vibPR7aose3Ev37uhAJvFQDCsxRuQONnii3GuuTrPxqKJTobnZqPVagiFw2rVNkRitd4VW51d7wrExXSq7QUCQecx67QMcqSx/KW97KxrUu95AYr7ZaHTwMgBNp7eeoTSPBsBKTKudyb2ROwKBBeWelcAnQaO1Lu5a4KTkCSTm2PlHwfPqO5wyn3eXRMKmPXkNlbPHKa61VU4HZj1kWpqxfFTuW+ZOqwv9vMQkwjXI4EgNTqthlBY5vAZN70yzfhDYbxBiVPNXnpkmNBpRYsegUBwfjR7gwnXFhSnlGaPEKMIBAKBQCAQdHe6RybqMiJRJdfg3pkcOtUaI0RJnFh2MCLPRnVdU8w+q2obWPriHhZNdHLrH7bHvWeZ086re06wZnMtEwt7sHRqESs21cQlihVHAU9AIoxMZXl+wqR4wkTtXz/i6v42Nh84E7d9VW0DRxs8pJv0YvFf0CXpbOKr0R1I2I4FInEyv01okIqAFGbRRCc9Mkw8MWcEZoMOWZZZF+WSoqCIKJZNGcJ9bcKHe5/fzZrZpSydUoTLHyIkhXm95qSaTKhwOlKK0b57nZM5T50bZ8qddpZPK+bWP7zHkDbXj+Uv700o2gD4yU1DYsQoS6cM4Ym3a2OEKOe2P8CqGSV4Q1LceDW3bADT11RxdX8bC8rz+fDTJkpzs/nlt4bHtKpRiBbvJGtXpJDo+WghUNxzbSK9TIs+5fe7ZHKhuq8ffn2w6gRj0ms53eon3RRx9kgzGRjZ36aOl1lWI7k2Kx+fccfsM8tq4icvxp/rqtoGHnx5Lw/fMrTLj7n1rgBbautZNn0IkgwPvBTbfinauWbN5kPcM6kQty/EwVMuhudmqzG0q64Rtz92bmvxpV5Ua+3geYFAkJh6VwCtBlWIkkxM+dCMEtZVfcKSSYVoNRo1XsNyatcsEbsCwYXF5Q+S2db6r8kTVNsC9s0yq653qrhkaF9+8LUrOdXiY35ZPhu2H+WuiU7V7c6s16m/MbfWNlBZlt9pgZhwPRIIOiYM1CdwRZHbHu+TJURbAoHg/Gj2BklLIEYx6rQYdBqaPIGv4KgEAoFAIBAIBJcSQoxyCZGqkssTCKmPtW+/obCzrpEj9W7+WDma063+mIRrVW0D908potxpj3MXiG5boYhFKsvyuXdyIcfOelV3g2hHAWVxMBHJErXfu74gpsVQNE3eIJ81eal3B4SVsqDL0dnEV4svlHI7RZiQigE5VhrdAc5ELTL2yTLHidQUlNhcO3ckobBMvj2NFZtq4sYJJWG/pbY+pRhNp9Xw6t3ltPpCpJl0nG7xM/vJbXgCEsPzbPiCUlJBxtbaBmQZNi0ux+0PEQrLmPRa7n9hb9Lt3QGJ0jwbP/jalTS1VdxEj1fKeDq5pHdMa50yp511c0ey/ZOzADEin2TtipTXVR9LfC5TCYWavUG0Gk3S5wFcvnPinnBYZoA9jVZfkAxztPgkLeFr+2RbmFjYk0Z3gBZfiEyLnrAMH51sZdFEJ6W52WpySJkXXO3ERF0RJfYsOh33v7AnRohiNeoozbNhMej43b9fTSgsk2bUoTHp2bTnOPe9sEfdtsxp55sjrojZt5J4S0ZGB88LBILENHsDWIx6trRzrVKwGnUMz7NxqsXHE3Ouxu0PsfXjhnOiyQIHq1O4IIjYFQguLDaLkZMt3oTPWY1a1fVO+f3Yz2bBH5RodAcp6ptF5TM7eP674ylz2tFqiWkb6w+FafYG+fi0q8O2O8L1SCDoGCmcWrDZ0fOCz49oISboqjR7g6QZ49MLGo2GNKOe1g7WuQQCgUAgEAgEXR8hRrlE6KiSa+WMEvWxRG06ot1SohO30UnkFm+QH08qZKE/hFGnwajXEZTCeAIS/3vnOIJSmBZviB1Hz6LTaDh21svCZ3clPeZEiddUidpQioUNk17LJw0eFj67S1gpC7ocnU18ZZpTD8mKMCyZUOKGwp7IwKvtWuBURI0D7d1MAE63+ln47C4WTXTyx/c+Sepa0r5SFWJbhvlDYTLNekJhmcpndqjvFT0+lQ9ypPyMrf4Q6SY9d23YxeqZwzDoUotvXP4QazbXckNhrxixSTSJXEu21jag1Wi4ur8NiBXRtW9XpKC4wijivfakEgqZ9FrSzbqUnyX6+SyLgUE901NuH00iMWNFgYMNt49l9ev74xwF1swujXP66IoosecKSDFCFEe6kXXzRtHqC7X1uI6IdDZsP8qC8vw48dbW2gaWv7Q3pqrakW7kmgIH7yRIfF1T4MCRLhaXBYLPg9Wop6XNZa80N5t1VUdUUV1ACpNrs7L70ya1xRy0E0124IIgYlcguLDIyEgyvLn/FEV9syjNzcYXlBjgSCPTbOBrQ3qqrnf+UJhWb4h/HjrXwgfA7Q9xW/lATjT7YpLhJr0WX1DiG//1rvpYst+KwvVIIOiYjqQmQory5SBaiAm6Mk2eIFZj4rUOi1GHyy/EKAKBQCAQCATdHSFGuURQKrnaJ3aVBFkwHFYXzhOJQJK5pUQnkTPMev7zbx8xb1w+vTPNce0KKpwOlk0rYqRso3e2GYNWg9WoS5i8hkiyNJqKAgdzxydP1Cr9QtujCFiUKjhhpSzoanQ28WVLM8a5FymUt8VJMqFEudPOT6YOYWmCtixbahsIQ1I3E0VEkUjoptDeDckfCqdoGRYrfoken75/w5XJThMAeq0GX1DikZnDWLf1SFIHJoUsi4G1c0cSkFK7fCQaN7ccquc71w4CYt1QlHZFq2cO497Jhbh9EpmWiMjmv/5Rm3BMrHDaOdXiS/je0WNcqu/3dEvEzeZ8k6HJxIxbDtWz4pUarsqz8VZUi7TIuK9hxYziTr/H5YoSe9EJKKtRx1NzR7H6jQMJ29E9ueUwd147kKAkx83FDe5ATHukR2YO497nd8fE9jVtrgxi/hIIPh9arYaMtnvMUFhOOM+UtZtnou9311UdYVhuNieafRyud8dVH4vYFQguLFIY/vjeJ9x9/ZX8518PxMRqudPOyptL1N+UJr0WnVbDuIF2ygc58AYldtY1EpLCfPhpE0P7ZaHXRpzkypx2Trf4+Kw59v4q2W9F4XokEHQWDa/tORHzm6TCaWfRxIKv8Ji6LqKFmKCr0+wNxq0PK1iNOlqFGEUgEAgEAoGg2yPEKJcILb5g0sRumdPOLaX9WD1zGD9+fnfC6vuOksgLr3Ni1GsZnptNTrqRZS8lSljX89ArNVSW5fNZoxedRsMri8pp8gY43erHpD/X3qE0L1Lx9vKiMj5t9JJtMdAv28JDm2oSJmrLnHbSjfqkbYI2bD8as72wUhZ0JTqb+OqVaeZntwzl/hf2xMRJudPOT28ZyopXIvF198ZqKsvzWXidE5Nei81qJN2sp8HlT9kCJ5Gwo8LpYM9nzUDqNjPtn8+2GJKK4NqLX6LHp6raeiqcDrbUxgtzKpwOqmrrmT68L79+8yBbaxsozbOlbJnjC0osWP8BLy8qS3nsyVxLjHotFU57jMinuq5JFcO0Fys8NL2EoCTz5v7TMY/PL89HgybuWKNFelajjmdvG8vKdm2Qyp12lk8rZvaT2z5XMjSVLX1VbQPzE3zvW2rrCUipv++ugBJ7zd5zYpTK8nz+868Hkoo3Rw3IYXJxHx7aVJNwLlZo9gTwBiT+7w0F3D9lCDqNBp1Wg72LWG4LK3HBV4Veq8Gi11JR4KBPljlOOAbxjl3KY7eXD+SqWdlx99Ltq4/7Zlt4bFYp9a6A2g7Nkd41r3ERy4IvG78k8d3rnAljtaq2geUv1VBZnk91XSOnW3z0yzbz72u3q78Zy512bhjSizH5ORypd3O82UeZ087iiQV4AxIrX90f956JfisK1yOBoHOs2XwoThyviNUf6gZi9YuNaCEm6Oq0+oL0yTInfM5s0Ik2PQKBQCAQCAQCIUa5VMg0J0/sbq1t4MGXa1gzq5Q1bQvnFU57jKtJR0lko17LrD9s49bRebT6gikT1vd8vZDb/riDR2YO46ev7WN4bjYTBvcEIsniKUP7EA7LzH36fdbPH80AuxWXP4QvJHHnNYO4rWIg737coFovK4uJOWlGVs8cxtEGD03eoNpyZMP2o8we019N1irOMA3uAJxxiUVzQZegs4mvPHsa//l/rqLRHaDFFyLTrMeWZowIVb4xLOHjCsfOes7rmMqcdhZOcPL+J5HxwGxI3UZGEXRUOCMJwptK+sQk+9o7O+XlWIHYFl1/eOcwa2aXAnKsM1OBg/llA1i0oZobi3urY1Tyljl2lk0rpskdYN28UaQb9Uwu6cWgnhlxbhb7jjcnbR/m9ocYnmfjnkmFnGj2sWRSIRkmPcsTCPa21jbw0Cs1rJxRzN3XF/Bpo1cdxxZtiDhCVZbnU1mWT4bZgD0tIhJa+sIePAEJT0BizlPbWD1zGEsmF+L2S2RbDKQZdTR7A/zPHeM+VzK0I1v6ZPNDSzdZFOqbbSEQlHj4lhJ6ZppJM+oZkWdjeJ4tpkUAoLr3rNxUk3QufmBaEXqtlmUv7o0RVSnJ7q4wXwkrccFXiT3NyBmXj1UziglIMpVl+cwZ018d05W4TSSyzLIa+OXfPoqL30TVx1nWrn9/KWJZcDHQaDS4/UFK82xUluXH3IOtqzqiOtGVOx30yDARlMK8vKiM+U/v4Fijl6raBmQOsGxKEb0GmvEFJb45oh/BsMyMNVuTunS2b7sjXI8Ego7xh8LsrGtS29+1j9eO1pUE549oISbo6rj8oeRtegw6XOIaFwgEAoFAIOj2CDHKJYIj3cj4gfak7iZb2iomBvVM51Srj/nlkcV3JZmbrOpfId2k5/f/fjU5aUbqXYGU27oDIZ6ZP4pf/f0g3x47ALNBG1fpVlHg4I+Vo0k36JA08PBrB2KSchVOBy8sHM+JJh8nWnwMyLGqi/7pJj31rgCNnoDamkdp7ZPIGUYsmgu6Cp1NfPXKNMeITDp6XCHdnHpI75dtaWtpE6ZHugmjXsvpVj9Th/albJADi1GX1LVEaTVT5rQzt2wAUx6rYk2bSARI0bLHwc1X9WP9/NHsOHqWdVVHWLQh4uwyry1hkZtjwe2XeGrLYTwBKcbFon3LHJdPIttqoNkbYPaT29Q2QFaDlh/dWMj7h2MTkP2yzNxS2o9/+/17cZ+p3GnHbNCp7i3f/59/UVmez5ShfWKEMtFsOVSPJxjmTKufH/3pQ1V888tvDVcXchdvrOaVReUM6pkOwEMzSvCHdquVbwvWf6AmRvq0jWu5pKX87lJVtre3pU8kClo00RknvEhLsmDUFZE18NqeEzHfa/s2HwqhsJxykT4QknngtT0Jk91Lnt/NsqlF6LSay1ZIKazEBV81WVYjre4AYTSseGVvyrhVkmbKuGcy6Jgzpj8LygfGCFfgXPUx0C2cQkQsCy4WGiAnzUh1XWPSdlomvZae6SbcwRCtPokMs54/LhjN8UYv3/uff7G1tgEZqGvwMLh3Bn2yLXx82pVUiAKJ2+50J9cjgeDz4PGHUra/84h2Ghcc0UJM0NVp9YWwGBKvRVmMum5TBCMQCAQCgUAgSI4Qo1wiZFmNGDsQlLT6gnx61sPeT5u5IsfKtOF9+fHkQk63+OmVaaaiwJHQ/rPC6WDzgVMM7p3Jr948mLBVR3tMeh3Dc7M50ezl1T0n4ttwHKoHeT8/u2Uoy9tVh0OkBcSqV/ezakYJpXnZMQuASkK+2RNg8cZqtXJt0URnjDNMdEJ1/4kW3P4QPTNMYjFRIEiCUatN2dJGp9OweGM1j84q5ddvHqS6rolHZ5Wy/OWIC4gj3ZiwjUyF08ED04v5rNELoCYBs6NiMXnLnnoeeHkvpXk2qusa1aSEsvh5Q2EPltw0hEAozO0VA1lQno/NasBq1OEJSFiNuqQtc37+zWFo0PBU1WEMOg2jB+Swqd14Vea0k98jnSU3FnLP87tjPtPSqUPQaOC5O8Zi0mt4cWEZKzfVUNQnM+V5bvUG2Xu8mbVzR7Lm7dq4hdx180bFWMF/0cRIR5Xt0bb0qdq9RSdwy5x20ozd4xag2RNg+Ys1cQKjRG0+ICLeTHUO3f7k7mI7jzbiC0qcafXzSb2bXJuVXpmX17wlrMQFlwJarYb7XtjTYdya9NpOj3sATd4AD75S06FTSFdobSNiWXAxWbVpX8p2WmkmPcdbvFiNehY+u7PN6dPBoolONtw+ltlPbqPVF6KfzaIKdT9v253u4HokEHxebFYjv2prhxqN8vfK6SVfxWF1aUQLMUFXRgrL6rpNIiwGHadafBf5qAQCgUAgEAgElxrdIxN1mWBrt2jWvrrdbNSx5dAZznoCvFD9GSPzc5DCMj0zzXgCIR6aXswDL8VWkFY4HayYUYw3IPHLv0dsy0vzbCkT1u8dbuDDukbumVTImVZ/0qTbltoGWnyhpAvdWw7VEwrLSRcD21spl+Zmq0mEZIkF4ZIiECSn0RNgfpvYrL0gY35ZPo3uQIxopL0A7NbReax+Yz9X5dmY3+ZaorSh+c+/HeCbV+fGuIA40o1cX9iDtw6ciYnf9mytbeDeyUMA2LD9qJpEnFjYg3smDeHBl2vinJfWzRtF5TM7UrYv0wKTh/Zha20DSyYVxjg4RY+fbn+IoVdk8drd5XiDEoFQmK0fNzDnqe08MnMYT289ooplttY2MK8DwV6WxcDNV/Vj6Qvx7hhbaxvQajQxrjHw+RMjna1sV8bSYbnZSc8XRBJC1XWNLJ5YQLa1e1Th1bsCCd1+IHJebi8fqLqgABh0Gk63+Kiua4rbFuDB6cUJ96XMW79440DMPNzRvHWpJb2FlbjgUqA1KCV1qFLa8yiOXZXl+WzYfjRhi5DoOQfAHwx3OJ5e6NY2X1WMi1gWXCzCMikdxRZe5+Rfx5oY1i+LFl+IjbePZdaT29rmZpmbhvZh9cxhEfeUDJO6X9F2RyC48ATDYapTxGswLNr0XGjEWCboyrgDEdcTSxIxitWoxyUclwQCgUAgEAi6PUKMcgnR2er2B6YW8/TWT1g2tZiHNkWSuFajjjuvHch9Nw3hXsDlC6HTath+pIHXa05w3ZU9+eHXBnPHNYMw6bV8bUgvfslHMQk6JWGtVJDeAx32DO7IbrGjhe5ox4AG97n2QckS0MJaXCBITppJz7fXvU9leb6akFPEJHdvrOaPlaNjRCPtBSTK35sPnInZrzIePbP1SKzYrcDB0ilFyHQ8Vhw766G6rpH5Zfn0zjSpif8Vm2qSCkg23j6WoBRO3r6snXBEWVgdmWejT7aZVZv2sWZzrbrfcQPt6LUaTPrIQsm3x/VXx5nKsnPJSqUdUSIhXrnTji3NiMsXStnK50JVnHe2sl0ZS080+zoQBRXSL9uitk7rDqRKyFqNOvpkm6muSt5aILpFwNbaBoIhOeG+Ps+8daGT3hcCYSUuuBRo7YSd96qbSzha76FXtpmr2oR47eN4flk+eo0GiMxZ7x5OPG5Ht/C5kK1tvsoYF7EsuFh4A6nbfhh0GlZu2sf/3DEWb1Dig0/Osm7eKG79wzb1Xq53phmb1RAXX6LtjkBwYXF10KZHJI2/HMRYJuiqKPfsFkMSZxSjrlP39QKBQCAQCASCrk23FKM88sgj3HfffXzve9/jN7/5DQA+n48f/vCHPPfcc/j9fm688UaeeOIJevXqddGOq7PV7Ss31bB65jBWRglRlAWFX//9kLptmdPOqhklSLLMQy/Htii4obAn908ZwvEmb1zCWkm8+YPhmOq0RKSbE//gUOjMQrfqGHDapT6WymVBWIsLBImxGHVcnWdLGDsVBQ4On3GRHhWT7QUkyQQlSVvwHKpnxaYaKsvy6ZmZeqzIzbFSmmdjw/ajLJ5YwIL1H7B27sgYIUqihdG1c0em3K9yzL6ApL4eoLqqMW58TCTs+/0/D8d99nVVR3i0zdkk+jOXO+387JahmPVa6jznxHOJuFAV5+dT2Z5lNXK43p1ye38wzE0lvbvV+JkqIVtZnt9ha4H28dTg9lPhdMS5rZzvvNVZ15uLjbASF1wKpJtS/0S5wmZBksN8UNfIDWk9UzpC/ejrg7mmINJubtpjVUn3qYynF6q1zVcd4yKWBReLbKuR//x78rYf904agicg4Q5ILNqwi9K8bPId6dx57UB+/fdD+ENh3IEQxf2yEu5ftN0RCC4c2RYjv0oRrytEm54vDTGWCboirjahSbI2PVajDrc/hCzLaNoE4gKBQCAQCASC7ke3E6Ps2LGD3//+9wwbNizm8e9///u8+uqr/OlPfyIrK4tFixbxjW98g61bt17U4+tMdfuW2gZ+PLlQFZdE25PfXj6QLKsBvU5LsydASJYJSTKLr7+Se2/SYdbrOHbWw4/+/CGN7gAL1n+Q9FhafEFOt/gSJt0AKpx2Trf4qShwJFy4P9+F7uhF845cFoS1uEAQS7MnQCAUZtFEJwsnDGLrxw2sqzqCJyBR4bSzoDyfDz9tYmLfTPU1Jr02Zh/t/1boqAVPZVk+f605ldRNpMJpBxn6Zpm5Os+Gue19ouM8meClowULc1sFTpbVwG83H4pzOUnVvuHnb+xXxQYmvTbGmSUUlrl30hD8IYkzLj8D7GnkpBmRwzKLNlYzb/yAlMd1oSrOz7eyvaPtbd1wEdSRbkw6T40faO/w2m6PTqvhhzcOJoyctI1dItrPW511vbnYCCtxwaWAQadNfv9Z4ADArNVxQ2EvjDpt0ljcWtvA0ik6HptVSoM7EON01J4Ms+GCtrb5qmNcxLLgYuEPhVPGoD8UiTub1cC6uVfjCcocrndxfWFPfv33Q5j02g7vXwQCwYUhGE4dr6JNj0AgOB+Ue+NkbXosBh1hGTwBibQOxOYCgUAgEAgEgq5Lt7oTdLlczJkzhyeffJJVq1apjzc3N7N27Vo2bNjAxIkTAXj66acZMmQI27ZtY+zYsRf1ODtT3e4LRJLO66qOMDLPxlW52WzYfpRR/W0cONFCrywLVoOOM61+3o1KSpc57Sya4OTP3xnH0QZP0oX+Mqed6mNNrKs60uZMIMe253A6mF8+gL/s/JSHbxnK/S/s+cIL3dGL5smS4grCWlwgOEeiNgAVBQ5eXFjG8SYvH9Q1svDZXXgCEkFJptxpp6q2Ia4dTfu/HelGVs8c1mG8+UNh1lUdYc3sUrTQbqywM788n3/7w3uU5mWzaIITrVZDmdMeE+fJBS/njrc95U476abIoofLH1KPO1rkooyPHbVv2Hu8mbVzR7Lm7dq47RZNcGLRazHrtSzaWM2WQ/UMz81mYmEPivpmxfVb/+hES5wQr9kToN4VoMUXJNNiwJHWOVHI+Va2i0r4eLKsRh6cVsyDr9TExojTjqGDuaa9MDJyzempfGYHP/z6YJZPLcLtl/AFJYznOW9dyKT3hUZYiQu+aly+AKtuKWHpi3vj4nbRBCd6nRYpLFPv9ie1BVfwBiT12v2i4+P53H9eCjEuYllwMXB1YL9/xuWnwmnHqNOi1WrplaHHatRhNer5nzvGIAM5aeKaFAguBh3Fa0fPCwQCQTSt/tRtehTHFJc/JMQoAoFAIBAIBN2YbnUneNdddzFlyhRuuOGGGDHKzp07CQaD3HDDDepjhYWF5OXl8d577yUUo/j9fvx+v/p3S0vLBT3WjqrDWnxBqusaeXRWKbY0Az//60dce6WDHhkmntpyOCYZrPT/vXtjtZqsnTq0D4N6prHi5mKWvxSboFOStErLngXrP2DT4nLcgRBuv4Req6Gqtp6N2+tYMaOEPhdwoVtZNG/yBC+Y44pAoPBlx+1XQbI2AFsO1bPy1X3cNLRPjLjiwIkWVt08lGUv7Y1rR6P8rQEOnGzl2dvGqm14UpFliYxXGjRMHtqHeW0OJCa9ltMtfq6wWfivOVez4+hZntxymJ/cVERpno0B9jQqnHa21DYkdUPKthiYV5aPTGzLnDKnnXll+WRZDVQUODjdeu57jRa5ZFkN/PJvHyW1ol4yqRAAWYYn3q5NuJ0WDcunFcVUuT/3fp16fqLPr9LKJ3r8SyQWuqbAwSMzh9E325L63J5nZXtXrIS/EHEbkiV+dnMJroDE8SYvEBFfddS/OvpaqihwsOrmElq9Af7njnEx89zxJi//PHgmqTtQonnrfF1vLjbCSlzwRfiicWtPMyHJMLmkN/PGD1DnlFMtPjwBiZ+9uo/l04oxabUdJrGVOaqz4+OFEvRdKjEuYllwPnye2M2wpF5SyDIbWDihgCmPVanuROVOOw9OL2ZnXSPvfXyW1TOHpdyHQCBIzvnEbYY5dbx29LxAIBBEc65NT+KxQ3FMafUF6ZVpvmjHJRAIBAKBQCC4tOg2vzSfe+45du3axY4dO+KeO3nyJEajkezs7JjHe/XqxcmTJxPu7+GHH+ahhx76Mg4VSF3drriWKAmvh6YXU13XxM9uHsr9L+5JmnRV2lEorQcsRj0/fXU/D00rxhuSONrgwaTXUn2sSRWiQMRO8bMmL9uPnKU0N5uAFKbc6WDG8L6cbvXhCUo40owM6pkOnHMAOFzvPi8HAAVl0Xx1F0uoCr56vuy4/SpI1QZgy6F6fjJlCG/83wpavEHSjHpOtvj49trtzCjtx8LrBmHSa/nJTUPwBCS8wYjYbH5ZPv3taTzw8l621jZQmmdLmmQvd9rJMOt5eVEZD71cEyOEUyhz2inNs1Fd18j8snwCkkRpbjbHznpYOMFJmOQtggIhmbs3VlNZnq+22Ykep/5YOZo1ba3NFKIdXvQ6TUorar1OS7nTztB+WfzmzUOJz2NtPVJYxhU8J1y4dXQeKzbVxO27qraBpS/u5bFZpWRZjUnFQu8cqufe53er26Wib7aFX3xrOI3uAC2+EJkWPTarMeliTlerhL8QcWvV66mqree1PSdirtFFE50p20v1yTLz/HfHkW0xqknokCRHHA/aOkhlWY30zbZwU0lvxg20s+ylvXHCo0TzlnCxEXRlvmjcSsDSF/eknFM8AYljTR6y0gwpW/qkRyXWOhofL6SgT8S44HLk88SuzZq8HV5FgQOtFiqf2RHTJquqtoEHX67hgenF/PyNgyx5fjerbi4h22K4bO9XBIKvivOJW4tBl9J1siO3MYFAIIim1RdCA5gMiddzlDGloyIQgUAgEAgEAkHXpluIUY4dO8b3vvc9/v73v2M2Xxgl9n333ccPfvAD9e+WlhZyc3MvyL4h+WJ4tGsJRJKpvmCYyvJ8Pmvypky6Rrsb+ENhXD6JN/efZtboPCwGHQuf3ZX0eIKSzJrNtViNOh6dVcqv3zwY817XFDj42S1DkcJyXPKgsw4A7elqCVXBV8+XHbdfNorQy+UPkm01EgiFafSktvk/fMbNxu11LJ06hJPNfk61+Fgxo4S7NuxizeZappT05jvXDcJk0GIx6pjyaBUArywuU2O8vYOKQpnTzsqbh/LTTTXMGtM/YdJQeU1lWb7qILJ0ShEL1n+A1ajj8dkjmDK0Dz0yTKpLSjRpJh2egJSkhU+k0uZkiw+rUacmQqKPt9Gd+vx4/CF+/s3hfNboTbmdOxCKqfZJ3lYoIjSpdwUAONHsSyoWeudQPSdaIiKaVOPa53FW6UqV8Bcibn2hMD0zzXHXV6pre25ZPt/83XuM7G/jsVmluANSyu9BOedrOpi3ols2LZ1SxM66RlZu2qcm6YToUtAV+KJx6wlIHc4pLl+IK3tlUPnMDjbcNpaH2gkEy5x25o4fwNIX9vDQjBJ1vEw0PkbHZZbFwC++NRyXL/SF7j+7olOVoOvzeWK3V6aZlTNK4sSYZU47y6YWcfPjW2OEKApVtQ0EQzIQEVDXu/wsfXEvK6YX0+D2k24yYDXq8EkSHl+IbKsRXyhMi7djYW57Pm+7RIHgcuB84tYdCKV0nXQHRMJYIBB0Hpc/iNWoQ9vW/rg9yhqKyy/GFoFAIBAIBILuTLcQo+zcuZPTp08zYsQI9TFJknjnnXdYs2YNf/3rXwkEAjQ1NcW4o5w6dYrevXsn3KfJZMJkMn2px62IMT5r8vJJEtcSAG8g4jLQ5E2ddI1ug2HSa0k369THq481Ja2QqXDa2VXXCETcVZ7eeiRO9PLOoXru+8tuJg/tE5c8OB8HgPZ0pYSq4KvnYsTtl4UiSNh5tJEn5ozgl3/9iC21DaydOzLl60x6LVtq61mxaZ/qTrJogpM7rx3I7/95mFuuvoKfv3GALbUN/O+d49TXuXznxhhPQIpzJ+mfY+Vki4+fv7Gfwr5ZpCWxZVVQxp+ttQ2EZVnd710bdlFZnk/fLAtLpxax6tX9MYmMNKM+ZfWeUa/la796h8klvVgxo5hlL+6lqrZBPV57B+0b0k16/nnwDH2yUicz/KEwb390QnXRSNZWSKHJG+DBV2qYNTov5XanW/2cdQUw6rXY0oxxyZEL4azyaaOHVl+IFm8kyZpu1nOFzZryNZcSFyJuW33BhN9Z9LW9ZFIh/mAYq1HH6zUn1bn2nUP1nG71s2LTvk59D6nmrWTCotfurqDFGyDNJESXgq7BF4nbZk+A1k7c06ab9RypdzO4dwav15ygNM9GZVk+aSY9bn8o5p7ZH0o+XqYS/Cmuf58XIawWXG583tg1ajWsurkET0DiSL1b/d16pN6dUIii0Oo7F+thGXYebWT5yzXcc+Ng/n3tdgp7Z7B8WjEtXolf/G0v1XVNVJbnU5qbzWeNXpo8AXpnmi+4qFcguJw4n7ht9cX/roteZ1o/f/SXfLQCgaAr4fKF1FY8iTjXpkeIUQQCgUAgEAi6M91CjHL99dezZ8+emMfmz59PYWEhS5YsITc3F4PBwFtvvcXMmTMB+Oijj6irq2PcuHGJdnnRyLIaOdniS+lakmbS0+ILJm1zoaA8X+a0c7rFF/O4Uh2uQRNjc17mtLN8WjGv7T3B2rkjSTPqGZFnozTPxrqqIzGLi1tqG5jX5r5iNerUhUJ/KIzZoKPJExSL7wLB5yBakHDPjVeydsthVfQV3ZKmPUpLL4h3J1kyqZCgJMeIyzKjWhkoYjWF9u4krywuA+D1vaeAU5TmZqf8DNHj01l3IOF+rUYdLywcHxFO+IJkmg0Ew+GU1XtKwnJQzwxWbtrHVXk25kctrEqynEJo50Cj0SDLMvY0U8pWRO9+3BDjotHReGvW65hflk+PdBNWoy5pIkav1TD7ye3q3+2TI6naMCkOLKnG1boGN1W19fTKNOMPhXEHJE7VeSlzOsizp6X8DF2JNJMekz5xclu5BscNtONINxKUImKpx2aVqvOXJMvsPNqY8PWd+R4gtbBo+Ut7P5dgUyDoitS7AmRYDCm3ybYY0AD9c6wsnlCANyixs66RxRur+eW3hsfdNyeL0wsh+OsIIawWdHWaPQG2HWmguF8WkiSz8f06NaY6Ek1Ht9EKhsJqW9nvXDuIZ28by5yntrHilRoWTXRSXdfEo7NKeXrrkZh70oo2t6FEwpKLEeMCweVEplmf0nUyw6zneJNXCLUEAkGnaOlAjGJV2/SkFpoLBAKBQCAQCLo2qbNpXYSMjAxKSkpi/ktLS8Nut1NSUkJWVhYLFizgBz/4AW+//TY7d+5k/vz5jBs3jrFjx36lx368yYsUlilz2hM+X+a08/ZHp+hns6hJ6WTbKc8vmlDAyPwcljy/W31cqQ6/96ZC1s0bxRNzRrB27khGDcgB4P0jZ1mw/gNufXIblc/s4MO6Jl5YOJ7180ezaKITq/Gcy4rSyqe6rpEF6z9g4bO7qHxmB0tf2svxptStMAQCQTzRgoRrr+wZ4z60ruoI88vy42Jfaem1ruqI+li0OwlEWs1Eiy+UHuIAp1v86r/bE+knricnzajGfmfGHwW9NrGF69X9bXx4rIn/WPc+YRl+/eZBDte7uXtjNaV5NtbOHamOTaV5Nu7eWM2pVr/6WTYfOMOazbXquLNg/Qc0u4Msn1Yc91nKnXaWTStizlPbeHXPCXzBECtnlCTcbvm0Yp57v04dJ0vzbPS3W6lI8XlfrzlJ5TM7+PkbB1g7d6R6ntrv29tOpKIkR5o9EcFOSweLNqkWdT5r9PBZk5dX95yIGYs37TnBZ01ePmv0pNx3V8Kg03K61Z/0Gi132skw65EBvU7D+EGRa/ZHf/qQymd28NNX9/PorNKE3yN0bnGtM8IigUAQcZZKN+pSzkE9Mky0eIOEZaj6uJ67Nuyiuq4xEqeGzsepiEuB4ItzutXP0CsiTiVvfXSKu68voKLAAaS+Pyx32jnVViBRUeDg3cMNqri52Rtk5aYaVs8cRlVtA2kmfVKHzi3t7p2iETEuEMRiNeqocDoSPlfhdGA16pLGk0AgELTH5Q9hNSSvc9VqNZj0Wlz+5C5pAoFAIBAIBIKuT7cQo3SGX//610ydOpWZM2dyzTXX0Lt3b/7yl7981YdFszfIsUYv88vy4xKfFQUOFk0o4PG3PyYUkjlwvCVhUrrC6WDF9BKmlPTmoenF+IMSs/6wjcG9M2KS1Z6ARJMnSFiW+dGfPmTB+g8IhWVWvFITv+jX1vZjx9Gz5xb/jTpMeu3nWigUCATJiRYkeIOxP+KjBRKvLC6LE2tEO3JEu3lE2hZExGOLJjpZO3ckZ1x+lk8rpsJpZ8nzu1k2NbGIY/m0Ym79w3v86m8H1djvrCimzGkn3aRPOJ4tKM/nRIuPx2aVIoVllkwqJC/HqlbvRYtM1myuxROQMOoinylZ25xMq4Gfv76fq9qJWa7Ks/Hz1w8wv2wApXk2gpLMqRYfSyYN4c/fGcd//fu57R55fT+3trXbUY7l4zNu5nbi826preeJtz9m6ZQhcdvNK8tHl6C3cnRyJNMc6w4Q/X09MWcEZqMu6ZjqD4VZ83Zt3Fi8tbaBNW/XdthqqCvR4PIzfqCdxRML4ufIAgf33FjI7//xMSadllPNft79uIF9x5vV63vLoXqe3nqEyvL8hPvPMKd2cYAvJiwSCLoTVqMeX1hi1c1D4+agCqeDFTNK+K9/1NIr08zxZi/jB9l57o6xfHSylWe2HkFqawXXnkRxKuJSIPjiSG2/F2Xg9/88jMsX4u7rnWy4bQzDrshi5YySuPs+5X7yx3/eTUWBg6VTilhXdUS9NzHptVTVNtAzM9J6xOWT4kTU0SQTlogYFwjaIcs8ML04/reY084D04tBloVQSyAQdJpWXxCzMXVqwWLU4faLNj0CgUAgEAgE3Zlu0aYnEf/4xz9i/jabzTz++OM8/vjjX80BJaHFG0Sv1bB4YzXP3TGWea1+tf3EgZMtpJl0XJ1n462PTnHvTYU88toBSvNsav/fbIuBfjYLvlCIU61+7OlGJFlm9cxhal9gJVld5rTz3uEGqusaVYvk8QPtSS1c27f9WDZlCA0uP18v6kVpbjZzxvTHbNCxq65RbenT2XYGAoHgHNGChHRT/LAdbbX8YV1TTKsthfbuJBlmPVkWPU/MGcFTWw7HtMpZOmUIP55cSKsvwIrpJfikMC3eIGkmHadb/Mx+cluk0rS2HhlZHS+U/uMLygeSZtQhhWXePdygjjOKUGP+Mzu4dXQe89rGqdwcC0adllueeFcdj5RWX18v6kWF0x7jBqNQ4XSQk2Zk3bxRONKNCdvhGHRa3jxwhjcPnEl4bn/w9cH87LV9cXbvS6cUcbI5Uq277fBZ5ozpH/M6vVbDkud3s3rmMO6dXIg3EKbVF4wbVyEiSPneDQWsnTsyri/7L781POFxKckRR7qRawocvHOoXnWdam9P3761j4I3ICVN2mytbYgTNnVl0s0GfJJEMBRm5YwS3IEQbr+EXquhqraex946yO3XDOLv+0/S4pMYN9BO+SAHMpF2PYs3VqtzXnuuKXDgSO94TmsvLGpPZwQtAkF3QAMYtTrWbjnMz24Z2ta6LUS6OTIH/fKvB7hnUiF1DR6ksIwvGOZofTPr5o3i1j9so7I8XuSXLE5FXAoEXxyZSLvWqwfkcHWejbs27OLOawcyubgP/7Wplv0nW1k9cxg/nlyI2y+RZTFgNWipd/n5Y+UY3v7oFCeavHgCEia9Nuae1eWL3Kukm3U0dlDQkEhYImJcIIglDLR4A9w1sYAfTy7E5ZNIN+tw+yVavAFy0iJzpRBqCQSCztDqC2FJ4kqoYDEIMYpAIBAIBAJBd6fbilEuF3pnmXH5QzwzbxRZZgO/+OtHMVbDT2/9hNUzh9E700yjJ8ji6wvQajR4gyHMUiQRrAhBKpx2bq8YRM8MEy2+EEV9MlkzewS76ho5cLyZW8f0V5OolWX5lDvt6HWpFe7RbT/umzwEo17LyldqYhLHZU47j84qVfctFjYEgvMjWpCg1UREGIkEJ/uON/PDGwcTRo4RISgikLs3Vqt/v773JP1tVl7+8DOqorb1BCTuf2EvZU47owbkEArL3Fjci1v/sE3dRnHnKM3Nxh8K0z/HCkRaBq3ZXEuF085tFQOpPtZEaW426+aNwu0PxQg1osUvz942hkAoxBOzR2BPNxGQJIKSTFiW2XzgNJXlAwlDzGeqaGuzc6TejUGnpeZ4C8/MH8W8p3fECEFc/o4rYqsSuDit2rSPqwfYqK5r5PHZI8hJM7J27kgCUpieGWayLXqemD2C324+xNbaBp6YM4KFz+5K+j6hsEz1sSaee7+OW0fnUZqbzS+/NZy8HCuLJjrVcVo5JzarkY9Pu3D5g6yYUcKyl/YyPDc7oeuU0trnF98ajssXosUXJNNiwNOB2MTTjaxyM406jrf6+f7//oun541GliHLbEAGxg9yMGFwT3RaDYW9svj9lo9jxUlOhzqHteeaAgerZw7rlMAyOo4T7aczghaBoDug0UCj289/jM/n4df3828j87gix4I/GCbDbOB7N1zJh8eauP+FvTGC6nxHOndeOxC5nTNKqjgVcSkQfHGUBJNRr+HBGUW8f/gsvbIsnGr1cd9NQ9BqNMjImPQ6DpxoxXZFFkcaPGRZDHzzd+9SmpeNNximoq1tT/Q9a7o50rLL7Q/FOPwlIpGwRMS4QBBLQApjsxrwhcLIaNBqJPRaLVkWDWa9loAUcc4UQi2BQNAZWn0hHOmmlNuYDTpcQowiEAgEAoFA0K0RYpRLjGZPgHpXgBZfEKtRx86jjfzq7wd5ZOYwfrephvllA4BIstRq1PHIzGGs23qE6romKsvzGZlnIyfdSIbZQL3Lz9V5NiiPJIl31jWxUK9h9RsHYpKvFQUOHppezP/5/Xvqon6m2cDSKUUkNjo/R2zbjxAPv34oYUsIQHVPEAsbAsH5kWU18sjMYdz7/G70Og0LJwyKE5xUOO3cf9MQzrT6WD61iLAc6d8bDIVj3EkqnHbml+ezaEM1/71gdELHETjnfLRg/QcU9clUnUpG5NlIN+pIN+vxBCTOuPycavXTL9vCpsXlnGrxsfXjBn74pw+5dXQeZoMOnUaDXqulNDebNbNH4AtKmA06dn/axPArsvnV3z5iZ10Tj84q5ZE39scJaYZdkcXo/BzVlaJvtoXqukZufnxrTCJy8cQCHppWxD3P71FfbzGmrtJJN+lVMQhExqnS3GxCYZmCHulUFDgISTKeQERMEy3uWzzRyagBkePqkZF6AcbtD7HveDPP3jaWFZtqYsQO0YI9gHXzRrH0xb2q4MiRbmTN7FKyLMakTlXvHKqnvtXP6zUn1WPccNuYlMeUaek+twAhZDLNen71ravwBkOkmw00e4Kkm/W0+oIsfH43Q3pnsHCCk+q6ppjXbqmtJ9zmAJSXY+WtH1xLqy9IhtmAI90Yk+COnsMzLQYcaeeej47j6KTY+QhaBILuQHZbLEhymMUTnZj1erxBiRZfiAyznk8bvfy95hRrZpeyaENkblPmjSWTCjHrdSnjNJpkcVlREGkHJBAIOkZxH7mmoAf1rQFe3XOCXXVN3HHNQMqdDqSwTLpZjz8YZkifDLRaGOSw0uKTeHreKLKsBvYdb+ZbI/rx4ofHWfXqfjwBSRWhLJ9WzMkmH6dafJQ57Qld35IJS8TcKxDEYtRqCRNpB51m0hOWI622XP4gpgwzRq2GZVOLhFBLIBB0Crc/RK7NknIbs0Eb52ArEAgEAoFAIOhedJ9M1GXA8SYvS57fHeN8Uua089TcUTz61kE2HzjD8Nxs7ptcyIIyP31tFla+UkN1WxJ3w/ajXJWbzeo3DsQlcx+dVUrN8WbWvF0bt4C35VA9D7y0l1tH56mJTrNRx+F6N7k2KxUFjphjit5vbNsPQ8qWEJVl+aICTSD4nPTNtvDYrFJOtvhYsP4DKsvz1XZcStuXW/+wjQ23jeWhTTVsrW1QBSTjBtopdzrwBCRONnvREGlhIIVTv6fifJRjNajtYdZVHeHRWaWqI4hCpCo9jV6ZJqYN68OM4X354JOz+IISLb4guTYruz9tYtWr+4GI6GP8QDs6rYbK8oHMk2XWJXD9UP4uzbOxYP0HALy6uJzjbS102m+3cnoJz393HEFJRgrLmHS6pImLMqedsAzVdY2smV2KBg1PVR1WP+Oyl/cmHEvv3ljNzromJBk++OQsv3nzEIsmOlO+T/WxJor6ZrGi7btJdOyV5fn0y7bw1DuHGZ6XzbyyAQSksHrubGmpXV6OnvVQXdeoHuP7n5xN6qJTUeCgd6Y55f66FGHIMOjQ2cw88HLsd1DutPPsbWOZ89Q21rxdqwono9la28Bd1znpGSU6avEFaQslsqzGhHN4+xZKShzXuwKdSpQLBN2RXplmwoEQkkZDUKtl6Ut742J22dRiHn3zo5h43VrbgEYDvTJNCWMqmVgsen79tNELQPWxJm56dAsj+9sStkETCATnyLIaqChwYDLoePj1A1TXNbFmdilPVx3hN28eUrcrc9pZNMFJutGMFI7cZ571BHAHJPwhGdDw+38eVoUoq24Zih7whCQyLXrK7HbGDbKz/KWauLk2lbBEzL0CwTl0Gg2hcJjHNtfGza0PTi/GoNUyIi9bxIdAIOgU7kAIcwcFQGa9cEYRCAQCgUAg6O4IMcolQrMnEJfEAqiua2L/8RYWXufkm1fnkpdjRSbSm/tMq58ttQ0smujk6a1HKM2zJWzhsLW2AZNey303DaGkbxZzxvTHbNCxq65RraDfUtvAvDbXgYoCB2aDFke6EbNRy6qbS/is0UuTN6i+bt/xZma3tfVRXpNu1PHSXeNw+SX6ZFoIhsPIcuRY3f4QOWlGfjJlCIfr3WRaAjEV48o5SFZRLhAIwB2QCMtEqkgtBiwGHWdafViMer45oh/Thveh0R1g6ZQi0ow6Glw+Mi0mPMEQZ1r9ZFmM7Kpr5M39p6gszyfdFD8FKAKW0txssiwGNv/wWgJSGHfAy4LySPuDZ7cfTSqoWDWjBBmZs+4A/R1pvPvxuVZhZU47j88egYzM2raWPgrP3jamQzGbQrTo4t7nd6ttb/yhMH4pzMFTLkb2t3G8yYdWB4smOGOOEZSESAENLj9baxvQApOH9mFr1JiaSjQCxIj7FAFLovdR7OYfm1Wa1Nlka20DS6cUYTVo6ZNl5qkth+PcU+6dXJjwtQomvTbmGA06DXclcNEpc9q5q+2cdBf0Gg0h4Gev7ac0z6YKuZQ57edv7Gf1zGERoVfUtRaNUa/ltMuHNxBGo9HQ6gvR4g1yptVPTpqBE00+fnxjId+7PuIYZNbrCMsyn5514/YH0Wu1eIMS7oBEtsVAviOtwzlOzIuC7opGoyEMrHx1X9KYnT2mf9zrfMFw3L1lkyeIKxDC5Yu0+dBrNXgDEh+5XGSa9Wg1oNVq2FXXxMpN+4DIGPrYrFL8oTBHG9zotBp6dScBn0BwHgSCEg9NL8bb5lKkOM4pTpzR95aegESzL0RIkvj2uh3tWm2l8cLC8cgy2NKMSWNuzecQlmRZxfwpEACENcnvhx9+bT/Lp0ViWSAQCDqD2y9hMXQgRjHocPmEGEUgEAgEAoGgOyPEKJcI9a5AnBDFatSpbgRbX4htq7PwukE0eSNV8qW52azZXEtlWXw1t7Kf2WP6s+LlmpiWHNFV/p6AhD8UpqLAwQPTivhrzUmG9s3mrBzgsc2H4tr6/OjGwSx4ZoeaYJ47fgAPvlLDkslDaPZK/Oy1fdw6pj9Pt7UQenRWKb9+82BMQjS6YrwzFeUCQXfmaIOb+1/Yk7A6fM5T2yjsncGyqcXctWEX9a4A5U47P715KNuPNGBPN7W5nATpm2Xm6jwbGiKmDtFuHtFjjiKuaB+3FU4Hc8sGsO3w2Tir1a21DXiCEt6AxFlPAJNeR98sM4/PHsFdG3axtbaBKUP78Nb+U+oCaCgs0zfLgjeYenFCcWmByGKGIrJbN28Uez9rVp870exDlmXOuv3869NGemb0whcMM2VonxgnmVMtPnxBSTG2iBHkKWNqIqKFMdHbeAISd2+sVh1rMs0GzEYdp1t8LHl+tzrGpuJIvZtmb5DXdh+PGXOV993zaTMVTnvC1krRTlXKMWZZDPzHuvcTuuhUPrODl+4q6zaJGVkDnqDE7LZ5qb3QZ35ZPr2zIq4ngSSWQSFJ5miDN06oVO60U1mejwZNXJup6wt7sGTyEOpd/jhnsoq2Su5kc1yyeXH1zGFYjTohUhF0aUJ0LmaPNnhjXmfUaalrcJNlMdDkCXKi2ctjbbEXc1+dQDT4930nkwomO4pXIRwTdGeksIwkh9X7wuj7qOi4ax9TmxaX8/reEzz+9sdqTP7s5qH0d6SlfD8hLBEIPj/eDuZWb1Ai0yLaKgsEgo6R5Ug7Y3MnxCj1Lv9FOiqBQCAQCAQCwaWIEKNcIrT44tsvVJbnJ6zO33KonrAs84OvXQmcS9ImS3Qm2090Bf2azbXk5ViZN34AdWe9FPbOpFeWiV1HG9lV1xRT0eYPhfH4Qzz/3fF4gxKt3hDpZj333VREk8fHH989QmHfLPU9v/+1Ak63+Kgsy49zZbn3+d384lvDE7rCvHOonnuf381js0rFgqOgW9E+qWXWa+OEKABVtQ2s3FSjOjpE/3tXXRPHm728/GGssEGxSLcadUjhED+9eSjLXtzDltoGKsvz2bD9KKV5NpZMKuQX7Vp+AWyprSeMnLCVCYDHH2nLo9Nq6JdtoU+WmVZvUG2do9NoGOhIJ4zM+0fOMjLPhjsQJNOSOsZNei0QSfxrgUUTnUwY3ANvQKK4bxYaTUSIAqBBQ06aiRuLeyOHYflLe3lwejE9M024fBLp5sjUd8+fP2TFjBL1PZKNpe3Hv54ZJkJhGatRFyPI8QQk9Zz86c5xTHusijKnnUdmDuPujdXqZ0j1GXtmmBKKTQBWvbqfF+8qY+UrscLCigIHS6cM4WSzX61G9ofChKTIMZbmZqufPcOspzQ3G6tRR7M3ddufrkQgHLHpSjUXLr2pCIAe6aa411c4HfTIMJER1HPnNYO4rXwgO+saee79Oq7Ks2HW65CBBeUDGZ2fg16rYUy+nbAsEwyFybIYGJ2fQ3Vdk3rNbEkxxyVyS7MadYzob+OsO8C+Ez40Go06l4pWIoKuRLMngF8Kdypmo8fVigIHAMebvchAkzdIWIalU4rQ6zQ0e4IYdFpGDYjEIqCO7QB3TShACkt8Uu9Jes+aKF6FoFrQ3QkDDa0BLMbI/VX0fVSq37MPvFzD/ZOHMLGwFyeavfz4z7vxBCWq6xqFqEsg+LLoYG5dNqWItATumQKBQNAebzDi3NuRM4rFoMUt2vQIBAKBQCAQdGvEr8xLhExzfPVJR9X5D043UO60qwvxyRKdnanyL3NGkmbrElSLpqoSXTa1iHcP1/P7fx5mRF42D04vYX5ZPllWAyPzbPz4xsFkmg0sf2lvUleWRne8K4zCO4fqqXcFxEKkoNuQKKn1+vcqkrawqaptYElb+5bof1eW58c5McC5hcaVM0owajWcaPFx09A+zCvLp1emiatysyNtv3Kzk4oi2rfNicag07J4YzWPziplxaYatRr98dkjONHspVemWbWD7pVhIjfHwkOb9jHsimwqnA621MaPBRVOB3s+a6bcaeeB6cUgw1NV8W1s5pfls2jDLkrzshngsPL+J2fZ82kza+eN4qFXauLGtkdmDkOv0aiPWQ06Fk10kptj4Yk5IzAbdOz5rImrrsjms6Zz1fdnXAFONfv4/b9fjRSWsWcYVaHHqTYnFEtb3+Ro0V/1sSauL+zBkL5ZqrBFSXR+fLoVgB4ZJvW9o1upKWg0MndNdLJk8hC8QYlgKMy7hxu45Yl3VaeqR2eV0ifLjE6rYePtY3mw3Wcvd9rZePtYJDm1U0tXIihJyGhStoKSNZFrLd0Uu5hW7rSzbFoRP31tH5sPnFEfr3Da+d87x/HBJ2e57Y8f4AlIWI061s8fRe1pF83eIP5QGJdB4mSzl7H5OQzrl81dG3ap32myOa69W5pSWf7M1iP85s1D6uPRc6kQbwq6CvWuAGGNjAwpYxYNqiNUhdPO/LIBLFi/g0dmDlMFnIqYcNxAOzqNhlZfgF4ZZn7/71cTDIdj7m2tRh3r5o7ilQ+PJ71nbR+vydpsCkG1oDshy/DY27WMGpBDhdMR85s01e/QLYfqCU2S+evek+w/3syzt43lWKOH29Z/AAhRl0DwZdDR3CoDLl+IXpkX9bAEAsFliKtNYNJhmx6jDndAiFEEAoFAIBAIujNCjHKJ4Eg3ck2Bg3eiFrMTOZ1EV+hLUpgHphXzwdFGtUVDdMuNVPtpz09vHsqqTTUJE9dThvbhtT0nEla0PfRKDVOG9lEX6R96uYb7biokKMnkpBs5cKKFlz48HtMGpLI8nxF5Nkx6LX/+zjgAXru7nIAURpZBp9VwotmHQadlV10jzd4AzR4hSBF0fZo9AZa/tJfhudnMGz9AFStogFybJcbdI1r44PKdEyso/+5IhOYPhdHotXj8Elf2zsSg06DTaNVKuTlj+qc81kTjSkWBA6tJxwsLx3O62c+9k4bgD0l4gxL2NCMWoxZfIIxGA1kWA70zTZxu9bNoQgFuX4ipw/rw4Ms1MU4uigDFpNMwqbg3kiTz01f3JWxjA8Q4tiyZFBmLooUojnQjq2cOo2emCbdPIttq4O8/uIZmdwCNRsNNJb3xBCSMei39c6z0zTbT4gkyPDebk80+Hn5tPzNK+zFuoB29VoPZoOOvNadU0Ui5085zd4wlGNXqRRHvvLDrU5ZPK2bpC3vihH0rZ5SwalMNb7YTO6yZXcqiDZFWandeO5BGV4DDZ9xc2SuDFm8QTZSYRnkvDTC/LJ/cHGucEAUioqWHXqnhkVuGpvyOuxIWg56TLamtgVu9Qe6a4ESr1bB27kgMei06jQZfUOKR1/fHCFEg0tpp2Ut7Y+bAO68dSCgss6ndnKk4Ep1p9cW5CrUmcEZz+YMsmuhURUu5Niur39jf4XXfWfGmaCkiuJRp9gZwpJto8qWOWbc/RPkgO+MH2nn3cAOLNlTHuDAkaw9S5rTzk5uG8Ku/fRQTp5Xl+Tz29qGUToLt4zVRm02Fdw7Vc6Il4tgl4kvQlfGFJLbWNvDRyVY23j6WD442qm0FO/od6vaH1BaSP39jP8unFvFf/z6CnhlmDDoNB0+24vaH6JlhEnEkEFwAOnIncPtD+LWalNsIBAIBRIRrEBGbpMJi0OH2Sym3EQgEAoFAIBB0bYQY5RIhy2rkkZnDuPf53aogxaTXxohPAlIkIfXx6VZ6Z5pBo6HZG2Rsfg5jB+bw01f3M7/NrSB6IT27g56/9jQTsizz7uGzCZ/vlWmOS4ApbK1t4N7JQzjd4uO/F4zh7Y9Oc7rFz7xndlDhtLNsWjEPvrIPSN4zXHE02LD9KLPH9Ff/rzgcTB/Wlx/96UMemlEiKuMEXZoGd4Bvj+3P8SgXDohUfv/3gtGs2BTrzFDutPPsbWMJy+d+2KebIwsBHS3+H6l38+z2o8wvy+fba7dTmpfN0ilF6tjRUUuZrHbjSkWBgwenFfNZo5eTLV6uyrPFuZGUO+3MK8vnh//7IZ6ARIXTwR3XDGRnXQM3Fvfm07Nelkwawv8NSdS7AjjSjW3tT2TufyHirrR27siUji33Ti7kxuJeuHwSBp2WGVf15aXqz1g00cmY/jlcYbew9MW9cce1fFoxx5u8/OrNg1TXNfHorFKWvxy7XYXTwdPzR1Pv8lFV26AKUCqcDl5YOJ6TzX52HD3Lw6/tZ/mUIv7+g2sISTKt3iBZVgM//PpgjtZ7mF8+kOF5Np57v45bR+dRmpvNJ/Vu7pk0hO9c5+SMy49JH3FG2bCtjjuuGchv3jzE14t60eQJ8uqeE9z/wl71uKKr9j0BiaraBirLByKFZarrmmJEDdGOK62B7rMg5A9LZFtS3/IYdFr+fe12nv/ueJY8v5vVM4fxH+s/YO3ckXFCFAVFaLRu6xEqy/OZMLgnqxO0t1L+XjKpkIE90mO+D1uC5FqWxUh1XaM6V66dOzLlPKw4FSUStrRHtBQRXOqkW/T4QudaqiXdzqRHp9Uw4/GtqttQtBAzVZvKh187wPC87BgBYGecBDPaORkmarMZzeEzbn726n4RX4IujTcg4Ug38t+VozHrtVyVm8XYgTksf6mmU/eTzp5pZJgMVJbnI8nQPyeNFm8Qs0FPvxwLlU/vIN+Rdt5xJISXAkE8HbXgSTPpMepSx61AIBAAqsCkQ2cUgw5PIIQsy3HFNAKBQCAQCASC7oEQo1xC9M228NisUupdAVp9QXKsRtbNG8Vjmw+pi+OOdCPP3TGOh17ey/6TrayeOQyTQYvXL3H/TUMISmF+fGMhhps0uP0SwXAYb1vrhkR2rGVOO28eOMWHdU1x1doKHSW1j531sPDZXer+pg3rg9WoY0ttAys37eOuCYP4xV8PpkwKAJTm2SLtQdr+rxzPyk01DM+zCbtzQZcnLMuY9NqErgr59jSu7m+LSYpX1TawclNNpHUNEVHF6Tb3B2sH1SkmvTbOVaHFey6plsxpCSLCkz6ZZp7/7jiCkowUlnnvcAPT1lRFxBkFDq7KtXFHxSBK82yqaKOqzfr5zmsHEpRkRuTZMOq1jM7P4dU9J1RxxriBduzpRqxGHVajnmUv7lEFKB2NR/WtAeY/syOmNcOv/+0qQmEZq1HHyk37EjqF7PzkrOoAtWiiM+FYtaW2ngde3ktpno3qukZVALKltp4Vm/apj9917SBkjYYHX9ob9z3OL8vn7o3VlOZl8+xtY1n9xn7WVR2JaWvUfvscq4Gdn5zFqNOx5u39Kav2lTHcF5Rw+4JJBYCPzirF7e9YuNAVaPYEyNDqCGoj124iF4Myp53NH53GE5Bw+0M8PX8Ux5sijgYdXXP+UDhGEJKyrQjQ7A2yoK0FAcQLQZo9AZa1E0x15hiAuER5e0RLEcGlTrMngCTJaDUaglI45f2rRgP/qmuMGfuiYyVle5DaeuaVDYh5rDNOgo702PhI1GYzGpNeK+JL0OVJN+t5ev4oZODethZZf/7OOK7ub+MKmyV5G8YCB8gysgynW/2EZVj2Urxg+JnK0fzb7987rzgSwkuBIDEa6HBubT/XCQQCQSKUNj1mQ2oBm9mgIyyDNyhhNYo0hEAgEAgEAkF3RNwFXmJkWc9VbDV7AvzkxT0xCwWrZw5ThSjP3jY2LnlZ7rSzYkYJsgw/fW1/jE25Fk3MQmB0YtQTkOIW5RU6qmiLfn5rbQOrXt3Pc3eM5dNGL2aDjgE5Vp7e+kmnKk7XbK6N+T9EWiHMKzu/FgQCweWITqPhsbdrk4oNlk8t4hd/PRjzXFVtAyFJ5uFbShiZn8OsP2wDoFemiXKnPaGbgtLWS9m3EmvRlXKKQCL6/SHiDrJogpOpa6qoLM+nuq4xcQuvTTUxoo17n9/NraPzGJFnI8OkJxgO8+7H59xFJhb2UMe06HHi2dvGxDihdDQe9co0x7kwKcKU8QPt3FExiG+PHcDONncQpZq+Z6ZZfZ/OjlVwTgCytbaB28sHApCTYeLjMy4WlA+MEeO0F42s3FTDVXk2ivpmpRTqLZtSxE3D+qo2+KmOK/o85aSb+PVbyVtOrJxRkvJcdhWaPEF0GvBLEiumF7P8pZqkcyFEKrv2nWhhcO8MoPNzoD8UxtOB/bAngRtN+0R1vSsQl7TrzDFcU+DoMHnQUUuRz5q81LsDcdXjorpccLGodwUIhUGvhbOuQELHPyVmNRoY0jeTnHST+lx0rHRWxJXotYm4wmaJu+4TtdmMPk5lrn3nUD31roD6GaNjKdFjIr4ElxMmnZbqo40xbV3TTHp+8+Yh/vDOYR6dVUoYOfZ+ssDBqptLeOT1/VQU9EADyMQLOqtqG3jgpb2snjmMBes/6NRvQSG8FAhSoCHl3ArgC4XJ+koOTiAQXE4obb86dkaJ3GO7/CEhRhEIBAKBQCDopoi7wEuYSEIqdkGuZ6ZJbVXRXogCkQW77YcbKLkiS33OE5C4e2M1z942hnllA/CHwpj0WqqPNalClFScbvGlrCZXFtoVthyqZ974AapbSoXTwZ/uHMdZdyDl+yhJgfb/j/53Z1oQXK6IZF/3ptkTwBNMLTYISXLC55o8QV7be5Kxg+z815wRpJsNaDQwryw/bmG/feIdzsWXUa9VBSzKuFFZnq+KHLIsBg6dciVsh5DoeO+dPASA53ce46m5o/jFXw8kdOi4e2M1RX2zEo5pzd7YmE/l2FLmtGM2aGNcmFK1B4tubZNovEmG8vzW2gYWtAlQrEYdfbLNVFc1pnyfaNFIVW2Duuib6jzKwE0lvTl42tWp41LG5X42S8rrKSB17ALQFXAHQlgMOkJhaPIFmFc2gO9cN4hmbzBuLqwocBCQwhT2ySAYCqvnMtU1p8yBJr0WvS617bBZr6MqQXW4kqjOshoTtv3o6BhOt/pZPXNYh3NGRy1FPmmIOJ1FV4+L6nLBxaTFFyQQCqPVavigrpF9x5spzbNRWZYfc//63Pt13D95CHqjFp3m3P1ldKx0JC5p38YyVZxdU+CItMhsR6I2m5B4rm3yBnjwlRo1lqxGHevmjeLxzbUxAjQRX4LLDV8oHCPqhVj3hej7SX8oTJbFQN8sMzuPNjKoZwa9EsRWNFW1DSyZXAh07rdgR8JLUdwg6M5ogA3bjyacWzdsP8qSSYU0ugMdxqVAIBC4A4ozSmoxiiJWcfslyPjSD0sgEAgEAoFAcAkixCiXMImSRi6fUsVvSppk7Jlpxu2LFZh4AhJn3YGY1gDxrzNR4bTHLCRWOB2MzM/h6gE5rHylJua5RAvtCtHJ3C219Sx/qYZ7Jg1O+t5wriK1/f+j/91RC4LLFZHs694o3//iiQUpt3MnEY6lm3VsORSJs+FtApGNt49VF//vnVzIsbPepCI0Jb6CkhQjYPEEJNZsrlVj/T/WvY+nre1XZXl+p1p4Vdc1cs+Nhax562DK9jLJhC3tk4nJHFuUY2xw+WP21VF7MMWlJNF4k4zo5xXxQWV5PqsStABK1ELnfIQvEKk4yrIayepEO4jocfm6K3uk3q8v1OF7dwXcAQmTXsdZV4AMs57FG6t5dFYpz24/GncNrZhezNEGD5IsY083Mr8sn43bj6asIL17Y7UqStFrNcnbETgd6LQa1lUdSXicSoItUduPpE5FBQ5WzijBZjV0KrHWmZYicK56/BffGi6qywUXFeUa9YUk9bpPJCZcNaOE061+1myuZcWMYjXprbxGQ8ft5jLM+pjn11UdYe3ckXFOgtcUOFKKvZQ2myeafRyudyeda/3BcEwsVZbn89jmePcqEV+Cyw23PxR3PyOFwzFzpxLDytzpC0pkWQyU5mZ36l5I+Q3cmd+CHQkvu3Jxg0DQEWFZZvaY/gnn1vll+YRlmVZf4t+cAoFAEI3LH0Kr6Xj9xKyKUbrH+oNAIBAIBAKBIB4hRrmESZQ0SjdHbuJdKRYIIhVn8dXZHVVWv7X/NPdMKmReq1+tkOmRYeKzs14WbthFZXk+90wq5NNGL7k5Fv5acyqps0r7HyNbauv5Xqigw+ryiqgqdKXaXPl3Z1oQXI4IK+nuTfT3f1+bk0gy0k3xQ3a5087pFj8QcSX68aTBTBjcE6tRp4pJgITtdCDW2aHJE+qUgKV9S5hkmPRattY2oOUjhudl8+aBM3HbKPtKlohoP25FO7YsvM6JJMsEQ2H1GP/83fGccZ2rku9Myx1oc4BqExF01gkDwGaNjNPjBto79T7KeUn072Qoc4HZoE3qUlVR4KBftpnSPJv6XRk72HempWuK+9pjsxrwhSSyrAaksExpXnZclbZJr+VUiw8Z+KCukdLcbEx6HVfYLPzoxkLqW/3cc+NgfjJFR5M7gC8UZlddI3dvrObqvGyWTSvmSL2bTLOem4b2iRNvVhQ4eGh6Mf/n9+8ldSNTEmyJ2n4o1/2yKUP4wdeu5FSLn2yLgf52K/1s1k6fi862FIHIHNToFtXllyOXs9OaI93IiRYfFoMuZayGkbGadGyprccfCrNyRgnegMSxRi96jYb5ZfmkmXTcfFU/HopyIwGocNq558bBPPGP2rjK8Pc/Ocu04X343g0FnG49F2d9UgiDlfOt1WrY+H5d4jHa6eDdw7FzSqr5ScTX5+NyvvYvZ9JMekztnOwkObX7wv03DVHv/Tp1L2TRd/q3YEfCy65a3CAQdAaTXpcyNpdOKUJjTu30JxAIBBARl1gMOjSa1GOG4oziEmIUgUAgEAgEgm6LEKNcwjjSjXGJx9MtfiqcdlWUkgiTXktVbX3ca6OrRasSVHdv2H6UUFiOqVwrzbNR1CdTTWqX5maz8NldLJro5F91jQmTaola9wCccflTVpdv2H6UZdOKeeT1/dxZMYiQHObVxeW4/CHSzXpuHZXbJReUhZV09yb6+z/V4k3pquDyxy70lzvtLJ9WzOwnt6mPHTvrZeGzu3jujjFqy52O3EQUZ4csi0GN9aI+mWqrrUT4Q2H2nWjplGhjS20988oGpNxXskSEcuzRleqegER1XSNX5WbHiGTKnXZCUuy+OtNyp9xpZ2R+Dn2yLYSRO3W+lL9DkkyF045Om3oBpn0LHeV4o0V3yntZjTrVLQZAo4kk2ALhMAuvG0RYluOOa+F1TiwGHbdc1Y8bCnuSYTaQbtanFK/0zDClPOauQobZQGODh3STnn0nW1g0wcmat2vjqkEXTyggjMy+483q6z6pd1HcNwtHhonjTV50Gg1hZHpmmCnNzeaxWaVUH2vi5se3qqKUurMe7plUyA/DEXcHT0BiV10jr+w+zuDeGdQnaQGiJNiUth/tRYqledkMcKTT4g3xoz99iCcgcU2B47wEi+fTUgSgpQP3HFFdfulxuTutZVmNHDzloke6MWmsLppQQIPLr1Zuu/whZJ9MmkmPI93IGZcfo07LkXoPPTJMLLmxkPsma/AEQgSlMFs/bqDymR08MnMYzySpDFecwICUcRZ9vpW2cLIsx91nL506hFueeDfmtR3NTyK+zo/L/dq/nNEAp1p8Mfcyb390mtsrBiadb93+ECa9FrNBx9EGNxogcTPKyP1ShknPzzvRjg5SCy+7anGDQNBZjFpN0thcNKEAo1aDuZsI1gUCwRfD5QthMaZu0QPnnFE8ASFGEQgEAoFAIOiuCDHKJc6iCU4AdWF1yfO7ee6OcdS7/GqiuT2nW3zs/bSZH904GGRZrc72BCSe236UlTeXcLLZR6MnqFbBbNx+lNlj+sckWZXE1GNtSdkKp4OeGWaemDMCq0HHDUN6IXMgZbI2GqNOy2LFdWFSIY3eIDarAb0mUuVa1DeLVl+QZVOGgEbD0hf3xrUjWDG9mGZvgAyLsctUOwor6e5N9Pd/z593s+H2sfGuCk47D84oxh8M8fr3KmjyBEk36zjd4mf2k9uoj3ICUYQYizZU89wdY3nw5RqqahvU6vJFE5wYdFr0Wi1aLZxs8fHHytFYjTosBp06rnRUpZplMXRatAGpk27KOJRoTPMEJDZsP8q8sgHMKxuARgN9syxUt7lSRAtRfnrLUH7z94/ol5OmJkQ6+hwDHGn8eFIhZ11+9DoNSyYVEgiFafIGWTWjBF8oTLM3iBSWee9wg/qeymc06rVMHtonoWtN+8/YXvyzfFoxP3/jAO9+3KCex+q6poRtKa4pcPDQjGIWb6zm1tF5cZWMC9bv4C8Lx1PYO1N9TbMnwF0TnAnFK3e1zS3dAZcvRIZZTygc5qq8bBpdAaYM7RNzDk+3+OmVaeKxtw6pc+GUoX3okWHiW79/jydmjyAnzUijJ4jVqMfVFrcajYaiPpk8NXckPTJMnHX5ybIYcPlDvPtxRAimXKNWoy7SAkSjiUuWtm8BkmbUcdPQPswbPyDuey7Ny1bbPn0ewaLSUqTeFaDRE6DZG0zYUgQg05z6uhbV5ReHzro9dBWnNatJh08K0yPDxNS4WPXRM9OEQaNh4bPvtm2vx6zXggySLGOzGtFrNez5rJmfvrqfOyoG0SvThEajBVnPuIF2ivpkqg4qCyc40Wk06HUa3v7oTFwsJIuz9uc72rnru+2cu040+eLiq6P5ScRX5+kq1/7l6uwiI9Mny8LiCZF2k1trG/j9Pw8zrF92XAyfavFhzzBS3+rndIuPq3Jt6LUafEGJsEycyFkRXq9+4wA/nlTIwVOthGUZT1sLw0TnKJnwsqOWWwJBd8AbluibZUk4v/bNNuMNS2jDHbfOEggEApdfUoUmqTjnjCJagAkEAoFAIBB0V4QY5RKmwR3AE5C458bBMQmpzQdOck1BD1bdPJSlL+6JSd6WO+2MH+Rg7CA7v/zrR9wzqZD7tFo+aTjXw/5bv3svshBnMdDsDTJhcA/KnXZ8gTC//NbwmLYcpXnZkfY5BQ7mjh/Av/3hvZjEmmKdnmkxkGnWs+toY8KEVoXTof5bcVhZsP4DANbOHan+uzQ3m4OnXLy2+3ic28KWQ/UsfWkvpXk21myujYhTZhSTY72wC7UXeyFYWEl3X5o9AfWHOURcUmY/uY3VM4fx48mFuHyR1iIt3iAGjYaH/3aQ2WP7s67qSEIhWrTrRr0rwK1/2MbGO8YSlGSa2wQsZoOOyqd3MKO0H5OLexOSZJq9Qf5x8Ayj+ucwrywfmdRtvSoKHOTaLPxl4XhcviArZ5TgD4U5Uu9O2NYHIDtJhZ1yzAdOtLDq5qEse3FvjDNMmdPO7DH9WbyxmhF52ay8eShuf4AxA+08/93xtPqCZJgNBKUwizfs4ns3XEnfLLPamqGjljuv7Tmhij7Wzh3J9Ke2qtXty1/ay/A8G/uON1PUN4vS3OyYMVIR8d3/wl4evmVoSheSgY40Vk4v4XCDW3XTmP3kNp69bQxhOSI6+8lNQ9BpNazatC/ueN85VM/yl2q4dXRe0rYO7Xsw17sCLNqwi9Uzh3Fv2/WUbtZzusXHog27+J87xnWLhEyLL0iGWU9Y1lB9tBG9VsPV/W2EJBlPQCLDrCcvx8qZVh9Thvdl8cZqru5vI82oo1emiavzbGrrngXrP8Bq1PHcHWPxBMPodRr6Z1jRaTUcb/Sx4+hZnnu/jkdmDqO6nXtYaV42vmCY5VOL0Go06rXrSI+fY+pdAe77y56En6d926fPI1jMaps3mz0BFm+sTlo9bksT1eVfNefj9tBVnNasBh2yDL/82wH+z6g8emaacbXFy6AeadS7fNjSTNS7ApQ57fiDEkcb3Pzwfz/kjmsGUu50EArLTCzsycTCnmg18PLu43x4rIl7Jw+h7qyHnhkmPEEJk17L0QYPY/JzmPpYVdI2WoniLNH5VtzF1myujbm/XTTRGTcXpZqfRHydH13h2r+cnV1Meh3Pbj/KjycNZvnUIkKSjMsfwqDTIGMm4p0SQQP862gjJVdkM36QBWQZk8FEQArj9odYMb2EoBSmyRMkrZ3w+jvXDeKR12MLIZKdo2jhZbL59nIV/wgEXwSTVsfjbx/itmsGEZJkWr1BMiwG8uxWnnj7EHdNiLSpo8dXfaQCgeBSR2nT0xEmg1bdXiAQCAQCgUDQPRFilEsQZWEsKIVZV3WYeWX56mK2ws/4CEe6kdUzh/GTKUW0eIOkm/RIsswjb+znnwfrqSzP56wrQD+bJa6H/eKN1Tw6q5QN24+yK6oSP8aJxOlg6dQhnGjyMaWkDzN/F6lAXTTRSWluNv5QGLNBx666RiYO7oFOB+MGObi6vy3mvcqcduaWDWDj9jpeuquMZm8Ai1HHc7ePJd2sx6DTcv9Ng7kq10aaSU+6Sc+SyUNY0Oqn5kQzhb0zCUhhemaYMeg0+AISI/Js7KprZOWmfXytqDfXXtmjUwu1yrl1+YNkW40EQmFc/pC6AOkJSPz4Ii8ECyvp7kP0oneaUc/OukZOt/qSJqM0ALKM2x/i8X8c4u4brsSg0/K96wtY0SYAafZEEu0Wo46AFKbJHWTK0D5YDFpavX7MOi0hSUKrAb1Wi0Gr4aaS3ny9qBdarYYe6UYc6Sb62Sw0e4J895mdVJbnMzLPxrRhfVi1aX+cOOSuCU62H27gnufPJcv//v1reHb70aSij342CxVOe6zjS4GDB6YVEwqH+berr+Dxtw8xPC+bH08ezGeNPvrZzFgMOhpcAf7nzrGcbvHzrd+9yzPzR6PTajDrtfzsrUMx+1TGym9c1Yef3VyCOyipwpT241Iy95bK8nx1PNyZxKlEeX3vLDOLJjr5z79/xP/eOY5lL+2NG0fnjh/AlCRJzk8bvfTLtvDbtw6xtbaBtXNHxnweiG3bk2bUq+NftOsGQJoxdkp3+YP84pvDOdHsVT+fNyhxqsXHL745HLe/e7guZZoNBKQwGSY9/e1WwjKsaCf4qXA6uGfSYOY98wFX97exYkYx24+cZXS+jR/dOJjfvnUQiHwXS6cMIRAK4wtK2AwRIebsp7ZzrNGr7k9xR6gsyyfNpMftD1F9rIm7NuzilUXlDOqZnvR4mz0B/CGJJ+aMUOfY9t91tNPQFxEsdlQ93ivTLKrLv0LO1+2hqzitaQCDTsMPvj5YdfdSKHfaeXB6CX+tOaEKpVu8Ia6wWbAadYTCEYGlPxTGE4jEz9RhfRjeL5ugJDP7yW08/53xhMKymqAe2d+mCsCTkSjOXP5gwntiJV6j4zSRk9i6qiOsmzeqU25JgtRc7tf+5e7sotdo+MlNQ/jgaCN/3XuSOWP7YzZo+c+/H4pzuVw0wUnvTDM6DbQGQpxq9pNhMXDwVCvj8nPwS2EsBh1efQiDTkuvTDN3XJvPIEcGbp/EgvKBlObZ1Dh751A9D7y0l1W3DMXlC9HiC5JlMZBm0qt/K7/zAD4+7VLvwz/8tInjzV5K+mZxotnHaauBvBwr/WzWr+pUCgRfOsGwxO3XOHnw5b0J59dgWMLaibYbFxohDhMILj9cgZAqNEmFVqPBbNAKMYpAIBAIBAJBN0Yjy3Ky9syC86ClpYWsrCyam5vJzMzs+AVJiK6KUyoqv/+1Anqkm+iVaVYXu/d81oQsw6gBOei1GtJMet6oOcmIPBuVz+yI2afVqGPN7FKerjoSk+S8YUhPlk4ZQqsvhDcokWUx4AuGCbQtAoZlmWZ3kNZAiHxHGn+tOcnwK7J5qupwXMuHVTNKaPQEsJr0GHVa/G0ijwxTRGzS4Paj02o5fMZFSd8MjHo9zd4g7kCInLSInXqTJ8CW2khbA4Cn541izeZD7KxrUhOxAL0yzby5/xS7P21i9pj+6DUa1m49wrKpRei0mqQLF8q53Xm0MaH45poCBwsnOKl8ZkdcQuKaAseXuhB8vMmbNNnX5xKvhrycuVBx2xlONHn5x8Ez9MwwqXF8qtnLyPwcmt2R2PGHJDLMRh56eS9bahtUEULZIDsGnZaAFGbHJ2cZ2i+LtVXnrl+rUceyKUMouSKLzxp99MgwYTFo8QYl0pUYdPlJb0umZRh0HG/1kmbSY9Lr1fdbN29UzPgRLYLwh8IMcKRh1Gk46wqQaTWgISKm6JlhRq+LLDIkEq/ML8vHZtHT5A3RK9OMLygRCIV59/C5NiYVTgcPTC/meKMXNPCd/7cz5r2jk3zrK0cjhcOkmQz82+/fU7dTRGtmvRaTQRd3HscPtKPVavAEpIQJ/pcXlVHvCpBp1vPN372X9Dzk5lj4a80p1lUd4ZffGs7G9+tYOqUIWQ4jt1X/NnuDpJn0yLLM/9v2CTcW96Fnpgm3X8JmNcR8J2lGHXOe2s6M0n58vagXnzZ61c+ruGy0H6+ixTRK26Cf3lyCFEZdxDVpNXzW7OPRzYfiXrtogpN+WRbyHGkX9kK/CJxv3J5q8XGyyUOGxUhAktjzaQuDe2cAERcDs0FHjtVAvctPiy9Ens2KUafhs0YPWWkmfvvWQWZencu9z+9mzewRPL45VgCVau4oc9pVNy9l21RzSaLK+PbfNZxzFLtQc5OSAOioejyVm4vgwvPxaRfX/+qfSZ9/6wfXxgibznf7i8n5xO3BUy3otVpC4TBn3UHSTLo2Zycdbr9EzwwjvmDkXnXVq/u458ZC9h1vZmDPDH7x1wNxLT7uubGQWU9uwxOQksZMRy5BiV7zSb2bn7y4J+nY/Nis0hgxudWoY9nUIkb2t+H2h9RYAkR8fUEu5Wu/M1zKx9+Z2D1wsgWPL4gjw0xACvPI6wcYekUWEwb3BMAbiDj9SZJMzfFmemSYeXb7Ue65cTAQaeV61h25J2r2hkg3RVpHvltbz9sfnWHZ1CLe3H+K4bnZhCSZdLMei0HHGzUneHrrJzwycxjrtx5R7/mUggvF1c4fCmOzGsi2Gvn22u1qe8sKp4OFEwaxYP0HMW0fH/nGMK7IEYIUweVLqrj96FQzFp2O4y3+uPm1b6YZrxTCrNPT/yL+RricnaEEgu7MvHXv4/KH+OHXB3e47Xef3UllWT53X19wEY5MIBAIBAKBQHCpIcQoF4gLkdRu9gRYtLFa/RH+xJwR/OhPH/LEnBGs3XJYTX5ZjTrWzRsVlxArc9q5e2IB//aHbXH7VpKpNxb34thZLwPsafzrWCOrXt2fNHn2r7pG5kUlwCqcdhZOcMYs2ClUOO0Mb0u4lTntLCjPJ82oY83m2lgnBKeDFTcXs3LTPjYfOBPznvPL8tnQ1vbio5MtbPs41pUgtordztyyfJ7bfpT7byrCFQjhC0i4AhINLj/jB9nxBcNqUjbdpOcnL+zhzf2nWTTRSXVdY1IHh+jEYTRfZCG4M5U+Itl38blYYpRmT4D9J1t5LIEo4O6JBaSZdOz+tJkre2XQ7A2i0WjY/WlTQvHXz24p4fU9J2LGg0QxEp0QK83LVv89Ii+bVTcP5azbh4yGJk/k/XbVNTKqv42nthyJEZMoVBQ4uKmkN/e9sFd9bGJhD5ZMGsKKTTVsjRJ9jBtoR9dO9PH8d8ez6tV9lObZOoy/8kF2XAEp6Wfqm2Xmp6/t5z/GDUCn1cRt1/4cRVPutHNVghiPjv3o1gqJeGLOCBY+uws4JwqocDq4eoCNnZ+cZdnUYuoaPfTKMAMyVqOeB9uEMYm+n3EDc1g6tYilL+6N+7z33FjIo28djBkv2x9zdV0jD0wr5uev7+fNqO0e/sZQqg6e5htX59Iz04TLF2lJc6rFx593HmPJpEIGOC7d5Fwyzidujzd5+emmfdw7uZCHXqnh+1+7kg8/bVbFnUrLpXVVR7g6z8b0q/oyKt/Gqlf288C0Ik63+kk3R8ST9jRT3PeoUFHg4KahfWJa65Q77TFzaEcCw/b3ANFEX5/Kv3cfaxKCxS5OdV0jtzzxbtLnX1w4nqvybOrfn0dQcbHobNweb/LS6A7wxNuHuOfGQt493JAwXkfkZbNyRgkf17t57v06/n1s/5h75Wg6GzPnIwxu9gRYtKE64XxZ5rQzdVhfgJgxQYiMvzwu5Wu/M5xvrF9MOhO7H3xylpw0Iw+8tJf9J1t5et4opDA0uP0Aatwqvx+VmCzNzWbxxuqkv2sXTyygV4aJs24/AUlm68cNPPd+HbeOzmPcQDt6rYYsq4E9x5p44JV9eAIS3/9aAX0yzVyVa2PlppqYfZY77fzoxkJmt4nToo8l+r6wosDBmkv8mhEIUpEqbo81uHnolRoKo8Rayvx64EQLD0wtItNiuGjXf6r738th/BYIujPf/K93sRh0LJzg7HDbH/zvv5g+vC/33TTkIhyZQCAQCAQCgeBSQ7TpuYSI7nduNeq4wmbhz3eO4+HX96sLaY50I8/MG83qN/bHLbhvrW3g/smJb+yVHvY3Fvfi2e1HWTKpkPujksrt91NZls+azbXIRFpWKKKScNTf0WypbWBeWb76ei0weWifuGPcUlvPshf3clWeLSa5Wl3XxNShfn709cG0+kLcMKQX1xT0pNkbQKvVtCVcm9SFQ+VYSvNsHGlws27rEeaX5Ucq12eN4N7nd8e1A/nRjYMpzcvmmit7JBSbKMf+kylFVDgdONJNeIIhPmv0YdRrCX9O3VZnK32yrEJ80lVp8gTjhChWo47SPBthWcYfDHNVbjaegERACmPS6+iVYcJq1FJd1xSzr16Z5phrO7qljLLfaCeh/14whrc/Os2G7UfV2F364h5WTC/hL//6LGZR35Fu5K4Jgwgjx4m/Fl7nZMH6WNelor5ZqhAFzo0z0QnzNZtrqXDa+VebAEUZWxKhPO9OIERRntcAP71lKKV5NvplW3j49f1x27U/R9FU1Tbw3eucsUkHp4Nl04r4pMHN898d32HfY5M+YkVb5rRTfawJiIxt37uhgN+8eYiVm/Zx/01DaPQG8AYk1m09kPCzQOT7A+KEKMo2Wj5ieF52QjHK1toGlk4p4v9cfQUrXqmJEaIAXJFl4e4bBsd8RxBJxiybWowvlLwlRVeg2RNgyZ93M69sAMcavRT2zaLJG0o6922prWfp1CHo0fDmgdNUlufTJ8vMylf3UdQ3i8klvZNeV1sO1bN8ahFv/eBaVVCYbo6059lw25hOCQyj7wHao8RGRYGDh6YXA3B7eb6YM7o4mR20YGrfOqajtkuX+vWitCr5yU1D+MbVuXzW7Esar1W1DfhDYVXs9f2vXZk0PrfWNrBsSlGHMdM328Jjs0o7JQyudwUSClGU91s+tYjemWZGD8gRIuOLwOV+7Z9vrF9qpJv1BKQwW2obWDTRyeo3DjA/QZtZBWVO84fCVJbnx90jK9sATB3ahzED7Zxp9XNTSW/1nif2Ps7Oo7NKuff53Uwu7sMHR8/yULt7H4iMGxo+4rG2llmK854j3RgjltlyqJ56V+CSv24Egs+DOyjx5oEzcb8bFH5042CMBh1ZF+l4Ut3/viNiUSC4pHH5Q9jSOhefZoMOd0C06REIBAKBQCDorggxyiWE0u9ccTr43T9qufuGK1XL4e9dX8C1g3sgSXLSBXezQUuF05HY2cDp4J2DZyjNs+ELhhO8+hxKn3tlsVCh/d+JXgOx4pT2VNU2MD/quWhnh/teOFdB2t7Z4dFZpWrSQUnkTyrujScgcd/kIYRlmQ23j2XFKzUx/Y8hsqiIDDcN7c2njd6Un72uwYNep4lrbVHRtqB9Plaxl3sPeMGFwR0IxQlRlGt+XdURHp1Vyq/ePBjnLqLXanjjexW4AxIt3iBZFgMmvRZHulG1GC/NzVYX5KP3G71Ir8SSXhNpIVNV24BfCjNlaB8qnA4yLQaCUpiTLT6++/92UVmeryYJTHotPTJM3PqHbXGOSNHv3R5lrCh32ll581C++btIxW/0OJEIJTGQyDlFOXZPQGLK0N7otJqE23X0HpIss3buSPyhMPmONKxGHd5gkAxT5PxqNHLScVQRoESPTwqhcESwtqW2nhMtPiqf2cGzt41J+lmix9Nk53FLbT3zygYk/SxufwiNBmaOzGX22AEx7Yd6ZZt56JXEyZiVm2pYOaMk5Xm63DnZ4mNLbT1LJhdSd9ZDaW42zd5gyte0eEPIRGKp2RfEnm5k9pj+3L2xmmsLeqR8rdsfOq/K9faOWc3eQMrtsywGUandzXCkG7mmwJHU7UFp8RLN+QgqLjWUhFSrL0TPTBPHzqa+X3NF9Z13+VIvbvuCUtJzEOdel27s0AlPuWdPRqQtyeVx3rsKl/O1/3li/VLCYtBxstkHnLs3nD2mf8rX+ENhemWayMvpRWluNnPG9I9pyegJSDEi5S219aqL2bJpRQzNzea/3zvKraPzVAH2HytHc9bjZ+gV2SmFp9+5bhBzntquPlZR4OCFheM52exnx9GzrKs6QmsHMS4QXK60elPPl63eEMgRcf/FoKP5VMSiQHDp4vaHOizkUTDptXj8XbsYRiAQCAQCgUCQHCFGuYRQquIUp4PbyvNp9gSxGnWsmV3K01VHePj1AzwxZ0TSfQQlmYUJnA3K2lrsvP9JA2s21zJhcOqkmlL5D/GJ3WSJ3ujXpNqu/XPtnR0Uop0D1myuRQvccc1Ant/5KevmjeJMq5+6sx7MBh3vHDrDvuPN3DtpCAdOtrJoolO1nY1e2JxXNoAsS+rqwn42M4+8Hu9ksOVzCEhEpY8AwN0moFIcSww6LXpdxPFn1IAc9fqPFpM8934dz942lvte2BPnavHsbWOZ89Q26l2B84qlH0X18m32Brk1qqVXudPO/73hStXdJJon5oyIE6JAx6KPNJOeq/JsrNq0j1tH57Fmc23cONEek16LL5h6keJIvZsN24/y0PQSrEZd3LF19B7BUJgF6z+gzGlnyaRCfvLCPpZNLWbxxh3UuwJcX9iDlTeXsPTFPTHCtooCB0unDOFEUyThoojjFKzGcwsxel1E+NOR+KGjc9jRNv5QmG/+Zov6d1lbdfDdG6sJheWUoh5fJ977cqXZE+CzJm/b9RHCpNeq4qpUWE06kCNzTa7NgssXUgWRGZbUt0znU7meyDFrw21jUr7GJhLb3Y7P6/ZwuYogVFG2SUeLN9RxvBp1PD57BHdt2EVmB/d2yeKzs+517bncnSy6KpfrtX+5O7uEw7Iag8o9S0fxm2UxcPCki3yHlcVR91PR9zGegIQ/FObYWQ/VdY3q4w+9UsOM4X159raxrNgUcUmJvoee04EQpv292ZZD9azYdK6V5KOzSmPGlPNpt5pqG4HgUiD690rC5006XP6LJwAR86lAcPni9ocwd1KMYjboYoTkAoFAIBAIBILuhRCjXEIoVXFKRdlPbioiFA5zxzUDebrqiOqGkmpxLwwsWP9BnLNB9bEmFqzfodoSt3iDHVb+K7R/v0Tv3/41HR1n9HOdcVeAiNvK/VOKmD68L8te2hsntplfls/P3zjAunmjWP3GgThniEdnlRIKyxj1Wsqc9oRJ2jKnHUsKV4bzFZCISh8BgM1q4PHZIzjRHKnydrVVkJQ77WRbjPzmzUNArJhk7dyRce1V4JyrxeqZw1iw/oPziqV7J2vUv9NMsYsGkfY1nROadfS4gtsfUo9n1pg8ANVVJFn8VR9rUitck2HSa6mqbeDBV2oStg3rzHuUOe0smlDA2x+djjunbx04A9Tw40mFzG/1q+PooB5prNi0L2HLnDKnPeZvm9WgHmtHn6Ujkgnoyp123jucvD2Ttu3xREIi6NrjT70rQKbZQGV5PmlGPe8cqmf8QDvvHm5Iem1UOB2Y9ToCUpjrBvdkz2ctjB2Yw7iBOfzw64OxWS9M5Xoyx6x3DzdQ7rTHOXud7/6j30ckxi5/Lme3h/Ml3RT5WWI1RERhWz+uTx6vBQ7STHqsxhAPTiui2RukosCRUACcLH6+iHvdhXayEPEquJxjPSyDSael3GlX72tS3YtVOB1kmPWsfHUfpXnZMfdy7YsRlP21f/z7N1wZc58cfQ+dzMVTIdG9V3QrSQ3wn//nKqBzgrXPK2rrLGJ8EFxILEZdh2sh1k4mly8El7szlEDQnXEHJCyGjtczIDL3ijY9AoFAIBAIBN2Xzt01Ci4KSlWcgssfQquBrw3pFdOWR1ncS0SrN6g6GyxY/wELn93FgvUfsGZzrVpdBqBBw/zyAVS0248i6lhXdUT9O1pkUuF0cKrFl/I1ke3scdsplLfbZ2fadii0eIM80E6IApEFxKe3HqGwbyYuXyjp832yzJxo9jG/LD/uHCqfo8GVulXC+SRwRaWPACLXgdmg5dU9J9S4nP/MDh7fXBtTHVKam61euz0zTSldLXq3WSdHjwcdxVKjJ3LtljvtnG7xxz3/3uEGKgoccY9XH2uiPMGYk+xxSCxQA1hXdSRl/K2rOtLp/W45VM+4gfHbras6wqIJzrjxrcLpYPnUIkbm2ZgytA++oMTv/3kYiJzTnpkmddu3DpxBo4mI+370pw+pPtZESJa5vWJgwmNXhC3K3yFJVs9RsvFa+Swdfd4Msz5uHxVOB/PazpdSDVxd18iC9R+wYP0HzPyvd/npa/t5dFZp0grIrjz+tPiCEeHhIDsnW3wcON7CFTYLB443J7z+yp127rupEJc/iNsfIiSF+c+/fcTyl2r4j3ED0Os09Mo088jMYVzTLkbOt3I9mWNWxL0rPy4GP09l/PEmL4s2VnP9r/7JLU+8y/X/+U8Wb6zmeFPqtieCS5Msa6RtzFV5Ngb1TO+ySUijLtJqEiDTrGd/inhdOaMEf0hizdu1jBmYwz1/+pC7JjjPK346416XDOWe/YuOByDiVXCOyzXWfSGJMy4fq24eyukWHxVOe9L7vQqnnRU3l1D5zA61FU97EbLyWFnb70nlvi962/bub9H30J2590qEch9dVduAyxfqULDW7Al0apsvghgfBBcaLbBogjPp7xkt4JfCF+0au5DzqUAguHiEpLDqRN0ZLAYdbtGmRyAQCAQCgaDbIpxRLjH6ZltwtyWn04w6Trf6CMux26yrOsKjbQ4n7d1B0kypv1KlEswTlPjRnz6ksjyf+eUDsRp1SGGZ9w43qLbI5U4788ryuXtjNRBZPFw+rYiwLPP69ypodAfItBjY/WlTTLuKMqed+eX5pBl1ce4rFU4HK28uYcWmmrhj6uiYATIshhhhTjRKRVtTkrYYW2sbCITC6LUaFm+sTugec/fGav7nzrEpj+d8Erii0kcA4A1EEmZxrZ9qG7h38rkf79FiEpcv9Q91lz/E2rkjCYVlvlHajwderukwlkKSTLnTzvJpxcx+clvc8+uqjvDyonIefHlvjDvDvuPNrLy5hGUvxj++bGpxnIOLIixRxg6AftkWtQrv7rb4u+s6J0a9lqAUZuvH58aeA+exX51WE1fdV5qXjS8Y5uoBOXz3OicGvZYMkx6jXkvtGRc6jYbjzT5Wvbo/xjWk/Tl3+aS41kkbbh/L1KF9YsaOUy0+VdiiHGNzm/BHGa+1aGLGwujPMm5gDqtuHsqyl/bGJDOUbSqf2cGto/OoLMsnzaTH7Q+Rk2ZkzlPb8QQkFk10JmzPtOVQPbIsJ3SPKXfaSevApvtyJtNsoPaMC3uakSXPV/Pn74zn2FkPS6cWs+rViBW/8h1mWQzkWI2cafUTlmV6ZpoISjKPzBzG3Rur+fGkwWp/6wtRuZ7MMcsTkLh7YzUv3VWGVqP53Pv/Im4PAsFXSZM3wA9vHMxZT4AMi56fTCnipwnitW+2GV8oSKsvzM6jjTS6QzwwvZhFG3bxv3eMQ4ZOxc8Xda+7EOOBiFdBV8AXkMi0GPEFQ/TJsrDi5hKWv1Sj3u8pTiW9M82EZfi0wRMj9kompl40oQBfUGLVq/vjtm0vtI3eR7LfyhUFDuaOHxBzHxlN9H20Ev+dEax9WS1Zxfgg+DLwh8M4Mkxxv2dOt/jokWHEHw7T6gvxs1f3X7Rr7HJ2hhIIuivutnUUy3m06XELIaVAIBAIBAJBt0WIUS5BemaYuKbAgV6nwWTQ4W3XYkFJWCmLexlmA62+INXHmnhz/6mk7XcqChwMsKfxpzvHkW7Wqw4qENvi4ZffGk5ejpVTLT50Gg2//NZwVawx4/GtrJk9gkUbdlFZns/IPBuleTb+985xuPwhsiwGNEBYljHotTw4vQhvMIzLH0Kv1VBVW483GKSobxZzxvQnIIUZYE+jwmlPKDKJrl6rcNrRyHGbxKAspiTjjMtPzfFI64VoNJpI+5KxA3M43eJPal17vgKSLKuR1TOH8Y+DZ+iZYVIrB061+JhwZQ+xwNJNcAfi3XoUguGwer1FX7vp5o77ef/bHyKCkmfmjWJySW/yHcljqcLpIC/HwqqbS/jm795LWPHtCUiEwmGuyrPxkylFHKl3q7H/H2vf58HpxSyZXIjbJ5FtNaDRwKeNHpZMKkSv1aptiBRhlyL0KHfaOdHspTTPxr2TCzl21otJr+Xdww08934dt47OU8ee3BwLsgxzntrGraPzYrZvv1+IjDXKfs+0+sk0GzDqtZxo9jG0XxbvHm5g//FmhvTNYnJJb+74486k57T9OU8362Js3wFmP7mN1TOH0TPThMcvkWkxcIXNwtGzHh6bVUr1sSY2bD9KUd8s9Zxu3H6UeWUDuPemyGfpZzNjMehocAX4nzvHcrrFz7fXbuepuSPxh8J82hj/eZWx+ok5I1j47C7Wzh2pnodU7Zki7ZecMc8rgqQGl588e1rS83E540g3cqpFj9Woo94VwBsM4sgwIclhbizqRX6PdEJhmR4Zke/8b/tP8uGxJr5W1Js8uxWLAX771kEqy/PxBcLYo8b9LOvnXxxv9gSwGHQ8MWcEZoOOXXWNrKs6gicgqfOwFJZxBUOf24q/M24PYu4RXIqkmwz82x/e478XjMGk07H69f3MHtOfnpkmXD6Jnpk6Trf4+cUbB5h5dS5X2Cw8OquUgCTx9NYj3Do6j7OeACMH5HT8Zpxzr4u+B1bu03bVNZLZ1iItVYuMLzIegIhXQdfAkWbEGwqj02p5suowo/NzmDasD9+7voBQWFaFI28eOMWHx5rUeySFRL/d+mRZ+Pv+k/z+n4dj7vtMbe1W2xO9j/a/lf2hMP3tVtKMOlZt2pewfWF7x5QMs6FTgrUOfpp+oZaIYnwQfBmY9ZH5debVuer8qvwG+tXfPmLJ5CGETRf/Gvui86lAcD6I9mdfHKWI0tRpMYpo0yMQCAQCgUDQnRFilEsQxap02+EG+mVbqD7WFJdgVpKTFU47d010sq4tWWo16thw+1hk5BgHgzKnnbnjB/Cz1/bx/a8N5kSzl3KnXd0mOtlZ4bQzeWgf7n9hb9yxlTnt7KprjHp/B5OH9la3XTTRSb8sM5v2nEjSI9zO0H5Z6ns9fMtQHn5tH3PL8gkT7/SiOAdUFDhYNrWIoJTaLSLbYuDdw4mT/gBZZgPPvV/Hs7eNZcWmmrgE7YPTS5j/9Ps8ML0Y2h3P57WKlYHXdp+IEQhdU+Dg2it7nNd+BJcviRa9FcJhmfltFaOKrfjW2gZOt/hjYjSa6DY7ZU47H9Q1smZzLT+fOZS7JjgTxtLcsgFMX7OVdfNGUtg7I+l+A6EwazbX8q2rr2DD9qMx2y1Y/4G63VV5tpj4caQbee6OcXGuKuVOOz+9ZSiPvL6f1/eeinzOusaY41P2U+a0U5pno1+WhSF9MtXH228f/bnSTXqq6xrVuF65qSbu/VfePJSVm2oYkZfdqXMa/Xd7oUe9K6CeB4ANt41h9lPbY14X6yjlYOnUIn766n5mXn0F3/1/u+LeW+FwvRujTsvCZ5Nvk9WWHI2+Vjpqz6TTanhlcZm62Hy6xc/sJ7exvnJ0ytddzmRZjfS3WznrDlDhtHO8yU/fbDMNrgB59jR+++ahOKeaRRMK6JlpRKeBlZv2UdQ3i9LcbKwmHQathkOnW5HCMh5/iCyr8bwXLY83eeMqnCucDjbcPpbFG3bxwPRint56JOZ6u6bAwSMzh9E329Lp9/mibg8CwVeFI91IUZ9MNIABuOfGQpa9FD+nLJ9WzG/fPMi4gXZerznJg9OLVXc8i1HHoVOt9MwwdRifjnQjv/jmMIZfkc3KBPeEs0bmJozbzxOXyRDxKugKaLQaZFkmEJJZPKGAp7Z8TPGY/vz6zYMdOtwlaptT7rTz2t4TcUJbpW3P/LJ83v7odMw9XfR9EbT7bVvg4KaS3qx8NdK+0BsKpzwupfhAr9Wwtk0o3F5ACp1zy/wiLRHF+CD4MjAAP/j6YB58Of4304PTSzAA2kidjrjGBF2SL/verrugiFHOxxnFI9r0CAQCgUAgEHRbLgsxyscff8zTTz/Nxx9/zG9/+1t69uzJ66+/Tl5eHsXFxV/14X0p9M22cH1hT060+FhXdYQ1s0uhXZuHigIHiyY48QfDLJrgBCIJ6NvW72DdvFF81xei2RtUK+yfe7+OeycPofKZHXiDUsLErbJPT0CKcwdpn2Qtc9pZNq2If9U1qgt1VoOOXlkm8nukq8ej7tvpYNXNJXhDIZ67Yyx6rQaNBu574QzvHj6rVq8FpDA90k0Y9Vp8QYnHZpXSJ8vM63tPMCY/J2UyuU+2mX3HmxOe04oCB7k2C3/+zjiWvbg3LrldVdvAQy/X8PT8UTS4AqyYXkIoHMYbkD63Vaxqr1wr7JW7K82eQMr2WXqdlg3bj1KaZ2Nkno1pw/qwatN+ljy/O6m4Qmmz037x/MFX9vHSXWVMGdqHBW3tt8x6HTqtBm8wxJP/MZJddY0sn1bMilfi97vqlqF8u01Y4fGH+P/snXl8E3X+/5+Tyd27CQUKFAop9ATKqbZVYT1ATtc9BHfl8viu4vF1VTy4BFx1XdddRdcL1N2f6B5+RcVrRdwVULyoyqkUKgWplJ5p7mRmfn+kCUlztLiycszz8fAhTWYmk0nek5nP+/V5vWZX5KMQW8fLppdy92s7o95HUa806lvd3DW1FJc/gMMjkWHWUd/qxuHxcfmZA7B7Akmt0xdNLuZQi5vPv2llXmU+KErSWLLOETYHWlysmF6Kyy9jd/tJMQSFF5ev+pBp5X3QCZq4cUOdo4tCAprfvrmbSUNzk36+eq2GVbNGYdZrcfslFEWJcZT66WMf8Je5Y7ucPasXg8snOsdVFVhJM2qpsFmijktX8UxOb4BLn4iOZaqwWbo9cHSy0ifLjNMXYE5lPi9+epD/vWAwvdKM+BWFKcN6c8N5R2dsazUCqXotGgHmPvsJXzU4mDG2PwAC0O4N/p7Oe/aTcBOqqsDK8mmlZJl1XZ7HE1ntb6xpREFh9ZzR3PVKdCyVWS8ytF8mXzc6+bbN3W0BTHoXja//pDGmonI8CYmx29w+fLIXnWBg2dRS3H4JuycQFtP9cf1X3HrhECY+tAmXT8IfOHp2lWSFaY9tZmT/LO7qEBZbEtSN0ycRkBTu6hQJB8FrwvdqGsNi4s7uKfubnIgagZ7pxv/oPav1qnIq4PRJGHQiD/zzS359wRAuKOlF7wwDCyYUIgjQ6vKTZdazrVO0a5XNyjXjbMx79uPwtio6rsnue2NX1GtU2awsmlLMG9vrWfHaLs4YmM2iySWsWLeTjTWN4esiAaLvbW0Wrjk3+BqdHVMALKkG1u86HN6vswus/PaSoTh9EgvXbo8Rrj40o5zrn69mVP+ssFvm8YpkVc8PKscDn+zFoDEwf7yNBRMLw2J1pzeAQRTQyQqyEvxdVb9jKqcaavzZ94ejQ4xi1CUfiwhh1ImqM4qKioqKioqKymnMCS9G+fe//83EiROpqKjgvffe4+677yYnJ4fPP/+cVatW8Y9//OOH3sXjRoZZT6vbz8j+WcxfU81VZw8MN8/SDMGm2WG7F0EQMOtFlk0txROQcHgCSLJMr3QjGgFaXH7K+2UCcM8bu1gytYTrn6/mj+u/5DcXl9HuCeDwBkgz6dCLAhpBwBuQWDipGAg2MlMMWoxaDXXNbp6ZM5p2T4DqA63MfDIYpWFJNQAgKQoGrYg/4GPx5GICkoLTJ5Fu1OLw+mlo9/D+viYefHsPEIycgOjZa5GEIinWXVfJ5wdamTS0N0unlsa4L1TZrNw1rYSr//wpt04sxBtnxtuSKSVc/Kf3eejS8rgxJhBsDPoCMtZUw/eSU6zaK5/ehGbd3DGxKGH0U7PDx8yx/cOOCKGG1/+cOwiHx8dvLi7D6ZNo9/hJNehI0YvsOeLgvkuGxsTWuHwSzU4fpX0y8AVknN4AWWYd7Z4Abe4Aedkmzh3ck3a3j7svLsPlk2h3+0k16QhIMt80uTjQEozacXhjLc6Piive58nLRzFjTF7U41f/v09ZOXMEc5/5OOxysnJDDc/MHo0CTOrIJg/ICgsmFCLL4AlIZJh0+AIy37S4eWrTPqrrWnlk5ggmlvVmduTyCgQkGbsnwNa6lpgIG4BVs0Zx3fPVrJo1it++sTtc6ys31FBts3DTeQXcNbUUryTT7g7a8pp0Gtx+iYdnjCDdpMWsEznY5OLqcwahDU0NTIBJJ/KTxz5gzRVjmfvMxwmXCygyikLC70FoZvDOQ20smlzCsk7N0QqbhUWTi/nD219SnpcVdRwNWk3ieDablcN2T8xrzR9XgKGL93YqoNNoWPNhHXdeVIRfkQEBo0bD2IGW4Pff40cnakjRiwgKvL6znq8aHEAw9q1/tpm3dx1m3JAcVr5bw9zK/PD3beOeRu5cu43JQ3M5Z3CPpDPpkv0WbKppwueXo37TzHqRh2aUfyenFGuq/rg1xlRUjje5mSYCkozTJ/DG53X8bHQeCEHhnMsn0TfLxI/L+9Dk9IV/+5qc3vC697+1O1ynS17ezrC8LL440BpTN6FmxOyzBiSM0ctJM4SFKPHqsarDMe8/mUWr1qvKqUC7248uTc8tFwzBI8lIcrCRrSig1Wg6hNFwls3Ki786K3jfadRi0GrwSzIv/uqs8P2mIICCwpIpJVx5tgefJKPVCPRIM3CoxcPYfAuluRlUH2jlsqe28OTlo/jVuYNQAH9A5tYJhVzjDdDqCk7I6JFm4NIntkRdK0fW8dv/ezYXD+/DeYU54ckHAPOfr465rgqdKxZNLubciLjVey8Zym0vfhFVx9/VUTMS9fygcjxwB7S88XkdPx2dh8MnIQoSWo2GPhkmvm5sp0eGGZNOVL9jKqck6vjc94ezw+Wk+84oGvySgi8go+9iMo2KioqKioqKisqpxwkvRrnttttYsWIFN910E2lpaeHHx48fz8qVK3/APfvv0Ory8esLhvC7t3bzh/V7+MP6oIhjfGEPbjxvMHqdBo0gIGo0HHF4kAIKuVkmvAGJpa9sjyu68AcU1l1XiQB4JIkUg5Z73tgV1yngT+/uYW7lIFy+ALIsMuvpj5g/3hYVm9F5YH72WQMQBIHD7V6yzDp0okCLy4cgCGiAx/+9L7x8KHIiEQathqoCKyhwXlFPqr9uoWe6ibmV+dx+URHtnkDY/eBnj39Ao8PH9c9Xs3BSEf973mAa2r3hRnlju4fVs0eHb5oS4QvIMVnm3xXVXvn0JXLWjTxRDkfxdBYYWNL0/PzxLTGijw/2NbF6Uy0Pzyhn3rOfhEUqk8p6c0VETExnHN4AP39iC1U2C8uml7Fi3Q7e2X0k/HylzcKK6WW4/QF8AYmcdCML125jU00T88fbwmKJFIM2asA+clZ4ce90dB0uHpF25aEYr86OLZ/UtbD7kJ3C3HR6phuRZIUj7V6qD7Sy61AbM8f2x6gTuXbNVuZW5rNgQiH3v7k77vmrqsDKRWW944rXQoKOkXlZ5KQbueOiIpw+iSMOL3pRw2G7B7tH4perN4X3ec0VY5kYEbMTeq+rZo3i/jd3MywvK6GApNJm4Vu7hwqbhR5phqSuTU6vxIe1TVEuVuH31DHb19OxT5c9FRT5XXOuDb2owawX+fxgK/Oe+ZinZo2OiZP4UWEPlk0vYWEnx6eqgqAb1Uf7msLuVQathga7hz6ZRrzyqW+T2+T0sXBSMZ4O15rbX491xYLgZ7RkSglP/Ls2/FimSUeKQeTxf+9jbL4lHAMSSeixRDPpQnnkTU4fq2ePjrH4D2H3RM8Sm1uZz9Md8XuRdGfWXshd4ng0xlRUjjdtLh/tXj9mvZbqg3Ye+te/YpapslmZWzkgfF7LMuu55+JSFFlhw+4jXNbharSxponZFUFhSue6aXX5mX3WAMx6bcLaDEWgJarHjd/DLFq1XlVOBdJMOm564TMenTmC3722m0vH9ufzA228lii2teNabuyAbL5pcZOTbuDB9V9RnpfFZ3UtLJ9eyn1v7ub6Hw2mvtXN+7XNrN5US3leZljoHKLZGYxPfPPGqrAIZuaTR6/rHr1sRNK4TKc3wPC8rKjH9jY4EjYrN9c0sXRKCb0jRGi5mSYenlFOo8NHu8f/nR01O6OeH1SOByl6Menv690Xl+LyS+p3TOWURB2f+/4IO6Poux/TA+D2SaoYRUVFRUVFRUXlNOSEF6Ns27aNNWvWxDyek5NDY2P8QaKTmVDjyu4JztjPNOn5xaoPeeSyEVzjDRCQFbLMOiQ5GA/RQ2egze1H1AjkpBnx+GXa3D6Mem1S948Wlw+dqEEQoN0fYPm0UjwBGYcnOFNNJwr4JJmZZ/RHrxNoaPfTIy04+/6Fj+oSxocsn1bKfW/u4o3th8OPVxVYWTKlmLd2fEtJ7wyevHxUOD7I44+NAwoRygWfddYA/rD+S26dWIQvIPH7t79iw+4j4ZmqKzfsidqP8rxMctKNXL76owgbaEvwuQ6HmGR8n3a0qr3y6UvkrJtv27w83xHFM7ciH71WQ7ZZz+cHW3lnVwPleZlxxRWVHeIKODqTs7xfZuIYF5uVPpkm1l5TgUYDXzc6ue2iIn59QSEur5/MFANuf4CvDreTm2kixaCjyeFh+bSgU4jd7WfK0N5srWvlg72NVBVY2bgn8azwSLvykXlZLJpSjKNjACfSsWXnoTYWTipm8cvbo9YPCWO8koRJKzKyo8FQ3i8z8flrTyO3ThhClc0StUxkzA/Azx//gEvH5HHu4B7oxeBgx6E2Dyte2xUVs9I3yxRzDirPy8Tjlxk5IJuKQdn8ZETfsGAnct/vvrgMnySxbGopdreXFdPL4i63fHoZgqJwQXEvDFoN140v4LawLbYWp9fP4TYPWw+0UN4vk/J+mWSYdPzrqyOMysti1tMfMbcyn6VTSzjQ4mLJ1BJ8ARm7OxCOQrp81UdMK+8TFjT1t5gRBYFb/v4Zv/3JMNz+oBNMmknHoB4pzF+zlUWTSxjYI+5hPmVINWjxBAKYdSIaQeC68QVAbETU8mmlNDk8/GXeWLQagRaXl97pJt7YWY/LJ4UdckLN6Ui8ATnuTLp4eeSRNRPZHEs1Rg/ilfeLf06A7s3aO16NMRWV403wOyuRZdAxf7wNUGLO9UunlOCXZURB4Ns2D2/u+Jbd9XaK+2Rg1otRdRr6d2TdHGp1s3DttqjtxqvNUATaf1qPXaHWq8rJTopOZMmUYtp9Eut3B+NXrz5nIEsml7B83Y7oGrZZWTatBG9Awu0P8P+27OfKswcypyKfFz6qY8X0MgKKxDmDe3Dxo5ujBM+RQufQY6HrZLs7wGcHmynvlx21b11FGeq1GvYecUTF4HXVrHR6Y2MGMszHp2bV84PK940nIDGncgAxv682C3MqB+AJSNjdfgp7pf9g+6iicrxQx+e+P0K/hd12RtEGl3P4gmMYKioqKioqKioqpxcnvBglMzOT+vp68vOjZyNXV1fTp0+fH2ivjg/xGldVBVbu/+kwrn1uK6vnjOaI3cuBZjcGrYbth9oY3jeTnhlGvulowFYfaEWrESjrk9zZQ1GIcR6YVNaLBRMKcfokmpw+0o06ctKMuP0BLClaskw67pxchN0dQFZklk8rpcXl53C7B32HS8KK13ayeHIx14yz4fQGG3gf1jbR7PSxZW8zj7y7l4dmlPPch/vZXNMUbnLH5HsXWFk2tQSXL4A3IFOYm8H0RzYDwRmql43tT5pRS4pB5NYJhVzp8mHQiqQZtHweJ498TuUA5q+p5nc/HcbOentCAUxVgRVJUWIGJb8LbS4fWo0Qbuh3RrW+PbWJHMhe8OIXYQFXKIrnkZkjEIDhfTOZWNqLFet2xeTSL5lawmd1reHZ30adSIvTy4rppSxcuz1G9LBoSjHznvmYaeV9GNU/i0yzFhEBPzIy4Jdk9KIGTUdjXSMIZJgNLH55e0yzb9nUEobnZXLzBUMw6jQ0tHm5bUIR3kDQacSgFdla18ILH+7nhavOYMchO0ZRw+dHnJT3y+TBnw8nN8OEpCj4JRmfJHPPxWU4/RJtrqDYzi/J/HLVhxxocUe5e8Rr+Efi9krMrshnTuVAPH4Jg1aDRoB2t4/6NnfYvcWoE6k94mDkgGyWvLIj5tx6/Y8KCMgy5XlZ3DaxkIMtbnqkGtBrNdS3eSjrk4HdI/Gnf+9geF4WczrFFd316g6KczNYuaGGVbNGceVf3ue+S4ZG5a832L389LH3+e1PhjF/zVZWzixn9abaTjFjFuZU5vP4v/dRnpfJnIr8sJguNKM3USM0FGUG0S5Vr11fiSQrfPR1K+f+7t9x1zUbujdwdDKTlaLH65do9/hI04v0yzCyYlopbr+E2ydhNmjxSzL7m1x8vD848/rMgdksmVKC3RNgeN9sXr62ghaXD7NejNvUCj0WOZMuUR556HcnMu6nwmahwe6N+q3oqga6M2vveDXGVFSOJ3aPn0yTDh3QJ93I0qnFBOSge5BRpwFF4M0d9QRkhVH9szHpRS4q7cW04bk8smEPcyvzo+o08t/tHv/R2ux0DRivNhvavZxdYP1e6rEr1HpVOZkRBDDrtRxqdYcf80sKh1rd3HTBEG6dqKHF5SPLpOOtnYf53VtfsmBCIa0BP78610aqUUuPVIHbLypk6/5mRvXPZkT/LJ68fBR2j5++WSa2HWyLur/rLE7xSzLnDelFfadowuoDrUknPry+/VtWbqiJisE70ZqV6vlB5fuk3R3g1n98wX2XDOXWCHF8g93Drf/4gsd/MVJtyKucsqjxZ98fTl8ArUZAJ3bP5cTU4aDiiiPoVFFRUVFRUVFROfU54cUol156KQsWLODvf/87giAgyzKbN2/m5ptv5vLLL+/WNv70pz/xpz/9ia+//hqAkpISFi9ezMSJEwH49ttvueWWW3j77bdpb29nyJAh3HnnnVxyySXH623FkKhxFfr7uSvGcrDFzcxOcRKrZo1CkiRyM40oCKQatGSl6jCKIm/cWIWggAK0ewJ4AxIBSaZHmgEBgWvHF3Dj+UPYuOcIL356kBvOG8ymmkZ6phvxBmQcXommdg9jB1qQZKi3ezHrRT7+uolH3t1LeV4m140rwKwT+dVzW8ODg7++YAipei0CAs0uH0W9MzjU6mb5xSW4fTJOr5/l00qDM/s9flINIkunltLQ7gnne1cfaOXu13dx6Zi8mBnkoSbBa9dVMumhTeHHxxf24KbzBzO8XyZ/v/pM2r3Bm6NNNY3MXxPchkGrYfWmWh6aUQ5Ez46vtFm4+YIh1Ld6EATw+CS+tXvQiRosEcKUzu411pTg46HHMkw69KKG21/axqf7W3hoRjmyokS91vdlrxxvX9TByhODyIHsRoePy57aEiVUyE7V0d9ixuOXaHP7uWtaCd6ATJvLT6pRy5F2D20uP6989k3MrLUeaUauqBzIgomFOL0S6UYd9W1uZj65JapWAjJ82+7h/b1N4fiBKpuVhZOL+LbNi0Mn8Jf3v2ZYXhazO4QWRl1QZLL8tZ1hocX4wh7cflERTQ4vKQYt2WY91lQDPdMMVNms6LUazsjPxq8ovPLZN3xa18pDM8q5981dMTU2u6Nx4PJJVBVYeXrOGAKyjC8gc8Pz1dxw3mB6Z5p49LIR4X3pHJ1gNmh5qMMRKRQfVDnIioLAAGsKm6Per4XR+dlcPDyX2WcNICAr9M4wIsvgDUh4/DLl/TKD2cWiBrsnEBTZfFTHpWPyuLCkJxt2H2FDRNRRJKE4iOoDrQzplca8OBFKVTYrh+0e5lbms2pTnKiHjr9fuOoM/rnzcMzM/MiIpMjPaPWm2rjiiAqbJTxDaWJpT34ysh856QYcHok0o5bDdg//+PRAt2cxncwYtcEGWLpRjwI8vbmW2RX5KDoRRQGHJ0C6SUu+1Uyry8ufLhuBrMDtL22LiVJ6evZottRGf3aRs7LTOtV8Mov/UNxPqJn20taD3HNxGXe8tI339jR2OZNbbRKonKqkG3VoBHDIEimCiCCK+AQFs17E5ZVIN2mZMjSXNpeHnz/5cfhcWWmzcNe0UhrbPeFzalVEfUKwbrpbm2cXWBk3uAfnDO7B143OpPus1qPK6U5AVmh2BUVZZr3IY5eNRK8TyDDpMGpFvJKMSSeiFTVMLuuNRgMuX4Brn9vKgY4JFSEhdN9sEz9+7H1cPil87XOo1RO8v/ufM2l2+vAFZKoPtEZdT36wr4lJZb35YF9TlPgk0X1fZzFLZAye2qxUOZVJMWpZ9cvhZKeacfgkNEJwAtHgnFRW/XI4BoMWk/bUv0dQOT1R48++P5xeKSww6Q6h+1tnkug8FRUVFRUVFRWVU5cTXozym9/8hmuvvZZ+/fohSRLFxcVIksTMmTNZuHBht7bRt29f7r33XgoKClAUhWeffZZp06ZRXV1NSUkJl19+Oa2trbzyyitYrVbWrFnDz372Mz755BPKy8uP8zsMkmxwfOOeRto9AdrizLxUgBSDjvf3NtEz3QhAz3QjS1/dwcyx/cMZ96Fm5lkDLbj9wWZmil7Lhi8Ps/1gG6tnj0aSZfpbUmhz+zHpREQBRudnc6jVjUEnYtKLNDt9jMjL5m9X5zD76Y94+N09TC7rHTWTtN0TQNJrCEhg0oqYzCK90w0se3VnVEO3qsDK/HE2fvLYFoCoZutZAy38fGRf7lq3M27Od1WBFZNOw6vzK/AEZFINWrwBCVmGbYdaWR4RxREi1DR0+SSuf76auZX5zK3IJ82oRZaDLgF/WP9V1D5W2CxcUTmQT/e3MLRvBu2eAH5JDje7AVbPHs0jG2rCrhbzx9v4rK4l7HwQ+VoAfbNMGLQa7F4/9fvdZJj130lEEs9JJ3JW3/FEFcF0TeeB7EaHLyxUOK+wB3dMKmbJy9vDwo0/vrMnJjpk0aRirqgaxLC8rLC4YmNNEzJQnpdFdV0L8yrzcfskVm8OPt9VnM7Gmkb+sP4rfnWuDVEQmHVWPilGLd+2eVj88nYaHb7wAL1OExwwGDUgCzFopoJGAEuqgaWvBN1UIs8tokZgbuVAZitK2P0ohFkvMjwvC5NO5LFfjCQgK2yta+E3rwdFLzsPtXH/T4ezbN2OmGZBZHRClc2KSSdy56QiAPSiyNJXtid5v00senkH144bhMcn09diYsW6neF9Dx2vzq/53BVncN+buyjundyiOjRjPmmzo3IA1lQ9pX3SEzqcbKxpYna7N+b5ZoeP1bNG8fC7NTHvcfWs0dQ1RzdJK2wW5o8rwC/JpOlEFkwo5M6122NEQSuml6EXkr61k55DrW4eeucrLi7viyEV/vbxQeZU5BNQglnV9o5YuoMtbv5v60FuvbAQrxRg+0F7HMFQIwgwsn9W+Dt/5kALoiDg9kvc8+OyqOZUVxb/Zr2WVbNGUX2glb9+VMeyaaX0jrDilxVFddVSOS3RiwLf2j0MSDfiBfyygitBvS68aAh3rN0JBN31lry8nWXTSlm9qTbKFQ+O1s2+LoQlKQYtb91YRa+Oa+pGhw+DTsPzV46NEjqGUOtRRQVcfgm3X6JnmoH/u+YsACRZwe2TQAk2nhodXto9AQ63eSjpk87j/9rLn+eN4fJVH3Ggxc3GPY0sfnkHt04YwpBeaWyuaYpyELtuXAE90vU8vbk2+n7SZmXhpGIufnQzFYOs4esxDQIbaxrD930LJxVx28RCXF4JhzcQJWYJEYrdGpSTqjYrVU5ZMrUa/Foz37S5STFokZVgvX7T5qZPuhkdCvUuL/1J+aF3VUXluKDGn30/OL2BY5rcYuxYNl7UnYqKioqKioqKyqmPoCiK8kPvRHeoq6tj+/btOBwOysvLKSgo+I+2l52dzf3338+8efNITU3lT3/6E7/85S/Dz1ssFu677z6uuOKKbm3PbreTkZFBW1sb6enHnq9bXdfCxY++n/D5v119JqlGLRf9cWPU4+t/fTZH7F5WvlvD5pomVs0axerNteFG9eYuGq7zx9nw+GWe+3A/4wtzuOOl7UCwcbx61iie2riPSyNELSEqbRZuvrCQmU9u4eEZ5YiCwOfftDJuSA6iRgjPXtUIIAMpWjHYUPBLuLxBK1ijToMGgYUvb4+Jz5g/zkYgIJObZWJx53gNm5V5Vflc0+HG8uhlI7Ck6Pn5E1sw60XWXVcZE8kROfuts0jltesr0WoE2tx+fv23z/nJqL6MG5IDgMcvYU01sKRTjEloezsOtfFRbXPUsVk1a1Rcd4QQz185lq8bndzecawhOLi5bFopbW4fqcZYcUdn8UeqQcudL21j/a6GmO2fXWDl4Rnl3b6ZPlZhyQ8pgvm++U/rtisOtbpjBrIrbBYWTCjkvjd3s7mmifnjbeFa7UyFzRKu5c7f31evq+CtHYfDoqi5lflcVNqLe17fFRM/ELmtFz6q47krzogRfVTaLCyaXMJlT20JC1IWTS7imxYPuZkmHN4AAUnG5ZN47sP9bNh9JOG5pcpmZVbFgPD+JjsHzanIRysIfFLXckzHYWReJrdOKOS3neLGOq8TamS8eWMVelETVcvJjn2lzcLwvCzK+2Umrec1V4wNO1aZ9SILJxVR1jeDgy3ucHzZ6k21PDyjHG9ADkfqxKPzuaPKZmHBxCLueyP+Z1pVYOW68bYO56tgfNBhu4feGSZ6ZxoxajUxQpTI97dieikDrKkJ9+dEpTt12+by8eu/f86iSUW4/DIaDaRpRXwK3Ll2G9V1rWEBJECfTBM769soyU1HJ4pMfnhTXCHkmzdWoRUE7np1R0y01X0R58C9DQ5+9Pv48UgAb95QhccvJR38jHf+CDXCep9k51oVle7U7WG7h817jlCUm0GGXsQrK2GXrpBYOdOso3e6kYc37OGm8wZTef+/orbxxg1V+AIy737ZwBPv7cPlk6LqpqvafPt/z6agZ1rca53O7l5qPaqcDnSndqv3N5OZoud3b+7m6nOD1yV2j59+WWa+ONjKiohJAqH7zzSjlj+8/RXXnzeYqSs3h7e1evZotta1hH+fczNN6EUNsiKj0QgoCnj9Mk5fAFEjsHFPI8P7ZjL7mY959boKpjy8GbNe5LkrxtLs9EXFK67eVMvvfjos6bXY2mvOYnheFnD0HkltVqqcbCSr20NNTtyyzIf7msOOuEadyOE2N2MGWjBpBNr9Er3SjerkExUVlYQsfWUH7+w+zG8vGdat5e1uP1f/v0954pcjuaCk13HeOxUVFRUVFRUVlRONE94ZJUReXh55eXn/8XYkSeLvf/87TqeTM888E4CzzjqLv/71r0yaNInMzEz+9re/4fF4OPfccxNux+v14vV6w3/b7fb/aL+6yqYOSDKvb6unymaJaoAJihAWogDkpBvCNuOhJuzcyvyYJjAcnb0/qaw3hb3Tw84qZr3IwzPKeeTdGoblZcVdd1NNEwJf8vCMcnLSDPgCMpPKerP0lR1hRxAINujmVAwIx+RU2Cz85uIyPtrXTHaqnoCscMuFQ/j1+UPwBSTMBi1mncj+ZhfXrgkOFL5w1RnMPmtA1GDiNRGxQAathjRT8Pi5fBKtLh+3TShkToUXb0AiL8vMF9+0xRWiVNgsvNGRFV5ls/LneWNobPd2KRQI/b1gQiEPvr0n6rmQU0IiBEGgd0Z04+K9PY3cuXZbuHkeKe441OpmwT++CDuvhI7rrLMG8P7epvBnHBnh0eLydWuw6FiFJYnipCKtrU/kQarvu267IjTr5mCrm/1NrvD3t9HhC3+HyvtlJnTLiFfLoX832I+6aJj1IrkZRhCEuKKFyG2V98uMEaJAsKaXr9vBfZcMZd6zn/Dlt+0YtVqe2VzbKSYoKDTZsq854bllY00jMkp4f7s6B918wZAuj8NtEwsBwnW8saaJ//EEuny/IfYdcdIz3RC1fLLX3FTTxJyKfKoPtEbZvUdSVWDFE5BYNWsUOq0GURD4YF9TVNMFgg3M6gOt4cZKIjJMOlbPHo1ZLyLJCh/sa6Kh3ZvwPW7c08gNP4oWZh5q8/DOrsMsnlyCyy/F3e/Q+3P7k5+rThS+S902OnwU9k7H6ZNJM4g4fRISR4Uo8RyEqmxWyvtl8fCGPbxw1RkIAlHRRgte/AJRELjr1Z0xn8nGTufAriz+e2cYuzxXqrP2VE5mvkvdtjh92HqmkaYXCQDLX9sZdvmLrNVKm4XFk0twBoKC5BS9loAsIwgCdref7BQ9Fw/vw/ghPUgxRNdNV7WZk2ZIeK2zqaYJQRB4+doKNIKg1qPKKcl3qV1LqoFlr+5g/o8Gh++hQnR2uAs9d+dFRRTmZqAVo+MITTqREXlZfNvmpneGCUVR+LC2ide21ScULAdkhQqbhQa7N3zN1uz0xRUTH0sMXoZZrXGVk4NjqVuJ4H1k55qqsFkYYE2hb6YJs07k13//PGriy8k6+URFReX48J2dUXyqM4qKioqKioqKyunICSlGuemmm7q97O9///tuLbdt2zbOPPNMPB4PqampvPTSSxQXFwPwt7/9jZ///OdYLBa0Wi1ms5mXXnoJm82WcHv33HMPd911V7f3syusqfqElvyVNgvv7wtag6+cWQ4dtsMQdO6IHERweIIN0EhBRHea3aFZMdZUPatnj0YUBGaM7U9ethmA6rrWGCHHxppG/ufcQUxZuTmhaCP0fkKz0xrsHrSAgkKGSUdADhrzaDTwQW0TXxxo5cbzBnPtmqNikx3ftPH69m/jHpvgwKOHHmmG8GPegIwlRY9WDL6ndm+A4f0yWTSpKCq+J5RN7pVkzi7oQYpe5LDdy6EWV0KhQORgqTcgoxM1zB9vi7Jt72qQU1YULKk6Vs0aRU66AYdHIsOkRafV0ObyMSIvi611LSx5eTuLJxdzx0vb4jY+ZUXh6nMGUpKbEbepunx6KVlmXcwgamiWX6vbh1ErctP5g7miciAf729m9abahMKSNpeP+jYPM8bkMacin611LeH3bU3VM+usARxq8/DlYQfpJi1ZZn1Y4HSi8H3XbXfIMOupb/NEzcJ89LIR4X93JV4KPR8prjDrRfItKbx2fSXt7gCZZh3VdS0caHZ1ua1+2aakAoUFHaKP+38ylEVr43z3IoQm3Tm3QNfnoNsmCvik5Meh1eWnvF8mv/vpMIw6ka11LTFRKJ3rMyfNEK5Pg1ZDQ7s3avnuHPtE8TtVNgvLp5Wy4rWdrN/VEHZ/+ayuJUaIcvfFZSx7dQdAYmGLzcpXh9u546XtUTEwWk3yLJ02tz+q2RKK4HF4fTh9yd9fexdRMicK36Vu7Z7g98Xp9ZNm0KAAjo4m2PzxNp7eXEt1XSvzx9uihHxbapu5/kcFcaONnrviDCRZiRIGRhKy9w81r74Pi3+1EaZysvLd6jaAUXe0Xos7rm/i1eqndS2MyMtk8cvbufeSobEOXQVW7rm4jL4d17EhulObexscSaMzNYLAoJyTz1VKRaU7fJfadfslCnMzeOCt3QmFx5GC6s01TQRkhfJ+mTjc/gQCUQvzxxfw7pcNBGSFJZNLWL5uJxtrGmPi8hRg2rBcGto8LJ5czIp1uxKKiasPtFJps0RNngihxm6pnKwcS936ZSU8manzvZNC0NX2t2/uprB3epQY5WSZfKKiovLfwekLYNR2X4yiEwU0Aji9se6jKioqKioqKioqpz4npBiluro66u+tW7cSCAQYMmQIAF999RWiKDJy5Mhub3PIkCF89tlntLW18Y9//INZs2bx73//m+LiYhYtWkRrayvr16/HarWydu1afvazn7Fx40bKysribu/222+PEs3Y7Xb69ev3Hd5tkAyznhXTS7nzpW0xziKzzjoad3HrP77g/p8M5Y5JRQhAuydaVZ5qDN4MRAoiutvsNutEnpo1ussZbZG0uYPNzGTN5tDsfZNOpKBnGg6/xJh8C8vX7eCDDneF8n6ZDOuTydShubS4/Dzws2EYtCKH2zyMys/kjEEWFr8cG70zf1wBvTOM/HLVh+HjlZth5HC7lz++syemMfHK/EpaXT5MOhGTXsQTkGn3BDDpRWQFDjQ5GZ2fjTVVj8snkZ2iZ9WsUXgDMiadiCVVzx/WfxUzWLr22gre2F7P4//eR/WB1oTCoiqblZ5pRnSiwB/X72F9ZOZ5gZVl00rw+t3kZhgZmZeF3etP6vwQGfcSdcxrGlm4dhtLppZw2O6lZ7qBDLM+rhNKKCpl56G28Occ2VSF+A4qoe/FXa/s4KlZo+PGvvzm4jLyLCdO3vT3XbfdxaDVcF5hDoW56ZT3y4wST3UlXupcy8EIrdEseSU2OmrJ5BLMejFuvEhoWyHBWiJCz/dMN3XpOtLdc0tXy7W4/PRINSRdpkeqAZ8kU9/mQRQEKm0WMkxHB0Ijo4Ai67PCZmH1rNFYOpyYIunOsXf5JK5/vpq5lfnh95xvTSFFL9Lq8nLLhUO4fWIRrW4fqQYt91xchsMn0e72k2bSkaoXOdzmYsmUoFPJ9OF9ghEvnaLJlk4pQacReOOGKuxuP5lmHXpRg8uf/PPqnWHijRuqaHf7STXpCEgyv1z1IY/+YkTU7N54dPX8icJ3qdt0o442lx9rqoE7XtrO9ecNRu74/Mv7ZYZFRvG+L6P7Z1Fd1xq1vZBz0A0/Gpz0ddvcfvY2OLB7/GSYdNz/02E4PAHV2UTltOO71a2WVpcfnaih3e3vslbPyM/mmTmjueeNONdBexq57f++4L5LhtInK1qQ0pXrUGehY2dOFiGfisp34bvUbrsn0G2BcgiXT8IbkOmTqeOPG/bEuZdpAkHgzouKcHgDKILCsmkluPwBjFotS1/ZHuOYNLsin8ue+pBLx+RRMcgS95pr56E2Fk0uiblvOVaxqIrKicSx1K27Q5yd6N4pNP6kFWIF8Z3HCFRUVE5fHJ5A2O2kOwiCgEkn4lKdUVRUVFRUVFRUTktOSDHKu+++G/7373//e9LS0nj22WfJygrmN7e0tDBnzhyqqqq6vU29Xh92Ohk5ciQff/wxf/zjH7n11ltZuXIl27dvp6SkBIBhw4axceNGHnnkER577LG42zMYDBgMyZunx0Kby8dv39zNrRMKuVPUUNvoxKDV0CvdyE8f/wCXT8KsF7n3kqH8Zcv+sG15KLoiRIPdG46ECM0G607D1aQTcfulbs9oi1wXum422z1+5j5zdPZ+VYGVFdNLQVZ4f1/Q9tyoE6m3e3h/b1PYcaOqwMrYgdnUt7pZOKko3Jx1eoNuIlqNwFV//oQDLW6qbBZmnTWAD2ubefXzQ3EbE3e9uoMpQ3uTnWqImUUbyjA/bPfw4E+H45Vlfv/PL6Ma8qGBzi37msMN/401Tdz16g4mlfXmoRnl3PbiF/zt6jNZ/PL2KGFRhc3CrIoBTFm5iRF5mcyuyOf9yO3saWTx2h3cOqGQ9btqubJqIKKQ/LOL/Hw6s6mmCY9fYsrDmzm7wMo9Py7jtv/bFiOSCbvAdEQyhT7nkNAokV19aL1HLhuRMPbljpe28cDPhp8wDinfd912l4Aic9tFhdz1yg5Wbqhh/nhbuD6TxcBUdNRyCINWw6LJxTzy7p4Yocjmjmb5wklF3PHS9oTbmthFPm9I0ObwJh8kCMVmJSOjIz6rq+UCkoLepEl6HAQB7ntzN/Mq85EVhYc37KU8Lyu8TrIoIA0CIwdkkZNmoMpmDTtbJDv2lRHH3uWTwue+CpuF8rwsPqtrYfGUEt7e+S3l/bLQaTUoCjh8Aa7686ccaHGHt3P39FIOt3sw6kTa3X5WTCvF5ZfCTdAUvUhAVrizkxNNhc3CggmFSd1UjFoNflmmzRNAFDXoRIGFk4vRIJCmFxPO/q20WUjTd3/w6Ifku9StNVWPRoDFL2/n6yYX6UYtIS2SNyAn/b7c9eqOuL93m2qauGNS8ssmj1/ix396P/x3yNJcdVFQOd34LnWblaLH45dp9/hJN+locvqS1uriV3Zw+8TCpNdB+5tcpBq0MY2zZK5DXUVnnixCPhWV78J3qd0UgzbGfa4zne8VtRqBTJMOrSgkrOGNexpRAKNOwCSKBBQlLETpfB28qaYJBbh0TB4rN9SwckMNPyrswZ0XFfFNqzsq7vWyp7Zw0/mD+d/zBtPm9pOXbSYnzaA22FVOWo6lbkNjDwnjVjscWG++YEjc9VVBpoqKCgTHi471mtikF3GozigqKioqKioqKqclJ6QYJZIHHniAf/7zn2EhCkBWVhYrVqzgggsu4Ne//vV32q4sy3i9XlyuYKyFRhPdLBVFEVlOLrD4Pml0+Hj3yyMU56ZzXlFPeqQaSDVqEYAXrhyLRqNBqxFw+QIsmVyC2y9x03mDyTDqolw4Frz4Bc9dcQa/fXMXczpmoHXV7G6wezhjkIXaRleXTgid1w01a7tqNudmmHjnpnNw+fwgaGhx+vi2zYMl1cBr2+qj3WBsFlbOLGf+mmo27mlk0cvbubJyIAICi1+OHnyssll57Jcj8UsybW4/po74kkanl5EDsinrkxG2dA/FytzwowIeXP9VQtHNpLLenDXIEhPTAEcHOjs3KkPHZ/XmWi4dk4fXLzE8L4sbOwY5Q59DyF0m0XY21jTya1mhODeDle/WsHxaadLj6u7kgNHZalcjCOGYkv1NroS286H9X7mhJioKBoLfzWTraUVN0kZQi9N3wohRfgjaXD4kGb5pcTGnciDD8rJ44aM67r1kKEDCGJiQY831zwedoqoKrORbU8jNNHH7/22L+1oba5q4dWKseCHkvHGk3YOkKEkFCiFBW4ohuVAhw6Tjg31NiYUSBVb6Zpl468YqFIWEbkEVNgtb61oYlZcVPmfFOw4Ob4Dl00ppcnhJN+u49+IynBFOI0ndmWoauXNy0E1qTL4lPEs2dOwFiDkHLZlawr1v7I55T3dNLcEnyVxY3BO318vI/lmkGLQ4PBKKopBq0PLE5aOwu4POGCl6kWanB2tK0NnFjYQnIGHUanACQsd/h1vd3DqhiBsCEo0OHz3SDOhEgTann2XTSln68o6oeJgqm4Vl00v57Zu7eGP74SjL+gyjjoZ2L34pwN3Ty7hzbbTjVig66IS/APgPyDDr+dbuoc3t54UrxoZtxyttFgxaTRfflyZmd/q9CyEowWMf77eyqsBKhknH07NH82nH782JamkeimuzdzT9rSmqY4vKD0/PdCM+v4TTr0EUBDJMui6d9265UIn7XIg2jz/G6a3N7Q+fo9NNOnIzTVHrWFP1nF1gjYrxCXE8YzzUulQ5WRE4KkBOROS9YpXNyr4jDs4caOFwuyfpeg5vgJwUPS5/gN++9SWXje2f9H51XuXAqFgvgIZ2L8vX7Qw34StsFnLSjTz2770sn1aKWS/S6PCxr9Gp1p7KKU+6KXgH0JWb0e0T44/vqIJMFRUVCP4+90g7tnE+g1bE1cWkJxUVFRUVFRUVlVOTE74XZbfbOXLkSMzjR44cob29vVvbuP3225k4cSJ5eXm0t7ezZs0a/vWvf/HWW29RWFiIzWbj6quv5ne/+x0Wi4W1a9fy9ttvs27duu/77STE4fWzcmY5T2+q5f63vgo/Pr6wBwsmFPHbN3cxon8WE0t7s3DtNj6ta+WhGeU88q8a5lXmgxJsuDY6fFz21BZWzhxBplnHoknFSIoc16a4wmbhunEFpBhE/LKMoiQf0I+c0RYZHwTJBS9VBVYMWg31bR6yU/W8s+swj7y7l7mV+VTXtcS3ZUbgqrMH8of1QdvmZVNL486C21jTyOKXtzM8Lys8mDK+sAd3XFTEXa/s4A/r90S934dmlCMrSkLxREiU4fbLXS4T7/hsrmnitomFtLkDrNxQQ3m/TOY9+0mcrSTejicghQeH3D4pqZDIHCEYSBZT8tCMctq74XQR+n+FzYKoCVrzdmlX707+fFfrn8okije695Kh3PbiF1w6Jo95lQPJNGm586IiZAVc3gB+WeGDfU1h8VKFzcI159qoa3Jh6sLN4mCLm/K8rHCkTH+LGQFweHz0SDPil2VWTC9jYRyBworpZbj9fuaPt2HWiUm/ex6/dNQC/ZVYocScigFM/ONGRuZlsXRaCcumlgSjtiKXK7CyaHIxh1rcpJm0/HLVR1FxOKEZrNc/X82LvzoLuzuApEAgoPCN043HJ+OVZG69sBB3F3E27e4AghCMBFo4qRhZUcKz7++cVMRhuxdvQKZflhmjTkOry8udFxVx84VDsLuCkTtmnUib24uoCTpJ5aaZOGgPOqBoBDDoRO54aVuMYG7p1BKOOLy8t6eRFz6q49IxeZw50IIoCNS3eWhs9zAm38Ida7dFi4hsVhZOLsIXkLh2/CDmVObj8UsYtBoO2z00Ozxce24B144rwO2TCMgymyOcpSaV9WLBhYXMH29jwcRCHB6JVKOI0xtAUECKdd8+pfD4AqycMQKPLOPzedHp9SyfXsqWvU3kdCGQS+T2JWjg2nE2ZKJFU5UdzlwhJ7Mqm5U1V57BFc9+fMyW5se7IR3vvBRycOnclFdR+W/j9kt8WW+nd4aR3Ewj+444ky6fKJYuRN8sE05v8Dpkf5OTO17aFlO7K6aXkWXWhessw6wP/06/1ylSbcnUEpqcvvBy3xdqXaqc1AiQZtQmFDtHTmCoslmYP95G7wwjT2+uZdKw3KSb9gVkFAF2HrLzyzMGoBWTX7ykGbTkZhi5LiJetspm4ZX5FbS6/Bh0IgFJps3l58KSXsiywvznq9XaUzlt0Gs1VBVYu3S2dfulmPjX4ynIVFFROblweAOYdF07OUdi0mtwqjE9KioqKioqKiqnJSe8GOXiiy9mzpw5PPDAA4wZMwaADz/8kFtuuYUf//jH3dpGQ0MDl19+OfX19WRkZDB06FDeeustzj//fABef/11brvtNqZMmYLD4cBms/Hss89y0UUXHbf31ZlMk5773/oyZgCvODeD+97cxcyx/Tli97Do5aBbx/zxNp7eXEt1XSvD+mWyYEIht2vA4ZVI0YukGEQUBbbUNrPui0OMyc9mytDe3PCjAgKyEna9ePfLBrYdbGPx5GL6dDHgNsCawp/njSEgKfTLNnHP67vCgxOJ3B2qbFbmj7PxdZOLa9dsDTfpHppRjlYQwqKJzo4eRp1In0wjT7y3D5cv6CSQaBbcppqmsKNC6JgteSU2Nib097KpJcwfb2NUXhYZZh06UYPDEwg70YiiQLs7+Q2ST5KjZt0ZdSLWVD1mvciBZjeZZh1mvUh2ip5Vs0bhDciYdCKyoiAKAi6/hEkn0ifLxDOzR+PyS2H3llSDSHNHo8PhDTCnIh+DVkNxbkb49TLNOlINWvYcdoRn6V99zkAa7B7mVuRz+ZkDyM0w4Q1IHHF4MetFeqQZogaUOh/zvGwz88fbyDLrmFORHxajhOzq431GW+tayOh4r/GeW72p9rSdPdU53ijyGAE88ctRtLh85KTpue+N3WysaeKZOaO55rmt4eV+99NhYUHGvGc/5snLR4U/l0ToRU24rqpsVi4q68Xy13bx0IxyfvvPoCOQNVXPfZcM5faJRTi8AcwGLU6vn7omF//z3Ke4fBJv3VjJdeMKAGJcVkICks17m8LiitkVA4DgzNh/fXWE+WuCTYCQYOzOiwq5dvwgbp1YiMsbINWgRa/TMOOJLTQ6fDw9exQjI0RlkVQVWPmsroXbI+KHqgqsXFk1kBUv72Tp1JIuGwapRi06UUCSFSRZxqgVcQL1rR7q29z0zzYzoGcq7+9tome6EUlRSDHoURRQAElWqLe7saQamPHEFob3zWDh5GJWbqhhU8c5Ob64rpHFr2ynPC+L6roW1lx5Bp/VteDxS+FaCcgKDe1uRg/IDgtxQjV035u7Kc7NoLquhTkV+dz898/DsW2rZ43mD+t3xUT7PDSjnOufr+bHI/pyeyeBS4hKm4V7Li5LesxOdrJTDHzT5qax3cvQPpnIKDz0zh7mVuajFbuOr+tMpc1Cq8vH3Gc/4eEZ5Vxzrg2p45weKR6D4OeuoLB69mgufWJLty3Nj3dDOlHs2onq4KJy+iEpMLhnGqkGLQ++/SVXnWNLurxWI0TFr0VSYbOw7WAbFYOsHGp1xwhRIHgNuXDtNu6cVIzTJ4XrLDfTxMMzyml0+Gh1+/D6Zd7f18SUhzfh8klqXaqoRJCqE/nN+q+4+cJCBL6MER4vm1pCo8PLxNJemLQiB1pc3PfGLm48fwgtLl/SGv5gXxOVg6wM65dJQ7s3HLmXCLvHz2vb6sPXQsFr0SaWvBKMVA3FCVXarPRMN3Lb/30Rc4+p1p7KqYygwI0/KuhSjKLTaKJcXM8usHLfJUPVmlBRUQHA6Q2OYR4LBq2IU43pUVFRUVFRUVE5LTnhxSiPPfYYN998MzNnzsTvDzZztFot8+bN4/777+/WNlatWpX0+YKCAl588cX/eF//E3ySHHcmWahx/fTmWuZW5IcH0cv7ZbJ6Uy2PzBxBs9OLRiPQ7vFjNmhJNWhp9/iRUTgjP5ux+dn85vVdlIzJiImnqSqwsmRKCXZ3cAZ2shltr2+rZ+WGmnBUxR0XFeOXgm4rLp/E9c9Xs2hyMYsmFdPuDZBm1KLTCDQ5fWSn6HlkZjnXrqnm07oWJpb1Yni/TB69bAQmnYglVc8f1n8V1Yg+rzCHF646g0aHD6cnwOrZo8MCh84zYSMHU7qynPUEZCaW9sKsEzni8OBwB0jtsKt1eAOkm3SkGJKXRr4lBZNWRAZ21ttZvamWkXmZPDSjnBS9SJpRy6pZo/j9P7+MGuCstFmYHdFQjvf3j8v7YO64qRM1Are9+AVPzRrNA2/tjnpflTYLy6aVMqxfBr95bRcTS3pz17odVHe45tz75q6Yz3r17NHMfeZjgLguKpU2C1OH5vLclq+4u6NRbU3Vc35RDj8fkxd3+Z+O7MvTs0fxUEc+e+R3ZtWsUaR14eRxqhIZb2TWizwycwT1bUEXDW9AptXtp8HuIS/bzKd1rQAYtdHHStQI5KQZOXdwD0py00k3ajHqkzuWHJ15amX+eBtznvk4YSa4jIIC6ESBnFQjjU4vL/7qTLSiBllR6JFuYPLQ3CinkmanD4OoYdXm2vD7W7mhhkqbhaVTS/D6JS4q602VzUq6SYdfkrn5b5+jE0Ue3rC7k1gt6BJz/fPV9EgzcM24QcgoMctce66Nuc9+HLXvodd+bt4YDtk96EVN0mbGa9vqw4KOOc98zIi8TBZNLuF///Yxvzp3ENmpBg7bvQywpvBRbTPD+maybF1Q1BYZgdPq8vPsnDF8a/ewbN3O8Pky2Xmnuq6VBRMKGZGXRYvTR99sM+9HOJiML+zB6PwiPvm6OcbNaU5FfpRwLzQoPLcyn4ff3ZNQdDe3Mp+cdEPSCC1HF44CJzsOn0SKQUtuhollr+5g6aRCbjxvMHeu3c7cigGJ43ZsVhrs0bEBlTYLy6eX8dPH3g///lz21IesmjWKyxO4X22qaeJXngBzK/O7Jcr7bzSkk8WuHauDi4rK8cDpDYqDjcCN5w/hw9rmpOf2fUcc/PrCISgoUdevkVF3L19bgcsvJT0fKhBTZ6H/L+3kLAhqXaqoROKWJW6dUMSKdTsYlpfJ7IoBeAMyGSYdaUYtP338A4p6p7NsagmegIzZoOW68wZj1GromWrgrmklLIkTR7hoSgmHWtwIAvglhdpGJw3t3sRunDYL275pi7oWCl0/hRwpy/Oy+KyuhctXf8TDM8oTTnb4LrWnRm2pnAwYNAKSoqDRkPD3tarAikYDk8t6c15hDmlGHdZU9fusoqISRFEUnN5Al669nTHqgi6tKioqKioqKioqpx8nvBjFbDbz6KOPcv/997N3714ABg0aREpKyg+8Z98vjgQX5N6AHG5yXja2f/jxgKzw+GUj6WcxYdBq2N/sJCfNiKIo1DW56GsxsXhtMNYm1EhN1YssmlyMLIPLF8AvBSMdpq4MzvI8ryiHFdPLWPTy9phYkdCAfpXNyjXn2pj88CYgOMj3P+cMQtQIuHwSW+ta2LTnCDdfOCT8+iGqCqw8etkIBODJjfu4I9LlwGZhTmU+W/Y1h2f9Xzo2j/vejG5eR876jxSkRM5i72qWT12zi1/9v61U2CxcP95Gz0wjd70a7aRyz8VlVBVY4zYHKmwWtta10DvDxBcHWzlzoIXKQVbcfolv29wMHJiNKGgQELhmXAELJhbR2O7lqwY7Zwy0IgjwzJwxiBqBLfsasbt8rL3mLAKygsMr4fZJ9Ms2c/8lQ/mwtolLx+Rx/1u7487mXfTydhZPLuaWCwtZ3tE4D7nmxDg0dLyXRZOL+abVzZoP90fFuRx1YtjFsmmlUc2YpVNLuPXFLxLMKN7Oz0f1ZenUEgKSEhY2aUWBVRv38b/nD0n6eZyqRMYTXX3OQIw6Da9tq4/5PudbU5g/fhAun0xmio6XrjmLFet2ht2GOouKzivK4e44MTtVNgt3TSul5oiDVbNG0dDuJStFx3NXjCXNqGVo3wxuOn8IJq0Gk0Fk4drtMfsypyKf2U8HhRrzKgeiEYiJ7/JLMvubXIzsn8X/nDMIvVaD0xvgy2/b0Wk0LHljRycxiZVHfzGSFqeXuRX5XFk5MMoJaM2H+5lbmY+swK3/+IJHLhuBTtSEv0epepHaRhd/verMsNOPQRtc9+Xqb5AReHhDDdV1raycWQ4onWJygiKCf+6sD8di/e3qM5EVhYY2D3+eO4bFL2+PPh8VWDlrkIXqulbMepE/XTaCQ61uPH6JNrcfo06kod3L7RcVcdP5Q/D4JQKSElcwFxIi7fimjZx0I01OH0adSG6GkccuG8nWAy1U2qzsb3Ixt3Igw/OywuuHjuOtFw4JOzGl6LWMyMvCmqpn9abauN+9UMPF4ZGSuhZ1163jZMXu9iMroNVouKisJ5JG5JF39/Cb6aW4AhLLE8RVLZ9eilaAV6+rxOEJkGHSYdJp2HvEQaMj6FoVGWuWjDa3n7MGWrplaf7faEh3Gbt2in8nVE58ssx6mp0+LDoRFBAFWDq1JEYQEoqa7JFuwC9JzKnIZ27lwHCUWSjizeWTcHoDXdZqu9sft87UulRR6RpZAr1WYemUEhx+iW9a3Bi0Gj7YFxTeBgXAxSx/bScbdh+N360qsHLtuYOY/3x1lMtenywTKTqRu17dwfqI5StsFq6oHMjwfploEKKa6BU2C9eMK8DTES0SLw7VG5CpbXSyrsM5xSd1cV44htpTo7ZUTha0QK80Ix9+3Rysy3U7YsZsVkwvZfe37fRMMzA8L+uH21kVFZUTEm9AJiArmI7RGcWo1SQc+1ZRUVFRUVFRUTm1OeHFKCFSUlIYOnToD70bx41EUSh52Wb8koxZL4YFF2a9yEBrCn5J5s44DeUFEwqjGs0un8TKDteKCpuF68bbeGRDTcxMsPW7GkCBeZX53D6xiG/tbtKNwRlt9a3Bpu1Xh9uZ9+zH4UZr5HbLOyI25o+3xTS6oUMMocBFZb1i3FeC+yJw1dkD+cP6PQmdHOLNdIt0g4D48QqR5FtTePPGKtrdAVIMIhoBrqwayLyOJoZRJ1LTYGfJlBLu6tT8qLJZWTot6P7g9EmcX9wTX0DG7g7wxTet9E434pMUvmlxIAhCuGH+xOUjMeoy+cP6r8JxOwFZYfyQnmg0cLDFjSAIyIqCyxtAUhSK+6Qz1pCNyx+IcVyI/J7YPUEXmmF5WXxa18qojgGjziKT1ZuCThaLJxczIi+T4f0yY5xOQoKEzk0bj19OOKP4cJuHsj6ZLFy7La4IwHOa3mymRzghjBuSEyOsgqPf5xXTSln4clAMEYp6SSQqWr+rAUVRgg4fEY4l1QdaWfHaTopzM6JqIxQPM6cin1+u+pBFk4p4vZMoJnJfQrV1UVlvXt9Wn9ApqTwvi8ue+pDKju/MmYMs3BEnEmZjTSNLOmJqPqtriXICinT+8Pklnpo1OuxGEiIkDrj/zd28vv1w+PEqm4VVs0dz35u7KM/L4srKgeRmGbmorDezI47LYbsHSZH5aF8zv3n9y6Prdwy0LnxpW8y5MFTz144bBATPKeviCIlGD8jmwbe/5J1OjZJIwVxIiBRv/bumlvDsB60xbiiR61fXtZJm1FFd1xJVq1UJhHkhvAGZXhkiK2eWs3pTbcy6K2eWk246tSO00k06ArKMy+tnzAArC17axoIJhWysaeS1bfV8+W07910ylAUTC3F4JFKNQdtgvyzxwdetfNPqZuWGGlbNGkWWWU9kQFbod6ar3xuDVoNBp+lWs/q/0ZBO78Kh5XSNVVM5cbCm6vEFJLwQjhkz60WuOntgOGoyxSCSqtdS3+bmntd3ccdFRVQfaI3rnAfB77Whi6ZzWsf5sHOdqXWpotI1elFDAAGHN8Dueju5WSYkGcbkZ3PmQAsev8S9b+yKEqJA8HpLVhQuHZMXfZ1SYOWi0l5RQhQ4eq16ZeVAJpb1CjuwREZaludlhq9lO9/P9M82o9EI3HzBELwBiSyzKSrCtDPdrT01akvlZMILLF23g+LcDHqkGrjpgiHcOlGgxeVHpxHol2nCGZDQagT190dFRSUuIXeTY43pMepEDndyIFVRUVFRUVFRUTk9OOHFKOPGjUMQhITPb9iw4b+4N8ePZFEoVTYrD80oZ/uhNqpsVoblZfLJ180JG8rtnkBC4cDmmiZum1iY0JJ4/e4Gbji/gHte3xleZtWsUcx79hNWzRoV5R7Qebuh2WfJ4io21jSGZ73Fe+6G8wr4w/o9XUbthF4rFHNz/fPV4eerD7QmjBuqslnZur8l/D7MepFVs0axauO+mBlBw/tmMrJ/FrPPCg50Zpp09M0ycd8bu3gtoileYbMwrzKfM/KzWbmhhltf3BYWi1QMsvCTEX35qLaJf33ZwI3nDabdE8Du8dMvy0ybx4fHJ+PpEI002D30yjBxXUeDuaojiufZOWP4eH8zL3xUxy/O6M95RT1pc/vJ6IhBaWjzUmmzMKmsJzpRy6pN+2JEJqHGtdMbwGzQJhX7LJ1SEvV4smbMY78YGSNECX6eTSxau517Li5NuO6pjDVVz9kFVt7rGJhOVpP1bZ7w9zr0uSWrgXd2H2Hm2P7MixMPEumg1HmbwegWY8L6j6ytnunGuDXUeblNNU0smFiIqNEkfY+h/VA4KngJLb9gQiFpBi0LX44VsW3q+B7NqciPEqNsrGnit2/uZv74Au5/azcA1Zta4u5Dpc3C8LysqKbGxj2NtDj9CY/Fxj2N3DphCHpRw7J1O+PWytJXtjM8LytKjBJa7qqzBxKQFSaV5bLkldj3FVx/R8L1I+N4lsQ5LhtrmpCJFuZFYtBqUGSBpzfVJhT/neq1mW7UcqDFRe8ME+0dTjNaUUPPdGP4eMaroTdvqCInzUBOmoEqm4UGu5ecNAPNTl/YMav6QGtYCNlVbNbFw/t0c3+Pf0O683kpkrMLrN1ycFFROZ5kmPW0uv3hmoWgqPoP6/dECffeuKGKXukm1u9qYMaYPD6ra4kr0At9r50dUYjxftcqbRYCHWKVznWm1qWKStd4AzJ6rYZ7Xt/FDecNps3tD987Xt7x/85ClBDxHEw27mlk9lkDEi5/28RCZj/zccLnQ9uLFIxW2Cy8sePb8CSKORX5PLXxS9ZeU4HTF0DUCNS3edCJGrbWtfBlvb3btadGbamcTLT7JDbsPpKwJt+4oQoBaGj3Mqq/6oqioqISi9MbvNY+ZmcUnYjzFI8KVlFRUVFRUVFRiU/yKb0nAMOHD2fYsGHh/4qLi/H5fGzdupWysrIfeve+N0JRKHHjVWoaeWZzLYoCv75wCGcOtCRtKLe5k8/SdHiSX/y7fXLUtrsbR3Csy8UjICvd2oZZr2X17NEsmFDICx/VRTUedh5qY+nUUqps1qh1qmxWlkwtpk+mmfnjbWHByMp3j7rEmPUi88fbmH3WANq9ElUFPag+0MrNf/+cmU99yB0vbSM/Jy283KpZo7hsbH9MOhFBENj1bTs3nlfAP/7nTCaV9iLLrOdIu5eh/TL53/OH8If1X3HZUx/yq/+3lckPb2LlhhoCisLNf/+cuc98zLpt9Rh1Gq4+ZyAQbBovfnkHH+9vZuehNp674gw++bqZyQ9v4rKnPuRnj3/Arno7Wak6XF4JjaChen8zn9a1Rr336rpWGuwe/jJvDAFZQZIV7phYxOvXV/LSNWfx7Jwx4WOyuaYJSY6OZknWjPHJcuKGfk0jDv/pebOZYdZz7yVDObvAisub/Bi0dtRs5Pf+u9ZR58dDf2+uaQo7LnVnu8fy+g6PRHsX553O+xFic00TokbAE0jsvrOppomcdEPM44W903mgI8KqvF9m0vUjXzOEp4vvpscnIyuJhUSJtru5pokLS3rxeV0LLl9icWCy9UOPl/fLTCoeird+SAQhKUrS2nSe4rUpSQq904y4fBL2ju9nu9vfdVyH52ikx/zxBfTKMPLH9XsYm5/Nby8ZSlWBldWbaplTkc/OQ23MqcinwmaJ2kao0XUszaxQQzoe31dDOvK81Hn7910yVG2WqZwQePxHazYR7R4/7kDwHOYNyGyqaeKZzbXMrTza1K4qsLJkaglNTh8pepG7Ly6jslOtVna4uF373Na4dabWpYpK17R7Arh9EhtrmghISsy15He5pk22Tlf3st6AHOWcGfpNDsUbbq5p4unNtRT2TueudTt4Z3cD9725G1EjMH/NVj6ra2HR5OJu154ataVyMtGd31cFGDe4h/r7o6KiEpdQ1I5Jf6xiFE3YVUVFRUVFRUVFReX04oR3RnnwwQfjPr506VIcDsd/eW+OL8miUDbVNHHNuTauePZjHr1sZFLBSVexAanG5DcMKZ1uKI4ljuBYlouHueO1u9qGyxdg3rOfYNaL/O2qM/mfcwYRkJXw+v/cWc/IAVnMqcwnzajFF5D5YF8TU1duDseDPDSjHK0ghF0FzHqRh2aUs+bD/UCwCXzY7uGsQRYuKO7J3Gc+ZlNNUzCrfEZsxE1VgZU1V57BH97+kqF9M3h6U21UIzjk4rJlX3NYPLOppimhU8SDbwdnAEe6yURGmIT29+nNtVGONZ3jOxItFxqYXfPhfmaO7c/OQ208MnMEnx9sRVYUqutaSDfpsKbok87abXcnv5ns6vlTmdxMEw/PKKe+CyvSeLXzXeuo8+ORf4eszI91X7p6nVSjiKjp/vKdGwxNTh96Mfn68RoPke4x36XRYTYkPxca9RpqG53HvF2AQ61uNtY0cW0XM3+6KyrqLlU2K4umFOPw+PF1sQ37KVybbS4fd67dxh2TCmn3SOFIojSTrsucarNBxODR0CfLRKpOZPqf3qfR4cMnyTw8o5yVM8ppaPfS7vGz8KJi/IrMosnFSJJCk8tHQFLYWtfCCx/VsXxaabcH8kMN6dte/CLqXPt9N6RD56VGh492j580ow5rql5tOKicMNg9gS5jxNKMOhyeaIvwTTVN3D6xiHMH98AXkHl/XxNTHt6EyydxdoGV+y8Zym9/Mow2tx+720+aSUdAkrl81YfkW1Pi1plalyoqXWM2iGHhtdkg4vRFX0t+l2vaZOt0dS+badKxZEoJdreP8n6ZVB9ojXFNinTt6+wiuHJDDQvXbu92vI4ataVyMtGt31dvgKLe6f+lPVJRUTnZcPwHMT2JovFUVFRUVFRUVFRObU54MUoifvGLXzBmzBh+97vf/dC78r3R1ayqgKJw3yVDMek0SQfoqg+0UmWzsrEmVjhQ0RE5kCxWwKCL3vaxxBGElk8Yk1NgTZgRGjmzvKtt9M0y8daNVZg7bB4VfzCSwagTw24HOlGgb5YRjSDgDcicU9CDSWW9kWSFd3YfZs2H+/nf8wp4ZvZo+mab8PplnD6JWycUsuObtnBUDgSFJE/NGs3MJ7eQYdbxu39+Getgs6eRZa/uCM+867zvnYUnITrbU2+uaUIjwPzxtrCTRa90IxcW9wrP6KNjO/GddKLjOxItF/q7PC+LpzfXMiY/G6NOwydfN0dZ4Z9dYOXeS4Zy3yVDWRCnGWM2iGGXmdD+GnUiW+taWL2ptsuG/6lOKHIgUU1WFVijaidUY8nqrTKi3iKp6PR4578NWk236/iw3UOVzRLXWSNyucqOc0pupjFhzcbbj0h6pMa6nnQmXuMhUrDxXcU7yY6Fovxn4jqAlC6+/8lERVU2K30yTUnXz800sWrWKLwBmb5ZJv658zDTH9nMwzPK6ZVuTLruqVybjQ4fG2uaaPfIpBl1mHSacBRHg92T8HOvKrBi1ok0tAe/0z5Z4dIxeazcUBO22h+UkxpuTrW5fMx/vppP97dEnQPL+2XSJ9MUFki2uXw0OnzYPf6wyC9eg+u/1ZDOMKtNbpUTF7NeJFUvJo3VSdWLCEJQgKcoR93c9je76J9t5iePfRC1znt7GrnlxS94eEY5uZmmcE36/BLPzBkTVWfx6lWtSxWVxKTqtWgiYnUPd/zOHus9ZIhQRF48qgqspOq1Sa+re2ca+eljH7Byxoi4cXwhOju3RN6THUu8jhq1pXIy0Z3fVxUVFZVkhNxNvktMj8sXQFEUhIjrBhUVFRUVFRUVlVOfk1aM8sEHH2A0Jm+0nWx0NavKH5CZ9+wnzB9vIzfDmHBQb+ehNn594RBklKjnQ04YC178gpUzRqCBqEZzhc3C/HEFHGx2R2179abasGPInI4Busjthhw/rn++Ovz6iyaXRLl4QHBw8NYLh+DyBWL2vcJm4brxBaQatKyaNQpREDivqCcKu2OWu+ZcG9+2eZAVeGrTvqjnxxf24JYLChlf2BO7y49fUvj8QAsrXtsVdglZNKmY8wp7UjHIil4r0jNdw29e3xWVm1xVYOW16yp5Y0c9KzfsZVNNE0btHh6ZWY5JJ3LduAJum1AICGz48jCP/3sfLp/EppomFkwsjDu4EzpunXPRIbqpbtaLGHVaqutaop1XbNYox5NIV4hkr9Od5VZuqGHBhELue3N3zHfqvT2N3NbRwInXjGly+nh69ij2Hol2kMjNMPL07FHHfIN6KtLq8jGncgAQHZtSZbOwbFoJd6/bBRyttc7/7lwDi6eUcO8bu6Jeo3Mdhuo98u/qA62s3lTL6lmj49Z/aPlKm4XeGSbmVOYjx3n9yOWWTy/lYJObr75t5+7pZdy5dlvU9z/Rfhw9Blaq61romZ5YzFKZoCmRETGz77uId3bX27lunA3ivMf54wp498sG/JJyzNutinhcUISEDZPE61vJt6Ywu2IAeq2QtIGjE4Vwo2XVrFFRTjEmvZh03VO5NkPizhR9UCy37WArS6eW8siGr7jhvMEMsKYAxPxG3XlREYIAZw20ICl+3H4pKgqps9V+o8PHxo7mU7zz7JgB2Th9Egte/CK8HBwV+eXGERupDWmV0500gxYDsGJ6GQs7/aZU2izcfXEZDq8Ho87A4inFfNPiDj9v0Gqwe+K7H0U2lxPV2aFWd8J6HZST+v29SRWVUwiPJGHWa6gqsPLulw2cmZ/N/HE2nty4L+zCGO8esqrAyrXjbMx95uPwYxU2C/PHF2CK0zCvtFlYMqUYUYCFk4tj7jUrbBZmnTWAu9ft4t5LhnbpoBLPuSXynqy78Tr/LQclFZXvg65+Xw2ArApSVFRUknDUGSX5xJzOmHQishJ0BT/WiB8VFRUVFRUVFZWTmxNejPLjH/846m9FUaivr+eTTz5h0aJFP9BeHR+SzaqKbOCu3lTLIzNHMD9BE3Xm2P5c8ezH/Pr8ISyaVMyhNjeKQtiieGReJpY0PVOG5TK7Ij8c3dFg96IR4OZ/fM69lwwNb9vlk7j++WoWTiqid4aB2yYUIooaWp0+slL0mHQa3H6JP88dE7ZFv+ypLVw6Jo9rz7Wh12pIMWgxajUcanHTO8vE5KG5zI16bQ890wzsb3IhCAKfHWxFrxW4bUKwMej2SaQatWFXk20HWhlf1DPqvVtT9dx43mCaXT7a3H6MOpH3ahrZeaiNh2aUc9uLX/Dbnwxl9aZabn9pW3i9KpuFOZXR8Tkb9zSy+OUd3DmpiKJeGdzyj8+ZeUYeqztF71TYLFw/3sb4IT054vDi8UtoBIH5422s3lQb14IyXvxG5CDo3Mp87nplexzHk0ZklLDjSXdjSY4lviRRTFQ8R4AQdrcfRYHXttXHCozGFaDTqDMeUg06Zjz5IXMr85ldkU+aUUe7x0/1gVYCssy8qnzcAYnNNU1c/3w1cyvzuWNiEQdb3SyYUIhfkvFLCpKs8MG+JmY+GayveZUD0WoEdKKGFpeP3Ewjz8weTUBWeH9fU1i4FCkIKc/LxBuQmFuZz20Ti3D6gnEIBlFDk9PL//3qLPTaYE07PAHumlpCQFKoa3bRO9OISSfS7PDxj1+dhVmn4Ui7F0uaAZ1OoNXl5p6Ly3D4JBzeAP6O80FoPzoLZqoKrCyaXEx9q4cd9a0snlLCsld3xAyMrphexn1vRotvKmwWss16qgqsbNzTmFC8U2WzsnhKMfe9uTtm/R5pRrwBmUllvTudj4K1/Pi/9wEk2K6FZdPLWL5uR8x2F00pYfojmwH4ps2dUIi0aEoJv42zX7MqBjD54U2U52WydEoJ148viHn9SPFg6O/O7jMGjcB14+Kve924AgyncG2GxJ0GnQZkhbI+mbS5vcwY2x+fLNMn08TyaaW4fRKujt8XUQgOkCmywv5mFzkZBnQaCMhHXRc6W+135WjW5vaz9NUdUY1tiBb5qU0qFZVoNIBAsGEW+k1p74jVSdUHazTNYMTpD/B1kxupo0ZD58ELS3ol3Hay5nKbyxcjRAG1XlVUuqLdHeCA28+tE4bwx/V7GN43kwa7lxvPG4xGgFsuLARg0aRiArJCs8uHTiOQk2bgnd2HefLyUYgaAZdP4ts2Ny6fxDPv13LnRcUowP5mJ3ox6O73m9d3cX5RT0YPzGZuRT7XnGujze0Pu/+Frjk9AYklU0u6dGRJ5t53LPE6atSWyslCst9XLaBwEgwSqqio/KA4v2NMT8iF2+ENqGIUFRUVFRUVFZXTjBP+PjM9PT3Kvk+j0TBkyBCWLVvGBRdc8APu2fdPaFZV54HwKpuFa8YVMO/Z4Kwxl0/i2jVbufqcgSybWorTF8Djl0g36tCKAi1OH3+eOxaTTkOz00v/7BRc/gBlfTL489wxmPUibS4fI/tn4wlIuH0SKQYtfbNM6EUN/2/eWJqdXhZPLkZWgjcaJp2IT5I5YvfxSV0LOw+1BUUvf3o/LLgIRbVMKOnFmPxsssw6BEUgxRBc9+smJzlpRnySxJgBWXj8ctitpE+mkZ8+/gGNDl/wPRdYufbcQfz8iQ/CyyydUsyI/lmML8zhzIFW0oxabrlwMIIAZ9ty0GoFvmlxIwgCO+vtrN5US3leJldUDqSx3cv/mzeWJqeP2yYW4Zdk7O4AH+9vDkbfbPqaq84eGBVPs7GmkWanjyPtHu67ZGjc6J3qulZkBX775q4YkUqki0kknWM5Og+CnjXQ0i3Hk+7Gh3R3uVDWeyISNXAkReGJjfsoz8sKN/RDMT1PbNzL4snFSbd7OmBN1TOqf1b4c101axRPbw5+n8YN6cG8Zz/pEIcUcrDFTY9UA06fxNV/+TS8jcgopOLe6eFIltWbanl4Rjnznv2E166vwOmTqG9zU94vkwd+NoweqQbMehG3X+L5K89AEKC+zYNWI/D5wVbGDszGH1BwB2RSDTrufm1nTDTPqlmjuCpiXzqzatYo5j37CZNKe3HbxGDDAUWhV4aRyUN7U2mzkmrQImpAIwg8PXs0Bq2IRgO1jU50ooYskwGNRuGuqaV4AhLt7gAZZh0pepG6JhdVBT34ych+YcHIYbuHI+1elk0rYdHa7WyKEPJce64NnVaDSSfS0O6hwe7hR4U5/OKM/qQYtPgCMh/sa+LaNVsBWDSpiD5ZJuxuP6kGHfmWFCav3BSu3dB2Q9/vfGsKBlFDu9vL7ROLuPmCQhzeQFhg9Mb2esrzMtlc04RWIzB/TXVYiBTa/+oDrcx8cgtPzx7D/HEFNDmDzi/VB1q565UdPDyjnJx0A00OH1kpOhZOKuKw3YvLJ4UFfNY0PTOe2BLjPjO+sAcAzoCEJVXP5LJo8d9huweNBhQUTlWsqUGh0sFmN/0tZr440MKwvpmka8AXkPH4JWSCv6dmg4hB1IAAM5/6kKVTS7iu4zM/a2BQ9PT07NF8a/fEWO135Whm1ot8ur8lKnYtMsasuxEA3wfdjQpSUfmhafdJuETI0IiE7wA6/qEAeo3A9X//jNkVAzFoNWg1QQeqOZUDeG7Lfi4o7plw2/Gay6HaaHb5mFORz7B+mTGC4mOJ7OgKtRZVTjVSjVra3H5+/viW8PVL7wwjMtDuCZCiF0gxaHH7Jdx+iZ5pBnSiQG2ji7MH56AXNTQ7ffTOMJKbYURWFG65sBCtJtiw0osavAGZEXlZAPTOCMarAlz21Idx92lTTRMur5Rw8sacinxe6HBsiefe913idVRnM5WTgYMOL73MOhA6xggitOkiCvVOH+nq91hFRSUJjo4xYs0xRu2EnFldvgBBWZyKioqKioqKisrpwgkvRnnmmWd+6F34r5KbaeJ3Px3G3gYHAUVBFAQ+/roZj18KNzch2ED7uLaZcUNyuP+tL2NmcQL85uJSNuw6TGFuBuX9MpFkhTa3n399dYSdh9o4v7gXw/pm8GFtE+/vbQpvO9RYDhHZBA+5CPTJNMUILVw+ic8PtDJ9eC7PbK7li4NtPDVrNItf3s7GmqbwdioGWcg06VEAt19CUhRyM4w8dfkoDrV5MOpEtn3Tyjetbv569Rk4vRKZ5qBzQ7sngMsnkWIQCUgKOWlGRvTP4jev7eSdiJidkBjkthe/wKjT8PLn33DLi19EPT+nIj/smnL989XccF5BlBgFoNXtZ2CPVAw6Tdz4kLmV+ax8tyZmxl3o75CLSYjOsRwxThE2K7ouxCMhksWSVNks9EgzsGrWKAb1SOnScafCZkErJr+RTDQ70O2XmDm2P09vro16r6Fj7PYnF7mcDnS2717w4hc8d8UZLF+3g4b2oMBg5YYainunc81zQYHEqlmjorYRWiZE50gWAA2amOiqED8q7MHMsf0BkGQFXUc96QSBD+ua6ZluJEWvZU7lQIblZQVFWgS/wzlpxoRRM1U2KzlpBtZdV4kvINHuDdZoo9NHu1ficJubkQOymfnkFop6pXPn5KBY5f5/xkZwDbCa8fplTHoNvTJM3Ll2G+V5Wew81EZxbgY9049Gsx1q87BhdwMXFvdkeF4WcysHYtaLSB2uMC98VMelY/I4a6AFUSPQK8PEJ/tbeLn6G5ZOLeHCkp5U2qxkmHT4JZn3a45Q1DuDu1/fSXleVsz5NnSsqwqsnDEwm4CsMGZANve9FbSInz/eRnVdC5s7znUhN5XqA62U58WPyqqwWXhr57eU98sMn3OtqXqeu+KMGOv5kENMs8ODQa+lvyWFVpeXP15azgcR7jPjC3uwYEJReP3QeTfPYibFoMXlkzjU5mHFa7t48VdnxezTqUKGWc/dF5dx3xu7uGacja+bXQzplc7v3vqSG88vIGR2IikKbp/E2zsP88R7wbi1PlkmVl8+kof/tTc6Kq3AyjmDe5BhPvo6yRzNzi6wImoEHppRHnV+NOtFFk4q4q9Xn0GT0wdHHMe9GZ0seiReVJCKyg+J3e2nd4aRr9vcOLwSOekGJBkCssyehuB3udHh48bzdOxvdlGam8HEsl40tXtZMLGIVmdsrBvEby53ro3I+jzY4sagPSoe625kRzLUWlQ5FUnRiTTYPZTnZXLd89W8cNUZPLD+S8r6ZTK5LBenLxAWQm+uaQz/3gK8cUMVfkkm3aSlttGFTtSwta6F3fV27ryoCEkmHDMZcvubMrQ3rS5/l+6Pdk/wXDK5LDfoJigKZJl1KAroNAKlfTPjugiq8ToqpzJmg5Ypj2/hvkuGRv2+ftXx+/rcFWPxS8lrS0VF5fQmJEY5VkJOKqGYHxUVFRUVFRUVldOHE16MMnDgQD7++GMsFkvU462trYwYMYJ9+/b9QHt2/OiZbkSSFZa8vJ2fj8nj46+beeK9feGZ+QC90oOzxh79Vw2LJhez/NWdUY3iCpsFW49UzhxkYfHa7dENNZuVpVNL+HR/Mz957APMepGnZo1Gw5dsrGmMETl0bsQum1rCsL4ZjMzLinrNqgIriycX4/D4uPXCQty+AFkmHXdMKsbuDs7+bPf48fhkVry+M6pReuZAC6IgkGrQkmXWMaGkFwdb3Bxp94WbACPyMsPCjdCg4fxxNpraPQztlxklRgnt+32XDE0qFinPy+LpzbXMrcyPimIIYdBqCMgKfk98QUV5v/hNZgi6piyYUBgW8WSZdfTJNOHyS1TZrKQZdZj1wcHb1bNH4/JJbK1r6XLwJy/bzDs3nUO7x88l5X1Y/Ep0BEQw5iOfP67/irumltI32xw3xzw06BrKca9tdIYjTzqTbHagIAg8vbk24TFWnVGCdLbv1mgU7pwUfWwMWk24JnqkGaiyWWJcSiB+JAuAIsSPWjLrRWaM7c8zm6Njps4ryuFHRT15Y1t9jLPPIzNHoBHgm1Y3TQ4vCycXsWLdrpiaXzqlhCMOD1v2NTOiXxa7v22nZ7oRrUZD/2wzuRlGWp0+/t+8sYiigKcjwmde5UDKO0QvLl8wosig1bDwomKc/gB7jziYVzmQbd+08osz+rNqU22MMOCuqSXsaXAwIs2Ioig8+34tg3LSGJGXReUgKzIK7+89Kkw5c6CFykFW3H6Jt3YEXWUenTmC+jY35w7pyZKXg/FY1XWtrJxZjgY6RetYuW68DZdXIjtVz31vHhXUdI4JCrmpVAyyMH14H+56ZUfMOfrKqoGgQE66keevPIM0oxaDTsPSV3bEfI6bappYuHYby6eVsujloBNM5Pnz3ME9CEgKTl8gSsgSOn+v3FBDlc3CrROKwtt0neKDQHnZZm6fUEi93RuM6XF5WTylmM17m1j3xaH4Qr4CK980uxjcMy3mO7oxTlRHZ6FZiFAjyx+Qo86PIaHS05trueOl7VHLH69mtBo9onKykWHWISoKuRkmbn9pW1wxcKXNQnaKHq0o0OTwsuK1Xfz16jOY+eQWHv/lyBiRWLzmcufaSFSfIYFzuqn7kR3xUGtR5VTF5ZcYnZ9Nfo9UHt6wh7nPfMyq2aP53Vtfoijw6dct8QXNBVYO2z18frCV0f2zyTDpcPsk8rJMXFjcC09Axu2Xuai0F5eU98HtDyArAm6fjEYjkJdtThqNqhEEBEHgnMFWnD6JumYXDXYv275pQxCgrE8GD/xsGL0zTGSatNjdfl6dX6nG66ic0qTrRIp6p0dNPgpRVWAlXSfSHlAnk6ioqCTG6Q1g1HdvEl0kxrAzinqOUVFRUVFRUVE53RAURTmhffo1Gg3ffvstOTk5UY8fPnyYvLw8vN74sx//29jtdjIyMmhrayM9Pf172Waby0eT04ckKwRkhTaXjxSDDp8k4fJJWFL06LQadIJAo9NLqkFHuyeA2SBi1onoNAKSrIAg4PJLtHv8pBi0GLQaZBR0ggavJKPVaLj39Z2U9s3kR0U5NLX7yM00sXzdjphG7LLpJWgFgXvf2EV+TlpYaJFp0tEny4TbH4zXSDGIpBm0bNnXRHaqAW9AxqQTMelFslJ07O+Y+WZJ1fOH9V+xoUNIYtaLLJpUxNB+GfglBVGjwe72k2HSsX7XYb442Epxbka4KV1hszCprDelfTKYunJzzDF89boKpjwc+3iIkAvMqlmj6JFmiNpGlc3KsLxMLijuiSgITHp4U8z6j142IuxkEUmoofFsp+Z/VYGVJZOL8UoyoiDQ4vKxeW9T1Gy/BRMK+e2bu+M2X84usMY0K75pcVHX7MIvK2SZdQQkBa9fol+2mT5ZR6fwh2zh29x+zPqgpaaCgkYQEDUClhQ9Tp+UsKnaO0GTdHe9nQl/3JjwGL95QxWFvb+fmvi+OR512x3aXD7mP1/Nxj2N3HheQXiQ/n/PL2DMgGxWvltDdV1ruCkWz1o8UpRVnheMAPrrVWfw8ye2hJcNiRXOHdyDNrcfQRDC4i6XT2L+eFvYdSRUy2kGLRlmHTpRg16r4aN9TeSkGwnIwcagrCj4JRlfQI5xIDHoNGSZ9Xj9MnaPH1lRor7fISeg2178IryOTqshzaAlRS/S6PBg1AfjxvY3udAIAt+2ucnLMuOTZXqkGXB6JDJTdFTvb2H5a7vCAxlVBVaWTCmh0eFhU01T2AElN8OITity1yvbYwQ38yrz6Zdt4uPaFsr6ZnCg2Y1RJ/LFwVZG9MviYKuLnunGcLxNg93DyAHZyCgEAgo/e/yDKNeoFL2WgCyTadbR6grOoK8+0Brel5x0A06vREZHLnub20OKIXh+bOsQ7AFMTFJPb9xQFfxsdSItLg/pJgN+Kbh+hkmHAkz4Q+L1V80axTPvf83/nDMQS6qewl4Zx/4F/oE51rr96rAdRVbI1mvxAW5JQtRoWPxytJCv0mbh2nEF9Mk04pMlpj78PuV5mVH1BvDOTecwKCc16jVC59d2j580oy7cyNpzuJ3zH3wvvFykg05n4p3fvw/2Njj40e//nfD5eO9HReX75ljqtr7FRUBWMMgKbo3AwrXbo+MrC6wsn1aKToBpf3qfP15aHnTakxWu/sunvHPTOVhT9XFrMpLOtZGsPittFh742fAoh65jRa1FlZOR7tTuJ183Y03R8/nBVob2zcTlD8bAZqfo8Xfcay59dUdMHa+YVopHkrC7AqQatQgCKIqCWa9l+as7WN/J9fKuqaXc+/rOqMcjXSYjm1tVBVZ+M72UdJMuXPuHWt3HfJ+jonIykqxujzQ5cStw58uxv613Ty/FBDgUhXyr+nukoqISn9v/bxsf1TaxYnrZMa3X5PAy//lqnp4zmnFDcrpeQUVFRUVFRUVF5ZThhHVGeeWVV8L/fuutt8jIONowkySJd955hwEDBvwAe/bfIzJ3et8RBw6fxMOdXD7GF/bg9olFPPavveE4nha3j4Ieqfg7nD627G0kJ92IX1JIN+n4/EArZX0zWfjKdsrzssID74W5Gdz7xm6q61q5+pyB3DqhiNuEoIWiqBHYVNPIpIc2xTiUhKi0WRjeMYt8/vhBXFTamz5ZZlrdfow6kU/rWth1qI0ZY/tz418/w+ULOoQsnFzErDPzqT7QwrC+mfxly9f0SDfGNOGrOhw/tBG5pJtrmsJuMfFwJHA0CZHI3rmqwMq8ynz+/MHXXFTaC5NejOsaYkgQqTO3Mj+uW8jGPY3ctW4nF5X15oF/fsnq2aM5c6CF4t7pGLQaqg+08ti/alg0uSQmpqMqgWV0nywzqQZtuOmSbtRhtabELNedHPMMM1HuHYkaOJEE814To856iKXR4Qt/l554bx8rZ5YDCooCj0bUeMhdY25FPoJw1DXpW7uHv8wbi0mnweEJ4PHLvHDVWNJNOh69bEQ46qqsT0aMo0hohvf1z1czKi+L4f0ywxEikRExY/KzOSM/m5BaUZIVjji8pBlFHlq/Jxy9FRLMhJxBfv/2VzHimVBk1vC8LMx6kUcvG0lAlnm/k1Bl+fRSLl/1Ef0tZhZNLuGyp7YwtG8Gd04q4qN9zUFbdVFDs9PHN20eIDpGbH+Tkz5ZJiYP7c3PRvTh9rXR57hINtc0oQHuuKiI17bVR82Cr7JZGJufzYoIsUuISpuFZVNLafd5Y+JXQu93yeQSfrnqI1w+KXyMVsc5ny2fXsYf1n/J2s/qw4+/cOUZSb87rS4/m/c2cuZACwathsN2L5v3NoaP4+rZo5Kun2LQMqxfJk9v/pq7ThPXolSdFpfkRQYCisKR9qDIc+mUErwBCYcnQJpJh14U+PkTWyjslcY9F5dx9TkDefDtYHRbZORavKiOROfXzhbEydy03tvTSKPD972LUexdRIt8H9EjKirfJwFZwSMHQKPlsN3NkinFeP1y+JyaahRpbHezr9HNr84ZRJvbz3Mf7mfBhMKwk1t3rnk610ay+txU04TDE6Dnf6BbVWtR5VTFkqLHI8kM6JHKJ183099ipuaIk14ZJrQaAWuqnsVTiglISvBexaRDr9FQ2+hEhrBQOiQAff7D/cwY25/39zWHr8M21zSx9JXtDM/LihKjhMT7kb/TiQQmnV0Ku3Ofo6JyquEHAorMdeNt3D6xCIc3QIpeRK/V4PZLCDqRu1/bxQM/HabWhoqKSlwc3kDY5eRYMOmD6zhPcYdWFRUVFRUVFRWVWE5YMcr06dOBYATIrFmzop7T6XQMGDCABx544AfYsx+GgKzEFTcU52Zwzxu7mDm2f7gh/MjMETS0e6lrctI328y6bfVmGv3LAAEAAElEQVSdmqBWcjNNVNe1Mrfi6MBdeb/McEP56c214SYcHHVjgOCgn0L0oF/o8bmVAxk5M4ue6QbuXLs9rqPDmg/3h9fdWNPIinU7uXVCET3TDJj1mo5GaRwhR00TMnDzBUOiHvcG5ISCh1Rj8hukkJikV7oRk17kb1efiUmv4b2vjvDnD77myqpBpOi1+GSJu6aWsKRTJM5hu4cqmzXGejpZQ2PjnkYWTipizRVn0O7x0yvdiCVVj8MTYNqwXNJNOhxeP4snFyMrwRu1DJOOnDRDwgGh7jRdusuxbivD1IXA5T+0tT8ViWxIuXwS89cERSc/KsrhD+v3RD23ckNNWNBw7+u74rp7CAg8vGFvVM385uJSnt6UOD5pbmU+GWYdv/vnl1GxViER1O0TC2n3BHit0/ljzRVjw/sQKbqaP96WMK7JoNXw1KzR3P/W7oTCmE01TSxau51HLhvB1JWbWb5uB/ddMpTth9posHtjzmOhKCEFJUZwU2mzsGJ6KXVNrqhzXGc21jTR7PInPNeEzlORghdvQMYrBV1a/vTvvXHf7/J1O8LrJhSmdbzfpVNLmDKsD96AjFEnkm5K/rOcadZRXdeS8Dh2VW9Ob4DquhbmVOTjPA0suNtcPnySjFGjxwPcuXZ7lOtU6Hfp8ff28usLhvDLM/vz4Nt7sHsCTCzpzeP/3hcjekwzdv+clt5p2UQCyBDHoxndeR86cyzvR0Xlv4HTL5Gu0+KSZP74TrQIu9JmYenUUgRBYEivNPRaDd+2ethc04SsEFe0m4j/dn2qtahyqiJqBAjA797czYIE14+ha9ZUg5a71+2MuZ4NXcdAbVSMaud7zTlxJiFsqmli4aRizivM6VJg8n3eM6monIxIQGO7F0mBuzuik0NU2azcMamIMflZx0UgraKicmrg9Pi/kxjFqFXFKCoqKioqKioqpysnrBhFloMDwvn5+Xz88cdYrdYfeI9+WGRZiWsbXt4vEyCqIXyk3cOrnx9iwYQi7n1zV5wmaCMyCnMr86MG3r0BOWHjNLKBvXJDTUJHEq0ocLDZxepN+xJuozwvK7zfwf1pYna7h3Xb6pk/zsaPCnOihDCdt3HbRCHqMYNWg1YjxCxbYbPg9EpxHU1Cz1cfaKXKZiHNqOVbuweHV6LRIVHYK53sFAN9Mo0ICjg9AUwGLcunleAJyLR7gjOIzDqRswZZWNQp7qEr2j1Bt5nMFD3WlNhB0Z58dxv4/zbWVD1nF1ijLK9DhGYoqwQ5bPfQ4vSh7+SoExKdFCeIM0pWlxpgYlnvmOd6phujBvo7rze3Ih+tKEStl5NuCP9t0Inc88bumO22uo824yJFV8kEWMW5GTzwVuy2Op9XNtU0cYcYPDabappYMLGQcWk53Pdm/HUnlfXm9U7NjtC6CzuELQdb3HH3KUSbO35zMXSMIt1fIt9fVYGVWWcNYEvErN0QG2uamN1xfkwqTKtp5LDdE5XZ/sJVZ1Bps8SN6aq0Bd1Qkh3HVIOWCpsl7u9F6JwXem7RpFPfGcXu9iMr4JMVlry8Pea4Rv4u3fPGbm65cAgPvr0Hpy/AsxGNsNBv5bGe0zqfH806kfnjbWFhk1EnRkVnHY9mtHqOVjnZaHcHMOtElryyI+75fekr27l7ehltHj9truBg+OrZozHrxG5HbbS5fMiKwqpZo8IRduYuBtX/0/pUa1HlVMXpkxAEGJaXhUmnjXv9GHnN2vn6tPP1YEhIHO9eM5Fo7JtWN0W908lV43ZUVJISkBVqjjhjBGMQvDf5zeu7WDqlRHXrUlFRSYjdE+jyujkeGo2AUafB4T31J8WoqKioqKioqKhEEz9j5ASitrb2tBeiQOIoFG9Aprxf5tGGWr9MBvZI5dO6VkRRiNvQhOCgX3m/zKiYGYNWE7WtROtEvnZnssy6LpvgoSZc5/exuaaJle/WYOjipqbFdXRgpMJmoaHdy74jjqhlqgqs3DW1lB5peuaPs1Fhs0Q9H5oNv7vezt0Xl/GHt7/ivT2NKEowlMSoExneL5OGdg8XPrSR6Y9+wIUPbuTOtds52OLmiX/vJdOsZ0CPVAZYU1k5o5x3bjqHtdecxTs3nUNetjnpe2hz+7n40ff50QP/5rrnqznUmrxhfiKTYdZz7yVDObsguk7PThArdLpS1+Tkpr99xoQ/buSN7d/GfCchcexTsrrcWNNEz/RY8VJXM7y9AZkWZ/QgY2Sslc8vx33NyH3sLGZLRHm/zC7PCSHaI8Qhof1J9N57phsTnuM21TShFTUJj2mIZM8nE+ht3NMYnrWbjK4+h9ZOYpj5a7aydGoJlZ2+H5U2C4unlOANxP8tCB3HNpefORX5Cc95qzfVhpdX4m3oFMPfEWvl9kldfgcjBYUpei1XnmNjVF4WEPyeJDuntbl87G1wUF3Xwt4jDtpcPiD6/GjWi2Sm6Kmua2Hes59wzXNbmfvMx1TXtfDQjHLOL8o5Ls1o9RytcrJh1os4fVLCc/+mmiZcfgmXT8LhDXDpk1uY+8zHLH9tZ9zrqcj63HfEwcFmF/Ofr+b8B99j3rOfhOswM0XP+MIecV/z+xCLqLWocqri9PjRajRU17Xg8Seu3UTXrBB9PRi6dop3DZXsum3Bi19w2O45xr1XUTm9cPkleqYbE9fpnkY8geMjkFZRUTk1aPcEwpE7x4pJJ+LwqM4oKioqKioqKiqnGyekM8pDDz3EVVddhdFo5KGHHkq67PXXX/9f2qsfhjaXjyanD7M+/kdl0GpiGsIBKeh60pXAwRuQ2VlvD8+irz7QGtUUTrRO5GtHUmGzEJCUbjXBO68b+ntzTRM+f/L1A5ISfr3rxhXgl2S2Hmhh1axR4W33zTIxdeUmHp05gh31bdw1tQSPPxjnk2IQ8UsyHp/E0qklLH15B+t3N8S8ToXNwqSy3lEW0ZGz6G978QsenlEetnuObCK0uXwJZ7+G3AlCvLenMWpbJyNqBntyDts93P7StvD3JxSHBdFCi8N2T1wnn+7UVGe6I8LQidGOQpGxVs4EArjqA63hc0ZnMdux7F+i59MiYmZSjSJOT+JZM11GKrj9UfvbmaoCa1QtdiYk0EvkbJLIIQqCLjMVHU4myej8fKPDx6VPbOH5q87A45dweCRSjSINdi8zn9zCwzNGJNyWNyDTI01k9jMfM7cynzsuKmJ/kwuDVkP1gVauf746ysXldLDHdfskWt1+dGLyzyH0XXL5JKpsVr61e3hm89fcdMFgqgqs2HqkJjxHH2p1s+DFL6Lq9uwCK/deMpTcTFP4/Njq8rNw7ba4s8UFji1e5FhRz9EqJxMmvciRdm/SZdo9foxakU0RMYkb41xPda7P+eNtVNe1xHdtEL7k7umlBKTtUddv36dYRK1FlVOR7FQDS14OxrMmun4MkezaLfRc6Nqo8zVSlS3+dVvo3mrjnkb2NjiQZEV1SFFRSYDTK3V5D+XySvRVa0hFRSUB7V4/5v9AjNLVtYKKioqKioqKisqpxwkpRnnwwQe57LLLMBqNPPjggwmXEwThlBajfNPi4kCzi+xUPa1uH1U2KxtropvU1QdaOXPg0RnwBq0Gs0HsUlQSWjbUFNcgsHpTLX+9+owu14HgLP3IwcBKm4XZFfm0ufxdNl8zTDo+2BedEx65LacvkLR53DvDyLrrKtEI8Pauwzz+731RDdYqm5WRA7Jw+SRcfgm3X45r9Q6w5oqxcYUocLTR3HkGX+jxlRtqEmYph2a/3vbiF1ENjZA7QTAT/Sjv7Wk86XOZ1Qz2xLQ4fVHfP5dP4vrnq5lbmc//njc4HBUjCgLzx9mQlehYrkxT8plp8WouqQjDZiHfmoKoEaKWabB7wxExYpzoKyDqnBH5GsleL6Ob+19psxCQ5PC/G+zepOt2da5JM+kSCn+qbFaWTSvh7td2xV23qsBKg93T5Xc63mBuhc3CO7saKM/LIt+aQpXNEteVo/O5L0Sjw8eeww6ueW5rzHORgqHOhI5HeV5QQDOprHfcbYRIM56QlwDfKy6fhEGrId2U/L2Gjp1RK3LXtBJ+9vgHNDp83DaxkOXTSulvTYm7XpvLFyNEgViRYYZZT6PDl9CdZVNNE54uhJj/Keo5WuVkQacRSO/idyPNqEOWlbDbU4jI66l49Zk0Om1PI35JOe5iEbUWVU41/JIc/n1LdP0YItm1m0GrCV8bxbvXXDa9hLvXRV+3VRVYufXCIbS6/Ey/6Wy8fpmDLW4cngA90w3hc0Gjw4fd4yfdpIsbkXqsxNsm8L2/jorK9026SdtlBE+aUat+d1VUVBLi8AQwJZgw2RUmvYjjNJgUo6KioqKioqKiEs0J2Ymqra2N++/TiYPNLhb83xeMyc9mzIBsnty4j1kVA5CJblLvPNTGT0b0CTeQqw+0clFpL6CLZnRHozXUFH/uirE0O32gwD0/LiMnzYA3IGPUiWyta2H1plrK8zLD27zlwkJcvgDnDu5BqiH4NfrdW19SmJtOboYx4etW2iykGbXh5kE8cYZBKzKnw20gqnlcYOXac21c8qf3AXhoRjkf1zZ3EqJYmFM5gPlrqju2ldzZoHNERmcSzRoKPZ5sIKfz7Fe9VsPr27+NcScIoeYyn7rY49iQunwSKzfUUN4vk3nPfgLAqlmjmNPharFgQiEHW9wYtBr6ZJqSCkviWZKv3lTLqlmj0EBUA7zCZmFWRT5H2r2Y9SLXjSsAgrW24MUveO6KM1i+bgcb9zTGFcC5fBIvfnqAWyYMQSMInDO4B/PHyXxY28wVlQPD2zq6f1b6ZJribiu0P6Gmw/LpZVy+6sNwJM3MJ7ewYEJhXLcYgIZ2b8LnKm0WBJQo4c8159rQazU4vQE+2d/CG9vrubJqIF5/dIRLlc3CDT8qwKjToNF0La7r/LqzO85pLp+ErUcK144vQI5zXGZVDIgRpoWI16yptFkSupmEjmNelonrxgc/0/e+OpLw+FQVWDHqTvikvv+INpePNJOWf+85giwrCY9F6NidV5SDJUXHviYny6aVYtSJaEUBh8cHxBejNDp8cbcJsSJDexfnePU3QEWFcMRVml4MX9t2ptJmIVUvcvGf3o+6njLrReZW5uMNSFTXtWDSiwzrl8mn+1vCy3XpqOXxMygnVW3CqagcA6FrE7NepM3tZ80VY2l1+6PuI4POY/GvWSH4W3zY7mFORT4vfLifOy4qQgAqbVYURaFPpgmfLHHn5CL+94LBHGh2oxMFdn9rJ8UQbGx1nnxQVWDl7umlLFu3k/W7jk4+iHQv+y50dlwy60VWzx7NIxtqoq51/9PXUTnK8RAUna6YdSLNDh+/ubiUnunGmDGfkf2zEJJrylRUVE5jFEXB6ZUwdRGvngijGtOjoqKioqKionJackKKUSJZtmwZN998M2azOepxt9vN/fffz+LFi3+gPTt+tLl83P5/XwSbwxMKue/N3WyuaWLLvmbmVuYzr3IgOlHAmmpAL2podflYMb2URS/vYPWmWiaU9KRXujGuI4BZL7JwUhEj+mfh8AR444YqBAABrnuqmpUzy3n9i0MxzevVs0eTnaLjUEtwAPHhDXtYMKGQJzfuZdaZAzDptdx+USF+WUEnahjYIzXqdSE4ILhocjEur5+HO/arc3REVYGVLLMOg07DoknFSIpCs9OHVhN8v6JG4MVfnYXDEyDVKLJgQiHzvRI+WSbLrEcDCAI8d8VYvAEJp1ciICsJj/WxRmh0fryrLOXI2a97GxwJRTHd2ZbKyUt6EgeKzjFbIXSiJuxq8Y//OTOuQKvCZmFOZT4CQkzTbmT/LDx+mZEDspldkR+OsArV3F+vPoOmdh+CAJPLejO3Y5mGNg93TipGEGDK0FyWvrojqtk+vrAHN543hGXrYgf8Jw/NZcW0UnySjN0dHCzWiRo8gUBcMV2VzcrCyUUowM9H9uVIu4fHfjkSk1bE7vHx3BVjMWg1jBmQzcK126MG+KtsVioHWThroIU7126Leu+VNgt3X1yGFnjjhira3X5SDFoOt3tQZIVDrW7K+2Xik2RSDFquHW9jXuVAXP6gi0ZDu5ecNAN3vbqDotyMpKK+vCxT8DU8fsx6LakGEU9A5unZo0k36TBpNQQUhclDc8PH2KDVkGLQ8tTGfXGFaSGxYCSVNgsrLi6LK6qoKrCycFIR9a0ePv+mld4ZJiaX9SY308x5RT1jPsOqAitLphTj9ieOQDoVaHT4MIgCu+vtjB2QzS0XDMGg1VDYO53yfpl4AzKZZh2pBi3PbK5l4aRibntpW8z3evm00oSvcSwCk/QuzvHqb4CKSrBuA4pEtlbknovLqGtx0xbR1N51qI3FU0q4ds1WGh2+8HpmvchDM8p5enNt1LVWhc3CQzPKw9eaXTpqqXWoonLMpBl1mPUij8wcQX2bO8pVMjfDyCMzR/CXLV/zizP6k6IXYwTKVTYrd00rweULoBM13HFRMYqgBJtVikKKQcuP//Q+Q3qlMbmsN/k9UtCKAtc9X83DM8rZsq+Z17bVx1yrbdzTyJ0vbWNYXlaUGOU/iUiN57g0tzKfhzfsiXn9UyGK9USgqzhElWPDL0uMzc9m895oMXVuhpFn5ozGkqInoCQeP1FRUTm9cfslJEX5zjE9Bq14yscF//mDr9l5yM6N5w2mV4ax6xVUVFRUVFRUVE4DBEU5se80RVGkvr6enJycqMebmprIyclBkk6MZprdbicjI4O2tjbS09P/o23tbXDwo9//G4BX5lcwdeXm8HOhgT6jTsOTG/dRnJvBqLwsMlN06EURly+ASSeSYdTywb4mLGlGtBqB7BQ9kqyQahQ50u4l3ahDIwgoBC0W081aTFqRdk8AuydAulGLpCi0OP04fQGyzDr6ZJpodHjDdoyKohCQFUQN/OvLIwCMzbcgKwo9Ug0AOHwBXF6JNKMOg1agyekjzaRDFASWv7ojxo1g0ZQS7n1jFxt2Hwk/3tlpIOSm8tKnB7lm3CBSDFo+2NtETsTMnm/b3PTOMPGXLV9z28Qi3theT2luRszMn7mV+XzekTHemQqbhUllvTnU5olpbJTnZfHFgdZjGlxsc/m47vnqqNieEGcXWE+KgcpTbVba91m3yThs9/Drv30Wd4b3mivGMvOpDwF4ZvZoAorC05tro4Ror15Xwc8f38LcyvywiKJHqgG9VkN9m4f+FjPftLjJzTRR2+jEoNUwOCeVpa/u4J2IWgpR2VFD1Qda2dvQzk9G9iMn3YDLKwVFG3YPC178ApdPYm5lPucO7hGOEgJYvbk2SuAW2i9BgD6ZJvSihhaXlyxz0GHJ7gkgyQouXwBREMKij+oDrew+ZOfOSUW4/TIOjx9rmgG3X8Ltk/BJMu/vbeK9Lxt44OfDCUhKWORi0GrQCdDkcGNJM+PwSTg9frJTDfgkmUMtbnqkGfEGJJqcPvpbzOg0GlrdPnwBmc17myJm6gYje2obXVjT9OhEDd+2eeiRZsCk04AgsPSVaEFHpHvLkF5p3DahkN+++WVc95fxhT24bGz/8N+ZZh37GhyMybfEiGwqbBaurBoICvRIM+D0Bkgz6Whz+5i/pjr8mZw50IKoEXD5JBRF4bkP94fPm6HP5NzBPXB7JXplGfEFZOzuoIivwe7l/7Ye5I4JhfSxxHf8OJHpbt1W17WQbtKiFQSWv7aLswZlM7GwF7IAPlnB5ZdweSXSTVpSdCKf1rVw+0vbYwRCVQVWfvfTYTGRbRD9ex2Pd246h0E5QXHmqfAboKLyXTmWus026ZAF+NuHdcw8oz8OnxQ+96foRUChvs1Dk/OoSEUnCnxU2xxXOBi6blu5oYb5421U17XEXU6tQxWVWLpTu/ubnHxU20zfLBNPbtzHsH6ZnFfYE1Ej0O4JkGIQSTVocQck2t0B0oxadKKGFqcPk17EpBMRCEb8OHwB6tu8ePwSGSYdHr/EdRETF1bNGkVOuoF739hNeV4WF5b0pMHuDbsMxmPVrFFxn4/8je4u8X73E23/P3kdlSBtLh/zn6+Oe6+unrMTk6xuDzQ5cfk8mPXGqN/XVL2Iy+eh3Qu90oz0PQnvEVRUVI4/h+0exv7mHW65cAgj8rKOef2V79bgl2T+dvWZx2Hvfnj+/skBbvnHFxh1GvplmXnrxrPRdBFhqKKioqKioqJyOnDCO6MoioIQxyf0888/Jzs7+wfYo+NP5Exrlze6KTa3Mp/6Njfrdx1m5tj+rPv8Gy4enotPCs4eSzGIGLUifllh5IBsnF4/gqDB4w8ONJj0JkSNgKxAi8tHhkmHTisgSQqbvm5kxWu7woN9IRHIzX//PNyw/fWFQzjc5sGoD6rZtx9qQ1GgrE9GuOm8ta6FSaW9kBSF7QfbgiIRSUYUBD7Y1xSO6Jlbmc/sDreHDJOOf311hJlPbuHSMXnceN5gDra4yTDpSDNqmfvMx+H9CjUQFkwoZMchO/2yzazrNBuuwmZh/jgbw/plsnzdTiaW9ooaJKywWXhk5ghaXF6mDO3NwRY3giBERRJdN74AjQArXtsVtd6cinz++lEd910ytFuDX5ECjoWTivm0roXl63aG38/ZBdZub+uHRJ2V9t3pmW7kNxeX8f/ZO/P4qMp7/7/PnNmzTCYzBAgQSJiwJCwGUZYkLqitCKLW2/sTaQsEtyra5daKCgKC261traKtVlDbutzeWhFwq0pvJbhLquwmgARlCVknk9nPOb8/JjPMZCYLi6zP+/XiRTJztjmZ5znnPN/P8/nc9Uqyg0eGWR+bIaq0C1HW1zSwr8Ufc0N578uDnD3QntJZp7zQyZ2XDqNqTzOZZkPMTeWtn5ayYGoxgXBq15AH39jKJ1818fx145NcTkpdDh68ehS3vVjFsrU1MZel59Z/xazSQVTVNjN3kouxeXZy7WY++6oJAH9I5ZtmP/tbfIwdlM2S17akFJZF+5Roe5ryWCUQid56bG11kjPTr75/Fg+8fkik5kw3suzaEmwWI0FNhzsQ6ft8QfhoZwO9bRasRj2tgRDvdxCd3DNtODp0XDaiL+cVOskwG9DLEgc9AWxWPXqdRFsgjD+sUNcaYH+Lj35ZFsYOsnPT+YMxG3SY9DL+kMK+Zh//c8OE9r4vxF1ThsdEI9EZ+6UuB9eOG8itL1ZRkpfF3AsLqayp57dvV3PRsF4svqKYxrZgTOxTtaeZm5/fcMgtyuXgspF9ufOVTbFzsmxtDcvW1sQKrNFIppCisa66PhYBtW2fm3mTh7Nw1aYU/WMhJ7US9RiQaTZQ3xqgn83ML787lAyDTGtYQVVBIxI5lWnRo6oav3n7S267eAhP/eBsbvjLZwmClHXV9eyo86CoWlJf50w3cl6hM0lgYjXKLJhahKpp7aKYiHjvwatHMe/lLxKWP1WuAQLB8SDTbACdxF8/3M2scQMJEXGjSzfp8fjD6CRIM8j0zjBxS3tfW+pysHBqMU/+a2fKba6vaeBnFw9hReWu2PVMgoRro2iHAsGRE1RUxg60s+S1Lfxw/CDSTDIPvrGVdTUNDLBbePKHZ6OoGmFFIzoNJqyopJv1hBWNT75qZJAzDUe6iYPuIBtqm9iyt4Vrxw1MijcNhFU8foX1NQ1UlObj8Svdxm919v6RxOOlckTrSfyX4Mg4nDhEQc/QqxrpBjOqqpHe7mzg9oWQgHSjmWyDRmP49HYtEAgER05re8SO9QhjeiyGiBj1dERVNZatreHc/GymjOzLwlWbeXdbHZcU9T7RhyYQCAQCgUBwwjlpxSh2ux1JkpAkiSFDhiQIUhRFwePxcNNNN53AI/z2iLfy18uHPrfVKPOdot5IEvS3WzEbJH5y8RDuWbkpqXg790JXxBEl3ciiVyMOJPEW5klRH6X5vLP1QIKVeWVNAxoR0ciy9gxsFY2SPDv9siy8u/UA15ybxzPrd/HIO9Wx7ZW5HHyvpB8zV3zMFSX9GNHfxt5mP/mOND6vbYoNKEaLqbNL8/nRio8TXi8ZkBUrqpe6HFxzbl5CIX59TQOt/jDD+mbG3CPiiRes/PbtamZNHJTwflVtM2aDjlc27OXnf/0i9np5oZPXbi1DliRs1sjfYfXcMlp8IaxGGVknIeskHv7+6B4NfHUm4Hj9tnLcviBpJgPO9JPfXSSVJTUI++nDIc+Rxq//8yya2oIR9yGLngyTnrZgmEXTilm0ejOSJMW+u3pdxH68oiyf4r42Lh7eh6WvbUmMW3E5ub68AKtJpqq2iZIBWbH3Wnwq81/5lMd/MIawotHqC5FhiQgvbn1hA+cNzeHHF7hY2kGIAofaT7Tte4MKL3y0myVXFtPgCcb6EYMsYTJkpxSD5TvTGD0gK0GMUlnTgCRJPH/dOBrbglTtaWbey19QUZbPd4p686s3tyX0ZdFjWbJmM2fl2Vm77SDOdGNKAU25y8HCacW8vfVAwj7jIxq27ncjIXFPB3FGmcvBnLICNCKihI6f5dYLC5k6qi97GnwoqsZv3/6SDbXNPDq9JOW2/nbTRHYc9GCzGGLuNY9NL6FqTzNznvskFlP27raD/Nd3VVp8oU5n1K6raYiJ9joSLcSU5GWhkyQqSgfF+jqbxUB2mpGZKz5m0bRi5k0ehsevkGGOON/c+uIG/jxnXMrtni440434QwqedieeBas3c824gUnXwEgUTzGPvrOdWy8awi0XDuZXb32ZsK1mXyhlX2ezJgtMrEaZFbPO4fG1Ndz5942xZaPF7seml1DvCdLqD5FhPjWuAQLB8cKZbmRvi5+K8QPxapH7qGX/rElqs/dOK+a3/3kWP1zxcft1YkvsmpWKFl8odi2ICor9IVW0Q4HgGKCoWkSQMshOH5uJ+187JET585xz2d/i577XtyZFNc4uG8QLH9Zy/XkFaJrGhq+a+KbFx+e1zfzXd4dy3XOfJLmVmfQ60s2RAlggrJKTKeMNHlns6pHEcqWK3BPxX98ehxOHKOgZkk5C00CRJO7uEE8ZnbSgniTuwwKB4OTD0x6xYznCmB6zQY5t43Tjw50N7G70MnPiIIb0zmBo7wyefX+XEKMIBAKBQCAQcBKLUR555BE0TaOiooLFixdjs9li7xmNRgYNGsSECaenrV/8TOvKmnrKXU4+q23i0eklCcXa5TPH8tS/tjA6z86s0nwCYTVmc5xliQx6hcIa8y4bzg1tQdKMerbtd7N9f2vM1cBmNaCXdbh9IeZeWMi2/W5uPL+A374dEZdEi51R1tc0MG/yMLbta+WOycNYtCq5kF1Z08CCVzdxRUk/lq2toahvJjc/vyEWHXH7pcNo9oXQSxLv72xImvFW6nJQtac5YZ8VKYqxLb4QGWZ9Sqv16HpROs5YqyjLTypuQGQG/MJVmxMKjkdanOhKwHHPq5tOKQGHmJV2bOidaU4Z9QHw0NWj+KbJF/u9ak8zJXlZscJatP1ExQY5mSbe2nyAA24/f3xvB+trGijJs1PqcrC+poE0k8yXdR4u+c17KWMJvvjGTcmArCTxR3zsTppRz5g8O/tbfPTPstLiC5Nm1tPqDzPv0uEYZIlvmn3MKSuIOXR4g0qSGKzjdoNhFUmS0OskfvP9s/i62YskwfRxA5ldVsCG2iZe+riWa87No2RAFoGwSl62FYCxefYkIQpERBuLVh0SrUSJF9aUDMhiYSd91mUj+/J6nKgm/njbgmFsYQMqGn95/ysqaxqYO8mVJGqIbmv+yo3MLs3nP/7wQcq/dXx/1OoLHfGMXoA0k54pI/viDSrc8kIVZ+fZWTitmG+afGhagIXTihNileCQ801b4PQewLdZjbQGwnj8Ye5ZuYkJLgdZFj33ThtBMKwgRaPqAmHcvjC3TCrEH1L4bnEfHv/njoTrkkmv67Svy82yJAhM7FZjUvwSRPrKO9oFLcKuXyBIjc1qZPv+VtKNMhu/aWZEro0lV4zAF1RibkYHW/08/I/t/OI7Q2PrraupZ1bpoC63/dz7X/HGbeVkWQ3inkUgOIZEXIs0Jo/oy95mP5+1O+hNGdmXQFihd6aZuy4bji+o4Egz4g+p1DZ5sRhkpo/L44/v7WDS8N6M6Gcjv1caj7xTjYqWNBmh1OWgzh2I/W7S66hzB6hz+2P3vx0p7/BcGeW8QifO9MPvB1I5olXtae50/0e6H0GEVOKfeITQ5/AJAaqmEdQUHrxqZEJUj6pqPPL2dn5+yVBavOL5XiAQJBMVAVqPUIxiMci0BU9PMcqqz/fS12ZmWJ8MAMYXOHj+o920BcKkmU7a8otAIBAIBALBceGkvRuaOXMmAPn5+UycOBGD4cwZaIifaf3UeztZdm0Jl7X0SSp85maZ+MnFQ1FUDX9IISdDBiSMevjygIfCnAzc7dE9uTYLexq9rKs+yAvXj2fT183kZllYsmZzkqvKkitGMLpfFgFFJddmQdbB738wBpNeZkNtEw2tQcYOtLO70cv6dseVaNE2EFYxGyLLjW3PD43OFotGR5QMyOLWF6tYeXMpVXFOKQCThvXipxcPod4T5IkZY2LbCqvJgRImvS4pxqgj0W13nLFWMiCr09mzx0pccToJOMSstG+fNKNMhlkf+95v/KaZOWUREdb6moZY+ylzObjn8mKu/eOHDOuTweWjIvafcye5GJNnp2ywk1sudGGUdbGB8WgsQXRbnRHvnrRsbU2sbZcOdqCXI20oHNbol23h012NkQiu9jbfz2bm9zPG8OP2iJn4/XS33UHONN7afCAmZpk0rFfM/aRjEeJ7Jf2oqm1OefyVNQ2xaCNnupGHrh5FTqYJb0ChV4aJoKLyw/GDmNMueInuDyJCoco4IUr88UYpL3Qyc+Ig3t/ZSMmALF76uJblM8eSk2lKcB254+UvyMk0dXqe4/ujDIuh25lBXc24tVkMjC9w0NgW4OUfT8RqlGnw+OltM2GUdTzybnWnzjdLrhjR5X5PB/rbrWzd52br/laWXjkCv6KgqCpIEm5fCEe6CbNBxzdNfmwWPX1tFnyhUILDQrxAsrO+zmY95Kqwo86TJESJcqr1/QLBiSDDYsCvKpzTL4ugBL6ggicQJtNiIBBW+fuGb/jZxUMI9HDmdrQNr6uuJ6xqov0JBMeYNJMes17H/JWb+NGEQTw6vYSXP9uDTtcHq16PT1HIMOnRSRJ1rQEyLQYG90rnH1v2seGrZq4ZNxB9uwtq9Jmv42SEaMRgn0wT33/yg1i7funjWv7nxgkMcqbF1otSXujkvitHsGTNloTjPZpYrlSOaCsqd7Fi1jnoJCnJDVPEfx0dncUhghD6HCk+RcGuk9Hp9AlClJCi8kzlTm67eAg+RSHoEddLgUCQjMcfdUY5snKCxSjT5j89xSjrqusZ3T8r5uw+ur+N5z7Q+HBnAxcNF+4oAoFAIBAIzmxOWjFKlPPPPz/2s9/vJxhMzJbMzMw83od0XMjNsvDw90fTFgjjDSlkmAz84juZ3HReCHuaEW9QRdMk0k0RJxS/QYcGtAXCGPV6ivraaPIGsVn1mOWI8jzToueX3x1GQFEZkJ1GWFW594oRkfUVlbZAmGyrkYCiYjXJ9LWYMco6fCGF/PYBvr62vngCYXwhhYHZVt76aTmqpmHWy+h0Eq3+ME3eIJcM702mWc9rt5aCJPGninNRVI39LT62H3Bzw3kFBBWFn1w0hLsukzHrZQLhMCGVpNidUpeDaaNysRrlWOG43OWkak8zFwzp1eV51OukJKcVOLJs7xZvkHpPELc/MmDjTOva0v10EnCIWWnfLvuaffzflwfJyTAh6yR6pZs4vzAHRVVYesUI/GEVty+EzWLAYpSpb/XzlznjyDDrafYG+MOMszEaJNJMenwBhZwMM0FF5ScXFbJgShFmg45d9V5+fslQ7r5MhycQxhNQyDTrGWC38PiMMehlHa3tg5GLphVT3+LHaTPR4AmSZtLj8StYDBKeQBirycSIfjY0oMETZEC2lT6ZZjyBEK/cPBGjrKOhLYBJr+Of/3U++9w+JHRUlOZzQ/lgMkw6HOlmvKHIAKjdauT7Y/ozaYiTFr+Cqmk88s52SvLsVJTmo2gaA7PTUFSNutYAf7tpAmFVo6ktREhVGeRIIxBWcPvCZFkNPDv7HPrZLSxatZmq2mb+MONs6lr9pJn0ZFkMZKUZGJTdj/MLnViMEQGJBrFzYWh3i5o/pYiQonLL8xvY0+RjXXU9qqZRUZaPBinjgspcDp6/bjyt/tQ5yPH90WUjemM16OhjM/PS9ePJtOgx6HV83ejDmW6KfCZ/CEeaibmTXAniGYgMwufazNisRgp6HXLaGOiI9Nfb97u7dI4KKV33g6cLbYEwj19zFkZJQq/Xo6ka6CQkCRragqSbZIb2TkeW4In/q+bGC1wxMWW5y8nM0kHc9mIV0LO+7nTq+wWCE0GaUcbS/rPU4b0si4FfXjyEyh11nJ2feA+Yk2mi3OVIElnPLs2PtWHR/gSCY48E+EIq62sauHPyMPY0erln8nACmkZY1ZDR0RoI4w0o2CwGTHodQUVl0rDeTB2Zi0wk6kcF4vW3GWY9L90wPjb7evv+VjLNeh66ehT97ZFe4vJRfck06cnolc7SK0bgDSmx/fTONGGzGnn4+6Op9wRjsas6nYQvpByx80NHR7Ro1NcyEcN3zEkl/gEh9Dka7HJqNwO7xcBdlwxl+Ye7uXb8QBrbUj/LCASCM5vWqBjFcOQxPf6wSlhRYxOeTgdqG7x80+zjmnMHxF7rYzOTk2HivS8PCjGKQCAQCASCM56TXozi9Xr55S9/yV//+lcaGpKLasppmme7r9nH7kYvj62tjrmPLLu2hGcqdyUMsk8a1ou7Livivte2JA2+z7t0GAadREhVI7nAKrj9Yf65vY7Xv9jHw/85OqHInZNu4mCrH6MhEsPxX3/9nF/9xyieXreTGeMHsa/FR+92JwSPQcZi1GEzG3D7w2hm+HxPM0te23pIMFLo5OYLBnPri1WxuI0+NgtjBtr5Yk8z71UfpMzVC0XVqPcEyLIa2HGwNcn1YH1NA0vWbI7NVC91OfjFd4fy6LtfotdJlLucKWehl7uc7Kpv49ZJhVQ8+0nCe4eb7b232ZcUuXNeoZMHrx5Fbpal4+rA6SXgELPSvj1avEF2N3pZ88VeqmqbeXR6CQ++uZXt+1t5/rrxzH91U5LYYcmVI/i60UdVbRPlLicmg4GFqzbF1v9dXL9RUZbPhAIHmWZ9ZPA/HJlxmmUxkG7W8/i1Z5Fp1uMLHRImGGUdDpuJFm+IXulmvKEwbn8Io15HpllPszciUDHKOtJMeha9uimp/7murIDKmgbOHZSNTqcjEFJIM8nY0/SYZQPrd9TH+pO2oMKBFh8TBjv4yf98zNDeGdx+6TDuXbM55uqSSvRxQ/lg8rKtLFyVfI4WTRvRHkk2mFy7mcWrkl2gZpfm8+PnN/BflwxhzCA7z8w+l8WrEj9LmcvBn+eM4+tGLx9+1ciKyl1UlOaT57B2GlO2ZM1m7rtqZJJle3xR9LIRvfnlpcO5e2VyVvuSK0fw329u441NBxJef3R6SSzWLLpcV4Pwbd04rrR14yx1upCdpscsmQhrENbC6HV6QqqGompIgKyTQAJJkrj1gkIe+1cNsybm8/x14/CHFG5tP+flPezrTqe+XyA4EdgsBoK+EAqRIrdJryPdpG+PAgHZIFM2OIewmhjx+O7WOm6/dBizWgMEwiomvY6qPc0JcZCi/QkExx5JisQOWo0ymUY9I3Jt1LX5yU4z420XC6eZZBzpRow6CZ+qoAF6nY7fvr2dWyYNwaiDRk8AR7oZZ7qxXdQRZs5znwKR+6C7LhvOf/zhg1h7jj6L9eokAjOKzWqkLaiwaPXmw3qW626bqe7BhDji2NOZ+Eec66NAI1nt2c6c8QNpVhVxvRQIBClpDYQxG3SRZ+gjwNouYmkLKtgsp48YpbKmHp0ERX0PTZiVJImivpl8tKvxBB6ZQCAQCAQCwcnBSS9Guf322/nnP//J73//e374wx/y+OOP88033/Dkk0/y4IMPnujD+1Zo8Qb5vy8PsuaLvbEiZUVZPisqd8ViJKIU5dqSCrEAVbXNeINhPIEwf1y3k6JcG2Py7BjDEtNG5zJtdC6+oEKr/5DtebMvSK7NwnXPfUpOponnKs7li6+b+eklQ/AGVUb1z0LTYO32Azz5r52MyctiVnth1RtUKO1QLF1XXY9Blnh65jn86q1tiXEXLicLpxVx/+tbWbvt4KHXC5384Qdnc9NfPktwAFhX08C8ycOZXNyHoKLi9oa49aIhPPrul8wuGwRoCQXk8kInS64Ygd1qwBtUGDvQniCkqGsNUF7oTBmj01Fc0eINJglRIBK3MO/lL3hseknKwbDTScAhZqV9ezR7QzHR2dxJrlgc1/KZY5MEGBAROyxYuYnZpfms2biPcfkOFrX3AfHrx0fNrKjcxePXjmHfgdaYAMRskNlV7+HsgXbuXpla8OILqvz2nU0pBRU/WP4xZ+dlcfOFLj7rICCrqm3GatTRO8OEL6QQCKtkmPTYLAZUVWJ/m5/RA7LY3+Lngde3ckVJPyYUODjgDvD0zLFARJA3p6wATdN4/qPdKc/D3EkuFq9OfY4WrdrEf//HKAZlW7n/9a2MzrMzqzQ/IUrs5c/2xPqnb1r8VNU2pdzW/JUbmV2aT1VtE49OLyGsagTDaqeuI5XtsUpLrxhBIKzG3JSMso4GT4C//Xgi6UaZO1/Z2OXfN16MUlnTgITE89eNo7EtSNWeZu5dvZn7vzeK3p0UYbobRM4wn/S3AMcEo05GBdpCYYyyDr8SxizrCUgSoGGQdQTDKvX+MJlmPbdc4MIbUvCFlATxzwNXjexRX3c69f0CwYkiDEgaqFLEzc4TCGOzGrAaZPa2BpCATLOeuZNcbNvn5ppz87jtxSp6Z5p5Y+M+0f4EguNIltWIqsGN5xeg00nUeQKkmQzscwfINOtxpBtp8gaRJAmdXodFJ9MaVmjwBLj+vMGEFJWwqpFmNvD+jnoeunoUz77/FTkZZp6YMYYsiwF7mpEfLv8o4fmwu2exKEf6LCc4eehM/CM4MjqTq4c10Etg0clkWIQYRSAQJNPqD2E1HPk4grldjOIJhLGdRv3Mp7sbGeRMw9ohvsiVk8666np8QQWL8cjcZAQCgUAgEAhOByRN07QTfRBdkZeXx5/+9CcuuOACMjMz2bBhAy6Xiz//+c+8+OKLvP766yf6EAFwu93YbDZaWlqOOjpoR52HrxrauPXFKirK8ikZkIXdoic73YwvdCjX1yjrMMsSrSGFtkAYu9UYG7DPNEdcC8KomHQybUEFjz+EI91ESFFx+8Okm/TIOokDLX6+bvYxIT8bSZLwhRXQiMX+pJn07b9ruP1h+mSYaAsqzHzmY0b1tzFj3EAgUizIshrwBQ/NJJ87ycXntU0JQpEoZS4HZ+XZE0QqEBGSjB6QlfT6738Qic+IL7ZXlOVTNtiBPc2IpoE3GLFl7jhbKhqxEz+bqi2odCqu6Bs3Q25HnYeLfvOvTv9e7/78fAbnpKd8b2+zr0f7OFVIdR5P5YHBY9luj5Qte1u47NFKAJbPHBtr95eN7MueRi+5NguBsMJBTwCTPiKiWFG5i/+5cTyXP7aeF64bx3V/+pSKsny+W9ybOneAAmca/rCKNxDCmWHGG5cHrpcldjd40UmRtl/Qy8rsZz+NDe5H29XEAgcakXa9obaJlz6ujTkcAfTONKNqGo2eIM4ME6oWidDpnWFGksAbCGMyyEgSHGyNHNOHOxvIyTQTVjVybZaIJbumkmUx0hYKs6/ZT26WBYtBpqktQJo5UnhsavMjy/rYeTDrZVRNI9du4ZsmHwXOtIhIrd3lySDraGoLYE+L9HfN3hDm9mOJHqOiagQVFVXTaAuE6WMzUdvoIyfDjEGW2NfixyDrks73f47tx60XFiZkrOtliVv+soEv6zyxv+tfbxxPqz9M1Z5mXvq4lhWzzuFgawBF0yjslY4/rLKrvi0mjOkYwbP61lIuf2x90vdl+cyxsVnCAG/+pJxhfVN/d3fXt3HXymTBC0RERfdfOZKB7RFspxKH2253HfQg6yT8ioJJ1uEPqbFZ2maDjFEnIWnw1rb9PPyPas4eaGfh5cUomsqOujayLAYGOqz0s1u73E98lFuaUc9ntU0sWbMlYQb3qdr3CwRHy+G026/qPZgkCb0GAQla4/rbdKOMXtWY+efP+LLOQ7nLyZIrR/Aff3ifor6Z/PfVo1Chx/dehxvBKBCcafS07e5paCOsRoYV2oIKqqpgt5rwBJVYDKTFKNPoCWAyyFgMMkFFJayq6JDQ6STMso5dDV56ZZiwmmSa2oLYLEb0OonJj65LuE+Kp6tnMTi6ZzmB4FSkq3Zb19CGpIFfIuF5Jt0oE5W3t6paQgSoQCAQRFm6ZgtvbNrPw98ffUTrf3mglYWrNvOPn53HkN4Zx/joThwX/fr/yHemM6csP+H1XfVt3PXKRv520wTGDso+QUcnEAgEAoFAcOI56adFNzY2UlBQAEBmZiaNjRF7u7KyMn784x+fyEP71nD7Q4RVLeZq8GrVN/xpzrkp4xyWXjmCX/7v5yy9aiT7Wvy0+CJF1399eZCdda388tLhLF69mTED7Vw4tDcHWwNYjTJmvczabQfISTcxon8WiqYRUFQ+39NMrwwzT1fuTOmG8PfP9vDLS4cRVjWemHE2VqPMxq87xPO4nDGHlJIUopIolTUNzC7NT3p9XXU9syYOSnrdKOtihfqK0nxsFgN2q7FHgohUs6lsVnpk+ev2h7rcdmsX759utsJiVtqxpy1uYD2+3UfjaR58c2tSW3x0egne9oiV1kA4ts5LH9fGon2iMT93vZLcbyyYWsyMpz9kaJ8M5l7o4sbzC/jt29UJbirx7bbU5eD568bz0Jtbk16fXZrPfz75AeMLspk3eTifftVI70wzQUUlJ8OMxaDDZjHgDUUs2e94+QsevHoUD765NRYr9Ou3v0yKFZIlif0tfva3+Cgd7OST3Y1kWSOiMw2oc/sx6HToJFjwarJ7yz1Ti1n62pYE56VJw3pxx6XDU0b+zCrN57/++nnM5Wl2aT5zX9hASV5W7HwPyUnn5gsKmZfinD71o7Hc8KdPY4KUDIuB96rrKRmQRcmALEKKxrb9bi4p6pMUvdTRVQrA409dcAmE1YTf3f7Oo3iafcFYH5uqP2/xBYFTT4xyJDz0xlZ+dslQ/vuNbXz/nDxyMs20+kJIkoRslJEl+M7wPgzvk8mcP33G4tWbuXdaMUN7Z/Soz+4syu3128px+4KkmU7tvl8gOJ60BRXSTHr8QF2rnzSzAZ0koWkaIVVD0kk8/6OxzPjTp6yrqWfBq5v4+48nYrMYYm2sJ/deRxLBKBAIUhNSNercfp5at5PSwU7Khjhp8oXQSRKqBpqmoWkaZqMety+EBlj0MgfdAVa8v4vrywvIshqwpxnwBMJs2dvCRJeTfnYrVbVNnQpRoOtnMTi6ZzmB4HRDAkKA2uF1FQhqYJQgED4zojwFAsHh4wmEsR6Fw4clzhnldMETCLPzYBsXD++d9N6AbAtGWce/9zQLMYpAIBAIBIIzmpNejFJQUMCuXbvIy8tj2LBh/PWvf+Xcc89l9erV2Gy2E314x5wWbxCLQaavzcxDb25jfU0Dq+aWMn9lchRPZU0DS9Zs4XfTS1iwclNChE80piYQDnPnZUU8+PoWfCGVMXl2AmEVR7qRi4b1Bgn8IQWDTiIYVhnRL4tWf4j5U4owyDr+sWUfy9buoKq2mStGB5g7aQg7Drah1+kIqxEXlpxMM3+9cQLvbD3AU+/tZF1NPSoaFWX5SYXTjnT2fsfXS10OqvY04w0qLFtbw3mFzmNiqdwTcUVmt1EXXb8vBByCrsiKsyaNb/fxkTvxRH9fPK04ts4j73xJSZ6deZOH8XWTlzllBTjTjTzyznZK8uxUdIin+e83t/LQ1aNiDht3XDqM375dTUVZPi9/toeK0nzmTR6Gx6+QYdZzwO3nd+9spyjXxtptB2OikZIBWciSxCs3lxJSVJraggxypvH+joaYk8rEAgcGWQcaFPRK49nZ5/CbdvHJ3EkuXvhoNyV5dq4vKyDXbuazr5rwt0f7mA0yGhDSVN7YuI932oUlVqPM/CnD6ZVpisX5lOTZE9xbvm7ycsuFhRTl2mKuI0W5tk6jjzQicWjL1tYkxKNFxTeLpxXz+A/GJIkCo+vPX7mRx38whkt+8x4XDeuFSdZRVduUFE82dmA25+ZnJ/1NXvhod8L+0s2pB3hM+sRc5cwuonbSTQam//GjmIAvEFYx6XVU7WnmtherWD23rNN1TxdavEHueXUTs0rzeXrdDu64dBi+sIoa5wknAYqqoddJ5NosMXFWSNEY0qf72Vpd2f8veHUTC6YWRXYiEAh6hDeoEDTp0TQVR5oJlUi/3xZQkHUSBp1MUII//HAMk379XsT6OqSQ5zgkruvu3kvEdggEx44Dbj8f7WzgveqD3HbREJa9+yUFvdI52OpnSO90emeaCCgq/lDk+U6nk0CLPO8NdFgZPzibZf+sYeqoXM4ZZMeo13BmmFm0ajMPf3/0UT+LHe36AsHphALo2v/FowPk9vclceMqEAg6we0PxaJ2joRYTE8Xk2pONTZ+3YIGDE7hKKXX6ch3pvHF1y3H/8AEAoFAIBAITiJOejHK7Nmz+fzzzzn//POZN28el19+OcuWLSMUCvGb3/zmRB/eMSU6Q3P0gCwuHtY7VvDUy7qk4qcz3RixG7eZOegOMH9qEWkGmbZojI/ZgE6KDPbpJPjZd4bSFogUloNhlfrWIHubfYzNt+P1hxmQbcUTUGjxhbBZ9OhliX1NPi4a1pvJxX35usnHk+/t4Jcvb4y5J7zw0W5G9c+izOXE4w9z8fDeXD4qlzc37+Pxf+6gojQ/pnrvjI6F1VSvR10LbnuxCjhktX68igTOdCPnFToT7N6jnFfoxJkuihWCIycnw0R5oZN11fUEwypVtc3MneTiu8W9KeqbyZyygqQYl/U1DfhCkZ/Disq14wYmuZmUFzpZMLWIf9c2Jewv12bm7Dw7OZnG2LaijBuYzeWjclM6h9xzeTHhcETQZjXIqGh88lUjYwfa2bC7MSF+5+LhvblgSC8yzHr0Oh31ngCSBDaLAUmSqCjL55YLC8myGLig0InVpEfVNFq8IUYPyMIbVGhsC9LXZmaQw0pTW4C7phTxi0sjkTuRbGGNOneAkKqRazNz8fDenD+kF2lGmX0tfhat2swVJf2YUOCgzOXEKOvItOg5a0AW8y4dnjL6qCLOqamqtpk7Lh1GyYAsAmGVkKJFhBy1zbFlov1wTqYJj18hrGi8dMM4+tssPPDG1pSinj+9/xWlhU7mPPdpghNM2WAn6e3Ckq17W6hzB5K+K1FRXvzfxZ7Wef/jTDcydmByFBqcOX1XvSfIupoG7poyjP+6sJAAoGrEIp3SjDJ+NYweGQ0NWZL4bnEOv327usezteo9waSCdpR11fXsafQy57lPheOCQNBD0k16dIBR0sViejyBMJmWSHRbWzBAmsGEWXfoXvHrJh99Ms09vjfsqt2+V11PvScoxCgCQQ9pagvS12bhxxe4eKZyJ3ddVkRQVelrM6MBTd5gJE5WUWn1RiJBrEaZg61+JAkuK+rLmn/vIyfDRCCkkmnWc8sLG/AGFeo9waN+FhPPcgLBIaKjM6n8gOR2DcpJneMtEAhOKC2+0NE5oxhPP2eUjd80Y9Lr6NfJc36ew8qWve7jfFQCgUAgEAgEJxcnvRjlZz/7Wezniy++mG3btvHZZ5/hdDr5y1/+0qNt/P73v+f3v/89X331FQDFxcXcc889TJ48ObbMBx98wN13381HH32ELMucddZZvPXWW1gsx6doFD9D87PdTZS5nLH3Wn2JQwXOdCPPXze+06iJaNTDxcNymHfZsKQIi3KXk9llg3h7634Kc9Lok2Xhzi6iPIb3zWTh5UVs298KRNwCXvhoNz8YP5BnKnfxyDvVsfVKXQ5uvdDF49eOQQL6ZJopdTmSxDTRfcQXVmPHV+jE1SudlTdPJMNsIN2spy0Q5oXrxp2QmBub1ciDV49i3stfJAxiHm9RjOD0xGaNCBrmvfwFjd5gpzE5HWNcvmnyA2CQdSkdVD7b3USDJ8Cqz/cmuCaVuhzMvdCFLB0q4vlDKstnjqWf3cKCVZ24MK3ewmUj+3DnK5tiry+bfhayJLFm475Y5E6qWKHrygpoC4ZZXrkrZZzOQ29u5cOdjTw6vYRH3q1OWCYardOxHytzOagoy8co65L2WeZy8Kc557JkzZaE89hZTE/0/Ibb7TKigrtfvbmNdR36zujfwWqUO+2Hl14xgp9cMpTFq5PfWzC1mLoWf6eRSJH4tZE88s72hL9BR1FemcvB/VeNpHemmc4Qfdcha36bXo8XUsZWLb1yJE+v28HU0f3om2kiwxApkvV0kK07+/+o05dwXBAIeoYsRR5QvFLnbRZNAylx5vbhCEgiMWVdvS9iOwSCnuL2h7FZDQTDYW67eAhL1myJCaU7RjJGiX/WHNYng0enj6G2wYs3qBBSlNj9bqs/xOCc9KO6nxH3QwJBIm3A3Z1cX9MkCIROnyKxQCA4trR4Q12OQXSHtX3C4ukUkbd1XysDHdaI81sK8rKtrN1aRyCsYNIfuZBHIBAIBAKB4FTmpBejdGTgwIEMHDiQzz//nOXLl/PUU091u07//v158MEHKSwsRNM0nnvuOa644gqqqqooLi7mgw8+4NJLL+XOO+/kscceQ6/X8/nnn6PTpXbt+DaIn6HpDSoJudgZlkTr4IeuHsVDb25NiN9IM+oJqyo2i8wbt5XjbXdICSkad1w6jF/89XO+M6JPbJa/xSAzY9xAVLSkIi9EI4A2x6I8Fq869HPJgCwAVlTuSihywyGXhSkj+zK+wIEeWHLFCBau2pwwA7W80MnCy4u5//UtCeuXFzr576tH0TfLwkDSOFnIzbLw2PQS6j1BWv2hEyKKEZy+RL9fjW1B5qdoj6liYwyyRHmhM6VzUmzZf9Z0uq0lV4yIvRZWVOY89ymv/6Q85bYA1tXUM6t0EEDM0cOVk8E37TE5mqbxt8/2xPqloKKSk2HGIEu0BcJoQEmenara5gSHlyVrNnNWnp2iXFtSRJDNosdikPm68VAUT9QhprKmAQmYPLJvyv5rwcpNnJVnZ217tA/QaUxP9Pc7Lh0WO3epBD7xEWQlA7I6jfzZ3xrgsbXVnfar91xe3Ok+KmsaWPDqJu67cgQ3XeDC7QuTadGTYdLjCYR5dva5ZJr12NOMPRoEOtP7rqg1v6KT+O3b27lz8nBknRQ7F2a9Dn84zI3nDWbh6s1cUtSHiYMdzJ8SWe5w9tEZ8U5fwnFBIOgevazDDzz4xlZuOX8wD141Ek9QiTkaaVokVis6hB11jXJ04RTVEaux60ego5nxKRCcaWSa9YRUlWyrhcVrNjNj3ECsRplffncoGWYDgbDKTy8ewvwp+kj4hwSBkEqTN8CKWedwzVMfMn/lRu6/aiT1niD73P7YtqMROkd7P3Om3w8JBFH8wBP/V829VxSj1+nwBhVafRHHosa2AIZ0E/Y004k+TIFAcJLS4g+R7zzysVqdTsJs0NF6GsX0bNnnZoDd2un7edlWFE2jps5Dca7tOB6ZQCAQCAQCwcnDKSdGORIuv/zyhN/vu+8+fv/73/Phhx9SXFzMz372M2677TbmzZsXW2bo0KHH9Rg7zqzeUNsUcxQJKyplLkdM+NHHZkqI5YjOsF/z+Tf85OIh3LUyeZbLUz8ayyPvbI8tX1GWz8QCB5Kk47oORd4olTUN3DE5UpxdV9PAL9t/DoRVSgZkpYx+gEhhN1qM3tMSIN9hZVnc4J/VqMdokNAjMX9KEf/1HQVvQCHToj8si/Xjjc0qBiwF3x42q5G61kCnYpBouwIodznolWHispF9aevE3rS7Nuprb+tlLgfv74zss6MLU0cCYRWrUWbZtSWsqEwdC/TgG1uTXF1mtzt6lORlJTm8VNY0MLs0H6NO12lEUHTm7NA+GQnrr6tpYFZctE480e0ezjkJtjtY9KR/y8k0dfq3SjPJnb5XWdNAWNW63Me66npCisawPpkp3z9czuS+y5lupLzQiU9R+MnFQ5i/MtlhZ/EVI/jtO1/yowmDQIsIQkf2t/VYjNKV/X/HaCU4vWaBCQTfBo40I/tb/fziu0ORJYl5KWZv33flSIKaQrnLyczSQdz2YhVXjM7t8T50OqlT575Sl6PH7V8gEIA9zUhNnQeDrOPacQN58aPdzBg/CJ0k8fBbmxJc5qL3hS99XMvt3x1KszfISzeMp+LZT2gLKrxXfZCR/SKFmo4ROkd7P3Mm3w8JBFH8qsKtFxQS1rRO3ccCitLFFgQCwZmM2xcmzXR0pQSrUX/aiFFCisqOOg8TCxydLtPfHnFc376/VYhRBAKBQCAQnLGcEWKUeBRF4X//939pa2tjwoQJ1NXV8dFHHzFjxgwmTpzIjh07GDZsGPfddx9lZWWdbicQCBAIBGK/u91Hl//YcWb1ispdPDq9BIBbnt/An+acy4KVm6isaUBHJJZj+/7W9mgNM0ZZ5meXDE0aUIBI8XP+yo0suWIE+b3SGdnPxvLK7mNAADz+Qz97AwpzJ7kYkG3B41dYMescNtQ2JYlYIFK03lXfxosf1cYiCaKDf3ubfbFIoijnFTp5UNgkC75ljnW7PdZ0l5sbCKuUuhzMLM3nmqc+xBtUePH6cZ0uG09UhBZ1R5IkeOB7I+mTaeaWFzZEluliUMFqlOlvt/DnOefS7A1RUVbAWXEitnXV9SxZs4WK0kHMGDeQQFjFbJDZUNvECx/t5sbzCwgpGhaDzB9+cDZhVWNDbRMvfVxLdpoRm9nAws4iguJcmiDRIabj50x1DqKfvVeGiSdmjIkdV8e+66An0NmmErAYZLyBzgdp4/vNVLT6Ql0eNwjBQjxH025tViNLrhiBTiIWR9exLRxw+/npxUN45O0v+eHEQbT6Q3gCYfLyOp/d1HEfqez/44VY8WR046QiEJwOHG27bWwLElY17ntjS4ITYLT/vnfNZhZdXszsskHMfaGKs/PsGOWeOxrqdVJMsNgxtm12ab4QowjOWI6k7fbONNPsDaJp8Mz6XZTk2TnY6kdRNWaV5jN93MCEey+ILHPvmkj7/ndtE0/PPIe2QIgvvm4mrGoiQkcgOAwOp91adDJhVeODnRGB/YwO7TM6biQQCAQd0TQtNsHwaLAa5dNGjLLzYBthVWNAdudjB1ajnt6ZJrbtbz2ORyYQCAQCgUBwcnHGiFE2btzIhAkT8Pv9pKen88orr1BUVMSHH34IwKJFi3j44Yc566yz+NOf/sRFF13Epk2bKCwsTLm9Bx54gMWLFx+z4+s4s9obVLjtxSoqyvK55QIXbl+QpVeOwBeKFDC372/l+evG88g72/npxUNZ8Oom5k0e1uVsfG9I4ZLhvXng9a09igEBSDdHbMqtRpleGSaqapt6JGIx6XVYDHLSIGKLN5gkRIFIdMG8l7+ICVcEgm+DY91ujzXdxX0MyLZQkmdPaG/rdzRQXuhMalPx0SBR96Som1KU8kIns9ujdwDq3P4EF6b49ZfPHMt/v7kt4b2O7X9ddT2/+M5Qrnh8fcIyc8ryGWC3smj15oT9TxrWi+evG8/SNZuZVZqfMHM2nniXpniHmI6fsyMmva7Tz56q78rLtvL6beV0rEF2FC/YLAZMhsi2Owrx4FC/2RnW9sidrhCChUMcbbtt8QUw6vUxIUpnbWHxtGIC4TBmgwGbxdCja1GLN0i9J4jbH2LB1CKMso4mbxBvUOH9nQ1J18aOs7wFgtOVo223YVQUjQQnwChRwUhbSGHjNy2MycviF98dSosvCD2MeHSkGfnNP7YnRMNlmPUccPt5ZcPX3HfVyCM+doHgVOZI2m6LN4jHH8JqMrC+poGbygeTa7dwTweRcfy9V0Vp5Jkz+r/EdhZfUcz8KUUAXF+WL54JBYIecjjtVkFDBV7buK/T9unvRjQvEAjOTDyBMKoG6aaji7OMiFFOj8k32/ZHxH9diVEA+tutbNt3ck2GEwgEAoFAIDienLRilO9973tdvt/c3HxY2xs6dCj//ve/aWlp4W9/+xszZ87kX//6F6oaedC+8cYbmT17NgAlJSW8++67rFixggceeCDl9u68805+/vOfx353u90MGDDgsI4pnlQzq71BhS/2NDPj3Dx8IYULHv4XAC/dMJ6Hrh7FvWs2U1GaH4u16HY2vj9MWyDcacG3Y5G3zOWgzh2ZYbNgynAWvprsWpBKxFLqclDXGmDSsBx6Z5oTlq/3BJOK5lHeq66n3hMUA4+Cb41j3W6PNd3Ffby1+UBStMuKyl2smlvG4tWbE9pWndsfE6lUlOXzzPpdSe13XXU9qqbF2u8dL3/B89eNZ8mazQmikwVThvPEP2uSRCqp2n8wrDJ3kivmOrK+pgEdcNnIvkn7L8q1xfqv6eMGdnlu4vu3qKtIucvBAbc/5fJl7fEonX32jsde6nJgNcjcs2ozZw+0U+5ysq6mvnPxgsvJ8pljmfPcp0mClLZAOKWoJ3pcbYEwVXuaO42IEIKFRI623WaYjDHXm67awsJVm1l8eTFN3gCunIxut9uVy5cE/OFfO5KEKGKWt+BM4WjbrVkn0xZWuuy/F0wp4jtFfbhwaA4Vz37C/9wwocfbt1mN/PLSYTHHpChlLgf3XzVStFPBGcuRtN261gAmg0xbIBxx0su2cE83z43Re7no/+tq6gkpKhaD3GNBqEAgiHA47VZV6XZc53QpEgsEgmOLu93N5GidUcwGudvJOacK2/e34kgzkt5NdFG/LAsff9V4nI5KIBAIBAKB4OTjpBWj2Gxd5yjabDZ+9KMf9Xh7RqMRl8sFwNlnn80nn3zC7373O+bNmwdAUVFRwvLDhw+ntra20+2ZTCZMJlOP998TcrMsPDa9hHpPkFZ/iAyzAWd6JN6mxRuMFan9IYWcTBPraxoS3FC6m42fZpJpbAt2uUx0QLDM5eCey4u59o8fUu5yUpJn585XNqVcJ17EUupyMPfCQvraTElCFAB3NwMbYuBD8G3ybbTbY0lncR/lhU5mThyUFPcBEdHaviYft1w4mLsuG06rP0ymWU+6UWZ8gYN7Vm2mZEBWkoglSnz7rfcEmfH0hzx09SjunDyctmCYdLMBHfSo/UNklktVbVOiY0pNA7PilokSf1xdOZxAYv9m0usoczmYXZaPhJQk6ihzObjvqpEsWb2F6ePyuv3spS4Ht04qxKiTuOXCwWSYDUwdlcvi1ZsZPSArtXihph6kiFAn/tyUuRw4M0wsvXIk81duTBCkxIqcFgM5GSauKunHolWbk8QMQrCQyNG226CqkmGJOM101RbWVdcTUFScaZF97ajz4PaHyLQYcKYZD9vlq7PreTzxziqp9iMQnKocbbv1RN2/uhBQa4CqabT4QiybPoZ0c88fa1q8Qe5emToabv7KTUfk1Cfas+B04EjabrMvhEmvwx9SqCjLZ1+Lv8eTH+Lv/9y+MA+9uY0bzhtM30wzzb4g6WbRlgSC7jicdusNKd22T+HQKBAIUtHijYzXpnUjvOgOi0Hudmz4VKG6zkM/u6Xb5frbLaz63B8bGxAIBAKBQCA40zhpxSjPPPPMt7p9VVUJBAIMGjSI3Nxctm/fnvD+l19+yeTJk7/VY0iFzZp6sC2+SP3vPc2Mz3cAiW4Bde5Al7Px69yBbgu+Ax1W3vhJOVaDTENbgD9VjMMbDNHajWo93aRn1dxSAP65vY6z+mdhT/FZuoshETflgjOdVKK0dLOe+a9sTBkJM2lYLzKsBn711raEotp5hU7uu3IE00bnknYYM1fqPUGeWb+LWaX5MTHJ8plju1wnKmIrdTkwGnQpHVMCKeye41/ryikk3qWpvNBJvjONe68YwTdNPgKKypJpIwiqKm5f5HylGWX++N4OhuVmdvvZ00x6po7KpV+Wha+bvTy2tiYW51JRls/k4j5dihd+eelQVt9aisevYLMY2Nfi45onP+R/b5zAr//zLJragrjbBUL2NGNMpBftG5f1QLAgODraAmEyzAbKXI6U38N4Wn0hfLLE/BQioQevHkVuVmSgqScuX4Nz0rv8W3blrBLdj0BwpuL2hdB1zEzrQFsgzH/84YPY74fTfo61U59oz4IzmTSjjNsX5sNdjUwocNDs677AFL3vq9rTHHstpKiM6p/FY2urmToyl5xME9P/+BFjB9pFWxIIjhFtga7ddAHM3YwZCQSCM5OogCTtqGN69NS3O5ee6nx5oJXivpndLtffHonx2XGwjbMGZH3LRyUQCAQCgUBw8nHSilGOJXfeeSeTJ08mLy+P1tZWXnjhBf7v//6Pt956C0mSuP3221m4cCGjR4/mrLPO4rnnnmPbtm387W9/O9GHnkC0SO32hvCEIoMI8W4BnUVslLkcLLlyJN//w/tcc25epwXf8kInJr0OX1Blzba9hFWNEbk2stOM+FIUwePxBML8v6c+jP2++tZSGtqSB/K7iiEpL3SiaBrVB1ox6nUEFRVF1WgLhEkz6dFJEnqdhEPMjhOc5vjDKiFFJahohFUVfzDMnZcNxx9WWVddHxNKTCxwkJ1m5L7XtiS16feq67nzlY2clWcnO63r9tIn08zqW0vxBhTSTXr2u/3c8fIXKcUvqTDpdTFXpEDo0Gz2zma+pnptReUuHp1eEls3SqJLk4PZpYOoc/u55o8fJW2v1OVg8bQR1HsCTBreh30tPtK6cYyyWQycP6QXoZDCo+1CFIg4zixbW0NRNwMLexp93Pz8BoBYbM95hU6y2/upVA5RCfvvRIAoOHakmfQcaPaz+IoR7G9JHesUxWLSo6pal44nNqvxqF2+euKsIr4XgjOZTIuh23vPjk4oh9N+jqVTn2jPgjMdq1EmGNZYUbmLssHObic/5GSa2LK3hdntwmeIPAe+v7OBi4bl8Mg71VSU5mM2yDGnPdGWBIJjQ6almygJu4Wg2rV4WyAQnJm0tItND2eyUyqsRvm0cMX2hxT2NHr5TlGfbpfNzTIjERGvCDGKQCAQCASCM5EzQoxSV1fHj370I/bt24fNZmPUqFG89dZbXHLJJQD89Kc/xe/387Of/YzGxkZGjx7N22+/zeDBg0/wkSdjsxpp8oZo9YdibgFRN5T4iI07Jg+jLRCZqR9SVHYc9FDvCXZZ8J05cRDXPPUh15ybx8QCB7JOwhtUkHUSH3/V2KmIpeOstnKXE02LzG5r8UZigaK25ekmPfMmD0PRtqbc/5WPr8cbVLh4WA7/9Z0h7HMH8IcUzIYw+1t89LVZeOD1rSy+YoSYHSc4LdnT0Ma6mnp6Z5oJqxoWg4wvGCYQVvjZxUO4a/IwjAaZRa9uYtnaGlbMOqdTq+XKmgYqygrYUNvUuQjN5USS4K1NB1hRuQtvUKHU5eDBq0fFnFG6ci0pdznolWFiysi+6CR4d1td7L2oC0V5oZO61uSZL1V7mmP9lzeocNuLVVSU5cdELLlZFvSyxO4GLw9dPYqqPc288GEtw3JTC0SqapvR0DAZdOSaLKyo3MneFn+Xn/2tzfv59KtGllwxgu37W5k7yUXJgCwCYRWzQSazm9iHaMGl3OVk4zctPYrZEVEOxxeDTsfXLT5ertrDTy8eQnmhM6UjQnmhk7ZAiF7pJqxGOUmMFe+WcLQuX8falUEgON1IN8ps/LqZcpczEovWgfJCJ6l8U3rafo6lU59oz4IzHR0SZoPE2Xl2vKFu7hsLnegliaJcW+w+s8zlYMHUIq58fD3nD+nFX28cj9WoJ6yqLK/cGXPa664tifsrgaB70gxyl/fC2/e5GdU/6/gfmEAgOOlxt4tRrEfpjGIxyni6cd8+Fdh5sA1Vi0TwdIdJL5OTaaKmznMcjkwgEAgEAoHg5OOMEKMsX76822XmzZvHvHnzjsPRHB0t3iALVm5k6/5Wnr9uPL97ZzsLphbH3FDqPUHmPPcpZS4Hi6aNYG+Tj5ue/4w//igSs5Gq4Nsvy4IvpODxh3l65jn86q1tLFtbk+C+UO5yMm10Lgs7RBeUuRyxOI/o7zNLI6IWb1ChvNDJLRe6mPvCBq45N4+SAVnIksS900bQ7AviCyqkmwzodLCvxc+ya8fwxdfNjO6fxdLXEwUrEecFFyP628TsOMFpSZ3bz9fNPl7buI+q2mYenV7Cg28mtoNyl4ObL3TxWW0zAAY5sRwXbbdRQUXvDBMGWWJOWaS9J27Lyfypw9nfEiDXZubxa8dw+98+pyTPjsUg89QPz8ZmMRJSFC4f1Zelr21NaP/R9fc1R9wmvEGFJ/+1M/b+IccUF1ajnFSY2LK3hQVTi7l3zWbWtwtSlq2tobzQyYKpRTz4xlbWbjsYW77U5eCeqUVc9cT7SefOaozMnr131WZG59mpqm1ifU0Dn7Wfx46fvbT9PH78VQOVNQ3c99pWls86h1+9uS0hlueBq0Z0WgyNCvFKXQ5umeQiN9PM7ImDRDTLSUaTN8iEgmzyHWk89OY2/us7Q0Ej4W9aXujk3iuK0TRQNY0bzy/gt29XJ20rOoOrK5ev8wqdONOPnyuDQHA6ElIVxuU7GOhIA7QE0WV5oZObzi9AkkgpHOtJ+znaNhyPaM+CM50mX5AWb5g7LxvG51+3xFxPIPm+c+6FLv6x9QAlA7J4+PujsVkM5GaZ+dHyj/EGFdKMet7eup/fvl0dux8MhhVWVO7qsi2J+yuBoGdIaCy9YgTzX93U4bku8szmSDcSUHrmjikQCM4sWnwhzAYdet3RRXlZjTIe/6kvRqmuawXo8X1GvywLX+5v/TYPSSAQCAQCgeCk5YwQo5xO7Hf7YwPyM57+kP+9cQL+cJglV4zAH1Zp9YdINxkw6CX+sWUfy9bu4Ow8O/2yLLGCarTgGxWszH7mY64o6ccFQ3rR4Alw03mDuXPyMKxGPQtf3cSKyl1UlOUzNs/O7d8dyl2XDafVH8ZqlJF1oJMknp19DmlGPUFFxe0LU1GWz4rKXayrrkfVNFbMOoeH3kwWuZgNMr6Qwvod9TFXhnKXg3H52VS1F9ujRAcz77h0GL99u1rMNBWcdrQFwiz7ZyQqZu4kF8+s3xX73seLTLxBhT/PGcc/t9fhTDfG3hubZ6dvlpmla7YkCCpKXQ5K+tuZU5bPXZcNp80fib7a7/Yz4+mPqPcEY6KRP1Wcw7/3tBAIq1gNMq2BEO/vaODVqm9YNK2YeZcOoy0YJsNswCBLfN3oQ9E09rb4Wfra1lhRMN4xBWBdzUFK8uzMmzyMPY0+THodVXuamfF0xI2pojSfQFilwJmGPc3IQ29sZca4gfzskiF4/AoZZj0H3H5avMGU8UEVZfmx8zWrND/2+TsK8AJhlQHZFt7afIA5z33CY+1ClRH9bTz85raEiDOAJa9tZcXMc0CiSyFOxbOfsHpuWbczdkWUw/En3axH02Bvi4+fXjwEVdO4e8pwNCJtziDryLYaUFSNqj3NyBJcODQnpRgl6pZgsxp58OpRzHv5i4Ridk+cceDYujIIBKcbLd4gEjrMEuRkmFh8xQh8QQVvMHItWF9TzzPrv+KykX245cLB/OqtLxPW70n7Odo2HI9oz4IznXSTgel//Ii/3jiecfl2xudnc99rWynJs3N9eQFmg4zNYkAngVHW8d2iPjT7QuRkytS5A3z/Dx9Q7wlS7nKw+ZsWJhf35cl/7WRddT2LV2/mytG5vHTDeIx6HVW1TUmuJ+L+SiDoOTokdBLMnzIcTSMyrmOSsRpkvm7y8du3v+QX3xl6og9TIBCchLj9YdJNR19GsBrlWDS1QT46YcuJpKbOg91q6PE56ZdlYUOHcW6BQCAQCASCMwUhRjmFaPEG+brJF/u93hPkgDvANX/8MOYK8Nz6XYmzR10OFlxeRKPHz8JpxYQUFbcvYl1slHVoaPzxR2Np9oUiwpBgmAHZFnxBhYWvboq5CjyzfldCcbu80Mn8KcMJhlXSTTK+gIInoLDxm2Y0DSYUOCgb7MQXUvistom2QJiq2mZ+enEhFw3Poc4dwB9WeX9nAysqd1GSlxXLBF9X04AKMUvmeOJn14mZpoLTDW9IiX3HSwZksaJyF3MnuZJEJlHxyYQCB20Bhb/dNIF/72nm82+a+WNlY5ItelVtMyaDxDcH/WhaJD7HE1Q44Pbzq/8YzS0vbGB9TQMmvY57phQBoGkaTb4gORlmLhvRh4uG9SLdZCCkaKgaSBLIksSAbCuLVic6JpUXOll0eTF7m30xkcpj00uY89ynAHy+pzlh+Wg7P6/QGSsY/PTiIdz5ysakOK97rxjBxcNyeCcuDih6vqLbicYDxc5ruwAvyhMzxiQtW+Zy8sg7yeIDb1Ch4rlPWDW3FF9ISRDSXPXE+wnCmCZvkBZv5yI5EeVwYsg0G9jb7GNMXhYLXk3+ri6eVsxXDV4++qqRbXtbuGtKEd5g8kytjm4JuVkWHpteQr0nSKs/RIbZgDO9Z5EAx9KVQSA43aj3BNEkhQB66tuCPPpudVK7/dnFhXyws4HzhvRKEKMcTvs5mjYcj2jPgjMdZ7qRsQPt1DZGnlMzjHruvGw4QUWNtS2DLNEWCLcXnmQeX1uT7FA2rZiDngANbUEem17CrS9WUVXbzMKpxSxud9GLEu96Iu6vBIKeo6ChqBpGWcc9q5Lvi5deOQJN1U7gEQoEgpMVty+E1XgMxCiGyDZa/WGy007d63NNnYd+h+G+1s9uZfUX+/AGw8fkPAoEAoFAIBCcSoi7n1OIek8w6bVoVmd09v9LN4xnVmuAoKLS325h49ctXPn4+kNuBYVObr5gMD9c/hEPXj0qwXkBIrP9b500GItJz7oU7gxR1lXXc++aLZybn02vdBMj+9v4psnPxcN788WeZq7/06exfZa6HEwb3Zdl15awonJXQsG31OWIiVBgV0yAsr6mIRYj1JHodi3Go8sp/bYRueWCw8UbOCRsCKtaTAgGUFUZiZ2JCs86CsRKXQ4WTi1OiMmJcuP5BagarNm4L2X0VTSSZPSALOpa/bH3NS1ixbqjzs9ZeXYWrd6ctP51ZQWcPdDOTecPxqjX0RYIs7fZR73Hj6JqsWNMM+mZO8nFjrpWll4xgvd3NpCTYSIQVjEbZA64/YzLz+agJ4DbF+LuDkIUgMqaBhau2sySK4oJhNWk6JyoSGdAtoUnZozBbJDZUNsUc12KYtLrkn4OK50PunqDCk1tIQyyjpuf39DpcgBzX6zi3mnFtPqD2K0mPEEFty+EzWLoNhe50RtEX99Gsy9Iujl51m+9J4gnECLLaiQYVvEEwj3qW870vig3y0JrIIROkrhsRB9mTRxEIKxi0uuocwcIqyqqBrk2M2fn2bn/9a1JM0I7c0uwWY/sXB5LVwaB4HTD7Q+Rk2bkV29v5ycXDUnRbv1kpxkpHezAHzwkPixzOVh65YjDaj9H2oY7bkO0Z8GZjM1q5N4rRvBNsw9/SCE7w4BRBxo6Hnu3OmGiROzecZCdmy4YjEHW4QmEGZRt5fVN+xjWJ5NAWMVuNfDSDeNZV32QJR2EKBARmdzx8hc8/P3R3UZlNbQF4aDnjLv/EQhSI3HQ4yfdbEh5XywBQooiEAhS0eILkWY6+nHY6Fhuqz90SotRqg94KOiV1uPl+9sjwpUddW2M7G/7tg5LIBAIBAKB4KREiFFOclq8QerbAuiQaGwLUrWnORa3E2XSsF4U5dooGZBFszcUubEPwoNvbEspIlE1jYeuHsWKVCKTmnomj+xD70wzkOg20JH4mWp3vbIp9nq8wMQbjDg9fPZVM69t3JsUgRHdf1SEEi9A6ehuEEWvkyh3Ofl8TzPpJj397NbuTmOM41WUFbnlgiPBZjlk5d/XZuahN7fFhFnRdhgfRxPP+poGlqzZnNJR6MKhObFtdVwHDkVfXTQsh1Z/mNc6iFbKXQ4GZFs7jc4qybMz4+mPKHM5mF2az12vbKLU5WDxtOLYsmkmPf1sZq4q6YcEEaVLPJrGnkYvP35+A0//aGxC4SKeddX1eAIKt186lLvk4bT6QjjSTYQUlRWzzkHVNN7afCAmQOnYH5W6HFTtiXyOUpeDTXtbmDvJRabV0KWAxWqSSTPKlLocSecxui2jXse66nruWbWZuy8bjjekYNbraJMiy2SYu77kRu1dVVXFG1DY5fei1HnItBhoaAviDylYDDKeQBiTXkcwrOILKmxtbWVQtpU+cX1LtK8LKUHSjCaCikowHPnn9oUIBhV6nSF9UYs3iFUvc9crG1N+r0pdDkry7FTVNjH3QldsYGjlLaUoqkqWxXhEbgndcaxcGQSC041Ms4HWkMItFxYy/9VNKfvcqFNWUFF5euZYJKBqTzNL1mzh4e+PPu7tSLRnwZlOY1sAf0ihsS1IgTMNVZJYvCr5utvx3rHc5WRsvp3+WRY+2NGQ4HRU5nKweNoIHv/njpT7XFddz446D45u3Ida/SH+88kPxLOYQEBkwkO6ycDS17amvL6WFzq578oRJ+DIBALByU5TW/CYxPQcEqN0PVnnZCasqHzV0EZ5obPH6+TaIvcf1XWtQowiEAgEAoHgjEOIUU5i9jb7ePjNbdx6cSEH3H56pZsZk2fnvMJe/PjCwby/o4EPd9Vz95ThfLSzEQBPIIzZIONIM6UcXIDIIOC8ycOS3o+6Cozsb6PNr7Bi1jnYrQasRjmhKBuloiw/5Uy1jgITgJxMU5IQJX75qAglXoAS714QpdzlZOdBDzdf6OLjrxo46AniDSoJDgFASsFJTwUiRytY6UlueWfHKDhzOeD2Y9BLlBc6WVddTzCsxtpSfLvoSiC2rqaBWZ04CnXVH+gkieUzx2Ix6HkglYitPTor+t2NuplERRvR9ltZ08Adk4fFthsIqcyd5GLL3hbe2ryfqtom8nulY7fqYzPuZJ1Er3QT2VYjgbDCX+aM69ZBxBdUqK5r5Tdvf8mDV4/id2urkxxbogKU+P6oqraJ2aX53PZiVWxmrobG8spkl5mOAhZF1dh+wMPs9s/acX+zS/PZ1xJxlVlXXc/eFj8Vz35CqcvBjeWDcQd8DMxOSxITRil3OQgrGu/X1jNxsIOPdjXiSI+ISEwGmZ0HPSx9bWuC49Ts0nxmP/sJY/KyWDRtRCwiKNrXoaksuXIU81LEHS29ciTGLiKFThf2N/toCykEw2qnAqeOgq87Lh1GayCMWS/z+P/t+FYL28fClUEgON1wphvZ1+LnoCfQ6bWrsqaBQFhl49fNONJNsRg44IRFcoj2LDhTOej2YzXp+cHyj/nHT8pRVY22oNLldfdnFw9hReUu1tXUc/ulQ1myZnPSs2JlTQOLVqcWWkdRNY0WXyh2/xx9ni0ZkEUgrJJlNeALKliNcsKzmGirgjMVVYNGb6jT6+u66nq8oeSxH0GEM91xUnBm09AWpFeG6ai3Yz0NxCi7G72EVe2wBK4Wo0yvdBNfHvB8i0cmEAgEAoFAcHIixCgnKS3eIOur67nh/MHsrveSa7ewaNWm2KCe1Siz+PJiRuXZ0DSJNzbuSxjwWz5zbJfb9/gTBxg6i/4odzkTirLxdFUU7xiz05nLScf3czJMWI0yZw+0U9caiB1bRVk+EwscGPQ6ZEniveqDnNU/iwZPgCvjChDlhU5uudBFxbOfxI73vEIn9181kkWrN3cpEDkcwUpXdJVb/unuJpq8IRa8ukm4pghig1lN3iBBReWTrxq5vrwANDjoCcSWixdmddeWUhEf/5Py/fa24g8rXYpWbr7AxYynP4q9FhVthONyxeP7lragwr9rm1hy5Qh+tPxj9jT5MOl1LJxSREGvNDLMekx6PYvj+jaA568b1+XxWowyBb3S+POccRxo8TPv0uEEwgoHPQFM+ohI5uXP9sTEM4qmUdgrnamj+uL2hXjl5okYZR2NbZHzHnHFaI6dh/U1DZj0Oh6bXoLZIGOUJcx6mQF2K7WNXn5+yVDuuFSirjVAToaJYFjloCfAALuVuZNcrKjchUGO2KFs399Krt3MwlWbqapt5tHpJahoscilirJ8JhQ4kCWJek8AWScRVjVWf7434ZyUu5w8MWMMNz+/IeY4BYdEf4tWbeKBq0YCxPqwdbdfwMLVmynJs1NRmp8gIlqyZjOLLi8+rQdPW7xBapu8aFr3duPRdhU9r25fiEfXVzOzdBANbae/aEcgOJkIhlU0oNnXdfRGqz9Esy/I8NzMpNcFAsHxIxBSQIOz87LQAEWKXEe7osUXij1jShJdOuLNmjio0+3YrUZkHcwpy8cgS1w7bmDKKMvovt6rrme/2y+u64IzFn9YoaWb9unxh2Mid8EhhPut4EynyRs8rFiazkgzRkoR3cXsnczU1EUEJf3sh9f2+9nNfHmg9ds4JIFAIBAIBIKTGiFGOUlp8oZ49fNvuP3lL5g7yUVV5c5YkcxqlFl2bQnPVO5id5OXqtqmTgvInZFuTsz57Cz6Y11NPRpalzPSOqM7l5N4THodpS4HXx5oZfnMsfTJNOP2h3h21jn0zTKzdM2WpEHFySP6sK/Zn3i87TFE8cf7XnU9d76ykdEDsnhna13Svt+rrqfeEwTo1tGkJwMyXT1QVZTls2BlsmW1mKl35hE/mBUvSjDrJZZeOSJhRlrVnuZYPEx3bSkn05QUJWM2dJ3ra5R1zHnuU/564/gul+s4cBkf8xMlvm/JtOj55aXD2F3v5fc/OJs3N+8n0yIT0jQeW1sTi0bp2O988lVjFw4iTt7ZeoBPv2qkoiwfCYkH30y0mZ40rBd3XDqcpWs281m7AKRj1EPUWeS2F6soyctKEN1ZjTLXjhvIs+t3xdZ/5N1k95V7phbz0JtbWbvtYMLrj04vwZluZMWsc3CmG7n/9UPHd9uLVVSU5XN9WQG5WRaWrNmc0Lfdf9UIFq3anNRHrKupBwluuXBwzMI+XvRXWdOAJ6gQVLRYH9YWUjotyswuzaftNJ/1WO8J4kgz8tz6XUwfP6jLZePblS+oULWnmXU19ahoLLq8uIs1BQLBscYXUmhrjyPrikyzgfOH5NDYlnhtyjAbOllDIBB8GwRVjec//Ir7rxqJBtyzchN3Tinqdr1n1u+ioiwfX/DwhdYQuZ95e+sBemeYOCvPzvwpRSxMEe3VUcD7dZOPPplm8cwlOCPxBZVur6+B9lhP0UYO0RP3W3G+BKc7TW1BMkxHf59tNUXGjLoTxp3M1NR5SDPKZFkO73z0t1vZUNv0LR2VQCAQCAQCwclL10+hghNCizfIgpUbY1bFJQOyEgbVKsry222NG5LeixItXqei1OWgzh1IeH9Mnr1LK/SJBYnbKnU5yMns2p4xfpCjzh2gvIvjOeD2M7s0n6WvbWX5up3oJNDpJHQ6iYa2IKPz7DErR4gMKi5dsxVFS57vvr79vMSzrro+6bV4Wv2hLh1N4gUr3ZHZRRGkZEBWpzP/DmcfglOb+MEsq1Hm8WvHkGszoyoa2ekm7lq5kdc27ou10RWVu5hdmk+py9Ft2353ax0leXaWzxzL8plj+dtNE2jyBih3pc6yLS90YjXJrJpbSno3AwupBi7X1zQQbBeelbX3LdHtvrv1AA+9uY2wpvGfT35AVW0T3y3qy6JVm2PtNFW/Y9RLLJxWRFmHz1nmcrBwWhFGvURlTQPPVO7iYKs/9nmfmDGGFbPOYca4gTz05lbW1TR0KrRbX9MQK4LE/wyHxHndrb9kzWaKcm1Jrz+7fhd7myMxPb96azvXjhsY67+8QYVla2v4tLaJxWuSRSe9M81dzg4+b0ivhNfiRX+t/lCiGE6jy8/erV3IKY4nECKkalSUFwBal9egqj3Nsd9tVgMvfVwLRM6Vop7mJ0ogOInY2+zjm2YfGWZ9l9e7cpcTnU4irGgJIsjzCp0400UxSCA4noQUhTnlg7nrlY34Qgqf1TajqhrlhanvPaPX3fXtz5hyNyMSUaF1PGUuB3dcOozR/bNYs3Efk3+3jl31bV1GA8U/C4pnLsGZSpqpm+troZMPdkZE7oJDHKuxIoHgVCUQVmgLKmSYj35Oq16nw2KQu3VRO5nZUeehn92CJEmHtd4Au5W9zX7h5CgQCAQCgeCMQzijnITUe4IJA2kdYzni43Hi34u6K4zJs2OUJb5X0o+FqxKjacpdTm6+0MWtL27gwatHAVBV24y1G+eEsKbx4vXjMMg6gmGV93c28O7WuiQHhijxxb1yl5MJBdmMK8hm4aubE9wOyl1OFk0rZs3GvSx9bSsA14wbyPyVibEd8fbK0SiNdTX1zC47FAUUT6ook67iTTLMhm4tInv6sOBMN3JeoZP3OhmsOBb7EJzaxA9m3Xh+AekmHQW90uiTaebulZEZndFIF4gMoEcdNcoGO7jyrH4sXt2xbTuYXZbP3BcSI7WWzxzLrS9WsezaEkDrEP3iYHbpIK58fD0leVnMu3R4p44kHQv28Rz0BChzObjn8mKu/eOHlLuczCnLj0XKwKHZqN80+WJCu87aZJmrF7/+x3Zml+Zzx+RhePwK6WY9dW4/v/7Hdn58gYv/5ks+q21mwdRiXv18b1K82MzSQXy4s7HbOLHrywqYO8lFyYAs0ox6xuTZI64mlbuAruPIKmsamF2a3AdV1jRw5+RhrJ5bRqDdCvvPc8bxz+11rKjchTeodLrd7mKYOs4ejhcIZZoNGOIqOhp0KjJcX9NwumtRsFmMHHD70SwG6t1Bllw5MikiLd4hByID8PubvDx49ajY9aZjRJ1AIPh2iAo1ry8vwCjDtn3uWB8b35eVuRz84rtD8fhD6CRoaHdGOa/QyUNXjxIzkwWC44zFoOfuVyLOj95gmBeuH88jb29n5sRBoGlJz3Tx112dTmJnXVuX959RoXVFaT7pJj0Wo4yEhMWk46E3D7mH9iQWNno/60gT/YTgzEQnwZa9LZ1eX++dVsyUxyo5rxMx2ZnKsRorEghOVZq9ke94+jEQowCkmeTYNk9Fvqzz0Nd2+PFcA7KtAFTXeRiTZz/WhyUQCAQCgUBw0iLEKCchHR90O7oRpIq/sRplHp1eEotjsBplfnqxiyVXFOMLqbT6Q2RaDJhkHUFF5S9zxuENhll6xQgA9rUkxt10pFe6CVkCk6xjX6ufCQUO2oJhLh+Vy6KORfFCJ4unFVPvCTBlZF/2tfj4jyc/YFR/G7dfOpQfBwajkyS8QYUNtU3safTy27erAZg7ydXpTH4gKS7I30nMRCoHh87sE3s6i7antu82q5EHrx7FvJe/SBCknFfopH83eaLCWv7MIL6NXzI8B6NeZvGqzfxy8rDYd90bVGIClDllBViNMqqq8eGuRsYMsDN5RB9mTRxEIKxi0us44PYjkTgrw2qUyU4z8tj0EsKqxvypxaiahtsXwmqKiDsWvrqZirJ8SgZk0eAJcO+VxdyzsoNorNDJzImDYoWDjgxypHHfVSOpb/Xz5znjsBh07Kr3suzaMWyobWJF5S5uKh9MycyxZFmNvHj9eDLMekwGHc50Y2wmWVRQZ5B1/PTiISxds6WDeMbJ/KnD0Yj0FXqdxJI1m1PGi6loPDa9hF4ZJp6YMQazQY4dS1RcYDXK9M0yU1XZ1EHMckj81lVhI3p+l88cSyCsxvbx0se16GUd967ZkhTtE91uUEm93e5ss9PiHKLiBUJlLgf2NCNmvS4mhmsLhLvcVnfvn8pEHMY2cfulQ3H7QnjDCno07p1WzK4GL1ajjKJqfLCzISY6KXU5WHh5EWZZx6P/rI5db2yHab0rEAiOjKhQ85eXDiWowt1ThrNkzZZYEToQVrFZDGSY9VQ8+wmPXlPCALsFgw7e/fn5ONONQogiEJwAvCGFdTUNWI0yjjQTd76ykfU1Dby/s5GXbhjPrNZA7H61ak9zwuQCm8VAWt8Mzh5UnHISxeyyQQlC6+Uzx/L/nvoQgBeuG5dwn9XdPZTNYogJYa46q9+xPg0CwSmBJMG14wbywke7U15fQ5qKN6h06fZ6JtLd+RDjOILTnca2yJhN5jETo+hP2ZgeTdPYedDDqH6Hfy/RL8uCToLt+1uFGEUgEAgEAsEZhRCjnIR0fNCN2qhGB9viB9qq9jRT7nIyOi8rJuKwGmV+P2MMJr2Ou1duijks/O7d6tj7j04v4YWPdlOUa+PS4j68v7OhU5eT8kInmqYhyTruenUTFaX5zHnuU+BQ8Ti+KN7XZmbqY5WxQcPyQidPzzyH6577hIfe3EZJnp2q2iZK8uwsW1vDEzPGxPbVnYtBRQcXglSDjqkcHM4rdDLQYU1yLOk4i7YzR5PDtX3PzbLw2PQS6j1BWv0hMsyG2PrHah+CU4MWb5B6TxB3uyDMmWZMKG5bTXoWvBJxArrZnyiuika6zJ3koqq2ifU1Dcyd5OLJdTs6dSSKFtCj8T/b9rnJyTQTVjS+afaxv8VH/ywrH+w8wLn52Tzy/87CYpTxBhUOegLUNvi4ZdJg7poynF31bbE29vxHu1M6RJQXOtn4dTO/fHlj7LUyl4MFU4uZ8fSHDO2Twe9njKG/w8L8dteX+OX+58bxvLlpP8P72rAaZFQ09jb7eP6j3UlW6+tq6rm3vTBZVdvEvMnDeOSd6pTnfX1NAzdf4GLasvUJ5yfeYamiLJ+lHQQjkf00oBIRv3VW2Ij2o7/5x/akGb9PzzyHX725rUtRXa/01DFnHfv7eEpdDsztLlbxM4vLXA7uv2okvTPNADExXLCbGcL207hoG3EYq+d2hpJpMZBhllEBvU7imfW7+Gx3U0yE9fD3R8cEXa9t3MfFw3rz4/Nd7G7win5ZIDiORIWaYUXDYdYR1OCSoj7kZJhi95gf7GxgReUuSvKyeH9nA5NH9KFXppWc9v5PIBAcf9y+iLi1oiyfr5t8CcLqf2w5ELuH7Ui5y8Fbm/fzyDvVCc+UUae3oKLwwc6GQ8+UHZ7xmjsUsbq6hyovdOIPRYTeYwfaxbVdcMai1+li40AlA7ISrq9b9rYwf0pRTOQuOERX7rfieUFwJtDULkY5VsKrNOOpK0bZ1+LHG1Tol3X4zihGvY6+Ngvb9rm/hSMTCAQCgUAgOHkRYpSTkI4PuisqdyXEdUQEKA4+q21Gr5O487LhtPpDjMmzU5JnxyBL7G32sWbjvljxOt5tpKIsnxc+2h2bEVPmcibtI0qpy8E9U4v4psmHxSgzp6wAu9WAtb14HS2Wx/PEjDEJRet11fVomsaKWedwzVMfUlEaKZZHhSXxxd6e2CtHKS90UtcaSHi/vNDJLRe6qHj2k9hrUcFJ304EIlEhSleOJkdi+26zpp6heyz3ITi52dvs446Xv0iY5XleoZP7rxrJJcNzeHtrHf6gGhMzpJtTx2XFi7R6Gjtz4dBehBWNgc403t/REHMEKXU5WDytmOc+aOap93ZGBBXvfJnU7n960RBufn4DEBFeLLu2hFBYTYr5mXuhiw93JQ76V9Y0sGTNZh66ehRznvuUqSP78vS6nUnFgQ21zdS5A3y4o4FfvfVl3HYPxex0FMBERWnL1tZw0J3Y/jvS0WWqo8PShAJHt+K3qj3NTBrWK2HA1myQ0TQtpWBmfU0DOrYzOi+Ld7Yd7HS7aUY5pSX9ispdLJ85Fh0kiVzmXliIQSfxj5+eh0GWaGgL8vcfT8SeZowJUeCQGK7ZG6K80Jky3/x0HzSN/u3/ub2O753VD0XT4VMU9DqZBVOKWLxmc8LfPiruWfraVs4d5KDFFyI73Sj6ZYHgOBIVY7d4Q+SkGUHTOGdQNgtXbUq6RkXFeBMKHN1GTQoEgm+XNFOkDZYMyEoSiHT2jFnucrBwWjH+kMJfPtxNvScYuy4/MWMMz3+0m4VTixndLwuAScN6sWBqEbvqvTHHO2e6MfZM2tW+ylwOfvGdoUz/44eMHWgX13bBGU1jW4Brxw2MOepGiV5bG9sCLI0TuQsiHOuxIoHgVKPRGxWjHJsygtUon7JilJo6DwD9unG+7owB2RY27xViFIFAIBAIBGcWkqZp2ok+iNMBt9uNzWajpaWFzMzMo97e3mZfwoOuM93IsmvHYLMYaPGGyEozoGnwq7e2sXbbQaxGmbmTBjO5uC8BRaXVFybNJGM1yOh1Eq1BhbZACEeaiaCi4m5/32KQMegkWoNh3L4wmRYDZr2OoKpikWVaA2GQoNUfJsvSLkIJKXj8IbJj24oIOyLrKVj1etqCCm5/mHSTjNkg4wuGsRj0tIXCtPkVvCEFu9XAsrXVDM+1xQZCXrhuHNc+/VGn52X5zLHMee7T2EO/1SindB/pTHDSE6JOFke6/smyD0H3HOt2G0+LN8jcF6s6FQI88L2R3Pn3jcydVMh/PvkBEPl+v/DRboa3Cx+CikpOhplMi57dDV56Z5iRdVDb6KNXhgmDLLGvxY9Zr8OeZkTWSQSCYRzpZtpCCm5vCJvVgF4n0eQN0hZQ+Ky2ia17WxieawMimeEdhRYbapuYMqIPV//hAyrK8hmTZ8dqjPQXkgR1rQFyMkyoKgTCSmwfDZ4gGWY9Br2O/U1+Bjqtkb7AF3GF0csS+5v9eEMKuTYLSBr1rUF6ZZgwG3Q0tQXJTjPhDYVp8oToZ7cQCKt4/CEc6ZHXv2ny089uoc7tJzfLTG2jj1ybhUBYodEbpG9m5Od6T5BBDitBRaWuNUDvDHPs9YEOK5qm0RYI4w2qhFUVSZLwh5SEOJ/Hppdwx8tf8Px147m3PQ7I2S5Q6JNpxhMIxz7v7gYvOklKWDfqIBWP1Sjz9x9PjHxHfCEUTUsSC91/5Ug+2tWAI/2QG0Cd28/Y/GwCwTAj+qe2c03lwuMLKvzzy4MxZwGzQeaA28+FQ3rR5whm8pwM9KTd7qjzcNFv/oXVKLNy7nhAR6YsEwI8cd/JNKNMfBm7sqaekQOy+KrBS4EzjWF9j22/IBCcqfSk3bZ4g9z6YhWjBmRxzdn9MQEBoDWuzaYbZYzAH9fv5On3a1l9ayk6SaK4/ZomEAiOLT1pu7UNbdz1ykZmjB+IUdZxx8tf8Nj/O4u8bCsqENY0AuHI82m6WSbNqMcXijwrphkjz4ohRUVVNfa2+DHqdPzwmY8pdzm4e0oR+1v85GSauP/1rVR2ELTcfKGLOc99mhDBWFGWz4QCB4qmoZck+tstuH1BLEbxzCU4M+iq3W7f78bt8dPXnpZwfU0zyhgAv6rQ4gtzVl72iTn4kxwxjiM4U/nzB1+xaPUW/lxxLpIkdb9CN/zhXzto9gV59ZayY3B0x5fllbt46I1tPDPrHHS6wz8Xq/79Da9+vpdNi757ROsLBAKBQCAQnIoIZ5STlPiYl7ZAiEyLkQUrNyXMoi9zOZhVms8XX7fwyH+eRT+7hXte3RSbTe9MN/LnOeNoagvS4g8xwG7lg50NLH1ta2zArrTd2cAfUrn9b59zzbl5TChwYNTrUIwaX3zdzK/f/pJrzs2jZEAWAL0zzZj0Mktf28LauJn/ZS4HS68cyeLVmxMcASYN68VdlxWxcNWmRFeFQicLLy9G0VSuGJ2LrJNIM+k7tT8tL3SSl23l3Z+fn+Ro0pGjGRDozNHkWHI89iE4sdR7gimFKADvVdfjD6k8Nr2Er5t9MYFDX5uZOy8rYvGqTUkz1e6ZWsxDb25NaHPRGWw/fn4DY/KyuKF8MH2yzNy1cmOns8jPzrMzf+pwFFVD1TTG5GWxvPLQzDirUWb+lOFIOok/zT4Hq0mPNxgRcRgydFgMOrKtBtoCCut31CeIKGaX5jP72U+4YIiTX146nDtf2Zg0M3VOWQEGWeLBN7cmzpBt7w+CisLB1gBGnY6PdjWQa7PQN8vMog79R1l7JJFFL7Pxm2b62Cxkmgy0BkIJ4o5Jw3pxx6XDY2KS2P5cDuaUF6BpsCLOOSp6vh6dXsKAbAt/vXECC1/dFBOixAtT4o8lPpbo0eklhNVknWc02ue+17awrj0yraIsn4kFDs4v7IXFGBGKXP+nT3nqR2fTGgjj8Ssxx5zpT33I/9wwIeV3qjMXnqVXjuDdLQd4Z1tdwuvnD+mVcjunC/EOY7/4n408MX0Mfg3ubm8b8cUqo15HplmPxaBj4mAnGhpWg4wnED7RH0MgOKOIzjq+/7UtmACvBovXbI4JJhvagmRZDfTPslAxsYD+2RbCikZY6dpVTyAQfLv4wgqzSiMRhJ/VNrDyxxNpCynsaw2QbtZTtbuJJa9tBeDR6SUJjp2Q+Dz6wke7mT+1iAF2C+tqGgipGvta/Cyv3JkgRIGog5zEgqlF3Pn3SFykN6jw+Z5mvlfSDwBHWvSZK+24nAuB4GQn2yBjyUrjvZp6emeaCYRV2oIKB1p8nFvgwKiTsVtEcbQzxDiO4EylsS1Epll/TIQoAGkmPbsb2o7Jto431Qda6We3HLGQZJAzDW9QYXejl3ynuD8RCAQCgUBwZiCcUY4RJ8phodTl4PqyAvKyrSyIszG3GmVeuH48v3prW6dF6XhBypSRfRnRz8ZDbyYu31khNyqEid9O9PWz8uwJhfS5k1ydZoVHl9++z82iacX4QypN3iAhRWV9XEE5Pmqn47np6ASQanCgp8sJziy+zXZbVdvEVU+8H/s9WvyOOpDkO6zkZllo8QYJqrB41SZG59m7bSsdY2VKXQ5K2l9/6YZxPLa2JuX68cuVuhxUlObT12bmgde3xkQeUbHEM+t3UVXb3GnBINqHlORlJfQn0X2UDMhKEnhEuf+qEby+cV9SQSH+GP9d28TSK0dw75otFOXaOj0n5S4Hd102nKWvb+30GCvK8jtfv9DJ5BF9uOuVTSnP9/wpRYRUlcsfWw9EnGs6+1xl7fuc89ynlLoc3H3ZcC57tDJhmfi+MP5cpzp2s0HHD57+OGH98wqdPDa9JKnv6uoa0dn3prNtnQr0tN1GHcbuuHQYelni3jVbujz3EUFUEWFVo7EtgN1ijDkICQSCo+NwrrcH3H5CIYV7Vm+ORQl0vAddeuVIDBJUH2yjV4YJe5qR3FPU7UkgOJnpSdvdsLuRG/78GS/fOAFNkliwcmNS1ODs0nw2723h412Nnd6nThnZl70tfv5d28SiacVMW7ae52afS7pZz+Tfrev0GP/x0/OQdZJwKhAI2umq3X7T0EZts49l/6xJKQqTdRK5WRb6263H+7AFAsFJzKJVm3l32wH+++rRx2R7f9/wNe9uPcCGe75zTLZ3PLnqifWkGfXccqHriNZ3+0Lc+JfPWHZtCVNH5R7joxMIBAKBQCA4ORHOKKcAXTksrK9pYPG0Yva7/QmDCRVl+fy6gxAlunz0/Whxcn1NAxWl+bT6w0nLF+XakoQoAJU1DWgdthN9fXZpfsKyJQOykgqh8cvffIGLswZk8cuXv0gqDK6+tQyJ+Flth+jMCeDBq0clFCR6upxAcCzJNBtiP8cXv+PbwnmFTh64aiSLV37BupoGZpXmd9lWOrYtONR+ITK7JNUAf8fl1tc0cMuFLva1+BOKBRVl+bGi39xJrqQCYHTd6LLRY43+HN1HTqap0+PonWlOKUSJP8Zla2uYv3ITZ7ULWzo7J+tqGmj0hro8xi7Xr65n1sRBKd+rrGmgyRukyXsox7irz1VZ08Adk4fF7V+i3OVIOL8TCxwJ56yr87tk2giWzxybFK1jsxqTxHWqqnV6jejse/NedT31nuBpXayJOoztc/tRtcTvRapzv666nsWrt7B4WjGONBPpBpmq2iYhYBQIjjNuX4iwqlGUa0vZVitrGljw6iYWTytmn9vP9gOtVNbU8/D3R9M703yCjlogOHOxWYxcc24eigYLX00UosCh6+8dlw7jt29Xp9xG9B6wd6aZZWtrCIRUKsryaQ2EOegJdLl/bzDMWXmpIwwFAkEiAVVLEqLAoXa6eFoxrX7hDigQCBI52BrAFjfGdbSkm/S4/WE0TTtmbivHA03TqD7gYeqovke8jUyLAUe6kU3fuIUYRSAQCAQCwRmD7kQfgKB73P5Ql+8HQirNvsRlSgZkJQ0ERllf0xCL3IltI6zS4kveT8mArC6L2x23E91WV793xKjXdV4YXLU5pRClxRtMEphApMA67+UvaPEGD2s5geBYE40Jgc6L3+9V19MaCMfaandtpbP3o697/ErK91OtH1a1pO3Ft/eetv2O/UAgrHZ5HD39jJXt2+1u+VT9VvxxHek5BZAkif52C1ZjJCanu/Mb/743GOaykX1ZPnMsT8wYw/KZY4m3Ievu/O53+5nz3Kfc/PwGKp79hDc27kMjIq6b+2IVF/3mX1z1xPtc9Ot/UdvoPaLP2NrNteV0wGY10uoL4477nnR17tdV1+MLKrT6w6jAjKc/4qJf/4tbX6xib7PvOB21QHBm0+IL4faFum+rIYVx+dlIUkSQt6POI9qpQHACcKYbmVjgIKiqXT5/dkcgrMbuWdz+MBMKHGyobcKk73rIIuMYFscEgtMdX1Dp8hkkEFLPiGcEgUBweBxw+4/p5Iw0k56wqiU4bZ8K7Hf78QTCR+0eNdiZTlVt0zE6KoFAIBAIBIKTH+GMcgqQ2c0AW1tQSRqkO9wCbGeDfEdSyO24rVTbjo8s0TSYU1ZASZ49FssTpbPZ+3WtgU6dAOLX6cpV5kxwBhCcOGxWIw9ePYp5L3/RpTuHO27mWXeD7Z29H3093Sz3eH271UAohXAs2jZ7ZZh4YsYYzAaZDbVNSW0zvu3H/2zS67o8jsP5jIGwesTn5Fis3xYI8/jaapbPHMuc5z7t9vzGvx8Mq9zZIf5n+cyxCcfWFR0Fhu9V1/N/Xx7k9S/2sa4mdZ/WGZ19xjOleGM1ygl5zvHf86hgqeP3PN2sxxNUYq4/UQHjqRptJBCcSqSZIo8nDW3BrttqQMHjD3H+kBzuf307zb6QaKcCwQnAZjVi1Oto9R26p03Vdg2yDqtR7rTwFH+/km6WaQsorKjcRUVZPqUuR8oC+nmFTpzpor0LBD3FG1S6vLa2BZUz5hlBIBD0nLrWAKP6H7sI2+j9fosvFPv5VODLAx4A+tuPzmW7sHc6L2/4mrCiopfFPGGBQCAQCASnP6fOHd8ZQsf4BWeaMeaw8F4KUUWZy4HFqOO96uaEQbruCrBWg8zcSa6Yo4Ez3US9JxAbIIwOUAzItvD7H4whJ8OMQZbY7/bjSDNhkCX2tfjpl2XhpxcXIkkwul8W2elGZJ3EnyrOBQ1CqordauT568ZhsxgIKSqtvjBZaQYeeedLlq2tie1rQoGDssFOfCGFz+KKgm2BUOy8tPiCWI16NA2em30un+xuTCqSw6EZ/925yohZP4Jvk2hMyJd1nk6XSTMdEjBU7Ulsx/EDhQBZViNzJ7kSvvOlLgdVe5qBiHiizOVIGYMTv1ypy0FY0ZL3Z0gdJ1TqcvD4tWP4/OtmRvazEVY1XL3S+dtNE2j0BhnsjPzc4guRZTWiaXDVWX2ZOrofOZkmPH6FDLOeA24/Ld5gQnxNZ5/x1apvKOydTjCs8tL140lvX/+Ol7+g3hNxNCovdLJpb0usL+s4mGrS66ja05xwTjruz5luSjqn8edrXXvkzvwpw6lzBzo9v2UuB3XuQOy43t+ZvEz8+T4SkUxOhimlEKXj37HjcUX/7vGcScUbi1HGoJNifzu7Wc8bt5XhC6tIgAa0+sJcNqIPV5f0Q6cDf0il2RtKcP0RAkaB4PggAWlGmfxsA//4STmtwYjoxJFuIqSoWA1OJhf3wWqS8QcVVA3mTnJhNciinQoEJ4gMs56wouFMN/Lw90fTJ9NMqz9MmknGbJAJqSpo8MrNE1FVjbAKa7cf4Ml/7cQbVCh1OTjg9rO3xR+7p5IkCW8wIkh5dHoJkOiwcl6hk4euHgXAjjoPbn+IdJMeo6yj2Rck3ZwYs5fqObunfUVP1z2afQgExwObVc+Lc84mO92KJ6jg9kW+q//v7P5cVdKbUFiKuUIKBAIBRKJp6lr92K29jtk209vHwVp8oVMqPn37fjdmg45eGaaj2k5hTgb+kMq2/a2M6HfsRD4CgUAgEAgEJytCjHISsbfZlxApYzXKLJhaxJi8LG67qJAfXzCY9TsaYkXTMpeDxVeMIBhSGJht5bvFvWloDdI704TZIPPabWV4AwqZFj1pBpmQptEWUPAGFWwWPd/L7sfeFh+qCm5fiEGONP73pgn4QwrZViP3vLopqSA9uzSfHy7/iJK8LGaX5vOfT37A2QPtLLy8CL0kseDVTayracBqPFTUjh80jG7jd+98ybXjBvLF1y08ePWolMXvR6eXMO/lL8iwGJj7QlVCETa6nS17W3h0egm3vViVUEiOzubpzlVGzPoRfNvYrEayuxiEtuhlJo/ozX+cPYCcTDPlLifzpxRh0EsoCixdszmhbZS7nLxycymKqmLSyzS2BbAY9ZS5HKiKxsJpxXy6q5GcTDNBRSUnw4zFoMPjD9MWVLj/qhH0tVlo8YZYUbmLx68dw5SRfemdaaZXhokH39iaJGqoqm3GYpT57KtGnnpvJ49OL+GeVZuoqm2O/Ry/TpnLwX1XjmTxms2s3XYw4fWlV44k32ll0bQR+MNh9Do56TNePDyHP88Zx4KVGxPs3ssLnbx0wwTCqhrJFkajxafw6Npqlq2twZlu5KGrRzG5uA/lLieZFgP5jjS+O7wX904zElAUDLLM4lWbOpxTBytvLmVXQxsGWcf+Fh99bRZueWEDAJ/VNjF/6nBCisZPLx7CzReqvN+hL77n8mLmPPsJD3xvJCV5Weyqb2PFrHMS3DaixRSdJHUpICntREDSmaPHF183c11ZATpJSnCCOq/QydIrR7BkzZaE7USLN2dKccRikFFCCr++aiQqEAQWr97MD8YP5JnKXQnfsVKXg1snFdIn04TNaqChLTHKTQgYBYJvH0kCM6CaLdzxysbYteaRd6sT+szyQicLphRx5RPrGZOXxcXDe2M1yqKdCgQnALNBRpM1/ueGCSxatSnp2jr3Qhf+kMqfP/yKa8cN5KWPa7l7ynAuH5XLsne/5PvnDMQfUnh36wHuubyY3729nfycDCDi5HDbi1VUlOUzb/Jw9jR6Mel1NLYFCSsq//W/mxjWN5OSAVnsa/GTZTWQbtLz/578gKK+mTx09Sg0SIpuPa/QyYNXj+q2CNbxGb2zdXu6nEBwIrHrZYxpVua9sjHp+W3plSPJNILRIsZIBALBITyBMP6QSpb12PUNacZIOaLZe2rdt2/b10pethWdJHW/cBfkO9PQ6yQ21DYJMYpAIBAIBIIzAknTNO1EH8TpgNvtxmaz0dLSQmZm5mGv3+INMvfFqgQhSioxR7nLyfypw1FUDbNept7jxxNUeKZyF5/VNvPkD87GIEs89s8a1reLQm44r4BLhvdmv9uPJEmx4ujZeXbmlOejQ8Kgl1jWvs7cSS6qaps6LZKW5NlZtrYm4edyl4PLRvaNxVL0ZBtVtU1UlOazosNnjF/u1kkuHl9bkzJ/PH470eOA9uLE1CJknUS6Sc/t//t5SleZ8wqdwsr9DOdo221PafEG+cX/fs7Q9oHyeAePq0r6Iksyd69MHBB84KoRvL5xX7ff/dml+dz2YhVj8rK478qRqJrGhzsbyMk0x/YTL644Oy+LuZMK+XBXA0/+ayfLri1hReUuKmsaYnE0HYlvz5393JEyl4Oz4tpl/OsLpxWzaNXm2GfouH5X2y13OTl7kJ1Pv2pk6ZUjefSd7fz93/twpht5/rrx3Ltmc8J6Fw3rxYKpxcxfuZGzOtlf/DmN9mezy/KZ+0IVAI9OL+G59buShDELLy8mrKpYDRFRkM1q5J6Vm1MK56KCOatR5o3bylE0DQ1YtGpzQtGivNDJzImDkgR2AM/OOoewpqUU+c0py2ewM42wGhFMZJgNONMjs3Gjs3Q7vn6qcrjttsUbpM0bIgyoaNy7Zgszxg3EbJBp8YWSInpKXQ6mjsqldLCD6jpPQpt49+fnMzgn/Vv8dALB6cnhtNsDbj+hkMId7YWyuZNcbNnbQlGuLekaum2fmxH9bDzyTnWsH7/qrH6inQoEx4ietN0Dbj+/+Ou/ufeKEdz32haGpWirW/a2cPHw3uxt8cee3T6vbeby0X0ZV+AgqCiEwhr7Wvz8/bOvuXpsf+a+kHgvFLn3KqK6zoNJL6NpGn/7bA9Xnz0g6d7oomG9IsKVJh8mvQ6dRMKkjijdPQ92fEaPJ37dni4nEBwPumq3exramNcu9OwocD/Q4qPc5cRgkOmdaT5BRy8QCE42auo8XPybf7FgahFFfY/NuJnHH+b6P3/KEzPGcNnIvsdkm8eDSx95j35ZFq4rLzjqbS1atZnBOWk8MePsY3BkAoFAIBAIBCc3whnlJKHeE0wYvKooy08aVANYV1PP0te2snhaMftb/PTONPNY++yzuZNcfN3kZc3GfTEhyqPTS3h2/S4eeac6to2o68htL1bBOrhl0mAeW1sT21fJgKykAnKU9TUNVJTmJ/28rqaBWe0/93Qby9bWMG/ysJSF4ehy86cUpSzGd9xO9DjKXZFC7pWPr8cbVLhkeA5LrxzB/JWbEgQpZ5ozgODEYrMauWdqUVLESj+bGZNOjhXc4snJNPfouw+R/mLZ2ho+3tXAgGxrrA+IEp2ReuP5Bfz27WqQJKaNyqWiLJ/llYf6mUBYTbm/+Pbc2c8dqaxpYHZcnxD/ejCkJn2GzvbXkXU19fzk4kIeeaea+Ss38stLh/H3f+/joatHJQlRAIbn2mJCn9md7A+S+zOVyHkFUvfF1fUsXr2Zy0f15Zcvb+xUQBP9Pfo3GjvQTpbVEOt7lk0vSRCKGGWJhas3JwlRAMxGmcfWVqfchwQ8cNVIBuekJa1ns57a4pOjxe0LoUrwfk09JQPtXDtuIM+uT3ZEiV4Xo9+FVn84waHmTIo2EghOJL0zzWzd5471dWPz7Jw1ICuli97s0nz62kw89d5O1tc0cMsFLtFOBYLjTJM3yGe1zQQUlWvGDey0reolid6Z5tiz27K1Nfzk4kLufmUj9101kgZPxNXkpgtcPPLO9kQhisvJzRe4mLZsfUzcu2BKET+5eAhuX4jbvzuURdP0+IMKbUEFm8XAp181suS1rQnRllHnzWvOzYsV4fe5/QAp75U6PqPHEx8LVtca6NFyAsGJxhNUYo5jqdrq+AIHbb6gEKMIBIIYda2R66T9GDqjWE0yOgkaOziRnswEwyo1dR4mFDiOyfaK+2Xy7tY6VFVDpzs6pxWBQCAQCASCkx0hRjlJcHewFO+yGFtdz1cNXjbUNvHd4t6xglrJgCwgsfiZqoi6vqYBHRLPXzeOJm8IZ7qJqtrm2PudFaRTvd+Tn7vahsefXHCNp8XXtWVjdDtWo57lM8fSK8PENU99GBt0fHtrHQC/+v5o2gJhFFVDUTW8wTDekMIBt5+2QJgWn8j1Fnx7tHiDfNPi57U4kYgz3ciya0toDSopBVk9bUPxIoqhfTN56M1tnQoi7rh0GL99u5p11fXcOXk4w3IzEvoZk17X6b6i8TC9Mkw8MWMMZoOM3WrAapRTCie6+gxuf7jL97v77GE1YuhVWdPAXXLkmHMyTSnPY3xfejh9W/x57aov/uWlw5g7ycWYPDsrKncxd5IraTbwispdVJTmpxTBdRSKfL6niWvHDSQQVpMERVkWQ6fivcqaBloD4S4/35mKp/37mZNpBi1RXBQfewTw5znj+Of2OsKqhi8UiVYCIWAUCI4nLd4g7vb7P6tRJtdu4WBrgBnjBjKnrCDWr8Zf26KCP5NBJ9qpQHCcafWFufH8AkyyDotB7rSt/uI7Q9nXEiloRe+5wqpGZU0D3qBCukmmttGH3Wrg7suK+MV3Vdy+EFaTTJpRzxub9gGJDqJ3vrIx9vuv3tqedO8UH+cafQZ+8YbxPPD61oT7u87idDo+o3ekLRBib7OP2kZv1+dIxIcJThLcvhA3nl9AndtPRWl+zC0w2l7vWbWZ+VOGn+jDFAgEJxEHWwMA2I/hPbZOksg0G04pMcrOeg9hVSPPYT0m2xuRa+PvG75h8143I/uLqB6BQCAQCASnN0KMcpKQaU5UmHdXNLUaZapqmxIsEjuu01HQEl90C6samWYDiqpR2+DlrzdO4J2tB/jLh7vpb7ewfObYpGJqtOA8INvCkz88m1ybBVkn8fsfjMGkl3GmG2OF6Y5F7fh9B8IqedlWfnpxITmZxi73lWaSY1FDZS4nqqrhyDAiIREIq3j8YV67tax9XS9GnY4bzitgZD8bQUUlJ8OMQZbY2+wly2rk3tWbE6M2XE4WXF7Ee9UHefJfOxk70C5yvQXHnGZvKMHRwplu5IXrx7Nk9WbmTipMuU5nwpBU73cUUaSi4+ueQJh6TyDhtao9zUwa1ispCiEnIyKcWV6ZOHuu3OVMGOTvSE6GKda+LQYZVdOQJQmDLLF6bhmyTmLFzHPId1rxhyMFh0yLAYtBx7mDspg/tRi9rKO1/fWQonLL8xuwGuXYPlrbC5Yev5LUz3QUzKQ6p/HrpBn1rJh1Dhtqm3jp41qy04zoZSkmvunYP1mNMmFFZUKBA71O4q83TuCLr5u5Ne58RAshdqshpUV7NELH7Y98xiyLkTnPfco15+ZRUZpPIKxi0uuo2tNMczfivM5EQWc60aJ2WNUw6nXcdP5gFkwpQgPaAmFCisb6HfUJMT3TRueiahp/vWEcFuOpH20kEJxK1HuCZFoM/Lh8ENeOH4TbH0YnSeRlWzng9rOjrjXByUiSDgmy00x6WrzCgUAgOJ5kWPRMLu7LPa9uSnrOevWWUsKqil7WoagavdJNvH5b5Nlt3e0XoAMG2C20+kI40k0MdFixGGRCigqahjPdhCcQ4psmH2cPtPPXGyfQFgiTaTGwaFoxTW1BbBYjS1K448VP0FhRuSt2v9foCfLzS4Yyqn8Wf/lwd8wlZes+N22BMDkZplgf0vEZHRLvHYOKxu6GNmyWrmeLZ6TYjkBwIrBZDVw+qi91rQHSTHo8foUMs54yl5PSwdnMee4z1E6CvDs+t4iJPALBmUGdO4DFIGM2yN0vfBhkWPSnlBhly143AHnZx0aMUpiTjtmg473qg0KMIhAIBAKB4LRHiFFOEpzpRs4rdMaiZDormt54fgEXDs1B1kn85KIhZFn1rJg1FpCwGmTSzXrmTnKxonJXzD0guv3ls87h4Te3saJyF49fO4aPdjXQO9NMIKxS1xqgd6aZV348kQ93NpCdbgJAliTKXE6mjOxLvSdAtjVSnC3MSee+17YwPNfG2Dw7NqsBWZL4U8W5pJv1mGQd7/xXOQadjC+o4AmECSkq63c08NLHtVxzbh4TCxw0toXok2nGqJdQNRjk6MfkEX1oCyhkWvRYDTIv/3gCbl+kEGEx6WjwBHns3eqkiIVbJxXSN9PMcx8kxxLdcekw5q/clDL2aPHqzUwZ2TdW2Fj46iaWXjUSjz9Miy+I1aRH1kkYZR3BsIqnfQDUmRYZeKn3BPEEQmRZjQRCKi3+EGnGiIhGh0STL0i6+fgP1ojBopOHtmCYqtrmmGtGH5uZJe3CqDsvK0pYNjrAbbMYWD5zLJIkJYkgSl2OhPiQaH/hDXQtRogXK6QZZVr1OgbYLTw+Ywx6WUebP8TVY/pxz8pNCaKTB743kte/2Nutm0S0bScUBX0h7nl1E8P7pHP/VaPwBBVafSGM+kibyrTIeEMK+1v8LFq1mStK+lE62MED3xvNolUdihqFTp6ZfS4aKutuvwBPUMHtC/HGT8rJMMqsmDmWZf+sSRTMFDp55eZS9rf4Casqk0f0ZnBORnsBQWVgthW3P4Q3qNLoDWI2yAzMtvLyTROZv3Jjp1EuAI9OL+E3/9je6TLeONebpVeMYL/bz876NtKMevSyRLM3QJbVREhRCYVV0MATDPPEjDHodTr0ssS+Fj96nY6SAVkpCyLx35vsNCPb9rsjoh6zgUyLgdwsC183eWn1h3H7QtgsBtLNevrbj80AyqlAnwwTsk4iJ8PEw29t45ZJrlibUTUZLaAwdVRf/t/Z/Xljyz4eebuGJWu2cO+0YhxWI7062JSLvlUg+HZp8gYZmGFixvhBtIUULO0CRG9Qob/dwl2Th/PH93Zww3kFPPJONU3eEIGwSqnLwZov9vHFnuYkYbFotwLBt4fVIHP3Kxv5LO5eNyoK/ndtE2MHZXPfa1sYOSCLC4fmoKgaDZ4gGWY9SPDHmWdjNejxhhS8QQVZJ7G/xc8v/vdzhvbJ4LZJLnrbzNy7ejOVHe655pTlYzXKzC7N59p2h4cvvm5GkiIzjqOTIL5T1JtH3vmyg6jawQvXj+ehN5JdUh66ehQ6nURIUXnphvFkmvUY9Dq+bvTRz27h3tWbE9a5/6oRlLucSZGc0e2J+DDByYLdIONXNfraLHiDCjop0ub62sygatx7eREef7Lb4t5mH3e8/EVCHFVnjkICgeD04oA7EqN3rMkwnVrOKBu/aaGvzYzVeGxKKXpZx8h+Nv6xZT+3XOg6JtsUCAQCgUAgOFkRYpSTBJvVyINXj2Ley1/wXnU9VXuaKXU5Eoq/j187BrNBF4vhiLcojl9uwZTh/O2mCeh0Eq/dVhZ7/b41WxidZ+enlwwhrGgMcqbx/o6GhNng5wyy8+bm/by77WBs+0+t3ZEg4igvdDL3Qhc/GD+IP3/4FWcNyOLhfyTaIl80rBcLLy/mm2YfTd5QzFVgy94Wnr9uPA+9uTWpYLzw8mKWvraFtdsOxl4vczmYVZofK+w+cNUIXt+4L6H4+//ZO/P4qMp7/7/PnNmzzCQzBBIgEJhgSFgMoCBJXHBFEbDWe0XaslmtirbXLlYFN3DrcmsV9dYKLreiv9taUHGrawUUBEllR8IWlkDINsmsZ87y+2Myw0xmEsCye96vly/JLOc858z5Puc5z/fzfL5waOXbuMH5lBQ4+DBhG8trGmkLyV06RkyvKGLB8h3cfEE/ygoc/OL//hXfh90sMn/KCJ75pCYlMX7bRR5mLlzDb74/hN+9n5qUnnmRh1BEZdKfVx4T15UjTabok0WnFsGIwtM3DKPOGwRAgPi1YjYaqPS4WNYhpjvW744JHMoLnUxrj4nYezFhyuFWqhjb69BWeFzYzCJ93Db+d8ZI7l28juU1jfzskmK+2tmUdO1PryxiUM9sHDYT0yr7sXZPC0N7OXl+2faUNi788Sj+VdsMRN1a6rwh6ltD/O3mUQiCgV8vWpdinz6toogfLVjF8D45vDjtXBp8YfySnCJEgWhZnAff3MBDE8rY6w0SklQCEQW/pNBmEXn6k5qkJEXsO3OXbGRaZV/sZpGfX1bCg29GxTYd+9DY8Y7u52J/a4jplf0Y2l5+J1FYMr0yWr7nxeU7UvYX+8zNF/QjomjxhEwoovL2+rqk/va+cdE+b8X2Jp6cVM4fE9xzYudnRmURqqaxYPkORvd3ccnAPErys5MSPWv3tHBev1zue2N9UnsqPS4evWYwda0hbGYRVYu6g+xpDqCpGr1dGV1eL2cCB1qCIAgc8IXYXu9n5sUD8IdlBEEloqg0+yOs2tXEgmU7GF6Yw0MTyijJc/CTV74irKiYIlEBV6zvbQ5IcWFl7LfU+1YdnWOHNyChqCoRQNM0TAYDQUmhNSSTZTWypznI39fs4VeXlxCSZZ77bDuyopFjN8XvjQFJ4devr427UeljIh2d44svLLOzMcAbMys42BqmJXjo2a9Prp2H397I5FF9sZsNrN/rjS+GaAvL7PcGGdE3lwfe2pD0DFhV7Ob/bj6P3U1BbGYjczoIUeBQ6dkrB/fg7kXrD33X4+bWi/oz46XVcSF27Jlyxfam+GtLaxp56K0NnF2Yw0cJ+169q5ldTQGe/jh58UOlx8WcCYOY9UbqAoe5b29i/pQRgJbyvKiX+dM5lVABQRDY5w2SYTGiaqCoGnXeIAXZNir6uWiTkxc4eANSyn0U4LOtDUn3Wx0dnTOT3c0BurUvWDyWZFlPL2eUdXu89HUf2zmUc/rm8syn29jvDdHDYT38F3R0dHR0dHR0TlMETdM6MeHUORpaW1txOBx4vV6ys7MP/4VOiCW8vEGJTIuJOUui7gkzx3gocFh5e11dfPJr5hgP1bXNSUKUJyeVs3DlLm4Y2Sfp/7+6ooQ9zcEk4QpEk503VfVD1TS6ZVkJSFGbVlXViCgqG+tamfv2pqSyFLFkrUY0+b1lf+pnYgne6tqWJAeF7tlWtuxvxZURfZBx2E0YRQPNfgmTaCAgyUklLmJtLC/MYd7HNcyfMoIZL61OW45jTW0zIwpzUDSNGS+tTjqvz0wexq2vrEl7zu1mkVduHEmTXyLLakJW1SSRTsfznEiFx8X0iiJeSJOUjr1/1eB89nlDzPu4hvOL3d96suZIkynegMTMV6tTJotin9cniw5xrOL2cOxq8EWvgU9qWF7TyGs/HsX1f14BwP9OP5d8p5UH3oxOhneM6cT4KXDa4tdBg0+KiznueLWaYYVO5k4cxOzFG9KuyqzyuBneN4dVO5uYeVExkqzQK8fOw+9sjJfl6Z5tZdxTy+L77ih2i27Hxa0XeeIT/B3b6M608MGmA0kOSGajAVlV8YcVBCG6yrTAYSMsK3iDUVehT7bUs6Pex7UjetE9y8qupkA8rhMdV2IrXNftaaGHw8Yv//Y1159byNiyHknfSXSSAXjlxpF8sb0x6fwmxnZnx5t4jmPbmz9lBDazyA1/Xpn297abRRbfVsGDb23ocluVHhdnF+YAsHGfN6U80praZjbv8zJmYHfuWbSe/512Lvk5Vu5/c0PKb3LbRR6mJyRdor+FmdduGpXy+UqPiwfGl2E3iRSchg4pRxq33oBEXWsIXyh6jT36ziYG93JyfnE3FE0jq93FKySr+MMymRYjGhpBKcLHWxq5cEA38rIsIAjcu2hdUp/a8bfU+1Ydna450rjdVu/DL8l0s5nQAAWIqBpBKeqYEHPNW1PbwrBCZ/vYS6PC42bJun3M+3hbvB/86M4LcGea9TGRjs6/wZHEbvWuJpx2M9W1zZT1dCArGv72eLUYDYQiCkaDgZaAhNkoIghQ5w1hEg3s9wYpzsvAF1YQENI+G4qCwNQXV3XaxtjzYSKJz49dvZbu+109+71y40gmP9/5+O+1m0ZxsC0cL7Po6ZZJn2OcuNLRORxdxe3eRj+qqoLBEHeZzLaZyDSLoKoYDFH3wJ4JovVt9T4u/u9/drq/j+68gP55mcfnYHR0dE46VzzxGb1ybMyo7HdMtzt/2Xb2tgR596fnH9PtHg8UVaPs/ve4dlgvxg0pOGbb9YVlfvKXr7hvXClTRvc9ZtvV0dHR0dHR0TnV0MUox4hjndT2BqSoNXF+NpeXdUfT4GBbOGmirKMw48IB3fAGI+TYzUiKglk00ByIkO+wkmUxIqsagYhCSFJwZZjRgFBEwWQUebBjOQyPi9lXl7F+r5cRfXLwhWUCkkKGxYgACAawG6PlNVpD0SSeQYiWCwnLKm3tK1hNooEmX5gsmwmbSSQoK2jRahT4w4c+0xyQyLQkf95uim6/LSQn7ddsiCYPfWEZm0lEFMBmEQlKarwMhc1soDUogyDQ7JfIzTDz3ob9LFi2AyCePJdVjSJ3RrxkSozERONTk8pTJjgTeev2Cq5+anmn70dXyRHfRrrJmgOtIZr9Eq0hmWybkRy7me4JpSGORmCiTxYdOSdKjLKzwce9CWWi3pxZwfh50Wvm77echy+kYDYJZNvM3Pzy6njZnLbY9WwSOdgWIsNqwmgQomWhbGbCikprIEKWzYTFaOBnr1Vz99gSujtshGUVXyiCO9OCpKh427dlEg20BiWybebo64EImVYj+70hNOCOV6vj/YkvLNPDYcUsGmjyh8myRr/f6Iv+224WafCFsJlNRBSV3763ifvGD0JWNNpCh/bX6AuT3R7T/ogSj9OO22ryh3DYLNHjav+MRTSgAA+8kVqyZ86EMhr9EkaDAUGAZl+Evm57tF9q/36GWaQlEMJpt8YnXGPntMEXwmoyomgaiqriDci4Mi2EZYWWYIQiVwaR9nOXbYse4y/+72v+OKkcVdPY1ZgsfrGbRR6/dgg9HFbaQjJOmwmbWaTJF8JhtyDJKvtbQ7gzLaiaxsG2MEXu6D5EgyFNP+xm1riBKKrGO+v3c015Afe9sSFtcmTi2fncNqaYiKLR1t5em8nAy5/vZOKwXvHrKXYcLyzbzs8uPYs+p6E7ypHG7dYDbYgGga92NuGwmyjpkR0VldQ04s4089K0czEbo+XX/O1CzLZQhPxsK5qqEVRVTAYD97+R6tID0WvwvnGlBCQZu8WIJKsEJCV63RqjSbdYiTYg7mqVaTFiFg20nKQSbjo6J4Mjjdvq2masJgM5ZiMqENGI9+mx/uvF5Tv42cXFaBoEFZW6lhCvrNzFzy87iwyTAQQBv6TgC8lkWEQs7cnvgCTjCyuYRANLtx7kuc+2s+T2SlwZZr2Ej45OJxxJ7O5u9HPQF0aSVZ5qF17HqCp289D4MgTAYBAIRKKxaTeL2ExidFwrJY/bgopCsy8ajwB2o4GtDX66Z1kJywoHfWEsxkPjr99dNzTtooN0IpV0r3VctJDuM519FpLF43azkWBEYU1tM1vqWvnddUP1/kTnhNNV3B5o9BPRoC0ioyHEnw9AI8tsxAL4VI2ibofmC6prm7nmmc873d/iW0fHBfY6OjpnFpqmUXb/+0w8uydXDz12IgyAv67ezefbGllxz8XHdLvHg60H2rj0D58x+6qBlBY4jum2f/v+ZmRV482Zlcd0uzo6Ojo6Ojo6pxJ6mZ5TFIfdzIMTBvHr19cCxFfLJxKW1U7LesSsiH/9+lp++/2heE0R/rx0O8P75DCmpDuyBg++uZ6hHZwYYiytaeTBtzYw68qB8eRdjFh5ifsT7JQ7K2UTKzPRFlb43xU7uWFkn06dB344/0vKC53cWNmPQETlz59tS7vfOQmlfGL7feSdTSlJ3Nsu6p/kFFDlcfPOHVWgacx+I1qmY+YYDy9/sTPl+GN/PzWpnLwsCy9PP5fcDDMRRaU1KMfLOgQkBV8o2ca2Ix1/t7ZQJOnv2kY/d3coX1LpcfHINYMpbE8UN/iktEIUiNrjNvik+ERna4ftd6Tj/nWOP4klXmLEynBJssadf/0Xj187hBy7llQ2J0alx8XscWVMfn4FQ3o5uOuKgSkW4VXFbv44qRwDAvcsXkd1bQtPTirniY9SS7/cf3UZD7+9McmSvKrYzYPjy3j7jkruW7w+ZSVprJxOrEzQlBdWMazQGW/X2b0czJk4hFmL1yW5BFV4XNxY2Y+2sMz8ZeljP7atORMHMWdJcqmu31w7mLe+3pe2ZM/sNzYwfkg+v3x9HZcMzGPWVaXMWrwupS94YHwZ+7whltY0xOM28ZyW9MhiRmU/DAKs2xt1XMm0GPGFZQQBWoIR/JLCAW+QP/9oBHOWbEgqBVbhcfE/k4fTw2lNcUOp9LjSHldimZ7SAkcn/XADDy3ZyDl9cylwWIkoGj8Y1YcZlf2S3F9659j46SUDmLV4fcq+504czENLNqSUP5szcRBhpeu+63RmX0uQ2qYABQ4r5/bNwWiIlola2u6C88K0c2gLRVKTZh43948vxWIwYBdF/LKcVogC0WswJCtYTSKqqiEK4LSbCEdUmnwSDruJ3U0BWvxhvjngJy/bEne9OdBelqCm3ke91Uhhrp2eHVxqvAGJ+rZouYMMs0iGxYjTZtKTWjpnNLl2MxFNQQUkjbT3wzkTB/HER1u58xIPmqxx28I1BCSFn15STFCGtbtbuP+tjUBUfHxePxdGg4DTbsJpFZA1jUsHdue8fi4iiso76/bz+w+2xB24djb46ZVjp0e2JSne0pVKBHQhi853HlnT2LK/jSUJDp4xlm5t4OG3N3LnZWfxaMLzmt0ssmDKOTz9SZpSOBMH8Yu/fs3u5mB8LNPXbUOKaORmmHFnmNnW4Gd0fxeXlXbH64/Etzm9sogRhTk47CYsRpGXp5+Lomp81T5u6vhcBtHFFImk+0xnn+3qWfyRawbr/YHOKYcGyGjMfXtTmueGQRgFAaHDd7Ktpi63mXWY93V0dE5fmvwSAUlJWix3rMiymmjyS2iahiB07HlOLf61uwUBjnmZHoDzB3TjiQ+3svVAG8Xds4759nV0dHR0dHR0TgV0McopTIHTxm+vG0pLQELTYG9LMOl9i9HAzRf0o8kX5u6xAxENAr6QTKZVxG42EoooPDt5OBkWEatJ5P5xZcxavI5gRI0nPqdWFKVYFcdYXtNIUyCSkohbXtPInCXRkiKxBOf0yqJ4CZKOnzUAYwfnU1rgSBGixD4T38bHNVw1OJ9319X9W/tdWtOAihbfZuy12YvXMXZwfnzb5b2dXR7/bRd6uHreIdeTWPJ84z4vT04q545Xq8m0iinftZtFbjq/H5UeN6oGGRaRt2ZW8vGWA9gsIt5AVDxyoDWUJERJXFm3td5HSFbpnmU5rMCkOSBRXdtMts1EltXIzDGelHIfscT18Zos2tcSxBuMxFc2ZttMSeWDvsv4peSk/ydb6pl5kQeATKvI764byvyl2/nlFSU8tGRdyvW8rP3af/zaIVTvbuGhJanuGEu3NnDfGxsYO6gHy9tLe3UWbw+9FY2jRDHK0q0NrNzRxNtr96WUnOoYo4n/TmxXx6Rh7LtXDc7nnTQJio7bnb14fVJ8A/TrltmlGGDWVQMBKMnPTrv/pTUN3Pfmeu66ooSxZT04v9hNtjW68nZfc4D/nXEuEUXDJBowiQaeX7o9rUvSzIVrGN4nh+F9c7nzsrO445IB1LeF46t0FVWjwRemvDCH6tqWuAhuWU0jc5ds5GeXDOC/Lh2ALxR14DjQGuKPH26Jl+bpqh+afVUpc9/eyD2L1ie1K9YHPT15WIoQJbbvWYvXpZzTZTWNzF68nkeuGZx2n6c7sbr2U0f3xWgUQDDQEjokKpleWcT6Pd70SbOaBh58cwMPjh9EvS9ET4cNd2bUNSEdvpBCMCLR6AtzdqGTB9/ckBQ/VR4X948v44NN21LESH3dGWze38ofPthKpcfFY98bQq/cqCBlX0uQu/62NqnkVoXHxe1jiumTaydf71t1zkD2tQSZs2Qjs8YNJJJGiAKH+q9fXVGCP6JhFg3x/nZ3U5BbX1lDlcfFvBvKERB4ftn2pP61qtjNrRf25/ZXq+Pik7xsC3+ZMZI1tc1JpSKrit083l4KMV2pxKpid7RE2our4t9JVz5RR+dMJyyr5GVb0zq3AZQUOJKEKBC9Fz/1ydZOY/zpycMYP295fCzzyDWDueTJT6nwuJh5kYfeuXZmvLiKWy/0UN7HyWs3jSLHbkLVNAyCgblLNqSIkxffVoEkR0tMxmK20uOiendLUhs6Ck4Sqd7dQlWxO94XTK8sSjvejrZ7vV4KTOeUIwJJjp0xYtfso9cMpt4XxmwS4/cyd6aZ84vdfNaJS6s7U7/GdXTOVHY1BQDonm055tvOshqRFDXuwn0q89WuZnrn2rGbj307hxXmkGU18tqq3cweV3rMt6+jo6Ojo6Ojcypwao/2voN4AxLNAQm/pESFAxYjCGAxGahvC8fdFAC2HGhl4tACIqrGg29uiK/4jq3OWt4hIXbbRR6+qm1JEqB0tfILwBtML4JYVtPItIqi+N9dJVOXtoteumdbu0y4Tm/fXvdsa6fJ56PZb+I2O7YlxuGOv6XD8cfOaXlhDi8s38GsqwZS3xqmyuNKWuk374ZyXli2gyc+3Br/biyRaBENfLS5nmGFThr9EvdeOZAMs5GgrMRLptS3hthY1wrA7qYAPQ+T1PAGI9z+ajU3X9CP8UMKqK5tTnG2eHJSOa99WYtRFOJimHSrfL/NhGlto59lNQ10z7YSbi95UV3bTIXHHXd3+S7jtJmShEayqpGXbeH2McVkWIw8/u5mltY0crdo6HQif1lNI3eNLek61rY2MLW9zmxXn+sYRzHysiwpQpQYifGU+O8jaVf3bOsRbTddu2Sl60pyrUEZOHxf0BaSmfz8yvhrsZW2mqZSXdvE4J5Ofv/+RoYW5jC1oihJyLVw5a64YObBtzZQXpjDxn1e7rpiYIowKFEkEpCiCY9JI/vw2/c2p6z8nT2ujHpviDZJ7vIY97YEuxQIGQ9z3aT7rZfVNKaIpM4UYk5SQ3s7GZCXyT2L1nH7xQPi75f3dgJ0es6W1jQSklUyLEZaQzKv3DiKyc+vSCtICUgyM15aTZXHRa8cG2tqW1K29cCbG1IEQbF933VFCX/4YCvLahq5e9E65k0qj77eQYiS+J1xQwq4clAPPbmlc0bhDUjc9be1DC10EoxEx2Zd9Wv3iNFyjI6MQ48yVpPIf11azEVn5SEIEJRU7rz0LM7pGy3JE5AUlm5twCQKPD/lHH77/ua0Y6VY/710a0PUYfC6oSlCFIjec1UtWfj8Wft39AS0zneJtqDc5TNVujHa4cap94iGpL9jY5ZYvzBhaAHzp5zDg0s2cPeidfHPPnLNoLSLGpbWNPDgWxsYNzg/HufD++Rw/9VlPPLOxqTPNvnCPHrN4CRHs5iwf9M+L7OvKuXB9vFfV8fx2dYG6rwhtjf4decknVMGXxrHzhjLahrxtSeFE+9lDruZx64dwq9fX5skSDm/XbSpX9c6OmcutY1RMUpe1rF3RomV42vyS6e8GGXVziYGdD8+5c5NooELBnTj/1bv5heXnYXNnLrgUUdHR0dHR0fndOfUHu19x6hrCbKrMZCySqyq2M3cCYMY3c9FUbslYHVtCxX9u7F8WyPvrKuLJyo7W521tKYRtf39xMnCrlZ+He79xO0cTtRxuPcTP3M02/o2+/13j395TSMzKvsx7+Ma7hk7kCkvfMlvvj8EEFha08D0yiIWLNvRafJ43OB8zu7tJCSrZFqMeIMymhZNony9pxlZ1bhwQB55WVbawjJ2k4EMs5i0Ci+RKo+bzftbeXJSOfWtoZTyLbF9C8Aj1wymwRemNRjBGYywtyVEU0DCahL5aHM9W+paeXDCIAqctiMWqtS1BPGHQlR53PGa79k2E8XdMvCFQtS1GL7zq/jzsiy8NO0caup9AAgCGBB4+uOt/GpsSXzCvK0T8VcMX0g54mv+342Nw30n8d+Ha9e/E9N2S9cP4vb2B/WjFdbFVto+es1g3l5XR0+nnevby4ilK1FkbLeNTRTPpHOo6ej20tWK2TlLNnDf1WVI7auNjpZYWw533XR2bs7Ukl0xJ6kFy3Zw1eB8vqptISPhOjqS+1FbKILaroN6+pOtPH7tEGa8tDrpMxUJq6kT77Edk1KdCYLSuRvFBC8dhSiJ35leUZRUmk1H50ygvi3M0poGplb0pfUwfRpE75eZViMWg4jdLFJe6KRPro3eOTYeeivVEWHeDeXMXBgVmZQWOPj9+5sP239DNJnc7O+8VGI64XPH8ok6Omc6douIJXRkz4xdvZZIx7FN4phleU0j/3XJAOakGYd1taghFq8vLt/B335yHpkWIyIwdlA+k0f2iZbANYl0d1h45O1N8YUesXJfVR43DruJT785wMSze3LfuFK8wa4Fxdsb/Nz6yhpAd07SOTU43D22LRRB01LvZQVOG09NKqfBJ9EWipBlNeHO1AVWOjpnOrsaAzhspuMikMiyRlMSjX6J3rn2w3z65NESkNh20M/lZT2O2z4uGdidt9fW8ca/9nL9uYXHbT86Ojo6Ojo6OieLrjPxOicMb0Di0y0H09oVL93awKzF61i5oxGnzcSvrxjIWzMr+d37m1McB8p7Oztd6RJbvZUosKje3UKFx5X281Ued4ptcSKJ2zkSUceRCj+ORiDybcQkR3r8FWlsm2MYxWhiuikg8cfry3HaTDw0oYy3b69kbFmPLl0g8rKtNAUiVO9q4cNNB/hs60H2t4bY2einwtONcYMLCEWiq/9cGWYcNhP7W4PcPbaEKo87aXtVxW7uvrKEsvxoCaTuXVhkx1YVZtvMtAYjLK1pwC/JCELUueN7Z/fk55cNoDUYYVNdK+v3tVLbFOCjzfX855++4J31+9myv5Xq2ma2HfThDUSTplpEIdNixSfJJHpY+CSZTIsVLXJmui8cLaoGb6+rY8ZLq9m4r5X73ljP0ppGgtKhyfgsW9cllDKt4nGJo65e6+z9xH8frl3/bkx3FaOxSZFvc7zLahppC0dXB7ozzZ2WNXph+Q4c9kO/TVhWj6ivha775GU1jciqdph+uPN+KNaW7MNcN52dmzO1vnusrn1AUmgLRpheWYTNFBX0wZHdjzKtRjKtIplWkWU1jfToUCM7JlJasGxH/LXE370jnSXdAh3cadpCkcOWZQvL6hkrJNL57hJzoov1aYfr17JtJmwmkbrWEHMnlDFnwiC+2tXMAx2EKBAVd72wbAfTK6OikfLezi6T1R3juDXUdbI5XXzrMarzXcJuEqlvDXU6lnGmiefD3Yc7jok7jllkVUsbx0cigF5a00idN1oq9bOaBs4tysVoiLYnN9OcJER5clI51bXNTH5+Jf/53AqueGIpS7c2cl4/F2f1yMaV0XUiPvE4Y85JsWeoRLwBiW31Pqprm9l6oI1dDX6+3p38zKWjcyw43P01y3roHtwckJKuP4fdTP+8TM4uzKF/XqYuRNHR+Q6weX8rBc5j74oCh8YHB9vCx2X7x4pVO5sBGNA967jto3u2lfJCJy98vhNN69odWEdHR0dHR0fndOQ7IUZ59tlnGTJkCNnZ2WRnZ3Peeefx7rvvpnxO0zTGjh2LIAgsXrz4hLaxwSeRl23psmyAK9OCpKhcPW8ZfklmaU1jyoTbkUzAJSY+FyzbwbSKIqo6TB5WeFz84vKz2LjPm3Y7HetrHy6ZeqA1dMTCjwOtoXjS8N/ZbzoxSawtMWLH33EbVR53SqIxkZz2xLQkq0x+fiVWk5FZb6znqqeWxWuqdkZYVvEGI+RlW7jorO5U1zYz46XVzHhpNVc9uYx7F63DYDBw3Z++YNXOJr7c0YSswu/+sYWhhU7mTxnBM5OH8cqNI7lnbAmg4coyszzN9dCRHQ1+Hn1nIz1zbHFhxC1/WcP3nv2c+95cjyAY+M37mxn7x6VMfn4l015cRXVtM6/cOIoPNu7n8ieWcs0zn3Px7//J7a9Ws68lCAYBBY25b2/iyj8u5T+fW8HYPy7l4bc3oaCBQeiyTd8FmgMS8z7eGhcpVRW74xPoGQkrTGRFpbKT67nS46K+NUz17pZOP1NV7Ka+/fruKjY6xlGMrmIvMZ4S/30k7TrQGkrpY9JtN127PtlSz8yLPCnHUuFxMfOiYszt11dX++9KWBZLGAoGoUtxSUyABtHEwrFyqGkLRjrthyo8LmZfXdZpPwTQO9eGxWjo8rpJd+yVHheZZ6j9a6yuvTvTTJbNRHlvJwZg7sRBVBVHRZZdJc2qPC78YRl/WKa+NTox5gvLzJ8ygld/PIr5U0ZQXpgTL+WRSGe/d2dJN2OH/jHLaoqLaTrDYjScsUIine8uGQnCQqvJgABd9muZZhGzQeCpj7dydu8cHnl7I67MrsfRMZHJ0TqHZVu7NpJMF996jOp8l9AAT14mt48pTvtMlWU1prx+uHGqrKhJf5vE5PulvZMxzJGKk8OyGl8kMGvRevq67PR12REFIT5G79RxdGsD9yxahzcgxccc6Ug3/vwswQUtxr6WIDNfrebi//4n1zzzOZf+4TPuWbyOgz6Jq59aduiZS0fnGJBlFru8v2aZRWymaJzESgHr15+OzneXdXu9FLmPT3mabJsJg0DSHO2pyPKaBrplWcjLshzX/Vxe1oMt+9tYsb3puO5HR0dHR0dHR+dk8J0Qo/Tq1YvHHnuMr776itWrVzNmzBgmTJjAhg0bkj73xBNPIAgnJ3HuDUpHNDluMRqo8rgJhKMJsI4TbkcyARcXoBS7CUhKtGZ231zevr2S+VNGxBNtM15axU1V/VLcOCo8Lu67uoxNCUKVBct2MPMiT0oiu8LjYlplEfkOG5v2eTtNuMaEHxUeF/kOGz+u6pd2W53ut6NjiMfN7RcVJyVxKxPaEmtD7PjHDc7nrdsrePYHw3jr9gqmVvRl4cpdKYnG2LZlRYsnNSGaqIxNVB6pS0xYVqnzBtOWVPr9+1uYXllE92wrZ+VnM++TGj7efJB5H9cw46XV3PrKGiY/v5K572zCZBTxhdJfD+n2PbDAwb2LU0v5xMqGlBY4kl5f3snrsdV9CnS6vVmL16P7okAwoiat3kyYX8diMsSvx9teWcOciYNSJggr26/9u15fy8Z9XmaPK0sbR/eNK2V431wqPa4uhFYuHhhfliI0q/C46N8tk9njSg8bo7F/d2zX3GsGp7Q9FtPTKovSvpe4rTkTB6e06+vdLeQ7rIwbnB8XYs2fMoJxg/Pp6bTywFvrAdi4z8sD4weliF6qirsWlsUShr7DrHxvDkTiba7e3XLEyY50q4ETybQY4/1QeWEOb99eGT/G8sIc3l1fR3mhM+13Kzwu3t9wgCkLvmRuJ9fNw9cMTuozY6/PmTiYsHpmRqfDbmbOxEE8M3nYoTJOqopFg0cnDmL80HxG9nN1mjR7YPwgumVaKMi2cdfra4FoCYIZL60mIMnMeGk18z6uSXt/SHdddCYIqvK4WZZQjicmoDlcYqu+LYw788SvRE1cta2v1NY51mSYjfH+tbEtRJZZZO7E1HtKrF8zAxaiY5SgrPDh5oNHLDJxHIWb1PnFbnIyji7ZHIvlE4kenzonk2a/RG6mhQOtQeZMGMTbd1Ty/24axd9+ch7D++Yw7cVVKc9rC5bt4PaLilOe4WJjlNvaS9tUelzMvWYwt/5lTfwzFR5XipgzxpG6fiaKUpbWNBCIKBgEIene3pW73WdbG6hrDdHkl3jkmsEpfUQ6B7UYic5J3oDEXa+vTSkFFnPmm15Z1KWjio7O0WIBHu7i/moheo1WedwIAgzvm8POBj9rdjXp9xcdne8YzX6JPc1B+rWXiz/WGASBHLuZ+lPcGWXp1oMMKnAc93zB4J4OeuXYePmLncd1Pzo6Ojo6Ojo6J4Oul/qdIVx99dVJfz/88MM8++yzrFixgrKyMgD+9a9/8fvf/57Vq1eTn59/wttoNxuxmrpOhlqMBiKKyt1XliDJUdu+6t0tVHlc8UR3bAIu3cRZlcdNfWs4nvh87aZR/OSC/iiahigIHGwL0+gL48q0UFaQzZ9+OByH3cQD40sJySpBSSHDYgQNVFXl/qvL+MXlCr6wTKbFiEGAhyaUEZZVfCGZTKsRs2igyR8m02ri/qvLCMoKs8eVomngD8tkWY2YRAPNAYm/3zo66fOPTBxEIKLQFpLJsBgRBNDQmHVVKb+4XMUXlrGaDBgNAnOuKcMXin7WYTORaRHxBiP87ZbRNPslIopKboaZJz78hhXbm5heWcT0iqhde162hY821TPnTysISAqPXjOIjzbXc0N77fDEc1nZ7hgTkGTuv7qMPU0Bnv3BMLJtRpbcXoGiRpPLVcXulElFiE5MHmgNsc8b6rScA0Qt5adVFsXL9XTl2CBFVDKt0YTrur1eqjxultak7rvK42bdXi/lvZ3M+7gm7faW1TQyrf28HMnrn21twCcpXZYh8aVJ2H7X6Ch0yLQc6nr3NAWZeZEHiP6eP5r/JU9PHsa9oiFej9tuEjnoC/Hy9JGYRAF/OMKsK0vxSzKqptEWkqne3cI76+vYXNfKtIoi7hprIRBWeHB8GcGIQkObhDvLjFEQMAgCl5b2YHL7NW4xGqhvDZGbaWb6C6u49cL+3DeulL0tQbKtJsxGAw1t4aQYff2W0dhNItsb/Dx+7RCqd7ew4LPtPHzN4Gh5lFAEh82ESTTQ5AuTaTPx6DWD8UcUWoMJ77VvK8Ms0uQPMfuqUn51hUprMEK2zYRFNOANhKnwuPG3bzfLamJAXiYNrQG+N7yQSSP7RuOqOcjwvrlMbb9WezisKKrGHz/8Jq1woNLjwmSMTih0tsI2hqxo8cTCHa9WM71dXJOuJFeVx01uhpn5U0aQaTV2+rlKj4ssq5F37qiKH2+WReSVlbviMRWziI9dHzES2xKQFH44/0v+d8a5BCIqbe3byjCL/OKv/2LWuDJ+fnkJbcEIWTYTsqLyo/kr+e//OBu6dXnYpy3BiEJepgVUjZ45NmxGkZW1zYwsdJJljP7WvR1W5k6I3mcCYYUsqxG7WSR2JUx7aTUNPinu/gOHuccmOBPFiE3sz12yMfmzHhfTKvsyc2E1EE1eP37tkLjd+WPXDklJTlV4ogKavrn2E26Lvq8lmNKe84vdPHbtEAqcthPaFp0zE6fdxO1jinnus21cN7wXJsAkwKPXDMaX0PdnmUUsREt0NMvRfr0zgXZHLEYDVcVucu3mTuM4UVxS1R6X3bOtPHbtEH79+lo+S4iBqmI3t13kYfqLq+KvdYzlE4EenzonmwyrEW9AorTAgT8sYzMbyHBYCEdUKjxuLi3tjs0k8vA10XuuLyRjN4vYTCKPXDMoKcYzzSJBReH3/zGULKsJi9HATS+t5pt6H3DIGc9uFtPG8YJlO5g/ZQQGSBKCx57hJv15RVKcx/qNHQ1+Xlm5i7vHDox/53ACt73NQap3t7ClrpVHvzeYUCRaRs9qElmyri6tgxokOyc1+KS0z4wQHffFnldjjip6WRSdfxcFsAOPXTOYtg6xZyXqdOQPK0yp6MvCFbXMqCpqF2RHr2X9/qKj891hffuiluMlRoHoM0DHZ+hTif3eENsO+rlqcMFx35cgCFw6sDsvf7GLOm+QfIfez+ro6Ojo6OicOXwnxCiJKIrCX//6V/x+P+eddx4AgUCAG264gaeffpoePXoc0XbC4TDh8CH1dmtr67/VLsEA+dmWLpKbURGDrGoU52ViM0UnwRcs28G8G8oBgaU1DSxYtoMnJ5UjQNJ2KjwuZo7x4M4y88ZtFUiyilkU6Om0MWfJBiaN7MPClbu4YWQfFnSwI670uJiakPSsKnZz8/n9yMs28PDbG5P2c3FJN2aNK0U2iextDiIIAu5MM2t3t/Dehv3cMLIPLyzfQXVtCzed349KjxtF1ci2GjEaoqKUgz6JDIuJsKJgEQ20qFp77e4G7GaR6ZVFnNfPhdlooC0k8/m2RhYs20FAUuKr6WobA/zPZ9tYU9vC9MoihhXmEI4o/OKyswhISrws0vo9Xm5PmCis8rjx5GUyvG8uj7+7ifLCHKZXFBGWVRw2E1lWI3/6dBvfG94LWVVxZpgQAgLvrd/Pef1ymfdxDV/VtrT/JqQkEmdeVEwoovDhpgP0dNjY6+3c8tYoClg0QzzJ0hmxBH2Fx4UgwK0X9UdFS0lc33qRh3BEITfTzDOTh2E1iaypbY6fuxidTbx29nprMJL29RiJq/9ONsc6bo8Eb0DCbkkWOggQn0D/xd++ZuGPRzFucH78WjvYFqa+NcS5/XLZ3RSkW6YFVQNV09jdHMQqGgjJGhkWEZPBwFMf17B0a0NcuNAxhqs8bmaNG0hdS4g6b5Bz+uVS4LDiyrQQkGSyrCb6d8vgYFuY5340AtAwi9DPncmsxeuSYjwWY795bxPvrj8Qf73C4+KOMcUcaA0RCCvkO63MXbIpSRhV5XEzZ2IZtiwr96bZ7iPXDGZNbTPdsixYjCKNPol/7W7m0tIe3LPo0OftZpHZVw2kvDAHl2ZAVjX2eUPMfXtTvB94YPwg/rGxjsH5TmaNKyUsr0/Z3wPjB7F2TwsQtYbttP8tdtMj25JUmiXmUPPQkg0p/eWUir5Mfn4lAUlh7KDuzJk4iNmLU/d/39VlfO/Zz+N27fOnjCBgM8WFX8trGuPiwVlXDeTXY0vwhxT8UlR8lJjk2N0cZGNdG7e2rySeP2UEPbKtfLmzhfHzlqe9Njtel6cq3yZuvYEIGdlWAoqC1WTg5c938sPz+rLLG6S2McjAgmwEAaSIQm6mBYMg0BqKoKga+1tD3PX62rgQ5b6ry7jhzysAOr3HVhW7eWhCGWbRwHs/q6I1KJNtNZKTYaZ7tpXfXTeUBp90aMLfasQflll440iyrCbcmeakBFOB08a8SeXUt4XxBiPYzSIZZiNOu+mEJ6I6W7UdW6n91KRyPTmmk8LRxq3DbqZPrp1LS3sQURXsBhEJUAFV1QABVdUIKSr7/BI9sqz8+OWvAMiyRR9nDicW65Vj4/vDeuKwGpk7YRD3vbkh6bquKnbz0PgymgMSE4YWkJdliV/bBU4bT00qT4rjmPvJWzMrk147kfGgx6fOsebb3HMtRgPr9vgBOKdPDgfaQuxqDFBW4CDLYmRve4mP6t0tLFi2g+GFOfzyirPY0xzkuc+2J40VKzwu7r1yIBkWkfrWEL1y7Dx5Qzm+kIytXcAiCtDkDzNnwiDuf2N9kuikvNBJKKIyvG8ut1zowSgaUFWNTKuRqS98yfDCHKZU9OWOV6tTRCnVtS0YDcSF/YcTuAFxkX9YVnlqUjn98zLxBiTW7m5JK0Tp6JzU2sVzkt0sxsXNYVlFkhW8AV2QopPK0cRtSFUwCiJtkoIGaFpUgOKTFASziKwq9HbauPSPSwlICkFZYXplUXwxi35/0dH57vCv2hbsZpHuDutx24fTbj6lxSifbKnHIEBZz+wTsr+q4m68tmo3r365mzsvHXBC9qmjo6Ojo6OjcyIQNE3TTnYjTgTr1q3jvPPOIxQKkZmZycKFC7nyyisBuPnmm1EUheeffx6IqpEXLVrExIkTO93eAw88wIMPPpjyutfrJTv76AepOw76ePjtjVw/sg8vLd+ZNClX6XExvbIIAQGz0YDTbuSf3xzkykEFzH5jPV/tak4WdtiMZJiNBCMKrUEZu0XEbhJZvauZx9/bzGPXDmHhyl2UFjgY2SeX7k4rauJloMVWxETdTWxGkUBExhdSsJlFGn1hNA1Eg0Bhrp1ARMEbjLRPGoa56/W1nN3byb1XDiQgKYRlhVy7GRV45J1NlORnc+GAbnjbRQyxicm4IKTYzZwJZfjDCqGIgivTjAaEIlHHlQyLSIbFSEhWopMnGnF3lkyzSIMvhM0SdZNoDcn4w9FzIAoCGRYje5uDtIZk+rjsWEQDfknGLykYDQLbD/rolmXlvjfW8/TkYbSFZLzBSHSCcncLG/d5uaFduFNa4ODr2hZ+fvlZ/PmfNfzgvL7MeGk1ALdd1J+xg3ogKRreQARbu+vCJ1vq+Xp3Cz+u6k++w8KVTy5LO1EJ8O5Pq9jXEhUijH86fTIZ4P2fVSErGgaDgKyq/OefVjC9sojy3s6460XsHP/5RyOY/PzK+Hc7uitANIkdO45EOnv93Z9WMfaPSztt37s/rWJg/ol5cDscxzpuD0dstfKD48uY/cahUkZ/v+U8mgKReB16d6aZx68dQo9sa9QxyBa9fht9IUwmI/UJyXGAMSXduPvKgdQ2BqLJaZuJB9/cmCTYGt3PhVEU4s4mTf4wNpMxJdH+8MRByGpUbOQLR8iyGDEaDWioZBqMKEJ0cjIxxh94cwMTynvG3X2cdhPf7PdR0d+FqkWThW3BCBntJWgO+sKYxaj7yjlFuYiChsEgxh1UsqxRF49mfwiH3UIwoqKpEbKsVnySQlCK4Mq0Rj/f7u5hN4uE5OjfrgwLYSXqppJhNnKgLYRRMPCnpdtSzq8vLGO3RIUAeVlmBATue3MDm+paeeXGUcxZsiFFNDJ34mDQNAIRlb0tAXo4rNhNRloC4fb2yuxtDtHHZWdfS5AeDgu7GoPx2Hujei8PjC8jL9uCL6TgsJuoa782Yr9phcdFeWEOVw7K5/v/83mnMfzUpPK0cQiHYjS2re+V90y67hKp8LiYM2EQ/bodn/rPx5JvE7eb6lqxm0VUVcNkENjrDfHppgNMHtUHhehqZr+kUOS2IysaraF2NxmTiF9SaA1Fr1+z0cB//2NLkvDq4pJu3HtlKZKq0hqM3nsiikpLIMI+b5ALB3Qj32nDG5Bo8EnxbbszTmyS+lixrd7Hxf/9z07f/+jOC+ifd+pfRzonlm97v91x0IdoELABaCAJEFY1gu0ORtk2I4qqcfP/fsXu5iBVHjePXDOIexZHx8NPTiqP31tjVBW7eeDqMmRNxWmLCsSAeIx2FJKcTrGrx6fOsebbxO6eRj/72y32s21Gvt7t5aweWQCEIgoOmwlfOPpMVeC0kWESCcgKvqBMToaZiKLSmuCWElFUVFXDYhIxCgLvbazjiQ+j5fGqPC7uH19GS0AiN8NMfZtEhkXEH1KijnpGA96ghM1sRAAQQBRge0OALEv0+fj2V6spL3TGn4GGFzoZWpgDQGGOjd65duZ9UkN5YQ7Vtc2djqPKC3Mozc+OC4ET421fSzDFTSnmnJSf4CjRWQzHBOYvLd+RJLbRXSl00nE0cVvX6OfllbXcMLIw6krU/myVaRZZuLKWH40sJKAqXPz7Q8/36eYB9PuLjs6ZQSii8OWOJgpz7fTt4IAy9o+fkW018bNLjp8o4vml26nzhnjnp1XHbR//DlNf+JL61jCzx5WesH0+v3Q7G/a1svzXYxA7KUuoo6Ojo6Ojo3O68Z0Ro0iSRG1tLV6vl7/97W88//zz/POf/6Smpoaf//znVFdXk5kZfZg+EjFKutUnvXv3/tZJ7c37W7niiaXYzSLPTh5G92wr+7xB8rKs2E0iD7+zkS+2N/HUpHIyLEae/riG8zy5jDmrO01+iZZ2wQTAwpW7+GJ7U9KEfGKC2mw0kJthRpJV2ton/kyiAZMo8Lt/bKGoW2Y8CZpjN9E925qy6iy2CryhLUxWewJvnzdEWFHJsZsxCGBoP5cH28Ks2d2M0SBwQXEeBgMEJYWcDDP3d1yV2u5WUN8WQtXgi+2NmESBr3e3UFrgYFhhDkZRIMduQlY0QhGFgKTwSrs45LLS7jz+3uZOJw3vHlvCexsOMKwwB7vJQF62lVB7KSCjKKCoGllWU3uZoehK9Ob2BKNZTBakxAQclR4Xv7qihCc+/IYRfXMY3b8bv39/M0trGvmvS4vJz7bSr1smsqrFS4F8sqWe/Gwrb62tS2vNXOVxM7xvDl/tbOahCWXsagygQoqTySUD87j7yoE80P77PDN5WHxCNB3p3o9Nps77uIZKj4uz2/+dSGevn1/s5pHvDebXr6/ttAzJY9cOoVeOvdM2nUiOddx2hTcgMfPVapZubeDjn59PfavEU59sZXlNIz+7pJj1e7yUFESFWa2hSFw0UucNYTQIGIT2ieiPa1JcbmZeVMyXOxv5wwdb+e/rhtA7105NvY+8bGtS2Z0Cp41bXlnD8MIcHp44iJ1NftyZ1kOJd7PIwbYQFlNUZFLgsNIWlnnqoxqW1jRwbl8nv7vubAIRBaPBwINvpa4if2B8GRFFRZJVbntlDbubgzz6vcF8tOkAJfnZ8b7EahLRtGgMKJpGttWEJKv4wzI9HFbaQjINPgl3ppksq5G6lhCKpqWIOfzt5VT2e0N4AxKLqveytKaRmWM8bNznpbTAQXlvJ7Kqke+wElFUFFXDLIoYDMTP74HWEEN6OQhKMlvr/XTPtqIBfVx2IrKGLxQhwxoVI0x/YRUTynt2KaKr9LiYOaaY659bwcwxnk6TF1UeN2MH9+CeReuTftNYQuTFaefEz3/Kd4vdXDk4n7v/vi7lvVgcV9c2x7f1/k+r2NMSZN4n6a+hQqeVnq7jZ7l7rPg2cbu3OUBYVjEABiHqrvDF9ibys624s8xkWIzc90bq9Tx34iBEDW59dQ13XzmQ+ct2JF3HTpuJXjk20DQa/SF+90Hquf3pxcUUOGzcvWjdGVE2o7q2mWue+bzT9xffOpqz25N4Ojoxvk3cegMS76yrY1DPbFw2M6Z2MYqkamiQdsx4//hBWEUBf0TloSUbqG53xIuJJQucNj7efICnP9lGQFIOG4enW8kbPT51jjXfJnZrD/pAEFi5o5FBPR3YzEZmv7E+1XloQhlNPgm7xYjVaCAUURAEUDTi5fIsRgNNfgmrWcRmFBEECMnR8jd2sxGbSURSFEDggDdEoz9MWU8HrUGZiKLyxfZDjpmJ46vhfXJ4aHz0mSo2voy5tMy+upSJTy/nqfayiLe3l2McUZhDr1w7D765IcW9JbbdRJFwx3jrTPCWiDcgcfur1UmiFaDLseT5xW7dlUIniaOJ2/pGPyEN7k0To49MHIQVCALn//bT+Hvp5hD0+4uOzunPnuYAP5z/JTsa/AjAjVVF3HVFCUbRwNYDbVz6h8+489IBnNM397i14fU1e/h0Sz2rZ1163PbxbWkLRRg25wOuP6eQKwfnn7D9bj/o497F61kwdQRjSrqfsP3q6Ojo6Ojo6BxPvjNilI5ccskl9O/fH5vNxpNPPonBcMiGV1EUDAYDVVVVfPrpp0e0vdbWVhwOx7dOan+5o5H/+FO0DMD8KSO46/W1/Ob70ZVTBgFsRpE9zUHawjL98zIQEZj79kZ+MKovdd4g3duT0HaTSF62hUff3cxXu5qTJuS7Z1vJtkatkltDMvkOK5Ks0hqKrlLbeqCNvq5MGv1hDAYBl92MwSDQ0BZGUlQEQQA0Cpw2REFgX0swvnK8vi1MXpYFSY4mpQG+3NnE0F5Onl+2PWWF6syLPNHjMket1Q+02zJW727htS9reeaGYagaPPXJVqprW9KudI1NBMbKC93xajUPXF1KYW5GPOmf+NnEBH6Vx82tF3mY8dKqeDI5nUtITMRzeVl3fCGFQHt5jI6lbd6+vZIDbSE87gz2tYbIsBjxhRScdlOnriF2s8iS2ytThD4d25ooFkmaVC108tDEQcxafMj5oDP3khgd3088vvrWMD1zbKzd3cIDb21McqqZO3EQc5ds5INN9fHvJq7uq230s7ymIS6GsJpEDniDVHjcFJ7Cye5/N267InGl45LbKwhKSlwwIqsaRe4M5ry1ka9q06/kHlPSjV+PHcjqnU3x+LYYDRxoDZHvsHHbwjWUFzq5sbIfRoPAnuYAedlWJEWlW6Yl6hwSUbBbjNhNInuaAjgzLNhMBhQNHnxrAwe8IZ770Yh4KZ7/urSYUUW5bDsYFWckbkuKyORkWAhE1Pikut0k0hYKYzBEBSYH29pdkwQBm1nkyY+3dl3yK00cQvS6nD9lBM98sq3Tyf9hhU5mVPZDQ2P+sh2d9hNVxW4enjiIldsbcWVZo2IYVeOL7Y289mUtv/3+UPZ7gylCngqPm4fe2kBJgSOeDIitUu24j5h7igG4d/E6vuqiz5pRWcS6vV7O6ZOL2Wggy2rEJEadazIsJrLMIrKmMXtxBwFg+z4UTUtNyBa7uf/qMgJhGYRDghtF1SjpnsXybQ1pj09RFYq6nRquRUfDkcTtvpYgd/3ta+4dV0pbMEyvbDtr61rp485gvzdEvsOKzdTuzhOWsZoMaFpUKPinf25nWKGTBycMIiQptAQjKJpGRFbJd1ipbw3zvyt2cveVA7n+uRVxd5sYj1wziHfX1SX9fjFOxwSS7rygcyw4krjdVu/j6nnLePf2SswGAUUDTYBARIm6hllNRBQt6oJlFfGHFUwGaAtF7x+ra5uTRCgAggA7GvxYjIdKE47ok5M2DhNFpB05VWNXj0+d482RxO7eRj8H/SGcdgv7WkL4JJk+uXYUNZrIiQmgw6qCIreXBAnJZFmNGEWBiKLSFlKwmQyIBgG7WeSJD79hZFFUDL/fG+K2hVFx9cwxHrpnWzjYFsZmNmI1GfjDP7bwyTcN8edeQYj2ASaDgeZAGKNB5NNv6vnLil1cf25hXGDqsJkIRRTkdrelZyYPA0hKuv/XpcV0y7QkjcVjz4Llhc74Mxp8+3hL56Lyyo0jk9wsO6LHtk5XdBW3Bxv9RDQIaRphWY07o1iMBqwGARMwY+Ea1u49VOpHd0bR0Tnz0DSNKQu+ZGNdKz+9eACb97fy6pe1XDCgG7+7biizFkcFa89MHoZJPHzZum/LR5sPMH/pDrY+PBbjcdzPt+Hva/Zw5/99zZPXl9Mty3JC9/3rv6+lND+bZ38w/ITuV0dHR0dHR0fneGE82Q04WaiqSjgc5sEHH+TGG29Mem/w4MH84Q9/4Oqrrz5h7cm2muL/DssqDT6JNbUtbFyxizsuHoDZYKBHthVHRGFbvZ8+Lhv3X13GvpYgA/OzMYoCzYEIJoNAlsXInAllBCNR1wFJVvm8fZWY3Swy74ZhFObaaW2feMi2mWgJhCkrcCCrKhkWI1kWI9W7W5izJFmU8NCEMtqCEg6bBYtJ5A8ffpM28auoGl/taua5z7YzvbKI6RVFAPTMsWERDciqhiQrIGj4wwpF7gwkRSXLamRsWQ8yLCImQWDOhEEEIwohSWHuhEHtpURkMiyHrJfvuqKEXU0BnppUTpMvTL7DyrghBUyvKDqUfG0Lk5dtpqRHNvOnjGDdXi97mvz8/ZbR7GwM4M40s/VAW5IQBSAgKVTXNgPRmuCdCT3awjImwYCKwFMJThaxSc10BCSFrfU+xg7O51djS9jTfMh9ZcZLq3hqUjl2s0h5YQ6Xl3WnND87LvL4v5tHsXaPl0BYSTr/1btbqPC4OnWGidVFB5IS64mOJ1XFbt6+vRJvMHkV3++uG9qpnX19W5h3OiRfq4rdFHfPwmEznXLJmxNBYg34jzbXM6ool5jyT1E16lpC3HNVCSDgD0ei13d7qZlsmwmLGJ1AH9XPFbUvb3+9yJ1Bkz/M328ZjVEU2NEQwCgKjPa442VsMizRlaUtfokfzP+S0f1d3HvVQO5bvCG5lM+FLtpCYR69ZjA+ScEfiuDOtLC9IQBES2B5gxG21vsYWZRLQFIwGUUMAgiArGoEIxo5GQYiskqf3AweeGsDX+1q5ukbhnHV4Px4HFpNInaziMNq5MVp5+CwRZ1gDAgMK3QmOesMK3TS02njmmEFTKsswm4WsRoPOZvMu2EYdrNIToaJtuChc+cLRZgzYRCSEi3pZTWJ+MIyqBqj+rmixxiO0D3Lyrgh+Zxf7CbbZsLTLQN/RIn2iVYTA/Iy+fPS7Uwa2YceDgsTz+4Zd4W5o3217G0XejAZDWRajNjN0ZJKGVYTcyYOwhdWCITleFtiv53RILCrMcCYkjxsJpFGn4SqRROlf/9qDxXFbvZ5Q2yrb2NqRRG/GluCL6SQaY061/zmvU1cWNKdob2d/PTi4iSXluuf+yKeYDEaBBw2E95ghEXVu/nesN5JNtwD8jL5v1W1/Mc5hScoGk4s3oAUdTaoacQXkpmzZDN/vmEYA/OzefnznfxodF/8ksKB1jCZViMOW1QQJMkq5/VzcXFJd3zhCBoa8z7ZyrXDe3NHu6V/eWEOlf3dTBrZh0ff2cTvvj80ngCPXefuTDNz396Utm2fbW2gwSedVn2iO9PM+cXulFXbEE3QuzNPn2PRObXxBiUCksKe5iADXHZUIZq0NosGNEu0PI8ggFEUMBkM7G1uY0hvJ88v20L/vKykscw7d1TRHJDwBiNYTVEhysZ9Xp6cVM4dr1anjcMGn5RWiAKnbuzq8alzKqCoGrl2Kyt3NHJWfjaZViMtgQiZViN5WRaaAxKqqmE0CliMBoKyGi0RK4BFNBBRNFRViwtq//7Vbv7rsrPiz4zZViOv3zIai2hANMC+5iDOTAuKGi2HetuYYqZV9uOgL+oMsd8bQkDgtoVRUcmTk8pZvbOJBp8U7yc6upsA2E0iuZlm5k8ZEb+nr9vbQg+HlfnL0i+KuOPVauDfi7cCp42nJpXHn7MyLMb4GK8z2kJdv6+j0xUiYDIIhBNeMxkEROD9zXVJQpSOcwig3190dM4EPt1ykM+2NvDLy87Ck5eJJy+TXjl2nvmkhuFzPwTgZ5cUH1chCkCOPVqWvdEvxUtpniq8vmYPA/OzTrgQBaDK043/t7qWloCE8xR7/tDR0dHR0dHR+TZ8J8Qod999N2PHjqWwsJC2tjYWLlzIp59+yvvvv0+PHj3o0aNHyncKCwspKio6YW3MyTBT6XGxpraFXjk25k8ZQYbZyEVn5bFpn5ceDhtFLjsGg8BfvtgVTyjfdlF/xpR0Z19LEIAv21dqDSt08tCEQWSZLYQVjbGDenDBgG5ktSepRYNAWI4mSe1mEavJyFe7msl3WJEVjQyzSHlvJ6/fMrq9ZE3UTlmSVUyiSFCWKci2xsUigbCC1Wzgs28OcsUfl3JFaXcenjgIv6TExQuZZhFV1QjICgICAUnFbhFxZ1qIqCqapuHOtGAUBBRNQxPg9//Ywtvr9iedK7tZZPa4Usrys1E0DaMoYDWJmAwC3fKz+cH8lTwwvgyrSURSVPKyLTT6wixZW8egAgdhWeXcvrlkWo388aNvuHZ4b37yl6947NohKUnx2ETjaytru/z9/GEZiLoiJE5UWoyGeOI/MVkZW51rNAjcs2h9kvtJDFnV0opFKjwuhvfJZe7bm+KTpzEWLNvBk+2vJYmEPG6mVPSNT5gCTK8sSnFuAFi6tYH739yQsgLYYU+1lgZoCURSREmx7aiaxiMTB59yyZsTQaLA7E//3M6Qns7434IAuZkm/lXbQlNAonpXCyUF0VIgqga5GWbCsoqsRsVpUStzFUGQ+fSbg+yob+OXVwwkICk4bSb8kow3GMEYU4kAEUXFH1F5efq57WKJML+6ooRfaBqSomIyGDAY4EBbGA0DqhZtWEhRqewfFW7EVrP2c2fQ7A/jzLAQUaLOR2ajgbAcdUxqDclsqWvjnH5OHppQRigS7VvOLcolGFHY2xxC0zSW1TSwYNkOXrtpFL6wgk3VCEeioo2YBXumJboyL6IqFDjtZFhEAuGoICMcUXFlmJFVDX9YJiQpSKpKltWEqoLSfr4MwqGY9IdlHvhsGx9vPpjyG40p6cYdFw9gzpKNSdfvJSXd+NXYgcx5awNLE8qc3XJBf4wGAZ+kxAV+8ZJZJd2487KzuPet9Wldmd7fsJ8/fLA1pQ0Lpp7Di8t3cv/4MlRNRdXAaOiZYglf6XFx39VlACz+1954rCfGcMw96f6ry3js3U2saC/X9utF61KcXB6+ZjCtwTBw6joXfVsSE8qZViN/vL6cg8EQVouJKe1CFF9Ixm4RsRgNmA0CqqphEQ0YDQKiQaB7lpU9zUH652XFhSixpNP5xd1YuHIXQ3s7KXTZ+fOy7cmCPo8ret5fX5u0AjvW9/vDp1cCyWE389i1Q1JWbcfcsb6L/bvO8cHe7pYXVFQUwAyEiQpSFFUjEFbIthnJtRuR1Kjj3xP/+IbrRvRm5sLqpG3VeYPMeGl1vP8+r5+Lyv5uNOCpSeVp47D1MMndUzH5q8enzqlAQ0BCVZQE4a+MpoGqaoTaHTMzLUaCshIdOwJiu4ObrGrYjAawm/C1l26887ISwrKCKEQXW9T7wpiDkRRHkuraZh4YX0b1rmZcmdFEUV62hfrWqJNKbIy2cOUufnVFCbeFZUSDAVXVsFtEpr+4ivJCJ9W7W7Cb291F39mU4lg5pKeTc/rmctuFHsxGAxFFZfm2xvgihmMRbx2fs7bV+7r8fFbCc4aOztEQVBVsgpj2vaXb6pn11pb431XFbm67yMP0F1fFX9PvLzo6ZwZ/WbGLIncGw/ocKrd1dm8nv/n+EKprW+jusFKaf/xdVHPa+5I6b+iUEqPsawnyeU0jPz6/30nZf4XHxcIvd7FkbR0/GNXnpLRBR0dHR0dHR+dY8p0Qo9TX1/OjH/2Iuro6HA4HQ4YM4f333+fSS0+dmpTds608es1g9rQE+c17m1MEEfePK+Om//2KP/9wOPddXcoDb21geU0jwYjKb9/fTGmBg/LeTkrzs5l3wzDW7mmhoS3EUx/XdCj14OaXV5xFWzCCKArxcjKZViO9c+3kZ1v4zXubuWZ477TlLuZMKOPLHU24Mi1x15FGX9S94aAvzMD8bP52y2jeWVfHFX9cmlJ646bz+3FZaQ+8oagIxmw0sKcpwIHWELkJ24yV63lqUjmtwUjSMZQXOhnRJwcVjT1NQUxGA6IgsHxbcnI4Zm3cUQxiMRrItBq5/rkVBCSF1pDM9ecWxh0P7h47kF1NgXg7Fq7cxU8vHsCTH32T9reLrRa6vKx7iiBj/T4v86eMYN4nNSmCkvlTRvDlziYgKhyJucfEyHdYefy9zSnbXF7TyJwlG5hemSqWCkhK/DimVxTFbbHrvCFeWbkLiNYgL+/tpFuWJalNiRzNCuBARE7rxBJra0CSD7uNM5HE1coBSeG2hWuYXllE92wruXYzBgGKumVQELFzycAezH17Y0pCe1plEVNfOFTCZv6UEfHP3DCqL5OfX8nL089l6gvRCcJYmZ3q2mYGdM/CIESdTT795mBSbIwp6cbkkX0QhKiTUltI5osEcUWFx8XNVf3Jz7GyasehMkHNQZn61hDD++byH3/6ggafRJXHzfTKvty9aF1cVFXgsPLxpvoEgY2GIERX1Izql8s/Nh5IKjs1ef4qZl9Vykeb9lNS4GBYYQ6ZZpFsq5GApNAUkPCFZQ60hvDkZbKmtomApDKiMIc+uXbmLtmYkjiYVlGELxS99tIJUQBKCxzM++gbygtzkhxc1tQ284cPtjC1ooipCQ5Lif1GRz7cfJD/umxAkhtMrCSOQYgKkjpSVeymT66dqRV9uf65L+LlXtyZZl69aRShiJLkjHLDn1fwu+8PjbdXVrW4+8qOBn/cWemGP6/g+nML+eXlJexrCSa57mRZTdhMBm5fuIZHrx1ydBf1aUJiQtlmEvlqZxPn9M0lIMs0BkO4MqwIArQGo7UCNJOIJmjUNYXo5rBgMRpoDUZwZpg4tyiXYYU5rKltjiedZEWltMDBRWflcf8b61OFeDWNmIwGnp9yDr99f3NK3//9Yb1O2Lk4VnRctZ3ojqWjc6wwGASqPG76uOxU7/MyKN/BAV+IXPuh1YiaBmZR5IA3gCAIzBzj4ZNv6tP2y506wHnczJk4KOXz2YdJ7p6qyV89PnVONhlWIyHJwANvbWB43xwuK+uBQRMIRpT4ooZgRKY1JGM0GLCaDNGxIXCgLUQ4opFti5bc8QYiiBkCmgYhWeEPH2xJeZa97+pS9jYHKe/t5NF3NiWN82LPffOnjEAQBDIsIrKiIRpg+bZGNu7zcsPIPjy3dBs/v3QAw/vmUt8WYuLZBTz45ga+qm2JPyvFxoV13iA9nTYKc+30yrXjDUjkZVm5pCTvuMWb7nqkc7wwYEACfJKCBvH//BGFUf3zeP9nLnwhGafdHL/O3ppZqd9fdHTOIA60hvhkSz1TR6fOKTrtZi4qyTthbYm5juxpDnB2e7nNU4FXVu7CahIZVeQ6Kft32s0M6eVgcfVeXYyio6Ojo6Ojc0bwnRCjzJ8//6g+r2na4T90HMi2mXhm0fokIQocEh9MrejLQb+EaIDywhx+PbYEgah6PV2plVFFuXxV25K0raU1DYyvy6d3rj2pnAzEVvB7uG5EIX9etj2t08Wsxes5uzCHX72+Lum9qmI3v7z8LNbUtlDem7Qih4Ck8MSHWxnQPStei3v+lBEA/LLD9mLMeGk1r900iqlt4SShyt7mIGajgRkvrWb+lBH8qEP5nHD7KrzYfju255nJw+KJi5gQJPa5C4q7xUtclLc/DP3PP2u4+8pSJHl9fELUbhaZddVABvdysKc5iKE9KZKY9Nc0eOaTmrSCEgMCw/seWoWQ2OYKjwtJVjsVeSytaWRqRRHVu1uoKnYn2crHjqPC42LckAJG93Nx+6vVPH3DMH5c1S8ujOmqhBAc+Qpgfzg1AZT0fpoE0XeBjquVE3+XB8aX8cCbG+K/b2zifOrovtHv2kx8+s1BZi5MLhuVeI3E7LtzMg4lxy46Ky8uYEpX2zvGx5sPMnlkH9bUNlNd25z2+rx9jCepjYlUelw8fu0QZry0mqU1Ddxz1UBmjvHw2pe13HVFCc3+CL++ciAPvrk+KfYqPS5mjytj8vMr4vuBqEtPvsPK9SP7pHUCmlZRxO3tQoAKj4u5EwYx6431AFQvS99+gF9cdhZ13lDacwAwojAnbf8Z26coCExNWAmY2G+ko7YpiEB0Ve6e5qhblWgQ6J5tSXFdip6LUv4zQYQSo8EnsfWAL95PJrZrdW1zksX8Ly47C1nVuOUvyZ+d93EN3yvvyYI07kex79pM6VdFnu4kJpT9YZniHlmoqoYoGPjtP7ayPMHt5rx+LsyR6CruupYgFrOA02bhN+9v6fS8fb69MX5vWNpJH11a4OD376cXE973xvoU56nTgc7csXR0jhVGg8DPLz+LiKwxqMDB/tYgmRYTda0hvMEIFqOBf249yOZ9rcy+upRrn/2cs3pkcdcVJfyGQ4LhqmI31btbOneAq2lIG4enc/JXj0+dk4lVFJn7flQY/NHmg/zmvWg82s0i86eM4OG3N6Z1npz6wqq489jk51fGx1gxcfPmfV7mXjOYBl+Y+rZwXHS7tznI1BdXMX/KiBTBcWy8Pe/jGuZPGcH1z62gyuPmzssGMKww+ty1cOUuSgscFHfPYuLTywlICgtvHMlXtS2dulLOnTiIXrl24MTEm+56pHO8EAwCIVWlurY5vuDAF44uODi3Xy4IUZfOom6Z8e/o15uOzpnFe+v3YxAEKjwnR2iRSKbFSIZZZHdT8GQ3JU4oorBwZS0XDOiGzXzy5kxG93fzzKfb2N0UoHf7GERHR0dHR0dH53TlOyFGOV1o8ElJZRkSWVrTyK/GlmBA4N0N+/m6toXS/GwKnLZOS62omsb0yqIUIcZZ+dmdOm4APDi+rFMRxLKaRqZVpKrnl25t4JeXnxVP0HWFxRh1SIg5inRl/RiQFPY0B5OSsokPTFXF7qQEfcd9HK4NMRK3YTeLbN3TFp+cKe/tpKfDRl1LkKuHFvDTSwbQ6JcozsvkvjfWc8+i9Ulte3JSeXwF/eCeDp74MLU0B0STIVMr+qa0KTZBG6t73hlhWWXBsh0sub2S2W+klgaJlZR447YKRvTJ4es9Laze2RT/3OHO0ZGsAPYGJDIsXXcjh3v/TCa2WrmuNURrIILZKGIwRB9uE3+vjoKpRAeURBJ/s9i/ZUWjwuOKby/2/3RxkUjs2u7MHSfDYuyyH7hrbEn87x0Nfjbu87Lwx6OY89YGhhbm8NzSVJHIsppGHlqygevPLYzvd3lNI/91yQAyzEbmvrOx034p1pdF3XYUygtzGNahtFXH790z1tDlde6wm/jdP1JFB7G/77qiJOl1p63rmOjrymCfaKCxTSKmaaxtDrJi21buvWogGgLeQIRMq4jVJDLpuRUpQpTO9lXpcTG1PaZjf993dRkHWkLkdJIgDUgyMy/yJB0THCodZDYIXR7P6UpiQtkfVhAEwCpw/6JD/WRisqqyvb+8e9F63rq9EklRmVZRhAEh6Z6c2K/+7rqhBLsQJnUVW0fjPKWj813ClWHmv/+xhR+N7ovVZGBrvZ+319WlvReFFZXffH8I019MFl1Wtgs+r35qGU9NKj+qONSTvzo63w6fJKcVZ06vLGJeJ6L8+PvtMZr479hCgXkf1xCU13HV4Hzysq1xYfKzP4gK6o9krFvhcTH76tK46CR2LzcK0THQaz8eicNmxi/J/M8PhmMUhfYSQC1JCxfue2MD876lkNQbkGjwSbS2l790ZxyZmEV3PdI5HsjAfW90vuDg4WsGdznG1dHROf35cNMBBuZnx0tknmy6ZVnY3Rw42c2I8/qaPbQEIlxW1v2ktuOcvrlYjDt4a+0+br3Qc1LboqOjo6Ojo6Pz73JqjDx1gMPXqt/THMRuEtlc18rPLz8Lf1jGYjR0WSKlY+mXxPc6ez0cOfzEXjoCUrQO+Ma61qTkeCJV7QKUWGL116+vZcHUc5g/ZURSiYxEd5HEZHJiMhBg8a0VNKQRbVTvbum0DTERTCKxfVR53AQjMucW5RKMKPjDCkaDwLq9Xj7YtJ8bRvbhRwu+ZHplEX/5YmdaFxs4NKF6JJOkEBXV5GVZ+NtPzsNsNFDnDVGY07Xy3WI0MLwwhzpvMKnMSMw9JiaI8YdlHrt2CLWNgSRhTFfn6EhXADf4JNDo8lyfmenuI8dhN9Pol/jjx1vj5yg2id4Z6a6bxOs28d/eQCQuEAskuNQciSArLKspZaxiMRg4jOONL5S8ryG9nMx5a0PctacrkUjHfskbjGAxHXlftqspQHVtM5X93V22sS0s0y3LQpXHlTZJYha73qdoEHjlxpEYDQIm0UBzQOryWjcI8MLyHSkOKA9fM5ivdjXjycvkmU+2srSmkZljPJzVI4uGdP1ksZse2Vb+9pPzOOgLYzGK2M0GHFYTf/rhcCRZpXp3C4+/u4kfn9+P5TUNKQ5JALf8ZQ1/mXEu49KUDipwWM9YMUpiQjnbZsQkGghIyQKwjtd9gdPGzDEeQpKMhsAdr1az6NbR7GkOkmEx4g/LSf2qw2bCae9cnHS4vv9Inad0dL5LOOxm7rmqFG8wQlBS6J5tpTpNyYzYOPHX7aLIcETl5RnnIgoCBU4rexqD8TFpV6SLQz35q6Nz9HRWJuuy0u6U93YyeWSflGe8xLFdurFhLH5j7y1YviP+fNUrx4bdLB52rNs718a4wfm8u74uSVgCUfc8h82E1SRy1+trk8ZQHRcXQHThxbcRku5rCaZs//xiN49dO4QCp+2w39ddj3SONQFJ6fLeGpAU9rUEyXdY9WtPR+cMxBeWWbG9kRvOLTzZTYnTLcvC7qZTQ4wiKyr/8+k2zi3KJd9x+Pv08cRqEhneJ4c3qnUxio6Ojo6Ojs7pjy5GOYU4XK16s2jgllfW8MzkYfz5n9v42WUD8Aa6Tmilm4g/XJK5s7IqseRd71wbz0weRobZiKyqCIJAKKLgsJrIthq57431PHbtECB1Nf6DEwaxuynIZaXdefbTGh67dgi/eW9zinVzbAJweJ8cumVZeGbysBSRBYCKRu8cG1Ued9IK9gXLdvDkpHIESGsLHROzxF6LlbuZVtGX5dsaqfK4sZpEsixGGgMSQ3o5kVUtvu/R/VxHlGw/EkFAtE19+WzrQUYWuQhICvkOK5kWMeW4Ettc3xZmzsRBBCS507ZA1OGkwGljX0uy7WXsHMXaHONoVgC3hiKYjUJcDJHOnUU4M/PdR0WwgxNKt0xLl593dHDFSLxuqzxuplT0jV/Dq2ub2bjPS3lhDtkJ3zsSQdaIwpxO7ci/V96T3jk2HhhfRl62BV9IIctq5EBriLteX0umVUza1oUDusXFTkcqwkqk2X/kfVlMhHe4B3JN07j+uRU8OakcldTr83AlpPxhhdqmABX9XTz41ga+2N6UNmZiv4+qajw0YRBhWaUtGCHTZkJWVH780mqe/sEw9jYFmTWulLlLNnUaf7H+4K11+xjRJ5ccu5kMs8j7Gw+wYNkOnppUnlR+6VdXlHBZaXeG9nKgalrStgpddlQtWtZNBdqCEbJs0f7AJEDeYQRvpzOxhLI3KKGo0Bo8dH25M828MPUcWkMy3mAEq0nk7XV1bN7n5dphPQnLKsMLc3h3/X5W7WyivIMDT6XHRU+nDZtZ7LSkx+FcdI7EeUpH57tIgdOGoqjsbw2jAf/vplFJsbqmtpnN+7zMu6GcQFgGINtmxN8is8cbYF9zgIgatab6tg5wevJXR+foyLYmTynYzSJPTirnt+9tZmlCabzR/VxcUNwNu1lkf2uIxMK4HceGifEbbi9dOr2iiAqPi3V7vEyvLOpyrFvV/rkRfXOZ+PTypPdi7nkZFiO/+OvXKWLedM4tcPRCUm9AShGiQNSZ6devrz0tS/bpnP74QxGenTwsZW6gp8PKs5OH4Q9Hr3PdxU9H58zki22NRBSNs3vnHP7DJ4humRY21LWe7GYA8ObX+9jdHOSWU0T8cV5/F7//xzds3t9KSY/OXcV1dHR0dHR0dE51dDHKKURXteqrPC66ZVn43XVDQYPvDe9Fa0Aiw5I6kR6b8BtWmEOmxch7P6vCajTglxQC4WhC+eM7L6AtHKE1KCOrKtk2ExFZI8MSFWC899MqIqqGLySTZTXS4AuTl2XGG5SRFY3CXDsBSUFWBT7f1hhfRVNV7Oa1m84joijcMaaYWVeVYjMZCEZk2kIKzQEJvySjahq3XOjhyQ+/6dRdZPa4Us7r52Luko18uLk+9ZwUu7EZRfa2BJlS0ReVQ4nYgKSwcOUufnVFCfcYBA60hsl3Wqne1ZwkZqnyuLl/fBmNvhDDCp3MXBh9b91uL3ddWUJDW5g//XNb0iRnlceN4TCOAtlWE6/dNAp3prlTQUmVx02+w0p5YU58vzEqPC7OLcpNOa7Ycc+ZMIgcuwmH3Yw3IHV63cQcTrwBCZspudZpQFK449VqplcWMb2iiGyridwM81GtAM62mtjZ6OfVlbvSurO8unIXj3xvyBFt60ymowDM3C5CSj957kY0CMyfMgJF0+iTGxUUtIUivH7LaEyiQF1ziP9303mEZYUGn8QVZT1Yu6eFT7YciF9vC5bt4OkbhnHV4Px4ySmrSWS/N0i+w8ZtC9dQOdXFnz/bnvTbxZJ9v//HFl6ecS73LV6fkkh49gfDMRkMfHznBbQEwpiMRoyiwLM/GIbFKOLONGM3i0nXdKITRYbZyIKp57Cmtplt9W0A5GSYeWbysLTuSJBa3gvgi+2NncdXsZsCp41Ft47GF4owd8IgwopKazBqkW4RDQQjXYtRsqxGRhbl0uQPcf/VZfglhbZQhAfHlxGMKOxtDmESBap3t/Daylp+fWUJOxr8/O2r3fTPy4onMCo8LoKSwtQXV8XPw91XDWR3U4C7rihBklUafBLuTHPcGWlMSR7vbzgkQIltq2Oypi0k47CZmP7S6ngsJ8bgdX/6gr/efB4hWUUQQGg/roIzWIgSw2E34w/LtIUjOOwm7rnyLC4Z2AOjILC/LUSO3YxJNJBhEZl4dgHC2T054A1jFAXuGlvCprpWRl9czNQXVsW3WeVxc/eVJdiMBrpnWzst6dHHZT9sv6yjo5MeE+CwG8nLMnOgLYzTbsJoEHDYTfR39yIwOJ/93hDZNhOPfm8wqqrx52Xbuf2iYtxZZlRNo8rjPiYOcDo6OofHJCaPa6dXFrFw5S6GFuYwrbIfdpOIisbn2xp57ctafnheHy4b2B0Egb/efB4ZFhGbSeSjn19AXUsQ0WAgIMnxsWSiMCUmzn5qUjm3v1qddvFBVbGb+8aV8s76OnY0+NM6t4QiCr6QnCIUiZHOreVohaQNPqnT7esl+84Mvm0JppOJO9OCMRCidz8XYfnQs1Ffl53WYBin3cKn3zTgyji1j0NHR+fb8cW2RrplWuie3fUCqRNJtywr+zYdQFW1w861Hk9kReXJj7Yyok8ORe6Mk9aORM7u5STTYuTNf+2j5ApdjKKjo6Ojo6Nz+qKLUU4hOqtVX+Vxc+tFHq5/bkV8Mi22Ej/DYkqa/IutRIs5HbgzzSyYeg6NPglfWKbAYcMXlmkNRujhsNLHZefxdzfx9voDQFQQ89pNo5jz9sakyftKj4sHxg9CNI3lkgABAABJREFUUSJsamhNSm4XOKw8fcMwblu4hqVbG3jgzfWcnbCS/OKSbsweV8ojb29OKpURc3j4fHtTyiTh8ppGZo8rRUPjF1ecxY/P7xd1fdBAQ6M1JNM9y0IgotAUiPCLv36dNhF7/XMreGnaufTOtSErKqP7u/jrzefRGpLJtIrUt4b5wwdb+OXlJQQjMi9OO5dsm5G1u1uY9NwKfnheH+66ogRBgJCkkmExsn5vS9pJzUQUVSM308xj72xOLyjxuLnv6lJaAhJf1yZvL1bCKOZ8ETsugMJcO3lZlqRJrs6um0SHk231PiRFTUnKBCSFeR/XUOFx8cDVZfTPy+zyuDrizjTzhw+28KsrBjJnyYYU94CHrxlM92zrUW3zTCTbltzV1nlDnbvJVPbFZjKQZbVgNBiYu2RjUtxUeFzcN66Mx9/bxMebD8Zfr/K4eGjCIK4clM+KHU3kZ1vJd1pZsGx7ctwVu7n/6jL+9pPzAPjBqL7UeZNXxhU4rFxW2p3fvbc5nkjINItkWo0EJIWDvjBtIZmGthDnFuVy7+L1Ha5vF/OnjGDGS6sJSEpKvxTj4pJuzBpXyqwO3+9ojx4ToHR0NlqwbAeLbh3NQ0tS+6s5EwYRlGT2toR44M0NTCjvyXn9XJiNBsIRFUlWMRqEtOVtosfgps4bxGKM2sDvbQny5c4mBvd08MePtqZ1Rhk/bznDCp1J8VvhcTHzomI+2RIV1MVirry3k/9NU+or9p2YG0fHsmIdV/rn2M20hiLx7aajKSBhNxvRtKgYxfAdsisqyLFT523ClWFmSE8n/6pt5uxCJ6BhNgpkaCJBScFmErn/jfUpsfLQ+DIW3Tqa1pBMhlkkw2zE2S4EhK5LehyuX9bR0UnPgUAYt91MSIWdDX48eZk42kWEsxavS4nTc/vmMvuqgby/8QB/+ud2ICpoHt7HyTXlPXngzQ0pJTL0ONTROXY0+sJJ49qRfXK5ZGB3fv/+5hTnvb/9ZDQhWaHFHyHTaqQ1FOGWV77irB5ZzLzIQ0TRuOl/V1Fe6OTJSeUsXLkrPg5y2Ez8aMGXBCSFbKuJl6adS7bdyP3jywhJCn4pWl51WU0DE55eTkBSmD9lRNo2O9vHT12RKAD+NgK2w21fL9l3evPvlmA6WYhAToYt7f107oRBGDSN176s5Zqze568Ruro6Bw3Pt/WwMD8LIRTaE4gL8tCRNE40BY6qaVxFlXvZWdjgJvO73/S2tARo2hgZFEui/+1l19cdtZJFevo6Ojo6Ojo6Pw76GKUU4zExFZTQKI1GGHdXi9f7mzk5enn4m0vNRArV7Po1tFJk3/TK4t4YfkOlrc7GTw/5Rwef28z1bUtPDmpnMfe25SSNP755SV88k0DAUnh8WuHcP+bG9LWEV61o5GK/m4Eg5Bkl75xn5cfV/Xj5gv68YcPtrKsppFpFUVxB4CLzurG3pYQd40dyJ2KSmtQZtWuJhYs24GKlmKBHMMXknn4w29SEvHTKop4deUuZo8rIxSR09qwJz5YWc0GApKCqoI/LJFtM+GwmWgNRsjLtjBldF8EAVQ1ukouKMm4Mi38/rqzsZgM+EIyn2+Pur8AzL5qIIW5qaWB4ue02E2+08rBthB3XzkQWVWjwhotWp8102JkvzfIHz7Ywn9ddhaj+ucyo6oIu9mIokYFL098+E1coDLv45p44iS/k4mtrhKiEJ0M9QYiXZbTEb/FQ43Dbuaeq0p55O2NTKso4q6xJfhCCtlWIw67iV7fAfeFIyHTJCaJHowGgdsTXGkSBVQzF0ZXe+ZmmHnkHxtTVnQvr2lkzpINnF2YkyRGWVrTyH1vbuCh8WW8s3YfQwtz2LjCy9DCHKZWFCGrGgUOG2FZobbRT77ThtVoINOSvoxBOKJw84UeHn9vMwuW7eCZycN46uOtKZOW/bplcE7f3BRnlflLdzDrqoHcs2h9Ur+UyMACR4oQJXaMEBVifb27hVlXDaSuJQSQ5GwUkBTqWkKUF+Zw24UeTEYDmRYjmWYRbyCE3WqhwGnj9/8xlGybCbNooCUQJstqJKKo+EIR5kwYxOw0k7EPjC8joqhYjQb2NgUpyLFxWWkP/OHod6R2l5VMiwmTKNASkPj7LaNpCUqEJJXfXTcUi9HAgdYQ7iwzXycISgD+vmYPcycOZtbidZ2WEusovukoTDnSxEgoonLts0vjf8eEYn1cp8Zqn+NNboaZL7Y3smRdHQ9NKEMAXBlWHngzKj6ZOcbDxn2HYiXxOn747Y0M6uVk7KAenNWJLW5nJT0O1y/r6OikxySKqAi0+EN48jLIsZtZ0R7DsfFtzGkrLKvUt0VdUob2dPK764bitJnon5cZF8PO0+NQR+e4kmk1MeWFVdx8QT/uvmIgRqPA3uYg0yr7MbQwJ+52t7ymkVmL1yUtWqjyuHn1x6OY/uIqdjT4Kc138D8/GI6sahzwBrnnqoHUNgaZP2UEgiAwvbKIBct2xJ/RMsxG7vn7urROZJUdxk0xjnT8FHvG/LYCtsOV4NVL9p2+nM4lmAwGgQff2pB23Dvn7Y08OK6Un192lu4epqNzBtISkNiyv40Lz8o72U1JoocjOmbfcdB/0sQokqzyxIdbObco95RxRYkx2uPmo831rN7VzLlFuSe7OTo6Ojo6Ojo63wpB0zTt8B/TORytra04HA68Xi/Z2cfGOm/Vziau+58v4n/HnAYSefv2Sp75tIbrhvemt8uOAVA0jYiiYhCEeC1us2hIWfUdo6rYza8uPwujQUAQBL737OdxF4OOooU5EwYRjijIqgZohGWNDIsxutreADajSGtIJhBRyLREE74t/giqpuHOtKAJGkEpmng3CALTXvySx68dknJcAAtvHMmNL69OSjjEyowc9IVZtaOJhycOZvHXezm3by7zPqlJae/tFxWzcmcjq3c2c//VZfhCEtlWM5Kqsrc5iCgIODPM/Pb9zWkFGou+2s0vriih2R/BL8k47SY0DTIsIhZR5J5FyZOfMVeTB9/cwPNTzmHOkg0sq2nEnWnm8WuH0CPbii8sk2Uz0RqUmLmwmgafBMCYkm787JIBNPsj5GSYMIlRi2qn7ehK56RjW72PRf/ay8Z9XkoLHPHzGRNAbK5r5ffXDf3W+4hZFJ9uiZ7jEbfp8AYkmgMRZr+xnqVbG5g5xkN1bXP8motdH3nZFiIRhW5ZVnySErdNtpkM7GgIoEG8hM1Tk8rjcZP4/UBYIcNixGYSOeANYhAFMiwmQpJMboaFQETG64/Qx2VP2kemWWRnQwAVsJgEsiwm/OEI3bKsaJC2/7CbRRZMPYen04hU7htXiskgEGoXbdjNRg60hrjr9bXxaz5dn5bI+z+rQtWItzGiqNz2yhp2N0edXKo8bu68bABNfon61hAVHjcfbDxAX3cGRoNA7xw7IVmlLRSJisBaD7mkjO7nwmQ0kG0TsRmN8RI8jnbRSoMvTLYtGodN/jB2swmTUaC2MYDDZsJuFglGFMKyGi9VZjeLzLthGA6bibZQhGxrdFtN/jDODDPhiEpIVnDazGRajfzhgy3cMLIPRtGAr/0YjaLAvuYQ2XYTH246EE/gJDomBSQlSaDmDUjc/mp1p4mYxKRP4uu/+f7QU3rlZmccbdxuqmvlgDdEkz/IyL5uFODeRYcESC9OPQdZ09Le86ZVFJFlEenrztRdnnR0/g2OJm53NfhB07AbBMLA3YvWMbWiiBkvrU5y2upYwnDK6L7xPvKjOy84arc3HR2dVI4kdnc3+nngrQ1MHtWHF5btSLuQIFFM3HH8V1Xs5vGJg9jbFsJqEvGFomVlfeEI3bIs/OefVnD9uYWU93YC0aSVPyxTmGOnh9OW4lBhN4vMumogwwpz2NsSRBCE+Ph5RJ+cw46f7GaR2eNKKe/tpC0s47SZUpwpj4SuxmfnF7tPacGCTtdsq/dx8X//s9P3T/Y9qKu4/eZAK7VNwU7HvYW5NjSNTkXYOjo6py8fbDzAj19ezZPXl9Mt69Qp06OoGtNe/JJ7rhwYX0B3ovnLil3MXryex68dQu/cU2tRnapp/Oz//YtLBubxqF4GXUdHR0dHR+c0RRejHCOOR1J74z4vVz65LP73M5OHcesra7CbRW46vx+VHjcA7gwz9W0hNEDV4M9Lt/PDUX1p9ocZ0ttJMKJgQOCqp5alrCaNrYK5anAPLEaRRp/EP7ceTEqUJ1JV7ObeKweiAZlmkVU7myjt6aAtJJNlNWIRDYRllbZwe0kDi5GQHK3JnWuPikA0FTQgEJbJzTATbndLybCIWI0iu5sC/PWr3QzokcWQnk7qvMGkskD7vUHO6ZvLhKeX89efnMeGvV7e+npfp0Kbh8aXARCMKAQkGYfNjKppaBqYjV2LdG67qD/TX1wdnzzt6FDSUYRhNRl44M0NfLCpPkmA4pdksq0mNKC+NUxIVsh3WFE1UBQ17trgDUpkWI69mMMbkPjFX7/mP88tTJl4qvS4eOx7Q+h1ij1wnQhOpBhlR0Mb2TZLND6CERx2E2tqW3jmkxpenH4OAkJS3e4Ms8iepgB5DhuSrBKQIuRmWJDaP5PVLlzY2xSkj8vOiu2N5GVbkRSVvCwrNpMBSVbIsJiIKBqBcITczPbvt1+vRjEqrnDazdhMBkKRaLkSBFBU2uMlet1e8cTSlOPqKKpJJNZXRFQ17pZjM4tIioo3ED1Gf1jm+wmCu4789ebzyLYZ2dUUwGgwcMAb5Lz+Lm56+SumVRZRXugkEI7gyrQSkBS8gUh7KSGZ/Cwry7Y1kJfQdxxojZYViigqArCnOUihy87qHU1Jn9vvDZLvsHHbwjUM75PD/VeXoWoqJoOBvc1BumVbUNSo45DTbsJkMNDoj4pXcuzmuGjhcCKtfS3BTsu42M1i0nczrUb87SXWjnRbHZOzHXn3p1UMzD/9JpqPNm6/2tWE227EKIhoAvgiStL1/MZto9mwL7n8XCxpVV7oZPa4UsyigX7d9MS2js635Wji1huQkAIREKAponD5E0vjY+CYk1GisDbRqa+0wMG8j2tYfOtozi7MOUFHp6Nz5nIksVvX6EcG7lm0Lq1wedZVAxnQPYv6tjBWk0iO3cTk51cmjU3e/WkVZjE6nhINBlbvbKK80EGW1UxbMEKG1ch+7yFRc1Wxm4cmlJFhNpKXbeVAa4ht9T7awjJFrgweal8QEKOq2M2cCYPISSi1B1DXEuTTbw6SlxUdo2eYjXTLMvPoO5uTHDC/bfmVrsZ6nTle6pz6VNc2c80zn3f6/sm+B3UVt5vrWvnN+5s7vY/+6ooSwhGZob311fc6Omcaj76zib+t2cO8ScNOdlNSuGfROkb1yz0pYotQROGC335C/26Z3D6m+ITv/0j4v9W7+XDjAVbNugSrSTzZzdHR0dHR0dHROWr0Mj2nMNkWI+/cUYmsagQkBYfNxC8vH8CgAgfPL93OEx9uZeYYDwXtlobG9jIrd11RgqxoZFqMKJpGptlIIKLwpx8Op8idwUNvbUip3z3x7J4YBMi2GSnv7UxbNgdg6dYG/JJMSyBaLqispwOr0YAl00xYVtnnDcWdBYxitDyOLySTaTUSllUEA2ho1LeFsRnFdoGIQqM/jC8cTRafV5TLTy8pRlY0/GGZt9tt2RPb28+dwcwx/REFgcG9nGRaTUyr7BdPIMYmN5dubUBSVH73/hY+2FQfF+OM7ufCYjKAYEwrRIl9994rB/L3W0bHz3/HBHC68gy/u25opwlob0DCLBpoC0XIMBvTiE6Ojx2kw27mwQmDuP+N9ZQX5sRLqjhtJvq47PTUy+kcVxr9Et6gwqa6xrjowdd+jb5640gUjXjd7sRr1GQSkRUVs1EgFI7Wrdfa/wOwiAZyM6KCr6JuGQQkFZNowGEzIckqkqJhUTWMImTbzCnCq6piNw+Oj5YtCckqEUXDZgaLUSQsq2hadF9tITntcR2ur9CAcESlJSghySoWk0CmxURTQMIvKeQeRnCVYTESlFQc1qhDSY9sCwfbwjx1QzlrdjXz45dW8/yUc7hn0boUgdXciYP4YNMBPt58MH5Oz+vnorEtTFa740m21YisaBR1y0hyKarwuJh5kSdeeuyxdzZxw6hCrCYRq1mk0S/F3VACkhJfSTj1hVWM6JMTT1Z0Vr4lJlJpDUXiQoeOQjRvQIr/3ghgNRront25GCJdSZiIovK9Zz9PK0QBaAtFujz/ZwrdMi1IioImaFFhV1iOl6GTVY1sqyntfebJSeXc8Wo1Wrs7j46OzokhLKvIApg08LfHa+/caNJ2RGEOZ/d28sLyHSlj2WkVRRjbyzTq5S90dE4c3oiCAGmFKDEno3sWrY+/XuVxx++xsTGKLyxjNxsQBYFcu4nKYjfB9veybCbQVPq5M/jbzechaxrhiEpDm4RkU2kOSGhArxwb9W0hHntvE2cX5nBjZT8cdhNG0UCzX2K/N4TNLOJIaKMGvLO2Li486Uxo/dnWBu5/Yz1zrxmMLyTTGooKq90ZXS8g0Ev2nZmc1iWYBLhhZJ9O76MATvup45igo6Nz7PhyZxMDumed7GakpafTxpb9bSdl369+WcvBtjC/urzkpOz/SKgqdrOoei/vb9jPhLN7nuzm6Ojo6Ojo6OgcNboY5RSlriXInpYQT32yNcWGfESfXL6qbQGik/K5mWYMgkAwomA3i3y1q5m5b28C4MlJ5by4fAfLahqZOcbDy1/sTJlcW17TyINvbuCeqwbGa2N3haxoaEBfVwaSrFDvk5AVleUJydmqYje3Xdif6S8dchW5ZGAes64qZfZb6/iqtoUnJ5Xzx4+3Ul3bEndrycuyEIgoWIwi6+uaKS3I5oGry5BVLe4YYTQIqKiMLcvnvo7JdY+bRbeOZr83zKpdTbz2ZS0GQeBXV5Rw0wX94+4t3qBEls2MomrYzWKnydpQRDnqVU2dJaAP997xpsBp61Ioo3P8UDQNs9HAkjQJ71FFufHr2G4WefqGYdR5gwQjCi3BCG0hmdagxLDeOdy9OFV0MWfiIPY2BXn2s21s2d/GKzeO4v431yd97tFrBvPOulT3oKVbG7j/zQ08cHUZ+1qCPPDmBh6aMCjuRiQpKkbRQKYl/a0iLKtdHve+lmBSWYXnPk525Xn0e4OpKnan1FuHaCz/a3dzUvIiNkl6yytruPPSAfz5RyPY0xxgRmU/ygtz4v3PsppG/vjhN9x95UB+eUUJJoOBB99cn3bCdcoLqxhW6OSVG0cx+fkVNPgkltc0YjEauPvKgQzu6cRuEtHQkvq4RLFC7JimVxYx7+OaLmvFd7SSh9TVtkfymXR07F821bV22rfBKT5RfoyobQrw+Lub+NmlZ/Hb9zZz5+VnkWM3U90uXJx3Qzn3vbE+7X0Ror+pLyyTZTFSXdt8RIknHR2dfw9/WCazvUSPsz1ejQaBKo8Lh93E7/6xpdOYvefKgVw6MA93ph6jOjonikAoQtRzLpnplUUpjowAS2saUNHi4yaICldAI9tuSvN85+K+q8to8IWQVZJKs9rNIrOvKmVQr2xqmoP0zLFx56Vn8fQnWzm7tzOlv6hqdyUpaC/Tc9fra5McUDoTWtvNIv95biG/+L9/JbXt24zPdE5/3Jlmzi92d1qC6VS+BwmQNi5jf983rhRJ7fz5QUdH5/QkFFFYt8fLD0f1OdlNSUvvHBtvra1D0zQEIXVMcbwIRRSe/qSGymL3KV3CON9hozQ/m4Ura3Uxio6Ojo6Ojs5piS5GOQXxBiQ+/eYgS9buS52829oAWnTybsGyHeQ7rcxdsjFpUmxMSTdeu2kUEUWlJRBhemU/zi7MYVhhTucuBjUNNPslbnx5Nf/v5lFdti/bZsJhN1Hb5MdhM2MxGmjyS4zu7+Ky0u5Mf3EVS7c2oGoaN1/Qj4iiUd7biUk0sM8bZGhhDiOKcnlh+Q6q20UpHVfmVBW7uX2MB4PBwANvbUhNwE8YxG/e35yaXK9p4KElGykvzGHjPi+v3DiKh97akLZ2+Q3Pf8nwPjkpK/MSyyBFFI2N+7xkWIw4babjPpGY6JhwPJKe+mToycEoCEkT5zGW1zTSGpLj1+fNF/TDajKkuDRUFbvpnm2lul2Ellhua/tBPwVOG+WFOdxYWcRDSw7FS+8cG09PHoZJNHD3onVp27Z0awM7G/28sHwHL00/l2Z/mDpv9D1NA28wQkRRuWpwD4q6ZSbZObszzV2KuWJ0TEbE2t/LYeOhCWXMXrw+yUq90uPi/vGlXP/cipTzBbBg6jk8/t7mFKFKLJZdGWbuuGQAsxZHnYDSrXDtKCCZs2QDj187JC6euWFkHx56M7Xv6EyAsrymkentqwk/29pAg09KibV40qPDxHXiatvWYITapgDTKooY2tsZF798trWhS5FLOhw2E5UeV9K5TTzHDtuZLUbxBiTu/ftaplcW8dBbG7ipqj8mg8DymkZ+XNUPu9lIhlns1B0r9pvKisoVfzxU1ufbWvXr6OgcGSFZxWIWUQSN+xevZ8v+Nu65soTLS7uDIKQtDQft/boG915VSpM/6i6lj3l0dI4/rixr3DUzcYzaLcvS6bNn4ripyuNCFMBkFPm6tiXpvmw3iwwtzOFgWxiT0UCmxcCMyiJmVPYjLCv0zrGzdk8L//mnFfHxaKXHxS8uL+HJj75J+ywdG081+KSUMVlnQuvOhDXfZnymc/rjsJt57NohnZZgOpWvBVWjy/uoqoHdoJeA0NE501i7x4usagzocWo6o/TKteMLy+xtCdLrBDo3/2XFLpr8Et8r73XC9vltuagkj6c/qWH7QZ9eQlhHR0dHR0fntEMXo5yCNPgk8rIsnU4SLK1pZGpFEdMri1KEKLEk6uPvbU5xYKjs7+5yvy3BCAFJ4aNN9VR5XGkTdFUeN9W16d0KbnxpNcMKncyfeg6fbqlncE8nWRYjEVXlyx1NCAIMKnAwojCHPi47VZ5uSEq0BMfsq0oxiQJhWaPRH0ZWNLbV+xEN/pTzsKymkdlvrGdaRRHvrj+Q0sbEyc3ExHzi+3Aoiaxph1bm2c0i824o54VlO3jiw61Jx3j7mGL65NqPur53OoEJkPJaQFKSapY3ByJ8uaOJCwd0OyE1xf9dIczxFtKczgQjSqfxnDjpfdFZeTzx4TdJpZRiNbxf/mInT00qRxAE7CYRFS2lVMy4wflJQpSXZ5zLfYvXM2lk16tfwrLKsppGHnlnE3deOiBFDHNJSR6zxpUya/G6ZNGYx8WCqecw/cVVKYKUCo+L6t0tQPIq00TLdoAXvvBydmEO09qP12I0UL27hUfe2cT15xamJDGW1zTSFpK7jOvLSrsza/H6eF9wJImQZTWN3DW2JL6NrlYMphOgxM5jjHQlcNIlPWLnJN1q20TxS0yQkk7k0hkFThsPXzOYexetSxH7PHzN4DNeTNHgk1ha08jdV5aytKaRhycOQlZJcih6ZvLh62WvqW1O+ltPPOnoHF98IZkss0gwovJVbQv/d/Mo2oIyT31Sw+TD3M+aAhKPvLuJ8sIc1u5u0YVjOjongICkYLeIXFLSjesTyn8c7h4bllUqPC5uu6gY0WAgEFYY1icnLnROHDPGntNifyeJtj1uFv54FDe+tIoGn8SymkYEtjC00MnHmw+m7PezrQ3Ut4XxhVPLUHbmEtpVacqjHZ/pnBmcriWY/Gmu+6N5X0dH5/Rk9a4mbCaRwlO0RHf/dnHFV7uaT5gYJSgpPPvPbZxf3I3u2dYTss9/h3P75vIXm4mXv9jFA+PLTnZzdHR0dHR0dHSOCl2McgrSGooctvxFWFbTTop1lUS99UJPl9uMTb4999l2npxUjgodJvpc3HqRhxkvrUrZdmzf8z6uQXh/C1cO6sH0F1clfNfNrRf15/ZXq3ns2iHMeXsjpQUOyns7aQ5IFObaqd7RzJy3N8WT2hUeF/ePK0vrvJCYOD6a85PY5sQk9K0Xepj3cU3ccaajk0DsGMcNKeDKQT2OeJKpriWYVmDSv1sGU184lMD/7feH0DvHnuKGU+FxUeTOwG4Wj+vE1rctC3Ksvn+m4+/COSSxBI6hixre940r47fvbeLDhEn1jk4de1uC8feenTyc+xavj4vXuiIW+yX52Tz6zqYUB5PLSrsze/G6lLhYWtMIgsDscaXc/fdDzitVHjdTKvpyx6vVQLJII7GPiglF0iUKgE6Tjt5gqtADDsW1UTTEj+FI+tIYvlD0dzrSvqPj9xMTGFlWU4pAyxuU0m7zSMUvkF7k0hV9XBn85vtD8QYj8Ylyh830nYjL1vZzFUs2aYLA/R1KXR2uNF2PbCtiGptgPfGko3P8sFuiK7LbQjLTK4swCAJPfZIqAEz7XbOYdH/RhWM6OseftlCELIvI7KvLuGfRofvs4e6xvXOjzn7TX1pFeaGT8sIcvt7dEh/bdhwfdVX2R0NjwdRzuP65qEPK0poGplb07XTfTX4Jpz3VIa56dwsVHlfKPg43njza8ZnOmcHp6Dqaae16CjDTaqS1k2ctHR2d05fVO5vx5GViMJy4EjhHg8NmoqfTxqqdTSesDM1fVuyiJRDhmvLTo+yN2Wjg4pI8/m/1bu68bADZ34Gyyzo6Ojo6OjpnDl3PEOmcFLKtpsNO3lmMhrSTYuW9nZ06MHyxvZEqT3p3lEQXg4CkcMer1ZQX5jB/yghe/fEo5k8ZwS+vKGHGS6kOCBBNmpb3dkatlHs7GdTLwTOTh7Fg6jnMHOPhq9pm5n1Sw+PXDmHhyl3cMLIP1bXNzHhpNbf8ZQ1XPbmMJevqeHJSeXvN8Og25yzZwPTK9ImHWOL4aM5PIonvm40GXrlxJJeVdk9b0iLWnrwsCw0+CW9AYlu9j+raZrYd9OENpCaZvQGJXU0Blqzdx4yXVnPrK2uY/uIqlqzdh6JGSxjFOKtHFk99sjVtMvqpj7fGra+PB12VDvn162vTHtux/P53AWcX5VAEovEHIAqGTkUJc5ZsoKTAkfL6C8t3pI0RoyjEXTZiE+vp6OhgsjRBiPLkpHKqa5s52BbutJTJ0q0NlBc6WXxrBa/dNIq3ZlZw3/jSpNJXif1ZYh91NDGaSFf9Y1hWaUuYQD2SvjRGplU86nbFvp94Hs8vdmM1GZj5ajUX//c/ueaZz7n49/8kFEm/3a767VjfGiPrW0w4FDhtDMzP5twiFwPzs78TQhQgPjmT0X5PiSanks9zV7FR5XHxwaYDuLLSJxn0xJOOzvEh6v4FUruwWFG1eB95uPuZSYz2ybF+OiYc09HROX5k20yYjEb2tQSTxjNd32PdoB1KiMXGO0u3NsTHth3HR12Nl5a1O+cljom7Gs/5wjJrdjVTVZz8bLxg2Q6mVRRR2aHdXY3l4duNz3R0TgYmg6Hr+6jBoF/POjpnGJqm8dWuZoq7n9qlXQZ0z+LLHU0nZF/+sMyz/9zGhQO6kXcauKLEuKS0OxFF5S8rdp3spujo6Ojo6OjoHBW6M8opiDvTzJc7m9KuygKoKnZT3xomL9uS8l5Xk24Llu1g0a2jeWjJxhRr40QXA4gm7WIr8edPGcGMl1bzzORh8eRyzDFhWGEORoNAboYZURD420/O41+7k+t2J7o35I21UFrgOGIXgK5cHbI7WdUTSwonJnDTkZiEzrAY+f7/LD0iO+mWoMQDb204rAtISyDCUx+nF5gA3HVFCX/4YGvK6x1ZXtOIXzoyu9xvUyqns9IhcGSr///d738XyMuyUFXsTnueIqrKtPZrXEXrcpJ9WppYSFwpXr27hSqPm6U1DbSGDl0zC5bt4MlJ5fHPx4iV2Dqcg8nkkX3iMV/e24msahQ4bIRlhYO+MBFZZWOdl/7dMhCEqN1peaEzJXm4vKaxUyeRdKR7v8rjjos+OvtOdkLCoLMVrrHjj22r0uOivjUMHD7h0FGAkngezy9288g1g7n/zUN9ROzcZVqN8d8nkSMVv5xf7Mad+d2OpaPBnWnm/GI3RlGgyuOOi5TsZpGbzu9HpceNrGqMHdQDAfhg0wH+9M/tBCSFSo+LX1xewqQ/r2BkUfoJe32iXkfn+CAKAn5J4YvtjZzXN5dMi5G3bq/AF1LIshqZcHYB//2PLUmlGqs8LqZUFFHnDQHJ9w9dOKajc3yxm0TuXbQupSxkuvGn3Swy66qBDOkVdcccW9aDqwblc7AtTJbNmORu1HF8dLjxkjcYSXr+c3QynouN3xYs28GCqeeApsXFqgFJYeHKXfzqihJuC8tYjCIOm4lMq5Hzi918lmYsr4/PdE4nWoISN1f15/YxHjIsxvi91ReOIEU0vEGJbpmp80w6OjqnL9sO+vEGIwzIyzrZTemSkh5ZfLKlnia/RG7G8b2vvvj5TtpCESaeJq4oMXLsZi4Y0I3nl+5g2ugibO0Lb3R0dHR0dHR0TnV0McopiMNu5sIB3ShyZwAdSuUUu3l44iAMQETVUhLcXSV3A5JCvTfM7WM8zLqqlJZAhGybEbPRQGObxO//Yyh5WVZMokCdN4RJNNDYFkYUBOZPGUGG2ciCqeewbm8Lg3s6mL8stZTItIoiPtx0IC4+CUhKksjEF1KOuARGLIHbLcvCM5OHYTWJrKlt5rUva/n5pQMQDdF2CYLAmtpmFizbwfBCJ1Pak8LTK6Or2tI5nSQmoSs8LmLr8o7IkSaipnUBuev1tcydOAinzYTDbsYnyV0KTJJ+m3DnLi8QLfPiDXQt6vi2pXJaD5OkOVwS59/9/ncBh93M49cO4devr02axK7yuDCLAq99WUt5YQ7+w1wHnU3Cx15fsGwHS26v5L431pNhEZMEJBFF4+6xA7GaDDT6JLJtpvg1ExOOJYowyns7ee3LWuZPGUGB08ZL084hN8NCSJYxGkTmLtmQ5DJR5XExc0wxVpPI2+vqmHlRtCzY8prGeDJCIDnGuhKKVHncHGgNJb1W4XHxi8vP4o8ffZP2PFR53PRx2TGJhnjsH4kQp9LjYs7EwWw/6ONvPzkPh91ElceV1g0m1ndUFbu5/+oyFE3l2vKeNPrDvH7LaBw2E1JEYcv+Nt6cWYFJNNAWjJBhNXKwNcyMqqIU0dGRiF/OL3bz+LVDvvPCrqPBYTfz2LVDWLG9kbuvLEEQBHrn2Fgw9RweemsDT3x4SAxYVezmwfFlTBhSQERVeWf9fpr9EgFJiTvmJKInnnR0jh/+iExrUOazLfVcN7wXsxatS77fFLuZO2EQt4/x0BpSyLaaUFWN/3juC56aVJ40xgNdOKajc7yJOY/NqOzHzDEeyns7CcsqVpPI5v1eZlQWcffYgURkhZwMC7P/P3t3Hh9VfS/+/zX7kslkmUnYJBCYyI4EcE2Cgtq6gEtt7xW99wLB2lbR7u471K3t/Vml7W2rqO1V+729XlTQ2tZiFbCuRNmVABIgQMg2k1nPbL8/JjPMZJYESCAJ7+fj0UclM3PmnJl5n+XzeZ/3+5VN3LVyc+L1NQ47i2vK+eWa+sQ1ZCAUSbsuO5rKodUOG/lGbdp5ZvL5n1cJU/vcR/zxxnNY2BFIvGfd3vZEu5+//+B8xpbG7iR/NMO5vJyfiYEm36SlwKTjwde2pB1b7583kQjRlMR+IcTA99GXrahV9PvKKFNPK0Ctgr9sOcj8s8r67H2c3iD/9c5OZo8rxT4Ak+/mTR3OPz4/zLPv7eamCxwne3WEEEIIIXpEklH6qWGFJsx6DQ9fNQW3EsIbCJNv0mLQqglFotz/6mY+aWhn+XWVECVxp33d3vasCRgXjS9lpM3Eva9sZm19S6INR9cqJfFBuiUvbmDGqCJuumAsi5//ODFh/fDVk3l2XfbKJpVlRYnyyvGkk3iSicWooa2bti2BUCSxbi9+ECs9WDmyEH8wzHljbXx9+gh++uZ27uwyiLn6lmr+vPlAYnBxW6OTh66czL2vbs46CBmvChPPRolVlsg+Cd3UEUibIE+e8D/Q7qex3cfIYjNuf+5qJsntjrSa3H1bVcAtL9VlTSzprlXOU/Mrsw6SdtdntLtJnON9/alieKGJp+ZX0uxWaPEE6PCHqNvbzr898yFPzq/k1//YyfSyopzLyDYIH//7jLIiQpEID10xGbUqVtVo+dv1aUljN1SP4Z+7WjhzdDHL50/HFwxzwOmjwHSkckcUeOGGc3ho9RbqGtp5cn4lT/x9B5VlRdQ1tKXFfyxmVDx45SQumTwUtUrF3CnDE3e2alUqlsxxUGIxJpLosiWK1FTYeWDeJFQqeOPWGg65/OQbY4lzTR0B7rhkAsFQJG0A9d65E9nX6mNIgZ5lV03hnlc2sa6+JZGcdtMFDvRaNRqVCqNOjT8Y5k/fOpeDLj/f+K/3Eq0czHoNzyyYmbRdne/hsHPP3AnEU4KuWL4OrxKmxmHj3nmTWPzcR9x0wVimjyri//vXabgDId7bGUuIiVfbePiqyTxy9RQ8SpgOX5B8kw6LXpO1ck5NhZ1yex6PXjOVYZ2xf8jlp82j4A6EsOXpCYajeJQQeQYtOrWKFo+CxailyKxniNXIvjYvHf4QLl8wcXfvaUXmnL+1wUKrVjFtZCFaFahVKv6w+GweWr2FM8qKWNj524wnOi57fSsXTRjCmBILU0cUEghHqHbY0pLEZOJJiL7l9ocpzNOx/LrprKtvZmFVOfPPHpWI1RXrdnPPq5u5cVY5//7MR9Q4bNwzdyLnjCnmkMufUvFLEseE6HvuQAizXkNhnp66hrbEeafdoucPtWfR5g2yt83LaFse7+1s5pOG9pTXr61vBhWcM6Y4cQ05sthEKBxNqSjXk2p3lSMLqXLY+PFXx+P0BLnn8olEo9DsCRAKR9nQ0JbSStKrhNnX5uOmFzZk3LbkpPrkc/kOf5B8ow67pfsqlEL0J3kaDXes3MQnDe1pyWMffdnG+WNteJUwBafGpYIQp4SPvmxltC0Ps75/TwEUmvVMGl7Aa5829mkyyq/eqScYjnD1AKuKEldqNTJnfCm/fnsn888so6iPq8gIIYQQQvSG/n0meopyehWaOgK0+4LkG7SYdRqMOg0PvLqZM7pMBG/a7+TSKUNZXF1OidUAUZhVUcJNsyMpk6A1FXbumTuRe185cndpchuOZF3b5USi0ZTEkiFWY8Zkjfhra6vKWbFuN7dfMj5lcMNu0dPSoaSUTM7U+kOjVvGbf5+BVq3mexedzhNvfcHyNfWJ5547xsaCqjF8fWYZn3ROSqytb+b+Vzfz40vGU263UGDSMcxqZMGKD7mycgS1VeUo4QglFgNmvYZWr8Lv/mMm/mCsFPN9cydR7bCxYt3uWIIPqpRWGlUOG0tmV6BRw1CrEbNeg1cJpyT0JE/41zjs3HnZhJzfs1Z9JAFlXX1zxvYd8WWtq2/OmVhyPK1y4q0sjrXs9PG+/lRSYI4NWCsHIvzLb95P/P2G5z+OVQGyZG/nU510p3dy3EDsov2Rqyczs7yY2mc/4srKEXxt2gh+9XZ9WnzXNbRj1qsZkm/AHwwn4q7IrMcdCHPHpeO5ORCkyKxnX5uXxdVjiEajvPDBnkR8Z6tstLa+mUAowjW/fo9fXjedKNHEY/EEjmA4zAPzJiVaXcUTRW6+wIFGHWvNcMjpY1+bl++8sIFzxhRz+yUTWLZ6ayI+zHoN914+gdsuHc++Nh96jZoWd6zFzu/W7eLzgx388VvncPflE4iiosMXa1tl0qlp9ypEovDG5oPUOGy0eoM8u353IhEFYEZZIWqVihmjixMJC/E7ZR97czvfu+j0lM9gbX0LS1dvZcWiM3ngtS0piXLJbcq2H+xAicD9r21K+V4uHF/C0isnc/crm9KScu6dO5G9rT4anT7OG1MMqLjrlU3UNbTzy+um88WhDoZYjQTDUU4rUqPRaVARRadW0+wO0OH3YtAcudsnCkQjUQ63eCix5WX8HgeLxnYfB50+isx6fCE/eToTB5w+/v2c0bR6Yq3ufEoEi0HD1ypHMG/qMCKRKHV729GqVUweUcBPrp5CQ4uXN26tRglFZOJJiBPAatKSp9WgRCKcPsRCsdmALxTGEwgzd+owrp42goXPfkhpvpElcxysWLebZau38ZOrJ9PhD7Hg2Q/xKmFJHBPiBLEYtdRWl/PTv2xPacfz7MIzqdvbzvACE0OtRkKRKKNsebz87XMJRaKoVCo6/CHyjVoOufwMLzTw0798wc0XOMjTa2nzKtx/xcREBYfkSnvrMtxo8McP9nDltOFUlhUx/3exyiZL5jg4d4yN/3jmw6zrn6viStek+vi5vBADlTsYxukL8uZ3a/AoYVy+IMMLdWg1Kt7c3Mg5Y4oJBoIMI3tlVSHEwPLh7lYmjyg42avRI+eOtfG7d3exp8XDqD4Yr9jf7uO59V9y+ZRhFA7g4/nXpp/G+vpmfvrXz3n46ikne3WEEEIIIbolySj9TGO7j9v/d2NKUsLj10xh1WeNrK1vYWGXieDJwwu45aU6npxfycNvbEudyHTYWXnTeUQiUT7d52RXsycliaSn7XKS/xu679cdikR5cn4lP31ze1rlgsXV5RSYtFQ7bGzorLbw7PrdicHFR99M3YZqh43F1WOYMaqIWaeX0OSKlVD+567YgGRlWWFiondtfQs/JtYf3GrUEiFKmc2csZXQix/s4bqzR/HHDxq4Z+4kbnlpAz/9xjSWrt7CkhfruHHWGL57UQXhSBSLUUs0Cn/ffojfvLOLyrLCRHJOtoSetfXNzNvfnr3aQWeCSdzGfe3cO28iS1ell8tdVDWaJS/G7vDNllhyPK1y4q0sjrXs9PG+/lRk1KlTKhh5lTDL19SzYt1unl14Zkq1I4j9DpZeOZllr2/NmAAVS86YSCAY4a7LJ2DQavCHwml3ngJ86/wxRKKwetOBRMWTTHG3MKmEebyC0Pu7WruNf7c/xOpbqnH5FIZYC/EqYQ67A0SjUXYf9nDuWDs3/v5jvjJ5KAvPG41KBSX5BoxaDY3tPqxGLXZLAQecfp6aX0nd3naeeOtzFlaN5tsXjEWjVlFo0tHuU2h1x5JmwpEoDW0+Hv/L51x7Vhk3XeCg3aNQaDagdK5vKBLloMuPEozy7Rc+SdwRu/2Ai8qyokQFF4NWzab9TiDKJ1+2prRyiSelvf15U9p2r93RzGFXIGty3x9vPIcCo5a7Xtmc9py/bz8MbOHRq6fgVsJ0+GPJMwaNmhZPAHu+ntF2M75gGKc3yL2XT8Ss19DmDTDUWoin8zNu6ghwyOnn3LHFLFu9FatRww8vGk/XGk0RIBiFw21eSgZphRSnV+GPH+zh8jOGA9DhVxGJxhIINWoVK9btT2szdf8VkwiGI6zdcZjvXzQOVFEMUYhEIU+vZeLwwZ28I0R/YdFpCEVi6YzhCNz32uaUieeaCjvP156F06tQ19CWOA/sCIRo9wX5fe1ZmHQaivNk0liIE8GgUXPuGFvKNdfNs8fiCYR4a9sh/v2c0Ww74GJIZ0JKSb6Bh99IveapdthYdtUU7BY9eq2aA+0+nlm/m4Xnjua+eZNQwpFEpdBHrp7CvnYfbd5gIlk4fm3X3BFIWY8V63bzlYlDslYOremsfJmJJNWLwUgJhnhq/nTu7toCr7Nt6X+9U8/C88pzLEEIMZAcdPrZ1+bj69NPO9mr0iNVY+387yf7+M+/fsEvOqvo9qbH/rwNs17DvM5xgoGqwKTj6zNO4/f/3MM100cwY1TxyV4lIYQQQoicJBmlH3F6lbREFIAxJZbEQEHyRLBZHxto/33tWTh9QRZXj6GyrChRDWVtfTPLXt/GbV8dx7gh+QTDEVYsPDNR4lwJ555UTn6v5P/url/3sAIjj725PT1BozNR4Z7LJ7Dsqin8c1dLIpFjyRxHxqSODQ3t3KxT8/7OFn76ly8Sf0+uNgBHWgL5g+FEosq3zh/D3CnDWFw9Bq1GRZFZT6wYSZTvXXQ6ALdfNp7/eOYD9rb5uP7p93nsmqncfqkBnxLGoNMQjUZp7VDoUEJMGRFLQlmxbnciOSdXQs8Dq7byys1VLF21NTWxwGHjx5eMo9Wt8MyCmRSYdPzji8Nc97v3ufasMhZWlWPWa/EqIYrz9Fz/9AcpLX0yJZYcb6uc4y07LWWrj1IU7p07iaWrt6QMjFeWxZI3Zowu4jsXjEWnVWMxaNFqVKxYt4sbasbw3QtP5/Gk5JHk5JQ7V25KLKumwp6IkeTfz+xxpYn4zBZ36+pbiHKkOtLa+mYixCokdRf/Ln+QX/z9C+6fNwklFMZs0FIciVJg1lFuz2NPq4fvXXw6Rp2GQy4/M0YVEQyHUatVnFZkTlRMSd6O++dNYn+bLxHb8btiS60GPP4wxXl65k0dxvkVdnQaDWo1HHT5ARVmvQaVCg53BBL7vvjnsWLdbl5bUs39r21OS1q7fOow5p0xPKUySpPLjz8Y5jfv7Mq47e2+zElf6+tb6PCH0KhVGUvLQywh5YdfCfP1/3qPJ+dX8ou/78iZIFTtsLH0qsksXb2VNdsPp6z7aLuZKSML+ddpI/BH4e6Vm9KWteyqKWgiUQarFo/CNTNH8vift7GwqhybxcDhjgA2i56f/WVLWnWvtfUt3P/aFuZOGca3znfw+Jvb+NFXx9MSDrLg2Q+ZVWHP2iZNCNF7Gtt9uINhjFoNG3a38tpnjWkTyGt3NHP/a1tYdtVk6hraiZ8H7mnxJlptxGNW2gwI0fdavQoa1ZGKj2a9hq9OGsr9r23hrPJijDo1r286kDj3/P0/v8x47nnPK5t47Jqp+JQw3/rvT7jn8gnY8vVEolEefWN7SoW8ey6fwJTTCujwhbhsyjDmThnGQacfrxJOaeXjVcLUPvcRTy84ExWfpyV73z9vEkaNiosnlPK3bUeSjSWpXgxWRWYDd3ZJRIHYufC9r2zmgSsmooSjOL3ZK6sKIQaO93bGjnsThllP8pr0jF6r5prpp/G7tbu4aOKQXk0aeX9XC699doBvzRqDUafpteWeLF+ZOJT3drbwwz99xhu31vT7NkxCCCGEOLXJmUo/0uxWMrZpCYWPTBjGJ4LjE9D/+dfPUwYSkpM0vEqYtTua+fb5Y7n+6Q/SnlNkzp2gYNCqE+1ARhab+NX10zF2JmjMGV+SMgGavGwlFMk64bp2RzPhKLgDQaaNLOTO/4tNnmdL6qitLmd5hlYjXVsJxZND3IFwYjmb9jq57dLxackg8eoGNouOK5evT0xMN7sVFj//MQD/861zOOTyp03Uxz+7+IBrrioRXiXM7mYPM0YXcdul42hyxe66q9vbzrW/fZ/KskIWVZXzHys+TKxDfN2fWTCTxc9/zDMLZqYkEkDmxJLeaJVzvGWnpWx1zxXn6bl75SYWVZVz9+UT+bLFQ4nFgC1PT7M7wJmji3lv15E2W1UOG7fOqcCnhCkw6VJiPmt1nh3NaS224uLP7Wl1pOR/1+1tTxnkz9QyaFpZEY/8eRsThxdQ19DGDdVj8AXDPL12V+rd7Q47Y+x5LHruI7xKGLtFz2PXTOW2S8bhCcS2tbHdx7W//WeijY5Zr6GsyMyDq7ekxebi6nJK8zVo1Wo0KhVOXxC9Vs3CZz9KiyOIxajLp1BZVsQdl45nb6svcYft/N/GksNslliLm2EFJkryDVz72/czLgtyJ+q5/MGU1lyZuAPBrN9n1wShdZ0DxtPKilL2xfHX3X7JeIKQ1vonvqx7XtnEI4O4nGsoEuX+VzezsKoci1FHMBLBatLhD0b4pKGdJXMcKW3k4olKtVXl/Pwvn3NGWSHBSBS3P/Zd52qTJoToHfGk7FsurCBihLGlFs4oK0okBSbH6todzfiC4cQ5b/LxCiRmhTiRLAYt+31+4Mg1qi8YZn19C7dfMp4n3voiUYUuT69lellRyg0UcevqW7jz0glo1Cpe/s556DVqDrh8aFQqFlaNZnFNOWq1iiKzjlA4itsfwhcMc8Ov3+P3tWex8LmPEu8PR86Jmt0Ky9fs4MeXjGORuxx/MIxBq6bMZuaK5euYOaqIh6+ewp2XTcDlk6R6Mbi5g+Gc58LBSBRPIESLR5JRhBgM3tvZwmibGasp9/hvfzJ7XAnbDrj43h8/pa6hncunDmW0LS92g2E3YyrZ+INh7l65iXFD8pl1ekkvr/HJoVar+Nb5Y7l75SYeeG0Lj3/9jJO9SkIIIYQQWUkySj+SrdWK2XAkYzs+EVxZVpRxwrJrkgaAs8vd+vHnPHDFpJRJ5WRVDhubG51p7UDijz14xSTUKhVvJd1BFm+B013LGI8/hMsfJC8paztbUkdPJ8sDoQg1Dht2i55fXT+dkcUmolG47nfv82/njOK7F1UQikQx62Of5dufN3HGiMKsk8r5Rl1aZYL4ewL85KrJ/O37s/CHMr8+sRyDlife2sFv391FbXU5542xMXtcCVdMHc4ne1rTqlZA7HOMf891e9tTHsuWWCKtcgaWArOeuy6fyB0vb+Tm2Q6+89+xO7n/343n4FXCHHD6qBxZyM++cQYGrZpDrtidnje/uIEVC85MWdbRJJQAeANHfm/dtdzp+nggFElUJgESbX4y7SMWVZUzosAIwGf72vnoy9aM7awA7r18Aneu3JxICIu1TJmckoQSd+/lE1naJRElvq0qYFFVORpVrCLK8rfrqSwrorKsMON+rsYRS+Bavqae4QVG/rzpQEqiT3ybqhw2br9kPH/deijnsrrGa7ISi4E8Q+5Dbr5Rd1Tf57r6FhZVpZfRjq+fWwlnTQxcV9+CO8v+bzCIRKKsrW/hjsvGY9FraHIHKLEYOOTyZ/3NPjm/klAkytr6ZhZWjabNq2DLO7LvzNYmTQjRO+JJ2XdcNh6PP0hhnoG6hraMsXrrS3V0+EI8uz5WFQVI2wdLzApxYhh1Gg45/SnXqLfMrgBArYLrzh6V9bjb9VrIo4R4bv1ubrtkPA0tPp7/527md74+V8W43Yc9ifaot75UR211eaIFY6FJhz8UTkkojp/beZUw7+5o5q6Vm3hqfiVjSiwn6FMT4uRw+0M5z4U9/hB5Bi3hQVxBUYhTRTQaZV19M9M7bxwaKFQqFd86fwzDCoz8v48aWLF+NxC7+Wfc0HwunjCEa88qoyTf0ONlPvHWDva0eHn46imoVceW0NIfjSg0sahqNP/1zi6mjSziurPLTvYqCSGEEEJkJMko/UiuVis1Dhtr64+0qDDpND2esMx0t/76+hYCwXBiIrNrhYFFVeVs3u/MmvDy4KqtLK4uZ3F1ORq1GpNeQygcIRAMU5SXe9C/IxCibm87l04amnMdoeeT5QUmHQuqyrn2t+8zo6yQM8qKgFgpyife2sETb+1IeV2Nw0aJJfOFS43DjlpF1knc9fUtBMNRKobks7/Nm7UH+awKO2NLLfz9B+dnbF1j1KmZXlaY8tr4Z//HDxu4ZU4Ftc99lLK8XIkl0ipnYIl/X41Of+JvZoOGRc99RG11OUMLTBTn6QlHojQ6/Sx7fVvG5KnuYqSr5HKk3bXc6fp4gUmHVwknBvnvvmwCj7yxLWvS1t2XTaByZCEl+Ya0GIxbW9/M3ZdP4M3v1iSS1A51+Gls8zFuaD7NXeJj6siClHZEydbVt1BbPYY/fdzAbZdMYO6UYQwrMDFv6jCWrd6W1i5rUfVolrxYR7XDxrACE4uqy4mQvj9cMruCtz9vSknE6fqcH311HE+tybyNVQ4beq0as06TdX9R7bBh1mmOKUEoE68SRkXuZJNMLb8GC68SAkCNGi0wxGLAGw5jtxh4Ikei4e2XjAdin6tZr0HfJQYG82cmxMkWT2Y26zSYdBruf3VzzqRrs16TOOcttRpYsW532jIlZoXoe95AiJmjiygvySMcibJ8TT3f72yJqlGpe3wDBcQSc2+/dAKtbj8Wo4brzxmFRqXm9kvGo9WoaXEHCEeiGHVq8o1all9XSUm+AQ0qRtnzgFh1wPgyayrsLJntYMmLdSmJKLd0ntvFSfKaOFUUmfX8f299kTUmH7piMqFIhHBUklGEGOh2NXs46PQz6ZyCk70qR02rVvO16adxxbTh7G310dwR4LA7QH2Tm+Vv1/PLf9Rz64UV3FgzBq0m97jWu18c5jfv7ORfzxzJyOLB18Pz/NNL2XXYw72vbmZYoZHZ40pP9ioJIYQQQqSRZJR+JFurlbc/b+K+eZN4YFWsGsCtL9XxX/82I+ey4hOUmaprxPmUSMqdY0o4QonFgF6r5oDTz1cnDc0+gbyjmbsum4BZr8GrhHD7w+SbtBSadWhUqsSdaV3VVNgZXmBkyOSh5Bu0ied1bf0R15PJ8hqHHX8wNkE+Y1QRi6piE8wAry2p5v7XNqcn21SXo0JFjcOe1jv8vrkTafcqae+VzBOITXSOKDLz6NemxvouZ6hIMsRqZEiW1qxltjx+/i/TaPMqdPhDWAxaNCoVWo2Kn38jVl5x1ZLqo0oskVY5A0uBWU+rV0kkKDS5AkwvO1IZI7kFzlPzKxlqNXKow5/yu+0uRgpMOv732+fi8ocoyddjNXYfd5C+76hx2BlRaGTVLVU0dygMLzQShbR+43Hr61tQqVQMKzB1OxnY4Q9Rmm9ACQUx6DQMtZrwBII8eMVkAsEw7kAIjxLmkNOHr5tqHma9hvPHlaJRQXlJHhaDjg5/kAeumIQ3GOJAu59hhUbMOi1t3gD/861zMes1KOEIbn+Qh66YjDcYYn+bH51GRZPLz4hCI5/tbU9JxLnpAgcGrRqNWsXftzfxm3fque2r4xNl6ZM/x0VV5Rxw+jFo1TxwxSQeeG1LSkJKtcPGA1dMxh8OH3WCULbna9WqHlViGawKTLH9YFQFvkiEfJWaQ94QKrUqZ6Kh0nnsLDTpGFFo4osmd8pzBvNnJsTJFk/K1qlVeILhnMeXmy5woNUcuavx79uaMiZsSswK0ffcgTDv7TrElWcMT1S0W1ffTI3DToRozuNu8g0U1Q4beXoN3mAIk15HFPAHY232mjr8aFRqdBoVdquBQCiMyxdiZLGZK5ev52ffOIMf/ekz3ri1mmA4yt42L6X5RrRq2LjPyVPzKwmEIhi0apo6AgRCYX7zzq6U9ZHkNXEqCEayt1ReX99CMBI7F97f5mOY1ShjC0IMYGu2NaHXqJk0PMug5ACgVaspt+dR3plwCuAOhFi5YR8/+8vn/G3rIZ68tjJrkkl9Uwe3vFTH1NMKmHfG8BO12ifcf5w7mlaPwnf+8AkrFp7JeQ77yV4lIYQQQogUkozSj8Rbrdz+8saUxIbP9rbztWkjmDtlGHdcOp4mV4CivNyD6watmpoKOwvOG82tL9VlfE6+ScuMUUUZK6zUVNj57oUVOd8jPmCnQoXVrCVPqyEYiRKKRnnwiknc/9qWlO2oqbBz/7xJGDQq7lq5iW0HO3hmYazlSLaKA00dgayJLVUOG00dAR68chLN7gAvf+c8Djp93Nx551uVw4bLpyR6lMcHIOv2tieSVVbfUo3LH+KA04deE3vssTe3J+7myyZ5cuO0YjPLj7EiSSxZxZj1cRn8OQVE4d65k1i6egu3v7yRF244h6WrY8kKXiXM8jX1VDtsLL1qCnUNbRSb9dwzdyLLXt/abUJJjcPOF4c6WPb6Np6cX8mjf95OXUM7y6+rhGg0a9wllz6HWKwtqBrNZU+uY8aoIu6fN4lQJILbH8q5aZ5ALMnKYsx9qLEYtQQjEfxB0Goi6DonGEPhCCa9Bl8wxLACIyOLTRxs9+dcVoFJx8xRxbT7/IwoMOFWwqhVoNWo0IbVFJp1aNVqtBoVKkCjVqFRq1BFQK8BvUaFElZTbNaRb9JRUWphX6uXey6byG2XRHD5YjFu0Ko52O7jhj98wvSyQpZeNYX/eOYDrqwckba/ufWlOp6aX4knEEvcWzLHwe2XjsftD2MxavAEQjS2+bAYNUeVIFSdJdmwpsLOrsNuzhljy1mJJb+b72Ugiyd3urxBGpUQ40osWIxadjV7cr7usDtAtcPGyCITDS1ekgv4ZmuTJoToHfG4berwEyV3+WytWsVb2w4BMNRq5JYM57oSs0KcGFaTlt+8s4uzRhVR3Fl58rfv7mL5dZU4vbkTPOI3UMQTc6ORKNGoKnYOadQyvMiIxx/CrNdi1GrQqFX8+zMfMG5oPktmV/DGpgN4lVgyr1cJ0+xWmDm6mDyDlvtf3czk0woSdwj7lDD5Rh3bD7pYunprWgKbJK+JU0F312/uzptkAKkWJMQA99a2Q0weYcWg1XT/5AHEYtDy7+eO5uwxNn75dj2X/WItD1wxia9NH4EqqQXP5v1OFj33EQUmLbfMqRhU7Xm60qhV3DKngv/82+csfPYjnpw/jUsmDzvZqyWEEEIIkTB4Z6IGqOGFJpbPr6SpI4DTF8Ri0GLQqmn1+DlvrJ3H/7KdWy88nQ17WnNOQJfb81h25WSWvp4+0AaxyUqzTsNDV07ivlfTk0YemDcJJZy7XYTVpMMTCGHUajBpNTS6fNz0Qh3NbgWzXsMjV0/mnssn4PKFMBtiJdf1ahX+SJi7L59IlFhZ5wfmTSIQinC4I8B9cyfS4Q9x2B1Ar1GzudHJktkOiJLWYuPeeZP48+YDzH1qHdPLCrntkvGJRJSaCjs3z3bw7o5m6hraMibczKqwY8vTY8vTk2/Q0uEPcvW0Ediry/GHIlmTYDJNbkhFEnGsivP03L1yE4uqyhlWaKLDr7Dsqsl4gxFc3iB5Rg1NrgBPvvU5i6rHcLgjwJctHn781XF898IKPIEQV08bwQOvbU65izxe5ScQCvP72rNo8wa5b+5EIlFw+2NVR5RwhA5/kKVXxv47OdHCHwyzYsGZWE06dBoVHf4gK286D51GTbs3QKHZ0G3lDbNeQyQaRavOXi2pymEjGI4QiahABWp1bJIRIBKFYDiC0xdCr9XgCUQoNOuyV15y2NBpVESI4guCRhNLRPEEIuQZwngCITr8IYbkG3EHQoQiEIlEUcIRIpEIgRCEo1GigEoVS+rzBsNotWoC4QhmnQaPKohKBdEoaLVqXv7OeTS2+6jb08poe17W5L4ml59N+51s3udk/HArlSMLCYQieJVYwsr2Ay6+MnFIjxOE4glKS1dvSfsMFleX89m+dto8PpZdNYV7XtmUVonlJ1dP4bSiwVeiNi6e3BlPKAlHo6hUKgq6mWgqMOp48IrJfPRlK/5QJNFGq7s2aUKI4xeP26aOAGZd7ipReQYtv3x7JzUVdqwmHTNHFaVUFpSYFeLEGWo1MmNUEYt//wlP/8eMxHnakhfr+MPis3K+dlSxmddvrabDH0Krgna/gj8YxWrSYtJq8AXDeJUIBSYdoXCURqePx66ZyiGXH38wVt0kOVnXrI9NuA0vNPGzb5zBQZeffW2+xPv95t2drNl+OG09JHlNnCq6S0bPN2qJAnV727F1035ZCNF/tXsVPv6yjQXnjTrZq9JnTh+Sz8NXT+G5977kh3/6jN+//yXfmDESu0XP+voWXvywgdE2Mz/6yrhux64GA71WzQ+/Mo5f/2Mn3/7vDXzvogpumVOBRj14k3CEEEIIMXCoolFpBtsbXC4XBQUFOJ1OrNbeLYHo9Cq0e4N4gyEMWg37W30MLTKiVam4r0v1kXhLiPiE5YoFM/nl2zvTEjluml3B4uc/AuCPN55Dhz+E0xdM3Mm/Yt1uaqvL+bShLeNd9TUOOzfPGYsnECYQik3SGvVqLIZYGeXhhSa27HfyyJ+38+g1U3nxgz1cd/aotJ7h8fX944cN3HHpeB5+Y1vKAGGsmspE/rLlIBOGFZCn1xCORHlvVwsr1u1OJJ785KrJhCNRnL4jlUkAWjwKUeCBLp9TfJJiWKEp6+fe2O7jjpc3ZpzcyPU6MXD0ZdwejfhvbesBV0pllLh44sHjb27jz5sPJf4ej587Xt7IDy4+nWkjizjo8hGNwqb9TipHFvL02l0pSSoXjS/ljssmcP9rm6lraOfJ+ZUpcWnWa7j38glUlhXxZYu3s1VNgBmjC3H7Q2g1alrdChajFr1WzeNvbs+6j7hn7gRWbTzAGJuZEUVmlq/ZkZYwc+/cibj9QdZ8fpitjU4mDi/g7NHF6DRqnnp7R9r+4tY5FRj1Gn7a5X2T932VZYUZ/9urhKlx2Jkxuogn3tqBWa9J2/74sm6ZU8HeFg8/fnlTxvdIroYyymbm4aunEI3CPa9uTkvue+jKSehUKtzBMDq1mgdWpScAPnjFJNSAPxzB4w9RnKcn0JkgZDXqsBi1uAOhRMJQnl5Dq8eP1WQgGI7gU8LkGbToNWqcPoU8w5EKTfvavHT4Q4nKTflG7YBORDmauD3k8rNmexNVY20EIxGCoSjLXt+a9Tf70JWTcPkUAqEotnx95+ev73G1KyFEZkcTt/taPGiA217ZnDXxcMboYj7Z08ZPrppMmS0Pp1c5pgp1Qojcehq7Da1e7l65iU/2tKWcWy2Z46CuoS3jDRTVDhtLr5xMu0/BatTT6gmghKP8M/k6z2HnvnkT+evWg8w6vYQmVwAgcb3a9Zzv4aumMCqplD+Q2D94AkGsJj33vbpZru/EoJYrbg+0eLIfXyvsPH7VZELAV3+xllVLqhlbajlBay2E6E0vfLCHe1/ZzC+vm07hKXBOvGm/k9c+3c/WAy4iUSgy67howhDmTh2OvptWyINNJBrllbr9/O8n+5g5uojHrpnKmBLZlwshhBDi5JJklF5yoia1nV6FJS/VsXZHM2a9hhtnjeGiCUPwBEKEIqmDd9UOG9+sGYO9s1zyQVfsDu9N+52ccVohz6zbxbr6FuwWfcYJ8AvHl3DP3Inc88rmtDv0b7tkPJrOEoe+YGwSVAVEidLhD1Fi0aNRq3ErYTz+ILY8A6FohEAwgkqlwh0IkWfQEo1GMejU7DzkpnJkIf5QBG8wjDcQJt+o5ZDLzw//9BnNboVZFXYev2YqJr3mqCccjnWSQiY3Brf+kowCsd/aIVeAQ04/Q4uMKKEIHb4Q+SYtZp2GxjYf+Z0tZg66/Fg7k0EOOP1o1Srq9razq6mD2y4Zz72vxKqkmPUaaqvLOW+MDb1WjVmvxaBVEQE+3t1GqdVAOBplVLGZSDTWesus16LpvFZ3+UIZk7+WXTWZh1Zt4bN9zqzJM/fNm8R1v3ufcUPzuaF6DGpVLOmm1GpMtLA55PIzrMDEzS9uSJlMOP90O7dfMp5/7mxJeX5TR4CzyotpbPOh08Za7rh8QawmHQaNmjavglqtRqNS4QuGCIXT1/2hKyfzk9e38ta2JuBI8s3k0wrY1xZr19Xk8nNmeTGtngAmvRZPIEyhOfYeLZ4AeQYdFr2GFo8fg05HgUnH8M4JjJ7sM2S/cnyONm73tnhw+gIUGA2oVBCKwr0ZkoaWXjkZFVEiUdCoVBSYdfK9CNFLjiZunV4FxavgQ8XdGWL1gXmTCEcj5Ou1DBvAiXVCDARHm0jmDoZp9yiUWo34gxF8wViCbaZKnEuvmkxrhx+jXku+QcsnDW2JyRKvEsao1VBo1vLvz3zI3jYfF40v5XsXn06UaFpSyoyyQm6eU8GEofl9dl0oxECRK24Pt3nxhaMZj68PXzUZI7DP5eX/W7OLp+ZXSmwIMUB9/dfvEYpEuP2SCSd7VU4oJRTBHwpjMWgHdVuenth2wMVv391FiydAbVU53z5/LEU9rHgVjUYJhCJo1Cp0mlMrmUcIIYQQfUOSUXrJiZzU7lqxw6zX8MC8iUwfVYQvGMGvhLEYtRi1apRwBE8gRJFZTxQ6Sx2HsZq06NVq/KHY4/kmLUaNBm8wnBiYy9NraPMGyDfqU1p4WPQa3EqYdm+QfKOWSDSKTwmhUasTbYWUcASfEqI4z8C+Nh/tviD5ei0FeTo0KhUeJZR4j0gkSlGeTNiKE68/JaPENbR4uGvlprTEsPvmTeLeVzaz9YCLF795Dg+tylw9ZW+Ll2FFRoKhKO5ArKpGnl6DO6BgMejxKGHcgdjEQDyuC0w6dBo1rZ2JFgDNHQEMulhMe5UwrR6F04rMGHVq3P4ARWYjbiWMTwlisxjxKrF9h8WgQ6dVxSo4FRjRa2PLtRh15Oli+44Of+p7WoxHEj3i/+30BSjOM+JRjuyTzHoNLW4/Jn2suodHCeH0hrAateTpNXT4FfIM+sT+p8CkT+wDzXotOo2KNq9CkTm+7bF9X74hVnXE6QthNcX2nW5/MLHv8wRCsg/qR44lbg+0eIhEo6BSoQbCQEeX35YOGG7L62ZJQohjcbRxe6jdhzocxo8qcdyIn4MagShQKvEqRJ872tg97PLHrjeDR9rEqlSgU6tjfw/ErkPzdBqIRomqVInzSbsl9bzPotfgDYVx+UIUGHWYdGoeXL2V93a2UFtdTuXIQgBKrQa2H+igxmFnqFQ3EaLbuD3c4iFA6rlw/PgajkRY9tcvuPvyiVItSIgBqr7JzUX/+Q43z3ZQ7bCf7NURJ1EgFGbVZ428vukAAFdXjmDeGcOZXlaEURdrbRiNRml0+tmwp42Pv2ylrqGdHU1ufJ3nckOsBs4aXcwV00YwZ3yptP0RQgghxDGRZJRecqInteOJGk5fELNeg1qlIqqKolerafUoGPUazJ0nlm1ehXyjjtJ8AwVmfVqSR75Riy8YxhMI4VViGeQatQqNWoUtKUkkucRx8iRvd5O0klQi+qv+mIwCsdYibR4Flz+WaFGUp2eI1ZiIJZ8Si0GPEk48x2rSEQiGU1pVHWucZYv1eEJHi0fBYoglgDh9Cib9kffLFe/Jj2VrKSNEd441bhvbvIQiUbzBMG5/KFHRS6WGQlMsxoQQfeNY4tbpVWjzKISj4A91JlMbtRSaJV6FOFGO9Zjr9Co0dQRw+oKJmxWC0QhalTpxc0S+UYtGpUKnVhEIR1CrwaDRJBJSrEZd4hw4ebnJ18CZrlmFONX1JG67tvG06DVoieKPQLHEkxAD2l0rN/HnTQf4xbWVUtVCAODyBfnr1oO888Vhmt0KGpWKUqsBrTo2vudVYoknwwqMOEoslNnMFJh0hCJRDjr9bNzXzpctXkbZzHzvogquPGMEaklKEUIIIcRRkGSUXtJfJ7WFENlJ3Aox8EjcCjHwSNwKMTBJ7Aox8EjcCnHqamz3Mftn/+DKaSO4unLEyV4d0c9EolH2tHjZedhNi1shEo2Sb9QyrMDE2JI8CnMkIu487OaVuv18vKeNScOt3Dt3IueMsZ3AtRdCCCHEQKY92SsghBBCCCGEEEIIIYQQQohj85PXt2HWa/jqpCEne1VEP6RWqSi351FuP/p2p2NLLPzwK+P4/GAH//3BHq797ftcOL6U7198OpNHFPTB2gohhBBiMJF6fUIIIYQQQgghhBBCCCHEAPS/n+zj9U0HmH9WGWa93Hsq+sa4ofk8eMUklsx2sKXRxdyn1nHtb9/n/zbso92rnOzVE0IIIUQ/JWenQgghhBBCCCGEEEIIIcQA80rdfu54eSOzx5VQ7bCf7NURg5xapaLKYeecMTY+2N3CW9sO8YP/+QwVsQoq44blc1qRiRKLgeI8PYVmHcV5BkryDZTmG9Bp5N5oIYQQ4lQjySi9JBqNArH+vEKI3pOfn49KpeqTZUvcCtE3JG6FGJj6KnYlboXoO3LMFWLgkbgVYuDp67jt6Og4qtc4fUE27HXxPxsOsH5XG1VjCrl2+hB8Pm+frKMQmUwbZmLasNG0eYNsOeBmd4uXnYdcfLS7BacvRCAUSXm+CijN1zOyyMTIIiOjik2cVmhkRKGR0nwDRWYdWnXvxVlfxq0QQgghek4VjV+piuOyb98+Ro4cebJXQ4hBx+l0YrVa+2TZErdC9I2mpiZKSkr6ZNkSt0L0nb465krcCtF35FxZiIFH4laIgacv49blclFQUNDt8wpr/o2C867N+JjS9CUgQ/yif1FpdKgMZtSGPNQ6Q68t98Dz30M5WN/t8/oyboUQQgjRc5KM0ksikQiNjY1HlXHrcrkYOXIke/fuPWVOjE61bZbtPX59mcV+LHHbU6fad5/sVN52OLW3P77t7e3tPRpMOxY9jdtT+XvIRD6PVPJ5HJH8WYwYMaJPjrk9idtT4TuRbRwc+ts29qdz5f722fQG2ab+byBuT3+I24H4uZ1s8pkdm8HyufW3yihHYyB9BwNpXWFgre9AWlfonfWVyihCCCFE/yBtenqJWq3mtNNOO6bXWq3WAXES2JtOtW2W7e2fjidue2qgfBZ94VTedji1t78vL/aPNm5P5e8hE/k8UsnncYTVau2z2D2auD0VvhPZxsHhVNjGYz1XHoyfjWxT/zfYtudYybly35PP7NjI55adSqU6IZ/NQPoOBtK6wsBa34G0rjDw1lcIIYQQ6dQnewWEEEIIIYQQQgghhBBCCCGEEEIIIcTgIckoQgghhBBCCCGEEEIIIYQQQgghhBCi10gyyklkMBi4//77MRgMJ3tVTphTbZtle09dp/JncSpvO5za29+ftr0/rUt/IJ9HKvk8jugvn0V/WY++JNs4OJwK23isBuNnI9vU/w227TlR5HM7evKZHRv53E6+gfQdDKR1hYG1vgNpXWHgra8QQgghslNFo9HoyV4JIYQQQgghhBBCCCGEEEIIIYQQQggxOEhlFCGEEEIIIYQQQgghhBBCCCGEEEII0WskGUUIIYQQQgghhBBCCCGEEEIIIYQQQvQaSUYRQgghhBBCCCGEEEIIIYQQQgghhBC9RpJRhBBCCCGEEEIIIYQQQgghhBBCCCFEr5FkFCGEEEIIIYQQQgghhBBCCCGEEEII0WskGUUIIYQQQgghhBBCCCGEEEIIIYQQQvQaSUbpJdFoFJfLRTQaPdmrIoToIYlbIQYeiVshBh6JWyEGJoldIQYeiVshBh6JWyGEEEIIMZgN+GSUn/zkJ5x33nmYzWYKCwszPufWW29lxowZGAwGpk2blnN59fX15OfnZ11WNh0dHRQUFNDR0XFUrxNCnDwSt0IMPBK3Qgw8ErdCDEwSu0IMPBK3Qgw8ErdCCCGEEGIwG/DJKIqi8I1vfIPvfOc7OZ9XW1vLv/7rv+Z8TjAYZP78+dTU1PTmKgohhBBCCCGEEEIIIYQQQgghhBBCnDK0J3sFjteDDz4IwHPPPZf1OU8++SQAhw8fZuPGjVmfd8899zB+/HguvPBC3nvvvV5dTyGEEEIIIYQQQgghhBBCCCGEEEKIU8GAT0bpLWvWrOFPf/oTn376Kf/3f//X7fMDgQCBQCDxb5fL1ZerJ4ToBRK3Qgw8ErdCDDwSt0IMTBK7Qgw8ErdCDDwSt0IIIYQQ4lQy4Nv09IaWlhYWLlzIc889h9Vq7dFrHnnkEQoKChL/GzlyZB+vpRDieEncCjHwSNwKMfBI3AoxMEnsCjHwSNwKMfBI3AohhBBCiFNJv0xGueOOO1CpVDn/t3379l57v29+85tcd911zJo1q8evufPOO3E6nYn/7d27t9fWRwjRNyRuhRh4JG6FGHgkboUYmCR2hRh4JG6FGHgkboUQQgghxKmkX7bp+eEPf8jChQtzPmfMmDG99n5r1qzhtdde42c/+xkA0WiUSCSCVqvlt7/9LbW1tWmvMRgMGAyGXluHY+X0KjS7FVz+IFaTDnuengKz/mSvlhD9Un+J2/5O9iuiPzkZcSsxIMTxOdFxKzErRO/o7diV2BSi7x1L3EpsCnFyDfaxKdnHCCGEEEKIZP0yGaWkpISSkpIT9n7//Oc/CYfDiX+/+uqrPPbYY7z33nuMGDHihK3H0Wps93H7yxtZu6M58bdZFXYevWYqwwtNJ3HNhBADlexXxKlOYkCIgUViVoj+SWJTiP5JYlMI0ZdkHyOEEEIIIbrql216jkZDQwOffvopDQ0NhMNhPv30Uz799FPcbnfiOfX19Xz66accPHgQn8+XeI6iKABMmDCByZMnJ/43YsQI1Go1kydPpqio6GRtWk5Or5J2cg/w7o5m7nh5I06vcpLWTAgxUMl+RZzqJAaEGFgkZoXonyQ2heifJDaFEH1J9jFCCCGEECKTflkZ5Wjcd999PP/884l/V1ZWAvD2229zwQUXAHDDDTfwzjvvpD1n9+7djB49+oSta29qditpJ/dx7+5optmtSAlEIcRRkf2KONVJDAgxsEjMCtE/SWwK0T9JbAoh+pLsY4QQQgghRCYDPhnlueee47nnnsv5nH/84x9HtcyFCxeycOHCY16nE8HlD+Z8vMUTgMNIX04hRI91t19x+oLsbHKn9f2VfsBisOguBjr8wVP2936qbrfo33oSszD4f7+DffvEwJD8OwxFojmf29FN7Aoh+kZPj5tCCHE04ucALZ7clU9aPAocdsu5qhBiwHp/VwvFeXpOH5J/sldFCCEGlAGfjHKqshhyf3Ud/hD/8pv3pS+nEKLHrEZdzsfVahVftngIhCK0eYNs2NPG2eXF3L1yM2vrpR+w6P/ig2TuQJBCsx4lFMEdCCUmb7uLAZNew5KX6k65/tfS91ucbE6vQrs3iEcJ4VHCFJp0lOYbuo3ZfKNu0P9+B/v2iYGhsd3Hfa9uZvwwK5UjCynJN+R8vkmvOUFrJoRI1pPjphBi8DveRObk1+fptXzS0MbS1Vt5an5lztd1+IP8y2/+KeeqQogBKRiO8J3//oSRRWZeXVKFSqU62askhBADhiSjDECN7T4+3tNGlcPG+voWAMx6DbXV5VSOLASg0Kzn+xdXoFWraOoIsL/NR55Bg0mnIc+gpdRq5JDLT5tHweUPYTVpKTLrGRL/u1ehwx8i36BFrQKtRo1NMteFGJTiAwnhaJSaCnvGsqo1Djv5Bi0t7gD3vbqZZrfCw1dP5u6Vm1jbuR+Ke3dHM7e/vJGHr5qMv8tkf3f7kGyDIrkGS47lNT15z+N1PMs9lu0VuTW1+/AFw+g0Kgw6DU0dASwGLSadhnAkQv1hN8OsRmZV2Hk3QwzMqrCzoaE9a//rp+ZXDsrvobu+34N1u0X/sL/NizsQwuULJc5jrQYt+9u9fLynjVkV9pwxazFq+dGfPhu0v1+JT9EfOL0KP3l9K7deWIFXCVFg0mHUaXjj1hrcgRD5Bg06rQanT6GpI0CBSUebN8iXh92YDFo8gRBOXxCLQYteo6bdp2AxyvmNEH3BpNfwrzNHcMOssYTCUVy+2PWEVqPi6Xd3SqKYEKeAY01kdnoVOnxBOpRwYt9hy9PT6PTy7hdNPDm/kk37nSlj1cmqHDbq9rYDqeeqHiWMyx/E5QthMWgw6TUUm+UcQAjR/6zdcZg2b5A2r5MNDe3MGFV0sldJCCEGDElG6Ye6TjSadJpYawBfiEKzllZPkOEFJu6bO5Flr2/jkz1t/Nf1M9DrVOQZtLj9YYxaNfOmDOfhN7by0798kVh2lcPGLXMq8AfDPLBqC2u2H048duH4Eu6dO4l7XtnEuqQLhyqHjUVV5TzyxjYevHKyZK4L0c8dTbJC8kCEWa/hyfmVRKPRtH1AbXU5qzY2cuboYn59/Qy8SoiiPD3LXt+WcblrdzTT0Obj+qc/SPytuwGOTIMiNRV2Hpg3iWa3n7X1LaxYtxuvEmZWhZ2lV04mHI1y/6ubUxJiLp5Qyr1zJ3L3K5u7HWDpqzvKj2e52V772DVTiYLcAX8U4rGgUkXRqtSEiPJhfQulViOBUIQOf4iDTh/DCky88MEe7rpsAo9cPYU7V25KmdyeVWHnoSsnc9mTazO+z2Dufy19v8XJsqfFw9JVWxg/vIDKkYUcdgcoNOsoMukYVmCmxRvkgNPHw1dP4a4MMfvYNVPxBEKD+vcr8Sn6gxaPwrfOH8vnB1xMG1VEq1vh4de3sba+BbtFz4qFZ9Lq8uP0BTHqNLy3s4WtjU7uuHQCS/9vI/PPHsWtL9XhVcKJ6875v/uAmaOK5PxGiF7mDwa56YIK1u9sZkjn+bBHCXPI6eOmCyrwB4OAxJwQg5XTq9DQ6uW2S8Zx0wUO8o1aDrn83P7yxpyJzIfbfXhDEe56ZVNKokm1w8YDV0zmx18dx0//8jnjh1lZVFUOkPK8+PH91pfqEn/b3ezB6Q2yfmdz4vrcHdBw0OnDUWphZJGZYXIOIIToR16ta2RkkYlAKMKrn+6XZBQhhDgKkozSz2SahKx22FhYVc4dL2/k0Wum8uIHe5g4vICZZUUsu3IyvlAIrVrN/a9tSbsoWFhVznu7WvEqYeDIxcDcKcOYOLwgJRllwvAC7u5yYZH8msqyIrnLUoh+7miSILreUe1Vwtz6Uh211eXceekEDrr8AGxudBIlykdftvLEWzsSr69x2HhyfmViAqErpy+153i8YsryDPuQbHd3r93RzH2vbaayrIi6hrbE+727o5n1O5t5Y9OBlMQZgHHDrNy5Mn1f1vVO8b66o/x4lpvrtf/44jBvbDyQ0hKpN9Z3sEqOhbU/voADLh+RCKzedCBtYGzJbAdTTivg/te28PBVk3lqfiXNboUOf5B8ow67Rc+XLZ6Mv/O4Dn8w62MDmaub7Rqs2y1Orn1tXpau2sK1Z4/i2fW7Wb6mPvFYtcPGXZdNwG4xEAxHiUaiGWO2wKynrqEt5/sM9N+vxKfoD8LRKN5AiGAkyke7WxPnZma9hqcXnMljb27POCH1+J+3M2F4Ac+u301tdTnL19Qnnhf/t5zfCNG79GoNe9t9vJ7hfHi0PY+RhSacXklkFGKwavMGeWrNjrSx4xduOIfrn34/YyKz06vgC6cnogCsq2/hgdc2c+uFFXx9xkgAbukcU6qtKiffqKXDH6Jub3vauNGv/206jU5f1uvzdfXNfGXiENkfCSH6jU37nUwaUUCrR2HnYffJXh0hhBhQ1Cd7BcQR2SYh19W38Oz63TzWmYhy3dmjqGtoY+FzH+EOhNBkSERJfl1tdXnK39d33hUeb+kTVzmyMGMpxfhrKkcWJu6yFEL0P90lQTi9qbGb6Y5qrxJm+Zp6IkRZ/PzHLH7+Y4LhKM+s2522f1ibZR8TZ9CmH2LW7mimqSOQtt4HnP6sd3fH9z/ru7zfEKsxLREFcu/LkvdhPbmj/Fgcz3JzvbY035CWiNLT5Z5qusaCWwlTnGfgqbfrMyZcLn+7ntnjSlm7oxmPEqbArGdsqYVpZUWMLbVQYNZjMehyvme+MffjA5W1m+0arNstTq4Of4jxnZPUmc5vH3ljO8V5epa/XU8wEs0YszD4f7+DffvEwKBVqXjq7XqGWI0p52a11eX8/C/bMx53n12/m/HDrYlztuTr0uR/y/mNEL1LiURZnuN8WIlEJeaEGKQOufwZb0BcV9/C0tVbeOyaqWk3FAEcdPnxKOGsYyzr6ls628EbCIQiiTGlxc9/jNWoY/HzH7N8TX3ajR1mvTbn9fm4oflpY0dCCHGyRKNR9rf7KM03MNRqZHez52SvkhBCDCiSjNKP5JqEjCWQGJjYZWDeo4QJhqLdJpF0FQhFCIQiaX/LJf643GUpRP90tEkQue6odvuPDBT0JFGtq+R+wF0lD3A0tvtY8lIdu7o5iY/vf5LfL9s+q7t9WXwf1ld3lB/PcnO9tqfbJdJjweULEghGcv6O4zr8oYzPsVv0zKqwZ3xsVoUdu2Vw3rF1qm63OLlcvmDOY8/a+uZETPtC2SsWDfbf72DfPjEweIOxCaqu15eVIwtT2igmi5/PxZ+f67pUzm+E6D2+HBPK6+tb8AXDEnNCDFJtHiVnQkmp1YBZr0n5+/42L/vafLgyJKkkc/tDuP3hlBuSqh02rCZd1nPV7vZHkF5tVwghTpbD7gCBUIQSSywZ5UC7H38w+1iEEEKIVJKM0o90N4Hp9ofTBuYtBm23gwWZJjANWnVa1YJMVQwyPS53WQrRPx1tEkSuO6otxiODEN0lQXRV47CzqKqcFet2Z3w8PsCRXL2ip/uf5PXJ9prulhXfh/XVHeXHs9xcr+3pdon0WLCadHhytNgBEndqmQ2ajI8XmPU8es3UtMG0WRV2Hrtm6qAtH3yqbrc4uawmXbfHnnhMewPhtMpfcYP99zvYt08MDN5ALBa7Xl/25EaH+PNzXZfK+Y0QvSdXy0mIxbPEnBCDkyvLTRdxnkAYjVqV+LfTq7CnxQvEzs1zyTfqsBq1iRuSqjrbxgeC4aznqt3uj5RwWnKMEEKcLPvafACU5BsYWmAkCuxt9Z7clRJCiAFEe7JXQBzR3QSmxaihrctgezAc6XawoOvgXpXDRpPLz36nP+XvdXvbqXbYMra9iFc5kLsshei/jjYJIn5H9bsZqql4AqHE/qC7JIjhhSbeuLWaA0n7lBc+2JNxcKHKYSNPHzv0JFevqNvbTpXDlvHOmK5VVuLrc8jlp8ZhS7vrNte+LHkflmv7j2dfdzzLzfXapo5An6zvYNQ1FvL0GiKRaM7XaNUqahw2zLrsA17DC008Nb+SZrdChz9IvlGH3aIf9BO+p+p2i5PHotdQ2M2gt0kfOxbkGbQZ+9vHDfbf72DfPtH/WYyx87q6ve0MLzAmzs26O38sNOl4b1dL2nle8r/l/EaI3mU15R4CtJq0EnNCDFJWY3fxr0tJRml2K7T7gmw94MJRask6xlLtsGHUqtFqVEwZUcAzC2ZSt7edW1+q48UbzmZaiSXjuepBlz9tWcmMWk1i7EgIIU625GSUeNL97mYPFUPyT+ZqCSHEgCGVUfqRXKW2YwkkAQq6DMy3eRQMWjXVDlvG11VnGNy7ZU4FVQ472xqdKc/d1uhk2VVT0pZV5bCxqKqczw+45C5LIfqxoy3Xn+2O6iqHDSUY5b55kxL7kKos+5iqzoEHjVqFWqXikMuPSqXixpqxvPjNs1kyx5G4myW+/yk0x/ZjydUrVqzbzaKq8rT3ie9/4lVWqpLWZ1iBiUXV6a/5/ICLh6+e0u2d4n11R/nxLDfXa2efXiJ3wPdQ11h4a/tBrEYtNVnio8ZhZ/dhD0vmVKBLGoDLpMCsZ2yphWllRYwttZwyn/uput3i5LCadJxWZMp6fltTYefdLw5TU2EnGo12WyVwsP9+B/v2if7NpFNz0fhSdBoVZ44uZsmcCmoq7DnPH6sdNvKNWrY1OtPO8+L/lvMbIXqfRafJfj5cYSdfp5GYE2KQKsrT5xw7dvuD2PKOxL/LH8SgVbNi3W58wRAPXDE57fXVDhsPXDEZvUbF9U9/wLf+8AmLn/+Y5Wvq8SpHKi0ln6vaLXqa3UrsZpAc1+dGnToxdiSEECfbvjYvFoMWs15LoUmHSafhy5bcLeeFEEIcoYpGo7lvFRY94nK5KCgowOl0YrVaj3k5je0+7nh5Y8qd79Wd5Q3veHkjKxaeyeNvbk9ko3//4gqqx9ooMBt44LXNKVnq1Q4bP7l6Cv5QGJc3hNmgwazTkGfQUmo1csjlp82j4PIHydNrOejy89ift3P7peMZajXiDYaxGLSoVaDTqLHlyV2WYnDprbjtTzLtQ+KD+cMKTRlf4/QqibtUTHoNbd4gfiVMKBpltC2PUCSCVq3mwVVbEpVM4EhyyfACI0adht3NHp5asyNlP1RTYefeuRNx+4N8fsjN7NNLGNq5Hjub3Fz4n+8knmvWa6itLmd6WRHmzkoW7+1qYcW63XiVMDUVdh68YhKhSIRP9rSzdPVWAGqryzlvjA2DTk2hSZ+4Izx5u3LdKd7T5x2t41lurtf21foOFD2N2+RYMOs1vHLzORg0Ou55dXPK77imws4DV0zis73tTBtZSIQoalTkGbS4/SFc/iBWkw67HAOFOGbHcrw92OrFF4lw36upx54ah53FNeX8/p9fcmPNWEqtBtq8ClajDr1GTbtPwWKUmBWiN/Qkdp1ehcNuhftf20xdQzvfOn8MF40fglarQqNKP3+sqbDz0BWTiBDFYtDhCYRw+YLkGbToNWqcPoU8w6l3fiNEb8kVtwdaPChRMp4PL7tqMgZgqC3vBK+xEOJEjU01tHi4a+WmtLHjB66YjFmrxh+O4PTFrn+1KhUrP93PR1+28vnBDv6w+CycvhB5Bg1ufxiLUYMnEGZEgZHdzV5C0SgbGtoS4zezKuw8Nb8y5Vje2O5LtGo26zX85t9n8Nt3d6Xtj+66bALFJh1DsoxhCSHEiXbXyk38c2cLD189BYA7Xt7I+eNKeOjKySd5zYQQYmCQZJRe0psXDvGJRqcviBKKYDZo0KjB5QtTaNZg0Gq5/9UtrK1v5nsXVfDJl61sO9jBY9dMpdRqSLkomDA0v0eDeE6vwkGXn3ZvkFAkyj+7TAA/ds1UhstFgBhkBmMyChxfskJju4/b/3cja+tTk1l+ctVk3t/dSnGenkAogkGrpsnlZ3ihie+8sIEHr5jEa5/uT2uZA0cGNwtNupT1cHoVbnmpLmPbmYsnlLLs6im4/SFJyBApjiZuk38nBSYdOrWKtfXNDCkwodWoKDLrCIWjOL1BDjh9TD2tkAXPfsij10zl2fW7U9pGzaqw86gcC4U4Jsd6vG1s89Lui1U9afcGyTdpEzH7cedgd2VZIZVlRSxfU5+oqnDrS3XMHFUkMSvEceppMsqSFzeknQOa9RoevGIS00YW4guG8SphtGoVu5s91DjsieRkIUTvyhW3h1x+HnptM1+bMTJl7KjJFeD/NuzjvnmTGGI1nqQ1F+LUdSLHppJvTMw36rDoNWjVKu59bQtvbWtKPG9WhZ0lcxy4AyGeWbebuoZ2aqvLqRxZCMCIIhMuX5CbXthAszvWUj5+Lv7/PmzgoSsnp9wQ5fQqLHmpLiXxxKzXcOOsMVwwrpRgOEKePtaap9Csk/EdIUS/8h/PfIBXCfPDr4wD4JE3tnFasYlfXT/jJK+ZEEIMDNJ8sR8qMB+ZVM1U5eDiCaU8es0U/MEIgVCYJ97aAcDi5z9OW9bff3B+j0/gNzS0s3pjY8rkG8DaHc3c8fLGtIx2IUT/lLwPORpOrxK7S6U+NTnk3R3N3P3KZn76jTNw+YI0tHoB2O/0s/T1bXiVMMV5+oyJKBDbh4TC0bR1irekyVTJ5aErJzPEamRIlnGYY91GcWrp+jvZ2eTmzpWbsz5/xcIzufassrREFIjFgRwLhTixhheZyTMoHHD6ue7pDzI+Z319C7VV5Yn/hljFrOVr6iVmhTgBmjoCGc8BvUqYH//vRlbdUoXbH8Ko01Bg0lExUVpJCXGyOL1BXt98iNc3H8r4+HcvPF2SUYQY5GLjLEfiPFOSCMSuf6PAFWcMp7KsiNqqcgKhCCadhjKbmQe6JK9A7FxcrVLxs2+ckbYvaXYrae/hVWJj2k+8tYO//+B8xpZaendjhRCilzS7FYYXHtmvFZh0NLkCJ3GNhBBiYJFklH5ueKGJp+ZXZq0AUNfQlvP1Hf5gj96n2a1Qmm9Im3yLe3dHM81uRQYOhRjEMg0OxL27oxm3P4RapcqY+BYIRXIuO9u+qLt9nBC9ydXNMdEfDFM5spDla+ozPi7HQiFOvAKznl3NuXsxJx+DkpNTJGaF6Hvx6kXZeAJhrnv6A5lkEqIfcHYTr909LoQYfHKNA63d0cx9cycyvawoZbym2a2kJaIkv8btD6XdWNTdtXhPx6+FEOJkaPUqjBuan/h3gVnHnn3ek7hGQggxsEgyygCQqwKA1ajL+dr8bh6Pc/mDxzyZLIQYHHoyOJCtr5tBq8752lz7IqlyIk6U7o6ZBq1ajoVC9EM9id1kyXEsMStE38rTa3I+bu58XGJRiJPPbOgmXrt5XAgx+HQ3DuQJhJhWVpTyt+4SxTMd83tr/FoIIU60aDRKm0fBajwylVpo0tPslsooQgjRU7lnD0W/Z7fomVVhz/jYrAo7dkvPJnitRt1xTSYLIQa+ngwOWI06zHoNS+Y4eGbBTH51/XRWLDwTiPUHzuRo9kVC9KVcx8wqh426ve1yLBSiH7IYtdR0E7vJkuNYYlaIvpWn12Y9B0z+e09j0elV2Nnkpq6hjZ2H3Ti9Sq+spxACzDpNzng16yQZRYhTTU+TRJKPz6ZuElEzHfN7a/xaCCFONF8wTCAUSdm3FZp1eJUwXiV0EtdMCCEGDklGGeAKzHqWXTWZ6i4DCtUOG8uumtzjagN2i56mjoBMJgtxCuvJ4IDdomfFwjOpa2hj8fMfc9MLG6h97iNe+GAPD14xOe31syrsPHbNVKl8IvqFXMfM++dN4o8fNlC3t12OhUL0I43tPu5euYkF541Oi80qh41FVeWsWLc75W/x5BSJWSH6XqFZxy1zKjLG55LZFbz9eVOPY7Gx3ceSl+q48D/f4epfvceFP3+HW16qo7Hd11erL8QpJc+gZclsR9Z4/XhPm8SbEKeYnowDdT0+r954IO2auutruiow63n0mqkyZiSEGHBaPbHk+PykyigFplhiSnOHJM4LIURPSJueAc7pVXho9VamlRWxqKqcQCiCQaumbm87S1dv5WffOKNHJ/QFZj0XnF5CuT0PgPX1LYnHairsPHz1FFo8CruaPVhNOux50lZDiMEmPjhwx8sbeTepZ3Dy4IDTq/DLNfUp+wiANdsPo1Zt46ffOAO3P5TST7i/7yucXoVmt4LLH5T92yCX65j52J+3ce1ZZaxYt5sn51eiAtYl/c5lkEyIE8/pVbj95Y2s3dHMeztbqK0up7aqHCUcoazIzMb9Tm59qQ6vEgaOJKfc+lKdxKwQJ0iBWc+oYjNzpw6nNunYesjlxx8Ms3mfs0exmBzvyd7d0cwdL2/kqfmVEs9CHKdSq5FAMMzcKcMyxuv9r21h5qgiiTchTgHxcRB3IMhDV07mvlc3ZxwHAtKOz/FrZji6a+bhhSaeml9Js1sZUGNGQohTW5sn1nosuTJKPBnlsNtPmc18UtZLCCEGEklGGeCa3QpvbWvirW1NGR9vdPpp9SjkGbQYtOqcE67DCk2Y9RoevmoKHiWEVwlTYIq15Njb6qXVG8So0/D37U18fsDFg1dOZnih6YRsp0wWC9F3kuOrwKRLSSjJM2jRa9Q0dfjxBsNEIlHW1jdnXM5b25q489IQY0stR/WevRnT2Zab7e+N7b7EwIpZr6G2upzzxtjQa9UU5ekzrpfsjwaupo5AzmPmj746novGl2I16fj5v0wbcIlVQgw2B13+xMC3VwmzfE194jGzXsPrt1Szakl1yvHK6VNYtaRaYlaIE2hYoYlZFXb2tvrIM8QGZ0cUmogS5a7LJuDyB2k/oGDUasgzaCm1GtOW0exW0hJR4t7d0UyzW5GYFqIXGDVqzhljY1ezJ/G3RqefZa9vw6uEJd6EGOScXgWXL4hbCdPhC5Jv0qFWwYNXTiIYiuBVwinXvzub3GnHZ68S5taX6qitLueeyyfiD4Z7fM1cYJZzdCHEwNLqTa+MUmjuTEbpCJyUdRJCiIFGklEGOJc/mPNxpy/IT9/dyTdrxlBqNfKvv/0nze7YAXRWhZ1Hr5maklASvyhwehUOuwOoUHHHyxtZm5TpHr/r9P5XN/e48srxSJ4sjsu07kKIo5crvsx6Dbd1eeyZBTNzLq/Vq2Bx+RmSYZKhJ+95LDHt9Cq0eBSiwAOvbk7ZX108oZR7507k7lc2p73fw1dP4YFVWxKJKE/Or+TZ9btTJju7rpfsjwauA+0+mrq5SOzwBxk3JD9xXBtiPRFrJoTIZG+LB5cve/9lrxJmV7OHmaOKuiRB5vX9ygkhUji9Cve/toX/OGc0pQUGWt0K7b7YjQwbGtrY2ujkurNH8eIHe/hmzRgCwTAjbamx2t11bUc3jwshuuf0KnhDERqdfhY//3HW50m8CTE4NbZ5cQVC7G/zoVKp2NDQxop1u5leVsgDV0zCqNVQZNZTkjSek+34HE8Uv2h8KdPKik7UJgghxAnX6omNJSYno+QZtGjVKklGEUKIHpJklAEiPtkaikSJRKN4AyHyjbGqJbkUmnQsOrecva1eTDoty+dPxxcME4lG0ahUbDvgwhMIUZpvSEy+Nbb7uO+Vzcw/uwyjTsP8s0exqHpM4iIl3p6jsqyoz++YkXLNQvSdTPFl1muYOrKQL5s9RIFFVeWcMbKQP37YwLVnlVFqNeRcpssX5Ef/8yk/uXoKBSZdWgURSC3xGq9GUjmykO0HXbgDIQwaNe0+BYux+6ojB9p9/OOLw1SUWnD6giyqHsMZZUWsWLcbrxJm3DArd67clNZW6N0dzdy5chNnjCzkrW1N1FaX8+z63RmfF9/XdF33TM+R/VH/5PTGfoe2vNzfT55ei9MblO9RiJNsT4uHX/ztc755vqPb58rd20KcfM1uhSmnFVBi1fOT1VtZW9+SOMerHmtj7tRhRCLw7fMdWAwaIlHYftCFyxc7Rywy67Emlb3OJL+bx4UQ3evwBWn3+rFZch83Jd6EGHwOtnrxhyNEo2AxaLGadHytcgT/UjmCDiVM3Z42RtnNDMk38WWzB2fnmIzFkHvqQPYXQojBrtUTxKBVY9AemYdTq1QUmHSSjCKEED0kySgDQGO7j/te3cy1Z5Xx7Prd1DW0JyZvjToNL33zbNbvbElMvsbVOGxEo2A0aJg0vAAlHCEYjgBR7BYDSiiCUa/BH4zw162HOG+MjXAkykOrt3D9OaM56PSllFAeXmDkl9dN5+YXN7C+voXaqvKUO2b6onWFlGsWp7pjiavk3r+FZj1KKIJXCSX+2x0IYenM4P73c0fxvYtOR6dR0dQRoKzYzEOrtqRUB6ly2Hjxm+fw2J+3Jf7dNWkj/ve6ve2srW/h7lc2c9nkody5cnPi8ZoKO0uvnMy2Ay6WzHEws6yIYYVGlq3emvZ+i6rKmf+7D5g5qohHr5lKnl6TMbFlT6uX1RsbWZ806XHeGBvnV5Rg1mvQ69T88cOGjJ/T2h3NLDxvNACVIwtZsW43S+Y4qBxZSCAUSdzNu2Ld7kRFqaPZH0k7n/7D5Q2iUavYuLedGoc90WrKrNfwrfPHMHtcKSoVeJQwWo0Kp1eOLUKcLE6vwi/+9jk/uOh0oioVNRX2tATGmWVFFFv0aNSqWMncw27ZxwpxErkDQeZNGY7LH+Lm2RXcdul4zDotn+1twxeM8NTqrSnnjjUOO/fOm8jHe1qJRmH2uFIMWk1KvCebVWHH3s3kuRCie9FIlGKzEW8ozOu3VOPyh8g3ajnk8nP7yxtpdisSb0IMQk6vQjASpanDT55BSyQK4UiUpo4AI6xGrAYNY0rMFOcZ8YXCvL/jENPLS/j8YAflNvMxHZ+PdjxExk+EEP1Vm0fBakpPvMs3amnzSjU5IYToCUlG6efilQvOGFmYSETJ1Eqi2mHjyfmV3PpSHV4lTE2FnZsucHDNf72XSFCZM76EOy6dwNJVW9La7twy24ESjqBVq7jr8on4gmEsBi0Wo5Yml5/NjU4qRxah06h5+Tvn0eTyU5SnR69R89neNqxGHe5ACLVahVGnoc2joIQiHHD60WhU6DVqXD4Fq+nIZLjVFMuw9wRCOH1BCkw68gxa3P5Q4uIjHI1it+j5t3NGUe2wEwpHyTNoABVrPj+EJyAHfDH4NLb7cPmDaNVqHnwtte1MvCVMpuSMArM+USlkmNXIsEIj96zcxCed+43H//J5ykRAlcPG4upy1tUfZuaoYkYUmFi6agvruiSarK9v4aFVW5hWVsQfP2zghRvOYenq1OdVO2zcN28S1/3ufQA+2dPGg1dMYtUtVexv82PQqolEozS5fPzxxnP4aHcrxRY9yzrvnu36fgC11eUsX1PP7S9v5LIpw7jz/zYBscnIe+dO5JzyYn65ZkciESXTvrHGYeNP3z6Xfa0+PviylVfr9vPAFZMotRpw+8MUW3T840fn4w9FeG1JNQ++tjnt9cuvq8QTCBKO5v7ekpPzjredz/EkIcngTSqnV+GQ28+z63bz3YvHMazQhE6r4oyRhVw2eRgt7gCPvbk9dZKsws5j0npJiJOi2a1w4wVjCKHioVVbWHDeaHSaWMxeOmkYj7+5jWkjC9PiVlqmCXHyFJj03PvKpvRrzDkV2PL03FgzlsqkynVr65t59M/b+PFXx+H0BvnJG9sS17mRaDTtfPWm2Q68SpgC88nYOiEGD41ahS8c5aEuCWLVnTcf/OKtL7j9kvFyDSHEIOPxBQlEIjy1pj4t9h+4YjJmjYoyi5EPGl04SizMnjAcr8/LTS9swKzXJNo1dx3fePyaqQDsbHKnjEN4lPBRjYdIO2QhRH/W6lXIz1AlymLQ0uZVTsIaCSHEwKOKRqPdTK+JnnC5XBQUFOB0OrFarb223J1Nbi78z3dYsfBMap/7iCVzHNQ1tGWsSlDjsPODr5xOOBLli0MdLHt9W0qllFyvrXLYuPKM4UweUcAjb2xLGUisqbBz8wVjqX3+Y7xKOHEh8qu36xOT3C9+sIfrzh6V1uYiXuFg5Sf7+O7Fp/Pgqi1pFz4Lq8q54+WNPHrN1LTXXzShlNsuGZ8xgWbJbAfDC0yMsqf2Gxeip/oqbnsqU/JAuy/IXSs3UVlWlD3WK+wpyRkQu1B/+OopNDp9/OLvO1Je391+49IpQ7lr5WaeW3QmC5/9KOv6PrNgJnV729na6GTi8IJEBRGDVp3y9xXrdvPk/EqeX787JW6rHTZ+fMl4/EqIJ9fUU1tVnrNX+TMLZiYej69bctLJN2vGJNa3u22srR7NH97fw71zJ3LvK5tZV9+C3aLnhRvO4aHVW3J/3g47S6+ahD8Y4ZJfrM26vn/7/iwqhuTj9Coseaku651D3bXzOZaBmFNp8OZo4/bzgy6iUdjf7mPF+t18frCDpxecybZGJ1Hg9U0HMn7vPfmuhBA9czRxu2FPKyUWA3d0tlizW/SJmF296UDO/bXErRC9qyex6/QqLHmxLlF1LFmVw8blU4bR6PRT19DGoqryxM0TS+Y4GFFgZHXScTi5fSNAgUnHP744zIp1u5k5qkjiW4geyBW3DS2ejC1MIXatds/lEzFq1RRJUrsQJ1Rfj011F/sPXz2FaBTMKmgNhVm6aiuPXj2FLw572NDQxh8/bOCHXxnHzFFFeDrbxtstmZNOHvnaFN7YeCDjeUGmc/XjHT8RQoi+9p3//oT97T7uvHRCyt+feOsLtBoVL9xwzklaMyGEGDjUJ3sFRG6uzjvttWoVEGslkeniAWBtfTOtHgWnL8hdKzenJKJ099r19S2MKbGkJaJALPP9qbfrqa0uBzqrFbxdz9r6Fmqry3l2/W4mDi9ISySJL/fZ9bv52ozT0hJRANZ1Pv5YhkQUgPHDrGmJKPHlLn+7nvd2teCUDFQxADW2+1jyUh0X/uc7XP2r97jw5+/Q6PRzV+cAQc5Y39FMab4h5W/v7mjmzpWbqG9yp72+u/3GkM52XMak3peZBEIRKkcWsmb7YZavqWfx8x9z0wsbWPz8xyxfU8+a7YepHFmY2C90jdt19S387C+fs/Owh/X1LQRCkW7fL86oi61bfNnr61tS1rf7bTQxcXgB93QmogA8ds1UHlq9pfvPu74ZfzDC6k0HqHLYMj6nymFDr4kdUnvSXiybeDWsrq9/d0czd7y8MeP+7lhec6o45PLT7Fbo8IcotRpYX9/CtWeV8dO/bKfUamSI1Zj1e+/uuxJC9I08gxa3Ek7EZnLMdre/lrgV4sRrdisZJ5wgds02xGpMxO2z63cnrikrRxYm4jrOq4QT55iLn/8Ypy/I8jX1eJWwxLcQvcCTdHztal19C+FoFF8oIrEmxCDTXex7lDDv7WzGDxCN/a1DCXP/q5upa2jj0WumsnT1VtQqFdPKihhbagHIOA5Rmm/Iel6Q6Vh+POMnQghxIrR5g+RlqIySb9TS5pGq/UII0ROSjNLPWY2xfnRFebH/V8K5J291WjUl+QZ+df10Viw8kyVzHJj1sQnb7iZ+Q5Fo2uRxXHzwH8g4yd1dokt8EvBoH68cWZhznUrzDXJhIgacbMkDUY60qDmaRI24tTuOJJYkP97TZVnN6f0vkxm06rRlmfUalsxxxKolXT+d0nwDF5xekjORZmiBiSVzHIwsNmXcVyW/X1y8N2fyviZ5fbvbRncglLafSt7vdPd6lz/EinW7WVRVnpaQEq8A5fQpnc/NfSGS3M7H6VXY2eSmrqGNnYfdNHUEjnogRgZvsov1ddVi1mvwBmJ3YX910pBEMlR333tHN9+lEKL36TRqXL4jsTe9rCglgVHiVoj+JX7+k03y8Tb5mrInx+Guj0t8C3F8Ony5Y8jtD9HhD0qsCTHIuLqJ/Q5/kFKrEbcSJtJZP92nBPnD4rO449LxWPRa/vfb56JJmkXINg5xtOfqRzN+IoQQJ4PLFyRPn34Dp8WgkzY9QgjRQ+kpfaJfsVv0zKqwEwpHmTO+hNOKcrdb0KhUXLF8feLfVQ4bT86v5NaX6lImdjPpOhHcVaZJgJ5ODLj94WN6vCcDlHJhIgaabBftHb5gojx5PFHDqNOwoaGNFet2p1Q7yhbP8ZhJfry72I8/rorG9hnZSrfW7W1PTCAAKS1zlq+pT/w93k84G7NeQ11DW8prkvdVXiVMVef7xR9TRVO3r+v6dreNeXoNze5Ayt+S9zs9eb1XCXPrS3XUVpdTW1VOIBRhZLGJv2w5xK0v1bFqSTVwJIkwm/zOxzO11unus8u0v5PBm+xc/hA6jYoikxaLUcu5Y2y4/WFWLDwTq1GLs5tBufxuvkshRO9r9QQSCYhmvQZLZ9LjyOLYOXB3+2tTN+ezQojeZc5wl2CyrjEbP5crMOnwB3NfI3Z9rRyXhTg++abur1NUKhIVH4UQg4O1B7EfqygaRK1SYbfoKc4zcvcrqa194i19ymx5WcchujtX73os7278xKiLjR/FW1tLyx4hxInW4Q9i1lvS/p5v1NLuPXXHXIUQ4mjIFWY/V2DW8+g1UwkEw3zvotPZtM+ZtU1EjcPGP3dlbpNTW13Opv1Oahz2LK+1o+tsBZRN/IIi0yR3dxcbFmPqxEDXagqFZl23lRGyrZMMSoqBIl4Fo8WjZKwGYjXreHJ+JXUNbcx7aj03vbCB2uc+oq6hjSfnVyaem5yo0VU8Zur2tif2Fcn/3VXysiJEslb+uG/eJLYfcKUsK7llztEIR6JZW3rVVpcnKo2sWLc78d8R0pNskte3u23UalRp+5Pk/VJPXg+p5eNvemEDe1t9LF9Tz8xRRdgtsUGReBJhJrMq7Ngt+qzVcbqTaX/X0+SXU5HVqMXtC6FSq7hr5Sauf/oD5v/ufWqf+4gvDnXQ5ArkPKZajJKzK8SJZjHosOg1VDts3Dx7LEV5euoa2vjLlkM92t9vaGg/pduTCXGiGbVqarKc91Q5bBxy+VPOWw1aNVUOG/lGLYdc/pzH4eTXxc+hhBDHLq/z+JpJtcNGnl5Dnl4jsSbEIJPfTexb9JrE+GqeQcMfFp+VlogCsfY9d63cxCGXP+s4RK5z9UzH8lzjJ9UOG6s3HUi0tr7lpToa233dba4QQvQqlz+U8SZui0GLLxgmEMqdYC+EEEKSUQaE4YUmyksseAIhlr2+LeNkcY3DzoNXTuaPHzYAqcke1589iksmDeW8scXcPHtsxonmm2bHJsV7MmFdt7edOeNLWDLHQYFJxzMLZlJo1vPw1ZMzHpirHDaaXIHEhU+8mkJdQ1tiQvfSX6zl0y4T7vH3qsmxTk0dARkoEQNCY7uPJS/VceF/vsO//OafGZNMtGpVxuSO5ESNWRV2bplTwYp1u9PeY1aFnaaOWPWP5JYy2drL1DhsLJl9ZFkHnQH+75O91FaVs+qWKl765jmsvqWa2qpyfv32Du66bAJzxpVw/9xJ1DjsaW1v4vudknxD1ritqbCnJc0lb+elk4dy+yXjCUeiPDW/ksqyIl76YA8d/hCQOrBx0BngpQ/2UFlWlLJeyaocNm6ZXcFb2w5Rt7c9ZQCmyRXgws592fSyIm6ZXcGL3zw7JUko+fWZGLRqZlXYeeyaqYk7dOJJhF0HVJKfl606ztEO3EDPkl9OVUV5enQ6FfesTB9IW/b6NsaUmLlltiM9Nirs/Oir43EH5A4HIU60ojw9WuAnV03hkknDeHDVFtbXtySOZVsbnVmPaffPm0Rxnp4DLr8kpAhxokRhyWxH2jlYjcPGg1dM4vQh+cwoK2LJHAcXjS+lJN9AZVkRtc99hKPEwi1zKjJe2y6qLk+co3Y91xJCHBsV8MAVk9Ku1WocNh64YjJqFWhAYk2IQSbfpGPZVVNSxkPMeg2PXD2Ze+ZOZF+7D7vFgFWvwaTTEokcaSHd9WbC2uoxeAOhrOMQf/ywgbsvm5CWqJrtWJ5t/KTaYWNhVXnK2Ne7O5q54+WNcp4vhDhhotEo7hzJKIBURxFCiB5QRaPR6MleicHA5XJRUFCA0+nEarX26rKdXoVmt0KLJ8C//OZ97BY9KxaeSYc/hNMXxKBVU7e3ne2NTq49exR3vLyRR6+Zmjap/eINZ3PD7z+mtrqcypGFBEKRxGtXrNvNr/9tOnqNml+9vZNPGtoSzwMYWmDkb1sP8dt3d2HWa/jjjedw/2tbUpZf47Bz0+yxLH7+40Q7kXhVg5Wf7OO7F5/Og6u2UFlWRF1DW9ZWINPKihLtOy6eUMq9cydy9yubUyZuqxw2bplTwehiM0MLc7cuEiKbvozbZE6vwpKX6jImH1Q5bFR2/uafrz2TBSs+yrqcN79bw7ACI14lzO0vb+TdpOXFL+yBxGPxlj/njbFh0qspNhtQwhE8gRB5Bi16rZpwJMr9r21h7Y5mRhaZ+MPis3hvZwtDrEYCoQhGnYZDTh/njbXz988PMmFoAUOtRvyhCJFolD0tXow6DRv3tXPGaYU8vW4XdQ3tifY9XUu63jt3Ilf/6r2UlkPJnlkwk8XPf5zymvvnTeJv2w4yboiVUCRKuS2Ppau3sO1gBy/ccA5LV29hQ0M7v7xuOgecvsS6G7Rqmlx+hhea+M4LGzhvjI175k3knpWbWFffwsgiE79ffBb3vLI5bV92/xWT2N/mo9Hpw1GSx4JnP0pb55oKO8uunEyhWZdxwDa+7+7wB8k36rBbjpSUrWto4+pfvZf2mniy3vPvfZnye4l/v8Oy7O8a233ckeU3ke01A9XRxu3nB1189Ym1GR8z6zWsvqUajUrF/nYf7UnH1K2NTu64ZAL5Ru2g+wyFONGONm4PNnsIq6DNH2LuU+sSf48f12aWFVFg1mHUaWhxBygw69i0z8my17cl9tWzKuw8es1Uhkv8CnHMehK72w+4+Nqv3+PGWWOodtgJR6JYTTo+bWhjaVJM1jhsLLt6CoddfiLRWHufLw52MH5YPuEIoIriD0YoNOmwmnR4AiFcvvRzKCFEbrnidtsBJ02uAI3tPkozXDOVWg0QjTBheNFJWnshTk0nYmxq32E3YbUKrxKmwxekwKxjw57UY/UjX5vCGxsbuWl2BfN/935Ka+aUMZMKO49fM5UopIxDmPUaViw8k6ff3cX44dbE2HOhSccom5kRReas65c8fmLUaVi96UBau+q4v31/FmqVCpc/KO17hBB9yquEmHjfX1gy20FVl+T7Lw51cP9rW3jzezWMH9p38wpCCDEYSP35fq6x3Zdo5bBi4ZkAXHtWGY+9uT1jMocvFOGxa6ayIkN1hXZfMNFiIhOzXostT89DV04iCtz/6uaU59ZU2HltSTUaFdz36ua05a+tbwYVrL6lmlaPQp5Bi0oFGpWK2y4dT4dP4eGrpuALZl+HdfUt3HP5RC4aX5oy8Lh8fiVNHQGcviBmvYY8vTbrBLAQ/U22KhgQu9uktqocgFA4d26gPximwKynwAw//cYZtHkUXP4QVpOWIrOeIVYjAE/Nr8yYBBG/uI8CarWKQpMuEV+xv0c56PTz+qYDKfFd5bBRXmLhgtOHECUKKlj2+tYuCRw2zi4vpq6hHa8S5taX6qitLk9s24giE1ajjg5/MGsiCsBpRSbe/G5NbLuMWvL0Gq57+gOurBwBxFr87G/3sbCqHI1KhU8J8vDVU/AoYTyBIKNsNoLhCO5ACI1aTUm+gQNOP0917kPyNGoe//oZOH1BVMB9r2Telz24agvLrpzMtJEFeJUwM0cVHXWiR+y7yryPylbSNv7Z/fnWGkKRaMZElkyGF5qyfu+nuly/N68SxukL8rO/fp7xmBoMR7nijOF8ZeIQ+SyFOIE80QjhcBRfl/jteh67YuFMolF49M/p58XxOyefml8p8StEH+rwh/AqYZ54awdPvLWDJXMcGW88WFvfwj2vbOYnV03GFwqjAmaMLsLpVcgzyHmLECeCWqXit2t3ZTzvrXLYuG/uRLSaU7fFpxCDWVgVqxi6tstYz5PzK7n1pTq8SpjSfANr61u47dLxQPbWzGt3NHN753l28jhEkVnPPa9sZm19M29tb0p5zawKe87z8uTxk7qGtqzjxgANrd6Um5gkCV0I0Vdcvlil7kyVUfI7K6O0eaQyihBCdGfAJ6P85Cc/4fXXX+fTTz9Fr9fT3t6e9pxbb72V9evXs3nzZiZMmMCnn36a9pxoNMrPf/5zfvvb37Jnzx7sdjs33XQTd999d99vRBZOr5JIRDF39u2tdtioHFmY9aR8fX0Ld182kfX1LYm7R+OZ6GXFZpbMcWTNLA92VjrY2+rl6XWZLzYeWrWFpVdOTrl46fo+SjhCRaklwwVGHhC7qMjFHwwzrSz1Tpxck7pC9Hcuf+6T0nyjjr//4Hwi3RSqyu9MYDjQ7uMfXxymNN9AIBRLvKhraOeC00sYVmjKGC/JiW1xyRfsBWY9e5o9PPV2fcY2QQAPXjEZrxLOmAy3tr6FCLHBiuVr6tMmDP/+g/MZXmjC6dVQU2FPrEfy/gNiA6SlVgPjh8WSZw67Azyz4EyWrt6Ssrx4daRye6Z9TUw8+WaY1UCBSU9ZsZl9Lj9Wk47hBUZaPErKvixle3Y0E4pEE8k/vZ3oES9p+26GJKWZo4qOKdlO9pOZdU38sVv0PHbNVEqtBtz+MEadprNiV3vasXHtjmZuu2QcLR5FPlshTiB7noH97T7yDBqeW3gmBWYdWo2aNo9COBLlk4Y2VqzbTTQKpVZDxkk1iCWkNLslfoXoS2ZDbGA2fk731UlDUs7Zul4rugMhPtvn5LLJQ2OxacvLuuz4uZzc+SxE74hEoa6hnSVzHImYNOo0bOg8rkainNItPoUYrJxepTNJJH2sRw3cOGsMT7y1AyUcAUi0Ws81/hw/zx6bNP6767CbM8oKWVg1Om3/cjTn5dlu3slGktCFEH2lo3NM36xPn0a1GONteqR1mBBCdGfAJ6MoisI3vvENzj33XJ555pmsz6utreWDDz5g48aNGR//7ne/y1//+ld+9rOfMWXKFFpbW2ltbe2r1e6R5GoKtdXlPPHWFyzsrDKQS7x6SLyUYkp1E4edF795Djc8/xHN7iMHyiqHjY/2tFJiMTBtZBHXnz2KxdVjEhcN8Qm6d3c041FCiddle59cWendXVTkH+VFhxD9XXe/eVuenrGlFg65/FQ7bKzL0sLKYtTi9CrsafWyemNjevUSex5mvSbt4js5sS1Z1wt2jxLKOTjpU0JoVKqU982U9AakJb3FT94LzLFEgDte3sjHe9qy7j+WXTWZh1ZvZfwwa8a7a9fXt6BWqVg+vzLrREX8f9kSce6/YhJmvSZr5YyOpCSi3k70iPdFztZaRwZQeo/dok8kQNktel785jk8uGpLWvwk3xGWrMkVoKw4/Q4IIUTfKTDrafUqLFu9lflnj0qrXlRTYeeVm6vwBEL4lEjOZXV0kxAqhDg+Zp2GSycP4VvnO/j5X7YzcdiREtVZr0kr7Jx/egkF2av1p5y/Jbee1GvVFOXpJTFFiGPgVUIZYzJ+LuxTQhJXQgxCzW4lVs06g3gllN++u4sSiwGItX5+4YZz2N/mzbncrufZUdKrmiRfa/f0vDzXzTtVDht1e9vT/i5J6EKIvuBKJKOkjwvm6bWogDavjDkIIUR3BnwyyoMPPgjAc889l/U5Tz75JACHDx/OmIyybds2fv3rX7N582bGjRsHQHl590kffS25mkI8G33jPie/rz075+ssRk32Uor1zUSJ8tKN57C31Yc/GKbQrMNq1NLsVvjd2l3ctXJz4vmZJuiSJ+qyvU+urPRcFxWzKuxyJ44YdHr6m/cEQiysKicKaRPlC6vK8QRC+BUVT63ZkbV6ycNXTUmLua5tgromkBxw+QFQQuGsSWwrbzoPAJe/+2S0TPuNPMORw028pUy7N8g9r2zKuP+4a+UmppUV5bwT55M9bbR5g9z76uasFV9yJeI88NqWRCWXTPo6MU5a65w4N892EIlG+WbNmLREFDgSP9l+D+FI7qpFQojedcjl575XNnNGWVHW0uBLV21lxugizhpdnHNZkuQsRN9SAbdfMp67O1sfJt88kau8f/K1YtfEYotBy+0vb+STPW1876IKLpxQSpMrgD8U4b1dLaxYt5uZo4qkJL8QR6nYrOc///ZF1nPhZVdOxumVyVwhBpvuqvU2uQLUVpej16qpcthYX9/C9U+/3+34s8mg4ZDLj9sfIhKN8lA319rJ40K5ZLt5p6bCzoLzRnPrS3UZXydJ6EKI3hYfB8+UjKJWq7AYtbRJZRQhhOjWgE9G6Q2rVq1izJgxrF69mksuuYRoNMpFF13E448/TnFx5gHuQCBAIBBI/NvlcvX6eiVXUwiEYnd9XntWGZ/ubUtcHHRV47BBFC6bPCzrBOu6+pbYhcZzHyX+9tNrpvDaZ409mqArMOkSE+s9KdnYdSBDKgKIk+VExG0mPf3NO31Bbn2pjtrqcmqrygmEIhi0aur2tnPrS3W8eMPZ6LXqrO0I1te34FFCaQP64WiUkUUmHrhiEkMLDBi0Wh58bXNaNZKHrpzMk2vS2/SsrW/modVbqSwr4twxtkQyy1cmDuGnGVr2dN1v1DjsGLVqDrn8tHkUXP4QVpMWo1bNJw3tGbdlXX0Lizo/g2xqq8u595VNaaVmk5PhuibipGzXjma+c/7YjPuwE5UYJ611une8cdvsVqh97iO+e2EFQ6zGnPFT26X6WPyuqyL5joQ4Kscbt22dbdQWVmVPGFxb38z3Lq7g7c8PZ60qVlNhR6tRycSaED10LLHrD0Vo9SiJ42vd3vbEtWp314qNTj9Of4i7V25KOV/74zfPZuboIu69fCJLV2/hibd2JB5LTno+WSX5pX2Q6E+OJm4D4UjOc+FAOCKVBYQ4AU702FRP2t5UjizkgNPPos5r4vX1LYQikZTx55FFJn55/XS0GjUefxC9Ws2P/udT1ta38L/fPjdrG+T19S3cdIEDrVpFXUNbj46dmW7e0apVXPrk2qzVbSUJXQjR21y+WJJbtmS6PL028RwhhBDZSTIKsGvXLvbs2cOf/vQnfv/73xMOh/n+97/P17/+ddasWZPxNY888kiiKktfSa6mYNCqMes1XHB6Cf+x4kOenF+JQatm4vCCRHWDQrOO0wpNPPbnbVx+xoicy3YmHSTNeg3jh1v58cubMj43eYJuVoUdq0nH3ZdPYEGbr9us9mxZ6VIRQJwMJyJus+nJb95q1OFVwjkrdbR2k23tUcIseXEDa+tbEkkjNQ4bzy46iwdfi91lnqntzbs7mrn31c2cMbKQNdsPpy03vh/4eE8rzyyYyfK366kcWZhzsKG2qpwqh40lcxxEgB/8z6esT1qv88bYWLHgTHzBMJ80tPHHDxu49qyyxD5taIERk07D/7vxHPIMWg65/Nz+8kZMOg2/vH46Oo2aicOsLMrSUiw+SZCLQadOq1rTXWKcTECcWMcbt21eBbM+1kLgoDM24Ne1OlC8HVUoqQJKjcPOvfMmct3v3ufqabmPqUKIVMcbt/F9dzwhMVvMEo21hXtyfiUqlSplMrvKYWPBeaO59BdrpYKCED10LLEbBdqTri3/+GEDKxaeSYc/hFqlYsXCM9PO0+I6/CE27WtnxqgiFp43mkAogkmnYYjVyDCrkQdXd1/NrKkjcELPy7K1f5R9jDhZjiZuXZ0tnTMdU1es243LFyRPr+rjNRZCnOixKbtFzyNfm0JpviEt7ivLCqnb287EYVYKTDq++fuPEzdI+ZRwIjmlocXLfy8+i8NuP1ajEaPWwH2vbk6MCYW6qSaqUato8yr82zMf4lXCPTp2dr15x+lVmDmqqNuqwzJmI4ToLbFrGjBo1RkfzzNoUubZhBBCZNYvk1HuuOMOHnvssZzP2bZtG+PHj++V94tEIgQCAX7/+99z+umnA/DMM88wY8YMPv/880TrnmR33nknP/jBDxL/drlcjBw5slfWJy65msLmRifPLJiJ0xfEq4S54+WNPL3gTH7+l+1p7TQWVY+mNN/AMwtmpl1kxAcAkw+gtdXlNLkCae+fTAlHeORrU6gsK2TLfieoVNTtbWd6WVHO1+XKSj+VKgLIhVD/cCLiNpdMF9I7m9xHSpIbtcydMpTRJZa0wcHPD7iwW/REorkv8IOhSCIRJd5CB0gkoOS8y3xHMwvPG53xMbNeQ3GenmqHnXZvkMXVYygy6zDrNVnvSskzaKksK+Kfu1r45MvWRCJKttY+L9xwDo+9uS3t74uqyln03EdMLyvkjzeeg0at4p7OUvDJz+vaGqjDH8TSTcKcxaDl0snDWNA5AWLQqmnqSN8fxmO4zasQDEdYv7MlsU+VCYi+dTxx6/Qq5Bm0PPWv0whHwGrW8f2LK7h00jCWrt6S9lu7pnIE//vtc3H6ghTn6Xn8z9u5/ZLxx1wlR/b94lR1vHFbYIqdP8aTsZdfV8mKdV1byNm4ZnosUezWl+pYedN5+JQILZ7YPjxeVcyrhHl3RzO3v7yRZVdNptCkkzgUIotjiV1PIJS4trRb9Dy94Ex++ub2lITlTOdpAO5AiJHFZlZ91phS/aTaYeP2S8dzWw9ulmho9bL4+Y8Tj/XleVmu9o8nq0qLEEcTt1aTLusxdfl1lVhNOvJ16WXohRC960SPTXmUMG9sbEw7Nj+zYCb+YIQf/+9n/M+3zsWjhHiqM8l7Q0MbM8uKEtV7H7lqMqFolEKzgbtf2UxtVXnK8robe/EqYQoKjNw4awxPvLXjmI6dPak6LEmjQoje1OEPkWfQolJlTtbN02slGUUIIXqgXyaj/PCHP2ThwoU5nzNmzJhee79hw4ah1WoTiSgAEyZMAKChoSFjMorBYMBgMPTaOmQTr6bQ7gtyz8pNiR7c155Vxk//kt4eY2197GT7silDuXPl5sTf4wOAd7y8kR9cfDoFJh2/un46Rp0Gu0VPqzu12kLXu2XKbXnUedr42q/eSwwgVjlsfGXikOwtgyrsqICPvmyl0KSjNN+Q8wJjsE7ayYVQ/3Gi4rYnsv0ull41mQ92xeJJpYq1xbps8lDmjCuhqSOAUaumpsKesfVMjcPOh1+2smSOgwtOL8HpiyWN2C16VqyLJaXkanuT7fF4Asl//vXzlIor542xsWLhmeg1arQaFQecfnQadSL5Ld8YO8RMO60wMcFQW13Os+t3Z7zLdenqLUwrK0qpzNL17tePdrfxxqaetRSLl3HNto+qctgIR6LcuTJ9omNWhT0xKJLpu0qeVJEJiL51PHHb7FZQqeC0YjNuJYhZp2OY1chDq7ektfRYX9/Cfa9t4e7LJuDyh9CoVby3q4UfXTKOXc0erCblqI5Lsu8Xp7Ljidt2bxC1SkWNw07d3nYemDeRZ9ftTovZtfUt3PfqFlYtqWb1pkZaPQpeJZwyKZ3y/B3N1De5ef69LyUOhcjiWGI336jlH18c5qppw/jeRadz7yub0yrnZTpPq+m8g/mJt75Ie/66+hYWdXOzRLZz2r48L8vV/jFbi1oh+trRxG2eTpP1mIpKxcNXTcYdzHyjgRCi95zIsalEImWGY7NapeKHXxnH87Vn8cGuVpa+vjUx5lvjsHPlGcN5duHMxD6iyeXnqc7WztefPSpleUadhhqHLWP13CqHjUMuP8MLjFQ77InxoWM5duaqOixJo0KI3ubyx6rKZWOWyihCCNEj/TIZpaSkhJKSkhP2flVVVYRCIXbu3MnYsWMB+OKLLwAYNWpUrpeeEAVmfWzgq76FM8qKqHLYcvbfXlvfzG2XjmfJHEfizv319S0YtGr+sPgsPm1oTzlIbtnvZNKIAuaML2HN9sOJO9q6Vl3pekfb+voWnnjrC3781fFAamJMjcPOTRc4mLt83ZELmc5M9UyD/301adc1wcVi0OIJhHD6TkzCi1wIiUxy/S7uWbmZS6cMTZlMi1cH+bdnPuScMcU8dOUk7n1lc8ogYo3DxoNXTqK5I0AoGuUfXxxOxH+Nw8Yvr5vOZ/vaGVlsSiSiZSqZXmg6Us0onnQST2xZVD2GGaOLOeO0Qp5etytjBZMlL26gsqyQZxbM5K1th6hraGPe1GGJCipd911dE9/Kis0AKeuVfPdrqdXQbWsgiN1RazFqOdDuTel53HV997b5Mi4rPigCZPyuuk6qyARE/+TyBzHpNXiCYUw6HUtXb+G7F5+e9U7rtTuaCUWibGhoY3uji+XXVeLyBfmX37wPxI5jj1w9hdM6f6eQOZESMv9uZN8vRPc8Sgi3P8Rtl4znibc+57LJw3LGrD8Y5isThhCORnH5QzmXHQhFJA6F6GUGrZrGNi/fu2gc+9t9Oc/T7r5sIjqNis/2tnP7JeP56Zufc93Zo3h/V2vWSnu53rfKYaNub3vaY311XtZd+8dsLWqF6C88wXDWGF27oxlPMEyHL/exVAgxsORKpFy7o5nvXljBdb/7kMqywsS40ZQRBQRCEQ67A/iUMNsbXYQro5TkG6hraAfSW1YEw2GWzKkAVIkbJSE29rJkdgVDrAZ8wVBaO59jOXZmq7QtSaNCiN7W4Q9i1mefQs3Tazno8p/ANRJCiIGpXyajHI2GhgZaW1tpaGggHA7z6aefAuBwOLBYLADU19fjdrs5ePAgPp8v8ZyJEyei1+u56KKLmD59OrW1tTzxxBNEIhFuvvlmLr744pRqKSdTfOBrxbrdPDm/stvn7231UtfQlpI8csbIQpzeIKs3HUiblB1tz+MHF8e29XsXnc5jb6ZXXek6+WrWa5g4vACdBn70lXHcc7kGTyCETqNmS6OTxc9/lDKouDbL4P/xJmw4vQotHoVQJEokGsUbCFFo1qPXqLlz5aaU5VY7bCysKk98Jn19l7pcCIlMcg4G1DezsGo0kJqoAfCHxWfz9udN/PTN7bHWNVXlWI06THoN7kCQhhYv3mAYo07D8AIjv7xuOje/uIFPGtq5Safm4y9bU0qgd00wq6mwM7zQSI3Dzied+4+u7XRqHDbOLi9ODEDEdd0/qFExY3QR6+tbWLZ6W+LvyXex5mrZ07WUe/x1PansUtUZ555ACKNOy60vfZjoeRxvxRNv3/BUjv1pfFAk23eVnPyS/HzRf1iNOoLh2G+msd3H+OEF3balcwdCbG10ct3Zo3jx/QbuvOxIS8C1O5q54/828ujXpnJasTlrIuXdl0+Qfb8Qx8ijhDHqNeg0cP+8SRzsJmY7AiF+9+5OfviVcYwoNOZ8bnzQXOJQiN7j9AX5/kWnc9fKTcw/O/eNHAecPs4ebWP2uFIi0Sjf/8rpqFUqnq89izy9Bo1azVvbDvLLt3dSt7c95x3WTS4/izqv6zLpi/Mya44WtJC7Ra0Q/YG7m6RNtz+E2SBteoQYTLpLpHT6golK2sYM40Y1DjsLqkaz9PWtXDxhaGKspm5ve0oVWrVahVcJM++MYXz3ogpCkShmvQatWkUkGqW5w89He9qoqUi9+bQ3j52SNCqE6G0uXyhnZZQ8g7TpEUKInhjwySj33Xcfzz//fOLflZWxicW3336bCy64AIAbbriBd955J+05u3fvZvTo0ajValatWsUtt9zCrFmzyMvL49JLL+XnP//5iduQbsQHvrxKmFtfquMPi8/K+XyDVp24IIhPtuYbdYQiESrLiqhraE+pOgBwxyUTuP7sUbj9oYwtLeLPra0qzzqJXFNh597LJ7Ls9W0Z7257d0czBzqzReMTAN0lbBxw+jtbJKRXMmls93Hfq5u59qyylNYfS+Y4qGtoS9uOdfUtREmtZnD7yxtZdtVkCk06uXtOnBDd/S4CoUjORI1FVeVoVCoWPvcRb363hjavwvK369OSzJbMdvCt88cQDEfTHofUBJK6hjYWVY2mocXHpVOGctsl43n8zW0ZWoG1ECG1zHry8uLJGclJNWvrm7lpdqzqVHJllmg0ygsf7OlRy534BGLXu2+6Gm3Po7Kzr/GLN5yN2aClsixzJal4C4hs8o26Hn1Xyc8X/Yvdoqeh1YtWo8bpDzKzrAiLMfepTzgSZeLwAp5dv5vKsiKC4dQ7t9bVt7Cn1QsquPP/NmVMpFyQpeJOnOz7hciu0KQjEIolVvpDoUTLt2zCkSgThhfwyBvbuWfuhJyt2ZL3+RKHQvQOi0FHhxKrtrAwKUk3m1++vYM7L5uASafhodVbU9oz1jjs3HnZeKaOKOT7//MpL95wDg+u3pJWgfPeeRNRQmH+5TfvZ62o0hfnZXaLnlkVdt7NcO06q7PtkBD9WXfnwRajFr0m9/WWEGJg6S6REuDZ9bt57JqpGceN1tY3EyFKZVkRpVYDK9bvpra6POVmyfX1LRg0al74YA/jh1mxWQwEQhE8gdiNQNsanXz/4nF80iUZpbePnZI0KoTobW5/EKMuRzKKXtr0CCFETwz4q8znnnuOaDSa9r94IgrAP/7xj4zPGT16dOI5w4cP5+WXX6ajo4ODBw/y7LPPUlxcfOI3KIv4wFecXqOhJunfyZIH29fXt2DUaVj8/Mf8y2/+yXW/+yBRMSU5q3N9fQuoYhNu7d0cQAOhCLXV5SnJH3FrdzSzdPUWaquzD0TuOuzhlpfqaGyPTdZ1N9m7q9nD1b96jwt//k7K6+IVVcYPs6atS+XIwpwJNfFKE/F1rm9ypyy7t8iFkMiku9+FQavOGmPr61t4dv1uCsyxZeg06qyJJsvfrmf2uNJu4+Grk4ZQWVbEpv1OfrN2J3et3EwglL2Ec9cYSpacnKHXqnlmwUx+8+8zGGo18llDG/OeWs9NL2yg9rmPeG79l1x39qiMGebJ75G8T2tyBahx2DK+d7XDxoF2H8vX1ONVwuQbdWjVKhZVlVPV5TVVDhs/+uo4tje6Mi4rPijSk+8q+fmifykw6yk06fAEQgy1GhhWaOSLQx3UOLIfP/+5qyURM5UjCzMeo5y+IE5vMGsiZXdk3y9EdqX5BvzBMB/tbqXNE+LThrYexeza+mYCoUjGfX5NZyLninW7E3/LFodOr8LOJjd1DW3sPOzG6VV6b+OEGITsFj0dndeP8bukM4mfz62tb+GA08+9r2xJOw9cW9/Mw29sw27Rc/tXx/HnLQeoLCvimQUzeWbBTFbdUsWM0UVc9cv1vLnlEJVlhRnfq6bCjlaj6vX4LTDrefSaqSnX5RA7D3zsmqlSbUn0e3qNOmeM6jVqDGrVCV4rIURf6jqenCx+bF5f30Kp1dDtOGogFEn8d/xmyfhxWqNSsbi6nM8a2lj8/Mfc9MIGFj//MZ81tHPrhafzy7d3cOOssUSjUVYsPJNHvjaFxzuPnb11/p1rW2XMRghxLDoCIUy5klEMWtz+EJEuLciEEEKkGvCVUU4V8YGvO17eyIzRRXiVIHdeOp5odBvrulRD6FquuGt2ZqaqAwA+JUyeXgvkLt1q0KqpHFnIinW7WTLHkbggMeo0bNrfTjQKF04oZeIwK0adhg0NbaxYtztx15pBq05pwdPTyV5Ibd0Tr6iy8LzRaVUPetLKo+u/e9oW6GjI3XMiE7tFT02FPeNEdnwwoHJk5moeEIvhOy6NDRL6guGcAwbQfTzsbY0lcDyzYCa/eWcXS+Y46O4cOtsyk+M136DjqmfW872LKvjvf36ZltwSv8MmU5WV+Hsk79OqHTbOLC/inLHF3PvK5pR9X7XDxn3zJnHd794HYvFlMWrxK2H++GEDlWVFaW16/uudepZcWIEvlPoZxicUALRqVbfflUxA9G9Wky7Wvkqr5p5XNvNJQzsrbzqPh1ZvTasmFP+t/ewbZwCx32BJhotOg1aNK0eZ81hrAXtKr+w42fcLkVuBWU++Uce4YVYee3M7dUcRs77OQfF4azaAUquBv29rSmn9li0Os7Xe6suWjkIMdIFQBKspdj0Xv0taDSnnfV2vUQOhSOcxMsqNs8aktANYV99CqzfIjFHFlLTGWlDGPfG3L/h7ZyWVrndkx1U7bPzoK+O49BdrmTmqqNfjd3ihKXEt2uEPkm/UYbfo5TxQDAhtHoVFncfHTMfUNo+Co9RyslZPCNEHCsx6Hr56SqyqZ9L1addjs9ufudJYXHwsJZlXCbN8TT0Xji/hR18dz+LnP+bGWWO4/dIJOH1BrCYtoXCUNo/CrNNLGZpv4LKn1iXapp9/egkH2n3c1kvn38lj5+92WZ6M2QghjoU7EKLAlH3fkWfQEiWWtFJgkhvPhBAiG0lGGUDiA1+tHoV7Xt3MN6vHMK2siNsvHc/eVl9igjV5sB0yt7VIbrdTW11O5chCVKgoMGvxKCHmjC9JKZkcF6/GUppvYMWCM4kQ5b2dLYk7TZ9ZMJNfvV2fMqBY5bAleorOKCtMVDh4d0czzW4lZ8JG15Lq8dc1dQRwB2ITgZkmxbtr5dH18fi/4+vUWxcociEksrl5toNINJpadrzCzs0XOKh9/qPExFo2nkCYZxbMzFqaHMCs16D7/9k78zApynNv31XV++zTzbDJwECPzDADOoCKzIwRXBEEDEmOYCKbUaOoOYmJGsUNkmiWL0aJiRpQk7jknHjighqXqAmgIAoqqzKCDLINs/b0Wl3L90d1F93T3QMqGpS6r8tLpru6qnp5q973eX7P75FEBpX2voBP/v4VTTdbA+VyPun5mtRrCECxx8GCiX427+lCTAyzBr8v7ZqQSmprn54M8eVx+9Ra2kMx/nb5ePZ2Rbjw/tW47RK/u2g0N0oi3dE4eQ4b+wJRZj2wmtagzGmVPhZPr+XGv2/g9Q/bem13dMnDa/nBWcdz/aQqYnGNIrcdh00kGld57f0W+ha5ufJ0P987fZh5rQvLKo2VPm6bWgPAdxsqrLF8FFPkMSq2g/GDbj97O6NZBUrJ+2fy913ktiOQXh2abO90Tk3fnMdctnIHT15Rn9FaoMHv5WcXjLR+LxYWhyAQUXDaD7acPNwx63FKZnvKYo8duyTym5c+SG8DkmMOlnTcy9Z660iLlS0svkq0h2UOdEdp8HtZ2dTG1Y+t5/FLxzGnO5Z1vMLBeeSKpjZ+MnkEAPf/e7v5fDKBNeehteZxJlb14bpzq4kmqrKTFdk3Ta7m+klVfNwRodBlp8Bl48pH1pnz0y17A4RiCmUFziO6xrOuBxZfRjxOG99Z9qYp2uw5Rp/43vj/9ClaWFh8Dsiqxo/OHc6c7iE57835rtyV/2Csjd/YbszNB5a4ef6aRlOUWeCQ2N0VJSyr3PXyNu7/93bzPpw8Xt2gYg4EY+bxknPsSSP7H/b8uyss0xqUCUTjWVu5gyUatbCwOLIEYwpue+48U57TSK8GInFLjGJhYWHRC5YY5UtGSFYJygqrmtq46JTBLFtpJI0fW9OctQI7m5gjiaxqWZO0DX4vC6cYSdbU4H2D38vi6bUsemYTr29vNxcWdYOK+Z/LTqWlO8oDK7ZnuB8kEwkLp4ygb6GTBY8edG3pjsYZVpbPzy4YyfX/916Gy8slDUN59+NOls4ea7qvrGvuYHdHhP7FLiC78CRpEZ3NMaLnZ9Lz7+5DtA36pFgLIYuetAZl5j20NiMI2K/IRWt3jDHlxYcUVKmazvyH3+Jvl5+a9XmPQ+LumXX87NnNnFBekjYecglIBha7+PnzWw3b1R6vSeXM6jLynDaWzTkJj11KE6WFZdW8huzqCAOgqL3brGQTlDX6vSiqxnMb9rJs5Q7umVnH/IffMp+fumQVAM9eXc/zG/dRN6iYO2eMoqzQSSimcvszm3l5awuAWSV/3blV7O6M0CfficMmsrcryp0zRrF+VyeLn93Cn+efzO3LNzN2SAknDyll+Ya96WIhv48nr6hHFKFP/pFLaFh8/hwIxognfma+fAel+Q4m1fQjEFXoW2hjfyDK4282E5ZV857Q4PdS6nFwoDtq7qfe7+WmKdXc+Y+t1A0qzumaU1debLYW6Bnol9XenYosLCwMUUkopvD9Mytp8PvQdJ1za/qh6/DK+/u5718Hk9bJMdtY6ePfHxzgly98ABgC6asS1/+LThkMGEH0Ired/lmqLJOOe9k40mJlC4uvEt0RhRv/vpE/zT/ZdK57cfN+tu7pompAEXWDihnRv5Als0bz3sedDChyUeS2c+9Fo3HZJaJxlY0fd5rFC0mBWVTW+Nvlp9IalBlU4ua9jzu56I+rufDkcubVVyAIMLDYjcsm0RaK4ZBE3tjexuNvNnPHjFEZa1zL5cjCAkQBxg0txS4J9ClwAoazwYThZZSXuJFEaAtZ9zsLi68aXZE4/9zawvrmDtY3d3LpaUM5/fg+NAzzkeeUsIkCgmjEel7e0pLx+ga/lwKXjWUrd9BY6cMhibQGYxS47HgcEqG4arqkwUHHlFSev6aRPGd6ovbf21qZPX5I1nPuOf/+JA6GlmjUwsLiSBGKqbgduVOoeYmWo12ROIO+qJOysLCw+BJiiVG+RCQrNq883Q+Ax24kmx9Zs5PZ9UPQ0DMSp7Prh6S17EmlT76T37z8QUayeWVTG4uWb2JuvRG8jymGW0D/Qhe/eH4Lr29vzypiaaz0MXv8EFZvb89wa1jV1MYNk6r41n2rCcuqmQx32SXWN3fgcUiGS0HDUMJxlUGlbl7Z2oKOztqP2jOcVqaOGsC7HxvtMbIJT5K2zULi/SRp8HuZk2JDma2tUcEh2gZ9GqyFkEUqgWg86+J82ZyTWPCoUcnZp8BJo9+bIe4C43ecrEhZ2dSatR3IvIYKHly1g1VNbbzd3GnamK9P/DubU8gFJw5kfXOncS4rd7BkVqbN+pnVZfz43Cpuf2ZThngsmURY2dTG7cs3cd25VYCRVOyNnsrxBr+XhefX8PIWQ2Ryz8w6UzCT2vILIBBJ/xyfuaqeUEwxhShwMBBSN6iY7/1lXc7ziMU1Vmxr5UfnDOfOf2zNuDauaGrltuWb+Nl0y9niy4bDJuEQDCHKI5eM4/YsjiWPXDKOu19+nwvGDOLxNc385LxqgrE4x5V6ePzScYRllXXNHbSHZGadMpi/vb2L26fVcMtTm9Kcr5L3wp4uZUnOrCr7Qt6zhcWXGbddwmOXeDvLHHDBBD+jBhZz5aPrqCsvZm59BY+v2cmCCX5W7zg4rmOKZl7/k/O9i5e9yTMLGrIeM3AIMfKRFitbWHxV8DglvjH2ON7b1cnPLhhJSFaJyHG+Mfo4bnxyQ/p60e9j3IRhXLzsTfMemVyzPrpmJ/MaKoxEWcKB7Px7VpkFCvu7Y9x9oTGfLXDbcNkkKvsWAIY7S1K0vGCi35wDp2K5HFlYgCDC9ZOqaQvGMtY7jZU+TqooRT1Uv1YLC4svHflOQ0jyu1mj8TgklryyLWOOfdUEP9dNqkLT9XRXQb+Pa88ZzryH1jK6vJifTq8FjNhpcn4cjCr0KXCYLmk9afB7yXdIRLMUZvTWVjq5f8vB0MLC4j9F6DCdUboiVrzAwsLCojcsMcqXiM5wnDnjh5DvMr42VdfNQNvqhFNJsgK7yG2n0GXjty9vy5oMa6z0UeSxZ3U9AEPAkWz/M6jUzYaPuyj12Hl24/6cAb4V21pBh3tm1nFVliTcro6IKUTprW3Gtf/7LvMaKhhQ5GLpyszjrGpqY9Hyzfz43CoqfHkoqs7ZI/py18sHbdjDsspf32zmjq+PYldHmI5wHJddQtd1Sjx208K9py3laZU+fPnWAsbi86Uwh+BpXXMHdeXFLHmlyRRUaZARJEwmusGwNF8yqw7Q00Qj44d6zfGVtDFPuoP8MovQYlVTG7c+vZF5DRXm6wQEJo3sz5z6CnRgsNeDJAjc8tTGjABDcn/J169qakNOCSrkcllp8HuJxlXT/SjpHvGL57cysboszQ0lVfCSy0o2GFVzBjN6c0xqrPTx+vaDj+e6Nq5qaiMkK1mfszh6cScqFZbMGp0hRIGDIsyfXTASRdW5cXI1z23cy+9e/ZD/vexU5j201mjP5PdRN6iYx99s5sKTy9F0nUXTagnJCmFZNdo8SSKT7l6Rs4XW5yF4tLD4qmETBW78+4acbntTRg7g2asaiCkaOlB7XDFzH1przu8ABpW6ufei0Wk25GMHl+Sc5+W6Nyexxq6FRXY8donzavtz2zObuOav7wLw/TMrefuj9qzCXg09bb6ZFFTPqR+CxyFxYuI+O6mmH0tnj2X9rk6ufHQdYwaXMHlkf2KKyrfuW50mLEsdv3WDijME30kslyOLYx2nKPH6h60ZDpBgxHNufmoTtydakVpYWHx1cEgideXFvPtxJ29/1J5zjj15ZH8uOmUwPzxrOLs6IvQrdFHottEWjPHg3JPYurebi/64hlun1lBW6ETVQNE0PA6JF7fsY/H0Wm56cmNGUeDi6SMRgZc278s4N5c9d/FScv5tORhaWFj8J1BUzXTqz0WewxKjWFhYWBwOlhjlKCRbD8ywrHLTk0ZQfsFEP/V+L4IgmAuGbC4L//e9U5nfWEFEUdMCDfV+LwunjGD7gVBauw5Z1SgrcGGXBPZ2RUGHHa1BqvsVUFdeQiASZ/lVDdglgWUrd2Q99xVNrVx++jB+N2s0737cyciBReZN25fvMI+XTcyyqqkNURB4ekEDL2zay6jjivnJ3zfmPM6c7qiZqG6s9HH7tBquO1cjFDMSgslWOKIo8PvX3jMr1z0OiWVzTuJ3rzZl2DveOWOUtYCx+Nzx5Ts4rdKX9puc11DB6PISGv0+Fkzws+rDNq5/4j0uPLmcK0/345AECtx2NB0+aguxZNZo1jV3sGzlDhY8aghN5tRXUOC0UZrvoCMUzzq+baKYFnhI3SamaJSXegCwSwL/81Yz3xgziH5FLpw2idue3sic+orDev2ylTs4EIwB8Or7LSyYYDg6ZXNvyiZeA5h5Snna3z0FLw1+Ly2BWNo2+S6JsJxdsZ4U+IiCkDH2b5law/n3rAQgHMsuIkiSS2RgcfTitkuoqiHUfH9fN0tnj6Ws0EkwqlLgMtr0XPfEe3THFH7z4gdc0jgURdMJyyrhuMqf559MVzjOgBI3DlHk4nGD+dPqnZxR3Zcb/m+DeZzTKn38/OsjGTu4JM0tJfV5S/BoYXFoonGNt5s7zTY9iqqT55QAgVfe30//IhchWWV/IMpDqz7ihPJiwrJKvtPG0tlj8TgkAhHFbO+4bOUO6sqLuWKC3xCOeTKP2fPenIo1di0seqenY97IgUVmxXXPuaLbLnFcqZux5SWE46o5Tj0OCV++kz+/9D43TR5BJK7Qr9BFg9/H6cf3YWVTK3c+v5WRg4pYOGUEmq6zvrmDQredfJeNs6rLeGlLS68V1mC5HFkc24RkheOKPcyrr+D6SVUZc+EV21qJxK21joXFV41AVOa6c6uQBIHR5SVcN6mauKoRiCis3dnOskQh4Lz6CiRRoDMSZ+OeLi7/y9s8f00j/9rWyvrmDhZNq2V4v4K0oqHGSh+3T61h9Yft1PYr4vZptcQUjWBUoSBRTBmJK6iShK4b84JkTKXB76V/kSvtsdT9JuffloOhhYXFf4JQIj6cLHDLhifxXMASo1hYWFj0iiVGOcrI1QPzigl+3k5pn3H3zDp0vXf7VFnVmf/wW2mOKcnq0D0dEZyS2KtDydyH1rJk5mhufmpjWvK50e/LcCdIJRhTKHTZeKuHtXpjpY/lVzUQV7Wc1WpG8ENhYlUZwUMkhFMDjSu2tXLLU5uyWjMOKHZzz8w6WoMy3dE4BS5DqLIky2OWEMXii6DI42Dx9Fp+8vcNrOvRNicZsB8/1MvXju9DocuGQxLRgFt7jMVUp5Dka5df1cAtT21kfsPQrON76eyx5r97cym69fwaJiWqXOvKS1jf3MGqpjZmnjL4sF5/98w6Bha5WDDRz5/f2MmogcVMGdmf6xOOS06bSGmeg4v+uCanuCNbMiEZIGnwe7n5/BpmPbDafC4pTsnlgJJ0TPrVN08gGFXSxn5bSDbPwyYJvX9/bqs6/stG/2I3LW0hInK81zY93VGZC08p5/4VH/L9M4/nNy9twy6KTL93lbltY6WPW86v4UfnDmf7gRDL5pzEuuYOHn+zmVGDimluD3P1GZV87/RhrPqwzXRRGT/Ui9Mm0hqSAaz7jYVFL0TiCktm1fHgyh1Z2/R48x0Eowrrd3UaAuX6IYDRLuTuV7ZlOIo9vaCB7mic1z9sZVd7mBMHFRvz1YToOzkeb5xczeyOCIIgmCKWsYNLLLGyhUUvxBQto8I6OYfLNVdMtk699n/fJSyr1Pu9nD9qAHf+Yys3nleNruu809zJome3pCWsrju3yizC6CkGXZxoG+C05bbRBsvlyOLYRlZUjvO6uenJjVnnwhf9cbUlvLew+ApS4HJw45MbMgoV59ZXsHlPlxlXiikavnwH/97WSt2gYsCI8SZdxxY+tZE7LxjJj84ZTiCi4HFKuGwSkgiXf20Yug4393DSTR7n0TU7ubRxGPd9u5jL/vI2deXFzKmv4NcvvM9Nk6tZ/OwWU7wK0K/IRVTRKMJyMLSwsPjP0B0zBCbuXpxRRFEgzyFZzigWFhYWh8ASoxxFdIVl/vXBAeaMH8LMk8vTqjnveWWb6QYQllWuf+I9Hpx7Uq/7K3DZsjqmgNFLu8Hv5Z5EO41Ukn/fOWMU97y67bDslVPpX+TK6D8MCcHI05u48bxq87HUSjlF0xlQ5EYSDAvGUo+DBRP9ZiIvWU1nVs/1mAj8e1srLd2xrMmCIk92oYmVWLD4Ikm6Hqm6zi+e38KJ5SX8OKVtTq6AfaPfmyZIS7KqqQ0R+Nvlp6JoOjZRpCUQ44bzqilw2rjlmcx2JHBw3J09om/Olj23L9/Mj84ZzvyGofjyHaYbUmqAvzeXIwGYW1/Bu7s6efzSU1E0jX5Fho3rY2t2sqKpjaWzx/Ya7MyVTCh021kw0c+sB1bTGjQS+6nilLCssmzOSRkOKI2VPq4/r5pQTMGX72BYWb75vdhFgTOq+lA9oAinTaLR7zNt41NprPThtIl8sK+bkJyZyDwayea2dTSf7+eFLAp481z8pEcQDg626fn5BSO597VNjBhQBBjiy9c+aEnb9u2dHazd0UZl3wKSmtDBJW7+dvmp3PTkxvSxW+njb5ePZ/HyTWmPn1bp4/ZptXRFZPJd2b8T63uzOJYpdNn55Qvv52wJN2XkAE4ZWmrem2KKRqPfx8aPu3LMPzdSV17Cu82dXDFhGDMfWG3OLXe2hRhY7Gbdzo60xHdjpY/nrm6kxGOnyOOwxqSFRQ6Cscz2hU6biMchcc/MOh7KMldc2dSGTnp7x8XPbuaEQcXEVI08SaR/sZvfXzSGQrchzI7EVZx2kZCsMrehgpmnpK+Xb3pyI7/85glEZZXGSl9WO3/L5cjiWKc0z8kNf889F75zxihLeG9h8RWjKyyzsIcADQ7Oq+vKS3hw1Q7mNVTgtIkUuOwsW7mDX33zBMBIwh7ojpmvaQnJ+Nx2BLcNVYe2UIxCt53+hS6z4GrBRH9aDHdfV4SxQ0rY3hpkxIAiHp57Mm6H4ZTyzZMGMajEzbNXNfDTZzdnrKfvnDHKcjC0sLD4j5B0RumtTQ9AntN2SAcnCwsLi2MdS4xyFNERjrP8vT0ZSvWkQn1efQVgJJLvmDGKDR93Za3+B2PC3lOskcqylTuYMrJ/1teCscC4flJVr88nzyeVer8XWdFyvm7FtlaSXgepifek28sd/9iS9tozqvqYVezpyXkfF5w4kP8+q5L7/rXdTBw0t4fJc9oYUOw2t7WSBxb/SZK/v46wTFzVWPVhG6PLS3h56wFe3nqAukHFvJ1YrOcSh6xoakPjYMC+p4jLbZcyHYwqfcweP4TV29vTBB8b93SxdPZYlrzaRN2g4oxKVvOY21qZM34I8x9+i8aU61Cq60iyOiYbK5vamNcwlBXbWrnt6U3cNq0GSRSIqSq3TK3ltmc25XQwAeNasn5XZ9Z9F7ps5Dtt/Hn+KQQixriOqxrzH1pLa1Cmwe+lrMDBrefXIKsawWgcb76TuKrR2h2jyG2nLSSzraWbfJfdEBToOlec7sftkIjGVW6aUs3i5VvSBCmNfi83TxnB6u1tePOdxBSNjnCcN3e0c/rxfeifct05WsjltnXHjFFp18mvOl1hme6ogg45708rm9oIRBW+f+bxdIXiRGSV+Y0VXPHIOnOb1PvWqpQ2co1+L8eVeljXQzC2YlsrNz25gRMTYz7Jv7e1cuOTG6grL2HJK00Z34n1vVkc68RULUOIkiQ5B40pmnl/K3LbWTS9lm/84fVeX7PklSbsNoE/zj6JX76wNaurV9L5b8W2Vm5+aiP3zKyzxqSFRS/kuzJDChv3dLFs9lhAyDnX7LmeTM49d3dEqCzLx+/LI6bppjNg2j04x3o5EIkjCgLfP6OSa88eTr5TQhcgJmuEZJViK8lucYwTktVe58I3TKqmrMD5BZ+VhYXF50lrUM5aaAPpc+SrJlSyo9UQaYdlFadNpLHSx78+aKGqX6H5mv2BKKGogt0mUuCSKHDa0TGuL2/3cP4FYw196/kjmFjVl90dEdpDclobzbn1FXzjD28wOuGU8npKDGvFtlauf+I97plZxx0zRnH9E++lCVKsdusWFhafJ0nRfW/OKJAQo0QyBfoWFhYWFgexxChHCYZSPbNCJfn3vIYK0+74sq8NpSUQZUCRm5un1LD42c1pwfF6v5dbzq9BVtWc1f115cUcCMZ6Padg9JPZsyatFw+1346QTL3fa6rvVzW1sWCiP6vDQvWAoox2CmC4s9z89EYmj+yfljgoctu55amNLL5gJKGYkXhMDWAm2584bCIleQ7ynTbCMYW4pqPpOuGYQpHHYQlWLI4I2ZJX9X4vDcN85t+KpnPvRaNZumI7YweXHDJg39M9ZcFEP39646OsleCanulgpOtw76tGBepFKS13spG85qSKYZLCsdTncxFN9Btf0dTKjtYQD67awc3n17C3K8Kk2n4MKvEw/cSB3Pb0pgzRx5UTK5n30NqMfTb6DWeS6594L0N88+Dck1E1jQK7hKzDjU9tYH0iGHLXP3u0bfB7mdtQwcXL1jK6vJj5DUPR0fl/L39gutTMa6jge6cPwyYJFLrsiInuPc+8uyejXVKFL8/sE3q0iN+6wnLG7w8MIUQyoHMsXOf2dkawxVW6I3HU3rvbEYgq3PvqNqPPtarx7gcdaWKuXG5APQVjqaxsamNuFvFmahIu9TsBrO/N4pgnGO09kBNTNEKJwFCj30c0rrJ4+SYuPLk8p0gyec8aMaCIX7+Q3RUM0sdx0nXv9uWbrTFpYZEDj12i0e9NmxvpOvzu1SaunFDZ62t7ziVlVcMhiXRHjbavqS0qe3PkSz7f3B5m/sNvmc81VvpYMMHP3IfWprkeLTqEO5mFxVeV7kNU7YZk5ZBrPAsLiy8Xh6rWT455myQwZkgxqqpTn2iBPL+hgj+98RGR+MHrgtMmcdlf3mbRtBpqBxbRGY5Tmu+guS2cca9Oxq8eWrWDHz9xsL1eqpAUdpjzb0EQeP6aRtDhhS17ueulJv69rZXWoMywsvysLdite7iFhcXnRTLm4Hb0LkbxWG16LCwsLA6JJUY5SjCU6rmT0N9tGEppvoOH5pzEcaUebnvaCMz991mVTKrtx5zxQ4gpmtnWYt3OdsBoxwN6RuJ0bn0F8UNk5fJdvd9oBxS7efSSU+iMxHHaRNbv6uTqx9abybRcxBSNufUVuO2SGezP5bDQm/NCMpG3LGEnub65g20tQa49ZzgdYZk9nRH+snqnKURJJvxT93dmVRk/nlTFbT3amTRW+rh9Wg15Dhtlha6MY39WtxXLreWrTy4hwKqmNq443W/+fVyxmzue38KKprbDCtj3XNwfzhhJZeTAIu56eRuQuw1OktTnk/sKy6rh1NRQwaDS3qvBB5V6WDDRz7KVO1A0nRPLS2jtjiGKAv2K3BR57Pz6xa2cUF7MnPohxFWdgSUuw9VJEBgzuCRDyHPlRD/rdnZwQnkJcxKV8UmL9p89t5kRA4qYPLIfi5/d0qvQzbgmClx62lDuenkb543sz3Mb9prbJVucLXmlicZKH1NH9WdnR4T1zR05kyCLp9eyens7ZQW9u6bs6YzQFYkTiMQpctspdNszKuuPxDWiNShntagHzIDOV/260xWWefOjdkYPKqbAbUfReg+u57skVjS1sbcrisMmcEZ1P+56uclMYH3S8ZYkV1BfVg8+nvxOgGP+e7OwyHP2vkRx2kTynDbTBeyqhCh5Zi8iy+Q97ZOO465I3BqTFha9EFFUbptWy8KnDrYASM43b5g8otfX9pyL9i908/LW/TT6fWg91rCHM3YFQUhzEIwpGpqu88yCBpoOBLFLIuuaO1i8fBNVA4qyupNZWHyVKXD17g5U4LJz2zObuHHyCGtMWFh8Rcg7RBI1eS/Oc9poC8r0LXRxy/k1PL9xLy9t2cesUwYnRCNGrFRAp668mCKPg0BEpjTPiZJY7/a8Vx+OkHTJK03m/HvFtlZ2d0QoK3QyqbofI/oWcelf3jaFdLlasH8SrHishYXF4ZJ0RjlUmx6PQyJgiVEsLCwsesUSoxwl9FSqexwSl31tKBOGlwEQi2voOujo/Oy5zWZgrnZAkVn95XFIXDlhGJNq+tOvyEU4plLksfGzC0YSklWCMQVZ0Xh9e5uZTG7we7PaoCdV8LlaaDT4veztijCw2M3vXmtK22Z/IJrTkaXR7zPtGP/w7THm47kSdYeqyoklWgJdcbqfEwcVc/Vj66krL6auvIR3d3Xy43OGM2ZwCacO9dERlpnbMJQTyktYtnIHYVmlakBhhhDF45AYM7iErrDCgW6Z1mAMl13CJgoUue2EZZUfp4gMPA6JhVNGMLq8mLCs5lzMdIVl2kJymltLEisI+tWjNyHAG9vbOLOqjKoBhYiiwOz6Cm6bVnvI37vTJmYs7g9njOT6+9O2yUkKNQYWuWis9GV9n/V+L7quM6mmH1+r9OHLd/KXNz7K6P97y/k1qJqGxy4hqzq3Pp3uZPS9rw3DbhORBAGbJLAvEKVmYBEDStyE5YPvZUCRizHlJQiAjkBzW5inF9Rjl0S+lqiAlVWNznCcYrcdhyTSGoxR4LYzcmARDlHM2RLi7Z0d3DS5mqoBhYzoX8j8hqHmdSwpUljV1EZM0XjuvT0Zji0nDS5hV1uIiKriFCVWfdhK30KXUdkvq6xv7mD8MB+SABFFozsax22XCMkqB4IxApE4sqKxNxAlEFEodNso8TjoW+hifyBKR0gmEE1/HIz7Ss+ETFK4s2zljkNWRn4VaAvJjB5UTFRTcdlE9nfLOe97DYn7HkBnJE55qYefP7fFFCzBJx9vSXIJv/rkp9ugd0fjHMK85Zj43iyObT7uCCNAr/eX/YEoA0vc/Gx6LbP+uCbNwSgbjSn3tE86jj2HCOB3hGXWN3dYwWyLYxYBaA/GqCsv4fpJVexqj5DnMMIMgs5hzzXr/V7yXZJh2z+omLaQnLb94bg1DC718D+XncpvXnqfZSt3mHOgj9pCDPF6sIkCBU6JySP7o2k6y1busFyOLI4pXDax17mwyyYyY8xx1piwsPiK0BWWybdLvc6r1+/qpNHvRQCK3Q4cokBrMMbYwaXEVd10om70+7hhUhVuu0RdeQmPrdnJNWcez+3PbOSaM4ezflcndYOK0/Z/uCLw1CKNzkice19rYvH0WvoUObj0tKGHFNIdLlbrTQsLi0/C4bbp8ThsdFmxOgsLC4tescQoRwmFKRNrX76DZXNOIhxTuPMfWzMcO+bWD2F1oodmMijncUj8/qLROG0iC5/emPaaM6r68ONzq7n3tSZGDCiiblAxv/rmCVR4PZxZ3Red9GMknVOue+I9lswajYiQJixp8Hu5+fwaZj2wml994wTqykuYl3AoGFzq4eWt+5nbMAQNPWO/t02rYco9KwnLKnE11eYxe6LucJ0bVP3gAim15ynAebX9+PrvXz/4Gfp9PPrdcVzy8NqMhZHHIbFkVh0PrtxhJh+T537VhEpCskIopvD2zg5z+2TLlBv+76DlZM/FTHLBc8Kg4qzOClYQ9KtHb1aoj7/ZzP9efio3P7nRbHuz8KmNjB/mzRkkaKz00RKIZvw+DjVGitz2nH+nttzJdg1IVr+kvvaRS04xK9JVTWNgiQcgw8Fk8fSRLFq+iVe2HmDBRH/W3/2Kba3c8vRG6spLGFjk4rkNe00hR09nkvNq+3HD3zemjbme57xggh+PQyImx/nT/JO56cmNOdv0JN/jnIeMNj2Lptfiy3eYzhRJksdbvHxzhsNUaoswgN0dkQyHqxXbWrlt+WamjupPzcAi2iIRnk1xYEnua2ifPMoKXezqiOBxSEiijsMmIgoCqg7vNHfQr8jNlY+uIyyrNCQ+48XLN/Hy1gPmvlIFPn3yHDxz1VgcopugrBKIGJU//zXmOKaOKsUmHZmAztGMJMBvXnqfq888nvZgjAGFbhZPH8lNT25IC8Kn3tfAGFddkThV/Qs5/fiyT+UmlLrvbMKuer/RMi6VwwmyHcoe1MLiy044roAAC6eMYNEzm9PmoMa1vpJoXEXXdX7y5EZ+/+0xfOu+NwjLKmWFzozEd6Pfx8LzRzD9d6uAzPtiT1LHcWOlz0yq56IrEjeF4VYw2+JYxCGKuJ02c021vrnDTC5paGarulU97rtzUuaayXnZ9tYQdeXFWRNah7oHlxU6OX/JSkaXFzOvoYJvjxvCH1emu2I2+r3Mrq9g9oNrGTO4hCWz6ljw6Hr+va2V3Z0RWkPyEReVWRXYFkcTsqaxcEoNi5ZvyjoXljWN/kVuy/nLwuIrghyJowG3T63hpqc2Zo2JPLpmJwuTRUIOGzP/uIa2kMxNk6s5r7Y/X6vsQ6HbRp5dYkdrmD5FTrbuCXD1Gccz76G1PHH5eMJxlWUrd/DXy8alHf9wReDHlbjxOCTCsorTZhQK7e6MclyJm7Oq++LL/+zXIquNsYWFxSclFFNw2kSkZN/2HOQ5JHa1h7+gs7KwsLD4cmKJUY4SfPkOTqv0sXlvgMcuHcdbO9pZ3iNhCYmEr66bVobJoNy8hgr2dEayvqZ6QBG3LzfcP15JSVoumOhny54uU0wiqxp98p04bCKhmMLdF9Yhx1WuOsPPjycNJxhVyXdJtARizHpgNVX9CnirucMM8DX6ffzo3OG8uaOd+/61nXkNFaZIxWkTaQnEaG4LU1dezKqmtjRXhlwODet3dfbq3pJM8MUVLa0qNrmgWbGtlTnjh6R/hk2t6Ogsm3MSuzsjac/Na6hg2codGcdLnteUkf05cVAxl31tKL95aVtOy8nUxQxgLnjmjB+Ssyrg39ta2RuIAvxHFkBWoPTIUtgjqZzqUOHLd3Lzk4YDSGobme82DGV0eQmaninkWjDBjyDo5DnT99ubu0mj34ckwtLZY83xXepxmIKX1JY7yaSBN8/Jy1v3p4kskvsq9tjZ2RbmmsfXm6KNM6r68KNzhputwob48lBUzRSiwOFXw+RqVZY6jg9l83rb1Bqcksj1f9/Qa5uenrawC5/cyJ0zRpkJxSSHayvbGyu2tXLNGZW47BI/fW5L5nWuuRNNx/xNJEkGhxY8uo668mIWTPCb156VTW3c9OQGTiwvSROjrNjWym1Pb+KWqTUE43FcNrf5WSRp8Hv56fSRh6z2/7KzuyPMTU9u5EfnVrHwyY3cPLUGTdcRgBsnV6NpEIgqafe11qBsOiicU9OXukHFxFWNpxfUs7crSp8CJ41+b9bfamOlj/2Ja7j5mN/LwvNruOP5LWmPJ7/bvV0Htz+t0mcG2U6r9PHvHJVr65o76Vfosq7PFl9J9nRGcEsSggRvbG/n/BP6c82ZlSiabl6zXn2/hQ27Orlx8gje3tnBnf/Yyu9m1eGwSXSG4/zgrOFcd67A3q4o+U4b0bjKjtaQUdFZ6aPU4zgsp4ZGv4+fXzCSApet1zGZKjazgtkWxyIa4LZJNPi9ptB5fyBKvd/Lvq4Yj63ZmVa84LJL6LoxppfNOYlQTDHbvf7x4rGmIHpeQ0XaWO19zuvln1taCMsqK5vaEIBJI/tnbdOokTJ/S1lXB6IK7W1hWjx2BpV4iKsanZHPti6yKrAtjjbcNok7nt9iFB9NqkqL8dz10vtcP6maqGKsAS03PguLLz9xoFtWEYApowYwv2EoNkmgxGNHUXW6wnHOrO7LO80dnDSklG8vXcOujgj1fi8Vvnw27e7i5IoSwnGNuKZTlGenO6LwnVMHM/OB1YRllXDc2H9deTH/3NKStl4+nGKOer+XDR93me3Xk3Prrkic0jwH+wKGKOWzYrUxtrCw+KSEYsohXVHAaHPWW1GqhYWFhYUlRjlqKPI4uHPGKDrCMi2BGGWFrqyBNjCCaHMSydtkUG50eQl6jwR2klyJ4GSw8OHX01tn1Pu9XNIwFB2dv6zeyaxTBvPbLK4CPzqnCkmEEf0LzQXG719rSlS/7UjbZ4Pfy0/Oq+biZW9yx4xRacfv+e/U42ze08XCKTUsXr4pa5L26sfWZ20nkrrgkVWNBRP9jC0vochjxyaJdIRkJFFgqC/PVN/39lklz2tefQXt4TgThpfxm5e29br9v7e1ciAYQxIE5owfwsyTyykrcLJgoj+tvUcSX74DURD4uCPC+/u7KXTb01pufJ4ciUCpJWZJJykw+/e2VjwOid/NGs3eLkP8FFNUs2XU6PIS8zfkLXDwX/etzhByrd/VydyH1nLPzDpcdltaIL43d5Pbp9fy1o42SvOd6LqxmN9xIMjt02q46UmjKibpQFLv9zK/oYKuiMy7zR1pv8/kvhYv38Q/U4QPDX4v102qNpP49X4v1549HJskpAnfPm1rk2zbHGqMxlWduKqan8XhCmFWNrXxk8kjWDbnpLQWPIf7+sYs16FUFE0nIqtZr9HzGipY8mrTYQlerju3it+8tM0852S1cSormlpp6Y7Sv9DFjU9uyNjvyqY2bnxyA3dcMDLn+X7Z6QrL7GwLs6KpjRskkRVNbSiajiSBTRBx2CQOBKIZn3uj38fchiH8ZfVOzqgqM8agXeTOf2xlbn0Fv3+tiZum1JgCT/N1lT6+2ziUfV0Rnrmqno87IjgkkT4FTmY9sJoLTy7nolMGp43pqx9bbwoWGyt93DljlHnNvH1abcZ3l3rfO3lI6TF9fbX4atIVlrnub+9x+7QaHMApQ0vZ3RnhNy9/kDFOF0z0o2iaeX384VnDmX7vKnObpIgzGte46rH1/PWycTx7VQOSKPDvbS3cMqWGRcvTXVeS4rEdrSGWzh7LUF8ex5Ua7l93zBjF9U+8lyZIyeUiZgWzLY41dB1auqNce04Vv37xfa5+bD2XfW0ot0yp4bcvf8CPz61m0fJNGWvDpCNZUuDcWOmjf5GLyQkXzZ5z3OTfAqQVDtQn3E5Sx2LqerknPYXQye1CMYUrHllnnt9t02oJRBT+ubWF9/cGuG1a7ScSkFgV2BZHIzbgB2dXcevTGzOcUW6dWosNsIsiHod0xNpiWFhY/OeIaTqBSJx8l8SpQ70sfHJjVtdBSYSIrHLDedU4bSL7A1EkEcYMKSEQjROJazjznTgko4VyvstIJzRW+gwHVpfNdFmZXV+BBr0WHiaPvT8QNefTD1w81my/DkZcNymKOxJz60Mlii0BnoWFRU+6YwquwxCjeBw2uqPKF3BGFhYWFl9eLDHKUYTHIfHjv21h5imDD7ltMjG7bOUOfjdrNB6HlNFXu+e2PUm6Ijxx+Xj2jI+kJcmufNQIxM1rqKBfoZOFk0cQ1zQ6wnFK3HZe2LyfmQ+s5p6ZdWbQLtnO4tEe1W9FbjuFLhuvfdDCnTNGIQkCi6fV0h6WaQvJXHduFbKi0R4++G8dyHfaeHbDXi7642q+PW4wP55URUsgBmAm8urKizMSAY1+L2UFLhZM9PP4m80M71vAoBIPkbhKJK7y+tYWM9HcWOlj6eyxzH/4rbS2R7197jFFoyCx8Opte49DQtdh9Y42yhKCkgNBmQFFLn43a7TZbgMM4cIjl4zjtmc2ZTgY/OyCkZR783o9rySfRhByJAKlVtVfJkUeh5m8GjOkBI9D4rkNe9OCfo1+L41+n/l3MKqa4pBsxBSNvV3RNLvz5Di+aXI1N543gn2BCLpujJGLl67h1qk1lBU6CcVUClw2yr0eLl76JtPqBmY4Iu3titK3xMmc+gqum1RNMKbgskmIInzUGuL686r54dlVBGNx8hw29gWiaUKUufUVdIXjOHtM1D9Na5Nc2xxqjO5oDeHNO/h7/SRCmO6IEXwYWOTi/u+MQVF1PM7eb5MxRaM+kcBMtoDIRqrorSeHK3jJFsDJ9f4UTSeqaDlFjSub2ujOcT5fBVqDMp2J7zP5vQbCcTojcUb0zcclCgwsdrN4Wi1BWSEUU7GJAiubWk0RZlc4TonHjtshmd/BlRMqeXnLPk6uKOXK0/04Ei2rZEXDZRfZ3RHmv+5bbX7XP7uglup+hVm/30a/j9I8B0tnj6WlO5bmVNMVkdPuo6kClrCsWoEyi68krUHZDI4HNQ0nAoOK3dx4XjWKphOWjXG6ozVE30InUUUz23jEFDVNTLiqqQ0RGDOklDGDS3DZJV7ctI8R/Yv41wet3PVyE/fMrOPy04fRFYmbY2z671YRllVOq/SZYjGAAcVu7plZR2tQpjsax2ETeW7jvgwXsSTWGLU4logqKh3hOLG4xk8mVbOnK0K+04aOznfGD6YlEOXm82tQNJ1gNE6By05XJJ4mRKn3e1k4ZQRRRTXHVE8HvwKXnXBMYW59BddPqqY7qhCWlbT7Yyq9zQFTn0vO5ZKiYo9D4sTyEvZ3RREEGD/My9kj+vKrF7Ya8+OoclhrLasC2+JoJKiquESRBRMr05xRQjEVpygQVFXiis7CKSOOSFsMCwuL/yyaDoVuO3s6Iwzz2fjZBbW0heNE46q5/nx/b4CThpQSS8SGPE6Job48OsMxPm6PoOk6fYtcROIqaCpOh4O2YIxnr2rALgp0J1rrPL5mJyMGFGETBK49ezjXTxLoisSZfuIAbns6uwj8+Y17WfzsFsKyipbSfj15X548sj//3tZKkdtOV/jgffPTxD57uhf3xBLgWVhY9CQUUw6rVXaeUyKmaETj6mGJVywsLCyORSwxylGEEYTPXcWVSrLffVhW2by3i/phfXImdHtL9IZlFUXXMlpTJFnyShN1g4oZUOzm/HuMROvS2WMP9gRPUbmnBgyTyYG+hU46w3HiikbtwGIkQeCN7W28u7uTN3e0Z02UNvi93D61lh1tId5p7qA1KHPXy9u4/9/bzX3XDSrm/743nnXNHWnBx2Rl3H/d/wbjhpby+KXjuPmpjRnVc3fPrOPqx9abAcKFU0Zww/9tOOykefJ4vW1/2deG0haMZbROSlbrJtttANw5Y1RGpT0k3Br+voGfXzCSQre918XVpxWEfNZAqVX1l5tk8qo9JGf8DsGoxrx+UrX5d76r9wlr8vd2VUpgPjVR/f9eWsOjl4zjtpTf0vyH3zKFIvMeWsu8hgrKvZ4cyXEvk0b25yd/38iCiX427+lixIAi6gYVE1M0mtvCrN/VyfaWbmaMGQQYDg7J4z+2ZifVA4o4p6Zv2n4PVQ2TDP4fzjaHM0YL3Pa0vw+1fRKPU+K/7l9Nvd/LVRMr2dsZolR39vr6Cl8edeUlvNvcwZjykrQAS+r5AxS6s99yP4lgpmeiJdf7K/HYCUSO3cqfQDRufjbJ30OB204wphDTdN7b1UH1gELQDaeUQrdIIBLnxEHFKJrO42t2csaIfjQM83LD3zdk3EOSjguX/Okt8zt59up6dndFeeDisai6jk0QkBWNuQ1D0MhsvTW7fggX/XGN+fpUt5N8p73X9k9WoMziq0hH2EhKx1SNPLvEz5/fwrfGlnNcqRs9riEKAk67iChAS3eMPvlO8/oYjCnm/c6c4zW1cfnpfmoHFjFtiSEySbbM+8mkajoihkD4vn99mOZ4cloPp6IkRZ6DQe4PW4LWGLWwSBCOGUmocPzgunLBRD+DS41WN30LXXx4IMhxJW7ynTZ2d0YoK3Dyh2+P4UAwhkMyKrDfbe5gsC9dgJ/q4HfduVV86761ANx70WicNjHnGhZ6nwMmn/M4JIZ4PVw/qYqPOyI8PPdkijx2fvnC1rQxfkZVH34yeQTX/s87aW6dva21rApsi6MRtyTx/156n7kNQ5FEEVFQkUQRjwP+38vv84OzhtMVijG6vJgij8NyP7Ww+JITVVQKnTaeeHsXN06qxgH08dgJyCLhmEKBy8a4oV72dEWIqTolHjuyonEgEOOt5g427+nirOq+DOuTx75AjAGFTrYdCFFW6CSmavx93R6+OdaIDX33tGEsebUpw3n76omV/PjcKm4Q4aO2cIYIPEmygCcZv3pszU4uOHEgy1buoG5QMVc9tp47ZoxCAH78KWKfqe7FPUltmWthYWGRJBRTcdkPXUDpcRjx3kA0bolRLCwsLHJgiVGOIpIBq/W7OhlQ5Oq1J3ahy8bz1zQSjinkuYyq7JZEb+6er1m/q5MGvzcjEQ7GwqAlEOs1CdwSiFLu9ZjbpCaWe9onJwOGjZU+rjjdz3eWvmkuLiZW9eG6c6t5p7mDdc2dWVuLJO1h/7F5L8tWfsQjl4xj0fJNrEzZd9LWef5Da5lWN5B7ZtaZDizRuMpVCXHKiAFF3PJ0psCjZ+uLFdtaufG8av7x/UZkRaOx0pdVnJG0kNzTFeXM6r7mZ5vrszurui8/fW5LzuOnttvo10tbppVNbXRG49z45Maci6vPIgj5rIFSq+qvd4o8DvZ0RdMC16k47aL5G2oJxHodq0lBRl15dieNRr+P5zftpa68hOuzOAklLc+Xzh6LCJmtrxoqEBCo93vNsf3gqh0ZwYRbzq/hjue3pLXiSX28blBx2vvorZVQqrORcV5CRsXMFRMqmf/wWvO9HEq00qfAaR7/cIUwDYlrYeo5LppWiygYn2s2kUljpQ9JFDivth/RuMoN51UxaVenWdmTPMaCCZW8+n4LZ1X1zXp9+SSCGZsomP9uyNEaqN7vRVF1Ct3HbuVPocvOP7e20FjpQ1E1Gv1eFNW4T76waS9Xnl7JvkAETSdrq54FE/0Uum2s3t6WMR5THRdSv2dZ0VnyihF8Wzp7LBc//BZLZ481xWM/Oa+anSnBt55V3KnXWitQZnGs0RWWkRPCku6IQold4rpzq7nxyQ1Z2wjoaNhFwewzn7wW9pzjqSkVlmAktt9t7gAMV6ruaDzN8aTAZceXf+hEmzVGLSwOUuC28a9tBzh1qDGXavT7WLZyB08tqKetW+aeV412r0kXzYdW7cgi8qykX6GT1mDmejZ1LpUkeS/tbb28PxDNer7Ja4bHIbFszkn8/Lkt5px4wUQ/65s7MvZZPaCIm5/amPF4b2stqwLb4mjECVxz5nBuynJ//ekFI3FijOmIrFrupxYWXwHCMZUyp40fn1tNRA5jc3hwAPkOiYJEtb8A+Apc3PrUpowWPldNqGRAsQs7xrVBAf76VjM/PHs4neE4Xx99HGJiH/0KXdw8ZQRdkTiKpuNxSDhtInFVY3dnhOF983l0zc6s8a4GvxdvwjU0Weh04+QRzF72JmPKS1i/q9O8504a2f9TxT5T3YsPR4huYWFhETzMNj15TmObQEShrODzPisLCwuLLyeWGOUoIhmwSrbeWTDBD6QnbxsrfSyaVsvi5Zt4eesBFkz0U5eo5B47pJQhiWqy1Nds2dPFwik1Gc4bDX6v0Y7jifdMdXm2wOCAIhff/dNb/HhSlXl+qYnlpBvKlaf7kUSBkKyyZW8XBS6JBy4em2Z/ftfL7/Pjc6sIxRScNpFbz68hrho9TPNcEqGYgk2AJa98SFhWueiPq3n0u+OIKSrBqEqRx87ezohp69wzIb909lgz4XC4rS/A6AF44/9t4CfnVXPb1BpueXpT2uIm+VlE4yqvbG3hO+MG849rGtnZHmbqqAEsWr4pLbHf6Pei69nbaiSPLwkCj186jrCsEoz13lcwGFV7XVwdShDS0h3LubD6rIFSq+rv0AR76RvZFpTNtjvXPfFemgArSWOljytP9zMvIcjIJeS69pzh/PafH/DK1gM8/mZz1n3VlRcTjWuMGVLK5af7sYkCdknklfdbWPCoIQqZ11DBggl+nDaRG8+rJhJXUVQdZ6JlT3N7mO+feTxXTzweWVHJc9l4fuM+Zj2wmgtPLiffKbF4+kgzyJl0TVo4uZqFk0fQHVOQFY03treZScIGvxdfgZNxw0q5/PRhSKJAWFZ57+NOonHVdB1JXn+yXa+Swpan1u/mT/NPZuGTGw9LCJMUuM16YLX5/KqmNoIxhd++/AG3T6/hpic3Zrx+9vghTP/dqpR2YW8ypryYJ6+oZ0dbCJso0BKI4Stw8O6uTv721sf8ef7JLHwq/frS0h3rVQSXTLI2+n2sTASHGvxeFk0fyaLlmzK2T7ZL8vVx5BQ3Nfi95B+G1eSXFV++g617A1x3bhXLVm5n8fSR/PafH3D1GZUM8eXxu9e2cenXhiEhcPvUWqMtQEwl32XDKYk4JIHpv3+dO2eMyrr/VBez1M88SdKtIaZoppByysj+Zlu7bKRea61AmcWxRmtQ5vXtbTT6vXgcEiLgAH5+wUiCidZUBS47eQ4JWVVxSRJ/Xr2TIb78jHaNqXM8SRDSRF+Nfh9zG4aw4NH1/OqbJ5DntKc5nhwu1hi1sDhInl1i694AZ4/oyyOrP+Lm80dw+/LNzHtwLX+efzK3nl+DrGiEZKNl5MIpIwAjWOtxSLgdEgLw239+wLQTj2PyyP5p7n/7A1GicZX7/rUdOCjGzTUnbPT7+NG5w3HbJRr93ow12uzENWPhlBH87pVtac/nWj/2tq7MJb63RGtffb6MriEChiDFvL9G4hS47eQ7JOyAoKk4JRHJKVjupxYWXwEMd1YdJwKSw4OoqSAa470zruKQRDyigFMQ+MHZx3PjlGq6Iwoep4THLuEQBURAAjx2iZCsMmPMIPYFoogICKKAAOiAgI5NFPjtP7dljW08e3U9c+or0MmMzcypryASV8lz2jinpi91g4r51n1vMLxfAbdNq2HKPSsB4xo0e/yQrO/1cIrherbePFwhuoWFxbFJMGq0rz8UeSnOKBYWFhYW2bHEKEcRqQGrKx9dx2VfG8p151YhCQLdMYVCl51gLM6i5Zv4Z8KRINkOxyYKbG8NsXVfwEysBaMKdpuI12MnEle56bwRIBiqznynDZdNJKZq/OHbY8hzSPzsgpFE4qoRGHRKuO0ScVVjV3uYPoXOtJ7dkiBw29QauiJxAhGF/sUuNnzcxZ6uCLUDiqjw5dMdVRhU4sYmCsRUjXNq+qJUlRFOHP/Bldu56szjCUTjdERkQrIhWPlapc9MHLQGZXa1h00L5nsvGt1rMq9n/+/eSH3eY5f4oCXInIfW4nFIXDlhGDdMqqY7Gjd7A776fgsbPu5i0bRa+ha62NUe5rI/v21avs9JCZr2KXDSfQiBSUhWmPfQWsKyyjNX1fe6bbJ9S67F1aEmO83tYfKctqwVTJ81UGpV/R0ajzP3xNXjlJibaJ8zr76CXR1hbplqBO0DEYVijx2PQySu6owpL2ZFDwGY3SbisUvsC0Rx2AzHnbiis6KplYv+uJo7Z4zihknVhGSFApcdUYCd7WFGDiyiuT3MqUO9LH52My9vOVht+m5zB1NPGEBze5iyAhGbKCKg4bAZSb24qnGgO0ZLIMpJFaW8uHkfdYOKGdG/kEGlbl7YtJ+n1u/mz/NPpjumEI6peJwSLYEY3166BjBaU51T05f6YT7jPdolmtvCVPUr5I3thutSqqvSnPohzKkfQkzROK7Ezdx6w2nioyxOE2E5wsVL3+TP808mHNcIReMsmlaLrGp0heMUeew4RJH2cIy/XjaOlkDMFLilEpbVxLV2M9edW4WsaHRFDjpYJY/XsxJ/0fLN/ODs42kPyZQVOvnNi+8zYkAR3x43hPZQjMXTagnHVQKJ4K8o6Jw0uITbntmU6VaTSJg0Vvq4fVoNB7pjnFFdRksgxsVL1zCtbiAXnTIYMNq3vfbBAbNd0lCvJ00UlCRZ+ahr+mf4VR/dFHkcLJwyggPdUa6Y4EfTVX5w5vFENJXjit1c/jU/kbghQCl02yh22nDYwYZIVFFpCym0BuVe7yMeh82s3Ho00R87SdLNJvn/0yp9lOR9smutFSizOJYIROMsW7mDJbPqcNslDkSi9HG50ABBAAEBTddRNB23JPGXNc1ceHI5gWicmQ+soWcLs5hiOCL1L3Sx/KoGgjEFmyiwsqmVBY8a1+5it/0zJYOtMWphYSBrKjeeV82vXnyfS7/m566XPuCShgr6FrroCMmU5jlRNB0jVQUuu0RHKEaBy4ZdEninuYO9gSjzGoYiCtC/yIWO0aM9rmrs6YqarnPJwoyIrHBapQ+7JBhz6IahROPGuI7EVS683xAY91yjVXjzaA3F+NO8k8lz2Ljh/zakvZdc9/1DrSuzie8t0dpXmy+ra0hHPEqJzUW2SIWkQ0QQkVUNlyRZ7qcWFl8B+hW6eO2DA4wfVIwCtCrQHYlSlGcnEFHIc0h0xBXK8hz0y3MQiB+cUyfv3DYgrhtz8u5onKsfW88fZ4/l9Q/bOKemLzZRxOuQQBBQdZ3F02q56amNadeQxkof4ZiWFldObTl99WPrWTbnJFY2tZptou+cMYqWQJTmtnDaXL+3e/LhFMN9GiG6hYXFsUlIVvDm9d6+HYw2Y8Ah26VbWFhYHMtYYpSjiGTAKhnU+M1L2/jNS9v42QW1PL9xH2/v7OBP8042hShgTMI37w0woMjFYG8ev3lpGxOrymjrllF1nXxstIVkXHaJsKyYfbmb9ndT7/dx/z+b+MfmFu6eWceja3bynXFD2NsVoW+hC02HYb48fIVOFk4egaZDWFbwOGw4bAKd4TiBqMK65g4e/1sz3x43mAa/D0XT6VMgke+wEY4byXRNxwzUu+wS+7uiDCjNY/LdKzMSCFNG9k9ri5FqwfxJ2lkc7rb1fi82STCPEZZVfvnCB/zu1Q+Z11DBqUO9qLrO2SP6MefUIWbv5KSdfLLqPZUFE/1mK59ciCnVur21Z0ltHwLZF1eHEoQAOSuYPmug1Kr6OzQeu5TTRjwUUxmdo+1Og9/LLVNr2B+I8fDrHzGnvoIfT6oiGFXJdxnijj+v/ohhZQUseaWJBRP9bN0T4ITyYubUD0FWjfZVTrvI3oBhLSgrGqqmI0kCuzsjfOMPr7NszknMPLmcmKLhskvous6ejghFLjvBqMLr29t4av1ubp1aQ79CF16PA7fTuH1ceP9BEUe930tdeYnZTuvDAyHmJ1qV/Nf9q9PeW1JgBvCPaxp58t3dnDyklD+8uiOjSmbWKYPN9lv1fi+3T61l/sNv8fSCeh5ZszPr5zrY6+Fv63ZnfK71fi/XnVvFuUtW8LfLT+X8e1bl/N6SLXH+ufUA3z/reLoi8bTzTiW1En9FUytz6ocw/+G3OLOqjBunVHPzk5tY8kqTKV47dagXp02kKyyz6sM2Hn/TSKwm3WqMSiQbHaEYT3xvPPkOiXBcoTTPaYhWEuNtyStNpmjl4mVvUldebLZLCo/qRyQW5/ZptcQUzax8dNpE2oNRyvJdOd/7V4HB3ryEOEmlxG0HHVyiRCiuIgCSKIAAiqYT03Tcoo0Xt+7jVy9s46+XjQN6v4+EZYX5D7+V0W4q6WaT/H/yWtq30PWJr7VWoMziWKHQZScsqyx4dD0vXNNITLLzUVeYfsUeAApcNnSgPSST77LhK3DSEZbpCMcz5pFgiPOunFDJgWCMuQnhbyoNfi+DvZ7PPL6sMWphAaIg0tod44dnD6e5Lcwlpw3FJgm0BGP0yXcSiMaxSQfbDKIbYzQkqxzojjO0LJ/h/QsJyyoum5EIf/X9Fv78xk4uPLmcc2r60uD3EYoptASi6LrO3kCUpSsz54w3TxnBBfe+bo75nvPAZGFDvd/LFaf7M95Lrvv+odaVucT3lmjtq8lnaZH7n0bV7fz85a384CzD9ZaUofn6rg6G9y/EbZfoDMvZd5DAcj+1sPhyUORxcPKQUoLxMHYxsf4XQNOMthKGO6idC+5bzZJZdRS5HejoiIKAlHA9UYC4rmMTBApcdkaXF5PvtLFs5Q7qh/kQBRVfIhGrA5IAN55XTTSuEVVUM0mbdL/N5TTmsklpz2VzsIXe78lWMZyFhcWRJBxTGVjc+zoAIC8RI++yxCgWFhYWObHEKEcZA4rd/Gx6LYGoQnNHmLICJwUuG6dUeLn1mU20dMfStnfaRLOtT6nHbgoXWgJRXtqyn2+PG8zyd/Ywp6ECj9NGqaZT6Lbj75PPno4wU04cyP6gbKrTBaB2YBE2SaQzJBNVVFq6Y5QVOHHYJN7dFWT5hj2samrL6Kl918vbuOvlbYARDJwyagBDvHlc9dg6fvmNE3DZRTZ83Imq6SzfsDdnf+9/bNrH7PohgM6KprY0C+beeoOntrMgsW0ugUdqsvCqCZW8vGW/2Solue+wrLK+uYMTBxXz+JqdXDep2gwqJe3kc53L1r0BLhw7KE1Uk/Y+K31sPxA0/87VniXb4ivb4qo3QUjyvfZWwfRZAqVW1d+hcYhC1rZb9X4v/QudOd0rFk2v5eO2CHsDEb5/5vHc/szmjB6+qUnwzXu6uPqM4/nlC1vTFvFJQVu26rYGv5cXN+/PCAg0+L0smlaLqutMqulHo99HSFbZtKeLMUNKufWpjTldPJLOG3c+vwXofdw2VvrYH4hy37+2M66ilCmjBjC/YSgeh4Sq6WmtfJLtsj7uCAOwtyuaMW6T53Lz+TX8InH8nue4tysKwMqm1pztcVJb4oDRKuuTuC3lO2387fJTKc1z8HF7hJumVKNqJNxQbDhsIq2BKANLPEwe2Z/T/D4K3Xbskkh7KMaBbpn9XV2MrShlZorg56E5J3HCoGLmjB9CXNUZWOLCbZdoD8r83/fGIwiwqz3MT86rRkenNM/N6x+2UlboMgQpiUTOeL+PkJqZwP2q0R2N88b2Nr554kAEQAPWN3cyrE8ed/9zW68CwEa/N+Oem6TR72NgiZtHLjkl4zc6t76Cx99sZtG0WgC+21BhXgetpJSFRXZS5zGtwSh98l280xzmrlc/zHnvKHDZaO6IZH1uYLGLFzfv48RBJYwZXJJRmfnzC0YysMTzub4nC4tjBV3HmMOIAn9cuT3t3rp09lgeXLWDlVnWjqk0+L2cmBA0p7K+uYOBRW6q+hegahI1A4v4uD3CgkeNtev1k6rY1R7huBI3L27ez97OaFaBWhKnTaTR72Xh+TVEsmyXa87a27ryUOJ7S7T21eNQLXKPZteQAodESzBOwy9fy3iuwe/ljgtGomg6+U7L/dTC4qtC/2I3XWEJOSSjJ4Qh3dE4ffKd2ESBPLtEVb8CLrx/TcZrG/xefn7BSJyJQp1oXOVH51SZLs+FLhti4jmHDlpCwOKwiaxv7qCs0EV7SMZpEylw2XLHXyp9FHtsPH9NI93ROHkOG/sC0QwH29MqfTnX6FYxnIWFxZEmKCu47Ydu0+O0iUiiYDmjWFhYWPSCJUY5Cil021F1eGT1TjPg5XFIXHraUIb48tK2Xb+rk7ryYq58dB0LJg7j9mm1/PrF97nmzOMZ4svjgRXbGXVcMbKiEZZV8pwSLptIeyhKUZ6TaFeU686tQlEN6/M8h4SsanSEYiiahhgx+t7ZJAF3wj1k3NBSbn56kykSgfREcGOlj9um1vDyln28+VGUO2eMQgD6F7roX2jYLlf0yc98nd/H3IYhLHjUSKxfetpQrp9Uzb5AFEkQmFtfgU0UmTpqAIue3ZyRWLjidD/zH15rPrZ1b4Bbp9Zyy9MbM87v5ikj2NMZZWCRm5ii8rtXPwRg4eQR/GRSNc0dYRySaLZfmHXKYKOKPkHSTj7b+6/3e/nJedUcV+rhp18fyY1/35Bxrr+YMQpF1cygZmtQ5qI/GpUIP5k8gkDE6Du9ryuStvjKtbjq6aqTei6pYoXeKpg+S6DUSrD2jigKlBU6mTKyf5odaUvit90aima4V3jsEm2hKCX5dsqKnOzrjPKjc4fzfbWSuKpnCDUaEg4ilzy8lgtPLufKCX7skohdFLFJMGZwKYt7jJukaOOOHqKNBr+XxdNH0haMIokSogidkTg2UUDRdHa3RxgzpJTLT/djlwTynXYcNoH2kGy6eOiayvWTqrnqjOMJxeLMqBvIzU9vSh8LfmMsznxgNXXlxYBAv0InggB2SaDQZeP04/tQM6AQh2R8XgOKXMz4w+uA4VxyVQ6b11kPrDYcX04ZnGH/ek9i3N7/7+08eWU9i3q0x2n0e9OuRWC0ygrLh+/MlOe0cefzWxg3zMuY8lJuX7454zpx1YRKPmoNYZNE7DbRrEyURIGyAie6rqcJURr9XvYGolkriVJdaZL/Hl1ezDPv7GZuw1Bskmj+tvoUOLnrpff54VnDe30/XwUKXHbu+9d2LjxhICpgBxqGednfHeXWqbXc+vTGrALA3770PgsmVtK/yEWj39vj9+Fj0fRa2oIR+uQ7mTKqPxOG9yHPYUMSjQqyX3/zhJzXPyspZWGRSeo8RkPACdT7fea8t+c87pozKikrcPLi8s1p+0m28GgLRvna8WX0K3SxxJqfWFh8rogYLoDv7u7KEFhf98R7PH7pOG49xNpx0fRaFvcYz8m5Umm+nT0dUd5q7mDzni5GDCgyiwbAcD9ZOnus6RKYSzTS6PdR4cvjB2cPZ09HBFXXM4QnqUUQqfvYujfAoum13PzkpjRhuCW+PzY5VIvco9k1xAn8dPpIbszRxtMJtIRlClx2fv71kSxavjlD4GUlfC0svnwUeRzgcXCgLYTLIVHgkBAAuyga14ULjNhlrutCssHvG9tbWfLKh2YMqtBloyMc54mt+/j6SKMARARsgsDxffPxOO3mHDzPIfHT6bXc+GSPFj5+L3Prh3D78s3MOmUw1z/xHnfMGMWDq3ZkCFHunDHK/LdVDGdhYfF5E44pOA9DjCIIAnkOiUA0WyNECwsLCwsAQdd1/dCbWRyKQCBAUVERXV1dFBYWfub97e+M0C2rae0YAH4xYyTPvLfXfMzjkLh7Zh0PrjJsij0OiXsvGo0vz0Ge07A0j8RVwjGVQrcNhyTSGZYp9jiIqRoCxqIiGFXId9lwSiJRRSMYU8h32lA1HUHQ6Y6qpjjijue2sviCWgrddkIxhSK3HVnRaQvFUDUdj0OiLN9JKHHcApeNA8Eo733cRVW/QiRBoNzrQdF04qqWsIW0seHjTm595mCwo9Hv4+oz/PQrdBGUVYJRBY9TwiGJiAJm+4Uijx1N1dEBVdeJyCrFHq2CXkkAAQAASURBVKM1STSuYhcloopKKPkZiCLtYZk8h/H+wrKCKArkOW1omo7LIRJXdAKJBZPHLhGJKwQT51nicRCKKUz89b/MlhvJnqbJhPf5J/Qn327j9Q/bGN6/ADDO122XKHTZiGsasbhKsdtBUFbpTnz+uq7THpQp8ti56+UPeCWlJVNycdW/lx7UO1tDNB0Ipp3LspU7zM/0pf8+DVEQCEQNsYsv78gkZLrCMq1B+Yjv9/PmSI/bXGzZG+A7S9dw54xRlBU6icoaimYIxCrL8mjpltm2P0hZofOgUKU7xvihXhY9u5nXP2zLGOfzGioYP9SL3SZS4LSR5zDajwQicQpdhsNGICqT77QTV3WC0Til+Q5iikZUVslzJa8HMYo9TmRVIxCJk++yJyzSFbojKiV5DhRVJxhTsCXG3t6uKDZRYH8gSv8iN1c+uo4xg0u4ecoIZFVFQCASV4nFNV7fbrSguetbJ+ItcKAj0J0QW7ntIq3BGIGoyr6uiLmvuvJivn9GJS67BAJpbYm6wjJ/f2cPK7a1fqoK256ijZMrSomrhvvLvoDhmNJz3DQkRF3rd3XyTnNHTrel5H4bK33ceF41sqqBDvkuG2t2tFNWkP79jhlcQjAax2WXiMRVPA4bBQ4JVSdngFjVdG7pIepJFZ3VlReb/55wfB9+PKkqZ1DJJQr0/RI6A3yScftxR9gIZl1Qi6QZwjAdUDHa8yAIhOOqGSBzSMZvf+PubsYMLqY7Giff6SAaVwnGFDxOGy2BKNc98R6tQZl//uBrDCvL/0Let4XFl5nDHbd7OiNEZAUEHY8goQkQ03RzLlvgtuGyiTgEATuwNxLnQHeMmKJR7LbTv8hFJK6S57Qx2JuX8zgWFhaHx+GM3Y8OBAnGZArdTjpDEUry3In1jTHfy7NLRrvXuEooFqc0z0lc1eiKxHE7bCiqRjCi4HSIFLntBCLGmk8AXtqyn/v+tT3NfaznfKeuvNicg51ZVcb151VnFCMk3fXe/KiN37xkOHl6HBJLZ4/l3leb0kSnZ1aX8cOzhrO/O4rLftCpL9lScfxQL067SLHbYYnbjlE+bAlyxv/7V87n/9Pzw97G7YG2EOgQFTDGaUKsnu+QcAE/+Pt73DSlhnPuWkFjpY8rJ/hNBwQ4vJiEhYXFJ+eLik0BHOgIg6YTBzQdHOgIgkAM6JYPro3zHRIOjLWzCFz66Dre2x0ADhZo7O0IU17qQQWee283k0cNRNN0JEFA1vWMdsFOQWDd7k4q+xYYrrEuO9G4Slc4zlvNHWYcJhnzmlTbj1jcaD+des/dH4jSHjooNvc4JGyAwy7Rp/Cr3Y7YwsLii0HXdSpvfJ6LTx3MWSP6HXL7H/7PO0w5YQA/Oa/6Czg7CwsLiy8flhjlCPF5LByaW0N0JqpqIrJKgctI3mrArSnJSI9D4qbJ1ZxwXDHdMQXQ6VvgynAh+PnXR/Lchr05LRFvPb+GjrBMntMI9EdVja5wnLiiMbDYTWswBgmlp5x47oOWAKdVlqGDuQhw2UXauqN4C1zsaY8QVTUkUSDPYaMgITrZE4hSO6AIHRiWqHrVdIgqhsCkwGlUmL+4eR+/e/XDtMXIqUO9qLpOXNHYsLsLQYCxg0tRdWPBU+Cy8fvXmpgxZhCPr9nJTVNGsPDJjZxQXpIzcd3o93JB3UBGDy7N2i5lTiLYmVT///SCkSxevpmXtrRk3dfCKSMAgUXLDzoueBwSC6eMoK68mK6wEZyNqxoPrdrBeaMGmPtPbpsUuXgcNko8RoLlUMHOrrDMVY+tz9qup7HSx9QTBrCzPWyKZ0o8dspLPZ/Jqn5PZyTDkeW0Sh93zBjFgKM8SPVFLfjf3N7Gt+4/2Grp2avr2dsV48FVO2huC/Pn+SfzxodtZiuVpGtK/TAf3TFDLJW0UY0lRCMFLjs2SeDKv6zj7ll1rG/uNNyHil0sXr6FFU2t+PId3DljFP0KXYRkxRRXdUeiFHlcxDWdnz23har+hRmCqq17A1x79nB0jN9jRyhKoduJnGj1YhdFRPGgMKUlEGPMkGL2dETZ2xWhwe9jZ0eIIreDcEylOM9OR0gmpmh4HDbynTacNqMlTb7TeC87WsOIAsZ79/u47ZlN/DNFkNXg9/KzC0ai6jo3P7WJt3d2pIl0kiQr4xc/u5mXU8ZoriTGmMElLJ5Wg46QdfwnW2WdOKiYmyaPYOFTGw8pBhlY7AKM62VnOEqxx0UoJTHjlETaQjHynAlRTncMj9OOXRKIxeMUuJ2EZTXR1seOgE5EVuhf6Oa1ba30LXThcUi4bBKSKBCJKzgkKe07aQvGaKjwEk+I90zXHYeEHRjwJU3UftJxu7M1xB9XfMilpw3jJ3/fwJZ93fziG4lxkRAZisLBai9RgNZgjKsfe4eH553M/kCMfV0RDgRjZgIryZNXjOfE8pLP4V1aWHy1+CTjdldrCFGAX7/8AfMbhiJJAt2J5LTbLqFoGoU2iYim8c37DPeoxkoft0+t4eP2CJc98jaPXnKKNTYtLI4AhzN21+1spzuqcFypC6coIWs6NkAQBVOUkkxqRTUVpyghaDqaKBjzk5SK6XA8zo7WCCVuO74CJ4qq0x1TKEgUTexsC6PquikcHlNewi1TR9AZlslz2nll635OPK6Y5o5Iugg4EGNonzzmpiTVG/xerju3in9ubeGM6jJ2tUcY2ieP9c2dLFq+mXkNFTnXjqdV+rhnZp0lRDlG6W3NfTT8NnoVoxwIgCgRJV2MkueQuPZ/38Fpt3HH9FrqE218TkusqzoSbimWAMvC4vPhixSjJNnTGaE7FsclingSTtA9BSkeh0Q4FmfFh60M8xWYAvB8l415D63lh2cdz4nlJZQ4JHQgEFdRNJ3/fvwd7r94DJG4ZsY0FFXjoZU7uOz0YeztNOJGoweXcM5dK3Ke4yOXnELfQhf5Dol+ifjins4I1/3t3Yy20Qsm+BlY5MYtQJ9ErOPLWjhnYWHxnyemqAy/6R9ccfowGiv7HHL7m5/ayOjyEu78xqgv4OwsLCwsvnxYYpQjxJFaOKROlEvzHCx8cmNGkMOX7+Av808mGteJKUYizW2TkDUNTdNx2iSckkBc0wmnuJOAjtMucfNTmzLaxiyaVmu4coRlClw2XDbDWiwYU7BLAqGoQmm+E1EAUTAqy0MxBbdDwmOXsIkCQVkhGDWqUVdsO8Bv/7ktzdL13otGc+3/vmuKLPKcNkIxxXRX+Ntbu7j2nOHsaAsTVzUqy/K55amNGQuMuSnCkFSWzh7L/IffMh0K1jd3UFdewplVfZl+7yrz+Vw8d3UjP3tu8yFdD+BgUjzTXtLHFRP8vPlRG2/uaGd9cyfzGioYXV6Cx2EkUQQBWrpj9C1woWo6iqah68Z30xqUicZVXHaJ9z7uRBBgYlUZEVnDm+84rIXTns4I1z/xXoZl5ZUT/IRllT+u3J4WVG3we7nj66M4rvSTC1K6wjILHlufVeB0NATiDsUX6Ywy6bcHF9h/v2I897yyjREDijh7RF/agjIVPk/GQv3KR9axqyPSqwNIvd/LSUNK6Vvgoq68mJCsUOgy3FACkTh5LolQTKVfgZNIXMVuE2kPyuS77DjtApIgsvDJDRltahZfMJLm1jDHlbqJKhqxuEKhy3BU6o7E8RUcdFMpchtOLO3BGHkpIpkPWoLmPp+7uoFANE6R20FEjuPNc6VVyMqqRmc4Tp5TIhRTcAg6vkKPKeBIJjGa28L0KXaiqjqKZlgm+vKNCttA1KiwDcXiDCx0sy9oVLSGYoZbkkNKiF9chhjkQDCW6Heuo+kwe9mbpntNMJpwUrIZiY+BxW6UxHvf2RGhZkAhuzuNNi0Om2gKQJLJkQfnnMR/pQiQAH587vGcPaIftz69iZUpDjenDvXisIm47RL/2LSPLXu6mHXK4IzrVWOlj4WTq7FLIjFVM9xu8pwIGIueFVl+H2dU9WHWKYMB0sRGW/Z0sWj6yKNeMJaNTzpu97aF0ICopmITJW7qed1O3AMl4KO2MP/9v+8YyW2/lzFDSrnrZUOAku0e8p+ufLWw+LLwScbt7rYQrSFDoHf78sy2jD+dXosT2B+WaQ3JFLntvPr+AZat3ME9M+uY//Bb1ti0sDhCHM7Y3bI3wIzfv87fLj8VQTDWooIgICuasYEAgg4amMIyT8LuuiMs47BJ2CWBuKqxu/PgfGrrni5+cPZw3HaJb973BmFZZV5DBZNH9jcdM502kU27uxjaJ5+5D62lNSjjcUjcev4IagcWE4jG8TgkRAScDpFd7RGicdWcD40tL2HOQ2u596LRXPHIOp67uoHz7l4JZL/vp/J5XWeS8YBgLE6xx4GccC21kmhHF7nW3EeDa0hv43br3gCRSAxvkSczllHpY9G0Gna3R7ho2Zvm49Y91cLi8+c/IUbJxoHOiBFriKvYRDHDrbvR72Xh+TXsaA1R4LThzXfQGY4ztMhFVIf9wQgg8o0/vGHGO0aXl+CQBHwFTjMmk+c04jvdUYWfPr8lZ6yrrryEd5o7zBa6LrvEjf+3gZe3tmQ4VZd47EiiwMBCF4gCoiDw4y9p4ZyFhcV/nvaQzOhFL/GDs47npCGlh9z+jue30L/IzR++M+YLODsLCwuLLx+2//QJWBykp8OExyGxbM5J6JA2ea7uX4jLJvGPTXs4s7ov0biKSFIgomLIiyR2tYe59m/vpvXYPLO6jJ9Nr02rUnt2w17Ou3tFmrgj2f7n4VUfpfXFTopBNu3p4s0d7WkLhkMF7Jw2kbCsmoKOv146jn6FLvoVuQhE4nx9zHHsbAsz76G1ANz3nTGcUF7CnPoKYorGcSVuXty8P6sQBYxEKxg9yOfVV7DklSbm1VcQjatpz+eiOxrPKkRJ3WeSlU1thGSVH50znOvPraI7ppDnlPhgXzfrd7UzeWR/JlaVEY1rOCQRIdHaJCwb7UjGDinljue3sHp7O5eeNpQzqss4EIhR5DHsnvd2RTmlopRit53vLHvT/A5zLZy6wjIt3TE6I3HynUYf1qiiEYoZbhg2UeDJd3dnfGceh8SJ5SU0t4dp6Y4ax/8EQc7WoJxViALw722ttAZlK2AK5DmktP71XeE4s04ZzIOrdlA3qNgcN7nGULJ/vSgIGeKnW6bWEFMU3tvVxdd//zqAKXCQRIED3TL7uiIoisbAEjdBWUFLSBAFBHa1hVk0vTZNCGOXBD5uD+NySnRFFCRRQBR07JJAW0hJc3npSTKY35NQYsy2dsdwOyS+9qvX8OU7ePS741iz3XCFkVUNm+ik0GUnElfZ3hqiNM/BFY+s48KTy6kbVIysang1O06bDV3REASIaxoOm9FCSMS4Ds5auoZpdQMZnxB6dIRk1uxo54TjivnNy9vMcZB0ljq5opTq/oVZP/9Gv48xQ0q46+VteBwST3xvPADf+0vm+0zidmT2FB3et5AL71/NnTNGcd2kqrT2Q9c8vp4H555kXh+/f9bxGa9fsa2VjzujzHtorXktvnjZWh64eGxWIQrAP7ceyCpsAbg2Ev/KB2FaAlE6ZRVRBHQ4/54VzGuoYM74IcQUDZddwpfv4Pblm9LaotX7vcxOCB+T9LyHnFbpw5dvXd8sLI4kezsjdMdVXA4b0363inkNFcytr8DjONgq49zfrjCd6n50ThUzH1htzgtjimaNTQuLLxgBqCsv5sXN+xlY5GZgiZtZf1yTc/vU+e593xmDJAqZLneJZNfzG/dywsBicy205JUmTj++DwVuiUBEQRLshGWV/7r/4HVgdHkxJ5aXMOuB1Wnr4Gzz7HsvGg0Y61Q4OF+FQ68dOyNyr89/GpLxgKT73y9eeD/tc7GSaEcPA4rd3DOzjtbgwTYRXwrXEAHaYhr3PLOJEwYVm3PiIredApeNmKpxzf+8k/aS7oRbr4WFxVefPon7y/rmDordIndMr6U7rhKKKkiiiMsu0h2N48t3oOs63VFDLNmlKeQJEn0LjFZ9QFr8N5VHLjmF1z7YS/0wH/MfXsvdM+sAMtrrpRYiXjepikBU4fUP27ho3GDe+biTX3xjFMtW7kg7RmOlj9un1aArOmt2tGfEK/+9rZXrn3jvqC+cs7Cw+M8TiikARgv5w8DjtNEZseZMFhYWFrmwxChHCV1hOaPVSVhWmffQWm6bWsPCySPMyi63XaI1JHPXy9s4vm8BkiiwYfcB6gYVU+Cy0x2LY7eJLF21Iy0A1+j3cs2ZlXx4IIS3wIEA5Dsk3t3VmSHuCMsqj67ZyZz6Ifx40nB2tUfMCrJkcq7ngmH9rs60hHsq9X4v63d1pj2maDp7A1EeXbOTWacM5tE1OxkxoMh83iYKaYuKRy45JetCJkkyiAgHg4cxRaNPgZTxfDayJY9T6RmQDETiaVbPjX4fN08dQVzRuO3pTVkdXRY8uo668mKG+vIYN7SUb48bzIMrd5iV96nbzn/4LUaXF/PH2ScxK5FoybZwMiwq38sQDV01sZLBpR76F7tZ39xB7YCitBYTScHRg6vSF2+fJMgZSASmku1gko4SBS4b+wNRIrI1CQPQNZ1bp9aYjhhvNXeweU8XdeUlDPXl0ej3saKplfW7Oqn3ezOqQsKyyuNrmplbP4Q544cAMLDEjTNRVdIdVSkrdLFk1mhiiprm1jGg2MW65g4WP7uFv142jvPvWWXu97mrG/nOg2+Si4fmnMRbzR3UDSpG0XQQJDzO3sdJrnFmuH4Ylet/vXQcYIiZZj2w2qwg7ArHcdpF7DaRixMirAUT/QzvV5D2G01WwIwf6kXRdbo7Iqzf1cnjbzabopUbzqvmuBI3qqZz9WPruXVqDWfX9KM7Eue2qTVE4iq7O6LYJaP69sL7V/M/l52a4cbU6Pcyt2EICx5db34XcVXD7ZCyfldgjL9sxBSN1qCcU7QXjKpZ/52KTTLsc5PHnddQYY7DXORKpnzVA8t7OiMEojKBiELfQic6MGZwScZ9xOOQWDi5mh+fW8WO1hAO6eC9LvXemPrbTla+WgEsC4sjR1dY5rUPDjDUl8fAIlfaeE2tfLxnZh3ePCeiCPNS5mEAxW67NTYtLL5oBJhbX8Hja3ZSN6iYkjzHYa8JbaLAVY+tZ15DBfPqK0znzPW7Opn+u1XUlRcTV9ONXPOcNvZ1RulT4OTbS9dw54xR/PWycYRjKvlOG/sC0QwhSra1KBj39uRzDX4vhS5b2nO9EYtr7GwLUey2f+ZrTrKwoLk9zNx6QzS7rIdAB6wk2tFGkedLID7pgVMSeXxNMyMGFKW1aX1jextb9wS4cXJ12tgBEk6SFhYWxxKFLkPsKbls/HT5Zn4yeQTvfnSAM47vR5FDYuHyzVx4cjnHJeJSd//zA35w5vFE4goum0RjpS9r4VqD30uBy8aylTs4p6YvYVnl6sfWs3ByNQsnj2BPV4RCl53XPjiQth4PRlU6wjLL39vDVRMq+etl4/j5s1sy5hortrVy81ObuH1qDWUFzqzvzSqcs7CwOBxCsiFGcdt7XxMkyXPY2N0Z/jxPycLCwuJLjSVGOUrozWHCm+9g0fJNvJ1o+VI3qJh+hS7gYJAsGax/aM5JKLrOH/+9NWuly9wH16YFFyZW9eHac4aDrmeIJ2adMpirHlvPTZOreX7DvjSxA5BYMIzgB2cdz/5AjFKPnamjBrBoeXYhRmqFeWOljz4FTvZ0RBgxoIjHE4KU5DYNPQKGjX4fBS5br8nf1O2Tn0vy/8nnP2nyOJWeAUlF05nXUGF+9iuaWnlrRwfPbdiT4VKQmjhObv/zC0Zy4983HHJbgffTXpe6cOoKyxlClNR9TBk1gPNq+1HosrO3K5qWzCnNc/D/Xnz/MwU5CxMVYI9cMo7bl2/KaP+zePrIXl9/rBBRNVq6YiyY6Oe6SVWEYyoz6gZy81ObeHN7K7/8xonc9OQG0wEFyKgOnTWunAWPrqeuvJgFEyp5YdM+3tzRznXnVvHAym292poueaWJBr+XlkAs7fn9gYgphOlJY6WPvV2RjMT9gon+nAmGnuM29Tz+sWkfS15pot5vOLYkSYozFkz0805zByub2kwBSmtTW9bPJCyrrG/uYOoJA5j+u1VpicjU8106eywAuzoi5jF6a3cUlhVOKC/hhvNGsLcrAhgiuwWPHgyCJK8VsbjKggn+tPNKPn/VhEpefb8l4xiHSmrku6Ss/07F63GwbM5JrGvuYNnKHcyrr6DY03uAONdxv8qB5aTA8xfTatDdArvaIgz2elg0rYaFPVrVjSkvYcSAIl7Zup9VTW1Zfx+NfuOe9bfLT6Uk4WBlBa8sLI4srUGZsgInhW47ErB4Wg03JcZrsrLScAQbwbMb9nLfv7anXf8bK30MK8unb2KObGFh8cXgsUs8tmYn15x5PIqq4bKJLJpWy81Pp99ve64Jk+uz5Phu8Hs5MaUtaq415OY9XciqzqBSD1X9CkyRb6rQPnW92+D3MqfHfpL7bwlEE0KaZm6fVousaGYC7VBrx9e3t1E3qJiHX//oM7mV9HRHBaMII9txwUqiWXw2nKLAd08bypJXtqW7Cfi9LJhYiTNlnQaWE6CFxbGKL9/BB/uDiKLAiqY2ROJMOL4fMcAJ3DxlBLcv38xPzqvmvd1dXDnxeP7fyx8wr2EoGhq3nF+TpcWPj2vPGc68h9YyurzYjE+NGVzCmCGlaLrKgkfXc8/Muow4VL5LIiyL5r3xxvOqufCUwby+vT2juHLFtlZiqmYUVOXgUIU5yZZ5gWjcapNnYXGMEooZ1xan7fCcUfKdEl2WM4qFhYVFTiwxylFCrsr2S08byoMrd/B2c6cZXFu2cgdPL6g3A3gDilxmoCzpuHDRKYNx2yU6I3FcdomyAid3PL8lIzA365TBzH9oLReeXM4VE/zYRJE8pw27TaAzJPO/l59Knl1i7JBSFj2zOS1pPaa8hL5FTr6z9E3GDC7htqk1/PKFrYwZUsp1k6qJxo3qtPXNHWmK9ka/j2vPHs6ejgiqrjOpph82UTC3afB7uXVqLYqmUTOgkEKXnWjccIm5Z2YdIkLW1kE9A5vJ/5eXuLlqgp/7V2xnbqLVTs9E/+z6Cl59vyVnYr6n2KXR76XQZefsEX1ZtnKH+d7KCp0Z4pJUAUiew8bo8hLWNXcQjqs5W2uktgVa0dTKnPohac8nF06tQTnr+abuozUo48t3cKDbnuaEsnR27tYehxvk9OU7WDJrdIYQBYxWRjc9uYFff+vEYzox1BWW6Y4qXP7I2+bvwOOw8XF7hB+dOxy7JBKKRfnZBSMJyiqhWJzF02qNPr1RBZtktJ/Z2xXlnpl1tARilBU6OWVIKXFVZ95Da7ljxigESBOIpI6L5Ji68P430s7tR397j8cvHZfh5NPo93LL+SOwi2KG8GTLni4WTa/lpic3ZoqPLhjJomc2pR0j9Tzq/V4WTKhE0/WM4P7mPV0snFLD7cs3ZQhQrk5UzF5xuh9JFAjLKuuaO3inuYMx5cVZf8epYzb5HnKJfZICki17Aix5pQm7JHDykFKWvNqUsd2CFKHJuIpSJo/sz7xEKzGnTWR/IEqRx8aGLKKc3tyjUsVC2YRDyeM/nyLquXtmHYqmU+i2f2KBUIPfS8Eh3KC+zCQFnrogkO+QWPJWM9edPRwduH1aDTFFozui4HFK5DttLFq+mdc/bMshBvMxt2EIF96/mkcvOYVhZfn/oXdlYfHVJhCNE1M08h0SL2zZy5lV/Vk4uQodge6ogqxovPlRO7vaI7y5oz1DiPKLGaOO6fmGhcV/ChG44bwRrN3Rxktb9vP9M48nFlc5r7ZfRput1PVeqkCkwe/lpxeMJBpX+drxfShw2jJcyhr9XhZM8CMIRmugOQ++yT2zRiPwPiuaWs3q6psmV/OTSdW0h2VKPA40Xeeulz9Iv2Yk2gDZRIG4qjGxuowp96wEDPdPXdd7nTcm57a/+uYJn8mtJJs7KnDIQPZX3d3O4vPDDvQvdHHeyP5mO2SnTaQlEKV/kYvH1zab2zZaToAWFscsRR4HhW4bnWHjflPqcIEOTl0lJkpEFJVbp4xgXyBK3aBiWoMx/uvkwSBARyhOoVvipsQ8fnfHwUKfmQ+sZnR5MYunj6QlEGH5VQ3kOSW6QjGKPE4WTByW4eza4PcSiqmIghFfXdXUhqLpPLhqR1rhXiq7OyL0L8q9LuitMCebSNRqk2dhceyRbNNzKCf9JHlOG4GI8nmekoWFhcWXmiMuRlEUhddee40PP/yQWbNmUVBQwJ49eygsLCQ//8gncH7605/y7LPP8s477+BwOOjs7MzY5uqrr2bVqlVs3LiR6upq3nnnnYxtXnjhBW655RY2bdqEy+XitNNO49e//jVDhgw54uecjcIcE+EGv4+7Xt7Ggol+s5f2gol+1u3sMAUWY8pLzAr9ZNDskTU7+fa4wTy2ppkVTa348o1q+nkNQ+lKCFR0XSfPIfGHb4/B45AQgP2BGN0xhWhcpdhtR0Tg0j+/zU/Oq+aWqTXE4iphWaXQbcMuiXSEZJ743nhUVac1GOXqM45H03QicaNVi9MmcspQL/97+amEYyoFbhtOSaQjLNOv2MWGj7vYuKeLkQOL+NU3T6DIbac0z0FcUfjOsrVU9ytg4fk17GgN8YtvnECJx87UEwYwtyF7YDMZHHx0zU6jyu3NZsaUlxBTNM6s7otNELj27OFcP0kgEFEoctt5ect+rn5sPacOLU0k2TekJXV7BkvrE+KV/7r/DcaUF3P3zDrz+D0XTbla4dT7vUw/cSAeh5Sh4k8SUzRTyNKnwMm9F43GZZdY19xBodv4vRxOe45kP9eyQhf3PHVQQHCoPuiHE+Qs8jgocttzVu6tbGqjIyQfs8mh5CL2psnVaf1yU3vWP3d1PU6Hg5VNrfQvctO/2MXNT21iRVOr+f2fOtRLkdue+K3o2AWBAped80b2p9Hvo8hj5/ZptURklZCsUOiyY5dEWoMx/nrZOCKyil3EdBtJUt2vEFEQuG1qLRFFpTuiUOSx47SJxBSFcFTm5wmRTLIXusch0RGKGoIZRSMQjZPvtOOwiXSGoiycMoJrz9EIxRRsoogopgppovQrdNIWinHjedVE4xqqrmNPbLerI8x151ah66BqWto2sqLxxvY2U/zV4PcypryEuQ0VaOROEowfZjj0JMd1NmHL/q4IA4pd9C9y0uD3ct+/tjNqYHFWoYmvwMGGXZ28vr2dUQOLM75zAWgPyvx4UjVRRcsQ8vw04YjU8xpz8/k1zHpgNQ1+L4umj+RX/9iStl+jXVCF2S4o+X5vm1qLrKjMqa9A7/E5NFb6WDy9ltt7CISSrkVqL5VCX3aS10YF2LSni+vOqeKuf37AVRP9oAs4JRFHvoNIXKW12/g9yqpGMKaweFotsqoRiCqIglFZlXTH+Sq7yVhY/KcpdNlpD8lIwMTq/ry8ZS9nVvcnmJhT2iWRs0b0JRpXzXG6PdFaq7zUg+crLLCzsDiakTUdmwDDyvI5ZaiXX7/wPt+bMIwTEm0eo3GVknwHZ1X35fTj+5DvtBlroLjKQ3NPosBlJ98hsT8QQZIkXHaRtlCMsYNL+PsV4wkkxKMum4QowEtb9hGJa9w2rZa4onLDeVVcI6scCMZwSMZ8raU7xpWPrmNeQwWb93QxYkARF50y2JzTrd/VyS+e38IZ1f0Y4vOw+Nkt5posOVf83ul+nDaR686tQlY0c/+pIpmk+9yndSvJ5Y56KDc9az5i8VlwY8SZgrJKdyROgdvO8WX5uIDJJw7ka1V9iSma5TZmYXGM06/QRTJkoAOCAJog4eSgU0Cey44bKMt3ElE0VE3HJmmAgE2UsAkwvCyfoGzM5yeP7I/TJrKrLcyane0sW7mDJbPqqOyTz9qP2plU05+dbQfbXCQLq2wCxFQb9317DJf95W3CsppWxJeNXOGO3hyfcolErTZ5FhbHHuFEmx7XYTqjeBw2gjEFVdPTHMEtLCwsLAyOqBhl586dnHvuuTQ3NxOLxTjrrLMoKCjgzjvvJBaL8Yc//OFIHg4AWZb55je/yamnnsrSpUtzbjdv3jzWrFnDe++9l/Hcjh07mDZtGj/4wQ945JFH6Orq4r//+7/5+te/zrp16474OWfDl+/grOoyhvcvNHv3uuwSmm7MnusGFZuJ7LpBxVz12HqeurKeM6v7ImCIHm6bWktMUYnGVRZOHoGsadw0pRod6I4q2CWB8hI3wTyHmVjOd0jENBVV0/HYbFAk0B2N07/IhcsuIgB3z6ojGFWIqxo2SaBPvgNZ05AVDU+ir3ee00ahzZiQS5JAgc2GQxSJaxoOSUIUBHR0dB0UXTfELwKccFwRI48rMivUPXbjBt8eUvjz/FPwOCSiikqpx4HHKeG0SYytKCEa1wjHFPrkO5k8sj9fSwQ2BQABfnj2cFq7ZS5pHMofV2zn9Q/bmNdQQb8iNzFFIyzraYntxkofPzx7OJf+6S2um1TF9ZOqE4lHQzwjaxp/vHgsUUVjXYrTS9KR4fFLx/FxR4QKn4enF9QjCALBqELfAie3PL0xQ6yxqqmN257ZxF8vPZUXNu9Lc1dJ4rFnF7I0+L1cOHYQkFvElMRpEylw2WkNyuzqCKedx5EKcnZHlTT3l+RvN9lGJBA9NlXBqYvYA93RtJ65qbbfL21pYfzQUir65JHntBOIxLltag2yqtEViVPkseOQRNpDMfoXuSgrcBLXNe54fov5+zMcauoocjuwicb1ICSrCALYRJG+hXY6glEWJZJ3waiC2yHhtIm0BWOU5jmRBAFRNAIMxqRZJKqqhOIqLptIKDGP1nWdjrBKWyhES3eMUytKkUSBoKyiaMaCXxRAFHRK8gwBTYnHjsdho8KXh6wZIiinTULTQVGN58NxlUKnkWzMc0iEYjFjTAsqETnOcSVuJo/sz2l+HwVu49oViqsEowedZAIRw0LVIYm0dhtCnFBMYU9HmHkNFdwwqZqwrFDoNsQ67UHjM63sk0dMU9ndFmPx9FoicY1QLM5gr9cQJST2O9SXR0c4xk8mj0h8jnEGlXoNkV5cRVE1ygpdxFWd5vYwt0ytQVY0uqNKolVWhO8+bFxjbphUTUhWDIGPXeJAMMqDc0/iQHeM3e1h6iv78PUxg9KEMALpi5lVTW3IikYwppqJk1TxzBBvHt99+C1+9a0T+OE5VWawWVE1vrN0DX/4zpgvbDx80SSvjSFZ5Y2mA4wZUMSPzjyebkVFQEfTjWtXntNGntNGTDU+s352JwJGsG3tR2384h8fmPu0bMotLD5ffPkO1jV3oOg6bkHgnOr+BOKqOc+wicbcqNhpwwG0RmN87y/raKz0cf25VVbbCguL/xBxTSXPZqPIbSeualx1ZiVgCHQFQUBOJKcKXDZEIKZphOMqug42UcBpEwnFVfJdDnSMKsRijyEY7QrF6ZYVilQ7hS6dbfu7OXWYj487Iui6zo7WEGWFLsoKXJR6dIrcdloChhAlLKssW7mDpbPHcu+rH/ZoSeJj4fkjmP67VdSVF6dVVicF5EteaeLlH5zGLU9nOkBCpnPmp3EryVVY0FuLIGs+YnEkEDL+Ycx/S+0SdgncdrslRLGwOMYp8jgIRhUaK33EMa4RzmTAR4eYAG67yNLVO5l7ymCwiXTLKrquIwoCDknAgeGg5nRI3P+vbfzfO/u496LRXPHIwVh7octOSFYZXV7C/u4oFX3y+J/LxpHvsmOXBF7cvJclr3zImPISbjiviisnDMOWSPRmK7JL3p8nDO/DaZU+/t3D4aSn41NqSx63Q+KEQcW8vbMjI0ZrtcmzsDi2CCba9LjsvedPkuQ5jZxWIBKnJM+6TlhYWFj05IiKUa655hrGjh3Lu+++i9frNR+/4IIL+O53v3skD2Vy2223AfDQQw/l3Obuu+8G4MCBA1nFKG+//TaqqrJ48WJE0bjBXHvttUybNo14PI7d/vlXHhV5HCycMoIb/74hLVD2zFX1QPoEW9F05jVUICsqdYNKQIBgTMGpauQ7bPzmxfepGlDE+uYO6spL+LClm2+MGYTLLrK7K0pJnp1+hS5Cssq+QMwUgSiaTihmBPx1TSeuaGg6BKIKeU4jcR1XNaKqhscuIWJUwmm6RDCqUOC2ga4TU1TynXYicaPKPM9piFA8DolfvrCVV7YeAIy/F04eQd3gYlRdw2mzE0kkHZw2iTyHxPrmDna0hRk5sIgDwRhFbjvHFbtQNZ2oovH+/m6zum3zni6+2ziMNz9q4zcvbTOPsXT2WGLxg64UHofEwikjOLemH6PLi+lT4OTfHxzA45DoW+QyHSvgoMOCTRCY89DarN/diqY25nTHuPmpjTxyyTju/MfBgGVvrXBWbGtlzvgo65s70txVksdVdd10w0llZVMbN/x9A3fOGIUv35GxuEo995Zuo6pwe2soYyF1pIKcRW5bTveXu2fWUeQ+NruBpVY6vre7i1vPH8Gtz2xmxbZW08FIANOFY29XhL6FLhRNx2mX0DQQRYF9XVFcdpE/v/4RFWUF1A0qRgduTggdQlEFt1OiJRBjwaNrcNslfnfRaOySISZz2kRCMZX9wThtYYUNu7sYPaiEbfuDlBU6kVUNQRDwOCQkUUDXdWTVSBqIgEMSianG9cdlE4kqGnlOiQKXnWF98rj6sfX89OsjOf+eVVxwYn+umFiJpkNcNYQAiqrxw/95l10Ja9afX1DLcxv2pglp7pwxirJCp1lto2g6wZiOLhiv/+vaXVw0bggHug2BSldEJq7aEAUBRTOuj3ZJRBIS56to6IBdFCnLd9EWilGU52BfV5Q7n9/KdZOqKCt0oumgaTodkThdoThhRaE1KOOwiURiGppHJxhVCMYUREFg+QcHzOraZKucuvISlrzSxMSqPtw8ZQQ39mhf1Oj3csWESr7xh9fNMZi8xjT6fdw+rYaQrHIgKOO0Seg6LHt9h3mdTKXe782woY3ICoVuW5rzTpL/vexUPmgJMnXJqqy/0XAsuyvTVwFfvoMpI/sRjCp89zQ/HYoKuk6B3UZ3XDWrrJ02EYcoEFZU2oIykijQtD9IudfDklc+NPdn2ZRbWHz+FHkcjB/mRVY0OnWVYlEi3y5hl0QicZX2kEyh24ZDlIhrGt95+B3jGnu6H4ddtNpWWFj8h3DbbNz6zCZumjwCFZ13mzsZNagISRAJx425hiAY7XAckoiuQzAhCPU4bMiqhtsu0RGKYbdJ+PIcIEBXRMHpECnO92ATBTrDMtvbwtz01CajmMDv5dpzqpj5wGp+9c0TuOKRdfz10nHs7orwv5edSltYpsRjR9N0rju3imuUg+4p/YpcyIrKr755Ai67ZLiIZikOaAvKzG+oQISMlpZJx7qkKN9lN9auhW47vjzHYc0ZchUWJNcKPdvTZkuiWVh8EgKaSqGQu8I3pKnYRYn+VisKCwsLYGCph8XTa1GAA91R+hW4cGIIUbpllWseW8/9F4/lwYQ7t82GGW/eG4hS4LJT4JDY3RlmQGk+nkRRVJJ6vxeHTWRPV5Rit41ij4NgNM5v/7ktI1a5oqkVnt/KzVNG8OyGvQAUudPvo41+H7Prh3D1Y+u54MSB3DOzjtagbBZk+vLT78/ZWvIkY5mpMdok1nrDwuLYISwr2CQBm3SYYhSHkf8IRC0xioWFhUU2jmiWeMWKFbz++us4HOkX3CFDhrB79+4jeagjypgxYxBFkQcffJA5c+YQDAb585//zJlnnplTiBKLxYjFYubfgUDgM51DV1jmxr9vzBQu6AL1fq85Wfc4JCq8efzpjY/SxBVLZ4/lp89uZk59BS9vPcDMUwaz5JUm5tUbicvnN+43d5lsEfL4d8dx4QOrc57T45cargKp4oyepLYbSf13KknVezJQ19MiuU+Hk4HFbna2hRNV6hIvbNrP4282c+HJ5abbhtMm8sb2NkYOLOK///oO8xoqOHtEXz7uiFA3qBiA+Q+v5Z5Eb28wKtvmP/wWj186jjndsbSeyKCz4NH11JUXU1degqbDzVNG0NweTju/ZD/w3ogpGnfOGMXty9Mr5w7VCiemaOb2ySRzUgAjCELO9jcrtrXyUWuI40rc3DFjVNbF04IJlUii8Rkkre9TydUH/ZMGOfOctqyimVVNbQjAHTNGHdZ+vgiO9LjtjdRKx+F9C/mv+1dz54xR/Pjc4URlo43N3PoK5jUIxDWNMYNLUDVDQW2TRPZ2G4vi1qDM0tljeXbjfmB/xnHuvWg0V9yf7uA0dcmqjGqTVDwOiUcuOQVJFPi4I0JXJM5rHxzICMLfe9FovnFf9mtE6v6DUeM1f39nL1NOGAiQ87pRVuhKu861BuW0bXNdR7596hDmP/wWz1xVz/n3ZBdX9Hx9rn8n///MVfVMvmdlzn09c1U93/jDG+brk9fci04ZDBi/8fkNQwF4ZesBfnS2Sl15SZo7SZ8CJxfevzprO64VTa3s6Ypy0R/XmI8lgyert7dnvCabDa3HacNjl9Kcd5LkOXu/xRd+SYRin2bcFnkcXH9eNd1RhaCsEowqHAjGzESV22E4j7UFZfITLeU0XUfUBar7F2K3CTzxvfEcCMZQVJ1hvjwrKG9h8Qn4tPfbaFwzHalmPrqG33zrRI4rOTj2dEDWdTrCMn+efwpxVePC+1fzxPfGW20rLCyOAJ9m7IZklZe3HuCyr8VYu7Ods0b0Q1Z0uqMx8l02AtE4l//lbW6fVps2N73vO2PYsLvLXOsdV+Lm+Y2GY+Q9M+uY//BbLJ09lm/dtzrNhfFX3zzBnGO1B+W0djkep8S7zR2cWdWXi5e+mfOcH7nklB7zr+yJp3yX0VJ10sj+zOnRulHAEHPfMWNUhij/tEofd8wYxYBDzB1yFRaEZZXH32zmZxfUElWMdUO2JJqFBXyycdvaHeeGFzfwq2+eaDyQ0oJjwf++w7XnVFFgGaJYWHzufJGxqc9KsdvOnq4oJR6n4XzikAjKKoFInA9aglz6p7f4/bfH0G22d4Z8h0SeTcQuCpz6y9cAI065dPZY3vyo3fx7bn0Fe7uiDCrxIIqg61DmtLNwygi6woZDbVzVuPKRdezqiLBiWyuyqnH/v7dT7/cSjatGAaKiUeS2E42rXPXYesYOLjHvmbnum7la8vSM0aZirTcsLI4dgjHFdPA/HJIx2K6IJVqzsLCwyMYRzURpmoaqZibcPv74YwoKCo7koY4oFRUVvPjii3zrW9/isssuQ1VVTj31VJ577rmcr/n5z39uurIcCVq6Y2lVT2aFlUPgqomVbD8QNKvwewoe5jVUsOTVJlY1tTEzkSRNiiCyiSGSj3mcvd9QPQ4pQ8CQa1+5jgUH28Fkq9wHI8kbVzXmP/wW9140mlBMNLfLtX1yX3WDijMS7j3PIyyrfNwRydjukUtOMRcX8+orDFeWSHbxzaFa2jhtImWFzgxBxuG8DozFznXnVnFOTV88dhvtoRh6j5YcPemMxPn9a03c8fVR/Gx6LW2hOFFFxeMwvtdX32/hvn9tZ+zgEn75zRNo6Y6lOaGE5YOtPa483Y/LLlHk/uRBzlBMySmaWdnURih29LTpOdLjtjdSKx1lVUsTXfQmFMn2fG+iply/sd5+e2FZNcd2b+fR2z5Sn8t3HbyWHI4A69M8nxS8JP9/OK8/1PXpUPtKfT7XvmzSwXHaHcu8xt170eisQpQkPRcpK5pa0dCzBj56Hrve7+XZDXvZvKeLRdNqWfjkhjShz7b9gawiFTCcPvI+waLqP8mnHbdxRUMQoCscJ99pS0tUtQRivQotn7+mER3MRNb/fW88g315n+r8LSyORT7tuA1E4yiqTqHbaDH4nWXZk8nPX9NIdyROa8hIRAejCuVez2c9bQuLY55PM3YDiblMnsvOL/7xQVqLu1R6zittopA210kV/vZcy2ZbR9570WjgoB1/g99LKKawYGIlpXl2GvxeVmZZozT4vbyxvWe1dRsgcM/MOq5KCFIa/F4KnHbuWLk1q9Nlvd/LnTNGsSyLKP/f21q5/on3uGdmXa/rqiKPgztmjOL6J97LaCOwaFqtJYS1OCw+ybgtdNt586NOTkskh3tS4LLTHY2ztzNi/f4sLD5HvsjY1GelyOPg/X3d5DltBBPxvWQrY4APWoKc8f/+lfG6569ppMRxMOawqqkNURC49uzh1A4oMov/Hrh4LKCDLhCWFbwOieXv7TXv+41+L3+afzIXL32TXR0RQjGV0eXFzKmvMO/ZSWHL1Y8ZBYe3T6s9ZFyzNSjz9s4OFkz0Z2053rMQKOkgndrW55O4oVlYWHy5CMdUXJ8gbpqfyLNZYhQLCwuL7Byez9RhcvbZZ3PXXXeZfwuCQDAY5JZbbuG888477P1cf/31CILQ639bt249Yue9b98+vvvd7zJ79mzWrl3Lv/71LxwOB9/4xjfQdT3ra2644Qa6urrM/3bt2vWZzqEz5UblcUjcPbOOzXu6+Lg9iiSAXRK5ZUoN44dmBtXqBhWbAbBkkK/n/1PpaYmYjeTjhyum6G3b9bs6aezlOPsD0bR9HGr7ZG/unn26ezuPbI91ReKmowoYk4xcAp1kS5vezilbYvtwXpckIhstikKywu//9eEh7R+dNpEVTW10RuLs744x/d5VXHj/aqYuWcXUJav4zUvbCMsq/97WSiimcPrxfbhqYmXa+YRllXd3dVLhy2P04BKGleV/4kVUINK72ORQz3+RHOlx2xvJSkeAsgJn2nOfZFwdavv1uzppTByn5+MNh/jt9fb7bOwxNrO9HoyAfkvgYEWP0yYetojlkzyfFLykCl8O9fpDXZ8Ota/U53Ptq8RzUHSU58jc36d5v6ua2tKuTdm2TwZblq00WvosWr6JOfUVLJ09lnsvGs3S2WMZU17C4mk1Gb+Pxkofi6fVHkLudvTwacbtns4INz+1EVEwgu5uhyFAqU/8XlsC0V5/+267SHeP+7KFhcXh82nvtx6HhMcpke+Qct7DGvxe8h0SBW67eU3Md9mOKvGrhcWXlU8zdpPJKJsoHNYa7nD+7m0tm7pNkdvO3PoKtuzpYtH/Z+/M46Oo7///nJm9k2yOTcIdCGwkIQENICBJPNCqyCHWtl9FWy7rCW2/vbxAENBqj2+t0sMLta1of/36FRWPVotVQBEVKrcSQAJy5tpkz9k5fn/M7rKb3SSgeME8Hw8fkj1mZnfnM/M5Xu/Xa8pQ9reG6eG280lzkDsmZugDeQuZHus/dWRVfSMOqzEOP7+8iLsvG0pU1TuNXF1T30TvPGenovw3dzTS6O+6sAOgd56TB66s5l8/PoflN47lXz8+hweurDaFACbHzPG022O6vzqs/PujI/iC3Z+/JiYmn44vcm7qRJDjtBquy05rQohyLNeTjqPoVTsaafQbhSFLVtYzvCSPvvlO9vvC6IDHZUMGRpbkH31PfRN3LN/CH68aARgOr3dNGUpPt53Hpp/JC3NquOXiClRN54Erq6kuyact1P31yx+Jcv+V1WxoaGHWE+9x45Prmfn4u4kodUU7uh4Qd5AOyiqzn9rA+f/zBpf94S3O/80bzHlqA/tbQ8f7lZqYmHzF8UeU4xKjmM4oJiYmJl1zQp1RfvOb33DRRRcxZMgQwuEwU6dOZceOHRQWFvLUU08d83Z+8pOfMH369C5fM3DgwM94tEf5/e9/T25uLr/85S8Tj/31r3+lX79+vPPOO4wZMybtPXa7Hbvdnvb4pyV5EXNmbSmPrdlNdUk+j6zexajSAiYO7Y2saRlz6pIr5eOLyx3/nzxJFhd7vP7hYeac5wVSY1riES+vf3iY4mw7dV5Pp9VgyZOGh9rCGV+7dPVuHpt+JghCxigZUTBcPOLbW7p6N8tvrOHODg4wyUr3Wq+H6bF/d3VMnT0GxgRm/Lvr6Xago6PppH1f8c/w6LSRiBk+Q/yYLqrskbaPzqJwkt8XJyCrrG9oYWRJPj+64DRy7JZOXQ2SP1NbWOl2Ubk1GMWSJdAjx87CyVWEFZWgrJLntFKcY/9MKv5uHXa6ef6L5ES3265IrnTMtltSzqtM7TJOpvO1q9dv2+9j8ZQq5j67OcVdaet+H/MmVqY5KdV5Pdx4XhmznngXIOP5Wev1MKO2FAEhbb8d2+EdkyqZGov7qu0gFst0vIfbIp1eU2o7aavJgpdAROm00rUzsVpn2+1qW8n77Gy7NV4PimpMUNSVFeJ2pLfZw+0R6ryFKb9NpuPtSCYnl7qyQjzZdh6dNjJRRRR3XfnX9iPccK43MegRBIHD/ghul5XbLilHR6A9FCXHaUVAR9E1mgNR+ni++m4fx9tuE3a79U1kWSQ0QNF0zijJZUChiz+//TE/vnAwpYXZAB3aRyELJleytymEx23ss8brQRK/LtIdE5OvBp/mfusLyqzf08rIAfnYgcVThjJ3+aaUa3St18Ndlw1F1HSybVLi/qjpesKdwcTE5NPzadpuls2ICzzUGmbhlCruWL4lpd/TcdzW8e+O46K6WP+oroPgOZkar4fD7RHGlBYQUTVuu2QI+5qDhKMqz32wn9++ugOXTeLZG8fSHlYS/aOCLBtXPfJOp651vlCUp9Y1cG8sYmdDQ0uXn7093LUIrrvigjhdxQiYmHTH8bTb7u6vdkCwSRTn2Gn0y+Z5aWLyOfFFzk2dCAqybAgcnbvOtknHdD3Z3xZMcx7Jd1lx2SRGlOTz04sGE5IVerrtBCIKWTY7UU0n15Uah7OqvpHbJYG6skLaQlEufnBV4rl4P+K///afxP39sjP6dPuZ8pw2fvmPDzNGjgMsurSK/3fdWbidFtwOK8GowseNQWbUlHJ6v7xEzPWxuqGZmJh8vQjKCg7rsdfxO6wSomCswZiYmJiYpHNCxSh9+/blgw8+4Omnn2bjxo34/X5mzZrFVVddhdN57JU9RUVFFBUVnchD65JgMIgopt5cJMnoYGta15ESJ4os29HF6up+eYnomPj/mwIyqqYTlNMnvJIrxuLih2Xv7GFGTWni/3C0Qx0Xhzy8ahdn9M1j4tDezOyQgR2Oqmza6+O6cwYyb1Ild76wJW2xuuMkYq9cJzNqS9EhZSBSXZJHUFa57Ize3HZJBb5QNFFlvv1AG0U5Dj7Y25piqfjylgNMHNqL28ZXsLclRGG2DZtF5IAvzJKpw+nltvObf36YMpFY5y3kxvO8iUX2TMcZJ74IXN0vjzqvB7fDgqLpvLenhdkZBDrVJXlIgsCccV5+eH4ZvlCUPrEJyviC8OG2SNrCdjwKZ96ECm6/ZAgHfIZavuNCco3Xw/qGFpasrDcWQy+tBGDh5ErueC51QrfO62HepEpe3nwAl00i2yHRiYFPyjly6/JNqYuuMWX/Zx0sOa1Sl8IK59ckCuTzIF7pWH/Yn9IO4+1UILWt1JUVcuO5qecwGG126fQzESFFxFHr9TB3YiVRTeVnFw/mNqkCf0Qhqmi8tauJqx5ZyxWjSrjpXC9Wi0hU1Vi3u5lwVKW6xHBUikc13XiuF0kUyLZb8EeiRKI6TpvI7ZdUEIqqKKpuRJ1YJVoCEf5+3VkcbAsz9eG1NPplar0eFk0ZyqIVW1i7q7lTkcvYQR4GFmWhkS7QWhx7fzLJgpdar4deOQ4WTK5iwfObU7675IWMjv9eMLmSe15KddOq8XqQozp3TKpk4Qtb0iZP4vvsarszakrxBaPUeQtZPKWK/a1BbrukHBAS1UKiAGMHeri9wwRNnbeQaTUD0q5NcfKcqZMvdd5C5k0cwpTfr+l0ASV+Hbjjuc00+mWWXTOaHy59l99fNTwlTiiq6lz753f503dHZNzO151Gv5wQBAVUFYsADlFCsloQXDBnXBmKqtHTbWfxpVUEoyrBiBpzVogiCfDUu3u4/lwvdWWFTBs7wBSjmJh8ATT6ZRa9uJV//LAOAXAAv7hsKH5ZTYjpsm0SVkAVBRzA9v1tzKgppbFdpnee48v9ACYmpyhhVWXhpZWs3dnMi5v2c/dlVQRi8acuu0SO3UIoqvLY9DPJtlvIskvsbQnx2PQzUTWdt3Y1JcZFdd5CZtQOYNnaPcweV0YPtz1tfFXj9TBnXBlFOTae37ifB9/YlWLRv/jFbYAxfosoGr5QNBHNF4977Qy7RWTVjkZCsdckx25mwu3oejolp5v3m5h80QgYgpTO7q8CoGgKsqods5jKxMTk5MQXlGkOyASjKnJUpSjbjh3AJuEA0Lu5nmgKTRGN/8TmOuPUlRWy/KYaXt58gCsfXsvD3xtJYbYNiyQSkFVskog1VogZj7Cv7pdHWyjK7RMq+Ne2QxRm27hiVElC5OKySYmoveElece0gCyrWqfuZmvqm2gOyHznwbeB1LnleJ/j/iurE3/H3dBMMYrJyYysaKzd1cTogQXYLSf/XH8goh7X5xQFY07ddEYxMTExyYygd5ZD8zWhoaGB5uZmnn/+eX71q1+xapWhjvZ6vWRnG1XP9fX1+P1+/vSnP/H666/zt7/9DYAhQ4Zgs9lYuXIlF1xwAQsWLODKK6+kvb2d2267je3bt7Nt27ZjEtK0tbWRm5uLz+fD7XYf9+c40Bri46YAS16v56rR/bnxyfX84arhif8P8LhQdZ1/bDnEhoaWlA7z7HHelMfinfWRJfkUZNuwSgKaDpoOLUEZiyggRzV8IZmK3m50HXQgGFHIc9mIKBr+sILbacFplWgOhHE77ciqRiCs4LBJZNsshBQjVibbbsFuEWnyR3DaJNwOK/6IQlBWybJbEAUQBLCKIrKqoemGI0GW3UJU1Wj2y7wXy+QcUZLP3IkVHGgN8599rYwd5OGBlfVpbiTX1A7kg32tnDmgAEkUyLFb2Lq/jf1tIap65xJRNPJdVnq6HSx6cSsrtx9JeX9cqPONIT0ZM7AAQYeIqmGVRBr9EeoP++nhdiQEOofbIniLs/BHVP669mN+cuFgvrd0HfdcPozHYjnhhdk2nrxmDItWZF7YnvX4u/zyW8P4w+s7U8UlZYXcObmSoKwQiBjfWZZN4pPWEPkuGwFZIctm4WAsMiXuHlNdksec87yEoxoF2TbufWV7xoFUnbeQ2yaUE44azjotMWHT+w0tfHigjV9/+/TPNGDac8SPis7857em/E51ZYUsnDwEEYH+RdmfevufJ5+13R4rW/f7+Naf3k4MoiOKhssqoeo6vXId+MMKNotEjkOiyR/hdyvr09wa7pg8BKskEJK1hNAhqmrc9OR6fvOdM/jOg2/zwpwaZjz2LvdePoxitx1/WCXXaTE6z4LOut0t9HA7yLZLZNstqLpOY7tMjsOSEHvZJRFV18mxW7BIAg6rRFBWaQ7K9HQ70DTQdI1cp42oahxLjtOK0yrSEojgsltjbTxKgcu4brTGjrctJHPH8i0suWo473/cTHFKGwtTM6iQg+1hnDaJQFjF7bKSZZVo9IexWQ2hQFGOnYOtYSRJIMtuIRAxHH7sFpGWoIwoiogCHPCFsYgCh9vCjB1UiKyqtIUVVE3HJkmIIhxsC9Mv30m2zWJMnoSj5DisZNmMfbrsVuySyL6WEE6blPiOLKLAhr2tbD/Qxu2XVADQFIiQ47Cyfk8Li17clljoMNpBJTqG20myfeuT7+xJuTbFqY1do4i9Jx5fVjPIw5UPv9PpebZ0+pksXb2LRVOq2NccQtd1bFaJB1buSBP+zBlXRt9cB32/Bs4oHemu3W5oaOGyP7wFwJs/OQdBFNjXHGRggYsoIGs6oahKJKqSH7vntcV+e7sk8sDKj/jRNwYT1VRsosR9r33E/EmV3V4nzexoE5POOZb7bbztrvpJDQ7RCjpEBGhPuj5n2yRsGDmjQU2hvjHMU+saGF/VkxH98xlQeHz9DbPdmph0zbG03YbGALqucyQQIdthZX9riB45DnIdFhRdJxTV8EeOjhlbAjIuuwW3TULWdIKy4diY7bCgajqCoLP1k3ZGDsjnwTd3cv05gwxRSTCKI1bQsGrHEYqy7VT0dhOIqKiaztu7mhJVyvHxnqrpqJrOjU+uBzofN8fjEfNcNl7/8DAXVhQzrF8+vqDMnKc28GYGp8qzywr51bdP52d//6DT580qaZMvg67a7ZGmAOgQFsAvqylxG3FJZ0N7BB3Id9kYVPzVHMebmJxsfFFzU8fKgdYQe5oCPPD60bmp04qzeXLaSHQM4ZqqgyRAGFLmU3JsEnZd59mtB1n1UWNGV9w6byEjBuRz32s7eGFODQICeQ4L+9vCZNkttIcVZj7+LvdfWZ2Yc41T4/Uwf1Il/2lowZNtTziuHPKFGVjkIihrLHtnT2Kus7P+fvK8QSb+ePVwbvjr+pT9VpfkJ4Q1dd5C7ry0En9E4aYn1/PAldWckRQvZGJyMhFRVC753Sp2Hgkw+zwvP71o8Jd9SJ8705euIyAr/Pgbx/5Zf/L3/zBhaC9unzDkczwyExMTk68nn9kZ5fnnnz/m106ePPmz7i6NO+64gyeeeCLxd3W1UY3/+uuvc+655wJwzTXX8MYbb6S9Zvfu3QwYMIBx48axbNkyfvnLX/LLX/4Sl8vFWWedxSuvvHJcji6fhbZwlHBUY+LQXvQrMPaZnJWtaUaMy7b9voxOJ49OG4mIwKr6RoKyajhsJLksJDtwzKgp5el1Dfzg/DKmxhY2/zprFG6HlfnPbU4ZKNR5C/nJRYNZsnIHP7rgNARBx2mxEJBV/BGFHIcFq2hMKmY7rDgsIrquI4oCug6aruOwSAhAc1DGZbNgswgIWNBjn61HroNzTyti4tBeRnSOqpHtsHBxVU8cFpGFkysJK0ZlTq7Tik0y3FvOryhG1XSsksDWT3x84gsztM9RIUrfPCchReXnF5Xzk28Mpikoo6g66xtaePqdPdx2SQWCIBCUFQ76IgiCwKZPWnFaRc4v72lE2cSq5Qd4XPgjCk5N52cXlfP9P7/HFaNKsAgCP71wMLeMF2kPRVFUlZsvLud2ScQXiiYW+fc0Bbnn8mHkOCyMGVTADecOwiqJ2K0iWTYL73/czPwXth5dwPZ6mFZTyrTH1vHXWaO568WtaQO4NfVNiILAzy8aTFTVufnicu577aOUxe26skLmnOflgC/Mo6vTB3AzYq47xzJJ2tkAziqAgMj4qp5MHzsgRVwgCWLslz61ybJZqC7JS6kGifP090cTimrUH/HTJ89FSYGTubFOa7LoZOZj77K35aiQIS5Wg6PXCl03MnKXvF6f9lv/8PwyzhpYQCCiElGM8yyqaPTOc7L4xQ5ColhMz32vfsRv/usMsgQBTdexW4wKldZgBCEmMov/p+nGZES8XcuK4SSh6roheNN1inMc3H9lNf6wzFhvIcGkCQtvcTbNgTCF2XaiqkZYVBGF2PsBiyjQy+2kJRghP8sW26+AJGo0BWTe/biZkSX5WCwaWXbDRjXbIZFll9jTFOT6J99nRP98FkyuRMAQeRS4bIiCwCe+EEFZIxw1BHaH28KcWVpAVFXZ0xSmd56TRRm+o3mTKjOK3ZIrZFbtaOSO57ewYFIlVotAlk3igdfr2dDQyv1XVhNRtLTfynBy2cZrHbZ76em9u4wV0nWd1fVNzFu+mRk1pRRk2fiff2a2nRUR+MVlVZlP2K85yVXMgihg0XT65Ls4GIggijoFTgeS3YIoCBzxGyKinm4HEVUlqmv89BuDjeuWIHLvK9v5+cXladfI5Oth/L5067ObUs6Rs8sKuSdm829iYtI9bodh2S2IVj5uCTAg3xDLCQIIGPchRdOxisZNZ1+rzJOx/hwCNAdkBhQe+/72t4aMSC+z3ZqYfCYEQUfWdH732o60ceTiKVW4bRIumyFubozdd7NsEiFVRRTERF8SjP4egsDIAflEVJWrRg/g3d3N9HA70dHJshsC/fKebjbsbWXBC1sZM7CAn11YznmDixjSy50Q8d7yzEaWTj8TSRD4w1XDcVglNu5r5ZpaI+433hd7bM3ulD56jdfDt4f3Tdzrf3h+GT+5cDBCTOxsjY1FzzutiB5uRyKW880O15IT4UBpYnLC0VRCgsTtz25Kba9lhdw1pQqnpuKyW/CHFTRdxxc0K/1NTE41fEGZf394hBWb9qfMJXx02A/oCLqAKhhClAipQpR4fM+OlhB1ZcXc/dKHGfexqr6R2ydW0DvXgabpyKqGqsjkOOwEZYVcp5WXf1hHUFb54bgy5k0YgqLp3PDX91lT38SiFVu5Y+IQmvwyxTkSINDYHkYQBEQB1uxsoikgE5DVTvv7uc6u3ctsUqq7ypr6JmbG5uPjn2F3Y4DH1uzmr7NGIwqw87DfFLmbnJSs+qiRnUcCDC/JZ+ma3cyoGYAn++sTNfZp8EcUnLbjc4DJtltoMWN6TExMTDLymcUoU6ZMSflbEAQ6mq0IsdklVe3cEvfT8vjjj/P44493+Zp///vf3W7niiuu4IorrjgxB/Up8IUUfva/H/D3685C1XXqvIVGJXwsTqZ/vpNRAwq49ZIKfvHSNqpL8plVOxCLJOBx2ciySSy8tDIlbsBhFbEIAn+/7ixCUWNSQQBCskJVn1zu/9cOnrxmNJFYtVrHCUQwOtcIcNeUKkJRhXtf+ZCN+3wsnX4m97/2Ee83tCaqyWTVqDYXBeid5zAmHSMqkiDgsokUZNkIRTWa/HJiElLVdFqDMgVZNmTFWFR2WCVynVa0WCVdIKIQVTXW7DSq3TxZNh763ghAIBiJYrdaGTGggKGKxn5fiOIcOzaLyNaD7eTYLRS77WzZ7yM/y45VEhhf1ZOJQ3shCQJ3ZBDfLLi0kl+8uJW3djWnOFnEHVKG9bXy+6uGc99rH6VNXM6fWMlLmw/w7sfNncbWVJfkM/WRd/jf689i8pI1icdTFrDrm9CAK0aVcLAtzPsNrWk5q+tjbjJH/DIzH3+XGq+Hn11UzvfGDEDV9USM0Fu7mtKOx2WTqC7Jx2WVaPLLRNU2bJJIe1gm12VHjol/XHYLkiBgk0TmLt+c4ugSH8BpgsCCF7YwpHcuPdxGPZUgCHziC3Pnii3cOanyBLSQrzd5LitzxpUBpFRizptQQV6WjWBLiME93TgNZQ+LX9yaFm/zyLQzueoRIxIHjgpQ6ryFiIJAjdeDwyLxq1c6ER8IApdU9WTRi9v4/dThbD/YTg+3gyN+mZ9dNJiffGMwEcVw5vFHooRljevP9dLQFORwW5i++S5e2XWQM/sXYJVEwlEVRdN58p099C3ISpybvpDChr2t7GsOGOrxKEgioMPuxgA3P7ORoKwm2pYkCBRm2wlFVVQNYmuMiWqbOEJMmBJVdaySsRiw4Pkt3DVlKDarwPkVxQQjKp5sG5GohiiAJIgU5zhoDkR49saxWCWR5kCELLvh5BII6xz2RcjNspJlF/CHFbIdxkDjiofW8tv/OgOrRaQ1KMdEbTqKpiGKArKi8suXt6U5m8S/+5m1pYnrw6odjRxqCxOKqsyJxSLdMr6c/a1hbr64HFnROOKPYJOMxZMrHlrL0ulncuXo/jH3F+M7PdgaZnqNEYXWUcAyvaYUKXavXV3fxG0ThiBAxiokMK7t7V3Y1H+dKcy2UVdWyKodjQSiKgJw/2sfctv4IUQ0ndaIQijm3JXrtBrOYLKKIILLInHDsvXcPmEIS16vZ/rYAcxdvplff/v0xPWt4wJ2xyrrOGZ2tInJ8VGYbWPexCG0yyrffuRdXpk9imynK+bgpyMKAhZRQAKaFJW8LCvD+uax80iAPnlOHMcRC+gLymkT02C2WxOTT4OiwZ3Pb8k4jpz73GYWTq5E0w0XzlyXFadFoj2iEIyo5DgsOG0SoijgC0bJsluwxqIFdU0gHFUo65FDc1BGFAQCskLPXAd2i0i23cLFlT3xR6IcaY9w/ZPvJ4T9LpvE0mkj+dUr21OOq8brYVjfXEaVFnDzxeX8KoOz5IaGVg60hdPGPnEh/+xl6xnZP59zTjNiheOxnI1+ObEYV5htLkCZfDVpUWHRik3p7XVHI7cv38y8CRX8c+tBfvvqDsAUaZqYnIo0+mWK3XbW1DelOIhFFI0mWSMqR+iZ7ULGcB3VdB0BQwQSiChctnQdV4/pz9A+uV3upz2kMLAoiyy7BYeu87tXd/GD80+jwGUjomrc9uymtHmxP88axfceXceqHY00NAcTMXx1ZYXMnzQEXzBCvwIXf7pqBKqmd9nf/9W3T+fsssKM7mbxufiORBQt7e/1Da3s94XSXLDN66fJycSKjfspKXBy3TkDueGv7/PKloNcNbr/l31YnysBWSE/6/j681l2C61B+XM6IhMTE5OvN59ZjKJpRztir732GjfffDN33303Z511FgBvv/02c+fO5e677/6suzqpcTssXDGqhHnPbeaa2oGGG8m/PkrEyZzZP5/GgMz7Dc3MmziEYFSjPeaa0B6OMu2xdTT65RQbwfig4dzTivCForSFlYSAIT5RF1E07ppSRTAqdr5ouaORI/4IkajG7PO8iSif+OvjC66PThuZGAhk4oU5NTS2yxRm29m0rzXFTnHHYT8VvXJRNZ1wVEUSBQTijgsaG/f5OPe0ImoHFZJll1BU2HHIR1GOA4sgEIqqBCIqvXOdWCWB3U1BLKKA0yZxoCXM3pYQp/XM4XBbhENtEXTdWETPNGm64LktnF6Sx2vbj2R0snh02kgeW7Ob6TWlrN3VnPguDXX+Fn54wWnc99qOjN9BspJe0fSUxyF1ATv+WosodFq1d/+V1dgsQtI2tjNhaC8qe+fSGpK59dnNPDptZMrxuGxSxu3VeT0snFLFohe2pDgy1Hk93Hiel/cbWlI+S3wAd9uECqaO7p/x+GbUlOKPnpwL3sdDrsvGgAIXc8aVccv4coIRlVyXlQ17WvhmzBZ0Zm0p4yt7srsxwK3jK3BYRZr8Mll2C4fawvzutQ+59/JhzHriPcZX9aAox86Lc2rxRxREAeZOqECjC/HBjkZ+fnE5D1xZzV/WfpwQUSRPLhTl2Ln35W2ZbVTLCrl1fDkvbT7Icxs+YcHkSnq6HUwd3Z8chyHuONIewWmzcHaZh8LsPuz3hciyW1A1Q1ySZZf47XfOQNdhX2sQMOKxAhEVTdfRENjvC/PWzlSb9R+OK8MViwtSdbBIAofbIjQFZK5/8n1m1pYydqAHURRoaA6R77Iy4/GjjlDJuGwST187hv2tIYpy7HzrwbWd/m6SIDD1kaOxOHVlhXy/biCapqPqeko7SaZjxQxAaywzNO5cNaSXO+Fsk4l9LaGUuDaAIVcN56d//4CZtaXMrClNifD5wVMb+PW3T0+83xeMIgqdbd3gZM6Bv+lcL5quI4nQGlR4cfNhvjmihFlPvMfscV4+aGjJeJ7XeD2cPbiYLLuFVTsaufniclbtaKQlINPD7ci4gF3dL7PrERjXyU9aQzQGZLMyysSkG3JdNob3z6MtpADwSbvGhq37umyv1SX5WCWRYERJCMaOhUa/nDYxHcfMfDcxOT6CUbXL/mdTQOZ7S9cl+nWLLzWc2bSY+52qG45HuS4r7SEFi8OCrGrYLAJZWIyqRKuFd3Y3IQhQ1dtY3OrhdvDatkNs/sTH9+sGpuz3zsmV7G0O8sMLTuOm83Sy7EbV9MoPD7Fs7R6q+ubRHlYyHvfM2lIeenMnp5fkMb1mQEohwLJ39iTGa8nCtfh/JiZfdbobL2rEHIpimCJNE5NTj7Zw1IiWzjB3+P+uO4tGv8KkP/478fq/XTuG/3oodV6lOyEKGHMziqbx7sctjB5QwM8vruC7j77DQ98bSWtQThOLxl1g/3DVcCYtWZMiDFm1o5EFz29h8ZQqNF2nd74DVdO77O/7w0pGd7P4XOYPntqQ9r54UVjy3zNrS9PcieP7MK+fJicDiqrx6tZDjB/aC7fDysDCLNbtbj75xSgRFedxFLyA6YxiYmJi0hWfWYySzI9+9CP+9Kc/UVtbm3jsoosuwuVyce2117Jt27YTubuTivwsG2cN9LBkZT1nlOSz83A7P7xgMBYJfn5ReSL6pi2scvvyzaypb8pYjV2cY1ikdSY4yBQhcbg9gi/U9Y1SUXV+8PQG7rl8GL959aOMHY64k0tnjiD/2HIoER9007mDmPnEewRlFZdN4tFpI/nlK9vSKtdm1ZaSZZNYveMIv3h5e+K5ceVFzJ9UiT+s0NAcRBCEhNBmREk+d15aybSl61h0aRW98py8v6o5RZBRV1bItLEDUsQkcVbVN/KziwczvCSfjftaE5OesqpRnOPA7bTw3bMG4LJJPHBlNXNi36Xx3iZuOq/rWJr4gMnVweot0wJ2RNHo73Ex//ktGd0uwJhsTX7slvHlNLXLiLFJpI7K/Zm1pWmZq/Fjn7d8M2eU5KcsssddWpKFMnHe3NHIbToZtxf/e56ZkwgYYozdRwIUuw0RVkBW0YE/XTUCDZ1HVu3KKOaZ8fi7DC/J445JlaiaxoSqnvzs4nJuX55eJfKjC07r8hj2Ngd56p0GptUY5z6Qcp14dNrILicnp48Ns6GhhT/PGsWiFanxNLVeD/MmVnLVI2u5bXw5BVl2HlhZn3aMd15aiaxoPLhqZ8Ie/Z5XtqU5fcSvUxsaWlF1Pd1K2uvh+ZtqOOKPEJQNN6grHlpLUFZ5dNrIjEIUMMQgcqxNqJqecNDoSK3Xw1u70isGNV3npxcO5oAv3OV37bJZWDr9TCMWbF0DffOdCAI8+N0R9M51IokCf7x6OHaLlCYShNSYtuTH4mKWTCS/1u00Ymi6IsfRtS3t15VGv8zMJ95lZm0pdkki7hwavxZ2JR5ZU9/ETed6ybJJzB7nJRwT07XFhDuZFrA7XmM78nFTkBufXJ+xMqqz+DMTk1MW3YiQA6NtHUt7jSgqOU7rcVnotnUjxvs0Yj2zPZucqgQjXQvPfaFool+3pr6JoKyypzmIwyrx9q4mzhpYwJKV9Yl+Xnxs+IfX69McLG88bxCzYmNI4zEj2vThVbt4YXYtzQEZt9OCRRJZ0MEBs8brYfZ5Xob1yaN3noMj7ZG0im+HVaI4x8bwkjweXZ1ZaG+J9a9M4ZrJ15H2mOCz0+fDCqNLPSmPvRkTlQHmfc7E5BTA7bDSHJAzzh1m2SXaw6mCDLfTiNr84fllnH1aEf6wcZ15Y8eRTueI67weXDYJCYGhvXOJ6hrXPPoee1tChjv0npa094AhSLk9Fp/TN9+ZmHNZuno3q+ub+KQ1TN88J7/+xzZm1g7MuI047eEog4qzU9zNsuwW3tvTkpgzT6ajW0r87+6KU8y+gsnXnd2NAQKySnnPHADKe7lZu6sJXdcTaQgnI4GIkiZA645su4VPWkKf0xGZmJiYfL05oWKUnTt3kpeXl/Z4bm4uH3/88Ync1UlHD7eDvc2GU8DS1bu5/8pqfvvah5zeL49vlPfAYZUQRBgbE6wAjCzJB0hUyDusEp4sG+eXF1HRO7dLgUCysCDZoaMzFE3n3suHsTS2zY6iieTjTt4PpKvKV+1oxCoJPBB7rVUS0YHTS/J5v6E10eHf0NDKIV+Yil65XDm6PzNqByYWdqeO7s/cDovTyQvY85/bzJ9njiIgqyxekcE2OraonElgAYYrwU///gHzJgxhWN9cGlqC2C0Sr394mK37fUwd3Z9rnniPESX5KeIeAJe968UQh1WizuvJ+FzHRU27RURWtIyDNzC+Z7nDewIRlWBUpV+BM7GNZLoaKK2ub2JGht+2s98cSIsM6fi+7s+ukx9fUGZPczAtc7fG6+H2Syr4n39+yOpu2urCF7aw6NIqrjt3UJoQBYzf7oZzu14Ut1tEVtU3omGc+5AqJOpuUT0SOxfjoqVkMcrq+iZ++co2lk4/E5sksqcpyKzagVSX5CeEFqvrm1i3q5l/bTtEdUk+/33BafhC0bTXJX92IGOVyar6Jua/sIWbLy7nyoffMWKsSvJYU9/UrTDu3x8Zrkcum8TyG2u4c0Wq2CsuVstUCbOmvolbx4vdDkiCssKsJ96jxuvh6WvH8IuXtvH2ruZuxTfxquH4JMfhtjAXlBdT3ttNrtPKo9NGpojv4ted5PfUej3YLCI2SaTW60k7t+Kv6SiIO1loC0cTop0JQ3vhsBjfg90i4rJJFGbbeGF2LYJgiJOy7RKaDv/afogH39iFKApMeGA11SV5XF7dB5dNSgh3Mi1gd3cuxJ/vWBnVMe4HTCtfExNNh7d2NXFBRTEl+S5EEZ7+/piEq8GbOw6jaDpVvXOJKBp5LitOq+GI8tO/f3DM7Sfb3vUQKKub5ztitmeTU5kcR2p7iQs8Rpbkk+uy4rBKNAdk/nrNaFZuP0xAVhKub3VeD6NLC3i/oTXx/s4qjJP7sIkoxJhovrokn4+bAjy+5mMWTalk3vLNaeO/+PYmDO2FJ9tGnsuasXjjF5dV8dKmA52Oo3964eDEYyezy5zJyUm2w0Jhto17Lx9GsduOP2zEZR1qC3PzMxvJtlvSCpVcNgkdmP3UBvM+Z2JyClCYbWPdx82JgslkrKLI4fZIynxLjk3iL7NGseuwn0BEQdV08rIM99w7J1cmnKytksiqHUfYuK+V2y6pIKKoZNss3PevrdwyviIheuuV6+DpdQ2dHl97KEqN18M/txoFj8nzKb5QlIIsG9ef602IYjojx2HNKCY/97QiXu6fn+KWUhuLRo7PESXPcyc71GY8XrOvYPI1Z+uBNgD6F2QBUN7TzYqNB9jbHKLE4/oyD+1zJSArx1XwAsY8gxnTY2JiYpKZEypGOfPMM/nxj3/MX/7yF3r06AHAoUOH+NnPfsaoUaNO5K5OSrJjE3lBWeUHT21gZm0pVb1z+bg5iN0iUlaUjSWWoe2ySfTKc7BhdUtq1EpZIXMnDKEpEEl5vGPVV0mB0VlYuno32XaJQERh2TWjaQ1FEzbES1fvBoz4D4dVxGYRuXV8BVFVQxTg6WtHE5Q1wlEVh1Vi0yet7Dzs57bxFQRlFbfTglUSafJHyLZb+Nu1Y4iqOrKqkeOwIAlw0BdB0XQCssI3Know5Yze7GoMYJck8rOs1B9qJ8shMcCSRXs4yoShvfjW8D7c99pHnU4wxicow4pGWyia9rrk7yLLZmF4SX7awq7DetRZ5tZnNyXeGx9wPP3OHv527Vn8Y+vBhF3z0tW7mVlbSpbNwgXlRZT3zk2pslvf0MK2/T56uR384PwyXv/wcNo5kLyoGV9czrIXdnnetHcYYGXbJVw2J5Ig8Oi0keS5bNx9WRWLX9xGUFaPSXBwPI8HIl0P8Lp7/lSgNRjlgZU7Mk5q/+Ll7ZzeLy/hRtNxcjDPZaW6Xx43P7ORsKIhCJ2Lf97e1cSEoT355vC+iffnOi1YLSIHW8K4HBZmj/Ma52pMXJR8nYgv1sfbR9wNyCoJHGwL0y/fxfOzazjgC9MnNukYbzcum8TVYwaw5RMfxW4HiqbTO9fBBeU9qPUWEpJV3m9ooW+ei6vGDOCAL4QvFE20j965Dh68egSKplGYYycQVsnLsmITRc4sKWDxpVWJNu12Wo2ontYw4ajG0ulnsnFfK9fEKl+OVRhXXZLHy1sOUF2Sz0++MZioqqFoOnlOK99+8O1O3VX8EaVbwUtcGLKmvokFz2/hjJL8LkWCdovIA1dW47BKSLHosV9cVsWogQWMGFDAnS9s6dTlqrokL/G5ar0e7phUyZ6mIPlOC/MmVrJoxZYUQUr8NU3+MP09WRk/49cZd5LjS1soiuCyMr2mFF9Q5rmbzkLVBQ60humZ66Aw22bcoxxWLjujDxOH9SSqwjPXj0VH51B7hMVTqsiODUCTtx1vK8cqEoKjlVFAl/nVppWvyalKIKLw9LoGnrl+LH/89w6uOXsQuS5r4to/eVhvbMDtL2zmte1G+6krK2Th5Ere29NyzO3HJomMKy9iSIa+2tb9PmzSsVdAZYrvArM9m5w6iAJcUFFMeS83Zw7Ix2WzkGWTyLEbETttsYUhiygwvrIHLpuFZ64/C6dNSlhfPzHjTFw2CzariKrp1Awq5NbxFYkF8vi9M5NAPv6YrGqcXpJHINJ5bNCa+ib++4LTkESwiGLGflmx29Hl+28Zf7QC9GR1mTM5eXFZRJ67/iwUQSAoq4iCEZHsLc42Htch2mHhZWZtaZrTEJj3OROTk5VclyHIaIgVTCbz6raDXFDRg9JCYx5hTX0TFgw37ec/2M+q+iYKs23873VncUlVT0Agy2ZBxLjmjK/qyTerexOQFX71j4+YO2EIVX1yufOFLcysLWVDQwsbGlq5YlRJp0V0OU4rs2pLmb1sQ+IY4vMpxW47baEo2XYLoajKuPKilCKqOHVlhTisYqciu2S3FIdN4pOWEP0KnImiynhUclBWuy1OMfsKJl93th5oozDblli3GtzDcEjZsLflpBWjqJpOOKrhsByfGCXHYcEXUk561xgTExOTT8MJFaMsXbqUyy67jJKSEvr16wfA3r17KSsrY/ny5SdyVycl+S5booq9YxSDITKpwO00OrEza0tZvGJresXYjkYWrtjCnPPKEo+5bBK/nzqcA76jNmGH2yP0yXXywpwaJEHk3jXb0yIwnrupBkXTuPvFbdz27ObEczVeD3dMrOR3/9qR6NS7bBJLp43k96/Xp8TpxKN2/BGVR1bvYkNDa2KhG6BnroNXtx7ioTd3JRwBZtSU8uEhH2eXGYsEDU1HY3ieXtfAT74xmOvP9XL1GCMqx2mV0HUjr7w5ICcmOwXBEPj84arhiQWGp9c1cM/lw1Iq4Fw2ibkTKvjbdWPY1xIi12kl227hvtc+6rQirrokn8PtRmTJjJpS7KLIGf3yeGzNbp5e18DfrjuLO57bnPIbxmNM7n/tQ64/t4wH39iVsu3kRcvkRfMLynt0ddogJWU6jysvwmWzcMdzm1MWn+u8hTw6bSSznnjvmKv4j+Xxs8sK06ohO9Ld86cCAVnpVECyakcjs2pKmT3Oy+j+BfT1OJkbi+KKU+v18OQ1YwhEZKJqurgsfn7/c/NBHvreyIRzSvx1Zw304LBJ+MNReuc6+P3U4ciqhqrpKduyW0Sen13DgufThQ8zakq5/I9vJYQP33nwbUb0z+f52bXIioqq6SiqgtOWRbbdSiiqouk6omjYszssErXeQvJdVlqDUSp75yIIJOJurKJIvwJnWiXruPIi7pg4hLd3NlHsdiQijg75Qpw1yMN3H13H3pYQdV4PZ/TLY1RpATNrSlE0nTsnV9IWUlB1HVnReHtXU4r7yIyaUm55ZiNXjCrBaZNoaZWNwULMMaMzsuwWlq7ezaPTRiJCmkNTx3zhZMehTBMqLpvE1NH9eXzN7tTrcFkhowd6+OXL6df6NfVNiAi8OKcWXYfmQIS/X38WB31hpj68lnsvH4bLbuWqR4x/3zy+HH9YJdshcbgtwtSH1/KXWaM7/YxfZwqzbZxdVsibOxpxO634wwq3PLORp68dw53PG05ZLpvEjy7wcn55T1x2C/6wgiiAyyphQeO3/97BxNP78NQ7e5g3sTJx741v+709Ld1G4SWLhJKJV0Z1lV9tWvmanKrkOq1cPaY/j67ayezzyvB3uBZrOigCLJxUxU3nBvnXR80sXb2bO57bkhAjH0v7aQvL3HxxBQtXbMnYV2sPy8CxifUyxXfFMduzySmBALdPqGDxiq2A4cJ4pD1CvstKT7eDRr/MP7cepLfbydB+uXzcFKQox87q+iOc0S+fHIcVTTfcFpv8EeSozvVPvk9QVhN94KseWZsQpGQSyCuaTllxNm6HlbawkmLb37FP5wtFyXW6Eo5/HelOuB/PgT+7rJDCbLNtm3y9cIkg6wJqB3dcVdOxCAIuUcfX4T1jM7gjxDHvcyYmJye98pz4MxSW/f71nUwe1hsxCxZdWkUoqhLSdB5YuYPVsXH2X2aOQkJFEiyoAvhlFX9Ewe20YrdIbNjbyul9ctm4z0dIVjm/opiH3tzF9ecM4ox+efzgqQ08/L2RGa87tV4PWTaJTZ/4jrpTd5hPSZ4Hu+EcL9+vG8SanY2JPkGN18OCyZXc/dK2LsXkg4qzAUN4fveL27j1ksEMKspi7vKj860um0SWXcpY3BmUVbOvYHJSsG1/W8IVBYy1loIsGx8dav8Sj+rzJSgb1z+H9fidUVRdpz2ipBSzmZiYmJicYDGK1+tl48aNvPrqq2zfbggSKioquOCCC0w14DHQw+3g7suGctuzm9Kq2BdeWsW+piAaOnVlhV1GrRgVW+WJv687ZyAOq8iLSXbDhdk2lkytRkBg3vJN6TE29U0sfGEL44f2yuhAsmjFlpSYjpm1pTyQwU7ZWDCF8UN7saGhNePiXV3S4l1c0T5v4hDuWJ6e8/3kNWO495VtCbeSTjPFywoZU1rAzKRM8Rqvh2XXjOGXSREZLttRB5RkwU2dt5CfXDSY740ZQDDm/BIfUMSr75InMOdPquTOF4yoj9njvCx4bnPad7G6vomFK7ZQXZKPPxJNmRit83pYMLmK5kCES6p64bCKtARklkwdTkswQp23kFX16Qsddd5CVscer/F6+NmF5WlCFOP3bAQBnp9dQ0TROt1ebYcq/uT9HGoLpzx2dlkh914+DF03zslMCzF1ZYXkHKfV/clIoAthg8sm0TvfQW+fg575jjQhChjnzqIVW7j7sqFoms6zN9aweEW6YGTJ1OEsikXOJJ/bqe2tkHmThmAVhYSriKbrvLWzCYso8O7HzccU7xX/953Pb2HEgHw27Wtl3sRK5i5PvX7VeD1cUzsQVdd4YOXOjE4ls5etZ0RJHn0LnCk27QCn98vjgC/Mig526TVeDwMKs3jouyMYf//qWPsXGDEgn1lPvEet18MZJfksWVmfcJu5uLIn55xWRFTReGtXE7c8szFNnAZGO+nK9URRNUb0z6fY7WDhpVVEVA1fMJpwTMmUL9zVwkamLGYwxArzn9+S4pyT8nx9I35ZYdM+H71ynXzvsXdTYn7KirMp75nDrCfeS3tvrddz0grFcl027rl8GLc8sxGbJCI6LFw9pn+KEOXBq0dglQTueD61vdV5C1k0pYr/Pv807vnHdip65zJ3+SZ+850zyE3a9hsfHenU5UYUBJ6+dgz/3Hoo47mQ47BmjPtJxrTyNTlVKc6xM668mAKnFUWHRS9uTRNnLp5ShYRAUY6Lrft3J/qQ02sGAMfWftwOG7d1Enm3cMUW7p4y9JiP2WzPJqc6TovEnS9s4crR/dP6VLVeDz+/uJwxpR6WrNzBz57ZCMQKGaafyQP/2pEyhquNLRDNHjeIX77yUaIPfO/lwxL9mbhAPr7YNLwknxyHhSPtEd7e1cTT6xq4YlQJZw30UDuokFDUcOdLFqYsfGErP7jgaPFGMt0J9xVVT4yDulqAz2T9by7Ym3zZqAhEgXnPpRc/LJ4yFAmBtbtTXQK6axPmfc7E5OSkOMeeKPKIE5RVblq2nj9MHU5I0SBWGBifA7rpvEFYJBGrKBLW4fZnN2XsyzcHIjwxYxThqEprKMrM2lJUXU+Mn22W9MjhWq+Huy4byitbD1DVOzfxePJ8SlfzYM/eOJYDrWHea2jhSFuYy4b35fUPj6SN1zuK7OJzAL/+x3Z+dH4Zt0+oQEcgEI5SmG3njufS567vv7Kav61rYOGlVea93+Rrz9YDbdSVFaU81jffyUeH/F/SEX3+HHXOP3bHVIDsmAClNRA1xSgmJiYmHTjhK1GCIHDhhRdy4YUXnuhNnxKUeLL4zXfOoCUg0xZWcDss5GfZcFhENM1JIBJl4aWV7DoS6HI7iqonFlPPG1zMva9sTxGiPHnNGBau2MLMmtJObYhX1TcxvYMNcpzkSn+gS3FMfDudLrjGsr7ji9tDeuemCVGgcxFMxkzxHY1oemqmuPH+rZxecnRht/NjakRHN1xPLCLhqErNIA+Xnt6bmY+/S0TREpMya+qbUDX9qGtKN0Kh/77gNAQBXvlRHftbw7gdFmwWkfojfiyiwBs7Gtm+38e4ih7c9uxmXDaJJVOrAb2De00hd0wawq7GAI9OG8nh9giiSJoQJfk72dMU5Ml39rBoSlVG0cAdkyq55+VtKe+r83qYUTuAJr/MP35UR0hWyXFYKcw+OqG6eEoVc5dvThGk1JUVsnhKFb3zT07LvuMhz9l5B/S6cwbSGsvGjap6RgEEGL+rLxxl0z4fL286kLl9vLiV0/vl8a/tR7o8txe9sJXxQ3t2EGB5+Pn4cu57bUfG/Sfboif/e1V9Iz+8oAxF0xOOLB3fN2FoL17qICaJPwdH237ydSBOx+tXx/cuurQq5bPdekk5xTl2ziwt4BcvGedyo19m1hPvJYQxH+xr5ayBHiYN65XRYarZLyeub5nEMw6LyOn98vivB9/milEljB3oIddl5b8eWpvxu3PZJPrmOxEE+OPVwxPRRwd8YaySSK7T0vn1c0cj08cOyPgcQDCist8X5n9e/ShR/ROP+dm239epwPGuy4bS9yRum73zDAvdI4EwOVaJ8wYXJ87tmbWl7GsJpgmcwDiH5i3fxG0ThvCtEf0QBIElK+tpCcj0cDsS2x7ZP59b/29T2n7B+M1uOGdQxt/0WCujTCtfk1OVXJcNmy+MqpPxnrK6vom5yzdz+4Qh2C0iQ2LxZzNrSxOiv2NpP7Ka2REBjOu+rHbtjJBMd5NMZns2OdnxyyrlnUQRrq5vYsInbby4aX9KX2RmbWnGCMvVsXjD+ZMr+eUrHyUeu+2SISydfiYHfSE27/d1utg0rrwoMc7N5Fy27J09bNjbGuszVmT8PF1FMdZ5CxlY6Oo2lmR/aygtvitu/d87FnVpYvJlEKWr++smfnHZUC4e0otR/T2JMX/clagzzPucicnJSXKRR7IgJc9lwy6JCKKAoun4k6LDzxtcjKprRBG76ctXYJMksmwSraEo1f3yiCpaYgHYaZX45ZShCWeVeGSnCGRbJZQkd6fkOdiu5sEWrthKdUk+GxpaANjQ0JI2/xSno8iud56T+ZMqOewP47BKhKMaOQ5rmhAFjhao/PrbpyfmEExMvq60h6M0+uVEVHucvvkuNn/S0Uvt5CHuDOX8FM4oAC1B+aSNMDIxMTH5tJxQMcrChQu7fP6OO+44kbs7aenhdmTssMYnvHxBmWDuUTvCTHEdUUVNEYskd8TvvXwYC2PuCVeN7t/lsXRVzZ/8XHd2xhFF61akEV/c7up1xyOCyZQpvqq+MVE5eyz7uuFcL1c98k7isVqvhydmjuJIezhlwJE8UOnuu2gLRxlYmM3KDw8yprSQX7y8PWWistbrYWZtKQJCYiJ09rINzKwt5YZzvditInlOI6sxEFHokWMnx2FlZP98djV2LVJyO6zMn1hJnsuaJHoyBnUbGlqZ+vBarhhVkjgv+uQ7kQQBSRQYUZLf6aRrf08Wv/jmUNrDCu3hKDkOKzkOy0m92H08FOfYO3WPuXBID1qDUV7cdIDSwuwut+MPq/ToIsc+WbjQtUDMEJA8Om1kynXjSHuky/131uYVTe9yfz3cjk5FUp2JXDq+prP3hqJqwor96XUNCILAkN65tAaj3HxxOfMmDKElGCHfZSeiarSFolxU2ZNsm4Q/oqR8l3EHlSK3nfaQwu2XVGC1iOxpCiJgLE48u34ft1xczoShvajzFpLrtOKwirSHlYyOQ3H3pl++sj1N/BV3hXn4eyMzfr44XV1Tcl1WLj29N5dV92FBh2iwurJC7r08n3svH0bbKdg2c102IorGf/a2kp907YrHxHUanVXfhA4Uu+0JK/62pAk2IKNlcTJ2q5hWRdaxirrj88mvM618TU5ldIwKy67EmToQiqqJe0/83nGs7ae7Nhzo5vlkkqPBOmK2Z5NTgfZwtMt+YLHbntYP7G4MFlVSI0R8oSgzH3+XOm8hM2oHcN05AzMuNg3pnZsY5yazpr4JAfj5xeVcERMPS2Lme3FXUYzTagag6aSNiZJdULLtFt7b08L7e1pSXpNs/W9WSZt8Wfjlru+vfllFAM4oyU95zrzPmZicmsSLPBr9cmI+IbkwrQ+wIxbVcVpxNqIgYJfEbq81OkYBjcMqsmFvK9X98lJiy9//uJlzBhez8IUtDOmdS3W/PJoCMnkuK7VlxWiahssmEZTVxHyJyyZx7mlFXc4R33iul8mn92bqw0b83y3jDWFqx1i/TCK7+Gee89QG3tzRyKPTRnY5N+cPK/Rwd/MFm5h8xdnbHAKMee1k+uU7eXnTAUKyitN2fIKNrwPBiHE9sB+nGCXuQN0aMl3jTExMTDpyQsUozz77bMrf0WiU3bt3Y7FYGDRokClGOUHkumz4Iwrnlxd1aod8+fA+uBWVeROG0N5hEa3YbU8MCrqzXO3q+eTnjmU7xyJYSf5/d6873td+mvf7OnQeVtc3Mf+5Ldx1WRVPr2tIPJ5lO9qUuvsuinLsLFqxhbkTh/Dy5gOM6J/P9ecMQhIFwlGVgiwbv3vtI97e1czM2tLEAkvffCc93Y4uJy/djq6rlgqybPQvNHIecyFF9NTL7WDUgIKMA8xj4VRY3P605LoMkUNyRYnLJjFv4hBsksSS17fF4rUyV2jGyXZItAS7/o2PtR35QtGU+JYar4eLKnt0+Z7O2rzLJtEc6Py4Pkubjg8AOiMYUZn5+LuJStjFK7akOQgtmlLJ4hVbU6Ju6soKmT+pkidmjOLdPc08t+ETHpl2ZtoCRq3Xw7yJlVz1yFrO6JfH7RMquO3Z1GizurJCFkyqZNGllcx7fkuK6GjexCH84fX6tEWYZFcYt7PrW3FnzjpnlxXSK9aGZz+1IT1uLWnRo88p2j6L3Q7Ke+bQnrSw3N35CNAeiqLpkO8yvnt3h0ij7pwQ3A5rl5N2nVWZHYvtv4nJyU4goqBqepevaQ9FEQUhpT0fbo9kbD+ZojJOpJuJ2Z5NTnXcTmuXzgndjccy0bEiOd5XikePLphUyW9fTXfz607kMtMvJxabrJKYse2O6J9POKoxYkAB02PRrHaLmIhiXHbN6JTtZnJBiTuxdIzr62j9b2LyRdPWzeJIeziKQGrEtnmfMzE5tcl1dT03GC++mj95CFFFJ6xo3V9rQlFEUcBqMYQgf7tuDEtX76bW62F6TSllRVncuWILM8aWYrUIZNkt+MMqOQ4L+30h+uQ6uem8QfzqHx+R67QmHNN8oSgum8R15wzkvMHFgBG3YZVEVu04go7OL1/extVj+nPfazvY1xJkQ0NLyj27rqwQiyTgCxr36+SxRK7TmnCfPd6+jInJ15GG5iBgzK0l06/AhQ7sONzOsL55X/yBfc58VmeU1m7m7k1MTExORU6oGGXDhg1pj7W1tTF9+nQuu+yyE7mrU4pDbWFagzLBiIIn205AVglFosyfVMltz6bbHq5vaGXtrmaqS/JoDRmd5dnjvAmltz98dEKsaxtiD4fawhmPqdbrSajWj3U73dkTxhe3c7uINEl+Xcd/d/faOMnb/zTvX1XfSHtY4Z7Lh/GDpzYwsn8+uU5rIs90w95W6ryejAr5Gq+HTft8VPTO5UBrmG9U9EQUhITLSVvIqKa7+5vD8Cc5GRyrMOSzVOd2N8A0+WwkV5QEIlHcThvzlm9mWJ/cRLvRdT0tFzdOrdfD4bZIt+ds/Pzu7nUdWVPfxKZ9vk4dXGqS2nzHf3e3v+NpZx1fa5GEji9PIb440Vkl7Kr6RuYu38wZJfkpYpRVOxqZ//xmqkvy+aChlSevGc2tGa6nq2PxYP93w1jAyDzOJPpY8MIWFl5amXAICkSieLLshBWt0ziXuBNMcqxaR2q8HvrkO9PatTHZU0lTQEbV9Iy/GRiLHgfbwhzwhY3oN6eFfJftlLKLLfFksfOwP3FuH0vbyHFa0TUdWdWo9XpwJ903fEEZSaTLtvLenhbOOa2IQcWdux11VmUGsPOwP2Xh3Lw2m5xK5DgsdKNFIcdpRRKOOpj0yXMyoNCFKAop7cdhEZn//BZe23aYfvlOfn/VcGRFQ6DzNpypv5RJ0JLcLrurGjUxOZmxS2KXkZSZ7rvd3Yuzk0SgtV4PrqSJ4FU7GgkrmcXK3S0MhaPG+84uK8QTa8cd265FFBh//6oUEUkyyWI1X1BOE6JAehRlMubilMmXibubuZYchxUxw/DLvM+ZmJh0xYJJlYhASySKrundX2tikTsui8SI/vloGiyZOhxd13E7JIJRjTEDC+iV52D+8+kFQwsmVzGxqheVvXIJR1UWTBrC4bYww/rmsXT6meQ6LWTbLASiKiFZxWWTuLy6D5qm8dauZn74jdN46M1d9M13sqGhFTBiPz9oaOWnFw7m8j++xZBebhZPqWLhiq28tu1wYv/fqChOzPsAXFBeyPxJVSlxQtk2CTE2oOluHGFi8lVmX0sQu0VMK9DqlWvMKe5uDJyUYpSgbLRvh/X45tbtFhGbJNLUTcShiYmJyanICRWjZMLtdnPnnXcyadIkvvvd737euzvpaGgKcPuzm9h2sJ0nrxnDbc9uYnV9E4XZNh6fMSpt0To5Pzt5AbQ2qTor23F0Mm/p6t3cf2V1RhviGbGomI6L4zVeD3dMquTel7elbGfptDMRBSGtKiy+nYO+UJcLrnFBS47D0unrjkcEU9PhtfHHkrd/vO+P0xZWeGzNbuZNHMK5pxXRK8/JPd8cxq3PbmLp6t0sv7GGOzssjMdjOX7w1AYeuLKa1lCUHm5Hp4uVn8bO0axa+moTF/z4gnLMyaKROfJRxwZfIMq8iZUsWrElpc3VxtpcPEapqxz7+Pn9ac7txS9u48U5tcx9bnOn527Hf88+r4zXPzxMVO1cSHOoLdylQCt+LHVlhWkCuF1H/J0uGNaVFZIVW5w4nnivOHExyJKV9bSHlS5tZAOySlTVeL+hNfF4x5g0WdE41BYmHDWij9rCSreLIlaLiM0iJI4v0/ceUVV+8c2hKVE7TqvIkfYITpul2ziJ9rBClt2CruvourEQc6ApQC9PVpfvO1nwBWUEARZMrmTB81vYsLeVPrnOLgWUAjouu0S7L8qiKUP59T+287OLytGBm/93I9sOtvHkNWO6bKsvbzrQrRV/RxFgpurqs8sKuefyYfTukNH7WTAnxEy+ytgtIuGo1qU4UwCyrBJ5WTYuKC/iX9sPMbinOy22I15d2dAU5MHvjWDucuP+Fu8v67qeso9M/aVjbZefVdRrtkuTrytt4Si9ch3H1Q/sqp9am1QQEV9wkrXU/pQ/nLnvcywC6I7tvGPb9QVlRvbPPyZxf6Nf7lQQ3Fn85PE4L5mYnGhybFKX99ccm4TciSLULF4xMTHJRKNfJqIoOKwWXDYJURC6vdYIgFUSCERVpo0dwJUPr02IQGeP81LnLeQbFT3T5qbAmJ9Z8Pxm7rpsKO/F4pr/33VnMe+5zdz27ObE6+rKClk4uZIeOXYimoYGWASRxZMrONwWYe6ECjbt8yWEo7dPGIJFFPjdvz5KFB/e9uwmo7ApSYzy6rbDRBSNX337dH5w3iC+OaIft3QobKr1erjrsqEcaQ3x4y9gfG9i8nnR0Bykh9uBIKQqVV02C3lOKx83Br+kI/t8iTujOI7TGUUQBHKd1i7dw01MTExOVT53MQqAz+fD5/N9Ebs6qdjXHExU4T86bWRi0asw28aT14xhX0v6Df/aswfyeIb87NX1TQiCwAuza5EVjTpvIavqGwnKKj94agPXnj2Qn48v53BbBDAmCGcv28CYgQXMn1RJk19G1jTyXVYUVedQa5irxvTnx98YzMHYZGE4qjLljN78/OLBHGmXKcqx4bRKNPll3A4LgwpdjBroYcFzWwyL5RjxuIxgROHCIT043B5hfmxBvmPcxsIpVdy1YkvisaOZ3kLqNr2F3Hiel1lPvJt4LL4gMfPxd7liVInhSKDpXHp6bxa+sDXt/dNqBvCDp9LdfsCIS1lT38SCSZX0ig0g+ha4WBKrFmoJylSX5DMzg7VzPNPUbhFpCshwxH9CFxzMqqWvPskT11JS6Vm7rPDfj6zl3suHceslFTERgcThtkgi07arHPufXjSYP/67nuqSfEaW5DNpWC8Wr9h2zOd2UFZpChjn7rwJQ9jvC+F2WHE7rfjDUZZdMwZRhAO+MA9cWR0TXqg8+MYuxg7ysHhKFXOXb04Tr/XKdTKjthQd0p6LC1vqvB5m1Zbywb5Wll0zGqtFJNtuwSoJjBxQwPwO8Td1ZYUsnlLF3phl5KeJ7ALDQvHRaSMJdFL9GscXjPL713ckRH0Af7pqBDarYRkbCKvowO4jARa9uC0xkfJkByv3jkiCwMubD7F1vy/jNWPZO3v40QWncefzGxMLqDNrSzlroAdJEDjSHiG/m7bttEoomoamg6rpHPCF6O12cqQtTNFJ7pBysDVENOaCYBUE7plShV9RkQSB0iJDjLOmw31m0ZQqLMCSN+q54Vwv97y8jUHFOfz7oyO8tHE/q+qbmD3Oy72vbOOMknxDMJT0m93z8jauGFXCkpX1x2XF31l19ZtJcUsn4jr+RQleTEw+LRFFI8cmsXjKUOYu35Qm+Fo8ZSg2ARRN59f/2M78SZW8tPlAmhAF4pn08PurhyeEKECi/zuztpQbzvXisIrkOW1p/SWzXZqYdE+23crOxgA/vagcQfgwrTChV66T2ePKIKloYenq3SydfmbaGC7expv8YV6YU8vhtjBXPPQ2f56Z2p/KcVgT49lkNuxt7XTxq66skEFFWcckFD1WcX9bNy4nHfuf3TlVmph83tiBu6YM5fYM99e7LhuKHTgYiDCwqHN3PxMTE5Nk2sJRsh0W5j+3mTGDPEwa1rvLa028L/9JWxgQkASBmbWlCTfv6n55uJ1WworWZcFQ/LUA8zOIVlbtaGTuc5uZOLQXIwYUcO/L2/j5xRWMLS1kR2OQ4f3zsUkiGjpPr2vgQGsIRdNZuf0IEUVLiFQyFTa9uaORQEThO6NKuPmZjRnHILc/u4lfXDaU6WMHsO1AWyLS8ESPI0xMPk/2NgcpyrFnfK6H28GepsAXfERfDEFZReD4XcfBcPBu9EdO/EGZmJiYfM05oWKU+++/P+VvXdc5cOAAf/nLXxg/fvyJ3NVJjy8o09AcTCw2F7vtiQ78vZcPY+GKLWmVVi6bxPkVxdz3Wnp+NhgdcVnV0NCYP7mSO18wFneDssp9r+1g475WfnTBaTT6ZYb0cvPAldVs2NvKlQ+vZdk1Y9KcPmq9HmbVDmTTJz6G9slF1jSqS/K5+8VtlPd2U90vD1nV6JXrxGGVOOI34iRuHl/O7ZLAgVYjusdpFYlqGtkOC6GoSq7Dio7OwilVhKMa/tiCfI7dQlBR+elF5fzkonL8EQWXTULXdWbWDmBGbSnhqIrdIrLpEx/rPm7ib9eN4XBbhB5uB7qu89vXPqLRLyccFGq8Hq6pHciIAflMrxlARDEEN33znCx4YUtGe+Z4XAqQ5kgQrxbaedjfqUsDGFEqb+9qorpfHt958O0TvuBgVi19tUmeuF61ozExmW63iDT6ZWY98V6iavuBlTtS2l11SV4ix/76c73YLSKyqvHWziZmPfEu91w+jMfW7GbJyvqEcOH6cwdhs4hEVY0cu5X/eujtTq3HBQGWrKxnSnVvnFaJ3772ERsaWnno6hF4ciR0BPKdhkBlgCeL1pDMMzeMxWkTaWoPM3ucl5vHVxCSFdwOK1ZJpNEfIdshcfuECgRBwBeMkuuyYpNEmgMR/u/GsYl/X1TZE7sk0hKUiUQ1oiq0BqIsurSSUFRLCKzsFhFV05jzt/8Any5yCyDLbuH+lTsyVq4mk5dl5efjywmEVf73+rNAB5tVYv7z6S4yyXnDb+9q6rIi6O1dTQmHqvjvlrytn19Uzn2vfZRSyd/xdXdfVtWle8wH+1pTKoSMKuNKpJPcJd4XlNnXGqQw244kCog6CECORUIDeuXaWXxpFcGoSjCikuO0kGWVCKsK97++i6mj+6NrOi9vPsSj0/oBRwVgcSeelUnRT8lcNbo/cHxW/F1VV7+5o/G4hC2d8UUtrJuYfBbawwpuScQlCvzisqH4ZTVx7c+2Sdgx2nK7prG6vglZ0ajqnctvX83c/11T34Si6mmTxEFZZcnKepasrOflH9ZldKoz26WJSfc4rCIuq8TUh9fy/649i5svhlBUJdtuQQAQQBRg4eRKwoqGP6KQbbfgtIr84rIq2pPauE0SeWnzfpas3JnSV/UnjblqvB42f9LKgksruaPD4tP2/W0smlLFHcu3pIuxxw7grhe3ceelVeS6uv5Mxyrud3fjcpLc/zSdKk2+CqiajktIur+GouTEIiUcgKbpOK0W9jUH6VvQTUMxMTExwbgXtoZkVtU38X5DK1OG9U5ca+65bGjKfT7el9c1nRynFbskMu2xdVSX5CXmUSKKhtMqcqit68Xc9nCUiKIxvCS/0/nXuEvZwhe2MLO2lLnLDYFI3wIHLcEoz205xOUj+/K368awvznM8JL8xPtm1Q4EOi9sagtFsVulLgUzflnlsTW7efKaMVz1yNoUQcqJGEeYmHze7GkKclrPnIzP9XDb2dV4copRAhEFh1VKc4Q5FtwOqylGMTExMcnACRWj/Pa3v035WxRFioqKmDZtGrfeeuuJ3NVJT6NfpjV0dBHLHz46GVfstrOmvonqknzGlRdxer88zi/vgSQKtIe6Xvja3xpKLHQ/e+NY9rWEyLJbsIgCHx1q54qH1qYtUtd4Pby85UCiat9plRAEYxH3vtc+SlmMu6SqB/MmDSGYNNhw2SSaAxFcdgv7W0P4ZRVN1+nvccViLSK4nVYsIuxvDZPvshJRND5uCmIRBQ63hRkz0EN7RGG/L0xxjh1ZMRal811W7nx+S8b4jzqvhwlDe3GoLcycmJPBzNpSZtUORBIFsu0WNu5r5aZl6xOfudbrYeGlVdy0bD33XVGNrGxJq9aLRzBA5zbLDqvYtf2tw8LW/UfdgswFh1OLbPvRS+9Db+5iydRqQE+xLE+u2p5ZU4ogQFGOHV03nEkuqOjBK1sOMrp/ARFNY0NDC41+OfGeOeeVYZEEIorG27uaeHpdA1eMKuGSqp6M6J+fWbjgPSpo2NjQyukleUwc2ouZNaUEoipuVUfTdDRgvy/M+oYWlq7ezWPTz2TzvgD9PVnsOuKnh9uBouk4rBLhqGYIUEIKb+1sSlS7xKnxeqiOTR7UeQtZMLkSiyjw7Pp9fK+mFEXVcdhEQlGNbJuEHI2iaTq7GwPc/MzGxGC+q0rYjvFeyY+7HRbumlKFrtOloANd5x9bDiWO/xffHMpLG/enTTzE/45X0cSFJkKHCLO6skJm1Axg9rINab+1oumUFWfTHJAJRVWuHjOAIb1zsUpCxsr/xS9u49FpI0HX09ykbjw31SEK4ra2W7jrsqFpn/VkotEvU5Rj5636JmoHFYIAmq4jCAIaGOKmkILLLuGJVSl/eNiPyypx9Zj+OO0WhNgaUscJqGN14jkeK/7uqquPR9jSGV/EwrqJyWfBF5SRFY0IEI6EyLKni3RVTadNUznQakzwBKNqt22yu/5xZ+3LbJcmJt2jazoFWTaGl+TxytaDbN3vY0jvXGpKC+hT4CIgq7SGji5CWSRoDSpouoTDIiEAxTl2vvWntxP9uo5Isftxsqve368fkxif5tgttEcUNuxt5dt/epsrRpUkCg3sFpGeuQ6+/ae3Yw6VxzbmOhZxf2G2jbPLCjuN9PEWZbP8xrGmU6XJV4YwICshnNb0+2tLNIxVsqPqOrc+u4kl5tyEiYnJMVCYbWNPs7EgHZRVbli2nvuvqE691iSlf7VGw1gkO69sPsj7e1qS3Gd3M7O2FLtF5AdPbeDeb53e5X5zHFaCERWHresYjYhiCNhvvaQiIRBx2STyXDamji7h4Td3MuvsgRS6bfhCRtFjMPYal03qtLAp5xgWnNvDUVbXN7FoxRbuvXwYs554L+U5E5OvMrqu80lriBpvYcbne7gdbPrk0Bd8VF8MgYiK8zgjeuK4ndZOxzQmJiYmpzInVIyye/fuE7m5U5q2cDSlw5vtOHoDjAtTntvwCX+ZOQpF1xOCjEenjTym7cejYpau2U11ST5b9/uYOro/1SV5ae4n02MTfkFZNaJyLq1kvy/E/f/awZDeuVw1uj8RRSPXaaXAZUMElqzcwYubDgKGY8tN5w1i4rDePLZmd2bb5Nh2//zW7tTF1LJCbjrPy7cffJurx/TngooetMWqd3rZLTT5I/zkonI0tqdFLdx4npcrHlprxCVMHU6WTULVdN6KOREAzJ1Qwd+uG8O+lhC5Diu985xEVJVbLxlCRFFYNKWSQESlLayQ7UiNS+nMZtkXlJn//Bam1xixJB2PKx6lMnV0/5SoFHPB4dTBJokpopPZywwhwsiSfCYP682iF7cmXIuWrKxPTLz/14OGWCxZwME4b8aIl9U7G9nXHGBm7UAuGdqLmkGFiXP4+3UDMwgXPMyoNcQRtV4PQ2N2p6VFWbidNj5uDHCkPcKGva1pgpJcp5XD/gjDB+Sz80g8I9NCe1hJCGHuuXwY/2loSROiJMf0LJpSxT+3HWBEv1xm1Q1i7vJNrG9o5fdTh1N/+KjIpdRj4/E1u1M691v3+5g3sZKFHRyc6ryFzJs0hHte3pbyG9TErm0hRWXhC1sZO8jDTecOQtP1NKeTG8/18o+th9jQcHSipDjHnlEEB0erb+BoFMTT145h+tijiyKH2yIIHFXYx3/ruPvJ/Oc2p8UwzZ9YyYNv7ErbX1BWmfXEe8Y+2iOJffT3uJi8ZE1GF5y4re3JTFs4itMmUex28HFTkH4FTiTRiAPokevEFbsnvLH5SOKcrisz7kWKquGwSLy89QCQ7qxzLE48yfcIX1Cm0S/TFo7idlozRrN1V119PMKWzvgiFtZNTD4Lh9sjvLWriatH9OVwRGD93kZ6uB1EFI32sMKhtjCDirLp6bbz079/ABgCz7ZuxCY5zmNvX8nt1dnN5LbZLk1MoF1W+e6j7/DU98dw7yvbmTq6f5qLW3xMecszG7liVAljB3oIYcRVbtjbyuH2MIN75tDYSbyOVRJ5YU4N/9hyKDEubQpEE/t4YU4N33lobeI9HSukX/lRXeLfJ3LM1V2kT688J/3J+sz7MTE5UYgitPoF3tuTdH+NGPdXb3E2vd0CvkCUVebchImJyTGS67LRJ8nleeMnbaxvaKFvgavTa00vt8BDb+4iGCtWjBfzzKwpZcPeVtxOK+h6lwVHDouIwyaianra88nEx+7+sOGyFu9bOywi7RGVWWcPJChrZNssNAeiiWPRNJ2l00fyzu7mtG3WlRWS7bAgq10L4uNjhdX1Tdw8vjzjcyYmX1Vag4b7kCcrc1+gZ67hMOQLRcntZrz9dSMgKzhsxx/RA8Yc+a4j/hN8RCYmJiZff06oGGXmzJn87ne/Iycn1b4rEAgwZ84cli5deiJ3d1KT67TiC0Z56Qe1KJpOSNZ4YXYNvpBMnstwG1kydThv7WripU0HEp3zZGeFjtR0cAc44Aszo6aUp9/Zw9TR/Vn2zp7EgrasavTOdZLvshKMqjw+40yy7BaybBK/euVDXv/oCDedN4izTysiLGuGWtwqsa85yEHgsuF98YWiiQXPX/3jI3YcamfxlKHMe25zWpb4Ty8azL7mED++cDC3jBcJRdWEAl1WNJ68ZjRtIQVJgL75ToJRlUBYwZNtJ6rq3HZJBQDBiIrLLuG0SASjCkumDkcUBKKqxtrdTeg6DC/J5+zphdgsEmLMjcUmiexpClDktnPVw+8kFrmTYzGSv9OubJYb/TKvbTvMWzubEk4sFkkg32XFIojGQkpxTmIiNRlzweHUoDUkJ3Jn44KUJSvrqSsrZM55Xkb0z2dGTWlisfztXU0pgrBpNQMSQqbOIl7qygw78iseWsuvv306Nz65PvFcPL5nek0pqma4FFklkWZ/hGduGIsvFOXK2IT+vZcPoyhb5Kl1DZ26hqypb+StnU2MGlDA2EEeglENfzhKkdvBRZU9qOqdi10SWXhpVWxB0RioWGPRPM/cMBZF1djXHOSNDxu5+8UPmVBVnLCPDkSi9Pd4iKoa+1tD7G8NMb2mlBm1A3FZJSySgM0isrclyM0XlyMrGkf8EfrkOVE1nakPG6K0uHDObhHZsLeVHzy1gSVTq1lT38R/X3Aa31u6LuFOkvy6WU+8ywNXViciIGbWlh6zMwYYYpEj7ZGUKhgwhDIvzqklFNVoCxk5y5quc+8r2zM6rixasSUxMZKJ5EkYQRCIKjp985z8+junY5FE2kOGECKqatz05PqT/nrjdlhpDshEFI2bn9vI09eNoTUQYay3kLnLN/P+nhZm1pZS3S+PB66spofbwbYDbVgFAbtFIqrp3PdqfeLe2TvXkYjU6u5ee7g9krhHHGgN8e+PjlCcYyeiaLQEo6zb3cy5pxXRK2nSrrvq6kzix0/znXSFOSFm8mWyvzVEQ3OQpat3M626D71yHOw8kmr7KwgCnmwbVz+6jka/TI3Xg0DX/d86byEWSehyMjvbJrGhoYUsm4X3G1pYtGKrIRYd5+30fWa7NDExaAtFafTLXPnwWv73+rM46Avz0wsHc8t4gZag4WinajpZNok/Xj2CbLuFbJtEUyAMQO0gD3tbglxc2YvFMUF2nLqyQr5fN5D7XvuIIb1zU/pAimr0e2piEapdjoEbWvnbtWfxj60HWbp69wntAx1rpI+JyVcBGwJWSUiznRcEgVyXFSs67XLqgq2JiYlJd/R0O1KcZuc+t4WHvzci47WmR46dkKqyYk4N1/9lfUoxT0TREoVGf/z3DhZNqWLe8s0pffFar4e7LhvK/pYgLruVV7cdOqZ5cHdssTzHYaU9HEWzWwhEFOxWK61BmSybE5ekM7Iknxqvh7d2NTF2oIdvVPTg9D55BKMqDqvEQV+IXrlO5j67iTsmDul2jBEn2fH8RI0jTEw+Tw62GX31/E7EKD3cDgD2NAUY1jfvizqsLwR/RMFh+ZTOKA7TGcXExMQkEydUjPLEE09wzz33pIlRQqEQf/7zn00xynFgl0ScNpH/7G1N3Nx75GQjiSJ2SWTRpZX8+h/bmV5TmtLpjS9MQ0dHDg/TYg4EcSyiwJxYxX6zX2bOOCPWoyUYRRAEXt1mRFI8cGU1uU4rd7+0jQ8PtvP0tWP45oi+RBSNw23pTgmPThvJsnf28POLy5npl3HZJAIx2+Rv/ektrh7Tnx9dUIamQ1TReGtXE1c+vDZRlX7HxCE0B2TsVolGv4zbacVhERFcFlTNqJrNsltw2Y0IkIiioqo6SpLrSdw9YkZSBV51vzyyYoONt2PClKF9csl1WglEFD7xhXn1pW2Gk0psojPuanDt2QOZN3EI/rBh2Zhlt+DqpFo2XuEaFxgk8+i0kWkL0smcyAWHY6nEN/lyyLZbufLhd9KED6WFWextDvL+x83c99qOhGhk7EAPtd5Csu0WnFaJSUtWJ9pbx4iX+DneO8/Br//xIUFZTXNx6HhuJp+Xf5kxCqtV4C+zRtMejuJ2WBEFmD+pkjtf2JK2QHDHxCF80hLCaZPQdFj+wX6qeuey7J09VPTOpbpfHpquI6sa/7fhE2oGeZj1xHsJEYCOUUmj6YagZNGlVciKhi8UpV1WsUoC1/91facd+a7aVNwpqtEvdyrgiC8CKqqesc3GiYtLkidKuiL5O+8oBIyzqr6RA21hpj78TuKxp74/utPM4VX1TcyI5RYnY4gTq/mff36Y4qYyYWhPHvreSG5fvinN8erPs0YR1boW1HzdKcy2EVZU7GGRRr/MFQ+u5VffGobTrrLo0ir2t4YScXgb9ray/UAb8yYOwQLsbg6Sl22juiSPGTWlLHtnDyNK8plVZ/z2nd5rywpZdGkV+S4ruS4bvqDMnuYgK5IinVw2ibkTKmgOyBzwhch12RLX566qqz/r9dsXlLGIQqdxVOaEmMmXiS8oc/MzG5k+dgBBWeWqv7zPk9NGUjfIQ7us4o8oRBWNT1pDLFqyNaWfp3O0TQqQ0i+uibluCbrO4ilDmbt8U9pk9uIpQ5n6yDvsbQkl2mfcNc9ltXBBRQ9ge8r7Pku77Ng/y3ZYPnchmonJ50l8cafRL/PdR9fxxMxRzH8+vc+46NIqwopKe1hB03XcTjsCRtRWtsOKLySzcHIlbWGF/b4QNskQBT/+1u40R8kar4f1DS2J68DNz2zknsuHAaQ53MVd+B64sjrhcuc+wdWbxxLpY2LyVSCgqRS67EB6xW6WReL7y9bzwwtOA0wxpImJybGT67KxcHIlc5/bnCi4+v6f32fexCH0yXPSHlFwOyzsaw4SUjRyrBIKOo98byTX/Pm9xHxLSYHLcOB+xCgo2t8S4u7LhhJIioLPtkn4IzIRRSeqRXluwyf8edYo7nhuS1rhY7wPUOv14LJJnF9eRE5sLtcfVshxWHGLErLLxr6WEP3yXXg0jWvrBnLDk+up7O3mj//eyY8vPA0tCOsbWti+v42rz+rPmp1N7DwS6HSMcddlQ3l399HjiTuen11WyMJLq/i4KUB2QDbnak2+ssTFKAXdiFEamoMnnRglEFFwfMqYnlyXlVBUJSgruGwndOnVxMTE5GvNCbkitrW1oes6uq7T3t6Ow+FIPKeqKi+99BLFxcUnYlenBL6gzFu7muib7+TFTQf48GA7y74/hjU7DXtDURCo6pPLT/6+kStH9095b8eFaZfNQpbduHle8dDaFCeODXtbqS7J45+x+IlOK7zbwmz6xMeMmlIeW7ObFRsP8P7HzRkjKuq8HgYWZvGNip6J/c0e5+U/DS2Jjvl9r+1ILLTPnVDBuacVUd0vj55uB9l2C/t9IdojClHNOKc0XadNEMiyW3ht2yE+PNjGDed6afTLhGPK9BKPk3BE45KqnlTH4kXizgfJUSeJaJOkz9fxsas6fKcAYwZ6uGvFNlbVpy4Q3nP5MHrnpeYtd1XhumFv6xeyELi/NcTNz2xM2U9nx2vyxVOYbWNk//w04cPfrzuLG55cn3AtiYtU4iKrX3/7dE7rkZ0Wp5VwVvEWcvuECgIRhdc/PMzN4yuIKNoxOybVeQvJzbLyz62HeHpdA7/7rzPQ0QkpGoFwlDsnVxJRNAIRBYsoIoqwqzGARTSqXQqybWza28pf3t7Dk9eMYdGKLWkW7d8a3pcRJXkZRR/x9vifhhbumFTJ1IfX8pNvnEZFL3dmVxavh0OxwVFXn6urz26LiUZc9q4HGcnikoiisfVA2zF+p+lCwGSUDpayUfXYLGaTufbsgTy2enfaNfmbw/umCVHAWKidt3wzv7hsaJf7+rqT67LR6Jc53BZO/FYzHjeES/3ynfzx6hHkZ9loD0eZMLQX3xnRFwmQNY1H3/qY2y6p4KcXDsYXjDKkdy43LTPchRZMGsIt48sJKyqLJlchaxrBiEquM70SujUY5YGVO1KEKHEno9ue3Zx4XfL1+fOoro7fE+KZ3B3jqE6U4MXE5NPS6JdZtaOR0/vlJfpJ33zkHf5w1XAskoiATs9cBxOH9WJMaQGyqrO+oYWn32ng9gkVPD5jFBFFZUZNKTNrB6LrOrlOKzsO+xPiMIBffut0Q+wYa18A3330qBAlU/scV17Ezy8uZ65FJCyrn6ldZuqffaOimMVTqpi7fPPnIkQzMfm8ybZJ1Hk9rKpvYm9LiO88+Db3Xj6Mn188OHF/tFtEmvwRHDaJLJvEq9sO8eAbu1LGprVeD3MnDCGiqPQvyGJvS5AJQ3vRJ9eR4ihZ5y1kweRK9rWEABLPdRwDB2UlZTwYUTTW1DchAL/5zhlfwjdlYvLlYxclNu1vM9wsZZX2WATyoKIsbly2nhynlQ17W00xpImJyXEjaxoTh/Xm+rMH4bBJZNstRjCxAJ4sG//+6BArtx3h/HJjfSAYUQnICn+4ejh7moLUej1oup4oMFyysp4lwMs/rOPyP77Fyz+s4/ZnU0Ufy64ZzYLJlSxasZVraku5/ZIKPmk1+gfxPsDwkjwWTK4iEImyYFIldkC3SbgsoAsiCiAK0BqK4rJZ8GRZ6eG2A1CUbWdVfSPTawYw64n3EgKXv769h2vPHsiNy9bzrx/UJVx949fUbJvE4bYgc5834qJrvR5y7Bb+8aM61je0csn9qxL9GnOu1uSryiFfGAHIc2Ve58iOOejvbQ59sQf2BWCIUT5dTI/bYSy3NvllXAWmGMXExMQkzgm5Iubl5SEIhtXnaaedlva8IAjceeedJ2JXpwSNfpnBPXMSUQ2PTz+TJn+EFzcdYE19E7PHeXHZCoHMC5PJ1f2PThuZmIgbUZKXslgZryJ96p09KZEhcWq8HuacV0ZEUVn0otGBnllbyhl987ikqheLOtgo18QWXb+3dB1/vWYUz9wwFn9EIcdh4fLhfdJU6iNK8hnRP58DrWEOt4Xp73ERiirku2zkuWyGJZpVJMtqQRN0dh4OMLRPLoqmpwlr/nDVcG58cn2nsTp1ZYXceK6XWU+8m3K8M7pYJI4zb+IQfr+yPkWIAkbe+C3PbDScY5IWCrqKWvjwQBu/uGwotz276XNbcIhXF3dcvO/seE2+eDpzQMhxWrp057BbRAIRhdnneYH09nrjeV4iippwClm6+mPuvXwYffKdTDmjT5qzSXIbqPF6uPPSSiY+cNR15ft/eZ+ZtaUML8nHZZM46Avz1q4mnl7XwBWjSjhroIdcp5Xc2KTllQ+tZen0MxEFgfawzMLJVYQUlbaQQrbDcEgSdZ0ZtaVopDtKzJ9USVRV+eYZffCFZR6fMYrG9gizakvTFs9rvB5m1JYiIKSJQjq290wOFvHPfsAXTnnsWCLO7BaRpat38+i0kYiQcl2t83qYN6mS3Y0BHp02kqIce9r1Kpn8DoO6/KyuKxALsmxpgrZzBxdz32s70l5b7LZ36rKyur4JfyfHdDIhCuAtzmbOuDLg6DmwtyXEvS9vZ0btAJat3cMtl1Sg66Cic9+/6vnZReU0B2R+99qOlGt/jddDiScLT5aNnscwWRSQlZTfYGZtadr9CdKvzyfyGt3xnpC8WAdG9Vlxjt28L5h8qcRd5Zau3s1LP6hlXqzPOGnJGlw2iUenjWT+c1tS2mNdWSE/vXAwh9rCXJHkMBW/vj/4xk4WXlqVcm73znMmJnp3HvZz/v+8kXius/a5cvsRIorGgkmVnFGS/6k/Y2f9s1e3HQbgV98+HX9YMWM+TL52qJoes9E32mijX04s2MwZV4bDKhKUFaKKSlGOnYNtEd77uCWlb1TnLeTWS8rxR6LkOCx891EjNrUw28aSqdX87w1j8QWj5LqsHGgNEdU1pj22LuU4Oo6BOzrnxcfNq+ub8IcVerg/5y/GxOQriKjpVPZys781xAOv12ccHz29rsEUQ5qYmBw3mqYzZmBBpw4la3Y0cddlQ7EDjbKKqkGBy05E1TjUFmZ6TSn3vfYRD8TmbxRNp3eukyybxPCSPMKKyq3jKxBFgfZQlCy7BatFJKporNx+hLW7mvnTVSPIshtu1jWDCrmosgeBiIpFgH1tMlKeiMMqoWs6DlEirOm0KSo64LJKhBUVvyxitxjFk/HipWSnXIDqknxqvYXc99oOjoRk+nuykFUdURRoDsi8uOmoa3et18Pdlw0l12ll9lMbzLlak68NB9vC5LqsWMTORRnFbgd7W4Jf4FF9MbSHFZyf1hkl4RoZoV+B60QelomJicnXmhMiRnn99dfRdZ1x48bxzDPPUFBQkHjOZrPRv39/evfufSJ2dUrQFo4iiUKik9u3wMn857cc7fT2y8MZsxXcsLc1UYnWkZqYa8B+X5ilq3fz2PQzQRASHd+grLLsnT387MJydHRuu6QCURAIRBScNgmLKCBJAr94cVtisjDuMHLLxeVMGtaL6WMHJNwbklXn9YcDKarxeFTO9LEDEAQozrFjlUTawwrFbgc9ch0EIypNgQg3LduQJjSxW0Su+8v7nX5n8cnFjlVxboeVPJeV4hw7gYjCI98biSgKidig5Cq7OCUFLv7143MSiwGarnPr/23KuN83dxgTrskDhq6iFhZeWkWvzzlXPF5dfKzHa/LlkOyA0BKUiSgaNknsVhAxsiQfHZgwtFdKxM+htjDhqEokevR8ji8GvDinll++spXT++UxfewAZFWjKNuOzSLS7Jf588xR/PujIwRlJcV1JXlS//zyIqaO7k91vzyG9HJjt4i8vauJbft9TB3dP3Ft+OfWQwBs3e9jSCymx+200hZSeHtXEyNL8nly7R6qS/KZWVOaOJacmGWrqhvOIE++s4eV248AJOKK4ovnniw7r20/xOxlhpAs+blcp5H/Kwp64rPErwk3nuvFIgpYJIHXPzzCD57awCPfM6J8Xv/wcKcin2TRWvx3qC7JIxzVGDGggOkdjmvK79ckriuzx3kZXpKXMUO4xutB6eCEoqh6l+eAKMCSDteP5mDmCKPkTOJMnAo58JIo0MPt4IN9rSy+tIpgVCUYUclxGlVamq5z24QhWEQBXdMJKColhVkcaA2BACMG5PPDC8pQNB2XTSLHbqHgOGx0Ax3uL9X9MrsCwed3fe54T+goePvXj88x7wkmXzpxV7mgrLK3KcTdU6poDUWJKBpZdguiAAsvrSSiagTCCqquU5xj51t/epsnZo5i6fQzsUgC+S4rVknEJon8+tund3lut3W4BnbVPtfUN6FqXTtXdUdX/bNXtx3mlvEKg4qzP9M+TEy+DDSgxR/m9gnlgEB7WMFll3BYJGRFZU9TkIdW7WJNfROF2TZ+/a3TuXOS0Z79Sa8VBLBIAqqm86erR3DEH8EmiayuNxZ0qkvyqC7JZ0NDCzdfXE6t19Np/6pjPGKdtzDlsVOhD2RikglNFAjJIfrlOVl0aRWhqEpIVsmyG1ODogh3ThxCL7NC38TE5DhxWS2EFJVFUyqJRDXawwpZSe4od06qxKJDVDDuw9l2KyFFJRBROOKPsDg293xN3UCa/SFO71uAX1aJqCqLplQZ24xE6ZHjAKeVqKpxpCWUcLkNyirXP/l+IhI6omgEZZHeuU4O+yO819DCOY4iItEoYUWnl9tJY0BG0yHHYUHVjTF/eziKZrcwtG8u+1vDsc8mMXucN7HdkgIXkijEYtytKQUlh9rCeLJsnF1WhNthIT/LRg+3g52H/V3O1R7whdnVGDBj1k2+MsTP5a4oyrbT0HTyiVH8EYWebkf3L8xAPA60qZO4eRMTE5NTlRMiRjnnnHMA2L17NyUlJQiCcCI2e8ridlg50h5J/B2JaikLkxFFS7gBLF29myVTqwEhrXp7znll9HTb2XEkwN+uG8O2/W3Uegu5dbwR4+Gwieg6/HPbQR58Yxcj+udz07leZj7xbmIhdXxVD+ZPqmRmbQhfKJoQnfzx3/X8/OKKjLmYi6cMZW9zkBfm1LBpny8tKmf+pErueXlbYqE5frzxBd+O4pD4PjtboK3tMOEY39fZZYUpyvJcl40su4XWYJS5z23uNCqnY4X4hoaWLn+vTJOZ3UUtfNrKd19QptEv0xaOdjpA6bi4cizHa/Ll0HHA2tAU6NSlKN4+ek9wsHLbYcp7uxP5nAD7fWFWbjvE+RU90/bT6JeZOro/j63ZnRZTNSPmZhSUVfoXuJiTQZBRV1bIHZMqWfjCFv6V1G5rvZ5EnE5H0cbS6Wfy+5U7WLKynp9ffBoXDunJfxpaeHpdQ8YIn/PLi5g3sZJ5yzfxfkMr919ZnbBTT75+zKotpSUo85+GlhSRXK3Xw6IpQ1m0YgtrdzXz+6nD0wQ7e5oC9Mp1ctOy9QRllbqyQvrmO6n1enjwjV0M65PHhKG9mFU7EJdNQtV03t7VlLguxW3hj7QbExLx7dR4PSyYZNjFb2hIrfTdvr+NeRMrWbhiS8bf1BdMbY++YLTLc0DT9bTrR2S/L+03h6OZxJ1xKuTA67qOTRA4o28eh9vDFGTZEQUBXzCKy27hcFuYZ97fy83jK2jyh7j6sfcZUZJHD7eDT1pDLFlZz0Nv7kpMaIWjGlps28m/QWfX5jxn6nccr6jqjM/j+mzeE0y+DiS7yv333//DyzeMJd9pNeyuwwoum4Qk6hxsDfP39/fys4sG891H11HRy7A2mB27HgMsv3EsvQodNPrlLid0O8Yqdtc+O3O4OlbMtmhysiIAigbrG1rp4XYgqxr9bC5kReVX//yQtbuaU4TDPfMcCAJs2tdKYY4DVddp1aK8HXPfu+fyYTy+ZnfKGDPeD4o7et705HoemXYmi1ZsSXldXVkhN53nZebjR90wa70efnrRYK58eG3isVOhD2Rikgk7oFmdvPNxC0P75SJgCMB0HZxWEasoYOOziS9NTExOTfJcVoKtMpIu4bKIaHYJHWiLRde0ywo5NguqFiHHYRQRCQjkOKz89tWjTq8WUaCqTwG3PGtEDj8+40ye/88nfP/sQWTZLTQFDOc0Xdc50BbmjFhUO6QXXgC8MKeGbIeVpat3M76yJ9l2Cy1NQfyyiiNW6IlAougzx2HFH1FojIlix5UXkZdlY0NDS8q267yFrJhTm+ae0MPtSJmri9PdWGBXY4AbnzSigc3oHpOvAgd9YfK6WbsoyrGz6ZPMc5JfZ/yxQu1Pg9thRRTgcNLanomJiYnJCRCjbNy4kaqqKkRRxOfzsWlTZgcJgGHDhn3W3Z0SFGbbaI8oib87VlbHM7dnn+flD//eyexlG7j27IH88IIyVE0n22HBaZFAMDI7++Q5CUSiDO9fQFTVCESiFGXbiWo6oajKWQMLOaesmDU7j/B+QzN/njnq6HasElagf76T9iwb7eEoE4b2wm7pQ0SNsnByFRFVoz0cJctm4WBbmO8++g6XVvehdpCHEf3zeeaGsQQiCoqqs3pnI1MfXssVo0qYVTsQm0VEEgS2H2zLKESJC03ikUKQvkB7R0zckkxnsTfxRdx7O3EuyfSejosVHelsMvNERy3sbw2l2btnGqB82uM1+XLp4XbQHJCZ9ti7iQn7ZAeTA74wf7tuDCu3H2bqmBIeW/NxmrBkVm0pkiCkuSV90hpk5bbDVJfkdyq0qPV6GF1agK7rTOwg4jjcFkYCfnZROT+5qJz2kLHg7rSKtARlfndFdQfRhoeoorFwShXhqHHNsQgCs8eVkWWXaAvJLJ5SRSgau3bYLRz0hfnjv3fws4vLEQQjP3jh5CqimlE1axFFRBEO+MLYJYHZ47zMnTiE1qBxLFlWiZZgmLmXDIlV2kY5a6AnUXUrKxr7feFEtU1dmSEsaQ9HmD+5EkXVCUSiDPB4Eu8vznEwaVgvzi4rJMdhxSIJPL5mF+eW96C6Xx6//vbpCbHcfxpa+MfWQwnHl/h3B/C71z5Me3zD3laeemcPFb1zU86D9xpa2Lbfl/H1y97Zwx0Th6SdO1k2S0ax3uG2SKdVw7VeT8I68mRG0yGKDjoU5TiIaCpZsQFlezhK7zwnt46vYFX9YRa/+CEjSvKZPc5LT7ed8fevTol+i7c3l01i3sQhDC/JIyirZNksvN/QwqIVW1Nyn+++bCiCYEQFCILA+oYWXN3YfH4e12fznmDydaCjq9yqjxupG1CIYJOwWURCUZWWQJReeQ6uPXsg3/rT25T3zGHRpVUc8IW4/8rqxD3IaZOYvWxDikg7U3+pY6xicvRl3JErXv3osErdVoV1h9kWTU5WBEhE9tokkVyn0WeySAK3XzKEcFQlGFXJcVhAB03TUCWB0/vlIasaclSjOMfOxZU9jT6X3cLiKVWEFQ1fMEq23YIkChxpjzB1dH8kQeDWSyo47Atz+4QhCEBb2IiGbQ9HeffjZh6IiZpznVZyHBZmPv5uyj26MNusNjY5dbEDowbk448ajgTGvI+IXRSwAiHM4jITE5PjJ9dlwx9W+PhIgNJCF9k2C35ZRRCMvkKOzYKsBXnv4xBjB3qIqoY7cFaHBd9cp5Xbl29KzG+4HRYmnt6bQEQhquq4nRau/fP7PDptJKNLC5BEoct5j0BEISirVJcYLuOypiKIxnxAYbYdAKso4BMEPjzQxpiBhiOtomrsbgzwowtO495XtqfNt6yqb2T+c5uZdHpvxgw04nzjRSr+SJQ8lw1Z0fBHFNxOKwUuGy6b1KnAPXksYkb3mHwVOOAL0ze/65iZ4hw7+1tDqJqOJJ48/Qf/Z4jpkUSBPJeNg77QCT4qExMTk683n1mMcsYZZ3Dw4EGKi4s544wzEAQBXU+vpBAEAVX9bBWFpwq5LhtFEYU6byGr6htx2lKz+TbsbaVmkIdwVOMH53uJKjqtoSi+UDQh3IhX6585oIDT++bhsIrMf34zG2KOA3/4985EhIai6fTLdzKuvAd7moK0hRVAx+2w0NAUpHe+k9+99hHfHN6XYredtlCUohw7iiLwcaufwmwbnmwbIVmjZ66D33znDJw2EYdFQhIF6o/4cUoSuVlWxscnGR1WfCGZG/76PkFZ5f4rq9OiLOpisTaLV2xJid+58Vwvdosx0WmTRNrDMvMnVnLreI1ARDmm2JvunEuSq9wLslIXK5I5u6wQiyiwoaHlc7VS9AXlNCEKZB6gdFxc6Xi85uTrVxNfUCY7loWbKSagxuuhuiSfJSvrcdkknr1xLI1+GbtFRFY03trVlBJb8/Px5Rxpj+B2WHFaJUaVerjz+S2J98+sLWXsQA/nnFZEtt3C/tYQr20/yHmnFXNGSR46QkJ04i3KpikQRhQtRBSV1lAUl11C0YxKul65DiYO7cWwPrlIosCGva1c99f3+fPMUXzrT2/zuyvOoCjbzu7GAMU5diKKRltY4VBbmF65TqY/9i7VJXkZXZPGlRdx6yUVNPkjZNktuB1Wsh0WLCKg64l4FUXTyXHamf98aj5xXVkhd02p4mBbmIsqe1AzqJBsh0QgovBJc4jrn3yf4SV5zKodiI7Ob1/bkbhO/va1HSkTDrVeD3ddNpQ7X9iS5uw0oiSfqaNLeHR1qvvMuPIibr64goUdnGCSXaKS2bbfF3OI2Zz2+tnnlSFmmBzOc1mZM64MSBXr/e/7e1k8ZWhGB6u7Lht6SlTZFGTZONweZsdhPxU93ciyji9qnEvxe0gwqjK4Ry7P3HAW6CDEhJwum8QVo0p4bM3uxPeaLE5Jjm+r8XpSFsPf3NHILf+3kTNibTb+mgsqejCuvCjl/InzeV2fzXuCydeFeN/sYFuYYERB1nV0QUBWNcKySkGWjaimEVV1/nj1CHrk2DnUFmLNziY2NLQws7aUD/a2sn5Pa4oQBYz+0s3PbGRJB8e8ZAFM3IUvfg/o6Cb2WSsUzbZocrJiEQVEQcBlFSly2/mkJYQ/FqcVnxvo6XZwpC1CnzwHsirS4jf6ktk2C1aHQFTTY4tVhqglEFHZ1xoiJyZEybJJ3L1qV8ZCguQ4EV9QxpNlpz0cxWmTWN/QmiYWzVR8YGJyKhGfXdJ10NERBcGIMk08bjqjmJiYfDr6FLgQBWiLKLSFDHfDwmw7NlEgpIRpDYqMGejBjjGXk22TeHPH4cT767yFWCUxZV5DVnRmL9vAU98fg90KTotEkdvOrCfe45HvjkDXdBZMrmLB85vT5j0WTK7CJgr84pVtzDmvDEmAoKyR77JiEUVsooCOsTiSbbdwZqmH5kCEjw4HGF1awLqPm2n0y6ypb8Jlk7junIGMG9zDKKCSVbLtEk6rZDhqCwK3P7uJ9/e0cP+V1fzyHx+mfI6zywpZOv3MFIFsnEwRg2bMusmXzaG2MEP75Hb5mqIcO4qmc7AtTJ+TaI4xICs4PqUYBcCTZWO/L3wCj8jExMTk64+gf8aR5p49exLRPHv27Onytf379/8su/pK09bWRm5uLj6fD7fbfUK2ua85yK3PbmLMwALW7mpOLLC6bBJLp53JzCfe5bpzBnJJVS8WrtiasgBb6/UwPSky485JlQzvn09AVghHVXKdVsJRjXBUxSIJuKwSkiSgahCIGLmeogA2i4isagi6kQfeHjJyvbOshvNKWNFoDylkO6REPqg/bFTXoOsQs30NyirZDgt2SSQYVQjJCm6nDUXVaQrK6BooscU/iyTitEpEVQ1/SMFqFbBKIofaIgmHgEnDehGU1c9FANLRgaQw28bT157V6cDmiofepjGWA/h5WSnuPOzn/P95o9Pn//XjcxhUnJ3yGTpzfjHzn4/yebTbrugY5ZFttxCMKOjA/Oc283FTkD/PGsW85ZszWpMnnEfKClkwaQgfNwVZ8PwW7ry0ksdW705xQxlf1YOfX1ye2FZhtuEI1NPtICAruGwW/JEovd1OfvHKNl7efAiXTeJPV43AZhWM/N6oSlTRWPdxM6f3zeOR1btS43u8Hm48r4xZT6QPpuu8hYwYkM/7Hzczb9IQjrRFKMi2oSPgj31+myTS6I+QbbdilQT8kShuh41QVCUcVRMim6fXNXDFqBLGDvQgigJBWeWgL0SvXCcWQeCdPc1U98tDB/p7XMiKkU+c4zAcV3732kcsvmwoVkmkPRzFHXM52d0YxCoJFGTZsIoCwaiK02pUqrSGogzwuFBUnbaYYC3bJqFpGm/taqaqby57m0OJa9LS1bsBQwg0vCQft8OS8fglUYhdcy1EFIUsu5WoqtMWipIVE8n0djt5d08zeS7bUXea9ghjSgto9EfIcVrJdxm5w76gTFPAuP68s7s5IfaJv+f804qI6jptYSUhvHM7LPTpprrhq8zxttu9jQE04I9v1HPd2YOQY245+Vk2BCAYVQnKKi6bxL8/PMyGhlZun1CBrOgc8IWY/thRq//Z47xsaGjJGBlX5/Xws4vL2dcSwmGVWN/QwsiSfKYnRQXExVFzl2/+Qq/P5j3B5MvmeNrt9gMt5FqsWESBMBCIqsQMjghEFNwOCzaryO9e/YgZtQO54qG1BGWVJ68ZTe9cBxMeWN1pxeGr/302ZT1yUu7H2XZLQtyc7bDx9q4mVmzcn7Gdd4yAPF7MtmjydeNY2u7HR/xo6Ly/p5WKntm4nTYCsmr0uZxW7BaRqKqhaobldbbdYjiF6TphVeXeVz5k5fYjKWJpiyjgsltw2SRe3nyAbfvbmDepEn9Sf6a74gM42vc+nveYmHzd6ardHmjyY9EFwgJGFF4sPiPbJuEAVHTaVI0e2Q6zrZiYfIF80XNTnze+oMzBtjBtsbhNh0VCFAwHEjuwzx+mKNtBUyDIlY+8n5jnmjuhgrZQlG8/eDRa7/nZNUxesobZ47zUej0caY9QlOPggZVGIdGj00YSVXScdpEsuxV/7J7vskoouoYvKLP9oJ8xAz1EFIVDPpmCbBsFTitxX8IIOlFVZ/pj7/LItJE8+fZupp5Vyq9f+ZA553uRBBGrRUDXQdV0mgIyqqbzfkMLu4/4uTk2D9AaitIv38W9r2zL6NRSV1bIJUN7pRW2dBZbv/zGsZxRkv95/EQmJl0SVTXKbn+Za+sGcl55caev+6Q1xE///gFPfX8MZw3yfIFH+PmhajqDbnuJa88eyHmDO//sXXHfax9hkQSevGbMCT46ExMTk68vn9kZJVlgcjKLTb4MchwWFl9aRTCqcHFVLxbEKv6DsppYPPvtqzt48I1dzKwtZfrYAQkr8X4FTva3hPnTd0fgybKhayArKjZJJKJoaLqxfUXVEAWBFzcfTDiqALzyozpe2HiAkSX55LqMhduWYBRF1XljxxEuKC/m4Td38fPxg0G30B5W0HRwWiVyHBY0XSPLZkXRdOY/tyWlQrXOW8iiKVVouoZFFPFk2QhGVFx2G+gw7bF1CXFHnEenjUxkZwIM6eVO/F1XVsidkytpCchk2S04rCIiAi0hmWzHUbFKRyFAJhFLJgeSK0aVcPdLWzmjJJ8ZHWIz7n5pK1eMKklUzn5eVordZYu2d3i+O+cXky+eTDFLtV4Pt46v4J6XtyWEJN97dB2/v2o4t0si/ohCNCZoSBaizKgZwKQlaxLtdfYywzVoVt1Asu0WIorG27ua+N6j61gwuZJbx1cQkA3XIJdNIqIoSKJAn1wnqqZz9mnFfGtEv9i1QQdELJKAEDVcTy6q7ElDc5CbLy5HVjQa/UZGr8MiYbMKjOifn+ZGcsfEIXzSEkLRdPa3hLlx2fpE5IGi6TisEv6wQlNAJstm4bkPDiSuQfE2LQkwYWgvagcV4nZakUTY0xxEEoRE5M7/3TCWDxpaU7N7ywpZdGkVi1ds5bXtRpXN5CVrUlxKOjqezKwtxSqKfHSwnWK3A1nR2NMU5HBbhLIeWfxz60FO75OHKAiUFmXhtEo89U5DWvX9kpX11HkLuWNSBZIoMnFYL6pjGcZv7WpKuc5OGNqTWy4uR9FURBEsokgvt5OmQJhhffMSIpVcl5XeeQ4amkNENY38LDvNAZlAROFwe5gsu4VQRGV0aQGyoiXEMxW93AiAphma07j0VNN0jrSGKDpFFj5lXcEpWrjxXC/BqJoQVQK0BCNc+5f1iXuO4chVSSCicM/L2/nhBaelbKu6X2bnIoBV9U1Mb48k7k01Xg+ThvVKseJdtaORqKp/4ddn855g8nUiy2YjoukENAWHaAhFQrLR781xWFi1o5G3dzYxd+IQvvWntxLtS9V1Gv2RToUoYPSXuos9jKpaygRxMp+1QtFsiyYnIwFZZdpj6/jNt0/HIkkcaY+Q57LhdloNhxSrhKwakTvvNbSw/UAbt4yvQNFUsq0W5k0Ywk8uVAlGjOIFmySwYtMBHnxjF7/+9un86h8fATB7XBmhaGoxQnfjuxMdm2pi8nXHF9WQZZncrNRxgAYcCISw2WzYLaJZjW9iYvKZyHXZOOAL0DPHjl9WaYoV1Ug2iXc+8TGsTy77fcH/z96Zx0dVnf//fefOnm2SGZawBAITzQYYRFmSuKCVVZZa+yvaCkTritrVFWQRXFr7rVVqWyuobVG7qgiuqK0EF1SiQgBJZAnIEjJJJpl9u78/JnOZPYARAe/79fJlmLlz75nlnHvOcz7P5yHbqOPpueeSoRMxakXc/iBZScoJV1rNrKzdxbmD8ijtl02H28/NXWWgVQhc/qf3YkpsdnoCctLQ368LbwZ/vLuVL+0eLirpjVatQgd4QhKugA+9Rsulyzdw9qBcMjUi155npdnh5ecTzsDu8vPn9Q3MGj1Idk2NCGgrh5qZcVZ/7n5+sxzTWzF7VFIhCoTjAfdMLeXNn51Pm8uHTqPitfpDSYUooJTxVPjmaO1Kessxpv8N9uoqdbW3zcVYTg8xisMbADjuMj0QdmnefrCzp5qkoKCgcFrwlcUo0axevTrp44IgoNfrsVqtFBYW9uQlT1viA+U//U4Rk8v7yoKTXpk6uYyPyxeM2RyrsppjSgOk2oCttpqZW1XIvGdiJ73VRRY6PYGUG24A5xX14paLi/hgZyuLXtqa1GLw9onF1Dz1IQ9eNpzbJp15JMCoVrGrxYklU4c3EOSww0t/k4FX4gQx0XgDoZh/R2ppGrUiIwaaOGD3EAxJ2D0BDtk9WHtn4PAGmfXnD6gcambB1FLufmFLwob5omllCECGLlxj3BsIJZTCiWw+JiurAHDl6EFyWyKLnx3NDvIytD3m2pLdzQIk2QJFCb6ePKQqsxRZoEY7muxtczNt+QYeu3Ik97y4hQcvG87kYfld5WXCAfqZj70b00+ix4BXbq3EZNQyqbwvVUMtZBnCfa7VEUIlBNCKAv6gxG/WbuX2icXctGoTv79yJGpRJWfGBYIhfvz0RyyaVsb3/vg+/7huLNf99eOk782oFVl1zWjmjBsMQH+TAY0oEJRgxYZdrG9oYd54KxUpyg9VWc3cP3MYIwtyqZ5jQacJZ8w0HnaQZ9SSZ9Ty+/82Jnx2EB5n3tx+iDmVg7ljUrEsuDFoVLQ6PNw9pYRfTjyTL9vdZOs1aNUq9rYdEdV0ePz0MxnwBUKoVQIOb4ARA024fOFxCcLW1Z2eICP6m/ioqY2t++2U9sthVEEu86eWsHTttthxxWpmbtVgJGDBi1uoqSzk6qc/SvrZrd18kCtGD+LKJz6I+TyWzhjGw+s+54VPDsSUhfn8YCerrhnDwtVbqLSauaS0L4++1RgzrkeyauY8+SETSntz68VnxtRbjr6G1vXtCDJrVWq8obAN8NI1W+V+FxmzH7xsOACWTB1ufwC1EHbfqW20cdeU2GlS/L0onujnNzTaWLpmGzVVhTG//U6Pn6G9M0/4Z6/cExROFZz+INkakVBQ5I7nN7OpqV2eXx12eBlsycCoU6PusseO4A+EsGTo0p7bZNB2W/YwEnxKRbwA+FhR+qLC6YbLF6TF4WN2lJNYhMg8sdXpwxsIyQLdB17exsJLS1EB+9vcrNq4h+vOt3LZH97l0VkV/PaNBuDImg/C4vxQCFxiEJvk41Cnh5ZOH9ldm1b7Wt1s3NXKBWf0UpyGFBRS4PAEuGrlR7Gbtt4jm7Z/rTkXY5YOu8fX/ckUFBQU0qARNXxx2En9ATvnn9FbnmNbe2Xi8AbI0Gl56NXtrIuKtVYXWVg6o5yLinvxZtfjb3/ezLwLrSx/u5HrV33MT75j5ZKSvixds5XifjlcUtqHs9OUu968z06hJROPP8hr9QfpbzIwtjCPL50eQCDLoOW1rQc5e1AuS6aV4QoGEQUVJqOWD3baeGPbIa4cPYinooQokRgNkOCc2l3MwOkNcFZBLnVNbbxWf4i6prakcfDqonAMUEHhm+BwZzgmmpNEHBaNVq0iz6hlX6vrRDTrhODsATGKOUPHAXszUlfZUgUFBQWFHhajzJgxQ64LHU3kMUEQqKqq4oUXXiA3V7GZS0X8xrVRG775nTM4j8Vd5Xgik18JKUZxXW01c++MYXiDQTnYV7e3nSufeJ+fX3ImC6aUYnf7UYsCWlHFw+t2xEx6K61mZo8bjD+YfvKsEuDKJz7gV98bztkFppjN9GqrmR+fN5QMncg/rhvLwhe3yM8btSILppRQUZDLYYcXtUpFf5MBrz98o390VoXs7rKpqU0Wp0SLTxZMKSHXqOGFm8ahU4u0On34AiE+bmrjuY1N/Ow7Z2DUqvEHJf5x3Vg0osDOw07mVhYyYqBJPuf6hhbueXELFQW5fNLUxpzKwqTv+2g2Hy2ZWlbOOYdOTwC720+nJ8D/dhzm8wMdLJ5e/pXL9lgytZxXZImxdY9wXpEFS6ayqXEy0+LwJRVTQDibNBlGjcgDlw1nZdeCN8J9M8sTnEgiVFrNrN18iOVvNcqihJqoEjorZo8ix6jhkSgBQ1GfTF7fekgORnr8IQKhEA9+bzihkMRL86pQi6knzi5fkFanj6fe3c09U0vRqATcgRAOj5/F08rw+EO4/QEuG9mfe17YEjNWVFnNLJs5DEISfbP1uHx+cg0anP4gOfpwGSNBgHunl7Pghc0J48yCS8vY1eIE4LN97YwdauFgh4tJv9sIwF+uPpfeWVoK8owsWl2ftMzW61sP8KtXd/D8jeP43ZsNScsyVFrN/OKSM1lZu0sOOix/q1EWM1x//lDErvJBm5raWPX+Hn4x4Uw2NNpksVoq7O7YTc3aRhvzX9jMbROLeeGTA9RUFcrZNytmj5JFhYumlbFwdX1CeyP/rqkq5JLSPglClOhr3D9z2Gm/IbqvzcX7O22c2TeTPL2WZTOHMb9LmBgRcVVbzcwbX4THH6RfjoFNe9sZ1j8Ho1ZEIPxbi/z2ojfFkhH//PrGFuZUDo55TMluUlDoBgmEkIRWJXDv9HLuWV0f635lNbN05jD++M4Xstir0mrmUIeHArORSqs55VjuCyWKjiNEXE+ORwCsoPBtJtuQOqTg8gVx+4IxwtzI/M/h89PU6iFLp+anl5xJzZMfUlFgom5vOxDus5G/ATo9Afk80SVpXb6gPO99Y+tBCi0ZGLXiaT/HUVA4HrL16oRkpmiy9OHSy8q9TkFB4aui16hodfqYWp6PoBLocPvZ1emU473bD3Rw03gr7+5sjXESnf/CFpbNKMcb2Exto40//W8nw/ubmDosn5rKQvIytCxds5UfdDmVROI0IYhLwLSweHoZLl+AN7c38+nedq6tHoo5S8t7O1sZNTgclxaASquF4QNyOdThJjdThwrwBUMU9srkZwNMqAT4uKkdICZGU1NZmDCedhcziIyv2XqN3Hbi2l5pDSdVzn9+c4/ElBUUjpVIgp6pGzEKQK8sHXvb3F93k04YsjOK9iuIUTK1ePwh7G4/JmVNoqCgoAD0sBjljTfe4O6772bZsmWce+65AGzcuJEFCxYwf/58cnJyuO666/jFL37BihUrevLSpxXRG9fRiuupw/NZNqMchzeI0xsgUyeybMYwXP4gdpefDL2a5g4PP1rxAX+rOZdcowaDVk2WXs3UYfnoNCp8wRAZOjVtLh+GDJEfnzeEH40djEolkGvUgARqUYVGVMnOK/FUWs00HOrk198bgVolMHlYPnOiytc0d3goyDOwcVcrL366X55QR7+XO5/fIp8vkqW/fb89YbPhhRsrOWT30CdHz9pbKtGp1Tzw8lZ6ZevlyX90u1ZdM4YHX93GXVHnj66/WVFg4pFZFXLgMnrxIAE/jSvJAN0vJIwakSdmn8ODr25P6lCw8MUtPHT5CDkgejTlguLJMWp54LLh3PHvz2IEKecVWXjwsuFKsPUkJ12ZpYwUk9ugJCX8xgGWrt3GitmjQJJixBnRv3OIFSVE+pU3EMLnD8nP1TYe5q7JJSzu2uiL7qN1Te08MquCP7y6jYqC3JSbe9VWCwNyDVRZLWhEgbtf2JLgwnHfzGHYHB7umlKChCA7sAhI2Bwedre4mP9iPY/MquC36zbL137krbA4xJKp5cHLhnNHV7mhDK2aoCRxoD3siKRWCXxp93DvmnrumlwKhMebfjl6PP4QDrePJdPKcQeCdLgDZOpFnN4A+9vcLH/rCwDUokBdU3vYxaVLmBMtilOLYbHJLc92lUSqGoJRKxIKSTHldyLfQ6hLlHms4oXw92LjLjH8eHRZmN7ZOvmz9QekpN9H5LuvqSxELapSHlPbaMORppTF6YLDG+CcwbmIKhXOQJBslch9M8pxRpXridSv9gVDCBK8vf0QfbL13HThUAQB5lYVIhH+zOr2tqfd6I7eNIsQLWhUxIMKCt0jAagE2l1uco0Gls4ox+ULymVtMrUiIlBgNjK0VxbVVgs3XmjF4w+yeZ+dm8cXAYkB3ZvHF+E6CteTQkuGIgBWUDgGsvUaqossSYVe1UUWBuYaeOXW6pg+7PAFsLuDmDO01Da28Nm+dn71veF4/CFuemYT1VYzs6PmtfH32NpGGxJw3flD8AclOQnjpguL+PxgB/2y9cr6SEEhCRkaMW1/zdSI7O/0UtQ78xtonYKCwumELxBi7OBcfMCdz29OGi/9/dtf8OBl5dz87Kfyc+sbWvAGQtw/cxgOX5BOt59sowZrrwy+bHejFgWK++XExMui4zTZejX+YAi3P0i7y4cvKHFxcR8uHd4PbyBIICgxfGAOvmAIc4aONpePTJ2a3lkiD732ObdPKmH+83HJUEUWnr12DFc/9SGjCsLJtTWVhRi1idsq6WIG0WsJS6aWUYNy5bbXRMXVD3V4eGXLAd7Y1ow30POl4BUUuuNonVEgLEZpOo2cUSJiFP1XckYJ99f97R5FjKKgoKDQRY+KUW699VYef/xxxo0bJz920UUXodfrufbaa6mvr+fhhx+mpqamJy972hG9cR1RXNc1tSMisL/dzaNvJy/LUPN0OJts4bQyHF4veRn6lOUZ7C4fy17elnJD7dzCPGZXDgZSb3gvmFrKy5/tj3k++jo/ufiMmPNHq8ejiWTpn1WQG2PPuL7Rxr1rtnLX5BJanF7cviArN2ynoiA36Xk2NNq4d009ZxXkxpTUSbYpH79BHznu9omqhEVDd5uPQUniN69tT9oegIqCXLnmcnz5JQgvRh64bHi3Svd+JgOPzqqgxeGTg7mWTMXq/VQgXZa1TpP4m4Owo1Sy35yrK7v0uWvHMKfTS4ZOjbPLWjm+zuyGRhs/vfgMKgaaCIQkrL0yaXMdsVwWBRWLV9fLfTi6j84bb5X/johDIueMUG21cM+lpcz68/v84NyCBCEKhPv3hi9srP1sf9K6uZVWM/dMLU15bQgL9CKZsNVWCyNSWLAC/GJCCKNWZMXsUTHvDcIBhAVTStnf7uajKOclCFtWR7ueRLfvkVkVsoNJdCbh+OJeXDl6EBUDTTx0+Qh0apX8PayYfQ7Q/fiRTLwA0Nl1vWghg8Nz5LvtrkyENxCSz5GKr1pq4mTH7vKhE1UgCNzx/GYqCnIZN8TMuzttCVa6EaqtFn458UwefHU7iy4tQxAEnvmgiQe+OxxvIITT6+d7Iwdwz4tbYjaq48Vg0UQER4p4UEGhe+wuHw5PAL1axf2vNaYcOxddWsZ3SvvS0ulj0bQy1mzez+Z9dpZOL0cCpg7vFxPQbe70MjjPmNKNLEKWXqMIgBUUjhGjVmTJ9LKkDngLppYy4Xfr5fnWvPHWlPdgf1DitgnFPDqrgl5ZOn7w+PsxQt/4e2xdUzsLp5axeE19wtxt7JDTo2a9gkJP4w9J3HjBUEKSlBBTuvECK76QRJZBrdzrFBQUvjIuXxC1Xs385xPdWqPjpWf0yU54rdMboKhPWPQRSeizOX30ytJjd/tiknYi11r+ViPzxlv5pKktZeypoqukfXWRhTnjBiMIMNicgTcQosnm5raJxQlCFAgLZJDg6bnnohZV1NXuZPlbjeFEsThSuZ3EryWi1xzx85i5lYX83xs75GSpni4Fr6DQHYc7vWTp1ajF9Al2AL2zdNQmSWY+VemJMj15XWKUA3Y3pf0SxzgFBQWFbyM9Kkb54osvyM5OHGCzs7PZuXMnAEVFRbS0nD43qK+D6I3ryAR73ngr7+20sWbzgbRlGSIT2AdmDuOOJBP+iPBj2Yxh3WbV3/xsnbzhHQnmR29452frGVGQK7uiRLsI1DbamHdhbLmm+MVCfLvmVhYmPL6+sYX9djdXP/0Rq64ZndIGsbvzRF4X/zfEOhMcsHuYW1mICuTFx8raXayYPQoVQoxTTLXVwk3jreGSP918lp0ef0L5pQjvNLRwx7+PTumeY1QWHqcilkxtygy0VqeXeRdagdiFany5s2hcviD72tzcuGoTj105khtXbUp5rN3t5+Zn63hkVgX3rN7C3ZNL5OcqrRbuf2W7/O/oPhr9d7QjSE1lIb5giEF5GYBEm8vHr783AnOmlpW1u5K2IbwwSd1HAl0ZrcmuHU+ysifRdHr8zJ9SwmNvNyYNINy7ZiuThvVNWOz3ztJzz+pEMU3k3/OjPjcI9/8rRg/i5jgBUIRMfXjRkioQUW21MLtycFLxAkBWV/ZB9PgUOWf47/S3b51aJZ8jFae7/Xa7y48nEGTp2m3yWNzu9nf7+7pLHS4lFwhJdHp8/OTiM3hvp41zB+cxyJxBjlEbIwzM0Kn5aE9bghgMwgKo/iYDr/6kmlyjlj7Z+uN+P8fjqqWgcKrR4vCRqVfj8gfTzlN9gRCBkIBBK2Jzepk2vB8Ty/rypd2NyahlfHFvnN4AHe6weHfUoFxyjFrsLl9a15NMvZovmh04vH7unVGOLxDC6Q0clwBY6bMK3xaaO724fX7u68pgtrv95Bg07G9309Tqirk3pr0HN7Rw12SB/2zax3XnD+Why0cwKM/IK/UHk95ja6oKuXdN8pKF96yuZ3nc2krpkwoK4PQHufnZui7XyWIcniCZXQ67Nz+7iadrziVL16NhQgUFhW8pLl8QlUroNl7a4U50LozEKpLduwUBDnZ4k54z3TwjOha8vqGFq6sK6ZWpQ6sS0GpFBJMehzd1fHd9Ywv3qFUseenI3CNV8lH9fjt3Ty5BksDlD2IyaOidpUuYd0SSDr9sd7Pb5kKvEZEkCb2o4p/XjeWA3UN7VCn47Qc6WDC1lEHmjKRtVFDoKVoc3qMq0QNhZ5TmTi8ef/AruYmcLDg8X12MkmvUohVV7LadPo4xCgoKCl+VHl1lnn322fzyl7/kL3/5C7169QLg8OHD3HbbbZxzTjhLvKGhgYEDB/bYNZctW8batWv55JNP0Gq1tLe3xzz/6aef8sADD1BbW0tLSwuDBw/m+uuv59Zbb4057r///S8/+9nPqK+vZ+DAgcyfP585c+b0WDuPFrvLR0iSWDF7FIIQLp1j1Iqy9XB3ApLI3w5f6iB+baMNlz99Zqg3EIrZ8I7HqBXJN+mpq21L6iJwy7N1GHWxN+3oDP9U10z3uD2JU8DxnCf673hnArVK4OZn67j2vCHcNqmY5q4FzsbdrZw9OJfrLxiKqBLI1KlZt+0Q/bL1HHIkXwRFXytLr4kpvxTPOw0tsnuKwunJTRdaEzLQqqxmvH6JgXkGuf5tRPhlztClPV9EpNBdGRgIB+uf+WAPFQW5GDQiVVYztY023HHjQLI+EiGSaRIp5XP/y1tjXUei+n78ZkF3fdbpC6a9djzpns/Saxgx0BRTqiua9Y0t3HpxEauuGY2oEjAZNGxqaqPd7Us7vgqCIGe95Bg02N1+nvlgT1IhSpXVLCvpo4U8P734DHkcA1iV5vWBYPg9Rgc3mju88nd3qMMj/x1PZFzrlaVLeUyV1UzmV6h/eirg9AUgymEo0rfS/X6MWhGNSkVdU1tc2TgLIwaYaGp1kecN0D/XGDNeZ+jUvDIoN2aDu9pqZm7lYL77h3dx+YIJLljHsin2VVy1FBROJTo8fkwGDYcdvrTHhR1OJFzeIIPMRu55MdaR4bwiC/fNHEZ213iNEH48letJdZGFJdPLufs/n8W49B1vP1P6rMK3iU6PH1Gl4q64TOJKq5mFU8swakV5vtPdHG9/u5s7JhbLbiov3VyZclOpO2FL9NpK6ZMKCmHcviAPXDaclUlKHj9w2XA8viAqQfgGW6igoHC6kG1Q0+7q3tE1PnYcKWWT6t5938xh5BkTN8mNWhFzppYVs0clJExG5iH+4JGkL62oIluvRgdIIQmNSsDpCaQs3ezyBfEFQzFznfjko+jS1799oyGm3Q9cNpwcY/LPQSUICEI4kWvHwQ4GFZhw+8NiQZVKQCOqGJRr5JLSPvz13d3MGTeYTl+Qji4BcJZeTf/cFCdXUDgODnd6yT5KMUrvrHD8el+bG+tpUOZPLtOj7T7engqVSqCfSU9js6OnmqWgoKBwytOjYpQVK1Ywffp0BgwYIAtO9u7dy5AhQ3jxxRcBcDgczJ8/v8eu6fP5uPzyyxk7diwrVqxIeP7jjz+md+/e/O1vf2PgwIG8++67XHvttYiiyLx58wDYtWsXU6ZM4frrr2fVqlW8+eabXHPNNeTn5zNhwoQea2t3JJtoV1stPDKrAl8wRBqjBCA2uNfh9mPUitRUFSadRHdXnqG7je6aqkKWrtma1qUFiFGId7dpnur5+LZ81fNE/o63fI5s4Lp8QR5e18Bn+9q59rwhqFUqinpnoVOreG+njZW1u3jo8hE8vK6BC87oRV43AhKTIZxNu7PFmfa4071kxreZFoePmqc+lJ1FsvQagqGw4CsYktjV4mLsUDNufwiHN4A/ECJDL6Ys71IVJaI6mjIwowpyGTUolxXrdzKpvA9XVw1h8rB8MuOy3uL7SDJSldta32gjRKxDU3fnipCpU8f8/nVqVdrxK1XN0iqrGaNGpP5AR9rrBUIS7+20sf1ABxPK+rLf7mFIr/QLpj2tLv7y3m7mVhZy1cqNjBtqZsHUMryBzTFijyqrmWUzh4EkyUKQiJBn234791xaxt3Pb2ZTV+kjbyCUIFBaNnMYv3vjc+BIcEMAbv/3Z6y6Zgz3rqnntn99xjM/HsOSl+pjrh89rn3Z6mTZzGHc/XzyNgZD6UWJpzpOX5BQ6MiNM+Lulc66v6aqkMVJHHLWN7aweE09FQW5fNLUxgPfHc6AvCPBHgGYNCyf2eMGEwhJDDAZUKkEHJ4A/7x+LHq1yN5WF/et3cqymcNw+oJHtSlmd/lo7vTS1OpibmUhIwaa5EDYsbhqxXOs2eFKNrnCiSJbr8EXCpFtSL9EydSpEQQ40O7mnhd3JmQwvtPQwp3/2cyYoXm4/SEqBprY3eJkYK6RHKOGey4txe72k2vQ4g2GsLv9HLB7KO6Xw7s7W+WAdXQ/A46qHxzq8LC7xcmscwuYW1ko37tS9Vmlfymc6piMWha8mNxd7t61W1k5ZxQ1T32Eyxc8KhG1o6v/3TezHBWCnKQRv6HUHZG5ZU+4UyoonC6YM7T8dt2OlHGcpdPLOdxNso2CgoLC0dA3W0+omzh2jkGDQSPy9Nxz+XBPK58f6GDJ9HKAlPfuu57fzH3fHRaTeBMRgfzmtc8ThLHRSVMFeUZZJJuboUUPIEk0OcMb73kZ2oTElJhzeGPnIPEuwv1NBpatTYyVR+Ycv758BA5PQJ7369UqFq6uZ922Zvl9/PuGsdg6fTz6VgO1XQKX684fwiWlfTCqRX44djC3xzmhR2I8imOKQk9xuNObMvYaT8QBeG+r67QRo2hFFWrV8YtRAPJNBhqbO3uoVQoKCgqnPj0qRjnzzDPZunUrr7/+Ojt27JAf+853voOqawCfMWNGT16SxYsXA/DUU08lfb6mpibm30OGDOG9997jP//5jyxG+eMf/0hhYSG/+c1vACgpKaG2tpbf/va3J0yMkipItr6xBQmJn8Rl1CcjOrhnytDwzI/H8JvXtiedRGcbNIwv7kVpv5yEzd6t++3yRvehDg/VVnNCkL8768MbL7Dy9ufNcsmcDY22tJvm0Zvr0VQm2XT/queptlrIz9FTUZArL0iqrGbmxAlTflw9FLcvyE3PfJQQ9Ix81hHb9lSW71VWM4PM4Qz6bH36LN/TvWTGt5kOj18WJAAMzDXwl6vPZcELWxJFAjOGsafViVoQuPnCIiCuvEuRhQVTS3nglW1AWKzw2JUj5TJSERHHuCFmDBoVeRk6XP6wZfrtk0rQq0XUKj8vbz5AxcDcmL4U3bdS9bOjtT2NprnDk7JMUbXVwqEOd8z1tuy3s2L2KJa/3Zgwfq2YPYosvTqhbVVWM4umlSNJUsJGR7ywJVOnpr/JwIyz+qFWCaz+dL/sPpUMo1akqHcmC6aW0uHy8+8bxpGpFXF4PdzXJSzodPvJMmjI0Ip4AgE8vgD3d9nVd3rCZSL8wRC//OcnzJ9ahkZU4fT6uXd6Ob5giA63n0y9BgGJL1tdFFgyZSeWgXkG5o23ohVFHB4fS6aV4w4Esbt8LJ1RjtsfotPjJ1uvQSuqsDm9/PP6sQRDEq9v+fJIO7ramKkVeX7TXr47qiDlez4dMBk0+KOiX3V729m+384lpX2osprZ1NSeIHjKM2q7/X0vf6uRO5/fzPwpJUiATlThDYYY2iuTLJ2IWgxb934cdX6Avjl6fj7hTGxOLwtXb+12U2xfq4s7//NZ2mDa8bhqHWt2uJJN/s3zbRIPWTK1tDp9aFQC1VYLHze1JfTTQ3YPLp+f3ll6ivOz01pp3z2lhGVrt8b064uKe/GTi89EpxbZZXPGbHKfXWBi+RUVzHvmiMvXR3vaaHP5WfDilm77wf52N7f/69OU/Ta+zyr9q2c5lX/7cOq23xcMpXSXW9/Qwg3nD+Vf14/lqpUbj0pEnaVX8+8bxnLf2m0xTnfRfWlkgYne2eldBCNrK8WdUkHhCOn664ZGG75gSIlLKCgo9Ag5Ri0dbn/SmDKEYzh5Ri0alcAQi5H1O5pZeGkZ+SYDXzQ70t67vf4QD3x3OHc+v5n1DS0pk6Y2NNpQCQLPXTuGP/y3EZCoqSqkrqkNlQAf7GunYoAJvUaNTi0yP0m5+8i/F08rIyuJYD461vfSzVUp1ybvNLTwRbODK574IOYzmFNZyLtfhJOYaqoK2ba/A28gRE3VEK47fyi9snToRBWHOjwYckTufiGxjbWNNpa8VM/iS8twB8JJbtFzyRMxx0x3jVN1jvtt5nCnl+K+WUd1bG6GFo0osMeWPgn3VMHpDWDoASfp/iYDb2471AMtUlBQUDg96PFisCqViokTJzJx4sSePnWPYbfbycvLk//93nvvcfHFF8ccM2HCBH7yk5+kPIfX68XrPZIx0tGRPhO/O1IFyYxakbMKcsk1amlodiQN3hm1IvOnlJBj0PDYlSPJNWoxqkUefGV7wiR4Q6MNAVg2cxi3TyxhyZr6mAB9lTWc7X/lE+9TaTWTn2Ng3vgiEISUC4Fk6NQqPtvbzp/+t1NWiIuCwHcr+rMwzkq92mphwaVHNtcjxDuXRBwCnvugKUbkEt32pTOHsfSl+pTnqbSamVs1mDe2HaJioIlHZ1VQkGck26Ch0+PnLzXnYtSKGLUiH+5uY+Hq+gQhSiRQGrGOTGf5fv/MYbJVYjrRSuRcCl8vPd1vj5bsuIDe3jY3V63YyO+vHMndooqOLpFAIBjix3/5iEvK+5LRlfV924RixEkC3kAQjz+Exx/kt298Tmm/HK4cPQgJGGI2cteUYkDA4Q0QCIao29vGxSV9ueuFxIyJRdPK2H6wE7c/ENOXol044u1Gj5dKq5l+JgP3TC1lyUv1CaV97p1RzoOvbuN/O1rk60kSPPZ2Y/JAAgJjhuYxdVg+C6aW0u7yk6ETcXoDtDm9GLRizEZHtE1qbNkVM32ydWgEFZPL+1JoyUgaJDFqRVbMHsU9L25JcCCZd6GV17d9yW/faKDSaqaiIJdP97Zz7/QydAY1dz2/mREFudQ1tcW8l2nLN8jfxVkFuQnih8i5Vm7YxdzKQg7ZvWjU4bJt8aUojrwfC2cPzuXD3a3MrSxk9pMfMm6omflTSrkzhTPKgFPExvV4+23vLB377UfKGT23sYkn55yD0xvklovOIEMn8unedm6OKi0VEQClbEuXA9n6hhb2tbm5+umPYsSMNVWFfNrUxsddzjfJfnf3zhjGx3vakp7/nYYWbE4fDm+A2//zWVr3sch5j8VVK112+O3//owFU0sRVUJM0ErJJv9mOVXFQ8fbb3OM2nAmUkjivpnlOLwBWl1+7G6/LJrefqCDsUNLUUO31t92tz9mzDRqRWaNHsSvXt2WUjBC7W6uPW8ID68LW2vXVBWy4IXNSd1XovuB3F9S9NtrzxtCICThDQSpa2ojL0PL/Oe3sL5R6V89wcny2z9eTpb2H0/fjdRVT0W7289j/23kn9eNZV+rO+V6cHblYG55to5JZX2pa2pP2pdUgsBL86pQqwRCSCnFztFrq45u7pOKO6XCqc6x9Nvu+munJyDb7SsoKHx9fFOxqRONBlg6YxjzX4id81ZbLdw5uRijVoUaaPUE+NHYwTy1YRc3X1R0VPfuoQW5LJ9VQYvDhzcQTFu674bzh3LbxGJ2t7gYO8TMWQNNtDp9DBtg4qDdhd0XQlQZ+LipPWWZnqXTy3l/py2luKbSaqajmyRSe9z7qm20IXFkfT+qIJeBeUbuiXJrnTfeSr8cPXtanMwYOSBpfC6yxrkzrmTieUUWFk8vY0+LixDI72XUoNwenWOmm8cKwG0nwRxX4diwOX1HXaZHJQj0ztKzp9X1NbfqxODwBjFoekaM0ubyY3N4MWcqcysFBQWFHhejvPnmm7z55ps0NzcTCsXWhF65cmVPX+6Yeffdd/n73//O2rVr5ccOHjxInz59Yo7r06cPHR0duN1uDIbEydH9998vu7L0BB2exLI6Bk243uXD63awsnYXL95UyWBzePMwMvmMbJQ+9nZjTOZYJKD3fpTVeITaRhutTh+/ef3zmElsRPjS5vTyt2tGy4+HQnDLRUXcPrEYfzCE3eUnU5/+p6PXiFw5ZhA//c6ZuH3hOpdaUYU3EOSOySXcJkGbK5z56vIFueLP7/ODcwu4cvQgNGoVoiDw3k6bnP0NYbX5Mx/s4crRgzBqRRZMKUUCWhxe/EGJTU1t7LW5uOXiM5hbNYQOj59emTp0ahGPP8ATV43i3Z02Odv1vCILD142nPyuyW/EVi6CVi0yalBujHgkImz5+8YmHrxsuLxJ0M9k4NGuRVDEBSEiVImQSrQSaYey4fD109P99mhJJkTa2+Zm2vINfKekN/OnlsaUbknYwC6ycPN4K/5AiN7ZeuaMKyRTr+ag3UOGWoVfkqhraqdPtl5eMF94Zm8eeGVb0oyJRavrefCy4bzT0MKWfXYqCnKpqSzEGwihFgTmVhZy52Q9hzu83D6xGEGAVoeP3Awtoip9DfH+JgNrb65iT6tLLotyw6pNGLUiD142nNsmFYfHBJ0GrVpFq9PDz75zJjePPwOHx8/iaWX4g5K8CRjP+sYW7ppSAkgctnvp9AVwelU0d3gYN9TCq/UHGVmQS9VQCzddaMXtC7Lqgz0pywr9csKZ3PnkFi4q7sWCqWUsXlMfc+z8KSU89nZjjJgDjozBt08s5rdvNLCh0caCKaX8uKtE2bxn61jfaGNOZWzZovhxvqCr1Eu07fyGRht3TCoG4JZn63h67rn0M+np9Aa6dQCYPqIfNqeP/9w4Dp2oIhDyJji0ZOnVp4wQBY6/3+YYtXS4/CydMYx719Qza/QgHnh1e8z9c/6UEv51/Tg6PH4ytCJiNzac0a47EWFKdAAp4hw0b7w1ZTmrBS9uYfW8Kva3uwmGJD6OKzsQDEnssbnSZq3ePrGYkQW5bGpqSxkgSJZ9lC47fH1DC3tbXVz99EdycMjtCyrZ5N8gxyoGOpnEQ1/lfusNhFCpBCRg6cvbZGFhxPXr/DN64Q0ECYmqbuej8RlN6bIm4Yhg5DulfTijTxZ6jUi2Xp0yuB3dD9L1r8i4/sArRxwLV8welSBESXZehe45mX77x8PJ1P7j6bsZuvT9sE+2jk1N7RywexiQZ8AvheSSdZ1d9yhRJfBlm5tHZ1UgAeX9c2Qb/Wgin9EgS9iG/sGjWFvFi8LjUVwgFE51jqXfdtdfM3VqvMHTu5SngsLJwDcVmzrhqATUQYkl08vwBkN0ugMYdSJGjYgggCRJdAZDHOzw0Nzh4apxg2lx+I763p1jDMddP9rdmvb4drefP/z3CxZPK2Of3c1zG5tYOLWUu57/jLunloHKj9PjTxqPi4jWXb4g967dxvIrKhISNqutZhZ2xbLSMSDXkDC/iV7f98rSsSiubHDFQBNalYpqq4X9dg+QGFcamGvkwVe3JcSt3mloYf4LW6joSoKKFuAnKxt0PI4l3c1jJw3LPynmuApHj7+rjO3RlukB6J2tY0/L6eGM4vD6e8wZBaCx2aGIURQUFBToYTHK4sWLWbJkCaNGjSI/Px9BSL9pmYo77riDBx98MO0x27Zto7i4+JjOu2XLFqZPn87ChQu55JJLjqttEe68805+9rOfyf/u6Ohg4MCBx32+HIMm6aQ3knH9/s5WdrY42X6wg9snht+32xfEnKll8er6hA3KSHB71TWjaXP52dTUxnMbm/jBuQVUDDThD4aYP6UUrajiQIebDK0ak0FDs8ODJVPPfWu38oPRgxIC9tVFFu6ZWsr+dndKNXi11cy6bYd4eF3YLeCaqiF0ev08Wbs7JuheaTVz7/Ry7nt5Gy0On/y+xxf34q7JpXy6tz1mgl5dZOGuyaX4A0ECIQiEQnz/T+/HHHP+dWMJhSQGmAz4gjo63H5UAmjVKgwaFRec0YuRBSb6mwz0zdannfRGC0zs7rBYSFQJiCqBhy4fkfDayCIoHUcjWlH4+ujpfnu0pBMiLZleTr7JwK++NwKPP8jCF7ckbmB3vWZSeV/mPPWh/HiV1cz9M4fxZbubtZsPJPTVuZWDGTHQRHm/HHzBEL2z9GhEgeZOL4WWDExGDWOHmMk2aNCpVdgcXjL1GkDCoFahzTPw0a5W8k0GMnRqnN4ALl8wrb26Vq1i+8FOfvHPT+XF8UOXj8CgEQlJErZOHzqNiNsfREBC1XWfEARQqQQOdXgJdlNYeLfNSbvTT8UgEzq3iky9BmuvDPa0unin4TD3v7I95nOYPS65MG9Do435U1Ry5ku728/PvnMmt08UOGD30M+kx+sPxQj94l8fjcsXzjQ8YPcw69wC5lYWkmvUyMGGVC4t8aVXAPa2uuVj8jK09M81UteU3E0jgscfpDg/m8JesY8f6vAg+IKAgCCARvxqdU9PNF+l37a6fXyw28aiS8u46/nN1HVlOo0qyCXfpGfpmq0x3+/9M8tTl5QqsrD94JGMtWhhSqSEjzcQwqgVueCMXmkzs/bYnFz99EdA4vcfDEm0d5NRta/NzY2rNlFlNfODUYmfRarMpFsuKkp73ojAJhIc+snF6Y9Xssm/Xo61tMTJVIriq/TbQEjCr4IlL22loiCXH1cNkftr/Nh5+8TitPckFUJMduPAPGPaUlzxghHo3jEp0g+6y+Bs7vDGtDPS31LR7k5f3lHhCCfTb/94OJnafzx9N0Mryi5k8VRazew45GDtzVX4uhJVBGBDYwv3rg27YkbmRjHz2CRzowjR956jWVsp7pQKpzvH0m91GlXaua5Oo8LrT39/UlBQ+Op8U7GpE03fXCNb9rVi1MZuakvAl21ufvL3T7hv5jBElUDvbD1OXxCXL4C1V+ZR37vtLh++bubVOrWK9Y0t+IIhREHgB+cWMOF36zl7UC7BoMTbO1qYcVZ/fvtmQ1LRerZezW0Tivn7dWNweILcOamYpdPVtDg8aNThTevXtx5k/Jl9Us6Jqq0Wth/o5PEfjeTav26Kmd9E1vdrk5T58QZCFPQx4vAFyTFq+MnFRVxU0pvmDi+CILD1QEeXg0s7t008g++U9sUfkLrmRWoOdnjQCCqWk+i0Gl826HgcS7qbx84eNzjlcyf7HP3bSpszvA49WmcUgD5ZenYc6vy6mnRC6fQEesQZJd+kR69RsampndFDzD3QMgUFBYVTmx4Vo/zxj3/kqaee4kc/+tFXOs/Pf/5z5syZk/aYIUOGHNM5t27dykUXXcS1117L/PnzY57r27cvhw7F1nA7dOgQ2dnZSV1RAHQ6HTpdz6kaM3TqpJma0RnXapXAb99o4LdvHHEMWDnnnLSZ8nMqB3P10x9xcXEv/nn9WO55YUtCyYC5VYV8fqiV8Wf2IVOnYeGLWxhRkJs8q7uhhSVrtrJkWhlLppcnlIyIiFV2tjhZMXsUdXvbuemZTUBYGDOncjDeQEh2TPj+n97jh2MGcf35QwlJoNeEN/de33qA+VNK6PQECIQkjF2K1LWb9/On/+3E5Qvy2JUjE4KTeRlahvbOjHkskh3e6fGTa9RS1DvzqCe7RyMwOVa+jnMqHB093W+Phe6C5f0idXFT9eeGFm6bWMy88VbZRaG20UanN8CjSUrarG9oAQkmD+srb3pDWOx1+8QSFsSJXiKuP7Of/JCRBSaWzRhGs93Fy5sPxLTp4pLeLJsxjAUvbolZcEZeb3N4MWqSiy4i4robn/4Qly9IdZGF+VNKcXgDiCoBhyfAILMRTzeBhMHmDNbuP8BvnvicZ64Zw+I19dRUFrIyxZgVkqSYsiYRjFoRjShS19SWsME5t7KQDrcfUUgv3IiMQUatSK5Ry7xn6mLtZ4ssLL+ignnP1HWbkR/dxojQITrQcjyZvU02J3c+n1iq6b6ZwygwZ6Q938nCV+m3mToN9639nGpr75jSOQB1tW0J38W9a7eFN54lKaGEx40XWPH6g/K/6/a2x7zWGwjJv317N2KS6E3oaEeGPtlhB5xooUsyIs/XNtq46/nNMRlF6TKTrj9/6FGdN3L8XZNL0h6vZJN/vRxraYmTqRTFV+m3oZCE0xeSRdGQvL9uaLTx8Lod/HJCMbA94Z52y/giQkgxY/xjV45Me+14wcjRkKFT80Wzo9t+G093x3v9IewuJUB7NJxMv/3j4WRq//H0XU8wyIKpZSxdU59w74yUSj17UC73dtnE98nRo1Wr+P0VI/l0X3tKJ7EQJJ2/xd97ultbKe6UCqc7x9Jv1YLAjRcMJSRJCffNGy+wohYEWrzpS/koKCh8db7J2NSJxqDVMv7//pfy+Qydmvd22ijNz6bT7ScQkvAEQiyZXp4Qd4rcuwG+aHbQ4Qk7GLj9QcYX9+Kt7YcTzl9ttchr905PAI8/yC3P1lFRYGL2uMFc/qf3qCgwMXVYftJ1gCVTy08uPpP5cTG0KquZpTOGgSSxz+7mgjN689Br25lTWYhEYln5OycXY3P46JWt488/Opsf//VjOZ4UWRfYnIlidJ1aRSAo4fD46Z2t5+PdrTFOwpVWM+dZLfzxyrPpl6tn4er6hGvfO6McS2bYyTGSSAMkJMEcj2NJd/PYdAL8k32O/m2lxRH+HR6LM0qfbB1vbj9EMCR166h9stPp7hlnFLVKRUnfbNY3HOaGC9LH4hQUFBS+DfSoGMXn8zFu3LivfJ5evXrRq1ev7g88Surr6xk/fjyzZ89m2bJlCc+PHTuWl19+OeaxN954g7Fjx/ZYG7rD4QmkteSvqSykbm97QvanOskNPtquz6hV8/Tcc7Fkanj/i3DJiFmjB8XUvaR2N8tmlnPn85upqSxMWloimvUNLbS6fPz+7UbumlxCUAqruC2ZWoxakUMdHjSiSj5/ZHLd6vTFbIpHeHhdAw+va2DF7FF8/09Hnr/wzD5874/vpfzM4oP4qTLbFPGHwslC/G/R7vLxRbMDh9ePyajFGwiLrKL7Z7Tgam+ri7qmtphM0UBISjl2RARp0ZT2y+HBV7fFlOaJXO+ZD/bIQf93v2hJEKIArNvWTEiSmDNuMNefPxSdWoVRK/LyloPc8mwd/7phHB2eQLfiuuVvNXaJ2+qpKMjl06Z2FlxagtMXREJixexRCIKQ8DlUWy0EQ5JsMbpu+0FqKgvpZzJ0O4bGU1NVyOLViU40kX8vurSMpm5qnmbp1cwbb2WI2ZggzoMjoqBrzxvCsP45aTPyI22MCB3iN0mONbP3UIcnQYgCRwQMv/n+WQnlyU43LJlaqossdHr81FQV8swHe6goyGVCWR9K87O5umpIzG/M5Qty9dMf8dy1Y5jT6Y0RT1799If8/boxMZtr0ejUKoKSxJMbdiX9vcUfG82GRht3TSrht2/soKh3ZtL7fYR4Icw7DS00d3rl30m6zKR3d9pSZsMmE9iIKkHJJv8GOVYB2ulSisLlC5CpU/NsN/0V4K3th7lqzGD5npat19Dh8XOow4M5U8uiuGDssQpGgLT9sbrIQiAYwhMIoVOLqe9dRZaE/tVdP393p40+3bj4KYQ51X/7p3r7Q0H41Wvb+OXE4ph755b9drZ8aefRWRV4AyGc3iC5mVp+uOIDSvpmc+OFQ6kusqQszZhs/na89x7FnVJBIYzDF+TmZ+vCJa4mFePwBMnSqznU4eHmZzfx16tHk9EDGzAKCgoKEdLFMaqsZrL1GtQqgYF5Bjy+EHpN2LX3qpUb+cG5BczpctYYkBt2uHb5guGyyHHJUfdMLQOIEaRUWs3Mn1rCzMfeBcCoE3F6Azw6q4K6ve1yXG1Do40DXSVw4nnwsuEsWVMvu6xGHBf1GpH3vmih2mpBCkFIgnXbD/PuzlZqqgr5cdUQcowa1KJAm9NPSIL+uQZAwpylleNi0euEkJToEly3t50snRpLlo75z29OiDltaLRx9+QSQvgThCgQjv8seGELD142XI7JRwQiydZG7zS0cMDuod3lp93tI1OfvnxPd/PYdOuvk32O+23F5vQC3X+30fTJ1uMPShzs8MjlaU5V7J6AnBT9VSnvn8PfP9yLxx9E3wNuKwoKCgqnMj0qRrnmmmt45plnWLBgQU+eNi1NTU20trbS1NREMBjkk08+AcBqtZKZmcmWLVsYP348EyZM4Gc/+xkHDx4EQBRFWfBy/fXXs3z5cm677TZqamp46623+Mc//sHatWtP2Ps4GiXxytpdPDKrAjiyWZqbETsxiC4DsbJ2F9eeN4TvlPZGp1YnbCxHlwbY1+ZmQ6ONK0cPkq8Xfc7oWpR6jUiGTs1n++wsfXkb00f044y+2Tzy5g5K++XIx1UONfP9swfgCQbpcIU3Ft76+fm0OX1k6NT4gyFuXLWJvW3uhE2wKquZDK2YMkhfFXe8ktmmcKoRKaPx8Z6wuORXr32ekJ0Wb0+uU6vkY567dgyCAC5vrDtQfH/tnaWLcVMZVZDLWQNNSUvFzK0sRN1VNqdPjoGPkyy2IxtsPxwzmCuf+EB22bjgjF6cU5CLXq2ib44+od9GtytDq2ZkQa58rprKQlbW7sLmCIvcalN8DmcX5HJ1dSH77W7un1nO8IE5aEQVH+9u67b+eWRMi25H72xdWnFIMCTRK0uXsiRZpdXMK1sO8klTG1OH5/Ozf36W9FzrG1v4+SVnst/u7raN1UUWFk8LB1F+XFUY43bR4vBxy0VF3HDBUDZ8YZO/01TjX5vTl1KgU9too83pO+3FKDlGLffNHIbDG2D04DxGFphYWRu+P0Z+BxUDTfz7hnG8tf0Qv3/7C1y+oGyTG4/bG6SiIDehbEDkHjayIJcNjTYqCnKPWkwS4VCnhx+MLmDHoU627+9gbtcGXDIHo3ghTFOriwydmn4mQ9r5xMraXbx0cxWLV9fHBANTnVdUCUo2+TfIsQrQTpdSFCajFn8oxJVjBrHq/T1AuFa6xx+kcqiZ6Wf1o+bJD9nbFh5TXf6gPJb/47oxiCqB/XYP+9rcCRbZaYUlKfpmZP6tggQ3wHkXWjnY4eWJ2p0p7+GjBuWyZHo5kx9Zn/S8kLqfX1zc+xg+uW8vp/pv/1Rvv0R44+f/nVPAtgMdVFktBEISE0r7UtfUxs1R98xqq5kVc85h1uPvs/ztRm696Iy0545ek37Ve4+SoKCgAE6Pn99+/yz2tYUF995ACLc/SHOHl99+/yycXj99sk7v9YGCgsKJJeJQFu/eGYl9tDq9TCrvi0EjolMHsbvC7kz/vG4sEnD9Xz9mR7OD84os/PryEdyWxAV0Q6ONJV2OuVeOHhSTVHKg3SOXmzZqxJjS1xGG98+mnyn52Nc7W09dlMtqfBxtzBAzGToVba4jriYGjYre2XoO2N0xQvWzB+Vy1+QSDBqRioEmqqxmFk8rY+qjtRi1InkZ2oQyPytrdzFlWD7+YCilk7LLFyRDp04b/7l9UrH8b51alTIuAbCzxcmqD/Ywt7KQWX/+gFGDcnngsuFkaEVaHD46PH6yDWGRSnfz2OZOb9JrnApz3G8rNkekTM/Rbxvm54QFKLsOO095MUqH248ls2ecq4b1z+Gv7+/h7e3NTBqW3yPnVFBQUDhV6VExisfj4fHHH2fdunUMHz4cjSZWKPF///d/PXk5AO655x6efvpp+d8VFeGg7ttvv80FF1zAv/71Lw4fPszf/vY3/va3v8nHDRo0iN27dwNQWFjI2rVr+elPf8rvfvc7BgwYwBNPPMGECRN6vL2p6E5tOiDXwF+vHo1WVHHD+UO5a1IJGrVASCImqB4pAxGZKD+1YReBUNiiPFn2v0oQ+P0VFXR2WbEaNSLzxlsZmGfgsStHYtCImDO1PLxuR1x5HwtPzD6HK/78PrdPLOaRdTu4ostOPb4sxy8mFHPVyo3hshxWC4unl7Gv1UWLw8ufZ4+iwx3OTjvU4eGpOecQlCT65xpoanVyTdUQ9GoVJf1yGFmQi1olYM7QohYFEOB7I/t3ZdoFcPmDKe3MIxu50RNmJRip8E0RXUZj3njrUZVuiV4o1jW1k6lT4w2EEIQQK+ecw6amNp7b2MQDlw1PukCObIrlGDU89PrnKa/3i0vOBEArCikX28uvqCA/R8eK2aMIhCQCIQlBAJ1W5GCHB4NGxKgVY0rYpDrXI7MqCIS6yugkKTcUGafW3FyFqBJo6fSQodNT0icLdzCAiIqXNx9gTjdOFLlGTUI7uivX8GW7m5ufreORWRWEiN0sjJQYcvkCXFLah45uyrL4gkEG5RnTHjPEksHyJHao+9vd3P6vzxLK/6yeV0VQCmEyaOmTrU8Y57oTOXZ4vh0W3Nl6NQ6Pn15ZOpat3cqmrvvjMx8c2eTe1eJkZEEuf792DHOf+jBl9k6mQcMnTW0xQpRI+albnq2j9PJsIPUmc7XVzOwkoo9wOzX8dt0O6praef7GcTz46nbZ7SFDp8bpDcRkb8UTsdNNN59w+YIIIGeH291+PP4g7+60JZz3vCIL5q57pZJN/s1wrKUlTpdSFBk6NYc7vDzz/h5mjR4U01fbXH4k4G/XjOaHT3zA3jZ3TH/N0mv4/p/eBwiX3IojXd9cPL2cqY/WJrzG5QtbeT937Rh+SdgNsHeWjoZDnby/y8bGXa0p712v3FKNyRjuk6MG5cZ8L5Hzzp9Swk8vPoPmTq8cNI/0RyVb8Og41X/7p3r7nd5w9mChJYO/vbc7wTo+Wly9vtEGr33OH64cyQ2rNpHZjZh4SK8MXr6lCqcviMmgkbMUlbWdgsLxYcnScdDuYc3mA9Q1tceI9PMytWTp1Jza5voKCgonI/1MBu6bUc7eNjcdXeWZdWoV3mCIYJeriNMXJFMj0in4EQQBtUpAD6y6ahSN7S427Gyl3ZXaBTTiqBbtyF1pNcv/v/nCIoJJnEeG98/mkVkjcfuCCYlIRq2IyxtIW3J52dptLLy0FIMviCVTy5NzzqHTE2C3zSkndG3db5fnQ/e9vI0l08vI1KlZNnMYDc0OILxGf2TdjoQyPy5fEKNGZH8K5xaAww4vJkP6eZDDc8RtuNXhS5qMEiE6ES4Sk7z9358xeVg+d/5ns3xcZK6abh4b+ftUnON+W2lxeLvEYUfv5NErS4daJfDFYQdVRZavsXVfP53ennNGGZBrYFj/HB58bTsXFvdW3FEUFBS+1fSoGOWzzz7jrLPOAmDLli0xzwnC17Okfeqpp3jqqadSPr9o0SIWLVrU7XkuuOAC6uqST8JOBOmUxJVWM69vPSRvSM+tLGT2kxtZfsVIjBoxJnu6YmA46/vRLiFKbaONud2U3LljYjFatQpLphZThpa6prYEQcmcykLe39kqb1RFNkVrqsLXLu6Xk7Ish8DnR8pyNLaw4MUtTB3Wj0KLke92WSXWVBUydogZURBw+4Os+ewAW/fbueH8odw9pZSFL25J6uLw7AfhjYpIgPO8IgsPXDacflEq3IgDRXyd0fjjFBROFNFlNCoGmrot3RKdJR0RVCyMKwlTaTXzxOxzeOTNHWmFLWpRSFvO5o5J4bG6b05irdnIMSoEJg/ry71rt/HIrAoWxNXOrbZaYjYe0i3cAW6fWIxaJbCydldKJxanL8iWfe30Mxn40coPGVlg4r6Zw7i7y6Z0RBoniiqrmT7ZelbPq4opy3M05Roim4U1VYWyVXyOQcOOQ53sb3dz0zObqCgwcffk0rTnydJrEAVSlkipLrKQmxGu4buzxSlvrAAJQhQIj90LV28Jlzja286908tZuqaedVGWtK/cWp22TceS5XAq0+70kK1T0+kPb4LNG2/lmQ/2pBRQ/qXmXN7YdijhPFVWMxkakSXTy3H7gnR6w45fkfI8T1w1iqyumrrxv5tIZlZ+jp7v/fG9BDFJtdWCNirgc6DdE+M01itL5PdvNaR06Knb2847DS20OHzdZiZFBCaRwM/+djd//N8XCUKU6OCQkk3+zXGspSVOh1IUDk8AUSVQ3C8nbV9dMeccHnhlmyzUrLKaMWpF/nEAq9pAAAEAAElEQVTdGLL0mqQ219F9845JJXS4/eRmaAkGQ0hInD0oN+kYXVFg4vWth6gYaOLGVZtYMXsUdz6/hRWzR/HbN5KXGFnf0EIgJMmffbIg7ahBuQzplSmLtqNRsgWPjVP9t38qtz9Dp6amqpB7X6pPah0PRzYyINw37ppcwu+vGIlKRWq3oiILHW4/7+20MaK/CbVKIBAM0eLwsWj1lgSnovtnDmNAN8JfBYVvO5IEj77dmDLLv7rIwr3Ty77BFp6+KCI6hW8zdpePu17Ywsd72njhprFoRRV3vxAbR6qymlk6YxiZejUzH3uXkQUmls4YhhGw5hoJDRHCJaHnnMPHScpaQzjusnpeJQfsHrL1avpm69nX6qZ/jh5zlpYDbWFBR7Rj7hm9M7nvlW1cUz2EuVWFIAjyeqCmqpBMnTpl3M6oFfnB6ALuen4zowrz+EvNuSx7eVtS18Posthef4hMnZp2lw+VEE4E02vEmDI/0XEEp8+f4IwejUEt0itLy4rZoxJiaZHPKFMfdh+/abyVHL2aX7/+edIkl+oiCyoh/N6iSyaub2iRSyZFeKehhdu7kmJ+ffkI2pw+OjwBsg1qco1a2Yn30VkVNHd6sbv9GLVht/We2uxX6HlsTh85hmNLihBVAvk5enYednxNrTpxOHqwTI8gCPxwzCDufn4zVQ++xajBeVx33hAqCnJ75PwKCgoKpxI9uhP19ttv9+TpvlWkyoiLt86va2pnyjAPq64ZzdI1W/nlxGKe+WCPnD2drVfLk9hIgC7a3jgZBzs8NHd4ePCy4fz6te1JBSUSsUFECAtS5lQOxuUNpt1QjxwXITKZffTtRq47fwhlXUKWZGKT3S1OfvdmQ4LNeqSNFQW5PLlhl9y2dxpa5OzwHKM2xoEimvjjFBROJNGOFd31zwydOqYsSDonFRWfM6LAFFMjN/r5mspC2pzp3TLaXOHnfQEppWgl0qdTiUzWN7YgIcn9sjvBjS8QIhCS0rqn7G93s2bzAW6+sIibLhzKr1/bwd42tzzOpSt3MKeykEMdniMZuV2kK9cQ7UTj8gVj2rRi9ijuen4LlVaz/B41opD2XDq1ipc+28+NFwwlJEkJbbzpQivv7DjML/91pNTPeUUW7p5SkiBEif7sarrEhvNf2MxZBbkxYhSVkHqDp9JqRid2L8Y5HVCpRDp8QRxdTjAVA00AKQWU97+ynWnDY+0zI4Gxva0uVr67i+EDTFxU0ptDHeFg1ueHOijum00fjZ5qq4X1jS0Jv5sqq5nbJhZTUWBK+P4XTy+TM6IAPmpqixGGRkRoQIIILXqO0OnxM7R35jFl2J/KG6DfFo5VDHSqi4faXD5CUvd99d41W/nlhDP5wePvU2U1s2haeXhhI0k0tboY0isjqQDQ5QvyaVMbl5T24b2dNrbtt3P7pBLmrPyQP/3obCRpW0K5uEgAOULk3t3dPbwz6n6fqq+5fMEE1xQlW/D4ONV/+6dq+wVg7BBzt+LqaDrcfg7Y3fQ36ZlbWYgAMf2u2mrmF5ecyeP/+4KbL7KiUqm496V6RhTkJnX8XN/Qwh3/+YwHLxtO/1xFkKKgkAqXP8iGLnF20nVcQwsLXqxP6taocPwoCVIK33YiCVl3TT4Tg1rNHc9vTjq/n//CZu6fOYxV14ymzeXnvS9aqLZakIArn/hAPrbaauH5G8dx0O7lwz2tsuii0+Pnd2/uYm6XQ8rZBbksnl5GUJKY9fj7/ODcAsYX94oRu79yazXfO3sg3kCIec/UcdOFQ7lzUok8Xz+QptxydEzs7MF53B8nRIHY+HVkfdPpDdA3S8e+djeSJPHkhl1cOXoQkBh/gnAC6/M3jEu6tjFqRXpn61i8uj4hVhBJEhtZYMKoCccWa576kL9fN4YrusoZxSeW3XiBFZUg8fsrRnLTM5ti1jvJ1j4f7WmjzeVnwYtb5LYZtSILppYyssAULiGkVfNxUxv3rtkqC2CUMfDkxebwkqU/9i3D/BwDjae4GCUYknB4Axi1PbdlWpBn5FeXDeeNbYeo32/nsj+8yz+uG8uowXk9dg0FBQWFU4GvLS163759AAwYMODrusRpR3SQus0Vts+PtuqOLjHRJ1vP+kYbP7R7YibRL82r4uE3G+RJLBxd9n/vbD29s3VpHRPig4gR9BqR1qjamMmIn7BGJry3TyzmwVe3U9fUnuCIcNDuprhvVoIQJb5Ny99qjGlbJDs8x6iNcaCIJ/o4BYUTSXQZje76p9MbiFmIHovwKx5vIIShG3V3IChRZTXj9KYv4eINhNK2pbbRxg0XWMNZH91s1h12eOlvMvDgq4liuGj3lMjf90wt5dev7cAeVRonmRPFgFwDr289xC3P1vHQ5SMSrns8pVQi7z3ymjsmlQDhzdNol6oIkU3MVqeP8n45XP30R3IbfcEQvTJ1aNUqDtg9lORnM2+8VQ6kvNPQwk2u9OKhSFsiLljR7Gl1pW1Tm8tH+uJGpwcOX5AOt5/MrsV0d7/d9Q0t3D25hJdursLhCZCtV2PQiHy6t40hvbKYM66QvAwtwZBEP5OeQEjCH8yiV5aOj/e0MrdqMCFiBUdVVjO/nFCM3ennF5ecyV2TVDh9QVy+APvb3TTZXGRFlSqI/21Gft//un4sc+yemBrY0eV1IiU9jlVgcqpugCqcnmTo1IRCEi0Ob7d99Y5Jxfz9urE4vX40Anywu5UCs5EhlgwCgRALLy1j8Uv1sfXprRaWzCjjcKeXCWV9qBho4oo/v09J32xaOn3cPrGYGzwB7G6/3M8iDi2R+4KpK1Osu3t4fJmdZH0tx4giCFM4tRFA7MYFNX4uaNCK9MnWE5Sgd6aOpTPK2dniwqgVcXoDDMg1EAiF+N6ogWxqamft5gNsaLQxJ43jZ22jjT02F5k69TfWfxTnA4WTHac3PGfs7v6qxEl6DiVBSkHhSELW+OI+OHzBlLHn2kYbDl8QvUZkUJ6GTJ2KYEjCHYydR6xvbGHJmq1hp9imNl64qZJPmtrYst+e4Mq24MWwo2yLw8fK2l08d+2YmNhTh9tP72wdzR1eRhaY+PVrO/j9219QU1VI1VALt//7M/52zeik7R3V5W5QU1lIryxdTKnCaCLx68h8yKgV0QCWTB3+YChtzB3CYp4DdhcLppZy75qtMePJgikl3P/ytpTudAumlHBOoZnXth6Qx323L5TUyTUvQ8uVT3xARYGJKcPyqakqjFnvJFv71FQVsuCFzfL1o/cuokv6xJduVMbAk5cWhy9t+edU9DPpeX9n69fQohOHoysW3tPOPfkmA1eNHUwwJHHPi1u4d+1WXrix8murJKGgoKBwMtKjYpRQKMTSpUv5zW9+g8MRVkJmZWXx85//nLvvvhuV6tuRhf1ViASp65raYupcQqziOiI2UasEbo6aQIqqROHI0WT/l+Zn4/Qk2vNFk2xDuW+2nky9Gpcv/cZ1/IQ1+t+pLGIrrWbOLQxbriezDoxuU3zbIpmo0Q4Uyejs5nkFha+D6DIa6fpntdUiOy8cLemEH4PNGWjUguzckOx6BXkG5lYWou1mg02nVnUrMglKEitmj6JXli7tcVpRRShEWjGcL0oAEghKchuiSeZgEvl3skVzvIAlS6+h0+Ond5ae//d4YimVCNE1Pve2uqhramNSWV/mPPlhwmI+Ihb453Vjae70ym2MLNB/u25HglAkeoHenXgo+n3Ffx+iEHt/iG/Tv64fm/bcpwsd7vCGkFZUyS413boZeAP4AyGyDRre2HaIx9/Z2WUt65HvzREnhh+teI8Wh0+2+u2dpef2icVoRRVOX4AMnRq9WoUnEEIUBdpcftkyt6LAxNzKQv718V7umFRCldVMbZT4JL48VIcnwFPv7k4qsowv6aEITBROVQRAp1GRY9DEiA6T4fQGCIWgT46eH67YyKJpZWTqNSx+qZ7aRpvcL284fygatYoMrZqDHW6uWrGR6RX9ZRH0735Qgccf5PpVHwPImXwOb4BxQ8K15qNLQg4yGzmvyJL2Hn4sZXaU/qpwKqMTVUgklsWKOSZqvlJpDfcpbyCE0xvgqpUbee7aMdQ89SGPXTmSG1dt4rlrxxAKSfTK0mLJ0nHX81vk16Sj3e3/xjbRFecDhVOBSJnOY3H2UvhqKAlSCgpHErK8gZDsWJqKTk/4Xv7sB03cObmYIGHX13njrYwqyCXHqEEtqmhz+sjrKm38wCvb+E5JX4b1z0koLxP9t8sXxOkNxMzdsw0a2l1+REFgTmUhAgKHOjzMOKsf/mDYIUSSYP0vL8DlD/Bq/SH+9L+dAOSb9NTVhh1NH7tyZNr3FYnHVFst6NUiNreX61d9wq+/F06cSreuqLKayc8x8uSGXSybUc7eNrcsnM8xaFjfNU+KJ5xAVcwPHn+PBy8bLj+e0RVnjxclrpg9CleXWKimspDB5gw27g6LC6Ldg6OJFzd2V6Y72nVdGQNPTlocXswZx/6d9DMZONjhwdFV0vpUJDL/+brKSIkqgf93zkDuf2U7H+9pU9xRFBQUvlX06J3h7rvvZsWKFTzwwANUVlYCUFtby6JFi/B4PCxbtqwnL3dak0yBGj3BiwT06va2U1Fw5PE//HCk/HhkEpuufEXE3j+sRE6veo3fzK0ustDc6aHVoSInQyNvosUTP2GNKX/hDaadqC5+qT6hPFCyNsW3LZKJ2p2SNz5jVUHhRBBdliu1O4eFuVWD2fylXa77Gllopj13iuerrRY0aoFOt5/5U0tZujY2m6LSamZ25WCmLd9ARYGJOyaWpBStRPpwxGI0Ff5AiKuf/oj7ZpZTbTUnZGqE22WmV5au2w1Htz8ofw5CVyBi85f2pDal0W1M9nf05xxZgFdazdw+sZjv/+kjVswexcgCU9LxrMpqRpKObLjo1Co2NNoQBGLG4vi2GDQivaNEOenGPZ1axcu3VOH2hxDgqN5jpC3RxN8f4l+b0YO2kycz2QYNGVqRYEji5gut7GxxMsickfY1Bo3I/3YcjqmxHB+0r220sXh1PT//zhnc+fwW+bcUKa+Rn2Tj6cs2F3tsLkrzs3l0VgV1e9v5+8YmlkwvJ99k4IHvDufO5zezvuFImZ/qIguLp5UBYM7Q8uAxlOBRUDglEUAtCHJwOR3ZBg06tYrL/xgWhXkDIb5sc8vjd6QfRfrm/d8dxgOv7GZvm1seGyutZm6bUIw/EOLRWRUMyDXQN1sv9ye7y0efbD0XF/eOcS154LLhLHxxS1IHKqVPKnybEID+JkPKuV78PGzehUW8/Xkz5f1yyNCpcfmCePwhjFpRnssYtSIOTwCdWmRni1M+V3duRAV5RvzBENsPduDyBsgxak+IO4nifKBwqpChEam2Wo7Z2UshzPG4HykJUgoKRxKynN4g2d3EtMJJQoFw+eeXt3H35BIydGq27rdz1kATD73+edLYtk5U8cd3vpBjyL5gYnmZKqsZTVy5YrUokK1Xs8/jZ/vBDpZNLyMkCNz9QmwpoSqrmWUzhjGprA8XFfchEArx61e3h0vfdDmjpCPHoKHJ5uTOycWoBFCr1Tx+1SjanGGn8XSxwdmVgznU6WFuZSGLX6rnB6MHseqDPWxotHUrgtnb6pbXSZHPS6dJvAfEx5e8gRA5Bg0aUeC8Igs3Xmil5qkP014L0jtvJXOAUcbAkw+bw8fgbmJmyYiIrxsOdVLR5Rp0qtHh/nqcUaIp75dDlk7Nfz8/rIhRFBQUvlX06E7U008/zRNPPMG0adPkx4YPH07//v258cYbFTHKMaDXqBLEHdEbYRGxSfxkVds1qY5/PJJhfeMFVkSVgMsXZFNTG7c8W0dFgYnmDg9n9M5MuflclURQMnvcYFZ90MStFxXh8QdYMr2chatjrdCrrGbmRJW7iBbAVFrNqEWhW4vYG84fmnIzNfI5RLctOhM12oEinmPJWFVQ6GkiZTT22z00tbq4fWIxvkCIww6v3I//9v4e3tp+OOZ188ZbUwq/qq1m8jK0CYKLSquZhdNKeeDlbazbfljOFL/xgqFoRBVqlQqVKhwo+/NVo3hvp425T23kV98bDkgxGwvVRRZuusBKzdNhF5DuXJcqrWbycwzMrSokRHLBzdufNzO+uHfaz0slCDFuUZVWM7eMtzJtRD8WvLglpdCuusjC7HGD5TEolTAvsjECyBkxUpLj5lQWylb00WPPG9sOcfP4opTnfmnzfnpl6mRhSapxz6gVuWL0IBa8sIX1XVn9j8yqQJKkmO88+j1C4hgN4fvAitmjUEFC7eB5FxZ167pyOmB3+cjUinhCQfQqEVEloFGp6JejT92Piiy8Wn8w4fuJiMEioii9RmRTUziT4c2fnX9U5TX65xrJ1Knlchwzz+qPpapQPn5AnpHl3ZTrUEp6KJzuZOrUEAohCBL52en7qsPj57UvbLQ4wkFcnVqFyZjYT1fW7uKdhhY8/lBM/8nQqdGKKuxuHwPzjEn7UirXkn4mAw9dPgKb08eiS8sIhiRcviA5BqVPKny78ARD3PLXOh6/ahQLXtwSVxbLzMJpZbQ6fYwsyOWg3Y3HH+TTve30ztRTYDYCYZe8+VNK5LmjWiXw7k4bn+5t55cTzpTP113WcEiSuHfttgRx2NftTqI4HyicKqiAe2eU8d4XttTunEUWubylwhGO1/1ISZBSUDiSkNXh9pOhFVPO76usZjK1ohzbqG200eryo9OIlPbLSeu4sWBKKXVN7bLYYUCuQXbaHpRn5OVbqxGQUMWVxbjpb5tYOXsUWksGf3lvN9NH9EsQogBsampnv93NY283sr7Rxl/nnsstF59BZ1d5TwEhpTC3ymqmv0mPOUNLp8ePQSNS29DCYEtGzNwmvnSOyaDBEwhy278+47lrx7K3zc267Yd5d2erfFx3IpjeWTpZ8Du+uBe/vKQYbyDEY1eOlNdKW/fbY0qSQnhdlalXc1FJH344ehAuX5BRg3ITkmIG5MaOf905b8U/r4yBJx82p7fbRMhkDMw1ohJg24FTV4xyxBnl65sHqVQCwwfk8Nb2Zn4Rtc5RUFBQON3p0ZG1tbWV4uLihMeLi4tpbT21a8adSOwuHwtX1ydshkZnrxwRm+yKmaz2yjqy2Rn9uC8YoiDXyGdf2rl3zVY507vSaubm8UX0y9Hz9w+bWHBpYv3J6iILS2eUc6DdTWl+tlzm4bmNTcyfUsrSNfUxG9zXnz8UUSXg8QcJhiQytCJPXDUKtz8ol4c4uyCXmy608sEuG2cNTD9BUamEpJvrcysLefaDPTEbsvGZqNEOFEoWucLJRo5RiycQYtnarQkLcaNWZMXsUfgDUoxAbOt+O/dcWsa9L9XHikS6SoZoRYGpw/JjyrIc6vCwv81D+QATc6uGoFOr0IgqRJWAPxgkQyfiD0poVSp6Zen4tKmdFoePec+Ex5DrL7AeeY0goNUInF2Qmzpzo8jCwkvL8AaCVAw08ct/fYrLF3ZBuukCKxB2OlEJ4fe5cVcr/qCUdnPhvZ2JAQeVIHBxcW9qKgu5c1IJLl+AbH04S97m8PLvG8ahV6t49K0GecxLVvqk4VAnHn+Qv763h3njrZiztOxv93DHxBK8gaAsEIqMXw9dPiJBDPKn/+1kUnnfpJ+9xx+UbVxXzB4FkoQ3EJLHzEiZCL1GRJIkVn2wR/5uo8u13ND1PXgDId7baZNLRkQ2eu57eVvMZ3R2QS6WLB2XjujHnCRt6vD46Z2tP9qf6ylJc6eXTJWAQSUSlCTyMnXsanFid/u4d3o598QJKKutFhZdWsaly2tjzlNpNdPc6eVQh0cuFRB5fOZZ/REEwgUKjqLca3flOI6mXIdS0kPhdMYfCOIPSWhFFYFQkHtnlHPPi/UJc9P5U0rY3+6Rx9dKq5nmDi/NHR7ujOunkfJnnR4/Q3tnkmPUytnNrS7fUWc3x6P0RQWFcLmsHc0OfrTiA1Zdcy5uf4h2V1jsZdSKeANB2lx+1CqB/XYP67Yd4sfVQ+lv0uMOBHnu2jFo1QLjhpp5eN0O5l1YhFatkt3JfnJxEffNLKdPtp5ASGLaiH4J8+Aqq5lfTijm4bjyh3Bi3EkU5wOFU4WAJCFJUDHIxJgh5vD9tTH2/rpkWhneFOVKv618FfcjJUFKQSFMP5OBkCTR6nCzdMYw5r+wOSYOVmU1s3TGMETCMe8Idrcfk1HTreNGMCRRUxWOe1QXWdi8z05NVSGf7m3HoBFBgBAS2/Z3xLjP7mh28N8dh3mt/iAbGm24/aGkcamaqkKWv93Ihq6koQF5Bt7faZNjKq1OH3dOLoFXtiesW+6dXo7L56f+yw5GDs5DoxIYNiAHOBLf16lVjBho4sIzw4labl8Qk1HDQbuHH44ZxOLVW5g1ehAQW6J63nhr2iSxHYc6WTl7FNl6Db+45Ezue3lrXCzRws8nnMk1T38ox82qrRaaO7wMyDWwr81NrlFLfldCXXxSDBAzxnXnvBX9vDIGnny4uxwLu3MwSoZWraKfycD2gx1fQ8tODJ2er98ZBWDEQBOP/fcLDnd6uxWUKSgoKJwuCFJ0vYGvyOjRoxk9ejSPPPJIzOM333wzGzdu5IMPPuipS510dHR0kJOTg91uJzs7+yud64tmBxf93//kjcpLSvvwZbubIZYMlq7dxvqGFoxakZsuHMp3SvoQlMDhCZClV6NTqwhKsPilxKD9vdPKkABPV41Oo07EoBHxB0NIgFEj8tm+dgotmUC45na2QYMKEEXQq0Xc/tCR2n8SdHr9dHoCsrOCJVOHRgyXAskxavH4Qzh9AbJ0ajSiQIvDh1En4vYF6Z+txxeSOGD3cMUTib+NyPufUNZXrgWqVgkEpRC+gESHO8Ags4GQBB3u9NnhkQ0HJYtcIZqe7LfHg93lo9XpQxAEeSEe+d2PHWLGoFFhMmrxBkLYXX5yjBqMWhEpJHGw00OGTo3DEyRTr8bp9ZOfrcfl9ZKp1+PwBbv6oQatqMLm9JKh02DQqgiEJD7c2UrvbD2+YIjeWXoMGhVuXxCjViRTp5Zfn20Iv35vm4tMfdiis7nTS58sPd5AuN5u3xw9Hn8IfzCEQSOi06hodfjIMmhQqwQ6PH70apFOb4D8bB0BCRatrufsQbl8vLuV9Y02LJlanv3xGJa8tDUhIPrj6iHU7W2jvF9OTKb7cxubeOaaMSxeUy8HBGqqChk3JGy9atSKdHr99Ms28OCr21i75VDMeRddWoY/GESvOTK2BYIhNnxhkzdAokUnkYX5K7dWs7/dzaLV9Uyv6C+LSQotGfiDIdqcfnQaFU5vQBbu/eDcAioGmvAFQww2Z6BTq9hjc/FE7c4EIc/8KSUctHv5cE+rHISJiFay9RqyDeGxvs3lw+0LsXF3KyMH5rKv3UWfbL0sOumVpeMX//iUh74/ArWokr9PfzDETas28dv/d9YpaQl5LP32w92tGLUiIkFMOh1+4Eu7hwydiMsbxJypxesP4fYHydKrydCI+EMSV63cyN42NxAWet10YREqAeY8dSRAE6HaamFEVDmkE5GBraBwqnGs/TYvQ022SsQLdPoCqEUVvq45pU4tYtCq0IkqJj1Si8sXpMpq5q7JJXR6AsxN0k8rrWYqCnKZeVZ/hvbOPO7sZgWFbxtH03e3H+xg4sPrWTa9DK1axbCB2Rg1GtyBIGqVik172ii0ZBAISRi1ImqVQJZOTTAkgQAefwi720+mXk22Tg0hiav/+jGXlPelYqCJTJ2akCTJ8zOA+VNKGDHARGfXuvTV+oOMKshlThr7+Dd/dj5De2d+LZ9TZP3+TVxbQSGedP12r80BksSXHT6y9CIqVEgCdHZt9mZoRA51ejFowy4ECmG+ah/f3+5OmSCVrLSnwrePbzo2dSLZ2+IkIEl4AkEkwvHjLIMGAYksrZq9rS5mrdgoH7+iS0hx2OHlxlWbUp73uWvH4PQG0GtE+mTrmba8lj93JUbe8mwdZw/K5d7p5eQaNTh9wZg+uWL2KNmJ9+/XjuH/Pf5+wvmjj1k2vYxLrBY8KgGHL0iH20+OQUOmVkQiiMsf3tSOxMdsXbFwpzdIlk5k9pMf0uLw8a/rx/K9P77HwFwDT9acg88f4v6Xt8WIRaYM68vPLzmTXS1OemXpmLZ8Q0y7Im66T2/YleCIG4llnT0ol6Uzypn//OaUJRUrCnJZ/lajvK463Omld7aO/e0eDBqRwZaMlOuk6DFu3ngrdU1tKcUxkesoY+DJyb42F1UPvs2dk4oZPsB0zK9/5K0G/IEQ/7phXM837gTwfN0+fvr3T3l67rlouxFWfRWaOzzc+vdPeHLuObIATUFBQeF0p0edUX71q18xZcoU1q1bx9ixYwF477332Lt3Ly+//HJPXuq0JpJZFVE6Vww0ccPfNskTTJ0ocOWYwRi1YtjFJG6yeV31UG6bcCbXnz8Uu9svO5ksWbuVK0YP4j8f7+W2iSUpVej+UBBJEvjjfxtjykQ888Eerhg9KMEWUS7Fsyq8WTu+uBe3TyxJKJ0RybJx+wM4vQEOOry0u/yYjFrum1nO0rXb5M2DyDWf3LArRvlebTUzO2pj+GgDe0rmqsLJQHSN6Qytmo+b2rh3zVaMWpEHLxvO/KmlqFUqFq/eImdH/OaNHYn9aHoZT7yzk3VRJXwi7hjugEBbu5t3kwgqrlr5IeOG5HHH5BLWbD6Q1G3oRys3MrLAxNVVQ5CQ+N1bDUmP+3+Pv0dFgYm5lYUcaPew9aCdS0r7snB1fYqSORs5uyCXJdPLaHV4uWtyCQaNir+9vweAmqrBqFQCk4b1ZU7lYFlQ0dzhoSDPQEunJ+az7Jej56m55/CrV7dRUZDLj6uGkG/Ss3TN1rgxw8KiaaXcNamEeRedgcPjx5ypwxcI0er0YjJq5ZI40Z/l8isqmPdMHRsabejUKh7tcoCB8Njc6vDx16vPZcELWxLGqIWXlnGww4MgCGhEgYcuH8ET63fGHldk4cYLhlLX1B7zvtY3tLBkzVYqCnL5tKmNP1w5kqAksaI2diyMbJpmaEXyMrTsaXVR3j9HLvUEEAqFePSKkSxZU58wZj8x+xwk0tunng5kaEU8/iB9MnR4JHjsvw3Mu7CITk8Ahzf8X93edlbW7goHaKaXs+r9Pfzl6nPZcciBWiVQaM7A5Q/w/T+9n7DBDbC+sYU5lYPlf5+IDGwFhdOZTJ1ItkrEJcHiNfWU9suRRX85Bg19szUYJHBJEo/MqkAgXLpDEISkQhQIZ0vedIEVS6b2K2U3KygoJKIVVVw1poBxRRZ+/ep2Kgbl8mW7mz+v38mPxgxGkiR0GhX2Dq8s1I3cd2+6YCg1T38k99vIevTxH53N/Bdj51jRLkdL125jwdRSKgpM2F1+RhbkyqLtZGMAfL3uJIrzgcKpgj4EbkHFzsOOGBF7c4eHcUMt6CTI1KvlzGCFMF/V/ahfCkcBZb6h8G0k26jB4faj0qpx+YIIQthgVCUIPPTG5wzIy5CPjZRGnlDWp1vHjWy9Gqc3gMcfxOMP4PIFCUqSHD9e39DCPS9u4dFZFXKf/LLdzW6bi4yokhypHCEi5WUsmVouKeqFE7j7+c0JsZalM4aRJ0q4VeHyoy5/EFElIElgztDy8Bs75BKjWrWKSeV9+PmEM/H7Qyx7ObbUoCVTyy0XncHCF7fwcVM7y6+oiHF1gSNuuv+6fixz7B55XI84+0beu9MbTCpEgfBa6faJxYwbYibfpOeVLQfYtLudOyeHSyiW5menXSdFj3FOr5/LRvZPcLY8r8jCkunldLh94XLFyhh4UmLr+m0eb/mkQXlG1nx2AEmSEISjsA4+yehwB1CrwrHcr5NeXeWztu7vUMQoCgoK3xp6VIxy/vnns2PHDn7/+9+zfft2AL773e9y7bXXsnTpUqqrq3vycqct8TVl4+tHvnxLFe99YWPt5gMJpT02NNqYMiyfx9cnPgfhyXNNZWHS+pe1jTbmv7CZ2yYW8+Cr2+Xna6oKeXLDLioKcpPW56xttCF1Hbf8rUZK++UkbH4C8sT/rimlrKzdlWALGFGZR8p5JLvW+kYboa5rfba3Xd5UiGzwH6/NuoLC14Xd5cPm9CEBi17ckiAee2RWBXf8+zPq9rYzMNfA4tXhY+aNtybvAw0tLHhhC2cV5MaIUdY32li4ul7OMogO2Ef3ZSBBMALEHLP8rUYmD8vn5TjBSrLjAH5xyZl8J4kQJdnx81/cEpNxseqaMVz5xPtMKM3nnjgBW4Qqq5l546384PEjDkqVVjPnFuZxxZhBrOjKkq2rTcy+WN/YwqKXtjJteD4LX9rKI7MqePjNsMDmJxcXyc4ssa+xAQLXnjeEx9/ZyRWjB/FUXJZJdZGFAXkGPo4XkzTaWPjSke/hV5cNY8X6nQnj8fqGFkKSFPM5Rn9mNZWR76EfazfvT2o5f/u/P2PpjHJyM7T85vXPeWNbc8wxq+dVJh2Laxtt3LumnvtnDkv4rE83MrRqMnVqAoQDRbdPLKa2sYW1SX7b6xtamP/iFu6bUc7iNVupKDDx69d28I/rxqZdiBq7BEErZo+Kce6xOX3KvUhB4TjI1KrxAnd1zVXfirrXQfiecP/MYbQ4vAyxZLC31Y1GFFAJsPyKkXj8QbkfRkSZADqNihyjli+aHQlClAjvNLTQ4lD6roLCsRAIhbi2egh3Pr+ZOZWFbNzVytrNB6goyOWJ2p1UFOTy4qeJc5lkc6HaRhsLXtjM5GH5Sde5ANeeN4Ty/jk8uWEXd/5ns/x8tdXC8isq2PylPcFNb2XtruMOqh8NSmlYhVMFr0rgzucTY0Fw5P7qD0rkHIc9/+lMfIwunqMZX5QEKQWFMDlGLU5fkAVx90w50bGrFHIksem5jU0MsRjZ2eJKW45GEAR6Z+v43boGbrmoCAB/IBQjUn2noQWbM7zZ3uLw4fSF1w1GnSgLWjO1IlVWc8I8JCKG+c3lI/AIiUIUOBJXf2DmMAxaNe5AELvLx+//+wX3zSjnzq6YRIfHz/pGG82dXm6bUEwgJNHq8iec71ffG86SNfV8frCTv187Boc3wE0XWLnhgqExCWhnD8qVE20iIv6RBblQhXxMd6I5ty/IuzvD56woMLHw0jJEIfz6R2dVdLtOioxx+9vd3PPiFkYMNDFnXDjRzGTQMMhspH+uEchI+nqFk4PWrv5xvPOAQeYMHN4Ae2wuBltOve+60xMuNfp1C2kEQWCQ2cjWA6duSSMFBQWFY6VHxSgA/fr1Y9myZTGPffrpp6xYsYLHH3+8py93WhKfWfXcxiZWzjmHTk8Au9uP2x+id7Y+qdgEoE+a5zY02rhjUnHSyTuEJ853iaqY5yN1OSObo6nOW1NZGHN8MtY32mh1+pJs/raAAC/Nq8Lm9JGpU6e91h2Tipk2vB8Ob4CFq+tZF7UJGwn6GbWiIlJR+EaJlAEYMdCU1KYy8u+Vc87hwVe3c8EZveS+ka4f1TbamNvV3+LPF+mH8SKQ6OeOph93N45EX+eOSQL+gJRyXIk/PvJ3RBTx4GXD8QWT1+WNHHf7pOKEc37YtdkRLd5IxvqGFm6beCZ/qTkXu9vP1VVDqCjIpbrIwsPrGpK/prGFWy8uIhCSUoqCuhOTAAzplZk2+6QmyfcIR7JuemfrUn4P6xtaaGx28PS7u1k2o5xLyvqSl6GVN180cWN5NLWNNpzfglrwJqOG/fawq86GRhtqUUWfbH3Kz2V9QwsOX5Di/GzGF/fhyQ27UQnwf6/vkMVd0cGsiIvX/73+eYLQbGZF/6/3zSkonKb4QuFyjOnGL4cviFGnYWFXybfRhXksTeIWGN1vTYbwHPCrZjcrKCjEohFVdPrC2ba3dc3XoudmR7uGjLC+0cacFPOjDY027p5cyn0vb00qQEaAmy4cSs1TR9xWKq1mVs4552t3J1GcDxROBTp9wW7vrw5PgIH5WSe4ZSc3ivuRgkLPkuyeqdOo2Nfq4qHLR8jOHs9tbOKOicVoRBUvbz4ox8GSufGGpBDb93eyYGopL3zyJdVWC5u/tMdc16gVkYB5z9bFlra3muUESYfXx9IZwxLcxJs7PFQXWeiTHS6LnW4s7fQFcXjDJev7ZOvRieDwBalttHGHIPCzS87ktkkCRo2a/XYPJoMGuzt2DWLUivTNMdBkc/GP68ayMC65rbrIwpqbqzjc6UEQwJyho66pLaWrnFGXfgsox6jhrIEmHp1VQd3edoLBEHvtPioKTNTtbQdi10nJEkMB2YFyXVyy1HlFFsWB8hQgItbK0h/flqG1V9i9vm5v2ykqRglg1Ion5FqD8jKojxujFBQUFE5nelyMovDVsLt8NHd6ueWiIu6cXAKAShWu4WvJ1JJn1ODwBuSNymSkew7A4Um/AdkZNwGOnK+78x7tcfET7AjrG1rYbXPy1IZd3DS+KO059ra6uXHVJlk5/+4XNjng+NGeNva0uvj9W43hoGQXkbIWqWpcJm3rCXRdURxeTi+iywDM7SYI7/AG2NBo4+YLJfnxo+1v6R6PD/B3d87oY47l+m0uPzoxvWVq9PHRf0eEJh0pxoUIycataFFed+1t7vDK9X0hvHA/v6hX2teEJIkJZX2OaQMlgi8YYt54KyEp6dMyqdodybrp7n1p1CrmVBay3+5hQK6BDVHZMStmj0r72o5vgf12jlHL54c6kbq+h063v9vPtNPtp2KgiTanj79dM5o3tx1ifWMLEonio1QuXhsabSxaXc/yJMEWZaxXUEiPRhBocfvSHtPp9oMQnjveOamE+17emtJFIdpNDxKzm41akZqqQjmLUK8VsbsUdxQFhaPFGwjJ8ziHJ5gwlzyeOW261zi9gbRC3evPHxojRNvQaEMlCCyPKrn4daE4Hyic7HS35ur0+FGLAm5fkBzjCWrUKYDifqSg0PMku2dm6dQc7PDQ4Q4wqbwvM8/qD4KEzenhnqmlfNnu5heXnMkdkwTaXH4CQYlNTW0888EeFkwpZfSQPNz+AJ/ubefGC61s3B07X6ipKkxwLIYj7rjzp5TQ3BlgUJ6GJdPL8QZCdLr9ZBk0GNQqqq0WDnR4CUnpAz0RdwWJcGxo0aXl7O8IJ8k4vAFqukqLvnBjJXa3H42oSihDVFNViMfr55lrRnPX85sT29zQwsIX67ltYjECsGh1otNw5N8LppSi16iotpqTJktVWs3U7Wmjd7aeq5/+iGqrme+N7M9zG/dwdVUh854Ju9VEXKAiiXfxZXjunlKiOFCe4rQ6vRi14eS24yFTr6a/ycDHe9qYWTGgh1v39WN3+8nQnSAxitnIa/UHcfkCGLXKFq2CgsLpjzLSnUTsb3dz+78+ixFQjC/uxR2TSvAHJZas2UpFQS5jh5jlSWpCAF0jYsnUpq2XnalPf1PNirNii1yru/qcx3pcMryBEOsbbdxwgfWozhFfIoiuvx99qyFpWYt0NS7jSTW5PlZBy9FwIq+lcGJocfjk71OtSm/vF+hSLBijJrzH24/iH48O5nd3zuhjjuX6gaCEOSP97ST6+PhzO71BcgyapONZxFo92bh1rO8tmohbSjoMWpG9re60x6TaLBlszuCZ9/dwSWmftK9P1u5IXeRUz0cjCgJXPbUx5rWRDRixm99dtuH0nwLsszljbLOzjRoMWjGhpE50KY8sg4YWpw+9RkQtCJT3y8GoFalttPGLCcUxx44dYk7ryBMfbFHGegWF7pEEQa7Vnuq+kG3UyCKzDo8/rZPXTRdYufLcArkvZurVPHPNaNrdfgwaEXOmlofX7Yjpy0q/VFA4ehyegNxnM/UiLp8Ko1ZkQK6BFbNHkaFVs3LOOQn32wjJ5jrp5j86Tfq5kd3tZ9UHe2LWh8nuyQoK30ayu1lzRebNTW0uDFpR6TNRKO5HCgpfPxGBSpPNicMbpNXpw6gVMRl1qAiXtnjo9c8TnFFuHl+ELxhCj4pmu4/Sfjlc/fSHPBonRE27fm9s4Y7JxahVAhLw0mf75bJ/Ll8QS5aOB1/Zxh2TSlB1E2vJ0mvQq1Xhkt0S6ESVPL7qNSLP3ziOUNfjmYZMAkEJjz/IMz8ezbtf2HhuYxPTR/QlQ62m0x9M6ba7vrGFX0oSGTo1cyoLmTV6UEKMI+Iu3urwsmBqGQ+8uo0RA01cVNwbnUbE5w+/P1EUyNGrubikN/OnlPJlm4vKol4IhN9rdZGFoCTRcKiTj/a08fGetpi2vNPQwuy29PEzxYHy5Mfm8H3lUn3W3pkJv49ThXaXn4wTJAwZkGtAAnYedlLeP+eEXFNBQUHhm+T034k6RbC7fAlCFIDSfjl8tDu2FMV7O230y9FzcXEvfjB6EE9u2BUzma62mlk5exQ1T3+UEOyrtJpp7vAmrX8J4RqdgWCIaqtFbkvd3nZ5czRdfc7I5mnd3vaU568ussjHJSMSeHx3p43qIktSRXX0tSDRnSBdeZOjVWJHu1rEv/5YBC1Hw4m8lsKJI7oMQG5G+ol8nlHLvPFWMnVqOVMhXX+riusDEeL7BhzpU9HPpeqf0ccc6vCkz5roOq7SamZTUxvnFVmO6rzJ2pilV5OhCQsElr/dmGArumL2KGydiVny0RsVRzs+RSNJpH2NJB2fKKjaaqauqY31jTbmq1Upr1FttdDc6U247tyoOsmHuqxgk42FVVYz7+1M7QRgydDFjOXx187UnBi1/zfFoQ4P++xu+uWEF3gXFfdCJ6q4L00pj7MH5WLQqDAZNLy708anTW1MGpYvb2jZHF5evqWaDrePDJ2GNld694b2KHcHZaxXUOgeu8sn12q/qLgXs5LMc6usZi4/ewCREHBmN5bTeo1IfpeoJJkgLOKy9/7OVnnerPRLBYWjJ0OnJkMrUtW1zmx1eFkxexS/enV7zLwwvnRW5LH4OVq11UxzV/ZwPJVWc7ft0alVSd3rlA0QBQXI0qZfc2VpRRy+II3NDvpk6ZV7YByK+5GCwokhx6Bh/gtbYpyIjFqR318xkqnD8qmpLMQbCKFTq2ju9NI3S8feNjc//ccntDiOrMGjk4eqrWZEIb2IZI/NxY2rNlFdZOHGC4ZydVdcfd54K592xXgWa0U0KiFtXD1TKyIQniOFJAmVIJChFakustBwsIMRBblc9tgGzi7IZd54K32z9eQa1bQ6A/Q3GfjX9WMJhEIESe9oZdSKGHVqFq7ekrZcqcsbJCQJNLW5+MnFZ+DxBXB6A3y2z06fbL0sTPz8YCe3XlzEr1/dTmHvTJa/1Uil1cyCKSX0ytYz4/cbcPmCSed0R0OW/quJHBS+fmxO33GX6IlQ1DuT9Q2HcXgD3a7VTzbaXL4T1uY+2XogPO4oYhQFBYVvA8fnuRXHd7/73bT//fSnP+2Jy5zWtDh8STcMKwaa6JOtlzcYvYEQK2t3kZ9j4BcTipOWB1jfaOP3b3/BgiklMY9HNjn//fFels4YRlVcMK/KauaeS8u4adUmfj7hTPn5lbW7mFtZyLb9duZWFiYEAau6zruydhcAW/fbWTC1LOG46iILv5xwJtv3dyT9DKLrea6s3cXCSxPPURl3rQipSoAko7tApN3l44Dd0621YE8R7aDxdV9L4cQRXQYgEJRSBs8rrWYytCJ1TW1MfbSW2V19LNLvkvWBey4tY+t+e8Lj8X0jWkQWeW7rfjv3XFpGdZq+VWU1k59jYG5V8utHjqu2WrhnaimjCnL5aE8ri6aVJ4wr0ccna2OV1cxBu4ddNiePvd2Y1Fb0sf9+Qf2BxDqaEaEGkPLzqk4xZgC8/XkzN19oTfoe511YxNufN7P9YId8jXiqiywcitssqbSaWXBpGUvXbgPAHwwxL9U1xls5tzCPlXPO4blrx/DMNaOpKMiVF/SVXd/D/CmlSd6XhTkp3teGRhsVA00YdCpuvHBo0mvfeKEVf3c1hE5x2pw+MnRqWhxeNMDCS8u4J4kl74ZGG09u2MWCqaUsm1HOivU7GZBrYGXtLtY32uiTradioAkIZ2IteHELJqOWTL2620Wq1x/C3iVYUcZ6BYXuaXH46HT70QOLLi1LOs+tbbQx/4UtYfF0kQV9Ny4JkcyuVIKw2q4xoKYqduNa6ZcKCkeHAHz2pZ2lM4bxn4/3cU5hHo+93Zi0dFZ0X6susnDzhUUJ88IFl5bRz2RIOad8+/PmtPPqiLglfk2obIAoKIAIaddcIqBVC/TJ1uP0nbiSnnaXjy+aHdQ1tfHFYYc8f1ZQUPh2EimNdV5ULMblC7Lq/d2MHWqmn8kgJwZ92e5m8qO1PL5+Jw9dPgKj9kjSzcA8A49dOZIVs0fxy4nFuPzphRORc65vaOGx/x6Jq1cMNMlxBBWgh5Rx9WUzh6EHnIEgnR4/Ro1IQJL4bF87i6eVYcnS86tXt/OHK0fycVMby99q4N0vWlCrVIQkiTe2HuSA3YPHH2Lxmq30ydYzMDe5W2NNVSGLV6eOcUTmXA5vAJcvQO8sHVu+tLOvzU22XsMgc0aMQ9YbWw/i9AQ4Iz9LjoFsaLRR1CcrRngSfX6jVmTeeCsrZo8iL0ObNn4WlCRljD/JaXV4v/KcuSQ/m5AEG3cld/Q5mWlzheOIJ4JMnZoMnchum/OEXE9BQUHhm6ZHRtecnPTqvZycHK666qqeuNRpS0cKgUR8EE2nVuHyBbnpmU3847qxchAhmdVqodnIy7dU4fQFydKr0YkqPIEQcyqH4PQGuHtyKYIKkEDqsvW7/5Vt7G1zc83TH7Jyzjnc5A3gD0mYMzTcPqmEYDDEgimlBEMSLn+QYEjCqBVpc/r504/OxpyhQ6sW6PT4WTKtHG8gSKcnQJZew9ufH6K28TBXVxfiDgQTbBWj63m6fEE8/gAVBbnUVBaSrdfQ4fFTt7c9qfI6XQmQePSa8MZ/tkGDJUObtITCrHML0p6jJzPrUn33X8e1FE4clkwt5xVZeKehBbvLz9yu7Mz43/1tE4pZtnar/Pgtz9ZRU1XI7ROLOWD3cPvEYvzBEP6gRDAk8d5OG1f8+X1+cG4BV1cNQVQJmAwaNjW1xfSN6iILC6aW4vD4mXFWP/a1unl0VgV1e9vl18+pLEQQBPrl6FGLAq1OH/+6fiwatYpgSMLh8bNsRjlufwi7y4/JqEGrVtHq9PLvG8ahEmBPqwu1SkWuQYsgSLJlqcPjJ1uvQS0KtDl9/PuGcXwS18aIAO6KP7/Pr743IrX1aEML158/NOaxiFBjwdRSFr9Uz4ZGm/zZ3XiBFVElhK1UM7X84PH3k2Zr/Ol/O5lcns+UJJk1vkCQP/1vJ49dMZKRBbmEJCnhu5t3oZUMXTi7MFOnxuENULe3nV0tTvl6X7Z70IqqhGsc6vDg8gU5YHdT89SHXFTci1svPoOKgSYeunwEOrWKur3t3PTMJh67YqQ8Fhq1aly+AHkZWq584oOUWSjeQAiPL8TVT39ETVVhzLXr9rZz9dMf8s/rxx7Vb/lUpcMTIBiSyDaoaXV50Gg0bD/YyYrZo+idrcflDZcVEFUCh+weBuQa+NdHe7m6eigOr1/+bCP34SqrGUmS+HhPGx5/iDuf30xFQW5ad513d4bFLDlGrTLWKygcBR2ecE32xlYX5iwdnx/sZOWcUfTJ1sulQDSiite3HsDm9LHw0jKcXn9aJ7GIWCWdICyZiwIo/VJB4agQoDw/mzanhzsnFeMPScypLOSHYwfTL8eAPxhEpRIQVSranD56Zem4rKI/ggBBSeI/N4zD7vZj1Ik0d3i54s/vM3xADrdNLOYmbwC1SkWmXs3azQdk57hHZlWgQohJ5oh3l4teE0aPBQoK32bs/iC7bS5Wz6tELarodPvJNmjwB0PctGoTdn8QjUoll6U4EShlLBUUFJIRKY11sMNDpydAhk6NQNjh9r61WxPiR+sbWkA6Usa90mrmtfpDsgvUc9eOQZKko3bUXd/Qwt1TSnjl1uoYdxJ3IICgFslQqbh/5jAcviCd7vAaJlMrogc6QkE6XAH6ZOnwdsXzyvpl4wkEeKJ2JxUFufTO1vP7K0Zy0zObmFs1hDaXnyy9yN1TSvD6Qxg0IsMG5HD3C1t49prRzPjDuzFCeaNWZEp5Xy48sxc3XxSO6evUKrz+EOu2H+JP/9tJTVfC1uYv7Qzrn4NapaJfjoECs5EFL25JiHHNrSzk8fU7+enFZ7K3zSU/19zpTbgnbGi08eOqIZw1yyQ7WRq1Io/MqkCSpBhRcpXVzOxxg2VnFWWMP3k57PBhydR9pXPk5+ixZGqpbbAxvjh96fKTDbvbT+ZXdIY5WgRBoG+2nj2KGEVBQeFbQo+Mrk8++WRPnOZbTXYK1Wm8sCK6FEVkYhiZ7CWU6ymysGBKKR6vD42o4t41W5NONJ/b2MRdk0v44rCTGy6wcnXVEA47vDi94cl+q9OHzekn1xh2ePD6g/TJ1iMhcajDC1I4kPjhriM1KV++tZrdNieCICBJEm5fkOK+2fQzGbjsD++m3ByN1POstJoJScgLiKurCnlqw+6k7jHxJUvC5UVSl6Z4tf4gD69rAGKDHNEZs3PGDU77fR2rStju8tHi8NHh8SeIYFJ998d7LYWTg0gmxx3//oyPmtrYut8uCwqif/duX4B12w/Lr3P5gix/q5GKgSau++vH8uPRgrPS/GwGWzJ4efMBth/o4JLSPuy3e/hLzbnYuxbJKgHanF4eeauRq6uGUPPUhzHtix4rVs45hxW1O5lbWcicJz+kosDEkullmIxa7n4h+QL1qpXh4+ZWFjLvmU2cXZBLvsnAjavCf994oZXv/uFdXL4gK2aP4oZVm3jwsuH88/qxXYGEIxsOLQ4fnm4yVIKSxIrZo2I+u1Xv72ZCeT4VBbn89OIz5Pf+3k6bPBbNG29lZIEpqX1qRYGJl7ccwB+U6JOtJ9eoxeULgAQCAteeN4TeOXr2trq4Y2IJ3kCQww4vWjF8/blPfcjfrxvD1U9/xGNXjuTGVZsAWDF7lHwNtUrgpmc2UVNVKFswAuy3e1i6dht/v24MVVYzd04u5QePv5eQhV9pNfNRU5v8fa2YPYqrn/6IFbNHpQ0Q69QqnL6g/HtKhst7YgLM3xTZejX+UAijRsQlgdsXYNU1Y1iypj7mN11lNbNgahkPvf45P/3OGUiSRHNUWSidWoVeIzKnshBREKipKuTernPUNbXzSNd9K1k/ueXZOi4u7t3VnlNrrE9331JQ+LrI1mvQiAKftLkJAc/8eIwsOIxQZTWzaFoZKkGg0+OjzRlIKficU1nIotX1PHT5iG4FYcmc9U62fnk8KH1Z4etGL6qwOX18sLuVKcP6cc+LW/i46/7423Wfc0VXua3o/lldFHbX04oq/EIQbdeaV6NW8avvjWBTU5ssJn7l1mokSYqZz9zybB2Pzqrg+guGYnf75blhtLtcZH1YbTUzt+rIWKD8/hW+zfj8Af5y9bnMj1vjVVnN/OXqc+l0+wiKIjq1ikydOmUST0+hlLFUUFBIR6Q01oE2Fx3eAO0uP1l6TepEpsYW5lQOThCoAji9AVZ9sId7ppYlxASSHQ/Q6QmQZ9TGOKLub/fy74/3MnfsIPqajOEHo6r/HHK7yTYYyNRLqAUBtxSOvWRrRUCQRfAdbn84OamqELUoYPf4MRk0qAQBUSWgUQlcPjyfP/1vJw5/kMd/dDbf/cN7QDg++OScc7jv5W0J5XnmXWhlTGEew/ubEICrqwoREHiididXjh5Efo6ehXFCFDiyjqooyEUlgDGqrHOqpM8co4aHXv9cfq3LF4xJEtNpwuKYd3faYhLTlDH+5KXV6WNIr4yvdA5BECjvl8P6hsPdH3ySYXf5T2hpod7Zena3uLo/UEFBQeE04NQq3HYaE3FR+GhPW4zDicmoxR8MMr64F29tP8zK2l3yxpdWDE8Ga6oK5QBfvEOKzenFkq1l6dptaSeaC1/cwi8nFrOvzY1eI9Lu9GHtncn/vf45Q3uH7fn2troxGTX0ytQhAA++sj1pTcrnNjZRv6+dPW1uuR05Bg0F2XpsTl/azVFvIBRTIiPinPDfzw9x44VDCRHrTlBtNXPn5BKaO7w8duVIdGoVW/bbmVs1GAkpoU75TeOtmAwaLirpg0Ej0uH2sf1gB05vAK2o4uM9bUCs6Cee84osWDKPfrLcXaZPtIPGV72WwslFJJNjv93NjLP6s3h1fcxvv8pq5vwzesW8JtKHcwwaVswehSAIbGoKC70ir620mvnFJWdS19TG7ROLcfsCvPjpfnl8eHLDLuqa2vnjlWdz83grWToNj105UrbejAg1ImTp1NwztZQ9rS7ZPWXzPjv//nhfynEjIhwD+OvVo3n782b+9t5unrt2DG9tb0YlSJxdELYyrdvbzpl9s7j66Y9k8dyjbzXEnNtkSL/hJwoCVz39kfzvSquZW8YXYc7UsvqT2Pcefd5I2bB0wYbIZxERehi7aql/tLtVFq5Fv+bmqNc4POH/Ry/Oo8ePur3tVBSYko55lVYzGpWKmqpC2pweSvKzY8aJ+IBI9MZK3d72lDWKI8dNKuub9jPNNpzeU4Bsg4amVidaMRzMydBpEwLvEC7Rce+aeuZWFrL4pa0smVYmf7+VVjPNHV56Z+u45dk6Hrp8BBUDj3yf0cGWiNBsYJ6B1+oPyb+tyGb2qTTWKxmqCt8UlkwtXxzupHKoGUmCu1/YnLTPLlpdz7zxRfTL0ROSBP634zAzRvTjZ985g0Md3oSN6RaHr1tBWHyQ9WTrl8eD0pcVTgQalUCGTo0vIDH/+c2sb7Tx0+8U0dzh4ScXn8GvX92eWFa2oYXFL9WzcFoZHW4/3//T+ynP3+4Krx+j5z0uX5Cbn63jkVkVPPPBnpj5ULXVzMJpZbQ6fYwsyOWg3Y2AwIYvbLQ4fPKmhyLUUvg2kmvUcefzye+tC17Ywv0zh9Hc6aW5w0vd3nZ5zvt13TuOpoyl0i8VFBTyc404D3fSN1tPc6c37bEZOnVM+WM4EiN5qysRrKaykJsusKIWBTo9gZQu3E5vgEfW7WDJ9HKqiyysb2jh9n9/xjM/HsOSl+oT3D8WTSsnW2/A5vBiydQhApkaEQHYcrCTot5ZQDj23StLpE+2visxSkOmVo0vGGLZ2m0smlrK5v12yvJz+M3l5XS6A+QY1ayeVwmE9wOWJXGH2dBoQwVcOqIfgZDE2YNyebX+IBt3tcoiGLUopBTzRI5x+oIEJSnms0uGWhQS7ieRmP/ytxp59dZqLvvD+qSvVcb4kxOb09vtuvloGD4gh//uOMz+dvcps+70+IN4AqETKkbJz9ZTmySZWkFBQeF05PTeiTqFyDFqefCy4eHN4LcaEhxOFl5aCsBb2w/LG1/5OXqqiyzyxlgqh5RnrhmdVFQBRyaay99qZE6nV87sry6yUNQnk+svsPLQq9tj22M1c9OFVj5uak84lwDMn1LKa1sPHhHUGDT0M+l5ecsBivumL+k0MM/A1GH9MGdquKSsL5IED7yyjRsvsHLVyo0Jjiqbv7RzqMPLytpdMU4o44t7cdvEYmq6HBcimxI1T4XdV65++iOqujJmIwuO6iILz/x4DO1OHwFJ4rsV/Vm4uj4hgP/gZcOParJsd/lo7vTS1OpibmUhIwaaZBFAvAo84qDxznFeS+HkJceopd3lZ/GaekYUmJhTOTjG3SNDe2QYTtWHI0KvW56tk91I9GpV2GnlqQ/59fdGMLWrDEwgJHH7xGKQwovwRXH1Y6PPFVlo+4MhJjx8ZIFYbbUwdXh+2gXqjRdYufKJD2LOO7eykFaHjw93tzJigImzB+dxwwVW9BoVl43szz0vhNsSkymhVhEISjh9gZQCsGqrBVFFjDNKc4eHvEwtc5/8kOkV/bljUjH72t3cPrEYQYDDnV4kKSzauObpD1k0rYy7JpfQ6Q6QqVdzqMPD7f/+LCE4AV22rilqqcvPd30/mfpwtkjd3nY5OBEtGoz+O5kY5qDdw8raXdw3cxhLppXS6vIT6CrJFJ09Ej1eQWqRTbXVwvypJYQkiWytWm5TwmdaZCErKtPldMTtD2DJCgeA/CEJjz+UVLwD4QD8HZNKWN/QgqfrN1ZpNXPz+CLcXaXxXL5g0oygeIHlY1eOjAncRzazT5WxXslQVfgmyTFqMWfo8IWCBAJ022f3tbt59wsbnzS1MWlqGVc+8X6CwxSEy+0UWjJSCsLiXfZOtn55PCh9WeFE4QtJbN7XzndK+vDwugaMWpFJZfksXlNPn2x9yvlkbaMNrz/UrQNRll7D397bw6Jp5SxcvSUm+/a5D5qYP6UUpy8QI0SbtnwDZxeYuG1iCZua2li37VBY/OvyUdfURoZWzZb9dnKNGixZOpo7vHj8QTq9AQbkGnv8M1JQOFlw+IJp760OX5C8DC2jh+Qx9dFa+bmv696hlLFUUFA4WgRJoMPn73bcCIakhCSwSHloCMfVr64aQr5Jz4uf7OfD3a1J41AXFfdiqCWDX0wopsnmYumMcua/sIX1DS1c8ef3eejyEdw9pZROT9itxagVaWpxkWUQ0apFAiEJvUpABA56/JTnZ9PpDQCQ05WMFYlvSRL0ytRgcwYozs/G6Q9ydn4OB1xuhvfLxRsKC0N+/epWPm5q5+/XjUnjDmPjtknF2Dp9dHoClPfL4bdvhJOs6va2M3aIOe3n5w2EyO8qQ31ekYUbL7QmOC1DeG/A7Ut0loymwxNI+7wyxp9cuHwBPP6Q/Pv8KgwfYEJUCby57RA/Gjv4qzfuBNDuCv8eT6QYpU+XwM7lC2DUKtu0CgoKpzfKKHcSYdSK/P6txA3Q9Q0tLF69lSXTy/j5JUHcviAZOjUqFSyZXsbOw+HactEOKdG0u4/Oljzannx9QwuTh+Xz5taDjCjIZU6XACTirPD4+p0xG7IRahttBCWJYf1NsghkX5uLvAwt5xX1JhAKpSmhE86C/dLu5jd//pwn55xDldXCGX2yMHS5RcQ7OkQ+t1XXjJY3+QflGXml/qBs7Zzq/dY22pA4srG8vqEFSZIYNTiP8v45LF27lREDTcwZN1gW1QwyG8k/CkVvskzUeBFAtAo84qDR4vDJCxlLppKdd7rgC4Z4f2crpf2OiLEEIezjqRKQRRip+nAku+G5a8fw+tZDPPf/2TvzwKiqs/9/7tzZs05mEkiQQGACWcGwYxIX6sIO/uwi+FY2d0Fbq+KGbGq19X3bCtpWBbWtaGutG7jUrUpQUCQqqxC2sISEbDOZzH5nfn/MzGUmM0nQyur9/AOZuXO3mXPuOc/5Pt9nQy3TR+VyXj8zFw7IxB8IkJWqRxAEpECAo+FMkd+8syMu2NhRUFFuNZOsV/PGvHLaPRJpBjVqUUVre9f9hq1DvxLZ74KJRSydUkJzu4dLi3vilwKsrNrDgJ4pzCzP465xhTg8IUGIVq3iYLOL3Awj6ZKGxZNLWPxGrACsMt/CoknFiAK4jQHs4brmA7KScXglHv3JYFINGlQC/PLvX8plgeaEXVQiAp+VHe5rudXMw1cMihH4RIQeZWHh2NwxVllUF+0qMztcDqIi7JoBIXHIry4dKNfGjQhubr7QikYUEpb5eXFDLfdMKGTh5GKueW4jD/2/UmwuH/5AkJw0AxcOyKQ4J5Vz0g00tHnISTfw6E8GywstVz29nitH5MoCvb7mJN7cUsflT3zCypnDUakE5o2xAsTd03ljrLT5z+4yPc3tPm782xf87qfnkmc2UmfvOoPqmNuNn77mJJZOKUEF/HzlZ7Llf73dTek5XYsqI4KVRIvZZ0Jfr2SoKpxKDrU4CQZBI6hocceLSqJxeiVanT5ZlL1k9VauHJGb0IkqRa/pUhD20OWleKUAFxdknZbt8rugtGWFk4XTJ3HI5iYjXF8+upzdVSP7dPlZh8dPr3CCRWfiWYBZFX1RC7B4cjEeX4B2rx9RJbDnaDt1NrcsGo1g1IoMzjWBEKSsdzpji3tCEBodHmY++zlGrcjKGcN5/MNdMYs5FVYzD15eSh/zMXtyxUFF4WzC0c3CoMPtR9KKCEIwLpZyIp4dZ1oZSwUFhVOHJVnL9iMeGuzuzhOZ8i24fVJcieffvLWdZ2aO4J1tR1hZtRcBUAFPfryHV28qZ8nqWJeTUIJjIXe/sll+/a+zRnD3uALunSDQ5vJj1IZKmumTddS1uqiXgqyo2sukwdnk90gBX2hR2xUI4PIGcHglBEGgwmomw6jl39uPUJKTRlaqDq1aRaPDi1Gn5vJzc3C4fXg1IjlJBpxBaGhz8/THexica+KOsQVyHKozGuweMpK0tDi9MbH+lVV7mVia3eVn0w0afFKAVL2aZdPKcHj8PH31MFpdPjkutu2wjekj++DwdP1MSdV3veyk9PGnF03hpI6Ubr634yFJp6YoO5V/bzuDxCiu0PWfTDFKZkpo/nS41YU17JykoKCgcLaiiFFOIxod3oQiDQjVvdzb5CQQDMYsVhu1Ii9cOwogpnRANJ3Vduz4vlErxiy+5mYYKc5J5ffv7YxzaZhVnodaEOL2ZdSGMu17pOpwuP2kGjRYM5MhGKTd58fnD7B4SnGc40hlvoXbLx1IVc1RhuWamDQohwdWb40JDlZazSyfXsbcVbHWiU6vRHO7V158fn1ueadlgDrej4gzTISqmiZ+cfEAfvfeTtbVNMkWjhHOz7d0mw3UWSZqIleFaBV4pBaqwtmHw+Nj+fSymFI7EPpNX1HWizkVod9gZ20YQtkNM9s8fHWglYWTivH4JdrcPrKStexpcsaJLZ6/ZmSnWW+R332kLV/55HqG5ppYMKkQlSDw+d5mSo5zwb3jfoPBkNNKs9OH3S1xxOZiyrnncMc/v+LKEbmU9U4nSaum3u6RxR2R0kDbDttiBGCRwMGDb26jKCdNdoB65abzuKuDvXSl1SKLvaIn210LfAT+cd1o6tvcCILAoz8ZjF4jYk7SsHx6GSvivi8Lr9x0Hn4pwK8vL2FYXgbTnlxPpdXCPeOLCAoB7p1QiD8QCuSm6NU4vX56pOi599XNcQ41s8rzmLisKuTMZLWws76Ne17ZErdNkyPUv7147SjZvSpC9Pm9eN0o+W+jVsTl8ZOVomNcSc+Ye9pgd5OVosPm7Hqh90wnSSvS6PBy/d++4NWbzyO5mwl15P0Ug5qZz3zGgRYXlfkWll81hGXv7eLOcQW8taWO97c3dBn8ys0w8v5tF3S6mH269/VKhqrCqcLm9LK/yYk5WYteFI6rzTo8fty+0Jiw45guQrRD0ZkgCPu+UNqywsnC6ZEYdE46wbCde/R4trt5aJpBw1cHW1kwsSjO7j7kDlrMwWYnG2tbePGzWnksGRnTlOSkccWfPokToiRyGqzMt/CrSwfKJTGXfbgr7lleVdPEva9s5n9/ei49UvWnRakrRQyj8H1yPM/WfU3tJHWyEPN9PzvOpDKWCgoKp5Y0o5a+GUYyU3T0tYREo+s6jBvmVORx0/ObEiYmThvZh+raFh6bVoY5RSuXX/YFJM7NNTEryoUbiHOhbfP6uf5PXzC7Io+y3ukcdRxzZIvEteaOseL0SticPpJ0ato1Kg40uzHqRNrdPow6DfeML+RoOLaXlayj2eFlWF8ThmQdQhDqHW6ykvX4A0EElUCdzUUQmD6qDyur9jIs10S6sXsRh1oU6JGqx+Y81m87vRJ1NheVVnNCZ5UKq5nMVB2Hml3kmo20eyXu+tdmeRxk1IosmFjEhNJsGh0ezElaXp9bTku7j8/3N8ckkJ6fb8GUpPTxZxJN7aEY4ffhjAIwJNfE8xv2Y3P5vrd9nkha2k++M4olLOY/2KKIURQUFM5+FDHKaUR3QWO1KPDsun2h0hxRTiUpulAphugF2GiqD7R2unAWKU1RYTXTJ8PIU2v3xNkZzizPY/2eZnlAGdnPHZcVsGLGMLJSdTjcEqlhp4NH3trOe1EijsiCasTN4bZ/fMXjVw3hhgv6Y3P55MH7svd3Macyj021Layo2hM3MA79LXDd+f34/Xu74q4hglErduq+kqjWZcf75g8EOy1rdDzZQF1lonZcKFFU4D8M0g1a/u/fO+PEIWtrmrj/9a1cfm4OZbmmbn8PyTo1g3unM3l5lezUMPciK8m6UMmeu8YVcKDZhU6tQgrbeHZGil7N/ROLsLt8PHX1MD7d08S72+oZkmti9eY6Dtk6zzbpqmbsUYeHP3+0m1nlecxdtYmhuSauPb8fv/3xYJ6u2hMnbHt8+hAyk3VcVtyT4pxUslL0aESBOptbdo/5+qCNX148kLLe6WhEFc3tXspyTVTXtsr9UqS9//260QQJYtSKOL1SNwKfRu4RBZJ0Ik5vAJUgkJWiI0Wn5tF3vol3qapp5IHV27hzbCGEz+3ZWcNx+wK0tHswJWl5cM22mO+53Grm1h/ls3BKMS5vALfXT0aSDqfPz6EWN8unD6He5qZfZhKzOlifRkqfzQ27m6jFeAFgNGqVIB8TwJykixPtRKiwmvn15aVd7u9Mx6ARqcy3MLh3OkFiXYg6Um41EwwGqbCaIQjLpg9h+lPr5b784f9XyqLXt/Lu9gZ5kUsgtoRIxAnleNyzTmeUDFWFU0Wjw0ury4c5RYuoUqESpC7bLMEglmQtzQnK8kRI5FB0ugvCvi+UtqxwskgxqJGCQapqGim3mmPmVl3NQyvzLeg1KrJSDfj8Ae6bUIhWLeLySji9EkadiAB4pABDck0AcS6Zb8yriFtw6kyIvHZXI4FgUF5E6mx8WFXTREu7F71addylrk6UYOR0EMMonF10Nx5WCaAVQ2VUE/F9PzvOlDKWCgoKpwc90w3YnF50KoGlU0pw+SScHok0gwaVCiYvX5dQiAKh2G8kxnLThf35+pAdgDaXFDcmWDlzeFw/qVOr4kr0RpOdZuDd7UfYfKCVgpw0JpZm8+M/r2fJlBKy0nRyLGvd7ka+OtDKtZX9EQXQqAKoVQIOryQ7AQsC6AUBpxRk2Yc13HlZAU98WMOOI230MRtx+aROXeUi8brz+pk5x6RHr46Nkd/xz6/55w3ncd+rm2NFwFYLi6cU89j7OxnZz0JhTiq3v/RVjBAlIva9+1+bY443qzyPbYdtcoLYsD4mHrliED1S9UoffwbR5Ag57qR+T8KR4X1NPPfpPj7YUc/lZed8L/s8kdgizijfgzPM8WJKCjl9H251n7RjKigoKJwqFDHKaUSioHEkc6usdzp6tchtlwzE6/djTtbhkQJoBBUeKcDCScU02BM/uFZW7eWxaWWoEGIEGpEB4wsb9rNkSgm/eWdHwuywIHD9Bf3wScGYkhWpejXL398ZIzyJiFc+6SBeUQHzxxYSIMiTVw+jud2L1x9gW509Jqjo8kvcNa6A376zM+G9GJybziVFPRjQIwW9RuSIzUV2moGbV4XcAiqtFtSCwE0X9SdArKgkcr2RUhwROmbsRdxdOqO7bKDuREWRAK2iAv/h4JECnddz3dXIPeMLebn6EJcV9+hyPw6PP2biG/l9L51Swpe1LUCoXExRTlq3+0rSqjnU6mL1l4eYMLgXlxX3wKhRc//rW1hX00R1bSuPTSuLOQ6E2tiM8r5x7SiCXzrW7mQXIAHuHV/I/LEFqAQBh9tPmjE04La7fLR5fKzb3SRnvI7uZybdoCHVoOGKsl5MPbcXi1/fIt9Do1bkvgmF/P36URxscaFTHyuho1ELBIMCz80ajjlZJ9tMdnpP3X7SDBp8Pi8+AYJBaPdKXdbfndnmZvXXh8kzJ5FmVPOfnUdRq4SEtYYjDizjS3uydM12Hp8+hF0NTfRI1RMIZxAjgCVFxz9vGE2dzc38l7+mMXzeVTVN3DWukHKrmaqaxi7KnFnkRaC5F+Xz4TcNXFrUk+ra1k7LDTk6CdKcLfgCAZZMLuZomxujWqTdJzErLAZM9GxwuH3MH1tAUIDl7++KKeHmDJeEirgppBo0/O9Pz8Xh9v/X7gqnW8azkqGqcKqwubyhmumBkDA4EAh22WZFUeB///0N06PKgESciU4n15NT1caVtqxwskjSiEi6oDznNGiOzaUir0HH8aSZJVOKEYKQrBPx+CX83iDmJBGVSsDtl3B4/NRFzfWG9jHx2s3lvLmljj9/tIehuSZZSBq9mNKV0CSSGNBZEkcEu9uPRjy+UlcnSjDSmdtmIjGMgsJxI9DlsxUhVN7hkM0V99ET9ez4IbmWKSgo/Pd0Jizf3eDoVIgCx2K/VTVNzK7oB4TiKGkdXEaMWpGkBHHh7hI939xSR3VtCwsnFfObt3cwvI8pNFYwaNCIKnxSAINW5OLCHkwszSFAgDaXD6NOx5E2D8k6dcg9QoA6u5vsFD0uv8S6miY041V8UdvKqmtHsTRcUn7x5GIWvLal07j3xEHZGIICghBkydRi7ns1tG2jw8u/t9exdEoJTq+E3e0n1SAiBUL9/7QRfTAZtdhdvpgxSFeuwwBluSae+2Qfb91SSbpRI39HSh9/5hBxRvk+yvQAmJN15PdI5s3NR84IMUpr2EUoSXvylkvVKhUZSaH5hIKCgsLZjiJGOY3oGDROZDFsSdby4nWj+c1bO7jl4oHctzo0mDRqRZZNK0u4UOn0SqzasJ87xg7kRk9//IEgJqMGvxTE5vRRmJPGQ2u2U5CTCtTHnVd1bSsLJxazePXWuJIVM8r7xghPIuKV6FI0EFrAvVMI8shbO+TznV2Rx+h+ZiqsFnRqFSajBpcvNBh/85YKjtjd3PnP0KJsp3bLVjOzwiVOKq1mbr9sIAeaXWw62BJe+A492KVAkKZ2L8FgkGXTyhAFAadPwmTUYE7W8dysEXy+v5lth23dfk/dZQN1l4mqU6vOShX46bagejrR5uqmNrfHz8TSbJK06i4nt5kpOt6YV47DLZGiV1NvDwkXXD6JeycUohYFfjzkHO59dbP8mc729dbWI3LJnyaHm3e21jO2uKe8vdMrccsL1cyuyGN2eR5eKUCeOQlRFHjkze0JJ/nlVjObwqKYaBegUCYqPPL2jk4nyuf1M/PSDaO5/9UtMW38octLeGtzXYwQJdIXdCxp89i0MuptHm58/gsem1bG79/flbBkQzR2t48/vL+T+ycVc7jVxcNvb+eqqIXNREQyagDmjy2guraFu8YVxDg2RbO2ppH54wp4fHoZRq2KNZvr4u5DntlIil5Dg93NqmtHMf2p9bIg5UCLkwml2Qzuncb40my+2N9CVopOFpfU290M7WPicIuLrBQdbp/Enz/aw3n9zJ2Wh1o+vYx2z9lbosHm9CJJQTQqyEzRY3f7MWhFXvysNsZdLOLM9eKGWq45P49knZqjDg8FOalyFjaAzeUjv0dKXJ/WI/W/O8/TMeNZyVBVOFUYtWo++OYoOekGpCBsPmTj31vrE7fZz2q587KBvL/jKFeN6guEXBayUnSn1W/0VLZxpS0rnCz8gSBfHWxlaG46t7xQHZqThrN1O44nIbQooRUFWtq9+KSQKMTtCxAIwu/fj19UmXuRlesv6Mfv3t3Fkje2MWlwNitmDMOcrKW+1cPM8jyCHFsM6U5oEm3B3xmpenW3CQYtTi8769tY+sbWOBHz9yEY6cpt83jcOhUUEqFXdzEe/qyWBROLqOifwZI122M+d6KfHT8U1zIFBYUTR6KYeiS5EyDdqGXuGCsrq/aSpBV59aZy0o1qapudMbH0B6YUJ3QaPpboSYeS8hbum1hIXaubnDQ9X9W2MH1kLsFgKGEzRa+mud2DTwqybncTyz+owZKs5ZmZw3H7ghyxt6PXiHy08yjbDtuYPrIPL26o5a7xBTg9obibw+MPxdnf38n0kX14Zt1eACaUZsf15be8UM3QPiYEwBt2WNlytJ2yXBPzxxZwtM2DOVlLm8fPjjo71qxkdGp1TAIYwIoZw2Ku/3jEvss/qMEfCHbanwcBujb8VTiFNDm8JOvUqFVdj5O/DSP6ZvDSxoO0e/ydlgA8XWh1+UjShYTxJxNzso5DihhFQUHhB8Dp/RT4gREJGkeC1olUx49cMYjFr29hZnleTP1Kp1di3gvVcuZZRweU6SP70OzwMrNDGYhopo3MTfj67Io8lnaolRk5RoBgnPCkYymaCA12D9W1rfzi4nx+VJhFg92Dxx+Qs/SH5KYzM7ww7fRKVFhDi9N2lw+jTs2S1xOdQxMIAv+68TyCwAc76vEHgny+t5k/f7RHXrSO/lzEveX2l76SS53MKs9j+2Eb88cW8v6OI50u4h9PNlBXmaiV+RasmclnXSbb6bigejph1HXttmPUiuRlJtHm8vDg1NI4u8wKq5kHLy/ld+9+w6tf1sW8/tINo/H4JYJBCAZgweubu3Q2iRaAOL0Si1/fwp1jC+mVbsDVQWDS0YL0xetGMfvZz3lsWhkefzCh01K0Y0r0YkCdzdVpBsXssKAskqkRTY9UfcyEuLtsjMWTi2O2Kcs1dVtuqKqmiS/2NfNmWCTSnYAlsoAR2ee6miYa7J4uP1Pb7OSFz2q56cL+VNe2Jjz3+WMLWL25jn6ZSTw7cwT1bW48/gB9zUmYjBpu+OsmFk8pZs3Xh2PtVPMtjOibQc80PdnpevY3O1k2rQxLso7H3t+VsDwUCCyZUtzlOZ/JNDq8uP0Bnvp4D9de0B+XT8Llk7hnfCELX9sSVy5qVnkes5/dyNA+JhZOKqKsdzpGrSiXe+rOMeu7cDpnPCvZSwqnApVKYMdhGxcOyCSgFamzu5lTmcfyD2vi2uy8MfmoxVBf7PZJVFotLJ1Sclr9Rk+HNq60ZYWTgdMn8cCa7bx6UzmLV29l3gvVvHlLpTyejYwnI8/b376zg7vHF6JTi+w40gaE1iWiBSURosdIv3t3F2trGrn14nz+8N5O5o8tpM3r5/aXvpLFLh5/gN4ZXc89Ios1nY0PK6xmTElaNO6uxeQ2lw+by9epm95/KxjpTgzTnVungkIipECQORWJn61zL8pHCgQRVUKMI6Dy7FBQUDgTiBZib9zfkjChMZLElGrUcLjFxTV/CcXIX77xPO59JTRuKTknjQPNLp6/ZiQ2ly/GXfaWF6pZNKmIeyYUyU4G1QdaufyJTxiaa2LBpCLe2lLHwje2sWxaGXdcVsDsZz/nr3NGohJgR50do1bk6RnDefjtBMma/S0ECcXnH35zO7+6rACAJK0oi2oisa5IzG9lh/hYudXMksnFeCQJTyBIUCVgTtay/IMaynqnM+e5jbz9i0r+5+kNXDkil6F9MljUQYiSiOMR+0L8+ESJF585NDk8IXee75HhfTN4fkMtH+88yrjS7O913983LU4vybqTX8rWnKTlYIvzpB9XQUFB4WSjiFFOM3LSDSyYWMSBZic90/RxquOsVF3IZWRcQVzwLJJ59vw1I7lz3EB5cTSijH70J4O7PHZnA8vjUT8fz77UosCqa0fxv+/siHEQiEwGbnmhmiB7ZXFLVU0TC17dwqzyPPSeLspm7GrkUKuLOc9tpNxq5pYx+YwryWbjvuaEi9Yd3VuiLQWXrt7KsL4ZCa1rjzcbqLtM1OyzbLB9Oiy2nO4kd+N4oleLfHW0lUqrhYVvbOXcXBOzOmQ3LHljK9NH9okRo0S3kWfX7eXeCUVyO4nORL1rXAEHml0xmRIRZ5OQaxEEg8Fu62KqVYLstDSzvC93jhvY6X4hvgRWIqL7kET9TMe+pLv+yB8uJxbZpjN7+I7imawo0Ut39qvVB1rlv7uygY1Gp1aFHWLiBXzR51Zd2xp2kdkem22Tb+H5a0bKAZJo1u5qZOHrWzg31xSz37d/UdlFuaFGvFLXwYQzGbvbh83p4+Yx+TQ6PKQaNEiBIM3tHu4YW8DMNk9c9pDTK7F2VyMun4THH8CoDX1X1bUtJ8Sm83TPeFYyVBVONmqVwJ1jC5GCQRxuPyU5acx5bmPMInOkzc5+9nP+ecNoAPIsSdw8xorJePIDR11xurRxpS0rnGicHgmnV+KtrXVyhq7L5084no08b391yUC8UoAeqfpu97+upglREFg5czibalsIBIOsrWniHlGgd4YxTjw9d4y123Hcyqq9rJwxPK6MbYXVzEOXl9IjVY9e7e00wSCyn6Lsri3SWl1dl4vsiu7cNrtz61RQSITLJ3X6bJ3z3Oe8dMNofCqBc8xJyrNDQUHhjCMixG5xelmQINlpXU0TAqFyZXOe2wiEYi1CIMiSySU0tLkRBRXPrNsbmwAUdpf92/r9ZCTr+PmKDdx2yQAG9EihKDuVZdPKyEzRMfXxdXKMKM2gYdpT6xmSm05yOLnlV5cO5HbgwTXbZCHKk/8zFHOKFgQBIQiCCkwBDb+6rAC9WkWl1YJWE4qtRce6OrrPefwB+lqS+OpAKzvr2yjomYrD60fQiHj9gZhYVpvbz5Ujcln+QQ2XFvVIGDfqGBfrLr4XeT96fKLEi88smtu9pBq+39hXj1Q9uRlG3tl65LQXozS2eUn7nq//eLAk69i4v/mkH1dBQUHhZKOIUU5DREFgznMb+cf1o+Pec7ilmH874vRKNLeHBnsv3TCaBa9ukQfQ3Q0cv6v6NZHwpOOxKq0W+pmTePit7XGD3Gh3hOUf1MSIW6pqmpgfXkg/nnNYV9OEShC4e1wBZbmmmDIeHY8ZfZx1NU3MH1vAkFwTuRkG9jU5mT+2gEAA/IEAJqNWzgY6nnI00ZmoNpcPozZk8+bySdicZ5el8umy2HI6k27UMG9MPhAviJh7UT5vb63D5Qvg8Ep8sOMoH+w4mnA/v7hkQNxrVTVN3D2+gMG5JnyBAE9cNUTO3Hjxs1oA2t0SNz2/qdPzO9DsZM3mOkblmROW+oJQG66qaaQy7LQ0Lzzpra5t6Va0UdlBwNGRrjIsOvYl3WVj2F2+mG0STdDPMRn497b6GPFM9GeOV8ACocVTCE3UK63mhJP46HuxrqaJe8YXcmlRD+psbjSiSv6uNKKKv8wegc3lY1ZFPwbnmlhZFbJfHdw7nTa3v1NxSVVNEzdeaI1ZjLE5u86YtXeT8Xsmk6rX8P6OBpqdXopyUiEYsrY1aEQOtri6bA+HWtykGzS4fRKXn9uLCaXZNLV78AeD32v5MSXjWUEhFnOSln9vq6e0Vxop+lDJrI6LzNG0uf1U5ltQCdA3wyi3zdOlbKDSxhV+KKSEg7YRV8qV6/Zy648GdNp2IeSm0u72dzuui9Dk9DJ31SbKctP5f2W9MGpFGh0eNtW28NDlJfRI1eMPBMlJM+CTJCYNyuGBNdti5iiVVgv3TyrC6fVzWXEPWp0+7p1YiFol0OTwkmrQkKwVSQ3PiTtLMIgeDy4Ljxc7w+MLfOe5X1dum8fj1qmgkIiIeKyz9un0SuE6CgoKCgpnJmlGbShO2UXs5NrK/swdY2V0PzN6tQAqaLR7UIsCi17fkthdVhC4piKPG5/fhNMrcc8rW1gxY5gcW3jiqiFyfKncauY/O48yJDedJVNKADhkc5GkU4eFsKH9/+ISK30zjPiCQVQCfLa3mXNzTWysbSYrVU8/SxJzx1jxSwF6poZccKPp2J+vumYko/plICLgDkg4PRJ6tYjd7YuJZRmjnFYipYA6srJqL6/eVM7S8HjqeJK2LinMIlmvZneDA7vbh0ErMrh3Ol/sb4lL5FLixacfjQ4PKSfAGWRYHxPvbqvHJwXQiN9fCaDvm0aHp1sx+InAkqyl3uaR3ekUFBQUzlYUMcppiCVZS2W+hUTP52S9GPNvIqoPtFKYncrBJhezyvO4a1whTq8fc5Ku04XmCquZc0yGuPfLrWay07q3Ou64r+iF58p8CwsnFdPq8vHLSwfy9SE7U8p6UdY7HY8/IC+cD8s1AaG643PHWBmSa0KtElCrVOSZk3hu1gg+39/Myqq9cYPY6HNYu6uRe8cL3S60dgx+RhYoo+t91tndjC/pKQ+Ov429YJpRS7tXYtEbW0+KHeGpWnxRFlu6J82opU+GkYmDcmIy0Brsbtw+iWfW7ePKEbnYXV3fK5c3wIoZw+Rgu8cv0ez0YtCo+bK2JWYSWmm18OJ1o/FJEkI3RVl1ahXraprYuL+ZuWOsQDDOleP+iUW0uX2MKcjiyifX4/RKxyXaKLeaWTi5mMnL13V5/M74ttkYyQYNbZ7Ytt9xgr5ixrC4AGz0fqMFLLddMoDWsKijo/tLRKADoYn66nkVLHwt1t40kYBlf5OTFzbUct/EQo7YPOSaDKy6dhRL39ga99nl08sQEHi6ak+32bcaUeCNeeU43BLJehGV0PX3nnQCSs+cLliStXxTZ2fSoB4YNCJOn0R2qg4EgXZP18+GFL2ac0wGvFIgzq72ePrv7vriyPv+QFDO8k70XFMynhV+aKQZtVRYLdg9PoxqkfRuRNI6jciiScVIBOgZbpPHO0473nb634ypFFcDhR8KohAaK67d1SiPn1K6cdtL1qn5aOdReSGk+2MIspPmwte3smxaGQa1ijEDs2hz+2nz+MmzJLHkja1sqm3l8elDGFfSk5nn9ZXH3fV2N0HgD+/vihF+h8ThVv697Qi/e3dXTJ/RsdSVVq3izS1H5PFgSIyceH5dbjXzyZ4meqTqv9OcrDu3zR/S4s3pIjI8G+gu4zlVr8YfUNQoCgoKZzZdxSmNWpFeJgP/+LyNkX0ysKQbOGJ3YdSIaNRil67c900olEv5QmxcOTMsEo3EwD1+PxVWMzqVgEsKsCzszP3EVUPkz0wszMYL1DS0yc+1RoeH1eES0n+dNYKeJj0t7R5S9dpu50cZyVo8/gDBQBCjRiTVoMbtl0jVa7h65WcA/Pr/lSIKAkatmheuHUWPVB3PzhyO0yfFlCRyeiV8AYl7xhVy+DwX/kCQyYNyWLo6Pm41qzyPVzcdZMHEIm5/6auYuVi0G3rHmIcSLz69aHR4yU7r3rXw2zKkj4l/VR9i0/4WRvYzf+/7/75oaHOT080a2InAnKRDCgY52uah5wm4/woKCgqnC4oY5TRlyZRiNu5tiQtuNdg9VFrNNNg9VFjNcWptgO2HbSyYUMTG/c2sCQ9gLclaHv3xYBZPKWbh61vjssRuv2wg//P0Bm68sD93jy+kznas9uW/tx3p9FgdhSflVjN3XFaAy+vnwgGZePwBPt3TxOTlVTi9EpX5Fp6bPYIH1myLq9s5aVA2lmQt1sxk/vLpvrj3Z5Xnse2wLW4Q27FsBsChVhfnmEIDiEj9zY7iF6MmdiG2rzmJ1fMqaHP78QeC9EjTYc1Mism0XVfTyK0/yufmC60k6URA4INv6ln42hYe/cnguIWMzuwI57/8NY/+ZPBxWVMfD6eyBqey2HJ8ZKcbGFOQRUu7F7vbT5JOJNdsJBAM8I/rR7NhT5OcidkZfinAvBeqeWxaGQ+/vZ11NU3MHWPluU/2xWUnrK0JlW4pyzWRk6bvMlAeaT+/eecbXr7hPMaXZjMzWjTT5qG53cusZz8PtaXcdNbVNMWINuZU9CNZK6LViKgEqLO5WTatjPqw4Cbyma6On6ifWVm1lxUzhqEilI3SVTZGhdWMXwp0u00il5YGuzvmHkUELCur9vLMzOEs/6Am5v5VWs3MqujL3FUhkcnQXBNvbaljfGk290woYm9je5fli9bWNLJk9Tb5++koRIGw0xMwrjS705Jo0STrNLQ6vWjVKhxuPz1T9V323cm6s3cIkGbUsnhKCR6fxIY9TYzqZ2ZTbQvDc030Nhm6vC+5JgO/e28n11T2Y/uRtpj3u7OT7a4vTvR+ouCMkvGs8EMlO92AvymACF221cp8C2ajhoOtLlk0fbw20N+lnX6XMZXiaqDwQ0Erqnhgagn3vbqFtbsaWf5BDWqVIAtUOlKZb0ErCuyos5MTDrgKdG7GUG418+meJqprW2QnzZsu7E9mql4uXzh3jJW/fLpPHhs/XbWn03HgubmmGDFKxFnz9ksHUtornU21LTFzu+hSV7sbHDFz1JVVe3nlpvNYsnpbp8LsiwuyvsNdDdFRDJOi18hunT8UTuU892wkWSN22TaTNSJeRYyioKBwhtNVnPK68/vxxAc7uX9sAT5B4O5XNrP9SBuv3Hge9W2eLvfb5vaz6tpRTH9qPY0Or5zUVGE1k52qp+pXlXhR0dzuIUmnoVeaAT/w0JvbKcs1Mbs8j8wUnbw/SYB7X9nMg1NLWPDqFu4YW8Ajb++QxxQb9jezd6ODnw7rTVayqssYT2W+hWAAWl0+Hv9wFw9MLSVFI3KozcNHO0PjnpUzhvP4h7u4+1+b5c9VWM3MLM/j9pe+wumVYuITbS4Jow65pFEkvj6roh9qUcBk1KBRqdCqVSyYVBwnRIF4N/RolHjx6UWjw0NBdsr3vt88SxJpBg0ffnP0tBajNDq83SYBnggywnGBOptLEaMoKCic1Zy+3lg/UA63upj7QjU/+dOnZKboWDK1mMp8i/z+/Je/ZuHkEv71xUEWTCymwhr7EK+wmpk/rpA5z33OUYeHxZNDn290eJn57OdMXFbFpEHZvHVrJStmDGPFjGEMzk1n2lPr6WtOop8lmatXbpAXa8t6p3NJURZLppQkPNaSKSUM75PBE1cNYcWMYZTlmpj21HqufGoDHn+Aq57ewPIPauQFtrW7Grn/tS0U5aTF7GtdTRMPrN7O8ullLHo9cV3PZ9btpSgnjefW7eP5a0aycuZwHrq8hGsq+smlLKLRiCp+VJDJY9PKqK5tYc5zG7np+U3MfvZzvqxtIT1JizHsDFCZb0GnVuH0SqTo1fznmwYefecb/EH4sraJb+rttLp8vP7lIX78p0+58qn1TFq+joff3s6IvhlMH9mHpnYvNqeX3Q0OqmtbqLO75UG4USsyd4yVFTOG8cRVQ5hVnkeTw0Nda0j0Y3N62d/YzrbDNj7f18yu+jZszuOrMd6d6GV/U3vMviLnuGl/MzuO2NlZ38aeo47jPl5HIk4+iahUFltkDre6uP0fXzH2D2v56Z8/ZcJjVSx5YxtaUSQIvLm5DrUoxLWzCBXh7MrZFXk8s26v3EbKeseKPKJ/a1eN7MPY4p4cdXiYO8ZKZYd9RwLlkfZz5Yhclq7eyiGbO2a7Qb3SMCVpGZKbzsqqvcwqz6M8vK9joo09ZKXq2bCniUaHl3SDBoNG5LDNzcxnPmdWeV7ctUUff9thGwsmFsv7jVCWm47bF2Bo3wyev2YkFw3MZOGk4rhrqbCaefDyUp6p2hN3jtHHWzq1lO2HbXGfHZaXwZzK+M+U5abj9EoM7Wti1bUjeemG0ay6ZiSDc03MXRUSD1RazSHr1ECQu1/ZQnVtCy9s2M+c5zbG9H+Rc4gu2VPWO50eqfrOs29qmmTRWkRkk4hyqxm9RkVOup52t590oxZfIMDMTu7DzPI83P7EdqxnCznpBtz+AAOzU5ECfsp6m2hwuCEIS6eUUGmN7bcqrRYevLwUDZBjMvLQm9tZOXO4/JyIELGT7Uh3C+H1dnfC9yPPt9kVIbHRDzHjWUEhGqdPQgOog/Dg5aVxY4xKq5kHppbw1No9rNvdxILXtrC/qZ2GNk+3ZQO/azuNvP9txkoRV4PzO5y/0sYVzjYCQfhiXzM/HtKLd35RyUvXj2ZcaU+WTI4fr1VazSyeXMzjH9Zw/QX96Z+ZTP/MZHLSDWSnGeLGLJVWC3deVsDKqr3yuAnAFwhyT1iIArHj4Y5j42iqovYRzdpdjahUMHfVJqprW/jZiFya2uPbe0RkFsHplahrdVOWa5LneZE5cURk+t8utKQZtfTPSubcXBP9s5J/UH1Hd332d52//pDRAg90Mg5+YGoJWkCjWMQrKCic4XR8XkdzSWEWd18yAK8g4PBJrK0JxbDc/gBJ3STsJOnULF29jWdmjmBMQaYco5lZnofTL6FTidS1uGh0+PAHAnilIC6fxPSRfeS49L+31cvjHYdXorq2lWAQZpbnIQpCzBhmZdVepo/M5emqvYxfVsXb2+pYMqUkfn6Ub2HRpCIQAiTrRapqQnOkj2oaSdWrKe2Vxj9vGM3jH+6Kiz1VdYhHROIT900oJNUYGsN0jP/NfvZzrl7xGQ+/tQOdWkW/zGQcbn+nc7F1CcZfijj/9CIQCNLc7u3Wfee7oBIEBp+Txgc76r/3fX9fBAJBmh1e0k7A9XdHRlKoHdTb3d1sqaCgoHBmc/amRZ+BRIItX+xv4bFpZfxl/T5+PqqvbIkXcSlweX38ZHhvEIIsnVKC2x+gze0jWafhiN0lK7Q37mth6uBe3D+xECkIDrefJJ06VLBDCGLNTMbpk0jSqhlX0hN9WIzxzKzhuLwB0gxqNKKKJocHtahiyZQSnF6JNrefZL1Iu8fPwWYXNz7/RZzVHoCtk5IjVTVNzEqQ4b+2ppF7JhR2uigbcQZY/kEN90woJDNZRVObm46xkshib5pBw60XD4hRlUefg8A3LJtWxgsbaplT2Y9JYfcWCA3k51TksXT1VhZMLGLN5jrW725K6FwAMLE0h/6ZSWyvs9Ps9JGkVaMVVayYMQyvFKC3ycjXB1uZF13iI9/CgolFtDe04fUHePDN7THnWRlerOgu46vR4e10wL92VyM1DQ6e+2Qfj1wxiCAkzMqfVZ7HQ29u574JRZiMmk6DnIkskgHmjbEyrqQnPVL1svvMEZsLa1Zyl+f+Q8Hm9DL/n1/F/X5C4qyt3HnZQGZV9MPvD7A0nBFR1eG3cN+EUEkXU5ImRnwVbQtq1Io8Nq2MZ9btjXMWGtQrnfljC5nZ5sbjD9DXksSbm+ti3BjKeofEJgVRYjFBEPBKAW742xcsmlzM/HE6XB6JpVNK8PoD2MNZmj4pwM9XbOBAi4tKq5lrKvtRfaCVst7pVFgtuL0Ss8rzmF3RD49fIjNZh04t4vL5efrqYXyyp4mrnl7PlSNyuelCK2qVQJpRgzbcB11WHOqj9jS2Y9SK3D+5GL8UxO7ykWLQhFxjVm3i1osHMLuyHy6vxJLJJfikAPuanGhEgeoDrVy9YgOLJhfzi0sG4HBLpBtD536kxU2SVmT+2ALUKhW1zU4syVq0ahV1NjelvdL4ZHcTL35Wy8qZwynrnc6jPxlM7wwDmw/acHolBvdKB+CBNdvDJXu2xpU961iyp2O5sEREtumuLFKTw4NXCmLUq3H5JAKBoOxcE10eKuLW8tfZI7o99pnM4VYXdleoTvHPV37By3NGkpWsxxcIohYEHrg88kwL/YaTtSI2lwenqKasdzrLP6jhRrc/YQZPIjvZrvrij3c10tLe+fvrapq4d3whl5/b6weX8ayg0BG7y4/T4+ccowZBUPHQ1BIcUW3VqBFZsXYPsyrymPBYaOxW0+Dodr+Rdvtd2+l3qWuuuBoo/BBw+iTSk3TMfvZz/jprBCoVGEQtLinAA5eXxjxr9WoV/mCAeT/KJxgMOaK0e/24fQG8ksT8sQV4/QGOOjxoRRUZSVpa2r1xdvgmoyZmLBQ9nupubNXZ+w12j/zM16lV3D+xiN0NjrjSMB1L52ysbYkrlxlBEeb/d3Q3tvq2fbICNLg8ZOl1PHR5+Nkanksla0X0QKPbg6BSY3Mq91ZBQeHMpbNSd2MKMjFp1bgRONDiQiOGcnSNWhF/IIgAnbrsllvNCITmEneNhfsnFLGvyQnALS9U89zs4RiSRfqZtBxxeknVa3B6JQSISSiLjus43D6WTy/j/te2sP1IG3+bM5IVM4bFuHrf+uKXLJhQwH0TCrG7/Hj8Eg9MLcHtk7C7/aTq1Rg0Aovf2M7tlw3EoAklqK2s2suNF/Tnve31fLK7iXvGdR9vj/77lxcP4IjNRVObh7kXWeXXo+/HvIvy0YbdYbor4a5Rq+RrMxk15GYYlefMaYTN5cMfCJJmODHfybm903nsgxoa7G6yvieX+O+TVpcPKXjirr8rUnRqNKJAnU0RoygoKJzdnPFilAcffJA1a9bw5ZdfotVqaW1tjXn/q6++4uGHH6aqqorGxkb69u3LDTfcwK233ipv869//Ys//vGPfPnll3g8HoqLi1m0aBGXXXbZSb2WSLBl7hgrz6zbS1muiaer9nDv+ELZEg9gxYxhMRZ511/Qj0sKszBqVWSnGXjiqiEk69RIgSC1TU7avP5QqQ27m+F5GSx8fSuzy/Ni9hmNUSvy6k3lcXUgK61m5lT246uDrZTkpOHxB0g3alg2rSxGZBEhYleYiM6CgN3Vi4x8rs3to83tp6hXGqIAf75qKFIwSEayFo2ootHhIUmnxi8Fqa5tTbivtTWN3Hhhf267dABXr/ws5vzX7mqEIAzOTafdI/Gjwh789p2dCfdTXdvK/LEFODwSoqiiZ5oejUrgiQ93kZeVwsi+GYiCQFmuib/MHiGXxhBFAUkK0hoOzi6eXMK/t9WxsmofV47Ipax3Otvr7LR7/GSl6DodpLd0kxHm8Qf4eFcj/9l5lDe/rosr1SJnEeaauPfVzUwclMMFAzLjRDCdWSTfN7EQp1firc11HX4vFuZU5tHU/sMOZNmc3pBLThd1Z++4bCAAXx60MSovgwfDgft2jx+1SoVKRVhQoWLrIRuPTx/Czas24fRKMe2so2tKhMjft186UG73L1w7Ki5oLgpCQjFLpdXCwsnFce3cqBV5fPoQdjc4yErVc/f4QnRqFfV2N4EgPPnxHpxeiYcuL+GtLUcSBpPHFGRy1cg+lPVOpyg7FZ1axad7QqKPp64exqF2F14pgEqlwicFwjXMBZocXkxJGn725PqY/UX3jS9eNwpRJRAIBhGE0H1qavfG9H3/vGE0P/7TpwC8Ma+cScvWMXeMleralk4DEP/eVi/fnyeuGsI9r2yh3Grm9ktD36PTK7GnsZ37Jxfhl4Ldluzpjsg20WWRIuKS3hkG3tlazy0vVPPS9aP5ybIq2Tp1Qmm2nLmSiGT9GT8E6JSQAOxr7plQiM3p40CLiwa3hzSDDncgQDAQKgfg8PhRiyo2H2wlM0VPTroem8snP2tsLl/CDOpEWc7d9cV2t7/L973+QJxrmILCDxGjTkRUQb1Xwu3zkqTXIBBqd+0eP4IAV43MRRSEmAXq7vrTFL2m2yBpd+30u9Q1jy7xoaBwNuL0SKhVAkatiDcQwKgW8QQCOFw+AjqOCcm0IkIwyPV/rWZng4OXbxwdHjd65EWXf2+rZ2XVXrltRxYtIujUKsqtZvxSbBmR6PbfXV/Q1ftlvdMxakWmj+zDgle3xIzfo0vDRIvMkvVqxpf0jEssKLeauTm8eKPw3eiuz/4uffIPHVGtZo/NSc90Y8zrAWBPq5O0JB1tbj8aReijcBaTKMlK+b2ffeSkG1g6pYSao46YuYKkEnB4JVpdPvpkhPrC2RV5HG510ctkkJMnEyUAEU6GPOrwQLKOmc9+Lm+TptcgBAMEVGpMSWrueWUzZbkmJoTLLkeIjutYUnQ89v4uvqhtZdW1o3hwzba4uOrTM4ZzzXOfk5Om5/HpQ3B4JeptblIMGrJSdGw7bCO/Rwrv7zjKHWMLuPLJTxl0ThovXjcKtUpgVJ6Z8SXZOH0ST1w1RBa5RI+3ID5O39Dm4f7XtvDCdaNodniYUJodk+TU0ObBkqLl129u58HLS7st4a4WBK6OisUpJfdOLxodoRJVJ8oZpDgc61q3u5HLy845Icf4bzjR198VgiBgTtJxRBGjKCgonOWc8StRXq+Xn/zkJ4wePZoVK1bEvf/FF1+QlZXF3/72N3r37s0nn3zCddddhyiKzJ07F4CPP/6YSy65hIceeoj09HSeeeYZJk2axIYNGygrKztp1xIJtkQysiMuIEatGFMTMmIDuK6mCadX4nfv7uJ37+4C4O1fVGJ3+/nD+7viBs53XlbAw+EgWVmuiYsLsijISaWsdzpeKUBWih6NKNDu8dPY7mFwrokvaluPldipaQJBYFxJz5jF3EqrRa4nKbt+WC1yKYpEdAwCRhZPU/SaLgfHkc+1uf3yOVRaLSyZWkyb2x/nglIZVesykXtLq8vH4/+p4coRuXELtmtrGplZ3he724+6E6vaiBPFb9/eETNhuLgwi/smFPHA6q2c2zudB97cFud6snBSET4pQLJOQ5vbh1GrZlxxNpNLczjQ6qLF6UOvEXntq8N8U2dn8ZSSuEG6zenFLwWYO8ZKWe/0GPV85N5F7llWii5OiBIh2nVmdnked738NcumlckT8q4skr2+ICvW7ol3/Qgf6/5JRQmP+UMgIuCZN6brQPTBFhc3Pb+JcquZvmYjn+1rJidVT+8MI797b2dcW557kZXrL+jH797dFdMfRPqORKyraeKuccd+x6kdhAiVVjPnZBhY+PrWOBHG2ppGggTjHCJmV+TxdNWehKKNSquZv183ms2HWhmQlUzZ+EIeWrOdtTWNcns/r58ZlSDg8kl8EfWbjYhcdtTZyUrVEwyGRAFHbC6y0wxc8cdPGJKbzqLJJRi1Ylzb7qxdlnfoDyqtFqrCv9MKq5kGe2jysbJqLytmDEOF0K2zSaR9dby/AnDJ/33MKzedx/Mb9ncqbIn0k51l31TmW2iIqlscLS4pt5opyzWx/IMaKqxmknVqfnlJPkN6mzjY4kQKBLm4MIuC7NS4/mFHnZ1kjRh3vLOFRoeXtTWNaEUVeq2IUSuSpNOyfk8TQaAgXAdWCgRRCQKHbW7e2XqEhZOKCQRDzxgIfb8dgzKJ7GRtTi/ebjKxO7a5jij1khUUQm0pVacmCCx6eyuDe6dz0cAsINT/aUQVXx9oZXSHWtORvq2zvvR4baCVdqqg8O1JNahJ0oXGXivX7WVEXgaZyTpy0gyoVCoMWjVH2zxIgSB1NhcTBmeTubeZnUcc9LUYY8TO0WO1stx02WUv8l693c2s8jxszlgRQvR4OPr/HamIGntFExmTFWWndirujpSGicyRIvOk3Q0OfvynTxM60c1+9nPemFuhLHJ+R7pb2FL65G+PUSOy7NP9/L+hvclK1SEFQ+PhmgYH//riIPPHFSCgCH0Uzl46S7JSFsXPTpqd3rjkTodXwu7yoVOrMGjFUFwlPNZI0oi8sGE/ZbmmuGf6Cxv2c/f4QgB6pumRAseEsZX5FlI0ImrACdz76ubQmKS2lQsGZMadl9MrsbJqLxMHZbO2pom5Y6w89VENM8vzuHNcAQ53qIR8vd3Nkx/VsHLmcB57bxcvVx9ieN8MPt3TxFe1Lcwoz+NXL33NP64fDcD+JidOb6gs0CNv76C6tpXHppXxfx3iipVWC6uuHcU1z30ulyDuGKfvnWFgyZQSjrS46WM2YknW4/ZJcnn7PmYjVz4Zcmb/5SUD5dJIHydIQouUHY+m47hK4dRytO3EijFSDRryLEaqdjWdlmKUE3393ZGRpFWcURQUFM56zngxyuLFiwF49tlnE74/e/bsmL/79evHp59+yr/+9S9ZjPL73/8+ZpuHHnqI1157jTfeeOOkilEiwZbIwlfk33aPn5nleQQJLXhGLP0EiCnlUWE10+b2d1qWoaXdy3s7jgLw4me1/P360dz/2pa4ch6Rxday3PQ4IcfaXY3MPK9vzHmvrWkkELVQXW41c/tlA/nD+4mdRDoGAbsqLRJ9/EiQsLzD59fWNHL/q1sY10FtHnqviQAkLLMAocH2upom5lT0S3iuHn+ArNTOF2w7C1YWZKdy76shFXyi99fuamTxG9u4d3wBqzfXyYvwEZtDnz/Izvo2SnulUdY7nbHFPVlX08jIvAya2r1y9kZzuxe9Vs22wzYgJGRy+yTO62/m0qIe/Pk/u+V75fEHZBFAIuFK5P2MJC0zzuvLzgYHGUlaLElamrqwrVephM5dP2oakYLBhO+d7UQLeO4aW9DlttGCBoD54e0TlZiK3uZ37+6Ksfjszpa8JRy4r7CaCQSDrJgxDAgNtv+z8yg2ly9h4B5Cfc1NF1pj2tHofuZOxS9ra5qY2ebmrc11DJ9UTL3NzbjSntx2aT5JWg1LV2/ttL1ff0E/9BoVqzfXdSnEWfz61oRtuzuHmNkVeXxV28Ksir7MXVVNhdXM/ZOKmf5UyGVlSG46lhQdo/pncOvF+XLJsY7OJh37osj9jX7d45O6zKyJCFtWzhiOKnzfYrfpSx9zEpX5loTltW554dj5X/GnT5h/2UA0osDqzXX4JIkFE4q479XNHZxuzDwwtZTuPVnOXCLizgabi3MyjCyYWMSDa7Yxf1whjQ5PAuGihVkVfXH6JAzhZ2ZEVDkk1yRvV241s2RKSVywpNHh5ZM9TV2KikxJnQdnlHrJCgohjtjdGDQih1td/HxUX/QaVcL2OrRvBn4pIP8dDAbl56FKEOIWGB65YpDcbrtqh0o7VVD49hg1IlIwyDPr9lJd28rCicU8/PZ2po/sw6P//iZu/LN4UjGDeqVz86pNlOWmx4zlItveN6GQrFQ9qzbsB0JjlwWTinlrSx0PrNnO7Iq8mGSN6PFwV2UN759UzMNvbY85/+gx1bJpZV2KuxOVhrG7fV060SmL+t+drha2lD75u+GRJG65eCBLVscmIFRYzSyYWIxHktCKKjSGs3mmoPBDpaskK2VR/Oyko6jR4w9gd4UccdbWNNLPksTci6w4vRLVB1pxef0smFgcF0OpsJp54PJSrntuI5X5FpI0IkfsHizJWgp7pvLA1BJ5W6dXkvtXp1fqNLlxdkUeh1tcAIzsk8GkQTmd9s2tTg+3XpzP797biT8QZHxpNmqVIMenHJ5QMo1aJcjxsOraVpZNK0OvEblqZB/mVPST47+RhLMXrh3FtKfWM7BnSkxsq9xq5p2txxyB/3H9aG56/gsaHV5WzBjGT/78KStmDKPR4cWoFQkEgzQ6vNzyo3xuvLA/63Y3yXHuynwLM87rG5PUFUEpuXf6cPQkOIMUZadRVXOUYDCIICRuF6eKiDNKuvHUiFFMSVrqbK5TcmwFBQWFk8UZL0b5LthsNjIyMjp9PxAI0NbW1uU2Ho8Hj+dYtrrdbv+vzysSbIksTB9zAYkvzaBRqVg0uZijbR5aXT56pOgxGTW4/YFOg2FPXDVE/v+VI3K5/7UtXS7WRvbRcbE30YL3upom5o8toKx3KINtznOf8/AVg/D6AzGCmcp8C/dPLIoJAh7PwnF1bQuzyvN4cUNtnDMBhBe+o+pbdtzP7ATvRS8YG8OZ8x0dFtIMGhrsHvpZ4heDgU6DlR3dbRKxdlcjgiAwtrgH5+dnkqQV0ahVBAIB1KLIU2t38/v3dsnbV1rNDO1j4pPdjRRmp3HE5saSrCVJJ3LHZQU0t4cy83fWtyEIMKxPBrMq8mj3+PnlJfn0yTDy9+tH0WD3IAgC2+rsrKzaK4uOdCpVSDH/72/iLKkXTi5OeH8AHJ5u7JNdXVven0xORLvtjOga51qNqsu6s9GTvuhtOhOGrKtpIjJuj1h83jehkDxLUpfn5JeCMcKLgT1TWBgOyn+w4yjFOandXtfKmcMxakWkQDAmEyQRHn+AtTVNLHljK3eOLaSHFEArinElwKKvdXZFHpcU9oizOo/eJiLEiZTa6tjGunOIWTChiIml2dhcPl6+8TyStCJuSeKJ6UNINmjQiAKPf7CLV7+sixHLdSUmgdD9rbSaWTi5GJvTx2XFPUnRiSxZvY2yXBNzKvqFJuqBIJ/saTrm0JJvweOXGNo3g5kdRIRzV1Xz8k3nMb6kJ/eOL6S22Ul2uh6DRqTJ4eWfN55HXauL6U+FMlIGZqfy+/d2UpZronJAFve9ujmBa1ET9722hYeiAianM9+l3UaCTilGLe1eH0Ny0znU6mLjvuY4kRNEnJyC3DuhCKdPYvthG7dfNpBl7+/ix0N68bdrRuDxBag+0Ird5QVi25rd7ety8Wvx5GJ6pOoT1q3uuFCuoHA28F3arc3p5WCLi5x0AzqNyJ7GdtZ00l4Xvb6Ve8cXUhEWQLe0e+Xn4RtzK6i3u9FpVKQbtFiSjzkYdFY/PtIOlXaq8EPnu7Tddp9EMBh69s0dY2Xp6q0M7kSQv66micWrtzG0j0lerOk4T1tX08QvLx7An/6zm/ljCwgEgvRKMzD18XXyXGTHYTsLJhbLizaR9r9gYhF3jSugzuZm/tgC1CoV+5vb0YqhcdX0p9Zz5YhcrhrZJ1zu0Mg7W4/EOLEUZXc9Hu4oLlHcO04c3fXZSp8c4tu0W7Wg4r7V8XGgqpomlq7eytIpJTS3ezknw9jJHhQUzlyiYzQdOdmL4iczNvVDpqOoUadWkWrQkKIV2XHYRl1eBr1MRlqcofn849OHoAoGWTqlBLc/QJvLR4pBg1oUuO65jWSl6nhgagntPolkvZp/3jAatSAgcmyBxeM7Nk4Y0TeddIOGSqslzqm6rHc6qeGF/x7peha/Ee9SHOmb759UjM8f4BcXD2D2s59TnJMaEy9WqwQ5tljWO12OTTy7bi9f1LZy/QX9uGhgFpeF489qlcDHu47S6PDwtzkjqbO5uXnVJiBxrMsnBVg5czi/f29nTJJoJF625I3YGF9lvoU35lUgAP5AMGYM1xFFtHt60OjwolOr0GtOnBi1OCeVNZvr2N/kpG838euTzdE2T/j6T42DtDlJS3Vtyyk5toKCgsLJ4gcnRvnkk0/4+9//zpo1azrd5tFHH8XhcPDTn/60021+/etfy64s3xeRYMtHO4/GuICoxXi1aCAYxO2TONDspCgnjdpmJy1OLxpRSCiaAEiPUrd2t1g7OyqTv2OAsLM620fbPDH2hy9+VstDl5fS5vHj9EjotSo+3nmU/3v3G269eABXjepLMBgkO93Q5bncO6GIHxVkYXP6uHmMlaue3pBwENuVK4RBEys2GVOQyS8uHkCjw8sTVw0hRadm2bSyGJvoSquFnHQ9v3l7B2MGZrFkcjH3v76108lronPpzqniYIuLIESVDTFz59gClq6JDxCtrWli4etbGV/Sk9nPft7pInml1cJNF/VnznMb5ZInK2YM4+G3tndaskQl7OPBqSXc+0r8wvXHu0KLPp25y3Q3UDPqTp9SICei3XZGdI1zj09ibrhmfHeCBggJTOjGUMbtDfDPG0Zz1OGht8lIvd2NFBZDJHKqqcy30Nds5MHLS6lpcPDIFYPigvI9U/VdHjMjSUsgCE6Pj8xUPV6p69937wwjc8dYWVm1l4Y2N8+u28ud4wo6ddIJlbopIBDsWogTjVatSjix74pDra6YvqrCamZmuDzVlSNyGd3PzFUj+/Kz4X1Yt7tRfv2OSwtoag8FjDq6pFRaLfTNMDKzPI8rn1zPI1cMYs6fPuXigkzmjy1kSdgJJuJONLqfmQqrhRS9GoNa5IE3t/FB2LkqmnKrGQJw9ytbqAj/XiYtWye//8RVQ7jp+U3y3yoBpo/swzPr9nLRwMzOXYt2NdLuSxwMON34Lu3WkqylMt+C0ysRCAQRxYBsvdvZb2ttTaiEjyAI3HrxAP78n93cOa6AJau38YuLBzD56dB9v/zcXnGfTdVrYmo/d3Qmi5CTbmDZtDIaHV7a3D5S9JqYhXIFhbOF79JuIxbRbS4fBp1Ij1R95+11VyNB4MHLS7nij5+wZEpIXFeWmw7A9Kc3cH6+JWGGa3ftUGmnCj9kvkvbtTt98rA1Msec2Y0gP9ppM9F8KRiEm8dYqWt189WhVoJBWDatTH62ZiRpuerp0Bh2dnkeKXo1fimIQSvysz+vl8dnHcdJQMx5/f26UQzNNZEzQU92moGbV23i6auHdXm9HcUlinvHiUXpk7vn27Rbtz/QpROm2x9ApxGxu3z06GZuqKBwpmHvZtH7ZC6Kn8zY1A+ZjqLG6gOhMtI6YMHEYpau3sZ5+RlcUtCToX1M3LxqE7+4xMqlhdn87zvfyKXtPf4ACycX09tkQA3Uu3xkJutY8NpWHp5agjoYxCsIqILQK0mPUStSkpPC//7kXPYcbee+iYU8sGZ7XDxZqw4lrvkDwS77Zn8gyP5mJ89v2M/DVwxCHeUqUWm1sOeoQ44tPvqTwTHuKH+ZOZQeaUa5PFFqWFxzpNVFRpKOlnYP/TOTePuWStp9fv69rT7OEfjTPU2M7mfmFxcP4Pfv7ZSP1Vly6dpdjSx+fav8/O5MiAKKaPd0odHhIc2gOaGOJQN7pqASYMPeptNOjNLQ5iH9FI4tM5K0HLG7T0vXGAUFBYXvi9NSjHLXXXfxyCOPdLnN9u3bKSjouvRFR7Zs2cKUKVNYuHAhl156acJtVq1axeLFi3nttdfIysrqdF933303t912m/y33W6nd+/e3+p8EpGTbmB8SU9G9zPzwJptzCrPo7apnZUzh7Psg11xNoEzy/MQhCA3Pb+JAVnJPHX1MG6+sD+BYDBOoJCiV8t2xt2JJKLfj/5/RxeHaHqlG1gzrwKnNyQ8USGwv9HJhv3NlPVOJxBUMzLPTGV+JnU2N2qVQPWB1m4XtPc1tstBxBUzhnU6iO1MJBPh9ZvL2d3Yjk5UkZ6kTWj7HhFnDO1j4v6JRfzhvZ1cMbQ3t7xQzXn9zMwbY+We8YXYXD6MWhG1KvExO7rbdMUz6/bKQo+1NU3c6PZ3ufgSCd52OujvUDZpdkUeyz+s6dYFp83t73Lh+sYL4h0oICT06cr1w3iKVMWJOFHtNhHRWZLv72hgVF4GE0qz5YB5m9sfJ2iI0JmNZzSCAD8Oix1uu3QgK8MTzcemlRGADr/tULmXicuq+Pv1o5gXnjSW9U6ntFcaOWkGPH4JUdW5mK3camb15jqqa1uYd5EVt09Cr+lcCBKy9TxCdW0Lj00rI92gYWZ5Ho1t3i6v60Czi4xuJgDR9ytZr2ZmeV/uGl+Ayyvh8QdINXy7R1tVWITwzMzhPPL2jtiSNvkWnr9mFEGC+KUAggArquJdUmaU92Xi8iqG5qbzmx8PwieFlmXe23GUALBocjE+KUhbePJv0Kg42OwiWafG5QvV9PV0CA5HxEoqET6+40Law8GDt26tJFkrcrjFSY7JyFu3VspBhWStyF8+3UdZrgm9RuSJq4bElOOKvncO9+njWtQV36Xdphm1PHLFIOwuHw6Pn2S9Wq4B2xUt7V4ykrVo1Sp+fl5f3tpSxwc7jvKLiwcAod9DokWl6IWojv3k+fkWrq04JupMMyoLKApnP9+l3drdPqoPtDKhNJujbZ5ux6rtHj9SICBncYXEuFakYOhzXWW4dtcOlXaq8EPlu7TdlKiEh+MV5Ee/n2i+pNeqYsS3HYlYw0eeuWtuqWBHnZ2inLSYsU53czGHx8+csOX+wknFvHLTeaQbv524RHHvOPEofXLXfJt2291ie5vbh0GjlsuUKiicTZxOTlYnMzb1Qyda1Gh3exFDKSgYBVg0qQiHT6KhzcOSKcXc/9pWHlrzDU9+tJdHrhhEz1Q97R4/OemhWIsKUBH6rYjh0qDtPj8elYgohpKDJODxaedizUqhqd1HskFNXaubB6aWhErquP3Y3T4ykrTU2dzMKs+jrZs+t83lk8vMCyCXgg6NX4pYs7mORW9sw+mV0KlVsjj4N1cUk5lm5K5XNseV/3lgail/+qiGnw3PxeWT8Pq8GPV6Li7I4q+f7pdLyUeEJ0XZqaQbNBTlpMlxzK5Kd3+8q5GGcAxmxYxhCIIQF5dSRLunD41tnhNaogfAqFWTZ0li/Z5mfjY894Qe69tyqMV1Sn+LGUYtPilIc7sXc7LulJ2HgoKCwonktBSj/OpXv2LmzJldbtOvX79vtc9t27bxox/9iOuuu4777rsv4TYvvvgi11xzDS+99BIXX3xxl/vT6XTodN//w8Hm9NLo8KJWwZiCLHqm6uhjNrLk9cR2fUFg4aRixhRksmBCEfe8upnq2ta4zOx6u5u1NUflUjbdBeai34/8v9Jq5qaL8pnz3Odx21daLazeXCcPQkO26QVsOtjCl7UtLP+ghrljrFTXtsRdx4oZXWefRR+/3u5OuE1X75VbzXyyp4mvDrQyrqQnh23uhOextqYRBFgzr4I2j59DLS7OyUiSB9nv7Whg2shcAgG48qn1AMwdY00oxKg+0EpFlLtNV+VZOrrPtHYzCYkEb4/X3eZ4t7N3szCt06jiArPn51vQiALzLsqX9xd9ffMuyud0EvSeqHabiOjF6T9/tIdBvdKPnYdaZNknNQlFH5VWC1VhcUdnQo/INuVWM7dePIBfR5W0iXZmgFCpqZ31bQiEvgiX18+KGcNY/mGNbN358Nuhz1uStTx/zSiWrt4aU14regIamTTOH1vAlkM2lk4t5r5Xt3S7/f0Ti/jdezu5d0JRl/dNp1YldIOKJiLWKbea0ahUrFy3N1S6572drKtp4vW55cddFinCupom2tz+mOuAkBDrgdXbWDy5mNVf17HtsI2yXBO3XTKAVmeorUaLikKCLoH5Y4+JJT/YcZRbxgygqT20uNrulai3uTivv4WH39rOj4f25oUN+ynLNcU5avzriwPMH1fI3Z0EDxa9sZX3oxxVflSQyYKJxdzbocZxtBNS5DtJ0p2WQ4A4vmu7TdKKuLwSdTYXg1NC9riubtxg3P4AgQCk6ETEZIE/f7QHQC6n9JtOFpWUhSgFhVi+S7tN1WtYWbWXnw49h1ZR6HasmqRT8+9tR6i0WshM0bFocjFN7R7e3VYvb6PYPisofDu+S9tN0or4pCCVCcrNdnqc8PuJxmWVVjMNds9xj+Uq8y0ka9X8+q0d/G3OSDn5AjiuuRgcy+B99KeDv1O5LsW9Q+FU8m3abXeL7Sl6DR/sqOeigZ0nZikonKmcTk5WJzM2pRArarQ5vXjDsZygEHJjk4JBvFKAX08twe7x0x52mdarxXAiYiiiJgAikKIV8YfLVje1+0kzCAjhUINOrSLHFHIicfskzCladh5pw5qZRIpWJFkr8of3d3Juronz+pm55i8beemG0V2ef7JOLccGq2qauHt8Ic9fM5J0o4YDzS4+29ssx3kipXqMWpHR/TLjhCiRfdz36mYWTS7G5ZOwufz0TDWw9bANc7KOl64fzf4mJxtrW+QYkk6tosnppax3Oo/+ZDApWjUasevx3oEWJ7OfPeZKHB2XGtbHpMRKTiMaHR65bNSJpKBnKp/ubjrtHEAOtDixnEIRiCkp1A6O2N2KGEVBQeGs5bRcicrMzCQzM/N729/WrVsZM2YMM2bM4MEHH0y4zQsvvMDs2bN58cUXmTBhwvd27G/D4VYX81/+mu11dv52zUgO29yU5abjl4JdlrXwhutG1tnc8gAzkfhgxYxhsiNCX3NSlw4IkcBcpdVCRpKWNfMqeHd7PW6fRFluepzo4L6JhVz+xCfyayGhzA6mntuLBy8v5b5Xt8gL35HzjtBgd3fprhAJIs6qyCNZq44778h7AkJM8FF+L2pR/NYf5VN6TlqXttF7GttjSnhE4/EHyEw55vTR2TVtO2xjwcRiHnl7u6xY76o8S3fZgdFE3j/ejMPj3S5Z37WDSbpBy29/MpiWdi92t59UgxqTUYvN5SUzVcvEsOtHZCG9we4mM1XbrfPN2UrHxembV21idkUefcxJePxSqOzTa1tjfveVVjOzKvoyd1U1o/tlsGRqMfe/uiWu9up9E4po9/i4tKgHQYh53+mVYn7fb8yr4LDNzQfbG3jrlkoCwSD3vxYqAzV3jDXGXefKEbk88vZ2zs01cc/4IvY1tcuiiGgRQ2T7ol5pLFm9jXNzTdw7oYi9jZ1vLwVC/cKXtS3d9j1qlXBcQpxZ5Xk0t3tYV9OEWlTJ5xXJLok+18j9nZGgLFKEzoRga2sacfr8cnt/Zt1eLhyQ2Wk/sbamkTuF2DpLTe2xZczKrWb6WpL42bBcFry2hb/MGcGCV7fEuV/9+vLSOCEKHAsenJtrihGjFOakce+r8dt3dEIqt5o5DgOeM5oWp5fmdg9l56TT5vWTna5n/e6mThelKq1mNtW2cOGATMw6kenPfC7/htONGpYnKPcRjbIQpaDw32FJ1nJefzMiIbe9deG+vrP2Kgiw+aCNO8cN5E8f1jD3RwPw+AI8/uFueTvF9llB4cQjElrMWTipmM/2NsXM37oSgVRaLdw8xsrsZ48lOlTmW1gypZiWdg9Lp5SwsEOJ1IgzaGQsV2E1s3hyMUtXb+U3Px7Eo+/sCDmHEhofR8ZuAnQ6R4ywtqYRh9tPj9Tv9kxX3DsUzgT0alVczCRChdWMXq0iK0WP4TRyN1VQ+L5QEggUIPQ7wKjF5vTic/lI1ob6u2AANtfZKcpOZdO+Ri4Y2BOHV6LN5SMl7EKrAQKAFtjb4gJCsdQ2t09OxAvo1Dg9EoFgEKNOxOOT6JlmQAdyOe4HppaydPVWLivqwZDcdI7Y3F32zVqNipVVe+XXapudbD1sZ3xpthxrjMRjjRqRrFQ9syvycHilLsv/+KQgLU4fZqOWAy0uSnLSsHv8HLa52RhOLIVjY7ey3unMeW4jlmQtq64d1a37bFZKqGRRdGxQJQismVeBUadWysGdRhxt85B1Er6PguwU1myu42CLi94ZxhN+vOPlUKuL/KzkU3b8jIgYxeamOCftlJ2HgoKCwonktBSjfBtqa2tpbm6mtrYWSZL48ssvAbBarSQnJ7NlyxbGjBnDZZddxm233caRI0cAEEVRFrysWrWKGTNm8Ic//IGRI0fK2xgMBtLSTs4DwOb0Mv/lr1m7q5FnZw5HI6rISdMjQLcWqc1OL2qVcFyOGpGF6tJeaSyeXMzC12IXuqMDc5HSE1c9vYGXbhjN79/bhVErxrmuVB9opa7VHVdqZF1NEwsmFGF3enhoagkOr0S7x8fSKSX4pAB1NjeBYMjicMGkIpa+sS12YT7fwqJJxfgCASaWZhMEjtrdTD03h3vHF3LY5iIYDKm+566qZlS/DO4cW8Bsh5dgMEiaQcN/dh6NWRS3uXx42o7fNroj0Vl862qacHqlOCeKnql6vjrYKtcxVwsCt186kLvGCdhdfvQaFR9+E3te0QKU6gOtxyUUOl7RyvFsV95NBuL5+Rb0GhW3v/RVzHmdn29h0eRiHlizjaKctJiB6yGbmwfXbOe+bpwwzmYigex6u4cDLU4APt0TCowbtSKPXDGIu8YV4PD4STNo0Igqmts9vHT9aI7Y3Vy94jOmlPXixgutqFQCGlFAK4rYXF60YigTw+np2unhQLOT6toWZpXnUWdz0S67d8S75kT+/mDHUSr6W+TyWIlweiXUKhUf7Dh6XNvbw/3T0jXbWTOvgvvCgpgIHZ2Xlk8vA4IJhTh1rS78gSC3vFDNX2aPAIixNVWrBFl4N39sAQdbXOjUKjJTdFz55PrvVOrL6Q2wbFoZXinAbZcMJBAIdrotQHs330vk2hdPLmZKWS9+8/YOZpXnMX9cAQ63RLJepMHu4VCrO2FAAkLBg1lRrkpwfE5IkX6+6ys482n3SgiCQJtXwuGWONTqZmQ/M3mZoQlmxzJtESHYxNJsfv7cRg6Eg0uV+RZEgeMKTioLUQoK3500o5YFE4uweyVcHh+j+pnJs3TeXoPBIPeOL6S6toU7xhZCMMimAy3ydorts4LCycEVkJAkCASDHHV4WDixmN90IsivtFq4b2Ihda2hkq0mo4Zl08rw+AP0NRvZcshGUzgYXmdzcdslA7hzbKgUYyAQpN3rRxQEHv3JYNINGlL0avY0tvPejqP88tKBvLfjKJ/saebF60YxM1zuSy0IzCrPY3ZFPwwaEae381KZ0W5KyjNd4WzE45e4f1IxS96IdcKssJq5f1IxHkliUO80pODZPlNQ+KGiJBAoREj0nE8zaHD4vFw4sCdtXin0GwkLUdSEhCgSoTj2L//xJZX5FhrsHnqm6VGF1SgOtx+jTkQlCPikAO0eiTv++RWv3jgCp1+gze0nRa9m0aRiXJLE0qklPPrONyyYWBznUlxhNbNkSgkzVn4WM2bpa04iw6hFBZTlxseAfnFxPuf1M8txuM5oc/nQqAS0GhW2Zh+OJC0Otx+by8eQXBNwbJ3gxQ215KSFYr6/+fEgFr+xlbJcU5fi482HbPzzhtG8ueWIXJ4nkgT63Cf7ePiKQeSkG47vC1M4odS3eRjQM+WEH6egRyoAn+1tPm3EKG6fRJPDe0qdUdINGlRCyBlFQUFB4WzljBej3H///Tz33HPy32VlIZeKDz/8kAsvvJB//vOfHD16lL/97W/87W9/k7fr06cP+/btA+DJJ5/E7/dz8803c/PNN8vbzJgxg2efffakXEejwysv8vc2Gzhqd4cyy/pb8Pi7DgT4pSBalepbld5RqwQaHR7GlWZz68UDQqIQAbRqFXU2N8umlcUE6dQqQR5gdhzkllvNnR6zze3H7ZdodrbjDwTJSdPT6PCQoleTnaaXBTJur8SiycV4pQAOtx+dRkWyVo3bJ6ESBXyBAC1OH0l6NQdbXfzPig38z6g+/Kgwi7Le6fL5Xvnkespy05lVnsfVHQbrAFmpOuyursvRdFYjMSTYcLPlsC0muBoR+FTmW5hTkYdKgJF5ZgqzU+NcDmaW53HtXzbGnFdHq+lth20snlzMgm4W64/Xdrq77ertIReJ+S9/zcNXDEKFECMKOj/fwkOXl7Lwta1xApmPdzXi8kmyICERv7q060X5s500oxab04dRK7L8wxr5e3B6JVau28vSKSX877vfyPcvIvgq653OosnFpBk0aNWqGBFF9DaZKV0PliN1ZQFuv3Qgzc5jk9GOwqvov4+nVE50wD6pG2edyPtOr0RTu4d7xxciCILsprL5kC3GeWnuqpCY5PbLCmh0hLItqg+0cvkT6+T7UJl/rKRRSlS7rT7QKk/Iy3qnyyKZuWOscc5OESrzLQnL90QwaFX89M/HnE3emFfe5fUatcfuR1elgTy+gCwgeWtLfdw2T1w1pMvjdPUdJiJJp6Ys18SqDfu587KCLrc903F6JD7b18SwPhmkGjTY3T5qm0KisHvHF4bEXOHnW1VNI3NXVTO0jwmPP8DOBgcQ+l1cW9lPtuBVUFA4sbh8EnaXj1SDhte/Pkx2ql4uexbdXldtqOXucYUsXbONX1w8gHtf3UxZronq2hYem1bG3z+rZcmUEmVhQUHhJKBVidQ7PJiTtXy2t5k/f7SH2RV5cYL8jCQtazbXcfkTn8hjuUuLezLnuY1UWi08MLWEoX0yQAhSH7ao9vgCuHwSGhGSk7QkeUXavRLJejVVNY08+fEeloVdKts9oTme0ytxsMWVUCS9YsawTp3tQHFTUjj70apFfvtOYhH87979hjsuK8AnBRDPdgtFhR80ithQoTOy0w3U2wUe/2AX11f2w6gRcfok6uxuUvQa9GoVGkFg5l82UtAzhQemlvDIW9uZWZ4XFtlqURnUqFUqNKLAhr02Ss9J4+ErBjH/le0ctXv44/8MxSsFOGxzk2rQYBQF7hlbQLsksXhyCV4pgN3lI1mn5ojdzU///CmNDq98jpX5FtZsrmP5BzU8O3N4QvHvloM2KqwWjN2UZk4xaNCJKpw+Pzq1irbwPMzh8ZOsU7NixjCqD7Ty4ob9zB9XiNPr5415FahVAutqmqiubU3oFl6Zb2HGeX255YVq/nXTeRxsbo8pG+3xB/h4VyN3vfw1y7pxoFU48UiBIE0OD6aT8D0k69XkZhj5bG8zVww954Qf73g43BpKROsuvn4iUYX7jyM2RYyioKBw9nLGi1GeffbZLgUjixYtYtGiRV3u4z//+c/3ek7fBXt0TfugwLIPa7h7XAFH7C7SDNpOS1ZUhMsKDMk1Hbc4IfL/iwt7MLSPibpWN70zDCx6fVunZTHe217faXbbjPK+nZa9aPP4ZevlCquZBy8vZfmHNeRlJlPWO112V9mwt4lReWaWf7ArzqnljssKWP7BLt7b3oBRK/LYtDIG9kzh9+/t4smPQ8HO8/qZOd9qYWxxTzYfsiXMdKvMt6BRqchO1XfpPJKVoou73+VWM3MvyseSouXXb26Xg6wRN5Re6aHamjc9H7JH3HbYxuDe6dw5diAHml2khgdbS97YFnNeHa2my61mrq3sz9tb6yjLNXHThVZElYDTK/H1wVZUQpChuSbW1jR2ajtdmW/h5gutzA6LVlZW7WXFjGFxIpNKq5kFk4p5a0sdD6zZjtMrsWrDfm4e05+7x4cmGSajFkuylvo2N+/taEj4HXfnzNGZC8UPCYfXz5znNiZ0FTrU7GL6yD54/IEYcVMk++DqlZ8xuyKPIbnpnTpkHI+TzrqaJu4aJ8SI0joK2KL/rqpp7LZUziVFPeTXGuyeLm1FG+zH7DuTdBrG/WEt7992Pi9sqJWPEe28BNDLZEAnqkjWqVn0RqwYqtJq5v6JRUx5fB0AfikgHz+6fFZ0v9hZWa2IxfsDa7bFnXvkPho1auaOscrZHMEgXfa38nnmW5hdnsdXB1tZMWMYHn8AvUZkU20LK6v20h6eiHfGtxEZHs/27R6/7JTDWR5jTjWo8UlBUg0a9BoVDW0eCAZ5d3s900f2iSlRBaHfwQNTS3D7JZ6bNZyeaXr8UpDfvbeTBT9ghycFhZOFzemlyeElI0lLslbk6wOtFI/swyNv74hrq4smFyMK8MGOo/zy4gGy89PyD2pQhV0TFNtnBYWTg9MrkaRTc7DZybwx+Sz7YFeMID8yj/r3tiNxQn2/FKDSamHxlGLq29y0uY8lDei1IkJQYN3uRj7b29ztHDdZd0xI0tl4qKv5suKmpPBDQKsSuHp035gkCTjWTrUqAY+fbhcwFRQUFM5WeqTqmV3Rj2fW7WNmeV8AAsGQA5w/GGR3YzuLJhfLceDbLi0gksv19SEbI3qn4Q1AMBhkZF4GUiDIM+v2ctTu4emrh+Hye0jR6hAEsLv8BINBDBqRVFHk6uc2cmlJT7YftjEtHLOIFqJUWM0snVLCA6u3ArCxtoUddXbKck1xsUa/FIyJk3WkwmomWSvi9PvZdthObYuLCaXZGDUi1QdamVCajSDAxNJsctL0THn8WGLY368bBRDnFh45fq90A/+zYgNOr8ShFhezK/rxyNs75LLRkXHax7saaXR4FTHKKaap3UMgGCpPfTIY2DOF9XsTx7dPBYfCYpRT6YwCoVI9ihhFQUHhbEaZYZ4mpIazsCLZ9LPL8zBq1Xx90Mb72/eweEpxXM3sSquZu8cXypaAq+rsCQUjHUvvzCrP48XPahnWx8T05zdRlpvOlME54VI5W+PKYsy9yMqssKBkdkVejEDCqFXx9Md7EgoOKqxmLMlauT5kVU0Ti9/YyqJJxRxscdHq8smLsn0yjDz18W4G55qYGR7ARt5b9v5OFk4qxuMPsHZXozzQvelCK2qVQJpRgzFc09jpkxjeN4OhfUyx9ypc3qPB5iZAkEWTiln0+tYO4gwL908qwh8IubT4AiE1ukEjYtCKCAI0O7zcO74Ijz/AEbubzBQt729vYH5YzT001yQveD+zLlTPs7q2hXU1TfQ2GfjjVUO5fexA2lx+UgxqWp0+3F6JR38yGJ06tFianaZDpzaHxAm7m+TF73KrmcHnpDOqfwbzxxVgc/lIN6q5f1IxXn8Am8tHmkFDIBjkw28aZMtrnVrFZ/uaGdrXxMzyvqToNaQa1FTvb2Vq1GQiIoRxeSXcXjdluelhVw8vB5pdnf52DdquHTFS9Eo34/RKssgkjjFWdhwOTR7nVPTDqBWRAkE+3dMki6p2HLbxwNRS7nt1M5tqW3l8+hDqbKHvpM7mZsHE+LYb3e4jtDh9MUH4jgH56L+f/HhP4lI5VrNczmTQOWnyxHb+y1/z/DWjEtqK3j+pmOlPrZf/NmpFVs4cTlCAeT+yysfoKMT5f098QlluOtdU9GNoHxM3XNAfUSWQpBPZctDGm1vqZKeTm5/fxF/mjGDBq1uoqmmS+4ny/mYuP7eX3N6j+w9RJeD2Seg1KkTgusr+sigo+j7OvSif1ZsPyxn3t7xQzYffNDD3IisQ39/OG5OPTq1i9bwKUvQi+xqdfL6vmd+/tytmu8emlZGkU9Hm7lxAUn2gtUsxYkfHleoDrZ0GGyqtZjJTdJTlmnhhw34WTSru9LhnA1q1ihF9M/BJAVK1IkP6pGN3+sizJPPk2t0xAZt0g4acdANawBWA/pYkGts9PLV2L3ePKzxpk3IFhR8yjQ4vqQY1SeF67BGr6ui2mmbQ0CvdQEu7m8zkkNjE5QsJ+iLCvrW7GnG4/YQdgBUUFE4wDo+fNrePf35xkDsuHcjEQTkxCxINdg8qAf780R75MxVWMw9cXkqj3cXNY/pT2+QkSa8mSasmWa+m3u7mTx/WUJ6fyZ8/2pM469ZqZkbUHFejFjod40aQRfqCEFd69JErBimLIQpnPRogJ83AxNLsDu3UTa90PRpC4vXuBO4KCgoKZzMmo4aBOak0Oz2kG3TYXD4CwSCNbV56pOrCZZmDlOSkoQIa291IARhyTip1Di89k3V4A0G0ArikANW1rbx9awUun0SbG5rb3TjDpQcbHR7SjRpMRi0rZgzjiN3F1HN78fCb2xPOg4RgkHsmFHHdBW483iA/HnIOC17bEicEnlCSzVNrd7N0aokcJ4sQSRYVgkHsTj+ZKXre2XqEnw09B5dfYtthG+NKepKiU/Pgmm0xMUGIdZJLFOt889ZKrhyRK7+uFlVy8kCl1UxWil5O9op2XFY4NUSSB0+GMwpAYc8U3t1WT0Obm6yUU59AcqjFhQCYk07tPMBk1CplehQUFM5qhGBQKQb7fWC320lLS8Nms5Ga+u2j3zanl9tf+oqfjcjluXV7WVvTxJp5FRyxu5nz3Eb+ft0oMlN0tHv98iBh8yEbQ3NNPLl2D/sa2/nLnBE8uHobBTlplPVOxysFyE4zkKpXs/toOyohtFC547Cd/xndh5ue38TQXBMLJhXx1pY6/vrpfq4ckUtZ73SSdGraPX42H7KhEQXOH5ApH7f6QCsrq/YytI+J+ycVoRYE7n9tS9yC9dKppbh9fkAgCNhcPlJ0ar4+2Mr/vrtTPhbAOSYDXn+A3723k/V7muUSJBBSpQvAut1HGd3fgscXwGTU4gsEsDl9aNQqPtjRwMqqkPjjmZnDSNFrqLO55eBKRpKWq57ekLDMiccf4ByTgX9vq5eFHxASsIwvzebuf22Ouq5QnfMjNg8GrYoknRiytg1nDjW0uclON+D0+Ek3avFKAdQqFYujXB1+cXE+X+xr4YvalphziIhReqXpSTWoeW/HUSaUZmNz+UJuBuH7HhIBHTv/JJ0ao1aUz3/ZtLIu7affv+0CBAFe/+owFVYL/kBQFkF9+E0Df/5oD6/cdB4De4Z+x7sbHOxrau90n3dcNoD1e5oTOnNU5ltYMqWYPEtyp+dzKvlv2+3xsqPOztg/rE34nlErsnpeBfe/FpocRr7b8/qZ0ahVGDQiR9vc6NUq0pN0qAQQEFi6+pj4xKgVuW9CIWW5JrnsTfTvJcLKmcOZu2qTLJaKWGpGHCIizkPPrtsbcy6j+5nRqlX4pACfRAmkJpT05I6xBdz36maqapqwJGt55IpB9EzV0+71k6QN2YrOf/lrGh3eUBbH1FKuXrGBAy0uLi7I4s5xBWzc18yAHinYwvVsO557udUsu6VASC3e3O4lK0WHTi2ydHXI1am3ycDjVw1BI4bsRVMMGnxSgNv/8RWXlvSU+5Q0g4b/7Dwac4wXrx2JyxegzuaiR6pebpP1djfZaQZuXrVJFoWVhUVnj08fwhGbm6xUXUwgt2fU9pX5Fm66sD9zntsYJ9qrsJq59eJ8PtrZKIvWOjKmIJOFk4q595XNCYMHS97YyvtRJbJ+VJDJgonFLHh1c6fipCG56aHAQ0AiN/PMW6093na7cV8zAL/8+5f8/ZqRSMEgnkCoTw4EwOWXcHpC2dwOj4/sVD3ugJ/fv1PD7ZcNxOUL9e1On0RBzzPvPikonE4cT7utrm0BIMUgEgz4MIp6JAHaI/Xaw9bYXx+yUXZOOp6AxCX/t5Y1t1Qw4bGqmPIbr950HueG65wrKCh8d46n7W47bOPqlZ/xwrWj+ON/apg3xopXCmJ3+dFrVQgIQBAQcHj8pBk0+KUAz1Tt5WcjcslI1jLtyfU8csUguQ1XhsXMda1u/rx2N9W1rTHzw6xUHe9vb+DJj/fIJVp31tsp621i+Yc1cWPcCJVWC3eOHcjO+jZKeqXh8kqk6DVYkpWSDQpnD12124NHbQiCGk8wiMcfkOdMOrUKnUogGPBj84FWVJHfI+UUXYGCwg+PkxWbUjh+Dre6mP/y13yxvyVuDNLk8JKqV+MPhOJxKuHYgn5mio5UvRoR0ATcHHAKNDt9cqw/wtUjc7nu/H54A0Fc4QS2VIOaFI1IIAj7WtoRVSpUQigJU69W8dh7u6jID8Vxe2cY8foDaFQCvTKMONx+2r0SSVoRnUZFq9ODJVnPY+/vCpUREo+V4UnWiqiCYPP6eHd7A18faOX+ScU4XU4CKg11Ng+9TAYCgSATHquKuzfv3nY+i1/f2qnjysLJxdQ2OXn2k30M7p3O+VYLP31yPc9fMxKvP8DNqzbJ47d+liT6ZZ6eMeMfCh/sqGf2sxt5fPoQMk6CIKO53cvNqzbx+PQhTBiUfcKP1x2PvL2DlzYeYNm0rsukn2ie/WQfuxscvPerC07peSgoKCicKBQxyvfE9zFxONTi5M6Xv5YDZv+4fjSNDg83Pb+JN+aVY9So+fVb2ynplSaLCNL0avQaEbc/gNfnJyNJhyMqaJ+kFQkEgrj8AdrcPlL1GrSiiqZ2D0k6Df/edoQnEzibzB1j5ava1rjyGZHBd056aFAqCOCTQouwgiCEBCfhxYJmpxejVo0AqFQAAk0ON1kpBrkGpk6tQlQJCELIaSXNoEYrqmh0eNBrREDgg2/q2XzQxl3jCvEHJLSiyMI48UtIJOL0Smyvs3PU4Ymxc+6uPnhn7//zhtF4/AG0ooo2j18urTE018R9Ewupa3WzMfxaZKH6gSklePwBWT3eUfhiMmowJ+viXWisZmZV5LFqQy23/CifycvX8Y/rR9Hm9nd77oC8zdwx1k4Xts/Pt8h11ee9UM3HCQQkkW0iAdnq2hbe39HQ6T4vLsziV5cO4KE1O+KcZub9yEpmso6803RicbIm/DuP2Fm8elvC+xcJiuu1In4pKNeq7Cg+em7WcGoaHOSHRRuCIMi/Rwi5Fk0o6clDb+1IKAyqsJo5N9fE8g9qYn6ToiDQO8OIxx/A7vZhMmpocXpJ1mmobXbKwpYXP6uVBWQef4A8SxI2l4+cFB2CSojpd9SiwJLXt3DH2MKQMCT8ukGjoq7Fhd0rkZNmACGI3emjd4aRdq+EPTwxTtKG3ET8waB8jU9dPYyrnt4gX0+51czCicVMf3p9zHlFn+/KmcM52uaJcVp6rfoQK2cNx+2TcLhDfY4/3JcdbnWTk2ZAVAnsb25HKya+9twMI29vPcKLn9Xyj+tG4/JLtHskpEAAp1dCFAScPkk+5rbDNopy0hI647z9i0ogFPBtaPPIArcUvRqHx48lRYtAEK1KjLnHyVoRp8+NUaPHEXXvkrUidpebAGo0ohDq9w0aNKKKJoeH5MhnPU4EUceAM9A64Hjarc3p5WCrC5UgMO4Pa3nnF5U02D3kmQ1IASD8HR17Vqp48uM9zKnoh0ogJFoRVHilAEkakd7mpJN7kQoKZxnH0253Nzg40Oykr8WIyxdgX6OdQTkmPIEgLl9IPJZqUKMS4I8f7ubWiwewePVWbvlRPr995xvKws84CPWt2al6ZXFZQeG/5Hjb7qI3tlJvc7NixjCcPolAMEiyVo2vQ/vViiqa270YdaH/qwQ40OziL5/u49aLB/D+jnq0aoEKayaCAC5vICSaD8IhW6j0qSVFh8cnUdvsihFgA7J7YI9UPf5AkOw0PYEgeHwS7V6JTWE7+0WTijknw3gyb6WCwkmjq3a756iD9TX1VA7oGZpbhMUoyVqRtTuPMMrag2AwFOMpyD7z5gkKCmcqihjl9GR/Yzs1Rx0xsabopKY35pXzu3d3Mn1kH17cUMuYwiwKs1PRa0QsWpFGr4RereK+17bQI1nD/EsK8AGqQBBJJeD0+7E5/aEEGbefVIMGnahCFEAC6lpdPPPJPi4t6kFprzTq7R56pusRhCCPvPUNlxT2IAj837s7eeSKQWSl6kIJk3oRVzhBbOthO/0yk+VExBSdGo1KwOELxZEi8aUth1p4adNh5o8t4JG3dzBvTD51Njc3Pb8p7r7cOXYAlxb1ZNHr8c7IiyaX8O9tdQzskUpWio6fPbmel288j3F/WMtbt1aSrBU50urkqNNPulFDX3MSOemGk/OFKiTkxc9quftfm/nrnJGIqpNTz/u2f3zJpUU9WDyl5KQcryvmPPc5TQ4v88cWnNLzeP2rw6z+6jCbF192Ss9DQUFB4USh1M84jXD7YstDGLQq2R61we6hryXkfnCo1U1DW0issX5PE3lmI+f2NhEMwmGbmySdGqdHorq2lfMHWCAooNOoeOj9XTHih4hoIVGJnW2HbfzqsoEECMZlouWkG5CkIIdsLjSiiiM2F9lpBv658QB3jC2Iy8ivzLewcFIx/kCAHqkGGts8GHVqknQiOrXIkk7Ki8x45nPKctOZe5GVQb3SefjN7dw3sZD7OtgLAqytaeSB1dsY2jeDLQdbuXdiERcX9MDh8aMWVdhdXsYUZPJBlINA9PE6lrqIIAWCmJO0HLV7sCRrKcpOZfn0ISEHiLd3xOwvYhW9s8FBH7ORWRV5ELaAjiyOVFjNzCzPY95ToQX0G6JKHm2qbeFv6/dzz/gidh91ACHrw493NXZa27zSapHPvdJqZm1Nk1wmCDpYWXewn374ikHc9fLXMYKURBbVqXpNp/sst5q5e1whh1pcjCvtyczyvjGOEu0eCbXKyzFPix8mQei0hNaM8r7MevZzrhyRy/iSniRp1QQIaQQjopFLCnvg9kn0sSTxn51Hea36EIsmFzOuuCeVVgupBg02l5dPdjdx84X9CQSDccdZNLmEh9/aDhyz0ay0mlk4uYSH3tzG+zuOxgiZEomaon/HCycXs+yDXSyeXMLDb2zlvai2EHFAsbt8+KQATo+EUavm091N5KQbSNKItHtDZaV6mYzc/crmmONUWM08MLWUYDC0iBC5xndvO5+b/7aJZqeX2eV5+KQAK64eQkbSMUFGmkHDgKxkLiu0hMp4GTS0u/3kZSbJ+/JLQdL0Gn7z1nZmlPeTs2ajnV3MSVrSDBryLElckG8hWR9yWbn5+U0caHFRbjXzx6vKaPf6CSX8BjElaREEL25vQL6WnDQ9I/pkkJGkpax3Ov5AkJw0Ax6/xFGHB58/QKvLSx+TkT1HHbIri8snUW9zkZ1qwS358PpDGcUEQ/2STwpiUOu4K8G9WzCxmKueXs/44h7MruzPva8mvr9JJ2eOeUpodHjl8m2V+RY8Pj95ZiOf7m2mt8nAsg9rYu7JmIJM7ri0ALvbj8cvkWbQoNMIaAQVKuCrAy0k6zVYkpTMaQWFE4UlWctn+5rJNulI1ar55xeH0ag1cc4GFVYz88cW8Nm+Zm6/tIA2t4+7xxXy3vZ6jFqRstx0Vn9dx9cHWnn4ikFKcFNB4QSjEuCXF+ejFUEKQku7D1EUaHX64kqORlzaot3mvqxtYf7YAn725KcM7WPigSklPPL2dt7cUi8fozLfwuLJxTQ6PBxsdmFO1nL7S1/FzWNvXrWJ2RV5nNvbhN3tw+sPYEnWEtCr0bl8XH5uLywVecqzXOEHi1Yl0L9HGmtrGuV5h8MrccTmon+PdLQqAU9YYK+goKDwQyfdqOG5T/YlTOSrzLcgBAWKctJ4YcN+fnXpQOrtHmxOHz5dAItWJFkr0u6T6JGs4c6LC/AD6mAQv0ogGAwiCiJ/eH+77FQc7UycoldjTtZRek4a5mQddrefczIMtDp9ZCVp+fnIXIJCaM2g0eGNS2IMJZm2xJXY+eUl+Yzom8HyDjGRSInqrw60Mn1kH7RqFVkpuoT3ZWXVPsaXZHP3uMKQG4tORKdW0dzu4XCLi+Uf7OblG8/j5ys2MCQ3Hb1GRYXVjFoUWPTG1lA51jc38fUhuxyrVuZsp46GNg9pBs1JE6IADOyRwqd74tc5TgXfHGnj3PCa16kkI0lLm8ePw+OXHfgVFBQUziYUZ5Tvie9DxV5d28LlT3wi/33HZQPISNLx/rYjDM8zc8HAzFAdO0Fg86FWgkEY0TcDTXiQmqQJDXIdbh/mJB0eKUCby0+KXg0CjP19bJmQSEmOjkH+SKDwrpe/Zv7YAob1McWV4YkOJg7tY2LplGKCQahv80AQ0owaCEKQIHU2N298dYhJg3uh14iIgoDLJ5GiFwkEgxi0atkJoD5c0mNgzxQ5w7XcamZiaQ5ZqTp6puqZsCzeIjDCG/PKaWzz8nTVnjghxpIpxTy4ZjvvbW+IeX3GeX3loGhHXr2pnBZnyD4uuvxNR7cTnVpFZoqOK59cz7JpZfTOMPLIW9sZ0sfERQN70Ob2YdSK6LUibS4frS4fWSl6GtrcqAhl6EdU9uNKenDE5uGZdXv59eWlXPaHtZ1+T0unlDAxfD9evamcxau3xk1iImKX/pYk+lhis/ttTi+NDq/sDpDIotrm9DLvhWo27o8vK1RvdzO4dxo/+dP6uPciGQOv3Vx+2lr8njRnlPo2Hn5rO0VRJbTOMRnYfNDG/727k//7ybloNUI4G+KYK0ZOqo4Ne5vJSNbJ7h6NbW5G5GXEiLKMWpEFEwo5N9dEbbNTFjs0OrxYkrVo1SqOtnnoZ0nC7Q/I2Q8GjYpDLS4yU/VIgSDBYFBuX0atyPLpZTxTtTfOwWfh5BLUAtg9fp6p2kOvjKQ4xxUIldj6+mCrXOamLDedW8bk0zNVxye7myg5J40Gu0d2eYl2INGLKnLNxhjXDwCDWoU3GKDZ4cUvhTI7DBqRdq+EzeWjjzkJrz+AwxO6Rq1aBUFY9HpsHxYRZPzlk72s/GQ/lmQtL14/isY2D0k6NS6PhCVFh9cfwObyyaVcclL1vLW1noKeqfQyGVgctd/I91ByThqHW930Sjdg0IjYXB7SjbpjZbs6nIvcP63eFiPqKbeaufVH+fRI0XNPWFDSXSmkyLXNKs+jf2YST6/dwzWV/XD5AvJ9NGhUrFi7h+vO739GOn4cb7mPdIMGAWjzeDEZdLgkCZ0o4gsE+GxPM1nhALxRI5KVquPXb26P+14emFLCUYcbrVpk9rOfU5SdqixuKyh8B473eVvX6gr1lYA/CEvXbKMgO1UeX6QbNKQZ1Bx1eHlq7Z4EAVQrbl9ALpXW0e1NQUHh23E8bXffUUdorJOk575XEiclNLa5qepCmBJxinO4Q9nBRo2K/1nxGQdaXPK+IuKViKD6pousCcsgxm5n4YGpJXFzIAWFs5mu2u2uBjsEhbiEoEhpLIQgh5rd9M4w0j/r9HQ3VVA4G1GcUU5fDre64hL5Kq1mFkwqZm9jOyk6Ndnpet7aUsfGfS0U5aQxoTQbS7gc+l67m16peiSgtd2JKcmIliABBLyAyychEHLL7Rh7r8y3sGBCEXub2jEnaUnWiTS1+8g1GRCBvS0uRJXA7Gc/jxsPdeYAbknWsnLmcLYetpOVEl1y2sOwPBNvbq7jqwOtFOWkkZOm563NR2JcsCNrCR3LDlVaLfzqsoFc89znFPZMYWjfDDbua2bp1FIa21zY3BLJWpEknYYth1o5r7+ZCx79CIh36FY4udz7ymbW1TTy6/836KQd8+OdR/njR7vZtOCSk1IaqDPaPX6KF77DDRf054IBmafsPAC219lZsnob7912Ptas03MdRUFBQeG/QRGjfE98HxOH3Q0OfvR/H8l/G7UiT/18KD3S4hc8V8wYxhMf1sQtrs6uyENA4OmqPTGOJklaNU6fJJe8iAxSI4vHlxb1oNHhISfdgEalos7uwusPYtSqePyDmjglNXQI9OVbWDSpmKMOD1pRRasrlJUuiiqSdSIGjTqhY8q8MVZmPnNs0Byd1R9dN3zFjGHoNSJatYqf/OnTTu/hihnDqLe7ueeVLXHvVVrNPHh5KYdb3ahUAklaEY1axW/e2sF7OxoSbG9hwaQipj6+DqdX6rL8TeReVNe2UJZrYmSfDLLS9CxdvZVNHZxlzjEZ2LS/haVrtsdNFiqsZuaOsbL3aDvlVgtpBk2nQpCGNg/9M5P4w/u7WFfTxC8vySczWSdnOEULQob1Mf1XA/tEk69IEPmbI3bW726OmZzI9zDfwvLTeEJxsib8e4462NPYHiMoMmpDTkej+mUQCMLC1+OFRDq1Cq2oAiGkVO+Rog+7YkjoNWpZcNLHHFoY9wdCQfwUvRqn109GspbP9jTLvwm9RuSIzcWwvhk8/NZ21u9p5qmfD0UUBdlR6WdPrpfPL6HVeQB8gQBOj5/DrS7O62/B5ffzztZ6SnulsaJqb9x1qFUCGlHFB980sLehjZvH5PPQm9tjHI4ik/mH39rOrnoHf5szgjq7u4NAx0dOqgGH18eDb+6gurZVFmp9c6SNVdeOYuO+2Ovtqg+rsJp56PJSzv/tf/jr7BHkmPRs2NNMzzQDRo1IgCCfRC2chAQsJfz27R3k90zli33NMf1yZ6Kx+ycW88jbITFSZ31IdBmlaCqtFsaV9uSeV7ZgSdby/DWjWBIWnUV/NtJvRgQpb8wrx6BVoRbETp1R/EEJa+aZF+g63pIBN/7tC574eRlaVciB61eX5JGmN+IOBFm/p4mcNANpRg06tUidzRVT+irSN1dazQwOZ23fflkB059a/1/3pwoKP0S+zfN2Z70dk6jChcC63Y1kdXgGeaUA/kAgpn+OUJlvYVS/DH77zk75tfdvu0BZUFNQ+I4cT9vd1WDHIIrc3UGIEuHiwizun1iEze2LS0CIjFuemTmcWc9+Ln8mMrc81OLis/3NcluPXliptFoYX9qTu6PmfdEilyG56dxxWQECQQb1Nn2ft0VB4bSmq3a784id37y9g4JwkkR0OdMdh+3cOXYgf/poNwsnFStjXQWFk4giRjm9iU7kS9KpUasE9jc5kYJBOe5alpvOrPI8Xtiwn4WTitEBQkCi2Q8Grch9r2zmoctLcQckDCqRNq9Eu9tHZrIOp1/ioTXbE46jKq1m7hhbgEZUoVereLn6EBNKs2XXlSaHl0/3NMXFkp64akhciZ1I3GrVhv1yspzHHyDNoCFFr2b2s59T2DOV2y8byNxVm7hi6DlcUtiDI3a3HC/RiAKf7W3uNDY+cVAOw/tm4AsE8PkDPFu1lwmDc5j3QjWP/mQwOrWKlev28sCUEiYsq5Lncsqc7dRx7V82crTNc1LL1DQ5PMx9oZo/XjWEcaXZJ+24HfnyQCtTH1/Hg1NL6Jd5an9/DXY3t/79S/4yewTnn2JhjIKCgsKJQPF8Oo2wJGs5P98iL/g7vRKf729h477dVEUt7l5a1IPfvr0jbpBaVdOEAIwrzY5ZpI0ekJZbzTw2rYy7Xv5adiDw+ANoRBXBIFz73EauGHoOFw7IwicGSNarEw6GIVRuZHa49MjaXY20OL3MDgcRF08qJq+vCadPwuGW8PqDDO2bwRe1rfJAc234Ov9x/Wje3nqElVV7qappYunqrTxyxSA8/mOlLjz+AB5/gJy07rPRe6TqE76+tqaJNo8flQoMGhWiKCBJAeaPK8DjD8SIKSLlUw63uOTz7apUzazyPFZt2C8HPwF2N7QxqzyP+eOO1exssHtYUbWHGy6wMiQ3PWFtTY0K+lmSyQpfR6ScTvT3GCmnY9SKPDi1lAWvbeHPH+3hsWllrOywGJ6o9M63JSfdwLJpZTS0eahtdgJQfaBVvtbl08uAYMxv5fs47tmCOUnLr9/cTlmuidnleTFiIYICC1/fIgs4Omu3EUHD+j3NPD59CLsaQiVdVCrQqUUWvpbI+aOEtbuO8taW+hhxSEu7l9suGYhWraLN7cPjD/DO1nqG5B4L1M+uyOOv6/eFsyEMnGM28MDqbXGCsuF5ZgQEpg7uxdE2N3ePK+RomxuVIPDk2j3ydViStSyfXsaE0mxsLh/3jC9Eo1axv8mJKjyp/f2733DVyD7kpBtocHgJBOGdrfVyWaKeqXqOtLlJ1WuYXZ7HN1Y7L39xgNnleeSkG7A5feRZkmIs4VddO7LTPqyqpon2cPvuk2HkkM3Fms11ce378elD+OpgK6W90th9tJ0bL8pHIwo8+fGemPu1asN++TuOLsdzqMXJzRflA8G4AEH0ucyu6Bf3+tqaRmaW9wXgkSsGxQlRIp+N9JuRBRqHWyJdr4kr4xPZ/r5XN/Pry0sTnsvZgCVZS880PQ02D3/8z26G5WVg0hvxAwtf28IX4Wfko//+Ju77XnXtKFrbvTh9UqjWc7KWlVV7+d93vmF2RR7LP6ih0eFV+jYFhRNEMAg+QeDe8MJ25Nn4yNs7um2vm2pbGN3PHLO/NrfvZF+CgsIPCp0o4vBJMQLdiIjeHwiSZ0mS23OEcquZh68YJDujZKfreevWCty+AHaXn8/3N/PrN7dTck5IyPvYtDJueaE6Zn64tqaRu8YV8NrN5Xj9AUSVgFoUqLO5eerqYfKiyt+vG33S74mCwulKUIDpo/qwsip2vllpNTOrIo+gALddMlAZ5yooKChEkWaMdZE+anfT15LEoVYXRdmpLJtWRvWBVl7YsJ+7xxehAaSAm/agliStgMMbGic5/RIGUeSzfc0Myk2nZ4oeP0G8/mCncau1NU3cJQgIgNsfYGXVXs7PtyAIQfSiyI46u5z8GHPOYXfhaGZX5MkJVB3L2FdazaycOZx/b6vnTx/V8PSM4SxZvZXfv7fr2DZhx7tzzzFRlmuKSwxYV9PELy8ewOTlVTFOePPC4z2dWoXHH2BdTRP3v7ZVjq+AMmc7lRyxuTGfZHcSc3LI/f7TPU2nVIyy80gbAtDLdOrdjyMOMUds7lN8JgoKCgonBkWMchqRZtTy0OWloayysFCjtFcav39vV8widVnv9LhBanTQL0mr5q9zRvLNETvVta0x262raUIF/P260WzY2yQPCOHYwPORt7bLg80nrhrS5TlHBwRtLh+PTx+CSgCtWsV9r22JLZVjtbB8ehlzV1XHCFJmlXuorm2JWfBN0WvooRWZO8bKyqq96NShOpgqVeg8O3NqqT7QSlF251kEDrcflQDtHokn39tJUU4aw3JN3HbpAO4aV4DLJ+GXglTtbuSWF6qZXZFHudXMupomnF5Jfm1ORT90ahUZSVrcPgmb00dRTpocUI0IV579ZJ/8XUJIoHHTRVZmrPyMxVOKuXt8YdjJQoM+XG4ps4OYJiIE6aycTppRy/Lw++0eHw9NLcUrBWj3+DstvfNdiEy+knTqOJeUFzbU8vAVg3D7Al2W/PmhkmbUsnhKSUJR0YTSbLmdRE8Mo1kXFhucm2ticO9QvdWIaGLuGCvPfbKvE8HBFmaV5/HRzkbZ5cTtk/D4A7SHa4Ofk25k04HQ4p1GVPHCtSNZt7uJkX0yOLd3Os+s2wtAdVW8o8faXY0sfH0LZbkmvqpt4f5Jxby7/QjnnmNCVAlcU9GPslwTr1UfYsXM4SwN20FH+qvz+plJM2hwevzkmgxMOTeHjXubAeQsvVyTgb/MGcH9r26JswBdOrWYS4uy48relFvNLJ9exuZDNtTd1Dx1ePzMHWPl/7N35vFRVWfj/87c2ZNMEmbYJRCYIJAABnFNokK1bojwWt9X9H3LZm2rqN3ealsVULT6tv11kfp2E7Wt2M3XfWlVbAUUFYmyKxEkIEvINpPJLHe23x+TO8xklgQIJIHn+/nwIXPvueeec+59zj3nOc95Hl84QjAU5c7Lxic8zgwsMGNS9ARCYc4uHZCyC7+mzMkzN5/PAXeQ93c3p7SXJv8Pvrotpc0enTs1Z1kMSuayav3sILs54+4TiD/vOy4/vIsh36LgVSN8fKCNR+dOZZDdnLYb2XsSx4IvtJl48JpJ7G5qZ3VdE9+/chwqcRecqzvk5rG1u6itb2XRdFfKztBt+9w0+9SEZ4WaDiPO256qTRgGZVOUaDumPIF4SCRnnvSDgnCkRGPQlrSwrX0bs8lrgzfIT1+Lj1urXA5mnTEcm0lJjDULLOmK2GyIDAvCkRONxmjzh7GZFG6ZNoZLy4fgD0VoD0bINxtY/1kzH2SYk0Jcvj+qb8VmVNjT5OORtz5lflUpW/e5uf6ckQywGRPz0gXVpYk5oYY/FGFDfTPFVhPjhtnZ2+LHpOh5Z2dTwjukM19kWBASxGDlut2cUVLM/I5NEpox58p19fz3ZafjVcO9XUpBEIQ+zUC7hWBTO4oeRgyw4g1EuLR8MNUuB2ZFRygaQ6ezYNPFUKMx2vxx/YFZr+eQ18/UkmLCADpo9ASI6fQ573fAE6DUmUeTN4hPjVBgMZJnUIjGYkwbN4hDbUEeuWFKoj/fus9NgcWQ0GdrVI4oyrpBanVdEws7PNYtrB7N3hYfCzt0epoeLFkHmGwsnGyQEonGEh5QtE2MmmFK7Z7WhOFM8sYrOLI5m9Cz7Hf7GTv4xHsFmTDMztoMXtZPJBvqWzit2IrZoPRqOQAMip4im5F9bn/XiQVBEPohYozSh9jX6mfJC1uYPKKIeeePIhiOkm+OP6LkReobzhmZcl0ubwqZBoar65r4rKmdVzbtTzEOWV3XxNIX4gver3dYSHdW+HXmtGIrK+adxYb6FoptRuoavESiMV7s5F0gft/4AONPN53L3hZ/YpCcb1b4+EAbFqOe9Z81p1hdV7kcPDp3KnuafdS3+Jk4vJC7Z5SztJN3gGSXzA93eC/JRLsaYdPnrVwybjB3XTEBfziCLxjBalLwBkJs2NPCRWMH81GHBxdtUVlHfLHXp0ZYvqoucb9/bD3A+7uaUzycQNyrzZ/fq+fH107GGwinGGj41AilzjwWPH44dqfmRaSzIYpGZyv8Iz3fk3RlHCNkJlu7fXywLZEm18RwTV0T86tKGVhgTtkd3tU1d1w+jq9eODrFgEWjyuVg6cxynninNUXuql0OZlcO5/6XtiY8IGW7x9q6Ju68fDwAP3v9E/770tNp9Koc8gYxGxSGFVp4bMFZLH1uS8oO9879VY3LwZSRxby27SCrth9KGKxcPG4wOw+1M796NJOTJ8F1jbzzaTMvbdqXJn9xozsdl08cgjeY2+Ai32xgWKGF//zduzx4zaQ0A5Iql4Ov1IyGGFxePoQal5M8c9yg42evf8KYQQXU1rdwTeXwhAGeZuiQzXAkG8W2+OQ72bgwGI5SMsAWN5jpVJfO6fQ6HYumu+KLs54gRVZD1rA+T954Lu1B9YjK198YVmTlQMckLt9kpKHNj06nZ9F0F5eWD07075m+nfddXcFjaz+j0auyuq6JKPHvsGYYlElRsq/Vzx1Pb0wzQHzwmkkMK+r9XRaC0F9oD8YXwbQ+7rLyITnldfFV5fz6XzvxqRHWdoxltV12F5Q5u70QLTIsCEeHoteRb46Hd7QY9YnQkxrZ5qRr65r47y+O48ySYu59YQt3zyjv2Eixi8qSYh5bu4u7rpyQSHvzRS7e2Zk6trKaFMYOtjMgz8RPX/skZZeveGkUhAzo4LpzRmb8ns6vKgUdeHxijCIIgtAVpznyMBoVWtpVFH0Eg17P0A5v3opeF/f2GINwDAo6vJTEAEeeBfTQ6AlitxqJxHTYLYbEZszO4dw1Fj+3hcUzy6l2OcgzKbSrYfJMBvyhCFajnu/89aP4ximXk29fejq3PLmBxTPLgcNGwMkbSjtjMykMLbJQu6Yl59rC2romvlIzOpEm2bsJgN16WK916YQhGBUdH+1p5fpzRrLy3d0p99TKcyRzNqFnUcNRmrwqjjzzCb/3xOGFrNrewL5Wf6/Nt9/+tInxOTY1n2gceSb2t4pnFEEQTk7EGKWP4PapCQX469saEse1nfTJC86dDUQyeVOwmRQqS4qxGhV+9Z9nEo7G2PR5K7FY/GNvMxmYXz2ag+4At0wbk9gBvqauibtmTOCLEwbT3K6i1+kSnhI6D4qrXA7+sfUgy1fVUe1ycEXFECpLitnT4s+6CLu6rpF5baMScSurXA6unjyMH39pMsvfrMvoEUKPju9eNo4lL2zl4TmVvLJlP1dOHJoW7uS2p2o5c2QxDZ7MH+0al4MN9fFB9U9f25FQUJYPsyaeQbHNTHswxLJZFQnvInarkZ/8+xkJo5I8swGTosftVxk9aRjXVJ7G95/ZlOIp5IIyJ/deXcFgu4XBncY0hTb6vTHHiTR+OZnI1G72pEXtXBPD5PPJctLVNd5AhGmnD0oLb6Dls+T5uAHaG0nK+zUdxyePKGLV9kMEw9E0wwfNmGzFml3safaxdZ+bOy4bz93PpnspOWe0I7EjNpv3l9VJZdFCEe13+2lqDybuN6zQwi+vn8ItKzfgUyMMspvZkGGnvFaueVWjiMViVLscaQYrEDfK2NcaD83zu7ln8Ys3PsnYRlajwo01pRzwBBJeZRo8Ab55yekcbA2wfFUd+92BLg2Eave0pu1M0ahxObAaFb55SRkThxfy6Jp0BfG/VQ7HmW/iurNLmFpSzNAiC8te3MryVXUpYZguGjsw3j4FFha/kD2sz/0ncZgejUJrXN5C0Rh5FiNmRaG2voUzRhTxp5vORdHpWDStjO9dPj7hMWZtXROLn0sNe6S5mzUb9Tw6dyrRWAy373ConuRveDJv7Wjkzqc38vCcSukzBaGb5JsNoCMRz/zyiiH8+avn4gtGuPOycYCOVR8f5Nf/2hn3HPbC1hQl6Oodjdz+hTI+3u/h3qsruiV7IsOCcPQEoxFsZgP73a28tGl/wovR1JJiCm1GDIoejz/EHxeew6qPG1LmlE3tQXQ6eHtnM3c9t5mbLhjNz17fkTCEjsUO30fR61ixZlfid3zhwozFoNAeDLF4Rjnfu7znvUMKwklFjESY0zsvH5fiOfFvH+zhGxePxW4RNaEgCEJ3iOt8D28q/LTBS4tP5Uu/egebSeH/bj6fy362mn9++0JqypxEY2DU6whHYzz8xo40778rv3IuNz7xPo3ewxuHNI8iq+saCYYi3D97Iv5wBNDhCYRR9DoG2Ez85j/P5KY/fsDqukaixLi6cnjCu7emPy915mWty4LqUpa9GN+Q5sw38dA1kxIedu1WA3+66VwWPB4vm81kwGZSEpvXNKpdDl7atD8xL6tyObh1WhnTTh/E//6zjuvPGZkI9w7x9Q0xHu5dDnoCcSOpXjAGqhhWiF4Hq3cc4j/OKjnh99/X6qe+2cc1U0474ffOxoA8k3hGEQThpEVmmX2ERq+apgCHw4uXyQvOnRc0Oy98ZvI8YDMpPDp3Ko+8WZfmeeTemRX88s1P8akRbCYFHaQtXFd3soSucTm5a8Z4DriDCevt+1/exoyJQxlebMtZ1+S6rK1r4qFXtnHXjAksqCrlhnNGpiwmax4Q5ne4ImzwBNi01811Z5ewotOCdrXLwX2zKvCr4bQF35oyJz+cHQ9fc/G4QRkVlF0ZWHQ2KoHDg/gjNS4RYw5BozjPlDCW6MoTkdmgxxeMpIXl0rwTZdpFkW9RiMXIaiCmeVzpzOodjcw7fxQANmNu70sGnY4JwwrTPHBAh8HLc1v4803n8fetB5haUtyl95dcnlwWTXPx1QtH89PXdhCOxnKWy24xoOh1LKguRQedJvrxuOh6dB1Gbx8ztbSYCcMKUwxbNu5tZfJpRfxyVV2akc0oZx5DiuOKh1b/4bAt2QyEtJ39+k5lqXI5WFA9mhc37ueisYNoag+muUPVDCT+sOBslr28DTgcOimrt5kyJ3PPH8W6nc1p74Xm6elkx5lvoqbMiV+Ne8C657nNfHygjTED87jr2c1p35AnbzyXG363jtV1jXz38tNT8nL7Qyz8VapHK81jQrZvOMQXsxu9qvT5gtAN3D4VNRKlyGrkqXd385/njuTBl7el9ZmLprmYNLyIW1ZuSHPxDHF5XXxVOUO7ucNKZFgQjh6LXqE9FGGw3UJtfWvCkOyMEUX8+B8fd+kl5bE1uxJGKLd/oYyfvb4jMZbyBg97aPCpkcQ12sJFJsN/QRByoINvXHx6Rs+Jd88oB12MYvneCYIgHBXOfBMb6luocTmZXFLEvpb4ovKeZj/3XV1BuxpGb1T44LNmbr94LLdMi5FnVtCM7R9+4xN+v+Bs8kwKvlAUjz8eOtRq1PPWxw34QhHqGtoYO8SONxAmHI1RZDPiDUVwFJgSBvqakYjm3RviY6cfXTuZC8qcKZspNc4b7WD5qjqc+aacHnZv+N068kxKYuymjdmqXQ7mdXgs19CunzFxGF86cwS3Jo3/asqcuAbmi9F/L7PfHd/QOyDvxD+DfIuBMQPz+dcnvWOM8van8fdz3NCCE37vbDjyzNQ1eHu7GIIgCMcFMUbpI3gCoYzHtcVLm1FJOwbxgZ028NMWqC8aOzC+aJa0mLmgujSr55Elzx92Z55sCZ3MmromdDod/3fz+TR5Vd7Z2cTsR95OxH3UlIoLqkop7mIAk7zgbjMpXHfOyIzeFJIVlYFQhJoyJxeUDeTC0wex+Ll4jErNurvIamSkw8bwYhtun8oDsybSrobxqREKrUYGFZiP6+BWjEuEo2Ww3cIDsyfy/Wc25fScUd2xG2L66QNzGmDc+fRGrju7JBGHVYcOg16HzaRkNT7IZjyhHY/EYhm9mayta0IHzK8q7SL2bCPz2gLU1rdw1aShXZYllycXgO9dNp5QJMaYgXksfT6zAQzAsqsraFcj5JtifO+K8dyuRjjkDWJS9Bz0BNChIxiJ1/GD+hbuvmoCS1/YkhY+6JzSAQnPLp3vcd/VFUBqv5bNqMinRrjtqVr+dNO53B6O4u4wYNm8z02MGO9nCFOW3A+urmukxRdKC52U1dvMjkaisVia21SNtizfnZOJQpuJZVdX0OgNoig6Vtc18ejcqdz17ObEzu0Urzq7m/nxlyYz7/H38QZyG+ske0zI9g3XOBXaWhB6gkavSrNXxWTQM35YISvW7ErzbFVb38quxnYmDC1MeP8rthnTvi3ff2ZTt5WbIsOCcPS0hyO0B8MMyDPx+wVn4/aHuGVaGR8f8HSE3TmMNlZZUF1KbX1Lx07fJr57+Th+89ZOwtG4KxRtLKV0DKlqypyMdtp49ubzxeuJIBwDJr2e+17amqJL0TYD/c+r27jrygkpu/wFQRCE7lNoM1HtclIywEYkGsMfis9N3t3dTLMvyGnFNmxGhec/2pfV2L4tEOLDPa18/5nNifPxDaJTCEXDeAMm/GoYm1khGovRHgwTi0GexZDQA0Kqni/ZiPfBayZx59MbEwYpNpPCXVeOp8hq5JEbplAywMYHu5vTxnCah93l11eiRqJUu5z87PUdjHLYeOX2GgKhCB5/mAXVpSkb5ZK9p2jHtNDII3N4ahFODPs7vHD0RpgegEmnFfH3LQdQw1FMXWzQ7Gmerf2c04cUpHgs720G5JnY7/YTi8XQ6XS9XRxBEIQeRYxR+gjZPnza4uXfb6/h4vGDGDfUTuWIItRIlG9dcjp3XKZD0Wf2hgJxxd2zN1dxyBPIvVDcsaNUs4TOmG5HI81elRt+927K8WSlohqJoujIGhZDcy+okW0RNTnP5avqGO3MY3nSgsKPr52c1ROJGIYI/Y0SRx4/+fczaPWpzDpjOEtf2JKyQ7vK5eCeq8p58JVtTCkpyiozZoOe3809ix/9fXtaP9B5F2oy2YwnCjtivep0urQwYMkhewbbLYQi0S6NTNbWNbHsxW0smj6G0wfbEy4/NdfQdzy9MVGWbJ5c1tY1oSg6autbqBxRlDKB75xuvzvA9Un9lRYLXduNUeVycMdl44B4X3NfhpA2q+uaiJIeB1e7h7+jvrV7Wqkpc7J6R2NOo6IzRxazZZ+HQQXmRAiYRdNdPLqm634QDntgSVYs5DIE6uw2NZmCPjThOp60+lUG5JlobFexmRRKnTYWXzUBg17P4uc2pxl13Xd1BTaTQr5FSTme/O3S0DwmdDV5PVXaWhCOFU8gxPr6Fi6yDGRqSTEAC6pHY1R0OPPNGA06iOlY+vzmFAVtjevwd66ypIjaPa1H5NFEZFgQjoEY2K1Glj6/JdULXZmT5xdVEwpH2OeOj/MavSpr65q4+SIXZ4woSuyebfAEWVBdis2kJL65Na74uEpbQOmupyNBELITjESZd34pv33r07T54ldqRicM9QVBEISjY2iRFZtJYUeDl7d3NlHlcrBizS5+ef0UivNM3PPc5jQ9lqb7uXLiUCpLihk3JB4eONlg8L4Xt7DkqnKG2ONeIPPMBox6BTWkYjMrWBQ9znxTQi832pmX0Yh3WJE14d27PRiiwGrknmc7za06xnDBUJhoTEeTNwiALxSh0GoiEokRioSpKXPy0V433/u/TYlrM3nBC4ajDMgz8cgNUzAb9NTuacXjV0n2OC70DvtaA+SZFKwmpevEx4Gpo4p5esNe3t3VRE3ZwBN2389b/ayta+QrF4w+YffsDs58M+1q3LCr0CY6CEEQTi7EGKWP4Mw3cUGZk/W7W1IWeS1GhYOeAHarkXtmTOB7z2xKURpUuxzce3UF911dzuNZdsbf++IWHpg9EWe+KSXuZDLBcJQqlwOlC6vL5FAUyWgLnoVWI3q9jnlVpcRIXVDWFoKTXfZ1ZxG1psxJcd6RhdQRhP5GcqzZ5Ulhn/LMBkyKnraAyuIZ5fhDkayGGhOGFfKTv6d7FFm9o5FYFg8Z1VkW2WvKnBRYDFS5HARChw1MbCaFX14/JWE9D9DQFuSgO8Avr5/CLSs3ZDRIsXR4d9p2wMO9s8ozhkj5003n8o+tB8gzDcjZVvta/ayta+KGc0bmTNe5v+ps3LG2rgm1w6gjU7iz5L64ZEA8/FjnUEja31v3uVk6s5y7n9uc5r1Ko6bMyfyqUWz63I1rYH7i+JEYk2jGOskGRNk82+Q6X+1ykNdLk80TTb7ZSIs/RIHFwP/eMIVDbSo7D3l5sVMYKCARDmnpVROIxeDRuVOBuHy+vu1gRoOrtkCIUmdeVne3F5Q5cfZC/F1B6I/YLUZWrNnFjElDKbAYqF3TktI//nD2RF7etC9NgavFRr/7ygkMKbTw0d5WHp07laZ2FQ55ceblHjdq43CRYUE4coyKngde2srkkmLmdfK08MDLW5kwrJCP6ltY+ZVzuf6362j0qkRisTQj6fNGOzDodcyvKuVP79Wz9OpyAOafP0rmfYLQQyg6+P3buzLK6+/f+Yw7Lju960wEQRCEnBTaTBTbTEm6oV3csnIDf/nqeTk3VC2oKiUajWFQ9InNS3BYn94eivDAK9tZfFU54WgUo6JjaIGZXU0+Vr63my+fX8ovr5/C0x/soTjPhDcQjnuA1B0ul/Z/oc2E26fy8uYDzKsqZc45IxPfgxVrdrH4+bhH8o/qW1g0vQyfGuE7f/0InxqhxuVg6dUV3DrdxbzH3k+rB6RuqjIb9LQHw9z85IZEutlnDO+p5haOgQNuP4783vGKAjBygI2BBWb+seXgCTVG+e1bO7EYFc4tdZywe3aHgQXxZ7GnxUehrbCXSyMIgtCziDFKH6HQZuKhayaxu9nHw6t2pO1SqXE5uTcpfE7yQumuxnYmDCvk23/dmDHvNXVN7Gnx89A1k1IGs8n5jHLm8dULxpBvMbBouittwVUjmweF5PwCobg3lwXVpQnXr4MKzHxysC1N6djVIirA3PNHcdczm1h6dQXDZEeccAqQ2dgqvmOgtr4l63W5jBrWdOxC7dy33Hd1Bcte3JqStsblYPFV5cx/7D2WzCxnaJGVR26YgsWooCOGzaTnpU4L+Zpb0a9eOJqfvrYjJb9ql4NYLO52/aFrJqUZomjlW/L8Fm77Qhn6TkZxzvx4/6h5UimyGXl07lRM+tz9Uab+qrNxx6GOHR7JZPM0VeVysPz6SjZ97qZiWCHBcJQCi4Efzq7gvDFO2gIqC6pKsU1TsBoVHpg1ETUSpT0YpsBiJN9i4K5nNvHatgaqv+5M5NtdY5KaMmfCcCjZ+0pX/bLm4UajusPTTqtPpcRx8u9Eceab2NviY4DVSJ5ZQQ3HKB9eyPeSdv4k80F9C/fNKuedT5sYZLcQDEdpaAsyqMCc0eCqwGKk0GZKc3cLh93hyiKaIHQPZ76JqSOLMSt67nku/q1IHvMOLDDzvWc2pV1nMylUlhRzRkkRbn+Is0sH8PanTYnx7AVlTh68ZlLWcaTIsCAcPcFwhOvOGclja3clwsNWjiiickQRl5UPgVjcmPe+F7fyk2snM/ex9wmFo4lvaU2SJxSbUWF4kYXFMyYQjERpC4SJddxH5FAQjp0YcMO5o1I2FgAMK7RwZklxQt4EQRCEY0Ob12j68e9fMZ42fzjnNcFwlCafijPfnNDBacYhsIu7r5zA6h2NLH1+C2eOKmb9Z80smzWRUQ4b3710HD9ftYMql5M7LhvHW58cotSZRzgSw6cPsy8UD6s4rCO8faNXpcWnMtJhS5k3JXs2+ebFY1mxJq4Xu2ry0ISByeq6JhY/v4UzRxZnXDtI1rtVuRwc9ATY5w4kzouxf99hnzvAgLzeexY6nY6zRhbzyub9cQ/GyvEP1bNtv4c/vLOb/zhrRK95hMmGZoyyt8VPxXAxRhEE4eRCjFH6EDaTwi87dusns3pHI997ZhOTRxTx+raGjAulj9wwJWfebn+IkR07+5Pvl2nBtTqDSz1IXQjNxCC7GX8oklAudvYy8Is5lVSWFKXUr8ia2+VYodXIl1e8h0+NEAxv5OGkUD2CcCqSK5RAV0YNFqPCG9+6MBHeympSuP/FLYwbZmfOOSUEw9GEy8xwJMLimeWs6ORxqabMyc0XjUmLH6ul+cEV41OMUapcDuZVlSa8Lg2ym7N6dllT18QPrpyAjrhHCp1Ox/YDbi4eP4SlnULoVHfsxLi8YjCvbD6Ylle2sCqd28mk6KlyORhkP7wTIFf4MD06Lp84JMWwr8blYPTAfNbtauL9Xc08MHtiViOPpVdXEAxvZPWOxrj7+brGLo1JzAY9F5Q5eWD2RJa+sAUgxftKrrBAVS4HBRYDL9xahTcQId+i0OAJcv1v1/Hnm87Led+ThUKbiaFFFmI6+PnrO1hd15Tzm/nVC0dzwB1I85ySyeAqWYmS7O42Uwg5QRC6ptBm4n+umUSLP8TqDkOU5LFqJtnNZUCojWff2tHInU/nHkeKDAvC0aHT6Xhs7S5q61uzho3VZPEHV45n+riBiTFalcvB3VeVM+uXa7m8Yghf+vU7PHjNpLRxWFcGZYIgdI8YYDFm31ggxiiCIAg9Q7Kx+/JVdRj0Os4eldsLsNmgJxyJ4fGHEl5Ikuc0Wh+9uq6R2y8u42ev7+CuZzdx79UV6HRw8zQXoUgMbzDM8x9+nuKFpcrl4LbpLkLRGHc9uzktNLh2j2TPJm5/KHH8G5eUpXjQWL2jkXnnj8paF80D+6JpZQRCEZa9tA2IjwvvvbriCFtTOF7safYxvJfH1+e7nLy8+QBvf9rEBWOPr3eUA+4ANz6xnuHFVi6rGHJc73U02C0GzAY9e1t8vV0UQRCEHkeMUfoQjV6V1XXp7sEhdZCXaaG0O4uZPjVCTZkzMeDMtuC6JoNLvSqXg+9dPo7/99onGfOvcTl4Y1sDZ4woIhKNJRZZNXxq3FvK3TMmsOSq8hRPAdncole5HPzzk0MJg5i3djTS6FVlUUA4pckVSqA7xl1jBh0OD7PjYBsvbT4IHDbm0HagR2M6dMDC6tFUlhQndkms3tFINEvIn7V1TUQ7QpskG7bc9lQtP752MgDtgfRdE8m4/SGu+826xO8/3XROmiEKxPupxc9tZvHMcjyBcKrBjMvJ3KpRKSHBktH6yxqXg4EFZipLitm2z5Pot3J5mFld18i8qlGdjjURBe64bBw/fW0Hdz27OeuCp7bY2dSuMnPyMBY/vyWnMUmNy8mYgXmJ/DRjlrd2NCZ22FSPcTD7jOEseWFLikIhbjjk4rrfrEvbrVLtcpBvOTWGAG6fillRuOvZTQllTK5v5rTTB/HQq+nhrrTf2nPO5DFBQsgJwrGj0+toD8Z3DXYeq2aS3VwGhNr55avqujWOFBkWhCMnFovL26LproyymDx2/LzFz5KryjnUFmRKSTEb6lvY1djOmSVFvLb1INedXZIxj+4YlAmC0DVGvZ7lb6ZvgNJ+3ycLhIIgCD1GsrF7ezBEkc2UopdPJtmDyPljMhuHaHMkgHA0bpqypq6JYDiKTgfRaAy9TkcoGksLB7S2rokrJw5NeDbpfE67hxbSekFVKTqdjhVrdrKgupRINH0DXK4NcaXOPB6YNRGrSaHVr/Jw0maqK36xmqkji8XQuJeJxWLsbfFTWVLcq+UY7cxjWJGV/9uw95iMUQ56Ajy5bje7mnwMsZs5d7SDc0Y7yDcbUMNRXtm8n3tf3AoxWDKzHOMJ8MJypOh0OgYWmPm81d91YkEQhH7GqbES1U9o8ak5z2uDvEwLpV3tjK/d08qVE4dy5shi5p0/imA4yogB1pwhPe64fDwThtoTC8pfXvEev19wNoFQJGGwouU/t6qUle/uJhyNJXbsR4mllGfqyGIuGjuQoZ0GmpncomvxMDsvJrcFQjnbSBBOdnKFEhjpsGU1VMnkBrPVnypP3dld7lMjaaFukvEGw2nhwCC+gFjlcmC35TaYyTMrnX4bcnpSCYSiVJYUJ8ozrMhKKBLlZ69/ktFdqNYfav3Wdb9Zx5SSIq4+YxiVJcUsfWFLt8PmJJNcxq4WPJMXO5fPqeSAJ8BVk4ay7MVtKUZ88TKOIhg5fL9cO/eXdzpuUHTsPNSe5pFK81aTrMg4mWn0qgTCkRSFS65vpk5H1ndubV0Til7HG9+6UDwmCMJxoqVdJd8cn6J0HvNmkt1cBoSdv1cyjhSEnsfbMZ7orizudwf49JCXfe4Ay1fVsfLGc5hfXcqmz91cWj44ax6yMUEQjh1/KJJznOsP5d44IAiCIBwZnY3d7589kR88synNM4nmQWTVtvhmsdr6ljTjkEBSH21LCi/S5g+h0+kwG/UY9DoMih5nvolGb+o6w2C7Jc0QRSOTns+Zb6K2vpUFVaXkmw1pc6lhRVZsJiVN93ZBmZNhhRYKbSbcPpUPdrcyqMBMMBxlSkkxVMe9/Yqhce/S6gvhDYYZXGDuOvFxRKfTcUGZk//b8DmLr1IpPoqwQat3HOKm33+AXgclDhvrPm3it6t3odeBM9+Mxx8iEI4ydWQxC6tLKerD75wz38zeFjFGEQTh5EOMUfoIbp+K2mmBU/NQUDmiiGA4ysgBNhZNdyWsn5NJDtnQedFxfoehyBUVQ/jZ64fDZzw6d2rOMu1p9nHzkxsS5XjomknUN/u5e8YEItEYe1r8GPQ6ave08qf36rn+nJGJxWptx/6CqlKC4SijnXkM7RiIdiZ5cbXFp+L2hxLeFDoPaAtyhCgRhFOFXAYJ2QxVOntwAMjrFBsz1+5ys0Gf2MkQDEcZVGBm0XRXwmMKxPusYpsx4RlFi2+7fb8n4YGkwROg2uVIMWjTqHY5aPAEU455u/Ck4g2EWb6qjhqXk/nVo/jH1gNcPH4Qd1w2jlA4lmLcUeNycteMCUCMWWcMo8mr8revn4/HrzKnwxvLQ93YmZHNq0Zyf9XdBc9Cm4mdje3c8Lt3WVBdyryqURm9ytz/4rbErpFsO/c7H6+tb+GWlRtS+uLkfFfeeE63ytjf8QRCRKMxFk13Jb6nVqPCFycM5mevf8K6nc2Jby2AotOlvdvJ+NUI5cMkdqsgHC88gTCD7WYuHj+IAXmmlG/Kxr2t3Fg9Gjg83g2Go2lj5uT46skGhDKOFISeJ99swJlvYsQAWyIsYIHFwEFPgDue3piyEFK7p5Vql5NlL23j4TmV1JQ5GVpo4bMmH+/taqZsUEHOe4lBmSAcG75gBGe+iYeumcQguzlNXn1BMUYRBEE4npQMsPHQv03EHQjj8Yewdujl3vy4gU173Fx/7kgWrYzrwzsbh6zb1QzEdf3JFFiNRKMx2oJh9Looeh08ddPZXL38nRR9nTa3UiNRBhVYMCo69rsDGBU9G+pbUtYbBtnN/PS1T1hQHS+DXkdKKOwal5Nt+z2smHcWCx5/P3EfTf8I8GmDlxafykiHjbc/bUroWJI3vImhce9R3xwPBTPIbunlksQ9FD+9YS9/en8PX79ozBFdu2mvm4WPr6d8mJ1bprnIMxuIxWIc8ATYtr+NJm+QPLOB8mF2RmYJqd6XcOab2NMsYXoEQTj5EGOUPkKjV+XtnU2J3Z7ZPBRUuxzMnDQszfI42QDk+5ePZ3ezL7HouPLd3Vx/zkj0eh0r5p2FzaQQUCMMLbKkLRonL76ZDfqs5ahxObh7Rjn7Wv1cWj6YKSVF/HHd7sS1PjWSSH9BmbNLS2dtEdXtU7n1qdpue3YQhFOVbAYJOuDyiUOZe/5ho4aGtmB6BkCeyZCywzzbjlabSeH6c0by+NpdaTFnl19fyabP3UweXsSwIiv3vbglJU21y8GyWRP5n1e38fLmg3z/itNZMrOcJc9vSTFIqXY5uOeqcq7/7bqUe+dbUg1mOlPUYfwC8Md1u1m1/RChSIyt+9xMLilKM+74n1e3MX5YYaKe2gLmz6+rxKTo8QRCmBR9Wqix5DonT8CTMeh1ib+PZMHTbjGm9JmdMRv0R+Wevqt8T5VF2UKrER1x45zO37GlV1cQjcHS5zfn9AbUOT9BEI4fdosBI3DXlRO4Oym8FsTl9oqJQ/lK9Wi+f8U4djf5Gemw5vTqZdDF+2YZRwrC8cFm0POnm85l8fOpYRWrXQ7+/NXzmLfiPfa0+BlkN7NizS6mlBQnvq1LZ5YD8Ls1O3N63tM4VcYugnC8KMwzsPIr56aFQa12OVj5lXNBl77xSRAEQehZhhXbyPOpmBU9XjUcN9AY4yQWI2GIAqleebVxlOZF5c2PG4B4/61tmIoSQw1HUcNRLIqZR66fQiQWIxyNUerM474XtqTp9OZXlbJo5QbOLCni3yqH881Lyti0p5U3tjWwavshbjhnJIPsZnR6+NN79Ynr7poxntmPvM3UkcW8clsNLT41sVHOp0ZY9FRtmveXTKGHxNC490gYo/SyZxQAu9VI1RgnK9buYn7VKCzG3LpgjfZgmK8/+QEjBlj55iVjE6F3dDodQwutDC3sf2GgBuabWbezmVgshk6n6/oCQRCEfoIYo/QRPIFQineTypLijB4K1tQ1cd9LW7n7yvF875nNKed8aoQP61uoHFGE2aAnGI4mdnr/6b16vvGFsRRajbR1WF7f9+LWtHA72sCwsqSI2j2tWT0lrK5r4r4XtzKvahT/8ev44vEv5lSihqMpeWbzyJCNI/XsIAhCHLdPpaEtSH2zj8F2S5pxWSajsCKbkVunlwHxHeZqJHN4mlweU/TouHziENbXt1DbsZCQzJq6Ju56dhP3z56IJxBmSskArvvNOh66ZhJ3XD4ObyBCvkXBYlSY85t1aW5EGzzBnJ5UrEY9C59YnzCcC4ajib403YjOyb2zKrj/pa2JYz41Qm19C9UuJ2/vbOSnr+3gm5eUsWi6C4h1WgR1cst0Fwsefz+tLDUuJ2s6jFeOdMHTmW/KGl4p2fjlSN3T58r3VFqUzTMb+M5fPsz4HVu3s4mXN+1Pe786x0zWqClzYrca+bTBiycQwm414syTcD2C0JMU55mIhCJphigQl9ulL2zhyolDGVZk5Tt//YiHO/r7TN8oHTC/qlTGkYJwHIlCmiEKxMeAi5/bzIr5Z/HQy9t4Y1sDZ5YUsXFvKxD39NfoDWC3mhLX5gqjdyqNXQTheGFRFL73zKaM8nrvC1t4YPbEXiqZIAjCqUWhLR5GZ+bytVnTaEYmNWVOolF4eE4lBz0BAqEIv/7XTqpdDu6fPREDoEZjGHQ6tjV4qW/xc+XEoYwYYOOZDz/HoNfx+3c+yzhfgsN6j7uf28yMiUP578vGc8PvDm8U276/DUUHv/3yVJrbVWr3tNLgCeJTI7y1o5FwNMYZJcVAXDf53ac3phiiZLqXZoQshsa9x54WH3lmhTxz31genHnGMN7acYin3qtnfhcG6ho//sfHHGoL8tA1kxKGKP2dwYUWvMEwTe0qzvzeNxQSBEHoKfrG10ZI7GDXvJvkipe9ekcj373s9DRFXY3Lwb2zJnL/i1t4ffuhxPEql4OlM8t5adN+fvraDh6YXcErORbf7p4xgVGOPBY+8T4Pz6nMXo66RuZVjUosdv/pvXoeumYSgVA0LXTIkZArBIkgCOnsa/VzR6fJXmfPDpkMGQptJkYOsDFj0jAWVJUytDCza8ZsHlPgcD8w2G7JmmZNXRP1TT7mV5USi8U9QS18Yn1KmkXTXZw+pIDGTv3SHU9v5E83nZvRk8qSmRX4Q5FEX6j1n4umuTAb9PzgivH41AiHvEFMip6DngCRWJQvThjMnLNLUjzHDCow8+t/7QTg1//ayaThRVw+cSjzksLbNLerDCu0cGZJUdpO/fnVo1i0svaoFjyzGeFpu1Rue6o2cexIdo2IcV8cbyCcNS7yYLslo6ETpMdMrnE5uPfqcn7wzCZe39aQOH5BmTMRQkkQhGNnsN3Cxwc8XcYzv++lrSyoLkWn02VcuIYOg8grJ0gsckE4jgTC0ZwyqIai3HbxWH72+ifcPK2MQCjCxeMGkWdSMBrMNLYdNkTOFnr2VBu7CMLxol2N5JTX9gwhKgVBEITjQ3c2JlW7HNw3qwJ3e5DhxVZKnXk0e4M8/fXzyTcpiYWdBo8fRa9nYIGFJS9s5QKXE4tJ4aP6Fr57+Th+9vqOjGVI1ntof9/74hauO7uE5avqGGK3YNDr+PqTG3h4TiULn1hPtcvB1Wcc9tre6j88lmv0qmmGKJnuldwGQu+wp9nH4ILeD9GjMbTQygVlA/n5GzuYXTmcoi7G/Zv2unni7c+Yc3YJg/tAqKGeQvPm8lljuxijCIJwUiHGKH2E5AHo8lV1TBhqz5l+b4ufypJiFlaPxmZSyDMr/H3LQa791dtcd3YJc84ZmRKa4tND7VQMKwTiSv5cCv7vXjqO5naVypKiFJeAmbBbjPzta+dRaDUyqMDcYwrCbCFIBEFIxe1T0wxRILNnh0yGDEOLrFxRMSThkSTTbtSu+oGuzgPEdLDwifU8v6gq43lt8UGv06XU5fQhBaCD275QluJJpT0Y4aDbz+1//pDrzi5hQYfRyCiHjeFF1kTYr/3uAIe8QQqtRgYWmPms0ce4oXbUcJRD3iDDi6xMHVlMU7uaEmbslpUbWFBdmpjQDC+yUjG8kHtf2MLkkmLmVZWiRqIMzDdjMuhp9qo8d0vVUfeDmhHefneAnY3tib67c5iYI901IsZ9cc9j2ejq3c0zG3jkhikJo6X1n7WkGKIARxVCSRCE3Hj84Zzng+Eoq3c08rULx+D25zbS+7zVn9XYUhCEY6crQ1lPIEyRzciEYYUsfOJ9ppQU8cDsiYSiUf6++QAXjh2USJu8OUMb24125jG00CLfWEHoAdq6+GZKuARBEIQTR7YNRDUuJ3fNGM/+1gAAy17YyqLpo8k3KrSpEZK1GO3RMJGojlue+pBf/9eZ3LJyAz41QoHViNsfYnVdE/M8mUN3ayTrRYIdRsYLqkqpKXPS0Bbglo7QQcFwlCqXg3lVpTS2BRP6nPaHjQABAABJREFUxmAoitsX3/yWS//S+V6nFVtlfNeL7Gr0MbAPhOhJ5j/OGsG7u5p56NXt/PDfJmVNF4nGuOvZTYwYYOPyiqEnsITHn8H2+DPZ1djO1FEDerk0giAIPYcYo/QROg9ANVd82TAp+sQC8/RxA/nmJWMTvzN5J3h07tTEgK+rxbfPW/18568fsaC6lBEDcu/0LrIZKRtckDONIAjHjyPZdZDNkEEz/qqtb064Qkw2SCmy5jaA6Kq/Aii2xfMw6HUZDV58aoSV7+7mnqsmUN/kSzGm+39//5hrzhxBWyBMntlAgydI7Z7WRBii5D7vjW9dmJjMav8/8PK2rKFqkg0IknekJOerpWv0qry06UDWOibf+2joTnmPZtfIqW7cZ89hwNPVu9seDHPzkxu4oMzJvVdXcMUvVmdMd6QhlARByI3dmnuKosmuotdR2MU3ChD5FITjSK7vLEC+RaEtEE6Mq9bUNeEJhJnx8BqqXQ6+OGFISkjGTGMwkV9B6BkKuvhmSrgEQRCEE0u2jUmzH3k7ZWPS/OpSZj3yz7Trq1wOrpw4lG9dMpY3Pz6ET41Q7XKQZ1JoD+Y28NdI1osk/z33/FEJQxSAEQOsVJYUc9tTtfz42slUjiiiyuXg7Z1NFOeZ2NnYjtWkdOteF5Q5GXISebPoj3x6yEu1y9nbxUihyGZiztklrFi7i+njBnPJhMEZ0z3x9mds3OtmycxyFL3uBJfy+GI2KDjyTexqbO/togiCIPQoJ0cwtZMEbQD6xrcupGSAjZqyzAMCzVUfxONG3lQzhje2NVCTZQChpdcGfF0tvpkN+oQS8O9bDlLlcmRMV1PmZFAfs6AVhFON7u466I4hg9Vk4LanaqksKebRuVN55IYpPDp3KvkWA9VZ+gGtf6nd05q1r6hyOQhHYtS4HOSbDdw6zZWWtsrl4KaaMdTubmHhE+u5+ckNLHxiPRv3tHLn5eP503v1LHxiPWvqGlmxdhfLV9WlTMwz1dHtU2lqV1k8szytP+3s7l0zCLwgR7qu2jrXTkK3T+XTBi+19S18esiL26dmTNedcghHhuZ5LBMHPQFqcnzjSgbYeONbF/LwnErcfjXtnUtGdpIKQs9hUnTdGgf71AiKvuu0Ip+CcPwozjNlHSdWuxw0eILkd4pFry2OrKlr4sFXtnHv1RVpecjYRxB6njyTklNe87pYRBQEQRB6nkKbCX8oktCDZdJ3KR0bu5Kp6QjtvOylbZwxopg/vVdPtcvB/bMn8vu3P+PdXXFD39o9rd2aWyX/XWg1pnjqrXI5+PuWg4myaWsL86tKWbFmF7sa25n9yNu8uHF/l/pDGeP1Pp5AiENtcW/RfY2Lxw/irFHFfONPtWzb70k7v/2Ah/95dTuXTBjM2JN0g/RQu4XPmsQYRRCEkwvxjNLHSN7B/lAGV33a7uzm9iCXlg9m2z4P737WxBmnFXHV5GEsfm5zYlcZxAd686tKWfnu7sQxbdE4U6zgmjJnYuAJErdbEPo6Xe1GNRv03ZZXvU5HZUlRmnclZ76JP910Lkue35LSv9S4HNwyrYwFT7wPkLGv0Pogk0HH5JJifvjKNr5+kYsrJw5NuF/XQqAMspsZYjfz6jdq8PjjIWUKrUaGFVn58bWTafSqtAdDfGnKadzz3Oa0vjG5jvta/YnwRc58U7w/vWwc7WoYu9VIsc2UFlN0WJGVH107mZZ2FU8gjN1qSEnXVVtn20mYXJbk8j54zSSGZZj4SWidnkUz8On8DKpcDoYWWplfXUqU9Pf23qvLKXXmJ47lezMbEGnITlJB6DkUnZ7vXno6QJrczq8q5banaqlyOTjo9sdDqF1dzj3Pbk4JQ5mcdvYZw094HQThVGGw3cL9syfyg2c2pYwTq10O7rmqnJ+/9glfmnpayjXJu2bf2H6Imy4I8OC/TSIYjsrYRxCOI3pgycwKljy/OU1el8yskN1qgiAIvUSXnubMBr7zxdO583IdHn+YQquR17cdTBiM7Hf7+dvXzsegg8fX7uKSiqGs6zBGWbFmFy/eWs3dz23OqK/T5lba39UuB//85FCKIYp2Tvtdu6eVi8YO5Msr3ksxTkleR0jRH5Y5WTqzHICvVJfKGK+X+bTBC5BRJ9nb6HQ6vn6hi3tf3MKc367j0blTOXNkPFzNnmYfNz6xnsGFFq4/p6SXS3r8GGy3sPOQGKMIgnByIcYofZhsC5IA0WiMxc+nKt2d+SYen382Hn+IVn8o4dpv5bu7uf6ckYlB44o1u3h07lT0kKa0/84XT+fnb3ySOJYct/uWi1yYjXqKrCZRDgpCH0Hz+pAprEtNmRPXwPxuuzc36HUZw/SMH2JnX0uABdWlfP/KCbh9IfItCo1tQYLhCJUlRayta0r0FTdf5ELR6/CpETbUt/Cnd+upOK0wYeRy/VkjGWy3MMhupj0YodBqZKQjj2ZvEDUa4+1PmxIheKpdDh6YPZESR15KHXIZjbh9asLwwGZSePCaSaxYu4u1dU3YTAoLqks5f7SD+mYfdouBoo5r97f6+ecnhxhUYCYcjWE1Kux3B9jb7KMoz0S+xZC1rbN5nkkuSzJv7Wjkzqc3Zn023Q2t4/apNHpVPIEQdqsRZ176dd1Jc7IzrMjK/bMq+LzVj1HRYzUqbNzbyi0rNwAkvnGKXke7GuGg20+eKXWIlEvWjjaEkiAImQnFohRZjSydOYFAKMZ+tx+IG1Tf9lQtZ5YUsXhmOYFQhEferOO2i8dyxaRhzEsyctTSTh1ZLPIpCMeZkY48fjh7Ip5AGE8gTL5FocET5Oevfcztl5zO9b9dl0ibyZOeGomhRqKMGZSfdk4QhJ4jFgO9Lsai6S7uuHwc3kCEfEs8lINeFz8vCIIgnHic+SZqypwZw3DXuBxYjHrqm32JeY6mM9MoshlZ9uIW7rmqnPlVpXx95QZuv3gsAJUlRfjUMJUlxSyoKkWng2GFVkxKPM+H51SmzLO+c+k4AqEwF40dSDAc5Z2dTQmjl86bXrVjyZ4rNd3gXVdOIBCKiJFxH6SuwxhlaGHfDJVkNSn84IoJ/Pi1j7n2V+9wWcUQHHlmnv3wc/LMBr576emYDSevN7dhRVbW1jUSicZOujBEgiCcuogxSh8neUHS7VNp8YW4+9lNzKsqZXXSwurUkmIG5JswGnTkWwwUWI341TBXVAxleKElxbVeZUkRgVCUmZOHc/vFY3H7QwwrsvLSpv0sfOJ9HrwmvittbVLc7o/qW6ke4yQSjWEzKTKAFIQ+gub1IZMXpYeumYTNpNDoVdnZ2N6lIYIjz8QPX95GZUkx37pkLK2+eFiDkY48Zi5fg0+NsGi6i9r6lkT/oPVBC6tHk2dSiERjvL3zsDFJlcvBomllvPfZ4fSDCi3c92K6l5X51aUsWllLZUkRv5hTyW1P1bKmronvP7OJn/z7GQljk32tfu7420ZW12X2NNLoVRMT+AXVpTyWZIjyizmVPNYR5kdDM3g54Anw4sZ91Na38os5lTz46rYUo5xLxg9i2awK7no2t1eWZJLL0pm3djTS6FWP2uikXY106XHlSL2ynMz4QxHm/PZdXry1ii373FQML+KvXz0PbzCMotexpq6R37y1kzNHFrP4qvK0kHZdyZp8FwWh5zDqdXgCYSKxGD97/RPOHFnMtNMHk2828MWOuNEvbdrPpr1uvn/FeOaueI8fXTuZR96sE/kUhF7Cbo3v6NXrdbj9IYYWWfmv80dx/W/X0djhXUwbF775cUPKtUU2I14JpyUIx51IF9YmXZ0XBEEQjg+FNhP3XV3BD57dlOa9ZG5VKS9t2s/6XS0pejCNapeDUCTKdeeMxBeKUGBU+N85U/iksZ0ql4Nbp5WhI76gfWvH+sCjc6fy5Lu7+cbFY2kPhrmgbCBTSooZmG/mP37zDj41wuUVg/nWJadz3mgHE4baM2567ew1BeLrCMtX1XHxuEGcUVKcOC4bpfoOnx5qZ1CBGYux7xp05FsM3H3lBF7bepC1dYcIhN1UuZzMPmN4Yt5xslIywEYgHGV3UzujB4qxviAIJwdijNJP2Nfq51+fHOLFjftYW9fEnHNGJhZWV767mzNGFPGjV7eneDp5dO5Ubn5yAyvmncVvvzwVd5K3lD+s+4zvXzGBvc0+BhaYicZi1Na30OhVExbMWgiNQquRQCjCgifep7KkiBmThnFFxRAZMApCHyGbFyWfGmHRU7XdNkQotJlYenUFdz69kYvGDmThE+sBeOnW6oT3k86hu7RJZo3LyYLqUj7a20rliCJ+fO3kRH+z8In3ebjjmpsuGM2yToYooHlp0nHTBaP52es7gLghyfJVdaypa6KlXWWw3RL3NNLJEAXihh13PL2R5XMq8SQtZlSOOBx2KNkwJZk1dU384NnNXF4xhLV1TSya7sqY7rVt8YWTH107GW8g3C038p4uFlbaurHwksmg5If/NpGXN+7P2A6axxXgqLyynIy4fSoefxiAN7Y3cPaoATz46jZq61tZUF1K5YgiJg4v5K9fPQ+7xcDs/32bP990Xlr7SAglQTgxBMJRvMEw63Y18ZWa0Sx/s45fvvlpQl4BLh43mH87YzgLn1jPnhY/Cx5/n1duqyEcjYl8CkIv0OhVOeDxEwrHGGw3o9eBzWTgsfln0eILEY7EOOD2EwhF+PW/diauq3I5UHQ6bCe5UlkQ+gLBSBSPP3PoySZv8KRf3BEEQejLFNuMzJg0LCWkteax5NzRA/j2pacTJZZmrDKvqpSAGuWxtbu447JxhI0Keh24BuZx5cSh+JN0+trGr2A4yqrthwiGo1SWFHPxuMFsqG+h2uVM5P3K5oP8x9QSPtzbyhcnDKE9GOby8iHMPmM43mCIv37tXF7ZfDBlA2wyJoOe7Qc8KDodJkXPXc9uzrqpTDixfHKwjWFFfdMrSjKKXsdlFUO4rGJIbxflhFIywAbA9gNtYowiCMJJgxij9AO0MA/zzh+VGHCaDfrEwmplSXHGhVOIKwWv+826hPI+GI4mlPh7m33Me/x9HrlhCiMd1pTwHNribbKFs0+NsLauiQVVpd3ezS8Iwomhc1gXt0/lu0dhiKAttn9y0Hv4oC6W0j90DseTbzYQikS54XfvZpyAAgTDUQAuGjsoYWzSmdV1jdx+cRk/e31Hoq/R8ATihgQNbcGMO0EAVu9opKEtSL758KdNuy+kGqZkunbe+aO6TPfatgbuvDzcbTfyXcX9LejifLYwP4MKzFnbQfO4AvSIV5aTgVZfCIsp7unk1//ayaThRVw5cWhCyQPQ4AkyYoCV9lAkYWySie6GUBIE4eiJRuNeFrLJ60FPgBKHjQOeAF+sGMInq+rwqRFafGrK7jtBEE4crX6VIpuJA+4Al/9iDY/PO4v19S1UjihCjUQ5rdjKAbefW1ZuSIwXtbnmnhY/T67bfUoZygpCbxCNxfAGo+x3+xlstxAMR/Gpeg56AgwttJJvERWhIAhCb1FoM3Hh2IFpOqAql4PrzxnJjU+8z3Vnl3Dn5ePY0+xPMVb58bWTWVvXhE4Hn7f4GV5sxR+KsM8dYNlL2xI6fYhv1NI8wWq6t0A4QuWIIiLRWGJjGICi6Jh8WhE/fHlrygbYKpeD/750HFv3uTPqAatdDva7Ayx8Yj01Lgc3T3PxQX1LSppTcaNUX2HLPjfnjk4PnSn0DexWI8U2I9v3e7hi4tDeLo4gCEKP0O9nmvfffz8vvfQSH374ISaTidbW1pTzH330EQ8++CBr1qyhsbGRUaNG8bWvfY3bb789Y35r167lwgsvpKKigg8//PD4V6AbaGEe5pxdkjhWu6eV80Y7WL6qjgVVpRkXTmv3tFLlcqQYl2gkx+o2G/So4Rgr391NZUkxd1w2jr0tqYPa5IFlMBylqT1I6EAUvU6HQa/DIa71BKFPcSzhYQptJvLMh101JvcPyTs03tnZxNZ9biYMK6RyRFFWQxSI9zM1LgdqJJo1DUA4etg1dLIhib1DMdrqz+1JpNUfYkiBmRqXk9V1jSmhVpLzy4R2vqt03fFmAnFDEoNelzXu7wVlTpz5ufvNbM+xO2Xsysl2d+txMtCuhonFSDyLW1ZuYEF1aSL0k8WoMMhuZl9LAFvHu9+VoZAgCMePGPFdUGeOLE6TV42XN+1n2umDmJJkfCJyKwi9h81kIHnwsb6+hdr6lsQ8VAvtqHlvK7Qa+ecnh7jtqVoenlN5yhnKCkJvYFIU/rDuMyYMK0z5ru5zB3h920F+cMWEXiydIAiCMKzIyo+vncynDV5akzyca7r52g6Djs56fk331eIL4Q2EKc4z4Q2E09KtrWvi5otcvLPzsGFJMBxlYIFCezDMOzubEptYARSdjsFFFm6sGcPkkuJESO61dU3odR9z39XlhMKb0wxV5lWVoujioYFW1zURhRQjFw0Z/514DrUFOegJUurI6+2iCDkYMcDGtgNtvV0MQRCEHqPfG6Ooqsq1117Leeedx6OPPpp2/oMPPmDQoEH88Y9/ZMSIEbz99tvcdNNNKIrCokWLUtK2trby5S9/mS984QscPHjwRFWhS7QwD8mLqivW7KJ6TNx1XrZFSS2chl6nS7Oo1rydVLkc1O5pZWpJMdefMzLuaWVEETc/uSFrecwGPW2BMP/+63WJvH748jaWXl2R07WexIYUhBNHV+FhWnwqbl98wpdJNvNMhoQxm9sXSvQPyRPH5L7krivHJwxAOlPjcjCwwMwVE4elGLlkwmY6fF7r86pdDorzTGnnM15vVFi3q5lbpo0hSizFKC+5D82Edr6rdN1Z7NRC63ywu4VfzKkkGkt1pXpBmZOHrpnUZR+Y7Tn2RBlPpUXbdjXC2582snRmOfc8t5k1GTyArXx3NxOGFXLlxKHdMhQSBOH44Q2GCQQj3Hd1OXc9uznjt+f1bQeZdvogbCYFm0lh6shikVtB6EV0QIQow4vj88FsoR01Gf7yivfwqZHEfBROLUNZQegN9DoS4e86f1sXTStDr+vFwgmCIAgADLZbiERj3Pn0Rt5K0udXdxh53PZUbUr65LFUOBLr0NuHyDNnXvZR9DpWrNmV+F3YEaJtkN3MijW7WDHvLBZNd7F1n5u3dzZRW99CZUkxH9W38szN53PAHeT93c2sWLOLQCjKdy8bx2KTws7GdhSdLsVbi0Zn78fJyPjvxLJ5nxuAUU4xRunLlAywsaGTNyFBEIT+TL83Rlm6dCkAjz/+eMbzCxYsSPk9evRo3nnnHf7v//4vzRjla1/7Gtdffz2KovDss88ej+IeMW6fitUYX3yt3dNKjcvB6g5lni8U90KQbVHSp0a47ala/nLTedx5GRzwBBL53PZULVNKig4PYqth+34PlSXFDCwwJ+7TmSqXg4a2IMMKLTw6dyrBcBSbSeG6s0tY/Nxmfnzt5IyLq9rCbLJRjMSGFITjR1fhYawmhX1uP41elSXPp+5iuKDMyQ//bSKLprmA+M7WrfvcCc8oaiTKwHwzJoOeZq/K7xeczdpPG5lfPSpj/Nq5VaVc95t1+NQIL99WnTAO6UyyxyZtMl1T5mTJzHLcHbHNbUYl5/U2k8K4oQXsaw1w52XjCUUiXDVpKMte3JZimNKZmo6+DciZ7oIyJ/kWA582eLMa1nUOrXPn0xt56JpJ3Hn5ONqDEYqsRorzTAy2W7o00sv2HHPWpcyJQdFhMSpcUOZMUV4k1+NUWrS1Wwz8+l87+eKEQdw3q4J9rQHcSbuMVr67m+vPGclT7+7m3888jftnVYixpCD0InaLgX99cohmXx4/uGI8zb5QmszedMEY3vy4gakjB3D3jAlcNHagyK0g9CIxYlgNBgyKLjGX1EI7Lqwejc2kEInGeGdnU2J3b7JhM5xahrKC0BsY9Dr0OlLC35kN8TA9el38vCAIgtD7aCG0tRDCBRYjFqOeJc9vSfFK3HnD6QG3n33uAFdOHIpRydyn+9RIIo9ql4MBNhObP2/lc3cAnxqhPRjmoz2tLLu6gje2H6C2vpXvXT4egB/9/WNunV5G1RgH/37mabh9Ad74uIkPPmvm7hnl7Gpqj3uurI7r75LJtplWxn8nli2fu8kzKwwqMPd2UYQcuAbm8+LG/TR4Agzq5CVWEAShP9LvjVGOBrfbzYABA1KOPfbYY+zcuZM//vGPLFu2rMs8gsEgwWAw8dvj8fR4OTUDjskjiqhyOVixZhfP3lzF0he3sLauiQ31LYkF22yLkpUlRWz6vJWzRzsw6HUYFD0ThtpZfv0UnPkmfvb6J/jUCCvW7OKpr5zLj/6+PbGLLQopecZjPMZ3y9itBv6x9SAThxcRjsQYYrfwrUtO52Bb3OAl18KshsSGFE40J0Ju+wrOfFNWQ4Qql4NXNh9gWKGFVzbtTzM8e2tHI3uafQRCUa6cOJRhhdaEQUfyDroal4NbppcldrZq7te13Q7DiqyJfgxg0XQXsRjcOr0MIM1oZdG0Mt78uIGaMieLrypHDUficWZb/Kyvb+GsUQNw5pu4dVrm62+dXsb6z5r576c3pRy/sXo0Z44q5sySYmadMZylz29J8eBS7XJw91XlHHQHqOnoa5N38ibqW+bk3qsr+MH/beT17YdSji+5qpxwLEqR1YQvGE70dzaTwoPXTGLF2l1peS2+qpwmb4DVdU0JV6eakZ5Br6O5XUWnI2OYH223iqLTpTzjKpeDueeP4vKfr2ba6QO5b1YFP3hmE2uS7l3tcrCsHxlbHKvc1je182mDlzNHFvP3LQ3UN3q59QtjAXD7QwkXtE+9u5u7Z5QTjcY42BbEbjX2mzYShL7GscptvlFh+34PZ5YU4wmE2N3UzmC7hWA4SuWIIoYXWRmYb2LTnlZCkRhXTx7GUDFuFoRj5lhkt8Bk4IP6FiYMt7N4ZgVLnk/1RPaFcQNZfFU5yhgnE4ba09zOa4ay4k1TEI6MI5FbA+AsMLPzUHvKcV3H8VNSQSgIvcCppJsSjp5CW/oY6MfXTuaAJ8DeFj9weMNpZUkRi6aVEQhFeGPbQb40ZTj/2HogLc8qlyPhbaHG5eR7V4zDGwzhLLCw+IWt1Lic1O5pZfWORu56bjMPzKpg/BA7ajhCSbGVM0uKMSiw8In1TCkp4v7ZE7ly8hD+uG43S1/cQmVJMctX1VHtcnDx+MHYTErC8CXTZtpTbaNUX+DDPa2UOvLQ6cQAtS8zdkgBAOt3t3DFxKG9XBpBEIRjRxeLxWJdJ+v7PP7443zjG9+gtbU1Z7q3336bCy+8kJdeeokvfvGLAOzYsYPq6mpWr17N2LFjWbJkCc8++ywffvhh1nyWLFmS8MqSjNvtxm63H0tV4vn4VBY9VcvqHY3YTAq/mFPJY2t38eXzRrHpczeVI4oIR2OUOvL4n1e3cV1HCI30hdNy9EAkFiMQimIyKCx9YUsi3wXVpVSNcWBU9BRYDVgNCntb/LQFwwwttKCGo3gCIYYWWolEYrzx8UF+/a+dnDmymCsqhvC9ZzYn7qdZY//5vfqUkD07DrZxyU/fylrXN751IWMG5R9zmwlCVxxvue1r7Gv1p7n1TN418fCcShY+sT7jtY/cMIXv/PUjFlSXUjmiiEgsxsgBNiJR8PjjiwMHPX4KLEZ+/vqOFOOOGpeDW79Qhi8YwWjQY9TrKMozce8LW9hQ38ovr5/Cfrc/sbBoNuhp8AQ5e/QAGjwB1n562DijyuVg6cxyfvjyNt7YfgibSeGX10/hgNvPoJTrA4wemM/8x99P2SWi1bmypBiATxva+NKZIxhkN+MLRrCZFRo8Qe54eiM+NZKobzgao2xQPu1qmENtKnaLgWgsxm/+9WmKIcrhOjs5c1Qx6z9rZtmsiXzpV2/T6FVZNN1FbX1LVk8ulSXF1Na3JJ6JT41QU+bk8oohfP+ZzXzzkjLOHjWA5W/WpRnf3P6FMkY68vD4Q9Q3+4C4IkJrO82t6oRhhVSOKEq0Ve2eVrbv9/CTLJ6s+hrHIrcHPQG+85cPuffqchraVH771qdcd85Inv5gT+I98AYi2C0G7BYDoWiUnY0+Hlu7iwdmTWSkuC0VhKPiWOTW7VP59l8+5Maa0fxuzS4mnlbItNMHAeBXI9itRmwmPT9/bQczzhjGopW1/HHhOUwZWXxc6iIIpxJHK7tun0pLu8rBtgAGvY58q4GW9jB5ZgVvIEK+RaE9GGGY3UxMp+OuZzanjR3vnVVBS7tKMBxNGQuKN01ByM2RyO3Wz5spMJv53OMnz2xIks8wwwuttAWCDC/O7xdzBEHoz5xquimh53H7VA54ArT6Qlg7wlm/+XEDG/e0cteMCZh0Opa8uJXXtjUkrqlxObj36gqa2oNYTQbMBn3ceGV7A7/+104qS4q4Z8YEZj/ydkKv9tKt1VhNCnod6GLQ7AtiNCi8uuUgy1fVUVPm5IFZFbgDIf791+tS9Iyazmv5qjpqXE4unxjXcx0uj5PFM8vJNykMkXHeCSEajXHGvf/gkglD+NKZp/V2cYQu+Mafa7ly4jDuuWpCbxdFEAThmOmTxih33nknDz30UM4027ZtY9y4cYnf3TFG2bx5M9OmTeP222/nrrvuAiASiXDuueeycOFCvva1rwF0yxglkxX7iBEjemzi8GmDly/8v38lfmuGI5eWD+aqh9emHZ9aUkyhzYjZqNDkDWIzKZgVhUgsboDS2HFsgM3Ecx/tY+LwwpSFSU3Z98o3agioEQyKHr8awRsMs6G+JXE+mUfnTk1byNYGmhv3tPLwnEra1Qjb9nuyLngDPHvz+ZxRIgsIwvHneMttX+SgJ0Bzu8quxvY0eX/khinc/OSGjNdlku9M520mhWduPj+xK2N4kZVXtxxg8mlFrFizk9V1TWkGGcn92Z5mf2J3xJPv7mZVBkOPapeDMzomsMnXXzxuME3t8ec5sMCcCAXUGZtJ4U83nYtOB3ua/ViMChvqW5g4vJCv/uGDrHV85IYpPPnubuZXlWLQ6ci3GPjSr97Jmv5vXzuPL/3qHao7DH4WPrG+2+2YPElPPv7o3Knc2uHivrNByYo1u3julipiMfjiz9IN/h6dO5U7OkIEaUYXBRYDBz0B7nh6I0/eeA6nD+n77/2xyO32/R4u+/lq/v6NGmY/8jYLqkuZMWkokWgMvU6HNxAm32LAatSzp8nPN//6IQ9dM4mFT6zn5duqmTCs8HhXTxBOSo5FbjUj5hHFVv5w49kEQlHa/GFsZgWrUUEHuP0q//ykkd+8tTM+fr29hmGFFlk8E4Rj5Ghlt76pHXcgxH/8eh1/+eq5AJiNCmo4ii8YIc9sIBqNb2y4sMxJgcXIZ00+7BYjdquBD3a3sOylbYlxXLLxtGaQIt40BSEzRyK32/Z7uONvH/GLOZX4Q1Ha/CEKrEasRj23PVXLQ1+ajEGvo2xwwYmuhiCcUpyKuinh+HDQE6ClPe5RrsBiJN+kJLy8at7mWnwqbn+I2j2tTC0pZt7j7yf0ap31TFdUDCESiyX0R3kmhWA4gtmgEIxEMSt6IMbORl9C1/XK7TXEYjFe3nyACUPtKXrGR+dOZcXaXSysLqWxLcj4YXb2tvgxKfEQcQa9jkgMrqgYIuO8E8DWfR6u+MVq7p4xgQlDpa/p6/zyzTpafSov3lbT20URBEE4ZvqkF85vf/vbzJs3L2ea0aNHH1GeW7du5Qtf+AI33XRTwhAFoK2tjfXr11NbW8uiRYsAiEajxGIxDAYD//jHP5g+fXpafmazGbP5+MXW8wRCKb99aiSxSJkckqfz8bNGDaBieCG/+tennULsOLl52hi2H2zjZ6/vyHrfXYfaE4PGGpeDuVWlGQ1RIHOsx7V1TSyoKmX5qjoa2oLc++JW5p0/KmddJTakcKI43nLb19BC5NxykSuj0UkmF5katXtaqXE5U3atamjhwSDeB7X4QolJ6Eu3VhOOxvjdmp2JPqhyRFFKeB+t30qepD46d2pGQxSANXVNzO8I/ZN8feWIosR9H7lhSlZDlF/MqeRHr25PCUdU5XIwc9KwFJehmdpnbV0TemDxzHKa29VszQVAOBpLlFeLp5stJq6Gdl7rOzsfD4ajKf18Z9z+ELlMSp+88Vzu7QjtplHtcvDkjefiU0PZL+xDHIvcat9Sjz+cCEn3pSnDufvZLd16twVBODqORW5b/SGc+SZ+N/csfvDMZtbWNfHMzefzwMvbsnqZCoQi3PpUrXhPEIRj5Ghlt12N0B6M4FMjvLrlYE6vcAPzzZxd6uDWp2p5fP7ZNHnVlF2ycDhM4oLq+LzyrR2NNHpVWaQQhAwcidzqgAKrkYt+/K+0c1UuBzpdfH4hCMLx5VTTTQnHj8F2C4PtlozntDA/tfUtCd3ZoumuxLpCZz1TtcvBlRVD+I9fH97oFQ/zPJFAOEwspuPu5zZz/+yJKbqutkAIi1FJhEBOxm4x8p0vno7bF6K+xc+SF7am6FoenTsVQMZ5J4h1O5swKjpcA8VDfX+gYnghv/7XpxxqCzKwQL4ZgiD0b/qkMcrAgQMZOHBgj+W3ZcsWpk+fzty5c7n//vtTztntdjZt2pRy7JFHHmHVqlX87W9/o7S0lN7AnsVAY8WaXfxiTiV6dCkLadrusS373GnhegBW1zUSJcY3Lx6b874jBlh55IYpCc8BK9/dnVACdibbQrY2IHX7Q6ze0cjkEUUpBjTJSGxIQTg+uH0qdzy9kdU7Grn9C2UZ09Tuac0qm1v3ufn2pacTJZYWHkbbqaph63AJCvHFiM7GJ9kMMpL7kO4abWQ7lq0/WlBdmrFPXFvXxH0vbuGuK8enLYBAqlHC6romdjf5GGTPPfBPbgdvMEyVy5HT4KdzuTPVp6vrbSaFbLYoo5153PfSVipLillQVUowHE307f/z6jbuuao8Z94nA5qxo/ZsFlSX8v6uZhZNd4EOVmcJYQXZv8OCIBxf8kwKD10ziXtf3EJtfSuLprswGXQJo8RM3yS3L8RbOxq58+mN4j1BEHqBNn+IPHP8W6vNV4G0ELLzq0axaGUtZ44sZkF1KXkmhd3NwYx5djbUbQvIArkgHCt6PTm/p3pd6pxGEARB6P8k6zayjdOqXA7mVZXyk398zJ9uOpe9LYe9Csd1ZxM45A2yob4VnxpJ0VUVWIwJQ0ZNj6ZhNuqZuXwt2VAjUWIxGeedKN7acYixgwswdaFrFPoGlSVF6HTwxraDXHd2SW8XRxAE4Zjok8YoR0J9fT3Nzc3U19cTiUQSoXVcLhf5+fls3ryZ6dOnc+mll/Ktb32LAwcOAKAoCgMHDkSv11NRUZGS56BBg7BYLGnHTyTOfBMXlDl5a0fqzm2fGmHlu7uZVzWKO68YF18gLTDzycE2bnuqlofnVPLT1zJ7Pllb18Qdl+mzLj5Xuxz8vSPmIxxWSBh0urS0yQu1ndEGpJoSI5dC8qFrJsmCgSAcBxq9amKhfU1dY0YvJyvW7OLRuVPRQ5rXkOvPGcmNT7zPdWeXcPeVE/i8NR6Gp3ZPa8JlupY2GatJT6M31XAkm0FFsjHMkRhtZDqWzbCms2FMMqvrmvju5ePSrstkcBMMxyfI2frPzu1gMxuoLClmYIGZGpcjpX2Tr0nuR7X6JB/PZTBU5XKQZzIQjcUypglGolx/zkgeW7srpQ20+vlDJ7/nD4tRT43LidWkUFPmpHJEEYpOh0+NsPSqcvZ7Arj9oYRLWu3d7o4hkSAIx4c8k4Ehdgu19a38Yk4lj63dBcSNJJON6zS5Xfnu7kRILfGeIAi9Q4HVSDhyeDxyW0eIQU1eC61Ghtot7G72AXFj0K9dOAaDosv5vU021BVvmoJw7Bj0ela+uzvr9/SuKydgVtL1P4IgCEL/JXmNwadGUsZpAIVWI//85FBCHzLnnJEJL8aa/igYiVJgMbKgupS2QCihs6opc5JnUmgPhhlkN7Niza7EfbujVxmYb8btD8k47wTQFgixtq6ROWLU0G+wW4ycPriAf2wVYxRBEPo//d4Y5Z577uGJJ55I/K6sjBs9vPnmm1x00UX87W9/49ChQ/zxj3/kj3/8YyLdyJEj+eyzz050cbtNoc3Eg9dMSng20KjuWCS+9alabrpgNB/sbuGD3S38Yk4llSVFXXoX2O8OZNwJU91hAZ28+Kqdv+OycSl51Lgc3DytjIVPvJ+Wv7aIekGZkzxT/PXqPNDVFB6ugfkMFVfqgnBcSA719Zu3drL8+koglmIUUVlSRCAU5cxRA/jaRS4UvY4Cs4FXthxITEKXr6rjmsrh2EwKy9+sSzPaWDStjDc/bgDik9C3PjnEGSOKU8qSzaAi2Rgml9FFdRbjtwZPgJoyJ6t3NGY1euuKvS1+Zkwcyt0zJrCrsR2Tok8zuIG4ocibHzewaJor7R6d26Ha5aA9GGL5qrpEuaIZrkk2eNH6zpoyJzdf5Er0r7l2rdw6vYwim5FgOJqxXMTI6hUG4O4rJ3S7nfotMbjnqgkoOlhy1QQ+a/JRaDPy4398zJfPG4Wi1/Hku7sze1rwq0Be75VdEE5RimxGDrYFUjxbJRumZDKuSx6/yq46QTjxFFqNfHKgLWWe2XmDw45DXn7/zmcJr5sGvY4t+zzsbGzPmq+2gCHeNAWhZ2hpV3Maq7e0q7gGiet+QRCEkwltjeHOpzcmDFKWr6pL9P1fXvFeiv4reW0hWX+UZ4qH4imwGFmxZhc1ZU6WXV3Bxr1u/GqYz92BRD41ZU5umeYiFInm3GBlMynsaPAydWRx2nmhZ3nz40OEIjHOGjWgt4siHAHnjHbwh3W7OegJZA3JJQiC0B/QxWKxbB7+hSPA4/FQWFiI2+3Gbrf3WL4HPQE+bfDiDoQoKbaRbzHgD0Xw+ELYbUasRoUfPLOJDfWtLKgu5dLywVz1cHb3d4/OncqtHYYhlSPixiulzjxe2rSfFWt2pQw+NV6+rYZILMreFj8mRc+mz92cN3oAy1fVpXlTmF9Vyp/fq+feqyuwmRRufao2zbsLxBWK4kZd6G2Ol9z2BT5t8PKF/3c4FrjNpLCgupSLxg7EEwhhtxgxGfTsdwcw6HXU7mnl4/0evvXF01n20taUieLLt1bjDYapO+RlsN2SMCg76AkwtNDKLSs3cObIYpZcVU5DW3zyuSLJCMJmUhKLiJ0X/W+sHs1He1s5t3QAzgILS1/YkmKAV1Pm5L6rK1j20lZe39aQOF7tcrB4ZjnE4N4XtrK6rjFRx/NHOzAZ9FhNCga9jkt/tjprO71yew1uv0qB2cgDr2zLOkGuLClmxZpd/PL6KRxw+xmUpR2mlBRxz1XlHGgN8OvVn7K2rilRrqoxDoyKnmA4yjs7mxJ9bo3LGa8LMQbmm/H4Q3zvmU2sSWq/u68cT2VJMZ81+TAqOhragkwbO5AhHQZ9e5raWVPXmFKuYUVWLv957rqPH9r/3vsjkVu3T+UHz2zizsvGEYuBLxxBjUS56uG1Gb+H2s7QFWt28cKiasaIMl4QeoQj/d5u3+/h81Z/IrY5HP6OXVo+mD3N/hR5TR6/vvGtC0V2BaGHOBLZ3X7Aw7898nbW7+rDcypZ+MR6Hp07lYVPrOel26rJNxu474UtvL79UFp+2vhr455WHrpmkmxiEIRukktut+33cM3/ZpfTp79+fr+cHwhCf+dk1k0JfQe3T6XRq9LiU3H7QxnnUkBirJbMK7fX0NwWJBCJUjYoH38ogl4XwxsIE47CsEIrwUgUT4eXE4OiQw1H+bzFTzgWy6gPnF9VSqHFwIhiW0K3JRw/Fjz+Prub2lk2a2JvF0U4AnxqmFtWbuCrF4zhm5eM7e3iCIIgHDX93jPKyc5gu4VINMadT29k/e6WhNJAp4NCm5EPdrdw5+Xj8fhDtPpDxGJ0GRJCs4CG+ELvbV8oyxrGAsAbDDHvsfdTrJtnnTGcmZOHM796NAZFR7HNSCwWD4nw42snJ4xMki2vNS6Q8DyCcNzpHOpLk/sVa3axYt5Z/OKNHRnlUgfMmDQsxYtROBrDH4qgOWzW6eI7YIcWWvAGw/zta+cRjYEnoFJoNWI26LlnRjnLXtrK6iQ3oHfPmMA9MybgVyPYTAaMio6mdpXLyoeQZ1JoC6gsu7oCNRLFEwgRDEV5e2cT//XouyyZWc43Lx5Luxomz2TggCfAnN+sA+ChayZxx+Xj8CWdu+PpjVxdOZwZE4ckvKd0pqbMmdhx2+xVuWfGBJa9tC0lbfKue58a4ZaVG3hhUTVqJEpbh1HPKEceTd4gf/v6+Xj8Ktf/dh0+NcKC6lJuuciF2ainyGrCmW8ioEbY1ezj0vLBVI1xkm9RaA9GKDApDO6YfBfaTPzk38+gpV3FEwhjtxiwW40EQxGG2M0UWIxMHVmc0oeOcOQx3aikXOMNhnO+Iz419/mTgUKbie9fOYEf/307X58+BqtRodGrAnFvPJUlmcM4yQ5sQehd7FZjIjycRvL4tba+JaPxoMiuIPQeQ+0WzhxZnPG7mhyCMBiOUlPmpMBs4MYn1nPv1eUEI7E0Y+SlM8sB+Ep1qcwbBaGH0OvIOv6tcjnQS4QeQRCEk5ZCm4lCmwm3T826ebRzOGmN9mCY2//yIX+88Rya24MYDQpWg4Ijz0ChzZg2VnP7VJa+sIWbLhyTMFbuHB7utqdqee6WKjFEOQHsafbx5vYGbqwZ3dtFEY4Qm8lATdlAnnjnMxZUlVJok5BWgiD0T8QzSg9xvK3YNevltkDcwtiZf3gAecgbJBKNEY2BXw1TbDNxz/Pp3gVumeZiweOHjUqqXA4WVpdSZDXxb//7dtZ7P/3182j1xV2en1ZsxWJUaPOrFFhMqJEo7cFwSpm6W3ZB6G1O9t0n+1r9GY3B/ueaSVhNSla5TJbZPLMBi0HP0he3Mm6oPW0H3bZ9bsYPK2T5qjqc+SYeumYSg+xm/GqEgflmQpEYPjV3H5GNbH1Htnr96JpJoNelGGQMyDMRjsbSQp5pixw6wGY24A2EaQ+GKLSaCIaj7GnxAaTtFKkpc3LvzHJsZgOD7RYOegKJ+xVaDRRYjPjVSM7+7kT1iZ2943Smv3oPOBq5dftUWvwqP3r1Y26ZXsYVP1+d1WNPTYeMyA5sQeg5jkZutx/wcFkGz1aa7D7x9mcp/bpmVCmyKwg9x5HK7r5Wf9qYq7Nh75M3noOi0zHIbkKNxBhgM2E26GW+KAg9RC65/eSgh/pmf9Yd6iUDrIwdfPLNiwWhr3Oy66aEvkcmvVrnMVsyf1hwNr9ZvZMvTRnO2aWObs259jb7eO+zJp7ZsI/VdeI1vTe594Wt/Hl9Pb+8fgpmg9LbxRGOkBafyrf/8hH/PvU0ll5d0dvFEQRBOCrEGKWH6AsTB22B0xNQMRsUfGqEQ94gJkVPc7tK9RgHnmCYvS3xnabaIuvdMybw8qb9WT0H3DNjAnqdTpSCwklHX5Db401PGT5kMwBZNquC+17cymtJIXROxILgkdbrSNNnq29/WujMtdulP0/6j0Vu9zb7eGdnE89/+Dmrk0IoaUZWRVYjYwblSxxWQehhjtaIbNFTtRnHpxePH8T9syfiDYRl8VoQjiNHI7tamNlWfygtnFaNy8kt08cw1G7FrOgYUmw7zjUQhFOPXHLr9ql8+68fZdxksH2/h58kebkVBOHEcSropoS+R+eNaOt3t3Dfi1vTDFGqXA5mTBrGeaMdFGfwgpKLfS0+/OEoS57bkmKQIpuAThx7mn1M/8k/mTl5OF8687TeLo5wlLy8aT9/WLeb3/zXmXyxfEhvF0cQBOGIEWOUHqKvTRxyLbxmOteuRvr9wqsgHCl9TW77Ork8NJ2Mu1lPhnqdDEY1nTlWuXX7VJp9Kvc8t0W8KgjCCeJo5TaTlwVRXArCieNYZLfz+KOmzMm9V5dj1OkosB7ZQoYgCN2nK7mVb6sg9D1ENyX0BbKN3+67uuKIjVCScftUWn0h2tUwPjVCodXIoAKzjAVPANFojLmPvcfmz938v38/A4tRvKL0V2KxGD97fQcf7W3lN1+eyoVjB/Z2kQRBEI4IMUbpIU6GicPJsPAqCEfCySC3gtAVJ1vf3lNye7K1iyD0ZY5FbkVWBaH3ENkVhP5Hd+RW5FMQ+haimxL6CvJ9OHmIxWL86O8f87///JQ7Lx/HpNOKertIwjGihqP8/I1P+HBPK9+8eCxfu2gMRkXf28USBEHoFobeLoDQdyi0yQBTEAThZEP69sxIuwhC/0BkVRD6JyK7gtB3EfkUBEEQMiHfh75FLBbjX58c4pnaz6mtb8XjD+HINzHptCIuOn0g08cNosBiTLvOp4ZZ9tI2Vr5bzw3nlIghykmCyaDn25eczl8/2MtPX/+Ep2v3cvOFLq6YNJR8syzzCoLQt5FeShAEQRAEQRAEQRAEQRAEQRAEQRB6mQ/3tLL4uc18tNdNyQAbFcMLKTAbaPGpfLinhWdqP8eo6Dh3tIOzRw3gtAFWIlHYus/Dsx9+jjcQ5saaUr4wbnBvV0XoQfR6Hf9x1gjOHT2Av67fyx1Pb+T7z2yibHA+gwss6PU62oNh2oNhojEoshkZO7iAapeTmrFOzAYJ1SQIQu8gxiiCIAiCIAiCIAiCIAiCIAiCIAiC0Et4g2F+/PePeeLtzxjpsPGDK8ZTPsyOTqdLSdfoDbL+s2Y+3NPKr976lPZgBICB+WbOGjWAKycOYWCBpTeqIJwARjry+M6lp3OoLciHe1rY3eTD7Q8RAywGPYPt8WffFgzzyub9PP72ZxTZjFx3Vglzzx/J0EJr71ZAEIRTDjFGEQRBEARBEARBEARBEARBEARBEIQTTCQa4/mPPuehVz6mxady/TklXF4xFEWvy5jemW/msoqhXFYxFIBAKIJep8Nk0J/IYgu9zMACM5dMGNJluj3NPv75cQO/f+czfrt6J5eWD+baqSM4f4xDvKUIgnBCEGMUQRAEQRAEQRAEQRAEQRAEQRAEQTgBRKMxPmloY9X2Bv78/h52N/k4a1Qx379i3BF7NbEYxaBAyM6IATb+67xRfOnMEfzrkwbe2N7Ay5sOYDHoOX1oAacV28gzKURjoIajBMMRwpEYil5HntlAsc3E0EILpxVbGTHAxohiG3arIc1jjyAIQjbEGKWHiMViAHg8nl4uiSCcXBQUFBy3gY3IrSAcH0RuBaF/crxkV+RWEI4f8s0VhP6HyK0g9D9EbgWh/3G85batra3LdP/a0cStf93arTyHFZox6eHP7+0+1uIJQk5Kis0ouhi7mwN8tMfNR3vcx/V+BWaF390wifFD8rtOexzlVhCE3kOMUXoIbfAxYsSIXi6JIJxcuN1u7Hb7cclb5FYQjg8NDQ0MHDjwuOQtcisIx4/j9c0VuRWE44eMlQWh/yFyKwj9D5FbQeh/HG+5LSws7DJd/uRLcVx2a840wYOfEgur7PwcdvZUAQWhx9GhWAswFA5GpxzZsnJbMMJFX7yCwO6Pukx7POVWEITeQxfTzK+FYyIajbJv374jstzzeDyMGDGCPXv2nDId7KlWZ6nvsXM8rWGPRm67y6n27JM5lesOp3b9tbq3trZ2a1J+NHRXbk/l55AJaY9UpD0Ok9wWw4cPPy7f3O7I7anwTKSOJwd9rY59aazc19qmJ5A69X36Y336gtz2x3brbaTNjo6Tpd36gtweLf3pGfSnskL/Km9/Kiv0THn7gmcUjf7W/n0Zacueoy+2pXhGEYSTE/GM0kPo9XpOO+20o7rWbrf3mc7+RHGq1Vnq2zc5FrntLv2lLY4Hp3Ld4dSu//GcNByp3J7KzyET0h6pSHscxm63HzfZPRK5PRWeidTx5OBUqOPRjpVPxraROvV9Trb6HC0yVj7+SJsdHdJu2TkRuinoX8+gP5UV+ld5+1NZoe+WV6fTHVW5+mp9+iPSlj2HtKUgCMcbfW8XQBAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQTh5EGMUQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEoccQY5RexGw2s3jxYsxmc28X5YRxqtVZ6nvqciq3xalcdzi169+X6t6XytIXkPZIRdrjMH2lLfpKOY4nUseTg1OhjkfLydg2Uqe+z8lWnxOFtNuRI212dEi79T796Rn0p7JC/ypvfyor9L/ydsXJVp/eRNqy55C2FAThRKGLxWKx3i6EIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCcHIgnlEEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRCEHkOMUQRBEARBEARBEARBEARBEARBEARBEARBEIQeQ4xRBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQhB5DjFEEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRCEHkOMUQRBEARBEARBEARBEARBEARBEARBEARBEIQeQ4xReohYLIbH4yEWi/V2UQRB6CYit4LQ/xC5FYT+h8itIPRPRHYFof8hcisI/Q+RW0Hof4jcCoIgCEL3EWOUHqKtrY3CwkLa2tp6uyiCIHQTkVtB6H+I3ApC/0PkVhD6JyK7gtD/ELkVhP6HyK0g9D9EbgVBEASh+4gxiiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgtBjiDGKIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiC0GOIMYogCIIgCIIgCIIgCIIgCIIgCIIgCIIgCILQY4gxiiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgtBjiDGKIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiC0GMYersAfYUlS5awdOnSlGOnn34627dvP6HlOOgJ0NKu4g2GceSZCEVitAVD5Jnij0qnB6tBIRCOEIlCezBMkc2I3WLEr0ZoV8P41Aj55nj6GDGsBgWDXkebGsGvhnDmW/CFIrQFwuSbDegAvR4sBgVfKILHH8JuNZJnVGgPRWjzhymwGLCaFHxqGG8gQoE1fp1RAaNewavGryu0GikwKUQBrxqhzR+i0GbEpOhp9akUWk2EolHaAmHyku4di4FOF/8/GothMSiY9TpikMjLGwjhzDejRuLXF1gMGPR6PH6VIpspcTzPpJBnNsTr6A9jtxmwGeJlbAvEy2hW9AQjUaKxeBsWWAyYFD1uf7yMANEYBMIRfMEIdmv8fLNPTTyLGDHMBoVQNIoOsBkUgtEI4Uj8OZn0eoLhKB5/mDyzgsWgoNcBOmj0qthMCgVmA+1am1uM2EwK/nCE9kCYImu8Tt6O8uUZFSLRGOGOcvnVSKLcwXA8XZ7ZgNLxjsTzDZNvVrAaFYx6He3heJtoba+1eYx4mxv0OgLhCOEoeANhCqwGzAY9+hiYjAqBUARf6HCbmA16iEGe2UAgFEl5D/ItBk4rtp1Q+enr7Gv14/aH8PhDFOUZyTMZ8AbDeANxeQ9GorR1yJ9R0dPkDVJgMZJnUvAGAuRZLLQnyUIwEk28O/kWA+1qmDZ/CEfe4XOFGfJqDwbIM1vS5MrtD1FkM2IzGWgLhAmGQjjyLCnPNc+k0OoLkG9JvYdJ0dPUHr+HUdHj8QcZkBcvb/I74Q+H8fri/VDnfFt8AczGeF5uX5AB+fHrNbk1ddQjP6m+re2Hr29XA+SZUvO1mhRa2wMUdyqL1aTQ2BagKM+I2RCvb3sghKNTu2ptn29JL28wGsCkT69j/Jlmfg5uXxCHVq+OZ62VJc8c7wNafQGKbEn52ozkm+P5tvlTn73WLi2+IMW2juO+eL42k0I0GkDRW05p2dzf4iMSjeENRfAGwtithsT3ztvpW2RN+lZofbIRUGMx2tUI7WqEIqsRu9VIezCMu+MZOvNMFNpMvV1VQQDA7VNp9Kp4Av3z/XT7VPztKopehwq0dRrPmPQ6TMDn7UGspvhYxO0PkW/pf3UVepb+/u73d/a2+PAGwniD4cRcFF0Mm9GAv2PumfzNNeoPz63MBj1qNIrVoKAAvnAERa8nGI6gIz4nbA+Gye+Y+7T6VPIsBkx6PYGOeZDdYqDAYiQQisT7BHM8rSegYreaULV08m4IAntbfLQFwgndT75JwQLE9Dq84aiMcQXhJCVZJ1VoNVJsUggB7eFIXIcZiabMkXV6yDco6ACPGomPsSzxPiNMjEAoSnsgzIA8EzogBhg7dLnhaCxNr5pnVAhEI0Sj2tw7jDcYn2MPKjBn7W96eozn9qm0+0JEdGTVlSTfM99swKDX0dqhMw4mjT3yjQom4rprfUcb6KIRdrZ15GlSMAMRQNHrGHgEuhgZ2wonCrdPRfWHCAL+cKRDlvUEIxEcRgUVHWqHTAfDEXwdaxK2jrUjt69jTchiYPhR6Bu78667fSqHvEHC0RixjvWc4jwjZoNCWyCcoscLRiIouvhcwqDoMCkK/lCkYy1LTygaId9gIAopa18mg56WdpU8c3wdrKU9iMXYsSangwFGhSDQph5ePyswxecvrWokoVdWI1E8gTB2q0KewYA3FIEYiTmNts5jAg74glhN6X2g26fS1K529KUxfMEwhTaT9AOCIPRbxBglifLycl5//fXEb4PhxDZPfVM733tmE7X1rfxiTiU/ff0T1tY1Jc5XuRzMryrlqXd3870rxnP9o+vwqRF+ef0ULMYgy9+sS0u/sLqUPJPC8lV1bDvQxsqvnMsPntnE6k7p7plRzpIXtrBq+yFsJoVfzKnksbW7UvKrdjmYV1XKbU/V4lMj1Lic3DergsUd1wHYTAor5p7FL9/ckXKP6eMGcsdl47n7+c0Z6/Snd+u5/twSVq6r5z/PG0mBWc+APDM6nY4fPLs50SY/e2NHyvVavnc9tzljWe98eiMPXjMppS42k8Kjc6fyyJt1ae2w+KpyGr0BwlEytuf8qlL+69H3qCwpYn5VKSvf3c1XakYTCEV58t3d3HXlBH75z4/5+rQy7n52c1r+i6a5UHQ6/KEIX17xHlNKilLatNrl4J6ryvEEwvzktdTnX1PmZPFV5Tzw8tasz8lmUnhs3lnct2prxnsHQlFuWbkBnxpJ1Gflu7u5/pyRPP3BHr79xdO594XUa6tdDpbNmsie5nZ+viq1TWpcTn4yqxyfGuH7z25Kewb3z57ISEceAuxuauf7z8TbKPnZdSXvcx97nyklRSybNZEfvrKNf33SmFEWtHfHG4zw09czy1lyXsvf3MELGw+k5JVcrvomH79feDZ3PpP+XJfNmshDr27jlc0H0+7x5RXvc+7oAdw9YwLff2YTazJcOyBPnzXfm36/njGD8vjuZeP4Xqc0iXt01OPuGeXcsnIDjV6VKyoG893LxmfNd+kLW3ijo5/Sji+ZWY5Bp+eOpzcefg4Z2nXZrIksyXD9slkTefCVbbyc1A7VLgcLqkvRoUt7DtPHDczaLnfPKOeG361j8mmF3D2jPGOahdWjiRHrdj9Y43Jw36yJKX20ltepIpt7m9oJx+DuZ+PfPZtJYfn1lTy2ZlfG72Cmtloys4KW9gBzH1uPT40A8b5vfvUoFq2M990XlDl58JpJDCuynvA6CkIy+1r93PH0RlbvaEwc60/v575WPy9v3MuMCcPwxeCuZ9PHrLdOK2NYoYUhNiMzfrWO8UPtLKwuZc5v32XqyOJ+U1ehZ+nv735/Z3dTOz/oNHbpPMdMPp48B/mvR99LjOsefGUbd1w2nub2EL9+61OuP2dk2py0yuXgxurRtPpDPLom93xVK8PSTmWQd0M4lUmel2poc5u8SIxDHj/zn/hAxriCcJLRWfbPHlXE/3zpDB56dRvfvOR07nkuXYd5WAc9gf989N3ExrrH5p3F8lU7+KC+lV9eP4VwNIpeB858C8FolMa2INFYul61pszJfVdX8LnHx2Nvf8Z/njuSm5+M6yhrypw8lKG/6ekx3v5WP4fafNitFn6QoS98YPZEDIo+7Z7amGLZS1tTxhQ1ZU6WXV2BGQgBRiCsU1BVL5f9Zl1c/zJrIjYgGIlxqKmdgd3QxcjYVjhRHGr1QziKD7jvxS3ccO4oLEY9v3/nM+69bBz+mI79Hn9Gme489j4afWN33vV9rX7ueXYz151TkpgbOPNNPHnjudzz3MaUMn2hQ/d674tbmXdeKcOKLSx9YQv/1VGvP79fz7cuHksoSVeokVj7WXF4fnLD79Zx+pAC/vviMbQbbPwgaf0jeY3pg0565RHFVv6w8BzueWFLxjmN1nc4rSb+49H3GOXMS/SB+1r93PPcZq47uyTtOukHBEHor+hisVistwvRF1iyZAnPPvssH3744VFd7/F4KCwsxO12Y7fbj/j6g54A3/rLh6yta2LRdBe19S0pHxqNKpeDypJiPqxvYX5VKbV7WhlWaOGlTfszpq9xObh84lC+/8xmHp07lcfXpi7AaVS7HJxRUszyVXXduv/yVXVp1wFZr+1Onh/VtzC5pJiP6lv5/pXjAFj20racbdJVvguqSlnR6aOd65oal4Nbprt4uJPRRab6a3/X1rdw5cSh7HMH+LC+he9eNo4fvbo9YztXuRxcOXEoOuBzdyAlH60Nfzi7gpc37T+q57RouosP61tSFMGd772v477J9amtb8nYVsntor1HnVn93xelGQAkl/fBayb1WS8Mxyq33WVfq5///ttHiTZKfnbdlbfqjgFx7Z7WrOlzvTud81o8s5znPtyXkldyWZ5fVMVDr27P+lznV5Wy8In1Ge8BZC1jtcvBoukurvvNuxnPLZ5ZTn2TL+u7mKlNFj6xnkfnTs16Ted+6nB7TeTlTftY3cVzyHZ9tnbIJi9d3aOr5/tAx/PtLN9HW/a+LJu56K7cxnd8hnjgpW0JmcjVR+Zqq0XTXaypa0o5V+NyMDkp/QVlTh6eUyk7FIRew+1TWfRUbYoSR6O338/uyK1W/gdmVQDw/U7G0xpVLgczJg6l2uXkkwYvC59YT43LyeSSIpavquv1ugonnr787vd3uiO7e1t83PH0xiMahyTPQTqP6xo8AV7atJ8zOs4fyZgoOe9s81UNeTeEk5VcctuVvP5w9kRC0Rj/V/u5jHEF4QRyvHVTnXVSAG/990V875lNLKgqzaor7qyDXvjE+pQ59aLpLoYVWgAYPTC++LzzUDtADj21k+9fOY6XNh3go/rWxBge0vubnh7juX0qL28+wPljHGlGeRpxvdoBVtel3zPbmKKmzMkDsyoS3lESx3/0z8R1Wv9q1OvQ6XU5dTEytu0fnCid8vHkoCdAq0/FZlS485lNVJYUJ9aZFlSVMnZQfkIWssl0prH3Q9dM6paHlO686wCLVtYyuaQoZW6QTQ+s6UgrS4qpdjl4eFVdSr3uuGwcRkWXoivMVp9kvW+m9Y9c+n1Nr16ZY05T02EA1+IPMXP5Wi4oc/Kjayfznb9+xOQRRVmvk35AEIT+iL7rJKcOO3bsYNiwYYwePZobbriB+vr6rGmDwSAejyfl37HQ0q4mPi6VI4oyfmgA1tY1UTmiiDV1TQyym6kcUcRguyVr+tV1TQy2xycGg+zmjB9ZgDUd+Xb3/pmuy3Vtd/Jcnfi/kRg6Yui6bJOu8h1kN6edz3XN6rom8syGbtVf+3ttRxtrz8Wg6LO2s5Z2UEf6znkCDLJbjvo5aWXIde/keyXXIVNbaSS/R53xqpGs162pa6ItEM54rjfoabntLm5/KKWNkp9dd+UtWeazpc/17nTOKxyJpeWV/Nug6HM+10F2c9Z75Crjmg4Zy3YuHInlfBcztQmQ85rO/ZRGcp/YVZkzXZ+tHbLJS1f36Or5DrZbMsr30Za9L8lmLo5WbtsCYWLoUmQiVx+Zq63yzIa0c6s7pX9rRyONXrVbZROE40GjV82oxIET/34ejdxq5feq8bB/ub5ng+zx8GNaH7y6rjEhjyKLpx596d3v7xyN7LYFwkc8Dkmeg3Qe1w3qGO8czZgoOe+uyiDvhnCycCRy25W8etUIwXBUxriCcJw50bqpzjopgPYOXV4uXXFnHTSkzqk1nfRgu4U8s4E8syHxO7t+Ma7z1fS/ufqbnh7jNXpVBhWY8eXQY8b1apnvmW1Moc1hfB3zGO1f8nVeNRIPxa5GutTFyNi2b9JbOuXjxb5WP9/5y4dc+rPVCd1+8jrTILsZrxrpUqYzjb093dQ3duddb/Sqib4iuQzZ9MDJcwxtjSe5XgZFn6YrzFaf5L4v0/pHLv2+plfvah3Kq0YwKPpEnVva422S6zrpBwRB6I+IMUoH55xzDo8//jivvvoq//u//8uuXbuoqamhra0tY/of/vCHFBYWJv6NGDHimO6f/JEOhqM502rnvYG4ouBI0ncnXXfzy/Q727XdzVP7v80fos0fOuZ8M9X5aK7Jdn1yuZPL3tX1nZ9bd9ow0z1zlS3XvTNdcyT1TsbTRX3bArnPn0h6Wm67S+c2OprnDYdlvjtpuzrf5g/llOWu3uNs70t3+qRc71qbP3RE76KW9mje36N9Dsnkaofu5pGc19E836Mte1+SzVwcrdx6On1H4Niec3eeaX9pU+HkxNPF+3ci38+jkVut/J6OOPa5CIajad+LlG+YyOIpRV969/s7RyW73ZDXXMezjXWPZcxrMxlYMe8sFk13xePG9/OxkCDk4kjktsu5e8f4Wca4gnB8OdG6qUyyrx3rri5FS9dZj6L98wYi3dZTJ/czufqbnh7jeQLx++bqC49av9LRfyb/61xWXawjXRfllrFt36S3dMrHA7dPjYfG6TB20GQiWX69gQieDlk9Urno7jvanXddS9P5Hl3pY7V+Sfs725pTrjyS75Op38ilV27zZy53ZzqXR1sj7PI66QeE40QgFOG1rQdZ/1lzbxdFOMkQY5QOLr/8cq699lomTZrEpZdeyssvv0xrayt/+ctfMqb/3ve+h9vtTvzbs2fPMd3fbjnsKcBsyP1YtPP5FgWzQX9E6buTrrv5Zfqd7dru5qn9X2A1UmA1HnO+mep8NNdkuz653Mll7+r6zs+tO22Y6Z65ypbr3pmuOZJ6J2Pvor4FltznTyQ9LbfdpXMbHc3zhsMy3520XZ0vsBpzynJX73G296U7fVKud63Aajyid1FLezTv79E+h2RytUN380jO62ie79GWvS/JZi6OVm7tnb4jcGzPuTvPtL+0qXByYu/i/TuR7+fRyK1WfrvV2OXYwmzQp30vUr5hIounFH3p3e/vHJXsdkNecx3PNtY9ljGvTw2z4PH3qa1v4RdzKrEZM4/X5N0QTgaORG67nLt3jJ9ljCsIx5cTrZvKJPt2qxGbSaHQ1r3vuDbu7qxH0f7lW5Ru66mT+5lc/U1Pj/Hslvh9c/WFR61f6eg/k/91LmtM15Gui3LL2LZv0ls65eNBZ48kmkwky2++xYC9Q1aPVC66+452513X0nS+R1f6WK1f0v7OtuaUK4/k+2TqN3LplQusmcvdmc7l0dYIu7xO+gHhOLCv1c+Mh9fwld+v50u/eoe7n91MLBbr7WIJJwlijJKFoqIixo4dS11dXcbzZrMZu92e8u9YKM4zUe1yAFC7p5Wqjr87U+VyULunlWqXgwZPkNo9rRz0BLKmr3E5OOgJANDgCVKTJV11R77dvX+m63Jdq5U5V541if+dHUF6Yl22SVf5NniCadflql+Ny0F7MNyt+mt/V3W0sVaWcCRKjcuZ9fqDngANHek75wnQ4Alkvb6r56S1Y657J98ruQ4NnmDWtkx+jzqTb1KyXlftclBgyRySpTfoabntLoVWY0obJT+77spbssxnS5/r3emcl0HRpeWV/DscieZ8rg2eYNZ75JLL6g4Zy3bOoOhyvouZ2gTIeU3nfkojuU/M1a7Zrs/WDtnkpat20Z5vtjQHPYGM8t1VvtnKXmDKbcDTVzhauS2wGNARS2mzXH1krrZqD4bTztV0Sn9BmRNnvsRrFXoPZ76JC8oyfwNO9Pt5NHKrlT/fpJBvUnKOZxo8AfJNSqIPrnE5E/JYI7J4ytGX3v3+ztHIboHFcMTjkOQ5SOdxXUPHeCfX2CzbmCg5b4i72X587S4iGRRo8m4IJwtHIrddzd3zTfGFZBnjCsLx5UTrpjrrpADyTAp3Xzkej1/t8puarPtInlNrOumDngDtwTDtwTANHb+z613jOl9N/5urv+npMZ4z30RDWxBbjr6wwROgJss9s41rajrmMLaOeYz2L/m6fJNCWyBMgUnpUk8qY9u+SW/plI8HnT2SaOOD5HWmWCzWMefOLdOZ1ors3VwL6M67rqXpPDfIpgfW+qzaPa20B8Np9QpHomm6wmz1Se77Mo2hcun3Nb167nWoeN8RjkQTdS7Oy1zfTG0jCD1JKBLllpUbaPWpPDB7IguqRvGHdbt5esPnvV004SRBjFGy4PV6+fTTTxk6dOgJud9gu4UHZk+k2uVgxZpdzK8qTfvgVLkczK8qZds+N0tmVnDH0xtZsWYXQwutLJrmypy+upQxA/OocTm54+mN3H1VedpidZXLwT1XlbNtnxsgcf/OH9jqjvuvWLMLiH8wl82emLhOu/bWaWVp99i6z83dM8qz1mn7Pg/zq+P/L6wpxRcMYTEoLJtVkbNNsuWrlfWOpzem1WXFml3cNr0sbXJR09EOio7s7dlRf+3vrfvcLJpWxtBCK9v3e7h/9kQeX7OLe66akJZ/lcvBomlluAbmM6zIyoo1u9LatNrlYGrpABbWpNe1pszJkpkVbM3xnFas2cWiDHXT7j200Jq4V3Id5leV8rcP9rBkZkXatdUuB8tmT8Q1MD+9TC4n5hjcP2tixvfl/tkTOa3YxqnOsCIr988+3EbJz64redfek/tnT+TpD/ZkTZ/r3cmU1+/e+jQtr+Ry3fLkBu7rkL/O91k2eyJ/+2BP1nts3efOeu39sycy1G7Jmu8tf9zA3z7Yk/H6zvW456py7nh6IwBPf7AnpY0755vcT2nHzy4tZtms3P2uVuZM12vPpPPx+dWlDC20ZuyvsrWLVpdt+9yJMnVOM7TQyvzq7veDNWXOnGXXRXO74+3vnFZso8Bk4L5ZExP92oo1u5hfXZqxj0z+DmpUuxwsmVmBotMl+k6I933zqw/33ReUOXnomkkU2mQyKPQehTYTD14zKU2Z01/eT638f9+yDyOwbNbEjGPWW6eVUeVyYozFuOPpjdSUOVlYE5fHGpeD+66u6PN1FXqW/v7u93dOK7ZlHINl+7Z2noMkj+ue/mAP549xsmh6WeJ8pnGtNibKNVbUWFPXhLWTAa68G8KpiiUayTl3twCHPH4Z4wrCScawImuanuE7f/2QypHFLFpZG9cVZ5gjd9ZBQ6reUdNJjxmYh6KDwQUWzh7tYMzAvIx61ZoyJ/fNqqDZq7J9vycxhtfOde5venqMV2gzMW3sQIjGsupdqlzxvDvfUxvXbO00rqkpc7JsVgVGwAgo0QhqNMJnh9yJPO+fPZFoNIzNpGCELvWkMrYVjjedPZKsXPcZ98+eyPZ97sQ6k18NY4zGqBrjzCrTmdY17p89keHdXAvozruupdm+35MyN7jj6Y3cPaM8TY41verWfW7UUIwlM+PzEa1eK9bsJM+oxHWFGfQNmfTOVS4HajSc1m+sWLOLRdNcif4wuXy3PLmBZbPiOtlMc5oal5N7Z5WjxOJptT5wsN3Cg9dM4uNO9c3UNoLQkzz1Xj0f7Wnl1ulllDrzuGTCEKpdTpa9tDXrxmJBOBJ0MfGzA8B3vvMdrrrqKkaOHMm+fftYvHgxH374IVu3bmXgwIFdXu/xeCgsLMTtdh+TZexBT4CWdhVvMIwjz0QoEqMtGMJmMqADdDqwGhUC4QiRKLQHwxTZ4i7L/GqEdjWMT42QZ46njxEjGoVYDNBBQA0zyG6hXY3gDYbJ70iHDox6Pa1+lXAkRiAUZUCeEYOixxsIk28xYDMp+NQw3mCEfLMBNRwloIYYVmTDq0Zo84firsVMCjGIHwuE4i7dlHjedouJcDRKWyAcL6MuXic6ykcMwtEosRi0toc4rdiKotfhVSO0B0MMyDMTikTxBsLkWQwY9Xo8fpUimwk1Es/XZlLIMxnwhcLsavRhMypEYjF0Oh3BcITBBWaKbSbW7WxikN1CMBzFbNDT0BbknNIB6DuKE41BIBzBp0awWwyYFD3NPjXxLGLEMBsUwtG49arNoBCMRghHQKcHk17fEYs0jM2sYDEoKLp4PZu8KlaTQoHZQHsoHoOxwGIkz6TgD0doD4QptJridQ3G2z/fqBCJxgh3lMuvRsi3GDAregLhKO3BeJsqerAYFHyhCG0d97YZFYx6Hfs9ASKx+K4I7bkTg1gshrkjTSAcIRwl8X5YjXp0MTAZFQKhCL5QBF8wQoHVgMWghxjkmQ0EQpHEMy+wGCmwGPq8IUpPyW13ONDq57NmH3lmBW8gQqHVgMWooIajCXkPRqK4fSGKbEaMip5mb5A8ixGrUc/dz25i2exJ+JJkQY1EE+9OgcVAuxqmzR/CkWcmGInS4AkwyG6Jv7sdeeWbFBo9Ppz2uNz61DBWo4JPjdDoVSkZYMViVOLxNEMhHHmWlOdqNerZ0+RnUKGZaIwUGW9uj9/DpOhx+4I48uN9jXZtvkkhEI3Q7k/PN9+k0OILYDLG83L7gxTnWfB16kea2oPkmePpfeEIbl/8+nAkSlANMaDASjAcTeRrMym4fQEKbYfz0o43eQPYbUbMeiXRx2htp7VrvknBGwyQb04vbyAcwGxIr2N7KII3kJqX3RpvF09SvbTjNpNCY1sAm9mIxaDngNvP8GIb7UlpCiwGvMEwHl8IZ8HhZ6+1S4svSLEttex6HTz5zi6+coErpY/ONyn88s1PuGbqSM4aNeC4vvfHgyOV2/0tPsLRWKKvtVuN2IzxPtKrfYuI99s2g3L429XxnkSiUUBHuxrBH4pQ1BFCpD0YTrS1M98kE0Ghz+D2qTR61US/1BfezyOR212HvOQBOr0OFWjrNJ4x6XVsO+ChON9MvtmAXqdjV1M7ik5HgyfABWUDGT6gb48/hONDX3z3+zvdld1DrX6CkSjejjlmXmKOGcNqjM8T2pK/uTpS5lYWgx41GsVqUCAWIxiJouj1BMMRdOiIEZ/35psNmAx6Wn0qeeb4/CzQMe5rC8S9mK1Yswufmmpw+7evnUdhx7db3g3hZCeX3G7Y3cwIm4GAXkmb21iIccCnkm8x4ZYxriCcUE6EbsrtU2lqVzv0lPF5sS8Y5ppfvYMz38SPvjSJIYXWhG5R0xk2eALoYjqK8o14AxHyLQpNbSofH2zjwtMH0h4MU2wzJXSpRn38ux2OxtL0qnnGuE4oGgWrQcGrhmkPRii0GhlUYM7a3/TkGM/tU/n2Xz7igtMdTBs7OEVXkmdSKLQaKbSZEvd0+0PYTAomQ1z/bLeaCHbo8ewduloTEAEeeauOKyefhqLXQSyW0MEpsRiftwWxmgwEQxFcg/K7VX4Z2/ZtTqROuadx+1RufaqWtzpC9TjzTbz4tfMJ6cCrhtHrdHgCYb7+xw/461enYtKbUKMxYpCij8wzKinrGke7FtCdd93tUznkDRLp6Fvag2GK84yYDXGvQ9q1eSaFYCSCoovPJQyKDpOi4A9F0AFGRU8oGiHfYCAKtHfoG/ItBswGPS0d8xObSaGlPYjZGF8DM+p1QJgCiyVtHUyNRFCj4A0cXrty+0OYFB2KXo+xY/1Em9Po9Tre+uQQF5Q5MSpgMab3gVqfHYnGiERj+NR4Xyn9gHA88Klhav7nTSqGFfK1C8ckjjd6g9z+p1qWziznv84b1XsFFE4K+k78jF5m7969zJkzh6amJgYOHEh1dTXr1q3rliFKTzLYbmGw3dJj+dXWtzD7kbdTjj1ywxRufnJD1muynX/khim0+lRe3rSfNXVNieOPzp3KwifWZ80v0/kql4PKkmK+MG4QP/7Hx6yta2LRdBclxVZGDLCx/M061ibdo8rl4AdXjKfVp3LIq1LqzOO+F7cm4hsumu6itr4l5ZpH507NWI9MaTVqXA6+d8V4PIEQCx5fn6bIvKDMycNzKhMTk0VP1abEWExO9+NrJ3PA087Dq3ak1eXWaWUYIlE+afAC4Mgzc/nPV2dtw9e+eQGnOfKynu8OnzZ4ufbX67Kef+NbFzLiGO8hZMbtU/nvpzdmfVeS36n7X9qWMV2Vy8FfP9hLbX0Lt04vw5GXe/D5aYOXL694P+v5R+dOjYfUSZKFRdNd/GHdZ2my8Y2Ly/jgs2ZWZ5KZMie3TnfRFggTDEexBCK0+lTOHFnM95/ZlNJXaBbmtz1VmyJbufqI5aviodL+8Y0LKB2Yz4A8E9/+60eMG2qnckQR4WgMi1Eh32zgf17dnrGMVR075RV9jEEFZu56djNr6pp45IYp/Nv/xmXCZlL4xZxKfvp6qryuvPEcrv/duznbMVP/9+jcqXzpV+sypvv9wrP52h8+YPn1lSxftSOlzFobXfbz1fjUSMr78d6uZv79N9ll+E83ncuausbEIsyr36jh9+/u5ffv7s2Yfl71mIzHTzZsZgPbD7Sh6HW0BcL8e0c/mPNbUOZk5uRhfN7q52ev76DG5eDBayZRUVz4/9l78/io6nv//3lmn8meGZawhCRMMAtbkE0mUUGt7Oq17RVsLxBcWkRtb607IktdanvbCnq/rYLaXtHf7bWC4NZabE2iIkoqEEAYiYQdMkkmmcy+/P6YnMPsA8rueT4efVRy5pw5M3M+2/vzer/eZ/v2ZWROGTF76ELE7vTi9AYQtEoWvb4taZ9eVZjH9OEFrN96WBonRCL7TZlvFxfys38hY3d6eW/3cUYOzJXWM6nGWIvZyPTh/RhXlE/dnlbGFOWjFARcvgCfH+hgxIBcnq/fG3Xu5aUmnrhxOP1y9Qnv4ctjDml8T0SeQcPg3pnf8JPKyFz45Bk0/GTd9qRtc/l1Q8nP0FDcS24vMjIXG93eAIvXNVFnPRFvWrvAAkCrw8u8F+PjGu/+pIbff7A3aZ/R5fFLc/Eas4l7J5dhc3iY++KJWNTbd9fw3/+wcs93LiEQgl4Zuq/laHK65nitDi/v7TrGe7uOATvjjv/9P6+Q3k+K/a5pjPreRGLjVgCTKgpQKxV8tNd24rspNbFkZiXrtx6itHcWuSf5eeS5rcyZQnQbuf+1rXywp5UnbxzOz9Zuo8FqY9Wc0dz32lb+NH8crQ4vE399Yl8p9Z6KianDC5g6tO8ZaeNnoz0caHOy+0gXhcYMDnUEouK9KWPjZhOXFuXx2/f2ACdiu/1ydEx9uj7p+40pyseYoWeQKX4/Rm7/MmeTP396gPZuLzeO6h/1d1OmljFF+bzw4Vf8YPwgBEE4R3coczEgi1F6ePXVV8/1LZwRYm3XALSq1NWZkh3P0asZmK/nwde3R/1drGGXbHHSuL8Dg0ZJbXUxowrzUCkE8jM0KAUBQQFVhXk0tnRQNTAXgPkvfUptdTG1luLw5rZaiSlTw6/++gUbdx0HwhvHi6aVc//kMlranQzI08dtRiS7r6qBuXGvFamz2phrd0v/XjjJTNXAXOk+trS0Y+v2kmPQ0OrwJhQNAHywpxWnx0+LrZufXj2EuyeFyM/U4PWHsHV7CBHeIL3zuU2smFXF4U5XwuuInA4rrNh6kLF0pTku8/VJ96y0OrzSJPOXNw7nH7uP0ztLKz13R+0u+uXq+fHLW3B6A2hVCpbMqKTV4aWzxzXEFCFOsTu9ePwBnr15lPTcxmaJevzBuLaQrG0M658jTahjqdvTyo+vHBw1Qa8pNTFiYC6P3TAs7Ebi8ePzB/lwry1OiBJbW1SkwWqj1lIs/Vt8flsdXt7beYz3dh6Lev3bd9ckXBCI1/L4g6iVCp58Z1e4hNeUMhQRE6ja6mJeaGiW+guxz9KoFKyaMxpBEOK+x2T1ghN9Jo8/KP13niHcLysFganDCphXXdLjwBHio4jvKNJ60e70kqVTpfxNuz1+GlvaeXpWFXe90ohGqUjZN2uU345Kfa0OL9l6Nb5AkKOdbq4u601Zv2yureyTfCzY08pD08ppd4Vrw9ZZbbh9wYSvlZGROX20Ory0Ob0oldqUffqCK81olApe/aQl7njkuCojI3PmaXV46Z2lRaNUUG02Um+1pVxvNVhtzK8uwRMI0idLy61/PJEAUFNqYsJgI40tHVHnfLCnlftf25pUaCbWkv8gifBbrqkuIxPGEwgmXBtAz5opEJTHUBmZixC708t9/7c1TlChUJAyZqBWKJhnKUYhCFExrchEI5E6ays/C4bIy1BHvU4AHppWwbtNh5l4SR/8jtA57WNONTba6vAmFKJAuN+8b3IZowrz2NLSzo5Ddg51uDhkd0sxbgjHF7yBIH/6aB9P3jhcjr/KnBf0y9X37Em4CYWQ+oFtB+2UF2SzcddRaswmPmtpp7a6mKqBufTK0qbYU2llrqXogp1H2J1eWtqc/Ppvu/nvmy/lo722qP4xZWzc2sp9U8oY0idLitmu2bSPB6aUU1NqSpp0+tFeGxNKjOg0ytOanC4jcyoEgyFW1zczrthIr6z453BSWW8ef3sX2w92MmyAnKQp8/WRxSgXGaKtmbhBnalTRQXmDD31smvMpoST6WSbq1eX9yY/I1w2JnYzdHV9M0/PqgKIWsDUlJq45zuXsHDNFp6eVcULDc1RE5bIeuFPz6rCH2E7Fvm6RKpbpzfAA69vl1S3CkGIE45sPdDBLdUlcfeVDo8/iCCQ9J5vqAorBFMtYAw9pYrWf36Iz1o6eHpWFb+LcUipKTVJnzvdpnBWAlHRqZJImHS630MmMaey2PUFgry19RB1VpskiJhQYkStUvDfN4+icX84Y/TBmIxxUbgQIlw3MzZQIAoUxGC/VqWIEkgAcf9O93cRhSCwas7oKNHWL97cQUW/HD5v6WDJdZUoBIGt+zuixBM1ZiNzYgIYIqZMDQPzDay/04LDHS49drTTjdPrw5Sp4ckbh9M7W4vTE8CUqcWdRnzT6fKh1yh5e/tR3t5+FIC376qRFhaRmyaiS0qi9i9+j1WFuTwyo5In3o7OpEkUlBG/b/G4PxDitstLeL5ur/Qbir911cBcVsyqojDfIFk0HupwxQWOYn9TUQAj9jG11cXYHB7m9Qh6Yt2Z5lmKaev2UPItyHp0eHzk6NW0dnoYPiCHCYONfPiljW53IOV5B9tdXFPelz/8s1myC5WRkTmzdLrDNtidrtTtTaUQaHV4eOLG4XEiR5AFtjIyZ5NOt49AKITT62fZdUP5eG8bGZrUYQaDRklLm5MN2w5HzWfE+esdEwfz1Lu7o85JJTSLze4UkWuqy8hEk2587XT5vjWCdRmZbxNHOt0JY8CH7e6UMYN2l5e7XmnktR9N4LDFTYZGicMTLouXaA5u6/ZQbMrAoFFSVZjLPEsxIeB4lweVQqDL7QuX+z6HnGpsNF08z+kNsKWlnZ2H7Dw4tYJD7S6Wv7mTVXPGsHruGCk+dajDxX9eM4TG/R3cMLJ/ymvKyJwtcgwa3P4gzce7pb8JAiyaVsET7+yktrqYOzWDsfYc7/akjqOJZdNFYvepTGlcvs8lrQ4vHS4fPxg/CH8wGLfflS427vD4uefPn0sx2nmWYvzBAItnVLL4je0J+9i7XmmkoiCb1i4PwWCIgiQukDIyZ5IP9hxnX5uT2urihMcr++WQrVOxYdshWYwi842QxSgXEYc6XHGb0NeU92b59UN5eO12Pt0Xzph/edM+5liKCBKKGwgfmVHJL2M2VyeV9eKhaeU8snZ7XDkJMXh41yuN1FYX8+CUcva1OdGqFDTu7+B3f9/NMzeP4sl3dsUJQsR/VxXm8UJDM/dNLuN4lyfuc8Vm1kVu2nr8QUp6ZaBVKvjjR1/FbRwPH5DD2OJ8Hp5WQbcnXEYknTPMgDw9aqWCf7W0x2XlNVhtPPpGE4umV6DXKFk4yZywLnltdTGPrAt/XwsnmaMcF0Tq9rQSDIW4b3IZf91xNGk2Qs1pyuaTMwbPHSe72D3a6eaB18O2iAaNkmdmj+Kw3YXLF6DD5UOnVtInS4tBo+CzBBmj/9h9nLe2Ho4LMkQKFFZutErChchMDUjuipSuzWRpVdwUUT5GnFQX5ITVtMvf3MGIgblMGVbAQ9PKcXnD4hKVQmDaivq49mPK1PDyLeNZsr4pqk1Um40sv34Yr9w2nkffaKKxR+j1dKzQy2zi9QUTOGL3sHlfG6vrm1EqhLj3CYSCUuAlclER65IS+T0qCJfD+euOo8x/cTOPzqzkp9cMweEOkGtQS/1wIveXGrOJh6eXEwiGuKq8d5SiPlKEZ9AoeeuualodXo52eVi2vinOISDyN21saY8SwIiuMhlaFXNe2BzlNCX2zXe90shrP56Q6me9aMjWq/l//7By+5VmGve10y9Xz5vbDkc57yTjkbXbefLG4cx/6VNZsCcjcxbI1qlxegNotKmtP8PtMcTv/r5HGtvij8vIyJwNcvVqcvRqHntzB7PHD+KtbYeYm2aMDQZD9M/Rx81RAT7b187iGRWU9c2OEjqvrm+OE5pFBrhz9Gqe+t4IHBE14+Wa6jIy0WTrU4+P2Xq1LEaRkbnIsDu9HGhP7MasUgjc2RPPTRQz+MuCCVQV5uILBtnS0s5lJcaUZdoBFr/RxBsLLRzqcKNWKjColTz17i7um1xGMMQ5F6Ocamw0U5t660R0qJ1nKeaJt3YwdEAuTm8Ap9fP/Jc+lWLnaoWCYQNyeH/XMUxJNvxkZM4FDrefDJ1S+vfQfjkc6nBR0S+Hvtla7C4fb247LJXwSYVOrZTW4on2qdKV3jyXdLp9ZGlUTB9egN3lY8WsKhSCQK2lmFurS8hPs28SCIakNY24xnloajkOj4+qwryEfazofn6k083Wg/avVeJIRuab8urm/RTmGyhNUtZWqRAYXZTPW1sPc//kMrlUj8zXRhajXCTYnd64AR7gbz2lLJ64cTgdTi+Pv7WTOquNj/e2RS02cvRq3L4As5/7mB+MH8RPr7kEu8tHll6FQa2KE6JA/Ab3v1raAeI2BOZXl6S0gq21FLO6vhmtSoExQxtXEiNykziRY8HCSWY+T1CvUPz39OH9gBC/e283dVYbP7m6NKkzjMVs5K87jkob9rFuEhAWkexvczL/pU+pTvKaCSVG6f7S2VR7/UF2HLInzEaoNht5/IZhp2UiImcMnjtOdrHb3u2Vfv/bryhBp1ZIE34Ri9nIwolmbr+ihN/8LdoesHeWNqV9aK2lOEp9XVtdLNmpQ/LSVo37O6JeF4nFbMQbiFaHN1htaFUK7p9SzmUlRqoHm8jUqXin6Qgbdx7lV98bQY5BQ/NxB1WFuXHv9+SNw1m6oSnu7/VWGw+v3cbCSWYaUgm9rK0s3bCjpwRYO6vnjKHb46dXti7KPSUUEnjts/3UWoqj1OfpSnndKwiolQJLrqtkVX1zXJuNzNQXBSj+YPg7uuHZD3F6A0kXcGIft6inz101Z3TKUhX3TykDiOuDPP4gBo2SUYWJP0u12UiGRhn394uNo51uHlm7nXnVxfxiww5+PrmMpRt20GC1MaYoP+lYUFNqYttBe/j3nlImC/ZkZM4SpkwNOw530lenS2pnW2M20XTIzpiivLiSbiALbGVkzjZatZL7/u9zRhTmSfOiEYV5KW3/P9xr49rKPkB0aUZxHrTkjaaESRCRG+mpAtyDkwTSZGS+7WhVipTj67YDdi4f0usc3JmMjMyZotXhTXqscX8HVUliBpeXmlAKAvMsxagUApbBRhq+tCVPpDObaNzfQd2eVvbZnJJopabUxJwJRQSDITK1KtpdPuzOc1fGQ4yNLl63nUsKsqVExzyDmsJ8Q9x9aVWKlPGwSIfaqsI8hg/IpSbCdbzBakMAFs+spL3by7LrhsrxV5nzik63j7Zur/Sce/xBdhzu5MtjXWhV/Vnx/k7pGU8WN4ZwnLEgR4cpU5N0nypd6c1zSY5eTYZGlXAdMs9SzMZdx1LuJ3201xaV9NlgtSEIAkpB4POWjoT9bGSyaO8s7QVb4kjmwqXV4eFvO47yg3GDUopMRg/KY+OuY+xt7Wbwt8BlXebMIItRLhJaHd6EAQUIC1J+9h0fh+1uaTCNLYUDsGrOaFodXv7wwV6GD8jhhfrmk9oMrbUUU1NqYtH0Ct7efhiDRhm1KWpPYwXrDQR5elYVyzfsSBh0VEV0hLGOBQaNku9U9KFqYC6zxg2KK9HRYLWxaHoF/kBIurYgwIKJgxM6w8S6C4jvGftdiQKZ+gSvsZiNKBVC3GuTcdzhYfa4QbyyaV+UUjZHr2Zgnp4B+YaU558KGRoly64bSrfXj9MbIEevlkqByJw5EgmBDBoli6ZXMKowl72t3WTrvXR5/NI5Ey/pndJR6L7JZXFilHTPWqZWRVVhniRc+OJwJ4/dMIyH127ngz2tkgWhAFEL7V2HO1k0vVISiIjuRJeVGFEKAmqlIsolyKBRMnvcoDhHD7GN2brDk+s2pzehCKtvti6pgK3eauO+HgHGqMK8lEKvWku4XSoEgRnDC5jzwmbpeE2pie+PGsDPvlPG4je2UxWxaZLue9zf5qRXppYX6uOFMPVWGwICL98yjrZub9iC9dkPWTGrKupek03wYvu49PfiSvgdaFUK2rq9LJpeybINTVG/Z3WPC5bN4aHQmJHy+hc67d1ePmvp4MFcPTeNG4Q3EJSe4REDcrimvA9zO91RAsiqwlzumGhmU3P4O+v2BGTBnozMWSLHoCHXoMEbCHLnxMFMGdqXPtk6yR3hiN3F4F6ZfNxsw+UL24NG9pOywFZG5uzT6fJRZ7Ux13JiPRRpa93Y0iG5WgL0ydbx3s6juGJEtJDanU4Afv39kUDyRIzzOcAtI3M+cDLjq8Ptp0/2ub5TGRmZ00Wn25d0A3l1fTOr54xGAVGxm5qeObXLF+CuVxp57geXotUok5Zpt5iNPDy9nBue/RAgKmFJdIVeNK0Ch9dHMBQ65xuu/XL1LJpRyUN/2RoVT4l1bbA7vWzaG45Bzev0RMUNRhXm8vNry5j1XNglWIxB6VQKFs2o5PpnGqTr1ltt+AMhMjQqUkd4ZGTOPtk6Nbf+8VP+70cTWLahid5ZWh5Z18Kfb78Mh9cf1dZX1zez5tbxCOxiS4I5/vaDdvrl6FLuU6UqvXkuydCquOd//5UwGVsB3DeljGnDCng0xsU7cj/pV98bEXVuq8NDfoaan117CSFCUbFZ8bw1m/YBUFGQLZcbljnrrG08iEKAarMp5evKC7JRKwU+2H1cFqPIfG1kMcpFQroalp1uf9pNTTGTfsWsKkmIIv49FRlaFSMG5nL9Mw1cWpjLytlVLFxzIks/XYmP3lla/utvu5OUxBBYen2llL0T6VggZs499c6upOWDnN4AnS5f1Mbv0H45zH/pU8kZJkOrCtsqJqh5mijjVvxMkeWCsnRqrq3oQwh4/4tjdEdcI93n1ygVvLxpHz+5egitDi9uX9ii7aO9NrLKe5+2zeJU2YM5p0/vIpOEfrl6VsyqotXhpdvjI1uvYdHa7Tzwl23Sa9bcMi7qnFSOQgpBYNWc0VH25Tp1aqeL/AwNN4zsz9VlvaOsy8X76uqp4fnr74+MsjjP1KlYur6JqsI8bq0uoSBXx/INO6IW7TVmo9T2U20kADw6oxIAg0bFD1d9EmcJ64gQ5STC5Q2yas7otHapYt9Vt6eVuROKoo7V7Wnl42Yb6z8/RIPVJpX8gfRtVqtS0Cdbl1SkV2dtZa6lSMoEskRkxYj9Rq8sTZwLlNMbiHNlOZl7iaWm1ERBjo4QcPPzH/PkjcO5b0oZDneATJ2SY50eZj/3MS/Vjk157YuBTref2upihBC82NDMwoml0tixOmKcg/AzvHaBhbebDlP74mZW9DwPuXq1XLdVRuYsolQIBEMhHN4Ab207HBWwqTEbmVddzIj+uXT2iJ1LTBmsXTBBLskhI3OO6HSH522Ra0anN7x5dfsVJSzuEcbGllOdOaKflMQgzmdSudPVW09skl+IAW4ZmfOBUIi046u8ESIjc3GRrVMnFZFUFeZizNJyaVE+cyNiMiWmDApy9didXkYPyiMIUrzYGwjyn9dcwn2TBQ7b3agUAo37Ozjc4ZbiqQPy9FGJig1WGyHAHxRQKjjn/czBdicP/WVrXEwnVtTa7vSx9vNDNLx2Im4XGTdo7/bGOdT2y9Xj9UeXiIbwZ1YrFdz32lZWyqJZmfOITJ2K8r7ZeAJ+7ptczuf72/nZNUM4ZHejjEmkc3oDdHR7GVOczyNJ5vhjivJxeFK38XPdByTC4faniPPamNvpQSkIaUvuiBg0SnL1GhxuP/f8+XP+55ZxHGh3YXf5pPPWbNrH7HGDuOuVRlbMqpLLDcucVUKhEP/76X4uHZRHpi71HotOreSSPln8c/dxKbFYRuZUkcUoFwnZaQarbJ0q7UCvU4c36HRqZdTgm24ztNvjlyYe4fMEbru8hN++F3ZtaNzfkbwUgtlEnkGTdNO9ztpKW7eXBVcOJhgKRQU50214i24lWTo1WqVCWgh5/MEoZ5hnbx7Fgpe3JP18sWIci9nItoP2uHJB4rF5lmK2Heygxmykzmpj20G79N9xn7/URK8sLRX9crjpDx9HLWIAbhjZP+l9nQpy9uD5QY4hvElmd3pZ+EpjXJv4cK9NskV0euIXr5F0e/xRtXotZiPThhWkLAeUzgUn1PM/nUpBnwh7c7vTyw8vK2LFxj2oFAKr6tvinufItj+sf05Kx5JAMASAUiChJexbd9Wk/Oxdbh8vNDTz0LSKlK+L7LvUKkWceKd3li7KLer+17by5I3DKcjRJxSKwAlhSWW/1OmCvkCIhZPMTCgxohAEXL4AP72mlGH9c1hVH99viAK6WFJZYNaYTfTL0UW50lxeamLBRDPf/X8fseHOai7pm5WwprPFbESfRrx0MZCtUzG6MA+E8CbWT64eknTsqLPaWLIhLLoSx4oas5G8DLlvlJE5m6gUAl5/KK4MGoTbaRC45zuXoFUrubxHfCfPYWRkzh3ZPYGr2DWj0xvAFwixJEHpxQarjcVvNHHb5SVs/qpNEu2mQ1zPpkvEOB8D3DIy5wUh0o6v8kaIjMzFhSlTw+hBeVKp5shN1KOdbt7adliK30I4VjB9eD/yM8Lxq1/2OKQsWrc9Lqt/fnUx2w7auazESCAYYvXcMRyxu9h5qDPKQdqgUaIQQAE4PAFyDOpzVqrH7vTS4fQl3XQWRa0Ai9ZuSx038EXH7XL1agQBnnnfGueynaVT09rpoW5PK8e6PPL6Rea8odvjZ9GMCrQqBUvWN/HD8UX0ztLS0h52hY7F6Us9x1+0bjvLrxua8j3PxFzD7vTS6vDS2ZNoaco4tUSVdOsLsXzR5/s7WF3fLCUoVxRks3L2KI7YXWw/ZAfCfd7qOWP45Ts7uXdKOddV9WfT3nA8vHePM53oKHPXK41UFeZyrMvD6EF5X/vzy8icKtsO2tl91MF9ky85qdcP65/D6/86iC8QRK1MvV8sI5MIWYxykWDK1KTchM7L0KTd1ByQp2fZ+iZmjRsUdSzVeYky/sNOISqGD8hlS0s7Xx7rYtGMCpaub4rJvjFxz7WXsLe1O+Vn8wVCkpPJwPwT2empMudERxOL2cib2w7z+f4OVs0ZzfyXPo0LlJ6K84B4z3anl+fq9iYVwowpymdedTFatZIRA3IZV5xPkOgMhJpSE0tmVnK8y53wfS8vNWHKPD2LEzl78Pwi2e9xokyOgEqZvE4fEFUKCsLP1q//+gWP3TCMB1/fFtUXpCpbkMoxR7QmbXV4WbhmC0/eOJz+ufqoQEUkddZW7r66lGNdnpT3Lgo71EqFpKSvt9owZWp48sbh6NSKtH1OvdWGSimkrd0rohQE/iNGvDNjeIEkUjNolDxx43BWx4gUIoUiVYW5kvXiH9O4ihSZDLz88VdRfdRjNwxlzcfR5bhEYcyrm/bx6m3j8caI35JlMNWYTTw8vZwjdg/9c/VsuLNa+pxTnq7r2QAKsnCiOe5ci9nIwoml+IIXv0FshkZJfqaGg+0uADY125hQYkw7dkA4kPSLG4bRJ1t31u5XRkYGjBkaDtndKd3BHpyiQAAeu2GYPH+RkTnH5GVoqOmZd8UK8FOt1+r2tHL/5DJuGNkPhzdAtdlEVpqMLDFwnS4RQ95Ml5FJTIjU7psPTlGcthiEjIzM+YFYNvq+16JL0tSUmlhwpZnlb+6U/hZZbmJsUT45Bg2CQuCRGCEKnHDsnTq0Lzc/vynquvOri+mdFV5Hi86ksaXZY+NOZ4sOp48jnYnjsCKiqDVdyfpIqs1GBuTpcfoCDO6dJW00i8d0KgU5GWoMGmXacvYyMmeTLrcPpUIgEAxR0S+H5+v38oPxg+iVqWVTs01yqxfRqhRp5/jeQDDlPtXpnmucTGw7HenWF1qVgtX1zWy4sxq3L8Djb+2McQw3Ma+6CINGyaJp5Tzz/h7qrDZ+HgpxWYmRW//4KStnV/FC/VdRyakWs5E7J5VSlG+QYxsyZ5X//XQ/xgwNw/vnntTrK/pl88rm/Ww/aKeqUBZOyZw6shjlIkFcXNz/2lZpoDdolCyaXsGowlwOdbgYPSiPEQNy+MVbO6MCENVmI7XVxXh9QeZaisnQqFg9d0x4g/STFlQKgfunlHEsoj7mq5+08J/XDGHkwDw63T6mVPZBr1HxyLrtUcr3RdMqmD6sgG63j2XXD+Vwh5uOCDuy3/19N/dPLo+yb4wlS6uKcjIRN6nTlQ8CpEWU0xuAEDw8rTxOXJNOpNMnS8f/d9t4ur0BtrS0s+Lve/jpNUPSLkoWrmnk3btrePD1bXzWU0cxNgNh/dZD/OZve6iOKS2USjzwdZCzB88vkv0eoqX5//3oMrQqRdyEX6TGbKI+gdPQezuP8eDU8qiyO6nKFtidXh5Zt50RA3OZO6EoShyxeN12fvW9EeQYNDg8PkmocefE0pSfzR8MkaOPn8BHitUCoRBfHnegVyv55btNjCzM4/aawQw06vnwSxt9srU8Mr2C5Rt2xk3QxTYN4A8EWBQhZhGpNht5ZEYls3tq91abjXy0Nz5wsnzDTilbJZXTkgJ49bbx/HXHUamNalSpBTPBYCiuj+ifa+CmcYPiHJVEYUkgGOK4y8eksl5s3HUcOPFM1FYXc++1ZXh8ATQqJQoFfGVzolYqCIVCtDo8XNIni72t3VJfGgyF8PlD3DnJzP09ZXqydCocHh9eX5Bgj0PNxUyHy4taGXaAMWiUDO2XEyfkisXjD1JTamJgnp6Bp6lMmoyMzMmTY9Cw55gj5WtcvgC5GaqoevQyMjLnhj7ZOpZeP5RlG3awaHplVJZkuvWaw+Onw+XD4w+ypaUdlSK50DgycJ0uEUPeTJeRSUx3unKovoC8ESIjcxHSL1fP0pmV7G3txhcIMchoIBAI8fcvjrJiVlXCchNinLC925u8dEWSksiE4J5rw5nWt11ewksNzWlL4pwtur2p+0EIi1rTxVEhnFUOJxIXn3x7F/dOLmP0oDwpLlNjNrJoRiUef4Buj5/bryjBoLn4XWplLhzy9BoO2l2oFCdEJvOrS9CqlBRk68JJbqETMc5tB+0M65+T8prdHn/cPhWkTpb8utidXu77v61x7uOn2seYMjVJ4/BiwuOEEiMqhRC3twb0vH+INxZW4/UHeeD17eH76/ah69n3WrimkdsuL+Huq0vxB0NSX6BTK+grlweXOYu4fQHWNR7iqvLeKNLEyUWKTBloVQo+aW6TxSgyXwtZjHIR0S9XL21Cd3t8ZOs1LFq7nQf+cqK25TXlvXn8hmE4PH6c3gBef5DN+9oQBHjynZ1Ri4NJZb14+ZbxLN3QFOWEUFNq4v9+NIHlG5p4sGdgXTjJTGNLuzQQi8r3FxqaeeD1bXHHI/H5gzw8rVy6ViQWs5FA6MSGaaRLQDpHkxy9mv9Y/Ym0AKiztvLA1HJGDgxx/cj+LFnfRN2eVlbXN7NydhUKhLiN7wUTzRx3eNCpFQRDIakUxqxxhSnfW61S8Px/jKbLc6LeYCLF8Ko5o4Fw+QhBEFh3hwWFICQVD3xd5OzB84tUv4fTG+Cw3c2drzSycnZV1IQfwu1vnqWIhWviS7oAdLp8lPTKPKnnx9bt5aaxhUnLTdm6w445uXoNv3z3CxqsNu6bXJbymlk6FYFgKGojIbI/iM3EmTOhiLteaWR0YR5He7JTjnZ68AdD3Du5jJ8FQ7j9AZw9YjBJXAYEgwJP/W0nIwvzmBdTr/OJt3dy09hCPm/pYI6lKGEJnDprKw9OLaNqYC69s3XJVf1WG/McXlZutGLQKFk4yYxCIKVgZn+PE0ckpkwNj72deMGydMMOqgrz+Lylg6XXV6IQdvLezmNA+JnYur+D71b157DXz1N/3ZXA6cSM3emLera0KiU6jYIVf98T/QyZTdx5lRmt6uIPgBg0atq6vTTu7+DRGRU8X7eXedUlKc/J0atZdt1QXH4/hzpcZz1TS0ZGBjK1qZcomVoVSgQ5q1BG5jyh0+Vj+IBcutw+qgrzmF8d3mjJStOWHRFlJy1mI7dUlzB8QDi4XR8z/40MXCdKxIAzE+CWkbmYOJnxVUZG5uLkuMMrjbmmTA0vzBvDb/6W2PUWTsQJO92pxRuJhKfhWEs48fCa8j5J3XXPhVNztzeQOinxJEWtfbN1DMzXM3JALp+2tDPruXDp9TsmmcnSqeidpWHVnNEc63Rjd3oxZWpZ+f5OHppaToZG7mtlzh+ChHiubi/3Ti7D1u3FlKlhUL4Bty9Aeb8cbvzvD6mtLmaupRh/MESJKSPO1TmWLJ06ap8qXbLkN+FIpztOiCJyKn1MjkHD4zcM44HXt0UJUsQ476ub9nHPtWUcaHcldZmrs9rwB4LYuk84hnd5/QQI7205vQF++96euD7xrbuq096fjMzpZMPWw3R5/FwxpPdJn6NSKBjSJ4uP99q4/YrBZ/DuZC5W5NnPRUaOITyo251eFr7SGDcY/23nMTz+oDQZuOq//slPri5lVd3eOJV6Rb8cliao/1e3p5WH125jZGEe7/Vk78fas8W6DKS0b7PauHdKWdxCQFSPB4JBSZka6RLQO0tHjdmUcMJhMRv5x+7jcW4rh+0uQsCSN5p45uZR/OiKwdhdPnpnaZk6rC9zLUVRG9rzX9rMqJ7SHC80NMfV/EyGUhCY/fwmnr15VMrXqVUKyRWmbk8rCkFgcO/MtNc/VeTswfOLVL+HqLYWFdO11cXM6wno61RKMnVKZq5sSOokpFEp+PK446RqY/qDoaRuIACPzqgEwBsIxokfkjmCBIIhfvvebhZNr2T5mzuo29Oa1HWkbk8rwVAoXILLqOdYp4c3tx1OKLRQEO5HfvW9EZJ7SwjYuOu45CISy8+vLeOqst7c/PymhN+XQaNEoRBY3dDMzTHlyWLR9bTVSFGN6PbyoysHo1QIUYKZFT2iuUgEhZDSFrvWEu5fHl67nVpL2ElJq1aQq9dgytRgc3hZ+b416e+1bObQqGdLpRDihCjQo9YX4LHrU9dwvRgwZWrwBYOsrm/mLz+ewL2vbWNEYV5cGQGRGrMJty/A8jd3cN+UMu57bStPngPrYBmZbztalSKpO0K12RgWJAvhcloyMjLnnkytGkGADJ0qyiVzxayqtBmGIuJ8ZmxxPiML83hwagWdbh+5ejUFObq4ee3ZCnDLyFxMnNT4KiMjc1GSo1excJKZqoG5ePxBvP4Qj90wlOVv7oyLl0TGCbPTlNBL1m84PH7WL6zmK1vq0uxn26k5V6/m1U9aeH7OGBR8EZdctOy6odJcIlncrsZs5G87j7L5q3B2eGScuNsTIEuvxOsPSeKfmlITy68fSoPVRgjINchrGJnzB6c3QEW/HBr3tTNyYC4vzBvDo29sp85q49Vbx0e51S+cZOaPH33FmKL8pHG1yP5D3Kc6U9idXg4kSAaM5FT6GJVCYMnMSpzeAHaXj2y9Cn8ghN3p4zuVfQHoSJMQc7DDRUHOiRiiVqXg033tTCrrRUW/HKkPFmPbuw51ym5JMmedP338FcMH5NA359RK01/SN4t3tx8hGAydtKOKjIyILEa5SGl1eBMG/eCEKlS0HKw2mxKq1FMJSOqtNuZF1MeMVcLHnht7PLJkh3is1lLMoukVdLn8KBUCGpWCQDCEwx3goWnlNLZ0sGzDDmkSVGI0sHhmBUve2JGylEcsLzQ0s+y6oWw/aKdPttjhChy0u1mWYBFWb7VRW10SVRM0UU10kciSIOmCOUpBiCrPc6YWYXL24PlF7O8htocJJUYUCgGNUsF3Kvpw2O5GrVSgAEKhEE/9dRdVhXlUFeYmFYO8tf0IKzdaT6o2ZjAYSimOCIZC2J1efIEg//2DUfTO0qFVKfjJVaXcMdHMh1/aJLcgi9nIXZNKaXN4uXncIA53uFgyo5I2pxeNSpG0L2mw2rh/SjmEBJ6r20tVYZ5UzkqcmD9Xt5d7vnMJU5+uj/qsVwzplfJ7trt8dPe4QCWitrqY5Rt2JKz3G0tehoZXbxvPU++ccCUR+6KVG61YzEYpCFFjNkVtrog4TjKjqMFq49aaEmY/v4nLS02SpeSBjuTq+warDZc/EPVsOX2BlHa6Tl/i7+ViIsegoa3by6WD8qSMrtX1zay9w8Ky9Tv4rKWd268oYeIlvREEcHmDqBQCQ/vnEAiGqDtH1sEyMt92PIEg86tLmDmiHyW9MvEHQmRolSgVAsFQCG8wyPINO78VojoZmQsBU6aGa8r70LivXVojOb0B7nylkQ13VrNo3faoOcyksl785OohtDq8PHvzKGnO9+onLdw3uYzjXR7sLh8uX4CC7HghSiwhgHMcC7M7vdIaO1uvPilhuIzM2cYTCHJbzWAWTjKToVXFlPEM4ZHL38nIXLTo1Er+1dIe51b74tzRfLi3jWH9c/D4g+QZ1BTmG6QxLFOnSpkIGBv7MGiU3HZ5CVk6FbZuL31zdKxfWM3GL47y+3/ujYvPnG2n5t5ZWlbOHsXTf9/NiMJcKSkxR68mP0NDXo9QRIyt3Pfa1jiXhDkRJeFjY0kZWhVapQJf4MTnrNvTiqOnTJrTI5dDkzm/6PYGqBqYy52vNPLO3TU89Po2KZZo0EaLJMT9nsaWDp6eVUUQYhKLTTx2w7Cz9oy3OrxpXxPZx0TO13P06p65kJ9Ot49MrYpP97UzqjCXxxO4Wi+cZMaYqT0p4e7nB07sGzXu7+BQu5OHplXwizd3AOHv0e0LMGGwke9fOgDFxV9FXeY84l/7O/h8v53/vGbIKZ87pE8W//fZAfa2OjD3zjoDdydzMSOLUS4Ajna6ae/20un2k61XkWfQSCKKE8fCQa8MtZJuX4BOl4/Vc8ew9UAHggBD++VEbe52e7yYMrRsuLMap8fP6rlj2NLSLm0sQ3hjNFY0Ip6/ur456vjAfH1UINEbE8SIHKiTleyoNhu559oyfvzyZ6ycNYrf/O2LuPIk6xdWIwjgCwRRKxU89uYORhTmMq+6GINGiV6tZMehzqhSHtL5ZhPbDtppbOmg0Gjgufq9cQ4MkcKQSNy+E98JnNjMXLI+2jkmsuwIkNL60dIjWmlsaZccV87kIkzOHjz3xE56n7hxOF0eH0pBwZI3tkcHBcwmHp5ezhG7h4F5epa/FRZNfHGki5dvGc+yDU1RZXAWTSunqjCPr2xOqT0vXredX31vRNLf2JmiVq5Bo0SlULBwTWNSsdelhXm8vmAChzvcHOtyk5+pYckbTdG26mYjCyeVpvxeXN4AGRolPxxfxPP1exOWDAqGomfmDVYbD05JrRzP1Kqot7YmVZ9bBhul99p20J40wFJjNqFRChy2R9dKju0fC/MN9M/RMb7EyIqNe6KyjnRqJVmnkFGkU4c/W6SlZDJRjYh4XGzrXxx1pHx9pyt9reSLgSBBlsysxOULYNAouWPiYFQKgQenlaFRKTne6ebJd3bFLaBnDO+HKVNzTqyDZWS+7bi8foyZGlbXRzv3WcxG7pxopne2jtFFeRyyu9ColRHiYhkZmXOBKP7sk6PnrquG8OOJQT780sarn7SgAKYPK5DExoX5evxB4sZei9nImlvH88u3d0rum3CiRE+swPpQhytug+hkxNhngvPpXmRkUuHy+umbq4uLY1SbjSy9bigOd/pNHRkZmQsPu9PLw69vi3NF+mxfO0EEPvuqLSpJURzDBOC/3t3F4pmVLH5je8ya2ciCiaXMf2mz9DeDRsmzN49iVd3eqOuJjrfD++dyx5otUuziZEvifF0SCUUBntlo5bOWdir65Uiv9fiD/Gt/B/1y+kp/65erZ9H0Cva3OaNctCPjxv5gSIr9AAhCWCTr9ETHW7o94ddn6ePjQrKgVeZckqNTc7wrXFam2xud1KZAiIqVivsikc71tRFl0/MzNHR7/RztdEsij0TP9Ol65jvdvpMqu2V3eml3+li0Niy0idybiu3XLisx8sCUMhyeAGqlgro9x/nDB3ulNp7y/XqSE1fXN/PmXdUsWrudVz9p4S8/nsDS9U3MHjco4X7YMjnJRuYs8vt/fklBjo5LC/NO+dzBvTIQgC0tHbIYReaUkcUo5zkttm4eeH1bXKDgiRuGEQTpWPJB1MSCiYOZ/9Kn0kR5UlkvvjtqAA/ELERixRgGdWLRiPg6tUKR8Piksl5MHVoQ9TkiB+pkJTvqrTZC7OKp7w5nb6uDuZZiZo0bFCWAefSNJqYM68shu5vPW9qps9qkYKVBo+TZ2aMoNhninCMsZiMLJprx+AK8ett4Fsdk58EJJW+iUjziJrH4/6MKcznU7opyccjVh8Ud1z/7ofRdr65v5umech2x9zMvRkl/NsrlnGl7PJnkJApSV5uN3Du5jCff2RFfwsbaytINO6gqzOP5+g7mTCji471t3DS2kCff2cnIwjzm9dTrLDZmsHRDEw+8vl06X3zGbN2JN9HtTi+GFDXBa6uLJVvGSGLbyfINO1k8sxJjpoalMUKU8Oew8eMrzSm/my63LyzemGhmbHF+nDPKmk37+Pm1ZXHneQPBlGIvXyDIq5+08PIt41m6oSlusn/9yP5SmSxBgAUTBxMklLDv8AdDZOlUkgBldGEeBbk6lm/YESMiMjK4dyZ3XzWEB9duizr2+A1DTzqjKDui9ESX24fd6U1rjxtZriLHoCFDq0wpKIzNcLgYOdLhAsJOCgoBXpg7mmAIFq3bTlVhHv1ydHGloSDc/h5d38RT3x3OvBc/PevWwTIy33aMGdqojCwRsa1OH96Pgmwd+Rla2p1eWYwiI3OO2Wfr5pF12+MSCf78o8vYsq+doQNyONbpQadWolYqePzt+Llvg9XG0vVNUaVgIZxNfN9rW1kZ4VJmd3rj5tUQFvGebUez8+leZGTSYczQxsWXIByLeWTddh6/Ydg5ujMZGZkzSavDm9A1tba6mBUb98T1CR/0jL0PTCmj0JRJm8NDVWEeD00t57DdDYQTety+QFT89bbLSxKWgRePTxtWIMWSLGYjS6+rPGNjZDKh6EPTyvmspT1pvPuyEmPUPSkFQSq3E4tBo6S0dyb/89FXcY4zS6+r5KV5Y9m8r43V9c1k9TjMaBQK7M4TcbpDHS4eWbedsoJsqgbmctju5liPO03/PMPp/lpkZOLQqhXkG9T8982j4hydWx0eFk4azJRhfemTraNXllY6Flm+R2TVnNF4/Co+29dOn2wdHn+QdqePT5rbuHJILwpy9adVxG3QKFPuvSy/bijd3gD/3H2cDVsPRcW0E5aTt9p4eO02RkaU36oxm1g5uwqPP0jToU52HLJL1QJi9+vuufYSZj33MU5vgH2tTpbMHIpCgC63n7J+OUn3wx5Z1xS11pGROVN8caSLd7Yfoba6+GuV2TFoVAzMN9DY0sH3Rw88A3coczEji1HOY452upMGCg7YXazYaD2JQbSVIKEogUVFvxweWht/3dhN5kAolPCaDVYbAnD/5HK2Huyg1lLMzT2ikW0HOxjWP4d/7W+P2iSOnBikKv/TYLWxaFoFz9c3x00gRKHMXEsRfbJ1Ca+Rn6mh1eHl9ssH88CUcnyBIHanj09b2pn/0mae+4/RHO/yJC1dkahcR43ZBIRFNqKoZm7Pa8R7EOuKhkJEuRfEKoUztCq6Pf44JT0gl8u5iEkWpK632vix25+y9EqtJdweg6GQJCpYXd8sZXD0ztJGuaREngvw6IzKuOuKE/8RA3OTijkmlBhTtlOxndRZW9ln68aYoU3arj7ca6Om1JSwdJgowqjb0wohmDKsb9RCXxTV9BiwR2F3+hIuAMRz7E4fN40tZOmGpoT96JI3mqT+bmi/HO58pZEnbxzO/VPKJMvqo51u7nxlCy/VjiUzQvQH0FjfnnDhgiDw82svYX51CVWFeZLj1LI3d7J67hggJCnxa6uLuazEiFIQcPkCLJxkZschO0LEx9VrlCx8pZFaS1FK95aMGHFRtlbFqjmjWfm+NS7AsmrOaLJTiJEuBuxOL1+1OWk+7uDNbYeZX13MEbubDT3ik9oEz04kdXtaeXBqOXD2rYNlZL7NHO10c6DdlXaulqNXs3TDDh6aWh4V0JWRkTm7HGh38nAC8VjdnlYeWdfE1KF9mbGiQfr72gWWpGNvbCnYyGsd7fRI7fxkStKeTXvw8+VeZGTS0e0NpGx/ne5wNrMs8pSRubjoTJJcIcZnkyWxBEMhLi81oVIq2HW4kylD+xACKVYsnrfgSjNKhUCuXp2wDDycmMOPGJDLlUN6sfe4g0+/aif/DCTNpRKKzml3JY2hN1htLFq3PWpT2JSpSRrPenhaOY+s2x6flNUzBxoxMJfGlnZWzx1DlkbJvOoiDnW6CBFOILI7vTyybjs3jS1M6JbwxL8NZ0C+LEiRObN0OL30ytLySXMbw3vcP0QEQcDlC/L2tsPUWW0snGROmRTYuL+DKZV945K+LGYjxaYMFArhtIm47U4vW1o6GF+ST9MhO/dNDicxOr0BVAqB5tZudBol9/z5c+ZOKIq6n1R7U7HrkXAMNsTdVw+R9rfWbNoXlaCco1czIE/PD57fJO31BEIhvIGwY4zD40/b3yZLJpWROV2EQiEee2snfXN0XDmk19e+zuBemXy2r+003pnMt4WLeyfqAqe925s0UJChVZ30IBorsDjZ1+o1ypSBCpVS4K1th6Mm3Y/fMJQX6pv5rKd2oHhNUZSxaHpF2k29gx2ulEIZ0RIuEtEZ5ql3dsXZuc+zFEubwXZX+uz2yOuLpVLanV4enVHJfpsTgLteaWT13DE8e/MoyaYRwouUy0tNfBAxqRKVwhazkaoIZW0kg4wGDBolXx5zyLaMFyGpgtTpnknxeRTbpj8YisrgWDVnNJ+1dMSVhBHdLwLBaBFH5KL8s33tSdXj6jQ1MCPbiTcQlEpZJWJ1fTNrF1hYEiMKiXQIgvAEf66lKOpc8fWiKCCST1va2XWoM2oBILbHVze1MKm8d8r+rs7ayo8nDgYgS6viiRuHs7ohXgj3xI3DcfsCmDK0vNhzXBQJJbzunlbmTihi/kufxjlO1b64mfULq/EFg6gU8eWZqs1GFk2vJBjqsa01G9myLyzWmW8pZl51EaKYRaTGbGRedRFdbh9fHgtIfYhCgFV1e6O+H/HZWFXfzEPT4r/Ti4lWh5cMrZLe2TrqrTZqq0vona2Tft9EY0ksDrf/rLhWycjInKC920vHSYyN/mCIuj2thEDe7JWROYd0uf1JxWPinCiSw52ulNdLNj5HzpmTbaqduKez52h2Pt2LjEw6utKMr51uP0qFIItRZGQuMrKTxGHF8uvJXEKuH9kflSLIL9/dxaMzKvl4r42/Nh2Ji8F8tNfGrkN2brticMr78PiD7GtzsmbTPpZeN5Q5qz9hVGHeaZ/Hp4rBQeq4eF0CIekDU8qAXVHXtJiNDBuQw4MRDsWx15k7oYiVG60oBIGl11WycE0jK2ePkuYGrQ4vZQXZSd0SHnh9m+yWIHPGydSqsbt99M7WoVMpo5Lgcg1qfvXuib2WZC4kNWYT91x7CSs27qFfji7pvs6i6RWnTcTd6vDy679+wZpbx7NkfRO/+dsJIVy12cjy64fhcPup29PKrLGFUeemiwfGHq+z2vj5ZAWXFuZKScdiDF6rUtBi6+ao3cV1Vf2lPaDG/R1k6VRoMrT4g6G0/e0NVf1P6nPLyHxd1v7rIP/cfZz/vHoIKmXqfZ9UDO6dwT93H8Pp9WPQyPICmZNHflrOYzpjrNEicbijN35PZRBN91qDJpxN7w/EOxFEcrDDFaf+7p2tkyYoiWoH9s/VEeLULaAgOhM29rtJpWoXj6/caJXK7KSi2JTBn390Gb5AuNb5DT1ld8J2a2WSsKXb42fBy1uAsAXjrdXF5Bg0PHHjcO5/bWuUICVRLVWRa8p7o1OFnQ/kOuMXJ6mC1LHPZKxCujDfwMJJZlbXN+PxBxmQp+fJd3ZJz3asOEVEFEF0e/1RGeORi/JENT4H5ut5t+koOnXqthJ5372ztKhTTGKc3gCHOsJlre6bXMaBdlfCWruQuH9qsNoIJuiOdh3u5IFp5Ty6rilKXX5ZiZFqswmNUoE/0YkRqJUKGlva+U5FH36XwJ5W/Pd9k8ui+ryT7XNj+yCnN8Bxh4e+2VoeXhufQVNvtbFsQxP3Ti7DYjayaEYl1z8TziZ2+gI8sm47T944nHt73FsydSqOdbq59/+2snL2KG76w8fStd75SQ03JahHKoqATkaMcSHT6fbh9gWlzxkrmDqZ8cCgVbL0uqFy8EdG5izS6fanbZ9alYLMHncnh8eP+mvYi8rIyJweOtNsbhs0KlbPHSMJpTVpAl/J2n9kecFkm2oiZ9PR7Hy6FxmZdGTpUz+PmTplyjiUjIzMhUkydw+DWsmKWVVS0k0kDVYbS9Y3sWRmJRt3HefeyQF6ZemYX1MS575aYzbxyIwK3P7kSUoQHuMFQWBkYR6H7W5+/f2ReAOB0+5ymCoG17i/g6oY94dYuj3hUsmtDi9tTi8ub4BfXD8Ulz9AR7cPR4/j9YH2kxPY1u1pxeMLMqHEyNYDHUwbViDd56kKY2RkTjeZOhW2bi8efxCHx8edV5lBCD9/aqVC2ucRY64KQZAc6VUKgRAh/AFQKeDhqeVMXVGf8H0arDa6XKnnGKlE3GKbFBPwAqEQ8yxFLFuf2A170brt/PTqUiB+fXEy8YZYPL4AS68fyiNrm+JirAsnluL2BRidoZWEOfNf2sy0YQW4/QE+3dfGNeV9ePW28TwVEdMXabDaePQNuVSPzJljS0s7D72+nWqziTHF+d/oWoN7ZRIMQdOhTsYUfbNryXy7kMUo5zHZuuQ/T6ZOGfXvUxlE073W6fUz/6VPWTVn9EncZTSRm5uxtQNNmRr+/KPL+PhLW1JLtxqzSXIZSYRerSRDqyIErLl1HB9+aWN1ffNJub2IqlQgxfsbebfpCJ9+1RZVHxDCExmBL6itLqaxpZ3G/R0YNEoWTStn1KA89rZ2k633YsrQsGJWFUd6bObhRC3VUYW5UZvPl5eaeHRmJff/ZZtcZ/wiJlWQunF/h6Q6T6WQfnpWFZkaJV5/MOrZLcjRRYlTRCJFFHe+0igJmzrdvqSWgKvrm/nNv48EIEOjSltaB8Jqc2OGBqc3yKo5oxEEQbqWKDKpMRv5tKWdlRutVA3MlURciUjWP7m9AUmUI4rDbhpbyL///iNuGlvIQ9PLEYDlG3ZEfXcv3zIu6XsB+Hq+z9jvNfa79PqDUZn66frRAXn6qI2XSHeqYDDEVzZnnBBFpN5q4yGlgqrCPJpbu6Xv0aBWpnRvyYoZF4CUIr1Hplek/AwXOtk6NSqlH6c3/FvF/maN+zvol6NLOR7o1Uo6XV4g42zc8nlL7KJfdu6SOZNk61R8sOd4yjHoaKebYmO4XerVSvTq+P7vQkFuXzIXOtlpNrfFtaU4n911pJMaszGhm0p1xBwzEovZiCGinSdyoxQ5245m59O9yMikw6BWUm02JlyHVJuNHOv00F9OhpGRuejIMWj4xfVDeej17ZLjgUGjpHe2lnanL6XDmcsXwKBRIgB/+vgrRgzMjSuHsfe4g2AoFI4jJRnjLT19TGX/bBp74kMipzsZL1UMbnV9M39ZMCHpcYNGSbZeE5cwWFNqYunMSnRqBfNeDCdVpYubR8YgHB4/P/vOJWw/aEfT8/dsnZrDdnfKa3S5ffJ6QeaM0u3xk6FV0uVW0N7tIwQsnVmJw+un2xMWjySLV9eUmlh23VAcQR/+EBx3eFk5e1RcXFgkUlyeiGQibrHcfGybfHRGJc+8/2XCc+r2tPJQj8t24/6OqNhf7L8jsSRZj3R5AmRo/dwxaTD3TrkkKkHwzle2cEnfLB6eVsGIwlx+9/fdrOiJ4x/t8jCsfw6Pv7WTuZbilP2tLD6TOR3YXT6Od7kJhaDL4+cfXxznD//8kmJTBvOr40vinioD8vRolAo+398hi1FkTglZjHIek5ehSRoo6Pb4o46dyiDauL8j6XUjX5vqmleX9wZg1ZzRePxB9GolwVCIfrlaXl8wAZVSQXu3l0AwxGct7bz6SQsvzB3Dw2u30xhTwkekxmxk0YwKyQUgGVN+Vyf9d3VPUNMbSJ/hH1kOJFlpkjk9r3F6A9xSXZKw9EmN2cj3Lx2A3e3jiiG98AeCrN96WJpkiYuoS/pm0zdbR6vDizFDQ7Zeza+/PxKH20+X20eWTo0pUyPXGf8WkChILQpCJgw2cnlpLxZMHIzTG+DlTfsSigcUCCyZWUlrtzfq2MmIKD7Y08p9r20NK6z16qgFRKybSK5Bjc3h4Z2mwyy4cjDBUChpaZ1qs5Fl1w9j+YYm3tt1POo1YmmaqsJcFs+sZObKcLv+OhN+CE+ePm/pYN0dFlodHhq+tEntGUClEHjirZ2MKMxjbkRJGrcvkLS/qzGb+HBv+O+tDm/c8UhaHd6oQEK6z/HXHUcla8anZ1VJDi3VZiMf7rVRUZAddU6sQCgUClE1MBeN4sR7BkKhpOISAbi3JyAkEgqR8tlIYxpzwWPK1LDziIdjnW4sZiPbDtrpk31CfLK6vplnZo9i4UQzEDMelZp4YGpY4JSh/XZnNCda9MvOXTJnkrwMDbsOd/Lzay+BEFLAHE5kHSkVIAjhturw+BmYd2E+i3L7krkYyNQok2481ZhN5GdoeGneWPzBIHqNknHFRq6t7MuSN5qiyw6Wmlg8o5LH3toRdQ2x3WdoT4QukrlRXl5q4skbh5/VtdP5dC8yMulQCLDsuqEsWhft0FhtNvLIjEp+995uHplReQ7vUEZG5kzh9gd4ZGYFXl+4VM7APAOPv7WTWeMGpTzP6QlQW11MMBhido/zamQ5DDFG5A8G2XrAzsJJpYCQcA5fkKPlq1Yn86tLqCrMk+KopzsZL5VQdPSgPPIMyY8vml7BorXbo+4fwhvFD6/bzvTh/Vhz63hueWlzyrhQTakJCMd6nN4AGVoVh+1uKvpl4+1J5DRlajjamTreoNfITtoyZxa7y0e2Ts3RzrAw6l/72/n+6EKOdXkYZDQAyR3p6/a0smjddqYO7csDESWrYkuWi2RolKcs4o4sNx/73o+ub5KcqBMhCOHrxpYXEv8tQNx8aG7E/pFIjdmIAtCpVfzirV0JEwTveqURfzAo3YvPH2LpdZXkGzT85r3dNFhtaftbubynzDfhq9bu8By/p5y1iFal4KryPnzv0gHoTkMil0qhYJDJwLaD9m98LZlvF7IY5TymT7aOx24YxoOvb4sbGAfk6KOOJRtEa8wm7phopjaiPMyOQ3aWXz+Mh9ZuixODRJaSSVYHcFJZLx6eVhF3frXZyKLplazYuJONERvTNWYjf/7RZRzucEuvT1TCZ0Cenre3H+bSwry4Sb/4WURFvjiRCbuVCDw4rTzld9k/T8+v3v1COi/y/QFy9Gr+sfu4NEkyaJQU5OporG+Pr+E3sj9L1yfffI9dRMUupPpE70Gzt7U75b1/nYmIrJo/v8gxaFh+/VCpvaZSlM+ZUMTHe9vi1ON11laabd1x2d/HHZ6U7y0er9vTyrEuD9l6tbSASOXEsnh6JbOf/zjsOjK1nCOdbrJ1ajQqBYftblb09A3LNjRFtXcQxTPw6m3j2d7jClRVmBs14RdfJ332FKWsasxGemVqGVGYy5Nv72JUUS5VA3P5/26/DKfHT6ZOhccfSFiSZlJZLxZNr2T5mzviFPSLZ1RysN3Fwklmemelbh+mTA3/2H08SsiQTNQWKXyLdKipMZuYV13EtoN2BuafCBok+x2qzUaWXTeUacP68ua2IwiCkFRcUm+1cUuMUMmRxmLb4bm4LbhzDBqK8g30ytJS3CuT/W1Oio16lswcyqNvhAPwd6zZwu1XlPDQ1HJChL8zrVpJnkGNJxBug5kpXMoudpIt+mXnLpkzSSgY4uFp5bR2eXhoWhluXwi3Pzw3A/jiSBdji/Jw+4Msv24oOpXignwO5fYlc7GgUytZfv0wHo7ZuKk2G5ljKeLWP37KEzcOjwpgGzRKHp5Wzr1TwuUbNUoFbQ4vHp+fq8v7cPO4QdIa8Vinm/65Onpn66Let1+unhWzqmh1eKOE/uei3ZxP9yIjkwpPMIBGULB05lBcvgCdbj+ZOiXHOj387r3d/PzaS+gT09ZkZGQuDjpdfjY127iqrA86lQKVUqDOamOuJXWWdJZOxYQSI8FQaufVRdMqOGR3MWpQLjNGFHD31aX4gyEMGiUqhYBCEPju//tISgSqMRtZObuKhWsaJUHK6UrGSycU7ZOtS3p8VGEuD/xlW8Lris7bv3p3F6vnjqH2xc08cePwqO8BepIdJxTx8qZ9PD2rijWb9iEQLh3s8PjJFlQc6nDRL1fPIKMhaQLV5aUmtrR0yOsFmTNKbo/LoblXBkoFXG420eUNoHX5yNSoqDYb05aTmjuhKOpvsSXLIZzYrFYoWDDRTCAm8bEmhYg7WRKvQaNkxMBcrq3sQ0VBdpTrtxhTDxGS4vGxe0ED8/VRScM6jZJQMMRTf/0iKiZfU2pifnUxpgwti9dtT9oH1lYX4/IGo1yynb4AAidiuelctuXynjJflxabk3/77w9RKwVurSmhIFeHgIBWraBfjl5y5DpdlJgy+VeK6hYyMon49u6wXCAUGjP49fdH0t7tpdPtJ1unIi9DIwUIThwLiw4ev2EY3b4ADrefDK0KgfBA95cFE+hy+TFolehUSjyBAFWFecyvLiFTq0KvURAMhgfySwtzqbPacHoD3PVKIw9PK+f+iEBhrywti2KEKBDeDF22oYmRhXlRm9N1VhuHOtzYI0pcxJbwgfDG9e//uZcNd1azeN32qEw5S08wU5zIRypr66ytKARS2s2+s/0IN40txOUL0NDz2UTXgnmWYv5j9SdRE43a6mKWb9iRtIbfiMLcKDFK7CQr3SIqUiyi1yijyo/EcqoTETnL9vzD7vSydMMORhbmMc9STH6Ghv/66xcJFeXBUCipqtvjD8Y5G2mUqScTkcftLh+KCEFDMmV7Q09bvmlsISs3Wrm2sg+1L34ad+1Vc0bHCVGkz2K1ca8gcMju5r/+tjtqgX7/a1t58sbhPDilnG6PH4NWxbEuNz5/UBKtiNSYTTw8vQJ/IMjVZX24cogJU6YuTgz3xkJLws8i3t8jMyposTmljY2jnW72tzm5Y80Wqgpz+beq/kwq65Xw81jMRjQqhSRAUfRk+YgLmQVXmtGqFHS6/WxpaY9T/jdYbagUCu659hLaur180tyGLxCShC3JfgexxunjNwyjy+3Hk6b+ssvXU86nx2UlnYgiU3vxTwH65uo5YOvGmKEm15DD33cepXFfu9QWxefhre1H2HHITkW/HFZutFJjNvHQ9HJsbg+PvbWTJdcN/Vb2n7Jzl8zZxu708vPXtrJ0ZiW/+/sePmvpkFyj2rrDDlX+QJBQCEJArkF9wT6DcvuSuVjo9PhQAEuuq6SlzUmGVoXXH+SjvTZprhQ7z3F6Azz4+nYsZiNVEaVRX5o3lkN2N0XGDLJ0ajI0Si7pm8WAPEPC904k/D9XnE/3IiOTDCUKHly7nS0R42u7Mzy+VptNcJE7J8rIfJsxZmj48EsbT727G4Bnbx4FpHZ9rSk1seOQnYHGDIKkdl71BUN80tzGtGEFVBTkECREyBeet3++v4Nlb+6MipOEY74Ct11ewm/fCzutnE5XgHRC0WTH0yUMevxB6q02fuz2c9PYQu56pZEVs6pYcKUZu8uHVqWgcX+HFBfy+oPcO7kMQYB8Q9g1W6NUsHjddn71vRH0zzPwxL8N54HXt8XFcZdeN5SpT9clvA95vSBzutCoFHR7/RgztfgCIbyBECqlwLFONwONehZNr+RAuzPlNTz+eMd6UbwF4ed5ycxK7v/LNj7b1x6XoHysyyMln8TSmaBfSFfmXnTqzlCrWBIRjxfvs3F/B69+0sKvvjeCPr0zAdiyz4ZGpeSe71zCT64eElWGLBx/CCUtsSN+VofHT+2Lm6X7cHr8UVOrVP1ttdn4rU6Gk/n6BIMhFrz8GRqVgqUzK8+KqKnYlMG7TUek8VNG5mSQe7gLgD7ZuqTZKamONba0c/Pzm6TBOTIT7bUfXca/emp0rpozmu///lPpWG11MXdfPUQSjzTu7+C//rabm8YWUjUwF6UgJB1866025iVQ1YsT8khiy1Lk6NUsmlbOO02HmWsp5kdJJvKA5MpwohRHkEdmVLJ0fVNKe7VIBWxBjp5/7Y/fOAa4rMSYXPFrbWWupSju75GTLEhe17PbG4gTi1SbE9vXnWqdcTnL9vyk1eHlvZ3HeG/nMSAs4kg3gU2ENkIQIfQIIk6l7I1Bo6TT7ZPa3rWVfVI85ycyZNq7fXHtVdfjHhHpVBSL3emVri9uRtxaXcKAfANL3ogXnM2vLmZscT4PTS2ny+3HHwyx+as2DnW4eL5+Lw1WG6vmjOY378WL4QQheWBk467j/PSaIcx/KVpQYzEbJeHP4nXb+fnkMjwxZY9EwdphuxunN8CaTfuYaylirqUIfzDE4F4ZdDh9+AOpI7f72roZlG+QPkdkubJUGQb1VhsOb4AVs6rS1hLulamNWpCNKsxNKdJLttC72HD4AghAp9PHuGIjz7z/JcMG5tIrSwuEN8QmXtIblUJg5IBcINzPh0LQ6Q7wt53H8Pi/nf1nokV/JLKFqMzpRhRouP3BKCGKPxhiYJ4Bjz+ANxDE5Q9esI4oInL7krlY8AVCPPXOLh6eVsHhDhdVg/LYsPUwowrzGPuDfHplabmqrDe31fjZvK8tSoAfO+/VqhXsPGTnxlH9+V5P9rSYKfltFIXKyJxu3P4gu450sWJWFb2ztTjcAfpkqzja6ebXf9vNn+aPO9e3KCMjcwawO708sq4pKtYhxmiTuteWmlh23VDWfX6Qfnl6FIJSKtUe6UAA4VirUoB7vnMJHl8Qh8ePyxfg8wMdTBnal2Vv7oyLJ4nn3311qSRGOd0bWumEoomOZ+tSl3AWvze7yyc5MvTO1jJjReKS8/VWG3d4/ChQc9NzHwMnXJFt3WExyYB8AysTCGO+snUnjbeBvF6Q+eYc6nBx/2tbuW9KGSqFQDAk4AkEQYC+OXqCAfjluztZcKU55XWSOX5k6dT8/T+vwJSpiUrGSBQDHVuUn7C9ZifoF1IlVwI8PK2ckl6ZuP1B3tt5jA+/tEXFFq6t6MuVQ3qx+5iD/AwNmVoVuQYtixI4n0A4Nnz3VUOSfn6DRkmvLC2+QJBnbx6FTq3kiN1FsSkfn/9EvDhZfyuWS3Re5A7WMmeGtf86yPZDnTw64+wIUSAsRgHYcaiTcSXGs/KeMhc+shjlAidVOZZsnRqnNyC5ENw/pQyHO0CWTkVrl5df3BC2U9520C7V+RYdQ1bXN7NydhUv1H/FZy3tkq0gQPVgU8p7SqSGFQUl4qZ50jIlPWUsFAjc/PymqGsYNEp+ek0pUyoLWLYhpsa42cT8mmJuqS7hvilluL1BdBoF7zYdjRJ4RL7XK7eO47LBRskJ5sS1jGhVioSb7+JiKdFnjP3seo2Sn/35c8oKsqkamMthuxuv0cBjb+2MEyKIm8WRjhhfp864nGV7fhIpAKkamItBo4q27YtZXIrPUeQ5ALkGDbXVxdz/2lae+4/RkiBi5vB+cW0itlyMxWwkQ6NCpw5Jba+iIKZmVBJCIZK210QiKhFTllYKVujVSoKhEH2ytXFCFDgxCa8qzEOjUvD0xj00WG0snGSWBBwAvbO1CRcG7d2pF+HtzvjjkRsgdVYb8xxeqgrzqLUU4w+GKMjR4fUHOe7wMDDPwGM3DKUgR88da7YAYVHRo29EC+CS1UUtMmZECWZE56na6mIyNKmH4i63D3J0IITfUxCEuGenxmwio+d5ERdkLTYnf5w/lkVr42vCL7t+GB1OD4OMGSnf+2LA4fbTJ0tLyBD+72dmj0KnVvDkO7uk8ai2upgJJUbUSgUvzB3DZy3tuLx+th3sAL69/WeiRX8ksvpd5nQjCjS6PT5p7qlSCFxV3ptjnW4EQaDpUCc/+9/PuXRQ3gW9QS23L5mLhVAI/r7rOD/7ToDhA3NQKRQ0tsSXOp1nKWbHIXvcPClqXRUKcffVQzhid/Nf3x+JPxhiS0s7j6zbzq+/N+JbNw7LyJxuXF4fa24dz6dftQHh9ufyBTjW6WbNreNxuFNvwsrIyFyYtDq8ceXQG/d3SLHg2FLqOXo1SoWALxhkRP9c9GoVT8TEMi1mI8/MHkWIEP/z8T5GDsyNKgldW13MZSVGOt1+/vf2y9h6oIM7I8Z/MXYSDIU3ak81Ge+bkiyebsrUcHmpKap8T+RnFhO+tCoFLm+QPIMGlzdxjFhErVRgd57oX0VX5EdnVEp/SySMyXSk7pPl9YLMN0FKaLXauNsX5Ef/8xnPzB5Fjl5Nm8PLHWu28JcFExg+IJcsnYoasymuH4H4RMhI8gxqBvc4j6RzHUomrjJlaqgpNUmOKlUDc8nQqBhVmEdVYV5cXL3BauPBKeWolAJdPfF4MaYtikGeeGdnfDxQpeCW6pLk15yaOKFPvP5T7+yK6yMvKzHiCwSpKTVRt6c1KhYsxqNz9Gr+sfs4T7y9kwemlKf8jmRkYgmFQjy9cQ9jivK4pG/WWXvf/rl6NEoF2w7aZTGKzEkji1EuYNKVYzFlarimvDf/PraQ1TFq0WqzkSf/bTjLZlbiC4YYX5yPWqWgol+OJL4wZmi5Y9JgemVqefytncweN4gXGpqljfFIIjfNMzQqXpg7hmAohFIQcPoC5Bo0DMozcGlhHhDecE6kYK2zthIkxD3fuSTu+k/PqqLN4eHTfW3MtRQza9ygKIFIsC4k2Tz/f7eNZ3+bK6nbAIQXA6EQPDitAofbj06jQCkI2BxeMnWqlHZvKkFIeE1RCVxjNvL5/g5uGlsYdY1Ujhj1VhsPT6vg6rLeX7vOuJxle36So1entQ+MnOTm6NUpLQd/+d3hqJXhZzAQDHHY7uLnk8t4SKXA7vThD4Yka3SnN4DFbOTOSaXkGtS4/UGp7SVyYIlsyzl6NW/fVY1apWDJG00J22uIxGWFaswmGve188Dr26W/VZuN3DelLK0rjNt3wp0k1jXE4Y4WvYj3m5ehkdTniUQ+yZxLIjdAQqEQKzdape9eFCuc+ExG5lWHv7NF0yp49n1rnOtIorqok8p6oVUqcPkCUff46ictAGToUjuUZOvVLFzTGLXoi7WeXDDRjFIpMH1YgfS+11X153fv7ebeyWU8qFTQ5QoHWXyBIL9774uUqv6LiRy9mmAoFBZEBUPsPtrFm9sOpxRGWsxGbqzqj0pxoq//NvafqQJxZztYKPPtQBRo5Gdo+Z+P93H/5HKWbWiSsiUhuv+7kF3f5PYlc7Hg6Mng6/YG0KoULHkjcS11BQL/+Z0huHxhxzdxQ0pcP00Z2of8DG1SgfVxh+eCbOsyMucTpkwdBztc0lxYxGI2UmTKoP8FKvCUkZFJTWyClMcfJEOj4oaR/XmkZ9wW18ORiU1/+fEE8jI0/OrdXQkTiqYPK+CtbYcZERHjPZnyGU5vQOqDlsys/FrJeN+EdPH0x/9tGPtsTjpcPil+s+OQndnjBnHXK43S5nvVwFzmv/Qpq+aMTvl+Bo0SdUyJ7QarjUAwtcOuvF6QOZNEJrQed3hwesMJzCHCboUGjRK1QsFnX7Xxhw/28szsUUwZ1pc+2Topafeo3UVJr0zmvbg57voWszGqdPzXTcbIMWh44oZhtLQ7Wfm+9aTi6vvanCx4eQuv3jqOFbOq0KmV3DxuEPdPLufJCCHKqcTqfYEgV5f1pqxfdlTicigU4oWG5oTx4UfWNbHsukoWz6hkcU9fKyaCi33tf6z+RHqfe68tS/kdycjEsqm5ja9anfxwfNFZfV+lQmCQ0UDToc6z+r4yFzayGOUC5WTLsSyeWck+m5Obxw1ifnWJtElbb7XxwOvbuH9KGb94aydfHOni+Tlj+PW7u+IG4IemVlDWL0daWFQV5kWVBUk2eIslcu758+c4vQFqSk0snGhmbHE+k8p6JxWKNFht3D8lWuxRW13Mmk3hTYklG5riAifiJKHWUky12Ui2Xi0FRhNh0CjJ1qnj7NcsZiNLZw7F7QsktXsTIGEpInExYjEbWTSjkre3H2bd54eirpHMUUXE7Qswskew83WQs2zPTzK0qpT2gZHCBYvZiNsX4PUFE1i+YUfSYP7UYX3jhB611cWoBAUHOpxUDczlV98bIdXeLMo3kGPQ0HrMQWNLBwsnmemVpZUyYYCEavHfvLebWktxShHVj680xzimGFkw0cz8lzbHvXZepyfld+XxB6Mm+7FtJjNCuHGyCweL2ciWlvaE7xdpJdk7W4vFbEwhlgv/+9XbxiMI8MDr2xJeM1Loc1VZLxZNr2TR2m1xGytrbh3Pv1raISQkzTCoKQ2LemKPhZ+D8L38dcdR5r+0mb/8eAIu34nvbnRhHmMG5dF00E7vnsVitzfAUbuLG6oG4A2m7o8uFjK0KvyBIA+9vo27rx5Cn2xdVNtL1jYfeaOJn197iVRT+3T1n6kczc43cgwanrhxOPe/tjUqAHa2g4Uy3x7EgGsgFOTuq4bwcAKb3Nix8+u4Fp0P7VBuXzIXC9k6FQaNElOGBrc/mHTOWGdtZV51MbUvbpbc9dZs2kfj/g6uLu/NvdeWpWzzj0yvOOOfRUbmYicQDLHyfWvSdrb8uqHn4rZkZGTOMMkSpF6cO0Zyh/X4g3Fl0p3eAHqNMunY3jtbJ5V4Fq+brnxGZPyrwWrDHwydVXF5qnj6Y2/u4P4p5bS0RQtRdh3q5K6rhnDLS5upKsxlnqVYcg8HUpbPrjYbydSq6E7gJtydpiSHvF6QOZNEJrTqVUr+++ZR6FRKbN1e9BoFf6ody6M9ztYGjZIQId7adpj6GEcRhUJg1ZwxNHzZKiUG1piNzLEUY3d5gbAj8zcRV2XqVDz7/pcn1a8AkuO9TqPimfetUh+2as7oKHf6k+2rAHRqBQ9PL+ehtduj/v7yLeMSlkcHMYkT2hxu7ptchiDA/jZXXF8rkqosl4xMIl75pIV+OTrKz6IrikiRKYOtBzrO+vvKXLjIYpQLlHTlWGzdXrq9AR7osVsTETdAO7q9OH0BBEGQSmE89e6uhAPwYbsryp1A3KRWIFBnbU06eNdbbYQ4MXjX7WmFUIjFMyuxpbEabHf6oibyohvLsg3x7gyRkwTx/zM0So51upMuBhZNK2dpkms9ur6Jh6aWJzxP/FwLJkbXSqwxm1g0owJHz0SuubWbof1y+M3f9kS9LlkNRZFvutkpq+bPTxxuf9LnKVK4IKqi73ylkZWzR6UM5s+1FEX9TWxvY4ry8QdD9MnWoVUpKMw3MHpQnrRIdfSUPogUnATF+4hoywsnmaX/vnncoJSfLxgKSeV4tCoFvbK03PSHj7/WJFqrUpCpVUX9O5JjnR6qzUbqY+43ksg+4fOWdhZMLI0TxkC0laTFbOTvO49RVZjHtZV9korl6qw27hUEulypXTIMGpWUHfPw2m0JFfLL1u9gyrC+/PsfPur5HUJxTiyLZ1Qwc2Xi2sN1VhtzuzzSvTo8fjJ1J747Y6aGbo+fDQkyH++cWEpif6eLj063j1Ao/H3NHu8hFJF8FOu8E0ndnlbuvTbs0nW6+s90GVjnI/1y9axIUL9aDnzJnAnEgKvHF+CQ3X1SY+epuhadT+1Qbl8yFwN5Bg2r547hkXXbufXywSlfq+px9hPd9ZZfP4xur5/vXzqA/e2ulG0+GDo/hGQyMhcyTl8gZTtz+gLYnd++0pQyMhc7yRKkAj3usLEYNEoWTjJj0CjpdPmSlpkWk4cik4hSrbETOfR2uf2U9T17fU6yeLpBo+SWywdz31+2xsVP5lmKWfn3Pbx623i63H48/gA/uXoItT1uEGJsDYg7d66lmKUbdvDza6MdwAG8gWDaPldeL8icKaISWgU41ulmbeMBbrvCjNcfpNPjk+LStdXFrKpP7X5UU2pi/cJqDrS7yMtQc9MfPmb9wmrp+DcRVyUqNSYS26+Isd7a6uK4va6v21dZzEY6nD6e2bgzrh+1p4kPu/1BujwBfrfRSq2lmAUvb5HEPCtmVUkOK1ta2sk1yEnEMieP1x/k7zuPMXloX4QkVRzOJEXGDP6+8yhOrx+DRpYZyKRHfkouUNKVYwkEQ1Ldv0jCzh67GNlTzubZm0cB4QF4dX0zCyeZo6zGtrS0IwgCbl+0SvOuVxp5+ZZxzLUU0StLe9KDd53VRkubi1AotRWhPxBifnUxip5zPP7gSU0S+ufqCYVCfPpVG2NL8ikyZUjHIby4WDStghGFuVGuEpHU7Wmlw5lmYyME6++0cKDdhUap4Ginm4PtLn7+f59z09hC+uXqcLgDcQu2VGr5b7rZKQZn77qqlB9fOZiGL23S+8qq+XNLuvYqChca93dw/2tbqa0uxqBOXbolkcuO2A7mv/Sp9Le//+cVUb97rl7DL9/9QnoGI2tV9s4+0ZYj21s6EZVereSHqz6R/i32K4n6k20H7VFuLJHUmI30ytISIsSksl5U9MshR69m1ZzRCILAlpZ2Hn2jiefnjGHZhqa0fcKDU8uZUtmXDK2SUYW5UYKQSOvZyP92egNUFGSn/Lz725zk6lMvEJxev2TXmkohP9dSlLRm6O6jXbQ5vClFPZHPQafbz+6jXZLLSoZGxRPvJBYZwrcn89Hh9iG632pirHHTulX5g1xT3pul1w39xv3nyTqanY8kql8tI3Om6JerZ+fhzrRBHbH9noqQ93xsh3L7krnQ0aoUPLNxT1iwOyW1tXReRIC13mqjy+3juMOLzxBK2+YdHj87DnfGrXHOZ0GnjMz5htOTOlnA6Ql8LccxGRmZ85tkCVKJYpSnUrpCjBXpIuJX6dbYscczNKljX6cbMT4XW7ZoYJ6BX74Tv9EsJUkW5uH0Bmh3+qSyPU/cOFz6PsSYzoNTyqVyJ95AkNoXN9Pq8MaVo7eYjTR8aaN3li5tnyuvF2TOBJEJrb2zNBgzNexrd+EPhFApBOwRsezI+GuyxMC6Pa08ur6Jn31nCIIAi6ZXxO1zfF1xVbq4utivRMZ3V8yqiosZR8a3T7avEq/p9CZ2gBSvGduniHFwh9svJYNXFeYxqawXs8cNSlhh4KbRA1Pek4xMJB/vteHw+Bk96OtXWfgmFJsyCIZg5+EuLj1H9yBzYSGLUS5Q0pVjCQRDSZ1Two4CJcCJAdMfDCVdbHynog8Od7R1oNMboK3by/yXPpU2npMRO7iHQqGUogyL2cjWAx2MGJDLlGEFzKsuoXeWlpY2Z8r3Adh6oIORA/N47O1d/Nf3RqLTCCy/bihOb4Burx9jhpbFb2wnJ43S1KBNvRjKy9Dg9PgZmGcgQ6NEIYAgwMpZowgS4t2mo1KQNHLBlkwt/03FIomyfGtKTay/sxoBMMpZg+eUdO01GAox/6VPoxb9ohtQMpIJRGLbW2TGuN3pxeWPzoYTa1UC/LF2bMLrpGuvhpjgQZ4hsQWsxWzkluoSpg4rYMn6aGeiGrORedXF3PSHj3nl1rHcNznsXhR7/uKZldzy0mae+t5wlEJqkcwRu5vG/R3sOGRnZGEe8yzFeANBemVq0agUtDm8/Gn+WDqcPpSCwK++NwKdWkm2LvXQODDfwLFOt+TQkug7ER1XTnZxE/k7AKyeO4YHX9/Oy7eMS3m++ByIpYhW1zf3OLKE4n7rSMTMx28Dxgwt7p7vuXF/B/1zdJJgJ53QyucPsvyGYfTJ1n3j+0jnaCYH/mVkTtDl9qVtn1qVgppTFPLK7VBG5vQTzlQMzzf8gVDKOaM/EJ2QsL/dxYKXt7Bqzui0bb7L7af2xc1Ra6sLQdApI3M+kZlmnZOpU6UVhsnIyFx4JNvIPeF8TZQDwsmWrjja6ebqsl6YMjUnvcaOPG4xG8k4y9nU2Tp1QsHNqjmjk7oTi4lfB3rmLeLm9JpN+6TvQ4zpTBtWgCBAMBTkqXe+4Mkbh3PnK410RZTkqTGbmGMp4q5XGrm6rPdZ+dwyMrGITiX3vbYVhaCQnPEDwSCuQIhemVrptSfrKFK3p5W5E4qY/9Kn1JSauGJIL3IM8e97qvP2dHH1QUaDlOQpCsQSxWMj49vp+qoBefqoa/7qeyMSvq5xf0dKgcmUoX057giXrF9d38yrt43nyQSJg/VWGw++vk1e18icNO/tPEqvLC2F+Yb0Lz4DDMjTo1II7Dhkl8UoMieFLEa5QElVjqWm1IQjTd3JTK2KhZPMbD9kp8ZsoiBHl3AgbLDa+O17u3l0RmXcxqs4gJ/sQkNUiBbk6MnSqvi3qv4sfqMpWkDRU+7G5fXzq3e/4LOWDp6eVcXWAx0UGTNSvk//ng7wl+/s5Kaxhfzo5c+4/YoSZgwvQKEQ+GBPK40t7TRYbcyLsYVMRKp6n29uO8zKjVYMGiWr54xmdX1zXDkkMUja2NLBtGFu/lg7lmNdHpSCwJ2TzPzsmiG0O30U5hvonaX92hONZFm+dXtaWfJGkzyJOQ9I1V4tZiNZOhUWs5Gqwjxp0V9VmJcymC+KHWKJbY9ixrgoWJo9rjDpfeZlqKV2OjD/RIZpKsvReZZiHB4/b95ZTbfXT5ZOjV6tYOmGHQn7EwG4vWYwtZZi7p9SRrcnnDHS5fYRCIS47fIStGoVB9qdzK8uoaowTxJ2NVhtKBD4U+1Yvmztpl9u+udaXCRt3HU84fH1d1pYHRNoeeyGoVIgJRaL2ci7TUfYccjOoumVLN+wI+p1kSp8SO8qk+y4aGP/0V4bNaWmhJun4nMginw+P9DBillVdLh8PDitAoHws3fT2MI4Zf7q+ua0mZEXE3q1gmqzkdX1zay7w8L4EiMPr9ueUmhVU2riw702+mTr6JPaLOekSJfJcaqlRmRkLmaydGo+2NOachw82ulm6XWVpzTHkduhjMzpJ1wLvue/nT5pnZVozmiPcZ/snaWVRM3pxmRTpoZnbx6FTq3kiN3F7VeU8Ju/7ZGFZDIyp4BOpUi6zqkxm9CpFN+aUp4yMt8mkm3kio4e/3v7eH7eoxdVCsJJuV9bzEYKcvTcffUQfvPebhZNr2DZhh1px/PIUsl3TixFf5adUUyZGhZNr4gT3JxMIpEYv4l0S4lMJKs2GzGolQgCPaWCW/nRlYN5elYVmVolq+aMpl+uHp1Kwfd+/1GPg4pclkPm3NEvV89j1w/F0eN25A+GyDNocHj8KARBikd+HUeRutMoGk+5D2Y20eX2xcV2E8VbJQGeIKTtq/6642hUX5gsfptOYCIAufoT8XbRJSUR8rpG5lT4YPdxRgzIPSclegDUSgUD8/VsP9h5Tt5f5sJDFqNcoCSrs2cxG5kzoSjKIjERXW4fjS3t3Du5jOnD+9HlSmzZCLBx13HumuThnmvLEPiCz1raqa0uZnRhHjOH9+Ozlva0m+aJVOdiyZwHp5bT6fKhVAjoVEquf6aBFbOqqLPaWDjJzAsNzTS2dPDmndVJnQhqSk0c6nCxZlMLs8cPQoHAapoZ1j+HxeuaGFGYx2UlRum90zk9vP/FsYSB1Oqeep/iRnNtdTEr3rcmzRi4/YoSKvvl8EJDMw9GlAWqMZtYOMlMZUE2fb+hrbSc5Xv+k6q9zrMUU/viZm4aW8jkyr7SM5pMAFJjNrJgYinzX9oc9z6xIhWx9FOkYGnuhKKk9xkMhlg1ZzQr37eiUghSoDJRGZlcg5r3vzjOXa808tx/jGbaivoT91hqYs6EIj7e2ybZt4qT7lGFeei0SpQBIc5BaMnMoWw/0Mxv39sT9Xn/++ZRNO7vYFj/HPzBECHgxYZmRpyEYCddyZ1jnZ6485e/uTPsLiIQ1bZiy/kALJpRwf42Jx5/kEH5Bt5uOhJ1vHF/x0k5qMQi2tivrm/mjYXVLH5je/RzUGri4WnlHO5wo1YKhAix+au26O+u1MTLt4znyXd2JrTXzTZ8O6YATl8Al9cfFg+9uYP3dx9lakUB04cVUJCjZ8bwApZv2BkVlK8pNbF0ZiXTVtSftkyldJkcchBKRuYEBo2SnYfsCediNWYTS6+vpHFfO0Lqqo9xyO1QRub0Y9CemE/860AH2w/YqSrMo9ZSLG3cNO7vYM2mfVT0y5FeazEb2X20i5Wzq1AgsOtQZ9L11wNTyvju//tIml9ZzEYWT6/k9//ci9MbkIVkMjIniVohsHCSGQhFJdPUmI0snGRGrRDwK2Q5iozMxYYpU8PV5b0pK8iOS1TZe6wLfxCeeje8kZrO/TpDe6LM9B1rtrBy9ig27jrOiIG53DFpMKEQCdfY1WYjy68fyp5jDlbNGc2xTg+ZOiUOj5+z6Q2SY9AwqjCXB/6yLerv6RKJcvRqPtp7ot8UhTnixnv48w0DQuiUCsmJ1u7ysWbTPn5xwzAG5hv4ytbNnz7cx01jC9m6v+MblWuXkfmm2J1ejna5AYFdRzq5trIvD6/dRp3VhkGj5PUFFpbHiMxOJenudO1LJIur15SaWHClmYVrtvDEjcOBE+uIRPFYMb79+oIJtHV7k/ZVj0yv4LpnGqLuIdlektMboNuTfF/tbzuPct3wfrwwdwwrNu5JG6eW1zUyJ8PRTjdf2ZxcN7L/Ob2PQfkZbDtoP6f3IHPh8O3YibpIEevsHevySCVsGvd3cP9rW/lT7bi0m7QNVhtPvbOLB6eU4/anzpA/3OnhZ//7ObdfUcKS6ypZvG675AyycNJgll83lEdiXE4ihRuJbB6d3gAPvL6NGrOJ//zOEFy+AMYMDQaNUprMjy4MWzzVWopx+fySICbWiWDOhCL+5+N9zB43iDUf7+Ouq4dQW13Myx/vY0RhHmMG5aNRnpgMpXN6iBSb3D+ljP1tLnL1aly+AHdGbDSnsqZrsNq4b3JZQmVsnbUVQYAVPffwTZCzfC8MIuti2l0+3L4AH+61ScKFlRutjC3Kl14fKwDx+IMU5ht4b+dR3L4AlxbmxgUQl14/lOOdHiZX9sXh8VOcbyDHoOHLYw6pbW47aE+aDadRKvjV+1/QYLUxtjifBRMHEyQULunSc48Ws5GFE0upt7aycqOVarMxakEOYQFHMBSS7EpPpuZwg9XGo29sZ2RhHu/tOh4tXlErGVucz4df2lArBf740Vc0WG181tLRs4FB9HdRamKepYiFaxrj2lhsDc+CHB2r5oxGEATcvoAUkLn3/7byP/PH4fQF6HD6yNSpONbp5r7Xtkrtf+Ou4/z0miHMf+lTAF69bTz/ammXjgPsOmTn0ZlDE4pJFlxpTioqEm3snd4APn8gblOnV5aWG579EKc3wMJJZlbVJ67XumxDEyML86KcYUSHml/cMCzuvS82jna6cXsDZGjV3LlmC8/PGU0I+MXbO/nJ1ZewbEMTW1o6qK0uZq6lCAi7bDXsaaWt23taM5VSZXJcfoqlRmRkLna8gQAPTC3n8bd2RvV/OXo1/XP1fL6/ndFF+Ryxu9ColfQ7SWGv3A5lZE4/2ginBUGA+TXFrHzfGmdRHSnoj1xzXVqYx4wRBdw0rpA1m/ZFtflcvZo8g4Yfrt4UNb9qsNpYtqFJmmvKQjIZmZPHlKVh6rAC5kasLY51ujFlhe34lbIYRUbmoiPHoOHhaRU8tHZb3Pj82A3DeOj1bVI8Id1Gc5ZWxYqNe6TXu3tEF7//516mDiugtcvDk+/sYkRhLnMtRdJ4np+hweZwU2TMoNPlZdtBOwVdOnINp6gu/wbYnd6emFy8m3iqpMXqHkfj1fXNUX+XkpLurkGlFPCFAjz51hc8NLUcR48TrValoL4nphYMBck3aKiztvLjKwdz89hCcgwa6b463T6y9WpMcql1mbNEq8OLXqNCIQhUFGSz5I0mKb7q9AY4Znczv6aYPjk6rh/ZnyXrm9Im+MYm3Z2ufYnIuHqX2yfN/2esrE8YQzeolXx31AAWrdsetV9VVZiLVhmOqz7+1s6ovkqMNwihkFTKXXSXXl3fzAtzx6AQhLjERUUKZ4o/fbSP60b2Y+XGPdSfhFu/vK6RORk+7tmLKU8jbjrTFJsyqLe24vEH0KrOrtOZzIWHLEa5wMkxaGh1eKXNUICFk8x0uLwpLZLFQOCWlg60aiWBNHWBNUpFeFM0EOKxN3cwojCPuZZivIEgA/MMbP6qjUsH5TF3Qs9Cw6Cmb5YOu9vLr743giJjRvJ6gtZW7r66FJc3wN7Wbv7nlnGoFQID8/QU5OporG9n5UYrCyeZ2XHIzlxLET++cjAdLh9alYJtB+00HbJz87hBAPx4YimZWhWTK/owcmAuazbtY+TAXPzBcBaQuAmtFARqLcXcWl2C0xdgYL6ed5uORrsatLQDRNUQjQyEprOmi/3+Izld6mA5y/fCIbIu5qEOF//vn19GPU/GjOhnQRSAiKy/08Jv39uDQaPk1dvGM7fLgzcQpNiYwZaWdqY9XS9dT8zKaD7uiLJPFwRYOGkwU4b1pU+2TsqIOWJ3ISgEadExtF8O81/6NGoiL2a2zn9pM3+sHcuYQfkoBAGXLyyIEF1OINq+9WRrDouT8lTiFTELVvo8CEyJC6Z6EHoMpiMXSbHXjfy3eLy2upjLSoxcUdqLQ3Y3DV+2Rrm3PHHj8Kg+oj3Cbt7pDTCyMI8FV5pRKxV4A0GydCoW/M9n/Or7I1ApFXS5wsEFrUrBgTYXD08rj/sdCnL0ko29xWwEQaCxpT3qu3jshqFcWphHnbU1pSgu2UKn3mpLW87tYqC920t+hoZPv2rjd7NG0mBtZURhHm9vP8rmr9r51XdHMCBfj8cXpNsbwKBRolUqcHj9ZGhV1JSa8AeDfHnc8Y0DQskyOS4vNfHkjcPlYJOMTA92p5dAEG59aTPP3DxK6jv75arxBYL8cNUmHp1ZidMX4AOrja37v0xo/ZssqCu3QxmZ04vXH2TR9AqWbGiS5o+3X1HCfZPLgPD8KFOrghC8OG8MXW5/VD33Omsrcy1F3NkTQBbXark9weC1nx+Mmi+L1FltzLUUy0IyGZlToMsb4LY/fho1vmbr1fTK0jJ39Sf84T9G0y9Hd65vU0ZG5jRjd3pZvqEpSvApbrIe7HDxWUuHFC/Nz9AkTWCqNhv5x+5jUtllhztAbo+rq9MbwOsP8MlXbfznNUPQqpV4fUG6vX5UCgXvNB3h2so+PPbWDir65bByo5Vnbx7FQJ3hrHwHYunquj2trJ47Ju64VL4DIc41dcnMSt5pOhx3Tq5eTQh4c9thdvaUcv7X/g6cvnA56hqzCQgnRTk8fgLBkFSeUKdWUpCrj7ovkctLTTxx4/CTFtzLyHxdOt0+vD0ijN5ZuqhEP4NGSXaGmhXv7WbYwFyuKuvN4hkV+AJBSZiSzE06ktNZiisyrg6w+asTjtyxMXSAd39aw8wR/bj7qlL8Pe1PQKDbF2DO6k948sbh9M3RokBBSIBOp499tm765+m558+f4/QGqDEbWXuHhX+1tJOpVTF1aF9p/0uMk7t8yZO8a6uL8PiCUj/bK0tLjdkY9V2LxK5rZKGaTDI+3mtjQJ6eHP253fcrNmXgD4bYfcTBsAE56U+Q+VYji1EuAmKdMaoG5tLwpY0vj3VRaynmoakV2F2JM/trq4t59I3tKctd1JiNHO10A2GnkpEDcxNuEs+zFEe5hljMRu6bXMaCl7fwp/ljU34Gu8sXJaipMRv5Y+1YfvHmDumexIWBTq1k9vObAKI2k3/ztz3SZvKEEiNqlYIMjYofjh9Ee7eXYmMen8ds6Ir3/ZctB7iqvE/CY5GTqFjxSWzGQKzrgtuXWqxyOtTBcpbvhUmsojtDqyIYCqVUl4tuGVWFuVLtysduGMrLH++LKwNTb7WxaO12Hp5egU6txKBR4vQGGNE/l25vgLe2HY46p8Zs5NJB+dLrPP5g1EQ+8tkW1eH/2H08SqixcnYV2w7aGdovB48/SO8sLQsnmRlVmHdSNYch3MZSiVcis2Brq4t5vn5v0syV2uriKBekqsK8qOtGvs/JureI54mvEX8Ti9nIlp7+ZeVGK+/cXUNjSzulvTNZMXsUSzc0Rd3nhjstaNWKhL/DwkmlfNxsk/qgY50eKWiUoVXR7fGz/ZCdedVFBAmddL3WWBJt7FxsdLr95OhV9M7WYXf62LDtMEWmTCD8+YOEePSNpujfoNTE4hkVqASBe75zieRAczoCQokyOUyZ8mJSRiaSVoeXbrefJddV8st3dsX1kUuuq8QXCOFw+6UxKFbcmy6oK7dDGZnTR7fHT521lenDCiQR/LD+OQnb78PTK5n7wua4OUjsvFNk1ZzRfNLcJs3FEs1dHrthmNx+ZWROkm63L+X42u3xkWM4txmWMjIypx9bt5ebxg1KGO+43GxKmLQTIhS3Tp5nKUIpCDxXt1c6tnCSWSqF0ekK8Pt/7pXKlceW2rxuRD/mXlaMMSs8bucb1GclZhlZuhogFAolLd/xv7ddxr1CKJzkJAiEQiFabE4u6ZPN/95+Ge/tPMofPtjLqMJc8gwa/rrzCI0t7cyzFLNsQxNP3jgcpzdAtk7FHEsRL2/ax9OzqsjSqQgB+3uczXP06rj7EvlgTyv3v7Y1oeBeRuZ0kq1Th2OGAnR7oxPWaquLefq93fxgfBE6tYLH394lxU9vv6KEB6aUcf9k6PYG0CgV/GP3sbj5ejhW2kHfbN1pf5btTi9ZWhXP3jwqysEkar0QgrwMDb95b3dUf/TqreNpdYTb3/NzxvDrd3fEuI+bpPVHndXGkvVNTB9WAEKIg3Y3vbPDwl2hxxElmKBPEZlcUYCt2yP1s2KcOkh0AnNsgowsVJNJxWf72rmkT9a5vg0KjQYUAmw/ZJfFKDJpkcUoCXjiiSd44IEHuPvuu/ntb397rm8nikSKyFhnDI8/yKuftLDm1vEsW98UNZjGZvaLm8SftXTw9KwqtCoFFf1yJDFFnkGNKUuLAFxd1ote2Voee2tnWoeDyL8BklI+VqwhThaUMXZmdVYbj7zRxNwJRXy4N6xyFRcG/+8Hl0qvO5nN5JpSEw9OKeORmDIZ4j0KwOKZlfh8Qd6+u4bm1m5J2Ro7iYpVG6ZyXYBwADUVp8O15HzJ8pXVuqeOqOgWJ5g/vbo0paOR3emLEklZzEaGDcjhwde3J7x+nbWVA+1OXmxo5oW5o/lobxt9cnQsjxB5nXitjeVv7pDacKTQKmnbMpt4fcEEjtg9bN7XxpqPW5hU3jtKWGYxG6kebEr5PUSKJbQqBaMK81hd3xzlZBS5sJhXXQKkLpNVb7Xx4yvNrNxolawap1T2jXp95Pnp3FvEcj9iuSSAHYfsbGlpTyhc63L7GV0cLg/2YITlrdgH6tVKnnx7V9xCpc5qA0Fg6cxKJl7SmzaHlyydiqqBuSgFgVAIcg0aJl7Sm2NdHu6fXI5KGd1/xvazhfmGOOcagGzdxT8FyNGrUCrC9pu/6Bm7HppaDsBtl5fwQn1z/G+wp5Ul63fwi+uHolQIUdaci9dt51ffG/GNHVLkvlFGJjmdbh/5GRp++97uxH0kAvdNLkOlEqQ+LVLce7JBXbkdysicHrJ0av700T7+fPtlePzB5ONrz1zztstLJKc/cb6SoVGxeu6YuCCyxx9MuM4UydGrWbK+iSXXDT3vgrLy2kjmfMSYqeV3f9+TdHxdel3lubkxGRmZM4o/GEoa73hoajm/iyi7E1nu4sdXmtGoFHj9QYyZGv6+8ygff2mL6kPEjVUBgUydMmlspc7ayqJ123loajneQJB/tbTzvUsHYOv2sre1+4yOla0Ob9TaQKVQMNdSTIjo2FtVYS56rYJNe9volxN2616+IX6T+oW5YzBolPxw9SaevHE4v/nbnp7z8+iTrUMQwg7jYkzZ6w+GyySHoE+WhmvKe5OpU3HY7o5bs4icLjdtGZlUmDI1dDi9PcKr6GOjCvMAOGx38ea2wzS2dPDTa0qZUlnAsg1N0nMP4XaxYOJg/vDB3qi/zbEUcdcrjYwtyv9Gz3LkvDpHr0ajVPDA69vinFkiBew1pSYESNgfZejCSZur547hyXd2JeyvgpwoP99gtXFrdQkalZL+MQ5y/XN09M3WJexTLGYjnkAQY4aW3/79RD8bWVIIwuXCCyIEO7JQTSYVDo+fPUcdXHlJ73N9K2hVSvrn6tl20M6sc30zMuc9F/9O1CmyefNmfv/73zN8+PBzfStxJFNEPv5vw6KcMbQqBT8YPyhOiALRopHV9c1k9FilOb0BlrzRxKo5Y1i2oSmuhugtNSXcdfUQCCUvOxPrcADg9QdYPXc0GWoVV5f15qZxhXEb2tVmI1eX95EcGUTq9rTyoysGx7kt5EWUMjmZzeS6Pa20XeFLet/1VhseX5B3mo6gUghs/qotqUNMvkFNTalJ+g1W1zezas5oFAiMKMyNe/90NUd16tS1WE+Wc53lK6t1vz6RE8yfX3sJazbti7JOFYVRr27ax0PTKlh+3VB2H3OwYlYVjfs7ONDuSnl9jz9sBQgCn33VxlXlvZO2BanNbbRGPbupgglLN+ygqjBPygRRxQjLGqw27rjSnPIeReFLdU9t0fHF+SldSvQ97SadI0gwFOKtu2v4qkdgZuv2Rm18GDQq6d/XVvahoiCb+dUlcRshDVYbC640c3OPI5N4r8uvH4bL56dqYG6U4xRApk7F8S4PeQYNjT1WjKMK8zColQQJ0e70JbRkFH8HpzfAX3ccle6j2mxkrqWYBS9tlpxo5lmK+fc/fMSSGRVSv3QyDi/i+Rrl6el/zmcytCoeeG0rP59cRoPVhilTQ5ZWRbXZSLXZxG/f25PwvLo9rbR1+7j+2Qbpb+J3buuWA0IyMmeSbJ0abyCYvI+0tnK/AjLUJ+x+I61/Y4PNkchBXRmZ049WqWDlrCoWrdvO/VPKuXJI75Tj691XlfKHD/ae1HxFnCMmWmdazEb+sfs4f9t5DI///ArKymsjmfOVdOOrN5C+DLGMjMyFRzAYShgHCpeMEeKOiW5lKzdaWXeHhX/uPs6UoX0ZX2LkqXd3x732rlcaefmWcRjUSiaUGFMmDbU5ffTJ1lJvtfHQ69sYGeGke6bGykhHcYNGiVal4McvfRZXlnr7ITuEBN7cdjgc56pvTxgHQ4DxJfm0OrxSXEqcqzi9AfRqJe4e1zfxc3d7/NhdPgpz9SyaXsE9f/6cWWMLU9736XDTlpFJRY5BQ5fLR6fHj1IhRJXoUikEqgbmAtDYk8Tc5vCwdENTAlFrKxDi1dvGc6DdRY5ejdsXkBz0v8mzHDuvXjjJTGNLfNuM3PPaur+DJ28cjt3lk2KykcmOoRAsmlZOl9ufcp9rfk8yJEBBrg5bl4cN2w7HCU5KemVGCUwi4/mdrrCAJvKcWEfItXdY6PYGyOmpWibHNGRSsfVAByHA3CvzXN8KAEWmDLYdsJ/r25C5ALj4d6JOAYfDwc0338xzzz1HXl7eub6dKFIpIh99o4nHbggLUiAsgLhySO+kQYYGq42qgbksmlZOIBiWvRo0Sp65eRRLNsQLWLa0dHC4w0UwGFberZ47hoWTzFKdy0jESbhBo+Sn15RizNDyQn0z01fWc9fVpQk3tOutNp56dxe11cVx17O7fLxQ/xW9MrXMf+lTFry8hXebjlBtNka9H/SUJ0ryme2u1JOeDqeXy0qMjC8x8sCUch67YWjU57OYjSy9fig2h5dLB+Wxas5oVs0ZzV9+PIEsnYoZIwq4trJP3Puvrm9mnqUYS8/9Rl5vrqWYR99owu70pry3kyXHoGFw70xGFuYxuHfmWXVESaXWPV2f72IlcoJ5pNPN7HGDaGxpl573+S99SmNLOzeNG0SX24dBq+JPH33F/Jc+ZeVGa0pBgUGjZECenj/NH4vD42dedQnd7tSlWTQqBTVmU9Szm6ptif1Jg9XGCw3N5Bji3X4+3GuTauXGYukRoNSUmnh05lB2HLLTN0eXNGvnhYZm+mTrsJiNcWWyYsnSqmnt8nDPnz+ncX8H+RkaVs8Zw4TB4ff0+4M8PauKxpZ2ZqxoYMHLW6h9cTONLe08Pasqqg+I7UPqrTYeWruNN7cdYXVDM0/cOBxTpoaFk8ysuWUcDo8fty+IIMDK2eH3qH1xMzc99zGzn9uUtk/a1+aMuo/6ns8u9pMNVhtrNu1jxawqemfrWTyjkhqzKaXDi3h+jdnI/Opi2rov7rZpd3o50OaizmrD6Qk/9099dzhL1jcx11IsjX/JcPuj24r4HaY7T0ZG5pthytTQ6fKnfI3TG5AWMaL1rzjfiC1fGYsc1JWROb24AwGeed/KlpYOdGpF2s1sfzB0UvMVcY4oErnuEwWiq+ubgRNB2fMBeW0kcz6TbnztdPnlZ1RG5iLE6U3c9muri7F1eVg1ZzTr77Twyq3j2XBnNavmjJbK57h8ARpb2gmFINkQ7/QGaOv28k7TEZQKIfGLerC7fFJJ83qrjWsr+0ox5jM1VkY6itdWFxMIhaTN4MjYmy8Q4tEeV+1RhXkpE7kmlfXBlKmJikt5/EGydCqOdLrpcvui4udOb4CV71vxAw+9vp26Pa1pY1oalYIvjzvkflnmjOILhttDS5uL+TXF1PTsL+VlhEv4ePxBbr+ihDaHh+EDchOWooGwy9rxLg+vbNqHx39CiALhRLEvjzlobGk/pWdanFd/tq+dhZPMrJozmu9UxO+/iDRYbUwbVsBT3xtBQa4er9/Pq7eN57ISY4zr9l6qBuWljc1GOlGrFQpWvG9NuH7xBxP3KSs3WqU4+f/96DJevXU86xdaWL+wmp9eUyrFnQ/bXVF9nxzTkEnFv/Z3oFeHHUnOB0pMmew60ok3TdKwjIzsjBLBHXfcwbRp07j66qtZvnx5ytd6PB48Ho/0787OzjN6b6kUkX/beYwHppZLzhjdHl9ax4BsnZr+uXo2bDsc3mwuzEuoBo3Msn8gohRIbNaaiFalkM451unm4XUnSuO0Oryn5KoiXq/O2spcS5HkYDC6MI8Zwwt46p0vGJCnZ9Wc0Xj8QTK0yR/n2JIjsaWC+uXqeXv7YZ55/8uwlZvZyPqF1ViPO1ApBI52uvH4Anza0h6V6ffPe65k+ZtfcElBNoX5GXHvG2lved/kMg60u+JKAJ2MmvV8tnm+kNS6Z7vdngyRE8x8g4Yf/c9nPHnjcO6fUobDHSBLp+Jop5v7XtvKK7eOp9vjj7L+S+a+Y9AoWTVndFw98JdvGZfyfvRqJVOG9WWupQh/MMR9k8ukQEEyIjNB7p8SH3hYXd/M6wsmsHRDdHmgmlITi2dU4vH7ubQwl6N2F9dU9MUXiM7aiW2zHn+QOyeZ6Zulo8ZsTCi8s5iNZOlU6DSKpJm3M4f344l3Tq7sWKIggaiSF8saPT9nDE+9uyvqfR7/t2G8tfVQ0r4vGTq1Mu4+IvtJg0bJ7HGDeLGhmbqeMmGJShHF3u/9U8rol6NDQCDjAinT83XbbavDS1vPQk5cQPbN0fPeruN8uLeN/739MiB5+bhMbbzgssFqk8UoMjInwTcZb3MMGgw97S9p+9Qo8QZDTCrrxexxg6Ksf2PLV8ZyOkokyshcrHydthsIhoPPCyeZWfJGEz+fXJby9VlaFddW9klZkvGOK83MGF7AEbtHKjVYbMrg2ZtHJS2ner4EZS+ktZHMxcGptFuDVpl0bF1d34xBq5SfURmZs8DZjk3l6BO36XGD8hlg1PPI2u0xpWiMrLl1PLOf+5hAMMQXR7rQqxUphSaN+zuYUGLE5QuPzcn6GoNaiSti/N4fkYhz1yuNZ2SsNGVqJEfxqoG5fLTXljCOFum+rUojqmnv9vLqbeP5644j0t9y9GoEoCBHh0IQ+P7vP5bi51l6FY0tHQSCIeZaipg1rpBcg4bHbhjK8jd3Rs1pIByzemv7EVZutMruaucJ52NM+Ztid3px+gJk6VVo1Qr+/fcf8+ad1exrc6JWKMgzqMnSqikxZWDr9uIPhhKWARfJ0qn50ZVmcvRqbru8hD98sJdLB+Xx6b52HvjLNul1J/tMtzq8fLavPSqu++zNo1J/JpePf+3vYOKQXuTotTy4dltUWxdF7Z1OnxTrTdZfGTPU0jkufyCuBLv4+hx9tJO+iEGjRK9W8vDr2+LKfT0wtYyqAXn86OXP0CgVfLCnlWNdHjmmIZOWrfs7KOmVgSLNOHW2KOmVgS8QnisMG5Bzrm9H5jzmwtiJOgu8+uqrbNmyhc2bN5/U6x9//HGWLFlyhu/qBOkUkZ0uHyW9Trhh7DiU2hopQ6uky+OXanvq1cqEatBUWWvicXGiLmaviefUWoqjznP7UjsyxApoasxGemVpefbmUfTN0bFuoYXjXR46nD6OdXq4d3IZyyKcXFbNGZ302qLzQuwEJvK97pho5pnZo7hjzRbqrDYeeWM7VYV5fN7SzqIZldidXq4p74M/GJImXXaXl6XXDeUfu4+ToVPy7M2jooI5Tm9AUsZWDcxlwctb4u4tXeD0fLd5vpDUume73Z4MkRNMnVrBillVrIxRWlvMRlbMqkKjFDjm8EZZ/+XoVHx31AAeXruN+ghRwtVlfbB1e6itLmFkYZ70PH6010a12ZhQyV5jNrL1QAcP9gjPIkvYpCJSqNHh9EWdK07MfYEQtZZibq0uwaBV0eX20bi/g5v+8BE3jS1kVGEeGpWCkQNycLhPZO0kKztTYzayeGYltdUlBImvyTnPUkxbt4dOtz9pH7Zsww5GFOaycdfxuM8UKfyIzcyNRBQ5VPTL4dfvxtcZ7Z2lTSiWSVfCK9RTrDVWqCf2k7F9s9jPVBRkJ7xPkW5PgEN2N3/feZRHZlwYNeG/brvtdJ9YWNZbW6kxm3B4ws+W0xsgFELayE5UPu7fqvrHlY8Tz5WRkUnNNx1vdSplyvb53VH9Wf7mTu6bXM7b2w8DJ+YbkcHmWC4vNUlZnjIyMvF8nbYrztvEDZx7EZLOcSxmI8EQHLK7U5bpUSkFbn5+E60OLxazkdVzx2BQKxKupUTOl6DshbQ2krk4OJV2a1CHExZWvm+Na3ur5ozGkCQuJCMjc3o527EpU6Ym4UZp31xdnBAFwiLTZet38OzNo9j8VRsv3zKeR99oYkRhXtIxfschOxMv6UWmLn2Z9siS5b2ztDS2dADNUoz5dI+VOQYNT9w4nPtf24rHH5Ri4ZC8FL0xU51UNOv0hjfvl7zRxLwe99rqnoQoBMjRqXD6TpR9FoDHrh/G07OqWLwuVvhjYtWc0cx/6VMgHOu5rMSIUhBw+QLSxv/9r51fJQm/jZyPMeVvSjipOUCfbC2ffdVOVWEuIeBwh5temRr6ZOsSPLNG1i6w0GzrRq1URLWLLrdPepZreubwKkXYBUlMJhbb0uJ12/nV90akfKY73T4p/imW2xmYn3ovpNvjZ8PWQ4wcmMtjb+6IS3asKswjQ6MkW6/mA2tryrjDDVX9pePdnhNCu9h1zM+vHcLCiWYIhaK+q0XTyuOEKBAuaxR6aycPTS3n0RkVUsy5pc1JhlYlxzRkUtJ0qJNhA3LP9W1IFBkzUAiw9WCHLEaRSYlcpgfYv38/d999Ny+//DI6ne6kznnggQew2+3S//bv339G7/FUFZEZGlVcaRgRi9mIXq0ko2eT7a5XGgkEQxjUSsny7NmbR7F67hiuHNIrpZvJlUN6SSVrFk2v4JryPkyu7EuD1RYnLklnPxh5vKbUxIKJpdz0h4+558+fc9ju5tE3mpj93CYWvLyFTV+1xZUUEjd3E7HrUCcPTCnj4WnlCTem66w2Vrxv5bDdFVUGY+qwAqYMK+D6Zxr43u8/ZtqKej5vaWfl7HDpjEydmhDw1tbDKct8pNrMThU4vRBsni8kte7ZbrcngzjBhHBm6bNJLP+eff9L/EHIM6j5zb+PZFRhHo37O9CpVSzd0MTIwjxenDuG1xdM4POWdq5/toH5L30a9zyurm9mrqU4rmyOxWxk0YxKlr+5EzgxuW5saefdpqMp+5PIZ9ugUUadK9oTTl9Rz+qGZvyhEEoBXmhoZnV9M099dwT9cnSEQiGOO7wc7vSEF/A9JBPE1VltLFm/g88PdFBVmCf1W6vmjKaqMI+7XmkkQ6dGEOLrH5+4RqtU/zQRHn+QmlJTlA18LKZMLQsnmRlVmJdQdJLMpUosg1STpIRXnkEtfabeWVrJ2lXsJ5OVTkrXz3Z7/DS2tDNr3KCkVr3nG1+33Wbr1JIQ8Q8f7OW2y0vIjHDQsjk8/Pw7lyQtH7d4XVPC8nE5+vOnT5OROV/5puOtWiGkbJ+L1jVRVpDNgXYnm79q4+lZVWT3tE0x2CyOrSKXl5p48sbhcgBXRiYFX6ftivM2sTyPzeFh4URzwjKlCyeWYnN4KEhTkjFTq+L5OWMwZWposNp45n0rrd1eyTY8llRBWbvT+7Vswb8uF9LaSObi4FTarUohpFxvqhSC/IzKyJwFznZsKsegYenMyrg4UCAYSlrivc7aSrZeTWVBDkt74q+pSpH/cHwR+QYNv31vN/dNKUs6j//Vu7vQ9cQtLGYju4929cSPOqT4zJnoh/rl6lkxq4oSU4YUC4+NJeXo1VLZeYNaFRXTioytTSrrhT8Q/u76Zut55dZxLJ5Ryer6vRCCbl+AA+2uqM/tC4Z4ocfZNvZ7fvb9L1m/0MIbC6v5vKWdm5/fxE3Pfcy8iPf8dF/7eVOS8NvK+RhT/qaEHdhVODx+lr+5k3mWYtq7PVQNykWjVvLIusRitSUbmth20B7VLq4u7y0lFa+eO4YRhXk8X7cXY4aWz75qi2tL/z62EFua8uHZOjVVA3NpbOk4pRh1g9VGW7c36t4jY9UNX9pobGlnxyE7P7l6SNL+6tE3mri5x4k1qyeemChOfVmJEac3wJRhBVF9SnlBTtI+tt5qo93lY9SgPCnmrFQI3P/aVgA5piGTkE63j/3tLoqMhnN9KxIalYLCfANb96c2R5CRkZ1RgM8++4xjx44xatQJm69AIMAHH3zAypUr8Xg8KJXRdv1arRatVnvW7vFUFZG5BjV3TioF4h0DFk4s5Y2th/AFQpKiPRgMkZuhobGlPUoFmsptBMLWZ6LiVbz+3VeV8tNrShnSJzNK9RoKhZIq6GtKTRQaDfz+h5fSK1ODNxDE6Q3wq++NoE+2ji+OdPYo5cNEWieKJFO215SamF9Twn+s/oT/94NLJdeHWEQHgj7ZJwRJhztcca8PTyIElsysJEOr4p4/f06dtTXuWhCeoHze0s6CiaXc+cqWOFX90U53SjXrhWDzfCGpdc92uz0ZIjM0FApSBgK+snWzuiG8+F+4ZgtVhblMHVrAxl3H2bjrOAsnmWmsb0/qZLSip334gyEWz6zA6w+yr82JRhm2O29u7ZZcHyIn1+KkP/JacMKB5K5XGqV/5+k1vHrbeJ56J94lRPz30plD+ek1l5BjUGHr8vLmtsNRr338hmFS1k6iti59J3ta+dEVg7n5+U1xxyxmIwrSW6umKmlWbMpg2XWVLN8Qb5kK4QwWnz9IY0s71YMTb47EikMiHWO8gSAPTqvAFwhy2O5GpRAk2/kVs6ri+tZVc0bzyVdtKe87leNK5KIMYNG0iqSf/Xzi67ZbU6aGLw53svz6ofzuvd30z9PjCwSjSjsFQskzocQScf8/e2ceJlV5pv3fqb2q1+oqduiNaugVbGSTrnbBjR0dJ/MJZgI0ahKCOonGHRcgLlkm+QSdiZGOJp/oJDpuoFERk9C4a0d26ZKGRvau3rv2qvP9UX1O115gULZzX1euSNepqvecOu973ud57ue+I3G6rWkKFJyu+Geft55gMPX8bGpl4ZRCvIGQ3G34q387T35dSja39vjo9vjJMmixZp4+FocKFJyu+DpzN8ugYUblIEoGhmM/a5aOY90+ZlYNoa6mCG8ghF6j4kiXB48/iAD4+uZuImx2OHH5gvxmw27qF07g2ic/YFNTK/fMKOPuGWX851tf8PbOo/LxqZKyp0Jh8kyKjRScHTiRedvjC6aMN3t8QYbmHF+DlgIFCr4+TkVuSi0I3DennGNdXjrcYRXRbk/qBpVudwBLli5KkTVSqdcbCDEiz8ib24/w+VcdmE06Nu46xvcuKEz6nG9wOOnts0dfZC9i6dpGqvNzqbOHP++bfFbmmHR4AiFqbVY+bWmPek0QBEJiiPoF49nT2st9EbbzEqS44/ZppbT1EUM63X7m/e5D7DYLy2ZVgCDS6fIzMCt6LfVEWHxAYjvqf+zv4NOI3Lf0nRDO0SnqaqcWp2NO+Z9FtkEbVuHu9lJnL0KjEhARCIVENCqBz2LuRwmRKs7SvPjZ1VVM/7+b5PyplDM+3OWJ23tI9/UDaRSbrZk69jp7jytHXWuzctuVo1n8TNj1oNPtj5pneRk6/vOtL+Sx3/RcI4/Nq6bHG0ibd3D5ggRDIrUlVsblm+Py1Eathl+9vTvuc9JZCqkEAY8/rKpvt1kYlmuUiWcjB2YqOQ0Fcdh1qBuAAkvGKR5JNIqsmTTub09/oIJzGgoZBbj00kvZunVr1N8WLVpEaWkpd9xxRxwR5VQgsmD995hkWqLkW45JR0GeiVljhkYlAY92efAFwpuC8flmZo8Zwsp1OwmKIr9MYDFxovjicDcDMvVMLMzjgVe3R1mBTC0dwH2zKli+bntcQXvBlEIefn0n351cQJZBywOvxR8j+Ye6fMGEhdjIoOju6WW0tIeL7AOzDPyfJ9/H5QtytNsb975IpCpMR2KTo5V7Z5XR4wkkJYtsdji5e0YZ0ysGo1YJ/HHxJNp6ffgCIXYc6qK+oZnzC8xcNGoAOUnIjGeCzPOJ3psK+tHp8tHa46PL4+feWeV0pZFE9kYk7iX50kOd/d0WqYgbmx1OllxsiyJu1JZYWTClkJv65lX9wgkJPytybt05rZTevk34+3uc8pyssVm4f3YFD72+g3mTCpImOTc7nPiDIa75r/d4+OoqXt96MG7dWbF+R5gIJ6afk2pVvBS8HPB0etBpUyuF5JoSd9zYbRb8wRD+YIjbp5XiDYSiSGc1NgtLLrGhVvVf20SIJIcksxySxiv9Domw2eFEhcD5hebwuJOoc0ikPBVC3HgjiUObHU7EFNflbECOSceDcyvp9fv58WWjuP+17dw3s5wll9gAAZc/yP52V8rPiLz/vs6aFjnHs41arBlK4KhAwfGg0x2gtSf9nq3fistJjyfAoAinshxTeL5J83BPay/ZRp8yDxUoOMnwBULcdmWpLOH9xHXjuO3Pn1Nnjyb5H+z0sHL9Tn75nbG4A6kt7471eGlwOPmhJyDveTvcfh7f2MTKq6u4a0YZXe7USdl0CpPflNS9EhspOJ3RnSbe7Pb4QSGjKFBwVsKgU9PbG2R+RE7ojVtqU74n06Ch09W/bsQSKAxaNYFg2Mp81bxqDnWF81PprG0lBQGBcPOQVBw2atXf+LPS6wty/5xyjnV74yzLfnHNGF79/ACL7MUp1QyWeoN80kdmyTFq5L+vWLedh66uImSAYCg6lyVZfEByO+rY3LcE6fooylUKTjasmToO93jIy4xvUK4tsSa8HyVE5ssaHE6cvb64+xbgtitGJ/zuzQ4nwVDqzGSOScdwc5hAHpmjvvPFLdQvnEC3J0BnH7mucX8H//ed3TxyzZiwWrZOEzXP1iwYL89rbyAk57r/+7vnpxyDdJ7tLh/L51QkrC2JJG6kSadeHRJF1CqVrJL96Bu7oohnUk4DiMlrKPnFcxU7DnaiUQsMzT299uslAzP52+6j9HgDUarkChREQrkzgKysLCorK6P+lpGRgcViifv7qcSJdHl2uny4/UHOLzATEkVc3iAmnZqtBzoZnGPko+Y2fv12EyadmhsvLGZIjjGKOCJBKqQ2tnTEBRyHO91sOxgtv/ToNWP4YI+TdTFKBwAbdx0D4N6ZZbT3+mUmvqQE4PIF8QdCzKgaklRRQUpGJnuYu3xBVm90cFnZIEaYTexvd9HrC8iboROyCkphrQPQ7QmgTqO6cKTLQ0gkTr5NCjDufHELf9t9jPEFZnq8gbjNxJki86x0IJ84EnVqPnv9pJTvke7PSAZ6JNIRN2L9vzc1tSKKojyvRFHEbrPQkMBmS5pb1SNyuePFLTx6zRiurBhEzUgr2UYNJq2ar9rczJ9cmFaNRCJZDczWJwzwXb4gi5/5hNeW2vGkKVaIokh1vjmKdNe4v4OXPv2KH18xGrWAfE6xsNssDMjSx71eY7Nw3+wKfv7GTjbsOiYnXH5w8UjUKgGXL8hnLe0sfuZj/lA3EYD39zgTejDXNzRTv2ACKmBsvjmpJD30r2/JbL02OVq5Y3opVcNyUKmEhN/n8gVZ++E+FtYUcueMUvY5XXHrrIRe75lh0/PPYGiukV2H/ezvdFM+NIdlr2zj05YO1t4wCbVK4GhX6mL3iDwjT1w3jkKLiWG5xhNa005FN7YCBWcDjnR5MGhV6NRpyIRGLe/t6V9PExF0lXmoQME3D39IZMVr/fapeo1K3jcmQrp4DJDnf6fbL0v2m3RqNjmc3PvyNlbPq6Z4QGbKzziVCpNKbKTgdEVWGrvJLIP2tFBfVaBAwclHrzcQlQMw6cL27cnyJTU2C3qNipy+Bp5kBIpaW7hgHRRFBmSGVSPSPeszdBrufmkbNTaLnAcBGDkwM4rI+k00d/T4Ahzp8lDfEJ+bycvUscnh5IYLR6b8DI1a4PmPWqi1WWV7dugjyHsDGLVqWnv6r3VtSfRxdfYi1n64LyqXZdCq+aylnbUf7ou6JpFQ1NUUnGzkmHS09fo42OHhukkFLLYX81lLO/UNzWxqaiUUkTOORew8T5Rj3Oxwcuf05DliKUeZaq4PzjZE2V4BXDsxn0cTKHJDODdeZy/CnKHl0b/sorGlg6VTbWTo+sug0thdviD+YOpc+sAsPSadmkyDhl5vkEACAk1PkvyqZB2eKCax2yxk6jUIArLdvMsXZN6k/Lh6j5LXUCBhx6EuRphNaFTpY+pvE7aBmYRE2PJVB1OSqMcrUKCQUc4wRDIikyHZA+rmS0sIhET++MFeecPrC4YYYTYRCIUw6dRxTNf6hmYenz8Oo07Nqo1NcQHHInth1PsGZocDj2QKKxt3HePWK0ZHMfEjscnhZGGCIrv0mVIBPpUVRa3Nyo6DnbK9TqTVUDoLiyNdHg52eqgtsfLgnApe23Iw4XWBcCCm16RWzck2aPn1hniZts0OJ3qNiqcWTOAXb+7irv/tV+aJ3EycSTLPx3NvKggjWafm+3ucKRMBEkHBpFOTl6FjzYLxaNUqnrthEpu/dGLQpr4fEyUEGhxOfnixjdUbHagFgYU1RYh9xybqelERtvtZHeM1Hqm88bvvpbb3ksaZijzj8gU51tcVn2zjXltixWyKZ+9PLR3AHdPKuP/VbdxgL5bPKXa8C2uK8AdCLKopos5ejMcflK/RI2/slAl0UkFFIopUR0gySmOsb2jm1aV27n81Wsq1Oj8XjQrumVlOCFIq19TVFMlqNZKCSSx6vQEWP/MJJp2a3y+cACJRCii1JVbuml6GIEAgKLLk2c+SXuNzhaksitDRV8xavdGBSadGrRJ4c/sRhuYYUjxLLLy5/QirNzp45ycXnbAiyqnoxlag4GxAh8uHKIZJKansHYebjbK3MsQTdJV5qEDBt4NQSIwiFx+PZeCwHCO1Nmuc1WnkMRDej3oDoShf+E1NrRzp8qadv6daYVKJjRScjjClKDzbbRZMOjXtvb5TMDIFChR80/CHRIxatZznuaJ8EMtf254wX2K3WfjplaW0u3xYM/XU2ixJm2s2OVrRagTumVHOp/vaqC2xps3ZJmq0Gm42RiuqfUPF115fEEEQEjZGeQPh3PjQXEOU7bxUnJdyw0atmp9cPorxhXlxBXiXL4hRqyajL99SW2JlxdwK1m89JF+T8flmzhuRm1Q1VyPEF++Hm0+sOUaBguPBwQ439/WpG0qIVOhJ1gxZa7MwMMvA0qk2eW4kU55udyXfc+eatGnneqQ6ioR0yuB1NUWoBUG29Pn95maZ4A7R8Uq62KXpaA+vLrXjD4YIhEK8v8cZd3yyPprnP2rhz9+/gGWvbItrglxYU8RvNuzm9itLGZdvBjtyfiOy3qPkNRREYtfh7rj5cDpgmNmISaemsUUhoyhIjnOjEvU18Ne//vWUffeJML+lYzvdPkx6DWoBgiGRWy4t4bpJBQzI0qNVCxzq9GDUqrmsdACXlQ3iV2/uipNee3z+OD7/qoOqYTlRG+4dhzr54EtnwoAjRDRDtseT2EInEl3u1N340vsTFcLNJi0mnVq2ohAg6mFe22edIfkDQvQGQ3ofxBemb7qkBEuWDrc3yNzzhjJrVQPj8nMTStLV9CVqzKbkZJEamwWdRpWUmFM+NIdfJbBGit1MKDLPZx+SdWrK97UgRL0eSfSQulH+860vooIFu83CFeWD0ib/EyEoiqxZMB69Vs2SZz7uk1XXU79gPKv6ZEul+TijcjAPv74zqbrHqnnVZBk0KQsMKgGWTrXJqhOJgnsAjUrAHwxx/+wKHnxte9Q1qbVZuH92Odc++QHXTsznx5eNilJ+kezArptUIEvFx6qn3PxcI7/8zliZsCGd45UVg/jX80fw3cmFceOKDcSkzl2XL4jHH5CJflkGDQatus/qR6Td5cMfTC0/maHX8NMrR3Ptkx8klbXNMmhYOtXG+Hwz5gwtM6oGs7CmMMKKzYuISEgkZZeT3WYhQ3fqLei+aXS6fPR6A3IxC+D7FxXT6w0wZaQFjSDw4JwKHnx1e9R8qi2xyr/F1yH+ncpubAUKznR0eQK892UrNcV5nDejjIdf3xVNurNZuWtGGSGxf01NNE+VeahAwbcDly8c20n7qHH5ZuwjrfzoEhvvfemU91G1Niv3zirjUIcHc4aW8qHZgBiX/Jb2vJJK5QXFFpZeUsK7XxyVj9vf7iLToElZjEqnMJlxjpByFSiIQkjkgUR7X5uF++dUQkg8bdRXFShQcHIRCokEQiHqF05g1cYmxuWb2bDrGP/4qpNHrxnDndNL6fEEyTFqCIREVIKALxBuiFl5dRWHOtxJi7/lQ3N44NVtjC/K467ppfzqrS9Y1Jc32Ry11lhZNrucTsyD0Q4AAQAASURBVJeX3y+cwKct7QRCIrUlVgbHKKJ8U8VXs0lLMEluxqQN59ti18jI4nx1fi6+YIjyITm8se0Qk4osUZ9h1KrxBkKohLANkkGjQi8IBEIiSy+xoRIEckxafvnWF0nzarG2JrHXR4GCkwF5nqVRb45Fjc3Cgpoi/s+T71PdVzdZ++E+AknmlTaJevaFJVYy9Bpu+/Pnaef64GxDVP0lXe0JwmoldfYimURXnW+Oqg89Pn8cM6uGMDTHyOwxQ1i5bmdc3mGRvZDbX9jCTy4fxahBWbj9wYS1pU1NrQnz4NdOzMcbDDGhKI87p5ei16rx+UP0+oJoVAJjhucSFEXqnv5YXmdG5EUTz5S8hgIJoijiONrD3POGneqhxEElCNgGZPLpvvZTPRQFpzGUDMxphhNhfscea9KpWbNgPE+860iY1Fvw+48SsjEBPt3XzpKLR/LJ3jZ+s6Ep6r0PzK7g8Xe/TDjezQ4nd0wLMzg/a2kny6CRE5LJkGlIr96QTv7x5ucaef6jFlZeVcmeVpesaDAgSx9XyK1vaGb1/GpUCGxytHLzc43U2YtYcrENjUogx6gNBwnbD/Pbv+1h1bxqBgh6XL6gfJ0iN2C1NitLp9r4eG87dps1IVlEUjc41OlJep6pWLyRmwlF5vnsQ6c7cbeZ5Ff5vz+cgj8Uki1EIi1Wlk61JexGaXA4MWxoYuXcSu57ZXvcBnpBTXK1DX8gxOJnPmHNgvGyCohGJfDp3jY2O5xR87F6RG5S79zNDidLLrZx7ZMf8Ni8akKIcaSvm6eWEBSJUzOJ9cattVnZfaSbu1/a1m+Tc9FIdBoVLl+AQFDEFwixfG4lBq0anUbFTX3vXbNgvPy96aTiC/JMLJ1q4/mPWnjkmjEJ15yXlkzhcKeXj/e1Ud/QLAc9kQSfGpuFkBhWPrm0T5nlwT5CjIR0NkwZeg1HuzxMLs6TVVkiUdMn4fh5nzdxY0N7UuJRdb6ZOecNZuVVVSx7eWtcsnnl1VW4g2e/TU9br48sg4a/7j7GBcVhEuH0iiFRv41Jp2bZzHLunllOl9tPTl9yqq3Hx/kF5q9F/DvV3dgKFJzJyNRr+OP7+5gzdig/W7eDsfm5Muku16hlSK6B7635iCe/N546exFb9ncknKfKPFSg4NtBjlGXMnZ7damdr9pcfNLSztVPvCfv1dSCwPmFedw+vTRuz1udn8uy2RX8/I1d2G1WLH1ks0jFynTFqFQKkzU2C5/saydDn5rQokDB2QZPKIRKEJhRNYSFEUT9o10eREQ8IZFco5JnUKDgbITLF0CnVvP4xjAJ4vsXjsSkU/PINWOo78sxRT7PNycgrCVTjpbymwtrivhe/UfUL5yA2xdg5dxKDnV6omzaH3ljJ/fOLOe79Q1U5+cyd+xQbppqw+ULkmMKf943WXw1aNUExcRF8xBiSmvle2eWMTDbQKfLT4ZBw2//toeaiA7wWpuVDIOa1m4fmQYV03+zGbvNwsqrqqi15dHpDnHntFJUKiFp42KsrUmNzcKDcyqUHLCCk45U8yyyES/HqOWNW2o52BG2yonMUW92OBGA26eV0t7rZ+lUW1RT8ZFON4Ozw0pDgiDIDX/j+3JtPZ7Acc31HJOOlVdVcvdLW2noU5tPhRyjFqNWHVV7iSSRNLZ0ICLy+tZDNPStfXX2In548Ui0GhW93gADs/T8dfdROVd83ohcDFq1nLuPbHo0adVcUjoQ3iTqfKYUW+hy+RkzLJduT4CH39gVR9CbWTUEk04tX8uf/+vYqHNR8hoKJBzocOPyBU9LZRSAUYOzeGv7YUIhEVUSEpqCcxsKGeU0wokwvxMdW2cvirPOgP5N86PXjOFQpydhl3yq9y5ftyMpGxbgq3Y3S579jBqbhWuqh5GhVydVRagtsdLrDaS0RTjS5YlirkYi/Jkiz984mUy9hr2tLuqe7ldBeeK6cXGBkcsXZOuBTqbHKAi8vyfMhD0/38z5hWaZhOMNhPD4+22LGhxO7pheSvWIXHJNWnYf7iZDr+b+V7czvsDMqnnVcWQRlQAzVzWwqm+TkwjpWLyRmwlF5vnsgkmXfOl1+YIc6HDz7If7uGNamazwIeGCYkvSubhh11EW1xYxNj+X26ePZn+bW96gP/vhvoRJg1qblSNdYdJUpIpQ1bAceU5EzsfrJhWkPLdOtz9uYw4wLNdIh9tHpl7DQ28kV1apsxfxeUt7lMJRpE2O3Wbh/jkVPPDq9rjkiERmiZxb6eQW39h+mH+0tFO/cEJCv9FNjlaWr9tBdb6ZxpZ2HptXjValiurcrbH1d+zW2CzcctkoVsT8bpDehunN7YdpbGnnwTmVqNjBhghCSq3NwoNzK1n+2nbZzizyPohVksrPM2FQazjS5WZ6TLL5SJeHw51uBmWdA501QpiQtPNgJ1eUD2LZrPK438blC3LXS1uptVlZWFNIh9vPH97fy7KZ5cwZOzTK2/l4ka4bW+k4VaAgOQTg1itGc9/LYbngDTHkvBqbhbnVw+jxBJhZNYT5E/MZkqCYrMxDBQq+Hei1KpbNLEsauz346jbGRlgcSmTe8iHZ/GZDE0/+fY+8hykfks2qedU07u/gYLubO6aX8vq2Q/z2b3uiOi8b93fw96ZWDvXtYRPFSckUJiP3cG/0xXJKnKXgnIEID7wWH6dAeG7cN6s8znJCgQIFZwdyjDraXT65UcWcoY3LvSbPxTp58NXtSXPDUg7GGwjR2uPj2ic/CNs7b9yWsJnJH+j/rOWv7eD8QjOf7XPIz+Rvqvja6fKxv82V0GbDpFMzMMuQkiTyk8tH8V/vfknl8BxqS6ycn2+moS/3XdOn1E0o3L1u1IRjjQaHk3tf3srDV1fx+LvbmT+pgBxj6jjE7QvxxHXjZAKPAgXfBNLNM28gRG2JlaYj3VQNz2XxM58kPK7B4WSpN8jArHgb9VqblUE5Brl5sNZmYf1NdvL6XAAaW1KrKEhzvdPlY/m6HZyXb2ZRTRF5GbqUity7j3QzoSgv6u+Rueo7ppXyi7/skvOzsXnnpVNtvL7tMFeUD5JzxRq1in+0tFNrs7DJ4YxbCy8rG8gd00r54UV+mYAXCIlkG7U4jvWwfuuhpDlnaT1scDjp8vgZSn9+Q8lrKJDQdKQHgBGnKRmlbHAWL3z6FV8c6aZsSPapHo6C0xAKGeU0wokwvxMdm84v787ppexvcyd8PdV7NzW1snBKYdJxS8XuxpYOjnZ7eerve1hQUxinilBbYuXemeWExBD3zCjjZzFWHzU2C4vsRQgIFFgyko/H4WRht5dco5ZDne6oACIZM7ZyaE7STdMmRysLa/rPT69REQiGolQawtYX8O9rPsLlC1K/cAIuX1D+XUYOzIxKYu4+0k11fm7KQni64EPZTJy9UKmEtHY6kjJGXU2Y0BEIiYwamInLH+S/vjuOgVkG2YJLp1YREkXUggAIjMs30+HyR9nPPDavGm8glGDOFbL1QCdrFownEBK5pnoY972yPYrQEbk+pGOfS6/HqpHUL5zA7xv2cPv00jQdIKVcUT4oqVVNg8NJa7dXtsOJtBRb++E+6uxFUWNMZc0lFSJcviBLPIGU46rrI38IwENXV3HHtFIOdXpYNa+ao11eLJlaxg7LxR8Uw+tzgs/qtxcTogKm2LE8+Op2bp82mlsuH0WPJ+y72un2caDNLRdlI3+fpN3IJVaWXDySlet3xl3LGpuFFXMrE57v2QSPPwSiyLxJBfzXXx3ccuko7vrfrQmPlZ4Fmr4uJRH46QtbGPeTi064SJWqG/vr2P4oUHAuQaWC6vzcpHNVWpMzDBo63X4ESKhsoMxDBQq+HfR4A1QOz+Gul7YlfF0i0UL0nkci7SdTsHt5SQ2vbzvEr98Ok6MjOy+vffIDAPYc6+Wh9TsTqohCeG1YMbcSx7GeOKvGyFgu9jl/Ira5ChScSQiJpIx5QiJ0uZUOWwUKzkZYM3VyIxJAIChyQbGF+oZmfnx5CZeMHohGpUqRi23lBxePTPi6lN+MzAeF35N4vYnMw0r//ZsNTfIz+Zsqvrb2+Gh3+Xn+oxaeWjABFV/IuZk6exFHujwJLeMlNYdAUOSGC4tpcbrYc7SX+2aXs6e1lzULxtO4v4PFz3zMn39wAb3eIC5DWNm4vqGZBoeTHl+Q704uxKhVpT2/Hm9AzufVlli5wV6U8ngFCr4O0t2HOUYty+dW8OrnB3H7+3OKieaIOUPLf/81vsl5k6OVEKJMttjkcHLfq9tZ3RcHHO9cb+3xsWHnUTbsPCqPIZEid63NwrLZFbyx7RCPb2ziBxfboj5PijtSqX43OJzcPbOc+oZmrigfJH9+IBhiQJaBJZfYCBFvQXbPzDKOdHqZ/9SH8t/XLBiPPxhk9OAs7k4WK8XU3brc0aRgJa+hQMLuI90YtCosmfpTPZSEsA3MQqsW+HCPUyGjKEgIhYxyGuFEmN+Jjk2ntNHjCSYtJB+P114iRNpU1NmLeGxjE5sdTt7b00advYjF9mJMOjXBkMj7e5xc/cRmXL4gl5UO4KGrq3D5gjS39sqJwaVrwzYi//3d81N+ryBAIBSiOj+X8YV5slpAMvJHuvOTXq+1WXD2eGlpd7PjYKecJM0yaHD7gqyaV41aENBr1dQvnMBnLe30euN/C41KYFFNEWs/3JfYJ7XEygizUdlMnKOQ7g9ITpAA2LjrGNdNKuCm5xpZPb+a+17ZltCC60drP2Ncfi4La4pY8szHuHxBXl5SIx+XSEKwwGLijW2Huf2FLVw7MR8AvVqFAMyoGsyIvP6E/okojSTr2vD4g+FiRJ8MezL0epOvUxIEQUho87OopgiNIPBJS7s8xshzv3N6KV+1uxmQqUenUXGo08Pq+eP4rKWd7jQdgNI1aHA46fEGEAQIhkQ0KoEDnW5WPL5DTrj813fHJfwMaSzPXj9JVmkakWfkze1H5KII9CV5PCO5LiKAkToVJcWmyGuUtHupqZWQKCbsXtrscEYFk2crws9NQb4Huj1pbOT0Gnq8AUw6tdwV+nW6rpJ1Y19YYv1atj8KFJxLMGrUstpBKoiiSKZeQ1eSea3MQwUKvh10uvy0uRJbUErIMmh55ycX8em+NnnPk25PuWHXEapH5Eb9vcHhpK7HJ++Z9BpVQhXRSLS5fEmbEiD+OX8itrnfNBRSjIKTjZ40e+EeTwBzhtIUo0DB2YjemAaVTpcfk17N4/PHYdKp+flfdjE/jRKuTqOitsQa9YysLbGSbdBQY7Ow7WAnD11dyaBsQ0pFYIjOM2n77NqlZ/I3VXzt8vgxacPWRI+9szvKDrQgz8Sxbm/CRh/JVloURT5obmNaxWDe3HGY+x/fHq/O7Q1izdLT7fHzeUs7q+dXs3RtI90ePyIiapVAh9sXdx0jv+uzPrUIu83Cw1dXKc9+Bd8IUs2zWpsVjz+Is8fHr99u4tLSQUCKZjiblVuvHM2bO47GzYlIyx8I5yol4tnxzvUujz+OBKNVqair6a899XoDNO7v4KrHN1Odn8uimiK0KiHhXEtXJ+p0hVW/Xd7+cxGBpxr20NjSwfcvKuaOaaVAeM4bdWpEETTqaGuSbQc7mVk1hH1OV8rvixxPtiF67VTyGgokNB3tYbjZiEo4PS1wdBoVIwdk8sGe/mYUBQoioZBRTiOcCPM70bHH45f3YbMzoYxZuvcOzNbHJQtjC+eR6gkS03TpVBuNLe1xScYNu47hDWzl7hnl3Pbnz+XNxK/+bSwDswwYtaltEYbkGDna5WXVuw4aWzqosxexsKYItSDIyg6R55hOhUSvUfVJKpZgydSxaqOD++dU8PTm5qjiv91miSr419gs/Ou44XGfZ8nQ8fDrOykfmoNGELjtitHcOV2g3eVHqxIoHpDJoGyDspk4RyHdH9X5Zu6YFiZIxHZqSvAGQtx4YTG/b+i/FyM34AB/XDyJd784KiuDrN7oQKUias5Gdp3W2CzcM6NMVuqQgoilU238rmEPmx1O/uOyEnmtOB6lkVqblQU1hfJ6EIt0a4yEXm+Ax9/dG6VMFItgSIxbU6R/3zGtNG6M0rmPzzejVav49YbdcWvZnDFDo/yPE9neSF0tne4Am79sZcfBTsqH5lA9Ipdffmes3DEzNCd5ocLlC9LWGy6K1NgsVEfI1keiM6YjcbPDycp1O+XfN7KAk04Vqy7JBtCd4NqebZCem9I9MKtqSMrje7wB6jeH75+svgDw63ZdDc01xlm4WTOVIpICBengD4lp592gbAMqATL0GrTq5IG4Mg8VKPjmkW3U0pOG1Nvt8bNqYxPLZpUzuTiPjbuORSjGEWVhGBlj/vI7Y+M+yxsIysdJJOhkCidwYjH2idjmftM4nUgxCs4eZBpSpwAzDRr06uOL2xQoUHDmQHq+jR2RK+cRPmlpZ+7Yoextbee1zw/S4HDKTVPJYNKqmVE5mIVT+i3Qj3Z5ae/1c2NtMQatmtUbm9jkcLJmwfiUnxWZI1ILAo/Nqya7L3f7TRVfsw1a2np9rP1wn5zLkZQd2l0+8jJ1Ce2bJXW2RTVFjBmeS7OzV1Zui0WWQUNIDO+Pwjk8gRsvLCbLoOX/vtPE3dPLEEW4c1opC6d4EARBVl45Pz+XZbMraG7tZe31kyiwmBhmNn2tc1WgIBFiic4PXV3FA69tlxVHILzHXlBTyE3PNfK/P5yCSadGLQjU2qyMzc9Nas0JpLXykiARz453rucYtUmJYotqisg2qMk2aMkyaJleORiNSsWGnYcZac3g0WvGxO2pc03p4oMwwS6WXCIRUaZXDGHFuu1RNaPaEit39hFUJIgiPPjq9rSFeWk9tNssmDPi1zcBmF41hAWRa2936mZPBWcfvjzWw5Ds0zsOrByWw5vbDhMIhtAoMYWCGChklNMIJ8L8TnRsuu6yz/e3c0FxHnPGDuW+V7ZFJf2OdHmSeu3VlljZdbBbtsXI0GtktmlksTgRqzSl/Y/DiaAi4Wbioasrk4/HZsGgUbHy3X75t8j3XlY2kIeurqTD48ftC5Jj1OIPiqxZMD5qky+Nu7bESoHFxPcvHInHH+STvW0sn1vBUw3xm6uGPusGaXO12eHkvle2xSUmc0w6HpxbyZ0vbokam7SZGpRtAJQiybmKyPujekSuLL+ZCHqNCrvNym82hAPdZCz0SGUQgEOdHq63FzOzagiDsg34gqE+opeKHk8AUYRXltbwaIRd1rgIYoQgwJJLRhJCjFpbYlVWAIblGlGrBR59Pd4ORhqbVCxo3N+RsgOkcX8Hm5paEZMoetTarLy/J7GUYmNLBxk6Df/z/cn0eoLcO7McrVrgQLubQEhkSK6B5et2JEwsrFi3nWUzy7nrpa0pr/Fj86rJNWl4/qMWnr1+MsvXbZePMenULJtZRrZBw2WlAyiNSW581tLOjoOd8jVYMKWfvJOK/ALIr2XqNYzLN7Plqw6utxcDx6/8FIsM/dm/BTBoVSAiP08EgaT3X63NCoTvI4FmfnZ11T+tUpVjUtZzBQpOFG5/kCy9BrvNErVXlWC3WXD5Anx5rJdiawb5eakTtMo8VKDgm0WWQcPRLk/KOBRg4ZRC9re5+Mnlo/mPy0ZxqNPDkBw9i2qKqLMX4/EHKbJmIAAiIqvnVzMkR88T142LksgfbjZyWelArp2UH0WCjlQ4iUy052Wkj7Gl49tcPhbVFDF2RG5UvAipCS8nG6cTKUbB2YVMnTrl8zVTp06rGKlAgYIzD5LV+6f72uXGnfqGZmZVDaF4QKZcUE2VV64tsfL5Vx0JrSbsNguL7UWs2thEg8OJSRduMEyW143MEdXYLLy/x8k/Wtr51b+dJx9zMvOl0nO+0+1jaI6B+ZMKovI9Jp2aV35Uw7Eeb1IrswaHk7o+BYahuQbe/smF/Oj/fcbuoz3918hmxahTcbTLh6Uvj7DJ0cqtV4wmU6fmi8PdGLRqHnh1W1wRe/3NdtSEVVNsAzKjzlVRSlNwMpCI6FxbYmX53ApuvWIUza2uuEZJo07FspnldHp83DurjNYeX0orL8l+KxaGmKZjnUbFl8d6sGbojmuuZ+g1CUkwElFs6VQb1z7Zry5da7OwdGoJOrWKoblGfvmdsXx5tIcOtx+9RkVminxDrc2CRiXw4JxKPL4gtTYLmxxO3L4gj82r5miXhwfXbU+oTj17TCcP/0sVA7P0fWrYJn6zoYmx+eaUa2vj/g5qbRZ+dnWVXDOS0OnycXuCuADCsYwSF5w72HOslysrBp/qYaTE2OG5vPDpVzTu72BCYd6pHo6C0wxnfyXqDMKJML8THVvf0MyaBeNRCULUAyqyu+z8AjP3zCzjvHwzi/rsOvQaFdsOdrLIXhjntWe3Wbh/djluf5CXPz8Qp3Zi0qlZOtVG9YhcMnQa2bpGSt6lK5IGQ2LCzcTK9Tv7WPRi9Aa9z/vP4w8mDRA27DzKopoiBmTpcfb4eOydprhuO0l14fwCM0sutjFn9WYgXPC9oNhCrkmb0ks5UmkgWWLyeAMnpUhybkK6P9p6ffLGNhZSgC4VySG5JYv0759eOZqlU20MNxvQqFTUN+xJaO2z8OmPOT/fzIKaQt7b04bLF0Sj6md8Vw7NYfEzn1BnL+LS0gHMHTuUT/a2MzBbL5MrDne6GTkgkzd3HOa3f9vDY/OqcQdCKRWU6huaWX+Tnfte3Z50nYJwoL/kYlscGeTeWWVc/cR7cddKIpAkCuoX9TH6/7h4YtJ5vcnh5J5Z5dhtFs7LN6cMcn52VRXXTsxneUTwEUlg+dXbu+OIKhBeT1deVYXHH2DueUN5uI+8k4r88vj8cYiIrGmIf23M8BwmFuVRkKIQa9KpGW42smbB+DhSzOkp6neSIYI3GGKRvRAECImwbGY5K9bviLv/FtQU8uyH++Tng9sX5BenUKVKSTgpOFfh8gbRqAWWzapgxbrtUXs4u83CfbMrCIZCrFi3g+r8XB66quprf5cyzxQo+Ocx3GxiykgrRQMygXgLyvtnV/DIGzvZuOuY/PfaEiu3XTGaQ51eru+z0JH2YNJ+x26zsHxuJfe9so3WHh81NgtrFoxn18Fubp9eylWPb44ii0gKJ7GJdpMubK8qQpzKyKPXjMHlC8YleCPjxcjv+DrWfV8HUtEwEb5NUoyCsw/uQDDl89UdCCZsLlCgQMGZDcnqPba5qNPtRxT7j0uqhFtiZcXcSv71v+NzMRDO39w1vUwmojw2r5pnP9zHgpr4PHNk7ifyv12+ID2eAIOy+z/3ZORLY/cFLy2ZEpfvqbMX8eBr29PaFHn8YcvkA+1ufr+5mSe/N56bn/uMLQe6qLVZuH9OBV3usP1Ru8snNxj5QyGO9bipXzghLmcF4f3Jspe38eg1Yxgzwpx0/FIT05RiCzqNCnOGTolfFBwXkhGdNzW1ct8r23lgdkXCRsmv2tyMK8zF6w/h9YcIhsS4YyKRqA5kt1kQIxaaGpuF17cdZvVGR5TqX6r7uMcTSEsUizqvPlWiq8cNo0YTbsrUaVSYTVo2f+lkQkEedfYiBIQowpykmm/QqHD5grT2eLl3Vjkr1+0k16Tl1xt2c0NtccKxWDN1jB6czS/f3CXP8SeuC9u4P/9RC08tmICKL6K+r9ZmYfmcCvY5XQzLNaJNoCShxAUKANp6fXS6/QzNMaQ/+BSi2JpBlkHDX784qpBRFMRBIaOcZjgR5nfksZ3usHeeIMADsyvY6+yViSaRjNZP97XT7QkkZLFKm9pY25CHXt/JrZePpq6miDunl+L1h5gzZig//8tOro1hk0N08s6kDZNVxuWb0agE8jJ0+IMhutwBPt7XBpDwAe7yBVn8zCc8f+NkFnZ78QZCFFkz6HT7ONLhYUC2ntdustPe6yMYEvk0Ru2k0+2nxdnL61sPxbFcNzucqAgHIJ/ua2fxMx8D0Qot0mYhGRLJyyUraCgbAgWp4AkEeWBuJR/taYsiehzp9DCxOI+v2txRChbpLFnumammsc9fNpFFlvRvSXUkRL8CiTlDK68DA7L0svVMtztAlkHL61sPxpHDVlxVya5DXVFJjZ9eUYqzNywXGKugdEFxHmqVwIzKwfz0ytEpLYo0apUs7Zpj1PLX3cc41OFJmCBNRtLZ1NQKItx4YXHaxOrBDg/n5Zu5smJw0mvc4HDS6wsws2oI5UOyWWwv5rOWdrRqQf7+pVNtPPqXnbKaVCQJZMW67dw3u4K2Xi/zJxXgDYSojiG/RKqk6DQqBAGq8800tnTI5xBexwTunVVGu8uXsOPIpFPz+4UT+MVfdsX9bsuvqsQXPPsTzXkZOr443M3StY28frMdXzCEWgU3TbXxw4tGyl0Rkfefv88aq9sTSCiP+W1AkeZXcC4jy6gBER79y8448nTj/g5+/sZObrsyLH+7uW9N/jpQ5pkCBScHnS4foiiSqVezfE4lnkCQfU6XLDcdS0QBZBW8FVdVYtKpGZefy8IIUjKE91z3vRIuzCx+5hN573N+oZnSQBaA3BShFgTUKth9uJv97a44dZO6pz/m3pll3HJpCZ1uP8PNRgb3dR0ufa4xLsEbu1+W8HWt+04UXWlIL98WKUbB2YeQCD9P8nx95I2d3D6tNK3FsQIFCs48RFrWRVo4r7/ZHlVcdvmC3PniFuoXTqDbE6AzIl5euX4Hj1wzJqmdsmTZd+OFxTy9uZkGh5MP9rTJxBdvIESOUUu+2cixHi9/XDyRTU2tUZ+XTOXs65LGExXgNWohLm8k5dnS2RTlGLW8v8fJ+AIzDQ4n9768lcfnj6PL66fbE0SvEjjmDTIoW4tKEGhsCSvRZOrVvLWjlfEFeQkb0SC879nndJGp10QpokQSURI1MSnxi4LjQSpCw6amVvyhUGLlDjH8v99s2M3dM8rSqqflxuwhamwWFtYUoe5T8Y5tRPx7Uyv3v7KNlVdX0eMJJJ3v6fbGHn/8mvRpSzsPzKngtj/9I65p8eJRA9j8ZTvTqwazsKYwaj+0+JmPOb/AzFXnDeXWP2+Rc7SBkMgXh7vJ0MWXUyXye6zNV4ZejUmn5pFrxvDYO7sZm58rf1+OUcvQHAPtLh8Lnw7XphIpnShxgQKAPcfCKlxDTvO1XqUSGDs8l7e2H+GnV5amf4OCcwoKGeU0xIkQGGKP/fJoDwc63Czu6zCLRZ29CH8StRIpIElkG3Lr5aOp7yuWPnHdOL5X/xGr5lXzdAqFhhsvLCY3Q8c/WtoT2onsPNjJ3LFDMenUCQMZly/cldO4v4P6hmb+UDeRJ/++h/mTClixfkccsz6ye02vUTEw25B0k7/J4cQfFDnY6WHVvGq0ahUatSAXfCO9SxMh9nWjTh2XyFQCAgWpIBXBPt3Xzur51azfejCKOFVrs1A8IIN/fNXOJaMHykFBOrWh9l6frN6TirQiqftE/ncoFLazWv2uI+q9D/9LFX94fy9j880sjCFXLF+3g+smFfCv54+QN+9/azrK2OG5rGlojpZ/tFn48eWjueelrbKHcCqLIoNWxb/9NryWrVkwXlZmSmSzktoSrJVbLitBr009r0VRZPVGB+VDslMe1+sN8remY3KBo8Zm4f5ZFfz2b3sAGJ9v5rwRuUmtlNz+IO0uP899uI/qfDMzqoZEycOmsgiKTNRscrRyoMPD0rWfJVSSenBOhezXHH09nNz38nYeuroy5XmeLcjqC4ZDImhUKu5/ZRu3XDaK+U99kPD4TQ4nt08vRaMSaO/1xUlkftNQpPkVnOvQq1V4AiE27joWV8CWcNu0Ur5/UTG/frvpa3VwK/NMgYKThw6Xn2WvbOvzQvcyzGyU93drFoxPOo8bHE5c3iBv3lLLsR4vR7q9rJ4/Lkpls8Hh5I7p/UksSf67w+Xn8fnjONTpRi0IDMk1sOzlbXFqgJF7p7tf2sZrN9lp3H2M5z9q4ZffGZsyMR+rhvnPWvedCLLTkF6+LVKMgrMPokjK5+tPrywl26TcXwoUnG2wZuoS5lF0GhWBQIham5VPW9qpsxdxRfmguIYWCW5/MKGdMoCpr+h6adlA2WY6kvgiYe31k/hobxsf722LI38kUzmDr5djTfSc73DFF26lPFsqW2m7zcLALD3Pf9SC3WbFpFPT4HDS4wti1Gg44vPiCgYxm8JNmKGQKKvrPnR1FRqVQKc7ddG4w+2PUjmIHH+yBiwlflFwPEhPaAhw89QSjBpVlOX4ULMRly/Ixl3HuG5SAfl5puT2NiVWhpkNrL/ZTq83QLcnIDd+Pb1oAmsWjI9rRDTp1PyfiflxhJHY+Z6ZxmY8UR3nxguLkyoR/eCikbIieCJsamrlrullcs1q9UYHF40awC//dWxC9ZI6exE93mj1FpNOTb7ZxAOzy2WCXuz+S1JUkpBI6USJCxRA2KJHALmh4nTGpOI8fvXWbnYf6WbUoKxTPRwFpxEUMspZBmumLiEbVEL1iFze2+NM6enduL8Dk07NjRcWU1tiJRgCtVpg+ZxK2l0+MvQabrywGEEQkpI9Njuc3DOjjIdf35lQmQTCnf7L1+1IGsgA9HoDMpM8EAxRPjQnpUVJnb2IxpZ2Gvd3pC0ou/1BGhMQZR6bV822g51pr5GEC0usfNbSoRQ0FBw3IotgS6faWNOQQNGj798PzKlkf5uLm6eWAIk32JGQguh0pJXI16X/Vgsqnnj3i7ixDMk2JFVBWlRTxNBcA/6gyKFOD0If210AZlQOZuGUfob5kS5P2EbsOPyIa2wW+b9NOjV5GTrWLBiPAMweM4TP9rWzYv3O4y5CiiJoBFVyj86IeZ3uGnd5/PK6dPNzjWx2OFmxbgf/c+MFvLnjMLkZWn7xZvx1lP69bGY5w3ONfHdyIU817KFm5PHbMMWulxq1wKp51XR5AjzQt0Z3efxkG7RkGTT89IUtCc9hk6OV3hTPirMFrT0+jJqwws79r2zj9umlbHI4+dHU1NKiR7u82AZkcqTbQ6fr25W8VCQ4FZzr6HT7SPMI40C7m0tGD+TXbzeRZTjxcEaZZwoUnDz0+gJscjgZm2/mX8cN470vncdNot7X5uK5D1tYUFPIrX/6HJcvyNTSATx/42Rae3x4/EHUgsB/XFbCk3/fI9vADjMb6ej1sX7roXAzQUN6NUCA/W0uGlvaWVRThLPXlzYxL40/kW3uNwlrpo4LS6xR1r0Svk1SjIKzD71pOpp7vYG0sZACBQrOPOSYdKyYW9lH3Ox/thxu91BoNXHzpSPJ1Gtpc/lxeYMsshczNt8cpUAN8URNCbU2C84eHw/OqeBolzflWDrcfuw2q0xYkZ7T0vPtZJLGEz3nTTp13N+kde/5j1p44QcXcLDDQ4fbLzdh7TrUxe3TSvmvd5v4+b+OQRT7FYa73X4OegPUb27mwTmVCBqRoCii7ks5NDicHOhwc2HJQI52e1KOV69RRakcRI4/VQOWEr8oSId0hIZMvQaNWuAnV4zm4dd3snqjA5NOzf/+cArOXh8mnZoReSZ+/fYX3DG9lJkHuhiYFanu7WZicR5uX4iWNhdmky6K6JGp1/Bvv41vCDtekpVOnTifa9KpuXdmGTlGLU9cN06es/UNzVHrTCze3+NkXH5uymtyqNMdFUdk6jVYMnR8kKCuNj7fjCrGDL3OXsQv39zF0ktHcfuLW+XxSkrY0rULhYhq1I5VOlHiAgUAX7b2MCBLj+4M2KePGZaLSadm3ZZD/ORyhYyioB8KGeU0RKfLx7EeL4E+qUSBcEd3rzdApkFDhlZNSBRx+YOoVQJi32u5Jh3eQAi3P8hzN0zG5QugFgRc/qD8MNaoBOobmlk9vzpcFI7xxl5UU8SdL25h9fxqft/QzG82NMkPyguKLagFgcOdHgZlGRJu4CPR6w2mJKtIyg0/uGhkwg21RPqQmOSLaooYF+Ejnugzl1xs47wRudz8XCOr+nxOk8EfCCVNWk4ozJMZ+pHH2G0WfnplKfN+F95AXdjnm/rSPw6wZsH4KMWI+oZm/t7UirPXB/BPy0ue7jgZEppnO6Rr5A0E5bmXWtHDyV5nL/Wbm1k+t4Krxg5luNmY0JIFoNZm5bM+i54TUfeRpJg9gcRzNsek5ZdvJSdX3HbFaH751hcsqili6drPWDaznKc27Un4WZLtDqT2I75vVjk93gA/vryE8flmDFoVQ3KNdLv9hEQ4Lz+X126qwXG0F41KYEgaz8Quj5+QGEo4r2tsFpbNruCqxzcDqUkytTYLA7MMVOebWfvhPjkw2eRoZWG3h8aWdmZUDUnqZbrZ4SQoirT1+jHq1Nx6+eiotTSdDdMN9mJZkt4bCGEx6Xhj+2E5SWS3WVg2q4LrnvqAJ647P2GgI61P3e6vZ21xJsEX8GPSa3niXQd7nS48vnBRKfs4itfuQJAMvZqbnms87g6sk7EOKhKcCs51ZBl0+IJp2Ch9qC2xolGp+GRvGzlGLQOz9Mc155R5pkDByUOvL4hJp0arFnD7Q6xcv5PH5lWHlSqz9Cnfq9eo2ORolW0j6xuamT+pIE7iutZmZfX8apaubSTXqEWnUvH4u47jUgP88WWj5H2SXqOSP/f+WRVpE/OFFhPv/OSipLa53xRyTDoeuWYMd764JSrx/G2TYhScfchI01mcodfQlaZz/1yGkvNQcCaj3eXlwbkVNLf227ob9Wr2trnINmhZ+frOlArUyVBjs7DkkhKyDRoG5xg40O5OOQ69RiXnuzc7nCy2hxshpefbl0d7UpLGD3WFCR3HM/cSPee1CYrajfs7mFo6gPmTCli5boesDOHxB6kZaWHu2KE8/m4Ts8YOY+0HLVw3OZ+LRw2gvqGZLKOW1j6F4gdf286PLhnJwEwDohr+WDeRD/e20eUJkG3QsvVAZ1LlldoSK/kWE11uP18e6wmvLxGWJ+kIvkr8oiAVUhEaakusqARweYNsP9jJpy0dQJhMcaDDjUal4vH51ax4bTs7D3fzHxo17+w4HDVPCq0ZaFQqguogZmP43l061UZ9QzPnF5hRqQTqF06IUkCE4ydZdbh9cflcyRrn8Y1N3P3Stv7zsVlZe8NkvCka8Oobmpm1ZEra61Y9Ilf+b706rLT8aUtHXC47x6SNWyel9x7scMvjTaSEXRuz1sYqnbh8QZZcYiMoinG5cyUuOHewr7WXwWlqH6cLdBoVEwrzeOGT/dxyaQlqlZD+TQrOCZyRZJQtWxJ3eifCmDFjvsGRnHwc7HBz38vbuHZSPms/3Mf8PjWC2IfN0kts+AIhnvz7l/JD8Fdv744jTiysKeK2P38uFymvqR4GwNYDnVGqBYOy9ew+3MPNzzXKicAGhzOlZcTd08tSnks6SwxZjUElxAUCsaSPz1o6uHO6Ia0KQlAU5Yf30S5PyqL9e3tSE2Vu6rsWdTVFZBm0qARocLSi06p4pm4ieSYd1kwdzl4fH+9ti2LbSkHbnS9uQSTei/xss/A5WRKaZzMir9ET142T/54uoNRqVNTVFLG31UXZ0Bw0aoFF9kJCxGxCbRYenFvBrFUNQHrVEUkFpMZmweMPsmbBeALBxGoRiTx1JWx2OLlzev/rdfYiBmbrkxLRIJoJ7guG+Mnlo7ljmsChTg8jzCb+sb+dr9rd/GjtZzx8dSXDzEbueXlb3Bqx8qpK9rX18ND6L3j7JxcmlYqUzrd6RK5sjVMX45H+eUs75xeY2dTUmpQkU2OzsKCmiP/z5PtU5+eyqKYIjdC/ofL2Edza+whoydDh8nPdUx/K/3746io5IZHqfjDp1AzJNdDYEK/otHp+NVsPdFI5NIev2l389t/HY9KpZAn7SAzNMfD4/HFkGlITCs8GZOh0uP1B9jpd/GHxRNnHWq9RU2uzJLxPpfsl26gl16jlk33tx9WBdbLWQUWCU8G5jiyDhh5vIO0crbVZuWt6KbNXN8j7Qykhk27OKfNMgYKTB7NJy2Pzqln74T4uKLLg8gW588UtrFkwgR3HqTYpFaOwk7A7MhzPiSybWUa2UcveNpe8PqTbS3e6/fL4Ir8vKIqYtOqUnYbDco2nLME7NNfIqnnVtPb46Pb4yTJov3VSjIKzDwKknJMCkGs8I9OE3ziUnIeCMx1GnQZRhKc375XzpEun2mRbnuNVZ7Vk6OVmPCmfsviZj3nqe+PRqlWpm3tKrDTu7+CK8kHy37INGlbOrWRI3zxKRxrfc6yXh9bvPK65l6gAf6jTw6KaIvQaFeV9xfRASORfxw1j+bodch4+8pztNgu3XVnKY+/spnxoDtlGLZ3uAGsWjMekVcnNXpuaWrl92mg8gSAi4AuG+LylnTljh9Lt9jOl2MKMqiEsf217VO6qxmZhwZRCHn59J/MnFfDvaz5ifIGZh66u4vKygby982jahjMlflGQCsmIzva+e+/qJ96TbcglYkT1iFzUgkCOSQsist36I2/sTDpPVlxVSd0zH8uftWbBeDz+EP+S4PNdvmDaJhSJZJWp1zLvdx/KdRpvIES+2cSjf9mZwJq8FRGRu2akqVupVWlzDpGq+5ENnHe+uIVHrxnDndNL6fGEm8Df2XU0au3zBkJRZJZkKjCbHE5Cfa9v2d8RpXTS6fJx+4tb+HRfe9S56zUqjnZ70zaKKzh70NzqIt9iOtXDOG5cXj6Iv+0+xl+/OMqlZYPSv0HBOYEzMso877zzEAQBUUxcOJVeEwSBYPDMsSHodPm444UtjM3P5febm6nONyd+SDW1gigyo2oImxxOlk61JTyuweFEpD9waHA4ue+Vbdw7s4xB2YYouTSTTs3q+dVMKc7j4lED5M1EogelSadmYlEeGXp1yuJvOkgbaZcvGFUcHpFn5M3tR2jv9eHq67R7bF41j76xs8+LPDn8gRAuX5Bam5XxRXkMM5sAMWpjIakuzO1TQUgEb9/nrN7ooMZmCcs/90k6H+nwUDY0m0HZBjpdPu5/ZVvSoO3Ra8bwwCvx/oR/b2rljhe3sPKqSnKN2jM6qXgyJTTPVsReo8ggMl1AqRYEvhcxV2ttFursxUwozKOupghfMER+npFuT4B2l49V86oRBIGtBzpYbE+sArKopoibn2uk1mbh5ktL6PYGGJClj5MUlNDemzoZ0N7nuSsRuVIVBbYd7KR+wXhWveuII1QsvcRGSAxx90vbqLFZqLMXMXZEbhwRBcLr270vb+Ohq6uwDcjGFwiysKYIMcX5PjF/HPOS2A2dn29m+ZwK7nt1O5uaWmVi3j0zymUyR6S3qfQdd88okxMx+Xkmlk614U8TTMVenxXrd1C/YAKIqe+HOnsRK9ftSLjeqBCYUTU4al1/99aLMGhVrN96KO6aLL3ERqbujNwCHDe+anex+ctWbAOzePy6cdz78jbumlZGrc2Ks8fLslkVPLhue9L7ZVrFYJa/1m8ll0r29mSug4oEp4JzHV5/kA6XL+0cnVE5BI8/EEVU3nScc06ZZwoUnDwYtGo5dpVUF747uYBfvrkrYecgRM9lCSadmvEpVDA3OZzcO6ucYEjEqO1PvB6PpcjTm5u5fVop1z7ZLw/e3NrLi5/uZ8VVYduC01GBJMekkE8UnFyIiEmVIhfVFCEiolYphY1YKDkPBWcD1IKAxx/gwbkV3PfKNhocTp7/qIWZfbnlRJDJon2otVnZsOtIwmd1h9tPQZ4pZXPP8jkV/Gz9TqaVD5b/nqHXkGvqJ1JkG7QpFV71GtVxz71EBXiNSuCOF7fw1IIJ/OrNXfK51C+ckNQavsHhROALxubnUj0iF39QZECWjp//5QvunFYaZeXe4wmiUYdza4Nzwo1aK9btYPncCu57eTs/uKiYh66u4kCHG39IxGzSEgiKdLkDlA/NiVLgvfulrTz8L1V4A6GUJB8lflFwPIgkOne6/agEAZUqTNBaPX+cPMegmTp7P+nhl2/uYuklYev4gdn6lPPkvle2y/dvOFcJ5xfmyTF7LMltSE4/oSzRvJfWBmumjvEF/XGCSafmf74/Oena1eBwYtSqkyoRPTi7ghanK23OIVJ1X1K+t2bq5PVD+v4/1E3k+Y9aeGrBBFR8wSZHK3qNCm8gxI5DXdTYLGnV/pdeYuO6ifnkmHQJldUTvXdiYZ6y/zgHIIoi+9p6mVCYd6qHctwYOSCTkQMyeGpTs0JGUSDjjKxENTc3n+ohfCNo7fGFrR5qClm90ZFScniTwykTM9LZOkT6eW5yOLl9emmch6fLF+T2F8KbcWePV94AXFkxKOqzJWLI0S4Py9ftSFj8lVRN3v3iaNpOuBqbRd7sSBuOHk+Qcflmckz9AYi0yRmbb07JsC+wmFh3U43sL27J1HHzZaO4e4YGbzBIR6+fT1racfZ4U6qsSEnNyA2IdPwd00rp8QQYlC39ZsmDtrunlyV9fVNTK46jPTzz3t4zupumtceXUkJT8S2Nv0aSBGj50BwGZOmTMrHtNgvvxyj4SIzp6nwzi5/5hB9fXkKuUcvqPqlyCTU2C9XDzSy2F3H39DJ6fQGyDFoMGhXNrS5e+MEFCIS7Y37zztbwxneqLeH8kiRUkyFSUUUKVpIlDzQqQZZVj4REqKizF8r/vnN6GW5/vJ2WhAaHk15fkLqnP2bNgvEygeSOaaV81e6Wu3Sk+evyB7ntz5/Hsckb93fwo7Wf8fSiCUyvHBye494AwZCIiBhF8IgdsxBB4Dna7WVojgFLhj5pwCOteZFw+YLUPfMxL/zgAgIhMen9MKXYkuKZEH52RMIXDMXdF9K4AVbOrUz4WWcLuj0BBmaHLeUEVVi9p7XHy5JLRpKh17Bh12FummqTOxmyDBqOdHm448UtnJ9vlq0DpOuaSvb2ZK6DijS/gnMdPb4gpnRztMBMKCTiS6DodTxzTplnChScPHR7AnLMebjLg91mobak36Nd2p/dOb2U/W39+7M7X9wStVfM0mvIy9BFeabH4qt2NyJEKdMdjxpgg8NJXY8v6nP1GhXv7DqGIOzgF98ZS48noCiQKDjrYdCoefHT/dTVFMU9X1/4dD+3X1lKTxo12nMRSs5DwdkAnUpAo9PwX39zsGJuJYc6PRh1ano8qe17JWJFjc3C/XMqmLO6IeFxJq0ak05NdX7YOv37FxVzx7RSIGz/YdSpUasEbriwmNYer/yZghBtuWPN1FG/cAKrNjbFNRGtWTCej/a2Acc/92KVxswmHbdeMZpfvBmtBuPxB9PYaIdzA95AiIHZaoxaDZuaWrl7htBXwA8j06BGo1IhCGE1KsmqxBsIscnRyl0zy3D5g/T6gnEFfSkHLe1z/t7UiscfYtW8apy9Pq6uHsYDfQ1UEpT4RcGJQCI6723t5Z6Xtya15rqhtphgKNzo3eBwcnvfXO7xpJknTa0snFLY/2+Hk0URhDYI5yTvmVHG1ecNQ63q26/3EdjjLGwilE8j4/c6e1FcbSsWQVHkRxePJBRjbzO1dABVw3Po8QZ4Y/sh5o4dGrVWaTUqNjUdY8pISxTRTKtSYc3U8dwNk3ngtWgCiyjCI9eM4bF3dnN+oZlbLishQ6+hvdcnE/TSNqOqBEw6dVJl9URQ7LnODRzr9uLxhxiUk9oC93TD3POG8Z9v7+bDPU4mFacXLlBw9uOMJKMUFBSc6iF8I5CkCKWu+XSSwyd6nISv2t0U5MXLOl07MZ9fvLmLG+zF8gYgUo7MpFOzal41T29uZmFNERt3HeODPW0JC7vtvT5++7c9KTvh1n64j0U1Rdz54pYknnlWHp8/jmyjpp+pnoJhf+/MMq598gMen1+NKMKyGMWSWpuVRfZCOUhIpupSa7MyIEvPCz+4gL/uPhZFRNnscOILhAiFwr9VOvnIXl/qoM4bCJ3x3TTproGyMYq/Rs9/1MLaGybz4Gvb5f9e8dr2OAWfBVMKozpGJUSSzC4ZPZBHE0iqNrZ0oNMI/O7d5qjPtdss3De7gkAghEar4t6I4CPZ/Grt9iT3tbVZ2fJVh/xvvUbFtoOdrFkwntUJ1E/unl7GL97cnfA6bXK0csf00bx2U024m0QlpE2MRN5fkppR9Yhcljz7Wdyxeo1KPiYRTDoNg3OMHOny8OyH+ygfmsOFJQNSfn+7yxdFVqmxWRg5IIMVc8NdtpE2YbV9Nj/SbxpL2AmGRN794ijX1xYzvWoIg7INMpHncKcbTRqPxdi1XrINSoTNDieuFP6pZwO63H68gRBGnZpj3eEgNQQsfuYT/nfJFM7Pz2P1xqao+VFjs7BqXjWZeg0hUcSkU8uyoQadmk5X4mTXyV4HFWl+Becyej1+8jL1Kefo0BwjTcd6GJSVOBg/njmnzDMFCk4Outzh+eYPitz78lbW3jCZTlf8/gzg85YONjlak3uml1ijpLsT4febm6mrKZIJKKniw0j1FU/EvifSImjDzqPcNT3AyIGZJ+FqfD1I3Y9dHj/ZRi3WDGUtUvDN4dYrRnP/q9FFFLvNwgNzwkT1Xq8Sv8dCyXkoOBug0ajYc7SXxfaRrFi/g/KhOUyrGIxBmzrPYM0M50c73X6OdXuSPp+Dohi2Kb/Exu827aFiaE5crqq2xMpiexGCIMjPaYh+DmbqNTQf66WxpSPq86UGpvMLzfLfjnfuxSqNeQJB7vrfrVHHSCoGqeANhMgxajFq1bT3evvGEIiyDO31BhiUZUAAPMEQjS3tPDavmt4+y2CXNxx3JFKWkJQkbruyNOocRw7MlMe/WolfFPyT6HT5WBZDRIH+++9/brwArUbg701HGWnNkl+vsVnINKhpd6W2JvcGQlH5zgydmvqFE+RmZJcviC8QonxoDp/vD6vQz6zyJFTmHzsil72tvRzudJNj0vGL74yl1xvA4w9yOA0ZxesPUffMJ1F1K5NWTV6mjoMdbqxZenYd6mJaxRCWr4vfF628qooOl5eKodkMyNQzIEvHHxdP4mi3N0ETZ4jfb26msaWD+ZMK+PWG3TS2dPDGLbWMy8/lzhe38IfFk1KON9OgpdPl555Xtsn5d4M2tVqdYs91bmCv0wXA4GzDKR7JiWF8gZliawYPvb6Tl5bUoEpT11Bw9uOMJKMkwo4dO2hpacHni34gzpkz5xSN6MQh+ddLTMl0jMkTPU6CTq3q6763RhVKJWZrXU2/EolU8JYShgatmk0OJ/MmhQlByQq7z90wmdXzx7H1QAcTCvNYbC9GoxYYkKlHFEU63X7Kh+bInXKJNuGftrSzRDuSjphkpvQeaSMx3GzkrR1HCIRCfHdyAV8e642zpYB+r/FV86pRCwKzxwxh5fqdUQX2GpuFH11iw9nj40drP0sYZB3r8TJqYHgzlp3moZ/udem3OZO7adKdo7Ixir9G351cwIo+FvWPLy+hvdfL9KohLIwgdQ3I0nPtkx+kVPCR0NjSwdKptigVElEUWZVAFaPB4WTFazuYUTWYquG5UYSs2PmVodfQ6w2gUQkJ2eTSfPm0pU3+d+P+DjQqgSeSKHIc7vKkPJcjXd4ocsfa61Nv1qX7K7IrNlmHbOP+jjgSmhQgXVBsoacvOeDs8XLn9DIefG074/LNpEJsskL6zgdmVzA2P5dbrxiNsy9JEfmbJivCTC0dwPTKIfxu056ocdbaLCybXZGyWzh2rXd5U9876V4/05Ft1NLjDeDyBcg2hu8Tjz987fVqVVyRG/qCb0FgcnEepYOzqbMXMSBTT43Nwroth9iyvyOhktU3sQ4q0vwKzlVYM/V4g6GUc/TemWUMzTGgTbIHPt45p8wzBQr+eWQbw2qWBRYT351cgC8QwqSPT5zWNzTz0pIpLF+3I6UdrSiKsnR3JKR9pmQZEGk1Iu1fl1xsQ60ScPmCfNbSHkVqSaR8KeFUFpIjux8lXFhiPaOVMxWcvvAFQwltPxscTh58dTv3ziojL+PM6rr8NqDkPBScDejyBMjN0MlF1427jjGxMI+B2fqUCmNqAb5X/xGr5lWz+Utn0mNNOjVtvT48/hA3XljM4xvjc0KS7fw9M8uZUJjH8x/u46fTSln6XGNcbjYROTVWEfbrzr1EOZXG/R1ckKZzO8eoJS9Dx8ZdR7CXDATAqA3vL8LF60r2tbrwBkPo1SpZPQ5geR/hb0Cmnh5vMOE1hLCSxD0zVXLuJ/YclfhFwT+LVCrvmxxOFnZ7eHrzXpbNLidMMYN3vzjK0kts9HoD5BhTzzuDNnG+M3JeS/d1pl7LvN99yB/qJnL3S9vkY5PlTKU9srPXhyiKSRuN7TYLPX1EsUhrn8fmVdPtCecID3V6+OHFNlbEEFEgvC9a9vI27phWilat4tcbdrNiTiXtvWGLo1gIgiArjkfGOB5/kHtmltHpChAIhlKutYFgiIBaJa+FJp0aa6Yu6Tkq9lznDvY6exGAgVlnFhlFEAT+/YICHnxtB89/vJ/5k/JP9ZAUnGKc8WSUPXv2cPXVV7N161YEQUAUww9JoU/SLhg8c4ptkn+9VEhNJTlca7PIcmTHI00swd73b61a4K4ZpfD6LpmQIhVVpQdo5GdLCcPr+kgo6QgwLl+Axc98Iif7lq79jOr8XK46bxgTC/Po9gT4vKUdly+5vFudvYjV7zqibIYSWX8A7DjYydAcA3ablU63P+Wm/gcX27juqQ/lz/rhRSPRaVToNSpEQCVA3TMfJy34Zhu0ZBrCU0f6zf6eQDHiwhIr5ozkr8f+NmdqN026a6BsjOKvkd3WL12eTNlkzYLxKYkoOUYtaxaMxxcIJdygP3v9pBTzIBzAJ/r8yI36E9eNY8mzn/HCDy7ge/UfJVRBqnvmY/5QN5Eam4Xr7cV8/lUHl5cNYtSgLBbZi6OY718H7+1xpgwuMnRqlk61MT7fLBPMknXI7jrYxbJZFXLyJVlwU2OzUGDJ4AZ7MXmZuqS2OYksd6Tv9AVDVI/IRaOGLIOGVe80MTbfTHV+bpjol4SEVz40hwdfiw+ENjmcrHhtO8tmlnFXRIAWOZbI9QT65XSTIct4xm8BUiLLoOFot5f1nx/gx5eP7rtfNDy1YAKd7kBKC7VFNUWIosiUYgsmnTrKri2RkpWyDipQcPIgAiGRlHM0EBLRqAR6E+ydapU5p0DBt4osg4YHZ5dj0Kj4dG8bT/59D6vmVfPs9ZPodPtlq8b6hmYOdXiozjfHWcFGosHhZMnFtri9WazKSaT1okmnQasW2H2km5Xrd8btO2tLrORl6FizYHyUhWP/OZyaQnKnyxdHRAHOeOVMBacvUj5fHa2ERGRVQAX9UPb6Cs4GdLn9mPTqqFyDOUOHIMD19mJmJlBnHZJjBCGcJ/IFQ9Q3NLP2hskI7IrK0dTYLAgIZOg1fK/+I/5QNzFlsTsYEplUlMfMMUNwdnvjnoPSGBORU6U16p+Ze7EEM5NOjVYt9OWVk+efhmQbaOv1Yjbq0KjC6i4GnVreX3j8QX7w7Kc8UzcRDGoy9Rr5fPzBEJeVDsTlD+HsTa0scaDDTZ29iM/3dyjri4KTjnRqX5Kl1Ip1O/jZVZXUllj57d/2cMnogTiOdDO+MC/pPKmxWRBFManyD8CyWeXyfZ1p0PDU98bjj7HfTZYzlfbId88oo9cTYGFNESLx6ogLa4rIMWkTfuZ1kwoYbjaSqdfQ2uNLeB4Q3hfdrRbkcfiCITrc/oQ1MUmBMbbGFRLhze1H+GRvG9f3ken1GhXlQ3Pk2lauSUumXkNnrx91hHJEnb2I32zYnfAc7TYLD11dpcQJ5wj2OXuxZOrQpanHno4oHZzNxaMH8NDrO7l49ACl2eIcxxlfibrlllsoKirinXfeoaioiI8++gin08mtt97KL3/5y1M9vBOC5F9/3yvbomxsIPqBU1tiZeklNnyBELUl1qSFV3vfw1dK2tX02XM88sZOPtjTRvVwMw/MqWCvsxdvIMSIPCMmnTrKCkL6bKNWLaumwPETYCSJtxd+cAECAq09Xv6xv53zRpiZ0acEYdIlvg3H5ZvDlhv55tT+gX2KARt2HiYkprctkhisUtF99UYHtTYLY/u+b+lUm1wwjkWtzUKWQSPLK0q/meRZKEHy7ByUbUj4eqKOvDO1mybdNVA2RvHXKBCzyU50r6Umo1lpcLTymw1NvPkftfx6w+644xIxtSPhDYQYatAkJHhJBQNpgx0IiintbUQRfja3kqAI9Q17ZKINxHe0NO7vSEnuiCVUSGuQgBCl5CRJJhISGZpjICiK7Gnt5T8uG4VRq8LlC3DntDK8gSDHerzo1CryMnRc99QHXDsxn7qaIvIydPznW18kDZDumFbKtU9+EF5fBSEqQVJrs7KgJrGNEoRVRwRBQCWEEzzTq4YwdkQOc8YMZcW6HUlJeNUjcqlvaI5TupF+k9unl8bdF7UlVpZcbGPxMx9HfdaHzc7k9kolVjLSyD2e6RhuNlFTbKHYmoErEGTZrApCoshD63cwf3Jqu78MnZoPmp1MGWmlzeWLKlolUrJS1kEFCk4ePIEgvWmUm9p6fQSCIsPMxijFqBqbhRVzK5U5p0DBt4lAgPGFedz3yjY+7YvX6mOSx9J+8Gi3h89bOqKsYBNBo1axZsH4KAJ0rMpJ5N709Vtq2d/mYnxBHufn58bZey2fW8HKdTt4Z9exuO86lYXk1h5fwn0anNnKmQpOX0hKkKleT8NnPyeh7PUVnA3IMWkRReTnq1GrRqsS0KrCKhyvbz0Up866dGoJWpWKqaUDGG424vIF6ej1cV6+mUUxjUouf5DBOj3nF5g51pPaPqPN5ePJv33JndPLaPgyubVwZHOihOFmI5eWDuD+2RVfe+7ptSq5mB7ZpPTCJ1/xTN1E7n9le8L801s7D/Gbtx3875IpdLq8LL2khNe3HuLXb4dzYBeWWHH5gmTqNejV0UXDHm+AZbPLOdThIRCKzgkmwgXFFq4+b9jXOj8FClLheFXcNzW10uMLsmJuJSvXbaetx8f+DjdjQiHunlHGwxENzhDOMy6qKUSFkLQ5crPDybJZ5eSYdFHqgGsWjI86LlnOFMJ75Ac1KkTEOPX8yLjh2esnRZFmIh0BDnV6GJJjwNOdeq3q8Qa4sXYk1flmujwB+fNj87LSNYutSXV7AlQNy+E3G5o4L9/Ml0e7ufnSUfzqzV1R52e3WbjtytIoW1FpvB/saUt4jgp5+NzB/jY3A5JYVJ8J+O6kArZ81cld/7uVpxdNkEUkFJx7OOPJKO+//z4bN27EarWiUqlQqVTY7XYefvhhbr75ZhobExcKT1cMzTXyq++M5ViPlzumlSIC980qJyRCrzdApkFDplZNty9AMCRy78wyRMDlDbBybiXeYIgD7W5ZJUYtCKyaV83AbD3v7DzK4qc/5oE5Ffz48lH0eoJ4A+HicH1DM9+/qJj6BeOJrJNLth3//d3zgf4C+fF6c0OfxFunh6c3N7NsdgWHOt3cHeFNGLvhgDAr3dRXKJW+K5F/oPT5K17bwfSqwXR7EjNUI5Ho9U0OJwv7gpxk51ZbYmXZrHIWP/0xq/pel36zVSk8O6XXj3Z7aWlzydcxMqF6pnfTpLsGCsLX6BffGUt7r4+g2D/JIjeakZDuQxXEJdSXXGLjo73hv/V4Awk3+cczD1Sq8Pxb/a4jrvu0fsEEjDoV9QsnyEpAST9Lq+L9PU5e33oooaUC9He01Dc0s+4me1xwn4zcIa1BLy2ZQlCEbrefLKOWQDDEtgMdWDP1cbZcNTYLN08twdnjDfsWi2HFJ0m2XTrXNQvGJ+3W2exwolGpqLMXsf7zAzx0VSUdHj89niCZBg1GrYo5qzcnVXzJNGj4zm/f5+GrK+XrUr9wAkvXfkadvYiMJCS8QEhk9fxq6hviSXer51dzqDPcUSwFISPyjLR2e1EJYhyJbkCmnpum2kAk7lrfNLUk4fefbXD5vQzK0nO0x8uSZz/jj4snRa33yRAMhe+bDJ2GLrc/7ndOpGSlrIMKFJwcCAj40hCLpc6pFet28OrSGnYf6ZGTMl1uH5DxLYxUgQIFAD0BCIpBNiWQppYgWWzV1RSyyF6IXpOaEKsSiCO0SIglL9fYLHj9Qb7/x09lkvXCmGRtp8vP/bMquKzcycAsvUz2PdLl4ZJRA07ZszpdZ+qZqpyp4PRFVpq4LsugwXAGdl1+G1D2+grOdJi0au55aWtUDuT5GyZhztCxemNTnDpA+DiBe2eV8dMrS+l0+5haOgCXP3Gj0tKpNobmGlg2qzytJbBJF7aAv1sl8ErjgaTHxRZ2a2wWth3o4rrJBaxYt4NffmfsCc/BTpePr9pcstpApHXg0qk2Vq7fwdj8XBbWFEbtJVau207l8FxcviCdLj/ZRi2HO7389m975M/OMmi5rGwgBo0KTyCEKmI5zTJocPuD5Ji0fLDdSa3NGpWniTzHxv0dXFgyAASR2/78OQ/OrVS6yRWcNKRS+4rdZ+9z9rL2wxbuml5KjyeAKMIDr2zns5aOvj13/zw52uVlYJaebk/q+d/tDnCg3cWd/7tVJmXHEjzSNRr3ev0MyTWmbJzsdPnl3F+Dwyl/ZuP+DqaMtFD39Mf8oS61NbwvEOLjfW00trQzvXIwf286xo6DnXHN45ItfGwu3qRTy9bt9Q3NPH/j5ITq6A0OJyK7eGB2hfzbSONNdo6XlQ5MOXYFZw9a2lwMyDxzySgZeg2La4r4xVtf8FLjAf5l3PBTPSQFpwhnPBklGAySlZUFgNVq5eDBg4wePZqCggK++OKLUzy6r4dU/o+dLl+cl6aEn19TxWufH0yqODCxKI/751Qk7VT74nAXj7/rYGyfEol0jMsXxN/HtuwnajRHsU8z9GG1kESyxyBJvDlZ/tp2bp9Wyu0vbpVfS8QorbMXyQV7qRgd6x8YCcl2pHF/B0NzDCntjWKVF6Sk5cBsPU9cNw6DVs2WrzqYWJQns/BzjFr+uvsYj7yxk/vnVJBtPDHPTun1DL3mrO2mUXxLU+Ngh5s7XtjCJkcrb//kQpmdnalPzEiX7vvnb5zMwm5vVBC8+Jl+QlQyBZR06kUAOpWKJ96N9/ENKxoJnF9o5jcbmnj9ZnvazxqYbUhJ7JDmUnV+Lq9tOcjY/Fx+cPFI1CqBTH1YoWXWqoak5A6tWsVXrWEVp15fkMOdbiYXW1j2yrak459RNTjK0qbWZmHNgvEsfuYTXL5g2uBmr7OXnQc7WTargrtjkjYPXV3J+QXmxKojNit6dZjIk23QsKlvDB5/UA4iqkfkJvzOYTlGHv3LzqSJoDumlXLjxk8BZAu1+oZmHp8/jlljhkYx5Qdk6XH2+JheNTgqQDzS5aHXG6DXf8ZvAVKi0+WjrTdEXmaILIMWly8oF3XCCj3Jkz/v7XEypdgiz7lYJFOyUtZBBQr+eYiELdqS7uVKrDh7vFgzdWxqaqXD5WfJs5/JrysdhAoUfLsQCe9bTTo1V5QPonpELtdNKohSdnP5gmxqauUHF43khj98wqp51Smfww2OsGWeAHFd2gsiGh8kMnOnK1r5MhZXnzcMjUbF61sORX3nhSVWLho14ORekBNAus7UM1U5U8HpC51alTKu06lVcVL5Cvqh7PUVnKnodPm456VtUTkNk05Nry9Ijim1fZcvGGL2qs1hte1ZFbT1JlYSqG9o5qJRA3jvy1ZmVA5JmZfVq8Ok1PZeHw/OraDu6U8SfmZkYTey+fF/vj+ZzV86v5aCWGuPjzaXX7b7i7QOlJQINiZQUgO45fJRPPn3PeSYtLh9AX609jM5hyXZSN85vQwQ6fb4ZZseu82CUaumxxPAoFUzLMfArDnl3P/q9qQNnuPyzaza2MSCKYXc/8q2r0W8UaAgESS1r1iryEQNxsPNRj7d187Dr+/kgTmVTBlpZczwXM5LYsmeqOE4Fia9mn1OV9R3RzYGN7Z0MDCNCkSOUccmR2tKRfFP+sZYZy/inpnliH11pvqGZuaeN5Sywdm8teNwUkXpGpuF9/c4ZeV+o1bNjoOdfHdyAWs/2BfVKJhn0vKd84fz3pfReYxgKES+2QSE45TWHl9K1Zhub4Dlcyu575VtaZtMlTjh3EFLm4tLRp/Z5KNxBWYuKLbws/U7uax8UNo4WMHZiTO+ElVZWcnnn39OUVERkyZN4uc//zk6nY4nn3yS4uLiUz28k45YKV+JSDE+38zgHINM8khkvTEiz8gDMRtd6LfSuX1aGb94c7csryy9Bv0MzwaHM4qEEgiJDMkxIAgCbb0+xuWbwU7chkR6gDY4nNwec06JPEerR+TyfkQhwuULcjSNdFqGXoNGJTA818TSS2xR44d+ubila/s3VZFyjIl8yZ/9cB/zJxXwvfqP5PPxBUL86t/OSzmWZFC6ac5NdLp8MhEFwuoeUhdGIBRKuvGtzs/lrR1H4pLqJp2avAwda6+fFOeBKaFfWSXe3uZnV1XxYbOToblG5k0qYJG9OKpYAP0EL4C3dx6JmlPS+nJBsQWNSiAQFNGqU2+SvYFQVGAjFQtqbBZuu2I0wZCKcfm5cSQMU5//7v2vRCdOam0WJheH7bsSIXL8/X9zgiBw78wy7n4p/cZ+RJ6J+ZMKuPflrXHjWrl+J/ULJ4Q/N8a+56ZLbbj8Aeqe/pj6iEBMr1FhzdTx6DVjGJxj4PkbJpNp0HCky8MdL26htceHKIgpE0G3C+Hgyd5nuzb/dx/g8gX54wd7uW5SAUatmmyjFn8whE6t4k+ftPCv549gYLaeHk9Q7ob80yct3HLpqJTnf6ajtcfHx/vamDNmKCpBYNnMMlmR5pXGAzx7/ST2t7vpdPvlgtnOg53cMb2M+b/7gItHDaDD7aO+oTnqc890JSsFCk539HoD1Dc081/XjWNW1VAGZverGBzudGMbkMmgbAO9vnDxOVIGXJmfChR8++j1BsgyaHhsXjW/+MuuOEW/SLtGtUqgOj+Xm55r5PH545heNZhB2YZ+pZJOD+OLzMx78gPOG57Liqsq2ed0YdSqMenVtHZ7MWrVPFM3EbUg0O7y8dyHLVSNyOGhqyujPkva244vMJNp0HDbnz+PI79IvvOr5lWfkngsVWeqsp4p+CbQ6fJx/+wKPt3bxsCouedmfFEenW4fAop0tgIFZxtae3xRz0ApF1q/uZn/uCx1XsDtC8mNe5/ua2PM8JyEhFKXL4hJq+aP7++jZqSVxfaiuHxUWOm3BJ1WwKRT4wmEGJydWPGjtsTK4GwDaxaMl9W+pf3E0S4vdfair6Ug1uUJx/9STirSOjBdw9LRLi/3ziyjsaWD0sFZUUSUB+ZUIohw/6vbeHBOBZl6Dd2eAFNLBzB/UgEufwCjTs3P39zFzZeO4qs2N7OqhnCDvZgckxaNWqC9108wJLJsZhkDs3R8uq+dkChSnW9WrPsUnFRk6NTMqBzMLZeWyE2OsQ3GtTYrW7/qlJWu9zp7WfxMmDgWuccH5BqUSadBp1Hx0NWVrFy/M46sUmOz4A+KdMbMXZcvyJ0vbuHpRRPQa9T8o6U9qcX7hSVWen1BVq7fmVS1f/ncCmb2NTyu3ujgstKBFFkz5Pz7wXYPy2aV8+sNX7B8bgXLXt4WlfeNzLnabVZMOjVftbn44UUjEUWYWjZIjjv0GhXNrS4GZxuYXJTH+flmVqzbziaHE7Wgotvrl2tqydTRJfgDIbrcPlbNq6bD5U9aL1DihHMHLl+Atl5fWoLWmYDrJuVz2wuf88S7X3Ln9NJTPRwFpwBnPBnl3nvvpbe3F4Dly5cza9YsamtrsVgs/M///M8pHt0/j06Xj9YeH12esARgt8fHf1xWgt1mJRASyTJoEABnt0/ePCQjWKy9flJC9qVJp2ZsvhmNWohTBpE2xVq1iotGDWDJJSHe+9IpF+fqF07g4dd3pkw6Rkq8mXRqtBEe4FKisNvlZ0JRHrdPCy9Ebl+QCQV5XFY2CAjLl6UrHPd6A3y6t43zRuTSuL+DO/o+y+MPkanX0OP18+TfvozaCNXZi5JLSSOwsKaQm2KUXhocTno8AQaltjpPCqWb5tzD0W5vVAB+pNvLrX8Kd2Fk6NVhoocYTUKoLbGyYEq8bY00v//zrS9kOfRETHCXL8jzH7Zw/5wKOlw+jvV40alVaFQCh7s8vPL5wSiFoth5C+ALhlg61UbVsFzUgsC9M8pw+4Nk6DWsXLcjan159vrU0oYj8oxU55vjlJM2O5zcOV3gcIeH+2ZXsPy17VEBwLKZZTzxriMuANnkcHL/q9t59vpJtLv8cWQaSJxI+HRfO/fNKue1m2pw+0I8d8MkNvetaZHvrbFZeHP7YS4otsQRUaTrW/f0x7zwgwtYOMUTIUvpwZKp583thwCiVJS+ONLF8zdOjut+sdssrL1hMm/vOIyK/nU40Tl5fCHWLBhP4/4OHnljJ9dOzKexpZ35kwrktarGZqGupgghE/7jstEsXxf/fctmVYBwdnc9dnn8GDRq/GKIXneA84vMCAg8f8MkBmQbuPulrTHd1lZuvXI0v377C66dmI+xzyou8vqfLUpWChSczpCs4cwZOp7atCeOiLjIXsST63dwR1/wKtl9KPNTgYJTg0yDBr1axTObm9PaNXr8QarzzdxgL2ZEnon6hvg5Xjwggz9//wLaXT6aj/UyMNuATi3Q5fGj14a7uJ/7sJnK4Tk8+fc9vPKjGtp6faze2BQXk9YvnEBhnolebyBhIhfChJRTVeCROlPPVuVMBacfzCYdR7q9rN96KE51qHhAJoOy9PhDZ3eMoEDBuYhYW7jIXOg9M1Jb5/V4A7IKYY3NwvjCPOrsRYQQ4wrAarXA4/PH4Q2E2HqgM06lVVL6Pb/AzAOzy/mspZ0LS6xx5JYam4UlF9sIiiI3PddIdX4u1fnmqNi8ekQuGfoTL2tkG7S09frk4nBkrtmgTX0tAKqG5/B/fvsBL/5wCs/dMJlMg5qjXV6uffJ9nqmbyGaHE48/hCiKZOo1/Mdlo6h7+mOevX4SPb4Af9vdynWTCwAYnGNEr1Xxy7e+iG6mtFmZUJTH4/PH8aO1n1FX8/WINwoUJENrj4+7XtoWVUOKvQfvnVXO1U9slpW5I3Os0rHfv6iYiqE5cTWoWps1Spka+ht/tWoSFtavnZjPlq862bDzCP8+uZAll9gIEd9o/Og1Y9jf7pIVxaWG6ch1prXHKys33nhhMQadGsexHpbPqeC+V7fT6wuwYddhbrm0hJ+t28F5+WYWxXzGz9/YyXcnFxAKiTw+v5p7Xt7Gc9dP4q4Y5WwJl5YO4IHZFRzp8vCTK0Zz+3QVapWAxxeizl6EgJC2tpVj0qJVqeTa0aNKnHDO46t2N5B4zpxpsGTqmVYxmKffa+b62iKsZ7D1kIKvhzOWjDJ+/Hiuv/565s+fT3Z2mBVgs9nYtWsXbW1tmM1mBOHM7ug42OGOkkwz6dS8urSGT/e28ZsNTfJxNTYLK+dWcqjLAyQnWHQksPNIpQyy2B5Wbki0KX5pyRT8AZFH/7IzZdKxsaVdVkKQvuuh9TviEoVzxg6hSsyJ882bWjqAO6aV0t7rZ0iuISkjtNZmYUCWnnmTCtBrVJQOzubaJ8OKAc9eP4nXtx2ivqGZdTfZcQf6bT0kCcZEkJQVEtmGKEGAghNB7NzTqVUyO/viUQNY9PTHsr99v9pQuOti9fxxUaSE2PkdKWUYmwi4dlI+c1Y3UJ2fy6KaIm56rpGXlkxh+bodCQlY0F8sAMjPM9Le60MURVp7ffT0beR/vW5H3Lx/f49TDuZjUWuz8ub2eIUXCW5fCF8oxBPvNrGopog7ppfS4wmSbdSgFoQoq51IbGpqZeGUQhY/80lCMk0ir87H5lWz/LXtUeO3JyDQSetWZJdMLFy+IHudrih7CAgHR5OL8wDQafqlsEsHZ8cRUQA+a+nA2ePlgy+d/OLN3fLfE52TSafmO799Xz7mtitLqR6Ryx0vbpGPCRN8ytCpVNybwMaoweFkxbrtrJhbmfTczgbkGrVcUjoAraAi06BBq1Jz78tbOS/fzOct7QmskFoJEe46mlJswaBV4wkEZfJkocXEsFyjEvApUPANI0Or5sE5FXEKCxAmIoYIe7v7AiFqbRaGm428frNdmZ8KFJwi6NUqvMFQWrtGu81CXoYu3NRghzUxRBTon+OzqoZwoNMj7x1rS6wsm1mOiMiGliO8t8fJvEn5uHxBtGohjogifa9KEFgxtwJvIMQf6iYSDIl8moDs+8/GdrENJNaM428+UJQzFXybEIHHNjbFxQfS3Fs5txJdmkKJAgUKzjzEyuFLuVCTTo0gCCntuz5raZf/vdnhZMVr2zm/MI8JhXn8+LJRBEMiGfpwo6Reo2L1xibG5puZMtLC/N99mHA8m5pauWdGGT9/8wtmVg1JTlrJN8s5Ksn6WWp4rB6Riy6NQm8iWDN1HOv2yGrFkRbXoigmtRGstVnYeqCT0cGwIsqxHi/fW/NR1DHS3sLtDyKGQux1ugiGxHBBWwS3N8jj88dhNurY1+Zi95FuPtnblmBNbuXBV7czvWoIdfZwgVyx5FBwMiER1JIROvIydIiImHRqmYQSm2Pd7HByx7TSuFoO0DeHRF5aMoWv2t2yutHaD/dxWdkgzi8wx607kp15+dAcnmrYQ2NLR9y4jnZ7MenU8nxIZtE5o3IIJp2a1fOr+X1Ds1xHM+nU3DuzjOIBGWTo1ARDsGHXMTYkseb68eWj2bDrCJeVDmL53Eq6fUE2RSiGS44EGToNOSZtnMW7lE9sOtrN9KrBDMjSJ1V8kewSLRk6Obbo8fpZcVUlvkCoT41SiRPONexvcwEw4CwgowDMrBrKm9uPUN/QLIsSKDh3cMaSUcaOHcvtt9/OrbfeyjXXXENdXR0XX3wxAHl5ead2cCcBnS5fnHdfnb2IB1/dnjDRdt+r21k4pZAamyUpwSIR+zK1MghMrxqScEOxfN0OfnL5qJRJx7unl3HJ6IFsajoW9V2NLR0snWqLshDqcPlp7fZSV1MU5zH+mw27ufnSUTzyxk4WTCkkJIpx5Jgll9hk8gmEE5aPzavm+Q9bGJxtQKMKE5Mcx3qiPP3SbeaTSTQqQYCCE0GGLrq7IjLYbe3xyX9XCwLDLUZWrosnbEmkhAuKLVHzOzJw+PFlo6LkFe98cYu8OQb44+JJhEQSJhmgv1gAcFnpAAQE1m89FHX8s9dPSjjvJVKMIAhxtjUPzq1g1qqGpNenxxugfnMz982q4NG/7OSDPW08Nq+a1e82cd2kgqTvg/45GkumiVRkkpBsvWtwOBEEgWevn0Rbry9KmlKvUSW0PZPWp4FZ+jilp/qGZm65tASAQ50errcXM7NqCINzjFw3qYDFMbZIdfYiVr/rSEsQqrFZ0KijSZZ7W3t57sN9/Pxfx7B0bT9p5at2F4WWjKS/dYPDiSeNBO2ZDoNWDX74YI+TKSOt3P3yVjY7nCyqKUpKjJLmgE6jQq8W2N/mlWVI3/nJRUrAp0DBtwCXP8iY4Tn89IUtCV+X5qnbF2TZ7AoOdXgYblaIKAoUnDIIcKCvYysVFtYU8ZsNu2Wrx3TP4iE5xqiYsbXXi9sXpHFfB6vnV6PTqFh7/SRcfUnhRNjU1MqBDg/XPdVfDEtE9k0U2x0vwSS2gQTCHYuPXDOGobmJrQdioShnKvi24PIHU8aCLn8Qg1YhoyhQcLbBmqmLau6T8ih19iI6XL4+Sx0SKhJGWp3Td8wiezEhUeTXG3bLa8qPLy/hyvLBbHI42et0cfGoASnH5Oz1cevlozBqVdydrAEpwn451vr5xR9O4ViPl4AonhAJNMekQ6NWcfNzn8i297PHDGHlup3oVCqWX5XYsuP+ORUcbPcQEsPqUaIYryKV1afUkmXQ0OP2c6jLQ+XQHMYMz8XlDZCXqcPR3MahTjcFlgyqhuVENZtGn7uThTVFDMo2YNSqFUsOBScVkQS1RISOZ6+fxK5DXTx6zRggnNvNy9BRv3BCnIpzsn3FJoeTQ50eFj/zCc/dMIkrywdxWdkg9re76PYEWFQTbeUlrUuRda1E8cLEwjwydeqkDZF2mwWTThXOATfEKzce7PRwgVqFTqPicF9jdzIc7vIgiuH/d/Z4ydCpEzZ2L51qo7GlPeG10GtUVA7NYfEzn8gEGen6SKgtsbKoppC2Xi8Grfqfji0UnD3Y3+ZCoxYwZ5wdz4BMg4aLRw9g7Uct3HxpyXEpkik4e3DGklHWrFnDqlWr+NOf/sTTTz/NpZdeSlFREXV1dSxYsIBhw4ad6iH+U2jt8cUpgKRU8Whq5c7ppfz0ylKcPd6Ex0QWwI/rM/s2vomw2eHklktTy7fuawsrBkgJvwydWi5YRz6wTTo19Qsm8NrnBxMW4Lcf7ORXb4a7Yz/Y00advYi7p5exr83FwCw9u490s/iZj6O62zY1tSKKIrdPK2V2nzLEY/Oq0QhC1Pm+fkttynNIROCpVXz5FJwgMnSaqLkXqWYyMEsnzwmAxob4zatkG/XSkikc6Yqf31LgUD0iVy6cJ1M9WrNgfMqxSgH+PbPKuffleFWNzgQKS9IYbn6ukT//4AIW1YSZ6sGQyPt7nLy25SDV+bkpO202O5ysXLeDFVeFu1dX9qm31CVZgyREzlHpeImgtviZj6OOTbeGLqopkjtsfvmdsRi0agREfr9wPI9tdMSpR9UvmMCeYz0J7Y6kBIVerSKEyOtbD0UlWCILIanGJZ1Tjc3CTZeUsGHnkbjzD6+bAjdeWCwnMoabjWm7fM92hSePP4gnEGJgtoFeX3/iPZ0PtDcQIkOvoTcQ5PMDHUBqP9Z/phtagQIF8eh0B0inbSjN073OXjJ1mrj5qcxLBQq+PXj86cmtOUYt36v/CJcvyGJ7cVrlBV8wRLE1g8aG9jjJ7wU1haz9oIVbrxjF9McaeOK6cSk/K3bvGkv2TfSMP16CSaIGEghb/9z54hZWzatW1h4FpxVc3njV19jXTUpSWIGCsw45Jh0rr6rknpe28llLhyz3Xz0il4/2tjG5KI/pVUNYGKFAcKTLg5BkV55t0OALhKKabS4tHUSn249Jp+bx68ahTqNWnm3UUF1gTpuXkOL3SOvn8/NzCQZFOt1+/vW/3z/hQq3ZpIsqwEsNSNYsfVLLjode38nlZYMYmG2g1mZhaK4Rk07d3xRps6IS4LKygejVAtoMPWOH5eIOBOn2+BmUY6DLHWBQtoGbnmvk+Rsn4/GnXpOl7y+wmJT9hIKTCmumjgtLrFH2LxLsNgtZBg0r1u/kz9+/gC1fdbCgppDrnvpQVpOW8pnp4gCVSsCkU5OXoYtS6a5fOIGbn2tk1bxqfnjxSDrcfkbkGTna5U2bs+v2+Mkyalg2q4IV67bHEcfum12BJxBkesXgqDgiMk8+rWIwq991pM05A1QNywHANjATk06TsNExWV63cX8Hw3KNURYrAkLcenu0b73NNGj52+5jfLqvXR6z1By581AXvd4AA7P0ynpwDuGrdjcDM/WoznAHkEhcUT6Yv2w7zKufH+Tfxo841cNR8C3ijCWjAJhMJhYuXMjChQv58ssv+f3vf89vf/tb7r//fq644goWL17Mv/zLv5zqYX4txPp5QvoC2j6nixc+3c9tVySWOJIK4IlYp8mQ6vUMXerbRyoSS4X0O6eXJlRHycvQ8Z9vfZHU7ueOaaX8+u1wgVUKFq4oH8SSZz9jzYLxSRn0DQ4nd6kEfvVvY9Fr1BzpdDMkpz8wqbFZ6HL7ksqj1dqsHIlhyNptFn52VaXy0FdwQsg1abnl0hJmVg1hULYBbyCEWhC4aaoNS6aeX729WyYdpLKN8gVD+IPJ52TkfE2mApIORdYMbppqo8cbSMroTgaXLwgifNbSHsUIlzb8EJ7X0mb6gmILakHA7Q+ydKqN+oZmnL0+dDLBIjGJTkIi9ZMMvYbzC814/EGZACN9X2YaP2GTTk1jS0zRo8TKjy4eSWNL9PdI69r5hea4vwM8OKeCpVNtDMk1sHLdjji2fmQhJN06nKHXMKtqCN5AkN9v3iuvnQC5Jp187W65rITfbGii1mZl21ednJdvTvm5Z7vCk8sfpMcTwBsIRSW40nq0GrXoNSqOdXsxaNRcXjaQB+ZU0NrjY09rb1Rh+2R0QytQoCAaGfr0RbBco5YMnZo8k45MgyZqX6bMSwUKvl10e/wp92u1Ngt/3X1MLtboNKq0e7IBmXqZmByJSEu9YF9fRLrneqLXpX13Is/1I10e7njh87j4MBHBJFEDSeTxrT0+JW5UcFoh05B67mUaNLjTFEcVKFBw5qHT5WPFa9uZUJTHfbMq+HRfGzU2C95ACI0q3LSXOC9q4fkbJ/NVuztKCdYXCDE/QnVsaukAZlUNwdiXe+n2BDDp1ClzOYGgiNvnJxRK3eg43Gxk7Q2TONrlpb6hmer8XJZcUsLOQ10c6Awrs50oCTS2EC/lmq+sGJTSsuO6SQUYdWqWza7A2e3l2esn0e7yc6TTzdBcI8GQyK2XjwYE/KEQlkwt3oAGs0nHz9bt4PoLR+INhMIKuU9/zB/qJqUcp16jYrjZyDCzKe05KVBwIugnqG2LsqWqtVm57crR1D0dbvrt9QU42uNlxfqdAFH5yOdumIwqjZia2aSlzl4Uta836cJKP+Pyc7npuUbW3jCZJ/7q4Lx8M0NzDBRYMlJ+ZpZBS5fbzw1/+IRHrxkjW71nGtQc7fIy/3cf8Pj8ccQuLbF58s0OJ9X55pS2OVLDYuP+DmZWDUGnUSVUeIzN60p56PH5ZvItJtx9tver5lVTn8CqFMI1p9unlbJuy0Eem1fNnS9u4ZFrxsQ1mSq5jXMLBzvdWDLPDoseCYNzDFQNz+GFT75SyCjnGM5oMkokRo4cycqVK1mxYgUvvvgi3//+9/nLX/5CMHhmBtKxfp5wfIm2jbuOcVnZoCj5RQkuX5DnPtzHwppC2YtzRF7qB1eq7zSlkESLLRJvcrRyt0rg4lED4tRR1iwYn9LuJxmbXgqcUmGfM6zOIh2/fE4l1kwdowdnsagmLDf5p+9fwP2vbo+6XjU2C4vshWw90ClbcEhs+IMdbjRq1Ul/6CtdvGcvckw6BmcbWPVOU5zs6V0zyo5bsaHHE0yZ7M819q8bqVjZqRICb24/zKAsPWNG5PLEdeOiEg4uX+rvr7FZMOnUzKwaQvmQ7Cg7GslK6AZ7McPzTDz46raYTlcLq+dX0+0O4IpIgEaqyER+Z6Q8ayR6vQF+s6FJ3vjfYC+WCSFSwJQMwZAYX/RoaiUkinL3bNRrEZKxkdjscBIIiTS2tFM9Ijfl+nY8LPwcoxYRuPXPnycMRCLVWGptFlkV5i+31KaUrYy1jzrb4PIGMek19HgDZEfMjVT3sNQB0uHyydKBM6uGcOf/bo0rbD90dRUPvLY9YTf0HS9u4ZffGcugbMM3d4IKFJylkLybU83TYWYjLn84oSOGRA60uxhmNikqBQoUnAJkG7Qp92vLZldw1eOb5b/5AiHW7z6UMvFr0qlT7p8W24tx+4OyokmtzRqVSI/8rFjisoQcozZuTTjY4WZva2/S744lmCRqIInE2a5Cp+DMg16jSjpfam1W9BoVbb2+BO9UoEDBmYzWHh8bdh2jdGgOD67bTmNL2PLOpFUzYtQAfvHm7oTv2+RwsrDbG5VXXbNgPB/tbYs6rnxoDgc63AzMMnBBsYVOtx9/IMSivnxHolxOlzvAwGx9SruNGpuFt3YcYfVGB7U2Cy8vqeEf+9tRCZBvMXGwq98m8ERIoDkmHcvnVnJPn5WvhC53IOX7vIEQOo2Kvc5ezMYwKf6GP3xC2eAslk4tQaNRsb/NzZAcA+29PoblGnH5gviCIUqH5hAMiXKevbXHx7tfHEm5hzna7aVyWA77Wnvp9QXo9QXJNWoVZQQF/zQ6XT6Wr9vBA3PL6e1TTXN5g2g1Kt794qhMIjfqNPz67aak6tsPXV2Z8h4OBMU48kadPWzdubCmCJFmrn/mY+oXTqDXG8CapUOrSr4mXFhiJdOgwR8K0drjk9XBY6FWCfTEqCNG5sml86tvaOblJTU8uG570pzz/3x/MvUNzcyoDDcJJlJ4lOa1SafmxguLubRsIEe7vKhVAhqVgF6ron7hBIIhMWmc0eBwcoPLJ4/j0WvGUJ+gyVTJbZxb2N/mxnKWWPREwm6z8sRfv2R/m4sReQrh8lzBWUNGAfjrX//K73//e1588UU0Gg033HDDqR7S10YiubTG/R1RSbtIqS7o75D/z7d389wNk3ngtegHaa3NyrxJBdwU4Y+9dKotRReblaNJvPNqbRY+3tvGwpoihAilFUheJG5u7UWvUcUxUdPKr3njg4FNTce46ZISggk8OiMRSaZpbOngo+Y2/t/1kzjW7SUQFLl2Yj57Wnu5f3Y5HS4/nW4/A7L0vLXjCEvXNkZZ/0goH5LNE389uQ99pYv37Eany8c9L2+L23BucjhZ6um/v9ORvwZk6RiXb6bWZmXpJTY2f+mUSSK1JVaGmY0yES3ZvEqkkAThebvYHp7PTzXs4a4kljLy+wUh6n612yysuKqKn63bHtVFEvne1Rsd/MdlJaxJwAKXrGbumFbKkW5P1PoWCIncOa0MbyCI2x9ELQi8t8fJzc9Fz1F7RMFB6mxZOtUmWx+NzTcnX+9KrLy/58RJI8mu88EON5sdTq6bVJDw9cj3d7p8CQmEEF6HN+46QungbP7f9ZM40O5msb2Y6nyz/NtL57N8TiU/nVbKtU9+gMsXxOUP9gV38cmfhTVFUaSfsxFZRg2HOty09fooGZDJw1dXMjDbQCAkMmfsUFa8tj2GHNbfAfJM3UT0GhXtvX5WrtsZF1j/vamVu17aytgRuWzYeTTuuzc1tfLl0R6CIVFZwxUoOEFk6DT0egPcPLUk3ru+xMpd00vp9frp8QZRqwT+fc1HjMvP5ZF/GYMvGEqpUnC4y6MkbBQoOMnI0Kk5v8AsE4/rYiT+39h2SN6v2W0W3t8T3r+G/dLj96NLLymJUmaI3BN6AyEMWjVDcgx0u308d+NkHn59JwtqCgkhxsW+C2oK42JSCWZTmPgvNQQERZEVr21nXpq9WyTBJFEDSSTOdhU6BWcmlk61AWLC56tAmKilQIGCswsSeTKyGLvrcBfTKobQ3Nqb8r2ROY9kCrFSXtrZ48WoU6PXqPikpZ0dBzupzjdH7Q0a93fw/Ect3Hr5KDJ1aryhII/8yxjuemlrfJNgRG55k8PJivU7uG92Oa9vPcRv/7aHVX1EWAknQgLtcHmZUJjHjy8bRSAkYtKp0arTN4EatWpMOjUdLj//953dPHv9ZK576gNWb3TwwJxyPP4gXW4/uSYdLn8Qlz9AMATj88MKvke7vGGybEsH5UNyGF+QR6I1+aapNqyZevY5e/m/7zRF73H6lN2UXIOCr4vWHh/vfekkGIJH/7IrLmf42Lxqnvtwn1wXSqS+bdKpOdbj5b7Z5Sx/bUfC2pCnL4cbCWkd+mBPmxw7HOr0MCTHgBiCX7y1k9uuLEXgixjVFgsPzq1g96FuhucZUzZK6jQqth7ojMq1+vqUxk06NTnGcEnU5QvyxvZDzKwaErdOSZZg7+w8SnV+Lr3eAFkGTUI7w8b9HUyvHMT3L7Lxqzd3yU2Sa2+YzF0vbWVCYR6f7m1LG2cIfddqs8PJndNLk6qdKwqM5w4OdroZNSjrVA/jpGNCYR5G7V5ebjzATZeWnOrhKPiWcMaTUb766iuefvppnn76afbs2UNtbS1PPPEE3/nOdzAaz9xNWY5JxyPXjOHOF7fIhJT6hmaeXjQRhCY+3deekJFaY7PwyDVj2NfmitvwWzJ1zP/dh1HF2+c/auGpBRNQxT3grdw/p5yQSBwb1W6zsPyqKr7z3+/h8gV59vpJLKwpJEMfLh5ID+xYIodUaI9VbEin+KJRCfIGQ0pGTi4Os2sHZuqTFnEjO+EiGbx3vbQ16pg5Y4bi7PHR4w2w+JlPWLNgfFKrFGm8J/Ohr3Txnv1IJeFtirAjSKbYYNKpqV8wgQdf3R4XoL661E4wFOKzfe1c81/v8cg1YwiJYtJ55fIFufm5Rl5aMoWQ2E8Sa9zfwdYDnXzU3Bb3/ZGWMqs3Onj+wxYW1RSycEqhvL4ArFi3nY0xcqax77XbrPxmQ1PCsW1ytHKPWmBoroFXl9rj1FNqbBZunmrDoNPwj5b2qDUmvGZV4OwJB0oSUWNKBAM/ZdfurPKort1YJCOdJLvO1kw99QsnkJ1GCrvYmkFQNDKuII+V66KDt/BaW8FXbW5++/cvo9bhSJKPREjxB0N81R7uDFo61UaPJ5CwMCSt0X+om5hybGc61IJAe6+fSYV5BBF5feuhKDLnvTPLuHN6Gd3eAC5fkM9a2pn3uw84v8DMrkNdjMs3k2lQJ+zwgDDhZOGUwqTf3+H2K2u4AgVfA72+AIIA1ix9Qi9lvVbNOzsPU1syEFEUcfmCYWvGl7aybFZ5ys/+qt3N4GyDMicVKDiJcPmDLJ9Twb2vRO/b7H3k15V9st6Sh/v834VJs0vXNnLjhcXcclkJgZBIll5DUBR594ujTCzMA0jahVlrs7JsVjnObi8bdx2LSmYHRZEiSwbBkEiH28+fvn9B2HrnxS209oQVHy4ssWLNjLbbk9Q6F6ZRrYskmCRqIJEgfYcCBacTVIBKELl7Rhltfc04Unzwn2/t5t5Z5WQeh12eAgUKzixI5ElvIIRJp2bp1JHMqhpKjzdIhk5D/cIJUYq4kRiRZ5KtgV2+YEKFWG8gxI5DXVxos5Bt1PL+Hie7DnYyf1JB3DPcbrPw0ytL+dXbu7n9ytHsb3czwmzil98ZS683QFuvj063P2FueVNTKy1Ol2zlHpunORESqFGnoXJYDr/esFvODy2dauOysoGUDsmOIsF+1kesOdLlYWiOAb1GzbtfHKPB4WTFuu08es0YFj/zCW5/+PgsgxadRqCt10eWQYsgQI5Rw6N/2cW/Ty5k6SU2mlt7+eMHe/n3yYUJYx6zSYfLF4gjokjXQck1KPhn0OXxc+OFxayIaWSGcL5UAB66uop9ThdLp9oYn2+OmseRe/Tf/m0PdfYieV2QLKt2HuxCk6UnNpsqzVupiTASaxaM593drZQMzuaWy0r40SU2MgxqjFo1n+1rZ+ZjDdTZiwiK5pTKS4c6PQgCLLl4JCExTFgf0Gd1UmcvQhRFuZ7027/t4bF51XEqJLUlVhZMKWTth/tYekkJWUYNOrWKD/Y443L3z3/Uwp9/cAH3RTSi1tmL+NWbu+QGxyf/vodbLhuV8PeQ6l0DMvX8oW4iwZCIRlBh0qkTNkqDosB4LsDjD+Ls8TEg6+xb5w1aNdX5uazfekgho5xDOGPJKH/605+or6/nnXfeYeDAgSxYsIC6ujpsNtupHtpJw9BcI6vmVdPa46Pb4yfLoCXLoOHu6aVoNSoefDXxhgHgjmmlcQ/0F35wQdQDzKRT88g1Y3jsnd2Mzc+VrXtyTVoGZenRqlQsX7ed8/LDD/jIQubP1u3g9wsn8uaOw7S7/Nz0XCOr51fz9Ht70xJDYq0y0tl+fNjs5I5ppfR6A+Rl6Fi5bod8blKRHkjJoE/E4JWu14p128NF7D452nTjkc7jZD30Fa/xsx+dbl/Crs7PWtoR6CdbJSNL3DuzjMffbYpXE2lq5cFXtzG9agh39ymZSOSDAVn6pFKJ5+eb8QVEPP4gt/35c3lcGToNVcNyo1Q3JEib59oSK9+9oIAlz34W9fqaBePjiCix74WwFU7qa+Vnk6OVxpb2hPNVhcDyuRXcP6eCwx0eDDo1WpUKlQq+PNaDVq1iWI6Rl380hYPt0cpOEhEnkpwx3GzkrR1HONjuTrrBh8Skk0gllkhESsmmkqystVn5rKWdu1/aJt8fP7h4JGqVQKZew1+2H+aT5nZe/fxAnDxlLMkHoMsTwGzSygHhzKohCYM7CRn6M3YLcFxQqwRKBmcSFEXueyVamcjlC3L3S9uotVk4vzBPJkjV2CwsudiGxx/kZ6/v5P5Z5SmDv1TKXiebuKhAwbkCtz+IQaPmgVfiFcUgvHbeM7OMTJ2ajoi92Kam1rSe84AyJxUoOMnocoe7BGdWDWGxvZhMvYZAKITbF+6E/OV3xsox5CNv7OTaifms3ujA5Qvymw1N8jP4ievGyTYAS6faqC2xMnZEbsIYbpOjlRXrdvCTK8IJXWm/Y83U8ez18QqhdptF7lwuH5LNo9eMAYhqCJCe6aliwViCSaIGEum4R68Zo6w1Ck47CMDAbAP3vpT4GesNhlg+p+LbH5gCBQq+UUjkSZNWzePzxzEoW889L22NWgdiG16kv725/TCNLe1Rr8XGwXqNivqGZmaNGcKOA51MKc5jUlEev9u0J6pRMteoZbjZyL/813u09vj4yRWjZZsNSR1aqxaSWm9AdAwemac5URKoTqOK22O80niA/7d4Epu/jM7fDMsx8J1xw/iq3Y3LH0QQws1OELbWuGN6KQAubwCTTkWmXs3Rbi+Zei0qAYxatXy9P9jTxtKpI5lWMYTzC8x9TUmJY5775pQryggKvhHkZei4smIwk4ss3D6tFBDY+MURfvu3PXKzR5cnwPf/36dU5+cye8yQqNxYbJ0lijReYuW2K0YzPM9Ipl7DJsexqEbnVE3JgZAo5zQjGxklkjuE60pBUUzZfLdqXjWVQ3NY/MwnfP+iYu6YVkqmTkONzSLn5e+aXooo7qTB4ZQ/a8nFNtQqAVEMqxy3tLq4vGwwmXo12w90Ujo4m5Xrd8bl7v/9ggJc3mDUXI5sxpbOa/eR7rg4Iyn5vsQatyZHQlFgPPtxqDNc17Bk6E/xSL4ZTCqy8OsNu2lu7aXImnGqh6PgW8AZW4n67ne/y8yZM3nppZeYMWMGKlVqdY0zFTl98sESvjzaQ7PTxcAsfUofbV+CAplOo4p64EVuHKQiskkXDkyOdnsJibBh17Eoy41IzJuUT2NLO9MqBvH7hRP43d+/ZMGUQplxKiGSGGLSqZlROSTqc5IV4GtLrCyfU0GPL8Cv397NdZMKCIk+5k0qYJG9WGbt1z3zMffOLOOWS0s42u1lRJ6RN7cfiXpYx6qxRGKTw4nbH5Q3Q0nH02fhMO93HwAn76GveI2f/cjQa5IqGc0ZO4TF9n42t7QB/ukVpTh7vUBYKvnuCNucSMR2cErJ+Mj7OFYqcUFNIf/nyfd58QcXJB3XY/OqufPFLVw7MV/eqA/OMfCjS2z4/PHri9Rhk4hwU9/QjDcQotYW9vaMROx7so1aZo8ZIgf28efbyp7WXn6/uZkVcys52u3lFxviJSULrSY+P9BB5dCcqPfHkjMkJaSlU20pbdCsmfqobiDJlmjFuu1Rnx9LhFu5fidrFownkezqkottLH7m46hxrd7ooMZm4bYrRrN6o0Pu0k2EWPugLIMGo1bFPS9vkzsZUhHrYpQyzzpYMnTsc7rQ5KiSXsNNDid3zShnaulA2R93U9MxtnzVQfnQHPa3u1MGf7lJZMy/CeKiAgXnCgQEPIFQinnbSluvjx2H3FQNy41Kikm2damI0Wej364CBacSJr2akCgyMNvAmoY9LLYXU/d0eH8TuZ8qH5LNuHwz1kxdws7ryMR0fUMzry61c6TLkyKGa+X26aOj/vboNWNYvi6+YUPqXH7ph1PINmrJMen48mhP1FqRNhZMQjBJ1EBizdQpxSEFpyVCwJEOb/JnbFPrWW/lqUDBuQiJPNnS5qLF2Ut9Auvk2IaXyNxGZAF69UZHXDG5cX8HU0ZaEEVoaXfzUuMBdh7u5tFrxjAwW0+PJ8igbA093gDeQFBWKuuJsK2W1KFXzK1MeS7Sd0fG3F+HBOoNhOL2CyvmVnKw0836rYcS5JgyGJxrxOcPgiBE7WN6POH/zjaFVVCcPV4y9BoydWqCokiXJxB1vUsHZ3P/K9tYWFOUMubxpFmPlVyDgq+Dgx3uPlJqrFWmjTHDcvnR2nDzYa83IOfDVq7fGdUMl7LO0tTK7dPCRLNx+bksn1uJ3TYA/rKLBoczJfF7hNnIz17fmXAvLxJegyQlpur8xGOo7VsbyodkAzB2eC6/+MsuvntBoaymolKp+F79Rzx342SOdnnp8QYYkmPAFwhxrMeLXqPmPUcrVcNz+HBfGyvW7wDgmUUTEzY6jhqUyT7n/2fvzAOjKu/1/zmzz2SZmcywBEhIYAJZwbBDEhXcQDat7b0CvWVzqQh6ba0roCyt2r2C9rYKSntF+2utIqioiL0mKCoQkbAokSXsIctkm335/TE5h9mDFkXgPH8ls5xz5sy87/t9v9/n+zyOiOsIJ85lGnU8sXEf1XX2mH1Gogbqyv0NBIPBiPsuQlZgvDRw3B5SP++WdnGSUQZnGdGqFLxZc4J5V148AhMyEuOCJaMcPXqU7t27n7Pj/fGPf+SPf/wjhw4dAqCoqIjFixczYcKEc3aOfxenWl14/AGsqVp8XXR+xiM4nGhxMbc8F0WnN3d04GDQKFkxrRSzQUNjh7vLrnkxcN9zvJX1O49TWdvIB2ESyW5fAKNejcvrZ0FnYfbxmwZRc8weUfR1ePw88PJnrJ41nDaXL0Iq9pNDTby9+yTTR/bl+S0HE7L2H3qlhtWzhjPvhR3MH2djZ509YmOQrHsdwOH208esl64rOqgQP0dzhweHx39OF33Za/ziR7yOC+hU5lm/l2G55hhbLY1KIXWDPD1jSNLjx/t9i8Hx3388mocFgeMtTtJ1ajQqBSdaXKycPoQUrSrhdQGsnjWcJzbuiyGqzB9r4/Yr+knSqAAGdXwmtzhO+5j13DmuPwa1MsJ2K5H0ejICgNsXoKqzo+T1XceTKkS9vefUWSkdra46yKvzyliyYbe0OYh3Xa/OKyNAkGPNTn606iOmlvZmRqfnp1Gv5l9fnI64bofHz9w123jptlHMaguRiywpWlJ1Sqas3BL384W8QQXpsyaD+Hy5zcJxuxNBEM58VoGkspUXO4wGDb1MOlqdvqSvO9HijOi8Eu+PShCwO72s/ehwws1fX4shpvAdTUiS53AZMr4agnSdWG1xeTHqNSzdsIcXbhlJs8PLjrpmTAY1y6YW8/CruxISo2+8rPc3/AlkyLi0oFcrcfv8vPRxHQ+OL8DZWThJHOfFdl5X2KzsOtYivSbUde3rUlGv2RE5V3RP1ybsIK6qbaTD6yfLEuq6it4vhyfGo/eCJr2a/t1T6ZGui3vs6AYSGTK+q3D5uiaaOJOoRcqQIePChQBkpKqxppqxpGqZNrJvRAORaP97//h8SrNMMTY5klquzcqp1kgV2n3HW1k0uZCTLS5Kehv58/vxbS/KbBaW31BMllnPkWYnBk2kLdj7+xvw+AMJLfAqbBa6p+l47HsljOlnocXp4cbLen8tEqjL42P+OJvUGJWiUZFjMbAwah8hfnaApVOKaXF56ZYaGQ+k60OKC2qFgqaOkDKyTqXA4fOjUMCR5jNF6jnluayuOkhVbSPTOnNJia8xeT5IzjXI+KpocXi4/x+fxag3i7/xiSWZUv4rRaviyc37pf9/fEV/KabvKldZ3+qW3vfIa7tZfkMxj91Ywv7THXj9AaYM6sWyDbsjGgIXTSpEoRASxvLhDXnJbNgfnVLMkSYHGaka7hzbn1WVB6RGzgWdluUdbh8N7R6ONDmZv3YHT04r5YmNsY2OQ/tmRJDoxQbL6EbH9fPLYq43nLTnCSO/he8zfIEg/bulUJplYkacObmqtpF5V9oiziUrMF46ONYcIqNkXKQNVVqVkkF9jGzac0omo1wiuGDJKOFElL/+9a/8z//8DwcPHuTDDz+kb9++/P73vyc3N5epU6ee1fH69OnD448/Tl5eHsFgkDVr1jB16lSqq6spKjr/MqV1jR08+MqZgDjUaZ8Y8Sw6UrUqth1uYkJJT2aV5WDQnPn6xWRhOOGjq3OIi6olVRtBLInn9+fw+Jk/zsZzWw5Khd4AZwKGm0dkxyz64nvzexmTFszF4MagUWLQKNl3vIWfXjeQAGcUWpJJwAG0u304PX5mluVK1yV+jvACxq9/MPicL/qy1/jFj3gdFyIqaxv48ZX9mfHsR8CZDtLwTWVXv99kz/sDQRRKgd4mfYz854u3jkwa5Le5fEmJHuFkFH8wmHCcCsBjN5awbHMtS6cWMX9sKMAozTYnlF4PEJ/9DSFfQQgVHKLta6KvU9ygKASByv0N0v0d3c+CUhBwekNz077jrQQJ8vD1BSgVAss37EkoCf/olEIAHry+IGKjsGJaadzrdXj8HG128sJHh7lrXB4qZah4kswWSCyunM13X26zsHhyEdOf2crSsC4itULBix8djiE6VR+x8+JHh1k4sTDpsS8GGNTKLsmb0RC/93uvHYi/I5h085dp0vPrHwzmy/p27GFESjFpJ8/hMmR8dXS4faQnUB0S0S1VS32bm8r9Dcwak8PcNdsot1m4eVgWaToVkwb1iivXO6yvWR6TMmScY2gUAm7gZ9flo1YKkvJawi6/2kYCnNnDVdiszBtr4+NDkYqU/kDIci8ZVFHPi53JiXCsOUTO7mXSxzQERCe1xXVfXPMTEVFkyLiQICDgDyaPjaOVLGXIkHHho8XhYfmGPdx73cAYC9toe54Otx8hiYzq3IrcTlu8kRHNhO1uH2qlArcvkNQqffG63Tw9Ywi/3LiP022umON3uH1xLfBCKr+5/OefP2RYXzNXDOjGoCzz174nJoOW6rpmKZ/85LRSjtmdSZVpXV4/Dk8Al88vqTOW2yzoVUpml+XS0O4mVatCq1bQ0akskWnURcQc4Y2hXeV71EpFQtVHOdcg4+ugod0T10YczpA9eqTrKLNZ0KkVbKlt5J6rB7C66iBKhSA18Xb12wUkpenK/Q14fAG6p2l5bsvBiNzsLJGQYU1hyfo9mAzJ8wDhqijxbHrq29xsP9zEfS/vYv44GxOKe/Krt74AQsTz0mwT//riNBM7VftdXn/S+WrZht0RuWm9Jv6YdHoDMYov4f83OTwR5DedWsmuY3ZKehtZ8trupHOyLxhk7S0j0aoVmPQaWYHxEsIxuxOzQY1aeXE6ggAMyTbz5/cP0NDuxpp6cSrAyDiDC36X+cc//pHFixfz3//93/z85z/H7w8loEwmE7///e/PmowyefLkiP9//vOf88c//pGtW7eedzLKqVZXBBEFkntZl9ksNLS5JdKJuCC7vH6G9c2Qit7hZJPwhVcMCIx6NatmDkMQhAhWpngOUVHgbDv3wwPu8IBBrVJgMWjiFnHdvkBS6bdwVqw/EGTRpEKG9TVzuMnB0inFtLl8nGh1dpJzLHE3FRV51tC5/AHu+dunEYFMtOVPP2sKK6aVntNFX/Yav/jhcCdXZtB0Egp2hClyiEE7JB/vFTYLje3umKD2s6N2RvfL4Jcb93FZtpnquuaY90d3k0ajxRn/+S21jSiikhN6jTJpJ+rxFhel2WY83gCWVA2TSnpxWQJJRfEcc+Kod5TbLAQ7E6hdKh55/JJCzPr55RxtdtLbrGfJazUR5w1Z7hRz+1+2c21xT64r6pFUKtXjD8YoaTw5rTQp6SHHmkJptpnZz3/CMz8ahk6dPJD0+UPHqm9zJ567bFZyrSksv6GYukYnz80aEVG08QYCzBjVl+eqYjuSZ5fn4vYnv38XOo7bnZxqc5GRoj0rdZxwbKlt5KEJCuk5nVrJuz+5gg53SI3B4w9wstWFw+vHmqIh15rC/fIcLkPGOUGaToVGqUg6bjUqhURMFNeCHXV2KmsbGNbXTF73VLz+UEJIjF/lMSlDxjeDNL2aE21ulIIfo17DzqN2yY89WZwndl5npGiY8exHrOgkglTYrMwsy+FP//qS+yfkR3jMh6PMZsGsV0fESak6ZczrovHAy5+xYlppTENAuOT2nVfa0KmVGPWy5Y6MiwtB4MMDjYn3lnlWNEoFLQ6P/LuXIeMiQkO7h/8YnhVDRIHYRj+Hx8fcNdtiCqIQspo41erit+98Idm9i7g8rxt6tRKTXt2lhcdD1xdw57g8vJ12z+GNOmmdpNFf/WAwzR0e7E4vKVoV9a0u7n/5Mxwev2Tp83Xzsy0OD4s67Y3Fz/7cloOS6m0iOL1+vP4A7S4fc8pz+bSumcWTizjZ5gwpE98+GoNGSV2TA21nHNHh8ZOmU0kF7HCL66S5apsVlVLgoQkF/Eb5OZv21Z95Tt7XyIiDFoeHhnYPrS4v6Xo11pTYGLbd7Y3JH4fXfMQa0vyxefgCQeaPs9HuCtn1pOtUTCjJZFZZLhkpmpgmaBFink20yQGoa3KgVytZMM5GIBiMIH7/4sZi/vfDQ2yvs3P31XlJP6NWpZAI5FrV4YjnDBoll/Ux8V+rQ3Wv1VUHuXJAN+l58X0vbz+CTt2bcpsFrUqRfL7qVFSBUO7Y5fWzeFIhS9dHEkhStaoYYvvqqoOsvXUUOtUX5GSksOaDQxHn+cWNxTxXdbDLOdnrC/CjNdu4PM96zmtSMr7bONniuuhtpkuzQ6TSzfvq+Y9hWef5amR807jgySgrVqzgmWee4YYbbuDxxx+XHh82bBj33nvv1zqm3+/n73//Ox0dHYwePTrua9xuN263W/q/tbX1a53rbNDc4YlJFCSTI5tdlouAwEOv1EiPGzRKbr+iH2P6WaXHwgvc4sKbSE65zGZh5fRSdh1rYVjfjAhFAbEYkAh9Mwz8/cejI2SWwxVUVs0cxuEmR9z3alWKsyK7lNksfHigkeuLMznQ0IFaqUAhhKQS1SoDTo+fR6cU8+hrsR0ACycW8MTGfTwwvgAg4nM/PWNIRFdcplEnLfpnE+SdLWSv8W8H3+a4DUdX0plpOhXzx9nonqrjkddCG+LSbLM0PpON97kV/ehl0rNu5/EYwsGo3Ax21NmZXRZfYeTfUVxxev1s/ukVNLa7SdWpae+CcCMIQkTXyZzyXPq7UpO+JxplNguzynJRKRTMH2cjK0PP0zOGxGyeRKRoQ3NTabaJVpeXTw438efKWFJOVW0jC1+t4fpBmfgCQTrcybtq210hOddwKVuAh68viPv6cpsFnz9ErHtq+hCyzXo6vP6kZL8ddc1U2KyMyM1gZG4Gj8TpXppZlsOkFVWUZptYPKmIJzbupbCXUfrdONw+BARpsyhuKk+1uhAQuiRJfVfwdcdti9OLyaDB6w+wYGyepI4joiLPyswxOZKdTjScXj+rqw4CIQum/t1TOW53cv/Ln0Uc5/I8K4/fNEiew2XICMO/s95qlCF562Q2Y03tHomYqFUpIuLXB/+5S3p9RZ6V9QvKEQDLvxGnyZBxqeDrjF2jQUOqVoXXH+oUXv76XiluTYajzU7mvbBDUtFM06pYe+tIUrUqmto9TBzcixMtLpZOLeaRdbsjkt0VeVYWjLPR4fHxwIQC7iNIfasbnVqZlLxSfcTO+/sbaGj30L97akxDgMPj57MjdmaMyCbTpD+bWyZDxnnHVxm3HW5fcmn7yUU0d3gIBpHXTBkyvkF827mpVpeXniYdg7PNUm4gPI8iNgOFN2tsqW1EgcCKaaUseLGaIdkmTrS4eH7LIWaW5bD1QFNE/iVVq6Spw0N2hoH99e1Jr6fN5WXO858wNNvMbZf34/ebQqq7otpHvH13mc3Cr74/mJ1HQ2oCbl+AE512QV91vopWhxBz4vEaosLh8QXobdQTFIJMLMlkYklP/vXFKfpb0xiSbUKvViIAu0+0cEVed7QqBY3tHp7/4CBLpxax8NUaUjQqad8izsfhyuHiZ51ZlsN//OlDhmabWHZDCT+9bgAdbj9GvZruaVp5jj4POF855bNBslxVr7CY1qjXSLlZEeHEM51aSaZRR2Obm5N2F9V1zUwZ1IsnNu7l1sv7SbUmcf8drkovHktUl18Rth/INOpodXnx+gP85JqB3D9eoL7NTfc0LWpl6O/7JuQTDJKQ5FJus7DrWAtzynPRKhQsnFjII+tqYj7L4zcNkkh0KdozpU+ReP7U9FLc/gCzynI51erqUv3Q7QtQYbOwZGoxgSA0truYVZbLfRPyaXf5SdWpEAQSqLXAA9cXsPS13TG56B7pOrbX2ROSg6LnZHEPI4/9SwfHW5xkpFzcaiFGvRpb91Tek8kolwQueDLKwYMHKS2NTXZptVo6Ojq+0rF27drF6NGjcblcpKam8sorr1BYGN/G4LHHHmPJkiVf65q/KlpdsQXD8M6thyYU0OjwYDFoeHP3SclORkR4gr6kt0l6/KWP63h25nAUfC4RPpJJkykEgSmDMiVlFQgt8hOLeyZlw765+yQ76+z89zXx2a1igTQeqo/YGd3PkvjmEJq0xECnMDOdeS/sAM50/+861kJxLyPtLh9Lpxbj9gVocXpxev1sO9zMCbuLzftO4+2UkgwPYsTriu6mPdsg76tA9hr/5vFtjttwJOvwrsiz4vEFcHgC2J1eiXAQnSQUx/u8K21oOzvCT7W62H28hWcqDySVQI8mdJ2N+lFFnjWuYoSIVqeX32/6gtllucx8LqT2kQwpGiWl2Waq6+wSGW1CUc+k7xGvL9pmYfWs4XE3T09NHyIlJgBUCgWP3VjCsFwTShRMKO6ZtEP3wQkFfHbUHiPbHg1BIHT+ToJeca9QIkQQBH5xYzHLX98r3cdym4XlN5bwxJt7qTnWyl/mjuCBKKWr8I3f0L5mHplcREOnXO3kFVUALJpYwIPXF3Ky1UkwSIQVjCgdeVm2OeJ3YzJo+M07XyRUFlgWZunzXcbXHbetTi+pWhUKRRBrmoaHrs8HBNpcPhQCmA0aJq+sSmiXpO/s0KrIs5KqU4W8daPmfSCiK6t/969GsJIh42LFv7PeNjvc6DQq7lr9cYzsrjj3/fOOMZxscVHRmZxJaAeyv4Elr+2WO4hkyDhLfN2xa9SrOVjfjkatlPapf5kzIul7sjL0rJo5DJNBw/xxNvQaJRqVkqUbIpO1V+d352fjBzK7PReDRkmH20e3NC0ZejWuQIANO0/wyaEmttQ2dtoGjOq0powsYIn7RQgVwUBuCJBxceCrjFsxvo0nbV99xI7b50enUUpjRIYMGd8Mvu3clEmvJhAkaREaiFgr4Yyt9KqZw3B5Axj1KrbXNcfYKhs0SlQKAbNBQ0O7m0xj8rxomk6Nw+OnsraBu6/O4/eb9kt5VyDuvru6zo5OrWDboSaJvAJfLxfb6vJKebHSLJNkY199xJ40v/3BgcZQHisYyiscamhn3MAefHbEzmM3lnCo0YFOreTKAd1J1apweL2k6lT85JqBEIQbBvein9UQkRMS5+N7rh4gqROH53sqaxtZtK6GlfJ+5rzjfOWUu8LZ5KqMBk2MIpAI8f9FEwvoZdSx66id0r4ZbN53Uso3Ds424/CcyS1HxxIpWhUdbp/02y3NNkk55QqbFb1GyScHm+iWpuPul7Zx33UDKelj4rjdSW+Tnh5pWv7zT1sBeHJaKUGCEeTyijwrP7+hmGAgyMJ1IUJMdZxmw2hVEQFi8vHd07W0uXzc9WI1t1/RjyHZye2+cqwpTCjJ5LWdx6k5amf+VQN4cnOtdEyDRslLt47qbKaJbO5+bX4ZTR3uuOpHvkAwYUP4k9NKUQpCzJwsx2eXFk7YXdgugTzz4CwTG2tO4vUHLmpLIhkXARklNzeXTz/9lL59I6X0Nm7cSEFB/C71RBg4cCCffvopLS0t/OMf/2DmzJn83//9X1xCyoMPPshPfvIT6f/W1laysr4Z9lZ6As9esaB7XVFPPj7YxMSSzLi+k2KCvrrOTjAYpMJmZXtdM4/fNIgn3/2CYblmBvRIZdXMYXRL0yaVUrxv/MAICcUttY385p0vWHZDMYvW1cSw1sVF0+Hx8+iUori+emLiI5404eqqg0ws7pmww63CZsXl9UvnCP/coWMJTCjpGWGpUWGzMG+sjWAQ9hxviXj9rDAWfEWelewMA+/+5IqIpOTZBnkyvnv4NsdtOOzO+B3e5TYLM8fkcPOft+Lw+HnhlpHSc/GShH3Met7ec4o9x1so7GWkuq6ZBybkS/6X0YhndZNM/UhMQgzJNvHolCJ+/vqeuMcVmdmhThn4222jqW9zJe1EfXP3SarrmiNkXlVKIakNw7++OB13PtKpYi2BEiUmrs7vxtAcM4vX1zD9LCRX39x1guLexqTXpVEppC6h66Pnlzwr6+4so6nDQ3vnRmzZ+t0U9DJyx5U2FibY+CmAl24bxe7jrQSDQdy+SCWpB1+poSLPyuAE8pFVtY3MLsuN+N24fYGE1klbahu7VJ36ruDrjtt0vZo2l4/uaVqWb9jNkBwz4wb2wJqi4Tdv7+On1+VTmm1K+D3rVEquLujOzSOyWfjKLu69bmBcr2aQOxRkyIjGv7PemgxaBEKdRfHmu4pOOV1/MMjs8lzmrw11XSWKX+XxKUPG2ePrjt0UjZLeGQY6OlXXHB4///ridMJ4qtxm4a3dp6RxW26z8L3S3ix/fU/M6zftq8fp8zOxJJPjLS5Wbq5l1cxhtLt89DbrKeltlGK/hnYPM57dygu3jORoszOGyCbuYcNVC8+mIeBcKmLKkHGu8VXGrUGtlMZl9LoZajTKRKkAXRfkfBkyZPx7+LZzU1q1kvv/sTNp4daoV/Oj1R/HNGu0OL28+NFhhuZkUNLbKOV0xFyTQaNk5fRSdh6xMywnA18gyNt7Tia1SteF5W4DwZBidn1bSHGiscMTd989pzyXle/VxnyGr5OLTdOpWDm9lNWdlsarZw0HQjnoV+aNYemGyHikwmbl3usGMnfNJ1wxoBtNHR56puvIsRhocXgo6WPiwVd2xRTPl00txuP34wsGUAkKBmeb6PD6I44t5vZLs0wRuaVwVO5voL7NLcce5xnnK6fcFRra448ZiNwLRysChWNLbSMPTMjn129/zvCcDJa8tptHpxTx+021Ur3E5Y2cG8JV75+eMSSsOdjK7PIc5q+tDinSTypg2YY9XF3QA4NGwapZw/nVxn3c9/IZRdPwnPQDL3/G6lnDucPlo8XplWL5pev3cEtFLtvr7MxKoAAufhYpFy4QkY+fU55LMCBIta3fvbO/U40lkV2WhU/rmnnolRpWzRwWIpC8+wWl2WbmlOXiCwTJtaaw/VAT7+w9JT0u7kGUCoHWBASSTKOOJzbuSzgvL5lSxJSVW2JszGRcOjjZ6mJEbsb5voxvHJdlmfjH9qNsO9TM6P7JRQlkXNi44MkoP/nJT7jzzjtxuVwEg0E+/vhjXnzxRR577DGeffbZr3QsjUaDzWYDYOjQoXzyySf84Q9/4E9/+lPMa7VaLVrttyOTZE7RJCzyltsspGiUVNc1A2fYntEWPGK3/AsfHWZmWQ6TB2fy/JaD7KizM31kXx59bTdVtY08PWNI0mupb3Uzpzw3JBfWySB3+0KSzIsnFXKk2YnL64+b9DvS5GDplCIWrossxp5qdbHveCuzy3NBENh+uFk6NoCgEHjsxhIeXb+bd8M8SUXZwgWdDNHHbizGqFfH2HbMKsuJ+AyiYsSkkkz+++oB3PznrdJzYnFW3DSYDeqYYP9sgzwZ3z18m+M2HKlaNdOe+SiCMe4PBPnwQKPEcJ4/zoY5ygcwPLCH0AZd/H/GyL6s3FxLfaubZHD7Auw50SrNB12pH21YUI7b58cfDDB9ZF+J0CB2jYzuZ4mw6VpddZD6NhcvfHSYRZOKWP76nqSkNDjDUN+09xQLxuZJ5xdRYbMwf1weWw82Ssoo4pjec7wFRRySbKLERH4vI0vWhzpsZ5+F5GplbSM/bHEltYc40RJSLamsbYidX/Y3sHTDHh4Yn89/ds4tBo2Su68egFqpSEgOqaxtZFabmwf/uYsym6VTRSaSvFO5v4FZY3Livh/OzF/i7+aKvG4JXwt0aa30XcHXHbdGfWiT5vT62bTvNJv2nWbswO54AwHuGJvHp3XNLJpYSFOnD7X4G9t3vIVbL++Px+9n4cRCrn+yEofHz80jspOeT+5QkCHjDP6d9dbjD5CiVnLXuDz0KgX5vYxSvGk2qDEbNCgF6G3W872nP5C8rZNBHp8yZJwdvu7YtTu9LHp1F/ePL6DCZmXvyVaGZpuYVJLJMbszQoVvSLaJWVFdflW1jTyybjeDs01sDtvviRAT5Mtf3xvhQ29OCVkEhaOh3cP6z05QXRfbLQmhDupUnYov69vPilzyTShiypBxLvFVxq1KIfDfV+UxqSST7uk6aZ91qsWJrXsqaVolHx9u5qr87t/wVcuQcWnj285Ntbq8cQutEFpj511po6q2ISLPK+6Pe6ZrpeIvwHNbDkYo8N52eT+eqzrI9jo7r9w5hlSdit5GHddPKmLZht0R5y23WVh+QzFv7T4hPaZTK/iPP4VIGJfnWVk4Kb5CeWmCxhz4arnY43YndoeH56oOSnl2sXGzsrYBp8fH/ePzaYsqhP/h3S94/KZBpOlUeHwBNEoBlxf8QVi6fndMzr5yfwOL1tWwdEoRAD9/fQ+LJhVxstNaSISYa+uWpk1qQy2qpsg4fzhfOeWukIjsIELcC0crAkXbwhxtdrJpbz0/uWYgf3h3Px7/GQX5aFX76OPkWlN4bX4ZSgUoEPD4A6yYVkr1EbukSD9jZF++PN3Bm7tOxIyXcGIcEJekAeD0+ZlTnovHn3z/Lz5/ssXFSx/VSSSRbmlalEqBU60ursrvRkEvI5f1MTGhuCfLN+yNsQVdNKmQG57aAoRyrsOyzQzJNklEtvnjbPzlw0NU19lZOb2U56oiGz9fvHUkfczx9wtef/IGQo8vGDEHiDZmMi4NtLm8tLt9WC6B7zzXmoLJoOZfX9TLZJSLHBc8GeWWW25Br9ezcOFCHA4H06dPp1evXvzhD3/g5ptv/reOHQgEIrwAzxd6pOv4xY0lPBTFsi63Wfj5jSX88s3QAl1dZ5fsGcKtGtyd9jNiAfqzoy38Zc5IHl2/hxXTSnl+y5kAPJFdTjiGZZu5bJopRlmhwmZlZlkO9/59Z1zbA38wiNsXYGJJJnPKQoFDH7OefcdbmT4qm//depj7rhuIQaPi0ddqYo790+sG8qPRuWjViphC/uqZw3nqvf1UdnoXwhlWrS8QjLkWkSXb0O6JuNa+GQZWzxrOjrpmrn+ykmF9zTGJxrMN8mTIEGFN1TCsr1n6Ta9fUMbkFaFgNlyp5JqCHkklQUWJQ4NGSUaKRlIzSraZ0KoUEfNBsg185f4GDjZ08PyWQyy9oYiXtx+hNNvMgxPyUSsVLN+wJ66aii8QlAoHSyYX0eH1caTJGZeUFs5Qf+q9L/njDCOTBvWKYI63OENdJh8fbOJ375xROSm3WVh2QzGHGhwx157oc4U/Hk7Si0aFzcqHB0KPqxQCCxJIV0f7nsbbAFXub+Ch6wXmj7MxJNuMQR2SlPf6Y+eicIjJHPEeidd9+xX98PqDknysOEe99HEdN4/Ilr737AyDRBByePykJVDVEtHV8xc6epn0uH1+TocRtrQqJfVtLtJ1ajJStfz89T0RybEKm5UHr88nVaNiQ81xKmzdEtr4RCNFe/aFLRkyZCRGm8sHBMlI1fCTawfy2Bt7I2PCPCtLpxahUyqk8dlV/KrTKGlxyGRhGTK+KTg7ZeR/2OLktsv7kWnS8UiUN3qFzcrrd5Xzxq4TEbGhiHgk33Aca3axcGIBJX2MHG12kmU24Pb5ccZZp6PtLkVcnmdl+Q3FPPzKLjbtrY94PB65RFbElHGxweHzYU3T8uS7+6NiYAtLbyimw+djaJZJ/l3LkHGRoc2ZvBFFrRAY3MfEs1UHIuLucpuFGy7rxf/MGEogGGRbJ9FzTlkulhQtq2YOI9OokxTK2l0+0nVqXtt5nEfW72FOea5EYumZriMQBKfHx+83hc5R1ql4KOL9/Q0E4uRwgXNCPhfX9fvH50fMgUpBYOGkApZu2EOKTs2jUTGMCI8vwM9vLKF7upbfvv05FXnduCzLnJDoU7m/AVfndef3MtLh9aPXKKXnz0a5WIyXxPfJam0yotGV1biopmHUq5P+3lSCAITG0pzyXNpdPqlR2KRXc7gplItN9rudP9aG2xvAHwwyd802KmxnCstuX4Ae6bqkxDgxX9yV6onYfJYI3VJDpKF7/76Tv98+msXrQnWmp2cMoVuqFqUgcN/4ApZu2M3KzbVSXv3HV/ZHo1KQqlWhVSki5hWtSoE5RcOv39on1dHEnLNBo0RAYEJJJrPC8sjd0rQ0dXhinAIMGmWXOeJW5xny0Jh+obmyocMDII/5SwAnOxthM1Iu/u9aIQgM6m3kvX31PDjhqzmdyLiwcFFUombMmMGMGTNwOBy0t7fTvftX7+J48MEHmTBhAtnZ2bS1tbF27Vr+9a9/8dZbb30DV/zVkW1J4Tf/cRnNHR5aXT7SdSrMKRocbh/vfXGa+eNslGaZ8PgD/PSagaTrVZxodrFkahFCUCArQy8t5DePyKa+1cWT00rRqZURQUD1ETtX53eL6EINVySoOd7ChOJMTre5mTGyL3PL+0lF78raBoJRvqEixEJ6j3Qt3dN1rO4kxoiLalaGgfuuy0ejUrB4XU0so7yzOP+Tawdgd3gJBIMMyzZT+IN0+pj1/GrjvphgRtw43D8+P+49NWhUpGqVUvG2NNvEzqN2Sf4Z4icazzbIO1+QNybfPRgNGh6/aRAPvPwZ7+9voN11Jmk+pzyXl7cfYU5ZLuk6FXeO7U+AYFxFjrterMagUfLU9CHsO9FK93QdwWCQ1TOHs+K9/TGbgNUzh1PX1BFh3RLdPRoNty9AZW0Di9ft5v7xA/nsaAs6tZIlr+3ucoxt3nean10X4EiTU5JnTHQOCCl4rPnwEDNG9kUQBDJSNJ2JBw0PvborZvNfVdvI4ld3s2RqUQTpIvyYic4FiQsTomzkjGc/Yv44G0a9mhXTSiO6eMXzhJOCAPqY9THqLS99XIdaqeDTKD/mcBumeBCTMOFkI48/1GFgd3hweQI0Ozzo1Ep6G3W8eNuomCJteNJCr1HEtUaDUDE3Ra2MefxiQ2O7hzS9GoNGyW2X98MfDJBp1NPi9PJs5YG4a03gjb1MGtQrpguh+og96f3cdriZB/95RmZU7pqWIePrQSTKVR9u5rWdx+N2GS5et5uf31DMm3dXcLjRQbbFkFBat8xmYcNnJ/jsiF0ekzJkfENod/swaJRYUjS0u30siVPEqaxtYNGrNZKtYDwkKzTlWAy8sPUQD4U1H1TkhWTzx+V3i1BUEWPfRRMLWDypEKfHT4pWhU6lYMn63WyKUl9JRC6RFTFlXGzQKJQsejVevqWRRa/WsGxqMUqFcJ6uToYMGd8UDNrke/90g5o/bN4fNwfz6Gu7efj6AjRqBfPWnsnz7D3RwoOv1PDSraNC59AoMRs0PLKuhu119piGqc+O2mlyeBiREypOhwrXeXijGnwcHj+X51l5P47FezKcTS5WXNcXjIuyG/H6OWF3UZptxpPE7riqtpF2t49fvLGX2WW5qAShS8XZdrcPARiSbabN6UWvUZ6VcjGcURWusFkQgGPNDh745y5ZrU1GBKypmrhjBiLVNFK0qoS/N4EzdjZ6jTLUDKdVcrrdTbnNQppOxcjcDMk+PNnv9oHxBRxpdlBms7BocpGkLKJVKbpUNImu1cTbM4SIHsqEzYZlNgsZKRpevHUUqTolbS43v7ixhHaPn0AwiICAQatk6YbdVNfZpZqa2xfA7QtwuLGDoX3N7DvZhtmg5qXbRjHn+U+oPmJnYklmRM5B3LvMKc/l2aoDEddj0Ch5fUE5AkEemVwouRKIr/cnIN6JMBrUrF9QzqPrIhu25TF/aeB4JxnFcgmQUSBk1fPk5lpOtDjJNMq/7YsVFzwZxel0EgwGMRgMGAwGTp8+ze9//3sKCwu59tprz/o49fX1/OhHP+LEiRMYjUYGDRrEW2+9xTXXXPMNXv1XQ490HT3SdRGP7TzSHJeNWm6zhHzwgvDoazVMH9lXeq40y0RGqoYnNu5jRtjjAOuqj/HXuSN5+NVd0vEMGiWLJhYwaVAmAgLL1kcWpcOLn1W1jdxxpS2mOCoW0m8s7YVZr2LhxELaOskSWqWChnY3gWAQp8cf144IznTLzV2zTTrmvX/fyYpppUlZtZ6ERWo/057Z2lm0H4bTG+DOtTsiVA8gNtF4tkHe+YAsI/3dRS+Tnl/9YDDNHR7Cw82RfTOYPKgXSzfsZk5ZLve//BlP3DSIh64vwOnxYU3V4fD6aXF4+X+3jyYYDOLy+tiw6wRbahv576vz2H6oKb7tDgJzynPYsKCcdrcPfyDYZXJR3OBvP9yMTq3kjV0numSuhycPOtw+cq0pZ3WOMpuF/xqVw86jdsptVlqcXgRBwKhXJ7GzCam3RFvYJEpMhD8eTsoRFU9yrSm8vusE9S1uHr9pUNKOlNJskzSXQajDt+ZoKAET/vpnZw7n8Tf2SHOZSLpL06m6VL4ROwx++/bnMXK2szrnPIfHT5nNQo41hcFZkXL24UmLYADuvLI/gWAsuenOsbYE387FhVStilSNkjWzh3OkyYFKoeCE3YlRr0m41mypbWRueT+6p2kjknarqw7y2p1lLN2wJ2L+vzzPyryxNuY8/0nEceSuaRkyvh60SgVuf4DCXsYIH+lwVO5voMPjR6tU0Muk42Szi5lluQQhYmxHW8XJY1KGjHOPk3Yn6QYVL902CrVSIFWrShg3VtU2cv+ERI0CSnIshhiS7+qqgwztG7IvjD5u5f4GCMJ94/Mla0kRpdkmuqXrWPHufh6YUMCDr+xi1picGCKKiHjkElkRU8bFBpcvkDAGrqptxOULIKi7VsuVIUPGhYU0rSqh/XtFnpVgIJiUgNHk8JIZlo/uma6TLNMNWqWUx3D7AmzvVO1e+9FhIJSDdnn99LWmMCrdgicQ4I0F5dS3u3F5AhxviVxLjXo1j32vhMONjgg73cZ2d8J8ytnmYsV1PUWjjFAY7pam5e09p9hZ18zled2Sqg873H7pXt177UA0yq5IMioEQvmoVJ2KTXtPMb8zH5NMuVhUgCizWXhkShGv7zrB8L4ZslqbjBhEN0CKuDzPyhM3DZJ+F+0uX9JxPqe8H2VhSiZ6tRKTXs3Prstn3gs7+OvcEVxf3JPiPsakv1sIkmNNYU5ZLnVNDimHWX3EzpUDktuJOzw+qVYTrQ4kwqBW4vT6mF2Wi1aloDCsodpkUJOqVdHi8KAUQKNUoNepeXT9bgp6Gbnxst74AgGCCAzPyeCBCfnUt7oRBIE9J1qlZuXR/SxS/rW8M897y5pPuCJqfuiWFlJgiTeW55TnsvVAI0Fg094DXJZtZnZnLjorQ89bu08lVe9O16lC5LOo5+Uxf2ngZIsTATBfIt9xSR8TCgH+9flppo3IPt+XI+MbwgVPRpk6dSrf+973+PGPf4zdbmfEiBFoNBoaGhr47W9/yx133HFWx1m1atU3fKXfDEx6Db986/O47HUBuL4kk6raRondCiHWpsj0nhP2uEGj5KkZQ1gUpkgQLr12rMUV13s7mrENSElE0drigZc/Y/nUIpSCgt9u2h9TGJ1dlsvM5z6JIYJEI9zGQjynUhAiWKzR/pqn22OtlsptFnqb9Bg0ylDRXhAY2tcMICkShB+rw31mc3S2Qd63DVlG+ruNcKLQqpnDpERAD5OOJetD3aM/Gp3D4zcNYvWWg3x+so0XbhnFw1EKIeU2C0umFjGmv4U5ZbmkaFQM6mNicLY5hjUuEri8/gArOrtc5o+zJWWPi6ofc8pzWdLJ2p4eRVqLRvg5FQoBjVKRMElQkWeVxljN8RaCBPnkUJMk6wqh+SMZwosN4rxzqtUV95zVR+wRSReHxy/NU2U2C8umFrNycy2lM4clZPYrEPjHj0fzRs1JaSNUYbMwb6yNuWs+ifP6zxmcbWLTvtMRc6iozJJM+SZRZ0xVbWgDI37e8I6DcCsj8RoenliIyxdgzpptce2G5jz/Cf+8Y0zS+3wxIEWjxBcI4g+EbOKWbdjD/ePzOdrsTPo+lVLA6wtC8Ax5qzTbxOs1J1g2tRhfIEiby0uaTo1KITDhycq4HRty17QMGV8dLU4Peo2KjrPoMgyoBTrcfjyBgDSHPjyxkIMNHXGt4uQxKUPGuYfHH0AtKFiycQ/DczIo6W1M+voOd2g8hidyPf6Q3eDOI3aWvb43QpFu1cxhWNO0TF25Je7xKmsbeFAh8MD4Atw+Pz5/EJVSQKNScKLFxe1X2Hjwn59RWdvYZWKrxRlZEPuuK2LKkPFV0e7qem0NBi9+9UQZMi4ltDg8LF2/h1mdxO1oC72lU4v44lR70mOElJdVzCnPZecRO+/sPRWx/104sYC1Hx3mjittzCnPZe1Hh/mvUTmcaDmz73Z5A2w90MiY/lZcPh9HmxyYUjRYw7q+K/KsaNWKGCLKvuOtXFvYg+7purj5lKVTi88qvhfXdYNGyaqZw1j5Xi0rN9cyf5yNPcdbWDSpiCDBpFYm6fpQKWVLbSMPTAjFG4kUVMtsFqrr7IzKzcCSouFUq4vPT7QydmB3JpX0IkWTvCyTolVRmm3G7vDy1w8Pk9c9Le7r3t/fwInWUBe9vM+5NBBPFX3FtFIa2j1SrsqaqvlKJOtgMMj8sXm893k9V+f3QACcXj+3/CWUV1z0ag2VtY08PWNI0uO4fAE++Lyejw828cikIsbld2P6yL7c9WI1E4szzyonHa/WBKE5S6dRoFEqeeDlHTw/ezjtbh8pWhXtrpBdebvbS480HRNXVDGnPJd9x1uYNrIvaz86zITiHhjUKvxB2NaZixb3JKP7WSjvb8Xp9bP1YBO3X9GP372zv7PG9jk3j8gmVRdpUTR/nI0KmxW3LxBDYsvKMFDf6kIQBDbvOx3RSPj0jCFdqne3e3yyQuMljBMtLowGNaouCI8XC1K1Kgb0SONfn9fLZJSLGBc8GWXHjh387ne/A+Af//gHPXv2pLq6mpdffpnFixefNRnluwQxoGh3ezHq1bh8AdpcPtK0KpQK0KqUePwBAkHwB4IxdjnihqCytpH7JuTz9Iwh9EzX8diNxXRP15GmVZGiVbFq5jBSNAr+794rcXj9tDq9qJUKfjY+n0Mv7OBIszOiMDqnLNZ+R0Q4scWSquHnG/ZIzE2DRslzs4aRrldHEF3C3wuhAKMrhKscbKltZP5YGz3SdTwT5WsazqA1RfkIltkszC3vx6d1dv4yZwT1bW50aiXd07QM7mOKUSQos1n4/pA+EcfoZdJ3GeR925BlpL+7aHF4WLyuhsFZJmaNycEXCLJ4ciFNHZ6IMdzbpGPphj1sqW1k1cxhLN0QK3G+o85OfaubrV828qu3vpAeT8Qad/sCqJSCJD04LNvM5EGZLN+wN4K4EU6IgEhWd1dyqCZDaIyV2yx4fH4mr/wohnQhKiyVZpuxO71kGnX075bC8s7P+1WgUyslAlqaTs34op44PD4WTymM8fbdd6KVR6cUs+S1mhhf9Pnj8iTlJJGYFg/b65rRq5VMKO5JWX8raToVWrWCOc99krBbZlZZDkAMuSRcmQWgt0mPxx+gvs3N324bjVIhUJiZHndOjyYQbqltREggdNPu8kndN4nm7bYuktEXA1wBP8qggmcqDzBvrI36djc7DjdT3MfE0zOGxJAXRWQYNKgUguSJGz4+rsjrxmXZZum11XXNCe0GQO6aliHjq6DF4UGnUaFRKkjXqZKSjVO1KjQqgSaHD0EQpPnu8jxrUqs4eUzKkHFu4QsE+c1b+7i1vB/ZFgMdHl/SNdaoV/PSbSPRqVX86q19CfdwDo+/k+QL916Xn3St7XD7+MGfPpSIxstf3yMlfFfNHCbFgF3FtAZNZBH+u6yIKUPG10GaXpW04z9Np+qSsCJDhowLCw3tHjbtq+eDAyFbmAcm5OP0BNBrFKgVClxeP5lGHQaNMuFa2y1VS4fbz5h+Fr5X2pv99e2snjWcHXXNVO4/zZUDunO8xUWKRkVplgm1UkCnVvB6p6KviDKbhdxuqfRI01LQy8iKd7/gp9eGFNMq8qw8MrmINpcXp9fP4nU1NLR7pL34k+/u5+6r8rh/fD4eX4DT7W40yhD53O7wAMkVegFSdSGFGIVC4On3zjT5iEXhUy0u+loNSa1MfnFjiWQh0uHykWpUsXBiAXaHtzOOENj8+Sl2HrFLBfiX7xiDSinw2qfH+Nn4fJZt2ENhLyPFfdLjXqc4T6dolRRmpqPXKHlu1nDsHYn3MQdOd/CL1/fKytiXAJKpovfvnprwfV2RrHuZ9JxudbPrSAvji3riCQRY0BmTD8k2R+SIk8USXl+AYDA0Zpa9voefXTeQOc9/wtC+ZpxeH7eU92Pq4F7kZ4Z+/w6PH71aiUIQmP38x9L1ROdAK2wW5lbkolYqUCkEHppQgE6t5LE398U0cT46pZj54/pT2NPI0GwTq6sOMjjbTFObF32GiiWvhSwLwxsIo/ckj0wq4k//dwCHx09lbQMPTSwI1bjC5ofVVQdZe+sonB5fjCJUu8uHxx+IIJ2J9y0rQy+pd992eT/uuXoAvkAQg0aJgIDXH8DlTW5p9E3nNeIRnuS60reHU61uMi6x+z04y8T6ncdx+/xoVTI5/mLEBU9GcTgcpKWFmMFvv/023/ve91AoFIwaNYrDhw+f56v76hADiu2HQxYU0aonFTYr88f1p8PjZ1XVwZigProYfaTJyb1/38nK6aW8seuEJJe4YvN+DjU6+MvcETwUR3nhL3NH8KNVH0cUpJN5eIvPV9isGNRKfjY+n58Bbm8Ao0FFU7uH5g5vUquPOWW5VB+xU2GzxH1dOENWhOhHmojgsmhiAT3Tdfx19gjaPD60KoWkxvDqzmMx93be2P5sr7PHHGvxupoYZRGj4bu1CMsy0t9dNHZ4uHlEthTcGjRK/mfGUDRqAV8ggNmgIU2nIhA889vtnq6NS46YU57LyrBNs4hErHGtSkGLwxsRXIvB70+vHUhjR0g5KLp7PBzVR+xJmes+f5CyThsZlycQY4ejUghkZxhYvK4mwtKmwmZlZlkOHxxoijhvV+ezpmqormuOYsdbmFvRjxG5GZIKSN8MAykaJd5gkAklmcwKUwepb3VhTtEQCPr5xY3FKBXxixPixmTxumgyi5VnZw7niY174xZRfJ3en9FSjdHkkFfnlTH92a08Oa2Uxzfu7XJOj56HEyWLUjRK6MLuvSvf6AsddY0d6FHQEQgwY1Rf2pw+JhRlsmTD7hhrpfD7XG6zoFUrON7spJdRx6qZwyLGR3QXtNw1LUPGucFxu5P7//EZlbUNbP7pFQQhZq4Xx+tLH9fh9QfQqlRY07TUNTqk1+g1yec2eUzKkHFu4fH7ufuagTS2u3kkKl6KXmMrbFaO251UH7GfteJmqMEi+TWk6s50KT+yrobB2WaJjBIeO3UVY0bbWX5XFTFlyPi6MKgi1QBEiCpEBpUSlaGLTYQMGTIuKIi5QofHz7rqY9xU2ptfvhnbrLN65nDmrPkkJsdQkWdFp1Li9vvxCwJHmpzsOtYi2VncXtEfh9fPsGyz1CwzdmB3nti4L+E6v3RKMcftTn4wLAuvP8ALt4zkwwONTFlZJe3JX7hlFDOe3Sq9pzTbDALc/KetMdc4qSTzrO5Fh9vHrLJc2t2+iM8v2nn+7bbRtHVhZdLq8rGzrpk/zhhCD6OOxa/Gxj4LxuYxdmB35jwfup8dbl+nskw/Fr0aKoKLcUp0XJKoOF5us7B4UlFC0pBWpZCVsS8B/Duq6MlI1mU2C5/WNaNUCNxS0Q+FQsDvD7By+hD2nmghXXemhFhzvCVpLPHxoSZJKbFyfwN3XNGf/3f7aNpcXrRqJW0uH1kZhpg5osJm5ZffH8T8tWdyoClaFU/PGNKZx3VjSdXQ4fajUQYZ2tccoyYOoXH66Gs1PDq1iEAgiNcXpLK2kdsq+tPHoo8Y/4nUqbfUNrJsw+6IPUlDu5vuaZG5eofHzy1rPuEfPx7N0g17mD6yrzR2n581nCyzgSCh/HD42BbvV3WdneLeRn636YuoexFSfEpGEvwm8xrJCE8y2e3bwakWp9T8e6mgNMvE3z45wicHmynPs57vy5HxDeCC1/mx2Wy8+uqrHDlyhLfeeotrr70WgPr6etLT4zOMv6sIDygSLYaVtQ18ebqD1VXxF8rnthyMUBjRqhTMKc9lVdVBKmsbpeNW1jby1IwhLHw1lshRVdvIoldreGrGkIjkXZfqCHo1j0wpYun63dz85628vecUrS4vgQA89V4tdmdyMoRapWBYtpmFk4qoiJpwRCb86qqDEY/7g8GkBJfiPkaWv74HY4qae/++k7lrtuH1B2OIPBC6tyvfq42r0CIqi3yXIRdEv7vwBYIR43n+uP70MutYsbmWySu2MO2ZrUxaUcVx+xkJ03ZX/GCzNMuUcGO8pbaR0iyT9H+FzUL3NB09jfqI84uEiOnPbsWgUfJ8Z6AcLoneM8wPeHXVQWZ3etWGQxyXOrWS0mwzd71YjU4TUi0BWLm5lrlrtuELBFkYVZyA0JiLnrPCz1cR53z3XZfP76OC9NCxGnmm8gBef5C5a7Yx74UdnGpzEwCWvLabh16pkR6fu2YbD75SwxNv7kWvVvPGrhMJyVrhc2b0tS/bsJvCXpFS9OI8nGUOBeddkfhOtDqTbn7izenhSI1DKCnrJFPoVcqY7yz8NfqLmGV8qtXFw6/sIqgQcPsCrK46SEaqhmVx1IbC77NIqvIHgjR2eKhvczN3zTZpfMTrghY39PEgd03LkHF2aHF4JCIKQDAIP39jb9zx+vyWgyyeVEhTu4dH1u1GpRDINIbWrDKbhfpWd8K5Tx6TMmSce2iVSrYdamLle7Ux8VL0GrtkahH3v/zZV4pnAZod3qQxTXjpvDLq/eGxU1cxbTQZBc4oYr77kyt4dd4Y3v3JFayYVkqmnIiVcQHCFwxGqAGI2FLbyNPvfYkvGESvvnj3CDJkXIoIt6Z5bvbwmEYbCK2dT71Xy8KJBRGPl9ks3DnWBkIQjVKJw+1j5nMfU10XaqCsrrNz1O7A5w/QLU2L3eEh0xhaH5Ot8y5fKPeUadTT4fYx49mPInJSVZ2F4CduGiS9Z0i2mfpWd0z+qMxmQZFIMjYKdoeHu16sjmsHevOIbB55rYYmR/Lcr93hobK2keN2VwwRRbzWFe/tp+ZYCzd3Wg2k6VToVUo63D7JQtqgUaJWCjwyqSgi95XMunnZhj1xc9bhzZsXQv5axtfH2aiiJ4LRoGHp1OKYXGuFzcLc8lyWvb6X/t1TmbPmE5a+tgePP8j8tTvYeqAJk0EtKQgGgySNJYLByFxoi8vL1gONeP1BtEoFx+zOuE2WlbUNPFcVmQPtcPvC8ri7+OWb+1ApBFRKBQ6vXxpP0aiqDVmubz/UzMlOC6seJh0LX62hIyzfnmxPEr2nMBnUtLp8GDrz3qtmDuPpGUP45fcH09ThpbCXMWbsKoXQ/SqzWSLGtrgnWTixIEH9r5HFr+1m0aTCuNf2TeY1uiI8tXQxR8o4NzjZ6sZ8iZEKszMMWFI1vLvv1Pm+FBnfEC54MsrixYu59957ycnJYeTIkYwePRoIqaSUlpae56v7aggPKJIthj3SdQkX2/DknRiMlmaZJIuO64p6SMdVKRVJ2d4qpSIieSd2ksVDhc1Kb5Oex9/YwwcHmjo3Jc3MXbONY3YnlbWNXZJZlILArOc/4cantzC0r5lX55Xxjx+P5oVbRkqF7nA2aJnNInmOJ8LRZifv7jtNm8snBTNfNfkp4ruuLCIXRL+7CAQiPW2vKezJI6/FFsXDkaqLnwQ8G4UiCI3JhZNCVkDtCTo7HB4/c9ds42fj86UgetXMYZRmm/nXF/USKUxUOinNNrNq5jBWzRzGP348mtJsM2s/OozXH5ASBx1un5SYEDcqiVReIP6YE8/3s/H5vDqvTDpnabaZ5g5PhM9momOV2ywEg0GONDsi5svwTcPMMbkctzu5LNvMrmMtcee3ZPNFVYL5YkttIwqFECKFdDHvaZSKs56TotWhKmzWGPGTMpuF+WPzcHn9CALMH2uLW3CZPzYvocXPxYDmDg/b6+y0e0L3oaq2EY8vkJS8eF1RD2mtaXf5KOyVjjLsJiXqgha7pqPnX7lrWoaMs0dDuyfCOs7l8yede9vcPrbVNVNZ24DbG8DjC0jF5Ptf/ixusVkekzJkfDNw+QL0SNcljWWuK+rBpJJM3qw5QUO756zjWRE+fzApiSQQDMZ9vxiLvnDLSJ6eMYSV04dQc6yFEbkZUnz5xl0VLJ1STKvTiyUl/vxgNGjo3z2Vy7LN9O+eKs8jMi5YOL3+hPFwZW0DTq+ftjhFWhkyZFy4EHOFt13eD7c38Z64sraBAT3SYnJDc57/BIVCwe83fU6gs8M/nGzay6hH32lDseXLRgSCOJNY6wE43H6qj9g5ZndiSrCmVtU20j2sScqgUaJRKph6WS/WLyjnL3NG8OKto7h/fD52hzuiuSsRDBoVDo8fXRzSXWmWSbLtSAZLqhaDRkn3dG3S/EKPdB1Dss2U2SxolAoCgSCqTkXeLLOedfPLGN43g0NNHSycVMSbd1Xw99tHM7EkM0lxvIEx/eLHQuHNm9/1/LWMr49/RxW9xeHB5fMzf1web95dwZt3VfD6XeXcOS4PrTL02+zw+CVbmjZXyHpm++Fmlqzfw6JOslpJb2PSeaSktzEiF9rHrOf1XSf4/v982OW+oTJJDlR8XqdWoFEqurQVdHr9EXOIvzM3r1KeyfOd7Z6kzGZBrVCQplOxcnopn3bWvOa9sIM5z39Cs8MTk9u1pGr51dufo1TA7LJcRvc7o4Ik5r0H9EhLfC/2NzAk2/St5xr/HcKTjHOHk52K7pcSBEGgNMvEpj2nCEbt72VcHLjgbXq+//3vU15ezokTJxg8eLD0+FVXXcWNN954Hq/sqyM8oEi2GJ7NQikGo3e9WM3v/vMySQasMPOMWkxbF0olbU5vhJSx6KEJocBatPoY3c+CRqWgw+Mjv5eRkiwTaz86HNq0lOVi6NyUJJNFrrBZSdUq2bCgnCBBOtx+lApI02lwen3sOd4SQ0SZXZYbsqJIAk1nMNXi9ErBzFdNfor4riuLyDLS3104PJEBstcXSU4Rx1K3NK1kU1Xf6qbcZokhnnVFbuibYZAkTm98+gMcHj+rZg5Lcm1+jjY7mffCjojH77kmjzuv7E8gGLpWUU1FHHs/Wv0xpdkmFozNo3J/iBwyobgH3dK0PDAhnw63n3/eMQZBgFanV/ITXl11MEZiMN6YG5ptAuD/9tdT0tvIc1sOUbm/gadnDEn6+T3+gKRuoRQEHB6/dH+HZJsxqJUECPLBl43StZTZLNxS3o9BfUIqJ8lIQtFINF+0uXxMLMmM+E6jIW6swuflROeosFlZNLmQgw0drJ41nFMtTmzdU6lvc7P2lpGoVQr0aiXtbi+9jTqeem8/d47NQ6UQmFSSKVkXiRZFKgWo4nT/Xixo7SQgtjq90nd0ut2d9D1HmpyS/GaqVoXL4yNFq+LVeWNI06mxpia2ZhO7phvaPbS5vF2+XoYMGZGITqo5uiAbO9x+KeHa7PCEiH1hxGXRKu7h6wvw+ALymJQh4xtEW9hamwgOt5/SbDNBYGh2hmSrkwjh8W6ZzSLFkHPKc5l3pQ1/MIjXF6D6iJ21Hx3m4esLmT/OJsV2BrWSe67JY0JRJss27I6Rz59dlsvajw4zbWRfvv8/oXi5Is/KqH4WjIbQ6xL5pMv+6TIuZJzN+noxE9ZlyLgUIaoh2B1eWrso3ta3uWNyQwCN7W7e3Xeae64ZKNlGiHbrRoOaX721j9sv78/qqoNcmWfFqE+eP03Xq1hddZAV00rxBhLHEI4wclwwGCQrw8CiV3dFrOvlNgv3XpfPI+tq+PUPBiddk8WmIQUCFXnWiIKrGMtolMqkln7BYJA55blnlVu2pGi4pbwfhxsd9OuWgkGjxJqqYc2cESxeVxOR7xPjk4aO5HkLnVrJxrsrONDQgValiGu5/V3PX8v4+vi6qujhlrgixN/cSx/V8ZNrB3DvtXl0S9WyYUE5p1pdOL1+iXS2cnMtD19fQJnN0uVvH6B7mo7542zsO97CrqMtUh2p3eU7q7Ej2rrf9WJ1zPPBoMCjr9Xw0MT4qiEiHG4/bl+APSdaKbNZaO2sg1XVNlBhs1JZ29Bljl2rUlBhszC7PBdfIECKRsVzVQfjNolHf64gQTbvO82gPiZqjrbwozE5kdfn8VPflny8Oz3+bz3X+O8QnmScG3j9AZo6PGRcgnvMIdlmNu2tZ399OwN6pJ3vy5FxjnHBk1EAevbsSc+ePSMeGzFixHm6mq+P8IAi2WLY1UKZa03hgfEFbP78FAaNkn7dUqhvdTNjZF+yMwxSoi6ty82BmpLeRqYM6iUl8cTk/vyxNrql6VjyWk2MP+Ajk4so7WPmqN0BgACsnjWcXcfszO1UJ9kStXGYU55Ls8PLqsoDMcnC+WNt/PfVA/jRqBwcXn9EsP3SbaOSbhJEBq1WpZCCgrMJNKJxoSiLyAXR7yaM+sj7Hx64hXtGioSvAHD/y5/xwi2jWLZhd0SQW9/qitk0i6jIs7LzqJ2HXqmJeU4kZJRmmXD7AujUSimxH+83X9zLyJw125hTnsucslw8/gDdUrVoVApOtLj42+2j2HW0BUEI2RBNLO7BvePzY6y/wolxpdkmnpxWGrNRNkXNReU2C0tvKOZokxOvP8h9//iM1bOGM2tMDj3CWO3x0DcjRSpI/voHg+mZro3rt1tms0jXIl7viNwMiUTn9gXoY9bj6WKTlGg+UQhwvMXFoD5GFk4qZPmGvRGbvvCN1crpyQk2udYUJpT05Iantkj3rcJmYfHkIu54YSsN7R7KbBZKs83srLOz4Cob44t74fT76ZmmxZKqxe0L0OoMFU1yrSmoBXD6kyejL2Sk61SUZplI16s50RKS5BTJiYkgfpflNgtBwGzQ4vD6KMg0Jn2fCKNBnmtlyPi6iE6qpemTb1HCnzdolNgd3og5XiRQ3nhZ7xg7NRkyZJxbpOnVXSopGLRKlr2+J8yy0haXdA2Re7gKm5V7rxvI3DWf4PD4+bSumWsKejDtma0SoXj+2Dw27DrOzjo7a28dxZotBzClaOiWqmVJAns+BQKzynJYEBaTVnZKT6+YVkqHxx/XJ335DcUs3bCHTXvrIx6X/dNlXCjoan3tiigmQ4aMCxPNDjdefzChAq+IrAw9T88YEpEvcnj8eP2hDuX6NpdUmIZQ8VWlFNhS28jc8n4ApBs07DvZmjRvpVEqeOZHw/AHggQCwQhCaTjEvHWZzYLD448hokBIQSXIPkqzzTS0e5LuyVUKgdlluTi8XuaPtUEQKU+jVSkwaJQ4PT5ureiHAiEmhzO3Ipcjzc6Eitrh0KoU+ANBdh61U9LbSFOHlz0nWlg5vZRHoogocCZPfs/VA5Ie16gP5Xl/8cbeiEZEERdK/lrG14OodPRVvnvJdqU28j3hMfFv3v6cxZOLsDs8TH3qA8ptFpbdUMznJ9u45+oBrK46iN3hZUx/CznWlKTX2Nuk53CTgzH9LfzH0D7MePYjIGRB5fEHuqzLZGXoeWRKIVNWbomZEwDUSoHK2kYESFoTStWpaHV5pVx7eud88uf3D7DuzjKWrt/T5VzVM13HhJJMUjVKmto9qIyKuM2G1UfsjI5SLXJ5/dL5Vk4vlRoCw/Pz3dK0Se9Fmk79recavy7hSca5g0hSMqdceve6qJcRnVrBO3tOyWSUixAX/E5z7NixCElaNzZv3vwtXs2/h/CAIpmKyKlWV9JO+9d3nWDl5lrG5Xfjb7eP5tFOP9BwJZPy/lYMamXCJGC5zYLXH+D2v26X3jerU+XE4fHh8Ph55LWauMm9X27cx0+uGcCfKr+MKUoP6m1ieE4Gc8pyUasUKAWBDw80svOonW2HmhIG4xNLMjne4oooNFTYrLz3eT2zy3JRCEJE4BBeABcTmtcV9eDpGUPIsaQkvH8VedYYVuqFpiwiF0S/e4jeLIQn+aL9YEXC15yyXOpbXCy/oZgOj59Wp49UXWhjvHhSIUvXR3Z5VtisLJ1SxMQVVTHnrznewqqZw1j5Xm0MIWPVzGF8fKgp7nWLxbx4eOm2URxvcbH89b3848djuLawRwwRBc6M4fCERfjf5Z0bBFG9xahX868vTrNswx4KexmprmvmV98fjKLTkEarViTdbIRvarIyDKRqlPzmnS8SXtdtl/fDFwhSmmUiRaPC4fVLSZcV00qTzsXlcSQjITSPfFoXejwIHGp0cN/4fB5UCByzO8nO0BMMhjoTVk4fgjVVk3QubnF6YghGlbWNPLp+N6tnDUcQoN3lx2RQU5plYs2WQ9x73UCUgB9YtK4mhgC4/IZidMIF79SXEOYUDcfsTlI1SmnNTPZdloU9P6ssl1+9tY9rCntS1j++NZ0MGTLOLaLXyRS1UupWikaFLRTDzinPZecROwa1kveOxNq3yYlYGTK+HaRqlNS3upIoYFqkjkgR4YqbVRHxrIVFk4to7ghJXVcfsbNi837+3+2jaWhzo1QqSNWq+N1/XoZKIdDY7iZVq6Kkt4m87mk4PD5+cs1AHl2/m+kj+yaVuZ9dnhuhoCcStk+2uthRZ2f74eaI97y/v4GHXtnFZZ3dWuGPiyQWeQ8m47uO1C7W11SNEof34iWsy5BxqUKnVqFVBalvdfPYjcV0T9fFNCkNyTbx1u5TUq5GbOB56aM6PjtqB0JFynAihlaloNUZIqTuqGtm0cQClq3fzS0V/ZgXprQrosxmYd6VNlSKUC44XK127a2jsHd4cHhDNjqnWpyolYKkZGtN1TKrLJdpI/vGkGVElZauOvYtKSESxx1X9uf2v27nl98fxAPX59Pu8tEtVcuiSYUgBOlt1nPnuP7cN2Eg7S4/qToVHW4vllQt9S0uWlw+7A5PwiJ2mc3CqVYXx1tcjO5n4cMDjZTbrCx/fS8v3zEmqb3PA+MTK7OI+xtZGfvSxdf57pPZrlTWNvDjK/szbWRfTtidpHYSDapqG1n0ag1P3DSIVpeXJ6eVYkpRM7EkEyAxgcNmYePuk/x+034glH9cMX0I05/ZSmmWiQ8PNNLLqOty31DS2xi3oXLfiVZJ4SkQDNl4AnGbInUqBfVtbkqzTdz1YjXr55dLuVe3z8/kwZmMzM1gSLY54Vx1us3NQ6/UUGGzsnBSvjTfRWN11UGuLewRkdtN1YbupcPjZ/7aap6eMYSrC7pz84hsqWHynmvy+MWNxfSIMycP62s+L/mMr0N4knFucao11FRpvgTnco1KweA+JjbWnOTOsbbzfTkyzjEueDLKZZddFvG/1+vl008/paamhpkzZ56fi/qaCA8ooi1xRFTkWRmem8HwnAyWbtgTsfBfnd+Nu68ewOl2D0/PGEKW2RBBRIlWB8gy6/nr3BEsfDWSkR0qVJZw21+2AZEF6VUzhzF3zTZWzRyWMLmXn5nOY2/sTVj8Lc02M3fNNtbeMpLpnezYVTOHSYFKNMRNRUkfI4WZ6aRoVGjVCkwGNYcaHKRqldx33UAenFDAiZaQR6ionFKabZJkmAFWbq7lf28ZwaJJRSzbsCciCSMGbgaNkhE5GbKyiIxzhujNwqlWlxSglmaZ4nZzixDHnAiDRslzs4ZxfUkmszoVS7LMBtrdXpocnris8WAQnn6vNn5nqCAweVBmxOMVNgu9TfqI5HwQyLGk4PaFiDFGfSgJYdAoaXV5SdEqE84J4hiO/rvCZuWRKUUcb3ZyqtVFH5OBqtoGSrNMlPQ2YuuWytiB3fD5g7S5Q7ZhQYjYbIST7JSCgMPrp5dRx3OzhrF53ymuyu+R9LoWTiykuq45ojM2nKSzuuogq2YOQwFR5B8Lj0wp4hdv7I04prhh6ZmuZeG6SOWoq/O78bPxBSyN6tK9Or87iyYVxTwuEiNcnvjqLFtqG2lz+aQuA/GePjq1CAgRUX69cR9zynJ5YEI+7S4/aToVp1pd/HLjPh6cUBD3uBcDeqTrsDs8uAN+xvTLoJ81hWfeP8DPrstHYF9U4cvKwkkFnLCHgn1RuWfGyL6SopYsyS9DxjcLcZ0UlQg6vH5ml+cAwZi5d3Z5SClvTD8L5TYrKqXAsGxzhB3csL5mORErQ8a3hGAgyJj+VqlLMjqWWTS5iBue2hLxngg7rYmFtDq96DVKBATerDnBZX3MEfGv0xuy+Vm5uZaKPCsLJxZS3+JiQM90Ht+4N2a/PHNMDr5Acp9pUSr/8ZsGJVXQC4+tq2obpTg0HKJ/ujznyPiuo72L9bXD60fbhZqgDBkyLjzoVAqChJTK3th1IkaRetXMYbi8Ae5ce8aiZ0un8sDD1xfy9t6TlNlCFu3iHrnMZqG+1U1pXxMQKsb+7fZRPPhKDQ9cX8AP/udDqdFKtAyuPmJn7ppPePmOMVTXNUeo1Qrs47LOtR6Q7PPKbBaCBPnlxr0x1x2+Vrs7rTmTwWjQsHBiIYcaOvjl9wexOsxuw6BR8s95Y9AoFZxqdbEyKocmKndnZxg4andiSdFw33UDAWKaI+ePzcPl9bP89b2U26xSodrh8dPehVVSm9vDz67LR8HnkcosUWQDWRn70sVX/e67sl1pcXp54aPD3HvtQBSCIFlxVdU2cv+EfAB+/84XPDq5iGN2J3+uPMDMMTkxBI6KPCuLJp2xFxf35r9563PJ2mp11UGemj4kpExEbM1r/lgbDo8fjUpBdV1zRHwuqrU43CEyuVql4MWPDkcoXIvzzIsfHWbJ5CJG98sg15rCn9//kjaXh8WTi1i6fjcgUNfspKiXn7lhquDRc9Xfbh8FhEg7/gDoNPFjJIfHz73/byfPzBzGwldrqNzfQJCgRAB2ePzsPt7CT68ZwPLOmplBowzZ0lcdjJnbVs8aTk6GAaNBw6lWF80dHlpdPtL1KswGTZeq4f8OZLLb+cepToVvc8qlea+H52Sw8r1ajtmd9JbVRy8qXPBklN/97ndxH3/00Udpb2//lq/m30d4QNHh9vLzG4px+wK0uXx4fAE+PtTEO3tOck1BT64v7smsMTm4fQEMaiXd07U89saZ4HzVzGHS39HqCwBHmp3c9pftPDNzGA6Pn1anlzS9Gp8/wO83fc59E/IjEnAVeVZJBSCZv190cT0cW2obeWhCAaP7WSIShGfjF3ikycm9f9/Jk9NK+fPmWNWVWyv60SdDj98fpFtaD8r7W1GrFHxZ38Z/jcqRNlXpOjXH7U5mleUwuzwXl9dP3wwDJoOazM4JTl5YZZxrCMCEkszOgB2W3VDMoldrzuq3H4455bk8ufnMpviea/LIMKg5cLqD0mxz3GOU9DYmJHtV7m/g7qvyIpRJvjjVhk6lkNRUXvq4jhduGRWjhlRus/DCLaNwery0u5J30IV/jjSdirW3jOSDA41MWVmFw+MP2c5MKaJXqw6lINDHoufR12piAvIpg3ox/dmt3Dwim1vL+5Fp0rF8w56YAsKCsXmoFEKXG/2DDR28vutERBJDlKocmmOmNNuEyxtgaE4Gszo3JjnWFNRKgXn/u4P7J+RzzzUDJKJHIBikpcPL6XYPc8v7UZpt5qWP67h5RDbXFvZgeRy5+E376gkQZE5Z7OZHtBtKhBZn5KZye10z9a0unnqvltsr+nPX1QNjSC7lNguLJhXh8F3cXY96tRKX14+LAN3StDw8qQC3L8CwnAzun5BPh9tPh9tH9RE7Nz79QQyRy+0L0OH2hbx140j1f11JfpnYIkNGfPQy6Vk+tZgOj49Wh5f5a6slZb7weXH+2mqenz0ctVLBzOc+5p/zxnDvP3bS0B7qTnzjrgrMBnXEuJLHnQwZ3xwanR7S9SqyTHqWTS3G6fHj8IaSxG/WnORgQ0dcsrRIwC7NMkUQT8ptFiYUZUqJcIgkM1fub2D5hj3MLs/hV2/ti4mrKvc3EAgGuX98ftLr9geCPHHTIFZH7ZHF80Gkmp+IRLG77J8u40LA2ayvsvy6DBkXF47bnbi8fhQKgRXv7o9R5QjlP2BoTkbMel1V28iJVhfDczIo6mXkRIsLrUohFZ3frDnBu3tPSSoJxzsbPNpdvqRKu20ub8xaG034rNzfwOJ1Ncwqy2VVVddrtanTvqYrmA1qvBl6lr4WaYnt8PjpcPnQpGhiiCjh51s2tRiTQc3JFjd3rt3B87NGcPdVfnyBIAZNyAbpvc/r+dP/HcDh8ePw+Bna14xWpcCaqklqh2bQKOmZrmf563sYnG1iVlko52/Sq+lj1ks5axGyMvali6/y3Xdlu6JVKUKqPBMETrY6I+LfDrefA6fbKehlpMnhZUXn2Nh6oCnC1j3bbOCzo/YIe/FwwtisshwgNM7uXLuD26/oJ8XqDo8flULAbFCzYdcJdh2xc1VBbGNhVW0ji1+tYcnUYhZOLOC3b3/OfeMLWLZhdwxpZenUYn7x5l4enlBAb6OORRML2XaoiRyrgf++2obD46M0y0RrF3NVeK7bFwiiVcVX684y6/nTfw1l2frdDM4yMWtMDgpBCBGABdh7opWxA3twotUVMXclmtsUgsDKaaXUNXbw4Cu7YnK6v7ixhGxLcrukfwcy2e384lSrC5VSIE17wZfuvxZKs02olQIba04ytzy2EUTGhYuL9hf9wx/+kBEjRvDrX//6fF/KV0Z0QNHi8LD89Wq2Hw6xxpva3SxZHxk0zx9no7quOWJx8nX6bor2E0OyzZRmmyN8OK8t7slDr+yius4eIX825bI+nGxxcvsV/fjdO/tD7NOpxSzfsAcgqb9fV8X1k60uFrxYzUu3jWLVzGG4fQGyMgxJ36NVKTDq1dx2eb8YUg2c8TlcfmMxC9fvitPlE5q4ymwW3t5zKqZwXZpt5rMjdlleWcY3ghaHh/uiitnWVA1PdFHMNmiU9DHrpXGiU4c6OFdXHZRec1V+d9pcPl7fdYLSbHPcoLirMVnf5mbeCyGy1qqZw3jolRre++kVkprKqpnDYggNENoILNuwm/vH55PaBSk7fM7w+oOSmodBo2T+OBtDss00tXvok2HA6fHzxMZ9cZMkyzbs5ifXDOChV2pC815Vc8Ikwf3j8/F08dnFTRdEFhy21zWzZGoR5TZrZ4GzG2qlwIkWF35/gN1HW1k5YwhL1scSPWaV5XLPC59y84hsRvezcMWAbmiUIf/h7Z32PdHYvO80M0b2jSjGiDColdJcHi3bGD0XzynPlRIniycXxVwfnPnelk0tTnpvLnQ0Ozxo1UpOtrjINOrQqZS0OLz4AkEefzOkGBPvfovQqhToNcoYIgpESvIDZ13kPtfEFhkyLjY0OTwY9WoEQUiaFErTqfEFAjg8fo41O/nV9wexvc5OaZaJL0+3k90ZVxoNGnncyZDxDcOo00AQPq9vZ+6abRg0Sl66bVQoibq5ViI8ny1CccqeGCJIeDxbWdvAA9fnJ1W/8/gCSe35PjzQyHVFyRX05sRRQUm0Dz5XBXyZPCfjm0SaXt3l+trhTk7mlyFDxoWDFoeH+1/+jFsr+pGRokloD1NZ28isOGsegMvrJ02r4oGXq3ny5lJSdSoemJAvqZ49NX0I1xT0ZMmG3fQ2hRJD6frka6K4ZkavtdG5q8raRu6bkHy9n1OWS7nNQi+TDofHjzF5ehmjQcPJVlfce2FO0eDsbFBKdD6nx4/LFyqoOzx+Nu45GZOPFyHa9Sy/oRiHx8fTM4YgCCSMTxZOLOCRToXzzfsibUgr8qyslHPWMr4GktmuiJbVAM0OL8EgDOt7ptHRqFez/PW9rJhWitsXkH634bHE/HE2/nfr4aSEMQip2ItKIb97Zz+/e+dM06RYm6mua2bZ1GImxbGgh9CcoAAG9zHx0Cs1fHKomSduGsT9nWrQqTol9a1ujjU5eaPmFPdel8/2Q01s3H2SH47qy5Pv1jKnPJeeRj0N7R56GpOXRo0GtUSQd7j96NSKGFUXg0bJmjkjWNw5djd1jt1X5o1h7Ud1PDQhH61aycGGjog5Llkzd+X+Bk61ulmSIBf/0Cu7+M1/XPaNK6TI8835QX2bmwyDBkEQzvelnBcYNCoG9Tax4bPjMhnlIsNFS0b58MMP0em+uQn524To7Td/nI21Hx3mnmsGcN/LuyJeE72AGTRKci0p/OXDQ0klh0uzTJIlUDx54mVTixmZk4ElVcttf9nGyulDcPr8VB+xJwyejV1sOgRB4Mlppfxy4xmrhPnjbFTYLHE3A2LwfqrVxVUF3RMrPNQ2cKzZGXOMytpGAsCiSYXkWFKYu+YT6bkKm5WZZTnS/ZDllWV8E4jnz9nQ7mHumm1s/ukVcceSQaNk1cxhEeMEQhvQV+aN4WSLm08ON6HTKPntO19Iqijx/DK7GpPhSXUxMO7w+KWx1D1dm3AzXlXbyP2CQH2Y9VA0wjc3FXlWDBolq2cNZ9cxOyW9jayqipx7xHG59UBTTGeOmIwos1m6VGEKXV9DQl/08OsKT4KItmZiEiD89bPLcvnPP29laLaJbIuB4TkZkpqJSBJ5efsRnp05nF+9tS/qc8WXfBcRjzR0dX43uqdrY+Qpw62EwhF+T3yBYNLvzdUFUedCR5pOTWO7B18giFKhYNmG3dx9zQDpHpVmm5OuO/VtbnKtKQm9dbcdbqbZ4WXRupqzKnKLicBkxBZ5/ZFxqSNdp0YgNA8nWlPKbZaQRZzzjMJeplFPdV3kWnJ5npXHvlfCA//cJY87GTK+QXgCAbydMYVBo2Tl9FJ+tXEfgztJ0sn2jeGxWDgqaxuYWxGZeNKplRH/d6V+d7rdHTcurrBZmFmWy10vVlPW35r0GNGxWXmC6z1X/ukyeU7GN42zWV9Viksz8S1DxsUIMRc1uyyXpg5P0tcmamLSqhSk6VQ8Nb2UFK2K//jTVv52+ygcHj/zx9n469ZDzB9rozTbTIpGxdUF3c9qrol33niET4e7a0XXh64v4I2aE2z9sums4vvWBDGExxfA6fUnbAYSlU5Ot7ulnI6YU1cgxNjqLJ1ahAIIBII4PH7SdWqCwWDc+KTMZqGkj5GHXqmJe22VsiWgjK+JaEtcEWKO864Xq0Ov06n51xf1jO4XytsOyTahVyskG6zMBCoNXeVm55TlYknRMizbzI2lvXlkXU1MnnvhxDPW2bWn2+PmTEXUt7kRZw0xtx6Nl24bxdMzhuD2+SnsnY45VYtGpaSytpEHry9EIYRy5VpV8rlKp1Tw5LRS1n50GJVSwN7hxeUNMLEkU8oF9zHraXP52F5nj5g7NCoFD0woQAAe6VR5ipd/T4QWpzdpTre5w/ONklFknD+canVjMlzaSoUj+2Xw9L++5LjdKe+DLyJc8GSU733vexH/B4NBTpw4wbZt21i0aNF5uqpzC9Hbb1i2mdIsU9xAPJ6VRzwlg2gFALcvENfCR3ztI+t2M6ssh5fe289TPxyCzx9k3pU2jHo1N5X25uev72XTvnrpPWU2C2k6VcKEY4XNQjAYjDnf6qqDrJxeCp3Bu0GjZE55LqP7WVApBFK0KjbtPUVjW/LNk90ZXx55S20jiyYVcqrFxd9uHyVZamhVCt7Ze1Ji+Hp8floccnAv49wimT9ns8MTdyO6aFIhT79XGxMQV+5vYOmGPRJj/KbS3vxwVF9Wdaql7DneEuOX6fL6z4ooAmc2/+EdcV1Z8DjcPjQKhWQ9VBWHwPHAy5/x2I3FFPcxcrTZiValZGRuBs++fyBWXr22IWRb0zlPifOBGNCDwJyy3C7l6tzeAIJAqJghxPr4hm+64Mw8mmxOFJ9fXXWQQBC2HWqKIMiV2SzcPz6fX22MIxvfSYxL9Ln6ZhiYP84mJTnKbBbuvnoAj3X6iUZfi2glFPGZw9aCtgTzofT8RS4nn6JV4fT4saRoeOS1GgZnm9l7vJX8zHSenzUcU4qaiSWZNHV48PgCbO9MMJVmm1gwLo+e6Voa2t0Jjz+nPJdFr+6KIbMkKnLHI6WFv0dOLMmQAak6FQ3tbrw+P49MKaKx3U2KViXFbe1uL9Y0Lc0dbk61eaiwWSNIheF4f38Dhxsd8riTIeMbRofbh9cfpPqIneVTizjV4mJWp2T3+KKe7D3ewoi+GUwsyaRHuk4q7JxqcdLTqJesVKNh0KikTsRymwVrqibCuietC7J1uk7NrX854wEPIYL1u3vrJWJwV0m+8ITx5XlWlt9QzLJOpdDwx8+Ff7pMWpXxbaC5w93l+pprTT3flylDhoxzBDEXtaOumTH9LUlfq4ujyHqyxUlju5u+FgMHGhw0dbgpzTbh84dI4cOyzVyWZWLfyTZ2HrFz5YBuPHx9AQ1tri7nGhHiWlths7DrWEvMdXWlsmJJ0aJSKlhddYiGdg8nWl0caOhIqi4WToYJR5vLizU1fjOQ2FiUplfR6vJG5HMEAR6eVIBBraTD4wt9Xr0KdWdX+y837mXB1QNodfqwpmpY+9HhmLxd9RE7p1sT5x/E65Mh4+ugl0nPr38wmC/r27E7vRHW4GL+cc/xVkp6G0nXq1gyuZDB2Wbc/gAGjRKjXo1CICaPqVOfHYk1I0XNb975nEMNHayePRxfZ24XAdrdPk62uNjWmZN7evqQpIQwvVbJ6S7qQylaJU0dICDgD4TqUD8c1RcIna/V5aV7mpbGdheLJhXxh02fc9PQLLqna2l3+UnXq0jTqjjZ6uD5LQe5b3w++060UtArnVnPf8Kc8lyJCKJUgMsTiGn0nj/OxsTiniAIVNY2MjjbTC+jLpSHr7PTPU2b9DMYtPHnKRGJSHUyLnzUt7m6bCy+2DG0rxmNUsGGz45z2+X9z/flyDhHuODJKEajMeJ/hULBwIEDWbp0Kddee+15uqpzi3RdSBIs06Rj+YY9caUTo9njZ8NKFd+XVBastoEHJgzk7qsHsPDVmhjW9s+uy+eea/M43OiUApk5z3/C4zcNks4loqLTs6+uyRlTMHB4/MxfW81tl/dj4aQClAoFS16riQn+ry+O9A/v6j6Eo83l45nKAzHs23lX9mfumm3SMb+N7jNZfvnSQjx/TjGAN2hU/Neqj5lTnssDE/I52uykW6qWVK2KB/+5K87RQuPqnqtDCg9tbh/PdXpMVtfZ46ocXZ3fnUWTimIIatGEjLLOzf/8cTaMYcn5VF3yADhVq2Lin6sk66GHJxbS6gz9trVKBXanh5duHcWj63fzYFiXh6iA8kEcBRRxnhJVSuIpNz06uSjpfNDq8vLxwSZG5WYwpyyHBybkc7jREbPpEiHOH2czf4bb4UQ/3+byJZS/7epzVdhCyjeBIHj9IWJJYindBsl3Vfw9ZWWcmbe6Cly7Supc6Ohw+9BrlHj8ASprG5lb3o8AQZ7c9AU3j+zLr976PHKNyrOyYUE5QYIoELA7PKRoE9+jZL+TeEXuZKQ0kBNLMmQctzu5/x+fseCqPIwGFcEgrNgcOc+W2ywsnlyEXqsizRNg4aQCbnz6A8kyKxqJSMoi5HEnQ8a/j1StCn8AXvq4jr/fPpqF6yL3jFfnd2PhpCKeqfwy0ko1z8rszjgmHlI0Sm6/oh/dUrWU9DFS3+rmr3NH8t7n9ew73oJepUiqfufy+vnnvDEca3YiCALBYJDGNg8DeqSxcvoQTrY4SdMoE8qWX55nxdYtlVfnjYnwSf/1DwZ/I/7pMmlVxrcBvbbr9VWGDBkXD8Rc1Oqqg0wo6klFnjXuWlORZ6WXUcuqKBJGRZ6VxZMKsTtCHfgFPdMpzDTS4gjF0EaDml+//TnVdXbeuKuCDo8Ply+A4SznmnAFtXlj83B5/RE5ngqbFaVCSLreb9p3iuq6Zm4ekc3KzbXYHV7mPP8JDo8/YX7XoFbGPaY5RcviqDgGzuS2F00qRKtUUHO8JW4+R7RtFnNNojrKfwzPZsfhZgb3MeEPBrn32oH84o29cXPeyXCuLAFlXJoQgO5pWv74ry8jfvvhueEh2SYenVJEaV8zN/95K3+cMZQlk4tCc4kAq2cOZ8V7+yN+uy/cMjLpeXub9Hzvjx9w33UDGZaTwS9e38PNI/vGNAGW2SysnF5KRqomISFs7UeHqW91c6rVlbQR+q3dp1i5uZbX7ypHEM7k0SG0x9BrFMx+7hP+OnckC9bu4E8/GhpT9yq3WVh+Qwn7TrbR7vbRLU1HqkZFaXZkLvCVeWMwGTT8dtMXVIepo/gCQXRqJfVtIZLZ6qqD/M+MoSydWkR9q5sDp9sTzm0VeVb0quS5+HSdHLNdrDjV6iLHknK+L+O8wqBRMaSviVeqj8lklIsIF/ys9dxzz53vS/jGYU3VsGhSIcs37JGYlNELbrT8sS8QTMoiFbvn61tddO9K0ksQWPxq/GBcIXzO8qlFvPhRXcTiedeL1cwpz2XelTYCwSAmg5pdR1t4c/cJhmZnxD2Nw+Pn95v2c11hT5ZuiH++ZRv2sGRyET97+bOY91fkWePKJovw+gJxVSYIBrnt8n6SusE33X0myy9ferCmarimoDsDM9OlgDTXmsLS9btRKQSGZptZubmWkt5G1EoFv9v0BTNG9k16zBanl7lrtrFq5jApse/w+KWxJ3ZYZGXoUQoCM57dys0jsplb3g+DRok/EOTDA40RLPi55bmoBAVH7Q5UCkGaU+pb3UllC0+2hqQURXnEf94xmja3jyc372dLbSOvzS/jkfWxSk2hOSPI324bzVt7Tkrzkwi1SsFLt42KqzKypbaRpRv2sHBiQVwZUzGpISqI3Dc+H7VCiJmrol8fD9HM/57pOm64rBclvY3MLe8XMbeK301XSKS+UlkbUr6599qB3Pj0B/zxh0OSHkcQiCC2iJ9lS20jGqUiqSy+RpmYvHcxwBcIYlApaGwPdQuIibLSbHP8e7+/gUfW1TCnPJdUbai4pFMnLlJ1hWaHh+q6ZolwGI+UFg45sSTjUkaLw8P9//iMytoGHp5YgF6l5KFXdsX1R166fje/uLGEbLOe2tMdDO1rjttFCclJyiCPOxkyzgU0SgWnHC5+es2AmAKOQaNk2si+cZXEKvc3EAyeUcILhyjhP3lQLxavq4mI9UKJ4WKcXh+PTCliyfrdcdXvXvy4jkUTC8ixplDf6oohEVfkWbk8r1vIZ/7lzyLWelHtJNOkpy9nEoHfZEOBTFqV8W3gbNbXU20umfgkQ8ZFAmuqhsvzrGw73Ixeo+TOK/sTCAZjCsB3Xmlj19GWuHvkpet3s2hSEQ6viyaHmxSNiq0HQ69TKQW21DZi0CgRgkEMaiUNHZ5QHjtOHjl8rrk6vzsPXl/AcbsTgLlrPqE02yTFBeU2CzPLcpj+zFap4TFRAd3h8UtNl/7AmdgiUX5XABZcZYs5ptvrT9pY9OCEAtz+AJNLevHIa/E/X5AzariV+xtYvG43S6YUcccLO3h9QTmN7S7SDRouyzZz/4R8jjQ5MWiU9Dbp+bSuOWEOR7QElJsbZXwdhBo/drK9zs6c8lypsa2XSY8/EOTdfaeA0G/Y4wvgCwRpaPeQplMxLMfMZ0ft5PVI46n39sf8Pj880JgwZ1xhs0jHqmt2sn5nSLU4kRq1AphQkhn3OQG4b3w+c57/hF99fzDzx9qk586cz8rs8hzmrw01XYbbk+k6SWhqpYBSoWRQHyOBYJClNxQlnK8WvrqLJ24ahC8Q5M61O3jjrnIWRJ1XpRTw+gNU19l5esYQVlUekPY2L8wZgSU9pIDi8PjZcaSZk606Xtt5nOG5Gcwb258A8edkh9eXNBdvTpHH/cWK021uLssyd/3Cixxj+lv57TtfsP9UG3k90s735cg4B7jgySgitm/fzt69ewEoKiqitDR+h+SFCKNBw5Bsk6SSIPpRwpmFb3XVQVbNHIZCENh+uJlcSwp/+fBQQlnBPmY9q2YOo0e6lmAX51d0yonFQ+X+Btrcfu4dPxA2ngniHR4/n9Y1c1mWic9PtmI2aOiRriMrw0BqJwM+nrTbjrpmEGKl1qXz1Tbw8KQCrs7vHmEPVJFn5d5rB7Ji8/6476uwWfngQKJjNnLfhHz+/P4BqZh8LrrP4m0QAFl++RKE0RAilD34yi5Wbq5l/jgbf/nwEFtqGxljs/Do1EI+PtBEltnAiRYnc8v7YU3VYE3VcPOI7LikMhHRFl0Ojz9i3P/ttlF0eHwU9EyXHg8feyumlWJJ0bJp3yn2nWxlaHYo2HF7/SydWszHB5oSWvCIXS3Tn9kacQ0KhRCzsUg8phuZ1eaiuq5Zmp/EcagUBE63uZPOPw9dXxATnEdfV2VtA3f7/Bi0au6ME+hX2ELdKo3tbiaWZCKEKUwmU2aZXZbL/LU7KM02RVx7V8VPo17NdUU9kqqvPDQhdIxuqcllG7ulafnbbaP55ca9Eeo4AE0ON4snFbFsw+6496fFmVzW8kJHIBDEGfCT2tktICbK5pTFFr1EiOtBIBCk1eULbUynFrN4XU1Ekaoiz0pvc3LyoEgYg1Di6LHvlSTtvramynO/jEsXDe0eKYY0aJR0ePxxky4QSgp1ePzo1Qrq21zcOdaG0+OPq5RVfcSesNtIHncyZJwbuHwBXJ4AJX1MEQp4YgylUysTxnJVtY3ccaUtbhe2NxBg+6FmdtTZY96zaN1uhvY1U5CZzpLJRZxoddESJjn+4keHmTayLxt2naB7qpZ1O4/HLbA99MouVkwrZcW00i7VTr7phgKZtCrj28DZrK8XOV9dhoxLCkaDhsdvGsSW2gacHj9z1myLaF6SFK7XfJJQabCythF/MIi5Uz33D51NhADNHV5pvf/gQCMVNitpWhUub2wzoAhxrlk8uZD5a3fw2bFW6bkttY08fH0BY/pZ+CCseequF6t54ZaRzCrLibju8PyR2xegzGbhwwONlGaZpGPGy++6AwFMBg0TSnpKxxQtdpLhSLMDa6r2rNRwpfu3v0HaqygAa5qeU60uVm6upTAznXv/vpOXbgspCVfX2Vk5vRQFxKjJ/eLGEhweP/fJzY0yviIkK8iwGpKYEz5ud2IyaOiWquWp6UO4c+0OOtx+/MFgp51MMwDDczNw++KTtcQ6lSAIMQTxmWW5HOsknInqwrO6yMnFcwSA0NzxsFLBimlDMOlDdp7LpxbT7vHh9PhJ16nZuPsk89eG5gWDRomlsxZzzzV5qJUC88b2p83thSA8MKGA5g43Rr0m6Xz1wIQCTrW6QvleQaCXUc+CcTYemJBPu8uPyxvAGQxw59j+rKo8IN0jg0ZJtsXA1gONkipVSW+jdNzZZbnMTTInPz1jCIsS5HR/cWOJZBMk4+KCxxeg2eHt0k72UsBlWSZStSr+WX2M+8fnn+/LkXEOcMGTUerr67n55pv517/+hclkAsButzN27FheeuklunXrdn4v8BwhPLkeT/2gj1nP5ydaWTa1CH8AHo3D0Bb/XzixgLf3hOTKnp4xhC9OtVFhs8QNKMpslggWaTycanXR2O5mcLZJCuLNBjU90nQcszuZUJzJorAuufnjbFyV341pnZJs0ZKGVwxI/p0da3Zy2xX9uGNsf3z+IGk6Nb5AgDnPf8JLt47C6fXHMEoXTS7khqe2JDxmfas7pisvuvvsq7DPEyUrl04tZvvh5rjvkeWXL160ODws3bBH8oRN0agYkm2mNNvMlQO60dDm4fVdJyIS+Ffnd+Ol20bzSBy7qlUzh/HxoSag665vlVKge5o2goQhElbKbBbuvioPlRKuK+pBikbFqVYXr+86wfLX97Jyeimv7zpOVW2jZMHz4IQCOjw+UjQq1CoF05/ZSkN7aI4QSS4alTJiDDrc8W10RLh9Aen14V0wHx5opDAzPel7T7Q4uSzbzOyowP3xN/dKMq0QIga0OL0sCJs7Pf7Q3LnraAsTV1RJ8+xj3yuRNgqJFEyir1cBksLLrmMtSWVkW5xeUvzJl1+n18/6BWXo1Mqk6iY6tRKX18+0kX2Z3anS8sDLn3HziGwyDFqWv7En4f1ZNLEw6TVc6HB4fBhQoRRCyZvmjtCcHk3gikZ9qxtb91ROtbpJ06k43NjBDaW9uX98PnXNDklR5rMoRbJwVEQp7by/v4FHXwt1gD30yq643dfy3C/jUka4IoDL56e9C//jdrePX7yxn9svz2XO858wJKyLMhx7j7ewaFIhS6Js6sQEjjzuZMj499Hu8uHw+ml2RO4ZxRiqK7U/AXjz7gpanF5JuW/qU1sk5b5osjKECjuzxuQA8EV9OzXHW7iuqAdOT4Ax/SxASKlTLKwlSjC/v7+B+jY3CkFIuseTkvjfYEOB2L0uk1ZlfJM4m/XVInfaypBxUaGXSc/gPiaaOjwxzUvhSNWqWL+gjHaXnzRdKDd0/8uf0dDuod3tQ69S8tkxO/mZ6RT0SueNuyskhbPnthxkTlku3mCQXUft5FhTk15Tu9tHq9PD8htLuPnPW2Ny3tOf/Sji9Q6Pn6YOj9TsEQ9GvVpSSvn1DwZHPNfh9kbkdA0aJdsON7H89b1S0Xrl9FJSurAq0ygVpGlVNHeRJ4/OOXR4/Dxx0yDa3H6e2LiXn3UW1XTqUA6tzeU7ozCDwISSTGaF5XDq29z4A0EefW233Nwo4ysj3AoyWcPd/LE2br+iH2l6FR5vgPuuy2faM1tZMa0UlzeA1x+/nVmsU/3zjjE0dnhQKoSQCkhdc0Q8Lo6LrnJyyZ4/1NjBHf+7Awjt6ZfdUIzD7ePJzbXcfdUASfUeYP64/qRpVbS5fXRL1eLzB1nwYjV/nTsSnUrBr9/ax91XD+zS2tfh8dM9TccvbizGD5xodfHM+wco6GWkNMtE9zQtXn+Qywd041dvfSG9b055Lq0uH8dbXTwyuZCPDzaRolHR1Lln8vgDSedkvVpJfZuLR6cU4QsEaXGE9iopGmWXtuwyLlw0tIdsncwyGQW1UsGofhm8Wn2Mn107EIVC6PpNMr7TuODJKAsWLKCtrY3du3dTUFAAwJ49e5g5cyZ33XUXL7744nm+wnOD6E6p6MVq/YIyBmWZCAZDSfxkDO0HJuSz/PWQioxWpeDP7x8IddLHYbCKBcyukJ+ZTo90Pb5ASL2h1eXDqPdjSdVyqKGDueX9GJ6TgSBASW8Tl+d1wxcIUJptprrOLm08Qt1xyc/XLU2LWqmAYJCUFBXjn6yUutl8wSBTB/eKYJTWt7kJBIMx3bLRCGfOQ2T32VfphEuWrFzUaQGRKNCQ5ZcvTjR2eLh5RHbcYP8HQ/vw8zdibWjyexnjyn6KtjNDc0IKJtEWXeEos1l47/PTlGaZIkgY4tjYdayFDrefFe/up7K2kXd+crkkYT5/nI1VVWdIGA6Pn+ojdmmcGDQqFAL8/MYS1EoFnx21M7iPiWerDjAiJ9KKS61KHixkZRiYP84WYueX5VJhszKzLEfatCRSUVpddZBgkITjKbr4ET2XpWhUtLl8HG9xRTy+bMMeVs0cBsGgxNyPh/COl3CFl7vG2Rib353AxlipxUcmF/H4m3u7LMzo1Eoef3Mv91w9gNmd54h3rCfe2MumfacjHn/8pkHc9WI1Ywd2Y/O+02wOez4c943vem6/kGHUa/D4A2iUCpZOKZLspHRqZdLflCAIOL1+HG4fj7z7BfPG2ijubeR373xO/+5prNxcy6qZw3h0/Z4YlTIQCZBFMQTId/bW8+D1BWfVfS1DxqWG8DjX54dUnSrpOE3Vqth+uBlrWgEGjZKq2kbmRakrlNksTBvZl1MtLokMGk7K8/gv7jlQhoxvC6k6Fa2u2O6tkX0zKM0ykWnS8/SMITG2sSKMBjV6tYJfvfUlhZ1J3V//YLD0+rUfHY67f0rRqlArFaiVAv/YfgS9WsFV+T0Y/4dKIJRs16gUtHVRfK9rckQUt+Lt8cKT+NE4Vw0FYvf6Awksg+RYQca5wNmsr05v8ryJDBkyLjy0dBIukyFVq+IHf/pQWqPLbRZeuGUUM57dikGjJEWt5LfvfMETNw3iVKuLuWu2MX+cjdH9LKzcXMuPRufg8QWwpukkddJk56qua0ZAiFnjDZr4+/Vgp1JD/GYQKy6vP65arUGjJF2vYf6L1TE577W3jsLe4cFoUPPbtz9nyZTihM2aFTYLTe0eUrQhtYNkiG4a02sUpGh0BAhSWdvIzzrPHwwGKesfajJaNXMYaqUClVLgeItLIspI58+zMjjLFKESLkJubpSRDOGNH1013D18fQHVdXbK+1todrhxePy4fQFc3tiGkehxGiRk2bPneAuFvYys3FxLhc0qNWqJ46Krpspkz/cx6yVF1KraRhav280D4/NZMM5Guj5y3rm2MJM2t4+fv7GXOWW5Um7e7w/iFgLcfqWNJzbu5d7rkisupOlU/NeqjyjITGdUPwvPf3CQBVcN4Ddv7ZPUz8cOtOLyROa+S7NMtLl8DO5tIhAMEfBTdEo6PAoMGiV9ulBbbnf7mLtmG+U2C7PCLMnKbBYWjMujb4aBTFkR6aLDqc7ctUmezwGoyOvGpr31bD3QyBib9Xxfjox/Exc8GWXjxo1s2rRJIqIAFBYW8tRTT3Httdeexys7t7CmaqRO/WiU2Sy8tftU5yJv4a6rByQ91tHmkDzaL24sxqhX8+sfDCZVq2TplCIONjpwef0RkodzynOTBONWuqVpUQrQ06hl+YY9bO+0ifj5G3tjrDDmje3P3DXbpIBa7HYTO/lLs0wYNMqkn1VUdanIs7J0ShFj+mWw61gLZTYLgUCQ7mk6/MEzbN0h2SY27T2VUKmgwmZl17EWRvWzSInSU60uqfvsq3bCJUtWhnfxxYMsv3xxwhcIJgz2jzU7426mk5EgKmsbJH/P1VUH48t42qzMrchl3gs7WDGtNC7bev44G89WHZDO7/GGPC7nj7NxXVEPCjPTmVvej13H7JT0NrKq6mCMjPrCiYWcbHHRI02LQaOgus6OLxCM2JSIvpyJlELe2n1Ssukx6lX85NoBzHj2IxwePzXHW1g1cxgr36uNKTKunjUcvVrB6lnD2VHXzEsf10XYGvU06lg1cxj/2H6E6iN2hmWbE3YAhHfdOjx+5q7Zxku3jTorVZfwv0Wy0Kj+GRHFz+wMA1q1gife2Mvmfacp7GVMmEgpt1mob3NRVdvInWMDEUpYHn+AbqlajAZ1DBEFQr8prUrBimmlCbsWRHRVnLnQYU3VsL++HUFQQRB6puu4uqA7PdK0rJ41nBWb98dVHUrTqThpd+EPhhJFAWBSSSbfH5olvdbtC8RVKRPXzoMNHXEJkK1OL/26pcpJIhkyohCuCNDu9mE2qFg9czgr3osdp6tnDkepgNuv6EeLw8uvvj+I2c9vQ6kQ+MePR1Pf5o6IY3/9g8Fx19Or87t/mx9RhoyLFqkaJfVtbrqlaaV4z5qqoY9Fz8Io//XomKvcZiFVoyIQDPLDUX1jYk2xOUIlxBKbOzoTtBU2C0unFtPY4Za6pQwaJX+cMYQMgwbPWTRWhCPeHq+1i4aBc9VQ0Mukl0mrMr5RqBQkXV9VCrA7ZTKKDBkXG1I0SpQKkuZadx61RxBDqmobWbZhNyunl5KmVfHRgUZuHpEt7XshlI8qt1kxaJTkWlIIBIM8W3WARRMLk+ZgBaBHug5BEOiefsaauCLPSopWFTdvMy6/G4snFbH89T1xrEByWBBWqA1XKV00qZBFr9bEXMuW2kYE9nFZtpnSLBOVtY00OTzMregXt1lz3tg8ehl1/HDVR8y7sn8F/EJjAAEAAElEQVTSexl+/oo8K+9/cZrhfTNwdcYk731ez/yxNhQCWFJ1LHmtJiKfF08Zrqt8stzcKCMRwhs/umq4CwbPNOiJNjBaVYg88a8vTkt5zK4szdUKBRU2C4snFzK1s1ErZKFrSdpUWWGzUN/qinlcPPauoy0R81Tl/gYeGB9qUpxbnhsx73j9AXyBUJPgjJF9USoESrNMKBQCwWAoJ1rQywjBYBfzVVAijLt9fuZdaePxjWeaSl/6uI6bSnvH5ADdvgCZRjUpWiVN7W427DrBsRYXvYw6Fk4sYNfRlqTNpeI8UlXbSJAzytzi6ycN6sX1xT3lfcJFhvq2kDKKSVa/ASCveyo903X8s/qYTEa5CHDBk1ECgQBqdezgVKvVBAIXT8ej0aBhyZQith5opEe6TmKGn2xxkmnUc+fakERZZW0j88Ym/9xaVYhx/fR7tTwUZgtS0enld+/fd0oLqEGjRK0UWDK1OMJqB87Y3/zunc/5/tAsnt9ykMpORYV4hffK2gYCBCOCBvE1L946iqXrd7Nycy0GjZJX55XFSKqLAc1dL1aHjre/gcXrdvPQ9QW0ujwM7mOixemll0nPDU9vkT7DS7eNwhcIMi/MpiT8mPPG2vj4UCMdbh/zXgjdx4o8K1cM6IbR8NU74bpKViaCLL988SIQCMYNLoGEcoBfRbYwroxnq4t+FgPr55fj8Qd48dZRMVKr0ZsQp9cXsZkQCSXXF/fksShyGYTG4PINexiaY+aTQ02SpOOuY/YIAom4SQkSjJBJDx/T4nhdNrWYdvcZkkQwCE+/VxuXyCMqxPx+037KOrt2nti4N8b6a9kNxfxo1ceUZpnOynJHhD8QJEWnTPo9hDP2xb9FslB4l+0bd1Xg9QX44EDIXkn0VA0/v3i9y28o4e29J4CQIs2QbFPEffzdpi+YU5YbQ0SB0Jw9fWRfnt9yMKHPqoj0LrqVLnS0u314fAHanD7S9Eq8QT8LJxZS3+biqc37E/6mJg/OJD8zXbKoExVwuqdrOdIUInOK33UiSc1VM4fFvaazIRx+FUs4GTIuFoiKAPe//BlpOhValZKn3tuTcJwumVrE2IHdUSkUdEvVhjootUrqW91SLCciUWfVVyEAy+NShozEUAOjczNw+QMsnFTA0g17mFOWG0NEgciYa2ednZllOSzZsJvbLu/Pc1WJY7R7rx0Y8XhF3pkuy8raRha/tpslU4pQKgQpfj1ud0oe9SHPe3tMl/WpVhdKQZA6LEVE7/GiVUqjcS4bCowGeX6R8c2hq/X15zcWk6Y7TxcnQ4aMbwwpGhUHGzpYMqUobm5XzMusnD4k4n1VtY08PLEQu9PLwMx0MlK16NRKPjxwRkFXrQypmyzdsJuHJxaypbYRnUqRNAerUylw+wJkpGho6cyJVdisPHZjCRqVIm7eZvO+0wjA4smFHGlykqZV4fUH+OBAYwTJdVZY3vjyPCtDsk08+M9dce9LVW1jhCK4XqNky5cN3HBZL+4bPxCnJ0CKRolOreRIk4NfvrWPm4b2Ydnre3l9QTmL1+2OKGBHn78iz8qyqcUs37CHKwZ0p90TKjL+9cPDjBvYA4NGyaNRRBRInKNKlieUmxtlJII1VcNj3yuhe5oWg0YlNfRFqxUCtLl9ODx+7E4vJoOGCpuVU60uuqfpIpohB2ebk+ZXF00sZEJJZkgOpBP7jreyaHIRv3xzX1wF6BB5JRTPR5M0wucp0fZHxMnWUDPfvpNtrL11FMvW76aytpE2l5dAZ4+eVqXA3/lPu9tLqlZNi9NLaZYJrVqZdL7SqpQ8t+UgT9w0CIUg0ObyRrzu5hHZtHt86NWqiOvWqhQoFaBTqyTySnWdnaemD6F7mpYf/OlDnpxWigIhYh6Jrn+J92lOWI5X/F9WRLr4cLrNjUKgSzWzSwWCIFCeZ+WNXSdYOrUIg+birmVc7Ljgv71x48Zx99138+KLL9KrVy8Ajh07xj333MNVV111nq/u3EIhCLyx60REMbfCZmF2+ZnFyKBRkqJVUW6zxPXGrsiz0t+awsJXd8UEu2IHuBjshrNcARaMszHvShstTq+U2P/dO59z09AsdGqldLyztbUIf6y+zS293+Hx8+buE0wsyWRueT9StUraXD6pwzXCL7y2gfp2F8++f4DJg3tR1+xkZ11zRMBu0CgZ3NvERwebuOfqASwYGyRFqyRIiI0+d80nPPOjYdJmCjqZtS9/xq9+MFjy8kuEaPZ5V8nKPmZ9jB+4LL98cSOcXBGNREWyrmQLRYbsnPLcCHUTEdZUDcNzR/HIa7tjyA6i1Gr0RjZNp+Z3m/bHsNzFLpF4qKxt4O6r8yRfzvvH57N5X30EgSRcQeKh6ws51NgR0bUujukttY10eHy0u3w8N2s4Do8fS6qGP79/IOG5RYWYLZ1dO5dlmyNsaapqG1n0ag2PTilCEISEpKDwuUn87L99+3MGZ5vPiqke3f3iCwSZP84m2RoJAnxa18JT04dw59odcVU1+pj1vL3nFMs37OG/rx4AfI5SEJhb3o/rSzIZ0CONFqeXueX9MBvUMYUTiJTcTHbtFTYrGmXy39iFjlaXjw8ONDKxpCc6lQqVAG0eP3qNKunveVZZDl5/QLLzCXlIq1AIgiQJGt7JES1NajKocXb6Tod/P2dDOPwqlnAyZFxs6GXSs3JaKU0dHjo8iS0nK2sbcHr9CAK8UXOCnUfsPDdrOHq1EmuqllUzh50pNLc4qTneEnOMr0IAlselDBnJ4QwG8QQCuDx+XF4/pdlmepn0SWOuhRMLJRtJh8fPPdcM6MJm9kwmu8xmYcmUIiatqJIeq9zfwMGGDtZ8cIjVs4bj9QYwpqhpaPeQqlGydGoRp1vdrHivNmQL2blud0/T0j1dy99uG83s5z+mof3Mvi98jxeu3hQNuaFAxoWErtbXDo+fFE1yMr4MGTIuPBg0SnoadTR1uOPaV4p5GdEiJ3wf2+b00ujw0NukRxCge5qWYdlmSVk6VatidD8Lq6sOSrkvlz/A3DXb4qqIzl3zCf+cN0YqDvfrlsJr88tI06rok2Hgy/r2hDHEu/tOM7eiH4FgEIUCqg81U5pl4i9zRmA2aEjVqehw+1h7y0hJXexAQ0fSe+Pxn1F6SdWoGNMvA2uqjuMtLlqcXto7rcz2Hm/hvvEFOD1efr9pP40dbh6YkM9DCoF2t48Ot4++GQbc/gCrZw3HqFejVSlQBIM8PLEAhSCw66idq/O7c/PIbB7fGLIOSRb/ROfPE3XKy7GIjGTo8Ph547PjXarvAKg6VQb7mPV0uL08eH0+p1rdaNQCYwd2k5ohS/oYk9Z+AsBDr9RQYbPywi0jaerwkJGiYfozW7l/fH6nOkghAKfb3fj8QXbUNTP1qS387j8vSzpPuX2BiDxcShjBZu7zn7BsajGPTCnCFwgSDITmPwgpRKXoVAiExqxWpcAXCOI5i/kqtB/JJxiMbSodkm3G6Q7Nn0umFPHoa7upqm2k+ogdlSBgTddG5MfvXLuDP/3XUCkv/OKto7hvwkA63H463PHrXwBqlSIi12E2qOlwy4pIFxvq29yYDBoUcZRBL1VU2Kz8Y/tR3t59ihtKe5/vy5Hxb+CCJ6OsXLmSKVOmkJOTQ1ZWSEL/yJEjFBcX87//+7/n+erOHVocHha9WhNDMIkmkMwpz+X3m75gVlkuQaIZplbuvNLGoUZHl8GuQaNkxbRSdGolM0b2JVWjIjNdR0OHB6WgIUWrxKBR8Z/Dsnmm6gAzRvaVjvFVFB2kzxe1kP/1w8OsvXUUzR1udOoQGaUwM52V04fEsHcNGhV7T7Zx34R0Hl2/B4fHLykCVNgsqASBTJOOZ6qapII5nGGaAigVAqurDkZcw/v7G/iyvr1Lz+Ro9nlXycqe6TpZfvkSgyFJQi8kU3hGDlAMqMOlzqNRZrPg9PpZe8tIjAZ13A3AEzcNiiGiwBmp1SduGhTjxesPRHZgrP3oMKXZZlK6YJ2K7HLxvSW9jRFjDc4oSBT3MsZ0rYejvtXN3DXbpOsa0y9kx6Pp9M490eJCrVRI80D4fCJ2tUSjqraRB68v6NKWRjxWOKFDtB0L/3wQyVQX/37g5c+YP87GkGwzaVoVY/pb+ODLRmm+KrNZJPWY372zP0ZVY9XMYdL/P7k2ZLfmCQRQKUJExEglKyv/M2MovmCAbmla2l3+kJqAWsFLH9cBIanIF24ZxbINuyPWjnKbhUemFOINXjzqYfHQ7vLx0sd1/OfQPjz0yi6WTS3G7fWhU6siNnDRa4powaMQzvhXOzw+pj2zlYowa7nHbxqETqVg2si+MdKk5VEb+7MhHH5VSzgZMi5GGA0aTrW5aHN543rFi+O1zelDqRQixu7C6wv49Vv7Ii3r8qzMH2vDoDkgve6rEIDlcSlDRtdwePyolAIZKaGxsO94CxVJJHQNGiUCoS6nX/9gMDq1EpWgiEuyFeH0BHh6xhApMdzc4WFOeW7M+v3+/gY0KgUPXV/AI+vOdBr/99V5bD/URHVnXBe9blfYLKyaOZx/fVFPcS9jaM7RKGlxeCSlElGeW24okHEho9XZxfrq8qLroilChgwZFx6cXr+kBpCogAyh3E60GkeqTi2pJfQ26dl11M7Cdbul9ff5WcOlhp40nQqDRklrHAVgIayw1ur0cqrVxfEWF2MHdsPjC+DxhY7n8vl56bZRqJUKKvef5s/vH4iID+wOLy98dJiHry8AYO6abbw6bwz9u6fG/UzpuuQNhn3Mejbvq6fCZsHl99MtTc/Dr+6KUWxYNLmInXXNDM/JACBFq8bu8JBl0uMLBGhxBnH7A7S5fFTVNnBFXjdS07T4CFJ9uJl3957iv68dSJnNyoOv7JKsQ5LNyeH5rjKbhV4mXYw9kByLyEgGaT97Fuo7FTYrVbUNVNis7Drawqh+Fo43O/nr1kNcXdCDO66wseuYnR7pOtpd/qS/3Q63j/njbKyuOkhTh4e5a7axauYw8numUZptpr7FicsfIF2n5kerPo64NrVSkXSeMqhD883ajw4DSOcf09/CdYU9aO7w8pu3P+e+6/JRqwVWzxrOn9//ktsq+tPQ5iY7IwVBgA++bODawh5nNV/dc00eHS4/CkUgpnlUpRAwGtR8WmfnzV0nuCzbzOyyXHyBID2MOlpdvjhNbKHx6vCEmmwef3Mfc8pyIxS2o6EUBH4U9nyFzcqyG4oTvl7GhYnTbW7ZoicK3dN15PdM45XqYzIZ5QLHBU9GycrKYseOHWzatIl9/5+9M4+Pqr7X//vMPpM9MywJEBKYQDbAIPskLrixo5f2VvDeAsG6IGpvrwsqoIJ1aXu7KNq6gNr+RLtYBRG1VawmqLiQKmERApGArNmX2c+c3x8n5zB70NYFPM/r5cswc+bM95w53+3zeT7Ps3s3AMXFxVx44YXfcMv+vZCVQyKT1eGTvSPVxNqaBlWV5L39LVRVFLCoYggGvUCfVDOd3gB6nYBep2fTDRWYDHqOdXiwGg0RSd7+aWaevWoCv3hVDugrm4rbo6SWK5127p5dxg8n5kdUhlqMp25rocBm1KsqAr5giH7pFjo88obh56/uTsre9QdDPDBnJIdaPRHBSMXT02jU8cCmXRGsWmWBtG7rAaoqCnD7xbjBzzZPgJ1HOhKqC8RjnycKVlYWOrhzVinN3X7sKaaEGyUNZx5STIaEz9DuIx3cNGU4vCqpxIcntzSoNi7xZAIXugrUStJEdiB90y0Jq0m2NbaxcnYKATHEkwvGIkoS7+xrxmo82TfH5GUxbnA2JqNAutWoVr3Ek3JM67F7kW29dPRJM8ccD/ImZ1C2Nem5lPMk8x5dsm4b5XmZPDi3HEMUUzgRGa7TG1QZ/okwMMvK2gVj6Z9uVtscT8GkwJGCzainsdnNQ3PLqT3YppIT4rVZGa+U3+PWKUX86u+RZJ1oZZXunt92QKaVn768M4aIuOtoB7lZlqTKN5ePy+OBV3epG6Fwdv+9m3apVQhnKlItBv5rwmC6/SK7jnYCkGE18dOXdzJiUCbnD+8LyBu46SNy2Lz7GA+/uQ+LUU+2zcQrO45SPigz4reprm/GaNDxwJyRpJgMXHueE6tRz00XD+eqyiAfHGhhbU0DNfXNCILA+utc6AThlAiHX9QSToOGMxUGnY40i5GH543mRKeXDKuRoCir2k0r68+U0n4Y9AIgRHioewIhdb0Yvlbu8gV5YfEkdIKAXidg77HYORXrHa1fatDQO9w+kZx0M+/ub2Z8fjZ3ziylyy/y+6pxZKeYCIghOjzyHPnc+43cP2ckP315ZwxxbOP1FRxu8xAQJT6KWiO6/cEIMnP5oExqG1sj9oTKHrMoJ50719fxUWObur9MMRkYOTCTBZLE2rh2ss0gfMq0sv4RQeBwFaTcTKtWUKDhtEeGzcjvrjgbk1EgxWxQCe0VTjvjBmeTYTXi7qUY56uAZoenQcNXCzEk8Xmrh5EDkxc9vbu/WVV3BTm+YDHo2P55O+cN64s/KPF87ecR868oSdjMBn6zeS/3XzZCVgWxGRPGdB6cW06GzUhOhpV7Xt6Fa6id3EwrR9o8LFsfHXd2sHpeOUvWyXYVSkxJKYicWpbDc+8fjCkSDB9TslNMMQSO8PbUfd7BrJG5zB6ZizcY4p5XdsSNH9//yi4uLO6HBFxY1BebUc89b9azanYZRp0Oq1FPlzdImtmAQSeQatEjIrHixTp+fNEw3tzTxPfH5tEn7aRKgs2o5+F5oznS7kEQBFLNBrJTTFxc0o9pZf0x6ORYQnH/dG6fXsSBZjd39BRapZj1mA16dAKIYkgl0Gr4buBU581k+9lw9Z0Kp53lM0v4zd/3sGJmCbMf3sJfr51EmtXIvPGDWfr8J/z56onk2VNo9wTol27guasm8Nt/yP27fFAm3oDIpKF2Li7ph8WkIy/LysPzRuMXQ1Q67fRJM3NWXhaPvV3PkvMLOdjqQZLgyQVjCUkSekHAJ4YY4rAlHKcqnQ5CSKzbeoB5CQrDbp9WzPfHDOJYh5cBmVYe3ryX6vpm/mtCPkZBhyiFsBn1TBpiRxAEMqzJx6t0q5E+qWYybUaCIYl39jVR6bSre5msFCN+USaYvL77RISl+s2XDOOC4n4x518y2am6GgTEEFvqmylPom5d0TM+h6O6vokV6+u0ApkzDEoMTEMkXE4HT25p4HinbBum4fTEaU9GAZmteNFFF3HRRRd90035SnC4zcPBVjeQOEmrVGsHexQK3H6RtTUNnDU3k6e2fMZtU4t56I29MaSOha4CfvT7rZTnZapJ3uUzSiLk28JVAsJRXd/M8vV1lOdlsf3zdiqcdrY1tuFINSW0CYpOuAJcWNyXzBQTtY2tkddU6GDxeUP5qDHy+GivcZtJrwY6lYq6QdlWyvOyWPT0B6yZP4bL4yxQlOtPNelJMRtVxm54YnxQto29xztZVBHrZZiMfR4erGzz+PEFZB/TmQ/VqFXymrz7dweZNiM3TC5EBzF9cMWMEo51eJk6IoebpxTx4Ot71I2vXwzxk4uGc+sUgZYuP33TLRxpl+0CElWOKnD7YpnXFqOe7Z+3MWJARkS1qNKWOeUD1D5kTzViNRliyA7RZLBKpx2LUc//XFTIiAEZ3BuVXHA57ayeV46AwBM1+xMSNRTlkNqDbQnHnGjmvtDzdzgS2RulmPVIEkktd/628xirN9dT6XREtCtaweSRK0bz7NZG7pxVyr2bdrJ59wmWTHaeUpu31Dej8GeU32fiEDt6QcATEFky2clz7zeS0lNN1OYOsLBiCKPysiJIPZeU9uNAs5tFFUMo73nP7RcjlG8AVm+uj7AtCsf/Xvz1B5q/Tuh1cFFJP7p8AZ5eOI5tja1s2n6EKybkYzHqeKDHs1VBZaGDJxeMRa87qZb10NzyCK9Wm0nPvPGDeWpLQ9z5dOfhdvXZqd7bhE4QTpl42OFNLq8ZbQmnQcOZCk9AJN0sV1W+9HGsnPCS850MyLTiFyPHMG9QUcyLv1ZW1l4ZNtMpW+9o/VKDht7RJ9VAQJL4x6fHGVtgZ9kL2yP2geFz5BPzx/LgG3ti7WL3Nqn7SnktZufF61y8UneETw62qSp8yvlqD7ZFrLFqG1upPSjvCy8u6cfovCyuM+oJIUWo1D1z5fiEZO3qvU0smJQf8Vq0CpLynwYNpyvSjHoMCQjtd80qxarXcbzL97W2SbPD06Dhq0W728+K9XUsm1HC0Q4vy2YUs3LjzhjSx02XDGfR0x9QkpMOyOPCTy8bgUGCvGwbmSkGDrV6qG1sAxrUGMenxzoYkCXb8wUliSfe3seKmaUJ4yMCcO9lI7j5Lx/j9otk2kx83uoBAcrzsqhtbDtJOK9vAiSuOmcIZQMyeCo6Fl7oUMmvCqLHFJtJz4brXNz5UmxsS9nrP/7DMQTEEP0zLEnjxwZBYMWGHayaXUZLt6/nXhBDsq1w2rn0rAH4grI12vUXwFXnDOGJ6v1cP7lQPU4Q5BjWy9uPxG3bc+838pdrJhKSJEDgser9cY97dusBbptWgscv0l8bN894fJF5s7f9rM0kKwfXHmzjZ6/u5sYLCmlsceP2i3R4AwiCoBbfrYiKI1c47SyfUcoDr+6KIYTcc+kIChw2QhL0t5pZMbOUTXVHeO79RtZdOUFVBwr/zAJXAbuPdtDtDXD7tCJa3AHaPQGVELbzcDtXnTOUdItMMn8qzhhTU9/Mfa/sZunUIkCiq8ee0GbSI0kSWalmQiEJXyjEY2/v49rzhtInzZJ0vFo1u4x+6Ra2H2pjbH42rqF2xhdkE+o5JihKsrqTGFkcaTPpGZGbSapRr57fZtJz9blDuKCoL3PKB/DhZ62EJKW4U+DOGaWs2rgjkrTvdDDfla/GJcOhFciceTje6cOeov2e0ZhQYOepdz7jle1HmR+1Z9dw+uC0JaM8+OCDp3TcDTfc8BW35KuFIqd24wXyYjUZMSQEqkxh+LHleVmsfGlHr5JsOuC5qyYAcNtft6vHKWor8aCwaK9/tpYH55YzfYQ3oU2QspF5bccR1SIh02aUfQKjVFdADgiGpFiJSOV7rzvPyVmDMrniia09SXGZMf//3jvAazuOqZ+xGPWsfrM+YZJ42fQS5vz2HVVpITwx/tqOo9Q2tjJyQCZj87NVZny+3caATGvSyV55766Xdmjy7t9xZNhM5GZYmDYihwU9JJOBWVbqDrUjhiQefWsfRbkZjBqUmXTje7zDy9otDdw/Z6T6nEbb/Jz8zvjM7nsvK+PJmoa448GKDTtYNr2Y21+oI81i5I44/TJ83PhnYyvLZ5byn4++y/9eNCzheXWCwLSy/knP9XFjK/N7AgEPzS3vdcwBeZPxk4uGq1WvAJk2UwyxrMJpR5JAJ8CKGSXcs3FXxP0KD0KAHPAIEX/sATlYUF3fxN0v7eD6yU6uGD+YPmnmU2ozgMcv8tdrJ5JqNrJq446Iz00u6sMfr57A+/tb6JtuocXtx2LUk5thUW151tQkVl9RCCm3Ti3iYIsnbnsUuH1nLhnlWIes9NXc5e1R2goyrH8a+5vdHGn3xAR7AHWc/umlZXT1yBDnZFj53u/eUZ+l3ohS5XlZPLnlZGDuiySq0y3Jme/R1V7/CrTqUw3fVhzr8OL2idiMeh7avDeG2Kz0tRkjc3ENtUe8p1jiJeqnytrr598fdcrWO1q/1KChdxgMBm57/hMWuApiZO0hco78v9c+ZVReZlyibPh6qbq+mbtf2sGMETncMqWY4+1eIHbNpuwJLyrux5J123hwbnlSVc1oa9hoxFPY04K8Gs4kiJDQyvWuDTu459Iy7KlmjnXIa+ivGpodngYNXz1kpe1mgiGJVIuB4+0+bp1SRKc3SLsnoKqn/uaNPdw/ZyQDMi28cmMlR9s9PPDKLq6fPIzyQZkcavYg6AR1TlXm7KL+6bR0+bGZ9HgDIYpyM+j0BROSP2vqm+n0BfmvCYP54LMWBGDeE1uB2NgGyGuCGy8cxq9f3xNrXd9DZlUKBeONKW6/yL6m7gi1E+Wale9p9wR49v1Glk0vTrrfv+ni4VTvbaLLF8RiMlBVURBT6KVc4/L1dSybLsfo0ywGLijuy2Nv7yfDdnL/kJNh5e6NsWNy+Npp2Yt13HfZCG5/Ifka664NdSyZXIjVpNfGzTMYX3Te7G0/6/YHI1QBF7oKKHCk4Eg1qXvdy8flxe0XSjHcWXlZEWv7mvpmlr9Yx3WTh7L/RDfjC+x83uohJ93Cc1dN4O4E6xAJuG1KMSaDLqZfVDod/O8lw7ny6Q94ZN5oLijuG2MPr6B6bxNLzg+SlWKitduvFqv85aOD3HjhMLq8QcxGPUW5GWSlmHsdr7r8QXzBEJ+3eyno9vHgG3v5qLFNVdA26QVMBgP6KDXuqooCDrW5cfSoIdlMshKSxajjl3/fw39NGEwwFCIohtT4/aNv7aeqooAFPeNr33QzqSYD33/03YRFqVqBzJmFE50+hjg0N4VopFoMjBqYwfp/fq6RUU5jnLZklF/96lcR/z548CA5OTkYDCcvSRCE056Mosip3XzJcFxOe6/EEFGSqHTa+aixjYtL+lE+KPOUk6TV9c3cIkCXN3JyS2R7Ef6+YmXx+6px3PZCnWoTFL3QP9LmIc1sjFjoPHPl+LgqKtHti4ZBr+OG338YxZiHW6YU8YPH3g07Tki4qNjSsyFz+8WY6jol0KmcX1Zakdv9xk/OPaXFvSbvrgHkzcLtL9RFECAcqSYemDMSbzDElZVDCSGBRK8b32gS2dqaBjZeX8GK9XUR/cgWxrwOR790S8xGWUH13iZumSKPNZ5AKGm/WTa9hNln5XK01cuj/z0GSZKofqEu4XmjK03jnWvuY+/h9ounNOYoECUpRlUpPIAxOi+Tey4bwcqXdrB59wlVjeSa84Zi0uvo8AYighDh7Vo6VQ4chBNbIixb9jZxx/RidIJAdy/EjvA26wSBt/c28dFnLTG/xahBmRzv8LExTmXMHdOK+b+/fdqr+gpAt09MqBKjIN162i4BekVrtx8E6JNmxhMQ8QZF9KKgkpaSVUV390gNVzod6HVEPBenQs5cvblenbe+SKLakWrinEJHhL2bgniWcF8WWvWphm8zWrv9pFoMuANir2vDbr+okg/L8zLV95P107f3NtHafeprM61fatDQOzq9QXkfObU46dpRmSMXuPITnit8vaR8ZuXGHSybVqJWbUav2Qx6Hb95Yw+/+6+z+eRQGwtcBcwdPzjKLlImiva2Nkr0vhbk1XCmoDss7hGNmvpmPAG5D7r94tdCRtHiJRo0fPVo6yFiuv0iBp2A0SjEqIQq8AdDLHQVsOjpD6l0OlhYkU+bx89nB7sZNSiTTXVywV5VRYE6Z/dJM2PoIal0egKUD8rstfDF7RO5qLgfowZmIkkn1c/ixTYAJImEe4Oa+mYONLtJNRsSjikGnZBwfwA9BUd7mwhJiWMFcoxITjZ3eAL0z7Ak3Xd8dKAVk17H2gVj6fIFsRr1rJ5XzvaDbapibyCUPO6mrJ0UdYfejrt1apE2bp7h+KLzZrL9bDz1+nZPgN/9o57V88qxGHRIJN9f19Q3szBO3qa6volbpg6nX7oFdyCI0SCw4ePD2FPNSZ9lnQ5WbdxBbZjdpqL0vfNwO/89cTBZqSYam90J7pCMdKsRfzBEutWoFquMzc/maJsXvV62+y0flIlfDPU6Xinx1REDMtAJOlVpRYHRoEcvCAgQUSw6Oi8LSZLUfURVRYFaHFeel8WamgaqXAV4AmJE/D5aZWbJ5MKIotRo/DsLZDR8s5AkiaYuH5k27TeNh4lDHTz8Zj2ft3kYoMXJTkskj8Z8i9HQ0BDxn9Vq5a233op4bf/+/d90M/9lKHJqR9q9cSf3aBxq9XBl5RDWLhjLz1/dzaKnP+RQa/Lq+PCg38EWD25/MOL9Uw3auf0ixzt96t+rN9ez6OkPWfzMNhY9/SGrN9fT4g7QNyqo8WUq1EBWOYiehKvrm/CLYsTrrd3Jz9/q9qt/b6lv5pLSfpTnZUVM8lvqT/qmfpHkgybvrgGUSpSTC3+bSc/9c0aydksDU39TzeWPv8e8x+VKkGQbUYNeUP9Wnsez87J4pe4IC10FbLy+gj9fM5F1V45PGGjsjejh9olUuQro6KVfNjR1c9eGHfhCIf57zdYv3Y+Vc63csIOH5pZjM+m/UKIgKMZu3rfUN/P0ls/YeH0FK2eX8fNXdqksfWVsuuKJrXR4A+rYFG9Bf7DFTW1jKw/2tEupxlXscgA6PUH+secEnl681ZU2Vzod1NQ3MWJARtwN2PnD+yZUcrpv026KczPinj/8mQC58qbAkUJloSPu8ZWFDky93OfTGR3eIB3uAP5giDa3nzSLAZtZjy8Y6rUPdHoDuH0iKy8tJRAls3mqRClfMPSFE9UZNhP3zxnJOVG/WTJLuC+K3qpo2sPmQw0avgl0eINYDLpTIvh1egPUNraydv4YbpxcyO4jHep7vX1HMoSvzbR+qUFD71DWjG5f8r4VPkcmQvQa0BcM9cj+hxKu2SxGHZ8casdm0vPy9iPq/rPqqQ/4uLGV1fPKqW1so3xQJrU9CaB4qHTa6ZtmYclkZ0SAGbQgr4YzB73t8Tq9Abq8wV7nyn9be7R4iQYNXzlSeua0VLOBdneAVLMxKSlNqe6vrm/iyZoGLEY9fdMtSMiFOkrsQZmzu7wiZr1cDJVmNeILhnotfEm3Gjja4aWpy4cxau6Pjm0AWEzJYxdtnoCqMBgPsqJw/Pk/PCHf1cvY1+qWz59pM+ELJt6vKEoMK9bXUfXUB3z/d+8y46EantzyGfY0M1dWDMHltJ9yUVNvY6FyXJdX1MbNMxxfdN7MsJm497IRMbHBePFNUJSgm8mwmjDowNtLrBMSr+27vHKxoTcQYvWb9dTUN/e6V+/2iXzU2MaDc8upbWyNWNe/vP0IU0tzCIXZdyaC1aij2xckzaSnYqiDLfXNnFPoIDfTik4QCIaknphCkLRexqs0s4Hag234xRDBUAibSSaW/bOnfYdbPQTEEN2+IMtmFKt7DYNOwBcMqfuI8kGZ9Eu3qGPclp77kWk1JR2TvQGRp3oUmKPx7yyQ0fDNo8MTJCBKZFq1vWc8jM7LxKgXeLXu6DfdFA1fEmduWfQZAkVOzaATuL5HeSQZ+qSa2drQzHv7mtVE5xdJ7CoKJi6nndoeybE+aWYqnfa4idNoFu2pfFf0wuPLVKhVFsoJ3XgIiJGLEiWBnwjBqOMPtnjiMn59wRCVhQ5Wzi5Ler5wfJ3y7hq+vWjzBFRFjvJBmWSnmPhlHIWL5u7kCSdl4wuQYjbw8vUVdPmCpFoMHG338vbe42ypb2ZLfTN/unpC3HP01t+6fEGe3NLAHdNLkh5nNugiqlZ6Q7LvVTY7CALLpherY1C8xXj4mFPhtCeUKayub6KhqZuntnzGfFc+b+5pSnhssnZtqW9WLcz+tvNYDBPdatJT29jKJSX949olKW0GWHfleHQ6AY9fJNNmxGbSx21TvAoApbL3VKqJK5x2Uk0Gmrt9LKooAImIdlU6HSyqKKC1209BfK7KaY90iwEJmezYL92CDgG9TiDDaux1M51mMRKSJPxiiNauyI18bxsC5TnPtBq/VKI6N9PKQ3PLaery0+kNkGYx4kj991l1aNWnGr7tSLcYMOiEXgPYZoOONItRtYK7dUoRg+02Kp2O3lWhLL0Em6LWZlq/1KAhOdKt8pom/QvMkfEQrzpTLXrwiXHXTZVOeSHz8++NjFEJBHr2rwJXnTMEXzDE2poGHpxbjk4QIvqdy2lnvquAHzz2box1qxbk1XAmobd+qsyBySMo/z5o8RINGr56WI16Jhf1QQBOdHrpm25WrdPDYw3KHKsLs5qQlc8EVT0gfB5W5ux0iwFfT5FQiklPls2ISa9LGB+pdDow6eX5fcSADA7FsRcOjxtXOh1IveSezQYdnd5AwjFlbU0Dq+eVQ5z5P9z+r7ekdFCUqCx0IIZktYNEldmJbEMVO/qx+dmU52Wd0p4Heh8LleNSLXpsRi3Vcyajt3kzxRz5+x9u83DXSzsYNSiTBZPyMRl06AWBd/Y3x8Q3K512tn/eDsjkVUGQ42eOVHPS70y0/0616HH7daSYDGpf6G2vrsTOE6mGr9q4k6XTipLGjSuddrxBkXSrkVBIwqAXsJn02EwGVvbYCuVmWMh3pJBqNmDuZbyymfWsrWlg3ZUTMBrkfcWTNQ3qvsMdEDnR6cOeauZIm1e1BOuTZiYYkjAaBCqd9ohxTfnbbNARDEkROYPosVmnE6ipb2bxec6IfNW/s0BGw7cDJ7pka9oMTRklLmwmAyMHZLJp+xE536HhtMOZWxZ9hkCRU6s92EZ5Xib/2HMiYTWXy2lnz7FOzhnWJ4I4kqwCLDzop/y9tqaBKyuGyFLIja1c/th7zHcVxJwjHov2WIc3YSW+cv7ohUfSCrVCh6q2or7mtLNiRgmPvR1f+UZeYOhZMtnJM1eOJ8tqUgOV8dq0rbE14rVEC6NB2VZGDcpk2oPVXP9sLYfbkivOwMnfLx60wOZ3B6lmfQSru6XbH5fcFeyF3R3O/hZDEtMfquEHj73H9AdreHJLA1PLcvj0aCeQeLN6KuNBTX0znd4AFacwbiiM7t4qTY91eHs9V/XeJob1S2NMXhYrZpTE9NvwMcfltLPAVYBeSBwq9QVDcjVPAgZ57cG2XscrkAMwJzp9MdW4LqedVLOeKlcBbn+AVZeWxtyzCqedn142gme3HmDeE1u5/LH3WPjUB/zyb3tUxZVweP1i3AoARaEl2TNiNuiocNpZdekI2j1+smwmbvrzx4zKy2TN/DE8csVo1swfw6i8TBY/sy1mk3omISvFRKpJj9Woxx8MIRLCK4qkWQwc7/AmfVbNeh02o54jrV76Zpgj7l1upiXhmK48M5WFDob2TSXnS0oGZthMDO2byll5WQztm/pv3Vhq1acavu3ISjHhDohyQChJXzve6VOrPKv3NqHXyVK7E4Zmk9dDSomHcwodZKV88bWZ1i81aEiMdIuBtfPHYNLrel07Vjod5GRaTmlfGb4WkyRYM39MxLrJ5bSzfGYJb356nH7p1sQ2lPVNVPQQ1dx+kee2HuDOmaW8emMlaxeM4S/XTOTWKUWIIYnV80ZTnpfFuq0HqKooOOUgb7vbz77jXdQ2trLvRFeEolGy9zRo+LqRYtIn7KcVTjspJj06QZ6Pvw5o8RINGr56iEjcPq0YnR7GFGRz36ZdcWMNyhybFZUEa3UHGJhljSBqDMi0MmJABmvmjyEkSXT1qKP5QyIDM620uP0srMiPUSOpdNpZWJFPq9tP7cE2unxBbvrLxzFtVuKyLqedFT1zfbK4bu3BNlLMhoRjitsv8uzWRh6YM5J1V45X9/fhqtgVPbGAZHuQo+0e7pxZypEOD2ajjk8OtcVtl6J6EA9b6psZMSCD1ZvrVdJOsuuSi430vcbxKp0OPH5RGzfPcCSbN11OOx8eaFXzFYoC5+u7jqvq9dsaW9HrBD4+2BYT31x8fiGjBmaqJPOjbV7SLDLpPFG/qIhDJgeZxNHtC3K8w4dfjCSxJXuWLUZ90v6jEEbW1jSwMEGu6u7ZZRxt8yEAoiAraj84t5wQstLI2poGBmbayM2wIkDv41W3n/K8TNKsemxGPecP7xux77AZ9WSlGNEJEkc6vGo8V68TsJl07DzcwcpLy8i0GtWxTfl/7cE20qyGpHFge4o8JgcliTXzx/Cnqyfyxk/O5aG55V867qjh2wklB5pp1cbxRBhXkM1HB1o5niDPpOHbjTM3E3WGQJEHv3N9HTdfUsRjb9WzYkYp92zcETHxhbO5H5pbHnEOpQIMIi1Awj8T/rfbL/LxoTY+/KxFPf6GZ2upqiigylWAIED/dCsfH4r07HY57eRkWFk+o4SVL+2IqExTzr9u64GYa0xWobbQlY89xcSa+WNIMRsIhSRSLQZaun1xFQVcTjtpZgMvLJ7EPRt3snpzvSqPKCHFbZPCgIfEiyiX085rO46pDFRFOv2hueVJg5PK77f0+U8i/Bk19up3CzajIYLVnUiWcFtjaxJmt4OcDCtLJjvZdbidd/dHHlNT38zyF+v4xfdG8WFjK2ZDfGb32poG1swfgw4hSi3DzsKKApask/vD4me28edrJrL8xbpe+01EpWnUeV095xUQYq4t3rnaPbJ1jiPVxANzRrJ0ahHdviCpFiN6HRxt9/KHReN489MT3PBsLb/4/qiE911Z3CseutFYW9PA+utcrNy4M2LsqXTaWVQ5hI8PtakVQ/3SZdl2pWLI5bRz96xS7tm4k9d3n2DJZCc7D7dzVl4WC12yf7Ly/Stf2qHaBClQ7tELi134giJH2r0Y9TqyU038ZvPeuBUAAHdMK457rZVOBwWOFBa6Cvj+796hqctPZaGD1fNGU/XUB7GVxIUOdF9XyeM3gH7pFo42dZNtM3K43Uv/DAvHO3xc+8xHrF0wlgJHKhA5J1YWOrhndhktbh/1x7oYnpNOpzfIoqc/VI/Z/L/ncu9lI1j610/i9otntx7gvstG0C/Kju7bAq36VMO3HaGQRIcniF6HWukQvTZccn4h/dLNEQTNhqZuntl6gIWuAo62epnvyo9Z9ylrr37plm/V2kzrlxpOd4RCIR5+s57bphVx16xS7toQfx/43NYDLKzI55W6I0wfkUOVqwC/GGJglpXth9pj9pXKGrHCaafbH+SpLQ08d9UEDrV6VDXPkCTx6Fv7mTgkudSbGJLU4Pfl4wcza3UNa+ePISTBr17fE3d9OjDLyo8qCnodEw63eWKsts4pdHD/nJEIwC0J3svVgscavgG0dPtYMbM0Jl5T4bSzYmYpLd0+rD1xl68DWrxEg4avHkadjmUvbOeey0aw7IXtMeTNcMXb2sbWGPVog06g7lA7LqeDJZOd7D7Swas7jvLr1/cCcMuUYZw/vJ/8XYKe9/Y3UzYwk/9e8z5VFQUsCIuP1B5sY8m6Wp6/dhJraxqYPiKHpq5Ikmal00F2Txy49mAb7Z4AZw3MYmppDis3Jo41f3/0wKRjysrZZeRkWgmIIX771r7IGFChg4WufFq6/aycVcqy9XWRsQKnneUzS3ml7ghiKETfVAuv7z7G2poGNl5fwfKo43uDLyhbfbR0y0lwkOLG+J/b2shds8r4+PN2br6kCNgd1S4H8135PLv1ALdNK1ItljScuUj0jIevnV8ZnKUqe0YrcJblZvDhgRamlfVnwaR8giGJnAwL/mCIE10+MqxG1i4YiyBAdqoJMSTx0Bt7WDW7jDte3B6zZl45u4x7X94V8R0up507Z5XS5vYxtiArwiIwUY6q0ungpkuGExBjcz3R6PaJlOdlRuSqlDHmWIcXCYk/vPcZt0wpwhcM0e0XeXJLAz++YBggk9O2H27jjx8c4IaLhmEzGXodr8rzsujyithtRvxRMX1Rkth5uIP39jVx7eRCChwpAAR6CuIGZFgRQxI6HRzv8FHZk3tyOe2srWlgTvmAhEowArBiZql8vqBsW/rGT85laN/UXu+ThtMPJ3rIKBmaTU9CjM7LQifA67uOM2983jfdHA1fEKctGaWjoyPi34Ig0NXVFfN6enr619msrwS5mVZ+8f1RHOnwMufsQfz81V3cPKWIBZ2+iAkyWl5NgdsvRkzQ6RYjggAmg44j7V7+fM1EXqk7GvH5EQMy1I2Fco5wKbC/XjuR0tx0HppbHtGG69Ztw55i4vdV4zjc7qXdE1Dfe27rAa6qHErV0x/EtG/d1gMsmJTPNecOVatbtzW2smSdTK55aksDt00r5ki7lyt//yFrF4yNm9hecn4hOgFWbtypvhd+/YvPc2Ix6kkx69nWGHnPzil0cM+lZazauDOiffES5nDq0ulftby7hm8/vEEx4llNpL6TjNAx35XPZY9sYXReJstnlHLFE+/FfH5bYxt5dhuP1+xXzxVCivjus/MycaSZmTkqhwWu/IgFuxAmyNzU5Wf/iW5umVLEHXodDU3dCccapdL0hmdr+eNVE7lDL/B5DxNfWbyDHNy4fWoxB1rcCc+Vm2ll0w0VmAx67t5QF7Ehr3DauWtWGX/beUQdj7JsxriWNjsPt0cQy+IRgCYOycak1zG1ZxMWfi9yM638/t3P+NXfT46DlU4HLyyexJE2LxaTjvs37eL1HpJJ+aBMVm+ujyGdrJk/JuY1BdX1TRxqdbN2i8zoX7JuG+uunJC0goYEpJ6qigLcPX7Dqy4to2+aBaNeoL07wPrrXLR7Apzo8mE26DnW7mHckGz0X5sA9zcDPxLeYIgMmxG3X8Ri0tHU5efyx97j6nOHcOuUIkCeIww6AXuKCXcgQIrZwJ0v7eQPi8ZxouukMtc5hQ7sKSaau/2clZdFVcUQDHqBLJuRoCjR7g5QnJuBX0zugftNQqmieTuOJUhloQNRkth3ogtHijZHafj60e72c8vzn3D7tCJ0gsB/rXmfq84Zwo0XFKrStQBvfnqcKWX9IwLliq2aAOqaraqigFunFuELhMiymSLWXt/U2qzd7Vf97NOtRhwppqT9UqsK13A6wB0IUV3fzI3+ED9c+wE/+95I7pheInuwWwxIksSJTj9FuRnqmvDBueU8uUWWt1ZkqZ/44RiMeh3d/iAfHmjlhmdrGZ2XyYqZpRxr81Jd38yCTh+Ln9kGyOvCOaMHAKhKSYmQajEwJi8LQF17mo16HnqzPiEB+K6ZpaekiBJNRIGThQtTR+QkfK+3ogYNGr4KWEwG5j3+Hg/MGcmtU4vo8oqkWvQc7/Ax7/H3+H9Xjsdq0HPr1/iMavESDRq+WviDIT5qbCMoSixwFTB3/OAYe54tPRYQZw3KpD3MHrqyp+Bv1cu7eP7aSfyzsZWVs8v4+Wu71WNK+mcgIMcluvxBslPNWIw6zh6cFdcCvbLQob6vF+CRK0ZHxHHmjR/MFU9sVeNEl5T243u/e5dLz8rhvstG0NwdwBuU7fsEBHYfaWfFjFKCkrwPTzSmADQ0dfHTjTtZNbuUz9tOxqwHZlk53ObFZNQhgUqaDY93X/rwFsrzMpkxIpd0q6DeOwGYEXV8b4nETJuRB+eWYzXq+eFaOQm+ME584dapRSBI3LVhB7/43ijV/sMXDDE424bRoONwq4fi3AyOd/gIAQadThs/z3DkZlpZNbuM+hNdcfNCSr4ingJnMCRxzrA+zHxoi1q8+8Cru2MKtVbMKCEohvD4RQr6ptHtD6jPX0CUGGy38fL2I/zno+9y+bg85o7Pi2iLKIXYsq+ZR9/az7LpxVQWOqje2xSTowKwp5hp7vbx0Oa93DG9uNfiLqtRx0JXAWZDZMGz1aSnbEAGHe4A88YP5vVdxxiXb0cQBLbUN7Ns+sk06MQhdob3TycYlOMMycYrs0HH6s31TC3rT6dfjFG4FgSBnHQrV59XyH2bdjEmP4u7Z5fS1i3HYYf1NXCsQ85nPTxvNHfOKuX+Tbu4+ZIiHnpjD95gKKlduz8YUhWQtPjAmY0TnT4sRh3WXva232WkWgwU56Tzt51HNTLKaYjTloySmZmJEGbPIEkS5eXlEf8WBAHxFBiVpwMybCb2Hu8C4PXdJ/iPswepgbhoKPJ84clshUxS6XTwk4uHcdkj76jv3XzJMAZkWFViicWoJzvFGNeXW4FflGh1+yIqxk9+l4fvP/ouf7lmItkpJjo8AaaPyOH7Zw8kFJI4Oy8rJtE+b/xgru9ZNLmcdsrzsnraK9t7LD6/kGMdPvUYt1+kPC+Lxec5VfLKsXYvqWY9R9p9MUFF5fpXb65n0w0VGPU6Jhf1ZVx+dkzA4xffH6VuWkwGHZuiiDrhOFXp9AybFkz5LqPLF4zwf8xOMcVVLVGJWa58brywkPYe9nj4pqKmvpmVG3fwwJyRABGLVINO4N6Xd6obhGBIYumUYnxBkW5fkP4ZFiQJGpvdONIsMd7ACqlBWYD3T7dw7yu7KM/LoraxNS5JIlxCvTwvk9d2HuWsQZkMyrKxKqpq5Z+Nrfzn2QN5ZuuBhOcKiCH+tvNY3O+rqW/m7g113DWrjOH90kk169HrdEwaauedfc3qtVQ47TGEncyoYIDLaefGC4ex7MXYyiCQExxn5WVFEEmq65u4Z+NO7pxViicgqkQU5XeIh0SvKzAadOoG7A+LxtPpSz6mHOv0RgQgzAYddYfbsZn03P/qroj7PbmoD7dOKeaul3bEVPUM6ZNK37Tkvq+nO4IStLuD2FNNNHX6yE41Uem0U13fzK/+vjeKaGTn1qnF/Orve1g+owS3X8TjF1Uv68qw6sz9Td1xN6kKLizq+5Vf25dFoiqaCqed+ZPyufThLbj9ola1reEbgVK5ZTMaeL+hmbPzsvj163sjyNEg98f/OGsAR90yWawybB6qqW+mqmKIuu4ryUknN8MSt3Lo616bJVJPeGDOSK0qXMNpjQ6PLM1/ostHU5efbY1tfNzYQHUP0UQhnoSvRZ7b2siq2WV0+0U6PAGyU010eQO4fUHEkERJjlz0UHuwjftf2cX1kwsBed1kM+kpz8tkUcUQPmxo5Q+LxqHXCQm93l1OO0adjgVPRRZEGPRCUgKweArKEPEqThW8vbeJ+ZPyE753KkUNGjT8u5Fq1FOckx43jlNZ6CDVqMcTEr/2Z1SLl2jQ8NXB7Q/y4Nxy7ooq9nE57Tw4t1yNNYmSxLqtByjJzQDkPeKds8o43CoXGnV6A7Ii7/o6bp1SRIc3yJb6ZjJTjIhSiIWuArp9Ir5gCKMgsOR8J0iRih+VTjtLzndiFASWzyjBpBO46c8f4/aLVDod/O8lw7ny6Q8iYlQ6QcBm0lPQJ5XD7V4e2rw3Iu5R6bSTZ09BEMBm9JCTaY0ZUw63eXhrzwk2fnKYLfXN/PiiIDoBNTb12o8reaJmP7dNKaal20/fdAtro9YuSqFim9uPI9WkJqRDEpQNlMkg4bG5Cqc9op1qewsdpJoN/PbNen5y8XDK8zLjxheU2HhtYyuXj8vDHYgsEn3uqglUPXryXj1yxWj1d9Jw5qPF7Y87lyvo9AbiKnDm9KgGg1wwGE+Ro3pvEytf2sH/XjIcm0FH+aBM3P6Q+vwtmexkcLaVjxtbaeryxzy/LqedOeUDCPQUj9zz8i42LHFx5wY5Nun2i6ytaWDZ9GJGDMzgUKuHQVk2inLS+enLu7hjWnHS/iMBS5//hLULxrLj83b1PW8gxGdN7YzNz+aKNe9jTzFx3vC+apGZPxhS9wtWk4En/76H26cVoe9lvHL75b1Ot08ESSI71RRRJOgLigzsk8rdL+2gtrGN/56Qz4kOHzaTAZNehzco0uYJ4PaL3PyXj3nuqglMLu6HUQ/zxg+mze1X90vh91IZozu9ARa6Cvjj+41afOAMh6xOpP2+vWF0XhbPfdBIty9Iivm0pTd8J3Ha/lpvvvnmN92ErxXtbj/+YEhNbCZSVoDE1hMVTjs3XTJcnUQBbCY5GPFkTQMfNbapyfLmLj9/unoir+86xmNv748hYqSaDdTUNyW0FBneP431Hx+mNDeDdVsPMG/8YJ7c0kBtz3cscOUD0DfdzBu7jkeQPRRLjcpCBytnlxIQQ/iDITq8AVbPG82xDi+OVCMXFvcj3WJg34ludAJ83u7h77uOclXl0KT38rNmN4uf2aYm26ITFOGbln3Hu5ImHTXpdA2nglSLIWJhmcg6qrLQoRKzHppbnnBjoVStXPHEVvU1l9PO7dOKKBuQEfE9VRUFTBpqx2Yy0NLtZ0sYaSM6+BBuZ1PpdPDJoTa21DdT29j2hay+Hv/hGK54In7F3eu7jqoJhehzXX9+IdV7T6gqI/FQXd/MZ83dPLmlgQWuAm549sOYa1EIO5ePy1NJeGkWAy9d76LLK5JpM3K4zUNLtz8uEQXkhObCONY+1fXNtHsCMdK1icbkZGM1gF4Q+GHY7/zMleOTHp+TbuHjxraI+3Pff4xgdVRABqAkN4OVG3fEbizrmwkBK2eVJf2u0x2egIjNrMcXDJFmNaIT4K5ZZTFBuEqnnZWXlvF5i4d397fg7ZHMdaSa8QVF1swfg7NPqurFerpbaoRXirV7AngDIu/sb46Yh7WqbQ3fBJTKLU9Q5MHN9fx+0ThWvBjbX++5tAydBKkWPZVOB8tnlnDpw1vUY7yBSOWub0OfTKaeoFSfa1XhGk5XKMkYhcAZvo4Lr35cVDEEm0lPKCTxzv5mpj9UIyefCh38qHIIT73TwNIpxVz6yJaYvef/XCTLahsEgRcXu3h991EkJF78+HO2hJFe4tmyLjm/kCPtHrWtyn7X7RNZu2BsDDlbQaKijHDEqzgNRzJSspYs0vBNwBsSWTm7NO78ump2Gd6QiEGQ+7L2jGrQcGYg02riZ699mlAJTClIMggCS6cW09DUrVrkHGpx83jNfqoqCkizyEWLW+qbaeryq0UyZoMeg07gua2NcszZF8QdlIsIp47IibC9ONbhlQsMgyKHW72YjTr1+6vrmwghqXEcJcbU4Qnw4Nxy+qVZuP/VXQnjG3dMk1V4bSZ9xBpaWYcvmJSvfvaN3ceZUJCtKqCEJCjPy8Ji1mE0CtTUN/E/Fw7j+vMlUsx6JGR1xhuereWFxZM42uHlV/95Fga9EBtfKHSwclYpFxT1RYpSnJBtTUo52i4rvl1VGeTOmaWsemlHzJi8fGYp8x5/j6YuP4sqhuBINcWsW6IVi+HbH4/Q8O/BqcSl4ilwBnqUfG0mPecN65M09rp0qo7mblmVP9VsUHNA5YMy+bzdy/Ikz+7+pm5qG1t5cG45S5//hJawMSMYkiiwp7By4w5uf6Eu7LMyIa25y8sCVwESkXHjCqedlbNK2fDJYW65ZDjdviAbtx+J6WNDHClcfe4QRgzIwGrUk2WT71VrmC0WPee2GvV0B4JJxyuzUSbD20x69DoBnUJe6TlHn1QzwZDEp0c7ee6qCez4vJ2N24+wYkYJxzq8pFuMav/8rwmDuXuDfM8euWI0ZoOOVLOR1UnUGu+ZXYYE/OL7o7T4wBmOpk5/TDGthliU52Xyh/cOsKW+iYtL+3/TzdHwBXDaklHOPffcf+v57rvvPv7617+ye/durFYrkyZN4oEHHmD48OH/1u/5IgiX8raa9HgCchIVUL3l4hFByvMy2VR3hOvOH8q15w6lzRPAYtSTn23j81YPA7OtKsO0qqKANTUNarI5moVZGZWsBnlitxr1ET5/tWFEFoB+6RaOd3r5f+/JrPpwpm00y7M8Lysm2JdmMXD24CyaOn38+o29apCxqqKASUPs+IMSRr3Aho8Px5BlFlUMSXpflQXAqSTb/hXp9HhS7Nqi4bsJq0Ef0QeiraOMeh1+MUT/dAs/3bQTt1/sVVGj3RMZHJQrOFG/J7wSNR6z+oZna2OCDyAHzSt7NsjTH6qJaW+VqwC/GGJwdgrBUIiWLj9/vHoCJr2OI21eHppbjjcgMrx/Wlwyzc/njESHFCN7eqzDiyDIkpG9XbsvGKKmvhkJuPrcIQRESR17/rBoPG9+epy1NQ0yqc1pZ/nMEu5/ZVeEyolCfunte+IhFCKGeZtoTK492JaQ0V/htPPu/sjX393fnPB4l9OOyaBj6oj+LHDl4xdD9Ek1k2o2cNtft8ccn4zUs6W+GW/wzFAOSwSvXyQ7xcSJLj856WbcAZF2t4+Zo3K5WbHo8YlYTXp8AZHjnV6qKgr4vNXD3TNLqD3YxrEOLx8daGX13JPKa/+qpcaXnRv+nXOKQrrcd7yL//jtO3GP0aq2NXzdUAJqXd4gq2aX0dTp5brJTm7pITWmWQx0+QIc7/AgIZCTbuGO6XLAPF4g1uW0c7zTx5jBWRHf83Wtz6LX8cnUE5q6/Aztm6r1Nw2nJawmvaqUV+m0x9jVKdWP5XMzEUMS7Z4Ao/OyoEIuoqje2wSSxKi8LFZt3BGxLlXQ6g7gctp5Z38ztY2tVLnkPWy8tfW15zkRJYlAMMSxDi/egCyhfypr4/CxxGbSU9vYmnSc6C0RkIyUrCWLNHwTMKDjRIL59USnhz6pFrw9eyDtGdWg4cyAXwwlVQKT4yYOPAFRVcpU8OerJzKuIJtx+dlYDCeJIwa9wOi8LAw6AW8giD3FzKLKAkx6HRaTHkmCJ2r2J1TEXTGjhB1H2hkxMFON5SjtuX1qMRcW9UOvE3jkH3sZ2jeN2sZWbptanPQ6giGJhzbv5aeXlkWs9Q2CwEcHWpk77qSc/6Nv7WfkAPl79YKAUa+jtrGV8fnZDMiy8sFnLRHqjAoxZtIQO5IkcaDZzfghdpa9EKu0W723ieXr67hrVimThtpZOrWIgy0e1b7kSJsHk0Em8oqSRGu3j6kjclhUMYQMmxGDXqDDEyQohvj590Zy3bpaLAYdf9t5TCXprF0wlq0NkQl4OaYmMGZw1r+839Hi2d9+nEpcKp4yrtsvUne4nTXzx8TElaPhCYjkZdk40uEFCW6+pAiB3fiCIfLtVkKhEP97cRHXnS+qpK3dRzpo7fbRP92i9tcH5owkEJLUosmH5pbHKGmDrEYtIbFqdhnXPCPb/0TbZXmDIf7w7gH+eNVEVmyoS0jguHVKEXqdQEiSGJhppbLQwYc9VmCj8rLo8smF2gICAVFKOl7dMa2YFxe7EAQw6gS8YghvIKTGtTNsRjrcfp65cgJNXT769lx7uztAToaVNKuBt/aeYHJRH6aU9WVyUV9u0+uQQhIHWtz4gmLSsc0XDGEy6PAGQzQd79L65RmME53eXm3eNEBOhpWcDAtvfnpCI6OcZjgtySgdHR2nfGx6evopHffWW29x3XXXMXbsWILBILfffjsXX3wxO3fuJCUl5cs29UsjnpS3y2nnntllVDodKhFEhxBjeaOoE/zh3QP86eqJ/PYf+/iosZWXlriwmvW0un389NIRvLO/mbIB6azeXM+Syc740mw9DHNlw6GcPxAKMWmonR2H27ltahE2k4E719dFElkKHSyfXoJfFJMmRKviqA/oBIHH3t7PuPzspIn1SqeDdT+awJVPf0BTlx+Qk7mKF2E0wi1FoPdkWyJLg96k0xNJsWu2B99NdPtjF5bh1lHP/Wg87+5vZmpZf26dUkQgKPWqqDEo28aSyc6ISk5BiKxuidenowko0X0w35HC1BE57DneFRGEUNqr4JErRqtWYWvmj6HucDsTCrLZd6Kb/hlWrj+/kOvOd0bY57icdopy0/jBo+9RVVEQ4QN6uN3LL/++h99XjUOUksuiK/emtrGNO2eWcvdLO+ImFTKsBlbMKuWBTZFEFOU+LD7PeUrfEw2bWY/ZoIsgn4ST88Lv+c7D7SyfURqjUFLpdDDflc8Nz9ZGnFs5jyAIMeP/QlcBTV0+bn+hTh0Tf/X6Hq4+J74aVG+kHs8pVPyezrCnmtAJAilmPS3dPtKsJn739n7mjR8c44lb4bSzcnYZ2SlmREli5KBMrnhiK2vmj6XC6cDtF8mwycd+mXmh3e2nuduPBNy1PrJy6lTmhq9qTumtoluriNXwdUIJqKVaDGSlGDne4YupEHL1SOX2TTfT5RfR6WBAplWdD8vzMlVy4PWTC8nPljvuvuNddPkCZFhNLH+xLmLt/FWsz6L7rCKbnQhaX9NwOsOsE7hhciG/f6eBlbPLYooMlDXLU1saEtoDVNc3s8Alr00XxNkbGnSCusd1+0WWTi1KurbesMTFiU4fh9u93PPyLh6aW37Ka2OQ1wUbtx9R/51onOgtEXC80xf3nml+7xq+KUhAMETC+VUCOj1B7RnVoOEMgpJ0TYabLhlOa7efX3x/lGo1s7amAZtJz4SCbPqkWfCHRMbkySRvvSDww6feB+AfN51LhzfIoqc/5OmFY8m0GQlJJE2uhiS4uKQ/wZBIQ5Mn4v0DLW6e2XqAJec7ue58J//56HsxKiDxoKj9dvqCzHzopGpiZaFDjp8LQsSx163bRlVFAWUDMri7J6l9+9Ri7lwfP8EtICe4D7R4GNo3FU9AZFRelqqkEH7fauqbOdbh44Kifry282iERfDovLFkp8jpGHuqmftf3aUWiP7ib5EKNkrbs1NNrK1pUNuiEwRuung4IwZkcqzDy9j8LAJiiAyLEY9f5OZ/IXagxbNPD5xqXCpcGbfTG8BslMlij7wZf80dDqNehyAIONLM+IMh6LHjys2yYjHoWPZiXcxaYqGrgEff3s/t04oB+XldNr2El7cf4dKzclh8XiFNXT7VzlMpbA7vQ8c6vKpCUjQqnQ7+ePUEvIHkBA6Qi1xaAiH6Zpi5c2Yp92/aqSr3Tyvrj82kxxsMRcTS455LEFi5cQf3XTYCCfi8ycP1PST4fukWWrr89M+wcPsL27li/GD1sxaTnv/988esmT+G3Uc6uPmSYVgMRlZt3M6W+maWTHYyaahdtRFKhHaPXNxyz8s7I2LbWr8883C808fALNs33YzTAmcNymTz7mNIUhlC2Pyu4duN05KMkpmZecoPmSieWrLt1Vdfjfj3U089Rd++ffnoo48455xzvnAb/xUkkvLeUt/Mqpd3cvfsUlasr+OGZ2t55srxLKooUNnTrd0BBOBPV0/EoBc42u7ltmkyWeSejTt4ffcJbCY9q+eVs+mTwyrbTqmgT7QQmF7Wnyml/TnW4eXW5z/hkSvKufmSIlZu3EFAlKhtbI3rMXj3xh3cdHFydZnohGml00HDiS42LHFxqMWjtik+WUZmza5dMJbLH3tPrbx7aUkFd7+0I2JBpkjFNTR1R0gb9pYAiF649SadnkyKXbM9+G4i2TNmM+nJSjFR29jK6s31OFJNPDBnJPn2FNXLMhoup53XdhxVJQ+VwHxrd0DtL5eU9qMkJ51FFUNi5MejCShKH6x02vm4sZXbX6hjzfwxCceDtTUNEUQNXzDEqAGZdPtFNm0/EmM9tGFJBf6gSDAk4faJMcQW5T48OLec+zbtYlReVkLlp3BCWVVFAateirWh2VLfjA64e3YZIUni+2PzmDchP+Y+JCOuVUQR19TrcToIihI7P2+LsBtSKnKXTS9m+fQSGpq7Mell5v4VT7zH5ePy+J8Lh6mVB9kpJq54YmtcSfgbnq3lr9dOossX5ESXTz3PDc/W8qerJ7JkspPzhvWh3RNgUcUQUsz6mHYCWIzxX1eQZjktlwCnhHa3n3f3tfDGrqMsm16CTy/gC4pcMX4wz249oMqDhj/X927axQ0XFPK3ncewmvTcP2ckrd1+BmRZ+flru7l1ajFd3iAd3gCpZgM/vbSMLn+Qbp9IhtVI3zRz3LFdCeaMGpQZd67sbW74KueU091ySMOZBSWg5vGLIMHj1fvj9tXHq/dzx7QSun0BHKkWrnxqK/l2G08uGCsHygIi3xs9gCybCbdfZMmztVTvbWLJZOeX6oNfFPH6bG8EU62vaTidoUO2EbxuciEfftZCMCRxYVEfinIzKB+UicmgQ68TGJWXxUeNbRHrUThJAlHWo/H2hp6e9ZHyWbcv+R7/UKtHJU2DvJ+zGPWnVCBR4bT3WEGeJAwrllorZpSg0wlqJWJviQDl7y9S1KBBw1eJ0CnMr6kWvfaMatBwBqG3Pd+ATCutbj/ugKiOBzt7lBO6/QH2nehmf5ObiqF2MmyhGIVXTyBEl0+2uUi1GOn09E5+6fIF+c3re/jpZSNIMUXGy8wGnbpGWDqlWJ37UxPEPRQYdHKeoDtqjVC9t4mQJFHlKogbZ9IJgkqW1emEpFbOVV1+DDoBSQKDTqfG8RSEF0y2e+T4vMWgD3vfTlOnl4GZFiqddiROWnt0eYNcMX5wRAyvem8TkiQrRYTHjqr3NrHQVUDVUx9Q6bRTkpvO3MfeY8zgLBaf7+SjA60RbY+334mnfgJo8ezTCKear1DWrO1uPwda3IzNz+bXr+9NGnutdNoRBHAHRDo8AfqnmwkY9Dy4uZ4fXziM1i5/zPMqx2IFFrjyCYSt5zu9QdbXfs4zV47nth7ChiPVxBPzx/J/r+2OKS6cOTJHJb5FQ5Qkun29k9N8wRDpViMv1x/hvGEOHClmrjp3CPZUMytnlaET4L7Lyuj0Bnot5OvyBqmpb6bDG0Cn06EXhIhYed90g0qGC4+1G3UCTy4ch16ApVOLMeoEbn9xe0RR4+yzcumlHhO9TuA3r+9h3vjBvLe/RbPXPoPR1OWjbEDGN92M0wJnDcrklbqj7DnWxfD+ad90czScIk7LTNSbb76p/v3ZZ5+xdOlSFixYwMSJEwF49913efrpp7nvvvu+9He0t7cDkJ2dHfd9n8+Hz3eSufhF1Fp6Q1OXP6GU9+bdJ5g/0cMtU4ro9AYx6gWCkqSyp9WE7iuRPpoVTjs3XVLEf03IJ8Nm5Jd/+1StQAN5kk4mXXzpWQNoPNFFmsXAH6rGYTbqWL5+hzrRJgvsLZ2anDgUHqCvdNpZMtnJew3NvLhhB9NH5PDg3HIMgpDwO2rqm7nWG1QDmW6/iCiF1AVZq9uP2ahj+6H2CMlJpRrPZtb3KkGoLNxOBcl+P8324JvFV9lvkyE9jsSaQvS4uKQf92zcqfbf++eMZO2Wk9ZZIaS4TPPwgLzy7EsSpyw/Hr7YVlQ+FE/aJZOdZNmMvLjYxaqNsaoja+aP4f3PWiI+n2EzxlRxgLxJvnNDHUunFPHzV3fz44uGJbwPP391N9X1zXzUc+1AwmuH5DY01fXNMvFsSwMLXQUsWbeN8rzMiPuwtqaBFxZPYtXGnREEGpfTzoqZpdz/yq6Ic7p6xqcsm5GhfVMRJYlbLilCmCKrjHT5gtQebMPe4uba/7ct4rOrN9erqidPb/mMBa78hJsnt1/kcLuXNTX7Wegq4Pqe9lY47bI0ZVTA477LRsQl1UiSlNQiyNoLWeXbgi/Tb5u6/PRNN/P67hPcOVMgXaenW5SwmfRc3lMJEf1cL3QVYDLoWFvTwPQROTz85k5unVLEypd2sGJmKTf96Z8xFd3K8zhmcBb3zxmpqqcoCE9KL5iUn/B5TTY3fJVzyr9qOaRBQyJ82fk2N9PK9oMtGAx6tWopXl/1iyL2FDPL19exduFYZq/ewuo361k6pYgTnT4G9Sii3BIWTE02Z/w712fx+mwye02tr2n4NuHL9F1PSGLr/mZe3n6Eqooh3PKXj/nT1RNZHqWaGW89Gh60VfaE4XtDl9POfFe+uhZSkGpOHsaI3F86ALm6MxlsJgMvX1/BKzuOxlj2gLym9QZFfvDoe+q8n5tp7TUR8EWKGjRo+DL4Iv3WL4q9zq+pJgM5WpWtBg1fKb7O2JTFqEtY6FRZ6GBbT0GSAmU8WFPdwI8vHKYq2nb5RUx6XQxhs8MTIMWsx2bSk2qSYwy91Y+mWQzU1DfzeauHrJST8bLKQgd90yyq6qHEySyt0PN+vL1xpdNBTc/1GfWxX76lvpkfVQxhYc+ao7axjYfnjeZEpzdCMbY3FRlJkqg92MalZ+WqairhCC+YbO0O0OaRbQZBvq+LKocwKNsKEiw+30kgKPLMlRO4Z+OOhOpxNfXNeIMhNixx4faLGPU6qveewGQQer6zGV79VI0LipIU1/IwfL+TSP1k5eyyGCJLvM9/V/FNxZST4YvmK9q7A1h6iF2JFJ4rnQ7umlXKvqYuMq1G9DqB7Z+388r2I9xwwTA1dqsgUu2wiWvOG4rRII8JMknNwMrZpRxq9bClvpmrKoeydsHYGMVipR33bNzFTy6OjB2DHMf0BkRSzYaERXnqfbEYsRp17DrczvdGD+B4hxdRghU9uazbpw/n3MK+SEB3L/0+3WJgzfwx6HU6un1BMsMKSgGWTHaq+43ag23kZVmpdDowGfSs2FDHihklHO/w4UgzR1yv2y9yuNVLVooxqV37nmOdzB0/mHVbD8T0ba1fnjkQQxIt3X7NpucUUdQ/HbNBx1t7jmtklNMIpyUZ5dxzz1X/XrlyJb/85S+ZO3eu+tqsWbMYMWIEjz32GPPnz//C5w+FQvz4xz/G5XJRVlYW95j77ruPu++++4s3/hTQm2x+tz/Itc98RFVFAf9x1gCe3LI7orIsnoKIPKHt5qy8LMoHZaqLBiU4bu7x/kwkXXz3hh2MypOD+IqdQW1jG9C7FYTi8Z2IadsnzcwjV4zGbNBxrMOL2y8yakAmv/r7XqpcBazd0tCrukq7J6D6jLqcdj460Ma0sv4M7ZvKsQ4vP/nTPxPKLC6ZXMhHB9q47a/b1ff+Fakzzfbg24uvst8mwrEOLyBb2QiCwLbGVp57v5H754zkyS0NEf0xug/e0CP7F+0xmzCIbxR4bPOpyY8rQXqlD5bnZdHY4uZn3xupyn/GqyBXmO5n58ssdUVB5JLSfkllDTu9Qarrm1kx6+QiO5wAF34fFHWQqooCtWJvULaV13Yci5scSARfMBT32sOJa8fbfSx0FXD79BLa3QHSLAaOdXhZ9NQHzC4fwBXjB+MLhhiYZWXz7uO4/SJ3RPkCVzod3D27lIVPfYDbL7Jm/pi47VGua8MSF/5gKKnyzbawe3/1uUPom2qmfHAWn7d6WDqlGF9Q5ESXD7NBz/bP21hyvhMkKaJdKSY9C1wFSMSSeha4Cuj2916x9G3Al+m3HT3VDTaTHndQxGbQc/eGOn5y8fAYWXI4eX+WTy+hPC8Tk16uxvIHQ1T3/D9RRffV5w4hIEp81tTN0XYPGTaTSmgMT0r3Nlcmmhu+yjnly1rRadDQG/6V+TbTZsYdEJPaaayYUYI/FOKjA634AyG1HwqCgM1swB8UaXMHIoKpX7YPflHE67OJAn1aX9PwbcOX6buegEjfdAs19c1cdc5QLh+Xl1DiHogJovqCISoLHdQebKPSaWew3cbvF43DIAi8s785Zu3ncto51ulNuo5S1O0qnQ5unjKcOb99h4d6+mAipJj1dPqCCUlrAMc7fGr7wysRkyUCvkiSQIOGL4Mv0m91gtDr/OoOnNlWnho0fBvwdcWm2t1+7t20S1XYjlaxvW1qEcc7fDxyxegIJVxooDwvixCSuobu9AbQCQJLn/8kYl5Osxgw6AXWzB9DMCTR6Q3QJ82cmADjdKjxqDZPQC3ecjnt3DWzlIMtbsoHZfKnqydi1AvYTHrOHpyFKEmsnF3KivU7IkgUlU47CyvyWbKulkqngw5P/DW9KElqnOm2qUV0+0TEkIRfPLlHSO9FPTYnw8pfPjqIPyglVVC51hskK8VIMBTC4w/x8g0V/H3nMXQIIAncuaGObY1tvHJDZYRSgoLoNVO3L8j3fvduxD2cNSpXTfZX1zexwJWvfrYqgf1KpzeQVHl1+fq6uESW8M9/l/FNxJT/nejwBvCHQqTr5T4XL/ZqNugosKfQ3O3DnmIixWwACUpzM8izpyQkkMDJ57XdE+B3b+2jqqKA2sZWJEmif4aVhqZuALJTjHR4gwljyNX1TSydWhTxmhLH7J9uocsXxGY0JB1jjAYBd0Bk7vjBSBJ81uxmw8eH1e909kmjzR2gb5oZyahPei69TmDR0x+y8XoXWSkmlkftc557v5HpI3IAed//l2smMijbhl8MUdvYRrsngARxx6Zuf5D//fM/eWL+WATh05ixTVH4t5n0XDF+MPo4TL/ver88U9Dq9hOSIFMjo5wSTAYdxTnpvLXnBFedM/Sbbo6GU8RpSUYJx7vvvsvvfve7mNfHjBnDlVde+aXOed1111FXV0dNTU3CY2677TZ+8pOfqP/u6Ohg0KBBX+r7opFhNbJksjPCGuOTQ20IApTlZpBuMfLnayYiIC/cwyfAZFWfNfXNLOxZXChQguPHOrxMHGJP+NmPGlu5ecpwtU3HOrw8NLec65+t7VV6PChKKvs8Wq1lvqtAtddR4HLaVfKJkkw+FXUVvygHMudPymfp858waYidpi4/LW4/iyqGUJ6XFWHRodyTqooh9E0zR5zvX5E602wPvr34KvttPDQ2d3PbC9tjiABPzB/Lg2/sYUt9c4SfZHT/VexsSnLSI6TGo+ELhnA57aSajUkJIcqGVAnSu6L64MYlFTzw6i51rEisOiJvdCuddu65bAQPvLKL1vz4KlIK2noW3b6AqBIkyvOy1GBo+H0Iv3YFa+aPiane65se2W+joYxN4dcujyeyd2nD8U4GZFtjgzJOB2sWjMEflPAGZNKHThAYMSCDP33YGNcX+N5Nu1g2vZjbX6hLWgE/cUg2Bp1AR0Bk2Yxi7nl5V8RmI1r9pbaxjVWzy7hzfR2rXt7Fg3PLuf/VXTHP1MgBmdw6pZgFnV4EAXIzrHT6gtzw7IcxG0uF0PT0wnFJ79+3BV+m36ZbjLS7AzwybzRWg54uv0h1fTNLpwlxZckVSVEJWOgqoNXtB1C9W5vdfnYebo+p6K5tbOPOGaXcHaUgdE6hg3svG0GbRz6PzaSPmWeikWhu+KrnlC9qRadBw6ngX5lvuwMikiT3r+j1sNJfQxIIkhzw6vIFmVqaE9MPFY91pc+eilVOb0p5p4J4fTY80Ld8egnegKj1NQ3fSnyZvuv2ifhFmQCam2nhktL+lOSks/AUrCJBDrgtm17Cz1/dzbIZJQTEEBkWI+kWAx8fbIvYu1U6HSybUUwoJLH4/KExCoKVTjt3zirlaIeX8kFjONbhRSfIfTDZ+szltPO3ncfU4oZkUI7RKhE1fFvwRfpt6BTm1y7v6UFY16DhdMbXFZtq6vJT2C+Ne1/eSWVhH5bNKKHDEyQkSQzIsHCozRNjz6Osn6tcBXgDIjajnhDyWjkUkrh/zsiIPbHVqMcXDPHIm/UsnVaMLxDCHwzFnaddTjuLz3eqBBCzQVYaWDN/DLUH2zjY4mbBUx+ox1cWOlgzfwzeQIhf/n0Pd0wv4ezBWVxz7lBMeh0d3gC1B9tYsq6Ws/MyWViRj0D8+PHALCtP/HAMbZ4AVqOeB17ZzcKKIRH2zTajnguL+1KUkx4zRu4+0sGuI+3878VFHO3wJL3v7Z4AmVYj2z9v56KSfugFgWBI4lCbm6xUoxp/cgfEiPsTbZWd16P2GG2xXF3fxJ0bdkQQR1LMBlVRJhEJP81iTKq8qii6JsJ3PZ79dceU/91ItxhpdcukMmVNHB17dTnt3DWrFJvZgNWoAwQ+/KyF8UPsdPYQSBJZuivWOmaDjuq9TVx73lDOGpRJW7dfVkox6rn5kmEY9Dp0vcgndfaMC9FxzCcXjCUrxUhDk5uFFfmAFFUsKJPTDjS5SbUYuOHZWp6/diIluRnc8vzJIuQ+aWYkCfxiCJ1OSDpeeQLyushi0kcUPSq4fFweNqNeLbwMSbDo6Q95euFYHpxbTrrFiNUUwqCT4xHh969PmpmmLj9XPv0Bz141AW9ApMsrkmkz0u7xM+/x92jq8vdcm7wPUkhoCr7r/fJMwYlOOf6sKaOcOkYOzODZ9xvx+EWsptND/f27jtOejDJo0CAef/xxfvazn0W8/sQTT3ypBcGSJUvYuHEjb7/9NgMHDkx4nNlsxmxOnlj6sjDp4/tOLj5/KIue/lCdcCqdDm68sDDis71VfSqTuAIlOH71uUMo7Btf0khRL4iWYat0ykH+usPtSQN7SmBDSYimWYykWQy8vP1IXJWDcPKJ0tZk6ipKYn1aWQ53zSzlB4+9y/1zRrLsxe0JZePCv9OboPLn7b1NHO3wfuEAo2Z78O3FV9lvo3GswxtDRIEeZRHhU0YNymTz7hMR/TFR/+0tgTYo20p5XhYHW91Jj1MqT5fPKOFwq7x5Du8PgoDaZ3obS1LNBqaOyOFYh5f/uXg4opjc5FK5hi7vyYTcJaX9YpRaEqFvupmXrnepi3KDXmDXkQ4qnfa4FSnhVbHR13Owxc0/G1tZNbuMZXEqdz9qbKW5y8/Db9ZHkFQuLOrLzVOKWBnHumihq4CBWVbuvayMX/59D/fPGQlEEvAuKJKDP3e8WBexgbv23KHodAJuv8i2xtYYC6Y719dRXd/MksnOpJWMN108nEVPf4jNpGfjkgrSLIaYjWU40qynxxLgy/RbR6oJvyjS7g7Q6Q3KlRMmPSa9PmZ+DZ8bun1Bbni2lr9cMwmQ52MAvSAwL44sZlVFAas27oj5Td7e28TSv37CLVOK1Dl0z7HOL2XT8XXMKVrVtoZ/N/6V+bbDE0QvJLed6/YFSTEbGJOXhT3VxN0bYvuh6g/f02e3f96eVKLcYtSx5NnaGLnqL6qUl6jPuv0inxxs40cVBVp/0/CtxZfpu+lWWSr7wbnl3L0hscx8PKvIykIHfjGEhMSPLxrGZY+8g9svUtljmXj9ZCe3TimiyxdEDEm8u7+Zyx55Ry2KiEe4nbV6Cw/NLWfR0x8CsGGJC0isUBROBK6qKOh1z1mSk66+plUiavg24Iv0W7cvmHR+dfuD2HqRvtegQcO/jq8rNtXhDVDhdPDr1/fyPxcNp9sbJM2iw2o0sjRO4dRCV4G65/UFQ9j1elLTDep+uqXbz1NbGtT1tc2kp9Xtx2oyUF3fzI1+kf4ZFoIhiUVPxy+MWfT0Bzx/7SR1Xr2ktB8/eOw9gBiV2eq9TSBJnJ2fzebdJ9AJu5g7Lg9vQLaryc200j/Dwpi8LD5sbGXJulp+8f1RMffhnEIH2xpPKmJvuqGC6vpmfnTOUNbWNPDy9RUsWy9bFS2dWsSdGyJjPhVOO3fNKuNvO49w/6Zd/KQX9W6FZFNgTyHVbOhZ2zhIMRvQC7LaC0QqJYQrB0d/9/fPHshTC8ZGEIfW1jREEEe6fUFqG1tVq/t498CRamJ/jzrFF4UWz/56Y8pfBRypJg62uPEFgqyYUcqqjTsiYp4VPevvjp4+bdDpONHlo0+aBU9ApN0TSPicupx2Zo7MYXJRHzUWa9Dp+NuOQ1x9rhN/QMRs1PHevmZ+/tqehIrSIPeFPqnmsFj1yec5GJKQJNl+c8m6bVRVFKjFgsoYs2RdLavnjSalp+/pBIEOX+SavcsrkmLWE5Lodbz649UT5HZIAl1e+R5cfe4Qzh/eFwC9TuBEl5dVl5ax/MU63H4Rt1/Enmrmt2/tY/n0Evy+EKkmPdPK+nHVuU7+77XdrN5cz5LJTiYX9WHe+MGsjLOPCif/Vdc3cc/GXRExSa1fnjlo6imGzLRpZJRTxciBmfz+3QNsbWjmvJ7+qOHbjdMjE5UEv/rVr5gzZw6vvPIK48ePB+D9999n7969PP/886d8HkmSuP7663nhhRf4xz/+QUFBfEm7rxrtbn/cBHZ1fRMhIn0fq+ub+N+eBbCS1ByUnTxY3jfNjMmgi0jguv0iv/r7XkYsyIz7mUT2PUqbxuZns9BVgA4hIsAfHthTEqIVTjtn5WUxLj87qQSyQj5RFjDZNhN3zSxl5Us7Y75jUUUBTT3swaYuH4/99xh2H+3gox4bIQWJpKGTJcEPtXron245paRBeDXtsuklfNTYyqqNO9XFkybF/t1Ca7c/seRgWLVBeJVmomext0rO13YcY/Xm+qSLeYB8Rwp3zSyhqcvH4zX7YwIP3WEkrd7IIXqdwO0v1OFy2lkxo4TaxrakSb7tn7ezZLKTNKuBX3x/FBajntbuk5uBZNdY6XSw/VB7hI9xhdPOTy8dQZbNRIjESYXo67GZ9AzKsrHQVcDxTl+EapLNpOeBOSPJybBypN1DVcUQzgpTVCrKTWdlHOKB8u+bLxmOAPy/RePp8ARYNasMfyhEuzuAzaxHkmDZiyfJL8q4uHpzPS6nnfK8rJhxMVwtJ5nylULiUzaGd26oY+nU4qRSk6nGMzvQ/M/GNjZuP8KNFwwjw2rkqnOGsPKlxL9fVUWBKgF8rMMTYRnw7v5mahtbGZufzeSivmoFyKBsW1I1sh8HQyybXsyTWxqobWz7UjYdmpWOhu8S2t1+Usx6rEY9v3x9T8L+unJWGa1uP9mpJjwBMaFM9pb6ZhZVDAFkwmWiiqfrznfyzr7muHLVX1QpT+uzGr5rsBn1fN7uSUqYjWcV6XLauXtWKSc6vWw/2MbZg7N5eN5oPvm8DUmC4z37O0EAnQBVv/8wgtCSjHAbTnjxBkJqpbBCOLl9ajGdvqCcuAmzwDwVwkq43Y9WiajhdENWiinp/LpqtmxTve9E15dSB9OgQcO3C+kWo5rg0usFbDoDTZ1eHns7sdVGeY+9u9Wo5x97jvPRZ62svLSUli4f7Z6gbENznpPVm+upqijAbNSpikr2FBMr1tdxwwXDks7TXb4gC10FPPd+I7NG5bJ2wViOtnuoO9wec2x1fTMLXPJe/fJxeTy1pSFGCWG+q0CN20RbHJxT6GDl7DKmPVitvtbtk9cTWSlG3H6Rpi4f5XlZBEISd8YhudfUN3PnhjpunVKE2x/i44OtSe3oaw+2MbWsPyPTM1gWbfPco96443C7alEEiWPvNfXNLHuxjlFh8SCFQBgMSeq/aw+2RdjRR98DZR+SbvHH/iBhGJhljSHWa/uYMwMZNhOD7TZ8QZGfv7qLs/KyVAV9hXxx/yu7WD69hA2fHOaS0v6kmA388u97WD69BLNBl/A53VLfzD0bd7FiZglzH5fJZQExxOLznNzxYh33zC7j3p5CO0gcA7aZ9KxdMJa7NtTF9PMnF4zlvYZmzh/eF0mSGJ0XP0Za4bQjSRIWg44Kpx2jXqf2FQWpFj1v7znB+AI7noCYdLzy+kM8csVovEGRdJuBh+eNxmLUqZZFf1g0DnuKEb8Y4sqKIaT0kHoDYoiS3AzaPX4sJgMeUWTp1OKIvN/amgaeu2rCKdkfwUmlcpDHEq1fnjk4qYyi/Z6nitwMC/ZUE9V7mzQyymmC056MMm3aNPbs2cNvf/tbdu/eDcDMmTO55pprvpAyynXXXce6detYv349aWlpHD16FICMjAys1lOvhvxXkUwuL56ssU6HyqB8cksDQNKE7p5jndzTY/UQncA91hHfe7u3BGiVq4Drn63lmSvHc+OFhXT0+INuP9QeUQVX4bRz8yVFzH38PUb3SLclgkEnqAG/SqeDgBjC7Q9w72VlHGz10OYJYDbo2P55OykmPU9+fDhCbi2RCkr0PXQ57Rzr8HK43ZuwLaciv3y4zRPjuXlOoYNNN1TS4fGTYtak2L9r6OhF3lgJkocHvRMtxtfWNLBm/hh0EMOSDiddyH04vlJIhdPOkTYPnx7r4JLS/swYkcPSqUUcbPGomw6L8SQBpTcCjCnMAicoSqx6eWcPGUaKaaOSaHiwh3ih4Jkrx0dcY7zgf2WPLOKip09KtYK8Ib/jxe0snVpEeV4W/3PhMNp7qkrCkwpKG2oPtmEz6Vkzfww/e3VXTBsfuWI0uZlW7o4iK4SPJb2NhXdM17Nx+xFuiyLNrLq0jJ9u3Mnc8YNPyUYpEXpTq+n2ify+ahztngALK4ZgMQmsurSMZS9uj6l2WHVZGd5QfFWoMwFNXX76plt6fhcDJoPARcX9+PXre+Mev6W+mevOcyIAd80s5Zd//5Rl04v5+aufsrCigCXr5Odp6dQifvbKSZWwR64YnbQdJ7p8jBiYoRKp4vnxOvukkpNAdSGc5Lh8RgkmvY52bU7RcAajqcuPzajHL8bK3yrYUt+MJxAk1WJAkuBIe3KZ7BSTHptJT1luRsKKp6qnPlATzPFkh9vcgS/U3zT7Kw3fJXT5Rdz+5H1WWeNUOh1kp5hYM1+20HH7g6zevI+z87Owt7hZsm4ba+aP4ZE36yPm7C9quxX+vk5ArRS+4dlaVm+uZ2pZf1LNBmrqmyJshMIttW6dUsShVk+ENHh5XqZaLKFVImo4HRHoZX4NiCFsRj2VP//Hl1IH06BBw7cLjlST3K9Nejo9QdIsBrJTzUmJ3MqcnWYx8Njb+9Uqf0mSECUJm0mPKEmsmT+GnAwzNpOBoCjHv/xiiOr6Zu6YkTzdkGYx8Ohb+7htajE/eOxdmrr8VDodLKzIj7GgADkWkrhQspkQcsL2k4NtDO2byhs/OTdiDf5Zc3eUrYXcvqAo4XLaSbEYWVvTwNTS/knHSJBj5Nc/W8vqeeUxMbpKp527Zpfxf6/tRgz1Y8WLdTH3unpvE5IksXRKMVajTo3DJ4s3Ve9tYqGrIMJizWbS0yfNrOYElLhgTX0zd84sjbkHyj6kN+XV/ukWbR9zBiOEBILA67tP8PruE3GPuXVKiL98eIhJQxykWw2qpXXtwTYmDrEntXQ/0eXj/jkjWbf1AO/ub2baiBy21DfT6g5E9IW1NQ1x+9Dy6cU8vHlvbL+pbwZBYMKQbNx+Eb0gqBbw0THcBa4C9IKATwyxwFWAJyDywWctETmv5k4/YwZnYTXp6cUxCJ0OFj+zjT9dPYGcdAtH2j28vP2IqnjdJ9WMxahj24FWinLSsRj0VDplW6MxeVlkWuUCmpAkcLDDE9FemQyXuJg1XqzYZjKw7srxDO2bSr90S/LGazhtcKLTJyt697LP1XASgiAwIjeDt/acYPk33RgNp4TTnowCslXPvffe+y+d47e//S0A5513XsTrTz75JAsWLPiXzv1F0NGL1G90MvJIu5cfXziMn/UwKBNVXocndMODbMqElpNhxaBHlR2OV0mfCClmWenAoBdo6farNhFVFQU8NLc8Itjf2u1XrSgSkmYKHXh62jg6L5P5rnyueGIrbr/IhUV9uXVqkewtKMAFRf144NVdEYnW8GuPVkEJv4cup53rzy9Ep4N7Xt4V0w4lgW1PSb7gbnf7Y4goIFfTrlhf94WqaTWcOUi3JB9eFQ/A8P44Ji+LWSNzWfXyzojn6ezBWXgDIc7Oz+bHFw0jKEoEe6TKlaC8y2knJ8PKwoqCGKKZIrX4951HOaewL/e9vIui3AxKB2Tw7NYD6iL/4pJ+ar9MVhm6qKIAqSf44AuGEAS5r93yl09Yu3AsQVGiwxNQ23ioxc3j1ftj+vu7+5tVT81441LfdDNWo55Zq7fEBCNA3mTrdDpWb65n95EObr6kKEa5JJyws2x6MY+8WR+zqdlS34wOgakjYgMP4WNJb2SQeGo4ShVLlauAFFPyZ8Jq1Kv31GLUc6zdy8Csk8Hf3hIvYkji8h55W4D7/mMEb+w8GrfaYdVLO1g2vSTp+U5ndHgD6u8lhST8QYmjHYlJhwAmg45jHT5ysizceGEhYkiibGCGSkQBON7hi3h+evtNTHodh1pPJsrjVVv85ZqJZNqMMfNEIpKjlhTQcCajwxugT4qJ5u7khM4Ob5A+aWba3QFyM5L3BzEksemGCo53+npVUnCkmnhi/lhVNleBUnX0RfqeZn+l4buCDk8goe2pAl8whMtpV/d15XmZ3DGtGG9AZO74PPKybQTEEFedM4TVb9YntN1SSGPZKaYk6m92+qSZeeSK0WTZjHT5gtQ2tgGyrUBtYyuv1B1VlemiCxiUcWLSEDuLn9mmnjd8TalVCGs4XdHZS8FEpzeI0LO8/TLqYBo0aPh2IcNmoqXbz5r5Y2S1ViTcvt7n7Hx7Cj9cu/Xk3OgT6Z9uYfU/dvDg3HIyLUZOdPmwGAwsf7GOhRUFVPQkXwHMel3S4iazXscd04rxiSGaumSljniK4AoGZVtJMeuTFgctOd/JFePy6JduoV965Ptp3f4IIocgwL2XldHlkRVadIIc34oXdwqH2y+qcQYBgakjciIsQo51eAmFJG6ZUsSBFndC0o8cyxLYdqCV63qUG3uLN9lMsZbDlYUOlk8vUWP9Cjq9QTKsRiS5oRE4VRVHbdw/M6ETBD7vxeL98zYPK2eXYjPrCUmyoki3L8jOw+1UDHUk/WybO8C6rQdYdWkZ3//du1Q65eOPdcbG4+L1IXuqOaLALxzVe5v48YWFpJgMtAMLn/ogbqHJDc/W8uSCsXR5ZRvupxeOi1FJ3XGknff2NXPXrDIEEhd2u3qUVUBWCO/0BemfYY2IFW8/1EbZwEyG9U/noc17WTqliOsnO2VbLp3AB5+18OqOo9x0SZFaRBmOU9lHhcNq0tM/zawRUc4wNHX5NIueL4GRAzP4x54THG330j9D6xPfdpwRZJTq6moeffRR9u/fz5///GcGDBjAH/7wBwoKCqioqDilc0iS1PtBXwPSe5H6jU58GXQyAUQhY0QndH3BEAWOFEx6HTNX18QE2RTSSP8MMwadgd+8sZtReZkscOXjF0PkZdkIhJLfm25fkMXPbFMX9uHnj4ZSRZ4s0X33rFL2Hu/iz1dP5GiHl1uf/0Rt9+u7j+MNitwypYhfv76Hxec5Y4goChIpDQzOtqne4dV7TzC9LIfyvMyECezLzhqQ9PqTqdm8vbfplJRVNJx5yEoxqUSLaLicdtIsBnWxq/QXl9POlRVDOHuwTB6wmfSEQhKegMgf3vuMzbtPMGJABtf39PHyQZmsmT8Wt1+WGL9unRwwD+//g+02pJBMmnpnXzNF/dP556F25o4fjE4QuOGCYSw+P8SWfc3U1J9gyflOQO4/yliy+Dwnep2A2y/yyaE2BAR+8dqnMRUgaxaM5YFNu5g7fnBEG7NSTHE34co4IAgC1Xub1PtQWejguvOc/ODR9/jF90clDQh0eQK8fEMFNqOeH659n9nlA7h1ShGft3nokyrbkh1p98pBVKsxwuonHOFSh9E4FdUSSKxcIlvoFOPrZYMBsOjpD9W/K512SnLTmVzUh827TyRVq6nosZIJR980c9Jqh5suSR7sOJ2RbjHS0i0HtVq6/fRJ793X12rS87u39vHjC4aRZjFQ39KlVoIlQlJ7qUIHu492UNQ/Pc4nT6LdE+D6Z2sjSCbJSI53rq/jnstG0OUN0uENkG41ajLqGs4YpFuMeEMiKebkW5Qsmwl/UCTVbOCN3ceSqgK+s7+ZSUPsONJMEUHocL91t1/2X1+7YGxcidzqXhJy4SpGWp/U8F1DulUmfCRDvj2FW6cUcaTdy+p5o8lJN/N/f/s0Yo1S6XRwy9ThSVXMFp/n5Iontqq2hBJSxFq70ulg8flOLn/sPXX+rnQ6WD2vnCXrall8npOzBmWq1cOJChjOKXREVFanmA2qOtlLSyq0CmENpy16m19TzAbEsKWvFs/QoOH0h18M8cib9dw1S7bh6q07Z1qN/G3nUdx+MSLO2+UXKcmVC5p+dM4QFj39IZturKS6xxZzgasAq0m2p2h1+1nYE0OJF2ttdfuxmQxqkldBvNhLpdNB3aF2CvulJW23yaBLqDhq0utiiRxOB7dNK+KhzXuZNz6Px97az7IZyQt2DDqBDKuRqooCnqiJLbhSzjttZA4FdlvSvUe3L0hmipmqHuXGQdm9E+zj7VFWbdzJUwvH8dInh9X4hU4ncMEv31KPiy5q0VQczzwk24+Gv2cx6hF6kQKRJMjJtGLS6zDo5GPTLAauPmeoSk5JBLNBR019M8fafVw+Lk+1ojJF9fWrzhnCmpr9MTFzJW8UT610W2MrkgRv7D5GUf/0Xq3A+qVbcPtFUi0GVSX16nOHcOuUIox6HT9/bQ+CIOe1ko1XSmFb9d4mJg11YNSfvH/lgzKpO9zOCGSC/tyxebyzr5lh/dMQAItBR1FuOjc/v51rznNiiWOX3luRm8WojxiLBUD8luQxNfz7cKLTpxYuazh1lA7IQABq6pv43tkDv+nmaOgFpz0Z5fnnn+e///u/ueKKK9i2bRs+n+yv1d7ezr333sumTZu+4RZ+MSSTy1OUOsJxrMNLvj0l4rXoyfj/LRqPyaCLSagpQTzF3uefja3U1DezOSwoKHv1jaGy0BGXcBHeprU1Dfzx6glJry+eGoSy0ciwGtl7rJPGZjdX/+Ej1swfw1NbPuOZK8dztN3HBwdaWFvTIPuTeoOU5GYQFJNPvtHJ4UqnHVGSONTqwWLUk51iJsVsYMbI3LhM2jGDs3qVX+5Nzaazl/c1nJnol27h3stGcPsLkRYpymK26qkPuHxcHlWuAlLNBrlqs4dQomz6axtbVdm/B3tUhnzBUEQfXzN/TASBAVDfc6SaeO6qifiCImIIbptajMWo49mrJnBXlA9updPBXbNKkQSJGSNyImxv3t3frG6Yl0x2xt10V9c3s/KlHZyVlxXTxkRWJso48MyV47nxgkL1+/Ltth7P3sxeF+WpViPTflNNpdPBnbNKVTuda//ftphje7NUSVaJ4guG2HmkIyHBqNJpZ1tja8LPH2xx0+b2J6zirXDaeWd/7D0NsZtbpxThC4YSWxkVOpg/KV9NrJzK9cCZPTY5Uk283yPDGZIkPm/1JCeOOO3ogDumF3OgyY1eL/DM1gMR1dKVTkfMHJyMWLn4PCcpZj1vfno8aZVF7cG2mMrTRCRHm0nPD8blcdOf/hlB8NIUUzScKXCkmmhx+xGFUNJ+oxOgtTtIToaFsYOzKO5R9otWBbvpkuHMffw9SnPTybQZY4LQiirCH99vlOXLvcGEErmJEnKaipGG7zrSTXr+2elL2metRh37mzyIIYmcDBM/3RSrbFld38SCjvyk36WsFcP3kovPcyJKEtk2E58caoupDpbXXXKltShJvdq4nlPoYOXsMo62e0i1GClwpIT1+8h9twYNpxt0QvLqX50Qq55yJu8ZNGj4LiAkybEFvQAh5D1lohhvhdNObqaVS0r6MmtULivW18WsnRe6CvAF5FhsZ8+87AmKLH+xjj9fM5HKQgcpZgP/teb9hKoFLyyehBgK0e6JLfwIj2O4nHbunl3KjIdq+PPVE5NeZyKyXbvbz20vbI8Tw2qCTbtYdWkZbe4g1fXNWIy6hPem0ulg/4kuinLSmTQ0uVXJlZVD6JduSbj3uOHZWmwmPZ6AGBE7S1boEl18FP59nzV389GBVtb9aAKPvlWPLoprEE/pSlNxPHOQbD8qALeEvffnqyfQJzVx8WRloYPtn7eTbjHSJ9WEOyBSWejAG5DVkY50eE4pR+QPhZg4xA5IVPS8rnzOZtJzQXHfuAR0s0EXka+K7j//UT6AEbmZbDuYWHFfacfFJf2ocNojLOlLczP49et7uKpyKCATO1ItRuY/mVhl5fdV4wB47O39TC3rTzB0klDiC4Yoy80AoNMXZFjfVB6r3s9tL9Tx2v9UIkgCx1tlMovNqKfTG6DCaWdbY5tKtkmm+Ohy2nGkmmLGkgqnnfv/YyQDs20xn9FweuJEl69XkQINsUi3GCnok0L13hMaGeU0wGlPRrnnnnv43e9+xw9/+EOee+459XWXy8U999zzDbbsy0GRy4teRFQ6HVx3vpOqpz9QX1NsObJ6sZHJSjFyrMMX83q432aVqyBCKSWcebq1oYWVs0pZsb4uRglhYUUBS9bJCVC3X+SNXceTLkqybUYqnXaqo9QgFroKeOQf9cwbP1i9XoCPGltZuXEn5XlZEV7f7Z4A5YMyVZ/PaCjXMSjbyiNXjJYtLzq8DHGkRFbKFTo4d1gfzhvWh1t7kShMhN4mijRtIvnOIs+ewv/951k0dfk40OyOWMyGbzj/eNUE1kZ534b7cIYH3PMdkUHwRAl2R6qJP149ka37m+mXbpEJIgGRY+1ehvSx9ciVn0R1fRN3bqhj5ewy+qZb6PaJDMyycs/GXREL4mT+oDX1zfz4wmGYjTp++1+j6ZtmwagXYjbC4VD8h3/0+w/VzcaPKoew+JltVFUU0CfNrI4Z0ahw2gmKIbX9Uk+iIdE96Y3Ykux9s0HHzsPtrJhZyqqNO6PGZ9kGqeqpD1gzfwx90824fSKOVDPuQJDDbV7ysm2Ioo2x+dmsjP58AjIJyAmSpi4/5XlZVLkKSLMYuHVKEf5giBNdPgZmWQmKkmpl9kWuN/0MZ1y7htoZX5CNJyBytN2b0JO20mnnzlmltLn96HQCHza2ck6hg827T6je1LWNrSyfWcKlD2+J+I7wvrl0ahEHWzxqP1/09AecnZfF2fmy0pHM1I6vwAWRie5EJMdEPtmajLqGMwkrXqzjjhklLKooiNtfF1bI3tD2VBMSEg9t3sesUbnqOOkLhsiwGkmzGKh6Sk5K900z89NNu+JasQnAA3NG0u0PUn+8O2nbohNyyVSMtD6p4bsCCRgzOIt8uw0dQsS6sdJpZ/nMUjq8AZ7d2khRbjqDsq3MGz+YqoohERXCp4LwtY2yll69uZ4188dwtMObWNa7vpkFrgICPYTpaGRYjby4eBJWk55tjW1Me7BaPU4jl2k4kyBBr/NralSMRYtnaNBweqO7R70sBARDIjaDgdumFiFJu2LUxW66ZDjf+907XD4uTy2OCofy75svGQ6AzWzAZtIzONvG/XNGYhIE7pxZgl4QKM/LjBs7cjntmPQ6gjqBlu5Y6w4ljqvY3nR6A7j9IilmfUwS96TFsYeUOIoDkFzNurq+mU5fkOZuXw85JMSPKofI70XFbFbMKGFT3RF+9tqnPHLF2YluNwCONBMr1tclvH/LpxcjQUQ8/7n3G3li/lh0fBq5lip0cNfMUmaurkn4fb5giOq9TUiSxKrZZexrit3TnIrSlab2ePqht/3o1BE5KgGkqqKANKuRw60elk4t5pNDbdzz8i51zVvhtLPq0jI2bT+MzaxHpxNo7faz+DwnWTYTK1/awTv7W3hxsSvGXl4ZPxb15K2ybEY8/hA3PvtPHv/hGLY2NDNjRA6ft3nom2bheAIb7dqDbSybXhw37rWlvpk7N+xg5sgcBIGkaiY3PFvL+MHZrJxdhlEQ6J9uUeNp5XlZhGQTKw62uBmYZePsZONVz/7D7RcRAL0gqDFns0GHLxii0xtkQIaVFevr1HHVatDT3XNvbSY9VqMeR4qZH1UOwWzQ8dCb9Tz3fiO/+N4o7p5dyp3rd8Tso+6eXca9L++Mawl/2wvbWa3FG84YHO/wMaRP6jfdjNMSIwZkyLa+IQldsiSUhm8cpz0Z5dNPP+Wcc86JeT0jI4O2travv0H/BuRmWlk5q5SGZjdGvUB2igmDTsCgF3ioRx1BSXZdt24bf712Ui/VLQJH2z0xCd3yQScnWl8wlJR5OmNkLjNH5cZ4YQpR5pN1h9rkTYlExATqctq5/vxCuv1Bzs7PZmHFEAx6AbvNhE4ncLzDR0luBjc8W8tDc8uZ78qPqEpXyDIgJ+OUyV6UpJhrT3QdlYUOFlVEyj2Gy65/WYnCZGo25xQ6elVW0XBmo1+6RV68PxOr1KGg2y9GJNHMBh1ilD2WEnAvH5QZwWBPpMzwyBXlHO/w8vL2IzEL8yXnO7n63CH86u+RLPSa+ma6/UGe3NLAAlcBVzyxlcvH5bF0qux3G69d0Wj3BFj0u5NKLS6nnZsvKVKtZqJR6XSg18FDPddwz+wymrr86vUq1xcituJ91aUj+OGarRHtv/Y8p0psib4nxzt9SatcEm2GKp0OBmZZGTkwk6NtXqaV9WfBpPyIsdAfFHmqaiwr1u+gtrGNB+eW8+DmvTH3flFFAWPys7j23KEYDToCYgirUR+XTKLAGzhJXPr9onH856Pvqecrz8uifFBm3M8mUwJRfJrPVDR1+QmEQkiSRLdPpPZgGxOGZMf1pD3e4cWo05FmNWIz6Hnu/Uamj8gBFIulIgBeqTsSY+kGct+s7VHFUX6ncGJnusVIutXAz743inaPn4amWGKaAiXRnYjkGD5vR+OLyKhrQSYN31Yc7/RR3UMQSTHp4/qwp5j0CMhe9F3+INX1TdwydTi5WVbaPQHMBl2Eolel006WzcQV4wezKE7yu6a+GW8gRLdP7JXEF52Q682q8UjPvKL1Lw1nMjr9Mtl559F27phRzOetnoj96qUPb8Fm0vPMlRNYuXFHwgpht1+erxORkKNVQsPnWpvJQKpZlrBORm6JVjhTkGoxkGUzcdOfP+ajA60xSa639pxgWll/rS9rOO1xKvNruJS+Fs/QoOH0RrvbryZSW7v9ZKeY6PKLXP2Hj3jmyvF0eoN0+4OkmAwc7fCy6OkP5GKYJPvOLfXN3D5NPufxDi/LZ5QgCPDklgZWzS7j3k27uPbcob3aXpgMupjiyspCB6/tOBZRuNjaLe+RvcEQiyqGcJ1Rx+o362MsdyYMtcdtb29q1sc7fOgFgQfnltPpCSCGJG6+ZDgLJuWTYjbQ3aNiPPvhLer6wmaKT3xRoBOEuGsZ5X7cNrUYkNSkdm1jG/fPGcmDb+xhVF4mC1xyvCnTaiTVYqC5y5uUuKvsYWrqmznc7mVQlpU188fE2AMlU7rS1B5PT/S2H50/KT9pnmTDkgp8gSCiBG9+ehxvQOS8YX2xGPW4AyJWk44rnnifNfPHUJSbweu7T9DY6uaeS8s41OpR99+1B9v4zRt7uH/OSNZtPUBQlAiGQtw2rZiAGOKljw9zy/PbAVnhOxEU1f2EFut7m7hlynBKctNp7gpQ5Srg9mnFHG330ifNzN92HlP3Fe6giChJdAdC/GPPcSb1FFdWuQrw+EUqnHbueLGO31x+FvNdBTFxZ2UMOtJ+MlYcQgJBUMe32oNtTBxix2bWE5KkiH4vhkJ0+4LUHW5nzfwxNHV70Qk6HKlmPjnUxjWVQxlot7LsxTpqe0h2/3NRIQa9DkGA1m6ZiFeUm8E7+1tixoBqzUrxjIKsEJ/1TTfjtMTIARms/+dhdh7poGxAxjfdHA1JcNqTUfr37099fT35+fkRr9fU1DBkyJBvplH/BjR3+6l6SmaT2kx6fvdfZ7P98zbe298SsciwmfRISEkX+SEpRE6GlYU9RAxlYgyXPjQbdAkrrrfUN3PX+h2MystUFw7h36F4bFcWOrjynKFUPfUBC135LJtRTFCU6PaLpFkMdPsC9Ekxc2FxP1rcfrJsRvxiiI6uoGrB4/aLZFiN/HDt+7j9olqVrrR1S30z153nJM1iRK+Df+w5zvXnO+WFfs99SXQd1XubQJJiPMGVBN7QvqlfagJX1GyWfkllFQ1nPswGHZOL+lCSmxHjd7n7SAfbouT2IPHi/NbnP+GZKyewauMOanoUhm54tpbl04tZPr2EDk+ATl8QR6qF5UkqMW6dUhRDRgHo8orU1DcjIPD4D8fQ0u2n0xtUyTTJNg3xIH/fbpZOKSIQDMVU4C2qlG2K0iwGDDodK9bXsSBMKj3a0ssXDFHgSCEghvjhmq0cbPWox9pMejJtRv5yzSQ6PAGWTS9BJ8gEmYAo8eGBFha68glJkV67lU47Ky8tRYAYqcoKp53lM0uY9/h7XD4uj0er9yWUTZ1a1p8t9c0smexMOJYClOdlMe+JrVxY1Jd5E/JIsxj4xfdHxXgIKwhPjmbZ5ERoONu/qqIgLulkbU0Da+aPial8DPdpjqTnnTno8Abw99ha2VNMrK1p4LmrJvDAq7tV6ysl0ZRhM3G43cPATCv3v7Kb/71oODbTyYTWwRaPqhz24Nzy2KrvQgc3XzI8Ys6Ot9FXZP9v+vPHCQNJSqI7Ecnx32G9pAWZNHyb0dYj9W3S63j87f0U5WbQL90CgCAIHG73snnXcW6fVoxfDPH2XpnkeKjVg1Gv45mtB2JszBaf52TGQzVqv4tOfoPcd9ItRt7YndhWqzJOQq634Pb+E93c+/IurX9pOKPR4QnwYWMruRkyAVuxj1Tm2ofmlmPU62ju9vWoXbapfU/pa8r+bG1NA+uvc7HypZ1RlYEO5rtOqsglK6KI7t8K+qdbWFvTENN+l9POxk+OMGmInY8OtCY878Qhdm1fp+G0R2/z6x3Ti2l1y6q6WjxDg4bTH01dfjIsBiqcdtJsBrzBEG5fkDtnlXLHC9tj4gT3zxnJDc/W9rrv9PhF1swfgwSU52USFOUYiycgsnn3Ca473xkTxwkvyPjT1RM43unDZjqZlqhw2lk1u4xPj3WyZv4Yag+2sW7rAUoUCwxPkI8PtfHhZy1xLXdWrN/B/f8xggFZkbYVp2J7IEqSSqb59Rt1VLkKWPT0hxH22eEQIGGMb+fhdrp8wfhf1INuX5DH3t7HzVOKWOgqYPoIr3qt4ed7Z38zOw+3c8X4wQltVaLJuu2eACkmQ4Sdt7I+SqSOeypqj4BW0PINIlFBUW/7Ub8YSponuXNDnapGv9BVQLc3iMWox6ATaO4KYu6xuAmEJMoHZQLQN83Mshe281GYSlFJTjqj87I41u7hJxcNo8sb5J19zeRmWFhbsz9irFFs0BP1oaZOf9JrOt7hIzvFxPwn38fVM27c+vwnPDBnZMT6PSfDwgObdnHt+U5KcjLQ9ygm+IIhbEY9C1wFHO/w4g9KSccr5fkHsBoNePwi67YeoDwvizF5WeRmWJCAE52RrgQ+UZLzdhI88mY9u4528qerJxKSJCSgf5aF5S+ejNuvrWlg1shc7t64Iya/l2h/o1kpnhkIiCHa3AEyz3AF868Kw/qlYe0pINHIKN9unPZklB/96EfceOONrF27Vt5EHz7Mu+++y0033cTy5cu/6eZ9KbS7/fh7Fv5KoE2vEyjNyWDM4GwAVWLtwbnlIKFOgtGT5rqtB1g+vYSb//IB/z1xMHfOKiUgSpzo8tEn1ax+p8LkTOZ5ucCVH/N6bWMbd80qZdqIHNq6Zcb9H6+egBiCVS/tiEw898gagsShFjeBoEVdbORmWHh43mieee8A/9hzArdfxGbSU56XxSWl/ej2iqxdMJZtja0Y9AI/eOxdzh6cxfIZJby+6yhnD85SWespZn2S62iOSHQr+Fcn79xM65dWVtFw5qPT62fp1GLufimyGrTCaeeeS0fwvd+9E/OZRKoWTV1+fv36p9wypYhbBZk8km7VI4bgaIcXq1FP3zQLnoAYN5kGJHwdIN0qJ+HLB2XiC4QQBIEMqxGbSa9WrCZK1E0u6gMQtwJDEGIVKY51eJEk+LzNS980M/f+TZYeHJWXFfEd4ZZGlYUORsWp0lHGw/s37YoJqCyqKGD75+2U5WYQECXunlVKl1fEHxRJsxp5efsRfAGRTm+AJZOd3DatmE5vkBSznuMdPuY9/l6v1UHVe5tYMCkfiFWviLY/y8u2YdAJjBqYGbMpi95khAcWXE47Bp1ODcwox6ytaeDJBWOYMSKHvuknx9VjHV4CQYmz87Mj7nu4T/OZinSLEXdApNsvy+2ePTiLpi6/SkR5eN5ojrSfJDJ5AiFq9jXzvTGDyM2y0NTpVa3hDIK8YVWIUX+5ZiIL2r0Rz7HHH+Tn3xvFdeu2xWz0w3//fSe6ePZHE/j7rmMxpKPwytNEJMfeNia9yahrliIavu1I6aky9IdCXDEhP6KfAuRmWDg7LwtPMIhO0FHUPx2bSY9Jr+P6KMusDKuRAZkW/nvN+xFVjOV5WViNMtE7GJLY1thKqsWAI9XEp0c64hK8K5x27rtsREz/6C24bTbotP6l4YxHutXI2poG/nLNRIIhSZXQP1WyyJb6Zn5UMYQlk51MGmKnudvP/1w0jFumFNHu9WNPMaMToM3tp6qigLU1DUmLKICY4oPKQgcmo46z87Ji1DsVcm9JTnrS8y5fX6dJYWs47dHb/OoPhci0mXnjJ+dq8QwNGs4AdHgD2Exy0tWq13Os20t2qplfvr4n6Rzam1pgqtnA9373LgB/vlqe/wHcPnluN+p1SW16rEYDfdIASeKRK0arFpvNXV6u/sNHgDx333LJcH7wmKwMm2oxMGJABr9+PbaoCuSYzIFmN6lmQ8TYlWoxJFXIrT3Yxui8LLbUN+MXQ2ypb6a8JyaVSIlYr4OlU4tZFRXjU+wJe1MUTrcaeWP3CZZOLeavHx3kuslOcjKsCcfmTJuBqooChKjCmGjrX5D3H0ejVH8Va9L/+8+z4ranN3WNox1e7nl5l1bQ8g0hXkHRRcV9uWtWKdYE9lQKBmZZsRnl9J+SMwqP1W6pb45Qo185q4wWtx+DXsBm1hMUJR6eNxp7ipEDzfLzGRBDfNTYFhNXA9kOMMVkINMi7w8emlseoxJkNuh47v1G1v1oQtw+NGNkTq/3xGzQq3miezft4mffG4kkwTNXjqfdE8Bi1KPXwbwJg8mymfjl3/ewqGKI+v3ugMhNf/6Y31eN4x97TiQcryoLHRQ4UvjjVRPQ6wR8QREEmDd+sLrPcaSaeO6q8aRF2RyadDoEPYzLz1bHLR2yGuzL248wcmBmBMGsqqKAVVFEFEi8vwGw9qLSpOH0QEu3HwnIsGlklC8Dg15HSW46//j0ONed7/ymm6MhCU57MsrSpUsJhUJccMEFuN1uzjnnHMxmMzfddBPXX3/9N928L4xjHV7a3H6Meh2bbqiQ2agCiIA3EKLLF+Smi4dz58wS/IEQHx9qIy/bylWVQ3nozb0xMoXLZ5bgE0P8oWocP//bpzz61n5eWDyJR9/aR3leFpVOOx81tmHQCWRajTxyxeiE1fnRzHgl+btyQyTp5N7Lynhl+5GYxUb13iYeeHU3P7loWELrkKXTipi1ekvSardZo3LV89390g7K87LUYx65YjQtsdaYEYjH8P93+CBn2LRgjYb4yLDJrPF4Ho/L19fx+6qxmAwGfEGRLm8QR6oZdyDIOU4HS853smVfmN1AoYM5Zw/i8sfeU0lbq+eVs7amQQ38P7h5L9efX5i0TfGUGS4o6oNRp6c2SqmlstDB2vljqXr6g4Sb8QuL+nDzlGJWJmBwN3X64sosupx27p5Vhj8YimCDx/uOykIHd88q5d6Xd8Wcp7ekRHleFoue/lD23JxVhk4HrZ4AaVajzO4XBCRJIN1iwBsQybIZuWfjroiNfm9QxpbwMSaZHOaEgmw+amyL296qigK1MuGGZ2vVIINOgLVR1zlpqJ2+6RZWb66PIQAuOd/JY2/vj/m9K5z2XjetpzMcqSYOt3tp7vIxxG7jrpmlfNYsTw5LJg+lf7oZk0GnblKVCqarKodi0unITjFT29iGQIOamAYYnZfJprqjMRtAl9PO9BE5Kukk3K4n3u9fEZWMi1d5Go/kmGox/Eu2cL0FmTSJz28nvku2SikmAxcW9wXAYtTx+q5jarWUNyCS70ghy2okJEl0+YMYBR3P/mgCTV1yFVK0ZVaF085ds0pZ9PSHSfvjnPIBdPuCrJpdxvL1dREE70yrkcF2W0yVJSS3agwnE2r9S8Op4HTt62kmPVNK+mHQC9R93s6tU4vo8AQRBHpVQgF5rszJtFBb0xqzl118/lDm/PadCC97hSiazD5g6dQiSnLSVXLuhIJsDrV6WODK59apRTS2xFrmmQ26XonHWj/WcNpDApspfpLZZtKBJFdR56RbaOrys+9EFzazAZ0gYNAJ2E+TcUmDBg0y0i1GWrr93PBsLc9fOwlHmhlvWOwlGkpiujfLX2OYnVd4MjTNKqcZjrR7kyp4t7nl8aV8cFaExebz107iT1dPJNWsx6jXYTHosJn0jM7LxNILQQZApxNocwfUcepwm4cV6+uYPylWIdfVo5A7/cEaSr6fDsjqKxAek2qIUEwAGJBlxayXVX1j4t71zfzsld3cMaM4MQGm0IHFoMORakKUQtx44XACoojFqEsYKzfqdRgEHStmluAXQ3R6gqRaDBzr8HLr85+o6ySFXHNJaT+1oExBTX0zXd4g/dJj71tv6hqHWj1aQcs3hHgFRTaTnh+My+OW5z9RczuJLC73Hu1k5KAsHqtuTUgQD1ejD4gh0iwGurxBslJMNLR3y8UfBj0ZPcVRbr/I1ecOSfjMDnGkMjDTwnXnD6VPmlnNNX1yqA1BgD5pZm69ZHhMETPIfWj7ofak/adfugUJiSXrtlGel8lCVwE5mVbu2bgzoi0bl1TwZE0Dy2aURJDMlIJst1/keKcvYQza5bSzbHox97y8k5LcDJ57v5FnrhxP7YFW/r7rmBozCIYkLHo9Rn2k2rZPDGE16DCGjV2iBA+9WS8rSUXFaXuzR6uKKq529RQA9E+3aH3wNIeiqpOhKaN8aYwamMHT7xyg3RPQ7uO3GKc9GUUQBO644w5uvvlm6uvr6erqoqSkhNTU1G+6aV8Yjc3dcWUS755Vyv2bdvH67hPq65OL+rB8egkbtx9h1cu7eHjeaKaPyGFRxRBsJj1iSOLd/c1c2uNpWel0sKiygFGDMtXJubaxjfXXuWjq8rH6zfoIdnm8qrVoZnyi5G+/dEtCb8yinHTu27QrYcL4tqnFuP1iUpuLVS/tUAOY0ZNxb+z9eMdoPsgavmp4A2LCPrHrSAdWk0H1iFTIJNEkjA1LKjjRKfvEPrP1gNovrzpnCE/WNFATZQ/zPxcOS9omiyGSiFDhtLNsRgnLXoy19pEtrmDZ9GJuf6FO3Yxfd54TnU7A7RcZlG3lrg2JGdy3TimK244t9c20e/y0u09ufuNZ8wzKtvLajmP856Pvcv+ckXiDYgSDPJmykzJOuHqqVO7dtDNiPK1w2lngKmDp859w+bg8Jg2xI0lB7ppVil8M8VlzNya9rtfFjDK2WMJIHsnkMENxbMOU9i6bXsKlZ+XS0uXnL9dM5Ei7l798dJAbLxgWM9Z7A2Li3w1YPr2Y28KIQEq1jjuQXDr2dEa3X+TZ9z7jysqhnOiW5X8zbbLCz5TSnBgLKyUo9lj1Pm6+eDjtniBXnzuEX/19L1U91RPRFgHhUJ6xfj3KNAoS/f419c0IgsD661zoBCFh5Wk8kuO/YgvXW5BJk/j89uG7ZquUaTNy54wSAiGJx6v3R1QcKVBUxfR6EaNex9zH36M8L5O188fiC4pc22MrB3Jfu3WqPP8k64/L1tcxY0QOlYV9+MX3R52y0l0iFaN4VYpa/9KQDKdzXzcDP75oGGJIYn2YHzwkVkIJ379VVRTEBI9BVuYMEblWUqwkb59WnLRNB1s8qsVkZaGD0XlZXP3/PqI8L5MVM0pUy7xw9bLsFBMt3cllwbV+rOF0h0mvIySRMOFp0uto6vbT4elkwZMfRFjcLXQVcN+mXdw9u+xbPy5p0KBBhiPVhDco4vaLdHgCWAxmOjzJ5zJfMKQmZwWIiLsoe2LFzgvAqBcwGXRUOO3okOddg05QVQvj2V48f+0khvZJYdP2I6p9tMtpx6TX8Z+Pvqv++/rJhTx/zSQEgR4LeKOqhhBdQAmy/c2y9XU8MGckKSa9urZ6Z19zRFvysm2kmPR0+4KU52WqsRxbD7EmXkwKIN+egkgIvxiKG+OzmfRcPj6PDxtaWHze0LgEmMXnOfnwQCvPXDmBUCjEqk07uHtWKfe9sjthPG3l7FKMhiAro5L34dZKo/MyuWt2Kfdt2kVeljVurKk9wW9/KlZG8aAR7r96xCsoqqooiLCJ+Y/yAXFjXFdXDiUn08LdGxJbuEcrIXV4A9hTzGSlmNABekFg9Zv1rJxVRnaKCZfTTqbNyPnD+6o22NHnNRt0LJ9ewnv7mvn5a3vU9xSiedVTH/CHReO5OWzPEI7jnV6uS9B/rjvPyVt7jvPevhZ1jwENLJteEtMWnU6gur6ZjiiS2fMfHWRO+QAqnHZZJSVOf1fGqyNtXjbvPsEPJ+RzcUk/xBD839/3sO5HE/josxYAcjOsdPpFBAEWuAqQeu5DhycAViOpZjn9ajPp8QZPqpgr5D0Fvdmjhb8fHm8Yl58d0wdP1yKH7ypO9BRXaTY9Xx7leVms3fIZ1XtPMGNk7jfdHA0JcNqSUaqqqk7puLVr137FLfn34FiHN4aIAvLkddeGHZyVlxWRPC3JzWBF2EJDsQYoG5DBz1/dHYdZ2gSCnBBWFvpuv7whebiHkRn9vXCyaq0iyoPSZtJz3rA+cZO/ySbP3liep3JctNVO+PfVHmxjQIYlIYO/Muo6NB9kDV8HOryJk/4PzBmpEgkSkbCq9zZx14Y6zsrLUhfPvp5qlgqnQyWSlQ/KZG1NA0smO7EY9EmlSFvdPtVOZ3C2jaMdXg40uxNWyFTXN3HjhYWsmT8GkNm6et3/Z+/MA6Mq7/X/ObPPZM8MW4BAwkSyCmGRJYkKdWETpN72J9gWCK4IdnUHF6Bqt9teRVsXEHsreG9rlU3UWrw2QUXQVCGskSUgayZ7Zj9zfn+cOSezR1uVxXn+UWYy55w5c973/b7f7/N9HoEbntuG0yvyl1vHJeyu8SaYF5xeMcq7NtSaB2TrH+XfyiZhweV2BEEg1aTF7UsctKeZdFRX5PHY5j1sCZlLLQYtw3OzSDFoWXvTWJbGkIhcNLGAdw/K93rlnFEIghCV/KgqsHE6KIUqSZLKhv+izHYFJ9pcVK/u9vittFtZMq2Epg43x9vcDB+YyaNBS6KVc0bF/90ONPHTK4eqv7ViK9PS5SEr5cKc95TOkTuvHsrWT5sYnpvFlr2nyE4xsmRqMQ+ui78RL8/NCnYqHFDXyxSDlhdvHEOvNKNK8IwFjz9AdoqB3undZKSeOqw1gsCQ3l+MPPvv2ML1lGT6MlTCkvjy8E21Ver0igQkieKcjLjkkSXrdvHItaX8+vV9aqyqQY5bI8dop1v+d0/z8d2TCjna4iS/V+oXGpfKmDzR5uZgU1eU2oKC5PhKIh7O97HuBmobmtgcUdyG+LLSofu3z0MoDkVNQxN+KXHcF5pUrznQxPJNu3n51vH4AwECAYmnZo/gZ3/5mMeuu1glvFkMWv7nlrEJj5scx0mc7whIsCJB/mfp9FIsBi0fH20NG7ehsfL5MC8lkUQSMjIsBpo6vVTYraSZ9XhEsce1TCnOvvRBI4/MLKPT46fLI5Jm1nGi1cXta+r47/mXqH8vSRI+McDcijx0Wg0LJ9g5eKYzoU2PAFhTjQzrn8lTN4wg0yIXbNtc3aRQZd6ZdnEOWoEeya5VBbIqSE0wflo2o1SNrSLzS3/7yaUsXreLn155EfMq8jjV7qbCbsVi0Kr55MjPVNitLJ9RioAGhzs2eVUhv1dX5DH/hR0xi9vzX9jOU7NH8NGRZoYH7YHcvsRqNUhC3LlbA7x081je3H0Kr0/kxsp8th12MCI3K+pYbp/I8VZXFKEwkdqjcl/jIUnU/WoRq6Fo9KBshg/MVGPYp78/MkzZ06jTsO9UOzlZJjy+ALPGDGJeZX5UHnNrg4MFl9t572D3c5Vm0uPo8pBm0qPRgBgkhLj9Ima9lmll/dAJGvzEf2aVmlWs2lQAie+NHUSnJ36efEyelR+s+iDm+Kl+YTt/rL6EX72xP4y0HssYSzmHxRhOMntiVjm/fmMvP7u6kD3H29UxHzlfVdllYh3I9ikdbj96rYbvjxtES5eXTTtPUNvg4LU7qnB7/YhSd776psp80s16nF4/thQDVQU2Rg3OUq3MAPQajXpui0FLn3Rj3HsCMNiWwu+/NwKDNjzfEDkGz+cmh28qksoo/z5sqUZys81s2Xs6SUY5h3HeklFWr17NoEGDKC8vR5ISezGeD2jp8sZVTqhtcITZBEB0Ql0JkssHZsY9Ts2BJu6ZFP6aRhPOcg+FkvyrsltZdm0ZyzfuBrptB2Ixqi0GLb3T4i+ePbE8XV6RVxaMx6DVfG7LoNBk4+7jbUwflsNgW4r6HRRUFdh4dGYZXjHAFYW9v1ABL4kk/h0oLOhY6J1u7E7uJSiUKfNAJGM71IrWH5BUz87TnR5un2BnweVDwm1+7FbmVQ7m9jXdm/Y1N45h/gs7eOqGEQm/x+kOj9plumFRJd5ghw10s3jjIdH7mRY9kkRCGdjQza8y363Y0sDmH1ZR19hC2YDMhOfv9IiY9NooIopi2QCyvUPk+T9sbCUAfHi4Oa561MjcTJZOL8EXkEkoWkFQ2fBfhNkeikjSQG2Dg2Ub6/n5zDLK/QGc3m61nZ7Ocbytuzs49PqXzShN+LnzFUrnyL1TiuidbpI3rUdamTN+ML3SjNz7SuzuC2XNc3lF6kLsk1KMOl6vP8nk0j5xiSggr0ViQEIAHp1ZRu90IymGxGFWi9NLm/OLdxL9q7ZwiZJMSZWwcw/fRFulpk4v7S4fGkHokczV6RW5fkwuOkFOENU0OLh7crRaQnqw46inufJYizxXVgWJyl8kUaP8Do+8tic5vpL4wjjfx3qnV0yojBmLUKLs3yrtVgw9KFvGGrstTt/njhtBnjOOj3cx/4Udcqf1hAJWzR3N797arx7D6RX5+57TcaXOz9Y4TnY2JvFlIrQjNxJKscmk09An3USfdFPU+9UVctHnRJCEn3wWk0ji3MapdjdaAe6YWIA5uN5qNQJVdltMS+Iqu43sFAMr54yi7mgrD22oZ/aYQXLeY1AW8yoGMz7fSrvLF9LwoqWpU7YC+uuC8cxbvZ1bLsvnwWklLNtYH2UlPGf8YCSgyyMyd/V29T0lRxFqL6PMO5EFukiyq6IqPu2JWkCOn7q84cXuUDW0li4f8yvzSTPrebbmIGUDMlh+bSlIsHCCXT2H8plx+VZ0GgGvGECv1eCLs69Q9i83jBkURWYJvY5+mSaerT2ozqGJCvMgk0jiN445+GFQzabSbqP6he08Masci0Ebdi+r7FbePejgD+98GkUojKf2eGmBjaUzSpnyeE3ca0sSdb9aROYGlbrL8te6VQV1mnD7SotBy5qbxvLw+mglnUgSl1YjsKpWzolW2q2caneTatTR6fHLyihBMobTK/LbN/dRmJPBxQMDuLz/eiPyT68citsXP7fmF6W44wfk3LdyrHuC+//OGKSdNJOcBzDrw0lmAJt2neLt/U1x56oKu5XbJ9jJDsb+eq2GNpebgVnmKFWYLo+fvulGOoNN36tqDzF8ViYWvRZTsCFw2YxSnF4/PrE7id/i9DKvQlam+f7YwVj02jCbn1BU2a28WX+SZ/5xUJ3Hfv2dYZj0WrJCxvL53uTwTcWZDg/pJh06bc+OD0nEx/CBWby99zR+MZC8l+cozlsyym233cbatWs5dOgQ8+bN43vf+x7Z2dln+7L+ZSRSToDoJFy8hLrHHwgLsD3+QFxCB0CrMzGDOdWoY/nMMm56YQdXlfalujKP7BQDP9+0O0ydBLoLu/tPdcRNDvYkN5Vm1rEsQqY5kWVQVYGNQVYLT90wQmXJzn72fb4/bhB3TypEKwh0ePyY9Fp6pRron2VJeP4kkvgqYNRp4o6JLndsklUsGHQadcOvjOurivuo7/fPNNHh9kfJLlcV2Hh1QQXHW13saGxhYQgRpcJuxeUTefHGMT2Oz8HWFP5y6ziZWCJJ+ESJKwp789be0xh6WOQz4mxQK+xWdBoNjk5PTE/hKruVORFWB6HoDH7f423uuPf4iqLeDM624PSJYSQ3vVZQu+6VxGokqivzeCLCNkm5Rg0CGxdVIgYkHF1ebv3Th7IcrFHHnOdlFv/A7MTFzFjWYhVxCjO1wY1TJHGoJ3uyWO9vbXDgSrD5O5+hdI50uHx4/AFanF7uuKIAr1+k43OstSlGnaquUGG38nr9SeoaW5gxLIdfXleGNdUYtbaW52bS3OnlkrxsXH6R3ulGBEEgxaRNeD6ARWvrvrYOhURJpqRK2LmHb6KtklJsFYSeSY4dbh/Pbz3Ez64aqr7m8onYUg1cf0ku5QMzATk5t3CiPcxCLRaUubLmX0zUJMdXEv8qzvex3h5cbxNBr9OoBZGqAht5thT+95ZxWAzahOp5EDuOyTLrefCaEpZt3B2WbI1lkaVAuUa107osh+KcjDCi8jP/OMjjs8oJEB6Pfp5x/FWQRpKdjUl82QjtyI35vldEkqSE+SaAg2e6eGTTnuSzmEQS5zhaurwYdRoyzDoMQLs/gE4HD00v4aH14coFlXYrcyoGq+q3Cjz+QLf6gCTxyMwyHt28h827TgGw+YdV6hrf4fbLReu/HeDpd+Si6dwQZYP+WWZ++fpe7p9SHKUusrXBwYPr66PU1HyipBafI//+7kmFlA/M5FS7G6fXH3bdXUE7PsWWT2lEilTCXTythPrPWjnd7karEXD7Akwt68dNlfn0yzSxfOPusM9sWlTJuwcdMfNPyhyZKEcTak+okHX1WiHu3yvfJRHaXD4en1WOUSeo12HSS2FknSXXlKhKr7GIzqEKrG0uHxaDFo1GwB8IsGRaMcs27o6qKfRE1E0Sav99RDYUVVfmRdnX1B1tDXseqyvz+M0b0ar5sRQLFdX8iqB9uUmnobahiUmlfdFrNHQF84ZpJh3vHmymMCcDQRJUokcs9LQv8AekuGMI6DGPlmrUqWP7aLOTusYWpg/LCSNfQXcu3hcIhJHMlOuLN1cNyDLz5u5TKrHrw8MtjB9iw6jTIAGCEL5PSDXp8AUkNIJM6Bs5OIvVWw9x96ShpBr1/NeWBpZcU4wEvL3vtEoGNOo13LFKVmo52ebiv98/HGbzo6DSbuXeKUX8YNUHMeex0L3B+d7k8E3FmQ4PGZYkse/fxejBWaz/+Dg7jrQwNt96ti8niRg4b8koTz75JP/5n//JX//6V1atWsW9997L1KlTmT9/PldddRWCkDiQO9eQnmARh+hANl5ga9HHDrAVQkeaWacu9rZUA71SE0uApZv1PLZ5D1eV9g1TIqhpcDAsNysq2Hl+6yHqGlt5fFY5EF1Uzsk0xWfgF9jYebS1R3lnpdutwm7lwWtKONbsjOr6/+3fDvDBoWbKc7PU+3BpgS3JAE3irKDN5Y1JtKiwW0m36FUCWU/EBVuqkeOtLh5Yt0uVWr12eI46DvVaTUzpzpoDTSzbuJspF/eLmhfmVeSxKEj0emRmadxu0Cq7jX8ebeG+V3aFff7h6SWAFLX5ifxsqkkX9b5y/lNtbgw6jar4cvekQo61uDDqNPRKM3L9M+/HVaQwGTRsbXDEnXcmFvbirkmFPBiRaKmwW3lwWglPv3MQiN4sKb/J1SV9EliGNXGizc0Nz23j5dvG0dTpZf4LO1i/sCKMxZ/ovpwKdhVG3pMTbd2vhxIM210+Vs0djS3VoG60Et37WN3BCnpKRp+vUDpH0sx6Oj1+LAYtZzq8/PHdQ/zkqsKEn8006wkEJMoHZpJh1nP3pEKqV2+nqdPL0o27mVLal/kvdNsnVditrJwzCp1GIMNi4P5Xd4Z1Mbx2R2XC38ag03ztHQr/js1PEl8vvom2SukmPXqtgIbPQWA26YPdUN0xv88f4Pm5o3ns9b0RiWYb15X3j2tfFzlX/quJmuT4SuJfwfk+1tOD620iaAWBx2eVs2bbEW6qGsKGT46r1rFy/Bl7fxgrjqmwW3lj9ylW1R5iydRi7po0lDMdXvplmNi080SURZaCUEKaUgAq6JMa1cBxz8ufcP0ludw7uYjGZieDrRb6Z5oTjuOvgjSS7GxM4quAohYW932TjhanL26+SXndeBZi2CSSSOKLo8Ptx6UBs0GHpBHocvuwpho51enmzkmFzOv0kmrUkWLQsrn+ZMw1NJQ0Udvg4LNWF0N6pwGngufwYUs1UmG3hqkCx1I22PzDKmaPGUSry0tqjDx4zYGmKGXwwVYLgTj2fMdaXLy47QgLJxTw972nw97z+WUSxb1/3cnNl+azOpYldoODpRvrWT6jlMXrdrFkWjE/WLWd6so8SvtnsHzj7qj8WFdQ+eAPN4xk0UQ7KUYdnW6RNJMOZVeSKEcTak+o/F2H2x83Fqq0W3usGQCs3nqIO75VwOOzykk36nj3oIOrivtQPjCTuqOttDq7yT/xiM4ZFgOdHj+n2kVOtrvV2Gjv8XZWzhnF/Bd2hNkiJSLqJgm1Xw4yLAaWX1vKfa/IuaZRuVl0RMTdip07yOP189qFVxXYGJBlZvXc0exobOGOtXWsmjuaT461MmNYDh4xQLbFQJXdhgbUetPI3EyaOjxxn9merEZSjNq4Y6jT4yPVEJ0/VlBht6IRUJuWe6cZqa7I42izk/+eP4a3951Wm7EV5RFJQiWZVVfk0StC0T+RTbzHH6CmoYnbLh9C3dFWBmVZVDV+BUadBpdXRK8XeGhGCW6vyO/eOsCNnT40Gjln3drpQ6sReOYfB1kxuxyQ+Mf+M4wcJNtp9U43sWXvGd4/2BzTnkirEfjplRfFtDEOjcfO9yaHbyrOdHqSFj1fAvJ7pWJNMfBG/ckkGeUcxXlLRgEwGo3MmjWLWbNmceTIEVavXs2CBQvw+/3U19eTmvr5/d7PNrJSDAkDz8gkXN3RVlW6K7RQ2SvNyGOb98QkdAjAz68tVQP76oo8PmpsiV8oLbDhFwO8s7+J/xg5EJCLoh1uubin0wjcM7mQ0+0eBEHAlmpQF/xQGxFl8cy3peDx+1k40Q5I4VKNdisPXlPM9BVbY94fJViqtFtZdm0pR5qcALS7vFjTDHGL3KFdcUkGaBJnC6lGPTc81+13qUge1h1t5Uy7Ww50305MXKiwW9m08wT/bGzhxRvHcsNz78skjCMtLJpYAMhBcnzpzibum1rEa3dUcaS5K8pjEmD5pj28uqCChzfWRxHJFkywM/+F7WHHVDpXHppewmfNLq65uB/LN+4Jm8cq7VYenlHCii0HqK7I457Jheom41S7m798eDSYyIARQU/h8oGZKsFs4UQ75bmZce+J4tIWa97Js6VwvNXFwxvqY86JyzbWc/Ol+fgDEgOzzapqyifHWhk2IJPnag9S3C897HORylPpZj0LJ9rpdPvD5BSV3zFyUxh67fMqB7PzszZWzhkFyBu2/9t/RvUwVc4Xs4OnwMbzc0fz/iEHw/pnxrz3igRuPFWZnpLR5yuUzhFRDHC6w4O9VyrP/mMPw3Kz+Pho/DWvMkiYXPfxcUpzMnij/iTbDzfz2HUXc8faOmoONDF3/OCwz2xtcKARBO6eVBjzOTva4opLRAslHcVan77KLqJ/1eYnia8X30RbJVuqgfYuL34gy6KPL1NbYFMVuVqCKn8VQenp8fnRY7ymoYmfb9rNvZMLCUh7eowZITpR83nHZHJ8JfFFcb6P9VSDltPt7riE5gq7lfcOOvi4sZXF04rZvOsEw0PsFZdv2iPHQgJhBYsquy0q/gwdr06vyL2v7KTSbuWuSYUYtRr+2dgSk4hSabeGEXlBjpvbXL4okqmy7l9d0ocFL37E339yWY+KKF8FaSTZ2ZjEVwGzXpvQnsOs1+LRBzh4ppPjbdGkeaVwquSnks9iEkmc20g16TDpNBxxOMm1WshONeL2B3j/YDNl/TOY/8IOLuqdymPXlQHwxKzymArboc07rS6fqkAIMqnWqBF4eHoJWkGIG79X2q2Y9XIT0p9vHYckRaudAOhCVEIq7FbqGlsZZ4+thD4w28zUsn64faLaaATdljTTLu7HSzeNIcNiCLNdDoWsGivn0ix6bVheKlZcYzbIam99M01ROYBHZ5ZSVWCLmweqslvDyH6rag+x9uaxCBLcOWko0utS2L1T1Cp2ftYWl1RfFZyTaxsc/PjKi1i99RCPzixjxMAsTrS5ueW/PwRkEozS5BmP6Hys2cndf/0k5l5pZc1BXrp5rNo4drrDg8UQW8EiSaj98tDm9LJ0w26G52ZxY2U+A7ItdEYo/kbmQxOploAcA1fYrcwZP5jHNu9h9phB6liXJIl7JhfR5vLiD0CGRc+Sa4rRa7qVpe+ZXMjta+p46eaxMZ/ZNJMu7jxQYbdi0mnjjqFKu5VHZ5apee7IZ3HRhALe3neadz91sGRqMftPdUQ1TSpEFYtBx/dXfsC628fz4rYjFPZLp0+6CTEgJYiFwmtwynh996CDvcfbua68Px4xnBzX6vQiBqC30YjT6+dksOlw98k2RhvlgrjbL9I3w4TTK7JwjfxblfTL4IqivhxrcYaptayqPRSWdx6Rm4XLJzK0bxr3hnzXUCjx2Pne5PBNxel2Nxnm5Jz470IjCIwclMXmnSdZMrUYjeb8Eqv4JuCCqURpNBoEQUCSJETx/Ov27pNu4uczS7n/1V1RUsMPTS/lsc17wv5+9/E2lkwr4Vdv7OX6S3LVQuXKOaPi+nXXNjhw+wMYNRoeml6CX5RYtLYubqH04eklPLJpDy/eOAadRuDHVxZQ1j+DQCCgFkdDg/kquy3MTieShbtyzigGW1Nw+7xMLusXJtV4qt1Nc6c3rvoByJJww3Oz8PgDzF29naoCG7lZZo63uSnPzaK6Io8Uo46uYJE/FqM/yQBN4mzApNMwLr978yzL+skL4t6THdQeOJNQ3SMy8b5sYz2/uO5i5r+wgwc37GbdwgqmlfXD7Usshdjm8vHk2w0snlqMTivQN8NExRCbSgy5++VPaGxxsmiincVTiznU1NWjOsnWBgc+v8SOxhZe+ksj11+Sy9yKwXj8AQbbUjjR6uLW//6QZ34wivtf3Rm1yVgyrYQbnpOP/fiscgSEsC6SRGSORRMKqG04IxNWBmbiD0jkZJjx+EXOdHoISBIZZn1Cb927Jhfy2ObIDnorY/KyqWtsVTsGIAExxG7j2uH9uW9KEf882ipfU4gEpLIpXHC5HYNOg08M8O6nDtUuSfl9f7DqA1m+3m6jd5qJhRPt6EI2fGHXHlwnppT2Ze7q7SpJ5rbLh6DXaejy+Mm2GHh8y4GYv1tVgY20HiwrzlcoVhnbDzczLl+2zalpcDA3qAIUOylkY+mMEgIS/Pd7R/jT/DGqYhB0K3PFkhutOdDEfVOEmM+ZTiOwKAY5U1mjFNIRhK9PyS6iJOCbafuSYTHQ6vLR5fEzZ9UHPDdnNAL7whJFFXYrS6eXcKzFBcie0qHrZCSJUMFbe8/wvbGDP3fMGJqoSY7JJL5KnO9jvcXpYdwQK6MHZ/Pwht1R4zU0hj3W4mT74WYml/ZViSGKBeErC8bjFQN0ukXSTTokCd45cFpdK0NJu6HjtbbBwe0eP3tPtPOzqwuBvTELOb97a3+YLHk8G0OAxVOL8IvS5yIDfVWkkWRnYxJfBbq8fuZVDiZWc9C8ysF0ef2kmXQM6ZXK8k3dOShlLK/ZdiSKwJl8FpNI4tyFRpALz9mpBlxeEZ1WVkcZNiCTvhkmbKkGVtwwAr1WoK6xJabC9h1r68LWzAFZ5jAlV6NOg1eS+O2b+7h7UiEPTS/l4Qhl2qpgbvuow0V5biYmnVaN5SORFbQqqLRbeeCaEmY/+z5/yh0T9XdVBTbSjDp0GoHb13wUZkWtWNKUD8xk52dtlPXPSHifOoPzWJfXr9pkePyBqGYkk15Lc6eXp24YEbMZZdmmPay7vYKlG+rDyAEA/TLMvL3vFL6IQnaKQUur04fZoGF4bhbzYuQNADYuqmTJul1ReTHlu4K8L6ptcNDu9nOs1cnIQVlq85NJr+Wq4j7sPdGOLdUQRbRPNeq4N4KIAt2xUXluFmc6PGHK5KMGZcWMcZKE2i8Ppzs8vLX3NG/tPc3CiXZW1h7kzkmFUU1WSh2mwm5lybTihMccmG2mPDdLjakVK66PG1txekWe3byHxVOL8foD+MUAv35jH/dPLaausZWFE+2qyrJWQ8xntnr1dh677mIEQYhJNEeQ4o6h2gYH7x90MMhqUZVMlGOfbveQYdGpNjn3TS7iO0+/F/Z52VIdXrp5LAatXJj+rMXNLZfm03C6U72nS64pZtmG+oh5ysaSa4qZ/ez7QLhC46raQ6xfWEmX149Bqwkjhxl0WvRagU6vH1fIHqWwb7qqALXjSAvTyvpRVWBjz4l2ygdmYk0z0Or00j/TzGet8nyYKO+8eFpRlBVRKDrcPvJsKXGbHKoKbIiSxKdnOpOWWecYznR4KMlJvE4l8fkwfoiNN3efYtuhZsYNSaqjnGs4r8koHo9Htempra1l2rRprFixgkmTJqHRxPdnPFeRa03h198ZRqvTS7vLj8WoxazXogV+9K2LmHVJbtjifuML2/nv+Zew5NXuYLQnX74jDicDsswsXV/P7RMK4qqY1B1tpSkY8MweO4jq1dt5ZGYpz9ceYm5FXuziaEMTAaSwJF8kvGIASZIX/kHWFDLMesx6LXVHW+mbkTiZbzZoWbGlgcsu6kVVgY37pxRxpsPDQxtk38pLC2zcP7WI6595P+4xkgzQJM4GnH4/i6cVs/jVXVGb+3smF/Lo5r3y38UYjwOzzbxRfyos8V7b4ODuyYXqZ9qcXsYNsfY4/nUaeSOwfNNuJpf2DWOPV9qtrLlpLAaNwOJ1u1g4sUDdZD51w4iERLHDji7qGlvUTlKFUHHXpELqjrZyU1U+iyOIKMr3WLqxnusvyWXFlgbuWFvHizeOoc3pk5U+Nu2h5kBTGJlDqxFwekVOtrnom26ksG86K2sPqaSVx14P73hXNt7xcLrdE1OqNYBMQAglxihWZLHmvgfX76I8N4uPG1tYck0JgiAxrSyHH19xEW0uObHx3kEHL30gE3ZG5GZRNdeGGPRKVe5bRdCn+f898x7luZncM7kwbgdPqFKHsvFcsaWBqgIbd19diCBIzK/Mw+UToxIWCy63c/7RNj8/cjLNDO2bxqEmp9oV4vEHEq55+0938qf3j/DTK4di0GrUZz5UxjSeZHm7K3YSvu5oK+W5sSVSq+w2dn7Wpv5bWZ+SXURJhOKbaPuiFKebOr3Mfvb9oH/z4LDx2uL08tHRFqoKbAyxpXBJXrY6jybyaHf6usnSCyfaqWtsiasOqBSgk2Myia8D5/NY1+vkrsM/Vl/CnZOGMrdjcFQRJVSNZGuDg+Ub90T51R9rcYWplFTZbcypGMyabUdYPLWY9Z8cp3xgJr/+zrCojm2dVsNDG3bz1OwRjBqczY+uuAh/QMJi0CIgsGXfKd4/2MwNYwYBcix0qt0dpfwA8rp//5RiJEnih98qoKnLi9sfoMvjp80VrYz0VZFGkp2NSXwV6PR0d+TOjYiFF66p44/Vl6DRCLx/yMFfbh2H2xfAbNDi9om0OX0U52REEcKSz2ISSZy70Gs1NHV60GoEPAEQBMhONfLbv+9i/BArK2aPYMfhZjbvPBGXhLB4apFakK2y29h5rE21lgBo6vSQatQxc+RAurwipzs8TIloQDzd7uZYixOzXsvD00vQCPDPYy1R11tht6ITNKycM4q6o608tnkP11+SS0eEEoSi6rB8427unVLM/94yls9a3eg0AqfbPfxt90l1nqq029ScTDykmHQ8P3c0AoJq1zfIao5ZFL52eD9uucwecw/h9IocaXaydEYpx9vc6nnrjrby4rYjzB4ziG2HmlVVhlsuy6epw4MtzcgRhzNuLh2gpcurkupD5+5Q+x0l99Hp8dMn3YTLG4iKrZZfW0qXV+Tuv3yiEogtBi0v3zY+bnOrkhOJJOi0OL0xC9tJQu2Xh3a3T23A65VmZMWWBr7X5k6owKsRSGhP+0b9qbBnbWuDg9svtzNjWA6znn2fpk4vd14dQKcRONnu4e97z/CTq0R1PEwq6Ut1ZR4n2zxxn9k71tbx8q3jab7MiwBkpxpAAp1OwCBoVMvdWMhONbJgjWwZ1OH2q7nuz9pc/O2Nk9wzqYgVWz6lxRm7qbmmwcHcDg/adBP3TSmi3eWlw+Nn084TKmHdYtCyZGoRd00uxOkRSTfrOdnm4peb97Bq7mjOdHjok27irT2nVAJIc6eHzFQ9BkHDg9cU8/D6IAlfAoNWQ7vLS1OXh/rj7VTYrfRJN2LSaVS1pCmlfVk+owS/JLHtYDOgkN50nG53U2G3Up6bFTfvHLl/ikSaSR+3yaHKbmNJUKHy6XcOMmpQVrKx5hzCmQ4PmZZkPP1l4KI+qfROM7Lun58lySjnIM5bMsqCBQt46aWXGDhwINXV1axduxabzXa2L+vfRp90E33STWw/3MzJYHIsO8VAc5c3LIC0GLSsmF3O8VZ3WLCYKAGvHOvnr8nF2nunyEzZUBWT0KBSEARWzR1NvyBbvk+6iZoGB/Mq8+MGDKFFu9BjAWRa5MDjT+8fYX5VHpkWPb95Yx+FORnUNcqbkEQWJX5RUo+7aKKddpeXQdkWXrppDKkmPSlGHW6vGDfgOh9krpO48NDm9GLQaMNIYwq2Njg43e4Jey1yPL5445iYifdOd3fAnWrSc/+ru1g6vSSh7HJt8PVYdiO1DQ6Wbqhn6fRShudmkWLsVs2InFciu0Nysy2U52axZtsRlU2/eFoRJ9s89M8wUTYwgztf/iTm/QmdM5xeUZ3rLAYta24aw7yKPCwGLSadFo0GTrTJCQaAgAQra+UgfeFEe8yA/V+Fcl2hShqfx3d1xZYGHt5Qz+jB2fgDEv0yTAzIsrB8025qDjSpMqZGnQaJ7u6Usv4Z5GSaeSPEpznW8xGJeEodP/yWiMWgY/4LO2ISL+a/sJ2Xbxv/792kcxwdbj+2VCOBoPyvUadRn90RuVnoNAJ90k34xACjB2WTlaLnwyMt3D+1CJFwyWBFxjTSMk+BxRhbZUYhSWkQojrFF0yw88Fh+XmtKrCpHRM9dRGdaHNzsKnrS7fuSeLcxTfN9qVdKfamGvjFdRfTO91Ip1ukT7qs4vXSB41MLetHToaZOeMzWLaxnuuDBeYquy3uOAWiZLHjyWc/PL2EQ02dpJkNBAJSsrMvia8F5+tYF4CCPqk4fX70Wo0axymxYmgMm2bQqQn1FIOOEblZamwbiZqGJhBgXsVgvGKADw4189u/dRN0w2W45SSxRwxQ2j+D3761Pyo5//iscvwBSVXXc/nEMOWHUJxoCyfGVAbVVZQYLVQZ6asijZzv9k1JnJtINeqwGLSUD8xU19c0k47ygZlYDFpSjLIq0W//doBpZTkU52TQ5vSyaG1d8llMIonzED5RthZudfrIsuiRgq8pyrwv39ZHzfXGgmLJsXzTHqoKbCyeWsTMp97lL7d25xK6vCJpJj3Pbz3E4inFPFNzMG5ed8nUYn775n7mVAymX7op6v15FXmcaneHrcE3jBmExahlzY1jaHX5osiuLv8uppb1o3e6iRe3HeGmqiGcPuxW9++VQ2xhTUaRqLLbONXupnr1DqqC6/sda+soH5gZM8d0zbD+tDnjkyl6pxl5YN2umPfU4w9wSV42i6cVsXzTHiYM7c3v3trP4inF9Ek38cfqS8hOMeATA7S7/Gw/0qwSb80RljiK2nKaWc//3jKO3cfbMOrlvU6GWY+jy8uJ9nD1mZqGJj443Mz6fx4Py09UV+ZxPESZIVIN5qPGFkRJwqSPrdoQqRiZJNR+eci2GFTVoqduGIHFoKVfhonmTi8/u2oo90zW0OL04hclPmps4Y61dayaM4qHp5fw4Lr6cCvvoJLOoaYuVs0dHUbslgB/IKA+/51uP06vX81TOT0iz289RF1jK0a9hsklfenw+Hn5tvHotQIn2tzotZqwYyKAxy+TYJ/7wShmP7cNgDuvvoiRubGtt0AeJ9dfkssvXt8bc8z6gkouWSnx4w+PP0CL00vfTBNpJj2Pb2mIUpK595VdcpPopEL+2djKqQ43b+09w+yxg9U5SNlDvPRBI2aDFr2gwdHlZdthBw9ML8bnl+jy+HD7/KSa9HR4/GqeQSto8EsBlk4v4YF1u3hz9ymuHZbD6XYPm0IIgBaDlufnjmLRhAJESYqbd65paOLWy4fEfD80HsvJNPPYt8tw+QI4fX663CJ6nYbXdp7gk2Ot6v4p2VhzbsDp9dPlFckwJ+fFLwOCIFBpt7Hh4+MsnlZMqvG8pT9ckDhvf40//OEP5Obmkp+fzzvvvMM777wT8+/++te/fs1X9uUgy6Kn3eWjT7oJp9dP/ywzP7qigEq7Db8okWHR8c/GVrJTjWGfSxRgV9itGHQa9T2tJpz8kUgK7Lk5oznV7sZi0JJl0bNyzqiYPqIK4h6rwMac8YNZWXOQ+6cW89beM7x7sJnHZ5Wrkq8Qm9nb5vRRZbdi1mn5zh/eY2RuJtOH9+eq4j50eUV+9ueP+fBIC4/PKicgSWHHONsy15Hyh8kC4jcDiqz/fVOKYnplJoIyhv7zzX1hm1glEM4MjkMIBuIVeXj9ARZPK1aJDwoU2eWFa7rllGORGGobHDh9IjkZJlIMOnV+CJ1X4s8TVuZV5pGTaeaq4j4cdjjRa2XCRaKNeui1VNmtqj3NqtpDODplYkpoQTLdpFfZwq1unyoTeXVJn7DrUTbQGWb5PgmCEDVXRXqBxrquUCWNFEPPvqvQTUxRijHPzx3FlNK+3DN5KHqNlmUb66MUchZOsCMgRRVtlARDPMQjIEpSt9RsKEKPd6F3o6QadYgBCXOwE2HfqXZevnUczU4fbS4fJr2W1+tPsvt4G7PHDOLxt/bz+KxyOt0+eqUZ2bCoQk3QW/RafvStAuY8vz0qOZNp0SNJMLGwF1v2ngm7BqdXZM22I9w+cQh3TR7KsRYXBm03IeiJWeVqV9fiV3by8IzSHruIDjZ1qapFSZuQJC5EpJv16HTwl1vH8Vmrm6PNLkx6LVs/bWLP8TbZmxqJsgEZNHV4+MG4PAx6DX++dRxdHhExIJPJImPTqqASgoLQ+X3B5XYCkkS2xcAnx1r57tPvcf0luWrBPDJZF4oLfS5NIokeIchJZZNOh1YjsPamMQgIBJB491OHOm4q7VZmlvdn93ttUXHQyjmj+OBwc9ShZYJtAcdaXHE7tpdMLcKskws0/TJMUYlri0FLeW4WZr0Wg07DkmnFmPVarvv9uwmV/0JR2+BAQi7WrKo9xMUDMznc1MXJNhdpJj2PfruMZRt3Rx3v3ynUn+/2TUmcm9Bq4X9uGRvWkevyiZxud/M/t4wlIEn4xfBnN/ksJnGh4ZuUn2t3++mbZsQR8JKq1+INSDR1ykoaTq9Iu8vXo8Ku0yPy1wXjQYITrW6cXjEs/t1xpIWpZf3Y2uBAEojbILS1wYEYkMjrnUqnRyS/Vyor54wKs8586YNGCiMsNwUBTDotU5+rjXvc6oo8Vm89zAPTi5n1zPs8fn05JUElpymlfRiRm8mUsn60O32khthUD+2bxpJrivGJIhaDlpoDTUiSrPgtCLGteHunG2NcRQikAHdPLuRhgw5XUPEx1aTjH/vP8F9/P0B1RR6n2zwsm1GKy+tn9phBPBi0NVJyDePyrRh1GiqG2LiquA9Pv/MpaUZdlJWSEltd81QtIwdlUZ6bxcTCXnIjjl5LrF82v1cKNQ1NYXmNFIOOVJOOH11RwLABmTxXezAqVvt2eX8CSDy8LtpaJVIxMkmo/XLQ5vTyQIg1kyVIBvrl69GWlPMq5Bh15KAsaj918O3yHKaU9WVukNSdZ0uh1enleIsLMSCh10JOhoknZ4/g9jUfyXtwrUZtMkwz6wC5oW3hRDupJh3zKvJYfm0KJ1rdtAZzah81Nqk5tYVrPqI8N1Mmb2xrxKDVICDnIFuDKkG2VAOXXtRLfT0WjDqN2gxoMWi55bJ8JgztDaAqoWaY9HjEAE/dMELNnypK1MoznWHR0+70Y9RrEs5LOq2GZZt28z+3jOWiPmn0STfyoysKeOYfMrFOAJbNKMUjynG+TqthxZZPcXoDqmKNXqtBkiT6pZsYkZvJHWvr2LSoEq1G4EyHh2G5WYwcmEUAWPF2NDFm3uodPHRNMfm9UhM+E0adhkdnltE73ajW5U61u5lwUS91HWtzeml1+dRm9MjnRPmNV2xpSDbWnANo6pDX5Mzk7/ClYWJhb17952e8WvcZ3xs76GxfzlmDTwzwybFWygdmodEkri99XThvySg/+MEPeizSnc8w6bQ8v/UQtcFgdO3NY/nwcHOYXUOF3cqD00rC/OLidXkqC06or+eRZmcY+SORBQXA3ZOHJiyOK6z0nEwz/3PzOH4ZYZcBckIxIEmU52ap1xxaDNAJQpDZK+DyBuj0+PmosYU1245wZVFfFk4s4LNWF06vSE2Dg7smF3Km0yP7kweD3EhfztxsC73TjGdtcVUICTURiZtkAfHCRqisf6JCVd3R1phqJvHGoxIIL5xoV8kOCjlka8jm9bbLhqAJWtp81NjCwjXhcsoKiSGysK6sTZ+e6VLnh9B5Jb5koAMQuHtyIdNXbFVfr7BbuW9yUcJ7ZdRpgvY0eao9jVKUsBi0PHbdxayKOGeF3cpPrrhI/e7FIQmLeISZ0LmqPDczzFs33nVBt1qNovLU099DNzFlydQiVmxpoKbBwaZFlSzbWB/VIbO1wYFGEJg5PIef/LlbQabCbuWq4j5x1W56UurQaoSE9yH9Amddp+m1nOryYDYYWDRhCKkmPcsTbMaKczJ4fushls8o5b5XwpWMKu1Wll9bxlXFvZk2rH9M0ubSa0vQsJu3QggpVQU2HphWwpu7T/C7txqiilQpRl2EV+8nPfr7hj5nSZuQJC5EZBi0+ID7XtkZc7w++toeHrimhPpT7fzszx/z+Kxynt7yadSYDY1NqwpsPHhNCVqBsDnV6RX5Z2ML143oj8vr55HX9vBRY2uPa0jSniCJJLqRotMQQMvJdjcrthyIu0+sbXDw4LpdDM/NCiNvyt7uAiMHZ8U6PP6AFPN15bP3TC6k1eWl0m7FG7QBUpCIRP3ErHLmv7Ajam2OF19tbXBwU2U+w2dlxowDVs4ZFXa8qi+hUH8+2zclcW7CpNXyWasrrCMX5Od+sC2F/plm2pwels4oDXvOks9iEhcKvmn5uVSjji6fyHsHHeSU98ftD4Speqab9XR6/AmOIFu+pJpkwumOoKp1mkmnNilaU/S4vH4WTrTT1cOxHE4v5QMzcftE3D6RF7cdoTw3ixVbGqiwW1l+bSlTHw8nneRkmNH2kP73+APUNDRxut3D9ZfkYtBpuPGPcr7MYtCzsnZv1F7hz7eOo+5IC5t3naA0J0MtztY2OFhwuV0tnkeiyy3i8vlj5mkGZpnJMBs53ubi0c3h56wqsPGH740kEJDwBgIseXUn900tDsvlxYpZlFzEqXYXdY2tYeerbXDw0Pp6br40n9+9dYClG3Zz56ShNDY7MRk0dHnCYxyLQYsYSBwfjcnLjjrP1mAM99D00rgqOqGKkUkS45eDpk5vGEmpf7aZh9ZHk4FCCdqjBmcz48mtTC7ty71Ba/YfX1lAplkfRYJQGuMeuqaYN+pP8nFjK3MqBpNq0GLWazFqTXhFibpgU8aam8ayOEL1OxbBQQCWXVtKw+kuVr97iOrKPEx6LbZUA2tuGsvxFhf9s8xU2q0xGzcdnR6sqUYsBi1Pzh6BSa9RyebKs/vrN/eFXcfEwl68eONYlkY0/62cM6rH/XqH24/TK3K02aU2flVF7GHcvoCcY/f5MegEVs4ZxYq3Zav0O6++iGkX59DU4WZV7SGWTCth2cZ6AhIcb3FhTTXw0geNXFHUJ8pGXYHTK3LXyzvZ/MOqhNdq1GvYtPN42H27tMDGZRf1Uv/d6vTx6GvRNTnl3+W5WWp+O9lYc/ZxplOu1WZe4Dn6rxPWVCMjB8n1q1mX5KI9R4gYXzd++fpenq05RElOOn9dMB6jLraq+9eJ85aMsnr16rN9CV8Z2pxe7v3rTnVhqa7M41cRrFeQF5FlG+tZMrVIDTBCiR23X27HL0n4/AFVwvCJYEEZQCsILAohbijeg7FQ09DEYm1R3OK4BoEXbxyDyyfS5vJh0Wt79Jp0+URVpllhc+4I6TpdNXc01au3U2W38dD0Ej4+2oLbJyKF5CNPt3sYkGUO20iG2pwA/P0nl51VRZTIjS4kC4jfBIRabaSb9XGlLl/6oJE1N47l4Y3hG4px+da447G2wUF1ZT4gzw9rght4xYrFpNfy7kEHe463UZSTEXWciYVykLpq7uhgt0R452qF3cr9U4r5jz+8q84PEvDQ9BL8okRxv3TmV+ZHdYnXNDRxX8QCv7XBwe7jbXHts6oKbAzMMocV5Lc2ODDrtSyZWsyVRX1lqfTKfMpzs9TzbW1wcP8ULf8ZlGFXyGfKPYk/V8FLN4/lzd2n2LzrBOW5mbGlWgtsnA7poAc43e7+3MSQQVYLGxZVohMEdX4WBOLOi0rnb+T1/u6t/Sy9tiRqw1dlt7JgQgHzX9ge81rEgIRGEBISmh6dWRbzWi4EHG910ekTyTQbeGjdLkYOzubDw80JN2MjgkmwNpc/6u9qGxwsfnUnP7+2jMWv7ox6v6ahicWv7mLRxAJ+fOVQGlucqgLK8hALkUh0efxh4/MfB5owaDVcWmBjx5GWqDnjZJuLXcfbwo6RtAlJ4kJCm9NLAFj8SvQ4Cx2vSkdUvPm+tsGBIAhsWlTJyXY3Wz91MH2FnNheMrWI+6cVcbjJiV4rUHe0lZ9v3M0dV1xEbQLbt9B4t8Xp46PGFvadaE929iXxjYfcOdzEayE+7AqUcRRa4JkXErOBXBAZlpvJt4p6c1GftCjlzRSjVrWajIVjLbKs/NyKPNqDhSMl7r78ol60uXxRcWRNgwMEgSVTi7n3lZ3qsRQVzzvW1oUdR1mLB8QpAsixocRLN4/lWIsLo07D6Q6Pas/47+B8tW/6JuB8VFfwB6SoYhR0j9VlM0oxG7SqzWUoks9iEuc7von5OZNOQ1Onl1W1h5hc0heXT8SaYuhWvtVr5TxHnHyNkue4zNSL3mkGVtUeotJuxR+Q1OaoJ2ePYLDVRF1jC6PjEEsV+EVJtQ+2GLQ8GLQMWTlnFHVHW3F0htsUV9it1DW2Mt5uTXhcpWGjzeWjfGAmGWY9IwdlMXf8YO6PsX+vbXCw5FWZXLF4XT1PzCoPaz7SagQG21JinivDrOeTz1q4fcIQAoSrcj/zg5Fs/bQpivAHqPf3wWuKOdPhoSaouhYaK8Xb1yxZt4vJpX25+dJ8/AEpKq94aYGN3711gJqGJuZ15pFu0mHSa3F7w7VRqivzMBvi76FqGhwE6I7bIt/ziolVdEIL20kS47+PdrcvjDg0IjcrocLH/VOKOR5UL2p3dRPDJg7tw1P/J6vy3DO5UFUAPtXu5o/vHeanVw3loQ2ywl8AiSXTimnu9JBhMbA8mKteONHOb96Its0J3aNfXdIXkBsam7u8ZFn0bA2Su9w+kV/9x8Us21DPnZMKeXTzHuYGc82RRLHx+VY6vSLVlXmcaAsn0MZ7dotzMli6MTpGBzDrYytKR74/MNuiKnVHjoXjbS7ybSkEJNhzvI1X6j5Tz1Wak4EWSDXpyeudxi9e38Pw3Cy8QcUls07Df1dfwq/e3Mdtl9kTXotAuItBKKrsVnYea4vab0WuYV1ef481OaWBMtlYc/ZxpkNe9zIsyd/iy8T0YTksWVfPpp0nmD4s52xfzteO3cfbWVl7SFUpe7P+FNecA/fhvCWjXMiQma/dmwBFmiwWahoc3DO5KGyhcnpFPj7ayndGDmDphnr+HtJ5Fmq3sfOzNkYOylJlz1bNGZ3wujwRnWbh19HE3ArZU6/CbuXh6aVhii2R8IoBslMMURKDSvfc2m1HGJRtYcOiCvyixNFmJwcdThavq+fX3xkWdqwEzXLA2WV5hhISInGhFRDPx2TcV4lQqw2TVhPGmlZQYbeyYlY5b+09SXluFvdPLeZwUxdGnUa1GYgHt08eW6Nysxg+MLpDU2Gnpxi0Ya9fUdiLe6YU8+D6aDa7wvqWyQoS4/PlDb9WEOiXaWLp+vqEqkgAne7oTpjj7W5uv3xIlH1Whd3KgsvteMVAlMXO9ZfksvjVnQnPp5BSIHxu62nOnNvhUee9J2ePYGpZP/qkm7olDttcDMiy8NHRFlbOGYVXDDDYmoJBJzA6z8rDG+rDxrVyr5XCRZXdhgaBUx1ucjJM6lzY6Yk9HyqI1fm7Ze8Z7rw6QHluFndPKlQLHDs/a8PtE6PINHJnQwFv7zvNsAGxiTYgJzQ6P6c0/fmINpdP9cOuaXAwtyIvTFksFMpmTO6a0kb5KiuobXDQ5RN73NQ9seWA2t2lwBX0tFWeO0V6VwxIUfYfbS4vv7juYo40O3liy4Gozut5lYOj1tdkN0MSFwqaOr34AoEex1mH28fek+2J5/sDTRxvc+PyiWHEyXtf2UVlcN5WkujVlXnoNBqeumEEA7MtCcnZSrxbabfyyMyyb3Ssk0QSAB1ekT7ppriWlJGk4VA7gJ7U7F7a1kiqQceq2kNxz987zUiHy8+CNR/xl1vHfS6FPKdXVInAa24co6oJDs62MG1FLU6vnDxeMbucVbXdx1k5Z1Tc76nEmEpHJcAlg7OTc8QFivNVXSFeRy7IY9XlE7HotTywvp4VF2BhPolvNr5J+TkFXR4vFqNWzZ+kmeQywMIJckFUlCTG5GczboiNxa/uDFvjKu1WHrimhNnPvs/Usn4EJCjPzWRuRR5tQfKnUixeVXuQusZWsiwGrijsTWFOehRhYvfxNj5qbGFcvpVGRxejBmcz69n3VdsggGkX91P3zVV2Gz+7eijzX9jOxQMvocpujblHCG0MUkgp6WY9t18+hDSTPmFOxCcGGJufTd3R1jC13S6vSKcntvqJXicwJs/GrGffV5u3FIINCPRJN8XPnR9oossjqhYlofmznvY1c8cPpmxABo9t3hsV31w7vL+aI9AgFzVdXhGjXhuWbygfmIkkJW5+i4zbQtERRy1GQWRhO0li/PeQbtJz86X5rA6SL26I0+SkoLHZya7jbUws7EVGSFOkTitQXZGPLxDgjfpTYRaaS6aV4PWL6l55a4MDvyhhDtpMKWMu0fOpPDNHm53UNbYE7a9FslLk50GrEUgxaEk3G6hpcPAjMcCWvWd4/2Bz1BiqO9pKm9uPPyDnpCPjlnjXEe/1uqOtTCntG0XwCM3LuXwB1tw0htPtbnYfbwvLjYeOhQfW13Pf1CIK+qaFzUW2NCM+SaLV6VOv4/2DzVw7vD+PbNrD0mtLaXP5mD1mEKmmxKVYQQMPTy/hofX1UfPx0mvL+M4f3o35udA1rKuHXK9yr5OWWecGTrV70GkE0ozJMv2XCXvvNMoHZvKr1/dyZVEfzF9Ck8j5hPUfHyfNpOfmS4dwpsPDmm2NSTJKErHR5vKG/dsnJi5KN7Y4w1QRlE6sDw83M2vMINwhJJJVtYdYOWcUZp2WYQMyGZuXrdrmiDG6X0Lh+hyLGchByMMb6mMyqRX0Szfz4LpdUQG60rF/16RCjjY7mbs6uutf2VwoG46MHmSszibLs72HAuGFUkA8X5NxXyXSQ547jxjgqTgdaBpBUElhZr2G9w82U3OgiZVzRiU8vjIOMiz6KHlC5dgA900pUuVTjToNggAPr4899qCb9e0PBLhnSiEPrq8HoK62pcfPADE7QEtzMqh+YUfMTcb8F7bzv7eMU6/RpNciSVKULU+s850J6ZoJtRLqyfM49H0JKaqTt8puZV5lHk+/c1DdkK25cQw3/nEHT84eweTSvtw9qZBOj59AQOLdg45uKwi7lXmVg7nuD++q1hDKRqan7th477c5fapNUGiBQ9k8Kfd0YLaZncdkksrT7xzkN98dFleRZ1XtoQtm/okFpTNasfP7PM+ESS97Qxu08bsmekq+KKTNyORNKOGlpwJZilFOGjy5JXrOqGloIoAUtb4muxmSuFDQ7vb1GPd6/AFyTHqK+6Xj8iUe2+1uH396/0jUmKltcHD35MKYY/KpG0b0eH7lGItf3XVBdtEmkcQXQbvL94Vir1C7uZ5sKZddW4okwcjczJgFqImFvfCJEr3TTfzmu8PQaARWzR3FEzHW0Fhx65lOD7f96SMq7FZGD87G3iuFEbmZ1DY4uPnSfJ6vPRQWI36R7wkXzl4viXCcz+oKzh7I8U6viEEr8OGRlguyMJ/ENxvflPxcKDLMRryixMTCXqSbdRi0GlqdXqypRqaV9cPjEwlI8Nu/yZ388yLyNY9t3sNPrxyKUafB6RNVRdtVc+VmxvKBmQiCQE2Dgx9fWUDDqQ4WTyvm/ld3RlnNLJlWwn+9tY+ppf3w+gP8YvMeVs0dzbEWl9oUZNFrmVzSl6uK+yAgsPdEG7/5zjDcXpGFEwtAEOI2Bik54unD+sk2QP4AGo8/qvkjFO0uH/dNKeL6Z97nF9ddDMgNILZUA797az9zKgZHqZ+0dHkBIUqVG+B/bh7bY6zg9ok4g81lZkO01XM8ePwBTrd7YsY3Sg5+Ve0hcq0WHly3K2ZjF8Db+04zPt8apVQeeo/iXUuaWc/U0r5s2nUSCFeQAwhIEm3O5NrxZcGWauDqkr5qY1VoDB0L/bNM/HzTbp6bM5pPgs11sZoiQ+1nlm2sZ+mM0rDjtLt8DMwyc7rDoz4nFkPiEqIyb6hKa9NLaWjqBOTYIt2kUy3BlHEYawwBVAyx4fGJpJh0UXZZ8Z7NeK+vqj3EpJI+KgEvkSWWMp+8tK2RJ2aVs2htHR5/QJ1bag40IUiySn8onB4/GkEgxajD0SXX9G6+NJ+H18vj0Bkkhj1Tc5Al04rjKm5X2W2YdFqWbaiPOR//fGM911+SG7fWpqxhPdm9ZJj1NDY7k5ZZ5whOd7jJSjGo+eskvjx8f+wg7v7rJ/zqjX08cE3x2b6crxVv7TnF8IGZaDUCl17Ui6f/cZDmLi/ZKWd3zCfJKOcgQhd4i0FLbrYl4d8btJqYC9HKOaNUG557JhdytFnuqP/gcDOzx+by4vuHKR2QyY+vuIiAJDNVH5lZyvJNewDCCphZFn2Pxa7QoKjmQBO3XTYk5nVV2q2km3Vxu15rGxxUd3pV5YdQKAFA6Ibjmov7xZWUPNssz/Qe7tmFUEA8n5NxXyVsqQZVCisgJbZnue2yIQA8v/UwL90ylqYOD7ZUU8KOEqXzQ6cVEna3BSRZPlXByjmjeuw4BzAbdDwYlCGvrohPLAv9TIXdijGG/KHHH4i7yQDZqiT0Gl+8cUxMxrpyH5Q50RzidRdqUTYwOzEBSiGwVVfmsbK2Z2nSCruVvhkmNi6qVDf1T39/JAathhNtLsoHZvLr7wzDqNNwqt2tdrmA/PtKkkwekKT4cosVCWRnle6lUPUX5Tsr97TKbuXOSYUcb3OzfNMenF6RvunGhMSHzAtYAjDdrEcCAkG1mZ427YOyLbxzQE7KvHsw9vhQjpsIynlibYQzzHrW3V7B0g0JPH6nFWNLNUQppEX+bSjZ5Wyvc0kk8WUi3aTH7U9cKOufaUarkTuQIhNBkRiQZaausTVmd1+nW4xZCO9pvgh9/0Ltok0iiS+CdLNeTS7Hg0mvZeFEe5gq2CfHWrmiqDflAzO5YcygqEKI4s/+/ZXbglazgro2WgxaHr6mhLKBGXzW4sLpFak/3s5P//djnvvBqISxceh8MCDLjMWgZWuDgzuvLmTpht2qZHil3RalqvZF5ge4MPZ6SUTjfFZXSDMnTgGmmXS4fbKi34VYmE/im41vQn4uEp1eEbNOw51XDcWo1SAJYDbqONbipGyATCRp6vDw1t4zvBWiqh2Kn141FJ0g4ArJPyj7Yo8/QJpJh8WgZXJpP3Ycbo5ri7NsYz0/n1nGpp3HWbHlU5xekVljBqkNN1UFNsYOsXKs1cUt//0hIOcuFk0owJpqwBeQuOvqQu6fKvBZ0KJPsaQvz81kXkUer350DItex71//aRHVV+QyRVdHpEnZpXzweFmKuxWll5bwrKNu2MqN2SY9ViC3euxGn8+T0xkMWqpaWiS82c6Db+8roz8XqmY9YmblxLFIIpyypJpxTywbhe1wYJ76PWlGLRkmA3c/fInXHNxTlyl8jvW1sU8V5XdhkaAOycV8v3xg9h2qJmy/hmsrA3PN33TGxO/THR5/GqjFcjPeyKFoJ3H2nhoeglLN9YzenA26z8+3iM5u7bBEZW/yrDo0QDZqd2K9j01TGaY9bwXzKVtbXDg9gfItsiWYB81tlAxxKrOG5mW+Fb2q2oPkWbSsfXTJqaW9Qv7/hB/HMR73ekVmfv8dv58yzimleVQXZFHdoqB/0zQ1Fmem4VJLxNWUgzaMCXsWG0ztlQjjqAtUWZQkeZbRb3VfYTTK2LSayjOyWDHoWYWfUsmxoTm/KrsNhZ9y47LJ6rzceg9Ku6XzojcLGyphpjEOuhew3qnGePWySrtVgZmmSnNST9nY9VvGk63e3okECXxr6FfpplZl+SyaushcjJNzK/MiyL9iAGJVqcXg05zwcSBjQ4nDac7mX6xrISiKL99dKSFK4r7nM1LS5JRzkVoNIJacLzlsnwEJCrt1piSwKFyhJGILADXNXYrG6yeO5rrxwzi+a2H1MXRYtCyZGoxf75lHB0ePwFJ4t1PHeoi9+jMsh59REOh1QhRQVKV3caD00uCTPL4cPvEqIm4ym5j8bQiTrS6AbhjbR0jcjPZvOskc8YPVi1AlMV6fL4c2DcFz3U2FtlQQkIkLpQC4vmcjPsqkWEx8Nh1F3PPy5/EtK4JhV6rocpuY5w9GySBJ7Y0sO9kBy/eOJalG+ujOkoeml7K9c+8B0BLV+IkYahP6OeBsglx+7oVlT5Ph0aF3codEwtwewNhKicfNbaQ3QPpQasJDwTaQjYbiRjrU0r7ckVRb97aczrs85JEAqa5FVuqkfULKxCgR5JNhd3KjZX5eHwifglmjRnEvMp8BmabeWh9bD/SCrs1rOu2tsHBbZfb2bLvVBgbP/TvFWudSFTaraSbdDw6s5Q+GWYqh9i4fYI9bG6uKrAxZ/xgrn/m/bANSZbFwK/f3B+32/jRmWUxv/uFgAyznnaXD0EjUBlcn+Jt2qvsNjo8Pq4s7kun28ee420xj1lpt+ITA59rPY61Ec4w69EEu8ZiYWuDgwemFZNhMXCwqSvh91PG5KUFts/dzZC0UkvifIAt1UBzlzduvFllt7L/VAfDBmTi9IhRJL1QKAm56sq8mOtYqkkbU863p2NGxrtftFiXHItJXGhINWhxdHoSxl69Uo1RRY8qu42xedksCikMRRaLHJ0emjq9zH9hB0umFnHP5EKOtbrIs6awbGM9d778iXo85bNtPYxJZT6osFvZFZwjVmxpwCcGeGvvad496KC6Mi+mFewXmR8ulL1eEtE4n9UVtALx19gCG1oBtAZ5fbxQErJJJKHgm5Cfi0SHy0dqhgkAUYKHIhQzqgpsLJ5alNBm3eUVMejlrn+Q1zsNcqNTrzQjWo3AzZfms2xDPXMr8hLa4nx6potfvr5ffS00Rq850MQD6+q5++pC9TXlWMtnlDIrqF6SZtRS0DuVYy0uivul88SscuqOtvI/HzTy0PQS7vnrzqg9dyx1tEq7FZ1GoMsf4E/vH+G2y+z4RImWLi9bgsScWE1VW356GQDPzx0dZau7+YeVnG53x40Vqgps+EWJVbWHeHL2CPRaDRs+Pk5Ng4OFE+0JP5di1OH2ilH5ttDC9LABmdz7151x82iPzizjp1cO5YE4SuUgN8icbneHvVdpt/Kzq4ciBgJ81urlvYMO+meYeT5Gc9c3vTHxy0Kb00uryxdm572q9hCvLqjg4Y31UTlFhTDxv7eMY2uDg5uq8nu0qlYQSvioslv52+5TzBiWwwPrdlHX2MrCiXb6pJsSxg+ZZn2YrWaH20eaSce8ijzWbjsCwDUX96PCbkUg9vipslt5fu5oJEliz/E2ppX141TEeIoXi9cdbY2bqxvaN431nxzHJ0r0yzBh1Gl6bNJsc/lYs+0Id00qZP4LO7qt6T3+qGvwigFSjDoCQHaKnpVzRoU1zaQadZzp8FA+MJOH1tfz3/MvYXJZX+ZWDO52OGh30yfNpCqAxxvDVXZbTGJd6BqWYTHw6Mwy7n1lZ9jvVVVg49GZZQzooek9ia8Xpzs8Pbo+JPGvY1JJX5q7vCzftIe3951mUmk/DFqBT8908eGRFuo/a8MdjEUKeqdyU1U+/zFyABrN+atU8/5Bue5T2j8DgF5pRrItenYkyShJxIJOIzCvIg8NMGFob3715j61Qyt0sa2y25hTMVhlZ0YitBgWamGxtcERZe0Rusjd+8pO9XOhCcFlm3azao4sxRhPFjEUXV6RuyYVcavbhyhJ9Eo1YtJp+PUbe5lXmZ/wHmSY9aSadGz+YRVHHE76Z5nYdayNmU+9qy62lXYrc0POW12Zx02V+eRkmlkWUcA/W8zsUELCPyIsbC4UObTzORn3VSMn08wTs8o5GbGRi0SaWcfksr5U2m3c+4rcRbJwop2lG6PJDrVBCc4Vs0fQ5fH3qG5h0mvYsKhCVUbqKcBR5o2ukG6OnrpAB2abuXZYDn0zTNz/SrTP8HXl/WOSRkCex2ojChefV7790df28vOZpbh9Ih81tqpzmDLfRcqpVtitzKnIY+ZTWynPzeSOiQUJv1eaSc9PrrwIvyjR6vKxNYQAsiZCvSXy2iK78AVgWP9MrKkGlY2vbDpOtbuxpRn4OKLAWWm38uD0Ejz+AK/tPBGVNFq/sJIzHW6cXpG/fHg0qqsgIEFdY/gxFdQ2OOjswXrtfEZOphmfP4DbL/KzqwtZ8fcDLJlWwrJNu6PWrzkVg3nmHwe5qSqf/hlm7p5chMcfiOmXPX/1dh6cXoIQ0p2tHCdSHjgUFXYrO460UNA7NeF1K3Z4PXXtDbZa+PtPLsOW+vmK2EkrtSTOF2RYDLR0eVk2o5Ql63ZFjdcFEwpw+0R++cZe7p1cpM73AoSN2dAx+UQw/g1Fpd3K6XZPTJJKZMwc65ih+CLFuuRYTOJCRIvTw5g8K4NtKSBE7xNvn1iAy+uLikliWc9FFosyLQa1QHbvK7tYNXc0Oz9r44/vHY5bRPnxFRclvF6jTkNVgY07rx5K9ertqix/StAuUSk8XRUjURNvfojcl15Ie70konE+qyuYdFoevKaYh9bXR8W6D15TjFGrodPrQyMIF2RhPolvNr4J+blIpJn1dHr9IAg8tH5XVAG25kATyzfuSWiz7vEH0KFBJKDGw1JQgXfhRDvTyvoxYaisAjB77KCE1+OJUECMzDXVHGji/ikCGxZWAAJb9p2S7ZN9IkP7pnG63U2B3cYvNu8hr3eamv8Yn29l1uiBuH2BuM1yoXmaKruNJdcUc6rVTWaqnlsuG0KHx8eq2kNcUdQ7IeFDQsKo0bBiy4GowvfNf/yQP80fI8dEROTwC2zcVJVPm9OH0yvy8bFWVtUeVH+TRHuQOeMH81yNnLOYt3p7TBJv/ywzJ9vk3GO8PNqyTbv58y3jwvL+kffonsmF7DnezoZFFRxrcZFu0pOTaeI7f3iPp24YSZvLp9ryxCvoJ2pMTBLzPx+aOr34AxLvHXSoxAenV+SQo4vy3KwoC3QlBnUFVeZNumilnVClDYtBp1pYpZq6iWZzgvHslcV9+DBWnlWKzrMuuNxOU6cnjByRbtbj6PLw0geNzBoziDvW1pFu0rJoYgHpJj2/2Lw3avzUNDhAELh70lBmjRmETwzQL8Mc1tQXb/+/+3gby68ti5lDCN3DD5+VyYm2xDl65b4qyv3K96qyWzFqNVFjtd3lJ92sw+sL8H/7T/P+pw7mhuSENYKsat7pCfDQ9BIWv7orJmlGtjOTrUTijeGahiakiP1TVcQadrzVxUMb6hk2MJO542XCS6ZZzyCrhf5ZSSLKuYZT7W4GJH+XrwyCIHDDmEFc1DuNDZ8c58F1uwhI0CvVyJDeKVw3cgB90k24fSI7jrRw18ufsGXvKX53fTmmHhTLzlXUHW1lYLYFczC/IQgCBX3S+PBI81m+siQZ5ZyENcXAo6/tYeTgbLQaIaY0oBKwr/2gMSZ7varARr8ME0/dMAKTXsvOz1r59HQn90wuxOWVA+rQBS1RwVd5f8WWBqpf2M7/3DyOuePdmPRaBODdg44oRqYiwzYu34rLJ/LStiPcPaWIZZt2M3vMIPaf6ojL+K60W9VA9/c3jESvFTje6qa0fyZrbhpLICBhMWjZuPNE2HlXbGlg4UQ7dbUHzylmtkJIaOr0BpnB+s9dQDwfcD4n474OZFgMuP2BhAzyj460cN8ru9j8w6puWcAY3doKag40cetlQ9TNf6Kx1Or00SvNCMiLT5vLx8TCXmqnRyhCbbBCf7dEXaBVBTYseh1D+6Vz3yux5VgXr9vF8mtL8fjEKELFvIrBLFwTXtgLPV/C+9DQxGetLobnZnHXpEJ+9fpe9fyKZc+Pr7hIVVpRNmhOr8jWBge3X26PeVwFBq0GR5dHTULkZJh4cvYIbl/zUZRvaSQiC5yZFgOiFKDN6WO83YrTK9Lm9Kmbvtv/9BF3Ty7kx1deRJdbJMOixycGqP+sjZc/PBYzafTw+nrmVQ4mJ9PIXZMKWfzqrijG/O9vGEFAkrClGel0i6SZdJxqd3P3y59c0ESxNqeXxa/u5ObLhiCKEvdNLcLtFVk+o5RjrS7aXL6wTbvii7xseik7j7Wy/NpSujwiXV4/ZoOO0+1uZj/7Pk2dXu5YW8eLN47htsuHICEreSnHGTkoi9sn2KlevV29ltDN73M/SCxtqoy7nrr2+meaP/cakrRSS+J8QpvTi1cMYNBqmFLaV02eKON1/gvbKc/NpDw3C38gwPNzR5Nh0fPzmWU0dXo43eHBoA0f20AYQazSbmXZtWV8eqYzpq1bqO3bkqnFOL0ibp8YM979Il20ybGYxIWKNJOBxa/uZFReNpNjjNvq1fK4jVXoikXgDVWn++RYa9jn3D4xYWy4tcHB3ZM08eNWu5V+GSbum1zInhPtPHbdxfgDEpcW2EgxRKdGIo+jzA+LpxaxZFqxmnxON+nx+GSy8oW21zsXcK4Vrs5ndQVfQOKR1/YwPDeLeRGFrEde28PiqcWYdDpyMjXJZziJCxIXen4uEikGLSfbPZgN2vjW0Q1N3HZ5bJv1CruV9w46mFrWD40g21es3XaE2WNk0smq2kNcM6wv3mD+o1eqMeH1hL4fT+m73e2n0+PnZJuLsXnZXNw/E7dXlO160gw8unkPm3edAk6Ffe7SAhs/uiJxw5HFoGPlnFH0zTDxX3/bx+VD+5CVYqDd7cOaauDlW8fzmzf3hlkWRaq2tXTJeZxY9/Noi4s39pzg0oLeLJtRissr4vSKpJl1GLUaznS62XFE/s5l/TPClCuUGOOJWeUsuNweO2fhC/DErHJVVS5UzaT+szZKcuQu6HixktMr0tjiTHiPjrW4uOvlnaoV9Ju7T1HWP4OmTi8pBi0dOg1eMYAUy68kBLHyTUli/udHu9uHxxeIIj7oNELcOHjhRDu+4FhMj2hcjKe0UWm3ct2I/vz1tnFs2XdGfda6PNGWtsoeWYndM8x6/m//Gea/sJ3/uWVs2DH9YgBripEHpxUz8/dyY7HLF0ArgDMiNxyKmgNN/PBbBdzz8if8d/UlpBi1WFMNLJ1eitsvX5dBK3DHtwr48ZUXcardoyqLHHU4GTYwk59eORRHl4deaUbe3H1K/U4LJ9p5fuuhmBa+ocg06/ngsFy0dfsUIoqNhRPtaDQCIwdlhd0Li0GLoAGXW6SoXwa/emM/w3KzwuoAqUYdkiSTdGIRUUDOnyvK6Yn2O7UNDu6eXEhxv3T5u3d4sASLzqE5h8iG0EsLbMmcwzmI0+1uyoIKFkl8dRidl83ovGzEoNpUpEo/QFVBL3YcbuaJLQ385H//yYpZI85LhZR/Hm1hSK+UsNcKeqfx5w+P4hcD6LSJm86/SiTJKOcgMiwGHp5Ryj0vf8LYfCsQWxrQYtCycVFldIHXbmPB5Xb+4w/v4fSKWAxanp87mhVbDvDo5r0APHXDiLBj9ZTUu6kyn4UT7ZQPzMTp9aMRBLYfbmZcfjYfN7aEJeZDC8yTS/vy/JuHmD12EG6vSHFOBs9vPURdkF2rHD/0s0tnlPCDlR/Q1OnFYtDynaffU9+vsFv5+bVltLm8Ma830fc4m5YxGZYLd3N7Pifjvi54vKLM5JakKN/apTNKmPp4LSBLqKqf6cEaRyFYxOueqCqwsWxGKUs31ocRTyrsVh6YVgIQ9nqkDVbNgTNq4JyoC3TZjFK+v3IbD00vSagU4vYFuG9qERqNQEuXDzEgkWbSRVnLhH4nDULM+xDK5geBEblZCAh8GNJxq8yZ5QMzmf/CjpjX9cHh5oQkof2n2rnr5XClqIUT7NxyWX6PajGh71cV2Pj4aAvLNu3h8Vnl/O7vO6M6CZ6YPYJfvL4n6je5f2pRwqTRD68owO2TWL4pWmb1w8YWbtfZefLtA2HHqLRbefHGsSD0kD04j9HU6aWmwcGPrxoqdwdJcOB0B/Y+adzw3LawZ+jX3xmmdjx5/CIl/TNw+wO4fCIpBh1/23OKZ/5xUH1OncGEkscvsvOzNkpzMqgYIm8W83uloAGemFUe1aXi9MrF7HjPXKSs5pfVtZe0UkvifEKL04coQZdPVkGI9EleMXsEHzW2MCo3i3a3H71WwOsPYNZp+dFL/+Ro0D8+FP2zzPTNMHJpgU0ll33nD+/S1OmNS+h0ekU+OdrKTZV5ZFgMHG918Yd3Po0ionyR8Zgci0lcqPCKAWoa5A7A0JhLGb/KmpgblISO9DmPF/MqRM4Vs7v3rUadpscY+USbW1YZFYQYamh5/Mcf3qM8N5O7g0Tmh6aX8sNvFeAVAzz67TKWbdyN0yvy9r7TMe0Vy3MzybOl8kb9SX77N7mIpBRShvdKrICWxBfHuVi4Op/VFVw+kfcPNlOc0530VrzT3z/YjMsnohEEDFoNbc7kupTEhYkLOT8XCUenh3SzLswyIhb0umgiZ2hThbxXNbLneBt3Ty5i9rPvA3LMbNHp8Brk3IIhxnFCj2cI5kkU5VHlOKEw6TV89+ntav7lTIebwdZs1n9ynBEDshjSO42VcwZGKZf840AT900pSvg9nV4/81/YwYs3juF74/LonW7kRIuLdo9MLv3F5r3Mrcjj3YPNaqyytcGBBoEnZpXz4rYjmA1aOhLYYf/ubw1cXdgXnwSPbN4TlUN74JoSVtUejhnPKOe84bltMY9d09DEgglDeOnmsTR1enH7REx6LXlWC0ebXUiSRJXdmjBWMvRQhFLer2lw8EO/TIZ4YlY5FXYrZoOWuqOtTCrpy+mOxM9UZGNim9PLO/vPMHf8YGZdkhv22yWJ+dFIN+lpCXjDGiWqK/LITjFwRWFvCnPSw5SRP2psoWKIla2fOlQrrVCr6nhNyLUNDh5YV0+l3abWUyrsVtLNuqgaS2RtauWcUeq/nR6Z7DEu34pRp0GrETje6mLtB408dt3F3LG2jtKcDOY8v53V8y5J+N1TTTr+8zvD0eu0/Ndr0WPooeklHG9xc+uLH+L0iupcJUoSK7Y0sKr2EK/dUcXxVlfY9Srfpzw3K2FTZ5bFwLABmVgMWgZkmVm/sIIte0/z/iEHowZmsWxGCUvWdSvy//0nl2HQCgRMqLZKq2oPsX5hJQ9vqEev1fD7/2vgR1fI5JlEaO7ycGNVXo/F4mMtLha8+JH670sGZ5NhMSRzDucZfGKAZqePrORv8rUhFgklFKMGZ3P7BDu/fWs/Kwce4qZLE7t7nGtweUX2n+ykYogt7PVBVgsef4AjzU6GnMWcRZKMco7i89h7OL0iTZ0eHphewul2D20uH73TjOw/1cH8F7pl+6or83giQj4wspDq8QfCEv6hwcxLHzTSL9NEXW24z3eF3crFAzJ4aEYJXR65OKfTCBw608XOz9oYmZuJXiNw/9QiOjx+NBqByiE2RuRm8VFjC/e8/AnXX5IbpvbSL8PEkSYnR1tcVBXI9h0KCUa5pvcOOqgIknQi0VNy8kJWAjhbOJ+TcV8XOr1+bl/zEb+47mLumlyIyyuqBbHmECnDtBALnc9LdojcmKQYdeg0sqzy8ggiCsgb6aUb66muyOOGMYMQBNk7TpLgsMOJSSd7hNtSjQwfmMmCy4ew9VOHOl5vv9yOViOg0wrotRoON3Xx+++NpLnLm/B6DzV1sfaDRh6YVswz//iULXvPyGM7NzNmEfClbY0snVGC0xdOVPmivpmJ5gRBgNsvHxJXZvKDw7GVoh6aXoLLK4Zt7EIR2uFTZbdx++V2ql/YnlCBatnGeobnZoX9XorySyL4AxJGvSbmJqq6Mo8n3j4Qc7O5bGM9j84sS3js8xntbh8Wg5ZMkx63V8Tt91Oem8XJdg+2VAPPzRnNb97YG7WmXTu8P8dbXHxwpJnygZmc7vAwJi+bbxX2Zt7q7TR1eqmy23D7RLUbCeRN+OqthxmWm5mQALWq9hAbFlXy8Pr6uPOl0v3r9Pr4+bWldHlF2t1ycizLYqBPuukL34tESK6LSZwraHN6WfLqTu644iKQSDhWr7m4HxJw8EwXJr2W+s/a+GP1JXzn6fdo6uxej6rsVjy+ADOe3MqaG8fwRv3JsGPFI1tGxjDxumg9/gB7T7R/rjGaHItJXKhQCNKhMZfFoGXF7HJW1UbGa1ZWzC5n4ZruNTRWzNs/y8z3ntsm7y+1csJIia9G5WZF7Q9DJfR1GoEXtx3h9gl27po0lC6P3JwhBOX+oXu81zQ4OOzoUtftqgIbq+aOpnr1dp5+5yAX989kWlm/sP3q6XY3bp/I0+8cVK83qXD01eBcVpQ6X9UV3F4x4dh0e0UkJNqDw/lc/z5JJJFEYhj0Wix6bY8EhFSjLq71h9MrkmbS0+b08sA1JXzvuW1h8bbTL5Jq0FFlt6mEUIhtd+nyiqycM4q6o608tnkP11+SGxXnmw1a1T7k2ZqD/OiKi/CK8qTUO8NEXWNLVC5o46JKGh1ODFpBbZaLzHFnWfTkZJj531vG4vEGuOnFHZTnZvLAtGI8LQFanD5qGxxIEKXmppBAlkwtxuULYDHGlu5XzukWAxxrcfGTK4dy9ySBE21u9FoNHzW28IvNe/jzLeNwx8lVJcphWQxaslOMLN9YH6U6vHCCHX8gwJJpJTR1xi941x1tpcpuC7MdDr3/oWo1bS4fK+eM4mizk4UTChCAPcfbqLRbkSQpbkE/VmNii9PHxk+ORz0XSh4vWSQPhy3VwEeNLepvpTyPtlQDL944lqUb66MUTi6/qJe8vs8uBwGWTCvh4aD9e0/q2/dNKeLZmoMM7ZvGwgkFPeakoftZtRi09E4z8nGMsfnQ9BI+PtrCLZfl4xUDOL0iKXHGjwKzVsPAbLOsZjKhgHsnF6kKz7UNDh5aX8/PZ5bxn98djl4rqHPVr78zDJBzym6/SLpZH/asK/NI3KZOu42fXT2U76/axtC+aSyZWsSbu09RPjCT3711AItBS8mscpZu3M3ya0tw+wK0u+QGmS6fSIpei0cfUK9BlAI8eE0JbU4vCyYUcKbDQ7o5sXp8qklPutnAmR7IXgOyzKqVKXTnEnrKOTi6vHCm86yrDCYhQ5mrMy3fbFeBcw2X5GUzpbQvv3pjHxOLep9V8sYXRf3xNkRJIj/imgcGG4P2n+xIklGS6Eab00ur04fTJ6uPiAF48cYxeP0BPozwqVSCdI9P5L2DcmBh0Gm475VdYceMFXBE2m5Y9LELvBV2K2tvGsvSDfVxLXx+dtVQZj71rvr38yryGJBtZmyezIYVJfjV63ujFCGemzOa1i4vziCT+6PGFvyBDFmSLOilKQYknqs9GHVNY/OzubKoN3+LkBzrKVg6W5YxX7e08Nd9vvM1Gfd1weUT+eV/XMyq2kNhpLAqu5WlM0rVANJi0PKtwl4U5WTQK834ucgOEM5O//Ot4/ivt/ar3RzxkvVUwJptR1gyrYTFr+6ktsERRvSIVFl58cYxdHn86LVaXF4/Hn+At/edUeekF28ck/AeGHUaag40sXTjbsbmZ7Nl75mEnrjXj8nliMPJjsaWsLkqkW9mAIkngsdTvm+6Kf4yV5qTQfULO6Is0BQriD9WX0JpTkbUvTvT7uHGP+7gydkjmFzWjz7pJvVvTrW7GZefzZkOD1PL+mHRa7nuD7IkZU8yi/N6kIqMBYtBS0tX7M1GT+frjGHxdqEg3aSnujKPo81Ocm0WvKJIl1ckxajh+bmjeSzE0knB1gYHD2+oZ+n0Ela/1xa1sX9uzmge//t+Zo8ZFEZEHnrEpAABAABJREFUqbLbONXuZvG0ImY+9a76DMaC0ysiQNz5Uun+/fBIC4/PKuex1/dFFci/aCdw0kotifMFiqLRvVN0aDWwau5ofhFnrC7fuIflM0t5YN0umjq9VNitDLal8Ov/GMbcoE2WooLQ0iW//24wVg5FKKHznsmFnG73kJttoXeaMSqGieyibXR0cW+EPV2l3cojM8vItYZLYkJyLCZx4UJ5dkP3YTdfms/zEXEvdHvB3z5hCL96Y39Mif4Ku5W6xla1izLLoqeqQFb+XLT2I64o6hNViFKKGWu2HeFUu5vZYwZRvXo7z/5gVFh3caTUPoQXfWoONCEAm++oosXpJd2spzgnnU63nw63D5Ne7ghetmlPlLJfstvwy8e53t15PqorZKca+N1b+2OPTQSWXlsCEjh9/iRJMokkLgCYdFqaOtzkZJqotFtj2kNU2q2kGrR83NgaM39QZbeRYpAJLYtf3cV1IwfgD0hqnsmgke1nbp8wBFGCm/4YO8eiqJ2FNm7cELT7AXmNXjSxgNd2nuC3fzug5pc1ArS7/PRKlUkYsXJBD66r5/aJQ9BpNCyaaOe6Ef25eGAmD0RYGStqK32zTFgMsn29PyBRd7SVEblZQGwLQZkEYmDJul3UNjjY8tPLoogYChH3+drovPq8ijwWrvmI8txM2SJNDJBi0MZUTU2U17750vwoIgqgHuPh6cUcON1FUd+0uIqsu4+38dOrhxIguilLUcIJxVP/9ymLJtrx+AKcandzw9hBuL0BtILAwgl2NBBFjIlsTFSaDuLVFaor85JrTgyMHJRFcb905ra7EQSBjxpb0GkElsUYB7UNDm67XI5pBQQMGg2NzU7Kc7NYPLUYJLmJKhaRG2QlurU3j2X7oWbcPhFfIEDv9MS2W8qzumRqEQ+s2xX9XDY08cD6XUwry2H6xTkIyOPEotfGJTJNLu2DJAgsfjV6n/3nW8fx6ZmuYL1Mwt47hTd3n1QVj3OzLSycaOelDxrRCPK57ptSiCAISEiATHCPbOpU5qleaUZVvbupwcGSacX8/fV9gPxcj83P5oWth6hpcPDQNbB8425qGhw8N2cUAzLNnGhzk5VqUMeeXtCw/LXdLJ5azOkON09uaWDZzNK4ZLAquw2LXsu9r+xMqN5SYbey61g7L944hhanj48aW1SSS7pJH7fZfFXtITrcPr779HtnXWUwCRmKalnmebaf+Cbgu6MH8mFjC0s37OaF6sRqTucS9pxoR6sRGJgVPrYzzHrSTTr2nepgclm/s3R1STLKOYUTrS6ONDt55h+fMnvMoKiCa2jyTAlgtQgY9Fo1IRdpvwOxWdWRRWBRkuJ27J/u8MS1idja4OCeyULYvwHumyKzVrd+2smmnSdiHldgL8Nzs8Jk4KZfnEOH28fyGaW4RZHlG3bH/OwD6+p57NtlePyBsO7y0x2ez2WB8HXi65YWPltSxudjMu7rgjXFwO/+Fjvp98D6epZMLeLeV3bh8YssmVbM/a/uUsdogGj7nTnjB0dtEBV4/bJU+vfGDY5LMHt8VjlpJh2zxwxSiSiQgOhxoInlG/cwuaxvGNktdE5676AjbnIjtNBQc6CJuyYN5Vdv7I+5ARiQZVY9PX/9nWHdlj1BqfWeLMUWXG4PKzo8kiDQh9gWaAraXL6wRInyfTs8siSrhMRrO09EEYz6pBvVjt8qu00tpvSk3BTr/Z66VgACccx6v8lKUSa9hnH5Vsx6LSdbXORkWzjZ5sbrF9FqYivJgPx8trn8XDwgM0ylprbBgSDs4+FrStiw87j6eoXdyoPTS9i08zgnWt04vWIU2TMUlxbYsAbJgZHzZWj3r+JlG3mMf6UTOGmllsT5AqWLRyPI8tBNnd74Y7WhiS6Pn1VzR3P9M++rf7dsRim//94IDFo56b1m2xGuKOqjJlZjkcWcXpG6xhYAVmxp4O8/uazH8XWq3R1FRAF5rrjvlZ385rvDoxRSkmMxiQsVqcFiSuj6V2m38bu3DsT8+5oDTdw7uYi6I63Mr8pn/gvb1feq7DbmVMhx7sjcLFbNHYVeq+HuSYVoNfDTq4byqzdik9QEYNm1paz/+DjLg2SRNpcv6u9Ajnl1QYneyKLPPw404Q9IDA8WpQD6pMv/jSTBROJCjq3OBpKKUl8+PMG9YizUNDTh8Qf4+aY93H11IWkJSP1JJJHE+QGNAEaDjiMOJ3Mr8jDoNBTnZKhFykyLnlSjDpcosmDCkJgEhQUT7AQCEv5AgJoDTdwzqZBHNu9R18PNP6zC6Q2w4MWPeGJWOSND8ryhqLRbsaUawrr5U406nrphhKw81uGhd5pRVR5TrmPx1GJSTRr6pJsSzl+3Xj6EE+0uUk06LuqTxuJXo62MlX9PK+vHL667mPkv7MDlFdl7op1JJX1ZONHOqtpDUbmU6so8lm/creZ+fGIgyspPIeJGXmNo7KHcl8VTixEDEg9PL+HBdfVh+Z5T7e64OaDLL+qdML7yihLpJh3+gMSSacUs27A77DgVdiuzxwzirj9/zNPfH0mby4+jSy6GhirhKH9bd7RVVc1IM0ogCLja3HjEADqNQKpRx9ghVuZV5qPTCmRZ9Og1Glw+Mczqrae6QnVFXpKYH4EWp48l68Kf4Sq7jfunFcV9Bt476ODh6SWsrD3IQ9OL6ZtpYtWaQ3y7vD8PRpBFYhG0faLE8TY3b+05xZXFfRnSKyVujUV5PirtVspzs7g3ojFagfL7Ll63i2ll/YKEGJFFEwvU90OPeedVhdwfg7hU2+Bgyau7mFeRR/Xq7VTZbSyYMIQJQ3tz3e/fU7/DFYW9ZBurDg/P1Bxk9phBrNl2hNljBnGm3a3uVSJzwRXB7xFKNm9z+rhz0lA8vgDfGdEfly/Ar97YD0CXtzueEgBBA4JGwOMTefCaEh5cvwuPGOD9g80IgBgIkrYCJJxrnT6RrQ0O6hpb1bpdXWNriFU99Ek38daeU/TLMFG9ejuVdivXjxoIyDmHVXNH88w/PgXkJkW3T2T8ECtTS/vy9n65qftcUBlMQp7vAbKSyijnHIw6LbMvGcRv39rPP/af4dKLep3tS/pc2Huyg/6Z5phWXwOzLew/1XEWrqobyd3lOYI2p5f/23+GjZ8cpzw3Ky4xRAOsW1jBun8e5461day9aSyPru8OTkKTaRaDlpsvzWew1cIfqy8hO8WAT5QlxLYfaebh9fU8NL2E+6cWIwWkuAn/yCReJFqc0Uk+AVhZe4jqiry4x41UAlCsKu6cVMh9r+7kwWklfNjYGvOzNQeacPsCMbvLL7uo1zljGfN1Swufy1LG32R4/VL8TfOBJu6fUkRlkFiwJGTDHErUAJnFmG0xsPy13VHdmCBvTN47KH+2X4Ypbkc5wMPTS4Ps6Fb1vYSyjQ1NzK0YHPNY1ZV5KmlEQIja7EZ2V7i83Rv7RJ6jRp0Gp1fklQ+Pcd+UQvwBCacnsZpH5Hy1fNMeVs0ZDQJh46LSbqV/1hcjZynf9+5JhVRX5rGyNnyethi0DMvNwqjT8ofvjcQfkPiosYWXPzzKE7PK6Z1u5KkbRsTsQlC8SCM7FV76oJE1QQnOUNKL4pP65u6TjMjNikkEOleVor5qtDm9PLiunhur8slO0YNFz7sNTQzPzcLl03CyLb79HcCJdlfMIlrNgSYOObrYcaiFVxaM50Srmx2NLRxrdvLBoWZ8Yrc3bCiBSkFP61Bo92+isfhFO4GTVmpJnC9INcrbEpNOg9sf6DH+dHpFPP6Amtjd2uDA5ROpP97Oii0NVBXYeGBaMa/tOqEWpgdkmaMSapHrVGRxM5baXEtXfKJMbYODli5vFBklORaTuFChQbY9fK7mkLq384uxibIKOtw+fnTlRbyz/zRPzConzaTHJwbCbPBqGpq47fIh6DQCkiSRatBR3C+de/+6M+YxaxscHGpy8tu/yTLaCyfaGZhtjoq9tjY4uP1yO7UNTTGVWZTri4WkwtHXi+T9/vLR4fInft/tp+ZAEz+7SkqSJJNI4gKAXiOQatBxKuDmnpc/iWuB+fD0UhatrYuyUldUY/986zi0gkziPNnuDouD210+NIKA0yuv4WtvHguvh+fAquxW5lXm8bu39oeRMtJMetk2AkACnUYIy3VtbXAgSWDWa3pstmlz+Wh1ehGAXGtK3FhdKY4rsXqKUcc9kwtx+0Q+Pd3B47PK0QlC2GfG5VvVa7YYZIsRty/A1BArv8HWlLgkgVC1la0NshWQbEktMXJwFvdPLaLN5SPVqMNi0DIu38oD6+vD9iwTC3vhExPfg3aXn17pRpo6PGg1Ag9NL+GwoytKoeaJWeUsXrdLLXi/8O7huPsj2eoQ0Ah0uP0Y9RoGZJvpcPn47Vv7+f7YwUhIPP3OwZiqrgLQ2OxMeN1Acs0JQZvTy/KN9WHWWUos29wZ3yL9pQ8a+dONY7jzL58QkOR99ZKpxVFEFAjP59Y1tlB3tBWLQUddY4v627+yYDz3Ti5EkvaEN+EV2HjwmhK8fpEZw3NojaPWrMDjD7C1wcH8ynyeevtTfniFnT5pxrDxY9RpONUuE50S7bPvnlwIdKtjTyvLCZtTSgdksv1QMxt3nlDra+W5WazZdoRhAzN5cFoJyyJtroKKqpFNn+kmPT5RosMjP/deMaCS6UL3CnVHWxnSKwWfP4AoSuh1Gqor8ujy+KmuzOOBdbuYFVSB6vKJzE+g0L16nqzAoDRv3nJZvnrNkfP25NK+WAxatSHmiVnluP0Bngtpco9UfL7z6kIshk9xesVzQmXwm45T7W60GqFH+6Ykzg5GD87ioj6p/PqNfVQV2BAiYoNzEXtOtKuWPJHon2lm38kkGSUJ5CJU7zSjGqDGLwg7cPu6i7caTbgcntKRVtfYqsoDhgbDSkD56ekOnpszmmXBAmcsRRUFPRU0YyUb5S7TVjxjvpgSQE2Dgx8Gg5RfvrGXDQsr8PgDdHpEUgxaAhJs2XeKp985SIfbx5DeqTFk1ONbIHzd+Lqlhc91KeNvKtp66Nprc/m4f2oRkhQ+nmMRNRxdXmaPGaQG8woq7FbVJgRkhZREm+9Wp5f/+vsBVswuV1U8vD1sbGO9r8xZSqD84o1juGdyIUeanVE+wwpSDLH9QUMLAsr/X1HUmx9fNZSH1styqKvmjk54jSZ9+LGdXpHqF7azbmEFjQ4nXjFAr1QjBp0Gv1/qkeUf6/sKAlxd0ifstwm1OAp9fWJhL+6eVBQlpRrahQCwas5ofvn63rANXoXdyhOzyvm//aeYV5HHvZOL6PL6MRt0nG5389u/7ee2y+x4RZG5FXlIhHcVnO7wxO2oUaR4L0Q0dXp5a+9pZo8dhNsfwKjV8Lc9pxiTb6XT7e9xTTPqtPgD8dVmahpku6ny3CzqGlvIyTCFJWqqK/PQCAI//FYB908tQisIaDWCqogSDy1Ob9h5EuGLdgInrdSSOB9g0GqosFvxBAJ0enoeqylGHac7usKsd5wekauK+1A+MJNT7W5e2yXLfIOcNOubbmL5jFIaznRGJWWVdSrdrFcJKC1OLz4xwNZPHSqB8NICGwsn2hNeW7s7drEvORaTuBDR7hWpfmEH6xdWcKzZxc+uGoqhh/GbatShEaC4XwZ/ev8wN12aj8snRsWMrS4fv3/nU+6eVEiHx8/xNlfC47p9YtyYLDT20mgEPjnWGlOOHuKTHJIKR18vkvf7y4fFmDj+txi0WAxavGIguTYlkcQFgDa3F6NBS06mmesvyY2rLvbwhnquvyQ3bi7a6RFJiTN/pJv1+APd+1eLXsvksn7MjSgyCwi8f7BZteapsFvxB2RFFQWv/6gq+txePxqNjt5pPVuGKASTnkjtXjGA0+tncmkfBAGmr9jKiNxMfnZ1IU8ErXkVVNitKhEH5OL9nhPt/G33Kb49YgC90410ukW6PInJfqF7/HaXD60g4PKJ/O6tA4welEWfDBMPrpftV5S8wq2XDcGg05Bu1qHTaHB6E5/DbNAQCEC6RY8UkDjS3MWqkGZX5bg5WWZ+dtVQdFoNrU4vi6cWqffNpNciILBl3yksBi2PXXcxP9+0O8qK56dXDeX9g81cUdSH12IooitNkdcMyyHPFm1hGooBWebkmhMCR5eX62OQCSrsVqYmsFe4/pJcPmuRY2W9RsPyjbv52dWF3PtKbCK3oi49fGAmd6ytY0pZP8pzs7jn5U+orswLNgT6WTajFI8Y4HirC0kCvVag/ngbJTnpPLi+PsrWKhLKvt5i0PJhYwsmvQ5/QCS/VyopRi2dbpHUoBpbqzPx2O10h5PVqivywuyELi3oRYvTG1Zfu6kyn+EDM3l+6yGefucg1ZV5zA1ec+90I4Ik8N1n3gvbg1QV2Pjn0ZYwxZeqApu6lwglTK+r+4xrLu7HuwcdXFXUB6NWy6qth7ipKl9tNFPO1+UREyp0h86zTq+IT5R4OIYtk2JdrBBxlJqPTwxQmJMRs8m9tsGBwL4w8k5SZfDs4mS7m2yLAc15QHL4JkIQBP5j5EAeeW0Pb+87zcTCPmf7khJCkiT2nexg+rCcmO8PyDLz9z2n8YkB9DGUU74OJMko5wja3T41MO2pCNXlEdUCY0sE+3RV7SGenD0Cs0HDk1sa4jNfK/JYGrKYJUr492QT8VFQ2jwUPlHuVO256Bf9/plOD7ZUA4u+VcBD66MLuAsn2Lm4f2ZC1uC5YhnzdUsLJ6WMz01Yeij6W4xafr5pDwuDMoXxoGzkF8Xwtqw72qrahIA8jhLhTKdHJmFIspzo7946QK/UxJv7eO8rc5bTK2LSy/6fL247Etfb0mLQ8ujMMnqnG1WG/8k2F7lZFryBAJsWVdLl9ZNm0vP/Rg7gsc3dLHxJkhJ6HUsxLGucXhFHp5dFa+t4fFY5v31rv7rBf3xWOZIkRVjtdEvEx8Lpdk9M2dZYwX5xTkbYXKsgtAshJ8PEk28fiPpOshqWwF2TCnns9T1R5KP5lXmkGLVo/QIZAYllM0px+wIcbXGi0wgM6ZXCyEFZLN0QrarywDUluMTEKjPnK5R5cOdnreRk9OWBdbsYlpuFo8uDxaDjnQNn4troVNltSJIUd8wqa5bSVX3t8Bw27ZRVF4CYxS+lIyjDYoipsKC87g15pr4KVZtzZV1MIol4aHV5mVeRhxQAnUaT0PKqym7DqNVg1IV3SaaadBxq6lKT2ivnjAKi1UdeePdwzOLmFUW90WoEFq6pi1L5UhJP/zjQxN2TChN+l/QE1gbJsZjEhYYOlw+nV+RMh5e5q2XLnfULKxL6nAsC+AISJ9tcPHhNCUeanFFEFJDXw5oDTcwd72b11kPcNTnx2DPqNHFjstDYK92k48qiPjHPqZAc4q3ZSYWjrw/J+/3lw6zXJhybZr1cqIzXPJBEEkmcX0gx6hED8NERB+ND1D0iIa+1g+MeJ82ko93tpypG045Jr6HTHaDKbmVYrpx/iKUMXGG3Ul0p57CURskTEaqlsdSbzHotHW4/B051JMxN1x1tpbif7Ktn0csKaYodUaQ6ba9UI2a9lp9eNRSfGMDpFdVC7bDcTPqmm1T7oLqjrUEVExmjcrPQCAI/vnIoD4Yola9fWBH3/kH4Ht+g1WAxaukKkksyLAaViALdjWkrtjRQGVSueWDdLu6eVJhwDpckWLZhN4/MLCUA9E4zsWhCAQsnFODxieRkmfnl5j1qYX5rRJ5obkUec5/fjtMrMrGwF2tvHsvpdg+zxgxiXmW+eg9rDjQRkCSqK2WFmVj5OZAJKXdOGsr+k/F/u0uDDQNJdMMfkOLGsgLEHwdDrGpc6xEDvLX3DHMiiCIKIUm16jLr2X64mXH52ZxodalKv5F5LVkNpZhHXtvDsIGZfHt4f+4J2taW52YlfC6VOUMMyM9Ml8ePUWfgyS0Homo+i6cWJ7w3qabw+CQyNytKUlR9LcOi59dv7lOvL5Lgc/ekwigiyu2X26kOsRIFeZ6Ugs+9UadRv/PSGSV0uP289EEj/zGiP0uCNkOjB2dz8YAMoLt5PFGeAOS5KzTn/UUUzDvcPryi1ONn5lV2PxNJlcGzi5NtHrJSkr/BuYzSnHQK+qTy1NufnvNklGMtLrq8IrnW2Moo/TLMiJLEsRZXjyTRrwpJMkoQ//jHP/jVr37Fhx9+yIkTJ3jllVe49tprv7bzp5v0NAelCXsqQgUkiZ9ePRQJKWb3toTU7UUXA1sbHNwzuTAsSEiU8N99vI2fXj00ys+uqsDGnPHhBVuLQcuSqUVkmA1cWtALnUbgkZmlqjx6KOIpDxh1WlbNHc2vXt8bl0wzrawfxTnpMb/fuYSvW1o4KWV8bsKsi5/0q7Jb2XmsjdoGB3f1UNxSNsLlubEDy0dmllJlt1LT4MDQA8NReb+moYl7pxThD0gYE1xnhd0at8tVmbMq7FZ8YoBDbe4o/1zl/TsmFuAPSGzaeTxK5nHZjFKWbqxny94z6uuVditLppWw7XALTZ1etIIQUwWkIrhx1sZhE+s0QlRxQlFzqa7M47bL7YiShM8foFeakeufeT+mFVLkd1ZQPjCTVbWHohIetlQDq2oPxTyGMhd7fAHui+OvWtPQxK1uXxzbNoEpZX3DmPqVdivLri3lBys/4DffHc6CFz/kF9ddzN2TC4PdBlpOt3uY/ez7/P6GkXG/3/kMZR6UJJCQ18K5FXlYDFpMOi17j7erNgKRyZefXT00brdRld0WtmYZdBpSDDo+PNyC0yuycKI9ZsJA6Qh69Ntl3PPXnVHWPY9ddzEur8i7Bx3q+Eu0Jic7gZO4UJFq1DPr2W28fNt4tBqBvScSj1VvICCviUFllKoCG2JAirCs1PH6D6vol2FSi5bxipsVdit3fKuA+16J9qiO9Ho/2e5OSIzMSkmO0SS+OUi3yAo/vdOMqt2gVhDiesEvnFDA3/acYmJhb3qnmzjY1IUYkBLuFWVlMgeTj7WpsW4kquw2BmSZ6Z9pjpuAVTpAxQD0yTAxIjczbBwrJAenV+SuCOtTZc1OKhx9vUje7y8XAsTdpy2cUIAAjM+3kmJIpgqTSOJCgEcMEAjI9sWK/UNPiCxWZ1n06LUaUo1all5bypl2j6zOIMn5Z68YwKDXMK8yD5NOm3ANVlQMynOzVLuYyHOHotJuxWzQotUKLN+0RyaaR1gwh1rKPDGrHK0gkJlioK6xJaZC2ksfNGIxaDnR5iYn04xJ131Opbjb4fGHNVgtnGhXVXUzLHr2nGjn6ZpPo/YMn6cgX1Vgw6zXYNBqEAOSrLyiERJak3iD1iVv7zvNognx46u3952mpqGJLp/IR0da6JNuUnNTFoOGZRvqGRa0LomlmiCBeg/7ZZpYGqNBVCHob21w8OMrLuqxEa7LI1LSP4Pifun86s19Yfm+qiS5NCbEgBT3edDpBBZMGBJVo5lY2AudVkPdUTmv1O7yq2pnCuKpB4bmEeORumsONPHw+t3cPnEIKUYdnV5R/RvVqhqinhdlbFbYrbx30EH5wEwyzHoe2hC7aa/D7Uu4zz7TEf68ReZmU4062oPKSMp7Om388bW1wYFOo2HTokqanV40gsCALDOT/6smZk64tsHBbZfbcXR5mV+ZhwaBPulmNBr47XeHB61G5XOlm7T0zzSH3SOQ4pKJquw2vGKA5deWsWTdLmoONPXYsJ5q1PHCvNFsP9JCulmP1x+gqYcxqTwXIwdlJXOLZxkn211kJee/cxqCIDB9WA6/eXM/2w83M3pw9tm+pLjYf0q24BmYFZuM0jdDJn4eaupMklHONrq6uhg2bBjV1dV8+9vf/trPb0s18MHhZrXYFS+ArbRbefdTB6Nysxiem0WvVGOYzUR1ZR5/ev8Ic8fHlkhTLQQQ+GP1JWSnGPCJATrdfq4o6gPsjQpoZ48ZxI0vbI/yDh2QZeZXr+9TF2eLQcvKOaN46u2GcBkzu42Vc0Yx/4Ud6t8qjOtI5YGKoLJBh9ufkExTXZFHp9tPn3Ocj/J1SwsnpYzPTei0QtwN45JrSrj2ya1hryXavKpBviCEbcCr7FZG52UzclA2yzfuTjiPRBLB2t0+6hpbuNRui1n4i9e1orwH8OKNY9BpBPyiRP2JNiaX9mNaiP9nmkGHLd2AIAg0NjuprsxneG6W2plSc6CJxa/uZHhuFlv2nglLgJxodfE/N48L2iVICP4A1RV5zK/Mx+0Tw2wWfv2dYVHXWGW3UdvQFJMdHtpxosxTCyfaKc/N7PHeTSzsRXFOBuUDM0k16nj19gqWbgj38awK2azH2sgcbU4sNw/xJWZrGpq4a3IhCyfa1ftY2+Bgyau7ePKGEWg1Ak2dXua/sCPm53uS6T5focyDZf0zaAtKfMpkKx0un5+7JxdS19iiKhs4vSImnRatRmDe6g94cvYI9p0I91BUit+znn1ffS3DrKdfSIHE4++W2oxM4Jn0WtpcPj48Eq4kphBVfnRFQcjmlLD/j/RdTiZrkrhQYUs1MHJQFi6vH78occ/kQj46En+s/uF7I9l9vA2Q59oHrynmk6OtHHR0e5JLkhRGRFGgFDdbnF46PH663CJ6nQYBgbrG1pjXF+r1fvfLn/CXW8ez+NWdUcpTj8wsUyXCk0jimwCzVsNLN4/jofXdfvQWg5YNiypYNNHOPSohVkeXx4fXF+Dpdw5SnpulJlgjE8mhyevQ95dv2sOrt1fwcEQCu6rAxvJrS5ECEp82dSW8Xq1G4Ldv7WPYwEyG52axeGoxbp+okhwAFq6ti7JxVNbsJ2aVJxWOvmYk7/eXh+YuLz6/FHdsNnd50Ws1ZFqSTSxJJHEhQArIljCRRelY6J9lZmppH2653E6H269atmz91MGe420snlbM5l0n+OXr+4HutTonWNz4846jzImTiw7FG/WnWLGlISovpagzKcTWLIueVKMOr1/EYtDh9IrMf2EHr/+wimMtLlpdvijLzdMdHob0SmHF3/dTnpul5qMUZZS1246weFoxzV1e7n75E1bMHoEpPVwB2OMPIAhi2Octei3fLu/PknW70GkF+qSbovJFb+87HZPsV2W38fCMElxekdwsM4OtKQgaAbdPRCsIzKvIU4vnCkLzCRKoFkD//d4Rppb14/5gQ5nTK6LTCBw804nbJ/L0OwcBaHP62BRhnfPijWPURp1EhKH7JhfJBfGNu+OrrQcJ+m0uHwOyzAl/7y6Pn+ufeZ8Ku5U7ry7kB2MH4wzm8U63u9FqkvYUoWhzetVm5Vjw+iTmv7AjTC3boteSmWLA6fGruaQ0k0w82XeyQ80NxyOa1DY4WPzqLmaU90+oqvFhYwvWFCNef4B2l49Vc0erajn3vPwJv/1/w7h/WjFtTh9mQ7fd09j8bGaPGaQSxgw6TVxyyI4jzTw0vYSHI4hQVQU2ls4o4W97TqqvVditnGp3czwkVy0Ap9rd6vxSYbdGuQpEot3t47mag9x8aT5dHhFHpycqnxeqrCRKEilGHVs/bWJyWV86PX56pRk42eYKs46/fGhvkASVfHLH2jp+dKWdZdeWsuTVneHfz25l+cxSHJ0uzrRLLJlaxNEWV495hS6vSPXq7VTarVw/aiCpRh0tTm/MnKTyHQKSxOKpReT3SsXpFcmIXbdO4mvAiVY3F/VNO9uXkUQPGJGbxYAsM8/+4+A5TkbpxGLQkh2nOS47xYBBq+FQkzPm+18HkmSUICZPnszkyZPP6jUM6ZXCvVMK+c2b+2IWhEOVSIq/k86KLQ289EEjL982nvtfkZPho3KzGD4wM2Ywl8g/e15FHi+98ykPXVPC8TYXFoMOi17L5vqTalAfGYy8/qMqbhiby20ThtDq9NErzcgvX98bxV6V2Z4SrywYz7EWF4IgYEs18Lu39kfJoM0ZPxitIPTo7+nxB2h1xQ/OzhV83dLCSSnjcxMmvRZBkJgaJGd4xQADsszsOtbGoaYudRzUNpzh4RhBd2hC3ukVWbvtCHdePZS54weTatRhMWrZeayNGStkUkt1ZR4VQ6xcO7x/8Fixu0YUWAxa1XbkjjgWQHesrePZH4wK+16KmsnyjfW8FaFmcnVJP3IyzWSnGvD6RKypJu5/dWfUpvyVBeNxekW0GoETbW76Z5rRawXK+mewsvaQupF6IET+NPR7/OzPH4fNIxlB+y4l8B6fL3eZOL0imRY9FoM2ruKJUhCJx+qvsluZE7x3FoOWF28cy9KNMvlk4UQ7dY0t0d0DDQ4CEObJGYqelLB6+ptjLU7qGlvCCC+1DQ7u02qw6DVhZMVQVBXYLlj5bWUe3HOiHbdf/q37Z5h5eP0ubq4aQv9sHes/Ps5dL3f75lbZrcyrzAtuxPQMEsz85dZxnO7wMCjbQopRy3/8odtDtqrAFuVZ3enpJmbGlDWNQ0z6x4Em7ptSFKbUU12RhyhJPDCtGDEAnR7ZIiDLYkgWuZO4YJFhMfCL6y7G4xM52e5Gkkg4Vi0GLfdNKVKTt20uHyMHZbF4XT0gr0WDrJao2KfN6cXR5UUCHlq3K2qeT0QgVNaJpk4vkiTxm+8Op6VLtvFIM+lJNWgT2kgmkcSFiADw0PpdUXvAQECWwa6NGGOKNLRflJWM+qSbsBi0bP5hFYeauqIKS6HFKqdXpN3lZfGUYgKShMPpxS9KfNTYwgcHm1n/8WeqJ3s8OL0iW/ae4YYxg5j/wg6uKOzN8Nws9f1PT3fGjJ0A1Y89uadK4nxFiklHm9vHM1sOxRybKSYjAiSf8SSSuEAgIVtUvHjjWD480hw3P1Bht5Kq13LnpKKovI2Se/n5xt3cOamQoX3Sw+yWczJMHG9z8+Mrh+LroYu/d7qRVbWHohoUFWUPryiGNdNU2W08cE2xapMjE05kAsPabUfCSLCPfruMYQMy6PKK/OjKoXxyrJVFITG98j2Ot7gwG2VyS4pRi8cXCCuqZ5r1HHZ0ReXNf3rlRZTnZuHyBvCK0d/z6XcOcnH/TK4dlsOSqcV81io3HtUdbWXaE7WU52ayaIIdl08kJaCjw+1HlCTuWFvHy7eNV3NYI3KzsOi1BJDYfriZiUN70+6WVS4eu+7iMDsf5R799Oqh3PjCdvW7php1lOdmUdfYqr6m5Nl7Ulo40uyUbRJ7aBBVsOd4e8LnSonh5GveS3luVti9fT03i97JHIcKudEp/m/k6PKoNRrlmbmquA+/en0vw3KzKM/N5I61dWy+o5Lntx5i38kOVs4dza/f2JeQaLK1wSHb1Xhi50stBi1Pzh7BjsPN9A5R3MnJMPGHG0YiIWFNMfLzCBJT5PPZP9NMc1d85Y7ivhmcaHVz35Qimp0+2kJIZz/fuIe7Jhfyu781MDI3k9snyopuim02AIJsRbFwgp1naw4yryIPnTYx4SnVqONHV1yEVgCPL0CWJb6y0h1r6/D5A7h9IqMGZXPDc9vYtKgSrz/A0H7pYWrdkiTQ1Onm4RklPLBO3iv97m8NFPfJ4L6pRUgIdLh8pJn1CEgcdTi5+U8fUp6bybIZpaz9oJEHphV/Lqv62gYH972ykydmlTMw08zKOaNY8XZD2HMyLt9Kld1GilHHyEFZ3PDcNor7patE+1DEsytN4svFqXY3Y/OtZ/sykugBGkFgcmk/nqs5yMEzneT3Sj3blxQTB053MCDLjBDHNUAjCPTNMHG4h+adrxJJMsq/CI/Hg8fTvXi2t7f/W8dr6vQy9/ntLJw4hMVTiznV7uZnVw3lnskCrU4f6SY9Jr2GmU+9C8CALHlhMes16IBHZpZxtMVFtsXA8td2x/Tr68k/uzw3i4c31LN0eik/f203RTkZ1DW2xEzGV9mtBAISZoMWk17D/Bd2sHLOqLg+kTUNDgIS6qZCWQhvGDNILXgPslqYvmIrv/7OsB4LtEadBo8vQJvz3E8Gft3Swkkp4/j4ssft50WH28+81TtUP1VJgtZOHyX9M9CF2OlIEjR3ephc1o95lflYDFrEgMR7Bx1qQr7SbuXuyUXMfvZ9mjq9rJo7ise3HAgb14rSx7cKezG3YjD3TiniRFv3Rji0yDaxsBfmoKqROSjTF2tzUmG34vaJapfKYFuKHPRHEFFADoIfXL+L6oo8Trd7yLOmRCU0QCaqLd24O7hJbmFeRR7fffo9Fk8t4vlaea6KZ30S2ZEBciDeP9PEX28bR6pJz7IolRIbT84ewcfHWinrnxHFDlcY7E6vyD0vf8KfbxmH2x+g1ekjzaTDqNcwK2jfU12Zx9KN3UmAnjZ11TEKI6Eb83ibi0hrmEj0SjXGvBedbh/pBhMLLh9CQJKikkkLLrejixOcnGv4V8ZtTqaZdrePjZ+coMpuA0FSu4AeXBddMFNIQ0umFqHXaDAbdPy/p+Xfes2NY/BL3fYBoeS+460u7g7K+K+cI5O14sqaJiAmaTWCqmqlbBYfn1XO0o27o5RRFIuAJJI4l/Gvrrc5mWb2nmjH6RVZuqE+4VhNNero8Pj43soPcHpFJpf0xRP0fK8qsPHozDL6R8hTKmN22MDMf4tAeGmBjewUA11ekZ+/tieulUcSSZxv+FfGrtsfUMdqaFK8pzF8ss2FEomsfvcwS2eU8NePjvHWntPq38ciUbe5/PzHH94Pe39V7SGemFVOTYODYQl866vsVj5qlFXKlER/pI1puztxU0RHD+8nkcTXjS8yblP0WlbWxo5TJeDRmWUxraCTSCKJLxdfV27K6fXTO9XIPa/spK6xlY2LKlmyLrrR566rC3nvoIP1Hx9PmDP2i1IYWaTCbuWiPmn0TjOy43AzAiRsiNFrNPzvLePo9PhwewNq/vdUuxu3T+RIc/gaq+SM7p9SpL6WYtTz/ZXbuP6SXOZW5OEPSORZU1i2sZ57/9pNYq+IIJkr32PJ1GLa3V5WzhmFo8NLqknHXX/5mKF901g1dzRpRi1nOj2snDOKNJNMGqk72kqXRyYAjAiqlEffa5Hb13zEuoUVUfY2ofdxalk/bGlGUow6Uoxanv3BKFKDOTmleKzg0ZmldLj9vHfQIefJYuYZmgggcf0luarizOb6k3zc2MqrCyo41eamw+tnYLa8L/o8+XZ/QIqyoA5VhvCKASrsVnYdb+Pi/pncHiPvVGW3Ma9yMAvXdMdwslW1/Fsqx2p3x7ZJPhfxdYzbTo8PSZJYc+MYWoPqRKH3Xq+Rf7/QJqjygZnUNDj4sLE1qLB7CIfTR11jK7+/YQRur5/bJ/x/9s48MKr6XP+fM/tM9sywhCUQmEBIWAw7JFFAKiAgWmuvYFs2tyLYzV1wAevS9v7aK2jrAmpb0dvqdQG3aqGaoKBIVHYIhAQIEDLZM/uZ8/tj5hxmD6Io4nn+UTIzZ86Zc77L+77P+zz90QpC0vt6pMkV83zIe/tJA7uQatSzpuJgTPPk0ksH8dnhZp4uPxjz3Ic/n5/XNnGsxUW39MRxsmyD9ZcPYm2wIGg99uL1Y0kx6NBrBNo8fv66YDStbh8ZZj0WvZa/bz7E0N6Z/HLyADRC0Lo3GWHq7Z3HWbWhirJ8GzdN6M/WQ40J58FloZqdVhCUHLJOK9Dq9mPUafCKAeW73H4Rk17Hf7+zh9umFvDzEKns//1rb1yyV4ndquQhPP4Av7lkAMvX7YxrVV8aUhrSaeHxa4Yr9/JkuwedRuBQQwcLSvL42bi+5NlS4qp4P3zlUG5+oTKGaB+e55Sh5ji+frR7/HR4xYQqFirOLZTabfxj62Ge2XSIFZcP/rZPJy72HW+jR0byMdot3Ui1Skb57uGhhx7i/vvv/9qOJye9vH6JJqeXrmkmPGKANrefLIuBVKOWdk+wC3TNvFG0uf34AxLd0s18eDCYOCjISQcBrhnThxSDliuLe/LAG7v4d6hQ3FmxdGFpP1ZtqOJ4q5tBPTIS2gSUhNQBfvSXjxiRm8WyGYVYDNpO2dXhsoPxlFb+ecM4/r5wDGaDlje2H0voBx4uw9YtPVZ6/VzENy0trEoZx8fXPW5PFy0ub8QzLwcNj7y9J4I4VpbfhUfePmWVFS6rt3J2MT0zzRxtdilEFAgqgSSSN/z3npPMGdOHd3cfZ3TfbFZtrIrxFL3r0kKlm9Vi0LJqTjFIUkyn+LVl/ag8HCRxAIhiAAn47EhLwmDGPEHLnKe3sG5JSVJ/zgVhMqEyYUf+/tMleZTZbSydMUgJjFesiw3+P61tYpE+GFj86b39yt9L7NYgycYXUH73lbOLuSdOx/zK2cUsfG5rzHl1Nv9FI7y4Mr5/0J/1ntd2RlovhWQof7t+d8JjGEKBYjThJc2kp80rsuSFSh65cqgiw51m0nGi1c2SF7bxt4VjvtQ5f1s403GrFQT2HGvl/llFyngxaDUMy81iXpRc75pQUn7Z9EI6PD6OtriVbuwPDzr4/HAzry0uobnDR1aKHo1GYP+JNmobncwvyWNY70y2H22hxG49I2KSViNEqFolIrREWwSoUHGu4kzHbV2zi1a3D61GoDy0LsWTlx3WOxOXX6S20RUkn9httHl8dDWYeOeXZUgEu8ZEScLrD9Du8ZNq1LG1polPa5qYN77vGRMIZUIaEJOkAXWcqvhu40zGrkzOiE6Kf1rbnHCPuCwk4e30iggEfejveW0nSybZuWXKQI42xSdRR0v6hxNyoxXuwl+HUzL5M1ZWAMFiSzwb03RTcnWjaPKKChXfNr7MuG3z+KlMMjbbPH5cXpG8Lmf5pFWo+J7jm8pNZZj1tIcRMRraPBH2M7LiQFOHl+xUY6dqGLJCSfjfb5uiwesP0C3dpFhZLnttR0xuY8WswXhEkR//+SNlfy+jrsXNA2/sZmVo/Q5H+f4GxFD3f4ndyme1zUoR1ekV+eXkfP720aEYAmy8pp1NVQ7EgIRFr+eRd/Ywtl82Fw/qxrPzR/HjJzbz2MYqSu02/vhuMF/0558M5+d/3wZA8dyRlIZIrZcO7h6X+Or0irh9YueqIlLQTtSo03DN01vYeMtFPB6VrwPomm6i2eVjTUU1/7hhHHe9siPi9fBYKcWgo6S/lRSjjgXPBlUoPq1pojg3kw6HiM8f4KErhrCjriUJadeGVhDobbPw148OJVSGyLOl8ODlg3E4fTR1eHH5glba15X2Uyx4TrS62X60JabB9XBjpLpvuum7U5r6JsZthtnA797ZG1OHeXR2MWu31NAlzUCZ3caw3EwlZ3TNmD4AEWq7flHi+gv7UdfsYv32Y9xY1p+u2aakih99si1IwINXDFbURuS9PRC3mWNTlYMmp4+uYXnccFgMWopzs5hW1J0JA7rg9IqY9RomFXRhQ1RjIwRzdvFssGSU729g3vi+eM0BfKLI7Kc+VvKrFoOOrYcaufPSQu5ft1MZx5YQ2Uv+fPi1h5Pey/c3EJAkxSY4GpuqHCybUcj2w83YUo20eYIx0PEWN90yTDR3+HD7RFbMGsyy13bg9IikGHW8seMEG/c1sHhSfyYN7HZaqkPtHj8WvZb39pzkw4ONcRXMmzq8OH0ii57fplzPZcN6IEmwfvsxKmubWTm7mJNtHuaM6cOC0n7Kfi+8CSecaN/i9Ko5jm8Ix0P2UioZ5bsBg07D5EHd+OfWw/z6BwPIOsfuWyAgUXWynQt6ZyV9X06Gma01jd/QWcXiu7Pin2O48847+fWvf638u7W1ld69e5/x8TLMelbNKeaZimr+9N5+bKkGXrx+HI/+ex/j+lvpmmpkZN9s/rpgNL99c7eiGPC/n9Ry57RBNLR7IorYEGRJ3jKlgJ+O7YtHDNAjw6yoGkSzXwHFQ1SjEbiksBtrKqqVTcyvJg9QJP3Ck4LlVQ2sWL+LBaV5nbKrUzvZYFqMWrz+ABaDlj3HWkMSzkKMxciSifm4fCIPvLGbyQVdz/AXV/F9xNc9bk8XFkPksx9eaK5UmOtBhI/haNLWmzeXUXm4OSKY84vJO9c8/gBPvH+Q4l5ZEd7gaSYdWg08/OauiKBdQGDakBylwyQnI6jk4hUDjM7L5sMDDmXeKLNbef7asTzy9u64wUwAidVzRyaUeQw/R/na5c119GuJkGrU8fy1Y/jooIMrHv9QUbII39xHS1fGS1JoELhjWgGLJ9m5dHB3HnpzdxxGvwMQeOnGcQq5QUZn81+GWc/quSPxigG6pBox6DQca3HzzxvHcbzFHZTU7J3J7VMLaPf4FUWcAyc7uHpMLi6/GBOMzi/J41iYN6r8W5XardQ1u8g063n4yqGsiSI1lIQY8E7Pd6MD5UzHrVGrYdmMQjz+oPyuLdVAjywzPZoiJWB7ZJh4bM5wblq7jboWFzkZZgzaoMXR/ZcV8daOY3xa08ThRheL125TgvHo3/Ta0n4M7ZVBKE+WsIgud5uGW0k1h1S+fn/VMDo8fty+WMKmDNUiQMV3AWcybuXEx12XDgp6X4ekgGVlLxk9MkwYdBpanD5lrC6dPogmpxetRuCz2hYefGsPD185NCaRV2a38udrhpNh0SfdE0dDng8ArivNI8NiiLDyiDfem50+dZyq+M7hTMauTM4I39/OHd9XWS/XVFQr46O4dyb/uGEcAuAPBPctGSEbxfL9DSwszSMQkOhrS+G+1zq3moRYAlm07Z2ctO2SZqTZ6VXIpvVtnrg2prZUg6JWFo145BUVKr5tfJlx6/KJCdfWx+YMx+0TMZ2nVp4qVJxL+KZyU0athsb2UzkDjUaIG2f+5ScjCEid55ZSjbF5XUEIqgF7/AGuHp2LRxS5b2YhbW4Rtz/YVCkg8HG1g6G9MpKq8QJx9+iNHd6IfUBxbiYLSoPKaBcP6hrRbBSOTVUO7po2iKlF3TnR6ub2l7/A4fSSbTHwaU0TN02wk6LX0haQFNLKLy7OVz5vCFMy1goC80ryWLulhgkDujA/tPeIzgu4vMlzWB5/AJdPxJpi4EhjcC4O5tfjqECE9jDye8JhSzUozaotLh+CILKttolddS387kdDERB4uuIgd74SbnlqY8nFdi4u6EqLK/g5+XfeVdfCnDF9qG9z8/fNhyJIS/J71m6pYdn0QUgSnGhzM++ZrTE2SOFW2jIBIBxGnSZMZWLQOVfQS4azPW5bnF6WvbojLuFDg8C8kr64fQEWTexPQEIZR+H5SDmPPKWoG6V2Gy0uH5uqHNw7s4gH39iV+L7OKKS+zc1NaysZ0SeL1XNHsq22iWdDe/vwJsJoNLviqwaGx/PHW93Kd24+2Kgo5IQTUkrsVjq8Yqd5YI8/gNcfICfDrNjOAyydXsjS13by1pJSHrp8MG1ekUOODrqkGjHpNdw9fRBHm1ykGHV0ePwxpHf5t06GY81uslONmI0atBo9pXYrGgQMWg0mvZaKAw1kmLUsv6wIrxhQamkWg5aLC7pzpMnZ6bVBMNfdEcrZxmvohqAiSvi931TlYMX6XVw6uDuVtc08Nmc4x1tcETZY4flP+b6GE+0b2r2qXek3hBOtKhnlu4ZLCrux7vM61n5cy00T7d/26UTgaLMLty9Az6zOlFFMHG9x4/UHlAbnbxIqGeUMYTQaMRpjZfnOFClGHc+EmIkWg5ZHrhzKH/+1hwWleeRkmGlx+nD7A+w82sLovGyuK+1H72wzlw3LwdHhicugrqhyILGH0XnZFPXI4ME3d8VIqYVLFgYCEi9ePxanR6TD4+fF68ey4NlPWLWhiuLemRFSjOEor2rg5xP68/GhxoRqJmWhDv5E7Gu5w3/6oxVYDFqemTeKp8oPMqJvFr+YnI8/IJFi1GLWa3ln53Ee23gAp1dUO9NUfCl83eP2dKHRCBHPfrhygmwJ88iVQ/H4AgkL2Gsqqjnk6IjoICjOzYybDAiHvDH1S4FYb/B8G3PH9+XDg42K9czTFQfZFKaS8rso8kb4vFFe5WDF+p1ckJsVEUAogcClg5j2aAXrlpSc1jlCZLAd/Vo8+AMS1zy9JeJv4YFQPOnKeCivauBujcDntU2dvm9eixuzPjJJW3m4OakkvFGnpfLwSV78uJarR+dS3DsTMSChEQR6ZJhYWNYPjz+ATwyweO025T1d0oxc/eTmuCz4m1+ojOgcMuo0lNqt3DOziDlPbeYfN4zj6YqDLCjJi1FGeenTw9w6JT7b/1zDmY5bjxhAlCRMWg1aIRionWx188b2YzEJo8UT7dxwUT+FSJJnTWFsv2xmrKxgRG4mj84uJtWo5YaL+iW1jRqdl01Jf1vEcxdN1PphcU9umzqAgu7prK6IfF2WvuzMAuDbsAhQPWNVfBmcybiVEx8WvRZbqpEbLuqHSa+JO2b7dUmla5qRTIuB26YU8N7uE/gDEiP7ZNM7O4W/LRzD9iPNVNY2R3zHp7XN3KTT8t/v7E26J+6ZaeaNm0txekUyTHq6pRtjnvfWOGoQEdK3IQWVb0LKVh2fKr4unMnYtRi0lNltEfvbPtkWlq/fpZCu442PueP7cvMLlYzIzVLGX6pRh14rUNfoYlhuJvNK+uLxB+iVZeZfu07EJIxlePwBdh1rVfZi0UnbEruV4twsinqkK93ZWRZ93HGSYTFEqJXJCLfpU6HiXMKXGbdWiwGvP5BwP5xtMSBovhtWnipUfJfxTeWmOnwiaeZTedOKqgbK7DaF7Cnnn/paLRxtdiU6DACZZj0CsGbeKHQagewUAz4xgNsnkpNp4miTi5G5WZi1Wjq8fjq8QbKD03uK7NDHauGuSwv47HALXdOMSs7rRIubkXlZVNY0YQ2zwJELp9YUA8W5WRGWOwtK8lhQGrSGToaaRieLnt9GaaiZqr7VTYfHz6OzizHoBJx+EX9AUpRa5OaRsnwbORkmxQJDqxVY9LdtLCjNI8Wo41f/+xmPXTMcnVZDmyu4B/eJAQJScosboy5YtL7n9Z0sm1FI7ywzHQkauIw6DTvqWlgzbyR+8VSB3mLQ8vTcUTFNqTIhZO3mGiYN6qbk9haU5jEyN4sMix6DVkO7x483tHdaU1HNiD5B1fOFz37CilmDuXpMn7i5jPkleeRkGPn9O3u45ZKBcW2QwpVookkF4Qp3m6oc3DOjkG7pkc1C5zLO9rhtaPdGELHDUV7VwLySvohS0CrrLz8ZobwWno+U77dWEBADEl3TjCyeZA9a5SS5rz0zTGw/2qLc08c3HmDpjEH8/p19QPJGQZNeixSHzJYsnu9rs3DX9EHcMqUgYvy0OH0YdZqkufEMs54PDzoY3+9UDL+pyoEkBXPsbV6RVIOWdrcPvVbDH9/bx97jbfzv9eMQBAEBEJLYlrsSNKgAdE0z8vmRZrqmG6lrdrN0RiFI0NjhwajTsaeulR8W98QnBhAEMIcav9fMG8Xy9TvjKrCGw6gLNtts2HOiU4WDDLOejw5GNVGGlGPk3359kvynrBwTTrRvdfuwGLRcf2E/yvJtiAGJFKMOo06DxxdAlAK0OFVCytcBec1VySjfHaSb9ZTl21izqZqFpXmKVde5gH0n2gDo3SkZxUhAgiNNTvp1Sf0mTi0CKhnlHEG7268QUeSi1y9+MDDGaqLMbmPxJDtun8hDb+7mtmmDEAMo74m3WPfONnPf6zsTFs8WlOZRWdvEhwcdEaSTUruV1XNHMfupzZ36RWo0AuP6ZTMmL5sAUZLI+TbumVFIjcMZlzleZreyaGI+npDco9MrMv/ZT1g2o5ARuVkcDrFGK6oaIrpW1c40Fd8V6DRCxLMfvom3GLSKesUtlwxIWMB+dHYxOkFQGPHrFpdyyNGBFHo9HgmiNBToLSjNi+8NHpIglIPF8CLC9Rf245mK6oRSpzIJwuMPkBvyng0fn5uqHEgIrJ47Eg1CRMIjHNFy6zLRYlJBFwp7ZNAlzZiE5GaL2XjLx5AR3qUrS1cmwtFmF+VVDmZ38j6PP8D2oy0R19SZrdl/PfkRY/tl8/y1Y1m+fmfc4O/mF4IdCL//0TA+PxL8TZqdPp6eO5IPDzhYElWACf/tyuw2+lpTmF+Sp9g4+QIBfjl5IMvXR87/pXYry2YU4Qt8OWuh7xqcXj8+MYAhxYhXFLGlGmP8seHU/br70kG8ueM46SY9v3+/ijmh50CWr7z70kFMHNhVCdiiUVnbzO1TCxAQWDa9MCFp5d7XdnDTpHwe27A/oQ3PilnJ/Se/aSKm6hmr4puATO5odnpINRn4waBuihpgOOR/PzBrMDNWVSg2PYsm9mfhc6e688qiCCYQXBNWbowde+F74s9rm/GJEu/sOs6aimqG52by4BVDYhIu6XHUIMJR/g1J2arjU8W3jQ63hweuGExVfbvyt4AExblZ3D61gN9HFUogdg8aIPj/Zr2WL460MLxPZsS+cvXckQm7MSG491tTUc2aeaPQQAzZTN5n/fOGcTz8wyH0zLIkvaYemWZWzi6mod1Lm9tHmkmPLVUlean47kOCGOtWiFxbvf7kqpYqVKj4DkECU6hppaLKwZMfHAxaMyPxaRhh9IrinqQYtQnzNqV2K72yzDQ7fSx49hPl7yV2K3dMK8CgDdqyDO2VgShJPBC1h5fX4mc/PMRtUwp484u6yFx3vo1ReVm8vfN4jFLC4ol26ls9MfsAjz8QYfWTCHJ+qCLUTHXb1AL8osQzm6pZMWswXjGA0yuiDxXALQYtZfk2Fk2w86O/fITTG1R3+ecN4xSy62XDcnhm/ijujcq1X1zQhXtmFiW0Qlm7pYYTrW6yUwyU72/gf97bx18XjE5Y6N9R18LYPCtiQOLDgw4l97egNI//fid2fxXMF8KIvtl0SzcpNYa1W2q4oHcmf/hXtGqkjVcWjed4i4dPDzXyl58MxxeQ+H2YumR4nQFAIwgU9sggIAV/w2gbpPBie3huLp7CXbKi//cRrZ00HXn8AZweEadXxBdGTpLzkUadhjlxCCdlditXFvdEJwgsLO1HcW6Wss+W7/OtUwZiTTWyJuyetrhOqSknK7pKkkR9qzuG6HbpkByWJ6lH3X3pIC59tEL5e6ndygOXD+aTQ03Bvf/GqphxtGbuSFJNOtZUVFOYk84LW2q4/sJ+/Om9/bSHlEQaO7yY9WZSjDr+/P4BSkLHvefVHUkbUmQkavossVsx6DW8tf1YhGXWG0tKSTHq+enqLayeNwqfGMCs1+L2i6QZdKyZNwptKJ9fnJuVtFG7vtXN3dMH8c7O43x00KHM3dEos9vomm7kxY9rY17z+ANMHNiVP723L64SzlPlB/nl5AH88d399MoyR8Q30c4N4ee2eKIdrSDQ7PTRO8tCjppr+Eo41uwmy6JHr/3m1SlUnDmmD8lhw556Xqk8yuzRud/26SjYd6Idi0HbKblJJoDWNH47ZBT1aQ+hvb2dzz77jM8++wyA6upqPvvsM2prYyf1swGZebhydjFmvZYMsyGGiAJBJuyqDfupa3ZR0CODFet2Rkh+PTq7mMraJhY+t5VFz29jwbOfcLLVk1BmbFOVg3H9ghvCNRXVERvgiioHf/jXXm6a2J88a0rMcWWFBotBi14rsHJDFQuf20pxblDO7fFrhrN67kguHZzD0SYXZr02pOYQfH313JG8saSUJRfnoxHg33vqle92ekXu/L/t6LQCg3LSee7DoFdlOBFF7UxT8V2BNcXA/35cqzz7vcJYiuFFLJ1Gk7CA/eymasUnt7yqgUOODtZsqgYkFk+0K5KmMkpCChm76loo7p2ZdA6Qg8rw8V9qtyX1sTTptcp8MGNlRcR8IONYi4uFz23lqic+Ym5JX0rjnKM898j/rjzczIsf13LXpYOorG3i6ic3M7ckL+b6Su1Wls4YpHw2HHJXABBx7Z2prMjo7H1GnQZBgEUT+yvfI0vCTx+SwxtLSll77RheunEct08tQAxIrJoznGvG9GHF+viBmBzEf1rThEmvYeuhRhY+t5WfrfmYOU9t4bOo37fEbmVhaR56rcDz147hpol2XD4xwsbJoNHEEFHgVBLGoDm/twDpJj3WFCP3vraDf+06gcsnJh0HEsEgPs2kY8Oek8o9kV+P7sS2GLQsnmRn9dyR/Pknw/nHDePYebSF+c9+zOBe6Yk9bqscpJv1zB7ThzXzRrF4kj1i3HywvwGvGODCfFvcz3/TRMzOPGNbnN4En1Sh4stBJnekWww0O71IUmKZ3E1VDlw+URmX5VUNrNpYpYzZ4N8cEeMY6HQ9nFzQjQWlQQKhvK5tq23mrle2KzKqMmQrj2THlKVszxbU8aniXECayYjb51ckYYOxoYbK2iZOtnmS7iflPagck3rFAIN7ZbCttpl/3DBWWe/C93bRkPePxbmZdEszMnNYj4hYVO6kLs7N5K2dx7nz/7Z/qbEhAahCESrOEzg72Q87fSJtYcUnFSpUfLchAS0uL8tmFFFqt+L0iixeW8mw3CxeunEcz22qprK2GUmCpz84yNySvjHrbVm+jRWXD6a+1YWjw6OssWvmjaI4N4vdda00O730yrRgMei4J0EDyDObqvmvUbnc89qO2Fz3/gbuf30XQ3tlxnxu1cYqemdblO+U42dlv5Fkj1Bmt5GdYlA+t622Ga0gkG7WUVnbjNcfQJJArxXQCsFmqjSjjksHd2fhc59EENq/ONLM5IIuLJ5kx6TTxhBRAAb1yODuV7YnzOndekmBon4O8MPhvVjxxi4MWoGyOPG/JMGqDftpdvlYU1HN/FBuLLmar4OLB3XFH7IeemZTNYU9MuKT56saWL5+F5/UNLJ++zFMei1G3Snbk+g6w8LntjLtf4L5P5M++BtGE4LkvGJZvo2emWbWLS7h9cUljM7Ljin6p3Si9Px9Q3onTUdGnQadNrgpDX/u5XzkNWP68Fzc++xg6Ws72FrbFFPPgeDzqddq8AekiP252RDMGVoMQdvr6JyujBSjlu4ZZpbNKKTEblWem5OtyeMAWYVIRjBXuYvRedlxHQA2VTl47D8HeH/fSUVlSH7eLQYtWk0wLvAHJNx+EbdPZM6YPgzPzWLpq7HzzqY4+YISuxWzXsva68ZE5OrK8m3cNqUgrqW7o8NLikFLQfc0Fj77CTqNhrtf2Y7bF0CrEXhsw35a3cG9Vfg4DkeZ3cYDswZTarfxwf56BvfIYE1FNfNK8iizR84NwabHvjz05m4evnJoRD4Rgs+JRoA5Y/rEreXNGdMHjRDMLXaPUiYKd26I/q0e31jFIUcHVfXt/GffSTXX8BVR1+yKUAJT8d1ATqaZUXnZ/OX9A4iB5PaG3yT2n2ijV5Y5qeoTQLbFgE4rUOtIbhl2tqCu+iFs3bqViRMnKv+WPQDnzp3Ls88+e9a/P8Os59HZxTy7KTjhr547MunGcl5JHl3TTazaUMW8EOs4UWdmIu8+GaIkKRvC6CJs+f4G7rp0UMICKsDS6YPQazUKUzNe19ra68bg8oqsnF2Mxx+ga7qRd3aeYE1FNWuvHUuT08sT7x+M+Vyry0e/LqlqZ5qK7zQyLAbunzWYO17+glUbqlg9d6TCbg5XIxFCTOl4qKhysKC0n/LvFKOO4twsHttYxc8n2JkxJIdfTR6gkNMqDzcz56nNXD06t1MVBTlYDB//fjH5gtoSNa/Ek+SUIQdGC0rzWDTBjkGnwesP8OFBhzL3hHdJLJteyP1hgb38WbnDItOip9npQyLWNxdOdQVoBCGCYJPUSiffhi3VyLrFpRh0nSu5yCpS8exzHnjjI/73hrE8/FZkp8rz146Jy2iXf7/ryoL3N163YkWVA0EQWHvdWPxiAJNOi1Gv4ZmK6giljnBmv9sfSPo8uTvxYf2uwxPqFCmvcrD7eBvj+8cnd8hweUVG9MkixaBVpHhtqQalY+Rku4es0LqTzIbn4SuHcrTJnehrADjU0MGi57cpn4nuxujw+M8ZiwDVM1bFNwWZ3OH1B0gx6pSETSJEz/+bqhz8avKAGBW/kbmn5G078592dHiQY7foda2pwxshIy1beew+1pr0mGfTVksdnyrOBbR7RXyiRIpBS5ndyrDcLO5/fcdpqdKFj0mDToNRJ+APSYrrtBr+eeM4frb6Y9ZUVIfU9ojppF46fRDHmt30zDDjEUVG9s2OUUILtwVyesVOx4aqOKTifEW82CkcLm/Q1lOFChXnB5xeP+X7G9h/oo35JXncHrLvTTVpESWJ8ioHiyfZuf/1YKH2w4ONETmOTLMerxjA7Q+Qajbw+Pt7YpRLRuRmYTHo2FrTiDXNkLT4vHR6YVJL5F9Mzo/oxpc/5/KJEfHz6rkj6ZVp5kizixc/ruX5a8eyYv3OiHxLaahge83TW5Sc06Ozi3F6/bS6g0SNVrcPQRCQJImPDjr4vLaZB68YzIo3dkfMl8W9M7n95S/4543jWPrqDqYPyYmbawnP70WjosqBKEn8Y2st/bumAdA9w8hPx/al2eXlpgn9CUhSxHFH9c3mT+/tZ15JXkReLcWQfJ6ub/WQk2HCGOq4n1LULeF5yWomayqqOdnmITwVeNPE/nHJDZuqHNy/bicLSvNi4ivZPnr5rMG8taOOVRsOUJybqahFyyixWzGoigARsKUaKMu3xY3v5FykWR+0cYlWZ3Z6RQRBSDr+FkSp1Yfnb5udPvKsKVgM2lP3VBKUvf2f3tvHvJI8JCIbRkrtVrQaDTet3crfF46OUEbsTHU63p7k33tOcssUMfE8sb+BX1ycz6SCLopSdH2rh2XTB1G+v4H5JXkcb3GRZtSh1wk8s6maO6YVJCXiyr9Lmd3G/NK+7D/Zzg1/+5SyfBuv3lSCTxRJM+qpa3ZFzH8y/AEJp19kxeWD2XzAwX2h+XRhuxe9NkiYuW3aKZv0HUdb+NXkASyZKJFi1CIRJPS0e/yY9BrG53WhtsmpjPl1i0s50eqm2eWLsG13ekU8/kDEfSzLt9HY4aVHhjmpxfiy6YU8cPngmHhIdm6I+9tXObhreiGiKNHQ7lFzDV8RR5tdWFWLnu8kLhvWg6Wv7mD9F3XMuqDnt306AOw53kbP08hTaDQC3dJM1KhklG8XEyZMiOtv900hxaiLWCQ6S5bLrysMzSif7nB01uVv0Z/qtA+3y5DR4vIl3czcMa2AuubkhTeNICgWE7Jnt3yurR4fN63dFncTIhfRMywq+UTFdxvhct9eUVQ28eFjva2Twpvbd2qMdHj8VNY2Mb8kD4NWoGu6iVSjjnSTnhXrd1Fe1aDMD4kkBmVkhjyEI3xGjcl97+LNK+Gb+Oj5RJYzXbWhird+UcZ/9h2juHcm/+/HF9Azy4RFr6PV7eWlG8chARkWPfNL+yl2YOFz2+q5I1mzqZrllw2OSy5xekXWbqlhxawinN5Y6Ur5XGWUhVRGGju8PF1xkFqHk78uHM2yV3dEJDPK7Dbuu6yIumYXqaEkbaKAvj6OIlU0gScaKQYdlxR2i6v2Aqe8Pxc+t5UHrxjMW9uPxWWrQzCo7KwAejYLpOcCWl0+NKGq8k/H9el0HKQYddx/WRG+gMQt//w8ZP1xiihi0GrQaQRlDUsW2P1q8oCk3xU+fuIlAtJM+q9sEdDi9NLQ7qXVHfTftaWc2TramVTs+f4cqfjmkGEx8MDlg6lv85Bm0p3WmLUYtBH7xxaXT7GbhOBaNHNojvK+01HIWl1xSpo4fF2LR47pkWmmw5N87T4dW60zHa/q+FRxLqDN5SMgQX2bhxWzBtPs9ivr2emozckwaDXsPNrKrS9vV/5WFrKNffjt3bh9AUb0zWZeGAm4S5qRKx7/kBG5WSyfVcTrX9Txt49quHp0rlJIyzDr6ZpuZNaqTcp8kWxsdKY4dLatt1SoOJs4nbVVLQ6qUHH+INNs4MkPDvLmzWUse21HxNq2eu5IIJJAIedtwvHGklL0WgHRL8UUYuVY9p4ZhUwc2I3GjuSd8p3tTaOVEmSE7/dl6+qZw3Kwphr59Q8G8Mjbu7kgN4sFpf1IN+nw+AN8FNb8FH6uyy8bzN4TbRT3ziTFqEMjCASkgNKE0ur2xzSLePwBrh6dyz2vBsmurQlyO53l8k+0uvnl5IFc8/RmALRC0BrI5ZO47q+xzU5y13V4rk622O4UEuRkmqisaKIwJz3pW+WC9sqNVSwMNcHZUg1MKujG79/ZF/czcn4qHGV2K13SjFyQm8WK9Tv5waBurJpTzOK1lcCpGKvUbuXWKQW0uLxASufX8j1BhsXA/ZcVce9rO/m0tinCHqlbuon3dp8AYPFEO6s27I9o3NMKgpKnTITw5zPaUsnjD7BifZBgZNRpKLPbEASJRRPtBKRg7nNzFFlNJkakGnU4vSIb9p7k89omRblnXhQBKRopRm1MPA9BQkQytLh83D51kDKOIGgPerzFjUcMMLxPJiDgE4MNeu3u5ERci0HH6rkjOdHqZvvRFgb3yACCz/iK9bu4dEgO/bukQALFgW21TaQYbHh9QYXHO0MWPltrmxRVk/pWDxcXdGF2yEYpnHRXardyz4wifGKAZqePLulG0kKEM6dXxO0XmfP0lrjfHUmmsXLvzEL0Gg0d3s6VoVes38UfrhoWEdd0ll+obujghY9rWTp9EB0eNdfwVXC02cWg7mnf9mmoOAP075LKBb0zefTf+5kxtAdazbcroyoGJA6cbGdEn6zO3wx0TTNS09hxls8qPlQyyjmCdrc/YpFIlLizGLTccFE/BnRLxecP8PriUh5+cxdzS/omPHYyNYASu5V9J9pYPXckbl+Am9Zui/udyXCkydVpwsLpFWPUD2ToBCEuEeXCfBupJh0H6tu/cjFNhYpzATKpqrK2SQka+lpPBV6pptMjgMhEj1PqRIWs2VTN7VMLuPrJzSwozWNhaR45mSYeWL9L+UxcRRC7DbdfZPXckWSY9Vw+rAf3r9uZ9DOJiGsAXjFAmd3K3KhxHo7wwCIgSZxs86LBi1Gv4Q//2hfjLRydCEgz6Zg+JAeNAMtnFfHxwUa6ppvwByR6ZJgRAxK+QCDYGa/TKNcR3kkib9YzzHr2nWjDlmrgoZCSyeq5I1mxfpeSzLAYtIiBYKfMzFUVIZKCLa63aDzIHru9s5MzVFvdPp79d3XS46Yadcq9CvcoDcemKgcLS/t1WgA9nQLpdxnpZj0yx3TSwG4IJH+m/WJQQvN3b+9R7kF5lYMAQQUwOTCdX5KHWa9N2ll0+1RN0jEXPX7CA8hwG54zJWJ+nR3dnUnFnu/PkYpvDi1OL8vX7+I3lwxEAjRC8jErAI/NGZ6Q0AzBsfXA+t1K4vNElJd19DHltTU6MQeQniC51zXNyIX5tggVIxmnY6v1VcarOj5VnAtIM+vp8PgxajQgCDjaPcprncWh8npYYreys66FMXlWbKkGxd6qvMrBijd2MW9835ixXma3cevUgbx043hMOg2ODi+j+mbjEyWloBT0N8/nze3HIj6bbGyoikMqzmcIna2tQjCeU6FCxfkBW6qBEX2yaHN7ufvSAty+oH2FxaBFF7Lt7YxAcbzVTZ7NgjdBA6dc2PSG4ulkSDfp4xafZaQZdYpKqdyY5AwpNq2ZN0r5W3lVA/NK+qIVBIb0yuCuV3awYc9JFk8Kqgb/6C/lCc/VKwZC1ssCTq+frmkmahtP7V06vCLPbKpmZaiRyeMP0DvbglGnUXIA6eb4+4jTIb4vX7+Tq0fnsmpDVeiY+5k9pk9cItCaeaOC/41qrKo83EyZ3Rq3cVTeX00Y0IUn3q+KiW0SnXdx70zWVFQjSRLPXzsGrSAgBiQWT7Ir9yEewvdyc0vyuPrJzcp7rxnTh2cqDnH9hf3403v7uX1qAcW9M6k83IxeK5BiUGOVaOiEINFq+awi7nltR8QzUWa3cfeMQfzw8Q9ZUJqnELQBbKnGTokEvbLMEeNI/myJ3cq22ibKqxzcOMFOjaOD5ZcXsWL9LjYfbOQvPxkBxCerAcwYkkOJ3cqLH9fyjxvGUd0QLHJ2pk6973hb3PxnZ6QaODWOKmubqDzcTIpBx7xnPwn+RtMHYTFoORKKJzrLtbt8otLQsvbaMXx48NT5lu9v4O7pgxBFiRNtnrifX1NRHbRsrzoWQRST/w4ElZVuGMfSODZmFVUO7l+/kxWzBrNh71GKe2eSm23BYtAyIjeLZmfy+yqTafpaU3B6/fi0Em1uX8S9jkf4eXd3fUxcczpWUeX7G3hg/W4euHxw0veqSAxJkjjW7KLUnlzBW8W5iyuH92LZazt47bOj/HB4r2/1XGobnXj8AXplnV6+v2u6iQP17Wf5rOJDbXk4RxC9YYj2vLQYtPzqB/m8dlMJY/pmc+/rO3ljx3HufX0H7+05yc0vVJKRYDMs+9FF+0/KxJAH3tjN4xur+PxIc8ziVGa3oklgkm0xaFk8yU6vLDN6rSbGT085RigRH+7ZDbB4kp21147BoNPwQtRnL8y38cDlg7n7le1c/P/e54rHP+Ti/36fJS9UUtfsOo1fVIWKcxfpJr2yifcHAspYr2/1JPTglANKedzK6hmyz2ZxbhZuX0A57tbaJpav30V5lSOJJ6WVZTML+fvmGhY+txWTXoOjw8O0ITmY9BqWTMyP6xcc/v3R6GtN4b7LBiclaaSadDG+lV3TjXHtaeJ5eKab9dS1uDnS5KKhzcsb24+x5IVKtBqBh9/ezeWPb+Kqv3zElD+VU9vojLh2+fdZs6kaCfjZmo+565UdtLhOEQK7phvZsOckqzZUsa22iSc/OMBHB4OWSn+4ahhr5o1iWG4mL2ypiTivU7/rKbJBuMfuOztPJPQylu9veZzrDUeaSc+aTdXUJwiClN/YqMMUkkeNh1K7FdNpJEq+yzBqg6vX5IIuaISgX3a8cSCPKUe7lxXrdjG4Z0bEPdhU5WBYr0xG5WUzvr+VFIMWbScejMda3HG/q9Ru5ZYpA+OOH48/8LXY8HTW0f1lfV1l65R4OJ1CuwoVp4uGdi/v7a5HIDh+fWIg6Zhtdno51uJSxmoiomR5VQOXFHZj9dyR6DUC988qoizBMeWxGZ6Ul6WmsxJIqMp2PdHj5HTG81cdr+r4VHEuwGLQUutw0jPbzLJXt0e8lmgPGj7mwmPS+17fyZp5oyLiyfL9DZijClcldis3TbKjEeBna7aw50QbP/rLR8x5agufH27mlUXjeXbeKKYPycHtEyPsYDsbG6rikIrzGRoh+X5YI0CC9I8KFSq+g8iwGHjoiiG0e0SOtXjYdayFDo+fI00u/IFgI9HpECja3CLHWpIoYkvBXEtFVUNM7lmGvFdfPXdk3KbHErsVUZKUHFFlbROPzi5mckFXDFoh4m+ylYjTJ3Kk6VSOuLh3Ju0ev5KvXj13JI9fM5w180YpOecOj5/tR1vomWmiR5oJHUE1b/m4qSYdm6ocmA1aJWf1zs7jEVOjWR8/11J5uLnTnN6mkF13id2K2x/g09pmuqYZ4/+skkSp3YrTK3LHy1+woCSPdUtKKLPbWD5rcNL9lccf4IcjeivnlSgXVWa30jXNhFGn4dHZxazZVM01T2/h6qc2M2NlRcRvHo2eWWaG9MyIyPeH79c8/gDlVQ1KwfVIk4uFz21l1YYqDjmcLHtth5rjj4JHDFDb5OKe13bEkI3KqxpobPcqeU35+VzyQiU6rcCHBx1Jc47/2nUichzptTFxsFGnQa8RaHZ62bDnJE6viK8TkqokwLWl/Xj8mmIa2t1KQ0XCXHS+jcUT7RxpdsXkP0vtVlIN2tMaR+P7WbltSgEvflxLhkXP+iWljOibxYlWDwKQlWLAYtAiSSTNjebZLPzqB/lYDFr0Ok1Mvq7d7UcQ4HiLK+7v6/SKSJLErroWuqYbI/6eErqWhnYvNY3OpGolXjGgfHd9m4el0wdx72WFnRIGnV4/C5/bSkO7h9+9vYdp/1POfz25mQXPfsLnh5t5fXEpbywpYd3iUuU6zaHxHB3XyFZR8RCebymvalDJy18BTU4fbn8AW2r8uV/FuQ9711RG983mD+/sjXAy+DawJ2QdnpttOa33d0s3crjJSSCBGt3ZhKqMco4gmnkYznqurG3m0dnF1Le6qaxt4vXP66isbebXPxgQIaX4n30nk1pW3DOzkFqHM0JKTd4ollc5+PkEe8Tnyuw2ls0sJCBJ/O7KIdy3bpeyqZQLrM9sirTPKI1SMSixW1k6fRDNTi/PbqqmvMqR8LNl+TbWLSlFICgPe/cr23lvd33EOanyyCrOB8jFow/2N7Bxbz2LJwbH3u0vf8Ha68ayYt3OiKCjLN/G0umFHAsFadEBXrPTy6oNVUwY0EVR4Zha1D1ifghXBPH4A+RmW3h753HmPLWZq0fncs2YPmgEDfOfDUqDnmj20NtqZsaQnAgJxjxrCg+8uSsu0aQkRHBw+QKsnF2MIAgxLOyyfBs7jjRTnJulHNek1xKQSLopD7f/MWiCHSlTFpfy8Nu72RTyOY5nmyJKknLtd0wr4HCjK2b+g0gLnXAJx5G5WVzQOzNmvpIDtp4ZJoCIDtxlMwuZ89RmFk+yM2FAl6BtRGk/vjjSzLUhydNo9ZdwxahEnSsldisGncDtUwvQd6JGlWbSIRJgxeWDY+yGSu1WVlw+BFH6djdLZxvNLi9mg457ZhZR3+Yhy2Jg7ZaaiGdPfhbWbqmhsEcG5VUN3DZtIE9+cDDiHnR4/cx9JtiRvaA0j2lF3ZN+d49MMxv2nGBU32wWTbAjShK6kGzqgmc/iTt++tlSvpa17evu6JYL7Xe8/EWE8sPXQZxRoSIccgFYowVfIECKQceLW/YnHbPFvTPplm6iLN/G3PF9EypydXhETHotA3PSmbvmYx67Zjjz2jxx98QQqURW3+rhwSuG0C3dlPDcz9RW66uOV3V8qjgX4BVFhvfJoq7ZRXmVg2G5WUlV6XpmBhsZ2j0+Hp8znK0hxcBgTNrAje7+EdZ1AHqNhn//+iJaXF6Mei06jcB7u0/w2MYDMRZc5fsbeOCN3fz28sFkphg41uJi1ZzhbKttYu+xVpbPivVHD4eqOKTifIZOo0m6H146vVDloqhQcZ6hV7YFSZK4f90uCnqkK3vaxnYvv5lSwO66ltNS2bj95S8SfodOB6noePKDg7x4/ViQpIjjhec8RuRmsXT6oAilV1nJbOPeUzngTVUOBIJKwD5RUrr814aagqJJNBaDFr1WQ6pJFzfnLKvuppp0lPTPxqDV0OoRMRs1fHa4iQ8POFg2vRAppACj02h4dt4onD6RFIOObhmnioYun597ZhaxfN3OiFzL7roWVlw+mHte2xmxx4/O+aQYg2q/Tk/QEmjfiba4+XytIDCvJA+DTsOcMX1YE5bz+tUP8pkxtEfMXH7zC5UU52by0UEHU4q6Acksq23MLenLfz35ES/dOI7fvrk7oRVx9N6szG5Dr9Fww98+JRFM+mDBW7Zf6p1tUZRWjDqNmuOPg3aPP8I6S4ac6w1vkFDyv4XdOdrsUu6zBhKOPzg1tu69rCiGRGQ2aBneN5tDDacsHDpTONEIkG7SkpViZNlrOygOiwWic9EZZj1un8j8Zz9h5exi/vjufiVGKLVbuXdmES4xOL5WrNsVoWgafR2iJPE//97HqjnDSTNoufzPH+L0iryyqITDTS66ZZh4Zt5Iqk608cDlQ1j66vaI8VpitzKvJI8V63cxeVA3Hp1dTEoc0pXFoKPD4+eC3pkMz83kiyMtWFONSj77eIuLdo+fOWP6sLuuNUKJ1RMQle/uTFW7oc3DiNwsKg83U5iTTmGPDN7YfgyfKJ2W0qQ/IMUSmPY3cO/rwXtSWdvE4ol2xuZlYzFoFfJduGVwhlkfVOV5dUdMXeKWSway8LlPlL91ZlesIjFkEp5VbR76TuO/RvXmtpe/4OnygyyelP+tncfuY61kmvVknuY62i3NhMcfoL7NQ/eMxHnOswGVjHKOINWko9RuVRZFmfX8yJVDyZuVwoNv7GLRRDt6nYb71u3isTnD8YuR7KVkm8ufjO3DiRa3IjuWCHInmmxJcfljQW/tsnwbq+eOZOFzW5ViXLzCb0WVAwGBl24cpxBkmp1euqWbWTajEFEKduM8sH5XzGfL9zdw3+s7+cNVw+jw+GOIKDLOpJgWvrCqdj8qvm2EF4+eeP8gQ3tmMn1IDkunDwLg0iE5ityiUaehvs2DIEgICdQY5A6FLdUOnpk3ipUb9sdIvUXLKb54/Vjl3/J///yT4cr7VgG9s8w8ds3wUMHAj88foK7FxZwxffD4A3EJFSda3XR4RWVT3iPDpNgoDM/N5J4ZhRxpcvHq53UR5/O/149N+pt5/IFTChYdIVUQQVLOIV6wBsGgqTg3+FphTjr3vLaDR64cypSibpT0t5Fm0nGi1Y1BcyqRkW4+tTRmWPT84V97Ewbkt1wykM9rg0xznyjiEyVcXh9rrx3LivU7Y5IfQ3tlMDovWwnEemebeWfniU7tfuRrb3V6saaacIWsleIRfkrsVt7YfoxSu40XP67htqkF3KXV0OYKzn8+McCj7+3llz8YmPQ3/67DpNchihJORLqmGGhwepkzpg/PJSAWyQFtfauHBaV5Ed0HflFi7XVjqW7oQKcRaPP4klp9vLPzOJW1TSyZmI81zQASmHQa7l+/S7EeCEdZvg2NRqAh5LH9Vdanr6ujOzog/f1Vw2h3+79UoV2Fii8DuQBs0WppaHcTQODWqQNZvn5XwjH7h6uGkZ0S9NaesbIi4TyaYdHzr53HefKDg0E/6z31fHqoKe4YLrPb2H60hTK7jfsuK2JbbVNC9cHI74g/JpLtQb+O8doZEUbdA6s429AJGpa9up3ZY/oAsTGpvLeUbRxrGp3c8LdPIzoxw8dui8sXIXENwbHy6L/3c/+sIvbXt6PXBsnPEF8VqXx/A0eaXBH+6mX5Nh66Ygg5ndhfhZPGo6EqDqn4rqOx3cOcMX0SEu0bOzykGFXC1dmAuh6r+Dahk+DemYW0e0VaXT56ZAbzAn/ffIifjevLmH5WlkXZR4TvuWcMzWFQTnpcEnWJ3YpJo0UURIpzM7nlH5/z1M9G0urx0e4WlZzL7S9/oRBPf3PJQFbPHankvE60umOUzCCYY/YHJH78xEcR1u9dU40EpOD+IMtiYPKgrlw9OhetRsCo1cTNV8sF+BWzBtMqCUhIiJKE2xdgwoCuPLbxAHdNGxSUVAV8YoCttU3KXLnh1xcpeXtHu49AwMfiSXZun1ZAu1sk1RRUXTna6OK+mUXUNDrRaQWyLHr8okSry8+C0uC+J82kY3DPDFIMOv7fe/uodTj568LRMY1EoiRxx8tfsGbeKH7/9p6Ia3ri/YO8vriUe19PfN9K+gfzgtHkYK8YoE92CnqdwMGTHayaMxytRqCytjnu8xPdMFVit3L/rCJcPn9ELSMcpXarQuyxGLSU2W1KnmT13JF8fKgRUC0Qo5Fu0seoEIU39U4fHLTEkRuXn9lUzYQBXZT37qxr4d7LinB6RTy+AIIAFVUNMTnHiioHhxtdMXuBL460MLx3RoRiQ6J6U4ndytzxfUEKEqzuDc0h8rnJpBj5O8KfTWcobwxBZed1S0ro8Pg51uxGo4VfvvgZL1w3lsNNroTjyOsPsGHPSXx+icsu6MHDVw5l7/FWHli/k9lj+pCTYSTdpCeQCR6/yKi8bG6bWgCA0yOi12ko33+SzQcbg5ZSm6pZftngiCbrUruVN3ccU66hLN/GndMKqG89pVYtCALpJj3znvmEGy7qx7KZhaxYv4vy/Q3oBS2P/2c/D14xhI5OyCgGnYaFZXksen4bK2cX4xMDypzYGcmoLD+Y3w63O5Uhj99VG6rQAJcN60nXNBN/WzASvUbD4hcqlbndYtDy1s2l3HXpIBqdPlpcPoXotvLf+1k9bxRN7V6cPhGTQUuLUx27Z4IjTU4AuiRQxVLx3UCPTDPTBgebwWdd0JPep6lM8nVj17HW01ZFARQFpxpHh0pG+b6iw+NnXkkeEsFFwmLQ8vCVQ1mzqZrrSvtx9Zg+6LQapUh2rMVFH2tKxDGiN5dppmBwkWHSc7Ldk7CQLSPNrMfV6ubJDw6wYc/JiNfK9zeAJPHSjeNodftJMeriFn4hKNU1v9WDJEl8VtvEjCE5GHUa+tqC53ugvj0u417+ngP17XTLMCX1Ef0y8sh1za4YCfQL8208fOVQRTpOhYpvGnLx6FiLm5pGJ4N7ZiAgcP+6nQnZzjKTOVp9SFbJyEk3sWrDfiqqHNw9vTDp98crrBnC1DYsBi33XlbEI2/voTg3i111LRT2yGBKUTeuf2JzBLM9vAPjqZ+NjCC9lditLJloZ/2SUqobOvAHAgQkiYWl/SjOzVIKEJ15C/exWhTG/ss/H8/quSNx+06RBRLJFq6pqOaxOcOZMSSH3GwLf75mBL5AgHd2nlC+u9Ru5f5Zg5k+uDtv7DiOIWTJUFHlQKcVkiq23DFNoLyqgfte38FvrxjCXa9sV+5TIgJLcW6W8hu9mICEk2HWRyRnKg83c8fLX/CPG8Zx16vbYwJA+Zkozs1UgpFRfbKZMawnj0QlLeSAxek9v1nsJp2GJqcbi8ZAh19Eq9Fw8wuVvHj92KSKCEBEEazMbuXDgw4+r21mRN8s/vTefiwGLavnjgQSd3w5vSIaYETfbGYO7UFvawrLZw3G649UMCgNBfAy+fOrrk9fR0d3snWzf9fUMzovFSo6gywJ2+YVsaaaaPeKHGt2x+3elseYUachEJAQgBG5WQkJYp/VNuEPSMo4FwRYNLE/AaSY+fGmiXYyLDomFXTlv578iIZ2L8N/fdEZJVk624N+XQoMiYgw6h5YxTcBtz9AeZWDeaEiRTxFPqNOQ5c0I1c/uZmVUclsudNW7u7snW2m3S0qHdC76lpCVoYN3PvaTuaV9GXx2m0U52Yq+8Gb1m6LOa9mV2S8WL6/gbte2d5p962qOKTifEaKSc/PnvkkYSz38s/H0652un7tUNdjFd8m6ptdeIC7X4nMI5TarTxw+RBONDtpaPMyPUoVV54XRuRmsf1wM/fOLEpIfPAGJAIBicUT+tM9w8zSV7fHxMkPXzlU2cO7/SLd0020e/ykGnVUHm7mgTd2x80BH2lyKX+Xv/v2qQVc/tgmIJi7evnGcTzw5m5G5wVj74Wl/bjhwv5kpxjwiQFaXX4+qWlkTUU1bn8gxDcRcPtEJN8phYnDTS66pBkosVsVq2YZTt+pvH2G2aCo9EajxG7l4SuGYNZrYqyoS+xW1swdxc6jLdz28nbe/kUZm6ocrJ47khXrd3FBbhbzw+5BlkXP7380jECAmDx6sJjvTxorpZt0Ee+X91ur547kwTd3RZBIyuw2Xr2phLd2HOOJ9w/G3ItUo47HrxkebJhrdVPrcJJq0nHnpQUcb/FENEoV52YyryQPrSBQYrei0wjcMmUgs5/aHMyTCAILSvoqeX/VAvEUbKkGTrRGxoDhDcGiFLSynT7ErfxtyUSJHXUtSnPiH9/dr3y2zG5lfgIbcI8/0gJTzmW9cN1Ysi16yvJtlO9vUPb2K2cXs2iCPYKgcPMLlaxfUkqry688o/L7O8u9yepG7R4///XkZuU87pg6iIHd0/D4xaTj6NPaIKGpvKqBeSV9WbOpmjumFfD7d/Zx40X9EQQND765k2G5WYzMzaKoR0bc/Oijs4sx6TVsq22myeVVrIM+q21iXljjGgTjCUnazQW5WREkmwt6ZzIiN5M/vrufJ94/yKuLSjjc5MTl83NJUQ6PvLWb26cNUn7TaJTareSkm1j43FaKczOpPNzM1KLuym918wuVXH9hP24LEWEEQUCSJLSCwMrZxfTINLOrroW1141lzlObYwgpcs68vMrBbdMKcPtFrKmxc/WC0jw+POBg/fZjcec3j19kWNi1q3uZM0NtoxOzXkuaUS3Nf9fxw+JebDno4PaXv+D5a8d0Wn8/G9h1rJXhuVmn/f6uaUECSk2jkzH94luYnS2oT/w5gmanV0naLZ1eiNsn8v9C3fi3XDKQP/xrL3dMC7I35c3wRyEvwPDFQek8CzE1393dwMUF3Xi64mCETFo05E7uz2ubmVvSl80HG2M2nuVVDm6TJNxekcaO5B72bl+wQDAvVPB88M3dSuKvsy7QZpePP79/IEYCMBynm5xvcXpjgn5Q7X5UnBvIsBg42NChyFq+FQpE4yGcyQzBDWJlbRPzS/I41uKmzG6lqGcGv/5nUDpVgKTjPd7SWHm4WVF7CA92rivtp1jVAIrSSDRKQwF79HkDPDBrsGLVFX4eMomioqohodJEmd3Gu7tOsGpDFaV2KwadhhSjVgmGTXottlRDQgKbhMSb249xZ5QM7KnvdnDvazv47eWDKcm34faJ/OLiASyaGKDDHXu8cDQ5g/NZRZWDo02umPsUjeiOkg6PP4ZgVGa3kWUx0OLycc9rO5Qg4qErBiudBtHH1BAktvxr1ymVlXSzLqmqy7IZyQlL33V4xAAmgyGYaBIEMi1aRuRm8a9dJ+KSheBUd3Vx70xFinTZzCKFKPKLyfn86b39OL0iC5/byovXj+UOQaCm0Rk3uJaLc3JyJVzBoMXlw+0T+fCgI+IzX3V9+qod3eq6qeLbQobFwH0zi3B0eDDpNAhAXYuLyrCOxHCU5duob/UwpFcGRxpdzC3pG5dcIifV5AI4wKg+2Sx8bmvcYtyC5z7hrwtG87M1Hyvj8kwSpKczls6mAoM6llV8U2h3BwvX4RLe0Yp8MqlaTq7KkPdFySxgl80o4pqngwnq8qoG7phWwIvXj2XBs5/w+MYqRvTNjrv/i5bvh9Pvvj1T6y0VKs51mHQahieJ5Uw6DaBTO12/RqjrsYpvEy1OL+0+MUb1BII5jKWvbufey4qYtWoTj84u5tkPD0U8q2V2GzdNtLP4hW2svW4MC0qC9sfRiid/WzAas15LapaFO6JILxBLQBUDEvVtHj4/0syUMIvpeIjeD2+qchBe63F6RerbvIzOy2ba4BzufW1H3IaRXXUtPDq7GJfXz6oNVdw/q4iUUCGw3R20RumabqTD7Y9QYZTR7j5FttVqkjct+QMSj0cV0OXXNAiM6BssGrWFyH9d041s2HMypjH02XmjMOk1p9SBo3C8xcNnCWKlUruVFKM2Rrlk2fRBPL6xKkbNpLyqgfvX7WT6kJyI/JQMi1HL/Gc/DyoezyxizlOb+Z+ri2l1+9hR18LgkIXq//18PMdaXNz6UlDtfcnEfNKMOvbWtyu2iWsqqrnxov7K96gWiKeQYTHQO8sccd/ClaADEqzdUsOiCXbF6spiDFpYys2J4QiOBYHrL+zHn97bH/FanjWFJ346Ap1GiMhlHW124fX7eeDywSx9JUhWkJ+Fa8JUB2W4fEGCWTicXvG0cm/RCoebqhzodQLTh+Tw/r6TbD7gSDiOFpT2Vf6m12lYUJKHyxskXfTKNrP0le18WtvMrVMLgg2Xr8c2f8r/Xn7ZYB6bMxxHh5dNVQ6Whho846lYV1Q5mB+W191U5eD37+wJ2aTvpLyqgUanl4XPbeWtX5QFyS0leWw56ODOaQVI0u64dkH3r9vJ/8y+gKPNbl7cUsOsC3ooz4HTK/Kn9/bz9801vHTDOHwBieXrdkZa6dht9Mqy8IcfDWPes59EnHN4XFTf6qFnppmjIYvVcMg1x0TzW3gDAqh7mTNFbaOTrunGb4W4oOLrhdmg5dqyfjz01h6e2XSIBQnIf2cLLS4fdc1uLr/g9JVRDDoN1hQDtQ7nWTyz+FDJKOcILAadkrQr6W/D6T3FKNVpNWyqctDU4aPycDMjc7PIsOgZmZvFzKE5PLB+d4yH3tzxffnZmo9ZNbuYNrcvQiYNYmXVwju5A0gJiSBHmlxIUvzkXjiMOg0ZZj0/W/Mxa+aNikj8ddYFatRpKN/fwM8v6h/3HL5Mcr6h3RuXcQqqFKCKbx7xpHnDx0Ob2690ZBT3zlSsbuRgTWYyBxU5guQ0uYNt2pAcWl2nutgkSVI2x/HGuyyXGY5ddS38ZspAAkgRwU64VU2ieaQs38bc8X0jGOMyNlU5aA+b08L/DsGExN8317D2urExnruldivLLy/iSKOLh64YzPj+NmoanTzxwYEoOzJrhJWYjAWleayuiC/RKr++akMV22qbESV4c/uxCO/iF64bE3M94Qi3S5M7cBOptMiQX5eDrvBzqaxtYm5JX2auqmB4bibPXzuWa57ezMDuaRTnZkUQasJRXuVgXpsnYs7sTNUlziNwXqHd7UeSgvZWqUYd/oDEoon9ear8YNKxcfMLlfzfz8eTnaKnZ4aZt3YcU54p2esYgsG11x9AI8Ci52M7smV4/IGI5IqsYHCgvp0f/vnDuJ/5KuvTV+3oVtdNFd8mfIHgeAlI0OT0kpNhZvFEOxBrQXnvzEJ8YoDdda0cdDhZU1GtkEsgqDD1n30nwzoD9bx04zgyzHra3L6YYnk46ts8EWvJmSRIT2cs9e+aetYUGNSxrOKbgsUYtItMJuE9vySPF7bU8NsrhvCvXccU9TezXkvPLDP/uGEsj7y1J2bfUlHlYPn6nVw9OlcZrzWNTtZuqeHpuaOY89TmiIRo+HdGW/fIOF1yWSLFIRUqvsvwBQIsm1HEivWxMdc9M4vwBYJ7W0eHukZ8XVDXYxXfJhravXjFAJW1zSyeZI+bZ/KLkqJksH5JKR5/gFaXD4tRi0mnZdZjm7jhon74RYk1UfY3suKJIAh8cqiRwh4ZnTZYldlteP0iEvDJoUYmFXRN2khliJN/lpuCIKhq0j3DSJdmY1yl4XCF2mc2VbNi1mDKqxrwhGJ5g1ZDwBi02Nl+pIWx/awsCNl/hue+dVpBiR+KeqQnzd3Fy3/JkFUcAMz64B6qPUETlDXVyMNv745oaApHsBN7bMI5/fq/fsrTc0fS4RWpa3YBwRipPEFeSb5Hz246xPPXjqHJ6VNU6nYcaeX1xSXUOJzMeSqYo/oopCA7bUj3CIXkstBz0SXNSLpJx7rtdazacEBRd350djEdXj/PfXiIZTMKVQvEKDj9Ir+9YghLX91B+f6GiBzjxr31XFfWj5YoBcCS/l34/Tv74h6vvKqB26YNZEjPDOVZPd7i4osjzdQ2uWJiYqNOQ6bFSF2ziwkDu3L3jEKONrlIMcQvI7q9QXJaNDqLDV7cUsO9M4uobuhQFBHXVFTT5vJT1+Lm0iE5nVxTgfJvrSDws+e28vriEgBcvgCfhvLXv397D7+YPCDp3NTm9nOsxcWQnplAsMCbjCQXnffdsOckt04RmVfSlxsn9FfUyAOSFJHDr2/1xCgghROBfjOlgBe31HLX9EJ+/86eGBeFR64cSrPLj6PDw/zSfgwLUxwP1gYl7r2sKOLc4sVFUugaO7uu07l2dS/z5VHrcNJVteg5bzC0VybTBnfnobd2c0Fu5pdSKfmq2Hm0BYC+UQ4qnaFrupGaRpWM8r2FRiMom+9Uk5Ym5ynlkY5Qt5k/IPHix7X8sLinwvSWN7+3TBlIQ3uQKS0vYgtK81i5sYprQv7d4ZLJd0wr4HCjK24nd3TnfjhkG4+ggoI17ua6xG6lvtVD5eFmnF4RiyG4uZYTf8m6QMMXSKNeE/O+L5uc70yFRZUCVPFNIZE070M/HKI856nG+F2hcrCmC2PMyt6eJXYrPjHAXa/s4I0lpcrrGo3A2i01cSU7126p4c5pgyLOr8RuZc6YPlz73CdcPTo3IsgIJzU4vSJ3vBzscJA7YjItegTgh3/+MKG1liu8oyIqaM/NtlDcO5OH39odd1P+2/W7+MXkgRxpcuH2izwZRUSBU2z/NfNG0uERlQAr22I4LZWSBaV53Pvajpgugk0HHAk9cEvsVrbVNin/lhMVp0PWCyc+yOcSTjByekUqqhysWL+Tf94wjpcrj9Ls9J0WWUlGU0fy+a3jPJfgTjFqMeu1HG/xgABuX0BRQsi26Ll9agGCEExk+UWJbbVNQRniPlmYDVp+984ebp9awBWPnyKMyOuZjKzTWIsyzfq4yZWzuT59lY5udd1U8a1CCnZtO30imw442FXXwrDemdwuezt7RXQageqGDkxaDUcaXeR3T2PpaztjyCWr546M+Hd2ikGxmTpQ3570NMLn8TNVKDndsXS2FBjUsazim4JJp1Ukp+VYc2FpP3RaAavFAAKcbPUwqEcGx5tdXDwwKDm9Ye8Jnnj/YLDDdkYRn9Y2xz1+dGxq1GmoqHIgsDdu51EygjScGblMhYrzBVpBwx/e3cP8kjxuD8VyqSYt9a0e/vjuXm65pABvQEQMnOes9W8Q6nqs4ttEq9uHXwzEzTOV2a28uqiEFpeXZ+aN4liLK2Rp4yPNpOOzw83065KK0yty8cBuPPRmrC3NpioHAnD39EKyU420ujvPMSybWYjL6+cP7wQbno61uJM2ixxrccccI7wpaEFpHr9/ew83hilFRCNcwdYrBvMm7W4/aWYdXjFAqkGH2arllpc+528LxyikifACbkVVg7LfMeuS5+46PMkVduXczfv76inLt5Fq0sZ9nxRSfUykdN7Q7uXRf+/joSuG0O4VaXX5SDXpON7iZk7IEgfgs9omuqab8PgDEQ02ic5NJswsfG5rhErdI1cOZeFzW2OaWmVyjYzyKgcScPf0QVQ3ONl2qJlXFo3neIuHT2oaWbulhtunFrCpysE9MwrVInYUWpw+RIOf5ZcV0e71RzSSPfH+QYb2zMSacqqQvXFvPeP62ZIe0+kRowhDNuaX9lXsGmTIz32PTDNtHj8XFXThk4ONdE03KQT0aGSnGpACxKhdy3WopdMH8esfDKDDK5Jl0aPXaBClAJMGdVNUiOXvfnR2MSlGbVD1397ZNQXnm2iV7hK7lTaXP0Lxe87Y+OpCMo61uuiWbgIh+GOnJLhWGfHyvu1uEa0gYEs1oNME875toTmx3S3iFQNIEklJLm0uH3dNH4Q/EOD9fQ28vy+oXH5jWX96ZJu5L0r5SZ7Hqx0d6LUattU24QvLDUfnnuX7m2HWx72GzvLZid6j7mW+HGobnRT2yPi2T0PF14g5o3M52NDB9X/dyiuLSuidffpKJV8Fnx1pDjb4fEmrrK5pJg41dJyls0oMlYxyjkCnEbihrD9LJtnRajT0CT2wFoOWLmlGVs8diU4j8NcFoyMkB+XE+4QBXSI2FRaDlgkDurBqQ1VE8k5+f2FOeqed3NEosVs50eqmrsXNmopqnpk3CgQhorheYreyeGI+qUYtK97YRYn9lO+UnPiTu7ajC/PRC2Sm2fCVk/OdqbCoyUgVZxstTi/1bR5qG53ML8ljWO9MhbX8wf4G7nt9Jw9eMYS7XtmOGJCUjXI45AB/flQiXh4zjSEbF7NBq2z+39t9guvL+rNy4/6Y4HjJxHx8/gBv3FxKjSPSXkRGikmrWOBoEBQLHItBy8NXDo3piHl1UUlCIgpAqjE41hLJsD9/7Zi4sqQyZo/pw/rtdYzsm8W2BMWK8qoGfnPJwIjAv8ObPBkiz3XhSjDhkJn8AkJEUFVmt7F0xiCOt3hYPMnOnrpWJVERLlMfjTK7lS5pRopzs2LkHmWCUTgqqhy0h+btaYO7nTZZCYIExmTklZTz3Jsy1aCj3SuyudrBtMHdcXpEZQ2U72v0eCuz21g+q4hAQGLRBDtNTp8iJburriXi+OEF6kQEy1K7lT5WS9x162yvT2fa0a2umyq+TQgC+KQATo8YMU7Dfa9L7Fbuv2ww7T4Rl19EDMDK2cVJCXrRhJLTJUZ/FYWSLzOWzoYCgzqWVXxTEARYMWswy17dQXlVQ4R/enihAqAwJx29TsNjG6u4vqw/Ewd2ZcGzn7Bi/c6kFq3RqnJwqrO4Z5aZ1xeXKGS1VrePtVtq4+5Lv6r9lQoV33U0O71cOaJ3XHWD+SV5NDu9dEsz0p4krlPx5aCuxyq+TaSb9AgC/Onf++M29Ny/fifFuVlK4XdsPyvzn/1EIWPcdWmwiUmjERIqfVRUOejw+PEHJDIsyZ/nrulG5jwVJDUoauAagSUhMmt0I1W01SbENgWNzM3igt6ZtLhOr3lHLg5bjFqQQBLAI4qsWLeLq0fn0u7xUxqyzQjPkT35wUFeX1zC/et2IUrJc3e3hyk2xINczH1s4wHeuLmUE63uuE1QzlBuK5HCRJndxi1TBuL0iwgC3LR2G3/5yQilPnDPjAL8AYn1248pn1s9d+RpnZtep8Fi0Eao1FkMOlbPHRnT1BqvhlBR5eBYi4fVFQcpzs1i+fpdFOdmKXbjMifGpa43Mcgw6zHoNHh8InuOteEXAxFWmDet3cbK2cVK/veJ9w8yuaBb0mOmGHUR1ublVQ0EkLjlkoHKe8pCNtXVDR10eHzkd0lFAuX5WTzJHjfX6RMDBKSgFfj96yPViYpzM+mabuKnq09Z4P7v9WN5dEPsnCSPn9tCjShp5k7WT7OeMruNuSWnSOgb99azZGI+FqNWyfNaDNpOVSiMOi1un4grNPfVt3oSWsknUmBMN+u46omPsBi0PDt/FPNL8kgL5V1TTVr0WmNcNZLoa2r3+Hn0vX2KjdWaimouKezGPXHs1qLn8RK7lcsv6MkTPx1Br0wzb+08rozV8NhsxtCcCIKdjMrDzfTMNCfNace7dnUvc/oQAxJHQ6pDKs4f6LQafjV5APe9vpOfrtnCP24YF0P2Oxv4rLaZ/l1S0Gi+nOVT93QTlWF7qW8K53cl6jsEa4qBgAT3vh5cWH45OZ/JBV25ekwu971+inyyeu5I5f/DN9liQFIkzV78uJaHrxyqLHDxCqOdMR0zoxb8EruVJZPysaYaONro4uJru5BpNnDXtEGIUyVcXpEUoxYBECWJec98QnFuJosn5rNxb31M4q9Hppk/XDWMA/XtNLt8MQot8vu/anI+WbFBTUaqONuIp4YiEwfkZ/3d3fXceekgVs4u5nirO6FsYEWVgwWl/QCYXNCFXKuFX00eQKsraPmzeu5I9BqBRRP7E0DisY0H+PM1GcwYkhMR0Ne3eeiSbsTjC0qO3vLPz5WAIBFRpMxuU845nFkejv/sq0+4US+z2/CFuk8Sfb6zDbnHH2BTlYPl65IXKxwdHhY+t1WZH6cVdU963M7mQpnJ//LPx+P1B/CJATz+AB8ddHDF40ElmFK7lQcuH8Lv39kDJE8UzC3py9VPbo5bIEl0Lk6Pn8WT7Bh1Wp5LkvBYPMke+Vu0uVkzd1RcQtLquSPRdk54/07DIwbo8PgY3juLgARp5lNbnnClMHl89LFaEACvP8AD63dFJNtK7Vbuu2ww/9p1DIgtUMez2SjLt/HQFUPomRWfDX2urk/n6nmp+H4g22KgyeUl3ayLO07l/WKry4soQa8sM797a0+ML3w4QS8eoSSZndXyWYNpdXm54oKeX0mh5NseS9/296v4/kAjQIvTzYNXDKbV7aem0RlXfROCe51mp485Y/rwZPkBfjCoG2vmjeLqJ+Pb7YR/LrpxQcbxFjdiQFKKTtuPtrB0eiFef+Brt79SoeK7DotRx0/XfJyw6Pvyz8cTANw+tTj4dUFdj1V8m7ClGjiWJM8Urj5WXtXAva+dyrfIeYYSu7VTVdVUkw6DTkOry0up3cq22uYYUsjxFhd76lp5+MqhEeoclYebKc6N3xxUGlX4LMu3sXiinfnPfqL8TbaVvr6s/2k171gMWkrsVsx6LS6vH4shqI5SXuVgfmk/0k06bptawJ/e2xexhynOzcTtE7lsaA9yMkxJc3e3S0JMkTfeNTm9Iu/uOs5FA7qy4vIgsTeckGIK2fgkiot6ZZlxtHtINelpdfm4enSu8ttaDFouLujOilChWv5c1zRTUqVz+dy0gqDkAeXnxOWLVNeQkSiXpdMKVNY2K6o08n8Blk4vDJ2nWpqKRppJz+0vfc71Zf0Z3ieThjZPhHWt0yuy5IXKUJMwlO9vICCR1O5KIxCRi5aPtXS6lj//ZDi9ssxsP9KiKJVs+M1FaAQh4vnxByQuG9aDFet2Rjw/gUBQhemjgw6mD8lJqsYPkG7WJx0/i0MkLJ1GSKhUXWq3YtJp+PUlA7jm6S3K8Z94/yCzhvZAFk+ypRp4eu4odte1MKmgC4U9MmLIarvrWrClGmhs95Ji0AXV417+gn/eMI5lUQra8YhqEMz3GnQa1i0pod0dtAie98wnvHTjOMrybdS3esjJMLH/RHtSoodOK/DAG0EFu7VbahQVyDa3/7Tm8U1VDu5ft5Npg7uTYdYzYUAXCnPSlXtxx8tfsGx6IZIEY/KymTE0h/tfP3U/11RUs25JKX2tFuV44dc4v7Qvi9dGXru6l/lyONHqxidKqk3PeYgMs547phWwfP0urn5yM89fO4acjC+nWPJlIEkSlYebGdfP2vmbo9A9w0Sr209Th5eslG9u/Kor/jkCtz+gEFEg2GH2m0sG8ECUDKLMNk5UNC6xW1kzbxSPvL1HWYjiFUYrDzcntZ5w+0VeunEckhRki+s0Au/tPsGnNU38dGxfvH6Je6KlwfJt3H9ZEQcbOnjkyqHUt7px+0R2HGmJm/jrlm5CDEhxCwFfV6IwWbFBTUaqOJtocXpjiChwagyGEypaXT76dUllXye2AW6fyKSCLtw6dRD3vR7JNi8NdYoveaGSq0fnsqAkD7c/QFHPDHxiAJ8oEQixb2eG/G/L7FbWzB3FgueCnS+JiCLlVQ1ISEoyIV6S4MkPDrJqTrESCMkos1uZX9oXZ0ihJNHnT8faJngujqTFCoicHyF5QCbPhcnkzJxeEX8gQEO7J6aLEILB0t2vbuf2qQW0uHxUVDmURMGiCXa0mqC3sCRJPL+lJoL8I/+mAJkWA4sn2RXlHBkZFj2VtU0U985M2o30qx8MYPXckfgDEjkZJtKMurjM+U1VDjQILL+8KO6xzhe0uf3YUowcE904PX66pBkjEkLhlh5ldhsj+mYxpag7EsT8zhWhgO6eGYVMKcyJKVCfic3Gubo+navnpeL7AQnQaTQIoIzX6DWjzG5DpxGYOrg722qaYsarnDhfPmswa+aNor8thZw4c3zycfvl/Fbj4dseS9/296v4/kArCKSbjTj9IgadhrVbahLGmJWHmynunckzm6opzs2ia7qJNrc/rt2OjGSqcj0zzfzunT0Rynqldis/Ku51VuyvVKj4rsOk1zAi1D0bjTK7DZNew5EmFz2+pNSzisRQ12MV3yYyLIZO80zhqhay6piMd3ef4BcX5ye055Bh0Wtx+wIsXlvJ2uvG0tjuYeXGqpgmp/mlfXl+cw2/mDxA+XuiZp4Su5VbpxTQ1OHl8WuGB5urWj2kGnUU52Yq75UJDz2yTNwflSeTjykA15b1o8xuw6zXcvOkfHQagXSTHrcoKiodqQYtBq2GKx7/kAWleVwzpk8EaW/eM5/w4vXjqHEkl7Z3tHu4/7Ii7n19Z0xz2j0zi3j4rd3K3/70XhXFvbM43OTi3suK8PklZe+SbjxlhRhtSSrnMKYWdUeSgvmN4t6ZirXwgtI8XH6Rq8f0iagdWAzaoDpKHKVzmfRbErI9qaxtishdSuF+MWGfi6eUAKcILTJBRn7W5Hs0qaALn9Y2kWrSqetOGFxekfIqB7dNHcQ9r+2ksraZGy7qF2Fda9FryTDruXRwd+aN70tACrBkUj4QO44WT8zn3d0n+Li6Maa5r8MTLEb+fXNNxOd8YgAvxH1+lk4fxG3TCnB6RNJMeiSCKkxPvH+QR2cXs/1IC2+EqfGEoyz/VLNiImg0As9fOwa/KLJsRhEr1u+MIYTcM7MIr1/EoNVExAZOr0iAIFm+R4ZJqZHtPd7G89eOZfn6nRHXL9tQ/c97e/nZ+DzMBm3Qvjs3k62HGhUreb1Ogy3FgNmg5bfrd0V8Z1BRppDmDi9X/PkjAF5fXEJxbiYmvYabJvTnbx8dYkFZP0bmZZFnS4m5T2V2G8tmFnK8+RR5sDg3S8kXn04Dp4zy/Q3cMbWAFKOWB9bv4r1QnGQxaFkzdySPbazizle2K3+T7+eRJhcGrQavX+TjQ40Rz5teK2DQavnje3sjrl3dy3x5HAqtH93Sz75qhopvHt3STSydPoiH3tzNlY9/yDPzRzOwe9pZ+a66Fjcn2zzYQ3bkXwY5GcHnr9rRoZJRvo9o6vBGLEKDe2RwrNUTs3DLBdlEReNNVQ7aQ2zJcE/JaAZ1lkXPZUN7xMinyZvPxWsrKc7NpDg3i2lF3Wlz+5lU0I2LC7ph1GliiCgQXOzufW0nt04diEGnITfbglYj8IerhiVclM6kgPdl8U18hwoV0Who98btgoBI1jKckrNLMSQP8PtYLdx56SCWxwmwK6oc3LcuKJ0ZnVxcPMnOrroWhQH+h6uGKQzwpysOsmz6IO58ZUdCooh8/EUT7DQn2AA7vSKL11byfz8fT7vHz8l2DwZtMGBfu7mWX0weQIndGle+E5Jb2yQLbuO9L3x+rKxtjq9Skm/j3plFePx+intn8u89J5J+/zs7T1DcOzPu6/KxG9q9XJCbxc8n2DHqNLS4gp0BMrlEDvp9osSnNU1Ju3bkYkuZ3UplTRObqhxcM6ZP0us/0erhln9+zqOzixVCYiLySnlVQ8J7cb4gPRQUr9pYxYpZg2nz+LhlykCQpBiP1fmlfVm7uZapg7vz/t74VlHl+xvQCAL9E2zyzkTJ61xdn87V81Jx/qPD4+fe13bwy8kDWDGrKGT7ETte/765hplDe/DAG7vjHqci1DHm9ok4vX5anN64z+/ZsMcJx7c9lr7t71fx/UCrx49eE1Q8uWntNl66cbxi2SNDjjHXbqkBTu2FPf6g4lxx70y6phtj9mJl+TYWTbDHVZUry7fxxZHmGIvHiioHd76ynZWzixOu2SpUfF8hSbKaYux+ePEkO5IEzS4ff37/AKtmF6vrxdcEdT1W8W2iszxTdGNQeBMkBAsrvjCbkGiU2K0IEvjFAA3tXt7ddZzNBxxxm5wCSBTnZqHTaBR1jmjVDwh2F/9n30lmPxW7/r/883HMGJLDshmFVDd04PEFeHzOcJweMS4ZFoJ7g6XTC1kyyY5RI9Ajw4QeqHf6SDfraHEGra9TjDp8gUAM8SMcR5tcnXY6d0kz8vbOYyy/rIi6FjcBScKs1yrqMXdfWsiiCV4lb1bd0EGXNCO1DqdCfjlwsp3SftaQFeL2pDFRk9PL1ppgA1OKQceL148NWjRBTO3A6Q2qm9w3s5C7pw/iaJMLQFGwKM7NjLBZXBhSaO6abqTdHamQU2a3smhiPguf+4RohBNa5KJ2+LPW7vHzy8kDuPrJzbzVJ4uV6pqjoNUdzLtKSMq9++O7+yOsaxdPsvN5bVOEev76JaXMGNIjQj3nRKhR+In3D+L0ivxq8oCIBjiNIDCgWxp3vbIj6hz8pJt0cZ+fu17ZQYndyrLphazffoyLBnQh22KgODeTm1+o5IaL+nFviEQS3cR865SB6Duxk2j3+NlW28Qlhd14bMPeoFLJtALa3SKpJi31rR7++O5e+ndNo0dGrNKP1x8gw6BFZ9RxuNmlWAwtXx8/l748pP6SZtJh0AiMyM3kvlmDlUZOCDoVGHVaVqzfRWGPDGZHEdUefms3d19aqBz3WIub+SV5eP0SC57byovXj0UrCBw82UFVfRv3zCjEL0p0eEVSjTpOtLqY89Rmls8aDETWDeTvSYbo150+Ea1WYPaYPvxkbF+cPjGoKvv2nph5sq7FTR9rCikGHf5AcK462uQEgtYjAiIWgw6/GGDWsB7cNqWAdo+fLItB3cucAQ6e7ECrEeiariqjnK/IyTBz78wi/vCvvfzw8U388b8u4JJOlPvPBB9WNSAABWdAdpHJUNUnOxiem/U1n1liqGSUcwStURu6ZAXbSQVdmDSwa8KNsVwsjmaXn+oAtzK3JI8lL2zm6tG53DGtAJc3oCz20ZvPC/O74PUFC6kPvrmHeSV9kxY479EXkt+t80HQ4vTS0O6l1R20GcmzpZy1BexsFxtUqIiGHDwkQopRx+JJdvYea1Xk7FKMuqRWN+/uOsGQnhmJx9/+BuaN7xvzd9lDNx7xYX5JHtmWoM2P7GWZCH5JUhjc8SBv0v/43j7ml+SxJDSXLJmYj0aAJZPsZJjjj0N5vtIgxC1ehEsg9sg08+L1Y3B7Azh9Iia9lhMtbrpnmBTvVPk648mZ5tlS8IkBHO0e/uvJzcApNRWIb61z8wuV/OGqYUl/H7cvmLBYtaGKt35Rxp/e2xexyR+em0m3dBO3Ty3ApNck7NqBIOHws9om7p81mBkrK4DTs1cLJ+J0Rl5p7YRZ/12HLdXAkVDg6RUDtLlEFr+wjTXzRnGj209LmEXc3zfXKAmS//n3/oTHbOtkXJ8JztX16Vw9LxXnN5y+YBfYXdN1NLR5uHVqQdzxOmdMH5pd3qTHqm10suj5bUCwY+fhK4d+Kx133/ZY+ra/X8X5D6dHxKiXMBu0NLR7Odrs5NapA/m5u3+EHevaLcGxK+/pohOr/95dr8iAQzCJs3HvCbqnGxmRmxWxPyyz27hnRiGzHtsU95w+2N9AQ3t8EpoKFd9nNHV4cXpFpg3JYV5UscrpFWnq8GLUaShXx9DXDnU9VvFtIcWg+1KNP0adJkJt9uKCboAUYRMS/vnFE/Nx+kVSQvmkgu7p/P6dfXHPRS6w1jR2MLckjwCnbEdWbaiiJKT4e9mqihgSigwxAN0zzHh9AfadaMPeNZVMix5He/LYoM3t55OaRjIGdaPJ6aNnhgmPP0CH1x/MxeXb+PxIM2P7WRNa7JTZbeyoa+YHhd2TvMdKQIJtNc1MK8rB5RMjlN0ev2Y4ZoOOH/3lo4jPrZ47MsYCZ92SUvx+MXlM5PQGFQ8qqvnnDePYVtvErS99wePXDCfPlhL3vju9Ire9vJ23f1mGNdWAo91LYU46K2cXx9iqWAxaJg/qygf7TjKqr5XXF5fg9om0uf1sP9qC2ydGKNXIv5OcR3N6Rbz+QMyzZtJrONkWXJPUfVskUkNjyelJbJkX3Uzo9Io0tHkoyEmjsePUWKhrcfPAG7uV+9ni8ikNcCNys/hg/0kGxKnhpBp1BCSSNuRJBHO504d0p9npZX5JHlDNH9/dzxPvH2RBaR43htSi00w6Uo06Hn5rN4sm5Ce1itpW28SqDVVKnvjZTdUxtrzheeJXF5WwYv0uyqsagiQ6AQKA0y8S1FxNrNItX8uCkjwaO7zoNAIj+mbT1OGJmINkAv3mg40U9shQ/i6E7L82H2yMqOXpNAJLXqjk2fmjcXpF/rXrBD0zTORaU/j9O/sSzpHhsZHHH6BbuhGTXstbO44nsfexxczjrS4fV/3lI8VW6JZ/fs7K2cUROepErgs/LO7BzRcP4O5Xt8cosj9w+RDcfh86TeJGPRXJceBkO93Sjeg0yXP8Kr7bsKYauWdGEX95/wDX/+1TbriwH7+5JCjg8HVhU1UDebYUpcn9y8Ck15KdYlCUer4pqGSUcwTppshbkajo+OLHtay9biwn2zwJjyV/NpGnZJc0o9JdVlnbRM9MM8NzM9EIxN18WgxaFj3/KT8d14dbpw7E7UveTd+ZlyhAXbMrxsLk2ywUqFDxdSO9k4Wgw+Pns9omHrxiiBJwmfVaFk3sTyCM/Q7BjfaiiXY+PuQ4IzUL2UM3EfHhlksGsvC5rbz1i7Kkx/H5Axi0QlLCjF4rhGxg4MXrx/KvXSdY8NwnrJxdzLMfHuLu6YPiBh1Or8jaLTXMK+nLbdMGUt8anOOiA+FSu5W6ZhfPbKpWNtROr0hZvo35ITnZ6N8ouqvlnzeM4/39JyOYn/Hmy97ZZupbPYq1zpdhond4fNw9vRAJaHMFCXcCEgFJ4qG3didVLdlU5eDu6YWU2m00Ob3KtXemHmNLMzKlqJtyrZ2d75lsVr5LyLAY2HciKEnc5vaj0wo0tHu5+snNEf7VsuxlU4eXbummhEkvOP9/MxUqvm3ICTeNADqtNuF4vfmFSl64bmyMzHA4wufAD/Y3cMfLX6gddypUnAWkm3U0dfiwGLWU2K2kmfT8z3v7+MXkAbh8YszYldfZTLOeQ44O6lrcTCnqxpMvBDs2S+xWlkzM593dx/m4uhF/QGJYbia3Th3IkSZXSKbfjShJSdfss0EgVaHiu44Uk46frvmYBaV5EfLgcrHq/xaNZ+O+oNqQOoZUqDg/kGnRJ7TviG78kYua4U0uzU4vgYCEyaBl+pCcGNUFrQAmnRaXL6js2lnOSv7skjj56srDzfhEMen6bjZoaPdIHG9zM3FgVz6raaJ3tqXTWD3FqGNMnlVpBm33ihj1GpzeAGlGDcsvK2L6ygr+eeM4bprQn4AUm5dbPMmOyaDld2/t5jeXDESSpIjibpndxi1TBvLn/+xnyaR8Gju8MRaDGWY9Fn2sWk28383tE0kz6fhhyDYoXkz0f4vGk6rXMiI3EwnolmbkrV+U4RMDMUom0Whz+fGKgRgSTDjEgMRvfjCQf+0+Tpvbx4+f2MpLN45TPiPbT4ffx15ZZq54/EPlulvd/ohnrcRuRZKC16eci7rmKDBoNZTYrei0iRVE4j0vJoOWk22epPcTgmo5y6YX0i3DyOK1lTz1s5Ex7zHqNJ0SvNrdfpxekVSDDgFB2V+EPwuyWvT/LRqP0+vnZ+P68vh/9vObKQXA3kiyeb6NueP7Ks+JnKd98fqxzGvzKHnad3aeiBhX1Y4O7plZiD8g4faJCIBPAikAASItohLB4w/QNd1Iq9vPkx8c5AeDusX8Hk1OT1KFa5fXr1ivVx5upjg3U1GmWlNRzeuLS9BpBErt1qSWpjIyzHo2hpSbd9W1hMg+xBBEbpkykNlPbY57nIoqB4Ig8PLPx8eMsUSuC/NK8mKIKPKxlr66nfsuKyLNlFzdRkViHDzZQU66Wvv8PsBs0PLLyfm8sf0YT1dUU1HVwP/78QVfi21PcP/RwLh+1jM+Rk6GieoGlYzyvURWiiFiMao83EyPDFNM4fHq0bksX7eT26cVJDxW5eHmhJ6SJXYrxblZSqJv8cR8emaa6PD4+dmaj2M2/CV2K18caebq0bmKJNzqubGblHB0FgC0OL0xRBRQCwUqzi/YUg1cmG+L8IWWIW8Mgxu5HawMKXI0dnhZ+NzWuAH5whChozN0T4+dNwxaTVI2+x3TgptIvxjodFOcZtQxv7Qv8aSd55f2xRFi4JdXOZjX5lHmH39AYljvTJo6vNx72WDuf31njALKnDF9WPJCJY/PGY5ErJxoSYjRrRUEKkIsfLkQWb6/AUmSWFCa1ykJQxCgMiT5GP5bhc+X8lwpM/E9/sBpWwlZDFqsKUaWvbYjMjGRb+OmCf2prG3GMyZ5IHSooYNFz2/j+WvHKH9L5qU8vySPuiYXzrBgPtn5ltqtiuzu+Yx0c3Cbk2bU8fbO4wqRKrp4XWq3ctWIXsEEWr6NT2uaIpI9Jr2WE61u9Fohod2HChUqvjrkMSsBWo3AiNysuGSTsnwbjR0exvezsmpDlZIElcdslkVP1zQTf50/mo9rGllTUa123KlQcZZg0Gow6bVs3FvPkol29JqgJPTOo62s316XcB/SK8uMxx9gw+56fljckz9cNYw8WwqfHW4mxajl88PNETLxxb0zFbUjgHVLSpKel0ogVaEiFkadhhF9Eq+tRp2GNRXVgDqGVKg4X5BhMdA708yMEJHEKwbolWVmx5GWmMYfuagpq81aDFq6ZZhobPcgSVLMsQUEslMNrFi/k1unFnBtWb9O99qZZj01jo64Vjgldiul9mTKJFYseh3HW1oY2iuDgAQDc9J55O093HLJwORWQkIwL5VhDtr5trl9mPRaUo06JCRFTcLpEVmQIC/30UEHnx5qpLzKwWdHWvjnjeM41uLGH5DIsujxixJOr58bJ9hZ8Own/OGqYRF59lK7lewUA/Ea4uPlsbQaofN5W6shANwypYB5zwTz+v+8YRw761q5IERaCUd43ATB3OVDPxzCivW74tYEPjroYHhuFiP7ZPPRweBvW1HVoORWou9jmd3GiL5ZEcfqkmbgv57YHFGL2Li3nsFhChPqmnMKza6gysjBk+0JmwEzzfF/rxOtbmUcRMfImRY9Lq9IZW0zy2YUcqLFzf8tGk+z0xszdpqcXlJNycuGqaag6lKHT0QMSEmfU60AvgC4fQHK8rvQ3OHl15cM4LZpA2l1+ckIXc+Pn/go4tkpzs3kX7tOKMpJxXHyA6lGHa99XscVF/SkqcNLhlnPRwcauCA3C5dXpNRu7TRPnGEOjt9Uo44FpXmY9NqIeajycDOzhvXgvnWJFa7vmVFIZW3Qkl2ubxl0GuW3Pdzo5KWth7llSgFCFBGn1G7l1ikFCqmk1G6la5qRFz+uxekVeXR2MS9uqVVUJD3+AJlmPVkWAz9ds0X5zcrsVpbNLOKtHcewpRq4enQuxb0zaXH6SDPpFLKMHFvFu1+6JDWEiioHPlGiR4ZqMXOmOHCyPe7crOL8hCAIzBjag8KcdP78/gGmP1rODRf146aJdiyGM6dmbD/aQkO7lyG9Ms/4GN3STRw8qZJRvpfolm7iwSuGcNcr26moCrJGH5szPEYGUV4ofiNKCTfZu+pauO+yIu55bUeM5/byWUU42r1MLeqOVoCAJOEMyetFyx+Hs+T/eeM4intnIggCtlRDwsDgwnybYjmSCA3t3rifBVVSWcX5gwyLgYevHModL38RQUiJ7j7ZWtNEk9PHstd2MHt0blJvWkhOMCizW9FqiOlWae9ErajJGWRHL3p+G39dOJplr8aSKGR2+oQBXVi8NtjBMi8qMF+8tpK/LhitfC7cazjPmsJfPzoUUTS8Y1oBNY1O5fNyIsTpE7nln5/HDf7D7XLCPTQhuCleNMHOhwcdSeULK6oa2FTl4E/v7ePWKQXAnqSSorJiysjcLGYOzeGBN3ZHzGHR93TZ9EHcE0VEgaCNUuA0CTPy6x8ddEQE+Ym8lNduqaGwR4aSUIDE5JVSu5X7LhvM+U9FgdRQAOkPSOw40hyfSJVvY9mMQk62uZEQ+O2swRxtcbFqY1VMUqVbupEXttRy/6zBqoqXChVnAebQmJUkePw/+7ll6kB4h4g5t8xuZfmsImoanOh1GiYP6srVo3MTWtHtqmtRpIjVjjsVKr5+NLR7MOl1bD/SwgW9Mml1+7k5RC6+b2YRy9dF+cXbbdx5aQFiQOL5zTVcPSaXZqeXRc9v46Ubx1HS30q7x8+88XmIgeC+aVddS4z8dJPTl3C/dzoxqQoV30d4/QGWTLKDRIz11ZJJdrz+gKI6qY4hFSrOH3jEAEdb3HRNNyFJ4Gjz0jXdxKo5w3H7RPpkW0g36ejw+fnDVcNICRVIrr+wH1urG1m//RiVtc0xqkogcaTRxXt7TvLrSwZiNujYXdeStCkm3axnRN/sGMVcee++eG2l0ogVnXeZW5LHzFUVjMjNZGw/KyfbPJgNWjZVObiu1BdXOUA+rsvrJ8WoxaTT4PYFSDPp2X6kmaG9MtFrtXi1Eo/OLibdrEuYl1s9dyRPfnCQxZPsFPfOpNXlo6bBSdd0I4cbXUreak1FNcNzM9la06R8Vt7/pOg1iFG9SfHsksrsNj7Yd5Kpg7snnbdb3T4MOi2zn9qsFKTnPvMxf1s4GotBG9FwlsiWoyzfptgEyccIz3MVXpVOqlHHyNwsHr9mOBa9lokFXeHt+E1qi9eeUtspsVsx67X84aphipqO2yfy+eFmfGKQ4KTu2yKRatQz+6kt3HBRP5bNLAxa0ITHw/k2emaZY8bZxr31jM3LZvFEO2adlqvHxI+RH51dTLvbT/8uqXxc3Uhhj7SYPbvFoEMMSAkbFkvtVix6LQtK8mh1+fnoYEPS59TjD/De7np2HGmmoEcG3dJNHGtxK2NmT10rkwZ1jSCilNltyvMUnqeNPo80k441FdVMHNBFyaWu336MPFsqWkFgXkleBEkn3rWkmXR0ePxkmvWM62fFJ4rcO7OIe18P1tbWVFQzY0hO0kZPMXBq7vnNDwZgSzMiSZKiTCUh8MaOE2zc1xDKqfeNyHfLVoqldiv3zCzi/727l4evHMrNL1Ryx8tf8LcFY6hrcSmf+fhQI3qtwDPzR0Woi1/+2CbG9svmf28Yx32v7Yi4/6Wh+3/zC5UJ1WLaOrF0b3P7yMhJT/oeFfHh9okcbXJx6eCcb/tUVHzD6NcllQevGMJrnx3lyQ8O8o+tR/jl5HyuGtH7jKx7Xv+sjgyznsKvMBZ7ZJj58EADgYCERvPNqB2pZJRzCBlmPYsn5XPXpYUca3EhEEwWLL9sMG6/SEeYV2CLM/Eme86YPtQ1uhS2ZLpJj8WgxajXcMThos3rj9ggPzNvFDqtwIi+WcpCmGHW4/aJLAkt8jqNwJqQSoG8eY2WI7ww38YjVw7tlEjS2kkhQC0UqDhf0CPTzMrZxRxtdnHIEUu6gKCyx7JXtweVRMb3TXq8DLOe0v5WrrigJ/dFKYuU2q3ML83jZJuX6OXDFEcCNBz+UAB4uMnFz1Z/zGPXDOdurYZWl480sx6jVsNDbwU9RiuqGhJ3qoeIHjLkIGDp9EEsX3+KPS4H9pcUdovocA3/XDJSTrSHZjg0GiFEGunBA2/siilghgfGG/ac5Gdj+ypzZZpJh1+UcPlExZon/HwBJhV0Yd74vswvycNi0CIGJD466FDuaWmIqX/nKzvinrtMoDldlZU1FdW8smg8y9fvivFSnl+Sx8/WfExxbqaSJFg2fVBS8kpOhplWt5fmDg/plvO/88Th9LD8siJqHE5umzqIh9/ezbDcLIVIlWHWk2bSMeepzayZNwqDToOIxGMbq2LuTXlVAwEkinOzVBUvFSrOEpqcXu6ZUYhXFLlyRG/+5719DOudybzxfSPGrMvrx+kT6ZJu5O5LB8UQsCGMyJ2bxTObqllQmqd23KlQcRaQatJz/V+38uz80Tz05i5+dclAnF6RRWu38dic4UwbkhNBYK5v9XCi1UOaUcvsMbkseaGS//5xkGicYtSx5aCDW1/erhy/1G5l2Ywirnl6c8T3+kWJa0v7MX1IDt3STRFKZhMHdFHXaBUq4iAQkOjwiEwb0j2iCHKi1U2HRyTNqKMs38aKWYPVMaRCxXmEFpcvadPT49cMJ8+WwrEWN4ue36YoYpfabbS4fMq+Ot4x1swbBYBPlFj57318WtsctymmLN/GfZcVUdfk4oujzayYNZgDDR0xzUdOr8jC54JWML+4WKQlVBgNf728ysGy13bwq8kDFJvPrbVN7KpriVAOkI/74se1XFLYjeF9smh0ekkx6kjRa+mSZgIhqJ67cW89Ww818uAVQxL+Tv6AFEHmCCd3VEQ1AN0zswgxIHFhfhdSDFp0WoGdR1spzs2MyNkFFXTtLHjuk1N/C+Wu/r65hgvzbUnnbadHxGIIRBTxB3ZPw6DTYAQeuHwIS18NNr4msuWQ82Yv3zgefyBobLJxb73yext1GsRAgHnPnjpHucns5xPs6LQCZoOW7UdaWLy2MobQ0tRxyu6lrsXNe7tPMGdMH25+oZKy06wlfJ9gSzUwsk8Wf3x3P0+8fzBIXAjFw1kWPX2sKbR7YutCT7x/kLF52RxpdLFsZiFLX4m1Wgnaqgv89orB9Mq24BcD+CSJz2qauXVqAXcIAm2eYN1o1b/3Rzw/MkrtVlZcPoSfrN7CfZcVkWLQ8sT7BxnaMzPuc5qVYqDF6WVKYTdmXdCDe16NJUgsKM1j+9EWVs8dGREzWFMMrJxdTB+rhYff2hPxnE8u6Mrt0wo42uziD1cNw2LUYdRquDeUG7hj2iAanV5u+efn3HBRP5ZfNjgmly5baz3xnwPcMKE/jU4vOo1AqkGHXqvh8gt6cse0AupbPcpclAitodc3VTn41eQBzFq1iYkDbNw2dRAzhuQo9lyJ8t0vXDeW1XNHUnm4mbomF2/tOEGr28+C0uB9bnR6YyyYFk+y8/Bbe2Luc2GPjLg5Evk+JmuSTEuguqO8ruZUzhiHHB1IQE6mqdP3qjj/oNdq+NGI3lyY34V/fHqYpa/s4NF/72d+SR5Xj+pN5mmug2JA4vXP6xjbz4r2K5BIemaZcfsCHG120TvbcsbH+TJQySjnEBravVz95GaFjSzDYtDy94VjmPfMx7z08/FAJ5vsLbUU9EhXCpYzhuRQ2CODq1Ztiuu7mWbS8+aOYzEL4eq5I3F6RRZPsvNAqBAKRBQ4F02wY9JryTDrsaUaTmvzmN7JoqUuairOJ2RYDDS0e+OSLoAIWbykqif5NrqkGsm06MmwGPjDj4fR1OGl1e2j3SMiSRJ6QUO3DCNHmpwRn3X5/IllTvNtnGhxKf8+3OTid2/v4cErhvDAG7sUAtrj1wzH4wvw5AcHWTWnmEQ2PTLRQyZUlNitDOmVwV0JyBnxrrfycPNpe2hGb55TjFqufnLzKfWVqZHqK+GBMYDTdyoI+NuC0Ww51MjI3Cxun1qAT5Riug+uK+vPwuc+wekVIyQvV84upkemmboQ8SgZPP5Ap5Y74R6px5rdFOdmceuUAho7PKSb9Bh0Go63uvnrgtEYdBoa27387w1jEYDhfbJ5YP2uCMnUMruNpTMGcazZzdbaJtZUVPNyaD05n5FmCq5J3TJMvLXzGJMHdVMKVuH+tSNys9BpBAxaDR5fIO6zB8F7tbC0H6s2VKkqXipUnAWkGvUcbnTSx2ph7ZZ9iuJT+JjdVdfCLycPINOs560dxynunRmxHoVDTgStqajmpgl2teNOhYqzgFSDlt/9aChev0hBjwws+lNduDet3RbRRW3Sa+mabuSmtdt46efj+dkzwT2VQauhLN+GANy7blfE8SuqHCxfv5OrR+dG2CluP9rMkJ4ZvLn9WEyDxEUDutDi9NLQHtwrp5v12FJOL1ZVoeJ8hlGv5W+bD1EY6kqWIRcHl04vZPmsIjz+2LyRChUqvrvoLA9r1Gloc/nokRFU/5RzOX5RStg9L8MdsgrWaFD25HLOeGFpv4gmnpkrKxSrlilFOSx5oTJuntrpFWl2+si0GPjRXz6K+72bqhzcPlWDnBKScyzxVD+WzSik2enlnZ3HmTCwK0atBrco8rfNh1g6vRAJiSc/OBj8Xldi5bXeWWZ+++buuPnxn0+wY9RpSDUGSy2/e2s37+05eeo87FYWT8rHKwYw67W8umg8aaZgLr3N5eOpn42kxeVTcld/31zDnDF9qKpv51+7TlCQkx533i7skcGlQ3IiivgnWt2s/+IYlxR2Z1BOOg9eMYR2r4gUkBKSksr3N1A33sXC57YqeSkI7rnqW91c0Dszxua6sraJkv42dBoN//XEZm64qB8vXj82+LpHRK/TUL7/JBeGbOC8YoAJA7pwSWE3jrW4WTm7mF5ZZnJU1dkIRCtty/fsB4O6smxGIbe//AXDemfGrQttrm6kR4YZt09MGCOXVzVwpMmFTqshK8XALf/8nIE56WSnGuiSZmTBs5+weFJ/lkzO52ijk9umFnCXNjhHpJn1+MUAP1u9hcNNLvQ6DWa9lhG5WTH7fhlvbj/G4B4Z/Ogvm5lc0IXbphawyONHq9GQZtLxxvZjMblaGavnjmTNpmpG9c3mrksHsaAkj2aXD4teS2aKIcY259VFJcp1+8VgM4vTK/LHd/fzt49qWDNvFDe6+0eMtUf/vY/bpw1izlObeW7BaHRGgV7WFAAuHNCFW/7xGeVVjk4tQlNMpxpB69s8OL0iA3LS2VLtoKhnBrp4/lxhcHr9Sj3w8WuGA5GK4B/FUQFPZLWT6O8QjK3uml6IThDi1gr8YiCpIk7K98Dy/Wxhz7E2AHpnfTOFfxXnJrqmm1g8MZ8rLujFui/q+O9/7eWP7+5j5rAezB3XlyG9MpJ+ft3nddS3ebhoQJevdB69soJrb1V9u0pG+T5CVgyJt9GX5VKPt7goy7cl3mTbrdwS8pgrs9u4f1YRBo3AQ1HsURmldiuGMF/gcMjnMa6fNWYBkwucqzZU8ebNpcRIMSSBLdXAhfm2COsSGao0n4rzEcme+XAkIijIqkPhAVq3dBPd0k0cqG/nx0+8DwQZ0ZXlTTFBs8WgDXa2SLEEksUT7WyudkQErpWHm3nkrd0RFjaLng8GFT+f2B+jTsOdlw7idgnaPX4lsSAHD3KwX9cUJLkcaXIRDxv31nPzpHw0EHFee+pauO+ywdz/+o64sq0yUSOamCInS6Bz9RUZMpmlxG5ly6FGZa6zGLQ8f+0Ypfugd7YZnSBwvNVDcW5mXJWSEy1uFj63VekiSvadySx3wpVzSuxWttY28XltE7Mu6IFZb1GIJsp9DHUvPvDGLt7bXX+KiHNpATVhijxXPP5hxDrQ0Yl90/kAjxhAEIKFsu1HWrh6dK6i8iWj1G7lzksLsOi1VDV0dLqcWQxaLAatquKlQsVZgC3VwMeHGunfJYXry/qxMsouS/YY33usldF5VtZUVCvWbYnQ4vLx6OxizAaNWohWoeIswO0XeXxjFb++ZCC76lpIN2l54PIhLHt1B+VVDREEEnkfNzw3k+MtLqUgdaLVzfJZRVz1l4/ixqzhiVj5ODuOtrC6Ira794P9Ddz+8hdcOiSHO//vlMLKhfk2Hr5yqGqzp+J7Db1G4LqyfjF2lPL62uz0YDHo445DFSpUfHeRzG5dzqtMH5KDXquhzG5VclMpJi0d3s4thkvsVlpdp/ILcq5k8SQ7lbWxOapNVQ7uf30nC0rzEhZM0816aho7kn73sRY3A7qlKtcWnmPx+ANkmvW4/SJur4hZr+XTmiamFuXQ0O7BqNMyZ0wfmp1efvHiZ8q8F7Qzy1fOU0ZZvg2NRoi5lvD8+Lu/uhCA4y1uLi7szuwxfSJUHtLNOkw6LQ3tbob3sSrHcPsDZKcY0GkE0s16cq0WLAYNL35cy/UX9uO2qQUsX7czriXpC1tqGJ6bqRSx5b8/8MZuxvezAUHVmkv/p1wpcCeCXAeQr3Hp9EH065JK93QjP/rLR1w9OjemIXbhc5/w0o3jGN/fSlGPDB55e09MvmXCwK4JiUfyb6YiErLSdkO7lza3jzSTnlSTjlv++Tnl+xv4tKYpbl3ownwbP76yN8db3UmP3+zyKYq/988azB0vf8GqDVX8cnI+I3Iz+d3b+1hTcYhn5o9i5spNCY+jFQTe2nmMOy8t4ME3d8d9RjfsqefC/C6K6sef3tvHshlFPPL2bmYO65lUtQlgfkkeL35cy7Si7mSl6Pnz+wcY1jsz7txyrPVU/rnF6SM71aAQK+QmcLmpEGByQTcuKezGnKc2M7B7GiadNqIu1e72K3np+lZPUpKGbJUDwXnRYtAyrSiH+9fv5LaXt7N4kv20FLLlz8uwGHRoNULcmkEismBnJMJWl4+uaUYeCSM9yXi2ojqpIk4gICU9torE2H2sla5pRlKMakleRVCZ5MaL+jN7dC4b99SzYc8JXvr0CCP7ZHHjRf2ZVNA1xj7H7RP507/3MSI3izxbylf6fmuKAbNey/76tqD13jcA9ck/hyAz1aM7/ReU5iFKwYn+tpe+4B83jOPe13fGFDK7p5tIMWqpOtnBUz8bSe8sMx1eH8fafdw8eQAtbl/MIrJ81mDmrvk47oZQDii0QvLS3CGHk0XPbzvtBF80w1fG6dr8qFDxXUOyZ15mIQIxBAWPP0A/Wwo5GaaE4yKc6JKI+SzLnL54/VjmtXmUwLFLmpGrn9ycMNl41cjcGJb9hwccvPhxLS9cNxZfQGLBs58oG3nZA3b70RaONrlYs6ma8v0NCckZf/uohmmDc+LIt7tBkLi2rB93TBuE0yfi8wf4MMoOZ14UMWV+SR4tzliCQGeb/WiSi/ybNXackkD83+vH0u7x8/yWmriKVGu31FDYI8hcrTzcrFjlRKPMbgteH6eSFmV2GysuP0UmUd4bIvVISPx0bB86PH5cPh8PXD4Yrxigw+NXOmkyLAb++6phEYGqxy8mJeJYvgds9g6Pn1STDgm4rqwfT31wMOL+ZZj1ZKcY0OsEDje6TotXKQYk1e5DhYqzhAyLgQkDuhAQA3RNNzFjaI+I+fZEqxutAKPzrLhFvyIb3Rme2VTNg5cnltxWoULFmUOUgqTi68v83D41aM345AfV/PdVw7h7xiCOhkjJsrz+8NxM7plZxJynNlNmt7JsZhGf1zbjD0g0tHsTfk+KUacksW9+oZKVs4v503v74763fH9DjP3lB/sbVJs9Fd97SAGJHhlmZgzJiVhf61vd9MgwoRGg3SeS0YlEuwoVKr5byLAY+O3lg7nrlcjiYjih4ZLCbvz3O3u497Ii7n19J3e8/AUvXj+WE63uxAq+IdWM5ZcFcxTRSNadX17VwM8n9E9gAW3lvd0nGNIzeXewTiNwxOFi2YxCVqzbSXmVQzleWcjKeu2WWm6fVsD/+9derivrjyAE7XbaOzzc/EIlf/7JcA6HNVAZ9RosgsB9M4vw+gN0eEVSjTraPT7FhiMRWt0+9h1v49976inOzWRwz3Rc3gApBi19rBYaOzxs3FvPpIHdlM/UOjq4M8pOpdRu5YHLh3DN6D4ANHV4efCKIRxuckUoOrywpYa7pxdytNHF49cMj7E7shiDOR85b9hZ3BT++qYqB8tmFJKTbuKQo4OGdm/Ce9nu8fPLyQN46K3dMc9J8HnbE5d4VJZvo2uaMek5fZ+RYYlU9TtQ364QyjrLH3dGKjXqNHywv4GGdi/9u6aycnYxjpCd0ti8bAIEn4F3dp5IOP5L7VZFbfiCXpncPb2Qxg5vxDP64pZals0sxOUXSfXrmFLUnVK7DVEK8NaOE/xoRO/kv4FZz+P/qeInY/vQ6vZyqMHFrVMGotNo4j6PBu2pZ3hrbRMH6tu4ZUoBAnsj1KPlmti6L+p44v2DFOdmsnhiPlqBiN+8NawJ7faXv+D5a8eyYv3OuLZcc54KWorKueYFpXmsCLOsP12F7GhiSpZFj1YjxL3nvbPj1+A6G+s6jUCry0e/LqkRpCezQUuT08vj/9kfVxFn5b/3ce/MoqTHVpEYu4+1fmMKFCq+O8gw67m8uCczh/Xg05om3tx+jGv/upUB3VK5/sL+zBiag0mvxeMXuf3lL6hrcvPzi+xf+XsFQaBnpon9J9q/hqs4PahklHMI8uYw2qqjuHdmhBTX3DUf8/drR+P0Bmhz+zEbNEgSvLfnBE+8f1DZcJTZrVw2rAc1TS5e/LiWR64cyp3TBtHu8ZNu1mPWa/j923sjNt0yyuxWuqQZKc7NwunrfAMDXy7BF4/he7o2PypUfBeR6JkHIlRTwr0jL8y3dTqewokuyZjPTq/IkSZXBDnh8WuGJw1QRCm+hGeZ3UpAApNWw4g+WXHfM6mgC/PG92Xe+L70zDTHJWdcPTqX5et2xmWVl+XbWH5ZETqNwGMb99OvaxrFvTP5y09HoNMIuLwiWkFQCDByID6oR2SyYuPeehZPDC7QEV0tdhvLZhbiDl3/zVFdGtGbf61GQCsI3D61gAfWx2f7y4HDrroWfjNlIAGkmITCspmFvLvrOM9fOwatRiDDrCfVoMUbCDCpoCuzR+fiFQN0STUG1TdcPixGLSadhm7pqbE3KQzRgWqtoyMpaz/1e8DEzrIYCEgSd7+ynU9rm7n+wn6U2m34AxJd0rRBax6dhj+8s4ey/C7UtQSJQonIRCWhYHt8P6uq4qVCxVlCTqaZo44ODBoBnRAMzPwBCYtBS5c0Ixv31vPF4WZ+OXkAkNziTp7LN1U54ibIVahQ8dXRFuqE9gYCEQnXuc98gi3VwCNXDqV7uokyu41pg7tj1mlpcnp48qcj2bC3nt+9tZtlMzu3Bcmy6Hl8Y5WyZ+6s4y/e63LS/WzFnKo1kIpzHRqNwENv7eaHI3rTNd1Iu1skNSQr/7t39nDn1AJ0GkHd56pQcR4i15rCQ1cMoTYOoWHpjCLFdgMEfnGxnUyzkcqaJvp3SYmfU8m3sXxWkLABEnptrOVDZ2u1Tqth9dyRCILAtpCdcHFuJnND+ZUFpXmdKroAHPikjVunFnCj2x9xbc9vDl7bEYeTsvwuaARoaHeTatSx8LmtFOdm8smhplPXZLdi0ms52ujCYgxa0gf7MyW6pZk6jSc8vgAr3tjNqzeVsHzdTn7/zr6I851fksenh5q4cniwAH+i1R1DRIEggWPpq9v57x9fQLf0U81pRr02ZNftZ8aQHP5rRC8efGs3b+34/+3deVhU1f8H8PcMs7IM+6qAKAioqLjxRTBNTTQ11H5qLoVpi7tluVUuLaZpfrO0svq6tGiLmWaWlZpaGuKSG0q4oViguLBvAzPn9wfOldkHmGG2z+t5fB6Ze+fOOWfm3HPuued+zk2d5aNaTkM1bnjwwi2TIzMAQJVcAU9XEdwNTBYGgNKqWpRVK3QeV5WfKX0itZZPWkEPpDZIiUZ03vrjxwCwY2pPrjwNReiu/12rIv6q3jd9y184kVvETXioVTKMiG+Bxd9nakTa9kNaUituLPV4biGCZRLEBMvgIRGgQq5An7b+EPB5ePWHc2gX4slNAnnp4Vj8nHkDgPq1fP2l0KtrlfB2rRs3f65/FGoVDCIBH22DPCC4N0FCl7qHA33xx6U73OSP9/ZdQKcwL0xIasVFTAqSSfDv3UpEBXhgzZh47qEXzZgf9Zc4u10mx7j/HcFbj3bEvEGxqKiuhbtYgBslVRj7yRHcLpOrjQ+vGROv9v3Un0wyKbk1PCQCrQcvNceXH4jyQ7CnBFW195fOqX/M6X0jdY77nrxepHc8uFeUHw5duo2hHUO4715zLHlEfEutKEdUZ5vufH4JekU1bWkV4rhc+Dz0iPBBjwgf/J1fgp2n8/Di1tNY9H0m2gXLcL2wArfL5Jjapw3CzDSpKcRLiuybpWY5likc/06UHVF1Dhd/n8mtzXj40h1U1yq1Zk8++mE6Nk7oDpGArzdc2h+X7mDuoBgs/uE8KuQKbDicwzVo303pidd2na8LSVhVo9W4TOsTyUVMaEgYsYYM8Gk2doQ4On2/+aZGClJNdMkvNhyGUXNmtLGZ0gEysVbdT4r0RVpSBIZ/cBg9W/tg4ZB2eFVjjc6kSF+MTQjnwnA+/1AUpvWN1JqcoWsJMJU/Lt7GldvlyL5ZgtkDYrBkZybW/nYJriIXvDcmHpsO56h1qlVP1i7fnaV2nLPXi5HaKQRD4tSfri8orQufKBW54HRuodZElPqd/16RfrhwsxQvbc/kLo7mDYpRW6JIdeHQK9IX8wfF4vrdCiwa0g5KVndx5yYWQCTg49qdCsQEyZB+5Q6y80vw9shOKKqowaLvz3EXX++Nicc7ey9oLdXU0NDycoUSE5IiwKA9635CUoRT3JiVK5SoVdxfnmr13otaT1FveToBTyRGoKpGgTd+rPv97JqRjIXfZ+p9WuHBaH9qvwixJCVDuUKJOdvO6t3l8cRWAEx/wsgZliYjxBpUT93yeDytAc/bZfejzO2cnoQbxVUoKKlCj9Y+uFsmx8MdgvB/XVoAyrqbPIaWcg2SSdQmdkuEhiO86evnWmqZvbyiSszbdkbthhktDURsTVmNAj9m3sSPOm5cAsDMflHg8UH9XEIcVKivG1xc+CiurEFpVQ0GxwXj0fgW2HM+n4uKsPfvAswZGI3Xd53DidwivD+2C26VVmHewBgAdTdUJUIXBLiL4CYW4G6FHIt2ZOJEbhHWjo0HGLgHO4yNOZVW1XD9hF6RvtgxNQm7z+Vz4yvn84qxYFAMwLIMLuH83ph4vLv3AmJDPLkb2T1b++LRLi1w+VYZeKhbXd7PQ4zqmlo8seEYuoR5qUXb7RXph+l9I6FUMgR6SnCrtArVNUp0bOnJnROLK+R6+yq9ovzw55W65ZwnbTqGzyb1QF5RldbEn0VD2yNQJgFQF/HE0ASOwnI5ty9wf7luleIKOdJ6RqCkqlbrOmhG3yh41zuXh3hJ8XCHICS29sXC7zPV+iu6ogUD4KLBmjKxoV2wTGc+VIQufPw4MxkV1XXRtwJlYmprGkhmJDpv/ei9qntMmn1Tze/aQ2Oyhaqe1R+rff6hKC6iterhOTeRAI+uu78UeIcQT64u6/Jc/7ZIbO0LH1cRXEV8vL//MoD71/JiAR9jE8K1lh2qn17VsvBzUqK56w9NGw7l1J2HeDy1pbsSW/tCLOCjVsHgJhbg+LW78HVXj8rDAK1oyZq//frXNg/FBuCN4XGQCF3w8ePdUFWjUJtYomsyXv0JRJsmdIeCMXQJ80byBD+t8eX69wXu3CrTOb57Pq8YC4e0xxs/nlf7nrPuvf7arnNaD0m+OCAaa3+7iCc1okiqCF34uH63Uuuc7+8uQjBd0zTa7bJq3C6TI5wioxATxATLEBMsQ35xJTKu3MU/hRWID/NG3+gAs0bXCfVxxdET/0ChZHDhmxIvvmloMoqNCfGS4u2RnXCnXI4lQ9ujVsmgZExnKK47ZXJIjCyzUClXYO3YeMgkQhy4cAszvzyJrmHe+Cu3EL/9fQtHrtzVWk9TrlCiskaB+DAvHK43k5QH6AznqNlZtdQAHyGOqjGRgnQ9eRnsqX8Qv1eUHzcBQ6WgtFrvUya9Iv0g5PMxb2AMeDxwa1/WD/m59+9bUAKYmBSB+YNicP1upVZYUAD46OAVDOoQhMH3wkGrLl6YkWUmq2uVWPvbZXQI9sL0vpF1E0CqFJBJXfDy4Hbg84B/6oV+H/vJETzWIwzPP9RWLS2qtW0DZHUXGsGeUvh7iFFQWgUXHg8TkiIwqVdrSIQuWp3/pEhfPJncCmf/LcbmpxIg4PPgLhGgUq7QWqJIInQBYwx5hZXwlArB5/EgEfIhFvCxeOc5rZsTqouKgtJq7uJgYnIENh7O0RqQaExo+dKqWszfdqZuDdB7ZecuEaCgpArztp3B/57QvXySo8grqkRBcRVEAsPtpJDPx59XbnORxXpF+YEP6FyOSfW78JLSoAkhlqTk81BcZrg/qWpjVH3khUPa4fn+bVFQWq2zLaKltQixDFehC5IifVFlJJpmVY0CQZ4StPJ1gwsAmVSA/KJqbM64hrdHdjJ5KVdTbgglRfoCANandUN1rRISoQv3xLUlzgXFFXKtwX6AlgYitqe0yvDEzJKqWgTSkgmEODTGGArL5SiqrEFZtQJ/5RbifF4x3hsTr3YTVXVTetqWvzAxOQK+7mLu2jhYJkGItyuKK+RY9P05bt/pW07imQda4/mH2kLBGHzdRCZFNgHqHqh8dde5ugjd98Zinu7VBrdL5ejaygfPPdQW1TVKeLmJUKtQorC8BhOTI7DhUA6+ysjFkkc6oLSqBlfvVMBDLEALLykqaxWQSQTwkAgR6e8Oxhhul9bg80kJELvwUVFTiw0TukPA5+HKrTJUyBV4ctMx7vrhl+d6qbXfhvoqix9pj6FrDgEArhdW4on1R/H+uC7wdhOhtLJu4s+QuGB41VsGrcSEc7Ihnq4ihPu4ai1tWlBajVY+rlp9D9UDcmvvjT8WV9Zo3UCvnydVlCxD+Z76YCQmbjqGNfceDtCntKoGvm4itA8xvPQS0c/QpKD635eK6h7T5YIyFNWbFFV/skP992hGXlH56OCVew8FXuUmmq1P66b2ezEasbBGiUCZBP/crYCXq0DrWn7NvYcONcdC64+VnrlehLce7QgAOKAnyk+FXIEtR65hydB2kCsYyqrqlu52E7mgSqGAkMdHtUKJji29wACuDqjUagxUG/rtv5ba4d4EsbrX8ooqse7gZS5vmpPxNCO/BHlKIBW5oLhCDneJEG5iAYJkEvSPCah7oNGFj4LSKlTUKKBQMszfdgaP9QjTGqcc978j+HxSAvKKKrnrHj93EVbvzdZaptxDIsDHBy/jtdQOOq9NiivkmPvtGd3Lzkf5YS1d0zTa6XvtXYSfm3UTQuxKsKcUw+JbWOz4EX5uqKpV4sqtMkQFeljsc1RoMooNUnUO/7lbgQXfnUGnMG+ugdWMIrDlqQSDx6pRMCgZ8MSGo1xnfknq/Q6yZkg3ANgwoTum37vYmNYnEmIhH96uIqwa1RllVbUorKjrsGoO8qvQYD8hDdeQSEH6nrx869GOBgfxXUUu6NHKh5vwIuTzECgTQ8mYjugnrXCjuAoTNh3D+rRueme4//b3LcxNiYELn6e2BFB9FXIF8gqrkFdchXBfN8ikQhRX1hhdi9xLKkSFXIHJm09gYnIEerb2hVjIh0QggJ+7SG1GuorqfHYyt1AtT9y6wVF+mNE3EqVVtfD3EOORtfcjS6kuDFSz0iUCF/D5QH5xFTqEeN5b9sYPMqkQRRU1epco0rW80loDk43K6j2tb2hN5YaGlveUCrH80Y7YoHFBlxTpi+WPdoTMgdeCL66QY+mP5/HCgGjcKDEcMaikqgbv7KmLlpIU6YvXUtvj1/M3cDK3UO/3S6HLCbGsMnmt0XNU/cGd+DAvJET44I0fz+O3v29p7Uv1lhDL4QGY/mAklEYmGZdW1WLUR3Vrqf8wIwmjPzqC/z3RTW0wtCETtA0NDi8a2h5v/qR+PkiK9MWGCd0tci64XSbXeaMNsPzSQIQ0hIfE8BCgu1iASiNLZhFC7Nc/dysw77szOqMJbsm4honJEVj72yVU19y/saxr3HjH1J4Ih5tW+1chV6hFI/3thd51D8dotNX6Hm48fOkOXno4FgPaBQKoW3b5o4NXAAAdW3pi46EcjaVCfLFzWhJcRS4I8XbF5YIyveNSALBvdm90CvXEnXI5FEoGAZ8PpbIWJXIFcgsrseReZHHgflQ2Tfr6KnfK5dx7XUUuWPxIe53LXNRNsK37W2bknGxsO1C3xOnDHYLU0tMt3Nvoct+q7Zo30FV514zUrC/fFXIFuoV7qy2Poikp0hcFpdXoFu5tND9EP1MnbtcXKJNwExmMvUdf5BXVhJFvJyfilXvLpnu7qk+MMRYFqaSqBiM/SkdSpC/eGNYBD8UGYE9WAXd8ADp/O0DdeWHh4HZ4OjmCS+/DHYLwn9Y+WLTjnNrEiV5RfngsIRxD7431vjcmHqv3qUee7hXphwUPx6CgpBoVNQou7RVyBfx52pEBTL0+0dyvfhmpomBrRn7RjKIYKNM95t/r3n667sMBQI1CqTY+rjnxJdzXFSIXPlz4PCwdHqf3/FBQWq1zIgpQF8G8oLSarmka6a/cQni5CuFPk76JDQn3rZscdS6vhCajOLPiCjkWfHcGf1y6gxO5RXrDj3u7ivR29npF+nHrz60ZEw9fNzH2/n0T+UWVOhsuFZlEgC1PJehsXANldWmb8eVJk2fiEkLMx9CTl/PuPXlpqJNcvz4XV8jxZUauzggQWzKuod29JxaMzXCvqlEgws/N4NOpx3MLcTK3EElt/CAV8jFy3XGDS4A9EOWHNgHu2De7t97O/pXb5TrTo4rmxL8XllGlV5Qfpj8YiQkb6550+WBcF7X31R9kcRW5YMfUJCgZg7erCDKJAB1aeHIhUT1dRToHVfRdBBqabFT/gs9YWTck8pRE6KIzysrhS3fAA7Di/zqZfCx7c7tMjhFdWmLx95lqEzo19Yr0hb+HGB+M68L99q/drkBljRJPJkWAD57ahVhDls8ihDReWbUCnhKBgT6uL8J8XbE+rRuCPSU4db0IP5/Lx9iEcFTXKrWWOaN6S4jl3KmQw00sQIW8Fr0i/XQ/SVfv6eekSF+c/acYE5MjIBG6aIV7bsgEbV2Dw+4SAV7+7qzWxLTDl+6Az+NhrZEndxtD35OkKhQ5lNgKd6GL/siYUX747e+b+E9rXyukjBBiaapxZn2RB+LDvBEf6oUHovzg5WraciDG2r/bZXL4uYuwcmQnlFXVoqhSDpGAj1/O3dR7U7WsqhajPz6i9tr0vpFYf0h7bOOPS3fw2q7zXFQOUyJHaPYz8ooqG7xstr6+iuqz9UW8/UMjYpq3mwjJkb5ayxwCdctpeLuZ+CBSA/pOmho6EVh7rAtYMyYed8rlGBHfQisqsGrJIF2RWkjDNSaytqnvMVR/4sO88FPmDZy5XsT9futPjDl5vUjvuFv9KEiHL93B4u/PYdmIOFTXKrnPMmXcWTNKUV0a4nDtTgUX+aWFlxQjPry/fJDmKgPhvq44n1eCPVk30aFelJ684irs/7sAb4/UHifVFZnc1HODqow6hnqZFAVb35j/HxdvgzHGTRisLzlSu9+mOYlw3+zeaBPgrjPN9RVVGj6nFxvZTvQ7ca0IUQHu4OmY8ESItbiLBQjwEONcXrFFI7Co0GQUG1RcIUd+cRXGJITjyeTW+Cu3UCsUV6iPFL+cu4nHN2Rgy1P/wasaa8CpIhs89vERdA33xmup7VFeXYuU9oEQ8A3PVnUTCxBrYK3H+h2O49cKuZmWABDqTeueEWJJpjx52SbA3aSLPE9XEV5N7YD5286odVKTI33V1s71MvJ0uptYgDvlcix+pD2WaFx49or0wytDYpFfVIUQTwmqahSovhcCUTVpBIDOG4f1wx3qYmzW/u6ZvVCrZNzFloDPw6D3/tAbMlGTWMBHrZJB6MKDUMCHRGN/HoBBccFI69lKLRxqQ9W/4DOWpoZEniqpqjG4/nBJVQ1C4JjrfZZU1SBAJjY6oTMtKQKPfXxEbRBsfVo3dGrhhYLSKswfFIOZcgXcxS5wFQng5SqkARRCmoGXVAgFY0hLioASuuvuv3crMenT4/hxRjICZBK88WMWAODVR9pj6bAOqJArUC5XwEsqhKuRZS0JIY0nFbrgTqkcCsbwamp7LP5e/QnFpEhfLBzaHsPeP6z2JPSaMfFGo+SZQnPQ93JBGfb+XaBz3z8sFKVEX59UhSKHEltRVqPAoiHt8NoP59WfJI70w6TkCEzd/Bf6xQRaMYWEEEu5XSY3GHlgYlIEAHBRdU1ZDsRY+ycV8fH3jVJ4uwoR5uOKrgE+uHizVG80WAA6oyOaGkHWlMgRum4sN/Tmvi6qz5637YzJ6Q2USfDm8Di8tP2s2oSU5EhfvDk8jnsYqqEacvO8IfvqU78vtnZMPApKq1FcWQNXkQvcaBzF7Bo6+cjQd1x/m6dUiDeHx2HB9rNaE4pU/fcKuYL7/daf5FJeXYP/69ISi77PNBoF6feLt1FVo1SrdxKh4et1fX3pFt6ucBcLuOMwQG18T3NSxs+zeiEp0g/zt53hIiQD+ieg6YtMXj+aiaHyVZVRfnGVwXNC/r2IzobG/A9duoOpfSK1Iqu8MawDMnLuGnzY09QHx92MjJvQuErj1CqUOH29qFlu9hPSUK183XDmn+Jm+SyajGJjdDVyqiUV6s8a/2BcF67xyblTjvgwb0x/MBICFz48xAIIXfgoqpRjy1P/QWFFNV7fdT9M8fapPQ3OVjVlfp6qMS2sqMHCHWcNhhgjhJiPuZ+81PdEaXl1LT6f1APVNUpU1ij0R5aI8sPxa4VY8N1ZLgzglN5twOfz6iZxuPBx9U4FBHwe8oqr8MaPWdg+tSc3sKGapa5aEsxLKjL5wt/QrP1u4d5aF7wncwvVLkr0zdx3Fblgw4TueGVHplZUDNW5rbhCjrk6Zqur9tNcpseQ+gMmhp4maGjkqZJKI+sPG9luz2QSITcxSDU5SfU0hFDAhwuPh3Qd6yInRfoiM68Yg9oHY/2hK5i77Sy3TfX9e9KcS0IsLsBDjPziKmzJuGY0eldpdS1Xlx+KDUBChA9e3pFpcMCIEGI+7iIBBJ58vLrrHE7mFmFicgQmJLUCAATIxNiXVYDCcjnWjInXWubVEhE1rRGlxJQnsQmxBWVVNRAJXDAoLggTku5PqL9ZUgXGgJ6tfeHCpyc2CXFExtrH6lologLcuYhlpiwHYqj9S470xS/nbnLjxcmRvlg+oiMCPMQG20wPiUArWkhDIsgaigJh6MayKVEDjAnxkmLhkHa4WyY3Ob1hvm5YNaozCsvlKKmqhUwigLebqNETUUy5ed6YfU3VlCgtxPwMfcc8QGtc86HYALwxrAMu3ypH1b0lbDT77/V/v5rft6ru3Smvq3+a71UprapRe5CyuELe6L50/TTcLKkyGm0oUCYxaQKaocjkqmgm5XKF0Trk6SrSG9lb5cqtcrz5YxZm9osyuJ9E6KIzgrinVIjurXyw8HvtcZCGRIl1EwkM3jN0E9Gt5MY4l1eCyhoFopthGRRCGqpNgDu2n/wHNQolhC6GH5JuKjqD2BB9jZyqAagfiqv+0/MCPg8ncwvROdQLM788jgq5AtP7RuJ8XjHG/yccEoGLWpji4ooaPHlvxruudUIbEi1q4feZWjPrNUOMEULMxxJPXuq6WCyukHPhNVVrWwLaEUymPhiJiZuOAbg/43ztb5eQHOmLzmHeWjO/VevumvPJE1NDqmqWnb7ILAuHtMP7v13SCjNf/9xmSoSahuRHNWByp1yO4fEttCLMNGaZCXOsP2yv/NxFqKrR/TSE6vd8+nqR1kSUJ5MikPlvMV7fdY7aNkKsyNNVhPySKoxNCMenh6+qtSWaT1d5SoVYMyYeYT6ukEmFeHHraYMDRlR/CTEvgYCP13ec5fpSmvU1PswbZdW1auuYA0BLb6lF6qM1opQ0tE9KiLX4uImx8PtMnTcZkiN9sXR4HBRKZoWUEUIszVj76CUVIsBDzP1tytIe+to/zWi7QN1T/Qu2n8XaMfEG28xgLymWDo/Dy/WihTQ0gqy+MS5jN5bN0V678Hgolxt+8EczvXVRgRs3+aS+huSxucqDWI+x73hQXLDWtj1ZBaiqVaKTgeg+hvrSXN0rKMOoj9L17qerzpqjL21qtCFTJk0ZG/ctqqjBKxqTP1TbNOuQsfOvWMDH7xdvY3LvNgb385QKdU6cU+VnbRPH2r1chZjRt25CjOY9wxl9o4wu4UZ0+/3CLbiKXNAmwM3aSSFES0yQB6pqlDiXV4LO91Y/sRTHvRNlhww1cvVDJtZfa69XlB/8PcSID/NWm2mqCgl45MpdfPR4V7VjHc8txPm8Yp1Pmn6ZcQ1vjujY5PQ25mYsIcS45nrysn791owsUV2rRGs/N0iFLmrL3tSnL3xg/YsIc5wfGrJeqmbZ1c/XtD6RkAhd4CkVQskYFnx3Vuv9wP1zmyWeutUMbdrUyTpuIheDTwQYC79ozzxdRSitrtWZ/wq5Al9mXMNLD8cgr6hKrQ2c+eVJ/O+Jbli996LO41LbRkjzkQpc8GXGNUxIaoXJfdqg+N460PWfrkqK9EWNQolJnx7Hvtm9UVZVS31TQppZSVWNwbD/U/tEIv2K+nbV5GRLsFaUkob0SQmxFrlCaXAZz7LqWrSkKGKEOCRjUUzCfV212ixTbtjWb//ulMshFfHxy7mbOqMhqJbLaxPgbrDNDNeIFuLvLkKvKD+9kWlNadubawzbz12Eo1fNs2RGQzUkjzSm7/iMfcdpPVvp3PbHxduY0ruNzskopv5+G9MfN1df2lzRhoyN+5bLTR97MFQe9e/z/XnlTpPOdU2NTOTpKkK4jyuGdAxRu2dYUFqNVj7abQQxzcELt9A+RAYB37JRJwhpjNZ+bhAL+DiWc5cmozgTU0Im1n8a9IEoPyx+pD2Grjmk1cFXhTCskCsg1whnqIoGsPFwjlrHoqFrUlojBDIhzq65nrzUrN+a62zumNoTlTUKnRNRVPSFDzQ3UzvbusquQq7AmetFGNcjjAtHezK30OBxSqtqLP7UrTlCmxZVyrFwSHu8vuuc1hMBi4a2R3GlHKFw3FnZLb1dtZ6oAuryP29QLG4UVWHD4Ryt2f5CI09dUdtGSPO4W1GNuQNjseLnLIxJCMfmjGtq9bVXlB/SerZCfnHd+sqqNaINofpLiPkZW/ZPwOdhw6Ec7m9LRwuxZpQSCk1PbF1JpeF2sFKuoN8wIQ5KX/vYK8oPy4bHoYV349ejrR8NIftmqd6ICsD9/rixNlMzWshbTWzbm2sM29NVhD5t/RHhVzfWonn9Ysm+SEPySGP6js+U+0z6iIV8rckTDalvje2Pm6svbY5oQ8bGfcsNjIcD2ssZ6SoPzaivGw7l4IcZyXh15zmrRVsM9pLi4Q5BapOCuoV7U/+wkYoranAyt4hbxpYQWyNw4SMywB0ZOXfw9AOtLftZFj06aRBjjVxrPze8OSwOxZVy/DA9GX7uItwpl+u8GVw/hOHJ60VqM7I1owEIBXx4iBs+S9QaIZAJIc3z5KU56re+8IHWZErZmZJ3az112xCuIiFGf5yOtx7tiHmDYlBWpYC7xAUFJdUY+8kRfP1MorWTaHHhvm54c3gciqtqtPJfIVdgYnIEXhncDjm3y7mICx5Gli+ito2Q5uEuFmLMJ0fw1qMdEeQpxisPtwPj1d1Iq6pRINhTguEf/Ik195ZbM6VuUv0lxPyMLfsncOFhzZh4LrJesKfE4oOZFKWEEN2MtYNuYhoiJMSRWbp99HMX4WaJZcaKm5r25hzDDvaSwlXkgjeHxaFcXosKuQKe95ZBsmRfpCF5pDF9x2fK0jD6eElFTT5X2Ht/3Ni4r5e0YXVIVR75xVW4Um8Msn4UqQq5AjzA6uVGE+zNZ3dmPhgYuoZ7WzsphOjVPsQTu87kobpWAbHAcpH06UrThhhr5O4P3LlpbdN8z8nrRdzyBKpIKADUJqRoRgMwd3pt4WYsIY7K0h1DU+u3PZ4DjJWdKXm35lO3pvJzF6FdsAyTPj2utc2Wvx9zEwtdsGJ7Jv64pP19ns4tQtcwL0zd/BeAunJ5/D/hdvm7JsTRBHiIEavnHNYr0g9dW3kjPswLJ68X2X27RIg983YT6V0WMCnSF/uzb2Htb5fwQJSf2trplkaDqIRok0kEBpfxNDa5jBBi/yzZPnq6ihDu66r3PNPU/nhT0t7cY9jW6Ic0JI80pu/4jH3HBaXVOt9Xf9yzqb9he+6PGxv3dRW5NLgOqcrizZ+y9L7P1808ZU9sw/aT/6JDC0940/dJbFi3cG98c/w60i/fQZ/oAIt9Di1UpeH9999Hq1atIJFIkJCQgKNHjzbbZ6sauQei/NReN3RzU997svNL8ObwODwQ5cdFQokP88aWpxKwbUoi9s3ujTVj4hs9EaWx6SWE2AdT6rejngNMzZdqVvu+2b2xY2pPs5xXzclRv5+GCpRJsHR4B/SK9FV7vVekLxYObYc5354BcL9cAmUSKjdCbICnqwhv6aiLvSJ98WRyK5z5pwhPJkUgO7/E4dslQmxZoEyCN4fHIVmjnVWFnd5wKMfiYekJIaZpcW8ZS836mhzpi6VNXKaDEEKAuvPM8hEd0cvG+uPOcJ3QkDw6Q3k4O2PfcZ+2/vT9G2Fo3LexdYjqnvO4erscR3PuomcbP+M7E2JFLb2lCJSJ8ev5mxb9HB5jzNjy6k7j66+/xhNPPIF169YhISEBq1evxtatW5GdnY2AAMMzgkpKSuDp6Yni4mLIZLImpaO4Qt7gUFz63tOYYzVHegmxBeast47KlPrtqOcAR8mXo+RDpbH19mZJFQrL5SipqoVMIoBMKkR1jQLFlbrLxdHKjRBrakp7q6qLxZU1cBW5gM/jgYGBz+PBhc/jnhzS9R6qv4Q0TUPq7s2SKhRWyFFaVQsPiQAiPh9FlXJ4SCwflp4Qcp8p9fbfwgqUVNVy7aRMIqCJKIRYkSOOTdlqf9xW02VODcmjM5SHpdhLvTX0HdP333SNLUMqe8f30vaz+PFMPt57LB4iA8tiEWILtmRcw8ELt5DxUn9IRZZZqocmo9STkJCA7t27Y+3atQAApVKJ0NBQzJgxA/Pnzzf4XnvpgBBC7qN6S4j9oXpLiP2hekuIfaK6S4j9oXpLiP2hekuI/aF6SwjR5/rdCvRbdRDD41tgWHwLayeHEKNullTh+a9P4a3/64hR3UIt8hm0IOw9crkcJ06cwIIFC7jX+Hw++vfvj/T0dK39q6urUV19f229kpKSZkknIaTxqN4SYn+o3hJif6jeEmKfqO4SYn+o3hJif6jeEmJ/qN4SQkzBGMMrOzIhkwowsEOQtZNDiEkCZRJ0DvXCuoOXMTy+BYQu5o/mQ/GB7rl9+zYUCgUCAwPVXg8MDMSNGze09l+2bBk8PT25f6GhlpktRAgxH6q3hNgfqreE2B+qt4TYJ6q7hNgfqreE2B+qt4TYH6q3hBBTvLfvEg5euIUnkyIgEVpmuRNCLGFU91Dk3CrHZ+nXLHJ8Wqbnnry8PLRo0QJ//vknEhMTudfnzp2LgwcPIiMjQ21/XbNhQ0NDKTQbITaM6i0h9ofqLSH2h+otIfaJ6i4h9ofqLSH2h+otIfaH6i0hxJDqWgVW/XoBH/9+BSO7tsSILi2tnSRCGmzTn1exL+smvngqAf9p7WvWY9MyPff4+fnBxcUFN2/eVHv95s2bCArSDqckFoshFoubK3mEEDOgekuI/aF6S4j9oXpLiH2iukuI/aF6S4j9oXpLiP2heksI0SWvqBK/nruBjX9exT+FlRifEI6H42h5HmKfxieEIa+oEo+vz8ALA6IxoWcrs0X4ocko94hEInTt2hX79u3DsGHDAABKpRL79u3D9OnTjb5fFWCG1gskxLw8PDzA4/Escmyqt4RYBtVbQuyTpeou1VtCLIfaXELsD9VbQuwP1VtC7A/VW0Lsj6XrbWlpqdH9rtyuwEs7s3HlTgUYAxRKhlql8QVGEsI9UVFVhW+PXTVDagmxjggfMc7+y7B899+4VVyOWX1aGX2PKfWWJqPUM3v2bKSlpaFbt27o0aMHVq9ejfLycjz55JNG36s6idF6gYSYlyXDHVK9JcQyCgoK4O/vb5FjU70lxHIs1eZSvSXEcqivTIj9oXpLiP2hekuI/aF6S4j9sXS99fT0NLqfe+dB8E2ZZvJxmVIBZVU5/syiyWnEcfCEYqx89WUsSt1ldF9T6i2PqaZxEgDA2rVrsXLlSty4cQOdO3fGe++9h4SEBKPvUyqVyMvLa9DMPdXagtevX3eatQWdLc+U36az5GzYxtRbUznbd1+fM+cdcO78q/JeVFRkUue+MUytt878PehC5aGOyuO++mXRokULi7S5ptRbZ/hOKI+OwdbyaEt9ZVsrG3OgPNk+e8yPLdRbeyw3a6MyaxxHKTdbqLeNZU/fgT2lFbCv9NpTWgHzpNeW6q29lb8to7I0H1ssS1uIjGIqWyw/a6GyuM8Zy4IiozTC9OnTTVqWRxOfz0fLli0b9ZkymcxpfpQqzpZnyq9takq9NZW9lIUlOHPeAefOv6UuGoCG11tn/h50ofJQR+Vxn0wms1jdbUi9dYbvhPLoGJwhj43tKzti2VCebJ+j5aexqK9seVRmjUPlpl9zjE0B9vUd2FNaAftKrz2lFbDd9FI/2fqoLM3HWcqSx+NZJJ/OUn6moLK4j8pCHd/aCSCEEEIIIYQQQgghhBBCCCGEEEIIIY6DJqMQQgghhBBCCCGEEEIIIYQQQgghhBCzockoViQWi7F48WKIxWJrJ6XZOFueKb/Oy5nLwpnzDjh3/m0p77aUFltA5aGOyuM+WykLW0mHJVEeHYMz5LGxHLFsKE+2z9Hy01yo3BqOyqxxqNysz56+A3tKK2Bf6bWntAL2l15jHC0/1kRlaT5Ulk1D5XcflcV9VBa68RhjzNqJIIQQQgghhBBCCCGEEEIIIYQQQgghjoEioxBCCCGEEEIIIYQQQgghhBBCCCGEELOhySiEEEIIIYQQQgghhBBCCCGEEEIIIcRsaDIKIYQQQgghhBBCCCGEEEIIIYQQQggxG5qMQgghhBBCCCGEEEIIIYQQQgghhBBCzIYmo1jR+++/j1atWkEikSAhIQFHjx61dpIsYsmSJeDxeGr/YmJirJ0ss/r9998xdOhQhISEgMfjYceOHWrbGWNYtGgRgoODIZVK0b9/f1y8eNE6iTUDY/mdMGGC1nc+cOBA6yTWSpyhfi9btgzdu3eHh4cHAgICMGzYMGRnZ6vtU1VVhWnTpsHX1xfu7u549NFHcfPmTSul2DKWL18OHo+H5557jnvN0fP977//Yvz48fD19YVUKkVcXByOHz/ObbeFc54z1EFNxtpbR/9dmqMtvnv3LsaNGweZTAYvLy9MmjQJZWVlzZgL8zFHW92c5eFIddbR+4XO0P5/+OGH6NixI2QyGWQyGRITE7F7925uu73nzxJspQ43V1tw5swZ9OrVCxKJBKGhoVixYoVWWrZu3YqYmBhIJBLExcXhp59+alSezFXncnNzMXjwYLi6uiIgIABz5sxBbW2t2j4HDhxAly5dIBaLERkZiU2bNmmlxxzftTnqmC3lx944Y571MUf/2ZTfor2zpXOrszN0/rx79y5mzJiB6OhoSKVShIWFYebMmSguLjZ4TEuN4Rk71/fp00frcydPnmzwmJbqRxtK69WrV7XSqfq3detWvcdszrFRc41LNcd1imZabe13aw5Lly5Fz5494erqCi8vL63tp0+fxpgxYxAaGgqpVIrY2Fi8++67avscOHBA52/uxo0bzZQL22COsgRM6xM6OmNlCQAzZ85E165dIRaL0blzZ63t+s6HR44csWzibRT1qU27VnVGutplZ0eTUazk66+/xuzZs7F48WL89ddf6NSpE1JSUlBQUGDtpFlE+/btkZ+fz/07dOiQtZNkVuXl5ejUqRPef/99ndtXrFiB9957D+vWrUNGRgbc3NyQkpKCqqqqZk6peRjLLwAMHDhQ7Tv/8ssvmzGF1uUs9fvgwYOYNm0ajhw5gj179qCmpgYDBgxAeXk5t8/zzz+PH374AVu3bsXBgweRl5eHESNGWDHV5nXs2DF89NFH6Nixo9rrjpzvwsJCJCUlQSgUYvfu3Th//jxWrVoFb29vbh9rn/OcpQ7qYqi9deTfJWCetnjcuHE4d+4c9uzZg127duH333/HM88801xZMCtztNXNVR6OVmcdvV/oDO1/y5YtsXz5cpw4cQLHjx9H3759kZqainPnzgGw//yZmy3V4eZoC0pKSjBgwACEh4fjxIkTWLlyJZYsWYKPP/6Y2+fPP//EmDFjMGnSJJw8eRLDhg3DsGHDkJmZ2eA8maPOKRQKDB48GHK5HH/++Sc+/fRTbNq0CYsWLeL2ycnJweDBg/Hggw/i1KlTeO655/DUU0/hl19+4fYx13fd1Dpma/mxJ86YZ2Oa0n825bfoCGzl3EoMnz/z8vKQl5eHt99+G5mZmdi0aRN+/vlnTJo0yehxLTGGZ+xcDwBPP/202ucam4BkqX60obSGhoaqpTE/Px+vvvoq3N3dMWjQIIPHbY6xUXOOS1n6OkVXWm3td2sOcrkcI0eOxJQpU3RuP3HiBAICAvDFF1/g3LlzePnll7FgwQKsXbtWa9/s7Gy1PAYEBFg6+TbFHGVpSp/QGRgrS5WJEydi9OjRBvfZu3ev2u+ya9eu5kyqXaA+dR1TrlWdjb522ekxYhU9evRg06ZN4/5WKBQsJCSELVu2zIqpsozFixezTp06WTsZzQYA2759O/e3UqlkQUFBbOXKldxrRUVFTCwWsy+//NIKKTQvzfwyxlhaWhpLTU21SnpsgTPV7/oKCgoYAHbw4EHGWN3vXCgUsq1bt3L7ZGVlMQAsPT3dWsk0m9LSUhYVFcX27NnDevfuzWbNmsUYc/x8z5s3jyUnJ+vdbgvnPGetg4baW0f/XWpqTFt8/vx5BoAdO3aM22f37t2Mx+Oxf//9t9nSbgmNaaubszwcuc46Q7/QWdp/b29v9r///c9h89cUtlqHLdUWfPDBB8zb25tVV1dz+8ybN49FR0dzf48aNYoNHjxYLT0JCQns2WefbXK+GlPnfvrpJ8bn89mNGze4fT788EMmk8m4fMydO5e1b99e7bNGjx7NUlJSuL8t+V03pI7ZQ35slTPm2ZCm9p9N+S06GmueW4luqvOnLt988w0TiUSspqZG7/ubcwyvflrrj6OYorn70YbKtXPnzmzixIkG398c5WrOcSlLl6++tOpia7/bxtq4cSPz9PQ0ad+pU6eyBx98kPt7//79DAArLCy0TOLsTFPK0pQ+oTMxpSz19Y9ycnIYAHby5EmLpM2eUJ9aN81rVWfTkLbO2VBkFCuQy+U4ceIE+vfvz73G5/PRv39/pKenWzFllnPx4kWEhISgdevWGDduHHJzc62dpGaTk5ODGzduqH3fnp6eSEhIcNjvG6gLfxcQEIDo6GhMmTIFd+7csXaSmoUz1m8VVQhNHx8fAHWz02tqatTKIiYmBmFhYQ5RFtOmTcPgwYPV8gc4fr537tyJbt26YeTIkQgICEB8fDw++eQTbru1z3nOXAcB/e2to/8ujTHld5meng4vLy9069aN26d///7g8/nIyMho9jQ3B0NtdXOVh7PVWWufIy3B0dt/hUKBr776CuXl5UhMTHS4/DWVPdVhc7UF6enpeOCBByASibh9UlJSkJ2djcLCQm4fzT5iSkqKWcqkMXUuPT0dcXFxCAwMVEtPSUkJ94S6sTRb6rtuTB2z5fzYMmfMsyma0n825bfo6Jrz3ErUaZ4/dSkuLoZMJoNAIDB4LEuP4elL6+bNm+Hn54cOHTpgwYIFqKio0HuM5upHGyvXEydO4NSpUyZF7rB0uZpzXMrS5asvrbrYyu+2ORUXF3N9u/o6d+6M4OBgPPTQQzh8+LAVUmZ/NMvSkv1yZ/XII48gICAAycnJ2Llzp7WT0+yoT62f5rWqs2lIW+dsDLfoxCJu374NhUKhdrEKAIGBgfj777+tlCrLSUhIwKZNmxAdHc2FMuzVqxcyMzPh4eFh7eRZnGotR13ft6Ou8zhw4ECMGDECERERuHz5Ml566SUMGjQI6enpcHFxsXbyLMrZ6reKUqnEc889h6SkJHTo0AFA3W9fJBJprUPpCL/9r776Cn/99ReOHTumtc2R8w0AV65cwYcffojZs2fjpZdewrFjxzBz5kyIRCKkpaVZ/ZznrHUQMNzeOvrv0hhTfpc3btzQCnkrEAjg4+PjkGVkrK1urvJwtjpr7XOkuTly+3/27FkkJiaiqqoK7u7u2L59O9q1a4dTp045RP7MxZ7qsLnaghs3biAiIkLrGKpt3t7euHHjhkXqeWPrnL70qLYZ2qekpASVlZUoLCw063fdlDpmi/mxB/ZUX5tLU/vPpvwWHV1znltJHX3nT023b9/G66+/bnSZTUuO4RlK69ixYxEeHo6QkBCcOXMG8+bNQ3Z2Nr777judx7J0P9rUcl2/fj1iY2PRs2dPg8ez9NioucelLFm+htKqyRZ+t83tzz//xNdff40ff/yRey04OBjr1q1Dt27dUF1djf/973/o06cPMjIy0KVLFyum1rbpKktjfUKpVNrcybRb7u7uWLVqFZKSksDn87Ft2zYMGzYMO3bswCOPPGLt5DUb6lPrputa1Zk0pK1zRjQZhVhc/fUzO3bsiISEBISHh+Obb74xaRY5sT+PPfYY9/+4uDh07NgRbdq0wYEDB9CvXz8rpoxYyrRp05CZmam2vrajun79OmbNmoU9e/ZAIpFYOznNTqlUolu3bnjzzTcBAPHx8cjMzMS6deuQlpZm5dQ5N0PtLV1cE03UVhNzcOT2Pzo6GqdOnUJxcTG+/fZbpKWl4eDBg9ZOFnFyjlTnqI4RW0D9Z2KP9J0/60+cKCkpweDBg9GuXTssWbLE4PEseV1gKK31JxvExcUhODgY/fr1w+XLl9GmTZsmfa6506pSWVmJLVu2YOHChUaPZ8lytadxqYak1VZ+t7rMnz8fb731lsF9srKyEBMT06DjZmZmIjU1FYsXL8aAAQO416OjoxEdHc393bNnT1y+fBnvvPMOPv/884Yl3sY0d1k6MkuVpT5+fn6YPXs293f37t2Rl5eHlStXOtVkFKKbI12rNpQ9tcvWQsv0WIGfnx9cXFxw8+ZNtddv3ryJoKAgK6Wq+Xh5eaFt27a4dOmStZPSLFTfqbN+3wDQunVr+Pn5OcV37oz1e/r06di1axf279+Pli1bcq8HBQVBLpejqKhIbX97L4sTJ06goKAAXbp0gUAggEAgwMGDB/Hee+9BIBAgMDDQIfOtEhwcrPV0UGxsLBfO2trnPGesg/rUb28dtT6aypTfZVBQEAoKCtS219bW4u7du05RRpptdXOVh7PVWWufI83J0dt/kUiEyMhIdO3aFcuWLUOnTp3w7rvvOkz+zMWe6rC52oKgoCCdx6j/Gfr2aUqZNKXONSXNMpkMUqnU7N91U+qYLebHHjhjnhuqof1nU36Ljq45z62kjr7zp0ppaSkGDhwIDw8PbN++HUKhsEHHN+cYnrG01peQkAAAej/X0v1oU9L67bffoqKiAk888USDj2/OcrXEuJSlytdYWhUKBQDb+t3q8sILLyArK8vgv9atWzfomOfPn0e/fv3wzDPP4JVXXjG6f48ePRxibL25y9JYn9CeWaIsGyohIcEhfpcNQX1qbfquVZ2FqW2dM6PJKFYgEonQtWtX7Nu3j3tNqVRi3759etf4dCRlZWW4fPkygoODrZ2UZhEREYGgoCC177ukpAQZGRlO8X0DwD///IM7d+44xXfuTPWbMYbp06dj+/bt+O2337RC6nbt2hVCoVCtLLKzs5Gbm2vXZdGvXz+cPXsWp06d4v5169YN48aN4/7viPlWSUpKQnZ2ttprFy5cQHh4OADrn/OcqQ4aU7+9ddT6aCpTfpeJiYkoKirCiRMnuH1+++03KJVKbmDUkWm21c1VHs5WZ619jjQHZ23/lUolqqurHTZ/jWVPddhcbUFiYiJ+//131NTUcPvs2bMH0dHR3DISiYmJap+j2qcxZWKOOpeYmIizZ8+q3Qzes2cPZDIZN8nYWJot/V03pI7ZQ35skTPmuaEa2n825bfo6Jrz3Ep0U50/gbqyHzBgAEQiEXbu3Nmop3MtOYZXP62aTp06BQB6P7e5+9G60rp+/Xo88sgj8Pf3b/DxzFmulhiXslT5Gkuri4uLzf9uAcDf3x8xMTEG/4lEIpOPd+7cOTz44INIS0vD0qVLTXrPqVOnHGJsvbnL0pz9cltj7rJsDEf5XTYE9anvM3at6ixMaeucHiNW8dVXXzGxWMw2bdrEzp8/z5555hnm5eXFbty4Ye2kmd0LL7zADhw4wHJyctjhw4dZ//79mZ+fHysoKLB20symtLSUnTx5kp08eZIBYP/973/ZyZMn2bVr1xhjjC1fvpx5eXmx77//np05c4alpqayiIgIVllZaeWUN46h/JaWlrIXX3yRpaens5ycHLZ3717WpUsXFhUVxaqqqqyd9GbhLPV7ypQpzNPTkx04cIDl5+dz/yoqKrh9Jk+ezMLCwthvv/3Gjh8/zhITE1liYqIVU20ZvXv3ZrNmzeL+duR8Hz16lAkEArZ06VJ28eJFtnnzZubq6sq++OILbh9rn/OcpQ5qMtbeOvLvkjHztMUDBw5k8fHxLCMjgx06dIhFRUWxMWPGWCtLTWKOtrq5ysPR6qyj9wudof2fP38+O3jwIMvJyWFnzpxh8+fPZzwej/3666+MMfvPn7nZUh1ujragqKiIBQYGsscff5xlZmayr776irm6urKPPvqI2+fw4cNMIBCwt99+m2VlZbHFixczoVDIzp492+A8maPO1dbWsg4dOrABAwawU6dOsZ9//pn5+/uzBQsWcPtcuXKFubq6sjlz5rCsrCz2/vvvMxcXF/bzzz9z+5jru25qHbO1/NgTZ8yzIU3tP5vyW3QEtnJuJYbPn8XFxSwhIYHFxcWxS5cuqbUZtbW13DGio6PZd999xxhjFh3DM5TWS5cusddee40dP36c5eTksO+//561bt2aPfDAA2rHqJ9WxizXjzbWLjHG2MWLFxmPx2O7d+/WeYzmKld9GjMu1Vzlayittva7NYdr166xkydPsldffZW5u7tz58/S0lLGGGNnz55l/v7+bPz48Wr5rX+v5J133mE7duxgFy9eZGfPnmWzZs1ifD6f7d2711rZsgpzlKUpfUJnYKwsGas7z508eZI9++yzrG3bttw+1dXVjDHGNm3axLZs2cKysrJYVlYWW7p0KePz+WzDhg3WypbVUJ+6jinXqs5Ks112djQZxYrWrFnDwsLCmEgkYj169GBHjhyxdpIsYvTo0Sw4OJiJRCLWokULNnr0aHbp0iVrJ8us9u/fzwBo/UtLS2OMMaZUKtnChQtZYGAgE4vFrF+/fiw7O9u6iW4CQ/mtqKhgAwYMYP7+/kwoFLLw8HD29NNPO11D7Az1W9dvAADbuHEjt09lZSWbOnUq8/b2Zq6urmz48OEsPz/feom2EM3OhaPn+4cffmAdOnRgYrGYxcTEsI8//lhtuy2c85yhDmoy1t46+u/SHG3xnTt32JgxY5i7uzuTyWTsySefVLswtyfmaKubszwcqc46er/QGdr/iRMnsvDwcCYSiZi/vz/r16+f2s0Ie8+fJdhKHW6utuD06dMsOTmZicVi1qJFC7Z8+XKttHzzzTesbdu2TCQSsfbt27Mff/yxUXkyV527evUqGzRoEJNKpczPz4+98MILrKamRqv8OnfuzEQiEWvdurXaZ6iY47s2Rx2zpfzYG2fMsz7m6D+b8lu0d7Z0bnV2hs6f+r4nACwnJ4c7Rv02xJJjeIbSmpubyx544AHm4+PDxGIxi4yMZHPmzGHFxcVqx9Bs7yzVjzbWLjHG2IIFC1hoaChTKBQ6j9Fc5apPY8almqt8DaXV1n635pCWlqYzP/v372eMMbZ48WKd28PDw7ljvPXWW6xNmzZMIpEwHx8f1qdPH/bbb79ZJ0NWZI6yZMy0PqGjM1aWjNXVTUN1cdOmTSw2Npa5uroymUzGevTowbZu3WqdDNkA6lObdq3qrGgyijoeY4xphUshhBBCCCGEEEIIIYQQQgghhBBCCCGkEfjWTgAhhBBCCCGEEEIIIYQQQgghhBBCCHEcNBmFEEIIIYQQQgghhBBCCCGEEEIIIYSYDU1GIYQQQgghhBBCCCGEEEIIIYQQQgghZkOTUQghhBBCCCGEEEIIIYQQQgghhBBCiNnQZBRCCCGEEEIIIYQQQgghhBBCCCGEEGI2NBmFEEIIIYQQQgghhBBCCCGEEEIIIYSYDU1GIYQQQgghhBBCCCGEEEIIIYQQQgghZkOTUQghhBBCCCGEEEIIIYQQQgghhBBCiNnQZBTilA4cOAAej4eioiJrJ4UQQgghhBC7cvXqVfB4PJw6dcraSSHEqfXp0wfPPfeczRyHEEIIcQStWrXC6tWrLXJsanMJaT6Wvm7l8XjYsWOHRY5NiL1w9nutND5mGpqMQmzShAkTwOPxMHnyZK1t06ZNA4/Hw4QJE8z2ec5+wiTEkAkTJmDYsGFar9tqvamsrISPjw/8/PxQXV1t7eQQYjXp6elwcXHB4MGDzXpcVSdb9c/Hxwe9e/fGH3/8YdbPIcRZNFddFYlEiIyMxBtvvAHGmFk/ixBnpLpmrV+/XnvtNdTW1lo7aXrp679/9913eP31162TKEKsyFJtsMqnn36K7t27w9XVFR4eHujduzd27dplkc8ixFnpG7MyxaZNm+Dl5aX1+rFjx/DMM89wf9MNZ0Iapyn10xxCQ0ORn5+PDh06ALDdsWxCLMmW7rVacrJnQ928eRNCoRBfffWVzu2TJk1Cly5dmjlVjosmoxCbFRoaiq+++gqVlZXca1VVVdiyZQvCwsKsmDJCiC3btm0b2rdvj5iYGBosIE5t/fr1mDFjBn7//Xfk5eWZ/fh79+5Ffn4+fv/9d4SEhGDIkCG4efOm2T+HEEfXXHX14sWLePXVV7F06VJs2LDB7J9TH2PMpm/IE2IuAwcO5OrXCy+8gCVLlmDlypXWTlaD+fj4wMPDw9rJIKTZWbINfvHFF/Hss89i9OjROHPmDI4ePYrk5GSkpqZi7dq1Zv0sQoh5+fv7w9XV1drJIIQ0kYuLC4KCgiAQCKydFEKsiu61agsMDMTgwYN1jo+Vl5fjm2++waRJk6yQMsdEk1GIzerSpQtCQ0Px3Xffca999913CAsLQ3x8PPeaUqnEsmXLEBERAalUik6dOuHbb79VO9ZPP/2Etm3bQiqV4sEHH8TVq1cNfrZqZvwvv/yC2NhYuLu7cwON9W3YsAHt27eHWCxGcHAwpk+f3vSME2KnVJNAxGIxWrVqhVWrVqlt1/UkiZeXFzZt2gQAkMvlmD59OoKDgyGRSBAeHo5ly5Zx+xYVFeGpp56Cv78/ZDIZ+vbti9OnT2ulY/369Rg/fjzGjx+P9evXa23/+++/kZycDIlEgnbt2mHv3r1aabt+/TpGjRoFLy8v+Pj4IDU11eh5gxBbUlZWhq+//hpTpkzB4MGDuXo2duxYjB49Wm3fmpoa+Pn54bPPPgNgWrsKAL6+vggKCkKHDh3w0ksvoaSkBBkZGdz2gwcPokePHlwbOX/+fLWb09XV1Zg5cyYCAgIgkUiQnJyMY8eOcdtVM+l/+eUXxMfHQyqVom/fvigoKMDu3bsRGxsLmUyGsWPHoqKignvft99+i7i4OEilUvj6+qJ///4oLy83S7kSYm7NWVfDw8Mxbtw4JCUl4a+//lLb53//+x9iY2MhkUgQExODDz74QG370aNHER8fD4lEgm7duuHkyZNq21X1dffu3ejatSvEYjEOHTqEPn36YMaMGXjuuefg7e2NwMBAfPLJJygvL8eTTz4JDw8PREZGYvfu3dyxCgsLMW7cOPj7+0MqlSIqKgobN25sdBkTYklisZirX1OmTEH//v2xc+dOFBYW4oknnoC3tzdcXV0xaNAgXLx4kXuf6npzx44diIqKgkQiQUpKCq5fv87to+tJ0ueeew59+vTRm57PP/8c3bp1g4eHB4KCgjB27FgUFBQAqIuW9OCDDwIAvL291Z6A01wywNT0G7teJsSWWbINPnLkCFatWoWVK1fixRdfRGRkJGJjY7F06VI899xzmD17tlp9P3z4MPr06QNXV1d4e3sjJSUFhYWF3GetWLECkZGREIvFCAsLw9KlSwHofvL01KlT4PF43PWrKecbQhzVf//7X8TFxcHNzQ2hoaGYOnUqysrKANTVnyeffBLFxcVcpLMlS5YAUH9yu1WrVgCA4cOHg8fjcX+b0k6Xl5fjiSeegLu7O4KDg7XGyYC66+IXX3wRLVq0gJubGxISEnDgwAEzlgIhtsnYmFGfPn0wc+ZMzJ07Fz4+PggKCuLqqIqxMd76y2cY6gvritbQuXNntc+7ePEiHnjgAe6z9uzZo5UnGk8mtsqa91oNyc3NRWpqKtzd3SGTyTBq1Ci1Bx1NaWuNjQMbGu+aNGkS9u3bh9zcXLXP2Lp1K2prazFu3Dj8/PPPSE5OhpeXF3x9fTFkyBBcvny50Xl2VjQZhdi0iRMnqg0+b9iwAU8++aTaPsuWLcNnn32GdevW4dy5c3j++ecxfvx4HDx4EEBdJ2DEiBEYOnQoTp06haeeegrz5883+tkVFRV4++238fnnn+P3339Hbm4uXnzxRW77hx9+iGnTpuGZZ57B2bNnsXPnTkRGRpop54TYlxMnTmDUqFF47LHHcPbsWSxZsgQLFy7kBvRM8d5772Hnzp345ptvkJ2djc2bN3MX+QAwcuRI7ib0iRMn0KVLF/Tr1w93797l9rl8+TLS09MxatQojBo1Cn/88QeuXbvGbVcoFBg2bBhcXV2RkZGBjz/+GC+//LJaOmpqapCSkgIPDw/88ccfOHz4MDfALpfLG11GhDSnb775BjExMYiOjsb48eOxYcMGMMYwbtw4/PDDD9wAHAD88ssvqKiowPDhwwEYb1c1VVZWcgPzIpEIAPDvvwJkYqAAABjLSURBVP/i4YcfRvfu3XH69Gl8+OGHWL9+Pd544w3ufXPnzsW2bdvw6aef4q+//kJkZCRSUlLU6jQALFmyBGvXrsWff/7JXdivXr0aW7ZswY8//ohff/0Va9asAQDk5+djzJgxmDhxIrKysnDgwAGMGDGCliQhNqs56yoAHD9+HCdOnEBCQgL32ubNm7Fo0SIsXboUWVlZePPNN7Fw4UJ8+umnAOpu1g0ZMgTt2rXDiRMnsGTJErU+cX3z58/H8uXLkZWVhY4dOwKoW6LAz88PR48exYwZMzBlyhSMHDkSPXv2xF9//YUBAwbg8ccf5yaVLVy4EOfPn8fu3buRlZWFDz/8EH5+fk0raEKaiVQqhVwux4QJE3D8+HHs3LkT6enpYIzh4YcfRk1NDbdvRUUFli5dis8++wyHDx9GUVERHnvssSZ9fk1NDV5//XWcPn0aO3bswNWrV7lB9tDQUGzbtg0AkJ2djfz8fLz77rs6j2Nq+g1dLxNi6yzZBn/55Zdwd3fHs88+q/W5L7zwAmpqarj6eOrUKfTr1w/t2rVDeno6Dh06hKFDh0KhUAAAFixYgOXLl3Pt45YtWxAYGNigvFrifEOIPeDz+Xjvvfdw7tw5fPrpp/jtt98wd+5cAEDPnj2xevVqyGQy5OfnIz8/X2c7pnpgYuPGjcjPz1d7gMKYOXPm4ODBg/j+++/x66+/4sCBA1qTwqdPn4709HR89dVXOHPmDEaOHImBAweqTQIlxNGYMmYE1F1Lurm5ISMjAytWrMBrr73GTQIxZYy3vob0hTUplUqMGDECIpEIGRkZWLduHebNm6e2D40nE1tnzXutuiiVSqSmpuLu3bs4ePAg9uzZgytXrmhNCjfE2DiwsfGuhx9+GIGBgVr3sDZu3IgRI0bAy8sL5eXlmD17No4fP459+/aBz+dj+PDhUCqVjcq302KE2KC0tDSWmprKCgoKmFgsZlevXmVXr15lEomE3bp1i6WmprK0tDRWVVXFXF1d2Z9//qn2/kmTJrExY8YwxhhbsGABa9eundr2efPmMQCssLCQMcbY/v371f7euHEjA8AuXbrEvef9999ngYGB3N8hISHs5ZdftkDuCbEtaWlpzMXFhbm5uan9k0gkXL0ZO3Yse+ihh9TeN2fOHLW6B4Bt375dbR9PT0+2ceNGxhhjM2bMYH379mVKpVIrDX/88QeTyWSsqqpK7fU2bdqwjz76iPv7pZdeYsOGDeP+Tk1NZYsXL+b+3r17NxMIBCw/P597bc+ePWpp+/zzz1l0dLRaOqqrq5lUKmW//PKL4cIixEb07NmTrV69mjHGWE1NDfPz82P79+/n/v/ZZ59x+44ZM4aNHj2aMcZMaldzcnIYACaVSpmbmxvj8XgMAOvatSuTy+WMsbq6qFmP3n//febu7s4UCgUrKytjQqGQbd68mdsul8tZSEgIW7FiBWPsftu8d+9ebp9ly5YxAOzy5cvca88++yxLSUlhjDF24sQJBoBdvXq16YVISDNozroqFAoZAPbMM8+ovadNmzZsy5Ytaq+9/vrrLDExkTHG2EcffcR8fX1ZZWUlt/3DDz9kANjJkycZY/fr644dO9SO07t3b5acnMz9XVtby9zc3Njjjz/OvZafn88AsPT0dMYYY0OHDmVPPvmkiSVIiPWorlkZY0ypVLI9e/YwsVjMhg0bxgCww4cPc/vevn2bSaVS9s033zDG7l9vHjlyhNsnKyuLAWAZGRlax1eZNWsW6927N/d379692axZs/Sm8dixYwwAKy0tZYxpX/fqOs6FCxdMTr+h62VCbJ0l2+CBAweyTp066f1smUzGpkyZwh07KSlJ534lJSVMLBazTz75ROd2XXX65MmTDADLyclhjJl2viHEnulqL/XZunUr8/X15f7euHEj8/T01NovPDycvfPOO9zfusazjLXTpaWlTCQScW0nY4zduXOHSaVSrs29du0ac3FxYf/++6/acfr168cWLFhgUp4IsWX66qexMSPGtK8lGWOse/fubN68eYwx08Z4VdfEmtetmn1hzTrPGGOdOnXixpR/+eUXJhAI1Orq7t27aTyZ2AVr3WvVvJekGkNW1bVff/2Vubi4sNzcXO5Y586dYwDY0aNH1dJeX/221tg4sLHxLsYYmz9/PouIiODq7qVLlxiPx1Mbj67v1q1bDAA7e/YsY0z7PEN0o8XSiE3z9/fnwqUyxjB48GC1JyMvXbqEiooKPPTQQ2rvk8vlXHiprKwstac/ASAxMdHoZ7u6uqJNmzbc38HBwVyI44KCAuTl5aFfv36Nzhsh9uTBBx/Ehx9+qPZaRkYGxo8fD6CunqWmpqptT0pKwurVq6FQKODi4mL0MyZMmICHHnoI0dHRGDhwIIYMGYIBAwYAAE6fPo2ysjL4+vqqvaeyspILi6ZQKPDpp5+qzWofP348XnzxRSxatAh8Ph/Z2dkIDQ1FUFAQt0+PHj3Ujnn69GlcunQJHh4eaq9XVVVRCDZiF7Kzs3H06FFs374dACAQCDB69GisX78effr0wahRo7B582Y8/vjjKC8vx/fff4+vvvoKgGntqsrXX3+NmJgYZGZmYu7cudi0aROEQiGAunNCYmIieDwet39SUhLKysrwzz//oKioCDU1NUhKSuK2C4VC9OjRA1lZWWqfo4quANSt5+nq6orWrVurvXb06FEAQKdOndCvXz/ExcUhJSUFAwYMwP/93//B29u70eVJiKU0Z12NjY1FTU0NMjMzMWPGDHh7e2P58uUoLy/H5cuXMWnSJDz99NPce2pra+Hp6QkAXJQTiUTCbdfXl+7WrZvWa/XrsIuLC3x9fREXF8e9pnq6W9XPnjJlCh599FEuasqwYcPQs2dPI6VJiHXs2rUL7u7uqKmpgVKpxNixYzFixAjs2rVL7RrU19cX0dHRam2cQCBA9+7dub9jYmLg5eWFrKwsrf6pqVTRi06fPo3CwkLuaa3c3Fy0a9fOpGNkZWVBIBAYTb+h62VCbF1ztMHMxMh8p06dwsiRI3Vuy8rKQnV1dZPHnixxviHEHuzduxfLli3D33//jZKSEtTW1qKqqgoVFRVwdXW16GdfvnwZcrlcrT318fFBdHQ09/fZs2ehUCjQtm1btfdWV1drjX8R4kiMjRmFhYUBUL+WBNT7m6aM8ZozvaGhoQgJCeFe07wmpvFkYuua+17rH3/8oVUf6i+vo6pXoaGh3Gvt2rXj+qj1+676GBoHNmW8C6iLGLN8+XLs378fffv2xcaNG9GqVSv07dsXQN0SXYsWLUJGRgZu376tdo3doUMHo2kkdWgyCrF5EydOxPTp0wEA77//vto2VdjUH3/8ES1atFDbJhaLm/S5qhtqKjwejxtMkEqlTTo2IfbGzc1Naxmqf/75p0HHqF+HVOqH+u7SpQtycnKwe/du7N27F6NGjUL//v3x7bffoqysDMHBwTrXzfXy8gJQFzr533//1QrlplAosG/fPq2OlD5lZWXo2rUrNm/erLXN39/fpGMQYk3r169HbW2t2kUyYwxisRhr167FuHHj0Lt3bxQUFGDPnj2QSqUYOHAggIa1q6GhoYiKikJUVBRqa2sxfPhwZGZmNrn91VS/PebxeDrbZ9WFgIuLC/bs2YM///yTW77n5ZdfRkZGBiIiIsyaLkKaqjnrqqoNj42NxeXLl7Fw4UIsWbKEO84nn3yiNaBgykRSTW5ublqv6aqzmvUaAFePBw0ahGvXruGnn37Cnj170K9fP0ybNg1vv/12g9NDiKWpJmyLRCKEhIRAIBBg586dZjk2n8832HfWVF5ejpSUFKSkpGDz5s3w9/dHbm4uUlJSLBIa3ND1MiG2ztJtcNu2bXHo0CHI5XJuGUuVvLw8lJSUcDefDY0vGRt74vP5XNpVDJ0nCHEmV69exZAhQzBlyhQsXboUPj4+OHToECZNmgS5XN7kySgNbad1KSsrg4uLC06cOKHV93Z3d29S+ghxBIbGf8zJXPWZxpOJrWvOe60RERHcfRsVgaBhUxKM1U1D48Cqdt7YeFdUVBR69eqFjRs3ok+fPvjss8/w9NNPc2NVQ4cORXh4OD755BOEhIRAqVSiQ4cOtPxWA/GtnQBCjFGtq6dad6++du3aQSwWIzc3F5GRkWr/VDPqYmNjuSemVY4cOdKkNHl4eKBVq1bYt29fk45DiKOIjY3F4cOH1V47fPgw2rZtyzXu/v7+yM/P57ZfvHgRFRUVau+RyWQYPXo0PvnkE3z99dfYtm0b7t69iy5duuDGjRsQCARadV01g3f9+vV47LHHcOrUKbV/jz32GNavXw8AiI6OxvXr13Hz5k3uMzXX++3SpQsuXryIgIAArc+qP2uWEFtUW1uLzz77DKtWrVKrB6dPn0ZISAi+/PJL9OzZE6Ghofj666+xefNmjBw5krvAN6Vd1eX//u//IBAI8MEHHwCoOyekp6erXTAcPnwYHh4eaNmyJdq0aQORSKR23qipqcGxY8dMfnJbHx6Ph6SkJLz66qs4efIkRCIR99QrIbbCWnUVqLvorq2thVwuR2BgIEJCQnDlyhWt46gmcMXGxuLMmTOoqqrijtHUvrQx/v7+SEtLwxdffIHVq1fj448/tujnEdJYqgnbYWFh3MBabGwsamtrkZGRwe13584dZGdnq7VxtbW1OH78OPd3dnY2ioqKEBsbC0C77wzURVDQ5++//8adO3ewfPly9OrVCzExMVqRSlQ3xRUKhd7jmJp+QuxVc7TBjz32GMrKyvDRRx9pff7bb78NoVCIRx99FEDdU9/6xpaioqIglUr1blfd3Kp/rtB1njB2viHEEZ04cQJKpRKrVq3Cf/7zH7Rt2xZ5eXlq+4hEIoNtoopQKNTaz1g73aZNGwiFQrX2tLCwEBcuXOD+jo+Ph0KhQEFBgdb5pH60B0IcjbExI1OYMsarSV9fWLM+l5SUICcnRy29169fV9tH85qYxpOJPbCle62qenX9+nXutfPnz6OoqIi77jTlmljfOLAp410qkyZNwrZt27Bt2zb8+++/mDBhAoD718GvvPIK+vXrh9jYWBQWFjYqv86OIqMQm+fi4sKFA9acJe7h4YEXX3wRzz//PJRKJZKTk1FcXIzDhw9DJpMhLS0NkydPxqpVqzBnzhw89dRTOHHiBDZt2tTkdC1ZsgSTJ09GQEAABg0ahNLSUhw+fBgzZsxo8rEJsTcvvPACunfvjtdffx2jR49Geno61q5dy92YBoC+ffti7dq1SExMhEKhwLx589RmuP/3v/9FcHAw4uPjwefzsXXrVgQFBcHLywv9+/dHYmIihg0bhhUrVnCDCD/++COGDx+O8PBw/PDDD9i5c6dWeLQnnngCw4cPx927d/HQQw+hTZs2SEtLw4oVK1BaWopXXnkFwP0ns8eNG4eVK1ciNTUVr732Glq2bIlr167hu+++w9y5c02+KCLEGnbt2oXCwkJMmjRJ62L30Ucfxfr16zF58mSMHTsW69atw4ULF7B//35uH1PaVV14PB5mzpyJJUuW4Nlnn8XUqVOxevVqzJgxA9OnT0d2djYWL16M2bNng8/nw83NDVOmTMGcOXPg4+ODsLAwrFixAhUVFZg0aVKj85+RkYF9+/ZhwIABCAgIQEZGBm7dukUD7cTmNGddvXPnDm7cuIHa2lqcPXsW7777Lh588EHIZDIAwKuvvoqZM2fC09MTAwcORHV1NY4fP47CwkLMnj0bY8eOxcsvv4ynn34aCxYswNWrVy0apWTRokXo2rUr2rdvj+rqauzatYvqMLErUVFRSE1NxdNPP42PPvoIHh4emD9/Plq0aKG2rKVQKMSMGTPw3nvvQSAQYPr06fjPf/7DhRfv27cvVq5cic8++wyJiYn44osvkJmZqbUUl0pYWBhEIhHWrFmDyZMnIzMzE6+//rraPuHh4eDxeNi1axcefvhhSKVSrSevTU0/IfaqOdrgxMREzJo1C3PmzIFcLsewYcNQU1ODL774Au+++y5Wr17NDeovWLAAcXFxmDp1KiZPngyRSIT9+/dj5MiR8PPzw7x58zB37lyIRCIkJSXh1q1bOHfuHCZNmsTdHFiyZAmWLl2KCxcuYNWqVVp5Nna+IcTeFRcXa92c8vPzQ01NDdasWYOhQ4fi8OHDWLdundo+rVq1QllZGfbt24dOnTrB1dVVZ8QU1QOJSUlJEIvF8Pb2NtpOu7u7Y9KkSZgzZw58fX0REBCAl19+mYtoBNRFURo3bhyeeOIJrFq1CvHx8bh16xb27duHjh07YvDgweYvLEKama76+cwzzxgcMzKFKWO8mvT1hfv27YtNmzZh6NCh8PLywqJFi9TuQ/Xv3x9t27ZFWloaVq5ciZKSErz88stqx6bxZGIPbOlea//+/REXF4dx48Zh9erVqK2txdSpU9G7d29uCWhjba2xcWBj410qI0eOxMyZM/Hss89iwIABXD/d29sbvr6++PjjjxEcHIzc3FzMnz+/Ufl1eowQG5SWlsZSU1P1bk9NTWVpaWmMMcaUSiVbvXo1i46OZkKhkPn7+7OUlBR28OBBbv8ffviBRUZGMrFYzHr16sU2bNjAALDCwkLGGGP79+9X+3vjxo3M09NT7TO3b9/ONKvMunXruM8NDg5mM2bMaGrWCbE5+uqjZr359ttvWbt27ZhQKGRhYWFs5cqVavv/+++/bMCAAczNzY1FRUWxn376iXl6erKNGzcyxhj7+OOPWefOnZmbmxuTyWSsX79+7K+//uLeX1JSwmbMmMFCQkKYUChkoaGhbNy4cSw3N5e9/fbbzMvLi8nlcq10VldXMy8vL/buu+8yxhjLyspiSUlJTCQSsZiYGPbDDz8wAOznn3/m3pOfn8+eeOIJ5ufnx8RiMWvdujV7+umnWXFxcRNLkxDLGjJkCHv44Yd1bsvIyGAA2OnTp9n58+cZABYeHs6USqXafsba1ZycHAaAnTx5Uu195eXlzNvbm7311luMMcYOHDjAunfvzkQiEQsKCmLz5s1jNTU13P6VlZVsxowZXD1LSkpiR48e5bZrnmMY090+L168mHXq1Ikxxtj58+dZSkoK8/f3Z2KxmLVt25atWbOmIUVISLNozrqq+ufi4sJatmzJnn76aVZQUKB2rM2bN7POnTszkUjEvL292QMPPMC+++47bnt6ejrr1KkTE4lErHPnzmzbtm1q5wFd9ZUxxnr37s1mzZql9lp4eDh755131F4DwLZv384YY+z1119nsbGxTCqVMh8fH5aamsquXLliQqkS0rwMXbPevXuXPf7448zT05NJpVKWkpLCLly4wG1XtWfbtm1jrVu3ZmKxmPXv359du3ZN7TiLFi1igYGBzNPTkz3//PNs+vTprHfv3tx2zTq2ZcsW1qpVKyYWi1liYiLbuXOnVpv92muvsaCgIMbj8bhras3jmJr++nRdLxNii5qjDVZZv34969q1K5NIJMzNzY316tWL7dy5U+tzDxw4wHr27MnEYjHz8vJiKSkpXJuqUCjYG2+8wcLDw7lr7TfffJN776FDh1hcXByTSCSsV69ebOvWrQwAy8nJYYyZfr4hxF6lpaWp9XlV/yZNmsT++9//suDgYK4t++yzz7T6rJMnT2a+vr4MAFu8eDFjTLu/unPnThYZGckEAgELDw/nXjfWTpeWlrLx48czV1dXFhgYyFasWKHV5srlcrZo0SLWqlUrbnx5+PDh7MyZM5YpMEKakaH6aWzMSNe1ZP17QowZH+PVNX6lqy9cXFzMRo8ezWQyGQsNDWWbNm1inTp14s4JjDGWnZ3NkpOTmUgkYm3btmU///yz2nUsYzSeTGyTte+11qfZvl67do098sgjzM3NjXl4eLCRI0eyGzduqL3HUFtryjiwsfEulWeeeYYBYN98843a63v27GGxsbFMLBazjh07sgMHDqjVfX3j5EQdjzFa1JcQQojzOnz4MJKTk3Hp0iW0adPG2skhhBBCCCEObtOmTXjuuedQVFRk7aQQQhwcnW8IIYQ4CxrjJYQQ20TL9BBCCHEq27dvh7u7O6KionDp0iXMmjULSUlJdJFCCCGEEEIIIYQQQgghdoDGeAkhxD7QZBRCCCFOpbS0FPPmzUNubi78/PzQv39/netqE0IIIYQQQgghhBBCCLE9NMZLCCH2gZbpIYQQQgghhBBCCCGEEEIIIYQQQgghZsO3dgIIIYQQQgghhBBCCCGEEEIIIYQQQojjoMkohBBCCCGEEEIIIYQQQgghhBBCCCHEbGgyCiGEEEIIIYQQQgghhBBCCCGEEEIIMRuajEIIIYQQQgghhBBCCCGEEEIIIYQQQsyGJqMQQgghhBBCCCGEEEIIIYQQQgghhBCzockohBBCCCGEEEIIIYQQQgghhBBCCCHEbGgyCiGEEEIIIYQQQgghhBBCCCGEEEIIMRuajEIIIYQQQgghhBBCCCGEEEIIIYQQQszm/wH1GQHbMg17sgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 2250x2250 with 54 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "sns.pairplot(df, diag_kind='kde', markers='o', corner=True)\n",
-    "plt.suptitle('Pairwise Relationships in California Housing Dataset', y=1.02)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b06faf9",
-   "metadata": {},
-   "source": [
-    "-   Plot histogram of `MedHouseVal` (median house value) and `MedInc` (median income)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "da19e945",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(12, 5))\n",
-    "plt.subplot(1, 2, 1)\n",
-    "sns.histplot(df['MedHouseVal'], bins=30, kde=True)\n",
-    "plt.title('Distribution of Median House Value (MedHouseVal)')\n",
-    "plt.subplot(1, 2, 2)\n",
-    "sns.histplot(df['MedInc'], bins=30, kde=True)\n",
-    "plt.title('Distribution of Median Income (MedInc)')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b3e219bf",
-   "metadata": {},
-   "source": [
-    "-   Plot correlation matrix of all numerical features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "aa50ffe3",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 8))\n",
-    "sns.heatmap(df.corr(), annot=True, fmt=\".2f\", cmap='coolwarm', square=True)\n",
-    "plt.title('Correlation Matrix of California Housing Features')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2ea0c258",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "In this exercise, you will apply the concepts learned in the theoretical introduction and practical demonstration to perform EDA on the Ames Housing dataset. Follow the steps below:\n",
-    "\n",
-    "-   Load the dataset using `scikit-learn` (`fetch_openml`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "0e743589",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 1460 entries, 0 to 1459\n",
-      "Data columns (total 81 columns):\n",
-      " #   Column         Non-Null Count  Dtype  \n",
-      "---  ------         --------------  -----  \n",
-      " 0   Id             1460 non-null   int64  \n",
-      " 1   MSSubClass     1460 non-null   int64  \n",
-      " 2   MSZoning       1460 non-null   object \n",
-      " 3   LotFrontage    1201 non-null   float64\n",
-      " 4   LotArea        1460 non-null   int64  \n",
-      " 5   Street         1460 non-null   object \n",
-      " 6   Alley          91 non-null     object \n",
-      " 7   LotShape       1460 non-null   object \n",
-      " 8   LandContour    1460 non-null   object \n",
-      " 9   Utilities      1460 non-null   object \n",
-      " 10  LotConfig      1460 non-null   object \n",
-      " 11  LandSlope      1460 non-null   object \n",
-      " 12  Neighborhood   1460 non-null   object \n",
-      " 13  Condition1     1460 non-null   object \n",
-      " 14  Condition2     1460 non-null   object \n",
-      " 15  BldgType       1460 non-null   object \n",
-      " 16  HouseStyle     1460 non-null   object \n",
-      " 17  OverallQual    1460 non-null   int64  \n",
-      " 18  OverallCond    1460 non-null   int64  \n",
-      " 19  YearBuilt      1460 non-null   int64  \n",
-      " 20  YearRemodAdd   1460 non-null   int64  \n",
-      " 21  RoofStyle      1460 non-null   object \n",
-      " 22  RoofMatl       1460 non-null   object \n",
-      " 23  Exterior1st    1460 non-null   object \n",
-      " 24  Exterior2nd    1460 non-null   object \n",
-      " 25  MasVnrType     1452 non-null   object \n",
-      " 26  MasVnrArea     1452 non-null   float64\n",
-      " 27  ExterQual      1460 non-null   object \n",
-      " 28  ExterCond      1460 non-null   object \n",
-      " 29  Foundation     1460 non-null   object \n",
-      " 30  BsmtQual       1423 non-null   object \n",
-      " 31  BsmtCond       1423 non-null   object \n",
-      " 32  BsmtExposure   1422 non-null   object \n",
-      " 33  BsmtFinType1   1423 non-null   object \n",
-      " 34  BsmtFinSF1     1460 non-null   int64  \n",
-      " 35  BsmtFinType2   1422 non-null   object \n",
-      " 36  BsmtFinSF2     1460 non-null   int64  \n",
-      " 37  BsmtUnfSF      1460 non-null   int64  \n",
-      " 38  TotalBsmtSF    1460 non-null   int64  \n",
-      " 39  Heating        1460 non-null   object \n",
-      " 40  HeatingQC      1460 non-null   object \n",
-      " 41  CentralAir     1460 non-null   object \n",
-      " 42  Electrical     1459 non-null   object \n",
-      " 43  1stFlrSF       1460 non-null   int64  \n",
-      " 44  2ndFlrSF       1460 non-null   int64  \n",
-      " 45  LowQualFinSF   1460 non-null   int64  \n",
-      " 46  GrLivArea      1460 non-null   int64  \n",
-      " 47  BsmtFullBath   1460 non-null   int64  \n",
-      " 48  BsmtHalfBath   1460 non-null   int64  \n",
-      " 49  FullBath       1460 non-null   int64  \n",
-      " 50  HalfBath       1460 non-null   int64  \n",
-      " 51  BedroomAbvGr   1460 non-null   int64  \n",
-      " 52  KitchenAbvGr   1460 non-null   int64  \n",
-      " 53  KitchenQual    1460 non-null   object \n",
-      " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
-      " 55  Functional     1460 non-null   object \n",
-      " 56  Fireplaces     1460 non-null   int64  \n",
-      " 57  FireplaceQu    770 non-null    object \n",
-      " 58  GarageType     1379 non-null   object \n",
-      " 59  GarageYrBlt    1379 non-null   float64\n",
-      " 60  GarageFinish   1379 non-null   object \n",
-      " 61  GarageCars     1460 non-null   int64  \n",
-      " 62  GarageArea     1460 non-null   int64  \n",
-      " 63  GarageQual     1379 non-null   object \n",
-      " 64  GarageCond     1379 non-null   object \n",
-      " 65  PavedDrive     1460 non-null   object \n",
-      " 66  WoodDeckSF     1460 non-null   int64  \n",
-      " 67  OpenPorchSF    1460 non-null   int64  \n",
-      " 68  EnclosedPorch  1460 non-null   int64  \n",
-      " 69  3SsnPorch      1460 non-null   int64  \n",
-      " 70  ScreenPorch    1460 non-null   int64  \n",
-      " 71  PoolArea       1460 non-null   int64  \n",
-      " 72  PoolQC         7 non-null      object \n",
-      " 73  Fence          281 non-null    object \n",
-      " 74  MiscFeature    54 non-null     object \n",
-      " 75  MiscVal        1460 non-null   int64  \n",
-      " 76  MoSold         1460 non-null   int64  \n",
-      " 77  YrSold         1460 non-null   int64  \n",
-      " 78  SaleType       1460 non-null   object \n",
-      " 79  SaleCondition  1460 non-null   object \n",
-      " 80  SalePrice      1460 non-null   int64  \n",
-      "dtypes: float64(3), int64(35), object(43)\n",
-      "memory usage: 924.0+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.datasets import fetch_openml\n",
-    "\n",
-    "data = fetch_openml(name='house_prices', as_frame=True)\n",
-    "df = data.frame\n",
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9679b9db",
-   "metadata": {},
-   "source": [
-    "-   Show descriptive statistics (mean, std, min, max) with Pandas."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "1680351e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                Id   MSSubClass  LotFrontage        LotArea  OverallQual  \\\n",
-      "count  1460.000000  1460.000000  1201.000000    1460.000000  1460.000000   \n",
-      "mean    730.500000    56.897260    70.049958   10516.828082     6.099315   \n",
-      "std     421.610009    42.300571    24.284752    9981.264932     1.382997   \n",
-      "min       1.000000    20.000000    21.000000    1300.000000     1.000000   \n",
-      "25%     365.750000    20.000000    59.000000    7553.500000     5.000000   \n",
-      "50%     730.500000    50.000000    69.000000    9478.500000     6.000000   \n",
-      "75%    1095.250000    70.000000    80.000000   11601.500000     7.000000   \n",
-      "max    1460.000000   190.000000   313.000000  215245.000000    10.000000   \n",
-      "\n",
-      "       OverallCond    YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1  ...  \\\n",
-      "count  1460.000000  1460.000000   1460.000000  1452.000000  1460.000000  ...   \n",
-      "mean      5.575342  1971.267808   1984.865753   103.685262   443.639726  ...   \n",
-      "std       1.112799    30.202904     20.645407   181.066207   456.098091  ...   \n",
-      "min       1.000000  1872.000000   1950.000000     0.000000     0.000000  ...   \n",
-      "25%       5.000000  1954.000000   1967.000000     0.000000     0.000000  ...   \n",
-      "50%       5.000000  1973.000000   1994.000000     0.000000   383.500000  ...   \n",
-      "75%       6.000000  2000.000000   2004.000000   166.000000   712.250000  ...   \n",
-      "max       9.000000  2010.000000   2010.000000  1600.000000  5644.000000  ...   \n",
-      "\n",
-      "        WoodDeckSF  OpenPorchSF  EnclosedPorch    3SsnPorch  ScreenPorch  \\\n",
-      "count  1460.000000  1460.000000    1460.000000  1460.000000  1460.000000   \n",
-      "mean     94.244521    46.660274      21.954110     3.409589    15.060959   \n",
-      "std     125.338794    66.256028      61.119149    29.317331    55.757415   \n",
-      "min       0.000000     0.000000       0.000000     0.000000     0.000000   \n",
-      "25%       0.000000     0.000000       0.000000     0.000000     0.000000   \n",
-      "50%       0.000000    25.000000       0.000000     0.000000     0.000000   \n",
-      "75%     168.000000    68.000000       0.000000     0.000000     0.000000   \n",
-      "max     857.000000   547.000000     552.000000   508.000000   480.000000   \n",
-      "\n",
-      "          PoolArea       MiscVal       MoSold       YrSold      SalePrice  \n",
-      "count  1460.000000   1460.000000  1460.000000  1460.000000    1460.000000  \n",
-      "mean      2.758904     43.489041     6.321918  2007.815753  180921.195890  \n",
-      "std      40.177307    496.123024     2.703626     1.328095   79442.502883  \n",
-      "min       0.000000      0.000000     1.000000  2006.000000   34900.000000  \n",
-      "25%       0.000000      0.000000     5.000000  2007.000000  129975.000000  \n",
-      "50%       0.000000      0.000000     6.000000  2008.000000  163000.000000  \n",
-      "75%       0.000000      0.000000     8.000000  2009.000000  214000.000000  \n",
-      "max     738.000000  15500.000000    12.000000  2010.000000  755000.000000  \n",
-      "\n",
-      "[8 rows x 38 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "print(df.describe())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "964815ce",
-   "metadata": {},
-   "source": [
-    "-   Check for missing values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "05dd8d56",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Missing values:\n",
-      " Id                 0\n",
-      "MSSubClass         0\n",
-      "MSZoning           0\n",
-      "LotFrontage      259\n",
-      "LotArea            0\n",
-      "                ... \n",
-      "MoSold             0\n",
-      "YrSold             0\n",
-      "SaleType           0\n",
-      "SaleCondition      0\n",
-      "SalePrice          0\n",
-      "Length: 81, dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Missing values:\\n\", df.isnull().sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "61b48100",
-   "metadata": {},
-   "source": [
-    "-   Create a correlation matrix heatmap for all numerical features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "95c16138",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 3600x3000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(36, 30))\n",
-    "sns.heatmap(df.corr(numeric_only=True), annot=True, cmap=\"coolwarm\")\n",
-    "plt.title(\"Correlation Matrix for Ames Housing Dataset\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d28f2df0",
-   "metadata": {},
-   "source": [
-    "-   Plot a histogram of the `SalePrice` target variable and a boxplot of `OverallQual`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "5648ee2c",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "# Plot histogram of SalePrice\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.histplot(df['SalePrice'], bins=30, kde=True)\n",
-    "plt.title('Distribution of SalePrice')\n",
-    "plt.xlabel('SalePrice')\n",
-    "plt.ylabel('Frequency')\n",
-    "plt.show()\n",
-    "\n",
-    "# Plot boxplot of OverallQual\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.boxplot(x=df['OverallQual'])\n",
-    "plt.title('Boxplot of OverallQual')\n",
-    "plt.xlabel('OverallQual')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "774a765b",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "In this section, we covered the importance of Exploratory Data Analysis (EDA) in the machine learning workflow. We demonstrated how to perform EDA using Python libraries such as Pandas, Matplotlib, and Seaborn on the California housing dataset. We also provided hands-on exercises to practice EDA on the Ames Housing dataset. EDA is a crucial step in understanding the data, detecting anomalies, and informing modeling decisions. By performing EDA, we can gain valuable insights into the data and ensure that we are building models on a solid foundation."
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S01 Introduction to Machine Learning/S01C04 Train-Test Splits and Cross-Validation.ipynb b/src/S01 Introduction to Machine Learning/S01C04 Train-Test Splits and Cross-Validation.ipynb
deleted file mode 100644
index bb2fcbd..0000000
--- a/src/S01 Introduction to Machine Learning/S01C04 Train-Test Splits and Cross-Validation.ipynb	
+++ /dev/null
@@ -1,615 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "ee1d9352",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Train-Test Splits and Cross-Validation\n",
-    "\n",
-    "Training a machine learning model involves using data to learn patterns and make predictions. There is, however, a risk to training a model too well on some particular dataset, which is that the model may not generalize well to new, unseen data. To mitigate this risk, we use techniques like train-test splits and cross-validation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f4929bfa",
-   "metadata": {},
-   "source": [
-    "## Train-Test Splits\n",
-    "\n",
-    "A train-test split divides the dataset into two parts:\n",
-    "\n",
-    "-   **Training Set**: Used to fit the model.\n",
-    "-   **Test Set**: Used to evaluate the model's performance on unseen data.\n",
-    "\n",
-    "The split is usually done before any preprocessing of the data to avoid data leakage from the test set into the training set.\n",
-    "\n",
-    "The split ratio can vary, but common practices include:\n",
-    "\n",
-    "-   **80/20 Split**: 80% training, 20% testing.\n",
-    "-   **70/30 Split**: 70% training, 30% testing.\n",
-    "-   **90/10 Split**: 90% training, 10% testing.\n",
-    "\n",
-    "This ensures that the model is trained on a substantial amount of data while still having enough data to evaluate its performance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bf6a8344",
-   "metadata": {},
-   "source": [
-    "## Cross-Validation\n",
-    "\n",
-    "Each machine learning model usually depends on a set of hyperparameters that need to be manually tuned to achieve the best performance. Cross-validation (CV) is a technique that helps to tune these hyperparameters and assess the model's performance more robustly.\n",
-    "\n",
-    "Cross-validation involves splitting the training dataset into multiple subsets (folds) and training the model multiple times:\n",
-    "\n",
-    "-   **$K$-Fold Cross-Validation**: The dataset is divided into $K$ subsets (folds). The model is trained $K$ times, each time using $K - 1$ folds for training and 1 fold for validation. The performance is averaged over all $K$ iterations.\n",
-    "-   **Leave-One-Out Cross-Validation (LOOCV)**: A special case of $K$-fold where $K$ is equal to the number of samples in the dataset. Each sample is used once as a validation set while the rest are used for training. This is computationally expensive but can be useful for small datasets.\n",
-    "-   **Stratified Cross-Validation**: Ensures that each fold has the same proportion of classes as the entire dataset. This is particularly useful for imbalanced datasets."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec65fbb1",
-   "metadata": {},
-   "source": [
-    "## Practical Demonstration\n",
-    "\n",
-    "We will demonstrate the use of train-test splits and cross-validation using the California housing dataset, which is a regression dataset available in the `sklearn.datasets` module.\n",
-    "\n",
-    "We will go through the Machine Learning workflow steps, but skip some of the steps that are not relevant for this demonstration.\n",
-    "\n",
-    "-   Loading the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "abb2e928",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.datasets import fetch_california_housing\n",
-    "data = fetch_california_housing(as_frame=True)\n",
-    "df = data.frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "93e6d468",
-   "metadata": {},
-   "source": [
-    "-   Data exploration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "c60bd096",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
-      "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
-      "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
-      "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
-      "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
-      "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
-      "\n",
-      "   Longitude  MedHouseVal  \n",
-      "0    -122.23        4.526  \n",
-      "1    -122.22        3.585  \n",
-      "2    -122.24        3.521  \n",
-      "3    -122.25        3.413  \n",
-      "4    -122.25        3.422  \n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "# Display the first few rows of the dataset\n",
-    "print(df.head())\n",
-    "\n",
-    "# Plot the correlation matrix of the features and the target variable\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.figure(figsize=(12, 8))\n",
-    "sns.heatmap(df.corr(), annot=True, cmap='coolwarm', fmt='.2f')\n",
-    "plt.title('Correlation Matrix of California Housing Dataset')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5b818f7e",
-   "metadata": {},
-   "source": [
-    "Since the only meaningful correlation with the target variable (house value) is with the `MedInc` (median income) feature, we will keep only this feature for our demonstration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "5b56d675",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   MedInc\n",
-      "0  8.3252\n",
-      "1  8.3014\n",
-      "2  7.2574\n",
-      "3  5.6431\n",
-      "4  3.8462\n",
-      "0    4.526\n",
-      "1    3.585\n",
-      "2    3.521\n",
-      "3    3.413\n",
-      "4    3.422\n",
-      "Name: MedHouseVal, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Select only the 'MedInc' feature for simplicity\n",
-    "X = df[['MedInc']]\n",
-    "y = df['MedHouseVal']\n",
-    "print(X.head())\n",
-    "print(y.head())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "82406cb0",
-   "metadata": {},
-   "source": [
-    "-   Train-test split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "1eadb4d4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split, cross_val_score\n",
-    "\n",
-    "# Simple train-test split\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bb2f5133",
-   "metadata": {},
-   "source": [
-    "This code splits the dataset into training and testing sets, with 20% of the data reserved for testing. The `random_state` parameter ensures that the split is reproducible.\n",
-    "\n",
-    "Let's now visualize the distribution of the target variable in the training and test sets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "9d4a88e3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1200x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "plt.figure(figsize=(12, 6))\n",
-    "plt.subplot(1, 2, 1)\n",
-    "sns.histplot(y_train, bins=30, color='blue', alpha=0.7, label='Train Set')\n",
-    "plt.title('Training Set Target Distribution')\n",
-    "plt.xlabel('Median House Value')\n",
-    "plt.ylabel('Frequency')\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "sns.histplot(y_test, bins=30, color='orange', alpha=0.7, label='Test Set')\n",
-    "plt.title('Test Set Target Distribution')\n",
-    "plt.xlabel('Median House Value')\n",
-    "plt.ylabel('Frequency')\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "434eac7c",
-   "metadata": {},
-   "source": [
-    "This visualization shows the distribution of house values in both the training and test sets, indicating that the split has preserved the overall distribution of the target variable.\n",
-    "\n",
-    "-   Model training and evaluation:\n",
-    "\n",
-    "We begin by training a simple linear regression model on the reduced training set and then evaluating its performance on the test set using the $R^2$ metric, which indicates how well the model explains the variance in the target variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e2a01e0a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test set R²: 0.46\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "import numpy as np\n",
-    "\n",
-    "model = LinearRegression()\n",
-    "model.fit(X_train, y_train)\n",
-    "score_test = model.score(X_test, y_test)\n",
-    "print(f\"Test set R²: {score_test:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b7ed05fe",
-   "metadata": {},
-   "source": [
-    "We can try and improve the model evaluation by using cross-validation, which will give us a better estimate of the model's performance by averaging the results over multiple train-test splits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "37325ab3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Cross-validation R² scores: [0.46458389 0.35374043 0.46252448 0.29716971 0.52896687]\n",
-      "CV R²: 0.42 +/- 0.08\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Cross-validation\n",
-    "model = LinearRegression()\n",
-    "cv_scores = cross_val_score(model, X, y, cv=5, scoring=\"r2\")\n",
-    "print(f\"Cross-validation R² scores: {cv_scores}\")\n",
-    "print(f\"CV R²: {np.mean(cv_scores):.2f} +/- {np.std(cv_scores):.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a4b15e0",
-   "metadata": {},
-   "source": [
-    "This code performs 5-fold cross-validation on the entire dataset, providing a more robust estimate of the model's performance. The `cross_val_score` function automatically handles the train-test splits internally, allowing us to focus on the model evaluation.\n",
-    "\n",
-    "We can use the `cross_val_predict` function to get the predicted values for each fold, which can be useful for further analysis or visualization."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "9d72be21",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Cross-validated predictions: [1.80873431 2.03897928 2.18214843 1.26025238 1.93141503 3.20923508\n",
-      " 1.50040842 3.77828872 1.07598144 3.12757286]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import cross_val_predict\n",
-    "\n",
-    "# Get cross-validated predictions\n",
-    "y_pred = cross_val_predict(model, X_train, y_train, cv=5)\n",
-    "print(f\"Cross-validated predictions: {y_pred[:10]}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "10aea7b1",
-   "metadata": {},
-   "source": [
-    "There was no real improvement in the model's performance, but this is expected since we are using a very simple model with only one feature. In practice, cross-validation is particularly useful when dealing with more complex models and larger datasets, as it helps to ensure that the model generalizes well to unseen data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1fa4667d",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "Using the Ames Housing dataset:\n",
-    "\n",
-    "-   Load the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "c673b101",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.datasets import fetch_openml\n",
-    "data = fetch_openml(name='house_prices', as_frame=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3e0b5410",
-   "metadata": {},
-   "source": [
-    "-   Explore the dataset and select a few relevant features (e.g., `GrLivArea`, `OverallQual`, `YearBuilt`, `TotalBsmtSF`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "a3a59e9d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 3600x2400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Retain only numerical features for simplicity\n",
-    "data.frame = data.frame.select_dtypes(include=['number'])\n",
-    "\n",
-    "# Plot the correlation matrix\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.figure(figsize=(36, 24))\n",
-    "sns.heatmap(data.frame.corr(), annot=True, cmap='coolwarm', fmt='.2f')\n",
-    "plt.title('Correlation Matrix of Ames Housing Dataset')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "0efd264a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   GrLivArea  OverallQual  YearBuilt  TotalBsmtSF\n",
-      "0       1710            7       2003          856\n",
-      "1       1262            6       1976         1262\n",
-      "2       1786            7       2001          920\n",
-      "3       1717            7       1915          756\n",
-      "4       2198            8       2000         1145\n",
-      "0    208500\n",
-      "1    181500\n",
-      "2    223500\n",
-      "3    140000\n",
-      "4    250000\n",
-      "Name: SalePrice, dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Select relevant features\n",
-    "X = data.data[['GrLivArea', 'OverallQual', 'YearBuilt', 'TotalBsmtSF']]\n",
-    "y = data.frame[data.target.name]\n",
-    "print(X.head())\n",
-    "print(y.head())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "132f9b7e",
-   "metadata": {},
-   "source": [
-    "-   Do a standard 80/20 train-test split."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "90f0e1df",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train set size: 1168, Test set size: 292\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "# Train-test split\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "print(f\"Train set size: {X_train.shape[0]}, Test set size: {X_test.shape[0]}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c65d8cc1",
-   "metadata": {},
-   "source": [
-    "-   Train a `LinearRegression` model and compute $R^2$ on the test set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "b2246904",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Ames Test set R²: 0.79\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "\n",
-    "# Train a Linear Regression model\n",
-    "model = LinearRegression()\n",
-    "model.fit(X_train, y_train)\n",
-    "\n",
-    "# Evaluate on the test set\n",
-    "score_test = model.score(X_test, y_test)\n",
-    "print(f\"Ames Test set R²: {score_test:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "43909205",
-   "metadata": {},
-   "source": [
-    "-   Use 5-fold cross-validation (`cross_val_score`) on the entire dataset and compare the average $R^2$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "e96e5200",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Ames Cross-validation R² scores: [0.80148355 0.77468437 0.77212679 0.77194906 0.62676943]\n",
-      "CV R²: 0.75 +/- 0.06\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import cross_val_score\n",
-    "import numpy as np\n",
-    "\n",
-    "# Cross-validation\n",
-    "cv_scores = cross_val_score(model, X, y, cv=5, scoring=\"r2\")\n",
-    "print(f\"Ames Cross-validation R² scores: {cv_scores}\")\n",
-    "print(f\"CV R²: {np.mean(cv_scores):.2f} +/- {np.std(cv_scores):.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3e8d59a5",
-   "metadata": {},
-   "source": [
-    "-   Optional: Try changing the `cv` value (e.g., 10) and see how scores vary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "244a3213",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Ames Cross-validation R² scores with 10 folds: [0.80286191 0.79327    0.85007737 0.70204391 0.78052723 0.77653697\n",
-      " 0.77356133 0.77244003 0.50186064 0.81342654]\n",
-      "CV R² with 10 folds: 0.76 +/- 0.09\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Change the number of folds in cross-validation\n",
-    "cv_scores_10 = cross_val_score(model, X, y, cv=10, scoring=\"r2\")\n",
-    "print(f\"Ames Cross-validation R² scores with 10 folds: {cv_scores_10}\")\n",
-    "print(f\"CV R² with 10 folds: {np.mean(cv_scores_10):.2f} +/- {np.std(cv_scores_10):.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "783f6338",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "In this section, we learned about the importance of train-test splits and cross-validation in machine learning. We demonstrated how to perform a train-test split, train a model, and evaluate its performance using the California housing dataset. We also explored the use of cross-validation to obtain a more robust estimate of model performance."
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S01 Introduction to Machine Learning/S01C05 Data Preprocessing.ipynb b/src/S01 Introduction to Machine Learning/S01C05 Data Preprocessing.ipynb
deleted file mode 100644
index 1ea3d57..0000000
--- a/src/S01 Introduction to Machine Learning/S01C05 Data Preprocessing.ipynb	
+++ /dev/null
@@ -1,1929 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "95d6f6b6",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Data Preprocessing\n",
-    "\n",
-    "A key step in any machine learning project is data preprocessing, which prepares raw data for model training and evaluation. Proper preprocessing can significantly improve model performance and robustness, while poor preprocessing can lead to misleading results or even model failure.\n",
-    "\n",
-    "A few common preprocessing tasks include:\n",
-    "\n",
-    "1.  **Handling missing values**: Missing data can skew results or cause algorithms to fail. Common strategies of handling missing values include:\n",
-    "    -   Removing rows/columns with missing values\n",
-    "    -   Imputing missing values using mean, median, or mode\n",
-    "    -   Using advanced techniques like KNN imputation\n",
-    "2.  **Feature scaling**: Many algorithms are sensitive to the scale of input features. Common methods include:\n",
-    "    -   Standardization (z-score normalization): Rescales features to have mean 0 and standard deviation 1\n",
-    "    -   Min-Max scaling: Rescales features to a fixed range, typically [0, 1]\n",
-    "3.  **Encoding categorical variables**: Machine learning algorithms typically require numerical input. Common techniques include:\n",
-    "    -   One-hot encoding: Converts categorical variables into binary columns\n",
-    "    -   Label encoding: Assigns a unique integer to each category\n",
-    "4.  **Removing duplicates**: Duplicate rows can bias model training and evaluation.\n",
-    "5.  **Feature engineering**: Creating new features from existing ones can improve model performance. Examples include:\n",
-    "    -   Polynomial features\n",
-    "    -   Interaction terms\n",
-    "6.  **Pipeline creation**: Using pipelines to chain preprocessing steps and model fitting can simplify the workflow and ensure consistent preprocessing across training and testing data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "33d6f368",
-   "metadata": {},
-   "source": [
-    "## Practical Demonstration\n",
-    "\n",
-    "We will demonstrate data preprocessing using the `pandas` library for data manipulation and `scikit-learn` for machine learning tasks. The dataset we will use is the Titanic dataset, which contains information about passengers on the Titanic and whether they survived."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6dee55a0",
-   "metadata": {},
-   "source": [
-    "### Loading the data\n",
-    "\n",
-    "-   Loading the Titanic Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ab39d4ab",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "**Author**: Frank E. Harrell Jr., Thomas Cason  \n",
-      "**Source**: [Vanderbilt Biostatistics](http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.html)  \n",
-      "**Please cite**:   \n",
-      "\n",
-      "The original Titanic dataset, describing the survival status of individual passengers on the Titanic. The titanic data does not contain information from the crew, but it does contain actual ages of half of the passengers. The principal source for data about Titanic passengers is the Encyclopedia Titanica. The datasets used here were begun by a variety of researchers. One of the original sources is Eaton & Haas (1994) Titanic: Triumph and Tragedy, Patrick Stephens Ltd, which includes a passenger list created by many researchers and edited by Michael A. Findlay.\n",
-      "\n",
-      "Thomas Cason of UVa has greatly updated and improved the titanic data frame using the Encyclopedia Titanica and created the dataset here. Some duplicate passengers have been dropped, many errors corrected, many missing ages filled in, and new variables created. \n",
-      "\n",
-      "For more information about how this dataset was constructed:\n",
-      "http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic3info.txt\n",
-      "\n",
-      "\n",
-      "### Attribute information  \n",
-      "\n",
-      "The variables on our extracted dataset are pclass, survived, name, age, embarked, home.dest, room, ticket, boat, and sex. pclass refers to passenger class (1st, 2nd, 3rd), and is a proxy for socio-economic class. Age is in years, and some infants had fractional values. The titanic2 data frame has no missing data and includes records for the crew, but age is dichotomized at adult vs. child. These data were obtained from Robert Dawson, Saint Mary's University, E-mail. The variables are pclass, age, sex, survived. These data frames are useful for demonstrating many of the functions in Hmisc as well as demonstrating binary logistic regression analysis using the Design library. For more details and references see Simonoff, Jeffrey S (1997): The \"unusual episode\" and a second statistics course. J Statistics Education, Vol. 5 No. 1.\n",
-      "\n",
-      "Downloaded from openml.org.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.datasets import fetch_openml\n",
-    "\n",
-    "# Load the Titanic dataset from scikit-learn\n",
-    "data = fetch_openml('titanic', version=1, as_frame=True)\n",
-    "print(data['DESCR'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "81c04a8d",
-   "metadata": {},
-   "source": [
-    "-   Convert the dataset to a pandas DataFrame and display the first few rows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "52fa664b",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 1309 entries, 0 to 1308\n",
-      "Data columns (total 14 columns):\n",
-      " #   Column     Non-Null Count  Dtype   \n",
-      "---  ------     --------------  -----   \n",
-      " 0   pclass     1309 non-null   int64   \n",
-      " 1   survived   1309 non-null   category\n",
-      " 2   name       1309 non-null   object  \n",
-      " 3   sex        1309 non-null   category\n",
-      " 4   age        1046 non-null   float64 \n",
-      " 5   sibsp      1309 non-null   int64   \n",
-      " 6   parch      1309 non-null   int64   \n",
-      " 7   ticket     1309 non-null   object  \n",
-      " 8   fare       1308 non-null   float64 \n",
-      " 9   cabin      295 non-null    object  \n",
-      " 10  embarked   1307 non-null   category\n",
-      " 11  boat       486 non-null    object  \n",
-      " 12  body       121 non-null    float64 \n",
-      " 13  home.dest  745 non-null    object  \n",
-      "dtypes: category(3), float64(3), int64(3), object(5)\n",
-      "memory usage: 116.8+ KB\n",
-      "None\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>pclass</th>\n",
-       "      <th>survived</th>\n",
-       "      <th>name</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>age</th>\n",
-       "      <th>sibsp</th>\n",
-       "      <th>parch</th>\n",
-       "      <th>ticket</th>\n",
-       "      <th>fare</th>\n",
-       "      <th>cabin</th>\n",
-       "      <th>embarked</th>\n",
-       "      <th>boat</th>\n",
-       "      <th>body</th>\n",
-       "      <th>home.dest</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
-       "      <td>female</td>\n",
-       "      <td>29.0000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>24160</td>\n",
-       "      <td>211.3375</td>\n",
-       "      <td>B5</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>St Louis, MO</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Allison, Master. Hudson Trevor</td>\n",
-       "      <td>male</td>\n",
-       "      <td>0.9167</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>113781</td>\n",
-       "      <td>151.5500</td>\n",
-       "      <td>C22 C26</td>\n",
-       "      <td>S</td>\n",
-       "      <td>11</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Allison, Miss. Helen Loraine</td>\n",
-       "      <td>female</td>\n",
-       "      <td>2.0000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>113781</td>\n",
-       "      <td>151.5500</td>\n",
-       "      <td>C22 C26</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
-       "      <td>male</td>\n",
-       "      <td>30.0000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>113781</td>\n",
-       "      <td>151.5500</td>\n",
-       "      <td>C22 C26</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>135.0</td>\n",
-       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>25.0000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>113781</td>\n",
-       "      <td>151.5500</td>\n",
-       "      <td>C22 C26</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   pclass survived                                             name     sex  \\\n",
-       "0       1        1                    Allen, Miss. Elisabeth Walton  female   \n",
-       "1       1        1                   Allison, Master. Hudson Trevor    male   \n",
-       "2       1        0                     Allison, Miss. Helen Loraine  female   \n",
-       "3       1        0             Allison, Mr. Hudson Joshua Creighton    male   \n",
-       "4       1        0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
-       "\n",
-       "       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
-       "0  29.0000      0      0   24160  211.3375       B5        S    2    NaN   \n",
-       "1   0.9167      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
-       "2   2.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
-       "3  30.0000      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
-       "4  25.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
-       "\n",
-       "                         home.dest  \n",
-       "0                     St Louis, MO  \n",
-       "1  Montreal, PQ / Chesterville, ON  \n",
-       "2  Montreal, PQ / Chesterville, ON  \n",
-       "3  Montreal, PQ / Chesterville, ON  \n",
-       "4  Montreal, PQ / Chesterville, ON  "
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = data.frame\n",
-    "print(df.info())\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4afca283",
-   "metadata": {},
-   "source": [
-    "### Exploratory Data Analysis (EDA)\n",
-    "\n",
-    "We'll start by doing a bit of EDA to understand the dataset better. We begin by visualising the distribution of the target variable `survived`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "69109ada",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "sns.countplot(x='survived', data=df)\n",
-    "plt.title('Survival Distribution')\n",
-    "plt.xlabel('Survived')\n",
-    "plt.ylabel('Count')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c6b354a5",
-   "metadata": {},
-   "source": [
-    "As a first guess, we might hypothesize that the survival rate is higher might depend on the socio-economic status, which we can infer from the `fare` column. Let's visualize the distribution of fare for survivors and non-survivors:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "ea6fcd5d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.boxplot(x='survived', y='fare', data=df, log_scale=True)\n",
-    "plt.title('Fare Distribution by Survival')\n",
-    "plt.xlabel('Survived')\n",
-    "plt.ylabel('Fare')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a258a5fb",
-   "metadata": {},
-   "source": [
-    "This seems to be borne out by the data, as we can see that survivors tend to have higher fares than non-survivors. Another indication of socio-economic status is the passenger class, which we can visualize as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "49d9a044",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.countplot(x='pclass', hue='survived', data=df)\n",
-    "plt.title('Passenger Class vs Survival')\n",
-    "plt.xlabel('Passenger Class')\n",
-    "plt.ylabel('Count')\n",
-    "plt.legend(title='Survived', loc='upper right')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "37f1e72d",
-   "metadata": {},
-   "source": [
-    "Another hypothesis we might have is that the survival rate might depend on the gender of the passengers, e.g. \"Women and children first\". Let's visualize the distribution of survival by gender:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "2951d028",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.countplot(x='sex', hue='survived', data=df)\n",
-    "plt.title('Gender vs Survival')\n",
-    "plt.xlabel('Gender')\n",
-    "plt.ylabel('Count')\n",
-    "plt.legend(title='Survived', loc='upper right')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7db2241d",
-   "metadata": {},
-   "source": [
-    "We will only keep the features mentioned in the hypotheses above, which are `fare`, and `sex`. We will also keep the target variable `survived` for our model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "f80599e9",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "df = df[['fare', 'sex', 'survived']]\n",
-    "X = df.drop(columns=data.target.name)\n",
-    "y = df[data.target.name]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7ad7743f",
-   "metadata": {},
-   "source": [
-    "### Train-Test Split\n",
-    "\n",
-    "Perform a train-test split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "8648eed9",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c6324afd",
-   "metadata": {},
-   "source": [
-    "### Data Preprocessing Steps"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d8baea23",
-   "metadata": {},
-   "source": [
-    "#### Handling Missing Values\n",
-    "\n",
-    "Missing values are common in real-world datasets. In the Titanic dataset, the `fare` column has some missing values. We will handle these missing values by imputing them with the mean fare.\n",
-    "\n",
-    "First, let's check for missing values in the training and test sets:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "4f678cf4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "fare    1\n",
-      "sex     0\n",
-      "dtype: int64\n",
-      "fare    0\n",
-      "sex     0\n",
-      "dtype: int64\n",
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 1309 entries, 0 to 1308\n",
-      "Data columns (total 3 columns):\n",
-      " #   Column    Non-Null Count  Dtype   \n",
-      "---  ------    --------------  -----   \n",
-      " 0   fare      1308 non-null   float64 \n",
-      " 1   sex       1309 non-null   category\n",
-      " 2   survived  1309 non-null   category\n",
-      "dtypes: category(2), float64(1)\n",
-      "memory usage: 13.2 KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(X_train.isnull().sum())\n",
-    "print(X_test.isnull().sum())\n",
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b4cfa82a",
-   "metadata": {},
-   "source": [
-    "We can see that the `fare` column has some (one) missing values. We will fill these missing values with the mean fare using the `SimpleImputer` class from `scikit-learn`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "efed8e5d",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "# Fill missing values in the 'fare' column with the mean\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "imputer = SimpleImputer(strategy='mean')\n",
-    "X_train[['fare']] = imputer.fit_transform(X_train[['fare']])\n",
-    "X_test[['fare']] = imputer.transform(X_test[['fare']])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d6ab681a",
-   "metadata": {},
-   "source": [
-    "#### Feature Scaling\n",
-    "\n",
-    "Feature scaling is important for algorithms that are sensitive to the scale of input features, such as logistic regression, k-nearest neighbors, and support vector machines. We will scale the `fare` feature using standardization (z-score normalization), which rescales the feature to have mean 0 and standard deviation 1."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "a88e0499",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.preprocessing import StandardScaler\n",
-    "\n",
-    "# Create a StandardScaler instance\n",
-    "scaler = StandardScaler()\n",
-    "\n",
-    "# Fit and transform the training data\n",
-    "X_train[['fare']] = scaler.fit_transform(X_train[['fare']])\n",
-    "\n",
-    "# Transform the test data\n",
-    "X_test[['fare']] = scaler.transform(X_test[['fare']])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "420d67a0",
-   "metadata": {},
-   "source": [
-    "#### Encoding Categorical Variables\n",
-    "\n",
-    "Categorical variables need to be converted into numerical format for machine learning algorithms. Depending on the type of categorical variable, i.e. whether it is ordinal or nominal, we can use different encoding techniques. For a nominal categorical variable, for which the categories do not have a natural order, we can use **one-hot encoding**. For an ordinal categorical variable, where the categories have a natural order, we can use **label encoding**.\n",
-    "\n",
-    "In the reduced Titanic dataset, the only categorical variable is `sex`, which is a nominal variable with two categories, `male` and `female`. We will use one-hot encoding to convert this categorical variable into numerical format. One-hot encoding creates binary columns for each category, indicating the presence or absence of that category in each row."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "5671f7a0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>fare</th>\n",
-       "      <th>sex</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>772</th>\n",
-       "      <td>-0.495582</td>\n",
-       "      <td>male</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>543</th>\n",
-       "      <td>-0.445269</td>\n",
-       "      <td>male</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>289</th>\n",
-       "      <td>0.890700</td>\n",
-       "      <td>female</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>3.747624</td>\n",
-       "      <td>male</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>147</th>\n",
-       "      <td>0.171035</td>\n",
-       "      <td>male</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         fare     sex\n",
-       "772 -0.495582    male\n",
-       "543 -0.445269    male\n",
-       "289  0.890700  female\n",
-       "10   3.747624    male\n",
-       "147  0.171035    male"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "2615185c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.preprocessing import OneHotEncoder\n",
-    "\n",
-    "# Create a OneHotEncoder instance\n",
-    "one_hot_encoder = OneHotEncoder(drop='first')\n",
-    "\n",
-    "# Encode the 'sex' column\n",
-    "X_train_encoded = one_hot_encoder.fit_transform(X_train[['sex']]).toarray()\n",
-    "X_test_encoded = one_hot_encoder.transform(X_test[['sex']]).toarray()\n",
-    "# Create a DataFrame with the encoded columns\n",
-    "import pandas as pd\n",
-    "X_train_encoded_df = pd.DataFrame(X_train_encoded, columns=one_hot_encoder.get_feature_names_out(['sex']))\n",
-    "X_test_encoded_df = pd.DataFrame(X_test_encoded, columns=one_hot_encoder.get_feature_names_out(['sex']))\n",
-    "# Concatenate the encoded columns with the original DataFrame\n",
-    "X_train = pd.concat([X_train.reset_index(drop=True), X_train_encoded_df], axis=1)\n",
-    "X_test = pd.concat([X_test.reset_index(drop=True), X_test_encoded_df], axis=1)\n",
-    "\n",
-    "# Drop the original 'sex' column\n",
-    "X_train = X_train.drop(columns=['sex'])\n",
-    "X_test = X_test.drop(columns=['sex'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7c85c70d",
-   "metadata": {},
-   "source": [
-    "We have now preprocessed the dataset, and it is ready for training a machine learning model. Here's how the preprocessed training data looks like:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "520c9917",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>fare</th>\n",
-       "      <th>sex_female</th>\n",
-       "      <th>sex_male</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>-0.495582</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-0.445269</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.890700</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3.747624</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.171035</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       fare  sex_female  sex_male\n",
-       "0 -0.495582         0.0       1.0\n",
-       "1 -0.445269         0.0       1.0\n",
-       "2  0.890700         1.0       0.0\n",
-       "3  3.747624         0.0       1.0\n",
-       "4  0.171035         0.0       1.0"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f1aa8030",
-   "metadata": {},
-   "source": [
-    "We've only kept two features: `fare` (numerical) and `sex` (categorical). The target variable is `survived`, which we want to predict."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "da5a7886",
-   "metadata": {},
-   "source": [
-    "### Training a Machine Learning Model\n",
-    "\n",
-    "Now that we have preprocessed the data, we can train a machine learning model. We are dealing with a binary classification problem (predicting survival), so we will use logistic regression as our model. Logistic regression is a simple yet effective algorithm for binary classification tasks, and we will discuss it in more detail in a later session."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "1813a9b7",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
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-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
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-       "  z-index: 9999;\n",
-       "  position: relative;\n",
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-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
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-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
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-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
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-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
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-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
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-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
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-       "  color: var(--sklearn-color-fitted-level-1);\n",
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-       "\n",
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-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
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-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
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-       "    \n",
-       "\n",
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-       "            <td class=\"param\">dual&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
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-       "    \n",
-       "\n",
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-       "    \n",
-       "\n",
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-       "    \n",
-       "\n",
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-       "    \n",
-       "\n",
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-       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
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-       "    \n",
-       "\n",
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-       "            <td class=\"value\">100</td>\n",
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-       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
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-       "    \n",
-       "\n",
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-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
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-       "    \n",
-       "\n",
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-       "            <td class=\"value\">False</td>\n",
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-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
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-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
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-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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-      ],
-      "text/plain": [
-       "LogisticRegression()"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import LogisticRegression\n",
-    "\n",
-    "# Create a logistic regression model\n",
-    "model = LogisticRegression()\n",
-    "\n",
-    "# Fit the model to the training data\n",
-    "model.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "40b3e030",
-   "metadata": {},
-   "source": [
-    "### Evaluating the Model\n",
-    "\n",
-    "After training the model, we need to evaluate its performance on the test data. We will use the accuracy metric, which measures the proportion of correct predictions made by the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "c7f9d8df",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model accuracy: 0.75\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Evaluate the model on the test data\n",
-    "accuracy = model.score(X_test, y_test)\n",
-    "print(f'Model accuracy: {accuracy:.2f}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "cdf9039e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Confusion Matrix:\n",
-      "[[121  23]\n",
-      " [ 42  76]]\n",
-      "\n",
-      "Classification Report:\n",
-      "                 precision    recall  f1-score   support\n",
-      "\n",
-      "did not survive       0.74      0.84      0.79       144\n",
-      "       survived       0.77      0.64      0.70       118\n",
-      "\n",
-      "       accuracy                           0.75       262\n",
-      "      macro avg       0.76      0.74      0.74       262\n",
-      "   weighted avg       0.75      0.75      0.75       262\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Make predictions on the test data\n",
-    "y_pred = model.predict(X_test)\n",
-    "from sklearn.metrics import classification_report, confusion_matrix\n",
-    "\n",
-    "cm = confusion_matrix(y_test, y_pred)\n",
-    "print(\"Confusion Matrix:\")\n",
-    "print(cm)\n",
-    "print(\"\\nClassification Report:\")\n",
-    "print(classification_report(y_test, y_pred, target_names=['did not survive', 'survived']))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "823805a3",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot the normalized confusion matrix\n",
-    "import numpy as np\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "sns.heatmap(cm_normalized, annot=True, fmt='.2f', cmap='Blues',\n",
-    "            xticklabels=['Not Survived', 'Survived'],\n",
-    "            yticklabels=['Not Survived', 'Survived'])\n",
-    "plt.title('Normalized Confusion Matrix')\n",
-    "plt.xlabel('Predicted Label')\n",
-    "plt.ylabel('True Label')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a44cfb26",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "In this hands-on exercise, you will apply the data preprocessing to the Iris dataset, with a twist: instead of considering the species as the target variable, you will consider the petal length as the target variable. The ultimate goal is to predict the petal length based on the other features (sepal length, sepal width, petal width, and species).\n",
-    "\n",
-    "-   Load the Iris dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "daea2b0f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal length (cm)</th>\n",
-       "      <th>sepal width (cm)</th>\n",
-       "      <th>petal length (cm)</th>\n",
-       "      <th>petal width (cm)</th>\n",
-       "      <th>species</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4.7</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4.6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
-       "0                5.1               3.5                1.4               0.2   \n",
-       "1                4.9               3.0                1.4               0.2   \n",
-       "2                4.7               3.2                1.3               0.2   \n",
-       "3                4.6               3.1                1.5               0.2   \n",
-       "4                5.0               3.6                1.4               0.2   \n",
-       "\n",
-       "  species  \n",
-       "0  setosa  \n",
-       "1  setosa  \n",
-       "2  setosa  \n",
-       "3  setosa  \n",
-       "4  setosa  "
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.read_csv('../../data/iris.csv')\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5c32a63e",
-   "metadata": {},
-   "source": [
-    "-   Extract the features and target variable\n",
-    "    -   Features: sepal length, sepal width, petal width, species\n",
-    "    -   Target variable: petal length"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "cd03de53",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X = df[['sepal length (cm)', 'sepal width (cm)', 'petal width (cm)', 'species']]\n",
-    "y = df['petal length (cm)']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8bac5613",
-   "metadata": {},
-   "source": [
-    "-   Perform a train-test split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "3c482ac6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7b279493",
-   "metadata": {},
-   "source": [
-    "-   Preprocess the data:\n",
-    "    -   Handle missing values (if any)\n",
-    "    -   Scale the numerical features (sepal length, sepal width, petal width)\n",
-    "    -   Encode the categorical variable (species) using one-hot encoding\n",
-    "    -   Remove any unnecessary columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "b64906c1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Scale the numerical features\n",
-    "scaler = StandardScaler()\n",
-    "X_train[['sepal length (cm)', 'sepal width (cm)', 'petal width (cm)']] = scaler.fit_transform(X_train[['sepal length (cm)', 'sepal width (cm)', 'petal width (cm)']])\n",
-    "X_test[['sepal length (cm)', 'sepal width (cm)', 'petal width (cm)']] = scaler.transform(X_test[['sepal length (cm)', 'sepal width (cm)', 'petal width (cm)']])\n",
-    "\n",
-    "# Encode the categorical variable using one-hot encoding\n",
-    "one_hot_encoder = OneHotEncoder(drop='first')\n",
-    "X_train_encoded = one_hot_encoder.fit_transform(X_train[['species']]).toarray()\n",
-    "X_test_encoded = one_hot_encoder.transform(X_test[['species']]).toarray()\n",
-    "\n",
-    "# Create DataFrames with the encoded columns\n",
-    "X_train_encoded_df = pd.DataFrame(X_train_encoded, columns=one_hot_encoder.get_feature_names_out(['species']))\n",
-    "X_test_encoded_df = pd.DataFrame(X_test_encoded, columns=one_hot_encoder.get_feature_names_out(['species']))\n",
-    "\n",
-    "# Concatenate the encoded columns with the original DataFrame\n",
-    "X_train = pd.concat([X_train.reset_index(drop=True), X_train_encoded_df], axis=1)\n",
-    "X_test = pd.concat([X_test.reset_index(drop=True), X_test_encoded_df], axis=1)\n",
-    "\n",
-    "df_train = X_train.copy()\n",
-    "df_train['petal length (cm)'] = y_train.reset_index(drop=True)\n",
-    "df_test = X_test.copy()\n",
-    "df_test['petal length (cm)'] = y_test.reset_index(drop=True)\n",
-    "\n",
-    "# Drop the original 'species' column\n",
-    "X_train = X_train.drop(columns=['species'])\n",
-    "X_test = X_test.drop(columns=['species'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cfe845e4",
-   "metadata": {},
-   "source": [
-    "-   Train a linear regression model to predict the petal length using the preprocessed data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "2fb2a3f2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model accuracy: 0.97\n",
-      "Mean Squared Error: 0.09\n",
-      "R^2 Score: 0.97\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "\n",
-    "# Create a linear regression model\n",
-    "model = LinearRegression()\n",
-    "\n",
-    "# Fit the model to the training data\n",
-    "model.fit(X_train, y_train)\n",
-    "# Evaluate the model on the test data\n",
-    "accuracy = model.score(X_test, y_test)\n",
-    "print(f'Model accuracy: {accuracy:.2f}')\n",
-    "# Make predictions on the test data\n",
-    "y_pred = model.predict(X_test)\n",
-    "df_test['predicted petal length (cm)'] = y_pred\n",
-    "df_train['predicted petal length (cm)'] = model.predict(X_train)\n",
-    "\n",
-    "from sklearn.metrics import mean_squared_error, r2_score\n",
-    "mse = mean_squared_error(y_test, y_pred)\n",
-    "r2 = r2_score(y_test, y_pred)\n",
-    "print(f'Mean Squared Error: {mse:.2f}')\n",
-    "print(f'R^2 Score: {r2:.2f}')\n",
-    "\n",
-    "# Plot the predicted vs actual petal lengths, colored by species\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "sns.scatterplot(data=df_train, x='petal length (cm)', y='predicted petal length (cm)', hue='species', alpha=0.5)\n",
-    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], color='red', linestyle='--')\n",
-    "plt.title('Predicted vs Actual Petal Lengths')\n",
-    "plt.xlabel('Actual Petal Length')\n",
-    "plt.ylabel('Predicted Petal Length')\n",
-    "plt.xlim(y_test.min(), y_test.max())\n",
-    "plt.ylim(y_test.min(), y_test.max())\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S01 Introduction to Machine Learning/S01C06 Feature Engineering.ipynb b/src/S01 Introduction to Machine Learning/S01C06 Feature Engineering.ipynb
deleted file mode 100644
index 3c5be49..0000000
--- a/src/S01 Introduction to Machine Learning/S01C06 Feature Engineering.ipynb	
+++ /dev/null
@@ -1,208 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "87ce56d5",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Feature Engineering\n",
-    "\n",
-    "Feature engineering is the process of transforming raw data into meaningful input variables (features) for machine learning models. It is a crucial step that can significantly impact model performance and interpretability.\n",
-    "\n",
-    "It combines **Domain knowledge**, **Data intuition**, and **Mathematical transformations** to create new features that help models learn better."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "785b23b7",
-   "metadata": {},
-   "source": [
-    "## Common Feature Engineering Techniques\n",
-    "\n",
-    "-   **Polynomial features** (e.g. $x^2$, $xy$)\n",
-    "-   **Log/exp/sqrt transformations**\n",
-    "-   **Ratios** and **derived metrics** (e.g., $\\text{BMI} = \\text{weight} / \\text{height}^2$)\n",
-    "-   **Discretization/Binning** (e.g. age $\\Rightarrow$ age groups)\n",
-    "-   **Time-based features** (e.g. time since last event, weekday vs weekend)\n",
-    "-   **Interaction features** (e.g. combining `income` $\\times$ `education level`)\n",
-    "-   **Domain-specific rules** (e.g. combining gene expression scores)\n",
-    "\n",
-    "Good feature engineering can:\n",
-    "\n",
-    "-   Improve model accuracy\n",
-    "-   Improve interpretability\n",
-    "-   Reduce model complexity"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9f6b526e",
-   "metadata": {},
-   "source": [
-    "## Practical Demonstration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "60c508c8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   MedInc   MedInc^2  Rooms_per_person\n",
-      "0  8.3252  69.308955          0.021623\n",
-      "1  8.3014  68.913242          0.002597\n",
-      "2  7.2574  52.669855          0.016676\n",
-      "3  5.6431  31.844578          0.010407\n",
-      "4  3.8462  14.793254          0.011099\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "from sklearn.datasets import fetch_california_housing\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import PolynomialFeatures\n",
-    "import numpy as np\n",
-    "\n",
-    "# Load dataset\n",
-    "data = fetch_california_housing(as_frame=True)\n",
-    "df = data.frame\n",
-    "\n",
-    "# Example: create new features\n",
-    "df['Rooms_per_person'] = df['AveRooms'] / (df['Population'] + 1)  # avoid division by zero\n",
-    "\n",
-    "# Polynomial features for MedInc\n",
-    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
-    "poly_features = poly.fit_transform(df[['MedInc']])\n",
-    "poly_feature_names = poly.get_feature_names_out(['MedInc'])\n",
-    "poly_df = pd.DataFrame(poly_features, columns=poly_feature_names)\n",
-    "poly_df = poly_df.drop(columns=['MedInc'])\n",
-    "\n",
-    "# Combine into single dataset\n",
-    "df = pd.concat([df, poly_df], axis=1)\n",
-    "print(df[['MedInc', 'MedInc^2', 'Rooms_per_person']].head())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72bf9a20",
-   "metadata": {},
-   "source": [
-    "-   Training a model with the new features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "6f42143b",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test R²: 0.5751137366759734\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Prepare data for training\n",
-    "X = df.drop(columns=[data.target.name])\n",
-    "y = df[data.target.name]\n",
-    "\n",
-    "# Split into train and test sets\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "\n",
-    "# Train a linear regression model\n",
-    "model = LinearRegression()\n",
-    "model.fit(X_train, y_train)\n",
-    "\n",
-    "# Evaluate the model\n",
-    "print(\"Test R²:\", model.score(X_test, y_test))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dd4b84b7",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "Add the following features to the California housing dataset:\n",
-    "\n",
-    "-   `Bedrooms_ratio = AveBedrms / AveRooms`\n",
-    "-   `Log_income = log(MedInc + 1)`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "f5c6b446",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test R²: 0.6338026669281586\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Starter code\n",
-    "from sklearn.datasets import fetch_california_housing\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "data = fetch_california_housing(as_frame=True)\n",
-    "df = data.frame\n",
-    "\n",
-    "# New features\n",
-    "df['Bedrooms_ratio'] = df['AveBedrms'] / df['AveRooms']\n",
-    "df['Log_income'] = np.log(df['MedInc'] + 1)\n",
-    "\n",
-    "X = df.drop(columns=[data.target.name])\n",
-    "y = df[data.target.name]\n",
-    "\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
-    "model = LinearRegression()\n",
-    "model.fit(X_train, y_train)\n",
-    "print(\"Test R²:\", model.score(X_test, y_test))"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
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diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02 Supervised Learning - Regression and Evaluation.pdf b/src/S02 Supervised Learning - Regression and Evaluation/S02 Supervised Learning - Regression and Evaluation.pdf
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diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02 Supervised Learning - Regression and Evaluation.tex b/src/S02 Supervised Learning - Regression and Evaluation/S02 Supervised Learning - Regression and Evaluation.tex
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-\title{Machine Learning in Python}
-\subtitle{Supervised Learning - Regression and Evaluation}
-\author[Marocico, Tatar]{Cristian A. Marocico, A. Emin Tatar}
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-
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-
-\section{Introduction to Regression}
-\label{sec:orgf91f67f}
-\begin{frame}[label={sec:orgc6a3624}]{Introduction to Regression}
-\begin{definition}[Regression]\label{sec:org3b9b537}
-\pause
-\alert{Regression} is a statistical method used to model the relationship between a dependent variable and one or more independent variables.
-\end{definition}
-\end{frame}
-\section{Simple Linear Regression}
-\label{sec:orga67890d}
-\begin{frame}[label={sec:org251cc3a}]{Simple Linear Regression}
-\begin{definition}[Simple Linear Regression]\label{sec:org4a9dd24}
-\pause
-\alert{Simple Linear Regression} is a method to model the relationship between two variables by fitting a linear equation to observed data.
-\pause
-Mathematically:
-$$y = \beta_0 + \beta_1 x + \epsilon$$
-where:
-\begin{itemize}
-\item \(y\) is the dependent variable (response).
-\item \(x\) is the independent variable (predictor).
-\item \(\beta_0\) is the y-intercept (constant term).
-\item \(\beta_1\) is the slope of the line (coefficient).
-\item \(\epsilon\) is the error term (residuals).
-\end{itemize}
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org427e879}]{Simple Linear Regression}
-A Simple Linear Regression Machine Learning model will learn the coefficients \(\beta_0\) and \(\beta_1\) from the training data to minimize the difference between the predicted values and the actual values.
-\end{frame}
-\begin{frame}[label={sec:orgf3e32f9}]{Assumptions of Simple Linear Regression}
-\begin{itemize}[<+->]
-\item Linearity: The relationship between the independent and dependent variable is linear.
-\item Independence: Observations are independent of each other.
-\item Homoscedasticity: Constant variance of the error terms.
-\item Normality: The residuals (errors) of the model are normally distributed.
-\end{itemize}
-\end{frame}
-\section{Evaluation Metrics for Regression}
-\label{sec:orga382958}
-\begin{frame}[label={sec:org4e7e5b3}]{Evaluation Metrics for Regression}
-\begin{definition}[Common Metrics for Regression]\label{sec:orgd739632}
-\pause
-Common metrics to evaluate regression models include:
-\begin{itemize}
-\item \alert{Mean Absolute Error (MAE)}
-\item \alert{Mean Squared Error (MSE)}
-\item \alert{Root Mean Squared Error (RMSE)}
-\item \alert{R-squared (\(R^2\))}
-\item \alert{Adjusted R-squared}
-\end{itemize}
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orgf02586e}]{Mean Absolute Error (MAE)}
-\begin{definition}[Mean Absolute Error (MAE)]\label{sec:orgea40cf0}
-\pause
-\alert{Mean Absolute Error (MAE)} is the average of the absolute differences between the predicted and the actual values:
-$$\text{MAE} = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i|$$
-\pause
-where:
-\begin{itemize}
-\item \(n\) is the number of observations.
-\item \(y_i\) is the actual value.
-\item \(\hat{y}_i\) is the predicted value.
-\end{itemize}
-\pause
-MAE is a linear score, which can be used when all errors are equally important; it is also less sensitive to outliers compared to MSE.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orgaa6015b}]{Mean Squared Error (MSE)}
-\begin{definition}[Mean Squared Error (MSE)]\label{sec:org7ee1fef}
-\pause
-\alert{Mean Squared Error (MSE)} is the average of the squared differences between the predicted and the actual values:
-$$\text{MSE} = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$$
-\pause
-where:
-\begin{itemize}
-\item \(n\) is the number of observations.
-\item \(y_i\) is the actual value.
-\item \(\hat{y}_i\) is the predicted value.
-\end{itemize}
-\pause
-MSE is more sensitive to outliers than MAE because it squares the errors, which can disproportionately affect the metric if there are large errors; however, it is useful when larger errors are more significant.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org8432970}]{Root Mean Squared Error (RMSE)}
-\begin{definition}[Root Mean Squared Error (RMSE)]\label{sec:orge33f2c4}
-\pause
-\alert{Root Mean Squared Error (RMSE)} is the square root of the average of the squared differences between the predicted and the actual values:
-$$\text{RMSE} = \sqrt{\text{MSE}} = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2}$$
-\pause
-where:
-\begin{itemize}
-\item \(n\) is the number of observations.
-\item \(y_i\) is the actual value.
-\item \(\hat{y}_i\) is the predicted value.
-\end{itemize}
-\pause
-RMSE is in the same units as the dependent variable, making it interpretable; it is also sensitive to outliers, similar to MSE.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orgc6b2ee1}]{R-squared (\(R^2\))}
-\begin{definition}[R-squared (\(R^2\))]\label{sec:org3c7e6b1}
-\pause
-\alert{R-squared (\(R^2\))} is a statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model:
-$$R^2 = 1 - \frac{\text{SS}_{\text{res}}}{\text{SS}_{\text{tot}}}$$
-where:
-\begin{itemize}
-\item \(\text{SS}_{\text{res}}\) is the sum of squares of residuals (errors).
-\item \(\text{SS}_{\text{tot}}\) is the total sum of squares (variance of the dependent variable).
-\end{itemize}
-\pause
-\(R^2\) values range from 0 to 1, where:
-\begin{itemize}
-\item 0 indicates that the model does not explain any of the variability of the response data around its mean.
-\item 1 indicates that the model explains all the variability of the response data around its mean.
-\end{itemize}
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org81496f2}]{Adjusted R-squared}
-\begin{definition}[Adjusted R-squared]\label{sec:org0a63597}
-\pause
-\alert{Adjusted R-squared} adjusts the \(R^2\) value for the number of predictors in the model, providing a more accurate measure when comparing models with different numbers of predictors:
-$$\text{Adjusted } R^2 = 1 - \left(1 - R^2\right) \frac{n - 1}{n - p - 1}$$
-where:
-\begin{itemize}
-\item \(n\) is the number of observations.
-\item \(p\) is the number of predictors in the model.
-\end{itemize}
-\pause
-Adjusted \(R^2\) can be negative, which indicates that the model is worse than a horizontal line (mean of the dependent variable); it is useful for comparing models with different numbers of predictors.
-\end{definition}
-\end{frame}
-\section{Robust Regression}
-\label{sec:org2e11120}
-\begin{frame}[label={sec:org17f8437}]{Robust Regression}
-\begin{definition}[Robust Regression]\label{sec:org74c1528}
-\pause
-\alert{Robust Regression} is a type of regression analysis designed to be less sensitive to outliers in the data. It provides a more reliable estimate of the relationship between variables when the data contains outliers or violations of assumptions.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org248e0bd}]{Types of Robust Regression}
-\begin{definition}[Types of Robust Regression]\label{sec:org272f361}
-There are several types of robust regression techniques, including:
-\pause
-\begin{itemize}[<+->]
-\item \alert{Huber Regression}
-\item \alert{RANSAC (RANdom SAmple Consensus)}
-\item Least Trimmed Squares (LTS)
-\item Theil-Sen Estimator
-\item Quantile Regression
-\end{itemize}
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org87401ae}]{Huber Regression}
-\begin{definition}[Huber Regression]\label{sec:orgdef91cc}
-\pause
-\alert{Huber Regression} is a robust regression technique that uses a loss function that is quadratic for small residuals and linear for large residuals. This makes it less sensitive to outliers compared to traditional least squares regression.
-\pause
-Mathematically, the Huber loss function is defined as:
-$$L(y, \hat{y}) = \begin{cases}
-\frac{1}{2}(y - \hat{y})^2 & \text{if } |y - \hat{y}| \leq \delta \\
-\delta |y - \hat{y}| - \frac{1}{2}\delta^2 & \text{if } |y - \hat{y}| > \delta
-\end{cases}$$
-where \(\delta\) is a threshold that determines the point at which the loss function transitions from quadratic to linear.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orgfec9ad9}]{RANSAC (RANdom SAmple Consensus)}
-\begin{definition}[RANSAC (RANdom SAmple Consensus)]\label{sec:org08e4a09}
-\pause
-\alert{RANSAC (RANdom SAmple Consensus)} is an iterative method used to estimate parameters of a mathematical model from a dataset that contains outliers. It works by randomly selecting a subset of the data, fitting a model to this subset, and then determining how many points from the entire dataset fit this model well.
-\pause
-The RANSAC algorithm consists of the following steps:
-\begin{enumerate}
-\item Randomly select a subset of the data points.
-\item Fit a model to this subset.
-\item Determine the inliers (points that fit the model well) and outliers (points that do not fit the model).
-\item Repeat steps 1-3 for a specified number of iterations or until a satisfactory model is found.
-\item Select the model with the highest number of inliers as the final model.
-\end{enumerate}
-\end{definition}
-\end{frame}
-\section{Multiple Linear Regression}
-\label{sec:orgecd3410}
-\begin{frame}[label={sec:orge3035d2}]{Multiple Linear Regression}
-\begin{definition}[Multiple Linear Regression]\label{sec:orgfa59908}
-\pause
-\alert{Multiple Linear Regression} is an extension of simple linear regression that models the relationship between a dependent variable and multiple independent variables. It is used when there are two or more predictors.
-\pause
-Mathematically, it is represented as:
-$$y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \ldots + \beta_p x_p + \epsilon$$
-where:
-\begin{itemize}
-\item \(y\) is the dependent variable.
-\item \(x_1, x_2, \ldots, x_p\) are the independent variables (predictors).
-\item \(\beta_0\) is the y-intercept (constant term).
-\item \(\beta_1, \beta_2, \ldots, \beta_p\) are the coefficients (slopes) for each independent variable.
-\item \(\epsilon\) is the error term (residuals).
-\end{itemize}
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org6dc22ba}]{Assumptions of Multiple Linear Regression}
-\begin{definition}[Assumptions of Multiple Linear Regression]\label{sec:org5c34d27}
-\pause
-The assumptions of multiple linear regression are similar to those of simple linear regression:
-\begin{itemize}
-\item Linearity: The relationship between the independent variables and the dependent variable is linear.
-\item Independence: Observations are independent of each other.
-\item Homoscedasticity: Constant variance of the error terms.
-\item Normality: The residuals (errors) of the model are normally distributed.
-\item No multicollinearity: The independent variables are not highly correlated with each other.
-\end{itemize}
-\end{definition}
-\end{frame}
-\section{Regularized Regression}
-\label{sec:org1477c4b}
-\begin{frame}[label={sec:orga39a2a4}]{How do Machine Learning Models Learn?}
-\begin{definition}[How do Machine Learning Models Learn?]\label{sec:org4fbea32}
-\pause
-Machine Learning models learn by adjusting their parameters (coefficients) to minimize the difference between the predicted values and the actual values in the training data, also known as the \alert{cost function} or \alert{loss function}.
-\pause
-This process is often done using optimization algorithms that iteratively update the parameters based on the loss function, which measures the error of the predictions.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orgc83c9c5}]{Cost Function}
-\begin{definition}[Cost Function]\label{sec:org7595458}
-\pause
-The \alert{cost function} (or \alert{loss function}) is a mathematical function that quantifies the difference between the predicted values and the actual values in the training data. It is used to guide the learning process of the model.
-\pause
-Common cost functions for regression include:
-\begin{itemize}
-\item \alert{Mean Squared Error (MSE)}
-\item \alert{Mean Absolute Error (MAE)}
-\end{itemize}
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org8c20945}]{Cost Function: Mean Squared Error (MSE)}
-\begin{definition}[Cost Function: Mean Squared Error (MSE)]\label{sec:org42128f4}
-\pause
-\alert{Mean Squared Error (MSE)} is a common cost function used in regression tasks. It measures the average of the squares of the errors, which are the differences between the predicted values and the actual values:
-$$J_\text{MSE}(w) = \frac{1}{2m} \sum_{i=1}^{m} (f(x_i, w) - y_i)^2$$
-where:
-\begin{itemize}
-\item \(m\) is the number of observations.
-\item \(f(x_i, w)\) is the predicted value for the \(i\)-th observation given the input features \(x_i\) and model parameters \(w\).
-\item \(y_i\) is the actual value for the \(i\)-th observation.
-\end{itemize}
-\pause
-MSE is sensitive to outliers, and is, therefore, useful when larger errors are more significant.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org7f09245}]{Cost Function: Mean Absolute Error (MAE)}
-\begin{definition}[Cost Function: Mean Absolute Error (MAE)]\label{sec:org2da3d4c}
-\pause
-\alert{Mean Absolute Error (MAE)} is another common cost function used in regression tasks. It measures the average of the absolute differences between the predicted values and the actual values:
-$$J_\text{MAE}(w) = \frac{1}{m} \sum_{i=1}^{m} |f(x_i, w) - y_i|$$
-where:
-\begin{itemize}
-\item \(m\) is the number of observations.
-\item \(f(x_i, w)\) is the predicted value for the \(i\)-th observation given the input features \(x_i\) and model parameters \(w\).
-\item \(y_i\) is the actual value for the \(i\)-th observation.
-\end{itemize}
-\pause
-MAE is less sensitive to outliers compared to MSE; useful when all errors are equally important.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org9c39a83}]{Gradient Descent}
-\begin{definition}[Gradient Descent]\label{sec:org59c3347}
-\pause
-\alert{Gradient Descent} is an optimization algorithm used to minimize the cost function by iteratively updating the model parameters in the direction of the steepest descent of the cost function.
-\pause
-The update rule for gradient descent is:
-$$w := w - \alpha \nabla J(w)$$
-where:
-\begin{itemize}
-\item \(w\) is the vector of model parameters (coefficients).
-\item \(\alpha\) is the learning rate, which controls the step size of each update.
-\item \(\nabla J(w)\) is the gradient of the cost function with respect to the parameters \(w\).
-\end{itemize}
-\pause
-Gradient descent continues until convergence, which is when the change in the cost function is below a certain threshold or a maximum number of iterations is reached.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org21d0e15}]{Gradient Descent Algorithm}
-\begin{definition}[Gradient Descent Algorithm]\label{sec:org5b43133}
-\pause
-The \alert{Gradient Descent Algorithm} consists of the following steps:
-\begin{enumerate}[<+->]
-\item Initialize the model parameters (coefficients) randomly or to zero.
-\item Compute the predicted values using the current parameters.
-\item Calculate the cost function (loss) using the predicted values and actual values.
-\item Compute the gradient of the cost function with respect to the parameters.
-\item Update the parameters using the gradient descent update rule.
-\item Repeat steps 2-5 until convergence (i.e., the change in the cost function is below a threshold or a maximum number of iterations is reached).
-\end{enumerate}
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org32d01d2}]{Overfitting and Underfitting}
-\begin{definition}[Overfitting and Underfitting]\label{sec:orgd4fb22f}
-\pause
-\alert{Overfitting} occurs when a model learns the noise in the training data instead of the underlying pattern, resulting in poor generalization to unseen data. It typically happens when the model is too complex relative to the amount of training data.
-
-\pause
-\alert{Underfitting} occurs when a model is too simple to capture the underlying pattern in the data, resulting in poor performance on both training and unseen data. It typically happens when the model is not complex enough relative to the data.
-
-\pause
-To mitigate overfitting, techniques such as \alert{regularization}, \alert{cross-validation}, and \alert{pruning} can be used. To address underfitting, more complex models or additional features can be considered.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org8d3c0b8}]{Regularization}
-\begin{definition}[Regularization]\label{sec:org91089d8}
-\pause
-\alert{Regularization} is a technique used in machine learning to prevent overfitting by adding a penalty term to the cost function. This penalty discourages overly complex models by constraining the size of the model parameters (coefficients).
-\pause
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orgcd36034}]{Types of Regularized Regression}
-\begin{definition}[Types of Regularized Regression]\label{sec:org1b54609}
-There are two common types of regularized regression:
-\begin{itemize}
-\item \alert{Ridge Regression (L2 Regularization)}
-\item \alert{Lasso Regression (L1 Regularization)}
-\end{itemize}
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orga59bded}]{Lasso Regression (L1 Regularization)}
-\begin{definition}[Lasso Regression (L1 Regularization)]\label{sec:orgc8880a3}
-\pause
-\alert{Lasso Regression} adds a penalty equal to the absolute value of the magnitude of coefficients to the loss function. The objective function for lasso regression is:
-$$J_\text{Lasso}(w) = \frac{1}{2m} \sum_{i=1}^{m} (f(x_i, w) - y_i)^2 + \lambda \sum_{j=1}^{p} |w_j|$$
-where:
-\begin{itemize}
-\item \(\lambda\) is the regularization parameter that controls the strength of the penalty.
-\item \(w_j\) are the coefficients of the independent variables.
-\end{itemize}
-\pause
-Lasso regression can shrink some coefficients to exactly zero, effectively performing variable selection and resulting in a simpler model.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org8b140d4}]{Ridge Regression (L2 Regularization)}
-\begin{definition}[Ridge Regression (L2 Regularization)]\label{sec:org43d7e1a}
-\pause
-\alert{Ridge Regression} adds a penalty equal to the square of the magnitude of coefficients to the loss function. The objective function for ridge regression is:
-$$J_\text{Ridge}(w) = \frac{1}{2m} \sum_{i=1}^{m} (f(x_i, w) - y_i)^2 + \frac{\lambda}{2m} \sum_{j=1}^{p} w_j^2$$
-where:
-\begin{itemize}
-\item \(\lambda\) is the regularization parameter that controls the strength of the penalty.
-\item \(w_j\) are the coefficients of the independent variables.
-\end{itemize}
-\pause
-Ridge regression helps to reduce model complexity and multicollinearity by shrinking the coefficients towards zero, but it does not set any coefficients exactly to zero.
-\end{definition}
-\end{frame}
-\section{Nonlinearities in Regression}
-\label{sec:orgb75a939}
-\begin{frame}[label={sec:org4bcea24}]{Nonlinearities in Regression}
-\begin{definition}[Nonlinearities in Regression]\label{sec:orge4a6e8a}
-\pause
-\alert{Nonlinearities in Regression} refers to the presence of nonlinear relationships between the independent and dependent variables in a regression model. In such cases, a simple linear regression model may not adequately capture the underlying relationship, leading to poor predictions and insights.
-\pause
-Nonlinear regression models can help address this limitation by allowing for more flexible relationships between the independent and dependent variables.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orgb28c184}]{Types of Nonlinear Regression Models}
-\begin{definition}[Types of Nonlinear Regression Models]\label{sec:org8f84522}
-There are several types of nonlinear regression models, including:
-\begin{itemize}[<+->]
-\item \alert{Polynomial Regression}: Fits a polynomial equation to the data, allowing for curves and bends in the relationship between the independent and dependent variables. The degree of the polynomial can be adjusted to capture more complex relationships.
-\item \alert{Exponential Regression}: Fits an exponential function to the data, which is useful for modeling relationships where the rate of change increases or decreases exponentially.
-\item \alert{Logarithmic Regression}: Fits a logarithmic function to the data, which is useful for modeling relationships where the rate of change decreases as the independent variable increases.
-\end{itemize}
-\end{definition}
-\end{frame}
-\section{Overfitting, Underfitting, and Hyperparameter Tuning}
-\label{sec:org3e06a11}
-\begin{frame}[label={sec:org04f8d35}]{Overfitting, Underfitting, and Hyperparameter Tuning}
-\begin{definition}[Overfitting]\label{sec:org7511425}
-\pause
-\alert{Overfitting} occurs when a model learns the noise in the training data instead of the underlying pattern, resulting in poor generalization to unseen data. It typically happens when the model is too complex relative to the amount of training data.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org36cc1a0}]{Underfitting}
-\begin{definition}[Underfitting]\label{sec:org93241a7}
-\pause
-\alert{Underfitting} occurs when a model is too simple to capture the underlying pattern in the data, resulting in poor performance on both training and unseen data. It typically happens when the model is not complex enough relative to the data.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orgdf7097b}]{Bias-Variance Tradeoff}
-\begin{definition}[Bias-Variance Tradeoff]\label{sec:org74963cb}
-\pause
-The \alert{Bias-Variance Tradeoff} is a fundamental concept in machine learning that describes the tradeoff between two sources of error in a model:
-\begin{itemize}[<+->]
-\item \alert{Bias}: The error introduced by approximating a real-world problem, which may be complex, with a simplified model. High bias can lead to underfitting.
-\item \alert{Variance}: The error introduced by the model's sensitivity to small fluctuations in the training data. High variance can lead to overfitting.
-\item The goal is to find a balance between bias and variance to minimize the overall error of the model.
-\end{itemize}
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org679dca4}]{Learning Curves}
-\begin{definition}[Learning Curves]\label{sec:org303d99d}
-\pause
-\alert{Learning Curves} are graphical representations that show how the performance of a machine learning model changes with varying amounts of training data. They can help diagnose overfitting and underfitting by comparing the training and validation errors as the size of the training set increases.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org6ac12e6}]{Hyperparameter Tuning}
-\begin{definition}[Hyperparameter Tuning]\label{sec:org3ccbedf}
-\pause
-\alert{Hyperparameter Tuning} is the process of selecting the best hyperparameters for a machine learning model to improve its performance. Hyperparameters are parameters that are not learned from the data but are set before training the model, such as the learning rate, regularization strength, and number of hidden layers in a neural network.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orgd3ef78f}]{Techniques for Hyperparameter Tuning}
-\begin{definition}[Techniques for Hyperparameter Tuning]\label{sec:org2cbdbf0}
-There are several techniques for hyperparameter tuning, including:
-\begin{itemize}[<+->]
-\item \alert{Grid Search}: Exhaustively searches through a specified subset of hyperparameters to find the best combination.
-\item \alert{Random Search}: Randomly samples hyperparameters from a specified distribution to find the best combination.
-\item \alert{Bayesian Optimization}: Uses probabilistic models to find the best hyperparameters by balancing exploration and exploitation.
-\item \alert{Cross-Validation}: Evaluates the model's performance on different subsets of the data to find the best hyperparameters.
-\item \alert{Automated Hyperparameter Tuning}: Uses algorithms to automatically search for the best hyperparameters based on the model's performance.
-\end{itemize}
-\end{definition}
-\end{frame}
-\end{document}
diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C01 Simple Linear Regression.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C01 Simple Linear Regression.ipynb
deleted file mode 100644
index fa6d65a..0000000
--- a/src/S02 Supervised Learning - Regression and Evaluation/S02C01 Simple Linear Regression.ipynb	
+++ /dev/null
@@ -1,780 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "69f3b539",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Simple Linear Regression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "157d8427",
-   "metadata": {},
-   "source": [
-    "## Introduction to Regression\n",
-    "\n",
-    "Regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It is widely used in machine learning for predictive modeling and forecasting.\n",
-    "\n",
-    "The most basic form of regression is **linear regression**, which assumes a linear relationship between the dependent variable and the independent variable(s). In this section, we will focus on simple linear regression, which involves a single independent variable."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "381c5c43",
-   "metadata": {},
-   "source": [
-    "## Simple Linear Regression\n",
-    "\n",
-    "Simple linear regression is a statistical method that models the relationship between a dependent variable and a single independent variable by fitting a linear equation to the observed data.\n",
-    "\n",
-    "The equation of a simple linear regression model can be expressed as: $$y = \\beta_0 + \\beta_1 x + \\varepsilon$$ where:\n",
-    "\n",
-    "-   $y$ is the dependent variable (the variable we want to predict),\n",
-    "-   $x$ is the independent variable (the feature used for prediction),\n",
-    "-   $\\beta_0$ is the $y$-intercept (the value of $y$ when $x = 0$),\n",
-    "-   $\\beta_1$ is the slope of the line (the change in $y$ for a one-unit change in $x$),\n",
-    "-   $\\varepsilon$ is the error term (the difference between the observed and predicted values)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "56c9f723",
-   "metadata": {},
-   "source": [
-    "## Assumptions of Simple Linear Regression\n",
-    "\n",
-    "Simple linear regression makes several key assumptions:\n",
-    "\n",
-    "1.  **Linearity**: The relationship between the dependent and independent variable is linear.\n",
-    "2.  **Independence**: The observations are independent of each other.\n",
-    "3.  **Homoscedasticity**: The residuals (errors) have constant variance across all levels of the independent variable.\n",
-    "4.  **Normality**: The residuals are normally distributed."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "43c9c1c9",
-   "metadata": {},
-   "source": [
-    "## Evaluation Metrics\n",
-    "\n",
-    "Evaluation metrics are used to assess the performance of a regression model. Common metrics include:\n",
-    "\n",
-    "-   **Mean Absolute Error (MAE)**: The average of the absolute differences between predicted and actual values. $$MAE = \\frac{1}{n} \\sum_{i=1}^{n} |y_i - \\hat{y}_i|$$ where $y_i$ is the actual value, $\\hat{y}_i$ is the predicted value, and $n$ is the number of observations.\n",
-    "-   **Mean Squared Error (MSE)**: The average of the squared differences between predicted and actual values. $$MSE = \\frac{1}{n} \\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2$$ where $y_i$ is the actual value, $\\hat{y}_i$ is the predicted value, and $n$ is the number of observations.\n",
-    "-   **Root Mean Squared Error (RMSE)**: The square root of the mean squared error, providing an error metric in the same units as the dependent variable. $$RMSE = \\sqrt{MSE}$$ where $MSE$ is the mean squared error.\n",
-    "-   **R-squared ($R^2$)**: The proportion of variance in the dependent variable that can be explained by the independent variable(s). It ranges from 0 to 1, where 1 indicates a perfect fit. $$R^2 = 1 - \\frac{SS_{res}}{SS_{tot}}$$ where $SS_{res}$ is the sum of squared residuals and $SS_{tot}$ is the total sum of squares."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "edbb3c7e",
-   "metadata": {},
-   "source": [
-    "## Practical Demonstration\n",
-    "\n",
-    "We will now demonstrate how to implement simple linear regression using Python's `scikit-learn` library. We will use a synthetic dataset for this purpose.\n",
-    "\n",
-    "-   Create a synthetic dataset following the equation $$y = 4 + 3x + \\varepsilon$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "82dc5e2c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "np.random.seed(42)\n",
-    "X = np.linspace(-2, 2, 100).reshape(-1, 1)  # Independent variable\n",
-    "y = 4 + 3 * X + np.random.randn(100, 1)  # Dependent variable with noise"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0ef3dff3",
-   "metadata": {},
-   "source": [
-    "-   Create a `pandas.DataFrame` for ease of processing and visualization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "6e57f2f5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "data = pd.DataFrame(np.hstack((X, y)), columns=['X', 'y'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "84e247a1",
-   "metadata": {},
-   "source": [
-    "-   Explore the newly created dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "b63238ad",
-   "metadata": {},
-   "outputs": [
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-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>X</th>\n",
-       "      <th>y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>1.000000e+02</td>\n",
-       "      <td>100.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>1.643130e-16</td>\n",
-       "      <td>3.896153</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>1.172181e+00</td>\n",
-       "      <td>3.670850</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>-2.000000e+00</td>\n",
-       "      <td>-2.337523</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>-1.000000e+00</td>\n",
-       "      <td>0.681941</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>1.110223e-16</td>\n",
-       "      <td>3.872699</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>1.000000e+00</td>\n",
-       "      <td>7.251063</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>2.000000e+00</td>\n",
-       "      <td>10.018631</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  X           y\n",
-       "count  1.000000e+02  100.000000\n",
-       "mean   1.643130e-16    3.896153\n",
-       "std    1.172181e+00    3.670850\n",
-       "min   -2.000000e+00   -2.337523\n",
-       "25%   -1.000000e+00    0.681941\n",
-       "50%    1.110223e-16    3.872699\n",
-       "75%    1.000000e+00    7.251063\n",
-       "max    2.000000e+00   10.018631"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "35dfc869",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 100 entries, 0 to 99\n",
-      "Data columns (total 2 columns):\n",
-      " #   Column  Non-Null Count  Dtype  \n",
-      "---  ------  --------------  -----  \n",
-      " 0   X       100 non-null    float64\n",
-      " 1   y       100 non-null    float64\n",
-      "dtypes: float64(2)\n",
-      "memory usage: 1.7 KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "data.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "7912b7c9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "sns.set(style='whitegrid')\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.scatterplot(x=data['X'], y=data['y'])\n",
-    "plt.xlabel('X')\n",
-    "plt.ylabel('y')\n",
-    "plt.title('Synthetic Dataset: y = 4 + 3X + ε')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b46cd85",
-   "metadata": {},
-   "source": [
-    "-   Split the dataset into training and testing data (80% / 20%)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "48866989",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "X_train, X_test, y_train, y_test = train_test_split(data[['X']], data['y'],\n",
-    "                                                    test_size=0.2,\n",
-    "                                                    random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "67c10f03",
-   "metadata": {},
-   "source": [
-    "-   Create and fit a simple linear regression model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "f2aeefe3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Intercept (β0): 3.88\n",
-      "Slope (β1): 3.03\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "\n",
-    "model = LinearRegression()\n",
-    "model.fit(X_train, y_train)\n",
-    "\n",
-    "print(f\"Intercept (β0): {model.intercept_:.2f}\")\n",
-    "print(f\"Slope (β1): {model.coef_[0]:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e79bf326",
-   "metadata": {},
-   "source": [
-    "-   Make predictions and evaluate the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "68685d2d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Absolute Error (MAE): 0.60\n",
-      "Mean Squared Error (MSE): 0.62\n",
-      "Root Mean Squared Error (RMSE): 0.79\n",
-      "R-squared (R^2): 0.95\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
-    "\n",
-    "y_pred = model.predict(X_test)\n",
-    "\n",
-    "mae = mean_absolute_error(y_test, y_pred)\n",
-    "mse = mean_squared_error(y_test, y_pred)\n",
-    "rmse = np.sqrt(mse)\n",
-    "r2 = r2_score(y_test, y_pred)\n",
-    "\n",
-    "print(f\"Mean Absolute Error (MAE): {mae:.2f}\")\n",
-    "print(f\"Mean Squared Error (MSE): {mse:.2f}\")\n",
-    "print(f\"Root Mean Squared Error (RMSE): {rmse:.2f}\")\n",
-    "print(f\"R-squared (R^2): {r2:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4899c57f",
-   "metadata": {},
-   "source": [
-    "-   Plot the results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "1d4c843c",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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cdu7cqbvvvlu9e/dW3bp1lZiYqL1792rHjh369ttvJUkPPfSQrrnmGjVr1kzlypXT4cOH9eqrr6pDhw4qXbq0kpKS1KFDB3Xp0kX16tVTUFCQvv76a+3Zs8dhv7WLderUSS1bttSiRYt0/PhxRUREaPv27dqyZYvuv/9+h4UqAMCbEKwAwEITJkzQpk2b9OWXX+qNN95Qenq6qlSponvvvVePPvqo2/aCutAdd9whHx8fvfzyyzp9+rQaN26sxx9/XBUqVLji4x5++GHVqlVLL730kp577jlJUqVKldS2bdtcG8Jezrp16y5Zd0WgfOKJJ9SwYUOtX79eixYtkq+vr6pWraoePXqoWbNmkqSwsDCtXLlS8+bN0+LFixUcHKwePXqodevWeQqGBdWnTx+tWLFCq1atUvv27dWyZUu98cYbWr58uV599VUlJyerfPnyaty4sfr06ZPzuIYNG2rVqlWaP3++lixZosqVK2vkyJE6fPhwrhUML+eaa67RW2+9peeee06ffvqp1q1bp9DQUNWtW1fjx4936PHDDz/Uiy++qOTkZFWqVEn9+/fX0KFDJZnXb91zzz3avn27YmJiZBiGatSooRkzZjjsJ3YxHx8frVixQkuXLtXHH3+sd999V1WrVtWECRM0cODAAr6jAGA9m8HVnwBQLB07dkw333yzJkyY4NYQAfcbOnSoDh06pJiYGKtbAYBii2usAADwIqmpqQ63jxw5oq1bt+rGG2+0qCMAgMRUQAAAvErnzp115513qnr16jp+/LjWr18vPz+/K14TBwBwP4IVAABepF27dvroo4/0999/y9/fX02bNtXYsWNVq1Ytq1sDgGKNa6wAAAAAwElcYwUAAAAATiJYAQAAAICTuMbqIj/99JMMw5Cfn5/VrQAAAACwUHp6umw2m66//vqr3pczVhcxDEOectmZYRhKS0vzmH6KI8bAeoyB9RgD6zEG1mMMrMcYWKu4vv/5yQacsbpI9pmqRo0aWdyJlJycrH379qlu3boKCgqyup1iiTGwHmNgPcbAeoyB9RgD6zEG1iqu7/+ePXvyfF/OWAEAAACAkwhWAAAAAOAkghUAAAAAOIlgBQAAAABOIlgBAAAAgJNYFdAJmZmZSk9Pd9vznz9/Pud3Hx8ysBWyxyAzM9PiTgAAAODJCFYFYBiGTpw4oYSEBLe+TlZWlkqUKKE///yTYGWRrKws+fr66ujRoypbtqwqVaokm81mdVsAAADwMASrAsgOVRUqVFBQUJDbftDOzMzU+fPnFRAQIF9fX7e8Bq4sMzNTqampysrK0qlTpyRJlStXtrgrAAAAeBqCVT5lZmbmhKpy5cq5/bUkqWTJkgQri1w4Bj4+Pjp58qQqVKjAeAAAAMAB88vyKfuaquK04zRM2WPuzuvqAAAA4J0IVgXEdTbFD2MOAACAyyFYAQAAAICTCFaQJPXo0UMRERH6/vvv8/3Y6Oho/fjjj27oyi4iIkJr1qxx62sAAAAABUWwgn799VcdOHBAkvThhx/m+/HLli3TTz/95Oq2AAAAAK9BsII+/PBD+fj4qGXLltq0aROLMwAAAAD5RLAq5gzD0IYNG9SqVSs9+OCDSkhI0LZt2xzu89tvv2n48OG68cYb1aRJE/Xo0UMbNmyQZE7Rk6T58+crIiJCERER2rlzp44dO6aIiAht2rTJ4bmefPJJderUKef2yZMnNXnyZN18881q3LixoqKitHDhQqWlpbn5KwcAAICniY+X9u+Xdu6UDhwwb3sL9rGyWHy8FBcnJSZKoaFShQpSWFjhvf6PP/6o48ePa9iwYbrpppsUGhqqDRs25ISfI0eOqE+fPqpcubKmTp2q8uXL6+DBg/rzzz8lSW+88Yb69Omj/v37q3v37pKkunXrKiEhIU+vHx8fr9DQUE2ePFnBwcE6cuSIoqOj9ffff2vu3Llu+ZoBAADgeWJjpUGDpJgYey0qSlq9Wqpe3bq+8opgZSFP+ObZsGGDAgICFBUVJT8/P3Xp0kUffPCB/vnnH5UqVUrR0dHy8/PTunXrVLp0aUlSmzZtch7ftGlTSVLlypVz/iwpz8EqIiJCEydOzLndrFkzBQYGatKkSZo+fboCAwOd/hoBAADg2eLjc/9cLJm3Bw2S1q8v3JMPBcFUQItc7ZunME57ZmRkaNOmTerQoYPKlCkjSbrtttuUkpKiTz/9VJL0zTffqEuXLjmhytUMw9BLL72kW2+9VY0bN1aDBg00fvx4ZWRkKDY21i2vCQAAAM8SF5f75+JsMTHmcU9HsLKIJ3zzbN++XWfOnFHHjh2VlJSkpKQkhYeHq3z58jnXUCUkJKhChQpu6+Hll1/WvHnzdPPNN2v58uV66623NH36dEnS+fPn3fa6AAAA8ByJic4d9wRMBbSIJ3zzZC+tPnnyZE2ePNnhWHx8vE6fPq3Q0FCdPHky388dEBAgSblWGExKSnK4vWnTJnXq1Enjxo3Lqf3222/5fj0AAAB4r5AQ5457AoKVRaz+5klJSdGWLVvUuXNnDRgwwOHYqVOnNHbsWH388cdq3bq1PvnkE40fP/6y0wH9/PxynV0qV66c/Pz8HEJSWlqavvvuO4f7paamys/Pz6FWkL20AAAA4L0qVjTXGrjUjK6oKPO4pyNYWcTqb54tW7YoOTlZ/fv3V8uWLXMdX716tTZs2KB58+bpiy++0L333qtBgwapfPny+u2335SSkqLBgwdLkmrXrq0tW7aoRYsWCgwM1LXXXqvSpUvr3//+t1577TXVrFlTYWFhevXVV2UYhmw2W87rtGnTRq+88opeffVV1apVSx988IH++OMP937xAAAA8ChhYeYCbpdb2M3TF66QuMbKMtnfPFFRjvXC+ubZsGGDqlSpcslQJUl33HGH/ve//8nHx0fr169X1apVNWvWLD366KN6++23VbVq1Zz7Tp8+XYZhaPDgwerVq5f27t0rSXr88cd14403as6cOZo+fbratWunzp07O7zOsGHDdNttt2np0qUaO3asAgICNG3aNPd94QAAAPBI1aubq//t2yd98435+/r13rHUuiTZDMMwrG7Ck+zZs0eS1KhRo0seT01N1e+//65rr71WJUuWdPr1LtzHKiTEPFOVHaoyMzOVmpqqkiVLytfX1+nXQv5dOAbp6ekuHXvkTXJysvbt26fIyEgFBQVZ3U6xxBhYjzGwHmNgPcbAWoX2/n/2mbRokVS1qrR0qeTv777XyoOrZYMLMRXQYmFh3nFqEwAAAHCbP/6Qxo+X3n7bXnv0UalJE+t6yieCFQAAAABrpKRIzzwjPf20+edsnTtL9etb11cBcI0VAAAAgMJlGNK770qRkdKMGfZQVaGC9MIL0iefSBetHO3pOGMFAAAAoPD88os0apS0ebO9VqKENGKEGbK8YdOqSyBYAQAAAHC/xERp5kwpOlrKzLTXO3eWlizxuql/FyNYAQAAAHCfrCzppZekyZOlkyft9Vq1pIULpTvukC7Y59RbEawAAAAAuMfOneYUv+++s9cCA82QNX68+ecigmAFAAAAwLVOnDDD00svOdZ79zZXAaxZ05K23IlgBQAAAMA10tLMa6hmzZLOnrXXGzY0N/zt2NG63tyMYAUAAADAeTEx5mp/+/fba6Gh0hNPmJv9lija0YN9rIqx6OhoRURE5Pxq1aqVBgwYoO+//95tr/nkk0+qU6dOObffffddRURE6MyZM3l+js2bN+u1115za18AAADIo8OHzQUounSxhyqbTXr4YengQfMaqyIeqiSCVbFXsmRJvfHGG3rjjTc0c+ZMJSQk6IEHHtDBgwcL5fX/9a9/6Y033lBwcHCeH7N582atW7fOjV0BAADgqv75R3r8cXOZ9Pfft9dbtzYXq/jPf6Ty5a3rr5AV/eiIK/Lx8VHTpk1zbjdu3FidOnXS+vXrNX36dIf7Goah9PR0+fv7u+z1y5Ytq7Jly7rs+QAAAOBmhiG99Za5ql9srL1eubI0f750331FYvn0/OKMFRxUqVJFZcuW1bFjxzRp0iR1795dX375pXr06KFGjRrps88+kyT99NNPGjBggJo2barmzZtr3LhxOn36tMNzxcXF6ZFHHlGTJk3Url07rVq1KtfrXWoqYFpamhYtWqSbb75ZDRs2VPv27TVp0iRJ0qRJk/Tee+/p119/zZnCmH3MlX0BAAD3i483Z47t3CkdOGDehmez7dljLkDRp489VPn5SRMmmIPYr1+xDFUSZ6xwkXPnzikhIUEVKlRQRkaGTp48qTlz5ujRRx9V5cqVVaVKFf3000/q37+/OnTooEWLFiklJUWLFy/W0KFD9cYbb+Q819ChQxUXF6eZM2eqTJkyWrVqlf766y+VuMoc2xEjRuibb77RkCFD1LRpU505c0YxMTE5z3nmzBkdPnxYCxYskKScM17u7gsAALhObKw0aJC53kG2qChp9WqpenXr+sJlnDmj6vPmqeQ775gb/mbr2lVavFgKD7esNU/BT5Ku8tZb0vTpjstKOslHUqBhyHa11F+mjDR7ttSrV4FeJyMjQ5J04sQJzZs3T5mZmerSpYs++ugjJSYmatWqVWrSpEnO/adOnaqGDRtq2bJlOb2Fh4fnnN3q0KGDtm7dqp9//lkvvfSSWrduLUlq2bKlOnTooNDQ0Mv2sn37dn3xxRd69tln1b1795x69p9r1KihsmXL6s8//3SYwihJzz77rNv6AgAArhMfnztUSebtQYOk9eulsDBresNFMjOl1asVOHWqgi6cBVSnjhmounUrtmeoLkawcpVnnnFcWtIFbP//K8+vX4BglZycrAYNGuTcDgkJ0fTp09WuXTt99NFHCg0NdQhVKSkp+vHHHzVhwgRlZmbm1GvVqqXKlStrz5496tChg3bv3q0yZcrkhBdJKlOmjNq0aaNffvnlsv3s2LFDgYGB6tatW76+Dnf3BQAAXCcuLneoyhYTYx4nWHmAr74yV/T73/9yfiY1goJkmzZNGjtWCgiwtD1PQ7BylQkTzFVRXHjGypC5YITNZrtywCpTRnrssQK9RsmSJfXqq6/KZrMpLCxMlStXlo+P/dK7a665xuH+SUlJyszM1Ny5czV37txcz/fXX39Jkk6ePHnJRSnKlSt3xX4SEhJUvnz5q5+lu4i7+wIAAK6TmOjccbjZ8ePSxInSRdvbnL7lFgUtXarA666zqDHPRrBylV69CjwV73KyMjOVmpqqkiVLytfX16XPnc3Hx0eNGjW67PGLA06ZMmVks9k0ZMgQde7cOdf9w/7/v5cqVKhwyb2pLl5I4mKhoaH6+++/cwJlXrm7LwAA4DohIc4dh5ucPy8tWiTNmWMupZ6tSROlPvOMjpQtq8iqVa3rz8MRrJAvQUFBatq0qQ4fPnzFQNaoUSOdPXtWO3bsyJl2d/bsWX399ddXvJapTZs2WrVqlTZu3Khbb731kvfx8/PT+fPnC7UvAADgOhUrmgtVXGo6YFSUeRyF7KOPpNGjpUOH7LWyZaUnn5QGD1bW+fPSvn2WtecNCFbItwkTJuj+++/X6NGj1a1bNwUHB+vEiRP6+uuv1bNnT7Vs2VLt27dXgwYN9Nhjj2n8+PEqU6aMnn/+eZUuXfqKz92mTRt16NBBU6ZM0dGjR9WkSRMlJCTok08+0eLFiyVJderU0TvvvKMNGzaoZs2aCgsLU7Vq1dzaFwAAcJ2wMHP1v8utCsj1VYXo4EFpzBjp44/tNR8f6ZFHzMXR2G80zwhWyLdmzZrp9ddfV3R0tCZPnqz09HRVqlRJrVq1Us2aNSWZUwiXL1+uGTNmaPr06QoODlb//v116tQpbdmy5YrPHx0drWXLlumNN97QsmXLVK5cObVt2zbneK9evbR7927Nnj1bCQkJuvPOO/X000+7vS8AAOA61aubq//FxZnXVIWEmGeqCFWF5OxZ82zUwoVSerq93r69tHSpdMHiZcgbm2EYhtVNeJI9e/ZI0mWnk6Wmpur333/Xtddeq5IlS7q1l8xCuMYKV3bhGKSnpxfa2MMuOTlZ+/btU2RkpIKCgqxup1hiDKzHGFiPMbAeY+AihmEuSjFhgvT/i3tJkqpWlRYsMDf+vcR17sX1/b9aNrgQZ6wAAACA4uDHH83l07/+2l7z9zdXl548WSpVyrreigCCFQAAAFCUnTolTZ0qrVplnrHK1qOHORWwTh3reitCCFYAAABAUZSRIa1cae61mpBgr4eHS0uWSLfcYllrRZHP1e/i2bZs2aLevXvr+uuv10033aRRo0YpNjbW6rYAAAAA63zxhdSsmTn1LztUlS4tPfOMtGcPocoNvDpY7dy5U8OHD1fdunX13HPPacqUKdq/f78GDhyo1NRUq9sDAAAACtfRo+YCFB07mgEq24AB5tLq48eb11XB5bx6KuBHH32kKlWq6KmnnpLt/1cvKVu2rO6//379/PPPatGihdtem8UUix/GHAAAeKzUVPNs1Ny5UkqKvd68uRQdLbVubV1vxYRXB6uMjAyVKlUqJ1RJUpkyZSS574dgPz8/SeaSk4GBgW55DXim5ORkSfbvAQAAAMsZhvT++9LYsdLvv9vr5cubIevBB80Nf+F2Xh2sevbsqffff1+vvfaaevTooYSEBC1cuFD169dXs2bN3PKavr6+Cg0N1cmTJyVJQUFBDsHOlTIzM3X+/Pmc10Xhy97HKjk5WadOnVJoaChjAQAAPMO+fdLo0VJMjL3m6ysNHy7NnCmFhlrUWPHk1cGqRYsWWrZsmcaNG6cnnnhCkhQZGanVq1c79cOvYRg5ZycuJTg4WOnp6Tpx4kSBXyOvfWRmZsrX19dt4Q1XZhiGMjIyVKJECQUHBys4OPiK3xtwvZT/n86QcuG0BhQqxsB6jIH1GAPrMQYXSEyU39y5KrFihWwZGTnlzA4dlLZggYz69c2CC39mKa7vv2EYef453GZ48YUjP/74o4YMGaK77rpL//rXv5SQkKDly5erRIkSev3111WyZMl8P+eePXuUlpbmhm4BAAAAJ2RlqdxHH6nqsmXyO306p3y+UiUdGzNGCZ06SfxnvMv5+/urUaNGV72fV5+xmjNnjlq1aqVJkybl1Jo2bap//etfev/999WnT58CPa+fn5/q1q3rqjYLLCUlRUeOHFGtWrW4nssijIH1GAPrMQbWYwysxxhYr7iPgc8PP8hv3Dj5fvddTs0oWVIZY8cqc8wYVQ4KUmU3vn5xff8PHTqU5/t6dbD67bffdPPNNzvUKlWqpLCwMB09erTAz2uz2RQUFORsey4TGBjoUf0UR4yB9RgD6zEG1mMMrMcYWK/YjUFcnDRlivTCC471u+6SbcEC+dWqpcJcVqu4vf/5uRzHq4NVlSpV9MsvvzjUjh8/rvj4eFWtWtWirgAAAAAnpadLzz0nzZghJSXZ6/XrS0uWSJ07W9cbLsmrg1Xfvn311FNPac6cOerUqZMSEhK0YsUKlStXTl27drW6PQAAACD/Nm+WRo40V/3LFhIizZolDR0qsfWLR/LqYDVgwAD5+/tr3bp1euedd1SqVCk1bdpUixcvVlhYmNXtAQAAAHl35Ig0bpz07rv2ms0mDRwoPfWUVKGCZa3h6rw6WNlsNt1zzz265557rG4FAAAAKJjkZGnePGn+fCk11V5v2VKKjpZuuMG63pBnXh2sAAAAAK9lGNI775hnqS5ceK1iRTNo9e8v+fhY1x/yhWAFAAAAFLa9e83rqD77zF4rUUIaNUp6/HHzmip4FYIVAAAAUFgSEsyV/p57TsrMtNejoszV/urVs6w1OIdgBQAAALhbZqb04ovS5MnSqVP2+rXXSosWST16mAtVwGsRrAAAAAB32rFDGjFC+uEHey0w0Nz4d/x4qWRJ63qDyxCsAAAAAHf46y9p0iTplVcc6336SM88I1Wvbk1fcAuCFQAAAOBKaWnS0qXSE09IZ8/a640amcund+jg1NPHx0txcVJiohQaam5vxRau1mP9RgAAAMBVNm0yA9Rjj9lDVViYtGyZ9OOPToeq2Fipb18pMlJq1cpc66JvX7MOaxGsAAAAAGf99pt0++1S167SwYNmzWaTHn7YvD1smLmcuhPi46VBg6SYGMd6TIxZj4936unhJKYCAgAAAAX1zz/SU09JCxaYUwD/X0bLtoqdEK2TVa9X6Gmpgq/z0/Xi4nKHqmwxMeZxpgRahzNWAAAAQH4ZhrR+vRQRYQar7FBVubJOL3lV3YK3qfZd17t0ul5ionPH4V4EKwAAACA/du0yr5W65x7p+HGz5ucnTZqk+G8O6N6P7lPMp457Urliul5IiHPH4V4EKwAAACAvTp82r5Vq1kzats1e79ZN2rtXmjtXccllrjpdr6AqVpSioi59LCrKPA7rEKwAAACAK8nMlFaskMLDpeXLpawss163rrRhg/nruuskuXe6XliYtHp17nAVFWXWub7KWixeAQAAAFzOtm3SiBHm9L9spUpJjz8ujR4tBQQ43N3d0/WqVzcv7crexyokxDxTRaiyHsEKAAAARY7Tm+geP27uRbVunWO9Xz9p3jypSpVLPix7ut6lpgO6arpeWBhByhMxFRAAAABFilOb6J4/L82da672d2Gouv566auvpLVrLxuqJKbrFWecsQIAAECRcbVNdNevv0y4MQzpo4/M6X2//WavlytnLqf+0EOSr2+eemC6XvFEsAIAAECRUaBNdA8eNAPVxo32mo+PNHSoNGuWVLZsvvtgul7xQ7ACAABAkZGvVfnOnpXmzJEWLZLS0+31Dh2kpUulxo3d0iOKJoIVAAAAiow8rcqXlSW99po0caL011/2g9WqSc8+K/XuLdlsl30O4FJYvAIAAABFxtU20a3y1w/STTdJAwbYQ1VAgDRtmrR/v3T33YQqFAjBCgAAAEXG5Vbl6/2vv/VOuYcVfPMN0o4d9gO33y798os0e7a5PxVQQEwFBAAAQJHisCrf6QzV2rhC5ZdNl88XCfY7RUSY11Fd7vQWkE8EKwAAABQ5YWFS2K4vpBEjpJ9/th8oU0aaOVMaPlzy97eqPRRBBCsAAAAULUePSuPHS2+95Vh/4AFz899KlRQfL8UdNlcJDA2VKlRgeXQ4h2usAAAAUDSkpJjXStWr5xiqWrQwr6t68UWpUiXFxkp9+0qRkVKrVubd+/aVYmOtax3ej2AFAAAA72YY0nvvSfXrS9OnmwFLksqXl9askXbuNBOUpPh4adCg3JsIx8SY9fj4Qu4dRQbBCgAAAN5r3z6pSxepZ0/pyBGz5usrjR4tHTwoDRwo+dh/5I2Lyx2qssXEmMeBguAaKwAAAHifxERp1iwpOlrKyLDXb77ZXO2vfv3LPuxqTwsUBMEKAAAA3iMry7xWatIk6eRJe71mTWnhQunOO6+4wW9IyJWf/mrHgcthKiAAAAC8QtDPPyugY0dzel92qCpZ0jxztW+fOR3wCqFKkipWvPzWVVFR5nGgIDhjBQAAgEuKj///TXatXpI8Lk7+jz2myLVrHeu9ekkLFphnq/IoLExavTr3AhZRUWadJddRUAQrAAAA5BIbe/nwUb16ITWRni4tWybNnKkSSUn2eoMG5nVUnToV6GmrV5fWr7eHxpAQ80wVoQrOIFgBAADAwdWWJF+/vhBCyKefSqNGmVP8/l9G6dLKmjFD/qNGSX5+Tj19WBhBCq7FNVYAAABwYOmS5L//bl4rFRVlD1U2mzIeeEB733tPGUOHOh2qAHcgWAEAAMCBJUuSJyebm/tGRpqb/WZr1Ur69lulPfecMjjFBA/GVEAAAAA4KNQlyQ1Devttadw488KubJUqSfPmSf36mRv8Jie78EUB1+OMFQAAABwU2pLke/aYG/refbc9VPn5SY89Jh04IA0YYIYqwAvwnQoAAAAH2UuSXxyuXLYkeXy8NHKkdP310uef2+tduphha/58KTjYyRcBChdTAQEAAJCLW5Ykz8yU1qyRpkyRTp+212vXlhYvlrp3v+oGv4CnIlgBAADgkly6JPnXX0sjRkg//mivBQVJU6dKY8dKJUu66IUAaxCsAAAA4D5//ilNnCi9+qpj/Z57zCl/1apZ0xfgYgQrAAAAuN7589KSJdLs2dK5c/Z648ZSdLTUvr11vQFuQLACAACAa23cKI0aJf36q71Wtqw0Z440eLBUgh9BUfTwXQ0AAADXOHRIGjNG2rDBXvPxkYYMMc9clStnXW+AmxGsAAAA4Jxz56Qnn5QWLpTS0uz1du2kpUulpk0taw0oLAQrAAAAFIxhSOvWmRv6/vmnvV6lirRggdS3L8uno9ggWAEAACD//vc/c/n0r76y1/z9pXHjzH2qSpe2rDXACgQrAAAA5N3p09K0adLzz0tZWfZ69+7SokVS3brW9QZYiGAFAACAq8vIMMPUtGlSfLy9ft110uLF0q23WtYa4AkIVgAAALiyL7+URo6Udu+210qXlh5/XBo92pwCCBRzBCsAAABcWmysNGGCtH69Y71/f+npp81FKgBIIlgBAADgYqmp0rPPSk89JSUn2+vNmpnLp7dta11vgIciWAEAAMBkGNKHH5qb/B4+bK+XK2eGrIceknx9resP8GAEKwAAAEj795vXS33yib3m6ysNHSrNmiWFhVnWGuANCFYAAADFWVKSNHu2ubJfRoa9/q9/mdP+GjWyqjPAqxCsAAAAiqOsLGntWmniRCkuzl6vXt28vqpXL8lms64/wMsQrAAAAIqb77+XRoyQvvnGXgsIMFcAnDhRKlXKut4AL0WwAgAAKC5OnpSmTJFeeMFcqCLbHXdICxdK115rWWuAtyNYAQAAFHXp6dLy5dKMGVJior1er560ZIkUFWVdb0ARQbACAAAoyrZskUaNkvbutdfKlJFmzjSnA/r5WdYaUJT4WN2AK7z33nu644471KhRI7Vs2VKDBg1Samqq1W0BAABY58gRcwGKzp0dQ9WDD0oHD0pjxxKqABfy+jNWK1as0KpVq/TII4+oadOmio+P144dO5SZmWl1awAAAIUvJUWaP196+mnpwv9ovuEGKTpaatnSut6AIsyrg9Xhw4e1bNkyLV++XB06dMipd+nSxcKuAAAALGAY0rvvSuPGSX/8Ya9XqGCGrPvvl3yKxGQlwCN5dbB69913Va1aNYdQBQAAUOzs3WteR7Vli71WooR5DdWMGVJIiHW9AcWEV/+3xa5duxQeHq7ly5erdevWatiwofr27atdu3ZZ3RoAAID7JSRIo0dLTZo4hqrOnaVdu8wl1AlVQKHw6jNWf//9t37++WcdPHhQM2bMUGBgoFauXKmBAwcqJiZG5cqVK9DzGoah5ORkF3ebfykpKQ6/o/AxBtZjDKzHGFiPMbCex41BVpZ8166V//Tpsp06ZS/XrKn0p59W5m23STab5AE/z7iKx41BMVNc33/DMGSz2fJ0X5thXLg7nHfp0qWLjhw5ovfff1/16tWTJCUkJKhTp066//77NWrUqHw/5549e5SWlubqVgEAAFyi1J49qv7MMyr1yy85tayAAP314IOK69dPRsmSFnYHFD3+/v5q1KjRVe/n1WesgoODFRoamhOqJCk0NFT169fXoUOHCvy8fn5+qlu3ritadEpKSoqOHDmiWrVqKTAw0Op2iiXGwHqMgfUYA+sxBtbziDE4cUL+06erxGuvOZQzevZU+pNPKqxGDYVZ01mh8IgxKMaK6/ufn0zh1cGqbt26Onr06CWPnT9/vsDPa7PZFBQUVODHu1pgYKBH9VMcMQbWYwysxxhYjzGwniVjkJZmLpM+a5Z09qy93rChtHSpSnTs6N0/0OUTnwNrFbf3P6/TACUvX7yiY8eOSkhI0L59+3Jq8fHx2rt3rxo0aGBhZwAAAC7wySdS48bS+PH2UBUaKi1dKv30k9Sxo6XtAbDz6v/g6Ny5sxo1aqSRI0dqzJgxCggI0PPPPy9/f3/de++9VrcHAABQMIcPS2PHSu+/b6/ZbNLgwdKcOVL58tb1BuCSvDpY+fj46Pnnn9fcuXM1ffp0paenq0WLFnrttddUnr9wAACAt/nnH2nuXGnBAunCyxpatzanAzZvbl1vAK7Iq4OVJJUtW1bPPPOM1W0AAAAUnGFIb75pTvk7dsxer1xZmj9fuu8+84wVAI/l9cEKAADAq+3eLY0cKX35pb3m5yeNGSNNmyaVKWNdbwDyjGAFAABghTNnpOnTpRUrpKwse71rV2nxYik83LLWAOQfwQoAAKAwZWZKq1dLU6dKp0/b63XqmIGqWzem/QFeiGAFAABQWL76ShoxQvrf/+y1oCBzyt/YsVJAgGWtAXAOwQoAAMDdjh+XJk6UXnvNsX7vvdK8eVK1atb0BcBlCFYAAADucv68tGiRuffUP//Y602amMunt2tnXW8AXIpgBQAA4A4ffSSNHi0dOmSvlS0rPfmkudGvr69lrQFwPYIVAACAKx08aC6V/vHH9pqPj/TII9Ls2Wa4AlDkEKwAAABc4exZ82zUwoVSerq93r69tHSpOf0PQJFFsAIAAHCGYZiLUkyYIP31l71etaq0YIHUpw/LpwPFAMEKAACgoH780Vw+/euv7TV/f+mxx6TJk6VSpazrDUChIlgBAADk16lT5ga/q1aZZ6yy9ehhTgWsU8e63gBYgmAFAACQVxkZ0rJl0uOPSwkJ9np4uLRkiXTLLZa1BsBaBCsAAIA8KP399yp5//3S3r0XFEtLM2ZII0eaUwABFFsEKwAAgCuJjZX/mDGKeOcdx/qAAdLTT0uVK1vTFwCPQrACAAC4lNRUc1W/p55SiZQUe715cyk6Wmrd2rreAHgcghUAAMCFDEP64ANzk9/ff88pp4eFKevJJxUwZIi54S8AXIBgBQAAkG3/fmnUKCkmxl7z9VX6kCHa27u3wm+8kVAF4JL4mwEAACApSRo/XmrUyDFUdeok7dql9GeeUWaZMtb1B8DjccYKAAAUX1lZ0iuvSJMmSXFx9nqNGuZ+VD17SjablJxsXY8AvALBCgAAFE/ffSeNGCHt3GmvlSwpTZwoTZggBQVZ1xsAr0OwAgAAxUtcnDRlivTCC471nj2lZ5+VatWypC0A3o1gBQAAiof0dOm558wNfZOS7PXISGnpUqlzZ+t6A+D1CFYAAKDo27xZGjlS2rfPXgsOlmbNkoYNk/z8rOsNQJFAsAIAAEXXkSPSuHHSu+/aazabNHCg9NRTUoUKlrUGoGghWAEAAElSfLx5+VFiohQaamaOsDCruyqg5GRp3jxp/nwpNdVeb9lSio6WbrjBut4AFEnsYwUAABQbK/Xta15u1KqVVK+eeTs21urO8skwpLffNr+QJ56wh6qKFaWXXpK+/ppQBcAtCFYAABRz8fHSoEGO++JK5u1Bg8zjXuHnn80FKHr3lo4eNWslSphTAQ8ckO6/X/LhRx8A7sHfLgAAFHNxcblDVbaYGMd9cz1SfLw0apTUtKn02Wf2elSUtGePtGCBFBJiWXsAigeusQIAoJhLTHTuuGUyM6UXX5QmT5ZOnbLXr71WWrRI6tHDXKgCAAoBwQoAgGLuaidzPPJkz44d0ogR0g8/2GuBgebGv+PHSyVLWtcbgGKJqYAAABRzFSuas+YuJSrKPO4x/vpLGjBAatPGMVT16WNeRzVtGqEKgCUIVgAAFHNhYdLq1bnDVVSUWfeIJdfT0qRnnpHCw6W1a+31Ro2kL76Q1q+Xqle3rD0AYCogAABQ9epmNsnexyokxDxT5RGhatMmc3GKgwfttbAwafZsacgQc+U/ALAYfxMBAABJZlbxiCCV7dAhaexY6cMP7TWbTXr4YWnOHOmaa6zrDQAuQrACAACe5Z9/pKeeMpdJT0uz19u2laKjpeuvt643ALgMghUAAPAMhiG98Ya5qt/x4/Z65crm9VX33svy6QA8FsEKAABYb9cuc/n0bdvsNT8/adw4cwn1MmWs6w0A8oBgBQAArHP6tDR9urRypZSVZa9362Zu8nvdddb1BgD5QLACAACFLzNTev55c9+pM2fs9bp1pcWLzWAFAF6EYAUAAArXtm3mtL9du+y1UqWkxx+XRo+WAgIsaw0ACopgBQAACsfx49Jjj0nr1jnW+/WT5s2TqlSxpi8AcAGCFQAAcK/z56WFC6UnnzSXUs92/fXm8ult21rXGwC4CMEKAAC4h2FIH31kTu/77Td7vVw5c5+qhx6SfH0taw8AXIlgBQAAXO/gQTNQbdxor/n4SEOHSrNmSWXLWtYaALgDwQoAALjO2bPSnDnmUunp6fZ6hw7S0qVS48bW9QYAbkSwAgAAzsvKkl57TZowQTpxwl6vVk169lmpd2/JZrOuPwBwM4IVAABwzg8/mMun79hhrwUEmCsATppkLqUOAEUcwQoAABTM339LU6dKq1ebC1Vku+MO8yxV7dqWtQYAhY1gBQAA8icjQ1qxQpo+XUpIsNcjIszrqKKiLGsNAKxCsAIAAHn3+efSyJHSzz/ba2XKSDNnSsOHS/7+lrUGAFYiWAEAgKs7elQaP1566y3H+gMPSHPnSpUqWdIWAHgKghUAALi8lBTpmWekp582/5zthhuk6GipZUvregMAD0KwAgAAuRmG9N//SmPHSkeO2Ovly5sh64EHzA1/AQCSCFYAAOBi+/aZ11Ft3myv+fqatenTpdBQy1oDAE9FsAIAAKbERGnWLHOKX0aGvX7zzeZqf/XrW9cbAHg4ghUAAMVdVpb00kvS5MnSyZP2eq1a0sKF5r5UNptFzQGAdyBYAQBQnH37rblM+nff2WslS5oh67HHpMBA63oDAC9CsAIAoDiKizPD04svOtZ79ZIWLJBq1rSmLwDwUgQrAACKk/R0adkyc0PfpCR7vUED8zqqTp0saw0AvBnBCgCA4uLTT6VRo8xV/7KFhEhPPCE9+qjk52ddbwDg5QhWAAAUdb//Lo0bJ733nr1ms0kPPSQ99ZS5NxUAwClFame/f/75R+3bt1dERIT27NljdTsAAFgrOdncdyoy0jFUtWplLlqxahWhCgBcpEidsVq+fLkyMzOtbgMAAGsZhvT22+ZZqthYe71SJWnePKlfP8mnSP3fKgBYrsj8rfrbb7/p9ddf14gRI6xuBQAA6+zZYy5Acffd9lDl52cunX7ggDRgAKEKANygyJyxmjNnjvr27atrr73W6lYAACh88fHSjBnS8uXShbM3unSRliyRIiKs6w0AioEi8V9WmzZt0sGDBzVs2DCrWwEAoHBlZkrPPy9dd50UHW0PVbVrSx98IG3cSKgCgELg9WesUlJS9PTTT2vMmDEqXbq0S57TMAwlJye75LmckZKS4vA7Ch9jYD3GwHqMgfUuNwY+33wj/3Hj5PO//+XUjKAgpU+YoIwRI6SSJSXGzSX4HFiPMbBWcX3/DcOQzWbL0329PlitWLFC5cqV01133eWy50xPT9e+C/f4sNiRI0esbqHYYwysxxhYjzGwXvYY+P39t6ouXapyGzc6HD/TpYuOjRyp9IoVzSXW4XJ8DqzHGFirOL7//v7+ebqfVwer48eP64UXXtBzzz2ns2fPSlLOmabk5GT9888/KlWqVL6f18/PT3Xr1nVprwWRkpKiI0eOqFatWgoMDLS6nWKJMbAeY2A9xsB6OWNQubLKvPCC/ObNk+3cuZzjWQ0bKu3ZZ1Xypptk/b9eRROfA+sxBtYqru//oUOH8nxfrw5Wx44dU3p6uh5++OFcxwYMGKAmTZrozTffzPfz2mw2BQUFuaJFlwgMDPSofoojxsB6jIH1GANrBW/frrDoaPlc+I982bLSnDnyGTxYJUt49T/pXoPPgfUYA2sVt/c/r9MAJS8PVpGRkXrllVccavv27dPcuXM1a9YsNWrUyKLOAABwkUOHFDBypK67cNqfj480ZIg0e7ZUrpx1vQEAcnh1sAoODlbLli0veaxBgwZq0KBBIXcEAICLnDsnPfmktHChfNPS7PV27aSlS6WmTS1rDQCQm1cHKwAAihzDkNatMzf0/fPPnHJahQoy5s1TwP33S/mYmgIAKBxFLli1bNlSBw4csLoNAADy73//k0aMkL76yl7z91f6yJHa26OHIpo3J1QBgIcqEhsEAwDg1U6flh59VGre3DFU3XabtHev0mfNUlYxulgcALwRwQoAAKtkZEjLl0vXXSetXCllZZn18HDp44+lDz6QPGD7DwDA1RW5qYAAAHiFL7+URo6Udu+210qXlqZPl0aNkvK4ISUAwDMQrAAAKEyxsdKECdL69Y71/v2lefOkypWt6QsA4BSCFQC4UXy8FBcnJSZKoaFShQpSWJjVXcESqanSs89KTz0lJSfb682aSdHRUps21vUGAHAa11gBgJvExkp9+0qRkVKrVlK9eubt2FirO0OhMgzzWqkGDaRp0+yh6pprpOefl779llAFAEUAwQoA3CA+Xho0SIqJcazHxJj1+Hhr+kIh279f6tpVuv126fBhs+bra15bdfCgNHiweRsA4PUIVgDgBnFxuUNVtpgY8ziKsKQkc4PfRo2kTz6x1zt2lH76SVqyhDmhAFDEcI0VALhBYqJzx+GlsrKktWuliRMd03ONGub1VXfdxQa/AFBEEawAwA1CQpw7Di/0/ffSiBHSN9/YawEBZsiaOFFig18AKNKYCggAblCxohQVdeljUVHmcRQRJ0+aF87deKNjqOrZU9q3T5o1i1AFAMVAgYPVrl27XNkHABQpYWHS6tW5w1VUlFnn8poiID3dvFYqPFxas8Zc/U8yl4GMiZHeeUe69lprewQAFJoCTwXs06ePatasqR49eqhHjx6qXr26K/sCAK9Xvbq5B2z2PlYhIeaZKkJVEfDZZ+bKfnv32mvBwebZqWHDJD8/63oDAFiiwGesnnnmGdWsWVMrVqxQVFSU+vbtq3Xr1ikhIcGF7QGAdwsLM/evatnS/J1Q5eX++EPq1Uu6+WbHUDVwoLl8+ujRhCoAKKYKHKxuu+02Pf/889q6daumTp0qSZo1a5batWunoUOHatOmTUpLS3NZowAAWCYlxTwbVa+eOcUv2403Sjt3mlMBuXAOAIo1p1cFLFu2rPr166d+/frp6NGj+vDDD/Xhhx9qzJgxKlOmjLp06aLbb79dLVq0cEW/AAAUHsOQ3ntPGjvWPFuVrUIF6emnpfvvl3xYBwoA4OJVAQMCAhQYGKiAgAAZhiGbzaYtW7aof//+uuuuu3To0CFXvhwAAO6zd6/073+be09lh6oSJcyQdfCg9OCDhCoAQA6nz1idO3dOn3zyiT788EN99913stlsat++vYYNG6aOHTvKx8dHn376qebNm6fJkyfrrbfeckXfAAC4R0KCOe0vOlrKzLTX//1vcxXAyEjLWgMAeK4CB6vNmzfrww8/1BdffKHz58+rUaNGmjJlim699VaFXXR19i233KKkpCQ98cQTTjcMAIBbZGVJL74oTZ4s/f23vV6rlrRokXT77ZLNZll7AADPVuBgNXz4cFWuXFkPPPCAbr/9dtWuXfuK969Xr55uu+22gr4cAADu88030ogR0vff22uBgWbIGj/e/DMAAFdQ4GD18ssvq2XLlnm+f+PGjdW4ceOCvhwAAK534oQ0aZL08suO9bvvlp55RqpRw5q+AABep8DBKj+hCgAAj5KWZl5DNWuWdPasvd6wobR0qdSxo3W9AQC8ktOLVwAA4FU++UQaNUo6cMBeCw2VZs+WHnnEXPkPAIB84l8PAEDxcPiwuVT6++/bazabNHiwNGeOVL68db0BALwewQoAULT98480d660YIF0/ry93qaNOe2veXPregMAFBkEKwBA0WQY0ptvmqv6HTtmr1euLM2fL913H8unAwBchmAFACh6du+WRo6UvvzSXvPzM6cCTp0qlSljXW8AgCKJYAUAKDrOnJFmzJCWLzc3/M3Wtau0eLEUHm5ZawCAoo1gBQDwfpmZ0urV5tmo06ft9bp1zUDVrZtlrQEAigeCFQDAu23fLo0YIf30k71WqpQ0bZo0ZowUEGBdbwCAYoNgBQBwi/h4KS5OSkw0t4mqUEEKC3PhCxw/Lk2cKL32mmP93nulefOkatVc+GIAAFyZj9UNAACKnthYqW9fKTJSatVKqlfPvB0b64InP3/eDE4REY6hqkkTaetWs0aoAgAUMoIVAMCl4uOlQYOkmBjHekyMWY+Pd+LJP/pIathQmjTJ3J9KksqWlVaskH74QWrXzoknBwCg4AhWAACXiovLHaqyxcSYx/Pt11+l7t3NX4cOmTUfH2noUPPYI49Ivr4F7hkAAGdxjRUAwKUSE5077uDcOWnOHGnhQik93V5v315autSc/gcAgAcgWAEAXCokxLnjkiTDkF5/XZowQfrzT3u9alVpwQKpTx/JZnOqTwAAXImpgAAAl6pYUYqKuvSxqCjz+BX99JN5rVS/fvZQ5e9v7lF14IC5CgahCgDgYQhWAACXCgsz9+q9OFxFRZn1yy65fuqUea1U8+bm3lTZevSQfvnFnBJYqpTb+r5QfLy0f7+0c6eZ5ZxacAMAUCwwFRAA4HLVq0vr19v3sQoJMc9UXTJUZWRIK1dKjz8uJSTY6+Hh0pIl0i23FFbbkswl4S9e1TA7FFavXqitAAC8CMEKAOAWYWF52BD4iy+kkSOlPXvstdKlpRkzzLq/vztbzOVqS8WvX+/iTY4BAEUGUwEBAIUvNtZcgKJjR8dQNWCAdPCgNH58oYcqyU1LxQMAigXOWAEACk9qqrmq31NPSSkp9nrz5lJ0tNS6tXW9ycVLxQMAihWCFQDA/QxD+uADacwY6fff7fXy5aW5c6UHHzQ3/LWYS5aKBwAUS9b/KwYAKNr27zcXoLjjDnuo8vWVRo0yp/099JBHhCrJBUvFAwCKLc/4lwwAUPQkJZnXSjVq5HjhUqdO0q5d0uLFUmioVd1dUoGXigcAFHtMBQQAuFZWlvTKK9KkSY6rPdSoIS1cKPXs6dEb/OZrqXgAAP4fwQoA4DrffSeNGGHurJutZElp4kRpwgQpKMi63vIhT0vFAwBwAYIVAMB5cXHSlCnSCy841u+6y1wFsFYtS9oCAKCwEKwAAAWXni4995y5oW9Skr0eGSktXSp17mxdbwAAFCKCFQCgYDZvlkaOlPbts9eCg6VZs6RhwyQ/P+t6AwCgkBGsAAD5c+SING6c9O679prNJg0caG78W6GCZa0BAGAVghUAIG+Sk6V586T586XUVHu9ZUspOlq64QbregMAwGIEKwDAlRmGQjdvVsnnnpNiY+31ihXNoNW/v8ds8AsAgFUIVgCAy/v5ZwUMH646X35pr5UoIY0aJT3+uLnJEwAAIFgBAC4hPl6aOVN67jn5Zmba61FR0pIlUr16lrUGAIAnIlgBAOwyM6UXX5QmT5ZOncopn69aVVq4UAG9e5sLVQAAAAcEKwCAaccOacQI6Ycf7LXAQKU99pj2dumiek2bEqoAALgMrjYGgOLur7+kAQOkNm0cQ1WfPtKBA8qYOFFGQIB1/QEA4AUIVgBQXKWlSc88I4WHS2vX2uuNGklffCGtXy9Vr25ZewAAeBOmAgJAcbRpk7my38GD9lpYmDR7tjRkiLnyHwAAyDP+5QSA4uTQIWnsWOnDD+01m016+GFpzhzpmmus6w0AAC9GsAKA4uDcOWnuXGnBAnMKYLa2baXoaOn6663rDQCAIsCrg9XGjRv1wQcfaO/evUpKSlLNmjXVv39/3XXXXbKxchUASIZhXiv12GPS8eP2euXK5vVV997LSn8AALiAVwerl156SVWrVtWkSZMUFhamr7/+Wo8//rhOnDih4cOHW90eAFhr1y5z+fRt2+w1Pz9zKuDUqVKZMtb1BgBAEePVwWrFihUqW7Zszu3WrVsrISFBL774ooYOHSofHxY9BFAMnT4tPf649J//SFlZ9vqtt0qLF0vXXWdZawAAFFVenTwuDFXZIiMjde7cOSUnJ1vQEQBYKDNTWrHCXD59xQp7qKpbV9qwQfroI0IVAABu4tVnrC7lhx9+UMWKFVW6dGmrWwGAwrN1qzRypDn9L1upUuaZq9GjJTb4BQDArYpUsPr+++/18ccfa+LEiU49j2EYHnHGKyUlxeF3FD7GwHqMwZXZjh+X39SpKvHWWw71jD59lD5njowqVcwzWU78ncYYWI8xsB5jYD3GwFrF9f03DCPPi+LZDMMw3NxPoThx4oR69+6tOnXq6IUXXijw9VV79uxR2oVLEQOAB7KdP6+Kr72mSi+8IN/U1Jx6cni4jk6YoH+aNrWuOQAAihB/f381atToqvcrEmeskpKSNHjwYIWGhio6OtrpRSv8/PxUt25dF3VXcCkpKTpy5Ihq1aqlwMBAq9splhgD6zEGFzEM+W7cKL+JE+Vz+LC9XK6c0mfMkB54QDV8fV36koyB9RgD6zEG1mMMrFVc3/9Dhw7l+b5eH6xSU1M1ZMgQnT17Vm+88YbKuGD5YJvNpqCgIBd05xqBgYEe1U9xxBhYjzGQdOCAeb3Upk32mo+P9Oijsj3xhPzLlpW/G1++KI1BfLwUFyclJkqhoVKFClJYmNVdXV1RGgNvxRhYjzGwVnF7//OzN65XrwqYkZGh0aNH6/Dhw1q9erUqVqxodUsA4HpJSdKECVKjRo6hqkMH6aefpGXLpEuskopLi42V+vaVIiOlVq2kevXM27GxVncGAPBmXh2sZs2apc8//1yPPPKIzp07p//97385v7hOCoDXy8qS1q6VIiKkZ56R0tPNerVq0vr10uefS40bW9ujl4mPlwYNkmJiHOsxMWY9Pt6avgAA3s+rpwJu375dkvT000/nOrZlyxZVq1atsFsCANf44QdpxAhpxw57LSBAeuwxadIkcyl15FtcXO5QlS0mxjzuDVMCAQCex6uD1WeffWZ1CwDgWn//LU2dKq1eLV24aGuPHtKiRVLt2tb1VgQkJjp3HACAy/HqYAUARUZGhrR8uTR9uuNP9xER0pIlUpcu1vVWhISEOHccAIDL8eprrACgSPjsM6lpU2nUKHuoKlNGWrBA2r2bUOVCFStKUVGXPhYVZR4HAKAgCFYAYJU//pB695Zuvlnau9def+AB6eBBadw4yd+dC6gXP2Fh5izLi8NVVJRZ5/oqAEBBMRUQAApbSoq5yt/TT5t/znbDDVJ0tNSypXW9FQPVq5uLKmbvYxUSYp6pIlQBAJxBsAKAwmIY0nvvSWPHmmerspUvb4asBx4wN/yF24WFEaQAAK5FsAKAwvDLL+Y1VJs322u+vtLIkeaCFaGhlrUGAACcR7ACAHdKTJRmzTKn+GVk2Os33ywtXSrVr29dbwAAwGUIVgDgDllZ0ksvSZMnSydP2uu1akkLF0p33CHZbBY1BwAAXI1gBQCutnOnNGKE9N139lrJkmbIeuwxKTDQut4AAIBbEKwAwFXi4qRJk8wzVRfq1cvck6pmTUvaAgAA7kewAgBnpaeb11DNmiUlJdnrDRqY11F16mRdbwAAoFAQrADAGZ9+aq7st3+/vRYaKj3xhPToo1IJ/poFAKA44F98ACiIw4elceOk//7XXrPZpEGDpCefNPemAgAAxQbBCgDyIznZ3Mx3/nzp/Hl7vVUrczpgixbW9QYAACxDsAKAvDAM6a23pPHjpdhYe71SJTNk3Xef5ONjXX8AAMBSBCsAuJo9e8zrqL74wl7z85NGj5Yef1wqU8aqzgAAgIcgWAHA5Zw5I82YIS1fbm74m+2WW6TFi6WICMtaAwAAnoVgBQAXy8yU1qyRpkyRTp+212vXNgNV9+7mQhUAAAD/j2AFABfavl0aMUL66Sd7LShImjZNGjNGKlnSut4AAIDHIlgBgCT9+ac0caL06quO9XvuMRenqFbNmr4AAIBXIFgBKN7On5eWLJFmz5bOnbPXmzQxl09v18663gAAgNcgWAEovjZulEaNkn791V4rW1aaM0d6+GHJ19e63gAAgFchWAEofg4dMq+X2rDBXvPxkYYMMc9clStnXW8AAMArEawAFB/nzklPPiktXCilpdnr7dpJS5dKTZta1hoAAPBuBCsARZ9hSOvWSY89Zi5Ska1qVemZZ6S+fVk+HQAAOIVgBaBo+9//zOXTv/rKXvP3l8aNM/epKl3astYAAEDRQbACUDSdPm3uPfX881JWlr1+223mVMC6dV3yMvHxUlyclJgohYZKFSpIYWEueWoAAOBFfKxuAABcKiNDWr5cuu46aeVKe6gKD5c+/lj64AOXharYWHMWYWSk1KqVVK+eeTs21iVPDwAAvAjBCkDR8eWXUvPm0rBh5qkkyZzqN3++tGeP1LWry14qPl4aNEiKiXGsx8SY9eyXBwAAxQNTAQF4v9hYacIEaf16x3r//tK8eVLlyi5/ybi43KEqW0yMedwVUwKZaggAgHfgjBUA75Waai6fXq+eY6hq1kzavl165RW3hCrJDDrOHM8LphoCAOA9CFYAvI9hmNdKNWhgLlCRnGzWr7nGXKzi22+lNm3c2kJIiHPHr4aphgAAeBeCFQDvsn+/ea3U7bdLhw+bNV9faeRI6eBBafBg87abVawoRUVd+lhUlHncGXmZaggAADwHwQqAd0hKMjf4bdRI+uQTe71jR+mnn6QlSwr14qOwMGn16tzhKirKrDvbSmFMNQQAAK7D4hUAPFtWlspu2KDA5culkyft9Ro1pGefle66S7LZLGmtenXz0q7sxSVCQswzVa7Id+6eaggAAFyLYAXAc33/vQKGDdO1335rrwUESBMnmr+Cgqzr7f+FhbnnRFn2VMNLTQd0xVRDAADgWkwFBOB5Tp40V2i48Ub5XhiqevaU9u2TZs3yiFDlTu6eaggAAFyLM1YAXMIl+y2lp0vLl0szZjhcRJRy7bWyLVmikrfd5tKePZ07pxoCAADXIlgBcFpsbO6lwbPPrFSvnscn2bJFGjVK2rvXXgsOVtrUqfqlQwdFNmrk0p69hbumGgIAANdiKiAApzi939KRI1KvXlLnzo6hauBA6eBBZQwfLpXg/4AAAIBnI1gBcEqB91tKSTGvlYqMlN55x16/8UZp505pzRpWaAAAAF6D/wYG4JR877dkGNK770rjxkl//GGvV6ggzZsnDRgg+fB/PgAAwLsQrAA4JV/7Le3da15HtWWLvVaihDRypDR9OpszAQAAr8V/CwNwSvZ+S5eSs99SQoI0erTUpIljqPr3v6Xdu82NfglVAADAixGsADjlivstPZ+lsHfXSOHh0pIlUmamebBWLem996RPPjGvsQIAAPByTAUE4LRL7bdU5eg3Cu41Qvr+e/sdAwOlKVPM66sCA61rGAAAwMUIVgBcIme/pRMnpEmTpJdfdrzD3XdLzzwj1ahhSX8AAADuRLAC4BppadLSpdITT0hnz9rrjRqZ9X/9y7LWAAAA3I1gBcB5n3xirvZ34IC9FhoqzZ4tPfIIG/wCAIAij592ABTc4cPSmDHSBx/Yazab9PDD0pw50jXXWNcbAABAISJYAci/f/6R5s6VFiyQzp+319u0kaKjpWbNrOsNAADAAgQrAHlnGNKbb0rjx0vHjtnrlStL8+dL991nnrECAAAoZghWAPJm925p5Ejpyy/tNT8/aexYaepUqUwZ63oDAACwGMEKwJWdOSNNny6tWCFlZdnrXbtKixebm/8CAAAUcwQrAJeWmSmtXm2ejTp92l6vW9cMVN26WdYaAACApyFYAcjtq6+kESOk//3PXitVSpo2zVwFMCDAstYAAAA8EcEKgN3x49LEidJrrznW773XXJyialVr+gIAAPBwBCsA5pLpixaZe0/984+93rSpuXz6TTdZ1hoAAIA3IFgBxd1HH0mjR0uHDtlrZctKTz4pDR4s+fpa1hoAAIC3IFgBxdXBg+b1Uh9/bK/5+EiPPio98YQZrgAAAJAnBCuguDl71jwbtXChlJ5ur7dvLy1dKjVpYl1vAAAAXopgBRQXhmEuSjFhgvTXX/Z6tWrSggXS3XdLNpt1/QEAAHgxH6sbcNZvv/2mBx98UE2bNlXbtm01f/58paWlWd0W4Fl+/NFcgKJ/f3uo8vc396jav1/q04dQBQAA4ASvPmOVmJio+++/X7Vq1VJ0dLTi4uL09NNPKzU1VdOnT7e6PcB6p06Z4WnVKvOMVbbbb5eefVaqU8e63gAAAIoQrw5W69ev1z///KNly5YpNDRUkpSZmalZs2ZpyJAhqlixorUNAlbJyJBWrpQef1xKSLDXIyKkxYulW26xqjMAAIAiyaunAm7dulWtW7fOCVWS1LVrV2VlZWn79u3WNQZY6YsvpGbNpBEj7KGqTBnzOqrduwlVAAAAbuDVwerw4cOqXbu2Qy04OFjly5fX4cOHLeoKsMjRo+a1Uh07Snv22Ov33y8dOCCNG2deVwUAAACX8+qpgElJSQoODs5VDwkJUWJiYoGf1zAMJScnO9OaS6SkpDj8jsLnFWOQmqoSixfLb8EC2S7oM7NZM6U/+6yybrzRLHjA93RBeMUYFHGMgfUYA+sxBtZjDKxVXN9/wzBky+MCX14drNwlPT1d+/bts7qNHEeOHLG6hWLPI8fAMBTy5ZeqvmiR/I8fzymnh4Xp+LBhOt2jh7nhrwd9LzvDI8egmGEMrMcYWI8xsB5jYK3i+P7753HGj1cHq+DgYJ09ezZXPTExUSEhIQV+Xj8/P9WtW9eZ1lwiJSVFR44cUa1atRQYGGh1O8WSp46Bbf9++U+cKN8tW3Jqhq+vMoYMUfrUqaoQGqoKFvbnSp46BsUJY2A9xsB6jIH1GANrFdf3/9ChQ3m+r1cHq9q1a+e6lurs2bP6+++/c117lR82m01BQUHOtucygYGBHtVPceQxY5CYKD3xhLR0qbnyX7ZOnWRbulR+DRrIz7ru3MpjxqAYYwysxxhYjzGwHmNgreL2/ud1GqDk5YtXtG/fXl9//bWSkpJyaps2bZKPj4/atm1rYWeAi2VlSS+9ZC6XvnChPVTVrCm98460ebPUoIGlLQIAABRnXh2s+vbtq1KlSmnYsGH66quv9M4772j+/Pnq27cve1ih6Pj2W6l1a+nBB6W4OLNWsqQ0c6b0yy9Sz55SPv43BQAAAK7n1VMBQ0JC9PLLL2v27NkaNmyYSpUqpV69emnMmDFWtwY4Ly5OmjJFeuEFx/pdd0nPPmuerQIAAIBH8OpgJUl16tTRSy+9ZHUbgOukp0vPPSfNmCFdMM1V9eub11bdfLN1vQEAAOCSvD5YAUXK5s3SyJGOS6SHhEizZklDh0p+RXVpCgAAAO9GsAIuEB9vzsBLTJRCQ6WwMD/5+BTCpYhHjkjjxknvvmuv2WzSwIHSU09JFYrK4ukAAABFk1cvXgG4Umys1LevFBkptWol1asn9e9fQkFBEe570eRkc8pfZKRjqGrZUtq5U1q9mlAFAADgBQhWgMwzVYMGSTExjvWYGJtGjgzUuXMunoJnGNLbb5uB6oknpNRUs16xorms+tdfSzfc4NrXBAAAgNsQrACZ0/8uDlXZYmJsOn3a13Uv9vPPUufOUu/e0tGjZq1ECWn8eOngQen++6XCmH4IAAAAl+EaK0DmNVXOHM+T+Hhz76nnnpMyM+31qChpyRJz7iEAAAC8EsEKkLnwnjPHrygzU3rxRWnyZOnUKXu9dm1p0SLpttvY4BcAAMDLMd8IkHlpU1TUpY9FRRkqVy7z0gevZscOcyGKwYPtoSooSJozR9q7V+rRg1AFAABQBBCsAElhYeYCfBeHq6goQ0uXpqh06fT8PeFff0kDBkht2kg//GCv9+kj7d8vTZ0qlSzpfOMAAADwCEwFBP5f9erS+vX2faxCQqSyZTN0/PgBSXlccj0tzbxe6oknpHPn7PVGjaToaKlDB7f0DgAAAGsRrIALhIWZv7IlJ6crKysrbw/etEkaNcpc2e/CJ5w9WxoyxFz5DwAAAEUSP+kBzjp0SBo7VvrwQ3vNZjPD1OzZ0jXXWNcbAAAACgXBCiioc+ekuXOlBQvMKYDZbrpJWrpUuv5663oDAABAoSJYAfllGObFWI89Jh0/bq9XqSI984x0zz2s9AcAAFDMEKyA/Ni1SxoxQtq2zV7z9zenAk6dKpUubV1vAAAAsAzBCsiL06fNM1QrV0oXLmbRvbu0cKF03XXW9QYAAADLEayAK8nM1DVvv63A55+Xzpyx1+vWNZdVv/VW63oDAACAxyBYAZezbZtKDhummnv22GulSkmPPy6NHi0FBFjW2qXEx9v34AoNlSpUcFw6HgAAAO7jY3UDgMc5fly6916pfXv5XBiq+vUz96iaONHjQlVsrNS3rxQZKbVqJdWrZ96OjbW6MwAAgOKBYAVkO3/eXD49IkJaty6nnBwRodTNm6W1a82V/zxMfLw0aJAUE+NYj4kx6/Hx1vQFAABQnDAVEDAM6aOPzOl9v/1mr5crp/MzZ2rfjTcqsmFDy9q7mri43KEqW0yMeZwpgQAAAO7FGSsUbwcPSt26SbfdZg9VPj7SsGHSwYPKHDhQ8vW1tserSEx07jgAAACcxxkrFE9nz0pz5kiLFknp6fZ6hw7S0qVS48bm7eRka/rLh5AQ544DAADAeZyxQvGSlWVeKxUeLs2fbw9V1apJb7whff65PVR5iYoVpaioSx+LijKPAwAAwL0IVig+fvxRuukmacAA6cQJsxYQIE2bJu3fL919t2SzWdtjAYSFSatX5w5XUVFmneurAAAA3I+pgCj6/v7bDE+rVpkLVWS74w7p2Wel2rUta81VqleX1q+372MVEmKeqSJUAQAAFA6CFYqujAxpxQpp+nQpIcFej4gwr6O63Pw5LxUWRpACAACwCsEKRdPnn0sjR0o//2yvlSkjzZghjRgh+ftb1xsAAACKHIIVipajR6Xx46W33nKsP/CAuflvpUqWtAUAAICijWCFoiElRVqwwAxPKSn2eosWUnS01KqVdb0BAACgyCNYwbsZhvTf/0pjx0pHjtjr5cubIevBB80NfwEAAAA3IljBe+3bJ40aJX36qb3m62teQzVjhhQaallrAAAAKF4IVvA+iYnSrFnmFL+MDHv95pvN1f7q17euNwAAABRLBCt4j6ws6eWXpUmTpJMn7fWaNaWFC6U77/TKDX4BAADg/QhW8A7ffmtO8fv2W3utZElp8mTpscekwEDregMAAECxR7CCZ4uLM8PTiy861nv1MlcBrFnTmr4AAACACxCs4JnS06Vly6SZM6WkJHu9QQPzOqpOnS770Ph4M48lJprrV1SoIIWFub1jAAAAFGOsQw3P8+mnUpMm5hLq2aEqJERaskT66acrhqrYWKlvXyky0ty6ql4983ZsbCH1DgAAgGKJYAXP8fvvUs+eUlSUuZS6ZC5GMWiQ9Ouv0siRkp/fZR8eH2/eNSbGsR4TY9bj493YOwAAAIo1pgLCesnJ0tNPS/PnS+fP2+utWplLqrdokaeniYvLHaqyxcSYx5kSCAAAAHcgWME6hiG9/bY0bpzjXL1KlaR586R+/SSfvJ9UTUx07jgAAABQUAQrWGPPHnNq3xdf2Gt+ftLo0dK0aVJwcL6fMiTEueMAAABAQXGNFQrXmTPmflRNmzqGqi5dzLA1f36BQpUkVaxoXp51KVFR5nEAAADAHQhWKByZmdLzz0vh4eYy6llZZr12bemDD6SNG6WICKdeIixMWr06d7iKijLrXF8FAAAAd2EqINxv+3bzLNVPP9lrQUHmlL8xY6SSJV32UtWrS+vX2/exCgkxz1QRqgAAAOBOBCu4z59/ShMnSq++6li/5x5zyl+1am552bAwghQAAAAKF1MBPVR8vPTHHyWVnNxIf/xR0rv2YDp/3lzVLzzcMVQ1aSJt3Sq9/rrbQhUAAABgBYKVB4qNlfr2lerX91H79v6qX99Hffs6rkjusT7+WGrUSJo0SfrnH7NWtqy0fLn0ww9Su3bW9gcAAAC4AcHKw8THS4MG5d7oNibGrHvsmatDh6Tu3aVu3aRffzVrPj7S0KHSwYPSo49Kvr7W9ggAAAC4CcHKw8TF5Q5V2WJizOMe5dw5afJkqUED6aOP7PV27cwzVM89J5UrZ11/AAAAQCFg8QoPk5jo3PFCYxjSunXSY4+Zi1Rkq1pVWrBA6tNHstms6w8AAAAoRAQrDxMS4tzxQvHTT+by6du322v+/tL48ebZq9KlresNAAAAsABTAT1MxYq5N7jNFhVlHrfMqVPSI49IzZs7hqrbbpN++UV68klCFQAAAIolgpWHCQuTVq/OHa6iosy6JfszZWSY10qFh0v/+Y85DVAyb3/8sfTBB1KdOhY0BgAAAHgGpgJ6oOrVpfXrpRMnsnTqVIauuaaEKlXysSZUffmlNHKktHu3vVa6tDR9ujRqlDkFEAAAACjmCFYeKixMCghIVXLyPtWsGamgoKBCff2EPbHymfiYgje+4XhgwADp6aelypULtR8AAADAkxGs4Cg1VQnTn1XJZ59SyazknPLB4OYKeTlaFe9obWFzAAAAgGfiGiuYDEN6/31l1quv0Gem5YSqv3WNBmmVIpN2asCK1p67QTEAAABgIYIVpP37pa5dpTvukO8fv0uSMuSrJRqpcB3UGg1Slnw9c4NiAAAAwAMwFbA4S0qSnnhCWrLEXPnv/32mjhqppdqrhrke4jEbFAMAAAAehDNWxVFWlvTyy+Zy6c8+aw9VNWro+OK3dLO2XDJUSR6yQTEAAADgYQhWxc1330lt2kgPPGCf1xcQYC6fvm+fggb0UlSU7ZIPtXyDYgAAAMBDEayKi5MnpUGDpJYtpZ077fWePaV9+6RZs6SgIM/coBgAAADwcF57jVVmZqZeeOEFffHFFzp06JAMw1BERIRGjRqlFi1aWN2e50hPl557Tpo50/ECqchI89qqf/8710OyNyiOizMfEhJinqkiVAEAAACX5rXBKjU1Vc8//7zuvPNODR48WD4+PnrzzTc1YMAArVmzRq1bs9+StmyRRo6UfvnFXgsONs9ODRsm+fld9qFhYQQpAAAAIK+8NliVLFlSmzdvVsgFqym0bdtW3bt318svv1y8g9WRI9K4cdK77zrWBw6UnnqKC6UAAAAAF/PaYOXr6+sQqrJrEREROnr0qEVdWSw5WZo/X5o3T0pNtddvvFGKjjZ/BwAAAOByXhusLiUjI0O7du1S8+bNnXoewzCUnJzsoq4KLiUlxeH3yzIM+b7/vvwmTZJPbKy9XL680mbPVuZ990k+PmbwQr7keQzgNoyB9RgD6zEG1mMMrMcYWKu4vv+GYchmu/SK2RezGYZhuLmfQrNy5UotXbpUb775pho2vPQ+TFezZ88epaWlubgz9yn522+qvmCBgr/7Lqdm+PrqZN+++nPwYGWVLm1hdwAAAIB38/f3V6NGja56P486Y3X27FmdPHnyqverXr26/P39HWrbt29XdHS0hg4dWuBQlc3Pz09169Z16jlcISUlRUeOHFGtWrUUGBjoeDAhQX5PPqkS//mPbJmZOeXMTp2U9swzKlOvniIKud+i6IpjgELBGFiPMbAeY2A9xsB6jIG1iuv7f+jQoTzf16OC1aZNmzRt2rSr3u/jjz9WnTp1cm7v3btXI0aMUPfu3TV8+HCn+7DZbAoKCnL6eVwlMDDQ3k9WlvTii9LkydLff9vvVKuWtGiRfG+/XYF5PF2JvHMYA1iCMbAeY2A9xsB6jIH1GANrFbf3P6/TACUPC1a9e/dW79698/WYP/74Q4MHD9b111+vOXPmuKkzD/HNN9KIEdL339trgYFmyBo/3vwzAAAAgELnUcEqv06ePKmBAweqcuXKWrp0qfyusC+TVztxQnriCenllx3rd98tPfOMVKOGNX0BAAAAkOTFwSo1NVWDBw9WfHy8pk6dql9//TXnmL+/v+rXr29hdy6SlqaKa9cq8IUXpLNn7fWGDaWlS6WOHa3rDQAAAEAOrw1Wp06d0v79+yVJjz76qMOxqlWr6rPPPrOiLZfy799f1TZssBdCQ6XZs6VHHpFKeO3QAQAAAEWO1/50Xq1aNR04cMDqNtzKd+dOSZJhs8k2eLA0Z45UvrzFXQEAAAC4mNcGq+Lg/OuvK/611xQ6aJAC27a1uh0AAAAAl0Gw8mBZbdroz7AwhURGWt0KAAAAgCvwsboBAAAAAPB2BCsAAAAAcBLBCgAAAACcRLACAAAAACcRrAAAAADASQQrAAAAAHASwQoAAAAAnESwAgAAAAAnEawAAAAAwEkEKwAAAABwEsEKAAAAAJxEsAIAAAAAJxGsAAAAAMBJBCsAAAAAcBLBCgAAAACcRLACAAAAACcRrAAAAADASTbDMAyrm/AkP/74owzDkL+/v9WtyDAMpaeny8/PTzabzep2iiXGwHqMgfUYA+sxBtZjDKzHGFiruL7/aWlpstlsatas2VXvW6IQ+vEqnvSNYrPZPCLgFWeMgfUYA+sxBtZjDKzHGFiPMbBWcX3/bTZbnvMBZ6wAAAAAwElcYwUAAAAATiJYAQAAAICTCFYAAAAA4CSCFQAAAAA4iWAFAAAAAE4iWAEAAACAkwhWAAAAAOAkghUAAAAAOIlgBQAAAABOIlgBAAAAgJMIVgAAAADgJIIVAAAAADiphNUNwC4zM1MvvPCCvvjiCx06dEiGYSgiIkKjRo1SixYtrvr4s2fPau7cudq8ebPS09PVrl07TZs2TRUqVCiE7ouG7du3691339WuXbsUGxur++67T9OnT7/q444dO6abb745V71JkyZ688033dFqkVXQMZD4DLjSZ599psWLF+v3339XlSpV9PDDD+uuu+664mP4HBTMb7/9pjlz5uinn35SqVKldPvtt2v06NHy9/e/4uMMw9CqVav0+uuv68yZM4qMjNTkyZPVtGnTwmm8CCnoGHTq1EnHjx/PVd+9e7cCAgLc1W6R88cff2jNmjXatWuXfv31V9WuXVsbNmy46uP4DLhOQceAz4AjgpUHSU1N1fPPP68777xTgwcPlo+Pj958800NGDBAa9asUevWra/4+NGjR+vQoUOaOXOmAgICtHjxYg0ePFjvvPOOSpRgqPNi27Zt2r9/v2644QYlJibm+/Fjx45Vy5Ytc26XKlXKle0VC86MAZ8B1/j+++81fPhw9erVS1OmTNE333yjqVOnqlSpUrrllluu+ng+B3mXmJio+++/X7Vq1VJ0dLTi4uL09NNPKzU19ar/obBq1SotXbpU48ePV0REhF577TUNHDhQ77//vqpXr15IX4H3c2YMJKlLly4aOHCgQ+1qgQyOfv31V3355Zdq0qSJsrKyZBhGnh7HZ8B1CjoGEp8BBwY8RkZGhpGQkJCrdssttxhDhgy54mN//PFHIzw83Ni2bVtO7bfffjMiIiKMjz76yC39FkWZmZk5f+7YsaMxa9asPD0uNjbWCA8PNzZu3Oiu1oqNgo4BnwHXGThwoNGnTx+H2tixY42uXbte8XF8DvJv5cqVRtOmTY34+Pic2vr1643IyEjjxIkTl31camqq0axZM+PZZ5/NqZ0/f97o2LGjMWPGDDd2XPQUdAwMI39/R+HyLvx7f+LEiUa3bt2u+hg+A65VkDEwDD4DF+MaKw/i6+urkJCQXLWIiAidPHnyio/dunWrgoOD1bZt25xa7dq1FRkZqa1bt7ql36LIx4ePhNUKOgZ8BlwjLS1NO3fuzHVm6tZbb9Vvv/2mY8eOWdRZ0bR161a1bt1aoaGhObWuXbsqKytL27dvv+zjfvzxR507d05du3bNqfn7++vf//433+/5VNAxgOsU5O99PgOuxc8/rsG76OEyMjK0a9cu1a5d+4r3O3z4sK699lrZbDaHeu3atXX48GF3togLzJw5U5GRkWrdurWmTZumhIQEq1sqNvgMuMbRo0eVnp6e6++cOnXqSFKe3ks+B3l3+PDhXO91cHCwypcvf8X3OvvYpcbpzz//VGpqquubLaIKOgbZPvzwQzVs2FDXX3+9Bg8erAMHDrirVVyAz4Dn4DNgx0UHHm716tWKi4vTAw88cMX7JSUlqUyZMrnqISEh+vnnn93UHbL5+/vrnnvu0U033aTg4GDt2rVLK1eu1M8//6y33npLfn5+VrdY5PEZcI3s69qCg4Md6tm3r3TdG5+D/EtKSsr1Xkvm9+2V3uukpCT5+/vnujg8ODhYhmEoMTFRJUuWdHm/RVFBx0AyL9xv3LixqlSpotjYWK1cuVL33nuv/vvf/3KNj5vxGfAMfAYcEazc7OzZs1edxidJ1atXz3Wh3/bt2xUdHa2hQ4eqYcOG7mqxSHPm/c+PChUqaObMmTm3b7zxRl133XUaMmSIPv30U916660Ffm5vV1hjgMvLzxg4g88Biptp06bl/LlFixZq27atunbtqjVr1jh8FoCiis+AI4KVm23atMnhm+5yPv7445ypNpK0d+9ejRgxQt27d9fw4cOv+vjg4GCdOHEiVz0xMTHXdVvFSUHff1fo0KGDgoKCtHfv3mL9A2VhjQGfgcvLzxhkv1dnz551OJaUlCRJ+X4v+RxcWXBwcK73Wrr6921wcLDS0tJ0/vx5h/+xT0pKks1mK/bf8/lR0DG4lAoVKqh58+bau3evq9rDZfAZ8EzF/TNAsHKz3r17q3fv3vl6zB9//KHBgwfr+uuv15w5c/L0mNq1a2vHjh0yDMPhGpPff/9d4eHh+Xr9oqQg7z9cq7DGgM/A5eVnDNLS0uTn56fDhw+rXbt2OfXLXc8A51zqGsCzZ8/q77//vuJ7nX3s999/V7169XLqhw8fVpUqVZgClQ8FHQNYi88APBGLV3iYkydPauDAgapcubKWLl2a52sS2rdvr8TERO3YsSOn9vvvv+uXX35R+/bt3dUuruDzzz9XcnKyGjVqZHUrxQKfAdfw9/dXy5Yt9cknnzjUs88oVqtWLV/Px+fgytq3b6+vv/4654ygZJ5h9PHxcVjh8mLNmjVT6dKltXHjxpxaenq6YmJi+H7Pp4KOwaXExcXphx9+4Pu9EPAZ8EzF/TPAGSsPkpqaqsGDBys+Pl5Tp07Vr7/+mnPM399f9evXz7ldv3593XHHHXrqqackSddff71uuukmTZkyRRMnTlRAQIAWLVqkiIgIRUVFFfrX4q2OHz+uPXv2SJJSUlJ09OhRbdq0SZIclp+++P1/+umnZbPZ1LRpUwUHB2v37t36z3/+o4YNG6pz586F/4V4sYKOAZ8B13n00Uc1YMAAzZw5U127dtXOnTu1YcMGLVq0yOF+fA6c17dvX61du1bDhg3TkCFDFBcXp/nz56tv376qWLFizv3uv/9+/fnnn/r0008lSQEBARoyZIiio6NVtmxZhYeHa926dUpISNBDDz1k1ZfjlQo6Bhs2bNDnn3+uDh06qEKFCoqNjdXzzz8vX19fPfjgg1Z9OV4pJSVFX375pSTz34Bz587l/L1/4403qmzZsnwG3KwgY8BnIDeClQc5deqU9u/fL8n8weZCVatW1WeffZZzOzMzU1lZWQ73Wbx4sebOnavp06crIyNDN910k6ZNm6YSJRjmvNq5c6cmT56cc3vbtm3atm2bJDksH3rx+1+nTh2tW7dOb775plJTU1WxYkX16tVLI0eO5P3Pp4KOgcRnwFVatGih6OhoLV68WG+//baqVKmiOXPmOOwXI/E5cIWQkBC9/PLLmj17toYNG6ZSpUqpV69eGjNmjMP9srKylJmZ6VAbPHiwDMPQCy+8oDNnzigyMlJr1qwplitxOaOgY1CtWjWdPHlSTz31lM6ePasyZcqoVatWGjlyJGOQT6dPn9aoUaMcatm3X3nlFbVs2ZLPgJsVZAz4DORmMwzDsLoJAAAAAPBmXGMFAAAAAE4iWAEAAACAkwhWAAAAAOAkghUAAAAAOIlgBQAAAABOIlgBAAAAgJMIVgAAAADgJIIVAAAAADiJYAUAgKTx48erUaNG+v3333Mde/755xUREaHPP//cgs4AAN7AZhiGYXUTAABY7fTp0+ratavq1aunV155JaceGxur7t27q0OHDlq6dKmFHQIAPBlnrAAAkFSuXDmNHz9eO3fu1HvvvZdTnzVrlkqUKKGpU6da2B0AwNMRrAAA+H+9e/dWs2bNNG/ePMXHx+ujjz7Stm3bNHr0aFWsWNHq9gAAHoypgAAAXODXX3/VnXfeqZtvvlk//PCDKlasqLfeeks+PvxfJADg8kpY3QAAAJ7kuuuu08CBA/Wf//xHvr6++s9//kOoAgBcFf9SAABwkbCwMElShQoVdN1111ncDQDAGxCsAAC4wF9//aWlS5cqPDxcf/31l1avXm11SwAAL0CwAgDgAk888YQkadWqVbrlllu0cuVKxcbGWtwVAMDTEawAAPh/n376qT777DONGjVKlSpV0pQpU+Tn56dZs2ZZ3RoAwMMRrAAAkHTu3DnNmTNH9evXV//+/SVJFStW1KhRo7Rt2zZt3LjR4g4BAJ6MYAUAgKTFixfr5MmTmjVrlnx9fXPq9913nxo0aKCnnnpK586ds7BDAIAnI1gBAIq9n3/+Wa+//rruvfdeNW7c2OGYr6+vZs6cqVOnTmnx4sXWNAgA8HhsEAwAAAAATuKMFQAAAAA4iWAFAAAAAE4iWAEAAACAkwhWAAAAAOAkghUAAAAAOIlgBQAAAABOIlgBAAAAgJMIVgAAAADgJIIVAAAAADiJYAUAAAAATiJYAQAAAICTCFYAAAAA4CSCFQAAAAA46f8AsR47ue58HfgAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "sns.set(style='whitegrid')\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.scatterplot(x=X_test.squeeze(), y=y_test, color='blue', label='Actual')\n",
-    "sns.lineplot(x=X_test.squeeze(), y=y_pred, color='red', label='Predicted', linewidth=2)\n",
-    "plt.xlabel('X')\n",
-    "plt.ylabel('y')\n",
-    "plt.title('Simple Linear Regression')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f4a3e2c9",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "-   **Exercise 1**: Generate a synthetic dataset with a linear relationship between the independent and dependent variables. Use the following parameters:\n",
-    "    -   Independent variable: $X$ (uniformly distributed between 0 and 10)\n",
-    "    -   Dependent variable: $y = 2X + 5 + \\varepsilon$, where $\\varepsilon$ is normally distributed noise.\n",
-    "    -   Split the dataset into training and testing sets (80% training, 20% testing)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "1df8203e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Generate synthetic dataset\n",
-    "np.random.seed(42)\n",
-    "X = 10 * np.random.rand(100, 1)  # Independent variable\n",
-    "y = 2 * X + 5 + np.random.randn(100, 1)  # Dependent variable with noise\n",
-    "\n",
-    "# Create DataFrame\n",
-    "data = pd.DataFrame(np.hstack((X, y)), columns=['X', 'y'])\n",
-    "\n",
-    "# Split the dataset\n",
-    "X_train, X_test, y_train, y_test = train_test_split(data[['X']], data['y'],\n",
-    "                                                    test_size=0.2,\n",
-    "                                                    random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e386a55",
-   "metadata": {},
-   "source": [
-    "-   **Exercise 2**: Fit a simple linear regression model to the training data and evaluate its performance using the following metrics:\n",
-    "    -   Mean Absolute Error (MAE)\n",
-    "    -   Mean Squared Error (MSE)\n",
-    "    -   Root Mean Squared Error (RMSE)\n",
-    "    -   R-squared ($R^2$)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "9cd6b681",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Absolute Error (MAE): 0.59\n",
-      "Mean Squared Error (MSE): 0.65\n",
-      "Root Mean Squared Error (RMSE): 0.81\n",
-      "R-squared (R^2): 0.98\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Fit the model\n",
-    "model = LinearRegression()\n",
-    "model.fit(X_train, y_train)\n",
-    "\n",
-    "# Make predictions\n",
-    "y_pred = model.predict(X_test)\n",
-    "\n",
-    "# Evaluate the model\n",
-    "mae = mean_absolute_error(y_test, y_pred)\n",
-    "mse = mean_squared_error(y_test, y_pred)\n",
-    "rmse = np.sqrt(mse)\n",
-    "r2 = model.score(X_test, y_test)\n",
-    "\n",
-    "print(f\"Mean Absolute Error (MAE): {mae:.2f}\")\n",
-    "print(f\"Mean Squared Error (MSE): {mse:.2f}\")\n",
-    "print(f\"Root Mean Squared Error (RMSE): {rmse:.2f}\")\n",
-    "print(f\"R-squared (R^2): {r2:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1fffede6",
-   "metadata": {},
-   "source": [
-    "-   **Exercise 3**: Visualize the results by plotting the actual vs. predicted values on a scatter plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "526bf3ce",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.scatterplot(x=X_test.squeeze(), y=y_test, color='blue', label='Actual')\n",
-    "sns.lineplot(x=X_test.squeeze(), y=y_pred, color='red', label='Predicted', linewidth=2)\n",
-    "plt.xlabel('X')\n",
-    "plt.ylabel('y')\n",
-    "plt.title('Simple Linear Regression - Actual vs Predicted')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bbb3e9d1",
-   "metadata": {},
-   "source": [
-    "-   **Exercise 4**: Explore the impact of outliers on the performance of the simple linear regression model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "d02db305",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Absolute Error (MAE): 7.41\n",
-      "Mean Squared Error (MSE): 197.19\n",
-      "Root Mean Squared Error (RMSE): 14.04\n",
-      "R-squared (R^2): 0.30\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "np.random.seed(42)\n",
-    "# Generate a dataset with outliers\n",
-    "X = 10 * np.random.rand(100, 1)  # Independent variable\n",
-    "y = 2 * X + 5 + np.random.randn(100, 1)  # Dependent variable with noise\n",
-    "\n",
-    "# Introduce outliers\n",
-    "y[::20] += 100 * np.random.randn(5, 1)  # Add large noise to every 20th point\n",
-    "\n",
-    "# Create DataFrame\n",
-    "data = pd.DataFrame(np.hstack((X, y)), columns=['X', 'y'])\n",
-    "\n",
-    "# Split the dataset\n",
-    "X_train, X_test, y_train, y_test = train_test_split(data[['X']], data['y'],\n",
-    "                                                    test_size=0.2,\n",
-    "                                                    random_state=42)\n",
-    "\n",
-    "# Fit the model\n",
-    "model.fit(X_train, y_train)\n",
-    "\n",
-    "# Make predictions\n",
-    "y_pred = model.predict(X_test)\n",
-    "\n",
-    "# Evaluate the model\n",
-    "mae = mean_absolute_error(y_test, y_pred)\n",
-    "mse = mean_squared_error(y_test, y_pred)\n",
-    "rmse = np.sqrt(mse)\n",
-    "r2 = r2_score(y_test, y_pred)\n",
-    "\n",
-    "print(f\"Mean Absolute Error (MAE): {mae:.2f}\")\n",
-    "print(f\"Mean Squared Error (MSE): {mse:.2f}\")\n",
-    "print(f\"Root Mean Squared Error (RMSE): {rmse:.2f}\")\n",
-    "print(f\"R-squared (R^2): {r2:.2f}\")\n",
-    "\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.scatterplot(x=X_test.squeeze(), y=y_test, color='blue', label='Actual')\n",
-    "sns.lineplot(x=X_test.squeeze(), y=y_pred, color='red', label='Predicted', linewidth=2)\n",
-    "plt.xlabel('X')\n",
-    "plt.ylabel('y')\n",
-    "plt.title('Simple Linear Regression - Actual vs Predicted')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "11f54807",
-   "metadata": {},
-   "source": [
-    "-   **Exercise 5**: Implement a function that takes in the independent and dependent variables, fits a simple linear regression model, and returns the evaluation metrics. Use this function to evaluate different synthetic datasets with varying noise levels."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "d9152174",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Noise level: 0.1, Metrics: (0.060194149099218296, 0.005214607031248922, 0.07221223602166688, 0.9998207908794612)\n",
-      "Noise level: 1.0, Metrics: (0.651759296479477, 0.5335195182655126, 0.730424204326166, 0.987900554863308)\n",
-      "Noise level: 5.0, Metrics: (3.8813180869516657, 22.589545022391185, 4.75284599186542, 0.48918651066956254)\n"
-     ]
-    }
-   ],
-   "source": [
-    "def evaluate_simple_linear_regression(X, y):\n",
-    "    # Split the dataset\n",
-    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "\n",
-    "    # Fit the model\n",
-    "    model = LinearRegression()\n",
-    "    model.fit(X_train, y_train)\n",
-    "\n",
-    "    # Make predictions\n",
-    "    y_pred = model.predict(X_test)\n",
-    "\n",
-    "    # Evaluate the model\n",
-    "    mae = mean_absolute_error(y_test, y_pred)\n",
-    "    mse = mean_squared_error(y_test, y_pred)\n",
-    "    rmse = float(np.sqrt(mse))\n",
-    "    r2 = r2_score(y_test, y_pred)\n",
-    "\n",
-    "    return mae, mse, rmse, r2\n",
-    "\n",
-    "# Example usage with different noise levels\n",
-    "noise_levels = [0.1, 1.0, 5.0]\n",
-    "for noise in noise_levels:\n",
-    "    X = 10 * np.random.rand(100, 1)  # Independent variable\n",
-    "    y = 2 * X + 5 + noise * np.random.randn(100, 1)  # Dependent variable with varying noise\n",
-    "    df = pd.DataFrame(np.hstack((X, y)), columns=['X', 'y'])\n",
-    "    metrics = evaluate_simple_linear_regression(df[['X']], df[['y']])\n",
-    "    print(f\"Noise level: {noise}, Metrics: {metrics}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0b54bd60",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "In this section, we introduced simple linear regression, a fundamental technique in machine learning for modeling the relationship between a dependent variable and a single independent variable. We demonstrated how to implement simple linear regression using Python's `scikit-learn` library and evaluated the model's performance using various metrics. Simple linear regression serves as a foundation for understanding more complex regression techniques and is widely used in many."
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C02 Robust Regression.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C02 Robust Regression.ipynb
deleted file mode 100644
index 2d35207..0000000
--- a/src/S02 Supervised Learning - Regression and Evaluation/S02C02 Robust Regression.ipynb	
+++ /dev/null
@@ -1,475 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "7102ea9c",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Robust Regression\n",
-    "\n",
-    "**Robust regression** is a type of regression analysis designed to be less sensitive to outliers in the data. Traditional linear regression methods, such as ordinary least squares (OLS), can be heavily influenced by outliers, leading to biased estimates of the model parameters. Robust regression techniques aim to provide more reliable estimates in the presence of outliers or violations of the assumptions of linear regression."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c61ba4b3",
-   "metadata": {},
-   "source": [
-    "## Types of Robust Regression\n",
-    "\n",
-    "There are several types of robust regression techniques, including:\n",
-    "\n",
-    "1.  **Huber Regression**: Combines the ideas of least squares and least absolute deviations. It uses a quadratic loss function for small residuals and a linear loss function for large residuals, making it less sensitive to outliers.\n",
-    "2.  **RANSAC (Random Sample Consensus)**: An iterative method that fits a model to a subset of the data, repeatedly selecting random samples and estimating the model parameters. It identifies inliers and outliers based on the fit.\n",
-    "3.  *Theil-Sen Estimator*: A non-parametric method that estimates the slope of the regression line by calculating the median of the slopes of all pairs of points. It is robust to outliers and provides a more reliable estimate of the slope.\n",
-    "4.  *Quantile Regression*: Estimates the conditional quantiles of the dependent variable, allowing for a more comprehensive understanding of the relationship between variables, especially in the presence of outliers.\n",
-    "5.  *Least Trimmed Squares (LTS)*: Minimizes the sum of the smallest squared residuals, effectively ignoring a specified percentage of the largest residuals, which are likely to be outliers."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "accfc7f4",
-   "metadata": {},
-   "source": [
-    "## Practical Demonstration\n",
-    "\n",
-    "We will now demonstrate how to implement robust regression using the Huber regression method from the `scikit-learn` library. We will use a synthetic dataset with outliers for this purpose.\n",
-    "\n",
-    "-   Create a synthetic dataset with outliers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "72e1710e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "np.random.seed(0)\n",
-    "X = np.linspace(0, 10, 100).reshape(-1, 1)  # Independent variable\n",
-    "y = 2 * X + 5 + np.random.randn(100, 1)     # Dependent variable with noise\n",
-    "\n",
-    "# Introduce outliers\n",
-    "y[::20] += 500 * np.random.randn(5, 1)      # Add large noise to every 20th point\n",
-    "\n",
-    "# Create DataFrame\n",
-    "data = pd.DataFrame(np.hstack((X, y)), columns=['X', 'y'])\n",
-    "\n",
-    "# Split the dataset\n",
-    "X_train, X_test, y_train, y_test = train_test_split(data[['X']], data['y'],\n",
-    "                                                    test_size=0.2,\n",
-    "                                                    random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "84f69b58",
-   "metadata": {},
-   "source": [
-    "-   Fit a traditional linear regression model for comparison"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "21d61fe5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Intercept (β0): -29.66\n",
-      "Slope (β1): 6.77\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "model = LinearRegression()\n",
-    "model.fit(X_train, y_train)\n",
-    "\n",
-    "print(f\"Intercept (β0): {model.intercept_:.2f}\")\n",
-    "print(f\"Slope (β1): {model.coef_[0]:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3e82f6bc",
-   "metadata": {},
-   "source": [
-    "-   Make predictions and evaluate the traditional linear regression model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "77b83797",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Absolute Error (MAE): 92.33\n",
-      "Mean Squared Error (MSE): 65633.92\n",
-      "Root Mean Squared Error (RMSE): 256.19\n",
-      "R-squared (R^2): -0.09\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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1Uq9evXTrrbcWaTtjxoxRq1at5O7urmrVqqlevXqFDpJT2L5pGEaBz8h8+ZfbluSzNP/LmWv9KYuyqBGo6AheAIrk4rM++U6dOqU2bdrI399fL730kqKiouTt7a0dO3boueeeU15enhMqLZ6Lz4hczPjffRflrY9Xqze/nocffli9e/cutG3+QeT8+fPVp08fde3aVc8884xCQkLk7u6uSZMmFRgAQyp8H7iSatWqKTY2VpIUFxenunXrqkuXLnr99dc1fPhwe70hISGXHcihOGfYrlZv/mvz/vvvFxpELj5AHjp0qO655x4tW7ZMn3/+uUaPHq1JkyZp7dq1uu2222SxWPTRRx9p06ZN+uSTT/T555+rX79+mjp1qjZt2qTKlStfc90Xu9r7XZhFixYVODt9pfZr167Vb7/9poULF2rhwoUFli9YsMAevErqcmdWcnNzHfpa3H2zKOLj49W3b1/t2rVLjRs31uLFi9WhQwf7fUyS1Lp1a/3444/6z3/+o9WrV+vdd9/Va6+9psTERD366KNX3UbDhg3t+/yVFLZvWiwWffbZZ4W+56W1P5WEK9QIlHcELwDXbN26dfr999+1ZMkStW7d2j7/4m/NpQu/JyNd+Mb04rMN2dnZSklJUaNGja64naJewhQcHKxKlSrpwIEDBZbt379fbm5uxb6xu6h9LC+Cg4Pl5+en3Nzcqx4AfvTRR7rxxhu1ZMkSh9fYrCHEO3furDZt2mjixIkaNGiQfH19FRUVpf/+97+68847rxjs8vehH374weFswe+//37Fsz8Xy79EMyQkpEgHx1FRURoxYoRGjBihgwcPqnHjxpo6darmz59vb9O8eXM1b95cEyZM0AcffKCePXtq4cKFhR6km7F/FiYuLq5Yo/AtWLBAISEh9tFDL7ZkyRItXbpUiYmJ8vHxUVRUlPbu3XvF9V3p7zUoKKjQH+b9+eefHc6GmLFvdu3aVYMGDbJfbvj9998XOsR7lSpV1LdvX/Xt21dnzpxR69atNXbs2CIFr2sVFRUlwzAUGRmpv/zlL5dtV5LP0vzX91rfv7KoEajouMcLwDXL/9bz4m/Ts7KyNGPGDId2zZo1U3BwsBITE5WVlWWfP3fu3EIPwi6V/xs3V2vr7u6ujh076j//+Y/D8MdpaWn64IMP1LJlS/vlZEVV1D6WF+7u7urevbs+/vjjQg+wLh6KvLC+bd68WcnJyabV99xzz+n333/XO++8I0l64IEHlJuba7/862I5OTn297xDhw7y8PDQzJkzHdrkDwNeFHFxcfL399fEiRMLvS8r/7U5d+5cgZESo6Ki5Ofnp8zMTEkXLvW79CxS/kh7+W0uZcb+WZjq1asrNjbWYbqcP/74Q0uWLFGXLl103333FZgGDx6s06dPa/ny5ZIu3Oe3e/duLV26tMC68l+PK/29RkVFadOmTQ6fAytWrCgwHLkZ+2ZgYKDi4uK0ePFiLVy4UF5eXuratatDm99//93hceXKlXXTTTdd9j0tLd26dZO7u7vGjRtXYL8yDMNeV0k+S4ODg9W6dWu99957Onz4cIFt5Lvc+1cWNQIVHWe8AFyzFi1aKCgoSL1799aQIUNksVj0/vvvF/hH2dPTU+PHj9egQYPUvn17Pfjgg0pJSdGcOXOKdM1/06ZNJUlDhgxRXFyc3N3dFR8fX2jb8ePHKykpSS1bttQTTzwhDw8Pvf3228rMzNTkyZNN62Nxpaena/z48QXmR0ZGqmfPniVa9yuvvKIvvvhC0dHRGjBggOrXr68TJ05ox44d+u9//6sTJ05Ikrp06aIlS5bo3nvvVefOnZWSkqLExETVr19fZ86cKVENl3PXXXfplltu0T//+U8lJCSoTZs2GjRokCZNmqRdu3apY8eO8vT01MGDB/Xhhx/q9ddf13333afQ0FA99dRTmjp1qv72t7+pU6dO2r17tz777DNVq1atSGdF/f39NXPmTPXq1UtNmjRRfHy8goODdfjwYa1cuVJ33nmn3nzzTX3//ffq0KGDHnjgAdWvX18eHh5aunSp0tLS7PvdvHnzNGPGDN17772KiorS6dOn9c4778jf31933333ZWso7f2zpJYvX67Tp0/rb3/7W6HLmzdvruDgYC1YsEAPPvignnnmGX300Ue6//771a9fPzVt2lQnTpzQ8uXLlZiYqEaNGikqKkqBgYFKTEyUn5+ffH19FR0drcjISD366KP66KOP1KlTJz3wwAP68ccfNX/+fIcBYyTz9s0HH3xQDz/8sGbMmKG4uDj7ICn56tevr7Zt26pp06aqUqWKtm3bpo8++kiDBw++5m0WRVRUlMaPH69Ro0bp0KFD6tq1q/z8/JSSkqKlS5dq4MCBevrpp0v8WTp9+nS1bNlSTZo00cCBAxUZGalDhw5p5cqV2rVrl6Q/P2+ff/55xcfHy9PTU/fcc0+Z1QhUaGU4giIAF3C54eQvN/z5119/bTRv3tzw8fExwsPDjWeffdb4/PPPDUnGF1984dB2xowZRmRkpGG1Wo1mzZoZGzZsKDCMdGFDIOfk5BhPPvmkERwcbFgsFof6VMhQ0jt27DDi4uKMypUrG5UqVTLatWtnbNy40aFN/nDdlw4Hnj8s9MW1F7WPxRlOXlKhU4cOHQzDuPxw8hcPxZ6vsCG609LSjISEBCMiIsLw9PQ0wsLCjA4dOhizZs2yt8nLyzMmTpxo1KpVy7BarcZtt91mrFixokA/8t+TKw2vXlhNlxuWe+7cuQXe41mzZhlNmzY1fHx8DD8/P6Nhw4bGs88+axw9etTeJicnxxg9erQRFhZm+Pj4GO3btze+++47o2rVqsZjjz1mb3e59zbfF198YcTFxRkBAQGGt7e3ERUVZfTp08fYtm2bYRiGcfz4cSMhIcGoW7eu4evrawQEBBjR0dHG4sWL7evYsWOH0aNHD6NmzZqG1Wo1QkJCjC5dutjXka8s9s+SuOeeewxvb2/j7Nmzl23Tp08fw9PT0/7zBL///rsxePBg44YbbjC8vLyMGjVqGL1793b4+YL//Oc/Rv369Q0PD48C7/XUqVONG264wbBarcadd95pbNu2rcDnQFH3TcMo2nDy+Ww2m+Hj42NIMubPn19g+fjx44077rjDCAwMNHx8fIy6desaEyZMMLKysq643vz35cMPP7xiu/y/6/T09EKXf/zxx0bLli0NX19fw9fX16hbt66RkJBgHDhwwKHdtX6WGoZh7N2717j33nuNwMBAw9vb27j55puN0aNHO7R5+eWXjRtuuMFwc3MrMLR8adYIXG8shlHCr20BAHCSU6dOKSgoSOPHj9fzzz/v7HIAALgs7vECALiEP/74o8C8adOmSZLatm1btsUAAFBM3OMFAHAJixYt0ty5c3X33XercuXK+uqrr/Tvf/9bHTt21J133uns8gAAuCKCFwDAJdx6663y8PDQ5MmTZbPZ7ANuFDZICQAA5Q33eAEAAACAybjHCwAAAABMRvACAAAAAJNxj1cx5eXl6ejRo/Lz8yvSD3YCAAAAqJgMw9Dp06cVHh4uN7crn9MieBXT0aNHFRER4ewyAAAAAJQTR44cUY0aNa7YhuBVTH5+fpIuvLj+/v5OrgYAAACAs9hsNkVERNgzwpUQvIop//JCf39/ghcAAACAIt2CVG4G19iwYYPuuecehYeHy2KxaNmyZQ7LDcPQmDFjVL16dfn4+Cg2NlYHDx50aHPixAn17NlT/v7+CgwMVP/+/XXmzBmHNt98841atWolb29vRUREaPLkyWZ3DQAAAMB1rtwEr7Nnz6pRo0Z66623Cl0+efJkTZ8+XYmJidq8ebN8fX0VFxen8+fP29v07NlT+/btU1JSklasWKENGzZo4MCB9uU2m00dO3ZUrVq1tH37dk2ZMkVjx47VrFmzTO8fAAAAgOtXufwBZYvFoqVLl6pr166SLpztCg8P14gRI/T0009LkjIyMhQaGqq5c+cqPj5e3333nerXr6+tW7eqWbNmkqRVq1bp7rvv1i+//KLw8HDNnDlTzz//vFJTU+Xl5SVJGjlypJYtW6b9+/cXWktmZqYyMzPtj/Ov48zIyOBSQwAAAOA6ZrPZFBAQUKRs4BL3eKWkpCg1NVWxsbH2eQEBAYqOjlZycrLi4+OVnJyswMBAe+iSpNjYWLm5uWnz5s269957lZycrNatW9tDlyTFxcXp1Vdf1cmTJxUUFFRg25MmTdK4cePM7SAAAAAqJMMwlJOTo9zcXGeXgmvk6ekpd3f3Eq/HJYJXamqqJCk0NNRhfmhoqH1ZamqqQkJCHJZ7eHioSpUqDm0iIyMLrCN/WWHBa9SoURo+fLj9cf4ZLwAAAOBKsrKy9Ntvv+ncuXPOLgUlYLFYVKNGDVWuXLlE63GJ4OVMVqtVVqvV2WUAAADAheTl5SklJUXu7u4KDw+Xl5dXkUa+Q/liGIbS09P1yy+/qE6dOiU68+USwSssLEySlJaWpurVq9vnp6WlqXHjxvY2x44dc3heTk6OTpw4YX9+WFiY0tLSHNrkP85vAwAAAJRUVlaW8vLyFBERoUqVKjm7HJRAcHCwDh06pOzs7BIFr3IzquGVREZGKiwsTGvWrLHPs9ls2rx5s2JiYiRJMTExOnXqlLZv325vs3btWuXl5Sk6OtreZsOGDcrOzra3SUpK0s0331zoZYYAAABASbi5ucThNq6gtM5Ulps94cyZM9q1a5d27dol6cKAGrt27dLhw4dlsVg0dOhQjR8/XsuXL9eePXv0yCOPKDw83D7yYb169dSpUycNGDBAW7Zs0ddff63BgwcrPj5e4eHhkqSHHnpIXl5e6t+/v/bt26dFixbp9ddfd7iHCwAAAABKW7m51HDbtm1q166d/XF+GOrdu7fmzp2rZ599VmfPntXAgQN16tQptWzZUqtWrZK3t7f9OQsWLNDgwYPVoUMHubm5qXv37po+fbp9eUBAgFavXq2EhAQ1bdpU1apV05gxYxx+6wsAAAAASlu5/B2v8qw4Y/UDAADg+nT+/HmlpKQoMjLS4UQBSubS3/stC1d6L4uTDcrNpYYAAAAAyo/k5GS5u7urc+fOxXpe7dq1NW3aNHOKcmEELwAAAAAFzJ49W08++aQ2bNigo0ePOrscl0fwAgAAAMqpkyel/fulzZulAwcuPC4LZ86c0aJFi/T444+rc+fOmjt3rsPyTz75RLfffru8vb1VrVo13XvvvZKktm3b6ueff9awYcNksVjsIwKOHTvW/jNQ+aZNm6batWvbH2/dulV//etfVa1aNQUEBKhNmzbasWOHmd0sUwQvF+asP0QAAACY78gRKT5eqldPat5cqlv3wuMjR8zf9uLFi1W3bl3dfPPNevjhh/Xee+8pf2iIlStX6t5779Xdd9+tnTt3as2aNbrjjjskSUuWLFGNGjX00ksv6bffftNvv/1W5G2ePn1avXv31ldffaVNmzapTp06uvvuu3X69GlT+ljWys2ohiieI0ekRx+VVq/+c17HjtK770oREc6rCwAAACV38mTBYz3pwuNHH5UWLpTM/Bna2bNn6+GHH5YkderUSRkZGVq/fr3atm2rCRMmKD4+XuPGjbO3b9SokSSpSpUqcnd3l5+fn8LCwoq1zfbt2zs8njVrlgIDA7V+/Xp16dKlhD1yPs54uaCr/SFy5gsAAMC1paUVPNbLt3r1heVmOXDggLZs2aIePXpIkjw8PPTggw9q9uzZkqRdu3apQ4cOpb7dtLQ0DRgwQHXq1FFAQID8/f115swZHT58uNS35Qyc8XJBRflDNPMbEAAAAJgrI6Nky0ti9uzZysnJUXh4uH2eYRiyWq1688035ePjU+x1urm56dJfscrOznZ43Lt3b/3+++96/fXXVatWLVmtVsXExCgrK+vaOlLOcMbLBTnzDxEAAADmCwgo2fJrlZOTo3/961+aOnWqdu3aZZ92796t8PBw/fvf/9att96qNWvWXHYdXl5eys3NdZgXHBys1NRUh/C1a9cuhzZff/21hgwZorvvvlsNGjSQ1WrV8ePHS7V/zsQZLxfkrD9EAAAAlI3Q0Av37xd2lVPHjheWm2HFihU6efKk+vfvr4BLDiq7d++u2bNna8qUKerQoYOioqIUHx+vnJwcffrpp3ruueckXfgdrw0bNig+Pl5Wq1XVqlVT27ZtlZ6ersmTJ+u+++7TqlWr9Nlnnzn86HCdOnX0/vvvq1mzZrLZbHrmmWeu6exaecUZLxeU/4dYGDP/EAEAAFA2goIuDJp26TFf/mBqZt1WMnv2bMXGxhYIXdKF4LVt2zZVqVJFH374oZYvX67GjRurffv22rJli73dSy+9pEOHDikqKkrBwcGSpHr16mnGjBl666231KhRI23ZskVPP/10gW2fPHlSTZo0Ua9evTRkyBCFhISY01EnsBiXXmyJK7LZbAoICFBGRoZDQi9rjGoIAABQfp0/f14pKSmKjIyUt7f3Na/n5MkL9+9nZFy4qik0lHv5y9qV3sviZAMuNXRREREXhhHlDxEAAKDiCgri+K6iIHi5MP4QAQAAANfAPV4AAAAAYDKCFwAAAACYjOAFAAAAACYjeAEAAACAyQheAAAAAGAyghcAAAAAmIzgBQAAAAAmI3gBAAAAKHN9+vRR165d7Y/btm2roUOHlnkd69atk8Vi0alTp0zdDsELAAAAgF2fPn1ksVhksVjk5eWlm266SS+99JJycnJM3e6SJUv08ssvF6ltWYWl0uTh7AIAAAAAlC+dOnXSnDlzlJmZqU8//VQJCQny9PTUqFGjHNplZWXJy8urVLZZpUqVUllPecUZLwAAAAAOrFarwsLCVKtWLT3++OOKjY3V8uXL7ZcHTpgwQeHh4br55pslSUeOHNEDDzygwMBAValSRX//+9916NAh+/pyc3M1fPhwBQYGqmrVqnr22WdlGIbDNi+91DAzM1PPPfecIiIiZLVaddNNN2n27Nk6dOiQ2rVrJ0kKCgqSxWJRnz59JEl5eXmaNGmSIiMj5ePjo0aNGumjjz5y2M6nn36qv/zlL/Lx8VG7du0c6jQTZ7wAAACAMtJsVjOlnkkt022GVQ7TtoHbSrQOHx8f/f7775KkNWvWyN/fX0lJSZKk7OxsxcXFKSYmRl9++aU8PDw0fvx4derUSd988428vLw0depUzZ07V++9957q1aunqVOnaunSpWrfvv1lt/nII48oOTlZ06dPV6NGjZSSkqLjx48rIiJCH3/8sbp3764DBw7I399fPj4+kqRJkyZp/vz5SkxMVJ06dbRhwwY9/PDDCg4OVps2bXTkyBF169ZNCQkJGjhwoLZt26YRI0aU6LUpKoIXAAAAUEZSz6Tq19O/OruMIjMMQ2vWrNHnn3+uJ598Uunp6fL19dW7775rv8Rw/vz5ysvL07vvviuLxSJJmjNnjgIDA7Vu3Tp17NhR06ZN06hRo9StWzdJUmJioj7//PPLbvf777/X4sWLlZSUpNjYWEnSjTfeaF+ef1liSEiIAgMDJV04QzZx4kT997//VUxMjP05X331ld5++221adNGM2fOVFRUlKZOnSpJuvnmm7Vnzx69+uqrpfiqFY7gBQAAAJSRsMphLrHNFStWqHLlysrOzlZeXp4eeughjR07VgkJCWrYsKHDfV27d+/WDz/8ID8/P4d1nD9/Xj/++KMyMjL022+/KTo62r7Mw8NDzZo1K3C5Yb5du3bJ3d1dbdq0KXLNP/zwg86dO6e//vWvDvOzsrJ02223SZK+++47hzok2UOa2QheAAAAQBkp6SV/ZaVdu3aaOXOmvLy8FB4eLg+PP2ODr6+vQ9szZ86oadOmWrBgQYH1BAcHX9P28y8dLI4zZ85IklauXKkbbrjBYZnVar2mOkoTwQsAAACAA19fX910001FatukSRMtWrRIISEh8vf3L7RN9erVtXnzZrVu3VqSlJOTo+3bt6tJkyaFtm/YsKHy8vK0fv16+6WGF8s/45abm2ufV79+fVmtVh0+fPiyZ8rq1aun5cuXO8zbtGnT1TtZChjVEAAAAMA169mzp6pVq6a///3v+vLLL5WSkqJ169ZpyJAh+uWXXyRJTz31lF555RUtW7ZM+/fv1xNPPHHF3+CqXbu2evfurX79+mnZsmX2dS5evFiSVKtWLVksFq1YsULp6ek6c+aM/Pz89PTTT2vYsGGaN2+efvzxR+3YsUNvvPGG5s2bJ0l67LHHdPDgQT3zzDM6cOCAPvjgA82dO9fsl0gSwQsAAABACVSqVEkbNmxQzZo11a1bN9WrV0/9+/fX+fPn7WfARowYoV69eql3796KiYmRn5+f7r333iuud+bMmbrvvvv0xBNPqG7duhowYIDOnj0rSbrhhhs0btw4jRw5UqGhoRo8eLAk6eWXX9bo0aM1adIk1atXT506ddLKlSsVGRkpSapZs6Y+/vhjLVu2TI0aNVJiYqImTpxo4qvzJ4txuTvaUCibzaaAgABlZGRc9lQqAAAArm/nz59XSkqKIiMj5e3t7exyUAJXei+Lkw044wUAAAAAJiN4AQAAAIDJCF4AAAAAYDKCFwAAAACYjOAFAAAAmIRx7Fxfab2HBC8AAACglHl6ekqSzp075+RKUFJZWVmSJHd39xKtx6M0igEAAADwJ3d3dwUGBurYsWOSLvzWlcVicXJVKK68vDylp6erUqVK8vAoWXQieAEAAAAmCAsLkyR7+IJrcnNzU82aNUscnAleAAAAgAksFouqV6+ukJAQZWdnO7scXCMvLy+5uZX8Di2CFwAAAGAid3f3Et8fBNfH4BoAAAAAYDKCFwAAAACYzGWCV25urkaPHq3IyEj5+PgoKipKL7/8ssO4+oZhaMyYMapevbp8fHwUGxurgwcPOqznxIkT6tmzp/z9/RUYGKj+/fvrzJkzZd0dAAAAANcRlwler776qmbOnKk333xT3333nV599VVNnjxZb7zxhr3N5MmTNX36dCUmJmrz5s3y9fVVXFyczp8/b2/Ts2dP7du3T0lJSVqxYoU2bNiggQMHOqNLAAAAAK4TFsNFfk67S5cuCg0N1ezZs+3zunfvLh8fH82fP1+GYSg8PFwjRozQ008/LUnKyMhQaGio5s6dq/j4eH333XeqX7++tm7dqmbNmkmSVq1apbvvvlu//PKLwsPDr1qHzWZTQECAMjIy5O/vb05nAQAAAJR7xckGLnPGq0WLFlqzZo2+//57SdLu3bv11Vdf6a677pIkpaSkKDU1VbGxsfbnBAQEKDo6WsnJyZKk5ORkBQYG2kOXJMXGxsrNzU2bN28udLuZmZmy2WwOEwAAAAAUh8sMJz9y5EjZbDbVrVtX7u7uys3N1YQJE9SzZ09JUmpqqiQpNDTU4XmhoaH2ZampqQoJCXFY7uHhoSpVqtjbXGrSpEkaN25caXcHAAAAwHXEZc54LV68WAsWLNAHH3ygHTt2aN68efq///s/zZs3z9Ttjho1ShkZGfbpyJEjpm4PAAAAQMXjMme8nnnmGY0cOVLx8fGSpIYNG+rnn3/WpEmT1Lt3b4WFhUmS0tLSVL16dfvz0tLS1LhxY0lSWFiYjh075rDenJwcnThxwv78S1mtVlmtVhN6BAAAAOB64TJnvM6dOyc3N8dy3d3dlZeXJ0mKjIxUWFiY1qxZY19us9m0efNmxcTESJJiYmJ06tQpbd++3d5m7dq1ysvLU3R0dBn0AgAAAMD1yGXOeN1zzz2aMGGCatasqQYNGmjnzp365z//qX79+kmSLBaLhg4dqvHjx6tOnTqKjIzU6NGjFR4erq5du0qS6tWrp06dOmnAgAFKTExUdna2Bg8erPj4+CKNaAgAAAAA18Jlgtcbb7yh0aNH64knntCxY8cUHh6uQYMGacyYMfY2zz77rM6ePauBAwfq1KlTatmypVatWiVvb297mwULFmjw4MHq0KGD3Nzc1L17d02fPt0ZXQIAAABwnXCZ3/EqL/gdLwAAAABSBf0dLwAAAABwVQQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMBnBCwAAAABMRvACAAAAAJMRvAAAAADAZAQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMBnBCwAAAABMRvACAAAAAJMRvAAAAADAZAQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMBnBCwAAAABMRvACAAAAAJMRvAAAAADAZAQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAk7lU8Pr111/18MMPq2rVqvLx8VHDhg21bds2+3LDMDRmzBhVr15dPj4+io2N1cGDBx3WceLECfXs2VP+/v4KDAxU//79debMmbLuCgAAAIDriMsEr5MnT+rOO++Up6enPvvsM3377beaOnWqgoKC7G0mT56s6dOnKzExUZs3b5avr6/i4uJ0/vx5e5uePXtq3759SkpK0ooVK7RhwwYNHDjQGV0CAAAAcJ2wGIZhOLuIohg5cqS+/vprffnll4UuNwxD4eHhGjFihJ5++mlJUkZGhkJDQzV37lzFx8fru+++U/369bV161Y1a9ZMkrRq1Srdfffd+uWXXxQeHn7VOmw2mwICApSRkSF/f//S6yAAAAAAl1KcbOAyZ7yWL1+uZs2a6f7771dISIhuu+02vfPOO/blKSkpSk1NVWxsrH1eQECAoqOjlZycLElKTk5WYGCgPXRJUmxsrNzc3LR58+ZCt5uZmSmbzeYwAQAAAEBxuEzw+umnnzRz5kzVqVNHn3/+uR5//HENGTJE8+bNkySlpqZKkkJDQx2eFxoaal+WmpqqkJAQh+UeHh6qUqWKvc2lJk2apICAAPsUERFR2l0DAAAAUMG5TPDKy8tTkyZNNHHiRN12220aOHCgBgwYoMTERFO3O2rUKGVkZNinI0eOmLo9AAAAABWPywSv6tWrq379+g7z6tWrp8OHD0uSwsLCJElpaWkObdLS0uzLwsLCdOzYMYflOTk5OnHihL3NpaxWq/z9/R0mAAAAACgOlwled955pw4cOOAw7/vvv1etWrUkSZGRkQoLC9OaNWvsy202mzZv3qyYmBhJUkxMjE6dOqXt27fb26xdu1Z5eXmKjo4ug14AAAAAuB55OLuAoho2bJhatGihiRMn6oEHHtCWLVs0a9YszZo1S5JksVg0dOhQjR8/XnXq1FFkZKRGjx6t8PBwde3aVdKFM2SdOnWyX6KYnZ2twYMHKz4+vkgjGgIAAADAtXCZ4eQlacWKFRo1apQOHjyoyMhIDR8+XAMGDLAvNwxDL774ombNmqVTp06pZcuWmjFjhv7yl7/Y25w4cUKDBw/WJ598Ijc3N3Xv3l3Tp09X5cqVi1QDw8kDAAAAkIqXDVwqeJUHBC8AAAAAUgX9HS8AAAAAcFUELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMBnBCwAAAABMRvACAAAAAJMRvAAAAADAZAQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMBnBCwAAAABMRvACAAAAAJMRvAAAAADAZAQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMBnBCwAAAABMRvACAAAAAJMRvAAAAADAZAQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMBnBCwAAAABMRvACAAAAAJMRvAAAAADAZC4bvF555RVZLBYNHTrUPu/8+fNKSEhQ1apVVblyZXXv3l1paWkOzzt8+LA6d+6sSpUqKSQkRM8884xycnLKuHoAAAAA1xOXDF5bt27V22+/rVtvvdVh/rBhw/TJJ5/oww8/1Pr163X06FF169bNvjw3N1edO3dWVlaWNm7cqHnz5mnu3LkaM2ZMWXcBAAAAwHXE5YLXmTNn1LNnT73zzjsKCgqyz8/IyNDs2bP1z3/+U+3bt1fTpk01Z84cbdy4UZs2bZIkrV69Wt9++63mz5+vxo0b66677tLLL7+st956S1lZWc7qEgAAAIAKzuWCV0JCgjp37qzY2FiH+du3b1d2drbD/Lp166pmzZpKTk6WJCUnJ6thw4YKDQ21t4mLi5PNZtO+ffsK3V5mZqZsNpvDBAAAAADF4eHsAopj4cKF2rFjh7Zu3VpgWWpqqry8vBQYGOgwPzQ0VKmpqfY2F4eu/OX5ywozadIkjRs3rhSqBwAAAHC9cpkzXkeOHNFTTz2lBQsWyNvbu8y2O2rUKGVkZNinI0eOlNm2AQAAAFQMLhO8tm/frmPHjqlJkyby8PCQh4eH1q9fr+nTp8vDw0OhoaHKysrSqVOnHJ6XlpamsLAwSVJYWFiBUQ7zH+e3uZTVapW/v7/DBAAAAADF4TLBq0OHDtqzZ4927dpln5o1a6aePXva/9/T01Nr1qyxP+fAgQM6fPiwYmJiJEkxMTHas2ePjh07Zm+TlJQkf39/1a9fv8z7BAAAAOD64DL3ePn5+emWW25xmOfr66uqVava5/fv31/Dhw9XlSpV5O/vryeffFIxMTFq3ry5JKljx46qX7++evXqpcmTJys1NVUvvPCCEhISZLVay7xPAAAAAK4PLhO8iuK1116Tm5ubunfvrszMTMXFxWnGjBn25e7u7lqxYoUef/xxxcTEyNfXV71799ZLL73kxKoBAAAAVHQWwzAMZxfhSmw2mwICApSRkcH9XgAAAMB1rDjZwGXu8QIAAAAAV0XwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMBnBCwAAAABMRvACAAAAAJMRvAAAAADAZAQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMBnBCwAAAABMRvACAAAAAJMRvAAAAADAZAQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMBnBCwAAAABMRvACAAAAAJMRvAAAAADAZAQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMJnLBK9Jkybp9ttvl5+fn0JCQtS1a1cdOHDAoc358+eVkJCgqlWrqnLlyurevbvS0tIc2hw+fFidO3dWpUqVFBISomeeeUY5OTll2RUAAAAA1xmXCV7r169XQkKCNm3apKSkJGVnZ6tjx446e/asvc2wYcP0ySef6MMPP9T69et19OhRdevWzb48NzdXnTt3VlZWljZu3Kh58+Zp7ty5GjNmjDO6BAAAAOA6YTEMw3B2EdciPT1dISEhWr9+vVq3bq2MjAwFBwfrgw8+0H333SdJ2r9/v+rVq6fk5GQ1b95cn332mbp06aKjR48qNDRUkpSYmKjnnntO6enp8vLyuup2bTabAgIClJGRIX9/f1P7CAAAAKD8Kk42cJkzXpfKyMiQJFWpUkWStH37dmVnZys2Ntbepm7duqpZs6aSk5MlScnJyWrYsKE9dElSXFycbDab9u3bV+h2MjMzZbPZHCYAAAAAKI5iB6/evXtrw4YNZtRSZHl5eRo6dKjuvPNO3XLLLZKk1NRUeXl5KTAw0KFtaGioUlNT7W0uDl35y/OXFWbSpEkKCAiwTxEREaXcGwAAAAAVXbGDV0ZGhmJjY1WnTh1NnDhRv/76qxl1XVFCQoL27t2rhQsXmr6tUaNGKSMjwz4dOXLE9G0CAAAAqFiKHbyWLVumX3/9VY8//rgWLVqk2rVr66677tJHH32k7OxsM2p0MHjwYK1YsUJffPGFatSoYZ8fFhamrKwsnTp1yqF9WlqawsLC7G0uHeUw/3F+m0tZrVb5+/s7TAAAAABQHNd0j1dwcLCGDx+u3bt3a/PmzbrpppvUq1cvhYeHa9iwYTp48GBp1ynDMDR48GAtXbpUa9euVWRkpMPypk2bytPTU2vWrLHPO3DggA4fPqyYmBhJUkxMjPbs2aNjx47Z2yQlJcnf31/169cv9ZoBAAAAQCrh4Bq//fabkpKSlJSUJHd3d919993as2eP6tevr9dee620apR04fLC+fPn64MPPpCfn59SU1OVmpqqP/74Q5IUEBCg/v37a/jw4friiy+0fft29e3bVzExMWrevLkkqWPHjqpfv7569eql3bt36/PPP9cLL7yghIQEWa3WUq0XAAAAAPIVezj57OxsLV++XHPmzNHq1at166236tFHH9VDDz1kvwxv6dKl6tevn06ePFl6hVoshc6fM2eO+vTpI+nCDyiPGDFC//73v5WZmam4uDjNmDHD4TLCn3/+WY8//rjWrVsnX19f9e7dW6+88oo8PDyKVAfDyQMAAACQipcNih28qlWrpry8PPXo0UMDBgxQ48aNC7Q5deqUbrvtNqWkpBSrcFdA8AIAAAAgFS8bFO00z0Vee+013X///fL29r5sm8DAwAoZugAAAADgWhQ7ePXq1cuMOgAAAACgwirR4BoAAAAAgKsjeAEAAACAyQheAAAAAGAyghcAAAAAmIzgBQAAAAAmI3gBAAAAgMkIXgAAAABgMoIXAAAAAJiM4AUAAAAAJiN4AQAAAIDJCF4AAAAAYDKCFwAAAACYjOAFAAAAACYjeAEAAACAyQheAAAAAGAyghcAAAAAmIzgBQAAAAAmI3gBAAAAgMkIXgAAAABgMoIXAAAAAJiM4AUAAAAAJiN4AQAAAIDJCF4AAAAAYDKCFwAAAACYjOAFAAAAACYjeAEAAACAyQheAAAAAGAyghcAAAAAmIzgBQAAAAAmI3gBAAAAgMkIXgAAAABgMoIXAAAAAJiM4AUAAAAAJiN4AQAAAIDJCF4AAAAAYDKCFwAAAACYzMPZBQAAAACoOAzDUHZetjJzMpWVm+UwZeYWnJeVm1XkthlnM5VxJktnz2epW50e+lvD9goKcnaPi4bgBQAAgGty8qSUliZlZEiBgVJIiFzmIPhqtZfXvuUZeVcNK8dPZenY8SydOp0lT59MVfLLkpd3McNPXvEC0cXtsvOyy+S1WP5OQ3UMaK9335UiIspkkyVC8AIAAECxHTkiPfqotHr1n/M6dlSpHgSbFX4uV/vbs3IVEp6pn37O0uChWVr/ZZbkfmGKaZmpf4zOUlC1yweQooSUq4WWq60718gt+QtQUbhnavXqC+/lwoXlIxhficUwDMPZRbgSm82mgIAAZWRkyN/f39nlAHBx5fUb1bJQnvvuqt+Em+167TcKOnlSio93DC75OnYseBBsGIZy8nKKFT6OpmVp+oxMfbPvz/Bzc4NMdX8gS5UqXyXIXCEQnc/O0i+/ZemPrEz7eu2TW17ZvYguzMvdyz5Z3a0Oj73cvWT1KGReIe2K8/y0o1Y92sdLyr1oOh0u/VFVkvTdd1LdumX/WhQnG3DGCygGVz7ocHbtzt6+s1yp36XxbbGrvq5l8U35tbpabeW5djNdr/12BYZhFH7Z2DWeWblcu6y8P+efyMjS+tAsqU+W5O4YYFZ7ZOrmWVnKszg+39A1fNd/8/+m/zkgaeLWUnjRvP83lTMWWYoUWi7X5txpL3248H+hJMcq5Xle+O//gsqLL1hV84ZrDz9Wd6s83DxksVjK/LXZfFLSz5dfnpFRZqVcM854FRNnvC5w1YM96dprd+WDDmfX7uztO8uV+l25cvG+LS7u+svz61rcb8rL0tVqmztX6tOnfNZupvL8npkt/36akgSWIrXLu/bnltX9NK7O3eIuL3cveciq06cuOXOS+2c4ue1WL+3cdsnyi8JLjwe9VLvGtYejKwUcd4t7iULN5s1S8+aXX75pkxQdfc2rd6r9+6V69S6/3BXOeBG8iong5boHe9K11+7KBx3Ort3Z23eWq/X7tdekBg0u//yr/QPiyq9ref3HU7p6bbt3S40aXX65M2s3kxnvmWEYyjVyS+VMzBXb5ZUsFHE/TRHluf8vnFwIKNVDvFTJWrTwUVgYSU+16p3Ey4efKa966db6RQ8/nm6ecndzl1Sx/87L8+drSZXXf/e41LAI3nrrLU2ZMkWpqalq1KiR3njjDd1xxx3OLqvcO3myYHCR5BI3Npak9rS0wv/Q85+fllZ+++3s2p29fWe5Wr9PnLjy8692yYQrv65X65szLxcpaW3l5VKX/KGcr3jZWDGCzQ8/Z0rtLrkX5qLLyx5fl6XKO4sfbK7p0rPrUGncS1Oi5xbS7txpLw3qb9XaJK8Ll7MZf/40bGkcBO/fL73z1eWXd6kj1Y26tnWHhl6o8XIH8MHBV14eGnpt2y0LV+tbea79aoKCLnxRfrkv0Mvrv3kXuy6D16JFizR8+HAlJiYqOjpa06ZNU1xcnA4cOKCQkBBnl1euufLBXklqd5WDrcI4u3Znb99ZrtavypWvvDwgoGTrL8+v69X6drXlZipSbZa8gjfk/y+EpBlZ2n60lM7W5F37c0259KzN5RetS5OUVvqbNNul99OUp2CT385Z99NclZ80d5Z5B8FmBoirHcBXr+66B/gVIZxcSUTEhVCff8tIQMCFfcFV+nVdBq9//vOfGjBggPr27StJSkxM1MqVK/Xee+9p5MiRTq6ufHPlg72S1F6eDxSvxtm1O3v7znK1fvn5leygwpVf1ysdUP21Y678q2Yp43zpDLt81ZByybxzmVnyeyZTp88VDFVuXllquixLejHnsn37++cmvnAuxsPNo3TDiQnBJv/SM1wbMw+CzQ4QV6vdlQ/wXbn2oggKct2+XHfBKysrS9u3b9eoUaPs89zc3BQbG6vk5OQC7TMzM5WZmWl/bLPZyqTO8sqVD/ZKUrsrn7p3du3O3r6zXK3fVaqU7KCiuK/rpUM5l+o9NMUMN1m5WTrbMVPWplnKzLko4HhkKsmSpxtmFP/1LlW+/5sukScpr5xcGXdpiLhc4Cg0dLhdW2DJOOGlaVO9tG3TnyOl3dncS69O8lJkhONzL76fBhWbmQfBZgeIq9Xuygf4rlx7RXbdBa/jx48rNzdXoZcclYSGhmr//v0F2k+aNEnjxo0rq/LKPVc+iC5J7a586t7ZtTt7+2Xh4vtp7GHEkqXnp2bpzAtZ2rjlz3thmt6RpR5DM7Uu9UIAuffFLLUckinbuSx5eGXJ2zdL81KylHnw6gHmj/uyFNQgUydtf4aXSv5Z+jY4Uw3eK3g/T7m7n8b6v6mcsbpb5enmJQ+LlzwtVnl5eMnH00veXn+GEzfDS0aOlyx5VlWyesmvkpcq+1x7qClOO083T6ddenb3rIr7LTrKJwIEKpLrblTDo0eP6oYbbtDGjRsVExNjn//ss89q/fr12rx5s0P7ws54RUREMKrhdTaqYb6Lh6J3tYMOZ9d+rdvPH8q5OGdYrumsTQlGPmMo56Jxs7gVCBae7p6yulsLhAuzLzW7XJuSDuUMALi+MKrhFVSrVk3u7u5KS3O8CzgtLU1hYWEF2lutVlmt5fArWSdy5WuHS1q7K3/zdmntFy49u/JQzs4eICArl6Gciyr/fprydg/NxY+59AwAcD277oKXl5eXmjZtqjVr1qhr166SpLy8PK1Zs0aDBw92bnEupCIFEDNcfD9NUYLLNQ8gUMKBB8rdpWflVGkED083T1k9rFf8jZnClhdp3e6ecrO4Xb0jAADAaa674CVJw4cPV+/evdWsWTPdcccdmjZtms6ePWsf5RDlW56Rp+zc7FIJIMVqV8zn4uouHcq5pGddzDhr48z7aQAAQMVxXQavBx98UOnp6RozZoxSU1PVuHFjrVq1qsCAG9ej3IsuPSuvoSYn7/JDOeNP7hb3QoNEYaGjrC5Fyz+rkz/Pw+26/AgCAADXoetucI2SKs4NdGab/PVknTp/yjGg5BX/crOLJ+6nKRoPN49rCi9WD6u83K7tpv/ihCeGcgYAADAfg2tcJ6Ztmqbfzvzm7DJKXVGDh1lnZa7WjvtpAAAAUFwELxdm9SjeaIv599OU1qhnlxsGuiShxsPNg/tpAAAAUOEQvFzYv7r+Szl5OUUON1x6BgAAADgHwcuFtarVytklAAAAACgCblQBAAAAAJMRvAAAAADAZAQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMBnBCwAAAABMRvACAAAAAJMRvAAAAADAZAQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMBnBCwAAAABMRvACAAAAAJMRvAAAAADAZAQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMBnBCwAAAABMRvACAAAAAJMRvAAAAADAZAQvAAAAADAZwQsAAAAATOYSwevQoUPq37+/IiMj5ePjo6ioKL344ovKyspyaPfNN9+oVatW8vb2VkREhCZPnlxgXR9++KHq1q0rb29vNWzYUJ9++mlZdQMAAADAdcolgtf+/fuVl5ent99+W/v27dNrr72mxMRE/eMf/7C3sdls6tixo2rVqqXt27drypQpGjt2rGbNmmVvs3HjRvXo0UP9+/fXzp071bVrV3Xt2lV79+51RrcAAAAAXCcshmEYzi7iWkyZMkUzZ87UTz/9JEmaOXOmnn/+eaWmpsrLy0uSNHLkSC1btkz79++XJD344IM6e/asVqxYYV9P8+bN1bhxYyUmJhZpuzabTQEBAcrIyJC/v38p9woAAACAqyhONnCJM16FycjIUJUqVeyPk5OT1bp1a3vokqS4uDgdOHBAJ0+etLeJjY11WE9cXJySk5Mvu53MzEzZbDaHCQAAAACKwyWD1w8//KA33nhDgwYNss9LTU1VaGioQ7v8x6mpqVdsk7+8MJMmTVJAQIB9ioiIKK1uAAAAALhOODV4jRw5UhaL5YpT/mWC+X799Vd16tRJ999/vwYMGGB6jaNGjVJGRoZ9OnLkiOnbBAAAAFCxeDhz4yNGjFCfPn2u2ObGG2+0///Ro0fVrl07tWjRwmHQDEkKCwtTWlqaw7z8x2FhYVdsk7+8MFarVVar9ap9AQAAAIDLcWrwCg4OVnBwcJHa/vrrr2rXrp2aNm2qOXPmyM3N8WRdTEyMnn/+eWVnZ8vT01OSlJSUpJtvvllBQUH2NmvWrNHQoUPtz0tKSlJMTEzpdAgAAAAACuES93j9+uuvatu2rWrWrKn/+7//U3p6ulJTUx3uzXrooYfk5eWl/v37a9++fVq0aJFef/11DR8+3N7mqaee0qpVqzR16lTt379fY8eO1bZt2zR48GBndAsAAADAdcKpZ7yKKikpST/88IN++OEH1ahRw2FZ/mj4AQEBWr16tRISEtS0aVNVq1ZNY8aM0cCBA+1tW7RooQ8++EAvvPCC/vGPf6hOnTpatmyZbrnlljLtDwAAAIDri8v+jpez8DteAAAAAKTr5He8AAAAAMBVELwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMBnBCwAAAABMRvACAAAAAJMRvAAAAADAZAQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMBnBCwAAAABMRvACAAAAAJMRvAAAAADAZAQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMBnBCwAAAABMRvACAAAAAJMRvAAAAADAZAQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMBnBCwAAAABMRvACAAAAAJO5XPDKzMxU48aNZbFYtGvXLodl33zzjVq1aiVvb29FRERo8uTJBZ7/4Ycfqm7duvL29lbDhg316aefllHlAAAAAK5XLhe8nn32WYWHhxeYb7PZ1LFjR9WqVUvbt2/XlClTNHbsWM2aNcveZuPGjerRo4f69++vnTt3qmvXruratav27t1bll0AAAAAcJ2xGIZhOLuIovrss880fPhwffzxx2rQoIF27typxo0bS5Jmzpyp559/XqmpqfLy8pIkjRw5UsuWLdP+/fslSQ8++KDOnj2rFStW2NfZvHlzNW7cWImJiUWqwWazKSAgQBkZGfL39y/dDgIAAABwGcXJBi5zxistLU0DBgzQ+++/r0qVKhVYnpycrNatW9tDlyTFxcXpwIEDOnnypL1NbGysw/Pi4uKUnJx82e1mZmbKZrM5TAAAAABQHC4RvAzDUJ8+ffTYY4+pWbNmhbZJTU1VaGiow7z8x6mpqVdsk7+8MJMmTVJAQIB9ioiIKElXAAAAAFyHnBq8Ro4cKYvFcsVp//79euONN3T69GmNGjWqzGscNWqUMjIy7NORI0fKvAYAAAAArs3DmRsfMWKE+vTpc8U2N954o9auXavk5GRZrVaHZc2aNVPPnj01b948hYWFKS0tzWF5/uOwsDD7fwtrk7+8MFartcB2AQAAAKA4nBq8goODFRwcfNV206dP1/jx4+2Pjx49qri4OC1atEjR0dGSpJiYGD3//PPKzs6Wp6enJCkpKUk333yzgoKC7G3WrFmjoUOH2teVlJSkmJiYUuwVAAAAADhyavAqqpo1azo8rly5siQpKipKNWrUkCQ99NBDGjdunPr376/nnntOe/fu1euvv67XXnvN/rynnnpKbdq00dSpU9W5c2ctXLhQ27ZtcxhyHgAAAABKm0sMrlEUAQEBWr16tVJSUtS0aVONGDFCY8aM0cCBA+1tWrRooQ8++ECzZs1So0aN9NFHH2nZsmW65ZZbnFg5AAAAgIrOpX7Hqzzgd7wAAAAASBX0d7wAAAAAwFURvAAAAADAZAQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMBnBCwAAAABMRvACAAAAAJMRvAAAAADAZAQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMBnBCwAAAABMRvACAAAAAJMRvAAAAADAZAQvAAAAADAZwQsAAAAATEbwAgAAAACTEbwAAAAAwGQELwAAAAAwGcELAAAAAExG8AIAAAAAkxG8AAAAAMBkBC8AAAAAMJmHswsAAAAArjcnT0ppaVJGhhQYKIWESEFBzq4KZuKMFwAAAFCGjhyR4uOlevWk5s2lunUvPD5yxNmVwUwELwAAAKCMnDwpPfqotHq14/zVqy/MP3nSOXXBfAQvAAAAoIykpRUMXflWr76wHBUTwQsAAAAoIxkZJVsO10XwAgAAAMpIQEDJlsN1EbwAAACAMhIaKnXsWPiyjh0vLEfFRPACAAAAykhQkPTuuwXDV8eOF+YzpHzFxe94AQAAAGUoIkJauPDP3/EKCLhwpovQVbG51BmvlStXKjo6Wj4+PgoKClLXrl0dlh8+fFidO3dWpUqVFBISomeeeUY5OTkObdatW6cmTZrIarXqpptu0ty5c8uuAwAAAIAuhKy6daXo6Av/JXRVfC5zxuvjjz/WgAEDNHHiRLVv3145OTnau3evfXlubq46d+6ssLAwbdy4Ub/99pseeeQReXp6auLEiZKklJQUde7cWY899pgWLFigNWvW6NFHH1X16tUVFxfnrK4BAAAAqOAshmEYzi7ianJyclS7dm2NGzdO/fv3L7TNZ599pi5duujo0aMK/d9diYmJiXruueeUnp4uLy8vPffcc1q5cqVDYIuPj9epU6e0atWqItVis9kUEBCgjIwM+fv7l7xzAAAAAFxScbKBS1xquGPHDv36669yc3PTbbfdpurVq+uuu+5yCFDJyclq2LChPXRJUlxcnGw2m/bt22dvExsb67DuuLg4JScnX3bbmZmZstlsDhMAAAAAFIdLBK+ffvpJkjR27Fi98MILWrFihYKCgtS2bVudOHFCkpSamuoQuiTZH6empl6xjc1m0x9//FHotidNmqSAgAD7FBERUap9AwAAAFDxOTV4jRw5UhaL5YrT/v37lZeXJ0l6/vnn1b17dzVt2lRz5syRxWLRhx9+aGqNo0aNUkZGhn06cuSIqdsDAAAAUPE4dXCNESNGqE+fPldsc+ONN+q3336TJNWvX98+32q16sYbb9Thw4clSWFhYdqyZYvDc9PS0uzL8v+bP+/iNv7+/vLx8Sl0+1arVVarteidAgAAAIBLODV4BQcHKzg4+KrtmjZtKqvVqgMHDqhly5aSpOzsbB06dEi1atWSJMXExGjChAk6duyYQkJCJElJSUny9/e3B7aYmBh9+umnDutOSkpSTExMaXYLAAAAABy4xD1e/v7+euyxx/Tiiy9q9erVOnDggB5//HFJ0v333y9J6tixo+rXr69evXpp9+7d+vzzz/XCCy8oISHBfsbqscce008//aRnn31W+/fv14wZM7R48WINGzbMaX0DAAAAUPG5zO94TZkyRR4eHurVq5f++OMPRUdHa+3atQr636/Nubu7a8WKFXr88ccVExMjX19f9e7dWy+99JJ9HZGRkVq5cqWGDRum119/XTVq1NC7777Lb3gBAAAAMJVL/I5XecLveAEAAACQKuDveAEAAACAKyN4AQAAAIDJCF4AAAAAYDKCFwAAAACYzGVGNSwv8scisdlsTq4EAAAAgDPlZ4KijFdI8Cqm06dPS5IiIiKcXAkAAACA8uD06dMKCAi4YhuGky+mvLw8HT16VH5+frJYLM4uRzabTRERETpy5AjD28NU7GsoK+xrKCvsaygr7GsVl2EYOn36tMLDw+XmduW7uDjjVUxubm6qUaOGs8sowN/fnz9klAn2NZQV9jWUFfY1lBX2tYrpame68jG4BgAAAACYjOAFAAAAACYjeLk4q9WqF198UVar1dmloIJjX0NZYV9DWWFfQ1lhX4PE4BoAAAAAYDrOeAEAAACAyQheAAAAAGAyghcAAAAAmIzgBQAAAAAmI3i5sLfeeku1a9eWt7e3oqOjtWXLFmeXhApm0qRJuv322+Xn56eQkBB17dpVBw4ccHZZuA688sorslgsGjp0qLNLQQX066+/6uGHH1bVqlXl4+Ojhg0batu2bc4uCxVMbm6uRo8ercjISPn4+CgqKkovv/yyGNfu+kXwclGLFi3S8OHD9eKLL2rHjh1q1KiR4uLidOzYMWeXhgpk/fr1SkhI0KZNm5SUlKTs7Gx17NhRZ8+edXZpqMC2bt2qt99+W7feequzS0EFdPLkSd15553y9PTUZ599pm+//VZTp05VUFCQs0tDBfPqq69q5syZevPNN/Xdd9/p1Vdf1eTJk/XGG284uzQ4CcPJu6jo6GjdfvvtevPNNyVJeXl5ioiI0JNPPqmRI0c6uTpUVOnp6QoJCdH69evVunVrZ5eDCujMmTNq0qSJZsyYofHjx6tx48aaNm2as8tCBTJy5Eh9/fXX+vLLL51dCiq4Ll26KDQ0VLNnz7bP6969u3x8fDR//nwnVgZn4YyXC8rKytL27dsVGxtrn+fm5qbY2FglJyc7sTJUdBkZGZKkKlWqOLkSVFQJCQnq3Lmzw+cbUJqWL1+uZs2a6f7771dISIhuu+02vfPOO84uCxVQixYttGbNGn3//feSpN27d+urr77SXXfd5eTK4Cwezi4AxXf8+HHl5uYqNDTUYX5oaKj279/vpKpQ0eXl5Wno0KG68847dcsttzi7HFRACxcu1I4dO7R161Znl4IK7KefftLMmTM1fPhw/eMf/9DWrVs1ZMgQeXl5qXfv3s4uDxXIyJEjZbPZVLduXbm7uys3N1cTJkxQz549nV0anITgBaBIEhIStHfvXn311VfOLgUV0JEjR/TUU08pKSlJ3t7ezi4HFVheXp6aNWumiRMnSpJuu+027d27V4mJiQQvlKrFixdrwYIF+uCDD9SgQQPt2rVLQ4cOVXh4OPvadYrg5YKqVasmd3d3paWlOcxPS0tTWFiYk6pCRTZ48GCtWLFCGzZsUI0aNZxdDiqg7du369ixY2rSpIl9Xm5urjZs2KA333xTmZmZcnd3d2KFqCiqV6+u+vXrO8yrV6+ePv74YydVhIrqmWee0ciRIxUfHy9JatiwoX7++WdNmjSJ4HWd4h4vF+Tl5aWmTZtqzZo19nl5eXlas2aNYmJinFgZKhrDMDR48GAtXbpUa9euVWRkpLNLQgXVoUMH7dmzR7t27bJPzZo1U8+ePbVr1y5CF0rNnXfeWeBnMb7//nvVqlXLSRWhojp37pzc3BwPtd3d3ZWXl+ekiuBsnPFyUcOHD1fv3r3VrFkz3XHHHZo2bZrOnj2rvn37Ors0VCAJCQn64IMP9J///Ed+fn5KTU2VJAUEBMjHx8fJ1aEi8fPzK3DvoK+vr6pWrco9hShVw4YNU4sWLTRx4kQ98MAD2rJli2bNmqVZs2Y5uzRUMPfcc48mTJigmjVrqkGDBtq5c6f++c9/ql+/fs4uDU7CcPIu7M0339SUKVOUmpqqxo0ba/r06YqOjnZ2WahALBZLofPnzJmjPn36lG0xuO60bduW4eRhihUrVmjUqFE6ePCgIiMjNXz4cA0YMMDZZaGCOX36tEaPHq2lS5fq2LFjCg8PV48ePTRmzBh5eXk5uzw4AcELAAAAAEzGPV4AAAAAYDKCFwAAAACYjOAFAAAAACYjeAEAAACAyQheAAAAAGAyghcAAAAAmIzgBQAAAAAmI3gBAAAAgMkIXgAAAABgMoIXAABFkJubqxYtWqhbt24O8zMyMhQREaHnn3/eSZUBAFyBxTAMw9lFAADgCr7//ns1btxY77zzjnr27ClJeuSRR7R7925t3bpVXl5eTq4QAFBeEbwAACiG6dOna+zYsdq3b5+2bNmi+++/X1u3blWjRo2cXRoAoBwjeAEAUAyGYah9+/Zyd3fXnj179OSTT+qFF15wdlkAgHKO4AUAQDHt379f9erVU8OGDbVjxw55eHg4uyQAQDnH4BoAABTTe++9p0qVKiklJUW//PKLs8sBALgAzngBAFAMGzduVJs2bbR69WqNHz9ekvTf//5XFovFyZUBAMozzngBAFBE586dU58+ffT444+rXbt2mj17trZs2aLExERnlwYAKOc44wUAQBE99dRT+vTTT7V7925VqlRJkvT222/r6aef1p49e1S7dm3nFggAKLcIXgAAFMH69evVoUMHrVu3Ti1btnRYFhcXp5ycHC45BABcFsELAAAAAEzGPV4AAAAAYDKCFwAAAACYjOAFAAAAACYjeAEAAACAyQheAAAAAGAyghcAAAAAmIzgBQAAAAAmI3gBAAAAgMkIXgAAAABgMoIXAAAAAJiM4AUAAAAAJvt/cZSbHnwHXqYAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "y_pred = model.predict(X_test)\n",
-    "\n",
-    "mae = mean_absolute_error(y_test, y_pred)\n",
-    "mse = mean_squared_error(y_test, y_pred)\n",
-    "rmse = np.sqrt(mse)\n",
-    "r2 = model.score(X_test, y_test)\n",
-    "\n",
-    "print(f\"Mean Absolute Error (MAE): {mae:.2f}\")\n",
-    "print(f\"Mean Squared Error (MSE): {mse:.2f}\")\n",
-    "print(f\"Root Mean Squared Error (RMSE): {rmse:.2f}\")\n",
-    "print(f\"R-squared (R^2): {r2:.2f}\")\n",
-    "\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.scatterplot(x=X_test.squeeze(), y=y_test, color='blue', label='Actual')\n",
-    "sns.lineplot(x=X_test.squeeze(), y=y_pred, color='green', label='Predicted', linewidth=2)\n",
-    "plt.xlabel('X')\n",
-    "plt.ylabel('y')\n",
-    "plt.title('Traditional Linear Regression - Actual vs Predicted')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "451f1c38",
-   "metadata": {},
-   "source": [
-    "-   Fit a robust regression model using Huber regression"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "2e29a3a6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Intercept (β0): 5.00\n",
-      "Slope (β1): 2.03\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import HuberRegressor\n",
-    "robust_model = HuberRegressor()\n",
-    "robust_model.fit(X_train, y_train)\n",
-    "\n",
-    "print(f\"Intercept (β0): {robust_model.intercept_:.2f}\")\n",
-    "print(f\"Slope (β1): {robust_model.coef_[0]:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba356dbc",
-   "metadata": {},
-   "source": [
-    "-   Make predictions and evaluate the robust regression model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "1db1c49a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Absolute Error (MAE): 77.33\n",
-      "Mean Squared Error (MSE): 61779.20\n",
-      "Root Mean Squared Error (RMSE): 248.55\n",
-      "R-squared (R^2): -0.03\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "y_pred_robust = robust_model.predict(X_test)\n",
-    "\n",
-    "mae_robust = mean_absolute_error(y_test, y_pred_robust)\n",
-    "mse_robust = mean_squared_error(y_test, y_pred_robust)\n",
-    "rmse_robust = np.sqrt(mse_robust)\n",
-    "r2_robust = robust_model.score(X_test, y_test)\n",
-    "\n",
-    "print(f\"Mean Absolute Error (MAE): {mae_robust:.2f}\")\n",
-    "print(f\"Mean Squared Error (MSE): {mse_robust:.2f}\")\n",
-    "print(f\"Root Mean Squared Error (RMSE): {rmse_robust:.2f}\")\n",
-    "print(f\"R-squared (R^2): {r2_robust:.2f}\")\n",
-    "\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.scatterplot(x=X_test.squeeze(), y=y_test, color='blue', label='Actual')\n",
-    "sns.lineplot(x=X_test.squeeze(), y=y_pred_robust, color='red', label='Predicted (Robust)', linewidth=2)\n",
-    "plt.xlabel('X')\n",
-    "plt.ylabel('y')\n",
-    "plt.title('Robust Regression - Actual vs Predicted')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ac52ce78",
-   "metadata": {},
-   "source": [
-    "-   Compare the performance of the robust regression model with the traditional linear regression model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "925a9f8c",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot the results for comparison\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.scatterplot(x=X_test.squeeze(), y=y_test, color='blue', label='Actual')\n",
-    "sns.lineplot(x=X_test.squeeze(), y=y_pred, color='green', label='Predicted (Traditional)', linewidth=2)\n",
-    "sns.lineplot(x=X_test.squeeze(), y=y_pred_robust, color='red', label='Predicted (Robust)', linewidth=2)\n",
-    "plt.xlabel('X')\n",
-    "plt.ylabel('y')\n",
-    "plt.xlim(0, 10)\n",
-    "plt.ylim(0, 20)\n",
-    "plt.title('Comparison of Traditional and Robust Regression')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2fd57c26",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "Create a model using RANSAC (Random Sample Consensus) robust regression method on the same dataset. Compare its performance with the traditional and Huber regression models.\n",
-    "\n",
-    "-   Fit a RANSAC regression model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "5b414114",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Intercept (β0): 5.16\n",
-      "Slope (β1): 2.00\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import RANSACRegressor\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "\n",
-    "ransac_model = RANSACRegressor()\n",
-    "ransac_model.fit(X_train, y_train)\n",
-    "\n",
-    "print(f\"Intercept (β0): {ransac_model.estimator_.intercept_:.2f}\")\n",
-    "print(f\"Slope (β1): {ransac_model.estimator_.coef_[0]:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b058bb28",
-   "metadata": {},
-   "source": [
-    "-   Make predictions and evaluate the RANSAC regression model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "041a7b2a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Absolute Error (MAE): 77.29\n",
-      "Mean Squared Error (MSE): 61762.22\n",
-      "Root Mean Squared Error (RMSE): 248.52\n",
-      "R-squared (R^2): -0.03\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "y_pred_ransac = ransac_model.predict(X_test)\n",
-    "\n",
-    "mae_ransac = mean_absolute_error(y_test, y_pred_ransac)\n",
-    "mse_ransac = mean_squared_error(y_test, y_pred_ransac)\n",
-    "rmse_ransac = np.sqrt(mse_ransac)\n",
-    "r2_ransac = ransac_model.score(X_test, y_test)\n",
-    "\n",
-    "print(f\"Mean Absolute Error (MAE): {mae_ransac:.2f}\")\n",
-    "print(f\"Mean Squared Error (MSE): {mse_ransac:.2f}\")\n",
-    "print(f\"Root Mean Squared Error (RMSE): {rmse_ransac:.2f}\")\n",
-    "print(f\"R-squared (R^2): {r2_ransac:.2f}\")\n",
-    "\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.scatterplot(x=X_test.squeeze(), y=y_test, color='blue', label='Actual')\n",
-    "sns.lineplot(x=X_test.squeeze(), y=y_pred_ransac, color='purple', label='Predicted (RANSAC)', linewidth=2)\n",
-    "plt.xlabel('X')\n",
-    "plt.ylabel('y')\n",
-    "plt.title('RANSAC Regression - Actual vs Predicted')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5afb7f2f",
-   "metadata": {},
-   "source": [
-    "-   Compare the performance of the RANSAC regression model with the traditional and Huber robust regression models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "c527ab88",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot the results for comparison\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.scatterplot(x=X_test.squeeze(), y=y_test, color='blue', label='Actual')\n",
-    "sns.lineplot(x=X_test.squeeze(), y=y_pred, color='green', label='Predicted (Traditional)', linewidth=2)\n",
-    "sns.lineplot(x=X_test.squeeze(), y=y_pred_robust, color='red', label='Predicted (Robust)', linewidth=2)\n",
-    "sns.lineplot(x=X_test.squeeze(), y=y_pred_ransac, color='purple', label='Predicted (RANSAC)', linewidth=2)\n",
-    "plt.xlabel('X')\n",
-    "plt.ylabel('y')\n",
-    "plt.xlim(0, 10)\n",
-    "plt.ylim(0, 20)\n",
-    "plt.title('Comparison of Traditional, Robust, and RANSAC Regression')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4c88a703",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "In this section, we introduced robust regression, a technique designed to be less sensitive to outliers in the data. We demonstrated how to implement robust regression using the Huber regression method from the `scikit-learn` library and evaluated its performance on a synthetic dataset with outliers.\n",
-    "\n",
-    "Robust regression techniques are essential for ensuring reliable estimates in the presence of outliers or violations of the assumptions of linear regression."
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C03 Evaluation Metrics for Regression.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C03 Evaluation Metrics for Regression.ipynb
deleted file mode 100644
index 7e5e045..0000000
--- a/src/S02 Supervised Learning - Regression and Evaluation/S02C03 Evaluation Metrics for Regression.ipynb	
+++ /dev/null
@@ -1,1902 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e0e7c898",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Evaluation Metrics for Regression\n",
-    "\n",
-    "We have, so far, used some common metrics to evaluate our regression models, but without a clear understanding of what they mean or how to interpret them. In this section, we will correct that omission and learn how to compute and interpret the most common evaluation metrics for regression models.\n",
-    "\n",
-    "**Evaluation Metrics** are essential for understanding how well a regression model performs. They help us quantify the difference between predicted and actual values, guiding model selection and improvement."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1eee141c",
-   "metadata": {},
-   "source": [
-    "## Common Regression Evaluation Metrics\n",
-    "\n",
-    "The most common regression evaluation metrics include:\n",
-    "\n",
-    "-   **Mean Absolute Error (MAE)**: Measures the average absolute difference between predicted and actual values.$$MAE = \\frac{1}{n} \\sum |y_i - \\hat{y}_i|$$\n",
-    "    -   Easy to interpret (same units as target variable)\n",
-    "    -   Robust to outliers\n",
-    "\n",
-    "-   **Mean Squared Error (MSE)**: Measures the average of the squares of the errors, penalizing larger errors more heavily.$$MSE = \\frac{1}{n} \\sum (y_i - \\hat{y}_i)^2$$\n",
-    "    -   Penalizes large errors more heavily\n",
-    "\n",
-    "-   **Root Mean Squared Error (RMSE)**: The square root of MSE, providing an error metric in the same units as the target variable.$$RMSE = \\sqrt{MSE}$$\n",
-    "    -   Same units as the target, easier to interpret than MSE\n",
-    "\n",
-    "-   **$R^2$ Score (Coefficient of Determination)**: Represents the proportion of variance explained by the model.$$R^2 = 1 - \\frac{SS_{res}}{SS_{tot}}$$\n",
-    "    -   Proportion of variance explained by the model\n",
-    "    -   Can be misleading if used alone\n",
-    "\n",
-    "-   **Metric choice depends on research goals**:\n",
-    "    -   MAE: if every error counts equally\n",
-    "    -   RMSE: if large errors are worse\n",
-    "    -   $R^2$: for model comparison, not error interpretation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "423758f2",
-   "metadata": {},
-   "source": [
-    "## Practical Demonstration\n",
-    "\n",
-    "For the practical demonstration, we will use the California Housing dataset, for which we want to predict the median house value based on various features. We will fit a linear regression model to this dataset and compute the evaluation metrics discussed above.\n",
-    "\n",
-    "-   Load the California Housing dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "dd8deb5a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 20640 entries, 0 to 20639\n",
-      "Data columns (total 9 columns):\n",
-      " #   Column       Non-Null Count  Dtype  \n",
-      "---  ------       --------------  -----  \n",
-      " 0   MedInc       20640 non-null  float64\n",
-      " 1   HouseAge     20640 non-null  float64\n",
-      " 2   AveRooms     20640 non-null  float64\n",
-      " 3   AveBedrms    20640 non-null  float64\n",
-      " 4   Population   20640 non-null  float64\n",
-      " 5   AveOccup     20640 non-null  float64\n",
-      " 6   Latitude     20640 non-null  float64\n",
-      " 7   Longitude    20640 non-null  float64\n",
-      " 8   MedHouseVal  20640 non-null  float64\n",
-      "dtypes: float64(9)\n",
-      "memory usage: 1.4 MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.datasets import fetch_california_housing\n",
-    "data = fetch_california_housing(as_frame=True)\n",
-    "\n",
-    "# Create a DataFrame\n",
-    "df = data.frame\n",
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e8b5aa4d",
-   "metadata": {},
-   "source": [
-    "-   Exploratory Data Analysis (EDA)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "ed9ba3ed",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>MedInc</th>\n",
-       "      <th>HouseAge</th>\n",
-       "      <th>AveRooms</th>\n",
-       "      <th>AveBedrms</th>\n",
-       "      <th>Population</th>\n",
-       "      <th>AveOccup</th>\n",
-       "      <th>Latitude</th>\n",
-       "      <th>Longitude</th>\n",
-       "      <th>MedHouseVal</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>3.870671</td>\n",
-       "      <td>28.639486</td>\n",
-       "      <td>5.429000</td>\n",
-       "      <td>1.096675</td>\n",
-       "      <td>1425.476744</td>\n",
-       "      <td>3.070655</td>\n",
-       "      <td>35.631861</td>\n",
-       "      <td>-119.569704</td>\n",
-       "      <td>2.068558</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>1.899822</td>\n",
-       "      <td>12.585558</td>\n",
-       "      <td>2.474173</td>\n",
-       "      <td>0.473911</td>\n",
-       "      <td>1132.462122</td>\n",
-       "      <td>10.386050</td>\n",
-       "      <td>2.135952</td>\n",
-       "      <td>2.003532</td>\n",
-       "      <td>1.153956</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.499900</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>0.333333</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>0.692308</td>\n",
-       "      <td>32.540000</td>\n",
-       "      <td>-124.350000</td>\n",
-       "      <td>0.149990</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>2.563400</td>\n",
-       "      <td>18.000000</td>\n",
-       "      <td>4.440716</td>\n",
-       "      <td>1.006079</td>\n",
-       "      <td>787.000000</td>\n",
-       "      <td>2.429741</td>\n",
-       "      <td>33.930000</td>\n",
-       "      <td>-121.800000</td>\n",
-       "      <td>1.196000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>3.534800</td>\n",
-       "      <td>29.000000</td>\n",
-       "      <td>5.229129</td>\n",
-       "      <td>1.048780</td>\n",
-       "      <td>1166.000000</td>\n",
-       "      <td>2.818116</td>\n",
-       "      <td>34.260000</td>\n",
-       "      <td>-118.490000</td>\n",
-       "      <td>1.797000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>4.743250</td>\n",
-       "      <td>37.000000</td>\n",
-       "      <td>6.052381</td>\n",
-       "      <td>1.099526</td>\n",
-       "      <td>1725.000000</td>\n",
-       "      <td>3.282261</td>\n",
-       "      <td>37.710000</td>\n",
-       "      <td>-118.010000</td>\n",
-       "      <td>2.647250</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>15.000100</td>\n",
-       "      <td>52.000000</td>\n",
-       "      <td>141.909091</td>\n",
-       "      <td>34.066667</td>\n",
-       "      <td>35682.000000</td>\n",
-       "      <td>1243.333333</td>\n",
-       "      <td>41.950000</td>\n",
-       "      <td>-114.310000</td>\n",
-       "      <td>5.000010</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             MedInc      HouseAge      AveRooms     AveBedrms    Population  \\\n",
-       "count  20640.000000  20640.000000  20640.000000  20640.000000  20640.000000   \n",
-       "mean       3.870671     28.639486      5.429000      1.096675   1425.476744   \n",
-       "std        1.899822     12.585558      2.474173      0.473911   1132.462122   \n",
-       "min        0.499900      1.000000      0.846154      0.333333      3.000000   \n",
-       "25%        2.563400     18.000000      4.440716      1.006079    787.000000   \n",
-       "50%        3.534800     29.000000      5.229129      1.048780   1166.000000   \n",
-       "75%        4.743250     37.000000      6.052381      1.099526   1725.000000   \n",
-       "max       15.000100     52.000000    141.909091     34.066667  35682.000000   \n",
-       "\n",
-       "           AveOccup      Latitude     Longitude   MedHouseVal  \n",
-       "count  20640.000000  20640.000000  20640.000000  20640.000000  \n",
-       "mean       3.070655     35.631861   -119.569704      2.068558  \n",
-       "std       10.386050      2.135952      2.003532      1.153956  \n",
-       "min        0.692308     32.540000   -124.350000      0.149990  \n",
-       "25%        2.429741     33.930000   -121.800000      1.196000  \n",
-       "50%        2.818116     34.260000   -118.490000      1.797000  \n",
-       "75%        3.282261     37.710000   -118.010000      2.647250  \n",
-       "max     1243.333333     41.950000   -114.310000      5.000010  "
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Print some basic statistics of the dataset\n",
-    "import pandas as pd\n",
-    "df.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "c205a9c3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualize the distribution of the target variable\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.histplot(df['MedHouseVal'], bins=30, kde=True)\n",
-    "plt.title('Distribution of Median House Value')\n",
-    "plt.xlabel('Median House Value')\n",
-    "plt.ylabel('Frequency')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "b5694b65",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualize the relationship between features and target variable\n",
-    "plt.figure(figsize=(12, 8))\n",
-    "sns.heatmap(df.corr(), annot=True, fmt='.2f', cmap='coolwarm')\n",
-    "plt.title('Correlation Heatmap of Features')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "290c9da7",
-   "metadata": {},
-   "source": [
-    "-   Split the dataset into features and target variable, retaining only the most informative feature."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "ade152ef",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X = df[['MedInc']]\n",
-    "y = df['MedHouseVal']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aed551aa",
-   "metadata": {},
-   "source": [
-    "-   Split the dataset into training and test sets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "60b535a4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training set size: 16512, Test set size: 4128\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "print(f\"Training set size: {X_train.shape[0]}, Test set size: {X_test.shape[0]}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c2282213",
-   "metadata": {},
-   "source": [
-    "-   Standardize the feature"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "2d197855",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.preprocessing import StandardScaler\n",
-    "scaler = StandardScaler()\n",
-    "X_train = scaler.fit_transform(X_train)\n",
-    "X_test = scaler.transform(X_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "26216e97",
-   "metadata": {},
-   "source": [
-    "-   Fit a linear regression model to the training data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "2478e9ce",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('copy_X',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">copy_X&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
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-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">1e-06</td>\n",
-       "        </tr>\n",
-       "    \n",
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-       "                 onclick=\"copyToClipboard('n_jobs',\n",
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-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
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-       "                 onclick=\"copyToClipboard('positive',\n",
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-       "            ></i></td>\n",
-       "            <td class=\"param\">positive&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
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-       "\n",
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-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
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-       "\n",
-       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
-      ],
-      "text/plain": [
-       "LinearRegression()"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "model = LinearRegression()\n",
-    "model.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "31ad18ab",
-   "metadata": {},
-   "source": [
-    "-   Run the model on the test set and compute the predictions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "c7dd8415",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Predictions: [1.14958917 1.50606882 1.90393718 2.85059383 2.00663318]\n",
-      "Actual values: [0.477   0.458   5.00001 2.186   2.78   ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "y_pred = model.predict(X_test)\n",
-    "print(\"Predictions:\", y_pred[:5])  # Display first 5 predictions\n",
-    "print(\"Actual values:\", y_test.values[:5])  # Display first 5 actual values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "108c2992",
-   "metadata": {},
-   "source": [
-    "-   Visualize the regression line"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "5bf66a1a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.scatterplot(x=X_test[:,0], y=y_test)\n",
-    "sns.lineplot(x=X_test[:,0], y=y_pred, color='red', label='Regression Line')\n",
-    "plt.title('Regression Line on Test Set')\n",
-    "plt.xlabel('Median Income (Standardized)')\n",
-    "plt.ylabel('Median House Value')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c7a524db",
-   "metadata": {},
-   "source": [
-    "-   Compute the evaluation metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "34128ef6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MAE: 0.63\n",
-      "MSE: 0.71\n",
-      "RMSE: 0.84\n",
-      "R²: 0.46\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
-    "mae = mean_absolute_error(y_test, y_pred)\n",
-    "mse = mean_squared_error(y_test, y_pred)\n",
-    "rmse = np.sqrt(mse)\n",
-    "r2 = r2_score(y_test, y_pred)\n",
-    "\n",
-    "print(f\"MAE: {mae:.2f}\")\n",
-    "print(f\"MSE: {mse:.2f}\")\n",
-    "print(f\"RMSE: {rmse:.2f}\")\n",
-    "print(f\"R²: {r2:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "713c637d",
-   "metadata": {},
-   "source": [
-    "-   Let's add some outliers to the target variable to see how the metrics behave"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "86fa91fe",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MAE with Outliers: 0.68\n",
-      "MSE with Outliers: 5.54\n",
-      "RMSE with Outliers: 2.35\n",
-      "R² with Outliers: 0.09\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "# Introduce outliers in the test set\n",
-    "y_test_with_outliers = y_test.copy()\n",
-    "y_test_with_outliers.iloc[0] += 100  # Add an outlier to the first element\n",
-    "y_test_with_outliers.iloc[-1] -= 100  # Add an outlier to the last element\n",
-    "\n",
-    "# Recompute predictions with outliers\n",
-    "y_pred_with_outliers = model.predict(X_test)\n",
-    "\n",
-    "# Compute metrics with outliers\n",
-    "mae_outliers = mean_absolute_error(y_test_with_outliers, y_pred_with_outliers)\n",
-    "mse_outliers = mean_squared_error(y_test_with_outliers, y_pred_with_outliers)\n",
-    "rmse_outliers = np.sqrt(mse_outliers)\n",
-    "r2_outliers = r2_score(y_test_with_outliers, y_pred_with_outliers)\n",
-    "\n",
-    "print(f\"MAE with Outliers: {mae_outliers:.2f}\")\n",
-    "print(f\"MSE with Outliers: {mse_outliers:.2f}\")\n",
-    "print(f\"RMSE with Outliers: {rmse_outliers:.2f}\")\n",
-    "print(f\"R² with Outliers: {r2_outliers:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1be22407",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "-   Fit a linear regression model to the Ames dataset (feature = `GrLivArea`, target = `SalePrice`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "342135dc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-2 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-2 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-2 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-2 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
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-       "    background-color: #f6f6f6;\n",
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-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
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-       ".user-set td.value pre {\n",
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-       "\n",
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-       "\n",
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-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
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-       "            <td class=\"value\">True</td>\n",
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-       "    \n",
-       "\n",
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-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">1e-06</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('positive',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">positive&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
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-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
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-       "\n",
-       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
-      ],
-      "text/plain": [
-       "LinearRegression()"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Load the Ames dataset\n",
-    "from sklearn.datasets import fetch_openml\n",
-    "data = fetch_openml(name='house_prices', as_frame=True)\n",
-    "df = data.frame\n",
-    "\n",
-    "# Select features and target variable\n",
-    "X = df[['GrLivArea']]\n",
-    "y = df['SalePrice']\n",
-    "\n",
-    "# Split the dataset into training and test sets\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "\n",
-    "# Standardize the feature\n",
-    "scaler = StandardScaler()\n",
-    "X_train = scaler.fit_transform(X_train)\n",
-    "X_test = scaler.transform(X_test)\n",
-    "\n",
-    "# Fit a linear regression model to the training data\n",
-    "model = LinearRegression()\n",
-    "model.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "952a3be1",
-   "metadata": {},
-   "source": [
-    "-   Compute and report the evaluation metrics for the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "0617ce60",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MAE: 38341.20\n",
-      "MSE: 3418946311.18\n",
-      "RMSE: 58471.76\n",
-      "R²: 0.55\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Make predictions on the test set\n",
-    "y_pred = model.predict(X_test)\n",
-    "\n",
-    "# Compute evaluation metrics\n",
-    "mae = mean_absolute_error(y_test, y_pred)\n",
-    "mse = mean_squared_error(y_test, y_pred)\n",
-    "rmse = np.sqrt(mse)\n",
-    "r2 = r2_score(y_test, y_pred)\n",
-    "\n",
-    "print(f\"MAE: {mae:.2f}\")\n",
-    "print(f\"MSE: {mse:.2f}\")\n",
-    "print(f\"RMSE: {rmse:.2f}\")\n",
-    "print(f\"R²: {r2:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ea92928a",
-   "metadata": {},
-   "source": [
-    "-   Change the target variable by multiplying it by 1000. Recompute the metrics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "13a7bc22",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MAE with scaled target: 38341204.48\n",
-      "MSE with scaled target: 3418946311180806.00\n",
-      "RMSE with scaled target: 58471756.53\n",
-      "R² with scaled target: 0.55\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Change the target variable by multiplying it by 1000\n",
-    "y_test_scaled = y_test * 1000\n",
-    "y_pred_scaled = y_pred * 1000\n",
-    "# Recompute the metrics\n",
-    "mae_scaled = mean_absolute_error(y_test_scaled, y_pred_scaled)\n",
-    "mse_scaled = mean_squared_error(y_test_scaled, y_pred_scaled)\n",
-    "rmse_scaled = np.sqrt(mse_scaled)\n",
-    "r2_scaled = r2_score(y_test_scaled, y_pred_scaled)\n",
-    "print(f\"MAE with scaled target: {mae_scaled:.2f}\")\n",
-    "print(f\"MSE with scaled target: {mse_scaled:.2f}\")\n",
-    "print(f\"RMSE with scaled target: {rmse_scaled:.2f}\")\n",
-    "print(f\"R² with scaled target: {r2_scaled:.2f}\")"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
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-   "name": "python",
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-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C04 Multiple Linear Regression.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C04 Multiple Linear Regression.ipynb
deleted file mode 100644
index 029b1db..0000000
--- a/src/S02 Supervised Learning - Regression and Evaluation/S02C04 Multiple Linear Regression.ipynb	
+++ /dev/null
@@ -1,1301 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "9ed72b66",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Multiple Linear Regression\n",
-    "\n",
-    "**Multiple linear regression** is an extension of simple linear regression that allows us to model the relationship between a dependent variable and **multiple** independent variables. It is used when we want to predict a continuous outcome based on several predictors. The model assumes that the relationship between the dependent variable and the independent variables can be expressed as a linear equation: $$y = \\beta_0 + \\beta_1 x_1 + \\beta_2 x_2 + ... + \\beta_n x_n + \\epsilon$$ where:\n",
-    "\n",
-    "-   $y$ is the dependent variable (the outcome we want to predict).\n",
-    "-   $\\beta_0$ is the y-intercept (the value of $y$ when all $x_i$ are 0).\n",
-    "-   $\\beta_1, \\beta_2, ..., \\beta_n$ are the coefficients for each independent variable $x_i$ (the change in $y$ for a one-unit change in $x_i$).\n",
-    "-   $x_1, x_2, ..., x_n$ are the independent variables (the predictors).\n",
-    "-   $\\epsilon$ is the error term (the difference between the observed and predicted values)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba42ff5a",
-   "metadata": {},
-   "source": [
-    "## Assumptions of Multiple Linear Regression\n",
-    "\n",
-    "The assumptions of multiple linear regression are similar to those of simple linear regression, but with some additional considerations due to the presence of multiple predictors:\n",
-    "\n",
-    "1.  **Linearity**: The relationship between the dependent variable and each independent variable is linear.\n",
-    "2.  **Independence**: The observations are independent of each other.\n",
-    "3.  **Homoscedasticity**: The variance of the errors is constant across all levels of the independent variables.\n",
-    "4.  **Normality of errors**: The errors (residuals) are normally distributed.\n",
-    "5.  **No multicollinearity**: The independent variables are not too highly correlated with each other."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77328f46",
-   "metadata": {},
-   "source": [
-    "## Practical Demonstration\n",
-    "\n",
-    "We will use a feature of `scikit-learn` to generate a synthetic dataset for linear regression. This will allow us to demonstrate the concepts of linear regression without focusing on data preprocessing or feature engineering.\n",
-    "\n",
-    "`scikit-learn` provides a simple way to create synthetic datasets for regression tasks, which can be useful for testing and learning purposes. We will use the `make_regression` function to generate a dataset with a specified number of samples, features, and noise level.\n",
-    "\n",
-    "-   Generate a synthetic dataset using the `make_regression` function from `scikit-learn`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "be9e3f8b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "True coefficients: [87.73730719 74.07686178]\n",
-      "   Feature 1  Feature 2      Target\n",
-      "0  -1.191303   0.656554  -48.265023\n",
-      "1   0.058209  -1.142970  -84.162387\n",
-      "2   0.586857   2.190456  212.225658\n",
-      "3   0.473238  -0.072829   30.196562\n",
-      "4   0.738467   0.171368   85.348096\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.datasets import make_regression\n",
-    "\n",
-    "# Generate synthetic dataset\n",
-    "X, y, coef = make_regression(n_samples=100,\n",
-    "                             n_features=2,\n",
-    "                             bias=1, noise=10,\n",
-    "                             coef=True, random_state=42)\n",
-    "# Print the coefficients\n",
-    "print(\"True coefficients:\", coef)\n",
-    "\n",
-    "# Create a DataFrame for better visualization\n",
-    "import pandas as pd\n",
-    "df = pd.DataFrame(X, columns=['Feature 1', 'Feature 2'])\n",
-    "df['Target'] = y\n",
-    "print(df.head())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4ff38655",
-   "metadata": {},
-   "source": [
-    "-   Explore the dataset and create a correlation matrix to visualize the relationships between the features and the target variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "d865cbff",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.heatmap(df.corr(), annot=True, cmap='coolwarm', fmt='.2f')\n",
-    "plt.title('Correlation Matrix of Features and Target')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cc2966c9",
-   "metadata": {},
-   "source": [
-    "-   Perform the train-test split to prepare the data for model training and evaluation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "74b47095",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7c68a506",
-   "metadata": {},
-   "source": [
-    "-   Fit a linear regression model to the training data and make predictions on the test data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "454edbf9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coefficients: [86.31018533 73.66927906]\n",
-      "Intercept: 1.0659548036431996\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Fit a linear regression model\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "model = LinearRegression()\n",
-    "model.fit(X_train, y_train)\n",
-    "\n",
-    "# Make predictions\n",
-    "y_pred = model.predict(X_test)\n",
-    "# Print the coefficients and intercept\n",
-    "print(\"Coefficients:\", model.coef_)\n",
-    "print(\"Intercept:\", model.intercept_)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3b51ea7b",
-   "metadata": {},
-   "source": [
-    "-   Visualize the predictions vs actual values, the residuals, and the residuals vs the predicted values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "89952d15",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualize the predictions vs actual values\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.scatter(y_test, y_pred, alpha=0.5)\n",
-    "plt.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=2)\n",
-    "plt.xlabel('Actual Values')\n",
-    "plt.ylabel('Predicted Values')\n",
-    "plt.title('Actual vs Predicted Values')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "9c598ea8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualize the residuals\n",
-    "residuals = y_test - y_pred\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.histplot(residuals, kde=True)\n",
-    "plt.title('Residuals Distribution')\n",
-    "plt.xlabel('Residuals')\n",
-    "plt.ylabel('Frequency')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "891e90ae",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualize the residuals vs predicted values\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.scatter(y_pred, residuals, alpha=0.5)\n",
-    "plt.axhline(0, color='k', linestyle='--', lw=2)\n",
-    "plt.xlabel('Predicted Values')\n",
-    "plt.ylabel('Residuals')\n",
-    "plt.title('Residuals vs Predicted Values')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2afa4958",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the model using metrics such as Mean Squared Error (MSE) and R-squared ($R^2$) score."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "fd89d618",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Absolute Error: 9.667801070655655\n",
-      "Mean Squared Error: 154.62772689490527\n",
-      "Root Mean Squared Error: 12.43493976241563\n",
-      "R^2 Score: 0.9836279308340317\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Evaluate the model\n",
-    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
-    "mae = mean_absolute_error(y_test, y_pred)\n",
-    "mse = mean_squared_error(y_test, y_pred)\n",
-    "rmse = np.sqrt(mse)\n",
-    "r2 = r2_score(y_test, y_pred)\n",
-    "print(\"Mean Absolute Error:\", mae)\n",
-    "print(\"Mean Squared Error:\", mse)\n",
-    "print(\"Root Mean Squared Error:\", rmse)\n",
-    "print(\"R^2 Score:\", r2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7662336f",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "-   Generate a synthetic dataset using the `make_regression` function from `scikit-learn`; use the following parameters:\n",
-    "    -   `n_samples` = 200\n",
-    "    -   `n_features` = 3\n",
-    "    -   `noise` = 10\n",
-    "    -   `random_state` = 42\n",
-    "    -   `bias` = 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "c8ff7567",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[72.00365365 22.92513952 72.22525684]\n"
-     ]
-    }
-   ],
-   "source": [
-    "X, y, coef = make_regression(n_samples=200,\n",
-    "                             n_features=3,\n",
-    "                             bias=2, noise=10,\n",
-    "                             random_state=42, coef=True)\n",
-    "print(coef)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "87394de3",
-   "metadata": {},
-   "source": [
-    "-   Explore the dataset and create a correlation matrix to visualize the relationships between the features and the target variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "f262f73f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   Feature 1  Feature 2  Feature 3      Target\n",
-      "0   0.130741  -1.430141  -0.440044  -62.558855\n",
-      "1   1.502357  -0.269407   0.717542  156.776889\n",
-      "2   0.647689   0.496714  -0.138264   63.896041\n",
-      "3   0.341756  -0.759133   0.150394   18.485672\n",
-      "4   0.856399  -1.514847  -0.446515  -29.907412\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create a DataFrame\n",
-    "df = pd.DataFrame(X, columns=['Feature 1', 'Feature 2', 'Feature 3'])\n",
-    "df['Target'] = y\n",
-    "print(df.head())\n",
-    "\n",
-    "# Create a correlation matrix and visualize the relationships\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.heatmap(df.corr(), annot=True, cmap='coolwarm', fmt='.2f')\n",
-    "plt.title('Correlation Matrix of Features and Target')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9803b432",
-   "metadata": {},
-   "source": [
-    "-   Train a linear regression model on the synthetic dataset; choose two features from the dataset as independent variables and the target variable as the dependent variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "f9faf4c1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Choose two features as independent variables\n",
-    "X = df[['Feature 1', 'Feature 3']]\n",
-    "y = df['Target']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed745829",
-   "metadata": {},
-   "source": [
-    "-   Split the dataset into training and testing sets (80% train, 20% test)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "17bdf658",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Train-test split\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7a203f56",
-   "metadata": {},
-   "source": [
-    "-   Fit a linear regression model to the training data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "88642f60",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('copy_X',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">copy_X&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">1e-06</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('positive',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">positive&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
-      ],
-      "text/plain": [
-       "LinearRegression()"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Fit a linear regression model\n",
-    "model = LinearRegression()\n",
-    "model.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c5b355bd",
-   "metadata": {},
-   "source": [
-    "-   Print the coefficients and intercept of the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "e999b887",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Coefficients: [71.10396609 73.12415711]\n",
-      "Intercept: 2.783140422715624\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Print the coefficients and intercept\n",
-    "print(\"Coefficients:\", model.coef_)\n",
-    "print(\"Intercept:\", model.intercept_)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cf03d7cd",
-   "metadata": {},
-   "source": [
-    "-   Make predictions on the `test` dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "35cfde39",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_pred = model.predict(X_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c291588f",
-   "metadata": {},
-   "source": [
-    "-   Calculate and print the Mean Squared Error (MSE) and R-squared ($R^2$) score of the model on the test set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "306f5c43",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Squared Error: 542.5939963482766\n",
-      "R^2 Score: 0.9387819694528106\n"
-     ]
-    }
-   ],
-   "source": [
-    "mse = mean_squared_error(y_test, y_pred)\n",
-    "r2 = r2_score(y_test, y_pred)\n",
-    "print(\"Mean Squared Error:\", mse)\n",
-    "print(\"R^2 Score:\", r2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ae352d15",
-   "metadata": {},
-   "source": [
-    "-   Visualize the predictions vs actual values, the residuals, and the residuals vs the predicted values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "a3e86c39",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualize the predictions vs actual values\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.scatter(y_test, y_pred, alpha=0.5)\n",
-    "plt.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=2)\n",
-    "plt.xlabel('Actual Values')\n",
-    "plt.ylabel('Predicted Values')\n",
-    "plt.title('Actual vs Predicted Values')\n",
-    "plt.show()\n",
-    "\n",
-    "# Visualize the residuals\n",
-    "residuals = y_test - y_pred\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.histplot(residuals, kde=True)\n",
-    "plt.title('Residuals Distribution')\n",
-    "plt.xlabel('Residuals')\n",
-    "plt.ylabel('Frequency')\n",
-    "plt.show()\n",
-    "\n",
-    "# Visualize the residuals vs predicted values\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.scatter(y_pred, residuals, alpha=0.5)\n",
-    "plt.axhline(0, color='k', linestyle='--', lw=2)\n",
-    "plt.xlabel('Predicted Values')\n",
-    "plt.ylabel('Residuals')\n",
-    "plt.title('Residuals vs Predicted Values')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "76f7f041",
-   "metadata": {},
-   "source": [
-    "-   Modify the synthetic dataset by introducing a non-linear relationship (e.g., quadratic or exponential) and observe how the performance metrics change. Fit a simple linear regression model to this new dataset and evaluate its performance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "dfc20923",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Absolute Error (MAE): 15.29\n",
-      "Mean Squared Error (MSE): 286.67\n",
-      "Root Mean Squared Error (RMSE): 16.93\n",
-      "R-squared (R^2): 0.93\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Generate a non-linear dataset (quadratic relationship)\n",
-    "X = 10 * np.random.rand(100, 1)  # Independent variable\n",
-    "y = 2 * X**2 + 5 + np.random.randn(100, 1)  # Dependent variable with noise\n",
-    "\n",
-    "# Create DataFrame\n",
-    "data = pd.DataFrame(np.hstack((X, y)), columns=['X', 'y'])\n",
-    "\n",
-    "# Split the dataset\n",
-    "X_train, X_test, y_train, y_test = train_test_split(data[['X']], data['y'],\n",
-    "                                                    test_size=0.2,\n",
-    "                                                    random_state=42)\n",
-    "\n",
-    "# Fit the model\n",
-    "model.fit(X_train, y_train)\n",
-    "\n",
-    "# Make predictions\n",
-    "y_pred = model.predict(X_test)\n",
-    "\n",
-    "# Evaluate the model\n",
-    "mae = mean_absolute_error(y_test, y_pred)\n",
-    "mse = mean_squared_error(y_test, y_pred)\n",
-    "rmse = np.sqrt(mse)\n",
-    "r2 = r2_score(y_test, y_pred)\n",
-    "\n",
-    "print(f\"Mean Absolute Error (MAE): {mae:.2f}\")\n",
-    "print(f\"Mean Squared Error (MSE): {mse:.2f}\")\n",
-    "print(f\"Root Mean Squared Error (RMSE): {rmse:.2f}\")\n",
-    "print(f\"R-squared (R^2): {r2:.2f}\")\n",
-    "\n",
-    "# Visualize the results\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.scatterplot(x=X_test.squeeze(), y=y_test, color='blue', label='Actual')\n",
-    "sns.lineplot(x=X_test.squeeze(), y=y_pred, color='red', label='Predicted', linewidth=2)\n",
-    "plt.xlabel('X')\n",
-    "plt.ylabel('y')\n",
-    "plt.title('Linear Regression - Non-linear Dataset')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C05 Regularized Regression.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C05 Regularized Regression.ipynb
deleted file mode 100644
index 9194c92..0000000
--- a/src/S02 Supervised Learning - Regression and Evaluation/S02C05 Regularized Regression.ipynb	
+++ /dev/null
@@ -1,697 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "96537c8e",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Regularized Regression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00c6fb77",
-   "metadata": {},
-   "source": [
-    "## How do Machine Learning Models Learn?\n",
-    "\n",
-    "Machine learning models learn from data by identifying patterns and relationships between the independent variables (features) and the dependent variable (target). The learning process involves adjusting the model parameters to minimize the difference between the predicted values and the actual values of the target variable. This is typically done by minimizing a cost function, which quantifies the error in the model's predictions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c749acc8",
-   "metadata": {},
-   "source": [
-    "### Cost Function\n",
-    "\n",
-    "The cost function, measures how well the model's predictions match the actual target values. In regression tasks, a common cost function is the Mean Squared Error (MSE), which is defined as: $$J(w) = \\frac{1}{2m} \\sum_{i=1}^{m} (y_i - \\hat{y}_i)^2$$ where:\n",
-    "\n",
-    "-   $J(w)$ is the cost function,\n",
-    "-   $m$ is the number of samples,\n",
-    "-   $y_i$ is the actual target value for the $i$-th sample,\n",
-    "-   $\\hat{y}_i$ is the predicted target value for the $i$-th sample,\n",
-    "-   $w$ represents the model parameters (coefficients)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "217c6242",
-   "metadata": {},
-   "source": [
-    "### Gradient Descent\n",
-    "\n",
-    "Gradient descent is an optimization algorithm used to minimize the cost function by iteratively updating the model parameters in the direction of the steepest descent. The update rule for gradient descent is given by: $$w := w - \\alpha \\frac{\\partial}{\\partial w}J(w)$$ where:\n",
-    "\n",
-    "-   $w$ is the vector of model parameters,\n",
-    "-   $\\alpha$ is the learning rate (a hyperparameter that controls the step size of each update),\n",
-    "-   $\\displaystyle\\frac{\\partial}{\\partial w}J(w)$ is the gradient of the cost function with respect to the model parameters."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "99b0b4cd",
-   "metadata": {},
-   "source": [
-    "### Gradient Descent Algorithm\n",
-    "\n",
-    "The gradient descent algorithm can be summarized in the following steps:\n",
-    "\n",
-    "-   **Initialize Parameters**: Start with random values for the model parameters (coefficients).\n",
-    "-   **Compute Predictions**: Use the current parameters to compute the predicted values for the training data.\n",
-    "-   **Calculate Cost**: Compute the cost function (e.g., MSE) using the predicted values and the actual target values.\n",
-    "-   **Compute Gradient**: Calculate the gradient of the cost function with respect to the model parameters.\n",
-    "-   **Update Parameters**: Update the model parameters using the gradient descent update rule.\n",
-    "-   **Repeat**: Repeat until convergence (i.e., when the change in the cost function is below a certain threshold or a maximum number of iterations is reached)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b370a9f8",
-   "metadata": {},
-   "source": [
-    "## Overfitting and Underfitting\n",
-    "\n",
-    "There are a few caveats to consider when working with machine learning models in general, and regression in particular. These include overfitting and underfitting, which can significantly affect the performance of your model.\n",
-    "\n",
-    "-   **Overfitting**: occurs when a model learns the noise in the training data rather than the underlying pattern. This leads to a model that performs well on the training data but poorly on unseen data.\n",
-    "-   **Underfitting**: occurs when a model is too simple to capture the underlying pattern in the data. This results in poor performance on both the training and test data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "feb1ab32",
-   "metadata": {},
-   "source": [
-    "## Regularization\n",
-    "\n",
-    "To address overfitting and underfitting, we can use regularization techniques. Regularization helps to constrain the model complexity by adding a penalty term to the loss function. This penalty discourages the model from fitting the noise in the data, leading to better generalization on unseen data. Regularization is particularly useful when dealing with high-dimensional datasets or when the number of features is greater than the number of observations.\n",
-    "\n",
-    "Some common regularization techniques include:\n",
-    "\n",
-    "1.  **Lasso Regression (L1 Regularization)**: Adds a penalty equal to the absolute value of the magnitude of coefficients. This can lead to sparse models where some coefficients are exactly zero, effectively performing feature selection.$$J(w) = \\frac{1}{2m} \\sum_{i=1}^{m} (y_i - \\hat{y}_i)^2 + \\lambda \\sum_{j=1}^{p} |w_j|$$ where $\\lambda$ is the regularization strength and $p$ is the number of features.\n",
-    "2.  **Ridge Regression (L2 Regularization)**: Adds a penalty equal to the square of the magnitude of coefficients. This helps to shrink the coefficients but does not lead to sparse models. $$J(w) = \\frac{1}{2m} \\sum_{i=1}^{m} (y_i - \\hat{y}_i)^2 + \\lambda \\sum_{j=1}^{p} w_j^2$$ where $\\lambda$ is the regularization strength and $p$ is the number of features.\n",
-    "3.  **Elastic Net**: Combines both L1 and L2 regularization, allowing for a balance between feature selection and coefficient shrinkage."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e2685be7",
-   "metadata": {},
-   "source": [
-    "## Practical Demonstration\n",
-    "\n",
-    "We will now demonstrate how to implement regularized regression using Lasso, Ridge, and Elastic Net regression methods from the `scikit-learn` library. We will use a synthetic dataset for this demonstration, but you can apply the same techniques to any regression dataset. In particular, we will introduce some correlations between features, which will allow us to see how regularization can help in feature selection and model performance.\n",
-    "\n",
-    "-   Creating a Synthetic Dataset with Correlated Features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "a234d6cf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original coefficients: 41.7411003148779\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.datasets import make_regression\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "n_samples, n_features = 100, 1\n",
-    "\n",
-    "X, y, coef = make_regression(\n",
-    "    n_samples=n_samples,\n",
-    "    n_features=n_features,\n",
-    "    noise=7.5,\n",
-    "    coef=True,\n",
-    "    random_state=42\n",
-    ")\n",
-    "\n",
-    "df = pd.DataFrame(X, columns=['feature'])\n",
-    "df['target'] = y\n",
-    "\n",
-    "print(f\"Original coefficients: {coef}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f3ab9f4e",
-   "metadata": {},
-   "source": [
-    "-   Plotting the Synthetic Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "368e5cc1-6964-4a88-b3e2-0db60ff13604",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.scatterplot(data=df, x='feature', y='target', color='blue', label='Data Points')\n",
-    "plt.title('Synthetic Dataset')\n",
-    "plt.xlabel('Feature')\n",
-    "plt.ylabel('Target')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "655a3eb8",
-   "metadata": {},
-   "source": [
-    "-   Introduce some correlation between features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "516f4866",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Correlated coefficients: [41.74110031 20.87055016]\n"
-     ]
-    }
-   ],
-   "source": [
-    "X = np.column_stack((X, 2 * X[:, 0] + 0.01 * np.random.randn(n_samples)))  # Add a correlated feature\n",
-    "y += 0.5 * coef * X[:, 1]  # Introduce correlation with the target variable\n",
-    "\n",
-    "# Adjust coefficients for the new feature\n",
-    "true_coefs = np.array([coef, 0.5 * coef])\n",
-    "print(f\"Correlated coefficients: {true_coefs}\")\n",
-    "\n",
-    "import pandas as pd\n",
-    "df = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(X.shape[1])])\n",
-    "df['target'] = y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "854080f9",
-   "metadata": {},
-   "source": [
-    "-   Train a Simple Linear Regression Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "f6354788",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Linear Regression R^2 Score: 0.9900\n",
-      "Linear Regression Coefficients: [37.12805251 24.11330234]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "# Split the dataset into training and testing sets\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "\n",
-    "# Train a simple linear regression model\n",
-    "linear_model = LinearRegression()\n",
-    "linear_model.fit(X_train, y_train)\n",
-    "\n",
-    "# Evaluate the model\n",
-    "print(f'Linear Regression R^2 Score: {linear_model.score(X_test, y_test):.4f}')\n",
-    "print(f'Linear Regression Coefficients: {linear_model.coef_}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3d23e0d9",
-   "metadata": {},
-   "source": [
-    "-   Train a Lasso Regression Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "0c01151d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Lasso Regression R^2 Score: 0.9901\n",
-      "Lasso Regression Coefficients: [8.53706697e+01 7.85428100e-03]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import Lasso\n",
-    "\n",
-    "# Train a Lasso regression model with alpha=0.1\n",
-    "lasso_model = Lasso(alpha=0.01)\n",
-    "lasso_model.fit(X_train, y_train)\n",
-    "\n",
-    "# Evaluate the model\n",
-    "print(f'Lasso Regression R^2 Score: {lasso_model.score(X_test, y_test):.4f}')\n",
-    "print(f'Lasso Regression Coefficients: {lasso_model.coef_}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "423bc24d",
-   "metadata": {},
-   "source": [
-    "-   Train a Ridge Regression Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "837311a5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Ridge Regression R^2 Score: 0.9900\n",
-      "Ridge Regression Coefficients: [17.42423493 33.94453533]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import Ridge\n",
-    "\n",
-    "# Train a Ridge regression model with alpha=0.1\n",
-    "ridge_model = Ridge(alpha=0.1)\n",
-    "ridge_model.fit(X_train, y_train)\n",
-    "\n",
-    "# Evaluate the model\n",
-    "print(f'Ridge Regression R^2 Score: {ridge_model.score(X_test, y_test):.4f}')\n",
-    "print(f'Ridge Regression Coefficients: {ridge_model.coef_}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d3f94eb7",
-   "metadata": {},
-   "source": [
-    "-   Train an Elastic Net Regression Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "50dd72a2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Elastic Net Regression R^2 Score: 0.9896\n",
-      "Elastic Net Regression Coefficients: [16.49644689 33.888562  ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import ElasticNet\n",
-    "\n",
-    "# Train an Elastic Net regression model with alpha=0.1 and l1_ratio=0.5\n",
-    "elastic_net_model = ElasticNet(alpha=0.1, l1_ratio=0.5)\n",
-    "elastic_net_model.fit(X_train, y_train)\n",
-    "\n",
-    "# Evaluate the model\n",
-    "print(f'Elastic Net Regression R^2 Score: {elastic_net_model.score(X_test, y_test):.4f}')\n",
-    "print(f'Elastic Net Regression Coefficients: {elastic_net_model.coef_}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0a607795",
-   "metadata": {},
-   "source": [
-    "-   Evaluate all the Models and visualize their predictions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "234efd5b",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "R^2 Scores:\n",
-      "Linear Regression: 0.9900\n",
-      "Lasso Regression: 0.9901\n",
-      "Ridge Regression: 0.9900\n",
-      "Elastic Net Regression: 0.9896\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "# Predictions from all models\n",
-    "y_pred_linear = linear_model.predict(X_test)\n",
-    "y_pred_lasso = lasso_model.predict(X_test)\n",
-    "y_pred_ridge = ridge_model.predict(X_test)\n",
-    "y_pred_elastic_net = elastic_net_model.predict(X_test)\n",
-    "\n",
-    "# Calculate R^2 scores\n",
-    "r2_scores = {\n",
-    "    'Linear Regression': linear_model.score(X_test, y_test),\n",
-    "    'Lasso Regression': lasso_model.score(X_test, y_test),\n",
-    "    'Ridge Regression': ridge_model.score(X_test, y_test),\n",
-    "    'Elastic Net Regression': elastic_net_model.score(X_test, y_test)\n",
-    "}\n",
-    "\n",
-    "print(\"R^2 Scores:\")\n",
-    "for model, score in r2_scores.items():\n",
-    "    print(f\"{model}: {score:.4f}\")\n",
-    "\n",
-    "# Visualize predictions\n",
-    "plt.figure(figsize=(12, 8))\n",
-    "plt.scatter(y_test, y_pred_linear, label='Linear Regression', alpha=0.5)\n",
-    "plt.scatter(y_test, y_pred_lasso, label='Lasso Regression', alpha=0.5)\n",
-    "plt.scatter(y_test, y_pred_ridge, label='Ridge Regression', alpha=0.5)\n",
-    "plt.scatter(y_test, y_pred_elastic_net, label='Elastic Net Regression', alpha=0.5)\n",
-    "\n",
-    "plt.plot([y.min(), y.max()], [y.min(), y.max()], color='red', linestyle='--')\n",
-    "plt.title('Model Predictions vs Actual Values')\n",
-    "plt.xlabel('Actual Values')\n",
-    "plt.ylabel('Predicted Values')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3d3e0143",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "-   **Lasso Regression**: Implement Lasso regression on a dataset of your choice. Experiment with different values of the regularization parameter `alpha` until you find the best model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "346da8e5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Linear Regression R^2 Score: 0.5758\n",
-      "Linear Regression Coefficients: [ 4.48674910e-01  9.62797774e-03 -1.23323343e-01  7.83144907e-01\n",
-      " -2.02962058e-06 -3.52631849e-03 -4.19792487e-01 -4.33708065e-01\n",
-      "  9.62797774e-04]\n",
-      "Best Lasso Regression Alpha: 0.01\n",
-      "Best Lasso Regression R^2 Score: 0.5845\n",
-      "Best Lasso Regression Coefficients: [ 4.08895633e-01  1.03084903e-02 -4.74445356e-02  3.63345953e-01\n",
-      " -3.08601298e-07 -3.35945603e-03 -4.07109935e-01 -4.14933167e-01\n",
-      "  0.00000000e+00]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Example code for Lasso regression\n",
-    "from sklearn.datasets import make_regression\n",
-    "from sklearn.linear_model import Lasso\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "# Load the California housing dataset\n",
-    "from sklearn.datasets import fetch_california_housing\n",
-    "data = fetch_california_housing(as_frame=True)\n",
-    "df = data.frame\n",
-    "df['HouseAgeDecade'] = df['HouseAge'] / 10\n",
-    "X = df.drop(columns='MedHouseVal')\n",
-    "y = df['MedHouseVal']\n",
-    "\n",
-    "# Plot the correlation matrix\n",
-    "plt.figure(figsize=(12, 8))\n",
-    "sns.heatmap(df.corr(), annot=True, cmap='coolwarm', vmin=-1, vmax=1)\n",
-    "plt.title('Correlation Matrix of Features')\n",
-    "plt.show()\n",
-    "\n",
-    "# Split the dataset into training and testing sets\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "\n",
-    "# Train a Regular Linear Regression model\n",
-    "linear_model = LinearRegression()\n",
-    "linear_model.fit(X_train, y_train)\n",
-    "# Evaluate the model\n",
-    "print(f'Linear Regression R^2 Score: {linear_model.score(X_test, y_test):.4f}')\n",
-    "print(f'Linear Regression Coefficients: {linear_model.coef_}')\n",
-    "\n",
-    "# Train a Lasso regression model with different alpha values\n",
-    "params = {'alpha': [0.01, 0.1, 1.0, 10.0]}\n",
-    "from sklearn.model_selection import GridSearchCV\n",
-    "lasso_model = Lasso()\n",
-    "grid_search = GridSearchCV(lasso_model, params, cv=5, scoring='r2')\n",
-    "grid_search.fit(X_train, y_train)\n",
-    "best_lasso_model = grid_search.best_estimator_\n",
-    "print(f'Best Lasso Regression Alpha: {grid_search.best_params_[\"alpha\"]}')\n",
-    "print(f'Best Lasso Regression R^2 Score: {best_lasso_model.score(X_test, y_test):.4f}')\n",
-    "print(f'Best Lasso Regression Coefficients: {best_lasso_model.coef_}')\n",
-    "\n",
-    "# Visualize the coefficients\n",
-    "plt.figure(figsize=(12, 6))\n",
-    "plt.bar(range(len(best_lasso_model.coef_)), best_lasso_model.coef_)\n",
-    "plt.title('Lasso Regression Coefficients')\n",
-    "plt.xlabel('Feature Index')\n",
-    "plt.ylabel('Coefficient Value')\n",
-    "plt.xticks(range(len(best_lasso_model.coef_)), [f'feature_{i}' for i in range(len(best_lasso_model.coef_))])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "513b6bb2",
-   "metadata": {},
-   "source": [
-    "-   **Ridge Regression**: Implement Ridge regression on the same dataset. Again, experiment with different values of `alpha` and compare the results with Lasso regression."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "ba96c0b5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best Ridge Regression Alpha: 10.0\n",
-      "Best Ridge Regression R^2 Score: 0.5764\n",
-      "Best Ridge Regression Coefficients: [ 4.47068597e-01  9.64485390e-03 -1.20293353e-01  7.66201258e-01\n",
-      " -1.99123836e-06 -3.52184782e-03 -4.19720065e-01 -4.33421864e-01\n",
-      "  9.64485390e-04]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Example code for Ridge regression\n",
-    "from sklearn.linear_model import Ridge\n",
-    "\n",
-    "# Train a Ridge regression model with different alpha values\n",
-    "params = {'alpha': [0.01, 0.1, 1.0, 10.0]}\n",
-    "ridge_model = Ridge()\n",
-    "grid_search = GridSearchCV(ridge_model, params, cv=5, scoring='r2')\n",
-    "grid_search.fit(X_train, y_train)\n",
-    "best_ridge_model = grid_search.best_estimator_\n",
-    "print(f'Best Ridge Regression Alpha: {grid_search.best_params_[\"alpha\"]}')\n",
-    "print(f'Best Ridge Regression R^2 Score: {best_ridge_model.score(X_test, y_test):.4f}')\n",
-    "print(f'Best Ridge Regression Coefficients: {best_ridge_model.coef_}')\n",
-    "\n",
-    "# Visualize the coefficients\n",
-    "plt.figure(figsize=(12, 6))\n",
-    "plt.bar(range(len(best_ridge_model.coef_)), best_ridge_model.coef_)\n",
-    "plt.title('Ridge Regression Coefficients')\n",
-    "plt.xlabel('Feature Index')\n",
-    "plt.ylabel('Coefficient Value')\n",
-    "plt.xticks(range(len(best_ridge_model.coef_)), [f'feature_{i}' for i in range(len(best_ridge_model.coef_))])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dade0907",
-   "metadata": {},
-   "source": [
-    "-   **Feature Importance**: Analyze the feature importance in both Lasso and Ridge regression models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "bb55ece1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     Feature  Lasso Coefficients  Ridge Coefficients\n",
-      "0  feature_0        4.088956e-01            0.447069\n",
-      "1  feature_1        1.030849e-02            0.009645\n",
-      "2  feature_2       -4.744454e-02           -0.120293\n",
-      "3  feature_3        3.633460e-01            0.766201\n",
-      "4  feature_4       -3.086013e-07           -0.000002\n",
-      "5  feature_5       -3.359456e-03           -0.003522\n",
-      "6  feature_6       -4.071099e-01           -0.419720\n",
-      "7  feature_7       -4.149332e-01           -0.433422\n",
-      "8  feature_8        0.000000e+00            0.000964\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1200x600 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Example code for feature importance analysis\n",
-    "import pandas as pd\n",
-    "\n",
-    "# Create a DataFrame to hold the coefficients\n",
-    "coefficients = pd.DataFrame({\n",
-    "    'Feature': [f'feature_{i}' for i in range(X.shape[1])],\n",
-    "    'Lasso Coefficients': best_lasso_model.coef_,\n",
-    "    'Ridge Coefficients': best_ridge_model.coef_\n",
-    "})\n",
-    "\n",
-    "print(coefficients)\n",
-    "\n",
-    "# Visualize feature importance\n",
-    "plt.figure(figsize=(12, 6))\n",
-    "coefficients.set_index('Feature').plot(kind='bar', figsize=(12, 6))\n",
-    "plt.title('Feature Importance in Regularized Regression Models')\n",
-    "plt.xlabel('Features')\n",
-    "plt.ylabel('Coefficient Value')\n",
-    "plt.legend(title='Models')\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C06 Nonlinearities in Regression.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C06 Nonlinearities in Regression.ipynb
deleted file mode 100644
index 7ac73b3..0000000
--- a/src/S02 Supervised Learning - Regression and Evaluation/S02C06 Nonlinearities in Regression.ipynb	
+++ /dev/null
@@ -1,561 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "f14bcbbe",
-   "metadata": {},
-   "source": [
-    "# Nonlinearities in Regression\n",
-    "\n",
-    "In this section, we will explore the concept of nonlinearities in regression models. While linear regression is a powerful tool for modeling relationships between variables, it has limitations when it comes to capturing complex relationships in data. Nonlinear regression models can help address this limitation by allowing for more flexible relationships between the independent and dependent variables.\n",
-    "\n",
-    "Nonlinear regression models can take various forms, including **polynomial regression**, **exponential regression**, and **logarithmic regression**. These models can capture relationships that are not well represented by a straight line, allowing for a more accurate representation of the underlying data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6ac5ceed",
-   "metadata": {},
-   "source": [
-    "## Types of Nonlinear Regression Models\n",
-    "\n",
-    "-   **Polynomial Regression**: This model fits a polynomial equation to the data, allowing for curves and bends in the relationship between the independent and dependent variables. The degree of the polynomial can be adjusted to capture more complex relationships.\n",
-    "-   **Exponential Regression**: This model fits an exponential function to the data, which is useful for modeling relationships where the rate of change increases or decreases exponentially.\n",
-    "-   **Logarithmic Regression**: This model fits a logarithmic function to the data, which is useful for modeling relationships where the rate of change decreases as the independent variable increases."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c18db580",
-   "metadata": {},
-   "source": [
-    "## Practical Demonstration\n",
-    "\n",
-    "In this section, we will demonstrate how to implement nonlinear regression models using Python's `scikit-learn` library. We will use a synthetic dataset to illustrate the concepts of polynomial regression, exponential regression, and logarithmic regression. We will also explore how to evaluate the performance of these models using metrics such as Mean Squared Error (MSE) and R-squared ($R^2$) score.\n",
-    "\n",
-    "Let's start by generating a synthetic dataset that exhibits a nonlinear relationship between the independent variable ($X$) and the dependent variable ($y$). We will then fit a polynomial regression model to this data and visualize the results.\n",
-    "\n",
-    "-   Generate Synthetic Data with the following mathematical relationship:$$y = 3X^2 + 8 + \\epsilon$$ where $\\epsilon$ is a normally distributed error term."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "dc5b006a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "n_samples = 100\n",
-    "np.random.seed(42)\n",
-    "X = np.linspace(-3, 3, n_samples).reshape(-1, 1)  # Independent variable\n",
-    "y = 3 * X**2 + 8 + 1.0 * np.random.randn(100, 1)  # Dependent variable with noise\n",
-    "\n",
-    "data = pd.DataFrame(np.hstack((X, y)), columns=['X', 'y'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f26362de",
-   "metadata": {},
-   "source": [
-    "-   Exploratory Data Analysis (EDA)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "bfb2a33b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "sns.set(style='whitegrid')\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.scatter(data['X'], data['y'], color='blue', label='Data Points')\n",
-    "plt.title('Synthetic Data with Nonlinear Relationship')\n",
-    "plt.xlabel('X')\n",
-    "plt.ylabel('y')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dbd7dc0f",
-   "metadata": {},
-   "source": [
-    "-   Train a Simple Linear Regression Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "40697ddd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Linear Regression Coefficients: [0.35997096]\n",
-      "Linear Regression Intercept: 17.055287073961274\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "X_train, X_test, y_train, y_test = train_test_split(data[['X']], data['y'], test_size=0.2, random_state=42)\n",
-    "\n",
-    "linear_model = LinearRegression()\n",
-    "linear_model.fit(X_train, y_train)\n",
-    "\n",
-    "print(f\"Linear Regression Coefficients: {linear_model.coef_}\")\n",
-    "print(f\"Linear Regression Intercept: {linear_model.intercept_}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d5049b37",
-   "metadata": {},
-   "source": [
-    "-   Make Predictions and Evaluate the Linear Regression Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "0483832e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Linear Regression Mean Squared Error (MSE): 61.11\n",
-      "Linear Regression R-squared (R^2): -0.06\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import mean_squared_error, r2_score\n",
-    "\n",
-    "y_pred_linear = linear_model.predict(X_test)\n",
-    "\n",
-    "mse_linear = mean_squared_error(y_test, y_pred_linear)\n",
-    "r2_linear = r2_score(y_test, y_pred_linear)\n",
-    "\n",
-    "print(f\"Linear Regression Mean Squared Error (MSE): {mse_linear:.2f}\")\n",
-    "print(f\"Linear Regression R-squared (R^2): {r2_linear:.2f}\")\n",
-    "\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.scatter(X_test, y_test, color='blue', label='Actual Data')\n",
-    "sns.lineplot(x=X_test.squeeze(), y=y_pred_linear, color='red', label='Linear Regression Prediction', linewidth=2)\n",
-    "plt.title('Linear Regression - Actual vs Predicted')\n",
-    "plt.xlabel('X')\n",
-    "plt.ylabel('y')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "caa89f7a",
-   "metadata": {},
-   "source": [
-    "The linear regression model does not fit the data well, as expected, since the relationship is nonlinear. Now, let's fit a polynomial regression model to capture the nonlinear relationship.\n",
-    "\n",
-    "-   Train a Polynomial Regression Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "4cb10890",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Polynomial Regression Coefficients: [7.81796635 0.01783482 3.02035046]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.preprocessing import PolynomialFeatures\n",
-    "\n",
-    "degree = 2  # Degree of the polynomial\n",
-    "polynomial_features = PolynomialFeatures(degree=degree)\n",
-    "X_train_poly = polynomial_features.fit_transform(X_train)\n",
-    "X_test_poly = polynomial_features.transform(X_test)\n",
-    "\n",
-    "polynomial_model = LinearRegression(fit_intercept=False)\n",
-    "polynomial_model.fit(X_train_poly, y_train)\n",
-    "print(f\"Polynomial Regression Coefficients: {polynomial_model.coef_}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "81183a80",
-   "metadata": {},
-   "source": [
-    "-   Make Predictions and Evaluate the Polynomial Regression Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e73dd032",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Polynomial Regression Mean Squared Error (MSE): 0.62\n",
-      "Polynomial Regression R-squared (R^2): 0.99\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "y_pred_poly = polynomial_model.predict(X_test_poly)\n",
-    "\n",
-    "mse_poly = mean_squared_error(y_test, y_pred_poly)\n",
-    "r2_poly = r2_score(y_test, y_pred_poly)\n",
-    "\n",
-    "print(f\"Polynomial Regression Mean Squared Error (MSE): {mse_poly:.2f}\")\n",
-    "print(f\"Polynomial Regression R-squared (R^2): {r2_poly:.2f}\")\n",
-    "\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.scatter(X_test, y_test, color='blue', label='Actual Data')\n",
-    "sns.lineplot(x=X_test.squeeze(), y=y_pred_poly, color='red', label='Polynomial Regression Prediction', linewidth=2)\n",
-    "plt.title('Polynomial Regression - Actual vs Predicted')\n",
-    "plt.xlabel('X')\n",
-    "plt.ylabel('y')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3271fed",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "-   Generate Synthetic Data with a Polynomial Relationship:$$y = X^3 + 3X + \\epsilon$$ where $\\epsilon$ is a normally distributed error term."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "f14269b3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_samples = 100\n",
-    "np.random.seed(42)\n",
-    "X = np.linspace(-5, 5, n_samples).reshape(-1, 1)  # Independent variable\n",
-    "y = X**3 + 3.0 * X + 1.0 * np.random.randn(100, 1)  # Dependent variable with noise\n",
-    "\n",
-    "df = pd.DataFrame(np.hstack((X, y)), columns=['X', 'y'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a752d3f5",
-   "metadata": {},
-   "source": [
-    "-   Exploratory Data Analysis (EDA)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "8c187c63",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.scatter(df['X'], df['y'], color='blue', label='Data Points')\n",
-    "plt.title('Synthetic Data with Polynomial Relationship')\n",
-    "plt.xlabel('X')\n",
-    "plt.ylabel('y')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "90a2dc96",
-   "metadata": {},
-   "source": [
-    "-   Split the Data into Training and Testing Sets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "603cb12f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training set size: 80, Testing set size: 20\n"
-     ]
-    }
-   ],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(df[['X']], df['y'], test_size=0.2, random_state=42)\n",
-    "print(f\"Training set size: {len(X_train)}, Testing set size: {len(X_test)}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c5b824c4",
-   "metadata": {},
-   "source": [
-    "-   Train a Simple Linear Regression Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "102b566e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Linear Regression Coefficients: [18.57974736]\n",
-      "Linear Regression Intercept: 0.5956463511219252\n"
-     ]
-    }
-   ],
-   "source": [
-    "linear_model = LinearRegression()\n",
-    "linear_model.fit(X_train, y_train)\n",
-    "\n",
-    "print(f\"Linear Regression Coefficients: {linear_model.coef_}\")\n",
-    "print(f\"Linear Regression Intercept: {linear_model.intercept_}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d710979",
-   "metadata": {},
-   "source": [
-    "-   Make Predictions and Evaluate the Linear Regression Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "538c2aa7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Linear Regression Mean Squared Error (MSE): 407.78\n",
-      "Linear Regression R-squared (R^2): 0.85\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "y_pred_linear = linear_model.predict(X_test)\n",
-    "\n",
-    "mse_linear = mean_squared_error(y_test, y_pred_linear)\n",
-    "r2_linear = r2_score(y_test, y_pred_linear)\n",
-    "\n",
-    "print(f\"Linear Regression Mean Squared Error (MSE): {mse_linear:.2f}\")\n",
-    "print(f\"Linear Regression R-squared (R^2): {r2_linear:.2f}\")\n",
-    "\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.scatter(X_test, y_test, color='blue', label='Actual Data')\n",
-    "sns.lineplot(x=X_test.squeeze(), y=y_pred_linear, color='red', label='Linear Regression Prediction', linewidth=2)\n",
-    "plt.title('Linear Regression - Actual vs Predicted')\n",
-    "plt.xlabel('X')\n",
-    "plt.ylabel('y')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "04690bc6",
-   "metadata": {},
-   "source": [
-    "-   Train a Polynomial Regression Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "3a16618a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Polynomial Regression Coefficients: [0.         3.10858311 0.00805488 0.99296385]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.preprocessing import PolynomialFeatures\n",
-    "from sklearn.linear_model import Lasso\n",
-    "degree = 3  # Degree of the polynomial\n",
-    "polynomial_features = PolynomialFeatures(degree=degree)\n",
-    "X_train_poly = polynomial_features.fit_transform(X_train)\n",
-    "X_test_poly = polynomial_features.transform(X_test)\n",
-    "\n",
-    "polynomial_model = Lasso(alpha=0.1)  # Using Lasso for regularization\n",
-    "polynomial_model.fit(X_train_poly, y_train)\n",
-    "print(f\"Polynomial Regression Coefficients: {polynomial_model.coef_}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "03c8271b",
-   "metadata": {},
-   "source": [
-    "-   Make Predictions and Evaluate the Polynomial Regression Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "c8eb2bdc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Polynomial Regression Mean Squared Error (MSE): 0.60\n",
-      "Polynomial Regression R-squared (R^2): 1.00\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "y_pred_poly = polynomial_model.predict(X_test_poly)\n",
-    "\n",
-    "mse_poly = mean_squared_error(y_test, y_pred_poly)\n",
-    "r2_poly = r2_score(y_test, y_pred_poly)\n",
-    "\n",
-    "print(f\"Polynomial Regression Mean Squared Error (MSE): {mse_poly:.2f}\")\n",
-    "print(f\"Polynomial Regression R-squared (R^2): {r2_poly:.2f}\")\n",
-    "\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.scatter(X_test, y_test, color='blue', label='Actual Data')\n",
-    "sns.lineplot(x=X_test.squeeze(), y=y_pred_poly, color='red', label='Polynomial Regression Prediction', linewidth=2)\n",
-    "plt.title('Polynomial Regression - Actual vs Predicted')\n",
-    "plt.xlabel('X')\n",
-    "plt.ylabel('y')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C07 Overfitting, Underfitting, and Hyperparameter Tuning.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C07 Overfitting, Underfitting, and Hyperparameter Tuning.ipynb
deleted file mode 100644
index 50a9441..0000000
--- a/src/S02 Supervised Learning - Regression and Evaluation/S02C07 Overfitting, Underfitting, and Hyperparameter Tuning.ipynb	
+++ /dev/null
@@ -1,524 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "bf7ff470",
-   "metadata": {},
-   "source": [
-    "# Overfitting, Underfitting & Hyperparameter Tuning\n",
-    "\n",
-    "We've already seen how to build regression models, and briefly touched on the concepts of overfitting and underfitting. In this section, we will explore these concepts in more detail, along with hyperparameter tuning techniques to improve model performance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f364e52e",
-   "metadata": {},
-   "source": [
-    "## Overfitting and Underfitting\n",
-    "\n",
-    "Overfitting and underfitting are common issues in machine learning models, particularly in regression tasks.\n",
-    "\n",
-    "-   **Underfitting** occurs when a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and test datasets.\n",
-    "-   **Overfitting** occurs when a model is too complex, capturing noise in the training data rather than the underlying pattern, leading to poor generalization to new data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "db08e296",
-   "metadata": {},
-   "source": [
-    "## Bias-Variance Tradeoff\n",
-    "\n",
-    "The bias-variance tradeoff is a key concept in understanding overfitting and underfitting:\n",
-    "\n",
-    "-   **Bias**: Error due to overly simplistic assumptions in the learning algorithm. High bias leads to underfitting.\n",
-    "-   **Variance**: Error due to excessive sensitivity to small fluctuations in the training set. High variance leads to overfitting.\n",
-    "-   **Tradeoff**: A good model balances bias and variance to minimize total error."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4776e796",
-   "metadata": {},
-   "source": [
-    "## Learning Curves\n",
-    "\n",
-    "Learning curves are a useful tool to visualize the performance of a model as the size of the training dataset increases. They can help identify overfitting and underfitting:\n",
-    "\n",
-    "-   **Underfitting**: High training and validation errors, indicating the model is too simple.\n",
-    "-   **Overfitting**: Low training error but high validation error, indicating the model is too complex and captures noise in the training data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "35242027",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.datasets import make_regression\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.model_selection import learning_curve\n",
-    "\n",
-    "# Generate synthetic data\n",
-    "X, y = make_regression(n_samples=100, n_features=1, noise=10, random_state=42)\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "\n",
-    "# Create a linear regression model\n",
-    "model = LinearRegression()\n",
-    "\n",
-    "# Calculate learning curves\n",
-    "train_sizes, train_scores, test_scores = learning_curve(model, X_train, y_train, cv=5, n_jobs=-1,\n",
-    "                                                        train_sizes=np.linspace(0.1, 1.0, 20),\n",
-    "                                                        scoring='r2')\n",
-    "\n",
-    "# Calculate mean and standard deviation of training and test scores\n",
-    "train_scores_mean = np.mean(train_scores, axis=1)\n",
-    "test_scores_mean = np.mean(test_scores, axis=1)\n",
-    "train_scores_std = np.std(train_scores, axis=1)\n",
-    "test_scores_std = np.std(test_scores, axis=1)\n",
-    "\n",
-    "# Plot learning curves\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.title(\"Learning Curves (Linear Regression)\")\n",
-    "plt.xlabel(\"Training examples\")\n",
-    "plt.ylabel(\"Score\")\n",
-    "plt.grid()\n",
-    "plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color=\"r\")\n",
-    "plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
-    "plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Training score\")\n",
-    "plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Cross-validation score\")\n",
-    "plt.legend(loc=\"best\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bc45fa46",
-   "metadata": {},
-   "source": [
-    "## Visualizing Overfitting and Underfitting\n",
-    "\n",
-    "We can visualize overfitting and underfitting using polynomial regression. A simple linear model may underfit, while a high-degree polynomial may overfit the data, trying to fit every point perfectly, but likely failing to generalize well to unseen data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "80b0b601",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.datasets import make_regression\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.preprocessing import PolynomialFeatures\n",
-    "\n",
-    "# Generate synthetic data\n",
-    "X = np.linspace(0, 1, 10).reshape(-1, 1)  # 10 random points in range [0, 1]\n",
-    "y = 2 * X + 0.3 * np.random.randn(10, 1)  # Linear relationship with noise\n",
-    "\n",
-    "# Create polynomial features\n",
-    "poly = PolynomialFeatures(degree=20)\n",
-    "X_poly = poly.fit_transform(X)\n",
-    "\n",
-    "# Fit linear regression model\n",
-    "model = LinearRegression()\n",
-    "model.fit(X_poly, y)\n",
-    "\n",
-    "# Generate predictions\n",
-    "X_test = np.linspace(X.min(), X.max(), 100).reshape(-1, 1)\n",
-    "X_test_poly = poly.transform(X_test)\n",
-    "y_pred = model.predict(X_test_poly)\n",
-    "\n",
-    "# Plotting\n",
-    "plt.scatter(X, y, color='blue', label='Data')\n",
-    "plt.plot(X_test, y_pred, color='red', label='Polynomial Regression (degree=10)')\n",
-    "plt.title('Overfitting Example')\n",
-    "plt.xlabel('Feature')\n",
-    "plt.ylabel('Target')\n",
-    "plt.xlim(X.min(), X.max())\n",
-    "plt.ylim(y.min(), y.max())\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1e8cb7b4",
-   "metadata": {},
-   "source": [
-    "## Regularization Techniques\n",
-    "\n",
-    "Regularization techniques are used to prevent overfitting by adding a penalty for large coefficients in the model. This encourages simpler models that generalize better to unseen data.\n",
-    "\n",
-    "-   **Ridge Regression** (L2 penalty): Shrinks coefficients to prevent overfitting.\n",
-    "-   **Lasso Regression** (L1 penalty): Can shrink some coefficients to zero (feature selection)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ae9accd2",
-   "metadata": {},
-   "source": [
-    "## Hyperparameter Tuning\n",
-    "\n",
-    "Hyperparameters are settings that are not learned from the data but are set before training the model. Tuning these hyperparameters can significantly improve model performance. Common hyperparameters include:\n",
-    "\n",
-    "-   Regularization strength (e.g., `alpha` in Ridge and Lasso).\n",
-    "-   Degree of polynomial features.\n",
-    "-   Number of trees in ensemble methods (e.g., Random Forest).\n",
-    "-   Learning rate in gradient boosting methods.\n",
-    "-   Etc."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "65bcef1a",
-   "metadata": {},
-   "source": [
-    "### Grid Search and Random Search\n",
-    "\n",
-    "Grid search and random search are techniques for hyperparameter tuning:\n",
-    "\n",
-    "-   **Grid Search**: Exhaustively searches through a specified subset of hyperparameters, evaluating model performance for each combination.\n",
-    "-   **Random Search**: Samples a fixed number of hyperparameter combinations from a specified distribution, often more efficient than grid search."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "57b26892",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best parameters (Grid Search): 0.10\n",
-      "Best cross-validation score (Grid Search): 0.94\n",
-      "Best parameters (Random Search): 0.10\n",
-      "Best cross-validation score (Random Search): 0.94\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
-    "from sklearn.linear_model import Ridge, Lasso\n",
-    "import numpy as np\n",
-    "\n",
-    "# Define hyperparameter grid for Ridge regression\n",
-    "params = {'alpha': np.logspace(-3, 3, 7)}\n",
-    "\n",
-    "# Create a Ridge model\n",
-    "ridge = Ridge()\n",
-    "\n",
-    "# Perform grid search\n",
-    "grid_search = GridSearchCV(ridge, param_grid=params, cv=5, scoring='r2')\n",
-    "grid_search.fit(X_train, y_train)\n",
-    "\n",
-    "print(f\"Best parameters (Grid Search): {grid_search.best_params_['alpha']:.2f}\")\n",
-    "print(f\"Best cross-validation score (Grid Search): {grid_search.best_score_:.2f}\")\n",
-    "\n",
-    "# Perform random search\n",
-    "random_search = RandomizedSearchCV(ridge, param_distributions=params, n_iter=7,\n",
-    "                                   cv=5, scoring='r2', random_state=42)\n",
-    "random_search.fit(X_train, y_train)\n",
-    "\n",
-    "print(f\"Best parameters (Random Search): {random_search.best_params_['alpha']:.2f}\")\n",
-    "print(f\"Best cross-validation score (Random Search): {random_search.best_score_:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "886d1b23",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "-   Generate synthetic data and visualize overfitting and underfitting using polynomial regression."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "903b72eb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Generate synthetic data\n",
-    "np.random.seed(42)\n",
-    "X = np.linspace(0, 1, 11).reshape(-1, 1)  # 11 random points in range [0, 1]\n",
-    "y = 2 * X + 0.1 * np.random.randn(11, 1)  # Linear relationship with noise\n",
-    "\n",
-    "# Create polynomial features\n",
-    "poly = PolynomialFeatures(degree=20)\n",
-    "X_poly = poly.fit_transform(X)\n",
-    "\n",
-    "# Fit linear regression model\n",
-    "model = LinearRegression()\n",
-    "model.fit(X_poly, y)\n",
-    "\n",
-    "# Generate predictions\n",
-    "X_test = np.linspace(0.05, 0.95, 10).reshape(-1, 1)\n",
-    "y_test = 2 * X_test + 0.1 * np.random.randn(10, 1)\n",
-    "X_test_poly = poly.transform(X_test)\n",
-    "y_pred = model.predict(X_test_poly)\n",
-    "\n",
-    "# Plotting\n",
-    "plt.scatter(X_test, y_test, color='blue', label='Data')\n",
-    "plt.plot(X_test_poly[:, 1], y_pred, color='red', label='Polynomial Regression (degree=20)')\n",
-    "plt.title('Overfitting Example')\n",
-    "plt.xlabel('Feature')\n",
-    "plt.ylabel('Target')\n",
-    "plt.xlim(X.min(), X.max())\n",
-    "plt.ylim(y.min(), y.max())\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3adf393f",
-   "metadata": {},
-   "source": [
-    "-   Implement regularization techniques (Ridge and Lasso) and compare their performance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "7e6509d1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Ridge Regression Score: 0.87\n",
-      "Lasso Regression Score: 0.66\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import Ridge, Lasso\n",
-    "from sklearn.metrics import r2_score\n",
-    "\n",
-    "# Create Ridge and Lasso models\n",
-    "ridge_model = Ridge(alpha=1.0)\n",
-    "lasso_model = Lasso(alpha=0.1)\n",
-    "\n",
-    "# Fit models\n",
-    "ridge_model.fit(X_poly, y)\n",
-    "lasso_model.fit(X_poly, y)\n",
-    "\n",
-    "# Predict using both models\n",
-    "ridge_pred = ridge_model.predict(X_test_poly)\n",
-    "lasso_pred = lasso_model.predict(X_test_poly)\n",
-    "\n",
-    "print(f\"Ridge Regression Score: {r2_score(y_test, ridge_pred):.2f}\")\n",
-    "print(f\"Lasso Regression Score: {r2_score(y_test, lasso_pred):.2f}\")\n",
-    "\n",
-    "# Plotting\n",
-    "plt.scatter(X_test, y_test, color='blue', label='Data')\n",
-    "plt.plot(X_test_poly[:, 1], ridge_pred, color='red', label='Ridge Regression')\n",
-    "plt.plot(X_test_poly[:, 1], lasso_pred, color='green', label='Lasso Regression')\n",
-    "plt.title('Regularization Techniques')\n",
-    "plt.xlabel('Feature')\n",
-    "plt.ylabel('Target')\n",
-    "plt.xlim(X.min(), X.max())\n",
-    "plt.ylim(y.min(), y.max())\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "90344108",
-   "metadata": {},
-   "source": [
-    "-   Perform hyperparameter tuning using grid search for Ridge and Lasso regression models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "69bf97fd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best parameters (Ridge Grid Search): 0.10\n",
-      "Best parameters (Lasso Grid Search): 0.01\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import GridSearchCV\n",
-    "\n",
-    "# Define hyperparameter grid for Ridge regression\n",
-    "ridge_params = {'alpha': np.logspace(-3, 3, 7)}\n",
-    "\n",
-    "# Create a Ridge model\n",
-    "ridge = Ridge()\n",
-    "\n",
-    "# Perform grid search\n",
-    "ridge_grid_search = GridSearchCV(ridge, param_grid=ridge_params, cv=5, scoring='r2')\n",
-    "ridge_grid_search.fit(X_poly, y)\n",
-    "\n",
-    "print(f\"Best parameters (Ridge Grid Search): {ridge_grid_search.best_params_['alpha']:.2f}\")\n",
-    "\n",
-    "# Define hyperparameter grid for Lasso regression\n",
-    "lasso_params = {'alpha': np.logspace(-3, 3, 7)}\n",
-    "\n",
-    "# Create a Lasso model\n",
-    "lasso = Lasso()\n",
-    "\n",
-    "# Perform grid search\n",
-    "lasso_grid_search = GridSearchCV(lasso, param_grid=lasso_params, cv=5, scoring='r2')\n",
-    "lasso_grid_search.fit(X_poly, y)\n",
-    "\n",
-    "print(f\"Best parameters (Lasso Grid Search): {lasso_grid_search.best_params_['alpha']:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5ef616b5",
-   "metadata": {},
-   "source": [
-    "-   Visualize the performance of the tuned models on the test set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "ab53d78f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best Ridge Regression Score: 0.94\n",
-      "Best Lasso Regression Score: 0.94\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Predict using the best Ridge model\n",
-    "best_ridge_model = ridge_grid_search.best_estimator_\n",
-    "ridge_test_pred = best_ridge_model.predict(X_test_poly)\n",
-    "\n",
-    "# Predict using the best Lasso model\n",
-    "best_lasso_model = lasso_grid_search.best_estimator_\n",
-    "lasso_test_pred = best_lasso_model.predict(X_test_poly)\n",
-    "\n",
-    "# Plotting\n",
-    "plt.scatter(X_test, y_test, color='blue', label='Data')\n",
-    "plt.plot(X_test, ridge_test_pred, color='red', label='Best Ridge Regression')\n",
-    "plt.plot(X_test, lasso_test_pred, color='green', label='Best Lasso Regression')\n",
-    "plt.title('Tuned Regularization Techniques')\n",
-    "plt.xlabel('Feature')\n",
-    "plt.ylabel('Target')\n",
-    "plt.xlim(X.min(), X.max())\n",
-    "plt.ylim(y.min(), y.max())\n",
-    "plt.legend()\n",
-    "plt.show()\n",
-    "\n",
-    "# Score the tuned models\n",
-    "print(f\"Best Ridge Regression Score: {r2_score(y_test, ridge_test_pred):.2f}\")\n",
-    "print(f\"Best Lasso Regression Score: {r2_score(y_test, lasso_test_pred):.2f}\")"
-   ]
-  }
- ],
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-  "jupytext": {
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-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
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-  "language_info": {
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-    "version": 3
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-   "file_extension": ".py",
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-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
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- "nbformat": 4,
- "nbformat_minor": 5
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diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03 Supervised Learning - Classification and Evaluation.pdf b/src/S03 Supervised Learning - Classification and Evaluation/S03 Supervised Learning - Classification and Evaluation.pdf
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diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03 Supervised Learning - Classification and Evaluation.tex b/src/S03 Supervised Learning - Classification and Evaluation/S03 Supervised Learning - Classification and Evaluation.tex
deleted file mode 100644
index 2bfb3e7..0000000
--- a/src/S03 Supervised Learning - Classification and Evaluation/S03 Supervised Learning - Classification and Evaluation.tex	
+++ /dev/null
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-\title{Machine Learning in Python}
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-\date[Jul 4\textsuperscript{th} 2025]{Friday, July 4\textsuperscript{th} 2025}
-\usepackage{mathtools}
-\newcommand{\intsum}{\mathop{\mathrlap{\raisebox{0.1ex}{\hspace{0.2em}$\textstyle\sum$}}\int}\limits}
-\setbeamercovered{transparent=0}
-\usepackage[timeinterval=60]{tdclock}
-\hypersetup{
- pdfauthor={},
- pdftitle={Machine Learning in Python},
- pdfkeywords={},
- pdfsubject={},
- pdfcreator={Emacs 30.1 (Org mode 9.7.11)}, 
- pdflang={English}}
-\begin{document}
-
-\maketitle
-\begin{frame}{Outline}
-\setcounter{tocdepth}{1}
-\tableofcontents
-\end{frame}
-
-\section{Introduction to Classification}
-\label{sec:org2864275}
-\begin{frame}[label={sec:orgd3d726f}]{Introduction to Classification}
-\begin{definition}[Classification]\label{sec:org2a2c932}
-\pause
-\alert{Classification} is a type of supervised learning where the model learns from labeled data to predict the class of new observations based on past data.
-\pause
-
-\alert{Classification vs. Regression} is a key distinction in supervised learning:
-\begin{itemize}[<+->]
-\item In \alert{classification}, the target variable is categorical (e.g., "spam" or "not spam").
-\item In \alert{regression}, the target variable is continuous (e.g., predicting a price).
-\end{itemize}
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org625fd0b}]{Classification Types}
-\begin{definition}[Classification Types]\label{sec:org04c184b}
-\pause
-\alert{Classification} can be broadly divided into two types:
-\begin{itemize}[<+->]
-\item \alert{Binary Classification}: The target variable has two classes (e.g., "yes" or "no", "spam" or "not spam"). Numerically, this can always be represented as 0 and 1.
-\item \alert{Multiclass Classification}: The target variable has more than two classes (e.g., "cat", "dog", "weasel"). In this case, the model predicts one of several possible categories.
-\end{itemize}
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orgab56005}]{Classification Algorithms}
-\begin{definition}[Classification Algorithms]\label{sec:org6186236}
-\pause
-\alert{Classification algorithms} are designed to learn from labeled data and make predictions about the class of new, unseen data. Some common algorithms include:
-\begin{itemize}[<+->]
-\item \alert{Logistic Regression}: Despite its name, it is used for binary classification. It models the probability that a given input belongs to a particular class.
-\item \alert{k-Nearest Neighbors (k-NN)}: A non-parametric method that classifies a data point based on the classes of its nearest neighbors in the feature space.
-\item \alert{Decision Trees}: A tree-like model that splits the data into subsets based on feature values, leading to a decision about the class label.
-\item \alert{Support Vector Machines (SVM)}: A method that finds the hyperplane that best separates the classes in the feature space.
-\item More advanced algorithms like \alert{Random Forests}, \alert{Gradient Boosting}, and \alert{Neural Networks}.
-\end{itemize}
-\end{definition}
-\end{frame}
-\section{Classification Basics}
-\label{sec:org9aaf9a1}
-\begin{frame}[label={sec:orgac7372d}]{Logistic Regression}
-\begin{definition}[Logistic Regression]\label{sec:org4b9112f}
-\pause
-\alert{Logistic Regression} models the probability that the target variable \(y\) belongs to a particular class. The \alert{logistic function (sigmoid)} is used to map predicted values to probabilities between 0 and 1. The decision boundary is determined by the threshold (commonly 0.5) for classifying observations into different classes.
-\pause
-
-The sigmoid function is defined as:
-$$\sigma(z) = \frac{1}{1 + e^{-z}}$$
-where \(z\) is a linear combination of the input features.
-\end{definition}
-\end{frame}
-\section{Evaluation Metrics}
-\label{sec:orgbb1232e}
-\begin{frame}[label={sec:org09cc5f8}]{Evaluation Metrics for Classification}
-\begin{definition}[Confusion Matrix]\label{sec:orge5d6fbc}
-\pause
-The \alert{confusion matrix} summarizes the performance of a classification algorithm by comparing predicted and actual labels.
-\pause
-\begin{center}
-\begin{tabular}{lll}
- & \alert{Predicted = 1} & \alert{Predicted = 0}\\
-\hline
-\alert{Actual = 1} & True Positive (TP) & False Negative (FN)\\
-\alert{Actual = 0} & False Positive (FP) & True Negative (TN)\\
-\end{tabular}
-\end{center}
-From these four numbers we obtain the core metrics summarized next.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orga882b0c}]{Accuracy (Overall Success Rate)}
-\begin{definition}[Accuracy (Overall Success Rate)]\label{sec:org48c6fe8}
-\alert{Accuracy} is a measure of how often the classifier is correct across all predictions. It answers the question: "What fraction of \alert{all} predictions are correct?"
-$$\text{Accuracy} = \frac{\text{TP} + \text{TN}}{\text{TP} + \text{FP} + \text{FN} + \text{TN}}$$
-
-Despite its simplicity, accuracy can be misleading, especially in imbalanced datasets where one class dominates.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org08322fb}]{Precision (Positive Predictive Value)}
-\begin{definition}[Precision (Positive Predictive Value)]\label{sec:org267eddc}
-\alert{Precision} is a measure of the accuracy of positive predictions. It answers the question: "When the classifier predicts 1, how often is it correct?"
-$$\text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}}$$
-
-High precision indicates that when the model predicts a positive class, it is likely correct, but it does not account for false negatives.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org5d76841}]{Recall (Sensitivity, True-Positive Rate)}
-\begin{definition}[Recall (Sensitivity, True-Positive Rate)]\label{sec:org5d5057e}
-\alert{Recall} is a measure of the model's ability to capture all positive instances. It answers the question: "Of all the actual 1 cases, how many did we catch?"
-$$\text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}}$$
-High recall indicates that the model is good at identifying positive cases, but it does not consider false positives.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orgf745617}]{F1-Score}
-\begin{definition}[F1-Score]\label{sec:orgb3a3de2}
-\alert{F1-Score} is the harmonic mean of precision and recall, providing a balance between the two metrics:
-$$F_1 = 2 \cdot \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}}$$
-It is particularly useful when the class distribution is imbalanced, as it considers both false positives and false negatives.
-High F1-Score indicates a good balance between precision and recall, while a low F1-Score suggests that either precision or recall (or both) are low.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org1d1a35b}]{Precision-Recall Trade-off}
-\begin{definition}[Precision-Recall Trade-off]\label{sec:orgab0fcbc}
-The \alert{Precision-Recall Trade-off} illustrates the inverse relationship between precision and recall:
-\pause
-\begin{itemize}[<+->]
-\item Increasing the threshold for classifying a positive instance will \textcolor{darkgreen}{increase precision} but \textcolor{darkred}{decrease recall}.
-\item Decreasing the threshold will \textcolor{darkgreen}{increase recall} but \textcolor{darkred}{decrease precision}.
-\end{itemize}
-\end{definition}
-\begin{definition}[Precision-Recall Curve]\label{sec:org835364a}
-\pause
-\alert{Precision-Recall Curve} is a graphical representation of the trade-off between precision and recall for different threshold values.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org51f895a}]{Receiver Operating Characteristic (ROC) Curve}
-\begin{definition}[Receiver Operating Characteristic (ROC) Curve]\label{sec:org76b65b5}
-\alert{ROC Curve} is a graphical representation of a classifier's performance across different thresholds. It plots the true positive rate (recall) against the false positive rate (FPR) at various threshold settings.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orga007f07}]{Area Under the Curve (AUC)}
-\begin{definition}[Area Under the Curve (AUC)]\label{sec:org55dcb35}
-\pause
-\alert{Area Under the Curve (AUC)} is a single scalar value that summarizes the performance of a classifier across all possible thresholds. It is calculated as the area under the ROC curve. AUC provides an aggregate measure of performance across all classification thresholds, making it useful for comparing different classifiers.
-\end{definition}
-\end{frame}
-\section{\(k\)-Nearest Neighbours}
-\label{sec:orge781805}
-\begin{frame}[label={sec:org808a2e4}]{\(k\)-Nearest Neighbours (\(k\)-NN)}
-\begin{definition}[\(k\)-Nearest Neighbours (\(k\)-NN)]\label{sec:org22fd718}
-\pause
-\alert{\(k\)-Nearest Neighbors (\(k\)-NN)} is a \alert{non-parametric}, \alert{instance-based} learning algorithm that classifies a new observation based on the classes of its \(k\) nearest neighbors in the feature space. It does not explicitly learn a model but stores the training instances.
-\pause
-
-The algorithm works as follows:
-\begin{itemize}[<+->]
-\item Compute the distance between the new observation and every point in the training set (Euclidean, Manhattan or Minkowski distances).
-\item Select the \(k\) closest neighbors.
-\item Predict the class by majority vote among those neighbors.
-\end{itemize}
-\pause
-
-The decision boundary is implicitly defined by the \(k\) nearest points, allowing the model to capture highly non-linear class boundaries without explicit training.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org302fe80}]{Mathematical Formulation}
-\begin{definition}[Mathematical Formulation]\label{sec:org9596c45}
-Given a training set \(\{(x_i, y_i)\}_{i = 1}^{n}\) where \(y_i \in \{1, \dots,C\}\), the prediction for a query point \(\hat{x}\) is:
-$$\hat{y} = \text{mode}\left(\{y_j | x_j \in N_k(\hat{x})\}\right),$$
-
-where \(N_k(\hat{x})\) denotes the set of \(k\) training points closest to \(\hat{x}\).
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org61b752e}]{Considerations for \(k\)-NN}
-\begin{block}{Considerations for \(k\)-NN}
-\begin{itemize}[<+->]
-\item \alert{Choosing \(k\)}:
-\begin{itemize}
-\item A small \(k\) can lead to noise sensitivity (overfitting).
-\item A large \(k\) can smooth out class boundaries (underfitting).
-\item Cross-validation is often used to select the optimal \(k\).
-\end{itemize}
-\item \alert{Advantages}:
-\begin{itemize}
-\item Simple and intuitive.
-\item No training phase; all computation happens at prediction time.
-\item Naturally handles multi-class classification.
-\end{itemize}
-\item \alert{Disadvantages}:
-\begin{itemize}
-\item Computationally expensive at prediction time, especially with large datasets.
-\item Sensitive to irrelevant features and feature scaling.
-\item Requires a good choice of distance metric.
-\end{itemize}
-\end{itemize}
-\end{block}
-\end{frame}
-\section{Decision Trees}
-\label{sec:orga6019f9}
-\begin{frame}[label={sec:orgeb21917}]{Decision Trees}
-\begin{definition}[Decision Trees]\label{sec:orgaa9420b}
-\alert{Decision Trees} recursively partition the feature space into axis-aligned regions and assign a class label to each region. It consists of \alert{nodes} (features), \alert{branches} (decisions), and \alert{leaves} (outcomes).
-\pause
-
-At each node, the algorithm chooses the split that maximizes a purity metric such as \alert{information gain} or \alert{Gini impurity}. The tree is built by recursively splitting the dataset into subsets until a stopping criterion is met (e.g., maximum depth, minimum samples per leaf).
-\pause
-
-By hierarchically splitting the space, a tree can approximate complex decision boundaries while remaining interpretable-each internal node corresponds to a human-readable rule.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orge4c336e}]{Information Gain}
-\begin{definition}[Information Gain]\label{sec:org41995c9}
-For a node containing a sample set \(S\), the entropy is:
-$$H(S)= -\sum_{c = 1}^{C} p_c \log_2 p_c,$$
-where \(p_c\) is the proportion of class \(c\).
-\pause
-A split of \(S\) into subsets \(S_L, S_R\) yields information gain:
-$$IG = H(S) - \frac{|S_L|}{|S|} H(S_L) - \frac{|S_R|}{|S|} H(S_R).$$
-The algorithm selects the split with the highest \alert{IG} (or lowest Gini).
-\pause
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org8530782}]{Considerations for Decision Trees}
-\begin{definition}[Considerations for Decision Trees]\label{sec:org5518491}
-\begin{itemize}[<+->]
-\item \alert{Pruning}: Techniques like cost-complexity pruning can be applied to reduce overfitting by removing branches that have little importance.
-
-\item \alert{Advantages}:
-\begin{itemize}
-\item Easy to interpret and visualize.
-\item Handles both numerical and categorical data.
-\item Non-parametric: does not assume any specific distribution of the data.
-\end{itemize}
-
-\item \alert{Disadvantages}:
-\begin{itemize}
-\item Prone to overfitting, especially with deep trees.
-\item Sensitive to small changes in the data (can lead to different trees).
-\end{itemize}
-\end{itemize}
-\end{definition}
-\end{frame}
-\section{Support Vector Machines (SVM)}
-\label{sec:org92a9f38}
-\begin{frame}[label={sec:org9d531e1}]{Support Vector Machines (SVM)}
-\begin{definition}[Support Vector Machines (SVM)]\label{sec:orgb62cd81}
-\alert{Support Vector Machines (SVM)} are powerful algorithms that find the optimal hyperplane that separates different classes in the feature space. The goal is to maximize the \alert{margin} between the closest points of each class, known as \alert{support vectors}.
-\pause
-The SVM algorithm works by solving the following optimization problem:
-$$\min_{\mathbf{w}, b} \frac{1}{2} ||\mathbf{w}||^2 \quad \text{s.t.} \quad y_i(\mathbf{w}^T \mathbf{x}_i + b) \geq 1, \quad \forall i,$$
-where \(\mathbf{w}\) is the weight vector, \(b\) is the bias term, and \(y_i\) is the class label of the \(i\)-th training example.
-\pause
-
-Visualization should help\ldots{}
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orgb134eaf}]{Linear SVMs}
-\begin{definition}[Linear SVMs]\label{sec:orgede5b50}
-\alert{Linear SVMs} are used when the data is linearly separable. The decision boundary is a hyperplane defined by the equation:
-$$\mathbf{w}^T \mathbf{x} + b = 0,$$
-where \(\mathbf{w}\) is the weight vector and \(b\) is the bias term. The SVM finds the hyperplane that maximizes the margin between the two classes.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orga7d6338}]{Non-Linear SVMs}
-\begin{definition}[Non-Linear SVMs]\label{sec:org9699cf0}
-\alert{Non-Linear SVMs} use kernel functions to transform the input space into a higher-dimensional space where a linear hyperplane can separate the classes. Common kernels include:
-\begin{itemize}
-\item \alert{Polynomial Kernel}: Maps the input features into a polynomial feature space.
-\item \alert{Radial Basis Function (RBF) Kernel}: Maps the input features into an infinite-dimensional space, allowing for complex decision boundaries.
-\item \alert{Sigmoid Kernel}: Similar to the activation function in neural networks, it maps the input features into a space that can capture non-linear relationships.
-\end{itemize}
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org62ea7b0}]{Kernel Trick}
-\begin{definition}[Kernel Trick]\label{sec:orgf46fb36}
-The \alert{Kernel Trick} allows SVMs to operate in high-dimensional feature spaces without explicitly computing the coordinates of the data in that space. Instead, it computes the inner products between the images of all pairs of data points in the feature space, which is computationally efficient.
-
-This is done using a kernel function \(K(\mathbf{x}_i, \mathbf{x}_j)\) that computes the inner product in the transformed space:
-$$K(\mathbf{x}_i, \mathbf{x}_j) = \phi(\mathbf{x}_i)^T \phi(\mathbf{x}_j),$$
-where \(\phi\) is the mapping function that transforms the input features into the higher-dimensional space.
-\end{definition}
-\end{frame}
-\end{document}
diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03C01 Introduction to Classification.ipynb b/src/S03 Supervised Learning - Classification and Evaluation/S03C01 Introduction to Classification.ipynb
deleted file mode 100644
index 5a9271c..0000000
--- a/src/S03 Supervised Learning - Classification and Evaluation/S03C01 Introduction to Classification.ipynb	
+++ /dev/null
@@ -1,2706 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "d6f86995",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Introduction to Classification\n",
-    "\n",
-    "Supervised learning is a type of machine learning where the model learns from labeled data. In this context, **classification** is a specific type of supervised learning where the goal is to predict a categorical label for new observations based on past data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b33f323b",
-   "metadata": {},
-   "source": [
-    "## Classification vs. Regression\n",
-    "\n",
-    "Classification is distinct from regression, which predicts continuous values. In classification, the target variable $y$ is categorical, meaning it can take on a limited number of discrete values (e.g., \"spam\" or \"not spam\", \"cat\", \"dog\", or \"weasel\")."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "27da1bd4",
-   "metadata": {},
-   "source": [
-    "## Classification Types\n",
-    "\n",
-    "Broadly, classification can be divided into two types:\n",
-    "\n",
-    "-   **Binary Classification**: The target variable has two classes (e.g., \"yes\" or \"no\", \"spam\" or \"not spam\"). Numerically, this can always be represented as 0 and 1.\n",
-    "-   **Multiclass Classification**: The target variable has more than two classes (e.g., \"cat\", \"dog\", \"weasel\"). In this case, the model predicts one of several possible categories."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0e583539",
-   "metadata": {},
-   "source": [
-    "## Classification Algorithms\n",
-    "\n",
-    "Classification algorithms are designed to learn from labeled data and make predictions about the class of new, unseen data. Some common algorithms include:\n",
-    "\n",
-    "-   **Logistic Regression**: Despite its name, it is used for binary classification. It models the probability that a given input belongs to a particular class.\n",
-    "-   **k-Nearest Neighbors (k-NN)**: A non-parametric method that classifies a data point based on the classes of its nearest neighbors in the feature space.\n",
-    "-   **Decision Trees**: A tree-like model that splits the data into subsets based on feature values, leading to a decision about the class label.\n",
-    "-   **Support Vector Machines (SVM)**: A method that finds the hyperplane that best separates the classes in the feature space.\n",
-    "-   More advanced algorithms."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4a5f7e69",
-   "metadata": {},
-   "source": [
-    "## Key Concepts\n",
-    "\n",
-    "-   **Decision Boundaries**: The boundaries that separate different classes in the feature space. These boundaries are determined by the classification algorithm and can be linear or non-linear.\n",
-    "-   **Probability vs. Hard Classification**: Some algorithms output probabilities for each class (e.g., logistic regression), while others provide a hard classification (e.g., k-NN). The output can be class labels or probabilities, depending on the algorithm and the method used to make predictions.\n",
-    "-   **Output**: The output of a classification model can be either class labels (e.g., `predict()` in `scikit-learn`) or probabilities (e.g., `predict_proba()` in `scikit-learn`). The choice depends on the specific use case and the algorithm used."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "06ada17f",
-   "metadata": {},
-   "source": [
-    "## Practical Demonstration\n",
-    "\n",
-    "In this section, we will demonstrate how to perform classification using the Iris dataset, a classic dataset in machine learning. The Iris dataset contains measurements of iris flowers and their species, making it suitable for both binary and multiclass classification tasks."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ef98b563",
-   "metadata": {},
-   "source": [
-    "### Binary vs. Multiclass Classification\n",
-    "\n",
-    "In the Iris dataset, we can perform both binary and multiclass classification. For binary classification we can consider only two species of iris flowers (e.g., \"setosa\" and \"versicolor\"), while for multiclass classification we can use all three species (\"setosa\", \"versicolor\", and \"virginica\").\n",
-    "\n",
-    "-   Loading the Iris dataset and filter out the 'virginica' species"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "33c74972",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "df = pd.read_csv(\"../../data/iris.csv\", header=0)\n",
-    "df = df[df['species'] != 'virginica']\n",
-    "\n",
-    "X = df.drop(columns=['species'])\n",
-    "y = df['species'].astype('category').cat.codes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "edea234d",
-   "metadata": {},
-   "source": [
-    "-   Exploratory Data Analysis (EDA)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "0dcca151",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1117.75x1000 with 20 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "# Visualize the dataset\n",
-    "sns.pairplot(df, hue='species', palette='Set1')\n",
-    "plt.title(\"Iris Dataset Pairplot\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1eb054f0",
-   "metadata": {},
-   "source": [
-    "-   Preprocessing the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ea2957b8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Missing values in each column:\n",
-      "sepal length (cm)    0\n",
-      "sepal width (cm)     0\n",
-      "petal length (cm)    0\n",
-      "petal width (cm)     0\n",
-      "species              0\n",
-      "dtype: int64\n",
-      "Encoded labels: [0 1]\n",
-      "Feature columns: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
-      "Feature data types: sepal length (cm)    float64\n",
-      "sepal width (cm)     float64\n",
-      "petal length (cm)    float64\n",
-      "petal width (cm)     float64\n",
-      "dtype: object\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Check for missing values\n",
-    "print(\"Missing values in each column:\")\n",
-    "print(df.isnull().sum())\n",
-    "\n",
-    "# Encode categorical labels\n",
-    "y = df['species'].astype('category').cat.codes\n",
-    "print(\"Encoded labels:\", y.unique())\n",
-    "# Features and target variable\n",
-    "X = df.drop(columns=['species'])\n",
-    "print(\"Feature columns:\", X.columns.tolist())\n",
-    "print(\"Feature data types:\", X.dtypes)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "59578ef0",
-   "metadata": {},
-   "source": [
-    "-   Split the dataset into training and test sets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a292540e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training set size: 80\n",
-      "Test set size: 20\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "print(\"Training set size:\", len(X_train))\n",
-    "print(\"Test set size:\", len(X_test))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "80f3cdea",
-   "metadata": {},
-   "source": [
-    "-   Train a Logistic Regression classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "2f440e7e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('penalty',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">penalty&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('dual',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">dual&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">C&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">intercept_scaling&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('solver',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">solver&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_iter&nbsp;</td>\n",
-       "            <td class=\"value\">200</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('multi_class',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">multi_class&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('l1_ratio',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
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-       "    const originalWidth = computedStyle.width;\n",
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-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
-      ],
-      "text/plain": [
-       "LogisticRegression(max_iter=200)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "# Train a logistic regression model\n",
-    "clf = LogisticRegression(max_iter=200)\n",
-    "clf.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cc2a3177",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the model's accuracy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "df708d7d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Binary classification accuracy: 1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Make predictions on the test set\n",
-    "y_pred = clf.predict(X_test)\n",
-    "\n",
-    "# Evaluate the model's accuracy\n",
-    "accuracy = accuracy_score(y_test, y_pred)\n",
-    "print(\"Binary classification accuracy:\", accuracy)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b2b9e1b1",
-   "metadata": {},
-   "source": [
-    "-   Plot decision boundaries (2D) using only 2 features (e.g. `petal length`, `petal width`)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "4c831c80",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "# Select two features for visualization\n",
-    "X_vis = X[['petal length (cm)', 'petal width (cm)']]\n",
-    "X_train_vis, X_test_vis, y_train_vis, y_test_vis = train_test_split(X_vis, y, test_size=0.2, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "2b84b442",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-2 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-2 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-2 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-2 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('penalty',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">penalty&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('dual',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">dual&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">C&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">intercept_scaling&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('solver',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">solver&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_iter&nbsp;</td>\n",
-       "            <td class=\"value\">200</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('multi_class',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">multi_class&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('l1_ratio',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
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-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
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-       "            setTimeout(() => {\n",
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-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
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-       "\n",
-       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
-      ],
-      "text/plain": [
-       "LogisticRegression(max_iter=200)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Train the model on the selected features\n",
-    "clf_vis = LogisticRegression(max_iter=200)\n",
-    "clf_vis.fit(X_train_vis, y_train_vis)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "5f35c1bd",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot decision boundaries\n",
-    "from sklearn.inspection import DecisionBoundaryDisplay\n",
-    "display = DecisionBoundaryDisplay.from_estimator(\n",
-    "    estimator=clf_vis,\n",
-    "    X=X_vis,\n",
-    "    alpha=0.8,\n",
-    "    grid_resolution=500,\n",
-    "    response_method=\"predict\",\n",
-    ")\n",
-    "display.ax_.scatter(\n",
-    "    df['petal length (cm)'], df['petal width (cm)'], c=df['species'].astype('category').cat.codes, edgecolor=\"black\"\n",
-    ")\n",
-    "plt.title('Decision Boundary')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77981d2f",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "Use the Iris dataset to perform multiclass classification. You will need to load the dataset again, since we've filtered out the 'virginica' species in the previous section.\n",
-    "\n",
-    "-   Load the Iris dataset and perform multiclass classification using all three species."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "fb9f0dce",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Feature columns: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
-      "Feature data types: sepal length (cm)    float64\n",
-      "sepal width (cm)     float64\n",
-      "petal length (cm)    float64\n",
-      "petal width (cm)     float64\n",
-      "dtype: object\n",
-      "Encoded labels: ['setosa' 'versicolor' 'virginica']\n"
-     ]
-    }
-   ],
-   "source": [
-    "df = pd.read_csv(\"../../data/iris.csv\", header=0)\n",
-    "\n",
-    "# Features and target variable\n",
-    "X_full = df.drop(columns=['species'])\n",
-    "y_full = df['species']\n",
-    "print(\"Feature columns:\", X_full.columns.tolist())\n",
-    "print(\"Feature data types:\", X_full.dtypes)\n",
-    "print(\"Encoded labels:\", y_full.unique())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "600714dd",
-   "metadata": {},
-   "source": [
-    "-   Exploratory Data Analysis (EDA)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "30cf2320",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Missing values in each column:\n",
-      "sepal length (cm)    0\n",
-      "sepal width (cm)     0\n",
-      "petal length (cm)    0\n",
-      "petal width (cm)     0\n",
-      "species              0\n",
-      "dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Check for missing values\n",
-    "print(\"Missing values in each column:\")\n",
-    "print(df.isnull().sum())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "f9a26c1c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1000x600 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1117.75x1000 with 20 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualize the dataset\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.pairplot(df, hue='species', palette='Set1')\n",
-    "plt.title(\"Iris Dataset Pairplot\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3547ed45",
-   "metadata": {},
-   "source": [
-    "-   Select two of the most relevant features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "c40c96cb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Selected features for training: ['petal length (cm)', 'petal width (cm)']\n"
-     ]
-    }
-   ],
-   "source": [
-    "X_full = X_full[['petal length (cm)', 'petal width (cm)']]\n",
-    "print(\"Selected features for training:\", X_full.columns.tolist())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c215ffa4",
-   "metadata": {},
-   "source": [
-    "-   Perform the train-test split."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "4fde3a6a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "X_train_full, X_test_full, y_train_full, y_test_full = \\\n",
-    "    train_test_split(X_full, y_full, test_size=0.2, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "12b761cb",
-   "metadata": {},
-   "source": [
-    "-   Train a logistic regression model for multiclass classification."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "17235d4f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-3 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-3 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-3 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-3 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('penalty',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">penalty&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('dual',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">dual&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">C&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">intercept_scaling&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('solver',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">solver&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
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-       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
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-       "    \n",
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-       "    \n",
-       "                  </tbody>\n",
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-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
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-       "\n",
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-       "});\n",
-       "</script></body>"
-      ],
-      "text/plain": [
-       "LogisticRegression(max_iter=200)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import LogisticRegression\n",
-    "clf_multiclass = LogisticRegression(max_iter=200, solver='lbfgs')\n",
-    "clf_multiclass.fit(X_train_full, y_train_full)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "760b63ec",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the model's accuracy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "caf1e370",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Multiclass classification accuracy: 1.0\n",
-      "Confusion Matrix:\n",
-      " [[10  0  0]\n",
-      " [ 0  9  0]\n",
-      " [ 0  0 11]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Make predictions on the test set\n",
-    "y_pred_full = clf_multiclass.predict(X_test_full)\n",
-    "\n",
-    "# Evaluate the model's accuracy\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "accuracy_multiclass = accuracy_score(y_test_full, y_pred_full)\n",
-    "print(\"Multiclass classification accuracy:\", accuracy_multiclass)\n",
-    "# Confusion matrix\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "cm = confusion_matrix(y_test_full, y_pred_full)\n",
-    "print(\"Confusion Matrix:\\n\", cm)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4709574d",
-   "metadata": {},
-   "source": [
-    "-   Plot decision boundaries (2D) using only the 2 features, `petal length` and `petal width`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "8a18a410",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot decision boundaries\n",
-    "from sklearn.inspection import DecisionBoundaryDisplay\n",
-    "display = DecisionBoundaryDisplay.from_estimator(\n",
-    "    estimator=clf_multiclass,\n",
-    "    X=X_test_full,\n",
-    "    alpha=0.8,\n",
-    "    grid_resolution=500,\n",
-    "    response_method=\"predict\",\n",
-    ")\n",
-    "display.ax_.scatter(\n",
-    "    X_test_full['petal length (cm)'],\n",
-    "    X_test_full['petal width (cm)'],\n",
-    "    c=y_test_full.astype('category').cat.codes, edgecolor=\"black\"\n",
-    ")\n",
-    "plt.title('Decision Boundary')\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03C02 Classification Basics.ipynb b/src/S03 Supervised Learning - Classification and Evaluation/S03C02 Classification Basics.ipynb
deleted file mode 100644
index 6f680d5..0000000
--- a/src/S03 Supervised Learning - Classification and Evaluation/S03C02 Classification Basics.ipynb	
+++ /dev/null
@@ -1,1185 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "01e34019",
-   "metadata": {},
-   "source": [
-    "# Classification Basics\n",
-    "\n",
-    "We will now explore some basic algorithms used in classification tasks, focusing on logistic regression, which is a foundational algorithm in this area."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8ee26b82",
-   "metadata": {},
-   "source": [
-    "## Logistic Regression\n",
-    "\n",
-    "Despite its name, logistic regression is a classification algorithm. It models the probability that the target variable $y$ belongs to a particular class. The [logistic function (sigmoid)](https://en.wikipedia.org/wiki/Logistic_function) is used to map predicted values to probabilities between 0 and 1. The decision boundary is determined by the threshold (commonly 0.5) for classifying observations into different classes.\n",
-    "\n",
-    "The sigmoid function is defined as: $$\\sigma(z) = \\frac{1}{1 + e^{-z}}$$ where $z$ is a linear combination of the input features. In a one-dimensional case, it can be visualized as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b97de385",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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XDGXFCfNrr72G1atXY/v27Zg4cSJCQkIe+t7cvn07mjVrZrhf8gw7Dw8PREVFYfHixZg3b17NN4aIREkQBAgCIADQG27f+13ydnFdFJXj3tUPih8H7te5f/t+HZS4WkLxzbKuoCDce7TkPsuKuSStVossdZWaXeOs6lTwAwcOICIiwqgsKioKb775ZrnbFBYWorCw0HA/KysLQNEZGQ+elaHRaCAIAvR6PfR6fZXjM7yJ7u2jtnh5eRluOzo6QiKRGJUBMMRz+PBhTJs2DefOnUPr1q2xdOlSNGrUCAAwa9YsbNy4EePGjcPs2bORkJAArVaLjIwMTJ48GX/88QcKCwvRrl07LFiwAK1atQIAnDx5EtHR0Thy5AgkEgkaNGiAxYsXo3379obn3bx5M6Kjo3H9+nV06tQJy5YtM1zPRK/X45NPPsEPP/yAW7duoUmTJvj000/x1FNPGcVe8rjExMQY9vfEE0/gxRdfLFXnQT///DMiIiKgUCgMdRYvXoyQkBBDAtKwYUPs2rULCxcuLDdhLt7W1dW13NcZAPr27YsPPvgAn332WZn7MQdzvUcrQ6/XQxAEaDSaap0Krtfp8VzD55Camgq9Ti/as66K28X2lU2vF5Cn0SFPrUO++t7ve/cLNToUaPUo1OpQoNGjUKuHuvhHV/Rbo9NDrROg0env/QjQ6vTQ6AVodQK0ej10egFavVD0W1f0WycI0N/7rdML0Aswui8IRcmKXhCg0cjwzpHtRXWE+4+VTECsXbCDDC/U8Hu0Ku8Jq0puUlJS4O3tbVTm7e2NrKws5OfnG66VUdLs2bMxa9asUuXbtm0rdcq3jY0NfHx8kJOTA7X6gbQzN7f8wGQyQKUy3M1OSSm/rlQKlIyzvP3a25e/jwoUFBRdwKw4iSuWl5cHAHj33Xcxa9YsuLu7Izo6Gi+99JJhWK+wsBCXL1/Gr7/+ipUrV0IqlSIrKwsvvPACVCoVfv31Vzg5OWHFihWIiIjAkSNH4OrqiqFDh6Jly5bYsWMHZDIZTp8+jcLCQmRlZaGgoAB5eXmYO3cuvv32W0ilUrz66qt488038cMPPwAAvv32W3z++ef44osv0LJlS6xatQr9+/fHgQMHEBoaipycnHsvVS6ysrKQlJSEAQMGYPTo0RgxYgSOHz+OadOmAQCys7PLvX7L7t27MWDAAKPXZt++fejcubNRWc+ePTFt2rRSr2Gx4nieeeYZFBYWIjQ0FBMnTkSfPn2M6jVt2hRJSUk4c+YMAgMDK3cAa0l2dra5QyhFrVYjPz8fe/bsgVarrdY+RtiNAEKAvbv21nB0lic2NtbcIZhUzNZYZGuAbDWQo5UgVwvkaopu52uBfC2QpwXydUX3C3VAgQ5Q6wEBln7mnQQw4z8XkjL6XyQP3KjoFSzrsQfLbKQ1/x4t/jtWGVaV3FTHtGnTEB0dbbiflZWFgIAA9OrVC05OTkZ1CwoKcP36dTg4OEBVIlkBAKmra7nPIfTuDeGvvyAIArKzs+HcsCEk5RwEoWtXCDt3Gu5LGjSA5PbtUvX01ZzwqlKpIJFISrWtOJH79NNP0bNnTwBFiU6/fv2gUCigUqmgVCqhVquxevVqeHp6Aij643/s2DGkpKRAoVAgOzsbCxcuxObNm7F161aMGTMGN27cwJQpU9C+fXsAQJs2bYzi0Wg0+P777xEaGgoAeP311/HRRx8ZYvzmm28wdepUjBw5EgDQrl07HDhwAEuXLsXXX38NBwcHAIC9vT2cnJywevVqhIaG4quvvjLUv3LlCubOnQtHR8dSbS+WlJSEkJAQo8dv376NgIAAQ5kgCPD09ER2djbkcnmZCbOPjw/mz5+Pjh07QiqVYv369fjPf/6D9evX45lnnjHUa9iwIQDgzp07aN68+UOOXO0ofo8W9/BZkoKCAtja2qJLly6lPn+VpdFoEBsbi8jISMjl8hqO0DKIoY0anR43MvJxPT0fNzMLkJxZgJuZBUjJLEBqVgFS7uYiT/do70+pBLBVyGAnl0Ell8FOIYNSLoXKRgaVXAqljQxKGykUNlLDb4Ws6LdcJoVcJjH8tpFKYSOTwEZ670cmhY1UAtm9+9Li35KiMqkEsJFKIZHAcL/4MZ1Oi/1//40unZ+EXC6HVFL0uEQigQSAVHrvt0QCiaQoaZCUeLyorOgx4F5SYfTY/boosW1tMtV7tLx/OMtiVcmNj48PUlNTjcpSU1Ph5ORU5h8hAFAqlVAqSy+iJ5fLS73oOp0OEokEUqm0SldvlUgkkEillermlwCQVGLf1b16bPF2D25ffL9169aG2/7+/gCK/sAHBgZCIpEgKCjIqHfs9OnTyMnJMSQ7xfLz83Ht2jVIpVJER0djzJgxWL16NSIiIjBw4EBDIiOVSmFnZ4cGDRoYtvXz80NaWpqhZ+jmzZt48sknjWLu1KkTTp48aXQsim9fuHABYWFhRvU7duxoVKcs+fn5sLOzK/V48TEHjIeVytuXl5cXJk2aZLgfFhaG5ORkfP755+jfv7+h3P5e71tBQYHFXA24uH0l22wppFIpJBJJmZ/NqqqJfVg6a2hjRp4a/6bm4GJqNi6lZuPa7Vwk3MnDjYx86PQVjb8U/TFWyKTwcFDAzUEBVzsF3OyLfjvbyuFsK4eTrRxOKhs4quRwVNnAQWkDe6UNHFU2UNpILS6BB4r+8P+rAoI9nSz++D2qmn6PVmVfVpXchIeHIyYmxqgsNjYW4eHhpn/ye0MRZXpgfoCQklJ+AvNgeXz8o8VVRSXfHMUf/JJ/0O0fGA7LycmBr68v4uLiSq1LVHyK98yZMzF06FBs2rQJmzdvxowZM/DLL7/gueeeK/Wcxc9rjrV/PDw8cPfuXaOyshLmW7duVZgwlyUsLKxUF2x6ejoAlEoMyTRy1blwmF3Uy3c34i5c5C7mDaiOSc9V42RSBk5eL/o5l5yF1KzCcuur5FIEudnDz0UFXxdb+LvYwtdZBQ97G5w/dhAv9I2Eu6OtRSYoZPnMmtzk5OTg8uXLhvvXrl3DiRMn4ObmhsDAQEybNg03btzAf//7XwDAa6+9hq+//hpTpkzByy+/jJ07d+LXX3/Fpk2bTB9sVebA2NuXTmJqYr9m0LZtW6SkpMDGxgaBgYHIysqCk5NTqf/6GzZsiIYNG+Ktt97CkCFDsHz5ckNyUxEnJyf4+fnh77//RteuXQ3lf//9Nzp06FDmNk2aNMEff/xhVFbyNOzytGnTBufOnTMqKyth3rVrF5544omH7q+kEydOlFrw8cyZM5DL5UZnVBGJxc2MfBy4cgcHrt7BoWvpSEwveyje38UWDb0d0NDbEaGeDghyt0Owhz28HJVlJi4ajQZ3LwDOtnImNlRtZk1ujhw5gu7duxvuF8+NGTFiBFasWIHk5GQkJiYaHg8JCcGmTZvw1ltv4csvv0S9evXw448/1unTwE0tIiIC4eHh6N+/P+bMmQM/Pz9kZWVh8+bNeO6559CsWTNMnjwZAwYMQEhICJKSknD48GGj060fZvLkyZgxYwZCQ0PRunVrLF++HCdOnMDq1avLrP/aa6/h888/x+TJkzF69GgcPXq0UhdzjIqKwsqVK0vtq2TCvGPHDmzYsAF//vmnoc7XX3+N33//HTt27AAArFy5EgqFwjC3aP369Vi2bBl+/PFHo33v3bsXnTt3rlIPEJGlKtTqcODKHcSeS8W+y7eRcKd0MhPqaY9W9VzQsp4zWtRzRkNvRziqxD30QpbJrMlNt27dKhyeKOsPVrdu3XD8+HETRkUlSSQSxMTE4L333sOoUaNw69Yt+Pj4oEuXLvD29oZMJsOdO3cwfPhwpKamwsPDA88//3yZZ6iVZ+LEicjMzMSkSZOQlpaGpk2b4o8//jCap1NSYGAgfvvtN7z11ltYtGgROnTogE8//RQvv/xyhc8zbNgwTJkyBRcvXjSc/l5WwvzVV18ZJcy3b9/GlStXjPb10UcfISEhATY2NmjcuDHWrFmDAQMGGNX55ZdfeAFFsmq5hVrEnktF7LlUxF1MQ676/okOMqkEzf2dEV7fHeGh7mgT6AInJjJkISSCOSY/mFFWVhacnZ2RmZlZ5tlS165dQ0hISLXO1tDr9eUO24iBGNo3efJkZGVl4bvvvivz8Zpq4+bNmzFp0iScOnUKNjaWM7XNko/ho37+jObcvH0XLvYuNRyhZdBoNIiJiUGfPn1MMiFVrxfwz9U7WHcsCZtPpyBfcz+h8XJUIqKpN3o29kKHEDeT9MqYun3mJvb2AaZrY0V/vx9kOd+6RLXgvffew7fffgu9Xm/SP+65ublYvny5RSU2RBW5nVOIVf8kYO2RJNzIyDeUh3jYo08LH0Q29UFLf2dIpZwHQ5aP37xUp7i4uODdd981+fM8OERFZKkup+Vg6b6r+O3YDai1RWdOOqps0K+VH15oWw9tA104sZesDpMbIhIFmVSG3qG9kXYrDTJp1ZdvqGtOXs/AVzsuYceFNENZqwAXvNwpGFHNfKCS8zUk68XkhohEQWWjwsZBGxETEwOVTfWucFwXJNzJxbytF/HXqaIFdyUSIKKJN8Z0qY/2Qa7spSFRYHJDRFQH3MkpxKKdl7H6YAI0OgESCfBcG39M6P4Y6ns6mDs8ohrF5IaISMQEQcDaI0n4aNM5ZBcULUjapaEn3nmqMZr6VXzGCZG1YnJDRKKQq86F13wv6HQ6pESkcPkFAEl38zBt/WnsvVS0OG9TXye826cJnmzgYebIiEyLyQ0RiUaepuwlAOoavV7A6oMJmLP5AnLVOihtpJjUqyFe7hQCG5llXd+IyBSY3BARicjdXDUm/nLc0FvzeLArPnuhJefVUJ3CFJ6qrH79+li4cGGl6gYHB1e6rph169YNb775ptme/+LFi/Dx8UF2drZZnv+dd97B66+/bpbnrkvO3MjE04v2Ye+l21DJpZj1TDOsGRPOxIbqHCY3IvHSSy9BIpFAIpFALpfD29sbkZGRWLZsGfR6fY0+18GDBzFmzJhK1T18+HCl61ZXXFycoe0SiQS2trZo1qwZvv/+e5M+rzWZNm0aXn/9dTg6Oppk/6dOnULnzp2hUqkQEBCAuXPnGj3+9ttvY+XKlbh69apJnp+AtUeu44XF+3EjIx9B7nbYML4TRnQM5hWFqU5iciMiTz31FJKTkxEfH4/Nmzeje/fueOONN/D0009Dq9XW2PN4enrCzs6uxus+qosXLyI5ORnnzp3Dq6++irFjxxpW8rZ2giBU+xgmJibir7/+wksvvVSzQd2TlZWFXr16ISgoCEePHsW8efMwc+ZMo+TSw8MDUVFRWLx4sUliqMs0Oj3e33Aak9edQqFWj56NvfDHhCfR2IdnQlHdxeRGRJRKJXx8fODv74+2bdvi3XffxcaNG7F582ajFdYzMjIwevRoeHp6wsnJCT169MDJkyeN9vXnn3/i8ccfh0qlgoeHB5577jnDYyWHpQRBwMyZMxEYGAilUgk/Pz9MnDjRUPfBYanExEQ8++yzcHBwgJOTE/7v//4PqamphsdnzpyJ1q1b43//+x+Cg4Ph7OyMwYMHV2o4xcvLCz4+PggJCcHEiRMREhKCY8eOGR4vLCzExIkT4eXlBZVKhSeffBKHDx82PL5ixQq4ubkZ7XPDhg1GFzWrTHy5ubkYPnw4HBwc4Ovri88//7xUrP/73//Qvn17ODo6wsfHB0OHDkVa2v0rxRb3Rm3evBnt2rWDUqnEqlWrIJVKceTIEaN9LVy4EEFBQeX20P36669o1aoV/P39DWU9evQw6u0q/omPj3/Iq1za6tWroVarsWzZMjRr1gyDBw/GxIkTsWDBAqN6/fr1wy+//FLl/VP5CjQ6jF11FKv+SYREArwV0RA/DG8PZ1txLshIVFlMbiopV51b7k+BtqDSdfM1+ZWqW1N69OiBVq1aYf369YaygQMHIi0tDZs3b8bRo0fRtm1b9OzZE+np6QCATZs24bnnnkOfPn1w/Phx7NixAx06dChz/7/99hu++OILfPfdd7h06RI2bNiAFi1alFlXr9fj2WefRXp6Onbv3o3Y2FhcvXoVgwYNMqp35coVbNiwAX/99Rf++usv7N69G3PmzKl0mwVBwJYtW5CYmIiwsDBD+ZQpU/Dbb79h5cqVOHbsGB577DFERUUZ2l1ZD4tv8uTJ2L17NzZu3Iht27YhLi7OKMkCilbN/eijj3Dy5Els2LAB8fHxZfasvPPOO5gzZw7Onz+PZ555BhEREVi+fLlRneXLl+Oll14qdyHQvXv3on379kZl69atQ3JysuHn+eefR6NGjeDt7Q0A6N27NxwcHMr9adasmWFfBw4cQJcuXaBQKAxlUVFRuHjxIu7evWso69ChA5KSkqqVQFWGVCJFl8AuaGbfDFKJ+L/acgq1GLn8MLafT4PSRorvX2yPNyIacBiKCDxbqtIcZpc/Ia9Pgz7YNHST4b7PAp9yT0ntGtQVcS/FGe4HfxmM23m3S9UTZgjVD/YBjRs3xqlTpwAA+/btw6FDh5CWlgalUgkAmD9/PjZs2IB169ZhzJgx+OSTTzB48GDMmjXLsI9WrVqV2TOQmJgIHx8fREREQC6XIzAwsNxEaMeOHTh9+jSuXbuGgIAAAMB///tfNGvWDIcPH8bjjz8OoCgJWrFihWF+yIsvvogdO3bgk08+qbCd9erVA1DUQ6PX6/Hhhx+iS5cuAIp6UxYvXowVK1agd+/eAIAffvgBsbGxWLp0KSZPnly5F/Mh8eXk5GDp0qVYtWoVevbsCQBYuXKlIbZiL7/8suF2/fr18dVXX+Hxxx9HTk4OHBzuv9c+/PBDREZGGu6PHj0ar732GhYsWAClUoljx47h9OnT2LhxY7nxJiQklEpu3NzcDMnQF198gZ07d+LgwYOwtbUFAPz444/Iz88vta9icvn9noGUlBSEhIQYPV6cJKWkpMDV1RUA4OfnZ4gnODi43H1Xl63cFtv/sx0xMTGwldvW+P4tSa4GGLHiCE4lZcFeIcPSlx7HE/XdzR0WkcVgclMHCIJgGFo5efIkcnJy4O5u/EWYn5+PK1euAABOnDiBV155pVL7HjhwIBYuXIj69evjqaeeQp8+fdCvXz/Y2JR+a50/fx4BAQGGxAYAmjZtChcXF5w/f96Q3AQHBxtNfPX19TUasinP3r174ejoiMLCQhw6dAgTJkyAm5sbxo4diytXrkCj0aBTp06G+nK5HB06dMD58+cr1dZiFcV35coVqNVqox4jNzc3NGrUyGgfR48excyZM3Hy5EncvXvXkDgmJiaiadOmhnoPJiX9+/fH+PHj8fvvv2Pw4MFYsWIFunfvXmGykJ+fD5Wq7LWWNm/ejHfeeQd//vknGjZsaCgvOYRVU4oTp7w8XovmUaRlF2LRWRmS87PgYifHypEd0CrAxdxhEVkUJjeVlDMtp9zHHlyBOCU6pdwhgge7y+PfiH/k2B7m/Pnzhv+sc3Jy4Ovri7i4uFL1XFxcANz/I1QZAQEBuHjxIrZv347Y2FiMGzcO8+bNw+7du43+u6+KB7eTSCSVOuMrJCTE0IZmzZrh4MGD+OSTTzB27NhKPa9UKoUgGPeYaTSaGouvWG5uLqKiohAVFYXVq1fD09MTiYmJiIqKglqtNqprb29vdF+hUGD48OFYvnw5nn/+efz000/48ssvK3w+Dw8Po+GhYufOncPgwYMxZ84c9OrVy+ix3r17Y+/eveXuMygoCGfPngUA+Pj4GM2bAmC47+PjYygrHv7z9PSsMF4q391cNV5cdgTJ+RJ4OSqxanQYGnqb5gw4ImvG5KaS7BX2D69Uom55yc2j7Lc6du7cidOnT+Ott94CALRt2xYpKSmwsbEp97/9li1bYseOHRg5cmSlnsPW1hb9+vVDv379MH78eDRu3BinT59G27Ztjeo1adIE169fx/Xr1w29N+fOnUNGRoZRb0VNkclkhqGV0NBQKBQK/P333wgKCgJQlLgcPnzYcP0ZT09PZGdnIzc3F05ORWeanDhxokrPGRoaCrlcjoMHDyIwMBAAcPfuXfz777/o2rUrAODChQu4c+cO5syZY3gdHpwkXJHRo0ejefPm+Pbbb6HVavH8889XWL9NmzY4d+6cUdnt27fRr18/vPDCC4b3RklVGZYKDw/He++9B41GYyiPjY1Fo0aNDENSAHDmzBnI5XKj+To1KVedi+CFwVCr1UiISBDd8gv5ah1GrTyMq7dz4aIQ8PPoxxHKxIaoTExuRKSwsBApKSnQ6XRITU3Fli1bMHv2bDz99NMYPnw4ACAiIgLh4eHo378/5s6di4YNG+LmzZuGScTt27fHjBkz0LNnT4SGhmLw4MHQarWIiYkpc17KihUroNPpEBYWBjs7O6xatQq2traGBKKkiIgItGjRAsOGDcPChQuh1Woxbtw4dO3atdTwS3WkpaWhoKDAMCz1v//9DwMGDABQ1AMyduxYTJ48GW5ubggMDMTcuXORl5eHUaNGAYChDR999BEmTZqEw4cPG51lVhkODg4YNWoUJk+eDHd3d3h5eeG9994zSnYDAwOhUCiwaNEivPbaazhz5gw++uijSj9HkyZN8MQTT2Dq1Kl4+eWXH9rTFhUVhdGjR0On0xmGJwcOHAg7OzvMnDkTKSkphrqenp6QyWRVGpYaOnQoZs2ahVGjRmHq1Kk4c+YMvvzyS3zxxRdG9fbu3YvOnTtXqWewqm7nl56/JgZanR6v/3wMxxIz4Gxrg7ENCxDoVjuXWCCyRuI/paAO2bJlC3x9fREcHIynnnoKu3btwldffYWNGzdCJisaOpNIJIiJiUGXLl0wcuRINGzYEIMHD0ZCQoJhEmi3bt2wdu1a/PHHH2jdujV69OiBQ4cOlfmcLi4u+OGHH9CpUye0bNkS27dvx59//llqTk/xc2/cuBGurq7o0qULIiIiUL9+faxZs6ZG2t+oUSP4+vrisccew9SpU/Hqq69i0aJFhsfnzJmDF154AS+++CLatm2Ly5cvY+vWrYbeBTc3N/z3v/9FbGwsWrVqhZ9//hkzZ86schzz5s1D586d0a9fP0RERODJJ59Eu3btDI97enpixYoVWLt2LZo2bYo5c+Zg/vz5VXqOUaNGQa1WG01MLk/v3r1hY2OD7du3G8r27NmDM2fOICgoCL6+voaf69evVykOAHB2dsa2bdtw7do1tGvXDpMmTcL06dNLXbzxl19+qfRcLrpPEAS8v+GM4ayo74a1gQ/zGqIKSYQHJxmIXFZWFpydnZGZmWkYeihWUFCAa9euISQkpNwJmBXR6/XIysqCk5NTpYelrInY2wdYTxs/+ugjrF271nAW3MN88803+OOPP7B582aztG/z5s2YNGkSTp06VeZkc+DRP3+56lzDWY13374LF3uXRwnZYiyI/Rdf7bgEqQRY/J926NHQHTExMejTp0+157VZMo1Gw/ZZOVO1saK/3w/isBSRFcnJyUF8fDy+/vprfPzxx5Xe7tVXX0VGRobZ1pbKzc3F8uXLy01sqGy/H0/CVzsuAQA+fLY5opr5lDnJnYiMWe6/pkRUyoQJE9CuXTt069atUkNSxWxsbPDee++ZbG2phxkwYIDR6fH0cP+mZuPd9WcAAOO7h+I/T5Sex0ZEZeO/UURWZMWKFVWe5EzWJ7dQi3GrjyFfo8OTj3kgOrLRwzciIgMmN0QkClKJFO182yEzI9Oql18QBAHv/n4al9Ny4O2kxMLBrSHjkgpEVcLkpgx1bI41kUV41M+drdwWB0YesPrlF34+dB0bT9yETCrB10PbwsNBae6QiKyO9f57YwLFs7p5eXii2lf8uRPrGSSVceZGJmb+WXTl5ylRjfB4sNtDtiCisrDnpgSZTAYXFxfDOkF2dnaGi55Vhl6vh1qtRkFBgUWfRlxdYm8fIP42WmL7BEFAXl4e0tLS4OLiYrgmU12Tr9Zhwk/HoNbqEdHEC690rm/ukIisFpObBxSvhVOZhRofJAgC8vPzYWtrW6WkyFqIvX2A+Ntoye1zcXExWouqqvI0eWj6TVPk5eXhUuQlOMudazA60/ti+7+Iv5MHX2cVPh/YGlLOsyGqNiY3D5BIJPD19YWXl1eVryeh0WiwZ88edOnSRZRd62JvHyD+Nlpq++Ry+SP32AiCgITMBMNta3LyegZ+3HsVAPDJc83hbGc5x4bIGjG5KYdMJqvyl61MJoNWq4VKpbKoPxw1ReztA8TfRrG3zxqptXpM/e0U9ALQv7UfejT2NndIRFbPMgbdiYjqqMVxV3AhJRtu9gpM72eaFdOJ6homN0REZvJvaja+3lW0vMKMfk3hZq8wc0RE4sDkhojIDHR6AVPWnYJGJ6BnYy8808rP3CERiQaTGyIiM1j1TwJOXM+Ag9IGHz/X3OLOXiOyZpxQTESiIJFI0MSjCXJyciw+UcjM12Dh9n8BAFOeagRfZ+u9ojKRJWJyQ0SiYCe3w8kxJxETEwM7uZ25w6nQN7su426eBo95OWBoh0Bzh0MkOhyWIiKqRdfT87Di73gAwLt9GsNGxq9hoprGTxURUS2as+UC1Do9Oj3mju6NvMwdDpEocViKiEQhT5OH9t+3R05ODrpFdrPI5ReOJtzFplPJkEiA9/o0tfi5QUTWiskNEYmCIAg4f/u84balEQQBH286BwAY0LYemvo5mTkiIvHisBQRUS2IOZ2C44kZsJXL8HZUI3OHQyRqTG6IiExMo9Pjsy0XAABjutSHt5PKzBERiRuTGyIiE/v92A0kpufBw0GJMV3qmzscItFjckNEZEJanR7fxl0GAIzpEgJ7Jac6EpkakxsiIhPadDoZ8Xfy4Gonx7CwIHOHQ1Qn8F8IIhIFiUSCIOcg5OXlWcwp1nq9gK93FvXajHqSvTZEtYWfNCISBTu5HS6Nv2RRyy9sO5eCS2k5cFTZYHjHYHOHQ1RncFiKiMgEBEHAonu9Ni91DIaTSm7miIjqDiY3REQmsOtiGs7ezIKdQoaRnULMHQ5RncJhKSIShXxNPjov74zMjEx0j+wOudx8PSWCIOCrHUW9Nv95Ighu9gqzxUJUFzG5ISJR0At6HE0+arhtTvuv3MGJ6xlQ2kgxujN7bYhqG4eliIhq2Hd7rgIABj8eAC9HXo2YqLYxuSEiqkFXbuVgz7+3IJEAo57k1YiJzIHJDRFRDfrv/ngAQM/GXgh0t4xT0onqGrMnN9988w2Cg4OhUqkQFhaGQ4cOVVh/4cKFaNSoEWxtbREQEIC33noLBQUFtRQtEVH5sgs0WHc0CQAwgte1ITIbsyY3a9asQXR0NGbMmIFjx46hVatWiIqKQlpaWpn1f/rpJ7zzzjuYMWMGzp8/j6VLl2LNmjV49913azlyIqLSfjuahFy1DqGe9njyMQ9zh0NUZ5k1uVmwYAFeeeUVjBw5Ek2bNsWSJUtgZ2eHZcuWlVl///796NSpE4YOHYrg4GD06tULQ4YMeWhvDxHVDR62HnCSOZnlufV6Af89kACgqNfGUpaAIKqLzHYquFqtxtGjRzFt2jRDmVQqRUREBA4cOFDmNh07dsSqVatw6NAhdOjQAVevXkVMTAxefPHFcp+nsLAQhYWFhvtZWVkAAI1GA41GU0OtgWGfJX+LjdjbB4i/jWJun0KiQMKEBMTGxkIhUdR6G/deuo2rt3PhoLRBvxbeJnt+MR9DgO0TA1O1sSr7kwiCINTos1fSzZs34e/vj/379yM8PNxQPmXKFOzevRsHDx4sc7uvvvoKb7/9NgRBgFarxWuvvYbFixeX+zwzZ87ErFmzSpX/9NNPsLPjZD8iqhnfnZfiXIYUXX30eD7EvNfZIRKjvLw8DB06FJmZmXByqriH1qou4hcXF4dPP/0U3377LcLCwnD58mW88cYb+Oijj/DBBx+Uuc20adMQHR1tuJ+VlYWAgAD06tXroS9OVWk0GsTGxiIyMtKsV0c1FbG3DxB/G9k+00hIz8P5f/YBAN4f3BnB7vYmey4eQ+sm9vYBpmtj8chLZZgtufHw8IBMJkNqaqpReWpqKnx8fMrc5oMPPsCLL76I0aNHAwBatGiB3NxcjBkzBu+99x6k0tJTiJRKJZRKZalyuVxusjeWKfdtCcTePkD8bRRj+/I1+ei9pjfu3LmD7pHda3Vl8J8P34AgAN0aeaKBj0utPKcYj2FJbJ/1q+k2VmVfZptQrFAo0K5dO+zYscNQptfrsWPHDqNhqpLy8vJKJTAymQxA0VouRFR36QU99iTuwdncs7W6/EKeWotfj1wHwNO/iSyFWYeloqOjMWLECLRv3x4dOnTAwoULkZubi5EjRwIAhg8fDn9/f8yePRsA0K9fPyxYsABt2rQxDEt98MEH6NevnyHJISKqTZtOJSO7QIsgdzt0beBp7nCICGZObgYNGoRbt25h+vTpSElJQevWrbFlyxZ4e3sDABITE416at5//31IJBK8//77uHHjBjw9PdGvXz988skn5moCEdVxa+9dtO//2gdAKuXp30SWwOwTiidMmIAJEyaU+VhcXJzRfRsbG8yYMQMzZsyohciIiCqWcCcXh66lQyoBnm/rb+5wiOgesy+/QERkrYqXWniygSd8nW3NHA0RFWNyQ0RUDTq9gN/uJTcD29UzczREVJLZh6WIiGqKndwOOp2uVp7r78u3cTOzAM62ckQ29a6V5ySiymHPDRGJgr3CHhmTM7Cm5RrYK0x3Eb1ixROJn23tB5WcZ2sSWRImN0REVZSZp8HWsykAgIHtAswcDRE9iMkNEVEV/XHqJtRaPRr7OKK5v3lWISei8nHODRGJQoG2AM+veR5pt9LQQ9vDpJe2X3fvisQD2tWDRMJr2xBZGiY3RCQKOr0Om69sNtw2lYsp2TiZlAkbqQTPteG1bYgsEYeliIiqYO29Xpsejb3g7lB6UV4iMj8mN0RElaTTC/jj5E0ARUNSRGSZmNwQEVXS4fh0pGUXwkllg66NuEgmkaVickNEVEl/3uu1iWrmA6UNr21DZKmY3BARVYJWp8fmM0XXtunXys/M0RBRRZjcEBFVwv4rd5Ceq4abvQIdQ93NHQ4RVYDJDRGJgr3CHup31djQeoNJll8oHpLq08IHNjJ+dRJZMn5CiYgeolCrw5Z7yy30a8khKSJLx+SGiOgh9v57G9kFWng7KfF4sJu5wyGih+AViolIFAq0BRi2fhhSklNqfPmFP08VDUk93dIPUimXWyCydExuiEgUdHod1l9Yb7hdU/LVOsSeSwUAPN3St8b2S0Smw2EpIqIK7LyQhjy1DvVcbdE6wMXc4RBRJTC5ISKqwF/3hqT6tfLjCuBEVoLJDRFRObILNNh5IQ0Az5IisiZMboiIyrHzQhoKtXrU97RHE19Hc4dDRJXE5IaIqBzbzhZNJO7d3IdDUkRWhMkNEVEZCjQ6xF0sGpLq1dTHzNEQUVXwVHAiEgU7uR3uvn0XW7duhZ3c7pH3t//KbeSqdfB1VqFlPecaiJCIagt7bohIFCQSCewV9lDJVDUyhLT1TNGQVK+m3hySIrIyTG6IiB6g0wvYfv5ectOMQ1JE1obDUkQkCoXaQrzy5ytISkpCT23PR1p+4WjCXdzJVcPZVo4OIVxLisjaMLkhIlHQ6rX43+n/GW4/im33VgDv2dgLchk7uImsDT+1REQlCIKAreeKkhsOSRFZJyY3REQlnE/OxvX0fChtpOjS0MPc4RBRNTC5ISIqYdu9XpsuDT1hp+DIPZE1YnJDRFTC1rP3TwEnIuvE5IaI6J7r6Xk4n5wFmVSCiCZMboisFZMbIqJ7tt47S6pDsBtc7RVmjoaIqosDykQkCnZyO9x44wa279he7eUXtp0rvnAfe22IrBl7bohIFCQSCTztPeFs41yt5RIy8zQ4mnAXADgkRWTlmNwQEQHYfekWdHoBDb0dEOD26AtvEpH5cFiKiEShUFuIN7e8iYSkhGotv7Dz3lpSPRqz14bI2jG5ISJR0Oq1WHJsieF2Vej0Anb/ewsA0KOxV43HRkS1i8NSRFTnnbh+F3fzNHC2laNtoIu5wyGiR8TkhojqvB3n0wAUXZXYhgtlElk9foqJqM7beaEouenJISkiUWByQ0R12s2MfFxIyYZUAnRt6GnucIioBjC5IaI6bdfFol6bNoGuvCoxkUgwuSGiOm3nvfk2PEuKSDx4KjgRiYKt3Bb/jvsXu3btgq3ctlLbFGh0+PvKbQBMbojEhD03RCQKUokUwS7B8FZ6Qyqp3Ffbgat3UKDRw9dZhcY+jiaOkIhqC5MbIqqzdt07S6p7Y69qrUdFRJaJyQ0RiYJap8Y7O97BihsroNapH1pfEATD9W14CjiRuHDODRGJgkanwYKDCwy3H+ZSWg5uZORDaSNFx1APU4dHRLWIPTdEVCfF3TsFPDzUHbYKmZmjIaKaxOSGiOqk4oUyeeE+IvFhckNEdU6eWovD1+4CYHJDJEZMboiozvnn6h2odXoEuNkixMPe3OEQUQ0ze3LzzTffIDg4GCqVCmFhYTh06FCF9TMyMjB+/Hj4+vpCqVSiYcOGiImJqaVoiUgMdl8sGpLq0sCTp4ATiZBZz5Zas2YNoqOjsWTJEoSFhWHhwoWIiorCxYsX4eVV+tRMtVqNyMhIeHl5Yd26dfD390dCQgJcXFxqP3gislp7LhVdlZhDUkTiZNbkZsGCBXjllVcwcuRIAMCSJUuwadMmLFu2DO+8806p+suWLUN6ejr2798PuVwOAAgODq7NkInIQtnKbXH8lePYu2dvhcsvJNzJxbXbubCRShAe6l6LERJRbTFbcqNWq3H06FFMmzbNUCaVShEREYEDBw6Uuc0ff/yB8PBwjB8/Hhs3boSnpyeGDh2KqVOnQiYr+1TOwsJCFBYWGu5nZWUBADQaDTSah18LoyqK91fT+7UUYm8fIP42ir19DV0aIsE2ATqtDhpJ2W3cdT4FANA20AUqmfW9FmI/hmyf9TNVG6uyP4kgCEKNPnsl3bx5E/7+/ti/fz/Cw8MN5VOmTMHu3btx8ODBUts0btwY8fHxGDZsGMaNG4fLly9j3LhxmDhxImbMmFHm88ycOROzZs0qVf7TTz/Bzs6u5hpERFbhhwtSnLkrxdOBOkT6m+Xrj4iqIS8vD0OHDkVmZiacnJwqrGtVVyjW6/Xw8vLC999/D5lMhnbt2uHGjRuYN29eucnNtGnTEB0dbbiflZWFgIAA9OrV66EvTlVpNBrExsYiMjLSMGwmJmJvHyD+Noq5fWqdGp/u/RRXrlzBkmFLYK8qfRaUWqvHu0d3AdBhdN9OaOZXs98BtUHMxxBg+8TAVG0sHnmpDLMlNx4eHpDJZEhNTTUqT01NhY+PT5nb+Pr6Qi6XGw1BNWnSBCkpKVCr1VAoFKW2USqVUCqVpcrlcrnJ3lim3LclEHv7APG3UYztUwtqfLr/UwDAEumSMtt3JPEOctU6eDgo0TLADVKp9Z4pJcZjWBLbZ/1quo1V2ZfZTgVXKBRo164dduzYYSjT6/XYsWOH0TBVSZ06dcLly5eh1+sNZf/++y98fX3LTGyIiEoqvipxlwYeVp3YEFHFzHqdm+joaPzwww9YuXIlzp8/j7FjxyI3N9dw9tTw4cONJhyPHTsW6enpeOONN/Dvv/9i06ZN+PTTTzF+/HhzNYGIrIhhyYVGPAWcSMzMOudm0KBBuHXrFqZPn46UlBS0bt0aW7Zsgbe3NwAgMTERUun9/CsgIABbt27FW2+9hZYtW8Lf3x9vvPEGpk6daq4mEJGVSMsqwPnkLEgkwJOPcRVwIjEz+4TiCRMmYMKECWU+FhcXV6osPDwc//zzj4mjIiKxKb5wXwt/Z7g7lJ6HR0TiYfblF4iIasMergJOVGcwuSEi0dPrBey9dG8yMZMbItEz+7AUEVFNUNmosP+l/fh7/99Q2aiMHjt7Mwt38zRwVNqgdYCLeQIkolrD5IaIREEmlaG9X3uk2aVBJjVejmXPvV6bJ0LdIZexw5pI7PgpJyLR23dvMnHnBjxLiqguYHJDRKKg1qnx+T+f4/e036HWqQ3leWotjiSkAwA6N+B8G6K6gMNSRCQKGp0G03YWXfRzoW6hofzgtXRodAL8XWwR7M7FconqAvbcEJGo7f23aEiqS0MPSCRccoGoLmByQ0Situ9y0WTiJx/jkBRRXcHkhohEKyWzAP+m5kAiATqGups7HCKqJUxuiEi09l0uGpJq6e8MV3uFmaMhotrC5IaIRGvfvevbPMlTwInqFCY3RCRKer1g6LnhKeBEdQtPBSciUVDZqBA7LBb//PMPVDYqXEjJxu0cNewUMrQNdDV3eERUi5jcEJEoyKQydA3qityzuZBJZYaFMp+o7w6FDTupieoSfuKJSJSKh6SefIzzbYjqGiY3RCQKGp0Gi48sRsytGGQXFODgteIlF5jcENU1VR6WysjIwO+//469e/ciISEBeXl58PT0RJs2bRAVFYWOHTuaIk4iogqpdWq8se0NAMCz8VOh1urh46TCY14OZo6MiGpbpXtubt68idGjR8PX1xcff/wx8vPz0bp1a/Ts2RP16tXDrl27EBkZiaZNm2LNmjWmjJmIqEL/XL0DoOgUcC65QFT3VLrnpk2bNhgxYgSOHj2Kpk2bllknPz8fGzZswMKFC3H9+nW8/fbbNRYoEVFlHbjKISmiuqzSyc25c+fg7l7x5cttbW0xZMgQDBkyBHfu3Hnk4IiIquNiag6kUKETJxMT1UmVHpZ6WGLzqPWJiGpSE18neDgozR0GEZlBtc6Wkslk6N69O9LT043KU1NTIZPJaiQwIqJH8eRj/AeLqK6qVnIjCAIKCwvRvn17nD17ttRjRETm9iSXXCCqs6qV3EgkEvz222/o168fwsPDsXHjRqPHiIhqm9JGiSVRv8CzcAYUMgU6BLuZOyQiMpNqLb8gCAJkMhm+/PJLNGvWDIMGDcL777+P0aNH13R8ppObC5Q1hCaTASqVcb3ySKWAra1RXVlBQdE2cnnFdfPygPJ6uSQSwM6uenXz8wG9vvyY7e2rV7egACgoKL99D9bV6crfr51dUdwAUFgIaLU1U9fWtuh1BgC1GtBoql5XoyndRpXq/nvlYfstWVejKapfHqUSsLGpel2ttui1KI9CcT/2B+s+2L6SdXW6omNXnuL6Va2r1xe912qiro1N0WsBFH0m8vLuPwTANqsp3AtsEBZiD1tBC0BWZt1SqvK5f8TviErXLe9zr9FA9uDxt5TviIo+95Wtq9EYt8XSviPKUpXviJJ/dyzxO6KiulX9jijrb0VZdavyHVFZQjVIJBIhNTXVcH/nzp2Cu7u7EBkZKUil0ursstZkZmYKAITMoo9P6Z8+fYw3sLMrux4gCF27GlXVe3iUX7d9e+P9BgWVX7dpU+O6TZuWXzcoyLhu+/bl1/XwMK7btWv5de3sjOv26VN+3QffRgMGVFw3J+d+3REjKq6blna/7rhxFde9du1+3bffrrjumTP3686YUXHdQ4fu1507t+K6u3bdr/v11xXX/euv+3WXL6+47q+/3q/7668V112+/H7dv/6quO7XX9+vu2tXxXXnzr1f99ChiuvOmHG/7pkzFdd9++37da9dq7juuHH366alVVx3xIj7dXNyKq47YIBgpKK6j/AdIdTQd0RmQICgVqvv1xXZd8Sfv/xyv30i+47QxMYKGzZsKGqfSL8j1Gq1EDdvXsV1q/EdYfj7nZkpPEy1hqWCgoKMJg53794d//zzD65fv16d3RERPTKNFFjRuuhHw4VliOo0iSAIQk3trKCgAKmpqQgKCqqpXda4rKwsODs7I/PmTTg5OZWu8AhdzpqMDGzduhVRUVGQi3BYSlNQUH77RDIspdFoSrdRRMNSpdonomGp/VeT0Gl9YwDA7deuw93Fs9y6pVjRsJRGo8HWbdsQ9dxz99+jFvIdURPDUhqNBjFxcejTt29R+yzsO6JMVfiO0MhkiNm6FX369IG8uH55rHRYSqPRIObPP9GnR4/SfyseqAug0t8Rhr/fmZll//0uodJzbgRBeOhkYZVKZdGJjRF7e+MPW0X1qrBPnUpVtE15B7RYyS+bh6lK3ZJfjjVZ996Ht1LtK/nF/zBK5f0/QDVZV6Go/BhtyboaTcVtrMp+5fKHvw+qU9fG5v6XWFXrVtQ+mazy7/eq1JVKTVNXIjGqu/fm/S9HmYOD8XvlgboPZQl1y/vcazTQPfg5sJTviJqoq9HcT1YAy/uOeNS6JRMfS/yOqEh1viMqE3NVPveVVOnO22bNmuGXX36BuqLMEcClS5cwduxYzJkz55GDIyKqrANXeFV0IipS6Z6bRYsWYerUqRg3bhwiIyPRvn17+Pn5QaVS4e7duzh37hz27duHs2fPYsKECRg7dqwp4yYiMsgu0OBkUibACxITEaqQ3PTs2RNHjhzBvn37sGbNGqxevRoJCQnIz8+Hh4cH2rRpg+HDh2PYsGFwdXU1ZcxEREYOXk2HTl9j0weJyMpV+To3Tz75JK5evYro6GiEhISYIiYioirZd/m2uUMgIgtSrYv4jRw5El9//TXUajVOnToFmUyGpk2b4uWXX37oDGYiopq299Itc4dARBak2lconjBhAvz8/NChQwfodDrExMTgk08+wbZt29CmTZuajpOIqEzJmfm4cisXMokcP/b9Hy6ePQWlDSffENVl1UpuAGD06NH49ttvDRfz02g0eOWVV/DGG29gz549NRYgEVFF9l4qGpJqVc8dw1v1RswNR9hIq/3VRkQiUO3reEZHRxtdpVgul2PKlCk4cuRIjQRGRFQZxclN5wYeZo6EiCxFtZIbFxcXJCUllSq/fv0659wQUa3R6wX8fW8ycXioK9adX4e/M/6GVl/BVWqJSPSqldxERkZi1KhRWLduHW7cuIHExET8/PPPGD16NIYNG1bTMRIRlelcchbSc9WwV8jQ1M8WQ38finnx81CoreBy8kQketUamP72228xceJEDBo0yFCmVCoxYcIEfPLJJzUWHBFRRYqHpMJD3SGXcbVMIipSreTG3d0dq1evxuLFi3H16lXI5XKEhoZCVZW1RYiIHtG+y0WngD/5GOfbENF9j3RKgZOTE1q3bl1DoRARVV6+WofD1+4CADo39DRzNERkSdiPS0RW6VB8OtQ6PfycVajvUbMrChORdWNyQ0RWae+/RUNSnRt4QiKRmDkaIrIkTG6IyCoVryf1JK9vQ0QP4GU8icjqpGUV4EJKNiQSoNO9ycQKmQI/Pv0jTp48CYVMYeYIicic2HNDRFanuNemuZ8z3OyLEhm5TI7hLYejp3tPyGVyc4ZHRGbG5IaIrA6XXCCiijC5ISKrIgiCIbkpOd9Gq9ci5nIMjmQe4fILRHUc59wQkVW5kJKN2zmFsJXL0C7I1VBeqC1E/1/7AwAmaSfBVmlrpgiJyNzYc0NEVmXfvV6bsPpuUNrIzBwNEVkiJjdEZFX2XLp/fRsiorIwuSEiq5Gv1uHgtXQAQFcuuUBE5WByQ0RW459rd6DW6uHvYotQTy65QERlY3JDRFZj98WiIakuDbnkAhGVzyKSm2+++QbBwcFQqVQICwvDoUOHKrXdL7/8AolEgv79+5s2QCKyCMXzbTgkRUQVMXtys2bNGkRHR2PGjBk4duwYWrVqhaioKKSlpVW4XXx8PN5++2107ty5liIlInO6np6Hq7dyIZNK0PEx91KPK2QKfNnrS4zxH8PlF4jqOLMnNwsWLMArr7yCkSNHomnTpliyZAns7OywbNmycrfR6XQYNmwYZs2ahfr169ditERkLsW9Nu0CXeGkKr28glwmx9j2Y9HHsw+XXyCq48ya3KjVahw9ehQRERGGMqlUioiICBw4cKDc7T788EN4eXlh1KhRtREmEVmA+/NtuOQCEVXMrFcovn37NnQ6Hby9vY3Kvb29ceHChTK32bdvH5YuXYoTJ05U6jkKCwtRWFhouJ+VlQUA0Gg00Gg01Qu8HMX7q+n9Wgqxtw8QfxuttX0anR5/Xym6eF+n+m5lxq/T6xB3LQ6ns0+jR2GP2g6x1ljrMawsts/6maqNVdmfVS2/kJ2djRdffBE//PADPDwq99/b7NmzMWvWrFLl27Ztg52dXU2HCACIjY01yX4thdjbB4i/jdbWvstZQG6hDRxsBMSf2IfEk6XrFOgKMPj0YABAg20NoJKpajnK2mVtx7Cq2D7rV9NtzMvLq3RdsyY3Hh4ekMlkSE1NNSpPTU2Fj49PqfpXrlxBfHw8+vXrZyjT6/UAABsbG1y8eBGhoaFG20ybNg3R0dGG+1lZWQgICECvXr3g5ORUk82BRqNBbGwsIiMjIZeLb8xf7O0DxN9Ga23f57GXAFxDj6Z+eLpvizLr5KpzgdNFt3v06AEXe5dai682WesxrCy2z/qZqo3FIy+VYdbkRqFQoF27dtixY4fhdG69Xo8dO3ZgwoQJpeo3btwYp0+fNip7//33kZ2djS+//BIBAQGltlEqlVAqlaXK5XK5yd5Ypty3JRB7+wDxt9Ha2rfvyh0AQPcmXuXGLRful1tb+6pD7G1k+6xfTbexKvsy+7BUdHQ0RowYgfbt26NDhw5YuHAhcnNzMXLkSADA8OHD4e/vj9mzZ0OlUqF58+ZG27u4uABAqXIiEodb2YU4c6PoPzauJ0VElWH25GbQoEG4desWpk+fjpSUFLRu3RpbtmwxTDJOTEyEVGr2M9aJyEz23jsFvLm/EzwcSvfCEhE9yOzJDQBMmDChzGEoAIiLi6tw2xUrVtR8QERkMfb8y6sSE1HVsEuEiCyWXi9gz6WiU8C7cEiKiCrJInpuiIjKcvpGJtJz1XBQ2qBtkGuFdeUyOWb3mI0LFy7wCsVEdRyTGyKyWDsvFK0x17mBB+SyijuaFTIFJj0xCTHpMVxbiqiO47AUEVmsXReLkpvujb3MHAkRWRMmN0RkkdKyCnAqKRMA0K3Rw+fb6PQ6HLl5BJfyLkGn15k6PCKyYByWIiKLFHdvocxW9Zzh5fjwpRQKtAXouKIjAGC0djRUSnEvv0BE5WPPDRFZpOL5NhySIqKqYnJDRBZHrdUbLt7Xg8kNEVURkxsisjiHrqUjV62Dh4MSzf2czR0OEVkZJjdEZHGKh6R6NPaEVCoxczREZG2Y3BCRxSk+BZxDUkRUHUxuiMiiXL2Vg2u3cyGXSfAkl1wgomrgqeBEZFGKh6Q6hLjBQVn5ryi5TI73n3wfly5d4vILRHUckxsisij3h6S8q7SdQqbA9C7TEZPD5ReI6joOSxGRxcgu0ODQtXQAnG9DRNXH5IaILMa+S7eh0QkI8bBHiId9lbbVC3qcvXUWifmJ0At6E0VIRNaAw1JEZDEMVyVuVPVem3xNPtr80AYA8KLmRSgVyhqNjYisB3tuiMgi6PQCdtxLbno24ZAUEVUfkxsisghH4tORnquGs60cHULczB0OEVkxJjdEZBG2nk0FUNRrI5fxq4mIqo/fIERkdoIgYNu5FABAr6Y+Zo6GiKwdkxsiMrtzyVlIupsPlVyKrg15VWIiejRMbojI7LbdG5Lq0sATtgqZmaMhImvHU8GJyOy2nr03JNWs+kNScpkc0WHRuHr1KpdfIKrjmNwQkVkl3snDhZRsyKQS9HyEqxIrZArM6TkHMYVcfoGoruOwFBGZVfFE4g7BbnC1Z1JCRI+OyQ0RmVXxfJuoZlVbKPNBekGP+Ix4pBamcvkFojqOw1JEZDa3cwpxOKFooczIR5hvAxQtv9Dw24YAgP/r+39cfoGoDmPPDRGZzfZzqRAEoIW/M/xdbM0dDhGJBJMbIjKbbeeKhqR6NX20ISkiopKY3BCRWeQUarHv0m0AQFRzXpWYiGoOkxsiMoudF9Kg1ukR7G6HBl4O5g6HiESEyQ0RmcWfJ28CAJ5u6QeJRGLmaIhITJjcEFGty8zXYPfFWwCAp1v5mjkaIhIbngpORLUu9lwq1Do9Gng5oJG3Y43s00Zqg9favoaExATYSPnVRlSX8RuAiGpd8ZBUv1Y1NySltFHiq6e+QkxMDJQ2vMYNUV3GYSkiqlXpuWrsu1x0ltTTLTkkRUQ1j8kNEdWqzWeSodMLaO7vhPqeNXeWlCAIuJV7C5naTAiCUGP7JSLrw2EpIqpVJc+Sqkl5mjz4f+kPAHjmqWegUHARTqK6ij03RFRrUrMKcPBa0VpSfVtwSIqITIPJDRHVmk2nkiEIQNtAFwS42Zk7HCISKSY3RFRr/jp1/ywpIiJTYXJDRLXienoejiVmQCLhkBQRmRaTGyKqFZtOJwMAnghxh5eTyszREJGYMbkholqx8cS9s6S43AIRmRhPBScikztzIxPnk7OgkEnRp7lpkhsbqQ1ebPEikpKSuPwCUR3HbwAiMrl1R5MAAJFNveFqb5rrzyhtlFjabymXXyAiDksRkWkVanXYcOIGAGBA+3pmjoaI6gImN0RkUjvOpyEjTwNvJyW6NPA02fMIgoBcdS4KdAVcfoGojuOwFBGZ1Noj1wEAz7etB5m0ZlYAL0ueJg+u810BAHej7nL5BaI6jD03RGQyqVkF2P3vLQDAwHYckiKi2sHkhohM5rdjSdALQPsg1xpdAZyIqCJMbojIJARBwLojRWdJDeREYiKqRUxuiMgkjiXexdXbubCVy9C3JdeSIqLaw+SGiExi7b1em94tfOCg5LkLRFR7mNwQUY3LU2vx16mitaQGtgswczREVNdYRHLzzTffIDg4GCqVCmFhYTh06FC5dX/44Qd07twZrq6ucHV1RURERIX1iaj2bTqVjJxCLQLd7BAW4lYrzymTyvB84+fR0bkjZFJZrTwnEVkmsyc3a9asQXR0NGbMmIFjx46hVatWiIqKQlpaWpn14+LiMGTIEOzatQsHDhxAQEAAevXqhRs3btRy5ERUFkEQsPJAPABgcIcASE14bZuSVDYq/PL8L5gSMgUqG646TlSXmT25WbBgAV555RWMHDkSTZs2xZIlS2BnZ4dly5aVWX/16tUYN24cWrdujcaNG+PHH3+EXq/Hjh07ajlyIirLscS7OHMjCwobKQY/HmjucIioDjJrcqNWq3H06FFEREQYyqRSKSIiInDgwIFK7SMvLw8ajQZubrXT9U1EFVuxPwEA8GwrP7iZaJFMIqKKmPUUhtu3b0On08Hb29uo3NvbGxcuXKjUPqZOnQo/Pz+jBKmkwsJCFBYWGu5nZWUBADQaDTQaTTUjL1vx/mp6v5ZC7O0DxN9GU7cvNasAm08XTSQe1qFerb6Ouepcw/ILaV3T4GLvUmvPXZv4HrVuYm8fYLo2VmV/Vn1+5pw5c/DLL78gLi4OKlXZY+yzZ8/GrFmzSpVv27YNdnZ2JokrNjbWJPu1FGJvHyD+NpqqfTGJUmj1UoQ4Ckg4sQ8JJ0zyNGUq0BUYbu/cuRMqmbjn3fA9at3E3j6g5tuYl5dX6bpmTW48PDwgk8mQmppqVJ6amgofH58Kt50/fz7mzJmD7du3o2XLluXWmzZtGqKjow33s7KyDJOQnZycHq0BD9BoNIiNjUVkZCTkcnmN7tsSiL19gPjbaMr2FWr1+HD+HgBqvNm7Ffq0qPgzXNNy1bnA6aLbPXr0EHXPDd+j1kvs7QNM18bikZfKMGtyo1Ao0K5dO+zYsQP9+/cHAMPk4AkTJpS73dy5c/HJJ59g69ataN++fYXPoVQqoVQqS5XL5XKTvbFMuW9LIPb2AeJvoyna99eZJNzJVcPHSYU+rfwhl9XulD65cL89Yj9+gPjbyPZZv5puY1X2ZfZhqejoaIwYMQLt27dHhw4dsHDhQuTm5mLkyJEAgOHDh8Pf3x+zZ88GAHz22WeYPn06fvrpJwQHByMlJQUA4ODgAAcHLsxHZC7FE4mHhQXWemJDRFSS2ZObQYMG4datW5g+fTpSUlLQunVrbNmyxTDJODExEVLp/S/KxYsXQ61WY8CAAUb7mTFjBmbOnFmboRPRPccT7+Lk9QwoZFIMCePp30RkXmZPbgBgwoQJ5Q5DxcXFGd2Pj483fUBEVCUr98cDAJ5u6QsPh9LDwEREtckikhsisl43MvIN60iN6BhstjhkUhl6h/ZG2q00Lr9AVMcxuSGiR7Ik7gq0egEdQ93RKsDFbHGobFTYOGgjYmJiuPwCUR3HWX9EVG2pWQVYc+Q6AOD1Hg3MHA0RUREmN0RUbT/suQq1Vo/2Qa54oj6XQCEiy8BhKSKqljs5hVh9MBEAMKHHY5BIamf17/LkqnPhNd8LOp0OKREpcJG7mDUeIjIfJjdEVC3L/r6GfI0OLfyd0bWhp7nDAQDkaSp/eXYiEi8OSxFRlWXmabDy3kX7LKHXhoioJCY3RFRlKw/EI6dQi0bejohs4m3ucIiIjDC5IaIqySnUYtnf1wAU9dpIpey1ISLLwuSGiKpk5f54ZORpUN/DHn1a+Jo7HCKiUpjcEFGl3c4pxOK4KwCAiT0bQMZeGyKyQDxbiogq7YvYf5FTqEXLes54ppWfucMxIpVI0SWwC+7cuQOphP+3EdVlTG6IqFIupWbj50NF17V5v29Ti5trYyu3xfb/bEdMTAxs5bbmDoeIzIj/3hBRpXwacx56AYhq5o0OIbwaMRFZLiY3RPRQ+y7dxq6Lt2AjlWDqU43NHQ4RUYU4LEVEFdLpBXy86RwA4D9PBKG+p4OZIypbrjoXwQuDoVarkRCRwOUXiOowJjdEVKHfjibhQko2nFQ2eKOnZa/8fTv/trlDICILwGEpIipXVoEG87ddBAC83qMBXO0VZo6IiOjhmNwQUblmx1xAWnYhgt3tMLxjkLnDISKqFCY3RFSm/VduG079nvNCSyhtZGaOiIiocpjcEFEp+Wodpq0/DQAYFhaIJ+q7mzkiIqLKY3JDRKV8sf1fJNzJg6+zCu/05qnfRGRdeLYUERk5eT0DP+69CgD45LnmcFTJzRxR5UglUrTzbYfMjEwuv0BUxzG5ISIDtVaPqb+dgl4Anm3thx6Nvc0dUqXZym1xYOQBLr9ARByWIqL7Fu28hAsp2XCzV2D6003NHQ4RUbUwuSEiAMCef2/h612XAQCznmkGdwelmSMiIqoeDksREZIz8/HmmhMQBGBoWCD6tfIzd0hVlqfJQ9NvmiIvLw+XIi/BWe5s7pCIyEyY3BDVcRqdHq//dBzpuWo083Oy2uEoQRCQkJlguE1EdReHpYjquHlbL+JIwl04Km3w7bC2UMl5sT4ism5MbojqsG1nU/D9nqLTvucNbIkgd3szR0RE9OiY3BDVUZdSszFp7UkAwKgnQ/BUc18zR0REVDOY3BDVQcmZBRix7BCyC7RoH+TKqxATkagwuSGqY/K0wOj/HsPNzALU97THD8PbQy7jVwERiQfPliKqQwo1Ovx4QYYr2TnwclTivy93gKu9wtxh1QiJRIImHk2Qk5MDiURi7nCIyIyY3BDVETq9gOh1p3ElWwIHpQ1WvtwB9VztzB1WjbGT2+HkmJOIiYmBnVw87SKiqmNfNFEdoNMLeHf9aWw7lwaZRMDioa3RxNfJ3GEREZkEe26IRE6t1eOtX09g06lkSCXAi4/p8UR9N3OHRURkMkxuiEQsX63D2NVHEXfxFuQyCT4f0AJC4jFzh2USeZo8tP++PXJyctAtshuXXyCqw5jcEIlUdoEGo1YewaFr6VDJpVjyn3boVN8VMYnmjsw0BEHA+dvnDbeJqO5ickMkQqlZBRi98ghO38iEo9IGy0Y+jseD3aDRaMwdGhGRyTG5IRKZQ9fSMW71MdzOKYSbvQL/fbkDmvtziIaI6g4mN0QiIQgClv8dj09jzkOrF9DI2xHfvdgOwR5cL4qI6hYmN0QikKfWYtr609h44iYA4JlWfpjzQgvYKfgRJ6K6h998RFbuSHw6pvx2Cldv5UImleC9Pk0wslMwr9JLRHUWkxsiK5Wn1mLe1otYsT8eggB4OSqxaEgbhNV3N3doZiGRSBDkHIS8vDwmdkR1HJMbIiu0//JtTF1/CtfT8wEAA9vVw/t9m8LZTm7myMzHTm6HS+MvcfkFImJyQ2RNrt3OxbytFxBzOgUA4O9ii0+fb4GuDT3NHBkRkeVgckNkBW7nFOKrHZfw08FEaPUCJBLgP2FBmNq7MRyU/BgTEZXEb0UiC3Y7pxD/3R+PpfuuIVetAwB0b+SJqb0bo7EPF74sKV+Tj87LOyMzIxPdI7tDLq+7Q3REdR2TGyILdOVWDn7cew3rjyWhUKsHALTwd8a0Po3RMdTDzNFZJr2gx9Hko4bbRFR3MbkhshBanR57Lt3CTwcTsf18mqG8VT1njOkSit7NfSCV8iwgIqKHYXJDZGYXUrKw7kgSNpy4ids5hQAAiQTo2dgbY7rUx+PBrjy1mYioCpjcENUyQRBw5kYWYs+lYNu5VFxIyTY85m6vwLOt/THsiUCEejqYMUoiIuvF5IaoFmTmafDPtTvYd+k2tp9PRXJmgeExuUyCno29MaBdPXRt5Am5TGrGSImIrB+TGyITSMsqwMmkTBy8egcHrt7BueQsCML9x+0UMnRp4InIpt7o0dgLrvYK8wVLRCQyTG6IHoEgCEjOLMDF1GycT87CqeuZOJmUYdQzUyzU0x7hoe7o0dgLHUM9oJLLzBCxuHnYekCtVps7DCIyMyY3RJWQW6hFwp08JNzJRfy935fScvBvSjayC7Wl6kslQAMvR7QNcsET9d0RXt8dXk4qM0Red9gr7HHzrZuIiYmBvcLe3OEQkRlZRHLzzTffYN68eUhJSUGrVq2waNEidOjQodz6a9euxQcffID4+Hg0aNAAn332Gfr06VOLEZNY6PUC7uapcSunELeyC5GSkYe9NyQ48td5pGSrkZyZj5sZBUjPLb83wEYqQX1PezT0dkTLes5oVc8Fzf2dYc8rBxMRmYXZv33XrFmD6OhoLFmyBGFhYVi4cCGioqJw8eJFeHl5laq/f/9+DBkyBLNnz8bTTz+Nn376Cf3798exY8fQvHlzM7SAzEUQBBRo9MhTa5Gn1iFfo0NOoRY5BVrkFmqRfe92VoEGWflFvzPzNcjIUyM9V427eUW39cKDe5YBiddLPZ+rnRxB7vYIdrdDoLs9HvNyQCNvR4R42ENhw0nARESWwuzJzYIFC/DKK69g5MiRAIAlS5Zg06ZNWLZsGd55551S9b/88ks89dRTmDx5MgDgo48+QmxsLL7++mssWbKkVmMvqVCrQ3JGPtILgRsZ+bCx0QCA0STSsu4DgIDShcX1BMN9wXD//j4Eo3r3b5coF+7fN7p9b5/3txOgF4p6MgQA+nuV9ELRbb0gQKPV4sxdCZTn0yCRyaDXC9AJ97fTFd8v+VsvQPvgb50emuLfOgFavR4arQCNTg+1Tl/0W1t0u1BT9Fut1aNAo0OBRo8Crc5wu6a42Svg6aCEu4Mc6szbaNckFPXc7ODnYgtfZ1v4u9jW6RW3rUG+Jh9PrXoKd+7cQXcNl18gqsvMmtyo1WocPXoU06ZNM5RJpVJERETgwIEDZW5z4MABREdHG5VFRUVhw4YNZdYvLCxEYWGh4X5WVhYAQKPRQKPRPGIL7jt5PQP/9/0hADaYdWxvje3X8sjww4UT5g6iFJVcClu5DPZKGzgoin7bK2VwUNrAyVYOZ5UcjiobOKps4Gonh5u9Aq52crjaKeBiJzecfq3RaBAbG4vI7sGl/jjW5PvFXIrbIIa2PKhQXYg9iXsMtzVy8bUREPcxBNg+MTBVG6uyP7MmN7dv34ZOp4O3t7dRube3Ny5cuFDmNikpKWXWT0lJKbP+7NmzMWvWrFLl27Ztg52dXTUjLy0hG5BLyz77pTLXlpWUunH/5oPbSx64I3nwMUnpbcsql5S8f+92qfsSoHjARSox3q74MalEMNyWSIrqSe/VL/6RlSiXSQCp9H6ZTALYSATY3CuTSQAbKWBT/PvebblUgEIKyEv8KGVFvx+6KoEWQE7RjwDgzr2f8sTGxj5kh9ZNjO0r0N0/Q23nzp1QycQ9gVuMx7Akts/61XQb8/LyKl3X7MNSpjZt2jSjnp6srCwEBASgV69ecHKq2VWVRxf/1x8ZKcoucY3I2weIv41ibl+uOhc4XXS7R48ecLF3MWs8piLmYwiwfWJgqjYWj7xUhlmTGw8PD8hkMqSmphqVp6amwsfHp8xtfHx8qlRfqVRCqVSWKpfL5SZ7Y5ly35ZA7O0DxN9GMbZPLtxvjxjb9yCxt5Hts3413caq7Musp3goFAq0a9cOO3bsMJTp9Xrs2LED4eHhZW4THh5uVB8o6voqrz4RERHVLWYfloqOjsaIESPQvn17dOjQAQsXLkRubq7h7Knhw4fD398fs2fPBgC88cYb6Nq1Kz7//HP07dsXv/zyC44cOYLvv//enM0gIiIiC2H25GbQoEG4desWpk+fjpSUFLRu3RpbtmwxTBpOTEyEVHq/g6ljx4746aef8P777+Pdd99FgwYNsGHDBl7jhohgJ7eDTqczdxhEZGZmT24AYMKECZgwYUKZj8XFxZUqGzhwIAYOHGjiqIjImtgr7JExOYPLLxCReefcEBEREdU0JjdEREQkKhYxLEVE9KgKtAV4fs3zSLuVhh7aHqI/zZaIysfkhohEQafXYfOVzYbbRFR3cViKiIiIRIXJDREREYkKkxsiIiISFSY3REREJCpMboiIiEhU6tzZUoIgAKja0umVpdFokJeXh6ysLFGehir29gHib6OY25erzgUKim5nZWVBqhPn/25iPoYA2ycGpmpj8d/t4r/jFZEIlaklIklJSQgICDB3GERERFQN169fR7169SqsU+eSG71ej5s3b8LR0RESiaRG952VlYWAgABcv34dTk5ONbpvSyD29gHibyPbZ/3E3ka2z/qZqo2CICA7Oxt+fn5GC2qXpc4NS0ml0odmfI/KyclJtG9aQPztA8TfRrbP+om9jWyf9TNFG52dnStVT5yD0kRERFRnMbkhIiIiUWFyU4OUSiVmzJgBpVJp7lBMQuztA8TfRrbP+om9jWyf9bOENta5CcVEREQkbuy5ISIiIlFhckNERESiwuSGiIiIRIXJDREREYkKk5sq+OSTT9CxY0fY2dnBxcWlzDqJiYno27cv7Ozs4OXlhcmTJ0Or1Va43/T0dAwbNgxOTk5wcXHBqFGjkJOTY4IWVE1cXBwkEkmZP4cPHy53u27dupWq/9prr9Vi5JUXHBxcKtY5c+ZUuE1BQQHGjx8Pd3d3ODg44IUXXkBqamotRVw18fHxGDVqFEJCQmBra4vQ0FDMmDEDarW6wu0s+Rh+8803CA4OhkqlQlhYGA4dOlRh/bVr16Jx48ZQqVRo0aIFYmJiainSqps9ezYef/xxODo6wsvLC/3798fFixcr3GbFihWljpVKpaqliKtm5syZpWJt3LhxhdtY0/EDyv5OkUgkGD9+fJn1Lf347dmzB/369YOfnx8kEgk2bNhg9LggCJg+fTp8fX1ha2uLiIgIXLp06aH7rernuKqY3FSBWq3GwIEDMXbs2DIf1+l06Nu3L9RqNfbv34+VK1dixYoVmD59eoX7HTZsGM6ePYvY2Fj89ddf2LNnD8aMGWOKJlRJx44dkZycbPQzevRohISEoH379hVu+8orrxhtN3fu3FqKuuo+/PBDo1hff/31Cuu/9dZb+PPPP7F27Vrs3r0bN2/exPPPP19L0VbNhQsXoNfr8d133+Hs2bP44osvsGTJErz77rsP3dYSj+GaNWsQHR2NGTNm4NixY2jVqhWioqKQlpZWZv39+/djyJAhGDVqFI4fP47+/fujf//+OHPmTC1HXjm7d+/G+PHj8c8//yA2NhYajQa9evVCbm5uhds5OTkZHauEhIRairjqmjVrZhTrvn37yq1rbccPAA4fPmzUvtjYWADAwIEDy93Gko9fbm4uWrVqhW+++abMx+fOnYuvvvoKS5YswcGDB2Fvb4+oqCgUFBSUu8+qfo6rRaAqW758ueDs7FyqPCYmRpBKpUJKSoqhbPHixYKTk5NQWFhY5r7OnTsnABAOHz5sKNu8ebMgkUiEGzdu1Hjsj0KtVguenp7Chx9+WGG9rl27Cm+88UbtBPWIgoKChC+++KLS9TMyMgS5XC6sXbvWUHb+/HkBgHDgwAETRFjz5s6dK4SEhFRYx1KPYYcOHYTx48cb7ut0OsHPz0+YPXt2mfX/7//+T+jbt69RWVhYmPDqq6+aNM6akpaWJgAQdu/eXW6d8r6PLNGMGTOEVq1aVbq+tR8/QRCEN954QwgNDRX0en2Zj1vT8QMg/P7774b7er1e8PHxEebNm2coy8jIEJRKpfDzzz+Xu5+qfo6rgz03NejAgQNo0aIFvL29DWVRUVHIysrC2bNny93GxcXFqCckIiICUqkUBw8eNHnMVfHHH3/gzp07GDly5EPrrl69Gh4eHmjevDmmTZuGvLy8WoiweubMmQN3d3e0adMG8+bNq3AY8ejRo9BoNIiIiDCUNW7cGIGBgThw4EBthPvIMjMz4ebm9tB6lnYM1Wo1jh49avTaS6VSRERElPvaHzhwwKg+UPSZtKZjBeChxysnJwdBQUEICAjAs88+W+73jSW4dOkS/Pz8UL9+fQwbNgyJiYnl1rX246dWq7Fq1Sq8/PLLFS7UbE3Hr6Rr164hJSXF6Bg5OzsjLCys3GNUnc9xddS5hTNNKSUlxSixAWC4n5KSUu42Xl5eRmU2NjZwc3MrdxtzWbp0KaKioh668OjQoUMRFBQEPz8/nDp1ClOnTsXFixexfv36Woq08iZOnIi2bdvCzc0N+/fvx7Rp05CcnIwFCxaUWT8lJQUKhaLUnCtvb2+LO15luXz5MhYtWoT58+dXWM8Sj+Ht27eh0+nK/IxduHChzG3K+0xaw7HS6/V488030alTJzRv3rzceo0aNcKyZcvQsmVLZGZmYv78+ejYsSPOnj1r8kWCqyosLAwrVqxAo0aNkJycjFmzZqFz5844c+YMHB0dS9W35uMHABs2bEBGRgZeeumlcutY0/F7UPFxqMoxqs7nuDrqfHLzzjvv4LPPPquwzvnz5x866c2aVKfNSUlJ2Lp1K3799deH7r/kfKEWLVrA19cXPXv2xJUrVxAaGlr9wCupKu2Ljo42lLVs2RIKhQKvvvoqZs+ebdGXR6/OMbxx4waeeuopDBw4EK+88kqF25r7GBIwfvx4nDlzpsI5KQAQHh6O8PBww/2OHTuiSZMm+O677/DRRx+ZOswq6d27t+F2y5YtERYWhqCgIPz6668YNWqUGSMzjaVLl6J3797w8/Mrt441HT9rUueTm0mTJlWYVQNA/fr1K7UvHx+fUjO+i8+i8fHxKXebBydRabVapKenl7vNo6pOm5cvXw53d3c888wzVX6+sLAwAEW9BrXxh/FRjmlYWBi0Wi3i4+PRqFGjUo/7+PhArVYjIyPDqPcmNTXVZMerLFVt482bN9G9e3d07NgR33//fZWfr7aPYVk8PDwgk8lKnZlW0Wvv4+NTpfqWYsKECYaTC6r637tcLkebNm1w+fJlE0VXc1xcXNCwYcNyY7XW4wcACQkJ2L59e5V7O63p+BUfh9TUVPj6+hrKU1NT0bp16zK3qc7nuFpqbPZOHfKwCcWpqamGsu+++05wcnISCgoKytxX8YTiI0eOGMq2bt1qUROK9Xq9EBISIkyaNKla2+/bt08AIJw8ebKGI6t5q1atEqRSqZCenl7m48UTitetW2cou3DhgkVPKE5KShIaNGggDB48WNBqtdXah6Ucww4dOggTJkww3NfpdIK/v3+FE4qffvppo7Lw8HCLnZCq1+uF8ePHC35+fsK///5brX1otVqhUaNGwltvvVXD0dW87OxswdXVVfjyyy/LfNzajl9JM2bMEHx8fASNRlOl7Sz5+KGcCcXz5883lGVmZlZqQnFVPsfVirXG9lQHJCQkCMePHxdmzZolODg4CMePHxeOHz8uZGdnC4JQ9KZs3ry50KtXL+HEiRPCli1bBE9PT2HatGmGfRw8eFBo1KiRkJSUZCh76qmnhDZt2ggHDx4U9u3bJzRo0EAYMmRIrbevPNu3bxcACOfPny/1WFJSktCoUSPh4MGDgiAIwuXLl4UPP/xQOHLkiHDt2jVh48aNQv369YUuXbrUdtgPtX//fuGLL74QTpw4IVy5ckVYtWqV4OnpKQwfPtxQ58H2CYIgvPbaa0JgYKCwc+dO4ciRI0J4eLgQHh5ujiY8VFJSkvDYY48JPXv2FJKSkoTk5GTDT8k61nIMf/nlF0GpVAorVqwQzp07J4wZM0ZwcXExnKH44osvCu+8846h/t9//y3Y2NgI8+fPF86fPy/MmDFDkMvlwunTp83VhAqNHTtWcHZ2FuLi4oyOVV5enqHOg22cNWuWsHXrVuHKlSvC0aNHhcGDBwsqlUo4e/asOZpQoUmTJglxcXHCtWvXhL///luIiIgQPDw8hLS0NEEQrP/4FdPpdEJgYKAwderUUo9Z2/HLzs42/K0DICxYsEA4fvy4kJCQIAiCIMyZM0dwcXERNm7cKJw6dUp49tlnhZCQECE/P9+wjx49egiLFi0y3H/Y57gmMLmpghEjRggASv3s2rXLUCc+Pl7o3bu3YGtrK3h4eAiTJk0yytx37dolABCuXbtmKLtz544wZMgQwcHBQXBychJGjhxpSJgswZAhQ4SOHTuW+di1a9eMXoPExEShS5cugpubm6BUKoXHHntMmDx5spCZmVmLEVfO0aNHhbCwMMHZ2VlQqVRCkyZNhE8//dSol+3B9gmCIOTn5wvjxo0TXF1dBTs7O+G5554zShYsyfLly8t8z5bstLW2Y7ho0SIhMDBQUCgUQocOHYR//vnH8FjXrl2FESNGGNX/9ddfhYYNGwoKhUJo1qyZsGnTplqOuPLKO1bLly831HmwjW+++abh9fD29hb69OkjHDt2rPaDr4RBgwYJvr6+gkKhEPz9/YVBgwYJly9fNjxu7cev2NatWwUAwsWLF0s9Zm3Hr/hv1oM/xW3Q6/XCBx98IHh7ewtKpVLo2bNnqXYHBQUJM2bMMCqr6HNcEySCIAg1N8hFREREZF68zg0RERGJCpMbIiIiEhUmN0RERCQqTG6IiIhIVJjcEBERkagwuSEiIiJRYXJDREREosLkhoiIiESFyQ0RERGJCpMbIiIiEhUmN0Rk9eLj4yGRSEr9dOvWzdyhEZEZ2Jg7ACKiRxUQEIDk5GTD/ZSUFERERKBLly5mjIqIzIULZxKRqBQUFKBbt27w9PTExo0bIZWyg5qormHPDRGJyssvv4zs7GzExsYysSGqo5jcEJFofPzxx9i6dSsOHToER0dHc4dDRGbCYSkiEoXffvsNQ4YMwebNm9GzZ09zh0NEZsTkhois3pkzZxAWFobo6GiMHz/eUK5QKODm5mbGyIjIHJjcEJHVW7FiBUaOHFmqvGvXroiLi6v9gIjIrJjcEBERkajwVAIiIiISFSY3REREJCpMboiIiEhUmNwQERGRqDC5ISIiIlFhckNERESiwuSGiIiIRIXJDREREYkKkxsiIiISFSY3REREJCpMboiIiEhUmNwQERGRqPw/2ex7RSXr8uUAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "z = np.linspace(-10, 10, 100)\n",
-    "sigmoid = 1 / (1 + np.exp(-z))\n",
-    "plt.plot(z, sigmoid)\n",
-    "plt.title(\"Sigmoid Function\")\n",
-    "plt.xlabel(\"z\")\n",
-    "plt.ylabel(\"σ(z)\")\n",
-    "plt.grid()\n",
-    "plt.axhline(0.5, color='red', linestyle='--', label='Threshold (0.5)')\n",
-    "plt.axvline(0, color='green', linestyle='--', label='Decision Boundary (z=0)')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ecef3f11",
-   "metadata": {},
-   "source": [
-    "### Mathematical Formulation\n",
-    "\n",
-    "The logistic regression model can be expressed as: $$P(y=1|X) = \\frac{1}{1 + e^{-(\\beta_0 + \\beta_1 X_1 + \\ldots + \\beta_n X_n)}}$$ where\n",
-    "\n",
-    "-   $P(y = 1|X)$: probability of the positive class given the features $X$\n",
-    "-   $\\beta_0, \\beta_1, \\ldots, \\beta_n$: the model coefficients\n",
-    "\n",
-    "The logistic regression model is trained by maximizing the likelihood of the observed data, which is equivalent to minimizing the negative log-likelihood (cross-entropy loss)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "acc25177",
-   "metadata": {},
-   "source": [
-    "## Practical Demonstration\n",
-    "\n",
-    "To illustrate logistic regression, we will use a very simple synthetic dataset, consisting of one feature and a binary target variable. This will allow us to visualize the decision boundary clearly.\n",
-    "\n",
-    "-   Generating a synthetic dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "88516595",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.datasets import make_classification\n",
-    "import pandas as pd\n",
-    "\n",
-    "# Generate a synthetic dataset with one feature and binary target\n",
-    "X, y = make_classification(n_samples=1000, n_features=1, n_informative=1, n_redundant=0,\n",
-    "                           n_clusters_per_class=1, random_state=0)\n",
-    "df = pd.DataFrame(X, columns=['feature'])\n",
-    "df['target'] = y\n",
-    "df['target'] = df['target'].astype('category')\n",
-    "df['target'] = df['target'].cat.codes\n",
-    "\n",
-    "X = df.drop(columns=['target'])\n",
-    "y = df['target']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be952a12",
-   "metadata": {},
-   "source": [
-    "-   Visualizing the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "13dc6287",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "sns.scatterplot(data=df, x='feature', y='target', hue='target', palette='Set1')\n",
-    "plt.title(\"Synthetic Dataset\")\n",
-    "plt.xlabel(\"Feature\")\n",
-    "plt.ylabel(\"Target\")\n",
-    "# plt.axhline(0.5, color='red', linestyle='--', label='Decision Boundary (0.5)')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "70f5e844",
-   "metadata": {},
-   "source": [
-    "-   Training a logistic regression model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "f8ccb9fe",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
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-       "    \n",
-       "\n",
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-       "            <td class=\"param\">dual&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
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-       "    \n",
-       "\n",
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-       "            <td class=\"param\">C&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
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-       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
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-       "            <td class=\"param\">intercept_scaling&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
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-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">solver&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
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-       "            <td class=\"param\">max_iter&nbsp;</td>\n",
-       "            <td class=\"value\">100</td>\n",
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-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('multi_class',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">multi_class&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('l1_ratio',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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-      ],
-      "text/plain": [
-       "LogisticRegression()"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "# Split the dataset into training and test sets\n",
-    "X_train, X_test, y_train, y_test = \\\n",
-    "    train_test_split(df[['feature']], df['target'], test_size=0.2, random_state=42)\n",
-    "\n",
-    "# Train a logistic regression model\n",
-    "clf = LogisticRegression()\n",
-    "clf.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "90be79a5",
-   "metadata": {},
-   "source": [
-    "-   Evaluating the model's accuracy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "56630bde",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model accuracy: 0.97\n",
-      "Classification Report:\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       1.00      0.94      0.97       101\n",
-      "           1       0.94      1.00      0.97        99\n",
-      "\n",
-      "    accuracy                           0.97       200\n",
-      "   macro avg       0.97      0.97      0.97       200\n",
-      "weighted avg       0.97      0.97      0.97       200\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import classification_report, confusion_matrix\n",
-    "# Make predictions on the test set\n",
-    "y_pred = clf.predict(X_test)\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "# Evaluate the model's accuracy\n",
-    "accuracy = accuracy_score(y_test, y_pred)\n",
-    "print(\"Model accuracy:\", accuracy)\n",
-    "\n",
-    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00e1f309",
-   "metadata": {},
-   "source": [
-    "-   Visualizing the decision boundary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "3d95a2ad",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x_range = np.linspace(X['feature'].min(), X['feature'].max(), 100).reshape(-1, 1)\n",
-    "X_range = pd.DataFrame(x_range, columns=X.columns)\n",
-    "y_prob = clf.predict_proba(X_range)[:, 1]\n",
-    "\n",
-    "plt.scatter(X_test['feature'], y_test, c='blue', label='Test Data', alpha=0.5)\n",
-    "plt.plot(X_range, y_prob, color='red', label='Probability of Class 1')\n",
-    "plt.axvline(X_range[y_prob < 0.5]['feature'].max(),\n",
-    "            color='green', linestyle='--',\n",
-    "            label='Decision Boundary (Threshold = 0.5)')\n",
-    "plt.axhline(0.5, color='black', linestyle='--', label='Threshold (0.5)')\n",
-    "plt.title(\"Logistic Regression Decision Boundary\")\n",
-    "plt.xlabel(\"Feature\")\n",
-    "plt.ylabel(\"Probability of Class 1\")\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "39e54bb9",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "Modify the synthetic dataset to include two features instead of one. Train a logistic regression model and visualize the decision boundary in a 2D plot.\n",
-    "\n",
-    "-   Generate the 2D synthetic dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "5eec6c57",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Feature columns: ['feature1', 'feature2']\n",
-      "Feature data types: feature1    float64\n",
-      "feature2    float64\n",
-      "dtype: object\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Generate a synthetic dataset with two features and binary target\n",
-    "X, y = make_classification(n_samples=1000, n_features=2, n_informative=2, n_redundant=0,\n",
-    "                           n_clusters_per_class=1, random_state=0)\n",
-    "df = pd.DataFrame(X, columns=['feature1', 'feature2'])\n",
-    "df['target'] = y\n",
-    "df['target'] = df['target']\n",
-    "\n",
-    "X = df.drop(columns=['target'])\n",
-    "y = df['target']\n",
-    "\n",
-    "print(\"Feature columns:\", X.columns.tolist())\n",
-    "print(\"Feature data types:\", X.dtypes)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d958ae1c",
-   "metadata": {},
-   "source": [
-    "-   Visualize the dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "94fe2fd4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.scatterplot(data=df, x='feature1', y='feature2', hue='target', palette='Set1')\n",
-    "plt.title(\"Synthetic Dataset with Two Features\")\n",
-    "plt.xlabel(\"Feature 1\")\n",
-    "plt.ylabel(\"Feature 2\")\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c6760c13",
-   "metadata": {},
-   "source": [
-    "-   Split the dataset into training and test sets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "87be5837",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, y_train, y_test = \\\n",
-    "    train_test_split(X, y, test_size=0.2, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2feaa447",
-   "metadata": {},
-   "source": [
-    "-   Train a logistic regression model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "e3a20a46",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model coefficients: [[3.36491657 0.60449285]]\n",
-      "Model intercept: [-0.47996048]\n"
-     ]
-    }
-   ],
-   "source": [
-    "clf = LogisticRegression()\n",
-    "clf.fit(X_train, y_train)\n",
-    "print(\"Model coefficients:\", clf.coef_)\n",
-    "print(\"Model intercept:\", clf.intercept_)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "02450a4a",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the model's accuracy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "ff3735e3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model accuracy: 0.93\n"
-     ]
-    }
-   ],
-   "source": [
-    "y_pred = clf.predict(X_test)\n",
-    "# Evaluate the model's accuracy\n",
-    "accuracy = accuracy_score(y_test, y_pred)\n",
-    "print(\"Model accuracy:\", accuracy)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b84ad3b1",
-   "metadata": {},
-   "source": [
-    "-   Visualize the decision boundary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "a846c6a8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot decision boundaries\n",
-    "from sklearn.inspection import DecisionBoundaryDisplay\n",
-    "display = DecisionBoundaryDisplay.from_estimator(\n",
-    "    estimator=clf,\n",
-    "    X=X_test,\n",
-    "    alpha=0.8,\n",
-    "    grid_resolution=500,\n",
-    "    response_method=\"predict\"\n",
-    ")\n",
-    "display.ax_.scatter(\n",
-    "    X_test['feature1'],\n",
-    "    X_test['feature2'],\n",
-    "    c=y_test.astype('category').cat.codes, edgecolor=\"black\",\n",
-    "    label='Test Data'\n",
-    ")\n",
-    "plt.title(\"Logistic Regression Decision Boundary with Two Features\")\n",
-    "plt.xlabel(\"Feature 1\")\n",
-    "plt.ylabel(\"Feature 2\")\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03C03 Evaluation Metrics.ipynb b/src/S03 Supervised Learning - Classification and Evaluation/S03C03 Evaluation Metrics.ipynb
deleted file mode 100644
index 05fa7fe..0000000
--- a/src/S03 Supervised Learning - Classification and Evaluation/S03C03 Evaluation Metrics.ipynb	
+++ /dev/null
@@ -1,1408 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "47d7f795",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Evaluation Metrics for Classification"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a4708bd9",
-   "metadata": {},
-   "source": [
-    "## Confusion Matrix\n",
-    "\n",
-    "In classification tasks, we often need to evaluate how well our model performs. The ****confusion matrix**** is a powerful tool that summarizes the performance of a classification algorithm by comparing predicted and actual labels.\n",
-    "\n",
-    "|                | **Predicted = 1**   | **Predicted = 0**   |\n",
-    "|-------------- |------------------- |------------------- |\n",
-    "| **Actual = 1** | True Positive (TP)  | False Negative (FN) |\n",
-    "| **Actual = 0** | False Positive (FP) | True Negative (TN)  |\n",
-    "\n",
-    "From these four numbers we obtain the core metrics summarized below."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "04f0b801",
-   "metadata": {},
-   "source": [
-    "## Accuracy (Overall Success Rate)\n",
-    "\n",
-    "Accuracy answers: \"What fraction of **all** predictions are correct?\"\n",
-    "\n",
-    "$$\\text{Accuracy} = \\frac{\\text{TP} + \\text{TN}}{\\text{TP} + \\text{FP} + \\text{FN} + \\text{TN}}$$\n",
-    "\n",
-    "High accuracy is desirable, but it can be ****inflated**** when one class dominates."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b974e8ad",
-   "metadata": {},
-   "source": [
-    "## Precision (Positive Predictive Value)\n",
-    "\n",
-    "Precision answers: \"When the classifier predicts 1, how often is it correct?\"\n",
-    "\n",
-    "$$\\text{Precision} = \\frac{\\text{TP}}{\\text{TP} + \\text{FP}}$$\n",
-    "\n",
-    "High precision $\\Rightarrow$ few false alarms, but it says nothing about missed positives."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1cb56a21",
-   "metadata": {},
-   "source": [
-    "## Recall (Sensitivity, True-Positive Rate)\n",
-    "\n",
-    "Recall answers: \"Of all the actual 1 cases, how many did we catch?\"\n",
-    "\n",
-    "$$\\text{Recall} = \\frac{\\text{TP}}{\\text{TP} + \\text{FN}}$$\n",
-    "\n",
-    "High recall $\\Rightarrow$ few misses, but it does not guard against many false alarms."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7567c4a7",
-   "metadata": {},
-   "source": [
-    "## F1-Score\n",
-    "\n",
-    "The **F1-score** is the harmonic mean of precision and recall, balancing the two:\n",
-    "\n",
-    "$$F_1 = 2 \\cdot \\frac{\\text{Precision} \\times \\text{Recall}}{\\text{Precision} + \\text{Recall}}$$\n",
-    "\n",
-    "It is 1 only when both precision and recall are 1; it drops sharply when either is low."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d7ed3d72",
-   "metadata": {},
-   "source": [
-    "## Precision-Recall Trade-off\n",
-    "\n",
-    "The precision-recall trade-off is a key concept in classification. By adjusting the decision threshold, we can increase precision at the cost of recall or vice versa. This trade-off is often visualized using the **precision-recall curve**, which plots precision against recall for different thresholds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "32cb1d55",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.metrics import precision_recall_curve, PrecisionRecallDisplay\n",
-    "\n",
-    "# Generating a synthetic and train a Logistic Regression model\n",
-    "from sklearn.datasets import make_classification\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "X = np.linspace(0, 10, 1001).reshape(-1, 1)\n",
-    "y = (X[:, 0] > 5).astype(int)  # Binary target based on a threshold\n",
-    "\n",
-    "# Introduce some noise so that not all points are perfectly separable\n",
-    "noise = np.random.normal(0, 0.35, size=y.shape)\n",
-    "y = (y + noise > 0.5).astype(int)  # Add noise to the target\n",
-    "\n",
-    "# Plotting the dataset\n",
-    "plt.scatter(X, y, color='blue', alpha=0.5)\n",
-    "plt.title(\"Synthetic Dataset\")\n",
-    "plt.xlabel(\"Feature\")\n",
-    "plt.ylabel(\"Target\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "581c6755",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model accuracy: 0.8905472636815921\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "clf = LogisticRegression()\n",
-    "clf.fit(X_train, y_train)\n",
-    "\n",
-    "# Evaluate the model's accuracy\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "y_pred = clf.predict(X_test)\n",
-    "accuracy = accuracy_score(y_test, y_pred)\n",
-    "print(\"Model accuracy:\", accuracy)\n",
-    "\n",
-    "# Plotting the Precision-Recall curve\n",
-    "display = PrecisionRecallDisplay.from_estimator(clf, X_test, y_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "097768c2",
-   "metadata": {},
-   "source": [
-    "## Receiver Operating Characteristic (ROC) Curve\n",
-    "\n",
-    "The ROC curve visualizes the trade-off between true positive rate (recall) and false positive rate (FPR) across different thresholds. The area under the ROC curve (AUC) quantifies the model's ability to distinguish between classes. AUC = 0.5 means random guessing, while AUC = 1 means perfect classification."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ff7bba05",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import roc_curve, roc_auc_score, RocCurveDisplay\n",
-    "display = RocCurveDisplay.from_estimator(clf, X_test, y_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2bd80704",
-   "metadata": {},
-   "source": [
-    "## Area Under the Curve (AUC)\n",
-    "\n",
-    "The **AUC** (Area Under the Curve) is a single scalar value summarizing the ROC curve. It represents the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance. AUC = 0.5 indicates no discrimination ability, while AUC = 1 indicates perfect discrimination."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "cb08955e",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AUC: 0.91\n"
-     ]
-    }
-   ],
-   "source": [
-    "auc = roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1])\n",
-    "print(f\"AUC: {auc:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0c8f82f",
-   "metadata": {},
-   "source": [
-    "## Practical Demonstration\n",
-    "\n",
-    "Below we create a simple synthetic binary dataset, train a classifier, and compute all four metrics.\n",
-    "\n",
-    "-   Generating a synthetic dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "9f3421ac",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.datasets import make_classification\n",
-    "import pandas as pd\n",
-    "\n",
-    "X, y = make_classification(n_samples=1000, n_features=5, n_informative=3,\n",
-    "                           n_redundant=0, n_clusters_per_class=1,\n",
-    "                           flip_y=0.02, class_sep=1.0, random_state=42)\n",
-    "df = pd.DataFrame(X, columns=[f\"feature{i}\" for i in range(1, 6)])\n",
-    "df[\"target\"] = y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "899b28ec",
-   "metadata": {},
-   "source": [
-    "-   Training/test split & model training"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "7233ef11",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=500)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">penalty&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
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-       "            ></i></td>\n",
-       "            <td class=\"param\">dual&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
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-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
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-       "            ></i></td>\n",
-       "            <td class=\"param\">C&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">intercept_scaling&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('solver',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">solver&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_iter&nbsp;</td>\n",
-       "            <td class=\"value\">500</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('multi_class',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">multi_class&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('l1_ratio',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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-       "</script></body>"
-      ],
-      "text/plain": [
-       "LogisticRegression(max_iter=500)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "\n",
-    "X_train, X_test, y_train, y_test = \\\n",
-    "    train_test_split(df.drop(columns=[\"target\"]), df[\"target\"],\n",
-    "                     test_size=0.25, random_state=0, stratify=df[\"target\"])\n",
-    "\n",
-    "clf = LogisticRegression(max_iter=500)\n",
-    "clf.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2c275519",
-   "metadata": {},
-   "source": [
-    "-   Evaluating the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "d2537744",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Confusion matrix:\n",
-      " [[108  17]\n",
-      " [  9 116]]\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0      0.923     0.864     0.893       125\n",
-      "           1      0.872     0.928     0.899       125\n",
-      "\n",
-      "    accuracy                          0.896       250\n",
-      "   macro avg      0.898     0.896     0.896       250\n",
-      "weighted avg      0.898     0.896     0.896       250\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import (\n",
-    "    classification_report, confusion_matrix,\n",
-    "    ConfusionMatrixDisplay, precision_recall_curve,\n",
-    "    accuracy_score\n",
-    ")\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "# Confusion matrix at the default 0.5 threshold\n",
-    "y_pred = clf.predict(X_test)\n",
-    "cm = confusion_matrix(y_test, y_pred)\n",
-    "print(\"Confusion matrix:\\n\", cm)\n",
-    "\n",
-    "# Detailed report includes precision, recall, f1 and support\n",
-    "print(classification_report(y_test, y_pred, digits=3))\n",
-    "\n",
-    "# Visualise confusion matrix\n",
-    "disp = ConfusionMatrixDisplay.from_estimator(clf, X_test, y_test,\n",
-    "                                             cmap='Blues')\n",
-    "plt.title(\"Confusion Matrix (threshold = 0.5)\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6dea2626",
-   "metadata": {},
-   "source": [
-    "-   Precision-Recall curve"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "a63e2d08",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import PrecisionRecallDisplay\n",
-    "\n",
-    "display = PrecisionRecallDisplay.from_estimator(clf, X_test, y_test)\n",
-    "plt.title(\"Precision-Recall Curve\")\n",
-    "plt.xlabel(\"Recall\")\n",
-    "plt.ylabel(\"Precision\")\n",
-    "plt.grid()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "38e614a1",
-   "metadata": {},
-   "source": [
-    "-   ROC curve"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "be75d59f",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import RocCurveDisplay\n",
-    "display = RocCurveDisplay.from_estimator(clf, X_test, y_test)\n",
-    "plt.title(\"ROC Curve\")\n",
-    "plt.xlabel(\"False Positive Rate\")\n",
-    "plt.ylabel(\"True Positive Rate (Recall)\")\n",
-    "plt.grid()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "11b7bf46",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "-   **Threshold Tuning**: Try several probability thresholds and observe how accuracy, precision, recall and F1 change."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "e1c6d1ba",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Threshold=0.1: Acc=0.864, Prec=0.794, Recall=0.984, F1=0.879\n",
-      "Threshold=0.2: Acc=0.880, Prec=0.819, Recall=0.976, F1=0.891\n",
-      "Threshold=0.3: Acc=0.900, Prec=0.852, Recall=0.968, F1=0.906\n",
-      "Threshold=0.4: Acc=0.900, Prec=0.862, Recall=0.952, F1=0.905\n",
-      "Threshold=0.5: Acc=0.896, Prec=0.872, Recall=0.928, F1=0.899\n",
-      "Threshold=0.6: Acc=0.880, Prec=0.874, Recall=0.888, F1=0.881\n",
-      "Threshold=0.7: Acc=0.876, Prec=0.879, Recall=0.872, F1=0.876\n",
-      "Threshold=0.8: Acc=0.860, Prec=0.902, Recall=0.808, F1=0.852\n",
-      "Threshold=0.9: Acc=0.780, Prec=0.917, Recall=0.616, F1=0.737\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n",
-    "\n",
-    "# Get predicted probabilities\n",
-    "y_scores = clf.predict_proba(X_test)[:, 1]\n",
-    "\n",
-    "thresholds = np.linspace(0.1, 0.9, 9)\n",
-    "for t in thresholds:\n",
-    "    y_pred_t = (y_scores >= t).astype(int)\n",
-    "    p = precision_score(y_test, y_pred_t)\n",
-    "    r = recall_score(y_test, y_pred_t)\n",
-    "    f1 = f1_score(y_test, y_pred_t)\n",
-    "    a = accuracy_score(y_test, y_pred_t)\n",
-    "    print(f\"Threshold={t:.1f}: Acc={a:.3f}, Prec={p:.3f}, Recall={r:.3f}, F1={f1:.3f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2a496ff5",
-   "metadata": {},
-   "source": [
-    "-   **Class-Imbalance Scenario**:\n",
-    "    -   Generate a highly unbalanced dataset (e.g. 95% zeros, 5% ones); check the documentation of `make_classification`.\n",
-    "    -   Train the same model and compute the metrics again.\n",
-    "    -   Plot the **precision-recall curve** and choose a better threshold for the minority class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "a1134e52",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.94      1.00      0.97       237\n",
-      "           1       0.00      0.00      0.00        14\n",
-      "\n",
-      "    accuracy                           0.94       251\n",
-      "   macro avg       0.47      0.50      0.49       251\n",
-      "weighted avg       0.89      0.94      0.92       251\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/camarocico/venvs/ml_workshop/lib/python3.13/site-packages/sklearn/metrics/_classification.py:1706: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", result.shape[0])\n",
-      "/home/camarocico/venvs/ml_workshop/lib/python3.13/site-packages/sklearn/metrics/_classification.py:1706: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", result.shape[0])\n",
-      "/home/camarocico/venvs/ml_workshop/lib/python3.13/site-packages/sklearn/metrics/_classification.py:1706: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", result.shape[0])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Threshold=0.01: Acc=0.056, Prec=0.056, Recall=1.000, F1=0.106\n",
-      "Threshold=0.02: Acc=0.199, Prec=0.065, Recall=1.000, F1=0.122\n",
-      "Threshold=0.03: Acc=0.390, Prec=0.079, Recall=0.929, F1=0.145\n",
-      "Threshold=0.04: Acc=0.486, Prec=0.086, Recall=0.857, F1=0.157\n",
-      "Threshold=0.05: Acc=0.566, Prec=0.101, Recall=0.857, F1=0.180\n",
-      "Threshold=0.06: Acc=0.645, Prec=0.121, Recall=0.857, F1=0.212\n",
-      "Threshold=0.07: Acc=0.713, Prec=0.138, Recall=0.786, F1=0.234\n",
-      "Threshold=0.08: Acc=0.785, Prec=0.177, Recall=0.786, F1=0.289\n",
-      "Threshold=0.09: Acc=0.821, Prec=0.196, Recall=0.714, F1=0.308\n",
-      "Threshold=0.10: Acc=0.880, Prec=0.265, Recall=0.643, F1=0.375\n",
-      "Threshold=0.11: Acc=0.912, Prec=0.346, Recall=0.643, F1=0.450\n",
-      "Threshold=0.12: Acc=0.948, Prec=0.533, Recall=0.571, F1=0.552\n",
-      "Threshold=0.13: Acc=0.956, Prec=0.800, Recall=0.286, F1=0.421\n"
-     ]
-    }
-   ],
-   "source": [
-    "np.random.seed(42)  # For reproducibility\n",
-    "X_unb = np.linspace(0, 1, 1001).reshape(-1, 1)\n",
-    "y_unb = (X_unb[:, 0] > 0.95).astype(int)  # Binary target based on a threshold\n",
-    "\n",
-    "# Introduce some noise so that not all points are perfectly separable\n",
-    "noise = np.random.normal(0, 0.5, size=y_unb.shape)\n",
-    "y_unb = (y_unb + noise > 0.95).astype(int)  # Add noise to the target\n",
-    "\n",
-    "df_unb = pd.DataFrame(X_unb, columns=[\"feature\"])\n",
-    "df_unb[\"target\"] = y_unb\n",
-    "\n",
-    "X_train_unb, X_test_unb, y_train_unb, y_test_unb = \\\n",
-    "      train_test_split(df_unb.drop(columns=[\"target\"]),\n",
-    "                       df_unb[\"target\"],\n",
-    "                       test_size=0.25,\n",
-    "                       random_state=0,\n",
-    "                       stratify=df_unb[\"target\"])\n",
-    "\n",
-    "clf_unb = LogisticRegression(max_iter=500)\n",
-    "clf_unb.fit(X_train_unb, y_train_unb)\n",
-    "\n",
-    "# Evaluate the model\n",
-    "y_pred_unb = clf_unb.predict(X_test_unb)\n",
-    "print(classification_report(y_test_unb, y_pred_unb))\n",
-    "\n",
-    "# Precision-Recall curve\n",
-    "display = PrecisionRecallDisplay.from_estimator(clf_unb, X_test_unb, y_test_unb)\n",
-    "plt.title(\"Precision-Recall Curve (Unbalanced Dataset)\")\n",
-    "plt.xlabel(\"Recall\")\n",
-    "plt.ylabel(\"Precision\")\n",
-    "plt.grid()\n",
-    "plt.show()\n",
-    "\n",
-    "y_scores_unb = clf_unb.predict_proba(X_test_unb)[:, 1]\n",
-    "thresholds_unb = np.linspace(0.01, 0.13, 13)\n",
-    "for t in thresholds_unb:\n",
-    "      y_pred_t_unb = (y_scores_unb >= t).astype(int)\n",
-    "      p = precision_score(y_test_unb, y_pred_t_unb)\n",
-    "      r = recall_score(y_test_unb, y_pred_t_unb)\n",
-    "      f1 = f1_score(y_test_unb, y_pred_t_unb)\n",
-    "      a = accuracy_score(y_test_unb, y_pred_t_unb)\n",
-    "      print(f\"Threshold={t:.2f}: Acc={a:.3f}, Prec={p:.3f}, Recall={r:.3f}, F1={f1:.3f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "571111b1",
-   "metadata": {},
-   "source": [
-    "-   **Multi-class Extension**\n",
-    "    -   Use the [classification\\_report](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html) on the Iris dataset and compare **macro** vs **weighted** F1."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "d2116ea3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 0.947\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "      setosa       1.00      1.00      1.00        12\n",
-      "  versicolor       0.92      0.92      0.92        13\n",
-      "   virginica       0.92      0.92      0.92        13\n",
-      "\n",
-      "    accuracy                           0.95        38\n",
-      "   macro avg       0.95      0.95      0.95        38\n",
-      "weighted avg       0.95      0.95      0.95        38\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.datasets import load_iris\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.metrics import classification_report, accuracy_score\n",
-    "\n",
-    "iris = load_iris()\n",
-    "X_train, X_test, y_train, y_test = \\\n",
-    "    train_test_split(iris.data, iris.target,\n",
-    "                     test_size=0.25, random_state=42, stratify=iris.target)\n",
-    "clf_iris = LogisticRegression(max_iter=500)\n",
-    "clf_iris.fit(X_train, y_train)\n",
-    "y_pred_iris = clf_iris.predict(X_test)\n",
-    "print(f\"Accuracy: {accuracy_score(y_test, y_pred_iris):.3f}\")\n",
-    "print(classification_report(y_test, y_pred_iris, target_names=iris.target_names))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "44ed35aa",
-   "metadata": {},
-   "source": [
-    "-   Plot the confusion matrix for the Iris dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "1564549f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
-    "cm_iris = confusion_matrix(y_test, y_pred_iris)\n",
-    "disp_iris = ConfusionMatrixDisplay.from_estimator(clf_iris, X_test, y_test,\n",
-    "                                                  cmap='Blues',\n",
-    "                                                  display_labels=iris.target_names)\n",
-    "plt.title(\"Confusion Matrix for Iris Dataset\")\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03C04 k-NNs and Decision Trees.ipynb b/src/S03 Supervised Learning - Classification and Evaluation/S03C04 k-NNs and Decision Trees.ipynb
deleted file mode 100644
index 1dc0a54..0000000
--- a/src/S03 Supervised Learning - Classification and Evaluation/S03C04 k-NNs and Decision Trees.ipynb	
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e3753a38",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# k-NN & Decision Trees Classification\n",
-    "\n",
-    "Machine learning classifiers can be broadly categorized into linear and non-linear models. Non-linear classifiers are particularly useful when the relationship between features and target classes is complex or not easily separable by a straight line. Two popular non-linear classifiers are:\n",
-    "\n",
-    "-   **k-Nearest Neighbors (k-NN)**\n",
-    "-   **Decision Trees**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "19724184",
-   "metadata": {},
-   "source": [
-    "## k-Nearest Neighbors (k-NN)\n",
-    "\n",
-    "k-Nearest Neighbors is a non-parametric, **instance-based** learning algorithm which does not explicitly learn a model but stores the training instances. For a new observation $\\hat{x}$, the algorithm:\n",
-    "\n",
-    "1.  Computes the distance between $\\hat{x}$ and every point in the training set (commonly the Euclidean distance, but can also use Manhattan or Minkowski distances).\n",
-    "2.  Selects the $k$ closest neighbors.\n",
-    "3.  Predicts the class by majority vote among those neighbors.\n",
-    "\n",
-    "The decision boundary is implicitly defined by the $k$ nearest points; therefore the model can capture highly non-linear class boundaries without explicit training."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "20299e17",
-   "metadata": {},
-   "source": [
-    "### Mathematical formulation\n",
-    "\n",
-    "Given a training set $\\{{(x_i, y_i)}\\}_{i = 1}^{n}$ where $y_i \\in \\{1, \\dots,C\\}$, the prediction for a query point $\\hat{x}$ is $$\\hat{y} = \\text{mode}\\left(\\{y_j | x_j \\in N_k(\\hat{x})\\}\\right),$$\n",
-    "\n",
-    "where $N_k(\\hat{x})$ denotes the set of $k$ training points closest to $\\hat{x}$.\n",
-    "\n",
-    "-   **Choosing $k$**:\n",
-    "    -   Small $k$ can lead to noise sensitivity (overfitting).\n",
-    "    -   Large $k$ can smooth out class boundaries (underfitting).\n",
-    "    -   Cross-validation is often used to select the optimal $k$.\n",
-    "-   **Advantages**:\n",
-    "    -   Simple and intuitive.\n",
-    "    -   No training phase; all computation happens at prediction time.\n",
-    "    -   Naturally handles multi-class classification.\n",
-    "-   **Disadvantages**:\n",
-    "    -   Computationally expensive at prediction time, especially with large datasets.\n",
-    "    -   Sensitive to irrelevant features and feature scaling.\n",
-    "    -   Requires a good choice of distance metric."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d0e14ca",
-   "metadata": {},
-   "source": [
-    "## Decision Trees\n",
-    "\n",
-    "A Decision Tree recursively partitions the feature space into axis-aligned regions and assigns a class label to each region. It consists of nodes (features), branches (decisions), and leaves (outcomes). At each node the algorithm chooses the split that maximizes a purity metric such as **information gain** or **Gini impurity**. The tree is built by recursively splitting the dataset into subsets until a stopping criterion is met (e.g., maximum depth, minimum samples per leaf).\n",
-    "\n",
-    "By hierarchically splitting the space, a tree can approximate complex decision boundaries while remaining interpretable-each internal node corresponds to a human-readable rule."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "924bc683",
-   "metadata": {},
-   "source": [
-    "### Information gain\n",
-    "\n",
-    "For a node containing a sample set $S$, the entropy is\n",
-    "\n",
-    "$$H(S)= -\\sum_{c = 1}^{C} p_c \\log_2 p_c,$$\n",
-    "\n",
-    "with $p_c$ the proportion of class $c$. A split of $S$ into subsets $S_L, S_R$ yields information gain\n",
-    "\n",
-    "$$IG = H(S) - \\frac{|S_L|}{|S|} H(S_L) - \\frac{|S_R|}{|S|} H(S_R).$$\n",
-    "\n",
-    "The algorithm selects the split with the highest **IG** (or lowest Gini).\n",
-    "\n",
-    "-   **Advantages**:\n",
-    "    -   Easy to interpret and visualize.\n",
-    "    -   Handles both numerical and categorical data.\n",
-    "    -   Non-parametric: does not assume any specific distribution of the data.\n",
-    "-   **Disadvantages**:\n",
-    "    -   Prone to overfitting, especially with deep trees.\n",
-    "    -   Sensitive to small changes in the data (can lead to different trees).\n",
-    "    -   Can create biased trees if some classes dominate.\n",
-    "-   **Pruning**: Techniques like cost-complexity pruning can be applied to reduce overfitting by removing branches that have little importance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "833fd16e",
-   "metadata": {},
-   "source": [
-    "## Practical Demonstration\n",
-    "\n",
-    "Below we illustrate both algorithms on the classic **Iris** data set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "845841c2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn import datasets\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.neighbors import KNeighborsClassifier\n",
-    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
-    "from sklearn.metrics import accuracy_score, classification_report, ConfusionMatrixDisplay\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "\n",
-    "# Load data and keep only two features for 2-D visualisation\n",
-    "iris = datasets.load_iris()\n",
-    "X = iris.data[:, 2:4]\n",
-    "y = iris.target\n",
-    "feature_names = iris.feature_names[2:4]\n",
-    "class_names = iris.target_names\n",
-    "\n",
-    "# Train-test split\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "857bccab",
-   "metadata": {},
-   "source": [
-    "-   Preprocessing the data and training the classifiers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "831d5e3d-ee44-4dd2-a4e6-14df7df29b77",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "k-NN accuracy: 0.9111111111111111\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "      setosa       1.00      1.00      1.00        15\n",
-      "  versicolor       0.82      0.93      0.88        15\n",
-      "   virginica       0.92      0.80      0.86        15\n",
-      "\n",
-      "    accuracy                           0.91        45\n",
-      "   macro avg       0.92      0.91      0.91        45\n",
-      "weighted avg       0.92      0.91      0.91        45\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Create a Pipeline for k-NN containing standardisation and classifier\n",
-    "knn_pipeline = Pipeline([\n",
-    "    ('scaler', StandardScaler()),\n",
-    "    ('knn', KNeighborsClassifier(n_neighbors=5))\n",
-    "])\n",
-    "\n",
-    "# Fit k-NN\n",
-    "knn_pipeline.fit(X_train, y_train)\n",
-    "\n",
-    "# Predict and evaluate k-NN\n",
-    "y_pred_knn = knn_pipeline.predict(X_test)\n",
-    "print(\"k-NN accuracy:\", accuracy_score(y_test, y_pred_knn))\n",
-    "print(classification_report(y_test, y_pred_knn, target_names=class_names))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "35870096",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Decision Tree accuracy: 0.9333333333333333\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "      setosa       1.00      1.00      1.00        15\n",
-      "  versicolor       0.88      0.93      0.90        15\n",
-      "   virginica       0.93      0.87      0.90        15\n",
-      "\n",
-      "    accuracy                           0.93        45\n",
-      "   macro avg       0.93      0.93      0.93        45\n",
-      "weighted avg       0.93      0.93      0.93        45\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Create a Pipeline for DecisionTree\n",
-    "dt_pipeline = Pipeline([\n",
-    "    ('scaler', StandardScaler()),  # Non-necessary for DT, but included for consistency\n",
-    "    ('dt', DecisionTreeClassifier(max_depth=3, random_state=42))\n",
-    "])\n",
-    "\n",
-    "# Fit Decision Tree\n",
-    "dt_pipeline.fit(X_train, y_train)\n",
-    "\n",
-    "# Predict and evaluate Decision Tree\n",
-    "y_pred_dt = dt_pipeline.predict(X_test)\n",
-    "print(\"Decision Tree accuracy:\", accuracy_score(y_test, y_pred_dt))\n",
-    "print(classification_report(y_test, y_pred_dt, target_names=class_names))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ff00f238",
-   "metadata": {},
-   "source": [
-    "-   Visualising the decision boundaries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "1edbae49",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.inspection import DecisionBoundaryDisplay"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "253f6e78",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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TppW5X2pqKjZt2oQNGzagT58+2LVrF1xdXSsd4/Lly+Hi4oI1a9Zgy5Yt6NatG3bu3Flmu5QffvgBJiYmWLNmDfLz89GpUyfs379fWQ7VRL9+/eDo6Ai5XI7XXnut0rETVZUgVnbGHxERUSUVFxfD09MT/fr1w2+//abvcMiIcY4QERFVuy1btiA1NRWhoaH6DoWMHEeEiIio2pw+fRqXLl3CV199BWdnZ7X3yiPSFY4IERFRtVmyZAnef/99uLq64vfff9d3OESGkwgtWbIEzZo1g52dHezs7NChQ4dnLjuv2P249MfCwqIaIyYioqetXLkSxcXFOHfunMariRPpgsF0jXl5eWHevHlo2LAhRFHEqlWr0L9/f1y4cAFNmjQp9xo7OzuVRcEqWpWViIiIjJPBJEL9+vVT+Xru3LlYsmQJTp06VWEiJAgC3N3dqyM8IiIiMkAGkwiVJpPJsHHjRuTk5KBDhw4Vnpedna1cPr5Vq1b45ptvKkyaFAoKClRWpZXL5UhLS0OdOnU4okRERGQgRFFEVlYWPD09n71QqmhALl26JFpbW4smJiaivb29uHPnzgrPPXHihLhq1SrxwoUL4uHDh8VXX31VtLOzE+/evfvMZ8yaNUsEwA8//PDDDz/81ILP837vG1T7fGFhIeLj45GRkYFNmzZh2bJliIiIUGtvoKKiIgQGBmLo0KHPXM796RGhjIwM+Pj4YN9rfWFtaqqV9yAiIiLdyikqwsvbdiI9PR329vYVnmdQpTEzMzP4+fkBKNnQ8ezZs/jhhx/w66+/PvdaU1NTtGzZstz9c0ozNzcvs00AAFibmsKGiRAREZFBed60FoNpny+PXC5/5i7TpclkMly+fBkeHh46joqIiIgMhcGMCM2YMQN9+vSBj48PsrKysHbtWhw+fBh79uwBULKTcd26dREWFgYA+PLLL9G+fXv4+fkhPT0d8+fPR1xcHMaNG6fP1yAiIqIaxGASoZSUFISGhiIxMRH29vZo1qwZ9uzZg5dffhkAEB8frzIr/NGjRxg/fjySkpLg6OiI4OBgnDhxQq35RERERGQcDGqytD5kZmbC3t4eJ94cwDlCREREBiK7qAgd/9qCjIwM2NnZVXieQc8RIiIiIqoKJkJERERktJgIERERkdFiIkRERERGi4kQERERGS0mQkRERGS0mAgRERGR0WIiREREREaLiRAREREZLSZCREREZLSYCBEREZHRYiJERERERouJEBERERktJkJERERktJgIERERkdFiIkRERERGi4kQERERGS0mQkRERGS0mAgRERGR0WIiREREREaLiRAREREZLSZCREREZLSYCBEREZHRYiJERERERouJEBERERktJkJERERktJgIERERkdFiIqQm2aMUyNKS9R0GERERaRETITW5fzMXACBLSy75PHoA2aMHeo6KiIiIqkKq7wAMxQPLF2ATvhUA4PHn58rjdyOiS/4gmMDE0VkfoREREVElMRGqhMShc5V/9ugfA5u4KNz84Q9l6czEyU1foREREZEGDKY0tmTJEjRr1gx2dnaws7NDhw4dsGvXrmdes3HjRgQEBMDCwgJNmzbFP//8o/W4Uq38EBs4ENLwrfBYuACAavmMiIiIai6DSYS8vLwwb948REZG4ty5c+jWrRv69++Pf//9t9zzT5w4gaFDh2Ls2LG4cOECBgwYgAEDBiA6OlpnMaZa+UEavhXS8K1oOHkEvLsEMikiIiKqwQRRFEV9B1FZTk5OmD9/PsaOHVvme0OGDEFOTg527NihPNa+fXu0aNEC4eHhaj8jMzMT9vb2OHkxDja2dhrH6JL7pHSmwNIZERGRbmUXFaHjX1uQkZEBO7uKf38bzIhQaTKZDOvWrUNOTg46dOhQ7jknT55Ejx49VI716tULJ0+efOa9CwoKkJmZqfKpimeWztiOT0REpFcGNVn68uXL6NChA/Lz82FjY4PNmzejcePG5Z6blJQENzfVkRc3NzckJSU98xlhYWGYM2eO1mIuTVE6A4D6VzchP+oCu86IiIj0yKBGhPz9/REVFYXTp0/j/fffx8iRI3HlyhWtPmPGjBnIyMhQfu7evavV+yvEBg5E4tC58Fi4AA0njwBEGUeIiIiIqplBjQiZmZnBz88PABAcHIyzZ8/ihx9+wK+//lrmXHd3dyQnqyYWycnJcHd3f+YzzM3NYW5urr2gnyPVyg+pgX7wWNgC0q0rnowQgXOJiIiIdM2gRoSeJpfLUVBQUO73OnTogAMHDqgc27dvX4VzivQt1coPiUPnPuk4CwlitxkREZGOGcyI0IwZM9CnTx/4+PggKysLa9euxeHDh7Fnzx4AQGhoKOrWrYuwsDAAwOTJkxESEoIFCxagb9++WLduHc6dO4elS5fq8zXUEhs4EAgciIYtNnGhRiIiIh0ymEQoJSUFoaGhSExMhL29PZo1a4Y9e/bg5ZdfBgDEx8dDInkywNWxY0esXbsWX3zxBT777DM0bNgQW7ZsQVBQkL5eQWMl3WYD4ZIbg8Sp05gQERERaZlBryNUHaq6jpA2sdOMiIhIPequI2QwI0L0pGQmHVqSFCnLZkyIiIiIKsWgJ0sbs9jAgfBYuADeIUFsvSciIqokJkIGrHSnmbLLjJ1mREREamMiVEuUbr1XjBBxlIiIiOjZmAjVMhXta0ZERERlcbJ0LaXY10yl04yTqomIiFRwRKiWU+xp9nTJjPOIiIiImAgZDUXJTDGxmp1mRERETISMEjvNiIiISjARMmLldpoxISIiIiPCRIiUZbPSJTOWzYiIyBiwa4yUEofOhXQo4JIbA+nWFew0IyKiWo8jQlSGYsXqMp1mHCkiIqJahiNCVKGSktlAACWbvAJ4stErABMnN73FRkREpA1MhEgtsYElCZE0fKBq6Qxg+YyIiAwWEyHSWKqVH/B4PlH9q5tURokUOFpERESGgIkQVUnp8hlQfmLEpIiIiGoqJkKkVYrEyCU3BgDKlNAAsIxGREQ1BhMh0olUK7+SP5QqoSk2f+VoERER1RRMhKhaxAYOBAIHKpMigB1oRESkf0yEqNqV7kADAI8/Py9TPgNYQiMiIt1jIkR6l/hU+QyASgmNo0VERKQrTISoxlCUzwCU25rPhIiIiLSNiRDVWKVb81XKZwA70IiISCuYCJFBUJTPgPI70DhaRERElcFEiAyOooTm0b9kraLEqdPYkk9ERJXCRIgMlmKtImn4VuWx8jrQWD4jIqKKMBGiWuXpDjQu4EhERM/CRIhqpafLZ4BqCY0JERERAUyEqJZTbvWBJyU0ZfmMpTMiIqPHRIiMTuLQuWjYgtt8EBERINF3AOoKCwtDmzZtYGtrC1dXVwwYMADXr19/5jUrV66EIAgqHwsLi2qKmGqy2MCBiA0cCI+FC+CxcAEAqMwlIiIi42AwiVBERAQmTJiAU6dOYd++fSgqKkLPnj2Rk5PzzOvs7OyQmJio/MTFxVVTxGQIUq38kGrlB2n4VniHBEGWllzyefRA36EREVE1MJjS2O7du1W+XrlyJVxdXREZGYkuXbpUeJ0gCHB3d9d1eFQLlO44Y8mMiMg4GMyI0NMyMjIAAE5OTs88Lzs7G76+vvD29kb//v3x77//PvP8goICZGZmqnzIuJRs7bGVJTMiIiNgkImQXC7HlClT0KlTJwQFBVV4nr+/P5YvX46tW7di9erVkMvl6NixI+7du1fhNWFhYbC3t1d+vL29dfEKZABYMiMiqv0EURRFfQehqffffx+7du3CsWPH4OXlpfZ1RUVFCAwMxNChQ/HVV1+Ve05BQQEKCgqUX2dmZsLb2xsnL8bBxtauyrGT4VKUzBRYMiMiqrmyi4rQ8a8tyMjIgJ1dxb+/DWaOkMLEiROxY8cOHDlyRKMkCABMTU3RsmVLxMTEVHiOubk5zM3Nqxom1UIlJbOBcMmNebI4o2DCdYiIiAyYwZTGRFHExIkTsXnzZhw8eBD169fX+B4ymQyXL1+Gh4eHDiIkY1G6ZObdJZAlMyIiA2YwI0ITJkzA2rVrsXXrVtja2iIpKQkAYG9vD0tLSwBAaGgo6tati7CwMADAl19+ifbt28PPzw/p6emYP38+4uLiMG7cOL29B9UeiUPnAgAatnjSZcZyGZF23M7IREpeHhrY28Hl8X/jiXTBYBKhJUuWAAC6du2qcnzFihUYNWoUACA+Ph4SyZNBrkePHmH8+PFISkqCo6MjgoODceLECTRu3Li6wiYjoCiZFb/Xny33RFWUlJuLGSdOI/JBySirCQT0f6EePgtuCTMTEz1HR7WRQU6Wrk6ZmZmwt7fnZGlSm3Ivs8eYFBGpRy6KGLRrLzKy8jBOcEZ9wRynxRyskj/A4IYNMD24pb5DJAOi7mRpg5kjRGQoEofORcPJI9Bw8ggAXIeISF1nklNwMzMTUyVu6CixhYdghgESRwySOOGvW7HIKSrSd4hUCzERItIBxV5mih3vFesQEVHF7mRlQQKgCVTnBDUTLJEvlyE5N08/gVGtxkSISMek4VvLLszIpIioDG8bG8gB3EC+yvErYj7MJRK4WnHSNGkfEyGiapI4dC48Fi5gyYyoAu3d3FDfxhYL5MmIlOfgkViM3fJ0rBcf4rX69WBjaqrvEKkWYiJEVI1SrfzKlMyIqISJRMDiri/C3t4Ks+QJGCG7jcXyFHTz9sInLVvoOzyqpQymfZ6otpGGb2WHGdFTvGxssK5XD1x59AgpuXlo6GAPLxsbfYdFtRgTISI9Shw6Fx79Y2ATF8VFGanGSMsvwMpr13H4XgIEAN28vTAqwB/25mYa3ys+KxvLr17D2aQUWJtK0be+L4Y29HvmmkCCIKCJkxOaOFXhJYjUxHWEnoPrCFF1Kn6vf/nfEMr+0uAeZ6QL6QUFeHvvATzMzUNn2EIOEceQDXcba/zxcnfYmqk/T+d2RiZC9x2EqUxEJ9ggHTIcF7PR3t0VP3fpDBOJoMM3IWNXazddJarNFHOHSqt/dRPyoy6UOV66pMbNX0lb1tyIQUpuHn6W+MBdKBkBel0sxKSsOGy8dQtjAgPUvtfiy9Gwlgn4XuIDm8fJ/Fl5NuYk3cexxESE1PXUyTsQaYKJEFENFxs4EAgcWOa4R/8YAFApqwGcZ0RVc+x+ItrDWpkEAYC3YIbWgjWOJiRqlAgdS0zCIMFBmQQBQBuJDbxghqOJSUyEqEZgIkRkoFKt/Er+N9AP0vCSRIn7nVFVmUokKEDZGRP5kMPcRLNGY6kgoECuei+5KKJAFGEqYdMy1Qz8fyJRLSIN34qGk0eoLN5IpImXfbxwRszBv+KTVZyj5DmIEnPR09tbo3v19PHGbmQgUSxUHvtHzECqWISXvb20FjNRVXBEiKiWUZTSGrbYxJIZaWxQgwY4eDcBnz64iyaCJeQAroh56ODmiv7162l0rwlNg3AmOQUf5MShqWCJdMhwWyzAEL8GaOlcRyfxE2mKXWPPwa4xqg1Kr1fEhIiep1Amwz9x8TiUcB8CgO5eXujt612pclZWYRH+vn0bZ1NSYS2Vom89X3T2cIcgsGOMdEvdrjEmQs/BRIhqEy7gSIYgt7gYB+8lICUvD/4ODujg7gbJcxKnWxmZOJ6YBKlEQLe6deFubVVN0VJNxfZ5IiqjvAUc2XpPNcnFBw/xYcQxpBcVwlqQIEeUI9DBAUu6doGThXmZ80VRxDeRF7A+5hbMBQFyEfj2fBSmtGiGUQH+engDMjScLE1kZErvd+YdEgSIMk6sphqhQCbDlCPH4VlsgmUm9bFO0gDzTLyQmJGNr86eK/eabbFxWB9zC+9IXLBO4oc1Jg0wQHDEwqhLOJ/6oJrfgAwREyEiI5Y4dC47zajGOHI/EQ8LCzBJ4gZ3wRSCICBIsMIwwQmHEu7jUUFBmWv+unUbrQVrvCZxhKkgwEqQYLTEGXUFM/x967Ye3oIMDRMhIkJs4EAkDp2LhpNHAAATItKLh/n5kADwgOo2Hl4wgxwl23+UuSYvH15Q3QNNEAR4wRQP8/N1GC3VFkyEiEhJUTJTbPXBhIiqU5CTE+QATovZKsdPiNmwNzWFp7V1mWuaODvhLHJQVKrvJ1OU4TLy0MSJu7bS83GyNBGVSxq+VbnPGTvNqDo0cXJERzc3LEpJxl15IerDHGfEHOwWMzClcVOYl7Nj/agAf4y4ew+fy+/hVcEBBZBjs5gOU6kJBjdsoIe3IEPD9vnnYPs8UYn6V0sWaATATjPSmdyiYsy/EIUdd+JQIJejjpk5RjX2R6h/owrXHjqTnILvzkfhWkYGAKCNiws+DW6Jhg721Rk61TBcR0hLmAgRqeLijFQd8oqLkVlYhDoW5pCqsZCjKIpIKyiAVJDA3tzsuedT7cd1hIhIJxKHzoV0KBdnJN2ylEphKVX/V5QgCKhjYaHDiKi2YiKkppwCwMZW31EQ1RyKhMglNwaJU6dxTzMiMkhMhNR05b4tLKwAEwlgw790ECmlWvkpu8yK3+vPhIiIDArb59UUdTIeWyKkkMmBjFx9R0NUM0nDt8Jj4QIuzkhEBoOJkAayk1OwekMaACZDRBVJtfIr2dNs4QIAT9Yikj3idgdEVPMwEaqE1RvScDXRFhm5JQlRNhcvJSpDUTJTbOGh2NOMiKgmYSJUSZFH47B6QxrLZURqKL3Jq3KEiEkREdUATISqiOUyIvUpNnl9umxGRKQvBpMIhYWFoU2bNrC1tYWrqysGDBiA69evP/e6jRs3IiAgABYWFmjatCn++ecfncTHchmR+hRlM5VNXjmHyCBcfPAQX52LxCfHT+KP6zeQVVik75CIqsRgEqGIiAhMmDABp06dwr59+1BUVISePXsiJyenwmtOnDiBoUOHYuzYsbhw4QIGDBiAAQMGIDo6usJrqoLlMiLNlC6ZKeYQcYSo5lp+9RpG7D+II7fu4v69h1h44RIG796LpBz+x44Ml8FusZGamgpXV1dERESgS5cu5Z4zZMgQ5OTkYMeOHcpj7du3R4sWLRAeHq7WcxRbbIyZHgUzc81WVHx7cMnOx/ZWGl1GZLQUizMCXIeoprmTmYXX/tmNgYIjQiXOkAgCksQifCq/i2Avd8zv1EHfIRKpUHeLDYMZEXpaxuPN9ZycnCo85+TJk+jRo4fKsV69euHkyZMVXlNQUIDMzEyVT2WVLpdl8i9MRM9VenFGlstqlr1378FKkGCYpA4kjzc/dRdM0U9wwP57CSiSy/UcIVHlGGQiJJfLMWXKFHTq1AlBQUEVnpeUlAQ3N9W/Vbq5uSEpKanCa8LCwmBvb6/8eHt7VynWyKNx2BIhhQgo5w8R0bM9XS4j/SuQyWAOCUyhugO8NSSQiSJkcoMsLhAZZiI0YcIEREdHY926dVq/94wZM5CRkaH83L17t8r3VHSWsbuMSH3lLcpI+tPB3Q2PxGIcF7OVx4pEOfaKmWjlXAcWUhM9RkdUeQa319jEiROxY8cOHDlyBF5eXs88193dHcnJqv/xTE5Ohru7e4XXmJubw9zcXCuxlmf1hjQEd/ZFoEcWBAB2nD9EVCFFqaz+1U24+cMfJcmQYAITR2d9h2Z0gl2c8VJdT8xPSMRpMRtuMMVxZCMZxVjanPODyHBVakQoPj4eR48exZ49e3D+/HkUFBRoO64yRFHExIkTsXnzZhw8eBD169d/7jUdOnTAgQMHVI7t27cPHTro919alsuINKPoLuMK1fojCAK+69gBk5o3RZwNsNc0B408XbDq5ZfQyoWJKRkutbvG7ty5gyVLlmDdunW4d+8eSl9mZmaGzp0745133sGbb74JiUT7FbcPPvgAa9euxdatW+Hv7688bm9vD0tLSwBAaGgo6tati7CwMAAl7fMhISGYN28e+vbti3Xr1uGbb77B+fPnnzm3qLSqdI2pi91lROpjZ1nNlZiTi7isLNS1toa3rY2+wyEjp9WusUmTJqF58+aIjY3F119/jStXriAjIwOFhYVISkrCP//8gxdffBEzZ85Es2bNcPbsWa29iMKSJUuQkZGBrl27wsPDQ/lZv3698pz4+HgkJiYqv+7YsSPWrl2LpUuXonnz5ti0aRO2bNmidhJUXdhdRqQ+dpbVPDlFRfj4+En03r4T7xw+gr47d2HC4aNIr4ZqAVFVqTUiNGPGDHz88ceoU6fOc2+4e/du5Obm4o033tBKgPpWHSNCpSlGh0wkgI2Fzh9HZNAUc4cUOEKkH58cP4mj9+5jjOCMFoIVror5WCamIsClDv7XLUTf4ZGRUndEyGAXVKwu1Z0IAVBOpgZYLiNSB8tl+pOUm4te23biA4kr+kgclMePybMwT56Iv3r3REMHe/0FSEar1i+oWJsptupguYxIPSyX6c/drGyIAJoJqn9ra/r46ztZWXqIikh9GidCDx8+xIQJE9C4cWM4OzvDyclJ5UPao0iIFN1l3MiV6Nme7ixTJEVMjHSnro01AOBfMU/l+NXHX3vbcNI01WwaryM0YsQIxMTEYOzYsXBzc4MgCM+/iKqk9NpDGbmcP0T0LCWt9gPhkhsD6dYVAIC7EdFlWu5ZQtMOT2trvOTpiRWJyTCVC4/nCOUhXExFsLMzAhwd9B0i0TNpPEfI1tYWx44dQ/PmzXUVU42ijzlCz8LFGIkqxyU3RvlnxXwiFVyosdIyCwsx48RpHC21fVGwszO+e7ED6ljwb22kH+rOEdJ4RCggIAB5eXnPP5F0IvJoHCJR0l2WkcvJ1ETqSrXyU/5ZMZ9IQWXlagXhyZYRTJCezc7MDIu7dkZsZibuZGahro01Gjk46DssIrVoPCJ09uxZfPrpp5g5cyaCgoJgamqq8v1nZV2GqKaNCJWmaLUHWC4j0haX3BjYxEUpvy7dnq9QE8tq+cUyrLlxE7vuxCFfJkM7DzeMCQhAXRtr7L17D+tu3ERiTi787O0xMtAfrV1d9BpvblExfr9+A3vj7qJILkMnTw+MDgyAm5WlxvdKzcvDiqvXcTThPqQSCV728UZoQCPYPPX7SRfkooitsXfwV8xtPMzPR5M6ThgTGIDGTo46fzY9m87a52/evIlhw4bh/PnzKsdFUYQgCJDJZJWLuIaqyYmQAstlRLr1zLJaDSipFcnlePdQBC6mPkQnwQa2MMExZAOmErxazxd/3LiJZoIVGsIcF5CLWLEA/+3YHr19vPUSb4FMhjH7D+FGejpehC0sIOAYsmFuborVPbvD3Ur9/5Cl5uVh2J4DyC0oRGfYoBAijiEb9e3tsLLHS7CU6nZLza/PRmLDrdtoI1jDG2Y4jRykoAi/dO2Cdm6uOn02PZvOSmPDhw+Hqakp1q5dy8nSNQTLZUS6VVFZzePPz8udiA1U76jRgXsJOJf6AN9IvNBMUvIfgKFiHUwqisPqGzcxUHDEKJOSEaCRooh58kR8dz4KPbzqQqqDLZGeZ8edOEQ/eoQFJj5oJJQMZQ8R6+DDgjisuHodM4Jbqn2vldeuI7egED9JfOAslIwA9RPz8VF6PLbFxmFIwwY6eQcAiMnIwIZbt/GuxAX9JCUjQCNEZ3whv4cF56OwvvfL/B1pADROhKKjo3HhwgWV/b6oZli9IU2ZDAEslxHpWuLQuZAOffK1oqxWer5RdSREx+4nooHEQpkEAYCdYILusMMmpOF1yZMyjUQQ8JrEAZ/m38PNjAwEOlZ/CedYYhKaCJbKJAgAnAQpusAWRxLua5QIHU1IxIuwUSZBANBAsEAzwQpHExN1mggdT0yCOQT0FhyUx0wFAX0FB3ybkYi0ggJOFjcAGidCrVu3xt27d5kI1VCrN6QBAGzcXDEgpBiZuSyXEVWXVCs/pAb6Kdv3E6dOKzMBWxdlNFOJBAWQK6coKORD/vh/RZRe27ng8YwIUz2MBimeW4CyszIKINc4porulQ8RdXT8fqYSCeQAiiDCFE9+7gWPf+5SgWsWGwKN/yl9+OGHmDx5MlauXInIyEhcunRJ5UM1Q3ZyispijERUvRSrXSs+3iFB8O4S+GShx3LKaZX1srcX7skLcUDMVB67KxbiALIgCAL+kD+A7HHykyfKsV5MQ30bWzTQU3PLy95euCnm45j8yarTt8V8HEE2emo4b6mnjzeOIws3xScrzp6WZ+OamIeXvb20FnN5utWtCxlErJU/hPzxzzdDlOFv8RHauLjA3txMp88n7dB4srSknAxbEAROlq7BuJErUc1RunxWWlVKaKIoYuaZc9gaewd+ggVsIcElMQ++tjYY1sgP30ReQB3BFPVhhqvIR7EEWNK1C1q56GeSt0wuYvqJU9h77x4aCRawhASXxVz4OzhgWbeusDVTv9srp6gI4w5G4OqjRwgSrFAIOa6J+XjJ0xMLX+wIE4lu5+j8fu0Gvou6CE/BDHVhimjkwVwqxfIeXeFnzz3W9ElnXWNxcXHP/L6vr68mt6vxakMiBDwplQFgdxlRDaHoRlPpRKtk+UwuiohIuI9d8XeRW1yM9m5ueP2FerA2NcW1R+nYeOuWsn1+sF8DeD3eGkNfZHIRB+7dw56791Aok+FFDw+8Vt+3Ul1e+cUy7LgThyP3H7fPe3uhp7e3zpMghagHD/D3rVg8zM9HYycnDPZ7AS6Wmi8DQNrF3ee1pLYkQqUpRojYXUakf9lZmdi/ZwfSHqaiy4MLeLG+J+4d+ReA7idai6KIcympuJyWBidzc/Tw9qqWtXeIqoPO2ufDwsLg5uaGMWPGqBxfvnw5UlNTMX36dM2jpWpVuruM5TIi/Tl1PAIfvfc2cnKyYSmR4nt5MVq1aot1i/+LnFmzdNp5ll1UhEnHTuBccgqsrayQl5+Pb6MuYUHH9ujgXvMWjCTSFY0nS//6668ICAgoc7xJkyYIDw/XSlCke6s3pGFLhBQyOZDJydRE1S4rMwNT3h2OhnnACpP6WC/UxxxJXVy/eAHTFy5TTrIG8GSC9aMHkD16oJXnL7hwEdezc7D8t99w8eJFHD1yBK3atcPUE6eQWViolWcQGQKNE6GkpCR4eHiUOe7i4oLExEStBEXVg51lRPqzb9c25OXmYorgCmfBFIIgIFhijTdFe+zcsgF5eSX/Uj7ddQZRVuWuswKZDDvi72L8u+8iJCQEgiDA3d0d//3vf5FfXIy9d+9p6zWJajyNEyFvb28cP368zPHjx4/D09NTK0FR9VKsPZSd/5wTiUhrHj5IgbWJFE4wUTnuDTMUFhUhJztL5Xji0LlIHDoXHgsXoOHkEQBQ6YQop6gYBcXFaPDCCyrHXVxcYGdri4d5/I8BGQ+NE6Hx48djypQpWLFiBeLi4hAXF4fly5fjo48+wvjx43URI1UDlsmIqleTZi2RJSvCZTFP5fgJMRvuru5wqlP+pqipVn6IDRxYfulMzfKZg7kZPG1tsGfPHpXjZ86cwaOMDDSp41TBlUS1j8aTpT/55BM8fPgQH3zwAQof15EtLCwwffp0zJgxQ+sBUvUoKZNxvzKi6tK+U1c0a9YKYf9exiC5PerCDMfEbBwSM/GfSbPLXbOtPE/vfQag3P3PSk+4lggCxgf4Y8727YAgoO8rr+DOnTtYsmQJgpzroCMnS5MRqXT7fHZ2Nq5evQpLS0s0bNgQ5ubm2o6tRqiN7fPPo9jNnh1lRLqVkZGOebP/D7t3bEaxrBhuLm54Z9L/YfDwMc+/+BkU6xMpqKxTVMrmtBwsvXYdSdnZkEok6O3jjektW3BFZKoVuI6QlhhjIgQ8WYCRiy8S6V7Go0d48CAFvvUbQKrmgoLZWZmQmprCwqJyC/cVv9cfACATRTwsKISNVAorqclzrnqKjvZOI9IGra4j9N577+GLL76Al9fz921Zv349iouLMXz4cPWjpRqHpTIi3cvOysT3/52N7ZvXIy8vF771XsC7H36Cfq+/VeE1Z04exaJvZ+PyxfMwMTFBt5dfwSefz4VHXc326FKU1KQAKtPmUroMx4SIDJlaiZCLiwuaNGmCTp06oV+/fmjdujU8PT1hYWGBR48e4cqVKzh27BjWrVsHT09PLF26VNdxUzVZvSENwZ19EVQ3i2UyIi0SRREfvjMM1/69hHfGj0P9F17Arl278Nm09yGXi+j/5tAy11y8cBbvjnoTzZo2xbfffovMjAz8tnw5Rg99FRt3HIGtXfXtbZU4dC4AoGGLTbj5wx86XfyRSJfULo0lJydj2bJlWLduHa5cuaLyPVtbW/To0QPjxo1D7969dRKovhhraay00vuUcWSISDtOnziCcW/3x/LffkNISAiAx8nRpEm4HH0FOw+dLzNheuK4t5CUcAdbt2yB6eOtMO7evYseL7+Mjz/7GsNHvVvt71GaotzGESKqCdQtjandPu/m5obPP/8cly9fxoMHD3D+/HkcP34c169fx6NHj7Bp06ZalwRRCcXCiwAXXiTSlstR52BnZ4cuXboojwmCgH79+uHe3TikPUwtc82lqHPo07u3MgkCStZ2a9GiBS5eOFstcT+LNHwrGk4eAe8ugVVe9JGoumi+zS8AR0dHODo6ajsWquEUZTJ2lBFVnb2jE3JycvDw4UM4Oz8ZPYmPj4epmRmsrG3KXOPg6IS4+HiVYzKZDPfuJaBRk1Y6j1kdsYEDgcCBLJmRwdB4QUUybpFH45SLL3J0iKjyer4yAGbm5vjs88/x8OFDAMC5c+fw66+/otcrA2BlZV3mmgEDh2P79u3YvmMH5HI5cnNzMW/ePCQlJeK1N8rOKdKn8hZ9JKqJ2D7/HJwjVLG3B5esPst5Q0SVc+TgHnz84RgUFxfBwcEBqampaBzUHL+u+hsOjmVXdy4qKsL/TRqD/Xt2wMHREQX5+cjPz8cnn8/FiDHv6+EN1Ofx5+clHWaPcYSIdI3rCGkJE6FnY0cZUdU8SnuI3Tv+RtrDBwhq1hIvdn0ZJiYVr+cjiiKiIk/j1PEIWFha4uU+/eHl7VuNEVeeS24MbOKicPOHPwAwGSLd0uo6QjXFkSNHMH/+fERGRiIxMRGbN2/GgAEDKjz/8OHDeOmll8ocT0xMhLu7uw4jNR7Xb+Qh0ANca4iokjasXYE/lv+C/Pw8eNb1hourOxo3bVHh+YIgoGXr9mjZur3OYiouLsb+3dtw+MBuCIKAbi/3RbeefZ+ZoKkj1coPqYF+kIYPVBkhYkJE+mRQc4RycnLQvHlzLF68WKPrrl+/jsTEROXH1dVVRxEaH3aUEVXeoFdD8PPCuTAzlaJFs2a4GxeLYW/0wLa/1+ktpqLCQkwYMwifTBqL6zt34uqOHZg6YSQ+eu9tFBcXa+05iUPnQhq+Fd4hQSqbxhJVN40ToeTkZIwYMQKenp6QSqUwMTFR+ehSnz598PXXX+P111/X6DpXV1e4u7srP+puZkjqW70hDVcTbZGRyx3sidRx+MBuXL96GSNGjMDxY8ewdu1aHD50CO5ubvjy8yl6i2vTulU4dTwCX0rqYhG88AO88B+JJw4d2I3tm9dr/XmJQ+fCY+ECNJw8AgAnVVP107g0NmrUKMTHx+M///kPPDw8IAiCLuLSqhYtWqCgoABBQUGYPXs2OnXqVOG5BQUFKCgoUH6dmZlZHSHWCpFH43D98eKLLJURPdtPC76GiYkJpk2dqvxLpIeHB9577z38Z+ZM3I65jhf8/Ks9rt3bNqGNYI1Wkidda+0kNmgOa+ze/hdeH6T97ZNKl8yK3+vPlnuqVhonQseOHcPRo0fRokULHYSjXR4eHggPD0fr1q1RUFCAZcuWoWvXrjh9+jRatSp/zY2wsDDMmTOnmiOtPbhHGZF6CgryIZVKYWmpummq7eNJnRnpj/QRFvLz8mAvlv0LrrUooCBP98O90vCtqH91E/KjLnAOEVULjWtE3t7eMJRGM39/f7z77rsIDg5Gx44dsXz5cnTs2BHff/99hdfMmDEDGRkZys/du3erMeLao3SpLDtf39EQ1Tz9Xn8L+fn52Lx5s/JYUVER1qxZA1NTUzRr2UYvcXV66WWcluQhVSxSHksSCxEp5KFj1x7VEkNs4EBlyQxguYx0S+NEaNGiRfj0009x584dHYSje23btkVMTEyF3zc3N4ednZ3Khyon8mgcribaQibXdyRENc+7Ez+GtbUNZnz2GT6cNAmLFi3Cq/364dy5c+jbf5DO51xWZPio92Dn7IIpSMD/ZClYKkvBR0iAq6cXhgwfW62xpFr5qSzIyISIdEGt0pijo6PKXKCcnBw0aNAAVlZWKnveAEBaWpp2I9SyqKgoeHh46DsMoxF5NA6BLJMRlevQmRsY/kYP7Nu3D3tFESYmUowcPxHTPv1SbzHVcXbBmi0H8L9fFuLQ7u0QBAH9X3kL4z6YCnsH/WytxHIZ6ZJaidCiRYt0HIZ6srOzVUZzYmNjERUVBScnJ/j4+GDGjBlISEjA77//DqAk7vr166NJkybIz8/HsmXLcPDgQezdu1dfr2CUSu9RJgCwY0JERqioqAjRFyMhk8vQrHlrmJmbw9LSEn/vOq7xvXJzshF96QIsLC0R1KyV1jthXd088Pmc+fh8znyt3lcTt2OuIzkpEX6NAuDi6q7cw8yjfwwSp04rGR3iLvekBWolQiNHjtR1HGo5d+6cygKJU6dOBVAS38qVK5GYmIj4UhsSFhYWYtq0aUhISICVlRWaNWuG/fv3l7vIIulW5NE4RIKTqMk4Hdz3D77+z1SkppSUdpycnPF///kGffsP0vhea1b+ip8XzkV2dhYAwMunHr769me0bltxN6whSUpMwKeTxiIy8jQAwEQiQf+Bw/D5nO9gZm6uLJcpFmSUpSVzdIiqROMtNkxMTMpdlPDhw4dwdXWFTCbTaoD6xi02tE+xRxl3sCdjcP3qZbzVvxtCQkIwccIEmJiYYOn//oedO3di5bqdaNWmg9r32vvPVkybOArDhw3D8LffRmZGBhYsXIh/r1zB1r2n4e5RV4dvontyuRyD+nRC+u1YjBOdUF8wx2kxB6uQhsGh4/DpzHllruEK1VQRdbfY0Hg8taK8qaCgAGZmZprejoyQoqOMk6jJGPy56n9wdXXF4p9/RrNmzdCkSRN8v3Ah/Pz8sHpluEb3+mP5L+jUqRO+/PJL+DdqhDZt2uB/S5dCgIC/1v2uozeoPqdPHMGNm9cwFS7oKLGFh2CGARJHDIID/vpzJXIej4KVxu4yqiq11xH68ccfAZTsc7Ns2TLY2NgovyeTyXDkyBEEBARoP0KqlSKPxgGP5w2xTEa1WWxsDFq3bq3SWCKRSNCubVuciYzS6F53YmMwZvQolWO2trYICmqCuNiKu2ENRVxsDCSCgCZQXVupmWCJtQUPkZx0v9xFJhXlMuVijJw7RBpQOxFSrL0jiiLCw8NVWjvNzMxQr149hIdr9rcbMm6lO8pYJqPaytunHs6fOY7i4mJIpSX/yRVFEZHnz8Pbp4HG94qMjFQ5lpOTgytXruCt0Be1FrO+ePvWh1wUcQP58C+VDF0R82FuagZXt2d3/HLuEFWG2qWx2NhYxMbGIiQkBBcvXlR+HRsbi+vXr2PPnj1o166dLmOlWqh0mYx7lFFtNHTEeCQk3MPUadNw48YNxMbG4tMZM3D16lUMG/mORvcaPuo9REREIGzePNy9exfR0dH4YMIEFBUV480hoTp6g+rTvlNX1K/XAAuEVETKc/BILMZueTrW4xFeGzQcNrbPX9eNpTLSlMaTpY0NJ0tXH8UkapbKqLbZtf0vfD3zY2RmpAMAbGxsMe2zrzDwLc06ckVRxG9Lvkf4T/NRUFCyZLuLqxu+nv8LOnbupu2w9eJu/B1Me28Erl4rmQAtCAJ6v/I6vpz/MywsLJ9z9RP1r27CzR/+YJnMiKk7WVqtREjRpq6OhQsXqn2uIWAiVL3YUUa1VX5+HiLPnIBcLkdwmw6wsrZ5/kUVyEh/hKjI07CwtEKrNh3KLGxr6ERRxJXLUUhOTkRD/8bw9qlXqfsokyGwo8wYaTURenrdnfPnz6O4uBj+/iWT1m7cuAETExMEBwfj4MGDVQy9ZmEiVP1sHu9gz5Ehqg4pyYlY8euPOBaxD6amZni5T3+Ejv0A1jaa//sec+Mqlof/gKjzZ2Bnb4/+bw7DoGGjlXODniaXy7F542r8veEPpD18gKCmLTH63cloHNQc584cx6plixFz4yrcPepiyNtj0euVASqr/NPzueSWLMAIMBkyNlptnz906JDy069fP4SEhODevXs4f/48zp8/j7t37+Kll15C3759tfYCZLyyk1MAABmcM0Q6lpx0H8Ne74GdW9fjxY7t0axpYyz/dRHGvd0f+fl5Gt3r30sXMOyNHrhw7jh69ngJXp5umPflp5jx0TsVLjvy1RdTMXvGZDg72qF3zx649u8FjBjYC7/88F+MHfYakhPu4JXePWEqkeOTD8cg/Cf9rfRsqJ7er4zoaRrPEapbty727t2LJk2aqByPjo5Gz549cf/+fa0GqG8cEdIflslI1+Z9+Sn+2boBO3fsgJtbyWjB5ehovPHGG/jiqwUYNHSU2vcaP2IA0h8mY9PGjbC0LJnLsm3bNnw0dSpWrf+nzMKJ169GY2DfzpgzezbefvttACXrsQ1/+21cuXoVXTp3xi+LFyu3z1iwcCGWLl2Kfcej4ezCkQ1NsUxmfHS2oGJmZiZSU1PLHE9NTUVWVtnFrogqa/WGNGyJkLKjjHTm2OF9eLVvX2USBABNg4LQtm1bHD20T+37FBUW4tTxCAwbOlSZBAHAq6++ChcXFxw9XPZex48cgLW1NYYMGaI8Zm5ujh7du6MgPx+jR41S2UNszOjRKC4uxqnjEZq+JgGIDRzIbjIql8aJ0Ouvv47Ro0fj77//xr1793Dv3j389ddfGDt2LN544w1dxEhGLDs5Bas3pEEES2WkfVJTU+Tl55c5npeXB1MNVsoXJBJIpVLk5qmW04qLi1FYWAhT07L3MjU1Q3FxMYqKilSOK75++l55j7/WJC5S9XSZTPbogZ4joppA40QoPDwcffr0wbBhw+Dr6wtfX18MGzYMvXv3xi+//KKLGImwekMagJJkKLvs7y2iSunZZwB27tyJ6Oho5bE9e/bg4sWLeLnPa2rfRyqVolvPvvj999+RlJQEoKTz6X/LliEjI6Pce3Xv1RdFRUX48aeflHOIHj58iK3btsHGxhaLFy9GdnY2gJKEasHChbC0skKnWtImr0/S8K1oOHkEINauvTGpciq9jlBOTg5u3boFAGjQoAGsra21GlhNwTlCNYuio0wAYMeuMqqi7KxMjB3+Gq5duYx27dohNzcPFy9G4eXe/TD/pxUqK+g/T8K9eIwc3Afpjx6iffv2SEi4j5iYmxg/YRomTfui3GtWLP0RC+fNQr169VC/fn2cPn0aFhZW+L//fIMvv/gIplIpWgcH48rVq0hOTsbX8xej3+tvaev1jRq7yWo/rbbPGzMmQjXT24Od2F5PWpGXl4vtm9fj6OGS9vmer/THy71f0ygJUniU9hB/rVuFC5GnYe/giNfeGIr2nUKeec35syfx94bVSHuYiqbNgzFo2Cg4u7gh4V481q/+DTE3rsLD0wsDh45CYJNmlX1NqoBy93ouvFjraDUReuONN7By5UrY2dk9dx7Q33//rXm0NRgToZop+PGGrewoI6Kq4irUtZO6iZBam67a29srF/Gyt7fXToREVRB5NA7XH5fJMnK5LQcRVV5s4EB4h1woGRkio8PS2HNwRKjm4x5lRKQNLJPVLjpbR2j58uWIjY2tUnBE2qTYwZ4dZURUFYlD5yq7ybjWkPHQOBEKCwuDn58ffHx8MGLECCxbtgwxMTG6iI1IbZFH45SLL3K9ISKqLJWFF7nOkFHQOBG6efMm4uPjERYWBisrK3z33Xfw9/eHl5eXcpl4In1QLL4IMBkiospLtfKDd0hQycgQk6Far0pzhHJzc3H06FH8+eefWLNmDURRRHFxsTbj0zvOETJM7CojoqoqvT8ZwPWGDI3O5gjt3bsXn332GTp27Ig6depgxowZcHR0xKZNm8rdg4xIH1gqI6Kqig0cCGn4Vu5eX8up1T5fWu/eveHi4oJp06bhn3/+gYODgw7CIqq6klJZSVcZW+wJKNn2IjMzA1ZW1jA1NdV3OGRApOFb2VVWS2k8IrRw4UJ06tQJ3377LZo0aYJhw4Zh6dKluHHjhi7iI6oydpWRKIrY+OdK9AlpgRdb1kfn4Bfw3y9nIC+Pw4WkPpWuMs4dqjU0ToSmTJmCv//+Gw8ePMDu3bvRsWNH7N69G0FBQfDy8tJFjERVxlKZcVv7+//w5ecfoVXL5vjhhx8wMjQUf61fhU8+HKPv0MjAlCy+GMQNW2sRjUtjQMnfri5cuIDDhw/j0KFDOHbsGORyOVxcXLQdH5HWlC6VkfEoKirC/xZ/h8GDBiEsLAwA8Grfvgjw98ekyZPx76ULaNKspZ6jJEOSOHQuvMEyWW2h8YhQv379UKdOHbRt2xZr1qxBo0aNsGrVKjx48AAXLlzQRYxEWqUok2VyZMgo3L8Xj4cPUvHqq6+qHO/duzdMTU1xMeqsniIjQ5Y4dC5HhmoJjUeEAgIC8O6776Jz587cd4wMEvcpMy529g4QBAFx8fHo1KmT8vj9+/dRVFQER8c6eoyODFni0LlARH/I0pLZWm/ANB4Rmj9/Pl599VUmQWTQuPii8XB0qoOQ7r3x888/49KlSwCA1NRUfP7FF7B3cETXHn30HCEZMmn4VniHBLG13oBpnAgR1SalO8pYKqu9Zn69ELb2Tnj9jTfQsVMndHrxRVy8eAkLF6+CpSWHBKlqivuPBsB1hgwVd59/Dq4sbRxsHpfKAJbKaquioiJEHNiNa1cvw9XVHb37vQk7O45sk/YUv9efk6drEJ2tLK1PR44cQb9+/eDp6QlBELBly5bnXnP48GG0atUK5ubm8PPzw8qVK3UeJxkelsq0Jz8/H2FzpqNXl+boE9IC3/93NgoLC/UdFkxNTdGjdz9M/OgzDB4+hkkQaZ1yfzKODBkUg0qEcnJy0Lx5cyxevFit82NjY9G3b1+89NJLiIqKwpQpUzBu3Djs2bNHx5GSoWKprGqyszLRs1MQ1q5aCgc7G1hbWWD5rz+gT0gL5OdzNUuq3RKHzn2ycz2TIYOhVmksMzNT7Rs+a/hJmwRBwObNmzFgwIAKz5k+fTp27tyJ6Oho5bG33noL6enp2L17t1rPYWnMeCnWG2KpTH0fvR+Kg/t2YuXKlejUsSMAYM/evfjggw8w8K2RmPXNIv0GSFRNWCbTP62WxhwcHODo6PjMj+KcmuTkyZPo0aOHyrFevXrh5MmTFV5TUFCAzMxMlQ8ZJ5bKNHf6RAS6d++uTIIAoFfPnmjbpg0O7vtHj5ERVS+WyQyHWusIHTp0SNdx6ERSUhLc3FTXdnBzc0NmZiby8vJgaWlZ5pqwsDDMmTOnukKkGm71hjQEd/ZFoEcWR4bUIJfLy/2bl729PWTFxXqIiEg/EofOhUf/GCROncZ1hmo4tRKhkJAQXcdRY8yYMQNTp05Vfp2ZmQlvb289RkT6Fnk0DoHcwV4tLzT0x65duzD1o4/g7u4OALhz5w4OHT6Mth276Dk6ouqVauUHafhWFL9XsugiS2U1U6X2GgOA3NxcxMfHl+kGadasWZWD0hZ3d3ckJ6sOSyYnJ8POzq7c0SAAMDc3h7m5eXWERwZk9YY0vP04GTKRADYW+o6oZpr9zSK81b87XunbFwPffBPFMhk2bdoEExMpZn79vb7DI9ILafhWePz5eG8yqnE07hpLTS3Zs8fW1hZNmjRBy5YtVT41SYcOHXDgwAGVY/v27UOHDh30FBEZMkVHmUzOjrKKNAoIwh+bdsPFzRN/rF6NdevWwdu3ATZsP4y6Xj76Do9Ib7joYs2lcSI0ZcoUpKen4/Tp07C0tMTu3buxatUqNGzYENu2bdNFjErZ2dmIiopCVFQUgJL2+KioKMTHxwMoKWuFhoYqz3/vvfdw+/Zt/N///R+uXbuGX375BRs2bMBHH32k0zip9oo8GofVG9IggpOoK9KkaUts3n0CkdeSce5qEjbuiMALfv76DotIrxRlMqAkGZI9eqDniEhB40To4MGDWLhwIVq3bg2JRAJfX1+8/fbb+PbbbxEWFqaLGJXOnTunMvI0depUtGzZEjNnzgQAJCYmKpMiAKhfvz527tyJffv2oXnz5liwYAGWLVuGXr166TROqv1Kd5Rlc3kcIlKTNHwrGk4eUdJRxmSoRtB4jlBOTg5cXV0BAI6OjkhNTUWjRo3QtGlTnD9/XusBlta1a1c8a9mj8laN7tq1Ky5cuKDDqMhYrd6QptyaIzMXsONEaiJSQ2zgQHiHXOCcoRpC4xEhf39/XL9+HQDQvHlz/Prrr0hISEB4eDg8PDy0HiBRTabYmoOlMiLSROLQuQBYJqsJNE6EJk+ejMTERADArFmzsGvXLvj4+ODHH3/EN998o/UAiQwBS2VEpCmVMhknUetNlXefz83NxbVr1+Dj4wNn59q3PgK32CBNKEplAlgqIyL1uOSWLLzIdYa0S2e7z3/55ZfIzX1SA7CyskKrVq1gbW2NL7/8snLREtUSpUtlRETqSLXye7IlB8tk1U7jRGjOnDnIzs4uczw3N5dbUxCVwjIZEakrcehclsn0RONESBRFCIJQ5vjFixfh5OSklaCIDN3qDWnYEiHl4otEpLbYwIHwWLgAABderE5qJ0KOjo5wcnKCIAho1KgRnJyclB97e3u8/PLLGDx4sC5jJTIo7CgjIk0pFl70DglimayaqL2O0KJFiyCKIsaMGYM5c+bA3t5e+T0zMzPUq1ePW1cQlaP0Dvbcp4yI1GHRoiUQEc2d66uB2onQyJEjAZSs1typUydIpZXer5XI6EQejcN1Lr5IRGoqKZO1QOLUaUyGdEzjOUIhISGIi4vDF198gaFDhyIlJQUAsGvXLvz7779aD5CotmCpjIg0oVIm45whndE4EYqIiEDTpk1x+vRp/P3338oOsosXL2LWrFlaD5CotlHsYs+uMiJSR+lVqEn7NE6EPv30U3z99dfYt28fzMzMlMe7deuGU6dOaTU4otoq8micsquMo0NE9Dyld64n7dI4Ebp8+TJef/31MsddXV3x4AFnuBOpS1EqA5gMEdHzlS6TsaNMezROhBwcHJR7jZV24cIF1K1bVytBERmT0qUyIqJnKb3wImmHxonQW2+9henTpyMpKQmCIEAul+P48eP4+OOPERoaqosYiWq9yKNxADgyRETPFxs4EADLZNqicSL0zTffICAgAN7e3sjOzkbjxo3RpUsXdOzYEV988YUuYiQyCiyTEZG6VMpkTIiqpNK7z8fHxyM6OhrZ2dlo2bIlGjZsqO3YagTuPk/VjYsvEpG66l/dhJs//AEAXGvoKeruPl/pVRF9fHzg7e0NAOXuPUZElVN68cWMXMCeiy8SUQViAwdCGj4Qxe/113coBkvj0hgA/PbbbwgKCoKFhQUsLCwQFBSEZcuWaTs2IqPFjjIi0gTLZJWncSI0c+ZMTJ48Gf369cPGjRuxceNG9OvXDx999BFmzpypixiJjFbpjjLuYk9EFUkcOpc711eSxnOEXFxc8OOPP2Lo0KEqx//88098+OGHtW4tIc4RoprA5nGpDGCpjIieTVEmM/Y5Q+rOEdJ4RKioqAitW7cuczw4OBjFxcWa3o6I1MBSGRGpi/uTaUbjRGjEiBFYsmRJmeNLly7F8OHDtRIUUUVkxQUoyM9CJZsdDR5LZUSkDu5Ppr5KdY399ttv2Lt3L9q3bw8AOH36NOLj4xEaGoqpU6cqz1u4cKF2oiSjl5ebhpN7v0HMvzshlxXC0aUR2r40BfUDeuk7tGpXuquMiKgi0vCtKH6vf0kyJJjAxNFZ3yHVSBonQtHR0WjVqhUA4NatWwAAZ2dnODs7Izo6WnkeW+pJW2SyIuz4IxSZjxLh3XAkzCxckZqwF3s2fIDeQ5ainn93fYdY7bKTUwA4sb2eiJ5JGr4VHn9+jrsR0ZClJRv9vKHyVHpBRWPBydL6d+vKP9i36UM0ezEctg4BAABRFPHv6WkwlRZi4Dtb9Ryh/igWXxQA2DEhIqIKuOTGIHHqNADGM4laZ5Oliapb8r0oWFrXVSZBQMmIo7NHNzxIioZMVqTH6PQr8mgcVm9IgwhOoiaiiqVa+UEaXvKXRu5er4qJENV4FlaOKCx4BFmx6m/6/NwEmJrbQiKp9ALptQY7yohIHYqOMogyLsD4GBMhqvEaNu0PubwQty5/j+Kiko6xRymnkRS3FQEtBnI+2mOlO8rYVUZEFVEsvthw8ggA7CzjHKHn4ByhmuHm5W04tPUTABKYmFqjqOARPHza4pVhy2BqZq3v8Gqktwc7qXzNTVyJqDzKfcpqWWeZzjddJapODZu+Bs967XHr350oyM+Au3dreL3QEYLAQc2KKMplwJNJ1Rm5gGL8jJOriQgoKZcpdrE3xs4yg/stsnjxYtSrVw8WFhZo164dzpw5U+G5K1euhCAIKh8LC/6V2FBZ27qiWfvRaNN1CrwbvKhMgrLSE3D64HfYu+lDnD4wH5mP7uo50ppHMal6S4QUmyOkuMISGhGVEhs40Gj3KjOoEaH169dj6tSpCA8PR7t27bBo0SL06tUL169fh6ura7nX2NnZ4fr168qvOZ+kdkmIPYl//hwPQTCBtb0/4mOO4tKpFej91q/wbtBZ3+HVOCXrDwGRyUDk42NvD3ZSTrLmmkRExkvRWWZsizAa1IjQwoULMX78eIwePRqNGzdGeHg4rKyssHz58gqvEQQB7u7uyo+bm3EN+dVmcrkMB7f+H2zsA9C6+wYEtV+A1t02wNapGQ5t/T+jbqvXxOoNaew6IyIlafjWkonUjzvLajuDSYQKCwsRGRmJHj16KI9JJBL06NEDJ0+erPC67Oxs+Pr6wtvbG/3798e///77zOcUFBQgMzNT5UM1U0pCFHIy78MnYBxMpCVDGSZSS/j4j0VudgqS7kY+5w5UGvcxIyIFYyqVGUwi9ODBA8hksjIjOm5ubkhKSir3Gn9/fyxfvhxbt27F6tWrIZfL0bFjR9y7d6/C54SFhcHe3l758fb21up7kPYUF+UDAKRS1a4xqakNAED2+PukPi7QSEQKZRZhrKULMRpMIlQZHTp0QGhoKFq0aIGQkBD8/fffcHFxwa+//lrhNTNmzEBGRobyc/cuJ97WVG5eLWBqZo3EO5tVdqNPvLMZUlNLuHkH6zE6w6YolXFkiIik4VtrdbnMYCZLOzs7w8TEBMnJqv8AkpOT4e7urtY9TE1N0bJlS8TExFR4jrm5OczNzasUK1UPUzNrtHnpI5zY8zXycu7Czqk5stIuI/3BObTv8SnMLbjuU1VsiZBiQEgxMnPZak9EinJZCyROnVar2uwNZkTIzMwMwcHBOHDggPKYXC7HgQMH0KFDB7XuIZPJcPnyZXh4eOgqTKpmzdqNRs9Bv8DSUoLUe9tgbiFHjzd/RIuO4/UdmsHLTk5hmYyIVNTGPcsMZkQIAKZOnYqRI0eidevWaNu2LRYtWoScnByMHj0aABAaGoq6desiLCwMAPDll1+iffv28PPzQ3p6OubPn4+4uDiMGzdOn69BWvZCYC+8ENhL32HUWqs3pClb7Lk6NREBtWsRRoNKhIYMGYLU1FTMnDkTSUlJaNGiBXbv3q2cQB0fHw+J5Mkg16NHjzB+/HgkJSXB0dERwcHBOHHiBBo3bqyvVyAySKs3pMHGzZWlMiJSqi2lMu419hzca4xIlWIPMy6+SEQKHn9+jrsR0TVqEUZ19xozmDlCRFQzsKOMiJ6WOHSuSleZIXWWMREiIo1tiZBCBJMhInpCsQijoS3EyESIiDTGjjIiKk+qlZ+ys8w7JMggOsuYCBFRpZXeoyybC3kTUSlPl8tqKiZCRFQlqzekYUuEFDI5S2VEpMoQ9ixjIkREVcZSGRFVpKaXygxqHSEyLKIo4ta/OxB9bg1yMpLg5OaPFh3GwcO3jVafEx8TgUunViDj4R3YOfmgWbvR8G30klafQepZvSENwZ19EeiRxcUXiUhF4tC5aNii5i3CyBEh0plzhxdh/99TkJ8rh12dTniQFIttvw/D7at7tPaMK5F/4p+1Y5Dx6CHsnF9EVnoGdq0bh+gzv2vtGaSZyKNx2BIhhVyu70iIqKapiaUyjgiRTmRnJuH8sSXwbjgSPv4lW6CI4nhcPfsFTuydi3r+PSCRmFTpGUWFuTi5/79w9X4Ffs0+gSAIJaNQlxfi9MHv0Kj5GzAzt9HG65CGspNTIKJkWw4uvEhEpSlKZcpFGAG9LsTIESHSiYTY4xBFGTxfGKQ8Jggm8HxhILIzEpD+4FaVn5GSEIWigix41h8IQRAeP0NA3RcGo6gwB0l3I6v8DKq81RvScDXRlh1lRFQuRVeZvjvLmAiRTkgkpgAAuaxA5bisuOQ3osTEtOrPMDEr/xmykmeYaOEZVDWKMplMzknURFRWbOBAxAYOVNnRvroxESKd8GnYFSZSS8Rd+x9EUQYAKC7KQULMaji5+sPeqV6Vn+Hm1QJWNm64e2OFMhmSywpx98YKWFg5w92ndZWfQVWn6CgDmAwRUcX01VnGTVefg5uuVt61qE04vO1TmFu5wsq2AbLSLkMQ5Hj17VVw82qplWfE3zyM3Rveh4nUGrYOjZGdcQ3FhRnoOegX1PPvrpVnkPawo4yInqf+1ZLOMgBV6ixTd9NVJkLPwUSoalITo3H1/DpkZyTCydUfTVoPh61DXa0+I/3BbfwbuRbpD2/D3skXTYKHw9HFT6vPIO1RJEOcRE1Ez1L8Xn8AlU+G1E2E2DVGZcjlxYi/GYFHD2Jg61AX9fxfhlRq/sxrUhOjceH4r5AV5aNR8zfQoHEfAICFlRMy0uKRnXEf5hb2MLdyUj7j0snlSIg7BWtbN7QJmQJru5L/s8ffjMC/59dCIpigWfux8PAJfuazbR294enbBlY2LrB38oWdk48WfgqkK5FH4xA4mB1lRPRsT3eW6WrdIY4IPYexjQhlZ9zHjtWjkP7wFqSmtiguyoKVjRv6Dl+OOm4B5V6z76/JuPXvTkCQQBBMIMoLYedYD35N+uL88XBAlEFiYgm5LA+CIMVLr/0XR3fNRlFhFiQmFpDL8iEIJmjXfTpuRm/Fw6R/IQglObooFsPrhc549e2V5T47I+0Odqwejaz0eGW8NvZ18erwlXBwfkFXPybSApbJiEgdlS2VsTSmJcaWCG1dNRxpKbHwD54DW4cA5GbH48aFryARCjB04v4ya//cvLwNBzZ/BHffAfANGAeJiQVS7u7GrcvfQRBMYG3nh0atZsLCyhNZj/7FtXNfoKgwEyZSCwQEfwl752AU5qfiZlQYMtIuAqIc9QLfh0e91yGKMtyLWY17MWvQvvt0tOj0jsqzRVHEX8sGICczHf7Bs2Ft54eczNu4cWEOLCwtMejdHcq2eqqZbNxcMSCkGABHh4jo2RSlMkC9hEjdRIhdY6SU+SgeiXGn4Bv4LmwdSkZ/rGx80CBoCrLS45EYd7rMNReOh8PU3AkvBH0IqakNJBIp3H1fhaW1D0RRBr/m02FpXReCIMDOKQi+Ae8AkMPFqw8cXFpDEASYW7qiYYsZgCiHpbUP6jYYAomJGUyklvDxHwdLG19En1td5tkPk6/hQWI06jWeCGu7kjlB1nYvoH7jD5GWcg2p9y/r9OdFVceOMiJSlzR8q2pnmZZa7ZkIkVJudkm7opWN6hwbSxvfku/nlG1nLMjPgKWNNwRBdaRIeLyOkOXT97ItudfT6wiZWbhAYmIBE1PVUTdBEGBlWw9FBTllnp2XU1G8Pirfp5qPiy8SkboUCzEC2ll3iIkQKTm6+MFEaoEHiUdUjj9MjAAAuHg0LXONk6s/stL+RWH+Q+UxUV6MosJMlWtV7iVIUJiTonI840Ek5LJ85OfcU1kgsbgoC+kpZ2Hv5Fvm2XXcAiBIpHiQVDZeQZCgjnugOq9NNUTk0ThcTbSFjHuUEdFzaHMRRnaNkZK5hR2ath2JqJP/g6w4Bw7OrZGVfhX3b6/HC437wqFO/TLXdOr1H6xf0huXTkyEl99wSKU2SIzbgqKCVAgSU8Rc/C/ycu7Bxr4RHqWcQlLcVkhNLfEg6SAkUaao4xGC3Kw43Iv5AyZSKxQXZeDyycnwrD8IorwI926tg1xegI69/lPm2VY2LghsNQRXI/+H4oJ02NVpjsy0y7gfuwH+LQbCxs6jOn5spEXsKCMiTWijs4yTpZ/D2CZLy+UynD/6Cy6fXoGC/AxITS0R0HIw2veYXmELffytozj490fIz3sEADCRWqLVi++hUfM3sOnXV1GQnwlABAQT2Dv6YOC7u3Bo61TEXtsHUV4EQICdozdeHbEa8TcO4NSB71BcVFIKMzO3Q5e+X8Ev6NUK4i3G2cOLEH12NYoKsmBqZoMmrYehzUtTucWGAVN0lAkA7JgQEdFzuOTGIHHqNABPkiF2jWmJsSVCCjJZIfJy0mBh5fjcNYQUMh/dRVFhDhxdGkEieVJ1zc5IwsOUa3D3aglzS3vl8eLiQqSlXIeNnQesbFR3HU5LvQGJxLTcUahy4y0uQF5uGiytnGCiZrxUs7GjjIg0pewsE0yQZ2PPREgbjDURIqop3h5csggnkyEiUoeiVJZdXIzOB08wEaoqJkJE+sdSGRFpwiU3BlnZ2WjY/iWuI0REhi/yaBxWb0iDCK43RETPl2rlhweW6u0uwESIiAwGF18kIm1jIkREBqX04ouZXICRiKqIiRARGRxFqezK4wUYOUJERJXFRIiIDJYiIQLA7TmIqFKYCBGRwVOUy2TyknIZEZG6DC4RWrx4MerVqwcLCwu0a9cOZ86ceeb5GzduREBAACwsLNC0aVP8888/1RQpEVWnpzvLmBARkToMKhFav349pk6dilmzZuH8+fNo3rw5evXqhZSUlHLPP3HiBIYOHYqxY8fiwoULGDBgAAYMGIDo6OhqjpyIqsvqDWnYEiFVJkQslxHRsxjUgort2rVDmzZt8PPPPwMA5HI5vL298eGHH+LTTz8tc/6QIUOQk5ODHTt2KI+1b98eLVq0QHh4uFrP5IKKRIZLsRCjiQSwsdB3NERUnbKzMtGhuW/tWVCxsLAQkZGR6NGjh/KYRCJBjx49cPLkyXKvOXnypMr5ANCrV68KzweAgoICZGZmqnyIyDBFHo1Tzh0iIiqPwSRCDx48gEwmg5ubm8pxNzc3JCUllXtNUlKSRucDQFhYGOzt7ZUfb2/vqgdPRHoTeTQOAMtkRFQ+g0mEqsuMGTOQkZGh/Ny9e1ffIRFRFSnmDbGrjIieZjCJkLOzM0xMTJCcnKxyPDk5Ge7u7uVe4+7urtH5AGBubg47OzuVDxEZvuzkFO5XRkRlGEwiZGZmhuDgYBw4cEB5TC6X48CBA+jQoUO513To0EHlfADYt29fhecTUe1XegFGlsuISKrvADQxdepUjBw5Eq1bt0bbtm2xaNEi5OTkYPTo0QCA0NBQ1K1bF2FhYQCAyZMnIyQkBAsWLEDfvn2xbt06nDt3DkuXLtXnaxCRnimSIRs3VwwIKUZmLmBnpeegiEgvDCoRGjJkCFJTUzFz5kwkJSWhRYsW2L17t3JCdHx8PCSSJ4NcHTt2xNq1a/HFF1/gs88+Q8OGDbFlyxYEBQXp6xWIqAYpKZcBbw92QkYuYM9kiMjoGNQ6QvrAdYSIjMPbg50AgGsOEdUStW4dISIiXWJnGZFxYiJERPQYO8uIjA8TISKipyh2s2dXGVHtZ1CTpYmIqkvk0Thcd3OFfyNLBHpkQQA7y4hqI44IERFVIDs5BZFH41guI6rFmAgREamB5TKi2omJEBGRmiKPxik7yzg6RFQ7MBEiItKAorMMYDJEVBswESIiqoSnS2UslxEZJiZCRESVpCiVRSfYslxGZKCYCBERVUHpzjKAyRCRoWEiRESkJewsIzI8TISIiLSInWVEhoWJEBGRlrGzjMhwMBEiItIRlsqIaj4mQkREOvR0qYwjREQ1CxMhIiIdU5TKWC4jqnmYCBERVaPS5bJMJkREesdEiIiominKZdzRnkj/mAgREekBO8uIagYmQkREesRSGZF+MREiItKzp0tlHCEiqj5MhIiIagB2lhHpBxMhIqIahuUyourDRIiIqAZS7GjPzjIi3WIiRERUg7FURqRbTISIiGo4lsqIdIeJEBGRAWCpjEg3mAgRERmQ0qUy7mpPVHVSfQdARESaUSRDwZ19EeiRhcxcwM5Kz0ERGSiOCBERGSiWy4iqzmASobS0NAwfPhx2dnZwcHDA2LFjkZ2d/cxrunbtCkEQVD7vvfdeNUVMRFQ9SpfLWCoj0ozBJELDhw/Hv//+i3379mHHjh04cuQI3nnnnedeN378eCQmJio/3377bTVES0RUvRSdZTI5O8uINGEQc4SuXr2K3bt34+zZs2jdujUA4KeffsIrr7yC7777Dp6enhVea2VlBXd39+oKlYhIbyKPxiESwNuDnZSlMnvOHSJ6JoMYETp58iQcHByUSRAA9OjRAxKJBKdPn37mtWvWrIGzszOCgoIwY8YM5OY++69KBQUFyMzMVPkQERmSp/csY7mMqGIGMSKUlJQEV1dXlWNSqRROTk5ISkqq8Lphw4bB19cXnp6euHTpEqZPn47r16/j77//rvCasLAwzJkzR2uxExHpy+oNabBxc8WAkGJ2lhFVQK8jQp9++mmZycxPf65du1bp+7/zzjvo1asXmjZtiuHDh+P333/H5s2bcevWrQqvmTFjBjIyMpSfu3fvVvr5RET6ptjVnp1lROXT64jQtGnTMGrUqGee88ILL8Dd3R0pKSkqx4uLi5GWlqbR/J927doBAGJiYtCgQYNyzzE3N4e5ubna9yQiMgSrN6SpzB0ykQA2FvqNiagm0Gsi5OLiAhcXl+ee16FDB6SnpyMyMhLBwcEAgIMHD0IulyuTG3VERUUBADw8PCoVLxGRIVPMG2K5jOgJg5gsHRgYiN69e2P8+PE4c+YMjh8/jokTJ+Ktt95SdowlJCQgICAAZ86cAQDcunULX331FSIjI3Hnzh1s27YNoaGh6NKlC5o1a6bP1yEi0iuWy4ieMIhECCjp/goICED37t3xyiuv4MUXX8TSpUuV3y8qKsL169eVXWFmZmbYv38/evbsiYCAAEybNg1vvvkmtm/frq9XICKqUUp3lmWyu4yMlCCKoqjvIGqyzMxM2NvbY8z0KJiZ2+o7HCIirbNxK+nKHRBSDAEsl1HtkJ2ViQ7NfZGRkQE7O7sKzzOYESEiItKN7OQUlsvIaDERIiIiJS7ESMaGiRAREalYvSENWyKkkMmfzB8iqq2YCBERURmKUhnLZVTbMREiIqJnYrmMajMmQkRE9Fyly2UslVFtwkSIiIjU8nRnGctlVBsYxO7zRERUcyhKZcGdfRHokcV9y8igcUSIiIgqJfJonEp3GZEhYiJERESVpiiXAZxMTYaJiRAREVXZ6g1puJpoi+gEWyZEZFA4R4iIiLQi8mgcAOC6mysGhBQjIxew575lVMNxRIiIiLTq6XIZUU3GRIiIiHRCUS5TtNqzXEY1EUtjRESkM5FH4xAJwIblMqqhOCJEREQ6x3IZ1VRMhIiIqNqULpexVEY1ARMhIiKqVk8vxMgRItInJkJERFTtFKUylstI35gIERGRXrFcRvrERIiIiPTu6XIZEyKqLkyEiIioRlCUy64m2nIjV6o2XEeIiIhqFMXaQ8GdfRHokQUBgB3XHiId4YgQERHVSIpymQiODpHuMBEiIqIa6+mFGDOZEJGWsTRGREQ13uoNabBxc4V/I0uWy0irmAg9hyiKAIDCgmw9R0JEZNzS4rNwMh645OKCV18sRk42YGup76iopsrJzgLw5Pd4RQTxeWcYudu3b6NBgwb6DoOIiIgq4e7du/Dy8qrw+xwReg4nJycAQHx8POzt7fUcTfXKzMyEt7c37t69Czs7O32HU22M9b0Bvjvf3bje3VjfGzCOdxdFEVlZWfD09HzmeUyEnkMiKZlPbm9vX2v/z/I8dnZ2RvnuxvreAN+d725cjPW9gdr/7uoMYLBrjIiIiIwWEyEiIiIyWkyEnsPc3ByzZs2Cubm5vkOpdsb67sb63gDfne9uXO9urO8NGPe7P41dY0RERGS0OCJERERERouJEBERERktJkJERERktJgIERERkdFiIvQMixcvRr169WBhYYF27drhzJkz+g6pWhw5cgT9+vWDp6cnBEHAli1b9B1StQgLC0ObNm1ga2sLV1dXDBgwANevX9d3WNViyZIlaNasmXJxtQ4dOmDXrl36DqvazZs3D4IgYMqUKfoORedmz54NQRBUPgEBAfoOq9okJCTg7bffRp06dWBpaYmmTZvi3Llz+g5L5+rVq1fmn7sgCJgwYYK+Q9MbJkIVWL9+PaZOnYpZs2bh/PnzaN68OXr16oWUlBR9h6ZzOTk5aN68ORYvXqzvUKpVREQEJkyYgFOnTmHfvn0oKipCz549kZOTo+/QdM7Lywvz5s1DZGQkzp07h27duqF///74999/9R1atTl79ix+/fVXNGvWTN+hVJsmTZogMTFR+Tl27Ji+Q6oWjx49QqdOnWBqaopdu3bhypUrWLBgARwdHfUdms6dPXtW5Z/5vn37AACDBg3Sc2R6JFK52rZtK06YMEH5tUwmEz09PcWwsDA9RlX9AIibN2/Wdxh6kZKSIgIQIyIi9B2KXjg6OorLli3TdxjVIisrS2zYsKG4b98+MSQkRJw8ebK+Q9K5WbNmic2bN9d3GHoxffp08cUXX9R3GDXC5MmTxQYNGohyuVzfoegNR4TKUVhYiMjISPTo0UN5TCKRoEePHjh58qQeI6PqlJGRAeDJxrvGQiaTYd26dcjJyUGHDh30HU61mDBhAvr27avy77wxuHnzJjw9PfHCCy9g+PDhiI+P13dI1WLbtm1o3bo1Bg0aBFdXV7Rs2RL/+9//9B1WtSssLMTq1asxZswYCIKg73D0holQOR48eACZTAY3NzeV425ubkhKStJTVFSd5HI5pkyZgk6dOiEoKEjf4VSLy5cvw8bGBubm5njvvfewefNmNG7cWN9h6dy6detw/vx5hIWF6TuUatWuXTusXLkSu3fvxpIlSxAbG4vOnTsjKytL36Hp3O3bt7FkyRI0bNgQe/bswfvvv49JkyZh1apV+g6tWm3ZsgXp6ekYNWqUvkPRK+4+T1SOCRMmIDo62mjmTACAv78/oqKikJGRgU2bNmHkyJGIiIio1cnQ3bt3MXnyZOzbtw8WFhb6Dqda9enTR/nnZs2aoV27dvD19cWGDRswduxYPUame3K5HK1bt8Y333wDAGjZsiWio6MRHh6OkSNH6jm66vPbb7+hT58+8PT01HcoesURoXI4OzvDxMQEycnJKseTk5Ph7u6up6ioukycOBE7duzAoUOH4OXlpe9wqo2ZmRn8/PwQHByMsLAwNG/eHD/88IO+w9KpyMhIpKSkoFWrVpBKpZBKpYiIiMCPP/4IqVQKmUym7xCrjYODAxo1aoSYmBh9h6JzHh4eZRL8wMBAoykNAkBcXBz279+PcePG6TsUvWMiVA4zMzMEBwfjwIEDymNyuRwHDhwwmjkTxkgURUycOBGbN2/GwYMHUb9+fX2HpFdyuRwFBQX6DkOnunfvjsuXLyMqKkr5ad26NYYPH46oqCiYmJjoO8Rqk52djVu3bsHDw0Pfoehcp06dyiyNcePGDfj6+uopouq3YsUKuLq6om/fvvoORe9YGqvA1KlTMXLkSLRu3Rpt27bFokWLkJOTg9GjR+s7NJ3Lzs5W+VthbGwsoqKi4OTkBB8fHz1GplsTJkzA2rVrsXXrVtja2irng9nb28PS0lLP0enWjBkz0KdPH/j4+CArKwtr167F4cOHsWfPHn2HplO2trZl5oBZW1ujTp06tX5u2Mcff4x+/frB19cX9+/fx6xZs2BiYoKhQ4fqOzSd++ijj9CxY0d88803GDx4MM6cOYOlS5di6dKl+g6tWsjlcqxYsQIjR46EVMo0gO3zz/DTTz+JPj4+opmZmdi2bVvx1KlT+g6pWhw6dEgEUOYzcuRIfYemU+W9MwBxxYoV+g5N58aMGSP6+vqKZmZmoouLi9i9e3dx7969+g5LL4ylfX7IkCGih4eHaGZmJtatW1ccMmSIGBMTo++wqs327dvFoKAg0dzcXAwICBCXLl2q75CqzZ49e0QA4vXr1/UdSo0giKIo6icFIyIiItIvzhEiIiIio8VEiIiIiIwWEyEiIiIyWkyEiIiIyGgxESIiIiKjxUSIiIiIjBYTISIiIjJaTISIqEY5fPgwBEFAenp6hecIgoAtW7ZUW0zPMnv2bLRo0aJS144YMUK58aeuvPXWW1iwYIFOn0FkyJgIEZFOrFy5Eg4ODvoOQ6u0mYBdvHgR//zzDyZNmqSV+1Xkiy++wNy5c5GRkaHT5xAZKiZCRER68NNPP2HQoEGwsbHR6XOCgoLQoEEDrF69WqfPITJUTISIqIyuXbti4sSJmDhxIuzt7eHs7Iz//Oc/KL0jT0FBAT7++GPUrVsX1tbWaNeuHQ4fPgygpLw1evRoZGRkQBAECIKA2bNnAwD++OMPtG7dGra2tnB3d8ewYcOQkpJSpXjv3r2LwYMHw8HBAU5OTujfvz/u3Lmj/P6oUaMwYMAAfPfdd/Dw8ECdOnUwYcIEFBUVKc9JTExE3759YWlpifr162Pt2rWoV68eFi1aBACoV68eAOD111+HIAjKrxX++OMP1KtXD/b29njrrbeQlZVVYbwymQybNm1Cv379VI4XFBRg+vTp8Pb2hrm5Ofz8/PDbb78BeFIy3LNnD1q2bAlLS0t069YNKSkp2LVrFwIDA2FnZ4dhw4YhNzdX5b79+vXDunXrNPypEhkHJkJEVK5Vq1ZBKpXizJkz+OGHH7Bw4UIsW7ZM+f2JEyfi5MmTWLduHS5duoRBgwahd+/euHnzJjp27IhFixbBzs4OiYmJSExMxMcffwwAKCoqwldffYWLFy9iy5YtuHPnDkaNGlXpOIuKitCrVy/Y2tri6NGjOH78OGxsbNC7d28UFhYqzzt06BBu3bqFQ4cOYdWqVVi5ciVWrlyp/H5oaCju37+Pw4cP46+//sLSpUtVErSzZ88CAFasWIHExETl1wBw69YtbNmyBTt27MCOHTsQERGBefPmVRjzpUuXkJGRgdatW6scDw0NxZ9//okff/wRV69exa+//lpmxGj27Nn4+eefceLECWUCuGjRIqxduxY7d+7E3r178dNPP6lc07ZtW5w5cwYFBQXq/2CJjIWeN30lohooJCREDAwMFOVyufLY9OnTxcDAQFEURTEuLk40MTERExISVK7r3r27OGPGDFEURXHFihWivb39c5919uxZEYCYlZUliqIoHjp0SAQgPnr0qMJrAIibN28WRVEU//jjD9Hf318l1oKCAtHS0lLcs2ePKIqiOHLkSNHX11csLi5WnjNo0CBxyJAhoiiK4tWrV0UA4tmzZ5Xfv3nzpghA/P7778t9rsKsWbNEKysrMTMzU3nsk08+Edu1a1dh/Js3bxZNTExUYr5+/boIQNy3b1+51yh+Lvv371ceCwsLEwGIt27dUh579913xV69eqlce/HiRRGAeOfOnQpjIjJWHBEionK1b98egiAov+7QoQNu3rwJmUyGy5cvQyaToVGjRrCxsVF+IiIicOvWrWfeNzIyEv369YOPjw9sbW0REhICAIiPj69UnBcvXkRMTAxsbW2VcTg5OSE/P18lliZNmsDExET5tYeHh3LE5/r165BKpWjVqpXy+35+fnB0dFQrhnr16sHW1rbce5cnLy8P5ubmKj/fqKgomJiYKH8eFWnWrJnyz25ubrCyssILL7ygcuzpZ1taWgJAmZIZEQFSfQdARIYnOzsbJiYmiIyMVEkuADxz8m9OTg569eqFXr16Yc2aNXBxcUF8fDx69eqlUsbSNJbg4GCsWbOmzPdcXFyUfzY1NVX5niAIkMvllXrm0zS9t7OzM3Jzc1FYWAgzMzMAT5IVTZ4lCIJaz05LSwOg+vMgohJMhIioXKdPn1b5+tSpU2jYsCFMTEzQsmVLyGQypKSkoHPnzuVeb2ZmBplMpnLs2rVrePjwIebNmwdvb28AwLlz56oUZ6tWrbB+/Xq4urrCzs6uUvfw9/dHcXExLly4gODgYABATEwMHj16pHKeqalpmXeqDMW6Q1euXFH+uWnTppDL5YiIiECPHj2q/IzSoqOj4eXlBWdnZ63el6g2YGmMiMoVHx+PqVOn4vr16/jzzz/x008/YfLkyQCARo0aYfjw4QgNDcXff/+N2NhYnDlzBmFhYdi5cyeAknJRdnY2Dhw4gAcPHiA3Nxc+Pj4wMzPDTz/9hNu3b2Pbtm346quvqhTn8OHD4ezsjP79++Po0aOIjY3F4cOHMWnSJNy7d0+tewQEBKBHjx545513cObMGVy4cAHvvPMOLC0tVcpX9erVw4EDB5CUlFQmSdKEi4sLWrVqhWPHjqnce+TIkRgzZgy2bNmifI8NGzZU+jkKR48eRc+ePat8H6LaiIkQEZUrNDQUeXl5aNu2LSZMmIDJkyfjnXfeUX5/xYoVCA0NxbRp0+Dv748BAwbg7Nmz8PHxAQB07NgR7733HoYMGQIXFxd8++23cHFxwcqVK7Fx40Y0btwY8+bNw3fffVelOK2srHDkyBH4+PjgjTfeQGBgIMaOHYv8/HyNRoh+//13uLm5oUuXLnj99dcxfvx42NrawsLCQnnOggULsG/fPnh7e6Nly5ZVinvcuHFlynlLlizBwIED8cEHHyAgIADjx49HTk5OlZ6Tn5+PLVu2YPz48VW6D1FtJYhiqYVBiIhQso5QixYtlGvoGKN79+7B29sb+/fvR/fu3bV+/7y8PPj7+2P9+vXo0KGD1u+vsGTJEmzevBl79+7V2TOIDBnnCBERATh48CCys7PRtGlTJCYm4v/+7/9Qr149dOnSRSfPs7S0xO+//44HDx7o5P4KpqamZdYVIqInmAgREaFkYcbPPvsMt2/fhq2tLTp27Ig1a9aU6crSpq5du+rs3grjxo3T+TOIDBlLY0RERGS0OFmaiIiIjBYTISIiIjJaTISIiIjIaDERIiIiIqPFRIiIiIiMFhMhIiIiMlpMhIiIiMhoMREiIiIio8VEiIiIiIzW/wOW8qKBuIhjJAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display = DecisionBoundaryDisplay.from_estimator(\n",
-    "    knn_pipeline, X, response_method='predict',\n",
-    "    cmap=plt.cm.coolwarm, alpha=0.85,\n",
-    "    xlabel=feature_names[0], ylabel=feature_names[1],\n",
-    "    grid_resolution=500)\n",
-    "plt.title(\"k-NN Decision Boundary\")\n",
-    "plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, edgecolor='k', s=20, cmap=plt.cm.coolwarm)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "d67d5844",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display = DecisionBoundaryDisplay.from_estimator(\n",
-    "    dt_pipeline, X, response_method='predict',\n",
-    "    cmap=plt.cm.coolwarm, alpha=0.75,\n",
-    "    xlabel=feature_names[0], ylabel=feature_names[1],\n",
-    "    grid_resolution=500)\n",
-    "plt.title(\"Decision Tree Decision Boundary\")\n",
-    "plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, edgecolor='k', s=20, cmap=plt.cm.coolwarm)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "16b75b6c",
-   "metadata": {},
-   "source": [
-    "-   Visualising the Decision Tree structure"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "33b9ad02",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(12, 8))\n",
-    "plot_tree(dt_pipeline.named_steps['dt'], filled=True, feature_names=feature_names, class_names=class_names)\n",
-    "plt.title(\"Decision Tree Structure\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c921e8ab",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "-   **k-NN hyper-parameters**\n",
-    "    -   Re-run the demo varying $k$ from 1 to 15."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "060b08dc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import precision_score, recall_score\n",
-    "accuracies = []\n",
-    "precisions = []\n",
-    "recalls = []\n",
-    "for k in range(1, 16):\n",
-    "    knn_pipeline.set_params(knn__n_neighbors=k)\n",
-    "    knn_pipeline.fit(X_train, y_train)\n",
-    "    y_pred = knn_pipeline.predict(X_test)\n",
-    "    accuracies.append(accuracy_score(y_test, y_pred))\n",
-    "    precisions.append(precision_score(y_test, y_pred, average='weighted'))\n",
-    "    recalls.append(recall_score(y_test, y_pred, average='weighted'))\n",
-    "plt.plot(range(1, 16), accuracies, marker='s', label='Accuracy')\n",
-    "plt.plot(range(1, 16), precisions, marker='x', label='Precision')\n",
-    "plt.plot(range(1, 16), recalls, marker='o', label='Recall')\n",
-    "plt.title(\"k-NN Metrics vs. k\")\n",
-    "plt.xlabel(\"Number of Neighbors (k)\")\n",
-    "plt.ylabel(\"Scores\")\n",
-    "plt.xticks(range(1, 16))\n",
-    "plt.legend()\n",
-    "plt.grid()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9f403d1b",
-   "metadata": {},
-   "source": [
-    "-   Try Manhattan distance (`metric='manhattan'`) and observe how the boundary changes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "508cc34c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "k-NN (Manhattan) accuracy: 0.9111111111111111\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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8UGEiIiLg5eVVoDy/yyEiIkKl3NXVVeV9/i/hxMTEEsXZpEkTBAYGIjAwEAMHDsSePXtQq1YtjBkzRjn+KSIiAk5OTspn96qY1PGq+CMiIiCRSFR+aQMo9BllZGRg6tSpynFMNjY2sLW1xfPnzwt97h4eHgXKvL290bhxY6xfv15Ztn79ejRr1gzVq1cv0T2JL4ybKsyAAQNw8+ZNXLlyBRs2bEC/fv2KTKgTEhIwbtw42Nvbw8jICLa2tsq4C7unkn4/vFwvv+6L9dR9nupQKBT48ccfUaNGDZVzX7169Y3u63WkpqYW+N5+Ud++fdGiRQuMGDEC9vb26NevHzZv3lzipCgyMhJDhgyBtbW1ctxPQEAAgIJfw/zxPi96+ety//59ODk5wdraushr3r17F6IookaNGgV+vt26dQtxcXEFjhFFUafWTyoOxwgRAOC9997DmjVrsH79enzwwQcqn82dO1ellcfNza3QqbPAf2OFZsyYgQULFrzyuvnJ0/Tp00u1Vej69esAoPxllv9DbM6cOahfv36hx5iamiIhIeG1rzlv3jwMGTIEu3btwoEDBzB27FgEBwfj7NmzcHZ2fu3zvujlFo18r/plXBSJRIK2bdti4cKFuHv3LmrXrl3iY4v6IfryQM8XlWb8n3zyCVatWoXx48ejefPmsLCwgCAI6NevX6G/tF78S/1FQUFBGDduHB4/foysrCycPXsWP//88yuvnz+2JjExsdivb9OmTeHp6Ynx48cjPDwcAwYMKLJunz59cPr0aXz66aeoX78+TE1NoVAo0Llz50LvqaTPsyT11H2e6vjuu+/w9ddfY9iwYZg1axasra0hkUgwfvz4N7ovdT1+/BhJSUnFJrlGRkY4fvw4jhw5gj179mDfvn3YtGkT2rVrhwMHDhQZG5D3vd+hQwckJCTgs88+g7e3N0xMTBAVFYUhQ4YUuNfizqUOhUIBQRCwd+/eQs9Z2NpsiYmJqFGjRqlcX9sxESIAeQmCnp6ecjDliz+sg4KCVLpuivqFAqgmNoMHDy7RtcePH48FCxZgxowZKgP63sTatWsBQDkzJ7+FwdzcvMhZPgBga2sLc3NzZSKlrrp166Ju3br46quvcPr0abRo0QJLly7FN998U2h9Nzc33L59u0D5P//8o/y8rOXm5gL4rxXNzc0NBw8eREpKispfzi/HlP9X+vPnz1XO9yYtRm5ublAoFLh//75KK1Bhz2jr1q0YPHiwynoomZmZBeJ5lX79+mHixIn4448/kJGRAX19ffTt2/eVx3l7ewMAwsPDUbdu3WLr9u/fH9988w18fHyKTMQTExNx6NAhzJgxA1OnTlWW57dmlrWSPs/iWhGK+mzr1q1o27YtfvvtN5Xy58+fl3hCRml4+edCUSQSCdq3b4/27dtj/vz5+O677/Dll1/iyJEjCAwMLPI+r127hjt37mDNmjUICgpSlr/J7CxPT0/s378fCQkJRbYKeXp6QhRFeHh4oGbNmq88Z25uLh49eoR33nnnteOqTNg1RgDyfoAtX74cvXr1wuDBg1VmflWrVk3ZnRIYGIgWLVoUe67x48fD0tISM2fOLNG185OnXbt2lcoCZRs2bMCKFSvQvHlztG/fHgDg5+cHT09PzJ07t9C1M/K7+yQSCXr06IG//vqr0BV/i/qLNDk5WZlQ5Ktbty4kEkmxq1y/9dZbOH/+vMo07bS0NCxfvhzu7u6oVavWq2/4DeTk5ODAgQMwMDBQdn299dZbkMvlBVpFfvzxRwiCgC5dugDISyptbGxw/PhxlXq//PLLa8eTf+6ffvpJpbyw1kWpVFrg67Fo0aJiW6QKY2Njgy5dumDdunVYv349OnfuXKJfzn5+fjAwMCjRytAjRozAtGnTil3ELv8v+ZfvqSQtq6WhpM/TxMQEQMEEOP+zwsoLO/eWLVsQFRX1ZkGr4fDhw5g1axY8PDwwcODAIusV1iqcn7zm/79c1DMo7GsoiiIWLlz42nG/9957EEWx0LGX+dfp2bMnpFIpZsyYUeA5i6JYYPmFmzdvIjMzUznTTdexRYiUJBIJ1q1bhx49eqBPnz74+++/0a5dO7XPY2FhgXHjxqm1wGJ+l9qVK1eUP2RKYuvWrTA1NUV2djaioqKwf/9+nDp1CvXq1VOZqiqRSLBixQp06dIFtWvXxtChQ1G1alVERUXhyJEjMDc3x19//QUgrxn/wIEDCAgIwKhRo+Dj44Po6Ghs2bIFJ0+eLLTV6vDhwxgzZgx69+6NmjVrIjc3F2vXroVUKsV7771XZPyff/45/vjjD3Tp0gVjx46FtbU11qxZg/DwcGzbtk3tBRtfZe/evcqWnbi4OGzYsAF3797F559/rhwb1q1bN7Rt2xZffvklHj58iHr16uHAgQPYtWsXxo8frzJ+Z8SIEZg9ezZGjBiBRo0a4fjx47hz585rx1e/fn30798fv/zyC5KSkuDv749Dhw4VuobM22+/jbVr18LCwgK1atXCmTNncPDgQWWXlTqCgoLQq1cvAMCsWbNKdIyhoSE6duyIgwcPvjLpd3Nze+U4OHNzc7Ru3Ro//PADcnJyULVqVRw4cADh4eEliudNlfR51q9fH1KpFN9//z2SkpIgk8nQrl072NnZwc/PD0uWLME333yD6tWrw87ODu3atcPbb7+NmTNnYujQofD398e1a9ewfv16VKtWrUzuJf/7PDc3F7GxsTh8+DBCQkLg5uaGP//8s9gFFGfOnInjx4+ja9eucHNzQ1xcHH755Rc4OzsrW8Y9PT1haWmJpUuXwszMDCYmJmjatCm8vb3h6emJyZMnIyoqCubm5ti2bdsbjW1q27YtBg0ahJ9++gl3795VdpOeOHECbdu2xZgxY+Dp6YlvvvkGU6ZMwcOHD9GjRw+YmZkhPDwcO3bswKhRozB58mTlOUNCQmBsbKxcWkTnlecUNap4CpuGnp6eLgYEBIimpqbi2bNniz3+xenzL0pMTBQtLCyKnT5fVCzqTJ/PfxkaGorOzs7i22+/La5cuVLMzMws9LjLly+LPXv2FKtUqSLKZDLRzc1N7NOnj3jo0CGVehEREWJQUJBoa2srymQysVq1auLo0aOVU3pfnj7/4MEDcdiwYaKnp6doaGgoWltbi23bthUPHjyoct6Xp8+Loijev39f7NWrl2hpaSkaGhqKTZo0EXfv3q1Sp6jnVtwU9hcVNn3e0NBQrF+/vrhkyRKVJQVEMW8K/4QJE0QnJydRX19frFGjhjhnzpwC9dLT08Xhw4eLFhYWopmZmdinTx8xLi6uyOnzL09Jzo8rPDxcWZaRkSGOHTtWrFKlimhiYiJ269ZNfPToUYFzJiYmikOHDhVtbGxEU1NTsVOnTuI///xT4BmXZEp1VlaWaGVlJVpYWIgZGRnFPssXbd++XRQEQYyMjFQpz58+X5zC4nr8+LH47rvvipaWlqKFhYXYu3dv8cmTJ2/0PAGIo0ePLnD9l59TSZ+nKIrir7/+KlarVk2USqUq/x/ExMSIXbt2Fc3MzEQAyqn0mZmZ4qRJk0RHR0fRyMhIbNGihXjmzBkxICBAZbp9aX+fGxgYiA4ODmKHDh3EhQsXisnJyQWOeXn6/KFDh8Tu3buLTk5OooGBgejk5CT2799fvHPnjspxu3btEmvVqiXq6empxHbz5k0xMDBQNDU1FW1sbMSRI0cqlyt4Mf6ilgp5OR5RFMXc3Fxxzpw5ore3t2hgYCDa2tqKXbp0US5Vkm/btm1iy5YtRRMTE9HExET09vYWR48eLd6+fVulXtOmTcX333+/2GepSwRRfMPRZ0REWi43NxdOTk7o1q1bgXEsxZHL5ahVqxb69OlT4pYkIk0KCwtDw4YNcenSpSLHq+kajhEiIp23c+dOPH36VGWAa0lIpVLMnDkTixcv5r5NpBVmz56NXr16MQl6AVuEiEhnnTt3DlevXsWsWbNgY2PzxgsHEpH2YYsQEemsJUuW4KOPPoKdnR1+//13TYdDRBqgNYnQkiVLlMvCm5ubo3nz5sUu9Z+/4/SLr+JmChCR7lm9ejVyc3Nx8eJFtVcTJ6LKQWumzzs7O2P27NmoUaMGRFHEmjVr0L17d1y+fLnI1XDNzc1VFmLjcuJERET0Iq1JhLp166by/ttvv8WSJUtw9uzZIhMhQRDg4OBQHuERERGRFtKaROhFcrkcW7ZsQVpaGpo3b15kvdTUVOWS/Q0bNsR33333yr2UsrKyVFYCVigUSEhIQJUqVdiiREREpCVEUURKSgqcnJyKX5xWk4sYqevq1auiiYmJKJVKRQsLC3HPnj1F1j19+rS4Zs0a8fLly+LRo0fFt99+WzQ3NxcfPXpU7DVeXqiPL7744osvvvjS3terfu9r1fT57OxsREZGIikpCVu3bsWKFStw7NixEu3HlJOTAx8fH/Tv37/Yhc9ebhFKSkqCq6srQt7pChN9/VK5DyIiIipbaTk56PDnHjx//hwWFhZF1tOqrjEDAwNUr14dQN6GhxcuXMDChQuxbNmyVx6rr6+PBg0aFLpn0YtkMhlkMlmBchN9fZgyESIiItIqrxrWojXT5wujUCiK3dn7RXK5HNeuXYOjo2MZR0VERETaQmtahKZMmYIuXbrA1dUVKSkp2LBhA44ePYr9+/cDyNs9umrVqggODgaQt4Nws2bNUL16dTx//hxz5sxBREQERowYocnbICIiogpEaxKhuLg4BAUFITo6GhYWFvD19cX+/fvRoUMHAEBkZKTKqPDExESMHDkSMTExsLKygp+fH06fPl2i8URERESkG7RqsLQmJCcnw8LCAqff68ExQkRERFoiNScH/tt2IikpCebm5kXW0+oxQkRERERvgokQERER6SwmQkRERKSzmAgRERGRzmIiRERERDqLiRARERHpLCZCREREpLOYCBEREZHOYiJEREREOouJEBEREeksJkJERESks5gIERERkc5iIkREREQ6i4kQERER6SwmQkRERKSzmAgRERGRzmIiRERERDqLiRARERHpLCZCREREpLOYCBEREZHOYiJEREREOouJEBEREeksJkJERESks5gIERERkc5iIkREREQ6i4kQERER6SwmQkRERKSzmAgRERGRzmIiRERERDqLiRARERHpLCZCREREpLO0JhFasmQJfH19YW5uDnNzczRv3hx79+4t9pgtW7bA29sbhoaGqFu3Lv7+++9yipaIiIi0gdYkQs7Ozpg9ezZCQ0Nx8eJFtGvXDt27d8eNGzcKrX/69Gn0798fw4cPx+XLl9GjRw/06NED169fL+fIiYiIqKISRFEUNR3E67K2tsacOXMwfPjwAp/17dsXaWlp2L17t7KsWbNmqF+/PpYuXVriayQnJ8PCwgKn3+sBU339UombiIiIylZqTg78t+1EUlISzM3Ni6ynNS1CL5LL5di4cSPS0tLQvHnzQuucOXMGgYGBKmWdOnXCmTNnij13VlYWkpOTVV5ERERUOWlVInTt2jWYmppCJpPhww8/xI4dO1CrVq1C68bExMDe3l6lzN7eHjExMcVeIzg4GBYWFsqXi4tLqcVPREREFYtWJUJeXl4ICwvDuXPn8NFHH2Hw4MG4efNmqV5jypQpSEpKUr4ePXpUqucnIiKiikNP0wGow8DAANWrVwcA+Pn54cKFC1i4cCGWLVtWoK6DgwNiY2NVymJjY+Hg4FDsNWQyGWQyWekFTURERBWWVrUIvUyhUCArK6vQz5o3b45Dhw6plIWEhBQ5poiIiIh0j9a0CE2ZMgVdunSBq6srUlJSsGHDBhw9ehT79+8HAAQFBaFq1aoIDg4GAIwbNw4BAQGYN28eunbtio0bN+LixYtYvny5Jm+DiIiIKhCtSYTi4uIQFBSE6OhoWFhYwNfXF/v370eHDh0AAJGRkZBI/mvg8vf3x4YNG/DVV1/hiy++QI0aNbBz507UqVNHU7dAREREFYxWryNUHriOEBERkfap1OsIEREREZUGJkJERESks5gIERERkc5iIkREREQ6i4kQERER6SwmQkRERKSzmAgRERGRzmIiRERERDqLiRARERHpLCZCREREpLOYCBEREZHOYiJEREREOouJEBEREeksJkJERESks5gIERERkc5iIkREREQ6i4kQERER6SwmQkRERKSzmAgRERGRzmIiRERERDqLiRARERHpLCZCREREpLOYCBEREZHOYiJEREREOouJEBEREeksJkJERESks5gIERERkc5iIkREREQ6i4kQERER6SwmQkRERKSzmAgRERGRztKaRCg4OBiNGzeGmZkZ7Ozs0KNHD9y+fbvYY1avXg1BEFRehoaG5RQxERERVXRakwgdO3YMo0ePxtmzZxESEoKcnBx07NgRaWlpxR5nbm6O6Oho5SsiIqKcIiYiIqKKTk/TAZTUvn37VN6vXr0adnZ2CA0NRevWrYs8ThAEODg4lHV4REREpIW0pkXoZUlJSQAAa2vrYuulpqbCzc0NLi4u6N69O27cuFFs/aysLCQnJ6u8iIiIqHLSykRIoVBg/PjxaNGiBerUqVNkPS8vL6xcuRK7du3CunXroFAo4O/vj8ePHxd5THBwMCwsLJQvFxeXsrgFIiIiqgAEURRFTQehro8++gh79+7FyZMn4ezsXOLjcnJy4OPjg/79+2PWrFmF1snKykJWVpbyfXJyMlxcXHD6vR4w1dd/49iJiIio7KXm5MB/204kJSXB3Ny8yHpaM0Yo35gxY7B7924cP35crSQIAPT19dGgQQPcu3evyDoymQwymexNwyQiIiItoDVdY6IoYsyYMdixYwcOHz4MDw8Ptc8hl8tx7do1ODo6lkGEREREpG20pkVo9OjR2LBhA3bt2gUzMzPExMQAACwsLGBkZAQACAoKQtWqVREcHAwAmDlzJpo1a4bq1avj+fPnmDNnDiIiIjBixAiN3QcREb3ag6RkxGVkwNPCHLb//ownKgtakwgtWbIEANCmTRuV8lWrVmHIkCEAgMjISEgk/zVyJSYmYuTIkYiJiYGVlRX8/Pxw+vRp1KpVq7zCJiIiNcSkp2PK6XMIjY8HAEghoHs1d3zh1wAGUqmGo6PKSCsHS5en5ORkWFhYcLA0EVEZU4gieu89gKSUDIwQbOAhyHBOTMMaRTz61PDEZ34NNB0iaZGSDpbWmjFCRERUuZ2PjcPd5GRMlNjDX2IGR8EAPSRW6C2xxrb74UjLydF0iFQJMREiIqIK4WFKCiQAakN1TJCvYIRMhRyx6RmaCYwqNSZCRERUIbiYmkIB4A4yVcpvipmQSSSwM+agaSp9TISIiKhCaGZvDw9TM8xTxCJUkYZEMRf7FM+xSXyGdzzcOU6TyoTWzBojIqLKTSoRsLhNS0w6eQbTnkcBAAQAnV1d8GmD+hqNjSovJkJERFRhOJuaYmOnQNxMTERcegZqWFrA2dRU02FRJcZEiIiIVCRkZmH1P7dx9HEUBADtXJwxxNsLFjIDtc8VmZKKlbf+wYWYOJjo66Grhxv616he7JpAgiCgtrU1alu/wU0QlRATISIiUnqelYWgkEN4lp6BVjCDAiL++OcOjjyOwtoO7WFmUPJxOg+SkhEUchj6chEtYIrnGXIsCLuGszGx+Ll1K0glQhneCVHJMBEiIiKl9XfuIS49Az9LXOEg5LUAvStmY2xKBLbcv49hPt4lPtfia9dhIhfwo8QVpkJeC9AFRSpmxDzByehoBFR1KpN7IFIHZ40REZHSySfRaAYTZRIEAC6CARoJJjgRFa3euaJj0EEwUyZBANBYYgpniQFORMeUWsxEb4KJEBERKelLJMhCwZ2XMqGAvlS9Xxl6goCsl3ZxUogiskQR+hL++qGKgd+JRESk1MHVGefFNNwQ/1vFOUyRhjAxHR1dXNQ6V0dXF+xDEqLFbGXZ32ISnoo56ODiXGoxE70JjhEiIiKl3p6eOPwoCp/HP0JtwQgKADfFDDS3t0N3D3e1zjW6bh2cj43Dx2kRqCsY4TnkeCBmoW91TzSwqVIm8ROpi4kQEREpGepJsaxta/wdEYkjUU8gABjoXBed3VzU7s6yMTLExk4dsP3BA1yIewp7PT1MdHdDK0cHCAJnjFHFIIiiWLAzmJSSk5NhYWGB0+/14PLuRETlID03F4cfRyEuIwNelpZo7mAPySsSp/tJyTgVHQM9iYB2VavCwcS4nKKliio1Jwf+23YiKSkJ5ubmRdZjixAREVUYV+Kf4ZNjJ/E8JxsmggRpogI+lpZY0qY1rA1lBeqLoojvQi9j0737kAkCFCLww6UwjK/viyHeXhq4A9I2HCxNREQVQpZcjvHHT8EpV4oVUg9slHhittQZ0UmpmHXhYqHH/BkegU337mOUxBYbJdWxXuqJHoIV5oddxaWn8eV8B6SNmAgREVGFcPxJNJ5lZ2GsxB4Ogj4EQUAdwRgDBGsciXqCxKysAsdsu/8AjQQTvCOxgr4gwFiQYKjEBlUFA2y//0ADd0HahokQERFVCM8yMyEB4AjV8ZjOMIACedt/FDgmIxPOUN0DTRAEOEMfzzIzyzBaqiyYCBERUYVQx9oaCgDnxFSV8tNiKiz09eFkYlLgmNo21riANOS8MO8nWZTjGjJQ25q7ttKrcbA0ERFVCLWtreBvb48FcbF4pMiGB2Q4L6Zhn5iE8bXqQlbIjvVDvL0w6NFjfKl4jLcFS2RBgR3ic+jrSdGnhqcG7oK0DVuEiIioQhAEAfNb+qOLhxs2IxEzFU9wQT8Tk+r7YmgRM8BqWVthSZvWUJgb4AdFNBYqYmFvY46V7dvCzsionO+AtBHXEXoFriNERFT+MnJzkZydgyqGMuiVYCFHURSRkJUFPUECC5nBK+tT5cd1hIiISGsZ6enBSK/kv6IEQUAVQ8MyjIgqK3aNERERkc5iIkREREQ6i11jJSRPjINcjWZaKoYghdTKRtNREBERMREqqVWNf4GBzEzTYWi9uU0P4+7CtZoOg4iICAATIdIQeUKs8t9Sa3sNRkJERLqMiRCVq8nn2sG0ez/l+8+ff4ZHx66zu4yIiDSCg6Wp3KXGxilfX2VNgktAHU2HREREOkprEqHg4GA0btwYZmZmsLOzQ48ePXD79u1XHrdlyxZ4e3vD0NAQdevWxd9//10O0RIRVU5X4p9h1sVQfHrqDNbevoOU7BxNh0T0RrQmETp27BhGjx6Ns2fPIiQkBDk5OejYsSPS0tKKPOb06dPo378/hg8fjsuXL6NHjx7o0aMHrl+/Xo6RExFVDitv/YNBBw/j+P1HePL4GeZfvoo++w4gJi1d06ERvTat3WLj6dOnsLOzw7Fjx9C6detC6/Tt2xdpaWnYvXu3sqxZs2aoX78+li5dWqLr5G+xMeyzMM4aKyPfyObh0fFbHCNEVIE9TE7BO3/vQy/BCkESG0gEATFiDj5XPIKfswPmtGiu6RCJVJR0iw2taRF6WVJSEgDA2tq6yDpnzpxBYGCgSlmnTp1w5syZIo/JyspCcnKyyouISNcdePQYxoIEAyRVIBEEAICDoI9ugiUOPo5CjkKh4QiJXo9WJkIKhQLjx49HixYtUKdO0QNtY2JiYG+vOjXb3t4eMTExRR4THBwMCwsL5cvFxaXU4iYi0lZZcjlkkEAfgkq5CSSQiyLkCq3sXCDSzkRo9OjRuH79OjZu3Fjq554yZQqSkpKUr0ePHpX6NYiItE1zB3skirk4JaYqy3JEBQ6IyWhoUwWGelINRkf0+rRuHaExY8Zg9+7dOH78OJydnYut6+DggNjYWJWy2NhYODg4FHmMTCaDTCYrlViJiCoLP1sbtK3qhDlR0TgnpsIe+jiFVMQiF8vrcXwQaa/XahGKjIzEiRMnsH//fly6dAlZWVmlHVcBoihizJgx2LFjBw4fPgwPD49XHtO8eXMcOnRIpSwkJATNm/N/WiIidQiCgLn+zTG2Xl1EmAIH9NNQ08kWazq0RUNbTnQg7VXiFqGHDx9iyZIl2LhxIx4/fowXJ5sZGBigVatWGDVqFN577z1IJKXf4zZ69Ghs2LABu3btgpmZmXKcj4WFBYyMjAAAQUFBqFq1KoKDgwEA48aNQ0BAAObNm4euXbti48aNuHjxIpYvX17q8RERVXb6UgmG+nhhqI9XoZ9Hp6UjIiUFVU1M4GJmWs7REb2eEmUsY8eORb169RAeHo5vvvkGN2/eRFJSErKzsxETE4O///4bLVu2xNSpU+Hr64sLFy6UeqBLlixBUlIS2rRpA0dHR+Vr06ZNyjqRkZGIjo5Wvvf398eGDRuwfPly1KtXD1u3bsXOnTuLHWBNRETqScvJweRTZ9D5rz0YdfQ4uu7Zi9FHT+B5OfQWEL2pEq0jNGXKFEyePBlVqlR55Qn37duH9PR09OzZs1QC1DSuI1T2vu6dgLhJkwp+wP3HiLTCp6fO4MTjJxgm2KC+YIxbYiZWiE/hbVsFv7YL0HR4pKNKuo5QibrG8ruaSqJz584lrksEALO2WAPNVqmU5S+ySEQVW0x6Og48eoyPJXboLLEEADgIBtBXCJgdF427z5NQw9JCs0ESFUPrZo2RDhHlkCfEFv4ZW4uIKoRHKakQAfgKxirldf99/zAlhYkQVWhqJ0LPnj3D1KlTceTIEcTFxUHx0mqiCQkJpRYc6a6vsiYBzQr/bG7Tw7i7cC3kCbGQWtsXXomIykVVUxMAwA0xA1UFA2X5LTEDAOBiykHTVLGpnQgNGjQI9+7dw/Dhw2Fvbw9BEF59EFEpmnyuHfw+HYpGc9opW4yYEBFphpOJCdo6OWFVdCz0FcK/Y4QysFR8Cj8bG3hbWWo6RKJiqb3pqpmZGU6ePIl69eqVVUwVCgdLV2ym9nYYuKsrgOKToSK72F7AZIro9SRnZ2PK6XM48cL2RX42NpjbsjmqGBpqMDLSZaU6WPpF3t7eyMjIeKPgiEpLamwcljVblTe4+tj1vEKh8KX+HefPw1Pj6oV/9seX/x1PlVMR3xfF4Ti0kjE3MMDiNq0QnpyMh8kpqGpqgpqWlpoOi6hE1G4RunDhAj7//HNMnToVderUgb6+vsrnxWVd2ogtQtrBr5UbfBxTUD9ia5F1wn16lWNEVFHYpt+DaUSY2sfdXbi2QFlFbjXMzJVj/Z272PswAplyOZo62mOYtzeqmprgwKPH2HjnLqLT0lHdwgKDfbzQyM5Wo/Gm5+Ti99t3cCDiEXIUcrRwcsRQH2/YGxupfa6nGRlYdes2TkQ9gZ5Egg6uLgjyrgnTl34/lQWFKGJX+ENsu/cAzzIzUbuKNYb5eKOWtVWZX5uKV9IWIbUTobt372LAgAG4dOmSSrkoihAEAXK5/PUirqCYCGmX9/tYw8L41fWISsI2/Z7y39ETX1jrqoLNWsxRKPDBkWO48vQZWgimMIMUJ5EK6Evwtrsb1t65C1/BGDUgw2WkI1zMwvf+zdDZ1UUj8WbJ5Rh28AjuPH+OljCDIQScRCpkMn2s69geDsYl/5/4aUYGBuw/hPSsbLSCKbIh4iRS4WFhjtWBbWGkV7aTo7+5EIrN9x+gsWACFxjgHNIQhxz80qY1mtrblem1qXhl1jU2cOBA6OvrY8OGDRwsTUSV2otdqXpLdwH4rxv15XFnmmwtOvQ4ChefxuM7iTN8JXlJRH+xCsbmRGDdnbvoJVhhiDSvBWiwKGK2IhpzL4Uh0Lkq9MpgS6RX2f0wAtcTEzFP6oqaQt4Yor5iFXySFYFVt25jil+DEp9r9T+3kZ6VjUUSV9gIeS1A3cRMTHgeiT/DI9C3hmeZ3AMA3EtKwub7D/CBxBbdJHktQINEG3yleIx5l8KwqXMH/o7UAmonQtevX8fly5fh5VX4XjNERJVZdP9vodf/v/e26fcQPXFSoQPyyys5OvkkGp4SQ2USBADmghTtYY6tSMC7kv+6aSSCgHcklvg88zHuJiXBx6r8u3BORsegtmCkTIIAwFrQQ2uY4XjUE7USoRNR0WgJU2USBACegiF8BWOciI4u00ToVHQMZBDQWbBUlukLAroKlvghKRoJWVkcLK4F1E6EGjVqhEePHjERIo16v4+1pkMgApDXaqS3dJdKNxqA/5KjcuhG05dIkAWFcohCvkwo/v2viBeXNMz6d0SEvgZag/Kvm4WCozKyoFA7pqLOlQkRVcr4/vQlEigA5ECEPv577ln/Pnc9QTPPl9SjdiL0ySefYNy4cfj0009Rt27dAoOlfX19Sy04ouJwLBBVJC/PSNRbuguOf3yp1jnUmrn4QoLVwcUZ2x6E45AkGYFCXsrzSMzGIaRAEASsVcRjgsQBUkFAhqjAJjEBHqZm8NTQ5JYOLs7YF/kIJxUpaCnJG3v5QMzEcaQiyFW9P7I7urpgxY2b6CZaosa/LUznFKn4R8zAEBfnUo/9Re2qVsUPl8KwQfEMwyQ2kAgCkkQ5touJaGxrCwuZwatPQhqndiLUt29fAMCwYcOUZYIgVNrB0kREryu6/7dq1XfsXvIZbvmrqwNAcwd7dPdwx4Lwh9gtJsEMElwVM+BmZooBNavju9DLuKHIhAcMcAuZyJUAS5o209j4lXZVq6KjszNmP36MmmIijCDBNTEdXpaWGOytXiL0vlcNHI16gkmJkagjGCMbCvwjZqKtkxM6uZTtYHAHE2NMrF8Pc8Ou4LwiDVWhj+vIgExPDwsblbx7jzRL7VljERERxX7u5ub2RgFVNJw1VjFxdhjRf+OTAECwssOxqCfYG/kI6bm5aGZvj3erucNEXx//JD7Hlvv3ldPn+1T3hPO/W2Noilwh4tDjx9j/6DGy5XK0dHTEOx5urzXLKzNXjt0PI3D8yb/T512c0dHFBVJJ+SR6YfHx2H4/HM8yM1HL2hp9qleDrZH6ywBQ6Sqz6fO6holQxfR+H2sIAMyZDJGWS01JxsH9u5Hw7Cnq+DZE42Yt1W6pyZ/Jpu7gbFEUcTHuKa4lJMBaJkOgi3O5rL1DVB7KbPp8cHAw7O3tVbrGAGDlypV4+vQpPvvsM/WjJVLTus0JHDBNWu/sqWOY8OH7SEtLhZFED+mKXDRs2AQ/r9wMM/OS79ie230ocGzSqyu+IDUnB2NPnsbF2DiYGBsjIzMTP4RdxTz/ZmjuUHEXjiQqbWoPaV+2bBm8vb0LlNeuXRtLly4tlaCIiCq7lOQkjP9gIGpkAKukHtgkeGCGpCpuX7mMOd+qN8g6f6B2SfbUyzfv8hXcTk3Dyt9+w5UrV3Di+HE0bNoUE0+fRXJ2tlrXJ9JmaidCMTExcHR0LFBua2uL6OjoUgmK6FXyu8aItFXI3j+RkZ6O8YIdbAR9CIIAP4kJ3hMtsGfnZmRkpKt1Pr2lu+ASUAfyhNhXJkRZcjl2Rz7CyA8+QEBAAARBgIODA77//ntk5ubiwKPHb3JrRFpF7UTIxcUFp06dKlB+6tQpODk5lUpQRCXB8UGkzZ7Fx8FEqgdrqG4G6wIDZOfkIC01Re1zRvf/Fo7z5wFAsQlRWk4usnJz4Vmtmkq5ra0tzM3M8CwjU+1rE2krtROhkSNHYvz48Vi1ahUiIiIQERGBlStXYsKECRg5cmRZxEhEVOnU9m2AFHkOrokZKuWnxVQ42DnAusrrbYqav8Bj/pYghSVDljIDOJmZYv/+/Srl58+fR2JSEmpX4fg70h1qD5b+9NNP8ezZM3z88cfI/rcf2dDQEJ999hmmTJlS6gESEVVGzVq0ga9vQwTfuIbeCgtUhQFOiqk4Iibj67HTISmFVZHzF3V8caFGqbU9JIKAkd5emPHXX4AgoOtbb+Hhw4dYsmQJ6thUgT8HS5MOee3p86mpqbh16xaMjIxQo0YNyGSy0o6tQuD0+YqJ6whRZZCU9Byzp/8P+3bvQK48F/a29hg19n/oM3DYqw9WQ/72H/lrDuXb9jgaKyKjEZOaCj2JBJ1dXfBZg/pcEZkqBa4jVEqYCFVMTISoMklKTER8fBzcPDyhV8IFBVNTkqGnrw9Dw9dfuC/3w+6QiyKeZcthqieFma3Da5+LqKIpaSJUorbXDz/8EI8fl2wWwaZNm7B+/fqSRUlEpMNSU5Ix66uJ6NCyDnp0aoYeHZvirx0biz3m/JkTGPBuezSv54ZmdV0w8eMgREc9eq3r6y3dBfc2deHXsT682tUt0YwzosqmRH962Nraonbt2mjRogW6deuGRo0awcnJCYaGhkhMTMTNmzdx8uRJbNy4EU5OTli+fHlZx01EpNVEUcQnowbgnxtXMWrkCHhUq4a9e/fii0kfQaEQ0f29/gWOuXL5Aj4Y8h5869bFDz/8gOSkJPy2ciWG9n8bW3YfV2sRxnwv7ofm2P1ege4zosquRInQrFmzMGbMGKxYsQK//PILbt68qfK5mZkZAgMDsXz5cnTu3LlMAiUiqkzOnzmBi+dOYeVvvyEgIAAA8HbXrvhk7Fgs/el7dHu3b4EB078ungfPatWwYf166P+7FUZgYCACO3TAn9s3YuCQD8r9Poi0XYmnJdjb2+PLL7/EtWvXEB8fj0uXLuHUqVO4ffs2EhMTsXXrViZBREQldC3sIszNzdG6dWtlmSAI6NatGx4/ikDCs6cFjrkadhFdOndWJkFA3tpu9evXx5XLF8olbqLKRv1tfgFYWVnBysqqtGMhItIZFlbWSEtLw7Nnz2BjY6Msj4yMhL6BAYxNTAscY2lljYjISJUyuVyOx4+jULN2w1KLrbhxQupu7EpU0b1WIkRERG+m41s9MOfbL/HFl18i+LvvUKVKFVy8eBHLli1Dp7d6wNjYpMAxPXoNxKJ53yAgIABd33oLmZmZ+PHHHxETE413ehYcU6Su/MUYi5L7YfdyG0zNhIvKC6fPvwKnz1dMnD5PlcHxw/sx+ZNhyM3NgaWlJZ4+fYpadeph2ZrtsLQquLpzTk4O/jd2GA7u3w1LKytkZWYiMzMTn375LQYN+6jc4/e4tbVMzpsZdlllEUgAgCCF1Mqm8AOICsF1hEoJE6GKiYkQVRaJCc+wb/d2JDyLRx3fBmjZpgOkUmmR9UVRRFjoOZw9dQyGRkbo0KU7nF3cyjHi8pG/CCQAmEaE4e7Ctcr3bC2ikihpIqRVXWPHjx/HnDlzEBoaiujoaOzYsQM9evQosv7Ro0fRtm3bAuXR0dFwcODCYUSkeZs3rMLalb8gMzMDTlVdYGvngFp16xdZXxAENGjUDA0aNSuzmHJzc3Fw3584emgfBEFAuw5d0a5j12ITtNL21Lj6f//2qQ69pb1gm543vf9V3XNMlEgdWpUIpaWloV69ehg2bBh69uxZ4uNu376tkg3a2dmVRXhERGrp/XYA/rl5Fba2tvCu6YvQS5cwoGcgZn7/M97p2U8jMeVkZ2PMiL44ffIoqusZQwSwe+dmtG3fGfOXrC3xytdlIX8MU3FdcspuNXalUQmp/R0dGxuLyZMn49ChQ4iLi8PLPWtyubzUgntZly5d0KVLF7WPs7Ozg6WlZekHRET0mo4e2ofbt65h0KBB+PqrryCVShEdHY2+ffti5pfjNZYIbd24BmdPHcNMSVU0RN6A7XOSVMw6tA9/7diEd3sP1EhcLwr36VX0hz69UKP+VtxduLbEA7vZgqTb1E6EhgwZgsjISHz99ddwdHSEIAhlEVepql+/PrKyslCnTh1Mnz4dLVq0KLJuVlYWsrKylO+Tk5PLI0Qi0jGL5n0DqVSKSRMnKrucHB0d8eGHH+LrqVPx4N5tVKvuVe5x7ftzKxoLJmgo+W/WWlOJKerBBPv+2lYhEqFXCffpBb2lxSRL/3qxq43JkO5SOxE6efIkTpw4gfr165dBOKXL0dERS5cuRaNGjZCVlYUVK1agTZs2OHfuHBo2LHzNjeDgYMyYMaOcI6XXkZSe918OmiZtlJWVCT09PRgZqW6aavZvN37S80RNhIXMjAxYiAX/wDURBWRlpGsgorKT39WW+2F3yBPj2ZWmo0q8snQ+FxeXAt1hFZWXlxc++OAD+Pn5wd/fHytXroS/vz9+/PHHIo+ZMmUKkpKSlK9Hj15vM0MqW+s2J2Dd5gRNh0H02rq92w+ZmZnYsWOHsiwnJwfr/90+w7dBY43E1aJtB5yTZOCpmKMsixGzESpkwL9NoEZiKmsuAXU0HQJpkNotQgsWLMDnn3+OZcuWwd3dvQxCKltNmjTByZMni/xcJpNBJpOVY0REpIs+GDMZq5YtxJQvvsDxEyfgWa0a9u7bh/v376P7e/3LdYbWiwYO+RB/bfsD4+Oj0EaeN1j6iCQNdk7O6DtwuEZiKheinGOKdFSJEiErKyuVsUBpaWnw9PSEsbGxyp43AJCQULH/Sg8LC4Ojo6OmwyAiwpHzdzCwZyBCQkJwQBQhleph8MgxmPT5TI3FVMXGFut3HsKvv8zHkX1/QRAEdH+rH0Z8PBEWlpVza6Xo/t+iRv2SLQ6pHITNWWmVRokSoQULFpRxGCWTmpqKe/f+W2QrPDwcYWFhsLa2hqurK6ZMmYKoqCj8/vvvAPLi9vDwQO3atZGZmYkVK1bg8OHDOHDggKZugYh0VE5ODq5fCYVcIYdvvUYwkMlgZGSE7XtPqX2u9LRUXL96GYZGRqjj27DALvVvys7eEV/OmIMvZ8wp1fOq48G924iNiUb1mt6wtSv7dd+KnYn2Ar2lveBx699ZaRxXVCmUKBEaPHhwWcdRIhcvXlRZIHHixIkA8uJbvXo1oqOjEfnChoTZ2dmYNGkSoqKiYGxsDF9fXxw8eLDQRRaJiMrK4ZC/8c3XE/E0Lq/rxdraBv/7+jt07d5b7XOtX70MP8//FqmpKQAAZ1d3zPrhZzRqUvRsWG0SEx2Fz8cOR2joOQCAVCJB914D8OWMuTCoIMMWwn16wSXgMh4dv6XpUKgUqL3FRv5aFy8vSvjs2TPY2dmV6TpCmsAtNio2brVBFd3tW9fQr3s7BAQEYMzo0ZBKpVj+66/Ys2cPVm/cg4aNm5f4XAf+3oVJY4Zg4IABGPj++0hOSsK8+fNx4+ZN7DpwDg6OVcvwTsqeQqFA7y4t8PxBOEaI1vAQZDgnpmENEtAnaAQ+nzpb0yEqOf7xJR4dv8UWoQqspFtsqN2eWlTelJWVBQMDA3VPR/RGbkWbKafRE1VEf6z5FXZ2dlj888/w9fVF7dq18eP8+ahevTrWrV6q1rnWrvwFLVq0wMyZM+FVsyYaN26MX5cvhwAB2zb+XkZ3UH7OnT6OO3f/wUTYwl9iBkfBAD0kVugNS2z7YzXS/m0FIypNJZ419tNPPwHI2+dmxYoVMDU1VX4ml8tx/PhxeHt7l36ERMUIPREBnz4Fd+kmqijCw++hUaNGKhNLJBIJmjZpgvOhYWqd62H4PQwbOkSlzMzMDHXq1EZE+L3CD9IiEeH3IBEE1Ibq2kq+ghE2ZD1DbMwTjSwyWSSxcvWA6KoSJ0L5a++IooilS5eqTO00MDCAu7s7li5V768bIqLKzsXVHZfOn0Jubq5yny5RFBF66RJcXD3VPldoaKhKWVpaGm7evIl+QS1LLWZNcXHzgEIUcQeZ8HohGbopZkKmbwA7+4oz4ze6/7dwAbvHKoMSJ0Lh4eEAgLZt22L79u2wsqqc0yiJiEpT/0Ej8deOTZg4aRLGjB4NfX19LF22DLdu3cLkr75X61wDh3yIzyeMQvDs2Xh/4EAkJSVhzty5yMnJxXt9g8roDspPsxZt4OHuiXmPo/CBogqqCTKcE1OxCYl4p3cQTM2KHudB9LrUXlDxyJEjZREHEVGlVNu3AWb/uBzfTJ2MPXv2AABMTc0w7bsFaNK8lVrneuudXoiOeoSli+ZgxYoVAABbO3ssXLYOVZ1dSz328iaVSrF49VZM+nAQpv1zHUDecIzOb72L/331rYajKwK7x7ReiWaN5U9TL4n58+e/UUAVDWeNVXycOUbaIDMzA6HnT0OhUMCvcXMYm5i++qAiJD1PRFjoORgaGaNh4+YFFrbVdqIo4ua1MMTGRqOGVy24uLprOqQiOf7xJR4du84FFiugks4aK1GL0OXLl1XeX7p0Cbm5ufDyyhu0dufOHUilUvj5+b1ByERE5S8uNhqrlv2Ek8dCoK9vgA5duiNo+McwMVX/D597d25h5dKFCLt0HuYWFuj+3gD0HjAUenp6MDQ0QovW7VXqKxQK7NiyDts3r0XCs3jUqdsAQz8Yh1p16uHi+VNYs2Ix7t25BQfHquj7/nB0eqsHBEGAhaUVAtp3Lq1HUOEIgoDavg1QGw00HcorvThWiLRTiRKhF7vD5s+fDzMzM6xZs0Y5TigxMRFDhw5Fq1bqNfMSEWlSbMwTDOzZAdlZGej61lvIyMzEymULcPzIfqzauAeGhkavPsm/bly9jKED3kYVa2t06tgRj6OiMHvm57h04Qx++Ok3lW2K8s36aiK2blyDdu3aoYlffRw8eBCDenXC8I8mYNmiH+Dl5YW3OnfEtWvX8OknwxB+/y4+Gvu/0nwEVFr+3auM+5BpH7UXVKxatSoOHDiA2rVrq5Rfv34dHTt2xJMnT0o1QE1j11jFx64xel2zZ36Ov3dtxp7du2Fvn/cL7Nr16+jZsye+mjUPvfsPKfG5Rg7qgefPYrF1yxYYGeUlUH/++ScmTJyINZv+LrBw4u1b19GrayvMmD4d77//PoC89dgGvv8+bt66hdatWuGXxYuV22fMmz8fy5cvR8ip67Cx5S/bisY2/R70dq3K6yYDAOG/mdXsMtOMMltQMTk5GU+fPi1Q/vTpU6SkcLErItIeJ4+G4O2uXZVJEADUrVMHTZo0wYkjISU+T052Ns6eOoYB/fsrkyAAePvtt2Fra4sTRwue69TxQzAxMUHfvn2VZTKZDIHt2yMrMxNDhwxR2UNs2NChyM3NxdlTx9S9TSoHT42r523eOm4QXALqwKW1j/IlT4iFPDFe0yFSEdSeNfbuu+9i6NChmDdvHpo0aQIAOHfuHD799FP07Nmz1AMkKon81aXZMkTq0NPXR0ZmZoHyjIwMmFuXfKV8QSKBnp4e0jMyVMpzc3ORnZ0Nff2C59LXN0Bubi5ycnJUBjvn5OQAQIFzZfz7Xp8r+Fdo4T69gJc2cOUYoopN7RahpUuXokuXLhgwYADc3Nzg5uaGAQMGoHPnzvjll1/KIkaiYq3bnIB1mxMAAKkFf6cRFaljlx7Ys2cPrl+/rizbv38/rly5gg5d3inxefT09NCuY1f8/vvviImJAZA38+nXFSuQlJRU6Lnad+qKnJwc/LRokXLromfPnmHXn3/C1NQMixcvRmpqKoC8hGre/PkwMjZGi1bt3uSWieglao8RypeWlob79+8DADw9PWFiYlKqgVUUHCOkPUzt7dAjIJetQlRiqSnJGD7wHfxz8xqaNm2K9PQMXLkShg6du2HOolUqK+i/StTjSAzu0wXPE5+hWbNmiIp6gnv37mLk6EkYO+mrQo9ZtfwnzJ89De7u7vDw8MC5c+dgaGiM/339HWZ+NQH6enpo5OeHm7duITY2Ft/MWYxu7/YrrduncsINWjWjpGOEXjsR0hVMhLQLB06TujIy0vHXjk04cTRv+nzHt7qjQ+d31EqC8iUmPMO2jWtwOfQcLCyt8E7P/mjWIqDYYy5dOIPtm9ch4dlT1K3nh94DhsDG1h5RjyOxad1vuHfnFhydnNGr/xD41PZ93dskDcpfa4gzyspXqSZCPXv2xOrVq2Fubv7KcUDbt29XP9oKjImQdnn/3w1YpRLA1FDDwRAR/YutQuWvVBdUtLCwUK6BYWFhUToREpWBdZsTlF1kyf8OoDZnCxERERWhRInQqlWrCv03UUWUGhuHdZv/GzOUlM7ZZEREVDi1Z42tXLlSuRM9UUWWlxAlaDoMIiKqwNROhIKDg1G9enW4urpi0KBBWLFiBe7du1cWsRERERGVKbUTobt37yIyMhLBwcEwNjbG3Llz4eXlBWdnZ+Uy8URERETaQO1ECMjbb2zgwIH48ccfsXDhQgwaNAixsbHYuHFjacdHREREVGbU3mLjwIEDOHr0KI4ePYrLly/Dx8cHAQEB2Lp1K1q3bl0WMRK9sfwtOAAOnCYiov+onQh17twZtra2mDRpEv7++29YWlqWQVhEpefFAdPv97HmLDIdJooikpOTYGxsorK/FxHpLrW7xubPn48WLVrghx9+QO3atTFgwAAsX74cd+7cKYv4iErVus0JuBVthqR07kumS0RRxJY/VqNLQH20bOCBVn7V8P3MKcjISH/1wURUqamdCI0fPx7bt29HfHw89u3bB39/f+zbtw916tSBs7NzWcRIVKpCT0Rg5zE9yBV5XWZJ/F1Y6W34/VfM/HICGjaoh4ULF2JwUBC2bVqDTz8ZpunQiEjD1O4aA/L+urp8+TKOHj2KI0eO4OTJk1AoFLC1tS3t+IjKRP6iiwC7yyq7nJwc/Lp4Lvr07o3g4GAAwNtdu8Lbywtjx43DjauXUdu3gYajJCJNUbtFqFu3bqhSpQqaNGmC9evXo2bNmlizZg3i4+Nx+fLlsoiRqEy92F1Glc+Tx5F4Fv8Ub7/9tkp5586doa+vjythFzQUGRFVBGq3CHl7e+ODDz5Aq1atuO8YVRqhJyLg8++GrVS5mFtYQhAERERGokWLFsryJ0+eICcnB1ZWVTQYHRFpmtqJ0Jw5c8oiDqIKgV1klY+VdRUEtO+Mn3/+GXVq14avry+ePn2KL7/6ChaWVmgT2EXTIZKuEOWQJ8ZzB/oK5rUWVCSqjF7sIktmN1mlMvWb+TCzsMa7PXvCv0ULtGjZEleuXMX8xWtgZMTMl8pedP9v4RJQR9NhUCFea7A0UWUVeiICt7lrfaVja+eAzX8dw7FD+/DPrWuws3NA527vwdyc3ftEuk6rWoSOHz+Obt26wcnJCYIgYOfOna885ujRo2jYsCFkMhmqV6+O1atXl3mcpN24a/2byczMRPCMz9CpdT10CaiPH7+fjuzsbE2HBX19fQR27oYxE75An4HDmAQREQAtS4TS0tJQr149LF68uET1w8PD0bVrV7Rt2xZhYWEYP348RowYgf3795dxpFQZsJtMfakpyejYog42rFkOS3NTmBgbYuWyhegSUB+ZmVzBkogqnhJ1jSUnJ5f4hObm5q8dzKt06dIFXbqUfGDj0qVL4eHhgXnz5gEAfHx8cPLkSfz444/o1KlTWYVJlUToiQiE4r91hgB2lb3K1/8bg6SkRPz+++9o4e8PANh/4AA+/vhjfD/zc0z7boFmAyQiekmJEiFLy7zpp8URRRGCIEAul5dKYKXhzJkzCAwMVCnr1KkTxo8fX+QxWVlZyMrKUr5XJwmkyim/m4wLL77audPH0L59e2USBACdOnZEk8aNcTjkbyZCRFThlCgROnLkSFnHUSZiYmJgb2+vUmZvb4/k5GRkZGTAyMiowDHBwcGYMWNGeYVIWmTd5gT4tXKDj2MKBADmTIgKUCgUhbYKW1hYQJ6bq4GIiIiKV6JEKCAgoKzjqDCmTJmCiRMnKt8nJyfDxcVFgxFRRcLusuJVq+GFvXv3YuKECXBwcAAAPHz4EEeOHkUT/9Yajo6IqKDXnj6fnp6OyMjIArNBfH193zio0uLg4IDY2FiVstjYWJibmxfaGgQAMpkMMpmsPMIjLfZyd5lUApgaajioCmD6dwvQr3t7vNW1K3q99x5y5XJs3boVUqkepn7zo6bDIyIqQO1E6OnTpxg6dCj27t1b6OcVaYxQ8+bN8ffff6uUhYSEoHnz5hqKiCqbF7vLktKh811mNb3rYO3Wffjq09FYu24dBEGAh6cXvl+wHFWdXTUdHhFRAWonQuPHj8fz589x7tw5tGnTBjt27EBsbCy++eYb5eysspKamop79+4p34eHhyMsLAzW1tZwdXXFlClTEBUVhd9//x0A8OGHH+Lnn3/G//73PwwbNgyHDx/G5s2bsWfPnjKNk3RLfncZwAHVAFC7bgPs2Hda02EQEZWI2onQ4cOHsWvXLjRq1AgSiQRubm7o0KEDzM3NERwcjK5du5ZFnACAixcvom3btsr3+WN5Bg8ejNWrVyM6OhqRkZHKzz08PLBnzx5MmDABCxcuhLOzM1asWMGp81Rm1m1OYHcZEZEWUTsRSktLg52dHQDAysoKT58+Rc2aNVG3bl1cunSp1AN8UZs2bSCKYpGfF7ZqdJs2bXD58uUyjIpIFbvLiIi0h9qJkJeXF27fvg13d3fUq1cPy5Ytg7u7O5YuXQpHR8eyiJFI67C7jIhIO6idCI0bNw7R0dEAgGnTpqFz585Yv349DAwMuI8XUSHYXUZEVHGpnQi9//77yn/7+fkhIiIC//zzD1xdXWFjY1OqwRFVFuwuIyKqmNTedHXmzJlIT/9vF0pjY2M0bNgQJiYmmDlzZqkGR1SZhJ6IwLrNCVi3OQEioFyQkYiINEftRGjGjBlITU0tUJ6ens6tKYhKKH9Bxvzd7VO5MTsRkUao3TWWv7nqy65cuQJra+tSCYpIF6zbnABT+7wZmD0CcpGczu4yIqLyVuJEyMrKCoIgQBAE1KxZUyUZksvlSE1NxYcfflgmQRJVVqmxcQCAdZs5u4yISBNKnAgtWLAAoihi2LBhmDFjBiwsLJSfGRgYwN3dnVtXEL2BF2eXCQAknGFGRFTmSpwIDR48GEDeas0tWrSAnt5r79dKREVgdxkRUflSe7B0QEAAIiIi8NVXX6F///6Ii8tr2t+7dy9u3LhR6gES6ZrU2DikxsapzC7jYGoiorKhdiJ07Ngx1K1bF+fOncP27duVM8iuXLmCadOmlXqARLps3eYE3Io2g1zBhIiIqCyonQh9/vnn+OabbxASEgIDAwNlebt27XD27NlSDY6I/lt/aOcxPcgVedPtiYiodKidCF27dg3vvvtugXI7OzvEx8eXSlBEVBC7y4iISp/aiZClpaVyr7EXXb58GVWrVi2VoIioaOwuIyIqPWonQv369cNnn32GmJgYCIIAhUKBU6dOYfLkyQgKCiqLGInoJewuIyIqHWonQt999x28vb3h4uKC1NRU1KpVC61bt4a/vz+++uqrsoiRiIrA7jIiojej9mJABgYG+PXXX/H111/j+vXrSE1NRYMGDVCjRo2yiI+ISuDl3e2lXIyRiKhEXntVRFdXV7i4uABAoXuPEVH5Cj0RgVAApvZ2XIyRiKiE1O4aA4DffvsNderUgaGhIQwNDVGnTh2sWLGitGMjotfwcndZEscPEREVSe0WoalTp2L+/Pn45JNPlHuLnTlzBhMmTEBkZCRmzpxZ6kESkfrWbU4AAGWXGbvLiIgKUjsRWrJkCX799Vf0799fWfbOO+/A19cXn3zyCRMhogom9EQEbv/bXcbd7YmIVKndNZaTk4NGjRoVKPfz80Nubm6pBEVEpSu/uwxgdxkR0YvUToQGDRqEJUuWFChfvnw5Bg4cWCpBERVFnpuFrMwUiKKo6VC00rrNCcoFGTndnojoNWeN/fbbbzhw4ACaNWsGADh37hwiIyMRFBSEiRMnKuvNnz+/dKIknZeRnoAzB77DvRt7oJBnw8q2Jpq0HQ8P706aDk0rvdxdBrDLjIh0k9qJ0PXr19GwYUMAwP379wEANjY2sLGxwfXr15X1OKWeSotcnoPda4OQnBgNlxqDYWBoh6dRB7B/88fo3Hc53L3aazpErZTXXZb37/f7WHP8EBHpJLUToSNHjpRFHERFeng7BM9ib8G35VKYWXoDAGyrBuLGuUm4eOwnJkKl4MUFGTm7jIh0yWutI0RUnmIfh8HIpKoyCQLyWhxtHNshPuY65PIcDUZXeYSeiFDuXcYB1USkK5gIUYVnaGyF7KxEyHNVfzNnpkdBX2YGieS1F0inl+TPLntxhhkRUWXGRIgqvBp1u0OhyMb9az8iNydvxlhi3DnEROyCd/1eHI9WRji7jIh0Af+UpgrPzMIJ7brPwZFdn+JZ9DFI9U2Qk5UIR9cmaNJ2gqbDq9Q4u4yIKjsmQqQVatR9B07uzXD/xh5kZSbBwaURnKv5QxDYqFnWOLuMiCozrfstsnjxYri7u8PQ0BBNmzbF+fPni6y7evVqCIKg8jI05HQYbWViZgffZkPRuM14uHi2VCZBKc+jcO7wXBzY+gnOHZqD5MRHGo608nqxu4xdZkRUGWhVi9CmTZswceJELF26FE2bNsWCBQvQqVMn3L59G3Z2doUeY25ujtu3byvfczxJ5RIVfgZ//zESgiCFiYUXIu+dwNWzq9C53zK4eLbSdHiVUuiJCIQCMOX+ZURUCWhVi9D8+fMxcuRIDB06FLVq1cLSpUthbGyMlStXFnmMIAhwcHBQvuzt7csxYipLCoUch3f9D6YW3mjUfjPqNJuHRu02w8zaF0d2/Y/T6svYy/uXERFpI61JhLKzsxEaGorAwEBlmUQiQWBgIM6cOVPkcampqXBzc4OLiwu6d++OGzduFHudrKwsJCcnq7yoYoqLCkNa8hO4eo+AVC+vSUKqZwRXr+FIT41DzKNQDUeoG17uLktmUkREWkRrEqH4+HjI5fICLTr29vaIiYkp9BgvLy+sXLkSu3btwrp166BQKODv74/Hjx8XeZ3g4GBYWFgoXy4uLqV6H1R6cnPyBqjo6ZmolOvpmwIA5DkcwFJeQk9EYN3mBOw8pgcRbCEiIu2hNYnQ62jevDmCgoJQv359BAQEYPv27bC1tcWyZcuKPGbKlClISkpSvh494sDbisreuT70DUwQ/XCHym700Q93QE/fCPYufhqMTje93F3GhIiIKjqtGSxtY2MDqVSK2NhYlfLY2Fg4ODiU6Bz6+vpo0KAB7t27V2QdmUwGmUz2RrFS+dA3MEHjthNwev83yEh7BHPrekhJuIbn8RfRLPBzyAzNNB2izspPhvL3LxMAmHNANRFVQFrTImRgYAA/Pz8cOnRIWaZQKHDo0CE0b968ROeQy+W4du0aHB0dyypMKme+TYeiY+9fYGQkwdPHf0JmqEDgez+hvv9ITYdG+G//MnaXEVFFpTUtQgAwceJEDB48GI0aNUKTJk2wYMECpKWlYejQoQCAoKAgVK1aFcHBwQCAmTNnolmzZqhevTqeP3+OOXPmICIiAiNGjNDkbVApq+bTCdV8Omk6DCpC/oKM+YsxApxuT0QVh1YlQn379sXTp08xdepUxMTEoH79+ti3b59yAHVkZCQkkv8auRITEzFy5EjExMTAysoKfn5+OH36NGrVqqWpWyDSWewuI6KKSBBfHGVKBSQnJ8PCwgLDPguDgYxjTohKQ/5ijABbh0h3OP7xJR4dvwWplY2mQ9EJqTk58N+2E0lJSTA3Ny+yntaMESKiyoOzy4iootCqrjEiqlxe7C6r5ZjCrjIiKndsESIijQs9EaGcWcaVqYmoPDERIqIK4eWVqdllRkTlgV1jRFRh5E+1z/fiDDOJBDA11FhoRFRJsUWIiCqs/D3MbkabQa4AUrl9HBGVMrYIUZkRRRH3b+zG9YvrkZYUA2t7L9RvPgKObo1L9TqR947h6tlVSHr2EObWrvBtOhRuNduW6jVIs0JPRACt3FCnaoqmQyGiSoYtQlRmLh5dgIPbxyMzXQHzKi0QHxOOP38fgAe39pfaNW6G/oG/NwxDUuIzmNu0RMrzJOzdOALXz/9eategikPxb6vQiy8iojfBFiEqE6nJMbh0cglcagyGq1feFiiiOBK3LnyF0we+hbtXICQS6RtdIyc7HWcOfg87l7dQ3fdTCIKQ1wp1bT7OHZ6LmvV6wkBmWhq3QxVAfqvQi3wcU5CUzkUZiej1sUWIykRU+CmIohxO1XorywRBCqdqvZCaFIXn8fff+BpxUWHIyUqBk0cvCILw7zUEVK3WBznZaYh5FPrG16CKJfREhMorfx0itgwR0etiIkRlQiLRBwAo5Fkq5fLcvN9YEqn+m19DalD4NeR515CWwjWo4rv170BqrkFERK+DiRCVCdcabSDVM0LEP79CFOUAgNycNETdWwdrOy9YWLu/8TXsnevD2NQej+6sUiZDCnk2Ht1ZBUNjGzi4Nnrja1DFl98ytG5zgnINIiKikuIYISoTMkNztHprOo7++TmSEi7D2MwTKQnXIAgKBPZco+zKehMSiR7adPsO+zZ/hIuH+8HMshZSk/5BbnYSOvb+BdJ/W4xId6zbnID3+1gjNZNrDhFRyTARojLjXb8Xqth749aljUhNioab5wDUbjQQZpZVS+0arjXaoM8He3AjdAOeP3sAJ7euqO03EFa21UvtGqRdbkWboZYjp9kTUckwEaICFIpcRN49hsT4ezCzrAp3rw7Q05MVe8zT6Ou4fGoZ5DmZqFmvJzxrdQEAGBpbIykhEqlJTyAztIDM2Fp5jatnViIq4ixMzOzROGA8TMztAQCRd4/hxqUNkAhS+DYbDkdXv2KvbWblAie3xjA2tYWFtRvMrV1L4SmQtgo9EQGfPtacTUZEJcJEiFSkJj3B7nVD8PzZfejpmyE3JwXGpvboOnAlqth7F3pMyLZxuH9jDyBIIAhSRNw9jHNW7qheuysunVoKiHJIpEZISgjHvRt70Pad73Fi73TkZKdAIjWEQp6J22Hb0LT9Z7h7fReexdyAIOR9a4b/sx/O1Vrh7fdXF3rtpISH2L1uKFKeRyrjNbWoircHroalTbWyekxUweV3keWPF5Jyew4iKgITIVJxaOenyEhPhW/LpTCz9EZ6aiTuXJ6FfZs+Qv8xBwus/XP32p+4f2M3HNx6wM17BCRSQ8Q92of71+bi8qmlMDWvjpoNp8LQ2AkpiTfwz8WvcHjXp5DqGaJ207mwsPFDduZT3A0LxtlDswFRAXefj+Do/i5EUY7H99bh8b31CDu1HPVbjFK5tiiKCNk2Drk5CtRvvQIm5tWRlvwAdy7PwIGtn6D3B7tLZSwSaaf8qfWm9nboEZCL5HTAnC1ERPQSzhojpeTESERHnIWbzwcws8xr/TE2dYVnnfFIeR6J6IhzBY65fGop9GXWqFbnE+jpm0Ii0YOD29swMnGFKMpRvd5nMDKpCkEQYG5dB27eowAoYOvcBZa2jSAIAmRGdqhRfwogKmBk4oqqnn0hkRpAqmcEV68RMDJ1w/WL6wpc+1nsP4iPvg73WmNgYp43JsjEvBo8an2ChLh/8PTJtTJ9XqQd8jZy5YwyIiocEyFSSk+NB5CX/LzIyDRvNd/0tPgCx2RlJsHI1AWCoNpSJPy7jpDRy+cyyzvXy+sIGRjaQiI1hFTfTPU8ggBjM3fkZKUVuHZGWlHxuqp8TgT810KUlM4FGInoP0yESMnKtjqkeoaIjz6uUv4s+hgAwNaxboFjrO28kJJwA9mZz5RloiIXOdnJKseqnEuQIDstTqU8KT4UCnkmMtMeqyyQmJuTgudxF2Bhrbq1AgBUsfeGINFDfEzBeAVBgioOPiW5bdIh6zYn4Fa02asrEpHO4BghUpIZmqNuk8EIO/Mr5LlpsLRphJTnt/DkwSZUq9UVllU8ChzTotPX2LSkM66eHgPn6gOhp2eK6IidyMl6CkGij3tXvkdG2mOYWtREYtxZxETsgp6+EeJjDkMSpo8qjgFIT4nA43trIdUzRm5OEq6dGQcnj94QFTl4fH8jFIos+Hf6usC1jU1t4dOwL26F/orcrOcwr1IPyQnX8CR8M7zq94KpuWN5PDYiItJiTIRIRZN2k6Cnb4Rr51YhOnwb9PSNUMuvH5oFflZofcsqHujSfwUOb5+A+1fnAgCkekZo3GYCatbria3L3sajO6sBiIAghYW1B3p9sBdHdk1E+D8hiHu8H4AAcysXvD1oHSLvHMLZQ3Nx5/IsAICBzBzt350PB5cGhV6/ZeepkBma4/qFdYh6sBH6Bqao12wYGredWAZPh4iIKhtBFEVR00FUZMnJybCwsMCwz8JgINOdJnW5PBsZaQkwNLZ65RpC+ZITHyEnOw1WtjUhkfzX65qaFINncf/AwbkBZEYWyvLc3GwkxN2GqbkjjE1tVM6V8PQOJBL9QluhCo03NwsZ6QkwMraGtITxkm7ya+UGH8cUrjFE5c7xjy/x6PgtSK1sXl2Z3lhqTg78t+1EUlISzM3Ni6zHFiEqlFRqAFNzB7WOMbdyKbTc1MIBphYFz6WnZwA7p4LjjgDA2ramWteW6snYFUYlwgUXiehFHCxNRDonf9A0Z5ARERMhItJJoSciOIOMiJgIERERke5iIkREREQ6i4kQERER6SwmQkRERKSzmAgRERGRztK6RGjx4sVwd3eHoaEhmjZtivPnzxdbf8uWLfD29oahoSHq1q2Lv//+u5wiJSIioopOqxKhTZs2YeLEiZg2bRouXbqEevXqoVOnToiLiyu0/unTp9G/f38MHz4cly9fRo8ePdCjRw9cv369nCMnIiKiikirEqH58+dj5MiRGDp0KGrVqoWlS5fC2NgYK1euLLT+woUL0blzZ3z66afw8fHBrFmz0LBhQ/z888/lHDkRERFVRFqTCGVnZyM0NBSBgYHKMolEgsDAQJw5c6bQY86cOaNSHwA6depUZH0AyMrKQnJyssqLiIiIKietSYTi4+Mhl8thb2+vUm5vb4+YmJhCj4mJiVGrPgAEBwfDwsJC+XJxKXz/LCIiItJ+WpMIlZcpU6YgKSlJ+Xr06JGmQyIiIqIyojW7z9vY2EAqlSI2NlalPDY2Fg4Ohe+S7uDgoFZ9AJDJZJDJZG8eMBEREVV4WtMiZGBgAD8/Pxw6dEhZplAocOjQITRv3rzQY5o3b65SHwBCQkKKrE9ERES6RWtahABg4sSJGDx4MBo1aoQmTZpgwYIFSEtLw9ChQwEAQUFBqFq1KoKDgwEA48aNQ0BAAObNm4euXbti48aNuHjxIpYvX67J2yAiIqIKQqsSob59++Lp06eYOnUqYmJiUL9+fezbt085IDoyMhISyX+NXP7+/tiwYQO++uorfPHFF6hRowZ27tyJOnXqaOoWiIiIqALRqkQIAMaMGYMxY8YU+tnRo0cLlPXu3Ru9e/cu46iIiIhIG2nNGCEiIiKi0sZEiIiIiHQWEyEiIiLSWUyEiIiISGcxESIiIiKdxUSIiIiIdBYTISIiItJZTISIiIhIZzERIiIiIp3FRIiIiIh0FhMhIiIi0llMhIiIiEhnMREiIiIincVEiIiIiHQWEyEiIiLSWUyEiIiISGcxESIiIiKdxUSIiIiIdBYTISIiItJZTISIiIhIZzERIiIiIp3FRIiIiIh0FhMhIiIi0llMhIiIiEhnMREiIiIincVEiIiIiHQWEyEiIiLSWUyEiIiISGcxESIiIiKdxUSIiIiIdBYTISIiItJZWpMIJSQkYODAgTA3N4elpSWGDx+O1NTUYo9p06YNBEFQeX344YflFDERERFVdHqaDqCkBg4ciOjoaISEhCAnJwdDhw7FqFGjsGHDhmKPGzlyJGbOnKl8b2xsXNahEhERkZbQikTo1q1b2LdvHy5cuIBGjRoBABYtWoS33noLc+fOhZOTU5HHGhsbw8HBobxCJSIiIi2iFV1jZ86cgaWlpTIJAoDAwEBIJBKcO3eu2GPXr18PGxsb1KlTB1OmTEF6enqx9bOyspCcnKzyIiIiospJK1qEYmJiYGdnp1Kmp6cHa2trxMTEFHncgAED4ObmBicnJ1y9ehWfffYZbt++je3btxd5THBwMGbMmFFqsRMREVHFpdFE6PPPP8f3339fbJ1bt2699vlHjRql/HfdunXh6OiI9u3b4/79+/D09Cz0mClTpmDixInK98nJyXBxcXntGIiIiKji0mgiNGnSJAwZMqTYOtWqVYODgwPi4uJUynNzc5GQkKDW+J+mTZsCAO7du1dkIiSTySCTyUp8TiIiItJeGk2EbG1tYWtr+8p6zZs3x/PnzxEaGgo/Pz8AwOHDh6FQKJTJTUmEhYUBABwdHV8rXiIiIqpctGKwtI+PDzp37oyRI0fi/PnzOHXqFMaMGYN+/fopZ4xFRUXB29sb58+fBwDcv38fs2bNQmhoKB4+fIg///wTQUFBaN26NXx9fTV5O0RERFRBaEUiBOTN/vL29kb79u3x1ltvoWXLlli+fLny85ycHNy+fVs5K8zAwAAHDx5Ex44d4e3tjUmTJuG9997DX3/9palbICIiogpGK2aNAYC1tXWxiye6u7tDFEXlexcXFxw7dqw8QiMiIiItpTUtQkRERESljYkQERER6SwmQkRERKSzmAgRERGRzmIiRERERDqLiRARERHpLCZCREREpLOYCBEREZHOYiJEREREOouJEBEREeksJkJERESks5gIERERkc5iIkREREQ6i4kQERER6SwmQkRERKSzmAgRERGRzmIiRERERDqLiRARERHpLCZCREREpLOYCBEREZHOYiJEREREOouJEBEREeksJkJERESks5gIERERkc5iIkREREQ6i4kQERER6Sw9TQdQ0YmiCADIzkrVcCREVNoy0pKRlpoC5Gg6EtIFKZnZSM3NhTSH33DlIe3f55z/e7wogviqGjruwYMH8PT01HQYRERE9BoePXoEZ2fnIj9ni9ArWFtbAwAiIyNhYWGh4WjKV3JyMlxcXPDo0SOYm5trOpxyo6v3DfDeee+6de+6et+Abty7KIpISUmBk5NTsfWYCL2CRJI3jMrCwqLSfrO8irm5uU7eu67eN8B7573rFl29b6Dy33tJGjA4WJqIiIh0FhMhIiIi0llMhF5BJpNh2rRpkMlkmg6l3OnqvevqfQO8d967bt27rt43oNv3/jLOGiMiIiKdxRYhIiIi0llMhIiIiEhnMREiIiIincVEiIiIiHQWE6FiLF68GO7u7jA0NETTpk1x/vx5TYdULo4fP45u3brByckJgiBg586dmg6pXAQHB6Nx48YwMzODnZ0devTogdu3b2s6rHKxZMkS+Pr6KhdXa968Ofbu3avpsMrd7NmzIQgCxo8fr+lQytz06dMhCILKy9vbW9NhlZuoqCi8//77qFKlCoyMjFC3bl1cvHhR02GVOXd39wJfd0EQMHr0aE2HpjFMhIqwadMmTJw4EdOmTcOlS5dQr149dOrUCXFxcZoOrcylpaWhXr16WLx4saZDKVfHjh3D6NGjcfbsWYSEhCAnJwcdO3ZEWlqapkMrc87Ozpg9ezZCQ0Nx8eJFtGvXDt27d8eNGzc0HVq5uXDhApYtWwZfX19Nh1JuateujejoaOXr5MmTmg6pXCQmJqJFixbQ19fH3r17cfPmTcybNw9WVlaaDq3MXbhwQeVrHhISAgDo3bu3hiPTIJEK1aRJE3H06NHK93K5XHRychKDg4M1GFX5AyDu2LFD02FoRFxcnAhAPHbsmKZD0QgrKytxxYoVmg6jXKSkpIg1atQQQ0JCxICAAHHcuHGaDqnMTZs2TaxXr56mw9CIzz77TGzZsqWmw6gQxo0bJ3p6eooKhULToWgMW4QKkZ2djdDQUAQGBirLJBIJAgMDcebMGQ1GRuUpKSkJwH8b7+oKuVyOjRs3Ii0tDc2bN9d0OOVi9OjR6Nq1q8r/87rg7t27cHJyQrVq1TBw4EBERkZqOqRy8eeff6JRo0bo3bs37Ozs0KBBA/z666+aDqvcZWdnY926dRg2bBgEQdB0OBrDRKgQ8fHxkMvlsLe3Vym3t7dHTEyMhqKi8qRQKDB+/Hi0aNECderU0XQ45eLatWswNTWFTCbDhx9+iB07dqBWrVqaDqvMbdy4EZcuXUJwcLCmQylXTZs2xerVq7Fv3z4sWbIE4eHhaNWqFVJSUjQdWpl78OABlixZgho1amD//v346KOPMHbsWKxZs0bToZWrnTt34vnz5xgyZIimQ9Eo7j5PVIjRo0fj+vXrOjNmAgC8vLwQFhaGpKQkbN26FYMHD8axY8cqdTL06NEjjBs3DiEhITA0NNR0OOWqS5cuyn/7+vqiadOmcHNzw+bNmzF8+HANRlb2FAoFGjVqhO+++w4A0KBBA1y/fh1Lly7F4MGDNRxd+fntt9/QpUsXODk5aToUjWKLUCFsbGwglUoRGxurUh4bGwsHBwcNRUXlZcyYMdi9ezeOHDkCZ2dnTYdTbgwMDFC9enX4+fkhODgY9erVw8KFCzUdVpkKDQ1FXFwcGjZsCD09Pejp6eHYsWP46aefoKenB7lcrukQy42lpSVq1qyJe/fuaTqUMufo6Fggwffx8dGZrkEAiIiIwMGDBzFixAhNh6JxTIQKYWBgAD8/Pxw6dEhZplAocOjQIZ0ZM6GLRFHEmDFjsGPHDhw+fBgeHh6aDkmjFAoFsrKyNB1GmWrfvj2uXbuGsLAw5atRo0YYOHAgwsLCIJVKNR1iuUlNTcX9+/fh6Oio6VDKXIsWLQosjXHnzh24ublpKKLyt2rVKtjZ2aFr166aDkXj2DVWhIkTJ2Lw4MFo1KgRmjRpggULFiAtLQ1Dhw7VdGhlLjU1VeWvwvDwcISFhcHa2hqurq4ajKxsjR49Ghs2bMCuXbtgZmamHA9mYWEBIyMjDUdXtqZMmYIuXbrA1dUVKSkp2LBhA44ePYr9+/drOrQyZWZmVmAMmImJCapUqVLpx4ZNnjwZ3bp1g5ubG548eYJp06ZBKpWif//+mg6tzE2YMAH+/v747rvv0KdPH5w/fx7Lly/H8uXLNR1auVAoFFi1ahUGDx4MPT2mAZw+X4xFixaJrq6uooGBgdikSRPx7Nmzmg6pXBw5ckQEUOA1ePBgTYdWpgq7ZwDiqlWrNB1amRs2bJjo5uYmGhgYiLa2tmL79u3FAwcOaDosjdCV6fN9+/YVHR0dRQMDA7Fq1api3759xXv37mk6rHLz119/iXXq1BFlMpno7e0tLl++XNMhlZv9+/eLAMTbt29rOpQKQRBFUdRMCkZERESkWRwjRERERDqLiRARERHpLCZCREREpLOYCBEREZHOYiJEREREOouJEBEREeksJkJERESks5gIEVGFcvToUQiCgOfPnxdZRxAE7Ny5s9xiKs706dNRv3791zp20KBByo0/y0q/fv0wb968Mr0GkTZjIkREZWL16tWwtLTUdBilqjQTsCtXruDvv//G2LFjS+V8Rfnqq6/w7bffIikpqUyvQ6StmAgREWnAokWL0Lt3b5iampbpderUqQNPT0+sW7euTK9DpK2YCBFRAW3atMGYMWMwZswYWFhYwMbGBl9//TVe3JEnKysLkydPRtWqVWFiYoKmTZvi6NGjAPK6t4YOHYqkpCQIggBBEDB9+nQAwNq1a9GoUSOYmZnBwcEBAwYMQFxc3BvF++jRI/Tp0weWlpawtrZG9+7d8fDhQ+XnQ4YMQY8ePTB37lw4OjqiSpUqGD16NHJycpR1oqOj0bVrVxgZGcHDwwMbNmyAu7s7FixYAABwd3cHALz77rsQBEH5Pt/atWvh7u4OCwsL9OvXDykpKUXGK5fLsXXrVnTr1k2lPCsrC5999hlcXFwgk8lQvXp1/PbbbwD+6zLcv38/GjRoACMjI7Rr1w5xcXHYu3cvfHx8YG5ujgEDBiA9PV3lvN26dcPGjRvVfKpEuoGJEBEVas2aNdDT08P58+excOFCzJ8/HytWrFB+PmbMGJw5cwYbN27E1atX0bt3b3Tu3Bl3796Fv78/FixYAHNzc0RHRyM6OhqTJ08GAOTk5GDWrFm4cuUKdu7ciYcPH2LIkCGvHWdOTg46deoEMzMznDhxAqdOnYKpqSk6d+6M7OxsZb0jR47g/v37OHLkCNasWYPVq1dj9erVys+DgoLw5MkTHD16FNu2bcPy5ctVErQLFy4AAFatWoXo6GjlewC4f/8+du7cid27d2P37t04duwYZs+eXWTMV69eRVJSEho1aqRSHhQUhD/++AM//fQTbt26hWXLlhVoMZo+fTp+/vlnnD59WpkALliwABs2bMCePXtw4MABLFq0SOWYJk2a4Pz588jKyir5gyXSFRre9JWIKqCAgADRx8dHVCgUyrLPPvtM9PHxEUVRFCMiIkSpVCpGRUWpHNe+fXtxypQpoiiK4qpVq0QLC4tXXuvChQsiADElJUUURVE8cuSICEBMTEws8hgA4o4dO0RRFMW1a9eKXl5eKrFmZWWJRkZG4v79+0VRFMXBgweLbm5uYm5urrJO7969xb59+4qiKIq3bt0SAYgXLlxQfn737l0RgPjjjz8Wet1806ZNE42NjcXk5GRl2aeffio2bdq0yPh37NghSqVSlZhv374tAhBDQkIKPSb/uRw8eFBZFhwcLAIQ79+/ryz74IMPxE6dOqkce+XKFRGA+PDhwyJjItJVbBEiokI1a9YMgiAo3zdv3hx3796FXC7HtWvXIJfLUbNmTZiamipfx44dw/3794s9b2hoKLp16wZXV1eYmZkhICAAABAZGflacV65cgX37t2DmZmZMg5ra2tkZmaqxFK7dm1IpVLle0dHR2WLz+3bt6Gnp4eGDRsqP69evTqsrKxKFIO7uzvMzMwKPXdhMjIyIJPJVJ5vWFgYpFKp8nkUxdfXV/lve3t7GBsbo1q1aiplL1/byMgIAAp0mRERoKfpAIhI+6SmpkIqlSI0NFQluQBQ7ODftLQ0dOrUCZ06dcL69etha2uLyMhIdOrUSaUbS91Y/Pz8sH79+gKf2draKv+tr6+v8pkgCFAoFK91zZepe24bGxukp6cjOzsbBgYGAP5LVtS5liAIJbp2QkICANXnQUR5mAgRUaHOnTun8v7s2bOoUaMGpFIpGjRoALlcjri4OLRq1arQ4w0MDCCXy1XK/vnnHzx79gyzZ8+Gi4sLAODixYtvFGfDhg2xadMm2NnZwdzc/LXO4eXlhdzcXFy+fBl+fn4AgHv37iExMVGlnr6+foF7eh356w7dvHlT+e+6detCoVDg2LFjCAwMfONrvOj69etwdnaGjY1NqZ6XqDJg1xgRFSoyMhITJ07E7du38ccff2DRokUYN24cAKBmzZoYOHAggoKCsH37doSHh+P8+fMIDg7Gnj17AOR1F6WmpuLQoUOIj49Heno6XF1dYWBggEWLFuHBgwf4888/MWvWrDeKc+DAgbCxsUH37t1x4sQJhIeH4+jRoxg7diweP35conN4e3sjMDAQo0aNwvnz53H58mWMGjUKRkZGKt1X7u7uOHToEGJiYgokSeqwtbVFw4YNcfLkSZVzDx48GMOGDcPOnTuV97F58+bXvk6+EydOoGPHjm98HqLKiIkQERUqKCgIGRkZaNKkCUaPHo1x48Zh1KhRys9XrVqFoKAgTJo0CV5eXujRowcuXLgAV1dXAIC/vz8+/PBD9O3bF7a2tvjhhx9ga2uL1atXY8uWLahVqxZmz56NuXPnvlGcxsbGOH78OFxdXdGzZ0/4+Phg+PDhyMzMVKuF6Pfff4e9vT1at26Nd999FyNHjoSZmRkMDQ2VdebNm4eQkBC4uLigQYMGbxT3iBEjCnTnLVmyBL169cLHH38Mb29vjBw5EmlpaW90nczMTOzcuRMjR458o/MQVVaCKL6wMAgREfLWEapfv75yDR1d9PjxY7i4uODgwYNo3759qZ8/IyMDXl5e2LRpE5o3b17q58+3ZMkS7NixAwcOHCizaxBpM44RIiICcPjwYaSmpqJu3bqIjo7G//73P7i7u6N169Zlcj0jIyP8/vvviI+PL5Pz59PX1y+wrhAR/YeJEBER8hZm/OKLL/DgwQOYmZnB398f69evLzArqzS1adOmzM6db8SIEWV+DSJtxq4xIiIi0lkcLE1EREQ6i4kQERER6SwmQkRERKSzmAgRERGRzmIiRERERDqLiRARERHpLCZCREREpLOYCBEREZHOYiJEREREOuv/B0TDRbmWKTIAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "knn_pipeline.set_params(knn__metric='manhattan')\n",
-    "knn_pipeline.fit(X_train, y_train)\n",
-    "y_pred = knn_pipeline.predict(X_test)\n",
-    "print(\"k-NN (Manhattan) accuracy:\", accuracy_score(y_test, y_pred))\n",
-    "display = DecisionBoundaryDisplay.from_estimator(\n",
-    "    knn_pipeline, X, response_method='predict',\n",
-    "    cmap=plt.cm.coolwarm, alpha=0.85,\n",
-    "    xlabel=feature_names[0], ylabel=feature_names[1],\n",
-    "    grid_resolution=500)\n",
-    "plt.title(\"k-NN Decision Boundary (Manhattan Distance)\")\n",
-    "plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, edgecolor='k', s=20, cmap=plt.cm.coolwarm)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f36413eb",
-   "metadata": {},
-   "source": [
-    "-   **Decision Tree pruning**\n",
-    "    -   Grow an unpruned tree (`max_depth=None`) and visualise it with `plot_tree`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "d641c8aa",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dt_pipeline.set_params(dt__max_depth=None)\n",
-    "dt_pipeline.fit(X_train, y_train)\n",
-    "plt.figure(figsize=(12, 8))\n",
-    "plot_tree(dt_pipeline.named_steps['dt'], filled=True, feature_names=feature_names, class_names=class_names)\n",
-    "plt.title(\"Unpruned Decision Tree Structure\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77c86b35",
-   "metadata": {},
-   "source": [
-    "-   What, in your opinion, is the disadvantage of an unpruned tree?\n",
-    "\n",
-    "-   **Multiclass confusion matrix**\n",
-    "    -   Implement and plot confusion matrices for both classifiers on the full four-feature Iris data set.\n",
-    "        -   Which mis-classifications are most common and why?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "8e92dd06",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import ConfusionMatrixDisplay\n",
-    "\n",
-    "# Confusion matrix for k-NN\n",
-    "disp_knn = ConfusionMatrixDisplay.from_estimator(\n",
-    "    knn_pipeline, X_test, y_test, display_labels=class_names, cmap='Blues')\n",
-    "plt.title(\"k-NN Confusion Matrix\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "4b26faa0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Confusion matrix for Decision Tree\n",
-    "disp_dt = ConfusionMatrixDisplay.from_estimator(\n",
-    "    dt_pipeline, X_test, y_test, display_labels=class_names, cmap=plt.cm.Blues)\n",
-    "plt.title(\"Decision Tree Confusion Matrix\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2a71f6fb",
-   "metadata": {},
-   "source": [
-    "-   **Real-world data**\n",
-    "    -   Load the **Wine** data set (`sklearn.datasets.load_wine`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "954effe7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.datasets import load_wine\n",
-    "wine = datasets.load_wine()\n",
-    "X_wine = wine.data\n",
-    "y_wine = wine.target\n",
-    "\n",
-    "# Train-test split\n",
-    "X_train_wine, X_test_wine, y_train_wine, y_test_wine = train_test_split(\n",
-    "    X_wine, y_wine, test_size=0.3, random_state=42, stratify=y_wine)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f12f3e34",
-   "metadata": {},
-   "source": [
-    "-   Explore the data set and visualise the features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "3099f293",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "          alcohol  malic_acid         ash  alcalinity_of_ash   magnesium  \\\n",
-      "count  178.000000  178.000000  178.000000         178.000000  178.000000   \n",
-      "mean    13.000618    2.336348    2.366517          19.494944   99.741573   \n",
-      "std      0.811827    1.117146    0.274344           3.339564   14.282484   \n",
-      "min     11.030000    0.740000    1.360000          10.600000   70.000000   \n",
-      "25%     12.362500    1.602500    2.210000          17.200000   88.000000   \n",
-      "50%     13.050000    1.865000    2.360000          19.500000   98.000000   \n",
-      "75%     13.677500    3.082500    2.557500          21.500000  107.000000   \n",
-      "max     14.830000    5.800000    3.230000          30.000000  162.000000   \n",
-      "\n",
-      "       total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
-      "count     178.000000  178.000000            178.000000       178.000000   \n",
-      "mean        2.295112    2.029270              0.361854         1.590899   \n",
-      "std         0.625851    0.998859              0.124453         0.572359   \n",
-      "min         0.980000    0.340000              0.130000         0.410000   \n",
-      "25%         1.742500    1.205000              0.270000         1.250000   \n",
-      "50%         2.355000    2.135000              0.340000         1.555000   \n",
-      "75%         2.800000    2.875000              0.437500         1.950000   \n",
-      "max         3.880000    5.080000              0.660000         3.580000   \n",
-      "\n",
-      "       color_intensity         hue  od280/od315_of_diluted_wines      proline  \\\n",
-      "count       178.000000  178.000000                    178.000000   178.000000   \n",
-      "mean          5.058090    0.957449                      2.611685   746.893258   \n",
-      "std           2.318286    0.228572                      0.709990   314.907474   \n",
-      "min           1.280000    0.480000                      1.270000   278.000000   \n",
-      "25%           3.220000    0.782500                      1.937500   500.500000   \n",
-      "50%           4.690000    0.965000                      2.780000   673.500000   \n",
-      "75%           6.200000    1.120000                      3.170000   985.000000   \n",
-      "max          13.000000    1.710000                      4.000000  1680.000000   \n",
-      "\n",
-      "           target  \n",
-      "count  178.000000  \n",
-      "mean     0.938202  \n",
-      "std      0.775035  \n",
-      "min      0.000000  \n",
-      "25%      0.000000  \n",
-      "50%      1.000000  \n",
-      "75%      2.000000  \n",
-      "max      2.000000  \n",
-      "target\n",
-      "1    71\n",
-      "0    59\n",
-      "2    48\n",
-      "Name: count, dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "wine_df = pd.DataFrame(X_wine, columns=wine.feature_names)\n",
-    "wine_df['target'] = y_wine\n",
-    "print(wine_df.describe())\n",
-    "print(wine_df['target'].value_counts())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "364a0c26",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 3308.87x3250 with 182 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualise pairwise relationships in the Wine dataset\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "sns.pairplot(wine_df, hue='target', palette='coolwarm', markers=[\"o\", \"s\", \"D\"])\n",
-    "plt.suptitle(\"Wine Dataset Feature Scatter Matrix\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5e1f1631",
-   "metadata": {},
-   "source": [
-    "-   Choose two features for training and visualisation (e.g., alcohol and malic acid)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "43ac0cfb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Select two features for visualisation\n",
-    "X_wine_2d = X_wine[:, [5, 6]]  # total_phenols and flavanoids\n",
-    "X_train_wine_2d, X_test_wine_2d, y_train_wine, y_test_wine = \\\n",
-    "    train_test_split(X_wine_2d, y_wine, test_size=0.3, random_state=42, stratify=y_wine)\n",
-    "# Visualise the selected features\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.scatter(X_train_wine_2d[:, 0], X_train_wine_2d[:, 1], c=y_train_wine, edgecolor='k', s=50, cmap=plt.cm.coolwarm)\n",
-    "plt.title(\"Wine Dataset - Total Phenols vs Flavanoids\")\n",
-    "plt.xlabel(wine.feature_names[5])\n",
-    "plt.ylabel(wine.feature_names[6])\n",
-    "plt.colorbar(ticks=[0, 1, 2], label='Wine Class')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b6aad5c6",
-   "metadata": {},
-   "source": [
-    "-   Build a pipeline with standardisation + k-NN vs. a depth-limited decision tree for the chosen features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "fdf918bf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "k-NN (Wine) accuracy: 0.7777777777777778\n",
-      "Decision Tree (Wine) accuracy: 0.8333333333333334\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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XUqnErFmzMGHCBNjb26Nu3bpo3749+vXrBwcHh3eu9+DBA5QtW1brDwgAVKxYUfP6m1xdXbWeZ4YWXcLRu8jlcjg7O2u13b17F3FxcZqf49uio6M1ywF451U5FhYWH11fYmIizM3NAQAxMTF4+fIlVqxYgRUrVry3ttDQUJQvX17nqyR++eUX9O/fHy4uLqhRowbatm2Lfv36oXTp0u9c58GDB3ByctLUmSmnnyfw+jPN6edZtmxZzekDAOjSpQsEQcD8+fMxaNAgVKlSRbPP8uXLa62rUChQunTpLDXp4kO/j5nbLlu2rNZyhoaG2f4c165dizlz5iA4OFjrj42Hh0eWZbNrk8lk6NOnD5YuXYrk5GSYmJhg3bp1MDIyQvfu3d/7Xgrys2vQoAECAgIQFBQEQ0NDxMXFwdfXF8Drq++ePHmC8PBwhIWFISMjI8dB5X2fh4WFBe7evQtRFLN8Hpk+9vQxoH3cBV5/SZwxYwb++ecfzf/JTJkh40MCAwMxbdo0nDlzJsuYm7i4OFhaWqJx48bo2rUrZsyYgXnz5qFJkybo1KkTPv30UyiVSgA5P54VdQwqEhEREYG4uDiUKVMGADSXkH755Zfw8/PLdp3MZXNr3Lhx6NChA7Zv3479+/djypQpmDlzJo4cOYJq1ap91LYzvXne/U3iW4P9ckOpVGYJTGq1GnZ2dlrfUN+U+Y028+f7119/ZRvMPvZSyvT0dNy5cweVK1fW2l/fvn3Rv3//bNf52EvTe/TogYYNGyIgIAAHDhzA7NmzMWvWLGzbtg1t2rT5qG1nyo/Ps3nz5li0aBFOnDiBKlWq6LTuu3qYVCpVtrXmZf1///03BgwYgE6dOmHixImws7PTDIB9c9Bzpje/Zb+pX79+mD17NrZv347evXtj/fr1aN++fZYenI/1Me/9zXEqCoUCJUqUQIUKFQAAPj4+MDExwalTpxAWFqa1/MfWpFarIQgC9u7dm+2yZmZmOdrPu7x93AVe/z86ffo0Jk6cCB8fH5iZmUGtVqN169Y5urQ/NDQUzZs3R4UKFTB37ly4uLhAoVBgz549mDdvnmYbgiBgy5YtOHv2LHbt2oX9+/dj0KBBmDNnDs6ePavZb06OZ0Udg4pE/PXXXwCgCSWZ394MDQ21voFmx9PTEzdu3MjVfj09PTFhwgRMmDABd+/ehY+PD+bMmZPl6qNMbm5uuHbtGtRqtVZICA4O1ryuT56enjh06BB8fX3f+Ychczng9TwRH/r55saWLVvw6tUrzedpa2sLc3NzqFSqHH2e586dQ3p6us7fGB0dHTFy5EiMHDkS0dHRqF69On788cd3BhU3NzccOnQICQkJWt/MC/LzzMjIAADN3DmZ+wwJCdHqxUhLS0NYWJjWz8/a2jrbCbcePHjw3p6kd8nc9927d7V629LT0xEWFgZvb29N25YtW1C6dGls27ZNKzBlNwHa+1SuXBnVqlXDunXr4OzsjIcPH+K3337LUa0F9dlVr15dE0aUSiXq1aunec9yuRy1atVCYGAgwsLCYGdnh3LlyuXJfj09PSGKIjw8PPJsm296+7j74sULHD58GDNmzMDUqVM1y2X2wL7pXSF5165dSE1Nxc6dO7V6jN51mqZu3bqoW7cufvzxR6xfvx59+vTBP//8gyFDhuT4ePa+eooCjlGRgCNHjuD777+Hh4eHZpS/nZ0dmjRpguXLl+Pp06dZ1nlz5tOuXbvi6tWrCAgIyLLcu74tJScnIyUlRavN09MT5ubmSE1NfWetbdu2RWRkJDZu3Khpy8jIwG+//QYzMzM0btz4/W82n/Xo0QMqlQrff/99ltcyMjI0f9T8/PxgYWGBn376Kdvzwx8zs+zVq1cxbtw4WFtba0b4GxgYoGvXrti6dWu2ofLtzzM2NjbLpc3Auz9PlUqVpVvazs4OTk5OH/w8VSpVln3NmzcPgiDkWU/M++zatQsANCGgRYsWUCgUWLhwodb7/eOPPxAXF4d27dpp2jw9PXH27FmkpaVp2nbv3p3rS1dr1qwJW1tbLFu2TGuba9asyRKIMr/hv1njuXPncObMGZ33+9lnn+HAgQOYP38+SpYsmaOfe0F+dnK5HHXq1EFgYCACAwOzTLZYv359nDhxAmfPntWcEsoLXbp0gYGBAWbMmJHld18URa1LhnWV3XE3u88UeD0J5dtMTU0BIEe/F3FxcVi9erXWci9evMiyn8wrxTL/z+b0eJZZj1Rmyc1r7FEpYHv37kVwcDAyMjIQFRWFI0eO4ODBg3Bzc8POnTthZGSkWXbx4sVo0KABqlSpgqFDh6J06dKIiorCmTNnEBERgatXrwIAJk6ciC1btqB79+4YNGgQatSogefPn2Pnzp1YtmyZ1rfATHfu3EHz5s3Ro0cPeHl5QS6XIyAgAFFRUejVq9c76//888+xfPlyDBgwAJcuXYK7uzu2bNmCwMBAzJ8/P8v58oLWuHFjDBs2DDNnzkRQUBBatWoFQ0ND3L17F5s3b8aCBQvQrVs3WFhYYOnSpfjss89QvXp19OrVC7a2tnj48CH+/fdf+Pr6ZhsU3nby5EmkpKRApVLh2bNnCAwMxM6dO2FpaYmAgACt00o///wzjh49ijp16mDo0KHw8vLC8+fPcfnyZRw6dAjPnz8H8PpUwJ9//onx48fj/PnzaNiwIZKSknDo0CGMHDkSHTt2zFJHQkICnJ2d0a1bN3h7e8PMzAyHDh3ChQsXMGfOnHfW36FDBzRt2hTffvstwsPD4e3tjQMHDmDHjh0YN27cOy+Fzq3Lly9reusSEhJw+PBhbN26FfXr10erVq0AvO59mjx5MmbMmIHWrVvjk08+QUhICJYsWYJatWppDTIdMmQItmzZgtatW6NHjx4IDQ3F33//neu6DQ0N8cMPP2DYsGFo1qwZevbsibCwMKxevTpLD0379u2xbds2dO7cGe3atUNYWBiWLVsGLy8vrZmVc+LTTz/FV199hYCAAIwYMSJHPWkF/dk1aNBA0yvwdhipX7++Zs6XnJ72yQlPT0/88MMPmDx5MsLDw9GpUyeYm5sjLCwMAQEB+Pzzz/Hll19+cDs5Pe5aWFigUaNG+OWXX5Ceno5SpUrhwIEDmlNab8q8HPvbb79Fr169YGhoiA4dOqBVq1ZQKBTo0KEDhg0bhsTERKxcuRJ2dnZaXzrXrl2LJUuWoHPnzvD09ERCQgJWrlwJCwsLzezNOT2eZdazdOlS/PDDDyhTpgzs7OzydWbsAlWg1xgVY5mXyWU+FAqF6ODgILZs2VJcsGCB5rLet4WGhor9+vUTHRwcRENDQ7FUqVJi+/btxS1btmgt9+zZM3HUqFFiqVKlRIVCITo7O4v9+/fXXAr79uXJsbGxor+/v1ihQgXR1NRUtLS0FOvUqSNu2rRJa7vZzcYZFRUlDhw4ULSxsREVCoVYpUoVzXYzvWsmUlHMeqnih7zr8mRTU9N3rrNixQqxRo0aorGxsWhubi5WqVJF/Oqrr7LMFHv06FHRz89PtLS0FI2MjERPT09xwIAB4sWLF99bU+blrpkPQ0ND0dbWVmzUqJH4448/ZrkMNVNUVJTo7+8vuri4iIaGhqKDg4PYvHlzccWKFVrLJScni99++63o4eGhWa5bt25iaGioZpk3f46pqanixIkTRW9vb9Hc3Fw0NTUVvb29xSVLlmht9+3Lk0Xx9aXaX3zxhejk5CQaGhqKZcuWFWfPnq11yXDm/rK7/Pldlwi/KbvLk+VyuVi6dGlx4sSJWrOeZlq0aJFYoUIF0dDQULS3txdHjBghvnjxIstyc+bMEUuVKiUqlUrR19dXvHjx4jsvT377cvK3/19kWrJkiejh4SEqlUqxZs2a4okTJ7JsU61Wiz/99JPo5uYmKpVKsVq1auLu3buz/Izf93/hTW3bthUBiKdPn37vcm8qiM8uU+al1HK5XExKStJ67dmzZ6IgCCIA8dy5c1nWffv/fOblyTExMVrLZR4n377kd+vWrWKDBg1EU1NT0dTUVKxQoYLo7+8vhoSEvLfm3Bx3IyIixM6dO4tWVlaipaWl2L17d81su28ft77//nuxVKlSokwm06p7586dYtWqVUUjIyPR3d1dnDVrlmbagcxlLl++LPbu3Vt0dXUVlUqlaGdnJ7Zv3z7bY09OjmeRkZFiu3btRHNz8yyX0hd2gijmwahGIiL6KJ07d8b169eznSmXqDjjGBUiIj17+vQp/v33X3z22Wf6LoVIcjhGhYhIT8LCwhAYGIjff/8dhoaGGDZsmL5LIpIc9qgQEenJ8ePH8dlnnyEsLAxr165972SLRMUVx6gQERGRZLFHhYiIiCSLQYWIiIgkq1APplWr1Xjy5AnMzc2L9PTBRERERYkoikhISICTk1OWe7a9Te9B5fHjx/j666+xd+9eJCcno0yZMli9ejVq1qz5wXWfPHkCFxeXAqiSiIiI8tqjR4/g7Oz83mX0GlRevHgBX19fNG3aFHv37oWtrS3u3r2rudX3h2RO1z5q6lkojT7uLppERERUMFJTErHou7o5uu2KXoPKrFmz4OLionWzJg8Pjxyvn3m6R2lkBqWRfu8xQ0RERLrJybANvQ6m3blzJ2rWrInu3bvDzs4O1apVw8qVK9+5fGpqKuLj47UeREREVHTpNajcv38fS5cuRdmyZbF//36MGDECY8aMwdq1a7NdfubMmbC0tNQ8OD6FiIioaNPrhG8KhQI1a9bE6dOnNW1jxozBhQsXcObMmSzLp6amIjU1VfM8Pj4eLi4umPDTDZ76ISIiKiRSUxIw55vKiIuLg4WFxXuX1WuPiqOjI7y8vLTaKlasiIcPH2a7vFKphIWFhdaDiIiIii69BhVfX1+EhIRotd25cwdubm56qoiIiIikRK9B5YsvvsDZs2fx008/4d69e1i/fj1WrFgBf39/fZZFREREEqHXoFKrVi0EBARgw4YNqFy5Mr7//nvMnz8fffr00WdZREREJBF6n5m2ffv2aN++vb7LICIiIgniTQmJiIhIshhUiIiISLIYVIiIiEiyGFSIiIhIshhUiIiISLIYVIiIiEiyGFSIiIhIshhUiIiISLIYVIiIiEiyGFSIiIhIshhUiIiISLIYVIiIiEiyGFSIiIhIshhUiIiISLIYVIiIiEiyGFSIiIhIshhUiIiISLIYVIiIiEiyGFSIiIhIshhUiIiISLIYVIiIiEiyGFSIiIhIshhUiIiISLIYVIiIiEiyGFSIiIhIshhUiIiISLIYVIiIiEiyGFSIiIhIshhUiIiISLIYVIiIiEiyGFSIiIhIshhUiIiISLIYVIiIiEiyGFSIiIhIshhUiIiISLIYVIiIiEiyGFSIiIhIshhUiIiISLIYVIiIiEiyGFSIiIhIsuT6LoCIiHIvMT4KJ/bORciNvVCrMlC6fBM0bD0eNvZl9F0aUZ5gjwoRUSGV8ioOfy/ujgd3DmBg/z7wHzkMCc+u4e9FXfEi9oG+yyPKE+xRISIqpILObEDCy6c4sH8fXF1dAQCf9e2Llq1a48yRZWjbY6aeKyT6eOxRISIqpB6EnkX9+vU0IQUALCws0LZta0SEndVjZUR5R69BZfr06RAEQetRoUIFfZZERFRoKJRmiImJzdIeGxMLhcJUDxUR5T2996hUqlQJT58+1TxOnTql75KIiAqFyjU64datm1i/fj3UajUA4OjRo9h/4AC8qnfSb3FEeUTvY1TkcjkcHBz0XQYRUaFTxqs5qtXvgylTp2LJ0uVQKpUID7+PMl5NUcO3n77LI8oTeg8qd+/ehZOTE4yMjFCvXj3MnDlT63zrm1JTU5Gamqp5Hh8fX1BlEhFJjiAIaN31R1Sq3gnBV/dArUpHreb/gWfFJpDJDPRdHlGe0GtQqVOnDtasWYPy5cvj6dOnmDFjBho2bIgbN27A3Nw8y/IzZ87EjBkz9FApEZE0CYIA19K14Vq6tr5LIcoXgiiKor6LyPTy5Uu4ublh7ty5GDx4cJbXs+tRcXFxwYSfbkBplDXYEBERkfSkpiRgzjeVERcXBwsLi/cuq/dTP2+ysrJCuXLlcO/evWxfVyqVUCqVBVwVERER6Yver/p5U2JiIkJDQ+Ho6KjvUoiIiEgC9BpUvvzySxw/fhzh4eE4ffo0OnfuDAMDA/Tu3VufZREREZFE6PXUT0REBHr37o1nz57B1tYWDRo0wNmzZ2Fra6vPsoiIiEgi9BpU/vnnH33unoiIiCROUmNUiIiIiN7EoEJERESSxaBCREREksWgQkRERJLFoEJERESSxaBCREREksWgQkRERJLFoEJERESSxaBCREREksWgQkRERJLFoEJERESSxaBCREREksWgQkRERJLFoEJERESSxaBCREREksWgQkRERJLFoEJERESSxaBCREREksWgQkRERJLFoEJERESSxaBCREREksWgQkRERJLFoEJERESSxaBCREREksWgQkRERJLFoEJERESSxaBCREREksWgQkRERJLFoEJERESSxaBCREREksWgQkRERJLFoEJERESSxaBCREREksWgQkRERJLFoEJERESSxaBCREREksWgQkRERJLFoEJERESSxaBCREREksWgQkRERJLFoEJERESSxaBCREREksWgQkRERJLFoEJERESSJZmg8vPPP0MQBIwbN07fpRAREZFESCKoXLhwAcuXL0fVqlX1XQoRERFJiN6DSmJiIvr06YOVK1fC2tpa3+UQERGRhOg9qPj7+6Ndu3Zo0aLFB5dNTU1FfHy81oOIiIiKLrk+d/7PP//g8uXLuHDhQo6WnzlzJmbMmJHPVRERFV0Z6Sk4d/x3XL8QgNRX8XD1rIX6LUbBvpSXvksjypbeelQePXqEsWPHYt26dTAyMsrROpMnT0ZcXJzm8ejRo3yukoio6BDVamxe9TlO7psPudIT1g6tEH7vKtYu7IKnj67puzyibOmtR+XSpUuIjo5G9erVNW0qlQonTpzAokWLkJqaCgMDA611lEollEplQZdKRFQk3A85gbCQ46hYayZK2NcDADiX/QzXA0fg2L+z0Xv4X3qukCgrvQWV5s2b4/r161ptAwcORIUKFfD1119nCSlERPRxwu6cgLGpE6zt6mraDAyUsHNui/DbSyCq1RBkeh+6SKRFb0HF3NwclStX1mozNTVFyZIls7QTEdHHk8uNoMpIhiiqIAj/P/xnZCTCQK4EBEGP1RFlj9GZiKiYqOjTHmmpL/H43nqIoggAeJX4CFEPdsDLpz0EBhWSIL1e9fO2Y8eO6bsEIqIiy76UF3xbjkHgwYWIebwfhkY2SHh+A1YlXNCk3Vf6Lo8oW5IKKkRElL8at5kAj/INceNiAFJT4uHSqAuq1OoGpZGZvksjyhaDChFRMeNaujZcS9fWdxlEOcIxKkRElGdEUUR6WopmDAzRx2KPChERfTRRrcbZYytw6dQqxL+MgqW1I2o2HITajYbwkmf6KAwqRET00Q7v+gEXT65G9+7dUatmTZw9dw5bt/6E5MQXaNr+a32XR4UYgwoREX2UxIQYXDr1J74YNw4jR44EAHTu3BmODg5YunwF6jT9HCam1nqukgor9scREdFHefrwGlSqdHTq1EmrvWOnTshIT0NUxA39FEZFAntUiIgoW9FPQ3Dt/CYkxkfD0aUKqtbqAWNTqyzLGZtYAgAeP34MJycnTfvjiAgAgJFJ1nWIcoo9KkRExZAoihDV6ne+fvX8Jvzxa2sEnduGiIfhOLr7F6z4pSWeRd/Psmwpt+qwsSuN73/4EU+ePAEARERE4KeZs2DnVB4OzrwtCuUee1SIiIqRlFfxOL731/9N+JYAZ4+aaNR6PNzL1tcsk5QQi72bv4Gtc2t4VhkPmUyO1FcxuHnuC+zb+h/0GbFea5uCTIZP+v6GjSv7oXHTpnB0dMLTJ49halYSPT//m1Pz00dhUCEiKibUqgxsWNYXMZH3YO/aEUpjW8Q8PogNy/qi9/C/NWHlzo0DENVquFccBpns9Z8JpbEtSnn2wb2rs5CU+AymZiW1tu3gXBnDJ5/ArSs78SI2HN71S8OrWgcolKYF/j6paGFQISIqJu7cPIinj66iSv1FsCjx+nSMg2sHXD8zBif2zdUElfT0FAiCAQzkJlrryw3NAQCq9NRst680MkO1ep/m4zug4ohjVIiIiolHoedhbOasCSkAIMjksC3VEhFhFzVjVtzL+kKtTkP0o72a5URRhagHO2FtWxrmVo4FXjsVX+xRISIqwh4/uIJbl3ciPS0Zr5JfIj31JdSqVMgMlJplUl/FQKE0A/43lsTOsTyq1uqOaxfnI+7ZVZiYu+FF1Gkkxt1B14HLOeaEChSDChFREXVsz2ycPrQISmM7yBXmSIoLBSDD/ZuLULrSKMgMlIh/fgNRD3fAu043rQDStucs2JXywpUz6xH98BIcXKqgQ+9pWoNuiQoCgwoRUREUEX4Jpw8tgmv5IXAu8ykEQYb4Fzdx8+wERD3chWdPj8FQaYlXiRFwdPFG4zZfaq0vkxmgdqNBqN1okJ7eAdFrDCpEREXQzcs7YGRirwkpAGBhXQn2Lm0QF3sK1et/itRXCXApXQtlK7eEgYGhnismyh6DChFREZSemgy5wlITUjIZKqyhykhFo9bj9VQZkW541Q8RURHkVqYeEl/eQcLLYE2bSpWCmCcH4cZxJlSIsEeFiKgIqlitPc6fWIVb5ybAzrkt5ApLxD4+gPTUZ2jYaqy+yyPKMQYVIqIiSC5Xos/I9Th1YAFuXHp9ebJ7OV808hsH+1Je+i6PKMcYVIiIiigjY0u06DgVLTpO1XcpRLnGMSpEREQkWQwqREREJFkMKkRERCRZDCpEREQkWRxMS0RUAJ48vIpH989BaWSO8lXawNjUSt8lERUKDCpERPkoIyMV2/8cjTs39sPAwAgqdRoObJuODn3moqJ3O32XRyR5DCpERPko8OBvuHfrKMpVmwIbpyZIT4vH/ZsLseOvsSjlWg0W1k76LpFI0jhGhYgoH1058w/sXdvBtlRzCIIBFEprlK06EYLMENcvbtV3eUSSx6BCRJRDolqNV0kvoVKl52x5UURyYiyMzdy12g3kJjAysUNiQkw+VElUtOgcVPbt24dTp05pni9evBg+Pj749NNP8eLFizwtjohICkRRxMWTa7Do+/qYN8Ub8/7jg0M7vkdGesp71xMEAfalKuF55AmIoqhpT04IR1J8OBydq+R36USFns5BZeLEiYiPjwcAXL9+HRMmTEDbtm0RFhaG8eN523AiKnrOHl2OAwHTYGxeFeWrT4Ot8ye4eOov7Fj3xQfXbdByNF7GXkLwpal4FhmIp+E7cOv8V7Aq6YaKPh0KoHqiwk3nwbRhYWHw8np9Q6utW7eiffv2+Omnn3D58mW0bds2zwskItKn9LQUnD60BA5uneBZZRwAwMapKUzM3RASNBPRT0Ng51j+neuXr9oaHfsuxLE9sxF88VsIggyeXs3QuusPMFQYFdC7ICq8dA4qCoUCycnJAIBDhw6hX79+AIASJUpoelqIiIqKF7HhSE2Jg22p5lrtNk7NcDdoJp48uPzeoAIAlap3hJdPByTERyE97RWuX9yKHX/5w0BuBGf3GihXtTXsnbwgCEKe1y+KImKehiA9LRl2Tl4MR1To6BxUGjRogPHjx8PX1xfnz5/Hxo0bAQB37tyBs7NznhdIRKRPxiZWAICUpMewKPH/MSUpSU8AACZmJXO0HUEmgyiqsWFZb2SkJcLT0wO3b19H+N1AnDq4ECXtyqLDp3Pg5OqdZ7U/fXQNO9ePx7OouwAAI2MrNG47ATV8++XZPojym85jVBYtWgS5XI4tW7Zg6dKlKFWqFABg7969aN26dZ4XSESkT+ZWDnAv1xAP76xCYtzrP/ipr6IRemMOTMxsULpC4xxv68S+OVDI1fj2m0m4fv06Sji2QNUGy+BV+xekpsmwYVnfPLsSKCkhFuuX9UVqqgG8av+Cqg2WwdLGF/u3TsHtq//myT6ICoLOPSqurq7YvXt3lvZ58+blSUFERFLTvtdsrF/aB1dPDoXSyAapqc+hNDJHjyF/QC5X5ng7obcOYdDAz7Bp81ZY29ZAmaoTNad7zKzK4+LhHrh6biN8W4z66Jqvnt+E9LQUeDf8GYYKq9f7sCyP1JQonD2ygrPiUqGRo6Ciy9gTCwuLXBdDRCRFFlaOGPrVfty9cRDRT4NhbukIr2rtoTQy12k7oihCAHDv3j3YuPTWGpNiqLCEmVV5PIu6lyc1P4u6BzPLspqQAry+XNrSpiae3v87T/ZBVBByFFSsrKxyPMhLpVJ9VEFERFJkYGCICt5tUcE791c3lvFqgY2bNsPe3gHxccFar6kykpEcfx+WJXJ+Kul9LKxLITlhH1QZyTCQm2jaE1/cgqU1xxNS4ZGjoHL06FHNv8PDwzFp0iQMGDAA9erVAwCcOXMGa9euxcyZM/OnSiKiIqBh6/H4e1Eg4h4/RGrqXRiHrIaDe0dkpMUh/PZyiGI6vOv0zJN9+dTpibNHlyPk8gy4VxwOucISkQ924VnkCbTp/nOe7IOoIAjim9Ml5kDz5s0xZMgQ9O7dW6t9/fr1WLFiBY4dO5aX9b1XfHw8LC0tMeGnGzp3wRIR6UNifBTOH1+FG5e2ITE+BsDrQ7CRiTU69pkHz4pN82xfobePYef6L/Aq6TkAQJDJUbfJ52jS7qt8uRSaKKdSUxIw55vKiIuL++CQEZ0H0545cwbLli3L0l6zZk0MGTJE180RERUrZhb2aNZhMlw9a+Pgjh/wIuY+AAFOrt4oaeeZp/vyrNgEo6edRfidQKSnvYJL6Vows7DL030Q5TedL092cXHBypUrs7T//vvvcHFxyZOiiIiKsgf3zmDzH0MgitYoX306SlcZhycPQ/DXoh5IeZW3E2fK5UqU8WqGij7tGFKoUNK5R2XevHno2rUr9u7dizp16gAAzp8/j7t372LrVt1uWb506VIsXboU4eHhAIBKlSph6tSpaNOmja5lEVERlJGegrA7p5CWkggXzzqwsHLUd0l5IvDgIphZloNXnV8gCAYAAGvb2rh8tC+uX9iKWo0G6rlCIunQOai0bdsWd+7cwdKlSxEc/HrUeocOHTB8+HCde1ScnZ3x888/o2zZshBFEWvXrkXHjh1x5coVVKpUSdfSiKgICb19DDvWjUNK8uu7sguCDDUbDkCLT6ZAkOncGfxOD+6dxcWTa/A8NhwlbUujVuOBcPGolWfbz5TyKh6REdehNLLAk4dX4ejRSxNSAMDIxBHm1hXwNOJanu+bqDDTOagAr0///PTTTx+98w4dtO8c+uOPP2Lp0qU4e/YsgwpRMRb/8im2rh4G8xLeqFh7JAwV1oh6+C8unFgB65JuqNlwQJ7s5+q5jfh341cwtfCEuXUlPAy7huBr3dGxz3xUqtEpT/YhiiICDy7E6cNLkZH+CgBgIDdC/PMbWsup1elISXoCU7P6ebJfoqIiR0Hl2rVrqFy5MmQyGa5de3/ar1q1aq4KUalU2Lx5M5KSkjSXPb8tNTUVqampmue8CSJR0XTt/CaIkKFctamQG5oCAJzL9EZS/D1cPPVnngSVtNRkHNz+Peyc/VDGexIEQYAoqnHnyvfYHzAd5b3b6DTr7LtcObMOJ/bNRanSvWDn0gZpKTEID16OFzHn8DR8Oxxc20OleoXw28uRlvoSVWv3+Oh9EhUlOQoqPj4+iIyMhJ2dHXx8fP73HzrrVc2CIOg84dv169dRr149pKSkwMzMDAEBAfDy8sp22ZkzZ2LGjBk6bZ+ICp+4F49hYuaqCSmZzK0q4OGdU3myj4iwC0hLTUCpMp9qLtUVBBlKeX6KqyeP4uT+BRAEwKqECyr6dIDSyCxX+zl37HfYODWFu9dwAICJuRsqmrvj0uGeuH9jPh4Er4BanQYBAtr2mAlbh3J58v6IioocBZWwsDDY2tpq/p2Xypcvj6CgIMTFxWHLli3o378/jh8/nm1YmTx5MsaPH695Hh8fzyuNiIogW4dyuH5hG9JSnkFh9PruxKIo4mXshbz7Q545j4io1mp+lfQYEAxw5vBiGJnYI+VVDI7unoVew/6Eo4tuPcaiKOJFbBg8q3TWalca2cDE3A0uHhXh6FIVhgpjVKjaGmYW9h/1loiKohwFFTc3t2z/nRcUCgXKlCkDAKhRowYuXLiABQsWYPny5VmWVSqVUCo/viuWiKStSs1uCDy0GLfOfw2XcgOhUFoj8uFuvIg+j079Fud4O6Io4vGDy0iIi4S9kxdK2HpoXnPxqA0jYys8uvsnylX7DwTBACpVOu5fnwsjEwd41foZxmYuSH0VjZDL07BtzUiM+PY4ZDKD9+xRmyAIsCzhgvjnN+Dg9ommPS31BV4lPoKze1/Ubjw4x9sjKo5yNZg2NDQU8+fPx+3btwEAXl5eGDt2LDw9P36yIrVarTUOhYiKH2NTK/QZsR67NnyJ4IvfAgCMTKzg1/V7ePm0z9E2nseEY+vqYYiJ/P89dcpXbYtPPp0LQ4UxDBVG8Ov6HXauG4fLcSEwt6qEuGdXkJEejzLeX8PY7HVvrdLYDh5eo3EtcCQehp6De1ndBrvWajQIh7bPgJFpKdhnjlG5vRSGCmNUrtlFp20RFUc6B5X9+/fjk08+gY+PD3x9fQEAgYGBqFSpEnbt2oWWLVvmeFuTJ09GmzZt4OrqioSEBKxfvx7Hjh3D/v37dS2LiIoYO6eKGDR+N57H3EdaahJsHcpBbmiUo3XVahU2rhyAlJQMVKo7F6YWnngedRr3bi7Ewe0z0LbH63vdVKreESVsPXDx5Bq8iH0AF48qCL19FEYm2vO1KP/3/NX/LpXWRa0GA5AYF4Xzx3/HozurAQAW1s7o+fkamJha67w9ouJG56AyadIkfPHFF/j555+ztH/99dc6BZXo6Gj069cPT58+haWlJapWrYr9+/frtA0iKroEQcjVtPJhISfxIjYMVRssg7lVBQCAvUsbpKc+x/ULa9GswzcwMn59fxFHl6ro8OlcAK/nOlk4vTZinxyGqUVpzfZiHx+CIMjg5Oqj+3uQydCsw2TUbjIETx4EQWlkDpfStXQ6hURUnOkcVG7fvo1NmzZlaR80aBDmz5+v07b++OMPXXdPRPRBL58/hCAYwMyyvFa7uXVlqFRpSIyP0gSVNxkZW6BOk6EIPLgQaakvYGVTAwkvbiLywQ541+kJS+tSua7JzNwW5SrzSxiRrnQOKra2tggKCkLZsmW12oOCgmBnx/tIEJH+lbTzhCiqEP/8KixL+mja42IvQ25oDAsrp3eu26j1eBgZW+DcsZWIfrQHxqYl0KDVGPi2GFUAlRPR23QOKkOHDsXnn3+O+/fvo37914PKAgMDMWvWLK1Lh4mI9MXNsx7snCrhbtCPcKswHKYWZfA86jQiQtehVsP+UChN37muIAio02QoajcajLS0ZCgUJnk6ZT8R6UbnoDJlyhSYm5tjzpw5mDx5MgDAyckJ06dPx5gxY/K8QCIiXQkyGXoOXY2d68bhzpXvAQAymRw+dXuhabtJOd5Gbid5I6K8I4jZTTGbQwkJCQAAc3PzPCtIF/Hx8bC0tMSEn25AaaSfGohI2p7HhCMhLhI2DmVhalZS3+UQEYDUlATM+aYy4uLiYGGRdbzYm3I1j0omfQUUIqKcKmHrjhK27voug4hySecTr1FRUfjss8/g5OQEuVwOAwMDrQcRERFRXtG5R2XAgAF4+PAhpkyZAkdHR83NvIiIiIjyms5B5dSpUzh58iR8fHzyoRwiIiKi/9P51I+Liws+YvwtEdE7xT2PwLPoUKjVKn2XQkQSoXOPyvz58zFp0iQsX74c7u7u+VASERU3UY9vYc+myXj6KAjA63vhNP/kG1T0bqffwohI73QOKj179kRycjI8PT1hYmICQ0NDrdefP3+eZ8URUdGXGB+NdUt6w8CwJMpXnw4DQ1NEhm9HwFp/GI+wgntZX32XSER6lKseFSKivBJ0dgPS01JQteEcGCqsAABWNjVwLXAEzhxexqBCVMzpHFT69++fH3UQUTEV9eQ2zKwraUIKAAiCDNa2dRH1ZI/+CiMiSfioCd9SUlKQlpam1fahGeaIiN5kYemIsDvnoVZnQCb7/yEpKf4ezC3t9VgZEUmBzlf9JCUlYdSoUbCzs4OpqSmsra21HkREuvCu2wvpqS9w7+ovSEt5BlXGK0Tc24DnUYGo4dtX3+URkZ7pHFS++uorHDlyBEuXLoVSqcTvv/+OGTNmwMnJCX/++Wd+1EhERZidY3m07z0HL6JO4MKhrji7ry0eBK9ArUaD4F2n10dtW1SrkZ72ilMqEBViOp/62bVrF/788080adIEAwcORMOGDVGmTBm4ublh3bp16NOnT37USURFWJWaXVDGqzlCbx1GenoqPMr5wqqka663l5GeghP75uHKmQ1ITYmDtY0HGrQcjSq1uuZh1URUEHQOKs+fP0fp0qUBvB6Pknk5coMGDTBixIi8rY6Iig1jE0tUrtklT7YV8NdohN4+Dge3TjC1KI3nkYHYtWE80tNTUL0+v0wRFSY6n/opXbo0wsLCAAAVKlTApk2bALzuabGyssrT4oiIdBUZcR13bxxAmapfwcNrBOyc/VCh5newdfbDyf0LoFKl67tEItKBzj0qAwcOxNWrV9G4cWNMmjQJHTp0wKJFi5Ceno65c+fmR41EJFEpr+IRdGY9wu6chIFcgYre7VGpekfIDD7qgsKPEhF2CYJMDhvHxlrtdqVa4mbEfsQ9f4wStu76KY6IdKbz0eSLL77Q/LtFixYIDg7GpUuXUKZMGVStWjVPiyMi6UpOeoF1i7vh5bOHaNiwARITk7Brw3iEXN+LLgOWQyYz0EtdxiZWENUZSE2JhZGJg6Y9JTkSgAClsble6iKi3NE5qDx69AguLi6a525ubnBzc8vToohI+s4eWYrkhEjs+Xe3ZtzaoUOHMGz4cIRc35er+/TERN7B7aDdyEhPgXu5BvAo2wCCTLcz1GUrt4TSyAL3b8xDWZ/JMFRYISn+HiLu/QnPis1galZS57qISH90Diru7u5o0KAB+vbti27dunHuFKJi6s6NfejU8RNNSAFe97JWqlQZIdd0DyqnDi7Eib1zYKiwgIHcGGePLkfpCk3QbdAKyOXKHG9HoTRF536LsGX1cFw81B0KYxukJD2FtW1ptO3+k041EZH+6RxULl68iPXr1+O7777D6NGj0bp1a/Tt2xcdOnSAUpnzgwkRFW6iWgX5WzclBQBDQ0OkimqdthURfgkn9s6Bc9l+cCn7GQRBjhfRZxByaRrOHVsJ3xajkJGRirCQU0hNiYezR01YlXB55/ZKV2gM/ymncPPyDiTGRcHeuRLKV/HTKfAQkTToHFSqVauGatWq4ZdffsGxY8ewfv16fP7551Cr1ejSpQtWrVqVH3USkcSUrtgc27fvxNAhQ+Do6AgAOHfuHIKCruCTT/vptK3rF7fByMQRruUGQBBen+opYV8fNk7NcO38VpRyq47tf41GcmLs/9YQUK3ep/Dr+v07x8KYmpVE7UaDcv3+iEgadL48OZMgCGjatClWrlyJQ4cOwcPDA2vXrs3L2ohIwuo394dgYILWbdri60mTMNLfH/36D4Bbmbqo6NNep22lvoqHwshGE1IyKY0d8Cr5JTb/MRgKIzdUa7wadfx2wcNrJK6c3YBzx1bm5VsiIgnKdVCJiIjAL7/8Ah8fH9SuXRtmZmZYvHhxXtZGRBJmbmmPfmN3oErtvgg8ews3Q6LQuO3X6DFkLQzkCp225exREwkvbuFV4iNNm1qVimdPj8LcwhYqlQrlqk+DibkH5IbmcCrdHXbOrXHxJL8cERV1Op/6Wb58OdavX4/AwEBUqFABffr0wY4dO3jlD1ExZGZui6btJwGY9FHbqVKzKy4cX4UbZ8fCwa0T5IbmiH60B6kpUXAr0xLJyWkwVGjfmd3cqgKiH+2BKIoQBOGj9k9E0qVzUPnhhx/Qu3dvLFy4EN7e3vlRExEVM0ojM3w2ehOO7P4Zt4PWQZWRBrcy9dGk/y+IenILwVf3IiU5UmtelJcxF1DSrixDClERp3NQefjwIQ8MRJTnzCzs8cmn89Ch1xyIolozu62NQzmc3L8Aty9Mgmu5wVAY2SA6Yh+eRZ5E+16/6rnqvJGc+ByP7p+H3NAIbmXr8eokojfoHFQyQ0pycjIePnyItLQ0rdc5Oy0RfQxBJoMAGdRqFW5e2o6blwNgYWmLhPhYBF+aAgBQGlmiRccpqFq7u56r/TiiKCLw4EKcPrwIGemvj6Wm5iXRrudslPFqrufqiKRBEEVR1GWFmJgYDBgwAPv27cv2dZVKlSeF5UR8fDwsLS1x8eYjmJlbfHgFIioURFHEhNGDsWfnVtSvXx8lS5bEkaNHYWxsgu9+no/6DZvByMhY32V+tF0BmzBx7FAMHzYMffv2RXxCAmbPno3AwNPYfegsXN1Lf3gjRIVQYkI8alZyQVxcHCws3v/3W+erfsaNG4e4uDicO3cOxsbG2LdvH9auXYuyZcti586duS6aiAoPlUqFIwf24PupEzH7xym4ce1Knm4/8OQR7Nm5FfPmzcNff/6J+fPm4dCBAzCQCTiwd1eRCCkA8Pea5WjcuDEmTpwIR0dHlC9XDr8tXAgTE2Ns3sArmoiAXJz6OXLkCHbs2IGaNWtCJpPBzc0NLVu2hIWFBWbOnIl27XS/vwcRFR6vXiVjxMCeOHv6BNzd3ZGUlIQ/li/E8NFfYtzEKXmyjyMH9sDV1Q0d2v9/PhY7Ozt0794df/75V57sQwoiHj1A0759tNqMjY1RsWJFRDx6oKeqiKRF5x6VpKQk2NnZAQCsra0RExMDAKhSpQouX76ct9URkeSsWv4brlw6h7Vr1uDwoUM4dfIkxo8fj2W//YqFc35CfNzLPNlPdmP2ZYIAHc9WS5qHZ1mcPn1a6z0lJCTg2vXr8PAsp8fKiKRD56BSvnx5hISEAAC8vb2xfPlyPH78GMuWLdNMo01ERdfOgI3o2LEjGjRoAACQy+UYMXw4nBwdsWTBLDSuXREBm9d/1D6atmyDBw8eYM+ePZq2mJgYbNy0Gc39ik6v7cCho3D+/HlMnjwZN27cwOkzZzB4yBCIoogen/bXd3lEkqDzqZ+xY8fi6dOnAIBp06ahdevWWLduHRQKBdasWZPX9RGRxCQlJGh6VTPJZDI4OjmhUuXKMDc3x7cT/VGxUhVU8KqSq300aNQcrdt1wpixY7Fp82bYlCyJw0eOwMjYBKO+mJwXb0MSmrdqh+k/zsX82d9j85YtAAA3D0/8/uc2ODiW0nN1RNKg81U/b0tOTkZwcDBcXV1hY2OTV3XlCK/6ISp440b0R/DNIOzetQvGxq8Htd69exdt27XDjBkz0KN7dzRu0hTNWrXD1B/m5Ho/KpUK27duwO6ATUhKSkSd+o3w2cDhsLN3eOc6yclJSIiPh42tHQwMsr9ZoRSlpLzCretXoTQyQsVKVSGT5fruJkSFgi5X/Xx0UNEnBhWighdy+wZ6d24JJ6dS6N6tK+Li47FhwwaULFECAQEBMDExwaDBg2GgMMWSPzYUSE1xL1/gpxmTsGfXNqSnpcHB0QnDR09Er768ezKRFOkSVHJ06mf8+PE53vncuXNzvCwRFT7lK1bGX5v3YuGcnzDrl18gk8nQrWtXjB8/HiYmJoiPj8elS5fx2aDhBVKPWq3GsAHdERZ6B1+MGwfP0qWxZ+9eTP/mCwiCgJ59BhZIHUSUP3IUVFavXo3KlStDLpdDeM+oe06tT1Q8VKrig+VrNuH82VMY0KsDYmJjERwcjKTkZCxZsqRAA8LZwOMIunwBf/75J3zr1wcAtGjRAqIoYtlvs9GtV79CdRqIiLTlKKjExcVh69atsLOzQ+nSpXHhwgWULFkyv2sjIomrXbcB5i1Zg1k/fIt+/V9fpeJVqSpWrd/+wcGgGRkZuBZ0CRkZ6ajqUyPXk7hdv3oZFhYWqF+vnlZ7mzZtsHPnTjyLjXnvuBYikrYcBRVra2uEhYXBzs4O4eHhUKvV+V0XUbFx8dxpbNn4F57FRKOydzX06jsY9g65v9T/QXgoNvy1CqF3guHk7IJefQehYqX8uweXX9uOaOHXHmGhd6FQKODi5vHB3tUTRw9i6qQxiHz6BABgaWWNr/7zA7r26Kvz/kva2CIxMRHR0dGwt7fXtN8PDYVCoYT5B85/E5G05WhoedeuXdGoUSN4eLw+ANWsWROlS5fO9kFEOffH8oXo270Nrl46A6VcxJ+rlqKjX33cCbmVq+2dO3MSHVv5YsfW9TBSCDhxZD+6tmuMXds353Hl2gwMDFCmXAW4upf+YEi5f+8O/Id+irJlPLFl82bs2rkTTZs0xrdf+uP0yaM679uvbUeYmJriq6+/RmRkJERRxKlTp7B8xQq069gVxsYmuX1bRCQBOb7qZ9++fbh37x7GjBmD7777Dubm5tkuN3bs2BzvfObMmdi2bRuCg4NhbGyM+vXrY9asWShfvnyO1udVP1SYPXn8CC18q2LQoEGY9PXXEAQBL168QK/en6KkrQPWbtyt0/bUajXaNK0Je9uSWPXHHzA2NkZGRgYmfvUVjhw9iuPng2FqapZP7ybnfpj6Ffbt3ooTx49DqVQCeH0Twk6dO6OkXSksX7NJ522eOXUMo4f1RXJSEkxNTZGQkIDqNetg2epNsLC0yuN3QEQfK8+v+gGA1q1bAwAuXbqEsWPHvjOo6OL48ePw9/dHrVq1kJGRgW+++QatWrXCrVu3YGpq+tHbJ5KyQ/v/hYFcjjGjR2t6IaytrTF0yGB8PWkSXr58DiurEjne3p3gm3gQForvZ0zTzG8il8vxxbhx2LlzJ86cOoYWfu0/sJX8Fx52D9WrV9eEFOD1QPw6derg0GHde1QAoF6DJjh29hb2792J57ExqFy1Gur6NuYAf6IiQOeZaVevXp1nO9+3b5/W8zVr1sDOzg6XLl1Co0aN8mw/RFKUkZEOA5kMhoaGWu1KIyMAgCpDpdP20tPTAQBG/1s/U+bzjP+9rm8uru44cuBfpKWlQaFQAHjdo3Lx4iW4uuX+9LGZuUWuxrgQkbRJavrDuLg4AECJEtl/i0xNTUV8fLzWg6iwatSkJVJSUrB+w/8nRUtNTcVff/2FSlV8UKKkbjM9V/CqAls7e6xatUprwPvvf/wBhUKJOr6N86z2j9Hrs8F49iwWY8eNQ0hICMLDwzF12jRcvRpUYHOvEFHhoXOPSn5Rq9UYN24cfH19Ubly5WyXmTlzJmbMmFHAlRHljzLlKuDTfkPx/fff48SJEyjj6YlDhw/j6dNIrPxrq86nLQwNDTFp6k/4cvQQfNKxI3x9fXH16lVcuHABEyZNh7V1zk8j5afyFSph7uLVmDZ5HNq2e32DQRNTU/znu9lo1LSlnqsjIqmRzBT6I0aMwN69e3Hq1Ck4Oztnu0xqaipSU1M1z+Pj4+Hi4sLBtFRoiaKIgC3rsWXDWjyLjUGlqtUwZPhYeFX2zvU2z589hTUrF+HenWCUcnZFnwGfS2JsyttSU1Jw8fxpZGRkoEatuvw/TFSMFLp7/YwaNQo7duzAiRMn4OHhkeP1eNUPERFR4ZMvV/3kB1EUMXr0aAQEBODYsWM6hRQiIiIq+vQaVPz9/bF+/Xrs2LED5ubmiIyMBABYWlpqLq8kKgrUajWCLl9AfNxLVPWpofNAWSKi4kqvp37eNVhw9erVGDBgwAfX56kfKgxuXLuCCaMH40FYKIDXg14/GzQcX07+DjKZpC68IyIqEIXq1A9RUZYQH4chn3WBq4sz/tmwAU5OTtgWEIAFCxbA1s4BA4eO0neJRESSJpnLk4mKol3bNyExIR5LlyyBg8PrO/iOHjUKERER+POPJZILKunp6ThycA+uXDoPKysrtO/YHc6u7voui4iKMfY7E+Wjhw/C4eLiogkpmWrVrImnTx5rZpOVgpcvn6NXpxYYO7wfjuzfhZVL58OvcXVs27xO36URUTHGHhWifOTuXhp/PXyIJ0+ewMnJSdN+9tw5lHJ2zTJ9vi5EUUTQ5Qt4GH4f7qXLoKpPjY+6t82cmdMR8TAMi377DampqTAyNsbhw4cx5avRqFu/EZxKueR620REucWgQpSP2nfqjoVzf8Kw4SMwadLXcHJywvbt2xEQEIBvpv2c6+1GRT7F6M/74FrQJU2bd7VaWLRyHWzt7HXenkqlwq6ATShd2gOjRo/WtNvZ2kIul+PfHVswdOQXua6XiCi3eOqHKB+ZmVvgj78DkJqWjn79+qFFixZYufJ3DBs14aPuazNh1EBEPX2MNatX4+aNG/jjjz/w9PEDTBwzJFfby0hPR0rKK4SEhGDG9OkIunIF/+7eDTc3N6hUKsRER+a6ViKij8EeFaJ8VrFSVew+fB43rl1BfNxLVKrqAyur3N93JyT4Ji6eP4OlS5eiYcOGAIAmjRvjP99+izFjxyL0bgg8y5bXaZuGCgUUCgV69eqFvn1f34G4QoUKWLhwIXwbNECahMbSEFHxwqBCVAAEQUAV7+p5sq0nEY8AAFWrVNFq9/Z+fX+gJ08e6RxUUlNTkJaWlmWbdnZ2sLe3h4mJ6UdUTESUezz1Q1TIlPlfCDl58qRW+4kTJyAIAkp7ltN5m0ZGxnB2ccOJt7Z5//59PH36FOXKe+W+YCKij8AeFaJCxsXNA35tO+L7H35AUnIyateqhXPnzmHuvHlo06ELSjm76rxNQRAwePgYzPh2AqytrdGpY0dERERgzty5cHJ2Qev2nfL+jRAR5YAk7p6cW5xCn4qrpKREfPfteOzesQUqlQpyuRztO3XH1B/m5Po0jSiKWLlkHlYumYeEhHgAQPWadTBz7lK4uXvmZflEVMzpMoU+gwpRIRYbE40nj1+PWTmwZwfC7t+Di5sHevYZCI/SZXK1zeTkJITeDYalpTVc3UvnZblERAB0Cyoco0JUiNnY2iE5KRH9erTD5g1rkJ6SgJ1bN+CTVvVw9NC+XG3TxMQUVbxrMKQQkSQwqBAVYiqVCt9OHAUfH2+cOnkSq1etwqmTJ9DA1xf/+WoU0lJT9V0iEdFHYVAhKsRuXLuCxxEP8cW4cTAxMQEAKJVKfDlhAp7FxuDCuUA9V0hE9HEYVIgKsbS01z0mpqbaA2gzn6eyR4WICjkGFaJCrHLVarC0tMLatWvx5rj4tWvXwtjYBDVr19NjdUREH4/zqBAVYsbGJhg/aTqmTR6H+2FhqFu3Li5duoSzZ89i4rffw8LSSt8lEhF9FPaoEBVyPfsMxLLVm6AwMsOWrduQIcqwYNmfGDxsjL5LIyL6aOxRoWIl5PYNBJ48CmNjY7Rs/QlsbO30XVKeaNLcD02a++m7DCKiPMegQsXC68t4/bF9ywYYGRkhIyMDP02fhCnf/4oenw7Qd3lERPQOPPVDxcLfq5dj57aN+OnHHxF05QrOnzuHrl27YtrkcQi5fUPf5RER0TswqFCxsOWftWjXrh169uwJQ0NDWFpaYvq0abC1tcW2Tev0XR4REb0DgwoVC7GxMfD01L6xnqGhIdzc3BATE6WnqoiI6EMYVKhYqFTFBwcPHoRKpdK0RUZGIujqVVSuWk2PlRER0fswqFCx8Ln/eAQHB2PgoEHYt28fNm7ciE/79IG1dQl06dFH3+UREdE78KofKhZq122Axb9vwK8/TYH/qFEAgAaNm+M/382GlVUJPVdX8CwSwxA7eaK+yyCiYiotPSPHyzKoULHRpLkfGjdrheioSCiNlMUyoLzN0KWMvksgomLIMC0NwNEcLcugQsWKIAiwd3DUdxmUR648jcS228F4mZKCqvb26OZVEdbGRvoui4jyEMeoEFGhtPLSZXy6NQDn4xPwytEJiy5eQpdNWxARH6/v0ogoDzGoEFGh8+BlHOaeOYfhw4bh8JEj+HPtWhw5cgRyc3P8fOq0vssjojzEoEL0HlGRTzF10ljUq+KOupVcMXnCCDyOePjO5RMTEzBn5jQ0qVMRtSu7YvTQPrh981oBVlw8HAgNhamxMUaPHg2Z7PVhzNHREQMGDcLRsHCkZuR8oB4RSRvHqBC9w4sXz/Fpx+ZIio5BC7UpDGCAg9u24NSRg9iy92SWsS5paWkY3Kcz7obcRNeuXWFjY4MdO3bg0y5+WLd1H7wqe+vpnRQ9aSoVFAoFFAqFVruZqSnUoogMtQilnmojorzFHhWid9jw5+94FhWJuSiFAQa2+MzABgvgjFcvX2LtH4uzLH9w305cvXIBa9eswfRp0zDK3x87d+yAk5MjFs//WQ/voOjydXXBi7g47NixQ9OWmpqKDevXo7qTI0wVhnqsjojyEntUiN7hzIkjqC4aw072/z961oIcdVXGOHv8CPCt9vJnTx1HhQoVUL16dU2bsbExOnfqhCXLlhVU2cWCt7092pYtg6++/hqHjxyBm6sr9u/bh8ePH2PVJ+31XR4R5SEGFaJ3MDY1xQuZmKU9TlDD2NQsS7uJqRlevoyDSqWCgYGBpv3FixcwMTbN11qLG0EQMKtlc/g42CPg0kUEBQbC284Ws7t0QiU7W32XR0R5iKd+iN6hfaceuK5KwnF1PETxdWA5p07EJTEJ7bv0yLJ8u47dEBn5FMuWL4darQYAXL9xAxs3bUa7Tt0LtPbiQC6T4TPvqtjWoxuO9OuDea1bMaQQFUHsUSF6h3Ydu+HYoX2YvXsbNsjjIIOAh+pkNG3mh269+mdZvqpPDQwf/SXmzv0V//zzD6ytrXHz5k14VfaG/9iv9PAOiIgKPwYVoncwMDDAnMWr0Kn7pzi0fzdEtRqTWrRB4+Z+Wqd23jRu4hQ0ad4au7dvRlJSIj4bPBptO3SBQslrUIiIcoNBhSQnKSkRTyIewcbOHtbW+r0fjyAIaNS0JRo1bZnjdXyq14JP9Vr5WFXeScnIwKO4eFgbG8HGxETf5RARZcGgQpKRkZGBebNmYP2aFXiVmgIDAwO0bd8Z036aBzNzC32XV6SIoog/7oRhzYFTiHv1CoIgoKmHO6Y3bgRbUwYWIpIODqYlyZgzcxrWrFiET9JM8IuBCwaJJXBk9w6MG9ZP36UVOSvW/o0Ft++hY/fu+GfDBvzw/fe4EZ+Az3fvgep/A4GJiKSAPSokCQnxcdiwdiV6wAp9DGwAAF6CMUqq5fj51FHcvnkNFStV1XOVRUNGRgaWrlqLnj17YtrUqQCAWrVqoWy5cujRowdOPXyExu5ueq6SiOg19qiQJISHhSIlLRW1Zdrzk9QWXs8/Enzrhj7KKpKeP4tFdGwsmjVtqtVevVo1WJqbIyT2mZ4qIyLKSq9B5cSJE+jQoQOcnJwgCAK2b9+uz3JIj+zsHSEIAsLEVK32+3j93MHRSR9lFUkWlpYwMjLC7eBgrfbHjx8jPjER9macnI6IpEOvQSUpKQne3t5YvDjrfVOoeLF3cETT5n5YK7zAJXUS1KKIe2IKfkMs3F09ULteQ32XWGQYGRmjR6cOWLFsGf79919kZGTg3r17+OKLL2BlbIyWnqX1XSIRkYZex6i0adMGbdq00WcJJCE//roEIwb0wLSgixAAiABcSrli2eqN75y3hD5MFEWkpLyCUmkEmez1d5OpX45H+LFjGDN2LARBgCiKsDEzxZK2fjAx5A39iEg6OJiWJMO6REls2HEIVy6ew52QW3Aq5YL6DZtCLuevaW6IoohN69dg5W9zEPHkEawsLNGz3xD4j5sECxNjLKrjgztKS9yMjkEJE2M0cnOFgoGQiCSmUP0FSE1NRWrq/8cwxMfH67Eayg+CIKB6rbqoXquuvksp9Nb8vhizvv8WjWUW6CFzQGhCClYtmYfHDx9gxc//AQBUsrPl/XGISNIK1VU/M2fOhKWlpebh4uKi75KIckylUmHjutXo9UlztG7gg28mjETo3ZB82VdqSgqWzZ+FtoIlJsoc0ExmgaEGdhgBW+zeuQV3Q+/ny36JiPJaoepRmTx5MsaPH695Hh8fDxcXFziE7IGFqbEeKyN6P1EUMeyX5dh45DRqycxQRZTj5OPN2L9zM/bM+RbVynrk6f5u3H+IuIR4NDHQDvNNBHMsRBRuHP0XHJ5MRIVBoQoqSqUSymxu7vboj40wMyxUb4WKmavPX+KfE+cxTmaPFjJLAEB/UY2JaY/w9fQFWNmgZp7uLyk5BQAQJabDS/h/iI9CBgAg/dQlCC6l8nSfRET5Qa9/3RMTE3Hv3j3N87CwMAQFBaFEiRJwdXXN8XbkpTwgVyjyo0SiPBH4+DysZHI0E/5/zyIjQYY2giWWxEZD5eAGZR4OGnYGUOfGPayLjIWnqISroMQLMQNLxWhYK5VoUr0WBykTUaGg1yPVxYsX0fSN2TEzT+v0798fa9as0VNVRHnPQJAhQxShErQHhqVDhIEgQBCEPN/njy2aYmDAToxMeAB7mQKx6nQYG8qxuG3bPA1FRET5Sa9HqyZNmkAURX2WQFQgWnp6YNH5CwgQX6A7rCEIAl6IGdiFODRxd9O6LFgtithy8zb+uX4DUYlJKG9TEkNqVkd9F2ed9lnKwgK7+vTCgdD7uPPsORzMzNCuXBlYGRnl9dsjIso3/FpFVADKlSyJz2tUw4pLV3BSlgA7tRxX8QpmSiUm+tbXWvbnk4H469p11BfMUF0wxYWnzzBkxy7M8WuJNmXL6LRfpVyODuXLAQCik5JwKyYW9qam8CxhnWfvjYgoPzGoULGiUqsR/jIORnI5SlmYF+i+x9Wtg9qlSiHgdjBepqRgiGNF9KxcCTYmJpplHsXF4+9r1zFIZoMushIAgJ5iCfwkPMWvp06jlWdpGMh0m1UgTaXCd8dOYHtwCFT/68Gs4eCAX1u3hIOZ2QfWJiLSLwYVKjb+vXMXcwLP4GlSEgDA284OM5o1QXmbkgWyf0EQ4OvqAl/Xd8//czYiAiKAtoKVpk0mCGgnWGJK0mM8iItDaWvdekN+OXUaO4NDMEiwQW2ZKcLEVPweHYvhO//F1l7dkZCWBhNDQ85KS0SSxKBCxULgw0f48sAh1BfM4C8rhUSo8U/scwwM2IHdfXujhLE05uEx+t8g12SoYfTGsNtEqAEASgPd/ssmpqVhy63b6CGUQEfZ64DjKChgIRpg0vMINF39J2JevYKRgQE+qVAeE33rwYxX0BGRhBSqmWmJcmvlxcuoIDPGJJkjfGSmaCAzxw9CKSSlpWHrrdv6Lk+jsbsbjAwMsFodg/T/naaJEzOwCc/hbWen8+mqqMQkpKpUqCJoB7FKMIYAwOKVGl/LHNFVtMLuWyEYsetfDnAnIklhjwoVCyGxz9AeZpC9cRmwlSBHeRgjOPaZHivTZqFU4rtmTTD50BEEiWFwERUIEVNgbGiIec2a6Lw9O1MTKGQy3BJTUFn4/1iYYKRABNBfZoPqMlMAQDm1EaY9fYzzj5+gjjMngyMiaWBQoWLB3swUYS9StdrSRTUeIg21zEz1VFX2OpQvh4q2Nth6KxiRiYloZlMS3bwqouQbg25zylypRKeKFbDxVjDM1TLUEcxwX0zFEnUUbCCH9xvhpbpgAlPBANeiohhUiEgyGFSoSEtKS8f24GDIZDIEiolYqIrEUMEWyYKIVeoYJEKFrl4V83y/oijibMRj7Ay5g8S0NNQq5YQuFSvkePxHmRIl8HWD+h9eMAcmN/RFUloalty9h8WIBgAYAOgiWMPgjR6mF1DhlajiPCtEJCkMKlRkPX/1Cp9t2Y7wuJeoLJjARVDggBiPA2I8AMDYQI5ZzZvrfBVNTvwSeAZrgq7CRaaEtWiAX+6HY8PV6/iza2fYmureM/IxjORy/OrXEuPq1cGdZ89gb2qGNVeCsP9eGGqIpqgEY8RBhd/UUTCSy+FXxrNA6yMieh8GFSqyFp+/iJj4BCw2cIeLoIAoitgtvsRydQxG1KyOgdV8YJ7NTS4/1rWoKKwJuopBMht0FqwhyAQ8FtPwVUIEFp47h++bNf3wRvKBs4UFnC1e32vom0YNEfbiJSbFRsBKJkeCWgWFgQHmt2kFi3z4mRAR5RaDChVZe+/cRStYwEV4fbpFEAS0gxW2y14iPjUtX0IKAOy7G4qSMkN0FKw19/ApJSjQGhb4906oJqikq1RYE3QVW27cwvNXKahsb4fhtWoUyPgQa2MjbOzRFacePsKN6GiUMDZGm7KcXp+IpIdBhYqsdJUaxoL2FfgyQYCRKEOaSpVv+01TqWAEQTP+QyWK2C/G4agYj5QMFf5z+CgGV/fBgrPncSg0DE0FczgLljj99BkGbd+Jxe3boom7W77Vl8lAJkNjdzc0LoB9ERHlFudRoSLL180Fh5GAJPH/oeSqOhkP1Klo4Oaab/tt4OaKx+o0XFQnQRRF/KqOxDJ1NNyhRBvBEsdDQtH1ny3YH3of42T2GGfggG6yEpgtuKCqYII5gWfyZS6ThNRU3IiKRlRiYp5vm4gov7BHhYqs0XVqo/fDCPirHqKR2gwvocJJMQG1nZzQzMM93/bbyM0Vvi7O+OHRY1SGEYLwCl/KHNBE9np8yGeiGuPVDxEJAY2E/0/gZiAI8BMsMevFU7xIScmz2XIz1GrMPX0W66/fQKpKBQFAYzdX/NiimWRm5CUiehf2qFCR5VnCGht7dEWDcqVxxjgNDy1l8K9bC8s/aQe5jjf204VMELCkfVuMqVcH4Uo1LGGAxm8EEhNBhraCJTIgIlnUPgUVBxVkAJR5eN+dhWfPY23QVXRRW2K+gStGy+xx9dETjNy1h7PQEpHksUeFijQPayvMbNGswPerMDDA0BrVkJSehn8uX4Mar+cuyZQGEQKA1epYjBDsYSgIiBTTsA0v0NjdDaZ5dL+d5PR0rLt2Hd2EEuhjYAMAKCMYwVYtx5Tox7j45ClqlXLKk30REeUH9qgQ5aNWnqURp87ADvGFpvfimZiBfxGHSra2OIh4DBTDMEH9CJ+rwiE3VuDbRg3zbP9PExKRnJGB6jLtuVt8BBPIANx9/jzP9kVElB/Yo0KUj7xsbTHQxxurgq7imCwRNmoDXMUrWBobYV6bVkjJyEDA7RA8f/UK3e3s0LFCuTy9e7GNiQnkgoC7YqrWvX7uIxVqAE7mut3kkIiooDGoUJGQplLhwcs4WCqVsNPTvXtEUcSh+2HYfecuXqWno66zM7pXqoiJvvVQ16UUdgTfQXxKCoY5VULPyl6agawTfevlW02WRkq0K1cWG+6EwkYtR23BFPeRikViNJzNzNHA1SXf9k1ElBcYVKhQE0URf1+7jqXnL+JF6uubDvo6O+O75k0KtLdAFEX85/AxbAsORlmZESxEGeY/jMDmGzfxd7fOaOTmhkZu+pmv5D+NG+J58ivMevRI0+ZmYYGl7dvk66BiIqK8wKBChdrW28H46WQg/ARLNDGwRbSYjnVPojAoYCd29ukFRR5ePfM+ZyIeY1twMMbI7NFKZgkAeCKm4cuER1h84SKmNm5UIHVkx0yhwIqO7REcG4vg2GewNzVF7VJOMGBIIaJCgEcqKrREUcTKC5fQUDDHaAN7VBFM0FxmiamCEx7Ex+Ng6P0Cq+VAaCicZAq0FCw0bU6CAi1ggf13QgusjvepYGODThXKo56LM0MKERUaPFpRofUqIwMPExJQW9Aek+IuKGEvUyA49lmB1aJSi1BA0NzbJ5MSAjLU6gKrg4ioqGFQoULLSC6HpUKB+2KqVvsLMQPP1OlwKMBBtQ3dXBGuTsVldZKmLV5U4ZCQgMYevJcOEVFucYwKFVoyQUCPypWw5koQ3NUKNBIsEIN0LFFHQyk3QLtyZQuslmYe7qjv7IwZEY9RXzSDBWQIRBIEhQH8a9cqsDqIiIoaBhUq1EbVqYVHcXGYF3of8xEFEYCVQonFbdvCysiowOqQy2RY2qEt1l27jt0hd/EoLQ2tXctjSHUflLKw+PAGiIgoW4JYiG/2ER8fD0tLS1z4fHCeTpJFhc+d2GcIioyCpZESjd3dYCRnBicikqrEtDTUWvEH4uLiYPGBL3M8mlORUM6mJMrZlNR3GURElMc4mJaIiIgki0GFiIiIJItBhYiIiCSLQYWIiIgki0GFiIiIJItBhYiIiCSLQYWIiIgki0GFiIiIJItBhYiIiCSLQYWIiIgki0GFiIiIJItBhYiIiCSLQYWIiIgki0GFiIiIJItBhYiIiCSLQYWIiIgkSxJBZfHixXB3d4eRkRHq1KmD8+fP67skIiIikgC9B5WNGzdi/PjxmDZtGi5fvgxvb2/4+fkhOjpa36URERGRnuk9qMydOxdDhw7FwIED4eXlhWXLlsHExASrVq3Sd2lERESkZ3oNKmlpabh06RJatGihaZPJZGjRogXOnDmTZfnU1FTEx8drPYiIiKjo0mtQiY2NhUqlgr29vVa7vb09IiMjsyw/c+ZMWFpaah4uLi4FVSoRERHpgd5P/ehi8uTJiIuL0zwePXqk75KIiIgoH8n1uXMbGxsYGBggKipKqz0qKgoODg5ZllcqlVAqlQVVHhEREemZXntUFAoFatSogcOHD2va1Go1Dh8+jHr16umxMiIiIpICvfaoAMD48ePRv39/1KxZE7Vr18b8+fORlJSEgQMH6rs0IiIi0jO9B5WePXsiJiYGU6dORWRkJHx8fLBv374sA2yJiIio+NF7UAGAUaNGYdSoUfoug4iIiCSmUF31Q0RERMULgwoRERFJFoMKERERSRaDChEREUkWgwoRERFJFoMKERERSRaDChEREUkWgwoRERFJFoMKERERSRaDChEREUkWgwoRERFJFoMKERERSRaDChEREUkWgwoRERFJFoMKERERSRaDChEREUkWgwoRERFJFoMKERERSRaDChEREUkWgwoRERFJFoMKERERSRaDChEREUkWgwoRERFJFoMKERERSRaDChEREUkWgwoRERFJFoMKERERSRaDChEREUkWgwoRERFJllzfBXwMURQBAIlpaXquhIiIiHIq8+925t/x9xHEnCwlUREREXBxcdF3GURERJQLjx49grOz83uXKdRBRa1W48mTJxBFEa6urnj06BEsLCz0XRYBiI+Ph4uLCz8TieHnIj38TKSHn0n+E0URCQkJcHJygkz2/lEohfrUj0wmg7OzM+Lj4wEAFhYW/KWSGH4m0sTPRXr4mUgPP5P8ZWlpmaPlOJiWiIiIJItBhYiIiCSrSAQVpVKJadOmQalU6rsU+h9+JtLEz0V6+JlIDz8TaSnUg2mJiIioaCsSPSpERERUNDGoEBERkWQxqBAREZFkMagQERGRZBWJoLJ48WK4u7vDyMgIderUwfnz5/VdUrF24sQJdOjQAU5OThAEAdu3b9d3ScXazJkzUatWLZibm8POzg6dOnVCSEiIvssq9pYuXYqqVatqJhWrV68e9u7dq++y6H9+/vlnCIKAcePG6buUYq/QB5WNGzdi/PjxmDZtGi5fvgxvb2/4+fkhOjpa36UVW0lJSfD29sbixYv1XQoBOH78OPz9/XH27FkcPHgQ6enpaNWqFZKSkvRdWrHm7OyMn3/+GZcuXcLFixfRrFkzdOzYETdv3tR3acXehQsXsHz5clStWlXfpRCKwOXJderUQa1atbBo0SIAr+//4+LigtGjR2PSpEl6ro4EQUBAQAA6deqk71Lof2JiYmBnZ4fjx4+jUaNG+i6H3lCiRAnMnj0bgwcP1ncpxVZiYiKqV6+OJUuW4IcffoCPjw/mz5+v77KKtULdo5KWloZLly6hRYsWmjaZTIYWLVrgzJkzeqyMSLri4uIAvP6jSNKgUqnwzz//ICkpCfXq1dN3OcWav78/2rVrp/V3hfSrUN+UMDY2FiqVCvb29lrt9vb2CA4O1lNVRNKlVqsxbtw4+Pr6onLlyvoup9i7fv066tWrh5SUFJiZmSEgIABeXl76LqvY+ueff3D58mVcuHBB36XQGwp1UCEi3fj7++PGjRs4deqUvkshAOXLl0dQUBDi4uKwZcsW9O/fH8ePH2dY0YNHjx5h7NixOHjwIIyMjPRdDr2hUAcVGxsbGBgYICoqSqs9KioKDg4OeqqKSJpGjRqF3bt348SJE3B2dtZ3OQRAoVCgTJkyAIAaNWrgwoULWLBgAZYvX67nyoqfS5cuITo6GtWrV9e0qVQqnDhxAosWLUJqaioMDAz0WGHxVajHqCgUCtSoUQOHDx/WtKnVahw+fJjneYn+RxRFjBo1CgEBAThy5Ag8PDz0XRK9g1qtRmpqqr7LKJaaN2+O69evIygoSPOoWbMm+vTpg6CgIIYUPSrUPSoAMH78ePTv3x81a9ZE7dq1MX/+fCQlJWHgwIH6Lq3YSkxMxL179zTPw8LCEBQUhBIlSsDV1VWPlRVP/v7+WL9+PXbs2AFzc3NERkYCACwtLWFsbKzn6oqvyZMno02bNnB1dUVCQgLWr1+PY8eOYf/+/fourVgyNzfPMm7L1NQUJUuW5HguPSv0QaVnz56IiYnB1KlTERkZCR8fH+zbty/LAFsqOBcvXkTTpk01z8ePHw8A6N+/P9asWaOnqoqvpUuXAgCaNGmi1b569WoMGDCg4AsiAEB0dDT69euHp0+fwtLSElWrVsX+/fvRsmVLfZdGJCmFfh4VIiIiKroK9RgVIiIiKtoYVIiIiEiyGFSIiIhIshhUiIiISLIYVIiIiEiyGFSIiIhIshhUiIiISLIYVIgozw0YMACdOnXKs+25u7tj/vz5eba93FqzZg2srKz0XQZRscKgQlRMNGnSBOPGjcv3dYiI8hKDChEREUkWgwpRMTBgwAAcP34cCxYsgCAIEAQB4eHhOH78OGrXrg2lUglHR0dMmjQJGRkZ711HpVJh8ODB8PDwgLGxMcqXL48FCxbkurYmTZpg1KhRGDVqFCwtLWFjY4MpU6bg7bt7JCcnY9CgQTA3N4erqytWrFih9fqjR4/Qo0cPWFlZoUSJEujYsSPCw8O1fgadOnXCr7/+CkdHR5QsWRL+/v5IT0/XLPPixQv069cP1tbWMDExQZs2bXD37t131n716lU0bdoU5ubmsLCwQI0aNXDx4sVc/yyIKCsGFaJiYMGCBahXrx6GDh2Kp0+f4unTpzA0NETbtm1Rq1YtXL16FUuXLsUff/yBH3744Z3ruLi4QK1Ww9nZGZs3b8atW7cwdepUfPPNN9i0aVOu61u7di3kcjnOnz+PBQsWYO7cufj999+1lpkzZw5q1qyJK1euYOTIkRgxYgRCQkIAAOnp6fDz84O5uTlOnjyJwMBAmJmZoXXr1khLS9Ns4+jRowgNDcXRo0exdu1arFmzRutGmQMGDMDFixexc+dOnDlzBqIoom3btlph5k19+vSBs7MzLly4gEuXLmHSpEkwNDTM9c+BiLIhElGx0LhxY3Hs2LGa5998841Yvnx5Ua1Wa9oWL14smpmZiSqVKtt13sXf31/s2rWr5nn//v3Fjh075riuihUratXx9ddfixUrVtQ8d3NzE/v27at5rlarRTs7O3Hp0qWiKIriX3/9leW9pKamisbGxuL+/fs1Nbm5uYkZGRmaZbp37y727NlTFEVRvHPnjghADAwM1LweGxsrGhsbi5s2bRJFURRXr14tWlpaal43NzcX16xZk6P3SUS5wx4VomLq9u3bqFevHgRB0LT5+voiMTERERER71138eLFqFGjBmxtbWFmZoYVK1bg4cOHua6lbt26WnXUq1cPd+/ehUql0rRVrVpV829BEODg4IDo6GgAr0/B3Lt3D+bm5jAzM4OZmRlKlCiBlJQUhIaGatarVKkSDAwMNM8dHR0127h9+zbkcjnq1Kmjeb1kyZIoX748bt++nW3d48ePx5AhQ9CiRQv8/PPPWvsiorzBoEJEOvnnn3/w5ZdfYvDgwThw4ACCgoIwcOBArVMs+eHtUyqCIECtVgMAEhMTUaNGDQQFBWk97ty5g08//TRH28iN6dOn4+bNm2jXrh2OHDkCLy8vBAQE5Hp7RJSVXN8FEFHBUCgUWj0UFStWxNatWyGKoqY3IzAwEObm5nB2ds52ncxl6tevj5EjR2raPrYn4dy5c1rPz549i7Jly2r1frxP9erVsXHjRtjZ2cHCwiJXNVSsWBEZGRk4d+4c6tevDwB49uwZQkJC4OXl9c71ypUrh3LlyuGLL75A7969sXr1anTu3DlXNRBRVuxRISom3N3dce7cOYSHhyM2NhYjR47Eo0ePMHr0aAQHB2PHjh2YNm0axo8fD5lMlu06arUaZcuWxcWLF7F//37cuXMHU6ZMwYULFz6qtocPH2L8+PEICQnBhg0b8Ntvv2Hs2LE5Xr9Pnz6wsbFBx44dcfLkSYSFheHYsWMYM2bMB09jZSpbtiw6duyIoUOH4tSpU7h69Sr69u2LUqVKoWPHjlmWf/XqFUaNGoVjx47hwYMHCAwMxIULF1CxYsUc101EH8agQlRMfPnllzAwMICXlxdsbW2Rnp6OPXv24Pz58/D29sbw4cMxePBg/Oc//3nnOg8fPsSwYcPQpUsX9OzZE3Xq1MGzZ8+0eldyo1+/fnj16hVq164Nf39/jB07Fp9//nmO1zcxMcGJEyfg6uqKLl26oGLFihg8eDBSUlJ06mFZvXo1atSogfbt26NevXoQRRF79uzJ9koeAwMDPHv2DP369UO5cuXQo0cPtGnTBjNmzMjx/ojowwRRfGuyAiKiAtSkSRP4+PhIYop8IpIe9qgQERGRZHEwLRHlm4cPH753IOqtW7cKsBoiKox46oeI8k1GRobWNPZvc3d3h1zO70tE9G4MKkRERCRZHKNCREREksWgQkRERJLFoEJERESSxaBCREREksWgQkRERJLFoEJERESSxaBCREREksWgQkRERJL1X95Fzm3DJxvZAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# k-NN pipeline for Wine dataset\n",
-    "knn_pipeline_wine = Pipeline([\n",
-    "    ('scaler', StandardScaler()),\n",
-    "    ('knn', KNeighborsClassifier(n_neighbors=5))\n",
-    "])\n",
-    "knn_pipeline_wine.fit(X_train_wine_2d, y_train_wine)\n",
-    "print(\"k-NN (Wine) accuracy:\",\n",
-    "      accuracy_score(y_test_wine, knn_pipeline_wine.predict(X_test_wine_2d)))\n",
-    "\n",
-    "# Decision Tree pipeline for Wine dataset\n",
-    "dt_pipeline_wine = Pipeline([\n",
-    "    ('scaler', StandardScaler()),  # Not strictly necessary for DT\n",
-    "    ('dt', DecisionTreeClassifier(max_depth=3, random_state=42))\n",
-    "])\n",
-    "dt_pipeline_wine.fit(X_train_wine_2d, y_train_wine)\n",
-    "print(\"Decision Tree (Wine) accuracy:\",\n",
-    "      accuracy_score(y_test_wine, dt_pipeline_wine.predict(X_test_wine_2d)))\n",
-    "\n",
-    "# Visualise decision boundaries for Wine dataset\n",
-    "display = DecisionBoundaryDisplay.from_estimator(\n",
-    "    knn_pipeline_wine, X_wine_2d, response_method='predict',\n",
-    "    cmap=plt.cm.coolwarm, alpha=0.85,\n",
-    "    xlabel=wine.feature_names[5], ylabel=wine.feature_names[6],\n",
-    "    grid_resolution=500)\n",
-    "plt.title(\"k-NN Decision Boundary on Wine Dataset\")\n",
-    "plt.scatter(X_test_wine_2d[:, 0], X_test_wine_2d[:, 1], c=y_test_wine,\n",
-    "            edgecolor='k', s=20, cmap=plt.cm.coolwarm)\n",
-    "plt.show()\n",
-    "\n",
-    "display = DecisionBoundaryDisplay.from_estimator(\n",
-    "    dt_pipeline_wine, X_wine_2d, response_method='predict',\n",
-    "    cmap=plt.cm.coolwarm, alpha=0.75,\n",
-    "    xlabel=wine.feature_names[5], ylabel=wine.feature_names[6],\n",
-    "    grid_resolution=500)\n",
-    "plt.title(\"Decision Tree Decision Boundary on Wine Dataset\")\n",
-    "plt.scatter(X_test_wine_2d[:, 0], X_test_wine_2d[:, 1], c=y_test_wine, edgecolor='k', s=20,\n",
-    "            cmap=plt.cm.coolwarm)\n",
-    "\n",
-    "plt.show()  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "53a36d24",
-   "metadata": {},
-   "source": [
-    "-   Compare performance using 5-fold cross-validation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "c63bc657",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "k-NN Cross-validation scores: [0.61111111 0.80555556 0.61111111 0.88571429 0.8       ]\n",
-      "Decision Tree Cross-validation scores: [0.63888889 0.86111111 0.72222222 0.91428571 0.77142857]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "k-NN accuracy (mean ± std): 0.74 ± 0.11\n",
-      "Decision Tree accuracy (mean ± std): 0.78 ± 0.10\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Cross-validation\n",
-    "from sklearn.model_selection import cross_val_score\n",
-    "knn_scores = cross_val_score(knn_pipeline_wine, X_wine_2d, y_wine, cv=5)\n",
-    "dt_scores = cross_val_score(dt_pipeline_wine, X_wine_2d, y_wine, cv=5)\n",
-    "print(\"k-NN Cross-validation scores:\", knn_scores)\n",
-    "print(\"Decision Tree Cross-validation scores:\", dt_scores)\n",
-    "plt.boxplot([knn_scores, dt_scores], tick_labels=['k-NN', 'Decision Tree'])\n",
-    "plt.title(\"Cross-validation Scores for k-NN and Decision Tree on Wine Dataset\")\n",
-    "plt.ylabel(\"Accuracy\")\n",
-    "plt.show()\n",
-    "\n",
-    "print(f\"k-NN accuracy (mean ± std): {knn_scores.mean():.2f} ± {knn_scores.std():.2f}\")\n",
-    "print(f\"Decision Tree accuracy (mean ± std): {dt_scores.mean():.2f} ± {dt_scores.std():.2f}\")"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03C05 Support Vector Machines.ipynb b/src/S03 Supervised Learning - Classification and Evaluation/S03C05 Support Vector Machines.ipynb
deleted file mode 100644
index 9bc6d5f..0000000
--- a/src/S03 Supervised Learning - Classification and Evaluation/S03C05 Support Vector Machines.ipynb	
+++ /dev/null
@@ -1,3144 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "c662f3b2",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Support Vector Machines\n",
-    "\n",
-    "Support Vector Machines (SVMs) are powerful supervised learning models used for classification and regression tasks. They are particularly effective in high-dimensional spaces and are known for their robustness against overfitting, especially in cases where the number of dimensions exceeds the number of samples.\n",
-    "\n",
-    "SVMs work by finding the optimal hyperplane that separates different classes in the feature space. The key idea is to maximize the margin between the closest points of the classes, known as support vectors."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4e06f01e",
-   "metadata": {},
-   "source": [
-    "## Mathematical Formulation\n",
-    "\n",
-    "Given a training set $\\{(x_i, y_i)\\}_{i = 1}^{n}$ where $y_i \\in \\{-1, 1\\}$, the SVM aims to find a hyperplane defined by the equation: $$w^T x + b = 0,$$ where $w$ is the weight vector and $b$ is the bias term. The goal is to maximize the margin defined as: $$\\text{Margin} = \\frac{2}{\\|w\\|}.$$ This leads to the following optimization problem: $$\\begin{align*} \\text{minimize} & \\quad \\frac{1}{2} \\|w\\|^2 \\\\ \\text{subject to} & \\quad y_i (w^T x_i + b) \\geq 1, \\quad \\forall i = 1, \\ldots, n. \\end{align*}$$ This is a convex optimization problem that can be solved using techniques like Lagrange multipliers or quadratic programming."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1a7ca668",
-   "metadata": {},
-   "source": [
-    "## Linear SVMs\n",
-    "\n",
-    "Linear SVMs are used when the data is linearly separable. The algorithm finds the hyperplane that best separates the classes by maximizing the margin. The support vectors are the data points that lie closest to the hyperplane and are critical in defining its position. The hyperplane is defined by the equation: $$w^T x + b = 0,$$ where $w$ is the weight vector and $b$ is the bias term. The decision function for a new data point $x$ is given by: $$f(x) = \\text{sign}(w^T x + b).$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d97c3685",
-   "metadata": {},
-   "source": [
-    "### Example of Linear SVM\n",
-    "\n",
-    "To illustrate the concept of linear SVMs, we will use the Iris dataset, and keep only two features for visualization purposes. We will train a linear SVM model and visualize the decision boundary along with the support vectors.\n",
-    "\n",
-    "-   Load the `iris` dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "7404af47",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.datasets import load_iris\n",
-    "data = load_iris()\n",
-    "X = data.data[:, 2:4]\n",
-    "y = data.target"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7e248745",
-   "metadata": {},
-   "source": [
-    "-   Train-test split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "db2a7a66",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec95773a",
-   "metadata": {},
-   "source": [
-    "-   Create a Pipline for scaling and SVM and fit"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ff387210",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
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-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;svm&#x27;, SVC(kernel=&#x27;linear&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('steps',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">steps&nbsp;</td>\n",
-       "            <td class=\"value\">[(&#x27;scaler&#x27;, ...), (&#x27;svm&#x27;, ...)]</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('transform_input',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">transform_input&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('memory',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">memory&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"scaler__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('copy',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">copy&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('with_mean',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">with_mean&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('with_std',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">with_std&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"svm__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">C&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('kernel',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">kernel&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;linear&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('degree',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">degree&nbsp;</td>\n",
-       "            <td class=\"value\">3</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('gamma',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">gamma&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;scale&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('coef0',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">coef0&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('shrinking',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">shrinking&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('probability',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">probability&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('cache_size',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">cache_size&nbsp;</td>\n",
-       "            <td class=\"value\">200</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_iter&nbsp;</td>\n",
-       "            <td class=\"value\">-1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('decision_function_shape',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">decision_function_shape&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('break_ties',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">break_ties&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
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-       "\n",
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-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
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-       "\n",
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-       "                element.style = originalStyle;\n",
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-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
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-       "\n",
-       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
-      ],
-      "text/plain": [
-       "Pipeline(steps=[('scaler', StandardScaler()), ('svm', SVC(kernel='linear'))])"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn import svm\n",
-    "\n",
-    "pipeline = Pipeline([\n",
-    "    ('scaler', StandardScaler()),  # Scale features\n",
-    "    ('svm', svm.SVC(kernel='linear', C=1.0))  # Linear SVM\n",
-    "])\n",
-    "pipeline.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "09b2ad60",
-   "metadata": {},
-   "source": [
-    "-   Plot the decision boundary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "c57e1a08",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1000x600 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.inspection import DecisionBoundaryDisplay\n",
-    "\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "DecisionBoundaryDisplay.from_estimator(pipeline, X, response_method='predict',\n",
-    "                                       cmap=plt.cm.coolwarm, alpha=0.8,\n",
-    "                                       grid_resolution=500)\n",
-    "plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train,\n",
-    "            edgecolors='k', marker='o', s=50, label='Training Data')\n",
-    "# plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test,\n",
-    "#             edgecolors='k', marker='o', s=50, label='Test Data')\n",
-    "# Scale the support vectors back to match the scaled features\n",
-    "support_vectors = pipeline.named_steps['svm'].support_vectors_\n",
-    "support_vectors_scaled = pipeline.named_steps['scaler'].inverse_transform(support_vectors)\n",
-    "plt.scatter(support_vectors_scaled[:, 0],\n",
-    "            support_vectors_scaled[:, 1],\n",
-    "            facecolors='none', s=100, edgecolors='k', label='Support Vectors')\n",
-    "plt.title('Linear SVM Decision Boundary on Iris Dataset')\n",
-    "plt.xlabel('Petal Length (cm)')\n",
-    "plt.ylabel('Petal Width (cm)')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b7124bb",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "7a07484e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SVM Accuracy: 1.00\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "      setosa       1.00      1.00      1.00        19\n",
-      "  versicolor       1.00      1.00      1.00        13\n",
-      "   virginica       1.00      1.00      1.00        13\n",
-      "\n",
-      "    accuracy                           1.00        45\n",
-      "   macro avg       1.00      1.00      1.00        45\n",
-      "weighted avg       1.00      1.00      1.00        45\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import accuracy_score, classification_report, ConfusionMatrixDisplay\n",
-    "y_pred = pipeline.predict(X_test)\n",
-    "accuracy = accuracy_score(y_test, y_pred)\n",
-    "print(f\"SVM Accuracy: {accuracy:.2f}\")\n",
-    "print(classification_report(y_test, y_pred, target_names=data.target_names))\n",
-    "ConfusionMatrixDisplay.from_estimator(pipeline, X_test, y_test, display_labels=data.target_names,\n",
-    "                                      cmap=plt.cm.Blues)\n",
-    "plt.title('Confusion Matrix')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1a3b7d12",
-   "metadata": {},
-   "source": [
-    "-   Cross-validation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e1e15ba2",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Cross-Validation Score: 0.96 ± 0.02\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.model_selection import cross_val_score\n",
-    "cv_scores = cross_val_score(pipeline, X, y, cv=5)\n",
-    "print(f\"Cross-Validation Score: {np.mean(cv_scores):.2f} ± {np.std(cv_scores):.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b037e99b",
-   "metadata": {},
-   "source": [
-    "## Nonlinear SVMs\n",
-    "\n",
-    "When the data is not linearly separable, SVMs can still be applied by transforming the input features into a higher-dimensional space where a linear separation is possible. This is achieved using kernel functions, which implicitly map the data into a higher-dimensional space without the need for explicit computation.\n",
-    "\n",
-    "Mathematically, the optimization problem remains similar, but the decision function is modified to include a kernel function: $$f(x) = \\sum_{i=1}^{n} \\alpha_i y_i K(x_i, x) + b,$$ where $K(x_i, x)$ is the kernel function that computes the inner product in the transformed space, and $\\alpha_i$ are the Lagrange multipliers."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2483ca7b",
-   "metadata": {},
-   "source": [
-    "### Example of Nonlinear SVM\n",
-    "\n",
-    "-   Generate synthetic data for non-linear classification"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "c0fff753",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.datasets import make_moons\n",
-    "X_moons, y_moons = make_moons(n_samples=100, noise=0.1, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0aea60b2",
-   "metadata": {},
-   "source": [
-    "-   Visualize the synthetic dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "8efc4052",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.scatter(X_moons[:, 0], X_moons[:, 1], c=y_moons, edgecolors='k', marker='o', s=50)\n",
-    "plt.title('Synthetic Moons Dataset')\n",
-    "plt.xlabel('Feature 1')\n",
-    "plt.ylabel('Feature 2')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "65ec448e",
-   "metadata": {},
-   "source": [
-    "-   Split the dataset into training and testing sets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "97d0aca0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train_moons, X_test_moons, y_train_moons, y_test_moons = \\\n",
-    "    train_test_split(X_moons, y_moons, test_size=0.3, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "58362709",
-   "metadata": {},
-   "source": [
-    "-   Create and train the SVM model with a radial basis function kernel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "ef44ccf6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-2 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-2 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-2 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-2 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
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-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">C&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('kernel',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">kernel&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;rbf&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">degree&nbsp;</td>\n",
-       "            <td class=\"value\">3</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">gamma&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;scale&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('coef0',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">coef0&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">shrinking&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">probability&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('cache_size',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">cache_size&nbsp;</td>\n",
-       "            <td class=\"value\">200</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_iter&nbsp;</td>\n",
-       "            <td class=\"value\">-1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('decision_function_shape',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">decision_function_shape&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('break_ties',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">break_ties&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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-      ],
-      "text/plain": [
-       "SVC()"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_nonlinear = svm.SVC(kernel='rbf', gamma='scale', C=1.0)\n",
-    "model_nonlinear.fit(X_train_moons, y_train_moons)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f025225e",
-   "metadata": {},
-   "source": [
-    "-   Plotting the decision boundary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "9b623c5d",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1000x600 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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yBUDFPuObNm2CSqXC8OHD1d8x9+7dg6urK/z9/bFv375KP97agImQHpk9ezYKCgrK7CsUGxsLmUyGBg0aFNvu6uoKW1tbxMbGFtvu7e1d4hx2dnZ48OBBleJ7/HxFSVHR+W7cuAEhBN5//304OTkVu82dOxcAkJSUBABQqVRYsmQJ/P39oVAo4OjoCCcnJ5w7d65EezVQ2HxYUTY2Nvi///s/xMTEICYmBt9//z0aNWqEZcuWYcGCBcXKjho1Sj3iCwCOHDmCmJiYJ1b5jxw5Ehs2bMCNGzdw5MgRjTaLAYVTAACAlZUVAKi/kENDQ0s8t6tWrUJubi7S0tKQnJyM9PT0Sk+74Ovri2nTpmHVqlVwdHRE7969sXz58lJfi0fFxsaiUaNGJbYXVdk/6T35+HvoSdq3b4+ePXuiZ8+eGDVqFLZv346mTZtiypQp6v5PsbGxcHd3Vz93T4qpMp4Uf9Fn1M/Pr1i50p6j7OxszJkzR93/pegzkJqaWuHPQOPGjdGuXbtizWM///wzOnbsWOJ74nHafu2AwqSjZ8+eCAsLw6ZNm6BUKkt0Kn40voq8jjXxvfjBBx8gNTUVDRs2RIsWLfDOO+/g3LlzFX6c5cnIyCj2mCr7PijNxYsX8eyzz8LGxgbW1tZwcnJSJ8JF56jIZ/z69esQQsDf37/E98zly5fV39/6hqPG9Ej9+vXx4osvYuXKlcXadh9X1q/6xz3+67GIeELfmaqeT6VSAQDefvtt9O7du9SyRV9WH374Id5//328/PLLWLBgAezt7SGTyTB16lT1eR5Vkdqg0vj4+ODll1/Gs88+i/r16+Pnn38uNg/QgAEDYGNjg7CwMIwcORJhYWGQy+Wl9i141AsvvICZM2diwoQJcHBwwNNPP12l+Mpy4cIFAP89X0XPySeffIKAgIBSj7G0tMT9+/erfM3PPvsMY8aMwdatW7Fr1y688cYbWLx4MY4dOwZPT88qn/dRmn5PymQydOvWDV988QWuX7+OZs2aVfjYsj5Hj3cwfZQm43/99dexevVqTJ06FUFBQbCxsYEkSRgxYkSlPgOjR4/Gm2++ibi4OOTm5uLYsWPFOuNqiqYe+8iRIzFhwgQkJCSgb9++sLW11UB0mv1e7NKlC27evKn+LKxatQpLlizBihUrMH78+CrHmJ+fj2vXrhX7oVLZ98HjUlNTERISAmtra3zwwQfw8/ODqakpTp8+jf/973/FzvGkz7hKpYIkSfjrr79KfZ4qMkdabcRESM/Mnj0bP/30Ez7++OMS+3x8fKBSqXD9+nX1ryKgsNNjamqquqOwrtSvXx9A4fD7nj17llt248aN6NatG77//vti21NTU4sNJ9cUOzs7+Pn5qROMIgqFAkOHDsXatWuRmJiIDRs2oHv37nB1dS33fN7e3ujUqRP279+PSZMmlRi1VF3r1q0DAHVCWVTDYG1tXe5z6+TkBGtr6xKPs6JatGiBFi1aYPbs2Thy5Ag6deqEFStWlDmJpI+PT6lzThVVx2vjPVlQUADgv1o0Hx8f7NmzBw8fPiz2y/vxmIpqNB7vDF6dGqOiz+jNmzeL1baU9hxt3LgRoaGh+Oyzz9TbcnJyyu2cXpoRI0Zg2rRp+OWXX5CdnQ1jY2M8//zzFYpVF6/ds88+i1deeQXHjh3Db7/9Vm58FXkda+p70d7eHmPHjsXYsWORkZGBLl26YN68edVKhDZu3Ijs7OxiPxQr+j4oK9Hbv38/UlJSsGnTpmJTgURHR5davrzPuJ+fH4QQ8PX1LbNrwJPiqY3YNKZn/Pz88OKLL+Lbb79FQkJCsX39+vUDgBK98z///HMAKHVkijY5Ozuja9eu+Pbbb0vt5/TorKpyubzEL8kNGzaUOsN2ZZw9e7bU+UhiY2Nx6dKlUpsCRo0ahfz8fLzyyitITk6u8EiYhQsXYu7cuXj99derFfPjwsLCsGrVKgQFBaFHjx4AgLZt28LPzw+ffvqp+g/+o4qeW5lMhsGDB2Pbtm04efJkiXJl/XpPT09XJxRFWrRoAZlMVu4s1/369UNERESxYdqZmZlYuXIl6tWrh6ZNmz75AVdDfn4+du3aBRMTE/UfwX79+kGpVJaoFVmyZAkkSULfvn0BFCaVjo6OOHDgQLFyX3/9dZXjKTr3l19+WWx7aSNqSvsMfPXVV+XWSJXG0dERffv2xU8//aSexqEiPyZ09dpZWlrim2++wbx58zBw4MBy46vI61gT34spKSklYm7QoEG1Znw/e/Yspk6dCjs7O0yePFm9vaLvg6J5ox5PkIpqbh49R15eXon3cUU+40OGDIFcLsf8+fNLxCSEKPa8WFhYVLjpTtdYI6SHZs2ahXXr1uHq1avFqvpbtWqF0NBQrFy5Ul0dGhERgR9//BGDBw9Gt27ddBh1oeXLl6Nz585o0aIFJkyYgPr16yMxMRFHjx5FXFycep6gAQMG4IMPPsDYsWMRHByM8+fP4+eff1bXKlXV7t27MXfuXAwaNAgdO3aEpaUloqKi8MMPPyA3N7fUtZ5CQkLg6emJrVu3wszMDEOGDKnQtUJCQhASElKteDdu3AhLS0vk5eUhPj4eO3fuxOHDh9GqVSts2LBBXU4mk2HVqlXo27cvmjVrhrFjx8LDwwPx8fHYt28frK2tsW3bNgCFzY67du1CSEgIJk6ciCZNmuDu3bvYsGEDDh06VGpTxN69ezFlyhQMGzYMDRs2REFBAdatWwe5XI7nnnuuzPhnzJiBX375BX379sUbb7wBe3t7/Pjjj4iOjsbvv/9e6qSX1fHXX3+pawSSkpIQFhaG69evY8aMGeo5ogYOHIhu3bph1qxZiImJQatWrbBr1y5s3boVU6dOLdZ/Z/z48fjoo48wfvx4BAYG4sCBA+rZx6siICAAL7zwAr7++mukpaUhODgY4eHhpc63M2DAAKxbtw42NjZo2rQpjh49ij179sDBwaHS1x09erS6r83j/eDKou3X7lGhoaFPLFPR17EmvhebNm2Krl27om3btrC3t8fJkyexceNGTJkypULHHzx4EDk5OeqBIIcPH8Yff/wBGxsbbN68uViNc0XfBwEBAZDL5fj444+RlpYGhUKB7t27Izg4GHZ2dggNDcUbb7wBSZKwbt26EolMRT7jfn5+WLhwIWbOnImYmBgMHjwYVlZWiI6OxubNmzFx4kS8/fbbAAp/nP3222+YNm0a2rVrB0tLy3ITW53S7iA1qoxHh5U+LjQ0VAAoNnxeCCHy8/PF/Pnzha+vrzA2NhZeXl5i5syZxYarC1FyKGqRkJAQERISor5fmeHzn3zySYnz4bEhwUIIcfPmTTF69Gjh6uoqjI2NhYeHhxgwYIDYuHGjukxOTo6YPn26cHNzE2ZmZqJTp07i6NGjZcb3pGHdRaKiosScOXNEx44dhbOzszAyMhJOTk6if//+xYbRPu6dd94RAMTw4cNL3V/ec/Coyg6fL7qZmpoKT09PMWDAAPHDDz+UeD2LnDlzRgwZMkQ4ODgIhUIhfHx8xPDhw0V4eHixcrGxsWL06NHCyclJKBQKUb9+fTF58mT18OfHX/eoqCjx8ssvCz8/P2Fqairs7e1Ft27dxJ49e4qd9/Hh80IUvt5Dhw4Vtra2wtTUVLRv3178+eefxcqU9TqWN4T9UaUNnzc1NRUBAQHim2++KTalgBCFw8Dfeust4e7uLoyNjYW/v7/45JNPSpTLysoS48aNEzY2NsLKykoMHz5cJCUllTl8/vHpDoriio6OVm/Lzs4Wb7zxhnBwcBAWFhZi4MCB4vbt2yXO+eDBAzF27Fjh6OgoLC0tRe/evcWVK1dKPMflfU8Uyc3NFXZ2dsLGxkZkZ2eX+1w+SpuvXXnxC1H6d1ZFX0dNfy8uXLhQtG/fXtja2gozMzPRuHFjsWjRIpGXl1fuYyh6ropuxsbGwsnJSXTp0kUsWrRIJCUllTimou8DIYT47rvvRP369YVcLi/2+T18+LDo2LGjMDMzE+7u7uLdd98VO3furNJnXAghfv/9d9G5c2dhYWEhLCwsROPGjcXkyZOLTSmSkZEhRo4cKWxtbdXTldRWkhBV7IVIRER6oaCgAO7u7hg4cGCJfndEho59hIiI6rgtW7YgOTkZo0eP1nUoRLUOa4SIiOqo48eP49y5c1iwYAEcHR2rvQQEUV3EGiEiojrqm2++waRJk+Ds7FyhBZWJDBFrhIiIiMhgsUaIiIiIDBYTISIiIjJYnFDxCVQqFe7cuQMrKyu9mjKciIjIkAkh8PDhQ7i7u5c7ASgToSe4c+cOvLy8dB0GERERVcHt27fLXRiaidATFC3mt+/F4bA0MVFvL0i+C/eQVvgk42VdhUZEpHfMHe3x7M6XIEmA3NFN1+FQHZaRl4duP60vtihvaZgIPUFRc5iliYk6ESpIigeMjbDK7gMoClLKO5yIiB6hzMiHxcc/QJo9EUaP/LgkqilP6tbCztKVpEyOh2ePNljTfiWykpkEERFV1oajlV84lqimMBGqhIKkeAgB3O72qq5DISLSe8rkeF2HQMREqKKU9xMBAOv7/44f/9BxMEREeiwrOQVi4UpwOl+qDdhHqIKEUgXPXoFsDiMi0oANRx0wXNdBABDGxhBm5gCnR9EvQkDKzoKUn1/tUzERqiDx/hdYuLf8nudERKQfBID8gLYQjZtCkssBMBHSLwJCqYR05RKMI09V69VjIlRBYTsAhamuoyAiqlsKkuJh5Oyh9evmB7SF1LI1nOztYWokh8RESK8ICOQUKHHPRIF8ACaRp6p8LiZCRESkdVnJKVjf/3cM3/6c1q8tjI0hGjeFk709bE0VWr8+aYapkRFgb4+kxk0hLp6rcjMZO0sTEZFBEWbmkORymBrJdR0KVZOpkRySXF7Yz6uKmAgREZFhkSQAEpvD6oDC11CqVmd3JkJERKQTRaNwC5I4nxDpjt4kQosXL0a7du1gZWUFZ2dnDB48GFevXn3icRs2bEDjxo1hamqKFi1aYMeOHVqIloiIKmJ9/991HQIZOL1JhP755x9MnjwZx44dw+7du5Gfn4+nn34amZmZZR5z5MgRvPDCCxg3bhzOnDmDwYMHY/Dgwbhw4YIWIyciorpGqVTi7337MG3uXLz67v+w+MsvcTu+5mq2zOv5lntbuGRptc79x85dlYrBsUlTtOjaDROnv43T589X+pq9nx+Bd+Z/UJVwNU5vRo39/fffxe6vWbMGzs7OOHXqFLp06VLqMV988QX69OmDd955BwCwYMEC7N69G8uWLcOKFStqPGYiIqoYZUoC5A6uug6jQk6dO4cXJ01GbHwcrIxsYAxjZKjSsWjJF3hp2FB8sXABTDS8oGxURIT6/7//+ScWLFmCyPBw9TZLi6p3Fq6Mbz/5BL1CQpCbm4vr0VH44ZdfEDL4Waz4v48x6jntjwDUBL2pEXpcWloaAMDe3r7MMkePHkXPnj2LbevduzeOHj1a5jG5ublIT08vdiMiopqRlZwCj7deg1AqdR1KhVy6dg19R4xEWkI22qEb2hf0RBtlVwSr+qGBaIGfNvyOsW9OhdDw+iGuzk7qm7WVFaTHtm3Ytg2te/SEXcNGCOjeA9+uW6c+Ni8vD2/NmQPfdu1h17ARGnXqhE+Wfw0AaNypMwBgxCuvwLyer/p+WWysreHq7AQfL0/07NIFYd98gxHPPINpc+fhwb9/l1MePEDo62/Ar0NHODRugna9+2D91v/Wppo4/W0cPH4cy1evVtcwxd6Og1KpxKvv/g9NOj8F+0aN0ap7dyz/YbVGn8fS6E2N0KNUKhWmTp2KTp06oXnz5mWWS0hIgIuLS7FtLi4uSEhIKPOYxYsXY/78+RqLlYiIyrfkWg8Mx9e6DqNCZi/+CFKuEQKUnWEkGau3G0lG8IY/TIQpNv/1Fw4eO44uQR21EtOvW7ZgwedL8PkH8xHQrBkiL17ElBkzYWFmjheHPoev16zB9j3h+Gn5Mni5uyPu7l3E3bkLADj4x1b4tA1U1/TI5ZWvH5kybhx+3rQJew8exHMDBiAnNxetWzTHtFdfhbWVJf7euxfjpk2Dr4832gUE4JO5c3A9OhpNGzXE+29NAwA4OdhDpVLBw9UVP329HA52djh26hSmzHwPrs5OeG7AAI0+Z4/Sy0Ro8uTJuHDhAg4dOqTxc8+cORPTpk1T309PT4eXl5fGr0NERPrldnw8du7fj8aiTbEk6FEu8ESs/ApWrluntURo4ZKl+GjWLAzu0wcAUM/LC1euX8f3YWF4cehzuH3nDhrUq4fgdu0gSRK8PT3Vxzo5OAD4r6anKhr5+QEAYuPiAAAerq6YOnGiev+kMWOw+8BBbNq+He0CAmBjbQ0TY2OYm5oVu6ZcLsf7095S36/n5YXjp0/j9+3bmQg9asqUKfjzzz9x4MABeD7yYpbG1dUViYmJxbYlJibC1bXsdmiFQgGFgjONEhFRcecuX4YQAo4o+2+IJEmwUzrj1NlzWokpMysLUbGxmPS//2HyzJnq7QUFBbCxLlwf88WhQzHwxZfQqnt39AoJQd/u3dGzjL61VSFQ2Awo/TuXj1KpxP8tX45N23fgTkIC8vLzkZuXB3OzJ69TtWLtWqxdvwFxd+4gOycHefn5aNm0icZiLY3eJEJCCLz++uvYvHkz9u/fD19f3yceExQUhPDwcEydOlW9bffu3QgKCqrBSImIqCp0te5YRRX9oa9I7x9JS6vZZ/w7cnr5R4vRLiCg2D65vHDm7NbNm+PSwQPYtf8f7D18CC9NnoJunTsh7JtvNBLDlRs3ABTW4ADAkm9X4uvVa/B/c95Hs0aNYGFujnc+WIC8vPKXwNjwxza8t+hDLJ49Cx1at4GVpQWWfLsSJyIjNRJnWfQmEZo8eTLCwsKwdetWWFlZqfv52NjYwMzMDAAwevRoeHh4YPHixQCAN998EyEhIfjss8/Qv39//Prrrzh58iRWrlyps8dBRETF6XLdscpo1awZZDIZ7qnuwBN+pZYRQuC+PBH92nTVSkwuTk5wc3FB9K1bGDF4cJnlrK2sMHTgAAwdOADP9u2LZ0LH4H5qKuxtbWFsbAyVquqd1Zf/8AOsrazQ7d+O1kdPnUT/Xr3wwrPPAijs13sjOgqNG/irjzExMYbysWsePXUSHdu2xSsvvaTeFn3rVpXjqii9SYS++Tdz7dq1a7Htq1evxpgxYwAAt27dgkz2X0ev4OBghIWFYfbs2Xjvvffg7++PLVu2lNvBmoiIqDQerq7o36Mn9u49ChelF4ylkkPk7yIWGcp0THzpRa3FNfutqXh73nzYWFkVDm3Py8Ppc+eRmp6GN8aPx5erVsHVyfnfRE7Cph074OLkBFtrawCAj6cH9h0+go5tA6FQmMDOxqbMa6WlpyMhKRl5ebm4Hh2N78PCsG3Xbqz6/DPY2hSer0E9X2z+6y8cO3UKtjY2+HLVKiTdSymWCHl7euJEZCRib8fBwsIc9ra2aFCvHsI2bcbuf/5BPS8vhG3ejFPnzsHnCd1gqktvEqGKDEXcv39/iW3Dhg3DsGHDaiAiIiIyNAtn/g9djj2LyKyDqK9sDns4Q5Ik5IlcxCMK0dJljBoyBEGBgVqLaeyIETA3M8OSb1fivcUfwcLMDM0aNcLkl18GAFhaWODzb7/FzZgYyOVytG3ZEptXr1ZXHCyeNQszFi7C6l9/hbuLC64cLnsg0iv/zstnqlDA3dUVwYGBOLB1C1o/UsHwv9enIPrWLQwaHQpzM1OMfeEFDOzVC2kPH6rLTJ0wAROmv402vXohOycHlw8exLiRIxF58RJGT3kdkiRh2KBBmPjii9hZyt92TZKEpic7qGPS09NhY2ODKfMuQGFqpetwiIjqJHMnBwzf/pxW+gipbGxRMOBZeLu5QVGFFegvXr2K0ZNfx+Ub12FuZAFjyQQZynTIZBJeCR2ND2fOhJGR3tQz6LXcAiVu3b0Loz83Q5aWWmxfRl4e2v3wE9LS0mD9b+1XafhKERERVUKzRo1wcvdOHDlxAjv37Ud2Tg68PTzw/OBn4OzoqOvwqJKYCBERkc49uhJ9bR45VkSSJHRq3x6d2rfXdShUTXq7xAYREdUtXImedIGJEBERERksJkJERGRYhEDhfMgcK6TvCl9D8e9rWjVMhIiIyKBI2VkQSiVyCvRjxXsqW06BEkKphJSdVeVzsLM0EREZFCk/H9KVS7hnogDs7WFqJIcE7SyJQZohIJBToMS9+/chXbkEKb/85TvKw0SIiIhqBW2OHDOOPIV8AEmNm0KSywEmQnpGFNYEXbkE48hT1ToTEyEiIqo1tLXmmATAJPIUxMVzEGbmgJYWSSUNEQJSdla1aoKKMBEiIiKDJeXnQ8pP03UYpEPsLE1EREQGi4kQERERGSwmQkRERGSwmAgRERGRwWIiREREtY4yOV7XIZCBYCJERES1RlZyCtb3/706KyYQVQoTISIiIjJYTISIiIjIYDERIiIiIoPFRIiIiIgMFhMhIiIiMlhMhIiIiMhgMREiIqJaiXMJkTYwESIiololKzkFYuFKziVEWsFEiIiIap0NRx10HQIZCCZCREREZLCYCBEREZHBYiJEREREBouJEBERERksJkJERERksJgIERERkcFiIkREREQGi4kQERERGSwmQkRERGSwmAgREVGtxfXGqKYxESIiolonKzkF6/v/zvXGqMYxESIiIiKDpVeJ0IEDBzBw4EC4u7tDkiRs2bKl3PL79++HJEklbgkJCdoJmIiIiGo1vUqEMjMz0apVKyxfvrxSx129ehV3795V35ydnWsoQiIiItInRroOoDL69u2Lvn37Vvo4Z2dn2Nraaj4gIiIi0mt6VSNUVQEBAXBzc0OvXr1w+PDhcsvm5uYiPT292I2IiIjqpjqdCLm5uWHFihX4/fff8fvvv8PLywtdu3bF6dOnyzxm8eLFsLGxUd+8vLy0GDERERFpk141jVVWo0aN0KhRI/X94OBg3Lx5E0uWLMG6detKPWbmzJmYNm2a+n56ejqTISIiojqqTidCpWnfvj0OHTpU5n6FQgGFQqHFiIiIiEhX6nTTWGkiIyPh5uam6zCIiIioFtCrGqGMjAzcuHFDfT86OhqRkZGwt7eHt7c3Zs6cifj4eKxduxYAsHTpUvj6+qJZs2bIycnBqlWrsHfvXuzatUtXD4GIiIhqEb1KhE6ePIlu3bqp7xf15QkNDcWaNWtw9+5d3Lp1S70/Ly8P06dPR3x8PMzNzdGyZUvs2bOn2DmIiIjIcElCcCWX8qSnp8PGxgZT5l2AwtRK1+EQERkMcycHDN/+HIycPXQdCumhjLw8tPvhJ6SlpcHa2rrMcgbXR4iIiIioCBMhIiIiMlhMhIiIiMhgMREiIiIig8VEiIiIarWCpHhdh0B1GBMhIiKqlbKSU7C+/++6DoPqOCZCREREZLCYCBEREZHBYiJEREREBouJEBERERksJkJERERksJgIERERkcFiIkREREQGi4kQERERGSwmQkRERGSwmAgRERGRwWIiRERERAaLiRAREREZLCZCREREZLCMdB0AEWmHSqXErRuHcD85CjK5Ebx8O8DBpaGuwyIi0ikmQkQG4MrZP3Bgx2I8TLsDmWQEIVQQUMGjXnv0GrIYDs4NdB0iEZFOMBEiquPORfyC3ZtmwAkeaIzusBZ2EBBIQjyib13BL18/ixde28xkiIgMEvsIEdVhWRn3EL7lfXigPlqiI2wke0iSBJkkg6vkhUBVCIzyJOzZPEvXoRIR6QQTIaI67PyJ3wAh0ADNIUlSif3Gkgl8VY0QF30MKYnXdBAhEZFuMREiqsNuRx2FvXCCsWRSZhlneEKChLjoCC1GRkRUOzARIqrDVAUFkENebhkJEiRJBqUyX0tRERHVHkyEiOowe5cGSJM9gEqoyiyThvtQCSXs2VmaiAwQEyGiOqxl+xeQo8pEAm6Vul8IgVhcg7WtJ3z8Omk5OiIi3WMiRFSHObs3Q6OWA3FFOoM7IqZYzVCeyMUVnEEy4tGl30xIMn4dEJHh4TxCRHVcn+GfQZIkXDr7B27KLsNWZQcllLgvJUGSydDrmY/QqOUAXYdJRKQTTISI6jgjIwX6v/AV2oVMwrmIMNxPugm53BhN/ELRPHA4zC0ddB0iEZHOMBEiMhDO7k3Rc/BCXYdBRFSrsFMAERERGSwmQkRERGSwmAgRERGRwWIiREREtdbw7c/pOgSq49hZmohqPaFSIfbmYdy+eQTKgjzYOdVH41YDoTC11nVoVIPMnQpHNBo5e+g4EqrLmAgRUa1259Zp/PXrVKTej4WpzAJyyQhZyofYv+0DdOzxOtp3nQxJknQdJhHpKb1qGjtw4AAGDhwId3d3SJKELVu2PPGY/fv3o02bNlAoFGjQoAHWrFlT43ESkWYkxp3HhpUvQDzIRCC6opOqD4JUvdAJfeFR4I1DOz/BoZ2f6DpMItJjepUIZWZmolWrVli+fHmFykdHR6N///7o1q0bIiMjMXXqVIwfPx47d+6s4UiJSBPCt74PM6U52ojOsJUc1TU/ppIZ/KWW8ENzROxfjgf3YnQbKBHpLb1qGuvbty/69u1b4fIrVqyAr68vPvvsMwBAkyZNcOjQISxZsgS9e/euqTCJaoXszAe4eHoj7t29AkgS3L3boHHAMzBRWOg6tApJvnsZd2+fQQt0hFwq/avKG/64JbuBs8d/Qtf+s7UcIRHVBXqVCFXW0aNH0bNnz2LbevfujalTp5Z5TG5uLnJzc9X309PTayo8ohohhMCJf1bgyO7PIFQqWEv2EBC4eGoj/tm+ED2eXYimrYdU+dx3bp3CtXM7kJudBjNLezRu9QxcPJpr+FEACXFnAQBOcC+zjFySw17lhITYMxq/PhEZhjqdCCUkJMDFxaXYNhcXF6SnpyM7OxtmZmYljlm8eDHmz5+vrRCJNC7in29w6O+P4YOG8EEjmEABAMhBFm7kXcRfv70FmcwIjVsNqtR501PvYNu6V5AQfw6mMkuYwgzZyMTJAyvhVT8IA0Yu1+i6ZUKICpWTIEGgYmWJiB6nV32EtGHmzJlIS0tT327fvq3rkIgqLCsjBUd2fQ4fNIS/1BImkkK9z1QyRzMEwhme2PfHfCiV+RU/b+Z9/LZiGNLuRiEAndFJ1RuBIgSdVH3QEkFIjInEhu9eQF5upsYei7N7MwBAChLKLKMSKtyXJcPFs4XGrktEhqVOJ0Kurq5ITEwsti0xMRHW1tal1gYBgEKhgLW1dbEbkb64eGoDIFTwQaNS90uShPpogqzMe7h5aXeFz3v60CpkpSWhjeopOEqu6k7LMkkGZ8kDbVSdkZJ0A+dP/KqRxwEArp4t4ezWDDHSNaiEqtQycYhCniobrTqM0th1iciw1OlEKCgoCOHh4cW27d69G0FBQTqKiKhmJd29BGvYF6sJepylZANTmSWS716q0DmVynycOxYGN+ENM6n0jtaWkg2c4Y7IIz9WKe6ydBs0Dw+lVJyVjiBDpKm354s8RIvLuIazaNXxJTi4NKzS+TPSE3Fs71f4M2wKtv/6Bs4cWYPcHPYLJDIketVHKCMjAzdu3FDfj46ORmRkJOzt7eHt7Y2ZM2ciPj4ea9euBQC8+uqrWLZsGd599128/PLL2Lt3L9avX4/t27fr6iEQ1ShJkgES8KQuM4V9aio2CWHmwyRkZz+AA5qWW85RuOLi/RPIz8+BsbFpxQJ+Ak/f9hgy9kfs+PVNHMvcDUvJDnIYIQOpUEGFtp3Ho0vfmZU+rxACR3Z/juP7lkMGGaxhDwEVrkZuw4Edi9Fj8AI0DxyukcdARLWbXiVCJ0+eRLdu3dT3p02bBgAIDQ3FmjVrcPfuXdy6dUu939fXF9u3b8dbb72FL774Ap6enli1ahWHzlOd5eYVgCtntiIX2VBIpTf/pon7yBWZcPMOqOBZK5YwFXVY1vQczz7+nTFx5lFcv7gTt6OOFi6x4eiLZm2HwtLa5cknKMWR3Z/j2N4v4Ysm8IY/jCUTAEAusnGj4CJ2bnwHciMTNAkYrMFHQkS1kV4lQl27di13JElps0Z37doVZ85waC0ZhqZthuDAjsWIKriExqJNiaUnVEKFKOkSrKzdUa9h1wqd09LaBRaWzkjOuANHuJVZLlm6CwdHfxhpqDboUXIjEzRuNRCNWw2s9rky0hNxfN9y+KIJ/KRmxfYpJDM0FW2hQgH+2bYQDVv0h1xuXO1rElHtVaf7CBEZGoWpNboNmod4ROMiTiJTPFTvSxcPcFY6gvtIQq8hiyGTySt0TplMjlYdX0SCFIcMUXr/mTSRgmRxBwHBozXyOGrShZPrIYMMPii9X5EkSaiHxsjMTEbU5T1ajo6ItI2JEJGeKSjIRdr9W0hPvQOhKjmaqmX7F9B76CdINU3DUezEYdkuHJL+RgTCkWcJPDvmB/g26lqpazZqNQDGCnOcwn7cFbegEkoAgFIUIF5E4Yx0GG5eAXrRryY54QpsYA8jqeyaHivJFgqZOZITrmoxMnqUuZMDhm9/TtdhkAHQq6YxIkP2MC0Bpw6uxPkTvyEvNwMAYG3jgVbBo9E6KBTGJv/1CWoeOByNA57B9Qt/IfnuFUiSDG7erVG/cfcK1wQVSbt/GxtWvgBlbi6MYYKLiMBVGMNEKJCDbKigRL0GIRgwanmNNItpmkySQYXSh+MXEUJASCqual8LGDl76DoEquOYCBHpgZSkG1j/7fPIz86Au8ob9nCGCiokpcXj8N//h+vnd2DYhDCYKCzVxxgZKdAkYDCaBFTv2tvDpkCVmY2OogcUkhkyRToSEYcC5EMGOZKkeDxMjS927drMzbsNrp79s9wO5am4hzxVDtx92mo5OiLSNjaNEdVyKpUSW1aPhSy7AB1VPeEvtYSD5AonyR3NpHYIFCG4F38ZezbP0vi1E+LO4W5cJPxVzdVJg4VkjfpSUzSUWqGB1ByNRWukJN/A7aijGr9+TWjaZgjkRia4iUulDr4o7FB+Gbb2PvD266SDCIlIm1gjRFTLRV3Zi9QHt9AO3aGQSjY9WUv2qC+a4MrZbejS770SQ8pTkq4jPuYkhEoJR9dGcPcJrHCTz81Lu2AiM4OjquzRYnZwgpnMCjcu7oK3X3DlHlw1CCFwO+oorkRuRXbmAyjMrNGo5QDU8+8CSVb2bzxTMxt0f+YD7Pr9XShRAF/RGJaSDYQQSMU9REmXkSY9wNChX7FpjMgAMBEiquWun98BK5k9bIR9mWXcUQ/XxXncuLQLAR1fAgDcS7yG8M2zERdz/N9ShTMt2js1QNcB71eow3RebiZMJNNyEwJJkmAChbrfkjY8TLuLLWvGI+nuBZjLrGGmMkeuLAcXT22AvVMDDA79HnaO9co8vkW75yE3MsY/2xbiWNZumEoWUEkq5KmyYWvng6HPfQmv+h219niISHeYCBHVctlZqVCoFOXOVGgkGcNYUiAnKxVA4cioX79+DiYFRmiODnCGOyTI8ABJiLl3DZvXjMWAkcvRsEW/cq9tae2CbFUGCkR+maOslEKJLOkhrGxcq/oQKyUnOw3rVzyPnLQUtEEX2KmcIEkShEogDSm4nHIG678djhff+BMWVs5lnqdp6yFo1HIgbl7ajXsJVyFJMrj7tIW3X3C5NUpEVLfw005Uy5lbOiBbll3uZKJ5Ihf5qlyYWzoCAP5ePx0mBcYIVIXAVfKCTJJDkiTYSy5oLTrDCe7YueHtJ64W3zhgMFRQ4g5iyiyTgFvIV+WiaRvtDHU+e+wnpKfGo42qM+wlZ3VtlSRJsJUc0VrVGTkZqTh16IcnnksuN0bDFv0Q3OstBPV8Ez7+nZkEERkYfuKJarkmAYORqUrFAySXWSYe0ZDJ5fBv1gcJt88i6c4F+KmalFqLI0kSGoqWyMvLwuUzm8u9tpWNK5q1HYYb0kXcE3dL7L8vknBNOoeGzfvBztG38g+ukoQQOHt0HVyEJ8yl0kepmUpmcBNeOH88DEplfo3HRET6jYkQUS3n7RcMJ9cmuCw7jSxRsh9OikhEtHQZzduNgJmFHW5FHYWRZFLuchimkjlsJUfcqsBIrx6DF6BeoxBE4jBOYD+ixRXEiCs4iX9wGgfg7tsOvYd9Wq3HWFH5eVl4mH4XDii7yQsAHOCCnJw0ZD4sO3mk2ouTKZI2sY8QUS0nyWQYPOYHbPj2eRxL3Q0X4fnfPEJSPFKQgHoNQtB1wPsAAFVBHmSSEaQnLH8qF3KoCp5cY2JkpMDg0atw4/JunD26FrdvRQIAnN2boXPwfPg36w2ZXDtfJZJU+NvtSRMiFu2v7OSRVLtwMkXSBiZCRHrA2tYdo974E+eOh+Hs0XW4m3YCAODi1hK9g99G09bPqpMRO6f6yFNlIRPpsJCsSz2fUiiRLktFfaeKNWdJMhn8m/WGf7PemnlAVWRsYgYn1yZISrwDd9Qrs1wS4mFt6wkLSyftBfcEKYnXEHlsHaIu7UF+Xg6s7TzQosMLaBIwGCYKC12HR2SwmAgR6QlTMxu07zoJ7UJeRUF+DmQyOeRGJiXK+TXtBVMzW8RmX0NTBJZ6rjuIRr4qBy3ajajpsDUuIDgUuzfNxAMkw04qmeikiwdIkuLROejdWtPx+fTh1di3bT4UMjM4q9xhDFs8zEnBns2zELHvawybEAZbBx9dh0lkkGrHtwQRVZgkSTA2MSs1CQIKm7I6934HdxCDG+I8CkSBep9KqBAvonFNOo/mgc9rpYOzpjVr8xy86gchUjqCWHEN+SIPAFAg8nFb3MBp6RCc3ZsjIChUx5EWun7hL+zbNg/eaIBOqj5oJAWgvtQUrRCMYDyNgvR0bPxuFPLzsnUdKpFBYo0QUR3UquOLyM3NwKG/P0YcomEvnCGDDKmyFOSoMtG09XPoOXihrsOsErmRCZ4d8wP2/jEHl05vwg3VBZhIZshHDgQEGrboj15DFhdbhFZXhBA4unspHCRX+IuWJSamNJes0EoVhKOpu3Dl7B9o0e55HUVKZLiYCBHVUe1DXkXjVgNx7ngY4qNPQKiUaOTWBS07jISzezNdh1ctxiZm6D30E3Tu/S6uX/gLWRkpMDW3hX/zPrCyKXu0nLYl372E5MQrCECnMmfntpCs4ABXXDjxGxMhIh1gIkRUBbk56bh46nfciT0FlaoADi4N0aLd87C2rV2jXKxtPdC59zu6DqPGWFg5ISBotK7DKNPD1MK5l6xgV245K2GDew/itRGSXuDQedImJkJElXTx1O/Ys/k9KAvyYCs5QoKE6IvhOL73K7R9ajy69JlZazrpkm4VNc/lIxcKlFwwt0g+cmtFU15tYO7kAIBD50l7mAgRVcLVc3/i7w3T4AYfNEBzKFD4x6tA5CMON3HqwHcQQqBr/9k6jpQqQqhUuBV1BMl3LgGSBFevAHj4BGps1Xk3n7ZQKKxwNzcW/mhZahmlKECSdAcBzcdq5JpEVDlMhIgqSKUswP5tH8AJHmiK4n8sjSRj1ENjQACnD32PNsFjYG3nqcNo6Umir+7D3i1zkPrgFuT/LkWiFPlwcPJHzyEfwtO3fbWvYWxsipYdR+H0gVVwEu6wlRyL7RdC4BrOoQD5aNl+VLWvR0SVx/p7ogqKvvYPMh4mwheNy6wx8EIDyGGEcxG/aDk6qowbF3dh85qXIU/NQyC6oqsYhK5iEFrjKSjvpWLDdyNx++aTlx+piKCeb8HVOwBnpEO4KiKRLh4gW2QiUcThlHQA8YjG0899DFsHb41cj4gqh4kQUQXdS7wKY5kprKWyO77KJSPYCAfcS7iixcioMgryc7Bzw9twhBsCRKfCfl6SBEmS4CC5oI14CjbCDn9veBtCVf5SHhVhbGyK58b/jMCuryDZ9B4iEI7D+AvncQzmnl4YMnYNmgcO18Ajqxveahiu6xDIwLBpjKiCJEkGIVQQQpTbh0RIKkhc46rWunZ+B3Jy0tAGHUt9HWWSDA1EM5xI3Yfoa/tRv3H3al/T2NgUnXu/i4493kRC3DkU5OfA2tYd9k5+1T53XWLu5ID4JV+zozRpFRMhogpy92mLApGH+0iCA1xKLZMncpAq3UMLn9KXtiDdi4uJgJXMHubCqswy1rCHqcwS8TEnNJIIFTEyUsCzXjuNnY+Iqo9NY0QV5OETCAdnf0RJl6EUyhL7hRC4iUuQZHI0aztMBxFSRaiUBZA94atPkiTIJTlUqoJyyxGR/mMiRFRBkiTh6aH/hwzZQ5yWDuK+SIQQAgCQIdJwARGIRxS6DZoHM4vyJ9Aj3XFwboCHIhV5IrfMMtkiE5nKdDg4+2sxMiLSBSZCRJXg7t0Gwyf+ArmjLU7jIA7ItuOgtAPHsBsPzbPQZ9jnaNWBw6Brs2ZthwIScBvXyywTi6swMbFAo5YDtBgZDQtK0XUIZIDYR4ioktx92mLMtD2Ij45AfOwJqJRKOLj4w69pL8jlxroOj57A3NIR7bu+hmN7v4Qk5PCGP4ykwq/CfJGHGFxBHKLQrfc8GJuY6zhaw2Hu5ABp9nPQ0FyWRBXGRIioCiRJgmf9DvCs30HXoVAVBPeaBpVKiRP/fINbuA5b4QABgVQpBSqo8FSfmWgdPEbXYRokuRNHjJF2MREiIoMjSRKe6vMuWnUchfMRvyAx/iIkCWjs1Rot2o+AhZWzrkMkIi1hIkREBsva1gOdnn5b12EQgGk5ixCn6yDIIFWqs3R2djYOHTqES5culdiXk5ODtWvXaiwwIiIyDOZODogLP82JFEknKpwIXbt2DU2aNEGXLl3QokULhISE4O7du+r9aWlpGDuWqycTERGR/qhw09j//vc/NG/eHCdPnkRqaiqmTp2KTp06Yf/+/fD25mKBRHXVbKsVJbb93WIeTh5J0EE0RESaVeFE6MiRI9izZw8cHR3h6OiIbdu24bXXXsNTTz2Fffv2wcLCoibjJKIaZu7kgOHbnyuxPQ6AJH9k7TSVEs3DB6F5KefweOs1LDocUFMhUh1U1vuOSFsqnAhlZ2fDyOi/4pIk4ZtvvsGUKVMQEhKCsLCwGgmQiGpG6CDAMyUS8Uu+Vm+TpOoNX45f8jXGPHJfLFyJH/+oeoxkONg/iHSlwolQ48aNcfLkSTRp0qTY9mXLlgEABg0apNnIiDQkK+MezkX8gosnNyAjPREmCkv4t+iDgKBQOLo01HV4WjfbagXiwk8DEcAdqbC2R+7gqpFzP/rHTJkcD8yeiDEAPHu0YXMaEdVKFe4s/eyzz+KXX34pdd+yZcvwwgsvqNddqknLly9HvXr1YGpqig4dOiAiIqLMsmvWrIEkScVupqamNR4j1R4Jt89i9Wc9cGz3Upjdl+Bb0BBOmfa4ErEJa5f2xrmI0t/TdYm5kwPMnRwwJmIixkRMRFz4aUhyOYycPSB38tBYEvQ4uZMHjJw9IMnliN97Gs2XDsKYiImY1SmyRq5HRFQVktBG9qIhv/32G0aPHo0VK1agQ4cOWLp0KTZs2ICrV6/C2bnkBGhr1qzBm2++iatXr6q3SZIEFxeXCl8zPT0dNjY2mDLvAhSmVhp5HKQdmQ+TseazHlDkGqOVCIKJpFDvUwkVruEs4hCF515ei3oNu+gw0pozq9N/TV/VbfbSlIKkeABsNqP/+gfVlvcm1S0ZeXlo98NPSEtLg7W1dZnl9GrR1c8//xwTJkzA2LFj0bRpU6xYsQLm5ub44YcfyjxGkiS4urqqb5VJgki/nYsIQ35uVokkCABkkgyNEAAbyQHH9y3XUYQ1a0zERMQv+bpY7U9tYOTsAUkCpNmFNVSBwTVTI0X6o7a8N8kw6U0ilJeXh1OnTqFnz57qbTKZDD179sTRo0fLPC4jIwM+Pj7w8vLCM888g4sXL5Z7ndzcXKSnpxe7kX66eGI9XIRHiSSoiCRJ8BT1ERd9DOmp8VqOrmY82gQGFCYdNdX0VR3qZjMJbDIzYE39uUgx6Z7eJEL37t2DUqksUaPj4uKChITSO2A2atQIP/zwA7Zu3YqffvoJKpUKwcHBiIsreyL3xYsXw8bGRn3z8vLS6OMg7cl4mARL2JRbxhKF1aUZ6YnaCKlGzeoUqW5mMHL20ItROEUJkZGzR+GIs38TOKr7zJ0c0HzpIK42TzqnN4lQVQQFBWH06NEICAhASEgINm3aBCcnJ3z77bdlHjNz5kykpaWpb7dv39ZixKRJxibmyENOuWXykAsAMFFYaiOkGvNoM5i+NjMUJW5sLjMs+vp+pbpDbxIhR0dHyOVyJCYW/+WemJgIV9eKfWkaGxujdevWuHHjRpllFAoFrK2ti91IP/k374sEWRxUQlVmmTtSLGztvOHg1ECLkWlOYLBrrW8Gq4xHm8tKm9Ga6g42i1FtUaVEaN26dejUqRPc3d0RGxsLAFi6dCm2bt2q0eAeZWJigrZt2yI8PFy9TaVSITw8HEFBQRU6h1KpxPnz5+Hm5lZTYVItEhA0GjmqLFzHuVKndkgUcUgSt9G688uQZHrzm0BtVqdIddOCPjSDVVRRc1lc+Gk2ldVRbBaj2qTS3/7ffPMNpk2bhn79+iE1NRVKpRIAYGtri6VLl2o6vmKmTZuG7777Dj/++CMuX76MSZMmITMzU73Y6+jRozFz5kx1+Q8++AC7du1CVFQUTp8+jRdffBGxsbEYP358jcZJtYOze1P0eOYD3MYNnJIOIEHcRoZIx32RiAuIwAUcR8MW/dE6KFTXoVZaXWgKexI2ldV9dfW9S/qlwjNLF/nqq6/w3XffYfDgwfjoo4/U2wMDA/H2229rNLjHPf/880hOTsacOXOQkJCAgIAA/P333+oO1Ldu3YLskV/2Dx48wIQJE5CQkAA7Ozu0bdsWR44cQdOmTWs0Tqo9AoJGw9rOExH7luNC7HH1dhtbL3TtPBcBQaP1qjYoMNgVzZcWzuJel2qBymLk7AFlcjyaLx2EPj3aYOHDV3UdEmkAm8WoNqn0hIpmZma4cuUKfHx8YGVlhbNnz6J+/fq4fv06WrZsiezs7JqKVSc4oWLdkXb/VuESG6ZWcHRuqFcJUJExERMNdvK5ookY17RfqeNIqDo4iSJpS0UnVKx0jZCvry8iIyPh4+NTbPvff/9dYh0yotrExt4bNvbeug6jyor6yxjqHw8jZw8UJMVjTMTEWj0r9cO0u7gTexoqVQEcXRvBybWxrkOqlQztfRyblo7fr1xDdGoaTOQydHB3xwD/+jA3Zu2YrlU6EZo2bRomT56MnJwcCCEQERGBX375BYsXL8aqVatqIkYig2ZozWHlKWoqw+yJmF3LmspSU25h/58LEHVlD8QjIxVdPVshpN8seNbvoMPoao/h25/TdQhala9U4YODR7DxyjXY28gRGKBAaqbAvIPR+DQiAh+GdEFPX58nn4hqTKUTofHjx8PMzAyzZ89GVlYWRo4cCXd3d3zxxRcYMWJETcRIZNCKRtcY2i/oshQ9D3HhpzEGE2tFU9mDezH45evBQE4+GolWcIYnZJDhPpIQG38dG1aNxDOjv0P9xt11HapOmTs5ADCshP79fw5iR1QUvvrQCWNHWMPMrLBJPuZ2Pt6Zdw9T/96Lb/s+jU5ehvOc1DaV6iRRUFCAtWvXomfPnrh+/ToyMjKQkJCAuLg4jBs3rqZiJDJYht4cVp5HR5WFDtJtLDs3vA0pR4l2qm7wlPxgIilgJBnDWfJAW9EF9sIZO355A/l5WTUWg1CpEHvjEE4dXIXTh1cj4fbZGrtWVRlabdCleynYeu0mln/shNfG2qqTIACo52WMX1e64qmOpvgk4nipU3yQdlSqRsjIyAivvvoqLl++DAAwNzeHubl5jQRGZOgenSiRSlfUVOa5bwUA3TSTJSdcQXzsCbRAx1LXtZNJMjQSrXA4929cjtyKlu1f0HgMNy/twf5t85H64BbkkjEEVFAJJZzdmqPXkA/h6tVK49esKkN6P6+/dAXuzsYIHV56R125XML/XrdD3xfuIDIxGa1dnbUcIQFVmEeoffv2OHPmTE3EQkT/KppV2ZD+aFSV3Om/yReLml60Kfb6QcglYzjBvcwyZpIFbCVHxF4/qPHrXz23HVvWjoc8NR+B6IquYhC6icFohWDkJNzBb98Ox91buv/ONsSZwm+kPkD3LqYwMip75sgeT5lDkoDr9x9oMTJ6VKX7CL322muYPn064uLi0LZtW1hYWBTb37JlS40FR2SIxkRMRByYBFVG0Yiy4duf0/qIMmVBLuSSEWRP+F1pJIxQUJCr0Wvn5WZi18Z34CJ5orloD+mRqZqd4A574YzTykPYueFthE7bU2y/tsWFnza497QECXn55Td5FRQICAHIZZxmW1cqnQgVdYh+44031NskSYIQApIkqWeaJqLKCwx2BSKYBFWFrkaU2dh7I0+VjSw8hLlU+lxjKqHCQ1kavDU8fcOVyK3Iy8tCA3QpNcmRS0bwE01wOvkg4qMjdDZyLXQQgAidXFqnWru4YH34RWRmqWBhXnqivGlHJgAgwIXNYrpS6aax6OjoEreoqCj1v0RUNbOtVnD9pWp6dJ0ybXWgbtD0aShMrXEL18ssk4BbyFVloUU7zY6sjYs+DhuZA8wkizLL2MEZJjJT3I4+ptFrV4Y0eyIkuVxn19eV55s0wsNMJT768n6p+9MfKrHo8wfo4OkKPztb7QZHapWuEXp8IkUi0gxDbDqoKZIEYPZEhGqhmczI2BRBPd7E/u0LoBBm8EFDyKTCP/pCCCQhHlelSDRqPgCOro00em2lqgByUX6CIUkSZDCCSqWb2vqifltyB8NbL87D2gpvtmuLD784hcRkJd5+zQ4N/UygVAr8tTcTsz+8j7g4FX4eVLGFw6lmVDoRWrt2bbn7R48eXeVgiAwVV1nXLLmTB5QpCcDsiRiDml+Wo03nccjJTsOxvV/itiwKjipXyCDDA9k9ZKrS4Ne4J3oP/0zj13VwaoAo7EKBKICRVPrXeaZ4iByRAQfnBhq/fkUY2pD5x01s0woWJsb4essZfB8WC2cHI+TkqJCeqUJzFwesG/gU/O3tdB2mQav0WmN2dsVfsPz8fGRlZcHExATm5ua4f7/0KkB9xbXGqKbNtlrB2qAaVLRG2fr+vyMrOaVGr5WSdB1nj/2MuKhjUCmVcHRrjICOL8LDt32NdFROT43Hqo87w080Qz2p9Nqmi+IkHpim4pXZETAyKjm8vyZxCoj/5BYUYE/MLcSkpsFELkcHdze0dHHSdVh1Wo2tNfbgQckhftevX8ekSZPwzjvvVPZ0RAbN3MkBcduZBNWkohFlw4JSaryZzMHZH90HzavZizzC2tYDrYPH4vThHyAJCZ7wg/zfZrl8kYdoXMZdxKBn3w+1ngQZ4izS5VEYGaF/g/q6DoNKUelEqDT+/v746KOP8OKLL+LKlSuaOCWRQTD0ZgNtkeRyrTWTaVtI/1lQqZSIPLoGMbJrsFXZQ0DggXQPKqjQtd/7aNVhlNbjKlphnqi200giBBTOOn3nzh1NnY6ozptttYLzBWlJUUfdotXrtdFMpi0ymRw9npmPNp3G4FxEGJLvXoFMJkcj7zZo0X4ELKx0NyybS8OQPqh0IvTHH8XrloUQuHv3LpYtW4ZOnTppLDCiuoxNYrpR1Ez2VsNwLEoO0HU4GmXn6IuQfrN0HQYAdv4n/VLpRGjw4MHF7kuSBCcnJ3Tv3h2ffab5URFE2pR89zKS7lyCJElw8WxZYyNt2CSmO5JcjvglX9fJZrLaYFanSMRzUlDSI5VOhFQqVU3EQaRTd2JPYf+2D3A3LrLYds96HdBt0Dw4uzfV2LU4kka3Hm8muzD1D5w8kqDjqOqO+CVfG+TkiaS/Kj2z9AcffICsrKwS27Ozs/HBBx9oJCgibbp98yjWr3wemfG30BJB6IZn0Q2D0RztkRp7Fb9+MwQJcec0ci2OpKk9jJw9IElA86WDCpc2oWorSvINcfJE0l+VToTmz5+PjIyMEtuzsrIwf/58jQRFpC1KZT62//IGbFT2CBQhcJY8IJfkkEtGcJW8ESi6wrzAHDt+eQOVnHKrVBxJU7sUdeZtvlRL63HUYbM6RQJgkk/6p9KJUNHiqo87e/Ys7O3tNRIUkbbcvLQbmRlJ8Bct1MsiPMpIMkID0RwPUqJx++aRal1L/WuZI2lqlaI/3GMiJrJmqBril3zNJIj0UoUTITs7O9jb20OSJDRs2BD29vbqm42NDXr16oXhw4fXZKxEGhd74xAsZbawkmzLLGMLRyhkFoi9cajK12GTWO32aDNZUc0GVUxgsCtHiZFeq3Bn6aVLl0IIgZdffhnz58+HjY2Nep+JiQnq1auHoCAuHEf6RVmQBzmMyy0jSRKMJGMoC/KqfB02idV+ReuTcURZ5TRfOgiSxJpO0l8VToRCQ0MBAL6+vggODoaxcfl/PIj0gZ1jPVwRqSgQ+TCSSn9P54psZCnTYetYr1rX4h+K2u/REWWBwa4cTfYEbO6luqDSfYRCQkLUSVBOTg7S09OL3Yi0JT31Du7EnkJywhWIKk7r0LTNUKigRByiyixzCzcgNzJB41ZV61DLZgP9w2ayJyvqT8XmXtJ3lZ5HKCsrC++++y7Wr1+PlJSSU9QrlUqNBEZUlls3DuPY3mW4HfVf52VrGw+07jwWbYLHQiav+NvaysYVAUGhiDzyI4yEMdxRDzKp8PeBSqhwGzcQi6sI6voWTM1snnC2kji5nH4qquFgM1npZlutQNzS02zupTqh0onQO++8g3379uGbb77BSy+9hOXLlyM+Ph7ffvstPvroo5qIkUjt4qmN2LnxHVjBDs3QDlawRR5ycCftFg5s/xBxUccx6MUVlUqGuvafjYL8bJw/8StiZFdhr3KCgECKLAl5qmy0fWoCgnq8WaV4ObmcfitakoMTLxYXF87lYajukEQlJ0fx9vbG2rVr0bVrV1hbW+P06dNo0KAB1q1bh19++QU7duyoqVh1Ij09HTY2Npgy7wIUpla6DsegPbgXjdWf9YCb8EYTtCkxjcM9cRdncRSd+7yL9l0nVfr8SXcu4uzxn5F4+xwkSYKbTxu06vgiHJz9qxRvYLArmi8dxD8YdYAyOR5CAB5vvYZFhwN0HY7OhA4CpNmcGZ30Q0ZeHtr98BPS0tJgbW1dZrlK1wjdv38f9evXBwBYW1vj/v37AIDOnTtj0qTK//Ehqqizx9bBSDJGIxFQ6lxWjpIb3IQPzhxejcCnJlSqVggAnN2bodezH2oqXPVoGtJ/bCr7tzls9mmOEKM6p9KdpevXr4/o6GgAQOPGjbF+/XoAwLZt22Bra6vR4Igedf38X3BVeUJeysSHRdzhg4yHiTh3/GdEX92PB/ditBfgIziapm4y1MkXx0RMVDeH8T1NdU2la4TGjh2Ls2fPIiQkBDNmzMDAgQOxbNky5Ofn4/PPP6+JGIkAAHm5mTCBXbllTGAKAAj/Y456m6dvR3R6ejo8fdvXaHyPY9NB3WTk7AFlcjyaLx2EPj3aYOHDV3UdUo0KHQSAHf6pDqt0IvTWW2+p/9+zZ09cuXIFp06dQoMGDdCyZUuNBkf0KEtrF2TklD9FQwYK97dCMKxgi1Tcw62Y61i/cgQGvbgCDZo9XeNxzrZagbgavwrpUlGtSFz4aYzBxDrbVPZoc5g2CCFw8m4i9sXeQmZePlwsLfCMvx88rNk/k2pOpZvGHpWTkwMfHx8MGTKESRDVuGaBw5As3UGuyC51vxACcbgBa9jBSXKHqWT+78KpIXASbtj+y+vIznxQ43FyRI3hqKtNZeZODlpvDotOTcOQTVsw+o8d2JVwFddlsfjxUiR6/bIBM/b9g5yCghqPgQxTpRMhpVKJBQsWwMPDA5aWloiKKpyI7v3338f333+v8QCJijQPfB4KMxuclR1Frsgptk8IgRs4j/tIQj00LrZPJsnQGK2hUubj4qkNNRpjKBcxNziPrlM222qFrsOpttBBhUvCANprDot/mIHR27YD1pnYtd4DsWd8cHKPF+LO+eKrxU7YFRONqbv3QlnFiVOJylPpRGjRokVYs2YN/u///g8mJibq7c2bN8eqVas0GhzRo0zNbTB0/E/INwUOS3/jojiJW+I6bogLOIjtiMU1NEQrOEslv7xNJAUchAtuXtpTozFKsydypJgBkjt5wMjZo7CpLGKi3s4mPttqhfo9rM1azWUnTsPYogD7t3igx1Pm6lGhFuYyTAq1xYbvXfFPbBz2x97WWkxkOCqdCK1duxYrV67EqFGjIH9korhWrVrhypUrGg2O6HHO7s0wZno4gntPR7a9QJTRVcQb30IectAePeAtlT3njzFMkJ+XVeMxclSN4TJy9ijWXKYPNYTmTg46aQorkpabi7+iovD6eBs4OZbebbVvDwu0DzDFL5cuay0uMhyV7iwdHx+PBg0alNiuUqmQn5+vkaCIymNuYY8OXV9Dh66vAQBirh3A7z+8BKDsuUGFEHgoS4erXeMyy1SXvtYCkOYVjSzD7ImY3aMNRMuOtXIixsIJEp9T39dF37ab91ORW6BC/14W5ZYb8LQFli67p6WoyJBUukaoadOmOHjwYIntGzduROvWrTUSFFFlePsFw9LKBbdwo8wyqbiHh6r7aN7u+RqNhZ2kqUhRc1n83tOFEzHWskT58WYwvnfJUFU6EZozZw6mTJmCjz/+GCqVCps2bcKECROwaNEizJkz58knqKbly5ejXr16MDU1RYcOHRAREVFu+Q0bNqBx48YwNTVFixYt6twSIATI5EYI6jkVCbiFKHEZKlG8Q2W6uI8LshNw8WgJ34ZdaySG2vZHjmqPooQIgLr/UOggaH2UmbmTAwKDXdUx1JYJEv3sbaEwkuHPXZnlltu2MxNNHR21FBUZkkonQs888wy2bduGPXv2wMLCAnPmzMHly5exbds29OrVqyZiVPvtt98wbdo0zJ07F6dPn0arVq3Qu3dvJCUllVr+yJEjeOGFFzBu3DicOXMGgwcPxuDBg3HhwoUajZO0r0X7FxDU8y1E4SKOyHbimjiLm+IiTuEgIrAXNs718OyYHyDJqjVjRLn4i5rKU1TrIkmFneqLRpnN6hRZo9cNHQTM6hSJ4dufQ/Olg4rFUhvYKBTo7+eHr1alIele6UPkt+/JxImzOXihaVMtR0eGoMKLrkZFRcHX17fUNZ60pUOHDmjXrh2WLVsGoLBfkpeXF15//XXMmDGjRPnnn38emZmZ+PPPP9XbOnbsiICAAKxYUbFhrlx0Vb8k3bmIyGPrEHv1AJTKfNg51kPLji+iYfO+kBuZPPkEVTDbagXnDqIqKVrM9VGamJyx6D1ZRJIAyOSQO9TOuY7uPMzAiK1/wN5ZhSULHdErxBwymYSMTBV+/C0d786/h47uHljeuxdkHJZJFaTxRVf9/f1x9+5dODs7AyhMMr788ku4uLhUP9oKyMvLw6lTpzBz5kz1NplMhp49e+Lo0aOlHnP06FFMmzat2LbevXtjy5YtZV4nNzcXubm56vvp6eXPZEy1i7N7Mzw95COtXpNJEFXV481SBUnxJZpZxcKVuHiv/ASmqKanSBz0q4bS3coSawf0x9t796HfyDtwdzaGk4McN2PzkJWtwuBG/pjTOYhJENWICidCj1cc7dixA4sXL9Z4QGW5d+8elEplicTLxcWlzGH7CQkJpZZPSEgo8zqLFy/G/Pnzqx8wGYSidZiINOHx5KVo5FnzKhyrb+rZ2mDDs88gMjEJ4TG3kJWfj27NLTCooR/cLC11HR7VYZUePl/XzZw5s1gtUnp6Ory8vHQYEdVm0uyJkB6ZT4tIk3TdkVnbJElCa1cXtHbVTksDEVCJREiSpBL9g7TZX8jR0RFyuRyJiYnFticmJsLVtfRqY1dX10qVBwCFQgGFQlH9gMlg1NZ+F6QfHubm4WZqKoQA/OxsYM3vHyKtqlTT2JgxY9RJQk5ODl599VVYWBSfBGvTpk2ajfBfJiYmaNu2LcLDwzF48GAAhZ2lw8PDMWXKlFKPCQoKQnh4OKZOnaretnv3bgQFBdVIjGRYZnWKRDybxaiKEjIysfzkGWy7HoVcZeFoKROZHP3962NyYGt4WLE5iEgbKpwIhYaGFrv/4osvajyYJ5k2bRpCQ0MRGBiI9u3bY+nSpcjMzMTYsWMBAKNHj4aHh4e679Kbb76JkJAQfPbZZ+jfvz9+/fVXnDx5EitXVn9UBlH8kq/ZLEZVcjs9HSM3b0dGjoCHaAQnuAEA7qkS8Pe1m9gfexs/D+4PX1sbHUdKVPdVOBFavXp1TcZRIc8//zySk5MxZ84cJCQkICAgAH///be6Q/StW7cge2SemODgYISFhWH27Nl477334O/vjy1btqB584p0PSQqW2CwKxDBZjGqPCEE3tq1Hzk5crQTIVBIZup9VrCFh6iHM7kH8Mbfe/HH84N1OmUJkSGo8DxChorzCNV+mQ+Tcf7Er4i9dhDKgjzYOfmiRYeR8PAJrLE/ImMiCpcmMLTOrFR9ZxOTMGLznwhAJzhKbqWWuS+ScBoH8OOgvmjvXnoZIiqfxucRIqqNLpxcj92b3oMkBOyFC4xghJg7N3HpzCbU8w/BwBe/homiZvpaMAmiqtgbcwumkikcRNm1iXZwgoXMHHtjbjERIqphTIRIb12/8Dd2bnwH7vCFP1rAWCqcOVqoBJJxB5duHMW2nyZhyMtrNVozVNQsRlQVmfn5UEgmkFD2e1KSJJhAgcy8fC1GRmSYam7hJaIaJITAwb8+ggPc0ARt1EkQUPhHxFnyQFMRiJjrBxAfrdmspc/5eWC3DaoqFwsLZIosFIiykxylKECmyICLhbkWIyMyTEyESC/Fx57Eg5Ro+MC/zNoeJ7jBQmaDsxE/a/TaceGn2SxGVTbQ3w8qKHEHMWWWuYtbyBf5GNSwgfYCIzJQTIRILz1IjgIA2MKxzDKSJMFGZYcHSVEau25gMEeJUfW4WlpgcMMGuCldwD1xt8T+FJGIG9I59PWrD2+bsjt4EpFmsI8Q6SW5kTEAQAUlZOXk80oUaHTV+T7n5yGezWLV9iAnByfuJCC3QAl3Kwu0cXUxqGHic54KQkp2Dv65dRh2sIe9cIMECSnSXTxACoLc3bGwa2ddh0lkEJgIkV7y9O0ASZIhQdyGJ+qXWqZA5OOelIh2DYZo9uIyTqJYVWm5ufi/oxHYfuMmcgtU6u2+dlaY3KYN+vv76TA67VEYGWF5nx7YF3sbYRcu43zSdQBAMycHjGzeHd3recNIxgp7Im1gIkR6ydrWA/Ub90DM1aNwUrkVm5QOKOxMfRMXoYISLTuM1Mg1QwcBcbNP6/0q37qSlpuL0du2417+Q8x91w4vDrWGva0MJyJzsXTlA7z99z+4l52D0JbNdB2qVshlMvT09UFPXx9dh0Jk0PiTg/RW92fmQ25ujhOyfxAvolAgCiCEQKq4h3M4itu4ge6D5sPKRjPzsHjtW8HRYtWw5PgpJOU+xIE/PPG/1+3h4WYEMzMZugSZ4fcf3DB9ki0+Pnoc0alpug6ViAwIEyHSW9a2Hhg5ZQvcG3bAZZzBfmxBODbhJPYjz1aOfiO+REDQaM1elM1iVfIwNw/bblzHm6/YoEnDkn22JEnCB+86wMFGjl8vXtFBhERkqNg0RnrN2tYDz475AWn3b+N21DGolPmwdfCBV/0gSBrsY1HULMZFVqsmMjEJWXlKjBxS9jI1pqYyPDfIEnt3xAHooL3giMigMRGiOsHG3gs29l41dn6vfSsQL3GR1arKVSoBADZW5Sen1lYy5Co5mzIRaQ+bxogqis1iVeZtXVgTdPRUTrnljp7IgZcl584hIu1hIkT0BKGDCmeTpqpr6GCPli4OWLIiFSqVKLVMxJkcHIrIxnONGmo5OiIyZEyEiJ6gaLQYm8WqZ0rbtjh4PBsTpiUhLV1ZbN+RE9kYPPoumjnboweHkxORFrGPEFFFsFms2p7y9sRH3brg/Y2HsOGPDAzsYw57WzlOnM7FibM5aOZsjxV9esOEHdKJSIuYCBGR1gxq2AAdPdyx4fJVHDx6G7nKArhZOGFZ74YI8fHibMo6dj4pGRF3EpCvVMLX1gbd6nkzMaU6j4kQUTlmW61AXDhnk9YkZwtzTA5sjcmBrXUdCv3rasp9vH/gIM4npsDSXAaFiQwpqQVwsFDgjbaBGN60ka5DJKox/PlF9AScO4jqsmsp9/HStu2Q7DOwda0b7l+rj6TLvji33xv9+5tg7oHDWHP2gq7DJKoxrBEiIjJgCw4fhbe3DP9s9YC11X9Jf7NGCqz+whVODnJ8+u0J9PXzhYulhQ4jJaoZrBEiKkNRsxhHi1Fdde3+A5y8k4jZ0+2KJUGPmv2WPUwVEtZfvqrl6Ii0g4kQUTnYLEZ12ZmERMhkwOA+lmWWsbaS4+luZjiTmKjFyIi0h01jRKRTeUolTtxJQEp2NmwUCnTwcIOpEb+atKFApYJMkmBsXH45hUJCgVBpJygiLeO3DVEZ4sK5yGpNUgmB7yPPY93FC0jO+G/pDVszE4xo0gSvtW0NYzkrrWuSv70dCpQC/xzNRtdg81LL5OcL7D+Ug+7ONbeWH5Eu8VuGqBSzrVYA4GzSNUUIgff3H8KSiJMYMkSB03u88fCmHy4e8MHY0eZYdfYc3toTjgIVayFqUjs3V9S3t8ZHXz4oc+mTNb+lIyG5AM9zCD3VUUyEiMrA2qCaszs6FpuuXsfqL1zw9cfOaNVMAXNzGRr7m+DTeU7Y/KMb9sXexsbL13Qdap0mSRLe7tAeew5k4aXJCYi7k6/el5OjwterU/H6zGQMaeyPhg72OoyUqOawaYyItC7s0iV0CjTDS8NKX2m+Xw8LDOxlgV9PXcLzTRtBkiQtR2g4uvl447MeXfH+jkPY8EcMOrU3g4W5hIhTuUhJU2JYk4Z4v3OwrsMkqjFMhIgeExjsiril7B9UU/KVKhyPS8CyKU7llhs11ArDdyYgOSsbzhal918hzejboD6e8vbE1ms3cOJOAvJUKgz2tcHQxg1Rz9ZG1+ER1SgmQkSP6XN+HuK52nyNyVMVrjxvY1V+y7yNdWEimqdUlluONMPSxASjmjfFqOZNdR0KkVaxjxBRabjafI0xNzKCg4UCx07llFvu2MlsmBnL4WBmpqXIiMgQMREiIq2SJAlDGzbCut8eIvleQallHmao8O2PDzGggR/MjFlxXZOEEDiTkIjfLl3B+ktXcS3lvq5DItIqfsMQPaJoWQ32D6pZI5s3xcZrV9FnxB2sX+UKv3om6n3xdwvw4msJeJgmMLZ7Cx1GWfcdvh2PTyKO42pyKmQyQIjCW1t3Z8wKDkITRweNXu92ejp+vXgVpxLvokCoUN/aDsObNkJbVxd2iCedYY0Q0WMkuZz9g2qYs4U5vu/XF3dvydAoOBb9XojHm7OTMDj0DnwDo3H+rBIr+vSGLzvq1ph9Mbfwyl+74NogF3//6o7c2w2QHdsAv33nijzrNLz0x3ZcTL6nset9d+Ysev+yEZujL6FF51wE9yrA5bzbeGnrDkzZuQfZ+aXXDhLVNNYIEZFONHKwx47hQ7HjRjS237iBXReyYW1igZnBLfBMwwawNDF58kmoSnIKCvDePwfQv5c5Nqxyg5FRYW2MTAYMHWCFPt0s0G1wPGb9cwCbn3u22rU1v126gs+Pn8LMN+zw3pv2MDcv/A2uUgls3pGBMa/HY+b+f7C0V49qPzaiymIiREQ6Y25sjKFNGmJok4a6DsWg/HUjGqnZefh03n9J0KMsLWT4cLY9+oy4g1MJiQh0q3oNaZ5SieWnT2P0cCssnOlYbJ9MJuG5AVbIzhEIfT0Wl1unaLw5juhJ2DRG9K+i/kFsFqO67tidO2jXyrRY36zH9XjKHPY2chyPv1utax24FYfkjBxMn2RXZpkRg63g7mzMmcRJJ5gIET2CnaTJEOQrVbAwL//rXyaTYG4mQ34113u7lZYOawsZmjdWlFnGyEhCh0AT3EpPq9a1iKqCiRARkYHxtbXB6XM5yMwqO8m5GZOH+MT8andYN5HLkZMnkJtbfkKV/lDAhD9ESAf0JhG6f/8+Ro0aBWtra9ja2mLcuHHIyMgo95iuXbtCkqRit1dffVVLERMRPVmeUokdN6Lw9p79mPTXbsw7cBhnEpIgROmrwWvCkMYN8TBLhZVry66B+XxFKqxNjfG0b71qXSvI0x15+QKbdmSWWSb+bgH2H85CsKdHta5FVBV601l61KhRuHv3Lnbv3o38/HyMHTsWEydORFhYWLnHTZgwAR988IH6vrk51yyikmZ1ikTcEs4fRNVToFJhb8wt/Hb5Mq49uA8ZJAQ4u2BE0ybo6OFWYvTV+aRkvLE7HAkPsxDY0hQuznIcv5KA37ZcRQdPVyzp2R12pqYaj9PDyhIjmzXBjIVXYGomYdwLNjAxKYwtM0uF/1v2ACt+TMOM4A7VntDSz84WHT1dMffj++j5lBmcHIufT6kUmD43GaZGRnimYYNqXYuoKvQiEbp8+TL+/vtvnDhxAoGBgQCAr776Cv369cOnn34Kd3f3Mo81NzeHqys7v1L5pHPHOH8QVUtmXj4m79qN43EJCA40w+sjLJBfAPy+LQEv/xmLZxr6YVHXpyCXFVbERz1Ixbgdf6NpEzl2fu6t7kOjUgls35OJ8W8m4ZW/duKnQQNqpMloRnAH5CmVmDLjGhZ8+gDdnzJDfoHArr1ZeJilwhvt2mB0C82sOzb/qc4Y9cef6Ng3Dv97ww7DB1nCzFTC3kPZ+GT5Axw6noPPe3bjlAmkE5KoyfpXDfnhhx8wffp0PHjwQL2toKAApqam2LBhA5599tlSj+vatSsuXrwIIQRcXV0xcOBAvP/+++XWCuXm5iI3N1d9Pz09HV5eXpgy7wIUplaae1BUq8y2WoH4/WeZCFGVTdm5GyeS4rHpRzd06/Tfd4wQAj9tfIhxUxMxpmVzvN2xPQDg7T37cTHnNs7s9YK1VclE50RkDjr2vY3F3Z7C4Eb+NRb3tfsPsP7SFVy7fx+SJCHA2RnDmzSCh7Vmv+/i0h9i8ZFj2B97G6pH/uo0dbbHtHbt0MmLzWKkWRl5eWj3w09IS0uDtbV1meX0okYoISEBzs7OxbYZGRnB3t4eCQkJZR43cuRI+Pj4wN3dHefOncP//vc/XL16FZs2bSrzmMWLF2P+/Pkai52I6r6rKfcRHn0b65a7FEuCgMK11V4aZo2bMfn45KvLmNC6FVQqgV3R0fjofYdSkyAAaBdgil5dzLH+ypUaTYQa2tthduegGjt/EU9rKyzv0wvxDzMQmZCIfJUKfna2aO7kyOU1SKd02ll6xowZJTozP367cuVKlc8/ceJE9O7dGy1atMCoUaOwdu1abN68GTdv3izzmJkzZyItLU19u337dpWvT/qhaP4goqrafPU6XByNMGxg2bUok8bYoEClwt83oxGTlo58pUCPLuX3WezV1Rw37j8ot4y+8bCyRH9/Pwxu5I8Wzk5MgkjndFojNH36dIwZM6bcMvXr14erqyuSkpKKbS8oKMD9+/cr1f+nQ4cOAIAbN27Az8+v1DIKhQIKRdnzXVDdxP5BVB0JGZlo3tgExsZl/1F3cTKCq5MREjIy0fTf2ZNzc8vvmZCbK9R9ioioZug0EXJycoKTk9MTywUFBSE1NRWnTp1C27ZtAQB79+6FSqVSJzcVERkZCQBwc3OrUrxERKUxNzbGzXvKcsvk5QmkpSth7mOEBnZ2sDY1xu/bMxAYUPqoMCEENvyRgTYuLjURMhH9Sy9+ajRp0gR9+vTBhAkTEBERgcOHD2PKlCkYMWKEesRYfHw8GjdujIiICADAzZs3sWDBApw6dQoxMTH4448/MHr0aHTp0gUtW7bU5cMhojqmq48Xzl3JReSF3DLLbPk7Aw+zVAjx8YaZsREG+zfEyh/TcT0qr9TyYZse4tzlXLzQrEml47mdno5Pjp5Av/UbEPJzGIZt3oKfzl9CRl7p1yIyZHqRCAHAzz//jMaNG6NHjx7o168fOnfujJUrV6r35+fn4+rVq8jKygIAmJiYYM+ePXj66afRuHFjTJ8+Hc899xy2bdumq4dAtVBgsCv7B1G1da/nDXdrc7z2bhLSH5asGYq7k48ZH6Sgg6crGtoXrrk1qW0A7I0t0GVQPL4PS0PWv7M8x98twOzF9zD2jUQMauiHTpWcZHDbtZvo/9vv2Bx9CU8PkGHSK+Zo2DYPHx87joEbNuF6HetzRFRdejF8XpfS09NhY2PD4fN11GyrFYjfexpyJw7dpeo5n5SMcdv/gpOLDFNfsUGvEHPkFwhs3p6B5d+nw6jABD8NGgA3S0v1MSnZ2ZjzzyHsi70NE2MJVhZy3E8rgJmREUY1a4o32rWpVB+h4/F38fKff+GlYVZYttgZ5o+sJ3YrLh+DXrqLpHgZtjw3BLam7AtJdVudGj5PVKNknE2aqq+FsxPCnhmIZSfP4K33Y6H8d7IcM2M5+vv54fV2beBsUXyUmIOZGZb36YW49IfYF3sLmXn5cLGwQK/6PlWaXPDbM5Fo08IU333uArm8eMdtb09j/PmzG/w7xmLTlWt4OaBF1R8sUR3CRIiISEMa2Nth6dPdkZyVhegHaZAkCY0c7GD9hJGontZWeKlFs2pd+25GBo7G3cWad0smQerruBtj6EBLbP6HiRBRESZCREQa5mRuDictr2uYkFG4qGmbluUnXW1aKLD1z1QtRESkH/SmszSRphVNpMj5g6guMDMq/F2bcl9Vbrl795XVXkiVqC5hIkQGjavNU13hb28Hd2tzrF2fXmaZggKBnzZkIMTLS4uREdVuTISIiOoAuUyGEU2a4KeND7Frf2aJ/UIIzPk4BXEJ+VWam4iormL9KBFRHTGmZQucSkjEoJfiMW6UNcaMsIaLoxznr+Rh+fep2Lk/C+8GtUMzJ0ddh0pUazARIoMUOgiIm32aTWNUpxjLZfjq6Z5YeeYsfvv9Mlb8+N+i0U2d7bG0Vwf09vPVYYREtQ8TITJIXvtWIF4CO0pTnWMsl2FyYGtMbN0K55OSkZmfDxcLc/jb23Gld6JSMBEiw8WJFKkOM5bL0MaNC7YSPQk7SxMREZHBYiJEBqdo/iAiIiImQmSQJLmc/YOIiIiJEBERERkuJkJERERksJgIkUEJDHZl/yAiIlJjIkQGpc/5eZA4fxAREf2LiRAZHs4fRERE/2IiRERERAaLiRAREREZLCZCZDCKJlJk/yAiIirCRIgMClebJyKiRzERIiIiIoPFRIiIiIgMFhMhMgihg8CJFImIqAQmQmQQvPat4ESKRERUAhMhMhycSJGIiB7DRIiIiIgMFhMhqvOK5g8iIiJ6HBMhMgiSXM7+QUREVAITISIiIjJYTISIiIjIYDERojrN3MmB/YOIiKhMTISoTpuWswgA5w8iIqLSMRGiOo8LrRIRUVmYCBEREZHBYiJEddasTpHsH0REROViIkR1lnTuGOcPIiKiculNIrRo0SIEBwfD3Nwctra2FTpGCIE5c+bAzc0NZmZm6NmzJ65fv16zgRIREZHe0JtEKC8vD8OGDcOkSZMqfMz//d//4csvv8SKFStw/PhxWFhYoHfv3sjJyanBSImIiEhfGOk6gIqaP38+AGDNmjUVKi+EwNKlSzF79mw888wzAIC1a9fCxcUFW7ZswYgRI2oqVKoFZnWKRNyS0xwxRkRE5dKbGqHKio6ORkJCAnr27KneZmNjgw4dOuDo0aNlHpebm4v09PRiN9I/7B9EREQVUWcToYSEBACAi4tLse0uLi7qfaVZvHgxbGxs1DcvL68ajZOIiIh0R6eJ0IwZMyBJUrm3K1euaDWmmTNnIi0tTX27ffu2Vq9P1Vc0bJ61QURE9CQ67SM0ffp0jBkzptwy9evXr9K5XV0L/wgmJibCzc1NvT0xMREBAQFlHqdQKKBQKKp0TaodiprFiIiInkSniZCTkxOcnJxq5Ny+vr5wdXVFeHi4OvFJT0/H8ePHKzXyjIiIiOouvekjdOvWLURGRuLWrVtQKpWIjIxEZGQkMjIy1GUaN26MzZs3AwAkScLUqVOxcOFC/PHHHzh//jxGjx4Nd3d3DB48WEePgrSBs0kTEVFF6c3w+Tlz5uDHH39U32/dujUAYN++fejatSsA4OrVq0hLS1OXeffdd5GZmYmJEyciNTUVnTt3xt9//w1TU1Otxk7aM6tTJOIjuNo8ERFVjCSEELoOojZLT0+HjY0Npsy7AIWpla7DoSeY1SkSd778lokQEZGBy8jLQ7sffkJaWhqsra3LLKc3TWNET2Lu5ID4JV/rOgwiItIjTISoznirYTgANosREVHFMRGiOkWSdB0BERHpEyZCVCeom8VknD+IiIgqjokQ1QlsFiMioqpgIkR1BpvFiIiospgIkd7jaDEiIqoqJkKk995qGA5JAuROHroOhYiI9AwTIdJ7rA0iIqKqYiJEem1Wp0gArA0iIqKqYSJEeo+dpImIqKqYCJFeY7MYERFVBxMh0ltsFiMioupiIkR6jc1iRERUHUyESC/N6hTJJTWIiKjamAiR3pIkLqlBRETVw0SI9I66NoiIiKiamAiR3olf8jVnkiYiIo1gIkR6hSPFiIhIk5gIkV6Rzh3jSDEiItIYJkKkN2Z1ikRc+GnWBhERkcYwESK9EDrov75BREREmsJEiPSC174V7CBNREQax0SogkadehOBwZyzRhfYJEZERDWFiVAFSRLQfOkgjImYqB65RDWPTWJERFSTmAhVkNzRDUbOHjBy9kD8kq8xJmKirkMyCGwSIyKimsREqAqMnAv/KI+JmMjmshrEJjEiIqppTISqyMjZQ91cxqYyzWOTGBERaQMToWqQOxVvKmPtkOawSYyIiLTBSNcB1AVGzh4oSIpH86WD0BzAhal/4OSRBF2HpZfMnRwwfPtziMN/TZBEREQ1hTVCGlLUkZrNZdXzVsNwAEyCiIhIO1gjpGFFTTnxS77GmH+3rWm/Umfx6JMxERMRHwFIcrmuQyEiIgPBGqEaUlRDBHB0WUUU1aAZOXtA7sDnioiItIM1QjXMyNkDyuT/+g959miDhQ9f1XVYtcqsTpEcIUZERDrBREgLHh35FBd+GmMwkR2q//VocxhrgoiISNuYCGnZ4yPMPHu0wd8t5hlcUlQ0Ogxgx2giItId9hHSAfUIM7kc8XtPo/nSQZhttULXYWnNrE6RGL79OUgSkyAiItIt1gjp0KNNQUVNZgAgFq7Ej3/oKqqaxaYwIiKqTfSmRmjRokUIDg6Gubk5bG1tK3TMmDFjIElSsVufPn1qNtAqenQeImn2RIyJmIjZVivqxGizwGBXzLZaoV6oliPDiIiottCbGqG8vDwMGzYMQUFB+P777yt8XJ8+fbB69Wr1fYVCURPhaUxRx2plSkJhs1n4f32JPjedhazkFN0GWAnmTg54q2E44pd8jXiJtUBERFT76E0iNH/+fADAmjVrKnWcQqGAq6v+/fF9PGGICz+N4SjsXCwWFk7QWJubz4pqf+K3MwEiIqLaS28Soarav38/nJ2dYWdnh+7du2PhwoVwcHAos3xubi5yc3PV99PT07UR5hMVdSpWJsdDen8ihADGoLCmCECtGHkWGOyKPufnIS78NAB2hCYiotqvTidCffr0wZAhQ+Dr64ubN2/ivffeQ9++fXH06FHIy1jGYfHixerap9ro0TmJlCkJiN9/FlAp1U1owH/NaEVqojnN3KkwmSxq+gIARIBNYEREpFckIYTQ1cVnzJiBjz/+uNwyly9fRuPGjdX316xZg6lTpyI1NbXS14uKioKfnx/27NmDHj16lFqmtBohLy8vnHj5RViamFT6mrpQkBRfYltRc9qjKtK0Fjqo5DZp9sTi95n4EBFRLZORl4d2P/yEtLQ0WFtbl1lOpzVC06dPx5gxY8otU79+fY1dr379+nB0dMSNGzfKTIQUCkWt71D9JI83SRU1pz2qqGntiSJQcukLqXjNFBERkb7SaSLk5OQEJycnrV0vLi4OKSkpcHNz09o1awMmLURERKXTm3mEbt26hcjISNy6dQtKpRKRkZGIjIxERkaGukzjxo2xefNmAEBGRgbeeecdHDt2DDExMQgPD8czzzyDBg0aoHfv3rp6GERERFSL6E1n6Tlz5uDHH39U32/dujUAYN++fejatSsA4OrVq0hLSwMAyOVynDt3Dj/++CNSU1Ph7u6Op59+GgsWLND7pi8iIiLSDJ12ltYH6enpsLGx0avO0kRERIauop2l9aZpjIiIiEjTmAgRERGRwdKbPkK6UtRymJGXp+NIiIiIqKKK/m4/qQcQ+wg9QVxcHLy8vHQdBhEREVXB7du34enpWeZ+JkJPoFKpcOfOHVhZWUEqMbPgkxXNTH379u1yO2tR9fG51h4+19rD51p7+FxrjzaeayEEHj58CHd3d8hkZfcEYtPYE8hksnIzyYqytrbmB0tL+FxrD59r7eFzrT18rrWnpp9rGxubJ5ZhZ2kiIiIyWEyEiIiIyGAxEaphCoUCc+fO5WzWWsDnWnv4XGsPn2vt4XOtPbXpuWZnaSIiIjJYrBEiIiIig8VEiIiIiAwWEyEiIiIyWEyEiIiIyGAxEdKSmJgYjBs3Dr6+vjAzM4Ofnx/mzp2LPK5hViMWLVqE4OBgmJubw9bWVtfh1DnLly9HvXr1YGpqig4dOiAiIkLXIdU5Bw4cwMCBA+Hu7g5JkrBlyxZdh1RnLV68GO3atYOVlRWcnZ0xePBgXL16Vddh1UnffPMNWrZsqZ5IMSgoCH/99ZdOY2IipCVXrlyBSqXCt99+i4sXL2LJkiVYsWIF3nvvPV2HVifl5eVh2LBhmDRpkq5DqXN+++03TJs2DXPnzsXp06fRqlUr9O7dG0lJSboOrU7JzMxEq1atsHz5cl2HUuf9888/mDx5Mo4dO4bdu3cjPz8fTz/9NDIzM3UdWp3j6emJjz76CKdOncLJkyfRvXt3PPPMM7h48aLOYuLweR365JNP8M033yAqKkrXodRZa9aswdSpU5GamqrrUOqMDh06oF27dli2bBmAwvX4vLy88Prrr2PGjBk6jq5ukiQJmzdvxuDBg3UdikFITk6Gs7Mz/vnnH3Tp0kXX4dR59vb2+OSTTzBu3DidXJ81QjqUlpYGe3t7XYdBVGF5eXk4deoUevbsqd4mk8nQs2dPHD16VIeREWlOWloaAPD7uYYplUr8+uuvyMzMRFBQkM7i4KKrOnLjxg189dVX+PTTT3UdClGF3bt3D0qlEi4uLsW2u7i44MqVKzqKikhzVCoVpk6dik6dOqF58+a6DqdOOn/+PIKCgpCTkwNLS0ts3rwZTZs21Vk8rBGqphkzZkCSpHJvj/+BiI+PR58+fTBs2DBMmDBBR5Hrn6o810RElTF58mRcuHABv/76q65DqbMaNWqEyMhIHD9+HJMmTUJoaCguXbqks3hYI1RN06dPx5gxY8otU79+ffX/79y5g27duiE4OBgrV66s4ejqlso+16R5jo6OkMvlSExMLLY9MTERrq6uOoqKSDOmTJmCP//8EwcOHICnp6euw6mzTExM0KBBAwBA27ZtceLECXzxxRf49ttvdRIPE6FqcnJygpOTU4XKxsfHo1u3bmjbti1Wr14NmYwVcpVRmeeaaoaJiQnatm2L8PBwdcddlUqF8PBwTJkyRbfBEVWREAKvv/46Nm/ejP3798PX11fXIRkUlUqF3NxcnV2fiZCWxMfHo2vXrvDx8cGnn36K5ORk9T7+kta8W7du4f79+7h16xaUSiUiIyMBAA0aNIClpaVug9Nz06ZNQ2hoKAIDA9G+fXssXboUmZmZGDt2rK5Dq1MyMjJw48YN9f3o6GhERkbC3t4e3t7eOoys7pk8eTLCwsKwdetWWFlZISEhAQBgY2MDMzMzHUdXt8ycORN9+/aFt7c3Hj58iLCwMOzfvx87d+7UXVCCtGL16tUCQKk30rzQ0NBSn+t9+/bpOrQ64auvvhLe3t7CxMREtG/fXhw7dkzXIdU5+/btK/U9HBoaquvQ6pyyvptXr16t69DqnJdffln4+PgIExMT4eTkJHr06CF27dql05g4jxAREREZLHZSISIiIoPFRIiIiIgMFhMhIiIiMlhMhIiIiMhgMREiIiIig8VEiIiIiAwWEyEiIiIyWEyEiIiIyGAxESIinRozZgwkSSpxe3R5iepYs2YNbG1tNXKuqjpw4AAGDhwId3d3SJKELVu26DQeIvoPEyEi0rk+ffrg7t27xW61ceHL/Pz8Kh2XmZmJVq1aYfny5RqOiIiqi4kQEemcQqGAq6trsZtcLgcAbN26FW3atIGpqSnq16+P+fPno6CgQH3s559/jhYtWsDCwgJeXl547bXXkJGRAQDYv38/xo4di7S0NHVN07x58wCg1JoZW1tbrFmzBgAQExMDSZLw22+/ISQkBKampvj5558BAKtWrUKTJk1gamqKxo0b4+uvvy738fXt2xcLFy7Es88+q4Fni4g0iavPE1GtdfDgQYwePRpffvklnnrqKdy8eRMTJ04EAMydOxcAIJPJ8OWXX8LX1xdRUVF47bXX8O677+Lrr79GcHAwli5dijlz5uDq1asAAEtLy0rFMGPGDHz22Wdo3bq1OhmaM2cOli1bhtatW+PMmTOYMGECLCwsEBoaqtkngIhqnk6XfCUigxcaGirkcrmwsLBQ34YOHSqEEKJHjx7iww8/LFZ+3bp1ws3NrczzbdiwQTg4OKjvr169WtjY2JQoB0Bs3ry52DYbGxv1iuPR0dECgFi6dGmxMn5+fiIsLKzYtgULFoigoKAnPdQyr0tEusMaISLSuW7duuGbb75R37ewsAAAnD17FocPH8aiRYvU+5RKJXJycpCVlQVzc3Ps2bMHixcvxpUrV5Ceno6CgoJi+6srMDBQ/f/MzEzcvHkT48aNw4QJE9TbCwoKYGNjU+1rEZH2MREiIp2zsLBAgwYNSmzPyMjA/PnzMWTIkBL7TE1NERMTgwEDBmDSpElYtGgR7O3tcejQIYwbNw55eXnlJkKSJEEIUWxbaZ2hi5KyongA4LvvvkOHDh2KlSvq00RE+oWJEBHVWm3atMHVq1dLTZIA4NSpU1CpVPjss88gkxWO/Vi/fn2xMiYmJlAqlSWOdXJywt27d9X3r1+/jqysrHLjcXFxgbu7O6KiojBq1KjKPhwiqoWYCBFRrTVnzhwMGDAA3t7eGDp0KGQyGc6ePYsLFy5g4cKFaNCgAfLz8/HVV19h4MCBOHz4MFasWFHsHPXq1UNGRgbCw8PRqlUrmJubw9zcHN27d8eyZcsQFBQEpVKJ//3vfzA2Nn5iTPPnz8cbb7wBGxsb9OnTB7m5uTh58iQePHiAadOmlXpMRkZGsXmRoqOjERkZCXt7e3h7e1fvSSKi6tF1JyUiMmyhoaHimWeeKXP/33//LYKDg4WZmZmwtrYW7du3FytXrlTv//zzz4Wbm5swMzMTvXv3FmvXrhUAxIMHD9RlXn31VeHg4CAAiLlz5wohhIiPjxdPP/20sLCwEP7+/mLHjh2ldpY+c+ZMiZh+/vlnERAQIExMTISdnZ3o0qWL2LRpU5mPYd++fQJAiVtoaGglnikiqgmSEI81khMREREZCE6oSERERAaLiRAREREZLCZCREREZLCYCBEREZHBYiJEREREBouJEBERERksJkJERERksJgIERERkcFiIkREREQGi4kQERERGSwmQkRERGSwmAgRERGRwfp/llth9sbh/xQAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "DecisionBoundaryDisplay.from_estimator(model_nonlinear, X_moons, response_method='predict',\n",
-    "                                       cmap=plt.cm.coolwarm, alpha=0.8,\n",
-    "                                       grid_resolution=500)\n",
-    "# plt.scatter(X_train_moons[:, 0], X_train_moons[:, 1], c=y_train_moons,\n",
-    "#             edgecolors='k', marker='o', s=50, label='Training Data')\n",
-    "plt.scatter(X_test_moons[:, 0], X_test_moons[:, 1], c=y_test_moons,\n",
-    "            edgecolors='k', marker='o', s=50, label='Test Data')\n",
-    "plt.title('Nonlinear SVM Decision Boundary on Moons Dataset')\n",
-    "plt.xlabel('Feature 1')\n",
-    "plt.ylabel('Feature 2')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8026504f",
-   "metadata": {},
-   "source": [
-    "## Kernel Trick\n",
-    "\n",
-    "SVMs can efficiently handle non-linear decision boundaries using the kernel trick. Instead of explicitly mapping the data into a higher-dimensional space, SVMs use kernel functions to compute the inner products in that space. Common kernel functions include:\n",
-    "\n",
-    "-   **Linear Kernel**: $K(x_i, x_j) = x_i^T x_j$\n",
-    "-   **Polynomial Kernel**: $K(x_i, x_j) = (x_i^T x_j + c)^d$\n",
-    "-   **Radial Basis Function (RBF) Kernel**: $K(x_i, x_j) = \\exp\\left(-\\frac{\\|x_i - x_j\\|^2}{2\\sigma^2}\\right)$\n",
-    "-   **Sigmoid Kernel**: $K(x_i, x_j) = \\tanh(\\alpha x_i^T x_j + c)$\n",
-    "\n",
-    "The choice of kernel allows SVMs to adapt to various data distributions and complexities."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c96ee10d",
-   "metadata": {},
-   "source": [
-    "## Advantages and Disadvantages\n",
-    "\n",
-    "-   **Advantages**:\n",
-    "    -   Effective in high-dimensional spaces.\n",
-    "    -   Robust against overfitting, especially in high-dimensional datasets.\n",
-    "    -   Works well with clear margin of separation.\n",
-    "    -   Can handle non-linear decision boundaries using kernel functions.\n",
-    "-   **Disadvantages**:\n",
-    "    -   Computationally intensive, especially with large datasets.\n",
-    "    -   Requires careful tuning of hyperparameters (e.g., regularization parameter $C$ and kernel parameters).\n",
-    "    -   Less effective on noisy datasets with overlapping classes."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "85e2e795",
-   "metadata": {},
-   "source": [
-    "## SVMs as Regression Models\n",
-    "\n",
-    "SVMs can also be used for regression tasks, known as Support Vector Regression (SVR). The main idea is similar to classification, where the goal is to find a function that approximates the relationship between input features and continuous output values. The optimization problem for SVR is formulated to minimize the error while maintaining a margin of tolerance around the predicted values. The SVR can also utilize kernel functions to handle non-linear relationships.\n",
-    "\n",
-    "-   Generate synthetic data for regression"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "85482d6c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.svm import SVR\n",
-    "X_reg = np.sort(5 * np.random.rand(80, 1), axis=0)\n",
-    "y_reg = np.sin(X_reg).ravel() + np.random.normal(0, 0.1, X_reg.shape[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0507ab5",
-   "metadata": {},
-   "source": [
-    "-   Split the data into training and testing sets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "d25a031a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train_reg, X_test_reg, y_train_reg, y_test_reg = \\\n",
-    "    train_test_split(X_reg, y_reg, test_size=0.2, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "131066b4",
-   "metadata": {},
-   "source": [
-    "-   Create a pipeline for scaling and SVR and fit"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "ba45823f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-3 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-3 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-3 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-3 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
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-       ".user-set td i,\n",
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-       "\n",
-       ".copy-paste-icon {\n",
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-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;svr&#x27;, SVR())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('steps',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">steps&nbsp;</td>\n",
-       "            <td class=\"value\">[(&#x27;scaler&#x27;, ...), (&#x27;svr&#x27;, ...)]</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('transform_input',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">transform_input&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('memory',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">memory&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"scaler__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('copy',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">copy&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('with_mean',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">with_mean&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('with_std',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">with_std&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVR</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"svr__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('kernel',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">kernel&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;rbf&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
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-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">degree&nbsp;</td>\n",
-       "            <td class=\"value\">3</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
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-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('gamma',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">gamma&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;scale&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
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-       "                 onclick=\"copyToClipboard('coef0',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">coef0&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
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-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
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-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.001</td>\n",
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-       "\n",
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-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">C&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
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-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('epsilon',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">epsilon&nbsp;</td>\n",
-       "            <td class=\"value\">0.1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('shrinking',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">shrinking&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
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-       "    \n",
-       "\n",
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-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('cache_size',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">cache_size&nbsp;</td>\n",
-       "            <td class=\"value\">200</td>\n",
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-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
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-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
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-       "            <td class=\"value\">-1</td>\n",
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-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
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-       "\n",
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-       "\n",
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-       "            element.style.width = originalWidth;\n",
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-       "            setTimeout(() => {\n",
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-       "                element.style = originalStyle;\n",
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-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
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-       "                element.style = originalStyle;\n",
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-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
-      ],
-      "text/plain": [
-       "Pipeline(steps=[('scaler', StandardScaler()), ('svr', SVR())])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "pipeline_reg = Pipeline([\n",
-    "    ('scaler', StandardScaler()),  # Scale features\n",
-    "    ('svr', SVR(kernel='rbf', C=1.0, epsilon=0.1))  # SVR with RBF kernel\n",
-    "])\n",
-    "pipeline_reg.fit(X_train_reg, y_train_reg)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8dca7ad6",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "175b3176",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SVR Mean Squared Error: 0.01\n",
-      "SVR R^2 Score: 0.96\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import mean_squared_error, r2_score\n",
-    "\n",
-    "y_pred_reg = pipeline_reg.predict(X_test_reg)\n",
-    "mse = mean_squared_error(y_test_reg, y_pred_reg)\n",
-    "r2 = r2_score(y_test_reg, y_pred_reg)\n",
-    "print(f\"SVR Mean Squared Error: {mse:.2f}\")\n",
-    "print(f\"SVR R^2 Score: {r2:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fc37ea68",
-   "metadata": {},
-   "source": [
-    "-   Plotting the SVR results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "9445b968",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "# plt.scatter(X_train_reg, y_train_reg, color='blue', label='Training Data')\n",
-    "plt.scatter(X_test_reg, y_test_reg, color='green', label='Test Data')\n",
-    "plt.scatter(X_test_reg, y_pred_reg, color='red', label='SVR Predictions')\n",
-    "plt.plot(np.sort(X_test_reg, axis=0), pipeline_reg.predict(np.sort(X_test_reg, axis=0)),\n",
-    "         color='orange', label='SVR Model')\n",
-    "plt.title('Support Vector Regression on Synthetic Data')\n",
-    "plt.xlabel('Feature')\n",
-    "plt.ylabel('Target')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "574d856b",
-   "metadata": {},
-   "source": [
-    "This code demonstrates how to use SVM for regression tasks, showing the model's predictions against the actual data points. The SVR is configured with a radial basis function kernel, allowing it to capture non-linear relationships in the data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6df291f4",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "-   **Experiment with Different Kernels**: Using the moons dataset, modify the SVM model to use different kernel functions (e.g., polynomial, sigmoid) and observe how the decision boundary changes. Compare the performance of each kernel using accuracy and confusion matrix."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "cc12aedb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Kernel: linear, Accuracy: 0.87\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Kernel: poly, Accuracy: 0.87\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Kernel: rbf, Accuracy: 0.97\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Kernel: sigmoid, Accuracy: 0.73\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Experiment with different kernels on the moons dataset\n",
-    "kernels = ['linear', 'poly', 'rbf', 'sigmoid']\n",
-    "for kernel in kernels:\n",
-    "    model = svm.SVC(kernel=kernel, gamma='scale', C=1.0)\n",
-    "    model.fit(X_train_moons, y_train_moons)\n",
-    "    y_pred = model.predict(X_test_moons)\n",
-    "    accuracy = accuracy_score(y_test_moons, y_pred)\n",
-    "    print(f\"Kernel: {kernel}, Accuracy: {accuracy:.2f}\")\n",
-    "    ConfusionMatrixDisplay.from_estimator(model, X_test_moons, y_test_moons,\n",
-    "                                          display_labels=['Class 0', 'Class 1'],\n",
-    "                                          cmap=plt.cm.Blues)\n",
-    "    plt.title(f'Confusion Matrix for {kernel} Kernel')\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c82bee34",
-   "metadata": {},
-   "source": [
-    "-   **Hyperparameter Tuning**: Use techniques like grid search or random search to find the optimal hyperparameters for the SVM model, such as the regularization parameter $C$ and kernel parameters. Evaluate the impact of these hyperparameters on model performance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "8a2c5885",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 5 folds for each of 42 candidates, totalling 210 fits\n",
-      "Best parameters: {'svm__C': 3.0, 'svm__gamma': 'scale', 'svm__kernel': 'rbf'}\n",
-      "Best cross-validation score: 1.00\n",
-      "Best Model Accuracy: 0.97\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import GridSearchCV\n",
-    "# Define the parameter grid for hyperparameter tuning\n",
-    "param_grid = {\n",
-    "    'svm__C': [0.1, 0.3, 1.0, 3.0, 10.0, 30.0, 100.0],\n",
-    "    'svm__kernel': ['linear', 'rbf', 'poly'],\n",
-    "    'svm__gamma': ['scale', 'auto']\n",
-    "}\n",
-    "grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='accuracy', verbose=1)\n",
-    "grid_search.fit(X_train_moons, y_train_moons)\n",
-    "print(f\"Best parameters: {grid_search.best_params_}\")\n",
-    "print(f\"Best cross-validation score: {grid_search.best_score_:.2f}\")\n",
-    "best_model = grid_search.best_estimator_\n",
-    "y_pred_best = best_model.predict(X_test_moons)\n",
-    "accuracy_best = accuracy_score(y_test_moons, y_pred_best)\n",
-    "print(f\"Best Model Accuracy: {accuracy_best:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ef26aa56",
-   "metadata": {},
-   "source": [
-    "-   **Visualizing Decision Boundaries**: Create visualizations of the decision boundaries for different SVM models on the moons dataset. Use contour plots to illustrate how the decision boundaries change with different hyperparameters and kernels."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "6e8fd166",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1200x800 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualizing decision boundaries for different SVM models\n",
-    "kernels = ['linear', 'poly', 'rbf', 'sigmoid']\n",
-    "plt.figure(figsize=(12, 8))\n",
-    "for i, kernel in enumerate(kernels):\n",
-    "      model = svm.SVC(kernel=kernel, gamma='scale', C=3.0)\n",
-    "      model.fit(X_train_moons, y_train_moons)\n",
-    "      DecisionBoundaryDisplay.from_estimator(model, X_moons, response_method='predict',\n",
-    "                                             cmap=plt.cm.coolwarm, alpha=0.8,\n",
-    "                                             grid_resolution=500)\n",
-    "      plt.scatter(X_test_moons[:, 0], X_test_moons[:, 1], c=y_test_moons,\n",
-    "                  edgecolors='k', marker='o', s=50, label='Test Data')\n",
-    "      plt.title(f'Decision Boundary with {kernel} Kernel')\n",
-    "      plt.xlabel('Feature 1')\n",
-    "      plt.ylabel('Feature 2')\n",
-    "      plt.legend()\n",
-    "      plt.tight_layout()\n",
-    "      plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "86aee470",
-   "metadata": {},
-   "source": [
-    "-   **Support Vector Regression**: Implement a regression task using SVR on the following synthetic dataset: $$y = x^2 + \\cos(2\\pi x) + \\epsilon,$$ where $\\epsilon$ is Gaussian noise. Experiment with different kernel functions and hyperparameters, and evaluate the model's performance using metrics like mean squared error and $R^2$ score. Visualize the predictions against the actual data points to understand how well the SVR captures the underlying relationship."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "194206a4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SVR Mean Squared Error: 2.15\n",
-      "SVR R^2 Score: 0.95\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Generate synthetic data for regression\n",
-    "n_samples = 100\n",
-    "X_reg = np.sort(5 * np.random.rand(n_samples, 1), axis=0)\n",
-    "y_reg = X_reg**2 + np.cos(2 * np.pi * X_reg) + np.random.randn(n_samples, 1)\n",
-    "y_reg = y_reg.ravel()\n",
-    "\n",
-    "# Split the data into training and testing sets\n",
-    "X_train_reg, X_test_reg, y_train_reg, y_test_reg = \\\n",
-    "    train_test_split(X_reg, y_reg, test_size=0.2, random_state=42)\n",
-    "\n",
-    "# Create a pipeline for scaling and SVR\n",
-    "pipeline_reg = Pipeline([\n",
-    "    ('scaler', StandardScaler()),  # Scale features\n",
-    "    ('svr', SVR(kernel='rbf', C=1.0, epsilon=0.1))  # SVR with RBF kernel\n",
-    "])\n",
-    "# Fit the SVR model\n",
-    "pipeline_reg.fit(X_train_reg, y_train_reg)\n",
-    "\n",
-    "# Make predictions on the test set\n",
-    "y_pred_reg = pipeline_reg.predict(X_test_reg)\n",
-    "\n",
-    "# Evaluate the SVR model\n",
-    "mse = mean_squared_error(y_test_reg, y_pred_reg)\n",
-    "r2 = r2_score(y_test_reg, y_pred_reg)\n",
-    "print(f\"SVR Mean Squared Error: {mse:.2f}\")\n",
-    "print(f\"SVR R^2 Score: {r2:.2f}\")\n",
-    "\n",
-    "# Plotting the SVR results\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "# plt.scatter(X_train_reg, y_train_reg, color='blue', label='Training Data')\n",
-    "plt.scatter(X_test_reg, y_test_reg, color='green', label='Test Data')\n",
-    "plt.scatter(X_test_reg, y_pred_reg, color='red', label='SVR Predictions')\n",
-    "plt.plot(np.sort(X_test_reg, axis=0), pipeline_reg.predict(np.sort(X_test_reg, axis=0)),\n",
-    "         color='orange', label='SVR Model')\n",
-    "plt.title('Support Vector Regression on Synthetic Data')\n",
-    "plt.xlabel('Feature')\n",
-    "plt.ylabel('Target')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ecc6bfe1-f990-49bc-ac0f-97030062e613",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S04 Ensemble Methods/S04 Ensemble Methods.pdf b/src/S04 Ensemble Methods/S04 Ensemble Methods.pdf
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diff --git a/src/S04 Ensemble Methods/S04 Ensemble Methods.tex b/src/S04 Ensemble Methods/S04 Ensemble Methods.tex
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-\section{Introduction to Ensemble Methods}
-\label{sec:orgc01af1e}
-\begin{frame}[label={sec:org81ed87b}]{Introduction to Ensemble Methods}
-\begin{definition}[What are Ensemble Methods?]\label{sec:orga978861}
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-\alert{Ensemble methods} combine multiple models to improve predictive performance. They leverage the strengths of individual models while mitigating their weaknesses.
-\end{definition}
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-\begin{frame}[label={sec:orgca46c90}]{Introduction to Ensemble Methods}
-\begin{example}[Why Use Ensemble Methods?]\label{sec:orge1b80ea}
-\begin{itemize}[<+->]
-\item \alert{Improved Accuracy}: Ensemble methods often outperform individual models by reducing variance and bias.
-\item \alert{Robustness}: Less sensitive to noise and outliers, leading to more stable predictions.
-\item \alert{Reduced Overfitting}: Reduce overfitting by averaging predictions from multiple models.
-\item \alert{Flexibility}: Ensemble methods can be applied to various base models, including decision trees, linear models, and neural networks.
-\item \alert{Interpretability}: Some ensemble methods, like Random Forests, provide insights into feature importance.
-\item \alert{Scalability}: Many ensemble methods can be parallelized, useful for large datasets.
-\item \alert{Combining Different Algorithms}: Ensembles can combine predictions from different types of models, enhancing overall performance.
-\end{itemize}
-\end{example}
-\end{frame}
-\begin{frame}[label={sec:org9390590}]{Types of Ensemble Methods}
-\begin{example}[Ensamble Methods]\label{sec:org21118a1}
-\pause
-Ensemble methods can be broadly categorized into several types, each with its own approach to combining models:
-\begin{itemize}[<+->]
-\item \alert{Bagging (Bootstrap Aggregating)}: Combines predictions from multiple models trained on different subsets of the training data. The most common example is Random Forests.
-\item \alert{Boosting}: Sequentially builds models, where each new model focuses on correcting the errors made by the previous models. Examples include AdaBoost, Gradient Boosting, and XGBoost.
-\item \alert{Stacking}: Combines multiple models by training a meta-model on their predictions. The base models can be of different types, and the meta-model learns how to best combine their outputs.
-\item Other methods include \alert{Pasting}, \alert{Bayesian Model Averaging}, \alert{Blending}, and \alert{Voting}.
-\end{itemize}
-\end{example}
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-\section{Bagging}
-\label{sec:orgecc9b8b}
-\begin{frame}[label={sec:org715fe26}]{Bootstrap Aggregating (Bagging) Overview}
-\begin{definition}[Bootstrap Aggregating (Bagging)]\label{sec:org6f36a28}
-\pause
-\alert{Bagging}, or \alert{Bootstrap Aggregating}, is an ensemble method that combins the predictions of multiple models trained on different subsets of the training data. The key steps in bagging are:
-\pause
-\begin{itemize}[<+->]
-\item \alert{Bootstrap Sampling}: Create multiple bootstrap samples from the training data. Each sample is created by randomly selecting instances with replacement, meaning some instances may appear multiple times while others may not appear at all.
-\item \alert{Model Training}: Train a separate model on each bootstrap sample. These models can be of the same type (e.g., decision trees) or different types.
-\item \alert{Aggregation}: Combine the predictions of the individual models. For regression tasks, this is typically done by averaging the predictions, while for classification tasks, a majority vote is used to determine the final class label.
-\end{itemize}
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orga8e78af}]{Bagging and Random Forests}
-\begin{example}[Bagging and Random Forests]\label{sec:orgd3e9774}
-\pause
-\alert{Random Forests} is a popular ensemble method that builds multiple decision trees using bagging. It introduces additional randomness by selecting a random subset of features for each tree, which helps to decorrelate the trees and improve overall model performance.
-\end{example}
-\end{frame}
-\begin{frame}[label={sec:org4691be4}]{Feature Importance in Random Forests}
-\begin{block}{Feature Importance in Random Forests}
-\pause
-\alert{Random Forests} can also provide insights into feature importance, which helps in understanding which features contribute most to the predictions. This is done by measuring the decrease in model performance when a feature is permuted or removed.
-\end{block}
-\end{frame}
-\section{Boosting}
-\label{sec:org047e568}
-\begin{frame}[label={sec:org97cd03b}]{Boosting Overview}
-\begin{definition}[Boosting Overview]\label{sec:org05c7f3c}
-\pause
-\alert{Boosting} is an ensemble method that builds models sequentially, where each new model focuses on correcting the errors made by the previous models. The key steps in boosting are:
-\pause
-\begin{itemize}[<+->]
-\item \alert{Sequential Model Training}: Train a series of models, where each model is trained on the residuals (errors) of the previous model. This allows each new model to learn from the mistakes of its predecessor.
-\item \alert{Weighted Predictions}: Each model's predictions are weighted based on its performance. Models that perform well have a higher weight in the final prediction.
-\item \alert{Final Prediction}: Combine the predictions of all models, typically by summing their weighted predictions. For classification tasks, a threshold is applied to determine the final class label.
-\end{itemize}
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orgd467fcd}]{Boosting Algorithms}
-\begin{example}[Boosting Algorithms]\label{sec:orgb1e34b2}
-\pause
-Some popular boosting algorithms include:
-\begin{itemize}[<+->]
-\item \alert{AdaBoost}: Assigns weights to instances based on their classification errors, focusing more on misclassified instances in subsequent iterations.
-\item \alert{Gradient Boosting}: Builds models that minimize a loss function by fitting to the residuals of the previous model. It can handle various loss functions, making it versatile for regression and classification tasks.
-\item \alert{XGBoost}: An optimized implementation of gradient boosting that includes regularization, handling missing values, and parallel processing for faster training.
-\end{itemize}
-\end{example}
-\end{frame}
-\section{Ensembles of Ensemble Methods}
-\label{sec:org31b8c8c}
-\begin{frame}[label={sec:org7bc0f1f}]{Ensembles of Ensemble Methods}
-\begin{definition}[Ensembles of Ensemble Methods]\label{sec:orgfd0c7c7}
-\pause
-\alert{Ensembles of Ensemble} methods combine multiple ensemble techniques to further enhance predictive performance. This approach can lead to even more robust models by leveraging the strengths of different ensemble methods.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org3a98c71}]{Ensembles of Ensemble Methods}
-\begin{block}{Advantages of Ensembles of Ensemble Methods}
-\pause
-Some advantages of ensembles of ensemble methods include:
-\begin{itemize}[<+->]
-\item \alert{Improved Accuracy}: By combining predictions from multiple ensemble methods, the overall accuracy can be significantly improved.
-\item \alert{Robustness}: It reduces the risk of overfitting by averaging out the predictions from different models, leading to better generalization.
-\item \alert{Flexibility}: It allows the use of diverse ensemble methods, which can capture different patterns in the data.
-\item \alert{Diversity}: The combination of different ensemble methods can lead to a more diverse set of predictions, which is beneficial for model performance.
-\item \alert{Enhanced Interpretability}: Some ensemble methods, like stacking, can provide insights into how different models contribute to the final prediction.
-\end{itemize}
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org7ae870b}]{Ensambles of Ensemble Methods}
-\begin{block}{Disadvantages of Ensembles of Ensemble Methods}
-\pause
-Some disadvantages of ensembles of ensemble methods include:
-\begin{itemize}[<+->]
-\item \alert{Complexity}: The model can become complex and harder to interpret, as it involves multiple layers of models.
-\item \alert{Computational Cost}: Training multiple ensemble methods can be computationally expensive and time-consuming.
-\item \alert{Risk of Overfitting}: If not managed properly, combining too many models can lead to overfitting, especially if the base models are too complex.
-\item \alert{Hyperparameter Tuning}: Each ensemble method may require its own hyperparameter tuning, adding to the complexity of the model development process.
-\end{itemize}
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org21e11b4}]{Ensembles of Ensemble Methods}
-\begin{block}{Types of Ensembles of Ensembles}
-\pause
-Some common types of ensembles of ensembles include:
-\begin{itemize}[<+->]
-\item \alert{Stacking}: Combines multiple ensemble methods by training a meta-model on their predictions. The base models can be of different types, and the meta-model learns how to best combine their outputs.
-\item \alert{Multi-level EoE}: A more complex version of stacking where multiple layers of models are trained, and predictions from one layer are used as inputs to the next layer. This allows for deeper integration of different ensemble methods and can lead to improved performance.
-\end{itemize}
-\end{block}
-\end{frame}
-\end{document}
diff --git a/src/S04 Ensemble Methods/S04C01 Introduction to Ensemble Methods.ipynb b/src/S04 Ensemble Methods/S04C01 Introduction to Ensemble Methods.ipynb
deleted file mode 100644
index 167cb4a..0000000
--- a/src/S04 Ensemble Methods/S04C01 Introduction to Ensemble Methods.ipynb	
+++ /dev/null
@@ -1,3007 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "a8e8bb5b",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Introduction to Ensemble Methods"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "298a5787",
-   "metadata": {},
-   "source": [
-    "## What are Ensemble Methods\n",
-    "\n",
-    "**Ensemble methods** are a powerful class of machine learning techniques that combine multiple models to improve predictive performance. The main idea is to leverage the strengths of different models while mitigating their weaknesses, leading to more robust and accurate predictions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c9c2c7b5",
-   "metadata": {},
-   "source": [
-    "## Why Use Ensemble Methods?\n",
-    "\n",
-    "Ensemble methods are used for several reasons:\n",
-    "\n",
-    "-   **Improved Accuracy**: They often outperform individual models by reducing variance and bias.\n",
-    "-   **Robustness**: Ensembles are less sensitive to noise and outliers, leading to more stable predictions.\n",
-    "-   **Reduced Overfitting**: By averaging predictions from multiple models, ensembles can reduce overfitting.\n",
-    "-   **Interpretability**: Some ensemble methods, like Random Forests, provide insights into feature importance.\n",
-    "-   **Flexibility**: They can be applied to various base models, including decision trees, linear models, and neural networks.\n",
-    "-   **Scalability**: Many ensemble methods can be parallelized, making them suitable for large datasets.\n",
-    "-   **Combining Different Algorithms**: Ensembles can combine predictions from different types of models, enhancing overall performance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8251800a",
-   "metadata": {},
-   "source": [
-    "## Types of Ensemble Methods\n",
-    "\n",
-    "Ensemble methods can be broadly categorized into two types:\n",
-    "\n",
-    "-   **Bagging (Bootstrap Aggregating)**: Combines predictions from multiple models trained on different subsets of the training data. The most common example is Random Forests, which builds multiple decision trees and averages their predictions.\n",
-    "-   **Pasting**: Similar to bagging, but uses subsets of the training data without replacement.\n",
-    "-   **Boosting**: Sequentially builds models, where each new model focuses on correcting the errors made by the previous models. Examples include AdaBoost, Gradient Boosting, and XGBoost.\n",
-    "-   **Stacking**, **Blending**, **Bayesian Model Averaging**, **Voting**, etc."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "20f6b5c3",
-   "metadata": {},
-   "source": [
-    "## Practical Demonstration\n",
-    "\n",
-    "To illustrate the concepts of ensemble methods, we will use the Iris dataset and apply both Random Forests and Gradient Boosting classifiers. We will compare their performance using accuracy and confusion matrices.\n",
-    "\n",
-    "-   Load the `iris` dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "a89e5089",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn import datasets\n",
-    "iris = datasets.load_iris()\n",
-    "X = iris.data\n",
-    "y = iris.target\n",
-    "feature_names = iris.feature_names\n",
-    "class_names = iris.target_names"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "652b92e3",
-   "metadata": {},
-   "source": [
-    "-   Train-test split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "7908436e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "73739028",
-   "metadata": {},
-   "source": [
-    "-   Train a Random Forest Classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "e1149087",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
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-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('criterion',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">criterion&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_depth',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_split',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
-       "            <td class=\"value\">2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
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-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
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-       "            ></i></td>\n",
-       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_features&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">bootstrap&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">oob_score&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">42</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
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-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_samples',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_samples&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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-      ],
-      "text/plain": [
-       "RandomForestClassifier(random_state=42)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "from sklearn.metrics import accuracy_score, classification_report, ConfusionMatrixDisplay\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
-    "rf_model.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2cfe3e51",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the performance of the RF Classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "6a0c0b3a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Random Forest Classifier Accuracy: 0.8888888888888888\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "      setosa       1.00      1.00      1.00        15\n",
-      "  versicolor       0.78      0.93      0.85        15\n",
-      "   virginica       0.92      0.73      0.81        15\n",
-      "\n",
-      "    accuracy                           0.89        45\n",
-      "   macro avg       0.90      0.89      0.89        45\n",
-      "weighted avg       0.90      0.89      0.89        45\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "y_pred_rf = rf_model.predict(X_test)\n",
-    "\n",
-    "print(\"Random Forest Classifier Accuracy:\", accuracy_score(y_test, y_pred_rf))\n",
-    "print(classification_report(y_test, y_pred_rf, target_names=class_names))\n",
-    "\n",
-    "ConfusionMatrixDisplay.from_estimator(rf_model, X_test, y_test,\n",
-    "                                      display_labels=class_names, cmap='Blues')\n",
-    "plt.title('Random Forest Confusion Matrix')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3b1108bd",
-   "metadata": {},
-   "source": [
-    "-   Train a Gradient Boosting Classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "9c8ab2a7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-2 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-2 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-2 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-2 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
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-       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GradientBoostingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('loss',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">loss&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;log_loss&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
-       "            <td class=\"value\">0.1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('subsample',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">subsample&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">criterion&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;friedman_mse&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
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-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
-       "            <td class=\"value\">2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
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-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">3</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">init&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
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-       "    \n",
-       "\n",
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-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">42</td>\n",
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-       "    \n",
-       "\n",
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-       "            ></i></td>\n",
-       "            <td class=\"param\">max_features&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
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-       "    \n",
-       "\n",
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-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
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-       "    \n",
-       "\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
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-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
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-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('validation_fraction',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">validation_fraction&nbsp;</td>\n",
-       "            <td class=\"value\">0.1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_iter_no_change&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
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-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
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-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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-      ],
-      "text/plain": [
-       "GradientBoostingClassifier(random_state=42)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.ensemble import GradientBoostingClassifier\n",
-    "\n",
-    "gb_model = GradientBoostingClassifier(n_estimators=100, random_state=42)\n",
-    "gb_model.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "22acc9df",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the performance of the GB Classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "bf5d19ea",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Gradient Boosting Classifier Accuracy: 0.9333333333333333\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "      setosa       1.00      1.00      1.00        15\n",
-      "  versicolor       0.88      0.93      0.90        15\n",
-      "   virginica       0.93      0.87      0.90        15\n",
-      "\n",
-      "    accuracy                           0.93        45\n",
-      "   macro avg       0.93      0.93      0.93        45\n",
-      "weighted avg       0.93      0.93      0.93        45\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "y_pred_gb = gb_model.predict(X_test)\n",
-    "\n",
-    "print(\"Gradient Boosting Classifier Accuracy:\", accuracy_score(y_test, y_pred_gb))\n",
-    "print(classification_report(y_test, y_pred_gb, target_names=class_names))\n",
-    "\n",
-    "ConfusionMatrixDisplay.from_estimator(gb_model, X_test, y_test,\n",
-    "                                      display_labels=class_names, cmap='Blues')\n",
-    "plt.title('Gradient Boosting Confusion Matrix')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f08ead77",
-   "metadata": {},
-   "source": [
-    "-   Feature importances for Random Forest"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "8c9b6b13",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Feature importances for Random Forest:\n",
-      "petal width (cm): 0.4549\n",
-      "petal length (cm): 0.4002\n",
-      "sepal length (cm): 0.1206\n",
-      "sepal width (cm): 0.0243\n"
-     ]
-    }
-   ],
-   "source": [
-    "importances = rf_model.feature_importances_\n",
-    "indices = importances.argsort()[::-1]\n",
-    "print(\"Feature importances for Random Forest:\")\n",
-    "for i in range(X.shape[1]):\n",
-    "    print(f\"{feature_names[indices[i]]}: {importances[indices[i]]:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "68254e26",
-   "metadata": {},
-   "source": [
-    "-   Plot feature importances for RF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "063be6ec",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.bar(range(X.shape[1]), importances[indices], align='center')\n",
-    "plt.xticks(range(X.shape[1]), [feature_names[i] for i in indices], rotation=45)\n",
-    "plt.title('Feature Importances from Random Forest')\n",
-    "plt.xlabel('Features')\n",
-    "plt.ylabel('Importance')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "177f61eb",
-   "metadata": {},
-   "source": [
-    "-   Feature importances for Gradient Boosting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "8b3b2d1e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Feature importances for Gradient Boosting:\n",
-      "petal width (cm): 0.6642\n",
-      "petal length (cm): 0.3106\n",
-      "sepal length (cm): 0.0139\n",
-      "sepal width (cm): 0.0113\n"
-     ]
-    }
-   ],
-   "source": [
-    "gb_importances = gb_model.feature_importances_\n",
-    "gb_indices = gb_importances.argsort()[::-1]\n",
-    "print(\"Feature importances for Gradient Boosting:\")\n",
-    "for i in range(X.shape[1]):\n",
-    "    print(f\"{feature_names[gb_indices[i]]}: {gb_importances[gb_indices[i]]:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "21fe83b4",
-   "metadata": {},
-   "source": [
-    "-   Plot feature importances for GB"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "23fda268",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.bar(range(X.shape[1]), gb_importances[gb_indices], align='center')\n",
-    "plt.xticks(range(X.shape[1]), [feature_names[i] for i in gb_indices], rotation=45)\n",
-    "plt.title('Feature Importances from Gradient Boosting')\n",
-    "plt.xlabel('Features')\n",
-    "plt.ylabel('Importance')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dfd08a48",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "**Voting Classifier**: Implement a Voting Classifier that combines the predictions of the Random Forest and Gradient Boosting classifiers. Evaluate its performance on the test set.\n",
-    "\n",
-    "-   Import, instantiate, and train a `VotingClassifier` model from `sklearn.ensemble`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "f0934c82",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-3 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-3 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-3 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-3 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;rf&#x27;, RandomForestClassifier(random_state=42)),\n",
-       "                             (&#x27;gb&#x27;,\n",
-       "                              GradientBoostingClassifier(random_state=42))],\n",
-       "                 voting=&#x27;soft&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>VotingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.VotingClassifier.html\">?<span>Documentation for VotingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">estimators&nbsp;</td>\n",
-       "            <td class=\"value\">[(&#x27;rf&#x27;, ...), (&#x27;gb&#x27;, ...)]</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('voting',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">voting&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;soft&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('weights',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">weights&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('flatten_transform',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">flatten_transform&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>rf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"rf__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('criterion',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">criterion&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_depth',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_split',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
-       "            <td class=\"value\">2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_features&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('bootstrap',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">bootstrap&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('oob_score',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">oob_score&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">42</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_samples',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_samples&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>gb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GradientBoostingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"gb__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('loss',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">loss&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;log_loss&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('learning_rate',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
-       "            <td class=\"value\">0.1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('subsample',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">subsample&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('criterion',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">criterion&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;friedman_mse&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_split',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
-       "            <td class=\"value\">2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_depth',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">3</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('init',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">init&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">42</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_features&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('validation_fraction',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">validation_fraction&nbsp;</td>\n",
-       "            <td class=\"value\">0.1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_iter_no_change&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
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-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
-      ],
-      "text/plain": [
-       "VotingClassifier(estimators=[('rf', RandomForestClassifier(random_state=42)),\n",
-       "                             ('gb',\n",
-       "                              GradientBoostingClassifier(random_state=42))],\n",
-       "                 voting='soft')"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.ensemble import VotingClassifier\n",
-    "\n",
-    "voting_model = VotingClassifier(\n",
-    "    estimators=[\n",
-    "        ('rf', rf_model),\n",
-    "        ('gb', gb_model)\n",
-    "    ],\n",
-    "    voting='soft')\n",
-    "voting_model.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a7519f69",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the performance of the Voting Classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "872752f0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Voting Classifier Accuracy: 0.9333333333333333\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "      setosa       1.00      1.00      1.00        15\n",
-      "  versicolor       0.88      0.93      0.90        15\n",
-      "   virginica       0.93      0.87      0.90        15\n",
-      "\n",
-      "    accuracy                           0.93        45\n",
-      "   macro avg       0.93      0.93      0.93        45\n",
-      "weighted avg       0.93      0.93      0.93        45\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "y_pred_voting = voting_model.predict(X_test)\n",
-    "\n",
-    "print(\"Voting Classifier Accuracy:\", accuracy_score(y_test, y_pred_voting))\n",
-    "print(classification_report(y_test, y_pred_voting, target_names=class_names))\n",
-    "\n",
-    "ConfusionMatrixDisplay.from_estimator(voting_model, X_test, y_test,\n",
-    "                                      display_labels=class_names, cmap='Blues')\n",
-    "plt.title('Voting Classifier Confusion Matrix')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "58563cd2-63bd-4666-a2bc-a64e644b8a50",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S04 Ensemble Methods/S04C02 Bagging.ipynb b/src/S04 Ensemble Methods/S04C02 Bagging.ipynb
deleted file mode 100644
index dfde99f..0000000
--- a/src/S04 Ensemble Methods/S04C02 Bagging.ipynb	
+++ /dev/null
@@ -1,5204 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "8b5cb16f",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Bagging\n",
-    "\n",
-    "**Bagging**, or **Bootstrap Aggregating**, is a type of ensemble method that improves the stability and accuracy of machine learning algorithms by training multiple models on different subsets randomly sampled (with replacement) from the training data and then aggregating their predictions.\n",
-    "\n",
-    "The main steps involved in bagging are:\n",
-    "\n",
-    "1.  **Bootstrap Sampling**: Create multiple bootstrap samples from the original training dataset. Each bootstrap sample is created by randomly selecting instances from the original dataset with replacement, meaning some instances may appear multiple times while others may not appear at all.\n",
-    "2.  **Model Training**: Train a separate model on each bootstrap sample. This can be any machine learning algorithm, but decision trees are commonly used due to their high variance and low bias.\n",
-    "3.  **Aggregation**: For regression tasks, the predictions from all models are averaged to produce the final prediction. For classification tasks, a majority vote is taken among the predictions of all models to determine the final class label.\n",
-    "4.  **Final Prediction**: The aggregated prediction is then used as the final output of the bagging ensemble."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d4761d4",
-   "metadata": {},
-   "source": [
-    "## Advantages and Disadvantages\n",
-    "\n",
-    "Bagging has several advantages and disadvantages that make it suitable for various machine learning tasks:\n",
-    "\n",
-    "-   **Advantages**:\n",
-    "    -   Can reduce variance and help prevent overfitting by averaging predictions from multiple models.\n",
-    "    -   Can improve accuracy, especially with high-variance models like decision trees.\n",
-    "    -   Robust to noise and outliers, as the aggregation process smooths out individual model errors.\n",
-    "    -   Can be parallelized, as each model can be trained independently on its bootstrap sample, making it efficient for large datasets.\n",
-    "-   **Disadvantages**:\n",
-    "    -   Requires more computational resources due to training multiple models.\n",
-    "    -   May not significantly improve performance if the base model is already stable and has low variance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e5ef4ee8",
-   "metadata": {},
-   "source": [
-    "### Example\n",
-    "\n",
-    "We will illustrate the concept of bagging using a simple example with a synthetic dataset. We will compare the performance of a single `DecisionTreeClassifier` model with a Bagging Classifier that uses the same model as its base estimator.\n",
-    "\n",
-    "-   Generate a synthetic dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "4146cdec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.datasets import make_classification\n",
-    "X, y = make_classification(n_samples=1000, n_features=2, n_informative=2,\n",
-    "                           n_redundant=0, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5b1808e1",
-   "metadata": {},
-   "source": [
-    "-   Split the dataset into training and testing sets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "6437b9d3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "X_train, X_test, y_train, y_test = \\\n",
-    "    train_test_split(X, y, test_size=0.3, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5e39705d",
-   "metadata": {},
-   "source": [
-    "-   Train a single DecisionTreeClassifier model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "579500bf",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('criterion',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">criterion&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('splitter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">splitter&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_depth',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_split',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
-       "            <td class=\"value\">2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_features&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">42</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
-      ],
-      "text/plain": [
-       "DecisionTreeClassifier(random_state=42)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.tree import DecisionTreeClassifier\n",
-    "single_model = DecisionTreeClassifier(random_state=42)\n",
-    "single_model.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aec342fd",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the single model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "b95c6bac",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Single DecisionTreeClassifier Accuracy: 0.90\n"
-     ]
-    }
-   ],
-   "source": [
-    "single_accuracy = single_model.score(X_test, y_test)\n",
-    "print(f\"Single DecisionTreeClassifier Accuracy: {single_accuracy:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1dc26f17",
-   "metadata": {},
-   "source": [
-    "-   Train a Bagging model with DecisionTreeClassifier as estimator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "68d18c00",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-2 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-2 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-2 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-2 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=100,\n",
-       "                  random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>BaggingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.BaggingClassifier.html\">?<span>Documentation for BaggingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('estimator',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">estimator&nbsp;</td>\n",
-       "            <td class=\"value\">DecisionTreeClassifier()</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_samples',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_samples&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_features&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('bootstrap',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">bootstrap&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('bootstrap_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">bootstrap_features&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('oob_score',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">oob_score&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">42</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: DecisionTreeClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('criterion',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">criterion&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('splitter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">splitter&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_depth',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_split',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
-       "            <td class=\"value\">2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_features&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
-      ],
-      "text/plain": [
-       "BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=100,\n",
-       "                  random_state=42)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.ensemble import BaggingClassifier\n",
-    "bagging_model = BaggingClassifier(estimator=DecisionTreeClassifier(),\n",
-    "                                  n_estimators=100, random_state=42)\n",
-    "bagging_model.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2464bf9f",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "622ce9f9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Bagging Classifier Accuracy: 0.92\n"
-     ]
-    }
-   ],
-   "source": [
-    "accuracy = bagging_model.score(X_test, y_test)\n",
-    "print(f\"Bagging Classifier Accuracy: {accuracy:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "415f32c0",
-   "metadata": {},
-   "source": [
-    "The Bagging Classifier typically achieves higher accuracy than the single Decision Tree model due to the reduction in variance and improved generalization."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d2a01483",
-   "metadata": {},
-   "source": [
-    "## Random Forests\n",
-    "\n",
-    "**Random Forests** is a popular ensemble method that uses bagging with decision trees as base models. It introduces additional randomness by selecting a random subset of features for each split in the decision tree, which helps to decorrelate the trees and further reduce overfitting.\n",
-    "\n",
-    "This method combines the strengths of bagging and decision trees, resulting in a powerful and flexible model that can handle both classification and regression tasks effectively."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6d97cb7d",
-   "metadata": {},
-   "source": [
-    "### Example: Random Forest Classifier on the Titanic Dataset\n",
-    "\n",
-    "To illustrate the use of Random Forest, we will use the Titanic dataset and apply a Random Forest classifier. We will compare its performance with a single decision tree classifier to highlight the benefits of bagging.\n",
-    "\n",
-    "-   Load the Titanic dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "419f4da9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from sklearn.datasets import fetch_openml\n",
-    "\n",
-    "data = fetch_openml('titanic', version=1, as_frame=True)\n",
-    "df = data.frame\n",
-    "\n",
-    "df_sub = df[['pclass', 'sex', 'age', 'survived']].copy()\n",
-    "\n",
-    "X = df_sub.drop('survived', axis=1)\n",
-    "y = df_sub['survived']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9961224e",
-   "metadata": {},
-   "source": [
-    "-   Split the dataset into training and testing sets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "13d29888-ab1d-48bd-a974-24014b144288",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training set size: 916, Test set size: 393\n",
-      "Features: ['pclass', 'sex', 'age']\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "X_train, X_test, y_train, y_test = \\\n",
-    "    train_test_split(X, y, test_size=0.3, random_state=42)\n",
-    "print(f\"Training set size: {X_train.shape[0]}, Test set size: {X_test.shape[0]}\")\n",
-    "print(f\"Features: {X_train.columns.tolist()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "53813391",
-   "metadata": {},
-   "source": [
-    "-   Preprocess the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "51091908-d794-44bd-a2d1-10d2d9c1d38b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
-    "from sklearn.compose import ColumnTransformer\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "\n",
-    "# Create a preprocessing pipeline\n",
-    "preprocessing_pipeline = ColumnTransformer(\n",
-    "    transformers=[\n",
-    "        ('age', Pipeline(\n",
-    "            steps=[\n",
-    "                ('imputer', SimpleImputer(strategy='mean')),\n",
-    "                ('scaler', StandardScaler())\n",
-    "            ]\n",
-    "        ), ['age']),\n",
-    "        ('sex', Pipeline(steps=[\n",
-    "            ('encoder', OneHotEncoder(drop='first', sparse_output=False))\n",
-    "        ]), ['sex']),\n",
-    "        ('pclass', Pipeline(steps=[\n",
-    "            ('encoder', OneHotEncoder(sparse_output=False))\n",
-    "        ]), ['pclass'])\n",
-    "    ],\n",
-    "    remainder='passthrough'\n",
-    ")\n",
-    "\n",
-    "# Fit and transform the data\n",
-    "X_train_transformed = preprocessing_pipeline.fit_transform(X_train)\n",
-    "X_test_transformed = preprocessing_pipeline.transform(X_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "31c94b82",
-   "metadata": {},
-   "source": [
-    "-   Train a single Decision Tree classifier with hyperparameter tuning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "5dfd0d36",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-3 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-3 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-3 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-3 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
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-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=5, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('criterion',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">criterion&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
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-       "            ></i></td>\n",
-       "            <td class=\"param\">splitter&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
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-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">5</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
-       "            <td class=\"value\">2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_features&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">42</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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-      ],
-      "text/plain": [
-       "DecisionTreeClassifier(max_depth=5, random_state=42)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.tree import DecisionTreeClassifier\n",
-    "from sklearn.model_selection import GridSearchCV\n",
-    "\n",
-    "# Train a single Decision Tree classifier\n",
-    "single_tree_model = DecisionTreeClassifier(random_state=42)\n",
-    "grid_params = {\n",
-    "    'max_depth': [None, 5, 10, 15],\n",
-    "    'min_samples_split': [2, 5, 10],\n",
-    "    'min_samples_leaf': [1, 2, 4]\n",
-    "}\n",
-    "\n",
-    "# Perform grid search for hyperparameter tuning\n",
-    "grid_search = GridSearchCV(single_tree_model, grid_params, cv=5, n_jobs=-1, verbose=1)\n",
-    "grid_search.fit(X_train_transformed, y_train)\n",
-    "single_tree_model = grid_search.best_estimator_\n",
-    "single_tree_model.fit(X_train_transformed, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "375b9808",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the single model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "1cbfe8c2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Single Decision Tree Classifier Accuracy: 0.76\n",
-      "Feature Importances from Single Decision Tree:\n",
-      "sex__sex_male       0.580959\n",
-      "pclass__pclass_3    0.226990\n",
-      "age__age            0.147395\n",
-      "pclass__pclass_1    0.042398\n",
-      "pclass__pclass_2    0.002258\n",
-      "dtype: float64\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "single_tree_accuracy = single_tree_model.score(X_test_transformed, y_test)\n",
-    "print(f\"Single Decision Tree Classifier Accuracy: {single_tree_accuracy:.2f}\")\n",
-    "\n",
-    "# Feature importances\n",
-    "single_tree_importances = single_tree_model.feature_importances_\n",
-    "st_indices = single_tree_importances.argsort()[::-1]\n",
-    "\n",
-    "feature_names = preprocessing_pipeline.get_feature_names_out()\n",
-    "feature_importances = pd.Series(single_tree_importances, index=feature_names)\n",
-    "print(\"Feature Importances from Single Decision Tree:\")\n",
-    "print(feature_importances.sort_values(ascending=False))\n",
-    "\n",
-    "# Plot feature importances\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "plt.figure(figsize=(12, 6))\n",
-    "sns.barplot(x=feature_importances.sort_values(ascending=False).index,\n",
-    "            y=feature_importances.sort_values(ascending=False).values)\n",
-    "plt.title('Feature Importances from Single Decision Tree')\n",
-    "plt.xlabel('Features')\n",
-    "plt.ylabel('Importance')\n",
-    "plt.xticks(rotation=45)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "65fb01ba",
-   "metadata": {},
-   "source": [
-    "-   Train a Random Forest classifier with hyperparameter tuning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "034a690b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 5 folds for each of 81 candidates, totalling 405 fits\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-4 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-4 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-4 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-4 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-4 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-4 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=5, n_estimators=50, n_jobs=-1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
-       "            <td class=\"value\">50</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('criterion',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">criterion&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_depth',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">5</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_split',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
-       "            <td class=\"value\">2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_features&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('bootstrap',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">bootstrap&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('oob_score',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">oob_score&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">-1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">42</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_samples',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_samples&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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-      ],
-      "text/plain": [
-       "RandomForestClassifier(max_depth=5, n_estimators=50, n_jobs=-1, random_state=42)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "\n",
-    "rf_model = RandomForestClassifier(random_state=42, n_jobs=-1)\n",
-    "rf_grid_params = {\n",
-    "    'n_estimators': [50, 100, 200],\n",
-    "    'max_depth': [None, 5, 10],\n",
-    "    'min_samples_split': [2, 5, 10],\n",
-    "    'min_samples_leaf': [1, 2, 4]\n",
-    "}\n",
-    "\n",
-    "# Perform grid search for hyperparameter tuning\n",
-    "rf_grid_search = GridSearchCV(rf_model, rf_grid_params, cv=5, n_jobs=-1, verbose=1)\n",
-    "rf_grid_search.fit(X_train_transformed, y_train)\n",
-    "rf_model = rf_grid_search.best_estimator_\n",
-    "rf_model.fit(X_train_transformed, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6a805863",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the Random Forest model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "364c431f",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Random Forest Classifier Accuracy: 0.77\n",
-      "Feature Importances from Random Forest:\n",
-      "sex__sex_male       0.528668\n",
-      "age__age            0.232155\n",
-      "pclass__pclass_3    0.112150\n",
-      "pclass__pclass_1    0.102198\n",
-      "pclass__pclass_2    0.024828\n",
-      "dtype: float64\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='Feature'>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1200x600 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "rf_accuracy = rf_model.score(X_test_transformed, y_test)\n",
-    "print(f\"Random Forest Classifier Accuracy: {rf_accuracy:.2f}\")\n",
-    "\n",
-    "# Feature importances\n",
-    "rf_importances = rf_model.feature_importances_\n",
-    "rf_feature_importances = pd.Series(rf_importances, index=feature_names)\n",
-    "print(\"Feature Importances from Random Forest:\")\n",
-    "print(rf_feature_importances.sort_values(ascending=False))\n",
-    "\n",
-    "# Create a dataframe for feature importances\n",
-    "feature_importances = pd.DataFrame({\n",
-    "    'Feature': feature_names,\n",
-    "    'Decision Tree Importance': single_tree_importances,\n",
-    "    'Random Forest Importance': rf_importances\n",
-    "}).set_index('Feature')\n",
-    "\n",
-    "# Compare feature importances\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "# Plot feature importances from Random Forest and Single Decision Tree in a stacked bar chart\n",
-    "plt.figure(figsize=(12, 6))\n",
-    "feature_importances.plot(kind='bar', stacked=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "012492dc",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "**Bagging with Different Base Models**: Implement bagging using different base models (e.g., logistic regression, SVM) and compare their performance on a classification dataset (e.g., `iris`).\n",
-    "\n",
-    "-   Load the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "f2b002cf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training set size: 105, Test set size: 45\n",
-      "Features: ['sepal length (cm)', 'sepal width (cm)']\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.datasets import load_iris\n",
-    "\n",
-    "iris = load_iris()\n",
-    "X_iris = iris.data[:,:2]\n",
-    "y_iris = iris.target\n",
-    "\n",
-    "# Split the dataset into training and testing sets\n",
-    "X_train_iris, X_test_iris, y_train_iris, y_test_iris = \\\n",
-    "    train_test_split(X_iris, y_iris, test_size=0.3, random_state=42)\n",
-    "print(f\"Training set size: {X_train_iris.shape[0]}, Test set size: {X_test_iris.shape[0]}\")\n",
-    "print(f\"Features: {iris.feature_names[:2]}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9bda8acd",
-   "metadata": {},
-   "source": [
-    "-   Train a Bagging Classifier with Logistic Regression as the base model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "3d679991",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-5 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-5 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-5 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-5 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-5 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-5 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BaggingClassifier(estimator=LogisticRegression(), n_estimators=100,\n",
-       "                  random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>BaggingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.BaggingClassifier.html\">?<span>Documentation for BaggingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('estimator',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">estimator&nbsp;</td>\n",
-       "            <td class=\"value\">LogisticRegression()</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_samples',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_samples&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_features&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('bootstrap',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">bootstrap&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('bootstrap_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">bootstrap_features&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('oob_score',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">oob_score&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">42</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: LogisticRegression</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('penalty',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">penalty&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('dual',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">dual&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">C&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">intercept_scaling&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('solver',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">solver&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_iter&nbsp;</td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('multi_class',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">multi_class&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('l1_ratio',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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-      ],
-      "text/plain": [
-       "BaggingClassifier(estimator=LogisticRegression(), n_estimators=100,\n",
-       "                  random_state=42)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import LogisticRegression\n",
-    "\n",
-    "# Train a Bagging Classifier with Logistic Regression as the base model\n",
-    "bagging_logistic = BaggingClassifier(estimator=LogisticRegression(),\n",
-    "                                     n_estimators=100, random_state=42)\n",
-    "bagging_logistic.fit(X_train_iris, y_train_iris)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7eb1315f",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the Bagging Classifier with Logistic Regression and visualize the results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "1b6d55a8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Bagging Classifier with Logistic Regression Accuracy: 0.82\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1000x600 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Evaluate the model\n",
-    "logistic_accuracy = bagging_logistic.score(X_test_iris, y_test_iris)\n",
-    "print(f\"Bagging Classifier with Logistic Regression Accuracy: {logistic_accuracy:.2f}\")\n",
-    "# Confusion Matrix\n",
-    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
-    "y_pred_logistic = bagging_logistic.predict(X_test_iris)\n",
-    "ConfusionMatrixDisplay.from_estimator(\n",
-    "    bagging_logistic, X_test_iris, y_test_iris,\n",
-    "    display_labels=iris.target_names, cmap='Blues')\n",
-    "plt.title('Confusion Matrix for Bagging with Logistic Regression')\n",
-    "plt.show()\n",
-    "\n",
-    "# Plot decision boundaries\n",
-    "from sklearn.inspection import DecisionBoundaryDisplay\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "DecisionBoundaryDisplay.from_estimator(\n",
-    "    bagging_logistic, X_train_iris, response_method='predict',\n",
-    "    grid_resolution=500, cmap=plt.cm.coolwarm, alpha=0.8,\n",
-    "    xlabel=iris.feature_names[0], ylabel=iris.feature_names[1])\n",
-    "plt.scatter(X_test_iris[:, 0], X_test_iris[:, 1], c=y_test_iris, edgecolor='k', s=20,\n",
-    "            cmap=plt.cm.coolwarm)\n",
-    "plt.title('Decision Boundaries for Bagging with Logistic Regression')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "341bae0f",
-   "metadata": {},
-   "source": [
-    "-   Train a Bagging Classifier with SVM as the base model and hyperparameter tuning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "0b8df626",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n",
-      "Best parameters for SVM Bagging Classifier: {'C': 0.1, 'gamma': 'auto', 'kernel': 'rbf'}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-6 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-6 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-6 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-6 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-6 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-6 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
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-       "}\n",
-       "\n",
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-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
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-       "}\n",
-       "</style><body><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=0.1, gamma=&#x27;auto&#x27;, probability=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
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-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">C&nbsp;</td>\n",
-       "            <td class=\"value\">0.1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
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-       "            ></i></td>\n",
-       "            <td class=\"param\">kernel&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;rbf&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
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-       "            <td class=\"value\">3</td>\n",
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-       "    \n",
-       "\n",
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-       "            ></i></td>\n",
-       "            <td class=\"param\">gamma&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">coef0&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
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-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">probability&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">cache_size&nbsp;</td>\n",
-       "            <td class=\"value\">200</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
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-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_iter&nbsp;</td>\n",
-       "            <td class=\"value\">-1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('decision_function_shape',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">decision_function_shape&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('break_ties',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">break_ties&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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-      ],
-      "text/plain": [
-       "SVC(C=0.1, gamma='auto', probability=True)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.svm import SVC\n",
-    "\n",
-    "# Train a Bagging Classifier with SVM as the base model\n",
-    "from sklearn.model_selection import GridSearchCV\n",
-    "svm_model = SVC(probability=True)\n",
-    "svm_grid_params = {\n",
-    "    'C': [0.1, 1, 10],\n",
-    "    'kernel': ['linear', 'rbf'],\n",
-    "    'gamma': ['scale', 'auto']\n",
-    "}\n",
-    "# Perform grid search for hyperparameter tuning\n",
-    "svm_grid_search = GridSearchCV(svm_model, svm_grid_params, cv=5, n_jobs=-1, verbose=1)\n",
-    "svm_grid_search.fit(X_train_iris, y_train_iris)\n",
-    "print(\"Best parameters for SVM Bagging Classifier:\", svm_grid_search.best_params_)\n",
-    "# Use the best estimator from grid search\n",
-    "bagging_svm = svm_grid_search.best_estimator_\n",
-    "bagging_svm.fit(X_train_iris, y_train_iris)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fd7e053a",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the model and visualize the results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "b092120a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Bagging Classifier with SVM Accuracy: 0.82\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1000x600 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "svm_accuracy = bagging_svm.score(X_test_iris, y_test_iris)\n",
-    "print(f\"Bagging Classifier with SVM Accuracy: {svm_accuracy:.2f}\")\n",
-    "\n",
-    "# Confusion Matrix\n",
-    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
-    "y_pred_svm = bagging_svm.predict(X_test_iris)\n",
-    "ConfusionMatrixDisplay.from_estimator(\n",
-    "    bagging_svm, X_test_iris, y_test_iris,\n",
-    "    display_labels=iris.target_names, cmap='Blues')\n",
-    "plt.title('Confusion Matrix for Bagging with SVM')\n",
-    "plt.show()\n",
-    "\n",
-    "# Plot decision boundaries\n",
-    "from sklearn.inspection import DecisionBoundaryDisplay\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "DecisionBoundaryDisplay.from_estimator(\n",
-    "    bagging_svm, X_train_iris, response_method='predict',\n",
-    "    grid_resolution=500, cmap=plt.cm.coolwarm, alpha=0.8,\n",
-    "    xlabel=iris.feature_names[0], ylabel=iris.feature_names[1])\n",
-    "plt.scatter(X_test_iris[:, 0], X_test_iris[:, 1], c=y_test_iris, edgecolor='k', s=20,\n",
-    "            cmap=plt.cm.coolwarm)\n",
-    "plt.title('Decision Boundaries for Bagging with Logistic Regression')\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S04 Ensemble Methods/S04C03 Boosting.ipynb b/src/S04 Ensemble Methods/S04C03 Boosting.ipynb
deleted file mode 100644
index 3ee0b7b..0000000
--- a/src/S04 Ensemble Methods/S04C03 Boosting.ipynb	
+++ /dev/null
@@ -1,5795 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "bdafbbe5",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Boosting\n",
-    "\n",
-    "Boosting is an ensemble learning technique that combines multiple weak learners to create a strong learner. The main idea is to sequentially train models, where each new model focuses on correcting the errors made by the previous models. This iterative process allows boosting to improve the overall performance of the ensemble.\n",
-    "\n",
-    "Boosting algorithms typically follow these steps:\n",
-    "\n",
-    "1.  **Initialize Weights**: Start with equal weights for all training instances.\n",
-    "2.  **Train Weak Learner**: Train a weak learner (e.g., a decision stump) on the weighted training data.\n",
-    "3.  **Update Weights**: Increase the weights of misclassified instances and decrease the weights of correctly classified instances. This ensures that the next weak learner focuses more on the instances that were previously misclassified.\n",
-    "4.  **Combine Models**: Combine the predictions of all weak learners, often using a weighted sum where the weights are determined by the performance of each learner.\n",
-    "5.  **Repeat**: Repeat steps 2-4 for a specified number of iterations or until a stopping criterion is met (e.g., no significant improvement in performance).\n",
-    "6.  **Final Prediction**: The final prediction is made by combining the predictions of all weak learners, typically using a weighted majority vote for classification or a weighted average for regression."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "316be885",
-   "metadata": {},
-   "source": [
-    "## Advantages and Disadvantages\n",
-    "\n",
-    "-   **Advantages**:\n",
-    "    -   Can significantly improve the performance of weak learners, often achieving state-of-the-art results.\n",
-    "    -   Focuses on difficult instances, allowing the model to learn complex patterns in the data.\n",
-    "    -   Can handle a wide range of base learners, including decision trees, linear models, and neural networks.\n",
-    "    -   Robust to overfitting, especially with regularization techniques like shrinkage (learning rate) and early stopping.\n",
-    "    -   Can be used for both classification and regression tasks.\n",
-    "-   **Disadvantages**:\n",
-    "    -   Sensitive to noisy data and outliers, as boosting focuses on correcting errors.\n",
-    "    -   Requires careful tuning of hyperparameters, such as the number of iterations, learning rate, and base learner complexity.\n",
-    "    -   Computationally expensive, especially with large datasets and complex base learners."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e1841b89",
-   "metadata": {},
-   "source": [
-    "## Common Boosting Algorithms\n",
-    "\n",
-    "-   **AdaBoost (Adaptive Boosting)**: One of the earliest and most popular boosting algorithms. It adjusts the weights of misclassified instances and combines weak learners in a weighted manner.\n",
-    "-   **Gradient Boosting**: Builds models sequentially, where each new model is trained to minimize the residual errors of the previous models. It can use various loss functions and is highly flexible.\n",
-    "-   **XGBoost (Extreme Gradient Boosting)**: An optimized version of gradient boosting that includes regularization techniques to prevent overfitting and improve performance. It is widely used in machine learning competitions.\n",
-    "-   **LightGBM**: A gradient boosting framework that uses a histogram-based approach to speed up training and reduce memory usage. It is particularly effective for large datasets."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "71cfd8aa",
-   "metadata": {},
-   "source": [
-    "## AdaBoost Overview\n",
-    "\n",
-    "AdaBoost, short for Adaptive Boosting, is one of the earliest and most widely used boosting algorithms. It works by combining multiple weak classifiers to create a strong classifier. The key steps in AdaBoost are:\n",
-    "\n",
-    "1.  **Initialize Weights**: Start with equal weights for all training instances.\n",
-    "2.  **Train Weak Learner**: Train a weak learner (e.g., a decision stump) on the weighted training data.\n",
-    "3.  **Update Weights**: Increase the weights of misclassified instances and decrease the weights of correctly classified instances. This ensures that the next weak learner focuses more on the instances that were previously misclassified.\n",
-    "4.  **Combine Models**: Combine the predictions of all weak learners, where each learner's contribution is weighted by its accuracy.\n",
-    "5.  **Repeat**: Repeat steps 2-4 for a specified number of iterations or until a stopping criterion is met (e.g., no significant improvement in performance)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0571bb23",
-   "metadata": {},
-   "source": [
-    "### Example of AdaBoost\n",
-    "\n",
-    "We will demonstrate AdaBoost using the titanic dataset, keeping only the `pclass`, `sex`, and `age` features. We will preprocess the data, train a single Decision Tree, and then use AdaBoost to improve the model's performance.\n",
-    "\n",
-    "-   Load the titanic dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "071f718d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Feature names: ['pclass', 'sex', 'age']\n",
-      "Class labels: ['Not Survived', 'Survived']\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn import datasets\n",
-    "\n",
-    "titanic = datasets.fetch_openml('titanic', version=1, as_frame=True)\n",
-    "df = titanic.frame[['pclass', 'sex', 'age', 'survived']]\n",
-    "X = df.drop(columns=['survived'])\n",
-    "y = df['survived'].astype('category').cat.codes\n",
-    "class_names = ['Not Survived', 'Survived']\n",
-    "feature_names = X.columns.tolist()\n",
-    "print(f\"Feature names: {feature_names}\")\n",
-    "print(f\"Class labels: {class_names}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "783cea64",
-   "metadata": {},
-   "source": [
-    "-   Train-test split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "d400de18",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "X_train, X_test, y_train, y_test = \\\n",
-    "    train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "63dde241",
-   "metadata": {},
-   "source": [
-    "-   Create a preprocessing pipeline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "dd9bee94",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Transformed feature names: ['age__age' 'sex__sex_male' 'pclass__pclass_1' 'pclass__pclass_2'\n",
-      " 'pclass__pclass_3']\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.compose import ColumnTransformer\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
-    "\n",
-    "preprocessing_pipeline = ColumnTransformer(\n",
-    "    transformers=[\n",
-    "        ('age', Pipeline(\n",
-    "            steps=[\n",
-    "                ('imputer', SimpleImputer(strategy='mean')),\n",
-    "                ('scaler', StandardScaler())\n",
-    "            ]\n",
-    "        ), ['age']),\n",
-    "        ('sex', Pipeline(\n",
-    "            steps=[\n",
-    "                ('encoder', OneHotEncoder(drop='first', sparse_output=False))\n",
-    "            ]\n",
-    "        ), ['sex']),\n",
-    "        ('pclass', Pipeline(\n",
-    "            steps=[\n",
-    "                ('enconder', OneHotEncoder(sparse_output=False))\n",
-    "            ]\n",
-    "        ), ['pclass'])\n",
-    "    ],\n",
-    "    remainder='passthrough'\n",
-    ")\n",
-    "X_train_transformed = preprocessing_pipeline.fit_transform(X_train)\n",
-    "X_test_transformed = preprocessing_pipeline.transform(X_test)\n",
-    "\n",
-    "feature_names = preprocessing_pipeline.get_feature_names_out()\n",
-    "print(f\"Transformed feature names: {feature_names}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d16a0ec5",
-   "metadata": {},
-   "source": [
-    "-   Train a single Decision Tree"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "98c04428",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 5 folds for each of 45 candidates, totalling 225 fits\n",
-      "Best parameters for Decision Tree: {'max_depth': 4, 'min_samples_leaf': 2, 'min_samples_split': 2}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=4, min_samples_leaf=2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('criterion',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">criterion&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">splitter&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">4</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_split',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
-       "            <td class=\"value\">2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
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-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_features&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">42</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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-      ],
-      "text/plain": [
-       "DecisionTreeClassifier(max_depth=4, min_samples_leaf=2, random_state=42)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.tree import DecisionTreeClassifier\n",
-    "from sklearn.metrics import accuracy_score, classification_report, ConfusionMatrixDisplay\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "dt_model = DecisionTreeClassifier(random_state=42)\n",
-    "grid_params = {\n",
-    "    'max_depth': [None, 2, 3, 4, 5],\n",
-    "    'min_samples_split': [2, 5, 10],\n",
-    "    'min_samples_leaf': [1, 2, 4]\n",
-    "}\n",
-    "from sklearn.model_selection import GridSearchCV\n",
-    "grid_search = GridSearchCV(dt_model, grid_params, cv=5, scoring='accuracy', verbose=1)\n",
-    "grid_search.fit(X_train_transformed, y_train)\n",
-    "print(\"Best parameters for Decision Tree:\", grid_search.best_params_)\n",
-    "\n",
-    "dt_model = grid_search.best_estimator_\n",
-    "dt_model.fit(X_train_transformed, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "33343b78",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the single Decision Tree model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "5c135f3b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Single Decision Tree Accuracy: 0.7990\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "Not Survived       0.83      0.85      0.84       243\n",
-      "    Survived       0.74      0.72      0.73       150\n",
-      "\n",
-      "    accuracy                           0.80       393\n",
-      "   macro avg       0.79      0.78      0.79       393\n",
-      "weighted avg       0.80      0.80      0.80       393\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "y_pred_dt = dt_model.predict(X_test_transformed)\n",
-    "\n",
-    "print(f\"Single Decision Tree Accuracy: {accuracy_score(y_test, y_pred_dt):.4f}\")\n",
-    "print(classification_report(y_test, y_pred_dt, target_names=class_names))\n",
-    "ConfusionMatrixDisplay.from_estimator(dt_model, X_test_transformed, y_test,\n",
-    "                                      display_labels=class_names, cmap='Blues')\n",
-    "plt.title('Single Decision Tree Confusion Matrix')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0a42a9fc",
-   "metadata": {},
-   "source": [
-    "-   Train an AdaBoost Classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "961c9610",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 5 folds for each of 540 candidates, totalling 2700 fits\n",
-      "Best parameters for AdaBoost: {'estimator__max_depth': 4, 'estimator__min_samples_leaf': 1, 'estimator__min_samples_split': 10, 'learning_rate': 0.01, 'n_estimators': 200}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-2 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-2 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-2 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-2 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=4,\n",
-       "                                                    min_samples_split=10),\n",
-       "                   learning_rate=0.01, n_estimators=200, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>AdaBoostClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.AdaBoostClassifier.html\">?<span>Documentation for AdaBoostClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('estimator',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">estimator&nbsp;</td>\n",
-       "            <td class=\"value\">DecisionTreeC...ples_split=10)</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
-       "            <td class=\"value\">200</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('learning_rate',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
-       "            <td class=\"value\">0.01</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('algorithm',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">algorithm&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">42</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: DecisionTreeClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>DecisionTreeClassifier(max_depth=4, min_samples_split=10)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('criterion',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">criterion&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('splitter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">splitter&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_depth',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">4</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_split',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
-       "            <td class=\"value\">10</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_features&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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-       "</script></body>"
-      ],
-      "text/plain": [
-       "AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=4,\n",
-       "                                                    min_samples_split=10),\n",
-       "                   learning_rate=0.01, n_estimators=200, random_state=42)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.ensemble import AdaBoostClassifier\n",
-    "grid_params_ab = {\n",
-    "    'n_estimators': [50, 100, 200, 500],\n",
-    "    'learning_rate': [0.01, 0.1, 1.0],\n",
-    "    'estimator__max_depth': [None, 2, 3, 4, 5],\n",
-    "    'estimator__min_samples_split': [2, 5, 10],\n",
-    "    'estimator__min_samples_leaf': [1, 2, 4]\n",
-    "}\n",
-    "grid_search_ab = GridSearchCV(\n",
-    "    AdaBoostClassifier(estimator=DecisionTreeClassifier(), random_state=42),\n",
-    "    grid_params_ab, cv=5, scoring='accuracy', verbose=1, n_jobs=-1\n",
-    ")\n",
-    "grid_search_ab.fit(X_train_transformed, y_train)\n",
-    "print(f\"Best parameters for AdaBoost: {grid_search_ab.best_params_}\")\n",
-    "\n",
-    "ab_model = grid_search_ab.best_estimator_\n",
-    "ab_model.fit(X_train_transformed, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0b861d9a",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the AdaBoost model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "5af52704",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AdaBoost Classifier Accuracy: 0.7990\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "Not Survived       0.83      0.86      0.84       243\n",
-      "    Survived       0.75      0.71      0.73       150\n",
-      "\n",
-      "    accuracy                           0.80       393\n",
-      "   macro avg       0.79      0.78      0.78       393\n",
-      "weighted avg       0.80      0.80      0.80       393\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "y_pred_ab = ab_model.predict(X_test_transformed)\n",
-    "print(f\"AdaBoost Classifier Accuracy: {accuracy_score(y_test, y_pred_ab):.4f}\")\n",
-    "print(classification_report(y_test, y_pred_ab, target_names=class_names))\n",
-    "ConfusionMatrixDisplay.from_estimator(ab_model, X_test_transformed, y_test,\n",
-    "                                      display_labels=class_names, cmap='Blues')\n",
-    "plt.title('AdaBoost Confusion Matrix')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8490ae34",
-   "metadata": {},
-   "source": [
-    "-   Feature importance for AdaBoost"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "98360d23",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Feature importances for AdaBoost:\n",
-      "            Feature  Importance\n",
-      "1     sex__sex_male    0.469367\n",
-      "0          age__age    0.202771\n",
-      "2  pclass__pclass_1    0.163301\n",
-      "4  pclass__pclass_3    0.151265\n",
-      "3  pclass__pclass_2    0.013297\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "importances = ab_model.feature_importances_\n",
-    "indices = importances.argsort()[::-1]\n",
-    "feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': importances})\n",
-    "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
-    "print(\"Feature importances for AdaBoost:\")\n",
-    "print(feature_importance_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5e1d28bb",
-   "metadata": {},
-   "source": [
-    "-   Plot feature importances"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "56bfa3dc",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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ZrTuDRbKU4fBOKWf3kuu1WCz69ttv5erqmmr5O4/3vVdpbevfxo07Th5lhjsf+38ZP3683nvvPb366qsaM2aMcubMKRcXF/Xt2zfN309GPLbk7fbv319NmjRJc5nk8FuvXj2dPHlSa9as0Xfffaf58+drypQpCgsLs9vL4H7d7Xm6W7/dKaN/98ePH9euXbskpf4gRvrnWO+0ZrL/jdVqVePGjXX16lUNGjRIpUuXlo+Pj86fP68uXbrc0/8/X19f1axZU2vWrDHtLO/e3t7aunWrfvzxR3399ddav369li1bpgYNGui7776763MKAI8iQjcAPCKKFy8uScqbN++/Xs7o2rVr2rRpk0aNGqXhw4fbxtPaVfhuYSd5JvXOM3XfOfP4X/UahqGiRYvqscceu+f1MsPx48f19NNP236+efOmLl68qOeee06SbGfhPnbsmIoVK2ZbLj4+XqdOnbrny0nd7flduXKlnn76aS1YsMBu/Pr163YznvcquTcOHjx419qSH4e7u/s91Z8zZ06FhIQoJCREN2/eVL169TRy5MgHCt13KlKkiJKSknT8+HHbTKwkRUZG6vr167bfg1mWLFkid3d3hYeHpwqZP/30k6ZNm6azZ8+qcOHCKlKkiA4ePCjDMOx+r8eOHbNb78CBA/rjjz/0ySefqFOnTrbxfzvze1oSExMl/dObyWfj//3335WUlGQ3E3306FFJ/+vZIkWK6Pvvv9eNGzfsZrvvXE6SXFxc1LBhQzVs2FCTJ0/W+PHjNWzYMP34449q1KjRPX8YAgBZHfv/AMAjokmTJvLz89P48eOVkJCQ6vbkM44nh4c7Z/mmTp2aap3kGbQ7w7Wfn59y586trVu32o3PmjXrnutt3bq1XF1dNWrUqFS1GIZhd/myzDZ37ly753D27NlKTEzUs88+K0lq1KiRPDw8NG3aNLvaFyxYoKioKDVr1uye7sfHxyfVcyv98zu68zlZsWKF7Zjq+1WlShUVLVpUU6dOTXV/yfeTN29ePfXUU5ozZ44uXryYahspz1h/5+/G19dXJUqUSPPyWA8i+UOOO3tz8uTJknTPz3N6LVmyRE8++aTat2+vNm3a2H0lX05v6dKltlovXLiglStX2taPjY3V3Llz7baZ1v8/wzBSXbrt31y9elU///yzgoKClDdvXtv9R0REaNmyZbblEhMTNX36dPn6+qp+/fq25axWq2bMmGG3zSlTpshisdh6PPmM6SklXxM8+fd8t9cHAHjUMNMNAI8IPz8/zZ49W//3f/+nKlWq6KWXXlKePHl09uxZff3113riiSc0Y8YM+fn52S6nlZCQoAIFCui7777TqVOnUm2zatWqkqRhw4bppZdekru7u1q0aCEfHx9169ZNEyZMULdu3VStWjVt3bpVf/zxxz3XW7x4cY0dO1ZDhgzR6dOn9fzzzyt79uw6deqUvvzyS/Xo0UP9+/fPsOfnfsTHx6thw4a2y0rNmjVLdevWVcuWLSX9c9m0IUOGaNSoUWratKlatmxpW6569ep65ZVX7ul+qlatqtmzZ2vs2LEqUaKE8ubNqwYNGqh58+YaPXq0QkJCVKdOHR04cEBLliyxm1W/Hy4uLpo9e7ZatGihSpUqKSQkRPny5dPRo0d16NAhbdiwQdI/J+mrW7euypcvr+7du6tYsWKKjIzUjh07dO7cOdt1wsuUKaOnnnpKVatWVc6cObV7926tXLlSvXv3Tld9d1OxYkV17txZc+fO1fXr11W/fn3t3LlTn3zyiZ5//nm7vREy2q+//mq7/FZaChQooCpVqmjJkiUaNGiQunfvrhkzZqhTp07as2eP8uXLp/DwcGXLls1uvdKlS6t48eLq37+/zp8/Lz8/P33xxRf/enz5ypUr5evrK8MwdOHCBS1YsEDXrl1TWFiYbba5R48emjNnjrp06aI9e/YoODhYK1eu1Pbt2zV16lTbrHaLFi309NNPa9iwYTp9+rQqVqyo7777TmvWrFHfvn1te0WMHj1aW7duVbNmzVSkSBFdunRJs2bNUsGCBW3XUC9evLgCAgIUFham7Nmzy8fHRzVr1kx1bD4AZHmZfbp0AIA50rpEVlp+/PFHo0mTJoa/v7/h5eVlFC9e3OjSpYuxe/du2zLnzp0zXnjhBSMgIMDw9/c32rZta1y4cCHVJbQMwzDGjBljFChQwHBxcbG7fFhsbKzRtWtXw9/f38iePbvRrl0749KlS3e9ZNjdLj30xRdfGHXr1jV8fHwMHx8fo3Tp0sYbb7xhHDt27L6fj86dOxs+Pj6plq1fv75RtmzZVONFihSxu/RV8ja3bNli9OjRw8iRI4fh6+trdOzY0fj7779TrT9jxgyjdOnShru7uxEYGGi8/vrrqS7Jdbf7Nox/LufWrFkzI3v27IYk2+XDbt++bbzzzjtGvnz5DG9vb+OJJ54wduzYYdSvX9/uEmPJlwxbsWKF3Xbvdkm3n376yWjcuLGRPXt2w8fHx6hQoYIxffp0u2VOnjxpdOrUyQgKCjLc3d2NAgUKGM2bNzdWrlxpW2bs2LFGjRo1jICAAMPb29soXbq0MW7cOLvLrKXlvy4ZllaPJCQkGKNGjTKKFi1quLu7G4UKFTKGDBlid1kzw0j9u0wmyXjjjTfuqY6U3nzzTUOScfLkybsuM3LkSEOS8dtvvxmGYRhnzpwxWrZsaWTLls3InTu38dZbbxnr169Pdcmww4cPG40aNTJ8fX2N3LlzG927dzd+++23VL+ztC4Z5uPjY9SuXdvu0mTJIiMjjZCQECN37tyGh4eHUb58+TQv53Xjxg3j7bffNvLnz2+4u7sbJUuWNCZNmmR3+bhNmzYZrVq1MvLnz294eHgY+fPnNzp06GD88ccfdttas2aNUaZMGcPNzY3LhwF4ZFkMIxPOEAMAQBbw8ccfKyQkRLt27UrzDPEAAAB34phuAAAAAABMQugGAAAAAMAkhG4AAAAAAEzCMd0AAAAAAJiEmW4AAAAAAExC6AYAAAAAwCRuji4gsyUlJenChQvKnj27LBaLo8sBAAAAADghwzB048YN5c+fXy4ud5/PfuRC94ULF1SoUCFHlwEAAAAAyAL++usvFSxY8K63P3KhO3v27JL+eWL8/PwcXA0AAAAAwBlFR0erUKFCtox5N49c6E7epdzPz4/QDQAAAAB4IP912DInUgMAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAEzi5ugCcHfBg792dAlwEqcnNHN0CQAAAADSwEw3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgkocidM+cOVPBwcHy8vJSzZo1tXPnznta7/PPP5fFYtHzzz9vboEAAAAAAKSDw0P3smXL1K9fP40YMUJ79+5VxYoV1aRJE126dOlf1zt9+rT69++vJ598MpMqBQAAAADg/jg8dE+ePFndu3dXSEiIypQpo7CwMGXLlk0LFy686zpWq1UdO3bUqFGjVKxYsUysFgAAAACAe+fQ0B0fH689e/aoUaNGtjEXFxc1atRIO3bsuOt6o0ePVt68edW1a9fMKBMAAAAAgHRxc+SdX7lyRVarVYGBgXbjgYGBOnr0aJrr/PTTT1qwYIH2799/T/cRFxenuLg428/R0dHprhcAAAAAgPvh8N3L78eNGzf0f//3f5o3b55y5859T+uEhobK39/f9lWoUCGTqwQAAAAA4B8OnenOnTu3XF1dFRkZaTceGRmpoKCgVMufPHlSp0+fVosWLWxjSUlJkiQ3NzcdO3ZMxYsXt1tnyJAh6tevn+3n6OhogjcAAAAAIFM4NHR7eHioatWq2rRpk+2yX0lJSdq0aZN69+6davnSpUvrwIEDdmPvvvuubty4oY8++ijNMO3p6SlPT09T6gcAAAAA4N84NHRLUr9+/dS5c2dVq1ZNNWrU0NSpUxUTE6OQkBBJUqdOnVSgQAGFhobKy8tL5cqVs1s/ICBAklKNAwAAAADgaA4P3e3bt9fly5c1fPhwRUREqFKlSlq/fr3t5Gpnz56Vi4tTHXoOAAAAAIAkyWIYhuHoIjJTdHS0/P39FRUVJT8/P0eX86+CB3/t6BLgJE5PaOboEgAAAIBHyr1mS6aQAQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJM8FKF75syZCg4OlpeXl2rWrKmdO3feddlVq1apWrVqCggIkI+PjypVqqTw8PBMrBYAAAAAgHvj8NC9bNky9evXTyNGjNDevXtVsWJFNWnSRJcuXUpz+Zw5c2rYsGHasWOHfv/9d4WEhCgkJEQbNmzI5MoBAAAAAPh3FsMwDEcWULNmTVWvXl0zZsyQJCUlJalQoUJ68803NXjw4HvaRpUqVdSsWTONGTPmP5eNjo6Wv7+/oqKi5Ofn90C1my148NeOLgFO4vSEZo4uAQAAAHik3Gu2dOhMd3x8vPbs2aNGjRrZxlxcXNSoUSPt2LHjP9c3DEObNm3SsWPHVK9evTSXiYuLU3R0tN0XAAAAAACZwaGh+8qVK7JarQoMDLQbDwwMVERExF3Xi4qKkq+vrzw8PNSsWTNNnz5djRs3TnPZ0NBQ+fv7274KFSqUoY8BAAAAAIC7cfgx3emRPXt27d+/X7t27dK4cePUr18/bd68Oc1lhwwZoqioKNvXX3/9lbnFAgAAAAAeWW6OvPPcuXPL1dVVkZGRduORkZEKCgq663ouLi4qUaKEJKlSpUo6cuSIQkND9dRTT6Va1tPTU56enhlaNwAAAAAA98KhM90eHh6qWrWqNm3aZBtLSkrSpk2bVLt27XveTlJSkuLi4swoEQAAAACAdHPoTLck9evXT507d1a1atVUo0YNTZ06VTExMQoJCZEkderUSQUKFFBoaKikf47RrlatmooXL664uDh98803Cg8P1+zZsx35MAAAAAAASMXhobt9+/a6fPmyhg8froiICFWqVEnr16+3nVzt7NmzcnH534R8TEyMevXqpXPnzsnb21ulS5fW4sWL1b59e0c9BAAAAAAA0uTw63RnNq7TjayI63QDAAAAmcsprtMNAAAAAEBWRugGAAAAAMAkhG4AAAAAAEyS7tAdHh6uJ554Qvnz59eZM2ckSVOnTtWaNWsyrDgAAAAAAJxZukL37Nmz1a9fPz333HO6fv26rFarJCkgIEBTp07NyPoAAAAAAHBa6Qrd06dP17x58zRs2DC5urraxqtVq6YDBw5kWHEAAAAAADizdIXuU6dOqXLlyqnGPT09FRMT88BFAQAAAACQFaQrdBctWlT79+9PNb5+/Xo9/vjjD1oTAAAAAABZglt6VurXr5/eeOMN3b59W4ZhaOfOnVq6dKlCQ0M1f/78jK4RAAAAAACnlK7Q3a1bN3l7e+vdd99VbGysXn75ZeXPn18fffSRXnrppYyuEQAAAAAAp5Su0C1JHTt2VMeOHRUbG6ubN28qb968GVkXAAAAAABOL12h+9SpU0pMTFTJkiWVLVs2ZcuWTZJ0/Phxubu7Kzg4OCNrBAAAAADAKaXrRGpdunTRzz//nGr8119/VZcuXR60JgAAAAAAsoR0he59+/bpiSeeSDVeq1atNM9qDgAAAADAoyhdodtisejGjRupxqOiomS1Wh+4KAAAAAAAsoJ0he569eopNDTULmBbrVaFhoaqbt26GVYcAAAAAADOLF0nUps4caLq1aunUqVK6cknn5Qkbdu2TdHR0frhhx8ytEAAAAAAAJxVuma6y5Qpo99//13t2rXTpUuXdOPGDXXq1ElHjx5VuXLlMrpGAAAAAACcUrqv050/f36NHz8+I2sBAAAAACBLSXfovn79unbu3KlLly4pKSnJ7rZOnTo9cGEAAAAAADi7dIXur776Sh07dtTNmzfl5+cni8Viu81isRC6AQAAAABQOo/pfuedd/Tqq6/q5s2bun79uq5du2b7unr1akbXCAAAAACAU0pX6D5//rz69OmjbNmyZXQ9AAAAAABkGekK3U2aNNHu3bszuhYAAAAAALKUdB3T3axZMw0YMECHDx9W+fLl5e7ubnd7y5YtM6Q4AAAAAACcWbpCd/fu3SVJo0ePTnWbxWKR1Wp9sKoAAAAAAMgC0hW677xEGAAAAAAASC1dx3QDAAAAAID/lq6ZbkmKiYnRli1bdPbsWcXHx9vd1qdPnwcuDAAAAAAAZ5eu0L1v3z4999xzio2NVUxMjHLmzKkrV64oW7Zsyps3L6EbAAAAAAClc/fyt99+Wy1atNC1a9fk7e2tX375RWfOnFHVqlX1wQcfZHSNAAAAAAA4pXSF7v379+udd96Ri4uLXF1dFRcXp0KFCun999/X0KFDM7pGAAAAAACcUrpCt7u7u1xc/lk1b968Onv2rCTJ399ff/31V8ZVBwAAAACAE0vXMd2VK1fWrl27VLJkSdWvX1/Dhw/XlStXFB4ernLlymV0jQAAAAAAOKV0zXSPHz9e+fLlkySNGzdOOXLk0Ouvv67Lly9rzpw5GVogAAAAAADOKl0z3dWqVbN9nzdvXq1fvz7DCgIAAAAAIKtI10x3gwYNdP369VTj0dHRatCgwYPWBAAAAABAlpCu0L1582bFx8enGr99+7a2bdv2wEUBAAAAAJAV3Nfu5b///rvt+8OHDysiIsL2s9Vq1fr161WgQIGMqw4AAAAAACd2X6G7UqVKslgsslgsae5G7u3trenTp2dYcQAAAAAAOLP7Ct2nTp2SYRgqVqyYdu7cqTx58thu8/DwUN68eeXq6prhRQIAAAAA4IzuK3QXKVJECQkJ6ty5s3LlyqUiRYqYVRcAAAAAAE7vvk+k5u7uri+//NKMWgAAAAAAyFLSdfbyVq1aafXq1RlcCgAAAAAAWct97V6erGTJkho9erS2b9+uqlWrysfHx+72Pn36ZEhxAAAAAAA4s3SF7gULFiggIEB79uzRnj177G6zWCyEbgAAAAAAlM7QferUqYyuAwAAAACALCddx3SnZBiGDMPIiFoAAAAAAMhS0h26P/30U5UvX17e3t7y9vZWhQoVFB4enpG1AQAAAADg1NK1e/nkyZP13nvvqXfv3nriiSckST/99JN69uypK1eu6O23387QIgEAAAAAcEbpCt3Tp0/X7Nmz1alTJ9tYy5YtVbZsWY0cOZLQDQAAAACA0rl7+cWLF1WnTp1U43Xq1NHFixcfuCgAAAAAALKCdIXuEiVKaPny5anGly1bppIlSz5wUQAAAAAAZAXp2r181KhRat++vbZu3Wo7pnv79u3atGlTmmEcAAAAAIBHUbpmul988UX9+uuvyp07t1avXq3Vq1crd+7c2rlzp1544YWMrhEAAAAAAKeUrpluSapataoWL16ckbUAAAAAAJClpDt0W61Wffnllzpy5IgkqUyZMmrVqpXc3NK9SQAAAAAAspR0JeRDhw6pZcuWioiIUKlSpSRJEydOVJ48efTVV1+pXLlyGVokAAAAAADOKF3HdHfr1k1ly5bVuXPntHfvXu3du1d//fWXKlSooB49emR0jQAAAAAAOKV0zXTv379fu3fvVo4cOWxjOXLk0Lhx41S9evUMKw4AAAAAAGeWrpnuxx57TJGRkanGL126pBIlSjxwUQAAAAAAZAXpCt2hoaHq06ePVq5cqXPnzuncuXNauXKl+vbtq4kTJyo6Otr2BQAAAADAoypdu5c3b95cktSuXTtZLBZJkmEYkqQWLVrYfrZYLLJarRlRJwAAAAAATiddofvHH3/M6DoAAAAAAMhy0hW669evn9F1AAAAAACQ5aQrdEvS7du39fvvv+vSpUtKSkqyu61ly5YPXBgAAAAAAM4uXaF7/fr16tSpk65cuZLqNo7jBgAAAADgH+k6e/mbb76ptm3b6uLFi0pKSrL7InADAAAAAPCPdIXuyMhI9evXT4GBgRldDwAAAAAAWUa6QnebNm20efPmDC4FAAAAAICsJV3HdM+YMUNt27bVtm3bVL58ebm7u9vd3qdPnwwpDgAAAAAAZ5au0L106VJ999138vLy0ubNm2WxWGy3WSwWQjcAAAAAAEpn6B42bJhGjRqlwYMHy8UlXXuoAwAAAACQ5aUrMcfHx6t9+/YEbgAAAAAA/kW6UnPnzp21bNmyjK4FAAAAAIAsJV27l1utVr3//vvasGGDKlSokOpEapMnT76v7c2cOVOTJk1SRESEKlasqOnTp6tGjRppLjtv3jx9+umnOnjwoCSpatWqGj9+/F2XBwAAAADAUdIVug8cOKDKlStLki38pteyZcvUr18/hYWFqWbNmpo6daqaNGmiY8eOKW/evKmW37x5szp06KA6derIy8tLEydO1DPPPKNDhw6pQIECD1QLAAAAAAAZyWIYhuHIAmrWrKnq1atrxowZkqSkpCQVKlRIb775pgYPHvyf61utVuXIkUMzZsxQp06d/nP56Oho+fv7KyoqSn5+fg9cv5mCB3/t6BLgJE5PaOboEmzoW9yrh6lvAQAA7te9Zsv7mulu3br1fy5jsVj0xRdf3NP24uPjtWfPHg0ZMsQ25uLiokaNGmnHjh33tI3Y2FglJCQoZ86c97Q8AAAAAACZ5b5Ct7+/f4be+ZUrV2S1WhUYGGg3HhgYqKNHj97TNgYNGqT8+fOrUaNGad4eFxenuLg428/R0dHpLxgAAAAAgPtwX6F70aJFZtWRLhMmTNDnn3+uzZs3y8vLK81lQkNDNWrUqEyuDAAAAACAdF4yLKPkzp1brq6uioyMtBuPjIxUUFDQv677wQcfaMKECfruu+9UoUKFuy43ZMgQRUVF2b7++uuvDKkdAAAAAID/4tDQ7eHhoapVq2rTpk22saSkJG3atEm1a9e+63rvv/++xowZo/Xr16tatWr/eh+enp7y8/Oz+wIAAAAAIDOk65JhGalfv37q3LmzqlWrpho1amjq1KmKiYlRSEiIJKlTp04qUKCAQkNDJUkTJ07U8OHD9dlnnyk4OFgRERGSJF9fX/n6+jrscQAAAAAAcCeHh+727dvr8uXLGj58uCIiIlSpUiWtX7/ednK1s2fPysXlfxPys2fPVnx8vNq0aWO3nREjRmjkyJGZWToAAAAAAP/K4aFbknr37q3evXunedvmzZvtfj59+rT5BQEAAAAAkAEcekw3AAAAAABZGaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwiZujCwAAwNGCB3/t6BLgJE5PaOboEgAAToaZbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkbo4uAAAAAPcvePDXji4BTuL0hGaOLgF4pDHTDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYxOGhe+bMmQoODpaXl5dq1qypnTt33nXZQ4cO6cUXX1RwcLAsFoumTp2aeYUCAAAAAHCfHBq6ly1bpn79+mnEiBHau3evKlasqCZNmujSpUtpLh8bG6tixYppwoQJCgoKyuRqAQAAAAC4Pw4N3ZMnT1b37t0VEhKiMmXKKCwsTNmyZdPChQvTXL569eqaNGmSXnrpJXl6emZytQAAAAAA3B+Hhe74+Hjt2bNHjRo1+l8xLi5q1KiRduzY4aiyAAAAAADIMG6OuuMrV67IarUqMDDQbjwwMFBHjx7NsPuJi4tTXFyc7efo6OgM2zYAAAAAAP/G4SdSM1toaKj8/f1tX4UKFXJ0SQAAAACAR4TDQnfu3Lnl6uqqyMhIu/HIyMgMPUnakCFDFBUVZfv666+/MmzbAAAAAAD8G4eFbg8PD1WtWlWbNm2yjSUlJWnTpk2qXbt2ht2Pp6en/Pz87L4AAAAAAMgMDjumW5L69eunzp07q1q1aqpRo4amTp2qmJgYhYSESJI6deqkAgUKKDQ0VNI/J187fPiw7fvz589r//798vX1VYkSJRz2OAAAAAAASItDQ3f79u11+fJlDR8+XBEREapUqZLWr19vO7na2bNn5eLyv8n4CxcuqHLlyrafP/jgA33wwQeqX7++Nm/enNnlAwAAAADwrxwauiWpd+/e6t27d5q33Rmkg4ODZRhGJlQFAAAAAMCDy/JnLwcAAAAAwFEI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASdwcXQAAAACAR0Pw4K8dXQKcxOkJzRxdQoZhphsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADDJQxG6Z86cqeDgYHl5ealmzZrauXPnvy6/YsUKlS5dWl5eXipfvry++eabTKoUAAAAAIB75/DQvWzZMvXr108jRozQ3r17VbFiRTVp0kSXLl1Kc/mff/5ZHTp0UNeuXbVv3z49//zzev7553Xw4MFMrhwAAAAAgH/n8NA9efJkde/eXSEhISpTpozCwsKULVs2LVy4MM3lP/roIzVt2lQDBgzQ448/rjFjxqhKlSqaMWNGJlcOAAAAAMC/c3PkncfHx2vPnj0aMmSIbczFxUWNGjXSjh070lxnx44d6tevn91YkyZNtHr16jSXj4uLU1xcnO3nqKgoSVJ0dPQDVm++pLhYR5cAJ/Ew9TN9i3tF38IZ0bdwRvQtnNHD1Ld3k1yjYRj/upxDQ/eVK1dktVoVGBhoNx4YGKijR4+muU5ERESay0dERKS5fGhoqEaNGpVqvFChQumsGnj4+E91dAXA/aNv4YzoWzgj+hbOyJn69saNG/L397/r7Q4N3ZlhyJAhdjPjSUlJunr1qnLlyiWLxeLAypAe0dHRKlSokP766y/5+fk5uhzgntC3cEb0LZwRfQtnRN86L8MwdOPGDeXPn/9fl3No6M6dO7dcXV0VGRlpNx4ZGamgoKA01wkKCrqv5T09PeXp6Wk3FhAQkP6i8VDw8/PjRQlOh76FM6Jv4YzoWzgj+tY5/dsMdzKHnkjNw8NDVatW1aZNm2xjSUlJ2rRpk2rXrp3mOrVr17ZbXpI2btx41+UBAAAAAHAUh+9e3q9fP3Xu3FnVqlVTjRo1NHXqVMXExCgkJESS1KlTJxUoUEChoaGSpLfeekv169fXhx9+qGbNmunzzz/X7t27NXfuXEc+DAAAAAAAUnF46G7fvr0uX76s4cOHKyIiQpUqVdL69ettJ0s7e/asXFz+NyFfp04dffbZZ3r33Xc1dOhQlSxZUqtXr1a5cuUc9RCQiTw9PTVixIhUhwwADzP6Fs6IvoUzom/hjOjbrM9i/Nf5zQEAAAAAQLo49JhuAAAAAACyMkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0A/eJq+zB0ehBPIqSkpIcXQIAAOnCdbqB/5CUlCQXFz6fwsPHMAxZLBZHlwGY6uzZsypcuLAkXo/hPDZt2qTt27crOjpajRo1UtOmTR1dEvCfdu/erWPHjslisah8+fIqX768o0vKMgjdwL9I+QZv2bJlOnz4sFxdXVWrVi0988wzDq4Oj5rFixfrzz//VGRkpLp166bKlSs7uiTAVJ999pneeOMNhYaGqmfPnpII3nj4zZ8/X0OHDlWtWrUUHR2tP/74Q6tWrVKtWrUcXRpwVwsXLtSIESMUGBioq1evKm/evJo8ebLq1Knj6NKyBP5qAf8i+Y3dwIEDNWDAAB0+fFhnzpxR06ZNtWjRIgdXh0fJwIEDNXToUB05ckRRUVGqWrWqFi1apISEBEeXBphi06ZNGjRokB577DHNmDFDc+bMkfTP6zK7muNhtXz5cg0aNEizZs3S2rVrNXfuXAUFBfFBER5qq1evVv/+/fXBBx9o27Zt+uyzz5QrVy798MMPkjisLSPwCgCkwWq12r5fu3atPvvsMy1fvlwrVqywzXDzAoTMsnr1ai1ZskRr1qzR0qVLbTN+Pj4+cnd3l0Q/Imu5deuWvvjiCzVt2lQffvihGjdurClTphC88VD7+++/tWDBAg0cOFBt2rSRJD322GPy9PTU7Nmz1alTJ82bN8/BVQL2rly5oiVLlqhXr15q3769vL29VatWLZUpU0arV69WUlISh7JlAEI3kELyGzpXV1clJiZKkk6dOqUnnnhCtWrV0qpVq9StWzeFhYXp1VdfVXR0tA4ePOjIkvEIuHTpkho0aKDKlStr2bJlevbZZzVr1iy1a9dOUVFRunLliiwWC8EbWYa3t7fefPNNtWnTRnXr1tUbb7yhpk2bpgre9DweJrly5dL48ePVsmVL29jzzz+v06dPy8vLSzly5NBrr72mSZMmObBKwJ6Li4vKli2r+vXrS/rfSSurVatmW4bX2gfn5ugCgIfFDz/8oNdff1379+/X7Nmz5eb2z3+PbNmyyc3NTZ9//rm6d++uSZMmqUePHpKkjRs3asuWLRo5cqRy5szpyPKRhV29elUXL17UV199pR49euj999+3zXZ//vnn+vXXXzVz5kx5e3s7uFIgYxiGoccff1yPP/64JKlEiRJ64403JElTpkyRJL322mu6cuWKtmzZYptVBBytatWqtu9Xr16t27dva9u2bSpRooQkyc/PT7Nnz1bXrl2VI0cOZhDhcDlz5lS3bt1UsGBBSbL1ZN68eW2TUB4eHpKkPXv22PU47h2hG/j/qlWrpoULF2ro0KEyDENhYWGSpPz58+uXX37RqlWrNGHCBFvYuXnzphYsWKDixYsrR44cjiwdWdDmzZtVu3ZteXp6qmnTplq9erVat26tSZMm6fXXX5ckxcTE6JtvvlG+fPnk5eXl4IqBjJMyiCSfpb9kyZLq1auXJGnq1Km6efOmVq9erQsXLujFF18kvOCh8/zzz6thw4bKnj27bczNzU2PP/44H9TjoZIcuKX/vf5ev35d169ftwXupk2bKioqSjt27HBIjc6O3cuB/8/Pz09t2rTRuHHjtHr1ar322muSpGbNmqlDhw5KSEhQUlKSfv75Z+3cuVMvvviiLl68qClTprBrLzLUsWPH1KBBA02ePFlJSUkqW7asnnnmGZUsWVJnz57VsWPHtHnzZrVt21ZnzpzRjBkz6EFkWSnD9GOPPaY333xT9evX14ABA3T79m0dPXqU/sdDJ3kX3ZSB+/bt29q1a5dKly7tqLKAe2a1WuXh4SGr1armzZvr9OnT2rp1q6PLclrMdAMp+Pr62nZTHDJkiKxWq+bPn6+xY8fqxo0bWrx4sQYOHKjq1asre/bs2rlzp9zc3GS1WuXq6urg6pEVhIaGKi4uTj4+Pnr33Xd18+ZNjRs3TsOGDZPVatXGjRs1ffp0Va5cWblz59auXbvoQTxSAgMDtWPHDlWtWlU7duyQm5ubEhMTbYcEAQ+DlGcrT0hI0OnTp9WvXz+dO3dOq1evlvS/vTiAh1FgYKC8vLxUv359RURE6MiRI3J3d+f1Np24TjceaXe73uu1a9e0evVqDRkyRM2aNdOCBQsk/XNStatXrypPnjwqWLCgXFxcePFBhhkzZoymTZumTz/9VLdv39bBgwc1atQo9e/fXxMmTFBSUpLi4uJ04MABFSpUSIGBgfQgHimJiYnq37+/Nm3apL179/IGEA89q9Wq9evXa9asWYqOjtYPP/wgd3d3PijFQ2/dunVq2bKlKleurF9++YXX2wdE6MYjK2XgXrNmjc6fPy+LxaL27dsrZ86cun79ur788ksNHjxYLVu2TPMyH3cL7cD9unXrlp577jk1btxYQ4cOtY0vXLhQ3bt313vvvachQ4bI09PTbj16EM4qvbN8J0+eVHBwsO0EP7wBRGZKT9+eOHFCR48e1bPPPkvfwiHS07eHDx/Wp59+qrFjx7JHUQYgdOORlPLFZ/DgwVq2bJny5MkjT09PRUREaNOmTSpcuLCuX7+u1atXa9iwYapbt66WLVvm4MqRVd24cUMVKlRQly5dNGLECBmGYTtG9ZVXXtHnn3+usWPH2gVywFml/LDo3Llzttff/5JydpAPnJDZ0tu3KTHDjcyWEX1L4H5w/LXCIyk5cE+bNk3h4eFasWKFdu7cqS5duujkyZN64okndPToUQUEBOiFF17Q0KFDFRsbazsxCpDRsmfPrg4dOmjJkiXat2+fLBaLLBaLXFxcVLRoUTVp0kTvvvuuFi1a5OhSgQeS8g1g8uETu3btktVq/df1DMOwhZVLly4RuJGp0tu3Kd83REZGEriRqdLbtylvj4iIIHBnAP5i4ZF1+fJlHTp0SB9++KGqVaumdevW6e2339a4ceNUvHhxNWnSRCdPnpS/v786d+6stWvXysXFheCNDBMdHa2///7b9nPLli1VokQJvfvuu/rtt99ksVgUGxurgwcPqkePHurXr58mT56sK1eucKZmOK3kN4CDBw/WrFmz9OKLL6pUqVJ2YeTO/k65d9L8+fP15ptv6tq1a5lXNB556e3b5PXmz5+vPn360LfIVOnt2+Tb58+fr7feeou+zQB8bIFHVp48edSuXTuVLFlSv//+u/r06aMJEyaoV69eypMnj3r06KHSpUvrxIkTKlKkiCT7P6DAg5gwYYLWrVuny5cvq1KlSpo6dapq1aqlN954Q7Nnz1a9evVUpUoV2yfML7zwgk6fPi13d3f5+/tzxls4tY0bN2rJkiX69ttvVaVKFSUmJioiIkInTpxQiRIlFBQUZJuhSRm4586dq7feekufffaZcuTI4eBHgUcNfQtnRN8+HAjdeCSk3L0m5fFUDRs2lPTPC1KJEiX0yiuvSJJy586tbt26KTAwUAULFrRth6CDjPDuu+9q4cKFGjp0qKpUqaKWLVuqW7duGj9+vJo1a6YKFSpoy5Yt2rt3rwIDA/X2229Lkv744w8VLlxY8fHxcnd3d/CjAO7dnSfxSUpKUlBQkPLkyaODBw9q6dKl+uyzz+Tq6qocOXLoiy++UOHChe3WmzNnjgYOHKglS5bohRdecNRDwSOEvoUzom8fTkzZIctLOTsdFhamnj17asqUKTp27JhtmQsXLmjHjh1ydXXVjRs39PHHH8vf319jxoyRq6vrfx77Atyr77//XqtXr9ayZcvUu3dvJSQk6NatW/rll1/UuXNn7du3T/nz59crr7yiyZMna9CgQbpy5Yr69etnO5maj4+Pox8GcM+SkpJsb+QuXbqkpKQkZc+eXRcvXlT37t1t14AdMWKEpk+frqioKNvrc/J6YWFhGjRokBYuXKjWrVs77LHg0UHfwhnRtw8xA3hEjBs3zvD39zfat29v+Pv7G61atTLWrl1rGIZhXLhwwahUqZLh5eVllC5d2ihTpoyRkJDg4IqRFW3bts2YOXOmYRiGsWHDBiNXrlzGJ598Yvz9999Grly5jOeee8746aefbMtfvXrVGDdunPHUU08Z+/fvd1TZQLpYrVbb96NHjzY6dOhg7Nu3zzAMw1i3bp0xYcIEY9WqVcbVq1cNwzCMa9euGRUrVjQ2btxoW2/JkiWGt7e3sXLlykytHY8u+hbOiL59uHHJMGRZd15OpmvXrurYsaMaNGig33//XX379pWnp6d69+6tZs2a6dKlS1qxYoU8PT3VpUsXubm5cWkPZLiEhARdvnxZOXLkUIsWLVS3bl2NHDlSN27cUMOGDbV792517tzZ7izlly9flouLi3LlyuXAyoH0GzRokD799FN98MEHatCggfLly2d3e3x8vGJiYvTKK6/o2rVr2rZtm1xdXZWUlKQpU6aobNmyatq0qYOqx6OKvoUzom8fToRuZEkpA/evv/4qDw8PTZs2Te+++66KFy8uSdq3b5/eeecdu+CdEoEbZrp06ZIaNGig9957T+3bt9etW7f0zjvvqH///ipSpAi9hyxjw4YNCgkJ0dq1a1WtWjUZhqErV67o3LlzCgwMVP78+TVq1Cht27ZN0dHR2r59u9zd3W3XhTXuOD4RyAz0LZwRffvw4kRqyHKMFMdw9+vXT4sXL9aNGzdktVpVtWpV9e7dW5JUuXJlTZ48WQMGDNCoUaPk5+enJ5980rYdQg/M5Ofnp7i4OC1atEg3btzQsmXLdPXqVRUtWlQWi4UPfZBlxMbGqmjRonrsscf0+++/64svvlB4eLgsFovKlCmjTz/9VE8++aTc3Nw0aNAgubm52d4ASpzAEo5B38IZ0bcPL2a6kaWk/ITuwIED6tSpk2bMmKHo6GjNnTtXFy9eVK9evdSpUyfbOrt27VJ4eLimTp3K5cCQKZL3xDh27Jhatmyp7NmzK2fOnPr666/l7u7OJ81wWsm9m7KHf/jhBzVq1EgtWrTQzz//rGbNmql+/fry9PTUkCFDtGzZMtWqVcu2DT5wQmajb+GM6FvnQuhGlrRgwQJ9++23KlCggD766CNJ0sGDBzVhwgSdOnVKPXv21P/93/+lWu/O48ABsyT3WlxcnG7evKmcOXPKYrHYfeIMOJOUr583b96Ut7e3LBaLXFxctGbNGv3666+qXLmynn76aeXOnVvXr1/XU089pcmTJ6tBgwYOrh6PKvoWzoi+dT6EbmQ5165d05AhQ/Tll1+qVq1aWrNmje22gwcPauLEiTp79qw6dOignj17OrBSPOru/JCHD33grFL27tSpU7Vx40bFx8erXLlyGjdunLJly6a4uDh5enoqISFBt2/fVvv27RUdHa0tW7Yw0wKHoG/hjOhb58S7O2Q5OXLkUP/+/dWxY0f98MMPmj59uu22cuXKadCgQfL19dVvv/3mwCoB+2On4uPjCdxwSinPozFkyBCFhoaqYcOGatSokb777js9//zzunXrljw9PRUTE6Px48erefPmunz5sn788Ue5urrKarU6+FHgUUPfwhnRt86LfRiRJZUoUUJvvvmmJGnmzJlydXVVr169JP0TvKdPn67g4GBJ4vhZOETKvps6dap2796tjz/+mF3L4TRu3Lih7Nmz2/p41apVWrt2rdauXauaNWtqzZo1OnPmjK5evaqnn35aP/74o3x8fFS9enXFx8dr1KhRqU7iA5iNvoUzom+dH9MqyLKKFi2qXr166bnnntP06dMVFhZmu61YsWJycXFRUlISgRuZLmXgnjt3rkaMGKHnnnuOP4RwGq1bt9aAAQP0999/28aSkpLUsmVL1axZU+vWrdOrr76qCRMmKCwsTAcOHFCrVq0UExOj5557TuPGjZObm5usVit9j0xD38IZ0bdZhAFkcSdOnDDeeecdIyAgwFi1apWjy8EjLikpyfZ9WFiY4efnZ3zxxRcOrAi4fwsWLDBcXFyMQYMGGZcuXbKN//XXX8aNGzeMOnXqGGPHjjUMwzAuX75slClTxrBYLEaXLl0Mw7D/fwBkFvoWzoi+zRr4uANO535PNlW8eHF1795dRYoUUcuWLU2sDPh3RooZ7jlz5mjgwIFauHChWrdu7eDKgHtnGIZeffVVZcuWTS+//LIMw9A777yjvHnzqmDBgjp06JDOnDmjxo0bS5Ju376tChUqaMaMGapfv74krgWLzEffwhnRt1kHoRtOJzlw79+/X+XLl5eLi8t/vqCUKlVKpUqVksQZopFxfvrpJyUkJCghIUHPPPPMvy6bMnDPmDFDQ4YM0ccff6wXX3wxM0oFMkTK18+XXnpJiYmJ6tSpkyRpwIAByp07t/Lly6ccOXLo/fffV9++fTV69Gi5urqqfv36cnFx4bqwyHT0LZwRfZvFOG6SHUi/r776yvDz8zOuX7/+n8tarVbb91evXjWzLDxCBg8ebBQvXtyoVKmSkSdPHqN169bGsWPH0lw25a5dM2fONFxcXIzly5dnVqlAhkj5Wrpjxw4jNjbWMAzDWLJkiWGxWIyBAwcaV65cMQzDMD799FOjbNmyRtGiRY169eoZ8fHxqbYBZAb6Fs6Ivs16CN1wCncej7J7926jWLFi/xmiU643e/Zso2/fvkZ0dLQpNeLR8dFHHxl58uQxdu3aZRiGYUybNs2wWCzG9u3bUy2b1jHcK1asyLRagYyQso+HDRtmVK1a1Zg5c6aRmJhoGIZhfPbZZ4bFYjH69+9vxMTEGIZhGNHR0cahQ4dsb/wSEhIyv3A80uhbOCP6NmsidOOhl/LFJ/lFJCoqyggKCjK++eYb2213fqKXcr05c+YYHh4ehB1kiB49ehgffPCBYRiGsWzZMiMgIMCYPXu2YRiGcevWLdtyyX8gDeN/gXvlypWZWyyQgUaOHGnkypXL2LJlixEREWEYxv9ea5PfCA4cONCIjIy0W48ZFzgSfQtnRN9mLRzTjYde8nGwEyZM0OHDh1W6dGl5eXkpODhYMTExtuVSHqdtpHHCqqVLl3LCKjyw+Ph4/frrr6pSpYp27Nihrl27atKkSerZs6cSExM1fPhw1a5dWy+88ILtOKp58+bZTprGMdxwVufPn9eGDRs0c+ZM1atXzzaefBmaDh06SJI6duyoIkWKqFevXrZlOI8GHIW+hTOib7MeQjecxt9//61cuXJp1apV8vLy0q+//qqXXnpJL7zwgry9vVWuXDkVKlRIDRo0UGBgoKR/AvegQYM4QzQe2LVr15QjRw55eHgoJCREc+fO1cGDBxUWFqaQkBBJ0s2bN/X7778rR44ctvXmzZun1157TV988YVeeOEFR5UPPLDbt2/r0KFD8vb2tht3c3NTbGysXF1d1aFDB+XMmVMNGzZ0UJWAPfoWzoi+zXr4KAQPpaSkpFRjkyZN0pQpU7R161b99NNP6tixo4KCgvTUU0/p2rVrWrVqlebNm6c8efJI+t8M94IFC5hdxANZvHixKleurGPHjkmSqlWrJm9vb1WpUkXly5eXJJ07d04dO3ZUVFSUBg4caFs3KCiIwA2nYxiG7d/k7yUpX758unjxot2YJG3btk0TJ05UQkKCmjRpIjc3NyUmJmZqzQB9C2dE3z4amOnGQyflJRIWL16s8+fP6/z58+rWrZuKFi2q7NmzS5Jq166tyMhIvfHGG3rjjTck2e9WHhERoUWLFjHDjQeyatUqRUVF6ezZs+revbsWLVqkJ554Qv369dO0adPUokULBQQEyNvbWx4eHtq2bZtcXV1tl+lo0aKFox8CcF9SvgbHxcXJ3d1drq6uKl68uOrWrathw4bp8ccf15NPPimLxaLY2FjNnDlTOXLkkJvb/95WpPweMBt9C2dE3z46LMadH58AD4mBAwcqPDxczZo10/Hjx3Xu3Dn16tVLb731ltzc3LR582a1bt1a+/fvV4ECBWzHz3JNQmSUwYMHKzw8XP3799fJkye1YcMGeXh46KuvvlKxYsV07NgxnTx5UsePH1fx4sX17LPPytXVVYmJifwBhNMLDQ3V+vXr5ebmpscff1yTJ0+Wh4eH2rVrp++++05t27aVt7e39u/fr6tXr2rfvn1yd3e3+/ATyGz0LZwRfZv1EbrxUFq1apX69u2rr776ShUrVtT333+vZ555xm433RMnTqhy5crau3evSpYs6eCKkdUcPnxYDRs21Ny5c22z1adOndKLL76o+Ph4rV69WiVKlEi1Hh/6wFmlnHH54IMPNGbMGL311luKi4tTeHi48ufPr6VLl6pkyZIaN26cjhw5omvXrql06dKaOHGibRdHPnBCZqJv4Yzo20cPoRsPpbCwMG3cuFFffPGFPvvsM73++uuaMGGCXn/9dcXExCgyMlJ+fn4aPHiw5syZQ8hBhtuzZ48aN26sn3/+WaVLl7b9gTx48KDq1aunihUrav78+SpevLjdH0/A2W3btk3r169XnTp11KxZM0nS1atXVa9ePWXPnl07duyQ9L9zbyT3Pm8A4Uj0LZwRffvo4F0iHC6tk6adP39erq6u2rVrl3r27GkL3JL0+eef65NPPpG3t7fmz59vO34WyEgVKlSQr6+vFi5cKOl/f+gKFiyokiVLat++fWrbtq3tNj6/RFbw448/6qWXXtKsWbOULVs2Sf9cJi9nzpxav369jh8/rtmzZ0v653KOyf8vDMPgDSAchr6FM6JvHy2EbjhUyhnCLVu26Pjx45Kkl19+WVu3blXNmjU1c+ZMW+C+ffu2Vq1apcjISPn4+Ni2w0w3MsL333+v1atX68svv5S7u7t69eqlLVu26MMPP7Qt4+npqccee0zr1q3T5cuXNXToUEnimCpkCQULFlSHDh0UHx+v7777TpLk4eEhq9WqgIAAFS1aVNHR0ZLse57+hyPRt3BG9O2jhY9J4DCGYdgC95AhQ7Rhwwb16tVL+fLlU+HChTVw4EBNnTpVR44c0blz53T69GmNHz9e58+f15o1a2zb4MUHGWHIkCEKDw9X3rx5deTIEXXt2lUvvPCCIiMjtXDhQm3fvl116tTRmjVrFBcXp2rVqqlMmTK6cuWKo0sHMkzJkiX11ltvSZLCw8OVI0cODRw4UK6ursqWLZtiY2PT3DsJcCT6Fs6Ivn20ELrhMMlhecSIEZo/f75Wrlyp6tWr23ax6dq1qyRp4sSJmjt3rgoUKKACBQpo9+7dcnNz44RVyDDvv/++PvnkE61evVo1atTQjBkz1KdPH926dUudOnVSjRo1NGvWLH311VcKDAzUZ599Jg8PD7m7uytXrlyS+AAIWUehQoX05ptvymKxaNy4cdq7d68KFCig06dPKz4+XgMGDHB0iUAq9C2cEX376OBEanCokydPqk2bNpowYYKaNGmiS5cu6fTp01qzZo3tpBJRUVE6fPiwAgMDFRwcLBcXF04ggQxz4cIFDR06VM8++6zat2+vVatWqVu3bnrjjTc0bdo0NW/eXOPHj1eRIkXs1hswYIDCw8O1bds2zp6PLOns2bOaNm2aPv30UxUrVkzDhw/Xc889J4mz9OPhRd/CGdG3WR+pBQ6VI0cOJSYm6sCBA/L399fs2bO1f/9+eXh4KDQ0VAsWLFBISIhq165tWycpKYnAjQyTM2dOtWrVSk8//bR2796td955RyNHjlSfPn0UEBCgAQMG6Pz58woPD1ehQoX022+/acGCBVq3bp2+/fZbAjeyrMKFC6t3795ydXXV+vXr9ccff9jeBLJXBx5W9C2cEX2b9XEiNTiUq6ur6tevr8WLF+vJJ59Ujhw5NGHCBO3cuVMtWrTQ0aNHU63DpZmQkby8vNS8eXMFBATo+++/V9myZdW5c2dJ/5w07ZVXXpG3t7cKFCggSapYsaJatmypLVu2qHLlyo4sHTBdcHCwevTooSZNmmjevHkaN26cJF6H8XCjb+GM6Nusjd3L4XCXL19WZGSkEhMTValSJdt4nTp19MILL3A8C0yXfDz2q6++quPHj+vrr7+Wh4eH2rZtq1deeUXt27eXJCUkJMjd3d3B1QKZJ/kKExcuXND48eO1Z88eff3118qZM6ejSwPuir6FM6JvszZCNx4qsbGx+vPPPzVgwABFRERo165d7EqOTPPLL7+oXr16KlWqlOLi4uTl5aW9e/fSg3gkJb89sFgs2rt3ryIiIlS9enXlyZPHwZUBd0ffwhnRt1kf+yvgoZGUlKTPP/9c7777ruLi4rRz507bWcqBzFCrVi398ssvatWqlbp162YL3ImJiY4uDchUyXt/WCwWzZgxQ3Xr1lX+/Pl5A4iHGn0LZ0TfPhqY6cZD5cKFCzpw4IAaN27MWcrxUKAH8ahJefm7OXPmaMiQIZozZ47atm3r4MqAu6Nv4Yzo20cHoRsPteTjWwAA9y69r513vgEcOHCgFi5cqBdffDGjSwRSoW/hjOhb3AvSDB4aSUlJtu9/+eUXGYZB4AaA+5TyDWDyteQTEhL+c7073wD279+fN4DINPQtnBF9i3tFosFDIWXADgsLU/v27XXo0CEHVwUAziXla+ngwYM1cOBA/f7774qNjf3P9ZLfAC5atEgTJkzQxx9/zBtAZAr6Fs6IvsX94EBFOFxau9csWrRI5cqVc3BlAOBckl9LJ02apIULF2rDhg2qUKGCXF1d7V5rU0o5HhYWpgEDBmjp0qVq3rx5ptaORxd9C2dE3+J+MNONDJFy1/D7kVbg5tM+AEi/+Ph47d69W/3791flypV17tw5ffXVV3r22Wc1ZMgQbd261bbsna/BgwYN0scff8wbQGQ6+hbOiL7FvWKmGxkiefeamTNnKjg4WM2aNfvPde52AonWrVubWisAZCV3nsQnKSlJJ0+eVGxsrAoVKqTFixfr9u3b8vX11bfffqurV6+qTp06kmQ7Mz8n8UFmo2/hjOhbpBcz3cgQhmHo+vXrGjNmjG7evHlPyycH7hkzZmjAgAG8+ADAfUr5BnDx4sXauXOnvLy8NGfOHB07dkwDBw5U9erVNXr0aK1Zs0Zt2rTRuXPn5OrqansDGBYWpkGDBvEajExD38IZ0bd4EMx0I92SX3yS/w0ICFD16tV14sQJSbqn41nmzJmjAQMGaPny5WrRokWm1g8AzuzOk/iEh4erT58+euyxx1S1alXt3r1bN2/eVP78+SVJVqtVP/30k4oUKSKLxSLDMHTo0CG99957WrBgAW8AkSnoWzgj+hYPiut044EdPHhQZcuWlcViUUhIiP7++2+tXbs21XLJrcY1CQEg40yYMEEffvihNmzYoPLly8vd3d1uRiY2NlYbN27U/Pnzdfr0ae3du1fu7u629U+ePKnixYs7qnw8ouhbOCP6FunFTDceSHh4uHr16qUCBQrI09NTRYoU0cWLF7V8+XI9+eST8vT0VM6cOSXJbtabwA0ADy4mJka//vqrxowZoypVqujMmTM6dOiQZs+erRo1aqhFixby9vbW4sWL5ebmZnsDmJiYKBcXF7m4uPAGEJmOvoUzom/xIJjpxn25c5fxY8eOydXVVb///ruOHDmiI0eO6LPPPlPZsmUVERFhC+JdunRR9+7dJf1zsrV3331X8+fPJ3ADwAOIj49X9erVVaJECfXo0UMzZsxQVFSUAgICtGvXLnXs2FEffPCBzp49q4IFC8rFxUWJiYm24wsBR6Bv4YzoWzwIQjfuWcrdZ65cuSI3Nze5uLjIz8/PFsZ/+eUXtWrVSj/++KOio6N18uRJnThxQsOGDZObm5u2b9+udu3aacqUKWrXrp2DHxEAOI87z5qb7IcfflDXrl118+ZNvfbaa3rmmWdUr149DR8+XPv379eqVatsb/rutg3ALPQtnBF9i4zGRy+4JylPIDF27Fht3bpVJ0+eVK1atdSuXTu1atVKklS+fHnlzp1bFy9eVMOGDVWrVi277eTNm1fffPONKlasmOmPAQCcVco3b1u3btX58+f12GOPqXDhwmrQoIEOHDigq1evqnDhwrblf/nlF5UqVcpuloU3gMhM9C2cEX0LMzDTjfvy3nvvafbs2Zo3b548PDw0efJk7du3TwcPHlT+/PlltVpVqVIltW7dWqNGjXJ0uQDg9FIe1jN48GAtXbpUHh4e8vT0VLVq1dS3b19VqlRJknTjxg1t375dM2bM0JkzZ7Rv3z65ubnd9WoSgFnoWzgj+hZm4SMY3LOzZ8/q+++/1/Lly/XCCy/Izc1Nu3fv1sSJE5U/f37Fx8fL1dVVxYoVU2xsrKPLBQCnl/LN26RJk7R48WItXrxYx48f17PPPqsvv/xS7733nvbt2ydJOnz4sD755BNZLBbt3btXbm5uSkxM5A0gMhV9C2dE38JUBnAXVqvV7udjx44Z+fLlMyIiIoy1a9cavr6+xuzZsw3DMIxbt24ZYWFhxpUrV4wtW7YYCQkJjigZALKEjz76yPa91Wo1Lly4YLRo0cL49NNPDcMwjHXr1hl+fn5Gt27djAoVKhjPPfeccejQIcMwDOP48eO2129ei5GZ6Fs4I/oWmYHQjTSlDNyrVq0yTp8+bVy+fNlo0KCBMXLkSMPf398WuA3DMPbv32+0bt3a2LJli20sMTExU2sGgKxg9+7dhsViMTp37mw3vnXrVuPixYvG7t27jYIFCxozZswwDMMwBg8ebPj4+Bi1atUyDh8+bFv+zg9OATPRt3BG9C0yC7uXIxUjxUnThg4dqjfffFNr165V7ty5VaJECY0aNUo9evRQz549Jf1z3cKhQ4cqNjZWdevWtW3H1dXVIfUDgDOrXLmy1q1bp7Vr16pz58628Zo1ayooKEhfffWVqlevbrsMY2BgoGrUqKHGjRurVKlStuU5iQ8yE30LZ0TfIrNw9nKkknwsypgxYzRv3jx98803euyxxyRJc+bMUUxMjD799FPdunVLbm5u2r9/v65cuaK9e/fKxcWFSyQAQDolf+jZtGlThYeH6+WXX1anTp306aefysPDQ5J0+/ZtnTt3ThcuXFBwcLC2bt2qNm3a6PXXX5fFYuE1GJmOvoUzom+RmTh7OdJ09epVtW/fXl26dFHHjh11/vx5/fHHH1q6dKnq1aunDRs2yGq16tatWypTpoxGjRplO4FEysslAADuTco3b/Hx8fLw8NC6devUsWNHPf/88/rkk08kSUuXLtWUKVMUExMjV1dXJSQk6MCBA5w1Fw5B38IZ0bfIbIRupOnatWsqV66cQkJC9Mwzz2jWrFk6deqUkpKSdPHiRb377rvq2bOn3QuO1Wpll3IASIeUbwDDwsJ08+ZNdejQQQUKFNDXX3+tjh07qkWLFgoPD5ckLV++XKdOnVJsbKzee+89ubm58RqMTEffwhnRt3AEQjfuasGCBRowYICsVqt69uypxo0bq1GjRnrllVfk6upq+xQQAJAxBg4cqI8//liTJk1SgwYNVKhQISUlJenbb79Vx44d1bx5cy1evDjVerwBhCPRt3BG9C0yE6Eb/+rs2bOKi4tTyZIlJf3z6eAzzzyjWrVqaezYsQ6uDgCyjpUrV+rtt9/WF198oRo1atjdZrVatX79ev3f//2fnnjiCX311VcOqhKwR9/CGdG3yGwc+Y9/VbhwYZUsWVI3b97UTz/9pFatWunSpUsaOXKko0sDgCzlyJEjKlWqlCpXrmwbS0pKkvTP1SCaNWumuXPnKi4uzjYOOBp9C2dE3yKzEbrxnwzD0O7duzVx4kQlJCRoz549tuNZAAD3L603cREREUpISJC7u7sMw7Add2i1WvXNN9/o/PnzatOmjb777jvblSKAzETfwhnRt3gYELrxnywWi2rXrq3Ro0frm2++kbu7uxITEzmeBQDSIeVJfE6ePKkLFy7IarWqQ4cO2rZtm1auXCmLxWJb5vr161q0aJF27dpltx0uU4PMRN/CGdG3eFhwTDfuG9ckBID0SXnFh8GDB2vNmjW6fPmyypYtq9atWysmJkZjxozR9OnT9fTTTysuLk79+/dXZGSkdu7cyYedcAj6Fs6IvsXDhNANAEAmSPmB5eeff663335bYWFhun79ug4fPqxp06apW7duqlChgvr27Ss/Pz8FBAQod+7c+uGHH+Tu7s5Zc5Hp6Fs4I/oWDxs3RxcAAMCjIPkN4ObNm7Vp0yYNHDhQrVq1kiRFRUWpSJEiGjRokFasWKFDhw7pzJkzcnd3V61ateTi4qLExES5ufFnG5mLvoUzom/xsGGmGwCATBIREaG6devq0qVLGjRokIYNG2a77erVq3r11VdVqFAhTZ8+3W49DuuBI9G3cEb0LR4mdBQAAJkkKChIq1atUt68ebVq1Srt27fPdlvOnDmVO3dunTx5MtV6vAGEI9G3cEb0LR4mdBUAAJmoQoUKWrVqlaxWq6ZOnar9+/dLkm7cuKEjR46oYMGCji0QSAN9C2dE3+Jhwe7lAAA4wL59+/TKK6/o6tWrqlatmjw8PHTq1Cn98ssv8vDwsDvzLvCwoG/hjOhbOBoz3QAAOEDlypW1bNkyeXt7KyoqSo0bN9bevXvl4eGhhIQE3gDioUTfwhnRt3A0QjcAAA5Srlw5rVq1SvHx8dq7d69OnDghSXJ3d3dwZcDd0bdwRvQtHIndywEAcLB9+/apZ8+eKlasmEaMGKHSpUs7uiTgP9G3cEb0LRyBmW4AAByscuXKmjFjhi5evCh/f39HlwPcE/oWzoi+hSMw0w0AwEPi9u3b8vLycnQZwH2hb+GM6FtkJkI3AAAAAAAmYfdyAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgDACXTp0kUWiyXV14kTJx542x9//LECAgIevEgAAJCKm6MLAAAA96Zp06ZatGiR3ViePHkcVE3aEhIS5O7u7ugyAAB4aDDTDQCAk/D09FRQUJDdl6urq9asWaMqVarIy8tLxYoV06hRo5SYmGhbb/LkySpfvrx8fHxUqFAh9erVSzdv3pQkbd68WSEhIYqKirLNno8cOVKSZLFYtHr1arsaAgIC9PHHH0uSTp8+LYvFomXLlql+/fry8vLSkiVLJEnz58/X448/Li8vL5UuXVqzZs2ybSM+Pl69e/dWvnz55OXlpSJFiig0NNS8Jw4AAAdiphsAACe2bds2derUSdOmTdOTTz6pkydPqkePHpKkESNGSJJcXFw0bdo0FS1aVH/++ad69eqlgQMHatasWapTp46mTp2q4cOH69ixY5IkX1/f+6ph8ODB+vDDD1W5cmVb8B4+fLhmzJihypUra9++ferevbt8fHzUuXNnTZs2TWvXrtXy5ctVuHBh/fXXX/rrr78y9okBAOAhQegGAMBJrFu3zi4QP/vss7p27ZoGDx6szp07S5KKFSumMWPGaODAgbbQ3bdvX9s6wcHBGjt2rHr27KlZs2bJw8ND/v7+slgsCgoKSlddffv2VevWrW0/jxgxQh9++KFtrGjRojp8+LDmzJmjzp076+zZsypZsqTq1q0ri8WiIkWKpOt+AQBwBoRuAACcxNNPP63Zs2fbfvbx8VGFChW0fft2jRs3zjZutVp1+/ZtxcbGKlu2bPr+++8VGhqqo0ePKjo6WomJiXa3P6hq1arZvo+JidHJkyfVtWtXde/e3TaemJgof39/Sf+cFK5x48YqVaqUmjZtqubNm+uZZ5554DoAAHgYEboBAHASPj4+KlGihN3YzZs3NWrUKLuZ5mReXl46ffq0mjdvrtdff13jxo1Tzpw59dNPP6lr166Kj4//19BtsVhkGIbdWEJCQpp1paxHkubNm6eaNWvaLefq6ipJqlKlik6dOqVvv/1W33//vdq1a6dGjRpp5cqV//EMAADgfAjdAAA4sSpVqujYsWOpwniyPXv2KCkpSR9++KFcXP45f+ry5cvtlvHw8JDVak21bp48eXTx4kXbz8ePH1dsbOy/1hMYGKj8+fPrzz//VMeOHe+6nJ+fn9q3b6/27durTZs2atq0qa5evaqcOXP+6/YBAHA2hG4AAJzY8OHD1bx5cxUuXFht2rSRi4uLfvvtNx08eFBjx45ViRIllJCQoOnTp6tFixbavn27wsLC7LYRHBysmzdvatOmTapYsaKyZcumbNmyqUGDBpoxY4Zq164tq9WqQYMG3dPlwEaNGqU+ffrI399fTZs2VVxcnHbv3q1r166pX79+mjx5svLly6fKlSvLxcVFK1asUFBQENcKBwBkSVwyDAAAJ9akSROtW7dO3333napXr65atWppypQptpOTVaxYUZMnT9bEiRNVrlw5LVmyJNXluerUqaOePXuqffv2ypMnj95//31J0ocffqhChQrpySef1Msvv6z+/fvf0zHg3bp10/z587Vo0SKVL19e9evX18cff6yiRYtKkrJnz673339f1apVU/Xq1XX69Gl98803tpl4AACyEotx58FaAAAAAAAgQ/CRMgAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYJL/BwPRCjC6IrYZAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.bar(range(X_train_transformed.shape[1]), importances[indices], align='center')\n",
-    "plt.xticks(range(X_train_transformed.shape[1]), [feature_names[i] for i in indices],\n",
-    "           rotation=45)\n",
-    "plt.title('Feature Importances from AdaBoost')\n",
-    "plt.xlabel('Features')\n",
-    "plt.ylabel('Importance')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "548e9078",
-   "metadata": {},
-   "source": [
-    "## Gradient Boosting Overview\n",
-    "\n",
-    "Gradient Boosting is a powerful ensemble learning technique that builds models sequentially, where each new model is trained to minimize the residual errors of the previous models. The key steps in Gradient Boosting are:\n",
-    "\n",
-    "1.  **Initialize Model**: Start with a constant model (e.g., the mean of the target variable for regression).\n",
-    "2.  **Compute Residuals**: Calculate the residuals (errors) of the current model on the training data.\n",
-    "3.  **Train Weak Learner**: Train a weak learner (e.g., a decision tree) on the residuals, focusing on the instances where the current model performs poorly.\n",
-    "4.  **Update Model**: Add the predictions of the weak learner to the current model, scaled by a learning rate (shrinkage).\n",
-    "5.  **Repeat**: Repeat steps 2-4 for a specified number of iterations or until a stopping criterion is met (e.g., no significant improvement in performance)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1bcbac8f",
-   "metadata": {},
-   "source": [
-    "### Example of Gradient Boosting\n",
-    "\n",
-    "-   Train a Gradien Boosting Classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "6449bb76",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 5 folds for each of 324 candidates, totalling 1620 fits\n",
-      "Best parameters for Gradient Boosting: {'learning_rate': 0.1, 'max_depth': 4, 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 50}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-3 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-3 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-3 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-3 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(max_depth=4, min_samples_leaf=2, min_samples_split=5,\n",
-       "                           n_estimators=50, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GradientBoostingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('loss',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">loss&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;log_loss&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('learning_rate',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
-       "            <td class=\"value\">0.1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
-       "            <td class=\"value\">50</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('subsample',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">subsample&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('criterion',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">criterion&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;friedman_mse&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_split',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
-       "            <td class=\"value\">5</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_depth',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">4</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('init',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">init&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">42</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_features&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('validation_fraction',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">validation_fraction&nbsp;</td>\n",
-       "            <td class=\"value\">0.1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_iter_no_change&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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-      ],
-      "text/plain": [
-       "GradientBoostingClassifier(max_depth=4, min_samples_leaf=2, min_samples_split=5,\n",
-       "                           n_estimators=50, random_state=42)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.ensemble import GradientBoostingClassifier\n",
-    "\n",
-    "# Gradient Boosting Classifier\n",
-    "grid_params_gb = {\n",
-    "    'n_estimators': [10, 50, 100, 200],\n",
-    "    'learning_rate': [0.01, 0.1, 0.2],\n",
-    "    'max_depth': [3, 4, 5],\n",
-    "    'min_samples_split': [2, 5, 10],\n",
-    "    'min_samples_leaf': [1, 2, 4]\n",
-    "}\n",
-    "gb_model = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, random_state=42)\n",
-    "grid_search_gb = GridSearchCV(gb_model, grid_params_gb, cv=5, n_jobs=-1,\n",
-    "                              scoring='accuracy', verbose=1)\n",
-    "grid_search_gb.fit(X_train_transformed, y_train)\n",
-    "gb_model = grid_search_gb.best_estimator_\n",
-    "print(\"Best parameters for Gradient Boosting:\", grid_search_gb.best_params_)\n",
-    "gb_model.fit(X_train_transformed, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5f08ee3d",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the Gradient Boosting Classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "335f4376",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Gradient Boosting Classifier Accuracy: 0.8092\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "Not Survived       0.83      0.87      0.85       243\n",
-      "    Survived       0.77      0.71      0.74       150\n",
-      "\n",
-      "    accuracy                           0.81       393\n",
-      "   macro avg       0.80      0.79      0.79       393\n",
-      "weighted avg       0.81      0.81      0.81       393\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "y_pred_gb = gb_model.predict(X_test_transformed)\n",
-    "print(f\"Gradient Boosting Classifier Accuracy: {accuracy_score(y_test, y_pred_gb):.4f}\")\n",
-    "print(classification_report(y_test, y_pred_gb, target_names=class_names))\n",
-    "ConfusionMatrixDisplay.from_estimator(gb_model, X_test_transformed, y_test,\n",
-    "                                      display_labels=class_names, cmap='Blues')\n",
-    "plt.title('Gradient Boosting Confusion Matrix')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "229c7869",
-   "metadata": {},
-   "source": [
-    "-   Feature importance for Gradient Boosting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "032ba3ef",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Feature importances for Gradient Boosting:\n",
-      "            Feature  Importance\n",
-      "1     sex__sex_male    0.574131\n",
-      "0          age__age    0.227912\n",
-      "4  pclass__pclass_3    0.156952\n",
-      "2  pclass__pclass_1    0.035021\n",
-      "3  pclass__pclass_2    0.005983\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "importances = gb_model.feature_importances_\n",
-    "indices = importances.argsort()[::-1]\n",
-    "feature_importance_df = pd.DataFrame({'Feature': feature_names,\n",
-    "                                      'Importance': importances})\n",
-    "feature_importance_df = feature_importance_df.sort_values(by='Importance',\n",
-    "                                                          ascending=False)\n",
-    "print(\"Feature importances for Gradient Boosting:\")\n",
-    "print(feature_importance_df)\n",
-    "\n",
-    "# Plot feature importances\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.bar(range(X_train_transformed.shape[1]), importances[indices], align='center')\n",
-    "plt.xticks(range(X_train_transformed.shape[1]), [feature_names[i] for i in indices],\n",
-    "           rotation=45)\n",
-    "plt.title('Feature Importances from Gradient Boosting')\n",
-    "plt.xlabel('Features')\n",
-    "plt.ylabel('Importance')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e5d51af6",
-   "metadata": {},
-   "source": [
-    "## XGBoost Overview\n",
-    "\n",
-    "XGBoost, short for Extreme Gradient Boosting, is an optimized version of gradient boosting that includes regularization techniques to prevent overfitting and improve performance. It is widely used in machine learning competitions and real-world applications. The key features of XGBoost include:\n",
-    "\n",
-    "-   **Regularization**: XGBoost includes L1 (Lasso) and L2 (Ridge) regularization to control model complexity and prevent overfitting.\n",
-    "-   **Tree Pruning**: XGBoost uses a depth-first approach to grow trees and prunes them based on a minimum loss reduction criterion, which helps in building more efficient models.\n",
-    "-   **Parallelization**: XGBoost can parallelize the tree construction process, making it faster than traditional gradient boosting methods."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7686d58a",
-   "metadata": {},
-   "source": [
-    "### Example of XGBoost\n",
-    "\n",
-    "-   Train a XGBoost Classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "6f3fff6e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 5 folds for each of 243 candidates, totalling 1215 fits\n",
-      "Best parameters for XGBoost: {'learning_rate': 0.1, 'max_depth': 4, 'min_child_weight': 1, 'n_estimators': 100, 'subsample': 1.0}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-4 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-4 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-4 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-4 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-4 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-4 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
-       "              colsample_bylevel=None, colsample_bynode=None,\n",
-       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-       "              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n",
-       "              feature_types=None, feature_weights=None, gamma=None,\n",
-       "              grow_policy=None, importance_type=None,\n",
-       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
-       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
-       "              max_delta_step=None, max_depth=4, max_leaves=None,\n",
-       "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
-       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
-       "              num_parallel_tree=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.0.0/python/python_api.html#xgboost.XGBClassifier\">?<span>Documentation for XGBClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('objective',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">objective&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;binary:logistic&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('base_score',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">base_score&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('booster',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">booster&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('callbacks',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">callbacks&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('colsample_bylevel',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">colsample_bylevel&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('colsample_bynode',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">colsample_bynode&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('colsample_bytree',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">colsample_bytree&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('device',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">device&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('early_stopping_rounds',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">early_stopping_rounds&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('enable_categorical',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">enable_categorical&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('eval_metric',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">eval_metric&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;logloss&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('feature_types',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">feature_types&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('feature_weights',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">feature_weights&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('gamma',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">gamma&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('grow_policy',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">grow_policy&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('importance_type',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">importance_type&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('interaction_constraints',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">interaction_constraints&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('learning_rate',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
-       "            <td class=\"value\">0.1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_bin',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_bin&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_cat_threshold',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_cat_threshold&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_cat_to_onehot',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_cat_to_onehot&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_delta_step',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_delta_step&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_depth',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">4</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_leaves',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaves&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_child_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_child_weight&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('missing',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">missing&nbsp;</td>\n",
-       "            <td class=\"value\">nan</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('monotone_constraints',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">monotone_constraints&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('multi_strategy',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">multi_strategy&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('num_parallel_tree',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">num_parallel_tree&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">42</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('reg_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">reg_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('reg_lambda',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">reg_lambda&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('sampling_method',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">sampling_method&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('scale_pos_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">scale_pos_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('subsample',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">subsample&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tree_method',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tree_method&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('validate_parameters',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">validate_parameters&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbosity',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbosity&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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-       "</script></body>"
-      ],
-      "text/plain": [
-       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
-       "              colsample_bylevel=None, colsample_bynode=None,\n",
-       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-       "              enable_categorical=False, eval_metric='logloss',\n",
-       "              feature_types=None, feature_weights=None, gamma=None,\n",
-       "              grow_policy=None, importance_type=None,\n",
-       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
-       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
-       "              max_delta_step=None, max_depth=4, max_leaves=None,\n",
-       "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
-       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
-       "              num_parallel_tree=None, ...)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from xgboost import XGBClassifier\n",
-    "\n",
-    "# XGBoost Classifier\n",
-    "grid_params_xgb = {\n",
-    "    'n_estimators': [50, 100, 200],\n",
-    "    'learning_rate': [0.01, 0.1, 0.2],\n",
-    "    'max_depth': [3, 4, 5],\n",
-    "    'min_child_weight': [1, 2, 3],\n",
-    "    'subsample': [0.8, 0.9, 1.0]\n",
-    "}\n",
-    "\n",
-    "grid_search_xgb = GridSearchCV(\n",
-    "    XGBClassifier(random_state=42, eval_metric='logloss'),\n",
-    "    grid_params_xgb, cv=5, n_jobs=-1, scoring='accuracy', verbose=1\n",
-    ")\n",
-    "grid_search_xgb.fit(X_train_transformed, y_train)\n",
-    "print(\"Best parameters for XGBoost:\", grid_search_xgb.best_params_)\n",
-    "xgb_model = grid_search_xgb.best_estimator_\n",
-    "xgb_model.fit(X_train_transformed, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec0aba95",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the XGBoost Classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "d61daade",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "XGBoost Classifier Accuracy: 0.8168\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "Not Survived       0.84      0.87      0.85       243\n",
-      "    Survived       0.78      0.73      0.75       150\n",
-      "\n",
-      "    accuracy                           0.82       393\n",
-      "   macro avg       0.81      0.80      0.80       393\n",
-      "weighted avg       0.82      0.82      0.82       393\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Feature importances for XGBoost:\n",
-      "            Feature  Importance\n",
-      "1     sex__sex_male    0.661201\n",
-      "4  pclass__pclass_3    0.226911\n",
-      "2  pclass__pclass_1    0.055751\n",
-      "0          age__age    0.030057\n",
-      "3  pclass__pclass_2    0.026079\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "y_pred_xgb = xgb_model.predict(X_test_transformed)\n",
-    "print(f\"XGBoost Classifier Accuracy: {accuracy_score(y_test, y_pred_xgb):.4f}\")\n",
-    "print(classification_report(y_test, y_pred_xgb, target_names=class_names))\n",
-    "ConfusionMatrixDisplay.from_estimator(xgb_model, X_test_transformed, y_test,\n",
-    "                                        display_labels=class_names, cmap='Blues')\n",
-    "plt.title('XGBoost Confusion Matrix')\n",
-    "plt.show()\n",
-    "\n",
-    "# Feature importance for XGBoost\n",
-    "import pandas as pd\n",
-    "importances = xgb_model.feature_importances_\n",
-    "indices = importances.argsort()[::-1]\n",
-    "feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': importances})\n",
-    "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
-    "print(\"Feature importances for XGBoost:\")\n",
-    "print(feature_importance_df)\n",
-    "\n",
-    "# Plot feature importances\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.bar(range(X_train_transformed.shape[1]), importances[indices], align='center')\n",
-    "plt.xticks(range(X_train_transformed.shape[1]), [feature_names[i] for i in indices],\n",
-    "           rotation=45)\n",
-    "plt.title('Feature Importances from XGBoost')\n",
-    "plt.xlabel('Features')\n",
-    "plt.ylabel('Importance')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "26a03e45",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercise\n",
-    "\n",
-    "-   Implement a boosting algorithm of your choice (AdaBoost, Gradient Boosting, or XGBoost) on the `house_prices` dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "3807c988",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load the house_prices dataset\n",
-    "from sklearn.datasets import fetch_openml\n",
-    "house_prices = fetch_openml('house_prices', version=1, as_frame=True)\n",
-    "df = house_prices.frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "7d8dffa5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Missing values in the dataset:\n",
-      "Id                 0\n",
-      "MSSubClass         0\n",
-      "MSZoning           0\n",
-      "LotFrontage      259\n",
-      "LotArea            0\n",
-      "                ... \n",
-      "MoSold             0\n",
-      "YrSold             0\n",
-      "SaleType           0\n",
-      "SaleCondition      0\n",
-      "SalePrice          0\n",
-      "Length: 81, dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Check whether the dataset has missing values\n",
-    "print(\"Missing values in the dataset:\")\n",
-    "print(df.isnull().sum())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "4d40fd47",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Select relevant features and target variable\n",
-    "X = df.drop(columns=['SalePrice'])\n",
-    "y = df['SalePrice']\n",
-    "\n",
-    "# Train-test split\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
-    "\n",
-    "# Preprocessing pipeline\n",
-    "preprocessing_pipeline = ColumnTransformer(\n",
-    "    transformers=[\n",
-    "        ('num', Pipeline([\n",
-    "            ('imputer', SimpleImputer(strategy='mean')),\n",
-    "            ('scaler', StandardScaler())\n",
-    "        ]), X.select_dtypes(include=['int64', 'float64']).columns),\n",
-    "        ('cat', Pipeline([\n",
-    "            ('imputer', SimpleImputer(strategy='most_frequent')),\n",
-    "            ('encoder', OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n",
-    "        ]), X.select_dtypes(include=['object']).columns)\n",
-    "    ],\n",
-    "    remainder='passthrough'\n",
-    "  )\n",
-    "\n",
-    "X_train_transformed = preprocessing_pipeline.fit_transform(X_train)\n",
-    "X_test_transformed = preprocessing_pipeline.transform(X_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "ed0d0184",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 5 folds for each of 18 candidates, totalling 90 fits\n",
-      "Best parameters for Support Vector Regressor: {'C': 10, 'epsilon': 0.01, 'kernel': 'linear'}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-5 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-5 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-5 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-5 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-5 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-5 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR(C=10, epsilon=0.01, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVR</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('kernel',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">kernel&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;linear&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('degree',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">degree&nbsp;</td>\n",
-       "            <td class=\"value\">3</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('gamma',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">gamma&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;scale&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('coef0',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">coef0&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">C&nbsp;</td>\n",
-       "            <td class=\"value\">10</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('epsilon',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">epsilon&nbsp;</td>\n",
-       "            <td class=\"value\">0.01</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('shrinking',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">shrinking&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('cache_size',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">cache_size&nbsp;</td>\n",
-       "            <td class=\"value\">200</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_iter&nbsp;</td>\n",
-       "            <td class=\"value\">-1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
-      ],
-      "text/plain": [
-       "SVR(C=10, epsilon=0.01, kernel='linear')"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Train a Support Vector Regressor\n",
-    "from sklearn.svm import SVR\n",
-    "grid_params_svr = {\n",
-    "    'kernel': ['linear', 'rbf'],\n",
-    "    'C': [0.1, 1, 10],\n",
-    "    'epsilon': [0.01, 0.1, 0.2]\n",
-    "}\n",
-    "grid_search_svr = GridSearchCV(\n",
-    "    SVR(), grid_params_svr, cv=5, n_jobs=-1, scoring='r2', verbose=1\n",
-    ")\n",
-    "grid_search_svr.fit(X_train_transformed, y_train)\n",
-    "print(\"Best parameters for Support Vector Regressor:\", grid_search_svr.best_params_)\n",
-    "svr_model = grid_search_svr.best_estimator_\n",
-    "svr_model.fit(X_train_transformed, y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "b723c4d6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Support Vector Regressor RMSE: 51669.9132\n",
-      "Support Vector Regressor R^2: 0.6174\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Evaluate the SVR\n",
-    "import numpy as np\n",
-    "from sklearn.metrics import mean_squared_error, r2_score\n",
-    "y_pred_svr = svr_model.predict(X_test_transformed)\n",
-    "mse_svr = mean_squared_error(y_test, y_pred_svr)\n",
-    "rmse_svr = np.sqrt(mse_svr)\n",
-    "r2_svr = r2_score(y_test, y_pred_svr)\n",
-    "print(f\"Support Vector Regressor RMSE: {rmse_svr:.4f}\")\n",
-    "print(f\"Support Vector Regressor R^2: {r2_svr:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "ccd5d203",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 5 folds for each of 243 candidates, totalling 1215 fits\n",
-      "Best parameters for XGBoost: {'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 2, 'n_estimators': 200, 'subsample': 1.0}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-6 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-6 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-6 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-6 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-6 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-6 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-       "             colsample_bylevel=None, colsample_bynode=None,\n",
-       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-       "             enable_categorical=False, eval_metric=&#x27;rmse&#x27;, feature_types=None,\n",
-       "             feature_weights=None, gamma=None, grow_policy=None,\n",
-       "             importance_type=None, interaction_constraints=None,\n",
-       "             learning_rate=0.2, max_bin=None, max_cat_threshold=None,\n",
-       "             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,\n",
-       "             max_leaves=None, min_child_weight=2, missing=nan,\n",
-       "             monotone_constraints=None, multi_strategy=None, n_estimators=200,\n",
-       "             n_jobs=None, num_parallel_tree=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.0.0/python/python_api.html#xgboost.XGBRegressor\">?<span>Documentation for XGBRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('objective',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">objective&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;reg:squarederror&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('base_score',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">base_score&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('booster',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">booster&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('callbacks',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">callbacks&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('colsample_bylevel',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">colsample_bylevel&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('colsample_bynode',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">colsample_bynode&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('colsample_bytree',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">colsample_bytree&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('device',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">device&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('early_stopping_rounds',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">early_stopping_rounds&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('enable_categorical',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">enable_categorical&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('eval_metric',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">eval_metric&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;rmse&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('feature_types',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">feature_types&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('feature_weights',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">feature_weights&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('gamma',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">gamma&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('grow_policy',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">grow_policy&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('importance_type',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">importance_type&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('interaction_constraints',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">interaction_constraints&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('learning_rate',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
-       "            <td class=\"value\">0.2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_bin',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_bin&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_cat_threshold',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_cat_threshold&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_cat_to_onehot',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_cat_to_onehot&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_delta_step',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_delta_step&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_depth',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">3</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_leaves',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaves&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_child_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_child_weight&nbsp;</td>\n",
-       "            <td class=\"value\">2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('missing',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">missing&nbsp;</td>\n",
-       "            <td class=\"value\">nan</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('monotone_constraints',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">monotone_constraints&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('multi_strategy',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">multi_strategy&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
-       "            <td class=\"value\">200</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('num_parallel_tree',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">num_parallel_tree&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">42</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('reg_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">reg_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('reg_lambda',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">reg_lambda&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('sampling_method',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">sampling_method&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('scale_pos_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">scale_pos_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('subsample',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">subsample&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tree_method',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tree_method&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('validate_parameters',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">validate_parameters&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbosity',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbosity&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
-      ],
-      "text/plain": [
-       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-       "             colsample_bylevel=None, colsample_bynode=None,\n",
-       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-       "             enable_categorical=False, eval_metric='rmse', feature_types=None,\n",
-       "             feature_weights=None, gamma=None, grow_policy=None,\n",
-       "             importance_type=None, interaction_constraints=None,\n",
-       "             learning_rate=0.2, max_bin=None, max_cat_threshold=None,\n",
-       "             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,\n",
-       "             max_leaves=None, min_child_weight=2, missing=nan,\n",
-       "             monotone_constraints=None, multi_strategy=None, n_estimators=200,\n",
-       "             n_jobs=None, num_parallel_tree=None, ...)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from xgboost import XGBRegressor\n",
-    "from sklearn.model_selection import GridSearchCV\n",
-    "# XGBoost Regressor\n",
-    "grid_params_xgb = {\n",
-    "    'n_estimators': [50, 100, 200],\n",
-    "    'learning_rate': [0.01, 0.1, 0.2],\n",
-    "    'max_depth': [3, 4, 5],\n",
-    "    'min_child_weight': [1, 2, 3],\n",
-    "    'subsample': [0.8, 0.9, 1.0]\n",
-    "}\n",
-    "grid_search_xgb = GridSearchCV(\n",
-    "    XGBRegressor(random_state=42, eval_metric='rmse'),\n",
-    "    grid_params_xgb, cv=5, n_jobs=-1, scoring='r2', verbose=1\n",
-    ")\n",
-    "grid_search_xgb.fit(X_train_transformed, y_train)\n",
-    "print(\"Best parameters for XGBoost:\", grid_search_xgb.best_params_)\n",
-    "xgb_model = grid_search_xgb.best_estimator_\n",
-    "xgb_model.fit(X_train_transformed, y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "39ee9403",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "XGBoost Regressor RMSE: 25782.8259\n",
-      "XGBoost Regressor R^2: 0.9047\n",
-      "Feature importances for XGBoost:\n",
-      "                   Feature  Importance\n",
-      "4         num__OverallQual    0.189638\n",
-      "244  cat__GarageFinish_Unf    0.075611\n",
-      "179       cat__BsmtQual_Ex    0.072828\n",
-      "26         num__GarageCars    0.061467\n",
-      "223    cat__KitchenQual_TA    0.057392\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Evaluate the XGBoost Regressor\n",
-    "y_pred_xgb = xgb_model.predict(X_test_transformed)\n",
-    "from sklearn.metrics import mean_squared_error, r2_score\n",
-    "mse = mean_squared_error(y_test, y_pred_xgb)\n",
-    "rmse = np.sqrt(mse)\n",
-    "r2 = r2_score(y_test, y_pred_xgb)\n",
-    "print(f\"XGBoost Regressor RMSE: {rmse:.4f}\")\n",
-    "print(f\"XGBoost Regressor R^2: {r2:.4f}\")\n",
-    "\n",
-    "# Feature importance for XGBoost\n",
-    "importances = xgb_model.feature_importances_\n",
-    "indices = importances.argsort()[::-1]\n",
-    "feature_importance_df = pd.DataFrame({'Feature': preprocessing_pipeline.get_feature_names_out(),\n",
-    "                                      'Importance': importances})\n",
-    "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
-    "print(\"Feature importances for XGBoost:\")\n",
-    "print(feature_importance_df[:5])\n",
-    "\n",
-    "# Plot feature importances\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.bar(range(5), importances[indices[:5]], align='center')\n",
-    "plt.xticks(range(5),\n",
-    "           [preprocessing_pipeline.get_feature_names_out()[i] for i in indices[:5]],\n",
-    "           rotation=45)\n",
-    "plt.title('Feature Importances from XGBoost')\n",
-    "plt.xlabel('Features')\n",
-    "plt.ylabel('Importance')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "73ba82ce-aa3f-4ce7-8922-7f0f44d36342",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S04 Ensemble Methods/S04C04 Ensemble of Ensembles.ipynb b/src/S04 Ensemble Methods/S04C04 Ensemble of Ensembles.ipynb
deleted file mode 100644
index bba5faa..0000000
--- a/src/S04 Ensemble Methods/S04C04 Ensemble of Ensembles.ipynb	
+++ /dev/null
@@ -1,2996 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "829d4723",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Ensemble of Ensembles"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e0b9e3fd",
-   "metadata": {},
-   "source": [
-    "## Ensemble of Ensembles\n",
-    "\n",
-    "Ensemble of Ensembles is a meta-ensemble method that combines multiple ensemble methods to improve predictive performance. It leverages the strengths of different ensemble techniques, such as bagging, boosting, and stacking, to create a more robust model. The idea is to use the predictions from various ensemble methods as inputs to a final model, which can be another ensemble method or a simple model like logistic regression."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "205b7fc8",
-   "metadata": {},
-   "source": [
-    "## Advantages of Ensemble of Ensembles\n",
-    "\n",
-    "-   **Improved Accuracy**: By combining predictions from multiple ensemble methods, the overall accuracy can be significantly improved.\n",
-    "-   **Robustness**: It reduces the risk of overfitting by averaging out the predictions from different models, leading to better generalization.\n",
-    "-   **Flexibility**: It allows the use of diverse ensemble methods, which can capture different patterns in the data.\n",
-    "-   **Diversity**: The combination of different ensemble methods can lead to a more diverse set of predictions, which is beneficial for model performance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "416cb66b",
-   "metadata": {},
-   "source": [
-    "## Disadvantages of Ensemble of Ensembles\n",
-    "\n",
-    "-   **Complexity**: The model can become complex and harder to interpret, as it involves multiple layers of models.\n",
-    "-   **Computational Cost**: Training multiple ensemble methods can be computationally expensive and time-consuming.\n",
-    "-   **Risk of Overfitting**: If not managed properly, combining too many models can lead to overfitting, especially if the base models are too complex.\n",
-    "-   **Hyperparameter Tuning**: Each ensemble method may require its own hyperparameter tuning, adding to the complexity of the model development process."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "12b1230c",
-   "metadata": {},
-   "source": [
-    "## Types of Ensemble of Ensembles\n",
-    "\n",
-    "Ensemble of Ensembles can be implemented using various ensemble methods, including:\n",
-    "\n",
-    "-   **Stacking**: Combines predictions from multiple base models using a meta-model that learns how to best combine their outputs (e.g., logistic regression, random forest).\n",
-    "-   **Multi-level Stacking**: A more complex version of stacking where multiple layers of models are trained, and predictions from one layer are used as inputs to the next layer."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9aa896a4",
-   "metadata": {},
-   "source": [
-    "### Example: Stacking"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "5d4739d6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Feature names: ['pclass', 'sex', 'age']\n",
-      "Class labels: ['Not Survived', 'Survived']\n",
-      "Transformed feature names: ['age__age' 'sex__sex_male' 'pclass__pclass_1' 'pclass__pclass_2'\n",
-      " 'pclass__pclass_3']\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(estimators=[(&#x27;rf&#x27;, RandomForestClassifier()),\n",
-       "                               (&#x27;gb&#x27;, GradientBoostingClassifier())],\n",
-       "                   final_estimator=LogisticRegression())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StackingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.StackingClassifier.html\">?<span>Documentation for StackingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">estimators&nbsp;</td>\n",
-       "            <td class=\"value\">[(&#x27;rf&#x27;, ...), (&#x27;gb&#x27;, ...)]</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('final_estimator',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">final_estimator&nbsp;</td>\n",
-       "            <td class=\"value\">LogisticRegression()</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('cv',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">cv&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('stack_method',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">stack_method&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('passthrough',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">passthrough&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>rf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"rf__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('criterion',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">criterion&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_depth',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_split',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
-       "            <td class=\"value\">2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_features&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('bootstrap',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">bootstrap&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('oob_score',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">oob_score&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_samples',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_samples&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>gb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GradientBoostingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"gb__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('loss',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">loss&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;log_loss&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('learning_rate',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
-       "            <td class=\"value\">0.1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('subsample',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">subsample&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('criterion',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">criterion&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;friedman_mse&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_split',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
-       "            <td class=\"value\">2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_depth',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">3</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('init',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">init&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_features&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('validation_fraction',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">validation_fraction&nbsp;</td>\n",
-       "            <td class=\"value\">0.1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_iter_no_change&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>final_estimator</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"final_estimator__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('penalty',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">penalty&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('dual',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">dual&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">C&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">intercept_scaling&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('solver',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">solver&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_iter&nbsp;</td>\n",
-       "            <td class=\"value\">100</td>\n",
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-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('multi_class',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">multi_class&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('l1_ratio',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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-      ],
-      "text/plain": [
-       "StackingClassifier(estimators=[('rf', RandomForestClassifier()),\n",
-       "                               ('gb', GradientBoostingClassifier())],\n",
-       "                   final_estimator=LogisticRegression())"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn import datasets\n",
-    "\n",
-    "titanic = datasets.fetch_openml('titanic', version=1, as_frame=True)\n",
-    "df = titanic.frame[['pclass', 'sex', 'age', 'survived']]\n",
-    "X = df.drop(columns=['survived'])\n",
-    "y = df['survived'].astype('category').cat.codes\n",
-    "class_names = ['Not Survived', 'Survived']\n",
-    "feature_names = X.columns.tolist()\n",
-    "print(f\"Feature names: {feature_names}\")\n",
-    "print(f\"Class labels: {class_names}\")\n",
-    "\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "X_train, X_test, y_train, y_test = \\\n",
-    "    train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)\n",
-    "\n",
-    "from sklearn.compose import ColumnTransformer\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
-    "\n",
-    "preprocessing_pipeline = ColumnTransformer(\n",
-    "    transformers=[\n",
-    "        ('age', Pipeline(\n",
-    "            steps=[\n",
-    "                ('imputer', SimpleImputer(strategy='mean')),\n",
-    "                ('scaler', StandardScaler())\n",
-    "            ]\n",
-    "        ), ['age']),\n",
-    "        ('sex', Pipeline(\n",
-    "            steps=[\n",
-    "                ('encoder', OneHotEncoder(drop='first', sparse_output=False))\n",
-    "            ]\n",
-    "        ), ['sex']),\n",
-    "        ('pclass', Pipeline(\n",
-    "            steps=[\n",
-    "                ('enconder', OneHotEncoder(sparse_output=False))\n",
-    "            ]\n",
-    "        ), ['pclass'])\n",
-    "    ],\n",
-    "    remainder='passthrough'\n",
-    ")\n",
-    "X_train_transformed = preprocessing_pipeline.fit_transform(X_train)\n",
-    "X_test_transformed = preprocessing_pipeline.transform(X_test)\n",
-    "\n",
-    "feature_names = preprocessing_pipeline.get_feature_names_out()\n",
-    "print(f\"Transformed feature names: {feature_names}\")\n",
-    "\n",
-    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, StackingClassifier\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "# Define base models\n",
-    "base_models = [\n",
-    "    ('rf', RandomForestClassifier(n_estimators=100)),\n",
-    "    ('gb', GradientBoostingClassifier(n_estimators=100))\n",
-    "]\n",
-    "# Define the final model\n",
-    "final_model = LogisticRegression()\n",
-    "# Create the stacking ensemble\n",
-    "stacking_ensemble = StackingClassifier(estimators=base_models, final_estimator=final_model)\n",
-    "# Fit the stacking ensemble\n",
-    "stacking_ensemble.fit(X_train_transformed, y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "e7232641",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Stacking Ensemble Accuracy: 0.81\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "Not Survived       0.84      0.86      0.85       243\n",
-      "    Survived       0.76      0.73      0.74       150\n",
-      "\n",
-      "    accuracy                           0.81       393\n",
-      "   macro avg       0.80      0.79      0.79       393\n",
-      "weighted avg       0.81      0.81      0.81       393\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Evaluate the stacking ensemble\n",
-    "accuracy = stacking_ensemble.score(X_test_transformed, y_test)\n",
-    "print(f\"Stacking Ensemble Accuracy: {accuracy:.2f}\")\n",
-    "# Predict using the stacking ensemble\n",
-    "y_pred = stacking_ensemble.predict(X_test_transformed)\n",
-    "from sklearn.metrics import classification_report\n",
-    "print(classification_report(y_test, y_pred, target_names=class_names))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1ed570c2",
-   "metadata": {},
-   "source": [
-    "### Example: Multi-level Ensemble of Ensembles"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d2778445",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-2 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-2 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-2 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-2 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(estimators=[(&#x27;first_level&#x27;,\n",
-       "                                VotingClassifier(estimators=[(&#x27;rf&#x27;,\n",
-       "                                                              RandomForestClassifier()),\n",
-       "                                                             (&#x27;gb&#x27;,\n",
-       "                                                              GradientBoostingClassifier()),\n",
-       "                                                             (&#x27;svc&#x27;,\n",
-       "                                                              SVC(probability=True))],\n",
-       "                                                 voting=&#x27;soft&#x27;)),\n",
-       "                               (&#x27;log_reg&#x27;, LogisticRegression())],\n",
-       "                   final_estimator=LogisticRegression())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StackingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.StackingClassifier.html\">?<span>Documentation for StackingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">estimators&nbsp;</td>\n",
-       "            <td class=\"value\">[(&#x27;first_level&#x27;, ...), (&#x27;log_reg&#x27;, ...)]</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('final_estimator',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">final_estimator&nbsp;</td>\n",
-       "            <td class=\"value\">LogisticRegression()</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('cv',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">cv&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('stack_method',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">stack_method&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('passthrough',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">passthrough&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>first_level</label></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>rf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"first_level__rf__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('criterion',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">criterion&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_depth',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_split',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
-       "            <td class=\"value\">2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_features&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('bootstrap',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">bootstrap&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('oob_score',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">oob_score&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_samples',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_samples&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>gb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GradientBoostingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"first_level__gb__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('loss',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">loss&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;log_loss&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('learning_rate',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
-       "            <td class=\"value\">0.1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('subsample',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">subsample&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('criterion',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">criterion&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;friedman_mse&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_split',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
-       "            <td class=\"value\">2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_depth',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">3</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('init',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">init&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_features&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('validation_fraction',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">validation_fraction&nbsp;</td>\n",
-       "            <td class=\"value\">0.1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_iter_no_change&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>svc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"first_level__svc__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">C&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('kernel',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">kernel&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;rbf&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('degree',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">degree&nbsp;</td>\n",
-       "            <td class=\"value\">3</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('gamma',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">gamma&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;scale&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('coef0',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">coef0&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('shrinking',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">shrinking&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('probability',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">probability&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('cache_size',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">cache_size&nbsp;</td>\n",
-       "            <td class=\"value\">200</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_iter&nbsp;</td>\n",
-       "            <td class=\"value\">-1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('decision_function_shape',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">decision_function_shape&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('break_ties',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">break_ties&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>log_reg</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"log_reg__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('penalty',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">penalty&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('dual',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">dual&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">C&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">intercept_scaling&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('solver',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">solver&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_iter&nbsp;</td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('multi_class',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">multi_class&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('l1_ratio',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>final_estimator</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"final_estimator__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('penalty',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">penalty&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('dual',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">dual&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
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-       "StackingClassifier(estimators=[('first_level',\n",
-       "                                VotingClassifier(estimators=[('rf',\n",
-       "                                                              RandomForestClassifier()),\n",
-       "                                                             ('gb',\n",
-       "                                                              GradientBoostingClassifier()),\n",
-       "                                                             ('svc',\n",
-       "                                                              SVC(probability=True))],\n",
-       "                                                 voting='soft')),\n",
-       "                               ('log_reg', LogisticRegression())],\n",
-       "                   final_estimator=LogisticRegression())"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.ensemble import VotingClassifier\n",
-    "from sklearn.svm import SVC\n",
-    "\n",
-    "# Define base models for the first level\n",
-    "base_models_level_1 = [\n",
-    "    ('rf', RandomForestClassifier(n_estimators=100)),\n",
-    "    ('gb', GradientBoostingClassifier(n_estimators=100)),\n",
-    "    ('svc', SVC(probability=True))\n",
-    "]\n",
-    "# Create the first level ensemble using voting\n",
-    "first_level_ensemble = VotingClassifier(estimators=base_models_level_1, voting='soft')\n",
-    "# Fit the first level ensemble\n",
-    "first_level_ensemble.fit(X_train_transformed, y_train)\n",
-    "\n",
-    "# Define base models for the second level\n",
-    "base_models_level_2 = [\n",
-    "    ('first_level', first_level_ensemble),\n",
-    "    ('log_reg', LogisticRegression())\n",
-    "]\n",
-    "# Create the second level ensemble using stacking\n",
-    "second_level_ensemble = StackingClassifier(estimators=base_models_level_2, final_estimator=LogisticRegression())\n",
-    "# Fit the second level ensemble\n",
-    "second_level_ensemble.fit(X_train_transformed, y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "3301ba8f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Multi-level Ensemble of Ensembles Accuracy: 0.81\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "Not Survived       0.84      0.86      0.85       243\n",
-      "    Survived       0.77      0.73      0.75       150\n",
-      "\n",
-      "    accuracy                           0.81       393\n",
-      "   macro avg       0.80      0.80      0.80       393\n",
-      "weighted avg       0.81      0.81      0.81       393\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Evaluate the second level ensemble\n",
-    "accuracy_level_2 = second_level_ensemble.score(X_test_transformed, y_test)\n",
-    "print(f\"Multi-level Ensemble of Ensembles Accuracy: {accuracy_level_2:.2f}\")\n",
-    "# Predict using the second level ensemble\n",
-    "y_pred_level_2 = second_level_ensemble.predict(X_test_transformed)\n",
-    "print(classification_report(y_test, y_pred_level_2, target_names=class_names))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ae6c6674",
-   "metadata": {},
-   "source": [
-    "In this example, we first create a first-level ensemble using a voting classifier that combines predictions from a random forest, gradient boosting, and support vector machine. Then, we use the predictions from this first-level ensemble as input to a second-level stacking ensemble that combines it with logistic regression. This multi-level approach allows us to leverage the strengths of different ensemble methods at multiple levels, potentially improving predictive performance."
-   ]
-  }
- ],
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diff --git a/src/S05 Unsupervised Learning - Clustering and Dimensionality Reduction/S05 Unsupervised Learning - Clustering and Dimensionality Reduction.pdf b/src/S05 Unsupervised Learning - Clustering and Dimensionality Reduction/S05 Unsupervised Learning - Clustering and Dimensionality Reduction.pdf
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diff --git a/src/S05 Unsupervised Learning - Clustering and Dimensionality Reduction/S05 Unsupervised Learning - Clustering and Dimensionality Reduction.tex b/src/S05 Unsupervised Learning - Clustering and Dimensionality Reduction/S05 Unsupervised Learning - Clustering and Dimensionality Reduction.tex
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-\title{Machine Learning in Python}
-\subtitle{Unsupervised Learning - Clustering and Dimensionality Reduction}
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- pdfauthor={Cristian A. Marocico},
- pdftitle={Machine Learning in Python},
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-
-\section{Introduction to Unsupervised Learning}
-\label{sec:org4ff1573}
-\begin{frame}[label={sec:org51587ae}]{Introduction to Unsupervised Learning}
-\begin{definition}[What is Unsupervised Learning?]\label{sec:org36ca64c}
-\pause
-In contrast to supervised learning, \alert{unsupervised learning} does not use labeled data. Instead, it identifies patterns and structures in the data without predefined labels. This is particularly useful for clustering similar data points or reducing the dimensionality of the data.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orgb279d42}]{Types of Unsupervised Learning}
-\begin{block}{Types of Unsupervised Learning}
-\pause
-Unsupervised learning can be broadly categorized into a few types:
-\begin{itemize}[<+->]
-\item \alert{Dimensionality Reduction}: Techniques that reduce the number of features while preserving the essential structure of the data.
-\item \alert{Clustering}: Algorithms that group similar data points together based on their features.
-\item \alert{Anomaly Detection}: Identifying rare items, events, or observations that raise suspicions by differing significantly from the majority of the data.
-\end{itemize}
-\end{block}
-\end{frame}
-\section{Dimensionality Reduction}
-\label{sec:org46c7029}
-\begin{frame}[label={sec:orge2ac36e}]{Dimensionality Reduction}
-\begin{block}{Dimensionality Reduction}
-\pause
-\alert{Dimensionality reduction} techniques aim to reduce the number of features in a dataset while retaining its essential characteristics. Useful for visualizing high-dimensional data or improving the performance of machine learning models by reducing noise and computational complexity. Common dimensionality reduction techniques include:
-\begin{itemize}[<+->]
-\item \alert{Principal Component Analysis (PCA)}: Projects the data onto a lower-dimensional space by maximizing the variance along the new axes (principal components).
-\item \alert{Linear Discriminant Analysis (LDA)}: A supervised technique that finds a linear combination of features that separates two or more classes.
-\item \alert{Autoencoders}: Neural networks that learn to encode the input data into a lower-dimensional representation and then decode it back to the original space. They are particularly useful for learning complex, non-linear mappings.
-\end{itemize}
-\end{block}
-\end{frame}
-\section[PCA]{Principal Component Analysis (PCA)}
-\label{sec:org031c515}
-\begin{frame}[label={sec:orge2d8a43}]{Principal Component Analysis (PCA)}
-\begin{block}{Principal Component Analysis (PCA)}
-\pause
-\alert{Principal Component Analysis (PCA)} is a widely used technique for dimensionality reduction. It transforms the data into a new coordinate system where the greatest variance by any projection lies on the first coordinate (principal component), the second greatest variance on the second coordinate, and so on. This allows us to reduce the number of dimensions while retaining most of the information in the data.
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org5574d5b}]{Steps in PCA}
-\begin{block}{Steps in PCA}
-\pause
-The PCA process involves several key steps:
-\begin{enumerate}[<+->]
-\item \alert{Standardization}: Scale the data to have a mean of 0 and a standard deviation of 1 to ensure that all features contribute equally to the analysis.
-\item \alert{Covariance Matrix Calculation}: Compute the covariance matrix to understand how the features vary together.
-\item \alert{Eigenvalue Decomposition}: Calculate the eigenvalues and eigenvectors of the covariance matrix. The eigenvectors represent the directions of maximum variance, and the eigenvalues indicate the amount of variance in those directions.
-\item \alert{Selecting Principal Components}: Choose the top k eigenvectors (principal components) based on their corresponding eigenvalues, which capture the most variance in the data.
-\item \alert{Transforming the Data}: Project the original data onto the selected principal components to obtain a lower-dimensional representation.
-\end{enumerate}
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org311362d}]{Applications of PCA}
-\begin{block}{Applications of PCA}
-\pause
-PCA is widely used in various fields for:
-\begin{itemize}[<+->]
-\item \alert{\alert{Data Visualization}}: Reducing high-dimensional data to 2D or 3D for visualization purposes.
-\item \alert{\alert{Noise Reduction}}: Filtering out noise by retaining only the most significant components.
-\item \alert{\alert{Feature Extraction}}: Creating new features that capture the essential patterns in the data.
-\item \alert{\alert{Preprocessing for Machine Learning}}: Reducing dimensionality before applying machine learning algorithms to improve performance and reduce overfitting.
-\end{itemize}
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:orgd2871a3}]{Limitations of PCA}
-\begin{block}{Limitations of PCA}
-\pause
-While PCA is a powerful technique, it has some limitations:
-\begin{itemize}[<+->]
-\item \alert{\alert{Linearity}}: PCA assumes linear relationships between features, which may not capture complex patterns in the data.
-\item \alert{\alert{Interpretability}}: The principal components may not have a clear interpretation in terms of the original features, making it difficult to understand the results.
-\item \alert{\alert{Sensitivity to Scaling}}: PCA is sensitive to the scale of the features, so standardization is crucial.
-\item \alert{\alert{Loss of Information}}: Reducing dimensions may lead to loss of important information, especially if too many components are discarded.
-\end{itemize}
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:orgec09706}]{Evaluating PCA Results}
-\begin{block}{Evaluating PCA Results}
-\pause
-To evaluate the effectiveness of PCA, we can use several methods:
-\begin{itemize}[<+->]
-\item \alert{\alert{Explained Variance Ratio}}: This metric indicates the proportion of variance explained by each principal component. A higher explained variance ratio suggests that the component captures more information from the data.
-\item \alert{\alert{Scree Plot}}: A graphical representation of the eigenvalues associated with each principal component. It helps to visualize the amount of variance explained by each component and to determine the optimal number of components to retain.
-\item \alert{\alert{Reconstruction Error}}: After transforming the data using PCA, we can reconstruct the original data and measure the error (e.g., mean squared error) to assess how well PCA captures the original structure of the data.
-\end{itemize}
-\end{block}
-\end{frame}
-\section{Autoencoders}
-\label{sec:org525151b}
-\begin{frame}[label={sec:orge4b3afe}]{Autoencoders}
-\begin{block}{Autoencoders}
-\pause
-\alert{Autoencoders} are a type of neural network used for unsupervised learning, particularly for dimensionality reduction and feature learning. They consist of two main parts: an encoder that compresses the input data into a lower-dimensional representation (latent space) and a decoder that reconstructs the original data from this representation.
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org1583777}]{Structure of Autoencoders}
-\begin{block}{Structure of Autoencoders}
-\pause
-Autoencoders typically have the following structure:
-\begin{itemize}[<+->]
-\item \alert{\alert{Encoder}}: Maps the input data to a lower-dimensional latent space using one or more hidden layers. The encoder learns to capture the most important features of the data.
-\item \alert{\alert{Latent Space}}: The compressed representation of the input data, which contains the essential features learned by the encoder.
-\item \alert{\alert{Decoder}}: Reconstructs the original data from the latent space representation using one or more hidden layers. The decoder learns to reverse the encoding process.
-\item \alert{\alert{Loss Function}}: The autoencoder is trained to minimize the difference between the original input and the reconstructed output, typically using mean squared error or binary cross-entropy as the loss function.
-\end{itemize}
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:orge3a1570}]{Applications of Autoencoders}
-\begin{block}{Applications of Autoencoders}
-\pause
-Autoencoders have various applications in unsupervised learning:
-\begin{itemize}[<+->]
-\item \alert{\alert{Dimensionality Reduction}}: Reducing the number of features while retaining important information, similar to PCA but capable of capturing non-linear relationships.
-\item \alert{\alert{Feature Learning}}: Learning meaningful representations of data that can be used for downstream tasks such as classification or clustering.
-\item \alert{\alert{Anomaly Detection}}: Identifying anomalies by reconstructing input data and measuring the reconstruction error; high errors indicate potential anomalies.
-\item \alert{\alert{Data Denoising}}: Removing noise from data by training the autoencoder to reconstruct clean data from noisy inputs.
-\item \alert{\alert{Generative Models}}: Variational autoencoders (VAEs) can generate new data samples by sampling from the latent space, making them useful for generative tasks.
-\end{itemize}
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org0ac620a}]{Limitations of Autoencoders}
-\begin{block}{Limitations of Autoencoders}
-\pause
-While autoencoders are powerful tools, they have some limitations:
-\begin{itemize}[<+->]
-\item \alert{\alert{Training Complexity}}: Training autoencoders can be complex and may require careful tuning of hyperparameters, such as the architecture, learning rate, and regularization techniques.
-\item \alert{\alert{Overfitting}}: Autoencoders can overfit the training data, especially if the model is too complex or the dataset is small. Regularization techniques such as dropout or weight decay can help mitigate this.
-\item \alert{\alert{Interpretability}}: The learned representations in the latent space may not have a clear interpretation, making it difficult to understand the features captured by the autoencoder.
-\item \alert{\alert{Non-Deterministic}}: The results can vary between runs due to the stochastic nature of training, especially if random initialization is used.
-\end{itemize}
-\end{block}
-\end{frame}
-\section{Clustering}
-\label{sec:org33b556b}
-\begin{frame}[label={sec:orga333182}]{Clustering}
-\begin{definition}[Clustering]\label{sec:org768bd88}
-\pause
-\alert{Clustering} is an unsupervised learning technique that groups similar data points together based on their features. The goal is to identify inherent structures in the data without prior knowledge of labels.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:orgae405b6}]{Types of Clustering Algorithms}
-\begin{definition}[Types of Clustering Algorithms]\label{sec:orgfb2401f}
-\pause
-Bases on their approach, clustering algorithms can be categorized into several types:
-\begin{itemize}[<+->]
-\item \alert{Partitioning Methods}: These algorithms divide the data into k clusters, where k is a predefined number. The most common example is k-means clustering.
-\item \alert{Hierarchical Methods}: These algorithms create a hierarchy of clusters, either by merging smaller clusters into larger ones (\alert{agglomerative}) or by splitting larger clusters into smaller ones (\alert{divisive}).
-\item Density-Based Methods: These algorithms group data points based on the density of data points in the feature space. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a popular density-based clustering algorithm.
-\item Model-Based Methods: These algorithms assume that the data is generated from a mixture of underlying probability distributions. Gaussian Mixture Models (GMMs) are a common example, where each cluster is represented by a Gaussian distribution.
-\end{itemize}
-\end{definition}
-\end{frame}
-\section[k-Means]{k-Means Clustering}
-\label{sec:orga1a053f}
-\begin{frame}[label={sec:orgbc2aecd}]{k-Means Clustering}
-\begin{block}{k-Means Clustering}
-\pause
-\alert{k-Means clustering} is a widely used partitioning method that aims to divide the data into k clusters, where k is a user-defined parameter. The algorithm iteratively assigns data points to the nearest cluster centroid and updates the centroids based on the assigned points.
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org45bf6fe}]{Steps in k-Means Clustering}
-\begin{block}{Steps in k-Means Clustering}
-\pause
-The k-means clustering algorithm follows these steps:
-\begin{enumerate}[<+->]
-\item \alert{\alert{Initialization}}: Randomly select k data points as initial cluster centroids.
-\item \alert{\alert{Assignment Step}}: Assign each data point to the nearest centroid based on a distance metric (e.g., Euclidean distance).
-\item \alert{\alert{Update Step}}: Calculate the new centroids by taking the mean of all data points assigned to each cluster.
-\item \alert{\alert{Convergence Check}}: Repeat the assignment and update steps until the centroids do not change significantly or a maximum number of iterations is reached.
-\end{enumerate}
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org7496a53}]{Applications of k-Means Clustering}
-\begin{block}{Applications of k-Means Clustering}
-\pause
-k-Means clustering is widely used in various applications:
-\begin{itemize}[<+->]
-\item \alert{\alert{Customer Segmentation}}: Grouping customers based on purchasing behavior to tailor marketing strategies.
-\item \alert{\alert{Image Compression}}: Reducing the number of colors in an image by clustering similar colors together.
-\item \alert{\alert{Document Clustering}}: Grouping similar documents based on their content for information retrieval or topic modeling.
-\item \alert{\alert{Anomaly Detection}}: Identifying outliers by detecting data points that do not belong to any cluster.
-\end{itemize}
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:orge18b664}]{Limitations of k-Means Clustering}
-\begin{block}{Limitations of k-Means Clustering}
-\pause
-While k-means clustering is a powerful technique, it has some limitations:
-\begin{itemize}[<+->]
-\item \alert{\alert{Choosing k}}: The number of clusters (k) must be specified beforehand, which can be challenging if the optimal number is unknown.
-\item \alert{\alert{Sensitivity to Initialization}}: The algorithm can converge to different solutions based on the initial centroids, leading to inconsistent results. Techniques like k-means++ initialization can help mitigate this issue.
-\item \alert{\alert{Assumption of Spherical Clusters}}: k-means assumes that clusters are spherical and equally sized, which may not hold true for all datasets.
-\item \alert{\alert{Sensitivity to Outliers}}: k-means is sensitive to outliers, which can skew the centroids and affect the clustering results. Preprocessing steps like outlier removal or using robust distance metrics can help address this issue.
-\end{itemize}
-\end{block}
-\end{frame}
-\section{Hierarchical Clustering}
-\label{sec:org351b01a}
-\begin{frame}[label={sec:org9265884}]{Hierarchical Clustering}
-\begin{block}{Hierarchical Clustering}
-\pause
-\alert{Hierarchical clustering} is a method that builds a hierarchy of clusters, either by merging smaller clusters into larger ones (agglomerative) or by splitting larger clusters into smaller ones (divisive).
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:orgca80759}]{Types of Hierarchical Clustering}
-\begin{block}{Types of Hierarchical Clustering}
-\pause
-Hierarchical clustering can be categorized into two main types:
-\begin{itemize}[<+->]
-\item \alert{Agglomerative Clustering}: Starts with each data point as its own cluster and iteratively merges the closest clusters until a single cluster remains or a stopping criterion is met.
-\item \alert{Divisive Clustering}: Starts with all data points in a single cluster and recursively splits the clusters into smaller ones until each data point is its own cluster or a stopping criterion is met.
-\end{itemize}
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:orgbfb60e4}]{Steps in Agglomerative Clustering}
-\begin{block}{Steps in Agglomerative Clustering}
-\pause
-The agglomerative clustering algorithm follows these steps:
-\begin{enumerate}[<+->]
-\item \alert{\alert{Initialization}}: Start with each data point as its own cluster.
-\item \alert{\alert{Distance Calculation}}: Calculate the distance between all pairs of clusters using a distance metric (e.g., Euclidean distance).
-\item \alert{\alert{Merge Clusters}}: Identify the two closest clusters and merge them into a single cluster.
-\item \alert{\alert{Update Distances}}: Recalculate the distances between the newly formed cluster and the remaining clusters.
-\item \alert{\alert{Repeat}}: Repeat steps 3 and 4 until a stopping criterion is met, such as a predefined number of clusters or a distance threshold.
-\end{enumerate}
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org28583c6}]{Applications of Hierarchical Clustering}
-\begin{block}{Applications of Hierarchical Clustering}
-\pause
-Hierarchical clustering is used in various applications:
-\begin{itemize}[<+->]
-\item \alert{\alert{Gene Expression Analysis}}: Grouping genes with similar expression patterns to identify co-regulated genes or functional groups.
-\item \alert{\alert{Document Clustering}}: Organizing documents into a hierarchy based on their content for information retrieval or topic modeling.
-\item \alert{\alert{Image Segmentation}}: Dividing an image into regions based on pixel similarity to facilitate object recognition or scene understanding.
-\item \alert{\alert{Market Basket Analysis}}: Identifying groups of products frequently purchased together to inform marketing strategies or product placement.
-\end{itemize}
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:orgf34b736}]{Limitations of Hierarchical Clustering}
-\begin{block}{Limitations of Hierarchical Clustering}
-\pause
-Hierarchical clustering has some limitations:
-\begin{itemize}[<+->]
-\item \alert{\alert{Scalability}}: Hierarchical clustering can be computationally expensive, especially for large datasets, as it requires calculating distances between all pairs of data points.
-\item \alert{\alert{Sensitivity to Noise}}: Hierarchical clustering can be sensitive to noise and outliers, which can affect the structure of the dendrogram and the resulting clusters.
-\item \alert{\alert{Choice of Distance Metric}}: The choice of distance metric can significantly impact the clustering results, and there is no one-size-fits-all solution.
-\item \alert{\alert{Interpretability}}: The dendrogram can be complex and difficult to interpret, especially for large datasets with many clusters.
-\end{itemize}
-\end{block}
-\end{frame}
-\section{Anomaly Detection}
-\label{sec:org4e1487d}
-\begin{frame}[label={sec:org4d95fbd}]{Anomaly Detection}
-\begin{block}{Anomaly Detection}
-\pause
-\alert{Anomaly detection} is the process of identifying rare items, events, or observations that raise suspicions by differing significantly from the majority of the data.
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org7562253}]{Types of Anomaly Detection Techniques}
-\begin{block}{Types of Anomaly Detection Techniques}
-\pause
-Anomaly detection techniques can be broadly categorized into three main types:
-\begin{itemize}[<+->]
-\item \alert{Statistical Methods}: These methods assume that normal data follows a specific statistical distribution (e.g., Gaussian) and identify anomalies as data points that deviate significantly from this distribution.
-\item \alert{Machine Learning Methods}: These methods use machine learning algorithms to learn patterns in the data and identify anomalies based on deviations from these patterns. Examples include one-class SVMs, isolation forests, and autoencoders.
-\item \alert{Clustering-Based Methods}: These methods use clustering algorithms to group similar data points together and identify anomalies as points that do not belong to any cluster or are far from the nearest cluster centroid.
-\end{itemize}
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org61d3a40}]{Steps in Anomaly Detection}
-\begin{block}{Steps in Anomaly Detection}
-\pause
-The anomaly detection process typically involves the following steps:
-\begin{enumerate}[<+->]
-\item \alert{Data Preprocessing}: Clean and preprocess the data.
-\item \alert{Feature Selection/Extraction}: Select or extract relevant features that are likely to contain information about anomalies.
-\item \alert{Model Training}: Train an anomaly detection model using the selected features.
-\item \alert{Anomaly Scoring}: Calculate an anomaly score for each data point based on the trained model.
-\item \alert{Thresholding}: Set a threshold to classify data points as normal or anomalous based on their anomaly scores.
-\item \alert{Evaluation}: Evaluate the performance of the anomaly detection model using metrics such as precision, recall, and F1-score, if labeled data is available.
-\end{enumerate}
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org1c4a546}]{Applications of Anomaly Detection}
-\begin{block}{Applications of Anomaly Detection}
-\pause
-Anomaly detection is widely used in various domains:
-\begin{itemize}[<+->]
-\item \alert{\alert{Fraud Detection}}: Identifying fraudulent transactions in financial systems by detecting unusual patterns in transaction data.
-\item \alert{\alert{Network Security}}: Detecting intrusions or malicious activities in computer networks by identifying unusual traffic patterns or behaviors.
-\item \alert{\alert{Fault Detection}}: Identifying equipment failures or malfunctions in industrial systems by monitoring sensor data and detecting deviations from normal operating conditions.
-\item \alert{\alert{Healthcare}}: Detecting anomalies in patient health records or medical imaging data to identify potential health issues or diseases.
-\item \alert{\alert{Manufacturing}}: Monitoring production processes to detect anomalies that may indicate defects or quality issues in products.
-\end{itemize}
-\end{block}
-\end{frame}
-\end{document}
diff --git a/src/S05 Unsupervised Learning - Clustering and Dimensionality Reduction/S05C01 Overview of Unsupervised Learning.ipynb b/src/S05 Unsupervised Learning - Clustering and Dimensionality Reduction/S05C01 Overview of Unsupervised Learning.ipynb
deleted file mode 100644
index 2cfbccb..0000000
--- a/src/S05 Unsupervised Learning - Clustering and Dimensionality Reduction/S05C01 Overview of Unsupervised Learning.ipynb	
+++ /dev/null
@@ -1,745 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "b7080c6b",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Overview of Unsupervised Learning\n",
-    "\n",
-    "**Unsupervised learning** is a type of machine learning where the model learns patterns from unlabeled data. Unlike supervised learning, where the model is trained on labeled examples, unsupervised learning algorithms try to infer the underlying structure of the data without any explicit labels. This makes it particularly useful for exploratory data analysis, clustering, and dimensionality reduction."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5d35e1ce",
-   "metadata": {},
-   "source": [
-    "## Types of Unsupervised Learning\n",
-    "\n",
-    "Unsupervised learning can be broadly categorized into two main types:\n",
-    "\n",
-    "-   **Dimensionality Reduction**: Reducing the number of features while preserving the essential structure of the data.\n",
-    "-   **Clustering**: Grouping similar data points together based on their features."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "23333c60",
-   "metadata": {},
-   "source": [
-    "## Dimensionality Reduction\n",
-    "\n",
-    "**Dimensionality reduction** techniques aim to reduce the number of features in a dataset while retaining its essential characteristics. This is particularly useful for visualizing high-dimensional data or improving the performance of machine learning models by reducing noise and computational complexity. Common dimensionality reduction techniques include:\n",
-    "\n",
-    "-   **Principal Component Analysis (PCA)**: Projects the data onto a lower-dimensional space by maximizing the variance along the new axes (principal components).\n",
-    "-   **t-Distributed Stochastic Neighbor Embedding (t-SNE)**: A non-linear technique that visualizes high-dimensional data by minimizing the divergence between probability distributions of pairwise similarities in high and low dimensions.\n",
-    "-   **Autoencoders**: Neural networks that learn to encode the input data into a lower-dimensional representation and then decode it back to the original space. They are particularly useful for learning complex, non-linear mappings."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a6bf05de",
-   "metadata": {},
-   "source": [
-    "## Clustering\n",
-    "\n",
-    "**Clustering** is often used to discover inherent groupings in data, such as customer segmentation in marketing or grouping similar documents in text analysis. Common clustering algorithms include:\n",
-    "\n",
-    "-   **K-Means**: Partitions data into K clusters by minimizing the variance within each cluster.\n",
-    "-   **Hierarchical Clustering**: Builds a tree of clusters by either merging or splitting clusters based on distance metrics.\n",
-    "-   **DBSCAN**: Groups together points that are closely packed together while marking points that lie alone in low-density regions as outliers.\n",
-    "-   **Gaussian Mixture Models (GMM)**: Assumes that the data is generated from a mixture of several Gaussian distributions and uses the Expectation-Maximization algorithm to estimate the parameters of these distributions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "619e3c86",
-   "metadata": {},
-   "source": [
-    "### Advantages and Disadvantages\n",
-    "\n",
-    "-   **Advantages**:\n",
-    "    -   Can discover hidden patterns and structures in data.\n",
-    "    -   Useful for exploratory data analysis and feature engineering.\n",
-    "    -   Can handle large datasets without the need for labeled data.\n",
-    "-   **Disadvantages**:\n",
-    "    -   Results can be difficult to interpret, especially in hierarchical clustering.\n",
-    "    -   No guarantee of finding meaningful patterns; results can be sensitive to the choice of algorithm and parameters.\n",
-    "    -   Evaluation of clustering results can be challenging without ground truth labels."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18bf8698",
-   "metadata": {},
-   "source": [
-    "## Example: PCA and K-Means\n",
-    "\n",
-    "To illustrate the concepts of unsupervised learning, we will use the Iris dataset and apply PCA for dimensionality reduction and K-Means for clustering.\n",
-    "\n",
-    "-   Load the iris dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d9e480ef",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1117.75x1000 with 20 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn import datasets\n",
-    "\n",
-    "# Load the Iris dataset\n",
-    "df = sns.load_dataset('iris')\n",
-    "X = df.drop(columns=['species'])\n",
-    "y = df['species'].astype('category').cat.codes\n",
-    "\n",
-    "sns.pairplot(df, hue='species', palette='viridis', markers=[\"o\", \"s\", \"D\"])\n",
-    "plt.suptitle('Iris Dataset Pairplot', y=1.02)\n",
-    "plt.xlabel('Features')\n",
-    "plt.ylabel('Features')\n",
-    "# plt.legend(title='Species', loc='upper right')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d9ae8556",
-   "metadata": {},
-   "source": [
-    "-   Preprocess the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "feea03d3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, Normalizer\n",
-    "\n",
-    "# Standardize the data\n",
-    "scaler = Normalizer()\n",
-    "X = scaler.fit_transform(X)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c563776",
-   "metadata": {},
-   "source": [
-    "-   Apply PCA for dimensionality reduction to 2 dimensions and visualize the results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "e231168d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.decomposition import PCA\n",
-    "\n",
-    "# Apply PCA to reduce to 2 dimensions\n",
-    "pca = PCA(n_components=2, n_oversamples=100, random_state=42)\n",
-    "X_pca = pca.fit_transform(X)\n",
-    "\n",
-    "# Plot the PCA results\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y, cmap='viridis', edgecolor='k', s=50)\n",
-    "plt.title('PCA of Iris Dataset')\n",
-    "plt.xlabel('Principal Component 1')\n",
-    "plt.ylabel('Principal Component 2')\n",
-    "plt.colorbar(label='Species')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a6b84379",
-   "metadata": {},
-   "source": [
-    "-   Apply K-Means clustering on the PCA-reduced data and visualize the clusters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "0b92c036",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.cluster import KMeans\n",
-    "\n",
-    "# Apply K-Means clustering\n",
-    "kmeans = KMeans(n_clusters=3, max_iter=10, random_state=42)\n",
-    "kmeans.fit(X_pca)\n",
-    "y_kmeans = kmeans.predict(X_pca)\n",
-    "\n",
-    "# Plot the K-Means clustering results\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y_kmeans, cmap='viridis', edgecolor='k', s=50)\n",
-    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1],\n",
-    "            s=150, c='red', marker='X', label='Centroids')\n",
-    "plt.title('K-Means Clustering on PCA-reduced Data')\n",
-    "plt.xlabel('Principal Component 1')\n",
-    "plt.ylabel('Principal Component 2')\n",
-    "plt.legend()\n",
-    "plt.colorbar(label='Cluster')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d2a563bb",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the clustering results using the Adjusted Rand Index, and the silhouette score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "37ff09a1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Adjusted Rand Index: 0.90\n",
-      "Silhouette Score: 0.61\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.metrics import adjusted_rand_score, silhouette_score\n",
-    "\n",
-    "# Calculate Adjusted Rand Index\n",
-    "ari = adjusted_rand_score(y, y_kmeans)\n",
-    "print(f'Adjusted Rand Index: {ari:.2f}')\n",
-    "\n",
-    "# Calculate silhouette score\n",
-    "silhouette_avg = silhouette_score(X_pca, y_kmeans)\n",
-    "print(f'Silhouette Score: {silhouette_avg:.2f}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "446d538c",
-   "metadata": {},
-   "source": [
-    "-   Visualize the decision boundary of K-Means clustering"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "ac3ae25a",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Decision Boundary of K-Means Clustering')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.inspection import DecisionBoundaryDisplay\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "DecisionBoundaryDisplay.from_estimator(\n",
-    "    kmeans, X_pca, response_method=\"predict\", cmap='viridis', alpha=0.8,\n",
-    "    grid_resolution=5000, xlabel='Principal Component 1', ylabel='Principal Component 2'\n",
-    ")\n",
-    "sns.scatterplot(x=X_pca[:, 0], y=X_pca[:, 1], hue=y_kmeans, palette='viridis', edgecolor='k', s=50)\n",
-    "plt.xlim(-0.5, 0.5)\n",
-    "plt.ylim(-0.1, 0.1)\n",
-    "plt.title('Decision Boundary of K-Means Clustering')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4e2dddd0",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "Apply PCA and K-Means clustering on a different dataset, such as the Wine dataset or the Breast Cancer dataset from scikit-learn. Visualize the results and interpret the clusters formed.\n",
-    "\n",
-    "-   Load the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "0c5cc372",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.datasets import load_breast_cancer\n",
-    "cancer_data = load_breast_cancer()\n",
-    "cancer_df = pd.DataFrame(data=cancer_data.data, columns=cancer_data.feature_names)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6766a29c",
-   "metadata": {},
-   "source": [
-    "-   Explore the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "dede08d2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
-      "0        17.99         10.38          122.80     1001.0          0.11840   \n",
-      "1        20.57         17.77          132.90     1326.0          0.08474   \n",
-      "2        19.69         21.25          130.00     1203.0          0.10960   \n",
-      "3        11.42         20.38           77.58      386.1          0.14250   \n",
-      "4        20.29         14.34          135.10     1297.0          0.10030   \n",
-      "\n",
-      "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
-      "0           0.27760          0.3001              0.14710         0.2419   \n",
-      "1           0.07864          0.0869              0.07017         0.1812   \n",
-      "2           0.15990          0.1974              0.12790         0.2069   \n",
-      "3           0.28390          0.2414              0.10520         0.2597   \n",
-      "4           0.13280          0.1980              0.10430         0.1809   \n",
-      "\n",
-      "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
-      "0                 0.07871  ...         25.38          17.33           184.60   \n",
-      "1                 0.05667  ...         24.99          23.41           158.80   \n",
-      "2                 0.05999  ...         23.57          25.53           152.50   \n",
-      "3                 0.09744  ...         14.91          26.50            98.87   \n",
-      "4                 0.05883  ...         22.54          16.67           152.20   \n",
-      "\n",
-      "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
-      "0      2019.0            0.1622             0.6656           0.7119   \n",
-      "1      1956.0            0.1238             0.1866           0.2416   \n",
-      "2      1709.0            0.1444             0.4245           0.4504   \n",
-      "3       567.7            0.2098             0.8663           0.6869   \n",
-      "4      1575.0            0.1374             0.2050           0.4000   \n",
-      "\n",
-      "   worst concave points  worst symmetry  worst fractal dimension  \n",
-      "0                0.2654          0.4601                  0.11890  \n",
-      "1                0.1860          0.2750                  0.08902  \n",
-      "2                0.2430          0.3613                  0.08758  \n",
-      "3                0.2575          0.6638                  0.17300  \n",
-      "4                0.1625          0.2364                  0.07678  \n",
-      "\n",
-      "[5 rows x 30 columns]\n",
-      "       mean radius  mean texture  mean perimeter    mean area  \\\n",
-      "count   569.000000    569.000000      569.000000   569.000000   \n",
-      "mean     14.127292     19.289649       91.969033   654.889104   \n",
-      "std       3.524049      4.301036       24.298981   351.914129   \n",
-      "min       6.981000      9.710000       43.790000   143.500000   \n",
-      "25%      11.700000     16.170000       75.170000   420.300000   \n",
-      "50%      13.370000     18.840000       86.240000   551.100000   \n",
-      "75%      15.780000     21.800000      104.100000   782.700000   \n",
-      "max      28.110000     39.280000      188.500000  2501.000000   \n",
-      "\n",
-      "       mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
-      "count       569.000000        569.000000      569.000000           569.000000   \n",
-      "mean          0.096360          0.104341        0.088799             0.048919   \n",
-      "std           0.014064          0.052813        0.079720             0.038803   \n",
-      "min           0.052630          0.019380        0.000000             0.000000   \n",
-      "25%           0.086370          0.064920        0.029560             0.020310   \n",
-      "50%           0.095870          0.092630        0.061540             0.033500   \n",
-      "75%           0.105300          0.130400        0.130700             0.074000   \n",
-      "max           0.163400          0.345400        0.426800             0.201200   \n",
-      "\n",
-      "       mean symmetry  mean fractal dimension  ...  worst radius  \\\n",
-      "count     569.000000              569.000000  ...    569.000000   \n",
-      "mean        0.181162                0.062798  ...     16.269190   \n",
-      "std         0.027414                0.007060  ...      4.833242   \n",
-      "min         0.106000                0.049960  ...      7.930000   \n",
-      "25%         0.161900                0.057700  ...     13.010000   \n",
-      "50%         0.179200                0.061540  ...     14.970000   \n",
-      "75%         0.195700                0.066120  ...     18.790000   \n",
-      "max         0.304000                0.097440  ...     36.040000   \n",
-      "\n",
-      "       worst texture  worst perimeter   worst area  worst smoothness  \\\n",
-      "count     569.000000       569.000000   569.000000        569.000000   \n",
-      "mean       25.677223       107.261213   880.583128          0.132369   \n",
-      "std         6.146258        33.602542   569.356993          0.022832   \n",
-      "min        12.020000        50.410000   185.200000          0.071170   \n",
-      "25%        21.080000        84.110000   515.300000          0.116600   \n",
-      "50%        25.410000        97.660000   686.500000          0.131300   \n",
-      "75%        29.720000       125.400000  1084.000000          0.146000   \n",
-      "max        49.540000       251.200000  4254.000000          0.222600   \n",
-      "\n",
-      "       worst compactness  worst concavity  worst concave points  \\\n",
-      "count         569.000000       569.000000            569.000000   \n",
-      "mean            0.254265         0.272188              0.114606   \n",
-      "std             0.157336         0.208624              0.065732   \n",
-      "min             0.027290         0.000000              0.000000   \n",
-      "25%             0.147200         0.114500              0.064930   \n",
-      "50%             0.211900         0.226700              0.099930   \n",
-      "75%             0.339100         0.382900              0.161400   \n",
-      "max             1.058000         1.252000              0.291000   \n",
-      "\n",
-      "       worst symmetry  worst fractal dimension  \n",
-      "count      569.000000               569.000000  \n",
-      "mean         0.290076                 0.083946  \n",
-      "std          0.061867                 0.018061  \n",
-      "min          0.156500                 0.055040  \n",
-      "25%          0.250400                 0.071460  \n",
-      "50%          0.282200                 0.080040  \n",
-      "75%          0.317900                 0.092080  \n",
-      "max          0.663800                 0.207500  \n",
-      "\n",
-      "[8 rows x 30 columns]\n",
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 569 entries, 0 to 568\n",
-      "Data columns (total 30 columns):\n",
-      " #   Column                   Non-Null Count  Dtype  \n",
-      "---  ------                   --------------  -----  \n",
-      " 0   mean radius              569 non-null    float64\n",
-      " 1   mean texture             569 non-null    float64\n",
-      " 2   mean perimeter           569 non-null    float64\n",
-      " 3   mean area                569 non-null    float64\n",
-      " 4   mean smoothness          569 non-null    float64\n",
-      " 5   mean compactness         569 non-null    float64\n",
-      " 6   mean concavity           569 non-null    float64\n",
-      " 7   mean concave points      569 non-null    float64\n",
-      " 8   mean symmetry            569 non-null    float64\n",
-      " 9   mean fractal dimension   569 non-null    float64\n",
-      " 10  radius error             569 non-null    float64\n",
-      " 11  texture error            569 non-null    float64\n",
-      " 12  perimeter error          569 non-null    float64\n",
-      " 13  area error               569 non-null    float64\n",
-      " 14  smoothness error         569 non-null    float64\n",
-      " 15  compactness error        569 non-null    float64\n",
-      " 16  concavity error          569 non-null    float64\n",
-      " 17  concave points error     569 non-null    float64\n",
-      " 18  symmetry error           569 non-null    float64\n",
-      " 19  fractal dimension error  569 non-null    float64\n",
-      " 20  worst radius             569 non-null    float64\n",
-      " 21  worst texture            569 non-null    float64\n",
-      " 22  worst perimeter          569 non-null    float64\n",
-      " 23  worst area               569 non-null    float64\n",
-      " 24  worst smoothness         569 non-null    float64\n",
-      " 25  worst compactness        569 non-null    float64\n",
-      " 26  worst concavity          569 non-null    float64\n",
-      " 27  worst concave points     569 non-null    float64\n",
-      " 28  worst symmetry           569 non-null    float64\n",
-      " 29  worst fractal dimension  569 non-null    float64\n",
-      "dtypes: float64(30)\n",
-      "memory usage: 133.5 KB\n",
-      "None\n",
-      "mean radius                0\n",
-      "mean texture               0\n",
-      "mean perimeter             0\n",
-      "mean area                  0\n",
-      "mean smoothness            0\n",
-      "mean compactness           0\n",
-      "mean concavity             0\n",
-      "mean concave points        0\n",
-      "mean symmetry              0\n",
-      "mean fractal dimension     0\n",
-      "radius error               0\n",
-      "texture error              0\n",
-      "perimeter error            0\n",
-      "area error                 0\n",
-      "smoothness error           0\n",
-      "compactness error          0\n",
-      "concavity error            0\n",
-      "concave points error       0\n",
-      "symmetry error             0\n",
-      "fractal dimension error    0\n",
-      "worst radius               0\n",
-      "worst texture              0\n",
-      "worst perimeter            0\n",
-      "worst area                 0\n",
-      "worst smoothness           0\n",
-      "worst compactness          0\n",
-      "worst concavity            0\n",
-      "worst concave points       0\n",
-      "worst symmetry             0\n",
-      "worst fractal dimension    0\n",
-      "dtype: int64\n",
-      "(569, 30)\n",
-      "Index(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
-      "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
-      "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
-      "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
-      "       'smoothness error', 'compactness error', 'concavity error',\n",
-      "       'concave points error', 'symmetry error', 'fractal dimension error',\n",
-      "       'worst radius', 'worst texture', 'worst perimeter', 'worst area',\n",
-      "       'worst smoothness', 'worst compactness', 'worst concavity',\n",
-      "       'worst concave points', 'worst symmetry', 'worst fractal dimension'],\n",
-      "      dtype='object')\n",
-      "mean radius                float64\n",
-      "mean texture               float64\n",
-      "mean perimeter             float64\n",
-      "mean area                  float64\n",
-      "mean smoothness            float64\n",
-      "mean compactness           float64\n",
-      "mean concavity             float64\n",
-      "mean concave points        float64\n",
-      "mean symmetry              float64\n",
-      "mean fractal dimension     float64\n",
-      "radius error               float64\n",
-      "texture error              float64\n",
-      "perimeter error            float64\n",
-      "area error                 float64\n",
-      "smoothness error           float64\n",
-      "compactness error          float64\n",
-      "concavity error            float64\n",
-      "concave points error       float64\n",
-      "symmetry error             float64\n",
-      "fractal dimension error    float64\n",
-      "worst radius               float64\n",
-      "worst texture              float64\n",
-      "worst perimeter            float64\n",
-      "worst area                 float64\n",
-      "worst smoothness           float64\n",
-      "worst compactness          float64\n",
-      "worst concavity            float64\n",
-      "worst concave points       float64\n",
-      "worst symmetry             float64\n",
-      "worst fractal dimension    float64\n",
-      "dtype: object\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(cancer_df.head())\n",
-    "print(cancer_df.describe())\n",
-    "print(cancer_df.info())\n",
-    "print(cancer_df.isnull().sum())\n",
-    "print(cancer_df.shape)\n",
-    "print(cancer_df.columns)\n",
-    "print(cancer_df.dtypes)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c458672",
-   "metadata": {},
-   "source": [
-    "-   Visualize the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "6b8c361a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2400x2000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Correlation heatmap\n",
-    "plt.figure(figsize=(24, 20))\n",
-    "sns.heatmap(cancer_df.corr(), annot=True, fmt='.2f', cmap='coolwarm', square=True)\n",
-    "plt.title('Correlation Heatmap of Breast Cancer Dataset')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "282d7c42",
-   "metadata": {},
-   "source": [
-    "-   Preprocess the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "ec5a07f5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scaler = StandardScaler()\n",
-    "X_cancer = scaler.fit_transform(cancer_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "44f0e4bd",
-   "metadata": {},
-   "source": [
-    "-   Apply PCA to reduce to 2 dimensions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "84c76837",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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R5fgSQowWERGBDh064NVXX9XY9KCoqEgRoMXFxeHRo0dYtWoV5syZo/Yzffp0/Pzzzzq7Z02dOhUFBQU4ePCg4rb6+nps374djo6OCAkJMevxHT16FAAwcOBAvdvOmzcPEokE7733ntp99fX1ig528llMVqnRQV1dHXbu3KnymMrKSkXgLRcQEAAOh6NyjhwcHLR2x2tozpw5CAgIwKZNm3DhwgW1+4VCIV5//XWt42RZFp9++qlBf0uT8PBwsCyLd955R+0++d+ZPXs2rKys8M4776g1g2BZFiUlJSb/fV0OHDiA3bt3Y+TIkYqZfmPOgbyOc8PnwsrKCgzDqJR1u3PnjlpXNU0dAgcNGgQAiud73rx5yM/P11gbWyQS4dGjRyrjMfR1QUh7RDO+hBCjubq6Ii4uDlOnTsWgQYNUOrdlZGTg+++/x8iRIwHI0hzc3d0xatQojfuaOXMmdu3ahePHj2P27Nkat3nxxRfxxRdfYMmSJUhPT0eXLl1w6NAhpKWl4ZNPPjFq4VZD+fn5+O677wDIAtHs7Gx88cUX8PDwUElz0CYkJAQvvfQSNm/ejKysLEycOBFcLhc5OTn46aef8Omnn2LOnDkYNWoUXF1dsXjxYrz88stgGAbffvutWpB35swZrF69GnPnzkXPnj1RX1+Pb7/9FlZWVggPD1dsN3ToUJw+fRrbtm2Dr68vunbtihEjRmgcI5fLxeHDhzF+/HgEBwdj3rx5CAwMBJfLxfXr13HgwAG4urpi06ZN6N27N7p164b169cjPz8fTk5O+Pnnn9XyXI0xZswYPP/88/jss8+Qk5ODyZMnQyqVIjU1FWPGjMHq1avRrVs3vP/++9i4cSPu3LmDsLAw8Pl83L59G3FxcXjxxRexfv16k8cAAIcOHYKjoyPq6uoUndvS0tIwcOBAlTJtxpwD+ev+5ZdfxqRJk2BlZYVnn30W06ZNw7Zt2zB58mT885//RFFRET7//HN0794dV65cUTz+3Xffxfnz5zFt2jR07twZRUVF2LlzJ/z8/BQ1l59//nn8+OOPiIiIwNmzZxEYGAiJRIIbN27gxx9/xKlTpxSpSca8Lghpl5qjlAQhpGWSlzO7dOmSQdvfv3+fjYqKYnv27Mna2dmx9vb27NChQ9lNmzaxFRUVbGFhIWttbc0+//zzWvdRXV3N2tvbq5SS0qSwsJB94YUXWA8PD9bGxoYNCAhgv/rqK7XtGlPOjMPhsF5eXuyCBQvYmzdvqmy7ePFi1sHBQeu+vvzyS3bo0KEsj8dj+Xw+GxAQwP773/9m79+/r9gmLS2Nffrpp1kej8f6+vqy//73v9lTp06xANizZ8+yLMuyf//9N7t06VK2W7durJ2dHevm5saOGTOGPX36tMrfu3HjBhscHMzyeDwWgEElrMrKyti33nqLDQgIYO3t7Vk7Ozu2f//+7MaNG9kHDx4otvvf//7Hjh8/nnV0dGQ9PDzYFStWsNnZ2SwAlXOu7ZzIy4cpq6+vZz/++GO2d+/erI2NDevp6clOmTKFTU9PV9nu559/ZkePHs06ODiwDg4ObO/evdlVq1axf/75p2KbkJAQtl+/fnqPt+F45D92dnasn58fO336dHbv3r2KUnLKDD0H9fX17Jo1a1hPT0+WYRiV496zZw/bo0cP1tbWlu3duzf71VdfqZ2blJQUNjQ0lPX19WVtbGxYX19fdsGCBexff/2lMp66ujr2o48+Yvv168fa2tqyrq6u7NChQ9l33nmHraioUGxnyuuCkPaEYdkW0mCcEEIIIYQQC6IcX0IIIYQQ0i5Q4EsIIYQQQtoFCnwJIYQQQki7QIEvIYQQQghpFyjwJYQQQggh7QIFvoQQQgghpF2gBhZ6SKVS3L9/H3w+n1pBEkIIIYS0QCzLQigUwtfXV6W1fUMU+Opx//59+Pv7N/cwCCGEEEKIHvfu3YOfn5/W+ynw1UPeCvXevXtwcnJq5tEQQgghhJCGKisr4e/vr7eFPQW+esjTG5ycnCjwJYQQQghpwfSlpdLiNkIIIYQQ0i5Q4EsIIYQQQtoFCnwJIYQQQki7QIEvIYQQQghpFyjwJYQQQggh7QIFvoQQQgghpF2gwJcQQgghhLQLFPgSQgghhJB2gQJfQgghhBDSLlDgSwghhBBC2gUKfAkhhBBCSLtAgS8hhBBCCGkXKPAlhBDSoolEIhQWFkIkEjX3UAghrRwFvoQQQlokgUCA2eHhcOTz4ePjA0c+H7PDw5GWltbcQyOEtFIU+BJCCGlxYmJiEBwcjERBOpxDlsIz/E04hyxFoiAdQUFBiI2Nbe4hEkJaoRYT+J4/fx4zZsyAr68vGIZBfHy84j6xWIzXXnsNAQEBcHBwgK+vLxYtWoT79+/r3Ofbb78NhmFUfnr37m3hIyGEENIYAoEAq1atguOQ6fBash1Ow0Nh330EnIaHwmvJdjgOmY7IyEia+SWEGK3FBL6PHj3CwIED8fnnn6vdV11djYyMDLz55pvIyMjA4cOH8eeff2LmzJl699uvXz88ePBA8SMQCCwxfEIIIWayLToatp6d4DpuBRhG9WOKYThwHbcCtp6dEB0d3UwjJIS0VtbNPQC5KVOmYMqUKRrvc3Z2RnJyssptO3bswFNPPYXc3Fx06tRJ636tra3h4+Nj1rESQgixDJFIhISEBDiHLFULeuUYhgNe/4mIi98LkUgEHo/XxKMkhLRWLWbG11gVFRVgGAYuLi46t8vJyYGvry/+8Y9/YOHChcjNzdW5fW1tLSorK1V+CCGENI3KykpIJRJYu+qesLB28YFUIqH3aEKIUVpl4FtTU4PXXnsNCxYsgJOTk9btRowYgX379uHkyZOIiYnB7du3ERQUBKFQqPUxmzdvhrOzs+LH39/fEodACCFEAycnJ3CsrFBfVqBzu/ryAnCsrHR+BhBCSEOtLvAVi8WYN28eWJZFTEyMzm2nTJmCuXPnYsCAAZg0aRJOnDiB8vJy/Pjjj1ofs3HjRlRUVCh+7t27Z+5DIIQQogWPx0NoaChE15LAslKN27CsFKJrSZgVFkZpDoQQo7SqwFce9N69exfJyclGf9N3cXFBz549cfPmTa3b2NrawsnJSeWHEEJI01kXFYXa4lyUpexSC35ZVoqylF2oLc5FVFRUM42QENJatZjFbfrIg96cnBycPXsW7u7uRu+jqqoKt27dwvPPP2+BERJCCDGH0aNHY+fOnYiMjIT43hXw+k+EtYsP6ssLILqWhNriXOzcuROBgYHNPVRCSCvTYgLfqqoqlZnY27dvIysrC25ubujQoQPmzJmDjIwMHDt2DBKJBAUFsvwvNzc32NjYAADGjRuHWbNmYfXq1QCA9evXY8aMGejcuTPu37+P//znP7CyssKCBQua/gAJIYQYLCIiAgEBAYiOjkZc/F5IJRJwrKwwKywMUVH7KeglhJikxQS+ly9fxpgxYxS/r1u3DgCwePFivP322zhy5AgAYNCgQSqPO3v2LJ555hkAwK1bt/Dw4UPFfXl5eViwYAFKSkrg6emJ0aNH4+LFi/D09LTswRBCCGm0wMBABAYGQiQSobKyEk5OTpTTSwhpFIZlWba5B9GSVVZWwtnZGRUVFZTvSwghhBDSAhkar7WqxW2EEEIIIYSYigJfQgghhBDSLlDgSwghhBBC2gUKfAkhhBBCSLtAgS8hhBBCCGkXKPAlhBBCCCHtAgW+hBBCCCGkXaDAlxBCCCGEtAsU+BJCCCGEkHaBAl9CCCGEENIuUOBLCCGEEELaBQp8CSGEEEJIu0CBLyGEEEIIaRco8CWEEEIIIe0CBb6EEEIIIaRdoMCXEEIIIYS0CxT4EkIIIYSQdoECX0IIIYQQ0i5Q4EsIIYQQQtoFCnwJIYQQQki7QIEvIYQQQghpFyjwJYQQQggh7QIFvoQQQgghpF2gwJcQQgghhLQLFPgSQgghhJB2gQJfQgghhBDSLlDgSwghhBBC2gUKfAkhhBBCSLtAgS8hhBBCCGkXKPAlhBBCCCHtAgW+hBBCCCGkXaDAlxBCCCGEtAsU+BJCCCGEkHaBAl9CCCEtnkgkQmFhIUQiUXMPhRDSilHgSwghpMUSCASYHR4ORz4fPj4+cOTzMTs8HGlpac09NEJIK0SBLyGEkBYpJiYGwcHBSBSkwzlkKTzD34RzyFIkCtIRFBSE2NjY5h4iIaSVYViWZZt7EC1ZZWUlnJ2dUVFRAScnp+YeDiGEtAsCgQDBwcFwHDIdruNWgGGezNOwrBRlKbtQlXEMqampCAwMbMaREkJaAkPjNZrxJYQQ0uJsi46GrWcntaAXABiGA9dxK2Dr2QnR0dHNNEJCSGtEgS8hhJAWRSQSISEhAbz+E9WCXjmG4YDXfyLi4uNpwRshxGAU+BJCCGlRKisrIZVIYO3qo3M7axcfSCUSVFZWNtHICCGtHQW+hBBCWhQnJydwrKxQX1agc7v68gJwrKxo/QUhxGAtJvA9f/48ZsyYAV9fXzAMg/j4eJX7WZbFW2+9hQ4dOoDH42H8+PHIycnRu9/PP/8cXbp0gZ2dHUaMGIHff//dQkdACCHEHHg8HkJDQyG6lgSWlWrchmWlEF1LwqywMPB4vCYeISGktWoxge+jR48wcOBAfP755xrv/+9//4vPPvsMsbGx+O233+Dg4IBJkyahpqZG6z4PHjyIdevW4T//+Q8yMjIwcOBATJo0CUVFRZY6DEIIIWawLioKtcW5KEvZpRb8yqs61BbnIioqqplGSAhpjVpkOTOGYRAXF4ewsDAAstleX19fvPrqq1i/fj0AoKKiAt7e3ti3bx+effZZjfsZMWIEhg8fjh07dgAApFIp/P39sWbNGmzYsMGgsVA5M0IIaR6xsbGIjIyErWcn8PpPhLWLD+rLCyC6loTa4lzs3LkTERERzT1MQkgL0KbKmd2+fRsFBQUYP3684jZnZ2eMGDECFy5c0PiYuro6pKenqzyGw+Fg/PjxWh8DALW1taisrFT5IYQQ0vQiIiKQmpqKaUHDUHFuL4oPv4eKc3sxLWgYUlNTKeglhBjNurkHYIiCAtkCB29vb5Xbvb29Ffc19PDhQ0gkEo2PuXHjhta/tXnzZrzzzjuNHDEhhBBzCAwMRGBgIEQiESorK+Hk5EQ5vYQQk7WKGd+mtHHjRlRUVCh+7t2719xDIoSQdo/H48Hb25uCXkJIo7SKwNfHR1bLsbCwUOX2wsJCxX0NeXh4wMrKyqjHAICtrS2cnJxUfgghhBBCSOvXKgLfrl27wsfHBykpKYrbKisr8dtvv2HkyJEaH2NjY4OhQ4eqPEYqlSIlJUXrYwghhBBCSNvVYnJ8q6qqcPPmTcXvt2/fRlZWFtzc3NCpUyesXbsW77//Pnr06IGuXbvizTffhK+vr6LyAwCMGzcOs2bNwurVqwEA69atw+LFizFs2DA89dRT+OSTT/Do0SO88MILTX14hBBCCCGkmbWYwPfy5csYM2aM4vd169YBABYvXox9+/bh3//+Nx49eoQXX3wR5eXlGD16NE6ePAk7OzvFY27duoWHDx8qfp8/fz6Ki4vx1ltvoaCgAIMGDcLJkyfVFrwRQgghhJC2r0XW8W1JqI4vIYQQQkjL1qbq+BJCCCGEENJYFPgSQgghhJB2gQJfQgghhBDSLlDgSwghLYBIJEJhYSFEIlFzD4UQQtosCnwJIaQZCQQCzA4PhyOfDx8fHzjy+ZgdHo60tLTmHhohhLQ5FPgSQkgziYmJQXBwMBIF6XAOWQrP8DfhHLIUiYJ0BAUFITY2trmHSAghbQqVM9ODypkRQixBIBAgODgYjkOmw3XcCjDMk3kIlpWiLGUXqjKOITU1FYGBgc04UkIIafmonBkhhLRg26KjYevZSS3oBQCG4cB13ArYenZCdHR0M42QEELaHgp8CSGkiYlEIiQkJIDXf6Ja0CvHMBzw+k9EXHw8LXgjhBAzocCXEEKaWGVlJaQSCaxdfXRuZ+3iA6lEgsrKyiYaGSGEtG0U+BJCSBNzcnICx8oK9WUFOrerLy8Ax8qK1hcQQoiZUOBLCCFNjMfjITQ0FKJrSWBZqcZtWFYK0bUkzAoLA4/Ha+IREkJI20SBLyGENIN1UVGoLc5FWcouteBXXtWhtjgXUVFRzTRCQghpe6ybewCEENIejR49Gjt37kRkZCTE966A138irF18UF9eANG1JNQW52Lnzp1UyowQQsyIAl9CCGkmERERCAgIQHR0NOLi90IqkYBjZYVZYWGIitpPQS8hhJgZNbDQgxpYEEKagkgkQmVlJZycnCinlxBCjGRovEYzvoQQ0gLweDwKeAkhxMJocRshhBBCCGkXKPAlhBBCCCHtAgW+hBBCCCGkXaDAlxBCCCGEtAsU+BJCCCGEkHaBAl9CiElEIhEKCwshEomaeyiEEEKIQSjwJYQYRSAQYHZ4OBz5fPj4+MCRz8fs8HCkpaU199AIIYQQnSjwJYQYLCYmBsHBwUgUpMM5ZCk8w9+Ec8hSJArSERQUhNjY2OYeIiGEEKIVdW7Tgzq3ESIjEAgQHBwMxyHT4TpuBRjmyfdmlpWiLGUXqjKOITU1lVrtEkIIaVKGxms040sIMci26GjYenZSC3oBgGE4cB23AraenRAdHd1MIySEEEJ0o8CXEKKXSCRCQkICeP0nqgW9cgzDAa//RMTFx9OCN0IIIS0SBb6EEL0qKyshlUhg7eqjcztrFx9IJRJUVlY20chIa0FVQAghLQEFvoQQvZycnMCxskJ9WYHO7erLC8CxsqJ8eKJAVUAIIS0JBb6EEL14PB5CQ0MhupYElpVq3IZlpRBdS8KssDDweLwmHiFpiagKCCGkpaGqDnpQVQdCZKiqAzEGvV4IIU3J0HjNugnHRAhpxUaPHo2dO3ciMjIS4ntXwOs/EdYuPqgvL4DoWhJqi3Oxc+dOCmIIAMOqgIjvXUF0dDS9ZgghTYZmfPWgGV9CVKWlpSE6Ohpx8fGQSiTgWFlhVlgYoqKiKIAhAGQL2Rz5fDiHLIXT8FCt21VeSkDFub2oEgopPYYQ0ig040sIsYjAwEAEBgZCJBKhsrISTk5OKkGLtttJ+2FKFRB6rRBCmgItbiOEmITH48Hb21sRsNDqfQLIXgcvvvQSAFAVEEJIi0OBLyGk0Wj1PgGevA6SLmSB69kVwuyTVAWEENKiUI6vHpTjS4hutHqfAOqvg9r8P1C4fwP4Q+l1QQixPMrxJYQ0CVq9TwD114GdXz+4TVyJ0qQY1NzJhuPASVQFhBDS7CjwJYSYTCQSISEhAc4hS9WCXjmG4YDXfyLi4vdCJBLRZe02SNvrgD94KrieXSC8nICys3uAx2kPYWFhWL9+PwW9hJAmR4EvIcRktHqfALpfB3Z+fWHn1xdScS2qbwhQciIasbGx8Pb2boaREkLau1azuK1Lly5gGEbtZ9WqVRq337dvn9q2dnZ2TTxqQsxHJBKhsLAQIpGouYei4OTkBI6VFa3eb+cMeR1wuLaQ1lTR64AQ0qxaTeB76dIlPHjwQPGTnJwMAJg7d67Wxzg5Oak85u7du001XELMpiWXCePxeAgNDYXoWhKt3m/H6HVACGktWk3g6+npCR8fH8XPsWPH0K1bN4SEhGh9DMMwKo8x5NJabW0tKisrVX4IaS6toUzYuqgo1Bbnoixll1rQI1+9X1uci6ioqGYaIWkK9DoghLQGrTLHt66uDt999x3WrVsHhmG0bldVVYXOnTtDKpViyJAh+OCDD9CvXz+d+968eTPeeecdcw+ZEKMJBAKsWrVKY5kw/rAZKEvZhcjISAQEBDTrIqHRo0dj586diIyMhPjeFfD6T6TV++0QvQ4IIa1Bq6zj++OPP+Kf//wncnNz4evrq3GbCxcuICcnBwMGDEBFRQW2bNmC8+fP4/r16/Dz89O679raWtTW1ip+r6yshL+/P9XxJU1udng4EgXp8FqyXWPFBJaVomjfGkwLGoZDhw41wwhVpaWlITo6GnHx8ZBKJOBYWWFWWBiioqIo2GlH6HVACGkOhtbxbZWB76RJk2BjY4OjR48a/BixWIw+ffpgwYIFeO+99wx+HDWwIM1BJBLBkc+Hc8hSOA0P1bpd5aUEVJzbiyqhUJE3KRKJUFlZCScnp2bJpWzuv09aBnodEEKakqHxWqvJ8ZW7e/cuTp8+jeXLlxv1OC6Xi8GDB+PmzZsWGhkh5mNKmbCWsgiOx+PB29ubgp12jl4HhJCWqNUFvl999RW8vLwwbdo0ox4nkUhw9epVdOjQwUIjI8R8jC0T9sMPPzTbIriWWGaNEEII0aRVBb5SqRRfffUVFi9eDGtr1XV5ixYtwsaNGxW/v/vuu0hKSsLff/+NjIwMPPfcc7h7967RM8WENAdjykMFjR6NqKgoOA6ZDq8l2+E0PBT23UfAaXgovJZsh+OQ6YiMjDT7zG9LmWEmhBBCDNWqAt/Tp08jNzcXS5cuVbsvNzcXDx48UPxeVlaGFStWoE+fPpg6dSoqKyvx66+/om/fvk05ZEJMZmh5KBaArWcntcoPgKxdsOu4FbD17ITo6Gizja01lFkjhBBCGmqVi9uaEi1uI80pNjYWkZGRsPXspLE81CeffIKodetMWgRnKoFAgODgYI1l1uQBeVXGMaSmptIqfkIIIU2izS5uI6Q9iYiIQGpqKqYFDUPFub0oPvweKs7txbSgYUhNTcX8+fONXgTXWNuio5t8hpkQQggxh1bZwIKQ9iQwMBCBgYEay0OJRCKjFsE19qqFSCRCQkICnEOWaqwtDMiCX17/iYiL3wuRSESr+gkhhLQYNONLSCuhqTyUMYvgZoWFNToINaXMGiGEENJSUOBLSCtn6CK4qKioRv8tY8usUV48IYSQloRSHQhp5UaPHo2dO3ciMjIS4ntXNC6C27lzp1kWmslnmBMFSeAPm6G1lbK5ZpgJIYQQc6IZX0LaAH2L4CIiIsz2t5pyhpkQQggxJypnpgeVMyOtjaZFcOamr8zazp07zRpsE0IIIboYGq9RqgMhbQyPx7N4ikFERAQCAgIQHR2NuPi9kEok4FhZYVZYGKKi9mtNq2iKoJwQQgjRhlIdCCEmCQwMxKFDh1AlFKKgoABVQiEOHTqkMeil9saEEEJaAgp8CSGNoqnMmrLGtDcWiUQoLCyESCSy1PAJIYS0IxT4EkIsRiAQYNWqVXAcMh1eS7bDaXgo7LuPgNPwUHgt2Q7HIdMRGRmpNvNLM8SEEEIsgQJfQojFmNLeuDEzxIQQQoguVNVBD6rqQIhpRCIRHPl8OIcshdPwUK3bVV5KQMW5vagSCpGeno7g4GA4DpmuFizLS6VVZRxDamqqWeoSE0IIaRsMjddoxpcQYhGmtDc2ZYaYEEIIMRQFvoQQizC2vTGXy0VCQgJ4/Sdq7AgHyIJfXv+JiIuPpwVvhBBCjEaBLyHEIuTtjUXXktQ6vMkptzcWi8VGzxATQgghxqDAlxBiMca0NzZ2hphy7gkhhBiLOrcRQixm9OjR2LlzJyIjIyG+d0Vre2P5QrXQ0FAkCpLAHzZDY7qD8gwxdX4jhBBiLJrxJYRYVEREBFJTUzEtaBgqzu1F8eH3UHFuL6YFDUNqaioiIiIU2xozQ9wSUcMNQghp2aicmR5UzowQ8xGJRKisrISTk5PWGdvY2FhERkbC1rOT1hli5WC5JRAIBNgWHY2EhARIJRJwrKwQGhqKV9eto7JrhBDSBAyN1yjw1YMCX0KaXlpaGqKjoxEXH68IJGeFhSEqKqrFBZIxMTFYtWrVk0Dd1Qf1ZS07UCeEkLbGYoGvVCoFh6OeISGVSpGXl4dOnToZP9oWjAJfQpqPITPEzUkgEFDDDUIIaQHM3sCisrIS8+bNg4ODA7y9vfHWW29BIpEo7i8uLkbXrl0bN2pCCFHC4/Hg7e3dIoNewLSWzIQQQpqPwYHvm2++iezsbHz77bfYtGkTvvnmG4SGhqKurk6xDWVNEELaC5FIRA03CCGklTE48I2Pj8cXX3yBOXPmYPny5bh8+TKKi4sxY8YM1NbWAgAYhrHYQAkhpCUxpSUzIYSQ5mVw4FtcXIzOnTsrfvfw8MDp06chFAoxdepUVFdXW2SAhBDSElHDDUIIaX0MDnw7deqEP/74Q+U2Pp+PpKQkiEQizJo1y+yDI6S1onqubZ+xLZlbap4yIYS0JwYHvhMnTsRXX32ldrujoyNOnToFOzs7sw6MkNZIIBBgdng4HPl8+Pj4wJHPx+zwcKSlpTX30IgFtPaGG4QQ0t4YXM6srKwM9+/fR79+/TTeLxQKkZGRgZCQELMOsLlROTNiKKrn2j61xoYbhBDS1lADCzOhwJcYguq5tm+tqeEGIYS0RRT4mgkFvsQQs8PDkShIh9eS7RpLW7GsFEX71mBa0DAcOnSoGUZImkJLb7hBCCFtldkbWBBCNKN6rkSupTfcIISQ9o4CX0Iaieq5EkIIIa0DBb6ENBLVcyWEEEJaB6MDXysrKxQVFandXlJSAisrK7MMipDWhOq5EkIIIa2D0YGvtrVwtbW1sLGxafSACGmNqJ4rIYQQ0vJZG7rhZ599BgBgGAa7d++Go6Oj4j6JRILz58+jd+/e5h8hIa3A6NGjsXPnTkRGRkJ874rWeq5U2oq0VVTRghDSGhhczqxr164AgLt378LPz08lrcHGxgZdunTBu+++ixEjRlhmpM2EypkRY1A9V9LeCAQCbIuORkJCguI1HxoailfXraPXPCGkyVisju+YMWNw+PBhuLq6NnqQrQEFvsQUNPtF2gPqVkgIaSmogYWZUOBLDEGBLmlvqFshIaQlsVgDC4lEgj179uCf//wnxo8fj7Fjx6r8WMrbb78NhmFUfvTlFP/000/o3bs37OzsEBAQgBMnTlhsfKR9EggEmB0eDkc+Hz4+PnDk8zE7PBxpaWnNPTRCLGpbdDRsPTupBb2ArGGL67gVsPXshOjo6GYaISGEqDN4cZvcK6+8gn379mHatGno378/GIaxxLg06tevH06fPq343dpa+/B//fVXLFiwAJs3b8b06dNx4MABhIWFISMjA/3792+K4ZI2Tvkyr3PIUsVl3kRBEuLjgugybxNpK7Ptrek45N0KnUOWGtCtcC9EIlGLPyZCSPtgdOD7ww8/4Mcff8TUqVMtMR6drK2t4eOjuzuW3KefforJkyfjX//6FwDgvffeQ3JyMnbs2IHY2FhLDpO0AwKBAKtWrdJ4mZc/bAbKUnYhMjISAQEB7e4yb1MFcG1lUVVrPA5TuhVS4EsIaQmMTnWwsbFB9+7dLTEWvXJycuDr64t//OMfWLhwIXJzc7Vue+HCBYwfP17ltkmTJuHChQs6/0ZtbS0qKytVfghpiC7zqmvKtI+YmBgEBwcjUZAO55Cl8Ax/E84hS5EoSEdQUFCr+XLbWo+DuhUScxCJRCgsLIRIJGruoZB2xOjA99VXX8Wnn36qtZGFpYwYMQL79u3DyZMnERMTg9u3byMoKAhCoVDj9gUFBfD29la5zdvbGwUFut+oN2/eDGdnZ8WPv7+/2Y6BtA3yy7y8/hMNuMwb3y7e1JsygFOebfdash1Ow0Nh330EnIaHwmvJdjgOmY7IyMgWn2fdmo+DuhWSxqC1EaQ5GZ3qIBAIcPbsWSQmJqJfv37gcrkq9x8+fNhsg1M2ZcoUxb8HDBiAESNGoHPnzvjxxx+xbNkys/2djRs3Yt26dYrfKysrKfglKugyr6qmTvswZLZdfO8KoqOjW2yqAND6j2NdVBTi44JRlrJLa1UHWbfC/c04StLS0NoI0tyMDnxdXFwwa9YsS4zF6HH07NkTN2/e1Hi/j48PCgsLVW4rLCzUmyNsa2sLW1tbs42TtD10mVdVUwZwbWVRVVs4DupWSIxFayNIS2B04PvVV19ZYhxGq6qqwq1bt/D8889rvH/kyJFISUnB2rVrFbclJydj5MiRTTRC0lbJL/MmCpLAHzZDY+DSXi7zNnUA11Zm29vKcURERCAgIOBxt8K9DboV7qfghaho7Vc5SNtgdI4vANTX1+P06dP44osvFDm29+/fR1VVlVkHp2z9+vU4d+4c7ty5g19//RWzZs2ClZUVFixYAABYtGgRNm7cqNj+lVdewcmTJ7F161bcuHEDb7/9Ni5fvozVq1dbbIyk/VgXFYXa4lyUpexSy3FUvcwb1UwjbBqmBHCN0VZm29vKcQBAYGAgDh06hCqhEAUFBagSCnHo0CEKXIgKWhtBWgqjZ3zv3r2LyZMnIzc3F7W1tZgwYQL4fD4++ugj1NbWWmwVcl5eHhYsWICSkhJ4enpi9OjRuHjxIjw9PQEAubm54HCe/GcaNWoUDhw4gDfeeAP/93//hx49eiA+Pp5q+BKzoMu8Mk0dwLWV2fa2chzKeDxeqxgnaR5t5SoHaf1MamAxbNgwZGdnw93dXXH7rFmzsGLFCrMOTtkPP/yg8/5ffvlF7ba5c+di7ty5FhoRUdaaiu+bC13mbZ4Arq0sqmorx0GIIdrSVQ7Suhkd+KampuLXX3+FjY2Nyu1dunRBfn6+2QZGWofWWHzfnAIDAxEYGNguA3+5pg7g5LPtK1euRN3dTPD6TQDX3a/VzbbTVQPSnrTFqxykdTI68JVKpZBIJGq35+Xlgc/nm2VQpHWgsjRPtOfLvE0dwAkEAiQlJ4PD4aD2YR5qz8kW3DIcDmbPmtWqZtvpqgFpT+gqB2kJGNbIThTz58+Hs7MzvvzyS/D5fFy5cgWenp4IDQ1Fp06dWkzVB3OprKyEs7MzKioq6NKLEoFAgODgYI1laeRvYFUZx5Camkof3u1EWlra4wAuvkEAF2W214Dyly1e/4mKL1uiq6dQ+/Beo79sNefMfXu+akDaj9jYWERGRj75P6zhS3J7mTAh5mVovGZ04JuXl4dJkyaBZVnk5ORg2LBhyMnJgYeHB86fPw8vL69GD74locBXs9nh4UgUpMNryXatl6yK9q3BtKBhOHToUDOMkDQXSwVwlvyy1d5TdghpSk3xJZm0PxYLfAFZObMffvgBV65cQVVVFYYMGYKFCxe2yVkKCnzViUQiOPL5cA5ZCqfhoVq3q7yUgIpze1ElFLbJ1wZpWpb6sqV1FplmoAixKLrKQczJ0HjN6BxfALC2tsZzzz1n8uBI60ZlaTSjN3HLsVSjDOokRUjzac9rI0jzMSnwzcnJwdmzZ1FUVASpVLV4/1tvvWWWgZGWi8rSqKLL5JZnqS9b1EmKEELaF6MD3127dmHlypXw8PCAj48PGIZR3McwDAW+7QCVpXmCKls0DUt82WrqdsuEEEKan9GB7/vvv49Nmzbhtddes8R4SCtBZWnoMnlTMveXLZFIhFu3blHKDiGEtDOapzl0KCsro25oRFG7tSrjGIr2rUHlpQRU5/yGyksJKNq3BlUZx9p88X1DLpPbenZCdHR0M42wbVkXFYXa4lyUpewCy6qmWKl+2YrSug+BQIDZ4eFw5PMREBAAAAbNIgPAwYMH1e4TiUQoLCyESCQy9nAIIYQ0A6MD37lz5yIpKckSYyGtTEREBFJTUzEtaBgqzu1F8eH3UHFuL6YFDUNqamqbvsQvv0zO6z/RgMvk8RQYmYGuL1v3d0VAmH4UAwcO1Pr4mJgYBAcHI1GQDueQpfAMfxNcz64QZp1QC6TlWFaKquyTsHbtgLVr1yItLQ2AagDt4+MDRz4fs8PDFfe3RBSkE0KICakO3bt3x5tvvomLFy8iICAAXC5X5f6XX37ZbIMjLV97bdlLlS2ah7zT2Zo1a5B5ZpfsRoaBjW9v8LsOxY28KwgKUs+t1paWwrFzROH+DTpTdsQlefD+52ZUnI5BdHQ0rly50qryumnxJSGEPGF0Hd+uXbtq3xnD4O+//270oFoSquNLNKFaxs1H3sjCYdAUOI+aD46tIzhcWwDaG1noqgFcefkIylK+hLWbH/iDpig6SVVln4K45B7cJq4Ef/BUVF5KQPkvewCWbTUdC6lGMSGkvbBYHd/bt283amCEtAWtvbJFa56hl+dWu02IMKgEmb7qDQ59glCW8iU4PD7Kzu4BWCnAcGDfcyTcJq+GnV9fALLZe1YqhV0rKX9Giy8JIUSd0Tm+yliWhQmN3whpE8yx2EpX3qUlcjJbY26qMlNyq/WlpTA29gDDgUOv0fCP+gl+q7+Ff9RP8AzbqAh6AUBckgcA4AVMahV53bT4khBC1JkU+H7zzTcICAhQdF0ZMGAAvv32W3OPjRCLMFdA2ZjKFroCUEsFp5oWdzmHLEWiIB1BQUGIjY1t1P6bgim51fpqAHO4trDv8TSEWYlgrLmwcnBVpE7IsawUouvJsn0b8bebCy2+JIQQzYwOfLdt24aVK1di6tSp+PHHH/Hjjz9i8uTJiIiIoJkD0qJZIqA0pbKFrgB09OjRCLJAcKp82dtryXY4DQ+FffcRcBoeCq8l2+E4ZDoiIyNb/MyvKY0s5GkpomtJWqs3OA6bifrSPN2z9w/zwOFwWkXHQlO+IBBCSHtg0uK2d955B4sWLVK5/euvv8bbb7/d5nKA2+rittac42kKSy7ykZ9LLpcLsVis85zKF2ZpyrsU3buGogMbwB86w+wLp+SLuzwWbgHENWBs7FVmNVlWiqJ9azAtaBgOHTpk5BloWroWqgGaj0XXeZefW2H6UTAM8+Q18niRm/JrJCk52ei/3Rxo8SUhpL0xNF4zesb3wYMHGDVqlNrto0aNwoMHD4zdHWlirT3H0xSWmu1seC49vbywMjISGRkZWh+jK++y6vIRcN39zZ6TKRKJEB8fDwnLQd4n85G343nci56L4rgPUJP3P8X+W8tlb1Nyqw1JS4mJidE7e2+OvO6mYMgstymLL6kWMCGktTM68O3evTt+/PFHtdsPHjyIHj16mGVQxDLaQo6nIRp+OFtikU/Dc+kRtgH8kc/iROolredSV96lVFyL6pyLcBw42ew5mZ999hlYqRSstB6uY5bBM/xNuI5ZBnFJHgr3vwZh5gkAreeytym51SKRCLNmzcLp06d1BraBgYE4dOgQqoRCFBQUoEooxKFDhxT7ak0dC80ZpLfHL8yEkLbJ6HJm77zzDubPn4/z588r3tzT0tKQkpKiMSAmLUN7KG2kqVD/9OnTcfToUbg8s8yAgHIvRCKR3tkv5XPJ6xWIqstHUJ1zUVEGy9rFBytXrlQ7l7ryLtm6aoCVmr0hhkAgwMaNGzWmT8if99KkGHA9u6C+vAAMhwORSGTQeWhO8kYW0dHRiIvfq3i+Z4WFISpqv+K8N3xNMBwOZsyYgeSkJPTr109jWoq+NCBD/3ZzkwfpkZGREN+7ojV9Q994ldOEWkPDDkII0cXoHF8ASE9PR3R0NP744w8AQJ8+ffDqq69i8ODBZh9gc2srOb6m5EW2JlpzeK+eQk1xLhwHT4X7xEitj6/O+Q3Fh99DQUEBvL29df4t+bnkDZyCsuQvwHX3h+PASYq/WZV9EuKSexg8eLBK2oOuvEupuBb3oufCdcwys+ZkGvK8P9izGhx7Z4iLboGtE4Fl2VbV3UtboCp/TXCdvcBy7SF+eFf25QQMAGD9+lfx8ccfK7Y3pcNZa8iVT0tLexykxzcI0qP0PreG5Ea3pIYdhJD2y9B4zaTAtz1pC4FvW1/oYtjCpWPwXviRSl1WZYYeu/xcOgycCmHGMfCH6l4slZKSgrFjxyru0xWIFsd9AHHJPXRY9rlZvpwY87yXndkFK5cOcBoyvU1095K/Jmz8+6M295ralxNhViLqS/Owfv16fPzxx62qw5mpwbYpj2vrX5gJIW2HxTq3AYBEIkFcXJxixrdv374IDQ2FtbVJuyMWZkppo9YU+BqSwyu6nYHKy/EaA19jFvnIz2Vt4U29C9FEf6djW3S0SuC7LioK8XHBKEvZpfZYx2EzUXRgg8b7VHMy9xt0Xox53gHAZ+GHsHZ0V9zemlNgtkVHg+vshdrcaxq/nMiPbcuWLejatStWr17d4tOATJmRViavu24ofR3vAOPThNq61nAFgJD2zujFbdevX0fPnj2xePFixMXFIS4uDosXL0aPHj1w7do1S4yRNJIptU9bC5FIhIT4eL2F+vmDpkL01wVI6lQXhRm7yMfJyQkMh4O6+zfgOFB3By/+4GlITExUWYima3FU5ekYAIDQTAunjHnewXDAsXVUO4bW2N1LHrCxXHu9X06sXTvi/U2bWnyHs+ZYmEq1gA1Hi/8IaT2MDnyXL1+Ofv36IS8vDxkZGcjIyMC9e/cwYMAAvPjii5YYI2kkS5U2agk+++wzSKWGLQoDy+LB3tWNCih5PB4mT54MsKzJAYFy04vyX/ag+PB7KP9lD6YFDYNAIIDAyIYYusZqyPMuzEqEfc+Rat3KgNZV5kxOHrCJH9414MvJVDy4fx92fca22A5nzdV8pC1/YTan9lIth5C2wujANysrC5s3b4arq6viNldXV2zatAmZmZlmHRwxn9ZSf9QYO3fuxIYNGwCGMXhWEwyDsjO7GhVQvrpunWyfjQgIWJaFlGXBMLKFVgzDQPo43V5fSS1jGPK815fmgz9Mew5wa5vRk8/KG1olAwCsHF30btdc58AS5fgM0Za/MJtLW+mISEh7YnTg27NnTxQWFqrdXlRUhO7du5tlUMT8WlP9UUPIFiOthrVbR/B6jERV9imdH85V2adg33MkOr74JWw9/TFt2jSTA8px48Zh0ODBEGYeNykgMHSGiMfjwdvbu1EBhc7n/as1EKYfhX2vUVoX/QGtb0aPx+NhxowZAAz8QgRAUlWud7vmOAfy5iO2PUeDrRdr3MaSM9Jt8QuzOTXXlxJCiOmMDnw3b96Ml19+GYcOHUJeXh7y8vJw6NAhrF27Fh999BEqKysVP6RlUb7E3tjL6M1JIBAgMjISYAD+oKlwGh4Kcck9nR/O4pJ74A8LBcNwYB8wGYknTzZqDNs/+wyS8gdGBwTNMUOk9XkPHobgkBBIy/La3Izev9avBwAIsxJ1Hlv11VPw7dgRNX+caXHnQCAQYM6cuWClUlQI9qt025OKayF5VAapuBbAkxnpW7dumTX4bWtfmM1JV0MaueZOkyGEqDO6nBmH8+Q/uPwyrXwXyr8zDAOJRGKucTabtlDOTJPWvPp4dng4Tpz/HbUP8+AZ/ibsu4+AMPOErBGDvGzV40L9wuyTqC+5B5dnlsB5xBwAxtXs1SU2NhaRkZGw9egEXoDm5gANv0w0d3mohs97W67Tun79emzdulVj8w7lY9uxY4fWqg7mPgeG/r9TlFeTv7Ya1IiW1SJmAYYDW7++kAhLUF8uaxlviRrMjakF3FYVFhbKWpU/fg/SxlzvN4QQ3SxWzuzs2bONGhhpGYwtbdRSyGdZnEYvQu35rxWXsvmDp4Lr2QXCywkoO7tH0UWN69EZAAP+kBmKfZjrsrWxHbxaQnmohs+7ubp7tURbtmwBwzDYsmULRH9ngD94quLYqq+eQt3De4ovJxwOx6LnwJhSZIZ0WRSmH4NLyBLUPchB9V9psHb1hevYFRbrqhYYGIjAwMBW/YXZ3GjxHyGtEzWw0KOtzvi2VsqzLI+upkBckocOy3aoBAdSca2sBTDXDoXfvAquhz88wzYCsNyMqiEBQUueIWrLM3ppaWnYsmULjhw9qvPYLHUOjG2OYUy3vdp7musUt/bZ+taiua/gEEKesGjntpqaGly5cgVFRUWQSlXz4mbOnGn8aFswCnxbFuVuZDYduqNw/wY93dOedGxr7mCgNXTQa8szeoYemznPgaZUEuUvZhXnv1F5PRrXbW83uO5+Zuv0R4zXllOFCGltLJbqcPLkSSxatAgPHz5Uu6+t5PWSlkteYilRkAT+sBlwm7gSpUkxqLmTrZrbm3kc9WX3Yd8rEFKREJWXEpr90n3DsWsLVppzMVlzpMA0VbBt6LGZ8xwor/qvzf8DwksJqM65qEjF4fV4GlxnL0RHRyMwMNDgphFWfHcALBwHTqauas2oLacKEdJWGV3VYc2aNZg7dy4ePHgAqVSq8kNBL2kKyiWWHAdNhvfCj8D18EfZWVkziLKzu1Ffdh8AUP1nmqyKweMGEc1dvYLKQz3R1rtdKa/6r8o6icL9GyAuyYPrmGXwDH8TrmOWob4kD3XlRTh8+DBEIpHBeaOyBW6grmoWIhKJUFhYaFAlhrZSLYeQ9sLoGd/CwkKsW7eOVqcSkzV2hk/TLItDv7Gw8ekJYdYJSCoKYd8rEA79x0L8MA+i68mofZiH8ePHN/vMC80QySjnvTqHLLXYoqzmJJ+9ZSV1KDuzW2NKzpPFakdx+vRpzJgxw6CrAo/+dw6G1immhVWGM2YRojJa/EdI62F0ju/SpUsRGBiIZcuWWWpMLQrl+JpOU+ksUz5UtGm4GAkArF07wH3KK7Dz76/YriXm2rXlxWT6WDIvsiUFHvJ8XSu3ToBUorYIU45lpbi/ayWmhwxHXFycQedHmH4Mtv79Ia2u0LlfyvE1nLGLEAkhLYvFFrdVV1dj7ty58PT0REBAALhcrsr9L7/8smkjbqEo8DWepgA3ICAAV7KzLfKhIhKJED5nDlJ+z4bn/A/AsXUEh2ursk1LDQJaUqDWVCyxEt7cX6oaMvV5Cg0Lw5GEI3Adu9yoBY2KGtHy/y/y3PWsRNSX5sNt4kpwPTvrXdzZkr7stWS0SI2Q1s9ige+ePXsQEREBOzs7uLu7K5pWALLFbX///bfpo26BKPA1jqZZk5rbWRBmHNXbSMDUD5WUlBSMnzBBvkOA4cC+x9PgDw9TacWrrVqCclADwKQApz0GsHLGHLslKltYcqausQH1kSNHEBoaalIJu4ZXBRgOB6xUChsXbzgMmQlrFx88un4G1X/K6vjyB08zqIkKUUdlyQhp/QyN14xe3Pb666/jnXfeQUVFBe7cuYPbt28rftpa0EuMo60dr6SqBFx3f4v0s//0008xfvx4WDn7wHXMcsWiIXFJHgr3vwZh5gnFtg0X+jRcXGXv4AAHBwejFloZu0DLmEUzLZ0pi9MMrVpg6KIsS7aAjomJQXBwMBIF6XAOWQrP8DfhHLIUiYJ0BAUFITY2Vu8+JkyYAIbDMSkXNzAwEIcOHUKVUIiCggI8qqqCQCDAjHGjFYuoam5eREhICAIH9qSFVSai1sOEtC9GB751dXWYP3++SutiQgDV0k3yDxCpuBbVORcNLLtk+IeKQCBAyDPPYO3atQAASUUhavOug2PHh9PwUHRYtgP8odNlpc7y/gdANbhQDmrsuj0NgIG1a0e4PA6eDQlwjAmM2loFA1ODQnN3u9L0mpNrzJcqcwXUPB4PYWFhqL56Sq2Kh5y+EnY8Hg/e3t7g8XhqwXCVUIhffvkFv5w9q3LboUOH6JK8gcz9ZYwQ0rIZHb0uXrwYBw8etMRYdNq8eTOGDx8OPp8PLy8vhIWF4c8//9T5mH379oFhGJUfOzu7Jhpx6yefnSwtLdU7S6lt1oStqwZYqVk/VORB18Vrt+A6doXGWV550MN194fwcoJKcJGenq4IapzGr0T1X7+CP3Q6fJfvVAtwHAZNwcqVK3HmzBmVMRgTGJlj5rAlEQgEiIyMhH3ABHgs3GJUUCivZSy6lmRyIChnyZk6cwbU66KiUPfwnllL2CkHw6RxqPUwIe2L0eXMJBIJ/vvf/+LUqVMYMGCA2uK2bdu2mW1wys6dO4dVq1Zh+PDhqK+vx//93/9h4sSJ+N///gcHBwetj3NyclIJkJVzkolmirzG+HiVznwMh4OwsDCN+Y3aZk0YG3uAMe1Sr7axrVq1Cg6DpsB51HyVhWzy0lClSTHgenaBnV9fOA6chLKze1CaHPs4uNiPrdu2KYKah/EfakzDqMm7/qTZAIDx48cjbNYsxbEbEhiJ713B66+/jvPnz2tcNCMfb2RkJAICAlrFDJ1AIMD8+c8CYPDoShIeXT2tkk+tfOzypgwNrYuKQnxcMMpSdmnN+a4tzkVk5F4UFhYqcocb5hKbMlNnSKAoD6idQ5aapTmEpUvYWXphX1vXGhrLEPNrz+sy2jujA9+rV69i8ODBAIBr166p3GfJoPLkyZMqv+/btw9eXl5IT09HcHCw1scxDAMfH90fjOQJ+UIhG3c/OD+zTLFQSJh9EvUleTia/Avi4+LUFs1omzXhcG1h3+NpVGWfMsuHyoaNG8HY8FCVdRJVmSfUFrK5jluBmjvZEF5OgJ1fX1i7+ACsFFWZJxATE4MhQ4Yoghq2XozqnItwHbNMZVzCzBOy4NndH65jnpyDxFRZjdlPPvnE4MDo3NndsPPQHyBrCxIB/W/QTfUGLn9tWLn4wnXscsV5qco+hcL9r8Ft4krwB081KCgcOHAgstKPQvR3utqirJqiuxg0eDAmTJwoC+Q4HPj4dMCDggdgpVJFYLd61SqLzNSZI6Bu+JxEREQgICDg8WK1vQ1K2O03OUBtD/WQm4KhX8aiovY34yiJOdAXRWJ04Hv27FlLjMNoFRUVAAA3Nzed21VVVaFz586QSqUYMmQIPvjgA/Tr10/r9rW1taitrVX83p7yuZQv32svtH8MvF6j1GYpdc2a8IeHonD/hkZ9qAgEAkSuWoWrV67A2s0PzoFTtAZe8lleqbgW9eUFYBgGp0+fxtixY1FYWKgIajSlYdTkXUdpUozOZgNr164Fy7KGtZVlWfACDLkUrx4k6nuDbso3cENeG8oz7dqCQuVAjT9kBmoLb6Ls7G5ZNQ4Afn7+yGcY3MgvVQnkirJOgJWycBw8FVzXjo8Du/EYOGgQblwz70xdYy5963tOzNnkwJDnpDVdTWhO1FimfaAvigQwIfBVlpeXBwDw8/Mzy2AMJZVKsXbtWgQGBqJ///5at+vVqxf27t2LAQMGoKKiAlu2bMGoUaNw/fp1rWPevHkz3nnnHUsNvUUz5PJ9zZ1sAKwiv1H5g0DbrImdXz+4TohAWXKMxhk+fR8qMTExiIyMBACNJdEaBl7yWV5pbRVEV5Mwe/ZsjB07FoBqUGPXeZBaGobwUoLeChTi3CuoeZhrkbay8kBI0xu0+GEeTpxPRtzh0Xj22Wdx8OBBcN06qryBHz+XaJE3cENfG/KZdm1BoaZATSquhbS2CqVJMcjLuajzORamH4P3wo8Uv2elHwXA6PxSVWPkTJ3Bl76vnsLMGTN0PmeaPlR5PJ5ZZuYNTbfRdTWBPGGpWXnSMtAXRSJndB1fqVSK999/H1u3bkVVVRUAgM/n49VXX8Xrr7/eJNUeVq5cicTERAgEAqOCbrFYjD59+mDBggV47733NG6jacbX39+/zdfxNaa+atnZPXAJXoxKwTdqdVa1Fd6XX8IePHgIsq9kG9ytTF5Y3srFBwzHGh2Wfa41EHmwZzW4Hv6w7dgXZWf3wHHQZDzKSlSrD6xcs/Nh/IcQl+Shw7IdYOvFuBc9F65jluk9B+Vnd8PGww/eL2gfT8HeSNQ9zIPr2BVG1axtWEy/5u4VCC8nQPR3OsBKAYYBWBY2/gHwWbBJc2evjGMQaKmLbOyMo7GvjY6rv8HD7zdgyuhhiIuLU9yvr1ZqUdwHED+8C9/lMXqfY8+wjbJz/OUK1JUXAgC47v5wHDhZ8Zqryj6p+PIhEAiM+jAzrHvaUUXe+/hx47B69eoma4BgiXrI5AnK/2x7qFZz22fROr47duzAhx9+iMzMTGRmZuKDDz7A9u3b8eabbzZq0IZYvXo1jh07hrNnzxo908zlcjF48GDcvHlT6za2trZwcnJS+WkPjMlrBCuFlYOLxioMERERSE1NxbSgYWp1RQUCATIy0o0qu7QtOho2Hn6oLy/UWxLNceAkVP91AZWZJ8CxscOjrETs3LkTQ4YMUalKsS4qCrXFuShL2QXHYTMhLpGtuJfWVhlcgYJlWdQW616pX/cwDwzXDlXZJ3VWMHiUnahyKV4+k8frFYgH+9ai6OAbEJfmw3XMsscVLJbD2q0j6u5dRVWWau67fKbP2qUDXn/jDZX7UlJSMG3adKNLqhn72sjf/hxqH+YhISEBU6dNw5kzZ/RWYJCKayHKuQj+oKkGPcdScS0YhgOWaw9r147w/ueH4Hp0QtnZPSg+/B7Kzu4B16MTvP/5Iey8Ohtdzkx+6bsq4xiK9q1B5aUEVOf8hspLCbi/eyWE6cfgOHgqXJ5ZhkRBOlatWg0bdz+L1KrWhEpwWRZVzWhbqFYzUWZ0qsPXX3+N3bt3Y+bMmYrbBgwYgI4dOyIyMhKbNm0y6wDlWJbFmjVrEBcXh19++QVdu3Y1eh8SiQRXr17F1KlTLTDC1s2YvEYwHEgelWtdMKQvl9HQS73yNyv+yGdRW7zf4MBLUpaPkJAQzJs3D0nJyVi1erVavqVyPp99r1EQph9D9d8ZAMMYnNsZvW0b1q5dqzUnkGEYOAZMgDDjmM5L8XUleYiM/FrlmO26PY2iAxsAGJbeodyhjmE44A+ehvNnd0MkEiE9PR2r16xBdlYWrF19jc5tM+q1AQaOg6agrvg26u7fQOKJE0g8cQL9+vfXGagZU/YOrBRsXTWkAMQP78J1zDLY+feHnX9/SMW1YOuqwdjYK6p91BlYfaEhlUvfcXtkFU4YBryeI+E+5RXFOXcYOBF5n8yD/QBDalUbPw5NWnIJLpotJS2NpSrAkNbJ6Bnf0tJS9O7dW+323r17o7S01CyD0mTVqlX47rvvcODAAfD5fBQUFKCgoEDlm9miRYuwceNGxe/vvvsukpKS8PfffyMjIwPPPfcc7t69i+XLl1tsnK2VofVVq7JPgdfzadT8cUbvgqHGzprI36y4Hv4Gl0QDgI8//hjz58/H6tWrtdbOBaCYma65eREAC0n5fYAFhJnHDaox+/LLL2ud3U5ISADLsrDrOghuE2UzhA/2rFaZOXywZzWE6ccAQLHgUn7M1X/9CmvXDnrzjbnufhBeTlAbp3xWeuvWrQgKCkJ2Vhb4Q2fAd0Ws0c0YDH1tCLMSwfXqiqqsRLA1j5500hu7An/my94bHl07o/HxxpS9A8MBY2OvMVjmcG1h5eCqCHrl58LUWU95w4jpM2bA1sMPfmt/hFfY/6l80YC4BjBgsaM5Z1/NWQ/ZXCzZpKUtdTxsS8fSWrTkL4qk6Rkd+A4cOBA7duxQu33Hjh0YOHCgWQalSUxMDCoqKvDMM8+gQ4cOih/lZhq5ubl48OCB4veysjKsWLECffr0wdSpU1FZWYlff/0Vffv21fQn2j3lFABtl+9lOZOM0QX3TSF/s5JUlihKoukOyhMR8swzePrppw1qLgFApQtW9aNHOH06GZLyBwY3G9DUSevQoUMYP3684o2WP3gqvBd+BK6Hf4NL8f7gD5mu8kbr5OQEMAysXH0NTO+YrLj0r0z+JeCtt94yKIDWdxnekNdGfWkexEW3wR86HR2W7VA5774rYsEfOgPVf6ah5t41tf1zuLbg9XgawqwT+r94dX8KbF01WIYBYPgMvakfZiKRCMeOHYP9gCmwslEPIM1dq9pQhjwnTfH/FDBPe2dN2lLHQ03HEhoWhqNHj1IQbGEt8YsiaT5GL247d+4cpk2bhk6dOmHkyJEAgAsXLuDevXs4ceKEYjatrTA0WbqtkC9Os3H3g/2AJwuF5HV8rfmukFSVNVnZF/mCBKfxK1F0YKPGMmMNFw9t3bbN4EUM3377rdplWV0L9OQVKAw5dk2LKZQvxTPWXNzftRJe9sCPBw8q0kMcHBzgFPhPVAj2wzP8Tdh3H6H1b1Tn/Ibiw+/Bb/W3sHJwVRzf/d2RqC/Nh62HH2pL8g1asKdvEZS28yLMPIH6svvgev0DkIjRYdkOref9/q4IACx8V3yh9hwWJ3wI0Z+/akzteHL/hceL++SL/ACuixc6vLjLYgtWCgsL4ePjo/O5KI77AHUl9+CrY/GlJRbOmOu12hiGLAQ0ZWGfcpUMXv+JivScpjw2c9F2LLL/O/lgGEalQQ4xP0u9TknLYWi8ZnTgCwD379/H559/jhs3bgAA+vTpg8jISPj6+po+4haqvQW+AJCWlvY4rzFOrXPb7FmzdFZhMDflNytrNz+UJcc+Xr0/SSnwOg5J+QPs3LkTixcvNmi1e2nyFxBmHgOHw9FYb1VxDuLjDa5AoWvs2isDHIONixfEFUXYuXMnZs2aBR8fH3iEbcDDhP8aFLCWnd0D/6ifwOHaqlQcAMPA2cgAuqCgAN7e3lq303RepBIJnEc/h4q0A4aN98wu2Hr6wz5gslqg9uyzz+KHH35QC+QqLv4IaXUFrN38wB/0pIbzo+xE1JXkwbZTALyf1VzhorEfZoZUUKjJu47C/RsM+mJm7v875nitNoYlVsu3pSDFlPeB1hLQtzYt4YsisRyLBr7tSXsMfOXki1S4XC7EYrHJi1VMXewif9zBgwexdu1a2Hp2AtdvAGoLb6Lu/g1F04PBg4dg+/bPEBgYaNDsnLwzm7VrB1lNYR0zSY1dqPPZZ5/hlVdegY2HHxwGTFEqtXUK4pJ7cB23Ava9A1Hx60E8ykrE8ePHMX3GDDiHLEVt3nVFqTWtM6i7V4KxsoFL0HNqJbwAGBVAG1P2Svm14enlBf7IZ40KsKdNm4bEkyc1BmoNAzmGYcCyrNaZYHmgb+PiDYchMy3yYWZIcHc/ZgnqhaWw8+rcLB+qzbGozFJl1dpS6SlDjuXBntWw9vCDtaN7qwnoW6vm/qJILMfsgW9OTg7eeustfPHFF2o7rKiowMqVK/H+++/jH//4R+NG3sK058C3sUztLKbpcaNHjwYDIFUgkAVDHA6mTpmCdevWKZpTAPo/iCszjqEsOVZnEGWOD56GxwDI23mzAMOBrX8/gGVRm/c/2WV7MADHCmAljxdKdYTblDV60zuE6Uef/FGGAceGB2ltNT799FNErVtncABd9NUaTAs2LYiYHR6OE6mXUPvQuJQKAAa1Yn7xpZeQdCFLZ+BQ+NVqeNhI8KCgwCIfZobOQH7++edISUlpNx+qhnzRBAy/ogC0rRrFxtbA9lt7EA/3r28VAX1rR9VH2h5D4zWDy5l9/PHH8Pf317gzZ2dn+Pv74+OPP0ZMTIxpIyZtiqmtIbU97vfHs2affPIJ5s+fr/XNSlfXLWHmCZQlfwFrt45GdbvS9Aap601T2zEIsxJRX5oP+56jUP1nmmzB2ZhlEJfmoSorEVZOHuAPnAJAivJz30D0Z9rjjnexqLmTrZbeUV/+AIMHD0ZWdjZYqRQMw2DW9CmKIOuXc+eQKEhS5EfrbBn90LjuZsrkHfusXXxQlX3SqPbB+qqCAMCxY8fgHLJU5yI/+4BJeHBuL4qLihp1dUIbQ1vaRkREYOXKle3mQ9USq+XbUukpY2tgQ1xj1rJ3RDtzdVAkrY/Bge+5c+fw3Xffab1/3rx5+Oc//2mWQZHWzdTWkIY8bu3atRg6dKjO2TNNrZNr8q6jNGknwDBamyTIF57Z9RmLuPhvkJKSgs937lSZeQ4MDATDMBA8nnluOIttyDEI04+C1ysQnqGvQZh5QtaEgmEgKS9A+fmvYd/jafCHh0F4KR7Wbh3BHzIdtYU3UXZ2d4P0jh911kuWnwd9AbQ8P9rUGUl5ULhy5UoA0B1gG9k+2NggSCwW651RNJUxLW3by4eqwe2djVgt35ZKTxlbH52xsW+SgL69fDEjRBODA9/c3Fx4eXlpvd/DwwP37t3Tej9pP7ZFR8NGT/mshrOq8sfZenaC67gVYOvFkCo1ItD1uIZUZudyr4AXMBGPbghg5dIBkvIHsHb1UamuUFd4E8JLCajOufi4WgAHYKUYP3487Lw6K2ZtH109g9Tz57U2gfjkk09w8tQp2Hjo7uAlup0BgEVV1kmUJcfKgttBUxX7q8o+heq/LoA/PAz1lYUQZhwDwMrSO6ZOVUvv0BZkKZ8HW89OegPoxpAHhWvWrEFm+lGI/k6X5U9rmBU15m+1tCBIX3OW9kjTF005U77wWCKYbi6GHktV9inY9xwJDtfWoq9lU9PPCGlLDM7x9fHxwYEDB1Q+cJWlpKRg4cKFKCjQ/QHV2lCOr3FSUlIwfsIEuI5ZblR+njwXzmHgVEiqSlSCUPkMqJ1fX6Py+tLS0rD5ww9x/JisSYRLyAsoP7cPXM8uED+8q1ISy8rJC07DZiqVGTqO+rL7cJ2wEk5DpulctS+6dw2liZ+ivuxxDWmGgX2PkYoxazr2srN7AFaqZ8HWMXgv/Ai1eX+gIvVrPCwuhpubmzFPh+I8KC/mAMNg8qRJ+Ne//qX1/3NjnDlzBtuio5GYmKgzz9XQ4NHUhU7GBqcUzJrO3Kvl22NVB++FH8G2Y2+LLdprS+XhCNHE7Dm+wcHB2L59u9YPys8++6zN1fAlxomJiVE0hjA2P09+SVuYcUyR+6o8A1q4/zW4TVxp1GXAwMBA/PTjj7B3cABYFvUVBQBYsJI6lf3Lc28Za65igY48LaEsORY2Xl0hvJSg1gRCKq6FMP0Iys99A667H1zHrtA4Zv5g1RbZ8nw+a1dfnTPDNXeyIbycAId+Y8FKpRCLxQCMD9ACAwPBsizE9fU4duwYpBIJkpKTwbO3h62trdkDh7Fjx2Ls2LFax2nsrJOxM4rG7p9mwRrPmDQQQxiaU90anh/lY6nLzYZ9wCS16i5uE1fCtmNvk9KBDGFq+hkhbZHBM76ZmZkYOXIkpk+fjn//+9/o1asXAODGjRv473//i+PHj+PXX3/FkCFDLDrgptYWZ3wtMbMln9VwGDQFVVknDa7nmpKSgrFjx8pmiseP1zsDyh8yHY+yTxi1iCk0LAxHEmRtfQ2ZYZXP0irKDLl1hOjmb4pjqsm7LkuN+OsCAH1ltlT3qXzsLiEvwPnpcN3n6OweuAQvRqXgGySdOqWWc2xIgNaSZnpMHYuhM4rG7r8lnZu2wpzvL22p9JT8WA7HxYGVyiq5cD27wKFvCBgrrkVfc22pPBwh2likju+xY8ewdOlSlJSUqNzu7u6O3bt3Y+bMmaaPuIVqS4GvJWe2ZoeHIzE1HV4vbMfD+A/1ls96sGcVJFUlmDV9Cg4dOoTZ4eE4fu53+CzbqfMx9ZVF6ODlgQKlslX6juHVV1/Ftm3bYO3WEb7LY3TW0uR6+MMzbKPiduW0BM/wNyERlqA0KQZcd3+AYwVWWq+zW1fDfcpr79aX5htcAsrWww99/DyQnZ1tdIDWki4ZN3Ys+oIgY/ffks6NMkq5UNeWzolIJMLp06exd+9eHDl61OIBfVsqD0eILmZPdQCA6dOn4+7duzh58iRu3rwJlmXRs2dPTJw4Efb29o0eNLEcU8uLGUIkEiEhIUFRcoo/PBSF+zfovDQtLskDf8h0xMXHo7S0VOXxmjAMB1z3ThCX3EOxCCrHcPxcotZjEAgE2LZtm85qDvL9Ow6chLKzeyAV14LDtQWgVGYIDGpuZ0GYcQz8odPhHLwIeZ/Mh+uYZQbvk7HmoixlF+pL8wHAwJXeDGof5iG7JN+ky5TKCwaNWWhoCY0di76FZcbuvyWdG4BSLnRpS1UyeDweZsyYgRkzZjRJQN+WysMRYg5GBb6A7D/trFmzLDEWYiGWzu9q+MZq59cPbhNXojQpRnP92bIHcJu4ElaO7hBmHMX9+/f1vjHX5F1H9V+/akwrkB/DSg3HsC06GrYefqh9mGdwLU22rhp4HPjKywzxug1H1bUURQ1gaXXF4zxdw/ZZ+fth1P4lQG1xLhYsWIDvv/8BwqwTOld6C7NOACyLQYMH40Z+qdEBWsMvJJowDMeidUOVO7yZayyagiBjj9XQL1tNVVPVkl9MScvVFAF9S6uMQkhz0/yOT9oUQ2a2bD07ITo62qT9a3pj5Q+eCu+FH4Hr4Y+ys3tQfPg9lJ3djfqy+7Dp2Adczy6oLy8Aw+HA2toaDIej841ZeClBb+MJa5cOeP2NNxS3y4MhXr8JAKN7/4BqLU3gcfCZeRy2/v3AHzoDbF01+IOmgJFvo2efUnEt6or+BgBU/vo9pgUNQ2pqKg4cOIDPP9+B+tJ8lKXsAstKVR7HslKUJseivjQf//3vf3Hlimxxj/4ALR4ikUhxuykzPeYiEAgwOzwcjny+rLOXlxekEglYidgiYzH2WG/dutVs56Yh5S+mXku2w2l4KOy7j4DT8FB4LdkOxyHTERkZibS0NIuNgbRd8pJqomtJau81cq2lPBwh5mD0jC9pXZpi1k9brUo7v76w8+sLqbgW0toqFHz/f+BY2YCtqULh/n+DsbYBWBZ9+vQBwzCoSNsPG59usPPvr7J/qbgW1TkX9aYV8AdPw/mzuxXHIA+GuB5+sO/xNKqyT+meYc0+qailKU/JqC+7D1YqgUQoy2uXB0ocrq3WfSoWvylKsjGYMWOGSv5eZGQkOBzO41rD2eA9XuldcycLj66nQFpbDQB4bcMGsFLDZpYbXqZsrpkerZ3rMk+g/NzX4Njaq1W6aOxYjGoUAGDE008DjCx9RVeedVPMgrW0lAtd2lKubXti7lrLhLRmNOPbxplz1k8kEqGwsFAxq6j8+7qoKNQW52qcwWSsuai8eAiS0vtwm7gSjkNkQY8V3wMuY5bDM/xNuIxZDo69CwoPbEBlxnGVx0trqwxOK2BZFoWFhQBkwZB8Jpk/PBTikntaZ1jLUnahvuQeWIkElZcS8GDPagjTZfV/JRWFKDkhmw1XmdXWsE9h5gkU7t8Acck9uI5ZBs/wN+E6ZjmSLmQhKCgIsbGxisdHREQgNTUV04KHo+LcXhQffg/CjKOwdnSD69gV8Ax/E85BiwEwJgWvzTHTo2v20ndFDPhDp8tSYPL+Z9axGHqswqxE2HTsA5dnlsHGrSOEGUfVXm/mGI+hSktLER8fD7s+Y42e0W9KDWfwHfl8zA4Pp1noVkJeUq0q4xiK9q1B5aUEVOf8hspLCSjatwZVGcdaTXk4QhqLZnzbOHPM+qktuuFw4OPTAQ8KHoCVSp8swnn1VWzdulWt7qZyrUqARVnyFzpzdcuSY1Bfkge7LoNQX16A6iuJsjEaOJsnl56eDlbKQpiVCN9hOx+3AY6D6HYm+IOmqI3PtlMARDcvQnTzN9j3GgW7zgMhzDwOsFKcPXsWY8aMgTD7pGKGt2Eus13ngYrFb4bmUssXbKWkpGDChAnqedjdR6D2wZ8QZiWa1MWqqWd6DJm9FN3OROXleJUSb/rGYshMoyHHWl+arygvp/x6s/HsrHKlwdKzYMr/p1ipFOXnv0bdgz+1Nj1pzoVHlH/cNpi71jIhrZVB5cyMyW9ra4nxbaGcWWNqOGqrcyrMOoH60nzY9x8HG/dOqPnjDGqLc/Hqq6/i5s2biI+Pl+2A4cC+50jwh4XCzq8viuM+0Fvq7P6uCNSX3QcAcDgczJw5E/EJCbB27Qjf5drLnd3fHYn6snxUP3oEHo8nK5F2Jg115UXgD52OemEJ6gpuwaZDN4j+UuoM93h8th1748GeVbB284PnrI14sGcVwLGGpDQXxUVF8PD0BCtl1QLbmrz/ofJyPER/XoC1awf4rog1+jzreo40dY2Tt1wG1w4V57/RWXZLaw3cq0mofWh83VBtQagxZZPKzuyGS8gScN39dHb4MrbSgbZjrco+CXFJnlpDEfnrTVpdDufAhSZ3HDMmBUDb/ynlL4gNU0Gaq9RUSy35RhqHUlZIW2TWcmYuLi5gGEbnNizLgmEYSCQS40ZK9Grsm5Sps37aqkHU5F0HN68z6svuo/paCqoZDng9noadqx+2bt2KhIQExMfHw31aFOx7jVaUBjMmV7fs7G7YuPmiriQfkyZNwt+3b+NKdrae2bw8hISEKFogy3ObHa25KE3aCYCB61hZK2V54MjY2CvGBwCOAyej7OwelCbHQlySBxsXL8wKC4ObmxvCwsJwNPkXCNOPqVWrqCu6A4AFf/A0o3Op9eVhK88sV/91ARw7/pOWy5D9v1y//lWtgYd8puf119/A+bO7ofiuyzAIDg5GQECAxsc1pC8INSatBmBRkfq14oqBplknU2YaNc1qgWFg33MU3CavUZtNlb/eys/uRvkve3SOx5Rzoml7fRVWSpNiwPXsotJIpbkWHrWm/GNiuLZUHo4QYxkU+J49e9bS4yAamKuup6ntPzV96FVeTkBZym5Zi94xy9Vmq6z5rti7dy84VlaQiqpUgkq2rtqIEmAsPJ/9AJUXD2FlZCTAsuDYO0OYfgyiO1ngD5z8pERaViLqS/MAAJs2bQIAFBYWKoIw++4jwHF0x8PD76ksToPS2FT/thRVmSdg07EP6vL/ULRhXhUZibjDh8Hr8TTA4SgaWwAAuLIPEVMWoRkSMPIHT4XodgZEORfBWNuqtFyuvnoKW7duRbdu3bTOTl65cgXnz5+DjYcf7HoFg+vhD0llCX6/loSgIP2Xq/UFoevWrUPOzZsADEtJ4VhZ6ey+15gSfPL0kTt37qBr167wCH0NDr1Gax2PPDf89t9/g8fjGfwF05TA3JBAUt6q2s6vb7MuPGoJ5fAIaUo0E94+GBT4hoSEWHocpAFz59UZm9/V8EOvJu86ys9/i9p713Tm5wrTjyHhyBHMmDEDSRdUqzwYUgIMeFJWjGPrKMsJ/TsdAAvfFV+gNv8GhJefdFMDwwCPZzBjYmLAsixmh4crUi3kf4vXZZDhfxsAGA7q8v8AAEyYMEGR0wwAopyL4Njaw67bcNTc/A2Og6fC2tUX5Wd2m5RLbUgedk3edYhyftN57rUFgo2t42zI47du3QobF29wPbuq5EE3pDx76ebmpvV4DQkQ63Kzdc40ent7g2NlBUllicb75eTPibe3t8EfdqacU0MDSXnTk4qLPytSiCy58Ejbh72pC2MpcCCtDTWPaV9MrupQXV2NGzdu4MqVKyo/pPEsVdczMDAQhw4dQpVQiIKCAlQJhTh06JDG/9jKH3rySgV1hX/D2s1Pdy1ddz+wUimWLV2KmqK7KDm5A5I62Up0DtcWvO5PQZh5XOfK+6rsU4qyYvJL0fXlhWDrxbDz6wvPsI3wj/oJfqu/hd/aH2Hj7oeQZ54By7IIDg5GYupl2ar9jn0gzDoBlpWqlB/Tver/BACA6+r7pLLCM8tQVAOwUhaOg6fCdewKWDu6oebmb7B29YV9n2CUn9kDa9cOqMo+qXv/mcchlUgwecoUxfNnSEWCyksJsHbzNakWs6l1nOVVOz7esgU27nqedzc/WHl3g9vECNSX5OmsniGbvYzSeJzyv5uQkKC3drF9wCQcjovTWunAklUtDDqnHqrn1KhUEFaKitSvFbWfLbF4TF+lBqMWxnI4ePGll6jqA2l1YmJiZJ8bgnQ4hyyVveeHLEWiIF2tEg9pG4wOfIuLizF9+nTw+Xz069cPgwcPVvkhjWfphhM8Hk/v7Jb8Q6/mdhZKk2LgOHgKWHGNooGDJgzDAX/gZADAl7t2geFw8OhKEvKi5+H+3jWouHgIHDs+6svu6wyMxCX3wB/2ZHGUSke1xzhcW1g5uMLKhgeHgVNw/tw5REZGyr4svLADTsND4dA3RKVJhEElzUrzwesViA7Ld6qW4louK8VVlZkImw494LUoWtYGefA0VF0+Aq67P9ymvAKxnqCvvuwBrPieOH/uHEaPHq14U9VVDk5SJ4Io54LelsuaSl4ZGkQqP7ZhQHQkIQH2Aybrft4HTYHor4uw8e4Ot4krIUw/hgd7VptUNsmYAJGVSnH69Gmt2+g6r4YG4g0ZfE4DJuLw4cM4c+YMAOMrrDwsLtb6xbSxDPmwN/SLQ/WVk5BKpUi6kEWBA2lVqHlM+2R0ObO1a9eivLwcv/32G5555hnExcWhsLAQ77//PrZu3WqJMbYrLSWvTv6hl3D8JLjufnAeNR9VmScMXLgEJP+aDuegxeB6+Ck1L9in2E62QCwLjkq5usqr2pUXITXsqKbpb7Isq9bZreZ2Jhh7ZwjTjyoWozkOmiLLE76dAf6gqWp5whx7F3iGvga2Xgyp0uI3RSmuv9NRevoLeM99G2BZWDm5Kxbs8fz7a2/V/Hj/Ls8shdNTYY/TQo5i5cqVCAgI0JmHXX0lEWBZi+UPKz/2s88+w8aNGxUpNhyeI0qORxvV7pk/eCq4nl1UU1IABAUFY/Nh/QvGjGtGwWDv3r2YMWOGxm1MzW/XxajAnGUxbtw4xMTEICIiQtHoxWHgREBco7a40tBUEF305Skak6Zh0MLYh/fA6xUIz9DXzN4OnRBLosWb7ZPRge+ZM2eQkJCAYcOGgcPhoHPnzpgwYQKcnJywefNmTJs2zRLjbDdMyauzVC6dfDGXc+BCcGwdjcqRrSt9gLrzX8O+x9PgDw+D77AYRemjp58eifQ/bsHKwx9lZ3YDYBVlxdwmr1ar7yrMSlSkPmgiq6gAxYxoTd51VP4eB9HN32QbMBxI6x49+VtgUF9RhLIzuxT74Di6AWDg0DsID+M/VOq6xlEcg51fX1nFiTO7UPvgFgAG5akHVBbsaQz6GA5sfHsBAMp/+Qp192+AP2wmau5kQVJVonhT1ZaHPXPGDBw5ct9i+cOKx3I42LBhg0oecX21LG/T2HbPyl37qm8IUHIiGk5OfK0fHg2DtenTp+PE+RM6c4Wrsk+B69kFR44e1fkFUFd+e2TkXvTr18+oL5BGBeYMB46DJiuCv/HjxiHucBzyPpkny01Xen3ZduzdqIVshuYpGvNhf+jQIZ1fHGqK7sKa76YW9GraFwUOpCVpKZNMpOkZnerw6NEjeHl5AQBcXV1RXFwMAAgICEBGRoZ5R9cONVebWU3695cV9Ld29VHkyMpzZjWRBSMnwdjwYNOxD1zHLIO4JA+F+19DVdZJuI5bASsXX1y48Cvqygth7egOz3nvAgAcB02GR+hrGpsa1JfmgT9UfUavJu86iuI+QIXgW8U45fnI9aX5ihxd1zHLwLFxAABZRQawsHb2gkvIC3CfFgWXkBfA4fIAsBBmHIO4JE+p69qTYxBmnlDMaJed2wuABSutBxjVzmoN85D9o36SVRVgOHAJWSLb34GN4Hp0grROpJKnqikPOy4uDqFhYSblqhqT5+rToQPsvDqrBEQMKytPKNSTu6ycl62Mw7WFtKYKYBicSExUy8fVlmc6auRI1Jfl602JcegborfjIKB+XpNOnYK4vh7jJ0wwOifV0HMqPyduEyJg69kJq1evxurVq2HrIauIIn991ZXcQ+H+13A/ZonJHbQMzVM0JfVF0WEwaJiiw2DFub2YPGowGA4H/OHhLbrrHCGamLOrKWldjJ7x7dWrF/7880906dIFAwcOxBdffIEuXbogNjYWHTp0sMQY2xX5h2qiIMmkTl3m1DAIdxw8FUUH39B52VNckgf+kOkQZh6H9/z31WqT8gdPRdnZPeB1HwFh+lGI/k6Hfa9AVGUmoubuFZWOavJL0QBQ/WcabP36Kv6mMPMESpNiYO3WES4hL6D8/NeouZ2lt3OaMP2oxsuyXO+uKP7xLZ2PLU2KAX/IdFmg+zBXMTP6MP5DVGWfUnu+5CXTlIMg56fD4TRilqICBsCCZVmNK+KVn9vGdGAz9LH3ATgH/hNsvRjM4wBWNoPLKBasaX/e78Ft8mq1vy0/dhvf3qjL/0PlCoXuyiVxsudZQ81k5ZQYtl5s1BdAHo+HN954A9u2yfPjZbOuVm6dcPxMGuLj4gyqmGLIOZWfE4bhgOs3AFkZR/VURDlqUhc0Y1IXunfvbtIVJXmJOOWZ+crKSsTHx7eIq1OEGKslTTKRpmV04PvKK6/gwQNZWaf//Oc/mDx5Mvbv3w8bGxvs27fP3ONrl0wJcixRf7BhEC4V1wDQH4xYObpDmHEUbF01OFxXldqkDv3GAqwU7pMiIR4eipLET1H9p2yWrb40D+WPGywol1q7evWqyqVWtr4O5ee/UQlS6x78iaprKbBy9YXT03NUgjdAuV1uBgBW7QvFo6yTajnCDR9bcycLVddSwNjyYcVzVGzLHx6Kwv0bDA4MlfOF5R3qBg8egsLCAkiVW0ArXaJuTK6qIY/t27cfrl+/hgrBflSkfa+S3mHfcyRq8v/Q8ryfVLR7tu3YW+XvqixWHDId9QV/KT48DAnWhOlHYe3oAmsPP5W0EXlKjG3H3ijat0brF0BN/ydeffVVbNu2DdZufrIvWUp1qOvKi2Dbqb9BOanK51T0dzr4g6fpzFWvLbgJa1fdVTnE964gJSUFK1eu1Pp3NTEmdeHbb79t1Id9wy9kFDgYjmrEtiwtaZKJNC2jA9/nnntO8e+hQ4fi7t27uHHjBjp16gQPDw+zDq69Uv5QrcvNkjUccPeHRFiiFuRYuv6gchAurigCwIA/ZDokj0o1BiN2fn1ReSlBJd9TuTapjU/PJ/cxDLieXR9/cMqC3enTp2P5smUYP3684o0mMDBQLUfT2lU1SLXx64vqv36FRCxC/ueL1XJz5ePgD5LNOEvFtUZ3lHMcOFmRF8wfNV+xrXJnNc2BYZ7agj1Fh7rHecfFtQycn1mms16zsbWYlWl77MABA5BZdBe3ioSyMm1KgWDh/tdk7XOHh6L6rwvg9RoFgFV53nk9nwasuajNvYoHe1ZpXKzoOiECouwTKh8ehtbprS3OBc/RHX5rD6osBtPXcVDT/4mQ4GBs27ZNbx1qGxcvg3JSIyIi0LNnT4wbPx5lZ3c/ydltkKsuFdei7sENuI5ZbvZcQmPzFAGY7cO+KQOH1hw0Uo3YlqsxV9JI68Wwiv6lxpM/VF8749bM0N7P5iYQCPD6G28g9fx5lRazIcHB2LRpEwIDA1UuFfP6T1QELcrBsTnqf8bGxj6ehWLA9ewCSCXosGwH2HoxJI/KwTAAx95FEZDc3xUBrkdneM1+XbGP6pzfUHz4PVi7doSNVxfY+vVVdIBzHDjZ4LGXlpbC09MTzs8sg9NwWckzRdqDawfZzFuDbnJuE1eCP3iqyjj8Vn8LKwdXAIDkURnydjwPz/A3Yd99hNbzIH8sAI3biu5kQ3g5XtZw43HuJ6/bcDg9PVetVS4AVFz4CeXnv9YYiMnfdKsyjiE1NVVjgxFTAwH5Y69du4YJEyZonHWV/31h+jF4L/wI4uI7j1NLfOEYMBFWDi6QPCpXNFhgWRbWrh1QX16o8mWIP3QGqv9MUzkOkUgERz4fziFLFc+hJpWXEhRthO28Omud5VZ+nWj7P/EoOxF1JXng2DvDb/W3WoO0B3tWAxwrSEpzUSUUGnRuZ4eH40TqJXjMex8cW0e1POf6qhLkf77Y4NdXQUEBvL299f5dQNah0MfHx6h95+TkIDg4WOfzru1115BAIDDbvrTtvzUHjU31Hk1MFxsbi8jIyCfPkZ73GNJyGRqvGT3jCwB79uxBdHQ0cnJyAAA9evTA2rVrsXz5ctNGS1Qov1m6KLUFFl1Nwvnz53H16lWwLNuoblzGiIiIQMeOHTFz5kw49A1G+blvUJzwIcDKupgpZv96PP14odd9uI5dprIP2Qp3BvVl+WC4tqj+M82kLmS7du2CVPqkikJN3nVZ7q2e3FyuZxfY+fXVWBrNuI5ysk5xytvW5F2H8FLCk0oQYGDt3gn1JbnwCN2gtRrFoz/OG3T5W9PsY8NLzsaQP3bl4zd75b8vFdeCfVzGTTlFxSP0NYjuZkP0ZxoqU79WpGQ0TEexlbdEfnyFouJ0jFoahrF1eo8cOYKvv/5a7yy3YekTx1Cbf0PjFxHlKxNgpQbnpMpnjSovHoLruBUq97GsFBW/HgRgeCtnY75gm5KnaM4Sb5YoFydn7u6VTa2xHRNJ02jMlTTSOhkd+L711lvYtm0b1qxZg5EjRwIALly4gKioKOTm5uLdd981+yDbE0PfLIOCg5u0/uD48ePBcDhgrGxg3ycI1X+cl+XEjnlyeV6YdQL1pfkAALvOgxSPVXREYx+XEisv0NsBTj72IUOGKGY209PTsWHDBpUqCsJLCeDq6SomD95sO/aGMOsEeD2fVglGn3R1091qV34M1h6dIMxKBH/YDFRlnZQF1u7+aucCAKqunobTEPUSf5I6EcTFd+A61vyXvw0hEokQHx8Pp1ELIBFVoTb/f3h0JRmiW5dUyrjZdR4IYeYxFH61CnUP8xATE4PFixerzTbL01G2bNmChCPfg20QGCu/Bo0N1saPH48ZM2boneU2JH1CdCcLwssJGgNf4Ek9YobDMTgA1RX8CTOPo77sPrhe/9C4AFLO1JQAU9MNzPlhb4nAoS0EjVQjtvXQtHiztaXUEMMZHfjGxMRg165dWLBggeK2mTNnYsCAAVizZg0Fvo1k0Jtl7hWcP3/eIjmD2vB4PHTw6YDCS3GQCEv0rk6vK7wFO7++Kh3RAMBx8BRUZZ3U2wGO6zcAPx8+jLi4eEilsg9Sby9v2Lh4w8qrG6qyT8Fh4ERU51zQex7ks3ilybGoL80H17MLWFaq8hjHYTNRdED3AjXZMcgqOgBAXswLkOo5F2XJMbDx7Aw7//5q+wNMa0rRWAKBAJs3fwhWKpUtZhPI89dkqSwOfUPAWHFRlX0K1X9dAMBi/FMDsHHj94oP6IZjkV+SPnL0qCLonT59OqKiojTOVpsSrOma5TY015U/cLJajrcyeVOM0JkzjTrfmoI/hmHA2PDg/c8PAYbRuwBSWy6hvg9jU/MUzflhb+7AobUHjVQjtnVqzJU00noYHfiKxWIMGzZM7fahQ4eivr7eLINqrwx+swyYiJozu2Dl5K5zf+YMmkQiER4UPAArVe+Qpjw2WeWETJT98hUceo1G9ZWTqH14Dxx7J1jxnA3qACfMPAFhxjG1nN3izOOoLy8Cv8coiP66gIdHPjK4oxlYKaoyZbOwoj/TcH93pErptKrsk7K//bjEmvIqffmsHcfBBc4j5j6Z1c08DikAazc/7efi73QUHXoXLqMXqlVCACxz+VsX+eVjWX4zA2u3jmrVDcrPfQ23iSvRYdkOxReZdTpyKrVdkk66kISjQZovSRsbrOkLqIxJn1C0v24Q+MqbpQAs1q9fb8DZVKUc/BUWFuIf3brBOXCh4kuP1q5+mScgKb+vlhJgaH5rY9MNzPlhb459tYWgsSU1IiKEqDI68H3++ecRExODbdu2qdz+5ZdfYuHChWYbWHtk1Ic3APHDe0Av7duZM2iqrKwEK5UCDKN3tpY/aArKzuyC+MGfmD1rFvz8ZuPTTz+F89Pz9XaAMyRnV3gpHvzhYRBeigPAGJabCwZe896F6NbvEKYfBcfBRUNVijUAK0XRoXeVOsrJcnp1tWQtS46FjVdXtcvnypUb1DrUTVqFokPvQpiluzuZOUvpyC8f2/UcCdGfFwzKi3YdtwK1dzKxc+dOjB07Vus+jb0kbWiwxrIsZoeH6w3+jOumxgBcO5XblZulrF+/vlGziPLgj1XKRQe0d/UDK8tjVm67bGx+a1vKU2wLQSPViCWk5TJ5cVtSUhKefvppAMBvv/2G3NxcLFq0COvWrVNs1zA4JroZ82bJMAxq/jwPVqmslrLGBk0NZ9icnJzAcDhqH+aayAPzHw8exJw5c1BYWIhPP/1UpQOcPFVBuUQVYHjOrnyRGtejs9Z9yc9DVfZJ2PcaBV7XwbDrMhCi25mwtneGd9RPioVcyo9xGb0QZWd2w3n0c6gQfAdrt446W7LKc4g15Y3KzgUL35f2gMO1Uflbjv3HQZhxtMlK6Xy8ZQts3DsCEoksJ9mAvGg7v75wGDhF68xaYy5J6wvWrly5guDHuez6gj9D0yeE2YkAy6Lwm1cbzLrKZvXXr1+Pjz/+uNHnWtv/ZeVWzmxdNaqunkGl4BuMHz9esY2pXybaSp5iWwgaqUYsIS2X0S2Lr127hiFDhsDT0xO3bt3CrVu34OHhgSFDhuDatWvIzMxEZmYmsrKyLDDcts2Y9rLBwSGoe5ins6WrLGiKMmoM2lrIZmRkPJ6RMnyGde7cuYiNjVX7ILPtPADiknvI+2Qe8nY8j3vRc1Ec9wFEd7JRnXMRjgMn683ZFd38DWClsO3YS+O+avL+p9pNblio4vH8QVMe564CVg6uarme8mC1QvDd4xnuqXrHU/3XBUjFtZrPBcOBlYOL2t+y6zIIgCy94v7ulai8lIDqnN9QeSkBRfvWmNy+tiGBQIDQsDAcSUhA7cM8iG7+BseBkww+Jm0tO01pf9uQvJVwcVERrl69iuKiIhw6dEilaonXku1wGh4K++4j4DQ8FF5LtsNxyHRERkaqtBheFxWF2uJcnf8n6kvy4TohElwPf5Sd3YPiw++h7OxuTA95CgKBwCxBL6D//zKHawuOvTNq/jijFvgY8mXC1rMToqOjG+5W8be9vb1bbTBlzPtgSw4aDXk9mvIeTQhpHKNnfM+ePWuJcZDHDM193HRYvaNZY8sI6bu8umbNGgAsKjN1X56vyj4JXq+RsHZ0V8xMyWc/YGWFsuQvYO3eEfyBT3JLhdknUXRQVvfX0DxNAKjKOqVxX4X7XwPHwQXSR+VqzSN05XkCTwJ3KxcfSMofmJw3qpht7jlSJeCVz/aJS/JkNzAMvHnAg1/2KMqEzZwxA0uXbsf48eMhEolw9+5dVFVV4R//+Afc3Nx0jkeZ4jn18Ifr2BXg8BxRcjzaqGPSNrNmjkvS2vJYS0pKjJ5J1pU+odxIhD94KtjBk1GaHIuqzBNIOX1aYxpHY5nagbG157eaQ1toLGDJUm+EENOZlOpALMeYN0tNHc1MzevTdXmV69MNpYmf4rPPPgMASMrycX/XS3Cf8orGagXikjy4TV4D2469FcHJuqgoxB0OQk1yrNYqCPJAxLAZ5ceP05ULnH4UrhMiFc0rVB7foJav8jHIF7rxB05G+fmvDcwbVd1fw3MBaK736+PTAfv3f4exY8dCJBIhOTkZe/buxZGjRxEfHw+AAaDaY8bd3QPvvfeu3ta2mp5TqbgWJSc+NfiYwLXTOrPW2EvS2r5onUg9idrie3Adqx70ymkL/jSlTwAAx9Ye/CHTYeXojspLCYr/SzExMRYJegHTAp+2kN9qDtrOnbgkD6Lryah9XFqvpQeNbSn3mpC2wqDAd/bs2di3bx+cnJwwe/ZsndsePnzYLANrz4x5szRXXt/HW7bIZgUbBJHyrmhcdz+VlrbCzOMoPLAB9r0C4dBvrNb2vPLgRB6k6WrY4DYhAo+u/6Ky4Eu5oYK8M5ww8zhgbQtrvrvePNXa3GxgyFTFfsC1gzDzOKxdvMFYc1UepxysAiy4Hn6KfGSdeaOZx8Fw7VCVdfJJzmhWIupL82Dr309WP1hxHlXr/ZZdPYXx48crFnLJA0G7bk+j+q80tcoWwqxElJTkITIyEqmpqThw4IDW51TTJXPlHGvds/anwOv5NCrOf6N1Zq0xeYy6vmjZ9xmN/M8Xmxz8BQYGgmVZiOvrcfRxeTVpnQhVmccgzDjapIGHsYFPW8hvNRflc3c4TtbBT47D4SApOblF1/GVayu514S0FQYFvs7OzpC3JXZ2drbogIiMsW+WppYREggE+HjLFhxJSFCbYTOowkL6UVT/maZSGaFhWoFUIsHHH28BwIA/eBrYejGkGhaVMQxHseBLX2c4PK6YYEj93qKf31NqzMA8nkBl8WDPatUFTo+DVZdnlqL83D7UlxWAPzxUb/3V+rIHsPXvr7JSn2Njhx49euLmzf+h4MsVqCsv0nkeIyMjwbIs+ENngNcrEEUHNuqtlfz9998jKChI48yvrkvmhhyTuOQepOIaiCp/xeDBg7UGF6ZektaVx6qv8oecITPJLs88+ZJRfVU2kxy9bRtefvllnfs2J2P+L9OiKFWBgYG4cuUKDh8+DDvPTuAFTIK1qw/ED/Nw4nwy4g6PRkxMTIvu4CZHNWIJaRkYlmVZ/Zu1HJ9//jk+/vhjFBQUYODAgdi+fTueeuoprdv/9NNPePPNN3Hnzh306NEDH330EaZOnap1+4YM7f3cGskDBBv3jqh9mAfP8Ddh332E4v7iuA8gLslDh2U7tH4A398dCa5rB62teSsvJaD8lz2y7aVS2HTsg7r7f6p0B+MPD1MEy9U5v6H48HsAACtXXzj2GwOuhz8klSWKznA2HfugLv8PtfE2JN+XlasvnJRnTbMTUV+SD653V4iL7ijGwljbgmUlcA1ejEc3BJDWCOG7fKdKdzb1QDlfkTcqFddCWluFil8P4lFWIlJTUwEA8+bPR1E14LsiRut5LNgTifqqUvi98gMexn+Iuod34btc+/YP9qxCvfAhfD3dkJeXp7ZNYWEhfHx8tJ4j+Qy0tbsf+AMnq1U3ABjY9xoFKwc3PMo+gSqhUOuHtrG97kUiERz5fDiHLIXT8FCN+yyO+wB1D3Phu3yn1nNQtG8NpgUNw6FDhxS3CwQCBAcHa5xJlgfiVRnHkJqa2mJnClvDMTTV7GXDc1Gb/4dqutDjL7I7d36uN/WHENK2GRqvGZ3je/v2bdTX16NHjx4qt+fk5IDL5aJLly5GD9ZQBw8exLp16xAbG4sRI0bgk08+waRJk/Dnn3/Cy8tLbftff/0VCxYswObNmzF9+nQcOHAAYWFhyMjIQP/+/TX8hfZD+VKzc/Ai5H0yX2WGTSquRXXORbiOWaa/Zu/ZPRrvl89MTZ0yBcePH5ftVyRUudRflX0KhftfUwSPNXeyAADWrh1QX14g6yr2OEB2n7wG1X+mQZh+FAZXl2AYdFjyKaxsnnw4P5k1PQav+e+D6+GHil8PKhpclJ3ZpdhWPpOpVn/1ce6tlZMX2HoxqnN+U5k1lteCFYlEKCgo0LtYyWHgFJSd3Q2JqMqg8+44cDLKzu5Gfn4+8vPzYW1trRKE6LtkLq8pW5oU8+R4GQ543UfAdewy2HUeBA7XFtU5v0GYcVRnLqmxl/MNyWM1ZFba2JlkYzt+Ndel6aZYFGXqsRnaVMNclJ9PXe3BIyMjwTBMq5j5JYQ0L6MD3yVLlmDp0qVqge9vv/2G3bt345dffjHX2NRs27YNK1aswAsvvABANtN0/Phx7N27Fxs2bFDb/tNPP8XkyZPxr3/9CwDw3nvvITk5GTt27EBsbKzFxtkaNAwQGuZ9snXVAGtgzV5WCmltlVr9XHlwMmFCFI4fP67z0n1pUgy4Hp0gfLywjOFYqwfIBzbCdUIErF19UV/2AMLM47pzb7NOgNdzpErQCyh1VbuThdKULyEV10BaUQiG5wSXkfNh5eQO8cN7eJR1QqWTm0O/sbDx6QlhdiIk5QXgD58FSWWRSooDr+fT4EjFuH37NgBjO4qxkD4qVZz3hvnNmrYHAP9OnRRtgpWDEH2XzG079gYrEcOu21PwmLJG498xNJfUmMv5huSx2vn1g32vURCmH4U49wp4AfqDP3NVRGjq4E4TbV8mlKt9mKIxx2ZsU43GUn4+a/P/0Jt2pam2MSGENGR04JuZmanxjeXpp5/G6tWrzTIoTerq6pCeno6NGzcqbuNwOBg/fjwuXLig8TEXLlxQaagBAJMmTXq8Wl6z2tpa1NY+qcfasHZpayYPSrhcrlqAoDbDZmNvcJ4lADz88Q3YB0zWGJx8/8MPOhe1yRaiZaH4yH8BiVhngFyWHAv+kOkQlh1Ffdl93bm3pffhPuUVjeOuzf8DDMca4uLbTx4nEqI65wJcghfBoddoOI+aj6Kf30fNrd9VZoG5nl3hsfC/ivSMhgFq5aUEHD68G2fOnMHIkSON6ijGcXADwKD8/HcQP9ykNSVE0YGMZeE0aiGsHF1QX1GEE6m/KoIQQ/Jv60vz4D559eMWxqpMySU1JI/R0DxWaVkeQkJC4OHhYbaZZEB3RYSmDu50Uf4y0bDahynBeGOOzdSmGo2h/HzKGtvobroizs02aCafENK+GR34MgwDoVCodntFRQUkj0sHWcLDhw8hkUjg7e2tcru3tzdu3Lih8TEFBQUaty8o0B6EbN68Ge+8807jB9yCaJrlkUokYCVixTZ2fv3gNnElSpNiUHMnG44DJ8GmQy8IsxL1LrIJeeYZeLi7awxOhgwZgsjISLiMWa7/0v2ZXQYEyNmoLbypuF15RvZJ7q0sF9hx8FSN3dSU81tVKlVkJaL23jUU7v+3bDa3ohA1f1+WjwDWbh1li9+Cn1PZL4drq1K/19rFByzLYty4cYiJiTGso1jWCTBcHqpvnAfAgpXUaU0JcRw0WVZyzYoLhsuTNdpQlDxjYMV3w8qVKyEQCPReMgeA6j/TYOvXt0lrpSoH5c7Bi1S67jWsV23OmWRA+yx2cwR3hti3b1+jg/HGHps5U0gMJX8+xQ/zDEr/4QVMatO1jQkh5mF04BscHIzNmzfj+++/h5WVFQBAIpFg8+bNGD16tNkH2NQ2btyoMktcWVkJf3//ZhxR42ib5RFmnkD5ua9l9U0f17l16D8OVnx3VF05rZTLCp0zhjXFudj0vizALSwsBACVrlF37twBy7J6Z+Gs+O4AYGClht0AAI+wDajNvY6qa6dVZmTBlf1trmtHtX0YWqlCeClOPZcwU5an/OjaGZ2L6uQ1cB0HTUZkZCR27NiB+Lg4vbPTACub0daTEiK6mw1xyb3H580D/JHzVIL3+lLZYrc1a9YgIyNDZ/6tuZugGGr06NFYt24dtm7dBmH6MQCsogU1I66GuKJI5W8rzyRrC4IbWxGhOYI7fcwVjDfm2JqrqYb8+TxxPtngtKu2XNuYEGIeRge+H330EYKDg9GrVy8EBQUBAFJTU1FZWYkzZ86YfYByHh4esLKyUgRXcvLV65r4+PgYtT0A2NrawtZWvTpBa2TIh2ZpUgxYlkXt3WylldIc2HUbDvH9G5BUV0CYfhQ1d7LgqLT6X1azVxZ8/d/rr0MgEOjMGdQ3Cyffl6G5sLxuw+HQazQceo2G6/gVKIrbhJqc3x7vTASAUakHLKfvkimvVyCE6Uf1lhKruXdNpXmHnLwGrn3PkXCbEIH6/Os4c+YMPvzwQ7z22msQ3clSqaJQlX0K4pJ7cJu4EsKsU2DFIp3Bieh2BkR/ytr06htjZmYmzpw5g7Fjx2qdNTVnExRjxMTEYNu2bbD19If94xJV8i9k4vJCrF+/Xm0W05D8VF3pHZI6kezLWtFdtVnspgzujFlYZo5gvLHH1pxNNeSNb2RlDKm2MSGk8TS/C+rQt29fXLlyBfPmzUNRURGEQiEWLVqEGzduWLRSgo2NDYYOHYqUlBTFbVKpFCkpKRg5cqTGx4wcOVJlewBITk7Wun1bo/yhydaLIXlUBqlYlr8s/9Dk2DujLDkG4pI8uI5ZBs/wN+E6ZhnqS/Mhqa6AjbsfvP/5IbgenVB2dg+KD7+HsrN7wPXoBP7jUlQXr92Cc8hSeIa/CeeQpUgUpCMoKAixsbGyVBOGgTArUa1fvRzLSlF1/RcA+gNkeU4xf1iY4raqrJOoyfkd1m4d4Tp2BTzD3wR/yHTUl+ajLGWX4u/KK1U4DpykNQCounwE1m5+OgMNa1dflCR+qnY8yjVw+cNClYKJeCxfvhwMhwOGY93gPPrDe+FHcOg/DuLi23pnvPmDZLPzVnpSQrju/mC4PGz+8EOIRCIAshk05dl4ucDAQBw6dAhVQiEKCgpQJRTi0KFDFgt6lb+Qeb+wA07DQ2HffQSchofCd0UM+ENnYOvWrUhLS1M8JiYmBsHBwUgUpGt9rQFPKiJUZRxD0b41qLyUgIoLP+H+3jXIi56HR1eSwHA42Lptm8r+TQnuTDnu2eHhcOTz4ePjA0c+H7PDw1XGoUwesPL6TzQgYI1XPM8NNfbYmrOpxujRsjq9YAFh1gmd7yHtpbYxIaRxTGpZ7Ovriw8++MDcY9Fr3bp1WLx4MYYNG4annnoKn3zyCR49eqSo8rBo0SJ07NgRmzdvBgC88sorCAkJwdatWzFt2jT88MMPuHz5Mr788ssmH7uhzFVCSf6h6TBwKh7Gf6gymytfKAWwkFZXaJw5dBg4EXmfzIPDwCmw8+8PG58ecB1TDpYFrBxcUFd4E4X7N+icdZRfgg0KCkLq+fM6L/VLyvLB8JwN6pLG9e4GXpeBALSnLth3HwFrdz+UJccocoA5do46L5kaXMJt8DSUndmF+7sjwR805UlucfZJ1Jfcg3PQc+C6doBUXKsIJsRiMcLCwpAoSIf32oMqOa0AIHlUZngVDQCO/cYYVO7sdHIyHBwdERYWpnchVFMV2Dd2FtPYy/0qHb8O736catMRrmOXa82PtXRwZ8rCMnPNtDb22Jq7qUZERASkUilWrVpldHk7QghpyKTAt7y8HL///juKiooglap+A1+0aJFZBqbJ/PnzUVxcjLfeegsFBQUYNGgQTp48qVjAlpubCw7nyRviqFGjcODAAbzxxhv4v//7P/To0QPx8fEtsoavuUsoyT80hRnHZO2GNSyU4np1lS3w0hCAQFwDsLJFVsVxH6gFzhJRpf5V1o+Dlw82bUJQUJDelAmX0f9EWXKs3i5p3gs/UtyuK3XBacg0sGBRnrJLJQdYWwBgVAk3AFzXDiqlzKycvQEwqBAcQEXqd09yVjkcODk5KS7DV5z/Rn28XDuDL+cCANdDd965crkzp1ELkJgqQHxcED755JMm7VrWkCmX3U253C9vW3z48GGDvpwZUv7N1ODO1DxdcwXj5ghcTe3QZy6RkZHgcDiyfPTcbFkHtybKRyeEtC1GB75Hjx7FwoULUVVVBScnJ8hbGQOyig+WDHwBYPXq1VrLpmmqITx37lzMnTvXomNqLEuUUPr+++8BQO8iLv4QzR+EjI09AAbl575RLPJS1Lf93y+oL82Hfa9A/cFL3B6I6+vBMAxYlkV95UPZ4jSWBRgGjLUdAMBtYiT4g6eCYRjZAi61Sg1PFm2Ji+/Azq+v3hlaYeYJlCd/AWu3jnAMmAArRxdU/hanMfdXccyGlnBjOPAIldWOZuuqUfW/cyg/sxvWbh3BHzRVpbg+K5Xi66+/RkREhM4qC2CB6isndQYnVdknATCQVJYYMEZZVyunp2aDGTUfZSm78Morr+Dnw4fxwaZNzRIkGDuLWVhYaHJ+qrEBs6WCO1PzdM0509rYY2uKphr6GNsopTk0V9MTQojhjA58X331VSxduhQffPAB7O3tLTGmdsUSJZRiYmIQFbVOlvOqa6HU3+kqpcGU1RXeBMCCP3QGeL0CUXX5iMqsr7VrB1T/mYaavP9pLBkGPA5epFKcSkuHy5jlqEw/AonwoWImEiwLViyC46ApisoSXM/OkJXmYlVmU+17joT75NWo/jNN1uzCswu4rh20ztBqS4GwdvHR2hGMsebC2sVbb2OMquyTsO85UpGmICrIQfmZ3QbNLOr68B479t9YvXq1zuBEXJIHW/9+WoN35TEyXB54XQcrxilbHJcJwcVLCApq2pq0csbOYgIw6XK/KTPLlgjuGruwzFzBuDmOrSUEnsY0SmlKLaHpCSHEMEYHvvn5+Xj55Zcp6DUTbbNB8qYIzsGLjCqhJBAIEBkZCTAM+IOm6s9VPbsbUnGtWscueQqBtVtHFB3YqFbaq+pxh7XS07HwXfKZxr8hn3X0WPAhqrJOQlJRCGs3P1lerFKJsKqsk7Dx6gr+4KkQXkqAtVtH+Cz+BNLqSjAMwLF3UYzP1q8vau5kQXg5Ae7T1mmdodWWAqFcr1h0O0M2O6s8q1z2AIDuEm7iknuw6zxI0aa4PPU7vfWHlZ9DXR/eVVVVGqs/CDOPo77sPrhe/4B97yCUJcfoHSMA8IeFqj7nj1tMOw6a0iw1aY2dxfT29jbpcr+p+bHmDu4am6drzmDcHMfWUgLPpspHN4QxV+ya+7wRQkwIfCdNmoTLly/jH//4hyXG065omg2qybsO4aUEldlVrkdnHI6LM6iE0rboaNh6+KH2YZ7hbXIbtBuWpxDwB09DWfIXetMlRHeyFYvN5OQL0WDFRf7252SP0VOf1tq1I6r/ugCuZxfkfTJfY9cy5YVb9r2DwPXorDb7qS8Fgj94KrieXVCaFKNa/xcMrPjukAhLIEw/pmjk0TAf2cXFBZXZJyDMOAqGwwHLsnqrMWia0dP04b1mzRps2LgRbG31k7ExDGx8e4PfdShq7majLDkWXM+uWpp3PEkJcZsYqTYbL28x7TxqPurzrzdLpytjZjFNvdzfmPxYcwZ35sjTNWcwbq5ja0mBZ3My9IqdVCrF6ZQUmhEmpAUwOvCdNm0a/vWvf+F///sfAgICwOVyVe6fOXOm2QbX1jWcDZJ3FFNrnJB9EqxUiu3bt+Pf//631v3JA2mn0YtQe/5rlQ9bqbgWkkflKjOo8oVS5YIDcBoxBxwOBxx7F8Uir9rCm3oXsIn+Tkfl5XiVwFd5IZo8kNSbdnE7E6XJMQDY/2/vzsOaurP/gb9vIIQAYUdQwX2t4t6qVdG6L7Va+bV2qm2tjq2itkX9tjpTpx2daXWqYrUVpy61tXbaGUdBUdxwA9RWUXAZ92oFFETWAAEScn9/hFyy5yYkJIHzeh6fmZLtk7Dcc889n3PAKhVGp5ZJ+k7kAvaniau55yk6tgV+z0+HQOTDa5OaZ/gz8B82EwV7V8F/xBwoq8tRcSsNtUWPIPAOgLKiGHAX6pVbeLbtg7KsQyh48gRyuRwymQzt27e3WY9TsViMoUOHIvXMGfj0nci9J/WJifqzlWYcAAAoih+j+MQ21E9uA0QRkfCPesNgCYq6Plkg8rH5wAG+LM1iWnO53xb1sbYI7mxVp2vrTKst3htlL/nVb9f8nokFCxbAs0Vbh4/BJoRYEfjOnTsXALBy5Uq92xiGsevY4qZGMxvEZ6LYsmXLuAOgIepAWhgcDq/Og1CedQTCsI4oTf0B1dnXoTnW1iOiB5TFufD2kaAi6wgqso5wt7n5tQAA1Dy6iQAzo4ZVrb22ofT8fyEMCtcbyuDV4wXkbHgVkj4TTGdE2/WF9JLpwRHq2t76jVss/Ie/jYobZ1B++RDKLx9SZUfDugAw3L1BXULCeHjVB/6ntnOBrWTC+xC17lYXXCahxfS/wSOkDdd6rPLOL5BeOgC5XI7Q0FDIZDKbt8FiGAbuAa0QOGYeGEZQd9JSzK1BfaIgEEvg3XUoKq8eRnVBNro/8wx+K5Ai9A9/N1H7e4SrT3bkpCtLspjWXu53dCcCW6xDN7h0hkwr1bOq8K3f9uo1HtUntyF4xlq4edR/7xw5BpuQ5sziwFe3fRmxnmY2qCrnOu/2YMb+QGoG0pJnpyB/90d48uMyVf2pRg9TaWYyarKvqR4U0AoBI18zOPIWPEYNq1p7sSg5/W3dYlUBZOD4hfAMf6auP63556l6fBtufqHwi3rT6PuvepCFsosJUDzNBuPuCcbTGyWnd6o+t5Fz9UYLl57/N5dlM1RCwghF8AjvgRZTl2n11AXAvV555iGETF3OfV03gLV1j9OioiKkpqbCb9hbqM69obdmddmHulY3dPrfuAPojYwDYBjGbO1v4PiFBt9LY7Mki2nN5X5n6ERg7TqcNbi0Rwcae7NXZtqS+m2wrKpFpEbgy/fvOiHEtqzq40tshxvJWfDQbHbV3KVpzSBMGNYRgPmxtkET3tMavat5G2DBJLXnoiHpOxFu3v5aAaS5FmFVOddR9us+yB/fAgDkbJiuVdOr+f59eo+rb4UGgJVXma0/LkhcA882kSg+9k/9EpLLB1GT8z9U3EiF7wDtEp3619vObf4zFsDaIrOYlpaGL9auRdKBJLBKJUrO7ARYwM23hcGyD58+EwBWCbamEgJhAHcA7d46EJmXklD94DK8e0/QqE+uz8J7hj/jVJOu+GYxrbnc7wydCCxdh7MGl/boQGNP9j55sKR+G4ygrkWkNj5/1wkhtsUr8N24cSPeeecdeHp6YuNGwzv41RzZHN8VDR06FGvWrMFHH31kkzpRdRBWlPyl2braqgeZkGYc0Ap8NW+TF+XyaO2VDACQ/vpfSC/s0wtaBUIRROHPGHye+prmcK2MrW5Nr7o8wU0SVN8KDYB7oOlOCrL7lyC7lQ7ZrXSTJwDFKd+g8vZZ+Ee9qRVsqzeCsTWVYN2FRgPYhmYWlyxZgvXr18M9oDX8RszWyb7ngnEXwqvTQK01SzOSADDcwVR9AM06vQPHjx/HypWrcJrbHKedhXf1SVeWXu53lk4EfNbBN7js0qULRo4c2ajrt7YfsSM0xskD36s9Up32h7ocWXJESHPEsKxGJGFE+/btcfHiRQQFBaF9+/bGn4xh8Ntvv9l0gY5WVlYGPz8/lJaW2u2SsEwmg4+PD/xGzIHvs1OM3q/sQiJKT+9AuVRq8g/kxo0b8f777yNg5Fyzz1d8cjsiYv+j90dZdZsqu2ooaNTcZCXpNxme7ftwQas6s6juzZv34zJUZ1/T2qxVP/JY/wBf/9xJEEX0QHXO/+ou9TMagS+DgJF/NP/+TmyDe2ArtPpjvNED06NtMagtLwJbI9Nat/oz8I+ahaobJ7gA1tgBMz09vS6jl6CT0Ys1G/Sa+4wDxsTAt99EjTXPByuvQnjMd9z9K+/8goK9q5CXl4fQ0FDu50AUEgGvSP3WaH369MFXX33l8CDFmVkTLDckwJ4WHY3ktAwEz1irN9YaqPveb50HRcljvPzyy41W+iCTyeAjkcBv+Gyb/I2yp7S0NERFRRk8eVD/TpVfSkJqamqDPzs+ryXNSELojDVG+507w2dGSFPAN17jlfG9f/++wf9PbEMsFmPK1Kk2qxOdPn063n//fZ61Z6qMJnQCX82Rt9JLSZA/vAJx5Fi94ClgzHz49pvEPU53I5pHaEdUZ1+HmyRIawMaI5LwGrBRk/8bd6lf/jQH0sxDqC3NB2C+bthNEgSANd/PWN3btu8Ebt2i1t0gzUwGWBalqd9h2ssvIyZmB3r06GH0kqSlmcW0tDSsXx8H90DDY6M1M9fFxzaDYVA33U4ASZ+JWmUYgH7N7nvvvYf+/ftj0aJFuGygNdrNnCt2H2Th6Cyrteuw5jJ5SkoK1q1fj8OHVV1YLL20LpPJkJCQAPegtibb+an7bx9KvWDX0gfNz6yh/YgbU2Nmps1d7al68jvcJYEQte5m8PHOVHJESHNhOBowQi6Xo2PHjrhx44a91tNsLY6NRXXBQxSnbAXLam8g1L40HWv2uWxRe8Z1TgAwbOhQTIoagNLTO1CwdxVKTm6DsrIEoa+v1gp6gfoDizAoAtKLiSjLSATAgvHwQsDIuQiJXgH/qFlgq6RmOz1I+k4CK6+CMKQtKq6moOTMd3VBb90azbw/9RAHvicAfs9PhzAoAmUXE1Tt2Ipy4eEfiqFDhkDJshgzdizCwsLgI5FgWnQ00tPTDT6fWCxGaGio2QPZF2vXAoD5z6HPRIBhUHR0M6py/qe1ZramEoDxAyjLssjMzIRP34loveA7RMTuQcuZXyBwzLtoMWsTfPq9iJiYGKPvxVppaWmYFh0NH4mE12dmL9asIz4+HlFRUUhOy4Df8NkIiV4Bv+GzkZyWgWHDhmHLli16r9G3bz+MHj0ayYcOgWVZeLTuDu/eE40+xpCNGzeCVSq5dn4h0SsQ8MIcyAtzkL/7I0gvHwJQf1Ia/Orf7PL9M/SZvfPuuxAI+I3zduSGSXWnBXHPsTz2SyRAJpM1+DXnzZuH1NRUTBpW/zey9PQOTBo2AF9//TVqy4tt8nedEGIbFm1uEwqFqKqqstdamjVb7kBX154dPJ1s0ehd3dsYoRjufqFIP3sW5VIpACA/Px8dOnaE35AZWrXBmriNYSe2Agbqa2tbdkHJ6W95B6RPfv4YwqA2Wpu8StJ+MFt/XPG/0wAY3icAqFXAu+colJzeCYBF4NgYVD3IQmpqKjxDbNuDUyaT4cCBA+CTuVYHOu4BrSG9mAjP8Ge0TlpM1eyqs1/q1mia7FWXGR8fj5iYGIiCw+E79E0Ig8MdsjnLmjpPSzdwqV/Dzb+lXp269FISAsbMg7Aox+ymr7S0NCxfvtyCdn6qXsy637+GZtiNfWZHzx2FUsmi8sphm1yVshdHZaZNXe0RCAQO7yxCCKlncVeHBQsWYM2aNdi2bRvc3akphC3Zcgc6n04D8sJsBI5boPU43ZG33s8MR8npnSgrK+OymKzS9HAIQN3mDBAaKGcw1+lBTd0xwqfvRL3ATRjWEU9+XGby/SmKciGK6InyrCOmN59kHgJYFrlb5qgCYLDwGz4LwpC2db2VjQcj1u5iLysrA6tU1S3zDcx9eo1VZb1rZJBmJsOjVVeUZx42egDl22fUlrvKN2/ejAULFgIMg+qnOag+8119C7ZG3PlvbQcCSy6Tsyxr9jWKj21B6Oufmz254PO69e38crROWMU9x2Lfvu2YMnUqkpKSrO5gYO4zK0hcDdmtsw7vi2yKLSblNYShjZfO0lmEEKJiceR64cIFpKSk4OjRo4iMjIS3t7fW7Xv37rXZ4pojW+1AHzp0KBYvXox169aZHGtbmLzR5MhbViHnDhAymYzXwAalvBrVj+8AALwjx0JZWaq1SUcgFHEDNswFpIyHl8FspTiiJwLGzEfxsXjI7l+CpM9EnfeQi8Cx8yEMaYv83eYDZM0NetLLB1F6+jtUtmhvtg7Z2mypUCjkNuvpjlzW/RzUQyeEQeEAt2bV90iRd9voAbSxs1/qbKF7YGvV98NAl47G2vlvTZ1nUVEREhIS4DfsLV4nCnKFAqJg88GqNOOAyZMLvicomu381L2Ygbrvn1KJI+kZDboqYe4zC5myDI/iZ0GaccBps5e27qttK87SWYQQYkXg6+/vj+joaHushWiwxYSm3+7fh4d/KBifIJ2xtgxEEZHw6jYExSe/NXibf9QbELXuhic7F2HY0KGYMXMmt9EHDIOKLP0yCr0hEQBKTu9UDbfQ2aSjGrDBLyA1Fgz49psERWEOpJeS6kcLgwHj4YnQ1z/nSjECx85H0dF4/RMAMxv01B0rLMmWmjuwqTdNJSQkACwLcdfnIbt1jtfQiZrHdwAwqLhyFN69xqHiyhHcu3sX7dq1M/z5NGL2S50tVGfHWYUcbE0lPNv20btUb+++pZZmulNSUvD15s1ITEys66H8HWoe39LrJa2mPlFISkriGaxuh1e3YUZPLiwdhOA/fJbWutT1+CFvxFk9GYzvZyZ5Nholp7Zj/PN9sf+Ac2YvnWVinyHOMHmPkObO4sD322+/tcc6iI1pHsh8n51SN/a2BAwDCLw0hkzU1qL4xFaEzfkabkJP7jb1AaLqye84U/AQopA28B36JgRiH1RlX0fl9RNaBxbNnrxenQej8vbZuszfBKP9eY0HpIegKM4FAHi272PyfXq26wPppQNo/e42MO4eqCn4HU9+XoHKW+kQhT+D6twbqLhxBgALRfEjVd0xAFWALEbo66v1apU1u0pU5981+frqIOjYsWPY+d13JrsAaNZPuge1BatUIGTKMpRnHkbR0c16mWvN1nCi1t1QeGgD3CSBCH7pI9Q8vgOZmxtCQ0ONrq0xs1/r4+IgCo6AuOsQPE1YrT9tbsBLquznxUR49xhp153/lma6x4wZo1fTqvuzqkldgsM/WFVCXpht9OTC0s2okv6Tua+pr4yIuwwGq5CjpjQfAu9AuHv5WnRVwpLPjFUqsWXLFvz4449Omb10lol9hBDnxDvwVSqV+OKLL7B//37U1NRg1KhR+OSTT5zqDx6pp3sgEwhFEPjrB0nqWtzixM/h1WuCXiseMAw8Ow1EbUUJt+lLTZpxALLfMiBu3x/SS0mQ9H8R4q5D8OTH5SYnqqkzfz59xqP0/H/BKqrrM7aMAO5+deu0oP5VHbCLvQPg02cCpBkHUHEzDcqKEtWgi7qNR/KnOSi7fBDKsifwH2p6g566bZRmyzDDr89gypQp8GxRvwFO/jQHh84cw769QxEfH4+ePXty9ZN+UW8iZ8N0BLwwp+51JkL28BpkN8/UT6bTGDohat1NlQEvfozQGWsgat0Npce+5hWsNkb2S32S5dlxEJ78uFxvQl551hHk/7gcXl2fR+Xtc/AI62LXnf98A8mqB5kAYLJGV/2zqs6wqgJN1dAWS4LVqltnjH6/+J6g6G5G1bwyopSVI3fT69z93XyC4Dt4Onz7TeSVYbfm6oCh7KWzXMqnulpCiDG8A9+///3v+PTTTzF69GiIxWJ8+eWXePLkCXbs2GHP9RErWXQgEwgwYegAvUuXd+8F4Mq165DdOQ/3gFYIGPlHrXG/iuJHUBQ/grT4MdwDWiJg1Fw8TVitCnx4bNJxvxmE2rInCJ2xBh6hHcHWVILx8ELVg0wU7F3F1b969x5rtJm/oc4UwsDWAABlRbH+xrROA+HdcwRyv36L96VlZXW5wcCXC4I0hnxU597QLvdgGMyfH4PefXpz9ZPKylKArd8gWJVzHbKbqRC1iUT1w6twD2gNn15jIQwKR83jOyhM3ghFUQ4kz77MBcF8g9XGyH6pT7Iqb581M0I6CQAL2fVjdq2v5BtIVlxPgUeQ6R7K6iy15sQ7dX31+HHjceKi+WDV3T8UNU9zEBv7L6Nr5rsZ1bNtH1Te+UX1/bt6BFUFDwEAArEP/Aa/qjH17xCKj21Gdc41eHcfbjbD3tCrA/YeD2wNqqslhBjCO/D9/vvvsXnzZrz77rsAgOPHj2PSpEnYtm0bBAKL2gGTRmDRgezll7Fnzx6tA8Q333yD//73vwBgsquBNEPVkkvSdxJYhRyVd85zmUxD6ludqeuKGUgvJMCn7yR4hLQBoNFDmGWhKMpFzoZX67OgdXXCwhbtNepfF2m9p/KsI3CTBEHg4W0wqBGIfCzqKlF69me9DXaaQZCbJAgBo+bWlSzE62U8pZmHkJWZCa+uQ8Co+yZrvL70QiKEQREIfe3vqM69CenFRJSc+Y7LgIu7DAKrqEHVg0xuale8BcGqvbNfvr6+AMPAPcDMQJL7l6EoykH10xzExv7UoNc0RPPn11wgWXRsC5TVlfAeMoNXjW7p+f+i4loK5IXZ3AnK2LFjkJx8yGywCqjKXEx9znxOUHr37o2rWYcgvXQAjECAbl274kaB+d9Ptu77bS7Dbu3VAUvbxjV2IEp1tYQQTbwD34cPH2LixPpat9GjR4NhGDx69Ajh4eF2WRxpGEsPZOoDRFpaGmJjY+Ee0BKMwN1kMFN57yJqSx7DPSBMNUyB5dvqjIXv4OlgqytRfi0FlbfP1j2xAIxQVBfoMvAIbA3v3hM0gshk5O/+UHMhkF5IAAAuGyovfAgwAvi+MM1gUKPuKiHNNN3nWHr5UN3EuWRUP7wGn97j9OpvAcD32Wmozr1R1/rMdMazKud/8Ax/hutq4d17rNbJgmf4M/AMfwZKeTWXARcIRXXjl7fCrS4LHRkZafIz1mXv7BcDPoM4JqD4xFZ8+eWXNl2L0WzjkiVYt26d0YlaAP/hJiWnd8Kr6/Nc6Une9hikpqYaDVbVV0T69u2LTZs28Tq5MHaCMmzoMLDd2yEtLY3bXMqybN0gIQYKaSGqc29qbXjTnPonu3se0S+bz7Bbc3XAkrZxLMs6XVaYENL88A58FQoFPD09tb4mFAohl8ttvihiG9Ze5l4fFwePoHBUF+aazd5Keo9HyelvoSjOg2fbPvwzqYwAbl5+KD73bwiDwuHTewYX3JZd2IvaGhkkJg6m0owk+A9/C4ybENKsw8jf/REE3v5QVpTAf/gslJzeaTKo4dVVolg1uS349c8hzTig1TlC3HEAfIe+hsLEf8A9IKwua2tu9PBl7rK55usbOlkQCEVaY6TVtdhhM9ag8D9/sagdmG6AyTfI5BuYlpWVgWV5DuIA0K5dO0yLjrZJAGQq21i9bx+WLFmC+/fv62W6Y2J2YMzYsbx/VlvO/hrufi24khfv3hOwL2EHdu3aZTBYnTBhAhbH7sLIkSMtej+6Jyg//fQTYmNjIQppA8+Og1SbRgNMbxpV40Zbn9iKOXPm8Hp9S68O8G0bt2jRImRmZlo0TIQQQuyBd+DLsixmzZoFkaj+YFxVVYV58+Zp9fKlPr7OxdIDmXqjkmTwa6hO2202mBEGqbL96ilqfPrzlmcdgSiiB4pTthrMkFblXIdc+NBs7WVN3h2ETF2udVk3YEwMfCJHoeTM9yaDGs/wHvDq+rxqg55uH+C6bF3r8HDk5uSg8lY6Aid+AK9uw1B+5RiqHlyG7N4FyH67qHrvWcdQdfcXBIz8o/mM58ntUMqr4Rneo66rxWZYMl1OIPLh3Q7M2rpLSx9nST05IxDobQS0NgDik21ct24dUlNTsWvXLr0gnk8pkHq4yePt87VKbTR7H9sjmy4Wi5GRkYHY2Fj49LNs06hm5ld9shEREcH7tfm+H0vaxl0+sdXgIJrGHGpCCCGABYHvW2+9pfe1mTNn2nQxxD4sOTCrNyoJgyMsqINlVK3CUrbCZ8BLePLjcrN1jyIvX4Ob4JTyasj41gnXBZECoYjLqFb87yRQKwfcRSjL2A/v3mO1eptqrqWm4HfAzR2Kotz6mmNGAGFwW7hJQpCbo9rEJM04UFfLDNUmP82OBZmHUHX3fN1t/C6bqzfLSfpOhDCkHQr2rzE7flk9xEIgFPEaOGHNuF5rH8e3nrzy6hGwSqXNJuFZMqRiz549ep8Vn1Ig3eEmquzqhxB3fBaMQKBVN2vrWlLN98d306j6ioKauk69VatWFr++ufdjUf9hAH7PT7f5IBhCCLEU78CX+ve6Pj7th9TZu9qyQog7PccrIBOGtIO84IGqhvVBZl0mNQmy+5dVl2TVmdSsw1AU5iBg1FwUn9hmMLi1qE6YVaruLxRp1ZDW5N4EANSWypCz4VWIOw+Gr8YwAt3d+ZL+k+EX9aZW5wj1fdQBr/p+hgK2omNbUH75EO/Ncnm7l8G334vc58IwbtyJg7khFurnMbVZydpxvdY+DuBfT+7hH2qTSXi2GMdsqhTI0HCTqpzrcM+5DnlRNmT3LgAMgxkzZ9qlRlXz/Vm0aVTjZFCdsfYUixEYGGjT9QGW9h9mVJtKjazd3kNNCCFEzeIBFsT1GMr0mrqcrc7e+Y6ejyc/mq6DlRdmw823Bby6Pg/JgCmQXkys26jGQlGUozEwAhCGtEfojDUQBrREcco3BoNbVccD/pf+GQ8v7mvyukBWd3CGNPMQ8nd/BJ8+EyAMbI3KK0dQU5iNXr164WZuUf1708gM12fRMiEvfgx3EwFb4Jh5KL963OxJgjQzGcLgdgDDaGSYGXh1eR5uXYdAeiEBVQ+yDG6iCxw7n2upZW7ghDXjehvyOEA7iKx5mAnPrlEQBkWgVlrI1ZMzDAPvfi9ZHahqstU4ZkOlQAADRuiJkFdXQdSiHZTyalRcS9Ho2PFHu9eoar4/a04GWXchl7FWMIxdAkq+mX7p5YPwaNXNaC9s9drtOdSEEELUKPBtwgwFty+++CJatWyJLVu2GK2zXLx4Mar37YPsVjokz06F9EKCXvZWHZCJ2vRE9cNrkExewnUkqLiZhqeJqwEAnp0HwidyLCquHIWi+DFErbuBVciNllHU5N8FWJjtuKB56R9QZePKMw/zaL0GgGEwZMgQnDt7Fn4jTGfRvHuORsnpbyHpM9Ho/apzbwCKGiiKH6Ho2BYTrc9yETpjDTzDn0FtjQzFKVtRceUoJAOmqDo9dBkM6cVEjU10gLizaoiFZh9ZUz18rc2E2iKD2rNnTwyLikLqmTOoLtjNfdbDo6KwZNsmvPTSSw0OVNVsOY5ZsxTo4MGDeOWVV+DuH4aC/3zCbWYEWJuVaPCh+f4s3TRafvUEyq8ehaJINf0QLIvjx49j8uTJJh9vDV7lIsWPIGnf3+za7TnUhBBC1CjwbaJ0azXZ2hpU/O8M9ifuh7p/LuPfGh4tO3MlAOqD+Pr167lWUKKQNhBFRKI6+6p+HaxvC1Q/vMZlI9VqpYVQBwtC3xbw6vQs3MQ+Wl0UjLUTk15IhJtviKruluelfwAou5ComtBmpquCwNMb3t2icCHrEJRK81k0N29/AMbrd6tyruNp4j+491t++RAqrp+CT89R8GzXp+4k4TDkhdkQhnbgPic3DzGCxi9ETV3fXvVJg7qVWeXNNBQeioNHaAcoZVKUXUjkNXDC2kxoQzOo6p83j+Bw+A55HcLgCNSWFUJ29SjOnDmDadOm2SxQBWw/jll9VeQvf/mL6rFKBVfLXXLmB7C1Nbwz4bbY5Kb7/vhsGpVePljXeu1bMCLvuisc4ZBePoQpU6bYpXOCqXKRiqxk1BSqrsLI7l8Cyyob/H0ihJCGosC3CdKt1SzPPIziE9tUl2k1pq+V17UBU7dB0jyI379/H6mpqXWXgBPqnrluXDGrhPzp7/DqMpjL9Kqps7HiroOhlEkhzTiAmodZ8IocVzdKOAmy+5fAuItUpRAawa1SXs3VMoJhUJzyjX6dcGYyFEW5WsF2bY0MsjvnEPACv64Koa99Bu/eY5Gz4VWzgVhtRQkAGLyf9PIhFB2NV02t05xql6kaMiC9dIAbPezZtg+klw9qjT9WZZRHoeT0Tigqy+DupQr2BEIRlFXlYBgGZWf/BVap5D1wwtpMaEMyqGlpaYiJiYF7QEtUP81VZXvrOiD4jZmPylvp+OCDDzAsKgq/XrNNoArwryuOidmB/Px8g4Go7lURQLuWWymvhvzp383W14p7jsW+fdsxZepUJCUlaZUPLVywAD169LA4ENZ8f3w2jSqKH0HUtg+CX1oKdy9/7nZ7d04wXC4CCERekPSbDDdJIEpOf2/XsdmEEMIXBb5NkGatJp/BCpptkFiFHKIuQ7F337+wa9cuvYluZ8+exejRo+HTZ7zRS/rqbKyodTfkfTMXwR61eHxadUBkBAL4MlUoepILUZvIug1xqrpWgacPV8soatkFxSnfoFZWplUPC5aFm29o3YYf1ejWsksHAL59ZOtqIN28AyDuPBjSzEOmx9peOw4I3PXuV5VzndfAihavroK4XW9U3vkF0ksHuM14VTnX60cbg0XuVzO5Vlmi1t0gu3YU06ZNM9iGyxRrM6ENyaAuXKSanMcI3LU7XmQdQf6PyxEwZh5EwW3AgEF1wUObBUDm+lRXPfkdffr2xZixYw22ZdO9KlLxv1NQVldorc2S+lqlUokj6Rla5UMHTydjX12LR0v7Feu+P6ObRjOToSjKgcA7AKHTVzqkc4K6XCQlJUX190GndZlA5I2io/F6a7fV2GxCCOGLAt8mRrdWUz0O11wbpJLUXXDzlKgCsbr60v/3/17Bn/60HEOGDOECnVGjRiE+Ph7z58+H7LdLkPSZAGFQuMGNWADg3e8lPD69AwVPnkAul8PX1xevvPoqjqRf0hrRq1nXytU1ggFbJYX/8FnwiRwJxsMLNfn3tO/PCOAeFME9zhTdDXG+PIZYqKezKYq0uy7w/VzLMw9B3K631murM8W6o43VgwhEbXqipi4AtKZFlrWjZ615XEpKCrIyM03WvxYf2wJJvxdx5kwS3nvvPWzcuNGigSqmGOtT3SsyElkFD3Ezt8hoHfv69eu5qyKsQo7ik9v1Mru646WNUXfsMFY+JM1IgnfviUhOy7BoM5z2+0sAwEJRnIuSk9vAsiwYgQCsUvV74zfw/+m1BtSc/mfPzgnqk+ONmzbBs0Vb/X69da37yi4mcCeythybTQghfFHg28Ro1mpqlg6YbYN0YivcA8O1ArETF44ieZj2QTotLQ1Hjx2DQCBAbWkeSk6r29wx3EhX3Qb6ytpayOVyhIaGQiaTITk5mQvM1XWt8rKnUJQ8Qun5vVwto7hDf8h+uwhhcDjcvAMAwOBI36oHmSjYuwrlWYct2hDnGd6jrvziAGS/ZUDSd5JeFg0AAsbMByuXoeTUTsgeZMKn52iL2kvV1sggvXwQwhbtUZN/l0em+ACWLl1qdTBg7cQ+ax63fn2cqrexmROA6vy7YFkWX375JZYuXWpwmpq1AZBun+pr165hzJgxZgdbaLZWUxrJ7KrHW5utr806DGFIe9QWPzZYPlT1IAu1FUVoMWuTxWUHhvpwA+D+//Hjx7U2DmpdTag7OfTqPAgeLbvYvHOCoVKRgJH6PwtA/e9u6fn/ojT1OxQ8eWJRmzV7jdsmhDQvFPg2MepaTfnTHLj7hfJvgwQg9A9/h7tPEPd13drAK1eu1F8aHjFHq1ZYXpgDz7a9tYJeoL4mVCgUIj8/HzKZTGsTVdmlJJSd+w9qyws1HsUg719/ht/gVyH77aLBbJvmSF/1EA15YY5FG+IAQBjYGgADN+/A+pKKOu07dMD9336DLOsQPLuPBMCAEbij5PROAPxLK9T1lwBQfHKn2dHGNQ+zcP/+fZPPbY6lE/useZxMJkPy4WT4m+mMoToB2AaAgU/fCVi3bh2OHz+OtWvXAgBCQ0NtEsios+PzY2LMtmWT/XYJrLsHd7upzC6v8daFOQidsQai1t30yoc0T4JYhdxs2YGxAE83+6/+/6NHj+bqs01dTai8fQ4Mw9isc4JuqYhA7IPCg3G8pj2ySiXvcffWTiAkhBBDKPBtYjIyMhAWGoZHp3dy2diSMz9A4CnRC0rVNMfhatKsDfzzn/+MM2fOmMyi6Y5MZVklKi7tR1hYGEJatOAOWmAYVN3PRMX/TqPyxhm4B7ZGwMi5WpvDarKv4mneHbhJgrT64+pevuUyuV2fh2fb3gbrCNWBuW73Cc3HhkxdDqW8GrUVJSi/chTSX/6D69eu4dKlS3VB4PcAWLCKGrRetAu5X73Je6pdxZWjELboAPmT31Dz6IbRjJj6M/eKHId9+7Y3+JK0taN0+T7u2LFjYHl0xlCdALAQd3wWXt2jUPm/Uxg9ejRYlrV5EMO3LZuk70StYQ+mMrv146Xj9cZbGyrvMTRFTbO+XCAMMFh2YG2Ap67PPnhiP2pKnpi9mnDp0qUGf9aGhp0o5dUoPPSlzTp3ANZPICSEEGMo8G1C6ltKReh1GdC8/KpJPVhBswRAk3rH+umT2+AZbDqLpnmwZ1kl8n/6M2pK8lEo1D5olWclqzoewPg0NPVBulZeBQAoSFwNsIBM4/KtuPMggGG4TK5n+DMQBrfBkz0rtbK37gEtEfr65/CM6Kn1vnWzwAKhCIxfCGrunce0l1+GWCzWCgKPHTuGqVOnouzszxDzbC/FiLzBVpcDtQr4DZ2B0rTdvDdKbdq0CR9++KHJ+/Jh7ShdY49TB8TfbN0K1bhqfvWvbv4t8eTH5aoBI0Mm2CWIsWiMrsbkP8B0Ztenz3jIfs+C7FZ6/VCWuo4duuU9hqao6daX67aFa2iAtzg2Fvv27lOdRJq5mmCLDW6Ghp3wLQvh27mjIZMECSHEGAp8mwg+BwlDGVn1YIWgCe8ZfW43SZAqYxc5lldNa+n5/6Iy6xBqSvKNBrbZcdPhJgk003f3EhRFuejUqRPu3jqrOqhrXL6VZh6CoigXXt2juGC78lY62BoZWkz/GzxC2qDwyNeQ3TmPoiNfw6f3eJNt0Uxt/BKLxXjppZe4GlihXwvIS56YaS/1GJqDD1iFHKXp/+I94vWjjz7iAm9nYKiekxF5m62tll4+CPeAVijPOGAyG2mLIMaiMbpgAKEn9zXNzG7Vg0yDPy+eHZ9F1b0LCJoUC6+uQ41OI9OdoqZbX66Z9bRFgNe/f38wAgaSPhPMX01o4AY3U1l1PmUhfDt3NGSSILEdqq0mTQ0Fvk0En4OE7P4lFB2Nh/+wmaoNS1ePoqrgd3h1HWK0DAIA19mAbxatNPU7tGzZEoVCw+uplZWDlctMTkNT9d2diOITW3H37l0zmeEkFIn9UPV7FnfZWdyuNwDAJ3IMZHfOw80/TKMThOG2aHw6C2jWwO7du7duY9wlSPrqX/4WtmgP1Cq4dTN8N0plHoK4y2CwxblOcVCXyWTYuHEjli9frpeRLLuYCHlhttn+sh6tukEocLd7EMO3LVvl1SMAWJSe+V5rTZK+EyEMboOCA19oZXbFnZ6D/4i3IWrdHblfzYRSVm5yBK86wwuhp96VBd2sJ98Ab+3atejUqZPBAKSsrIx32UlDN7iZyqprlYU8yIRE4+TBks4dtpgkSBqGaqtJU0WBbxPAu66xLpAs2LuK27BU8LQdfr3xwORUpapbZ8Aw/C5pC9zc8PD339GmbVuj61FWFAHgGUgDZi/fyn7LgPRSEsSdB8Jv+FsQt+ujtSYAcPcNQfgHPwPyKq4tWtnFBNWmK5aFQCDAyy+/zKuzgGb5w/Hjx7Fjxw4kJqraS6kvfweMfhdP/r1Cr/MDr41SRbkIHBMDecEDhx7U1Qe+hIQEsEql0ZOP/J/+DGnGAb0sqbq2GgBqHt/mNwTCBu+XT1u2mqfZWLp0KdatW2ewg4VSWgSPoNbwjZqFimspkN39VVVmwwjg5h2AsouJ5rs8BLdF/veLterLdbOelgR4CQlbkZCQYDAAMZbp1q2Jt8VoYHNZdXXrsqKj8dzJg6WdOxo6SZA0DNVWk6aMAt8mwKK6RgBXr15Fx44dIRaLkZaWhqgoc0FCjkVTt9zd3U2uR+CtamFkLpBWB01mM8N9J6H4xDbI7vzCBSdenQdBMuAlyK4dRd++/ZCZmQxF7nWtAIctzgVYFmvWrMGiRYssPnCKxWJMnjwZkydPrm/cXzfYQ1lZarCjhvbldNXgDt1MMQB4hLQBK69y2EFd88DnHtRWNcLXyMlH6Gt/R+6WuVDWVGr1VxZ3GQSlvAYeIW0gu3eBdxBz79497ufTGnzbss2bNw9Tp07V62Dx0uTJ2L8/G+5B7fB032cGOiSoRlDn//RnhL72dyNdHlTfR9X0shfh5hNkcOx0fn6+Rb+7QZNioZSV6wUgupnu6twbei3NxJ0HofbJvQaPBuaTVRe17gY3RolJU6diy5YtFl8mb8gkQdIwVFtNmjoKfJsASw8SmkEF3yChZ8+eZgNkdRbL3HrcvXzh5hNkdmqaNCtZdX9eQQELv6Ez4BHaoX4c84/LAAB/3bYJ3t7e2Lx5MxfgMAIBJk6YgMWLd2DkyJEmn58P9WCPmJgYKHKvq9qfGWuPVZcRUw3iUGWc60cb91ZtivPwcthBXfPA5xf1JnI2TDebrfUd8BKKT25Hq3k7wFaXQ+Dtj7KzP0NWdg4evcZotaXTzUICqt6zJak/AAAiIyMbfFmVb1s2Qx0sysrKkJCQgMrbZ812SMiNfxu+z07TOXl5CDAMDh08iO3bt2NfQgKklw4YfH2LapIZAVdXbCgAUWe683/6M6ofXtML2KWZyVCU5KN9+/YWf566+A47Wbp0N0JDQy1+/oZMEiQNQ7XVpKljWJZlzd+t+SorK4Ofnx9KS0udOqswfMQInL1yB63mbjF6kHi0dR6G9O6CUydP6t2enp7OTYfSDhJiuT9uW7ZsQUxdj1RTWTQAmBYdjeS0DLSYtcngekozklByfIvBy+fqA6c04wDAMAh44Y/wfXaK0fdedkE1yS0i9j9cICXLvoai5C/rNpjVj4sdHhWFo0eP4fCRw3apW9P9HN0DWqPV3Hi9z6Aq57oqA3j7bN1XGIi7DEZN3j2IWnZC8JSP8GTnIkwaNgB79uxp8Losofm9U1aWIuerNxASvQJenQYafUzlnV9QsHcVAAaqbhrq/63DMHCXBEMY1lmrM4dX50Fw8wuF9EIC3ANbqVqF1QVqhn6uzDG0EcfSzTkymQxe3t6q790fNxv/fdoWoxpywgi4qYPizoMhatUVJad3Ii8vjxvaYur1zf2usKwSj7cvhDA4AiFTl2t9XfdnRF2+Yer3qvxSElJTUxv8827J3wNrqK9GGco82vq9EBWZTAYfiQR+w2eb/ZtbenoHyqVSOukgToNvvEYZ3yaCZVkoih+Z3WQEtrPBx/Pp3Tpv3jx06dIF6+PikJxseriBuYxQbXEuAKg2h+n0RlV3awAAccfnzGaGy7MOa+2Yr2/iH67VH/jQmcPYt3cv3CWBdqtbM9T+TPczUK9Pv39xMmrL8uHWdbBFu99tSbfm1LKRvQz8h8+CMDgciuI8VF49gpqn2Vi9ejVyc3OxceNGwF2klYUsu5iI2ttnTW5e5HNZ1dxGHEsPzgxgtkOCpM8EFJ/YhtYLf0DVvQsoPBSHoHExqPjfGa1Mvbl2cnyyp4aGrxiqi/7t/v1Gy9ZZOySFL2snEBLrUW01aQ5cIvB98OABVq1ahRMnTiAvLw+tWrXCzJkz8ec//xkeHh5GHzdixAicPn1a62vvvvsutmzZYu8lNyqZTIb09HR4dR0CaUaS0dpRr65DkJqWZnLzkLGDtG5gwQgEmDBxIpYuWWKwVGDo0KFYvHgx1q1bpzdQQh3YBoyZD4BB2fmf63fQAxBIgiDwCoAoojsAQFGUayYoyEHg+EUAVJlU8yOBk+DRsjPXycIedWu67c/UB25WUYOSM9+bXt+FBACqOtvGPqjrHvh4j+zNTIa462D4DYrmvq5+Px999BEYhjEY3FblXIfc3aNBgZqtN+KUlZWBZXlO5gMLhq2Fsqqc6+Jg6eV3UwGeNOswFAaGr2iuQR2AAGj0TgjWDknhy97BNdFGtdWkOXCJwPfmzZtQKpX45z//iU6dOuHatWuYO3cuKioquLGnxsydOxcrV67k/tvLy8vey2106mDFu+dISAZMqasdrd9kpG6yr5RJUXkr3eKzdGOBxcmLR3F49GijgcVv9+/Dwz8UbiFttNYjiugBRVEuFEU5CBg1F779JkJRWQZlRRFXGyrNSELN43uoLXsCgZef4czw5YNQFD/WCgqkFxJVtY08B21oft0edWuGDtzuAa1Mrq/6wWUMjuzkkF3Thg58fDtR6PaCVr+fimspcPfR79mslFdDdud8g7o92GMjjqV1txB6ojzrCMRdBqH0zPdWZeoN/ZwAgDCkPUJnrDE5dVEdgDgyW2ftkBQ+7B1ck3pUW02aA5cIfMePH4/x48dz/92hQwfcunUL8fHxZgNfLy8vhIWZPhC4Os0Dte+zA+EZ/ozBDURlFxItPku3NrCQyWRITEiA34g58H12it561Jf79bLBlw+qSjLAQCDyAuPjD0V5McRdhwBg6wNoMKo19HuRm0anlFejkkcgpTtVS/11e/UEVR+4i4qKEBISAknfSSbX5917AlJPO6aNmaEDn6lOFNLLh6AofmQ0I8kq5FDWyODdW79sgK2pNNj5QpduoKYZANljIw7fg79msCsvzAZTW42q2+esvvyuG+C98+67OHouE6LW3Qze31AA0pSzdfYMrkk9vhsXG7sMixBbMXz0dQGlpaUIDAw0e7/du3cjODgYPXv2xPLly1FZWWny/tXV1SgrK9P65+zUB2rZtaNg6zbZCIQiuHkHcIGd5kESAPLz8yGTycw+N5/AQhTSBnFxcVq3bdy4EUqNhvq665H0nYjQGWsgEEu43sKlp7ajhRiqARNgoSj8HSG+YohC2iBkykdoMfVPaL3wB6hrSb26DEbV71nce7YkkOLG1ep8XfOysa1t3bpV6zMxtT57rsOcxbGxqC54iOKUrdxnq/5+uQeHo/jkNu77pSjOhf/wt/RGYaupvieGywYsqR8WuLnh2rVrmBYdDR+JBGFhYfD28UHCvn0Q9zQ9UVB1QpPA6+fd1GfAvSeNutuaR7chzTgAhgEmjxqK1NTUBmfqxWIxQkND8X9Ll5pdgyoAieUep/t3QBdl64g56tKb8ktJeLJzEcouJKLyzi8ou5CIJzsXofxSEtVWE5fmkoHv3bt3sWnTJrz77rsm7/f666/jhx9+wMmTJ7F8+XLs2rULM2fONPmYzz//HH5+fty/iIgIWy7dbvgcqKue/I6Cp0+5wMFHIsG06Gikp6cbfE71RidLA4u0tDQsW7YMMDP0wjP8GXh3HQpGIMD9+/dRXl6On3/+GS+99BIEbm5glUo8fvQIXpHjuNdn2FoALITB4ZA8O4WbGsaySss2Yqk3bul83V6ZML6fib3XoUkmkxk8ATJ24Kt5fEer93FBQQEEbm5g3IRGX0P1PTH8njXrh80Far179cKYMWOQnJYBv+GzERK9Ar7P/4F3La6lJxKmDv6Pts7XCnb379+PiooK7Nq1C506dbIowLZ2DcYCED5/BzSDZUIMmTdvHlJTUzFp2ACUnt6hOtE9vQOThg2wyckdIY7k0MB32bJlYBjG5L+bN29qPSY3Nxfjx4/HK6+8grlz55p8/nfeeQfjxo1DZGQkZsyYge+//x779u3DvXv3jD5m+fLlKC0t5f5lZ2fb5L3am7mDpLo12K83HnCBg9/w2UhOy8CwYcMMbvizpmYQUGWJPVu0hbjzYF5BzbSXX0a7du2wc+dOREVF4ei5TPgNn42gSaqDs+brawa36kvw0owkPN6+EOWZh+HRsiukmckmX7M864hWFwjNtdgrE2bpZ2LPjFxaWppW5tTQCZCpA19aWho+/PBDBAYGms0wMu5CCDzEqMgy/D3RPXnRpBmoXb58Gd59JiDolZXw6TMeXp0Gwve5aRZljC09kTD2Gbw4/Fku2N23bx8CAgIwY+ZM3ieUtliDoQBEJpOhc+fO2LBhA2XrSIMNGTIEe/bsQblUiry8PJRLpdizZw/97BCX59A+vgUFBSgsLDR5nw4dOnCdGx49eoQRI0Zg0KBB2LlzJwQCy+L2iooK+Pj44PDhwxg3bhyvx7hKH181Q/14hw0dijNnzljcD9Oano4AuMd4tOyE/N3LDHYw0H1dlmX1enYq5dXIjnsFAS/M0Xr9gn2fQV6Yg5ZzvgLDCFCV8z9ILyai8vY5rp+q6f7ASVobhuzdE1Tzc7TkM7HHAUZzo6K451hePXNNbSri02tVlR1ljN5HNXDhKkQhbeAVOU6vZVXHjh3xW/YjKGuqtPr/Sp6dCumFBMgLs9FyztdGa3Ft0Q9ZnR0HgNDQUO5zsObzbMgaDH0fDLVyGzp0KBiGQWpqqtG+3IQQ0pS4RB/fkJAQhISE8Lpvbm4uXnjhBfTv3x/ffvutxUEvAGRmZgIAWrZsafFjXYWhHdAzZs60agOQNTt8NUewmtwUlXUYisJsrFmzBkOGDMG06Gi9NRprpaXbZcAz/BluQ5+yuhxP9/8D0owDqHmYpRVIVV45jOqn2XCXBKLm8R0oZdJG6QmqmTm35DOxNWs3KpraVMSn12p8fDwAGL1PTcFDLF26FPfv39drWdW69RRs3LgR7oHh8BsyQWNs8BHk7/4IkmenovL2ObtuxDHWI3j0qFFYuHAh78+zoV0JDH0fjHVc+bXus9+wYQOmT59OnRAIIaSOS0xuy83NxYgRI9C2bVt89913cHNz425Td2zIzc3FqFGj8P333+O5557DvXv38OOPP2LixIkICgrClStXEBsbi/DwcL3evqa4WsZXV0Mn8Vg6PcnQ6+llZBkBhMFtoSj8HRXl5QBgdI1VOdcNZkg1h0BodoVQB1tLliypC6S0J9GNGjUKKSkpJifU2Zo1n4k9ghQ+E8KszY7ymfzH5z6awWFGRgaPbHISl/l1D2gJSd9JNp0gZiqjW/XkIUTB4QidbTrbPLB7OwQGBRkdrmEtmmxGCCH1XCLjy9exY8dw9+5d3L17F+Hh4Vq3qeN2uVyOW7ducV0bPDw8cPz4cWzYsAEVFRWIiIhAdHQ0Pv7440ZfvyM1tLenpdOTDLfDekarxRqEnni6eyleevllvSyxLs0MqexBJiS9x8PdPwysQg43SSAURTkoObUdrFJpsLG9oSzb/PnzG7UnqDWfia3pTmQzpCEt3fj0WuVzH82s5vq4OIiCTV+pqHqQhdqyJ5D0exHSy0koPW27IQemMuTevcciZ8Or8Oo13uTnKfBXnWh7hrS1+bTAL9auhSjYdM9qe/SmJoQQV+YSge+sWbMwa9Ysk/dp164dNJPXERERFmV2mypfX18IBA3bAGTp9CRjfSAFQhFYd6He5WdzAwMkfSdCGNIORUfj6ye8MQIIPDwxfPhwJCcn8wqk+HzdXsz1xiw99a1de2M21nADPp8rn/vwDdTVPZlFwa0RPW0adu3aZbMTGlOt/CCvMtqmTa0q5zoqbTCOWVdaWhq+WLsW+xMTETDSwNrq2LM3tSuhwReEEE0uEfgS6+3cuRNKJQtp5qEGTeKxZHoSnyzxhg0buNZPfGqJRa27AcpaiDsPQtC4BWA8vFCeeRipp3cAUG04agh7Hxx1PxNheC9U591FzeObQN0JW5++fW3+umrqkwv50xzUtuyiNdhEk7MMN7AkUAerRHVBNmJj/2WzExpzgTef1nnSC4lwD2xt04ysuvTCI6g1ADhkSpurMFab3dASE0KIa3PJPr6EH/WlWnHXwVAUPbJJb091c31zB1FjbZgGdm+HYVFRiF28WKv10+hRo3gNDPB9bho3CMMWgx74tPayFfVn0q1VAKSXDkApK0XAC39ESPQKBIyci5u5RUZbyzVURkYGwkLDUHJ6J3K+egPZca+gYN9nqMr5H3cfZxpuYNHYYABffvmlTYMZc4G3euOlNOuwwTZt6imCkj76E+vULB2uoVl6EfJGnF1bubm6+Ph4REVFafV9Nte+kRDSPFDGtwlTX6oNmbIM5ZmHDXcSyDwERVEu+vTpY/PX180S//TTT4iNjdXbga6qd9yH1157DT/99BPkD69AHFmfJS7POgJ5YbbeWNyGHtSN7Yi3Rf2lMSzLIisry+aXv03hsoTBEQgY+Ue9zgiBY+fDp894pxpFyrejSEVWMoaPGIH33nvPpq/PJ/DW7S6iuUZldblV45hN0S29MNTxRJMzncg0Jmu7lxBCmgeX6OrgSK7a1cGSTgLygvsQhUSg5mmOXYI9gP8O9K+//hopKSnYu3evqmabEcCry2BIBkzRCnob2p/VUTvi7dlZwRB+fXaT4OHfAvLSJ3b7/lvD0V0L+HyvHsXPgkJapBpOolHSU3n1MKoLshEwcq5V3VR0Gf59NtzxRL225trVobF/xwghzqFJdXUgljN0qVa3kwDj4YWqB5ko2LsKwa/+DWXn99gtE2Jqo5BmvWNKSgr27NmDlJQUjB49Gj59xiNwzDyb92flux5b7YhXD0CwZ2cFQ/i8T9lvlxDiBfyc5FwBkqUdRWzN3IbE4pStqC0vxubNm+ta5Glv/Cx42gG/XuPfA9sUw7/PxntCy64eQfXT7GY3pc3e3UsIIa6PAt8mRHODlqlLtQKhCKjb2KQoyVN1SBD52K39kTUHo1GjRiE+Ph4xMTFQ5F63adDTmAdH3Q02QONtSOL7PiV9J+Lx6R3o16+f1a9lL5Z2FLElzcC75mEmPLtGQRgUgVppoV6PYEMt8tQZa1sM1zD2+6zueCK9mIjik9u5yYVTp07F0qU/NqugF2i87iWEENdFgW8TYGz38tChQ81mnMqzjsCry2Buh789MiHWHozsFfQ01sFRt4bYzTcITxPXNNqGpKYSBFjSUcTWevbsiWFRUUg9cwbVBXUBKsNgeFQU/r5X+2dQt6OELTPWpmqe1VdyamtkKNgViwlDB2Dfvn22+xBciCWbIpvjpj9CCAW+Ls/UBq2qJ78DDGMy4yQvzEbg+IXc1+0RBDXkYGSPoKcxDo7GNthUdj6D8qzD8O49FpBX6bUVa+iGJL5Zf02uEgQ0du9lzd8t/xfqNwXKrh7FmTNncPXqVbNBqy1P3sz2gj7zPWoKc7F06U9Wv2dXZ82YdVugXsGEuA4KfF0Yn93L0owDKL+U1GidEgyxxcHIlkFPYxwcjdXWitr2QuXtc8jZ8Kqqf2/d7nzJs1Mhat3N6trlhmT9nW3nvzMEEbbsDGCrkzdH1zy7Cj612bbqXkK9gglxPdTVwQxn7urAd/fywO7tEBwcbPdOCaY4eod+Y67H0A58AJBePoSio/FwD2oNSe8JXAZRmnUYisIcuEsCuM1SlnRW0MxMinuOrc9M1gVDLMsabJ/mbDv/nSmIaEhnAHsH7unp6XUZ5ASdDHKsw7+HhjjiRGbLli2IiYmp/50wcILQ0O4l5n7vnKlDCiHNAd94jQJfM5w18DUWXOnSbJd09uxZVaeEvhONdkqwZxDUGAcjZ1hPfn4+wsLCEBK9Al6dBgLg13pKmnEAmzdvxvz583m/Fq92ZZeSAJaFKKQNvCI1ejhfPgRFcS6WLl2KL774wuL3aUvOFERY87ul3szWmIG7M2TGTXH0iYw9TxCc7USeEEKBr804a+BrKLgypPLOLyjYuwp5eXkIDQ11ePDpbNkqe6zHUOBUsO8zyAtz0HLOVzbtLco3MxnhL8Kd27cBMABYroczI690eP9eZwsirPnd2rt3r9ME7s7A3icylgT99jhBoF7BhDgfCnxtxFkDX2uzUoBzBJ/Olq2y9Xo0D4ysQo7suFcQ8MIcmwwz0Fwz35+B4hNb4dN3IvxHvK21qc4ZslPOFkRY+rt19MgRjBkzxmkCd0ez54mMo7PIQMP+9jYFzva3mxA1vvGa4eaexOmpN2jJrh0FW9e7U5exjUtDhgzBnj17UC6VIi8vD+VSKfbs2dOoB2WxWIzQ0FCn+cNp6/Usjo1FdcFDFKdstWp8LR+WtCsDAL/np8PNQww37wCuk4R6iIUopA3i4uJ4va4tqXsNi3uO5dFTOQEymczgc+Tn5xu8zRqW/m59vXmz2SEhjvp8HYHP0BRrPo/4+HhERUUhOS0DfsNnIyR6BfyGz0ZyWgaGDRuGLVu22PJtGGVNm8CmIC0tDdOio+EjkSAsLAw+EgmmRUcjPT3d0UsjxCIU+LowzeBK9wCtvXs51uDjnS34bErUO/DLLyXh6b8/BhjG5m3FLGlXBoaBQORj8HZzgaU9NSSIsOeBmO/vVkxMTIMD96bEFicyhmh22WgxaxN8n50Cr04D4fvsFLSYtQk+/V5ETExMowRhTa1NIB/OctJBiC1Q4OvCNIOrJzsXoexCIirv/IKyC4l4snMRyi8lUXsjB5o3bx5SU1PxYtRzAADp5YMWZ+dN4ZuZlF4+CI9W3bT6BetyVHbK2iDC3gdic79b0owDWL16NTp06NAss3/G2Csbaq8ssjUacrXNFTnTSQchtkCBr4tTB1eThg1A6ekdKNi7CqWnd2DSsAFITU1tVhtqnJG6rOT4sWOoLXlsdXbeGD6ZSUXxI4hCO5l8Hkdlp6wJIhrrQGzod6vk1HYEChUQCAT46KOP0KFjRzB2yOa7KntkQ+2VRW6Ihl5tcyXOdNJBiC3QAIsmwJEjXQk/o0aNssvwAT5DDfr06YObOVfAskqnHGJh6cABPgdiefYVrF27Fp06deKCK2t+N4YMGYJ+/fohPz8fP/zwA/7yl7+gSO4OvxFzuE4Fpem7Ib180KWGhNiLPYbDOOPo7eYyTER90uE3fDaPkw7bjronxF4o8G1CGnukK7GMLcfXWvK8LMsiKqpxJllZw5Iggs+BuDr3BmpZARISEpCQkAAwjKqJG8ta1AVAt4MAAIODQIRhHfHkx2VO+/k2NltPTnPWmlp7/T47E2c86SCkoSjwJaQR2Ss7b+55nT07xTeIMHcgrp+OFw6vLkNQefss3ANaQ9KnflJectpRJOwbZrKXrGYfWr/hs1FxKw1KmdRgllkc0RMBY+aj+Fg8qh9chnfvCVZ9vk3lio2ts6GNMWLcWk39apuznnQQ0hDUx9cMZ+3jS4ilnKF/Mx+mgghTPVQ1p+OJuw7Bkx+Xm5yUZ6yXrG4fWr59mIuO/RPSy0kQCAQWfb626k3rbMGXLX/enG3ISXPibH22CTGGBljYCAW+pKlxtgDJUsYOxJrT8Z4mrLZ6Up7u89dWFCPnqzd4T3K7f/8+xGIxr8/XFhPOGho42/vnwVbP7+ipk4Dr/+5Yg046iKugARaEEINcvX+zoR31Snk1Ku+ch0/vcWAVcu7/W9oFwFAHAcbDC2AEvC/3hoaG8vp8bdGdoiFt3RprIIGtft4c2cGmOQ9voLaZpKmhGl9CiEsxVEMq8PThpuOxNZUWT8pTB2WGaogFQhG8Og9CedYRm9aY8u1OERcXZzCo0AycdZ9DMmAyilO2IiYmBpGRkXqP161htqT+2ZEcUVPrqp+VLTWHjXyk+aBSBzOo1IGQxmFpMKNbQwoAASPnwqfPeF41uWUXElF6egfKpVLu9YzVEGvWD9vicq+pWmVza1SztvaSLl3zR5+VvuZY7kFcA5U6EEJcgrWXkdXDQcqlUuTl5eGlukEYjLuQy9BaOlnL2EANz/AeCBw7H9KMJDzevqDBl3sbOuGsIUMdaCABf/RZ6XP1UilCKPAlhDiMLUYPqw/E/7d0KVf76zPgJcgLs62arGVsKpek70SEvv45WKUCxSe2NqjGtKFtoqwNnJ1xCpqzos+KkKaJanwJIQ7RkBpVQ3Rrf726Pg9pRhJk9y+r+vjy7CVrrg9tbUkevvzyS0yfPt3qy70N7U1rbeBMAwn4o8+KkKaJMr6EEIewx2VkzZ3/VXfOAWChKMrhMrQlJ7dh4DPtzWZozXUQeO+99xp8uddYZhkwn5U2VpKh+xy6gTMNJOCPPitCmiYKfAkxQyaTIT8/ny5l2pA9LyMPGTIEo0aNglKphCg4HP7D30bw1GXwGzoDHsHhOHP6NK5evcrreTRriMulUuzZs8dmm5ga2ibKmsDZ2oC5OaLPipCmiQJfQoxozr077a2hm7tMUZdQSPpPRujszfAbFA3vrkPhP+QPCH37a179cTXZczNPQ3rTWhs4NyTT3NzQZ0VI00PtzMygdmbNky0mahHjbNHOyxhXHbFqbZsoa0YDO8MUNFdBnxUhroFGFtsIBb7ND/XubBz2CFDtGVDbky16oza0DzKfgLm5os+KEOfHN16jrg6E6GjoRC3Cz+LYWCTsi0JxylajJxiqy8i7eT+nq+3ET0tLw/q4OCQmJnIB1ZQpU7Bk8WKLf7bEYrFF78URU9BcFX1WhDQdFPgSokG96cpv+Gwem652QCaT0QHQSubahplqOWaMK+3Ed5ZRuJYGzM0ZfVaEuD4KfAnR4GoZQ1c3b948REZG1l1G3qFzGXm3VVnPhvTHbSy27mFMCCGEH+rqQIgGV8oYNhW2bhvmCjvxaRQuIYQ4BmV8CdHgKhnDpshWl5HtUUJhS1ROQwghjkMZX0J0uELGkJjWkP649mbPHsaEEEJMo4wvITqcPWNI+HHWnfhUTkMIIY5DGV9CDHDmjCGxjD0nr1mDRuESQojjuEzg265dOzAMo/Vv9erVJh9TVVWFBQsWICgoCD4+PoiOjkZ+fn4jrZi4OltvuiJEjcppCCHEMVyq1GHlypWYO3cu998SicTk/WNjY3Hw4EH85z//gZ+fHxYuXIhp06YhPT3d3kslTQj17iT20Lt3b2RmHIDstwxI+k6ichpCCGkELhX4SiQShIWZ3hCiVlpaiu3bt+PHH3/EyJEjAQDffvstunfvjvPnz2PQoEEGH1ddXY3q6mruv2ljCSHEljQHV0j6TUZ1/l0Un9wG1E2P79u3HzbttbyHcXPlbDXchBDn5jKlDgCwevVqBAUFoW/fvvjiiy+gUCiM3jcjIwNyuRyjR4/mvtatWze0adMG586dM/q4zz//HH5+fty/iIgIm74HQkjzpTm4osWsTQgc8y5azvwCEbF70HrBd/DpOxGZmZcdvUyXkJaWhmnR0fCpS4j4SCSYFh1NV/QIISa5TOD73nvv4aeffsLJkyfx7rvv4rPPPsOHH35o9P55eXnw8PCAv7+/1tdDQ0ORl2d8N/Xy5ctRWlrK/cvOzrbVWyCENHPGBlcIhCK4+wQhcMw8GlzBQ3x8PKKiopCclgG/4bMREr0CfsNnIzktA8OGDcOWLVscvURCiJNyaKnDsmXLsGbNGpP3uXHjBrp164bFixdzX+vVqxc8PDzw7rvv4vPPP4dIJLLZmkQikU2fjxBCABpcYSs07pkQ0hAODXyXLFmCWbNmmbxPhw4dDH594MCBUCgUePDgAbp27ap3e1hYGGpqalBSUqKV9c3Pz+ddJ0wIIbZizeAKCnz18Rn3LM++gri4OAp8CSF6HBr4hoSEICQkxKrHZmZmQiAQoEWLFgZv79+/P4RCIVJSUhAdHQ0AuHXrFh4+fIjBgwdbvWZCCLEGDa5oOMqaE0IayiVqfM+dO4cNGzYgKysLv/32G3bv3o3Y2FjMnDkTAQEBAIDc3Fx069YNv/76KwDAz88Pc+bMweLFi3Hy5ElkZGTg7bffxuDBg412dCCEEHuhwRUNp86au/kGobaiGEp5tcH70bhnQogxLtHOTCQS4aeffsKnn36K6upqtG/fHrGxsVp1v3K5HLdu3UJlZSX3tbi4OAgEAkRHR6O6uhrjxo3D5s2bHfEWCCEEi2NjkbAvCsUpW/Uu1WsPrtjtwFU6r2vXrgEMg6eJa1Tt3xgBvDoPguTZqfAMf4a7H2XNCSHGMCxb1zySGFRWVgY/Pz+UlpbSH1FCSINt2bIFMTExEIW0gbjnWIODK2gktj51/2NhYGt4954A94AwKIrzUJ51BPLCbASOnQ9J34lgWSWe7FyEScMGYM+ePY5edoNQj2JC+OMbr7lExpcQQpqKefPmITIyEnFxcdiXsAPK2loI3Nzw8tSpiI2lwRWG8OnkUHQ0HsLgNqi8le7yWfO0tDSsj4tDYmIi9/MxZcoULFm8mH4+CGkgyviaQRlfQoi9UEaPn2nR0UhOy0CLWZsMbmpjWSUebYuBsqIIbI3MpbPmmpP9xD3HcpltuiJAiGl84zUKfM2gwJcQQhxHJpPBRyKB3/DZ8H12itH7lV1IRMnJbTh+/Dg3pt7VpKWlISoqymBmW10DXn4pCampqZT5JUQH33jNJbo6EEIIaZ4s6X/Msix69OjRSCuzPT49immyHyENQ4EvIYQQp9Vc+h+rexSLe47l0aM4ATKZrJFXSEjTQIEvIYQQp9Vc+h9bM9mPEGI5CnwJIYQ4tcWxsagueIjilK16wa92/+NYB62w4ZpLZpsQR6N2ZoQQQhqVpd0shg4dis2bNyMmJgby7CtG+x+78oYvdWY7Oe0oJAMmG+1e4eqZbUIcjTK+hBBCGkVaWhqmRUfDRyJBWFgYfCQSTIuORnp6utnHzps3D6mpqZg0bABKT+9Awd5VKD29A5OGDUBqamqTaPHVHDLbhDgatTMzg9qZEUJIw9myP21T7n9Mk/0IsQ718bURCnwJIaRhqD+tZdLT0+sm+yXoTPaLpc+HECMo8LURCnwJIaRh+Exee7JzESYNG4A9e/Y4YIXOqSlntgmxNRpgQQghxOGoP631xGIxQkNDKeglxIYo8CWEEGI31J+WEOJMKPAlhBBiN9SflhDiTCjwJYQQYjfNZfIaIcQ1UOBLCCHErqg/LSHEWdDkNkIIIXbVHCavEUJcAwW+hBBC7G7evHmIjIys60+7Q6c/7W4KegkhjYL6+JpBfXwJIcS2qD8tIcTW+MZrlPElhBDSqMRiMQW8hBCHoM1thBBCCCGkWaDAlxBCmgmZTIb8/HyajkYIabYo8CWEkCYuLS0N06Kj4SORICwsDD4SCaZFRyM9Pd3RSyOEkEZFgS8hhDRh8fHxiIqKQnJaBvyGz0ZI9Ar4DZ+N5LQMDBs2DFu2bHH0EgkhpNFQVwczqKsDIcRVpaWlISoqCj79XkTAqLlgmPpch3pwRPmlJKSmplI7MUKIS+Mbr1HGlxBCmqj1cXEQhbTRC3oBgGEECBg1F6KQNoiLi3PQCgkhpHFR4EsIIU2QTCZDYmIixD3H6gW9agwjgLjnWOxLSKANb4SQZoECX0IIaYLKysqgrK2Fe0CYyfu5+4dBWVuLsrKyRloZIYQ4DgW+hBDSBPn6+kLg5gZFcZ7J+ylK8iBwc6M9DISQZoECX0IIaYLEYjGmTJkC2bWjYFmlwfuwrBKya0fx8tSpNEmNENIsUOBLCCFN1OLYWFQXPERxyla94Ffd1aG64CFiY2MdtEJCCGlc7o5eACGEEPsYOnQoNm/ejJiYGMizr0Dccyzc/cOgKMmD7NpRVBc8xObNm6mVGSGk2aDAlxBCmrB58+YhMjIScXFx2JewA8raWgjc3PDy1KmIjd1NQS8hpFmhARZm0AALQkhTIZPJUFZWBl9fX6rpJYQ0KXzjNcr4EkJIMyEWiyngJYQ0a7S5jRBCCCGENAsU+BJCCCGEkGaBAl9CCCGEENIsUOBLCCGEEJcjk8mQn58PmUzm6KUQF+ISge+pU6fAMIzBfxcuXDD6uBEjRujdf968eY24ckIIIYTYUlpaGqZFR8NHIkFYWBh8JBJMi45Genq6o5dGXIBLtDOrqalBUVGR1tdWrFiBlJQU3Lt3DwzDGHzciBEj0KVLF6xcuZL7mpeXl0VtyaidGSGEEOIc4uPjsWDBAohC2qgGsgSEQVGsPZCFElzNU5NqZ+bh4YGwsDDuv+VyORITE7Fo0SKjQa+al5eX1mMJIYQQ4nrS0tKwYMEC+PR7EQGj5oJh6i9aSwZMRnHKVsTExCAyMpIGsxCjXKLUQdf+/ftRWFiIt99+2+x9d+/ejeDgYPTs2RPLly9HZWWlyftXV1ejrKxM6x8hhBBCHGt9XBxEIW30gl4AYBgBAkbNhSikDeLi4hy0QuIKXCLjq2v79u0YN24cwsPDTd7v9ddfR9u2bdGqVStcuXIFH330EW7duoW9e/cafcznn3+Ov/71r7ZeMiGEEEKsJJPJkJiYCL/hs/WCXjWGEUDccyz2JeyATCajYS0O5qyTIh2a8V22bJnRTWvqfzdv3tR6TE5ODo4cOYI5c+aYff533nkH48aNQ2RkJGbMmIHvv/8e+/btw71794w+Zvny5SgtLeX+ZWdnN/h9EkIIIcR6ZWVlUNbWwj3AdOmiu38YlLW1dLXWgZx986FDM75LlizBrFmzTN6nQ4cOWv/97bffIigoCC+99JLFrzdw4EAAwN27d9GxY0eD9xGJRBCJRBY/NyGEEELsw9fXFwI3NyiK80zeT1GSB4GbG21GdxDNzYd+w2dzmw+T044iYd8wp9h86NDANyQkBCEhIbzvz7Isvv32W7z55psQCoUWv15mZiYAoGXLlhY/lhBCCCGOIRaLMWXKFCSnHYVkwGSD5Q4sq4Ts2lG8PHWqU11aby5cZfOhS21uO3HiBO7fv48//vGPerfl5uaiW7du+PXXXwEA9+7dw6pVq5CRkYEHDx5g//79ePPNNxEVFYVevXo19tIJIYQQ0gCLY2NRXfAQxSlbwbJKrdtYVonilK2oLniI2NhYB62weXOVzYcutblt+/bteP7559GtWze92+RyOW7dusV1bfDw8MDx48exYcMGVFRUICIiAtHR0fj4448be9mEEEIIaaChQ4di8+bNiImJgTz7iqqPr38YFCXafXyplVnjc6XNhy4V+P74449Gb2vXrh00Z3FERETg9OnTjbEsQgghhDSCefPmITIyEnFxcdiXsAPK2loI3Nzw8tSpiI3dTUGvg1iz+ZACX0IIIYQQM4YMGYIhQ4Y4bbus5siVNh+6VI0vIYQQQgig2vAWGhpKQa8TUG8+lF07qld/reYsmw8p8CWEEEIIIQ3iKpsPqdSBEEIIIYQ0iKtsPqTAlxBCCCGENJgrbD5kWM1WCERPWVkZ/Pz8UFpaSpNgCCGEEEJ4aOzNh3zjNcr4EkIIIYQQmxKLxU658ZA2txFCCCGEkGaBAl9CCCGEENIsUOBLCCGEEEKaBQp8CSGEEEJIs0CBLyGEEEIIaRYo8CWEEEIIIc0CBb6EEEIIIaRZoMCXEEIIIYQ0CxT4EkIIIYSQZoECX0IIIYQQ0ixQ4EsIIYQQQpoFCnwJIYQQQkiz4O7oBTg7lmUBAGVlZQ5eCSGEEEIIMUQdp6njNmMo8DVDKpUCACIiIhy8EkIIIYQQYopUKoWfn5/R2xnWXGjczCmVSjx69AgSiQQMwzh6OSaVlZUhIiIC2dnZ8PX1dfRySCOj7z+hnwFCPwOkuf4MsCwLqVSKVq1aQSAwXslLGV8zBAIBwsPDHb0Mi/j6+jarH3aijb7/hH4GCP0MkOb4M2Aq06tGm9sIIYQQQkizQIEvIYQQQghpFijwbUJEIhE++eQTiEQiRy+FOAB9/wn9DBD6GSD0M2AabW4jhBBCCCHNAmV8CSGEEEJIs0CBLyGEEEIIaRYo8CWEEEIIIc0CBb6EEEIIIaRZoMC3iWrXrh0YhtH6t3r1akcvi9jR119/jXbt2sHT0xMDBw7Er7/+6uglkUby6aef6v2+d+vWzdHLInZ05swZTJ48Ga1atQLDMEhISNC6nWVZ/OUvf0HLli0hFosxevRo3LlzxzGLJTZn7vs/a9Ysvb8J48ePd8xinQwFvk3YypUr8fjxY+7fokWLHL0kYic///wzFi9ejE8++QSXLl1C7969MW7cODx58sTRSyONpEePHlq/72lpaY5eErGjiooK9O7dG19//bXB2//xj39g48aN2LJlC3755Rd4e3tj3LhxqKqqauSVEnsw9/0HgPHjx2v9TfjXv/7ViCt0XjSyuAmTSCQICwtz9DJII1i/fj3mzp2Lt99+GwCwZcsWHDx4EDt27MCyZcscvDrSGNzd3en3vRmZMGECJkyYYPA2lmWxYcMGfPzxx5gyZQoA4Pvvv0doaCgSEhLw2muvNeZSiR2Y+v6riUQi+ptgAGV8m7DVq1cjKCgIffv2xRdffAGFQuHoJRE7qKmpQUZGBkaPHs19TSAQYPTo0Th37pwDV0Ya0507d9CqVSt06NABM2bMwMOHDx29JOIg9+/fR15entbfBD8/PwwcOJD+JjQjp06dQosWLdC1a1fMnz8fhYWFjl6SU6CMbxP13nvvoV+/fggMDMTZs2exfPlyPH78GOvXr3f00oiNPX36FLW1tQgNDdX6emhoKG7evOmgVZHGNHDgQOzcuRNdu3bF48eP8de//hXDhg3DtWvXIJFIHL080sjy8vIAwODfBPVtpGkbP348pk2bhvbt2+PevXv405/+hAkTJuDcuXNwc3Nz9PIcigJfF7Js2TKsWbPG5H1u3LiBbt26YfHixdzXevXqBQ8PD7z77rv4/PPPaYwhIU2M5iXPXr16YeDAgWjbti3+/e9/Y86cOQ5cGSHEETTLWSIjI9GrVy907NgRp06dwqhRoxy4MsejwNeFLFmyBLNmzTJ5nw4dOhj8+sCBA6FQKPDgwQN07drVDqsjjhIcHAw3Nzfk5+drfT0/P5/qu5opf39/dOnSBXfv3nX0UogDqH/v8/Pz0bJlS+7r+fn56NOnj4NWRRypQ4cOCA4Oxt27dynwdfQCCH8hISEICQmx6rGZmZkQCARo0aKFjVdFHM3DwwP9+/dHSkoKpk6dCgBQKpVISUnBwoULHbs44hDl5eW4d+8e3njjDUcvhThA+/btERYWhpSUFC7QLSsrwy+//IL58+c7dnHEIXJyclBYWKh1ItRcUeDbBJ07dw6//PILXnjhBUgkEpw7dw6xsbGYOXMmAgICHL08YgeLFy/GW2+9hQEDBuC5557Dhg0bUFFRwXV5IE3b0qVLMXnyZLRt2xaPHj3CJ598Ajc3N/zhD39w9NKInZSXl2tl9O/fv4/MzEwEBgaiTZs2+OCDD/C3v/0NnTt3Rvv27bFixQq0atWKOzkmrs3U9z8wMBB//etfER0djbCwMNy7dw8ffvghOnXqhHHjxjlw1U6CJU1ORkYGO3DgQNbPz4/19PRku3fvzn722WdsVVWVo5dG7GjTpk1smzZtWA8PD/a5555jz58/7+glkUYyffp0tmXLlqyHhwfbunVrdvr06ezdu3cdvSxiRydPnmQB6P176623WJZlWaVSya5YsYINDQ1lRSIRO2rUKPbWrVuOXTSxGVPf/8rKSnbs2LFsSEgIKxQK2bZt27Jz585l8/LyHL1sp8CwLMs6KugmhBBCCCGksVAfX0IIIYQQ0ixQ4EsIIYQQQpoFCnwJIYQQQkizQIEvIYQQQghpFijwJYQQQgghzQIFvoQQQgghpFmgwJcQQgghhDQLFPgSQgghhJBmgQJfQkiz1K5dO2zYsMFmzzdr1iybj4M9deoUGIZBSUmJTZ+XEEKaKwp8CSEubdasWWAYBgzDwMPDA506dcLKlSuhUChMPu7ChQt45513bLaOL7/8Ejt37rTZ81ni8uXLeOWVVxAaGgpPT0907twZc+fOxe3btx2yHmfF92Tnm2++wYgRI+Dr60snHoQ0MRT4EkJc3vjx4/H48WPcuXMHS5YswaeffoovvvjC4H1ramoAACEhIfDy8rLZGvz8/ODv72+z5+MrKSkJgwYNQnV1NXbv3o0bN27ghx9+gJ+fH1asWNHo62kKKisrMX78ePzpT39y9FIIITZGgS8hxOWJRCKEhYWhbdu2mD9/PkaPHo39+/cDqC9B+Pvf/45WrVqha9euAPSzfwzDYNu2bXj55Zfh5eWFzp07c8+hdv36dbz44ovw9fWFRCLBsGHDcO/ePa3XURsxYgQWLlyIhQsXws/PD8HBwVixYgVYluXus2vXLgwYMAASiQRhYWF4/fXX8eTJE97vu7KyEm+//TYmTpyI/fv3Y/To0Wjfvj0GDhyItWvX4p///Cd339OnT+O5556DSCRCy5YtsWzZMq2s+IgRI7Bo0SJ88MEHCAgIQGhoKLZu3YqKigq8/fbbkEgk6NSpE5KTk7nHqEsxDh48iF69esHT0xODBg3CtWvXtNb53//+Fz169IBIJEK7du2wbt06rdvbtWuHzz77DLNnz4ZEIkGbNm3wzTffaN0nOzsbr776Kvz9/REYGIgpU6bgwYMH3O3qz3/t2rVo2bIlgoKCsGDBAsjlcu79/f7774iNjeWuEBjzwQcfYNmyZRg0aBDv7wUhxDVQ4EsIaXLEYjGX2QWAlJQU3Lp1C8eOHUNSUpLRx/31r3/Fq6++iitXrmDixImYMWMGioqKAAC5ubmIioqCSCTCiRMnkJGRgdmzZ5ssqfjuu+/g7u6OX3/9FV9++SXWr1+Pbdu2cbfL5XKsWrUKWVlZSEhIwIMHDzBr1ize7/PIkSN4+vQpPvzwQ4O3qzPQubm5mDhxIp599llkZWUhPj4e27dvx9/+9je99QYHB+PXX3/FokWLMH/+fLzyyit4/vnncenSJYwdOxZvvPEGKisrtR73f//3f1i3bh0uXLiAkJAQTJ48mQs4MzIy8Oqrr+K1117D1atX8emnn2LFihV6ZSHr1q3DgAEDcPnyZcTExGD+/Pm4desW9zmNGzcOEokEqampSE9Ph4+PD8aPH6/1fT558iTu3buHkydP4rvvvsPOnTu519m7dy/Cw8OxcuVKPH78GI8fP+b9ORNCmhCWEEJc2FtvvcVOmTKFZVmWVSqV7LFjx1iRSMQuXbqUuz00NJStrq7Welzbtm3ZuLg47r8BsB9//DH33+Xl5SwANjk5mWVZll2+fDnbvn17tqamxuw6WJZlhw8fznbv3p1VKpXc1z766CO2e/fuRt/LhQsXWACsVCplWZZlT548yQJgi4uLDd5/zZo1LAC2qKjI6HOyLMv+6U9/Yrt27aq1lq+//pr18fFha2trufUOHTqUu12hULDe3t7sG2+8wX3t8ePHLAD23LlzWuv76aefuPsUFhayYrGY/fnnn1mWZdnXX3+dHTNmjNZ6/u///o995plnuP9u27YtO3PmTO6/lUol26JFCzY+Pp5lWZbdtWuX3vqrq6tZsVjMHjlyhGVZ1efftm1bVqFQcPd55ZVX2OnTp2u9jub33Bxznz8hxPVQxpcQ4vKSkpLg4+MDT09PTJgwAdOnT8enn37K3R4ZGQkPDw+zz9OrVy/u/3t7e8PX15crPcjMzMSwYcMgFAp5r2vQoEFal9QHDx6MO3fuoLa2FoAqGzp58mS0adMGEokEw4cPBwA8fPiQ1/OzGmUTpty4cQODBw/WWsuQIUNQXl6OnJwc7mua79/NzQ1BQUGIjIzkvhYaGgoAeuUYgwcP5v5/YGAgunbtihs3bnCvPWTIEK37DxkyROtz0H1thmEQFhbGvU5WVhbu3r0LiUQCHx8f+Pj4IDAwEFVVVVypCQD06NEDbm5u3H+3bNnSotIRQkjT5+7oBRBCSEO98MILiI+Ph4eHB1q1agV3d+0/bd7e3ryeRzeoZRgGSqUSgKp8wpYqKiowbtw4jBs3Drt370ZISAgePnyIcePGaV2+N6VLly4AgJs3b2oFn9Yy9P41v6YOnNWfiS2Z+uzLy8vRv39/7N69W+9xISEhvJ6DEEIAqvElhDQB3t7e6NSpE9q0aaMX9NpKr169kJqaytWu8vHLL79o/ff58+fRuXNnuLm54ebNmygsLMTq1asxbNgwdOvWzeLs5NixYxEcHIx//OMfBm9Xt+Hq3r07zp07p5UhTk9Ph0QiQXh4uEWvacj58+e5/19cXIzbt2+je/fu3Gunp6dr3T89PR1dunTRys6a0q9fP9y5cwctWrRAp06dtP75+fnxXqeHh4dWlpkQ0vxQ4EsIITwsXLgQZWVleO2113Dx4kXcuXMHu3bt4jZgGfLw4UMsXrwYt27dwr/+9S9s2rQJ77//PgCgTZs28PDwwKZNm/Dbb79h//79WLVqlUVr8vb2xrZt23Dw4EG89NJLOH78OB48eICLFy/iww8/xLx58wAAMTExyM7OxqJFi3Dz5k0kJibik08+weLFiyEQNPwwsHLlSqSkpODatWuYNWsWgoODuQ4XS5YsQUpKClatWoXbt2/ju+++w1dffYWlS5fyfv4ZM2YgODgYU6ZMQWpqKu7fv49Tp07hvffe0yrVMKddu3Y4c+YMcnNz8fTpU6P3y8vLQ2ZmJu7evQsAuHr1KjIzM7mNjoQQ10WBLyGE8BAUFIQTJ06gvLwcw4cPR//+/bF161aTNb9vvvkmZDIZnnvuOSxYsADvv/8+NzQjJCQEO3fuxH/+8x8888wzWL16NdauXWvxuqZMmYKzZ89CKBTi9ddfR7du3fCHP/wBpaWlXNeG1q1b49ChQ/j111/Ru3dvzJs3D3PmzMHHH39s3YehY/Xq1Xj//ffRv39/5OXl4cCBA1xNdb9+/fDvf/8bP/30E3r27Im//OUvWLlypUXdK7y8vHDmzBm0adMG06ZNQ/fu3TFnzhxUVVXB19eX9/OsXLkSDx48QMeOHbVKJHRt2bIFffv2xdy5cwEAUVFR6Nu3r157O0KI62FYvrsjCCGE8DZixAj06dPHpmORnc2pU6fwwgsvoLi42CHDOwghxFKU8SWEEEIIIc0CBb6EEEIIIaRZoFIHQgghhBDSLFDGlxBCCCGENAsU+BJCCCGEkGaBAl9CCCGEENIsUOBLCCGEEEKaBQp8CSGEEEJIs0CBLyGEEEIIaRYo8CWEEEIIIc0CBb6EEEIIIaRZ+P8qSTVtOTY4MQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pca_cancer = PCA(n_components=2, random_state=42)\n",
-    "X_cancer_pca = pca_cancer.fit_transform(X_cancer)\n",
-    "\n",
-    "# Plot the PCA results\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(X_cancer_pca[:, 0], X_cancer_pca[:, 1], edgecolor='k', s=50)\n",
-    "plt.title('PCA of Breast Cancer Dataset')\n",
-    "plt.xlabel('Principal Component 1')\n",
-    "plt.ylabel('Principal Component 2')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4d1e0cbf",
-   "metadata": {},
-   "source": [
-    "-   Apply K-Means clustering and plot the results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "89d14d67",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "kmeans_cancer = KMeans(n_clusters=2, random_state=42)\n",
-    "kmeans_cancer.fit(X_cancer_pca)\n",
-    "y_cancer_kmeans = kmeans_cancer.predict(X_cancer_pca)\n",
-    "\n",
-    "# Plot the PCA results with K-Means clustering\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(X_cancer_pca[:, 0], X_cancer_pca[:, 1], c=y_cancer_kmeans,\n",
-    "            cmap='viridis', edgecolor='k', s=50)\n",
-    "plt.scatter(kmeans_cancer.cluster_centers_[:, 0], kmeans_cancer.cluster_centers_[:, 1],\n",
-    "            s=150, c='red', marker='X', label='Centroids')\n",
-    "plt.title('K-Means Clustering on PCA-reduced Breast Cancer Data')\n",
-    "plt.xlabel('Principal Component 1')\n",
-    "plt.ylabel('Principal Component 2')\n",
-    "plt.legend()\n",
-    "plt.colorbar(label='Cluster')\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S05 Unsupervised Learning - Clustering and Dimensionality Reduction/S05C02 Dimensionality Reduction Techniques.ipynb b/src/S05 Unsupervised Learning - Clustering and Dimensionality Reduction/S05C02 Dimensionality Reduction Techniques.ipynb
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "a79c77d6",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Dimensionality Reduction Techniques\n",
-    "\n",
-    "**Dimensionality reduction** is a crucial step in data preprocessing, especially when dealing with high-dimensional datasets. It aims to reduce the number of features while preserving the essential structure and relationships in the data. This can help improve the performance of machine learning models, reduce computational costs, and facilitate data visualization. Common techniques for dimensionality reduction include:\n",
-    "\n",
-    "-   **Principal Component Analysis (PCA)**: A linear technique that transforms the data into a new coordinate system where the greatest variance lies along the first axis (principal component), the second greatest variance along the second axis, and so on.\n",
-    "-   **Autoencoders**: Neural networks that learn to encode the input data into a lower-dimensional representation and then decode it back to the original space. They are particularly useful for learning complex, non-linear mappings and can be used for both dimensionality reduction and feature extraction.\n",
-    "-   **Linear Discriminant Analysis (LDA)**: A supervised technique that projects the data onto a lower-dimensional space while maximizing the separation between classes. It is particularly useful when the goal is to improve classification performance by reducing dimensionality.\n",
-    "-   **Independent Component Analysis (ICA)**: A technique that separates a multivariate signal into additive independent components. It is often used in signal processing and blind source separation tasks, such as separating mixed signals in audio or image processing.\n",
-    "-   **t-Distributed Stochastic Neighbor Embedding (t-SNE)**: A non-linear technique that visualizes high-dimensional data by minimizing the divergence between probability distributions of pairwise similarities in high and low dimensions. It is particularly effective for visualizing clusters in high-dimensional data.\n",
-    "-   **Feature Selection**: While not a dimensionality reduction technique in the strictest sense, feature selection involves selecting a subset of relevant features from the original dataset. This can be done using various methods, such as filter methods (e.g., correlation-based), wrapper methods (e.g., recursive feature elimination), or embedded methods (e.g., LASSO regression). Feature selection can help reduce dimensionality by eliminating irrelevant or redundant features, leading to improved model performance and interpretability."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c93bb6b7",
-   "metadata": {},
-   "source": [
-    "## Principal Component Analysis (PCA)\n",
-    "\n",
-    "PCA is a widely used linear dimensionality reduction technique that transforms the data into a new coordinate system where the axes (principal components) are ordered by the amount of variance they capture from the data. The first principal component captures the most variance, the second captures the second most, and so on.\n",
-    "\n",
-    "This allows PCA to reduce the dimensionality of the data while retaining as much information as possible. PCA works by computing the covariance matrix of the data, finding its eigenvalues and eigenvectors, and then projecting the data onto the eigenvectors corresponding to the largest eigenvalues.\n",
-    "\n",
-    "The number of principal components to retain can be chosen based on the cumulative explained variance, which indicates how much of the total variance is captured by the selected components. PCA is particularly effective for linear relationships and can be used for both feature extraction and data visualization.\n",
-    "\n",
-    "**It is important to note that PCA assumes that the principal components are orthogonal and that the data is centered (mean-centered) before applying PCA.**\n",
-    "\n",
-    "This means that PCA is sensitive to the scale of the features, and it is often recommended to standardize or normalize the data before applying PCA. PCA can also be used for noise reduction by retaining only the most significant principal components and discarding the ones that capture less variance, which are often associated with noise. This can help improve the performance of machine learning models by reducing overfitting and improving generalization."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2a17d66d",
-   "metadata": {},
-   "source": [
-    "### Mathematical Formulation\n",
-    "\n",
-    "Given a dataset $X \\in \\mathbb{R}^{n \\times p}$ with $n$ samples and $p$ features, PCA aims to find a lower-dimensional representation of the data by projecting it onto a new set of axes (principal components). The steps involved in PCA are:\n",
-    "\n",
-    "1.  **Standardization**: Center the data by subtracting the mean of each feature, $$X' = X - \\bar{X},$$ where $\\bar{X}$ is the mean vector of the features.\n",
-    "2.  **Covariance Matrix**: Compute the covariance matrix of the centered data, $$C = \\frac{1}{n-1} X'^T X'.$$\n",
-    "3.  **Eigenvalue Decomposition**: Perform eigenvalue decomposition on the covariance matrix to obtain $$C = V \\Lambda V^T,$$ where $V$ is the matrix of eigenvectors and $\\Lambda$ is the diagonal matrix of eigenvalues.\n",
-    "4.  **Select Principal Components**: Choose the top $k$ eigenvectors corresponding to the largest eigenvalues to form the projection matrix, $$W_k = [v_1, v_2, \\ldots, v_k],$$ where $v_i$ are the eigenvectors.\n",
-    "5.  **Project Data**: Project the original data onto the lower-dimensional space using the projection matrix, $$Z = X' W_k,$$ where $Z \\in \\mathbb{R}^{n \\times k}$ is the reduced representation of the data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9e967efb",
-   "metadata": {},
-   "source": [
-    "### Advantages and Disadvantages\n",
-    "\n",
-    "-   **Advantages**:\n",
-    "    -   Reduces dimensionality while preserving variance.\n",
-    "    -   Helps in noise reduction by discarding less significant components.\n",
-    "    -   Can improve the performance of machine learning models by reducing overfitting.\n",
-    "-   **Disadvantages**:\n",
-    "    -   Assumes linear relationships between features.\n",
-    "    -   Sensitive to the scale of features; requires standardization or normalization.\n",
-    "    -   May not capture complex, non-linear relationships in the data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be31b15e",
-   "metadata": {},
-   "source": [
-    "### Evaluation of PCA\n",
-    "\n",
-    "To evaluate the effectiveness of PCA, we can use the explained variance ratio, which indicates how much variance is captured by each principal component. The cumulative explained variance can help determine the number of components to retain for dimensionality reduction. A common approach is to plot the cumulative explained variance against the number of principal components and choose a threshold (e.g., 95% or 99%) to decide how many components to keep. This ensures that we retain enough information while reducing dimensionality.\n",
-    "\n",
-    "-   **Explained Variance Ratio**: The explained variance ratio for each principal component can be computed as: $$\\text{Explained Variance Ratio}_i = \\frac{\\lambda_i}{\\sum_{j=1}^{p} \\lambda_j},$$ where $\\lambda_i$ is the $i$-th eigenvalue. The ****cumulative explained variance ratio**** can be computed as: $$\\text{Cumulative Explained Variance Ratio}_k = \\sum_{i=1}^{k} \\text{Explained Variance Ratio}_i.$$\n",
-    "-   **Scree Plot**: A scree plot can be used to visualize the explained variance for each principal component. It is a line plot where the x-axis represents the principal components and the y-axis represents the explained variance ratio. The \"elbow\" point in the plot can help determine the optimal number of components to retain.\n",
-    "-   **Reconstruction Error**: The reconstruction error can be computed as the mean squared error between the original data and the reconstructed data from the reduced representation. This can help assess how well PCA captures the structure of the data. The reconstruction error can be computed as: $$\\text{Reconstruction Error} = \\frac{1}{n} \\|X - Z W_k^T\\|^2,$$ where $Z$ is the reduced representation and $W_k$ is the projection matrix. A lower reconstruction error indicates that PCA has effectively captured the structure of the data.\n",
-    "-   **Visualization**: Visualizing the reduced data in 2D or 3D can help assess how well PCA separates different classes or clusters in the data. This can be done using scatter plots or pair plots, where the axes represent the principal components. If the reduced data shows clear separation between classes or clusters, it indicates that PCA has effectively captured the underlying structure of the data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "42918989",
-   "metadata": {},
-   "source": [
-    "### Example: Synthetic Dataset\n",
-    "\n",
-    "To illustrate PCA, we can create a synthetic dataset with two features and visualize the results of PCA.\n",
-    "\n",
-    "-   Generate the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "82ac88eb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "# Create a synthetic dataset with two features\n",
-    "np.random.seed(42)\n",
-    "X = np.dot(np.random.rand(2, 2), np.random.randn(2, 100)).T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a176b08a",
-   "metadata": {},
-   "source": [
-    "-   Plot the original data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "7286444c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(X[:, 0], X[:, 1], label='Original Data', alpha=0.75)\n",
-    "plt.title('Synthetic Dataset')\n",
-    "plt.xlabel('Feature 1')\n",
-    "plt.ylabel('Feature 2')\n",
-    "plt.legend()\n",
-    "plt.grid()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7750a695",
-   "metadata": {},
-   "source": [
-    "-   Apply PCA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "1d4dad58",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.decomposition import PCA\n",
-    "pca = PCA(n_components=2)\n",
-    "X_pca = pca.fit_transform(X)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a6b912df",
-   "metadata": {},
-   "source": [
-    "-   Evaluate PCA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a9b161eb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Principal Components:\n",
-      " [[ 0.75710263  0.65329596]\n",
-      " [-0.65329596  0.75710263]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Print the principal components\n",
-    "print(\"Principal Components:\\n\", pca.components_)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "6586709d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Explained Variance Ratio: [0.92485706 0.07514294]\n",
-      "Cumulative Explained Variance: [0.92485706 1.        ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Print explained variance ratio\n",
-    "print(\"Explained Variance Ratio:\", pca.explained_variance_ratio_)\n",
-    "# Print cumulative explained variance\n",
-    "print(\"Cumulative Explained Variance:\", np.cumsum(pca.explained_variance_ratio_))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1a5df240",
-   "metadata": {},
-   "source": [
-    "-   Visualize the PCA results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "cd2a87d5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(X_pca[:, 0], X_pca[:, 1], label='PCA Reduced Data', alpha=0.75, color='orange')\n",
-    "plt.xlim(-3, 3)\n",
-    "plt.ylim(-3, 3)\n",
-    "plt.title('PCA of Synthetic Dataset')\n",
-    "plt.xlabel('Principal Component 1')\n",
-    "plt.ylabel('Principal Component 2')\n",
-    "plt.legend()\n",
-    "plt.grid()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b77dfd44",
-   "metadata": {},
-   "source": [
-    "-   Visualize the explained variance ratio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "a9c08cab",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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f//5XgwcPVoUKFXT+/HmlpKRo9OjRGjVqlK2vo/8vV8JlxYoVHf7/7tu3z7Y8r3VzcnJ08OBB25Vc6Y8PlV24cCHPdW+Vv7+/HnzwQa1du1Y///yzwsPD8+y/YcMG/fLLLxozZoxdrdIf72L06dNHy5Yty7d5kAEzYMwscAdISkrSli1bNGPGDI0dO1aNGjVS3759c82CIEnvv/++3VueS5Ys0YkTJ/TII49cd/uenp6yWCx2dyk6duyYli1blm/HEB0draCgII0fP15WqzXX8mvTeJUpU0Z16tTR3Llz7d6yTk5OznPcpCOdO3fWt99+q4SEBFksFj377LO2ZdeuiP75CqhhGJoyZYpL+/ir/D6XHh4eat++vT799FN98803uZZfq//pp5/W1q1bHY65vHDhgq5evXpT+3/ppZdktVqVlJQkyfF5k+RwtoSAgADb/m+kTZs22r59u7Zu3WprS09P14wZMxQREZHneOk2bdo4rOFazQX9tn1CQoIMw1DXrl11+fLlXMt37NihuXPnSvq/IQZDhgxRhw4d7L569+6tqlWrav78+QVaL3C74cosYHKfffaZ7erTnzVq1Eh33XWX9u7dq5EjRyo2NlZt27aV9MfcrnXq1FG/fv304Ycf2q1XokQJNWnSRN27d9epU6c0efJkValSRb17975uDY8++qiSkpL08MMP69lnn9Xp06c1depUValSRd99912+HGdQUJCmTZumrl276r777tMzzzyjUqVKKTU1VStXrlTjxo319ttvS5ISExP16KOPqkmTJurRo4fOnTunt956S/fee6/DsHA9Xbp00ZgxY/TJJ5+ocePGdvPBVqtWTZUrV9bgwYN1/PhxBQUFaenSpbnGzrqqIM7l+PHjtWbNGjVv3lx9+vRR9erVdeLECS1evFibNm1SsWLFNGTIEC1fvlyPPfaYYmNjVbduXaWnp2vPnj1asmSJjh07ZpseyxU1atRQmzZtNGvWLI0cOVIlS5ZUs2bN9Prrr8tqtapcuXJas2aNjh49mmvdunXrSpJefvllPfPMM/Ly8lLbtm1tIffPhg0bpg8++ECPPPKIXnjhBZUoUUJz587V0aNHtXTp0jyHeNSuXVsxMTGaMWOGLly4oObNm2v79u2aO3eu2rdvrwceeMDl43ZFo0aNNHXqVPXr10/VqlWzuwPY+vXrtXz5cr322mvKzMzU0qVL1apVq1zjkK9p166dpkyZotOnTys0NLRA6wZuG+6aRgHArclrai5JxuzZs42rV68a999/v1G+fHm7aaoMwzCmTJliSDIWLVpkGMb/Tc31wQcfGMOHDzdCQ0MNPz8/49FHH7WbZsowHE8n9e677xpVq1Y1fHx8jGrVqhmzZ882EhISjL++zFxvaq6/Tht1rZ5169blao+OjjaCg4MNX19fo3LlykZsbKzxzTff2PVbunSpUb16dcPHx8eoUaOG8dFHHzms+0buv/9+Q5Lxzjvv5Fr2448/Gi1btjSKFi1qhISEGL1797ZNtTR79mxbv7ymc7qVcynJeP7553Nt86/n2DAM46effjK6detmlCpVyvDx8THuuusu4/nnnzcyMzNtfS5dumQMHz7cqFKliuHt7W2EhIQYjRo1Mt544w0jKysrz/PUvHlz495773W4bP369YYkIyEhwTAMw/jll1+MJ554wihWrJgRHBxsPPXUU8avv/5q1+easWPHGuXKlTM8PDzspulydIyHDx82OnToYBQrVszw9fU16tevb6xYsSLPuq+xWq3G6NGjjUqVKhleXl5GeHi4MXz4cOPKlSt2/W5mai5n7dixw3j22WeNsmXLGl5eXkbx4sWNhx56yJg7d66RnZ1tLF261JBkvPvuu9fdxrVz/eep9IC/O4thuPgpAQB/S+vXr9cDDzygxYsXq0OHDu4uBwAApzBmFgAAAKZFmAUAAIBpEWYBAABgWoyZBQAAgGlxZRYAAACmRZgFAACAad1xN03IycnRr7/+qsDAwAK5DzcAAABujWEYunTpksqWLZvnTU+kOzDM/vrrrze89zUAAADc7+eff1b58uXz7HPHhdnAwEBJf5ycoKAgN1eDvyur1ao1a9aodevW8vLycnc5AJCveI1DQUtLS1N4eLgtt+Xljguz14YWBAUFEWZRYKxWq/z9/RUUFMQLPYC/HV7jUFicGRLKB8AAAABgWoRZAAAAmBZhFgAAAKZ1x42ZBQDAEcMwdPXqVWVnZ7u7lNue1WpVkSJFdOXKFc4XbpqXl5c8PT1veTuEWQDAHS8rK0snTpxQRkaGu0sxBcMwFBYWpp9//pk523HTLBaLypcvr6JFi97SdgizAIA7Wk5Ojo4ePSpPT0+VLVtW3t7eBLQbyMnJ0eXLl1W0aNEbTmgPOGIYhs6cOaNffvlFVatWvaUrtIRZAMAdLSsrSzk5OQoPD5e/v7+7yzGFnJwcZWVlydfXlzCLm1aqVCkdO3ZMVqv1lsIsz0AAACRCGVDI8usdEH5yAQAAYFqEWQAAAJgWY2YBALiOBx4o3P2tW1e4+3NWRESEBg4cqIEDBzrVf86cORo4cKAuXLhQoHWtX79eDzzwgM6fP69ixYoV6L5up33fTiwWiz7++GO1b9/ebTVwZRYAAJOKjY2VxWLJ9fXwww+7ta6OHTvqwIEDbq0hKytLISEhmjBhgsPlY8eOVenSpWW1Wm9q+40aNdKJEycUHBx8K2UWuDlz5tieFx4eHipTpow6duyo1NRUl7bz6quvqk6dOrnaT5w4oUceeSSfqr05hFkAAEzs4Ycf1okTJ+y+PvjgA7fW5Ofnp9DQULfW4O3trS5dumj27Nm5lhmGoTlz5qhbt27y8vJyedtWq1Xe3t4KCwszxTRuQUFBOnHihI4fP66lS5dq//79euqpp/Jl22FhYfLx8cmXbd0swiwAACbm4+OjsLAwu6/ixYtL+uOtcG9vb23cuNHW//XXX1doaKhOnTolSWrRooXi4uIUFxen4OBghYSEaOTIkTIM47r7fPPNN9WoUSMFBgYqPDxc/fr10+XLl23L58yZY/fW+7WrevPmzVNERISCg4P1zDPP6NKlS7Y+OTk5SkxMVKVKleTn56fatWtryZIldvtdtWqV7r77bvn5+emBBx7QsWPH8jw3PXv21IEDB7Rp0ya79g0bNujIkSPq2bOnvv76a7Vq1UohISEKDg5W8+bNtXPnTrv+FotF06ZNU7t27RQQEKBx48Zp/fr1slgstqEUv/32mzp16qRy5crJ399ftWrVyvVHRYsWLfTCCy/opZdeUokSJRQWFqZXX33Vrs+FCxf0r3/9S6VLl5avr69q1qypFStW2JZv2rRJTZs2lZ+fn8LDw/XCCy8oPT09z/NgsVgUFhamMmXKqFGjRurZs6e2b9+utLQ0W5+hQ4fq7rvvlr+/v+666y6NHDnSdtV6zpw5Gj16tHbv3m27yjtnzhzbtpctW2bbzp49e/Tggw/Kz89PJUuWVJ8+feyeGwWBMAsAwN9UixYtNHDgQHXt2lUXL17Ut99+q5EjR2rWrFkqXbq0rd/cuXNVpEgRbd++XVOmTFFSUpJmzZp13e16eHjo3//+t/bs2aO5c+dq7dq1eumll/Ks5fDhw1q2bJlWrFihFStWaMOGDXZDABITE/X+++9r+vTp+uGHHzRo0CB16dJFGzZskCT9/PPP+uc//6m2bdtq165d6tWrl4YNG5bnPmvVqqX7779f7733nl377Nmz1ahRI1WrVk2XLl1STEyMNm3apG3btqlq1apq06aNXdCW/gjkTzzxhPbs2aMePXrk2teVK1dUt25drVy5Ut9//7369Omjrl27avv27Xb95s6dq4CAAH311Vd6/fXXNWbMGCUnJ0v6I9A/8sgj2rx5s/73v//pxx9/1IQJE2xzsB4+fFgPP/ywnnzySX333XdatGiRNm3apLi4uDzPw5+dPn1aH3/8sTw9Pe3mdg0MDNScOXP0448/asqUKZo5c6befPNNSX8MG3nxxRd177332q7+d+zYMde209PTFR0dreLFi+vrr7/W4sWL9cUXX7hU300x7jAXL140JBkXL150dyn4G8vKyjKWLVtmZGVlubsUADfw+++/Gz/++KPx+++/51rWokXhfrkqJibG8PT0NAICAuy+xo0bZ+uTmZlp1KlTx3j66aeNGjVqGL1797bbRvPmzY3q1asbOTk5trahQ4ca1atXtz2uWLGi8eabb9oeZ2dnG+fPnzeys7MNwzCMxYsXGyVLlrQtnz17thEcHGx7nJCQYPj7+xtpaWm2tiFDhhgNGjQwDMMwrly5Yvj7+xtbtmyxq61nz55Gp06dDMMwjOHDhxs1atSwWz506FBDknH+/PnrnqPp06cbRYsWNS5dumQYhmGkpaUZ/v7+xqxZsxz2z87ONgIDA41PP/3U1ibJGDhwoF2/devW3XDfjz76qPHiiy/aHjdv3txo0qSJXZ/777/fGDp0qGEYhrF69WrDw8PD2L9/v8Pt9ezZ0+jTp49d28aNGw0PDw+Hz1/D+OP/QpIREBBg+Pv7G5IMScYLL7xw3boNwzAmTpxo1K1b1/Y4ISHBqF27dq5+koyPP/7YMAzDmDFjhlG8eHHj8uXLtuUrV640PDw8jJMnT+ZaN6+fPVfyGrMZAABgYg888ICmTZtm11aiRAnb997e3po/f74iIyNVsWJF29W2P2vYsKHd2M+oqChNmjRJ2dnZDu/M9MUXX2jcuHE6dOiQ0tLSdPXqVV25ckUZGRnXvYtaRESEAgMDbY/LlCmj06dPS5IOHTqkjIwMtWrVym6drKws/eMf/5Ak7d27Vw0aNLBbHhUV5XBff9apUycNGjRIH374oXr06KFFixbJw8PDdmXx1KlTeuWVV7R+/XqdPn1a2dnZysjIyPUBqXr16uW5n+zsbI0fP14ffvihjh8/rqysLGVmZuY6H5GRkXaP/3wedu3apfLly+vuu+92uI/du3fru+++0/z5821thmHYbslcvXp1h+sFBgZq586dslqt+uyzzzR//nyNGzfOrs+iRYv0n//8R4cPH9bly5d19epVBQUF5XnMf7V3717Vrl1bAQEBtrbGjRsrJydH+/fvt3s3ID8RZgEAMLGAgABVqVIlzz5btmyRJJ07d07nzp2zCxuuOnbsmNq1a6cePXooMTFRISEh2rRpk3r27KmsrKzrhtm/ftDKYrEoJydHkmxjKleuXKly5crZ9bvVDxcFBQWpQ4cOmj17tnr06KHZs2fr6aefVtGiRSVJMTEx+u233zRlyhRVrFhRPj4+ioqKUlZWlt12bnTOJk6cqClTpmjy5MmqVauWAgICNHDgwFzbyes8+Pn55bmPy5cv61//+pdeeOGFXMsqVKhw3fU8PDxsz5Hq1avr8OHD6tu3r+bNmydJ2rp1qzp37qzRo0crOjpawcHBWrhwoSZNmpRnPbcLwiwAAH9jhw8f1qBBgzRz5kwtWrRIMTEx+uKLL+xu3/vVV1/ZrXNt7Kijq7I7duxQTk6OXnvtNRUrVkweHh768MMPb6nGGjVqyMfHR6mpqWrevLnDPtWrV9fy5ctz1emMnj17qkWLFlqxYoW2bNmiiRMn2pZt3rxZ77zzjtq0aSPpj7G5Z8+edfkYNm/erMcff1xdunSR9Mf41wMHDqhGjRpObyMyMlK//PKLDhw44PDq7H333acff/zxhn+83MiwYcNUuXJlDRo0SPfdd5+2bNmiihUr6uWXX7b1+emnn+zW8fb2VnZ2dp7brV69uubMmaP09HRb+N+8ebM8PDx0zz333FLNeSHMFoLCnnQb7uftLfXrJz32mPSXP8rxN3a7TniPv7fMzEydPHnSrq1IkSIKCQlRdna2unTpoujoaHXv3l0PP/ywatWqpUmTJmnIkCG2/qmpqYqPj9e//vUv7dy5U2+99dZ1r8pVqVJFVqtVM2bMUIcOHbR161ZNnz79lo4hMDBQgwcP1qBBg5STk6MmTZro4sWL2rx5s4KCghQTE6PnnnvOVnevXr20Y8cO2yfqb6RZs2aqUqWKunXrpmrVqqlRo0a2ZVWrVtW8efNUr149paWlaciQITe8QupI1apVtWTJEm3ZskXFixdXUlKSTp065VKYbd68uZo1a6Ynn3xSSUlJqlKlivbt22ebO3jo0KFq2LCh4uLi1KtXLwUEBOjHH39UcnKy3n77baf3Ex4erieeeEKjRo3SihUrVLVqVaWmpmrhwoW6//77tXLlSn388cd260REROjo0aO2oRCBgYG5rpp37txZCQkJiomJ0auvvqozZ86of//+6tq1a4ENMZAIswAAXJcZ/kD5/PPPVaZMGbu2e+65R/v27dO4ceP0008/2aZ2KlOmjGbMmKFOnTqpdevWql27tiSpW7du+v3331W/fn15enpqwIAB6tOnj8P91a5dW5MmTdLEiRM1ZswYNWvWTImJierWrdstHcfYsWNVqlQpJSYm6siRIypWrJjuu+8+jRgxQtIfb6MvXbpUgwYN0ltvvaX69etr/PjxDmcW+CuLxaIePXpoxIgRGj58uN2yd999V3369NF9992n8PBwjR8/XoMHD3a5/ldeeUVHjhxRdHS0/P391adPH7Vv314XL150aTtLly7V4MGD1alTJ6Wnp6tKlSq2WR8iIyO1YcMGvfzyy2ratKkMw1DlypUdzixwI4MGDVJUVJS2b9+udu3aadCgQYqLi1NmZqYeffRRjRw50m7asCeffFIfffSRHnjgAV24cEGzZ89WbGys3Tb9/f21evVqDRgwQPfff7/8/f1twbwgWf7/J9HuGGlpaQoODtbFixddHth8s7gye+fx9raqX79VeuedNsrKcn1CbpiTGYIPcrty5YqOHj2qSpUqydfX193lFLoWLVqoTp06mjx5stPr5OTkKC0tTUFBQXbDFQBX5PWz50pe4xkIAAAA0yLMAgAAwLQYMwsAwB1s/fr17i4BuCVcmQUAAIBpEWYBANAfd1ICUHjy62eOMAsAuKNduyNTRkaGmysB7izX7o7m6OYcrmDMLADgjubp6alixYrp9OnTkv6YK9Nisbi5qttbTk6OsrKydOXKFabmwk3JycnRmTNn5O/vryJFbi2OEmYBAHe8sLAwSbIFWuTNMAz9/vvv8vPzI/jjpnl4eKhChQq3/BwizAIA7ngWi0VlypRRaGiorFaru8u57VmtVn355Zdq1qyZbZgG4Cpvb+98ubJPmAUA4P/z9PS85fF7dwJPT09dvXpVvr6+hFm4HQNdAAAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJiW28Ps1KlTFRERIV9fXzVo0EDbt2/Ps//kyZN1zz33yM/PT+Hh4Ro0aJCuXLlSSNUCAADgduLWMLto0SLFx8crISFBO3fuVO3atRUdHa3Tp0877L9gwQINGzZMCQkJ2rt3r959910tWrRII0aMKOTKAQAAcDtwa5hNSkpS79691b17d9WoUUPTp0+Xv7+/3nvvPYf9t2zZosaNG+vZZ59VRESEWrdurU6dOt3wai4AAAD+noq4a8dZWVnasWOHhg8fbmvz8PBQy5YttXXrVofrNGrUSP/73/+0fft21a9fX0eOHNGqVavUtWvX6+4nMzNTmZmZtsdpaWmSJKvVKqvVmk9Hkzdv70LZDW4jXl5Wu39xZyiklxTA7a79/iys36O487jy3HJbmD179qyys7NVunRpu/bSpUtr3759Dtd59tlndfbsWTVp0kSGYejq1at67rnn8hxmkJiYqNGjR+dqX7Nmjfz9/W/tIJzUr1+h7Aa3od69k91dAgrRqlXurgAoXMnJvMahYGRkZDjd121h9masX79e48eP1zvvvKMGDRro0KFDGjBggMaOHauRI0c6XGf48OGKj4+3PU5LS1N4eLhat26toKCgQqn7sccKZTe4jXh5WdW7d7Jmzmwlq9XL3eWgkKxY4e4KgMJhtVqVnJysVq1aycuL1zjkv2vvpDvDbWE2JCREnp6eOnXqlF37qVOnFBYW5nCdkSNHqmvXrurVq5ckqVatWkpPT1efPn308ssvy8Mj9xBgHx8f+fj45Gr38vIqtB/ArKxC2Q1uQ1arl7KyeKG/U/A7HXeawvxdijuLK88rt30AzNvbW3Xr1lVKSoqtLScnRykpKYqKinK4TkZGRq7A6unpKUkyDKPgigUAAMBtya3DDOLj4xUTE6N69eqpfv36mjx5stLT09W9e3dJUrdu3VSuXDklJiZKktq2baukpCT94x//sA0zGDlypNq2bWsLtQAAALhzuDXMduzYUWfOnNGoUaN08uRJ1alTR59//rntQ2Gpqal2V2JfeeUVWSwWvfLKKzp+/LhKlSqltm3baty4ce46BAAAALiR2z8AFhcXp7i4OIfL1q9fb/e4SJEiSkhIUEJCQiFUBgAAgNud229nCwAAANwswiwAAABMizALAAAA0yLMAgAAwLQIswAAADAtwiwAAABMizALAAAA0yLMAgAAwLQIswAAADAtwiwAAABMizALAAAA0yLMAgAAwLQIswAAADAtwiwAAABMizALAAAA0yLMAgAAwLQIswAAADAtwiwAAABMizALAAAA0yLMAgAAwLQIswAAADAtwiwAAABMizALAAAA0yLMAgAAwLQIswAAADAtwiwAAABMizALAAAA0yLMAgAAwLQIswAAADAtwiwAAABMizALAAAA0yLMAgAAwLQIswAAADAtwiwAAABMizALAAAA0yLMAgAAwLQIswAAADAtwiwAAABMizALAAAA0yLMAgAAwLQIswAAADAtwiwAAABMizALAAAA0yLMAgAAwLQIswAAADAtwiwAAABMizALAAAA0yLMAgAAwLQIswAAADAtwiwAAABMizALAAAA0yLMAgAAwLQIswAAADAtwiwAAABMizALAAAA0yLMAgAAwLQIswAAADAtwiwAAABMq8jNrnjmzBnt379fknTPPfeoVKlS+VYUAAAA4AyXr8ymp6erR48eKlu2rJo1a6ZmzZqpbNmy6tmzpzIyMgqiRgAAAMAhl8NsfHy8NmzYoOXLl+vChQu6cOGCPvnkE23YsEEvvvhiQdQIAAAAOOTyMIOlS5dqyZIlatGiha2tTZs28vPz09NPP61p06blZ30AAADAdbl8ZTYjI0OlS5fO1R4aGsowAwAAABQql8NsVFSUEhISdOXKFVvb77//rtGjRysqKipfiwMAAADy4vIwgylTpig6Olrly5dX7dq1JUm7d++Wr6+vVq9ene8FAgAAANfjcpitWbOmDh48qPnz52vfvn2SpE6dOqlz587y8/PL9wIBAACA67mpeWb9/f3Vu3fv/K4FAAAAcIlTYXb58uV65JFH5OXlpeXLl+fZt127dvlSGAAAAHAjToXZ9u3b6+TJkwoNDVX79u2v289isSg7Ozu/agMAAADy5FSYzcnJcfg9AAAA4E4uT831/vvvKzMzM1d7VlaW3n///XwpCgAAAHCGy2G2e/fuunjxYq72S5cuqXv37vlSFAAAAOAMl8OsYRiyWCy52n/55RcFBwfnS1EAAACAM5yemusf//iHLBaLLBaLHnroIRUp8n+rZmdn6+jRo3r44YcLpEgAAADAEafD7LVZDHbt2qXo6GgVLVrUtszb21sRERF68skn871AAAAA4HqcDrMJCQmSpIiICHXs2FG+vr4FVhQAAADgDJfvABYTE1MQdQAAAAAucznMZmdn680339SHH36o1NRUZWVl2S0/d+5cvhUHAAAA5MXl2QxGjx6tpKQkdezYURcvXlR8fLz++c9/ysPDQ6+++moBlAgAAAA45nKYnT9/vmbOnKkXX3xRRYoUUadOnTRr1iyNGjVK27ZtK4gaAQAAAIdcDrMnT55UrVq1JElFixa13UDhscce08qVK/O3OgAAACAPLofZ8uXL68SJE5KkypUra82aNZKkr7/+Wj4+PvlbHQAAAJAHl8PsE088oZSUFElS//79NXLkSFWtWlXdunVTjx498r1AAAAA4Hpcns1gwoQJtu87duyoihUrasuWLapataratm2br8UBAAAAeXH5yuxfNWzYUPHx8Wrbtq2++eYbl9efOnWqIiIi5OvrqwYNGmj79u159r9w4YKef/55lSlTRj4+Prr77ru1atWqmy0fAAAAJuZymL18+bJ+//13u7Zdu3apbdu2atCggUvbWrRokeLj45WQkKCdO3eqdu3aio6O1unTpx32z8rKUqtWrXTs2DEtWbJE+/fv18yZM1WuXDlXDwMAAAB/A06H2Z9//llRUVEKDg5WcHCw4uPjlZGRoW7duqlBgwYKCAjQli1bXNp5UlKSevfure7du6tGjRqaPn26/P399d577zns/9577+ncuXNatmyZGjdurIiICDVv3ly1a9d2ab8AAAD4e3B6zOyQIUN05coVTZkyRR999JGmTJmijRs3qkGDBjp8+LDKly/v0o6zsrK0Y8cODR8+3Nbm4eGhli1bauvWrQ7XWb58uaKiovT888/rk08+UalSpfTss89q6NCh8vT0dLhOZmamMjMzbY/T0tIkSVarVVar1aWab5a3d6HsBrcRLy+r3b+4MxTSSwrgdtd+fxbW71HceVx5bjkdZr/88kt99NFHatiwoZ5++mmFhYWpc+fOGjhw4M3UqLNnzyo7O1ulS5e2ay9durT27dvncJ0jR45o7dq16ty5s1atWqVDhw6pX79+slqtSkhIcLhOYmKiRo8enat9zZo18vf3v6naXdWvX6HsBreh3r2T3V0CChHD93GnSU7mNQ4FIyMjw+m+TofZU6dOqVKlSpKk0NBQ+fv765FHHnG9uluQk5Oj0NBQzZgxQ56enqpbt66OHz+uiRMnXjfMDh8+XPHx8bbHaWlpCg8PV+vWrRUUFFQodT/2WKHsBrcRLy+revdO1syZrWS1erm7HBSSFSvcXQFQOKxWq5KTk9WqVSt5efEah/x37Z10Z7g0NZeHh4fd99638P55SEiIPD09derUKbv2U6dOKSwszOE6ZcqUkZeXl92QgurVq+vkyZPKyspyWI+Pj4/Dmzl4eXkV2g9gVlah7Aa3IavVS1lZvNDfKfidjjtNYf4uxZ3FleeV0x8AMwxDd999t0qUKKESJUro8uXL+sc//mF7fO3LWd7e3qpbt67tBgzSH1deU1JSFBUV5XCdxo0b69ChQ8rJybG1HThwQGXKlLmlYA0AAABzcvrK7OzZs/N95/Hx8YqJiVG9evVUv359TZ48Wenp6erevbskqVu3bipXrpwSExMlSX379tXbb7+tAQMGqH///jp48KDGjx+vF154Id9rAwAAwO3P6TAbExOT7zvv2LGjzpw5o1GjRunkyZOqU6eOPv/8c9uHwlJTU+2GNoSHh2v16tUaNGiQIiMjVa5cOQ0YMEBDhw7N99oAAABw+3P5drb5LS4uTnFxcQ6XrV+/PldbVFSUtm3bVsBVAQAAwAxu+Xa2AAAAgLsQZgEAAGBahFkAAACY1k2H2aysLO3fv19Xr17Nz3oAAAAAp7kcZjMyMtSzZ0/5+/vr3nvvVWpqqiSpf//+mjBhQr4XCAAAAFyPy2F2+PDh2r17t9avXy9fX19be8uWLbVo0aJ8LQ4AAADIi8tTcy1btkyLFi1Sw4YNZbFYbO333nuvDh8+nK/FAQAAAHlx+crsmTNnFBoamqs9PT3dLtwCAAAABc3lMFuvXj2tXLnS9vhagJ01a5aioqLyrzIAAADgBlweZjB+/Hg98sgj+vHHH3X16lVNmTJFP/74o7Zs2aINGzYURI0AAACAQy5fmW3SpIl27dqlq1evqlatWlqzZo1CQ0O1detW1a1btyBqBAAAABxy+cqsJFWuXFkzZ87M71oAAAAAl7h8ZXbVqlVavXp1rvbVq1frs88+y5eiAAAAAGe4HGaHDRum7OzsXO2GYWjYsGH5UhQAAADgDJfD7MGDB1WjRo1c7dWqVdOhQ4fypSgAAADAGS6H2eDgYB05ciRX+6FDhxQQEJAvRQEAAADOcDnMPv744xo4cKDd3b4OHTqkF198Ue3atcvX4gAAAIC8uBxmX3/9dQUEBKhatWqqVKmSKlWqpOrVq6tkyZJ64403CqJGAAAAwCGXp+YKDg7Wli1blJycrN27d8vPz0+RkZFq1qxZQdQHAAAAXNdNzTNrsVjUunVrtW7dOr/rAQAAAJx2U2E2JSVFKSkpOn36tHJycuyWvffee/lSGAAAAHAjLofZ0aNHa8yYMapXr57KlCkji8VSEHUBAAAAN+RymJ0+fbrmzJmjrl27FkQ9AAAAgNNcns0gKytLjRo1KohaAAAAAJe4HGZ79eqlBQsWFEQtAAAAgEtcHmZw5coVzZgxQ1988YUiIyPl5eVltzwpKSnfigMAAADy4nKY/e6771SnTh1J0vfff2+3jA+DAQAAoDC5HGbXrVtXEHUAAAAALnN5zCwAAABwu7ipmyZ88803+vDDD5WamqqsrCy7ZR999FG+FAYAAADciMtXZhcuXKhGjRpp7969+vjjj2W1WvXDDz9o7dq1Cg4OLogaAQAAAIdcDrPjx4/Xm2++qU8//VTe3t6aMmWK9u3bp6effloVKlQoiBoBAAAAh1wOs4cPH9ajjz4qSfL29lZ6erosFosGDRqkGTNm5HuBAAAAwPW4HGaLFy+uS5cuSZLKlStnm57rwoULysjIyN/qAAAAgDy4/AGwZs2aKTk5WbVq1dJTTz2lAQMGaO3atUpOTtZDDz1UEDUCAAAADrkcZt9++21duXJFkvTyyy/Ly8tLW7Zs0ZNPPqlXXnkl3wsEAAAArsflMFuiRAnb9x4eHho2bFi+FgQAAAA4y6kwm5aWpqCgINv3ebnWDwAAAChoToXZ4sWL68SJEwoNDVWxYsVksVhy9TEMQxaLRdnZ2fleJAAAAOCIU2F27dq1tuEF69atK9CCAAAAAGc5FWabN28uSbp69ao2bNigHj16qHz58gVaGAAAAHAjLs0zW6RIEU2cOFFXr14tqHoAAAAAp7l804QHH3xQGzZsKIhaAAAAAJe4PDXXI488omHDhmnPnj2qW7euAgIC7Ja3a9cu34oDAAAA8uJymO3Xr58kKSkpKdcyZjMAAABAYXI5zObk5BREHQAAAIDLXB4zCwAAANwuXL4yK0np6enasGGDUlNTlZWVZbfshRdeyJfCAAAAgBtxOcx+++23atOmjTIyMpSenq4SJUro7Nmz8vf3V2hoKGEWAAAAhcblYQaDBg1S27Ztdf78efn5+Wnbtm366aefVLduXb3xxhsFUSMAAADgkMthdteuXXrxxRfl4eEhT09PZWZmKjw8XK+//rpGjBhREDUCAAAADrkcZr28vOTh8cdqoaGhSk1NlSQFBwfr559/zt/qAAAAgDy4PGb2H//4h77++mtVrVpVzZs316hRo3T27FnNmzdPNWvWLIgaAQAAAIecvjJ77WYI48ePV5kyZSRJ48aNU/HixdW3b1+dOXNGM2bMKJgqAQAAAAecvjJbrlw5xcbGqkePHqpXr56kP4YZfP755wVWHAAAAJAXp6/MPv/881qyZImqV6+upk2bas6cOcrIyCjI2gAAAIA8OR1mR44cqUOHDiklJUV33XWX4uLiVKZMGfXu3VtfffVVQdYIAAAAOOTybAYtWrTQ3LlzdfLkSU2aNEl79+5VVFSU7r33XiUlJRVEjQAAAIBDLofZa4oWLapevXpp06ZN+vTTT3Xy5EkNGTIkP2sDAAAA8nTTYTYjI0Nz5sxR8+bN1a5dO5UsWVLjxo3Lz9oAAACAPLk8z+yWLVv03nvvafHixbp69ao6dOigsWPHqlmzZgVRHwAAAHBdTofZ119/XbNnz9aBAwdUr149TZw4UZ06dVJgYGBB1gcAAABcl9NhduLEierSpYsWL17Mnb4AAABwW3A6zP7666/y8vIqyFoAAAAAlzj9ATCCLAAAAG43Nz2bAQAAAOBuhFkAAACYFmEWAAAApuXUB8DS0tKc3mBQUNBNFwMAAAC4wqkwW6xYMVksFqc2mJ2dfUsFAQAAAM5yKsyuW7fO9v2xY8c0bNgwxcbGKioqSpK0detWzZ07V4mJiQVTJQAAAOCAU2G2efPmtu/HjBmjpKQkderUydbWrl071apVSzNmzFBMTEz+VwkAAAA44PIHwLZu3ap69erlaq9Xr562b9+eL0UBAAAAznA5zIaHh2vmzJm52mfNmqXw8PB8KQoAAABwhtO3s73mzTff1JNPPqnPPvtMDRo0kCRt375dBw8e1NKlS/O9QAAAAOB6XL4y26ZNGx04cEBt27bVuXPndO7cObVt21YHDhxQmzZtCqJGAAAAwCGXr8xKfww1GD9+fH7XAgAAALjkpu4AtnHjRnXp0kWNGjXS8ePHJUnz5s3Tpk2b8rU4AAAAIC8uh9mlS5cqOjpafn5+2rlzpzIzMyVJFy9e5GotAAAACpXLYfa1117T9OnTNXPmTHl5ednaGzdurJ07d+ZrcQAAAEBeXA6z+/fvV7NmzXK1BwcH68KFC/lREwAAAOAUl8NsWFiYDh06lKt906ZNuuuuu/KlKAAAAMAZLofZ3r17a8CAAfrqq69ksVj066+/av78+Ro8eLD69u1bEDUCAAAADrk8NdewYcOUk5Ojhx56SBkZGWrWrJl8fHw0ePBg9e/fvyBqBAAAABxyOcxaLBa9/PLLGjJkiA4dOqTLly+rRo0aKlq0aEHUBwAAAFzXTc0zK0ne3t6qUaOG6tevf8tBdurUqYqIiJCvr68aNGig7du3O7XewoULZbFY1L59+1vaPwAAAMzJ5Suz6enpmjBhglJSUnT69Gnl5OTYLT9y5IhL21u0aJHi4+M1ffp0NWjQQJMnT1Z0dLT279+v0NDQ66537NgxDR48WE2bNnX1EAAAAPA34XKY7dWrlzZs2KCuXbuqTJkyslgst1RAUlKSevfure7du0uSpk+frpUrV+q9997TsGHDHK6TnZ2tzp07a/To0dq4cWOeU4JlZmbabuwgSWlpaZIkq9Uqq9V6S7U7y9u7UHaD24iXl9XuX9wZCuklBXC7a78/C+v3KO48rjy3XA6zn332mVauXKnGjRu7umouWVlZ2rFjh4YPH25r8/DwUMuWLbV169brrjdmzBiFhoaqZ8+e2rhxY577SExM1OjRo3O1r1mzRv7+/jdfvAv69SuU3eA21Lt3srtLQCFatcrdFQCFKzmZ1zgUjIyMDKf7uhxmixcvrhIlSri6mkNnz55Vdna2SpcubddeunRp7du3z+E6mzZt0rvvvqtdu3Y5tY/hw4crPj7e9jgtLU3h4eFq3bq1goKCbrp2Vzz2WKHsBrcRLy+revdO1syZrWS1et14BfwtrFjh7gqAwmG1WpWcnKxWrVrZ3Q0UyC/X3kl3hsthduzYsRo1apTmzp1baFc2r7l06ZK6du2qmTNnKiQkxKl1fHx85OPjk6vdy8ur0H4As7IKZTe4DVmtXsrK4oX+TsHvdNxpCvN3Ke4srjyvXA6zkyZN0uHDh1W6dGlFRETk2tnOnTud3lZISIg8PT116tQpu/ZTp04pLCwsV//Dhw/r2LFjatu2ra3t2gfQihQpov3796ty5cquHA4AAABMzOUwm5/TYHl7e6tu3bpKSUmxbTcnJ0cpKSmKi4vL1b9atWras2ePXdsrr7yiS5cuacqUKQoPD8+32gAAAHD7cznMJiQk5GsB8fHxiomJUb169VS/fn1NnjxZ6enpttkNunXrpnLlyikxMVG+vr6qWbOm3frFihWTpFztAAAA+PtzOczmt44dO+rMmTMaNWqUTp48qTp16ujzzz+3fSgsNTVVHh43fW8HAAAA/I05FWZLlCihAwcOKCQkRMWLF89zbtlz5865XERcXJzDYQWStH79+jzXnTNnjsv7AwAAwN+DU2H2zTffVGBgoCRp8uTJBVkPAAAA4DSnwmxMTIzD7wEAAAB3uqUxs1euXFHWXyZRLawbEQAAAAAuf7IqPT1dcXFxCg0NVUBAgIoXL273BQAAABQWl8PsSy+9pLVr12ratGny8fHRrFmzNHr0aJUtW1bvv/9+QdQIAAAAOOTyMINPP/1U77//vlq0aKHu3buradOmqlKliipWrKj58+erc+fOBVEnAAAAkIvLV2bPnTunu+66S9If42OvTcXVpEkTffnll/lbHQAAAJAHl8PsXXfdpaNHj0r64/ayH374oaQ/rtheuxsXAAAAUBhcDrPdu3fX7t27JUnDhg3T1KlT5evrq0GDBmnIkCH5XiAAAABwPS6PmR00aJDt+5YtW2rfvn3asWOHqlSposjIyHwtDgAAAMjLLc0zK0kVK1ZUxYoV86MWAAAAwCVOhdn//Oc/Tm/whRdeuOliAAAAAFc4FWbffPNNpzZmsVgIswAAACg0ToXZa7MXAAAAALcTl2cz+DPDMGQYRn7VAgAAALjkpsLsu+++q5o1a8rX11e+vr6qWbOmZs2ald+1AQAAAHlyeTaDUaNGKSkpSf3791dUVJQkaevWrRo0aJBSU1M1ZsyYfC8SAAAAcMTlMDtt2jTNnDlTnTp1srW1a9dOkZGR6t+/P2EWAAAAhcblYQZWq1X16tXL1V63bl1dvXo1X4oCAAAAnOFymO3ataumTZuWq33GjBnq3LlzvhQFAAAAOOOm7gD27rvvas2aNWrYsKEk6auvvlJqaqq6deum+Ph4W7+kpKT8qRIAAABwwOUw+/333+u+++6TJB0+fFiSFBISopCQEH3//fe2fhaLJZ9KBAAAABxzOcyuW7euIOoAAAAAXObymNkzZ85cd9mePXtuqRgAAADAFS6H2Vq1amnlypW52t944w3Vr18/X4oCAAAAnOFymI2Pj9eTTz6pvn376vfff9fx48f10EMP6fXXX9eCBQsKokYAAADAIZfD7EsvvaStW7dq48aNioyMVGRkpHx8fPTdd9/piSeeKIgaAQAAAIdcDrOSVKVKFdWsWVPHjh1TWlqaOnbsqLCwsPyuDQAAAMiTy2F28+bNioyM1MGDB/Xdd99p2rRp6t+/vzp27Kjz588XRI0AAACAQy6H2QcffFAdO3bUtm3bVL16dfXq1UvffvutUlNTVatWrYKoEQAAAHDI5Xlm16xZo+bNm9u1Va5cWZs3b9a4cePyrTAAAADgRly+MvvXIGvbkIeHRo4cecsFAQAAAM5yOsy2adNGFy9etD2eMGGCLly4YHv822+/qUaNGvlaHAAAAJAXp8Ps6tWrlZmZaXs8fvx4nTt3zvb46tWr2r9/f/5WBwAAAOTB6TBrGEaejwEAAIDCdlPzzAIAAAC3A6fDrMVikcViydUGAAAAuIvTU3MZhqHY2Fj5+PhIkq5cuaLnnntOAQEBkmQ3nhYAAAAoDE6H2ZiYGLvHXbp0ydWnW7dut14RAAAA4CSnw+zs2bMLsg4AAADAZXwADAAAAKZFmAUAAIBpEWYBAABgWoRZAAAAmBZhFgAAAKZFmAUAAIBpEWYBAABgWoRZAAAAmBZhFgAAAKZFmAUAAIBpEWYBAABgWoRZAAAAmBZhFgAAAKZFmAUAAIBpEWYBAABgWoRZAAAAmBZhFgAAAKZFmAUAAIBpEWYBAABgWoRZAAAAmBZhFgAAAKZFmAUAAIBpEWYBAABgWoRZAAAAmBZhFgAAAKZFmAUAAIBpEWYBAABgWoRZAAAAmBZhFgAAAKZFmAUAAIBpEWYBAABgWoRZAAAAmBZhFgAAAKZFmAUAAIBpEWYBAABgWoRZAAAAmBZhFgAAAKZFmAUAAIBpEWYBAABgWoRZAAAAmBZhFgAAAKZFmAUAAIBpEWYBAABgWoRZAAAAmNZtEWanTp2qiIgI+fr6qkGDBtq+fft1+86cOVNNmzZV8eLFVbx4cbVs2TLP/gAAAPj7cnuYXbRokeLj45WQkKCdO3eqdu3aio6O1unTpx32X79+vTp16qR169Zp69atCg8PV+vWrXX8+PFCrhwAAADu5vYwm5SUpN69e6t79+6qUaOGpk+fLn9/f7333nsO+8+fP1/9+vVTnTp1VK1aNc2aNUs5OTlKSUkp5MoBAADgbkXcufOsrCzt2LFDw4cPt7V5eHioZcuW2rp1q1PbyMjIkNVqVYkSJRwuz8zMVGZmpu1xWlqaJMlqtcpqtd5C9c7z9i6U3eA24uVltfsXd4ZCekkB3O7a78/C+j2KO48rzy23htmzZ88qOztbpUuXtmsvXbq09u3b59Q2hg4dqrJly6ply5YOlycmJmr06NG52tesWSN/f3/Xi74J/foVym5wG+rdO9ndJaAQrVrl7gqAwpWczGscCkZGRobTfd0aZm/VhAkTtHDhQq1fv16+vr4O+wwfPlzx8fG2x2lpabZxtkFBQYVS52OPFcpucBvx8rKqd+9kzZzZSlarl7vLQSFZscLdFQCFw2q1Kjk5Wa1atZKXF69xyH/X3kl3hlvDbEhIiDw9PXXq1Cm79lOnTiksLCzPdd944w1NmDBBX3zxhSIjI6/bz8fHRz4+Prnavby8Cu0HMCurUHaD25DV6qWsLF7o7xT8TsedpjB/l+LO4srzyq0fAPP29lbdunXtPrx17cNcUVFR113v9ddf19ixY/X555+rXr16hVEqAAAAbkNuH2YQHx+vmJgY1atXT/Xr19fkyZOVnp6u7t27S5K6deumcuXKKTExUZL073//W6NGjdKCBQsUERGhkydPSpKKFi2qokWLuu04AAAAUPjcHmY7duyoM2fOaNSoUTp58qTq1Kmjzz//3PahsNTUVHl4/N8F5GnTpikrK0sdOnSw205CQoJeffXVwiwdAAAAbub2MCtJcXFxiouLc7hs/fr1do+PHTtW8AUBAADAFNx+0wQAAADgZhFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmRZgFAACAaRFmAQAAYFqEWQAAAJgWYRYAAACmVcTdBQAAzO+BB9xdAQqTt7fUr5/02GNSVpa7q0FhWbfO3RU4xpVZAAAAmBZhFgAAAKZFmAUAAIBpEWYBAABgWoRZAAAAmBZhFgAAAKZFmAUAAIBpEWYBAABgWoRZAAAAmBZhFgAAAKZFmAUAAIBpEWYBAABgWoRZAAAAmBZhFgAAAKZFmAUAAIBpEWYBAABgWoRZAAAAmBZhFgAAAKZFmAUAAIBpEWYBAABgWoRZAAAAmBZhFgAAAKZFmAUAAIBpEWYBAABgWoRZAAAAmBZhFgAAAKZFmAUAAIBpEWYBAABgWoRZAAAAmBZhFgAAAKZFmAUAAIBp3RZhdurUqYqIiJCvr68aNGig7du359l/8eLFqlatmnx9fVWrVi2tWrWqkCoFAADA7cTtYXbRokWKj49XQkKCdu7cqdq1ays6OlqnT5922H/Lli3q1KmTevbsqW+//Vbt27dX+/bt9f333xdy5QAAAHA3t4fZpKQk9e7dW927d1eNGjU0ffp0+fv767333nPYf8qUKXr44Yc1ZMgQVa9eXWPHjtV9992nt99+u5ArBwAAgLsVcefOs7KytGPHDg0fPtzW5uHhoZYtW2rr1q0O19m6davi4+Pt2qKjo7Vs2TKH/TMzM5WZmWl7fPHiRUnSuXPnZLVab/EInOPh9j8ZUPisysjIkPSbPDy83F0MCslvv7m7Avfhde5Ow2vcnagwX+MuXbokSTIM44Z93Rpmz549q+zsbJUuXdquvXTp0tq3b5/DdU6ePOmw/8mTJx32T0xM1OjRo3O1V6pU6SarBpyzZo27K0BhCwlxdwVA4eE17s7jjte4S5cuKTg4OM8+bg2zhWH48OF2V3JzcnJ07tw5lSxZUhaLxY2V4e8sLS1N4eHh+vnnnxUUFOTucgAgX/Eah4JmGIYuXbqksmXL3rCvW8NsSEiIPD09derUKbv2U6dOKSwszOE6YWFhLvX38fGRj4+PXVuxYsVuvmjABUFBQbzQA/jb4jUOBelGV2SvcesoJ29vb9WtW1cpKSm2tpycHKWkpCgqKsrhOlFRUXb9JSk5Ofm6/QEAAPD35fZhBvHx8YqJiVG9evVUv359TZ48Wenp6erevbskqVu3bipXrpwSExMlSQMGDFDz5s01adIkPfroo1q4cKG++eYbzZgxw52HAQAAADdwe5jt2LGjzpw5o1GjRunkyZOqU6eOPv/8c9uHvFJTU+Xxp4/JNmrUSAsWLNArr7yiESNGqGrVqlq2bJlq1qzprkMAcvHx8VFCQkKuIS4A8HfAaxxuJxbDmTkPAAAAgNsQMwMCAADAtAizAAAAMC3CLAAAAEyLMAsAAADTIswC+ejLL79U27ZtVbZsWVksFi1btszdJQFAvkhMTNT999+vwMBAhYaGqn379tq/f7+7ywIIs0B+Sk9PV+3atTV16lR3lwIA+WrDhg16/vnntW3bNiUnJ8tqtap169ZKT093d2m4wzE1F1BALBaLPv74Y7Vv397dpQBAvjtz5oxCQ0O1YcMGNWvWzN3l4A7GlVkAAOCyixcvSpJKlCjh5kpwpyPMAgAAl+Tk5GjgwIFq3Lgxd+CE27n9drYAAMBcnn/+eX3//ffatGmTu0sBCLMAAMB5cXFxWrFihb788kuVL1/e3eUAhFkAAHBjhmGof//++vjjj7V+/XpVqlTJ3SUBkgizQL66fPmyDh06ZHt89OhR7dq1SyVKlFCFChXcWBkA3Jrnn39eCxYs0CeffKLAwECdPHlSkhQcHCw/Pz83V4c7GVNzAflo/fr1euCBB3K1x8TEaM6cOYVfEADkE4vF4rB99uzZio2NLdxigD8hzAIAAMC0mJoLAAAApkWYBQAAgGkRZgEAAGBahFkAAACYFmEWAAAApkWYBQAAgGkRZgEAAGBahFkAAACYFmEWwN9eRESEJk+enG/bi42NVfv27fNte9Ifd4+zWCy6cOFCvm4XAP7uCLMATCM2NlYWi0UWi0Xe3t6qUqWKxowZo6tXr+a53tdff60+ffrkWx1Tpkxx2+2Jv/32Wz311FMqXbq0fH19VbVqVfXu3VsHDhxwSz23q/z+AwbA7YswC8BUHn74YZ04cUIHDx7Uiy++qFdffVUTJ0502DcrK0uSVKpUKfn7++dbDcHBwSpWrFi+bc9ZK1asUMOGDZWZman58+dr7969+t///qfg4GCNHDmy0OsBgNsBYRaAqfj4+CgsLEwVK1ZU37591bJlSy1fvlzS/739P27cOJUtW1b33HOPpNxX6SwWi2bNmqUnnnhC/v7+qlq1qm0b1/zwww967LHHFBQUpMDAQDVt2lSHDx+22881LVq0UFxcnOLi4hQcHKyQkBCNHDlShmHY+sybN0/16tVTYGCgwsLC9Oyzz+r06dNOH3dGRoa6d++uNm3aaPny5WrZsqUqVaqkBg0a6I033tB///tfW98NGzaofv368vHxUZkyZTRs2DC7q9ctWrRQ//79NXDgQBUvXlylS5fWzJkzlZ6eru7duyswMFBVqlTRZ599Zlvn2jCIlStXKjIyUr6+vmrYsKG+//57uzqXLl2qe++9Vz4+PoqIiNCkSZPslkdERGj8+PHq0aOHAgMDVaFCBc2YMcOuz88//6ynn35axYoVU4kSJfT444/r2LFjtuXXzv8bb7yhMmXKqGTJknr++edltVptx/fTTz9p0KBBtiv5AP6+CLMATM3Pz892BVaSUlJStH//fiUnJ2vFihXXXW/06NF6+umn9d1336lNmzbq3Lmzzp07J0k6fvy4mjVrJh8fH61du1Y7duxQjx498hzOMHfuXBUpUkTbt2/XlClTlJSUpFmzZtmWW61WjR07Vrt379ayZct07NgxxcbGOn2cq1ev1tmzZ/XSSy85XH7tSvHx48fVpk0b3X///dq9e7emTZumd999V6+99lquekNCQrR9+3b1799fffv21VNPPaVGjRpp586dat26tbp27aqMjAy79YYMGaJJkybp66+/VqlSpdS2bVtbiNyxY4eefvppPfPMM9qzZ49effVVjRw5MteQjEmTJqlevXr69ttv1a9fP/Xt21f79++3nafo6GgFBgZq48aN2rx5s4oWLaqHH37Y7v953bp1Onz4sNatW6e5c+dqzpw5tv189NFHKl++vMaMGaMTJ07oxIkTTp9nACZkAIBJxMTEGI8//rhhGIaRk5NjJCcnGz4+PsbgwYNty0uXLm1kZmbarVexYkXjzTfftD2WZLzyyiu2x5cvXzYkGZ999plhGIYxfPhwo1KlSkZWVtYN6zAMw2jevLlRvXp1Iycnx9Y2dOhQo3r16tc9lq+//tqQZFy6dMkwDMNYt26dIck4f/68w/7//ve/DUnGuXPnrrtNwzCMESNGGPfcc49dLVOnTjWKFi1qZGdn2+pt0qSJbfnVq1eNgIAAo2vXrra2EydOGJKMrVu32tW3cOFCW5/ffvvN8PPzMxYtWmQYhmE8++yzRqtWrezqGTJkiFGjRg3b44oVKxpdunSxPc7JyTFCQ0ONadOmGYZhGPPmzctVf2ZmpuHn52esXr3aMIw/zn/FihWNq1ev2vo89dRTRseOHe328+f/cwB/X1yZBWAqK1asUNGiReXr66tHHnlEHTt21KuvvmpbXqtWLXl7e99wO5GRkbbvAwICFBQUZHvbf9euXWratKm8vLycrqthw4Z2b2dHRUXp4MGDys7OlvTHVcu2bduqQoUKCgwMVPPmzSVJqampTm3f+NOQhbzs3btXUVFRdrU0btxYly9f1i+//GJr+/Pxe3p6qmTJkqpVq5atrXTp0pKUayhEVFSU7fsSJUronnvu0d69e237bty4sV3/xo0b252Hv+7bYrEoLCzMtp/du3fr0KFDCgwMVNGiRVW0aFGVKFFCV65csQ3zkKR7771Xnp6etsdlypRxadgGgL+PIu4uAABc8cADD2jatGny9vZW2bJlVaSI/ctYQECAU9v5a1C1WCzKycmR9MfQhfyUnp6u6OhoRUdHa/78+SpVqpRSU1MVHR1t99Z5Xu6++25J0r59++wC5c1ydPx/brsWhq+dk/yU17m/fPmy6tatq/nz5+dar1SpUk5tA8CdhSuzAEwlICBAVapUUYUKFXIF2fwSGRmpjRs32saCOuOrr76ye7xt2zZVrVpVnp6e2rdvn3777TdNmDBBTZs2VbVq1Vy+iti6dWuFhITo9ddfd7j82vy01atX19atW+2u5G7evFmBgYEqX768S/t0ZNu2bbbvz58/rwMHDqh69eq2fW/evNmu/+bNm3X33XfbXUXNy3333aeDBw8qNDRUVapUsfsKDg52uk5vb2+7q8EA/r4IswDwF3FxcUpLS9Mzzzyjb775RgcPHtS8efNsH1JyJDU1VfHx8dq/f78++OADvfXWWxowYIAkqUKFCvL29tZbb72lI0eOaPny5Ro7dqxLNQUEBGjWrFlauXKl2rVrpy+++ELHjh3TN998o5deeknPPfecJKlfv376+eef1b9/f+3bt0+ffPKJEhISFB8fLw+PW3/JHzNmjFJSUvT9998rNjZWISEhtpkdXnzxRaWkpGjs2LE6cOCA5s6dq7fffluDBw92evudO3dWSEiIHn/8cW3cuFFHjx7V+vXr9cILL9gNk7iRiIgIffnllzp+/LjOnj3r6mECMBHCLAD8RcmSJbV27VpdvnxZzZs3V926dTVz5sw8x9B269ZNv//+u+rXr6/nn39eAwYMsN2ooVSpUpozZ44WL16sGjVqaMKECXrjjTdcruvxxx/Xli1b5OXlpWeffVbVqlVTp06ddPHiRdtsBeXKldOqVau0fft21a5dW88995x69uypV1555eZOxl9MmDBBAwYMUN26dXXy5El9+umntjHK9913nz788EMtXLhQNWvW1KhRozRmzBiXZm3w9/fXl19+qQoVKuif//ynqlevrp49e+rKlSsKCgpyejtjxozRsWPHVLlyZbvhCQD+fiyGs58qAAA41KJFC9WpU+dvfcep9evX64EHHtD58+fdcsMIALgerswCAADAtAizAAAAMC2GGQAAAMC0uDILAAAA0yLMAgAAwLQIswAAADAtwiwAAABMizALAAAA0yLMAgAAwLQIswAAADAtwiwAAABM6/8BOnr3iuqCGAsAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.bar(range(1, pca.n_components_ + 1),\n",
-    "        pca.explained_variance_ratio_,\n",
-    "        alpha=0.75, color='blue',\n",
-    "        label='Explained Variance Ratio')\n",
-    "plt.xlabel('Principal Component')\n",
-    "plt.ylabel('Explained Variance Ratio')\n",
-    "plt.title('Explained Variance Ratio of PCA')\n",
-    "plt.xticks(range(1, pca.n_components_ + 1))\n",
-    "plt.legend()\n",
-    "plt.grid()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "70f165b1",
-   "metadata": {},
-   "source": [
-    "## Autoencoders\n",
-    "\n",
-    "Autoencoders are a type of neural network used for unsupervised learning, particularly for dimensionality reduction and feature extraction. They consist of two main components: an encoder and a decoder. The encoder compresses the input data into a lower-dimensional representation (latent space), while the decoder reconstructs the original data from this representation.\n",
-    "\n",
-    "Autoencoders can learn complex, non-linear mappings and are particularly useful for high-dimensional data. They can be trained using backpropagation to minimize the reconstruction error, which is the difference between the original input and the reconstructed output.\n",
-    "\n",
-    "Autoencoders can be used for various tasks, including:\n",
-    "\n",
-    "-   Dimensionality reduction: By training the autoencoder to compress the input data into a lower-dimensional representation, we can effectively reduce the dimensionality of the data while retaining important features.\n",
-    "-   Feature extraction: The latent space representation learned by the encoder can be used as a new set of features for downstream tasks, such as classification or clustering.\n",
-    "-   Denoising: Denoising autoencoders can be trained to reconstruct the original data from noisy inputs, effectively learning to filter out noise and retain important features."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17db1f75",
-   "metadata": {},
-   "source": [
-    "### Mathematical Formulation\n",
-    "\n",
-    "Autoencoders can be mathematically formulated as follows:\n",
-    "\n",
-    "1.  **Encoder**: The encoder maps the input data $X \\in \\mathbb{R}^{n \\times p}$ to a lower-dimensional representation $Z \\in \\mathbb{R}^{n \\times k}$ using a function $f$: $$Z = f(X) = \\sigma(W_e X + b_e),$$ where $W_e$ is the weight matrix, $b_e$ is the bias vector, and $\\sigma$ is an activation function (e.g., ReLU, sigmoid).\n",
-    "2.  **Decoder**: The decoder reconstructs the original data from the lower-dimensional representation $Z$ using a function $g$: $$\\hat{X} = g(Z) = \\sigma(W_d Z + b_d),$$ where $W_d$ is the weight matrix, $b_d$ is the bias vector, and $\\sigma$ is an activation function.\n",
-    "3.  **Loss Function**: The autoencoder is trained to minimize the reconstruction error, which can be defined as the mean squared error (MSE) between the original input $X$ and the reconstructed output $\\hat{X}$: $$\\mathcal{L}(X, \\hat{X}) = \\frac{1}{n} \\sum_{i=1}^{n} \\|X_i - \\hat{X}_i\\|^2,$$ where $X_i$ and $\\hat{X}_i$ are the $i$-th samples of the original input and reconstructed output, respectively."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00253ccf",
-   "metadata": {},
-   "source": [
-    "### Advantages and Disadvantages\n",
-    "\n",
-    "-   **Advantages**:\n",
-    "    -   Can learn complex, non-linear mappings.\n",
-    "    -   Effective for high-dimensional data.\n",
-    "    -   Can be used for various tasks, including dimensionality reduction, feature extraction, and denoising.\n",
-    "-   **Disadvantages**:\n",
-    "    -   Requires a large amount of data for training.\n",
-    "    -   Can be computationally expensive, especially for deep autoencoders.\n",
-    "    -   May suffer from overfitting if not properly regularized."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f79bdfa8",
-   "metadata": {},
-   "source": [
-    "### Evaluation of Autoencoders\n",
-    "\n",
-    "To evaluate the effectiveness of autoencoders, we can use the reconstruction error as a metric. A lower reconstruction error indicates that the autoencoder has effectively learned to compress and reconstruct the input data. Additionally, we can visualize the latent space representation learned by the encoder to assess how well it captures the structure of the data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9fa99f0d",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "Use a synthetic dataset with 4 highly correlated features and apply PCA to reduce the dimensionality to 2 dimensions. Visualize the results and evaluate the explained variance ratio.\n",
-    "\n",
-    "-   Generate a synthetic dataset with 4 features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "0a6c29d6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   Feature 1  Feature 2  Feature 3  Feature 4\n",
-      "0   0.374540   0.505088   0.538319   0.480642\n",
-      "1   0.156019   0.158119   0.083270   0.158809\n",
-      "2   0.601115   0.669310   0.824426   0.874517\n",
-      "3   0.832443   0.801416   0.812983   0.715228\n",
-      "4   0.304242   0.336659   0.454589   0.464522\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "# Create a synthetic dataset with 4 features\n",
-    "np.random.seed(42)\n",
-    "X = np.random.rand(100, 4)  # 100 samples, 4 features\n",
-    "# Introduce high correlation between features\n",
-    "X[:, 1] = X[:, 0] + np.random.normal(0, 0.1, 100)  # Feature 2 is correlated with Feature 1\n",
-    "X[:, 2] = X[:, 1] + np.random.normal(0, 0.1, 100)  # Feature 3 is correlated with Feature 2\n",
-    "X[:, 3] = X[:, 2] + np.random.normal(0, 0.1, 100)  # Feature 4 is correlated with Feature 3\n",
-    "# Create a DataFrame for better visualization\n",
-    "import pandas as pd\n",
-    "feature_names = [f'Feature {i+1}' for i in range(4)]\n",
-    "df = pd.DataFrame(X, columns=feature_names)\n",
-    "print(df.head())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ee4b2b5b",
-   "metadata": {},
-   "source": [
-    "-   Visualize the correlations between features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "9fe68a85",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "# Visualize the correlations between features\n",
-    "plt.figure(figsize=(10, 8))\n",
-    "sns.heatmap(df.corr(), annot=True, cmap='coolwarm', fmt='.2f', square=True, cbar_kws={\"shrink\": .8})\n",
-    "plt.title('Feature Correlation Matrix')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2053812f",
-   "metadata": {},
-   "source": [
-    "-   Apply PCA with 4 components and visualize the results (first 2 PCs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "ab48d89c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.decomposition import PCA\n",
-    "pca = PCA(n_components=4)\n",
-    "X_pca = pca.fit_transform(X)\n",
-    "# Plot the PCA results\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(X_pca[:, 0], X_pca[:, 1], edgecolor='k', s=50)\n",
-    "plt.title('PCA of Synthetic Dataset with 4 Features')\n",
-    "plt.xlabel('Principal Component 1')\n",
-    "plt.ylabel('Principal Component 2')\n",
-    "plt.grid()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "48206e08",
-   "metadata": {},
-   "source": [
-    "-   Print the principal components and explained variance ratio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "92323412",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Principal Components:\n",
-      " [[ 0.43388756  0.48467323  0.5186208   0.55485666]\n",
-      " [ 0.6954338   0.33780634 -0.29920899 -0.55922508]\n",
-      " [-0.30317307  0.22769328  0.69475196 -0.61119684]\n",
-      " [-0.48600361  0.77403779 -0.39853     0.07641877]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Print the principal components\n",
-    "print(\"Principal Components:\\n\", pca.components_)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "39fbe5ea",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Explained Variance Ratio: [0.95355226 0.03281419 0.00854854 0.00508501]\n",
-      "Cumulative Explained Variance: [0.95355226 0.98636645 0.99491499 1.        ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Print explained variance ratio\n",
-    "print(\"Explained Variance Ratio:\", pca.explained_variance_ratio_)\n",
-    "# Print cumulative explained variance\n",
-    "print(\"Cumulative Explained Variance:\", np.cumsum(pca.explained_variance_ratio_))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "53398f6b",
-   "metadata": {},
-   "source": [
-    "-   Make a scree plot to visualize the explained variance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "dcae4754",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Scree plot to visualize the explained variance\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.plot(range(1, pca.n_components_ + 1), pca.explained_variance_ratio_, marker='o', linestyle='--', color='blue')\n",
-    "plt.title('Scree Plot of PCA')\n",
-    "plt.xlabel('Principal Component')\n",
-    "plt.ylabel('Explained Variance Ratio')\n",
-    "plt.xticks(range(1, pca.n_components_ + 1))\n",
-    "plt.grid()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b1bf7d05",
-   "metadata": {},
-   "source": [
-    "-   Visualize the correlation between the principal components"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "c93dad41",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualize the correlation between the principal components\n",
-    "plt.figure(figsize=(10, 8))\n",
-    "sns.heatmap(np.corrcoef(X_pca.T), annot=True, cmap='coolwarm', fmt='.2f', square=True, cbar_kws={\"shrink\": .8})\n",
-    "plt.title('Correlation Matrix of Principal Components')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7fa5a69a-01d3-44bd-b4f2-a0b747b11c19",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S05 Unsupervised Learning - Clustering and Dimensionality Reduction/S05C03 Clustering Algorithms.ipynb b/src/S05 Unsupervised Learning - Clustering and Dimensionality Reduction/S05C03 Clustering Algorithms.ipynb
deleted file mode 100644
index a7eb92d..0000000
--- a/src/S05 Unsupervised Learning - Clustering and Dimensionality Reduction/S05C03 Clustering Algorithms.ipynb	
+++ /dev/null
@@ -1,776 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "7c723c8c",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Clustering Algorithms\n",
-    "\n",
-    "Unsupervised learning focuses on finding patterns in data without labeled outputs. Clustering is a key unsupervised learning technique that groups similar observations together based on their features. It is exploratory in nature, meaning the results can vary significantly depending on the algorithm, distance metric used, and parameters set. Common clustering algorithms include:\n",
-    "\n",
-    "-   **k-Means**: Partitions data into k clusters by minimizing the variance within each cluster.\n",
-    "-   **Hierarchical Clustering**: Builds a hierarchy of clusters, either agglomerative (bottom-up) or divisive (top-down).\n",
-    "-   **DBSCAN**: Density-Based Spatial Clustering of Applications with Noise, which finds clusters of varying shape and density.\n",
-    "-   **Gaussian Mixture Models (GMM)**: Assumes data is generated from a mixture of several Gaussian distributions and uses the Expectation-Maximization algorithm to estimate parameters."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "11358bb9",
-   "metadata": {},
-   "source": [
-    "## Advantages and Disadvantages\n",
-    "\n",
-    "-   **Advantages**:\n",
-    "    -   Can discover hidden patterns and structures in data.\n",
-    "    -   Useful for exploratory data analysis and feature engineering.\n",
-    "    -   Can handle large datasets without the need for labeled data.\n",
-    "-   **Disadvantages**:\n",
-    "    -   Results can be difficult to interpret, especially in clustering.\n",
-    "    -   No guarantee of finding meaningful patterns; results can be sensitive to the choice of algorithm and parameters.\n",
-    "    -   Evaluation of clustering results can be challenging without ground truth labels."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9e0dd93a",
-   "metadata": {},
-   "source": [
-    "## K-Means\n",
-    "\n",
-    "k-Means is a popular clustering algorithm that partitions data into k clusters by minimizing the variance within each cluster. It works as follows:\n",
-    "\n",
-    "-   Initialize $k$ centroids randomly from the data points.\n",
-    "-   Assign each data point to the nearest centroid based on a distance metric (commonly Euclidean distance).\n",
-    "-   Update the centroids by calculating the mean of all points assigned to each cluster.\n",
-    "-   Repeat steps 2 and 3 until convergence (i.e., centroids do not change significantly)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a6227d29",
-   "metadata": {},
-   "source": [
-    "### Advantages and Disadvantages\n",
-    "\n",
-    "-   **Advantages**:\n",
-    "    -   Simple and efficient for large datasets.\n",
-    "    -   Works well when clusters are spherical and of similar size.\n",
-    "-   **Disadvantages**:\n",
-    "    -   Requires specifying the number of clusters (k) beforehand.\n",
-    "    -   Sensitive to outliers and noise.\n",
-    "    -   Assumes clusters are spherical and equally sized, which may not hold in practice."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2d6562fd",
-   "metadata": {},
-   "source": [
-    "### Example\n",
-    "\n",
-    "To illustrate the concepts of unsupervised learning, we will use the Iris dataset and apply PCA for dimensionality reduction and K-Means for clustering.\n",
-    "\n",
-    "-   Load the Iris dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b24574b5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1117.75x1000 with 20 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Load the Iris dataset\n",
-    "df = pd.read_csv('../../data/iris.csv', header=0)\n",
-    "X = df.drop('species', axis=1)\n",
-    "y = df['species'].astype('category').cat.codes\n",
-    "\n",
-    "sns.pairplot(df, hue='species', palette='viridis', markers=[\"o\", \"s\", \"D\"])\n",
-    "plt.suptitle('Iris Dataset Pairplot', y=1.02)\n",
-    "plt.xlabel('Features')\n",
-    "plt.ylabel('Features')\n",
-    "# plt.legend(title='Species', loc='upper right')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "56dc2836",
-   "metadata": {},
-   "source": [
-    "-   Preprocess the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "16cfb71f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, Normalizer\n",
-    "# Standardize the data\n",
-    "scaler = Normalizer()\n",
-    "X = scaler.fit_transform(X)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "898447f4",
-   "metadata": {},
-   "source": [
-    "-   Apply PCA for dimensionality reduction to 2 dimensions and visualize the results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "7b86d5f9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.decomposition import PCA\n",
-    "# Apply PCA to reduce to 2 dimensions\n",
-    "pca = PCA(n_components=2, random_state=42)\n",
-    "X_pca = pca.fit_transform(X)\n",
-    "# Plot the PCA results\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y, cmap='viridis', edgecolor='k', s=50)\n",
-    "plt.title('PCA of Iris Dataset')\n",
-    "plt.xlabel('Principal Component 1')\n",
-    "plt.ylabel('Principal Component 2')\n",
-    "plt.colorbar(label='Species')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b7632f7a",
-   "metadata": {},
-   "source": [
-    "-   Apply K-Means clustering on the PCA-reduced data and visualize the clusters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "ab0f2f60",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_1304995/1003231196.py:16: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
-      "  plt.legend()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.cluster import KMeans\n",
-    "# Apply K-Means clustering\n",
-    "kmeans = KMeans(n_clusters=3, random_state=42)\n",
-    "kmeans.fit(X_pca)\n",
-    "y_kmeans = kmeans.predict(X_pca)\n",
-    "\n",
-    "# Plot the K-Means clustering results\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, cmap='viridis', edgecolor='k', s=50)\n",
-    "#plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1],\n",
-    "#            s=200, c='red', marker='X', label='Centroids')\n",
-    "plt.title('K-Means Clustering on PCA-Reduced Data')\n",
-    "plt.xlabel('Principal Component 1')\n",
-    "plt.ylabel('Principal Component 2')\n",
-    "plt.colorbar(label='Cluster')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "35350457",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the clustering results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "27a9ebe9",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Silhouette Score: 0.61\n",
-      "Adjusted Rand Index: 0.90\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import silhouette_score\n",
-    "# Calculate the silhouette score\n",
-    "silhouette_avg = silhouette_score(X_pca, y_kmeans)\n",
-    "print(f'Silhouette Score: {silhouette_avg:.2f}')\n",
-    "\n",
-    "# Calculate the adjusted Rand Index (ARI) to compare with true labels\n",
-    "from sklearn.metrics import adjusted_rand_score\n",
-    "ari = adjusted_rand_score(y, y_kmeans)\n",
-    "print(f'Adjusted Rand Index: {ari:.2f}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "08b9e6df",
-   "metadata": {},
-   "source": [
-    "## Hierarchical Clustering\n",
-    "\n",
-    "Hierarchical clustering builds a hierarchy of clusters, either agglomerative (bottom-up) or divisive (top-down). It does not require specifying the number of clusters in advance. The algorithm works as follows:\n",
-    "\n",
-    "-   Start with each data point as its own cluster.\n",
-    "-   Merge the two closest clusters based on a distance metric (e.g., single-linkage, complete-linkage, average-linkage).\n",
-    "-   Repeat step 2 until all points are in a single cluster or a stopping criterion is met (e.g., a desired number of clusters)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c3ec76da",
-   "metadata": {},
-   "source": [
-    "### Advantages and Disadvantages\n",
-    "\n",
-    "-   **Advantages**:\n",
-    "    -   No need to predefine the number of clusters.\n",
-    "    -   Produces a dendrogram that visualizes the hierarchy of clusters.\n",
-    "-   **Disadvantages**:\n",
-    "    -   Computationally expensive for large datasets.\n",
-    "    -   Sensitive to noise and outliers."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b7fcba6c",
-   "metadata": {},
-   "source": [
-    "### Example\n",
-    "\n",
-    "To illustrate hierarchical clustering, we will use the Iris dataset and visualize the dendrogram."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e0f3a7f5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x700 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.cluster import AgglomerativeClustering\n",
-    "from scipy.cluster.hierarchy import dendrogram\n",
-    "import numpy as np\n",
-    "\n",
-    "# Perform hierarchical clustering\n",
-    "cls = AgglomerativeClustering(linkage='ward', compute_distances=True, n_clusters=3)\n",
-    "cls = cls.fit(X_pca)\n",
-    "\n",
-    "def plot_dendrogram(model, **kwargs):\n",
-    "  counts = np.zeros(model.children_.shape[0])\n",
-    "  n_samples = len(model.labels_)\n",
-    "  for i, merge in enumerate(model.children_):\n",
-    "    current_count = 0\n",
-    "    for child_idx in merge:\n",
-    "      if child_idx < n_samples:\n",
-    "        current_count += 1  # leaf node\n",
-    "      else:\n",
-    "        current_count += counts[child_idx - n_samples]\n",
-    "    counts[i] = current_count\n",
-    "\n",
-    "  linkage_matrix = \\\n",
-    "    np.column_stack([model.children_, model.distances_, counts]).astype(float)\n",
-    "\n",
-    "  # Plot the corresponding dendrogram\n",
-    "  dendrogram(linkage_matrix, **kwargs)\n",
-    "\n",
-    "# Plot the dendrogram\n",
-    "plt.figure(figsize=(10, 7))\n",
-    "plot_dendrogram(cls, truncate_mode=\"level\", p=3)\n",
-    "plt.title('Hierarchical Clustering Dendrogram')\n",
-    "plt.xlabel('Species')\n",
-    "plt.ylabel('Distance')\n",
-    "plt.show()  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e5fd07f5",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the clustering results using silhouette score and adjusted Rand index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "b3a0e069",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Silhouette Score: 0.56\n",
-      "Adjusted Rand Index: 0.80\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Calculate the silhouette score\n",
-    "silhouette_avg = silhouette_score(X_pca, cls.labels_)\n",
-    "print(f'Silhouette Score: {silhouette_avg:.2f}')\n",
-    "\n",
-    "# Calculate the adjusted Rand Index (ARI) to compare with true labels\n",
-    "ari = adjusted_rand_score(y, cls.labels_)\n",
-    "print(f'Adjusted Rand Index: {ari:.2f}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "715cf171",
-   "metadata": {},
-   "source": [
-    "-   Visualize the clusters formed by hierarchical clustering"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "d31af652",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot the clusters formed by hierarchical clustering\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=cls.labels_, cmap='viridis', edgecolor='k', s=50)\n",
-    "plt.title('Hierarchical Clustering on PCA-Reduced Data')\n",
-    "plt.xlabel('Principal Component 1')\n",
-    "plt.ylabel('Principal Component 2')\n",
-    "plt.colorbar(label='Cluster')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "98502167",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "Use K-Means and Hierarchical Clustering on the Breast Cancer Wisconsin dataset\n",
-    "\n",
-    "-   Load the Breast Cancer Wisconsin dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "54395656",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.datasets import load_breast_cancer\n",
-    "# Load the Breast Cancer Wisconsin dataset\n",
-    "data = load_breast_cancer()\n",
-    "X_bc = data.data\n",
-    "y_bc = data.target\n",
-    "\n",
-    "# Standardize the data\n",
-    "scaler_bc = StandardScaler()\n",
-    "X_bc_scaled = scaler_bc.fit_transform(X_bc)\n",
-    "\n",
-    "# Apply PCA to reduce to 2 dimensions\n",
-    "pca_bc = PCA(n_components=2, random_state=42)\n",
-    "X_bc_pca = pca_bc.fit_transform(X_bc_scaled)\n",
-    "\n",
-    "# Plot the PCA results\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(X_bc_pca[:, 0], X_bc_pca[:, 1], c=y_bc, cmap='viridis', edgecolor='k', s=50)\n",
-    "plt.title('PCA of Breast Cancer Wisconsin Dataset')\n",
-    "plt.xlabel('Principal Component 1')\n",
-    "plt.ylabel('Principal Component 2')\n",
-    "plt.colorbar(label='Malignant/Benign')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6a0abb35",
-   "metadata": {},
-   "source": [
-    "-   Apply K-Means clustering on the PCA-reduced data and visualize the clusters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "1b8a75e1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Apply K-Means clustering\n",
-    "kmeans_bc = KMeans(n_clusters=2, random_state=42)\n",
-    "kmeans_bc.fit(X_bc_pca)\n",
-    "y_kmeans_bc = kmeans_bc.predict(X_bc_pca)\n",
-    "\n",
-    "# Plot the K-Means clustering results\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(X_bc_pca[:, 0], X_bc_pca[:, 1], c=y_kmeans_bc, cmap='viridis', edgecolor='k', s=50)\n",
-    "plt.scatter(kmeans_bc.cluster_centers_[:, 0], kmeans_bc.cluster_centers_[:, 1],\n",
-    "            s=200, c='red', marker='X', label='Centroids')\n",
-    "plt.title('K-Means Clustering on PCA-Reduced Breast Cancer Data')\n",
-    "plt.xlabel('Principal Component 1')\n",
-    "plt.ylabel('Principal Component 2')\n",
-    "plt.colorbar(label='Cluster')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e36ce8b5",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the clustering results using silhouette score and adjusted Rand index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "a56302b1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Silhouette Score: 0.51\n",
-      "Adjusted Rand Index: 0.66\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Calculate the silhouette score\n",
-    "silhouette_avg_bc = silhouette_score(X_bc_pca, y_kmeans_bc)\n",
-    "print(f'Silhouette Score: {silhouette_avg_bc:.2f}')\n",
-    "\n",
-    "# Calculate the adjusted Rand Index (ARI) to compare with true labels\n",
-    "ari_bc = adjusted_rand_score(y_bc, y_kmeans_bc)\n",
-    "print(f'Adjusted Rand Index: {ari_bc:.2f}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "603e58e8",
-   "metadata": {},
-   "source": [
-    "-   Visualize the clusters formed by K-Means clustering and compare with true labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "2fdf1f5a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot the clusters formed by K-Means clustering\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(X_bc_pca[:, 0], X_bc_pca[:, 1], c=y_kmeans_bc, cmap='viridis', edgecolor='k', s=50)\n",
-    "plt.scatter(kmeans_bc.cluster_centers_[:, 0], kmeans_bc.cluster_centers_[:, 1],\n",
-    "            s=200, c='red', marker='X', label='Centroids')\n",
-    "plt.title('K-Means Clustering on PCA-Reduced Breast Cancer Data')\n",
-    "plt.xlabel('Principal Component 1')\n",
-    "plt.ylabel('Principal Component 2')\n",
-    "plt.colorbar(label='Cluster')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8314f791",
-   "metadata": {},
-   "source": [
-    "-   Apply Hierarchical Clustering on the PCA-reduced data and visualize the clusters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "9eee01d0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x700 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Perform hierarchical clustering\n",
-    "cls_bc = AgglomerativeClustering(linkage='ward', compute_distances=True, n_clusters=2)\n",
-    "cls_bc = cls_bc.fit(X_bc_pca)\n",
-    "\n",
-    "# Plot the dendrogram\n",
-    "plt.figure(figsize=(10, 7))\n",
-    "plot_dendrogram(cls_bc, truncate_mode=\"level\", p=3)\n",
-    "plt.title('Hierarchical Clustering Dendrogram for Breast Cancer Data')\n",
-    "plt.xlabel('Malignant/Benign')\n",
-    "plt.ylabel('Distance')\n",
-    "plt.show()\n",
-    "\n",
-    "# Plot the clusters formed by hierarchical clustering\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(X_bc_pca[:, 0], X_bc_pca[:, 1], c=cls_bc.labels_, cmap='viridis', edgecolor='k', s=50)\n",
-    "plt.title('Hierarchical Clustering on PCA-Reduced Breast Cancer Data')\n",
-    "plt.xlabel('Principal Component 1')\n",
-    "plt.ylabel('Principal Component 2')\n",
-    "plt.colorbar(label='Cluster')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e0e1391a",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the clustering results using silhouette score and adjusted Rand index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "63ef84cd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Silhouette Score: 0.50\n",
-      "Adjusted Rand Index: 0.66\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Calculate the silhouette score\n",
-    "silhouette_avg_bc_hc = silhouette_score(X_bc_pca, cls_bc.labels_)\n",
-    "print(f'Silhouette Score: {silhouette_avg_bc_hc:.2f}')\n",
-    "\n",
-    "# Calculate the adjusted Rand Index (ARI) to compare with true labels\n",
-    "ari_bc_hc = adjusted_rand_score(y_bc, cls_bc.labels_)\n",
-    "print(f'Adjusted Rand Index: {ari_bc_hc:.2f}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a60f05a4",
-   "metadata": {},
-   "source": [
-    "-   Compare the clustering results of K-Means and Hierarchical Clustering"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "a9932d83",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Compare the clustering results\n",
-    "plt.figure(figsize=(12, 6))\n",
-    "\n",
-    "# K-Means Clustering\n",
-    "plt.subplot(1, 2, 1)\n",
-    "plt.scatter(X_bc_pca[:, 0], X_bc_pca[:, 1], c=y_kmeans_bc, cmap='viridis', edgecolor='k', s=50)\n",
-    "plt.scatter(kmeans_bc.cluster_centers_[:, 0], kmeans_bc.cluster_centers_[:, 1],\n",
-    "            s=200, c='red', marker='X', label='Centroids')\n",
-    "plt.title('K-Means Clustering on PCA-Reduced Breast Cancer Data')\n",
-    "plt.xlabel('Principal Component 1')\n",
-    "plt.ylabel('Principal Component 2')\n",
-    "plt.colorbar(label='Cluster')\n",
-    "plt.legend()\n",
-    "\n",
-    "# Hierarchical Clustering\n",
-    "plt.subplot(1, 2, 2)\n",
-    "plt.scatter(X_bc_pca[:, 0], X_bc_pca[:, 1], c=cls_bc.labels_, cmap='viridis', edgecolor='k', s=50)\n",
-    "plt.title('Hierarchical Clustering on PCA-Reduced Breast Cancer Data')\n",
-    "plt.xlabel('Principal Component 1')\n",
-    "plt.ylabel('Principal Component 2')\n",
-    "plt.colorbar(label='Cluster')\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b3bde955-1832-4cc9-b502-52e5518e5278",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S05 Unsupervised Learning - Clustering and Dimensionality Reduction/S05C04 Anomaly Detection.ipynb b/src/S05 Unsupervised Learning - Clustering and Dimensionality Reduction/S05C04 Anomaly Detection.ipynb
deleted file mode 100644
index 7ebaaa2..0000000
--- a/src/S05 Unsupervised Learning - Clustering and Dimensionality Reduction/S05C04 Anomaly Detection.ipynb	
+++ /dev/null
@@ -1,1175 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "4fd14668",
-   "metadata": {},
-   "source": [
-    "# Anomaly Detection"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "657349fd",
-   "metadata": {},
-   "source": [
-    "## Introduction to Anomaly Detection\n",
-    "\n",
-    "**Anomaly Detection** is the process of identifying patterns in data that do not conform to expected behavior. It is crucial in various fields such as fraud detection, network security, and fault detection. Anomalies can indicate critical incidents, such as a structural defect or a security breach, and detecting them early can prevent significant damage. Anomaly detection can be categorized into three main types:\n",
-    "\n",
-    "-   ****Supervised Anomaly Detection****: Requires labeled data where anomalies are known.\n",
-    "-   ****Unsupervised Anomaly Detection****: Does not require labeled data and identifies anomalies based on the distribution of the data.\n",
-    "-   ****Semi-supervised Anomaly Detection****: Uses a small amount of labeled data to guide the detection process.\n",
-    "\n",
-    "****Applications of Anomaly Detection**** Anomaly detection is widely used in various domains, including:\n",
-    "\n",
-    "-   ****Fraud Detection****: Identifying fraudulent transactions in banking and finance.\n",
-    "-   ****Network Security****: Detecting intrusions or abnormal behavior in network traffic.\n",
-    "-   ****Fault Detection****: Monitoring industrial systems to identify potential failures.\n",
-    "-   ****Healthcare****: Detecting unusual patterns in patient data that may indicate health issues.\n",
-    "\n",
-    "****Challenges in Anomaly Detection**** Anomaly detection poses several challenges:\n",
-    "\n",
-    "-   ****High Dimensionality****: Data with many features can make it difficult to identify anomalies.\n",
-    "-   ****Imbalanced Data****: Anomalies are often rare compared to normal instances, leading to class imbalance.\n",
-    "-   ****Dynamic Environments****: Anomalies can change over time, requiring adaptive detection methods.\n",
-    "\n",
-    "****Common Techniques for Anomaly Detection****\n",
-    "\n",
-    "-   ****Statistical Methods****: Use statistical tests to identify outliers based on the distribution of the data.\n",
-    "-   ****Machine Learning Methods****: Employ algorithms like clustering, classification, and neural networks to detect anomalies.\n",
-    "-   ****Distance-Based Methods****: Measure the distance between data points to identify anomalies based on their proximity to normal instances.\n",
-    "-   ****Density-Based Methods****: Identify anomalies based on the density of data points in a given region.\n",
-    "-   ****Ensemble Methods****: Combine multiple anomaly detection techniques to improve robustness and accuracy.\n",
-    "\n",
-    "****Clustering for Anomaly Detection**** Clustering algorithms can be used for anomaly detection by grouping similar data points together. Anomalies are identified as points that do not belong to any cluster or are far from the nearest cluster center. Common clustering algorithms used for anomaly detection include:\n",
-    "\n",
-    "-   ****k-Means Clustering****: Groups data into k clusters and identifies anomalies as points that are far from their assigned cluster centers.\n",
-    "-   ****DBSCAN (Density-Based Spatial Clustering of Applications with Noise)****: Groups data based on density and identifies anomalies as points that do not belong to any cluster.\n",
-    "-   ****Hierarchical Clustering****: Builds a hierarchy of clusters and identifies anomalies based on their distance from the nearest cluster."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17ac94c1",
-   "metadata": {},
-   "source": [
-    "### K-Means Clustering for Anomaly Detection\n",
-    "\n",
-    "K-Means clustering can be used for anomaly detection by following these steps:\n",
-    "\n",
-    "1.  ****Data Preparation****: Normalize or standardize the data to ensure that all features contribute equally to the distance calculations.\n",
-    "2.  ****Clustering****: Apply the k-Means algorithm to cluster the data into k groups.\n",
-    "3.  ****Distance Calculation****: Calculate the distance of each data point to its assigned cluster center.\n",
-    "4.  ****Anomaly Identification****: Define a threshold distance (e.g., mean distance + 2 standard deviations) to classify points as anomalies if their distance exceeds this threshold.\n",
-    "5.  ****Evaluation****: Validate the identified anomalies using domain knowledge or additional data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9ff33c1b",
-   "metadata": {},
-   "source": [
-    "#### Example: K-Means Clustering for Anomaly Detection\n",
-    "\n",
-    "-   Create a synthetic dataset with clusters and some anomalies."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "936e30b1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.cluster import KMeans\n",
-    "from sklearn.datasets import make_blobs\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "n_clusters = 5\n",
-    "\n",
-    "# Generate synthetic data\n",
-    "X, _ = make_blobs(n_samples=300, centers=n_clusters,\n",
-    "                  cluster_std=0.5, random_state=42)\n",
-    "\n",
-    "# Add some anomalies\n",
-    "anomalies = np.array([[10, 10], [12, 12], [15, 15]])\n",
-    "X = np.vstack((X, anomalies))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6f8715df",
-   "metadata": {},
-   "source": [
-    "-   Visualize the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "b22150ee-1c66-48da-ade3-2f1250731681",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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n5wfg72/6tx+3sLAQmzZtMjqGt7e3yfrlcrlRXZ9++qnZ/Yn69+8Pd3d3xMfHIyAgAJ07dwZQGXaOHTuGr7/+2qxWGm9vb4svp9Tm+vXr2LFjByZMmID77rvP6Pb444+juLjYoP+XOYKDg9GjRw988MEHBq9peno6vvrqK/zjH/+w2s9g6nd8/PhxpKSkWHysyMhI+Pn54dVXXzU5iaPuPV9WVoby8nKDx9q0aQNfX19UVFRYfF6SJrbUkOQ88cQTKCsrw913342OHTvixo0bOHr0KD7++GOEhoZi5syZBvv37NkTXbp0waeffopOnToZzXFiid69ewMA5s2bh8jISMjlckyePNmiY2zYsAGDBw9G165dMWvWLISHhyMvLw8pKSn4/fff9XO19OjRA3K5HPHx8SgsLIRCodDPDVPV9OnTsXnzZsyfPx+pqakYMmQISktLsX//fsyZMwcTJ06ssabCwkL9N+GysjKcP38eO3bsQGZmJiZPnoylS5fq9x07diw8PDwQHR2tD4rvvPMOgoKCjIJa7969kZCQgGXLlqFt27YICgrCyJEjMWHCBCxZsgQzZ87EwIEDcerUKWzbtk3fklUbLy8v9O7dG8eOHdPPUQNUttSUlpaitLTUrFDTu3dvfPzxx5g/fz769u0LHx8fREdHm1VDdXbv3o3i4mL90PKq7rjjDgQGBmLbtm2YNGmSRcd+7bXXMG7cOAwYMAAPP/ywfki3SqWy6vIdEyZMwI4dO3D33Xdj/PjxyMrKwltvvYWIiAh9Hytz+fn5ISEhAdOmTUOvXr0wefJkBAYG4uLFi/jiiy8waNAgrF+/Hr/88gtGjRqF+++/HxEREWjUqBE+//xz5OXlWfz/GEmYI4ZcEdnSl19+KR566CHRsWNH4ePjIzw8PETbtm3FE088IfLy8kw+Z9WqVQKAePXVV40e0w2pNTWUFFWGq966dUs88cQTIjAwUMhkMv3wbkuOIYQQmZmZYvr06UKtVgt3d3fRvHlzMWHCBPHZZ58Z7PfOO++I8PBwIZfLDYYjVx3SLUTlMOuXX35ZhIWFCXd3d6FWq8V9990nMjMzTb4mOsOGDRMA9DcfHx/Rrl07MXXqVPHVV1+ZfM7u3btFt27dhFKpFKGhoSI+Pl4/rFw3lFeIymG748ePF76+vgKAvuby8nLxzDPPiODgYOHp6SkGDRokUlJSTP5c1Xn22WcFABEfH2+wvW3btgKA0c9takh3SUmJeOCBB4S/v78AoB/erdv3008/NTiG7ve8adOmauuKjo4WSqVSlJaWVrvPjBkzhLu7u7hy5YrF7539+/eLQYMGCU9PT+Hn5yeio6NFRkaGwT66Id1//vmnwfaYmBjh7e1tdJ6qw/q1Wq149dVXRevWrYVCoRA9e/YU//d//ydiYmKMhsBXrbHqkG6dgwcPisjISKFSqYRSqRRt2rQRM2bMEN9//70QQogrV66IuXPnio4dOwpvb2+hUqlE//79xSeffFLdy0gNkEyIevQyI5KIN998E08//TSys7ONRoQQEZFrYKihBk8Ige7du6NJkyZmTfNORETOiX1qqMEqLS3F7t27cfDgQZw6dQq7du1ydElERFQPbKmhBis7OxthYWHw9/fHnDlzsHz5ckeXRERE9cBQQ0RERJLAeWqIiIhIEhhqiIiISBIaVEdhrVaLP/74A76+vjab/pyIiIisSwiB4uJihISEwM2t+vaYBhVq/vjjD7Rs2dLRZRAREVEd/Pbbb2jRokW1jzeoUOPr6wug8kXRrVNDREREzq2oqAgtW7bUf45Xp0GFGt0lJz8/P4YaIiIiF1Nb1xF2FCYiIiJJYKghIiIiSWCoISIiIkloUH1qzKHVanHjxg1Hl0F25u7uDrlc7ugyiIioHhhqbnPjxg1kZWVBq9U6uhRyAH9/f6jVas5hRETkohhq/iKEQE5ODuRyOVq2bFnj5D4kLUIIlJWV4fLlywCA4OBgB1dERER1wVDzl1u3bqGsrAwhISHw8vJydDlkZ56engCAy5cvIygoiJeiiIhcEJsj/qLRaAAAHh4eDq6EHEUXZm/evOngSoiIqC4Yaqpgf4qGi797IiLXxstPREREVC8arUBqVgEuF5cjyFeJfmEBkLvZ/4ui07TUfPPNN4iOjkZISAhkMhl27txp8PiMGTMgk8kMblFRUY4plmxu8eLF6NGjh6PLICKiWiSl52Bw/AFMeecYnvwoDVPeOYbB8QeQlJ5j91qcJtSUlpaie/fu2LBhQ7X7REVFIScnR3/bvn27HSt0TreHPXd3dzRr1gxjxozB+++/b/HQ9MTERPj7+1ulruHDh+vrUiqViIiIwMaNG81+/oIFC5CcnGzROUNDQ7FmzRoLKyUiorpKSs/B7K0nkFNYbrA9t7Acs7eesHuwcZrLT+PGjcO4ceNq3EehUECtVtuporpxRBNcVFQUNm3aBI1Gg7y8PCQlJeHJJ5/EZ599ht27d6NRI8f8mmfNmoUlS5agrKwMmzdvxty5c9G4cWNMmTKl1uf6+PjAx8fHDlUSEVFdaLQCcXsyIEw8JgDIAMTtycCYCLXdLkU5TUuNOQ4dOoSgoCB06NABs2fPRn5+fo37V1RUoKioyOBmS45qgtOFvebNm6NXr1546aWXsGvXLnz55ZdITEzU77d69Wp07doV3t7eaNmyJebMmYOSkhIAla/tzJkzUVhYqG9hWbx4MQBgy5Yt6NOnD3x9faFWq/HAAw/o53SpiZeXF9RqNcLDw7F48WK0a9cOu3fvBgBcvHgREydOhI+PD/z8/HD//fcjLy9P/9yql59mzJiBu+66C//+978RHByMJk2aYO7cufqRSsOHD8evv/6Kp59+Wl8/APz666+Ijo5G48aN4e3tjc6dO2Pv3r31ebmJiAhAalaBUQvN7QSAnMJypGYV2K0mlwk1UVFR2Lx5M5KTkxEfH4+vv/4a48aN0w/FNmXFihVQqVT6W8uWLW1Wn7M1wY0cORLdu3fHjh079Nvc3Nywdu1anD59Gh988AEOHDiA5557DgAwcOBArFmzBn5+fvrLewsWLABQOcR56dKlOHnyJHbu3Ins7GzMmDHD4po8PT1x48YNaLVaTJw4EQUFBfj666+xb98+XLhwAZMmTarx+QcPHkRmZiYOHjyIDz74AImJifrQtmPHDrRo0QJLlizR1w8Ac+fORUVFBb755hucOnUK8fHxbAEiIrKCy8XVB5q67GcNTnP5qTaTJ0/W/7tr167o1q0b2rRpg0OHDmHUqFEmn/Piiy9i/vz5+vtFRUU2CTbO2AQHAB07dsRPP/2kv//UU0/p/x0aGoply5bhsccew8aNG+Hh4QGVSgWZTGZ0ie+hhx7S/zs8PBxr165F3759UVJSYlZA0Gg02L59O3766Sc88sgjSE5OxqlTp5CVlaX/fWzevBmdO3fGd999h759+5o8TuPGjbF+/XrI5XJ07NgR48ePR3JyMmbNmoWAgADI5XJ9a5LOxYsXce+996Jr1676+omIqP6CfJVW3c8aXKalpqrw8HA0bdoU58+fr3YfhUIBPz8/g5stOGMTHFA5/f/tc6/s378fo0aNQvPmzeHr64tp06YhPz8fZWVlNR7nhx9+QHR0NFq1agVfX18MGzYMQGVgqMnGjRvh4+MDT09PzJo1C08//TRmz56NM2fOoGXLlgYBMyIiAv7+/jhz5ky1x+vcubPBTL/BwcG1XgabN28eli1bhkGDBiE2NtYg5BERUd31CwtAsEqJ6r6qywAEqyr7ltqLy4aa33//Hfn5+U6xTo8zNsEBwJkzZxAWFgYAyM7OxoQJE9CtWzf897//xQ8//KAfaVbTquSlpaWIjIyEn58ftm3bhu+++w6ff/55rc8DgAcffBBpaWnIyspCaWkpVq9eXa81tdzd3Q3uy2SyWkd4/etf/8KFCxcwbdo0nDp1Cn369MG6devqXAMREVWSu8kQGx0BAEbBRnc/NjrCrlconCbUlJSUIC0tDWlpaQCArKwspKWl4eLFiygpKcGzzz6LY8eOITs7G8nJyZg4cSLatm2LyMhIxxYO52yCO3DgAE6dOoV7770XQGVri1arxeuvv4477rgD7du3xx9//GHwHA8PD6M+SmfPnkV+fj5WrlyJIUOGoGPHjmZ1EgYAlUqFtm3bonnz5gZhplOnTvjtt9/w22+/6bdlZGTg2rVriIiIqOuPbLJ+AGjZsiUee+wx7NixA8888wzeeeedOp+DiIj+FtUlGAlTe0GtMvx8U6uUSJjaC1Fd7Nvw4DR9ar7//nuMGDFCf1/XFyYmJgYJCQn46aef8MEHH+DatWsICQnB2LFjsXTpUigUCkeVrKdrgsstLDfZr0aGyl+wrZrgKioqkJubazCke8WKFZgwYQKmT58OAGjbti1u3ryJdevWITo6GkeOHMFbb71lcJzQ0FCUlJQgOTkZ3bt3h5eXF1q1agUPDw+sW7cOjz32GNLT07F06dJ61Tt69Gh07doVDz74INasWYNbt25hzpw5GDZsGPr06VPn44aGhuKbb77B5MmToVAo0LRpUzz11FMYN24c2rdvj6tXr+LgwYPo1KlTveonIqK/RXUJxpgINWcUvt3w4cMhhDC6JSYmwtPTE//73/9w+fJl3LhxA9nZ2Xj77bfRrFkzR5cNwPFNcElJSQgODkZoaCiioqJw8OBBrF27Frt27dL3QenevTtWr16N+Ph4dOnSBdu2bcOKFSsMjjNw4EA89thjmDRpEgIDA7Fq1SoEBgYiMTERn376KSIiIrBy5Ur8+9//rle9MpkMu3btQuPGjTF06FCMHj0a4eHh+Pjjj+t13CVLliA7Oxtt2rRBYGAggMpOynPnzkWnTp0QFRWF9u3bWzQJIBER1U7uJsOANk0wsUdzDGjTxCGBBgBkQghTjQuSVFRUBJVKhcLCQqNOw+Xl5cjKykJYWBiUyrpdJkpKz0HcngyDTsPBKiVioyPs3gRHlrPGe4CIiKyvps/v2znN5ScpcKYmOCIiooaGocbKdE1wREREZF9O06eGiIiIqD4YaoiIiEgSGGqqaED9pqkK/u6JiFwbQ81fdEOfa5sll6RLt1xE1ZmLiYjINbCj8F8aNWoELy8v/Pnnn3B3d6/XdP7kWoQQKCsrw+XLl+Hv72+wvhQREbkOhpq/yGQyBAcHIysrC7/++qujyyEH8Pf3N1qhnIiIXAdDzW08PDzQrl07XoJqgNzd3dlCQ0Tk4hhqqnBzc+NsskRERC6IHUeIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGjm6ACIiooZCoxVIzSrA5eJyBPkq0S8sAHI3maPLkgyGGiIiIjtISs9B3J4M5BSW67cFq5SIjY5AVJdgB1YmHbz8REREZGNJ6TmYvfWEQaABgNzCcszeegJJ6TkOqkxaGGqIiIhsSKMViNuTAWHiMd22uD0Z0GhN7UGWYKghIiKyodSsAqMWmtsJADmF5UjNKrBfURLFUENERGRDl4urDzR12Y+qx1BDRERkQ0G+SqvuR9VjqCEiIrKhfmEBCFYpUd3AbRkqR0H1CwuwZ1mSxFBDRERkQ3I3GWKjIwDAKNjo7sdGR3C+GitgqCEiIrKxqC7BSJjaC2qV4SUmtUqJhKm9OE+NlXDyPSIiIjuI6hKMMRFqzihsQww1REREdiJ3k2FAmyaOLkOyePmJiIiIJIGhhoiIiCSBoYaIiIgkgaGGiIiIJIGhhoiIiCSBoYaIiIgkgaGGiIiIJIGhhoiIiCSBoYaIiIgkgaGGiIiIJIGhhoiIiCSBoYaIiIgkgaGGiIiIJIGhhoiIiCShkaMLIJIqjVYgNasAl4vLEeSrRL+wAMjdZI4ui4hIshhqiGwgKT0HcXsykFNYrt8WrFIiNjoCUV2CHVgZEZF08fITkZUlpedg9tYTBoEGAHILy/HY1hNYsuc0UjLzodEKB1VIRCRNbKkhsiKNViBuTwZMxRXdtvePZOP9I9lsuSGyA14GblicpqXmm2++QXR0NEJCQiCTybBz506Dx4UQWLRoEYKDg+Hp6YnRo0fj3LlzjimW6DYarUBKZj52pV1C4pEsoxaa6uQWlmP21hNISs+xcYVEDVNSeg4Gxx/AlHeO4cmP0jDlnWMYHH+A/89JmNOEmtLSUnTv3h0bNmww+fiqVauwdu1avPXWWzh+/Di8vb0RGRmJ8nLzPkCIbKHqH82lX5wx+7m6lpu4PRm8FEVkZTVdBuaXCelymstP48aNw7hx40w+JoTAmjVr8Morr2DixIkAgM2bN6NZs2bYuXMnJk+ebM9SiQD8/UezPnFEAMgpLEdqVgEGtGlirdKIGrTaLgPLUPllYkyEmpeiJMZpWmpqkpWVhdzcXIwePVq/TaVSoX///khJSan2eRUVFSgqKjK4EVlDTX806+JyMVsciawlNaugxsvAt3+ZIGlxiVCTm5sLAGjWrJnB9mbNmukfM2XFihVQqVT6W8uWLW1aJzUctf3RtFSQr9JqxyJq6Mz9ksAvE9LjEqGmrl588UUUFhbqb7/99pujSyKJsOYfQzcZcLX0htWOR9TQmfslgV8mpMclQo1arQYA5OXlGWzPy8vTP2aKQqGAn5+fwY3IGqz5x1ArgLkfsuMikbX0CwtAsEqJ6nrLyFA5GWa/sAB7lkV24BKhJiwsDGq1GsnJyfptRUVFOH78OAYMGODAyqihqu2PpqUEOAqKyFrkbjLERkcAgNH/o7r7sdER7CQsQU4TakpKSpCWloa0tDQAlZ2D09LScPHiRchkMjz11FNYtmwZdu/ejVOnTmH69OkICQnBXXfd5dC6yTXdPrdMXWb31f3RtGYEySksx7pkzr1EZA1RXYKRMLUX1CrDVlW1SomEqb046aVEOc2Q7u+//x4jRozQ358/fz4AICYmBomJiXjuuedQWlqKRx55BNeuXcPgwYORlJQEpZLXRMky1lqXaUyEGirPRii8fstqta1JPofrN2/hxX9EWO2YRA1VVJdgjIlQc0bhBkQmhGgw7d1FRUVQqVQoLCxk/5oGqrq5ZXR/4iz5Bvfm/nN4Y/8vVq1PZ+MDPfGPbiE2OTYRkasx9/PbaS4/EdmaOesy1davRXfZasme0zYLNADwyq509q8hIrKQ01x+IrI1Sybkqjq7r0YrsP7AeWw6koVr12/auFKgoPQmZxkmIrIQQw01GHWdkCspPQcv7DiFa2W2DzO3yy0qR0pmPvsCEBGZiaGGGgxz55Zp6qPQh4nsK6V4Y79jRiQt/b/TKCj9O0jVpTMzEVFDwo7C1GBotAKD4w8gt7DcZL8aGQB/L3coGrkht6jC3uXVqi6dmYmIpIAdhYmqqG1CLgHgatlNpww0gPmdmYmIGiqGGmpQapqQy9/L3UFVmY+rCxMRVY99aqjBMTUhl1Yr8OB7xx1dmtm4ujARkTGGGmqQ5G4yg+HSu9IuObAay3F1YSIiYww1RHCdkCBD5aUyri5MRGSMfWqIULnqttpP4egyasTVhYmIasZQQ4TKy1FT+rVydBk14urCREQ14+Unor+ENvV2dAnVmju8DeaP7cAWGiKiGrClhugvztyvZnC7QAYaIqJasKXGhjRaYTBs2NK1e+r7fLJMv7AABKuU1c447CjB7BhMRGQWhhobSUrPQdyeDINVoS1Zu6e+z2cgspxuxuHZW084uhQDd3YP5u+OiMgMXPvJBpLSczB76wmjb/vmrt1jjefXJxA1dKZeP0cKVinx7fMjGWyIqMHi2k8OotEKxO3JMHn5wpy1e+r7fF0gqvqBnFtYjtlbTyApPcesn6Mhi+oSjG+fH4nts+7AQ4NC4aOQO7QeLotARGQehhorO3Yhv8Zv+LWt3ZOaVVDn59c3ENHfdDMOL4rujP9M7ePocrgsAhGRGRhqrCgpPQdzt5nXH6Pqh5RGK5CSmY8vzWxJ2ZeRa7StPoGIqndHmyYIVjl2ZJQzj8wiInIW7ChsJdX1g6nO7R9SdenD8f6RbPQLCzDoI2Put3ndfuxMbB5dB+LHHNCBmMsiEBGZj6HGCmq67GPK7UN0k9Jz6vRhKUPlpaQxEWoAla005/JKzHpukK+SnYktFNUlGG9N7YUXdpzCtbKbVj12Yy93XC27CRlg8B7isghERJZhqLGC2i77VDWoTRPI3WTQaAVe2HGqTufUXUpaf+AcPvruN7POr/vWf7W0AnM//NEohOk6E3MqftOiugRjTIQa6w+cx6YjWbh2vf7hZuH4TpgxKAz7MnKNQqaaIZOIyCIMNVZgaSfOvOIKpGTm42jmlXp/639j/zmz9tN9z184PgJLv6i+M/HtLUBsHTAmd5PhydHt8PjItvpLd9lXSrE99SJyiyosOpafshFmDAqD3E2mD0y8HEhEVHcMNVZgaSfOw+eu4PC5KzaqxjTdt36Vp4fZnYkHtGlivwJdjG50lM7jI9shNasAR87/ifUHM806xvK7uxqElqrHJCIiyzDUWIFuen1nmaztdo+PaINBbQP13/p3pV0y63kcQmwZXSDpFxaA/564VOt7YXSnQER3D7FTdUREDQOHdFuBbnSMM14oaNfMFwP+6sMDmN+qxCHEdWPOe2F0p0C8G9PPbjURETUUDDVWEtUlGAlTe8Hfy93RpRioGk50rUrVfejKwAUU60v3Xqg6t42fshHWTenJQENEZCO8/GQFuvlevssqsPpw3/rw93SHVghotELfUnP7oo0cQmw77PhLRGR/XNCynpxt8UNTTM0/w3lqiIjIVZj7+c1QUw+WziLsKNWt7s0ZhYmIyBWY+/nNy091ZOkswo5U3fwzHEJMRERSwo7CdWTpLMKOxsUsiYhI6hhq6shV53Fx1bqJiIhqw1BTR9lXSh1dQp1kXylzdAlEREQ2wVBTBxqtwPbUi44uo07e2P8LktJzHF0GERGR1THU1EFqVoHFixc6kxd3nIJG6wpdnImIiMzHUFMHrt4v5WrZTRy7kO/oMoiIiKyKoaYOpLAuUkomQw0REUkLQ00d1LZ+kmvg5SciIpIWhpo60K2fBMCsYOOM4WdAeFNHl0BERGRVDDV1pFuJWV1lJebGXu5GK3WrVUpsfKAnFI2c4+X293LHHZxJmIiIJIbLJNRDdSsxAzDalppVgIpbWgdXXGnlPV25xhMREUkOQ009Vbd+0u3bNFqBI+ev2LMsk9R+Ciy+szNX4SYiIkliqLGxpPQcxO3JsHidKBkALw85Sm9o6l3D4yPaYFDbQK7CTUREksZQY0NJ6TmYvfWEReOMWgd4YuodoYgZGAq5mwypWQXYl5GLT3/4HcXltyw6vwyV/XmeHtOBYYaIiCTPOXquSpBGKxC3J8PigdO/FlzH+0eycOBsnv7S1qLozlhyZ2eLjqOLMLHREQw0RETUIDDU2EhqVoHFl5x0cgvLMXvrCYM1mtQqT4uOoVYpkTC1F/vPEBFRg8HLTzZSn6UUBCpbWuL2ZGBMhBpyN5l+wr/cwvJqW38CvN2xcEJnqP2U7D9DREQNDltqbKS+SykIADmF5UjNKgBQ84R/sr9ur97dFXf3bI4BbZow0BARUYPDUGMj1lpK4fYWn+om/OOlJiIiIl5+shldy8rsrScgg+FKS1Xv16Rqi091E/6xZYaIiBo6ttTYUE0tKxsf6FljS44MQLDq7xmKb6cbFTWxBy81ERER6bClxsZqallxc5NV25IDcDg2ERGRJWRCCEunUnFZRUVFUKlUKCwshJ+fn6PLAWB6xuFglRKx0RHsI0NERATzP7/ZUuNg7CNDRERkHQw1TqC6RTGJiIjIfBZ1FL5+/Tq+/fZbZGRkGD1WXl6OzZs3W60wIiIiIkuYHWp++eUXdOrUCUOHDkXXrl0xbNgw5OT8PY1/YWEhZs6caZMiiYhup9EKpGTmY1faJaRk5kOjbTBdA4moBmZffnr++efRpUsXfP/997h27RqeeuopDBo0CIcOHUKrVq1sWSMRETRagdSsAuzPyMXnaZdQUHpT/xg71xMRYMHop2bNmmH//v3o2rUrAEAIgTlz5mDv3r04ePAgvL29ERISAo1GY9OC68MZRz8RUe1MjRI0ZeMDPfGPbiF2qoqI7MXcz2+zLz9dv34djRr93bAjk8mQkJCA6OhoDBs2DL/88kv9KiYiMiEpPQezt54wa9X7x7f/iL0/5dS6HxFJk9mhpmPHjvj++++Ntq9fvx4TJ07EnXfeadXCiIhu3NLipc9Pmb2siFYAcz48gaR0BhuihsjsUHP33Xdj+/btJh9bv349pkyZAlvO47d48WLIZDKDW8eOHW12PiJyrKT0HNyxItmg74y54vZksPMwUQPkMjMKL168GJ999hn279+v39aoUSM0bdrU7GOwTw2Ra0hKz8FjW0/U6xjbZ93B+Z+IJEKSMwo3atQIarXa0WUQkQ1ptAIv7DhV7+NcLq69Dw4RSYtLhZpz584hJCQESqUSAwYMwIoVK2ocTl5RUYGKigr9/aKiInuUSUQm6IZk17YcyLEL+bhWZvklp6qCfJX1PgYRuRaXCTX9+/dHYmIiOnTogJycHMTFxWHIkCFIT0+Hr6+vyeesWLECcXFxdq6UiKqyZOHWlMz8ep/PTQb0bt243schItfiMn1qqrp27Rpat26N1atX4+GHHza5j6mWmpYtW7JPDZEd6YZkV/1Do2ujSZjayyDY/Pt/Z7H+YGa9z7vt4f5wc5NxoVgiCZBkn5rb+fv7o3379jh//ny1+ygUCigUCjtWRUS302gF4vZkmBySLVAZbOL2ZGBMRGVfudSsAljra9bcD0/g2nXOOkzUkFi0oKXOli1bMGjQIISEhODXX38FAKxZswa7du2yanE1KSkpQWZmJoKD+QeKyFmlZhXUOGmeAJBTWI51yefQd/l+THnnGDYcqn8rDQCDQAMAuYXlmL2Vc9gQSZnFoSYhIQHz58/HP/7xD1y7dk2/LIK/vz/WrFlj7fr0FixYgK+//hrZ2dk4evQo7r77bsjlckyZMsVm5ySi+tmXkWvWfmuSz6Gg9IZNa9E1AHEOGyLpsjjUrFu3Du+88w5efvllyOVy/fY+ffrg1Kn6D8Oszu+//44pU6agQ4cOuP/++9GkSRMcO3YMgYGBNjsnEdVdUnoO3j+S7egyDOhahlKzChxdChHZgMV9arKystCzZ0+j7QqFAqWlpVYpypSPPvrIZscmIuvS9aVxVpzDhkiaLG6pCQsLQ1pamtH2pKQkdOrUyRo1EZGLq60vjaNxDhsiabK4pWb+/PmYO3cuysvLIYRAamoqtm/fjhUrVuDdd9+1RY1E5GKcuSXE39Md/cICHF0GEdmAxaHmX//6Fzw9PfHKK6+grKwMDzzwAEJCQvDmm29i8uTJtqiRiFyMM7eEzBwUyvlqiCTKolBz69YtfPjhh4iMjMSDDz6IsrIylJSUICgoyFb1EZEL6t26MdxkgLMNMmrs5Y7HR7ZzdBlEZCMWhZpGjRrhsccew5kzZwAAXl5e8PLysklhROS6Eg6dd7pAIwOw4p6uDm2lMXf9KyKqG4svP/Xr1w8//vgjWrdubYt6iMjFJaXn4I395xxdhgFnmE3YkvWviKhuLA41c+bMwTPPPIPff/8dvXv3hre3t8Hj3bp1s1pxROQadC0QuUXlWPp/px1djgEfhRxfPzsCHo3qNIG6VVS3/pVuluOq618RUd1YvKClm5vxHwaZTAYhBGQymX6GYWdk7oJYRGQ+Uy0Qzmb7rDswoE0Th5xboxUYHH+g2tdHBkCtUuLb50fyUhRRNWy2oGVWVla9CiMi6aiuBcLZOHKIubnrX6VmFTgseBFJhcWhhn1piAioeQVuZ+PIIebmBipnntuHyFVYHGo2b95c4+PTp0+vczFE5DrqOmuwStkIheW3bFCRacEqpUMn2zM3UDnz3D5ErsLiUPPkk08a3L958ybKysrg4eEBLy8vhhqiBqKuLQsr7umKk79fw3++sc+l7NjoCIf2VekXFoBglRK5heUmW7V0fWo4yzFR/Vk8HODq1asGt5KSEvz8888YPHgwtm/fbosaicgJ1bVlobG3At1bNLZyNcY83IC3HDCqSKMVSMnMx660S0jJzAdQGayAygBzO919RwcvIqmwuKXGlHbt2mHlypWYOnUqzp49a41DEpGTq60Fojq5hdex9IszNqtL59moTnYPNDXNRZMwtZfRY2rOU0NkVVYJNUDlbMN//PGHtQ5HRE5O7iZDbHQEZm89ARlgdrApKL2BgtIbtiwNANDUV2Hzc9zOnLlovn1+JGcUJrIhi0PN7t27De4LIZCTk4P169dj0KBBViuMiJxfVJdgky0Qpuj6jgT42CdsqP3s1/G2ppFgApU/e9yeDIyJUHPYNpENWRxq7rrrLoP7MpkMgYGBGDlyJF5//XVr1UVELiKqSzDGRKiRmlWA/Rm5eO9IttE+t/cdUXl62Lwme4944lw0RM7B4lCj1WptUQcRuTC5mwwD2jTBgDZN0DcsoMa+IxqtgNpPgdyiCpvVY++Ot5yLhsg5WBxqlixZggULFhitzn39+nW89tprWLRokdWKIyLXc3vLjam+I3I3Gab0a2XWopcB3u4oKL1p0fkndAu2e8fbusxFwxW7iazP4rWf5HI5cnJyEBQUZLA9Pz8fQUFBXPuJiGq1K+0Snvwordb93pjUA2o/JS4Xl6OpjwLPfJKGvKKKGjslywC7LxCpW9+ptrlodOs7ccVuIsuY+/lt8Tw1uoUrqzp58iQCAjh5FBHVztyWDbWfEgPaNMHEHs0xqG1TLL6zs1mjrOL2ZECjtd8CDrqRYEDtc9HoRklV7YOjGyWVlJ5j+4KJJMrsUNO4cWMEBARAJpOhffv2CAgI0N9UKhXGjBmD+++/35a1EpFE6Oa4qe5iiwymO/tGdQnG06Pb1Xjs2zvl2lLVSfbGRKiRMLUX1CrDwKZWKfUtR7WNkgLsH8iIpMTsPjVr1qyBEAIPPfQQ4uLioFKp9I95eHggNDQUAwYMsEmRRCQtNc1xU9ssu6FNvc06hy075dZ0+ej2uWia+igAAVwprUBKZj60WsFRUkQ2ZHaoiYmJAQCEhYVh4MCBcHd3t1lRRCR91c1xU9ssu45eINKcSfaiugQjKT0HCz49afCzebnLzToHR0kR1Y3Fo5+GDRum/3d5eTlu3DCcGZQdcInIXLWNlDLFkQtEmnP56IX/nsLZnGK8mXzOaL+ym+YNpOCK3UR1Y3FH4bKyMjz++OMICgqCt7c3GjdubHAjIrKEbo6biT2aY0CbJrUOa7akU6611TbJHgBcu34Ta0wEGnOp/RRcsZuojiwONc8++ywOHDiAhIQEKBQKvPvuu4iLi0NISAg2b95sixqJiAzoLl3V1CnXFuxxWaj8lhb7MnJtfh4iKbJ4nppWrVph8+bNGD58OPz8/HDixAm0bdsWW7Zswfbt27F3715b1VpvnKeGSFrsPYFdSmY+prxzzGbHB/5ubbL3XDtEzszcz2+L+9QUFBQgPDwcQGX/mYKCymGTgwcPxuzZs+tYLhGR5XSXruylX1iAzZd4qLoApi6kcQZiotpZHGrCw8ORlZWFVq1aoWPHjvjkk0/Qr18/7NmzB/7+/jYokYjIOezLyEVx+S2bn0c3tPtYZj7c3GTYn5GLz9MuGSwZwRmIiYxZfPnpjTfegFwux7x587B//35ER0dDCIGbN29i9erVePLJJ21Va73x8hMR1VV1Q7ltyd/THdeum177ipepqCEx9/Pb4lBT1a+//ooffvgBbdu2Rbdu3epzKJtjqCGi2lS9zNO7dWN8l1WAuR+eqDZgOErVNaWIpMpmfWpuV15ejtatW6N169b1OQwRkVMwNVNw1RmPnQlnICYyZPGQbo1Gg6VLl6J58+bw8fHBhQsXAAALFy7Ee++9Z/UCiYjsobqFJp010NyOMxATVbI41CxfvhyJiYlYtWoVPDw89Nu7dOmCd99916rFERHZQ00zBbsCzkBMVMniULN582a8/fbbePDBByGX/72OSffu3XH27FmrFkdEZA/mzBTsjKpbzZyoobI41Fy6dAlt27Y12q7VanHzpnN1oiMiMoezXb6RyYyXgKiOrZaEIHJFFoeaiIgIHD582Gj7Z599hp49e1qlKCIie3K2yzePDAkDUHOwCbbxkhBErsji0U+LFi1CTEwMLl26BK1Wix07duDnn3/G5s2b8X//93+2qJGIyKZqW/nbXlSejfDQoDA8PrIderZqbDQSq4m3Byb2CMGYCDVnFCYyoU7z1Bw+fBhLlizByZMnUVJSgl69emHRokUYO3asLWq0Gs5TQ0TV0Y1+Ahw/4kk3W/CYCDWXRiCCDSbfu3DhAsLCwiCTue7/UAw1RFQTU/PUOAJnCyYyZO7nt9l9atq1a4c///xTf3/SpEnIy8urX5VERE4kqkswvn1+JLbPugNvTu6Bp0e3h7+nu93r0H3TjNuTAY3W0e1GRK7D7FBTtUFn7969KC0ttXpBRESOpFv5e2KP5nh8ZFso3S0eT2EVt88WTETmccz/rURELiA1qwC5RRUOrcHZhpsTOTOzQ41MJjPqT+PK/WuIiGrjDIHC2YabEzkzs4d0CyEwY8YMKBQKAJWLWT722GPw9vY22G/Hjh3WrZCIyEEcGSh0K3BztmAi85kdamJiYgzuT5061erFEBE5k35hAQjw9kBB6Q2HnL/qbMEareAQb6IamB1qNm3aZMs6iIicjtxNhrt6hOD9I9l2PW8Tbw8sv7uLwXBuU8PNdfPZcNg3USV2FCYiqsGYCLVdz+erlCPlxVFGgWb21hNG8+fkFpZj9tYTSErPsWuNRM6KoYaIqAa6JRTs5f7eLeHR6O8/zRqtQNyeDJOzHHM+GyJDDDVERDWQu8kQGx0BGcxfORsAJnSr2yWh0VVahlKzCmqc4Zjz2RD9jaGGiKgWUV2CkTC1F9RmtNjIUNnX5c3JPbHxgV4I8PYw6xy651Ud7WTusHJnGH5O5GgWr9JNRNQQRXUJ1i8wuT8jF++Z6Dysa8nRjVr6R7dgRHb5e1HK7CuleGP/OchguGhm1efdztxh5ZzPhoihhojIbLolFAa0aYK+YQFGo5HUJkYj6Z6j00Hta9bzdHR9enILy032q+F8NkR/M3uVbingKt1EZE11nTfG0ufpRj8Bplt4uJo3SZ25n98MNURELoDz1FBDZu7nNy8/ERG5gNv79HBGYSLTGGqIiFxE1f45RGSIQ7qJiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIElwu1GzYsAGhoaFQKpXo378/UlNTHV0SEREROQGXCjUff/wx5s+fj9jYWJw4cQLdu3dHZGQkLl++7OjSiIiIyMFcKtSsXr0as2bNwsyZMxEREYG33noLXl5eeP/99x1dGhERETmYy4SaGzdu4IcffsDo0aP129zc3DB69GikpKSYfE5FRQWKiooMbkRERCRNLhNqrly5Ao1Gg2bNmhlsb9asGXJzc00+Z8WKFVCpVPpby5Yt7VEqEREROYDLhJq6ePHFF1FYWKi//fbbb44uiYiIiGzEZZZJaNq0KeRyOfLy8gy25+XlQa1Wm3yOQqGAQqGwR3lERETkYC7TUuPh4YHevXsjOTlZv02r1SI5ORkDBgxwYGVERETkDFympQYA5s+fj5iYGPTp0wf9+vXDmjVrUFpaipkzZzq6NCIiInIwlwo1kyZNwp9//olFixYhNzcXPXr0QFJSklHnYSIiImp4ZEII4egi7KWoqAgqlQqFhYXw8/NzdDlERERkBnM/v12mTw0RERFRTRhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSGGqIiIhIEhhqiIiISBIYaoiIiEgSXCbUhIaGQiaTGdxWrlzp6LKIiIjISTRydAGWWLJkCWbNmqW/7+vr68BqiIiIyJm4VKjx9fWFWq12dBlERETkhFzm8hMArFy5Ek2aNEHPnj3x2muv4datWzXuX1FRgaKiIoMbERERSZPLtNTMmzcPvXr1QkBAAI4ePYoXX3wROTk5WL16dbXPWbFiBeLi4uxYJRERETmKTAghHHXyF154AfHx8TXuc+bMGXTs2NFo+/vvv49HH30UJSUlUCgUJp9bUVGBiooK/f2ioiK0bNkShYWF8PPzq1/xREREZBdFRUVQqVS1fn47NNT8+eefyM/Pr3Gf8PBweHh4GG0/ffo0unTpgrNnz6JDhw5mnc/cF4WIiIich7mf3w69/BQYGIjAwMA6PTctLQ1ubm4ICgqyclVERETkilyiT01KSgqOHz+OESNGwNfXFykpKXj66acxdepUNG7c2NHlERERkRNwiVCjUCjw0UcfYfHixaioqEBYWBiefvppzJ8/39GlERERkZNwiVDTq1cvHDt2zNFlEBERkRNziVBDtqHRCqRmFeBycTmCfJXoFxYAuZvM0WURERHVCUNNA5WUnoO4PRnIKSzXbwtWKREbHYGoLsEOrIyIiKhuXGpGYbKOpPQczN56wiDQAEBuYTlmbz2BpPQcB1VGRERUdww1DYxGKxC3JwOmJifSbYvbkwGN1mHTFxEREdUJQ00Dk5pVYNRCczsBIKewHKlZBSYf12gFUjLzsSvtElIy8xl+iIjIabBPTQNzubj6QFPTfhqtwPoD57HpSBauXb+p385+OERE5CzYUtPABPkqLd4vKT0HvZftwxv7fzEINAD74RARkfNgS00D0y8sAMEqJXILy032q5EBaOangFYI7Eq7hOwrpXhj/7lqjyf+ek7cngyMiVBzSDgRETkMQ00DI3eTITY6ArO3noAMMAg2uvvlt7R48N3jZh/z9n44A9o0MbkP58QhIiJbY6hpQHTBouKWFk+Nbo/tqReRW/R33xlvRSOUVNzCtbKbNRyletX11+GcOEREZA8MNQ2EqWCh9lPg6dHtUHT9Jnb8+Duult2q1znO5RUjJTPfoBVGNydO1Utdur44CVN7MdgQEZFVyIQQDWZMblFREVQqFQoLC+Hn5+focuymumBR9fKTtehaYcZEqDE4/kC1Q8hlANQqJb59fiQvRRERUbXM/fzm6CeJM2eyPWvTtcKsP3CuXnPiEBERWYKhRuJqm2zPFnRhadORbLP2N3fuHCIiopow1EicowKDAIzmtKmOuXPnEBER1YQdhSXOmQODrk9Nv7AAR5dCREQSwJYaidNNtuds3XB19cRGR7CTMBERWQVDjcTpJttzNgHeHhzOTUREVsVQ0wBEdQlGwtReCPB2d3Qpeq+M78RAQ0REVsVQ0wBotAIqTw+8/I8IKN2d41euVnk6ugQiIpIYdhSWOFMzCTsSOwcTEZGtMNRIWHUzCTsKOwcTEZEtMdRIVE0zCTuKmotYEhGRDTHUuAjdCtuXi8sR5Ks0WDTSFEfMJFydmQNbY2zn4FprJiIiqg+GGhdgql9McC2tHs609MDoCDUGtGni6DKIiEjinGMoDFVL1y+maquLbtHIpPQck89zppmEj2ZecXQJRETUADDUODFzVtiO25MBjdZ4D2eaSfi9b7Ow96c/HF0GERFJHEONE6utX4wAkFNYjtSsAqPHnGkm4fKbWsz58Ees2Jvh6FKIiEjCGGqcmLn9YqrbL6pLMB4ZGmbNkurlP99kYe9Ppi+XERER1RdDjRMzt19MdftptAK7T9o2RDSycDTTwl3pJi+XERER1RdDjROrrV+MDJWjoKqbndcew7rXP9ATT49uZ/b++aU3kHgki8GGiIisjqHGid3eL6ZqsDFndl5bDutW+ynw1l+rbIc29bbouUu/OIPB8QeqHblFRERUFww1Tk63wrZaZXiJSa1SIuGvUFEdaw/rlsmAGQNbY/usO3DkhVH6c9flPLUNSSciIrIUJ99zAVFdgjEmQm3RjMLA35evcgvLa10uIVilxJ3dg/H2N1kAYHL/DVN64R/djEOU7jyWXOoSqGxtituTgTERas40TERE9cZQ4yLkbjKLZ+XVXb6avfUEZDAdVB4eFIrREWp9SOrZqrHFsxfffh5LesrcPiSdMw4TEVF9yYQQDabHZlFREVQqFQoLC+Hn5+focuzG0mUWLF1nqqbzmOPNyT0wsUdzi55DREQNh7mf32ypaQAsvXxVl1ahqufJLSrHiV8LsOXYxVqf50xLOhARketiqGkg6hpU6nOeO7uHYP+Zy9X26ZGhssNzdUPSiYiILMHRT2Qz9R2STkREZAmGGrKp+gxJJyIisgQvP5HN1XVIOhERkSUYasgu7NWnh4iIGi5efiIiIiJJYKghIiIiSWCoISIiIklgqCEiIiJJYKghIiIiSWCoISIiIklgqCEiIiJJYKghIiIiSWCoISIiIkloUDMKC1G5VnRRUZGDKyEiIiJz6T63dZ/j1WlQoaa4uBgA0LJlSwdXQkRERJYqLi6GSqWq9nGZqC32SIhWq8Uff/wBX19fyGT1X0yxqKgILVu2xG+//QY/Pz8rVEjV4WttH3yd7Yevtf3wtbYPW77OQggUFxcjJCQEbm7V95xpUC01bm5uaNGihdWP6+fnx/9R7ISvtX3wdbYfvtb2w9faPmz1OtfUQqPDjsJEREQkCQw1REREJAkMNfWgUCgQGxsLhULh6FIkj6+1ffB1th++1vbD19o+nOF1blAdhYmIiEi62FJDREREksBQQ0RERJLAUENERESSwFBDREREksBQU0fLly/HwIED4eXlBX9/f5P7XLx4EePHj4eXlxeCgoLw7LPP4tatW/YtVIJCQ0Mhk8kMbitXrnR0WZKwYcMGhIaGQqlUon///khNTXV0SZKzePFio/dvx44dHV2Wy/vmm28QHR2NkJAQyGQy7Ny50+BxIQQWLVqE4OBgeHp6YvTo0Th37pxjinVxtb3WM2bMMHqPR0VF2aU2hpo6unHjBv75z39i9uzZJh/XaDQYP348bty4gaNHj+KDDz5AYmIiFi1aZOdKpWnJkiXIycnR35544glHl+TyPv74Y8yfPx+xsbE4ceIEunfvjsjISFy+fNnRpUlO586dDd6/3377raNLcnmlpaXo3r07NmzYYPLxVatWYe3atXjrrbdw/PhxeHt7IzIyEuXl5Xau1PXV9loDQFRUlMF7fPv27fYpTlC9bNq0SahUKqPte/fuFW5ubiI3N1e/LSEhQfj5+YmKigo7Vig9rVu3Fm+88Yajy5Ccfv36iblz5+rvazQaERISIlasWOHAqqQnNjZWdO/e3dFlSBoA8fnnn+vva7VaoVarxWuvvabfdu3aNaFQKMT27dsdUKF0VH2thRAiJiZGTJw40SH1sKXGRlJSUtC1a1c0a9ZMvy0yMhJFRUU4ffq0AyuThpUrV6JJkybo2bMnXnvtNV7Wq6cbN27ghx9+wOjRo/Xb3NzcMHr0aKSkpDiwMmk6d+4cQkJCEB4ejgcffBAXL150dEmSlpWVhdzcXIP3t0qlQv/+/fn+tpFDhw4hKCgIHTp0wOzZs5Gfn2+X8zaoBS3tKTc31yDQANDfz83NdURJkjFv3jz06tULAQEBOHr0KF588UXk5ORg9erVji7NZV25cgUajcbke/bs2bMOqkqa+vfvj8TERHTo0AE5OTmIi4vDkCFDkJ6eDl9fX0eXJ0m6v7mm3t/8e2x9UVFRuOeeexAWFobMzEy89NJLGDduHFJSUiCXy216boaa27zwwguIj4+vcZ8zZ86wU58NWPLaz58/X7+tW7du8PDwwKOPPooVK1ZwGnRyeuPGjdP/u1u3bujfvz9at26NTz75BA8//LADKyOyjsmTJ+v/3bVrV3Tr1g1t2rTBoUOHMGrUKJuem6HmNs888wxmzJhR4z7h4eFmHUutVhuNHMnLy9M/Robq89r3798ft27dQnZ2Njp06GCD6qSvadOmkMvl+veoTl5eHt+vNubv74/27dvj/Pnzji5FsnTv4by8PAQHB+u35+XloUePHg6qquEIDw9H06ZNcf78eYYaewoMDERgYKBVjjVgwAAsX74cly9fRlBQEABg37598PPzQ0REhFXOISX1ee3T0tLg5uamf53Jch4eHujduzeSk5Nx1113AQC0Wi2Sk5Px+OOPO7Y4iSspKUFmZiamTZvm6FIkKywsDGq1GsnJyfoQU1RUhOPHj1c7gpWs5/fff0d+fr5BoLQVhpo6unjxIgoKCnDx4kVoNBqkpaUBANq2bQsfHx+MHTsWERERmDZtGlatWoXc3Fy88sormDt3Li+R1ENKSgqOHz+OESNGwNfXFykpKXj66acxdepUNG7c2NHlubT58+cjJiYGffr0Qb9+/bBmzRqUlpZi5syZji5NUhYsWIDo6Gi0bt0af/zxB2JjYyGXyzFlyhRHl+bSSkpKDFq7srKykJaWhoCAALRq1QpPPfUUli1bhnbt2iEsLAwLFy5ESEiIPsST+Wp6rQMCAhAXF4d7770XarUamZmZeO6559C2bVtERkbavjiHjLmSgJiYGAHA6Hbw4EH9PtnZ2WLcuHHC09NTNG3aVDzzzDPi5s2bjitaAn744QfRv39/oVKphFKpFJ06dRKvvvqqKC8vd3RpkrBu3TrRqlUr4eHhIfr16yeOHTvm6JIkZ9KkSSI4OFh4eHiI5s2bi0mTJonz5887uiyXd/DgQZN/k2NiYoQQlcO6Fy5cKJo1ayYUCoUYNWqU+Pnnnx1btIuq6bUuKysTY8eOFYGBgcLd3V20bt1azJo1y2B6E1uSCSGE7aMTERERkW1xnhoiIiKSBIYaIiIikgSGGiIiIpIEhhoiIiKSBIYaIiIikgSGGiIiIpIEhhoiIiKSBIYaIiIikgSGGiKymhkzZkAmkxndrLVYY2JiIvz9/a1yrLr65ptvEB0djZCQEMhkMuzcudOh9RDR3xhqiMiqoqKikJOTY3ALCwtzdFlGbt68WafnlZaWonv37tiwYYOVKyKi+mKoISKrUigUUKvVBje5XA4A2LVrF3r16gWlUonw8HDExcXh1q1b+ueuXr0aXbt2hbe3N1q2bIk5c+agpKQEAHDo0CHMnDkThYWF+hagxYsXA4DJFhN/f38kJiYCALKzsyGTyfDxxx9j2LBhUCqV2LZtGwDg3XffRadOnaBUKtGxY0ds3Lixxp9v3LhxWLZsGe6++24rvFpEZE1cpZuI7OLw4cOYPn061q5diyFDhiAzMxOPPPIIACA2NhYA4ObmhrVr1yIsLAwXLlzAnDlz8Nxzz2Hjxo0YOHAg1qxZg0WLFuHnn38GAPj4+FhUwwsvvIDXX38dPXv21AebRYsWYf369ejZsyd+/PFHzJo1C97e3oiJibHuC0BEtmeXZTOJqEGIiYkRcrlceHt762/33XefEEKIUaNGiVdffdVg/y1btojg4OBqj/fpp5+KJk2a6O9v2rRJqFQqo/0AiM8//9xgm0qlEps2bRJCCJGVlSUAiDVr1hjs06ZNG/Hhhx8abFu6dKkYMGBAbT9qteclIsdhSw0RWdWIESOQkJCgv+/t7Q0AOHnyJI4cOYLly5frH9NoNCgvL0dZWRm8vLywf/9+rFixAmfPnkVRURFu3bpl8Hh99enTR//v0tJSZGZm4uGHH8asWbP022/dugWVSlXvcxGR/THUEJFVeXt7o23btkbbS0pKEBcXh3vuucfoMaVSiezsbEyYMAGzZ8/G8uXLERAQgG+//RYPP/wwbty4UWOokclkEEIYbDPVEVgXsHT1AMA777yD/v37G+yn6wNERK6FoYaI7KJXr174+eefTQYeAPjhhx+g1Wrx+uuvw82tcgzDJ598YrCPh4cHNBqN0XMDAwORk5Ojv3/u3DmUlZXVWE+zZs0QEhKCCxcu4MEHH7T0xyEiJ8RQQ0R2sWjRIkyYMAGtWrXCfffdBzc3N5w8eRLp6elYtmwZ2rZti5s3b2LdunWIjo7GkSNH8NZbbxkcIzQ0FCUlJUhOTkb37t3h5eUFLy8vjBw5EuvXr8eAAQOg0Wjw/PPPw93dvdaa4uLiMG/ePKhUKkRFRaGiogLff/89rl69ivnz55t8TklJicG8O1lZWUhLS0NAQABatWpVvxeJiOrH0Z16iEg6YmJixMSJE6t9PCkpSQwcOFB4enoKPz8/0a9fP/H222/rH1+9erUIDg4Wnp6eIjIyUmzevFkAEFevXtXv89hjj4kmTZoIACI2NlYIIcSlS5fE2LFjhbe3t2jXrp3Yu3evyY7CP/74o1FN27ZtEz169BAeHh6icePGYujQoWLHjh3V/gwHDx4UAIxuMTExFrxSRGQLMiGqXIgmIiIickGcfI+IiIgkgaGGiIiIJIGhhoiIiCSBoYaIiIgkgaGGiIiIJIGhhoiIiCSBoYaIiIgkgaGGiIiIJIGhhoiIiCSBoYaIiIgkgaGGiIiIJIGhhoiIiCTh/wEleEca8yYL1QAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(X[:, 0], X[:, 1], marker='o', label='Data Points')\n",
-    "plt.title('Synthetic Data with Anomalies')\n",
-    "plt.xlabel('Feature 1')\n",
-    "plt.ylabel('Feature 2')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "69d89305",
-   "metadata": {},
-   "source": [
-    "-   Apply K-Means clustering and identify anomalies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "da669ca8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=5, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KMeans</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_clusters',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_clusters&nbsp;</td>\n",
-       "            <td class=\"value\">5</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('init',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">init&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;k-means++&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_init',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_init&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_iter&nbsp;</td>\n",
-       "            <td class=\"value\">300</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">42</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('copy_x',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">copy_x&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('algorithm',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">algorithm&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;lloyd&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
-      ],
-      "text/plain": [
-       "KMeans(n_clusters=5, random_state=42)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n",
-    "kmeans.fit(X)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "2618c79e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Calculate distances to cluster centers\n",
-    "distances = np.linalg.norm(X - kmeans.cluster_centers_[kmeans.labels_], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "36c707fd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define threshold for anomalies\n",
-    "threshold = np.mean(distances) + 2 * np.std(distances)\n",
-    "# Identify anomalies\n",
-    "anomalies = X[distances > threshold]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8f2f0104",
-   "metadata": {},
-   "source": [
-    "-   Visualize the results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "5cf67075",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot results\n",
-    "plt.scatter(X[:, 0], X[:, 1], c=kmeans.labels_, cmap='viridis',\n",
-    "marker='o', label='Normal Data')\n",
-    "plt.scatter(anomalies[:, 0], anomalies[:, 1], color='red', marker='x',\n",
-    "            label='Anomalies')\n",
-    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], color='black',\n",
-    "            marker='x', s=200, label='Cluster Centers')\n",
-    "plt.title('K-Means Clustering for Anomaly Detection')\n",
-    "plt.xlabel('Feature 1')\n",
-    "plt.ylabel('Feature 2')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7d404a22",
-   "metadata": {},
-   "source": [
-    "## Evaluation of Anomaly Detection Models\n",
-    "\n",
-    "Evaluating the performance of anomaly detection models can be challenging due to the imbalanced nature of the data. Common evaluation metrics include:\n",
-    "\n",
-    "-   ****Precision****: The proportion of true anomalies among the detected anomalies.\n",
-    "-   ****Recall****: The proportion of true anomalies that were correctly detected.\n",
-    "-   ****F1 Score****: The harmonic mean of precision and recall, providing a balance between the two.\n",
-    "-   ****ROC Curve****: A graphical representation of the true positive rate against the false positive rate at various threshold settings.\n",
-    "-   ****AUC (Area Under the Curve)****: A single scalar value that summarizes the performance of the model across all thresholds."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d568c462",
-   "metadata": {},
-   "source": [
-    "## Example: DBSCAN for Anomaly Detection and Evaluation\n",
-    "\n",
-    "-   Create a synthetic dataset with clusters and anomalies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "792d8b7d-bc8e-4943-aa26-125355e0201f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.datasets import make_blobs\n",
-    "X_core, _ = make_blobs(\n",
-    "    n_samples=600,\n",
-    "    centers=[(-2, -2), (3, 3)],\n",
-    "    cluster_std=[0.5, 0.6],\n",
-    "    random_state=42,\n",
-    ")\n",
-    "# Add anomalies\n",
-    "rng = np.random.RandomState(42)\n",
-    "X_noise = rng.uniform(low=-6, high=6, size=(40, 2))\n",
-    "X = np.vstack([X_core, X_noise])\n",
-    "y_true = np.zeros(X.shape[0], dtype=int)\n",
-    "y_true[-40:] = 1  # Last 40 points are anomalies\n",
-    "\n",
-    "# Shuffle the X and y_true data together\n",
-    "df = np.hstack([X, y_true.reshape(-1, 1)])\n",
-    "rng.shuffle(df)\n",
-    "X, y_true = df[:, :-1], df[:, -1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b6c6bd28",
-   "metadata": {},
-   "source": [
-    "-   Visualize the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "8984ebc0-5eaa-4c5f-9a83-a670156e342a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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y5cs4fPgw/vzzT0VvkBtvvBGurq5YvHgxKioq4OHhoejNom7ChAnYuHEjnn32WRw5cgS33HILqqur8d133+GJJ57AqFGj9I6poqIC//73vwEANTU1yMrKwvbt25GdnY1//vOfWLBggeLc4cOHw93dHXfffbciUPzggw8QFhamEaj17dsXq1evxsKFCxEXF4ewsDCkpKRg5MiReO2115CamoqBAwfi9OnT2Lx5s2ImSx9TXt9Yzz//PDZt2oQ77rgDTz31FHx8fPD+++8jMjISv/zyi97nbtmyBUII3HPPPVof/8c//oE2bdpg8+bNGDBggNFjcnNzw+LFi5GamoqhQ4di3LhxuHz5Mt555x1ERUXhmWeeMelr1Mec74e62NhYLFy4EHPmzEFeXh5Gjx6Ntm3bIjc3Fzt27MDjjz+O2bNnY+/evXjyySfxwAMPoEuXLmhoaMCmTZvg6uqK++67z2JfGzmJli63ImopX331lXj00UdFt27dhK+vr3B3dxdxcXFi5syZ4vLly1qf89Zbb2mUvEqkMtUlS5ZoPAa10teGhgYxc+ZMERoaKmQymaKU1ZRrCCHvQzNhwgTRvn174ebmJjp27ChGjhwpPvnkE5XzPvjgAxETEyNcXV1VyrvVS7qFkJc5v/zyyyI6Olq4ubmJ9u3bi/vvv19kZ2drfU8kQ4cOFQAUH76+viI+Pl48/PDDir4y6nbt2iV69+4tPD09RVRUlFi8eLGirFy5/PnSpUvirrvuEm3bthUAFGO+du2aeO6550SHDh2El5eXGDRokDh8+LDWr8uc14+MjBR33XWX1q9Z/XV++eUXMXToUOHp6Sk6duwoFixYINatW2ewpDshIUF07txZ73iTk5NFWFiYqK+vV5R0q5cyayuXFkKIrVu3isTEROHh4SGCgoLE+PHjxZ9//qlyzsSJE4WPj4/Wr1Nbub76+2Ls98PYPjVCCPHpp5+KwYMHCx8fH+Hj4yO6desmZsyYIc6dOyeEECInJ0c8+uijIjY2Vnh6eoqgoCAxbNgw8d133+l8H6n1kglhwSw4Igf3zjvv4JlnnkFeXp7WahgiIrJfDGqI/iaEwA033IDg4GCzkkmJiMg2mFNDrV51dTV27dqFjIwMnD59Gjt37rT1kIiIqBk4U0OtXl5eHqKjoxEQEIAnnngCr7/+uq2HREREzcCghoiIiJwC+9QQERGRU2BQQ0RERE6hVSUKNzU1obCwEG3btjW7jT0RERG1DCEE/vrrL4SHhyt2itemVQU1hYWFiIiIsPUwiIiIqBkKCgr0bmLaqoKatm3bApC/KdL+NERERGTfKisrERERobiP69KqghppycnPz49BDRERkYMxlDrCRGEiIiJyCgxqiIiIyCkwqCEiIiKnwKCGiIiInAKDGiIiInIKDhXUXLhwAQ8//DCCg4Ph5eWFhIQEHDt2zNbDIiIiIjvgMCXdZWVlGDRoEIYNG4avvvoKoaGhyMzMRGBgoK2HRkRERHbAYYKaxYsXIyIiAuvXr1cci46OtuGIiIiIyJ44zPLTrl270K9fPzzwwAMICwtDYmIiPvjgA73Pqa2tRWVlpcqH08jeC5QX6D+nvEB+HhERUSvgMEFNTk4OVq9ejfj4eHzzzTeYPn06Zs2ahY8//ljncxYtWgR/f3/Fh9Ps+5S9F9gyFvh4pO7AprxA/viWsQxsiIioVZAJIYStB2EMd3d39OvXD4cOHVIcmzVrFo4ePYrDhw9rfU5tbS1qa2sVn0t7R1RUVDj2NglSwFKWBwRGARO/AAIijH+ciIjIgVRWVsLf39/g/dthZmo6dOiAHj16qBzr3r07zp8/r/M5Hh4ein2enGq/p4AIeaASGCUPXJRnbBjQEBFRK+UwQc2gQYNw7tw5lWN//PEHIiMjbTQiG9MW2Jz/iQENERG1Wg4T1DzzzDP46aef8MYbbyArKwtbtmzB+++/jxkzZth6aLajHth8NIIBDRERtVoOE9QkJSVhx44dSE9PR69evbBgwQKsWLEC48ePt/XQVLV0VVJABDBmreqxMWsZ0BARUavjMInClmBsolGzSVVJfuG6Z0qknJfKQuChrUBsinmvqZxDI+FMDRERORGnSxR2CMHx8oBGPXlXohyA+IXLzzeHelLwo99oTx4mIiJqBRjUWFJLViVpu17nm3S/PhERkZNjUGNpLVGVpC9A0hdYEREROTEGNdZg7aqk0kx5To6u6ym/fmWh/HwiIiInx0Rhazr/kzygkTz6jXyJyBKy98pzcvQFSOUF8oDG3GRkIiIiG2KisK2VFwA7pqoe2zG1eUtB2srEY1M0t0ZQLxMPiGBAQ3LcAJWIWgEGNdZgyaokbl5J5uLPEBG1EgxqLM3SVUktXSZOzoc/Q0TUSjCosSRrVCVx80oyF3+GiKiVYFBjSdaqSuLmlWQu/gwRUSvA6idLs2ZVErdEIHPxZ4iIHBCrn2xFvSpJm+ZWJXHzSjIXf4aIyIkxqHEkliwTp9aJP0NE5MQY1DgKbl5J5uLPEBE5OQY1joCbV5K5+DNERK0Agxp7x80ryVz8GbJ/7PhMZBEMauwdN68kc/FnyL6x4zORxbCk2xFw80oyF3+G7JehBohskEhk9P2bQQ0Rka3pClwY0BABYJ8aIiLHwY7PRBbBoKYlMRmQiHRRD2w+GsGAhshEDGpaCpMBicgQdnwmMguDmpYSHA/4hesum1VeO/cLl59PRK0LOz4TmYVBTUvR1w+EyYBExI7PRGZjUNOSmAxIRNqw4zORRTCoaWlMBiQiZez4TLbmREUsDGpsgcmARCRhx2eyJScrYmFQYwtMBiQiSWwK8NBW/TO1UmDz0FZ2fCbLcrIiFgY1LY3JgESkLjbF8ExtQAQDGrI8JytiYVDTkpgMSERE9saJilgY1LQUJgMSEZG9cpIiFgY1LYXJgEREZM+coIiFu3S3pOy98iQrfT8g5QXygIZr50TkwHKKq5B/pQZRwT6IDvGx9XDIGMorChI7makx9v7dpgXHRMYEKgERNv/hISJqrvKaOsxKP4n9mcWKY0PiQ5E2LhH+3m42HBnppZ4iMWatvCpXSomwg8DGGFx+IiIii5mVfhIHs0pUjh3MKsHM9BM2GhEZ5ERFLAxq7JUTdXgkotYhp7gK+zOL0aiW1dAoBPZnFiO3pNrqr59xrsis17HENRyKkxWxcPnJHkkdHv3CdU/5ST+IlYVsyEVEdiH/So3ex/NKq62SX2OJJa9Wu2xmbBGLdL8pzbTrZSjO1NgjJ+vwSEStQ2SQt97Ho4KtkzBsiSWvVrts5mQdrRnU2CMn6/BIRNZnD8smMaG+GBIfCleZTOW4q0yGIfGhiA7xsfg4LbHkZetlM5tzoo7WXH6yV8pTflJgo5yNzoCGiGB/yyZp4xIxM/2EyngGxYVg4eiemLDuiMXHaYklL1stm5HlMaixZ+qBzUcj5McZ0BDR3/Qtm2yc3L/Fx+Pv7YaNk/sjt6QaeaXVij41E9Ydscg41fvfmLrkpa1/jq2WzcjyGNTYO6nDoxTQAA7X4ZGIrENaNlGnvGxiqxmG6JDrQYOp49QWeOibkRoSH4qDWSUqy0euMhkGxYUY9Xxp2czQNcj+MafG3pUXyJeclO2YavdldURkfcYsm9gDY8dZXlOHCeuOIGXZD0hdfxTDlu7DhHVHUFFTr3dGKm1cIgbFhag8NiguBGnjEhWfG0oENuYaZP84U2PPnKTDIxFZh6Msmxg7Tl2Bx2Mbj+JoXpnG86SZnis1dVqXvCTGzhTpuwY5Bs7U2Csn6vBIRNZhTLWRPdA1ThcASZGBiqooXRVI2gIaZdJMT3SID4Z1DdP4uk2Z0dJ1DXIMDGrskZN1eCQi63GUZRNt42wCcDS/DCPf/RE/nCtq9rW1zUgpl447yowWmY/LT/bIyTo8EpH16Ko2sjfSOB9YcwjH8sqgPB9zprASZwor9T4/KTIQx8+XG0zk1ZYQnBQVaLGvQxvuSG4/GNTYI6nDY3C84Q6PpZkO0RCJiKxLudqopajfzA3d3E+eLzO4lKROClwWju6JUasOoqymXvGYn1cbvD66l8r52vJyfs43vHzVnPfO3noEkQMHNW+++SbmzJmDp556CitWrLD1cCzPmEAlIIIzNERkEkvMKmi7mQd6u6kEHNpu7q/sPGPya0lLadP+/bPK9QGgrKYez3/6C9IfvwmA7oTgJqFxSEVzl5/srUcQOWhQc/ToUaxduxa9e/e29VCIiByCJWcVtN3M1QMO9Zt7TnEVzlzQv8Qkee72LmiCQJ/OgbglPhQ5xVU4nFOq9dzDOaWK6iVDCcGWZM89glozh0sUrqqqwvjx4/HBBx8gMNC666RERM7CUhs26qpSUqe+b5IpAceyPX/g7T2ZeGTdEUxYdwT7zmkGD8p+yilFTnEVLlVcM/o1lDWnn4+j9AhqbRwuqJkxYwbuuusu3HbbbbYeChGRQzB1w0Z9m06aOhsi3dwNVSDpcjCrBOlHzus956MDuUhZ9gPmbD+t9XEXmdbDCs1ZfmJFlX1yqOWn//znPzh+/DiOHj1q1Pm1tbWora1VfF5ZadzUJxGRMzF2w8aT58vwys4zKstE6ktUpgYn0s1d6lWjbclGn0YhkFlUpfec7GL9jw+OC0V9YxOO5F6x2DYI3FrBPjnMTE1BQQGeeuopbN68GZ6enkY9Z9GiRfD391d8RERYKKk2e6/h3jDlBfLziIhszFAgEuTthgnrjmD0e4c08l7Ul6h0NdJTp60BYNq4RAyMDW7GVwB4uem+XelKBH7z3gRkzE7Gxsn9sebhvhbv5+MoPYJaE5kQBhZG7cRnn32GMWPGwNXVVXGssbERMpkMLi4uqK2tVXkM0D5TExERgYqKCvj5+TVvINl7gS1jAb9w3dsUSM3zKgvlpdksuSYiG5N2ydY2qwAABzKL0aTn+RmzkxUByvnSao3y6jYuMjQoRRf6kpBzS6rx/g/ZSD9qXuPQXuF+evvbrE9NwrCuYRqvrd7Pp7kVYdLzpK+dfWqsp7KyEv7+/gbv3w6z/HTrrbfi9GnV9dLU1FR069YNL7zwgkZAAwAeHh7w8PCw7ECC4+UBja79l9S7AQfHW/b1iYiaIW1cImamn1BZ/hkUF4Lnhsdj1KpDBp+v3Mvllc9+ReXVBpXHhZA3uXtiWJzBm3t0iA+yDCwZ6eMiA/pGBuKVu7rrHbu2vBblv+ObWxGm73nasDlfy3GYoKZt27bo1Uu1yZKPjw+Cg4M1jluVcjdf9cBG3/YGREQ2pKvzcIaR2xNIAYK+UuajeWVG3bhziqtMbsKnrEkAR/PK8MoO3X1vAr3dDHYaDvR2Q+VV1VL0A1nFBvvMGNufxmmb82Xv1d8cFpDfD23QHNZhcmrsirb9l87/xICGiOye+oaNhvJtXGRQyY2xRCmzpfrJnNaz9FRWU69SvaWrt06jWgJGkwD2Zxbjq9MXtVaAmVJJZqkyersipWDo23dQ+gN/y9gWzy11mJkabfbt22e7F1efsflohPx4awho7DhKJyLT6KrikQyOU11WMbeUubymDu9lZDVvsCaSlsx0zS7pM33zccW/lWdXjK0kc9rmfHaegsGZGnMERABj1qoeG7PW+QMaO47SiVoTff1kTKGtiqdXuB92zRiEjZP7qyyVBPm4I1DL0omr2oyOLrPST+J4frlZ4zWWFGCZOzN08O8lKcD4oO7Xi/pbiDhscz5tKxXSvcAOUjAceqbG5soLgB1TVY/tmOrcMzV2HqUTtQaWztUwZafvWeknUaG2JQIA+Hm54bnh8cg4V4SoYB8IITSSY5szY9JcUoCVX1qNp8xc7mn8e0kqt6Rab3+aPpEBimBl46E8vdd06OZ82nJLx6yV3/9snILhMCXdlmBsSZhR1G/edvINbRG6onE7iNKJWgN95dnW3Egxp7gKKct+MPl5UsB1vKAMqeuNa56qzN1Vhjr15Bc9fD3a4KtZtyAi2BuJr32rsS+VRAbAlBugVCJeUVOvUUmmvqGnPkmRgdg2faAJr2ynlH/nS6z0u9/Y+zeXn5pD28278026p+ScDROliWzG1C0PLKm5yzhScmxzt0owJaABgKraBtyz6gB2n76oN9Do3sEPN8cEG30jlGZXSqtrkTo4Cpsm98f61CTcGBFgdEADAJMGRhl9rl2zwxQMBjWm0jcboW+t0dmof60fjWBAQ9QCbLmRYnODEingkv3dZdjQXkyWUFZTj4Vf/Kr3nOq6Bqx5uC8Gx4cavF5SVCAC/+68nLLsB6SuP4pH1h3BG1/+hpMF5SaNrUdHf5POt1u6UjBseN9jUGOq0kx5p2BdN2/lm31lofx8Z2WHUTo5GG45YjJbbqQo5ZM098aRV1qNtHGJ6BFu5vK/kQoravU+nl9agys1ddg4uT8yZiejV0c/aIu3Ar3d8OGEJK0l2plFxgeR2raOcFjqf+A/+o1d/EHPoMZUsSnyrQ/0zUZIgY2zb5Fgh1E6ORBW0jWLrr2XrHHD1FZdlTYuEV7umh3cjREV7AN/bze8+8+W2xvJ0E1OmtmKDvHB5sk34Ra1WZukyEDsmz0MpdW1Wpf9TOE0+0LZcQoGq5+aw5hAJSDCuWcs9CVKa6uKIlLHSrpm07XlgaVumLqqq54f0RUPffgTqusaTb5mfLvrVVDN3bG7OfTtZwWozmzpqwI7XtC8DsibJvd3rn2hjEnB0NZxv4Ww+olMx+onshT+LJnFmBLs5tBVXSWTQWXTSlMpl51X1NRj2r9/xuGcUpVzBsYGo+SvWvxR1Py9oYzVq6Mfvph5i1F7MzWn8isxIgCzbot3noAGsNmmzsbevxnUkGkM3Wx4MyJTteb2CHaouWXbxlAuOy+vqcNjHx/DsfzrMyBJkYH4cGISJn50BCf/LLfKGJRtnjwAa/fnGN3vZ9z7P2kEYbqol3g7xZ5PEqmrfGmm7u7yUld56TwzAxuWdJN1MFGaLI2VdHbFUvsyaSNVQZ0qKMewpftUAhoA+Dm/DJM/PtoiAQ0ArPjuDxxQWwLTtzdTQ5OhxSzA18MVSZGBGruYO/yeT8piU+S/2/XlxAVEyAOaFs6JY1BDpmGiNFkDK+nsRnPLtk0xe9sprX1dmgCNQMeajuaXaeTcSIFX+pHzKgnSxuws7uvhinl398DR/DKb9BFqUeo5ceqBjfIMrF94i+XEMagh08WmGL7ZBEQwoCHjsZLObsSE+qJfZKBVXyOzBfJlzDVn+2kMW7oPE9YdQUVNPf6Xe8Xgc6pqGzH7k9N6z3HYPZ/U2ekeUAxqiMi27LTfRWv2YD/OkEmuLxtZJv3Uofd8UmeH3eUZ1BCR7dhxv4vW7L/H9L/fS+7vjSeGxiChhZro2ZK0bNQxwLxlOadqvKfMznLiGNQQkW1wyxG7lFNcpTevJSkqEA/0i8Dzd3bH57NuQZKVl6rsRaMQGBgb3Ozn9+kc4ByN97Sxo5w4BjVEZBuspLNLhqqfJiptxphTXIWjLZjYa0tRwT544Y6u6NWxebNTT6TEOUc5tzZ2lBPHjsJEZBtSJZ2uPhfA9cDGAn0uyDiGqp96hl/fjNFQANSrox/OXKi0yLhsKcDLDXN3/mpWB2RTc2mMaQhoF+ysuzyDGiKyHW45YnekLQy0dRQeFBeicoM1FAC9MSYBi3b/bnTDOntVfrVeo5+NKQK93YwOTLRtUZEUFYiJA6PQM9zfvgIcXUvINtwqgctPRESkIm1cIgbFhagc07a3lKHNNXt3CkD64zchY3YyXh3ZHZFBXlYfu7UYbrunW1lNvdH9abTtBH40rwxPbjmhUmJuc3aaE8eghoiIVEgbO2bMTsb61CRkzE7Gxsn9teaE6AqAnhveRbHDd3SID/adK8GfZdda6kuwO8b0p8kprjK4E7jddCa205w4Lj8RETkYa+dbKF9/WNcwveeq72wd5O2OZd/+gVGrDirO6RcZ2KKdgu2RMTk1xmxRodyZ2KZLUXaaE8eghojIQWjLt7DkRonmXD86RB5gTVh3RCP/5HgrDmi05SLpCkoDvYz/HuaV2jioAewyJ47LT0REDkJbvoUllyPMvf7J82XYn1mskX9iTj5Kc3UJ87XBq2pSzkUqr6nDhHVHkLLsB6SuP6qRI7N8j/FLNE7VmdiCOFNDROQApHwLdZZajrDE9V/ZeUbv4zIZoCddxKL+7+4e6BTojS9/KcSpP8ux57cis6/55r0JaOfviahgHxRcqcaEj47qPXdATLDKe6YvaJx3Tw+jS8aTogJtP0tjpzhTQ0TkAAzlW5i7UaK5188prsKZC5UY7HIa4SjReo6PuysAIBwlGOxyfeNHmdazzXOh7CqiQ3zwZEo8enX0N/wEIwyICcawrmGIDvFBo4HgrJ2/p8aSk7YkYCloPGLEhpkS5QaIpIpBDRGRAzDUE8bc5Qhzr59/pQaDXU5jndsSpLsv1BrYVNU2IhwlSHdfiHVuSxSBjY+H5RcNiqtqAciXfL48ddGsa7nIoLFvk6nvl6GgseivWqPHo9wAkVQxqCEicgCGesKYuxyh6/ouMhi1NUBkkDdymjrgkghCpEuR1sBGCmgiXYpwSQQhp6kDAKCqtsGssWtzpaoOpwrKkLxkH/4oqjLrWj3C/Uzu0aP+/TAUBC3f84fBcTjtppgWxKCGiMhBGNsUz5LXbxLAmQuVRjV+K0QIxtW9gvymMI3ARjmgyW8Kw7i6V1CIEJ3X0qVTgCeW3d8b8QYSgdcfysOoVYdQftX8RnVp4/oY3aMnsXMAxvbrpNFsT1cQZApLfq+dlUyIlkrbsr3Kykr4+/ujoqICfn7N25SMiMjWpJ4w1upTk1tSjZlbjuO3wkqVyiWpPHnj5P4az8k4V4TU9fLEWfUA5tn66VjuttrsgEbSrX1btPVsg6N55pWKpw6MwoGsEmTqmcnp1r4tvn56iN7r7P+jCF+dvoTDOaXIK72+zKReDl9RU4+Z6SeMTgjeNLk/hABOFJShT+dA3BIfatTznJGx928GNUREpCKnuAopy37Q+XjG7GSNYEr9OcqBjcQSAY0lPTe8C/pEBGL8uv/pPMfXow0OvpCiMVOTU1yF3wor8eGBXJwsKNf6XF1BoBSUXq64hhe3n9b6XEBzQ1BL9iRyNMbev7n8REREKppTCSUtr0g3lUKE4Nn66SrnPFs/3W4CGgBY9u0fmL75Z73nVNU24LGPr5duK/eaeTL9hM6ABlAth1cWHSLv1Nw/Okjva/9WqLrDud1skWDHGNQQEZGK5lZCpY1LxOC/l0jCUYLlbqtVHl/utlpnubetVF4znKR8NL9MEZho6zVjiHoQmFNchYxzRZD9nfirkZz993+b1NZRdAVJdB2DGiIie5G91/BuxuUF8vOs6HpSq+Zjvh6u+O+x8/hRS16ItA/U1rEdsTtgsXzpKTAKePQb1PtF6qyKcgQz04/jVEG5wQ0ntZGCQG0dhesbmzAgRnXGpke4/vQIc3sSOTMGNURE9iB7L7BlLPDxSN2BTXmB/PEtY60e2KSNS4Sflr2IqmobsXpfDh5ZdwSJr32LAqXE2PKaOjyz9gu03/EAAq5dQH5TGJ7xfB0VIX3h9uiXuOjS3mBg0719W4OVTbbw64VKzN52yqTnqPe30TbLcyT3Ctq4uKjsiP6ugQonbpGgG4MaIiJ7EBwP+IUDZXnaAxspoCnLk58XHG/V4ZRW16JMT/k2AJTV1OOeVQcUn8/f9C2eLnxGpcppV56LPA8kIAI+j3+N80Kz3FvZ2Ut/6a1GshUBmDyuwXGhihJsQx2FASi6FVu7J5EzY1BDRGQPAiKAiV/Il2vUAxvlgCYwSn6elXc+NpQsLCmrqcePmcXIKa5Ccf4ZtMcVlSon5TwQv/bRaJP6Jc6LMLSXXUGMi3mdfu2Vj7srtjw2ABsn91dUKpmafG3tnkTOihtaEhHZCymwkQKYj0cCY9YCO6a2aEADGE4WVnYouwT9o4NxoCkBk+v/hZymDhpVTnml8g0xw6O6YGfKJmz7Zh8ONCVYethmcYE8n+WMWtWRqa7VN2HNDzkYqBSUmJp8LeUnWbsnkbPhTA0RkT1Rn7H5aESLBzSAaR1wD2SWKG7aB5oStJZtK9+0E3r00hvQGMqp6WUgkba5BseHYvNjN2GIEU3u4tv54P9GdtP6mLYqpeYuKUnl3wxojMOghojI3gREyGdolI1Z22IBjUTbEog2py9U6ixP1nbTjgn1xcDYYK3XGhgbjKUP9NY/rof6YMtjA4z4Ckwzf1RPlF+twy9/lhs8N/NyNf5z5E+95+SVVivKt3NLqrmk1AK4/EREZG/KC+RLTsp2TG3RmRpAdQlk06FcfHQoX+e5eaXVWDi6F0atOqCSYOzn1Qavj+6lcf7q8X01tgwYEh+KqUOi8fKOM1pfw1UmQ5/IAOSVVmstNzdXXmk1nt160uj9ogwlDr+3NwtH869v5SB1BL5SU8clJSvhNglERPZEPSnYRjk16ozZOmHuzl9xMKtEpcJH335RwPUtA4K83bHs2z/07osU6O1msCJLEu7vgcKKWqPOlSy+LwEvfKp72wJTuMgACBi9dxbpx20SiIgcjbYqp8436a6KshDlJRJdDOWEiL/zSHSVLO//o1jra0g5I8u+/QMHsvRv9Fhhwo7bpgY0Q+JDcanymlHnuspk6BcZqPecJrWABmBH4JbA5SciInugr2xbW1WUBWZsymvqMCv9pMYSkK5NE9PGJWosGfXpHIC0cYk4XqB/x+wJHx1ReY3nhnfBlZo6RAX7KAIiQ9S3DbCk2cO7oKymzqhzpTyYmekncCCr2ORxSZVgZHmcqSEisgelmUBloe4lJuWqqMpC+flm0tbhVt+mif7ebnh33I1Iiro+S3E0vwwz008gyISdo/dnFmPUqoOKrQJmWXGTRk83425zpTV1GNo1DIFGfB3zR/WEv7cb0sYlGtzSQBt2BLYeBjVERPYgNgV4aKv+GRgpsHloq/x8MxjqcKtriWRW+kkczy9XOXYwqwTLvs1EUlSgPJfEROq7UaszJyf4Wr36IpB2UqCxa8Zg+Hq46j1XapTn7+2Gd/9pfOUSOwJbH4MaIiJ7EZtieEkpIMLsgAYwvcMtYDgQOppX1qwlIins0BUQ3RIfqjI7ZKpe4X46++2oBxoRwd5Y9VAfvddTnmnRnWsEjVkflm9bH3NqiIhaIVM73ALGb53QXD3C/XDmgvZZG5kZ8zVvjEnAUh2VVcqBhrYcI2VS9ZL6TIu2XKNBcSzftgUGNURErZA0w6CrBFvbDdiUrROa44U7uuHd7zLxc36ZSuXQgcxijUoiYw2JD0XviABsnNwf+/8oxt7fi+AqA+LbtcWAmGDF15lTXIVZ/zmhdylMV88dfVsa+Hu7MZhpQQ6z/LRo0SIkJSWhbdu2CAsLw+jRo3Hu3DlbD4ucTfZew+Wy5QXy84gcnKkdbmNCfZEUFQgjdk5olj/LruKoWkADaJZGG2tgbDDSxiWivKYOD33wEyZ8dAQbDuVh3cE8vLj9NF7afhrnS6sxYd0RpCz7AWcuVOpdPqu82oCXP9PeGBDglgb2wGFman744QfMmDEDSUlJaGhowEsvvYThw4fjt99+g48Pf4DIArL3AlvGAn7hupM1pbLbykKLJGsS2ZIpmyZKSzNH8/SXbpvDkrHSv4Z3RY+OfrhSU4e5O3/FoexSjXMO55Ri1KqDqLzaYNQ1lZOoGbjYJ4cJar7++muVzzds2ICwsDD8/PPPGDJkiI1GRU4lOF4e0OjqA6LeRyQ43kYDJbKs6BDD+R7ayr9NEejthoqaeq2zLtKSV//ooGZfX92Sb42byTe2Q7Ey9pmxXw6z/KSuoqICABAUpPt/gtraWlRWVqp8EOmkvjuycudWfY3RiJycrqonU3z8aH8M1rH7tbTkFRPqa7BTrz1gnxn75TAzNcqamprw9NNPY9CgQejVSzNpS7Jo0SLMnz+/BUdGDk9b51Y72XuHyFYsUfVUWl2nstTVxkWGhiahWPLKKa7C8YIy3NW7A47lG7/E5YLm59w0B/vM2DeH3NBy+vTp+Oqrr3DgwAF06tRJ53m1tbWorb2+/0dlZSUiIiK4oSUZpjwzI2FAQ62Uoc0sF92bgI4BXipbIajLmJ2sNRgwVEZtSFJkoMpO2KYK9HZD5dUGo2ehds0YhN4RAc1+PWoep93Q8sknn8QXX3yBjIwMvQENAHh4eMDPz0/lg8goARHyGRplY9YyoKFWydBmluP6d8aQLqF6z9E1u9HcXB0XyJvqvfXADdj4aPN2vb45Jhi7ZgzWqADTp9TI/aHINhxmpkYIgZkzZ2LHjh3Yt28f4uNNT9I0NtIj4kwNkaqKmnqNBnPqm18ac44yQzNAygK93XQm9faLDER1bQPOXf5La0m2lIg8f1RP/C+nFALATUo9agAolsVcZbJmzTiRdRl7/3aYnJoZM2Zgy5Yt2LlzJ9q2bYtLly4BAPz9/eHl5WXj0ZFTUU8KVs6psdDuyESOxpjyb1NKxAHDuTqL7k1Ae39PxXVyS6oxM/04fitU7SdjKAdHSkTW1whPuQJMW1NCF6BZm1dSy3KYmRqZjm5P69evx6RJk4y6BmdqyCBdVU6sfiIyWk5xFfKv1BgMagzN1KjPihgzsyMFHy/c2U0lEdkU2maclOmbfSLrcLqZGgeJvciR6QtctFVFMbChVsZQsKIt6VdfAGDqVg3GVGE1AThTWIlOgd7NXiZSnnHSNjN0MKsEM9NPYOPk5uXykPU4XKIwkdWUZso7BeuaiVHuY1NZKD+fqBUor6lTbCWQuv4ohi3dhwnrjqBCLcdFW9KvFADoYspWDabsPaVtl3FTCSG0bp2g3FmY7IvDzNQQWV1sinzrg+B43TMwUmBTmsktEqjV0BesSLMVUoM+dYa2FjAlD0fXzI42lmiQZ2hmiJ2F7Q9naoiUxaYYXlIKiGBAQ62Grm7C6rMVxgQA+hi7GaS2mR1lhkrITWFoZoidhe0PgxoiItLJ2GDF3AAgp7gKGeeKDC7pSDM7GbOTsfKhRCRFqW6roG+XcVMZ6s/DWRr7w+UnIiLSydhgxdSkX4mpycUSqQR7ZO9wo0vImyNtXKJGJZQlAyeyLIcp6bYElnQTEZluwrojOoMV5QogU5vvmXJtW7Nm4ESGGXv/ZlBDRER6mRqsGBsAmNqnhlovp+tTQ0REtmFqp2Dl7rz6sLqILI1BDRERGcXYYMVYrC4iS2P1ExER2QSri8jSGNQQEZHNmNJR2NKMLSMnx8HlJyIishlT83UsQV8ZeWl1rVGbcZJ9YvUTERHZTvZe/VuTAPLNZi24NYn2MnLAz8sNZUr7WXE3bvth7P2by09ERGQb2XuBLWPlu96XF2g/p7xA/viWsfLzzaR72weoBDSA4c04yf4wqCEiItsIjgf8woGyPO2BjRTQlOXJzwuON/slDZWRK+Nu3I6HQQ0REdmGtOt9YJRmYKMc0ARGyc8ztNmsEQyVkWtjaDNOsh8mBTVXr17FgQMH8Ntvv2k8du3aNWzcuNFiAyMiolZAW2Bz/ierBDSA7jJyfdgvx3EYHdT88ccf6N69O4YMGYKEhAQMHToUFy9eVDxeUVGB1NRUqwySiIicmHpg89EIqwQ0Em1l5IHebho3RPbLcTxGBzUvvPACevXqhaKiIpw7dw5t27bFoEGDcP78eWuOj4iIWoOACGDMWtVjY9ZaPKABrpeRZ8xOxvrUJGTMTsa+2cMwOD5U5Tzuxu14jC7pbteuHb777jskJCQAAIQQeOKJJ7B7925kZGTAx8cH4eHhaGxstOqAzcGSbiIiO6WcQyOx0kyNPtyN2z5ZvKT76tWraNPmeq8+mUyG1atX4+6778bQoUPxxx9/mDdiIiJqndSTgh/9RnvycAuIDvHBsK5hDGgclNFBTbdu3XDs2DGN4ytXrsSoUaNwzz33WHRgRETUCmircup8k+6qKCI9jA5qxowZg/T0dK2PrVy5EuPGjUMrak5MRETm0le2ra/cm0gHbpNARES2IXUU9gvXnTsjBT6VhcBDWy22VQI5FmPv39zQkoiIbCM2RR6o6Nv7SZqxseDeT+S8GNQQEZHtGBOoBES0aAUUOS5uk0BEREROgUENEREROQUGNUREROQUmhXUbNq0CYMGDUJ4eDjy8/MBACtWrMDOnTstOjgiIiIiY5kc1KxevRrPPvss/vGPf6C8vFyxLUJAQABWrFhh6fERERERGcXkoCYtLQ0ffPABXn75Zbi6uiqO9+vXD6dPn7bo4IiIiIiMZXJQk5ubi8REzV1LPTw8UF1dbZFBEdm97L2Gu5uWF8jPIyKiFmFyUBMdHY2TJ09qHP/666/RvXt3S4yJyL5JXVD1tW2XuqBuGcvAhoiohZjcfO/ZZ5/FjBkzcO3aNQghcOTIEaSnp2PRokX48MMPrTFGIvsSHC9v6y7tR6Pe3l19P5vgeBsNlIiodWnW3k+bN2/GvHnzkJ2dDQAIDw/H/PnzMXnyZIsP0JK49xNZjK6N+PRt0EdERM1i7P3bpKCmoaEBW7ZswYgRI9CuXTvU1NSgqqoKYWFhFhm0tTGoIYtSD2DGrAV2TGVAQ0RkYVYJagDA29sbZ8+eRWRkpNmDbGkMasjilAMbCQMaIiKLMvb+bXKicP/+/XHixAmzBkfkNAIi5DM0ysasVQ1oWClFRNQiTE4UfuKJJ/Dcc8/hzz//RN++feHj46PyeO/evS02OCK7V14gX3JStmPq9ZkaqVLKL1z37I0021NZCDy01bhdi4mISIPJy08uLpqTOzKZDEIIyGQyRYdhe8TlJ7IoY3JqAM3E4dJMeUWUvsTi8gL5eQxwiIisl1Mj7fWkiz3n2jCoIYsxpfoJuH7Mtx1w9Qrg1xEY8wGwY4rua3DmhogIgBWDGkfGoMYBZe+9PquhS0vPahgq2zYU2Li0AZoarv+XJeFERHoZe/82Oadm48aNeh+fMGGCqZck0s5e81FKM+WvpyvoCIiQH5fGJQVc0jGpUkoKbMZ8wICGiMgCTJ6pCQwMVPm8vr4eNTU1cHd3h7e3N65cuWLRAVoSZ2ocTHNmRFoqCGjuDNL5n4CPRqiexx43RER6Wa2ku6ysTOWjqqoK586dw+DBg5Genm7WoIlUSDMegVHXtySQSqNtPasRm2L49QIiVAMabZVSLm3kX8NHIxjQEBGZyeSgRpv4+Hi8+eabeOqppyxxOaLrtAU2539yvGUa9SDs0W/k/21qUD1PvccNEREZzSJBDQC0adMGhYWFlroc0XXqgY2jzWpom1XqfJM8l8ZFLa1tx1TDjfqIiEgrkxOFd+3apfK5EAIXL17EypUrMWjQIIsNjEiF1LlXOR/FEWY19JV+75iiWgUlLUVp2/mbiIgMMnmmZvTo0Sof9957L+bNm4fevXvjo48+ssYYVaxatQpRUVHw9PTEgAEDcOTIEau/Zqtiry39dXXutfdZDW2VUuqBzqSv5P+FTN7HRj1/iIiIjOJQfWq2bt2KCRMmYM2aNRgwYABWrFiBbdu24dy5c0btFM7qJwPstYTa0XfDVq6UMtRBODje8fKFiIiszGrVT6+99hpqamo0jl+9ehWvvfaaqZczyfLlyzFlyhSkpqaiR48eWLNmDby9vVtkhqhVCI6XBzS6ZgqUb8h+4fLzrU1XPoquqih7pFwppavHjVQppZw/JPW4MVFOcRUyzhUht6TaYl8CEZEjMHmmxtXVFRcvXtSYGSktLUVYWJjV9n6qq6uDt7c3PvnkE4wePVpxfOLEiSgvL8fOnTs1nlNbW4va2lrF55WVlYiIiOBMjT6mtP+39gyCPfepMYeVuiSX19RhVvpJ7M8sVhwbEh+KtHGJ8Pd2M2fEREQ2ZbWZGmnjSnWnTp1CUFCQqZczWklJCRobG9GuXTuV4+3atcOlS5e0PmfRokXw9/dXfEREOMANz9bsqYTa2M69Zsxq2ERzetwYYVb6SRzMKlE5djCrBDPTT5g6QiIih2R09VNgYCBkMhlkMhm6dOmiEtg0NjaiqqoK06ZNs8ogm2vOnDl49tlnFZ9LMzVkgHKbf6mEGmj52ZDYFHnejr5ZDWmsrXxH65ziKpUZGkmjENifWYzckmpEh/jYYGRERC3H6KBmxYoVEELg0Ucfxfz58+Hv7694zN3dHVFRUbj55putMkgACAkJgaurKy5fvqxy/PLly2jfvr3W53h4eMDDw8NqY3Jq9lJCbUygEhDhGMtOVpR/RTPPTVleKYMaInJ+Rgc1EydOBABER0dj4MCBcHNr2TV6d3d39O3bF99//70ip6apqQnff/89nnzyyRYdS6ugq4TaUfJWWpnIIG+9j0cFM6AhIudnck7N0KFDFQHNtWvXUFlZqfJhTc8++yw++OADfPzxxzh79iymT5+O6upqpKamWvV1Wx1dLf0dodKolYoJ9cWQ+FC4quW7ucpkGBIfylkaImoVTA5qampq8OSTTyIsLAw+Pj4IDAxU+bCmsWPHYunSpXj11Vdx44034uTJk/j66681kofJDM5QQt1KpY1LxKC4EJVjg+JCkDYu0UYjIiJqWSaXdM+YMQMZGRlYsGABHnnkEaxatQoXLlzA2rVr8eabb2L8+PHWGqvZ2HzPAGctoW5lckuqkVdajahgH87QEJFTMPb+bfLeT59//jk2btyI5ORkpKam4pZbbkFcXBwiIyOxefNmuw5qyABjS6iljsKlmQxq7FB0CIMZImqdTA5qrly5gpiYGACAn58frly5AgAYPHgwpk+fbtnRUctiCTURETkwk3NqYmJikJubCwDo1q0b/vvf/wKQz+AEBARYdHBkA1ZqDEdERGRtJgc1qampOHXqFADgxRdfxKpVq+Dp6YlnnnkG//rXvyw+QCIiIiJjmL1Ld35+Pn7++WfExcWhd+/elhqXVTBRmIiIyPFYLVFY2bVr1xAZGYnIyEhzLkNERERkNpOXnxobG7FgwQJ07NgRvr6+yMnJAQD83//9H9atW2fxARIREREZw+Sg5vXXX8eGDRvw1ltvwd3dXXG8V69e+PDDDy06OCIiIiJjmRzUbNy4Ee+//z7Gjx8PV1dXxfEbbrgBv//+u0UHR9Qa5RRXIeNcEXJLqm09FKLrsvca7iReXiA/j8hGTM6puXDhAuLi4jSONzU1ob6+3iKDImqNymvqMCv9JPZnFiuODYkPRdq4RPh7t+wGskQqsvcCW8YCfuG6O4lLHccrC+X9rtj2gWzA5JmaHj164Mcff9Q4/sknnyAxkXvMEDXXrPSTOJhVonLsYFYJZqafsNGIiP4WHC8PaHTt/aa8hYpfuPx8Ihsweabm1VdfxcSJE3HhwgU0NTVh+/btOHfuHDZu3IgvvvjCGmMkcno5xVUqMzSSRiGwP7MYuSXV3PqAbEd5ixQpsJFmbLgnHNkRk2dqRo0ahc8//xzfffcdfHx88Oqrr+Ls2bP4/PPPcfvtt1tjjEROL/9Kjd7H80rl+TXMtyGbkQKbwKjrgc35nxjQkF0xeqYmJycH0dHRkMlkuOWWW7Bnzx5rjouoVYkM8tb7eJC3GyasO8J8G7It9Rmbj0bIjzOgITth9ExNfHw8iouv/0IdO3YsLl++bJVBEbU2MaG+GBIfCleZTOW4q0yGIfGhWPZtJvNtyD4ERABj1qoeG7OWAQ3ZBaODGvXdFHbv3o3qak6BE1lK2rhEDIoLUTk2KC4Ezw2Px/7MYjSq/T+onG+jDZeqyCrKC4AdU1WP7ZhquNybqAWYtU0CEVmOv7cbNk7uj9ySauSVViMq2AfRIT7IOFek93l5papJxOU1dZiy8RiO5pUpjnGpiixCPSl4zFp5QKOePExkI0bP1MhkMsjUpsbVPyci3YydOYkO8cGwrmGKQMVQvk1UsGpAM2zpPpWABgAOZhVzqYrMo63KqfNNmsnDnLEhGzJ6pkYIgUmTJsHDwwOAfDPLadOmwcdHtcx0+/btlh0hkYMzt6melG9zMKtEZQnKVSZDYucARWVUdIgPHvv4GMpqNJtgNgqwNJyaT1/Ztr5yb6IWZnRQM3HiRJXPH374YYsPhsgZ6Wuqt3Fyf6OukTYuETPTT6gERn5ebXAsvwyp648CAJKiAnEsv0zXJQBoLlURGaU0U94pWFeVk3JgU1koP59BDdmATKhnADuxyspK+Pv7o6KiAn5+frYeDrUCOcVVSFn2g87HM2YnmxRkSPk272Vk4Xh+ucrMjUwGGPq/+c17ExAe4IlGAUXODpFRsvfKOwXrC1bKC+QBDbdIIAsz9v7NRGEiM+UUVyH/So3WIMFQU71dpy7gnhs6Gh1cRIf4QAihkTMDGA5oAODF7adVPmcCMRnNmEAlIIIzNGRTDGqImsmYXBlDSb5v78nE23syTQouDAVKpjB1GUwffcEdEVFLMHmbBCKSM2YDSl1N9dQZ00hPqp5ytWDRoaFeN8Yor6nDhHVHkLLsB6SuP4phS/dhwrojqNCSsExEZE2cqSFqBlM2oNSW5GvM8yTaZoR8PVxRVduo83qL7k1Ae39PXK64prHkpI05CcSWSIQmIrIEztQQNYOxG1ACQGl1LVIHR2HT5P545vZ4o58nmZV+EgeyVAOimjrdAQ0A3BQTjGFdw9A/OkjveRLlXjemkII7U7sdExFZA2dqiJrBmIZ42mZYkqICDT5P2amCMq0zPE1/xxAyAMrhhAuAPpGByCutRsGVajQK+WuqV0pJXGUyDIoLafYsjTHBHfNriKilMKghagZ9DfGkIGHCuiMayzLH88sR6O2GyqsNOp+n7IVP9S8debu7oLquSfF5E6DSu0YS6O2mtSnfoLgQpI1LNPj16mJKt2MiImtjUENkgHpVj/T57OFdAEBlJkUKEvTl3JTV1CMpMhBHlRrlaQsucoqr8Pulv/SOTTmg0afiaj3i2/lg8qAYdAz0QkOTsEiVkjHBHRFRS2FQQ6SDtuUj9RmPIfGh2PXkIJRW16kECccL9Hf2fSIlDlHBPiobV6r7X+4VC30l8uWqzMvVeHH7aYv3ptGWCG3uDBARUXMwqCHSQVtVj/oSzv7MYjQ0NWHLlJtUjhuzLBMdYmimxDrNvi1dmaRrd3EiopbG6iciLXRV9WhzKLtUo8pHV38aV5kMQ+JDDd70y2vq8OnPF0wfuBGsVZmkvrs4EVFLY1BDpIWpXXv3/V6kcSxtXCIGxYWoHNOVO5NxrkglyHjs42M4bmBzSnNpKx+3J9reFyIifbj8RKSFoeUjdVuO5CN1cLTKMUPLMtpydhIj/AHIcKKg3JzhGyUq2MfiWxtY4nrGbD9BRKQNd+km0kEqyTZmCQowfcdt+fWL0WjB/wN93F1RbaAxn6tMhv7RQXBzddEbOJgSoFgyENH2vkvVVOxQTNQ6GXv/5vITkQ7alo/0OblvO87nntN/UnkBkL1XKWfHzEGqMRTQAPIlMJkMGl2KpQRifXs56VoSMmYfLGOwQzERmYPLT+TUzFkOUV8+ulbXiOmbj2s9d7DLafzj9BJc+iUIz3R8G/MeGaE5Q1FeAHw8EqgsRH6fNABtm/lVNc+kgZHo1t4Pfl5ueELL1yEFDlM+Pobj58tVHjuYVYzkpRka5exp4xJRWl1r9D5YhrBDMRGZg0ENOSVLLocol14PiQ/FgcxiqLe8y2nqgEsiCJEuRXi68BnM2/Q23p46UhFUxbqXofOuB4GyPFx27YCXf6xFSwc1Gw7lG3XeUS0Jyo1Cs5xdmolJHRyl93qmBCLsUExE5uDyEzklSy2HqEsbl4jB8aEaxwsRgnF1ryC/KQyRsiI8feEZTF/1GVKW/YCX138FsX6kIqAZUz0HhTB+WcteSTMxrjL955kSiJhbCk/UIrL3ymde9fl7qZlaFoMacjrWzMuQlqQyZidjenKMymMqgY1LEV68/C/0lZ1DuvtCRLoUIb8pzGkCGmWNAhYNRIwthSeyiey9wJax8qVkXYGNtNS8ZSwDmxbG5SdyOtbIy1DPzYkO8cGBzBKN86TARgpkPvWYLx9TUxjG1b3idAENIJ+JWTi6F0atOqCyROXn1Qavj+5l8vXYoZjsWnA84BcOlOXJA5eJXwABEdcflwKasjwgMEp+PrUYBjXkdCyZl6EtN+fmmGBcq2/E6QuVWp9TiBA8Wz9dEdAAwLP10x0qoAn2dUNpleau3spcAPQIl5dWvvLZGVRebVB5vPJqA17+7Eyzy7ANbyNBZAMBEfJARgpclAMb9YBGPeAhq+PyEzkdS+ZlaMvNOZxTqrc5XjhKsNxttcqx5W6rEQ7NmR17FezjbvCcJgBnCisxbOk+lmFT6yIFNoFR1wOb8z8xoLEDDGrIKVkiL8OU/Z8k4ShRyaG5r3auIscm3X2hQwQ2fp5t8MdlywUi9r4dA1GzqAc2H41gQGMHuPxETskSeRmm7v+kHtBIOTTKOTbp7gvtOrcm0NsNL93ZDf/69LTFrskybHJaARHAmLXygEYyZi0DGhviTA05NXN2jjZl/yddAQ2gWRVljzM2t3cPw5L7e+PEq8PRNyrIItdkGTY5vfICYMdU1WM7phou9yarYVBD9Df1LQCk3Bxj/ieJcbmI9rIrOquclAOb9rIriHG5aIWvoPn2nC3Cvz75BRPWHUGwj4fWnCRTsQybnJp6UvCj36jm2DCwsQluaEmtnr7uwwAwZEkGKq7qrwQC5Fsl5DR10Lu0FI4SxLhcxIGmBPMHbgUuMmBwnPxrn5l+Quv2B7oMjA3G62MSWIZNzk9XlROrn6zGqTa0zMvLw+TJkxEdHQ0vLy/ExsZi7ty5qKurs/XQyAno6z5cWl1rVEADAAeaEgzmyhQixG4DGgBoEsD+zGJ899slNDSpbwahX01do1nLfUQOQV/goq0qijM2Lcohgprff/8dTU1NWLt2LX799Ve8/fbbWLNmDV566SVbD818bLdtU4a6D3/5S6GNRmZbz33yCw5ll5r0nJMF5XhgzSFU1BgXBBI5pNJMoLJQ90yMcmBTWSg/n1qMwy4/LVmyBKtXr0ZOTo7Rz7G75Sep3bZfuO5pSqWdnfHQViA2peXH6cQyzhUhdf1RnY97tHFBbYNpMxatmbR81dyGe0QOIXuvvFOwvqWl8gJ5QMPf2RbhVMtP2lRUVCAoSH+VRm1tLSorK1U+7Ip6u231GRvlaU6/cLbbtoIgAzt2mxrQuADo3r4tooK9zBiV45KWr9hwj5xabIrhXJmACAY0NuCQQU1WVhbS0tIwdepUvectWrQI/v7+io+ICDtL2NK3/sqEsxax7FvLTg33jQpEYcU15JVeNes6CR398NzwLhYaleUZqotiwz0isgWbLj+9+OKLWLx4sd5zzp49i27duik+v3DhAoYOHYrk5GR8+OGHep9bW1uL2tpaxeeVlZWIiIiwn+UniXoAM2atvNcBAxqryimuQsqyH8y6RlJkIN564AbklVbDVQY8sfk4qmobLTRC++Xn2QaV1xp0Pp4xO5nJwkRkMcYuP9k0qCkuLkZpqf5kxJiYGLi7y/ehKSwsRHJyMm666SZs2LABLi6mTTTZXU6NMuXARsKAxqoM5dMYEujthn2zh0FAaJSEOztXmQw+Hq6oqm1Ak1A9PiguhDk1RGRRxt6/bbpNQmhoKEJDQ40698KFCxg2bBj69u2L9evXmxzQ2D22225xpnQMVpcUFYhX7uqO4wVleG9vFo6fL7fcwBxAoxCovNaApKhAHM0rUxxnwz0isiWH2PvpwoULSE5ORmRkJJYuXYri4ut/Ebdv396GI7MgXe22OVNjNVLH4AOZxTA2HbhXRz9MujkSGw7lY9SqQ1YdnyN4YlgcooJ92HCPiOyCQwQ1e/bsQVZWFrKystCpUyeVxxy0Il2Vvpyaj0cysLGS8po6NDQ1GR3QzL+nB7745SJmf2K5zR4dnRTIMJghInvgsH1qmsMuc2rYbttmJqw7goNZJRqN98gw5s4QUUty+j41ToHttm1GVydhMg5zZ4jIHjnE8pPTMrbdttRRuDSTszXNlFNchfwrNYrlkvwrNbYekkN65vZ43HNDR73LTT+cK8LJP8vRp3Mgbok3rhCAiMgSGNTYUmyKfOsDfe22pcCG7babRdcO3M8Nt3x35qSoQAyOD8Hbe5x3rxd9AU1+aTVGrzqInteOI6epA95GCAK93bBrxmBEBKtVmrGFPBFZAZefbI3ttq1K1w7cy77NxJD4ULjKVHvjuspkGBIfio2Pmp4r8n8je+Du3uFmjddavNzM+19del/0zdBIAc06tyVId1+IcJSgrKYe96w6oHqitOy6ZSw3aiUii2JQQ07L0A7cs4d3waC4EJXHuoe3xewRXTCkS6jWoEefl3acRrCPh8nPawlX683blNNQDs0P54pQVlOPnKYOuCSCEOlSpBLY/CjNlHE/MyKyIgY15DByiquQca7I6M0SDeXNlNbUYePk/tg5YyB6hcuz6c9cqMQ9Kw9iwrojeH10L42gR5/fCisxM/0E0sYlIrFzgNHPs2evjuyBjNnJ2Di5P/z1bP558s9yAEAhQjCu7hXkN4WpBDbHz5exoo+IrI45NWT3dOXFpI1L1HujNdQxOCpYvpSy7NtMnL34l8pjB7NK8PJnZ7Bxcn/kllQrmss9v+0UjuWXQVvNlLRD9ZWaOsxIiTNrCwZbkwG4JT4Ujw6ONur8GzsFKP4tBTbp7gsVgU25Zxrw8QsMaIjIqjhTQ3ZPV17MzPQTep8ndQzWlTcTHeJjcIkqt6Qa0SE+GNY1DNEhPvhwYhJ6huvvcfRTTgkuVVwz4Su0P/0iA00q2R7aNQyBSgGm+ozNDXvGMqAhIqtjUEN2zZigQ5+0cYkaS0jK+SGGlqjySlWv7+/thncN3OznbD+DOdvtv+uwqws0Aj4XmbyKa9v0gSqzYMYs/e2aMVgjsJnrOlP1JO5nRkRWxOUnsmvGBB36KnL8vd00lpCUzzd2iUqZNAPk6N2IG5uApKgAlQ0pB8eFqszQmLL0FxHsjROvDsePmcU4fr4MNwdfRf8fXgLKlE7ifmZEZEUMasiuNSfo0EbX/kS6AhRpGwBdAdNzw+NxpaYWZy5UGvX69uqJYXHoVn0MuQhHh87xiq9Xalb4XkYWjueXIxwliHG5iANNCYqlP11bJNwSH4pbQq8BHz/A/cyIqEVx+YnsmjF5MeYytESlrLymDhPWHcGoVYcUAY2vh6vZY7AVl5wMdPhyIgb+OBHRba4ovr6UZT8gdf1RHM0rQztRjHT3hVjntgSDXU4bXvrTVuXU+SZu+0FEVseZGrJ7aeMSMTP9hMoSiCX3HjK0RKVMW9JyVW2jRcZhCYvuTUB7f09EBftg7s5f8WNmsdZKLUl2UwcM9QtXBBrzPV/HwbzrAWQ4ShRVTPlNYchp6qB4TOvSnzH7mUmPc8aGiCyMQQ3ZPVOCDnPoWqKSSEnL9uymmGDF15A2LhH3rzmIzCLdyb3xXboDg64HGk83PYP/iVdQiBCNgGZcnfy4pI2LlgaD3M+MiGyIQQ05DENBh7XZ8yaY6jlA5TV1eOzjY3oDmkBvt+sbTk78Alc/vBORVQVId1+IZ+unY7nbap0BDQD8WXZV86Lcz4yIbIhBDZGRDCUtK5MBepd9TBEf5ovMoiq95ygvx5XX1GHY0n0oq6nXeb600aRCQASK7v0U2DASkS5F+NRjPgDoDGgA+deolTGBSkAEZ2iIyOKYKExkJN1Jy1DpzwLIK4C6t29r9mtumtwfax/pq/ect+7rjdTBUbhSUwcAmLLxmN6AZsn9vXHi1eGob2pS6T0TGdMVH4Q8r3Lus/XTtQY0ADAgJtiUL4WIyOo4U0NkAu1Jy/K+LVdq6lRyfk4VlGHUqkN6r3dzTDDcXF10lpRLy0Pay84BPy83PP/pL4pj/SIDcSxfuTGMJi83V0xYd0Sj98zKu0Ixv2mlyrnL3VZrnakZGBts1FKgVBpurTwoIiJlMiEcuHuYiSorK+Hv74+Kigr4+elvdU+kj7FJyxPWHcGBzGLo2iN7YGwwVo/vqxEoqTe4q6ip1zjHz7MN/rrWoLLM5QLofC1JUmQgjp8vVwmQOslK8an3G2jXeBGNAVGYK3sSU0rf0ppTo2/fLSmICfJ2w7JvM03er4uISBtj798MaoisqKKmHpM/Pqp39iRjdjKiQ3yMCpT2/1GEQzml2Hv2Mv64bNxu5coSOvrhtFrDQOUqp3q/SLg9+iUQEIHzuecQ+ul98KoqQL1fJI4mb1Rp0KdMW+dhddLsk66mfUREuhh7/2ZODZEFqe+R5O/thhkpcXqfI+0vpbxxpjqpKd6Ej45izb4cgwGNtmrrQG83TB0aq3JMvWz7aPJGRQJv5+iu8HrsKyAwCm6V+YoGfdpo69+jztj9uoiImos5NUQWoG+PJEts9WBM0KCsb2Sgyp5OSVGB+HBCEkqra1XOi3G5iPayK4olps2d41UvZERfGVP79xjar4uIqLkY1BBZgLagQ3mPpObsLyUxJWiQQV55pa1ZoZTvopxTc6ApAZPr/4U8EY64+G7ax2Kgr4yp/XuM3a+LiMhUDGqIzKQr6FBebjFnqwdTgoa4dj6Ka0rNCqWlK+XXDvR2U5R9H2hKUMwq6aSnr4yx/XuMDeKIiJqLQQ2RmQwFHdJyi7FbPaiXQZvS9O/9R5I0qou0zSJVXm1AUlQgnhgWZ3a5ta6dztVZcr8uIiJtGNQQmcmUnBl9Wz3oy8sxJmi4OUazd4y+WaSjeWUW6x+jbSZqSHwoZo/ogtLqOvapIaIWwaCGyEy6ZipMXW7Rl5ejLWhQpmv5yNhZJHO11KajRET6MKghsgBzcmYAw3k5V2rqNIIGAAYDCEtUXpnC1puOElHrxqCGyALMnakwdkZFPWgw9BqWmkUiInIEbL5HZEH6GujpY80ZlbRxiRgUp7p3E5N2icgZcaaGyA5Yc0aF+S5E1FpwpobITlh7RqW5s0hEpEP2XqC8QP855QXy86hFcKaGyE5wRoXIgWTvBbaMBfzC5R23tTWnLC+4vsXIQ1u1duQmy+JMDZGd4YwKkQMIjpcHNGV58sBFfcZGCmjK8uTnBcdruwpZGIMaIiIiU0l7ogVGaQY2ygFNYJTumRyyOAY1REREzaEtsDn/EwMaG2JQ42yYuEZErZUtfv+pBzYfjWBAY0MMapyJlLimbX1XIk2LbhnLwIaInIctf/8FRABj1qoeG7OWAY0NMKhxJkxcI6LWypa//8oLgB1TVY/tmGp41ogsjkGNM2HiGhG1Vrb6/ad+7Ue/0T4GahEMapwNE9eILI+5ao6hpX//aQuWOt+kO7giq2NQ44wcKXGNNwuyd8xVcywt9ftP3+yPvlkjsioGNc7KERLXeLMgR8BcNcfTEr//SjPlnYJ1BUvKgU1lofx8sjoGNc7KERLXeLMgR8BcNcfTEr//YlPkWx/o+55LPzvcIqHFMKhxRo6SuMabBTkK5qo5jpb8/RebYvh7HhDBgKYFMahxNo6WuMabBTkKR8pVa60c7fcfWRyDGmfiqIlrvFmQo2jJXDUm0ZvGUX//kUUxqHEmjpy45giJzUQtlavGJHrTOfLvP7IYmRBC2HoQLaWyshL+/v6oqKiAn5+frYdjHdl75Qm1+oKB8gL5/9D2tM6r/FeWhDM1ZE/UZwLGrJUHNC1dLmzM462Vo/7+I4OMvX873ExNbW0tbrzxRshkMpw8edLWw7E/jpi45iiJzdR6tXSuBpPom8cRf/+RRTlcUPP8888jPDzc1sMgS2FiH9k7W+VqMImeyGQOFdR89dVX+Pbbb7F06VJbD4V0MSW5kYl95AhsmavBJHoik7Sx9QCMdfnyZUyZMgWfffYZvL29jXpObW0tamtrFZ9XVlZaa3gEXE9u9AvX/QtXCmQqC4HkOcbdLKTzSzP5S5xantRkTV+uhvSzao1cDSmJ/qMR148xiZ5IK4eYqRFCYNKkSZg2bRr69etn9PMWLVoEf39/xUdEBH8JWJWpHYITHmBHTnIMtszVcITu4ER2wqZBzYsvvgiZTKb34/fff0daWhr++usvzJkzx6Trz5kzBxUVFYqPggL+ErCq5iQ3MrGPSDcm0ROZxKYl3cXFxSgtLdV7TkxMDB588EF8/vnnkMlkiuONjY1wdXXF+PHj8fHHHxv1eq2ipNsetGTpK5Gz0vWHAKufqBUy9v7tEH1qzp8/r5IPU1hYiBEjRuCTTz7BgAED0KlTJ6Ouw6CmBbHvDFHzsU8NkQpj798OkSjcuXNnlc99fX0BALGxsUYHNNTCmNxI1HzKFVfDXtJ8XD2JPv8QUBrKZVpq9RwiqCEHpCu5kX9REhkmVVxVFwM7n9ReUSgFNvmHgH1vyIMbJtRTK+eQQU1UVBSsuWrW2NiI+vp6q13f6VVeBD6bBtQ3AuE3AbfNB76bC1ReAP77GDB6DeDXwdajVOHm5gZXV1dbD4PoutgU+R8HyhWF2v4o2PfG9WWo4HgbDJTIfjhETo2lGFqTE0Lg0qVLKC8vb/nBOYumBqCqSP5flzaAb5j8v7qO25GAgAC0b99eJSGdyOaYMEzkXDk1LUUKaMLCwuDt7c2bm6ka6oDyfKBtIODiBgREAm3clR6PlD/eVC9vJhDQUfVxGxFCoKamBkVFRQCADh3saxaJWjnl/BlpxoYVhURaMaj5W2NjoyKgCQ4OtvVwHNO1OsClEXDzkE+DawQsnoBnV3kSZGO9/KfP09MWI9Xg5eUFACgqKkJYWBiXosi+qAc2UgI+AxoiFQ7RUbglSDk0xm7BQFp4+gFBMToCmr+1cZc/HhQjP9+OSN975lORXZIqCpWxopBIBYMaNVxyMpOnn+ElpTbudhfQAPzek53jdglEBjGoIbs0b9483HjjjbYehuMwZXd0cjzcLoHIKAxqHNykSZMU+2S5ubmhXbt2uP322/HRRx+hqanJpGtt2LABAQEBFhlXcnKyYlyenp7o0aMH3nvvPaOfP3v2bHz//fcmvWZUVBRWrFhh4kidgLQ7ur6bm3RT3DKWgY2j0Vbl1Pkm3fusEbViDGqcwB133IGLFy8iLy8PX331FYYNG4annnoKI0eORENDg83GNWXKFFy8eBG//fYbHnzwQcyYMQPp6elGPdfX15cJ28YydXd09jJxHPrKtvVtIEvUSjGocQIeHh5o3749OnbsiD59+uCll17Czp078dVXX2HDhg2K85YvX46EhAT4+PggIiICTzzxBKqqqgAA+/btQ2pqKioqKhQzLPPmzQMAbNq0Cf369UPbtm3Rvn17PPTQQ4ryZ328vb3Rvn17xMTEYN68eYiPj8euXbsAyPfzGjVqFHx9feHn54cHH3wQly9fVjxXfflp0qRJGD16NJYuXYoOHTogODgYM2bMUCT1JicnIz8/H88884xi/ACQn5+Pu+++G4GBgfDx8UHPnj2xe/duc95u+9Oc3dHJMShvl6Dte6f8va8slJ9P1IoxqLGCnOIqZJwrQm5Jtc3GkJKSghtuuAHbt29XHHNxccG7776LX3/9FR9//DH27t2L559/HgAwcOBArFixAn5+frh48SIuXryI2bNnA5BXAy1YsACnTp3CZ599hry8PEyaNMnkMXl5eaGurg5NTU0YNWoUrly5gh9++AF79uxBTk4Oxo4dq/f5GRkZyM7ORkZGBj7++GNs2LBBEbRt374dnTp1wmuvvaYYPwDMmDEDtbW12L9/P06fPo3Fixcr9g5zKtoCm/M/MaBxdNJ2Cfq+d9L3nlskELFPjSWV19RhVvpJ7M8sVhwbEh+KtHGJ8Pd2a/HxdOvWDb/88ovi86efflrx76ioKCxcuBDTpk3De++9B3d3d/j7+0Mmk6F9+/Yq13n00UcV/46JicG7776LpKQkVFVVGRUgNDY2Ij09Hb/88gsef/xxfP/99zh9+jRyc3MRESH/Rb1x40b07NkTR48eRVJSktbrBAYGYuXKlXB1dUW3bt1w11134fvvv8eUKVMQFBQEV1dXxWyS5Pz587jvvvuQkJCgGL/TYi8T52RMoBIQwe8vEThTY1Gz0k/iYFaJyrGDWSWYmX7CJuMRQqiUKX/33Xe49dZb0bFjR7Rt2xaPPPIISktLUVNTo/c6P//8M+6++2507twZbdu2xdChQwHIAwZ93nvvPfj6+sLLywtTpkzBM888g+nTp+Ps2bOIiIhQBDQA0KNHDwQEBODs2bM6r9ezZ0+VpngdOnQwuAw2a9YsLFy4EIMGDcLcuXNVgjynxF4mRNSKMaixkJziKuzPLEaj2lZajUJgf2axTZaizp49i+joaABAXl4eRo4cid69e+PTTz/Fzz//jFWrVgEA6urqdF6juroaI0aMgJ+fHzZv3oyjR49ix44dBp8HAOPHj8fJkyeRm5uL6upqLF++HC4uzf+Rc3NTne2SyWQGK7wee+wx5OTk4JFHHsHp06fRr18/pKWlNXsMzdKS5dbsZUJErRiDGgvJv6J/tiOvtGWDmr179+L06dO47777AMhnW5qamrBs2TLcdNNN6NKlCwoLC1We4+7ujsbGRpVjv//+O0pLS/Hmm2/illtuQbdu3YxKEgYAf39/xMXFoWPHjirBTPfu3VFQUICCgus32t9++w3l5eXo0aNHc79kreMHgIiICEybNg3bt2/Hc889hw8++KDZr2Gyliy3Zi8TImrlGNRYSGSQ/u0VooJ9rPbatbW1uHTpEi5cuIDjx4/jjTfewKhRozBy5EhMmDABABAXF4f6+nqkpaUhJycHmzZtwpo1a1THGBWFqqoqfP/99ygpKUFNTQ06d+4Md3d3xfN27dqFBQsWmDXe2267DQkJCRg/fjyOHz+OI0eOYMKECRg6dCj69evX7OtGRUVh//79uHDhAkpK5MuATz/9NL755hvk5ubi+PHjyMjIQPfu3c0av0laqtyavUyIiBjUWEpMqC+GxIfCVa3VvqtMhiHxoYgOsV5Q8/XXX6NDhw6IiorCHXfcgYyMDLz77rvYuXOnIgflhhtuwPLly7F48WL06tULmzdvxqJFi1SuM3DgQEybNg1jx45FaGgo3nrrLYSGhmLDhg3Ytm0bevTogTfffBNLly41a7wymQw7d+5EYGAghgwZgttuuw0xMTHYunWrWdd97bXXkJeXh9jYWISGhgKQJynPmDED3bt3xx133IEuXbqY1ATQbC1Rbs1eJkREAACZEGpJIE6ssrIS/v7+qKiogJ+f6t5D165dQ25uLqKjo+HZzJ2jK2rqMTP9hN1UP5FpLPEzoJN64DFmrTzXxRLl1tISl1+47utIr19ZyNJfInI4+u7fyhjU/M2SN7TckmrklVYjKtjHqjM0ZFlWDWoA1cBGYqly6+y98qUrfdcpL5A3Z2NAQ0QOxtighn1qrCA6hMEMaSGVW0v9YwDLlVuzlwkREXNqiFoMy62JiKyKQQ1RS2C5NZF1tWQ/KLJbDGqIrI3l1kTW1ZL9oMiuMaghsiaWWxNZX0v1gyK7x6CGyJpKM+Vl1LqqnJQDm8pC+flEZJqW6AdFDoHVT0TWFJsi7wujr9xa+oXMcmui5lPfpf7jkZbtB0UOgUENkbWx3JqoZagHNlL7BAY0rQaXn8imoqKisGLFCsXnMpkMn332mc3GQ0QOTuoHpcxS/aDI7jGocRKHDx+Gq6sr7rrrLlsPxSwXL17EnXfeaethEJGjYj+oVo1BjSXYQX+EdevWYebMmdi/fz8KCwut9jrW1r59e3h4eNh6GETkiNgPqtVjUGMuO+iPUFVVha1bt2L69Om46667sGHDBsVj+/btg0wmw/fff49+/frB29sbAwcOxLlz51SusXr1asTGxsLd3R1du3bFpk2bVB6XyWRYu3YtRo4cCW9vb3Tv3h2HDx9GVlYWkpOT4ePjg4EDByI7O1vxnOzsbIwaNQrt2rWDr68vkpKS8N133+n9WtSXnwoKCvDggw8iICAAQUFBGDVqFPLy8lS+vv79+8PHxwcBAQEYNGgQ8vPzTX8TicixsR8UgUGN+eygP8J///tfdOvWDV27dsXDDz+Mjz76COr7lL788stYtmwZjh07hjZt2uDRRx9VPLZjxw489dRTeO6553DmzBlMnToVqampyMjIULnGggULMGHCBJw8eRLdunXDQw89hKlTp2LOnDk4duwYhBB48sknFedXVVXhH//4B77//nucOHECd9xxB+6++26cP3/eqK+rvr4eI0aMQNu2bfHjjz/i4MGD8PX1xR133IG6ujo0NDRg9OjRGDp0KH755RccPnwYjz/+OGQymRnvJlELsoNZXqfAflAkEa1IRUWFACAqKio0Hrt69ar47bffxNWrV02/cNl5IVb0FmKun/y/Zef1H7ewgQMHihUrVgghhKivrxchISEiIyNDCCFERkaGACC+++47xflffvmlAKD4WgcOHCimTJmics0HHnhA/OMf/1B8DkC88soris8PHz4sAIh169YpjqWnpwtPT0+9Y+3Zs6dIS0tTfB4ZGSnefvttldfZsWOHEEKITZs2ia5du4qmpibF47W1tcLLy0t88803orS0VAAQ+/bt0/uaxjLrZ4DIVFnfC/FaiP7fDdLvkNdC5OeTdnwvnZ6++7cyztRYgra/BM7/1CINn86dO4cjR45g3LhxAIA2bdpg7NixWLduncp5vXv3Vvy7Q4cOAICioiIAwNmzZzFo0CCV8wcNGoSzZ8/qvEa7du0AAAkJCSrHrl27hsrKSgDymZrZs2eje/fuCAgIgK+vL86ePWv0TM2pU6eQlZWFtm3bwtfXF76+vggKCsK1a9eQnZ2NoKAgTJo0CSNGjMDdd9+Nd955BxcvXjTq2kQ2ZwezvE5D6gel7/es9Hv6oa3sB+XE2KfGUmzUH2HdunVoaGhAeHi44pgQAh4eHli5cqXimJubm+Lf0vJMU1OTSa+l7Rr6rjt79mzs2bMHS5cuRVxcHLy8vHD//fejrq7OqNerqqpC3759sXnzZo3HQkNDAQDr16/HrFmz8PXXX2Pr1q145ZVXsGfPHtx0000mfW1ELU5bszjpdwW74JqO/aAIzKmxrBbuj9DQ0ICNGzdi2bJlOHnypOLj1KlTCA8PR3p6ulHX6d69Ow4ePKhy7ODBg+jRo4dZ4zt48CAmTZqEMWPGICEhAe3bt1dJ8jWkT58+yMzMRFhYGOLi4lQ+/P39FeclJiZizpw5OHToEHr16oUtW7aYNW6iFmPDWV4iZ8SgxpJauD/CF198gbKyMkyePBm9evVS+bjvvvs0lqB0+de//oUNGzZg9erVyMzMxPLly7F9+3bMnj3brPHFx8dj+/btikDroYceMml2aPz48QgJCcGoUaPw448/Ijc3F/v27cOsWbPw559/Ijc3F3PmzMHhw4eRn5+Pb7/9FpmZmejevbtZ4yZqUeqBzUcjGNAQNRODGkuxQX+EdevW4bbbblOZtZDcd999OHbsGH755ReD1xk9ejTeeecdLF26FD179sTatWuxfv16JCcnmzW+5cuXIzAwEAMHDsTdd9+NESNGoE+fPkY/39vbG/v370fnzp1x7733onv37pg8eTKuXbsGPz8/eHt74/fff8d9992HLl264PHHH8eMGTMwdepUwxcnsifsgktkETIh1Gp/nVhlZSX8/f1RUVEBPz8/lceuXbuG3NxcREdHw9PT07QL61r/5rq4QzHrZ4DIHMq/KyT8nUGkoO/+rYwzNeZifwQiMge74BJZDIMac5VmApWFuv+qUg5sKgvl5xMRAeyCS2RhDGrMxf4IRNQcnOUlsjgGNZYQm2J43TsgggENEV3HWV4ii2PzPSIiW5BmeYPjDc/ylmbyjyIiIzCoUdOKisFIDb/31OLYBZfIorj89Dep3X9NTY2NR0K2In3vlbd+ICIix8GZmr+5uroiICBAscmjt7e3Yi8jcm5CCNTU1KCoqAgBAQFwdXW19ZCIiKgZGNQoad++PYDru1dT6xIQEKD4GSAiIsfDoEaJTCZDhw4dEBYWhvr6elsPh1qQm5sbZ2iIiBycQwU1X375JV577TX88ssv8PT0xNChQ/HZZ59Z/HVcXV15gyMiInIwDhPUfPrpp5gyZQreeOMNpKSkoKGhAWfOnLH1sIiIiMhOOERQ09DQgKeeegpLlizB5MmTFcd79Ohhw1ERERGRPXGIku7jx4/jwoULcHFxQWJiIjp06IA777yTMzVERESk4BAzNTk5OQCAefPmYfny5YiKisKyZcuQnJyMP/74A0FBQVqfV1tbi9raWsXnFRUVAORbmBMREZFjkO7bBpukCht64YUXBAC9H2fPnhWbN28WAMTatWsVz7127ZoICQkRa9as0Xn9uXPnGrw+P/jBD37wgx/8cIyPgoICvXGFTAjb9YYvLi5GaWmp3nNiYmJw8OBBpKSk4Mcff8TgwYMVjw0YMAC33XYbXn/9da3PVZ+paWpqwpUrVxAcHGyRxnqVlZWIiIhAQUEB/Pz8zL5ea8H3rXn4vjUP37fm4fvWPHzfTGfMeyaEwF9//YXw8HC4uOjOnLHp8lNoaChCQ0MNnte3b194eHjg3LlziqCmvr4eeXl5iIyM1Pk8Dw8PeHh4qBwLCAgwa8za+Pn58Ye3Gfi+NQ/ft+bh+9Y8fN+ah++b6Qy9Z/7+/gav4RA5NX5+fpg2bRrmzp2LiIgIREZGYsmSJQCABx54wMajIyIiInvgEEENACxZsgRt2rTBI488gqtXr2LAgAHYu3cvAgMDbT00IiIisgMOE9S4ublh6dKlWLp0qa2HouDh4YG5c+dqLHGRfnzfmofvW/PwfWsevm/Nw/fNdJZ8z2yaKExERERkKQ7RfI+IiIjIEAY1RERE5BQY1BAREZFTYFBDREREToFBjQV9+eWXGDBgALy8vBAYGIjRo0fbekgOo7a2FjfeeCNkMhlOnjxp6+HYtby8PEyePBnR0dHw8vJCbGws5s6di7q6OlsPze6sWrUKUVFR8PT0xIABA3DkyBFbD8muLVq0CElJSWjbti3CwsIwevRonDt3ztbDcjhvvvkmZDIZnn76aVsPxe5duHABDz/8MIKDg+Hl5YWEhAQcO3as2ddjUGMhn376KR555BGkpqbi1KlTOHjwIB566CFbD8thPP/88wgPD7f1MBzC77//jqamJqxduxa//vor3n77baxZswYvvfSSrYdmV7Zu3Ypnn30Wc+fOxfHjx3HDDTdgxIgRKCoqsvXQ7NYPP/yAGTNm4KeffsKePXtQX1+P4cOHo7q62tZDcxhHjx7F2rVr0bt3b1sPxe6VlZVh0KBBcHNzw1dffYXffvsNy5YtM6//nCU2pmzt6uvrRceOHcWHH35o66E4pN27d4tu3bqJX3/9VQAQJ06csPWQHM5bb70loqOjbT0Mu9K/f38xY8YMxeeNjY0iPDxcLFq0yIajcixFRUUCgPjhhx9sPRSH8Ndff4n4+HixZ88eMXToUPHUU0/Zekh27YUXXhCDBw+26DU5U2MBx48fx4ULF+Di4oLExER06NABd955J86cOWProdm9y5cvY8qUKdi0aRO8vb1tPRyHVVFRgaCgIFsPw27U1dXh559/xm233aY45uLigttuuw2HDx+24cgcS0VFBQDwZ8tIM2bMwF133aXyc0e67dq1C/369cMDDzyAsLAwJCYm4oMPPjDrmgxqLCAnJwcAMG/ePLzyyiv44osvEBgYiOTkZFy5csXGo7NfQghMmjQJ06ZNQ79+/Ww9HIeVlZWFtLQ0TJ061dZDsRslJSVobGxEu3btVI63a9cOly5dstGoHEtTUxOefvppDBo0CL169bL1cOzef/7zHxw/fhyLFi2y9VAcRk5ODlavXo34+Hh88803mD59OmbNmoWPP/642ddkUKPHiy++CJlMpvdDym8AgJdffhn33Xcf+vbti/Xr10Mmk2Hbtm02/ipanrHvW1paGv766y/MmTPH1kO2C8a+b8ouXLiAO+64Aw888ACmTJlio5GTM5oxYwbOnDmD//znP7Yeit0rKCjAU089hc2bN8PT09PWw3EYTU1N6NOnD9544w0kJibi8ccfx5QpU7BmzZpmX9Nh9n6yheeeew6TJk3Se05MTAwuXrwIAOjRo4fiuIeHB2JiYnD+/HlrDtEuGfu+7d27F4cPH9bY76Nfv34YP368WdG6IzL2fZMUFhZi2LBhGDhwIN5//30rj86xhISEwNXVFZcvX1Y5fvnyZbRv395Go3IcTz75JL744gvs378fnTp1svVw7N7PP/+MoqIi9OnTR3GssbER+/fvx8qVK1FbWwtXV1cbjtA+dejQQeW+CQDdu3fHp59+2uxrMqjRIzQ0FKGhoQbP69u3Lzw8PHDu3DkMHjwYAFBfX4+8vDxERkZae5h2x9j37d1338XChQsVnxcWFmLEiBHYunUrBgwYYM0h2iVj3zdAPkMzbNgwxaygiwsnXZW5u7ujb9+++P777xWtFZqamvD999/jySeftO3g7JgQAjNnzsSOHTuwb98+REdH23pIDuHWW2/F6dOnVY6lpqaiW7dueOGFFxjQ6DBo0CCNlgF//PGHWfdNBjUW4Ofnh2nTpmHu3LmIiIhAZGQklixZAgB44IEHbDw6+9W5c2eVz319fQEAsbGx/OtQjwsXLiA5ORmRkZFYunQpiouLFY9xFuK6Z599FhMnTkS/fv3Qv39/rFixAtXV1UhNTbX10OzWjBkzsGXLFuzcuRNt27ZV5B/5+/vDy8vLxqOzX23bttXIO/Lx8UFwcDDzkfR45plnMHDgQLzxxht48MEHceTIEbz//vtmzTwzqLGQJUuWoE2bNnjkkUdw9epVDBgwAHv37jWv3p5Iiz179iArKwtZWVkawZ8Qwkajsj9jx45FcXExXn31VVy6dAk33ngjvv76a43kYbpu9erVAIDk5GSV4+vXrze4NEpkqqSkJOzYsQNz5szBa6+9hujoaKxYsQLjx49v9jVlgr8FiYiIyAlwIZ6IiIicAoMaIiIicgoMaoiIiMgpMKghIiIip8CghoiIiJwCgxoiIiJyCgxqiIiIyCkwqCEiIiKnwKCGiCxm0qRJWncXz8rKssj1N2zYgICAAItcq7n279+Pu+++G+Hh4ZDJZPjss89sOh4iuo5BDRFZ1B133IGLFy+qfNjjxoj19fXNel51dTVuuOEGrFq1ysIjIiJzMaghIovy8PBA+/btVT6kXYp37tyJPn36wNPTEzExMZg/fz4aGhoUz12+fDkSEhLg4+ODiIgIPPHEE6iqqgIA7Nu3D6mpqaioqFDMAM2bNw8AtM6YBAQEYMOGDQCAvLw8yGQybN26FUOHDoWnpyc2b94MAPjwww/RvXt3eHp6olu3bnjvvff0fn133nknFi5ciDFjxljg3SIiS+KGlkTUIn788UdMmDAB7777Lm655RZkZ2fj8ccfBwDMnTsXAODi4oJ3330X0dHRyMnJwRNPPIHnn38e7733HgYOHIgVK1bg1Vdfxblz5wBc39ndWC+++CKWLVuGxMRERWDz6quvYuXKlUhMTMSJEycwZcoU+Pj4YOLEiZZ9A4jI+gQRkYVMnDhRuLq6Ch8fH8XH/fffL4QQ4tZbbxVvvPGGyvmbNm0SHTp00Hm9bdu2ieDgYMXn69evF/7+/hrnARA7duxQOebv7y/Wr18vhBAiNzdXABArVqxQOSc2NlZs2bJF5diCBQvEzTffbOhL1fm6RGQ7nKkhIosaNmwYVq9erfjcx8cHAHDq1CkcPHgQr7/+uuKxxsZGXLt2DTU1NfD29sZ3332HRYsW4ffff0dlZSUaGhpUHjdXv379FP+urq5GdnY2Jk+ejClTpiiONzQ0wN/f3+zXIqKWx6CGiCzKx8cHcXFxGserqqowf/583HvvvRqPeXp6Ii8vDyNHjsT06dPx+uuvIygoCAcOHMDkyZNRV1enN6iRyWQQQqgc05YILAVY0ngA4IMPPsCAAQNUzpNygIjIsTCoIaIW0adPH5w7d05rwAMAP//8M5qamrBs2TK4uMhrGP773/+qnOPu7o7GxkaN54aGhuLixYuKzzMzM1FTU6N3PO3atUN4eDhycnIwfvx4U78cIrJDDGqIqEW8+uqrGDlyJDp37oz7778fLi4uOHXqFM6cOYOFCxciLi4O9fX1SEtLw913342DBw9izZo1KteIiopCVVUVvv/+e9xwww3w9vaGt7c3UlJSsHLlStx8881obGzECy+8ADc3N4Njmj9/PmbNmgV/f3/ccccdqK2txbFjx1BWVoZnn31W63OqqqpU+u7k5ubi5MmTCAoKQufOnc17k4jIPLZO6iEi5zFx4kQxatQonY9//fXXYuDAgcLLy0v4+fmJ/v37i/fff1/x+PLly0WHDh2El5eXGDFihNi4caMAIMrKyhTnTJs2TQQHBwsAYu7cuUIIIS5cuCCGDx8ufHx8RHx8vNi9e7fWROETJ05ojGnz5s3ixhtvFO7u7iIwMFAMGTJEbN++XefXkJGRIQBofEycONGEd4qIrEEmhNpCNBEREZEDYvM9IiIicgoMaoiIiMgpMKghIiIip8CghoiIiJwCgxoiIiJyCgxqiIiIyCkwqCEiIiKnwKCGiIiInAKDGiIiInIKDGqIiIjIKTCoISIiIqfAoIaIiIicwv8Dui4kMdb4MOsAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.scatter(X_core[:, 0], X_core[:, 1], marker='o', s=20, label='Data Points')\n",
-    "plt.scatter(X_noise[:, 0], X_noise[:, 1], marker='x', s=60, label='Anomalies')\n",
-    "plt.title('Synthetic Data and Anomalies')\n",
-    "plt.xlabel('Feature 1')\n",
-    "plt.ylabel('Feature 2')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17385373",
-   "metadata": {},
-   "source": [
-    "-   Standardize features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "0631d8ad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.preprocessing import StandardScaler\n",
-    "X_scaled = StandardScaler().fit_transform(X)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "df9d8dd1",
-   "metadata": {},
-   "source": [
-    "-   Fit DBSCAN Clustering Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "9224e940",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 0  0  1  1 -1  0  1  1  0  0  1  1  1  1  1  1  1  0  1  1  0  0  0  0\n",
-      "  0  0  1  1  1  0  0  0  1  0  0  0  0  0  1  0  1  0  0  0  1  0  0  0\n",
-      "  1  1  0  1  1  1  0  0  0  0  1  0  0  1  0  1  1  1  0  1  1  1  1  0\n",
-      "  1 -1  1  1  1  1  1  1  0  0  0  0  0  1  1  0  1  0  1  0  1  0  1  0\n",
-      "  1  0  0  1  1  1  1  1  1  0 -1  0  1  1  0  1  1  1  0 -1 -1  1  0  0\n",
-      "  0  0 -1  1  1  0  1  1  0  0  1  0  1  1  1  0  1  1  1  0  0  1  0  0\n",
-      "  1  0  1  0  0  1  1  1  0  0  0  1  1 -1  1  0  1  0  1 -1  1  0  0  1\n",
-      "  1  1  0  1  0  0  0  0 -1  0  1  1  0  1  1  0  0  0  0  0  0  1  1  0\n",
-      "  0  0  0  1  1 -1  0  0 -1  0  0  1  0  1 -1  1  0  1  0  0 -1  1  1  1\n",
-      "  0  1  1  0  1  0  1  0  0  0  1  1  1  0  0  1  1  0  1  0  1  1  0 -1\n",
-      "  1  0  1  1  1  1  1  1  0  1  0  1  1  0  0  0  0  1  0  0  0  0  1  0\n",
-      "  1  0  0  0  1  1  0  0  0  1  0  1  0  0  1  0  0  1  1  1  1  1  0  1\n",
-      "  1  0  0  0  1  0 -1  0  1  1 -1  0  1  1  0  0  1  1  1 -1  1  0  1  0\n",
-      "  1  0  0  1  0  1  1 -1  1  0 -1  0  1  1  0  0  1 -1  0  1  1  0  1  0\n",
-      "  1  0  0  1  1  1  1  0  1  1  1  1  1  0  1  1  0 -1  1  1  0  0  1  1\n",
-      "  1  0  1  1  0  1  1  0  0  0  1  1  0  1  0  0  0  0  0  1  1  0  0  0\n",
-      "  1  1  1  0 -1  1  0  0  1  0  1  0  0  0  1  1  1 -1  0  0 -1  0  1  1\n",
-      "  1  0  0  1  0  1  1  1  1  0  1  1  0  0  1  1  1  1  1  0  1  0  1  1\n",
-      "  0  0  1  1  1  0  0  1  1  0  0  1  0  1  1  1  0  0  0  1  0  1  1  0\n",
-      "  1  0  1  0  1  0  1  1  0  0  1  0  1  1  1  1  0  1  0  0  1  1  0  0\n",
-      "  0  0 -1  0  1  0  0  1 -1  0  0  1  1  1  0  0  1  0  1  1  1  0  1  0\n",
-      "  1  0  0  0  1  1 -1  0  1  1  1  0  0  1  0  0 -1  1  0 -1  0  0  0  0\n",
-      "  0  0  0  1  1  0  0  1  0  1  1  0  1  0  0  1  1  0  0  0 -1  0  1  1\n",
-      "  0  0  0  0  1  1  1  1  0  0  1  1  1  1  1  1  0  1  0  1  1  0  1  0\n",
-      " -1  0  0  0  0  0  1  1  1  0  0  1  1  0  0  1  0  0  0 -1  0  1  0  0\n",
-      "  1  1  1 -1  0  1  0  0  1  0  0  0  0  1 -1  0  0  0  1  0  0  0  1  1\n",
-      "  1  1 -1  0  1  0 -1  1  0  0  0  0  1  1  0  0]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.cluster import DBSCAN\n",
-    "dbscan = DBSCAN(eps=0.25, min_samples=8)\n",
-    "labels = dbscan.fit_predict(X_scaled)\n",
-    "print(labels)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "03e53696",
-   "metadata": {},
-   "source": [
-    "-   Collect results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "bee34f9d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Detected 36 anomalies out of 640 total points\n"
-     ]
-    }
-   ],
-   "source": [
-    "core_mask     = labels != -1        # regular points\n",
-    "anomaly_mask  = labels == -1        # DBSCAN calls these “noise”\n",
-    "\n",
-    "print(f\"Detected {anomaly_mask.sum()} anomalies out of {X.shape[0]} total points\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "96169cc0",
-   "metadata": {},
-   "source": [
-    "-   Visualize the results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "0e0085db",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(6, 6))\n",
-    "ax.scatter(*X[core_mask].T,  marker='o', s=20, label='normal')\n",
-    "ax.scatter(*X[anomaly_mask].T, marker='x', s=60, label='anomaly')\n",
-    "ax.set_title(\"DBSCAN clustering & detected anomalies\")\n",
-    "ax.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "71d91741",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "07a06a3c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Classification report:\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "      normal      0.992     0.998     0.995       600\n",
-      "     anomaly      0.972     0.875     0.921        40\n",
-      "\n",
-      "    accuracy                          0.991       640\n",
-      "   macro avg      0.982     0.937     0.958       640\n",
-      "weighted avg      0.991     0.991     0.990       640\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import classification_report, confusion_matrix\n",
-    "\n",
-    "# ground-truth labels: 0 = normal, 1 = anomaly\n",
-    "# y_true = np.zeros(X.shape[0], dtype=int)\n",
-    "# y_true[-40:] = 1\n",
-    "\n",
-    "# model predictions: DBSCAN marks anomalies with label -1\n",
-    "y_pred = (labels == -1).astype(int)\n",
-    "\n",
-    "print(\"Classification report:\")\n",
-    "print(classification_report(\n",
-    "    y_true, y_pred,\n",
-    "    target_names=[\"normal\", \"anomaly\"],\n",
-    "    digits=3))\n",
-    "\n",
-    "# Visualize the confusion matrix\n",
-    "import seaborn as sns\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "sns.heatmap(confusion_matrix(y_true, y_pred), annot=True, fmt='d',\n",
-    "            xticklabels=[\"normal\", \"anomaly\"], yticklabels=[\"normal\", \"anomaly\"],\n",
-    "            cmap=\"Blues\")\n",
-    "plt.title(\"Confusion Matrix\")\n",
-    "plt.xlabel(\"Predicted\")\n",
-    "plt.ylabel(\"True\")\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S06 Artificial Neural Networks and Deep Learning/S06 Artificial Neural Networks and Deep Learning.pdf b/src/S06 Artificial Neural Networks and Deep Learning/S06 Artificial Neural Networks and Deep Learning.pdf
deleted file mode 100644
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-\title{Machine Learning in Python}
-\subtitle{Artificial Neural Networks and Deep Learning}
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-
-\section{Introduction to Neural Networks}
-\label{sec:org0110412}
-\begin{frame}[label={sec:org4318739}]{What are Neural Networks?}
-\begin{definition}[What are Neural Networks?]\label{sec:org8b68126}
-\pause
-\alert{Neural networks} are computational models inspired by the human brain, consisting of interconnected nodes (neurons) that process and transmit information. They are used to recognize patterns, classify data, and make predictions based on input features.
-\end{definition}
-\end{frame}
-\begin{frame}[label={sec:org8f41c5f}]{Artificial Neurons: A Biological Inspiration}
-\begin{block}{Artificial Neurons: A Biological Inspiration}
-An \alert{artificial neuron} is a simplified model of a biological neuron. It receives inputs, applies weights to them, sums them up, and passes the result through an activation function to produce an output. This process roughly mimics how biological neurons transmit signals in the brain.
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org4b69327}]{Activation Functions}
-\begin{block}{Activation Functions}
-Activation functions introduce non-linearity into the model, allowing it to learn complex patterns. Common activation functions include:
-\begin{itemize}
-\item \alert{Sigmoid}: Maps input values to a range between 0 and 1, useful for binary classification.
-\item \alert{ReLU (Rectified Linear Unit)}: Outputs the input directly if it is positive; otherwise, it outputs zero. It is widely used due to its simplicity and effectiveness in deep networks.
-\item \alert{Tanh (Hyperbolic Tangent)}: Maps input values to a range between -1 and 1, often used in hidden layers.
-\end{itemize}
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:orgb9a3bd7}]{Structure of a Neural Network}
-\begin{block}{Structure of a Neural Network}
-A neural network consists of artificial neurons organized in layers:
-\begin{itemize}
-\item \alert{Input Layer}: Receives the input features.
-\item \alert{Hidden Layers}: Intermediate layers that process the inputs through weighted connections and activation functions.
-\item \alert{Output Layer}: Produces the final output, which can be a classification, regression value, or other predictions.
-\end{itemize}
-
-Information flows from the input layer through the hidden layers to the output layer, with each neuron applying its activation function to the weighted sum of its inputs (\alert{feedforward} process).
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:orgf931a59}]{Training Neural Networks}
-\begin{block}{Training Neural Networks}
-Training a neural network involves adjusting the weights of the connections between neurons to minimize the difference between the predicted output and the actual target values. This process typically involves:
-\begin{itemize}
-\item \alert{Forward Propagation}: The input data is passed through the network, and the output is computed.
-\item \alert{Backpropagation}: The error (difference between predicted and actual output) is propagated backward through the network to update the weights using gradient descent.
-\end{itemize}
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org3051d8f}]{Forward Propagation}
-\begin{block}{Forward Propagation}
-Forward propagation is the process of passing input data through the network to compute the output. Each neuron computes a weighted sum of its inputs, applies an activation function, and passes the result to the next layer. This process continues until the output layer is reached.
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:orgd0ae497}]{Backpropagation}
-\begin{block}{Backpropagation}
-Backpropagation is the algorithm used to update the weights of the network based on the error calculated during forward propagation. It involves:
-\begin{itemize}
-\item Calculating the gradient of the loss function with respect to each weight.
-\item Propagating the error backward through the network to compute the gradients.
-\item Updating the weights using gradient descent, which adjusts the weights in the direction that reduces the error.
-\end{itemize}
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org901d280}]{Loss Functions}
-\begin{block}{Loss Functions}
-A loss function quantifies the difference between the predicted output and the actual target values. It guides the training process by providing a measure of how well the network is performing. Common loss functions include:
-\begin{itemize}
-\item \alert{Mean Squared Error (MSE)}: Used for regression tasks, it calculates the average of the squared differences between predicted and actual values.
-\item \alert{Cross-Entropy Loss}: Used for classification tasks, it measures the dissimilarity between the predicted probability distribution and the actual distribution of classes.
-\end{itemize}
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:orgf607122}]{Gradient Descent}
-\begin{block}{Gradient Descent}
-Gradient descent is an optimization algorithm used to minimize the loss function by iteratively updating the weights of the network. The weights are adjusted in the direction of the negative gradient of the loss function with respect to the weights. The learning rate determines the size of the step taken in each iteration.
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org40c39ec}]{Deep Learning}
-\begin{block}{Deep Learning}
-Deep learning is a subset of machine learning that focuses on training deep neural networks with many layers. These networks can learn complex representations of data, enabling them to perform tasks such as image recognition, natural language processing, and speech recognition. Deep learning has achieved significant breakthroughs in various fields due to its ability to automatically learn features from raw data without extensive feature engineering.
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:orgb0bee0c}]{Deep Neural Networks}
-\begin{block}{Deep Neural Networks}
-Deep neural networks (DNNs) are neural networks with multiple hidden layers between the input and output layers. The depth of the network allows it to learn hierarchical representations of data, capturing complex patterns and relationships. DNNs have been successful in tasks such as image classification, speech recognition, and natural language processing.
-\end{block}
-\end{frame}
-\section[CNNs]{Convolutional Neural Networks (CNNs)}
-\label{sec:org6cd92e8}
-\begin{frame}[label={sec:org9a66191}]{Convolutional Neural Networks (CNNs)}
-\begin{block}{Convolutional Neural Networks (CNNs)}
-Convolutional Neural Networks (CNNs) are a specialized type of neural network designed for processing structured grid data, such as images. They use convolutional layers to automatically learn spatial hierarchies of features, making them particularly effective for image recognition tasks. CNNs consist of:
-\begin{itemize}
-\item \alert{Convolutional Layers}: Apply convolution operations to extract features from the input data.
-\item \alert{Pooling Layers}: Downsample the feature maps to reduce dimensionality and retain important features.
-\item \alert{Fully Connected Layers}: Connect every neuron in one layer to every neuron in the next layer, typically used in the final layers for classification tasks.
-\end{itemize}
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org26b946d}]{Applications of CNNs}
-\begin{block}{Applications of CNNs}
-CNNs are widely used in various applications, including:
-\begin{itemize}
-\item \alert{Image Classification}: Identifying objects or scenes in images.
-\item \alert{Object Detection}: Locating and classifying multiple objects within an image.
-\item \alert{Image Segmentation}: Dividing an image into meaningful segments for detailed analysis.
-\item \alert{Facial Recognition}: Identifying and verifying individuals based on facial features.
-\end{itemize}
-\end{block}
-\end{frame}
-\section[RNNs]{Recurrent Neural Networks (RNNs)}
-\label{sec:orgd234b19}
-\begin{frame}[label={sec:orgb122f60}]{Recurrent Neural Networks (RNNs)}
-\begin{block}{Recurrent Neural Networks (RNNs)}
-Recurrent Neural Networks (RNNs) are a type of neural network designed for processing sequential data, such as time series or natural language. They have connections that loop back on themselves, allowing them to maintain a hidden state that captures information from previous time steps. This makes RNNs suitable for tasks where context and order matter.
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org10fb6ad}]{Applications of RNNs}
-\begin{block}{Applications of RNNs}
-RNNs are commonly used in applications involving sequential data, including:
-\begin{itemize}
-\item \alert{Natural Language Processing (NLP)}: Tasks such as language modeling, text generation, and sentiment analysis.
-\item \alert{Speech Recognition}: Converting spoken language into text.
-\item \alert{Time Series Prediction}: Forecasting future values based on historical data.
-\end{itemize}
-\end{block}
-\end{frame}
-\section[LSTMs]{Long Short-Term Memory (LSTM) Networks}
-\label{sec:org13904e4}
-\begin{frame}[label={sec:orgeeebde5}]{Long Short-Term Memory (LSTM) Networks}
-\begin{block}{Long Short-Term Memory (LSTM) Networks}
-Long Short-Term Memory (LSTM) networks are a type of RNN designed to overcome the limitations of traditional RNNs, such as vanishing gradients. LSTMs use special gating mechanisms to control the flow of information, allowing them to capture long-term dependencies in sequential data. They are particularly effective for tasks that require remembering information over extended periods.
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org7477c34}]{Applications of LSTMs}
-\begin{block}{Applications of LSTMs}
-LSTMs are widely used in applications that involve long sequences of data, including:
-\begin{itemize}
-\item \alert{Machine Translation}: Translating text from one language to another.
-\item \alert{Speech Synthesis}: Generating human-like speech from text.
-\item \alert{Video Analysis}: Analyzing sequences of frames in videos for tasks such as action recognition or event detection.
-\end{itemize}
-\end{block}
-\end{frame}
-\section[GANs]{Generative Adversarial Networks (GANs)}
-\label{sec:orge57429e}
-\begin{frame}[label={sec:org38473db}]{Generative Adversarial Networks (GANs)}
-\begin{block}{Generative Adversarial Networks (GANs)}
-Generative Adversarial Networks (GANs) are a class of neural networks used for generating new data samples that resemble a given training dataset. GANs consist of two components:
-\begin{itemize}
-\item \alert{Generator}: A neural network that generates new data samples from random noise.
-\item \alert{Discriminator}: A neural network that distinguishes between real data samples from the training set and fake samples generated by the generator.
-\end{itemize}
-The generator and discriminator are trained together in a competitive process, where the generator aims to produce realistic samples while the discriminator tries to correctly classify real and fake samples.
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org76a19f7}]{Applications of GANs}
-\begin{block}{Applications of GANs}
-GANs have been successfully applied in various domains, including:
-\begin{itemize}
-\item \alert{Image Generation}: Creating realistic images from random noise or low-resolution images.
-\item \alert{Image-to-Image Translation}: Converting images from one domain to another, such as turning sketches into realistic images.
-\item \alert{Data Augmentation}: Generating additional training samples to improve model performance, especially in scenarios with limited data.
-\end{itemize}
-\end{block}
-\end{frame}
-\section{Autoencoders}
-\label{sec:orge0468f2}
-\begin{frame}[label={sec:org6bb72c8}]{Autoencoders}
-\begin{block}{Autoencoders}
-Autoencoders are a type of neural network used for unsupervised learning tasks, such as dimensionality reduction and feature extraction. They consist of two main components:
-\begin{itemize}
-\item \alert{Encoder}: A neural network that compresses the input data into a lower-dimensional representation (latent space).
-\item \alert{Decoder}: A neural network that reconstructs the original input data from the compressed representation.
-\end{itemize}
-The goal of an autoencoder is to learn a compact representation of the input data while minimizing the reconstruction error between the original input and the reconstructed output.
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org271a9a6}]{Applications of Autoencoders}
-\begin{block}{Applications of Autoencoders}
-Autoencoders are used in various applications, including:
-\begin{itemize}
-\item \alert{Dimensionality Reduction}: Reducing the number of features in high-dimensional data while preserving important information.
-\item \alert{Anomaly Detection}: Identifying unusual patterns in data by comparing the reconstruction error of input samples.
-\item \alert{Data Denoising}: Removing noise from data by training the autoencoder to reconstruct clean data from noisy inputs.
-\end{itemize}
-\end{block}
-\end{frame}
-\section[LLMs]{Large Language Models (LLMs)}
-\label{sec:org532e9ef}
-\begin{frame}[label={sec:org0743131}]{Large Language Models (LLMs)}
-\begin{block}{Large Language Models (LLMs)}
-Large Language Models (LLMs) are advanced neural networks designed to understand and generate human language. They are trained on vast amounts of text data and can perform a wide range of natural language processing tasks, such as text generation, translation, summarization, and question answering. LLMs leverage deep learning techniques, including transformers, to capture complex language patterns and relationships.
-\end{block}
-\end{frame}
-\begin{frame}[label={sec:org4acd5e6}]{Applications of LLMs}
-\begin{block}{Applications of LLMs}
-LLMs have numerous applications in various domains, including:
-\begin{itemize}
-\item \alert{Chatbots and Virtual Assistants}: Providing conversational interfaces for customer support and information retrieval.
-\item \alert{Content Generation}: Automatically generating articles, stories, or other written content based on prompts.
-\item \alert{Sentiment Analysis}: Analyzing text to determine the sentiment or emotion expressed.
-\item \alert{Language Translation}: Translating text from one language to another with high accuracy.
-\end{itemize}
-\end{block}
-\end{frame}
-\end{document}
diff --git a/src/S06 Artificial Neural Networks and Deep Learning/S06C01 Introduction to Neural Networks.ipynb b/src/S06 Artificial Neural Networks and Deep Learning/S06C01 Introduction to Neural Networks.ipynb
deleted file mode 100644
index c42a4c1..0000000
--- a/src/S06 Artificial Neural Networks and Deep Learning/S06C01 Introduction to Neural Networks.ipynb	
+++ /dev/null
@@ -1,526 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "60f471fd",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Introduction to Neural Networks\n",
-    "\n",
-    "Neural networks are a class of machine learning models that are particularly powerful for tasks involving complex patterns and large datasets. They are inspired by the structure and function of the human brain, consisting of interconnected nodes (neurons) that process information in layers.\n",
-    "\n",
-    "Neural networks can learn to approximate complex functions by adjusting the weights of connections between neurons based on the data they are trained on."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "97fa90ac",
-   "metadata": {},
-   "source": [
-    "## Artificial Neurons: A Biological Inspiration\n",
-    "\n",
-    "An artificial neuron is a mathematical function that mimics the behavior of a biological neuron. It receives inputs, applies weights to them, sums them up, and passes the result through an activation function to produce an output. This process allows the network to learn complex relationships in data. Mathematically, an artificial neuron can be represented as: $$ y = f\\left(\\sum_{i=1}^{n} w_i x_i + b\\right) $$ where:\n",
-    "\n",
-    "-   $y$ is the output of the neuron,\n",
-    "-   $x_i$ are the inputs,\n",
-    "-   $w_i$ are the weights associated with each input,\n",
-    "-   $b$ is the bias term,\n",
-    "-   $f$ is the activation function (e.g., sigmoid, ReLU)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "badfba54",
-   "metadata": {},
-   "source": [
-    "## Activation Functions\n",
-    "\n",
-    "Activation functions introduce non-linearity into the model, allowing it to learn complex relationships. Common activation functions include:\n",
-    "\n",
-    "-   **Sigmoid**: Maps inputs to a range between 0 and 1, useful for binary classification.\n",
-    "-   **ReLU (Rectified Linear Unit)**: Outputs the input directly if it is positive; otherwise, it outputs zero. It helps mitigate the vanishing gradient problem in deep networks.\n",
-    "-   **Softmax**: Converts a vector of values into probabilities, often used in the output layer for multi-class classification tasks.\n",
-    "-   **Tanh**: Maps inputs to a range between -1 and 1, often used in hidden layers."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f46ffd20",
-   "metadata": {},
-   "source": [
-    "## Structure of Neural Networks\n",
-    "\n",
-    "Neural networks consist of layers of neurons:\n",
-    "\n",
-    "-   **Input Layer**: The first layer that receives the input data.\n",
-    "-   **Hidden Layers**: Intermediate layers that perform transformations on the input data. Each neuron in a hidden layer applies a weighted sum of its inputs followed by an activation function.\n",
-    "-   **Output Layer**: The final layer that produces the output of the network, which can be a single value (for regression) or a vector of probabilities (for classification)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ddb910d5",
-   "metadata": {},
-   "source": [
-    "## Training Neural Networks\n",
-    "\n",
-    "Training a neural network involves adjusting the weights and biases of the neurons to minimize the difference between the predicted outputs and the actual targets. This is typically done using a process called backpropagation, which computes gradients of the loss function with respect to each weight in the network. The training process can be summarized as follows:\n",
-    "\n",
-    "1.  **Forward Pass**: Input data is passed through the network to generate predictions.\n",
-    "2.  **Loss Calculation**: The loss function computes the error between predicted outputs and actual targets.\n",
-    "3.  **Backpropagation**: The algorithm calculates gradients of the loss with respect to each weight in the network, propagating the error backward through the layers.\n",
-    "4.  **Weight Update**: Weights are adjusted using an optimization algorithm (e.g., Stochastic Gradient Descent, Adam) to minimize the loss function.\n",
-    "5.  **Epochs**: The entire training dataset is passed through the network multiple times (epochs) to iteratively improve the model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aa57430a",
-   "metadata": {},
-   "source": [
-    "## Forward and Backward Propagation\n",
-    "\n",
-    "Forward propagation is the process of passing input data through the network to obtain predictions. Each neuron computes a weighted sum of its inputs, applies an activation function, and passes the result to the next layer. Backward propagation is the process of calculating gradients of the loss function with respect to each weight in the network. It involves:\n",
-    "\n",
-    "1.  Calculating the gradient of the loss with respect to the output of the network.\n",
-    "2.  Propagating this gradient backward through the network, layer by layer, using the chain rule of calculus.\n",
-    "3.  Updating the weights using the calculated gradients and a learning rate, which determines the step size in the weight update process."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "90fb6ceb",
-   "metadata": {},
-   "source": [
-    "## Loss Functions\n",
-    "\n",
-    "Loss functions measure the difference between the predicted outputs and the actual targets. They guide the training process by providing a signal for how well the model is performing. Common loss functions include:\n",
-    "\n",
-    "-   **Mean Squared Error (MSE)**: Used for regression tasks, it calculates the average of the squares of the errors between predicted and actual values.\n",
-    "-   **Binary Cross-Entropy**: Used for binary classification tasks, it measures the dissimilarity between predicted probabilities and actual binary labels.\n",
-    "-   **Categorical Cross-Entropy**: Used for multi-class classification tasks, it measures the dissimilarity between predicted class probabilities and actual one-hot encoded labels."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "216942ab",
-   "metadata": {},
-   "source": [
-    "## Optimization Algorithms\n",
-    "\n",
-    "Optimization algorithms are used to update the weights of the neural network during training. They aim to minimize the loss function by adjusting the weights in the direction of the steepest descent (negative gradient). Common optimization algorithms include:\n",
-    "\n",
-    "-   **Stochastic Gradient Descent (SGD)**: Updates weights using the gradient of the loss function computed on a single sample or a small batch of samples.\n",
-    "-   **Adam (Adaptive Moment Estimation)**: Combines the benefits of both SGD and RMSProp, adapting the learning rate for each weight based on the first and second moments of the gradients.\n",
-    "-   **RMSProp**: Adapts the learning rate for each weight based on the average of recent gradients, helping to stabilize training."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "364d14b1",
-   "metadata": {},
-   "source": [
-    "## Deep Learning\n",
-    "\n",
-    "Deep learning is a subfield of machine learning that focuses on neural networks with many layers (deep networks). These deep architectures can learn hierarchical representations of data, enabling them to capture complex patterns and features. Deep learning has led to significant advancements in various fields, including computer vision, natural language processing, and speech recognition."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d088a71d",
-   "metadata": {},
-   "source": [
-    "## Example: Building a Simple Neural Network with Keras\n",
-    "\n",
-    "To illustrate the concepts of neural networks, we will build a simple feedforward neural network using Keras, a high-level neural networks API in TensorFlow. We will use the Iris dataset, a classic dataset for classification tasks, to demonstrate how to create, train, and evaluate a neural network model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "7c475073",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-07-10 08:59:43.378288: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
-      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1752130783.396552   24762 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1752130783.401871   24762 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "W0000 00:00:1752130783.415848   24762 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
-      "W0000 00:00:1752130783.415872   24762 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
-      "W0000 00:00:1752130783.415873   24762 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
-      "W0000 00:00:1752130783.415875   24762 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
-      "2025-07-10 08:59:43.420278: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
-      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
-      "I0000 00:00:1752130786.535223   24762 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6022 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 2070 SUPER, pci bus id: 0000:09:00.0, compute capability: 7.5\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model summary:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">40</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">27</span> │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)              │            \u001b[38;5;34m40\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)              │            \u001b[38;5;34m27\u001b[0m │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">67</span> (268.00 B)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m67\u001b[0m (268.00 B)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">67</span> (268.00 B)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m67\u001b[0m (268.00 B)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "None\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "I0000 00:00:1752130787.608152   24885 service.cc:152] XLA service 0x7421a40049a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
-      "I0000 00:00:1752130787.608171   24885 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 2070 SUPER, Compute Capability 7.5\n",
-      "2025-07-10 08:59:47.625303: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
-      "I0000 00:00:1752130787.692249   24885 cuda_dnn.cc:529] Loaded cuDNN version 90300\n",
-      "I0000 00:00:1752130787.929256   24885 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Input, Dense\n",
-    "from sklearn.datasets import load_iris\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from tensorflow.keras.utils import to_categorical\n",
-    "\n",
-    "# Load and preprocess data\n",
-    "data = load_iris()\n",
-    "X = StandardScaler().fit_transform(data.data)  # Standardize features\n",
-    "y = to_categorical(data.target)  # One-hot encode labels\n",
-    "\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "\n",
-    "# Build model\n",
-    "model = Sequential([\n",
-    "    Input(shape=(X.shape[1],)),  # Input layer\n",
-    "    Dense(8, activation='relu'),\n",
-    "    Dense(3, activation='softmax')  # 3 output classes for Iris dataset\n",
-    "])\n",
-    "print(\"Model summary:\")\n",
-    "print(model.summary())\n",
-    "\n",
-    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
-    "history = model.fit(X_train, y_train, epochs=50, batch_size=8, verbose=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "82b81985",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the model on the test set to see how well it performs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "4c163645",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test accuracy: 0.93\n",
-      "Classification Report:\n",
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0       1.00      1.00      1.00        10\n",
-      "           1       0.89      0.89      0.89         9\n",
-      "           2       0.91      0.91      0.91        11\n",
-      "\n",
-      "    accuracy                           0.93        30\n",
-      "   macro avg       0.93      0.93      0.93        30\n",
-      "weighted avg       0.93      0.93      0.93        30\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "loss, acc = model.evaluate(X_test, y_test, verbose=0)\n",
-    "print(f\"Test accuracy: {acc:.2f}\")\n",
-    "\n",
-    "print(\"Classification Report:\")\n",
-    "from sklearn.metrics import classification_report\n",
-    "y_pred = model.predict(X_test)\n",
-    "y_pred_classes = tf.argmax(y_pred, axis=1)\n",
-    "y_true_classes = tf.argmax(y_test, axis=1)\n",
-    "print(classification_report(y_true_classes, y_pred_classes))\n",
-    "\n",
-    "# Visualize training history\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
-    "plt.title('Model Accuracy')\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Accuracy')\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c04477ce",
-   "metadata": {},
-   "source": [
-    "-   Visualize the confusion matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "f24ec39e",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualize the confusion matrix\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "import seaborn as sns\n",
-    "import numpy as np\n",
-    "cm = confusion_matrix(y_true_classes, y_pred_classes)\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=data.target_names, yticklabels=data.target_names)\n",
-    "plt.xlabel('Predicted')\n",
-    "plt.ylabel('True')\n",
-    "plt.title('Confusion Matrix')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "adff9ce8",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "-   Build a feedforward neural network using Keras for the titanic dataset.\n",
-    "-   Use ReLU activation in the hidden layer and `sigmoid` activation in the output layer.\n",
-    "-   Train the model and evaluate its accuracy on the test set.\n",
-    "-   Add a hidden layers to increase model complexity.\n",
-    "-   Implement dropout regularization to prevent overfitting."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2a8cbf97",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense, Dropout\n",
-    "from tensorflow.keras.utils import to_categorical\n",
-    "\n",
-    "# Load and preprocess the Titanic dataset\n",
-    "from sklearn.datasets import fetch_openml\n",
-    "titanic = fetch_openml('titanic', version=1, as_frame=True)\n",
-    "import pandas as pd\n",
-    "df = titanic.frame\n",
-    "X = df[['pclass', 'sex', 'age']]\n",
-    "y = df['survived'].astype('category').cat.codes\n",
-    "\n",
-    "# Preprocess\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
-    "from sklearn.compose import ColumnTransformer\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "preprocessing_pipeline = ColumnTransformer(\n",
-    "    transformers=[\n",
-    "        ('age', Pipeline(\n",
-    "            steps=[\n",
-    "                ('imputer', SimpleImputer(strategy='mean')),\n",
-    "                ('scaler', StandardScaler())\n",
-    "            ]), ['age']),\n",
-    "        ('sex', Pipeline(\n",
-    "            steps=[\n",
-    "                ('encoder', OneHotEncoder(drop='first'))\n",
-    "            ]), ['sex']),\n",
-    "        ('pclass', Pipeline(\n",
-    "            steps=[\n",
-    "                ('encoder', OneHotEncoder())\n",
-    "            ]), ['pclass'])\n",
-    "    ],\n",
-    "    remainder='passthrough'\n",
-    ")\n",
-    "\n",
-    "X_train = preprocessing_pipeline.fit_transform(X_train)\n",
-    "X_test = preprocessing_pipeline.transform(X_test)\n",
-    "\n",
-    "# Build the model\n",
-    "model = Sequential([\n",
-    "    Input(shape=(X_train.shape[1],)),  # Input layer\n",
-    "    Dense(16, activation='relu'),\n",
-    "    Dropout(0.2),  # Dropout layer for regularization\n",
-    "    Dense(8, activation='relu'),  # Hidden layer\n",
-    "    Dense(1, activation='sigmoid')  # Output layer for binary classification\n",
-    "])\n",
-    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
-    "history = model.fit(X_train, y_train, epochs=50, batch_size=8, verbose=0)\n",
-    "loss, acc = model.evaluate(X_test, y_test, verbose=0)\n",
-    "print(f\"Test accuracy: {acc:.2f}\")\n",
-    "print(\"Classification Report:\")\n",
-    "from sklearn.metrics import classification_report\n",
-    "\n",
-    "y_pred = model.predict(X_test)\n",
-    "y_pred_classes = (y_pred > 0.5).astype(int)\n",
-    "y_true_classes = y_test.values.reshape(-1, 1)\n",
-    "print(classification_report(y_true_classes, y_pred_classes))\n",
-    "# Visualize training history\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
-    "plt.title('Model Accuracy')\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Accuracy')\n",
-    "plt.legend()\n",
-    "plt.show()\n",
-    "\n",
-    "# Visualize the confusion matrix\n",
-    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
-    "import seaborn as sns\n",
-    "cm = confusion_matrix(y_true_classes, y_pred_classes)\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "ConfusionMatrixDisplay(confusion_matrix=cm,\n",
-    "                       display_labels=['Not Survived', 'Survived']).plot(cmap='Blues')\n",
-    "plt.title('Confusion Matrix for Titanic Survival Prediction')\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S06 Artificial Neural Networks and Deep Learning/S06C02 Convolutional Neural Networks (CNNs).ipynb b/src/S06 Artificial Neural Networks and Deep Learning/S06C02 Convolutional Neural Networks (CNNs).ipynb
deleted file mode 100644
index 900a23b..0000000
--- a/src/S06 Artificial Neural Networks and Deep Learning/S06C02 Convolutional Neural Networks (CNNs).ipynb	
+++ /dev/null
@@ -1,520 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "931ba52b",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Convolutional Neural Networks (CNNs)\n",
-    "\n",
-    "Convolutional Neural Networks (CNNs) are a specialized type of neural network primarily used for processing grid-like data, such as images. They are designed to automatically and adaptively learn spatial hierarchies of features from input data, making them particularly effective for tasks like image recognition, object detection, and more.\n",
-    "\n",
-    "CNNs leverage the spatial structure of images by applying convolutional layers, which use filters to detect patterns and features at various levels of abstraction. This hierarchical feature learning allows CNNs to achieve state-of-the-art performance in many computer vision tasks."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8c5c8653",
-   "metadata": {},
-   "source": [
-    "## Key Concepts of CNNs\n",
-    "\n",
-    "-   **Convolutional Layers**: The core building blocks of CNNs, where filters (also known as kernels) slide over the input data to produce feature maps. Each filter learns to detect specific patterns, such as edges, textures, or shapes.\n",
-    "-   **Pooling Layers**: Used to reduce the spatial dimensions of feature maps, retaining the most important information while discarding less significant details. Pooling helps to make the model invariant to small translations and distortions in the input data. Common pooling methods include max pooling and average pooling.\n",
-    "-   **Activation Functions**: Non-linear functions applied after each convolutional layer to introduce non-linearity into the model. The Rectified Linear Unit (ReLU) is the most commonly used activation function in CNNs, defined as $ f(x) = \\max(0, x) $. Other activation functions include Sigmoid and Tanh, but ReLU is preferred for its simplicity and effectiveness in deep networks.\n",
-    "-   **Fully Connected Layers**: After several convolutional and pooling layers, the high-level reasoning in the neural network is performed by fully connected layers. These layers connect every neuron in one layer to every neuron in the next layer, allowing the model to learn complex relationships between features.\n",
-    "-   **Dropout**: A regularization technique used to prevent overfitting by randomly setting a fraction of the input units to zero during training. This forces the network to learn more robust features that are not reliant on any single neuron.\n",
-    "-   **Batch Normalization**: A technique to normalize the inputs of each layer, which helps to stabilize the learning process and can lead to faster convergence. It reduces internal covariate shift by normalizing the activations of a layer for each mini-batch.\n",
-    "-   **Transfer Learning**: A powerful technique where a pre-trained CNN model (trained on a large dataset like ImageNet) is fine-tuned on a smaller, task-specific dataset. This allows leveraging the learned features from the pre-trained model, significantly reducing training time and improving performance on the new task.\n",
-    "-   **Data Augmentation**: A technique to artificially increase the size of the training dataset by applying random transformations (e.g., rotations, translations, flips) to the input images. This helps improve the model's robustness and generalization by exposing it to a wider variety of input variations."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a635b46d",
-   "metadata": {},
-   "source": [
-    "## Example: Building a Simple CNN with Keras\n",
-    "\n",
-    "To illustrate the concepts of CNNs, we will build a simple convolutional neural network using Keras. We will use the MNIST dataset, a well-known dataset for handwritten digit recognition, to demonstrate how to create, train, and evaluate a CNN model.\n",
-    "\n",
-    "-   Load and preprocess the MNIST dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "22f51f78",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "from tensorflow.keras.datasets import mnist\n",
-    "from tensorflow.keras.utils import to_categorical\n",
-    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
-    "X_train = X_train.reshape((X_train.shape[0], 28, 28, 1)).astype('float32') / 255.0\n",
-    "X_test = X_test.reshape((X_test.shape[0], 28, 28, 1)).astype('float32') / 255.0\n",
-    "y_train = to_categorical(y_train, num_classes=10)\n",
-    "y_test = to_categorical(y_test, num_classes=10)\n",
-    "X_train.shape, X_test.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1abad3c3",
-   "metadata": {},
-   "source": [
-    "-   Build the CNN model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d0c450f6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
-    "\n",
-    "model = Sequential([\n",
-    "    Input(shape=(28, 28, 1)),  # Input layer for MNIST images\n",
-    "    Conv2D(32, (3, 3), activation='relu', padding='same'),\n",
-    "    MaxPooling2D((2, 2)),\n",
-    "    Conv2D(64, (3, 3), activation='relu', padding='same'),\n",
-    "    MaxPooling2D((2, 2)),\n",
-    "    Conv2D(128, (3, 3), activation='relu', padding='same'),\n",
-    "    Flatten(),\n",
-    "    Dense(1024, activation='relu'),\n",
-    "    Dropout(0.2),  # Regularization to prevent overfitting\n",
-    "    Dense(10, activation='softmax')  # Output layer for 10 classes\n",
-    "])\n",
-    "model.summary()  # Display the model architecture"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0174f984",
-   "metadata": {},
-   "source": [
-    "-   Compile the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9bfa6486",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.compile(optimizer='adam',\n",
-    "              loss='categorical_crossentropy',\n",
-    "              metrics=['accuracy'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "463c6f46",
-   "metadata": {},
-   "source": [
-    "-   Train the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "16757b2e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "history = model.fit(X_train,\n",
-    "                    y_train,\n",
-    "                    epochs=10,\n",
-    "                    batch_size=64,\n",
-    "                    validation_split=0.2\n",
-    "                    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be73ff3b",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2b3c0981",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loss, accuracy = model.evaluate(X_test, y_test)\n",
-    "print(f\"Test Loss: {loss:.4f}, Test Accuracy: {accuracy:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5c4fe899",
-   "metadata": {},
-   "source": [
-    "-   Save the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "92f3fde1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save('mnist_model.keras')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e4fa76de",
-   "metadata": {},
-   "source": [
-    "-   Visualize the training history"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "20850970",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "history = model.history.history\n",
-    "plt.figure(figsize=(12, 4))\n",
-    "plt.subplot(1, 2, 1)\n",
-    "plt.plot(history['loss'], label='Training Loss')\n",
-    "plt.plot(history['val_loss'], label='Validation Loss')\n",
-    "plt.title('Loss over Epochs')\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Loss')\n",
-    "plt.legend()\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "plt.plot(history['accuracy'], label='Training Accuracy')\n",
-    "plt.plot(history['val_accuracy'], label='Validation Accuracy')\n",
-    "plt.title('Accuracy over Epochs')\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Accuracy')\n",
-    "plt.legend()\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a19d0d22",
-   "metadata": {},
-   "source": [
-    "-   Use the model on an image and show the image with the predicted label"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a57a74fb-07a2-4bfa-adb2-e405c310922a",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from tensorflow.keras.preprocessing import image\n",
-    "\n",
-    "# Predict the class of the image\n",
-    "img_index = 12\n",
-    "img = X_test[img_index]\n",
-    "label = np.argmax(y_test[img_index])\n",
-    "predictions = model.predict(np.expand_dims(img, axis=0))\n",
-    "predicted_class = np.argmax(predictions[0])\n",
-    "print(f\"Predicted Class: {predicted_class}\")\n",
-    "print(f\"Actual Class: {label}\")\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.imshow(img)\n",
-    "plt.title(f\"Predicted Class: {predicted_class}\")\n",
-    "plt.axis('off')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c788e8c0",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercises\n",
-    "\n",
-    "-   Load the CIFAR-10 dataset and preprocess it for training a CNN."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e50ea689",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "from tensorflow.keras.datasets import cifar10\n",
-    "from tensorflow.keras.utils import to_categorical\n",
-    "# Load CIFAR-10 dataset\n",
-    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()\n",
-    "# Normalize pixel values to [0, 1]\n",
-    "X_train, X_test = X_train / 255.0, X_test / 255.0\n",
-    "# Convert labels to categorical format\n",
-    "y_train, y_test = to_categorical(y_train, num_classes=10), to_categorical(y_test, num_classes=10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4a57580d",
-   "metadata": {},
-   "source": [
-    "-   Build a simple CNN model using Keras."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c454162d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
-    "# Build the CNN model\n",
-    "model = Sequential([\n",
-    "    Input(shape=(32, 32, 3)),\n",
-    "    Conv2D(32, (3, 3), activation='relu', padding='same'),\n",
-    "    MaxPooling2D((2, 2)),\n",
-    "    Conv2D(64, (3, 3), activation='relu', padding='same'),\n",
-    "    MaxPooling2D((2, 2)),\n",
-    "    Conv2D(64, (3, 3), activation='relu', padding='same'),\n",
-    "    Flatten(),\n",
-    "    Dense(64, activation='relu'),\n",
-    "    Dropout(0.5),  # Regularization to prevent overfitting\n",
-    "    Dense(10, activation='softmax')  # Output layer for 10 classes\n",
-    "])\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eab2622a",
-   "metadata": {},
-   "source": [
-    "-   Compile the model with an appropriate optimizer and loss function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "129c579e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.compile(optimizer='adam',\n",
-    "              loss='categorical_crossentropy',\n",
-    "              metrics=['accuracy'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a6d069cc",
-   "metadata": {},
-   "source": [
-    "-   Train the model on the CIFAR-10 training set and evaluate it on the test set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5229d7b0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.fit(X_train,\n",
-    "          y_train,\n",
-    "          epochs=16,\n",
-    "          batch_size=64,\n",
-    "          validation_split=0.2)\n",
-    "loss, accuracy = model.evaluate(X_test, y_test)\n",
-    "print(f\"Test Loss: {loss:.4f}, Test Accuracy: {accuracy:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "724298ca",
-   "metadata": {},
-   "source": [
-    "-   Save the trained model for future use."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8b52bb2b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save('cifar10_cnn_model.keras')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0806aba7",
-   "metadata": {},
-   "source": [
-    "-   Visualize the training history (loss and accuracy) using Matplotlib."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "066650d6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "history = model.history.history\n",
-    "plt.figure(figsize=(12, 4))\n",
-    "plt.subplot(1, 2, 1)\n",
-    "plt.plot(history['loss'], label='Training Loss')\n",
-    "plt.plot(history['val_loss'], label='Validation Loss')\n",
-    "plt.title('Loss over Epochs')\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Loss')\n",
-    "plt.legend()\n",
-    "plt.subplot(1, 2, 2)\n",
-    "plt.plot(history['accuracy'], label='Training Accuracy')\n",
-    "plt.plot(history['val_accuracy'], label='Validation Accuracy')\n",
-    "plt.title('Accuracy over Epochs')\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Accuracy')\n",
-    "plt.legend()\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "235b6763",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Implement data augmentation techniques to improve model generalization.\n",
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
-    "# Create an ImageDataGenerator for data augmentation\n",
-    "datagen = ImageDataGenerator(\n",
-    "    rotation_range=20,\n",
-    "    width_shift_range=0.2,\n",
-    "    height_shift_range=0.2,\n",
-    "    horizontal_flip=True,\n",
-    "    zoom_range=0.2\n",
-    ")\n",
-    "# Fit the generator on the training data\n",
-    "datagen.fit(X_train)\n",
-    "# Train the model using the augmented data\n",
-    "model.fit(datagen.flow(X_train, y_train, batch_size=64),\n",
-    "          epochs=10,\n",
-    "          validation_split=0.2,\n",
-    "          steps_per_epoch=len(X_train) // 64)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c2cafd5e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Explore transfer learning by using a pre-trained CNN model (e.g., VGG16, ResNet50) and fine-tuning it on the CIFAR-10 dataset.\n",
-    "from tensorflow.keras.applications import VGG16\n",
-    "from tensorflow.keras.layers import GlobalAveragePooling2D\n",
-    "# Load the pre-trained VGG16 model without the top layers\n",
-    "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(32, 32, 3))\n",
-    "# Freeze the base model layers\n",
-    "for layer in base_model.layers:\n",
-    "    layer.trainable = False\n",
-    "# Add custom layers on top of the base model\n",
-    "model = Sequential([\n",
-    "    base_model,\n",
-    "    GlobalAveragePooling2D(),\n",
-    "    Dense(64, activation='relu'),\n",
-    "    Dropout(0.5),\n",
-    "    Dense(10, activation='softmax')  # Output layer for 10 classes\n",
-    "])\n",
-    "# Compile the model\n",
-    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
-    "# Train the model on the CIFAR-10 dataset\n",
-    "model.fit(X_train, y_train, epochs=10, batch_size=64, validation_data=(X_test, y_test))\n",
-    "# Evaluate the model\n",
-    "loss, accuracy = model.evaluate(X_test, y_test)\n",
-    "print(f\"Test Loss: {loss:.4f}, Test Accuracy: {accuracy:.4f}\")\n",
-    "# Save the fine-tuned model\n",
-    "model.save('cifar10_vgg16_model.h5')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "39a75110",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Visualize the learned filters of the first convolutional layer to understand what features the model is learning.\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "# Get the weights of the first convolutional layer\n",
-    "filters, biases = model.layers[0].get_weights()\n",
-    "# Normalize the filter values to [0, 1] for visualization\n",
-    "filters = (filters - filters.min()) / (filters.max() - filters.min())\n",
-    "# Plot the first 16 filters\n",
-    "n_filters = 16\n",
-    "plt.figure(figsize=(8, 8))\n",
-    "for i in range(n_filters):\n",
-    "      ax = plt.subplot(4, 4, i + 1)\n",
-    "      plt.imshow(filters[:, :, :, i], cmap='viridis')\n",
-    "      plt.axis('off')\n",
-    "plt.suptitle('Learned Filters of the First Convolutional Layer')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S06 Artificial Neural Networks and Deep Learning/S06C03 Autoencoders.ipynb b/src/S06 Artificial Neural Networks and Deep Learning/S06C03 Autoencoders.ipynb
deleted file mode 100644
index 76e82a5..0000000
--- a/src/S06 Artificial Neural Networks and Deep Learning/S06C03 Autoencoders.ipynb	
+++ /dev/null
@@ -1,420 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "be71aedf",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Autoencoders\n",
-    "\n",
-    "Autoencoders are a type of artificial neural network used for unsupervised learning tasks, particularly for dimensionality reduction, feature learning, and data compression. They consist of two main components:\n",
-    "\n",
-    "-   **Encoder**: Compresses the input data into a lower-dimensional representation (latent space).\n",
-    "-   **Decoder**: Reconstructs the original data from the compressed representation.\n",
-    "\n",
-    "Autoencoders are trained to minimize the difference between the input and the reconstructed output, effectively learning to capture the essential features of the data while discarding noise and irrelevant information."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f224867e",
-   "metadata": {},
-   "source": [
-    "## Example: Autoencoder on MNIST Dataset\n",
-    "\n",
-    "To illustrate the use of autoencoders, we can apply them to the MNIST dataset, which consists of handwritten digits. The goal is to compress the images into a lower-dimensional representation and then reconstruct the original images.\n",
-    "\n",
-    "-   Load the MNIST dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6f552230",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "from tensorflow.keras.datasets import mnist\n",
-    "from tensorflow.keras.utils import to_categorical\n",
-    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
-    "X_train = X_train.reshape((X_train.shape[0], 28, 28, 1)).astype('float32') / 255.0\n",
-    "X_test = X_test.reshape((X_test.shape[0], 28, 28, 1)).astype('float32') / 255.0\n",
-    "y_train = to_categorical(y_train, num_classes=10)\n",
-    "y_test = to_categorical(y_test, num_classes=10)\n",
-    "X_train.shape, X_test.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "abd46379",
-   "metadata": {},
-   "source": [
-    "-   Visualize some samples from the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c09464b6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Visualize some samples from the dataset\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.figure(figsize=(10, 4))\n",
-    "for i in range(10):\n",
-    "    plt.subplot(2, 5, i + 1)\n",
-    "    plt.imshow(X_train[i].reshape(28, 28), cmap='gray')\n",
-    "    plt.title(f'Label: {y_train[i]}')\n",
-    "    plt.axis('off')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "104a307e",
-   "metadata": {},
-   "source": [
-    "-   Define the autoencoder architecture"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "95fadf83",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.models import Model\n",
-    "from tensorflow.keras.layers import Input, Dense, Flatten, Reshape\n",
-    "\n",
-    "# Define the encoder\n",
-    "input_img = Input(shape=(28, 28, 1))\n",
-    "x = Flatten()(input_img)\n",
-    "encoded = Dense(64, activation='relu')(x)\n",
-    "\n",
-    "# Define the decoder\n",
-    "x = Dense(28 * 28 * 1, activation='sigmoid')(encoded)\n",
-    "decoded = Reshape((28, 28, 1))(x)\n",
-    "\n",
-    "# Create the autoencoder model\n",
-    "autoencoder = Model(input_img, decoded)\n",
-    "autoencoder.compile(optimizer='adam', loss='binary_crossentropy')\n",
-    "autoencoder.summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4a500e5d",
-   "metadata": {},
-   "source": [
-    "-   Train the autoencoder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "274e4965",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Train the autoencoder\n",
-    "autoencoder.fit(X_train,\n",
-    "                X_train,\n",
-    "                epochs=50,\n",
-    "                batch_size=256,\n",
-    "                shuffle=True,\n",
-    "                validation_split=0.2)\n",
-    "print(\"Autoencoder training completed.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7a84b8c7",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the autoencoder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e23f5ab0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Evaluate the autoencoder\n",
-    "import numpy as np\n",
-    "reconstruction = autoencoder.predict(X_test)\n",
-    "reconstruction_error = np.mean(np.square(X_test - reconstruction))\n",
-    "print(f'Reconstruction Error: {reconstruction_error:.4f}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "33d927f7",
-   "metadata": {},
-   "source": [
-    "-   Visualize the reconstructed images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cfb68a30",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Visualize the original and reconstructed images\n",
-    "n = 10  # Number of images to visualize\n",
-    "plt.figure(figsize=(10, 4))\n",
-    "for i in range(n):\n",
-    "    # Original image\n",
-    "    plt.subplot(2, n, i + 1)\n",
-    "    plt.imshow(X_test[i].reshape(28, 28), cmap='gray')\n",
-    "    plt.title(f'Original: {y_test[i]}')\n",
-    "    plt.axis('off')\n",
-    "\n",
-    "    # Reconstructed image\n",
-    "    plt.subplot(2, n, i + n + 1)\n",
-    "    plt.imshow(reconstruction[i].reshape(28, 28), cmap='gray')\n",
-    "    plt.title('Reconstructed')\n",
-    "    plt.axis('off')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b7c850aa",
-   "metadata": {},
-   "source": [
-    "-   Save the autoencoder model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6e2be5ad",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "# Save the autoencoder model\n",
-    "autoencoder.save('autoencoder_mnist.h5')\n",
-    "print(\"Autoencoder model saved as 'autoencoder_mnist.h5'.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "99eb602e",
-   "metadata": {},
-   "source": [
-    "## Hands-on Exercise\n",
-    "\n",
-    "Repeat the above steps with the CIFAR-10 dataset, which consists of 32x32 color images in 10 classes. The goal is to compress the images into a lower-dimensional representation and then reconstruct the original images.\n",
-    "\n",
-    "-   Load the CIFAR-10 dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "14e1bdfa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.datasets import cifar10\n",
-    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()\n",
-    "X_train = X_train.astype('float32') / 255.0\n",
-    "X_test = X_test.astype('float32') / 255.0\n",
-    "y_train = to_categorical(y_train, num_classes=10)\n",
-    "y_test = to_categorical(y_test, num_classes=10)\n",
-    "X_train.shape, X_test.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6b589a34",
-   "metadata": {},
-   "source": [
-    "-   Visualize some samples from the CIFAR-10 dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1424d322",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Visualize some samples from the CIFAR-10 dataset\n",
-    "plt.figure(figsize=(10, 4))\n",
-    "for i in range(10):\n",
-    "    plt.subplot(2, 5, i + 1)\n",
-    "    plt.imshow(X_train[i])\n",
-    "    plt.title(f'Label: {y_train[i]}')\n",
-    "    plt.axis('off')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a69ebee",
-   "metadata": {},
-   "source": [
-    "-   Define the autoencoder architecture for CIFAR-10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "48898da3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define the encoder for CIFAR-10\n",
-    "input_img = Input(shape=(32, 32, 3))\n",
-    "# Add a Convolutional layer for better feature extraction\n",
-    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
-    "x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)\n",
-    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
-    "x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
-    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
-    "x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)\n",
-    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
-    "x = Flatten()(x)\n",
-    "encoded = Dense(256, activation='relu')(x)\n",
-    "\n",
-    "# Define the decoder for CIFAR-10\n",
-    "x = Dense(32 * 32 * 3, activation='sigmoid')(encoded)\n",
-    "# Add a Convolutional transpose layer to reconstruct the image\n",
-    "from tensorflow.keras.layers import Reshape\n",
-    "x = Reshape((4, 4, 128))(x)  # Reshape to match the last pooling layer's output\n",
-    "x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)\n",
-    "x = UpSampling2D((2, 2))(x)\n",
-    "x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
-    "x = UpSampling2D((2, 2))(x)\n",
-    "x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n",
-    "x = UpSampling2D((2, 2))(x)\n",
-    "x = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(x)\n",
-    "# Reshape the output to match the original image dimensions\n",
-    "decoded = Reshape((32, 32, 3))(x)\n",
-    "\n",
-    "# Create the autoencoder model for CIFAR-10\n",
-    "autoencoder_cifar = Model(input_img, decoded)\n",
-    "autoencoder_cifar.compile(optimizer='adam', loss='binary_crossentropy')\n",
-    "autoencoder_cifar.summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3bec8ba3",
-   "metadata": {},
-   "source": [
-    "-   Train the autoencoder on CIFAR-10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "702960d9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Train the autoencoder on CIFAR-10\n",
-    "autoencoder_cifar.fit(X_train,\n",
-    "                      X_train,\n",
-    "                      epochs=50,\n",
-    "                      batch_size=256,\n",
-    "                      shuffle=True,\n",
-    "                      validation_split=0.2)\n",
-    "print(\"Autoencoder for CIFAR-10 training completed.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7aca49e8",
-   "metadata": {},
-   "source": [
-    "-   Evaluate the autoencoder on CIFAR-10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "adb02f18",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Evaluate the autoencoder on CIFAR-10\n",
-    "reconstruction_cifar = autoencoder_cifar.predict(X_test)\n",
-    "reconstruction_error_cifar = np.mean(np.square(X_test - reconstruction_cifar))\n",
-    "print(f'Reconstruction Error for CIFAR-10: {reconstruction_error_cifar:.4f}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3d994b96",
-   "metadata": {},
-   "source": [
-    "-   Visualize the reconstructed images from CIFAR-10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "14731b71",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Visualize the original and reconstructed images from CIFAR-10\n",
-    "n = 10  # Number of images to visualize\n",
-    "plt.figure(figsize=(10, 4))\n",
-    "for i in range(n):\n",
-    "    # Original image\n",
-    "    plt.subplot(2, n, i + 1)\n",
-    "    plt.imshow(X_test[i])\n",
-    "    plt.title(f'Original: {y_test[i]}')\n",
-    "    plt.axis('off')\n",
-    "\n",
-    "    # Reconstructed image\n",
-    "    plt.subplot(2, n, i + n + 1)\n",
-    "    plt.imshow(reconstruction_cifar[i])\n",
-    "    plt.title('Reconstructed')\n",
-    "    plt.axis('off')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S06 Artificial Neural Networks and Deep Learning/S06C04 Recurrent Neural Networs (RNNs).ipynb b/src/S06 Artificial Neural Networks and Deep Learning/S06C04 Recurrent Neural Networs (RNNs).ipynb
deleted file mode 100644
index c79c05a..0000000
--- a/src/S06 Artificial Neural Networks and Deep Learning/S06C04 Recurrent Neural Networs (RNNs).ipynb	
+++ /dev/null
@@ -1,119 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "aa9ec796",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Recurrent Neural Networks (RNNs)\n",
-    "\n",
-    "Recurrent Neural Networks (RNNs) are a class of artificial neural networks designed for processing sequential data. Unlike traditional feedforward neural networks, RNNs have connections that loop back on themselves, allowing them to maintain a hidden state that can capture information about previous inputs in the sequence.\n",
-    "\n",
-    "RNNs are particularly useful for tasks such as time series prediction, natural language processing, and speech recognition, where the order and context of the data points are crucial for understanding the underlying patterns."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "db872dfd",
-   "metadata": {},
-   "source": [
-    "## Example: LSTM to Predict Sine Wave"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "20472717",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import LSTM, Dense\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Generate a noisy sine-wave dataset\n",
-    "np.random.seed(42)\n",
-    "TIMESTEPS = 50        # input sequence length\n",
-    "TOTAL_POINTS = 2000   # total sine points\n",
-    "TEST_SPLIT = 0.2\n",
-    "\n",
-    "x = np.linspace(0, 50, TOTAL_POINTS)\n",
-    "sine = np.sin(x) + 0.1 * np.random.randn(TOTAL_POINTS)  # add some noise\n",
-    "\n",
-    "# Transform the data into (samples, timesteps, features)\n",
-    "def make_dataset(series, window):\n",
-    "    X, y = [], []\n",
-    "    for i in range(len(series) - window):\n",
-    "        X.append(series[i : i + window])\n",
-    "        y.append(series[i + window])\n",
-    "    return np.array(X), np.array(y)\n",
-    "\n",
-    "X, y = make_dataset(sine, TIMESTEPS)\n",
-    "X = X[..., np.newaxis]   # shape → (samples, timesteps, 1)\n",
-    "y = y[..., np.newaxis]\n",
-    "\n",
-    "# Train / validation split\n",
-    "split_idx = int(len(X) * (1 - TEST_SPLIT))\n",
-    "X_train, X_test = X[:split_idx], X[split_idx:]\n",
-    "y_train, y_test = y[:split_idx], y[split_idx:]\n",
-    "\n",
-    "# Define a simple RNN model (or LSTM)\n",
-    "model = Sequential([\n",
-    "    LSTM(64, activation=\"tanh\", input_shape=(TIMESTEPS, 1)),\n",
-    "    Dense(1)                      # one-step forecast\n",
-    "])\n",
-    "\n",
-    "model.compile(optimizer=\"adam\", loss=\"mse\")\n",
-    "model.summary()\n",
-    "\n",
-    "# Train\n",
-    "history = model.fit(\n",
-    "    X_train, y_train,\n",
-    "    validation_split=0.2,\n",
-    "    epochs=20,\n",
-    "    batch_size=32\n",
-    ")\n",
-    "\n",
-    "# Predict the test set\n",
-    "preds = model.predict(X_test).squeeze()\n",
-    "\n",
-    "# Plot actual vs. predicted\n",
-    "plt.figure(figsize=(10, 4))\n",
-    "plt.plot(range(len(y_test)), y_test.squeeze(), label=\"Actual\")\n",
-    "plt.plot(range(len(preds)), preds, label=\"Predicted\", linestyle=\"--\")\n",
-    "plt.title(\"RNN one-step forecast on noisy sine wave\")\n",
-    "plt.xlabel(\"Time step\")\n",
-    "plt.ylabel(\"Value\")\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/S06 Artificial Neural Networks and Deep Learning/S06C05 Generative Adversarial Networks (GANs).ipynb b/src/S06 Artificial Neural Networks and Deep Learning/S06C05 Generative Adversarial Networks (GANs).ipynb
deleted file mode 100644
index 2e24539..0000000
--- a/src/S06 Artificial Neural Networks and Deep Learning/S06C05 Generative Adversarial Networks (GANs).ipynb	
+++ /dev/null
@@ -1,166 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "b1217939",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Generative Adversarial Networks (GANs)\n",
-    "\n",
-    "Generative Adversarial Networks (GANs) are a class of machine learning frameworks designed to generate new data samples that resemble a given training dataset. They consist of two neural networks, the **generator** and the **discriminator**, which are trained simultaneously in a game-theoretic framework. The generator creates new data samples, while the discriminator evaluates them against real samples from the training set. The goal of the generator is to produce samples that are indistinguishable from real data, while the discriminator aims to correctly classify samples as real or fake. This adversarial process leads to the generator improving its output quality over time, resulting in realistic data generation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "afc6718e",
-   "metadata": {},
-   "source": [
-    "## Example: DCGAN on MNIST\n",
-    "\n",
-    "This example demonstrates how to implement a Deep Convolutional GAN (DCGAN) to generate handwritten digits from the MNIST dataset. The DCGAN architecture uses convolutional layers in both the generator and discriminator to effectively learn spatial hierarchies in the data. The generator takes a random noise vector as input and produces a 28x28 grayscale image, while the discriminator takes an image and outputs a probability indicating whether the image is real (from the dataset) or fake (generated by the generator). The training process involves alternating between training the discriminator to distinguish real from fake images and training the generator to produce images that can fool the discriminator. The result is a trained generator capable of producing realistic handwritten digits that resemble those in the MNIST dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "36251570-bfcc-4998-9ff3-ed605aa0b77c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import tensorflow as tf\n",
-    "from tensorflow.keras import layers\n",
-    "\n",
-    "# ───────────────────── 0.  GPU-friendly startup ──────────────────────\n",
-    "os.environ[\"TF_GPU_ALLOCATOR\"] = \"cuda_malloc_async\"    # ↓ fragmentation\n",
-    "gpus = tf.config.list_physical_devices(\"GPU\")\n",
-    "print(gpus)\n",
-    "for g in gpus: tf.config.experimental.set_memory_growth(g, True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f014821d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "LATENT_DIM, BATCH_SIZE, EPOCHS = 100, 128, 50\n",
-    "SAVE_EVERY, GRID_N = 5, 25\n",
-    "\n",
-    "(x_train, _), _ = tf.keras.datasets.mnist.load_data()\n",
-    "x_train = (x_train.astype(\"float32\") - 127.5) / 127.5    # → [-1, 1]\n",
-    "x_train = np.expand_dims(x_train, -1)\n",
-    "dataset = (tf.data.Dataset.from_tensor_slices(x_train).\\\n",
-    "           shuffle(60_000).batch(BATCH_SIZE, drop_remainder=True))\n",
-    "\n",
-    "def build_generator():\n",
-    "    return tf.keras.Sequential([\n",
-    "        layers.Input((LATENT_DIM,)),\n",
-    "        layers.Dense(7*7*256, use_bias=False),\n",
-    "        layers.BatchNormalization(), layers.LeakyReLU(0.2),\n",
-    "        layers.Reshape((7, 7, 256)),\n",
-    "        layers.Conv2DTranspose(128, 5, 1, \"same\", use_bias=False),\n",
-    "        layers.BatchNormalization(), layers.LeakyReLU(0.2),\n",
-    "        layers.Conv2DTranspose(64, 5, 2, \"same\", use_bias=False),\n",
-    "        layers.BatchNormalization(), layers.LeakyReLU(0.2),\n",
-    "        layers.Conv2DTranspose(1, 5, 2, \"same\", use_bias=False, activation=\"tanh\")\n",
-    "    ], name=\"generator\")\n",
-    "\n",
-    "def build_discriminator():\n",
-    "    return tf.keras.Sequential([\n",
-    "        layers.Input((28, 28, 1)),\n",
-    "        layers.Conv2D(64, 5, 2, \"same\"),  layers.LeakyReLU(0.2), layers.Dropout(0.3),\n",
-    "        layers.Conv2D(128, 5, 2, \"same\"), layers.LeakyReLU(0.2), layers.Dropout(0.3),\n",
-    "        layers.Flatten(),\n",
-    "        layers.Dense(1)\n",
-    "    ], name=\"discriminator\")\n",
-    "\n",
-    "gen, disc = build_generator(), build_discriminator()\n",
-    "\n",
-    "bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
-    "g_opt = tf.keras.optimizers.Adam(1e-4)\n",
-    "d_opt = tf.keras.optimizers.Adam(1e-4)\n",
-    "\n",
-    "@tf.function\n",
-    "def train_step(real_imgs):\n",
-    "    noise = tf.random.normal([BATCH_SIZE, LATENT_DIM])\n",
-    "\n",
-    "    # ---- Discriminator ----\n",
-    "    with tf.GradientTape() as d_tape:\n",
-    "        fake_imgs = gen(noise, training=True)\n",
-    "        real_logits = disc(real_imgs, training=True)\n",
-    "        fake_logits = disc(fake_imgs, training=True)\n",
-    "\n",
-    "        d_loss_real = bce(tf.ones_like(real_logits), real_logits)\n",
-    "        d_loss_fake = bce(tf.zeros_like(fake_logits), fake_logits)\n",
-    "        d_loss = (d_loss_real + d_loss_fake) * 0.5\n",
-    "\n",
-    "    d_grads = d_tape.gradient(d_loss, disc.trainable_variables)\n",
-    "    d_opt.apply_gradients(zip(d_grads, disc.trainable_variables))\n",
-    "\n",
-    "    # ---- Generator ----\n",
-    "    noise = tf.random.normal([BATCH_SIZE, LATENT_DIM])\n",
-    "    with tf.GradientTape() as g_tape:\n",
-    "        fake_imgs = gen(noise, training=True)\n",
-    "        fake_logits = disc(fake_imgs, training=True)\n",
-    "        g_loss = bce(tf.ones_like(fake_logits), fake_logits)   # want \"real\"\n",
-    "\n",
-    "    g_grads = g_tape.gradient(g_loss, gen.trainable_variables)\n",
-    "    g_opt.apply_gradients(zip(g_grads, gen.trainable_variables))\n",
-    "    return d_loss, g_loss\n",
-    "\n",
-    "os.makedirs(\"generated\", exist_ok=True)\n",
-    "fixed_noise = tf.random.normal([GRID_N, LATENT_DIM])\n",
-    "\n",
-    "def save_grid(epoch):\n",
-    "    imgs = gen(fixed_noise, training=False)\n",
-    "    imgs = (imgs * 127.5 + 127.5).numpy().astype(\"uint8\")\n",
-    "    plt.figure(figsize=(5, 5))\n",
-    "    for i in range(GRID_N):\n",
-    "        plt.subplot(5, 5, i+1)\n",
-    "        plt.imshow(imgs[i, :, :, 0], cmap=\"gray\");\n",
-    "        plt.axis(\"off\")\n",
-    "    plt.tight_layout();\n",
-    "    plt.savefig(f\"generated/epoch_{epoch:03d}.png\");\n",
-    "    plt.close()\n",
-    "\n",
-    "for epoch in range(1, EPOCHS+1):\n",
-    "    for real_batch in dataset:\n",
-    "        d_loss, g_loss = train_step(real_batch)\n",
-    "    if epoch == 1 or epoch % SAVE_EVERY == 0:\n",
-    "        print(f\"Epoch {epoch:>3}/{EPOCHS}  D: {d_loss:.4f}  G: {g_loss:.4f}\")\n",
-    "        save_grid(epoch)\n",
-    "\n",
-    "print(\"Finished training — sample images are in the 'generated/' folder.\")"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,md"
-  },
-  "kernelspec": {
-   "display_name": "ML Workshop",
-   "language": "python",
-   "name": "ml_workshop"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From 6e01e8cdd0d3b9a97b6e66eb25860d803fa9af64 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@rug.nl>
Date: Wed, 28 Jan 2026 17:01:10 +0100
Subject: [PATCH 03/81] Added markdown to jupytext

---
 src/jupytext.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/jupytext.toml b/src/jupytext.toml
index cdc7434..efee013 100644
--- a/src/jupytext.toml
+++ b/src/jupytext.toml
@@ -1 +1 @@
-formats = "ipynb,py:percent"
\ No newline at end of file
+formats = "ipynb,md,py:percent"
\ No newline at end of file

From 559ab21a378449e1a61d36af9fca4e2166cfc5ed Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@rug.nl>
Date: Wed, 28 Jan 2026 17:07:15 +0100
Subject: [PATCH 04/81] Updated .gitignore

---
 .gitignore | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/.gitignore b/.gitignore
index ea6fc63..52cf91a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,8 +1,7 @@
-data/
+.venv
+.ipynb_checkpoints
 *.org
 *.db
-src/*/*.md
-.idea/
-.ipynb_checkpoints/
-__pycache__/
-*.pyc
+*.md
+uv.lock
+data/
\ No newline at end of file

From 592aca7227617b2ae348f94923edf97d2270d8d2 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@rug.nl>
Date: Wed, 28 Jan 2026 17:07:54 +0100
Subject: [PATCH 05/81] Added README.md

---
 .gitignore | 2 +-
 README.md  | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)
 create mode 100644 README.md

diff --git a/.gitignore b/.gitignore
index 52cf91a..8ab28a1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,6 +2,6 @@
 .ipynb_checkpoints
 *.org
 *.db
-*.md
+src/*.md
 uv.lock
 data/
\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..2118855
--- /dev/null
+++ b/README.md
@@ -0,0 +1 @@
+# Machine Learning in Python

From 5d5f63dc213f1182e977c54d26fda95b3b66256e Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@rug.nl>
Date: Wed, 28 Jan 2026 17:14:05 +0100
Subject: [PATCH 06/81] Updated requirements.txt

---
 src/requirements.txt | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/requirements.txt b/src/requirements.txt
index 6c44947..6000c69 100644
--- a/src/requirements.txt
+++ b/src/requirements.txt
@@ -1,7 +1,11 @@
 jupyter
-ipykernel
 pandas
 scikit-learn
+scikit-image
 matplotlib
 seaborn
 xgboost
+statsmodels
+torch
+torchvision
+transformers

From c0937fa9186bd1ef0f582a4e94fa29aa897da691 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@rug.nl>
Date: Wed, 28 Jan 2026 17:16:47 +0100
Subject: [PATCH 07/81] Removed requirement_gpu.txt

---
 src/requirements_gpu.txt | 2 --
 1 file changed, 2 deletions(-)
 delete mode 100644 src/requirements_gpu.txt

diff --git a/src/requirements_gpu.txt b/src/requirements_gpu.txt
deleted file mode 100644
index 1cc345e..0000000
--- a/src/requirements_gpu.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-tensorflow[and-cuda]
-keras

From a9f4804fb618ec768be2d0bcd32497b906be49b2 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@rug.nl>
Date: Wed, 28 Jan 2026 17:18:11 +0100
Subject: [PATCH 08/81] S01 Introduction to Machine Learning

---
 .../S01 Introduction to Machine Learning.pdf  | Bin 0 -> 264687 bytes
 .../S01 Introduction to Machine Learning.tex  | 350 ++++++++++++++++++
 2 files changed, 350 insertions(+)
 create mode 100644 src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.pdf
 create mode 100644 src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.tex

diff --git a/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.pdf b/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.pdf
new file mode 100644
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HcmV?d00001

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+% Created 2026-01-28 Wed 17:16
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+\author{Cristian A. Marocico}
+\date{\today}
+\title{Machine Learning in Python}
+\subtitle{Introduction to Machine Learning}
+\author[Marocico, Tatar]{Cristian A. Marocico, A. Emin Tatar}
+\institute[CIT]{Center for Information Technology\\University of Groningen}
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+\date[Feb 3\textsuperscript{rd} 2026]{Tuesday, February 3\textsuperscript{rd} 2026}
+\usepackage{mathtools}
+\newcommand{\intsum}{\mathop{\mathrlap{\raisebox{0.1ex}{\hspace{0.2em}$\textstyle\sum$}}\int}\limits}
+\setbeamercovered{transparent=0}
+\usepackage[timeinterval=60]{tdclock}
+\hypersetup{
+ pdfauthor={Cristian A. Marocico},
+ pdftitle={Machine Learning in Python},
+ pdfkeywords={},
+ pdfsubject={},
+ pdfcreator={Emacs 30.2 (Org mode 9.7.11)}, 
+ pdflang={English}}
+\begin{document}
+
+\maketitle
+\begin{frame}{Outline}
+\setcounter{tocdepth}{1}
+\tableofcontents
+\end{frame}
+
+\section{Introduction to Machine Learning}
+\label{sec:orgb50ff7a}
+\begin{frame}[label={sec:orga13367f}]{What is Machine Learning?}
+\begin{definition}[Machine Learning]\label{sec:org12455b9}
+\pause
+\alert{\alert{Machine Learning}} is the study of algorithms that learn patterns from data to make predictions or decisions without being explicitly programmed.
+\end{definition}
+\begin{alertblock}{ML vs Traditional Programming:}
+\pause
+\begin{itemize}[<+->]
+\item Traditional: input + rules → output
+\item ML: input + output → rules (the model)
+\end{itemize}
+\end{alertblock}
+\end{frame}
+\section{The Machine Learning Workflow}
+\label{sec:org5e5ee6c}
+\begin{frame}[label={sec:org3959082}]{The Machine Learning Workflow}
+\pause
+A typical \alert{Machine Learning} workflow has the following structure:
+\pause
+\begin{itemize}[<+->]
+\item Problem Definition
+\item Data Collection
+\item \alert{Train-Test Split}
+\item \alert{Data Exploration and Preprocessing}
+\item \alert{Model Selection}
+\item \alert{Training and Validation}
+\item \alert{Evaluation}
+\item Deployment and Monitoring
+\end{itemize}
+\end{frame}
+\section{Train-Test Splits and Cross-Validation}
+\label{sec:orgc457ea9}
+\begin{frame}[label={sec:org57de8e7}]{Train and Test Datasets}
+\begin{definition}[Splitting the dataset]\label{sec:org9efa658}
+\pause
+In machine learning, we split the dataset into two parts:
+\pause
+\begin{itemize}[<+->]
+\item \alert{\alert{Training Set}}: Used to train the model.
+\item \alert{\alert{Testing Set}}: Used to evaluate the model's performance.
+\end{itemize}
+\end{definition}
+\begin{alertblock}{Why Split?}
+\begin{itemize}[<+->]
+\item To ensure the model generalizes well to unseen data.
+\item To prevent overfitting, where the model learns noise in the training data instead of the underlying pattern.
+\item The \alert{\alert{test}} dataset is put aside and not used until the end, when the model is evaluated.
+\end{itemize}
+\end{alertblock}
+\end{frame}
+\begin{frame}[label={sec:org660a832}]{Cross-Validation}
+The \alert{\alert{train}} dataset can be further split into \alert{\alert{training}} and \alert{\alert{validation}} sets to tune the
+model's hyperparameters and prevent overfitting.
+\end{frame}
+\begin{frame}[label={sec:org3ae42b0}]{Cross-Validation}
+\begin{definition}[Cross-Validation]\label{sec:org9fc488e}
+\pause
+Cross-validation is a technique to assess how the results of a statistical analysis will generalize to an independent dataset.
+\pause
+\begin{itemize}[<+->]
+\item \alert{\alert{K-Fold Cross-Validation}}: The dataset is divided into K subsets. The model is trained on K-1 subsets and tested on the remaining subset. This process is repeated K times, with each subset used as the test set once.
+\item \alert{\alert{Stratified K-Fold}}: Ensures that each fold has the same proportion of classes as the entire dataset, which is particularly useful for imbalanced datasets.
+\end{itemize}
+\end{definition}
+\end{frame}
+\section{Exploratory Data Analysis (EDA)}
+\label{sec:org3578fff}
+\begin{frame}[label={sec:orgd07b658}]{Exploratory Data Analysis (EDA)}
+\begin{definition}[Exploratory Data Analysis (EDA)]\label{sec:orgf78e5cb}
+\pause
+\alert{\alert{Exploratory Data Analysis}} (EDA) is the process of analyzing datasets to summarize their main characteristics, often using visual methods.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org63ab2a7}]{Exploratory Data Analysis (EDA)}
+\begin{example}[Purpose of EDA]\label{sec:orgc39fb48}
+\pause
+\begin{itemize}[<+->]
+\item To understand the data distribution and relationships between variables.
+\item To identify patterns, trends, and anomalies.
+\item To generate hypotheses and inform feature engineering.
+\end{itemize}
+\end{example}
+\begin{example}[EDA Techniques]\label{sec:org88bfcbb}
+\pause
+\begin{itemize}[<+->]
+\item \alert{\alert{Descriptive Statistics}}: Mean, median, mode, standard deviation, etc.
+\item \alert{\alert{Data Visualization}}: Histograms, scatter plots, box plots, etc.
+\item \alert{\alert{Correlation Analysis}}: Identifying relationships between variables.
+\item \alert{\alert{Missing Value Analysis}}: Identifying and handling missing data.
+\item \alert{\alert{Outlier Detection}}: Identifying and handling outliers in the data.
+\end{itemize}
+\end{example}
+\end{frame}
+\section{Data Preprocessing}
+\label{sec:orgd25d3c3}
+\begin{frame}[label={sec:orge6baf51}]{Data Preprocessing}
+\begin{definition}[Data Preprocessing]\label{sec:orgde988c7}
+\pause
+\alert{\alert{Data Preprocessing}} is the process of transforming raw data into a format suitable for analysis and modeling.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org4d8d25e}]{Data Preprocessing}
+\begin{example}[Purpose of Data Preprocessing]\label{sec:org41288a0}
+\pause
+\begin{itemize}[<+->]
+\item To clean the data by handling missing values, outliers, and inconsistencies
+\item To normalize or scale numerical features
+\item To encode categorical variables into numerical formats
+\end{itemize}
+\end{example}
+\begin{example}[Common Data Preprocessing Techniques]\label{sec:orge1ddcb3}
+\begin{itemize}[<+->]
+\item \alert{\alert{Handling Missing Values}}: Techniques include imputation (mean, median, mode), deletion, or using algorithms that can handle missing data.
+\item \alert{\alert{Outlier Detection and Treatment}}: Identifying outliers using statistical methods (e.g., Z-score, IQR) and deciding whether to remove or transform them.
+\item \alert{\alert{Feature Scaling}}: Normalization or standardization.
+\item \alert{\alert{Encoding Categorical Variables}}: Techniques include one-hot encoding, label encoding, or using embeddings for high-cardinality categorical variables.
+\end{itemize}
+\end{example}
+\end{frame}
+\section{Feature Engineering}
+\label{sec:orgc0cab37}
+\begin{frame}[label={sec:org6297419}]{Feature Engineering}
+\begin{definition}[Feature Engineering]\label{sec:org975b9c2}
+\pause
+\alert{\alert{Feature Engineering}} is the process of using domain knowledge to select, modify, or create features (variables) that improve the performance of machine learning models.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgee3bd4e}]{Feature Engineering}
+\begin{example}[Purpose of Feature Engineering]\label{sec:orgf4a69f4}
+\pause
+\begin{itemize}[<+->]
+\item To improve model performance by providing more relevant information.
+\item To reduce dimensionality by selecting the most important features.
+\item To create new features that capture important patterns in the data.
+\end{itemize}
+\end{example}
+\begin{example}[Common Feature Engineering Techniques]\label{sec:orgb6cce2c}
+\begin{itemize}[<+->]
+\item \alert{\alert{Feature Selection}}: Techniques include filter methods (e.g., correlation), wrapper methods (e.g., recursive feature elimination), and embedded methods (e.g., LASSO).
+\item \alert{\alert{Feature Transformation}}: Techniques include logarithmic transformation, polynomial features, and interaction terms.
+\item \alert{\alert{Feature Creation}}: Creating new features based on existing ones, such as aggregating time series data, extracting date/time components, or creating domain-specific features.
+\end{itemize}
+\end{example}
+\end{frame}
+\end{document}

From 1a4d13ac84b75e0af2c1647b596d75e98f33d05b Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@rug.nl>
Date: Wed, 28 Jan 2026 17:32:28 +0100
Subject: [PATCH 09/81] Updated .gitignore

---
 .gitignore | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.gitignore b/.gitignore
index 8ab28a1..44a1954 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,4 +4,6 @@
 *.db
 src/*.md
 uv.lock
+.python-version
+pyproject.toml
 data/
\ No newline at end of file

From dec12f6f98db077da8a5ddd3dd23cc79bca415c4 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@rug.nl>
Date: Wed, 28 Jan 2026 17:44:53 +0100
Subject: [PATCH 10/81] S01C01 What is Machine Learning

---
 .gitignore                                    |   2 +-
 .../S01C01 What is Machine Learning.ipynb     | 498 ++++++++++++++++++
 2 files changed, 499 insertions(+), 1 deletion(-)
 create mode 100644 src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb

diff --git a/.gitignore b/.gitignore
index 44a1954..481866b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,7 +2,7 @@
 .ipynb_checkpoints
 *.org
 *.db
-src/*.md
+src/S*/*.md
 uv.lock
 .python-version
 pyproject.toml
diff --git a/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb b/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb
new file mode 100644
index 0000000..c401efd
--- /dev/null
+++ b/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb	
@@ -0,0 +1,498 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c01aa2be",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# What is Machine Learning?\n",
+    "\n",
+    "**Machine Learning** (ML) is a subfield of **Artificial Intelligence** (AI) that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit instructions. Instead, ML systems learn from data, identifying patterns and making decisions based on that data.\n",
+    "\n",
+    "At its core, ML is about **generalization**: the ability to learn a concept from specific examples and apply that knowledge to new, unseen data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "601411ef",
+   "metadata": {},
+   "source": [
+    "## ML vs. Traditional Programming\n",
+    "\n",
+    "In traditional programming, a human programmer analyzes a problem and writes explicit rules (code) for the computer to follow. In machine learning, the computer analyzes data to find the rules itself.\n",
+    "\n",
+    "To illustrate this, consider building a ****Spam Filter****:\n",
+    "\n",
+    "-   **Traditional Approach**: You write rules like \"If the email contains 'lottery', mark as spam.\" You have to manually update these rules constantly.\n",
+    "-   **ML Approach**: You feed the algorithm thousands of emails labeled \"Spam\" or \"Not Spam.\" The algorithm learns which words (and combinations of words) are statistically correlated with spam.\n",
+    "\n",
+    "We can formalize this difference:\n",
+    "\n",
+    "-   **Traditional Programming**: Data + Rules $\\Rightarrow$ Output\n",
+    "-   **Machine Learning**: Data + Output $\\Rightarrow$ Rules (Model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e4ac328",
+   "metadata": {},
+   "source": [
+    "## Key Concepts in Machine Learning\n",
+    "\n",
+    "Before writing code, it is essential to understand the vocabulary of the trade:\n",
+    "\n",
+    "-   **Data**: The raw material. It can be structured (tables, Excel sheets) or unstructured (images, audio, text).\n",
+    "-   **Observations (Samples/Instances)**: The rows in your dataset. Each row represents a distinct entity (e.g., one customer, one flower, one house).\n",
+    "-   **Features (Attributes/Predictors)**: The columns in your dataset used to make predictions. These are the measurable properties (e.g., height, weight, color).\n",
+    "-   **Labels (Targets/Response)**: The answer key. This is what the model tries to predict (e.g., \"Spam\" vs. \"Not Spam\", or the price of a house).\n",
+    "-   **Model**: The mathematical engine that learns the relationship between Features and Labels.\n",
+    "-   **Training**: The phase where the model iterates over the data to minimize errors and learn patterns.\n",
+    "-   **Generalization**: The ability of the model to perform well on new data it has never seen before."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48cda2df",
+   "metadata": {},
+   "source": [
+    "## Types of Machine Learning\n",
+    "\n",
+    "Machine learning tasks are generally categorized by the nature of the available signal (feedback) used for learning:\n",
+    "\n",
+    "| Type                       | Description                                                                                  | Typical Tasks                                     |\n",
+    "|-------------------------- |-------------------------------------------------------------------------------------------- |------------------------------------------------- |\n",
+    "| **Supervised Learning**    | The data comes with \"Labels\" (answers). The model learns to map inputs to these targets.     | Classification (categories), Regression (numbers) |\n",
+    "| **Unsupervised Learning**  | The data has no labels. The model looks for hidden structures or groups on its own.          | Clustering, Dimensionality Reduction              |\n",
+    "| **Reinforcement Learning** | An agent learns by interacting with an environment and receiving \"rewards\" or \"punishments.\" | Game playing, Robotics, Navigation                |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bcbb1995",
+   "metadata": {},
+   "source": [
+    "## The Machine Learning Workflow\n",
+    "\n",
+    "Building a model is rarely a linear process, but it generally follows this cycle:\n",
+    "\n",
+    "1.  **Data Collection**: Gathering raw data.\n",
+    "2.  **Data Preparation**: Cleaning, formatting, and handling missing values.\n",
+    "3.  **Exploratory Data Analysis (EDA)**: Visualizing data to understand relationships.\n",
+    "4.  **Model Selection**: Choosing the right algorithm (e.g., Linear Regression, Decision Tree).\n",
+    "5.  **Training**: Fitting the model to the data.\n",
+    "6.  **Evaluation**: Testing the model on unseen data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "544aa712",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: The Iris Dataset\n",
+    "\n",
+    "The `scikit-learn` library is the industry standard for classical machine learning in Python. It includes datasets, preprocessing tools, and algorithms.\n",
+    "\n",
+    "We will use the **Iris dataset**, the \"Hello World\" of machine learning. It contains 150 samples of iris flowers with four features and a species label."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7f2013a",
+   "metadata": {},
+   "source": [
+    "### 1. Loading the Data\n",
+    "\n",
+    "Import the necessary libraries and load the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "25736e96",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Keys: dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# Load the raw object\n",
+    "iris_data = load_iris()\n",
+    "\n",
+    "# The dataset is essentially a dictionary\n",
+    "print(\"Keys:\", iris_data.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18e7c777",
+   "metadata": {},
+   "source": [
+    "### 2. Understanding the Structure\n",
+    "\n",
+    "It is crucial to read the description to understand what features and classes we are dealing with."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b2e552fa",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ".. _iris_dataset:\n",
+      "\n",
+      "Iris plants dataset\n",
+      "--------------------\n",
+      "\n",
+      "**Data Set Characteristics:**\n",
+      "\n",
+      ":Number of Instances: 150 (50 in each of three classes)\n",
+      ":Number of Attributes: 4 numeric, predictive \n",
+      "...\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(iris_data['DESCR'][:193] + \"\\n...\") # Printing just the header"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f989d78",
+   "metadata": {},
+   "source": [
+    "### 3. Creating a DataFrame\n",
+    "\n",
+    "We convert the data into a Pandas DataFrame for easier manipulation. We also map the numeric target values (0, 1, 2) to their actual species names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "230736ee",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
+      "0                5.1               3.5                1.4               0.2   \n",
+      "1                4.9               3.0                1.4               0.2   \n",
+      "2                4.7               3.2                1.3               0.2   \n",
+      "3                4.6               3.1                1.5               0.2   \n",
+      "4                5.0               3.6                1.4               0.2   \n",
+      "\n",
+      "  species  \n",
+      "0  setosa  \n",
+      "1  setosa  \n",
+      "2  setosa  \n",
+      "3  setosa  \n",
+      "4  setosa  \n"
+     ]
+    }
+   ],
+   "source": [
+    "df_iris = pd.DataFrame(data=iris_data['data'], \n",
+    "                       columns=iris_data['feature_names'])\n",
+    "\n",
+    "# Add the target column\n",
+    "df_iris['species'] = iris_data['target_names'][iris_data['target']]\n",
+    "\n",
+    "# Display the first 5 rows\n",
+    "print(df_iris.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3976b65",
+   "metadata": {},
+   "source": [
+    "### 4. Exploratory Analysis\n",
+    "\n",
+    "Before modeling, always inspect your data.\n",
+    "\n",
+    "Check for missing values and data types:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f6a1b135",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 150 entries, 0 to 149\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   sepal length (cm)  150 non-null    float64\n",
+      " 1   sepal width (cm)   150 non-null    float64\n",
+      " 2   petal length (cm)  150 non-null    float64\n",
+      " 3   petal width (cm)   150 non-null    float64\n",
+      " 4   species            150 non-null    str    \n",
+      "dtypes: float64(4), str(1)\n",
+      "memory usage: 6.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_iris.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "184dd71c",
+   "metadata": {},
+   "source": [
+    "Look at statistical summaries (mean, std, min, max):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bf1d24af",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
+      "count         150.000000        150.000000         150.000000   \n",
+      "mean            5.843333          3.057333           3.758000   \n",
+      "std             0.828066          0.435866           1.765298   \n",
+      "min             4.300000          2.000000           1.000000   \n",
+      "25%             5.100000          2.800000           1.600000   \n",
+      "50%             5.800000          3.000000           4.350000   \n",
+      "75%             6.400000          3.300000           5.100000   \n",
+      "max             7.900000          4.400000           6.900000   \n",
+      "\n",
+      "       petal width (cm)  \n",
+      "count        150.000000  \n",
+      "mean           1.199333  \n",
+      "std            0.762238  \n",
+      "min            0.100000  \n",
+      "25%            0.300000  \n",
+      "50%            1.300000  \n",
+      "75%            1.800000  \n",
+      "max            2.500000  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df_iris.describe())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d786dac7",
+   "metadata": {},
+   "source": [
+    "Finally, save the processed data for future sessions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "cdac3f4f",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iris dataset saved to 'iris.csv'\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_iris.to_csv('../../data/iris.csv', index=False)\n",
+    "print(\"Iris dataset saved to 'iris.csv'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2d491a1",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "Now it is your turn. You will explore the ****California Housing dataset****, a regression problem where the goal is to predict house prices."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d5cc07b",
+   "metadata": {},
+   "source": [
+    "### Task 1: Load and Inspect\n",
+    "\n",
+    "-   Load the California housing dataset from `scikit-learn`.\n",
+    "-   Convert it to a DataFrame.\n",
+    "-   Print the shape of the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "df66e475",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset Shape: (20640, 9)\n",
+      "Features: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude']\n",
+      "Target: ['MedHouseVal']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import fetch_california_housing\n",
+    "\n",
+    "# fetch_california_housing allows loading directly as a frame\n",
+    "housing_data = fetch_california_housing(as_frame=True)\n",
+    "df_housing = housing_data.frame\n",
+    "\n",
+    "print(f\"Dataset Shape: {df_housing.shape}\")\n",
+    "print(f\"Features: {housing_data.feature_names}\")\n",
+    "print(f\"Target: {housing_data.target_names}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37a2ebed",
+   "metadata": {},
+   "source": [
+    "### Task 2: Statistical Summary\n",
+    "\n",
+    "-   Use `.head()` to look at the raw values.\n",
+    "-   Use `.describe()` to check the distribution of the \"MedHouseVal\" (Median House Value)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "55fa2c8b",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   MedInc  HouseAge  ...  Longitude  MedHouseVal\n",
+      "0  8.3252      41.0  ...    -122.23        4.526\n",
+      "1  8.3014      21.0  ...    -122.22        3.585\n",
+      "2  7.2574      52.0  ...    -122.24        3.521\n",
+      "3  5.6431      52.0  ...    -122.25        3.413\n",
+      "4  3.8462      52.0  ...    -122.25        3.422\n",
+      "\n",
+      "[5 rows x 9 columns]\n",
+      "\n",
+      "Statistics for Median House Value:\n",
+      "count    20640.000000\n",
+      "mean         2.068558\n",
+      "std          1.153956\n",
+      "min          0.149990\n",
+      "25%          1.196000\n",
+      "50%          1.797000\n",
+      "75%          2.647250\n",
+      "max          5.000010\n",
+      "Name: MedHouseVal, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "pd.options.display.max_columns = 5 # limit output width\n",
+    "print(df_housing.head())\n",
+    "\n",
+    "print(\"\\nStatistics for Median House Value:\")\n",
+    "print(df_housing['MedHouseVal'].describe())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4760e224",
+   "metadata": {},
+   "source": [
+    "### Task 3: Saving the Data\n",
+    "\n",
+    "-   Save the dataset to a CSV file named `california_housing.csv`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c58232f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "California housing dataset saved to 'california_housing.csv'\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_housing.to_csv('../../data/california_housing.csv', index=False)\n",
+    "print(\"California housing dataset saved to 'california_housing.csv'\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 48513d626689e1101ea0573e2255fa011df5865b Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@rug.nl>
Date: Thu, 29 Jan 2026 09:22:10 +0100
Subject: [PATCH 11/81] S01C00 Working with Jupyter on Habrok

---
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diff --git a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter on Habrok.ipynb b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter on Habrok.ipynb
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+++ b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter on Habrok.ipynb	
@@ -0,0 +1,399 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b21229c0",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Working with Jupyter Notebooks\n",
+    "\n",
+    "Throughout this workshop, we will be working in **Jupyter Notebooks** and **JupyterLab**. These are industry-standard tools for data science and machine learning that allow you to combine executable code, rich text documentation, visualizations, and mathematical equations in a single, interactive document.\n",
+    "\n",
+    "While standard Python scripts (`.py` files) are excellent for production code, Jupyter Notebooks (`.ipynb` files) excel at:\n",
+    "\n",
+    "-   **Exploratory Data Analysis (EDA):** Seeing results immediately after running a block of code.\n",
+    "-   **Documentation:** Keeping notes alongside your logic.\n",
+    "-   **Visualizing:** Rendering plots and graphs directly within the workflow.\n",
+    "\n",
+    "We will cover three ways to access this environment:\n",
+    "\n",
+    "1.  **The Habrok HPC Cluster:** (Recommended for this course)\n",
+    "2.  **Your Local Machine:** (For working offline)\n",
+    "3.  **Google Colab:** (For quick prototyping)\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09370ff1",
+   "metadata": {},
+   "source": [
+    "# Using the Habrok HPC Cluster\n",
+    "\n",
+    "For this course, we recommend using the university's High Performance Computing (HPC) cluster, **Habrok**. This ensures everyone has access to the same computational resources and hardware."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3778075",
+   "metadata": {},
+   "source": [
+    "## Accessing the Portal\n",
+    "\n",
+    "1.  Navigate to the Habrok web portal: [https://portal.hb.hpc.rug.nl](https://portal.hb.hpc.rug.nl).\n",
+    "2.  Log in with your university credentials.\n",
+    "3.  We will use the **Jupyter Python Interactive App**, but first, we need to configure a custom environment to ensure all necessary libraries are installed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9d49ccd",
+   "metadata": {},
+   "source": [
+    "## Building your own Jupyter Kernel on Habrok\n",
+    "\n",
+    "A \"Kernel\" is the computational engine that executes the code contained in a Notebook document. By creating a custom kernel, you ensure your notebook uses the specific libraries required for this workshop."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03dcf3ba",
+   "metadata": {},
+   "source": [
+    "### Step 1: Open a Shell Session\n",
+    "\n",
+    "-   From the Habrok portal, go to the **\"Clusters\"** tab.\n",
+    "-   Select **\"Habrok Login1 Shell\"** (or similar) to open a terminal."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bc86ccc",
+   "metadata": {},
+   "source": [
+    "### Step 2: Load Python\n",
+    "\n",
+    "We need to load a specific version of Python available on the cluster. Run the following command:\n",
+    "\n",
+    "```shell\n",
+    "module load Python/3.13.1-GCCcore-14.2.0\n",
+    "```\n",
+    "\n",
+    "> **Note:** If this specific version is unavailable in the future, you can search for available versions using `module spider Python`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "691efbbf",
+   "metadata": {},
+   "source": [
+    "### Step 3: Create a Virtual Environment\n",
+    "\n",
+    "It is best practice to keep project dependencies isolated. We will create a virtual environment (`venv`) in your home directory:\n",
+    "\n",
+    "```shell\n",
+    "# Create the environment in a folder named 'venvs/ml_workshop'\n",
+    "python3 -m venv $HOME/venvs/ml_workshop\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6141e6bc",
+   "metadata": {},
+   "source": [
+    "### Step 4: Activate the Environment\n",
+    "\n",
+    "Before installing packages, you must activate this environment:\n",
+    "\n",
+    "```shell\n",
+    "source $HOME/venvs/ml_workshop/bin/activate\n",
+    "```\n",
+    "\n",
+    "*(Your terminal prompt should now show `(ml_workshop)` at the start.)*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79f68a3d",
+   "metadata": {},
+   "source": [
+    "### Step 5: Install Dependencies\n",
+    "\n",
+    "Assuming you have the `requirements.txt` file in your current directory (if not, you may need to upload it or create it), install the packages:\n",
+    "\n",
+    "```shell\n",
+    "pip install -r requirements.txt\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80ef1ee1",
+   "metadata": {},
+   "source": [
+    "### Step 6: Install the Jupyter Kernel\n",
+    "\n",
+    "This step links your new virtual environment to the Jupyter interface so you can select it from a dropdown menu later.\n",
+    "\n",
+    "```shell\n",
+    "python -m ipykernel install --user --name=ml_workshop --display-name=\"ML Workshop\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20483da0",
+   "metadata": {},
+   "source": [
+    "## Starting the Jupyter App\n",
+    "\n",
+    "1.  Return to the **Habrok Web Portal**.\n",
+    "2.  Select the **Jupyter Python Interactive App**.\n",
+    "3.  **Configuration:**\n",
+    "    -   **Python Version:** Select `3.13.1` (matching the module you loaded).\n",
+    "    -   **Python Environment:** Select \"Custom virtual environment\".\n",
+    "    -   **Path:** Enter the path to your environment (e.g., `$HOME/venvs/ml_workshop`).\n",
+    "4.  Launch the app. Once inside the interface, you can ensure you are using the correct environment by checking the top-right corner of your notebook—it should say **\"ML Workshop\"**.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f74adec5",
+   "metadata": {},
+   "source": [
+    "# Building your own Kernel on a Local Machine\n",
+    "\n",
+    "Running Jupyter locally is great for offline work. However, the setup differs slightly depending on your operating system (Windows vs. macOS/Linux).\n",
+    "\n",
+    "**Prerequisite:** Ensure Python is installed ([python.org/downloads](https://www.python.org/downloads/))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a724d64e",
+   "metadata": {},
+   "source": [
+    "## Step-by-Step Local Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90e81963",
+   "metadata": {},
+   "source": [
+    "### Open your terminal\n",
+    "\n",
+    "-   **Windows:** Command Prompt or PowerShell.\n",
+    "-   **Mac/Linux:** Terminal."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6198026",
+   "metadata": {},
+   "source": [
+    "### Create the Virtual Environment\n",
+    "\n",
+    "Navigate to your project folder and run:\n",
+    "\n",
+    "```shell\n",
+    "# Windows, Mac, and Linux\n",
+    "python -m venv venv\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "198c3471",
+   "metadata": {},
+   "source": [
+    "### Activate the Environment\n",
+    "\n",
+    "This step differs by OS:\n",
+    "\n",
+    "-   **Windows:**\n",
+    "    \n",
+    "    ```shell\n",
+    "    .\\venv\\Scripts\\activate\n",
+    "    ```\n",
+    "-   **macOS / Linux:**\n",
+    "    \n",
+    "    ```shell\n",
+    "    source venv/bin/activate\n",
+    "    ```\n",
+    "\n",
+    "\n",
+    "### Install Dependencies\n",
+    "\n",
+    "Install the course requirements plus the Jupyter Lab interface (which is not included in Python by default):\n",
+    "\n",
+    "```shell\n",
+    "pip install -r requirements.txt\n",
+    "pip install jupyterlab ipykernel\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df12da40",
+   "metadata": {},
+   "source": [
+    "### Register the Kernel\n",
+    "\n",
+    "Just like on the cluster, we register this environment as a kernel:\n",
+    "\n",
+    "```shell\n",
+    "python -m ipykernel install --user --name=ml_workshop_local --display-name=\"ML Workshop Local\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "000ee778",
+   "metadata": {},
+   "source": [
+    "### 6. Launch Jupyter Lab\n",
+    "\n",
+    "Start the interface by running:\n",
+    "\n",
+    "```shell\n",
+    "jupyter-lab\n",
+    "```\n",
+    "\n",
+    "This will open a tab in your default web browser. When creating a new notebook, simply select **\"ML Workshop Local\"** from the Kernel menu.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18bdafdb",
+   "metadata": {},
+   "source": [
+    "# Working with Google Colab\n",
+    "\n",
+    "Google Colab is a cloud-based notebook environment that requires no setup to start, though it has some limitations regarding session length and file storage. It is excellent for quick prototyping or if you are having trouble with local/HPC setups."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7612aa09",
+   "metadata": {},
+   "source": [
+    "## Setup Instructions\n",
+    "\n",
+    "1.  Log in to [Google Colab](https://colab.research.google.com/) with your Google Account.\n",
+    "2.  Create a **New Notebook**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1870b283",
+   "metadata": {},
+   "source": [
+    "## Installing Requirements\n",
+    "\n",
+    "Unlike local machines, Colab does not persist your environment after you close the tab. You must install libraries **every time you start a new session**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "161372a4",
+   "metadata": {},
+   "source": [
+    "### Option A: If you have a `requirements.txt` file\n",
+    "\n",
+    "You need to upload the file to the Colab session first.\n",
+    "\n",
+    "1.  Click the **Folder icon** (Files) on the left sidebar.\n",
+    "2.  Upload your `requirements.txt`.\n",
+    "3.  Run the following in a code cell:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5b91ea5",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -r requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9d60ac0",
+   "metadata": {},
+   "source": [
+    "### Option B: Direct Installation\n",
+    "\n",
+    "If you only need a few specific packages, you can install them directly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37c3cfd2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install pandas numpy scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d413fa07",
+   "metadata": {},
+   "source": [
+    "> **Pro Tip:** Google Colab offers free access to GPUs. To enable this, go to **Runtime > Change runtime type** and select **T4 GPU** under Hardware accelerator. This can significantly speed up training for deep learning models.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c2545ab",
+   "metadata": {},
+   "source": [
+    "# Summary Checklist\n",
+    "\n",
+    "| Platform          | Best For                  | Persistence        | Computation Power         |\n",
+    "| :---              | :---                      | :---               | :---                      |\n",
+    "| **Habrok HPC**    | **Coursework & Research** | High (Files saved) | High (Cluster CPUs/GPUs)  |\n",
+    "| **Local Machine** | Offline work / Small data | High (Files saved) | Dependent on your laptop  |\n",
+    "| **Google Colab**  | Quick tests / Prototyping | Low (Resets often) | Medium (Free tier limits) |"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 238477394bf9ff596a838a2defdd66bdeb411554 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@rug.nl>
Date: Thu, 29 Jan 2026 09:32:32 +0100
Subject: [PATCH 12/81] S01C01 What is Machine Learning

---
 .../S01C01 What is Machine Learning.ipynb     | 213 +-----------------
 1 file changed, 12 insertions(+), 201 deletions(-)

diff --git a/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb b/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb
index c401efd..884b8d1 100644
--- a/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb	
@@ -108,20 +108,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "25736e96",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Keys: dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.datasets import load_iris\n",
     "import pandas as pd\n",
@@ -146,29 +138,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "b2e552fa",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      ".. _iris_dataset:\n",
-      "\n",
-      "Iris plants dataset\n",
-      "--------------------\n",
-      "\n",
-      "**Data Set Characteristics:**\n",
-      "\n",
-      ":Number of Instances: 150 (50 in each of three classes)\n",
-      ":Number of Attributes: 4 numeric, predictive \n",
-      "...\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(iris_data['DESCR'][:193] + \"\\n...\") # Printing just the header"
    ]
@@ -185,32 +160,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "230736ee",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
-      "0                5.1               3.5                1.4               0.2   \n",
-      "1                4.9               3.0                1.4               0.2   \n",
-      "2                4.7               3.2                1.3               0.2   \n",
-      "3                4.6               3.1                1.5               0.2   \n",
-      "4                5.0               3.6                1.4               0.2   \n",
-      "\n",
-      "  species  \n",
-      "0  setosa  \n",
-      "1  setosa  \n",
-      "2  setosa  \n",
-      "3  setosa  \n",
-      "4  setosa  \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "df_iris = pd.DataFrame(data=iris_data['data'], \n",
     "                       columns=iris_data['feature_names'])\n",
@@ -236,29 +191,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "f6a1b135",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.DataFrame'>\n",
-      "RangeIndex: 150 entries, 0 to 149\n",
-      "Data columns (total 5 columns):\n",
-      " #   Column             Non-Null Count  Dtype  \n",
-      "---  ------             --------------  -----  \n",
-      " 0   sepal length (cm)  150 non-null    float64\n",
-      " 1   sepal width (cm)   150 non-null    float64\n",
-      " 2   petal length (cm)  150 non-null    float64\n",
-      " 3   petal width (cm)   150 non-null    float64\n",
-      " 4   species            150 non-null    str    \n",
-      "dtypes: float64(4), str(1)\n",
-      "memory usage: 6.0 KB\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "df_iris.info()"
    ]
@@ -273,36 +209,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "bf1d24af",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
-      "count         150.000000        150.000000         150.000000   \n",
-      "mean            5.843333          3.057333           3.758000   \n",
-      "std             0.828066          0.435866           1.765298   \n",
-      "min             4.300000          2.000000           1.000000   \n",
-      "25%             5.100000          2.800000           1.600000   \n",
-      "50%             5.800000          3.000000           4.350000   \n",
-      "75%             6.400000          3.300000           5.100000   \n",
-      "max             7.900000          4.400000           6.900000   \n",
-      "\n",
-      "       petal width (cm)  \n",
-      "count        150.000000  \n",
-      "mean           1.199333  \n",
-      "std            0.762238  \n",
-      "min            0.100000  \n",
-      "25%            0.300000  \n",
-      "50%            1.300000  \n",
-      "75%            1.800000  \n",
-      "max            2.500000  \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(df_iris.describe())"
    ]
@@ -317,20 +227,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "cdac3f4f",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iris dataset saved to 'iris.csv'\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "df_iris.to_csv('../../data/iris.csv', index=False)\n",
     "print(\"Iris dataset saved to 'iris.csv'\")"
@@ -358,36 +260,6 @@
     "-   Print the shape of the dataset."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "df66e475",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset Shape: (20640, 9)\n",
-      "Features: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude']\n",
-      "Target: ['MedHouseVal']\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.datasets import fetch_california_housing\n",
-    "\n",
-    "# fetch_california_housing allows loading directly as a frame\n",
-    "housing_data = fetch_california_housing(as_frame=True)\n",
-    "df_housing = housing_data.frame\n",
-    "\n",
-    "print(f\"Dataset Shape: {df_housing.shape}\")\n",
-    "print(f\"Features: {housing_data.feature_names}\")\n",
-    "print(f\"Target: {housing_data.target_names}\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "37a2ebed",
@@ -399,48 +271,6 @@
     "-   Use `.describe()` to check the distribution of the \"MedHouseVal\" (Median House Value)."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "55fa2c8b",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   MedInc  HouseAge  ...  Longitude  MedHouseVal\n",
-      "0  8.3252      41.0  ...    -122.23        4.526\n",
-      "1  8.3014      21.0  ...    -122.22        3.585\n",
-      "2  7.2574      52.0  ...    -122.24        3.521\n",
-      "3  5.6431      52.0  ...    -122.25        3.413\n",
-      "4  3.8462      52.0  ...    -122.25        3.422\n",
-      "\n",
-      "[5 rows x 9 columns]\n",
-      "\n",
-      "Statistics for Median House Value:\n",
-      "count    20640.000000\n",
-      "mean         2.068558\n",
-      "std          1.153956\n",
-      "min          0.149990\n",
-      "25%          1.196000\n",
-      "50%          1.797000\n",
-      "75%          2.647250\n",
-      "max          5.000010\n",
-      "Name: MedHouseVal, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "pd.options.display.max_columns = 5 # limit output width\n",
-    "print(df_housing.head())\n",
-    "\n",
-    "print(\"\\nStatistics for Median House Value:\")\n",
-    "print(df_housing['MedHouseVal'].describe())"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "4760e224",
@@ -450,25 +280,6 @@
     "\n",
     "-   Save the dataset to a CSV file named `california_housing.csv`."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "c58232f1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "California housing dataset saved to 'california_housing.csv'\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_housing.to_csv('../../data/california_housing.csv', index=False)\n",
-    "print(\"California housing dataset saved to 'california_housing.csv'\")"
-   ]
   }
  ],
  "metadata": {

From 8e7356605aff60a49704c3e61624fe9a44c6e1f0 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@rug.nl>
Date: Thu, 29 Jan 2026 09:37:50 +0100
Subject: [PATCH 13/81] S01C02 The Machine Learning Workflow

---
 ...S01C02 The Machine Learning Workflow.ipynb | 341 ++++++++++++++++++
 1 file changed, 341 insertions(+)
 create mode 100644 src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb

diff --git a/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb b/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb
new file mode 100644
index 0000000..1256ce2
--- /dev/null
+++ b/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb	
@@ -0,0 +1,341 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8de14fd2",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# The Machine Learning Workflow\n",
+    "\n",
+    "A typical machine learning workflow is a structured process that guides the development of robust models. It is rarely a linear path; rather, it is an iterative cycle of improvement.\n",
+    "\n",
+    "The workflow generally consists of seven key stages:\n",
+    "\n",
+    "1.  **Problem Definition**: clearly defining the business or research problem. Is it a regression task (predicting a number) or classification (predicting a label)? What is the metric for success?\n",
+    "2.  **Data Collection**: gathering raw data from databases, APIs, web scraping, or sensors.\n",
+    "3.  **Data Exploration & Preprocessing**: analyzing data structure, handling missing values, cleaning outliers, and normalizing features. This is often the most time-consuming step.\n",
+    "4.  **Model Selection**: choosing algorithms suitable for the data and problem (e.g., Linear Regression vs. Random Forest).\n",
+    "5.  **Training & Validation**: fitting the model to the data.\n",
+    "    -   **Training Set**: used to teach the model.\n",
+    "    -   **Validation Set**: used to tune parameters.\n",
+    "6.  **Evaluation**: assessing performance on a \"held-out\" test set using metrics like Accuracy or Mean Squared Error (MSE).\n",
+    "7.  **Deployment & Monitoring**: integrating the model into a production system and monitoring for \"drift\" (when data changes over time)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5a2180c",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration\n",
+    "\n",
+    "We will walk through this workflow using the ****California Housing dataset****. This is a classic regression problem where the goal is to predict the median value of houses in a district based on features like average income, house age, and location."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9277026b",
+   "metadata": {},
+   "source": [
+    "### Load the Data\n",
+    "\n",
+    "We use the built-in loader from `scikit-learn`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31cb41ce",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_california_housing\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Set the visual style for plots\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "# Load data as a Bunch object containing the dataframe\n",
+    "housing_data = fetch_california_housing(as_frame=True)\n",
+    "df = housing_data.frame\n",
+    "\n",
+    "# Display basic metadata\n",
+    "print(\"Feature names:\", housing_data.feature_names)\n",
+    "print(\"Target variable:\", housing_data.target.name)\n",
+    "print(\"Shape:\", df.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11b4c1d7",
+   "metadata": {},
+   "source": [
+    "### Exploration (EDA)\n",
+    "\n",
+    "Before modeling, we must understand our data. We look at the first few rows and calculate the correlation between features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d28ea4f6",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "# Peek at the data\n",
+    "print(df.head())\n",
+    "\n",
+    "# Visualize the correlation matrix\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "sns.heatmap(df.corr(), annot=True, fmt=\".2f\", cmap='coolwarm', square=True)\n",
+    "plt.title('Correlation Matrix of California Housing Features')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c31103ee",
+   "metadata": {},
+   "source": [
+    "### Data Splitting\n",
+    "\n",
+    "To evaluate our model fairly, we cannot test it on the same data it learned from. We must split the data into a **Training Set** and a **Test Set**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a147acb",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Separate Features (X) and Target (y)\n",
+    "X = df.drop(columns=[housing_data.target.name])\n",
+    "y = df[housing_data.target.name]\n",
+    "\n",
+    "# Split: 80% for training, 20% for testing\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "print(f\"Training samples: {X_train.shape[0]}\")\n",
+    "print(f\"Testing samples: {X_test.shape[0]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbc9a4b5",
+   "metadata": {},
+   "source": [
+    "### Model Training\n",
+    "\n",
+    "We will start with a **Linear Regression** model. It assumes a straight-line relationship between the input features and the house price."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "358a8b18",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "# Initialize and train the model\n",
+    "model = LinearRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "print(\"Model trained successfully.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d81fd1e",
+   "metadata": {},
+   "source": [
+    "### Evaluation\n",
+    "\n",
+    "We predict values for the Test Set and compare them to the actual values. We use ****Mean Squared Error (MSE)**** and ****$R^2$ Score**** as our metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e39b94d2",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "\n",
+    "# Generate predictions\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "# Calculate metrics\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "r2 = model.score(X_test, y_test)\n",
+    "\n",
+    "print(f\"MSE on test set: {mse:.2f}\")\n",
+    "print(f\"R-squared on test set: {r2:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca2f9c89",
+   "metadata": {},
+   "source": [
+    "### Visualization\n",
+    "\n",
+    "A scatter plot of Predicted vs. Actual values helps us visually assess accuracy. A perfect model would align all points on the diagonal red line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb9fc1b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.regplot(x=y_test, y=y_pred, \n",
+    "            scatter_kws={'alpha':0.5}, \n",
+    "            line_kws={'color':'red'})\n",
+    "plt.xlabel(\"Actual Values (Target)\")\n",
+    "plt.ylabel(\"Predicted Values (Model Output)\")\n",
+    "plt.title(\"Linear Regression: Actual vs Predicted Values\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "443a43a8",
+   "metadata": {},
+   "source": [
+    "Finally, we save the dataset for future modules."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6219e7e",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "df.to_csv('../../data/california_housing.csv', index=False)\n",
+    "print(\"California housing dataset saved.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49db65bd",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "Now, you will apply this workflow to the ****Ames Housing dataset****. This is a more complex dataset containing 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f7f28d9",
+   "metadata": {},
+   "source": [
+    "### Task 1: Load and Inspect\n",
+    "\n",
+    "-   Load the dataset using `fetch_openml`.\n",
+    "-   Print the number of samples and features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24d1a856",
+   "metadata": {},
+   "source": [
+    "### Task 2: Feature Selection via Correlation\n",
+    "\n",
+    "The dataset is large. To keep things simple for this exercise:\n",
+    "\n",
+    "1.  Calculate correlations for numeric columns only.\n",
+    "2.  Select only the feature with the highest correlation to `SalePrice`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c331bbf8",
+   "metadata": {},
+   "source": [
+    "### Task 3: Train a Random Forest\n",
+    "\n",
+    "Instead of Linear Regression, use a ****Random Forest Regressor****. This is a more powerful model capable of capturing non-linear relationships.\n",
+    "\n",
+    "-   Split the data (80/20).\n",
+    "-   Train the model.\n",
+    "-   Evaluate with MSE and $R^2$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdc2cdb2",
+   "metadata": {},
+   "source": [
+    "### Task 4: Visualization\n",
+    "\n",
+    "Visualize the Random Forest predictions. Note how the points might cluster differently compared to Linear Regression."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "272198e4",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this module, we moved from theory to practice. We executed the full ****Machine Learning Workflow****:\n",
+    "\n",
+    "1.  Loaded and cleaned data.\n",
+    "2.  Visualized relationships (Correlation).\n",
+    "3.  Split data to prevent overfitting.\n",
+    "4.  Trained two different models (Linear Regression and Random Forest).\n",
+    "5.  Evaluated success using standard metrics."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From f39ea12f7ed927d039366e4d768a7ad24fb1904e Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@rug.nl>
Date: Thu, 29 Jan 2026 09:50:56 +0100
Subject: [PATCH 14/81] S01C00 Working with Jupyter on Habrok

---
 .../S01C00 Working with Jupyter on Habrok.ipynb                 | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter on Habrok.ipynb b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter on Habrok.ipynb
index a62617e..bdfbbd1 100644
--- a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter on Habrok.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter on Habrok.ipynb	
@@ -257,7 +257,7 @@
    "id": "000ee778",
    "metadata": {},
    "source": [
-    "### 6. Launch Jupyter Lab\n",
+    "### Launch Jupyter Lab\n",
     "\n",
     "Start the interface by running:\n",
     "\n",

From f826d1bea4db5aaf30374c5674a211536b30463a Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@rug.nl>
Date: Thu, 29 Jan 2026 10:10:13 +0100
Subject: [PATCH 15/81] S01C01 What is Machine Learning

---
 .../S01C01 What is Machine Learning.ipynb        | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb b/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb
index 884b8d1..313d724 100644
--- a/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb	
@@ -22,7 +22,7 @@
     "\n",
     "In traditional programming, a human programmer analyzes a problem and writes explicit rules (code) for the computer to follow. In machine learning, the computer analyzes data to find the rules itself.\n",
     "\n",
-    "To illustrate this, consider building a ****Spam Filter****:\n",
+    "To illustrate this, consider building a **Spam Filter**:\n",
     "\n",
     "-   **Traditional Approach**: You write rules like \"If the email contains 'lottery', mark as spam.\" You have to manually update these rules constantly.\n",
     "-   **ML Approach**: You feed the algorithm thousands of emails labeled \"Spam\" or \"Not Spam.\" The algorithm learns which words (and combinations of words) are statistically correlated with spam.\n",
@@ -101,7 +101,7 @@
    "id": "f7f2013a",
    "metadata": {},
    "source": [
-    "### 1. Loading the Data\n",
+    "### Loading the Data\n",
     "\n",
     "Import the necessary libraries and load the dataset."
    ]
@@ -131,7 +131,7 @@
    "id": "18e7c777",
    "metadata": {},
    "source": [
-    "### 2. Understanding the Structure\n",
+    "### Understanding the Structure\n",
     "\n",
     "It is crucial to read the description to understand what features and classes we are dealing with."
    ]
@@ -153,7 +153,7 @@
    "id": "2f989d78",
    "metadata": {},
    "source": [
-    "### 3. Creating a DataFrame\n",
+    "### Creating a DataFrame\n",
     "\n",
     "We convert the data into a Pandas DataFrame for easier manipulation. We also map the numeric target values (0, 1, 2) to their actual species names."
    ]
@@ -182,7 +182,7 @@
    "id": "b3976b65",
    "metadata": {},
    "source": [
-    "### 4. Exploratory Analysis\n",
+    "### Exploratory Analysis\n",
     "\n",
     "Before modeling, always inspect your data.\n",
     "\n",
@@ -253,7 +253,7 @@
    "id": "3d5cc07b",
    "metadata": {},
    "source": [
-    "### Task 1: Load and Inspect\n",
+    "### Load and Inspect\n",
     "\n",
     "-   Load the California housing dataset from `scikit-learn`.\n",
     "-   Convert it to a DataFrame.\n",
@@ -265,7 +265,7 @@
    "id": "37a2ebed",
    "metadata": {},
    "source": [
-    "### Task 2: Statistical Summary\n",
+    "### Statistical Summary\n",
     "\n",
     "-   Use `.head()` to look at the raw values.\n",
     "-   Use `.describe()` to check the distribution of the \"MedHouseVal\" (Median House Value)."
@@ -276,7 +276,7 @@
    "id": "4760e224",
    "metadata": {},
    "source": [
-    "### Task 3: Saving the Data\n",
+    "### Saving the Data\n",
     "\n",
     "-   Save the dataset to a CSV file named `california_housing.csv`."
    ]

From b89b2d8e4803a649da486863971c904f7a0a5533 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@rug.nl>
Date: Thu, 29 Jan 2026 10:13:09 +0100
Subject: [PATCH 16/81] S01C02 The Machine Learning Workflow

---
 .../S01C02 The Machine Learning Workflow.ipynb            | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb b/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb
index 1256ce2..a336f3d 100644
--- a/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb	
@@ -254,7 +254,7 @@
    "id": "8f7f28d9",
    "metadata": {},
    "source": [
-    "### Task 1: Load and Inspect\n",
+    "### Load and Inspect\n",
     "\n",
     "-   Load the dataset using `fetch_openml`.\n",
     "-   Print the number of samples and features."
@@ -265,7 +265,7 @@
    "id": "24d1a856",
    "metadata": {},
    "source": [
-    "### Task 2: Feature Selection via Correlation\n",
+    "### Feature Selection via Correlation\n",
     "\n",
     "The dataset is large. To keep things simple for this exercise:\n",
     "\n",
@@ -278,7 +278,7 @@
    "id": "c331bbf8",
    "metadata": {},
    "source": [
-    "### Task 3: Train a Random Forest\n",
+    "### Train a Random Forest\n",
     "\n",
     "Instead of Linear Regression, use a ****Random Forest Regressor****. This is a more powerful model capable of capturing non-linear relationships.\n",
     "\n",
@@ -292,7 +292,7 @@
    "id": "bdc2cdb2",
    "metadata": {},
    "source": [
-    "### Task 4: Visualization\n",
+    "### Visualization\n",
     "\n",
     "Visualize the Random Forest predictions. Note how the points might cluster differently compared to Linear Regression."
    ]

From e8460d832970068d95eb42db7ee2e1c703a6e978 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@rug.nl>
Date: Thu, 29 Jan 2026 10:22:15 +0100
Subject: [PATCH 17/81] S01C03 Exploratory Data Analysis (EDA)

---
 ...1C03 Exploratory Data Analysis (EDA).ipynb | 348 ++++++++++++++++++
 1 file changed, 348 insertions(+)
 create mode 100644 src/S01 Introduction to Machine Learning/S01C03 Exploratory Data Analysis (EDA).ipynb

diff --git a/src/S01 Introduction to Machine Learning/S01C03 Exploratory Data Analysis (EDA).ipynb b/src/S01 Introduction to Machine Learning/S01C03 Exploratory Data Analysis (EDA).ipynb
new file mode 100644
index 0000000..621ecc6
--- /dev/null
+++ b/src/S01 Introduction to Machine Learning/S01C03 Exploratory Data Analysis (EDA).ipynb	
@@ -0,0 +1,348 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "110f4a09",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Exploratory Data Analysis (EDA)\n",
+    "\n",
+    "**Exploratory Data Analysis** (EDA) is the detective work of data science. Before any modeling begins, we must understand the nature of the evidence we have collected. It involves summarizing the main characteristics of a dataset, often using visual and statistical methods.\n",
+    "\n",
+    "The primary goal is not just to make pretty charts, but to \"get a feel\" for the data—understanding its structure, its quirks, and its potential pitfalls."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f751ea9c",
+   "metadata": {},
+   "source": [
+    "## Goals of EDA\n",
+    "\n",
+    "EDA helps us answer critical questions:\n",
+    "\n",
+    "-   **Structure**: What are the data types? How many observations do we have?\n",
+    "-   **Quality**: Are there missing values? Are there duplicate rows?\n",
+    "-   **Outliers**: Are there values that don't make sense (e.g., a house with 0 rooms or negative price)?\n",
+    "-   **Patterns**: How is the data distributed? Is it skewed?\n",
+    "-   **Relationships**: Do certain features correlate with the target variable?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dafedba",
+   "metadata": {},
+   "source": [
+    "## Typical Techniques\n",
+    "\n",
+    "We typically alternate between statistical summaries and visualizations:\n",
+    "\n",
+    "| Technique                | Purpose                                                                       |\n",
+    "|------------------------ |----------------------------------------------------------------------------- |\n",
+    "| **Summary Statistics**   | Quantitative overview (Mean, Median, Standard Deviation, Quantiles).          |\n",
+    "| **Histograms**           | Understanding the distribution of a single variable (is it Normal/Gaussian?). |\n",
+    "| **Box Plots**            | Identifying outliers and visualizing the spread of data.                      |\n",
+    "| **Scatter Plots**        | Visualizing the relationship between two continuous variables.                |\n",
+    "| **Correlation Heatmaps** | Quickly identifying which variables move together.                            |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68d420ea",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: California Housing\n",
+    "\n",
+    "We will dissect the California Housing dataset to see what insights we can glean before modeling."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "760e31b4",
+   "metadata": {},
+   "source": [
+    "### Load and Inspect\n",
+    "\n",
+    "First, we load the data and look at the basic info."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4791037",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_california_housing\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Set plot style\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "# Load the dataset\n",
+    "data = fetch_california_housing(as_frame=True)\n",
+    "df = data.frame\n",
+    "\n",
+    "# Check data types and non-null counts\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d37fd39",
+   "metadata": {},
+   "source": [
+    "### Descriptive Statistics\n",
+    "\n",
+    "We use `.describe()` to look at the central tendency and spread. **Tip**: Look at the min and max values to check for sanity. For example, if 'AveRooms' had a max of 10,000, that would be suspicious."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8259d18f",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "print(df.describe().T) # Transpose (.T) makes it easier to read with many columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddee77a7",
+   "metadata": {},
+   "source": [
+    "### Checking for Missing Values\n",
+    "\n",
+    "Real-world data is rarely clean. We must check for nulls."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03ba5c36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "missing_counts = df.isnull().sum()\n",
+    "print(\"Missing values per column:\")\n",
+    "print(missing_counts[missing_counts > 0]) # Only print columns with missing data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c0e5ce2",
+   "metadata": {},
+   "source": [
+    "**(Note: This dataset is pre-cleaned, so we expect 0 missing values.)**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "755d2393",
+   "metadata": {},
+   "source": [
+    "### Univariate Analysis (Distributions)\n",
+    "\n",
+    "Let's analyze the Target Variable (`MedHouseVal`) and a key feature (`MedInc`). We use histograms to see the shape of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e5529d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(12, 5))\n",
+    "\n",
+    "# Plot 1: House Value\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.histplot(df['MedHouseVal'], bins=50, kde=True, color='teal')\n",
+    "plt.title('Distribution of Median House Value')\n",
+    "plt.xlabel('Value ($100k)')\n",
+    "\n",
+    "# Plot 2: Income\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.histplot(df['MedInc'], bins=50, kde=True, color='orange')\n",
+    "plt.title('Distribution of Median Income')\n",
+    "plt.xlabel('Income (Tens of Thousands)')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e028a9f",
+   "metadata": {},
+   "source": [
+    "**Observation**: Notice the spike at the far right of the House Value graph? That indicates \"censored\" data—values higher than 5.0 were likely capped at 5.0 during data collection. This is a critical finding for modeling!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bc93b82",
+   "metadata": {},
+   "source": [
+    "### Bivariate Analysis (Relationships)\n",
+    "\n",
+    "We use a Correlation Matrix to identify linear relationships."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d25c87a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10, 8))\n",
+    "\n",
+    "# Compute correlation\n",
+    "corr_matrix = df.corr()\n",
+    "\n",
+    "# Plot heatmap\n",
+    "sns.heatmap(corr_matrix, annot=True, fmt=\".2f\", cmap='coolwarm', vmin=-1, vmax=1)\n",
+    "plt.title('Correlation Matrix')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "788b3e7a",
+   "metadata": {},
+   "source": [
+    "We can also visualize specific relationships using scatter plots. Since the dataset is large (20k+ rows), we sample it to make the plot clearer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98b950a4",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "# Sample 5% of the data for cleaner plotting\n",
+    "df_sample = df.sample(frac=0.05, random_state=42)\n",
+    "\n",
+    "sns.pairplot(df_sample, \n",
+    "             vars=['MedHouseVal', 'MedInc', 'AveRooms', 'HouseAge'], \n",
+    "             diag_kind='kde', \n",
+    "             plot_kws={'alpha': 0.5})\n",
+    "plt.suptitle('Pairplot of Selected Features (5% Sample)', y=1.02)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5117a39f",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "Now, apply these techniques to the ****Ames Housing dataset****. This dataset is much \"dirtier\" and wider than California Housing, making EDA even more important."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2aeb3d66",
+   "metadata": {},
+   "source": [
+    "### Load and Inspect\n",
+    "\n",
+    "-   Load the dataset.\n",
+    "-   Print the shape.\n",
+    "-   Identify how many columns are numerical vs. categorical (object)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4591e35",
+   "metadata": {},
+   "source": [
+    "### Missing Value Hunt\n",
+    "\n",
+    "Unlike California, Ames has missing data.\n",
+    "\n",
+    "-   Calculate the percentage of missing values for each column.\n",
+    "-   Display the top 10 columns with the most missing data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "411fb3c4",
+   "metadata": {},
+   "source": [
+    "### Visualizing Categories (Boxplots)\n",
+    "\n",
+    "We want to see how categorical features affect the price.\n",
+    "\n",
+    "-   Create a boxplot for `OverallQual` (Overall Quality) vs. `SalePrice`.\n",
+    "-   This shows how the median price changes with quality rating."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15b0334e",
+   "metadata": {},
+   "source": [
+    "### Target Distribution\n",
+    "\n",
+    "-   Plot the distribution of `SalePrice`.\n",
+    "-   **Bonus**: Is it skewed? (Real estate prices usually are)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bfa5837",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this module, we practiced the art of ****Exploratory Data Analysis****.\n",
+    "\n",
+    "1.  We inspected data structure and types using `.info()` and `.describe()`.\n",
+    "2.  We visualized distributions using ****Histograms**** and discovered data capping in the California dataset.\n",
+    "3.  We analyzed relationships using ****Heatmaps**** and ****Scatter Plots****.\n",
+    "4.  In the exercises, we tackled real-world issues like missing values and categorical data visualization in the Ames dataset.\n",
+    "\n",
+    "Remember: **Garbage In, Garbage Out**. EDA is your primary defense against bad models."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 5e908c543a4e1fad102acd8cfed1fb30b9f2861b Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@rug.nl>
Date: Thu, 29 Jan 2026 15:20:24 +0100
Subject: [PATCH 18/81] S01C00 Working with Jupyter

---
 ...pynb => S01C00 Working with Jupyter.ipynb} | 54 +++++++++----------
 1 file changed, 27 insertions(+), 27 deletions(-)
 rename src/S01 Introduction to Machine Learning/{S01C00 Working with Jupyter on Habrok.ipynb => S01C00 Working with Jupyter.ipynb} (95%)

diff --git a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter on Habrok.ipynb b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb
similarity index 95%
rename from src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter on Habrok.ipynb
rename to src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb
index bdfbbd1..4167b46 100644
--- a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter on Habrok.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b21229c0",
+   "id": "5e23e866",
    "metadata": {},
    "source": [
     "\n",
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09370ff1",
+   "id": "a2bd4e99",
    "metadata": {},
    "source": [
     "# Using the Habrok HPC Cluster\n",
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b3778075",
+   "id": "bf3ebf57",
    "metadata": {},
    "source": [
     "## Accessing the Portal\n",
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c9d49ccd",
+   "id": "56772ca4",
    "metadata": {},
    "source": [
     "## Building your own Jupyter Kernel on Habrok\n",
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "03dcf3ba",
+   "id": "20481db1",
    "metadata": {},
    "source": [
     "### Step 1: Open a Shell Session\n",
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4bc86ccc",
+   "id": "0083affb",
    "metadata": {},
    "source": [
     "### Step 2: Load Python\n",
@@ -86,7 +86,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "691efbbf",
+   "id": "44d9ac12",
    "metadata": {},
    "source": [
     "### Step 3: Create a Virtual Environment\n",
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6141e6bc",
+   "id": "297627a9",
    "metadata": {},
    "source": [
     "### Step 4: Activate the Environment\n",
@@ -117,7 +117,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79f68a3d",
+   "id": "d1555713",
    "metadata": {},
    "source": [
     "### Step 5: Install Dependencies\n",
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80ef1ee1",
+   "id": "caf1c879",
    "metadata": {},
    "source": [
     "### Step 6: Install the Jupyter Kernel\n",
@@ -145,7 +145,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "20483da0",
+   "id": "b013ac18",
    "metadata": {},
    "source": [
     "## Starting the Jupyter App\n",
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f74adec5",
+   "id": "7de946ae",
    "metadata": {},
    "source": [
     "# Building your own Kernel on a Local Machine\n",
@@ -175,7 +175,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a724d64e",
+   "id": "a63057e4",
    "metadata": {},
    "source": [
     "## Step-by-Step Local Setup"
@@ -183,7 +183,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "90e81963",
+   "id": "6b3e739e",
    "metadata": {},
    "source": [
     "### Open your terminal\n",
@@ -194,7 +194,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e6198026",
+   "id": "f7d11316",
    "metadata": {},
    "source": [
     "### Create the Virtual Environment\n",
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "198c3471",
+   "id": "9623a60e",
    "metadata": {},
    "source": [
     "### Activate the Environment\n",
@@ -240,7 +240,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "df12da40",
+   "id": "c9196874",
    "metadata": {},
    "source": [
     "### Register the Kernel\n",
@@ -254,7 +254,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "000ee778",
+   "id": "7b771a0a",
    "metadata": {},
    "source": [
     "### Launch Jupyter Lab\n",
@@ -272,7 +272,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18bdafdb",
+   "id": "f189d5fc",
    "metadata": {},
    "source": [
     "# Working with Google Colab\n",
@@ -282,7 +282,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7612aa09",
+   "id": "8ca1d6dc",
    "metadata": {},
    "source": [
     "## Setup Instructions\n",
@@ -293,7 +293,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1870b283",
+   "id": "012054e4",
    "metadata": {},
    "source": [
     "## Installing Requirements\n",
@@ -303,7 +303,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "161372a4",
+   "id": "334efc17",
    "metadata": {},
    "source": [
     "### Option A: If you have a `requirements.txt` file\n",
@@ -318,7 +318,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d5b91ea5",
+   "id": "7910fbba",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -329,7 +329,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9d60ac0",
+   "id": "da060376",
    "metadata": {},
    "source": [
     "### Option B: Direct Installation\n",
@@ -340,7 +340,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "37c3cfd2",
+   "id": "693806b0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -349,7 +349,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d413fa07",
+   "id": "4a390f02",
    "metadata": {},
    "source": [
     "> **Pro Tip:** Google Colab offers free access to GPUs. To enable this, go to **Runtime > Change runtime type** and select **T4 GPU** under Hardware accelerator. This can significantly speed up training for deep learning models.\n",
@@ -359,7 +359,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c2545ab",
+   "id": "4e3ebfca",
    "metadata": {},
    "source": [
     "# Summary Checklist\n",

From e014d7a9cba384dac01682f460727e28e3c18862 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@rug.nl>
Date: Thu, 29 Jan 2026 15:28:14 +0100
Subject: [PATCH 19/81] Added info about HB accounts

---
 .../S01C00 Working with Jupyter.ipynb                     | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb
index 4167b46..d4d5375 100644
--- a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb	
@@ -32,7 +32,13 @@
    "source": [
     "# Using the Habrok HPC Cluster\n",
     "\n",
-    "For this course, we recommend using the university's High Performance Computing (HPC) cluster, **Habrok**. This ensures everyone has access to the same computational resources and hardware."
+    "For this course, we recommend using the university's High Performance Computing (HPC) cluster, **Habrok**. This ensures everyone has access to the same computational resources and hardware.\n",
+    "\n",
+    "**NOTE**:\n",
+    "\n",
+    "> You need to have a University of Groningen account to use Habrok. Your account also needs to be enable on Habrok, which you can request by following the [info](https://wiki.hpc.rug.nl/habrok/introduction/policies) on the wiki.\n",
+    "> \n",
+    "> If you don't have a University account, you will have to use a local Jupyter installation, or Google Colab, as explained below."
    ]
   },
   {

From f545c310ca0f8fefa4ffb407914435c5fbb79333 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@rug.nl>
Date: Thu, 29 Jan 2026 16:26:25 +0100
Subject: [PATCH 20/81] Updated README.md

---
 README.md | 44 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 44 insertions(+)

diff --git a/README.md b/README.md
index 2118855..471155a 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,45 @@
 # Machine Learning in Python
+
+This repository is associated with the "Machine Learning in Python" training. The lastest iteration of this training is February 2026, and the material for this iteration can be found in the `feb2026` branch of the repository.
+
+
+## Introduction
+
+The training is structured in six sessions spread over three weeks, with two sessions per week.
+
+Each session is four hours long, with a total in-class time for the entire training of 24 hours. To get the most benefit from the training, we recommend that you spend as much time outside of the class working on your own, with your own datasets.
+
+Each session is made up of several concepts, and each concept is structured as follows:
+
+-   A concept is introduced via slides and in a Jupyter notebook
+-   You will be shown some practical examples that illustrate the concept
+-   You will be asked to write your own code that uses the concept to solve a small problem
+-   I will show you how I solved the same problem
+
+
+## Working with Jupyter
+
+Python is a very popular programming language for Machine Learning, and it is what we will use in this training.
+
+When dealing with heavier computations, the Python code is usually placed in a `.py` file which is then executed as a whole. In a prototyping or teaching situation, though, it is best to do things more intearctively, which is why we will use [Jupyter](https://jupyter.org/) notebooks via `JupyterLab` to arrange our code.
+
+During the first session, we will cover three ways of running Jupyter, but we will only use one during the training (as much as possible):
+
+
+### Jupyter on Habrok
+
+For this training, we recommend using the University of Groningen's High Performance Computing (HPC) cluster, **Habrok**. This ensures everyone has access to the same computational resources and hardware.
+
+**NOTE**:
+
+> You need to have a University of Groningen account to use Habrok. Your account also needs to be enable on Habrok, which you can request by following the [info](https://wiki.hpc.rug.nl/habrok/introduction/policies) on the wiki.
+
+
+### Jupyter locally
+
+If you don't have a University account, or prefer to run things locally on your computer, you will need to install Jupyter (and Python) locally. We give a few pointers on how to do this, but it depends on the operating system you work with, so we do not go into details.
+
+
+### Jupyter in Google
+
+If neither of the above options is workable for you, you can use [Google Colab](https://colab.google/), which is an online platform provided by Google to run Jupyter. Although this is easy to set up and requires minimum configuration, we do not recommend it for long-term usage.

From f49eff571bc5e3aeb6685de3e8f07e86b221719e Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Fri, 30 Jan 2026 06:18:02 +0100
Subject: [PATCH 21/81] Modified .gitignore

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 481866b..2ce5ae6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,4 +6,5 @@ src/S*/*.md
 uv.lock
 .python-version
 pyproject.toml
+jupytext.toml
 data/
\ No newline at end of file

From f73d40880757eb6495ca9867a9710765e81c5b2c Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Fri, 30 Jan 2026 06:29:12 +0100
Subject: [PATCH 22/81] S01C00 Working with Jupyter

---
 .../S01C00 Working with Jupyter.ipynb         | 40 ++++++++++---------
 1 file changed, 22 insertions(+), 18 deletions(-)

diff --git a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb
index d4d5375..8143c16 100644
--- a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb	
@@ -30,15 +30,7 @@
    "id": "a2bd4e99",
    "metadata": {},
    "source": [
-    "# Using the Habrok HPC Cluster\n",
-    "\n",
-    "For this course, we recommend using the university's High Performance Computing (HPC) cluster, **Habrok**. This ensures everyone has access to the same computational resources and hardware.\n",
-    "\n",
-    "**NOTE**:\n",
-    "\n",
-    "> You need to have a University of Groningen account to use Habrok. Your account also needs to be enable on Habrok, which you can request by following the [info](https://wiki.hpc.rug.nl/habrok/introduction/policies) on the wiki.\n",
-    "> \n",
-    "> If you don't have a University account, you will have to use a local Jupyter installation, or Google Colab, as explained below."
+    "# Using the Habrok HPC Cluster"
    ]
   },
   {
@@ -66,19 +58,31 @@
   {
    "cell_type": "markdown",
    "id": "20481db1",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
    "source": [
     "### Step 1: Open a Shell Session\n",
     "\n",
     "-   From the Habrok portal, go to the **\"Clusters\"** tab.\n",
-    "-   Select **\"Habrok Login1 Shell\"** (or similar) to open a terminal."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0083affb",
-   "metadata": {},
-   "source": [
+    "-   Select **\"Habrok Login1 Shell\"** (or similar) to open a terminal.\n",
+    "-   Clone the GitHub repository\n",
+    "    \n",
+    "    ```shell\n",
+    "    git clone https://github.com/UG-Team-Data-Science/ml-workshop.git /scratch/$USER\n",
+    "    ```\n",
+    "-   Go to the code folder\n",
+    "    \n",
+    "    ```shell\n",
+    "    cd /scratch/$USER/ml-workshop\n",
+    "    ```\n",
+    "-   Checkout the most current branch\n",
+    "    \n",
+    "    ```shell\n",
+    "    git checkout feb2026\n",
+    "    ```\n",
+    "\n",
+    "\n",
     "### Step 2: Load Python\n",
     "\n",
     "We need to load a specific version of Python available on the cluster. Run the following command:\n",

From 5902f4638607b7802251024fc5e3c8f2723c420e Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Fri, 30 Jan 2026 06:31:11 +0100
Subject: [PATCH 23/81] S01C00 Working with Jupyter

---
 .../S01C00 Working with Jupyter.ipynb                       | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb
index 8143c16..f702abe 100644
--- a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb	
@@ -58,9 +58,7 @@
   {
    "cell_type": "markdown",
    "id": "20481db1",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "### Step 1: Open a Shell Session\n",
     "\n",
@@ -69,7 +67,7 @@
     "-   Clone the GitHub repository\n",
     "    \n",
     "    ```shell\n",
-    "    git clone https://github.com/UG-Team-Data-Science/ml-workshop.git /scratch/$USER\n",
+    "    git clone https://github.com/UG-Team-Data-Science/ml-workshop.git /scratch/$USER/ml-workshop\n",
     "    ```\n",
     "-   Go to the code folder\n",
     "    \n",

From c9dcb9bd88a4aeebbc376660549ac8dbc3e16184 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Fri, 30 Jan 2026 08:12:54 +0100
Subject: [PATCH 24/81] Updated S01C00 Working with Jupyter

---
 .../S01C00 Working with Jupyter.ipynb                           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb
index f702abe..0584b6b 100644
--- a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb	
@@ -103,7 +103,7 @@
     "\n",
     "```shell\n",
     "# Create the environment in a folder named 'venvs/ml_workshop'\n",
-    "python3 -m venv $HOME/venvs/ml_workshop\n",
+    "python3 -m venv $HOME/venvs/ml-workshop\n",
     "```"
    ]
   },

From 3920b3f5a39327d6dca8115ade53a16917128f5e Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Fri, 30 Jan 2026 08:14:11 +0100
Subject: [PATCH 25/81] Removed comment, updated venv name

---
 .../S01C00 Working with Jupyter.ipynb                        | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb
index 0584b6b..867550e 100644
--- a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb	
@@ -102,7 +102,6 @@
     "It is best practice to keep project dependencies isolated. We will create a virtual environment (`venv`) in your home directory:\n",
     "\n",
     "```shell\n",
-    "# Create the environment in a folder named 'venvs/ml_workshop'\n",
     "python3 -m venv $HOME/venvs/ml-workshop\n",
     "```"
    ]
@@ -117,10 +116,10 @@
     "Before installing packages, you must activate this environment:\n",
     "\n",
     "```shell\n",
-    "source $HOME/venvs/ml_workshop/bin/activate\n",
+    "source $HOME/venvs/ml-workshop/bin/activate\n",
     "```\n",
     "\n",
-    "*(Your terminal prompt should now show `(ml_workshop)` at the start.)*"
+    "*(Your terminal prompt should now show `(ml-workshop)` at the start.)*"
    ]
   },
   {

From be8272b76ac0553c5321e215b0b45da484b40fb9 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Fri, 30 Jan 2026 08:31:42 +0100
Subject: [PATCH 26/81] Removed torch entries in requirements.txt

---
 src/requirements.txt | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/src/requirements.txt b/src/requirements.txt
index 6000c69..b39773b 100644
--- a/src/requirements.txt
+++ b/src/requirements.txt
@@ -6,6 +6,3 @@ matplotlib
 seaborn
 xgboost
 statsmodels
-torch
-torchvision
-transformers

From 07f1b2ed51edff6c5089b0444182834044452c4b Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Fri, 30 Jan 2026 09:40:33 +0100
Subject: [PATCH 27/81] Added .placeholder for data

---
 .gitignore        | 2 +-
 data/.placeholder | 0
 2 files changed, 1 insertion(+), 1 deletion(-)
 create mode 100644 data/.placeholder

diff --git a/.gitignore b/.gitignore
index 2ce5ae6..6fdbd90 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,4 +7,4 @@ uv.lock
 .python-version
 pyproject.toml
 jupytext.toml
-data/
\ No newline at end of file
+data/*.csv
\ No newline at end of file
diff --git a/data/.placeholder b/data/.placeholder
new file mode 100644
index 0000000..e69de29

From e4c413d3cfc5134b6d48f66c7c6aaebf94fa7d0c Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Fri, 30 Jan 2026 09:41:11 +0100
Subject: [PATCH 28/81] S01C00 Working with Jupyter

---
 .../S01C00 Working with Jupyter.ipynb         | 20 ++++++++++++-------
 1 file changed, 13 insertions(+), 7 deletions(-)

diff --git a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb
index 867550e..5d6cc98 100644
--- a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb	
@@ -99,10 +99,10 @@
    "source": [
     "### Step 3: Create a Virtual Environment\n",
     "\n",
-    "It is best practice to keep project dependencies isolated. We will create a virtual environment (`venv`) in your home directory:\n",
+    "It is best practice to keep project dependencies isolated. We will create a virtual environment (`venv`) in your `/scratch/$USER` directory:\n",
     "\n",
     "```shell\n",
-    "python3 -m venv $HOME/venvs/ml-workshop\n",
+    "python3 -m venv /scratch/$USER/venvs/ml-workshop\n",
     "```"
    ]
   },
@@ -116,10 +116,16 @@
     "Before installing packages, you must activate this environment:\n",
     "\n",
     "```shell\n",
-    "source $HOME/venvs/ml-workshop/bin/activate\n",
+    "source /scratch/$USER/venvs/ml-workshop/bin/activate\n",
     "```\n",
     "\n",
-    "*(Your terminal prompt should now show `(ml-workshop)` at the start.)*"
+    "*(Your terminal prompt should now show `(ml-workshop)` at the start.)*\n",
+    "\n",
+    "**Optional**: Update the `pip` Python package manager to the latest verstion:\n",
+    "\n",
+    "```shell\n",
+    "pip install --upgrade pip\n",
+    "```"
    ]
   },
   {
@@ -129,10 +135,10 @@
    "source": [
     "### Step 5: Install Dependencies\n",
     "\n",
-    "Assuming you have the `requirements.txt` file in your current directory (if not, you may need to upload it or create it), install the packages:\n",
+    "You can install the necessary dependencies from the `requirements.txt` file, which can be found in the `src/` folder in the repository. If you've followed the above steps precisely, you should now be in the root of the repository (`/scratch/$USER/ml-workshop`), and you can install the dependencies with:\n",
     "\n",
     "```shell\n",
-    "pip install -r requirements.txt\n",
+    "pip install -r src/requirements.txt\n",
     "```"
    ]
   },
@@ -158,7 +164,7 @@
     "## Starting the Jupyter App\n",
     "\n",
     "1.  Return to the **Habrok Web Portal**.\n",
-    "2.  Select the **Jupyter Python Interactive App**.\n",
+    "2.  From the **Interactive Apps** menu, select the **Jupyter (basic)** entry.\n",
     "3.  **Configuration:**\n",
     "    -   **Python Version:** Select `3.13.1` (matching the module you loaded).\n",
     "    -   **Python Environment:** Select \"Custom virtual environment\".\n",

From 6b8567d906f23d46241da364d1231f4120c3341b Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Sat, 31 Jan 2026 07:01:31 +0100
Subject: [PATCH 29/81] S01C01 What is Machine Learning

---
 .../S01C01 What is Machine Learning.ipynb                       | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb b/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb
index 313d724..3086bef 100644
--- a/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb	
@@ -76,7 +76,7 @@
     "\n",
     "Building a model is rarely a linear process, but it generally follows this cycle:\n",
     "\n",
-    "1.  **Data Collection**: Gathering raw data.\n",
+    "1.  *Data Collection*: Gathering raw data.\n",
     "2.  **Data Preparation**: Cleaning, formatting, and handling missing values.\n",
     "3.  **Exploratory Data Analysis (EDA)**: Visualizing data to understand relationships.\n",
     "4.  **Model Selection**: Choosing the right algorithm (e.g., Linear Regression, Decision Tree).\n",

From 8111a6862a292e3c00fdbb44d530fb1d60c7c0a2 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Sat, 31 Jan 2026 07:05:13 +0100
Subject: [PATCH 30/81] Updated ML Workflow

---
 .../S01C01 What is Machine Learning.ipynb                      | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb b/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb
index 3086bef..7532814 100644
--- a/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb	
@@ -81,7 +81,8 @@
     "3.  **Exploratory Data Analysis (EDA)**: Visualizing data to understand relationships.\n",
     "4.  **Model Selection**: Choosing the right algorithm (e.g., Linear Regression, Decision Tree).\n",
     "5.  **Training**: Fitting the model to the data.\n",
-    "6.  **Evaluation**: Testing the model on unseen data."
+    "6.  **Evaluation**: Testing the model on unseen data.\n",
+    "7.  *Deployment*: Using the model in the real-world."
    ]
   },
   {

From 577c2f77ba36d9cb3a5e53ff3a9b6906b33ff439 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Sat, 31 Jan 2026 09:12:57 +0100
Subject: [PATCH 31/81] Updated iris_data display

---
 .../S01C01 What is Machine Learning.ipynb                       | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb b/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb
index 7532814..d8ab124 100644
--- a/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb	
@@ -146,7 +146,7 @@
    },
    "outputs": [],
    "source": [
-    "print(iris_data['DESCR'][:193] + \"\\n...\") # Printing just the header"
+    "print(iris_data['DESCR'] + \"\\n...\")"
    ]
   },
   {

From 7521f85b8e380c6ff7597bc5e041d69df2ccd0a1 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Sat, 31 Jan 2026 12:33:27 +0100
Subject: [PATCH 32/81] Small updates to the Machine Learning Workflo

---
 ...S01C02 The Machine Learning Workflow.ipynb | 47 +++++++++++++------
 1 file changed, 32 insertions(+), 15 deletions(-)

diff --git a/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb b/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb
index a336f3d..845aa04 100644
--- a/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb	
@@ -12,15 +12,15 @@
     "\n",
     "The workflow generally consists of seven key stages:\n",
     "\n",
-    "1.  **Problem Definition**: clearly defining the business or research problem. Is it a regression task (predicting a number) or classification (predicting a label)? What is the metric for success?\n",
-    "2.  **Data Collection**: gathering raw data from databases, APIs, web scraping, or sensors.\n",
+    "1.  *Problem Definition*: clearly defining the business or research problem. Is it a regression task (predicting a number) or classification (predicting a label)? What is the metric for success?\n",
+    "2.  *Data Collection*: gathering raw data from databases, APIs, web scraping, or sensors.\n",
     "3.  **Data Exploration & Preprocessing**: analyzing data structure, handling missing values, cleaning outliers, and normalizing features. This is often the most time-consuming step.\n",
     "4.  **Model Selection**: choosing algorithms suitable for the data and problem (e.g., Linear Regression vs. Random Forest).\n",
     "5.  **Training & Validation**: fitting the model to the data.\n",
     "    -   **Training Set**: used to teach the model.\n",
     "    -   **Validation Set**: used to tune parameters.\n",
-    "6.  **Evaluation**: assessing performance on a \"held-out\" test set using metrics like Accuracy or Mean Squared Error (MSE).\n",
-    "7.  **Deployment & Monitoring**: integrating the model into a production system and monitoring for \"drift\" (when data changes over time)."
+    "6.  **Evaluation**: assessing performance on a \"hold-out\" test set using metrics like Accuracy or Mean Squared Error (MSE).\n",
+    "7.  *Deployment & Monitoring*: integrating the model into a production system and monitoring for \"drift\" (when data changes over time)."
    ]
   },
   {
@@ -30,7 +30,7 @@
    "source": [
     "## Practical Demonstration\n",
     "\n",
-    "We will walk through this workflow using the ****California Housing dataset****. This is a classic regression problem where the goal is to predict the median value of houses in a district based on features like average income, house age, and location."
+    "We will walk through this workflow using the **California Housing dataset**. This is a classic regression problem where the goal is to predict the median value of houses in a district based on features like average income, house age, and location."
    ]
   },
   {
@@ -77,7 +77,27 @@
    "source": [
     "### Exploration (EDA)\n",
     "\n",
-    "Before modeling, we must understand our data. We look at the first few rows and calculate the correlation between features."
+    "Before modeling, we must understand our data. We look at the first few rows and calculate the correlation between features.\n",
+    "\n",
+    "-   Peek at the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2555cf5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8942c8c",
+   "metadata": {},
+   "source": [
+    "-   Visualize the correlation matrix"
    ]
   },
   {
@@ -89,10 +109,6 @@
    },
    "outputs": [],
    "source": [
-    "# Peek at the data\n",
-    "print(df.head())\n",
-    "\n",
-    "# Visualize the correlation matrix\n",
     "plt.figure(figsize=(10, 8))\n",
     "sns.heatmap(df.corr(), annot=True, fmt=\".2f\", cmap='coolwarm', square=True)\n",
     "plt.title('Correlation Matrix of California Housing Features')\n",
@@ -156,7 +172,8 @@
     "model = LinearRegression()\n",
     "model.fit(X_train, y_train)\n",
     "\n",
-    "print(\"Model trained successfully.\")"
+    "print(\"Model trained successfully.\")\n",
+    "model"
    ]
   },
   {
@@ -198,7 +215,7 @@
    "source": [
     "### Visualization\n",
     "\n",
-    "A scatter plot of Predicted vs. Actual values helps us visually assess accuracy. A perfect model would align all points on the diagonal red line."
+    "A scatter plot of Predicted vs. Actual values helps us visually assess accuracy. A perfect model would align all points on the red line."
    ]
   },
   {
@@ -246,7 +263,7 @@
    "source": [
     "## Exercises\n",
     "\n",
-    "Now, you will apply this workflow to the ****Ames Housing dataset****. This is a more complex dataset containing 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa."
+    "Now, you will apply this workflow to the ****Ames Housing dataset****. This is a more complex dataset containing 80 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa."
    ]
   },
   {
@@ -289,12 +306,12 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bdc2cdb2",
+   "id": "8694902a",
    "metadata": {},
    "source": [
     "### Visualization\n",
     "\n",
-    "Visualize the Random Forest predictions. Note how the points might cluster differently compared to Linear Regression."
+    "Visualize the Random Forest predictions."
    ]
   },
   {

From 081a0546dee37c35af2c3fb0f472241dcbd977c3 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Sat, 31 Jan 2026 15:01:15 +0100
Subject: [PATCH 33/81] S01C03 Exploratory Data Analysis (EDA)

---
 .../S01C03 Exploratory Data Analysis (EDA).ipynb  | 15 ++++++---------
 1 file changed, 6 insertions(+), 9 deletions(-)

diff --git a/src/S01 Introduction to Machine Learning/S01C03 Exploratory Data Analysis (EDA).ipynb b/src/S01 Introduction to Machine Learning/S01C03 Exploratory Data Analysis (EDA).ipynb
index 621ecc6..360c093 100644
--- a/src/S01 Introduction to Machine Learning/S01C03 Exploratory Data Analysis (EDA).ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C03 Exploratory Data Analysis (EDA).ipynb	
@@ -10,7 +10,7 @@
     "\n",
     "**Exploratory Data Analysis** (EDA) is the detective work of data science. Before any modeling begins, we must understand the nature of the evidence we have collected. It involves summarizing the main characteristics of a dataset, often using visual and statistical methods.\n",
     "\n",
-    "The primary goal is not just to make pretty charts, but to \"get a feel\" for the data—understanding its structure, its quirks, and its potential pitfalls."
+    "The primary goal is not just to make pretty charts, but to \"get a feel\" for the data - understanding its structure, its quirks, and its potential pitfalls."
    ]
   },
   {
@@ -142,7 +142,7 @@
    "id": "0c0e5ce2",
    "metadata": {},
    "source": [
-    "**(Note: This dataset is pre-cleaned, so we expect 0 missing values.)**"
+    "**Note**: This dataset is pre-cleaned, so we expect 0 missing values."
    ]
   },
   {
@@ -185,7 +185,7 @@
    "id": "3e028a9f",
    "metadata": {},
    "source": [
-    "**Observation**: Notice the spike at the far right of the House Value graph? That indicates \"censored\" data—values higher than 5.0 were likely capped at 5.0 during data collection. This is a critical finding for modeling!"
+    "**Observation**: Notice the spike at the far right of the House Value graph? That indicates \"censored\" data - values higher than 5.0 were likely capped at 5.0 during data collection. This is a critical finding for modeling!"
    ]
   },
   {
@@ -207,11 +207,8 @@
    "source": [
     "plt.figure(figsize=(10, 8))\n",
     "\n",
-    "# Compute correlation\n",
-    "corr_matrix = df.corr()\n",
-    "\n",
     "# Plot heatmap\n",
-    "sns.heatmap(corr_matrix, annot=True, fmt=\".2f\", cmap='coolwarm', vmin=-1, vmax=1)\n",
+    "sns.heatmap(df.corr(), annot=True, fmt=\".2f\", cmap='coolwarm', vmin=-1, vmax=1)\n",
     "plt.title('Correlation Matrix')\n",
     "plt.show()"
    ]
@@ -239,7 +236,7 @@
     "sns.pairplot(df_sample, \n",
     "             vars=['MedHouseVal', 'MedInc', 'AveRooms', 'HouseAge'], \n",
     "             diag_kind='kde', \n",
-    "             plot_kws={'alpha': 0.5})\n",
+    "             plot_kws={'alpha': 0.8})\n",
     "plt.suptitle('Pairplot of Selected Features (5% Sample)', y=1.02)\n",
     "plt.show()"
    ]
@@ -300,7 +297,7 @@
     "### Target Distribution\n",
     "\n",
     "-   Plot the distribution of `SalePrice`.\n",
-    "-   **Bonus**: Is it skewed? (Real estate prices usually are)."
+    "-   Is it skewed? (Real estate prices usually are)."
    ]
   },
   {

From b305eb1055fe45ac1def4f6094c8b6c617368151 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Sat, 31 Jan 2026 15:06:03 +0100
Subject: [PATCH 34/81] S01C03 Exploratory Data Analysis (EDA)

---
 .../S01C03 Exploratory Data Analysis (EDA).ipynb            | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/S01 Introduction to Machine Learning/S01C03 Exploratory Data Analysis (EDA).ipynb b/src/S01 Introduction to Machine Learning/S01C03 Exploratory Data Analysis (EDA).ipynb
index 360c093..afd0c1f 100644
--- a/src/S01 Introduction to Machine Learning/S01C03 Exploratory Data Analysis (EDA).ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C03 Exploratory Data Analysis (EDA).ipynb	
@@ -307,11 +307,11 @@
    "source": [
     "## Summary\n",
     "\n",
-    "In this module, we practiced the art of ****Exploratory Data Analysis****.\n",
+    "In this module, we practiced the art of **Exploratory Data Analysis**.\n",
     "\n",
     "1.  We inspected data structure and types using `.info()` and `.describe()`.\n",
-    "2.  We visualized distributions using ****Histograms**** and discovered data capping in the California dataset.\n",
-    "3.  We analyzed relationships using ****Heatmaps**** and ****Scatter Plots****.\n",
+    "2.  We visualized distributions using **Histograms** and discovered data capping in the California dataset.\n",
+    "3.  We analyzed relationships using **Heatmaps** and **Scatter Plots**.\n",
     "4.  In the exercises, we tackled real-world issues like missing values and categorical data visualization in the Ames dataset.\n",
     "\n",
     "Remember: **Garbage In, Garbage Out**. EDA is your primary defense against bad models."

From be077ceca72793f7c6c3e58aadf9bb36c5982ffb Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Sat, 31 Jan 2026 17:53:08 +0100
Subject: [PATCH 35/81] S01C04 Train-Test Splits and Cross-Validation

---
 ...ain-Test Splits and Cross-Validation.ipynb | 308 ++++++++++++++++++
 1 file changed, 308 insertions(+)
 create mode 100644 src/S01 Introduction to Machine Learning/S01C04 Train-Test Splits and Cross-Validation.ipynb

diff --git a/src/S01 Introduction to Machine Learning/S01C04 Train-Test Splits and Cross-Validation.ipynb b/src/S01 Introduction to Machine Learning/S01C04 Train-Test Splits and Cross-Validation.ipynb
new file mode 100644
index 0000000..cce8919
--- /dev/null
+++ b/src/S01 Introduction to Machine Learning/S01C04 Train-Test Splits and Cross-Validation.ipynb	
@@ -0,0 +1,308 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1380a956",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Train-Test Splits and Cross-Validation\n",
+    "\n",
+    "The ultimate goal of a machine learning model is not to memorize data, but to **generalize**. Imagine a student preparing for a math exam:\n",
+    "\n",
+    "-   If they memorize the answers to the homework questions (Training Data) but don't understand the concepts, they will fail the final exam (Test Data) which has new questions.\n",
+    "-   If they understand the underlying patterns, they will succeed on both.\n",
+    "\n",
+    "To ensure our models are learning patterns and not just memorizing, we use **Train-Test Splits** and **Cross-Validation**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f3a8bbb",
+   "metadata": {},
+   "source": [
+    "## Train-Test Splits (The Holdout Method)\n",
+    "\n",
+    "The simplest way to evaluate a model is to split the dataset into two mutually exclusive parts:\n",
+    "\n",
+    "1.  **Training Set** (typically 70-80%): Used to \"teach\" the model. The model sees the answers (labels) and adjusts its internal parameters.\n",
+    "2.  **Test Set** (typically 20-30%): Used to evaluate performance. The model never sees these answers during training. It makes predictions, and we compare them to the actual values.\n",
+    "\n",
+    "**Important**: The split must be random to ensure both sets are representative of the overall population."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e9e36de",
+   "metadata": {},
+   "source": [
+    "## Cross-Validation (CV)\n",
+    "\n",
+    "A single train-test split has a flaw: it depends heavily on **which** specific data points end up in the test set. If the test set happens to be \"easy,\" the model looks great. If it's \"hard,\" the model looks bad.\n",
+    "\n",
+    "**Cross-Validation** solves this by repeating the split process multiple times.\n",
+    "\n",
+    "The most common method is **K-Fold Cross-Validation**:\n",
+    "\n",
+    "1.  Split the data into $K$ equal parts (folds).\n",
+    "2.  Train the model $K$ times.\n",
+    "3.  Each time, use a different fold as the \"Test Set\" and the remaining $K-1$ folds as the \"Training Set\".\n",
+    "4.  Average the $K$ scores to get the final performance metric.\n",
+    "\n",
+    "This gives a much more reliable estimate of how the model will perform in the real world."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02a7842d",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: California Housing\n",
+    "\n",
+    "We will use the California Housing dataset to demonstrate how moving from a single split to Cross-Validation changes our perspective on model performance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0c46ed6",
+   "metadata": {},
+   "source": [
+    "### Setup and Exploration\n",
+    "\n",
+    "We load the data and focus on a single feature, `MedInc` (Median Income), to predict `MedHouseVal` (House Value). We do this to keep the model simple and interpretable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0dd627f9",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_california_housing\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Load data\n",
+    "data = fetch_california_housing(as_frame=True)\n",
+    "df = data.frame\n",
+    "\n",
+    "# Check correlations to confirm MedInc is a good predictor\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.heatmap(df.corr()[['MedHouseVal']].sort_values(by='MedHouseVal', ascending=False),\n",
+    "            annot=True, cmap='coolwarm', vmin=-1, vmax=1)\n",
+    "plt.title('Correlation with House Value')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b593275",
+   "metadata": {},
+   "source": [
+    "### The Train-Test Split\n",
+    "\n",
+    "We split the data 80/20. Note the use of `random_state` for reproducibility."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed4f4a79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Select Feature (X) and Target (y)\n",
+    "X = df[['MedInc']] # Double brackets to keep X as a DataFrame (2D)\n",
+    "y = df['MedHouseVal']\n",
+    "\n",
+    "# Split the data\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "print(f\"Training samples: {X_train.shape[0]}\")\n",
+    "print(f\"Testing samples:  {X_test.shape[0]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59fe51e5",
+   "metadata": {},
+   "source": [
+    "Let's visualize the split to ensure the distribution of the target variable looks similar in both sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e31c257",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(12, 5))\n",
+    "\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.histplot(y_train, bins=30, color='blue', alpha=0.6)\n",
+    "plt.title(f'Train Set (Mean: {y_train.mean():.2f})')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.histplot(y_test, bins=30, color='orange', alpha=0.6)\n",
+    "plt.title(f'Test Set (Mean: {y_test.mean():.2f})')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66894f27",
+   "metadata": {},
+   "source": [
+    "### Single Split Evaluation\n",
+    "\n",
+    "We train a Linear Regression model and check the $R^2$ score."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12e98cf0",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "# Train\n",
+    "model = LinearRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Evaluate\n",
+    "score = model.score(X_test, y_test)\n",
+    "print(f\"Single Split Test R²: {score:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bf89eab",
+   "metadata": {},
+   "source": [
+    "### Cross-Validation Evaluation\n",
+    "\n",
+    "Now, we use 5-Fold Cross-Validation. This simulates training and testing 5 distinct times on different subsets of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58a5ad1d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_val_score\n",
+    "import numpy as np\n",
+    "\n",
+    "# Initialize a fresh model\n",
+    "model_cv = LinearRegression()\n",
+    "\n",
+    "# Perform 5-Fold CV\n",
+    "# Note: We pass the FULL X and y here. The function handles the splitting.\n",
+    "cv_scores = cross_val_score(model_cv, X, y, cv=5, scoring=\"r2\")\n",
+    "\n",
+    "print(f\"Individual CV Scores: {cv_scores}\")\n",
+    "print(f\"Average R²: {np.mean(cv_scores):.4f} (+/- {np.std(cv_scores):.4f})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d0a6870",
+   "metadata": {},
+   "source": [
+    "**Observation**: Notice that the scores vary! In one fold, the model performed much worse (or better, depending on random seed) than others. The **Average $R^2$** is a more honest reflection of the model's true capability than the single split score."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05695bd0",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "Now apply these concepts to the **Ames Housing dataset**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0473e686",
+   "metadata": {},
+   "source": [
+    "### Load and Clean\n",
+    "\n",
+    "The Ames dataset contains missing values (`NaN`). Linear Regression cannot handle these. We must drop rows with missing data in our selected columns."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f866509",
+   "metadata": {},
+   "source": [
+    "### Standard Split\n",
+    "\n",
+    "Perform an 80/20 split and train a Linear Regression model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8214ff0f",
+   "metadata": {},
+   "source": [
+    "### Robustness Check (Cross-Validation)\n",
+    "\n",
+    "Run 5-fold and 10-fold cross-validation to see if the performance holds up."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c0708ea",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "We have moved from trusting a single \"exam result\" (Train-Test split) to looking at the \"student's average performance\" (Cross-Validation).\n",
+    "\n",
+    "-   **Train-Test Split**: Good for quick checks and large datasets.\n",
+    "-   **Cross-Validation**: Essential for reliable evaluation, hyperparameter tuning, and smaller datasets."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 819c069ad40c22608b85b41ac36c6bf2e06090f4 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Sun, 1 Feb 2026 07:57:05 +0100
Subject: [PATCH 36/81] S01C05 Data Preprocessing

---
 .../S01C05 Data Preprocessing.ipynb           | 376 ++++++++++++++++++
 1 file changed, 376 insertions(+)
 create mode 100644 src/S01 Introduction to Machine Learning/S01C05 Data Preprocessing.ipynb

diff --git a/src/S01 Introduction to Machine Learning/S01C05 Data Preprocessing.ipynb b/src/S01 Introduction to Machine Learning/S01C05 Data Preprocessing.ipynb
new file mode 100644
index 0000000..d076393
--- /dev/null
+++ b/src/S01 Introduction to Machine Learning/S01C05 Data Preprocessing.ipynb	
@@ -0,0 +1,376 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ade35cc5",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Data Preprocessing\n",
+    "\n",
+    "Real-world data is messy. It contains missing values, text labels, and features on vastly different scales. **Data Preprocessing** is the process of converting raw data into a clean dataset that machine learning algorithms can understand.\n",
+    "\n",
+    "Without this step, even the most powerful algorithms will fail. The most common tasks include:\n",
+    "\n",
+    "1.  **Handling Missing Values**: Data holes cause errors. We can drop them or fill them (Imputation).\n",
+    "2.  **Feature Scaling**: Algorithms like KNN and SVM calculate \"distance.\" If one feature is in thousands (Salary) and another is small (Age), the large one will dominate. Scaling fixes this.\n",
+    "3.  **Encoding Categorical Variables**: Computers speak math, not English. We must convert text labels (e.g., \"Male\", \"Female\") into numbers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc0950bd",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: Titanic Survival\n",
+    "\n",
+    "We will use the famous Titanic dataset to demonstrate these steps. We want to predict if a passenger survived based on their Fare and Sex."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8d1a60e",
+   "metadata": {},
+   "source": [
+    "### Load and Inspect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e77be15a",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_openml\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# Load data\n",
+    "data = fetch_openml('titanic', version=1, as_frame=True, parser=\"auto\")\n",
+    "df = data.frame\n",
+    "\n",
+    "# Keep only relevant columns for this demo\n",
+    "df = df[['fare', 'sex', 'age', 'survived']]\n",
+    "\n",
+    "print(df.info())\n",
+    "print(\"\\nFirst 5 rows:\")\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9c1f12d",
+   "metadata": {},
+   "source": [
+    "### Exploratory Data Analysis (EDA)\n",
+    "\n",
+    "Before changing the data, we visualize it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3adf1589",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Set style\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "plt.figure(figsize=(12, 5))\n",
+    "\n",
+    "# Plot 1: Survival by Fare\n",
+    "plt.subplot(1, 3, 1)\n",
+    "sns.boxplot(x='fare', y='survived', data=df, orient='horizontal')\n",
+    "plt.xscale('log')\n",
+    "plt.title('Survival Distribution by Fare')\n",
+    "\n",
+    "# Plot 2: Survival by Age\n",
+    "plt.subplot(1, 3, 2)\n",
+    "sns.boxplot(x='age', y='survived', data=df, orient='horizontal')\n",
+    "plt.title('Survival Distribution by Age')\n",
+    "\n",
+    "# Plot 3: Survival by Gender\n",
+    "plt.subplot(1, 3, 3)\n",
+    "sns.countplot(x='sex', hue='survived', data=df)\n",
+    "plt.title('Survival Count by Gender')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09766e06",
+   "metadata": {},
+   "source": [
+    "### Train-Test Split\n",
+    "\n",
+    "**Crucial Rule**: Apply preprocessing **after** splitting the data. If you calculate the mean for imputation using the whole dataset, you are leaking information from the Test set into the Training set (\"Data Leakage\")."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3d9c086",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X = df[['fare', 'sex', 'age']]\n",
+    "y = df['survived']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9c304d2",
+   "metadata": {},
+   "source": [
+    "### Preprocessing Steps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1e1050b",
+   "metadata": {},
+   "source": [
+    "#### Handling Missing Values (Imputation)\n",
+    "\n",
+    "All columns have some missing values, which we have to deal with. We will do so by *imputing* the missing values in the following way: for *numerical* columns (`fare` and `age`), we fill in the missing values with the average of the respective column in the **training set**. For *categorical* variables (`sex`), we will fill in the missing values with the most frequent value from the **training set**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f4b539d",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.impute import SimpleImputer\n",
+    "\n",
+    "# Create numerical imputer, train on TRAIN, transform TRAIN and TEST\n",
+    "num_imputer = SimpleImputer(strategy='mean')\n",
+    "X_train[['fare', 'age']] = num_imputer.fit_transform(X_train[['fare', 'age']])\n",
+    "X_test[['fare', 'age']] = num_imputer.transform(X_test[['fare', 'age']])\n",
+    "\n",
+    "# Create categorical imputer\n",
+    "cat_imputer = SimpleImputer(strategy='most_frequent')\n",
+    "X_train[['sex']] = cat_imputer.fit_transform(X_train[['sex']])\n",
+    "X_test[['sex']] = cat_imputer.transform(X_test[['sex']])\n",
+    "\n",
+    "print(\"Missing values in Train after imputation:\", X_train[['fare', 'age', 'sex']].isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95ca04f2",
+   "metadata": {},
+   "source": [
+    "#### Feature Scaling (Standardization)\n",
+    "\n",
+    "We use `StandardScaler` to force the `fare` and `age` to have a mean of 0 and a variance of 1, which this helps the model converge faster."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1643a3d4",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "# Fit on TRAIN, transform on TRAIN and TEST\n",
+    "X_train[['fare', 'age']] = scaler.fit_transform(X_train[['fare', 'age']])\n",
+    "X_test[['fare', 'age']] = scaler.transform(X_test[['fare', 'age']])\n",
+    "\n",
+    "print(X_train[['fare', 'age']].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09e09b66",
+   "metadata": {},
+   "source": [
+    "#### Encoding Categoricals (One-Hot Encoding)\n",
+    "\n",
+    "The `sex` column is text. We use One-Hot Encoding to create a binary column (e.g., `sex_male`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe2085bd",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "\n",
+    "# drop='first' removes one column to avoid collinearity (dummy variable trap)\n",
+    "# If 'female' is 0, 'male' must be 1. We don't need two columns.\n",
+    "encoder = OneHotEncoder(drop='first', sparse_output=False)\n",
+    "\n",
+    "# Fit and transform\n",
+    "sex_train_encoded = encoder.fit_transform(X_train[['sex']])\n",
+    "sex_test_encoded = encoder.transform(X_test[['sex']])\n",
+    "\n",
+    "# Convert back to DataFrame to keep things readable\n",
+    "feature_names = encoder.get_feature_names_out(['sex'])\n",
+    "\n",
+    "df_train_sex = pd.DataFrame(sex_train_encoded, columns=feature_names, index=X_train.index)\n",
+    "df_test_sex = pd.DataFrame(sex_test_encoded, columns=feature_names, index=X_test.index)\n",
+    "\n",
+    "# Concatenate back to main dataframe and drop original 'sex' column\n",
+    "X_train_final = pd.concat([X_train.drop(columns=['sex']), df_train_sex], axis=1)\n",
+    "X_test_final = pd.concat([X_test.drop(columns=['sex']), df_test_sex], axis=1)\n",
+    "\n",
+    "print(X_train_final.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59e48dc2",
+   "metadata": {},
+   "source": [
+    "### Training and Evaluation\n",
+    "\n",
+    "We train a **Logistic Regression** model (used for classification)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3d30aedc",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
+    "\n",
+    "# Train\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train_final, y_train)\n",
+    "\n",
+    "# Predict\n",
+    "y_pred = model.predict(X_test_final)\n",
+    "\n",
+    "# Evaluate\n",
+    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
+    "\n",
+    "# Plot Confusion Matrix\n",
+    "cm = confusion_matrix(y_test, y_pred)\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Died', 'Survived'], yticklabels=['Died', 'Survived'])\n",
+    "plt.xlabel('Predicted')\n",
+    "plt.ylabel('Actual')\n",
+    "plt.title('Confusion Matrix')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1caa5b97",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "Apply these techniques to the **Iris Dataset**. **Twist**: Instead of classification, perform a **Regression** task. Predict the `petal length` using the other features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a10ae0d",
+   "metadata": {},
+   "source": [
+    "### Setup\n",
+    "\n",
+    "-   Load the Iris dataset using `load_iris`.\n",
+    "-   Convert it to a DataFrame.\n",
+    "-   Define `X` (Sepal Length, Sepal Width, Petal Width, Species) and `y` (Petal Length)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "342f0104",
+   "metadata": {},
+   "source": [
+    "### Split and Preprocess\n",
+    "\n",
+    "1.  Split the data (80/20).\n",
+    "2.  Scale the numerical columns (`sepal length`, `sepal width`, `petal width`).\n",
+    "3.  One-Hot Encode the `species_name` column."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15b4919d",
+   "metadata": {},
+   "source": [
+    "### Train Linear Regression\n",
+    "\n",
+    "Train a Linear Regression model and verify the Mean Squared Error (MSE)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06954b8d",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "Preprocessing is the \"plumbing\" of Machine Learning. It's not glamorous, but it is essential.\n",
+    "\n",
+    "-   We handled **Missing Values** with Imputation.\n",
+    "-   We normalized ranges with **Scaling**.\n",
+    "-   We translated text to numbers with **One-Hot Encoding**."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 7f7d4e37d92bacc4e03a29b2d902acc319a0a2a0 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Sun, 1 Feb 2026 09:29:14 +0100
Subject: [PATCH 37/81] S01C06 Feature Engineering

---
 .../S01C06 Feature Engineering.ipynb          | 263 ++++++++++++++++++
 1 file changed, 263 insertions(+)
 create mode 100644 src/S01 Introduction to Machine Learning/S01C06 Feature Engineering.ipynb

diff --git a/src/S01 Introduction to Machine Learning/S01C06 Feature Engineering.ipynb b/src/S01 Introduction to Machine Learning/S01C06 Feature Engineering.ipynb
new file mode 100644
index 0000000..de3a868
--- /dev/null
+++ b/src/S01 Introduction to Machine Learning/S01C06 Feature Engineering.ipynb	
@@ -0,0 +1,263 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ffaf6fbb",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Feature Engineering\n",
+    "\n",
+    "If data is the fuel for machine learning, **Feature Engineering** is the refinery. It is the process of using domain knowledge to extract new variables from raw data that make machine learning algorithms work better.\n",
+    "\n",
+    "While algorithms (like Linear Regression or Random Forest) are mathematical commodities, feature engineering is an **art**. It is often the deciding factor between a mediocre model and a winning one."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74017aac",
+   "metadata": {},
+   "source": [
+    "## Why do we need it?\n",
+    "\n",
+    "Models typically assume a linear relationship or have difficulty seeing complex patterns.\n",
+    "\n",
+    "-   **Example**: A model might not understand that \"Total Bill\" and \"Tip\" are related to \"Percentage.\" If you create a new feature `Tip_Percentage = Tip / Total Bill`, you make the pattern obvious to the model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75c8ff92",
+   "metadata": {},
+   "source": [
+    "## Common Techniques\n",
+    "\n",
+    "| Technique            | Description                                           | Example                                   |\n",
+    "|-------------------- |----------------------------------------------------- |----------------------------------------- |\n",
+    "| **Transformation**   | Changing the scale or distribution of a feature.      | Logarithm (for skewed data), Square Root. |\n",
+    "| **Interaction**      | Combining two features to capture their joint effect. | `Income` $\\times$ `Education`.            |\n",
+    "| **Ratio/Derivation** | Creating rates or densities.                          | `Price` / `SquareFoot` = `PricePerSqFt`.  |\n",
+    "| **Polynomials**      | Adding curvature to linear models.                    | $x^2$, $x^3$ allowing the line to curve.  |\n",
+    "| **Binning**          | Grouping continuous numbers into categories.          | Age 25 $\\rightarrow$ \"Young Adult\".       |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80d51bbb",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: California Housing\n",
+    "\n",
+    "We will demonstrate how adding a few simple features can improve model performance on the California Housing dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c166c54f",
+   "metadata": {},
+   "source": [
+    "### Establish a Baseline\n",
+    "\n",
+    "First, we train a model on the raw data to get a \"score to beat.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b043b41b",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.datasets import fetch_california_housing\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "# Load Data\n",
+    "data = fetch_california_housing(as_frame=True)\n",
+    "df = data.frame.copy() # Use copy to avoid pandas warnings later\n",
+    "\n",
+    "# Split\n",
+    "X = df.drop(columns=[data.target.name])\n",
+    "y = df[data.target.name]\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Baseline Model\n",
+    "base_model = LinearRegression()\n",
+    "base_model.fit(X_train, y_train)\n",
+    "base_score = base_model.score(X_test, y_test)\n",
+    "\n",
+    "print(f\"Baseline R² Score: {base_score:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73831af2",
+   "metadata": {},
+   "source": [
+    "### Engineering New Features\n",
+    "\n",
+    "Now we apply our domain intuition.\n",
+    "\n",
+    "1.  **Ratio**: Total rooms in a block isn't useful. Rooms **per person** is useful.\n",
+    "2.  **Polynomial**: Income often has a non-linear relationship with house price (diminishing returns). We add $Income^2$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f28053c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1. Create Ratio Feature: Rooms per Person\n",
+    "df['Rooms_per_person'] = df['AveRooms'] / df['AveOccup']\n",
+    "\n",
+    "# 2. Create Polynomial Feature: Income Squared\n",
+    "df['MedInc_Squared'] = df['MedInc'] ** 2\n",
+    "\n",
+    "print(df[['MedInc', 'MedInc_Squared', 'AveRooms', 'Rooms_per_person']].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8237028d",
+   "metadata": {},
+   "source": [
+    "**Note**: Adding new variables that could be correlated with existing ones can lead to *multicolinearity*, to be discussed later."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1164e341",
+   "metadata": {},
+   "source": [
+    "### Evaluate Improvement\n",
+    "\n",
+    "Let's retrain the model with these new features included."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16f6a8c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prepare new X\n",
+    "X_eng = df.drop(columns=[data.target.name])\n",
+    "y_eng = df[data.target.name]\n",
+    "\n",
+    "# Split (same random_state ensures fair comparison)\n",
+    "X_train_e, X_test_e, y_train_e, y_test_e = train_test_split(X_eng, y_eng, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Train New Model\n",
+    "eng_model = LinearRegression()\n",
+    "eng_model.fit(X_train_e, y_train_e)\n",
+    "eng_score = eng_model.score(X_test_e, y_test_e)\n",
+    "\n",
+    "print(f\"Baseline R²:   {base_score:.4f}\")\n",
+    "print(f\"Engineered R²: {eng_score:.4f}\")\n",
+    "print(f\"Improvement:   {(eng_score - base_score) * 100:.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6cd3c046",
+   "metadata": {},
+   "source": [
+    "**Result**: By simply adding two logical features, we slightly improved the model's predictive power."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c409398",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "Now it's your turn to squeeze more performance out of this dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8e68169",
+   "metadata": {},
+   "source": [
+    "### Create a Bedroom Ratio\n",
+    "\n",
+    "The raw dataset has `AveBedrms` (Average Bedrooms) and `AveRooms` (Average Rooms).\n",
+    "\n",
+    "-   Create a feature `Bedrooms_Ratio` = `AveBedrms` / `AveRooms`.\n",
+    "-   Logic: This acts as a proxy for \"Apartment vs. House\". High ratio might mean small apartments."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4292687",
+   "metadata": {},
+   "source": [
+    "### Log Transformation\n",
+    "\n",
+    "Income distributions are often \"skewed\" (a few very rich people, many average). Linear models prefer \"Normal\" (bell-curve) distributions.\n",
+    "\n",
+    "-   Create `Log_Income` = `log(MedInc + 1)`.\n",
+    "-   **Note**: We use `np.log1p(x)` which calculates `log(1+x)` accurately.\n",
+    "\n",
+    "**Note**: If the correlation increases, the linear relationship is stronger!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f3c93b4",
+   "metadata": {},
+   "source": [
+    "### Final Test\n",
+    "\n",
+    "Train a final model with all your new features combined."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4514c630",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "Feature Engineering is about translating real-world logic into math.\n",
+    "\n",
+    "1.  We created **Interaction/Ratio** features (`Rooms_per_person`).\n",
+    "2.  We used **Mathematical Transformations** (`Log`, `Square`).\n",
+    "3.  We showed that **Better Data > Better Algorithms**. A Linear Regression with good features can often beat a complex Neural Network with raw data."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From f2f7284e531d334f3d296ede76fdab68acc38a41 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Sun, 1 Feb 2026 16:26:27 +0100
Subject: [PATCH 38/81] S02 Supervised Learning: Regression and Evaluation

---
 ...d Learning - Regression and Evaluation.pdf | Bin 0 -> 471543 bytes
 ...d Learning - Regression and Evaluation.tex | 631 ++++++++++++++++++
 2 files changed, 631 insertions(+)
 create mode 100644 src/S02 Supervised Learning - Regression and Evaluation/S02 Supervised Learning - Regression and Evaluation.pdf
 create mode 100644 src/S02 Supervised Learning - Regression and Evaluation/S02 Supervised Learning - Regression and Evaluation.tex

diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02 Supervised Learning - Regression and Evaluation.pdf b/src/S02 Supervised Learning - Regression and Evaluation/S02 Supervised Learning - Regression and Evaluation.pdf
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+\author{Cristian A. Marocico}
+\date{\today}
+\title{Machine Learning in Python}
+\subtitle{Supervised Learning - Regression and Evaluation}
+\author[Marocico, Tatar]{Cristian A. Marocico, A. Emin Tatar}
+\institute[CIT]{Center for Information Technology\\University of Groningen}
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+\date[Feb 6\textsuperscript{th} 2026]{Friday, February 6\textsuperscript{th} 2026}
+\usepackage{mathtools}
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+ pdfauthor={Cristian A. Marocico},
+ pdftitle={Machine Learning in Python},
+ pdfkeywords={},
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+ pdfcreator={Emacs 30.2 (Org mode 9.7.11)}, 
+ pdflang={English}}
+\begin{document}
+
+\maketitle
+\begin{frame}{Outline}
+\setcounter{tocdepth}{1}
+\tableofcontents
+\end{frame}
+
+\section{Introduction to Regression}
+\label{sec:orged6a9f7}
+\begin{frame}[label={sec:org6b1927d}]{Introduction to Regression}
+\begin{definition}[Regression]\label{sec:org762680b}
+\pause
+\alert{Regression} is a statistical method used to model the relationship between a dependent variable and one or more independent variables.
+\end{definition}
+\end{frame}
+\section{Simple Linear Regression}
+\label{sec:orgfb00269}
+\begin{frame}[label={sec:org52fb825}]{Simple Linear Regression}
+\begin{definition}[Simple Linear Regression]\label{sec:orgca6daa0}
+\pause
+\alert{Simple Linear Regression} is a method to model the relationship between two variables by fitting a linear equation to observed data.
+\pause
+Mathematically:
+$$y = \beta_0 + \beta_1 x + \epsilon$$
+where:
+\begin{itemize}
+\item \(y\) is the dependent variable (response).
+\item \(x\) is the independent variable (predictor).
+\item \(\beta_0\) is the y-intercept (constant term).
+\item \(\beta_1\) is the slope of the line (coefficient).
+\item \(\epsilon\) is the error term (residuals).
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org917409d}]{Simple Linear Regression}
+A Simple Linear Regression Machine Learning model will learn the coefficients \(\beta_0\) and \(\beta_1\) from the training data to minimize the difference between the predicted values and the actual values.
+\end{frame}
+\begin{frame}[label={sec:orgfb99e7d}]{Assumptions of Simple Linear Regression}
+\begin{itemize}[<+->]
+\item Linearity: The relationship between the independent and dependent variable is linear.
+\item Independence: Observations are independent of each other.
+\item Homoscedasticity: Constant variance of the error terms.
+\item Normality: The residuals (errors) of the model are normally distributed.
+\end{itemize}
+\end{frame}
+\section{Evaluation Metrics for Regression}
+\label{sec:orga0f8934}
+\begin{frame}[label={sec:org3adfd86}]{Evaluation Metrics for Regression}
+\begin{definition}[Common Metrics for Regression]\label{sec:orge65a72a}
+\pause
+Common metrics to evaluate regression models include:
+\begin{itemize}
+\item \alert{Mean Absolute Error (MAE)}
+\item \alert{Mean Squared Error (MSE)}
+\item \alert{Root Mean Squared Error (RMSE)}
+\item \alert{R-squared (\(R^2\))}
+\item \alert{Adjusted R-squared}
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org90fd26d}]{Mean Absolute Error (MAE)}
+\begin{definition}[Mean Absolute Error (MAE)]\label{sec:orgeb2e92d}
+\pause
+\alert{Mean Absolute Error (MAE)} is the average of the absolute differences between the predicted and the actual values:
+$$\text{MAE} = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i|$$
+\pause
+where:
+\begin{itemize}
+\item \(n\) is the number of observations.
+\item \(y_i\) is the actual value.
+\item \(\hat{y}_i\) is the predicted value.
+\end{itemize}
+\pause
+MAE is a linear score, which can be used when all errors are equally important; it is also less sensitive to outliers compared to MSE.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org4fb08d5}]{Mean Squared Error (MSE)}
+\begin{definition}[Mean Squared Error (MSE)]\label{sec:orge7b0e78}
+\pause
+\alert{Mean Squared Error (MSE)} is the average of the squared differences between the predicted and the actual values:
+$$\text{MSE} = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$$
+\pause
+where:
+\begin{itemize}
+\item \(n\) is the number of observations.
+\item \(y_i\) is the actual value.
+\item \(\hat{y}_i\) is the predicted value.
+\end{itemize}
+\pause
+MSE is more sensitive to outliers than MAE because it squares the errors, which can disproportionately affect the metric if there are large errors; however, it is useful when larger errors are more significant.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgd850ab0}]{Root Mean Squared Error (RMSE)}
+\begin{definition}[Root Mean Squared Error (RMSE)]\label{sec:org7b318b8}
+\pause
+\alert{Root Mean Squared Error (RMSE)} is the square root of the average of the squared differences between the predicted and the actual values:
+$$\text{RMSE} = \sqrt{\text{MSE}} = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2}$$
+\pause
+where:
+\begin{itemize}
+\item \(n\) is the number of observations.
+\item \(y_i\) is the actual value.
+\item \(\hat{y}_i\) is the predicted value.
+\end{itemize}
+\pause
+RMSE is in the same units as the dependent variable, making it interpretable; it is also sensitive to outliers, similar to MSE.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org79ba332}]{R-squared (\(R^2\))}
+\begin{definition}[R-squared (\(R^2\))]\label{sec:orgbb57536}
+\pause
+\alert{R-squared (\(R^2\))} is a statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model:
+$$R^2 = 1 - \frac{\text{SS}_{\text{res}}}{\text{SS}_{\text{tot}}}$$
+where:
+\begin{itemize}
+\item \(\text{SS}_{\text{res}}\) is the sum of squares of residuals (errors).
+\item \(\text{SS}_{\text{tot}}\) is the total sum of squares (variance of the dependent variable).
+\end{itemize}
+\pause
+\(R^2\) values range from 0 to 1, where:
+\begin{itemize}
+\item 0 indicates that the model does not explain any of the variability of the response data around its mean.
+\item 1 indicates that the model explains all the variability of the response data around its mean.
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orga45f723}]{Adjusted R-squared}
+\begin{definition}[Adjusted R-squared]\label{sec:orgaf103c7}
+\pause
+\alert{Adjusted R-squared} adjusts the \(R^2\) value for the number of predictors in the model, providing a more accurate measure when comparing models with different numbers of predictors:
+$$\text{Adjusted } R^2 = 1 - \left(1 - R^2\right) \frac{n - 1}{n - p - 1}$$
+where:
+\begin{itemize}
+\item \(n\) is the number of observations.
+\item \(p\) is the number of predictors in the model.
+\end{itemize}
+\pause
+Adjusted \(R^2\) can be negative, which indicates that the model is worse than a horizontal line (mean of the dependent variable); it is useful for comparing models with different numbers of predictors.
+\end{definition}
+\end{frame}
+\section{Regression Diagnostics}
+\label{sec:org91035e8}
+\begin{frame}[label={sec:orge23eb2e}]{Regression Diagnostics}
+\begin{definition}[Regression Diagnostics]\label{sec:org6960cf4}
+\pause
+\alert{Regression Diagnostics} is the process of verifying that a regression model's assumptions are met. A high \(R^2\) does not guarantee a valid model; if assumptions are violated, predictions and confidence intervals can be unreliable.
+\pause
+The primary tool for diagnostics is analyzing the \alert{Residuals} (the errors: \(y - \hat{y}\)).
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgacd5959}]{Key Diagnostic Checks}
+\begin{definition}[Key Diagnostic Checks]\label{sec:org165f384}
+\pause
+\begin{itemize}[<+->]
+\item \alert{Linearity}: Residuals should be randomly scattered around 0. A U-shape indicates non-linearity.
+\item \alert{Homoscedasticity}: Residuals should have constant variance. A "funnel" shape indicates heteroscedasticity.
+\item \alert{Normality}: Residuals should follow a Normal distribution (check with Q-Q plot).
+\item \alert{Independence}: Residuals should not be correlated (important for time-series data).
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org34568f7}]{Residual Plot}
+\begin{definition}[Residual Plot]\label{sec:org0f80518}
+\pause
+A \alert{Residual Plot} graphs the residuals (\(y - \hat{y}\)) against the predicted values (\(\hat{y}\)).
+\pause
+Interpretation:
+\begin{itemize}
+\item \alert{Random scatter around 0}: Assumptions are likely met.
+\item \alert{Funnel shape} (variance increases): Heteroscedasticity—consider log-transforming the target.
+\item \alert{U-shape or pattern}: Non-linearity—consider polynomial features or non-linear models.
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgd70c460}]{Q-Q Plot (Normality Check)}
+\begin{definition}[Q-Q Plot]\label{sec:org90d3ead}
+\pause
+A \alert{Q-Q Plot} (Quantile-Quantile Plot) compares the distribution of residuals against a theoretical Normal distribution.
+\pause
+Interpretation:
+\begin{itemize}
+\item \alert{Points on the diagonal line}: Residuals are normally distributed.
+\item \alert{Tails curving away}: Heavy tails or skewness—consider transformations or outlier removal.
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org39f4290}]{Influential Points (Cook's Distance)}
+\begin{definition}[Cook's Distance]\label{sec:orge5dbce2}
+\pause
+\alert{Cook's Distance} measures how much the regression coefficients change when a single observation is removed. It identifies influential outliers that disproportionately affect the model.
+\pause
+A high Cook's Distance (typically \(> 4/n\)) indicates that the observation has a strong influence on the fitted model and should be investigated.
+\end{definition}
+\end{frame}
+\section{Multiple Linear Regression}
+\label{sec:orga3c7d0d}
+\begin{frame}[label={sec:orgb60cc7d}]{Multiple Linear Regression}
+\begin{definition}[Multiple Linear Regression]\label{sec:org3c215ea}
+\pause
+\alert{Multiple Linear Regression} is an extension of simple linear regression that models the relationship between a dependent variable and multiple independent variables. It is used when there are two or more predictors.
+\pause
+Mathematically, it is represented as:
+$$y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \ldots + \beta_p x_p + \epsilon$$
+where:
+\begin{itemize}
+\item \(y\) is the dependent variable.
+\item \(x_1, x_2, \ldots, x_p\) are the independent variables (predictors).
+\item \(\beta_0\) is the y-intercept (constant term).
+\item \(\beta_1, \beta_2, \ldots, \beta_p\) are the coefficients (slopes) for each independent variable.
+\item \(\epsilon\) is the error term (residuals).
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org700e4bc}]{Assumptions of Multiple Linear Regression}
+\begin{definition}[Assumptions of Multiple Linear Regression]\label{sec:org64dc7af}
+\pause
+The assumptions of multiple linear regression are similar to those of simple linear regression:
+\begin{itemize}
+\item Linearity: The relationship between the independent variables and the dependent variable is linear.
+\item Independence: Observations are independent of each other.
+\item Homoscedasticity: Constant variance of the error terms.
+\item Normality: The residuals (errors) of the model are normally distributed.
+\item No multicollinearity: The independent variables are not highly correlated with each other.
+\end{itemize}
+\end{definition}
+\end{frame}
+\section{Regularized Regression}
+\label{sec:orgc15ca4b}
+\begin{frame}[label={sec:org087d73b}]{How do Machine Learning Models Learn?}
+\begin{definition}[How do Machine Learning Models Learn?]\label{sec:orgdebeb90}
+\pause
+Machine Learning models learn by adjusting their parameters (coefficients) to minimize the difference between the predicted values and the actual values in the training data, also known as the \alert{cost function} or \alert{loss function}.
+\pause
+This process is often done using optimization algorithms that iteratively update the parameters based on the loss function, which measures the error of the predictions.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org889e11c}]{Cost Function}
+\begin{definition}[Cost Function]\label{sec:org4ddcbd2}
+\pause
+The \alert{cost function} (or \alert{loss function}) is a mathematical function that quantifies the difference between the predicted values and the actual values in the training data. It is used to guide the learning process of the model.
+\pause
+Common cost functions for regression include:
+\begin{itemize}
+\item \alert{Mean Squared Error (MSE)}
+\item \alert{Mean Absolute Error (MAE)}
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org6236599}]{Cost Function: Mean Squared Error (MSE)}
+\begin{definition}[Cost Function: Mean Squared Error (MSE)]\label{sec:org8c5f5cd}
+\pause
+\alert{Mean Squared Error (MSE)} is a common cost function used in regression tasks. It measures the average of the squares of the errors, which are the differences between the predicted values and the actual values:
+$$J_\text{MSE}(w) = \frac{1}{2m} \sum_{i=1}^{m} (f(x_i, w) - y_i)^2$$
+where:
+\begin{itemize}
+\item \(m\) is the number of observations.
+\item \(f(x_i, w)\) is the predicted value for the \(i\)-th observation given the input features \(x_i\) and model parameters \(w\).
+\item \(y_i\) is the actual value for the \(i\)-th observation.
+\end{itemize}
+\pause
+MSE is sensitive to outliers, and is, therefore, useful when larger errors are more significant.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgb2843fb}]{Cost Function: Mean Absolute Error (MAE)}
+\begin{definition}[Cost Function: Mean Absolute Error (MAE)]\label{sec:org35c2d51}
+\pause
+\alert{Mean Absolute Error (MAE)} is another common cost function used in regression tasks. It measures the average of the absolute differences between the predicted values and the actual values:
+$$J_\text{MAE}(w) = \frac{1}{m} \sum_{i=1}^{m} |f(x_i, w) - y_i|$$
+where:
+\begin{itemize}
+\item \(m\) is the number of observations.
+\item \(f(x_i, w)\) is the predicted value for the \(i\)-th observation given the input features \(x_i\) and model parameters \(w\).
+\item \(y_i\) is the actual value for the \(i\)-th observation.
+\end{itemize}
+\pause
+MAE is less sensitive to outliers compared to MSE; useful when all errors are equally important.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgc1a5f1c}]{Gradient Descent}
+\begin{definition}[Gradient Descent]\label{sec:orgd119251}
+\pause
+\alert{Gradient Descent} is an optimization algorithm used to minimize the cost function by iteratively updating the model parameters in the direction of the steepest descent of the cost function.
+\pause
+The update rule for gradient descent is:
+$$w := w - \alpha \nabla J(w)$$
+where:
+\begin{itemize}
+\item \(w\) is the vector of model parameters (coefficients).
+\item \(\alpha\) is the learning rate, which controls the step size of each update.
+\item \(\nabla J(w)\) is the gradient of the cost function with respect to the parameters \(w\).
+\end{itemize}
+\pause
+Gradient descent continues until convergence, which is when the change in the cost function is below a certain threshold or a maximum number of iterations is reached.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgdd8ec35}]{Gradient Descent Algorithm}
+\begin{definition}[Gradient Descent Algorithm]\label{sec:org6cbf71f}
+\pause
+The \alert{Gradient Descent Algorithm} consists of the following steps:
+\begin{enumerate}[<+->]
+\item Initialize the model parameters (coefficients) randomly or to zero.
+\item Compute the predicted values using the current parameters.
+\item Calculate the cost function (loss) using the predicted values and actual values.
+\item Compute the gradient of the cost function with respect to the parameters.
+\item Update the parameters using the gradient descent update rule.
+\item Repeat steps 2-5 until convergence (i.e., the change in the cost function is below a threshold or a maximum number of iterations is reached).
+\end{enumerate}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgd4705a1}]{Overfitting and Underfitting}
+\begin{definition}[Overfitting and Underfitting]\label{sec:orgceddfb7}
+\pause
+\alert{Overfitting} occurs when a model learns the noise in the training data instead of the underlying pattern, resulting in poor generalization to unseen data. It typically happens when the model is too complex relative to the amount of training data.
+
+\pause
+\alert{Underfitting} occurs when a model is too simple to capture the underlying pattern in the data, resulting in poor performance on both training and unseen data. It typically happens when the model is not complex enough relative to the data.
+
+\pause
+To mitigate overfitting, techniques such as \alert{regularization}, \alert{cross-validation}, and \alert{pruning} can be used. To address underfitting, more complex models or additional features can be considered.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgbd60243}]{Regularization}
+\begin{definition}[Regularization]\label{sec:orgcc9440a}
+\pause
+\alert{Regularization} is a technique used in machine learning to prevent overfitting by adding a penalty term to the cost function. This penalty discourages overly complex models by constraining the size of the model parameters (coefficients).
+\pause
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgb9a23e1}]{Types of Regularized Regression}
+\begin{definition}[Types of Regularized Regression]\label{sec:orgff4c053}
+There are two common types of regularized regression:
+\begin{itemize}
+\item \alert{Ridge Regression (L2 Regularization)}
+\item \alert{Lasso Regression (L1 Regularization)}
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgd23f2fa}]{Lasso Regression (L1 Regularization)}
+\begin{definition}[Lasso Regression (L1 Regularization)]\label{sec:org79c3126}
+\pause
+\alert{Lasso Regression} adds a penalty equal to the absolute value of the magnitude of coefficients to the loss function. The objective function for lasso regression is:
+$$J_\text{Lasso}(w) = \frac{1}{2m} \sum_{i=1}^{m} (f(x_i, w) - y_i)^2 + \lambda \sum_{j=1}^{p} |w_j|$$
+where:
+\begin{itemize}
+\item \(\lambda\) is the regularization parameter that controls the strength of the penalty.
+\item \(w_j\) are the coefficients of the independent variables.
+\end{itemize}
+\pause
+Lasso regression can shrink some coefficients to exactly zero, effectively performing variable selection and resulting in a simpler model.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orge185f78}]{Ridge Regression (L2 Regularization)}
+\begin{definition}[Ridge Regression (L2 Regularization)]\label{sec:org74204ab}
+\pause
+\alert{Ridge Regression} adds a penalty equal to the square of the magnitude of coefficients to the loss function. The objective function for ridge regression is:
+$$J_\text{Ridge}(w) = \frac{1}{2m} \sum_{i=1}^{m} (f(x_i, w) - y_i)^2 + \frac{\lambda}{2m} \sum_{j=1}^{p} w_j^2$$
+where:
+\begin{itemize}
+\item \(\lambda\) is the regularization parameter that controls the strength of the penalty.
+\item \(w_j\) are the coefficients of the independent variables.
+\end{itemize}
+\pause
+Ridge regression helps to reduce model complexity and multicollinearity by shrinking the coefficients towards zero, but it does not set any coefficients exactly to zero.
+\end{definition}
+\end{frame}
+\section{Nonlinearities in Regression}
+\label{sec:orgb900328}
+\begin{frame}[label={sec:orge433515}]{Nonlinearities in Regression}
+\begin{definition}[Nonlinearities in Regression]\label{sec:orgda6c12e}
+\pause
+\alert{Nonlinearities in Regression} refers to the presence of nonlinear relationships between the independent and dependent variables in a regression model. In such cases, a simple linear regression model may not adequately capture the underlying relationship, leading to poor predictions and insights.
+\pause
+Nonlinear regression models can help address this limitation by allowing for more flexible relationships between the independent and dependent variables.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgddf6a28}]{Types of Nonlinear Regression Models}
+\begin{definition}[Types of Nonlinear Regression Models]\label{sec:orgb627b15}
+There are several types of nonlinear regression models, including:
+\begin{itemize}[<+->]
+\item \alert{Polynomial Regression}: Fits a polynomial equation to the data, allowing for curves and bends in the relationship between the independent and dependent variables. The degree of the polynomial can be adjusted to capture more complex relationships.
+\item \alert{Exponential Regression}: Fits an exponential function to the data, which is useful for modeling relationships where the rate of change increases or decreases exponentially.
+\item \alert{Logarithmic Regression}: Fits a logarithmic function to the data, which is useful for modeling relationships where the rate of change decreases as the independent variable increases.
+\end{itemize}
+\end{definition}
+\end{frame}
+\section{Overfitting, Underfitting, and Hyperparameter Tuning}
+\label{sec:orgda7dfc8}
+\begin{frame}[label={sec:org6bb2f59}]{Overfitting, Underfitting, and Hyperparameter Tuning}
+\begin{definition}[Overfitting]\label{sec:orgdd52bf8}
+\pause
+\alert{Overfitting} occurs when a model learns the noise in the training data instead of the underlying pattern, resulting in poor generalization to unseen data. It typically happens when the model is too complex relative to the amount of training data.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org22a49c4}]{Underfitting}
+\begin{definition}[Underfitting]\label{sec:org92273ab}
+\pause
+\alert{Underfitting} occurs when a model is too simple to capture the underlying pattern in the data, resulting in poor performance on both training and unseen data. It typically happens when the model is not complex enough relative to the data.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org7c30f75}]{Bias-Variance Tradeoff}
+\begin{definition}[Bias-Variance Tradeoff]\label{sec:org2ffffd4}
+\pause
+The \alert{Bias-Variance Tradeoff} is a fundamental concept in machine learning that describes the tradeoff between two sources of error in a model:
+\begin{itemize}[<+->]
+\item \alert{Bias}: The error introduced by approximating a real-world problem, which may be complex, with a simplified model. High bias can lead to underfitting.
+\item \alert{Variance}: The error introduced by the model's sensitivity to small fluctuations in the training data. High variance can lead to overfitting.
+\item The goal is to find a balance between bias and variance to minimize the overall error of the model.
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgbe2a44d}]{Learning Curves}
+\begin{definition}[Learning Curves]\label{sec:org8c67211}
+\pause
+\alert{Learning Curves} are graphical representations that show how the performance of a machine learning model changes with varying amounts of training data. They can help diagnose overfitting and underfitting by comparing the training and validation errors as the size of the training set increases.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org68e0231}]{Hyperparameter Tuning}
+\begin{definition}[Hyperparameter Tuning]\label{sec:org62672c3}
+\pause
+\alert{Hyperparameter Tuning} is the process of selecting the best hyperparameters for a machine learning model to improve its performance. Hyperparameters are parameters that are not learned from the data but are set before training the model, such as the learning rate, regularization strength, and number of hidden layers in a neural network.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org3e74da0}]{Techniques for Hyperparameter Tuning}
+\begin{definition}[Techniques for Hyperparameter Tuning]\label{sec:org9fa8330}
+There are several techniques for hyperparameter tuning, including:
+\begin{itemize}[<+->]
+\item \alert{Grid Search}: Exhaustively searches through a specified subset of hyperparameters to find the best combination.
+\item \alert{Random Search}: Randomly samples hyperparameters from a specified distribution to find the best combination.
+\item \alert{Bayesian Optimization}: Uses probabilistic models to find the best hyperparameters by balancing exploration and exploitation.
+\item \alert{Cross-Validation}: Evaluates the model's performance on different subsets of the data to find the best hyperparameters.
+\item \alert{Automated Hyperparameter Tuning}: Uses algorithms to automatically search for the best hyperparameters based on the model's performance.
+\end{itemize}
+\end{definition}
+\end{frame}
+\end{document}

From b4e47052c0cc6ebf77bd9cd4f0b37016af929b3c Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Sun, 1 Feb 2026 16:30:49 +0100
Subject: [PATCH 39/81] S02C01 Simple Linear Regression

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1b249d79",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Simple Linear Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8912d42c",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "Regression is the \"Hello World\" of machine learning. It is a statistical method used to model the relationship between a dependent variable (target) and one or more independent variables (features).\n",
+    "\n",
+    "**Simple Linear Regression** is the specific case where we model the relationship between **one** input and **one** output using a straight line."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f575c27c",
+   "metadata": {},
+   "source": [
+    "## The Mathematical Model\n",
+    "\n",
+    "We assume the data follows this formula:\n",
+    "\n",
+    "$$y = \\beta_0 + \\beta_1 x + \\varepsilon$$\n",
+    "\n",
+    "Where:\n",
+    "\n",
+    "-   $y$: The Target (what we predict).\n",
+    "-   $x$: The Feature (what we know).\n",
+    "-   $\\beta_0$: The Intercept (where the line crosses the Y-axis).\n",
+    "-   $\\beta_1$: The Slope (steepness of the line).\n",
+    "-   $\\varepsilon$: The Error term (random noise in the real world).\n",
+    "\n",
+    "The goal of the algorithm is to find the \"Best Fit Line\" by estimating the optimal values for $\\beta_0$ and $\\beta_1$ that minimize the error."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1813124",
+   "metadata": {},
+   "source": [
+    "## Evaluation Metrics\n",
+    "\n",
+    "How do we know if our line is \"good\"? We measure the distance between the actual data points and our predicted line.\n",
+    "\n",
+    "| Metric    | Name                    | Formula                                      | Interpretation                                                                     |\n",
+    "|--------- |----------------------- |-------------------------------------------- |---------------------------------------------------------------------------------- |\n",
+    "| **MAE**   | Mean Absolute Error     | $\\frac{1}{n} \\sum{\\lvert y - \\hat{y}\\rvert}$ | Average error magnitude. Easy to interpret.                                        |\n",
+    "| **MSE**   | Mean Squared Error      | $\\frac{1}{n} \\sum{(y - \\hat{y})^2}$          | Penalizes large errors heavily (due to squaring).                                  |\n",
+    "| **RMSE**  | Root Mean Squared Error | $\\sqrt{MSE}$                                 | Same unit as $y$. Most common metric.                                              |\n",
+    "| **$R^2$** | R-Squared               | $1 - \\frac{SS_{res}}{SS_{tot}}$              | \"Percent of variance explained.\" 1.0 is perfect, 0.0 is baseline, can be negative. |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd1c8fa8",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration\n",
+    "\n",
+    "We will implement Simple Linear Regression via `scikit-learn` using a synthetic dataset to see exactly how it works."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa9bd6a0",
+   "metadata": {},
+   "source": [
+    "### Generate Synthetic Data\n",
+    "\n",
+    "We create data that follows the rule: $y = 4 + 3x + \\text{noise}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c8b4fb1",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Set visual style\n",
+    "sns.set_theme(style='whitegrid')\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# Generate 100 points between -2 and 2\n",
+    "X = np.linspace(-2, 2, 100).reshape(-1, 1)\n",
+    "\n",
+    "# Generate y with some randomness\n",
+    "# Equation: y = 4 + 3x + noise\n",
+    "noise = np.random.randn(100, 1) * 1.5\n",
+    "y = 4 + 3 * X + noise\n",
+    "\n",
+    "# Convert to DataFrame\n",
+    "df = pd.DataFrame(np.hstack((X, y)), columns=['Feature (X)', 'Target (y)'])\n",
+    "\n",
+    "# Visualize\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "sns.scatterplot(data=df, x='Feature (X)', y='Target (y)', alpha=0.6)\n",
+    "plt.title(\"Synthetic Data: y = 4 + 3x + noise\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b08514c4",
+   "metadata": {},
+   "source": [
+    "### Train the Model\n",
+    "\n",
+    "We split the data and train the regressor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1435f7d",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "# Split 80/20\n",
+    "X_train, X_test, y_train, y_test = train_test_split(df[['Feature (X)']], df['Target (y)'], test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Initialize and Train\n",
+    "model = LinearRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Check the learned parameters\n",
+    "print(f\"True Intercept: 4.0  | Learned Intercept: {model.intercept_:.2f}\")\n",
+    "print(f\"True Slope:     3.0  | Learned Slope:     {model.coef_[0]:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a84de243",
+   "metadata": {},
+   "source": [
+    "### Evaluate and Visualize\n",
+    "\n",
+    "We predict values for the test set and plot the regression line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8896af1d-8695-4674-a0a6-0f1e4fd3a960",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
+    "\n",
+    "# Predict\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "# Metrics\n",
+    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "mae = mean_absolute_error(y_test, y_pred)\n",
+    "\n",
+    "print(f\"RMSE: {rmse:.2f}\")\n",
+    "print(f\"R²:   {r2:.2f}\")\n",
+    "print(f\"MAE:  {mae:.2f}\")\n",
+    "\n",
+    "# Visualization\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# 1. Plot Training Data (faint)\n",
+    "plt.scatter(X_train, y_train, color='gray', alpha=0.3, label='Training Data')\n",
+    "\n",
+    "# 2. Plot Test Data (distinct)\n",
+    "plt.scatter(X_test, y_test, color='blue', label='Test Data (Actual)')\n",
+    "\n",
+    "# 3. Plot Predictions (Line)\n",
+    "# Plot over the entire range of X\n",
+    "X_line = np.linspace(X.min(), X.max(), 100).reshape(-1, 1)\n",
+    "plt.plot(X_line, model.coef_[0] * X_line + model.intercept_, color='red',\n",
+    "         linewidth=2, label='Regression Line')\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.title(\"Simple Linear Regression Fit\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45fc9014",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "The best way to understand the limitations of Linear Regression is to see it fail."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6ad0cc8",
+   "metadata": {},
+   "source": [
+    "### The Outlier Effect\n",
+    "\n",
+    "Linear Regression is highly sensitive to outliers.\n",
+    "\n",
+    "1.  Generate a clean small dataset.\n",
+    "2.  Add **one** extreme outlier.\n",
+    "3.  Fit the model and see how the line shifts.\n",
+    "\n",
+    "**Observation**: Notice how the red line is \"pulled\" down significantly by just one point?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d0e1a5e",
+   "metadata": {},
+   "source": [
+    "### Automated Experimentation\n",
+    "\n",
+    "Write a function `evaluate_noise(noise_level)` that:\n",
+    "\n",
+    "1.  Generates data with the specified noise level.\n",
+    "2.  Trains a model.\n",
+    "3.  Returns the $R^2$ score.\n",
+    "\n",
+    "Run this for noise levels [1, 5, 10, 20] to see how performance degrades."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59280dbc",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "Simple Linear Regression attempts to draw a straight line through data.\n",
+    "\n",
+    "1.  It is **interpretable**: We know exactly what the intercept and slope mean.\n",
+    "2.  It minimizes the **Squared Error** (MSE).\n",
+    "3.  It is **sensitive to outliers**, as demonstrated in the exercises."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 1befb16195db4c347018dc14934832d77b50b351 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Sun, 1 Feb 2026 20:04:25 +0100
Subject: [PATCH 40/81] S02C02 Regression Diagnostics

---
 .../S02C02 Regression Diagnostics.ipynb       | 452 ++++++++++++++++++
 1 file changed, 452 insertions(+)
 create mode 100644 src/S02 Supervised Learning - Regression and Evaluation/S02C02 Regression Diagnostics.ipynb

diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C02 Regression Diagnostics.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C02 Regression Diagnostics.ipynb
new file mode 100644
index 0000000..a86a279
--- /dev/null
+++ b/src/S02 Supervised Learning - Regression and Evaluation/S02C02 Regression Diagnostics.ipynb	
@@ -0,0 +1,452 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "09ea93f7",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Regression Diagnostics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4dbfd96",
+   "metadata": {},
+   "source": [
+    "## Trust, but Verify\n",
+    "\n",
+    "In the previous session, we trained a Linear Regression model and checked the $R^2$ score. However, a high $R^2$ does not necessarily mean the model is valid.\n",
+    "\n",
+    "Linear Regression relies on specific assumptions (Linearity, Independence, Normality, Homoscedasticity). If these are violated, your predictions—and especially your confidence intervals—can be completely wrong. ****Regression Diagnostics**** is the art of checking these assumptions, primarily by analyzing the ****Residuals**** (the errors)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77f1f63a",
+   "metadata": {},
+   "source": [
+    "## Key Diagnostic Checks\n",
+    "\n",
+    "| Assumption           | What it means                                 | How to Diagnose                                                                                                               | Potential Fix                                                        |\n",
+    "|-------------------- |--------------------------------------------- |----------------------------------------------------------------------------------------------------------------------------- |-------------------------------------------------------------------- |\n",
+    "| **Linearity**        | The relationship is actually a straight line. | **Residual Plot**: Residuals should be randomly scattered around 0. If you see a curve (U-shape), the assumption is violated. | Polynomial features, Non-linear models.                              |\n",
+    "| **Homoscedasticity** | The error variance is constant.               | **Scale-Location Plot**: If the residuals \"fan out\" (get wider) as prediction increases, you have heteroscedasticity.         | Log-transform the target ($y$), or use Weighted Least Squares (WLS). |\n",
+    "| **Normality**        | The errors follow a Normal Distribution.      | **Q-Q Plot**: Points should fall on the diagonal line.                                                                        | Log-transform, remove outliers.                                      |\n",
+    "| **Independence**     | Errors are not correlated with each other.    | **Autocorrelation Plot**: Crucial for time-series data.                                                                       | Time-series models (ARIMA).                                          |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37c95602",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration\n",
+    "\n",
+    "We will generate a dataset that **looks** linear but violates the assumption of **Homoscedasticity** (constant variance), a very common real-world issue (e.g., as income grows, the variance in spending grows)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ba716cf",
+   "metadata": {},
+   "source": [
+    "### Generate \"Bad\" Data (Heteroscedasticity)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58b68639",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# Generate X\n",
+    "X = np.linspace(1, 100, 200).reshape(-1, 1)\n",
+    "\n",
+    "# Generate y: The noise increases as X increases\n",
+    "# Notice the noise term: np.random.randn() * X\n",
+    "y = 3 * X + 10 + (np.random.randn(200, 1) * (X * 0.5))\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(X, y, alpha=0.6, color='teal')\n",
+    "plt.title(\"Heteroscedastic Data (Variance increases with X)\")\n",
+    "plt.xlabel(\"X\")\n",
+    "plt.ylabel(\"y\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db6e8cbd",
+   "metadata": {},
+   "source": [
+    "### Train and Evaluate (The Trap)\n",
+    "\n",
+    "We fit a standard Linear Regression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5355a715",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = LinearRegression()\n",
+    "model.fit(X, y)\n",
+    "y_pred = model.predict(X)\n",
+    "\n",
+    "print(f\"R² Score: {r2_score(y, y_pred):.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "869dc80d",
+   "metadata": {},
+   "source": [
+    "**Observation**: The $R^2$ is likely high (around 0.8-0.9). A beginner would stop here and declare success."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ee290b1",
+   "metadata": {},
+   "source": [
+    "### Diagnostic: Residual Plot\n",
+    "\n",
+    "We plot the **Residuals** ($y_{true} - y_{pred}$) against the **Predicted Values**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e97b337",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "residuals = y - y_pred\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(y_pred, residuals, alpha=0.6, color='crimson')\n",
+    "plt.axhline(0, color='black', linestyle='--')\n",
+    "plt.title(\"Residual Plot\")\n",
+    "plt.xlabel(\"Predicted Values\")\n",
+    "plt.ylabel(\"Residuals (Errors)\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a513b2c4",
+   "metadata": {},
+   "source": [
+    "**Diagnosis**: Notice the **Funnel Shape**? The errors are small on the left and huge on the right. This confirms **Heteroscedasticity**. The model is untrustworthy for high values of $X$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3020bbff",
+   "metadata": {},
+   "source": [
+    "### Diagnostic: Scale-Location Plot (Homoscedasticity)\n",
+    "\n",
+    "The **Scale-Location Plot** (also called Spread-Location) is specifically designed to detect heteroscedasticity. It plots $\\sqrt{|\\text{standardized residuals}|}$ against fitted values. If homoscedasticity holds, the points should form a horizontal band with no trend."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb6dcf5c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standardize residuals (subtract mean, divide by std)\n",
+    "residuals_standardized = (residuals - residuals.mean()) / residuals.std()\n",
+    "\n",
+    "# Scale-Location: sqrt of absolute standardized residuals\n",
+    "sqrt_abs_resid = np.sqrt(np.abs(residuals_standardized))\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(y_pred, sqrt_abs_resid, alpha=0.6, color='purple')\n",
+    "\n",
+    "# Add a lowess smoothing line to show the trend\n",
+    "from statsmodels.nonparametric.smoothers_lowess import lowess\n",
+    "smoothed = lowess(sqrt_abs_resid.ravel(), y_pred.ravel(), frac=0.3)\n",
+    "plt.plot(smoothed[:, 0], smoothed[:, 1], color='red', linewidth=2, label='Trend')\n",
+    "\n",
+    "plt.title(\"Scale-Location Plot\")\n",
+    "plt.xlabel(\"Fitted Values\")\n",
+    "plt.ylabel(r\"$\\sqrt{|\\mathrm{Standardized\\ Residuals}|}$\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b890dc6d",
+   "metadata": {},
+   "source": [
+    "**Diagnosis**: The red trend line should be roughly horizontal. An upward slope (as seen here) confirms \\*Heteroscedasticity\\*—the spread of residuals increases with fitted values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40b55f8f",
+   "metadata": {},
+   "source": [
+    "### Diagnostic: Q-Q Plot (Normality)\n",
+    "\n",
+    "We check if the errors are normally distributed using a Quantile-Quantile (Q-Q) plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8560c7f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.stats as stats\n",
+    "\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "stats.probplot(residuals.ravel(), dist=\"norm\", plot=plt)\n",
+    "plt.title(\"Q-Q Plot\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "605903e3",
+   "metadata": {},
+   "source": [
+    "**Diagnosis**: If the blue dots deviate significantly from the red line (tails curving off), the errors are not normal."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "527d209b",
+   "metadata": {},
+   "source": [
+    "### Diagnostic: Autocorrelation (Independence)\n",
+    "\n",
+    "For time-series data, errors from one observation may be correlated with errors from previous observations. This violates the **Independence** assumption and makes standard errors unreliable.\n",
+    "\n",
+    "We generate synthetic \"time-series\" data where the error at time $t$ depends on the error at time $t-1$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "63d8354d",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from statsmodels.stats.stattools import durbin_watson\n",
+    "from statsmodels.graphics.tsaplots import plot_acf\n",
+    "\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# Generate time-series X (e.g., months 1-100)\n",
+    "X_time = np.arange(1, 101).reshape(-1, 1)\n",
+    "\n",
+    "# Generate autocorrelated errors: e[t] = 0.8 * e[t-1] + noise\n",
+    "errors = np.zeros(100)\n",
+    "for t in range(1, 100):\n",
+    "    errors[t] = 0.8 * errors[t - 1] + np.random.normal(0, 5)\n",
+    "\n",
+    "# True relationship: y = 2*X + autocorrelated_error\n",
+    "y_time = 2 * X_time.ravel() + 50 + errors\n",
+    "\n",
+    "# Fit model\n",
+    "model_time = LinearRegression()\n",
+    "model_time.fit(X_time, y_time)\n",
+    "y_pred_time = model_time.predict(X_time)\n",
+    "residuals_time = y_time - y_pred_time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "558cab86",
+   "metadata": {},
+   "source": [
+    "#### Durbin-Watson Test\n",
+    "\n",
+    "The Durbin-Watson statistic tests for first-order autocorrelation. Values range from 0 to 4:\n",
+    "\n",
+    "-   **~2**: No autocorrelation (good)\n",
+    "-   **<1.5**: Positive autocorrelation (errors follow each other)\n",
+    "-   **>2.5**: Negative autocorrelation (errors alternate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b75f2b0c",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "dw_stat = durbin_watson(residuals_time)\n",
+    "print(f\"Durbin-Watson statistic: {dw_stat:.3f}\")\n",
+    "\n",
+    "if dw_stat < 1.5:\n",
+    "    print(\"⚠️  Strong positive autocorrelation detected!\")\n",
+    "elif dw_stat > 2.5:\n",
+    "    print(\"⚠️  Strong negative autocorrelation detected!\")\n",
+    "else:\n",
+    "    print(\"✓ No significant autocorrelation.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d8cfa05",
+   "metadata": {},
+   "source": [
+    "#### Autocorrelation Function (ACF) Plot\n",
+    "\n",
+    "The ACF plot shows correlation between residuals at different lags. Significant spikes beyond the confidence band (blue shaded area) indicate autocorrelation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd8f72b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 5))\n",
+    "plot_acf(residuals_time, lags=20, ax=ax)\n",
+    "ax.set_title(\"Autocorrelation Function (ACF) of Residuals\")\n",
+    "ax.set_xlabel(\"Lag\")\n",
+    "ax.set_ylabel(\"Autocorrelation\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d5f00f8",
+   "metadata": {},
+   "source": [
+    "**Diagnosis**: If you see significant spikes at lag 1, 2, etc., your residuals are not independent. Standard OLS confidence intervals and p-values will be wrong. Consider using time-series models (ARIMA) or Newey-West standard errors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1cccc72",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "Now, you will diagnose a different problem: **Non-Linearity**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2a25b4b",
+   "metadata": {},
+   "source": [
+    "### Generate Non-Linear Data\n",
+    "\n",
+    "Generate data following a parabola: $y = x^2 + \\text{noise}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "469e048f",
+   "metadata": {},
+   "source": [
+    "### Fit Linear Model & Plot Residuals\n",
+    "\n",
+    "Fit a `LinearRegression` model. Then, plot the residuals vs. predictions. **Question**: What shape do you see in the residual plot?\n",
+    "\n",
+    "**Answer**: You should see a clear ****U-shape**** (Parabola). This pattern screams \"You need a non-linear model\" (which sets the stage for your future module on **Nonlinearities**)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a892345f",
+   "metadata": {},
+   "source": [
+    "### Outlier Detection (Cook's Distance)\n",
+    "\n",
+    "Influential points can skew the model. We can detect them using **Cook's Distance**. **Note**: We use statsmodels here as it has built-in diagnostic tools."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f795a165",
+   "metadata": {},
+   "source": [
+    "#### Interpreting Cook's Distance\n",
+    "\n",
+    "Common thresholds to identify influential points:\n",
+    "\n",
+    "-   **D > 4/n**: Point may be influential (n = number of observations)\n",
+    "-   **D > 1**: Point is highly influential (conservative rule)\n",
+    "\n",
+    "**What to do with influential points?**\n",
+    "\n",
+    "1.  **Investigate**: Is it a data entry error? Correct it.\n",
+    "2.  **Domain knowledge**: Is this observation genuinely unusual but valid?\n",
+    "3.  **Sensitivity analysis**: Fit the model with and without the point. If coefficients change drastically, report both results.\n",
+    "4.  **Robust regression**: Use methods like RANSAC or Huber regression (not covered in this training) that down-weight outliers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48f629d8",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **Don't trust $R^2$ alone**. It hides many sins.\n",
+    "2.  **Residual Plots** are your best friend.\n",
+    "    -   Random scatter = Good.\n",
+    "    -   Funnel shape = Heteroscedasticity (Needs Log transform or WLS).\n",
+    "    -   U-shape = Non-linearity (Needs Polynomials).\n",
+    "3.  **Q-Q Plots** check for Normality.\n",
+    "4.  **Durbin-Watson & ACF Plots** check for Independence (autocorrelation). Critical for time-series data.\n",
+    "5.  **Cook's Distance** identifies influential outliers. Use threshold $D > 4/n$ to flag suspects."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 2138cc53e36818eb5f78fcad43138197d5853efd Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Sun, 1 Feb 2026 20:39:10 +0100
Subject: [PATCH 41/81] S02C03 Advanced Evaluation Metrics

---
 .../S02C03 Advanced Evaluation Metrics.ipynb  | 369 ++++++++++++++++++
 1 file changed, 369 insertions(+)
 create mode 100644 src/S02 Supervised Learning - Regression and Evaluation/S02C03 Advanced Evaluation Metrics.ipynb

diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C03 Advanced Evaluation Metrics.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C03 Advanced Evaluation Metrics.ipynb
new file mode 100644
index 0000000..9039acc
--- /dev/null
+++ b/src/S02 Supervised Learning - Regression and Evaluation/S02C03 Advanced Evaluation Metrics.ipynb	
@@ -0,0 +1,369 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "13a7c7f2",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Advanced Evaluation Metrics\n",
+    "\n",
+    "In \"Simple Linear Regression,\" we introduced standard metrics like RMSE and $R^2$. However, real-world problems often require more nuance.\n",
+    "\n",
+    "For example, $R^2$ has a flaw: it **always** increases on the *training set* when you add features, even if they are useless. And RMSE can be dangerously sensitive to outliers. In this module, we explore advanced metrics that protect us from these pitfalls."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e3db4d4",
+   "metadata": {},
+   "source": [
+    "## MAE vs. RMSE: The Penalty Game\n",
+    "\n",
+    "Both measure the \"average error,\" but they treat mistakes differently.\n",
+    "\n",
+    "-   **MAE (L1 Norm)**: Treats all errors linearly. Being off by 10 is exactly twice as bad as being off by 5.\n",
+    "-   **RMSE (L2 Norm)**: Squares the errors. Being off by 10 is **four times** as bad as being off by 5.\n",
+    "\n",
+    "**Rule of Thumb**:\n",
+    "\n",
+    "-   Use **MAE** if you want a robust metric that reflects \"typical\" performance.\n",
+    "-   Use **RMSE** if large errors are unacceptable (e.g., in medical dosages or safety systems)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18aa7059",
+   "metadata": {},
+   "source": [
+    "## Adjusted $R^2$: The \"BS Detector\"\n",
+    "\n",
+    "Standard $R^2$ has a major weakness: if you add a feature filled with random noise, $R^2$ will stay the same or slightly increase. It never decreases, at least on the *training set*, though it may do so on the *test set*. This incentivizes adding infinite garbage features (Overfitting).\n",
+    "\n",
+    "**Adjusted $R^2$** penalizes the model for adding useless features. $$R^2_{adj} = 1 - (1-R^2) \\frac{n-1}{n-p-1}$$ Where $n$ is samples and $p$ is features.\n",
+    "\n",
+    "-   If you add a feature and the model doesn't improve significantly, Adjusted $R^2$ **drops**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9323e54",
+   "metadata": {},
+   "source": [
+    "## RMSLE: The Log Error\n",
+    "\n",
+    "When predicting prices (like houses or stocks), an error of $50k is huge on a $100k house, but negligible on a $10M house. **Root Mean Squared Logarithmic Error (RMSLE)** cares about the **ratio**, not the absolute difference."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a84c2713",
+   "metadata": {},
+   "source": [
+    "### Why RMSLE Penalizes Under-Prediction More\n",
+    "\n",
+    "RMSLE computes $\\log(pred + 1) - \\log(actual + 1)$. Due to the asymmetry of logarithms:\n",
+    "\n",
+    "-   Predicting **2x too high**: $\\log(2) \\approx 0.69$\n",
+    "-   Predicting **2x too low** (half): $\\log(0.5) \\approx -0.69$ → same magnitude\n",
+    "\n",
+    "But for the **same absolute error**, under-prediction hurts more:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91e05c29",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "actual = 100\n",
+    "over_pred = 150   # +50 error\n",
+    "under_pred = 50   # -50 error\n",
+    "\n",
+    "error_over = np.log1p(over_pred) - np.log1p(actual)\n",
+    "error_under = np.log1p(under_pred) - np.log1p(actual)\n",
+    "\n",
+    "print(f\"Over-predict by 50:  log error = {error_over:.4f}\")\n",
+    "print(f\"Under-predict by 50: log error = {error_under:.4f}\")\n",
+    "print(f\"Under-prediction penalty is {abs(error_under)/abs(error_over):.2f}x larger\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8567e5c1",
+   "metadata": {},
+   "source": [
+    "### Outlier Resistance (Compared to RMSE)\n",
+    "\n",
+    "RMSLE compresses large values via the logarithm, reducing the influence of extreme predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e4c2078",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import mean_squared_error, mean_squared_log_error\n",
+    "\n",
+    "y_true = np.array([100, 100, 100, 100])\n",
+    "y_pred_normal = np.array([110, 90, 105, 95])    # Typical errors\n",
+    "y_pred_outlier = np.array([110, 90, 105, 500])  # One massive outlier\n",
+    "\n",
+    "# RMSE comparison\n",
+    "rmse_normal = np.sqrt(mean_squared_error(y_true, y_pred_normal))\n",
+    "rmse_outlier = np.sqrt(mean_squared_error(y_true, y_pred_outlier))\n",
+    "\n",
+    "# RMSLE comparison\n",
+    "rmsle_normal = np.sqrt(mean_squared_log_error(y_true, y_pred_normal))\n",
+    "rmsle_outlier = np.sqrt(mean_squared_log_error(y_true, y_pred_outlier))\n",
+    "\n",
+    "print(f\"RMSE  (normal): {rmse_normal:.2f}  → (with outlier): {rmse_outlier:.2f}  ({rmse_outlier/rmse_normal:.1f}x increase)\")\n",
+    "print(f\"RMSLE (normal): {rmsle_normal:.4f} → (with outlier): {rmsle_outlier:.4f} ({rmsle_outlier/rmsle_normal:.1f}x increase)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26fb86f5",
+   "metadata": {},
+   "source": [
+    "**Observation**: RMSE explodes with the outlier; RMSLE increases but remains more stable.\n",
+    "\n",
+    "**Caveat**: RMSLE fails if predictions are near zero (log blows up). Always ensure positive predictions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8752cbdf",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration\n",
+    "\n",
+    "We will compare a \"Good Model\" vs. a \"Bloated Model\" (one with random noise features) to see Adjusted $R^2$ in action."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "644089e0",
+   "metadata": {},
+   "source": [
+    "### Setup Data with \"Noise Features\"\n",
+    "\n",
+    "We add 10 columns of pure random noise to the California Housing dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4250afdd",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_california_housing\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import r2_score, mean_squared_error\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Load Clean Data\n",
+    "data = fetch_california_housing(as_frame=True)\n",
+    "X = data.data\n",
+    "y = data.target\n",
+    "\n",
+    "# Generate 10 columns of random noise\n",
+    "np.random.seed(42)\n",
+    "noise = np.random.normal(0, 1, (X.shape[0], 10))\n",
+    "df_noise = pd.DataFrame(noise, columns=[f'Noise_{i}' for i in range(10)])\n",
+    "\n",
+    "# Create \"Bloated\" dataset\n",
+    "X_bloated = pd.concat([X, df_noise], axis=1)\n",
+    "\n",
+    "print(f\"Original Features: {X.shape[1]}\")\n",
+    "print(f\"Bloated Features:  {X_bloated.shape[1]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ceaba03",
+   "metadata": {},
+   "source": [
+    "### Compare $R^2$ vs Adjusted $R^2$\n",
+    "\n",
+    "We define a helper function to calculate Adjusted $R^2$ since scikit-learn doesn't have it built-in. Then we compare the **Clean Model** vs the **Bloated Model** side-by-side."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78584266",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def adjusted_r2(r2, n, p):\n",
+    "    \"\"\" Calculates Adjusted R-squared.\n",
+    "    n: number of samples\n",
+    "    p: number of features\n",
+    "    \"\"\"\n",
+    "    return 1 - (1 - r2) * (n - 1) / (n - p - 1)\n",
+    "\n",
+    "# Split BOTH datasets with same random state for fair comparison\n",
+    "X_train_clean, X_test_clean, y_train, y_test = train_test_split(\n",
+    "    X, y, test_size=0.2, random_state=42)\n",
+    "X_train_bloated, X_test_bloated, _, _ = train_test_split(\n",
+    "    X_bloated, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Train Clean Model\n",
+    "model_clean = LinearRegression()\n",
+    "model_clean.fit(X_train_clean, y_train)\n",
+    "y_pred_clean = model_clean.predict(X_test_clean)\n",
+    "\n",
+    "# Train Bloated Model\n",
+    "model_bloated = LinearRegression()\n",
+    "model_bloated.fit(X_train_bloated, y_train)\n",
+    "y_pred_bloated = model_bloated.predict(X_test_bloated)\n",
+    "\n",
+    "# Calculate Metrics for Clean Model\n",
+    "n = X_test_clean.shape[0]\n",
+    "r2_clean = r2_score(y_test, y_pred_clean)\n",
+    "adj_r2_clean = adjusted_r2(r2_clean, n, X_test_clean.shape[1])\n",
+    "\n",
+    "# Calculate Metrics for Bloated Model\n",
+    "r2_bloated = r2_score(y_test, y_pred_bloated)\n",
+    "adj_r2_bloated = adjusted_r2(r2_bloated, n, X_test_bloated.shape[1])\n",
+    "\n",
+    "print(\"=\" * 45)\n",
+    "print(f\"{'Metric':<15} {'Clean (8 feat)':<15} {'Bloated (18 feat)':<15}\")\n",
+    "print(\"=\" * 45)\n",
+    "print(f\"{'Standard R²':<15} {r2_clean:<15.5f} {r2_bloated:<15.5f}\")\n",
+    "print(f\"{'Adjusted R²':<15} {adj_r2_clean:<15.5f} {adj_r2_bloated:<15.5f}\")\n",
+    "print(\"=\" * 45)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01fc3c08",
+   "metadata": {},
+   "source": [
+    "**Observation**: Standard $R^2$ may look similar (or even slightly higher for the bloated model on training data), but Adjusted $R^2$ reveals the truth: the noise features add no value. As $p$ approaches $n$, this gap widens dramatically, though it is quite small here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e1d0f5c",
+   "metadata": {},
+   "source": [
+    "### RMSLE (Handling Skew)\n",
+    "\n",
+    "House prices vary wildly. Let's see how RMSLE handles the error compared to RMSE. **Note**: RMSLE requires positive targets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9834eb72",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import mean_squared_log_error\n",
+    "\n",
+    "# Calculate RMSE\n",
+    "rmse = np.sqrt(mean_squared_error(y_test, y_pred_clean))\n",
+    "\n",
+    "# Calculate RMSLE\n",
+    "# (California housing target is in 100k, so values are small, e.g., 2.5. \n",
+    "# This metric is usually for raw prices like 250,000, but works here too).\n",
+    "# We ensure no negative predictions for RMSLE (clip at 0)\n",
+    "y_pred_safe = np.maximum(y_pred_clean, 0)\n",
+    "rmsle = np.sqrt(mean_squared_log_error(y_test, y_pred_safe))\n",
+    "\n",
+    "print(f\"RMSE:  {rmse:.4f} (Absolute Error)\")\n",
+    "print(f\"RMSLE: {rmsle:.4f} (Relative/Ratio Error)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdf04eb9",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d61a52b",
+   "metadata": {},
+   "source": [
+    "### The \"Kitchen Sink\" Experiment\n",
+    "\n",
+    "Add 100, then 500 noise features to the dataset.\n",
+    "\n",
+    "1.  Train a model for each.\n",
+    "2.  Plot $R^2$ vs Adjusted $R^2$.\n",
+    "3.  Watch them diverge. $R^2$ will decrease much slower than Adjusted $R^2$, which will tank."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0c709fe",
+   "metadata": {},
+   "source": [
+    "### Prediction Error Plot\n",
+    "\n",
+    "A powerful visual metric is the \"Prediction Error Plot\".\n",
+    "\n",
+    "-   Plot `y_true` on the x-axis.\n",
+    "-   Plot `y_pred` on the y-axis.\n",
+    "-   Draw a 45-degree identity line.\n",
+    "\n",
+    "Points below the line are **Underestimations**; points above are **Overestimations**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4eb23b2c",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **Context Matters**: Don't use RMSE just because everyone else does. If outliers are rare anomalies you want to ignore, use MAE.\n",
+    "2.  **Model Selection**: When comparing models with different numbers of features, **always use Adjusted $R^2$**, never standard $R^2$.\n",
+    "3.  **Visualization**: A Prediction Error Plot tells you more in 1 second than a single number ever could."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From aaf887a140cd9b74b0523e8123df6895c9817ba1 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Mon, 2 Feb 2026 06:38:41 +0100
Subject: [PATCH 42/81] S02C04 Multiple Linear Regression

---
 .../S02C04 Multiple Linear Regression.ipynb   | 470 ++++++++++++++++++
 1 file changed, 470 insertions(+)
 create mode 100644 src/S02 Supervised Learning - Regression and Evaluation/S02C04 Multiple Linear Regression.ipynb

diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C04 Multiple Linear Regression.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C04 Multiple Linear Regression.ipynb
new file mode 100644
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--- /dev/null
+++ b/src/S02 Supervised Learning - Regression and Evaluation/S02C04 Multiple Linear Regression.ipynb	
@@ -0,0 +1,470 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c64622a6",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Multiple Linear Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69a5d640",
+   "metadata": {},
+   "source": [
+    "## Beyond the Simple Line\n",
+    "\n",
+    "In the real world, a single variable is rarely enough to predict an outcome. House prices depend on size, location, **and** condition. Health depends on diet, exercise, **and** genetics.\n",
+    "\n",
+    "**Multiple Linear Regression** extends the simple model to accommodate multiple predictors ($x_1, x_2, \\dots, x_n$). Instead of fitting a **Line** in 2D space, we fit a **Hyperplane** in multidimensional space."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c646402a",
+   "metadata": {},
+   "source": [
+    "## The Mathematical Model\n",
+    "\n",
+    "The equation expands to sum the contributions of each feature:\n",
+    "\n",
+    "$$y = \\beta_0 + \\beta_1 x_1 + \\beta_2 x_2 + \\dots + \\beta_n x_n + \\epsilon$$\n",
+    "\n",
+    "Where:\n",
+    "\n",
+    "-   $\\beta_0$: The Intercept (The baseline value when all features are 0).\n",
+    "-   $\\beta_i$: The Coefficient (The change in $y$ for a 1-unit increase in $x_i$, **holding all other variables constant**)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "859c16a8",
+   "metadata": {},
+   "source": [
+    "## Assumptions (Extended)\n",
+    "\n",
+    "Multiple Regression relies on the **same core assumptions** as Simple Linear Regression, and adds an additional one:\n",
+    "\n",
+    "1.  **Linearity**: The relationship between each predictor and $y$ is linear.\n",
+    "2.  **Independence**: Observations are independent of each other.\n",
+    "3.  **Homoscedasticity**: Constant variance of errors across all predicted values.\n",
+    "4.  **Normality**: Errors are normally distributed (important for inference).\n",
+    "5.  **No Multicollinearity**: The predictors ($x_i$) should not be highly correlated with **each other**.\n",
+    "\n",
+    "**Multicollinearity**: In simple regression with one predictor, multicollinearity cannot exist by definition. With multiple predictors, it becomes a real concern. If $x_1$ and $x_2$ are highly correlated, the model cannot reliably determine which one is truly affecting $y$. This leads to:\n",
+    "\n",
+    "-   Unstable, unreliable coefficients\n",
+    "-   Large standard errors\n",
+    "-   Coefficients that flip signs or change dramatically with small data changes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7488f069",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration\n",
+    "\n",
+    "We will generate a synthetic dataset with 2 features so we can visualize the model as a **3D Plane**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e847bf5f",
+   "metadata": {},
+   "source": [
+    "### Generate 3D Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d6a43fc7",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.datasets import make_regression\n",
+    "#  from mpl_toolkits.mplot3d import Axes3D\n",
+    "\n",
+    "# Generate data: 100 samples, 2 features, 1 target\n",
+    "X, y, coef = make_regression(n_samples=100, n_features=2, noise=10, coef=True, random_state=42)\n",
+    "\n",
+    "# Wrap in DataFrame\n",
+    "df = pd.DataFrame(X, columns=['Feature_1', 'Feature_2'])\n",
+    "df['Target'] = y\n",
+    "\n",
+    "print(f\"True Coefficients: {coef}\")\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f518044",
+   "metadata": {},
+   "source": [
+    "### Correlation Analysis\n",
+    "\n",
+    "Before modeling, check if features are correlated with the target (Good) or with each other (Bad - Multicollinearity)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0780a5c",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.heatmap(df.corr(), annot=True, cmap='coolwarm', vmin=-1, vmax=1)\n",
+    "plt.title('Correlation Matrix')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e79acd7",
+   "metadata": {},
+   "source": [
+    "### Detecting Multicollinearity with VIF\n",
+    "\n",
+    "The correlation matrix gives a quick visual check, but **Variance Inflation Factor (VIF)** is the standard diagnostic for multicollinearity.\n",
+    "\n",
+    "VIF measures how much the variance of a coefficient is \"inflated\" due to correlation with other features:\n",
+    "\n",
+    "$$\\text{VIF}_i = \\frac{1}{1 - R^2_i}$$\n",
+    "\n",
+    "Where $R^2_i$ is the R² from regressing feature $x_i$ against all other features.\n",
+    "\n",
+    "**Rule of Thumb**:\n",
+    "\n",
+    "-   VIF = 1: No correlation (ideal)\n",
+    "-   VIF < 5: Acceptable\n",
+    "-   VIF > 5-10: Problematic multicollinearity\n",
+    "-   VIF > 10: Severe — consider removing or combining features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a2ec1d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "\n",
+    "def calculate_vif(df, features):\n",
+    "    \"\"\"Calculate VIF for each feature in the dataframe.\"\"\"\n",
+    "    vif_data = pd.DataFrame()\n",
+    "    vif_data['Feature'] = features\n",
+    "    vif_data['VIF'] = [\n",
+    "        variance_inflation_factor(df[features].values, i)\n",
+    "        for i in range(len(features))\n",
+    "    ]\n",
+    "    return vif_data\n",
+    "\n",
+    "# Calculate VIF for our features\n",
+    "features = ['Feature_1', 'Feature_2']\n",
+    "vif_results = calculate_vif(df, features)\n",
+    "print(\"Variance Inflation Factors:\")\n",
+    "print(vif_results.to_string(index=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56ef1c7c",
+   "metadata": {},
+   "source": [
+    "**Interpretation**: Since our synthetic data was generated with independent features, VIF values should be close to 1. In real-world datasets, high VIF indicates you may need to:\n",
+    "\n",
+    "1.  Remove one of the correlated features\n",
+    "2.  Combine correlated features (e.g., via PCA)\n",
+    "3.  Use regularization (Ridge/Lasso regression)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e96b519b",
+   "metadata": {},
+   "source": [
+    "### Train and Visualize (3D)\n",
+    "\n",
+    "We fit the model and plot the resulting plane."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba9d9108",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(df[['Feature_1', 'Feature_2']], df['Target'], test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Train\n",
+    "model = LinearRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "print(f\"Intercept: {model.intercept_:.2f}\")\n",
+    "print(f\"Coefficients: {model.coef_}\")\n",
+    "\n",
+    "# --- 3D Visualization ---\n",
+    "fig = plt.figure(figsize=(10, 8))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "# Plot Data Points\n",
+    "ax.scatter(X_test['Feature_1'], X_test['Feature_2'], y_test, color='red', label='Actual Data')\n",
+    "\n",
+    "# Plot the Plane\n",
+    "x0_surf = np.linspace(X_test['Feature_1'].min(), X_test['Feature_1'].max(), 20)\n",
+    "x1_surf = np.linspace(X_test['Feature_2'].min(), X_test['Feature_2'].max(), 20)\n",
+    "X0, X1 = np.meshgrid(x0_surf, x1_surf)\n",
+    "Y_pred_surf = model.intercept_ + model.coef_[0] * X0 + model.coef_[1] * X1\n",
+    "\n",
+    "ax.plot_surface(X0, X1, Y_pred_surf, color='blue', alpha=0.3)\n",
+    "ax.set_xlabel('Feature 1')\n",
+    "ax.set_ylabel('Feature 2')\n",
+    "ax.set_zlabel('Target')\n",
+    "ax.set_title('Multiple Linear Regression Plane')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b885abc2",
+   "metadata": {},
+   "source": [
+    "### Evaluation and Residual Analysis\n",
+    "\n",
+    "We check the standard metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02511726",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "# Metrics\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "\n",
+    "print(f\"MSE: {mse:.2f}\")\n",
+    "print(f\"R²:  {r2:.4f}\")\n",
+    "\n",
+    "# Residual Plot (Homoscedasticity Check)\n",
+    "residuals = y_test - y_pred\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "plt.scatter(y_pred, residuals, alpha=0.6)\n",
+    "plt.axhline(0, color='black', linestyle='--')\n",
+    "plt.xlabel(\"Predicted Values\")\n",
+    "plt.ylabel(\"Residuals\")\n",
+    "plt.title(\"Residual Plot (Check for random scatter)\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2160ec9",
+   "metadata": {},
+   "source": [
+    "### Interpreting Coefficients: The Scale Problem\n",
+    "\n",
+    "The coefficients we printed earlier have a hidden issue: **they are not directly comparable** when features have different scales.\n",
+    "\n",
+    "For example, if Feature<sub>1</sub> is measured in meters and Feature<sub>2</sub> in kilometers, a coefficient of 50 for Feature<sub>1</sub> might actually represent a smaller effect than a coefficient of 2 for Feature<sub>2</sub>.\n",
+    "\n",
+    "**Standardized Coefficients** solve this by expressing coefficients in terms of standard deviations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1555e0e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "# Standardize features (mean=0, std=1)\n",
+    "scaler = StandardScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "\n",
+    "# Train on standardized data\n",
+    "model_scaled = LinearRegression()\n",
+    "model_scaled.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "# Compare coefficients\n",
+    "print(\"Raw Coefficients (original scale):\")\n",
+    "print(f\"  Feature_1: {model.coef_[0]:.4f}\")\n",
+    "print(f\"  Feature_2: {model.coef_[1]:.4f}\")\n",
+    "print()\n",
+    "print(\"Standardized Coefficients (comparable):\")\n",
+    "print(f\"  Feature_1: {model_scaled.coef_[0]:.4f}\")\n",
+    "print(f\"  Feature_2: {model_scaled.coef_[1]:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "789dc573",
+   "metadata": {},
+   "source": [
+    "**Interpretation**:\n",
+    "\n",
+    "-   **Raw coefficients**: \"A 1-unit increase in $x_i$ leads to a $\\beta_i$ change in $y$\"\n",
+    "-   **Standardized coefficients**: \"A 1 standard deviation increase in $x_i$ leads to a $\\beta_i$ standard deviation change in $y$\"\n",
+    "\n",
+    "Standardized coefficients let you answer: **\"Which feature has the strongest influence on the prediction?\"** — something raw coefficients cannot reliably tell you."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "858b505a",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "You will now deal with a higher-dimensional dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "667984bd",
+   "metadata": {},
+   "source": [
+    "### Generate 3-Feature Data\n",
+    "\n",
+    "Generate a dataset with **3 features** and slightly higher noise.\n",
+    "\n",
+    "-   `n_samples` = 200\n",
+    "-   `n_features` = 3\n",
+    "-   `noise` = 20"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64e2b87b",
+   "metadata": {},
+   "source": [
+    "### Feature Selection & Training\n",
+    "\n",
+    "Train a model using **only** Feature 1 and Feature 3. Ignore Feature 2. Evaluate the $R^2$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86d56bba",
+   "metadata": {},
+   "source": [
+    "### The \"Kitchen Sink\" Model\n",
+    "\n",
+    "Now train a model using **all 3 features**. Compare the $R^2$. Does it increase?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ed80916",
+   "metadata": {},
+   "source": [
+    "### The Problem with R²\n",
+    "\n",
+    "**Observation**: The $R^2$ is higher (or the same). But this reveals a trap!\n",
+    "\n",
+    "**Training** $R^2$ will **always** increase (or stay the same) when you add features - even useless ones. The model simply has more knobs to turn to fit the data. This doesn't mean the model **generalizes** better. On **test** data, adding irrelevant features can actually hurt performance due to overfitting."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0ad8c1b",
+   "metadata": {},
+   "source": [
+    "### Adjusted R²: The Honest Metric\n",
+    "\n",
+    "**Adjusted R²** penalizes the addition of features that don't improve the model proportionally:\n",
+    "\n",
+    "$$R^2_{adj} = 1 - \\frac{(1 - R^2)(n - 1)}{n - p - 1}$$\n",
+    "\n",
+    "Where:\n",
+    "\n",
+    "-   $n$ = number of samples\n",
+    "-   $p$ = number of features\n",
+    "\n",
+    "If a new feature doesn't contribute enough explanatory power, Adjusted $R^2$ will **decrease**.\n",
+    "\n",
+    "**Key Insight**: If the Adjusted $R^2$ doesn't improve (or decreases) when adding a feature, that feature likely isn't contributing meaningful information. This is a simple form of **feature selection**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9fb1df5",
+   "metadata": {},
+   "source": [
+    "### Non-Linearity Challenge\n",
+    "\n",
+    "Modify the target variable to be non-linear: $y = 3x_1^2 + 2x_2 + \\text{noise}$. Fit a linear model and examine the Residual Plot to see why linear models fail on non-linear data. **Observation**: You should see a distinct \"U-shape\" (parabola) in the residuals. This systematic pattern proves the linear model is insufficient—it's missing the quadratic relationship in the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62686a97",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **Hyperplanes**: Multiple Regression fits a plane, not a line.\n",
+    "2.  **Multicollinearity**: Watch out for correlated features; they destabilize the model.\n",
+    "3.  **Coefficients**: In multiple regression, a coefficient represents the effect of a feature **assuming all other features stay constant**.\n",
+    "4.  **Adjusted R²**: Penalizes unnecessary features; use it to compare models.\n",
+    "5.  **Standardized Coefficients**: Required to compare feature importance across different scales.\n",
+    "6.  **Beyond Prediction**: For formal hypothesis testing about coefficients (`p`-values, confidence intervals), see `statsmodels.OLS`. This becomes important when the goal is inference rather than prediction."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 4ba941575b82db5c49d648db0833688e887b7f70 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Mon, 2 Feb 2026 15:05:06 +0100
Subject: [PATCH 43/81] S02C05 Regularized Regression

---
 .../S02C05 Regularized Regression.ipynb       | 393 ++++++++++++++++++
 1 file changed, 393 insertions(+)
 create mode 100644 src/S02 Supervised Learning - Regression and Evaluation/S02C05 Regularized Regression.ipynb

diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C05 Regularized Regression.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C05 Regularized Regression.ipynb
new file mode 100644
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--- /dev/null
+++ b/src/S02 Supervised Learning - Regression and Evaluation/S02C05 Regularized Regression.ipynb	
@@ -0,0 +1,393 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "81489688",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Regularized Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74a0769b",
+   "metadata": {},
+   "source": [
+    "## How do Models Learn? (The Engine)\n",
+    "\n",
+    "Before understanding Regularization, we must peek under the hood of Linear Regression. How does the computer find the \"best\" line?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18718004",
+   "metadata": {},
+   "source": [
+    "### 1. The Cost Function ($J$)\n",
+    "\n",
+    "The computer needs a score to know how bad its predictions are. We call this the ****Cost Function**** (or Loss Function). For regression, we use ****Mean Squared Error (MSE)****:\n",
+    "\n",
+    "$$J(w) = \\frac{1}{2m} \\sum_{i=1}^{m} (y_i - \\hat{y}_i)^2$$\n",
+    "\n",
+    "*Note*: The $\\frac{1}{2}$ factor is a convenience which cancels with the exponent when computing derivatives, making the math cleaner. The minimum stays in the same place.\n",
+    "\n",
+    "Think of this as a \"Bowl.\" The lowest point of the bowl represents the smallest error."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0379855",
+   "metadata": {},
+   "source": [
+    "### 2. Gradient Descent\n",
+    "\n",
+    "To find the bottom of the bowl, algorithms use ****Gradient Descent****.\n",
+    "\n",
+    "1.  Start at a random point (random weights).\n",
+    "2.  Calculate the slope (gradient).\n",
+    "3.  Take a step downhill.\n",
+    "4.  Repeat until you reach the bottom."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d79cdc0a",
+   "metadata": {},
+   "source": [
+    "## The Problem: Overfitting\n",
+    "\n",
+    "Sometimes, the model tries too hard. It finds a complex \"bottom of the bowl\" that fits the training data perfectly (including the noise) but fails on new data. This is ****Overfitting****."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5b362d6",
+   "metadata": {},
+   "source": [
+    "## The Solution: Regularization\n",
+    "\n",
+    "Regularization acts as a \"penalty\" or a \"tax\" on complexity. We modify the Cost Function:\n",
+    "\n",
+    "$$\\text{Total Cost} = \\text{Error (MSE)} + \\text{Penalty}$$\n",
+    "\n",
+    "The model now has to balance two goals:\n",
+    "\n",
+    "1.  Fit the data well (minimize Error).\n",
+    "2.  Keep coefficients simple (minimize Penalty)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8278197a",
+   "metadata": {},
+   "source": [
+    "### Types of Regularization\n",
+    "\n",
+    "| Type      | Name           | Penalty Formula               | Effect                             | Best For                                                           |\n",
+    "|--------- |-------------- |----------------------------- |---------------------------------- |------------------------------------------------------------------ |\n",
+    "| **L1**    | **Lasso**      | $\\lambda \\sum\\lvert w \\rvert$ | Pushes coefficients to **Zero**.   | **Feature Selection**: When you suspect many features are useless. |\n",
+    "| **L2**    | **Ridge**      | $\\lambda \\sum w^2$            | Pushes coefficients **Near Zero**. | **Multicollinearity**: When features are highly correlated.        |\n",
+    "| **L1+L2** | **ElasticNet** | Combination                   | Hybrid of above.                   | When you have many features and correlations.                      |\n",
+    "\n",
+    "**Note**: $\\lambda$ (Lambda) is the \"Tax Rate\". High $\\lambda$ = High Penalty = Simpler Model. In scikit-learn, this parameter is called `alpha`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f5e500c",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: The \"Sparse\" Signal\n",
+    "\n",
+    "We will demonstrate the superpower of **Lasso**: automatically removing useless features. We generate a dataset with **50 features**, but only **5** of them actually predict the target. The rest are pure noise."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "726e7e61",
+   "metadata": {},
+   "source": [
+    "### Generate Noisy Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ea49258",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.datasets import make_regression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "# Generate 50 features, but only 5 are informative\n",
+    "X, y, true_coef = make_regression(n_samples=100, n_features=50, n_informative=5, \n",
+    "                                  noise=10, coef=True, random_state=42)\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "print(f\"Total Features: {X.shape[1]}\")\n",
+    "print(f\"Useful Features: 5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f83352b6",
+   "metadata": {},
+   "source": [
+    "### Linear Regression (The Control)\n",
+    "\n",
+    "Standard regression tries to use **all** 50 features, fitting noise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7dae3291",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "lr = LinearRegression()\n",
+    "lr.fit(X_train, y_train)\n",
+    "\n",
+    "print(f\"Linear Train R²: {lr.score(X_train, y_train):.2f}\")\n",
+    "print(f\"Linear Test R²:  {lr.score(X_test, y_test):.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ebb6bddb",
+   "metadata": {},
+   "source": [
+    "**Expected**: High Train score, lower Test score. Classic Overfitting."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c512cfbb",
+   "metadata": {},
+   "source": [
+    "### Lasso vs. Ridge\n",
+    "\n",
+    "Let's see how they handle the noise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a39d5dbb",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import Lasso, Ridge\n",
+    "\n",
+    "# Lasso (L1) - High alpha for demonstration\n",
+    "lasso = Lasso(alpha=1.0)\n",
+    "lasso.fit(X_train, y_train)\n",
+    "\n",
+    "# Ridge (L2)\n",
+    "ridge = Ridge(alpha=1.0)\n",
+    "ridge.fit(X_train, y_train)\n",
+    "\n",
+    "print(f\"Lasso Test R²: {lasso.score(X_test, y_test):.2f}\")\n",
+    "print(f\"Ridge Test R²: {ridge.score(X_test, y_test):.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7733fb2f",
+   "metadata": {},
+   "source": [
+    "### ElasticNet: The Hybrid\n",
+    "\n",
+    "ElasticNet combines both penalties. The `l1_ratio` parameter controls the mix:\n",
+    "\n",
+    "-   `l1_ratio=1` → Pure Lasso\n",
+    "-   `l1_ratio=0` → Pure Ridge\n",
+    "-   `l1_ratio=0.5` → Equal mix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01f84aee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "# ElasticNet with 50% L1, 50% L2\n",
+    "elastic = ElasticNet(alpha=1.0, l1_ratio=0.5)\n",
+    "elastic.fit(X_train, y_train)\n",
+    "\n",
+    "print(f\"ElasticNet Test R²: {elastic.score(X_test, y_test):.2f}\")\n",
+    "print(f\"ElasticNet features at exactly 0: {np.sum(elastic.coef_ == 0)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08aaf8bf",
+   "metadata": {},
+   "source": [
+    "**Expected**: ElasticNet zeros out some features (like Lasso) while shrinking others smoothly (like Ridge)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95b7ab2e",
+   "metadata": {},
+   "source": [
+    "### Visualizing Coefficients\n",
+    "\n",
+    "This is the most important visualization in this module.\n",
+    "\n",
+    "-   **Lasso** should have many coefficients exactly at 0.\n",
+    "-   **Ridge** should have many small coefficients, but none at 0.\n",
+    "-   **ElasticNet** should be in between: some zeros, some small values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c6609d9",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(15, 4))\n",
+    "\n",
+    "# Plot Lasso\n",
+    "plt.subplot(1, 3, 1)\n",
+    "plt.plot(lasso.coef_, marker='o', linestyle='none', color='crimson')\n",
+    "plt.title(\"Lasso Coefficients (L1)\")\n",
+    "plt.xlabel(\"Feature Index\")\n",
+    "plt.ylabel(\"Coefficient Value\")\n",
+    "plt.axhline(0, color='black', linestyle='--')\n",
+    "\n",
+    "# Plot Ridge\n",
+    "plt.subplot(1, 3, 2)\n",
+    "plt.plot(ridge.coef_, marker='o', linestyle='none', color='navy')\n",
+    "plt.title(\"Ridge Coefficients (L2)\")\n",
+    "plt.xlabel(\"Feature Index\")\n",
+    "plt.axhline(0, color='black', linestyle='--')\n",
+    "\n",
+    "# Plot ElasticNet\n",
+    "plt.subplot(1, 3, 3)\n",
+    "plt.plot(elastic.coef_, marker='o', linestyle='none', color='green')\n",
+    "plt.title(\"ElasticNet Coefficients (L1+L2)\")\n",
+    "plt.xlabel(\"Feature Index\")\n",
+    "plt.axhline(0, color='black', linestyle='--')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Count zero coefficients\n",
+    "print(f\"Lasso features set to exactly 0: {np.sum(lasso.coef_ == 0)}\")\n",
+    "print(f\"Ridge features set to exactly 0: {np.sum(ridge.coef_ == 0)}\")\n",
+    "print(f\"ElasticNet features set to exactly 0: {np.sum(elastic.coef_ == 0)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09a759aa",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "Use **Lasso** to perform \"Feature Selection\" on the real California Housing dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1cbb7d3f",
+   "metadata": {},
+   "source": [
+    "### Load Data\n",
+    "\n",
+    "Load the data and add some random noise features to trick the model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81c0382e",
+   "metadata": {},
+   "source": [
+    "### Feature Scaling\n",
+    "\n",
+    "Regularization penalizes coefficient magnitude. If features have different scales (e.g., income in thousands vs. age in years), the penalty unfairly targets high-scale features. Standardizing ensures all features compete equally:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "317823b9",
+   "metadata": {},
+   "source": [
+    "### Tuning Alpha with GridSearch\n",
+    "\n",
+    "We don't guess the penalty strength ($\\lambda$ or `alpha`). We search for it. **Note**: `GridSearchCV` tries every combination of parameters to find the best one."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa683208",
+   "metadata": {},
+   "source": [
+    "### Which features survived?\n",
+    "\n",
+    "Retrieve the best model and inspect which coefficients survived. Did Lasso kill the \"Noise\" columns? **Expected Result**: The `Noise_X` features should have coefficients very close to or exactly Zero."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e39a6ac",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **Overfitting** happens when models memorize noise.\n",
+    "2.  **Regularization** prevents this by penalizing large coefficients.\n",
+    "3.  **Lasso (L1)** zeros out useless features (Feature Selection).\n",
+    "4.  **Ridge (L2)** shrinks all features together (good for correlation).\n",
+    "5.  **ElasticNet** combines both, controlled by `l1_ratio`."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From dd61c1494898fd5ca5a2cefa62035929373b3093 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Mon, 2 Feb 2026 16:28:44 +0100
Subject: [PATCH 44/81] S02C06 Nonlinearities in Regression

---
 .../S02C06 Nonlinearities in Regression.ipynb | 323 ++++++++++++++++++
 1 file changed, 323 insertions(+)
 create mode 100644 src/S02 Supervised Learning - Regression and Evaluation/S02C06 Nonlinearities in Regression.ipynb

diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C06 Nonlinearities in Regression.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C06 Nonlinearities in Regression.ipynb
new file mode 100644
index 0000000..c78065c
--- /dev/null
+++ b/src/S02 Supervised Learning - Regression and Evaluation/S02C06 Nonlinearities in Regression.ipynb	
@@ -0,0 +1,323 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "09d0ec5c",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Nonlinearities in Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4120303d",
+   "metadata": {},
+   "source": [
+    "## The World isn't Straight\n",
+    "\n",
+    "So far, we have assumed that the relationship between variables is a straight line ($y = mx + c$). In the real world, relationships are rarely this simple.\n",
+    "\n",
+    "-   Bacteria growth is **Exponential**.\n",
+    "-   Diminishing returns are **Logarithmic**.\n",
+    "-   Gravity and braking distances are **Quadratic** (Polynomial).\n",
+    "\n",
+    "If we try to fit a straight line to a curved dataset, we face **Underfitting** (High Bias). The model is simply too rigid to capture the reality."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8241d621",
+   "metadata": {},
+   "source": [
+    "## The \"Linear\" Paradox\n",
+    "\n",
+    "Surprisingly, to fit curves, we often still use **Linear Regression**. How?\n",
+    "\n",
+    "We transform the **input data**, not the model. Consider a quadratic relationship: $y = \\beta_0 + \\beta_1 x + \\beta_2 x^2$. If we create a new feature $z = x^2$, the equation becomes: $y = \\beta_0 + \\beta_1 x + \\beta_2 z$.\n",
+    "\n",
+    "This is now a **Linear Equation** with respect to the coefficients ($\\beta$), even though it describes a curve with respect to $x$. This technique is called **Polynomial Regression**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33cc23b8",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: The Parabola\n",
+    "\n",
+    "We will generate synthetic data representing a Parabolic/Quadratic trajectory and try to model it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c0e396c",
+   "metadata": {},
+   "source": [
+    "### Generate Non-Linear Data\n",
+    "\n",
+    "$$y = 3x^2 + 8 + \\text{noise}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "266997d7",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme(style='whitegrid')\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# Generate 100 samples from -3 to 3\n",
+    "n_samples = 100\n",
+    "X = np.linspace(-3, 3, n_samples).reshape(-1, 1)\n",
+    "\n",
+    "# Quadratic equation with noise\n",
+    "y = 3 * X**2 + 8 + np.random.randn(n_samples, 1)\n",
+    "\n",
+    "# Plot\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "plt.scatter(X, y, color='teal', alpha=0.6, label='Data')\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('y')\n",
+    "plt.title('Synthetic Quadratic Data')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5358c6d0",
+   "metadata": {},
+   "source": [
+    "### The Failure of Linear Regression\n",
+    "\n",
+    "Let's see what happens if we stubbornly use a standard line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3bf7b62",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "# Split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Train Linear Model\n",
+    "linear_model = LinearRegression()\n",
+    "linear_model.fit(X_train, y_train)\n",
+    "\n",
+    "# Predict\n",
+    "y_pred_lin = linear_model.predict(X_test)\n",
+    "\n",
+    "print(f\"Linear R² Score: {r2_score(y_test, y_pred_lin):.2f}\")\n",
+    "# Note: A low score is expected"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b285e43",
+   "metadata": {},
+   "source": [
+    "### Success with Polynomial Features\n",
+    "\n",
+    "We use `PolynomialFeatures` to generate $x^2$.\n",
+    "\n",
+    "**Best Practice**: We use a scikit-learn `Pipeline`. This ensures that when we feed new data to the model, it automatically calculates the powers ($x^2, x^3$) before predicting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2fe47826",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "# Create a Pipeline:\n",
+    "# 1. Generate Polynomial Features (Degree 2)\n",
+    "# 2. Fit Linear Regression\n",
+    "poly_model = make_pipeline(PolynomialFeatures(degree=2), LinearRegression())\n",
+    "\n",
+    "# Train\n",
+    "poly_model.fit(X_train, y_train)\n",
+    "\n",
+    "# Predict\n",
+    "y_pred_poly = poly_model.predict(X_test)\n",
+    "\n",
+    "print(f\"Polynomial (Deg 2) R² Score: {r2_score(y_test, y_pred_poly):.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1aebfcc0",
+   "metadata": {},
+   "source": [
+    "### Visualizing the Difference\n",
+    "\n",
+    "**Visualization Tip**: When plotting curves, we cannot just plot the Test Set points connected by lines, because they are in random order (it will look like spaghetti). We generate a sorted \"smooth grid\" of X values to draw the perfect regression curve."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2335de6",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# 1. Plot actual data\n",
+    "plt.scatter(X_test, y_test, color='gray', label='Test Data')\n",
+    "\n",
+    "# 2. Create smooth grid for plotting regression curves\n",
+    "X_grid = np.linspace(-3, 3, 100).reshape(-1, 1)\n",
+    "\n",
+    "# 3. Plot Linear Model\n",
+    "plt.plot(X_grid, linear_model.predict(X_grid), color='red', linewidth=2, label='Linear Fit')\n",
+    "\n",
+    "# 4. Plot Polynomial Model\n",
+    "plt.plot(X_grid, poly_model.predict(X_grid), color='blue', linewidth=2, label='Polynomial Fit (Deg 2)')\n",
+    "\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('y')\n",
+    "plt.title('Linear vs Polynomial Regression')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "913e2c82",
+   "metadata": {},
+   "source": [
+    "## The Danger: Overfitting with High Degrees\n",
+    "\n",
+    "If Degree 2 is good, is Degree 20 better? NO. High-degree polynomials create wild oscillations (\"wiggles\") to hit every single noisy data point. This is classic **Overfitting**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b286e339",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "# Fit a Degree 20 model\n",
+    "high_degree_model = make_pipeline(PolynomialFeatures(degree=20), LinearRegression())\n",
+    "high_degree_model.fit(X_train, y_train)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(X, y, color='gray', alpha=0.3, label='Data')\n",
+    "\n",
+    "# Plot the wiggle\n",
+    "y_wiggle = high_degree_model.predict(X_grid)\n",
+    "plt.plot(X_grid, y_wiggle, color='green', label='Degree 20 (Overfitting)')\n",
+    "\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('y')\n",
+    "plt.ylim(0, 40) # Limit y-axis to see the curve clearly\n",
+    "plt.title('The Danger of High Degrees')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e45804da",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ff60cb4",
+   "metadata": {},
+   "source": [
+    "### The Cubic Challenge\n",
+    "\n",
+    "Data follows a cubic rule: $y = x^3 - 3x$.\n",
+    "\n",
+    "1.  Generate the data.\n",
+    "2.  Fit a Linear Model.\n",
+    "3.  Fit a Polynomial Model (Degree 3).\n",
+    "4.  Compare the $R^2$ scores."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddbd8ad0",
+   "metadata": {},
+   "source": [
+    "### Regularized Polynomials\n",
+    "\n",
+    "If you **must** use a high degree polynomial (e.g., Degree 10), you should use Regularization (Ridge/Lasso) to stop the wiggling.\n",
+    "\n",
+    "1.  Create a `make_pipeline` with `PolynomialFeatures(degree=10)` and `Ridge(alpha=100)`.\n",
+    "2.  Fit it to the cubic data above.\n",
+    "3.  Observe if it is smoother than a standard Linear Regression with Degree 10."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50fe181a",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **Linearity in Parameters**: We can fit curves using Linear Regression by squaring/cubing the features.\n",
+    "2.  **PolynomialFeatures**: The sklearn tool that creates these powers for us.\n",
+    "3.  **Pipelines**: The clean way to package transformation + modeling.\n",
+    "4.  **Balance**: Low degree = Underfitting (Lines). High degree = Overfitting (Wiggles)."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 127e6475bc1e9239a82de96f593cbeff5bd062ad Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Mon, 2 Feb 2026 17:02:40 +0100
Subject: [PATCH 45/81] S02C07 Overfitting, Underfitting, and Hyperparameter
 Tuning

---
 ...erfitting, and Hyperparameter Tuning.ipynb | 400 ++++++++++++++++++
 1 file changed, 400 insertions(+)
 create mode 100644 src/S02 Supervised Learning - Regression and Evaluation/S02C07 Overfitting, Underfitting, and Hyperparameter Tuning.ipynb

diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C07 Overfitting, Underfitting, and Hyperparameter Tuning.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C07 Overfitting, Underfitting, and Hyperparameter Tuning.ipynb
new file mode 100644
index 0000000..5a19453
--- /dev/null
+++ b/src/S02 Supervised Learning - Regression and Evaluation/S02C07 Overfitting, Underfitting, and Hyperparameter Tuning.ipynb	
@@ -0,0 +1,400 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "428480e1",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Overfitting, Underfitting & Hyperparameter Tuning\n",
+    "\n",
+    "Building a machine learning model is a balancing act.\n",
+    "\n",
+    "-   If the model is too simple, it fails to learn the pattern (**Underfitting**).\n",
+    "-   If the model is too complex, it memorizes the noise instead of the signal (**Overfitting**).\n",
+    "\n",
+    "Our goal is to find the \"Goldilocks Zone\"—the sweet spot where the model generalizes well to new, unseen data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8cb0a88",
+   "metadata": {},
+   "source": [
+    "## The Bias-Variance Tradeoff\n",
+    "\n",
+    "To understand **why** models fail, we look at two sources of error:\n",
+    "\n",
+    "1.  **Bias (Underfitting)**: The error introduced by approximating a real-world problem with a too-simple model (e.g., trying to fit a straight line to a curve).\n",
+    "    -   **Symptoms**: High Error on Training Set, High Error on Test Set.\n",
+    "2.  **Variance (Overfitting)**: The error introduced by the model's sensitivity to small fluctuations in the training set.\n",
+    "    -   **Symptoms**: Low Error on Training Set, High Error on Test Set."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a2e099d",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: The Polynomial Wiggle\n",
+    "\n",
+    "We will visualize this using a simple sine wave. We will try to fit it with:\n",
+    "\n",
+    "1.  A Line (Underfitting)\n",
+    "2.  A High-Degree Polynomial (Overfitting)\n",
+    "3.  A Regularized Model (The Solution)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d703520a",
+   "metadata": {},
+   "source": [
+    "### Generate Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "08023d0c",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# Generate a sine wave with noise\n",
+    "n_samples = 30\n",
+    "X = np.sort(np.random.rand(n_samples))\n",
+    "y = np.cos(1.5 * np.pi * X) + np.random.randn(n_samples) * 0.1\n",
+    "\n",
+    "X = X[:, np.newaxis] # Reshape for sklearn\n",
+    "\n",
+    "# Ground Truth function for plotting\n",
+    "X_plot = np.linspace(0, 1, 100)[:, np.newaxis]\n",
+    "y_plot = np.cos(1.5 * np.pi * X_plot)\n",
+    "\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "plt.scatter(X, y, color='navy', s=30, marker='o', label=\"Training Data\")\n",
+    "plt.plot(X_plot, y_plot, color='black', linewidth=1, label=\"True Function\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "077bbd9a",
+   "metadata": {},
+   "source": [
+    "### Visualizing the Extremes\n",
+    "\n",
+    "We will compare a Degree 1 model (Line) vs. a Degree 15 model (Complex Curve). **Note**: We use \\`Pipeline\\` to keep our code clean."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fecf5000",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "plt.figure(figsize=(12, 5))\n",
+    "\n",
+    "# --- Model 1: Underfitting (Degree 1) ---\n",
+    "ax1 = plt.subplot(1, 2, 1)\n",
+    "model_simple = make_pipeline(PolynomialFeatures(degree=1), LinearRegression())\n",
+    "model_simple.fit(X, y)\n",
+    "\n",
+    "plt.scatter(X, y, color='navy', s=30)\n",
+    "plt.plot(X_plot, model_simple.predict(X_plot), color='red', linewidth=2, label=\"Degree 1\")\n",
+    "plt.title(\"Underfitting (High Bias)\")\n",
+    "plt.legend()\n",
+    "\n",
+    "# --- Model 2: Overfitting (Degree 15) ---\n",
+    "ax2 = plt.subplot(1, 2, 2)\n",
+    "model_complex = make_pipeline(PolynomialFeatures(degree=15), LinearRegression())\n",
+    "model_complex.fit(X, y)\n",
+    "\n",
+    "plt.scatter(X, y, color='navy', s=30)\n",
+    "plt.plot(X_plot, model_complex.predict(X_plot), color='green', linewidth=2, label=\"Degree 15\")\n",
+    "plt.title(\"Overfitting (High Variance)\")\n",
+    "plt.ylim(-1.5, 1.5) # Limit y-axis as overfitting tends to explode values\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "657bd1f1",
+   "metadata": {},
+   "source": [
+    "**Observation**:\n",
+    "\n",
+    "-   The Red Line (Left) is too rigid. It ignores the curve.\n",
+    "-   The Green Line (Right) hits every point but oscillates wildly. It has memorized the noise."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2acecd34",
+   "metadata": {},
+   "source": [
+    "## Diagnosing with Learning Curves\n",
+    "\n",
+    "How do you detect this if you can't visualize the data (e.g., in 100 dimensions)? We use **Learning Curves**.\n",
+    "\n",
+    "-   We plot the Training Score and Validation Score as we add more data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3511227a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import learning_curve\n",
+    "\n",
+    "def plot_learning_curve(estimator, title, X, y):\n",
+    "    train_sizes, train_scores, test_scores = learning_curve(\n",
+    "        estimator, X, y, cv=5, n_jobs=-1, \n",
+    "        train_sizes=np.linspace(0.1, 1.0, 5), scoring='neg_mean_squared_error')\n",
+    "\n",
+    "    # Convert negative MSE to positive\n",
+    "    train_scores_mean = -np.mean(train_scores, axis=1)\n",
+    "    test_scores_mean = -np.mean(test_scores, axis=1)\n",
+    "\n",
+    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Training Error\")\n",
+    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Validation Error\")\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(\"Training examples\")\n",
+    "    plt.ylabel(\"MSE (Lower is better)\")\n",
+    "    plt.legend(loc=\"best\")\n",
+    "    plt.grid()\n",
+    "\n",
+    "plt.figure(figsize=(12, 5))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plot_learning_curve(\n",
+    "    make_pipeline(PolynomialFeatures(degree=1), LinearRegression()),\n",
+    "    \"Learning Curve (Underfitting)\", X, y)\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plot_learning_curve(\n",
+    "    make_pipeline(PolynomialFeatures(degree=15), LinearRegression()),\n",
+    "    \"Learning Curve (Overfitting)\", X, y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62a53944",
+   "metadata": {},
+   "source": [
+    "**Interpretation**:\n",
+    "\n",
+    "-   **Underfitting**: Both errors are high and converge. Adding more data won't help.\n",
+    "-   **Overfitting**: There is a huge \"Gap\" between Training error (low) and Validation error (high). Adding more data **might** help, or we need to simplify the model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45b6dd9e",
+   "metadata": {},
+   "source": [
+    "## Hyperparameter Tuning\n",
+    "\n",
+    "To fix these issues, we need to tune the \"knobs\" of the model (Hyperparameters).\n",
+    "\n",
+    "-   **Grid Search**: Tries **every** combination of parameters. Thorough but slow.\n",
+    "-   **Random Search**: Tries **random** combinations. Faster and often just as effective."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94bc7a43",
+   "metadata": {},
+   "source": [
+    "### Demonstration: Tuning a Ridge Pipeline\n",
+    "\n",
+    "We will try to find the perfect polynomial degree and regularization strength ($\\alpha$) simultaneously."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2f0f4cdf",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best Parameters: {'polynomialfeatures__degree': 10, 'ridge__alpha': 0.01}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "# Create a Pipeline\n",
+    "# Steps: 1. Create Polynomials -> 2. Fit Ridge Regression\n",
+    "pipeline = make_pipeline(PolynomialFeatures(), Ridge())\n",
+    "\n",
+    "# Define the Grid\n",
+    "# Note: We access pipeline steps using 'stepname__parameter'\n",
+    "param_grid = {\n",
+    "    'polynomialfeatures__degree': [2, 3, 4, 5, 10],\n",
+    "    'ridge__alpha': [0.001, 0.01, 0.1, 1, 10]\n",
+    "}\n",
+    "\n",
+    "# Run Grid Search\n",
+    "search = GridSearchCV(pipeline, param_grid, cv=5, scoring='neg_mean_squared_error')\n",
+    "search.fit(X, y)\n",
+    "\n",
+    "print(\"Best Parameters:\", search.best_params_)\n",
+    "\n",
+    "# Visualizing the Best Model\n",
+    "best_model = search.best_estimator_\n",
+    "\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "plt.scatter(X, y, color='navy', s=30, label=\"Training Data\")\n",
+    "plt.plot(X_plot, y_plot, color='black', linewidth=1, label=\"True Function\")\n",
+    "plt.plot(X_plot, best_model.predict(X_plot), color='gold', linewidth=3, label=\"Tuned Model\")\n",
+    "plt.title(\"The Goldilocks Model (Tuned)\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fcc4054f",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "We will use the **California Housing Dataset**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c23a8382",
+   "metadata": {},
+   "source": [
+    "### Random Search vs Grid Search\n",
+    "\n",
+    "Grid Search explodes in complexity. If you have 4 parameters with 10 options each, that's $10^4 = 10,000$ fits.\n",
+    "\n",
+    "-   Use `RandomizedSearchCV` to tune a `Ridge` model on the housing data.\n",
+    "-   Search over `alpha` values ranging from $10^{-3}$ to $10^3$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5aa1d82",
+   "metadata": {},
+   "source": [
+    "### Fixing Overfitting\n",
+    "\n",
+    "Train a Decision Tree Regressor on the housing data. Decision Trees are notorious for overfitting.\n",
+    "\n",
+    "1.  Train an unconstrained tree (`max_depth=None`). Check Train vs Test score.\n",
+    "2.  Tune `max_depth` using `GridSearchCV` to fix the overfitting.\n",
+    "\n",
+    "**Observation**: The unconstrained tree likely had a Train Score of 1.0 (100%), but a lower Test Score. Limiting the depth (Regularization) lowered the Train Score but improved the Test Score."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f27fc329",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **Bias vs Variance**: Underfitting vs Overfitting.\n",
+    "2.  **Learning Curves**: A diagnostic tool to visualize the gap between Train and Validation error.\n",
+    "3.  **Tuning**: We use Grid Search (small space) or Random Search (large space) to find the best Hyperparameters."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From fdd2bfc39f4d9583c7c2342b44879a75fe0f02dd Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Wed, 4 Feb 2026 17:43:16 +0100
Subject: [PATCH 46/81] S04 Unsupervised Learning Slides

---
 ...lustering and Dimensionality Reduction.pdf | Bin 0 -> 430634 bytes
 ...lustering and Dimensionality Reduction.tex | 502 ++++++++++++++++++
 2 files changed, 502 insertions(+)
 create mode 100644 src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04 Unsupervised Learning - Clustering and Dimensionality Reduction.pdf
 create mode 100644 src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04 Unsupervised Learning - Clustering and Dimensionality Reduction.tex

diff --git a/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04 Unsupervised Learning - Clustering and Dimensionality Reduction.pdf b/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04 Unsupervised Learning - Clustering and Dimensionality Reduction.pdf
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diff --git a/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04 Unsupervised Learning - Clustering and Dimensionality Reduction.tex b/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04 Unsupervised Learning - Clustering and Dimensionality Reduction.tex
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+\author{Cristian A. Marocico}
+\date{\today}
+\title{Machine Learning in Python}
+\subtitle{Unsupervised Learning - Clustering and Dimensionality Reduction}
+\author[Marocico, Tatar]{Cristian A. Marocico, A. Emin Tatar}
+\institute[CIT]{Center for Information Technology\\University of Groningen}
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+\date[Feb 13\textsuperscript{th} 2026]{Friday, February 13\textsuperscript{th} 2026}
+\usepackage{mathtools}
+\newcommand{\intsum}{\mathop{\mathrlap{\raisebox{0.1ex}{\hspace{0.2em}$\textstyle\sum$}}\int}\limits}
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+ pdfauthor={Cristian A. Marocico},
+ pdftitle={Machine Learning in Python},
+ pdfkeywords={},
+ pdfsubject={},
+ pdfcreator={Emacs 30.2 (Org mode 9.7.11)}, 
+ pdflang={English}}
+\begin{document}
+
+\maketitle
+\begin{frame}{Outline}
+\setcounter{tocdepth}{1}
+\tableofcontents
+\end{frame}
+
+\section{Introduction to Unsupervised Learning}
+\label{sec:org2f2069c}
+\begin{frame}[label={sec:orgb56e413}]{Introduction to Unsupervised Learning}
+\begin{definition}[What is Unsupervised Learning?]\label{sec:org9202d9f}
+\pause
+In contrast to supervised learning, \alert{unsupervised learning} does not use labeled data. Instead, it identifies patterns and structures in the data without predefined labels. This is particularly useful for clustering similar data points or reducing the dimensionality of the data.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgdb32e40}]{Types of Unsupervised Learning}
+\begin{block}{Types of Unsupervised Learning}
+\pause
+Unsupervised learning can be broadly categorized into a few types:
+\begin{itemize}[<+->]
+\item \alert{Dimensionality Reduction}: Techniques that reduce the number of features while preserving the essential structure of the data.
+\item \alert{Clustering}: Algorithms that group similar data points together based on their features.
+\item \alert{Anomaly Detection}: Identifying rare items, events, or observations that raise suspicions by differing significantly from the majority of the data.
+\end{itemize}
+\end{block}
+\end{frame}
+\section{Dimensionality Reduction}
+\label{sec:org47c7319}
+\begin{frame}[label={sec:orgf913ae3}]{Dimensionality Reduction}
+\begin{block}{Dimensionality Reduction}
+\pause
+\alert{Dimensionality reduction} techniques aim to reduce the number of features in a dataset while retaining its essential characteristics. Useful for visualizing high-dimensional data or improving the performance of machine learning models by reducing noise and computational complexity. Common dimensionality reduction techniques include:
+\begin{itemize}[<+->]
+\item \alert{Principal Component Analysis (PCA)}: Projects the data onto a lower-dimensional space by maximizing the variance along the new axes (principal components).
+\item \alert{Linear Discriminant Analysis (LDA)}: A supervised technique that finds a linear combination of features that separates two or more classes. (Included here for comparison; requires labels.)
+\item \alert{Autoencoders}: Neural networks that learn to encode the input data into a lower-dimensional representation and then decode it back to the original space. They are particularly useful for learning complex, non-linear mappings.
+\end{itemize}
+\end{block}
+\end{frame}
+\section[PCA]{Principal Component Analysis (PCA)}
+\label{sec:orgf9e2e6b}
+\begin{frame}[label={sec:orgdaac264}]{Principal Component Analysis (PCA)}
+\begin{block}{Principal Component Analysis (PCA)}
+\pause
+\alert{Principal Component Analysis (PCA)} is a widely used technique for dimensionality reduction. It transforms the data into a new coordinate system where the greatest variance by any projection lies on the first coordinate (principal component), the second greatest variance on the second coordinate, and so on. This allows us to reduce the number of dimensions while retaining most of the information in the data.
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:orgfaab9df}]{Steps in PCA}
+\begin{block}{Steps in PCA}
+\pause
+The PCA process involves several key steps:
+\begin{enumerate}[<+->]
+\item \alert{Standardization}: Scale the data to have a mean of 0 and a standard deviation of 1 to ensure that all features contribute equally to the analysis.
+\item \alert{Covariance Matrix Calculation}: Compute the covariance matrix to understand how the features vary together.
+\item \alert{Eigenvalue Decomposition}: Calculate the eigenvalues and eigenvectors of the covariance matrix. The eigenvectors represent the directions of maximum variance, and the eigenvalues indicate the amount of variance in those directions.
+\item \alert{Selecting Principal Components}: Choose the top k eigenvectors (principal components) based on their corresponding eigenvalues, which capture the most variance in the data.
+\item \alert{Transforming the Data}: Project the original data onto the selected principal components to obtain a lower-dimensional representation.
+\end{enumerate}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org46f652c}]{Applications of PCA}
+\begin{block}{Applications of PCA}
+\pause
+PCA is widely used in various fields for:
+\begin{itemize}[<+->]
+\item \alert{\alert{Data Visualization}}: Reducing high-dimensional data to 2D or 3D for visualization purposes.
+\item \alert{\alert{Noise Reduction}}: Filtering out noise by retaining only the most significant components.
+\item \alert{\alert{Feature Extraction}}: Creating new features that capture the essential patterns in the data.
+\item \alert{\alert{Preprocessing for Machine Learning}}: Reducing dimensionality before applying machine learning algorithms to improve performance and reduce overfitting.
+\end{itemize}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:orgd8319bf}]{Limitations of PCA}
+\begin{block}{Limitations of PCA}
+\pause
+While PCA is a powerful technique, it has some limitations:
+\begin{itemize}[<+->]
+\item \alert{\alert{Linearity}}: PCA assumes linear relationships between features, which may not capture complex patterns in the data.
+\item \alert{\alert{Interpretability}}: The principal components may not have a clear interpretation in terms of the original features, making it difficult to understand the results.
+\item \alert{\alert{Sensitivity to Scaling}}: PCA is sensitive to the scale of the features, so standardization is crucial.
+\item \alert{\alert{Loss of Information}}: Reducing dimensions may lead to loss of important information, especially if too many components are discarded.
+\end{itemize}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org1eb5ee7}]{Evaluating PCA Results}
+\begin{block}{Evaluating PCA Results}
+\pause
+To evaluate the effectiveness of PCA, we can use several methods:
+\begin{itemize}[<+->]
+\item \alert{\alert{Explained Variance Ratio}}: This metric indicates the proportion of variance explained by each principal component. A higher explained variance ratio suggests that the component captures more information from the data.
+\item \alert{\alert{Scree Plot}}: A graphical representation of the eigenvalues associated with each principal component. It helps to visualize the amount of variance explained by each component and to determine the optimal number of components to retain.
+\item \alert{\alert{Reconstruction Error}}: After transforming the data using PCA, we can reconstruct the original data and measure the error (e.g., mean squared error) to assess how well PCA captures the original structure of the data.
+\end{itemize}
+\end{block}
+\end{frame}
+\section{Autoencoders}
+\label{sec:org9242cbb}
+\begin{frame}[label={sec:org9c98984}]{Autoencoders}
+\begin{block}{Autoencoders}
+\pause
+\alert{Autoencoders} are a type of neural network used for unsupervised learning, particularly for dimensionality reduction and feature learning. They consist of two main parts: an encoder that compresses the input data into a lower-dimensional representation (latent space) and a decoder that reconstructs the original data from this representation.
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org85f82aa}]{Structure of Autoencoders}
+\begin{block}{Structure of Autoencoders}
+\pause
+Autoencoders typically have the following structure:
+\begin{itemize}[<+->]
+\item \alert{\alert{Encoder}}: Maps the input data to a lower-dimensional latent space using one or more hidden layers. The encoder learns to capture the most important features of the data.
+\item \alert{\alert{Latent Space}}: The compressed representation of the input data, which contains the essential features learned by the encoder.
+\item \alert{\alert{Decoder}}: Reconstructs the original data from the latent space representation using one or more hidden layers. The decoder learns to reverse the encoding process.
+\item \alert{\alert{Loss Function}}: The autoencoder is trained to minimize the difference between the original input and the reconstructed output, typically using mean squared error or binary cross-entropy as the loss function.
+\end{itemize}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:orgf12c362}]{Applications of Autoencoders}
+\begin{block}{Applications of Autoencoders}
+\pause
+Autoencoders have various applications in unsupervised learning:
+\begin{itemize}[<+->]
+\item \alert{\alert{Dimensionality Reduction}}: Reducing the number of features while retaining important information, similar to PCA but capable of capturing non-linear relationships.
+\item \alert{\alert{Feature Learning}}: Learning meaningful representations of data that can be used for downstream tasks such as classification or clustering.
+\item \alert{\alert{Anomaly Detection}}: Identifying anomalies by reconstructing input data and measuring the reconstruction error; high errors indicate potential anomalies.
+\item \alert{\alert{Data Denoising}}: Removing noise from data by training the autoencoder to reconstruct clean data from noisy inputs.
+\item \alert{\alert{Generative Models}}: Variational autoencoders (VAEs) can generate new data samples by sampling from the latent space, making them useful for generative tasks.
+\end{itemize}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org2d4a1f0}]{Limitations of Autoencoders}
+\begin{block}{Limitations of Autoencoders}
+\pause
+While autoencoders are powerful tools, they have some limitations:
+\begin{itemize}[<+->]
+\item \alert{\alert{Training Complexity}}: Training autoencoders can be complex and may require careful tuning of hyperparameters, such as the architecture, learning rate, and regularization techniques.
+\item \alert{\alert{Overfitting}}: Autoencoders can overfit the training data, especially if the model is too complex or the dataset is small. Regularization techniques such as dropout or weight decay can help mitigate this.
+\item \alert{\alert{Interpretability}}: The learned representations in the latent space may not have a clear interpretation, making it difficult to understand the features captured by the autoencoder.
+\item \alert{\alert{Non-Deterministic}}: The results can vary between runs due to the stochastic nature of training, especially if random initialization is used.
+\end{itemize}
+\end{block}
+\end{frame}
+\section{Clustering}
+\label{sec:orgd09f944}
+\begin{frame}[label={sec:org3462e54}]{Clustering}
+\begin{definition}[Clustering]\label{sec:org78cea45}
+\pause
+\alert{Clustering} is an unsupervised learning technique that groups similar data points together based on their features. The goal is to identify inherent structures in the data without prior knowledge of labels.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org2d45aa5}]{Types of Clustering Algorithms}
+\begin{definition}[Types of Clustering Algorithms]\label{sec:org57f7871}
+\pause
+Based on their approach, clustering algorithms can be categorized into several types:
+\begin{itemize}[<+->]
+\item \alert{Partitioning Methods}: These algorithms divide the data into k clusters, where k is a predefined number. The most common example is k-means clustering.
+\item \alert{Hierarchical Methods}: These algorithms create a hierarchy of clusters, either by merging smaller clusters into larger ones (\alert{agglomerative}) or by splitting larger clusters into smaller ones (\alert{divisive}).
+\item \alert{Density-Based Methods}: These algorithms group data points based on the density of data points in the feature space. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a popular density-based clustering algorithm.
+\item \alert{Model-Based Methods}: These algorithms assume that the data is generated from a mixture of underlying probability distributions. Gaussian Mixture Models (GMMs) are a common example, where each cluster is represented by a Gaussian distribution.
+\end{itemize}
+\end{definition}
+\end{frame}
+\section[k-Means]{k-Means Clustering}
+\label{sec:orga774d9e}
+\begin{frame}[label={sec:org396ab67}]{k-Means Clustering}
+\begin{block}{k-Means Clustering}
+\pause
+\alert{k-Means clustering} is a widely used partitioning method that aims to divide the data into k clusters, where k is a user-defined parameter. The algorithm iteratively assigns data points to the nearest cluster centroid and updates the centroids based on the assigned points.
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org9eba3d8}]{Steps in k-Means Clustering}
+\begin{block}{Steps in k-Means Clustering}
+\pause
+The k-means clustering algorithm follows these steps:
+\begin{enumerate}[<+->]
+\item \alert{\alert{Initialization}}: Randomly select k data points as initial cluster centroids.
+\item \alert{\alert{Assignment Step}}: Assign each data point to the nearest centroid based on a distance metric (e.g., Euclidean distance).
+\item \alert{\alert{Update Step}}: Calculate the new centroids by taking the mean of all data points assigned to each cluster.
+\item \alert{\alert{Convergence Check}}: Repeat the assignment and update steps until the centroids do not change significantly or a maximum number of iterations is reached.
+\end{enumerate}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:orgf57c0d8}]{Applications of k-Means Clustering}
+\begin{block}{Applications of k-Means Clustering}
+\pause
+k-Means clustering is widely used in various applications:
+\begin{itemize}[<+->]
+\item \alert{\alert{Customer Segmentation}}: Grouping customers based on purchasing behavior to tailor marketing strategies.
+\item \alert{\alert{Image Compression}}: Reducing the number of colors in an image by clustering similar colors together.
+\item \alert{\alert{Document Clustering}}: Grouping similar documents based on their content for information retrieval or topic modeling.
+\item \alert{\alert{Anomaly Detection}}: Identifying outliers by detecting data points that do not belong to any cluster.
+\end{itemize}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:orgd20a1af}]{Limitations of k-Means Clustering}
+\begin{block}{Limitations of k-Means Clustering}
+\pause
+While k-means clustering is a powerful technique, it has some limitations:
+\begin{itemize}[<+->]
+\item \alert{\alert{Choosing k}}: The number of clusters (k) must be specified beforehand, which can be challenging if the optimal number is unknown.
+\item \alert{\alert{Sensitivity to Initialization}}: The algorithm can converge to different solutions based on the initial centroids, leading to inconsistent results. Techniques like k-means++ initialization can help mitigate this issue.
+\item \alert{\alert{Assumption of Spherical Clusters}}: k-means assumes that clusters are spherical and equally sized, which may not hold true for all datasets.
+\item \alert{\alert{Sensitivity to Outliers}}: k-means is sensitive to outliers, which can skew the centroids and affect the clustering results. Preprocessing steps like outlier removal or using robust distance metrics can help address this issue.
+\end{itemize}
+\end{block}
+\end{frame}
+\section{Hierarchical Clustering}
+\label{sec:org1c3a6a2}
+\begin{frame}[label={sec:org9ef3320}]{Hierarchical Clustering}
+\begin{block}{Hierarchical Clustering}
+\pause
+\alert{Hierarchical clustering} is a method that builds a hierarchy of clusters, either by merging smaller clusters into larger ones (agglomerative) or by splitting larger clusters into smaller ones (divisive).
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org1648d65}]{Types of Hierarchical Clustering}
+\begin{block}{Types of Hierarchical Clustering}
+\pause
+Hierarchical clustering can be categorized into two main types:
+\begin{itemize}[<+->]
+\item \alert{Agglomerative Clustering}: Starts with each data point as its own cluster and iteratively merges the closest clusters until a single cluster remains or a stopping criterion is met.
+\item \alert{Divisive Clustering}: Starts with all data points in a single cluster and recursively splits the clusters into smaller ones until each data point is its own cluster or a stopping criterion is met.
+\end{itemize}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org7fd81c9}]{Steps in Agglomerative Clustering}
+\begin{block}{Steps in Agglomerative Clustering}
+\pause
+The agglomerative clustering algorithm follows these steps:
+\begin{enumerate}[<+->]
+\item \alert{\alert{Initialization}}: Start with each data point as its own cluster.
+\item \alert{\alert{Distance Calculation}}: Calculate the distance between all pairs of clusters using a distance metric (e.g., Euclidean distance).
+\item \alert{\alert{Merge Clusters}}: Identify the two closest clusters and merge them into a single cluster.
+\item \alert{\alert{Update Distances}}: Recalculate the distances between the newly formed cluster and the remaining clusters.
+\item \alert{\alert{Repeat}}: Repeat steps 3 and 4 until a stopping criterion is met, such as a predefined number of clusters or a distance threshold.
+\end{enumerate}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org2b5c3ff}]{Applications of Hierarchical Clustering}
+\begin{block}{Applications of Hierarchical Clustering}
+\pause
+Hierarchical clustering is used in various applications:
+\begin{itemize}[<+->]
+\item \alert{\alert{Gene Expression Analysis}}: Grouping genes with similar expression patterns to identify co-regulated genes or functional groups.
+\item \alert{\alert{Document Clustering}}: Organizing documents into a hierarchy based on their content for information retrieval or topic modeling.
+\item \alert{\alert{Image Segmentation}}: Dividing an image into regions based on pixel similarity to facilitate object recognition or scene understanding.
+\item \alert{\alert{Market Basket Analysis}}: Identifying groups of products frequently purchased together to inform marketing strategies or product placement.
+\end{itemize}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:orged94729}]{Limitations of Hierarchical Clustering}
+\begin{block}{Limitations of Hierarchical Clustering}
+\pause
+Hierarchical clustering has some limitations:
+\begin{itemize}[<+->]
+\item \alert{\alert{Scalability}}: Hierarchical clustering can be computationally expensive, especially for large datasets, as it requires calculating distances between all pairs of data points.
+\item \alert{\alert{Sensitivity to Noise}}: Hierarchical clustering can be sensitive to noise and outliers, which can affect the structure of the dendrogram and the resulting clusters.
+\item \alert{\alert{Choice of Distance Metric}}: The choice of distance metric can significantly impact the clustering results, and there is no one-size-fits-all solution.
+\item \alert{\alert{Interpretability}}: The dendrogram can be complex and difficult to interpret, especially for large datasets with many clusters.
+\end{itemize}
+\end{block}
+\end{frame}
+\section{Anomaly Detection}
+\label{sec:orgb2761af}
+\begin{frame}[label={sec:org086416d}]{Anomaly Detection}
+\begin{block}{Anomaly Detection}
+\pause
+\alert{Anomaly detection} is the process of identifying rare items, events, or observations that raise suspicions by differing significantly from the majority of the data.
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org2d38fa3}]{Types of Anomaly Detection Techniques}
+\begin{block}{Types of Anomaly Detection Techniques}
+\pause
+Anomaly detection techniques can be broadly categorized into three main types:
+\begin{itemize}[<+->]
+\item \alert{Statistical Methods}: These methods assume that normal data follows a specific statistical distribution (e.g., Gaussian) and identify anomalies as data points that deviate significantly from this distribution.
+\item \alert{Machine Learning Methods}: These methods use machine learning algorithms to learn patterns in the data and identify anomalies based on deviations from these patterns. Examples include one-class SVMs, isolation forests, and autoencoders.
+\item \alert{Clustering-Based Methods}: These methods use clustering algorithms to group similar data points together and identify anomalies as points that do not belong to any cluster or are far from the nearest cluster centroid.
+\end{itemize}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:orgc21167a}]{Steps in Anomaly Detection}
+\begin{block}{Steps in Anomaly Detection}
+\pause
+The anomaly detection process typically involves the following steps:
+\begin{enumerate}[<+->]
+\item \alert{Data Preprocessing}: Clean and preprocess the data.
+\item \alert{Feature Selection/Extraction}: Select or extract relevant features that are likely to contain information about anomalies.
+\item \alert{Model Training}: Train an anomaly detection model using the selected features.
+\item \alert{Anomaly Scoring}: Calculate an anomaly score for each data point based on the trained model.
+\item \alert{Thresholding}: Set a threshold to classify data points as normal or anomalous based on their anomaly scores.
+\item \alert{Evaluation}: Evaluate the performance of the anomaly detection model using metrics such as precision, recall, and F1-score, if labeled data is available.
+\end{enumerate}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:orga93eb69}]{Applications of Anomaly Detection}
+\begin{block}{Applications of Anomaly Detection}
+\pause
+Anomaly detection is widely used in various domains:
+\begin{itemize}[<+->]
+\item \alert{\alert{Fraud Detection}}: Identifying fraudulent transactions in financial systems by detecting unusual patterns in transaction data.
+\item \alert{\alert{Network Security}}: Detecting intrusions or malicious activities in computer networks by identifying unusual traffic patterns or behaviors.
+\item \alert{\alert{Fault Detection}}: Identifying equipment failures or malfunctions in industrial systems by monitoring sensor data and detecting deviations from normal operating conditions.
+\item \alert{\alert{Healthcare}}: Detecting anomalies in patient health records or medical imaging data to identify potential health issues or diseases.
+\item \alert{\alert{Manufacturing}}: Monitoring production processes to detect anomalies that may indicate defects or quality issues in products.
+\end{itemize}
+\end{block}
+\end{frame}
+\end{document}

From 916443c05b66c6710cc50713175b610404c509fa Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Wed, 4 Feb 2026 17:45:52 +0100
Subject: [PATCH 47/81] S04C01 Overview of Unsupervised Learning

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2a87c026",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Overview of Unsupervised Learning\n",
+    "\n",
+    "In Supervised Learning, we played the role of a teacher: we gave the model questions ($X$) and answers ($y$).\n",
+    "\n",
+    "In **Unsupervised Learning**, there is no teacher. We give the model only the data ($X$) and ask: **\"What patterns can you find here?\"**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb0521d0",
+   "metadata": {},
+   "source": [
+    "## Why Use Unsupervised Learning?\n",
+    "\n",
+    "1.  **Labels are expensive**: Labeling 1 million images takes humans years. Unsupervised learning can process raw data immediately.\n",
+    "2.  **Exploration**: Finding segments in customer data or anomalies in server logs where we don't know the ground truth.\n",
+    "3.  **Preprocessing**: Reducing the size/complexity of data before feeding it to a supervised model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b4fe275",
+   "metadata": {},
+   "source": [
+    "## Types of Unsupervised Learning\n",
+    "\n",
+    "Unsupervised learning can be broadly categorized into two main types:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2daf221a",
+   "metadata": {},
+   "source": [
+    "### Clustering (Grouping)\n",
+    "\n",
+    "The goal is to separate data into groups (clusters) such that items in one group are similar to each other and different from items in other groups.\n",
+    "\n",
+    "-   **K-Means**: Partitions data into K clusters by minimizing variance within each cluster. Works well for circular/spherical blobs.\n",
+    "-   **DBSCAN**: Groups points based on density; finds arbitrary shapes and identifies outliers.\n",
+    "-   **Hierarchical Clustering**: Builds a tree of clusters by merging or splitting based on distance metrics.\n",
+    "-   **Gaussian Mixture Models (GMM)**: Assumes data is generated from a mixture of Gaussian distributions; provides soft cluster assignments."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e588f586",
+   "metadata": {},
+   "source": [
+    "### Dimensionality Reduction (Simplification)\n",
+    "\n",
+    "The goal is to reduce the number of features (columns) while preserving the essential **information**.\n",
+    "\n",
+    "-   If you have 2 variables, \"Height in cm\" and \"Height in inches\", you don't need both. Dimensionality reduction merges them.\n",
+    "-   **PCA (Principal Component Analysis)**: Rotates and projects data to maximize variance along new axes.\n",
+    "-   **t-SNE / UMAP**: Non-linear methods for visualizing high-dimensional data in 2D/3D.\n",
+    "-   **Autoencoders**: Neural networks that learn compressed representations of data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6298d15f",
+   "metadata": {},
+   "source": [
+    "## Advantages and Disadvantages\n",
+    "\n",
+    "-   **Advantages**:\n",
+    "    -   Can discover hidden patterns and structures in data.\n",
+    "    -   Useful for exploratory data analysis and feature engineering.\n",
+    "    -   Can handle large datasets without the need for labeled data.\n",
+    "-   **Disadvantages**:\n",
+    "    -   Results can be difficult to interpret.\n",
+    "    -   No guarantee of finding meaningful patterns; results are sensitive to algorithm choice and parameters.\n",
+    "    -   Evaluation is challenging without ground truth labels."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c570cdb5",
+   "metadata": {},
+   "source": [
+    "# Practical Demonstration: Iris Dataset\n",
+    "\n",
+    "We will take the Iris dataset (4 dimensions: Sepal Length, Sepal Width, Petal Length, Petal Width) and compress it to 2 dimensions using **PCA**, then group the flowers using **K-Means**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9dfab89",
+   "metadata": {},
+   "source": [
+    "## Load and Inspect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba31e479",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "# Load Data\n",
+    "iris = load_iris(as_frame=True)\n",
+    "X = iris.data\n",
+    "y = iris.target  # We will NOT use this for training, only for validation later\n",
+    "\n",
+    "print(f\"Original Shape: {X.shape} (4 Dimensions)\")\n",
+    "\n",
+    "# Visualize relationships between features\n",
+    "df = pd.concat([X, pd.Series(y, name='species')], axis=1)\n",
+    "sns.pairplot(df, hue='species', palette='viridis', markers=[\"o\", \"s\", \"D\"])\n",
+    "plt.suptitle('Iris Dataset Pairplot', y=1.02)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef109d88",
+   "metadata": {},
+   "source": [
+    "## Preprocessing (Standardization)\n",
+    "\n",
+    "**Critical Step**: PCA calculates variance. If one feature is measured in \"Kilometers\" and another in \"Millimeters\", the Millimeter feature will dominate just because the numbers are bigger. We must scale everything to the same range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96d9d960",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "X_scaled = scaler.fit_transform(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca38609f",
+   "metadata": {},
+   "source": [
+    "## Dimensionality Reduction (PCA)\n",
+    "\n",
+    "We compress the 4 features into 2 \"Principal Components\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d99a0fc8",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "# Reduce to 2D\n",
+    "pca = PCA(n_components=2, random_state=42)\n",
+    "X_pca = pca.fit_transform(X_scaled)\n",
+    "\n",
+    "print(f\"New Shape: {X_pca.shape} (2 Dimensions)\")\n",
+    "print(f\"Variance Explained: {pca.explained_variance_ratio_}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06da083f",
+   "metadata": {},
+   "source": [
+    "## Clustering (K-Means)\n",
+    "\n",
+    "Now we apply K-Means to the compressed data. We tell it to look for 3 clusters (since we know there are 3 species, though in real life we use the \"Elbow Method\" to decide)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e584405",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "# n_init='auto' suppresses warnings in newer sklearn versions\n",
+    "kmeans = KMeans(n_clusters=3, random_state=42, n_init='auto')\n",
+    "kmeans.fit(X_pca)\n",
+    "\n",
+    "# Get the cluster labels (0, 1, 2)\n",
+    "y_pred = kmeans.labels_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c54eca5f",
+   "metadata": {},
+   "source": [
+    "## Visualization and Evaluation\n",
+    "\n",
+    "Let's see if the computer rediscovered the 3 species without being told what they were."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ef74dc7",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "\n",
+    "# Plot 1: Ground Truth\n",
+    "sns.scatterplot(x=X_pca[:, 0], y=X_pca[:, 1], hue=y, palette='viridis', s=60, ax=axes[0])\n",
+    "axes[0].set_title(\"Actual Species (Ground Truth)\")\n",
+    "axes[0].set_xlabel(\"PC 1\")\n",
+    "axes[0].set_ylabel(\"PC 2\")\n",
+    "\n",
+    "# Plot 2: K-Means Clusters\n",
+    "sns.scatterplot(x=X_pca[:, 0], y=X_pca[:, 1], hue=y_pred, palette='viridis', s=60, ax=axes[1])\n",
+    "\n",
+    "# Plot Centroids\n",
+    "centers = kmeans.cluster_centers_\n",
+    "axes[1].scatter(centers[:, 0], centers[:, 1], c='red', s=200, marker='X', label='Centroids')\n",
+    "\n",
+    "axes[1].set_title(\"K-Means Clusters (Unsupervised)\")\n",
+    "axes[1].set_xlabel(\"PC 1\")\n",
+    "axes[1].legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1488add0",
+   "metadata": {},
+   "source": [
+    "## Evaluation Metrics\n",
+    "\n",
+    "Since we actually have the labels, we can measure how good the clustering was using **Adjusted Rand Index (ARI)**. If we didn't have labels, we would use **Silhouette Score** (how distinct the clusters look)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6764d98",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import adjusted_rand_score, silhouette_score\n",
+    "\n",
+    "print(f\"Adjusted Rand Index (Needs Truth): {adjusted_rand_score(y, y_pred):.2f}\")\n",
+    "print(f\"Silhouette Score (No Truth needed): {silhouette_score(X_pca, y_pred):.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f639370b",
+   "metadata": {},
+   "source": [
+    "## K-Means Decision Boundaries\n",
+    "\n",
+    "K-Means essentially draws straight lines (Voronoi tessellation) between the centroids."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "945a012c",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.inspection import DecisionBoundaryDisplay\n",
+    "\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    kmeans, X_pca, response_method=\"predict\", cmap='viridis', alpha=0.3\n",
+    ")\n",
+    "sns.scatterplot(x=X_pca[:, 0], y=X_pca[:, 1], hue=y_pred, palette='viridis', edgecolor='k')\n",
+    "plt.scatter(centers[:, 0], centers[:, 1], c='red', s=200, marker='X')\n",
+    "plt.title('K-Means Decision Boundaries')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24e3bf06",
+   "metadata": {},
+   "source": [
+    "# Exercises: Breast Cancer Dataset\n",
+    "\n",
+    "We will use the **Breast Cancer** dataset. This is a much harder problem:\n",
+    "\n",
+    "-   It has **30 features** (dimensions).\n",
+    "-   Humans cannot visualize 30 dimensions.\n",
+    "-   We will use PCA to visualize it, and K-Means to see if we can separate Benign vs Malignant without labels."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "822f18cb",
+   "metadata": {},
+   "source": [
+    "## Load and Scale"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03d7bfe4",
+   "metadata": {},
+   "source": [
+    "## PCA Visualization\n",
+    "\n",
+    "-   Use PCA to reduce the 30 features down to 2.\n",
+    "-   Plot the result, coloring points by the actual diagnosis.\n",
+    "-   **Question**: Do the classes look separable in 2D?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67558ddb",
+   "metadata": {},
+   "source": [
+    "## Unsupervised Classification\n",
+    "\n",
+    "-   Ignore the labels. Train K-Means ($K=2$) on the PCA data.\n",
+    "-   Check the Adjusted Rand Index against the real labels."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0979b31d",
+   "metadata": {},
+   "source": [
+    "## Visualize Clustering Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fc37854",
+   "metadata": {},
+   "source": [
+    "# Summary\n",
+    "\n",
+    "1.  **Unsupervised Learning**: Finding patterns without labeled answers.\n",
+    "2.  **StandardScaler**: Crucial first step for distance-based algorithms (PCA, K-Means).\n",
+    "3.  **PCA**: Compresses data by projecting onto directions of maximum variance. Useful for visualization and speeding up training.\n",
+    "4.  **K-Means**: Finds groups by minimizing within-cluster variance. Simple and fast, but assumes spherical clusters.\n",
+    "5.  **Evaluation**: Use Silhouette Score when no labels exist; use ARI when ground truth is available."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 9ecd1d5f9908cba5b7bd23b6da0f37d3d01ad794 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Wed, 4 Feb 2026 17:46:18 +0100
Subject: [PATCH 48/81] S04C02 Dimensionality Reduction Techniques

---
 ... Dimensionality Reduction Techniques.ipynb | 383 ++++++++++++++++++
 1 file changed, 383 insertions(+)
 create mode 100644 src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C02 Dimensionality Reduction Techniques.ipynb

diff --git a/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C02 Dimensionality Reduction Techniques.ipynb b/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C02 Dimensionality Reduction Techniques.ipynb
new file mode 100644
index 0000000..001cb0d
--- /dev/null
+++ b/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C02 Dimensionality Reduction Techniques.ipynb	
@@ -0,0 +1,383 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5fe6059a",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Dimensionality Reduction Techniques\n",
+    "\n",
+    "In modern data science, we often face the \"Curse of Dimensionality\": datasets with hundreds or thousands of features (columns).\n",
+    "\n",
+    "-   **Visualization**: We cannot plot 100 dimensions.\n",
+    "-   **Computation**: Algorithms become slow.\n",
+    "-   **Overfitting**: With too many features, models find spurious patterns.\n",
+    "\n",
+    "**Dimensionality Reduction** is the process of compressing this data into fewer features while keeping the important \"signal\". Common techniques include:\n",
+    "\n",
+    "-   **Principal Component Analysis (PCA)**: A linear technique that projects data onto directions of maximum variance.\n",
+    "-   **Autoencoders**: Neural networks that learn compressed representations through encoding and decoding.\n",
+    "-   **t-SNE / UMAP**: Non-linear techniques primarily used for visualization in 2D/3D."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "017c6ddf",
+   "metadata": {},
+   "source": [
+    "## Principal Component Analysis (PCA)\n",
+    "\n",
+    "PCA is the most popular linear technique. It doesn't just delete columns; it finds a **new coordinate system** for the data.\n",
+    "\n",
+    "1.  It finds the direction of **Maximum Variance** (the \"spread\" of the data). This becomes **Principal Component 1 (PC1)**.\n",
+    "2.  It finds a second direction perpendicular (orthogonal) to the first that captures the remaining variance. This is **PC2**.\n",
+    "3.  And so on."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9581b1cf",
+   "metadata": {},
+   "source": [
+    "### Why \"Variance\" Matters\n",
+    "\n",
+    "Information $\\approx$ Variance.\n",
+    "\n",
+    "-   If a feature has zero variance (all values are 5), it carries zero information.\n",
+    "-   PCA keeps the directions with high variance (signal) and discards directions with low variance (noise)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52ce19ad",
+   "metadata": {},
+   "source": [
+    "### Mathematical Formulation\n",
+    "\n",
+    "Given a dataset $X \\in \\mathbb{R}^{n \\times p}$ with $n$ samples and $p$ features, PCA involves:\n",
+    "\n",
+    "1.  **Standardization**: Center the data by subtracting the mean: $$X' = X - \\bar{X}$$\n",
+    "2.  **Covariance Matrix**: Compute $C = \\frac{1}{n-1} X'^T X'$\n",
+    "3.  **Eigenvalue Decomposition**: $C = V \\Lambda V^T$, where $V$ contains eigenvectors and $\\Lambda$ contains eigenvalues.\n",
+    "4.  **Project**: Select top $k$ eigenvectors and project: $Z = X' W_k$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56890496",
+   "metadata": {},
+   "source": [
+    "### Advantages and Disadvantages\n",
+    "\n",
+    "-   **Advantages**:\n",
+    "    -   Reduces dimensionality while preserving variance.\n",
+    "    -   Helps in noise reduction by discarding less significant components.\n",
+    "    -   Can improve model performance by reducing overfitting.\n",
+    "-   **Disadvantages**:\n",
+    "    -   Assumes linear relationships between features.\n",
+    "    -   Sensitive to feature scale; requires standardization.\n",
+    "    -   Cannot capture complex, non-linear relationships."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79f15690",
+   "metadata": {},
+   "source": [
+    "### Evaluating PCA\n",
+    "\n",
+    "-   **Explained Variance Ratio**: How much variance each component captures.\n",
+    "-   **Scree Plot**: Visualizes variance per component; the \"elbow\" helps choose $k$.\n",
+    "-   **Reconstruction Error**: How well the reduced data reconstructs the original."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05d22f0f",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: PCA from Scratch\n",
+    "\n",
+    "We will generate a 2D dataset that is highly correlated (shaped like a cigar) and use PCA to rotate it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1ccd87f",
+   "metadata": {},
+   "source": [
+    "### Generate Correlated Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2c5fe97",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# Generate 2 features with high correlation\n",
+    "mean = [0, 0]\n",
+    "cov = [[1, 0.9], [0.9, 1]]  # High covariance (0.9)\n",
+    "X = np.random.multivariate_normal(mean, cov, 200)\n",
+    "\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.scatter(X[:, 0], X[:, 1], alpha=0.6, color='teal')\n",
+    "plt.title(\"Original Correlated Data\")\n",
+    "plt.xlabel(\"Feature 1\")\n",
+    "plt.ylabel(\"Feature 2\")\n",
+    "plt.axis('equal')\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9aed22b",
+   "metadata": {},
+   "source": [
+    "### Apply PCA\n",
+    "\n",
+    "We use scikit-learn's `PCA` to find the axes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16e6adde",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "pca = PCA(n_components=2)\n",
+    "X_pca = pca.fit_transform(X)\n",
+    "\n",
+    "# The Components (The new axes directions)\n",
+    "print(\"Principal Axes (Eigenvectors):\\n\", pca.components_)\n",
+    "\n",
+    "# The Variance Explained (Eigenvalues)\n",
+    "print(\"\\nExplained Variance Ratio:\", pca.explained_variance_ratio_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37ed5096",
+   "metadata": {},
+   "source": [
+    "**Observation**: PC1 explains ~95% of the variance. PC2 explains only ~5%. This means we can drop PC2 and lose very little information."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c85858a",
+   "metadata": {},
+   "source": [
+    "### Visualizing the Rotation\n",
+    "\n",
+    "Let's plot the data in the new \"PCA Space\". Notice how the correlation is gone! The blob is now aligned with the axes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "075d03a0",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.scatter(X_pca[:, 0], X_pca[:, 1], alpha=0.6, color='purple')\n",
+    "plt.title(\"Data in PCA Space (Rotated)\")\n",
+    "plt.xlabel(\"Principal Component 1 (High Variance)\")\n",
+    "plt.ylabel(\"Principal Component 2 (Low Variance)\")\n",
+    "plt.axis('equal')\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a09d4642",
+   "metadata": {},
+   "source": [
+    "### Dimensionality Reduction (Compression)\n",
+    "\n",
+    "Now we keep only PC1. We effectively flatten the data from 2D to 1D. We can then **inverse transform** it back to 2D to see what was lost."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6a8215f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop PC2 by setting it to 0\n",
+    "X_pca_1d = X_pca.copy()\n",
+    "X_pca_1d[:, 1] = 0\n",
+    "\n",
+    "# Reconstruct original data from just 1 component\n",
+    "X_reconstructed = pca.inverse_transform(X_pca_1d)\n",
+    "\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.scatter(X[:, 0], X[:, 1], alpha=0.3, label='Original')\n",
+    "plt.scatter(X_reconstructed[:, 0], X_reconstructed[:, 1], alpha=0.8, color='red', label='Reconstructed (1D)')\n",
+    "plt.title(\"Compression: 2D -> 1D\")\n",
+    "plt.legend()\n",
+    "plt.axis('equal')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44a192e8",
+   "metadata": {},
+   "source": [
+    "**Result**: The red dots lie perfectly on a line. We lost the \"width\" of the blob (noise), but kept the \"length\" (signal)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f9185f0",
+   "metadata": {},
+   "source": [
+    "## Autoencoders (Non-Linear Reduction)\n",
+    "\n",
+    "PCA is linear. It can only rotate and stretch. If your data lies on a curve (like a Swiss Roll), PCA fails.\n",
+    "\n",
+    "**Autoencoders** are Neural Networks that can learn **curved** compressions. They consist of:\n",
+    "\n",
+    "-   **Encoder**: Compresses input ($X$) into a bottleneck representation ($Z$).\n",
+    "-   **Decoder**: Reconstructs ($X'$) from the bottleneck.\n",
+    "-   If $X \\approx X'$, then $Z$ is a good compressed representation.\n",
+    "\n",
+    "The network is trained to minimize reconstruction error: $$\\mathcal{L}(X, \\hat{X}) = \\frac{1}{n} \\sum_{i=1}^{n} \\|X_i - \\hat{X}_i\\|^2$$\n",
+    "\n",
+    "Autoencoders excel at:\n",
+    "\n",
+    "-   Learning non-linear manifolds\n",
+    "-   Denoising (reconstructing clean data from noisy inputs)\n",
+    "-   Feature extraction for downstream tasks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98172371",
+   "metadata": {},
+   "source": [
+    "## t-SNE (Visualizing High Dimensions)\n",
+    "\n",
+    "t-SNE (t-Distributed Stochastic Neighbor Embedding) is strictly for **visualization**. It preserves local neighbors: \"If points A and B are close in 100 dimensions, keep them close in 2D.\"\n",
+    "\n",
+    "**Warning**: Do not use t-SNE for clustering or feature engineering. Distances in t-SNE are distorted."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf789ddd",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "We will use the **Digits Dataset** (8x8 pixel images of handwritten numbers). This is a 64-dimensional dataset ($8 \\times 8 = 64$). We will crush it to 2 dimensions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66baa783",
+   "metadata": {},
+   "source": [
+    "### Load and Inspect"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30f354ff",
+   "metadata": {},
+   "source": [
+    "### PCA Reduction\n",
+    "\n",
+    "-   Reduce from 64D to 2D using PCA.\n",
+    "-   Plot the 2D result. Color the points by the digit label (0-9).\n",
+    "\n",
+    "**Observation**: You should see that visually similar digits (like 4 and 9, or 3 and 8) tend to cluster together, while very different ones separate."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03ee6478",
+   "metadata": {},
+   "source": [
+    "### The Scree Plot (How many components?)\n",
+    "\n",
+    "How much information did we lose by going to 2D?\n",
+    "\n",
+    "-   Fit PCA with `n_components=64` (Full).\n",
+    "-   Plot `cumsum(explained_variance_ratio_)`.\n",
+    "-   Find how many components are needed for 95% variance.\n",
+    "\n",
+    "**Result**: Usually around 20-30 components are enough to capture 95% of the image data, reducing the size by half without losing quality!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abfabb17",
+   "metadata": {},
+   "source": [
+    "### Correlated Features Exercise\n",
+    "\n",
+    "Create a synthetic dataset with 4 highly correlated features and apply PCA. **Question**: Given the high correlations, how many components capture most of the variance?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35d81b03",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **Curse of Dimensionality**: High dimensions cause computational and statistical problems.\n",
+    "2.  **PCA**: The gold standard. Rotates data to align with variance. Linear.\n",
+    "3.  **Components**: We choose $k$ components to balance compression vs. information loss (using a Scree Plot).\n",
+    "4.  **Autoencoders**: Neural networks for non-linear dimensionality reduction.\n",
+    "5.  **t-SNE/UMAP**: Better for visualizing complex clusters, but tricky to interpret."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 20585cf86a61996e8e2f95032024710ccfc7db4e Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Wed, 4 Feb 2026 17:46:46 +0100
Subject: [PATCH 49/81] S04C03 Clustering Algorithms

---
 .../S04C03 Clustering Algorithms.ipynb        | 502 ++++++++++++++++++
 1 file changed, 502 insertions(+)
 create mode 100644 src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C03 Clustering Algorithms.ipynb

diff --git a/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C03 Clustering Algorithms.ipynb b/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C03 Clustering Algorithms.ipynb
new file mode 100644
index 0000000..aa536fb
--- /dev/null
+++ b/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C03 Clustering Algorithms.ipynb	
@@ -0,0 +1,502 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5f8a5ee9",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Clustering Algorithms\n",
+    "\n",
+    "Unsupervised learning focuses on finding patterns in data without labeled outputs. Clustering is a key unsupervised learning technique that groups similar observations together based on their features. It is exploratory in nature, meaning the results can vary significantly depending on the algorithm, distance metric used, and parameters set.\n",
+    "\n",
+    "Common clustering algorithms include:\n",
+    "\n",
+    "-   **k-Means**: Partitions data into $k$ clusters by minimizing the variance within each cluster.\n",
+    "-   **Hierarchical Clustering**: Builds a hierarchy of clusters, either agglomerative (bottom-up) or divisive (top-down).\n",
+    "-   **DBSCAN**: Density-Based Spatial Clustering of Applications with Noise, which finds clusters of varying shape and density.\n",
+    "-   **Gaussian Mixture Models (GMM)**: Assumes data is generated from a mixture of several Gaussian distributions and uses the Expectation-Maximization algorithm to estimate parameters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "293caa78",
+   "metadata": {},
+   "source": [
+    "## Advantages and Disadvantages\n",
+    "\n",
+    "-   **Advantages**:\n",
+    "    -   Can discover hidden patterns and structures in data.\n",
+    "    -   Useful for exploratory data analysis and feature engineering.\n",
+    "    -   Can handle large datasets without the need for labeled data.\n",
+    "-   **Disadvantages**:\n",
+    "    -   Results can be difficult to interpret (no \"ground truth\").\n",
+    "    -   No guarantee of finding meaningful patterns; results can be sensitive to the choice of algorithm and parameters.\n",
+    "    -   Evaluation of clustering results is challenging."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02d867f9",
+   "metadata": {},
+   "source": [
+    "## K-Means\n",
+    "\n",
+    "K-Means is a popular clustering algorithm that partitions data into $k$ clusters by minimizing the variance within each cluster."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f790675b",
+   "metadata": {},
+   "source": [
+    "### The Algorithm\n",
+    "\n",
+    "Mathematically, K-Means aims to minimize the within-cluster sum of squares (WCSS), formally: $$ \\sum_{i=1}^{k} \\sum_{x \\in C_i} ||x - \\mu_i||^2 $$ where $\\mu_i$ is the centroid of cluster $C_i$.\n",
+    "\n",
+    "The steps are:\n",
+    "\n",
+    "1.  Initialize $k$ centroids randomly from the data points.\n",
+    "2.  Assign each data point to the nearest centroid based on a distance metric (commonly Euclidean distance).\n",
+    "3.  Update the centroids by calculating the mean of all points assigned to each cluster.\n",
+    "4.  Repeat steps 2 and 3 until convergence (i.e., centroids do not change significantly)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f903b6f",
+   "metadata": {},
+   "source": [
+    "### Advantages and Disadvantages\n",
+    "\n",
+    "-   **Advantages**:\n",
+    "    -   Simple and efficient for large datasets.\n",
+    "    -   Works well when clusters are spherical and of similar size.\n",
+    "-   **Disadvantages**:\n",
+    "    -   Requires specifying the number of clusters ($k$) beforehand.\n",
+    "    -   Sensitive to outliers and noise.\n",
+    "    -   Assumes clusters are spherical and equally sized, which may not hold in practice."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fc73b12",
+   "metadata": {},
+   "source": [
+    "### Example\n",
+    "\n",
+    "To illustrate the concepts, we will use the Iris dataset. We will use the **Elbow Method** to determine the optimal number of clusters, apply PCA for dimensionality reduction, and then perform K-Means.\n",
+    "\n",
+    "-   Load the Iris dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3031aa26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "# Load the Iris dataset\n",
+    "iris = load_iris(as_frame=True)\n",
+    "X = iris.data\n",
+    "y = iris.target  # We will NOT use this for training, only for validation later\n",
+    "\n",
+    "df = pd.concat([X, pd.Series(y, name='species')], axis=1)\n",
+    "sns.pairplot(df, hue='species', palette='viridis', markers=[\"o\", \"s\", \"D\"])\n",
+    "plt.suptitle('Iris Dataset Pairplot', y=1.02)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8f89102",
+   "metadata": {},
+   "source": [
+    "-   Preprocess the data and Apply PCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "019c30d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "# Standardize the data (Crucial for K-Means)\n",
+    "scaler = StandardScaler()\n",
+    "X_scaled = scaler.fit_transform(X)\n",
+    "\n",
+    "# Apply PCA to reduce to 2 dimensions for visualization\n",
+    "pca = PCA(n_components=2, random_state=42)\n",
+    "X_pca = pca.fit_transform(X_scaled)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a01b5d13",
+   "metadata": {},
+   "source": [
+    "-   Determine optimal k using the Elbow Method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05a3c1bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "wcss = []\n",
+    "range_k = range(1, 11)\n",
+    "for i in range_k:\n",
+    "    kmeans = KMeans(n_clusters=i, init='k-means++', random_state=42, n_init='auto')\n",
+    "    kmeans.fit(X_scaled)\n",
+    "    wcss.append(kmeans.inertia_)\n",
+    "\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "plt.plot(range_k, wcss, marker='o', linestyle='--')\n",
+    "plt.title('Elbow Method')\n",
+    "plt.xlabel('Number of Clusters')\n",
+    "plt.ylabel('WCSS')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccb296b3",
+   "metadata": {},
+   "source": [
+    "-   Apply K-Means clustering (with k=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b3f41ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Apply K-Means clustering\n",
+    "kmeans = KMeans(n_clusters=3, random_state=42, n_init='auto')\n",
+    "y_kmeans = kmeans.fit_predict(X_scaled)\n",
+    "\n",
+    "# Plot the results on PCA-reduced data\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y_kmeans, cmap='viridis', edgecolor='k', s=50)\n",
+    "\n",
+    "# Transform centroids to PCA space for visualization\n",
+    "centroids_pca = pca.transform(kmeans.cluster_centers_)\n",
+    "plt.scatter(centroids_pca[:, 0], centroids_pca[:, 1],\n",
+    "            s=200, c='red', marker='X', label='Centroids')\n",
+    "\n",
+    "plt.title('K-Means Clustering on Iris (PCA projection)')\n",
+    "plt.xlabel('PC 1')\n",
+    "plt.ylabel('PC 2')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4bfb34f",
+   "metadata": {},
+   "source": [
+    "-   Evaluate the clustering results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81d0c71b",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import silhouette_score, adjusted_rand_score\n",
+    "\n",
+    "# Calculate the silhouette score\n",
+    "silhouette_avg = silhouette_score(X_scaled, y_kmeans)\n",
+    "print(f'Silhouette Score: {silhouette_avg:.2f}')\n",
+    "\n",
+    "# Calculate the adjusted Rand Index (ARI) to compare with true labels\n",
+    "ari = adjusted_rand_score(y, y_kmeans)\n",
+    "print(f'Adjusted Rand Index: {ari:.2f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b18ff72",
+   "metadata": {},
+   "source": [
+    "## Hierarchical Clustering\n",
+    "\n",
+    "Hierarchical clustering builds a hierarchy of clusters. It does not require specifying the number of clusters in advance and produces a dendrogram."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4377b1ec",
+   "metadata": {},
+   "source": [
+    "### Advantages and Disadvantages\n",
+    "\n",
+    "-   **Advantages**:\n",
+    "    -   No need to predefine the number of clusters.\n",
+    "    -   Visual hierarchy via dendrograms helps interpret data relationships.\n",
+    "-   **Disadvantages**:\n",
+    "    -   Computationally expensive for large datasets ($O(n^3)$ or $O(n^2)$).\n",
+    "    -   Sensitive to noise and outliers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08d53a5d",
+   "metadata": {},
+   "source": [
+    "### Example: Dendrogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e54079e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import AgglomerativeClustering\n",
+    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
+    "\n",
+    "# Compute linkage matrix using Ward's method\n",
+    "# Ward minimizes variance within clusters\n",
+    "linkage_matrix = linkage(X_scaled, method='ward')\n",
+    "\n",
+    "# Plot the dendrogram\n",
+    "plt.figure(figsize=(10, 7))\n",
+    "dendrogram(linkage_matrix, truncate_mode=\"level\", p=3)\n",
+    "plt.title('Hierarchical Clustering Dendrogram')\n",
+    "plt.xlabel('Cluster Size')\n",
+    "plt.ylabel('Distance')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d11f2a67",
+   "metadata": {},
+   "source": [
+    "-   Apply Agglomerative Clustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e407a838",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "# Perform hierarchical clustering\n",
+    "hc = AgglomerativeClustering(n_clusters=3, linkage='ward')\n",
+    "y_hc = hc.fit_predict(X_scaled)\n",
+    "\n",
+    "# Evaluate\n",
+    "print(f'Silhouette Score: {silhouette_score(X_scaled, y_hc):.2f}')\n",
+    "print(f'Adjusted Rand Index: {adjusted_rand_score(y, y_hc):.2f}')\n",
+    "\n",
+    "# Visualize\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y_hc, cmap='viridis', edgecolor='k', s=50)\n",
+    "plt.title('Hierarchical Clustering Results')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e26b491",
+   "metadata": {},
+   "source": [
+    "## DBSCAN\n",
+    "\n",
+    "Density-Based Spatial Clustering of Applications with Noise (DBSCAN) groups points that are close to each other based on a distance measurement (epsilon) and a minimum number of points (min\\_samples). It marks points in low-density regions as outliers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce5639a5",
+   "metadata": {},
+   "source": [
+    "### Advantages and Disadvantages\n",
+    "\n",
+    "-   **Advantages**:\n",
+    "    -   Can find arbitrarily shaped clusters (e.g., crescents).\n",
+    "    -   Robust to outliers.\n",
+    "    -   Does not require specifying $k$.\n",
+    "-   **Disadvantages**:\n",
+    "    -   Difficult to find optimal \\`eps\\` and \\`min\\_samples\\` if density varies across clusters.\n",
+    "    -   Struggles with high-dimensional data (curse of dimensionality)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fd16c39",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec443e73",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "\n",
+    "# Apply DBSCAN\n",
+    "# eps: max distance between samples\n",
+    "# min_samples: min samples in neighborhood to be a core point\n",
+    "dbscan = DBSCAN(eps=0.8, min_samples=5)\n",
+    "y_dbscan = dbscan.fit_predict(X_scaled)\n",
+    "\n",
+    "# Visualize (Label -1 indicates noise)\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y_dbscan, cmap='plasma', edgecolor='k', s=50)\n",
+    "plt.title('DBSCAN Clustering (Dark points = Noise)')\n",
+    "plt.xlabel('PC 1')\n",
+    "plt.ylabel('PC 2')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a889ec39",
+   "metadata": {},
+   "source": [
+    "## Gaussian Mixture Models (GMM)\n",
+    "\n",
+    "GMM assumes that the data is generated from a mixture of a finite number of Gaussian distributions with unknown parameters. Unlike K-Means (hard clustering), GMM provides \"soft clustering\" where each point belongs to a cluster with a certain probability."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4fe5bc",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "97c36d16",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.mixture import GaussianMixture\n",
+    "\n",
+    "# Apply GMM\n",
+    "gmm = GaussianMixture(n_components=3, random_state=42)\n",
+    "gmm.fit(X_scaled)\n",
+    "y_gmm = gmm.predict(X_scaled)\n",
+    "\n",
+    "# Visualize\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y_gmm, cmap='viridis', edgecolor='k', s=50)\n",
+    "plt.title('Gaussian Mixture Models')\n",
+    "plt.show()\n",
+    "\n",
+    "# Check probabilities for the first 5 samples\n",
+    "probs = gmm.predict_proba(X_scaled)[:5]\n",
+    "print(\"Cluster Probabilities for first 5 samples:\\n\", probs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "859184f2",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "Use K-Means and Hierarchical Clustering on the Breast Cancer Wisconsin dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cd4e816",
+   "metadata": {},
+   "source": [
+    "### Load and Preprocess"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "245cb377",
+   "metadata": {},
+   "source": [
+    "### K-Means"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ca563fe",
+   "metadata": {},
+   "source": [
+    "### Hierarchical Clustering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddc1f229",
+   "metadata": {},
+   "source": [
+    "### Comparison Visualization"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 70e6a4ceda1cb1fab9babd6f7bc1a3351b798318 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Wed, 4 Feb 2026 17:47:19 +0100
Subject: [PATCH 50/81] S04C04 Anomaly Detection

---
 .../S04C04 Anomaly Detection.ipynb            | 382 ++++++++++++++++++
 1 file changed, 382 insertions(+)
 create mode 100644 src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C04 Anomaly Detection.ipynb

diff --git a/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C04 Anomaly Detection.ipynb b/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C04 Anomaly Detection.ipynb
new file mode 100644
index 0000000..f2e1ae3
--- /dev/null
+++ b/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C04 Anomaly Detection.ipynb	
@@ -0,0 +1,382 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "37cc9b24",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Anomaly Detection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3982a907",
+   "metadata": {},
+   "source": [
+    "## What is an Anomaly?\n",
+    "\n",
+    "An anomaly (or outlier) is a data point that differs significantly from the majority of the data.\n",
+    "\n",
+    "-   **In Finance**: A $10,000 credit card transaction at 3 AM.\n",
+    "-   **In Security**: A user logging in from three different countries in one hour.\n",
+    "-   **In Manufacturing**: A machine vibrating at a slightly different frequency than usual.\n",
+    "\n",
+    "Detecting these rare events is critical because they often represent high-risk or high-value incidents."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dccc5144",
+   "metadata": {},
+   "source": [
+    "## Types of Detection\n",
+    "\n",
+    "1.  **Supervised**: We have a dataset with labels (\"Fraud\", \"Normal\"). This is just standard Classification (usually with imbalanced data).\n",
+    "2.  **Unsupervised**: We have no labels. We assume the majority of data is \"Normal\" and look for points that are statistically far from the rest.\n",
+    "3.  **Semi-Supervised**: We train only on \"Normal\" data. Anything that looks different from the training set is flagged as an anomaly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1c1da48",
+   "metadata": {},
+   "source": [
+    "## Techniques Overview\n",
+    "\n",
+    "| Method             | Description                          | Best For                    |\n",
+    "|------------------ |------------------------------------ |--------------------------- |\n",
+    "| **Statistical**    | Z-Scores, Interquartile Range (IQR). | Simple, univariate data.    |\n",
+    "| **Distance-Based** | k-Nearest Neighbors (k-NN), k-Means. | Clusters of normal data.    |\n",
+    "| **Density-Based**  | DBSCAN, Isolation Forest.            | Complex, non-linear shapes. |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6c23616",
+   "metadata": {},
+   "source": [
+    "## Evaluation Metrics\n",
+    "\n",
+    "Evaluating anomaly detection models can be challenging due to imbalanced data. Common metrics include:\n",
+    "\n",
+    "-   **Precision**: The proportion of true anomalies among the detected anomalies.\n",
+    "-   **Recall**: The proportion of true anomalies that were correctly detected.\n",
+    "-   **F1 Score**: The harmonic mean of precision and recall.\n",
+    "-   **Confusion Matrix**: Shows true/false positives and negatives at a glance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc80f577",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: Unsupervised Detection\n",
+    "\n",
+    "We will generate a dataset containing distinct clusters of normal data and then sprinkle in some random noise (anomalies). We will try to find the noise without being told which points are which."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0556e9d",
+   "metadata": {},
+   "source": [
+    "### Generate Synthetic Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b97ddbc8",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.datasets import make_blobs\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# 1. Generate Normal Data (3 blobs)\n",
+    "X_normal, _ = make_blobs(n_samples=300, centers=3, cluster_std=0.6, random_state=42)\n",
+    "\n",
+    "# 2. Generate Anomalies (Uniform noise)\n",
+    "X_anomalies = np.random.uniform(low=-6, high=6, size=(20, 2))\n",
+    "\n",
+    "# Combine\n",
+    "X = np.vstack([X_normal, X_anomalies])\n",
+    "\n",
+    "# Construct True Labels for later evaluation: Normal=0, Anomaly=1\n",
+    "y_true = np.array([0] * 300 + [1] * 20)\n",
+    "\n",
+    "# Plot\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(X[:, 0], X[:, 1], color='grey', alpha=0.6, label='Data')\n",
+    "plt.title(\"Unlabeled Data (Can you spot the anomalies?)\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7f88441",
+   "metadata": {},
+   "source": [
+    "### Method 1: k-Means (Distance Based)\n",
+    "\n",
+    "k-Means assumes anomalies are points that are **far away from any cluster center**.\n",
+    "\n",
+    "1.  Fit k-Means to the data.\n",
+    "2.  Calculate the distance of each point to its nearest centroid.\n",
+    "3.  Points with large distances are anomalies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d33f230e",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "# Fit k-Means (assuming we guess 3 clusters correctly)\n",
+    "kmeans = KMeans(n_clusters=3, random_state=42, n_init='auto')\n",
+    "kmeans.fit(X)\n",
+    "\n",
+    "# Calculate distances to assigned cluster center\n",
+    "# transform() returns distance to ALL centroids; min(axis=1) gets the nearest one\n",
+    "distances = np.min(kmeans.transform(X), axis=1)\n",
+    "\n",
+    "# Define threshold (e.g., 95th percentile)\n",
+    "threshold = np.percentile(distances, 95)\n",
+    "\n",
+    "# Predict: True = Anomaly\n",
+    "anomalies_kmeans = distances > threshold\n",
+    "\n",
+    "print(f\"Threshold Distance: {threshold:.2f}\")\n",
+    "print(f\"Anomalies Detected: {np.sum(anomalies_kmeans)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9ed0199",
+   "metadata": {},
+   "source": [
+    "### Visualizing k-Means Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b438b708",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "\n",
+    "# Plot Normal\n",
+    "plt.scatter(X[~anomalies_kmeans, 0], X[~anomalies_kmeans, 1], c='blue', alpha=0.6, label='Normal')\n",
+    "\n",
+    "# Plot Anomalies\n",
+    "plt.scatter(X[anomalies_kmeans, 0], X[anomalies_kmeans, 1], c='red', marker='x', s=100, label='Anomaly')\n",
+    "\n",
+    "# Plot Centroids\n",
+    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], c='black', s=200, marker='*', label='Centroids')\n",
+    "\n",
+    "plt.title(\"k-Means Anomaly Detection\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50e67d03",
+   "metadata": {},
+   "source": [
+    "### Method 2: DBSCAN (Density Based)\n",
+    "\n",
+    "DBSCAN clusters data based on density. Points that do not find enough neighbors within a radius `eps` are explicitly labeled as **Noise (-1)**. This is often superior to k-Means because we don't need to pick a threshold manually."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "934bf17f",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import DBSCAN\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "# DBSCAN is sensitive to scale\n",
+    "X_scaled = StandardScaler().fit_transform(X)\n",
+    "\n",
+    "# eps=0.5 (radius), min_samples=5 (neighbors required to be a core point)\n",
+    "dbscan = DBSCAN(eps=0.5, min_samples=5)\n",
+    "labels = dbscan.fit_predict(X_scaled)\n",
+    "\n",
+    "# DBSCAN labels noise as -1\n",
+    "anomalies_dbscan = (labels == -1)\n",
+    "\n",
+    "print(f\"Anomalies Detected by DBSCAN: {np.sum(anomalies_dbscan)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "653dda1c",
+   "metadata": {},
+   "source": [
+    "### Visualizing DBSCAN Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "411045ec",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "\n",
+    "# Plot Normal (labels != -1)\n",
+    "plt.scatter(X[~anomalies_dbscan, 0], X[~anomalies_dbscan, 1], c='blue', alpha=0.6, label='Normal')\n",
+    "\n",
+    "# Plot Anomalies (labels == -1)\n",
+    "plt.scatter(X[anomalies_dbscan, 0], X[anomalies_dbscan, 1], c='red', marker='x', s=100, label='Anomaly')\n",
+    "\n",
+    "plt.title(\"DBSCAN Anomaly Detection\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "892f8502",
+   "metadata": {},
+   "source": [
+    "## Evaluation (When Truth is Known)\n",
+    "\n",
+    "In the real world, you rarely have labels. But for testing, we created the data, so we know the truth. Let's verify our accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30de3830",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "\n",
+    "print(\"--- DBSCAN Performance ---\")\n",
+    "# Convert boolean to int (False->0, True->1)\n",
+    "print(classification_report(y_true, anomalies_dbscan.astype(int), target_names=['Normal', 'Anomaly']))\n",
+    "\n",
+    "# Visualize the confusion matrix\n",
+    "plt.figure(figsize=(6, 5))\n",
+    "sns.heatmap(confusion_matrix(y_true, anomalies_dbscan.astype(int)), annot=True, fmt='d',\n",
+    "            xticklabels=[\"Normal\", \"Anomaly\"], yticklabels=[\"Normal\", \"Anomaly\"],\n",
+    "            cmap=\"Blues\")\n",
+    "plt.title(\"Confusion Matrix: DBSCAN\")\n",
+    "plt.xlabel(\"Predicted\")\n",
+    "plt.ylabel(\"True\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed4d81b3",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56507e7c",
+   "metadata": {},
+   "source": [
+    "### Isolation Forest\n",
+    "\n",
+    "**Isolation Forest** is the industry standard for high-dimensional anomaly detection. It works by randomly slicing the data; anomalies are easier to isolate (require fewer cuts) than normal points clustered together.\n",
+    "\n",
+    "-   Use `sklearn.ensemble.IsolationForest`.\n",
+    "-   Fit it to the synthetic data `X`.\n",
+    "-   **Note**: Isolation Forest returns -1 for anomalies and 1 for normal."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1db638fa",
+   "metadata": {},
+   "source": [
+    "### Visualization\n",
+    "\n",
+    "-   Plot the results of the Isolation Forest. Compare it mentally to DBSCAN."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a42ef7d6",
+   "metadata": {},
+   "source": [
+    "### The \"Swamping\" Effect\n",
+    "\n",
+    "Add a **dense** cluster of anomalies (instead of scattered noise) to the data and see if k-Means can detect them.\n",
+    "\n",
+    "-   **Hint**: k-Means might just think the dense anomaly cluster is a valid new cluster! This is a common failure mode."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce272ed7",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **k-Means**: Simple, but requires setting a manual distance threshold. Fails if anomalies form clusters.\n",
+    "2.  **DBSCAN**: Excellent for spatial data. Automatically finds \"Noise\" based on low density.\n",
+    "3.  **Isolation Forest**: Powerful, modern standard for higher dimensional data."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 78c18522cab70c0c96ff563d9d470d2a6fd73c55 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Wed, 4 Feb 2026 19:43:06 +0100
Subject: [PATCH 51/81] S02C07

---
 ...erfitting, and Hyperparameter Tuning.ipynb | 67 +++----------------
 1 file changed, 8 insertions(+), 59 deletions(-)

diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C07 Overfitting, Underfitting, and Hyperparameter Tuning.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C07 Overfitting, Underfitting, and Hyperparameter Tuning.ipynb
index 5a19453..b14a1ee 100644
--- a/src/S02 Supervised Learning - Regression and Evaluation/S02C07 Overfitting, Underfitting, and Hyperparameter Tuning.ipynb	
+++ b/src/S02 Supervised Learning - Regression and Evaluation/S02C07 Overfitting, Underfitting, and Hyperparameter Tuning.ipynb	
@@ -55,23 +55,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "08023d0c",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -110,21 +99,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "fecf5000",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.pipeline import make_pipeline\n",
     "from sklearn.preprocessing import PolynomialFeatures\n",
@@ -181,21 +159,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "3511227a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.model_selection import learning_curve\n",
     "\n",
@@ -265,30 +232,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "2f0f4cdf",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best Parameters: {'polynomialfeatures__degree': 10, 'ridge__alpha': 0.01}\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.model_selection import GridSearchCV\n",
     "from sklearn.linear_model import Ridge\n",

From 4d7294f9abd44ddb2ec185e4bcc48f97e3e04bf7 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Wed, 4 Feb 2026 19:43:17 +0100
Subject: [PATCH 52/81] S03 Supervised Learning: Classification and Evaluation

---
 ...arning - Classification and Evaluation.pdf | Bin 0 -> 376312 bytes
 ...arning - Classification and Evaluation.tex | 465 ++++++++++++++++++
 2 files changed, 465 insertions(+)
 create mode 100644 src/S03 Supervised Learning - Classification and Evaluation/S03 Supervised Learning - Classification and Evaluation.pdf
 create mode 100644 src/S03 Supervised Learning - Classification and Evaluation/S03 Supervised Learning - Classification and Evaluation.tex

diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03 Supervised Learning - Classification and Evaluation.pdf b/src/S03 Supervised Learning - Classification and Evaluation/S03 Supervised Learning - Classification and Evaluation.pdf
new file mode 100644
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diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03 Supervised Learning - Classification and Evaluation.tex b/src/S03 Supervised Learning - Classification and Evaluation/S03 Supervised Learning - Classification and Evaluation.tex
new file mode 100644
index 0000000..37439d6
--- /dev/null
+++ b/src/S03 Supervised Learning - Classification and Evaluation/S03 Supervised Learning - Classification and Evaluation.tex	
@@ -0,0 +1,465 @@
+% Created 2026-02-04 Wed 19:40
+% Intended LaTeX compiler: pdflatex
+\documentclass[hyperref={pdfpagelabels=false},aspectratio=169]{beamer}
+\usepackage[utf8]{inputenc}
+\usepackage[T1]{fontenc}
+\usepackage{graphicx}
+\usepackage{longtable}
+\usepackage{wrapfig}
+\usepackage{rotating}
+\usepackage[normalem]{ulem}
+\usepackage{amsmath}
+\usepackage{amssymb}
+\usepackage{capt-of}
+\usepackage{hyperref}
+\usetheme{Madrid}
+\usecolortheme{beaver}
+\useinnertheme{rounded}
+\useoutertheme{tree}
+\author{Cristian A. Marocico}
+\date{\today}
+\title{Machine Learning in Python}
+\subtitle{Supervised Learning - Classification and Metrics}
+\author[Marocico, Tatar]{Cristian A. Marocico, A. Emin Tatar}
+\institute[CIT]{Center for Information Technology\\University of Groningen}
+\RequirePackage{pgfcore}
+\usepackage{amsfonts}
+\usepackage{amsmath}
+\usepackage{amssymb}
+\usepackage{blkarray}
+\usepackage{eurosym}
+\usepackage[english]{babel}
+\usepackage{xcolor,colortbl}
+\usepackage[utf8]{inputenc}
+\usepackage[OT1]{fontenc}
+\usepackage{multirow}
+\usepackage{listings}
+\usepackage{fancyvrb}
+\usepackage{tikz}
+\usepackage{graphicx}
+\usepackage[makeroom]{cancel}
+\usepackage{bbm}
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+\newcommand{\givenTitle}{Info}
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+\begin{block}{\givenTitle}}{
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+\DeclareMathOperator*{\argmax}{argmax}
+\setbeamercovered{transparent}
+\usecolortheme{beaver}
+\RequirePackage{pgfcore}
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+\mode<presentation>{
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+\definecolor{rugcolor}{rgb}{0.8,0,0}
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+\date[Feb 10\textsuperscript{th} 2026]{Tuesday, February 10\textsuperscript{th} 2026}
+\usepackage{mathtools}
+\newcommand{\intsum}{\mathop{\mathrlap{\raisebox{0.1ex}{\hspace{0.2em}$\textstyle\sum$}}\int}\limits}
+\setbeamercovered{transparent=0}
+\usepackage[timeinterval=60]{tdclock}
+\hypersetup{
+ pdfauthor={Cristian A. Marocico},
+ pdftitle={Machine Learning in Python},
+ pdfkeywords={},
+ pdfsubject={},
+ pdfcreator={Emacs 30.2 (Org mode 9.7.11)}, 
+ pdflang={English}}
+\begin{document}
+
+\maketitle
+\begin{frame}{Outline}
+\setcounter{tocdepth}{1}
+\tableofcontents
+\end{frame}
+
+\section{Introduction to Classification}
+\label{sec:orgd9633ab}
+\begin{frame}[label={sec:orga6fc109}]{Introduction to Classification}
+\begin{definition}[Classification]\label{sec:orgc9ef722}
+\pause
+\alert{Classification} is a type of supervised learning where the model learns from labeled data to predict the class of new observations based on past data.
+\pause
+
+\alert{Classification vs. Regression} is a key distinction in supervised learning:
+\begin{itemize}[<+->]
+\item In \alert{classification}, the target variable is categorical (e.g., "spam" or "not spam").
+\item In \alert{regression}, the target variable is continuous (e.g., predicting a price).
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org38505ef}]{Classification Types}
+\begin{definition}[Classification Types]\label{sec:org4a38c73}
+\pause
+\alert{Classification} can be broadly divided into two types:
+\begin{itemize}[<+->]
+\item \alert{Binary Classification}: The target variable has two classes (e.g., "yes" or "no", "spam" or "not spam"). Numerically, this can always be represented as 0 and 1.
+\item \alert{Multiclass Classification}: The target variable has more than two classes (e.g., "cat", "dog", "weasel"). In this case, the model predicts one of several possible categories.
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgde053fd}]{Classification Algorithms}
+\begin{definition}[Classification Algorithms]\label{sec:org51b9712}
+\pause
+\alert{Classification algorithms} are designed to learn from labeled data and make predictions about the class of new, unseen data. Some common algorithms include:
+\begin{itemize}[<+->]
+\item \alert{Logistic Regression}: Despite its name, it is used for binary classification. It models the probability that a given input belongs to a particular class.
+\item \alert{k-Nearest Neighbors (k-NN)}: A non-parametric method that classifies a data point based on the classes of its nearest neighbors in the feature space.
+\item \alert{Decision Trees}: A tree-like model that splits the data into subsets based on feature values, leading to a decision about the class label.
+\item \alert{Support Vector Machines (SVM)}: A method that finds the hyperplane that best separates the classes in the feature space.
+\item More advanced algorithms like \alert{Random Forests}, \alert{Gradient Boosting}, and \alert{Neural Networks}.
+\end{itemize}
+\end{definition}
+\end{frame}
+\section{Classification Basics}
+\label{sec:org1299dfb}
+\begin{frame}[label={sec:org5e16198}]{Logistic Regression}
+\begin{definition}[Logistic Regression]\label{sec:org28f7f82}
+\pause
+\alert{Logistic Regression} models the probability that the target variable \(y\) belongs to a particular class. The \alert{logistic function (sigmoid)} is used to map predicted values to probabilities between 0 and 1. The decision boundary is determined by the threshold (commonly 0.5) for classifying observations into different classes.
+\pause
+
+The sigmoid function is defined as:
+$$\sigma(z) = \frac{1}{1 + e^{-z}}$$
+where \(z\) is a linear combination of the input features.
+\end{definition}
+\end{frame}
+\section{Evaluation Metrics}
+\label{sec:org5e195ec}
+\begin{frame}[label={sec:org79c547f}]{Evaluation Metrics for Classification}
+\begin{definition}[Confusion Matrix]\label{sec:org2ce2ee1}
+\pause
+The \alert{confusion matrix} summarizes the performance of a classification algorithm by comparing predicted and actual labels.
+\pause
+\begin{center}
+\begin{tabular}{lll}
+ & \alert{Predicted = 1} & \alert{Predicted = 0}\\
+\hline
+\alert{Actual = 1} & True Positive (TP) & False Negative (FN)\\
+\alert{Actual = 0} & False Positive (FP) & True Negative (TN)\\
+\end{tabular}
+\end{center}
+From these four numbers we obtain the core metrics summarized next.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org5a4fff9}]{Accuracy (Overall Success Rate)}
+\begin{definition}[Accuracy (Overall Success Rate)]\label{sec:org4a60261}
+\alert{Accuracy} is a measure of how often the classifier is correct across all predictions. It answers the question: "What fraction of \alert{all} predictions are correct?"
+$$\text{Accuracy} = \frac{\text{TP} + \text{TN}}{\text{TP} + \text{FP} + \text{FN} + \text{TN}}$$
+
+Despite its simplicity, accuracy can be misleading, especially in imbalanced datasets where one class dominates.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org587f3d2}]{Precision (Positive Predictive Value)}
+\begin{definition}[Precision (Positive Predictive Value)]\label{sec:orgc7da7af}
+\alert{Precision} is a measure of the accuracy of positive predictions. It answers the question: "When the classifier predicts 1, how often is it correct?"
+$$\text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}}$$
+
+High precision indicates that when the model predicts a positive class, it is likely correct, but it does not account for false negatives.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgd5b10a2}]{Recall (Sensitivity, True-Positive Rate)}
+\begin{definition}[Recall (Sensitivity, True-Positive Rate)]\label{sec:org64f96d9}
+\alert{Recall} is a measure of the model's ability to capture all positive instances. It answers the question: "Of all the actual 1 cases, how many did we catch?"
+$$\text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}}$$
+High recall indicates that the model is good at identifying positive cases, but it does not consider false positives.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgd4d5710}]{F1-Score}
+\begin{definition}[F1-Score]\label{sec:orga5ba869}
+\alert{F1-Score} is the harmonic mean of precision and recall, providing a balance between the two metrics:
+$$F_1 = 2 \cdot \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}}$$
+It is particularly useful when the class distribution is imbalanced, as it considers both false positives and false negatives.
+High F1-Score indicates a good balance between precision and recall, while a low F1-Score suggests that either precision or recall (or both) are low.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org9b08fde}]{Precision-Recall Trade-off}
+\begin{definition}[Precision-Recall Trade-off]\label{sec:orgd8d2cee}
+The \alert{Precision-Recall Trade-off} illustrates the inverse relationship between precision and recall:
+\pause
+\begin{itemize}[<+->]
+\item Increasing the threshold for classifying a positive instance will \textcolor{darkgreen}{increase precision} but \textcolor{darkred}{decrease recall}.
+\item Decreasing the threshold will \textcolor{darkgreen}{increase recall} but \textcolor{darkred}{decrease precision}.
+\end{itemize}
+\end{definition}
+\begin{definition}[Precision-Recall Curve]\label{sec:orgab37166}
+\pause
+\alert{Precision-Recall Curve} is a graphical representation of the trade-off between precision and recall for different threshold values.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org9bfe5f2}]{Receiver Operating Characteristic (ROC) Curve}
+\begin{definition}[Receiver Operating Characteristic (ROC) Curve]\label{sec:org91171ab}
+\alert{ROC Curve} is a graphical representation of a classifier's performance across different thresholds. It plots the true positive rate (recall) against the false positive rate (FPR) at various threshold settings.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgad7ebc5}]{Area Under the Curve (AUC)}
+\begin{definition}[Area Under the Curve (AUC)]\label{sec:org2325939}
+\pause
+\alert{Area Under the Curve (AUC)} is a single scalar value that summarizes the performance of a classifier across all possible thresholds. It is calculated as the area under the ROC curve. AUC provides an aggregate measure of performance across all classification thresholds, making it useful for comparing different classifiers.
+\end{definition}
+\end{frame}
+\section{\(k\)-Nearest Neighbours}
+\label{sec:org7ff66de}
+\begin{frame}[label={sec:org2aa512a}]{\(k\)-Nearest Neighbours (\(k\)-NN)}
+\begin{definition}[\(k\)-Nearest Neighbours (\(k\)-NN)]\label{sec:orgcf719d6}
+\pause
+\alert{\(k\)-Nearest Neighbors (\(k\)-NN)} is a \alert{non-parametric}, \alert{instance-based} learning algorithm that classifies a new observation based on the classes of its \(k\) nearest neighbors in the feature space. It does not explicitly learn a model but stores the training instances.
+\pause
+
+The algorithm works as follows:
+\begin{itemize}[<+->]
+\item Compute the distance between the new observation and every point in the training set (Euclidean, Manhattan or Minkowski distances).
+\item Select the \(k\) closest neighbors.
+\item Predict the class by majority vote among those neighbors.
+\end{itemize}
+\pause
+
+The decision boundary is implicitly defined by the \(k\) nearest points, allowing the model to capture highly non-linear class boundaries without explicit training.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org8ea0056}]{Mathematical Formulation}
+\begin{definition}[Mathematical Formulation]\label{sec:orga7e09fa}
+Given a training set \(\{(x_i, y_i)\}_{i = 1}^{n}\) where \(y_i \in \{1, \dots,C\}\), the prediction for a query point \(\hat{x}\) is:
+$$\hat{y} = \text{mode}\left(\{y_j | x_j \in N_k(\hat{x})\}\right),$$
+
+where \(N_k(\hat{x})\) denotes the set of \(k\) training points closest to \(\hat{x}\).
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgd2b8339}]{Considerations for \(k\)-NN}
+\begin{block}{Considerations for \(k\)-NN}
+\begin{itemize}[<+->]
+\item \alert{Choosing \(k\)}:
+\begin{itemize}
+\item A small \(k\) can lead to noise sensitivity (overfitting).
+\item A large \(k\) can smooth out class boundaries (underfitting).
+\item Cross-validation is often used to select the optimal \(k\).
+\end{itemize}
+\item \alert{Advantages}:
+\begin{itemize}
+\item Simple and intuitive.
+\item No training phase; all computation happens at prediction time.
+\item Naturally handles multi-class classification.
+\end{itemize}
+\item \alert{Disadvantages}:
+\begin{itemize}
+\item Computationally expensive at prediction time, especially with large datasets.
+\item Sensitive to irrelevant features and feature scaling.
+\item Requires a good choice of distance metric.
+\end{itemize}
+\end{itemize}
+\end{block}
+\end{frame}
+\section{Decision Trees}
+\label{sec:orgf28f0e6}
+\begin{frame}[label={sec:orga977ef4}]{Decision Trees}
+\begin{definition}[Decision Trees]\label{sec:org5df3a34}
+\alert{Decision Trees} recursively partition the feature space into axis-aligned regions and assign a class label to each region. It consists of \alert{nodes} (features), \alert{branches} (decisions), and \alert{leaves} (outcomes).
+\pause
+
+At each node, the algorithm chooses the split that maximizes a purity metric such as \alert{information gain} or \alert{Gini impurity}. The tree is built by recursively splitting the dataset into subsets until a stopping criterion is met (e.g., maximum depth, minimum samples per leaf).
+\pause
+
+By hierarchically splitting the space, a tree can approximate complex decision boundaries while remaining interpretable-each internal node corresponds to a human-readable rule.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org687cde3}]{Information Gain}
+\begin{definition}[Information Gain]\label{sec:orgab1df6e}
+For a node containing a sample set \(S\), the entropy is:
+$$H(S)= -\sum_{c = 1}^{C} p_c \log_2 p_c,$$
+where \(p_c\) is the proportion of class \(c\).
+\pause
+A split of \(S\) into subsets \(S_L, S_R\) yields information gain:
+$$IG = H(S) - \frac{|S_L|}{|S|} H(S_L) - \frac{|S_R|}{|S|} H(S_R).$$
+The algorithm selects the split with the highest \alert{IG} (or lowest Gini).
+\pause
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgc8f7495}]{Considerations for Decision Trees}
+\begin{definition}[Considerations for Decision Trees]\label{sec:orgc17aaa8}
+\begin{itemize}[<+->]
+\item \alert{Pruning}: Techniques like cost-complexity pruning can be applied to reduce overfitting by removing branches that have little importance.
+
+\item \alert{Advantages}:
+\begin{itemize}
+\item Easy to interpret and visualize.
+\item Handles both numerical and categorical data.
+\item Non-parametric: does not assume any specific distribution of the data.
+\end{itemize}
+
+\item \alert{Disadvantages}:
+\begin{itemize}
+\item Prone to overfitting, especially with deep trees.
+\item Sensitive to small changes in the data (can lead to different trees).
+\end{itemize}
+\end{itemize}
+\end{definition}
+\end{frame}
+\section{Support Vector Machines (SVM)}
+\label{sec:org6321666}
+\begin{frame}[label={sec:orga40162a}]{Support Vector Machines (SVM)}
+\begin{definition}[Support Vector Machines (SVM)]\label{sec:orge52c844}
+\alert{Support Vector Machines (SVM)} are powerful algorithms that find the optimal hyperplane that separates different classes in the feature space. The goal is to maximize the \alert{margin} between the closest points of each class, known as \alert{support vectors}.
+\pause
+The SVM algorithm works by solving the following optimization problem:
+$$\min_{\mathbf{w}, b} \frac{1}{2} ||\mathbf{w}||^2 \quad \text{s.t.} \quad y_i(\mathbf{w}^T \mathbf{x}_i + b) \geq 1, \quad \forall i,$$
+where \(\mathbf{w}\) is the weight vector, \(b\) is the bias term, and \(y_i\) is the class label of the \(i\)-th training example.
+\pause
+
+Visualization should help\ldots{}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org5ec5c47}]{Linear SVMs}
+\begin{definition}[Linear SVMs]\label{sec:orgf40ba85}
+\alert{Linear SVMs} are used when the data is linearly separable. The decision boundary is a hyperplane defined by the equation:
+$$\mathbf{w}^T \mathbf{x} + b = 0,$$
+where \(\mathbf{w}\) is the weight vector and \(b\) is the bias term. The SVM finds the hyperplane that maximizes the margin between the two classes.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgf3635ff}]{Non-Linear SVMs}
+\begin{definition}[Non-Linear SVMs]\label{sec:orge90af98}
+\alert{Non-Linear SVMs} use kernel functions to transform the input space into a higher-dimensional space where a linear hyperplane can separate the classes. Common kernels include:
+\begin{itemize}
+\item \alert{Polynomial Kernel}: Maps the input features into a polynomial feature space.
+\item \alert{Radial Basis Function (RBF) Kernel}: Maps the input features into an infinite-dimensional space, allowing for complex decision boundaries.
+\item \alert{Sigmoid Kernel}: Similar to the activation function in neural networks, it maps the input features into a space that can capture non-linear relationships.
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orga97e919}]{Kernel Trick}
+\begin{definition}[Kernel Trick]\label{sec:orga5dcc43}
+The \alert{Kernel Trick} allows SVMs to operate in high-dimensional feature spaces without explicitly computing the coordinates of the data in that space. Instead, it computes the inner products between the images of all pairs of data points in the feature space, which is computationally efficient.
+
+This is done using a kernel function \(K(\mathbf{x}_i, \mathbf{x}_j)\) that computes the inner product in the transformed space:
+$$K(\mathbf{x}_i, \mathbf{x}_j) = \phi(\mathbf{x}_i)^T \phi(\mathbf{x}_j),$$
+where \(\phi\) is the mapping function that transforms the input features into the higher-dimensional space.
+\end{definition}
+\end{frame}
+\end{document}

From 254711dd93a14217a3e55d099c2b454d300b72d2 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Wed, 4 Feb 2026 19:50:07 +0100
Subject: [PATCH 53/81] S03C01 Introduction to Classification

---
 ...03C01 Introduction to Classification.ipynb | 332 ++++++++++++++++++
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+++ b/src/S03 Supervised Learning - Classification and Evaluation/S03C01 Introduction to Classification.ipynb	
@@ -0,0 +1,332 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5675842d",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Introduction to Classification\n",
+    "\n",
+    "In the previous modules, we predicted numbers (house prices, petal lengths). That was **Regression**. Now, we shift gears to **Classification**. Here, the goal is not to predict **how much**, but **which one**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53613f08",
+   "metadata": {},
+   "source": [
+    "## What is Classification?\n",
+    "\n",
+    "Classification is a type of Supervised Learning where the target variable ($y$) is **Categorical**. The model learns to assign a label to a new observation based on its features.\n",
+    "\n",
+    "Examples:\n",
+    "\n",
+    "-   **Email**: Spam or Not Spam?\n",
+    "-   **Medical**: Benign or Malignant tumor?\n",
+    "-   **Biology**: Setosa, Versicolor, or Virginica?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf3d8997",
+   "metadata": {},
+   "source": [
+    "## Types of Classification\n",
+    "\n",
+    "We generally divide problems into two categories:\n",
+    "\n",
+    "| Type                          | Description                     | Example                                     |\n",
+    "|----------------------------- |------------------------------- |------------------------------------------- |\n",
+    "| **Binary Classification**     | Two possible classes (0 or 1).  | Is this transaction Fraud (1) or Legit (0)? |\n",
+    "| **Multiclass Classification** | Three or more possible classes. | Is this image a Cat, Dog, or Weasel?        |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb2424af",
+   "metadata": {},
+   "source": [
+    "## Algorithms Overview\n",
+    "\n",
+    "While there are many algorithms, they all strive to find a \"Decision Boundary\" that separates the classes.\n",
+    "\n",
+    "1.  **Logistic Regression**: The \"Linear Regression\" of classification. It fits a logical S-curve (Sigmoid) to separate two classes.\n",
+    "2.  **K-Nearest Neighbors (KNN)**: Looks at the closest data points to decide.\n",
+    "3.  **Decision Trees**: Asks a series of Yes/No questions to split the data.\n",
+    "4.  **Support Vector Machines (SVM)**: Finds the widest possible street (margin) between classes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c601fa5",
+   "metadata": {},
+   "source": [
+    "## Key Concept: The Decision Boundary\n",
+    "\n",
+    "The Decision Boundary is the line (or surface) that separates one class from another.\n",
+    "\n",
+    "-   On one side of the line, the model predicts Class A.\n",
+    "-   On the other side, it predicts Class B.\n",
+    "-   Points lying exactly on the line have a 50/50 probability."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "915a756b",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: Binary Classification\n",
+    "\n",
+    "We will use the **Iris Dataset**. To start simple, we will filter the data to include only **two species** (Setosa and Versicolor) to perform Binary Classification."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc11c958",
+   "metadata": {},
+   "source": [
+    "### Setup and Data Loading\n",
+    "\n",
+    "We use `load_iris` directly from scikit-learn for reliability."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "343ecb9c",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "# Load Data\n",
+    "iris = load_iris(as_frame=True)\n",
+    "df = iris.frame\n",
+    "\n",
+    "# Filter: Drop 'virginica' (class 2) to make this a Binary problem\n",
+    "df_binary = df[df['target'] != 2].copy()\n",
+    "\n",
+    "print(f\"Original Classes: {df['target'].unique()}\")\n",
+    "print(f\"Binary Classes:   {df_binary['target'].unique()}\")\n",
+    "print(f\"Shape: {df_binary.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13d7ab80",
+   "metadata": {},
+   "source": [
+    "### Visualization\n",
+    "\n",
+    "Let's see if these two classes are easy to separate. We will look at `petal length` vs `petal width`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8eeaf214",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.scatterplot(\n",
+    "    data=df_binary, \n",
+    "    x='petal length (cm)', \n",
+    "    y='petal width (cm)', \n",
+    "    hue='target', \n",
+    "    palette='viridis',\n",
+    "    s=100\n",
+    ")\n",
+    "plt.title(\"Binary Classification: Setosa (0) vs Versicolor (1)\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0937b98",
+   "metadata": {},
+   "source": [
+    "**Observation**: The two groups are clearly separated. A simple straight line could easily divide them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00d9c88d",
+   "metadata": {},
+   "source": [
+    "### Training Logistic Regression\n",
+    "\n",
+    "We will split the data and train our model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a6b27d53",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "# Define X (Features) and y (Target)\n",
+    "# We use only 2 features for easier visualization later\n",
+    "features = ['petal length (cm)', 'petal width (cm)']\n",
+    "X = df_binary[features]\n",
+    "y = df_binary['target']\n",
+    "\n",
+    "# Split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Train\n",
+    "clf = LogisticRegression()\n",
+    "clf.fit(X_train, y_train)\n",
+    "\n",
+    "# Evaluate\n",
+    "y_pred = clf.predict(X_test)\n",
+    "print(f\"Accuracy: {accuracy_score(y_test, y_pred):.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd043e45",
+   "metadata": {},
+   "source": [
+    "### Visualizing the Decision Boundary\n",
+    "\n",
+    "This is the most important visualization in classification. We will draw the \"line in the sand\" that the model has learned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1e711c3",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.inspection import DecisionBoundaryDisplay\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# 1. Draw the decision boundary (background color)\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    clf, \n",
+    "    X, \n",
+    "    response_method=\"predict\",\n",
+    "    cmap=\"coolwarm\", \n",
+    "    alpha=0.8, \n",
+    "    ax=plt.gca()\n",
+    ")\n",
+    "\n",
+    "# 2. Plot the actual data points\n",
+    "scatter = plt.scatter(\n",
+    "    X['petal length (cm)'], \n",
+    "    X['petal width (cm)'], \n",
+    "    c=y, \n",
+    "    cmap=\"coolwarm\", \n",
+    "    edgecolor=\"k\", \n",
+    "    s=50\n",
+    ")\n",
+    "\n",
+    "plt.title(\"Logistic Regression Decision Boundary (Binary)\")\n",
+    "plt.xlabel(\"Petal Length\")\n",
+    "plt.ylabel(\"Petal Width\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f90c1f7",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "Now, you will tackle the full **Multiclass Classification** problem using all three species."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4684c4d5",
+   "metadata": {},
+   "source": [
+    "### Load Full Data\n",
+    "\n",
+    "-   Reload the Iris dataset (all 3 classes).\n",
+    "-   Select `petal length` and `petal width` as features.\n",
+    "-   Split into Train/Test."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5d77b61",
+   "metadata": {},
+   "source": [
+    "### Multiclass Logistic Regression\n",
+    "\n",
+    "-   Train a `LogisticRegression` model.\n",
+    "-   **Note**: `scikit-learn` handles multiclass automatically (using a strategy called \"One-vs-Rest\" or \"Multinomial\")."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b60c871",
+   "metadata": {},
+   "source": [
+    "### Multiclass Decision Boundary\n",
+    "\n",
+    "-   Visualize the boundaries. You should see **three** colored regions instead of two."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ea3986d",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **Categorical Targets**: Classification predicts labels, not numbers.\n",
+    "2.  **Binary vs Multiclass**: Binary = Yes/No. Multiclass = A, B, or C.\n",
+    "3.  **Decision Boundary**: The line drawn by the algorithm to separate the classes.\n",
+    "4.  **Logistic Regression**: A simple but powerful linear classifier."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 5a0ab993fc9a4a5e8ed7eba898246faf1f0db199 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Wed, 4 Feb 2026 19:55:40 +0100
Subject: [PATCH 54/81] S03C02 Classification Basics

---
 .../S03C02 Classification Basics.ipynb        | 302 ++++++++++++++++++
 1 file changed, 302 insertions(+)
 create mode 100644 src/S03 Supervised Learning - Classification and Evaluation/S03C02 Classification Basics.ipynb

diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03C02 Classification Basics.ipynb b/src/S03 Supervised Learning - Classification and Evaluation/S03C02 Classification Basics.ipynb
new file mode 100644
index 0000000..e12ab66
--- /dev/null
+++ b/src/S03 Supervised Learning - Classification and Evaluation/S03C02 Classification Basics.ipynb	
@@ -0,0 +1,302 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c2221493",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Classification Basics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbf59d65",
+   "metadata": {},
+   "source": [
+    "## Logistic Regression\n",
+    "\n",
+    "Despite its name, **Logistic Regression** is a classification algorithm, not a regression one. It is used to estimate the probability that an instance belongs to a specific class (e.g., \"What is the probability this email is spam?\")."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62ee5f30",
+   "metadata": {},
+   "source": [
+    "### Why not Linear Regression?\n",
+    "\n",
+    "If we used Linear Regression to predict probabilities, we would run into a problem: a line extends to infinity in both directions. It could predict probabilities of -5 or 1.2, which makes no sense.\n",
+    "\n",
+    "To fix this, we need a function that \"squashes\" any number into the range $[0, 1]$. That function is the **Sigmoid** (or Logistic) function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "078738e9",
+   "metadata": {},
+   "source": [
+    "### The Sigmoid Function\n",
+    "\n",
+    "The Sigmoid function $\\sigma(z)$ takes any real number $z$ and outputs a value between 0 and 1.\n",
+    "\n",
+    "$$\\sigma(z) = \\frac{1}{1 + e^{-z}}$$\n",
+    "\n",
+    "-   If $z$ is a large positive number, $\\sigma(z) \\approx 1$.\n",
+    "-   If $z$ is a large negative number, $\\sigma(z) \\approx 0$.\n",
+    "-   If $z = 0$, $\\sigma(z) = 0.5$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a158a5b",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "# Generate a range of values\n",
+    "z = np.linspace(-10, 10, 100)\n",
+    "sigmoid = 1 / (1 + np.exp(-z))\n",
+    "\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "plt.plot(z, sigmoid, color='navy', linewidth=3)\n",
+    "plt.title(\"The Sigmoid Function\")\n",
+    "plt.xlabel(\"Input (z)\")\n",
+    "plt.ylabel(\"Probability σ(z)\")\n",
+    "\n",
+    "# Add guide lines\n",
+    "plt.axhline(0.5, color='red', linestyle='--', label='Threshold (0.5)')\n",
+    "plt.axhline(0.0, color='grey', linestyle=':', alpha=0.5)\n",
+    "plt.axhline(1.0, color='grey', linestyle=':', alpha=0.5)\n",
+    "plt.axvline(0, color='green', linestyle='--', label='Decision Boundary (z=0)')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97dc48c6",
+   "metadata": {},
+   "source": [
+    "### Mathematical Formulation\n",
+    "\n",
+    "In Logistic Regression, the input to the sigmoid is just our standard linear equation: $$z = \\beta_0 + \\beta_1 x_1 + \\dots + \\beta_n x_n$$ $$P(y=1) = \\sigma(z) = \\frac{1}{1 + e^{-(\\beta_0 + \\beta_1 x_1 + \\dots)}}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8faa20e6",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: 1D Classification\n",
+    "\n",
+    "To visualize exactly how the \"S-curve\" fits data, we will start with a dataset that has only **1 Feature**. Imagine predicting if a tumor is malignant (1) or benign (0) based solely on its Size."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b15094e",
+   "metadata": {},
+   "source": [
+    "### Generate Synthetic Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee8cfa96",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import make_classification\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Generate 1 feature, 2 classes\n",
+    "X, y = make_classification(\n",
+    "    n_samples=200, \n",
+    "    n_features=1, \n",
+    "    n_informative=1, \n",
+    "    n_redundant=0, \n",
+    "    n_clusters_per_class=1, \n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "# DataFrame for convenience\n",
+    "df = pd.DataFrame(X, columns=['Feature_Size'])\n",
+    "df['Target_Class'] = y\n",
+    "\n",
+    "# Visualize raw data\n",
+    "plt.figure(figsize=(8, 4))\n",
+    "sns.scatterplot(data=df, x='Feature_Size', y='Target_Class', hue='Target_Class', palette='Set1', s=50)\n",
+    "plt.title(\"Tumor Size vs Malignancy (0 or 1)\")\n",
+    "plt.yticks([0, 1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8e749dc",
+   "metadata": {},
+   "source": [
+    "### Train Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de5e3f85",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(df[['Feature_Size']], df['Target_Class'], test_size=0.2, random_state=42)\n",
+    "\n",
+    "clf = LogisticRegression()\n",
+    "clf.fit(X_train, y_train)\n",
+    "\n",
+    "print(f\"Intercept: {clf.intercept_[0]:.2f}\")\n",
+    "print(f\"Coefficient: {clf.coef_[0][0]:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "520083e6",
+   "metadata": {},
+   "source": [
+    "### Visualizing the Probability Curve\n",
+    "\n",
+    "We will plot the predicted probabilities ($\\sigma(z)$) over the data. This shows us the S-curve fitting the points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a893d11",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "# Generate a smooth range of feature values\n",
+    "X_range = np.linspace(X.min(), X.max(), 300).reshape(-1, 1)\n",
+    "\n",
+    "# Predict PROBABILITIES (not just class 0 or 1)\n",
+    "y_prob = clf.predict_proba(X_range)[:, 1] # Probability of class 1\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Plot scatter of test data\n",
+    "sns.scatterplot(x=X_test['Feature_Size'], y=y_test, hue=y_test, palette='Set1', s=50, edgecolor='k')\n",
+    "\n",
+    "# Plot Sigmoid Curve\n",
+    "plt.plot(X_range, y_prob, color='blue', linewidth=3, label='Logistic Model')\n",
+    "\n",
+    "# Plot Threshold\n",
+    "plt.axhline(0.5, color='red', linestyle='--', label='Decision Threshold (0.5)')\n",
+    "\n",
+    "# Find Decision Boundary (Where Probability = 0.5)\n",
+    "# This corresponds to z = 0\n",
+    "boundary_x = X_range[np.abs(y_prob - 0.5).argmin()][0]\n",
+    "plt.axvline(boundary_x, color='green', linestyle=':', linewidth=2, label=f'Boundary (x={boundary_x:.2f})')\n",
+    "\n",
+    "plt.title(\"Logistic Regression Fit\")\n",
+    "plt.xlabel(\"Feature (Size)\")\n",
+    "plt.ylabel(\"Probability of Malignancy\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "093b6164",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "Now we move to **2 Dimensions**. Logistic Regression works the same way, but the decision boundary becomes a **Line** instead of a single point."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c984193",
+   "metadata": {},
+   "source": [
+    "### Generate 2D Data\n",
+    "\n",
+    "Create a dataset with 2 informative features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01477448",
+   "metadata": {},
+   "source": [
+    "### Train & Evaluate\n",
+    "\n",
+    "Train a Logistic Regression model on this new data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "390da85b",
+   "metadata": {},
+   "source": [
+    "### Visualize the Linear Boundary\n",
+    "\n",
+    "Use `DecisionBoundaryDisplay` to see the \"Line\" that separates the two classes.\n",
+    "\n",
+    "-   Notice that even though the probability function is curved (Sigmoid), the **cut-off line** (where `prob = 0.5`) is straight. That is why Logistic Regression is a **Linear Classifier**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e73f6320",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **Squashing**: Logistic Regression uses the **Sigmoid** function to squash linear outputs into probabilities (0 to 1).\n",
+    "2.  **Thresholding**: We typically cut off at **0.5** to decide between Class 0 and Class 1.\n",
+    "3.  **Linearity**: It draws a straight line (or plane) to separate classes. It cannot handle circles or moon-shapes (unless we add polynomial features!)."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From cffa9df8c0ee7bb799437a154cb0e1f20d90d9aa Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Wed, 4 Feb 2026 20:03:28 +0100
Subject: [PATCH 55/81] S03C03 Evaluation Metrics

---
 .../S03C03 Evaluation Metrics.ipynb           | 355 ++++++++++++++++++
 1 file changed, 355 insertions(+)
 create mode 100644 src/S03 Supervised Learning - Classification and Evaluation/S03C03 Evaluation Metrics.ipynb

diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03C03 Evaluation Metrics.ipynb b/src/S03 Supervised Learning - Classification and Evaluation/S03C03 Evaluation Metrics.ipynb
new file mode 100644
index 0000000..bd0d4ae
--- /dev/null
+++ b/src/S03 Supervised Learning - Classification and Evaluation/S03C03 Evaluation Metrics.ipynb	
@@ -0,0 +1,355 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2144b251",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Evaluation Metrics for Classification\n",
+    "\n",
+    "In classification, \"Accuracy\" is rarely enough. A model that predicts \"No Fire\" 99.9% of the time is highly accurate but useless as a fire alarm. To truly understand performance, we need finer metrics."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2947337",
+   "metadata": {},
+   "source": [
+    "## The Confusion Matrix\n",
+    "\n",
+    "Everything starts here. It breaks down predictions into 4 buckets:\n",
+    "\n",
+    "|                         | **Predicted Negative (0)**          | **Predicted Positive (1)**         |\n",
+    "|----------------------- |----------------------------------- |---------------------------------- |\n",
+    "| **Actual Negative (0)** | **True Negative (TN)**              | False Positive (FP) (Type I Error) |\n",
+    "| **Actual Positive (1)** | False Negative (FN) (Type II Error) | **True Positive (TP)**             |\n",
+    "|                         |                                     |                                    |\n",
+    "\n",
+    "-   **TN**: Correctly said \"No\". (Safe)\n",
+    "-   **TP**: Correctly said \"Yes\". (Success)\n",
+    "-   **FP**: Cried Wolf. (False Alarm)\n",
+    "-   **FN**: Missed the danger. (Miss)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20a7fc7c",
+   "metadata": {},
+   "source": [
+    "## Core Metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eadadf95",
+   "metadata": {},
+   "source": [
+    "### 1. Accuracy\n",
+    "\n",
+    "\"Overall, how often are we right?\" $$\\text{Accuracy} = \\frac{\\text{TP} + \\text{TN}}{\\text{Total}}$$\n",
+    "\n",
+    "-   **Pitfall**: If 95% of data is Class 0, a model that **always** predicts 0 has 95% accuracy but 0% intelligence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1462a047",
+   "metadata": {},
+   "source": [
+    "### 2. Precision (Quality)\n",
+    "\n",
+    "\"When the model claims it's Positive, is it actually Positive?\" $$\\text{Precision} = \\frac{\\text{TP}}{\\text{TP} + \\text{FP}}$$\n",
+    "\n",
+    "-   **Use Case**: **Spam Filter**. We don't want to accidentally mark a real email as Spam (FP). We prefer to miss some spam (FN) than lose an important email."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf863f93",
+   "metadata": {},
+   "source": [
+    "### 3. Recall (Sensitivity/Quantity)\n",
+    "\n",
+    "\"Did we find all the Positives?\" $$\\text{Recall} = \\frac{\\text{TP}}{\\text{TP} + \\text{FN}}$$\n",
+    "\n",
+    "-   **Use Case**: **Cancer Detection**. We cannot afford to miss a sick patient (FN). False alarms (FP) are acceptable if it saves lives."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37ecda44",
+   "metadata": {},
+   "source": [
+    "### 4. F1-Score (Balance)\n",
+    "\n",
+    "The Harmonic Mean of Precision and Recall. $$F_1 = 2 \\cdot \\frac{\\text{Precision} \\times \\text{Recall}}{\\text{Precision} + \\text{Recall}}$$\n",
+    "\n",
+    "-   **Use Case**: When you need a balance and cannot simply maximize one."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d889f605",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: Imbalanced Data\n",
+    "\n",
+    "We will generate a dataset where **90%** of the data is Class 0. This mimics Fraud Detection."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3444c579",
+   "metadata": {},
+   "source": [
+    "### Generate Imbalanced Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0442488f",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import make_classification\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# Generate 1000 samples, but only 10% are Positive (weights=[0.9, 0.1])\n",
+    "X, y = make_classification(\n",
+    "    n_samples=1000, \n",
+    "    n_features=5, \n",
+    "    n_informative=3,\n",
+    "    n_redundant=0, \n",
+    "    weights=[0.9, 0.1], \n",
+    "    flip_y=0, \n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "# Check imbalance\n",
+    "print(f\"Class 0 count: {sum(y==0)}\")\n",
+    "print(f\"Class 1 count: {sum(y==1)}\")\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56da85d9",
+   "metadata": {},
+   "source": [
+    "### The \"Lazy\" Accuracy Trap\n",
+    "\n",
+    "Let's train a standard Logistic Regression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "890eafe9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
+    "\n",
+    "clf = LogisticRegression()\n",
+    "clf.fit(X_train, y_train)\n",
+    "y_pred = clf.predict(X_test)\n",
+    "\n",
+    "print(f\"Accuracy: {accuracy_score(y_test, y_pred):.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8e4a534",
+   "metadata": {},
+   "source": [
+    "**Observation**: Accuracy looks amazing (likely >90%). But is it good?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b21de91",
+   "metadata": {},
+   "source": [
+    "### The Truth (Confusion Matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "170e5e33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import ConfusionMatrixDisplay\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "cm = confusion_matrix(y_test, y_pred)\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
+    "\n",
+    "disp.plot(cmap='Blues')\n",
+    "plt.title(\"Confusion Matrix\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Detailed Report\n",
+    "print(classification_report(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f299274",
+   "metadata": {},
+   "source": [
+    "**Observation**: Look at the Recall for Class 1. It is likely low. The model is ignoring the \"Fraud\" cases to maximize accuracy on the \"Normal\" cases."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "faadf15d",
+   "metadata": {},
+   "source": [
+    "## Threshold Tuning (Precision-Recall Tradeoff)\n",
+    "\n",
+    "By default, `predict()` uses a threshold of 0.5. If probability > 0.5, predict 1. We can move this threshold to capture more Positives (higher Recall) at the cost of more False Alarms (lower Precision)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cc7cb76",
+   "metadata": {},
+   "source": [
+    "### Precision-Recall Curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01d85e6f",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import PrecisionRecallDisplay\n",
+    "\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "display = PrecisionRecallDisplay.from_estimator(clf, X_test, y_test)\n",
+    "plt.title(\"Precision-Recall Curve\")\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c699c8bd",
+   "metadata": {},
+   "source": [
+    "## ROC Curve and AUC\n",
+    "\n",
+    "The **ROC (Receiver Operating Characteristic)** curve plots Recall (True Positive Rate) against the False Positive Rate.\n",
+    "\n",
+    "-   **AUC (Area Under Curve)** summarizes this into one number.\n",
+    "-   AUC = 0.5 (Random Guessing).\n",
+    "-   AUC = 1.0 (Perfect)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "975ba1b4",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import RocCurveDisplay, roc_auc_score\n",
+    "\n",
+    "auc = roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1])\n",
+    "print(f\"AUC Score: {auc:.4f}\")\n",
+    "\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "RocCurveDisplay.from_estimator(clf, X_test, y_test)\n",
+    "plt.plot([0, 1], [0, 1], \"k--\", label=\"Random Chance\")\n",
+    "plt.title(\"ROC Curve\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "948ae39b",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77996463",
+   "metadata": {},
+   "source": [
+    "### Threshold Moving\n",
+    "\n",
+    "Manually change the decision threshold to `0.2` (making the model more sensitive).\n",
+    "\n",
+    "-   Calculate Recall and Precision at this new threshold.\n",
+    "\n",
+    "**Result**: Recall should go UP, Precision should go DOWN."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1836861",
+   "metadata": {},
+   "source": [
+    "### Multiclass Evaluation\n",
+    "\n",
+    "Load the Iris dataset. Print the `classification_report`.\n",
+    "\n",
+    "-   Note the difference between `macro avg` (treats all classes equally) and `weighted avg` (weights by class size)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66324a76",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **Context is King**: Are False Positives worse (Spam), or False Negatives (Cancer)?\n",
+    "2.  **Imbalance Kills Accuracy**: Always check the Confusion Matrix on imbalanced data.\n",
+    "3.  **Thresholds**: You are not stuck with 0.5. You can tune the threshold to suit your business needs using the PR-Curve."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 5bec81c763a56693fd9cd118b261ed9a7f8531f9 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Wed, 4 Feb 2026 20:18:38 +0100
Subject: [PATCH 56/81] S03C04 k-NNs and Decision Trees

---
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 1 file changed, 507 insertions(+)
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diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03C04 k-NNs and Decision Trees.ipynb b/src/S03 Supervised Learning - Classification and Evaluation/S03C04 k-NNs and Decision Trees.ipynb
new file mode 100644
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@@ -0,0 +1,507 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "dedfee1b",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# k-NN & Decision Trees Classification\n",
+    "\n",
+    "While Logistic Regression is powerful, it is limited to drawing straight lines (linear boundaries). Real-world data is often messy and non-linear.\n",
+    "\n",
+    "In this module, we explore two algorithms that can model complex shapes:\n",
+    "\n",
+    "1.  **k-Nearest Neighbors (k-NN)**: The \"Birds of a feather flock together\" algorithm.\n",
+    "2.  **Decision Trees**: The \"20 Questions\" algorithm."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a5ee6b4",
+   "metadata": {},
+   "source": [
+    "## k-Nearest Neighbors (k-NN)\n",
+    "\n",
+    "k-Nearest Neighbors is a non-parametric, **instance-based** learning algorithm which does not explicitly learn a model but stores the training instances. It is arguably the simplest machine learning algorithm - it memorizes the training data.\n",
+    "\n",
+    "To predict the class of a new point $\\hat{x}$:\n",
+    "\n",
+    "1.  Compute the distance between $\\hat{x}$ and every point in the training set.\n",
+    "2.  Select the $k$ closest neighbors.\n",
+    "3.  Predict the class by majority vote among those neighbors.\n",
+    "\n",
+    "The decision boundary is implicitly defined by the $k$ nearest points; therefore the model can capture highly non-linear class boundaries without explicit training."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "289419c5",
+   "metadata": {},
+   "source": [
+    "### Key Concept: Distance\n",
+    "\n",
+    "\"Closeness\" is usually measured by **Euclidean Distance** (straight line): $$d(p, q) = \\sqrt{(q_1-p_1)^2 + (q_2-p_2)^2 + \\dots}$$\n",
+    "\n",
+    "Other distance metrics include Manhattan and Minkowski distances.\n",
+    "\n",
+    "**Critical Note**: Because it calculates distance, k-NN is highly sensitive to the scale of data. You **must** scale your features (e.g., using `StandardScaler`) before using k-NN."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8c83887",
+   "metadata": {},
+   "source": [
+    "### Mathematical Formulation\n",
+    "\n",
+    "Given a training set $\\{(x_i, y_i)\\}_{i = 1}^{n}$ where $y_i \\in \\{1, \\dots,C\\}$, the prediction for a query point $\\hat{x}$ is: $$\\hat{y} = \\text{mode}\\left(\\{y_j | x_j \\in N_k(\\hat{x})\\}\\right),$$\n",
+    "\n",
+    "where $N_k(\\hat{x})$ denotes the set of $k$ training points closest to $\\hat{x}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c37e59a2",
+   "metadata": {},
+   "source": [
+    "### The Hyperparameter $k$\n",
+    "\n",
+    "-   **Small $k$ (e.g., 1)**: The model is erratic and sensitive to noise (Overfitting). The boundary looks like a jagged coastline.\n",
+    "-   **Large $k$ (e.g., 100)**: The model is too smooth and ignores local patterns (Underfitting).\n",
+    "-   Cross-validation is often used to select the optimal $k$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb37ab34",
+   "metadata": {},
+   "source": [
+    "### Advantages and Disadvantages\n",
+    "\n",
+    "-   **Advantages**:\n",
+    "    -   Simple and intuitive.\n",
+    "    -   No training phase; all computation happens at prediction time.\n",
+    "    -   Naturally handles multi-class classification.\n",
+    "-   **Disadvantages**:\n",
+    "    -   Computationally expensive at prediction time, especially with large datasets.\n",
+    "    -   Sensitive to irrelevant features and feature scaling.\n",
+    "    -   Requires a good choice of distance metric."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "191ac92a",
+   "metadata": {},
+   "source": [
+    "## Decision Trees\n",
+    "\n",
+    "A Decision Tree splits data into smaller and smaller groups by asking a series of Yes/No questions:\n",
+    "\n",
+    "-   **Is the petal length > 2.45 cm?**\n",
+    "-   **Is the petal width <= 1.75 cm?**\n",
+    "\n",
+    "It mimics human decision-making and is highly interpretable. The tree recursively partitions the feature space into axis-aligned regions and assigns a class label to each region. It consists of nodes (features), branches (decisions), and leaves (outcomes)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8477bb1",
+   "metadata": {},
+   "source": [
+    "### How Does It Learn? (Purity)\n",
+    "\n",
+    "At each node the algorithm chooses the split that maximizes a purity metric. The tree tries to create splits that result in \"Pure\" nodes (groups containing only one class). It measures purity using:\n",
+    "\n",
+    "1.  **Gini Impurity**: A measure of how often a randomly chosen element would be incorrectly labeled. (0 = Pure).\n",
+    "2.  **Entropy (Information Gain)**: A measure of disorder.\n",
+    "\n",
+    "For a node containing a sample set $S$, the entropy is: $$H(S)= -\\sum_{c = 1}^{C} p_c \\log_2 p_c,$$\n",
+    "\n",
+    "with $p_c$ the proportion of class $c$. A split of $S$ into subsets $S_L, S_R$ yields information gain: $$IG = H(S) - \\frac{|S_L|}{|S|} H(S_L) - \\frac{|S_R|}{|S|} H(S_R).$$\n",
+    "\n",
+    "The algorithm selects the split with the highest **IG** (or lowest Gini)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "482dc868",
+   "metadata": {},
+   "source": [
+    "### Advantages and Disadvantages\n",
+    "\n",
+    "-   **Advantages**:\n",
+    "    -   Easy to interpret and visualize.\n",
+    "    -   Handles both numerical and categorical data.\n",
+    "    -   Non-parametric: does not assume any specific distribution of the data.\n",
+    "    -   No feature scaling required.\n",
+    "-   **Disadvantages**:\n",
+    "    -   Prone to overfitting, especially with deep trees.\n",
+    "    -   Sensitive to small changes in the data (can lead to different trees).\n",
+    "    -   Can create biased trees if some classes dominate.\n",
+    "-   **Pruning**: Techniques like cost-complexity pruning can be applied to reduce overfitting by removing branches that have little importance. We control this with `max_depth`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73289762",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration\n",
+    "\n",
+    "We will compare k-NN and Decision Trees on the Iris dataset. We use only 2 features (`petal length` and `petal width`) so we can visualize the decision boundaries."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "739fca20",
+   "metadata": {},
+   "source": [
+    "### Setup and Data Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77f7b45e",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
+    "from sklearn.metrics import accuracy_score, classification_report\n",
+    "from sklearn.inspection import DecisionBoundaryDisplay\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Load data and keep only two features for 2-D visualisation\n",
+    "iris = datasets.load_iris()\n",
+    "X = iris.data[:, 2:4]\n",
+    "y = iris.target\n",
+    "feature_names = iris.feature_names[2:4]\n",
+    "class_names = iris.target_names\n",
+    "\n",
+    "# Train-test split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72aea3af",
+   "metadata": {},
+   "source": [
+    "### Training k-NN\n",
+    "\n",
+    "We use a Pipeline to ensure scaling happens automatically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2be8c426",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "# Create a Pipeline for k-NN containing standardisation and classifier\n",
+    "knn_pipeline = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('knn', KNeighborsClassifier(n_neighbors=5))\n",
+    "])\n",
+    "\n",
+    "# Fit k-NN\n",
+    "knn_pipeline.fit(X_train, y_train)\n",
+    "\n",
+    "# Predict and evaluate k-NN\n",
+    "y_pred_knn = knn_pipeline.predict(X_test)\n",
+    "print(\"k-NN accuracy:\", accuracy_score(y_test, y_pred_knn))\n",
+    "print(classification_report(y_test, y_pred_knn, target_names=class_names))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "459e4ca5",
+   "metadata": {},
+   "source": [
+    "### Training Decision Tree\n",
+    "\n",
+    "Decision Trees don't need scaling, but we include it for consistency. We limit depth to 3 to keep it readable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd6c8ccc",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "# Create a Pipeline for DecisionTree\n",
+    "dt_pipeline = Pipeline([\n",
+    "    ('scaler', StandardScaler()),  # Not necessary for DT, but included for consistency\n",
+    "    ('dt', DecisionTreeClassifier(max_depth=3, random_state=42))\n",
+    "])\n",
+    "\n",
+    "# Fit Decision Tree\n",
+    "dt_pipeline.fit(X_train, y_train)\n",
+    "\n",
+    "# Predict and evaluate Decision Tree\n",
+    "y_pred_dt = dt_pipeline.predict(X_test)\n",
+    "print(\"Decision Tree accuracy:\", accuracy_score(y_test, y_pred_dt))\n",
+    "print(classification_report(y_test, y_pred_dt, target_names=class_names))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6dd8cfb5",
+   "metadata": {},
+   "source": [
+    "### Visualizing Boundaries (The Face-off)\n",
+    "\n",
+    "Notice the difference in shapes:\n",
+    "\n",
+    "-   **k-NN** creates organic, curvy blobs.\n",
+    "-   **Decision Tree** creates distinct, rectangular boxes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3638ede9",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "\n",
+    "# Plot k-NN boundary\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    knn_pipeline, X, response_method='predict',\n",
+    "    cmap=plt.cm.coolwarm, alpha=0.8, ax=ax[0],\n",
+    "    xlabel=feature_names[0], ylabel=feature_names[1])\n",
+    "ax[0].scatter(X_test[:, 0], X_test[:, 1], c=y_test, edgecolor='k', s=20, cmap=plt.cm.coolwarm)\n",
+    "ax[0].set_title(\"k-NN (k=5) Boundary\")\n",
+    "\n",
+    "# Plot Decision Tree boundary\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    dt_pipeline, X, response_method='predict',\n",
+    "    cmap=plt.cm.coolwarm, alpha=0.8, ax=ax[1],\n",
+    "    xlabel=feature_names[0], ylabel=feature_names[1])\n",
+    "ax[1].scatter(X_test[:, 0], X_test[:, 1], c=y_test, edgecolor='k', s=20, cmap=plt.cm.coolwarm)\n",
+    "ax[1].set_title(\"Decision Tree (Depth=3) Boundary\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a767eb12",
+   "metadata": {},
+   "source": [
+    "### Visualizing the Tree Structure\n",
+    "\n",
+    "We can actually read the rules the tree learned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87109c69",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(12, 8))\n",
+    "plot_tree(dt_pipeline.named_steps['dt'], filled=True, feature_names=feature_names, class_names=class_names)\n",
+    "plt.title(\"Decision Tree Structure\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54fdef3f",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e165a4ad",
+   "metadata": {},
+   "source": [
+    "### The Effect of $k$\n",
+    "\n",
+    "Train k-NN models with $k=1$ and $k=50$. Visualize the boundaries.\n",
+    "\n",
+    "-   **Hypothesis**: $k=1$ will look like a fragmented puzzle (Overfitting). $k=50$ will look like simple blobs (Underfitting)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1906e38f",
+   "metadata": {},
+   "source": [
+    "### Varying $k$ Systematically\n",
+    "\n",
+    "Re-run the demo varying $k$ from 1 to 15 and plot the metrics."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30983016",
+   "metadata": {},
+   "source": [
+    "### Distance Metrics\n",
+    "\n",
+    "Try Manhattan distance (`metric`'manhattan'=) and observe how the boundary changes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3929ce22",
+   "metadata": {},
+   "source": [
+    "### Tree Pruning\n",
+    "\n",
+    "Train a Decision Tree with `max_depth=None` (Unlimited).\n",
+    "\n",
+    "1.  Check the accuracy on Train vs Test.\n",
+    "2.  Compare it to `max_depth=3`.\n",
+    "3.  **Observation**: The unlimited tree will likely have 100% Training accuracy but might perform worse on Test data.\n",
+    "\n",
+    "What, in your opinion, is the disadvantage of an unpruned tree?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "156fc52c",
+   "metadata": {},
+   "source": [
+    "### Confusion Matrices\n",
+    "\n",
+    "Implement and plot confusion matrices for both classifiers to see which mis-classifications are most common."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5904a55f",
+   "metadata": {},
+   "source": [
+    "### Real-World Data: Wine Dataset\n",
+    "\n",
+    "Load the Wine dataset and compare k-NN vs Decision Tree performance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "498a8c73",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "# Select two features for visualisation\n",
+    "X_wine_2d = X_wine[:, [5, 6]]  # total_phenols and flavanoids\n",
+    "X_train_wine_2d, X_test_wine_2d, y_train_wine, y_test_wine = \\\n",
+    "    train_test_split(X_wine_2d, y_wine, test_size=0.3, random_state=42, stratify=y_wine)\n",
+    "\n",
+    "# Build pipelines for Wine dataset\n",
+    "knn_pipeline_wine = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('knn', KNeighborsClassifier(n_neighbors=5))\n",
+    "])\n",
+    "dt_pipeline_wine = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('dt', DecisionTreeClassifier(max_depth=3, random_state=42))\n",
+    "])\n",
+    "\n",
+    "knn_pipeline_wine.fit(X_train_wine_2d, y_train_wine)\n",
+    "dt_pipeline_wine.fit(X_train_wine_2d, y_train_wine)\n",
+    "\n",
+    "print(\"k-NN (Wine) accuracy:\", accuracy_score(y_test_wine, knn_pipeline_wine.predict(X_test_wine_2d)))\n",
+    "print(\"Decision Tree (Wine) accuracy:\", accuracy_score(y_test_wine, dt_pipeline_wine.predict(X_test_wine_2d)))\n",
+    "\n",
+    "# Visualise decision boundaries\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    knn_pipeline_wine, X_wine_2d, response_method='predict',\n",
+    "    cmap=plt.cm.coolwarm, alpha=0.8,\n",
+    "    xlabel=wine.feature_names[5], ylabel=wine.feature_names[6], ax=ax[0])\n",
+    "ax[0].scatter(X_test_wine_2d[:, 0], X_test_wine_2d[:, 1], c=y_test_wine,\n",
+    "              edgecolor='k', s=20, cmap=plt.cm.coolwarm)\n",
+    "ax[0].set_title(\"k-NN Decision Boundary on Wine Dataset\")\n",
+    "\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    dt_pipeline_wine, X_wine_2d, response_method='predict',\n",
+    "    cmap=plt.cm.coolwarm, alpha=0.8,\n",
+    "    xlabel=wine.feature_names[5], ylabel=wine.feature_names[6], ax=ax[1])\n",
+    "ax[1].scatter(X_test_wine_2d[:, 0], X_test_wine_2d[:, 1], c=y_test_wine,\n",
+    "              edgecolor='k', s=20, cmap=plt.cm.coolwarm)\n",
+    "ax[1].set_title(\"Decision Tree Decision Boundary on Wine Dataset\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49440861",
+   "metadata": {},
+   "source": [
+    "### Cross-Validation Comparison\n",
+    "\n",
+    "Compare performance using 5-fold cross-validation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c9aee1b",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **k-NN**: Memorizes data. Needs **Scaling**. Good for complex patterns but slow on large data.\n",
+    "2.  **Decision Trees**: Learns rules. **Interpretable**. Good for mixed data but prone to **Overfitting**.\n",
+    "3.  **Boundaries**: k-NN makes curved boundaries; Trees make boxy boundaries."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 72ab363f9a4d06377314ed73d6d6eaa32b731d50 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Wed, 4 Feb 2026 20:29:23 +0100
Subject: [PATCH 57/81] S03C04 k-NNs and Decision Trees

---
 .../S03C04 k-NNs and Decision Trees.ipynb     | 53 -------------------
 1 file changed, 53 deletions(-)

diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03C04 k-NNs and Decision Trees.ipynb b/src/S03 Supervised Learning - Classification and Evaluation/S03C04 k-NNs and Decision Trees.ipynb
index a9598f8..0ba80f0 100644
--- a/src/S03 Supervised Learning - Classification and Evaluation/S03C04 k-NNs and Decision Trees.ipynb	
+++ b/src/S03 Supervised Learning - Classification and Evaluation/S03C04 k-NNs and Decision Trees.ipynb	
@@ -404,59 +404,6 @@
     "Load the Wine dataset and compare k-NN vs Decision Tree performance."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "498a8c73",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "# Select two features for visualisation\n",
-    "X_wine_2d = X_wine[:, [5, 6]]  # total_phenols and flavanoids\n",
-    "X_train_wine_2d, X_test_wine_2d, y_train_wine, y_test_wine = \\\n",
-    "    train_test_split(X_wine_2d, y_wine, test_size=0.3, random_state=42, stratify=y_wine)\n",
-    "\n",
-    "# Build pipelines for Wine dataset\n",
-    "knn_pipeline_wine = Pipeline([\n",
-    "    ('scaler', StandardScaler()),\n",
-    "    ('knn', KNeighborsClassifier(n_neighbors=5))\n",
-    "])\n",
-    "dt_pipeline_wine = Pipeline([\n",
-    "    ('scaler', StandardScaler()),\n",
-    "    ('dt', DecisionTreeClassifier(max_depth=3, random_state=42))\n",
-    "])\n",
-    "\n",
-    "knn_pipeline_wine.fit(X_train_wine_2d, y_train_wine)\n",
-    "dt_pipeline_wine.fit(X_train_wine_2d, y_train_wine)\n",
-    "\n",
-    "print(\"k-NN (Wine) accuracy:\", accuracy_score(y_test_wine, knn_pipeline_wine.predict(X_test_wine_2d)))\n",
-    "print(\"Decision Tree (Wine) accuracy:\", accuracy_score(y_test_wine, dt_pipeline_wine.predict(X_test_wine_2d)))\n",
-    "\n",
-    "# Visualise decision boundaries\n",
-    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
-    "\n",
-    "DecisionBoundaryDisplay.from_estimator(\n",
-    "    knn_pipeline_wine, X_wine_2d, response_method='predict',\n",
-    "    cmap=plt.cm.coolwarm, alpha=0.8,\n",
-    "    xlabel=wine.feature_names[5], ylabel=wine.feature_names[6], ax=ax[0])\n",
-    "ax[0].scatter(X_test_wine_2d[:, 0], X_test_wine_2d[:, 1], c=y_test_wine,\n",
-    "              edgecolor='k', s=20, cmap=plt.cm.coolwarm)\n",
-    "ax[0].set_title(\"k-NN Decision Boundary on Wine Dataset\")\n",
-    "\n",
-    "DecisionBoundaryDisplay.from_estimator(\n",
-    "    dt_pipeline_wine, X_wine_2d, response_method='predict',\n",
-    "    cmap=plt.cm.coolwarm, alpha=0.8,\n",
-    "    xlabel=wine.feature_names[5], ylabel=wine.feature_names[6], ax=ax[1])\n",
-    "ax[1].scatter(X_test_wine_2d[:, 0], X_test_wine_2d[:, 1], c=y_test_wine,\n",
-    "              edgecolor='k', s=20, cmap=plt.cm.coolwarm)\n",
-    "ax[1].set_title(\"Decision Tree Decision Boundary on Wine Dataset\")\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "49440861",

From b58405cba8fc10bcd21b0c08fd1e66229f535d50 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Wed, 4 Feb 2026 20:35:09 +0100
Subject: [PATCH 58/81] S03C05 Support Vector Machines

---
 .../S03C05 Support Vector Machines.ipynb      | 390 ++++++++++++++++++
 1 file changed, 390 insertions(+)
 create mode 100644 src/S03 Supervised Learning - Classification and Evaluation/S03C05 Support Vector Machines.ipynb

diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03C05 Support Vector Machines.ipynb b/src/S03 Supervised Learning - Classification and Evaluation/S03C05 Support Vector Machines.ipynb
new file mode 100644
index 0000000..2af9d79
--- /dev/null
+++ b/src/S03 Supervised Learning - Classification and Evaluation/S03C05 Support Vector Machines.ipynb	
@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f9f2da00",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Support Vector Machines (SVM)\n",
+    "\n",
+    "A Support Vector Machine (SVM) is a powerful supervised learning algorithm used for both classification and regression. Unlike other classifiers (like Logistic Regression) that just try to find **any** line to separate classes, SVM tries to find the ****best**** line."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2693b395",
+   "metadata": {},
+   "source": [
+    "## The Core Intuition: Maximizing the Margin\n",
+    "\n",
+    "Imagine you have red balls and blue balls on a table. You want to place a stick to separate them. You could place the stick anywhere in the gap.\n",
+    "\n",
+    "-   **Logistic Regression**: Might place the stick close to the red balls.\n",
+    "-   **SVM**: Places the stick exactly in the middle of the gap, maximizing the distance to the nearest balls on either side.\n",
+    "\n",
+    "This distance is called the **Margin**. The specific data points (balls) that touch the edge of the margin are called **Support Vectors**. They \"support\" or define the boundary."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5d9e277",
+   "metadata": {},
+   "source": [
+    "## Mathematical Formulation\n",
+    "\n",
+    "Given a training set $\\{(x_i, y_i)\\}_{i = 1}^{n}$ where $y_i \\in \\{-1, 1\\}$, the SVM aims to find a hyperplane defined by the equation: $$w^T x + b = 0,$$ where $w$ is the weight vector and $b$ is the bias term.\n",
+    "\n",
+    "The decision function for a new data point $x$ is: $$f(x) = \\text{sign}(w^T x + b).$$\n",
+    "\n",
+    "The goal is to maximize the margin defined as: $$\\text{Margin} = \\frac{2}{\\|w\\|}.$$\n",
+    "\n",
+    "This leads to the optimization problem: $$\\begin{align*} \\text{minimize} & \\quad \\frac{1}{2} \\|w\\|^2 \\\\ \\text{subject to} & \\quad y_i (w^T x_i + b) \\geq 1, \\quad \\forall i = 1, \\ldots, n. \\end{align*}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11a19f34",
+   "metadata": {},
+   "source": [
+    "## The Kernel Trick (Handling Non-Linearity)\n",
+    "\n",
+    "What if the data is not linearly separable (e.g., a red circle inside a blue ring)? You cannot draw a straight line to separate them.\n",
+    "\n",
+    "SVM solves this by projecting data into a **Higher Dimension**.\n",
+    "\n",
+    "-   Imagine lifting the red dots (in the center) up into the air (3D).\n",
+    "-   Now you can slide a flat sheet (Hyperplane) underneath them to separate them from the blue dots.\n",
+    "-   When you project this back to 2D, the straight sheet looks like a circle.\n",
+    "\n",
+    "This mathematical shortcut is called the **Kernel Trick**. The decision function becomes: $$f(x) = \\sum_{i=1}^{n} \\alpha_i y_i K(x_i, x) + b,$$ where $K(x_i, x)$ is the kernel function and $\\alpha_i$ are the Lagrange multipliers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bed0b5d3",
+   "metadata": {},
+   "source": [
+    "### Common Kernel Functions\n",
+    "\n",
+    "-   **Linear Kernel**: $K(x_i, x_j) = x_i^T x_j$\n",
+    "-   **Polynomial Kernel**: $K(x_i, x_j) = (x_i^T x_j + c)^d$\n",
+    "-   **Radial Basis Function (RBF) Kernel**: $K(x_i, x_j) = \\exp\\left(-\\frac{\\|x_i - x_j\\|^2}{2\\sigma^2}\\right)$\n",
+    "-   **Sigmoid Kernel**: $K(x_i, x_j) = \\tanh(\\alpha x_i^T x_j + c)$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "278185d5",
+   "metadata": {},
+   "source": [
+    "## Advantages and Disadvantages\n",
+    "\n",
+    "-   **Advantages**:\n",
+    "    -   Effective in high-dimensional spaces.\n",
+    "    -   Robust against overfitting, especially in high-dimensional datasets.\n",
+    "    -   Works well with clear margin of separation.\n",
+    "    -   Can handle non-linear decision boundaries using kernel functions.\n",
+    "-   **Disadvantages**:\n",
+    "    -   Computationally intensive, especially with large datasets.\n",
+    "    -   Requires careful tuning of hyperparameters (e.g., $C$ and kernel parameters).\n",
+    "    -   Less effective on noisy datasets with overlapping classes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84edcb12",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: Linear vs Non-Linear SVM\n",
+    "\n",
+    "We will compare a Linear SVM on simple data vs. a Kernel SVM on complex data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4fbde383",
+   "metadata": {},
+   "source": [
+    "### Linear SVM (Iris Data)\n",
+    "\n",
+    "We use the Iris dataset (2 classes, 2 features) to visualize the margin and support vectors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ee3d1f9",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn import svm, datasets\n",
+    "from sklearn.datasets import load_iris\n",
+    "from sklearn.inspection import DecisionBoundaryDisplay\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.metrics import accuracy_score, classification_report, ConfusionMatrixDisplay\n",
+    "\n",
+    "# Load Data (Binary Class)\n",
+    "data = load_iris()\n",
+    "X = data.data[:, 2:4]  # Petal length, Petal width\n",
+    "y = data.target\n",
+    "# Filter to only 2 classes\n",
+    "mask = y != 2\n",
+    "X = X[mask]\n",
+    "y = y[mask]\n",
+    "\n",
+    "# Split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
+    "\n",
+    "# Pipeline: Scale -> Linear SVM\n",
+    "# C is the regularization parameter.\n",
+    "# Large C = Hard Margin (No errors allowed, narrow margin).\n",
+    "# Small C = Soft Margin (Errors allowed, wide margin).\n",
+    "pipeline = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('svm', svm.SVC(kernel='linear', C=1.0))\n",
+    "])\n",
+    "pipeline.fit(X_train, y_train)\n",
+    "\n",
+    "# Visualize Decision Boundary\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    pipeline, X, response_method='predict', cmap=plt.cm.coolwarm, alpha=0.8\n",
+    ")\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.coolwarm, s=30, edgecolors=\"k\")\n",
+    "\n",
+    "# Highlight Support Vectors\n",
+    "support_vectors = pipeline.named_steps['svm'].support_vectors_\n",
+    "support_vectors_original = pipeline.named_steps['scaler'].inverse_transform(support_vectors)\n",
+    "plt.scatter(support_vectors_original[:, 0], support_vectors_original[:, 1], s=100,\n",
+    "            linewidth=1.5, facecolors='none', edgecolors='k', label='Support Vectors')\n",
+    "\n",
+    "plt.title(\"Linear SVM: Margin and Support Vectors\")\n",
+    "plt.xlabel('Petal Length (cm)')\n",
+    "plt.ylabel('Petal Width (cm)')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e342ed1a",
+   "metadata": {},
+   "source": [
+    "### Evaluate the Linear SVM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b1e45e43",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "y_pred = pipeline.predict(X_test)\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "print(f\"SVM Accuracy: {accuracy:.2f}\")\n",
+    "print(classification_report(y_test, y_pred, target_names=['setosa', 'versicolor']))\n",
+    "\n",
+    "ConfusionMatrixDisplay.from_estimator(pipeline, X_test, y_test,\n",
+    "                                      display_labels=['setosa', 'versicolor'],\n",
+    "                                      cmap=plt.cm.Blues)\n",
+    "plt.title('Confusion Matrix')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23452bdc",
+   "metadata": {},
+   "source": [
+    "### Non-Linear SVM (The Kernel Trick)\n",
+    "\n",
+    "We generate \"Moons\" data which is impossible to separate with a straight line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d71d7f86",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import make_moons\n",
+    "\n",
+    "# Generate Data\n",
+    "X_moon, y_moon = make_moons(n_samples=100, noise=0.15, random_state=42)\n",
+    "X_train_moons, X_test_moons, y_train_moons, y_test_moons = \\\n",
+    "    train_test_split(X_moon, y_moon, test_size=0.3, random_state=42)\n",
+    "\n",
+    "# Model: RBF Kernel (Radial Basis Function)\n",
+    "# RBF is the default and most powerful kernel for general use.\n",
+    "clf_rbf = svm.SVC(kernel='rbf', C=1.0, gamma='scale')\n",
+    "clf_rbf.fit(X_train_moons, y_train_moons)\n",
+    "\n",
+    "# Visualize\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    clf_rbf, X_moon, response_method=\"predict\", cmap=plt.cm.coolwarm, alpha=0.8\n",
+    ")\n",
+    "plt.scatter(X_moon[:, 0], X_moon[:, 1], c=y_moon, cmap=plt.cm.coolwarm, s=50, edgecolors=\"k\")\n",
+    "plt.title(\"SVM with RBF Kernel (Non-Linear Boundary)\")\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b0ee6ef",
+   "metadata": {},
+   "source": [
+    "## Support Vector Regression (SVR)\n",
+    "\n",
+    "SVMs can also be used for regression tasks. Instead of a margin separating classes, the margin tries to fit as many points as possible **inside** a tube (epsilon-tube) around the prediction line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9beda078",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.svm import SVR\n",
+    "\n",
+    "# Generate Regression Data (sine wave with noise)\n",
+    "X_reg = np.sort(5 * np.random.rand(80, 1), axis=0)\n",
+    "y_reg = np.sin(X_reg).ravel() + np.random.normal(0, 0.1, X_reg.shape[0])\n",
+    "\n",
+    "X_train_reg, X_test_reg, y_train_reg, y_test_reg = \\\n",
+    "    train_test_split(X_reg, y_reg, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Create Pipeline for SVR\n",
+    "pipeline_reg = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('svr', SVR(kernel='rbf', C=100, gamma=0.1, epsilon=0.1))\n",
+    "])\n",
+    "pipeline_reg.fit(X_train_reg, y_train_reg)\n",
+    "\n",
+    "# Evaluate\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "y_pred_reg = pipeline_reg.predict(X_test_reg)\n",
+    "print(f\"SVR Mean Squared Error: {mean_squared_error(y_test_reg, y_pred_reg):.4f}\")\n",
+    "print(f\"SVR R² Score: {r2_score(y_test_reg, y_pred_reg):.4f}\")\n",
+    "\n",
+    "# Plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(X_reg, y_reg, color='darkorange', label='Data')\n",
+    "X_plot = np.linspace(0, 5, 100)[:, np.newaxis]\n",
+    "plt.plot(X_plot, pipeline_reg.predict(X_plot), color='navy', lw=2, label='SVR Model')\n",
+    "plt.title(\"Support Vector Regression (SVR)\")\n",
+    "plt.xlabel('Feature')\n",
+    "plt.ylabel('Target')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ea97daa",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56c0dfaa",
+   "metadata": {},
+   "source": [
+    "### The Effect of C (Regularization)\n",
+    "\n",
+    "The parameter **C** controls the trade-off between a smooth boundary and classifying training points correctly.\n",
+    "\n",
+    "-   Train two SVMs on the Moons dataset: one with `C=0.1` and one with `C=1000`.\n",
+    "-   Visualize the boundaries.\n",
+    "-   **Hypothesis**: `C=0.1` will be smooth (Underfitting). `C=1000` will be wiggly (Overfitting)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd3110a2",
+   "metadata": {},
+   "source": [
+    "### Different Kernels\n",
+    "\n",
+    "Experiment with different kernel functions on the Moons dataset and compare their performance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e61a5f4",
+   "metadata": {},
+   "source": [
+    "### Hyperparameter Tuning with GridSearchCV\n",
+    "\n",
+    "Use grid search to find optimal hyperparameters for the SVM model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e2046d8",
+   "metadata": {},
+   "source": [
+    "### Advanced SVR Exercise\n",
+    "\n",
+    "Implement SVR on the following synthetic dataset: $$y = x^2 + \\cos(2\\pi x) + \\epsilon$$ where $\\epsilon$ is Gaussian noise."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9003cf0b",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **Maximum Margin**: SVM finds the widest street separating the classes.\n",
+    "2.  **Support Vectors**: Only the difficult points (on the edge of the street) matter. The rest are ignored.\n",
+    "3.  **Kernel Trick**: Allows SVM to draw circles and complex shapes by mathematically projecting data to higher dimensions.\n",
+    "4.  **C Parameter**: Controls overfitting. Small C = Smooth boundary; Large C = Complex boundary."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 9af273e020356a8d1f490d6aff113ad5a8f725a0 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Thu, 5 Feb 2026 09:52:32 +0100
Subject: [PATCH 59/81] S05 Neural Networks I

---
 ...tworks I: Foundations and Optimization.pdf | Bin 0 -> 745195 bytes
 ...tworks I: Foundations and Optimization.tex | 698 ++++++++++++++++++
 2 files changed, 698 insertions(+)
 create mode 100644 src/S05 Neural Networks I: Foundations and Optimization/S05 Neural Networks I: Foundations and Optimization.pdf
 create mode 100644 src/S05 Neural Networks I: Foundations and Optimization/S05 Neural Networks I: Foundations and Optimization.tex

diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05 Neural Networks I: Foundations and Optimization.pdf b/src/S05 Neural Networks I: Foundations and Optimization/S05 Neural Networks I: Foundations and Optimization.pdf
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diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05 Neural Networks I: Foundations and Optimization.tex b/src/S05 Neural Networks I: Foundations and Optimization/S05 Neural Networks I: Foundations and Optimization.tex
new file mode 100644
index 0000000..f6e871a
--- /dev/null
+++ b/src/S05 Neural Networks I: Foundations and Optimization/S05 Neural Networks I: Foundations and Optimization.tex	
@@ -0,0 +1,698 @@
+% Created 2026-02-04 Wed 20:55
+% Intended LaTeX compiler: pdflatex
+\documentclass[hyperref={pdfpagelabels=false},aspectratio=169]{beamer}
+\usepackage[utf8]{inputenc}
+\usepackage[T1]{fontenc}
+\usepackage{graphicx}
+\usepackage{longtable}
+\usepackage{wrapfig}
+\usepackage{rotating}
+\usepackage[normalem]{ulem}
+\usepackage{amsmath}
+\usepackage{amssymb}
+\usepackage{capt-of}
+\usepackage{hyperref}
+\usetheme{Madrid}
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+\author{Cristian A. Marocico}
+\date{\today}
+\title{Machine Learning in Python}
+\subtitle{Neural Networks I: Foundations and Optimization}
+\author[Marocico, Tatar]{Cristian A. Marocico, A. Emin Tatar}
+\institute[CIT]{Center for Information Technology\\University of Groningen}
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+\date[Feb 17\textsuperscript{th} 2026]{Tuesday, February 17\textsuperscript{th} 2026}
+\usepackage{mathtools}
+\newcommand{\intsum}{\mathop{\mathrlap{\raisebox{0.1ex}{\hspace{0.2em}$\textstyle\sum$}}\int}\limits}
+\setbeamercovered{transparent=0}
+\usepackage[timeinterval=60]{tdclock}
+\hypersetup{
+ pdfauthor={Cristian A. Marocico},
+ pdftitle={Machine Learning in Python},
+ pdfkeywords={},
+ pdfsubject={},
+ pdfcreator={Emacs 30.2 (Org mode 9.7.11)}, 
+ pdflang={English}}
+\begin{document}
+
+\maketitle
+\begin{frame}{Outline}
+\setcounter{tocdepth}{1}
+\tableofcontents
+\end{frame}
+
+\section{Introduction to Artificial Neural Networks}
+\label{sec:org04297b1}
+\begin{frame}[label={sec:orgcd8d6cb}]{What is an Artificial Neural Network?}
+\begin{definition}[Definition]\label{sec:org425637c}
+\pause
+An \alert{Artificial Neural Network (ANN)} is a computational model composed of layers of interconnected nodes ("neurons") that process information by transmitting signals to one another.
+\pause
+\end{definition}
+\begin{example}[Key Properties]\label{sec:org264a45f}
+\begin{itemize}[<+->]
+\item Foundation of \alert{Deep Learning}
+\item Can learn complex, non-linear relationships
+\item Powers image recognition, voice assistants, medical diagnosis, and more
+\end{itemize}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:orgc94818c}]{Key Components of a Neural Network}
+\begin{block}{The Building Blocks}
+\pause
+\begin{enumerate}[<+->]
+\item \alert{Neurons (Nodes)}: Basic processing units that receive inputs, perform a calculation, and produce an output
+\item \alert{Weights}: Connections between neurons that determine influence; learning = adjusting weights
+\item \alert{Layers}:
+\begin{itemize}
+\item \alert{Input Layer}: Receives raw data
+\item \alert{Hidden Layers}: Feature extraction (the "magic")
+\item \alert{Output Layer}: Final prediction
+\end{itemize}
+\item \alert{Activation Function}: Introduces non-linearity, allowing the network to learn curves
+\end{enumerate}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org30143e6}]{Biological Inspiration}
+\begin{block}{From Biology to Mathematics}
+\pause
+ANNs are loosely inspired by biological neural networks in the brain:
+\pause
+\begin{center}
+\begin{tabular}{lll}
+Biological Neuron & Artificial Neuron & Role\\
+\hline
+\alert{Dendrites} & Inputs (\(x\)) & Receives signals\\
+\alert{Synapse} & Weights (\(w\)) & Controls signal strength\\
+\alert{Cell Body} & Summation (\(\sum\)) & Aggregates signals\\
+\alert{Axon} & Activation & Transmits output\\
+\end{tabular}
+\end{center}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org984a1bc}]{The Perceptron (1958)}
+\begin{definition}[The Simplest Neural Network]\label{sec:orgee6bc15}
+\pause
+The \alert{Perceptron} is a single-layer network that takes inputs, weighs them, and makes a binary decision.
+\pause
+\end{definition}
+\begin{block}{Mathematical Formulation}
+\begin{enumerate}
+\item \alert{Weighted Sum}: \(z = w_1 x_1 + w_2 x_2 + \dots + b\)
+\item \alert{Activation}:
+$$\text{Output} = \begin{cases} 1 & \text{if } z > 0 \\ 0 & \text{if } z \le 0 \end{cases}$$
+\end{enumerate}
+\pause
+This can solve \alert{linearly separable} problems (drawing a straight line between classes).
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:orgd231bbb}]{The XOR Problem}
+\begin{block}{The Fatal Flaw of Single Perceptrons}
+\pause
+\begin{center}
+\begin{tabular}{rrrr}
+\(x_1\) & \(x_2\) & AND & XOR\\
+\hline
+0 & 0 & 0 & 0\\
+0 & 1 & 0 & 1\\
+1 & 0 & 0 & 1\\
+1 & 1 & 1 & 0\\
+\end{tabular}
+\end{center}
+\pause
+\end{block}
+\begin{example}[The Limitation]\label{sec:org5c11fa3}
+\begin{itemize}[<+->]
+\item \alert{AND Gate}: Linearly separable (one line can separate classes)
+\item \alert{XOR Gate}: NOT linearly separable (needs a curve or multiple lines)
+\item This limitation froze AI research until \alert{Multi-Layer Perceptrons (MLPs)} emerged
+\end{itemize}
+\end{example}
+\end{frame}
+\section{The Multilayer Perceptron (MLP)}
+\label{sec:orgc289329}
+\begin{frame}[label={sec:org0461d23}]{From One to Many}
+\begin{definition}[Stacking Neurons]\label{sec:orgcb28804}
+\pause
+A \alert{Multilayer Perceptron (MLP)} or \alert{Feedforward Neural Network} connects many neurons in layers to solve complex, non-linear problems.
+\pause
+\end{definition}
+\begin{block}{Architecture}
+\begin{enumerate}[<+->]
+\item \alert{Input Layer}: Receives raw feature vector \(X\) (no computation)
+\item \alert{Hidden Layers}: Perform computation and extract features; more layers = "deeper" network
+\item \alert{Output Layer}: Produces final prediction (e.g., probability of class)
+\end{enumerate}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org3583b53}]{Activation Functions}
+\begin{block}{Why Activation Functions?}
+\pause
+Without activation functions, an MLP is just a giant Linear Regression (linear + linear = linear).
+\alert{Activation functions} introduce \alert{non-linearity}, allowing networks to learn curves.
+\pause
+\end{block}
+\begin{example}[Common Activation Functions]\label{sec:orga684aa9}
+\begin{center}
+\begin{tabular}{llll}
+Function & Formula & Range & Usage\\
+\hline
+\alert{Sigmoid} & \(\frac{1}{1+e^{-z}}\) & \((0, 1)\) & Output (Binary Class.)\\
+\alert{Tanh} & \(\frac{e^z - e^{-z}}{e^z + e^{-z}}\) & \((-1, 1)\) & Hidden layers (Legacy)\\
+\alert{ReLU} & \(\max(0, z)\) & \([0, \infty)\) & \alert{Standard for Hidden}\\
+\alert{Softmax} & \(\frac{e^{z_i}}{\sum e^{z_j}}\) & \((0, 1)\) & Output (Multiclass)\\
+\end{tabular}
+\end{center}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:orgf3acf1b},fragile]{Forward Propagation}
+ \begin{block}{How Data Flows Through the Network}
+\pause
+For a single layer \(l\):
+\pause
+\begin{enumerate}[<+->]
+\item \alert{Linear Step (\(Z\))}: Weighted sum of inputs from previous layer
+$$Z^{[l]} = W^{[l]} \cdot A^{[l-1]} + b^{[l]}$$
+\item \alert{Activation Step (\(A\))}: Apply activation function
+$$A^{[l]} = g(Z^{[l]})$$
+\end{enumerate}
+\pause
+This process repeats until the output layer. The magic: \texttt{np.dot} handles all connections automatically.
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org21c385f}]{Network Capacity: Width vs Depth}
+\begin{block}{Architectural Choices}
+\pause
+\begin{center}
+\begin{tabular}{lll}
+Architecture & Structure & Characteristics\\
+\hline
+\alert{Wide} & 1 layer, 100 neurons & Simple patterns, fast\\
+\alert{Deep} & 5 layers, 10 neurons & Hierarchical features, complex\\
+\end{tabular}
+\end{center}
+\pause
+\end{block}
+\begin{example}[Trade-offs]\label{sec:orgdd4a53d}
+\begin{itemize}[<+->]
+\item Deeper networks learn hierarchical representations (edges \(\rightarrow\) parts \(\rightarrow\) objects)
+\item Wider networks can approximate functions but may need more parameters
+\item In practice: start simple, add complexity if needed
+\end{itemize}
+\end{example}
+\end{frame}
+\section{Training Neural Networks}
+\label{sec:orge8d9a82}
+\begin{frame}[label={sec:org2891de2}]{The Goal of Training}
+\begin{block}{From Random to Learned}
+\pause
+A neural network starts with \alert{random weights}. It knows nothing.
+\alert{Training} adjusts these weights to minimize prediction error.
+\pause
+\end{block}
+\begin{example}[Three Components]\label{sec:org63cf105}
+\begin{enumerate}[<+->]
+\item \alert{Loss Function}: How we measure error
+\item \alert{Optimizer}: How we update weights
+\item \alert{Backpropagation}: How we calculate the updates
+\end{enumerate}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:org30387d1}]{Loss Functions}
+\begin{definition}[The Scorecard]\label{sec:orgac2f699}
+\pause
+The \alert{Loss Function} (Cost Function) calculates a single number representing "how bad" the model is. Goal: drive it to zero.
+\pause
+\end{definition}
+\begin{block}{Common Loss Functions}
+\begin{center}
+\begin{tabular}{lll}
+Problem Type & Loss Function & Formula\\
+\hline
+\alert{Regression} & Mean Squared Error & \(\frac{1}{n}\sum(y - \hat{y})^2\)\\
+\alert{Classification} & Cross-Entropy & \(-\sum y \log(\hat{y})\)\\
+\end{tabular}
+\end{center}
+\pause
+\begin{itemize}
+\item MSE: Penalizes large errors heavily (prices, temperatures)
+\item Cross-Entropy: Penalizes confident wrong answers (probabilities)
+\end{itemize}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org30588bb}]{Stochastic Gradient Descent (SGD)}
+\begin{block}{Optimization Strategy}
+\pause
+\alert{Gradient Descent} minimizes loss by following the slope downhill.
+\alert{Batch GD} calculates gradient for entire dataset — slow and memory-intensive.
+\pause
+\end{block}
+\begin{definition}[Stochastic Gradient Descent]\label{sec:org4ec2235}
+\alert{SGD} updates weights after seeing just \alert{one example} (or a small "mini-batch").
+\pause
+\begin{itemize}[<+->]
+\item \alert{Pros}: Faster, noise helps escape local minima
+\item \alert{Cons}: Zigzaggy path to convergence
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org45e5301}]{Backpropagation}
+\begin{block}{The Chain Rule in Action}
+\pause
+How do we know which weight in Layer 1 caused the error in Layer 5?
+We use the \alert{Chain Rule} of calculus.
+\pause
+\end{block}
+\begin{example}[The Process]\label{sec:org57f346c}
+\begin{enumerate}[<+->]
+\item \alert{Forward Pass}: Data flows through network, loss is calculated
+\item \alert{Backward Pass}: Gradient propagates backwards layer by layer
+\begin{itemize}
+\item "Layer 4, you contributed X to the error. Adjust your weights."
+\item "Layer 3, based on Layer 4's error, you contributed Y\ldots{}"
+\end{itemize}
+\end{enumerate}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:org0d588de}]{The Vanishing Gradient Problem}
+\begin{block}{Why Deep Networks Failed (Initially)}
+\pause
+In deep networks, gradients are multiplied repeatedly during backpropagation.
+\alert{Sigmoid} has max derivative of 0.25:
+$$0.25 \times 0.25 \times 0.25 \times \dots \approx 0$$
+\pause
+\end{block}
+\begin{example}[The Consequence]\label{sec:org2c10375}
+\begin{itemize}[<+->]
+\item Gradient "vanishes" before reaching early layers
+\item Early layers never learn; deep network fails
+\item \alert{Solution}: Use \alert{ReLU} (derivative = 1 for positive inputs)
+\end{itemize}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:org95e930c}]{The Learning Rate}
+\begin{block}{The Most Critical Hyperparameter}
+\pause
+The \alert{Learning Rate} (\(\alpha\)) controls step size during gradient descent.
+\pause
+\end{block}
+\begin{example}[The Goldilocks Zone]\label{sec:org351e223}
+\begin{center}
+\begin{tabular}{ll}
+Learning Rate & Behavior\\
+\hline
+\alert{Too Small} & Loss decreases very slowly (0.0001)\\
+\alert{Too Large} & Loss oscillates or explodes (10.0)\\
+\alert{Just Right} & Smooth, rapid descent to minimum (0.01-0.1)\\
+\end{tabular}
+\end{center}
+\end{example}
+\end{frame}
+\section{Optimizing Neural Networks}
+\label{sec:org07e48ca}
+\begin{frame}[label={sec:org656381a}]{Weight Initialization}
+\begin{block}{Starting Right}
+\pause
+\begin{center}
+\begin{tabular}{ll}
+Initialization & Problem\\
+\hline
+All Zeros & Symmetry: every neuron learns same thing\\
+Too Large & Gradients explode\\
+Too Small & Gradients vanish\\
+\end{tabular}
+\end{center}
+\pause
+\end{block}
+\begin{example}[Strategies]\label{sec:org806a4f0}
+\begin{itemize}[<+->]
+\item \alert{He Initialization}: Best for ReLU networks
+\item \alert{Xavier (Glorot)}: Best for Sigmoid/Tanh networks
+\item Both keep variance of activations constant across layers
+\end{itemize}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:org700831d}]{Advanced Optimizers}
+\begin{block}{Smarter Descent}
+\pause
+Standard SGD is like walking down a mountain blindfolded. Advanced optimizers add intelligence.
+\pause
+\begin{center}
+\begin{tabular}{lll}
+Optimizer & Mechanism & Analogy\\
+\hline
+\alert{SGD + Momentum} & Accumulates past gradients & Heavy ball rolling downhill\\
+\alert{RMSProp} & Adapts learning rate per parameter & Slows on steep, speeds on flat\\
+\alert{Adam} & Momentum + RMSProp combined & Smart ball, adapts speed \& direction\\
+\end{tabular}
+\end{center}
+\pause
+\alert{Adam} is the default choice for most tasks.
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org94ff7b3}]{Regularization: Dropout}
+\begin{definition}[Fighting Overfitting]\label{sec:org05e9325}
+\pause
+\alert{Dropout} randomly "kills" (sets to zero) a percentage of neurons during training.
+\pause
+\end{definition}
+\begin{example}[Why It Works]\label{sec:org3ac43f0}
+\begin{itemize}[<+->]
+\item Prevents neurons from co-adapting
+\item Forces network to be redundant and robust
+\item Each training pass uses a different "thinned" network
+\item At test time, all neurons are used (with scaled weights)
+\end{itemize}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:org7dc11f4}]{Regularization: Batch Normalization}
+\begin{definition}[Stabilizing Training]\label{sec:org042a064}
+\pause
+\alert{Batch Normalization} normalizes inputs of each layer to have mean 0 and variance 1 during training.
+\pause
+\end{definition}
+\begin{example}[Benefits]\label{sec:org3248e9f}
+\begin{itemize}[<+->]
+\item Stabilizes training (allows higher learning rates)
+\item Reduces sensitivity to initialization
+\item Acts as a form of regularization
+\item Enables training of much deeper networks
+\end{itemize}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:org97fbc0f}]{Early Stopping}
+\begin{block}{The Simplest Regularization}
+\pause
+Monitor the \alert{Validation Loss}. If it stops improving (or starts getting worse), stop training immediately.
+\pause
+\end{block}
+\begin{example}[How It Works]\label{sec:org51198f3}
+\begin{itemize}[<+->]
+\item Training loss keeps decreasing (memorization)
+\item Validation loss starts increasing (overfitting)
+\item Early stopping saves the model at the sweet spot
+\item No hyperparameter tuning needed (just patience parameter)
+\end{itemize}
+\end{example}
+\end{frame}
+\section{Building Networks in PyTorch}
+\label{sec:org0a8b05a}
+\begin{frame}[label={sec:org0b7b644}]{Why PyTorch?}
+\begin{block}{Beyond Scikit-learn}
+\pause
+Scikit-learn is great for standard ML, but Deep Learning needs:
+\pause
+\begin{enumerate}[<+->]
+\item \alert{GPU Acceleration}: Matrix multiplication is slow on CPUs
+\item \alert{Automatic Differentiation}: Backpropagation is hard to code by hand
+\end{enumerate}
+\pause
+\end{block}
+\begin{example}[PyTorch Advantages]\label{sec:org6f1abea}
+\begin{itemize}
+\item Readable, "Pythonic" style
+\item Research and education standard
+\item Dynamic computation graphs (easy debugging)
+\end{itemize}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:org816bc5d},fragile]{Tensors: The Foundation}
+ \begin{definition}[NumPy Arrays on Steroids]\label{sec:org2637cd9}
+\pause
+A \alert{Tensor} is like a NumPy array that:
+\begin{itemize}
+\item Can live on a GPU
+\item Remembers how it was created (for backprop)
+\end{itemize}
+\pause
+\end{definition}
+\begin{example}[Key Operations]\label{sec:orgaa85b47}
+\begin{itemize}[<+->]
+\item \texttt{torch.tensor([1, 2, 3])} — Create from list
+\item \texttt{torch.ones(2, 3)} — Shape (2, 3) of ones
+\item \texttt{x.to("cuda")} — Move to GPU
+\item Math: \texttt{x * 2 + 5} — Same as NumPy
+\end{itemize}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:orgf18e81b},fragile]{Defining a Network (\texttt{nn.Module})}
+ \begin{block}{The Blueprint}
+\pause
+In PyTorch, a network is a Python class inheriting from \texttt{nn.Module}:
+\pause
+\end{block}
+\begin{example}[Two Required Methods]\label{sec:orgf40c96f}
+\begin{enumerate}[<+->]
+\item \texttt{\_\_init\_\_}: Define the layers
+\begin{itemize}
+\item \texttt{self.layer1 = nn.Linear(input\_dim, hidden\_dim)}
+\item \texttt{self.activation = nn.ReLU()}
+\end{itemize}
+\item \texttt{forward}: Define data flow
+\begin{itemize}
+\item \texttt{x = self.layer1(x)}
+\item \texttt{x = self.activation(x)}
+\item \texttt{return x}
+\end{itemize}
+\end{enumerate}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:org88e8805},fragile]{The Training Loop}
+ \begin{block}{The 5-Step Boilerplate}
+\pause
+Unlike Scikit-learn's \texttt{model.fit()}, PyTorch requires manual control:
+\pause
+\begin{enumerate}[<+->]
+\item \alert{Forward Pass}: \texttt{y\_pred = model(X)}
+\item \alert{Compute Loss}: \texttt{loss = criterion(y\_pred, y)}
+\item \alert{Zero Gradients}: \texttt{optimizer.zero\_grad()}
+\item \alert{Backward Pass}: \texttt{loss.backward()}
+\item \alert{Update Weights}: \texttt{optimizer.step()}
+\end{enumerate}
+\pause
+Wrap in a \texttt{for epoch in range(epochs):} loop.
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org7821349},fragile]{Loss Functions and Optimizers}
+ \begin{block}{PyTorch Components}
+\pause
+\end{block}
+\begin{example}[Loss Functions]\label{sec:orgba42141}
+\begin{center}
+\begin{tabular}{ll}
+Task & Loss Class\\
+\hline
+Binary Classification & \texttt{nn.BCELoss()}\\
+Multiclass & \texttt{nn.CrossEntropyLoss()}\\
+Regression & \texttt{nn.MSELoss()}\\
+\end{tabular}
+\end{center}
+\pause
+\end{example}
+\begin{block}{Optimizers}
+\begin{verbatim}
+optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
+\end{verbatim}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:orgf5619b3},fragile]{Evaluation Mode}
+ \begin{block}{No Gradients Needed}
+\pause
+When evaluating, we don't need to calculate gradients. Use \texttt{torch.no\_grad()} to save memory:
+\pause
+\begin{verbatim}
+with torch.no_grad():
+    test_preds = model(X_test)
+    predicted = (test_preds > 0.5).float()
+    accuracy = (predicted == y_test).float().mean()
+\end{verbatim}
+\end{block}
+\end{frame}
+\section{Summary}
+\label{sec:orgcc87042}
+\begin{frame}[label={sec:org57b0d05}]{Key Takeaways}
+\begin{block}{What We Learned}
+\pause
+\begin{enumerate}[<+->]
+\item \alert{ANNs}: Layers of neurons with weights and activation functions
+\item \alert{Perceptron}: Single neuron, limited to linear problems
+\item \alert{MLP}: Stacked neurons solve non-linear problems (XOR)
+\item \alert{Training}: Forward pass \(\rightarrow\) Loss \(\rightarrow\) Backprop \(\rightarrow\) Update
+\item \alert{Vanishing Gradient}: Sigmoid fails deep; ReLU fixes it
+\item \alert{Optimization}: Adam is the default; Early Stopping prevents overfitting
+\item \alert{PyTorch}: Tensors + nn.Module + Training Loop
+\end{enumerate}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:orga0cb69e}]{Next Section Preview}
+\begin{block}{Neural Networks II: Deep Learning \& Transformers}
+\pause
+\begin{itemize}[<+->]
+\item \alert{Convolutional Neural Networks (CNNs)}: Image processing with filters
+\item \alert{From RNNs to Transformers}: Sequence modeling revolution
+\item \alert{Large Language Models}: GPT, BERT, and modern NLP
+\item \alert{Generative AI}: Text generation, RAG, Diffusion models
+\end{itemize}
+\end{block}
+\end{frame}
+\end{document}

From a8bc1962435a69df4fdcfb018b59ee2d4a9914a5 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Thu, 5 Feb 2026 10:38:07 +0100
Subject: [PATCH 60/81] S05C01 Introduction to Artificial Neural Networks

---
 ...uction to Artificial Neural Networks.ipynb | 455 ++++++++++++++++++
 1 file changed, 455 insertions(+)
 create mode 100644 src/S05 Neural Networks I: Foundations and Optimization/S05C01 Introduction to Artificial Neural Networks.ipynb

diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05C01 Introduction to Artificial Neural Networks.ipynb b/src/S05 Neural Networks I: Foundations and Optimization/S05C01 Introduction to Artificial Neural Networks.ipynb
new file mode 100644
index 0000000..79cf6de
--- /dev/null
+++ b/src/S05 Neural Networks I: Foundations and Optimization/S05C01 Introduction to Artificial Neural Networks.ipynb	
@@ -0,0 +1,455 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9fa7625e",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Introduction to Artificial Neural Networks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84c18a7f",
+   "metadata": {},
+   "source": [
+    "## What is an Artificial Neural Network?\n",
+    "\n",
+    "An **Artificial Neural Network (ANN)** is a computational model designed to recognize patterns. It is composed of layers of interconnected nodes, or \"neurons,\" which process information by transmitting signals to one another.\n",
+    "\n",
+    "ANNs are the foundation of **Deep Learning**. They are capable of learning complex, non-linear relationships that traditional algorithms (like Linear Regression) struggle with. They power technologies ranging from image recognition and voice assistants to medical diagnosis systems."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40a708a9",
+   "metadata": {},
+   "source": [
+    "## Key Components\n",
+    "\n",
+    "1.  **Neurons (Nodes)**: The basic processing units. They receive inputs, perform a calculation, and produce an output.\n",
+    "2.  **Weights**: The connections between neurons. A \"weight\" determines how much influence one neuron has on the next. Learning involves adjusting these weights.\n",
+    "3.  **Layers**:\n",
+    "    -   **Input Layer**: Receives the raw data.\n",
+    "    -   **Hidden Layers**: Intermediate layers where the \"magic\" happens (feature extraction).\n",
+    "    -   **Output Layer**: Produces the final prediction.\n",
+    "4.  **Activation Function**: A mathematical gate that decides if a neuron should \"fire.\" It introduces non-linearity, allowing the network to learn complex curves instead of just straight lines."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f78028db",
+   "metadata": {},
+   "source": [
+    "## Biological Inspiration\n",
+    "\n",
+    "ANNs are loosely inspired by the biological neural networks in the human brain.\n",
+    "\n",
+    "| Biological Neuron | Artificial Neuron      | Role                      |\n",
+    "|----------------- |---------------------- |------------------------- |\n",
+    "| **Dendrites**     | **Inputs ($x$)**       | Receives signals.         |\n",
+    "| **Synapse**       | **Weights ($w$)**      | Controls signal strength. |\n",
+    "| **Cell Body**     | **Summation ($\\sum$)** | Aggregates signals.       |\n",
+    "| **Axon**          | **Activation**         | Transmits output.         |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55df2476",
+   "metadata": {},
+   "source": [
+    "## The Simplest ANN: The Perceptron\n",
+    "\n",
+    "The **Perceptron** (1958) is the building block of all neural networks. It is a single-layer network that takes inputs, weighs them, and makes a binary decision.\n",
+    "\n",
+    "Mathematically:\n",
+    "\n",
+    "1.  **Weighted Sum**: $$z = w_1 x_1 + w_2 x_2 + \\dots + b$$\n",
+    "2.  **Activation**: $$Output = \\begin{cases} 1 & \\text{if } z > 0 \\\\ 0 & \\text{if } z \\le 0 \\end{cases}$$\n",
+    "\n",
+    "This simple mechanism can solve linearly separable problems (drawing a straight line between two classes)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90cf10b9",
+   "metadata": {},
+   "source": [
+    "## Limitations: The XOR Problem\n",
+    "\n",
+    "Despite early hype, the single Perceptron has a fatal flaw: it cannot solve non-linear problems. The classic counter-example is the **XOR (Exclusive OR)** gate.\n",
+    "\n",
+    "-   **AND Gate**: Linearly Separable (Can be solved by a line).\n",
+    "-   **XOR Gate**: Not Linearly Separable (Needs a curve or two lines).\n",
+    "\n",
+    "This limitation froze AI research for years until the development of **Multi-Layer Perceptrons (MLPs)**, which stack neurons to solve non-linear problems."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1f1ca57",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: Perceptron vs. MLP\n",
+    "\n",
+    "We will use `scikit-learn` to demonstrate the limitation of a single neuron and the power of a network of neurons."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b0ea147",
+   "metadata": {},
+   "source": [
+    "### The Perceptron on Logic Gates\n",
+    "\n",
+    "We define the data for AND and XOR gates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "32b78c0c",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.linear_model import Perceptron\n",
+    "from sklearn.neural_network import MLPClassifier\n",
+    "from sklearn.inspection import DecisionBoundaryDisplay\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "# Data\n",
+    "X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
+    "y_and = np.array([0, 0, 0, 1])  # AND\n",
+    "y_xor = np.array([0, 1, 1, 0])  # XOR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c03f459",
+   "metadata": {},
+   "source": [
+    "### Training the Single Perceptron\n",
+    "\n",
+    "We verify that it can learn AND but fails at XOR."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8dfd2c87",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Perceptron AND Accuracy: 1.00\n",
+      "Perceptron XOR Accuracy: 0.50\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train on AND\n",
+    "clf_and = Perceptron(random_state=0)\n",
+    "clf_and.fit(X, y_and)\n",
+    "print(f\"Perceptron AND Accuracy: {clf_and.score(X, y_and):.2f}\")\n",
+    "\n",
+    "# Train on XOR\n",
+    "clf_xor = Perceptron(random_state=0)\n",
+    "clf_xor.fit(X, y_xor)\n",
+    "print(f\"Perceptron XOR Accuracy: {clf_xor.score(X, y_xor):.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b77dce93",
+   "metadata": {},
+   "source": [
+    "### Visualizing the Failure\n",
+    "\n",
+    "The Perceptron tries to draw a straight line but fails to separate the XOR classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d76c6082",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 5))\n",
+    "\n",
+    "# Plot AND\n",
+    "plt.subplot(1, 2, 1)\n",
+    "DecisionBoundaryDisplay.from_estimator(clf_and, X, cmap=\"coolwarm\", alpha=0.8)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y_and, cmap=\"coolwarm\", s=200, edgecolors='k')\n",
+    "plt.title(\"Perceptron (AND) - Success\")\n",
+    "\n",
+    "# Plot XOR\n",
+    "plt.subplot(1, 2, 2)\n",
+    "DecisionBoundaryDisplay.from_estimator(clf_xor, X, cmap=\"coolwarm\", alpha=0.8)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y_xor, cmap=\"coolwarm\", s=200, edgecolors='k')\n",
+    "plt.title(\"Perceptron (XOR) - Failure\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afcd496d",
+   "metadata": {},
+   "source": [
+    "### The Solution: Multi-Layer Perceptron (MLP)\n",
+    "\n",
+    "We use `MLPClassifier`, which adds a \"Hidden Layer\" of neurons. This allows the network to learn non-linear boundaries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3127bc88",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MLP XOR Accuracy: 1.00\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# MLP with 1 hidden layer of 4 neurons, using ReLU activation\n",
+    "mlp = MLPClassifier(hidden_layer_sizes=(100,), activation='relu', max_iter=2000, random_state=42)\n",
+    "mlp.fit(X, y_xor)\n",
+    "\n",
+    "print(f\"MLP XOR Accuracy: {mlp.score(X, y_xor):.2f}\")\n",
+    "\n",
+    "# Visualize MLP Boundary\n",
+    "plt.figure(figsize=(6, 5))\n",
+    "DecisionBoundaryDisplay.from_estimator(mlp, X, cmap=\"coolwarm\", alpha=0.8)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y_xor, cmap=\"coolwarm\", s=200, edgecolors='k')\n",
+    "plt.title(\"Multi-Layer Perceptron (XOR) - Success\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b726a2e",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb29c31f",
+   "metadata": {},
+   "source": [
+    "### Moons Dataset\n",
+    "\n",
+    "The \"Moons\" dataset is a classic non-linear classification problem.\n",
+    "\n",
+    "-   Load `make_moons`.\n",
+    "-   Train a `Perceptron` (Linear).\n",
+    "-   Train an `MLPClassifier` (Non-Linear).\n",
+    "-   Compare their boundaries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "eca34a00",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import make_moons\n",
+    "\n",
+    "# Generate Data\n",
+    "X_moon, y_moon = make_moons(n_samples=200, noise=0.2, random_state=42)\n",
+    "\n",
+    "# Train Models\n",
+    "perc = Perceptron().fit(X_moon, y_moon)\n",
+    "mlp_moon = MLPClassifier(hidden_layer_sizes=(10, 10), max_iter=2000, random_state=42).fit(X_moon, y_moon)\n",
+    "\n",
+    "# Visualize\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "\n",
+    "DecisionBoundaryDisplay.from_estimator(perc, X_moon, ax=ax[0], cmap=\"viridis\", alpha=0.8)\n",
+    "ax[0].scatter(X_moon[:,0], X_moon[:,1], c=y_moon, cmap=\"viridis\", edgecolors='k')\n",
+    "ax[0].set_title(\"Perceptron (Linear)\")\n",
+    "\n",
+    "DecisionBoundaryDisplay.from_estimator(mlp_moon, X_moon, ax=ax[1], cmap=\"viridis\", alpha=0.8)\n",
+    "ax[1].scatter(X_moon[:,0], X_moon[:,1], c=y_moon, cmap=\"viridis\", edgecolors='k')\n",
+    "ax[1].set_title(\"MLP (Non-Linear)\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1c49c09",
+   "metadata": {},
+   "source": [
+    "### Architecture Tuning\n",
+    "\n",
+    "Try changing the `hidden_layer_sizes` of the MLP on the Moons dataset.\n",
+    "\n",
+    "-   Try `(2,)` (Simple) vs `(100, 50)` (Complex).\n",
+    "-   Does the boundary become smoother or more complex?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "f283523a",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mlp_complex = MLPClassifier(hidden_layer_sizes=(100, 50), max_iter=2000, random_state=42)\n",
+    "mlp_complex.fit(X_moon, y_moon)\n",
+    "\n",
+    "plt.figure(figsize=(6, 5))\n",
+    "DecisionBoundaryDisplay.from_estimator(mlp_complex, X_moon, cmap=\"magma\", alpha=0.8)\n",
+    "plt.scatter(X_moon[:,0], X_moon[:,1], c=y_moon, cmap=\"magma\", edgecolors='k')\n",
+    "plt.title(\"Complex MLP (100, 50)\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dc8b504",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **ANNs**: General-purpose pattern recognizers consisting of Layers, Weights, and Activation Functions.\n",
+    "2.  **Perceptron**: A single neuron. Fast, simple, but limited to linear problems.\n",
+    "3.  **MLP**: A network of neurons. Can solve non-linear problems (like XOR) by combining simple decisions into complex ones.\n",
+    "4.  **Activation Functions**: The key ingredient that allows networks to \"bend\" the decision boundary."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 281a951c246fd7d4fe75ec1564ae022b3cf8de86 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Thu, 5 Feb 2026 11:00:19 +0100
Subject: [PATCH 61/81] S05C02 The Multilayer Perceptron (MLP)

---
 ...5C02 The Multilayer Perceptron (MLP).ipynb | 369 ++++++++++++++++++
 1 file changed, 369 insertions(+)
 create mode 100644 src/S05 Neural Networks I: Foundations and Optimization/S05C02 The Multilayer Perceptron (MLP).ipynb

diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05C02 The Multilayer Perceptron (MLP).ipynb b/src/S05 Neural Networks I: Foundations and Optimization/S05C02 The Multilayer Perceptron (MLP).ipynb
new file mode 100644
index 0000000..6202404
--- /dev/null
+++ b/src/S05 Neural Networks I: Foundations and Optimization/S05C02 The Multilayer Perceptron (MLP).ipynb	
@@ -0,0 +1,369 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "61bb23c1",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# The Multilayer Perceptron (MLP)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e80ec700",
+   "metadata": {},
+   "source": [
+    "## From One to Many\n",
+    "\n",
+    "A single Perceptron is limited to linear decisions. To solve complex problems, we connect many neurons together in layers. This structure is called a **Multilayer Perceptron (MLP)** or a **Feedforward Neural Network**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e2f5132",
+   "metadata": {},
+   "source": [
+    "## Architecture\n",
+    "\n",
+    "An MLP consists of at least three layers:\n",
+    "\n",
+    "1.  **Input Layer**: Receives the raw features vector $X$. (No computation happens here).\n",
+    "2.  **Hidden Layers**: Layers between input and output. They perform the computation and extract \"features\" from the data. The more hidden layers, the \"Deep\" the network (Deep Learning).\n",
+    "3.  **Output Layer**: Produces the final prediction (e.g., probability of \"Cat\")."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c11791dc",
+   "metadata": {},
+   "source": [
+    "## Activation Functions\n",
+    "\n",
+    "Without activation functions, an MLP is just a giant Linear Regression model (because linear + linear = linear). **Activation Functions** introduce **Non-Linearity**, allowing the network to learn curves and complex shapes.\n",
+    "\n",
+    "| Function    | Formula                             | Range         | Usage                                                          |\n",
+    "|----------- |----------------------------------- |------------- |-------------------------------------------------------------- |\n",
+    "| **Sigmoid** | $\\frac{1}{1+e^{-z}}$                | $(0, 1)$      | Output layer (Binary Classification).                          |\n",
+    "| **Tanh**    | $\\frac{e^z - e^{-z}}{e^z + e^{-z}}$ | $(-1, 1)$     | Hidden layers (Legacy). Zero-centered.                         |\n",
+    "| **ReLU**    | $\\max(0, z)$                        | $[0, \\infty)$ | **Standard for Hidden Layers**. Fast and efficient.            |\n",
+    "| **Softmax** | $\\frac{e^{z_i}}{\\sum e^{z_j}}$      | $(0, 1)$      | Output layer (Multiclass). Returns probabilities summing to 1. |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "749203bc",
+   "metadata": {},
+   "source": [
+    "## Forward Propagation (The Math)\n",
+    "\n",
+    "How does the data flow from Input to Output? It is a series of **Matrix Multiplications**.\n",
+    "\n",
+    "For a single layer $l$:\n",
+    "\n",
+    "1.  **Linear Step ($Z$)**: Weighted sum of inputs from previous layer ($A^{[l-1]}$). $$Z^{[l]} = W^{[l]} \\cdot A^{[l-1]} + b^{[l]}$$\n",
+    "2.  **Activation Step ($A$)**: Apply the function. $$A^{[l]} = g(Z^{[l]})$$\n",
+    "\n",
+    "This process repeats until the output layer."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3953d5c0",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: Anatomy of an MLP\n",
+    "\n",
+    "We will first visualize the activation functions, then build a \"Forward Pass\" from scratch using NumPy to understand the matrix math."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "274c6a6a",
+   "metadata": {},
+   "source": [
+    "### Visualizing Activation Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e816e6fd",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "# Define functions\n",
+    "def sigmoid(z): return 1 / (1 + np.exp(-z))\n",
+    "def tanh(z): return np.tanh(z)\n",
+    "def relu(z): return np.maximum(0, z)\n",
+    "\n",
+    "# Generate range\n",
+    "z = np.linspace(-5, 5, 100)\n",
+    "\n",
+    "# Plot\n",
+    "plt.figure(figsize=(12, 4))\n",
+    "\n",
+    "plt.subplot(1, 3, 1)\n",
+    "plt.plot(z, sigmoid(z), color='navy')\n",
+    "plt.title(\"Sigmoid (0 to 1)\")\n",
+    "plt.grid(True)\n",
+    "\n",
+    "plt.subplot(1, 3, 2)\n",
+    "plt.plot(z, tanh(z), color='purple')\n",
+    "plt.title(\"Tanh (-1 to 1)\")\n",
+    "plt.grid(True)\n",
+    "\n",
+    "plt.subplot(1, 3, 3)\n",
+    "plt.plot(z, relu(z), color='crimson')\n",
+    "plt.title(\"ReLU (0 to ∞)\")\n",
+    "plt.grid(True)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c239e849",
+   "metadata": {},
+   "source": [
+    "### Forward Propagation from Scratch\n",
+    "\n",
+    "Let's manually calculate the output of a tiny network:\n",
+    "\n",
+    "-   **Input**: 3 features.\n",
+    "-   **Hidden Layer**: 4 neurons (ReLU).\n",
+    "-   **Output Layer**: 1 neuron (Sigmoid)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d85fbbd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(42)\n",
+    "\n",
+    "# 1. Inputs (Batch of 2 samples, 3 features each)\n",
+    "X = np.array([[0.5, 0.1, -0.2],\n",
+    "              [0.9, -0.5, 0.1]])\n",
+    "print(f\"Input Shape: {X.shape}\")\n",
+    "\n",
+    "# 2. Hidden Layer (Weights: 3 inputs -> 4 neurons)\n",
+    "W1 = np.random.randn(3, 4)\n",
+    "b1 = np.zeros((4,))\n",
+    "\n",
+    "# Linear Step Z1 = X dot W + b\n",
+    "Z1 = np.dot(X, W1) + b1\n",
+    "# Activation A1 = ReLU(Z1)\n",
+    "A1 = np.maximum(0, Z1)\n",
+    "\n",
+    "print(f\"Hidden Activation Shape: {A1.shape}\")\n",
+    "\n",
+    "# 3. Output Layer (Weights: 4 inputs -> 1 neuron)\n",
+    "W2 = np.random.randn(4, 1)\n",
+    "b2 = np.zeros((1,))\n",
+    "\n",
+    "# Linear Step Z2\n",
+    "Z2 = np.dot(A1, W2) + b2\n",
+    "# Activation A2 = Sigmoid(Z2)\n",
+    "output = 1 / (1 + np.exp(-Z2))\n",
+    "\n",
+    "print(f\"Output Shape: {output.shape}\")\n",
+    "print(f\"Predictions:\\n{output}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a3233e4",
+   "metadata": {},
+   "source": [
+    "**Insight**: The magic of Neural Networks is that \\`np.dot\\` (Matrix Multiplication) handles the connections for us automatically."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cdf84ed8",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: Sklearn MLPClassifier\n",
+    "\n",
+    "Now we use `scikit-learn` to solve the non-linear \"Moons\" dataset using an MLP."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fe24034",
+   "metadata": {},
+   "source": [
+    "### Generate Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85429180",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import make_moons\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X, y = make_moons(n_samples=500, noise=0.2, random_state=42)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
+    "\n",
+    "plt.figure(figsize=(6, 5))\n",
+    "plt.scatter(X[:,0], X[:,1], c=y, cmap='coolwarm', edgecolors='k')\n",
+    "plt.title(\"Moons Dataset (Non-Linear)\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d49da12f",
+   "metadata": {},
+   "source": [
+    "### Train MLP\n",
+    "\n",
+    "We use 2 hidden layers with 10 neurons each.\n",
+    "\n",
+    "-   `hidden_layer_sizes=(10, 10)`\n",
+    "-   `activation='relu'`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da6034fb",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.neural_network import MLPClassifier\n",
+    "from sklearn.inspection import DecisionBoundaryDisplay\n",
+    "\n",
+    "# Initialize\n",
+    "mlp = MLPClassifier(hidden_layer_sizes=(10, 10), \n",
+    "                    activation='relu', \n",
+    "                    solver='adam', \n",
+    "                    max_iter=1000, \n",
+    "                    random_state=42)\n",
+    "\n",
+    "# Train\n",
+    "mlp.fit(X_train, y_train)\n",
+    "\n",
+    "print(f\"Training Accuracy: {mlp.score(X_train, y_train):.2f}\")\n",
+    "print(f\"Test Accuracy:     {mlp.score(X_test, y_test):.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80b86802",
+   "metadata": {},
+   "source": [
+    "### Visualize the \"Neural\" Boundary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7026e80c",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "DecisionBoundaryDisplay.from_estimator(mlp, X, cmap=\"coolwarm\", alpha=0.8)\n",
+    "plt.scatter(X[:,0], X[:,1], c=y, cmap=\"coolwarm\", edgecolors='k')\n",
+    "plt.title(\"MLP Decision Boundary (ReLU)\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "734f7b80",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bf3d9d9",
+   "metadata": {},
+   "source": [
+    "### Activation Function Impact\n",
+    "\n",
+    "Train an MLP using `activation='tanh'` and another using `activation='logistic'` (Sigmoid).\n",
+    "\n",
+    "-   Compare the decision boundaries.\n",
+    "-   **Note**: Tanh often creates smoother curves than ReLU (which creates piecewise linear shapes)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d34a2127",
+   "metadata": {},
+   "source": [
+    "### Network Capacity (Width vs Depth)\n",
+    "\n",
+    "Compare two architectures:\n",
+    "\n",
+    "1.  **Wide**: 1 Hidden Layer with 100 neurons `(100,)`.\n",
+    "2.  **Deep**: 5 Hidden Layers with 10 neurons each `(10, 10, 10, 10, 10)`.\n",
+    "\n",
+    "Do they perform differently?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1619639e",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **Layers**: Input (Passive) $\\rightarrow$ Hidden (Active Feature Extraction) $\\rightarrow$ Output (Prediction).\n",
+    "2.  **Activations**:\n",
+    "    -   **ReLU**: The default for hidden layers.\n",
+    "    -   **Sigmoid/Softmax**: The default for the output layer.\n",
+    "3.  **Forward Prop**: Data moves through the network via matrix multiplication ($Z=WX+b$) and activation functions ($A=f(Z)$)."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 498e8e8a0c1185040f6841ae09f0dc3283cfc549 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Thu, 5 Feb 2026 11:01:08 +0100
Subject: [PATCH 62/81] S05C03 Training Neural Networks

---
 .../S05C03 Training Neural Networks.ipynb     | 281 ++++++++++++++++++
 1 file changed, 281 insertions(+)
 create mode 100644 src/S05 Neural Networks I: Foundations and Optimization/S05C03 Training Neural Networks.ipynb

diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05C03 Training Neural Networks.ipynb b/src/S05 Neural Networks I: Foundations and Optimization/S05C03 Training Neural Networks.ipynb
new file mode 100644
index 0000000..c475d4b
--- /dev/null
+++ b/src/S05 Neural Networks I: Foundations and Optimization/S05C03 Training Neural Networks.ipynb	
@@ -0,0 +1,281 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "285af708",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Training Neural Networks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7e99912",
+   "metadata": {},
+   "source": [
+    "## The Goal of Training\n",
+    "\n",
+    "A Neural Network starts with random weights. It knows nothing. \"Training\" is the process of adjusting these weights to minimize the error between the network's predictions and the actual targets.\n",
+    "\n",
+    "This process has three main components:\n",
+    "\n",
+    "1.  **Loss Function**: How we measure error.\n",
+    "2.  **Optimizer**: How we update weights.\n",
+    "3.  **Backpropagation**: How we calculate the updates."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22026089",
+   "metadata": {},
+   "source": [
+    "## Loss Functions (The Scorecard)\n",
+    "\n",
+    "The Loss Function (or Cost Function) calculates a single number representing \"how bad\" the model is. The goal is to drive this number to zero.\n",
+    "\n",
+    "| Problem Type       | Loss Function                | Formula                          | Intuition                                                                          |\n",
+    "|------------------ |---------------------------- |-------------------------------- |---------------------------------------------------------------------------------- |\n",
+    "| **Regression**     | **Mean Squared Error (MSE)** | $\\frac{1}{n}\\sum(y - \\hat{y})^2$ | Penalizes large errors heavily. Standard for predicting prices, temperatures, etc. |\n",
+    "| **Classification** | **Cross-Entropy (Log Loss)** | $-\\sum y \\log(\\hat{y})$          | Penalizes confident wrong answers. Used when outputs are probabilities (0 to 1).   |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfe379dd",
+   "metadata": {},
+   "source": [
+    "## Optimization: SGD and Mini-Batches\n",
+    "\n",
+    "We use **Gradient Descent** to minimize the loss. However, calculating the gradient for the **entire** dataset at every step (Batch GD) is slow and memory-intensive.\n",
+    "\n",
+    "**Stochastic Gradient Descent (SGD)** solves this by updating weights after seeing just **one** example (or a small \"Mini-Batch\" of examples).\n",
+    "\n",
+    "-   **Pros**: Faster, adds noise that helps escape \"Local Minima\" (valleys that aren't the lowest point).\n",
+    "-   **Cons**: The path to the bottom is zigzaggy and noisy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "459523f0",
+   "metadata": {},
+   "source": [
+    "## Backpropagation Intuition (The Chain Rule)\n",
+    "\n",
+    "How do we know which specific weight in Layer 1 caused the error in Layer 5? We use the **Chain Rule** of calculus.\n",
+    "\n",
+    "1.  **Forward Pass**: Input data flows through the network to produce a prediction. Loss is calculated.\n",
+    "2.  **Backward Pass**: We calculate the gradient of the Loss with respect to the Output. Then we propagate this gradient **backwards** layer by layer.\n",
+    "    -   **\"Hey Layer 4, you contributed X to the error. Adjust your weights.\"**\n",
+    "    -   **\"Hey Layer 3, based on Layer 4's error, you contributed Y&#x2026;\"**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "579d4f95",
+   "metadata": {},
+   "source": [
+    "## The Vanishing Gradient Problem\n",
+    "\n",
+    "In deep networks (many layers), gradients are multiplied repeatedly during backpropagation. If we use activation functions like **Sigmoid** (which has a maximum derivative of 0.25), we multiply many small numbers together: $$0.25 \\times 0.25 \\times 0.25 \\times \\dots \\approx 0$$\n",
+    "\n",
+    "The gradient \"vanishes\" before it reaches the early layers. The input layers never learn, and the deep network fails.\n",
+    "\n",
+    "**Solution**: Use **ReLU** (derivative is 1 for positive inputs), which allows gradients to flow unchanged."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e25d1426",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: Monitoring Training\n",
+    "\n",
+    "We will train an MLP on the Digits dataset and visualize the **Loss Curve** to see learning in action."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98818dc5",
+   "metadata": {},
+   "source": [
+    "### Load Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "815214be",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_digits\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "# Load Digits (10 classes)\n",
+    "digits = load_digits()\n",
+    "X, y = digits.data, digits.target\n",
+    "\n",
+    "# Scale (Crucial for Neural Nets!)\n",
+    "scaler = StandardScaler()\n",
+    "X_scaled = scaler.fit_transform(X)\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "print(f\"Data Shape: {X_train.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0071010f",
+   "metadata": {},
+   "source": [
+    "### Training with SGD\n",
+    "\n",
+    "We use `MLPClassifier` with `solver='sgd'`. We set `verbose=True` to print the loss at each step (iteration)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51c044f6",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.neural_network import MLPClassifier\n",
+    "\n",
+    "# Simple MLP\n",
+    "# learning_rate_init controls the step size\n",
+    "mlp = MLPClassifier(hidden_layer_sizes=(64,), \n",
+    "                    activation='relu', \n",
+    "                    solver='sgd', \n",
+    "                    learning_rate_init=0.01, \n",
+    "                    max_iter=500, \n",
+    "                    random_state=42)\n",
+    "\n",
+    "mlp.fit(X_train, y_train)\n",
+    "\n",
+    "print(f\"Final Accuracy: {mlp.score(X_test, y_test):.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05b750f9",
+   "metadata": {},
+   "source": [
+    "### Visualizing the Loss Curve\n",
+    "\n",
+    "The `loss_curve_` attribute stores the history of the loss. If this curve goes down, the network is learning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bd4d308",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(8, 5))\n",
+    "plt.plot(mlp.loss_curve_, color='blue', linewidth=2)\n",
+    "plt.title(\"Training Loss Curve (SGD)\")\n",
+    "plt.xlabel(\"Iterations (Epochs)\")\n",
+    "plt.ylabel(\"Loss (Cross-Entropy)\")\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfe93267",
+   "metadata": {},
+   "source": [
+    "**Observation**: The loss drops sharply at first and then flattens out. This is a healthy learning curve."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e096ec5",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e72ea7a2",
+   "metadata": {},
+   "source": [
+    "### The Learning Rate (Goldilocks Zone)\n",
+    "\n",
+    "The **Learning Rate** is the most critical hyperparameter.\n",
+    "\n",
+    "-   Train 3 models with rates: `0.0001` (Too Small), `0.1` (Just Right), `10.0` (Too Large).\n",
+    "-   Plot their loss curves on the same graph.\n",
+    "\n",
+    "**Result**:\n",
+    "\n",
+    "-   Small rate: Loss goes down very slowly.\n",
+    "-   Large rate: Loss oscillates or gets stuck (high loss).\n",
+    "-   Good rate: Smooth, rapid descent."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12d67964",
+   "metadata": {},
+   "source": [
+    "### Vanishing Gradients (Sigmoid vs ReLU)\n",
+    "\n",
+    "Train a **Deep Network** (5 layers) using Sigmoid vs. ReLU.\n",
+    "\n",
+    "-   **Hypothesis**: The Sigmoid network will struggle to learn (Vanishing Gradient). The ReLU network will learn quickly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "baa002b2",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **Loss Function**: Cross-Entropy for classification, MSE for regression.\n",
+    "2.  **SGD**: Updates weights using small batches to be faster and escape traps.\n",
+    "3.  **Backprop**: Passes the \"blame\" (error) backwards to update weights.\n",
+    "4.  **Vanishing Gradient**: Sigmoid fails in deep networks; **ReLU** fixes this by keeping gradients alive."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 8818351076fd3ce4881a7a209c4fc8ba218424a6 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Thu, 5 Feb 2026 11:01:40 +0100
Subject: [PATCH 63/81] S05C04 Optimizing Neural Networks

---
 .../S05C04 Optimizing Neural Networks.ipynb   | 321 ++++++++++++++++++
 1 file changed, 321 insertions(+)
 create mode 100644 src/S05 Neural Networks I: Foundations and Optimization/S05C04 Optimizing Neural Networks.ipynb

diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05C04 Optimizing Neural Networks.ipynb b/src/S05 Neural Networks I: Foundations and Optimization/S05C04 Optimizing Neural Networks.ipynb
new file mode 100644
index 0000000..65696bc
--- /dev/null
+++ b/src/S05 Neural Networks I: Foundations and Optimization/S05C04 Optimizing Neural Networks.ipynb	
@@ -0,0 +1,321 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3a35dd1a",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Optimizing Neural Networks\n",
+    "\n",
+    "Training a neural network is an optimization problem. We want to reach the minimum error as fast as possible without getting stuck. Basic Gradient Descent is often too slow or unstable for deep networks. To fix this, we use advanced **Optimization** and **Regularization** techniques."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cc3de06",
+   "metadata": {},
+   "source": [
+    "## Weight Initialization: Starting Right\n",
+    "\n",
+    "If we initialize all weights to Zero, every neuron learns the same thing (symmetry problem). The network becomes a linear model. If we initialize them too large, gradients explode. Too small, gradients vanish.\n",
+    "\n",
+    "**Strategies**:\n",
+    "\n",
+    "-   **He Initialization**: Best for ReLU networks. Keeps the variance of activations constant across layers.\n",
+    "-   **Xavier (Glorot) Initialization**: Best for Sigmoid/Tanh networks."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88970393",
+   "metadata": {},
+   "source": [
+    "## Optimizers: Smarter Descent\n",
+    "\n",
+    "Standard SGD is like walking down a mountain blindfolded. Advanced optimizers add \"momentum\" or adaptive steps.\n",
+    "\n",
+    "| Optimizer          | Mechanism                                               | Analogy                                                 |\n",
+    "|------------------ |------------------------------------------------------- |------------------------------------------------------- |\n",
+    "| **SGD + Momentum** | Accumulates past gradients to smooth out the path.      | A heavy ball rolling downhill (builds speed).           |\n",
+    "| **RMSProp**        | Adapts the learning rate for each parameter separately. | Slows down on steep slopes, speeds up on flat plains.   |\n",
+    "| **Adam**           | Combines Momentum + RMSProp. The default choice.        | A smart ball that adapts speed and direction perfectly. |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a87eed7e",
+   "metadata": {},
+   "source": [
+    "## Regularization: Fighting Overfitting\n",
+    "\n",
+    "Deep networks are prone to memorizing data. We need techniques to force them to generalize."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ca5c56e",
+   "metadata": {},
+   "source": [
+    "### Dropout\n",
+    "\n",
+    "Randomly \"kill\" (set to zero) a percentage of neurons during training. This prevents neurons from co-adapting and relying too much on specific features. It forces the network to be redundant and robust."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f82ce35",
+   "metadata": {},
+   "source": [
+    "### Batch Normalization\n",
+    "\n",
+    "Normalizes the inputs of each layer to have mean 0 and variance 1 **during training**.\n",
+    "\n",
+    "-   Stabilizes training (allows higher learning rates).\n",
+    "-   Reduces sensitivity to initialization."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed78641b",
+   "metadata": {},
+   "source": [
+    "### Early Stopping\n",
+    "\n",
+    "Monitor the Validation Loss. If it stops improving (or starts getting worse), stop training immediately. This prevents the model from overfitting in the late stages."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b1fd6c1",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: Adam vs SGD\n",
+    "\n",
+    "We will train a network on the \"Digits\" dataset using standard SGD versus Adam to see the speed difference."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e77391cb",
+   "metadata": {},
+   "source": [
+    "### Setup Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b8a6d8d",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_digits\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "# Load and Scale\n",
+    "digits = load_digits()\n",
+    "X, y = digits.data, digits.target\n",
+    "scaler = StandardScaler()\n",
+    "X_scaled = scaler.fit_transform(X)\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "982f42ce",
+   "metadata": {},
+   "source": [
+    "### Training Comparison\n",
+    "\n",
+    "We train two identical MLPs, changing only the `solver`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6ff2b4e",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.neural_network import MLPClassifier\n",
+    "\n",
+    "# SGD Model\n",
+    "mlp_sgd = MLPClassifier(hidden_layer_sizes=(64, 32), \n",
+    "                        solver='sgd', \n",
+    "                        learning_rate_init=0.01, \n",
+    "                        max_iter=200, \n",
+    "                        random_state=42)\n",
+    "\n",
+    "# Adam Model\n",
+    "mlp_adam = MLPClassifier(hidden_layer_sizes=(64, 32), \n",
+    "                         solver='adam', \n",
+    "                         learning_rate_init=0.01, \n",
+    "                         max_iter=200, \n",
+    "                         random_state=42)\n",
+    "\n",
+    "mlp_sgd.fit(X_train, y_train)\n",
+    "mlp_adam.fit(X_train, y_train)\n",
+    "\n",
+    "print(f\"SGD Accuracy:  {mlp_sgd.score(X_test, y_test):.3f}\")\n",
+    "print(f\"Adam Accuracy: {mlp_adam.score(X_test, y_test):.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b957918",
+   "metadata": {},
+   "source": [
+    "### Visualizing Convergence Speed\n",
+    "\n",
+    "We plot the Loss Curves. Adam usually drops much faster."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ad47ade",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(mlp_sgd.loss_curve_, label='SGD', color='red', linestyle='--')\n",
+    "plt.plot(mlp_adam.loss_curve_, label='Adam', color='blue')\n",
+    "plt.title(\"Convergence Speed: Adam vs SGD\")\n",
+    "plt.xlabel(\"Iterations\")\n",
+    "plt.ylabel(\"Loss\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d45b1134",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: Early Stopping\n",
+    "\n",
+    "We will force a network to overfit and see if Early Stopping saves us."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "332b0444",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "# Model WITH Early Stopping (default in sklearn)\n",
+    "# early_stopping=True sets aside 10% of training data for validation\n",
+    "mlp_early = MLPClassifier(hidden_layer_sizes=(100,), \n",
+    "                          max_iter=500, \n",
+    "                          early_stopping=True, \n",
+    "                          validation_fraction=0.1,\n",
+    "                          n_iter_no_change=10, \n",
+    "                          random_state=42)\n",
+    "\n",
+    "mlp_early.fit(X_train, y_train)\n",
+    "\n",
+    "print(f\"Stopped after {mlp_early.n_iter_} iterations.\")\n",
+    "print(f\"Best Validation Score: {mlp_early.best_validation_score_:.3f}\")\n",
+    "\n",
+    "# Plot the Validation Curve\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "plt.plot(mlp_early.loss_curve_, label='Training Loss')\n",
+    "plt.plot(mlp_early.validation_scores_, label='Validation Score (Accuracy)')\n",
+    "plt.title(\"Early Stopping Monitor\")\n",
+    "plt.xlabel(\"Iterations\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "533ee73c",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89bd52f5",
+   "metadata": {},
+   "source": [
+    "### The Effect of Batch Normalization (Simulated)\n",
+    "\n",
+    "Scikit-learn's MLP doesn't have a simple \"Batch Norm\" flag (it's built into the solvers implicitly or requires libraries like PyTorch/TensorFlow). However, we can simulate the **need** for it by using unscaled data.\n",
+    "\n",
+    "1.  Train an MLP on `X_train` (Scaled).\n",
+    "2.  Train an MLP on `X` (Unscaled raw data).\n",
+    "3.  Compare the loss curves."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d20e6038",
+   "metadata": {},
+   "source": [
+    "### Regularization Strength (Alpha)\n",
+    "\n",
+    "The `alpha` parameter controls L2 Regularization (Weight Decay).\n",
+    "\n",
+    "-   Train a model with `alpha=0.0001` (Default).\n",
+    "-   Train a model with `alpha=1.0` (High Regularization).\n",
+    "-   Compare accuracies.\n",
+    "\n",
+    "**Result**: High alpha should slightly reduce Training Accuracy (preventing overfitting) but might maintain or improve Test Accuracy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab3be707",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **Adam**: generally the best default optimizer. Fast and robust.\n",
+    "2.  **Early Stopping**: The easiest way to prevent overfitting without tuning hyperparameters.\n",
+    "3.  **Normalization**: Neural Networks fail without scaled data (Batch Norm automates this deep inside layers)."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 0d4f96bc8600b0c041418486339432accd1d2574 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Thu, 5 Feb 2026 11:01:55 +0100
Subject: [PATCH 64/81] S05C05 Building a Network in PyTorch

---
 ...S05C05 Building a Network in PyTorch.ipynb | 354 ++++++++++++++++++
 1 file changed, 354 insertions(+)
 create mode 100644 src/S05 Neural Networks I: Foundations and Optimization/S05C05 Building a Network in PyTorch.ipynb

diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05C05 Building a Network in PyTorch.ipynb b/src/S05 Neural Networks I: Foundations and Optimization/S05C05 Building a Network in PyTorch.ipynb
new file mode 100644
index 0000000..f41f2ab
--- /dev/null
+++ b/src/S05 Neural Networks I: Foundations and Optimization/S05C05 Building a Network in PyTorch.ipynb	
@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4247f644",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Building a Network in PyTorch\n",
+    "\n",
+    "Scikit-learn is fantastic for standard algorithms, but for Deep Learning, we need more power. We need libraries that can:\n",
+    "\n",
+    "1.  **Run on GPUs** (Matrix multiplication is slow on CPUs).\n",
+    "2.  **Calculate Gradients Automatically** (Backpropagation is hard to code by hand).\n",
+    "\n",
+    "The two industry giants are **PyTorch** (Meta) and **TensorFlow** (Google). We will focus on **PyTorch**, as it is the standard for research and education due to its readable, \"Pythonic\" style."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8db00b5",
+   "metadata": {},
+   "source": [
+    "## Tensors and Operations\n",
+    "\n",
+    "The fundamental building block of PyTorch is the **Tensor**. Think of a Tensor as a NumPy array that can live on a GPU and remembers how it was created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d462956c",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import numpy as np\n",
+    "\n",
+    "# 1. Creating Tensors\n",
+    "x_np = np.array([1, 2, 3])\n",
+    "x_pt = torch.tensor([1, 2, 3])  # From list\n",
+    "x_ones = torch.ones(2, 3)       # Shape (2, 3)\n",
+    "\n",
+    "print(f\"PyTorch Tensor: {x_pt}\")\n",
+    "\n",
+    "# 2. Operations (Similar to NumPy)\n",
+    "y_pt = x_pt * 2 + 5\n",
+    "print(f\"Math Result: {y_pt}\")\n",
+    "\n",
+    "# 3. GPU Support (Check if available)\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "print(f\"Running on: {device}\")\n",
+    "\n",
+    "# Move tensor to device\n",
+    "x_pt = x_pt.to(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aafd5b7b",
+   "metadata": {},
+   "source": [
+    "## Defining the Architecture (`nn.Module`)\n",
+    "\n",
+    "In PyTorch, a neural network is a Python class that inherits from `nn.Module`. You must define two things:\n",
+    "\n",
+    "1.  `__init__`: Define the layers (e.g., Linear, ReLU).\n",
+    "2.  `forward`: Define how data moves through those layers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "644570be",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import torch.nn as nn\n",
+    "\n",
+    "class SimpleMLP(nn.Module):\n",
+    "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
+    "        super().__init__()\n",
+    "        # Define layers\n",
+    "        self.layer1 = nn.Linear(input_dim, hidden_dim)\n",
+    "        self.activation = nn.ReLU()\n",
+    "        self.layer2 = nn.Linear(hidden_dim, output_dim)\n",
+    "        self.sigmoid = nn.Sigmoid() # For binary classification\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Define the flow\n",
+    "        x = self.layer1(x)\n",
+    "        x = self.activation(x)\n",
+    "        x = self.layer2(x)\n",
+    "        x = self.sigmoid(x)\n",
+    "        return x\n",
+    "\n",
+    "# Initialize\n",
+    "model = SimpleMLP(input_dim=2, hidden_dim=10, output_dim=1)\n",
+    "print(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3377a6fb",
+   "metadata": {},
+   "source": [
+    "## The Training Loop (The \"Boilerplate\")\n",
+    "\n",
+    "Unlike Scikit-learn's `model.fit()`, PyTorch requires you to write the training loop manually. This gives you total control.\n",
+    "\n",
+    "The standard loop has **5 Steps**:\n",
+    "\n",
+    "1.  **Forward Pass**: Compute prediction.\n",
+    "2.  **Compute Loss**: How bad is the prediction?\n",
+    "3.  **Zero Gradients**: Reset errors from previous step.\n",
+    "4.  **Backward Pass**: Calculate gradients (Backpropagation).\n",
+    "5.  **Optimizer Step**: Update weights."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f1206c5",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: PyTorch Classification\n",
+    "\n",
+    "We will solve the \"Moons\" dataset using our custom PyTorch model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db185f50",
+   "metadata": {},
+   "source": [
+    "### Prepare Data (Tensors)\n",
+    "\n",
+    "PyTorch models expect Float32 Tensors, not NumPy arrays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa37b767",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import make_moons\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Generate Data\n",
+    "X_np, y_np = make_moons(n_samples=500, noise=0.2, random_state=42)\n",
+    "\n",
+    "# Convert to Tensors (Float32)\n",
+    "# Note: unsqueeze(1) changes shape from (N,) to (N, 1) to match output layer\n",
+    "X_tensor = torch.tensor(X_np, dtype=torch.float32)\n",
+    "y_tensor = torch.tensor(y_np, dtype=torch.float32).unsqueeze(1)\n",
+    "\n",
+    "# Split (Using standard slicing)\n",
+    "train_split = int(0.8 * len(X_tensor))\n",
+    "X_train, X_test = X_tensor[:train_split], X_tensor[train_split:]\n",
+    "y_train, y_test = y_tensor[:train_split], y_tensor[train_split:]\n",
+    "\n",
+    "print(f\"X_train shape: {X_train.shape}\")\n",
+    "print(f\"y_train shape: {y_train.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "356ca50e",
+   "metadata": {},
+   "source": [
+    "### Setup Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e6200a1",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import torch.optim as optim\n",
+    "\n",
+    "# 1. Model\n",
+    "model = SimpleMLP(input_dim=2, hidden_dim=16, output_dim=1)\n",
+    "\n",
+    "# 2. Loss Function (Binary Cross Entropy)\n",
+    "criterion = nn.BCELoss()\n",
+    "\n",
+    "# 3. Optimizer (Adam)\n",
+    "optimizer = optim.Adam(model.parameters(), lr=0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88cb7f2f",
+   "metadata": {},
+   "source": [
+    "### The Loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee7efdda",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "epochs = 1000\n",
+    "losses = []\n",
+    "\n",
+    "for epoch in range(epochs):\n",
+    "    # --- Step 1: Forward Pass ---\n",
+    "    y_pred = model(X_train)\n",
+    "\n",
+    "    # --- Step 2: Compute Loss ---\n",
+    "    loss = criterion(y_pred, y_train)\n",
+    "    losses.append(loss.item())\n",
+    "\n",
+    "    # --- Step 3: Zero Gradients ---\n",
+    "    optimizer.zero_grad()\n",
+    "\n",
+    "    # --- Step 4: Backward Pass ---\n",
+    "    loss.backward() # Calculates dLoss/dWeights\n",
+    "\n",
+    "    # --- Step 5: Update Weights ---\n",
+    "    optimizer.step()\n",
+    "\n",
+    "    if epoch % 100 == 0:\n",
+    "        print(f\"Epoch {epoch} | Loss: {loss.item():.4f}\")\n",
+    "\n",
+    "# Plot Loss Curve\n",
+    "plt.figure(figsize=(8, 4))\n",
+    "plt.plot(losses)\n",
+    "plt.title(\"Training Loss\")\n",
+    "plt.xlabel(\"Epochs\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8557b277",
+   "metadata": {},
+   "source": [
+    "### Evaluation (No Gradients)\n",
+    "\n",
+    "When evaluating, we use `torch.no_grad()` to save memory (we don't need to calculate backprop)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e791672c",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "with torch.no_grad():\n",
+    "    # Forward pass on test data\n",
+    "    test_preds = model(X_test)\n",
+    "    # Convert probabilities to 0 or 1\n",
+    "    predicted_classes = (test_preds > 0.5).float()\n",
+    "    # Calculate accuracy\n",
+    "    accuracy = (predicted_classes == y_test).float().mean()\n",
+    "\n",
+    "print(f\"Test Accuracy: {accuracy:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae7e9fe0",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6b3c78a",
+   "metadata": {},
+   "source": [
+    "### Changing the Architecture\n",
+    "\n",
+    "Modify the `SimpleMLP` class to be **Deeper**.\n",
+    "\n",
+    "-   Add a second hidden layer (`layer3`).\n",
+    "-   Use =ReLU activation between them.\n",
+    "-   Train the new model on the same data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47f0e280",
+   "metadata": {},
+   "source": [
+    "### Regression with PyTorch\n",
+    "\n",
+    "PyTorch isn't just for classification.\n",
+    "\n",
+    "-   Generate regression data (`y = 3x + 2`).\n",
+    "-   Create a model **without** Sigmoid at the end (Linear output).\n",
+    "-   Use `nn.MSELoss()` instead of `BCELoss`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "206b382b",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **Tensors**: The math engine. Always check shapes and types (`float32`).\n",
+    "2.  **nn.Module**: The class where you define layers (`__init__`) and flow (`forward`).\n",
+    "3.  **The Loop**: Zero Grad $\\rightarrow$ Forward $\\rightarrow$ Loss $\\rightarrow$ Backward $\\rightarrow$ Step."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 1efdab17da68de650f24803188cfd3cf41588069 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Thu, 5 Feb 2026 16:03:12 +0100
Subject: [PATCH 65/81] S06 Neural Networks II

---
 ...Deep Learning, Transformers, and GenAI.pdf | Bin 0 -> 401478 bytes
 ...Deep Learning, Transformers, and GenAI.tex | 448 ++++++++++++++++++
 2 files changed, 448 insertions(+)
 create mode 100644 src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06 Neural Networks II: Deep Learning, Transformers, and GenAI.pdf
 create mode 100644 src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06 Neural Networks II: Deep Learning, Transformers, and GenAI.tex

diff --git a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06 Neural Networks II: Deep Learning, Transformers, and GenAI.pdf b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06 Neural Networks II: Deep Learning, Transformers, and GenAI.pdf
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+\author{Cristian A. Marocico}
+\date{\today}
+\title{Machine Learning in Python}
+\subtitle{Neural Networks II: Deep Learning, Transformers, and GenAI}
+\author[Marocico, Tatar]{Cristian A. Marocico, A. Emin Tatar}
+\institute[CIT]{Center for Information Technology\\University of Groningen}
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+\date[Feb 20\textsuperscript{th} 2026]{Friday, February 20\textsuperscript{th} 2026}
+\usepackage{mathtools}
+\setbeamercovered{transparent=0}
+\usepackage[timeinterval=60]{tdclock}
+\hypersetup{
+ pdfauthor={Cristian A. Marocico},
+ pdftitle={Machine Learning in Python},
+ pdfkeywords={},
+ pdfsubject={},
+ pdfcreator={Emacs 30.2 (Org mode 9.7.11)}, 
+ pdflang={English}}
+\begin{document}
+
+\maketitle
+\begin{frame}{Outline}
+\setcounter{tocdepth}{1}
+\tableofcontents
+\end{frame}
+
+\section{Convolutional Neural Networks (CNNs)}
+\label{sec:org23f1c19}
+\begin{frame}[label={sec:org2d737c2}]{Why CNNs?}
+\begin{definition}[The Problem with MLPs for Images]\label{sec:org4197c45}
+\pause
+Standard MLPs flatten images into vectors, causing two major problems:
+\begin{itemize}[<+->]
+\item \alert{Spatial Structure Lost}: A nose is defined by being \emph{above} a mouth, not just by pixel values.
+\item \alert{Parameter Explosion}: A \(1000 \times 1000\) image connected to 100 neurons requires \alert{100 million weights}.
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org937c7ec}]{The Convolution Operation}
+\begin{example}[Filters and Feature Maps]\label{sec:org506b2fb}
+\pause
+Instead of learning weights for every pixel, CNNs learn small \alert{Filters} (Kernels):
+\begin{itemize}[<+->]
+\item A filter might be \(3 \times 3\) pixels.
+\item It slides (convolves) over the image, computing dot products.
+\item This creates a \alert{Feature Map} showing where patterns appear.
+\item Early filters detect edges; deeper filters detect complex shapes.
+\end{itemize}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:org2a26b38}]{Controlling Dimensions}
+\begin{block}{Padding and Stride}
+\pause
+As filters slide, image dimensions change:
+\begin{itemize}[<+->]
+\item \alert{Valid Convolution}: No padding. Output shrinks (\(5 \times 5 \to 3 \times 3\)).
+\item \alert{Padding}: Add zeros around border to maintain size.
+\item \alert{Stride}: How many pixels to jump. Stride=2 halves dimensions.
+\item \alert{Formula}: \(O = \frac{I - K + 2P}{S} + 1\)
+\end{itemize}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org33e3911}]{Pooling Layers}
+\begin{example}[MaxPool2d]\label{sec:orgdeb1226}
+\pause
+Pooling reduces spatial size to control overfitting and computation:
+\begin{itemize}[<+->]
+\item \alert{Max Pooling}: Takes the largest value in a window.
+\item Asks: "Is there a feature in this patch? Yes/No."
+\item Provides translation invariance (feature can shift slightly).
+\item Typical: \(2 \times 2\) pool with stride 2 halves dimensions.
+\end{itemize}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:orge33b29c}]{Famous Architectures}
+\begin{block}{VGG and ResNet}
+\pause
+Modern Deep Learning is built on famous architectures:
+\begin{itemize}[<+->]
+\item \alert{VGG (2014)}: Simplicity. Stacks many small \(3 \times 3\) filters. Very deep.
+\item \alert{ResNet (2015)}: Introduced \alert{Skip Connections} (Residuals).
+\item Skip connections let gradients "skip" layers to avoid vanishing.
+\item Enables training networks with 100+ layers.
+\end{itemize}
+\end{block}
+\end{frame}
+\section{From RNNs to Transformers}
+\label{sec:orgb90aff8}
+\begin{frame}[label={sec:org5e2af73}]{The Sequence Problem}
+\begin{definition}[Why Standard Networks Fail]\label{sec:org46a4380}
+\pause
+MLPs expect fixed-size input, but language is dynamic:
+\begin{itemize}[<+->]
+\item "Hello" (1 word) vs "The quick brown fox\ldots{}" (5 words)
+\item We need models that handle variable-length sequences.
+\item We need models that "remember" context.
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgfe4eb84}]{Recurrent Neural Networks}
+\begin{example}[RNNs and Their Limitations]\label{sec:orgf7403e6}
+\pause
+RNNs process words sequentially, maintaining a \alert{Hidden State} (memory):
+$$h_t = \tanh(W x_t + U h_{t-1})$$
+\pause
+\alert{Problems}:
+\begin{itemize}[<+->]
+\item \alert{Sequential}: Cannot process word 100 until word 99 is done. Very slow.
+\item \alert{Vanishing Memory}: By paragraph end, the beginning is forgotten.
+\item \alert{LSTMs} improved memory with "gates," but remained slow.
+\end{itemize}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:org847670b}]{The Attention Mechanism}
+\begin{block}{Looking Back at Everything}
+\pause
+In 2014, researchers asked: \emph{Why squash everything into one tiny memory vector?}
+\pause
+\alert{Attention} lets the model "look back" at any word:
+\begin{itemize}[<+->]
+\item When predicting, assign a "weight" to every previous word.
+\item "I poured cereal into the\ldots{}" \(\to\) Focus on "cereal" and "poured" \(\to\) Predict: "bowl".
+\item No more sequential bottleneck for context.
+\end{itemize}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org356b39b}]{The Transformer (2017)}
+\begin{definition}["Attention is All You Need"]\label{sec:org02dbfe5}
+\pause
+The paper proved we don't need recurrence at all:
+\begin{center}
+\begin{tabular}{lll}
+Feature & RNN/LSTM & Transformer\\
+\hline
+\alert{Processing} & Sequential (Slow) & Parallel (Fast)\\
+\alert{Context} & Local (Nearby words) & Global (All words see all words)\\
+\alert{Distance} & Linearly far & Constant (1 step away)\\
+\end{tabular}
+\end{center}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orge493cdc}]{Transformer Architecture}
+\begin{example}[Key Components]\label{sec:orged5d393}
+\pause
+\begin{itemize}[<+->]
+\item \alert{Self-Attention}: \(Attention(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V\)
+\item \alert{Encoder}: Reads input text, creates rich representation (\alert{BERT}).
+\item \alert{Decoder}: Generates output text step-by-step (\alert{GPT}).
+\item \alert{Positional Encoding}: Injects word order since parallel processing loses it.
+\end{itemize}
+\end{example}
+\end{frame}
+\section{Large Language Models (LLMs)}
+\label{sec:org7b4eede}
+\begin{frame}[label={sec:orgd52442c}]{Evolution of LLMs}
+\begin{definition}[From BERT to Llama]\label{sec:orgf95261e}
+\pause
+\begin{itemize}[<+->]
+\item \alert{BERT (2018 - Google)}: Encoder-only. Good at "understanding" (Classification, Q\&A).
+\item \alert{GPT (2018-Now - OpenAI)}: Decoder-only. Good at "generation" (Writing text).
+\item \alert{Llama (2023 - Meta)}: Open-source. Brought GPT-level performance to researchers.
+\item Today's frontier: Claude, GPT-4, Gemini, Llama-3.
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org15c11cb}]{The LLM Lifecycle}
+\begin{example}[Pre-training, Fine-tuning, Inference]\label{sec:orgb3c88e1}
+\pause
+\begin{itemize}[<+->]
+\item \alert{Pre-training}: The expensive part. Read the internet (TB of text), learn to predict next token. Learns grammar, facts, reasoning.
+\item \alert{Fine-tuning}: Specialization.
+\begin{itemize}
+\item \alert{SFT}: Train on Q\&A pairs to make it a chatbot.
+\item \alert{RLHF}: Align to be helpful and harmless.
+\end{itemize}
+\item \alert{Inference}: Using the model. Feed prompt, generate response.
+\end{itemize}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:org78ea468}]{Tokenization}
+\begin{block}{Tokens and Context Windows}
+\pause
+LLMs don't see words; they see \alert{Tokens}:
+\begin{itemize}[<+->]
+\item A token can be a word ("apple"), part of a word ("ing"), or a character.
+\item \alert{Rough rule}: 1000 tokens \(\approx\) 750 words.
+\item \alert{Context Window}: Limit on how much text the model remembers at once.
+\item GPT-4: 128k tokens. Claude: 200k tokens.
+\end{itemize}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org9e0299a}]{Prompt Engineering}
+\begin{example}[Guiding the Predictor]\label{sec:org6ca44ac}
+\pause
+LLMs are "next token predictors." They are sensitive to \emph{how} you ask:
+\begin{itemize}[<+->]
+\item \alert{Zero-Shot}: Just asking. "Translate to Spanish: Hello"
+\item \alert{Few-Shot}: Giving examples first. "Red \(\to\) Rojo, Blue \(\to\) Azul, Green \(\to\) ?"
+\item \alert{Chain of Thought}: "Think step by step." Dramatically improves reasoning.
+\end{itemize}
+\end{example}
+\end{frame}
+\section{Generative AI and Modern Applications}
+\label{sec:orgfba8ff4}
+\begin{frame}[label={sec:org56adcf2}]{Text Generation}
+\begin{definition}[It's Just Statistics]\label{sec:orga163662}
+\pause
+At its core, a Generative LLM is a \alert{Next-Token Prediction Engine}:
+\pause
+Given "The sky is", it calculates probabilities:
+\begin{itemize}[<+->]
+\item "blue": 80\%
+\item "gray": 15\%
+\item "falling": 1\%
+\item It samples from this distribution to generate text.
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org575fc53}]{Retrieval-Augmented Generation (RAG)}
+\begin{example}[Solving LLM Limitations]\label{sec:org37c623c}
+\pause
+LLMs have two major flaws:
+\begin{itemize}[<+->]
+\item \alert{Hallucination}: They confidently make things up.
+\item \alert{Cut-off Date}: They don't know current events or private data.
+\item \alert{RAG} connects LLM to external knowledge:
+\begin{enumerate}
+\item \alert{Retrieve}: Search database for relevant documents.
+\item \alert{Augment}: Paste documents into prompt context.
+\item \alert{Generate}: LLM answers using provided context.
+\end{enumerate}
+\end{itemize}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:orga782429}]{Diffusion Models}
+\begin{block}{Image Generation (Stable Diffusion, DALL-E)}
+\pause
+Image generation works differently from LLMs, using \alert{Diffusion}:
+\begin{itemize}[<+->]
+\item \alert{Forward Process (Training)}: Take clear image, slowly add noise until pure static. Model learns destruction pattern.
+\item \alert{Reverse Process (Generation)}: Start with noise. Ask: "If this was a cat, how would you remove noise?" Repeat 50 times.
+\item Result: Clear image emerges from noise.
+\end{itemize}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org31c84ae}]{Ethical Considerations}
+\begin{example}[Bias, Hallucination, and Copyright]\label{sec:org6fee54d}
+\pause
+With great power comes great responsibility:
+\begin{itemize}[<+->]
+\item \alert{Bias}: Models trained on internet data reflect stereotypes. "Doctor" \(\to\) men, "Nurse" \(\to\) women.
+\item \alert{Hallucination}: LLMs optimize for plausibility, not truth. Dangerous for medical/legal advice.
+\item \alert{Copyright}: Image models trained on artists' work. Code models on GitHub. Legal framework still evolving.
+\item \alert{Mitigation}: Data curation, RLHF, human oversight.
+\end{itemize}
+\end{example}
+\end{frame}
+\section{Summary}
+\label{sec:org89d6fcb}
+\begin{frame}[label={sec:org158a95d}]{Key Takeaways}
+\begin{block}{Neural Networks II}
+\pause
+\begin{enumerate}[<+->]
+\item \alert{CNNs}: Use convolutions and pooling to efficiently process images. ResNet enables very deep networks.
+\item \alert{Transformers}: Replaced RNNs with parallel self-attention. The foundation of modern NLP.
+\item \alert{LLMs}: Giant Transformers (BERT, GPT, Llama) that predict next tokens. Power chatbots and assistants.
+\item \alert{Generative AI}: Text (next-token), Images (diffusion). RAG grounds LLMs in facts.
+\item \alert{Ethics}: AI reflects training data. Requires human oversight and responsibility.
+\end{enumerate}
+\end{block}
+\end{frame}
+\end{document}

From 318b7688d99f864aaff8826e89c4328f4b83a5c4 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Thu, 5 Feb 2026 16:03:25 +0100
Subject: [PATCH 66/81] S06C01 Convolutional Neural Networks

---
 ...Convolutional Neural Networks (CNNs).ipynb | 561 ++++++++++++++++++
 1 file changed, 561 insertions(+)
 create mode 100644 src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C01 Convolutional Neural Networks (CNNs).ipynb

diff --git a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C01 Convolutional Neural Networks (CNNs).ipynb b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C01 Convolutional Neural Networks (CNNs).ipynb
new file mode 100644
index 0000000..1ea9a5c
--- /dev/null
+++ b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C01 Convolutional Neural Networks (CNNs).ipynb	
@@ -0,0 +1,561 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "681dd101",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Convolutional Neural Networks (CNNs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55f7c04e",
+   "metadata": {},
+   "source": [
+    "## Why not MLPs?\n",
+    "\n",
+    "If you want to recognize a face using a standard Multi-Layer Perceptron (MLP), you have to flatten the image into a long line of pixels.\n",
+    "\n",
+    "-   **Problem 1**: You lose spatial structure (a nose is defined by being **above** a mouth, not just by pixel values).\n",
+    "-   **Problem 2**: Too many weights. A $1000 \\times 1000$ image has 1 million pixels. Connecting that to just 100 neurons requires **100 million weights**.\n",
+    "\n",
+    "**Convolutional Neural Networks (CNNs)** solve this by looking at local \"patches\" of the image rather than the whole thing at once."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35e948cc",
+   "metadata": {},
+   "source": [
+    "## 1. The Convolution Layer (`Conv2d`)\n",
+    "\n",
+    "This is the heart of the CNN. Instead of learning a weight for every pixel, we learn small **Filters** (or Kernels).\n",
+    "\n",
+    "-   A filter might be $3 \\times 3$ pixels.\n",
+    "-   It slides (convolves) over the image, calculating the dot product at each position.\n",
+    "-   This creates a **Feature Map** representing where that pattern appeared."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba83a350",
+   "metadata": {},
+   "source": [
+    "### Visualizing the Convolution\n",
+    "\n",
+    "Let's see how a simple edge-detecting filter works on an image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8d1a55c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.signal import convolve2d\n",
+    "\n",
+    "# Create a simple 8x8 image with a vertical edge\n",
+    "image = np.zeros((8, 8))\n",
+    "image[:, 4:] = 1  # Right half is white\n",
+    "\n",
+    "# Define a vertical edge detector (Sobel-like)\n",
+    "filter_v = np.array([[-1, 0, 1],\n",
+    "                     [-1, 0, 1],\n",
+    "                     [-1, 0, 1]])\n",
+    "\n",
+    "# Convolve\n",
+    "output = convolve2d(image, filter_v, mode='valid')\n",
+    "\n",
+    "# Visualize\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n",
+    "\n",
+    "axes[0].imshow(image, cmap='gray')\n",
+    "axes[0].set_title(\"Input Image (8x8)\")\n",
+    "axes[0].axis('off')\n",
+    "\n",
+    "axes[1].imshow(filter_v, cmap='RdBu', vmin=-1, vmax=1)\n",
+    "axes[1].set_title(\"Filter (3x3)\")\n",
+    "for i in range(3):\n",
+    "    for j in range(3):\n",
+    "        axes[1].text(j, i, f'{filter_v[i,j]:+d}', ha='center', va='center')\n",
+    "axes[1].axis('off')\n",
+    "\n",
+    "axes[2].imshow(output, cmap='gray')\n",
+    "axes[2].set_title(f\"Feature Map ({output.shape[0]}x{output.shape[1]})\")\n",
+    "axes[2].axis('off')\n",
+    "\n",
+    "plt.suptitle(\"Convolution: Filter Detects Vertical Edge\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8891d863",
+   "metadata": {},
+   "source": [
+    "**Observation**: The filter \"fires\" (produces high values) where it detects a vertical edge in the image."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a5bf3be",
+   "metadata": {},
+   "source": [
+    "## 2. Controlling Dimensions: Padding & Stride\n",
+    "\n",
+    "As the filter slides, the image shrinks.\n",
+    "\n",
+    "-   **Valid Convolution**: No padding. The output shrinks (e.g., $5 \\times 5 \\to 3 \\times 3$).\n",
+    "-   **Padding**: Adding zeros around the border to maintain the size ($5 \\times 5 \\to 5 \\times 5$).\n",
+    "-   **Stride**: How many pixels we jump. Stride=2 halves the size (downsampling)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5a791fb",
+   "metadata": {},
+   "source": [
+    "## 3. Pooling Layers\n",
+    "\n",
+    "Pooling progressively reduces the spatial size (and parameters) to control overfitting and computational cost.\n",
+    "\n",
+    "| Type                       | Operation           | Use Case                                       |\n",
+    "|-------------------------- |------------------- |---------------------------------------------- |\n",
+    "| **Max Pooling**            | Takes largest value | Feature detection (\"Is there an edge here?\")   |\n",
+    "| **Average Pooling**        | Takes mean value    | Smoother downsampling, less aggressive         |\n",
+    "| **Global Average Pooling** | Mean of entire map  | Replaces Fully Connected layers in modern CNNs |\n",
+    "\n",
+    "-   **Max Pooling (`MaxPool2d`)**: The most common. Keeps the strongest activation in each patch.\n",
+    "-   **Average Pooling (`AvgPool2d`)**: Smoother, sometimes used instead of max pooling.\n",
+    "-   **Global Average Pooling (`AdaptiveAvgPool2d(1)`)**: Reduces entire feature map to a single value per channel. Used in ResNet/EfficientNet to avoid massive Linear layers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b475552",
+   "metadata": {},
+   "source": [
+    "## 4. Common Architectures\n",
+    "\n",
+    "Modern Deep Learning is built on famous architectures:\n",
+    "\n",
+    "-   **VGG (2014)**: Simplicity. Stacks many small $3 \\times 3$ filters. Very deep, very heavy.\n",
+    "-   **ResNet (2015)**: Introduced **Skip Connections** (Residuals). Allows training networks with 100+ layers by letting gradients \"skip\" layers to avoid vanishing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30f3eeff",
+   "metadata": {},
+   "source": [
+    "### The Skip Connection (Residual Block)\n",
+    "\n",
+    "The key innovation in ResNet is simple: add the input back to the output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "66085083",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input: torch.Size([1, 64, 28, 28]) -> Output: torch.Size([1, 64, 28, 28])\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "class ResidualBlock(nn.Module):\n",
+    "    \"\"\"A basic building block of ResNet.\"\"\"\n",
+    "    def __init__(self, channels):\n",
+    "        super().__init__()\n",
+    "        self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)\n",
+    "        self.bn1 = nn.BatchNorm2d(channels)\n",
+    "        self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)\n",
+    "        self.bn2 = nn.BatchNorm2d(channels)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        residual = x  # Save the input (the \"skip\")\n",
+    "\n",
+    "        x = F.relu(self.bn1(self.conv1(x)))\n",
+    "        x = self.bn2(self.conv2(x))\n",
+    "\n",
+    "        x = x + residual  # Add the original input back!\n",
+    "        return F.relu(x)\n",
+    "\n",
+    "# Test it\n",
+    "block = ResidualBlock(channels=64)\n",
+    "test_input = torch.randn(1, 64, 28, 28)\n",
+    "output = block(test_input)\n",
+    "print(f\"Input: {test_input.shape} -> Output: {output.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28e633ad",
+   "metadata": {},
+   "source": [
+    "**Key Insight**: The `x + residual` allows gradients to flow directly through the addition, bypassing the convolutions if needed. This prevents vanishing gradients in very deep networks."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b07fc31",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: Building a CNN in PyTorch\n",
+    "\n",
+    "We will build a simple CNN to classify digits (`MNIST`) or clothing (`FashionMNIST`). We focus on tracking the **Tensor Shapes**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51648666",
+   "metadata": {},
+   "source": [
+    "### The Anatomy of a CNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5e01f8ec",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Output Shape: torch.Size([1, 10]) (Should be [1, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "\n",
+    "class SimpleCNN(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Block 1: Feature Extraction\n",
+    "        # Input: (1, 28, 28) -> Output: (32, 28, 28)\n",
+    "        self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, padding=1)\n",
+    "        self.relu1 = nn.ReLU()\n",
+    "        # Input: (32, 28, 28) -> Output: (32, 14, 14)\n",
+    "        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
+    "\n",
+    "        # Block 2: Deeper Features\n",
+    "        # Input: (32, 14, 14) -> Output: (64, 14, 14)\n",
+    "        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1)\n",
+    "        self.relu2 = nn.ReLU()\n",
+    "        # Input: (64, 14, 14) -> Output: (64, 7, 7)\n",
+    "        self.pool2 = nn.MaxPool2d(2)\n",
+    "\n",
+    "        # Block 3: Classification (The Flattening)\n",
+    "        self.flatten = nn.Flatten()\n",
+    "        # Why 64 * 7 * 7? Because that's the shape coming out of pool2\n",
+    "        self.fc1 = nn.Linear(64 * 7 * 7, 10) # 10 classes\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.pool1(self.relu1(self.conv1(x)))\n",
+    "        x = self.pool2(self.relu2(self.conv2(x)))\n",
+    "        x = self.flatten(x)\n",
+    "        x = self.fc1(x)\n",
+    "        return x\n",
+    "\n",
+    "model = SimpleCNN()\n",
+    "# Print shape check with dummy data\n",
+    "dummy_input = torch.randn(1, 1, 28, 28) # Batch=1, Channel=1, H=28, W=28\n",
+    "output = model(dummy_input)\n",
+    "print(f\"Output Shape: {output.shape} (Should be [1, 10])\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2e13e5d",
+   "metadata": {},
+   "source": [
+    "### Training on FashionMNIST\n",
+    "\n",
+    "Let's train our CNN on real data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "78c26775",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████| 26.4M/26.4M [00:00<00:00, 46.7MB/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████| 29.5k/29.5k [00:00<00:00, 1.67MB/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████| 4.42M/4.42M [00:00<00:00, 13.7MB/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████| 5.15k/5.15k [00:00<00:00, 23.8MB/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1, Loss: 0.4587\n",
+      "Epoch 2, Loss: 0.3067\n",
+      "Epoch 3, Loss: 0.2667\n",
+      "Training Complete!\n"
+     ]
+    }
+   ],
+   "source": [
+    "from torchvision import datasets, transforms\n",
+    "from torch.utils.data import DataLoader\n",
+    "import torch.optim as optim\n",
+    "\n",
+    "# 1. Load FashionMNIST\n",
+    "transform = transforms.ToTensor()\n",
+    "train_data = datasets.FashionMNIST(root='./data', train=True,\n",
+    "                                    download=True, transform=transform)\n",
+    "train_loader = DataLoader(train_data, batch_size=64, shuffle=True)\n",
+    "\n",
+    "# 2. Setup\n",
+    "model = SimpleCNN()\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
+    "\n",
+    "# 3. Training Loop (3 epochs for demo)\n",
+    "for epoch in range(3):\n",
+    "    running_loss = 0.0\n",
+    "    for images, labels in train_loader:\n",
+    "        # Forward\n",
+    "        outputs = model(images)\n",
+    "        loss = criterion(outputs, labels)\n",
+    "\n",
+    "        # Backward\n",
+    "        optimizer.zero_grad()\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        running_loss += loss.item()\n",
+    "\n",
+    "    print(f\"Epoch {epoch+1}, Loss: {running_loss/len(train_loader):.4f}\")\n",
+    "\n",
+    "print(\"Training Complete!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f75fe6de",
+   "metadata": {},
+   "source": [
+    "### Visualizing What the CNN Learned\n",
+    "\n",
+    "After training, we can see what the first layer's filters detect."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "470024b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Get a sample image\n",
+    "sample_img, label = train_data[0]\n",
+    "\n",
+    "# Hook to capture activations\n",
+    "activations = {}\n",
+    "def get_activation(name):\n",
+    "    def hook(model, input, output):\n",
+    "        activations[name] = output.detach()\n",
+    "    return hook\n",
+    "\n",
+    "# Register hook on first conv layer\n",
+    "model.conv1.register_forward_hook(get_activation('conv1'))\n",
+    "\n",
+    "# Forward pass\n",
+    "with torch.no_grad():\n",
+    "    _ = model(sample_img.unsqueeze(0))\n",
+    "\n",
+    "# Plot first 8 feature maps\n",
+    "fig, axes = plt.subplots(2, 5, figsize=(12, 5))\n",
+    "axes[0, 0].imshow(sample_img.squeeze(), cmap='gray')\n",
+    "axes[0, 0].set_title(\"Original\")\n",
+    "axes[0, 0].axis('off')\n",
+    "\n",
+    "for i, ax in enumerate(axes.flat[1:9]):\n",
+    "    ax.imshow(activations['conv1'][0, i].numpy(), cmap='viridis')\n",
+    "    ax.set_title(f\"Filter {i+1}\")\n",
+    "    ax.axis('off')\n",
+    "\n",
+    "plt.suptitle(\"Feature Maps from First Conv Layer\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b68d0a3c",
+   "metadata": {},
+   "source": [
+    "**Observation**: Each filter detects different patterns (edges, textures, shapes)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d89bc91e",
+   "metadata": {},
+   "source": [
+    "### Using Pretrained Models (ResNet)\n",
+    "\n",
+    "In practice, we rarely build from scratch. We use **Transfer Learning** with models like *ResNet*. `torchvision` makes this easy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "525fdb77",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /home/camarocico/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████| 44.7M/44.7M [00:00<00:00, 55.4MB/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Output Layer: Linear(in_features=512, out_features=1000, bias=True)\n",
+      "New Output Layer:      Linear(in_features=512, out_features=2, bias=True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from torchvision import models\n",
+    "\n",
+    "# Load a ResNet18\n",
+    "# weights='DEFAULT' loads weights trained on ImageNet (1000 classes)\n",
+    "resnet = models.resnet18(weights='DEFAULT')\n",
+    "\n",
+    "print(\"Original Output Layer:\", resnet.fc)\n",
+    "\n",
+    "# Modify for our task (e.g., 2 classes instead of 1000)\n",
+    "# in_features allows us to keep the connection to the previous layer\n",
+    "resnet.fc = nn.Linear(resnet.fc.in_features, 2)\n",
+    "\n",
+    "print(\"New Output Layer:     \", resnet.fc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68503a57",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5a8f1c6",
+   "metadata": {},
+   "source": [
+    "### Calculating Shapes\n",
+    "\n",
+    "Understanding shape changes is crucial. Given an input of $(1, 32, 32)$:\n",
+    "\n",
+    "1.  Apply `Conv2d(1, 16, kernel_size=5, stride=1, padding=0)`. What is the new size? **Formula**: $O = \\frac{I - K + 2P}{S} + 1$\n",
+    "2.  Apply `MaxPool2d(2)`. What is the new size?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfe29542",
+   "metadata": {},
+   "source": [
+    "### Implementing a VGG Block\n",
+    "\n",
+    "A \"VGG Block\" consists of: `Conv3x3` -> `ReLU` -> `Conv3x3` -> `ReLU` -> `MaxPool` Implement this as a `nn.Sequential` module."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "844a7516",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **Conv Layers**: Look for local patterns (edges, textures). Use `padding` to keep size, `stride` to shrink.\n",
+    "2.  **Pooling**: Summarizes features and reduces size.\n",
+    "3.  **Shapes**: Always track your $H \\times W$ dimensions. If they reach 0 or mismatch the Linear layer, the code crashes.\n",
+    "4.  **ResNet**: The standard architecture for most image tasks today."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 33ca7d51b8043d61437a92a0fbd6211aad35333c Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Thu, 5 Feb 2026 16:03:41 +0100
Subject: [PATCH 67/81] S06C02 From RNNs to Transformers

---
 .../S06C02 From RNNs to Transformers.ipynb    | 407 ++++++++++++++++++
 1 file changed, 407 insertions(+)
 create mode 100644 src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C02 From RNNs to Transformers.ipynb

diff --git a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C02 From RNNs to Transformers.ipynb b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C02 From RNNs to Transformers.ipynb
new file mode 100644
index 0000000..18c43f5
--- /dev/null
+++ b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C02 From RNNs to Transformers.ipynb	
@@ -0,0 +1,407 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d2526f7d",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# From RNNs to Transformers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91a30549",
+   "metadata": {},
+   "source": [
+    "## The Problem with Sequence Data\n",
+    "\n",
+    "Standard Neural Networks (MLPs) expect a fixed-size input. But language is dynamic:\n",
+    "\n",
+    "-   \"Hello\" (1 word)\n",
+    "-   \"The quick brown fox&#x2026;\" (5 words)\n",
+    "\n",
+    "We need models that can handle variable-length sequences and \"remember\" context."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7544465f",
+   "metadata": {},
+   "source": [
+    "## Recurrent Neural Networks (RNNs)\n",
+    "\n",
+    "The RNN was the first solution. It processes words one by one, maintaining a **Hidden State** (memory).\n",
+    "\n",
+    "-   **Step 1**: Read \"The\". Update Memory.\n",
+    "-   **Step 2**: Read \"quick\". Update Memory based on \"quick\" + \"Old Memory\".\n",
+    "\n",
+    "$$h_t = \\tanh(W x_t + U h_{t-1})$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "deb89289",
+   "metadata": {},
+   "source": [
+    "### The Bottleneck\n",
+    "\n",
+    "1.  **Sequential**: You cannot process the 100th word until you calculate the 99th. This makes training very slow (cannot parallelize).\n",
+    "2.  **Vanishing Memory**: By the time the RNN reaches the end of a long paragraph, it has forgotten the beginning.\n",
+    "\n",
+    "**LSTMs (Long Short-Term Memory)** improved this with \"Gates\" (Forget Gate, Input Gate) to keep memory longer, but they were still slow."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59b16d64",
+   "metadata": {},
+   "source": [
+    "### Visualizing Sequential Processing\n",
+    "\n",
+    "Let's see how the hidden state evolves as an RNN reads a sentence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52b05736",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "\n",
+    "# A simple RNN cell\n",
+    "rnn_cell = nn.RNNCell(input_size=4, hidden_size=4)\n",
+    "\n",
+    "# Simulate 3 word embeddings (simplified)\n",
+    "words = [\"The\", \"cat\", \"sat\"]\n",
+    "embeddings = [torch.randn(1, 4) for _ in words]\n",
+    "\n",
+    "# Process sequentially\n",
+    "hidden = torch.zeros(1, 4)  # Initial memory (blank slate)\n",
+    "\n",
+    "print(\"RNN Processing (Each step waits for the previous):\")\n",
+    "for i, (word, emb) in enumerate(zip(words, embeddings)):\n",
+    "    hidden = rnn_cell(emb, hidden)  # Update memory\n",
+    "    print(f\"  Step {i+1}: Read '{word}' -> Hidden: {hidden[0,:2].tolist()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4310daf1",
+   "metadata": {},
+   "source": [
+    "**Key Point**: Notice how each step **must wait** for the previous hidden state. This is the sequential bottleneck."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7d28adb",
+   "metadata": {},
+   "source": [
+    "## The Attention Mechanism\n",
+    "\n",
+    "In 2014, researchers asked: **\"Instead of trying to squash the whole sentence into one tiny memory vector, why not let the model 'look back' at any word it wants?\"**\n",
+    "\n",
+    "This is **Attention**. When predicting the next word, the model assigns a \"weight\" to every previous word.\n",
+    "\n",
+    "-   **\"I poured cereal into the&#x2026;\"** -> Focus on \"cereal\" and \"poured\". Predict: \"bowl\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73dbefd6",
+   "metadata": {},
+   "source": [
+    "## The Transformer (2017)\n",
+    "\n",
+    "The paper **\"Attention is All You Need\"** changed everything. It proved we don't need recurrence (RNNs) at all. We can process the **entire sentence at once** using **Self-Attention**.\n",
+    "\n",
+    "| Feature        | RNN/LSTM             | Transformer                      |\n",
+    "|-------------- |-------------------- |-------------------------------- |\n",
+    "| **Processing** | Sequential (Slow)    | Parallel (Fast)                  |\n",
+    "| **Context**    | Local (Nearby words) | Global (All words see all words) |\n",
+    "| **Distance**   | Linearly far         | Constant (1 step away)           |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e2252da",
+   "metadata": {},
+   "source": [
+    "### The Architecture\n",
+    "\n",
+    "1.  **Encoder**: Reads the input text and creates a rich representation. (Used in **BERT**).\n",
+    "2.  **Decoder**: Generates output text step-by-step. (Used in **GPT**).\n",
+    "3.  **Positional Encoding**: Since Transformers process parallelly, they don't know word order. We inject \"Position\" numbers so \"Dog bites Man\" $\\ne$ \"Man bites Dog\".\n",
+    "4.  **Feed-Forward Network (FFN)**: After attention, each position passes through a small MLP independently. This adds non-linearity and increases model capacity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "401ce085",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: Self-Attention in PyTorch\n",
+    "\n",
+    "We will implement the mathematical core of the Transformer: **Scaled Dot-Product Attention**.\n",
+    "\n",
+    "$$Attention(Q, K, V) = \\text{softmax}\\left(\\frac{QK^T}{\\sqrt{d_k}}\\right)V$$\n",
+    "\n",
+    "-   **Query (Q)**: What I am looking for?\n",
+    "-   **Key (K)**: What can I offer?\n",
+    "-   **Value (V)**: What information do I actually hold?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b79b4867",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# 1. Setup Dummy Data\n",
+    "# A sentence with 3 words, each represented by a vector of size 4\n",
+    "batch_size = 1\n",
+    "seq_len = 3   # \"I love AI\"\n",
+    "d_model = 4   # Embedding Dimension\n",
+    "\n",
+    "torch.manual_seed(42)\n",
+    "x = torch.randn(batch_size, seq_len, d_model)\n",
+    "\n",
+    "print(f\"Input Shape: {x.shape}\")\n",
+    "\n",
+    "# 2. Define Projection Matrices (Wq, Wk, Wv)\n",
+    "# In a real model, these are learned weights\n",
+    "W_q = torch.nn.Linear(d_model, d_model)\n",
+    "W_k = torch.nn.Linear(d_model, d_model)\n",
+    "W_v = torch.nn.Linear(d_model, d_model)\n",
+    "\n",
+    "# 3. Create Q, K, V\n",
+    "Q = W_q(x)\n",
+    "K = W_k(x)\n",
+    "V = W_v(x)\n",
+    "\n",
+    "# 4. Calculate Attention Scores (Q * K^T)\n",
+    "# transpose(-2, -1) swaps the last two dimensions for matrix multiplication\n",
+    "scores = torch.matmul(Q, K.transpose(-2, -1)) / np.sqrt(d_model)\n",
+    "\n",
+    "print(f\"Raw Scores (Word vs Word):\\n{scores[0].detach().numpy()}\")\n",
+    "\n",
+    "# 5. Softmax (Turn into probabilities)\n",
+    "attention_weights = F.softmax(scores, dim=-1)\n",
+    "\n",
+    "print(f\"\\nAttention Weights (Sum to 1):\\n{attention_weights[0].detach().numpy()}\")\n",
+    "\n",
+    "# 6. Final Output (Weights * V)\n",
+    "output = torch.matmul(attention_weights, V)\n",
+    "print(f\"\\nOutput Shape: {output.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8052804",
+   "metadata": {},
+   "source": [
+    "### Visualizing Attention\n",
+    "\n",
+    "This heatmap shows how much each word \"attends\" to every other word."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ff58c94",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "plt.figure(figsize=(6, 5))\n",
+    "sns.heatmap(attention_weights[0].detach().numpy(), \n",
+    "            xticklabels=[\"I\", \"Love\", \"AI\"], \n",
+    "            yticklabels=[\"I\", \"Love\", \"AI\"], \n",
+    "            annot=True, cmap='Blues')\n",
+    "plt.title(\"Self-Attention Weights\")\n",
+    "plt.xlabel(\"Key (Source)\")\n",
+    "plt.ylabel(\"Query (Target)\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40867a45",
+   "metadata": {},
+   "source": [
+    "### Causal Masking (For Decoders/GPT)\n",
+    "\n",
+    "In generation tasks, a word should **not** see future words (that would be cheating). We use a **Causal Mask** to block the future."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f342176c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a mask that blocks \"future\" positions\n",
+    "# Upper triangle filled with -infinity (becomes 0 after softmax)\n",
+    "seq_len = 3\n",
+    "mask = torch.triu(torch.ones(seq_len, seq_len), diagonal=1) * float('-inf')\n",
+    "\n",
+    "print(\"Causal Mask (0 = can see, -inf = blocked):\")\n",
+    "print(mask)\n",
+    "\n",
+    "# Apply mask to attention scores\n",
+    "masked_scores = scores + mask\n",
+    "masked_weights = F.softmax(masked_scores, dim=-1)\n",
+    "\n",
+    "print(\"\\nMasked Attention Weights:\")\n",
+    "print(masked_weights[0].detach().numpy())\n",
+    "# Word 1 sees only itself, Word 2 sees 1-2, Word 3 sees all"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13429c1d",
+   "metadata": {},
+   "source": [
+    "**Key Point**: This is why GPT models generate text one token at a time - each new token can only \"see\" previous tokens."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82fe9389",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: Using a Pretrained Transformer (Hugging Face)\n",
+    "\n",
+    "We rarely build Transformers from scratch. We use libraries like `transformers`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7833fa2d",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import BertTokenizer, BertModel\n",
+    "\n",
+    "# 1. Load Pretrained BERT\n",
+    "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
+    "model = BertModel.from_pretrained('bert-base-uncased')\n",
+    "\n",
+    "# 2. Tokenize Input\n",
+    "text = \"Transformers are fast.\"\n",
+    "inputs = tokenizer(text, return_tensors=\"pt\")\n",
+    "print(f\"Token IDs: {inputs['input_ids']}\")\n",
+    "\n",
+    "# See what BERT actually sees (word pieces + special tokens)\n",
+    "tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0])\n",
+    "print(f\"Tokens: {tokens}\")  # ['[CLS]', 'transformers', 'are', 'fast', '.', '[SEP]']\n",
+    "\n",
+    "# 3. Forward Pass\n",
+    "with torch.no_grad():\n",
+    "    outputs = model(**inputs)\n",
+    "\n",
+    "# The \"Hidden States\" representing the meaning of the sentence\n",
+    "# Shape: (Batch, Seq_Len, Hidden_Dim) -> (1, 6, 768)\n",
+    "last_hidden_states = outputs.last_hidden_state\n",
+    "print(f\"Contextual Embeddings Shape: {last_hidden_states.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77ce1e76",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "820a10c1",
+   "metadata": {},
+   "source": [
+    "### Positional Encoding\n",
+    "\n",
+    "Without this, the model treats \"I eat\" the same as \"Eat I\".\n",
+    "\n",
+    "**Real Transformers** use sinusoidal functions that encode position smoothly: $$PE_{(pos, 2i)} = \\sin(pos / 10000^{2i/d})$$ $$PE_{(pos, 2i+1)} = \\cos(pos / 10000^{2i/d})$$\n",
+    "\n",
+    "This allows the model to learn relative positions (e.g., \"3 words apart\") more easily than naive integers.\n",
+    "\n",
+    "**Exercise**: Try a simplified version:\n",
+    "\n",
+    "-   Create a random tensor.\n",
+    "-   Add a \"positional vector\" to it (e.g., adding 1 to the first word, 2 to the second).\n",
+    "-   Check how this changes the attention scores (Run the QK calculation again)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0dac2ccc",
+   "metadata": {},
+   "source": [
+    "### Multi-Head Attention\n",
+    "\n",
+    "The real power comes from having **multiple** sets of Q, K, V (Heads), allowing the model to focus on grammar AND meaning simultaneously.\n",
+    "\n",
+    "-   Use `torch.nn.MultiheadAttention(embed_dim=4, num_heads=2)`.\n",
+    "-   Pass `x` as both query, key, and value."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3307f758",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **RNNs**: Sequential, slow, forgetful. Good for time-series, bad for long text.\n",
+    "2.  **Attention**: The ability to look at the whole sequence at once.\n",
+    "3.  **Transformer**: Uses Self-Attention + Positional Encoding to process text in parallel.\n",
+    "4.  **BERT/GPT**: Giant Transformers pre-trained on the internet."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 8c03fc9bb5870a6d7d25c7a4eae83f7b9aeca704 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Thu, 5 Feb 2026 16:03:58 +0100
Subject: [PATCH 68/81] S06C03 Large Language Models (LLMs)

---
 .../S06C03 Large Language Models (LLMs).ipynb | 349 ++++++++++++++++++
 1 file changed, 349 insertions(+)
 create mode 100644 src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C03 Large Language Models (LLMs).ipynb

diff --git a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C03 Large Language Models (LLMs).ipynb b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C03 Large Language Models (LLMs).ipynb
new file mode 100644
index 0000000..afa0b09
--- /dev/null
+++ b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C03 Large Language Models (LLMs).ipynb	
@@ -0,0 +1,349 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a531b9bf",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Large Language Models (LLMs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fa499b7",
+   "metadata": {},
+   "source": [
+    "## Evolution of LLMs\n",
+    "\n",
+    "The field of NLP has evolved rapidly—and **scale** has been the key driver.\n",
+    "\n",
+    "| Model       | Year | Parameters | Training Data | Notable Achievement                  |\n",
+    "|----------- |---- |---------- |------------- |------------------------------------ |\n",
+    "| **BERT**    | 2018 | 340M       | 16GB          | Revolutionized \"understanding\" tasks |\n",
+    "| **GPT-2**   | 2019 | 1.5B       | 40GB          | \"Too dangerous to release\"           |\n",
+    "| **GPT-3**   | 2020 | 175B       | 570GB         | Emergent few-shot abilities          |\n",
+    "| **Llama-2** | 2023 | 70B        | 2TB           | Open-source revolution               |\n",
+    "| **GPT-4**   | 2023 | ~1.7T?     | Unknown       | Multimodal (text + images)           |\n",
+    "\n",
+    "Key architectures:\n",
+    "\n",
+    "1.  **BERT (Encoder-only)**: Good at \"understanding\" (Classification, Q&A). Reads text and produces vectors.\n",
+    "2.  **GPT (Decoder-only)**: Good at \"generation\" (Writing text). Predicts the next token.\n",
+    "3.  **T5 (Encoder-Decoder)**: Treats everything as \"text-to-text\" (Translation, Summarization).\n",
+    "\n",
+    "**The Scaling Hypothesis**: Performance improves predictably with more parameters, more data, and more compute. This drove the race from millions to trillions of parameters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0519d0f9",
+   "metadata": {},
+   "source": [
+    "## The Lifecycle of an LLM\n",
+    "\n",
+    "1.  **Pre-training**: The expensive part. The model reads the internet (TB of text) and learns to predict the next token. It learns grammar, facts, and reasoning.\n",
+    "2.  **Fine-tuning**: The specialization part.\n",
+    "    -   **SFT (Supervised Fine-Tuning)**: Train on Q&A pairs to make it a chatbot instead of a document completer.\n",
+    "    -   **RLHF (Reinforcement Learning from Human Feedback)**: Aligning the model to be helpful and harmless.\n",
+    "3.  **Inference**: Using the model. We feed a prompt, and it generates the response."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b1f0521",
+   "metadata": {},
+   "source": [
+    "## Tokenization & Context Windows\n",
+    "\n",
+    "LLMs do not see words; they see **Tokens**.\n",
+    "\n",
+    "-   A token can be a word (\"apple\"), a part of a word (\"ing\"), or a character (\"a\").\n",
+    "-   **Rough rule**: 1000 tokens $\\approx$ 750 words.\n",
+    "-   **Context Window**: The limit on how much text the model can remember at once (e.g., GPT-4 has 128k tokens)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0762388f",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: Tokenization\n",
+    "\n",
+    "We will use `transformers` to see how text is broken down."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56e35e9d",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import AutoTokenizer\n",
+    "\n",
+    "# Load a standard tokenizer (GPT-2 style)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\n",
+    "\n",
+    "text = \"LLMs are fascinating!\"\n",
+    "\n",
+    "# 1. Encode (Text -> Integers)\n",
+    "tokens = tokenizer.encode(text)\n",
+    "print(f\"Token IDs: {tokens}\")\n",
+    "\n",
+    "# 2. Decode (Integers -> Text)\n",
+    "decoded = [tokenizer.decode([t]) for t in tokens]\n",
+    "print(f\"Token Parts: {decoded}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab7af6fc",
+   "metadata": {},
+   "source": [
+    "### Why Subwords? Common vs Rare Words\n",
+    "\n",
+    "Tokenizers use **Byte Pair Encoding (BPE)** to handle any word, even ones never seen in training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bacba15a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compare token counts for different words\n",
+    "test_words = [\"hello\", \"AI\", \"Anthropic\", \"TensorFlow\", \"🤖\"]\n",
+    "\n",
+    "print(\"Word -> Token Count -> Subwords\")\n",
+    "print(\"-\" * 40)\n",
+    "for word in test_words:\n",
+    "    tokens = tokenizer.encode(word)\n",
+    "    decoded = [tokenizer.decode([t]) for t in tokens]\n",
+    "    print(f\"'{word}' -> {len(tokens)} tokens: {decoded}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a8505a0",
+   "metadata": {},
+   "source": [
+    "**Observation**: Common words like \"hello\" are single tokens. Rare words like \"Anthropic\" get split into subwords. This is why LLMs can handle **any** text, even made-up words."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4172e7ef",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: Using a Pre-trained LLM\n",
+    "\n",
+    "We will use a small GPT-2 model to generate text. (We use GPT-2 because it fits in memory easily; modern models like Llama-3 require GPUs)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab765e90",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import AutoModelForCausalLM\n",
+    "import torch\n",
+    "\n",
+    "# Load Model\n",
+    "model = AutoModelForCausalLM.from_pretrained(\"gpt2\")\n",
+    "\n",
+    "# Prepare Input\n",
+    "prompt = \"The future of AI is\"\n",
+    "inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
+    "\n",
+    "# Generate\n",
+    "# max_new_tokens=20: Generate 20 words\n",
+    "# do_sample=True: Add randomness (creativity)\n",
+    "# temperature=0.7: Controls randomness (Lower = More focused)\n",
+    "output = model.generate(\n",
+    "    **inputs, \n",
+    "    max_new_tokens=20, \n",
+    "    do_sample=True, \n",
+    "    temperature=0.7,\n",
+    "    pad_token_id=tokenizer.eos_token_id\n",
+    ")\n",
+    "\n",
+    "result = tokenizer.decode(output[0])\n",
+    "print(f\"Prompt: {prompt}\")\n",
+    "print(f\"Generated: {result}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39f812de",
+   "metadata": {},
+   "source": [
+    "### Seeing the Probability Distribution\n",
+    "\n",
+    "Let's peek inside and see what the model **actually** considers as next-token candidates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b35ce99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch.nn.functional as F\n",
+    "\n",
+    "# Get model's raw predictions (logits) for our prompt\n",
+    "with torch.no_grad():\n",
+    "    outputs = model(inputs.input_ids)\n",
+    "    # Logits for the LAST position (predicting next token)\n",
+    "    logits = outputs.logits[0, -1, :]\n",
+    "\n",
+    "# Convert to probabilities\n",
+    "probs = F.softmax(logits, dim=0)\n",
+    "\n",
+    "# Get top 5 candidates\n",
+    "top_probs, top_indices = torch.topk(probs, 5)\n",
+    "\n",
+    "print(f\"Prompt: '{prompt}'\")\n",
+    "print(f\"\\nTop 5 next-token predictions:\")\n",
+    "for prob, idx in zip(top_probs, top_indices):\n",
+    "    token = tokenizer.decode([idx])\n",
+    "    print(f\"  '{token}': {prob.item()*100:.1f}%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91f5487f",
+   "metadata": {},
+   "source": [
+    "**Key Insight**: The model doesn't \"choose\" one word—it produces a probability distribution over **all** 50,000+ tokens. Generation is just sampling from this distribution repeatedly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c447a169",
+   "metadata": {},
+   "source": [
+    "## Prompt Engineering Basics\n",
+    "\n",
+    "Since LLMs are just \"next token predictors,\" they are sensitive to how you ask.\n",
+    "\n",
+    "1.  **Zero-Shot**: Just asking. **\"Translate to Spanish: Hello\"**\n",
+    "2.  **Few-Shot**: Giving examples. **\"English: Red, Spanish: Rojo\"** **\"English: Blue, Spanish: Azul\"** **\"English: Green, Spanish: &#x2026;\"**\n",
+    "3.  **Chain of Thought**: Asking the model to \"think step by step.\" This dramatically improves math and reasoning."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76899986",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "353818f8",
+   "metadata": {},
+   "source": [
+    "### Temperature Playground\n",
+    "\n",
+    "Temperature controls the \"risk\" the model takes.\n",
+    "\n",
+    "-   Generate text with `temperature=0.1` (Deterministic, boring).\n",
+    "-   Generate text with `temperature=1.5` (Chaotic, creative/nonsense)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "494a2de3",
+   "metadata": {},
+   "source": [
+    "### Sampling Strategies: Top-k and Top-p\n",
+    "\n",
+    "Temperature alone isn't enough. Modern LLMs use additional sampling strategies:\n",
+    "\n",
+    "| Parameter       | What it does                                           | Typical Value |\n",
+    "|--------------- |------------------------------------------------------ |------------- |\n",
+    "| **Temperature** | Sharpens or flattens the probability distribution      | 0.7           |\n",
+    "| **Top-k**       | Only consider the k most likely tokens                 | 50            |\n",
+    "| **Top-p**       | Only consider tokens until cumulative probability >= p | 0.9           |\n",
+    "\n",
+    "-   **Top-k=50**: \"Only consider the 50 most likely next words.\"\n",
+    "-   **Top-p=0.9** (Nucleus Sampling): \"Consider words until their probabilities sum to 90%.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b27157b7",
+   "metadata": {},
+   "source": [
+    "### Few-Shot Prompting\n",
+    "\n",
+    "Try to make GPT-2 solve a pattern matching task.\n",
+    "\n",
+    "-   Without examples, it might just continue the sentence randomly.\n",
+    "-   With examples, it should follow the pattern."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8c8777d",
+   "metadata": {},
+   "source": [
+    "### Chain of Thought (CoT)\n",
+    "\n",
+    "Adding \"Let's think step by step\" dramatically improves reasoning on complex problems.\n",
+    "\n",
+    "-   **Without CoT**: \"What is 17 × 24?\" → Model guesses (often wrong).\n",
+    "-   **With CoT**: \"What is 17 × 24? Let's think step by step.\" → Model shows working.\n",
+    "\n",
+    "**Key Point**: CoT works because LLMs predict **one token at a time**. By forcing intermediate steps into the output, we give the model \"scratch space\" to reason before producing the final answer."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68128b48",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **LLMs**: Predict the next token based on vast training data.\n",
+    "2.  **Architecture**: BERT (Encoder) for understanding, GPT (Decoder) for writing.\n",
+    "3.  **Tokens**: The atoms of language models. Not always full words.\n",
+    "4.  **Prompting**: The skill of guiding the predictor to the output you want by providing context and examples."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 5d19b2fffc9c31bcf6b0a802f8195ec0be998eea Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Thu, 5 Feb 2026 16:04:14 +0100
Subject: [PATCH 69/81] S06C04 Generative AI and Modern Applications

---
 ...enerative AI and Modern Applications.ipynb | 453 ++++++++++++++++++
 1 file changed, 453 insertions(+)
 create mode 100644 src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C04 Generative AI and Modern Applications.ipynb

diff --git a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C04 Generative AI and Modern Applications.ipynb b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C04 Generative AI and Modern Applications.ipynb
new file mode 100644
index 0000000..928a10c
--- /dev/null
+++ b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C04 Generative AI and Modern Applications.ipynb	
@@ -0,0 +1,453 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "567f1e3b",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Generative AI and Modern Applications"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2022154",
+   "metadata": {},
+   "source": [
+    "## Text Generation: It's Just Statistics\n",
+    "\n",
+    "At its core, a Generative LLM is a **Next-Token Prediction Engine**. It doesn't \"know\" facts; it knows probabilities. Given the sequence \"The sky is\", it calculates the probability distribution for the next word:\n",
+    "\n",
+    "-   \"blue\": 80%\n",
+    "-   \"gray\": 15%\n",
+    "-   \"falling\": 1%\n",
+    "\n",
+    "It samples from this distribution to generate text."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18bb893c",
+   "metadata": {},
+   "source": [
+    "## Retrieval-Augmented Generation (RAG)\n",
+    "\n",
+    "LLMs have two major flaws:\n",
+    "\n",
+    "1.  **Hallucination**: They make things up.\n",
+    "2.  **Cut-off Date**: They don't know current events or private company data.\n",
+    "\n",
+    "**RAG** solves this by connecting the LLM to an external library (like a Vector Database).\n",
+    "\n",
+    "-   **Step 1 (Retrieve)**: User asks a question. System searches the database for relevant documents.\n",
+    "-   **Step 2 (Augment)**: System pastes those documents into the prompt context.\n",
+    "-   **Step 3 (Generate)**: LLM answers the question using the provided context."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fcfa32f8",
+   "metadata": {},
+   "source": [
+    "### What are Embeddings? (The Secret Sauce)\n",
+    "\n",
+    "Before diving into RAG, we need to understand \\*Embeddings\\*—vectors that capture meaning.\n",
+    "\n",
+    "-   An **Embedding** is a list of numbers (e.g., 768 floats) representing a word, sentence, or document.\n",
+    "-   **Key Property**: Similar meanings → Similar vectors (measured by cosine similarity).\n",
+    "-   \"King\" and \"Queen\" are close in embedding space; \"King\" and \"Banana\" are far apart.\n",
+    "\n",
+    "Modern RAG uses **Sentence Embeddings** (from models like BERT) instead of keyword matching. This allows semantic search: \"What's the price?\" matches \"Product A costs $50\" even without shared words."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cc8c0bb",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: Building a \"Tiny RAG\"\n",
+    "\n",
+    "We will simulate a RAG system that answers questions about a fictional company using a private knowledge base."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cea98569",
+   "metadata": {},
+   "source": [
+    "### The Knowledge Base"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2a8f119",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.metrics.pairwise import cosine_similarity\n",
+    "import numpy as np\n",
+    "\n",
+    "# Our \"Private\" Database (LLM doesn't know this)\n",
+    "documents = [\n",
+    "    \"Product A costs $50 and includes a 1-year warranty.\",\n",
+    "    \"Product B is a subscription service costing $10/month.\",\n",
+    "    \"Support is available 24/7 via email at help@example.com.\",\n",
+    "    \"The CEO of the company is Jane Doe.\"\n",
+    "]\n",
+    "\n",
+    "# Indexing (Simple TF-IDF)\n",
+    "vectorizer = TfidfVectorizer()\n",
+    "doc_vectors = vectorizer.fit_transform(documents)\n",
+    "\n",
+    "print(f\"Indexed {len(documents)} documents.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "052edeb2",
+   "metadata": {},
+   "source": [
+    "### The Retrieval System\n",
+    "\n",
+    "When a user asks a question, we find the most similar document."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d211882a",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "def retrieve_info(query):\n",
+    "    # Vectorize query\n",
+    "    query_vec = vectorizer.transform([query])\n",
+    "\n",
+    "    # Calculate similarity\n",
+    "    similarities = cosine_similarity(query_vec, doc_vectors).flatten()\n",
+    "\n",
+    "    # Find best match\n",
+    "    best_idx = np.argmax(similarities)\n",
+    "    return documents[best_idx]\n",
+    "\n",
+    "user_query = \"How much does Product A cost?\"\n",
+    "retrieved_doc = retrieve_info(user_query)\n",
+    "\n",
+    "print(f\"Query: {user_query}\")\n",
+    "print(f\"Retrieved Context: {retrieved_doc}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d72fc06",
+   "metadata": {},
+   "source": [
+    "### The Limitation of Keyword Matching\n",
+    "\n",
+    "TF-IDF matches **words**, not **meaning**. Watch it fail on a paraphrased query:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f318b9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Same question, different words\n",
+    "queries = [\n",
+    "    \"How much does Product A cost?\",    # Direct match (works)\n",
+    "    \"What's the price of Product A?\",   # Paraphrased (may fail)\n",
+    "    \"Tell me about the warranty\",       # Different focus\n",
+    "]\n",
+    "\n",
+    "print(\"TF-IDF Retrieval Results:\")\n",
+    "print(\"-\" * 50)\n",
+    "for q in queries:\n",
+    "    result = retrieve_info(q)\n",
+    "    print(f\"Q: {q}\")\n",
+    "    print(f\"→ {result[:50]}...\")\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "663123a5",
+   "metadata": {},
+   "source": [
+    "**Observation**: TF-IDF struggles with synonyms (\"cost\" vs \"price\"). Real RAG systems use **semantic embeddings** (sentence-transformers) that understand meaning, not just keywords."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a0eb818",
+   "metadata": {},
+   "source": [
+    "### The Generation (Augmented Prompt)\n",
+    "\n",
+    "Now we feed this to the LLM."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f024163",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import pipeline\n",
+    "\n",
+    "# Load a small text generator\n",
+    "generator = pipeline('text-generation', model='gpt2')\n",
+    "\n",
+    "# Construct the Augmented Prompt\n",
+    "prompt = f\"Context: {retrieved_doc}\\nQuestion: {user_query}\\nAnswer:\"\n",
+    "\n",
+    "output = generator(prompt, max_new_tokens=10, pad_token_id=50256)\n",
+    "print(f\"--- Final LLM Output ---\\n{output[0]['generated_text']}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5717ebd",
+   "metadata": {},
+   "source": [
+    "**Observation**: The LLM answers correctly because we **gave** it the answer in the context."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10dddf3e",
+   "metadata": {},
+   "source": [
+    "## Introduction to Diffusion Models (Image Gen)\n",
+    "\n",
+    "Models like **Stable Diffusion** and **DALL-E** work differently from LLMs. They rely on **Diffusion**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9429ee46",
+   "metadata": {},
+   "source": [
+    "### How it works\n",
+    "\n",
+    "1.  **Forward Process (Training)**: Take a clear image and slowly add \"static\" (Gaussian Noise) until it is pure random noise. The model watches this and learns the mathematical pattern of how the image was destroyed.\n",
+    "2.  **Reverse Process (Generation)**: Start with pure random noise. Ask the model: **\"If this was a picture of a cat, how would you remove the noise?\"** Repeat this 50 times until a clear image emerges."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d19ddb6",
+   "metadata": {},
+   "source": [
+    "### How Text Guides Generation (Conditioning)\n",
+    "\n",
+    "But how does \"a cat wearing a hat\" become an image? The secret is **Conditioning**:\n",
+    "\n",
+    "1.  **CLIP Encoder**: A separate model (trained on image-text pairs) converts your text prompt into an embedding vector.\n",
+    "2.  **Guided Denoising**: At each step, the diffusion model asks: \"What noise should I remove to make this image **more similar** to the text embedding?\"\n",
+    "3.  **Iterative Refinement**: After 20-50 steps, the noise becomes a coherent image matching your description.\n",
+    "\n",
+    "This is why prompt engineering matters for image generation too—different phrasings produce different CLIP embeddings, leading to different images."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c00e49b",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration: The Forward Process\n",
+    "\n",
+    "We can visualize how the model learns by destroying an image ourselves."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4cb08c6e",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from skimage import data\n",
+    "import numpy as np\n",
+    "\n",
+    "# Load sample image (Astronaut)\n",
+    "img = data.astronaut()\n",
+    "\n",
+    "# Normalize to 0-1\n",
+    "img = img / 255.0\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 5, figsize=(15, 4))\n",
+    "axes[0].imshow(img)\n",
+    "axes[0].set_title(\"Step 0 (Clean)\")\n",
+    "axes[0].axis('off')\n",
+    "\n",
+    "# Simulate Forward Diffusion (Adding Noise)\n",
+    "current_img = img.copy()\n",
+    "noise_level = 0.3 # How much noise to add per step\n",
+    "\n",
+    "for i in range(1, 5):\n",
+    "    # Add Gaussian Noise\n",
+    "    noise = np.random.normal(0, noise_level, img.shape)\n",
+    "    current_img = current_img + noise\n",
+    "\n",
+    "    # Clip to stay valid color range\n",
+    "    current_img = np.clip(current_img, 0, 1)\n",
+    "\n",
+    "    axes[i].imshow(current_img)\n",
+    "    axes[i].set_title(f\"Step {i*25} (Noisier)\")\n",
+    "    axes[i].axis('off')\n",
+    "\n",
+    "plt.suptitle(\"Forward Diffusion: What the Model Learns to Undo\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17842893",
+   "metadata": {},
+   "source": [
+    "## Ethical Considerations and Bias\n",
+    "\n",
+    "With great power comes great responsibility."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e46919a",
+   "metadata": {},
+   "source": [
+    "### Bias\n",
+    "\n",
+    "GenAI models are trained on the internet. The internet contains stereotypes, racism, and sexism.\n",
+    "\n",
+    "-   **Example**: If you ask an image generator for a \"Doctor,\" it might generate only men. If you ask for a \"Nurse,\" it might generate only women.\n",
+    "-   **Mitigation**: Curating training data and using \"RLHF\" (Human Feedback) to penalize biased outputs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31886995",
+   "metadata": {},
+   "source": [
+    "### Hallucination\n",
+    "\n",
+    "LLMs are confident liars.\n",
+    "\n",
+    "-   They optimize for **plausibility**, not **truth**.\n",
+    "-   **Danger**: Using LLMs for medical or legal advice without verification (RAG helps reduce this)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e31fb1b",
+   "metadata": {},
+   "source": [
+    "### Copyright\n",
+    "\n",
+    "-   Image models are trained on billions of copyrighted images (artists' work).\n",
+    "-   Code models are trained on GitHub repositories.\n",
+    "-   The legal framework for this is still being debated globally."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14f0be3c",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3bd9d0e",
+   "metadata": {},
+   "source": [
+    "### RAG Query Robustness\n",
+    "\n",
+    "Test how well TF-IDF handles different query phrasings.\n",
+    "\n",
+    "1.  Add more documents to the knowledge base.\n",
+    "2.  Try queries with synonyms, typos, or completely different phrasing.\n",
+    "3.  Which queries fail? Why?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d245889",
+   "metadata": {},
+   "source": [
+    "### Diffusion Noise Schedule\n",
+    "\n",
+    "The rate at which noise is added affects image quality.\n",
+    "\n",
+    "1.  Modify the `noise_level` parameter in the diffusion demo (try 0.1, 0.5, 1.0).\n",
+    "2.  How does the \"destruction speed\" change?\n",
+    "3.  Real diffusion models use carefully tuned **noise schedules** (linear, cosine, etc.)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d29fcb5c",
+   "metadata": {},
+   "source": [
+    "### Emerging Paradigm: AI Agents\n",
+    "\n",
+    "Modern GenAI goes beyond chat. **AI Agents** combine LLMs with tools:\n",
+    "\n",
+    "-   **Code Execution**: The LLM writes Python and runs it.\n",
+    "-   **Web Search**: The LLM searches the internet for current information.\n",
+    "-   **Tool Use**: The LLM calls APIs (calculators, databases, etc.).\n",
+    "\n",
+    "This is the frontier of GenAI—systems that **act**, not just **respond**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "086c346f",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "1.  **Next-Token Prediction**: The simple math behind the complex magic of LLMs.\n",
+    "2.  **RAG**: The bridge between a generic LLM and your private data.\n",
+    "3.  **Diffusion**: Creating images by learning to reverse-engineer noise.\n",
+    "4.  **Ethics**: AI reflects the data it was fed. It requires human oversight."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 5b735d34e47f23c86c85e0fe9b416289592829a3 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Thu, 5 Feb 2026 17:50:42 +0100
Subject: [PATCH 70/81] Some updates

---
 README.md                                     |   2 +-
 .../S01 Introduction to Machine Learning.pdf  | Bin 264687 -> 316295 bytes
 .../S01 Introduction to Machine Learning.tex  |  91 +++++++++-----
 ...ain-Test Splits and Cross-Validation.ipynb | 112 ++++++++++++++++++
 4 files changed, 174 insertions(+), 31 deletions(-)

diff --git a/README.md b/README.md
index 471155a..778e566 100644
--- a/README.md
+++ b/README.md
@@ -21,7 +21,7 @@ Each session is made up of several concepts, and each concept is structured as f
 
 Python is a very popular programming language for Machine Learning, and it is what we will use in this training.
 
-When dealing with heavier computations, the Python code is usually placed in a `.py` file which is then executed as a whole. In a prototyping or teaching situation, though, it is best to do things more intearctively, which is why we will use [Jupyter](https://jupyter.org/) notebooks via `JupyterLab` to arrange our code.
+When dealing with heavier computations, the Python code is usually placed in a `.py` file which is then executed as a whole. In a prototyping or teaching situation, though, it is best to do things more interactively, which is why we will use [Jupyter](https://jupyter.org/) notebooks via `JupyterLab` to arrange our code.
 
 During the first session, we will cover three ways of running Jupyter, but we will only use one during the training (as much as possible):
 
diff --git a/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.pdf b/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.pdf
index b41b0c70b0a670b5542516ec0f01244aa790a70c..7b1ab94a04ef3a2696e93f9f5b74e893296236e2 100644
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diff --git a/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.tex b/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.tex
index d8f9545..0006f47 100644
--- a/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.tex	
+++ b/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.tex	
@@ -1,4 +1,4 @@
-% Created 2026-01-28 Wed 17:16
+% Created 2026-02-05 Thu 17:24
 % Intended LaTeX compiler: pdflatex
 \documentclass[hyperref={pdfpagelabels=false},aspectratio=169]{beamer}
 \usepackage[utf8]{inputenc}
@@ -202,9 +202,9 @@
 \end{frame}
 
 \section{Introduction to Machine Learning}
-\label{sec:orgb50ff7a}
-\begin{frame}[label={sec:orga13367f}]{What is Machine Learning?}
-\begin{definition}[Machine Learning]\label{sec:org12455b9}
+\label{sec:org9e13c8c}
+\begin{frame}[label={sec:org2142dfe}]{What is Machine Learning?}
+\begin{definition}[Machine Learning]\label{sec:orgabdbe09}
 \pause
 \alert{\alert{Machine Learning}} is the study of algorithms that learn patterns from data to make predictions or decisions without being explicitly programmed.
 \end{definition}
@@ -217,8 +217,8 @@ \section{Introduction to Machine Learning}
 \end{alertblock}
 \end{frame}
 \section{The Machine Learning Workflow}
-\label{sec:org5e5ee6c}
-\begin{frame}[label={sec:org3959082}]{The Machine Learning Workflow}
+\label{sec:org0ff8e9c}
+\begin{frame}[label={sec:org53a9c8b}]{The Machine Learning Workflow}
 \pause
 A typical \alert{Machine Learning} workflow has the following structure:
 \pause
@@ -234,9 +234,9 @@ \section{The Machine Learning Workflow}
 \end{itemize}
 \end{frame}
 \section{Train-Test Splits and Cross-Validation}
-\label{sec:orgc457ea9}
-\begin{frame}[label={sec:org57de8e7}]{Train and Test Datasets}
-\begin{definition}[Splitting the dataset]\label{sec:org9efa658}
+\label{sec:org3d13c8b}
+\begin{frame}[label={sec:org368c0e3}]{Train and Test Datasets}
+\begin{definition}[Splitting the dataset]\label{sec:org379dd21}
 \pause
 In machine learning, we split the dataset into two parts:
 \pause
@@ -253,12 +253,12 @@ \section{Train-Test Splits and Cross-Validation}
 \end{itemize}
 \end{alertblock}
 \end{frame}
-\begin{frame}[label={sec:org660a832}]{Cross-Validation}
+\begin{frame}[label={sec:orgf3c3d6f}]{Cross-Validation}
 The \alert{\alert{train}} dataset can be further split into \alert{\alert{training}} and \alert{\alert{validation}} sets to tune the
 model's hyperparameters and prevent overfitting.
 \end{frame}
-\begin{frame}[label={sec:org3ae42b0}]{Cross-Validation}
-\begin{definition}[Cross-Validation]\label{sec:org9fc488e}
+\begin{frame}[label={sec:org0d89e14}]{Cross-Validation}
+\begin{definition}[Cross-Validation]\label{sec:orgb5bad86}
 \pause
 Cross-validation is a technique to assess how the results of a statistical analysis will generalize to an independent dataset.
 \pause
@@ -268,16 +268,47 @@ \section{Train-Test Splits and Cross-Validation}
 \end{itemize}
 \end{definition}
 \end{frame}
+\begin{frame}[label={sec:org50cbab0}]{Data Leakage}
+\begin{definition}[Data Leakage]\label{sec:org3649ac6}
+\pause
+\alert{Data Leakage} occurs when information from outside the training set improperly influences the model, leading to overly optimistic results that fail in deployment.
+\end{definition}
+\begin{alertblock}{The Exam Analogy}
+\pause
+A student who accidentally sees the answer key scores 100\%, but learns nothing and fails the next exam.
+\end{alertblock}
+\end{frame}
+\begin{frame}[label={sec:orge53ff51},fragile]{Common Sources of Leakage}
+ \begin{example}[Common Sources]\label{sec:orgc8637fa}
+\pause
+\begin{itemize}[<+->]
+\item \alert{Preprocessing before splitting}: Scaling or imputing using the full dataset leaks test statistics into training.
+\item \alert{Temporal leakage}: Using future data to predict the past.
+\item \alert{Target leakage}: Features that are consequences of the outcome (e.g., \texttt{treatment\_response} when predicting \texttt{needs\_treatment}).
+\item \alert{Duplicate samples}: Same subject in both train and test (multiple measurements per patient).
+\end{itemize}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:orgf88419f},fragile]{Preventing Leakage}
+ \begin{alertblock}{Prevention Strategies}
+\pause
+\begin{itemize}[<+->]
+\item \alert{Always split first}: No preprocessing touches the full dataset.
+\item \alert{Use Pipelines}: Scikit-learn's \texttt{Pipeline} enforces correct ordering automatically.
+\item \alert{Think causally}: "Would I have this feature at prediction time?"
+\end{itemize}
+\end{alertblock}
+\end{frame}
 \section{Exploratory Data Analysis (EDA)}
-\label{sec:org3578fff}
-\begin{frame}[label={sec:orgd07b658}]{Exploratory Data Analysis (EDA)}
-\begin{definition}[Exploratory Data Analysis (EDA)]\label{sec:orgf78e5cb}
+\label{sec:orgd6fb5ad}
+\begin{frame}[label={sec:org51cd569}]{Exploratory Data Analysis (EDA)}
+\begin{definition}[Exploratory Data Analysis (EDA)]\label{sec:orge0b934e}
 \pause
 \alert{\alert{Exploratory Data Analysis}} (EDA) is the process of analyzing datasets to summarize their main characteristics, often using visual methods.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org63ab2a7}]{Exploratory Data Analysis (EDA)}
-\begin{example}[Purpose of EDA]\label{sec:orgc39fb48}
+\begin{frame}[label={sec:org79be5f5}]{Exploratory Data Analysis (EDA)}
+\begin{example}[Purpose of EDA]\label{sec:org7f51a89}
 \pause
 \begin{itemize}[<+->]
 \item To understand the data distribution and relationships between variables.
@@ -285,7 +316,7 @@ \section{Exploratory Data Analysis (EDA)}
 \item To generate hypotheses and inform feature engineering.
 \end{itemize}
 \end{example}
-\begin{example}[EDA Techniques]\label{sec:org88bfcbb}
+\begin{example}[EDA Techniques]\label{sec:org691e58b}
 \pause
 \begin{itemize}[<+->]
 \item \alert{\alert{Descriptive Statistics}}: Mean, median, mode, standard deviation, etc.
@@ -297,15 +328,15 @@ \section{Exploratory Data Analysis (EDA)}
 \end{example}
 \end{frame}
 \section{Data Preprocessing}
-\label{sec:orgd25d3c3}
-\begin{frame}[label={sec:orge6baf51}]{Data Preprocessing}
-\begin{definition}[Data Preprocessing]\label{sec:orgde988c7}
+\label{sec:org3ce6678}
+\begin{frame}[label={sec:org375377a}]{Data Preprocessing}
+\begin{definition}[Data Preprocessing]\label{sec:orgb81ddbb}
 \pause
 \alert{\alert{Data Preprocessing}} is the process of transforming raw data into a format suitable for analysis and modeling.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org4d8d25e}]{Data Preprocessing}
-\begin{example}[Purpose of Data Preprocessing]\label{sec:org41288a0}
+\begin{frame}[label={sec:orgcf84454}]{Data Preprocessing}
+\begin{example}[Purpose of Data Preprocessing]\label{sec:org2282950}
 \pause
 \begin{itemize}[<+->]
 \item To clean the data by handling missing values, outliers, and inconsistencies
@@ -313,7 +344,7 @@ \section{Data Preprocessing}
 \item To encode categorical variables into numerical formats
 \end{itemize}
 \end{example}
-\begin{example}[Common Data Preprocessing Techniques]\label{sec:orge1ddcb3}
+\begin{example}[Common Data Preprocessing Techniques]\label{sec:orga78c06d}
 \begin{itemize}[<+->]
 \item \alert{\alert{Handling Missing Values}}: Techniques include imputation (mean, median, mode), deletion, or using algorithms that can handle missing data.
 \item \alert{\alert{Outlier Detection and Treatment}}: Identifying outliers using statistical methods (e.g., Z-score, IQR) and deciding whether to remove or transform them.
@@ -323,15 +354,15 @@ \section{Data Preprocessing}
 \end{example}
 \end{frame}
 \section{Feature Engineering}
-\label{sec:orgc0cab37}
-\begin{frame}[label={sec:org6297419}]{Feature Engineering}
-\begin{definition}[Feature Engineering]\label{sec:org975b9c2}
+\label{sec:org21c1fe8}
+\begin{frame}[label={sec:org71172f8}]{Feature Engineering}
+\begin{definition}[Feature Engineering]\label{sec:org30e3857}
 \pause
 \alert{\alert{Feature Engineering}} is the process of using domain knowledge to select, modify, or create features (variables) that improve the performance of machine learning models.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgee3bd4e}]{Feature Engineering}
-\begin{example}[Purpose of Feature Engineering]\label{sec:orgf4a69f4}
+\begin{frame}[label={sec:org58a8542}]{Feature Engineering}
+\begin{example}[Purpose of Feature Engineering]\label{sec:orgd6583ec}
 \pause
 \begin{itemize}[<+->]
 \item To improve model performance by providing more relevant information.
@@ -339,7 +370,7 @@ \section{Feature Engineering}
 \item To create new features that capture important patterns in the data.
 \end{itemize}
 \end{example}
-\begin{example}[Common Feature Engineering Techniques]\label{sec:orgb6cce2c}
+\begin{example}[Common Feature Engineering Techniques]\label{sec:org10f219a}
 \begin{itemize}[<+->]
 \item \alert{\alert{Feature Selection}}: Techniques include filter methods (e.g., correlation), wrapper methods (e.g., recursive feature elimination), and embedded methods (e.g., LASSO).
 \item \alert{\alert{Feature Transformation}}: Techniques include logarithmic transformation, polynomial features, and interaction terms.
diff --git a/src/S01 Introduction to Machine Learning/S01C04 Train-Test Splits and Cross-Validation.ipynb b/src/S01 Introduction to Machine Learning/S01C04 Train-Test Splits and Cross-Validation.ipynb
index cce8919..f268a4f 100644
--- a/src/S01 Introduction to Machine Learning/S01C04 Train-Test Splits and Cross-Validation.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C04 Train-Test Splits and Cross-Validation.ipynb	
@@ -227,6 +227,118 @@
     "**Observation**: Notice that the scores vary! In one fold, the model performed much worse (or better, depending on random seed) than others. The **Average $R^2$** is a more honest reflection of the model's true capability than the single split score."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "cd0806ff",
+   "metadata": {},
+   "source": [
+    "## Data Leakage\n",
+    "\n",
+    "**Data Leakage** occurs when information from outside the training set improperly influences the model. This leads to overly optimistic performance during development that collapses when the model encounters real-world data.\n",
+    "\n",
+    "Think of it like a student who accidentally sees the answer key before an exam—they score 100%, but learn nothing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59234970",
+   "metadata": {},
+   "source": [
+    "### Common Sources of Leakage\n",
+    "\n",
+    "1.  **Preprocessing before splitting**: If you scale or impute missing values using the entire dataset, the test set statistics \"leak\" into training.\n",
+    "2.  **Temporal leakage**: Using future information to predict the past (e.g., using 2024 measurements to predict 2023 outcomes).\n",
+    "3.  **Target leakage**: Including features that are consequences of the target (e.g., `days_in_hospital` when predicting `will_be_hospitalized`).\n",
+    "4.  **Duplicate samples**: The same subject appearing in both train and test sets (common with multiple measurements per patient/sample)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6041a6a3",
+   "metadata": {},
+   "source": [
+    "### Example: The Wrong Way\n",
+    "\n",
+    "Here we scale the data **before** splitting: a subtle but serious mistake."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92460a3f",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# WRONG: Scaling before split leaks test set statistics into training\n",
+    "scaler = StandardScaler()\n",
+    "X_scaled = scaler.fit_transform(X)  # Learns mean/std from ALL data, including test!\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9dde575",
+   "metadata": {},
+   "source": [
+    "### Example: The Right Way (Using Pipelines)\n",
+    "\n",
+    "Scikit-learn's `Pipeline` ensures that preprocessing is fitted **only** on training data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5d08bfd2",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "\n",
+    "# CORRECT: Split first, then create a pipeline\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Pipeline bundles preprocessing + model\n",
+    "pipe = Pipeline([\n",
+    "    ('scaler', StandardScaler()),  # Will be fit ONLY on training data\n",
+    "    ('regressor', LinearRegression())\n",
+    "])\n",
+    "\n",
+    "# Fit the pipeline (scaler learns from X_train only)\n",
+    "pipe.fit(X_train, y_train)\n",
+    "\n",
+    "# Evaluate (scaler transforms X_test using train statistics)\n",
+    "print(f\"Test R²: {pipe.score(X_test, y_test):.4f}\")\n",
+    "\n",
+    "# Cross-validation also handles this correctly\n",
+    "cv_scores = cross_val_score(pipe, X, y, cv=5, scoring='r2')\n",
+    "print(f\"CV R²: {cv_scores.mean():.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d20c87c0",
+   "metadata": {},
+   "source": [
+    "### Preventing Leakage: A Checklist\n",
+    "\n",
+    "-   **Always split first**: No preprocessing touches the full dataset.\n",
+    "-   **Use Pipelines**: They enforce correct ordering automatically.\n",
+    "-   **Think causally**: \"Would I have this feature available at prediction time?\"\n",
+    "-   **Use GroupKFold**: When samples are related (e.g., multiple cells from the same patient), use `GroupKFold` to keep all samples from one subject in the same fold."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "05695bd0",

From 9c9e768cb031e86b9019c08e9b38d2dc2961040e Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Mon, 16 Feb 2026 18:06:33 +0100
Subject: [PATCH 71/81] S05 Neural Networks I - Foundations and Optimization

---
 ...tworks I: Foundations and Optimization.pdf | Bin 745195 -> 771656 bytes
 ...tworks I: Foundations and Optimization.tex | 180 ++++++------
 ...uction to Artificial Neural Networks.ipynb | 266 ++++++------------
 ...5C02 The Multilayer Perceptron (MLP).ipynb |  53 +++-
 .../S05C03 Training Neural Networks.ipynb     |  28 +-
 .../S05C04 Optimizing Neural Networks.ipynb   |  83 ++++--
 ...S05C05 Building a Network in PyTorch.ipynb |  45 +--
 7 files changed, 319 insertions(+), 336 deletions(-)

diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05 Neural Networks I: Foundations and Optimization.pdf b/src/S05 Neural Networks I: Foundations and Optimization/S05 Neural Networks I: Foundations and Optimization.pdf
index a0d2360ecbcfc40cc312c12d412c2abc019f945c..a894f0313086949f503558d6d8e412be9269d97f 100644
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diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05 Neural Networks I: Foundations and Optimization.tex b/src/S05 Neural Networks I: Foundations and Optimization/S05 Neural Networks I: Foundations and Optimization.tex
index f6e871a..45a681b 100644
--- a/src/S05 Neural Networks I: Foundations and Optimization/S05 Neural Networks I: Foundations and Optimization.tex	
+++ b/src/S05 Neural Networks I: Foundations and Optimization/S05 Neural Networks I: Foundations and Optimization.tex	
@@ -1,4 +1,4 @@
-% Created 2026-02-04 Wed 20:55
+% Created 2026-02-15 Sun 20:39
 % Intended LaTeX compiler: pdflatex
 \documentclass[hyperref={pdfpagelabels=false},aspectratio=169]{beamer}
 \usepackage[utf8]{inputenc}
@@ -202,14 +202,14 @@
 \end{frame}
 
 \section{Introduction to Artificial Neural Networks}
-\label{sec:org04297b1}
-\begin{frame}[label={sec:orgcd8d6cb}]{What is an Artificial Neural Network?}
-\begin{definition}[Definition]\label{sec:org425637c}
+\label{sec:orgfa4b015}
+\begin{frame}[label={sec:orgbdcaabd}]{What is an Artificial Neural Network?}
+\begin{definition}[Definition]\label{sec:org1ab2f4a}
 \pause
 An \alert{Artificial Neural Network (ANN)} is a computational model composed of layers of interconnected nodes ("neurons") that process information by transmitting signals to one another.
 \pause
 \end{definition}
-\begin{example}[Key Properties]\label{sec:org264a45f}
+\begin{example}[Key Properties]\label{sec:orgb971635}
 \begin{itemize}[<+->]
 \item Foundation of \alert{Deep Learning}
 \item Can learn complex, non-linear relationships
@@ -217,7 +217,7 @@ \section{Introduction to Artificial Neural Networks}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:orgc94818c}]{Key Components of a Neural Network}
+\begin{frame}[label={sec:orgd95f9b3}]{Key Components of a Neural Network}
 \begin{block}{The Building Blocks}
 \pause
 \begin{enumerate}[<+->]
@@ -233,7 +233,7 @@ \section{Introduction to Artificial Neural Networks}
 \end{enumerate}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org30143e6}]{Biological Inspiration}
+\begin{frame}[label={sec:org089ab53}]{Biological Inspiration}
 \begin{block}{From Biology to Mathematics}
 \pause
 ANNs are loosely inspired by biological neural networks in the brain:
@@ -250,13 +250,13 @@ \section{Introduction to Artificial Neural Networks}
 \end{center}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org984a1bc}]{The Perceptron (1958)}
-\begin{definition}[The Simplest Neural Network]\label{sec:orgee6bc15}
+\begin{frame}[label={sec:orgca28863}]{The Perceptron (1958)}
+\begin{definition}[The Simplest Neural Network]\label{sec:org310cae6}
 \pause
 The \alert{Perceptron} is a single-layer network that takes inputs, weighs them, and makes a binary decision.
-\pause
 \end{definition}
 \begin{block}{Mathematical Formulation}
+\pause
 \begin{enumerate}
 \item \alert{Weighted Sum}: \(z = w_1 x_1 + w_2 x_2 + \dots + b\)
 \item \alert{Activation}:
@@ -266,7 +266,7 @@ \section{Introduction to Artificial Neural Networks}
 This can solve \alert{linearly separable} problems (drawing a straight line between classes).
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:orgd231bbb}]{The XOR Problem}
+\begin{frame}[label={sec:orgc0c1cda}]{The XOR Problem}
 \begin{block}{The Fatal Flaw of Single Perceptrons}
 \pause
 \begin{center}
@@ -281,18 +281,18 @@ \section{Introduction to Artificial Neural Networks}
 \end{center}
 \pause
 \end{block}
-\begin{example}[The Limitation]\label{sec:org5c11fa3}
+\begin{example}[The Limitation]\label{sec:org59d29f4}
 \begin{itemize}[<+->]
 \item \alert{AND Gate}: Linearly separable (one line can separate classes)
 \item \alert{XOR Gate}: NOT linearly separable (needs a curve or multiple lines)
-\item This limitation froze AI research until \alert{Multi-Layer Perceptrons (MLPs)} emerged
+\item This limitation highlighted the need for \alert{Multi-Layer Perceptrons (MLPs)} and better training methods
 \end{itemize}
 \end{example}
 \end{frame}
 \section{The Multilayer Perceptron (MLP)}
-\label{sec:orgc289329}
-\begin{frame}[label={sec:org0461d23}]{From One to Many}
-\begin{definition}[Stacking Neurons]\label{sec:orgcb28804}
+\label{sec:org5d3f846}
+\begin{frame}[label={sec:org880307e}]{From One to Many}
+\begin{definition}[Stacking Neurons]\label{sec:orgec20826}
 \pause
 A \alert{Multilayer Perceptron (MLP)} or \alert{Feedforward Neural Network} connects many neurons in layers to solve complex, non-linear problems.
 \pause
@@ -305,14 +305,14 @@ \section{The Multilayer Perceptron (MLP)}
 \end{enumerate}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org3583b53}]{Activation Functions}
+\begin{frame}[label={sec:org79ba5a5}]{Activation Functions}
 \begin{block}{Why Activation Functions?}
 \pause
 Without activation functions, an MLP is just a giant Linear Regression (linear + linear = linear).
 \alert{Activation functions} introduce \alert{non-linearity}, allowing networks to learn curves.
-\pause
 \end{block}
-\begin{example}[Common Activation Functions]\label{sec:orga684aa9}
+\begin{example}[Common Activation Functions]\label{sec:orgd817691}
+\pause
 \begin{center}
 \begin{tabular}{llll}
 Function & Formula & Range & Usage\\
@@ -325,7 +325,7 @@ \section{The Multilayer Perceptron (MLP)}
 \end{center}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:orgf3acf1b},fragile]{Forward Propagation}
+\begin{frame}[label={sec:org41cf858},fragile]{Forward Propagation}
  \begin{block}{How Data Flows Through the Network}
 \pause
 For a single layer \(l\):
@@ -340,7 +340,7 @@ \section{The Multilayer Perceptron (MLP)}
 This process repeats until the output layer. The magic: \texttt{np.dot} handles all connections automatically.
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org21c385f}]{Network Capacity: Width vs Depth}
+\begin{frame}[label={sec:org212c40f}]{Network Capacity: Width vs Depth}
 \begin{block}{Architectural Choices}
 \pause
 \begin{center}
@@ -353,7 +353,7 @@ \section{The Multilayer Perceptron (MLP)}
 \end{center}
 \pause
 \end{block}
-\begin{example}[Trade-offs]\label{sec:orgdd4a53d}
+\begin{example}[Trade-offs]\label{sec:org1499594}
 \begin{itemize}[<+->]
 \item Deeper networks learn hierarchical representations (edges \(\rightarrow\) parts \(\rightarrow\) objects)
 \item Wider networks can approximate functions but may need more parameters
@@ -362,15 +362,15 @@ \section{The Multilayer Perceptron (MLP)}
 \end{example}
 \end{frame}
 \section{Training Neural Networks}
-\label{sec:orge8d9a82}
-\begin{frame}[label={sec:org2891de2}]{The Goal of Training}
+\label{sec:orge394614}
+\begin{frame}[label={sec:orgcee9169}]{The Goal of Training}
 \begin{block}{From Random to Learned}
 \pause
 A neural network starts with \alert{random weights}. It knows nothing.
 \alert{Training} adjusts these weights to minimize prediction error.
 \pause
 \end{block}
-\begin{example}[Three Components]\label{sec:org63cf105}
+\begin{example}[Three Components]\label{sec:org83d1a93}
 \begin{enumerate}[<+->]
 \item \alert{Loss Function}: How we measure error
 \item \alert{Optimizer}: How we update weights
@@ -378,13 +378,13 @@ \section{Training Neural Networks}
 \end{enumerate}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org30387d1}]{Loss Functions}
-\begin{definition}[The Scorecard]\label{sec:orgac2f699}
-\pause
-The \alert{Loss Function} (Cost Function) calculates a single number representing "how bad" the model is. Goal: drive it to zero.
+\begin{frame}[label={sec:org0c0fba7}]{Loss Functions}
+\begin{definition}[The Scorecard]\label{sec:orgefaf011}
 \pause
+The \alert{Loss Function} (Cost Function) calculates a single number representing "how bad" the model is. Goal: reduce it enough to generalize well on unseen data.
 \end{definition}
 \begin{block}{Common Loss Functions}
+\pause
 \begin{center}
 \begin{tabular}{lll}
 Problem Type & Loss Function & Formula\\
@@ -400,30 +400,29 @@ \section{Training Neural Networks}
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org30588bb}]{Stochastic Gradient Descent (SGD)}
+\begin{frame}[label={sec:org3478b90}]{Stochastic Gradient Descent (SGD)}
 \begin{block}{Optimization Strategy}
 \pause
 \alert{Gradient Descent} minimizes loss by following the slope downhill.
-\alert{Batch GD} calculates gradient for entire dataset — slow and memory-intensive.
-\pause
+\alert{Batch GD} calculates gradient for entire dataset - slow and memory-intensive.
 \end{block}
-\begin{definition}[Stochastic Gradient Descent]\label{sec:org4ec2235}
-\alert{SGD} updates weights after seeing just \alert{one example} (or a small "mini-batch").
+\begin{definition}[Stochastic Gradient Descent]\label{sec:orgd23dda0}
 \pause
+\alert{SGD} updates weights after seeing just \alert{one example} (or a small "mini-batch").
 \begin{itemize}[<+->]
 \item \alert{Pros}: Faster, noise helps escape local minima
 \item \alert{Cons}: Zigzaggy path to convergence
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org45e5301}]{Backpropagation}
+\begin{frame}[label={sec:orge5ded9d}]{Backpropagation}
 \begin{block}{The Chain Rule in Action}
 \pause
 How do we know which weight in Layer 1 caused the error in Layer 5?
 We use the \alert{Chain Rule} of calculus.
 \pause
 \end{block}
-\begin{example}[The Process]\label{sec:org57f346c}
+\begin{example}[The Process]\label{sec:orgee60b7b}
 \begin{enumerate}[<+->]
 \item \alert{Forward Pass}: Data flows through network, loss is calculated
 \item \alert{Backward Pass}: Gradient propagates backwards layer by layer
@@ -434,7 +433,7 @@ \section{Training Neural Networks}
 \end{enumerate}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org0d588de}]{The Vanishing Gradient Problem}
+\begin{frame}[label={sec:orgced0470}]{The Vanishing Gradient Problem}
 \begin{block}{Why Deep Networks Failed (Initially)}
 \pause
 In deep networks, gradients are multiplied repeatedly during backpropagation.
@@ -442,35 +441,36 @@ \section{Training Neural Networks}
 $$0.25 \times 0.25 \times 0.25 \times \dots \approx 0$$
 \pause
 \end{block}
-\begin{example}[The Consequence]\label{sec:org2c10375}
+\begin{example}[The Consequence]\label{sec:orgfe3c32f}
 \begin{itemize}[<+->]
 \item Gradient "vanishes" before reaching early layers
 \item Early layers never learn; deep network fails
-\item \alert{Solution}: Use \alert{ReLU} (derivative = 1 for positive inputs)
+\item \alert{Mitigation}: ReLU helps (derivative = 1 for positive inputs)
+\item In practice, combine with good initialization, normalization, and residual connections
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org95e930c}]{The Learning Rate}
+\begin{frame}[label={sec:org3f82fc1}]{The Learning Rate}
 \begin{block}{The Most Critical Hyperparameter}
 \pause
 The \alert{Learning Rate} (\(\alpha\)) controls step size during gradient descent.
-\pause
 \end{block}
-\begin{example}[The Goldilocks Zone]\label{sec:org351e223}
+\begin{example}[The Goldilocks Zone]\label{sec:org841dc56}
+\pause
 \begin{center}
 \begin{tabular}{ll}
 Learning Rate & Behavior\\
 \hline
 \alert{Too Small} & Loss decreases very slowly (0.0001)\\
 \alert{Too Large} & Loss oscillates or explodes (10.0)\\
-\alert{Just Right} & Smooth, rapid descent to minimum (0.01-0.1)\\
+\alert{Just Right} & Stable descent (optimizer-dependent; e.g. Adam 1e-3, SGD 1e-2 to 1e-1)\\
 \end{tabular}
 \end{center}
 \end{example}
 \end{frame}
 \section{Optimizing Neural Networks}
-\label{sec:org07e48ca}
-\begin{frame}[label={sec:org656381a}]{Weight Initialization}
+\label{sec:orge829fdd}
+\begin{frame}[label={sec:org8ba08a6}]{Weight Initialization}
 \begin{block}{Starting Right}
 \pause
 \begin{center}
@@ -484,7 +484,7 @@ \section{Optimizing Neural Networks}
 \end{center}
 \pause
 \end{block}
-\begin{example}[Strategies]\label{sec:org806a4f0}
+\begin{example}[Strategies]\label{sec:org4b01a4a}
 \begin{itemize}[<+->]
 \item \alert{He Initialization}: Best for ReLU networks
 \item \alert{Xavier (Glorot)}: Best for Sigmoid/Tanh networks
@@ -492,7 +492,7 @@ \section{Optimizing Neural Networks}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org700831d}]{Advanced Optimizers}
+\begin{frame}[label={sec:org87cc935}]{Advanced Optimizers}
 \begin{block}{Smarter Descent}
 \pause
 Standard SGD is like walking down a mountain blindfolded. Advanced optimizers add intelligence.
@@ -510,13 +510,13 @@ \section{Optimizing Neural Networks}
 \alert{Adam} is the default choice for most tasks.
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org94ff7b3}]{Regularization: Dropout}
-\begin{definition}[Fighting Overfitting]\label{sec:org05e9325}
+\begin{frame}[label={sec:org5d89534}]{Regularization: Dropout}
+\begin{definition}[Fighting Overfitting]\label{sec:org4d63258}
 \pause
 \alert{Dropout} randomly "kills" (sets to zero) a percentage of neurons during training.
 \pause
 \end{definition}
-\begin{example}[Why It Works]\label{sec:org3ac43f0}
+\begin{example}[Why It Works]\label{sec:org2af5a66}
 \begin{itemize}[<+->]
 \item Prevents neurons from co-adapting
 \item Forces network to be redundant and robust
@@ -525,13 +525,13 @@ \section{Optimizing Neural Networks}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org7dc11f4}]{Regularization: Batch Normalization}
-\begin{definition}[Stabilizing Training]\label{sec:org042a064}
+\begin{frame}[label={sec:org82a17ab}]{Regularization: Batch Normalization}
+\begin{definition}[Stabilizing Training]\label{sec:orgb5b57f6}
 \pause
-\alert{Batch Normalization} normalizes inputs of each layer to have mean 0 and variance 1 during training.
+\alert{Batch Normalization} normalizes layer activations per mini-batch during training (with learnable scale and shift).
 \pause
 \end{definition}
-\begin{example}[Benefits]\label{sec:org3248e9f}
+\begin{example}[Benefits]\label{sec:orgcb9133c}
 \begin{itemize}[<+->]
 \item Stabilizes training (allows higher learning rates)
 \item Reduces sensitivity to initialization
@@ -540,35 +540,35 @@ \section{Optimizing Neural Networks}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org97fbc0f}]{Early Stopping}
-\begin{block}{The Simplest Regularization}
+\begin{frame}[label={sec:org30f3510},fragile]{Early Stopping}
+ \begin{block}{The Simplest Regularization}
 \pause
 Monitor the \alert{Validation Loss}. If it stops improving (or starts getting worse), stop training immediately.
 \pause
 \end{block}
-\begin{example}[How It Works]\label{sec:org51198f3}
+\begin{example}[How It Works]\label{sec:orgba5a44a}
 \begin{itemize}[<+->]
 \item Training loss keeps decreasing (memorization)
 \item Validation loss starts increasing (overfitting)
 \item Early stopping saves the model at the sweet spot
-\item No hyperparameter tuning needed (just patience parameter)
+\item Requires light tuning (mainly \texttt{patience}, optionally \texttt{min\_delta})
 \end{itemize}
 \end{example}
 \end{frame}
 \section{Building Networks in PyTorch}
-\label{sec:org0a8b05a}
-\begin{frame}[label={sec:org0b7b644}]{Why PyTorch?}
+\label{sec:orge369199}
+\begin{frame}[label={sec:org25ab4e9}]{Why PyTorch?}
 \begin{block}{Beyond Scikit-learn}
 \pause
 Scikit-learn is great for standard ML, but Deep Learning needs:
 \pause
 \begin{enumerate}[<+->]
-\item \alert{GPU Acceleration}: Matrix multiplication is slow on CPUs
+\item \alert{GPU Acceleration}: Deep learning workloads are large matrix operations, where GPUs are typically much faster
 \item \alert{Automatic Differentiation}: Backpropagation is hard to code by hand
 \end{enumerate}
-\pause
 \end{block}
-\begin{example}[PyTorch Advantages]\label{sec:org6f1abea}
+\begin{example}[PyTorch Advantages]\label{sec:orgcc557c1}
+\pause
 \begin{itemize}
 \item Readable, "Pythonic" style
 \item Research and education standard
@@ -576,8 +576,8 @@ \section{Building Networks in PyTorch}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org816bc5d},fragile]{Tensors: The Foundation}
- \begin{definition}[NumPy Arrays on Steroids]\label{sec:org2637cd9}
+\begin{frame}[label={sec:orgabf51eb},fragile]{Tensors: The Foundation}
+ \begin{definition}[NumPy Arrays on Steroids]\label{sec:org4ad692c}
 \pause
 A \alert{Tensor} is like a NumPy array that:
 \begin{itemize}
@@ -586,7 +586,7 @@ \section{Building Networks in PyTorch}
 \end{itemize}
 \pause
 \end{definition}
-\begin{example}[Key Operations]\label{sec:orgaa85b47}
+\begin{example}[Key Operations]\label{sec:org837f6ac}
 \begin{itemize}[<+->]
 \item \texttt{torch.tensor([1, 2, 3])} — Create from list
 \item \texttt{torch.ones(2, 3)} — Shape (2, 3) of ones
@@ -595,13 +595,13 @@ \section{Building Networks in PyTorch}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:orgf18e81b},fragile]{Defining a Network (\texttt{nn.Module})}
+\begin{frame}[label={sec:orgfb3215d},fragile]{Defining a Network (\texttt{nn.Module})}
  \begin{block}{The Blueprint}
 \pause
 In PyTorch, a network is a Python class inheriting from \texttt{nn.Module}:
 \pause
 \end{block}
-\begin{example}[Two Required Methods]\label{sec:orgf40c96f}
+\begin{example}[Two Required Methods]\label{sec:orgbd04975}
 \begin{enumerate}[<+->]
 \item \texttt{\_\_init\_\_}: Define the layers
 \begin{itemize}
@@ -617,7 +617,7 @@ \section{Building Networks in PyTorch}
 \end{enumerate}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org88e8805},fragile]{The Training Loop}
+\begin{frame}[label={sec:org2fd7327},fragile]{The Training Loop}
  \begin{block}{The 5-Step Boilerplate}
 \pause
 Unlike Scikit-learn's \texttt{model.fit()}, PyTorch requires manual control:
@@ -633,21 +633,35 @@ \section{Building Networks in PyTorch}
 Wrap in a \texttt{for epoch in range(epochs):} loop.
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org7821349},fragile]{Loss Functions and Optimizers}
- \begin{block}{PyTorch Components}
+\begin{frame}[label={sec:org4a3b823},fragile]{PyTorch Outputs and Loss Pairings}
+ \begin{block}{Match Model Output to Loss}
 \pause
+\begin{itemize}[<+->]
+\item \alert{Binary Classification}: model outputs logits of shape \texttt{[N, 1]}, use \texttt{nn.BCEWithLogitsLoss()}
+\item \alert{Multiclass Classification}: model outputs logits of shape \texttt{[N, C]}, use \texttt{nn.CrossEntropyLoss()}
+\item \alert{Regression}: model outputs raw values, use \texttt{nn.MSELoss()}
+\end{itemize}
 \end{block}
-\begin{example}[Loss Functions]\label{sec:orgba42141}
+\begin{example}[Inference Rule]\label{sec:orgc81bec3}
+\pause
+\begin{itemize}[<+->]
+\item \alert{Training}: keep the final layer linear (no sigmoid/softmax) and use the matching PyTorch loss
+\item \alert{Prediction}: apply sigmoid (binary) or softmax (multiclass) only if you need probabilities
+\item \alert{Class label}: use threshold/argmax directly
+\end{itemize}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:org3a48938},fragile]{Loss Functions and Optimizers}
+ \begin{example}[Loss Functions]\label{sec:orgc448c4a}
 \begin{center}
 \begin{tabular}{ll}
 Task & Loss Class\\
 \hline
-Binary Classification & \texttt{nn.BCELoss()}\\
+Binary Classification & \texttt{nn.BCEWithLogitsLoss()}\\
 Multiclass & \texttt{nn.CrossEntropyLoss()}\\
 Regression & \texttt{nn.MSELoss()}\\
 \end{tabular}
 \end{center}
-\pause
 \end{example}
 \begin{block}{Optimizers}
 \begin{verbatim}
@@ -655,22 +669,24 @@ \section{Building Networks in PyTorch}
 \end{verbatim}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:orgf5619b3},fragile]{Evaluation Mode}
+\begin{frame}[label={sec:org8bdca76},fragile]{Evaluation Mode}
  \begin{block}{No Gradients Needed}
 \pause
-When evaluating, we don't need to calculate gradients. Use \texttt{torch.no\_grad()} to save memory:
+When evaluating, set \texttt{model.eval()} and use \texttt{torch.no\_grad()} to disable gradient tracking:
 \pause
 \begin{verbatim}
+model.eval()
 with torch.no_grad():
-    test_preds = model(X_test)
-    predicted = (test_preds > 0.5).float()
+    test_logits = model(X_test)
+    predicted = (test_logits > 0).float()
     accuracy = (predicted == y_test).float().mean()
 \end{verbatim}
+If you continue training afterward, switch back to \texttt{model.train()}.
 \end{block}
 \end{frame}
 \section{Summary}
-\label{sec:orgcc87042}
-\begin{frame}[label={sec:org57b0d05}]{Key Takeaways}
+\label{sec:org90ac091}
+\begin{frame}[label={sec:orgdf0017d}]{Key Takeaways}
 \begin{block}{What We Learned}
 \pause
 \begin{enumerate}[<+->]
@@ -678,13 +694,13 @@ \section{Summary}
 \item \alert{Perceptron}: Single neuron, limited to linear problems
 \item \alert{MLP}: Stacked neurons solve non-linear problems (XOR)
 \item \alert{Training}: Forward pass \(\rightarrow\) Loss \(\rightarrow\) Backprop \(\rightarrow\) Update
-\item \alert{Vanishing Gradient}: Sigmoid fails deep; ReLU fixes it
+\item \alert{Vanishing Gradient}: Sigmoid can fail deep; ReLU helps mitigate it
 \item \alert{Optimization}: Adam is the default; Early Stopping prevents overfitting
 \item \alert{PyTorch}: Tensors + nn.Module + Training Loop
 \end{enumerate}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:orga0cb69e}]{Next Section Preview}
+\begin{frame}[label={sec:orgecb1a8c}]{Next Section Preview}
 \begin{block}{Neural Networks II: Deep Learning \& Transformers}
 \pause
 \begin{itemize}[<+->]
diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05C01 Introduction to Artificial Neural Networks.ipynb b/src/S05 Neural Networks I: Foundations and Optimization/S05C01 Introduction to Artificial Neural Networks.ipynb
index 79cf6de..001735a 100644
--- a/src/S05 Neural Networks I: Foundations and Optimization/S05C01 Introduction to Artificial Neural Networks.ipynb	
+++ b/src/S05 Neural Networks I: Foundations and Optimization/S05C01 Introduction to Artificial Neural Networks.ipynb	
@@ -61,12 +61,12 @@
    "source": [
     "## The Simplest ANN: The Perceptron\n",
     "\n",
-    "The **Perceptron** (1958) is the building block of all neural networks. It is a single-layer network that takes inputs, weighs them, and makes a binary decision.\n",
+    "The **Perceptron** (1958) is a foundational neural network model. It is a single-layer network that takes inputs, weighs them, and makes a binary decision.\n",
     "\n",
     "Mathematically:\n",
     "\n",
     "1.  **Weighted Sum**: $$z = w_1 x_1 + w_2 x_2 + \\dots + b$$\n",
-    "2.  **Activation**: $$Output = \\begin{cases} 1 & \\text{if } z > 0 \\\\ 0 & \\text{if } z \\le 0 \\end{cases}$$\n",
+    "2.  **Activation**: $$\\textrm{Output} = \\begin{cases} 1 & \\text{if } z > 0 \\\\ 0 & \\text{if } z \\le 0 \\end{cases}$$\n",
     "\n",
     "This simple mechanism can solve linearly separable problems (drawing a straight line between two classes)."
    ]
@@ -83,7 +83,7 @@
     "-   **AND Gate**: Linearly Separable (Can be solved by a line).\n",
     "-   **XOR Gate**: Not Linearly Separable (Needs a curve or two lines).\n",
     "\n",
-    "This limitation froze AI research for years until the development of **Multi-Layer Perceptrons (MLPs)**, which stack neurons to solve non-linear problems."
+    "This limitation highlighted the need for **Multi-Layer Perceptrons (MLPs)** and better training methods for non-linear problems."
    ]
   },
   {
@@ -108,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "32b78c0c",
    "metadata": {
     "lines_to_next_cell": 2
@@ -142,21 +142,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "8dfd2c87",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Perceptron AND Accuracy: 1.00\n",
-      "Perceptron XOR Accuracy: 0.50\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Train on AND\n",
     "clf_and = Perceptron(random_state=0)\n",
@@ -181,57 +172,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "d76c6082",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plt.figure(figsize=(10, 5))\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "bg_cmap = ListedColormap([\"#8ecae6\", \"#ffb703\"])\n",
+    "pt_cmap = ListedColormap([\"#023047\", \"#d00000\"])\n",
     "\n",
     "# Plot AND\n",
-    "plt.subplot(1, 2, 1)\n",
-    "DecisionBoundaryDisplay.from_estimator(clf_and, X, cmap=\"coolwarm\", alpha=0.8)\n",
-    "plt.scatter(X[:, 0], X[:, 1], c=y_and, cmap=\"coolwarm\", s=200, edgecolors='k')\n",
-    "plt.title(\"Perceptron (AND) - Success\")\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    clf_and,\n",
+    "    X,\n",
+    "    ax=ax[0],\n",
+    "    response_method=\"predict\",\n",
+    "    plot_method=\"pcolormesh\",\n",
+    "    cmap=bg_cmap,\n",
+    "    alpha=0.4\n",
+    ")\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    clf_and,\n",
+    "    X,\n",
+    "    ax=ax[0],\n",
+    "    response_method=\"decision_function\",\n",
+    "    plot_method=\"contour\",\n",
+    "    levels=[0],\n",
+    "    colors=\"black\",\n",
+    "    linewidths=2\n",
+    ")\n",
+    "ax[0].scatter(X[:, 0], X[:, 1], c=y_and, cmap=pt_cmap, s=200, edgecolors='k')\n",
+    "ax[0].set_title(\"Perceptron (AND) - Success\")\n",
     "\n",
     "# Plot XOR\n",
-    "plt.subplot(1, 2, 2)\n",
-    "DecisionBoundaryDisplay.from_estimator(clf_xor, X, cmap=\"coolwarm\", alpha=0.8)\n",
-    "plt.scatter(X[:, 0], X[:, 1], c=y_xor, cmap=\"coolwarm\", s=200, edgecolors='k')\n",
-    "plt.title(\"Perceptron (XOR) - Failure\")\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    clf_xor,\n",
+    "    X,\n",
+    "    ax=ax[1],\n",
+    "    response_method=\"predict\",\n",
+    "    plot_method=\"pcolormesh\",\n",
+    "    cmap=bg_cmap,\n",
+    "    alpha=0.4\n",
+    ")\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    clf_xor,\n",
+    "    X,\n",
+    "    ax=ax[1],\n",
+    "    response_method=\"decision_function\",\n",
+    "    plot_method=\"contour\",\n",
+    "    levels=[0],\n",
+    "    colors=\"black\",\n",
+    "    linewidths=2\n",
+    ")\n",
+    "ax[1].scatter(X[:, 0], X[:, 1], c=y_xor, cmap=pt_cmap, s=200, edgecolors='k')\n",
+    "ax[1].set_title(\"Perceptron (XOR) - Failure\")\n",
     "\n",
     "plt.show()"
    ]
@@ -248,51 +246,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "3127bc88",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MLP XOR Accuracy: 1.00\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 600x500 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# MLP with 1 hidden layer of 4 neurons, using ReLU activation\n",
-    "mlp = MLPClassifier(hidden_layer_sizes=(100,), activation='relu', max_iter=2000, random_state=42)\n",
+    "# For XOR, use a solver/activation combo that converges reliably on tiny datasets\n",
+    "mlp = MLPClassifier(\n",
+    "    hidden_layer_sizes=(4,),\n",
+    "    activation='tanh',\n",
+    "    solver='lbfgs',\n",
+    "    alpha=1e-5,\n",
+    "    max_iter=5000,\n",
+    "    random_state=42\n",
+    ")\n",
     "mlp.fit(X, y_xor)\n",
     "\n",
     "print(f\"MLP XOR Accuracy: {mlp.score(X, y_xor):.2f}\")\n",
     "\n",
     "# Visualize MLP Boundary\n",
-    "plt.figure(figsize=(6, 5))\n",
-    "DecisionBoundaryDisplay.from_estimator(mlp, X, cmap=\"coolwarm\", alpha=0.8)\n",
-    "plt.scatter(X[:, 0], X[:, 1], c=y_xor, cmap=\"coolwarm\", s=200, edgecolors='k')\n",
-    "plt.title(\"Multi-Layer Perceptron (XOR) - Success\")\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(6, 5))\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    mlp,\n",
+    "    X,\n",
+    "    ax=ax,\n",
+    "    response_method=\"predict\",\n",
+    "    plot_method=\"pcolormesh\",\n",
+    "    cmap=bg_cmap,\n",
+    "    alpha=0.4\n",
+    ")\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    mlp,\n",
+    "    X,\n",
+    "    ax=ax,\n",
+    "    response_method=\"predict_proba\",\n",
+    "    plot_method=\"contour\",\n",
+    "    levels=[0.5],\n",
+    "    colors=\"black\",\n",
+    "    linewidths=2\n",
+    ")\n",
+    "ax.scatter(X[:, 0], X[:, 1], c=y_xor, cmap=pt_cmap, s=200, edgecolors='k')\n",
+    "ax.set_title(\"Multi-Layer Perceptron (XOR) - Success\")\n",
     "plt.show()"
    ]
   },
@@ -319,49 +315,6 @@
     "-   Compare their boundaries."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "eca34a00",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.datasets import make_moons\n",
-    "\n",
-    "# Generate Data\n",
-    "X_moon, y_moon = make_moons(n_samples=200, noise=0.2, random_state=42)\n",
-    "\n",
-    "# Train Models\n",
-    "perc = Perceptron().fit(X_moon, y_moon)\n",
-    "mlp_moon = MLPClassifier(hidden_layer_sizes=(10, 10), max_iter=2000, random_state=42).fit(X_moon, y_moon)\n",
-    "\n",
-    "# Visualize\n",
-    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
-    "\n",
-    "DecisionBoundaryDisplay.from_estimator(perc, X_moon, ax=ax[0], cmap=\"viridis\", alpha=0.8)\n",
-    "ax[0].scatter(X_moon[:,0], X_moon[:,1], c=y_moon, cmap=\"viridis\", edgecolors='k')\n",
-    "ax[0].set_title(\"Perceptron (Linear)\")\n",
-    "\n",
-    "DecisionBoundaryDisplay.from_estimator(mlp_moon, X_moon, ax=ax[1], cmap=\"viridis\", alpha=0.8)\n",
-    "ax[1].scatter(X_moon[:,0], X_moon[:,1], c=y_moon, cmap=\"viridis\", edgecolors='k')\n",
-    "ax[1].set_title(\"MLP (Non-Linear)\")\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "e1c49c09",
@@ -375,45 +328,6 @@
     "-   Does the boundary become smoother or more complex?"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "f283523a",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 600x500 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mlp_complex = MLPClassifier(hidden_layer_sizes=(100, 50), max_iter=2000, random_state=42)\n",
-    "mlp_complex.fit(X_moon, y_moon)\n",
-    "\n",
-    "plt.figure(figsize=(6, 5))\n",
-    "DecisionBoundaryDisplay.from_estimator(mlp_complex, X_moon, cmap=\"magma\", alpha=0.8)\n",
-    "plt.scatter(X_moon[:,0], X_moon[:,1], c=y_moon, cmap=\"magma\", edgecolors='k')\n",
-    "plt.title(\"Complex MLP (100, 50)\")\n",
-    "plt.show()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "5dc8b504",
@@ -433,9 +347,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05C02 The Multilayer Perceptron (MLP).ipynb b/src/S05 Neural Networks I: Foundations and Optimization/S05C02 The Multilayer Perceptron (MLP).ipynb
index 6202404..adfbb4e 100644
--- a/src/S05 Neural Networks I: Foundations and Optimization/S05C02 The Multilayer Perceptron (MLP).ipynb	
+++ b/src/S05 Neural Networks I: Foundations and Optimization/S05C02 The Multilayer Perceptron (MLP).ipynb	
@@ -28,7 +28,7 @@
     "\n",
     "An MLP consists of at least three layers:\n",
     "\n",
-    "1.  **Input Layer**: Receives the raw features vector $X$. (No computation happens here).\n",
+    "1.  **Input Layer**: Receives the raw feature vector $X$. (No computation happens here).\n",
     "2.  **Hidden Layers**: Layers between input and output. They perform the computation and extract \"features\" from the data. The more hidden layers, the \"Deep\" the network (Deep Learning).\n",
     "3.  **Output Layer**: Produces the final prediction (e.g., probability of \"Cat\")."
    ]
@@ -42,12 +42,12 @@
     "\n",
     "Without activation functions, an MLP is just a giant Linear Regression model (because linear + linear = linear). **Activation Functions** introduce **Non-Linearity**, allowing the network to learn curves and complex shapes.\n",
     "\n",
-    "| Function    | Formula                             | Range         | Usage                                                          |\n",
-    "|----------- |----------------------------------- |------------- |-------------------------------------------------------------- |\n",
-    "| **Sigmoid** | $\\frac{1}{1+e^{-z}}$                | $(0, 1)$      | Output layer (Binary Classification).                          |\n",
-    "| **Tanh**    | $\\frac{e^z - e^{-z}}{e^z + e^{-z}}$ | $(-1, 1)$     | Hidden layers (Legacy). Zero-centered.                         |\n",
-    "| **ReLU**    | $\\max(0, z)$                        | $[0, \\infty)$ | **Standard for Hidden Layers**. Fast and efficient.            |\n",
-    "| **Softmax** | $\\frac{e^{z_i}}{\\sum e^{z_j}}$      | $(0, 1)$      | Output layer (Multiclass). Returns probabilities summing to 1. |"
+    "| Function    | Formula                             | Range         | Usage                                                                       |\n",
+    "|----------- |----------------------------------- |------------- |--------------------------------------------------------------------------- |\n",
+    "| **Sigmoid** | $\\frac{1}{1+e^{-z}}$                | $(0, 1)$      | Binary probability output (commonly via logits + loss in PyTorch training). |\n",
+    "| **Tanh**    | $\\frac{e^z - e^{-z}}{e^z + e^{-z}}$ | $(-1, 1)$     | Hidden layers (Legacy). Zero-centered.                                      |\n",
+    "| **ReLU**    | $\\max(0, z)$                        | $[0, \\infty)$ | **Standard for Hidden Layers**. Fast and efficient.                         |\n",
+    "| **Softmax** | $\\frac{e^{z_i}}{\\sum e^{z_j}}$      | $(0, 1)$      | Multiclass probabilities (commonly via logits + loss in PyTorch training).  |"
    ]
   },
   {
@@ -186,7 +186,7 @@
    "id": "7a3233e4",
    "metadata": {},
    "source": [
-    "**Insight**: The magic of Neural Networks is that \\`np.dot\\` (Matrix Multiplication) handles the connections for us automatically."
+    "**Insight**: The magic of Neural Networks is that `np.dot` (Matrix Multiplication) handles the connections for us automatically."
    ]
   },
   {
@@ -284,10 +284,33 @@
    },
    "outputs": [],
    "source": [
-    "plt.figure(figsize=(8, 6))\n",
-    "DecisionBoundaryDisplay.from_estimator(mlp, X, cmap=\"coolwarm\", alpha=0.8)\n",
-    "plt.scatter(X[:,0], X[:,1], c=y, cmap=\"coolwarm\", edgecolors='k')\n",
-    "plt.title(\"MLP Decision Boundary (ReLU)\")\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
+    "bg_cmap = ListedColormap([\"#8ecae6\", \"#ffb703\"])\n",
+    "pt_cmap = ListedColormap([\"#023047\", \"#d00000\"])\n",
+    "\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    mlp,\n",
+    "    X,\n",
+    "    ax=ax,\n",
+    "    response_method=\"predict\",\n",
+    "    plot_method=\"pcolormesh\",\n",
+    "    cmap=bg_cmap,\n",
+    "    alpha=0.4\n",
+    ")\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    mlp,\n",
+    "    X,\n",
+    "    ax=ax,\n",
+    "    response_method=\"predict_proba\",\n",
+    "    plot_method=\"contour\",\n",
+    "    levels=[0.5],\n",
+    "    colors=\"black\",\n",
+    "    linewidths=2\n",
+    ")\n",
+    "ax.scatter(X[:,0], X[:,1], c=y, cmap=pt_cmap, edgecolors='k')\n",
+    "ax.set_title(\"MLP Decision Boundary (ReLU)\")\n",
     "plt.show()"
    ]
   },
@@ -337,7 +360,7 @@
     "1.  **Layers**: Input (Passive) $\\rightarrow$ Hidden (Active Feature Extraction) $\\rightarrow$ Output (Prediction).\n",
     "2.  **Activations**:\n",
     "    -   **ReLU**: The default for hidden layers.\n",
-    "    -   **Sigmoid/Softmax**: The default for the output layer.\n",
+    "    -   **Sigmoid/Softmax**: used when interpreting outputs as probabilities (while many training setups optimize logits directly).\n",
     "3.  **Forward Prop**: Data moves through the network via matrix multiplication ($Z=WX+b$) and activation functions ($A=f(Z)$)."
    ]
   }
@@ -347,9 +370,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05C03 Training Neural Networks.ipynb b/src/S05 Neural Networks I: Foundations and Optimization/S05C03 Training Neural Networks.ipynb
index c475d4b..24305bb 100644
--- a/src/S05 Neural Networks I: Foundations and Optimization/S05C03 Training Neural Networks.ipynb	
+++ b/src/S05 Neural Networks I: Foundations and Optimization/S05C03 Training Neural Networks.ipynb	
@@ -32,7 +32,7 @@
    "source": [
     "## Loss Functions (The Scorecard)\n",
     "\n",
-    "The Loss Function (or Cost Function) calculates a single number representing \"how bad\" the model is. The goal is to drive this number to zero.\n",
+    "The Loss Function (or Cost Function) calculates a single number representing \"how bad\" the model is. The goal is to reduce it enough to generalize well on unseen data.\n",
     "\n",
     "| Problem Type       | Loss Function                | Formula                          | Intuition                                                                          |\n",
     "|------------------ |---------------------------- |-------------------------------- |---------------------------------------------------------------------------------- |\n",
@@ -81,7 +81,7 @@
     "\n",
     "The gradient \"vanishes\" before it reaches the early layers. The input layers never learn, and the deep network fails.\n",
     "\n",
-    "**Solution**: Use **ReLU** (derivative is 1 for positive inputs), which allows gradients to flow unchanged."
+    "**Mitigation**: ReLU helps (derivative is 1 for positive inputs), which improves gradient flow. In practice, combine this with good initialization, normalization, and residual connections."
    ]
   },
   {
@@ -123,11 +123,11 @@
     "digits = load_digits()\n",
     "X, y = digits.data, digits.target\n",
     "\n",
-    "# Scale (Crucial for Neural Nets!)\n",
+    "# Split first, then scale using training data only (prevents data leakage)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
     "scaler = StandardScaler()\n",
-    "X_scaled = scaler.fit_transform(X)\n",
-    "\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
+    "X_train = scaler.fit_transform(X_train)\n",
+    "X_test = scaler.transform(X_test)\n",
     "\n",
     "print(f\"Data Shape: {X_train.shape}\")"
    ]
@@ -211,21 +211,21 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e72ea7a2",
+   "id": "dd65c04c",
    "metadata": {},
    "source": [
     "### The Learning Rate (Goldilocks Zone)\n",
     "\n",
-    "The **Learning Rate** is the most critical hyperparameter.\n",
+    "The **Learning Rate** is one of the most critical hyperparameters.\n",
     "\n",
-    "-   Train 3 models with rates: `0.0001` (Too Small), `0.1` (Just Right), `10.0` (Too Large).\n",
+    "-   Train 3 models with rates: `0.0001` (Too Small), `0.01` (Reasonable), `10.0` (Too Large).\n",
     "-   Plot their loss curves on the same graph.\n",
     "\n",
     "**Result**:\n",
     "\n",
     "-   Small rate: Loss goes down very slowly.\n",
-    "-   Large rate: Loss oscillates or gets stuck (high loss).\n",
-    "-   Good rate: Smooth, rapid descent."
+    "-   Large rate: Loss often oscillates or becomes unstable.\n",
+    "-   Reasonable rate: Usually smoother, faster descent."
    ]
   },
   {
@@ -250,7 +250,7 @@
     "1.  **Loss Function**: Cross-Entropy for classification, MSE for regression.\n",
     "2.  **SGD**: Updates weights using small batches to be faster and escape traps.\n",
     "3.  **Backprop**: Passes the \"blame\" (error) backwards to update weights.\n",
-    "4.  **Vanishing Gradient**: Sigmoid fails in deep networks; **ReLU** fixes this by keeping gradients alive."
+    "4.  **Vanishing Gradient**: Sigmoid can struggle in deep networks; **ReLU** helps mitigate this by improving gradient flow."
    ]
   }
  ],
@@ -259,9 +259,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05C04 Optimizing Neural Networks.ipynb b/src/S05 Neural Networks I: Foundations and Optimization/S05C04 Optimizing Neural Networks.ipynb
index 65696bc..87b866e 100644
--- a/src/S05 Neural Networks I: Foundations and Optimization/S05C04 Optimizing Neural Networks.ipynb	
+++ b/src/S05 Neural Networks I: Foundations and Optimization/S05C04 Optimizing Neural Networks.ipynb	
@@ -120,13 +120,15 @@
     "\n",
     "sns.set_theme(style=\"whitegrid\")\n",
     "\n",
-    "# Load and Scale\n",
+    "# Load data\n",
     "digits = load_digits()\n",
     "X, y = digits.data, digits.target\n",
-    "scaler = StandardScaler()\n",
-    "X_scaled = scaler.fit_transform(X)\n",
     "\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)"
+    "# Split first, then scale using training data only (prevents data leakage)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "scaler = StandardScaler()\n",
+    "X_train = scaler.fit_transform(X_train)\n",
+    "X_test = scaler.transform(X_test)"
    ]
   },
   {
@@ -219,8 +221,14 @@
    },
    "outputs": [],
    "source": [
-    "# Model WITH Early Stopping (default in sklearn)\n",
-    "# early_stopping=True sets aside 10% of training data for validation\n",
+    "# Baseline model WITHOUT Early Stopping\n",
+    "mlp_no_early = MLPClassifier(hidden_layer_sizes=(100,),\n",
+    "                             max_iter=500,\n",
+    "                             early_stopping=False,\n",
+    "                             random_state=42)\n",
+    "\n",
+    "# Model WITH Early Stopping\n",
+    "# early_stopping=True sets aside validation data for monitoring\n",
     "mlp_early = MLPClassifier(hidden_layer_sizes=(100,), \n",
     "                          max_iter=500, \n",
     "                          early_stopping=True, \n",
@@ -228,18 +236,30 @@
     "                          n_iter_no_change=10, \n",
     "                          random_state=42)\n",
     "\n",
+    "mlp_no_early.fit(X_train, y_train)\n",
     "mlp_early.fit(X_train, y_train)\n",
     "\n",
-    "print(f\"Stopped after {mlp_early.n_iter_} iterations.\")\n",
+    "print(f\"No Early Stopping - Iterations: {mlp_no_early.n_iter_}, Test Acc: {mlp_no_early.score(X_test, y_test):.3f}\")\n",
+    "print(f\"With Early Stopping - Iterations: {mlp_early.n_iter_}, Test Acc: {mlp_early.score(X_test, y_test):.3f}\")\n",
     "print(f\"Best Validation Score: {mlp_early.best_validation_score_:.3f}\")\n",
     "\n",
-    "# Plot the Validation Curve\n",
-    "plt.figure(figsize=(8, 5))\n",
-    "plt.plot(mlp_early.loss_curve_, label='Training Loss')\n",
-    "plt.plot(mlp_early.validation_scores_, label='Validation Score (Accuracy)')\n",
-    "plt.title(\"Early Stopping Monitor\")\n",
-    "plt.xlabel(\"Iterations\")\n",
-    "plt.legend()\n",
+    "# Plot training losses together; show validation score separately\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "\n",
+    "ax[0].plot(mlp_no_early.loss_curve_, label='No Early Stopping', color='gray')\n",
+    "ax[0].plot(mlp_early.loss_curve_, label='With Early Stopping', color='blue')\n",
+    "ax[0].set_title(\"Training Loss Comparison\")\n",
+    "ax[0].set_xlabel(\"Iterations\")\n",
+    "ax[0].set_ylabel(\"Loss\")\n",
+    "ax[0].legend()\n",
+    "\n",
+    "ax[1].plot(mlp_early.validation_scores_, label='Validation Accuracy', color='green')\n",
+    "ax[1].set_title(\"Early Stopping Validation Monitor\")\n",
+    "ax[1].set_xlabel(\"Iterations\")\n",
+    "ax[1].set_ylabel(\"Validation Accuracy\")\n",
+    "ax[1].legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
     "plt.show()"
    ]
   },
@@ -248,7 +268,9 @@
    "id": "533ee73c",
    "metadata": {},
    "source": [
-    "## Exercises"
+    "## Exercises\n",
+    "\n",
+    "**Note**: The exercises below all use the Wine dataset."
    ]
   },
   {
@@ -256,29 +278,30 @@
    "id": "89bd52f5",
    "metadata": {},
    "source": [
-    "### The Effect of Batch Normalization (Simulated)\n",
+    "### The Effect of Feature Scaling\n",
     "\n",
-    "Scikit-learn's MLP doesn't have a simple \"Batch Norm\" flag (it's built into the solvers implicitly or requires libraries like PyTorch/TensorFlow). However, we can simulate the **need** for it by using unscaled data.\n",
+    "This exercise demonstrates why **feature scaling** matters for optimization using a dataset with naturally heterogeneous feature scales.\n",
     "\n",
-    "1.  Train an MLP on `X_train` (Scaled).\n",
-    "2.  Train an MLP on `X` (Unscaled raw data).\n",
-    "3.  Compare the loss curves."
+    "1.  Load the Wine dataset.\n",
+    "2.  Train an MLP on scaled features.\n",
+    "3.  Train the same MLP on unscaled features.\n",
+    "4.  Compare the loss curves and test accuracy."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d20e6038",
+   "id": "1a2b8fb7",
    "metadata": {},
    "source": [
     "### Regularization Strength (Alpha)\n",
     "\n",
-    "The `alpha` parameter controls L2 Regularization (Weight Decay).\n",
+    "Using the same Wine dataset, the `alpha` parameter controls L2 Regularization (Weight Decay).\n",
     "\n",
-    "-   Train a model with `alpha=0.0001` (Default).\n",
-    "-   Train a model with `alpha=1.0` (High Regularization).\n",
-    "-   Compare accuracies.\n",
+    "-   Train three models with different `alpha` values.\n",
+    "-   Compare train/test accuracy and total weight norm.\n",
+    "-   Interpret which model appears underfit vs. better balanced.\n",
     "\n",
-    "**Result**: High alpha should slightly reduce Training Accuracy (preventing overfitting) but might maintain or improve Test Accuracy."
+    "**Prompt**: Which alpha gives the best trade-off? Look for smaller weights with minimal drop in test accuracy."
    ]
   },
   {
@@ -289,8 +312,8 @@
     "## Summary\n",
     "\n",
     "1.  **Adam**: generally the best default optimizer. Fast and robust.\n",
-    "2.  **Early Stopping**: The easiest way to prevent overfitting without tuning hyperparameters.\n",
-    "3.  **Normalization**: Neural Networks fail without scaled data (Batch Norm automates this deep inside layers)."
+    "2.  **Early Stopping**: A practical way to limit overfitting, with light tuning (e.g., patience/validation settings).\n",
+    "3.  **Normalization**: Neural networks usually train faster and more stably with scaled inputs."
    ]
   }
  ],
@@ -299,9 +322,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05C05 Building a Network in PyTorch.ipynb b/src/S05 Neural Networks I: Foundations and Optimization/S05C05 Building a Network in PyTorch.ipynb
index f41f2ab..1ae86d9 100644
--- a/src/S05 Neural Networks I: Foundations and Optimization/S05C05 Building a Network in PyTorch.ipynb	
+++ b/src/S05 Neural Networks I: Foundations and Optimization/S05C05 Building a Network in PyTorch.ipynb	
@@ -10,7 +10,7 @@
     "\n",
     "Scikit-learn is fantastic for standard algorithms, but for Deep Learning, we need more power. We need libraries that can:\n",
     "\n",
-    "1.  **Run on GPUs** (Matrix multiplication is slow on CPUs).\n",
+    "1.  **Run on GPUs** (Deep learning relies on large matrix operations, where GPUs are typically much faster).\n",
     "2.  **Calculate Gradients Automatically** (Backpropagation is hard to code by hand).\n",
     "\n",
     "The two industry giants are **PyTorch** (Meta) and **TensorFlow** (Google). We will focus on **PyTorch**, as it is the standard for research and education due to its readable, \"Pythonic\" style."
@@ -114,9 +114,9 @@
     "\n",
     "The standard loop has **5 Steps**:\n",
     "\n",
-    "1.  **Forward Pass**: Compute prediction.\n",
-    "2.  **Compute Loss**: How bad is the prediction?\n",
-    "3.  **Zero Gradients**: Reset errors from previous step.\n",
+    "1.  **Zero Gradients**: Reset gradients from the previous step.\n",
+    "2.  **Forward Pass**: Compute prediction.\n",
+    "3.  **Compute Loss**: How bad is the prediction?\n",
     "4.  **Backward Pass**: Calculate gradients (Backpropagation).\n",
     "5.  **Optimizer Step**: Update weights."
    ]
@@ -152,20 +152,26 @@
    "source": [
     "from sklearn.datasets import make_moons\n",
     "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "# Generate Data\n",
     "X_np, y_np = make_moons(n_samples=500, noise=0.2, random_state=42)\n",
     "\n",
+    "# Split first, then scale using training data only\n",
+    "X_train_np, X_test_np, y_train_np, y_test_np = train_test_split(\n",
+    "    X_np, y_np, test_size=0.2, random_state=42\n",
+    ")\n",
+    "scaler = StandardScaler()\n",
+    "X_train_np = scaler.fit_transform(X_train_np)\n",
+    "X_test_np = scaler.transform(X_test_np)\n",
+    "\n",
     "# Convert to Tensors (Float32)\n",
     "# Note: unsqueeze(1) changes shape from (N,) to (N, 1) to match output layer\n",
-    "X_tensor = torch.tensor(X_np, dtype=torch.float32)\n",
-    "y_tensor = torch.tensor(y_np, dtype=torch.float32).unsqueeze(1)\n",
-    "\n",
-    "# Split (Using standard slicing)\n",
-    "train_split = int(0.8 * len(X_tensor))\n",
-    "X_train, X_test = X_tensor[:train_split], X_tensor[train_split:]\n",
-    "y_train, y_test = y_tensor[:train_split], y_tensor[train_split:]\n",
+    "X_train = torch.tensor(X_train_np, dtype=torch.float32)\n",
+    "X_test = torch.tensor(X_test_np, dtype=torch.float32)\n",
+    "y_train = torch.tensor(y_train_np, dtype=torch.float32).unsqueeze(1)\n",
+    "y_test = torch.tensor(y_test_np, dtype=torch.float32).unsqueeze(1)\n",
     "\n",
     "print(f\"X_train shape: {X_train.shape}\")\n",
     "print(f\"y_train shape: {y_train.shape}\")"
@@ -221,16 +227,16 @@
     "losses = []\n",
     "\n",
     "for epoch in range(epochs):\n",
-    "    # --- Step 1: Forward Pass ---\n",
+    "    # --- Step 1: Zero Gradients ---\n",
+    "    optimizer.zero_grad()\n",
+    "\n",
+    "    # --- Step 2: Forward Pass ---\n",
     "    y_pred = model(X_train)\n",
     "\n",
-    "    # --- Step 2: Compute Loss ---\n",
+    "    # --- Step 3: Compute Loss ---\n",
     "    loss = criterion(y_pred, y_train)\n",
     "    losses.append(loss.item())\n",
     "\n",
-    "    # --- Step 3: Zero Gradients ---\n",
-    "    optimizer.zero_grad()\n",
-    "\n",
     "    # --- Step 4: Backward Pass ---\n",
     "    loss.backward() # Calculates dLoss/dWeights\n",
     "\n",
@@ -255,7 +261,7 @@
    "source": [
     "### Evaluation (No Gradients)\n",
     "\n",
-    "When evaluating, we use `torch.no_grad()` to save memory (we don't need to calculate backprop)."
+    "When evaluating, switch to `model.eval()` and use `torch.no_grad()` to save memory."
    ]
   },
   {
@@ -267,6 +273,7 @@
    },
    "outputs": [],
    "source": [
+    "model.eval()\n",
     "with torch.no_grad():\n",
     "    # Forward pass on test data\n",
     "    test_preds = model(X_test)\n",
@@ -332,9 +339,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {

From e09c51fc2a09548abef4f962a6237e03ae169378 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Tue, 17 Feb 2026 08:36:44 +0100
Subject: [PATCH 72/81] Updated requirements.txt

---
 src/requirements.txt | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/requirements.txt b/src/requirements.txt
index b39773b..6000c69 100644
--- a/src/requirements.txt
+++ b/src/requirements.txt
@@ -6,3 +6,6 @@ matplotlib
 seaborn
 xgboost
 statsmodels
+torch
+torchvision
+transformers

From 546428ddb1648f5e01a38249416bb538d07748ea Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Thu, 19 Feb 2026 21:55:36 +0100
Subject: [PATCH 73/81] S06 Neural Networks II

---
 ...Deep Learning, Transformers, and GenAI.pdf | Bin 401478 -> 677692 bytes
 ...Deep Learning, Transformers, and GenAI.tex | 394 ++++++--
 ...Convolutional Neural Networks (CNNs).ipynb | 126 +--
 .../S06C02 From RNNs to Transformers.ipynb    | 377 +++----
 .../S06C03 Large Language Models (LLMs).ipynb |  30 +-
 ...enerative AI and Modern Applications.ipynb | 183 +++-
 ...es Forecasting with Feature-Based ML.ipynb | 924 ++++++++++++++++++
 src/requirements.txt                          |   2 +
 8 files changed, 1629 insertions(+), 407 deletions(-)
 create mode 100644 src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C05 Time-Series Forecasting with Feature-Based ML.ipynb

diff --git a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06 Neural Networks II: Deep Learning, Transformers, and GenAI.pdf b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06 Neural Networks II: Deep Learning, Transformers, and GenAI.pdf
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diff --git a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06 Neural Networks II: Deep Learning, Transformers, and GenAI.tex b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06 Neural Networks II: Deep Learning, Transformers, and GenAI.tex
index b1f1343..ef73616 100644
--- a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06 Neural Networks II: Deep Learning, Transformers, and GenAI.tex	
+++ b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06 Neural Networks II: Deep Learning, Transformers, and GenAI.tex	
@@ -1,4 +1,4 @@
-% Created 2026-02-05 Thu 11:04
+% Created 2026-02-19 Thu 21:06
 % Intended LaTeX compiler: pdflatex
 \documentclass[hyperref={pdfpagelabels=false},aspectratio=169]{beamer}
 \usepackage[utf8]{inputenc}
@@ -201,9 +201,9 @@
 \end{frame}
 
 \section{Convolutional Neural Networks (CNNs)}
-\label{sec:org23f1c19}
-\begin{frame}[label={sec:org2d737c2}]{Why CNNs?}
-\begin{definition}[The Problem with MLPs for Images]\label{sec:org4197c45}
+\label{sec:org70519a5}
+\begin{frame}[label={sec:orge37f4f6}]{Why CNNs?}
+\begin{definition}[The Problem with MLPs for Images]\label{sec:org9ac777c}
 \pause
 Standard MLPs flatten images into vectors, causing two major problems:
 \begin{itemize}[<+->]
@@ -212,8 +212,8 @@ \section{Convolutional Neural Networks (CNNs)}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org937c7ec}]{The Convolution Operation}
-\begin{example}[Filters and Feature Maps]\label{sec:org506b2fb}
+\begin{frame}[label={sec:org8c54d23}]{The Convolution Operation}
+\begin{example}[Filters and Feature Maps]\label{sec:org0b6d308}
 \pause
 Instead of learning weights for every pixel, CNNs learn small \alert{Filters} (Kernels):
 \begin{itemize}[<+->]
@@ -224,20 +224,20 @@ \section{Convolutional Neural Networks (CNNs)}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org2a26b38}]{Controlling Dimensions}
+\begin{frame}[label={sec:org0769352}]{Controlling Dimensions}
 \begin{block}{Padding and Stride}
 \pause
 As filters slide, image dimensions change:
 \begin{itemize}[<+->]
 \item \alert{Valid Convolution}: No padding. Output shrinks (\(5 \times 5 \to 3 \times 3\)).
 \item \alert{Padding}: Add zeros around border to maintain size.
-\item \alert{Stride}: How many pixels to jump. Stride=2 halves dimensions.
+\item \alert{Stride}: How many pixels to jump. Stride=2 usually reduces dimensions by about half.
 \item \alert{Formula}: \(O = \frac{I - K + 2P}{S} + 1\)
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org33e3911}]{Pooling Layers}
-\begin{example}[MaxPool2d]\label{sec:orgdeb1226}
+\begin{frame}[label={sec:orge0f3f6b}]{Pooling Layers}
+\begin{example}[MaxPool2d]\label{sec:org388c6ac}
 \pause
 Pooling reduces spatial size to control overfitting and computation:
 \begin{itemize}[<+->]
@@ -248,7 +248,7 @@ \section{Convolutional Neural Networks (CNNs)}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:orge33b29c}]{Famous Architectures}
+\begin{frame}[label={sec:org43db836}]{Famous Architectures}
 \begin{block}{VGG and ResNet}
 \pause
 Modern Deep Learning is built on famous architectures:
@@ -261,86 +261,157 @@ \section{Convolutional Neural Networks (CNNs)}
 \end{block}
 \end{frame}
 \section{From RNNs to Transformers}
-\label{sec:orgb90aff8}
-\begin{frame}[label={sec:org5e2af73}]{The Sequence Problem}
-\begin{definition}[Why Standard Networks Fail]\label{sec:org46a4380}
+\label{sec:orgd6c93a0}
+\begin{frame}[label={sec:org14d23b8}]{Why This Matters}
+\begin{definition}[Sequence Data Is Everywhere]\label{sec:orgeb04f7e}
 \pause
-MLPs expect fixed-size input, but language is dynamic:
+Many research problems are sequential:
 \begin{itemize}[<+->]
-\item "Hello" (1 word) vs "The quick brown fox\ldots{}" (5 words)
-\item We need models that handle variable-length sequences.
-\item We need models that "remember" context.
+\item Clinical notes and patient timelines
+\item Instrument logs and time-ordered measurements
+\item Reports, interviews, and long documents
+\item Key question: \emph{which earlier tokens matter right now?}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgfe4eb84}]{Recurrent Neural Networks}
-\begin{example}[RNNs and Their Limitations]\label{sec:orgf7403e6}
+\begin{frame}[label={sec:org7576a41}]{Running Example}
+\begin{example}[Clinical Sentence]\label{sec:org0e3f8c8}
 \pause
-RNNs process words sequentially, maintaining a \alert{Hidden State} (memory):
-$$h_t = \tanh(W x_t + U h_{t-1})$$
+\emph{"Patient admitted three days ago with acute chest pain, treated with aspirin, now reports reduced discomfort."}
 \pause
-\alert{Problems}:
+When interpreting \alert{"discomfort"}, the model should retrieve context from:
 \begin{itemize}[<+->]
-\item \alert{Sequential}: Cannot process word 100 until word 99 is done. Very slow.
-\item \alert{Vanishing Memory}: By paragraph end, the beginning is forgotten.
-\item \alert{LSTMs} improved memory with "gates," but remained slow.
+\item "chest pain"
+\item "aspirin"
+\item other relevant earlier tokens
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org847670b}]{The Attention Mechanism}
-\begin{block}{Looking Back at Everything}
+\begin{frame}[label={sec:org2772e15}]{RNNs: First Main Solution}
+\begin{block}{Sequential Hidden-State Memory}
 \pause
-In 2014, researchers asked: \emph{Why squash everything into one tiny memory vector?}
-\pause
-\alert{Attention} lets the model "look back" at any word:
+RNN update:
+$$h_t = \tanh(Wx_t + Uh_{t-1})$$
 \begin{itemize}[<+->]
-\item When predicting, assign a "weight" to every previous word.
-\item "I poured cereal into the\ldots{}" \(\to\) Focus on "cereal" and "poured" \(\to\) Predict: "bowl".
-\item No more sequential bottleneck for context.
+\item \(x_t\): current token representation
+\item \(h_{t-1}\): previous memory
+\item \(h_t\): updated memory
+\item Strength: natural for ordered data
+\item Limits: sequential bottleneck + fading long-range memory
+\item LSTMs improved memory with gates, but remained sequential
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org356b39b}]{The Transformer (2017)}
-\begin{definition}["Attention is All You Need"]\label{sec:org02dbfe5}
+\begin{frame}[label={sec:org6343a19}]{Attention as Weighted Lookup}
+\begin{example}[Query-Specific Relevance Weights]\label{sec:org07ec361}
+\pause
+Attention replaces "single compressed memory" with dynamic lookup.
+\begin{itemize}[<+->]
+\item Weights are non-negative and sum to 1
+\item Different query tokens produce different weight patterns
+\item For query token "today", weights are distributed across ["Patient", "reports", "chest", "pain", "today"]
+\item Self-weight can be < 1 because context tokens also contribute
+\end{itemize}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:org5cc7223}]{Transformers: Attention-Centered Architecture}
+\begin{definition}["Attention Is All You Need"]\label{sec:orgcb3de4a}
 \pause
-The paper proved we don't need recurrence at all:
+Transformers remove recurrence and rely on attention:
 \begin{center}
 \begin{tabular}{lll}
-Feature & RNN/LSTM & Transformer\\
+Feature & RNN / LSTM & Transformer\\
 \hline
-\alert{Processing} & Sequential (Slow) & Parallel (Fast)\\
-\alert{Context} & Local (Nearby words) & Global (All words see all words)\\
-\alert{Distance} & Linearly far & Constant (1 step away)\\
+Processing & Sequential & Parallel in training\\
+Context & Compressed in one hidden state & Direct weighted access across positions\\
+Long distance & Degrades with sequence length & One attention step away (subject to masking rule)\\
 \end{tabular}
 \end{center}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orge493cdc}]{Transformer Architecture}
-\begin{example}[Key Components]\label{sec:orged5d393}
+\begin{frame}[label={sec:orgc4d3363}]{Core Formula and Transformer Block}
+\begin{block}{Q, K, V and Scaled Dot Product}
 \pause
+$$\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V$$
 \begin{itemize}[<+->]
-\item \alert{Self-Attention}: \(Attention(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V\)
-\item \alert{Encoder}: Reads input text, creates rich representation (\alert{BERT}).
-\item \alert{Decoder}: Generates output text step-by-step (\alert{GPT}).
-\item \alert{Positional Encoding}: Injects word order since parallel processing loses it.
+\item Query: what this position is looking for
+\item Key: what each position offers
+\item Value: information to aggregate
+\item \(\sqrt{d_k}\) scaling stabilizes softmax/gradients
+\item Block components: self-attention, feed-forward, residual, layer norm, positional encoding
+\end{itemize}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:orgf47baab}]{Multi-Head and Model Families}
+\begin{example}[Parallel Attention Heads]\label{sec:org1821832}
+\pause
+Multi-head attention runs several Q/K/V projections in parallel.
+\begin{itemize}[<+->]
+\item Different heads can focus on different relations (syntax, semantics, locality)
+\item Outputs are concatenated and projected back to model dimension
+\item Encoder-only: BERT (understanding/classification)
+\item Decoder-only: GPT (generation with causal masking)
+\item Encoder-decoder: T5 (translation/summarization)
 \end{itemize}
 \end{example}
 \end{frame}
-\section{Large Language Models (LLMs)}
-\label{sec:org7b4eede}
-\begin{frame}[label={sec:orgd52442c}]{Evolution of LLMs}
-\begin{definition}[From BERT to Llama]\label{sec:orgf95261e}
+\begin{frame}[label={sec:orga77f255}]{Why GPT Is Left-to-Right}
+\begin{block}{Causal Masking}
 \pause
+For token \(t\), attention is allowed only to tokens \(\le t\).
 \begin{itemize}[<+->]
-\item \alert{BERT (2018 - Google)}: Encoder-only. Good at "understanding" (Classification, Q\&A).
-\item \alert{GPT (2018-Now - OpenAI)}: Decoder-only. Good at "generation" (Writing text).
-\item \alert{Llama (2023 - Meta)}: Open-source. Brought GPT-level performance to researchers.
-\item Today's frontier: Claude, GPT-4, Gemini, Llama-3.
+\item Future positions are masked out
+\item Training remains parallel across positions (with masking)
+\item Generation remains token-by-token
+\item Early tokens have less context than later tokens
 \end{itemize}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:orgb107b84}]{Practical Research Workflow}
+\begin{example}[Pretrained-First Strategy]\label{sec:orga32f035}
+\pause
+Most researchers do not train transformers from scratch:
+\begin{itemize}[<+->]
+\item Choose a pretrained model for the task/domain
+\item Start with prompting and retrieval
+\item Evaluate on domain-specific examples
+\item Fine-tune only if needed
+\item Keep expert review in high-stakes settings
+\end{itemize}
+\end{example}
+\end{frame}
+\section{Large Language Models (LLMs)}
+\label{sec:org0717edf}
+\begin{frame}[label={sec:orgd621ae1}]{Evolution of LLMs}
+\begin{definition}[Scale as the Main Driver]\label{sec:org6c1e75b}
+\pause
+LLM progress accelerated with scale (parameters, data, compute):
+\begin{center}
+\begin{tabular}{lrlll}
+Model & Year & Parameters & Training Data & Notable Point\\
+\hline
+\alert{BERT} & 2018 & 340M & 16GB & Strong "understanding" performance\\
+\alert{GPT-2} & 2019 & 1.5B & 40GB & High-quality open-ended generation\\
+\alert{GPT-3} & 2020 & 175B & 570GB & Emergent few-shot behavior\\
+\alert{Llama-2} & 2023 & 70B & 2TB & Open-weight ecosystem growth\\
+\alert{GPT-4} & 2023 & Undisclosed & Unknown & Strong multimodal capabilities\\
+\end{tabular}
+\end{center}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org15c11cb}]{The LLM Lifecycle}
-\begin{example}[Pre-training, Fine-tuning, Inference]\label{sec:orgb3c88e1}
+\begin{frame}[label={sec:org2967028}]{Architecture Families}
+\begin{block}{Encoder, Decoder, Encoder-Decoder}
+\pause
+\begin{itemize}[<+->]
+\item \alert{BERT (Encoder-only)}: understanding tasks (classification, QA)
+\item \alert{GPT (Decoder-only)}: generation tasks (next-token prediction)
+\item \alert{T5 (Encoder-Decoder)}: text-to-text tasks (translation, summarization)
+\item \alert{Scaling hypothesis}: performance improves predictably with more parameters, data, and compute
+\end{itemize}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:orgdb25937}]{The LLM Lifecycle}
+\begin{example}[Pre-training, Fine-tuning, Inference]\label{sec:org4356ec1}
 \pause
 \begin{itemize}[<+->]
 \item \alert{Pre-training}: The expensive part. Read the internet (TB of text), learn to predict next token. Learns grammar, facts, reasoning.
@@ -353,33 +424,73 @@ \section{Large Language Models (LLMs)}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org78ea468}]{Tokenization}
-\begin{block}{Tokens and Context Windows}
+\begin{frame}[label={sec:orgce35b27}]{Tokenization and Context Windows}
+\begin{example}[BPE and Subword Units]\label{sec:orgb995ac7}
 \pause
 LLMs don't see words; they see \alert{Tokens}:
 \begin{itemize}[<+->]
 \item A token can be a word ("apple"), part of a word ("ing"), or a character.
 \item \alert{Rough rule}: 1000 tokens \(\approx\) 750 words.
-\item \alert{Context Window}: Limit on how much text the model remembers at once.
-\item GPT-4: 128k tokens. Claude: 200k tokens.
+\item BPE/subword tokenization handles rare or unseen words by splitting them.
+\item \alert{Context Window}: Limit on how much text the model can use at once (model/version dependent).
+\end{itemize}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:orgb8769d1}]{Next-Token Prediction Mechanics}
+\begin{block}{From Logits to Text}
+\pause
+At each decoding step, the model outputs logits \(z \in \mathbb{R}^{|V|}\) over the vocabulary.
+\pause
+
+Probabilities are computed as:
+\[
+p_i = \frac{e^{z_i}}{\sum_j e^{z_j}}
+\]
+\pause
+\begin{itemize}[<+->]
+\item This is a full distribution over all candidate next tokens.
+\item Decoding selects/samples one token from this distribution.
+\item The selected token is appended, and the process repeats autoregressively.
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org9e0299a}]{Prompt Engineering}
-\begin{example}[Guiding the Predictor]\label{sec:org6ca44ac}
+\begin{frame}[label={sec:org42e5ea8}]{Sampling Controls}
+\begin{example}[Temperature, Top-k, Top-p]\label{sec:org55a51a2}
+\pause
+Generation quality/style depends on sampling strategy:
+\begin{center}
+\begin{tabular}{llr}
+Parameter & Effect & Typical\\
+\hline
+\alert{Temperature} & Flattens/sharpens token probabilities & 0.7\\
+\alert{Top-k} & Restricts choices to k most likely tokens & 50\\
+\alert{Top-p} & Restricts to smallest set with cumulative prob \(\ge p\) & 0.9\\
+\end{tabular}
+\end{center}
+\pause
+\begin{itemize}[<+->]
+\item Low temperature: focused/deterministic
+\item High temperature: diverse/chaotic
+\item Top-k/top-p reduce low-probability tail sampling
+\end{itemize}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:org8171ca6}]{Prompt Engineering}
+\begin{example}[Guiding the Predictor]\label{sec:org150d137}
 \pause
 LLMs are "next token predictors." They are sensitive to \emph{how} you ask:
+\pause
 \begin{itemize}[<+->]
 \item \alert{Zero-Shot}: Just asking. "Translate to Spanish: Hello"
-\item \alert{Few-Shot}: Giving examples first. "Red \(\to\) Rojo, Blue \(\to\) Azul, Green \(\to\) ?"
-\item \alert{Chain of Thought}: "Think step by step." Dramatically improves reasoning.
+\item \alert{Few-Shot}: Give examples to induce pattern-following behavior.
+\item \alert{Chain of Thought}: "Let's think step by step." Can help some multi-step tasks (model/task dependent).
 \end{itemize}
 \end{example}
 \end{frame}
 \section{Generative AI and Modern Applications}
-\label{sec:orgfba8ff4}
-\begin{frame}[label={sec:org56adcf2}]{Text Generation}
-\begin{definition}[It's Just Statistics]\label{sec:orga163662}
+\label{sec:org5157b4f}
+\begin{frame}[label={sec:org9ce0a6c}]{Text Generation}
+\begin{definition}[It's Just Statistics]\label{sec:org586c2af}
 \pause
 At its core, a Generative LLM is a \alert{Next-Token Prediction Engine}:
 \pause
@@ -392,14 +503,14 @@ \section{Generative AI and Modern Applications}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org575fc53}]{Retrieval-Augmented Generation (RAG)}
-\begin{example}[Solving LLM Limitations]\label{sec:org37c623c}
+\begin{frame}[label={sec:orgcebf702}]{Retrieval-Augmented Generation (RAG)}
+\begin{example}[Solving LLM Limitations]\label{sec:org97c7202}
 \pause
 LLMs have two major flaws:
 \begin{itemize}[<+->]
 \item \alert{Hallucination}: They confidently make things up.
 \item \alert{Cut-off Date}: They don't know current events or private data.
-\item \alert{RAG} connects LLM to external knowledge:
+\item One practical mitigation is \alert{RAG}, which connects LLMs to external knowledge:
 \begin{enumerate}
 \item \alert{Retrieve}: Search database for relevant documents.
 \item \alert{Augment}: Paste documents into prompt context.
@@ -408,7 +519,7 @@ \section{Generative AI and Modern Applications}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:orga782429}]{Diffusion Models}
+\begin{frame}[label={sec:org9907df3}]{Diffusion Models}
 \begin{block}{Image Generation (Stable Diffusion, DALL-E)}
 \pause
 Image generation works differently from LLMs, using \alert{Diffusion}:
@@ -419,8 +530,8 @@ \section{Generative AI and Modern Applications}
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org31c84ae}]{Ethical Considerations}
-\begin{example}[Bias, Hallucination, and Copyright]\label{sec:org6fee54d}
+\begin{frame}[label={sec:org7f503aa}]{Ethical Considerations}
+\begin{example}[Bias, Hallucination, and Copyright]\label{sec:org87b58a3}
 \pause
 With great power comes great responsibility:
 \begin{itemize}[<+->]
@@ -431,15 +542,146 @@ \section{Generative AI and Modern Applications}
 \end{itemize}
 \end{example}
 \end{frame}
+\section{Time-Series Forecasting with Feature-Based ML}
+\label{sec:org6311b41}
+\begin{frame}[label={sec:org3470a35}]{Forecasting Problem Setup}
+\begin{definition}[Formal Objective]\label{sec:org6327625}
+\pause
+For a univariate series \(\{y_t\}_{t=1}^T\), one-step forecasting is:
+\[
+\hat{y}_{t+1|t} = f_{\theta}(\phi_t)
+\]
+where \(\phi_t\) uses only information available at time \(t\).
+\pause
+Training is empirical risk minimization:
+\[
+\hat{\theta} = \arg\min_{\theta}\frac{1}{N}\sum_{t \in \mathcal{T}_{train}} \mathcal{L}\left(y_{t+1}, f_{\theta}(\phi_t)\right)
+\]
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org6898795}]{Why Time Series Is Not i.i.d.}
+\begin{block}{Temporal Dependence and Structure}
+\pause
+Time series typically combines:
+\[
+y_t = T_t + S_t + R_t
+\]
+\pause
+\begin{itemize}[<+->]
+\item \(T_t\): trend (long-term movement)
+\item \(S_t\): seasonality (repeating calendar pattern)
+\item \(R_t\): residual/noise
+\end{itemize}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:orgdf0cb2f}]{Why Random Splits Fail}
+\begin{example}[Leakage Through Time Mixing]\label{sec:org10432bb}
+\pause
+Information sets are ordered:
+\[
+\mathcal{F}_{t_1} \subset \mathcal{F}_{t_2}, \quad t_1 < t_2
+\]
+\pause
+Random shuffling violates this causal flow and can leak future-conditioned patterns into training.
+\pause
+\begin{itemize}[<+->]
+\item Correct: train on past, validate on future block.
+\item Incorrect: random train/test split across timestamps.
+\end{itemize}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:orga612268}]{Strong Baselines First}
+\begin{definition}[Naive and Seasonal Naive]\label{sec:org62e79d9}
+\pause
+\[
+\hat{y}_{t + 1}^{naive}=y_t,\qquad
+\hat{y}_{t + 1}^{seasonal}=y_{t + 1 - s}
+\]
+\pause
+for monthly data, \(s=12\).
+\pause
+\begin{itemize}[<+->]
+\item Random walk \(y_t=y_{t - 1} + \varepsilon_t \rightarrow\) naive is optimal (MSE).
+\item Seasonal random walk \(y_t=y_{t-s}+\varepsilon_t \rightarrow\) seasonal naive is optimal.
+\item If ML cannot beat these, complexity is not justified.
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org9d7df7d}]{Feature Engineering with Temporal Constraints}
+\begin{block}{Building \(\phi_t\) Safely}
+\pause
+Example feature vector:
+\[
+\phi_t = [y_{t-1}, y_{t-2}, y_{t-12}, \mu_{t-1}^{(3)}, \mu_{t-1}^{(12)},
+\sin(2\pi m_t/12), \cos(2\pi m_t/12), t]
+\]
+with rolling mean
+\[
+\mu_{t-1}^{(w)} = \frac{1}{w}\sum_{j=1}^{w} y_{t-j}
+\]
+\begin{itemize}[<+->]
+\item Shift first, then roll (prevents target leakage).
+\item Cyclical encoding respects December-January adjacency.
+\end{itemize}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org56c118b}]{Model Families and Objectives}
+\begin{example}[Ridge vs Boosting]\label{sec:orgfce97c4}
+\pause
+\begin{itemize}[<+->]
+\item Ridge: stable and interpretable with correlated lags. Ridge objective:
+\end{itemize}
+\[
+\hat{\beta}=\arg\min_{\beta}\left[\frac{1}{n}|y - X\beta|^2+\alpha|\beta|^2\right]
+\]
+\begin{itemize}
+\item Boosting: captures nonlinear interactions. Boosting (additive model):
+\end{itemize}
+\[
+f_M(x)=\sum_{m=1}^M \eta h_m(x),\qquad
+r_i^{(m)} \approx y_i - f_{m-1}(x_i)
+\]
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:org7b42126}]{Walk-Forward Backtesting}
+\begin{example}[Estimating Future Performance]\label{sec:org1967bd1}
+\pause
+Choose ordered cutoffs \(\tau_1 < \dots < \tau_K\). For fold \(k\):
+\begin{itemize}[<+->]
+\item Train on \(\{1,\dots,\tau_k\}\)
+\item Test on \(\{\tau_k+1,\dots,\tau_k+h\}\)
+\item Compute metric \(M_k\)
+\end{itemize}
+\pause
+Aggregate:
+\[
+\bar{M}=\frac{1}{K}\sum_{k=1}^K M_k
+\]
+This is usually more reliable than a single holdout split.
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:orgf7650b2}]{Diagnostics After Forecasting}
+\begin{block}{Residual Checks}
+\pause
+Even with good MAE/RMSE, verify residual structure.
+\begin{itemize}[<+->]
+\item Residual mean near zero (bias check).
+\item No strong temporal pattern in residual plots.
+\item Low residual autocorrelation at small lags.
+\item If autocorrelation remains, predictive signal is still unmodeled.
+\end{itemize}
+\end{block}
+\end{frame}
 \section{Summary}
-\label{sec:org89d6fcb}
-\begin{frame}[label={sec:org158a95d}]{Key Takeaways}
+\label{sec:orgd620280}
+\begin{frame}[label={sec:org04a9aa1}]{Key Takeaways}
 \begin{block}{Neural Networks II}
 \pause
 \begin{enumerate}[<+->]
 \item \alert{CNNs}: Use convolutions and pooling to efficiently process images. ResNet enables very deep networks.
 \item \alert{Transformers}: Replaced RNNs with parallel self-attention. The foundation of modern NLP.
 \item \alert{LLMs}: Giant Transformers (BERT, GPT, Llama) that predict next tokens. Power chatbots and assistants.
+\item \alert{Time-Series ML}: Formalize \(f_\theta(\phi_t)\), beat strong baselines, and validate with walk-forward backtesting.
 \item \alert{Generative AI}: Text (next-token), Images (diffusion). RAG grounds LLMs in facts.
 \item \alert{Ethics}: AI reflects training data. Requires human oversight and responsibility.
 \end{enumerate}
diff --git a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C01 Convolutional Neural Networks (CNNs).ipynb b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C01 Convolutional Neural Networks (CNNs).ipynb
index 1ea9a5c..55543d7 100644
--- a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C01 Convolutional Neural Networks (CNNs).ipynb	
+++ b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C01 Convolutional Neural Networks (CNNs).ipynb	
@@ -29,7 +29,7 @@
    "id": "35e948cc",
    "metadata": {},
    "source": [
-    "## 1. The Convolution Layer (`Conv2d`)\n",
+    "## The Convolution Layer (`Conv2d`)\n",
     "\n",
     "This is the heart of the CNN. Instead of learning a weight for every pixel, we learn small **Filters** (or Kernels).\n",
     "\n",
@@ -50,29 +50,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "8d1a55c1",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Uz1BaAdbyXnV+pjLjVd6zTNFedwOcF599AP73aKkDwFyRCkyCkbSWSbiS8T/WXXfdqpKUMT3SWiADj0aufKfbR1r2pNKUqadvvPHGaorzDGJcD2A6O/L6mbY5V/wztk5a37TLeB0ZJDUBTqYFT9esTGmeyvJRRx01w+dOYHXeeeeVbbfdtgoIElSl1UT7wMX1scjYQKkkpiKeym+mSu9tXJJ0scn+JnxIBTbHJoM5J3hKF5zO5+6rWZ3SfE6lEpsWRwnm0hUv+5p9PuKII6oBiPP/DCabQa8TumTg26yf/8+JTEWe9yDBQFpmpSVOPmupSGdb8l7V3v/+91fT3mdA3GzTpptuWgUICcCyPC1E6nGF5nR/8vlPS5kMBp7Pdgb7TWiUdfK5y+C96dKUxyeAzADGmT47fzOTJ0+uWhPlM5b78xnNGDkJH9tbns1I3vuMJZPPUV6nnjK9Njfel7QcShCT8X8yPkxa16Sly3LLLTfNun05Hglz6/cs3cYyrk8G8M54NbfddluZ089Ip7Tm23rrrauWQykXEoBl+1OGpIVOWgh1jqmTdRJMJTTMGEUJjs8666yqvIm69U7KnVNOOaUKZTNG15577lkNsJxAKMc94fFFF1002/sEAN36NEcWAPTRXXfd1dp3331bq6++ejX9dqYK33TTTaspt59//vnu9SZNmtT68pe/XE3vm6nIV1lllWpa8vZ16qmLt9tuu5lOmVy7++67u6csvvrqq3vdxltuuaWaanixxRZrLbLIIq0tttiide211/ZYp7cpzespjVdaaaVq2ujs180339zrtvz85z9vvfrVr24NGTKkx5TgnVOax7PPPts64IADWiuuuGJ1LDIVcqazbp8KeUZTePc2vfOsTmmeadh709cpze+8885qSviFF164uq99e8aNG1dtd97j7N8KK6zQ2mqrraopozuPd6bJ7qv26akzVXmmr99ggw2qqcz/8pe/9PqYTF995JFHttZZZ53qPVxqqaVaI0eOrD6LzzzzzFzbn3r66kMOOaT7M571MlX9vffe271OPnd5/fyt1NObZ5rxPH+myc7U6plifNSoUa2f/OQnrVnxqU99qnrOXXfdtdf758b7cvLJJ1fTnQ8ePLjH30tvfxN9OR6nnnpq9fnPe5P9z+eut8/b3JjSvHP7jj/++Grb8tobbbRR68orr+x1P+67776qTMpnY9lll20ddNBBrfPPP796zuuvv77Hurfeemvr3e9+d2uZZZapnjfbnffjsssum+m2A0BfdOWf/0Y8AADArEhX0gMOOKA89NBDVYscAJhXhDoAANBHGX+pfTawjKmTGcnSlS9dvQBgXjKmDgAA9FHGJ1p11VWrMYUyllDG68m4TBlbBwDmNaEOAAD0UWbAyiDICXHSOieDTJ9zzjnVAMsAMK/pfgUAAADQQD3neQUAAACgEYQ6AAAAAA0k1AEAAABoIKEOAAAAQAMJdWAOPfjgg2WhhRYq11xzzYA4lq9//evLpz/96f7eDOB/2N/+9rfS1dVVzjjjjO5lX/rSl6plA8FAK7c/+9nPllGjRvX3ZgAwQNx4441lwQUXLA888EB/b0qZNGlSWWWVVcrxxx/f35vCdAh1Bqh8Ec6X35tvvrkMBBMmTKi+kF9xxRV9Wj/rZfvPO++8Mr/7yle+Un0Z33TTTXss/+1vf1u22GKL8rKXvawMGzasvO51rytnnnnmbL/OP//5z/KhD32orLHGGmXhhRcua621VjnwwAPLE0880WO9z3zmM+W73/1ueeSRR2b7tQD6co7q7ZaAoq8OP/zwcsEFFwyIcvtnP/tZNVX1iiuuWIYOHVpWXnnlsvPOO5c///nPs/UaEydOLHvvvXdZd911y5JLLlkWW2yx8trXvrZ861vfqr4gt/vkJz9Zxo4dWy688MI53jeAl6K8nhXXXnttVW94+umnB/TxuPrqq6e5v9VqVQFG7n/HO95R+sshhxxSdt9997Laaqv1WD516tTyve99r6y//vpVfWCZZZYpW265ZXUOmV233HJLede73lWWXnrpssgii1TnreOOO677/gUWWKCqc3zta18rzz///BztFy+NIS/R8zKfSajz5S9/ufr/m9/85v7enAHjscceK9///verW7t8Md9hhx3KG97whu6r0z/5yU/KBz7wgfL444+XAw44YJZeZ/z48dVzPffcc+WjH/1odbJJ4f2d73ynXH755eUPf/hDGTTo3xnt9ttvX5ZYYokqTU/FBeClkjImQXO7fBnMl9CEGvkiOLNQJ8FJysv+Lrf/9Kc/laWWWqp84hOfqML4BOOnnXZaFchfd911VSAzK7L/f/nLX8rb3/72svrqq1dldCo6Kf9vuOGG8qMf/ah73RVWWKEqu7/xjW9UX6wB5lV5/VJIWZd6wwc/+MHqwuZAlNaaKYff+MY39lj++9//vjz00ENVuN9f/vjHP1YXh3McO+21117lrLPOquoU+++/f1U3uPXWW8ujjz46W6/1m9/8przzne8sG2ywQfniF79YXYC49957q2PQbs8996xCwByzbAMDi1AH5sAPf/jDMmTIkKowbJewZfjw4eV3v/td90nhwx/+cBkxYkR1hWBWQ52ERGl+efHFF5ftttuue3kS9ZykE/CkMI5UHFJJ+sEPflCdUAdKdwdg/vO2t72tbLTRRtP9wtwfchUxTdbroLuv5fb//d//TbPuPvvsU7XYyVXRE044YZa2I+Xz9ddf32PZfvvtV7XayTnimGOOqcKc2q677lp22WWXct9995U111xzll4LYE7K66ZIgLHooovOledK4H7uuedWLVJyTqgltBg5cmR1Eba/nH766WXVVVethlRolwvEuSDx05/+tOy4445z/Dr/+te/qnAodYv0rpjeeTMSzr31rW+t6jFCnYFH96sGSdqd9PQf//hHdVUz/1922WXLwQcfXKZMmTLNWAa54nfsscdWV0zTPG/zzTefphl5Wt301vImr5Uri/Xz5XWiDglySwuUWVG3WLnrrrvK+973vuqLbZ43qXCaOmaMg7qVSb7ojhkzpsfjX3zxxepLdwraPDaF+pve9KaqpUqndEl6//vfXz1XCqE99tijCj46x3iIO++8swpB8gU8lZCc8PraBD7dBtKEP+9FZyGZK77tKX9OGLn6m/eilu3Ka95xxx09Hp8uAHn8ww8/3P18sfzyy/dYL8FRtD9nbL311lUIlKQfYCCMqdMp9+cLer6g1ueVnHtqOdfli2PKvZSl66yzTtVypreuvuecc075whe+UFZaaaWq6XhdZs5Kud2b5ZZbrnq+9i4Ehx56aPXF97LLLuuxbrrHJkyaWRP4+tza2S3hLW95S/Xz5z//+Uy3C2Buu+SSS6rv1fl+vfjii1cV/bQ2bHfbbbdV5XSC53x/zff1lNPtQwHk+/6nPvWp6v9pGVSX7zkvzOjc0Fm3qOsNt99+e3nve99bfS9ub1WTgD51gnwHznf43XbbrapL9FW6NmW7L7300h51jYQbeb3epG61ySabVF2e8rp5/d6Gmsh2pxVNWtS88pWvrI5V1r3yyiv7tG05T6VLVeeF2VwMSOvRBDrphpVzaKfUqTL8Q+pY7a13sm/rrbdeNXxD/bgEWOPGjau6VeW8luV53ulJ/SJd1p588sk+7QfzjlCnYRLepMKfwiQFS4KahB8nnXTSNOumpUbS59GjR5fPfe5zVaCTAiJ/vLMihUKuUkYKkYwLk9u73/3u2dqH97znPVWBccQRR1RfrA877LDyzW9+syoo8oX8yCOPLC9/+cursKq98MuX9FNOOaUKobJOCvs0o8/xaA8v8ty5Anv22WdXoUkKqoxHk/93yskqKXhClTQpzLHMySyhWcZXmJGMiXDTTTeVDTfccJr7so157gRW99xzT9WM8atf/Wo1RlL7IMYZWyHHN9tWB3Mnnnhi1RTy29/+djW2Q2y22WZVYZtuAbnymyaRv/zlL6t9y7amBVC7nDhioAwCCsyfnnnmmepqZvutr3IeSViTSkR9XkmLxsh5KmVzmp/ni3HKypwXMkZNzhedUr7+4he/qM4b6dKVcGVWy+1awpacW9IdKy11cu7Zaqutuu9PeJSxDLItzz77bLXs17/+dTn55JOrCw+d3bTyRTrHJZWNnFdy7s7FluxPu1ysyJdt5TYwr8vrlL8JcRJ25zt2vr8mTEmIkiCmlgAkrQnTFSffUxOkJFRPq5eECZH6QQKTyMXlunyvLxDPqrRgzDAQKdv33Xffalm+/6aFydprr10FHRmXLEF7vi/3dRyfBOwZ2iD1hfZgK8cp+9WbnIvSMj6t5LM9uWCb7cv5p1O6cWW7ciE76ydA2nbbbWc6TlsuaPz973+f5jyVc1EGT954443L5z//+e6x2hKwpQVPLUFQLoCk1Wpah7ZfkEjdJK2A6tZOOcfmAnheM+FTni+/f+QjH+l17JzUL/I+99YtjH7WYkA6/fTTUzK2brrppu5le+yxR7XsK1/5So91N9hgg9bIkSO7f7///vur9RZeeOHWQw891L38hhtuqJYfcMAB3cs233zz6tYpr7Xaaqt1//7YY49Vjz300EP7tP2XX355tf65557bvSyPzbIPfehD3csmT57cWnnllVtdXV2tI444onv5U089VW1/tqN93RdeeKHH62S95ZdfvrXXXnt1Lzv//POr1/nmN7/ZvWzKlCmtLbfcslqeY1vbaqutWuutt17r+eef7142derU1iabbNJae+21Z7iP99xzT/V83/72t6e5b/z48a1dd9212q+sk9siiyzSuuCCC6ZZ99e//nV1/2GHHda67777Wosttlhrhx12mGa9U045pTVs2LDu58stx2fSpEm9bt+CCy7Y+shHPjLDfQCYk3NUb7f281B7eVufA9otuuiiPcr52t57790aPnx46/HHH++xfLfddmstueSSrQkTJvQ416y55prdy2a33K698pWv7N6XlMdf+MIXqnNIuz/96U9VGbvPPvtU56GVVlqptdFGG/VaHp999tk9jk/Wu+2223p97be+9a2tV73qVTPdD4C5VV4/++yz1ffLfffdt8fjHnnkkaq8bV/eWzlbl3FXXnll97Kjjz66WpZzQbvezg21znpGfc7Yfffde6z3t7/9rTV48ODW1772tWnK5SFDhkyzfEZ1rO985zutxRdfvHu/dtlll9YWW2xR/T/1oO22267HYzv3/8UXX2ytu+66VR2jc19yu/nmm7uXPfDAA62FFlqoteOOO85w+377299Wj73ooot6LL/llluq5csss0xV9zn++ONbZ511Vut1r3tdVd+45JJLeqx/4oknVuv/8Ic/bF1//fXVMfvkJz/ZY53XvOY1Vf0kt4997GNVHSo/87icbzs9/PDD1X1HHnnkDPeBec+YOg3UnrpGfZWzU1pwpOVLLc310jImLTySaveXXPmsDR48uOrulJYnuepZS5epJMa5GtC+bm51a5wk8fmZx2fU9tqvfvWranDOOs2PtHJJi6WMcVNL08H8nvQ8V1vrK66R1j9JtJNctx/DdnVT0zQH7ZSrz694xSuqbl25YpFWOGlNlbQ+Vzna+8imf2quTmc70oQzTTTTWqdTtiPvYa6G5CrvVVddVbXESpeuXPntlO3qz/7AwPwvM+2lrJub8n34/PPPr8aYyf/by7GUzbkqnDK/feaqtHbs7IY6q+V2LVcxc0U055/8PwMepwxvH2sgg4umO3JawaY7QrYxLSzbx2WopRl8yv2cs3IlOd2zemsyX29XBrwEmFfldV0+pXVNe3mb79ypN7QPc9BezqYlRybyqL/TplxOneSlrvdkPJl8/885on170xUsLXeyvWnJ0hd5jrSmyZiVaUWTn+2zPnVq3/+nnnqqOjdkn9tb+9TSCqhuOR8ZIyfDTFx00UXV4+o6TV/PUznW9f1ptZ/3JjK4frq5pedD9qG9S3CO1cc+9rGqrpCWoGld1PmcaQWVY1zvd+otaWGaukjqJjmmtXqb1C8GHqFOw6TC39l8MX9gKVg6tf8R1lKYtzfR6w8p1Nql+WD2KwVO5/LO6boz9kK6SGUcnPYpYdtH889YMhlrJuMgtOts6p5uUakwpIlpbr1JX9TphTq1urlpu3QXSIGbE1xdEciJI2NCpAtVZj5pl1Am4yikG1n6t2Ych3Zpjp9pFfOc9SB3Ce3SRDIVi/RnfvWrXz3NdhkkGXgpJWie2wNvputTKhgJwnvrWhyds3x0zugyM72V2+1fxGtpgv+qV72q+n9neJ4xIxIwpTl8vih3lsG1jAlUj4eWoD/rprvx3Xff3WOg5Hq7lNvAvCyvUxZFhmjoTb5rtl8QzffOlH2d5XC6Lb0UOsv3bG/Kyt7qOTGzWRfbpU6V8czy3TvhRsKWlNPTk9An4Um+r7/wwgvdy3srt6dXD8vr5DzXWf7P7DxVB0o5HnWgE+kylWEnMsbQ5MmTe1xcOPXUU6swJ8csXaY6L37Uv9fd5WoZUyihTmZ+bN+PepucpwYeoU7DTC/VnV35o+zty237wMvzYh+mt1/t25bCKoOzJczIl+kEH3nc17/+9WrMmllVDwSWMRhy9bc3nUFQu4xrFJ2BWtLtFKIZO6f9ym5OMpl5ILOeZJ32MR/apyLMOA6dhWsK1lQKOk/GSecztlAK6s4KRSpFnUEZwEBXl81p2djbWGjxmte8psfvfWmlM6Nye3py0SQVnQx22RnqpCVPXRlKud1XqTAccsghVZBfjyFUy3Ypt4H+KHPT6r+3oKE9JMgFynznzPfwjC2WQCGPTwuRGQ2wW5teGDCjekdn+Z7XyfNk/Jve6g99GQS/M8BI6/5HHnmk+p4+vSnY00I+37szbs/xxx9fXUDOd/u06EwoNLdM7zxVj7PZOWlKpE6Ui91pBZqL4u2TCdThU85T7Rct6ufMODudz1lfXO7chvp356mBR6gzH6u/bLbLzFP1zBv1F9b2Lk7trV3aDYRENl2TMhhYmhK2b0+6SbVL16Q0vUwS3t5aJy1z2tVTxqZArmcdmdUWRznR3H///T2Wp3VRkvLeTlApcHMyar8vBXAGnEsokxH1jzrqqGpA6gyEVsugodN7vsjrtUu3sQRH9RVmgIGot3NLrpxm5pWUebNTNs9OuT0j6X7VeQU65XguMuQKdprup/VN3d22L883vava2a7OgZYBXkppyVFX5GdU5qZCny6kaamTQeFnVN+YXr2h7r7TOZhxZ71jZtubi75psTI3uv/mO3cC9rSG//GPfzzd9dItOD0LMjB+++y2CXVmpR6WusmMBo2uJz/pPE8lgEnolu/4nTJbbrYt585aJolJ16sM85ALyfVF7NSTaukelu539UDJ7c8XndtZb5P6xcBj9qv5WKbDa//DTxPxdPtJCt1eMKYrU5oB1tLfv3P2jToc6euI8i+FOo1vb72T/UnTwHYpsBJ2ZCaS9i/g6UvcLievzFKVVjAp+Dq1H5PeJAxKy5nMaNX5vEn5M8tJgpX2fqvpR5vCuv2qw2c+85lqlPt0LctYRwndcnW6vVlnTloJdpK4t6v78GYk/nZ/+MMfqp8JiQAGqszA0XleSVm/0047VV+ge5slZGZl8+yU29HZlSAy60sqMZ2tJFNW52p1uodl5q2UtZktpH2cgfy/t5awmcUxOp8zIU9anSq3gXkp35sTUCecbh/aoLPM7e17ePQ2I2E9u1Jn+Z7XSSuPzqm90/KlrxKeZ1sSLnVuS37vHLphZtKyJ7P8puV7ujFNT14zYVX7RdacI1Lf6k3qJ+1jfmYGxLTQTMgyo54XGfZhlVVW6fU8lRmE8zzt07DnXJPnTavS9h4CaX2U+k96D+RclRZXGb+0/Zil5VVknc7zVNZPPamzfpFj0Nnih/6npc58LF2HMhVhvmgmIEihmyZ97VNqZyyWfDlNgZ4/9HypPeGEE6qxXzJQZC0hRFqSJMFOwLD00ktXA0XmNq9kTJm00kminmkXkxZnW7Nd9eBhke5Z6Td80EEHVa1zEqJceOGFVT/gzqsHCXpyjNZbb72q8EvrnYQnKYgzeHMCrhnJgGdpRp9jVfc5TkGdNDzT3mbwuEy5mBNACsw8Z7qR1TJQc05kaW1UT12YxD+FaMb5SaudeoyeLM/JJql7UvZMlZhQJ2MztPetjRT2uSLdGfYADCS5SpgpVXMeylXIeqyAI444ompxmf+nbE45nzI8X5Czfl2ez47eyu3IeSBTl6dLQa4m5ypryu1UcrI9tTvuuKMqn9NSp64AnHHGGdXjPvrRj3aPW5eyPueonJNybslg/LnCm/I5j+scvyL7lS/b2T6AeSXlYEKN97///dV30YwllhYaueCYqbozKH2GDsh66XqU76YpFxM+ZID43lo+1gMEp6zN8yVQT7mXsCcTpqRMzc+E2wl40oKlr3JBOuPaZKD6hCopY9NCJduRC6oZIDjfw2fF9Lr6tkvdI+eqdDVLl63UmVKPSH0rA+Z3Sh0p9auPf/zjVcueOrhKGDUzOQ9kXzrHWcs+5xyTCx8HHnhg1dUq55m8H+2DIKfOkPcu56aVV165WpYp6NOtOe91zlWRekLqgpkCPa3+N9988+oC8rnnnlu9Vt3lq5bzVz4PdRcxBpB+mHGLOZjSPNO/duqcJraeLjDTCY4ZM6a1yiqrtIYOHdp605ve1Bo7duw0j89Ud5kONtOzrr/++tUU251Tmse1115bTZ2e9WY2vfmMpjTP9OjtprdfmWp9nXXW6THV+OGHH15tV/YnU7lffPHFvW5rXuO9731vNU1hpmP84Ac/2Lrmmmuq1z/nnHN6rHvvvfe2PvCBD7RWWGGF1gILLFBNTfuOd7yjdd5557VmZty4cdX0iWeeeeY099XTDGaayEzPPmrUqB7P+a9//ava7g033HCaaXAz7fygQYNa1113XfeyO++8s7XzzjtX72e2M489+OCDW88991yPx2bq3UwFnGl4AebVOapdX6c0T7m22WabVWVk7muf3jzl6+jRo7vLvJTRW221Veukk06a4blmdsvtbF+mG19qqaWq+1dcccVqStf26ccnT57c2njjjVsrr7xy6+mnn+7x+G9961vVtvz4xz+ufs+xyfS4q666anXOynku5f0xxxzT69Tn73nPe1pvfOMb+7wfAHOjvG4vT7fZZpvqe3Om3l5rrbWq78/t03I/9NBD1ZTc+W6b9VLG1dNcd9YLvvrVr1bfqfN9tn1680wLvvfee1ePz/f0XXfdtfXoo49Od0rzznpDLdNvp8xM2ZrbiBEjqnPGX//617lyPHqb0vzUU09trb322lWZntfLc/V2bsvv2ZbUser1U2/JMe6Levryq666apr7Um/Je7DEEktU585Mp37jjTd23//ggw9Wx/ad73znNI/N43Ks7rvvvh7Tsn/pS1+q9jfn2pe//OWtY489dprH5pyXOuApp5zSp31g3urKP/0dLDF3JbXO1c6jjz56lpPq+VmaR6aVz9VXX91jKtw5lRZOucKQAdQGyn7mCkKa8WcQNwAGdrmdATpz3s6MMlrqADRbWteMHj26auE0u9JyNC1lMoD1QJAeH2mllfpFXycnYN4xpg7zpXogylq6P6XZYZqO1t2c5pZ0nbrpppumGYeovxx55JFVdy2BDkAzyu18WU73L4EOAJHuVBn2YlYGkX6ppHtXup5laAmBzsBkTB3mSxl3JsFOBvLKeEIZiyeDWqaAnNuFUcauef7558tA0TlwNAADu9xuH7MHADKmXPuEK/0pYyJljCUGLqEO86UMQDlmzJhy8cUXV1/cM4hZWuqkBQsAAADMD4ypAwAAANBAxtQBAAAAaCChDgAAAEADCXUAAAAA5ueBkru6ul7aLYHZ0Gq1HDcYQPbrWr2/N4HZdOyEOxy7Blpyk9H9vQnMphdvPc2xo3HUCZtJnWn+pqUOAAAAQAMJdQAAAAAaSKgDAAAA0EBCHQAAAIAGEuoAAAAANJBQBwAAAKCBhDoAAAAADSTUAQAAAGggoQ4AAABAAwl1AAAAABpIqAMAAADQQEIdAAAAgAYS6gAAAAA0kFAHAAAAoIGEOgAAAAANJNQBAAAAaCChDgAAAEADCXUAAAAAGkioAwAAANBAQh0AAACABhLqAAAAADSQUAcAAACggYQ6AAAAAA0k1AEAAABoIKEOAAAAQAMJdQAAAAAaSKgDAAAA0EBCHQAAAIAGEuoAAAAANJBQBwAAAKCBhDoAAAAADSTUAQAAAGggoQ4AAABAAwl1AAAAABpIqAMAAADQQEIdAAAAgAYS6gAAAAA0kFAHAAAAoIGEOgAAAAANJNQBAAAAaCChDgAAAEADCXUAAAAAGkioAwAAANBAQh0AAACABhLqAAAAADSQUAcAAACggYQ6AAAAAA0k1AEAAABoIKEOAMzEfWVC+UUZV84oD5YTywPl8fKiY9ZATz75ZNlzzz3L8ssvX4YPH17222+/Mn78+P7eLPpgyiNjy+S7Li6Txv6gTLrtLMcMAP5DqAMAMzG5TC0rlKFlVBnmWA1w22yzTTnzzDN7vS+Bzu23314uuuiicv7555drrrmm7L///vN8G+nd5LsvKVOfuLv3O1tTS9ewNcqgl41w+ACgzZD2XwCAab2iLFb9fLZMdnga6s477yyXXnppueqqq8rIkSOrZWPGjCk77rhjOfzww8uKK67Y35vIDAwevkH1c7qhDwD8j9JSBwCY791www1l2LBh3YFObLnllmXQoEHlpptu6tdtAwCYXUIdAGC+N27cuLLsssv2WDZkyJCy9NJLV/cBADSR7lcA0ObuMr5cWZ7s/v3tZbkyvCzkGA1QRx11VDn66KO7f584cWK58cYby4EHHti97JZbbumnrWNGpowbW6aOu+2/C6ZOKVMmPFam/OP67kVDRuxYuhb8d/dHAGBaQh0AaLNaWaTsXIZ2/75oGez4DGD77LNP2WmnnXoMhrzDDjuU7bffvntZZrrKjFePPfZYj8dOnjy5mhEr9zHvDVpmRBk0bI3u36c88PvSteTqZdCw1f670gKLeGsAYAaEOgDQZsEyqLrRDOk+lVtt4YUXrrpZrbXWWj3WGzVqVHn66aerVjsbbrhhteyKK64oU6dOLRtvvPE8325K6RoytJTcal1DSteQhUrX0CUcHgDoI6EOAMzE82VKGV+mlAllSvX702VS9XORMri6MfCNGDGibL311mX06NHluOOOq1rppIvWLrvsYuarBmi9OL6UyS+U1qTn0k+rtCY88e87hi5RugYv0N+bBwD9RqgDADPxQJlYrij/qUSWUi4rj1c/R5Yly0ZlmOPXEKeffnoV5Gy33XbVrFfpopVpzRn4pvzz1tJ66p7u3yffdWH1c/Ba25auxYf345YBQP/qarVarT6t2NX10m8NzKI+fnyBeWS/rtUd64Y6dsId/b0JzIYlNxntuDXUi7ee1t+bALNMnbCZ1JnmbwYNAAAAAGggoQ4AAABAAwl1AAAAABpIqAMAAADQQEIdAAAAgAYS6gAAAAA0kFAHAAAAoIGEOgAAAAANJNQBAAAAaCChDgAAAEADCXUAAAAAGkioAwAAANBAQh0AAACABhLqAAAAADSQUAcAAACggYQ6AAAAAA0k1AEAAABoIKEOAAAAQAMJdQAAAAAaSKgDAAAA0EBCHQAAAIAGEuoAAAAANJBQBwAAAKCBhDoAAAAADSTUAQAAAGggoQ4AAABAAwl1AAAAABpIqAMAAADQQEIdAAAAgAYS6gAAAAA0kFAHAAAAoIGEOgAAAAANJNQBAAAAaCChDgAAAEADCXUAAAAAGkioAwAAANBAQh0AAACABhLqAAAAADSQUAcAAACggYQ6AAAAAA0k1AEAAABoIKEOAAAAQAMJdQAAAAAaSKgDAAAA0EBCHQAAAIAGEuoAAAAANJBQBwAAAKCBhDoAAAAADSTUAQAAAGggoQ4AAABAAwl1AAAAABpIqAMAAADQQEIdAAAAgAYS6gAAAAA0kFAHAAAAoIGEOgAAAAANJNQBAAAAaCChDgAAAEADCXUAAAAAGkioAwAAANBAQh0AAACABhLqAAAAADSQUAcAAACggYQ6AAAAAA0k1AEAAABoIKEOAAAAQAMJdQAAAAAaSKgDAAAA0EBCHQAAAIAGEuoAAAAANJBQBwAAAKCBhDoAAAAADSTUAQAAAGggoQ4AAABAAwl1AAAAABpIqAMAAADQQEIdAAAAgAYS6gAAAAA0kFAHAAAAoIGEOgAAAAANJNQBAAAAaCChDgAAAEADCXUAAAAAGkioAwAAANBAQh0AAACABhLqAAAAADSQUAcAAACggYQ6AAAAAA0k1AEAAABoIKEOAAAAQAMJdQAAAAAaSKgDAAAA0EBCHQAAAIAGEuoAAAAANJBQBwAAAKCBhDoAAAAADSTUAQAAAGggoQ4AAABAAwl1AAAAABpIqAMAAADQQEIdAJiJ+8qE8osyrpxRHiwnlgfK4+VFx6yBnnzyybLnnnuW5ZdfvgwfPrzst99+Zfz48f29WfTBlEfGlsl3XVwmjf1BmXTbWY4ZAPyHUAcAZmJymVpWKEPLqDLMsRrgttlmm3LmmWf2el8Cndtvv71cdNFF5fzzzy/XXHNN2X///ef5NtK7yXdfUqY+cXfvd7amlq5ha5RBLxvh8AFAmyHtvwAA03pFWaz6+WyZ7PA01J133lkuvfTSctVVV5WRI0dWy8aMGVN23HHHcvjhh5cVV1yxvzeRGRg8fIPq53RDHwD4H6WlDgAw37vhhhvKsGHDugOd2HLLLcugQYPKTTfd1K/bBgAwu4Q6AMB8b9y4cWXZZZftsWzIkCFl6aWXru4DAGgi3a8AoM3dZXy5sjzZ/fvby3JleFnIMRqgjjrqqHL00Ud3/z5x4sRy4403lgMPPLB72S233NJPW8eMTBk3tkwdd9t/F0ydUqZMeKxM+cf13YuGjNixdC347+6PAMC0hDoA0Ga1skjZuQzt/n3RMtjxGcD22WefstNOO/UYDHmHHXYo22+/ffeyzHSVGa8ee+yxHo+dPHlyNSNW7mPeG7TMiDJo2Brdv0954Pela8nVy6Bhq/13pQUW8dYAwAwIdQCgzYJlUHWjGdJ9KrfawgsvXHWzWmuttXqsN2rUqPL0009XrXY23HDDatkVV1xRpk6dWjbeeON5vt2U0jVkaCm51bqGlK4hC5WuoUs4PADQR0IdAJiJ58uUMr5MKRPKlOr3p8uk6uciZXB1Y+AbMWJE2Xrrrcvo0aPLcccdV7XSSRetXXbZxcxXDdB6cXwpk18orUnPpZ9WaU144t93DF2idA1eoL83DwD6jVAHAGbigTKxXFH+U4kspVxWHq9+jixLlo3KMMevIU4//fQqyNluu+2qWa/SRSvTmjPwTfnnraX11D3dv0++68Lq5+C1ti1diw/vxy0DgP7V1Wq1Wn1asavrpd8amEV9/PgC88h+Xas71g117IQ7+nsTmA1LbjLacWuoF289rb83AWaZOmEzqTPN3wwaAAAAANBAQh0AAACABhLqAAAAADSQUAcAAACggYQ6AAAAAA0k1AEAAABoIKEOAAAAQAMJdQAAAAAaSKgDAAAA0EBCHQAAAIAGEuoAAAAANJBQBwAAAKCBhDoAAAAADSTUAQAAAGggoQ4AAABAAwl1AAAAABpIqAMAAADQQEIdAAAAgAYS6gAAAAA0kFAHAAAAoIGEOgAAAAANJNQBAAAAaCChDgAAAEADCXUAAAAAGkioAwAAANBAQh0AAACABhLqAAAAADSQUAcAAACggYQ6AAAAAA0k1AEAAABoIKEOAAAAQAMJdQAAAAAaSKgDAAAA0EBCHQAAAIAGEuoAAAAANJBQBwAAAKCBhDoAAAAADSTUAQAAAGggoQ4AAABAAwl1AAAAABpIqAMAAADQQEIdAAAAgAYS6gAAAAA0kFAHAAAAoIGEOgAAAAANJNQBAAAAaCChDgAAAEADCXUAAAAAGkioAwAAANBAQh0AAACABhLqAAAAADSQUAcAAACggYQ6AAAAAA0k1AEAAABoIKEOAAAAQAMJdQAAAAAaSKgDAAAA0EBCHQAAAIAGEuoAAAAANJBQBwAAAKCBhDoAAAAADSTUAQAAAGggoQ4AAABAAwl1AAAAABpIqAMAAADQQEIdAAAAgAYS6gAAAAA0kFAHAAAAoIGEOgAAAAANJNQBAAAAaCChDgAAAEADCXUAAAAAGkioAwAAANBAQh0AAACABhLqAAAAADSQUAcAAACggYQ6AAAAAA0k1AEAAABoIKEOAAAAQAMJdQAAAAAaSKgDAAAA0EBCHQAAAIAGEuoAAAAANJBQBwAAAKCBhDoAAAAADSTUAQAAAGggoQ4AAABAAwl1AAAAABpIqAMAAADQQEIdAAAAgAYS6gAAAAA0kFAHAAAAoIGEOgAAAAANJNQBAAAAaCChDgAAAEADCXUAAAAAGkioAwAAANBAQh0AAACABhLqAAAAADSQUAcAAACggYQ6ADAT95UJ5RdlXDmjPFhOLA+Ux8uLjlkDPfnkk2XPPfcsyy+/fBk+fHjZb7/9yvjx4/t7s+iDKY+MLZPvurhMGvuDMum2sxwzAPgPoQ4AzMTkMrWsUIaWUWWYYzXAbbPNNuXMM8/s9b4EOrfffnu56KKLyvnnn1+uueaasv/++8/zbaR3k+++pEx94u7e72xNLV3D1iiDXjbC4QOANkPafwEApvWKslj189ky2eFpqDvvvLNceuml5aqrriojR46slo0ZM6bsuOOO5fDDDy8rrrhif28iMzB4+AbVz+mGPgDwP0pLHQBgvnfDDTeUYcOGdQc6seWWW5ZBgwaVm266qV+3DQBgdgl1AID53rhx48qyyy7bY9mQIUPK0ksvXd0HANBEul8BQJu7y/hyZXmy+/e3l+XK8LKQYzRAHXXUUeXoo4/u/n3ixInlxhtvLAceeGD3sltuuaWfto4ZmTJubJk67rb/Lpg6pUyZ8FiZ8o/ruxcNGbFj6Vrw390fAYBpCXUAoM1qZZGycxna/fuiZbDjM4Dts88+ZaedduoxGPIOO+xQtt9+++5lmekqM1499thjPR47efLkakas3Me8N2iZEWXQsDW6f5/ywO9L15Krl0HDVvvvSgss4q0BgBkQ6gBAmwXLoOpGM6T7VG61hRdeuOpmtdZaa/VYb9SoUeXpp5+uWu1suOGG1bIrrriiTJ06tWy88cbzfLsppWvI0FJyq3UNKV1DFipdQ5dweACgj4Q6ADATz5cpZXyZUiaUKdXvT5dJ1c9FyuDqxsA3YsSIsvXWW5fRo0eX4447rmqlky5au+yyi5mvGqD14vhSJr9QWpOeSz+t0prwxL/vGLpE6Rq8QH9vHgD0G6EOAMzEA2ViuaL8pxJZSrmsPF79HFmWLBuVYY5fQ5x++ulVkLPddttVs16li1amNWfgm/LPW0vrqXu6f59814XVz8FrbVu6Fh/ej1sGAP2rq9Vqtfq0YlfXS781MIv6+PEF5pH9ulZ3rBvq2Al39PcmMBuW3GS049ZQL956Wn9vAswydcJmUmeavxk0AAAAAKCBhDoAAAAADSTUAQAAAGggoQ4AAABAAwl1AAAAABpIqAMAAADQQEIdAAAAgAYS6gAAAAA0kFAHAAAAoIGEOgAAAAANJNQBAAAAaCChDgAAAEADCXUAAAAAGkioAwAAANBAQh0AAACABhLqAAAAADSQUAcAAACggYQ6AAAAAA0k1AEAAABoIKEOAAAAQAMJdQAAAAAaSKgDAAAA0EBCHQAAAIAGEuoAAAAANJBQBwAAAKCBhDoAAAAADSTUAQAAAGggoQ4AAABAAwl1AAAAABpIqAMAAADQQEIdAAAAgAYS6gAAAAA0kFAHAAAAoIGEOgAAAAANJNQBAAAAaCChDgAAAEADCXUAAAAAGkioAwAAANBAQh0AAACABhLqAAAAADSQUAcAAACggYQ6AAAAAA0k1AEAAABoIKEOAAAAQAMJdQAAAAAaSKgDAAAA0EBCHQAAAIAGEuoAAAAANJBQBwAAAKCBhDoAAAAADdTVarVa/b0RAAAAAMwaLXUAAAAAGkioAwAAANBAQh0AAACABhLqAAAAADSQUAcAAACggYQ6AAAAAA0k1AEAAABoIKEOAAAAQAMJdQAAAABK8/x/lVc5UCwy9F8AAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1200x400 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from scipy.signal import convolve2d\n",
     "\n",
-    "# Create a simple 8x8 image with a vertical edge\n",
-    "image = np.zeros((8, 8))\n",
-    "image[:, 4:] = 1  # Right half is white\n",
+    "# Create a simple 64x64 image with a vertical edge\n",
+    "image = np.zeros((64, 64))\n",
+    "image[:, 32:] = 1  # Right half is white\n",
     "\n",
     "# Define a vertical edge detector (Sobel-like)\n",
     "filter_v = np.array([[-1, 0, 1],\n",
@@ -86,7 +75,7 @@
     "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n",
     "\n",
     "axes[0].imshow(image, cmap='gray')\n",
-    "axes[0].set_title(\"Input Image (8x8)\")\n",
+    "axes[0].set_title(\"Input Image (64x64)\")\n",
     "axes[0].axis('off')\n",
     "\n",
     "axes[1].imshow(filter_v, cmap='RdBu', vmin=-1, vmax=1)\n",
@@ -118,13 +107,13 @@
    "id": "1a5bf3be",
    "metadata": {},
    "source": [
-    "## 2. Controlling Dimensions: Padding & Stride\n",
+    "## Controlling Dimensions: Padding & Stride\n",
     "\n",
     "As the filter slides, the image shrinks.\n",
     "\n",
     "-   **Valid Convolution**: No padding. The output shrinks (e.g., $5 \\times 5 \\to 3 \\times 3$).\n",
     "-   **Padding**: Adding zeros around the border to maintain the size ($5 \\times 5 \\to 5 \\times 5$).\n",
-    "-   **Stride**: How many pixels we jump. Stride=2 halves the size (downsampling)."
+    "-   **Stride**: How many pixels we jump. Stride=2 usually reduces size by about half (downsampling)."
    ]
   },
   {
@@ -132,7 +121,7 @@
    "id": "a5a791fb",
    "metadata": {},
    "source": [
-    "## 3. Pooling Layers\n",
+    "## Pooling Layers\n",
     "\n",
     "Pooling progressively reduces the spatial size (and parameters) to control overfitting and computational cost.\n",
     "\n",
@@ -152,7 +141,7 @@
    "id": "9b475552",
    "metadata": {},
    "source": [
-    "## 4. Common Architectures\n",
+    "## Common Architectures\n",
     "\n",
     "Modern Deep Learning is built on famous architectures:\n",
     "\n",
@@ -172,18 +161,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "66085083",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Input: torch.Size([1, 64, 28, 28]) -> Output: torch.Size([1, 64, 28, 28])\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import torch\n",
     "import torch.nn as nn\n",
@@ -242,20 +223,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "5e01f8ec",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Output Shape: torch.Size([1, 10]) (Should be [1, 10])\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import torch\n",
     "import torch.nn as nn\n",
@@ -309,33 +282,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "78c26775",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████| 26.4M/26.4M [00:00<00:00, 46.7MB/s]\n",
-      "100%|████████████████████████████████████████████████████████████████████████████| 29.5k/29.5k [00:00<00:00, 1.67MB/s]\n",
-      "100%|████████████████████████████████████████████████████████████████████████████| 4.42M/4.42M [00:00<00:00, 13.7MB/s]\n",
-      "100%|████████████████████████████████████████████████████████████████████████████| 5.15k/5.15k [00:00<00:00, 23.8MB/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1, Loss: 0.4587\n",
-      "Epoch 2, Loss: 0.3067\n",
-      "Epoch 3, Loss: 0.2667\n",
-      "Training Complete!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from torchvision import datasets, transforms\n",
     "from torch.utils.data import DataLoader\n",
@@ -377,9 +329,9 @@
    "id": "f75fe6de",
    "metadata": {},
    "source": [
-    "### Visualizing What the CNN Learned\n",
+    "### Visualizing CNN Responses\n",
     "\n",
-    "After training, we can see what the first layer's filters detect."
+    "After training, we can inspect how the first convolutional layer responds to an input image."
    ]
   },
   {
@@ -402,11 +354,12 @@
     "    return hook\n",
     "\n",
     "# Register hook on first conv layer\n",
-    "model.conv1.register_forward_hook(get_activation('conv1'))\n",
+    "handle = model.conv1.register_forward_hook(get_activation('conv1'))\n",
     "\n",
     "# Forward pass\n",
     "with torch.no_grad():\n",
     "    _ = model(sample_img.unsqueeze(0))\n",
+    "handle.remove()\n",
     "\n",
     "# Plot first 8 feature maps\n",
     "fig, axes = plt.subplots(2, 5, figsize=(12, 5))\n",
@@ -416,7 +369,7 @@
     "\n",
     "for i, ax in enumerate(axes.flat[1:9]):\n",
     "    ax.imshow(activations['conv1'][0, i].numpy(), cmap='viridis')\n",
-    "    ax.set_title(f\"Filter {i+1}\")\n",
+    "    ax.set_title(f\"Channel {i+1}\")\n",
     "    ax.axis('off')\n",
     "\n",
     "plt.suptitle(\"Feature Maps from First Conv Layer\")\n",
@@ -439,40 +392,17 @@
    "source": [
     "### Using Pretrained Models (ResNet)\n",
     "\n",
-    "In practice, we rarely build from scratch. We use **Transfer Learning** with models like *ResNet*. `torchvision` makes this easy."
+    "In practice, we rarely build from scratch. We use **Transfer Learning** with models like *ResNet*. `torchvision` makes this easy. Typical workflow: replace the classifier head, decide whether to freeze early layers, and ensure inputs use the preprocessing expected by the pretrained weights."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "525fdb77",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /home/camarocico/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████| 44.7M/44.7M [00:00<00:00, 55.4MB/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original Output Layer: Linear(in_features=512, out_features=1000, bias=True)\n",
-      "New Output Layer:      Linear(in_features=512, out_features=2, bias=True)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from torchvision import models\n",
     "\n",
@@ -530,7 +460,7 @@
     "1.  **Conv Layers**: Look for local patterns (edges, textures). Use `padding` to keep size, `stride` to shrink.\n",
     "2.  **Pooling**: Summarizes features and reduces size.\n",
     "3.  **Shapes**: Always track your $H \\times W$ dimensions. If they reach 0 or mismatch the Linear layer, the code crashes.\n",
-    "4.  **ResNet**: The standard architecture for most image tasks today."
+    "4.  **ResNet**: A widely used and strong baseline family for image tasks."
    ]
   }
  ],
@@ -539,9 +469,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C02 From RNNs to Transformers.ipynb b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C02 From RNNs to Transformers.ipynb
index 18c43f5..3d2972c 100644
--- a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C02 From RNNs to Transformers.ipynb	
+++ b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C02 From RNNs to Transformers.ipynb	
@@ -14,14 +14,17 @@
    "id": "91a30549",
    "metadata": {},
    "source": [
-    "## The Problem with Sequence Data\n",
+    "## Why This Matters\n",
     "\n",
-    "Standard Neural Networks (MLPs) expect a fixed-size input. But language is dynamic:\n",
+    "Many researchers work with sequence data:\n",
     "\n",
-    "-   \"Hello\" (1 word)\n",
-    "-   \"The quick brown fox&#x2026;\" (5 words)\n",
+    "-   clinical notes and patient timelines\n",
+    "-   instrument logs and time-ordered measurements\n",
+    "-   reports, interviews, and long text documents\n",
     "\n",
-    "We need models that can handle variable-length sequences and \"remember\" context."
+    "This chapter is for conceptual understanding, not deep model implementation.\n",
+    "\n",
+    "Guiding question: **How can a model decide which parts of a long sequence are relevant at a specific position?**"
    ]
   },
   {
@@ -29,14 +32,13 @@
    "id": "7544465f",
    "metadata": {},
    "source": [
-    "## Recurrent Neural Networks (RNNs)\n",
+    "## Running Example\n",
     "\n",
-    "The RNN was the first solution. It processes words one by one, maintaining a **Hidden State** (memory).\n",
+    "Consider this clinical sentence:\n",
     "\n",
-    "-   **Step 1**: Read \"The\". Update Memory.\n",
-    "-   **Step 2**: Read \"quick\". Update Memory based on \"quick\" + \"Old Memory\".\n",
+    "*\"Patient admitted three days ago with acute chest pain, treated with aspirin, now reports reduced discomfort.\"*\n",
     "\n",
-    "$$h_t = \\tanh(W x_t + U h_{t-1})$$"
+    "When interpreting \"discomfort\", the model should connect to earlier words like \"chest pain\" and \"aspirin\". The challenge is retrieving useful context across long distances."
    ]
   },
   {
@@ -44,12 +46,27 @@
    "id": "deb89289",
    "metadata": {},
    "source": [
-    "### The Bottleneck\n",
+    "## RNNs: First Main Solution\n",
+    "\n",
+    "An RNN reads tokens one by one and keeps a hidden state (memory): $$h_t = \\tanh(Wx_t + Uh_{t-1})$$\n",
+    "\n",
+    "Interpretation:\n",
+    "\n",
+    "-   `x_t`: current token representation\n",
+    "-   `h_{t-1}`: previous memory\n",
+    "-   `h_t`: updated memory\n",
+    "\n",
+    "RNN strengths:\n",
+    "\n",
+    "-   natural for ordered/time-dependent data\n",
+    "-   simple repeated update rule\n",
     "\n",
-    "1.  **Sequential**: You cannot process the 100th word until you calculate the 99th. This makes training very slow (cannot parallelize).\n",
-    "2.  **Vanishing Memory**: By the time the RNN reaches the end of a long paragraph, it has forgotten the beginning.\n",
+    "RNN limitations:\n",
     "\n",
-    "**LSTMs (Long Short-Term Memory)** improved this with \"Gates\" (Forget Gate, Input Gate) to keep memory longer, but they were still slow."
+    "1.  **Sequential bottleneck**: token 100 waits for token 99.\n",
+    "2.  **Memory bottleneck**: early details can fade in long sequences.\n",
+    "\n",
+    "**LSTMs** (Long Short-Term Memory networks) improved on this with gates that control what to remember and forget. They were the dominant sequence model from roughly 2014&#x2013;2017, but remained sequential and still struggled with very long contexts."
    ]
   },
   {
@@ -57,9 +74,7 @@
    "id": "59b16d64",
    "metadata": {},
    "source": [
-    "### Visualizing Sequential Processing\n",
-    "\n",
-    "Let's see how the hidden state evolves as an RNN reads a sentence."
+    "### Demo: Sequential Processing in an RNN"
    ]
   },
   {
@@ -72,20 +87,16 @@
     "import torch\n",
     "import torch.nn as nn\n",
     "\n",
-    "# A simple RNN cell\n",
-    "rnn_cell = nn.RNNCell(input_size=4, hidden_size=4)\n",
+    "torch.manual_seed(42)\n",
     "\n",
-    "# Simulate 3 word embeddings (simplified)\n",
-    "words = [\"The\", \"cat\", \"sat\"]\n",
+    "rnn_cell = nn.RNNCell(input_size=4, hidden_size=4)\n",
+    "words = [\"Patient\", \"reports\", \"chest\", \"pain\"]\n",
     "embeddings = [torch.randn(1, 4) for _ in words]\n",
     "\n",
-    "# Process sequentially\n",
-    "hidden = torch.zeros(1, 4)  # Initial memory (blank slate)\n",
-    "\n",
-    "print(\"RNN Processing (Each step waits for the previous):\")\n",
-    "for i, (word, emb) in enumerate(zip(words, embeddings)):\n",
-    "    hidden = rnn_cell(emb, hidden)  # Update memory\n",
-    "    print(f\"  Step {i+1}: Read '{word}' -> Hidden: {hidden[0,:2].tolist()}\")"
+    "h = torch.zeros(1, 4)\n",
+    "for i, (word, emb) in enumerate(zip(words, embeddings), start=1):\n",
+    "    h = rnn_cell(emb, h)\n",
+    "    print(f\"Step {i}: {word:>8} -> h[:2] = {h[0, :2].tolist()}\")"
    ]
   },
   {
@@ -93,7 +104,7 @@
    "id": "4310daf1",
    "metadata": {},
    "source": [
-    "**Key Point**: Notice how each step **must wait** for the previous hidden state. This is the sequential bottleneck."
+    "**Takeaway**: each step depends on the previous one. This gives sequence awareness but also limits speed and long-context reliability."
    ]
   },
   {
@@ -101,13 +112,17 @@
    "id": "c7d28adb",
    "metadata": {},
    "source": [
-    "## The Attention Mechanism\n",
+    "## Why Attention Was Introduced\n",
+    "\n",
+    "RNNs must compress the entire past into one vector. Attention changes this:\n",
     "\n",
-    "In 2014, researchers asked: **\"Instead of trying to squash the whole sentence into one tiny memory vector, why not let the model 'look back' at any word it wants?\"**\n",
+    "Instead of \"remember everything in one state\", the model does a dynamic lookup: **Which earlier tokens matter most right now?**\n",
     "\n",
-    "This is **Attention**. When predicting the next word, the model assigns a \"weight\" to every previous word.\n",
+    "Attention weights:\n",
     "\n",
-    "-   **\"I poured cereal into the&#x2026;\"** -> Focus on \"cereal\" and \"poured\". Predict: \"bowl\"."
+    "-   are non-negative\n",
+    "-   sum to 1\n",
+    "-   are different for each token/query"
    ]
   },
   {
@@ -115,15 +130,28 @@
    "id": "73dbefd6",
    "metadata": {},
    "source": [
-    "## The Transformer (2017)\n",
+    "### Intuition Demo: Weighted Lookup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "63f7f2c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
     "\n",
-    "The paper **\"Attention is All You Need\"** changed everything. It proved we don't need recurrence (RNNs) at all. We can process the **entire sentence at once** using **Self-Attention**.\n",
+    "tokens = [\"Patient\", \"reports\", \"chest\", \"pain\", \"today\"]\n",
     "\n",
-    "| Feature        | RNN/LSTM             | Transformer                      |\n",
-    "|-------------- |-------------------- |-------------------------------- |\n",
-    "| **Processing** | Sequential (Slow)    | Parallel (Fast)                  |\n",
-    "| **Context**    | Local (Nearby words) | Global (All words see all words) |\n",
-    "| **Distance**   | Linearly far         | Constant (1 step away)           |"
+    "# Illustrative weights for interpreting \"today\"\n",
+    "w = np.array([0.05, 0.25, 0.35, 0.30, 0.05])\n",
+    "w = w / w.sum()\n",
+    "\n",
+    "print(\"Attention weights:\")\n",
+    "for tok, weight in zip(tokens, w):\n",
+    "    print(f\"  {tok:>8}: {weight:.2f}\")\n",
+    "print(f\"       sum: {w.sum():.2f}\")"
    ]
   },
   {
@@ -131,12 +159,7 @@
    "id": "0e2252da",
    "metadata": {},
    "source": [
-    "### The Architecture\n",
-    "\n",
-    "1.  **Encoder**: Reads the input text and creates a rich representation. (Used in **BERT**).\n",
-    "2.  **Decoder**: Generates output text step-by-step. (Used in **GPT**).\n",
-    "3.  **Positional Encoding**: Since Transformers process parallelly, they don't know word order. We inject \"Position\" numbers so \"Dog bites Man\" $\\ne$ \"Man bites Dog\".\n",
-    "4.  **Feed-Forward Network (FFN)**: After attention, each position passes through a small MLP independently. This adds non-linearity and increases model capacity."
+    "**Takeaway**: attention is token-specific relevance weighting, not a fixed memory vector."
    ]
   },
   {
@@ -144,185 +167,156 @@
    "id": "401ce085",
    "metadata": {},
    "source": [
-    "## Practical Demonstration: Self-Attention in PyTorch\n",
+    "## Transformers: Attention-Centered Architecture\n",
     "\n",
-    "We will implement the mathematical core of the Transformer: **Scaled Dot-Product Attention**.\n",
+    "Transformers (Vaswani et al., 2017) remove recurrence and build around attention.\n",
     "\n",
-    "$$Attention(Q, K, V) = \\text{softmax}\\left(\\frac{QK^T}{\\sqrt{d_k}}\\right)V$$\n",
+    "Practical difference:\n",
     "\n",
-    "-   **Query (Q)**: What I am looking for?\n",
-    "-   **Key (K)**: What can I offer?\n",
-    "-   **Value (V)**: What information do I actually hold?"
+    "| Feature       | RNN / LSTM                     | Transformer                                       |\n",
+    "|------------- |------------------------------ |------------------------------------------------- |\n",
+    "| Processing    | Sequential                     | Parallel in training                              |\n",
+    "| Context       | Compressed in one hidden state | Direct weighted access across positions           |\n",
+    "| Long distance | Degrades with sequence length  | One attention step away (subject to masking rule) |"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b79b4867",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "id": "babde8cc",
+   "metadata": {},
    "source": [
-    "import torch\n",
-    "import torch.nn.functional as F\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# 1. Setup Dummy Data\n",
-    "# A sentence with 3 words, each represented by a vector of size 4\n",
-    "batch_size = 1\n",
-    "seq_len = 3   # \"I love AI\"\n",
-    "d_model = 4   # Embedding Dimension\n",
+    "### One Core Formula\n",
     "\n",
-    "torch.manual_seed(42)\n",
-    "x = torch.randn(batch_size, seq_len, d_model)\n",
+    "$$\\text{Attention}(Q, K, V) = \\text{softmax}\\left(\\frac{QK^T}{\\sqrt{d_k}}\\right)V$$\n",
     "\n",
-    "print(f\"Input Shape: {x.shape}\")\n",
+    "Plain-language mapping:\n",
     "\n",
-    "# 2. Define Projection Matrices (Wq, Wk, Wv)\n",
-    "# In a real model, these are learned weights\n",
-    "W_q = torch.nn.Linear(d_model, d_model)\n",
-    "W_k = torch.nn.Linear(d_model, d_model)\n",
-    "W_v = torch.nn.Linear(d_model, d_model)\n",
+    "-   **Q (Query)**: what this token is looking for\n",
+    "-   **K (Key)**: what each token can offer\n",
+    "-   **V (Value)**: the actual information each token holds\n",
     "\n",
-    "# 3. Create Q, K, V\n",
-    "Q = W_q(x)\n",
-    "K = W_k(x)\n",
-    "V = W_v(x)\n",
+    "Attention works like a soft dictionary lookup:\n",
     "\n",
-    "# 4. Calculate Attention Scores (Q * K^T)\n",
-    "# transpose(-2, -1) swaps the last two dimensions for matrix multiplication\n",
-    "scores = torch.matmul(Q, K.transpose(-2, -1)) / np.sqrt(d_model)\n",
+    "1.  Compare each Query against all Keys (dot product) to get relevance scores.\n",
+    "2.  Normalize the scores with softmax to get weights that sum to 1.\n",
+    "3.  Use the weights to take a weighted sum of Values &#x2014; retrieving a blend of information from the most relevant positions.\n",
     "\n",
-    "print(f\"Raw Scores (Word vs Word):\\n{scores[0].detach().numpy()}\")\n",
-    "\n",
-    "# 5. Softmax (Turn into probabilities)\n",
-    "attention_weights = F.softmax(scores, dim=-1)\n",
+    "The $\\sqrt{d_k}$ scaling prevents dot products from growing too large, which would push softmax into extreme regions where gradients vanish."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b2c9959",
+   "metadata": {},
+   "source": [
+    "### Transformer Block (Conceptual)\n",
     "\n",
-    "print(f\"\\nAttention Weights (Sum to 1):\\n{attention_weights[0].detach().numpy()}\")\n",
+    "Each block combines:\n",
     "\n",
-    "# 6. Final Output (Weights * V)\n",
-    "output = torch.matmul(attention_weights, V)\n",
-    "print(f\"\\nOutput Shape: {output.shape}\")"
+    "1.  **Self-attention** (aggregate information across positions, as described above)\n",
+    "2.  **Feed-forward network** (a small MLP applied independently at each position, adding non-linearity and capacity)\n",
+    "3.  **Residual connections** (the input to each sub-layer is added back to its output, helping gradients flow through deep networks)\n",
+    "4.  **Layer normalization** (stabilizes training by normalizing activations within each layer)\n",
+    "5.  **Positional encoding** (injects token order, since attention itself treats input as a set &#x2014; without it, \"Dog bites Man\" = \"Man bites Dog\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a8052804",
+   "id": "b33a3a50",
    "metadata": {},
    "source": [
-    "### Visualizing Attention\n",
+    "### Multi-Head Attention\n",
     "\n",
-    "This heatmap shows how much each word \"attends\" to every other word."
+    "Rather than computing a single set of attention weights, transformers run multiple attention operations in parallel &#x2014; called **heads** &#x2014; each with its own Q, K, V projections. Different heads can learn to focus on different aspects: one might track syntactic relationships (subject-verb agreement), another semantic similarity, another local proximity. The outputs of all heads are concatenated and projected back to the model dimension, giving the model a richer, multi-faceted view of token relationships."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0ff58c94",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "id": "12d29d98",
+   "metadata": {},
    "source": [
-    "import seaborn as sns\n",
-    "\n",
-    "plt.figure(figsize=(6, 5))\n",
-    "sns.heatmap(attention_weights[0].detach().numpy(), \n",
-    "            xticklabels=[\"I\", \"Love\", \"AI\"], \n",
-    "            yticklabels=[\"I\", \"Love\", \"AI\"], \n",
-    "            annot=True, cmap='Blues')\n",
-    "plt.title(\"Self-Attention Weights\")\n",
-    "plt.xlabel(\"Key (Source)\")\n",
-    "plt.ylabel(\"Query (Target)\")\n",
-    "plt.show()"
+    "### Model Families\n",
+    "\n",
+    "-   **Encoder-only** (BERT): bidirectional attention over input, suited for classification and understanding tasks\n",
+    "-   **Decoder-only** (GPT): causal masking (past-only attention), suited for text generation\n",
+    "-   **Encoder-decoder** (T5): combines both, used in translation and summarization tasks"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "40867a45",
+   "id": "2cc89193",
    "metadata": {},
    "source": [
-    "### Causal Masking (For Decoders/GPT)\n",
+    "## Why GPT Generates Left-to-Right\n",
+    "\n",
+    "GPT uses **causal masking**:\n",
+    "\n",
+    "-   token $t$ can attend only to tokens $\\leq t$\n",
+    "-   future positions are blocked\n",
     "\n",
-    "In generation tasks, a word should **not** see future words (that would be cheating). We use a **Causal Mask** to block the future."
+    "So:\n",
+    "\n",
+    "-   training can still be parallel across positions\n",
+    "-   generation remains token-by-token"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d21b8ac8",
+   "metadata": {},
+   "source": [
+    "### Demo: Causal Masking"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f342176c",
+   "id": "b79b4867",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Create a mask that blocks \"future\" positions\n",
-    "# Upper triangle filled with -infinity (becomes 0 after softmax)\n",
-    "seq_len = 3\n",
-    "mask = torch.triu(torch.ones(seq_len, seq_len), diagonal=1) * float('-inf')\n",
+    "import torch\n",
+    "import torch.nn.functional as F\n",
     "\n",
-    "print(\"Causal Mask (0 = can see, -inf = blocked):\")\n",
-    "print(mask)\n",
+    "torch.manual_seed(42)\n",
+    "n = 5\n",
+    "scores = torch.randn(n, n)\n",
     "\n",
-    "# Apply mask to attention scores\n",
-    "masked_scores = scores + mask\n",
-    "masked_weights = F.softmax(masked_scores, dim=-1)\n",
+    "# Upper triangle marks future tokens\n",
+    "mask = torch.triu(torch.ones(n, n), diagonal=1)\n",
+    "masked_scores = scores.masked_fill(mask == 1, float('-inf'))\n",
+    "weights = F.softmax(masked_scores, dim=-1)\n",
     "\n",
-    "print(\"\\nMasked Attention Weights:\")\n",
-    "print(masked_weights[0].detach().numpy())\n",
-    "# Word 1 sees only itself, Word 2 sees 1-2, Word 3 sees all"
+    "print(\"Row 0:\", [f\"{v:.2f}\" for v in weights[0].tolist()])\n",
+    "print(\"Row 3:\", [f\"{v:.2f}\" for v in weights[3].tolist()])"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "13429c1d",
+   "id": "a8052804",
    "metadata": {},
    "source": [
-    "**Key Point**: This is why GPT models generate text one token at a time - each new token can only \"see\" previous tokens."
+    "**Takeaway**: early rows have less context; later rows can attend to more past tokens."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "82fe9389",
+   "id": "c07a2402",
    "metadata": {},
    "source": [
-    "## Practical Demonstration: Using a Pretrained Transformer (Hugging Face)\n",
-    "\n",
-    "We rarely build Transformers from scratch. We use libraries like `transformers`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7833fa2d",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import BertTokenizer, BertModel\n",
+    "## Practical View for Domain Researchers\n",
     "\n",
-    "# 1. Load Pretrained BERT\n",
-    "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
-    "model = BertModel.from_pretrained('bert-base-uncased')\n",
+    "You typically do **not** train transformers from scratch.\n",
     "\n",
-    "# 2. Tokenize Input\n",
-    "text = \"Transformers are fast.\"\n",
-    "inputs = tokenizer(text, return_tensors=\"pt\")\n",
-    "print(f\"Token IDs: {inputs['input_ids']}\")\n",
+    "Common workflow:\n",
     "\n",
-    "# See what BERT actually sees (word pieces + special tokens)\n",
-    "tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0])\n",
-    "print(f\"Tokens: {tokens}\")  # ['[CLS]', 'transformers', 'are', 'fast', '.', '[SEP]']\n",
+    "1.  choose a pretrained model appropriate for your domain and task\n",
+    "2.  start with prompting and retrieval\n",
+    "3.  evaluate on your domain-specific examples\n",
+    "4.  fine-tune only if necessary\n",
+    "5.  keep expert review for high-stakes decisions\n",
     "\n",
-    "# 3. Forward Pass\n",
-    "with torch.no_grad():\n",
-    "    outputs = model(**inputs)\n",
-    "\n",
-    "# The \"Hidden States\" representing the meaning of the sentence\n",
-    "# Shape: (Batch, Seq_Len, Hidden_Dim) -> (1, 6, 768)\n",
-    "last_hidden_states = outputs.last_hidden_state\n",
-    "print(f\"Contextual Embeddings Shape: {last_hidden_states.shape}\")"
+    "For example, a geologist classifying rock descriptions does not need to train a transformer from scratch &#x2014; they can use a pretrained model and fine-tune on a few hundred labelled examples."
    ]
   },
   {
@@ -338,45 +332,58 @@
    "id": "820a10c1",
    "metadata": {},
    "source": [
-    "### Positional Encoding\n",
+    "### RNN Sensitivity to Early vs Late Tokens\n",
+    "\n",
+    "Goal: compare how perturbing early vs late tokens changes final hidden state.\n",
     "\n",
-    "Without this, the model treats \"I eat\" the same as \"Eat I\".\n",
+    "**Questions to consider**:\n",
     "\n",
-    "**Real Transformers** use sinusoidal functions that encode position smoothly: $$PE_{(pos, 2i)} = \\sin(pos / 10000^{2i/d})$$ $$PE_{(pos, 2i+1)} = \\cos(pos / 10000^{2i/d})$$\n",
+    "-   Which perturbation (early or late) produces a larger difference? Why?\n",
+    "-   What would happen if you increased the sequence length to 30? Would the gap between early and late grow or shrink?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ef6837e",
+   "metadata": {},
+   "source": [
+    "### Build Attention Weights\n",
     "\n",
-    "This allows the model to learn relative positions (e.g., \"3 words apart\") more easily than naive integers.\n",
+    "Goal: compute a self-attention matrix and verify row-wise probability behavior.\n",
     "\n",
-    "**Exercise**: Try a simplified version:\n",
+    "**Questions to consider**:\n",
     "\n",
-    "-   Create a random tensor.\n",
-    "-   Add a \"positional vector\" to it (e.g., adding 1 to the first word, 2 to the second).\n",
-    "-   Check how this changes the attention scores (Run the QK calculation again)."
+    "-   Each row is one token's attention distribution. Which row attends most uniformly across all tokens? Which is most concentrated on a single token?\n",
+    "-   What does it mean when a token assigns high weight to itself?"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0dac2ccc",
+   "id": "7e92bc45",
    "metadata": {},
    "source": [
-    "### Multi-Head Attention\n",
+    "### Compare Unmasked vs Masked Attention\n",
+    "\n",
+    "Goal: see how causal masking changes attention for early and late positions.\n",
     "\n",
-    "The real power comes from having **multiple** sets of Q, K, V (Heads), allowing the model to focus on grammar AND meaning simultaneously.\n",
+    "**Questions to consider**:\n",
     "\n",
-    "-   Use `torch.nn.MultiheadAttention(embed_dim=4, num_heads=2)`.\n",
-    "-   Pass `x` as both query, key, and value."
+    "-   Row 0 in the masked version can only attend to token 0. What does this mean for the first token's representation compared to later tokens?\n",
+    "-   How do row 4's masked weights compare to its unmasked weights? Where did the \"lost\" weight from future tokens go?"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3307f758",
+   "id": "58d8373d",
    "metadata": {},
    "source": [
     "## Summary\n",
     "\n",
-    "1.  **RNNs**: Sequential, slow, forgetful. Good for time-series, bad for long text.\n",
-    "2.  **Attention**: The ability to look at the whole sequence at once.\n",
-    "3.  **Transformer**: Uses Self-Attention + Positional Encoding to process text in parallel.\n",
-    "4.  **BERT/GPT**: Giant Transformers pre-trained on the internet."
+    "1.  **RNNs**: sequence models with sequential memory updates.\n",
+    "2.  **Attention**: weighted lookup over relevant positions.\n",
+    "3.  **Transformers**: attention-first models with better long-context scaling.\n",
+    "4.  **GPT-style LLMs**: decoder transformers with causal masking.\n",
+    "5.  **Applied use**: pretrained-first workflow + domain evaluation + expert oversight."
    ]
   }
  ],
@@ -385,9 +392,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C03 Large Language Models (LLMs).ipynb b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C03 Large Language Models (LLMs).ipynb
index afa0b09..46fdcb9 100644
--- a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C03 Large Language Models (LLMs).ipynb	
+++ b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C03 Large Language Models (LLMs).ipynb	
@@ -18,13 +18,13 @@
     "\n",
     "The field of NLP has evolved rapidly—and **scale** has been the key driver.\n",
     "\n",
-    "| Model       | Year | Parameters | Training Data | Notable Achievement                  |\n",
-    "|----------- |---- |---------- |------------- |------------------------------------ |\n",
-    "| **BERT**    | 2018 | 340M       | 16GB          | Revolutionized \"understanding\" tasks |\n",
-    "| **GPT-2**   | 2019 | 1.5B       | 40GB          | \"Too dangerous to release\"           |\n",
-    "| **GPT-3**   | 2020 | 175B       | 570GB         | Emergent few-shot abilities          |\n",
-    "| **Llama-2** | 2023 | 70B        | 2TB           | Open-source revolution               |\n",
-    "| **GPT-4**   | 2023 | ~1.7T?     | Unknown       | Multimodal (text + images)           |\n",
+    "| Model       | Year | Parameters  | Training Data | Notable Achievement                  |\n",
+    "|----------- |---- |----------- |------------- |------------------------------------ |\n",
+    "| **BERT**    | 2018 | 340M        | 16GB          | Revolutionized \"understanding\" tasks |\n",
+    "| **GPT-2**   | 2019 | 1.5B        | 40GB          | \"Too dangerous to release\"           |\n",
+    "| **GPT-3**   | 2020 | 175B        | 570GB         | Emergent few-shot abilities          |\n",
+    "| **Llama-2** | 2023 | 70B         | 2TB           | Open-weight ecosystem growth         |\n",
+    "| **GPT-4**   | 2023 | Undisclosed | Unknown       | Multimodal (text + images)           |\n",
     "\n",
     "Key architectures:\n",
     "\n",
@@ -60,7 +60,7 @@
     "\n",
     "-   A token can be a word (\"apple\"), a part of a word (\"ing\"), or a character (\"a\").\n",
     "-   **Rough rule**: 1000 tokens $\\approx$ 750 words.\n",
-    "-   **Context Window**: The limit on how much text the model can remember at once (e.g., GPT-4 has 128k tokens)."
+    "-   **Context Window**: The limit on how much text the model can remember at once (varies by model/version)."
    ]
   },
   {
@@ -164,7 +164,7 @@
     "inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
     "\n",
     "# Generate\n",
-    "# max_new_tokens=20: Generate 20 words\n",
+    "# max_new_tokens=20: Generate 20 tokens\n",
     "# do_sample=True: Add randomness (creativity)\n",
     "# temperature=0.7: Controls randomness (Lower = More focused)\n",
     "output = model.generate(\n",
@@ -237,7 +237,7 @@
     "\n",
     "1.  **Zero-Shot**: Just asking. **\"Translate to Spanish: Hello\"**\n",
     "2.  **Few-Shot**: Giving examples. **\"English: Red, Spanish: Rojo\"** **\"English: Blue, Spanish: Azul\"** **\"English: Green, Spanish: &#x2026;\"**\n",
-    "3.  **Chain of Thought**: Asking the model to \"think step by step.\" This dramatically improves math and reasoning."
+    "3.  **Chain of Thought**: Asking the model to \"think step by step.\" This often helps on multi-step tasks, but is model/task dependent."
    ]
   },
   {
@@ -295,17 +295,17 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f8c8777d",
+   "id": "6b2fad6c",
    "metadata": {},
    "source": [
     "### Chain of Thought (CoT)\n",
     "\n",
-    "Adding \"Let's think step by step\" dramatically improves reasoning on complex problems.\n",
+    "Adding \"Let's think step by step\" can improve performance on some multi-step problems, depending on the model.\n",
     "\n",
     "-   **Without CoT**: \"What is 17 × 24?\" → Model guesses (often wrong).\n",
     "-   **With CoT**: \"What is 17 × 24? Let's think step by step.\" → Model shows working.\n",
     "\n",
-    "**Key Point**: CoT works because LLMs predict **one token at a time**. By forcing intermediate steps into the output, we give the model \"scratch space\" to reason before producing the final answer."
+    "**Key Point**: CoT can provide useful intermediate \"scratch space,\" but gains are not guaranteed and depend on model capability and prompt quality."
    ]
   },
   {
@@ -327,9 +327,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C04 Generative AI and Modern Applications.ipynb b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C04 Generative AI and Modern Applications.ipynb
index 928a10c..70b905b 100644
--- a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C04 Generative AI and Modern Applications.ipynb	
+++ b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C04 Generative AI and Modern Applications.ipynb	
@@ -37,7 +37,7 @@
     "1.  **Hallucination**: They make things up.\n",
     "2.  **Cut-off Date**: They don't know current events or private company data.\n",
     "\n",
-    "**RAG** solves this by connecting the LLM to an external library (like a Vector Database).\n",
+    "**RAG** helps mitigate this by connecting the LLM to an external knowledge source (such as a vector database).\n",
     "\n",
     "-   **Step 1 (Retrieve)**: User asks a question. System searches the database for relevant documents.\n",
     "-   **Step 2 (Augment)**: System pastes those documents into the prompt context.\n",
@@ -57,7 +57,7 @@
     "-   **Key Property**: Similar meanings → Similar vectors (measured by cosine similarity).\n",
     "-   \"King\" and \"Queen\" are close in embedding space; \"King\" and \"Banana\" are far apart.\n",
     "\n",
-    "Modern RAG uses **Sentence Embeddings** (from models like BERT) instead of keyword matching. This allows semantic search: \"What's the price?\" matches \"Product A costs $50\" even without shared words."
+    "Modern RAG uses dedicated **sentence embedding models** instead of plain keyword matching. This enables semantic search: \"What's the price?\" can match \"Product A costs $50\" even without exact shared words."
    ]
   },
   {
@@ -87,7 +87,7 @@
    },
    "outputs": [],
    "source": [
-    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer, ENGLISH_STOP_WORDS\n",
     "from sklearn.metrics.pairwise import cosine_similarity\n",
     "import numpy as np\n",
     "\n",
@@ -99,8 +99,13 @@
     "    \"The CEO of the company is Jane Doe.\"\n",
     "]\n",
     "\n",
-    "# Indexing (Simple TF-IDF)\n",
-    "vectorizer = TfidfVectorizer()\n",
+    "# Indexing (Simple TF-IDF with basic cleanup)\n",
+    "# Keep single-letter product IDs (A/B) by excluding them from stopwords.\n",
+    "custom_stopwords = list(set(ENGLISH_STOP_WORDS) - {\"a\", \"b\"})\n",
+    "vectorizer = TfidfVectorizer(\n",
+    "    stop_words=custom_stopwords,\n",
+    "    token_pattern=r\"(?u)\\b\\w+\\b\",\n",
+    ")\n",
     "doc_vectors = vectorizer.fit_transform(documents)\n",
     "\n",
     "print(f\"Indexed {len(documents)} documents.\")"
@@ -133,13 +138,15 @@
     "    similarities = cosine_similarity(query_vec, doc_vectors).flatten()\n",
     "\n",
     "    # Find best match\n",
-    "    best_idx = np.argmax(similarities)\n",
-    "    return documents[best_idx]\n",
+    "    best_idx = int(np.argmax(similarities))\n",
+    "    return best_idx, float(similarities[best_idx]), similarities\n",
     "\n",
     "user_query = \"How much does Product A cost?\"\n",
-    "retrieved_doc = retrieve_info(user_query)\n",
+    "best_idx, best_score, _ = retrieve_info(user_query)\n",
+    "retrieved_doc = documents[best_idx]\n",
     "\n",
     "print(f\"Query: {user_query}\")\n",
+    "print(f\"Best Match Score: {best_score:.3f}\")\n",
     "print(f\"Retrieved Context: {retrieved_doc}\")"
    ]
   },
@@ -150,7 +157,7 @@
    "source": [
     "### The Limitation of Keyword Matching\n",
     "\n",
-    "TF-IDF matches **words**, not **meaning**. Watch it fail on a paraphrased query:"
+    "TF-IDF still matches **surface words**, not true semantics. Watch where it works vs fails:"
    ]
   },
   {
@@ -160,19 +167,27 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Same question, different words\n",
-    "queries = [\n",
-    "    \"How much does Product A cost?\",    # Direct match (works)\n",
-    "    \"What's the price of Product A?\",   # Paraphrased (may fail)\n",
-    "    \"Tell me about the warranty\",       # Different focus\n",
+    "tests = [\n",
+    "    (\"How much does Product A cost?\", 0),  # Expected: Product A doc\n",
+    "    (\"How much is the monthly plan?\", 1),  # Expected: Product B doc (often fails)\n",
+    "    (\"What's the refund policy?\", -1),     # Expected: no relevant doc in KB\n",
     "]\n",
     "\n",
     "print(\"TF-IDF Retrieval Results:\")\n",
     "print(\"-\" * 50)\n",
-    "for q in queries:\n",
-    "    result = retrieve_info(q)\n",
+    "for q, expected_idx in tests:\n",
+    "    best_idx, score, sims = retrieve_info(q)\n",
+    "    result = documents[best_idx]\n",
+    "\n",
+    "    if expected_idx == -1:\n",
+    "        status = \"NO-GOOD-MATCH\"\n",
+    "    else:\n",
+    "        status = \"OK\" if best_idx == expected_idx else \"MISMATCH\"\n",
+    "\n",
     "    print(f\"Q: {q}\")\n",
-    "    print(f\"→ {result[:50]}...\")\n",
+    "    print(f\"Expected: {expected_idx if expected_idx >= 0 else 'none'} | Retrieved: {best_idx} | {status}\")\n",
+    "    print(f\"Best score: {score:.3f}\")\n",
+    "    print(f\"→ {result[:60]}...\")\n",
     "    print()"
    ]
   },
@@ -181,7 +196,7 @@
    "id": "663123a5",
    "metadata": {},
    "source": [
-    "**Observation**: TF-IDF struggles with synonyms (\"cost\" vs \"price\"). Real RAG systems use **semantic embeddings** (sentence-transformers) that understand meaning, not just keywords."
+    "**Observation**: Even after basic cleanup, TF-IDF can fail on paraphrases or missing concepts because it relies on lexical overlap. Real RAG systems use **semantic embeddings** (sentence-transformers) that capture meaning beyond exact words."
    ]
   },
   {
@@ -191,7 +206,9 @@
    "source": [
     "### The Generation (Augmented Prompt)\n",
     "\n",
-    "Now we feed this to the LLM."
+    "Now we feed the retrieved context to a small LLM.\n",
+    "\n",
+    "To keep the classroom output less confusing, we use deterministic decoding and a small post-processing step."
    ]
   },
   {
@@ -202,15 +219,36 @@
    "outputs": [],
    "source": [
     "from transformers import pipeline\n",
+    "import re\n",
     "\n",
-    "# Load a small text generator\n",
+    "# Load a small text generator (for demonstration only)\n",
     "generator = pipeline('text-generation', model='gpt2')\n",
     "\n",
-    "# Construct the Augmented Prompt\n",
-    "prompt = f\"Context: {retrieved_doc}\\nQuestion: {user_query}\\nAnswer:\"\n",
-    "\n",
-    "output = generator(prompt, max_new_tokens=10, pad_token_id=50256)\n",
-    "print(f\"--- Final LLM Output ---\\n{output[0]['generated_text']}\")"
+    "# Construct a constrained augmented prompt\n",
+    "prompt = (\n",
+    "    \"Answer using ONLY the context below. \"\n",
+    "    \"If the answer is not present, reply: I don't know.\\n\"\n",
+    "    f\"Context: {retrieved_doc}\\n\"\n",
+    "    f\"Question: {user_query}\\n\"\n",
+    "    \"Answer:\"\n",
+    ")\n",
+    "\n",
+    "output = generator(\n",
+    "    prompt,\n",
+    "    max_new_tokens=20,\n",
+    "    do_sample=False,          # deterministic output (less confusing in class)\n",
+    "    return_full_text=False,   # keep only generated continuation\n",
+    "    pad_token_id=50256,\n",
+    ")\n",
+    "\n",
+    "raw = output[0][\"generated_text\"].strip()\n",
+    "# Keep only the first line/sentence to avoid drift into extra continuation.\n",
+    "answer_line = raw.split(\"\\n\")[0].strip()\n",
+    "answer = re.split(r\"(?<=[.!?])\\s+\", answer_line, maxsplit=1)[0]\n",
+    "\n",
+    "print(f\"Prompt question: {user_query}\")\n",
+    "print(f\"Retrieved context: {retrieved_doc}\")\n",
+    "print(f\"Model answer: {answer}\")"
    ]
   },
   {
@@ -218,7 +256,7 @@
    "id": "c5717ebd",
    "metadata": {},
    "source": [
-    "**Observation**: The LLM answers correctly because we **gave** it the answer in the context."
+    "**Observation**: Augmented context steers the answer, but small/base models may still drift. Production RAG systems add stronger retrieval, better models, and grounding checks."
    ]
   },
   {
@@ -272,9 +310,7 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "4cb08c6e",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -312,6 +348,87 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "38897882",
+   "metadata": {},
+   "source": [
+    "**Note**: This is a simplified visualization. Real diffusion models use a carefully designed noise schedule and closed-form noising process."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb883be7",
+   "metadata": {},
+   "source": [
+    "## Optional Demonstration: Real Image Generation (Diffusion)\n",
+    "\n",
+    "The next cell is optional and intended for GPU-enabled environments.\n",
+    "\n",
+    "-   It requires the `diffusers` package.\n",
+    "-   It is much slower than the forward-noise demo.\n",
+    "-   On first run, the model weights must be downloaded.\n",
+    "\n",
+    "If generation cannot run, the cell falls back to a cached image if available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ad82120",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import matplotlib.pyplot as plt\n",
+    "import torch\n",
+    "\n",
+    "prompt = \"A watercolor painting of a robot scientist in a Dutch lab, soft morning light, detailed\"\n",
+    "out_path = Path(\"data/diffusion_example.png\")\n",
+    "\n",
+    "generated = False\n",
+    "try:\n",
+    "    # Use an explicit pipeline import. AutoPipeline can import optional\n",
+    "    # modules that may fail in some environments.\n",
+    "    from diffusers import StableDiffusionPipeline\n",
+    "\n",
+    "    if torch.cuda.is_available():\n",
+    "        model_id = \"runwayml/stable-diffusion-v1-5\"\n",
+    "        pipe = StableDiffusionPipeline.from_pretrained(\n",
+    "            model_id,\n",
+    "            torch_dtype=torch.float16,\n",
+    "        ).to(\"cuda\")\n",
+    "\n",
+    "        image = pipe(\n",
+    "            prompt=prompt,\n",
+    "            num_inference_steps=25,\n",
+    "            guidance_scale=7.5,\n",
+    "            height=512,\n",
+    "            width=512,\n",
+    "        ).images[0]\n",
+    "\n",
+    "        out_path.parent.mkdir(parents=True, exist_ok=True)\n",
+    "        image.save(out_path)\n",
+    "        generated = True\n",
+    "        print(f\"Generated and saved: {out_path}\")\n",
+    "    else:\n",
+    "        print(\"Skipping live generation: CUDA GPU not available.\")\n",
+    "except Exception as e:\n",
+    "    print(f\"Skipping live generation: {e}\")\n",
+    "\n",
+    "if out_path.exists():\n",
+    "    img = plt.imread(out_path)\n",
+    "    plt.figure(figsize=(6, 6))\n",
+    "    plt.imshow(img)\n",
+    "    plt.title(\"Diffusion Output (live or cached)\")\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.show()\n",
+    "elif not generated:\n",
+    "    print(\"No cached diffusion image found. Keep using the forward-noise demo in this environment.\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "17842893",
@@ -342,7 +459,7 @@
    "source": [
     "### Hallucination\n",
     "\n",
-    "LLMs are confident liars.\n",
+    "LLMs can confidently produce false statements.\n",
     "\n",
     "-   They optimize for **plausibility**, not **truth**.\n",
     "-   **Danger**: Using LLMs for medical or legal advice without verification (RAG helps reduce this)."
@@ -391,7 +508,7 @@
     "\n",
     "The rate at which noise is added affects image quality.\n",
     "\n",
-    "1.  Modify the `noise_level` parameter in the diffusion demo (try 0.1, 0.5, 1.0).\n",
+    "1.  Modify the `noise_level` parameter in the diffusion demo (try 0.1, 0.3, 0.6).\n",
     "2.  How does the \"destruction speed\" change?\n",
     "3.  Real diffusion models use carefully tuned **noise schedules** (linear, cosine, etc.)."
    ]
@@ -431,9 +548,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C05 Time-Series Forecasting with Feature-Based ML.ipynb b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C05 Time-Series Forecasting with Feature-Based ML.ipynb
new file mode 100644
index 0000000..0c0f182
--- /dev/null
+++ b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C05 Time-Series Forecasting with Feature-Based ML.ipynb	
@@ -0,0 +1,924 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b81c4de7",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Time-Series Forecasting with Feature-Based ML"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d6c6b44",
+   "metadata": {},
+   "source": [
+    "## Why time-series forecasting is different\n",
+    "\n",
+    "Time-series problems require a different workflow than standard tabular ML:\n",
+    "\n",
+    "-   Observations are *ordered in time*, so shuffling usually causes leakage.\n",
+    "-   Most useful predictors are *past values* (lags, rolling statistics, seasonal patterns).\n",
+    "-   Evaluation should mimic deployment with *future-only* validation.\n",
+    "\n",
+    "Guiding question: **Can we outperform strong naive baselines with feature-based ML while avoiding leakage?**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4cbb7a5",
+   "metadata": {},
+   "source": [
+    "### Forecast framing terminology\n",
+    "\n",
+    "In forecasting, three terms are essential:\n",
+    "\n",
+    "-   **Forecast origin**: the last timestamp you are allowed to use for training.\n",
+    "-   **Horizon**: how far into the future you predict (1-step, 6-step, etc.).\n",
+    "-   **Window**: the historical context used to build features.\n",
+    "\n",
+    "For this notebook, we use a 1-step horizon and evaluate repeatedly at new forecast origins."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96c352b8",
+   "metadata": {},
+   "source": [
+    "### Leakage checklist (use this in every project)\n",
+    "\n",
+    "Before trusting any metric, verify:\n",
+    "\n",
+    "1.  No random shuffle across time.\n",
+    "2.  No feature uses future values (directly or indirectly).\n",
+    "3.  Preprocessing statistics (if any) are computed on train data only.\n",
+    "4.  Hyperparameter decisions are made on validation, not test.\n",
+    "5.  Final test is run once, after model selection."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c68915e",
+   "metadata": {},
+   "source": [
+    "### Mathematical problem statement\n",
+    "\n",
+    "Let $\\{y_t\\}_{t=1}^{T}$ be a univariate time series. For one-step forecasting, at time $t$ we predict: $$ \\hat{y}_{t+1|t} = f_{\\theta}(\\phi_t) $$ where:\n",
+    "\n",
+    "-   $\\phi_t$ is a feature vector built only from information available at time $t$,\n",
+    "-   $f_{\\theta}$ is the model with parameters $\\theta$.\n",
+    "\n",
+    "Training solves empirical risk minimization: $$ \\hat{\\theta} = \\arg\\min_{\\theta}\\frac{1}{N}\\sum_{t \\in \\mathcal{T}_{train}} \\mathcal{L}\\left(y_{t+1}, f_{\\theta}(\\phi_t)\\right) $$\n",
+    "\n",
+    "Important interpretation:\n",
+    "\n",
+    "-   with squared loss, the optimum approximates $E[y_{t+1} \\mid \\phi_t]$,\n",
+    "-   with absolute loss, the optimum approximates the conditional median.\n",
+    "\n",
+    "So feature engineering is not \"extra\"; it defines what conditional signal the model can learn."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1910842",
+   "metadata": {},
+   "source": [
+    "## Running example: Monthly atmospheric CO2\n",
+    "\n",
+    "We will use the classic Mauna Loa CO2 dataset from `statsmodels`. This is a useful teaching dataset because it clearly shows long-term trend and seasonal structure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea53db66",
+   "metadata": {},
+   "source": [
+    "### Signal decomposition intuition\n",
+    "\n",
+    "A common conceptual decomposition is: $$ y_t = T_t + S_t + R_t $$ where:\n",
+    "\n",
+    "-   $T_t$: trend (slow long-term movement),\n",
+    "-   $S_t$: seasonal component (repeating calendar pattern),\n",
+    "-   $R_t$: residual/noise component.\n",
+    "\n",
+    "Feature-based ML does not explicitly estimate these components, but lag and calendar features try to capture them implicitly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81a3b785",
+   "metadata": {},
+   "source": [
+    "### Load and visualize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f0ebeae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "from statsmodels.datasets import co2\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# Weekly CO2 -> monthly start frequency\n",
+    "y = co2.load_pandas().data[\"co2\"].resample(\"MS\").mean().interpolate()\n",
+    "y.name = \"co2\"\n",
+    "\n",
+    "print(f\"Range: {y.index.min().date()} to {y.index.max().date()} | n={len(y)}\")\n",
+    "\n",
+    "ax = y.plot(figsize=(11, 4), lw=2, title=\"Monthly Atmospheric CO2\")\n",
+    "ax.set_ylabel(\"ppm\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60ade6d7",
+   "metadata": {},
+   "source": [
+    "**Interpretation**:\n",
+    "\n",
+    "-   The long-term upward slope indicates trend.\n",
+    "-   Repeating within-year oscillation suggests seasonality.\n",
+    "-   A \"last value only\" model may already do reasonably well, which is why baseline comparison is mandatory."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1b60db8",
+   "metadata": {},
+   "source": [
+    "### Quick autocorrelation check\n",
+    "\n",
+    "If lagged values are informative, sample autocorrelation at those lags should be non-trivial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd974ab4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for lag in [1, 2, 3, 6, 12, 24]:\n",
+    "    print(f\"lag={lag:>2} autocorr={y.autocorr(lag=lag):.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ee97624",
+   "metadata": {},
+   "source": [
+    "High autocorrelation at lag 12 is a strong hint that yearly seasonality features are useful."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3081a978",
+   "metadata": {},
+   "source": [
+    "### Train/validation/test split (time-aware)\n",
+    "\n",
+    "We split by date, not randomly:\n",
+    "\n",
+    "-   Train: 1958-03 to 1989-12\n",
+    "-   Validation: 1990-01 to 1995-12\n",
+    "-   Test: 1996-01 to 2001-12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5df259d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_end = \"1989-12-01\"\n",
+    "val_end = \"1995-12-01\"\n",
+    "test_start = \"1996-01-01\"\n",
+    "\n",
+    "y_train = y.loc[:train_end]\n",
+    "y_val = y.loc[\"1990-01-01\":val_end]\n",
+    "y_test = y.loc[test_start:]\n",
+    "\n",
+    "print(\"Train:\", y_train.index.min().date(), \"->\", y_train.index.max().date(), f\"(n={len(y_train)})\")\n",
+    "print(\"Val:  \", y_val.index.min().date(), \"->\", y_val.index.max().date(), f\"(n={len(y_val)})\")\n",
+    "print(\"Test: \", y_test.index.min().date(), \"->\", y_test.index.max().date(), f\"(n={len(y_test)})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a959849d",
+   "metadata": {},
+   "source": [
+    "**Why this split design?**\n",
+    "\n",
+    "-   Training block is long enough to learn multiple seasonal cycles.\n",
+    "-   Validation block is contiguous in time, so it reflects genuine forward prediction.\n",
+    "-   Test block is held out for a single final comparison after model choices are fixed.\n",
+    "\n",
+    "If a model does great on validation and fails on test, that usually indicates:\n",
+    "\n",
+    "-   over-tuning on validation\n",
+    "-   regime shift (data-generating process changed)\n",
+    "-   hidden leakage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e822867",
+   "metadata": {},
+   "source": [
+    "### Why chronological splits are statistically coherent\n",
+    "\n",
+    "Forecasting assumes an information flow: $$ \\mathcal{F}_{t_1} \\subset \\mathcal{F}_{t_2} \\quad \\text{for } t_1 < t_2 $$ where $\\mathcal{F}_t$ is the information set available at time $t$. Random shuffling violates this temporal filtration by mixing \"future-conditioned\" and \"past-conditioned\" samples in train and test."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "639905d7",
+   "metadata": {},
+   "source": [
+    "## Baselines first: naive and seasonal naive\n",
+    "\n",
+    "Before training any model, create strong baselines:\n",
+    "\n",
+    "-   **Naive (t-1)**: predict next month as last month.\n",
+    "-   **Seasonal naive (t-12)**: predict next month as same month last year.\n",
+    "\n",
+    "In notation:\n",
+    "\n",
+    "-   $\\hat{y}_{t+1}^{naive} = y_t$\n",
+    "-   $\\hat{y}_{t+1}^{seasonal} = y_{t+1-12}$ for monthly data\n",
+    "\n",
+    "Seasonal naive is often surprisingly strong for environmental, retail, and energy data. If your ML model cannot beat it consistently, the feature/model complexity is not justified."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "035c063e",
+   "metadata": {},
+   "source": [
+    "### When these baselines are theoretically strong\n",
+    "\n",
+    "-   If $y_t$ follows a random walk $y_t = y_{t-1} + \\varepsilon_t$, then naive forecast is optimal under squared loss.\n",
+    "-   If $y_t$ follows a seasonal random walk $y_t = y_{t-s} + \\varepsilon_t$, then seasonal naive is optimal.\n",
+    "\n",
+    "This is why baselines are not \"toy\" methods. They are implied by explicit stochastic assumptions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee49a8c8",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
+    "\n",
+    "def forecast_metrics(y_true, y_pred):\n",
+    "    y_true = np.asarray(y_true)\n",
+    "    y_pred = np.asarray(y_pred)\n",
+    "    mae = mean_absolute_error(y_true, y_pred)\n",
+    "    rmse = np.sqrt(mean_squared_error(y_true, y_pred))\n",
+    "    mape = np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
+    "    return {\"MAE\": mae, \"RMSE\": rmse, \"MAPE(%)\": mape}\n",
+    "\n",
+    "naive_pred = y.shift(1).loc[y_test.index]\n",
+    "seasonal_naive_pred = y.shift(12).loc[y_test.index]\n",
+    "\n",
+    "baseline_results = pd.DataFrame({\n",
+    "    \"Naive (t-1)\": forecast_metrics(y_test, naive_pred),\n",
+    "    \"Seasonal Naive (t-12)\": forecast_metrics(y_test, seasonal_naive_pred),\n",
+    "}).T\n",
+    "\n",
+    "baseline_results.round(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a2a20e4",
+   "metadata": {},
+   "source": [
+    "### Why these metrics?\n",
+    "\n",
+    "-   **MAE**: average absolute error in original units; robust and easy to explain.\n",
+    "-   **RMSE**: penalizes large misses more strongly; useful when big errors are costly.\n",
+    "-   **MAPE**: percentage error; intuitive but can be unstable when true values are near zero.\n",
+    "\n",
+    "For CO2 (values far from zero), MAPE is stable enough for comparison.\n",
+    "\n",
+    "In formulas, with errors $e_i = y_i - \\hat{y}_i$: $$ MAE = \\frac{1}{n}\\sum_{i=1}^n |e_i|,\\quad RMSE = \\sqrt{\\frac{1}{n}\\sum_{i=1}^n e_i^2},\\quad MAPE = \\frac{100}{n}\\sum_{i=1}^n \\left|\\frac{e_i}{y_i}\\right| $$\n",
+    "\n",
+    "Metric choice is a business/research decision:\n",
+    "\n",
+    "-   choose MAE when typical absolute error matters,\n",
+    "-   choose RMSE when large misses are disproportionately costly,\n",
+    "-   report both when stakes are mixed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fb36579",
+   "metadata": {},
+   "source": [
+    "## Feature engineering for time-series ML\n",
+    "\n",
+    "We convert the series into a supervised table using:\n",
+    "\n",
+    "-   lag features\n",
+    "-   rolling means/std (computed on *past-only* values)\n",
+    "-   cyclical month features (sin/cos)\n",
+    "-   simple trend index\n",
+    "\n",
+    "Conceptually, we are transforming:\n",
+    "\n",
+    "-   input: one column $y_t$ over time\n",
+    "-   into: many explanatory columns at time $t$ that are known at or before $t$\n",
+    "\n",
+    "The model then learns a mapping $f(X_t) \\to y_t$, where $X_t$ contains history-derived features.\n",
+    "\n",
+    "More explicitly, for monthly data: $$ \\phi_t = \\left[ y_{t-1}, y_{t-2}, y_{t-3}, y_{t-6}, y_{t-12}, \\mu_{t-1}^{(3)}, \\mu_{t-1}^{(6)}, \\mu_{t-1}^{(12)}, \\sigma_{t-1}^{(3)}, \\sigma_{t-1}^{(6)}, \\sigma_{t-1}^{(12)}, \\sin\\left(\\frac{2\\pi m_t}{12}\\right), \\cos\\left(\\frac{2\\pi m_t}{12}\\right), t \\right] $$ where: $$ \\mu_{t-1}^{(w)} = \\frac{1}{w}\\sum_{j=1}^{w} y_{t-j} $$ and $\\sigma_{t-1}^{(w)}$ is the corresponding rolling standard deviation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3de84334",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "def make_feature_table(series, lags=(1, 2, 3, 6, 12), rolling_windows=(3, 6, 12)):\n",
+    "    df = pd.DataFrame({\"y\": series})\n",
+    "\n",
+    "    for lag in lags:\n",
+    "        df[f\"lag_{lag}\"] = series.shift(lag)\n",
+    "\n",
+    "    shifted = series.shift(1)\n",
+    "    for w in rolling_windows:\n",
+    "        df[f\"roll_mean_{w}\"] = shifted.rolling(w).mean()\n",
+    "        df[f\"roll_std_{w}\"] = shifted.rolling(w).std()\n",
+    "\n",
+    "    month = series.index.month\n",
+    "    df[\"month_sin\"] = np.sin(2 * np.pi * month / 12.0)\n",
+    "    df[\"month_cos\"] = np.cos(2 * np.pi * month / 12.0)\n",
+    "    df[\"trend\"] = np.arange(len(series), dtype=float)\n",
+    "\n",
+    "    return df.dropna()\n",
+    "\n",
+    "feature_table = make_feature_table(y)\n",
+    "feature_table.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc337dc8",
+   "metadata": {},
+   "source": [
+    "### Why use sin/cos for month?\n",
+    "\n",
+    "Month is cyclical: December (12) and January (1) are adjacent in time. Encoding month as integer would incorrectly treat 12 and 1 as far apart. Sin/cos preserves circular geometry so nearby seasons stay nearby in feature space."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb6a8aec",
+   "metadata": {},
+   "source": [
+    "### The most common leakage mistake\n",
+    "\n",
+    "Rolling features must use only prior observations. In this notebook we use:\n",
+    "\n",
+    "-   `shifted = series.shift(1)`\n",
+    "-   then rolling statistics on `shifted`\n",
+    "\n",
+    "This avoids using the current target value in its own predictors.\n",
+    "\n",
+    "If we incorrectly used `series.rolling(w).mean()` at row $t$, then: $$ \\tilde{\\mu}_t^{(w)} = \\frac{1}{w}\\sum_{j=0}^{w-1} y_{t-j} $$ which includes $y_t$ itself. This leaks target information directly into predictors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66949b48",
+   "metadata": {},
+   "source": [
+    "### Split the feature table by time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84f423a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df = feature_table.loc[:train_end]\n",
+    "val_df = feature_table.loc[\"1990-01-01\":val_end]\n",
+    "test_df = feature_table.loc[test_start:]\n",
+    "\n",
+    "X_train, y_train_tab = train_df.drop(columns=\"y\"), train_df[\"y\"]\n",
+    "X_val, y_val_tab = val_df.drop(columns=\"y\"), val_df[\"y\"]\n",
+    "X_test, y_test_tab = test_df.drop(columns=\"y\"), test_df[\"y\"]\n",
+    "\n",
+    "print(\"Feature rows -> train:\", len(X_train), \"| val:\", len(X_val), \"| test:\", len(X_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80605d5e",
+   "metadata": {},
+   "source": [
+    "Pick one timestamp and manually verify that `lag_12` really corresponds to the value 12 months earlier. This simple manual check catches many indexing bugs early."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9abd1ae4",
+   "metadata": {},
+   "source": [
+    "## Model comparison on validation\n",
+    "\n",
+    "We compare a regularized linear model against a tree-based nonlinear model.\n",
+    "\n",
+    "Why these two families:\n",
+    "\n",
+    "-   **Ridge**: fast, stable baseline with interpretable linear effects.\n",
+    "-   **XGBoost / HistGBR**: captures nonlinear interactions and threshold behavior.\n",
+    "\n",
+    "In many real projects, this pair already gives a strong \"simple vs flexible\" benchmark."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92d38283",
+   "metadata": {},
+   "source": [
+    "### Ridge regression objective\n",
+    "\n",
+    "Ridge solves: $$ \\hat{\\beta} = \\arg\\min_{\\beta} \\left[ \\frac{1}{n}\\|y - X\\beta\\|_2^2 + \\alpha \\|\\beta\\|_2^2 \\right] $$ Interpretation:\n",
+    "\n",
+    "-   first term: fit training data,\n",
+    "-   second term: shrink coefficients toward zero to reduce variance.\n",
+    "\n",
+    "The penalty is especially useful when lag features are correlated (common in time series)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7fbe854",
+   "metadata": {},
+   "source": [
+    "### Gradient boosting objective (intuition)\n",
+    "\n",
+    "Boosting builds an additive model: $$ f_M(x) = \\sum_{m=1}^{M}\\eta \\, h_m(x) $$ where each weak learner $h_m$ is fit to current residual structure.\n",
+    "\n",
+    "For squared error, each stage approximately learns what previous stages missed: $$ r_i^{(m)} \\approx y_i - f_{m-1}(x_i) $$ This allows nonlinear interactions (for example, different behavior in different seasonal regimes)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2625a8e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
+    "\n",
+    "try:\n",
+    "    from xgboost import XGBRegressor\n",
+    "    HAS_XGBOOST = True\n",
+    "except Exception:\n",
+    "    HAS_XGBOOST = False\n",
+    "\n",
+    "def make_models():\n",
+    "    models = {\n",
+    "        \"Ridge\": Ridge(alpha=1.0),\n",
+    "    }\n",
+    "\n",
+    "    if HAS_XGBOOST:\n",
+    "        models[\"XGBoost\"] = XGBRegressor(\n",
+    "            n_estimators=300,\n",
+    "            learning_rate=0.05,\n",
+    "            max_depth=3,\n",
+    "            subsample=0.8,\n",
+    "            colsample_bytree=0.8,\n",
+    "            objective=\"reg:squarederror\",\n",
+    "            random_state=42,\n",
+    "        )\n",
+    "    else:\n",
+    "        models[\"HistGBR\"] = HistGradientBoostingRegressor(\n",
+    "            learning_rate=0.05,\n",
+    "            max_depth=4,\n",
+    "            random_state=42,\n",
+    "        )\n",
+    "    return models\n",
+    "\n",
+    "models = make_models()\n",
+    "val_rows = []\n",
+    "\n",
+    "for name, model in models.items():\n",
+    "    model.fit(X_train, y_train_tab)\n",
+    "    pred = model.predict(X_val)\n",
+    "    scores = forecast_metrics(y_val_tab, pred)\n",
+    "    val_rows.append({\"Model\": name, **scores})\n",
+    "\n",
+    "val_results = pd.DataFrame(val_rows).set_index(\"Model\").sort_values(\"MAE\")\n",
+    "val_results.round(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c57802e9",
+   "metadata": {},
+   "source": [
+    "**How to read validation results**:\n",
+    "\n",
+    "-   If metrics are close, prefer the simpler model for maintainability.\n",
+    "-   If one model wins on MAE but loses badly on RMSE, inspect whether it occasionally makes very large errors.\n",
+    "-   A single split can still be noisy; backtesting below provides a more reliable estimate.\n",
+    "\n",
+    "Also check rank stability across metrics. A model that wins only one metric by a tiny margin may not be practically better."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "842beb47",
+   "metadata": {},
+   "source": [
+    "### Refit the best model and evaluate on test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19b8718b",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "best_model_name = val_results.index[0]\n",
+    "print(\"Selected model:\", best_model_name)\n",
+    "\n",
+    "X_dev = pd.concat([X_train, X_val])\n",
+    "y_dev = pd.concat([y_train_tab, y_val_tab])\n",
+    "\n",
+    "best_model = make_models()[best_model_name]\n",
+    "best_model.fit(X_dev, y_dev)\n",
+    "test_pred = best_model.predict(X_test)\n",
+    "\n",
+    "test_results = pd.DataFrame({\n",
+    "    \"Seasonal Naive (t-12)\": forecast_metrics(y_test_tab, y.shift(12).loc[y_test_tab.index]),\n",
+    "    best_model_name: forecast_metrics(y_test_tab, test_pred),\n",
+    "}).T\n",
+    "\n",
+    "test_results.round(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f559889d",
+   "metadata": {},
+   "source": [
+    "### Optional interpretation: feature influence\n",
+    "\n",
+    "For teaching, it is useful to show what signals the selected model relies on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "163cbfbb",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "if best_model_name == \"Ridge\":\n",
+    "    coefs = pd.Series(best_model.coef_, index=X_dev.columns).sort_values(key=np.abs, ascending=False)\n",
+    "    print(\"Top Ridge coefficients (absolute value):\")\n",
+    "    print(coefs.head(8).round(3))\n",
+    "elif best_model_name in (\"XGBoost\", \"HistGBR\"):\n",
+    "    if hasattr(best_model, \"feature_importances_\"):\n",
+    "        importances = pd.Series(best_model.feature_importances_, index=X_dev.columns).sort_values(ascending=False)\n",
+    "        print(\"Top feature importances:\")\n",
+    "        print(importances.head(8).round(3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0850c5e",
+   "metadata": {},
+   "source": [
+    "### Plot forecast vs actual on test period"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b49fe7e7",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(11, 4))\n",
+    "plt.plot(y_test_tab.index, y_test_tab.values, label=\"Actual\", lw=2)\n",
+    "plt.plot(y_test_tab.index, y.shift(12).loc[y_test_tab.index].values, label=\"Seasonal Naive\", ls=\"--\")\n",
+    "plt.plot(y_test_tab.index, test_pred, label=best_model_name, lw=2)\n",
+    "plt.title(\"Test Forecast Comparison\")\n",
+    "plt.ylabel(\"CO2 (ppm)\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b6f823e",
+   "metadata": {},
+   "source": [
+    "### Residual diagnostics (simple)\n",
+    "\n",
+    "Good forecast plots can still hide systematic errors. We inspect residuals:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e7098da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "residuals = y_test_tab.values - test_pred\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+    "axes[0].plot(y_test_tab.index, residuals)\n",
+    "axes[0].axhline(0.0, color=\"black\", ls=\"--\", lw=1)\n",
+    "axes[0].set_title(\"Residuals Over Time\")\n",
+    "\n",
+    "axes[1].hist(residuals, bins=15, edgecolor=\"black\")\n",
+    "axes[1].set_title(\"Residual Distribution\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"Residual mean:\", round(residuals.mean(), 4))\n",
+    "print(\"Residual std: \", round(residuals.std(), 4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd8668b0",
+   "metadata": {},
+   "source": [
+    "If residuals show clear temporal structure (e.g., long runs above zero), the model still misses dynamics."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d598e95f",
+   "metadata": {},
+   "source": [
+    "### Residual autocorrelation check\n",
+    "\n",
+    "For a well-specified one-step model, residual autocorrelation should be close to zero at small lags."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cbb4a082",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for lag in [1, 2, 3, 6, 12]:\n",
+    "    ac = pd.Series(residuals).autocorr(lag=lag)\n",
+    "    print(f\"Residual autocorr lag={lag:>2}: {ac:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28d4e549",
+   "metadata": {},
+   "source": [
+    "Persistent residual autocorrelation means \"there is still predictable structure left in the errors.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d5ab090",
+   "metadata": {},
+   "source": [
+    "## Walk-forward backtesting\n",
+    "\n",
+    "Single splits can be noisy. We also do rolling-origin backtesting on train+val.\n",
+    "\n",
+    "There are two common backtesting setups:\n",
+    "\n",
+    "-   **Expanding window**: training set grows as time moves forward.\n",
+    "-   **Sliding window**: fixed-size recent history only.\n",
+    "\n",
+    "`TimeSeriesSplit` behaves like expanding-window validation by default."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97adfda8",
+   "metadata": {},
+   "source": [
+    "### Backtesting in mathematical form\n",
+    "\n",
+    "Choose ordered cutoffs $\\tau_1 < \\tau_2 < \\dots < \\tau_K$. For fold $k$:\n",
+    "\n",
+    "-   train on $\\{1, \\dots, \\tau_k\\}$,\n",
+    "-   test on $\\{\\tau_k + 1, \\dots, \\tau_k + h\\}$.\n",
+    "\n",
+    "Compute fold metric $M_k$, then aggregate: $$ \\bar{M} = \\frac{1}{K}\\sum_{k=1}^{K} M_k $$ Optionally also report dispersion (std or IQR) across folds.\n",
+    "\n",
+    "This estimates \"expected future performance under repeated re-training\" better than a single holdout."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e884138d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import TimeSeriesSplit\n",
+    "\n",
+    "def walk_forward_backtest(model_factory, X, y, n_splits=5):\n",
+    "    tscv = TimeSeriesSplit(n_splits=n_splits)\n",
+    "    rows = []\n",
+    "\n",
+    "    for fold, (tr_idx, te_idx) in enumerate(tscv.split(X), start=1):\n",
+    "        model = model_factory()\n",
+    "        X_tr, X_te = X.iloc[tr_idx], X.iloc[te_idx]\n",
+    "        y_tr, y_te = y.iloc[tr_idx], y.iloc[te_idx]\n",
+    "\n",
+    "        model.fit(X_tr, y_tr)\n",
+    "        pred = model.predict(X_te)\n",
+    "        scores = forecast_metrics(y_te, pred)\n",
+    "        rows.append({\"Fold\": fold, **scores})\n",
+    "\n",
+    "    return pd.DataFrame(rows)\n",
+    "\n",
+    "cv_df = feature_table.loc[:val_end]\n",
+    "X_cv, y_cv = cv_df.drop(columns=\"y\"), cv_df[\"y\"]\n",
+    "\n",
+    "factories = {\"Ridge\": lambda: Ridge(alpha=1.0)}\n",
+    "if HAS_XGBOOST:\n",
+    "    factories[\"XGBoost\"] = lambda: XGBRegressor(\n",
+    "        n_estimators=250,\n",
+    "        learning_rate=0.05,\n",
+    "        max_depth=3,\n",
+    "        subsample=0.8,\n",
+    "        colsample_bytree=0.8,\n",
+    "        objective=\"reg:squarederror\",\n",
+    "        random_state=42,\n",
+    "    )\n",
+    "else:\n",
+    "    factories[\"HistGBR\"] = lambda: HistGradientBoostingRegressor(\n",
+    "        learning_rate=0.05,\n",
+    "        max_depth=4,\n",
+    "        random_state=42,\n",
+    "    )\n",
+    "\n",
+    "for name, fn in factories.items():\n",
+    "    bt = walk_forward_backtest(fn, X_cv, y_cv, n_splits=5)\n",
+    "    print(f\"\\n{name}\")\n",
+    "    print(bt.round(3))\n",
+    "    print(\"Mean:\", bt[[\"MAE\", \"RMSE\", \"MAPE(%)\"]].mean().round(3).to_dict())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "332eba29",
+   "metadata": {},
+   "source": [
+    "**How to use backtest output in practice**:\n",
+    "\n",
+    "-   Report both mean and fold-to-fold variability.\n",
+    "-   If one fold is much worse, inspect that calendar period for anomalies or regime changes.\n",
+    "-   Select models that are not only accurate on average, but also stable across folds."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27e6c5e3",
+   "metadata": {},
+   "source": [
+    "## Practical takeaways\n",
+    "\n",
+    "1.  Start with **seasonal naive** before complex models.\n",
+    "2.  Build lag/rolling/calendar features using only historical information.\n",
+    "3.  Use date-based splits and walk-forward validation.\n",
+    "4.  Keep a robust baseline in production monitoring.\n",
+    "5.  Combine performance with interpretability and stability when choosing the final model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a6b4d3f",
+   "metadata": {},
+   "source": [
+    "## Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e120684",
+   "metadata": {},
+   "source": [
+    "### Leakage trap (random split)\n",
+    "\n",
+    "Run a random train/test split and compare to proper time split. Why is random splitting over-optimistic?\n",
+    "\n",
+    "Which assumptions of forecasting are violated by random shuffling?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c10dbca",
+   "metadata": {},
+   "source": [
+    "### Direct vs recursive multi-step forecasting\n",
+    "\n",
+    "Implement a 6-step forecast:\n",
+    "\n",
+    "-   **Recursive**: one-step model called repeatedly.\n",
+    "-   **Direct**: separate model per horizon.\n",
+    "\n",
+    "Recursive forecasts accumulate error; direct forecasts can reduce accumulation but require more models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22625fe8",
+   "metadata": {},
+   "source": [
+    "### Add domain-specific covariates\n",
+    "\n",
+    "Add a new exogenous feature (for example, policy period indicator, temperature proxy, or instrument change marker) and compare:\n",
+    "\n",
+    "-   original model predictions (without the covariate)\n",
+    "-   augmented model predictions (with the covariate)\n",
+    "\n",
+    "Keep model family, hyperparameters, and split fixed so the comparison is fair.\n",
+    "\n",
+    "Do the augmented predictions improve MAE/RMSE/MAPE consistently, or only one metric? If improvements are small or unstable, could the covariate be spurious?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a220bdb8",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "-   Time-series ML is mostly about *correct framing* and *leakage control*.\n",
+    "-   Feature-based models are strong, transparent, and often hard to beat on medium-size datasets.\n",
+    "-   Backtesting is essential for believable error estimates.\n",
+    "-   Explanation quality matters: each modeling step should be defensible in terms of what would be known at prediction time."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "ML Workshop",
+   "language": "python",
+   "name": "ml_workshop"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/requirements.txt b/src/requirements.txt
index 6000c69..8b3ef2d 100644
--- a/src/requirements.txt
+++ b/src/requirements.txt
@@ -9,3 +9,5 @@ statsmodels
 torch
 torchvision
 transformers
+diffusers
+sentencepiece

From de481cf05ef858eb5c71609eb8b5a69257569aef Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Mon, 23 Feb 2026 10:00:40 +0100
Subject: [PATCH 74/81] Session 01: Introduction to Machine Learning

---
 .../S01 Introduction to Machine Learning.pdf  |  Bin 316295 -> 316299 bytes
 .../S01 Introduction to Machine Learning.tex  |   70 +-
 .../S01C00 Working with Jupyter.ipynb         |   10 +-
 .../S01C01 What is Machine Learning.ipynb     |  272 ++++-
 ...S01C02 The Machine Learning Workflow.ipynb | 1022 ++++++++++++++++-
 ...1C03 Exploratory Data Analysis (EDA).ipynb |  252 +++-
 ...ain-Test Splits and Cross-Validation.ipynb |  178 ++-
 .../S01C05 Data Preprocessing.ipynb           |  238 +++-
 .../S01C06 Feature Engineering.ipynb          |  144 ++-
 9 files changed, 2063 insertions(+), 123 deletions(-)

diff --git a/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.pdf b/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.pdf
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diff --git a/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.tex b/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.tex
index 0006f47..5bc336f 100644
--- a/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.tex	
+++ b/src/S01 Introduction to Machine Learning/S01 Introduction to Machine Learning.tex	
@@ -1,4 +1,4 @@
-% Created 2026-02-05 Thu 17:24
+% Created 2026-02-23 Mon 08:41
 % Intended LaTeX compiler: pdflatex
 \documentclass[hyperref={pdfpagelabels=false},aspectratio=169]{beamer}
 \usepackage[utf8]{inputenc}
@@ -202,9 +202,9 @@
 \end{frame}
 
 \section{Introduction to Machine Learning}
-\label{sec:org9e13c8c}
-\begin{frame}[label={sec:org2142dfe}]{What is Machine Learning?}
-\begin{definition}[Machine Learning]\label{sec:orgabdbe09}
+\label{sec:org7a8c6b4}
+\begin{frame}[label={sec:org5fc047d}]{What is Machine Learning?}
+\begin{definition}[Machine Learning]\label{sec:org4fcc272}
 \pause
 \alert{\alert{Machine Learning}} is the study of algorithms that learn patterns from data to make predictions or decisions without being explicitly programmed.
 \end{definition}
@@ -217,8 +217,8 @@ \section{Introduction to Machine Learning}
 \end{alertblock}
 \end{frame}
 \section{The Machine Learning Workflow}
-\label{sec:org0ff8e9c}
-\begin{frame}[label={sec:org53a9c8b}]{The Machine Learning Workflow}
+\label{sec:org856314d}
+\begin{frame}[label={sec:orge2722b6}]{The Machine Learning Workflow}
 \pause
 A typical \alert{Machine Learning} workflow has the following structure:
 \pause
@@ -234,9 +234,9 @@ \section{The Machine Learning Workflow}
 \end{itemize}
 \end{frame}
 \section{Train-Test Splits and Cross-Validation}
-\label{sec:org3d13c8b}
-\begin{frame}[label={sec:org368c0e3}]{Train and Test Datasets}
-\begin{definition}[Splitting the dataset]\label{sec:org379dd21}
+\label{sec:orgb4e55fa}
+\begin{frame}[label={sec:orgedde426}]{Train and Test Datasets}
+\begin{definition}[Splitting the dataset]\label{sec:org851ed06}
 \pause
 In machine learning, we split the dataset into two parts:
 \pause
@@ -253,12 +253,12 @@ \section{Train-Test Splits and Cross-Validation}
 \end{itemize}
 \end{alertblock}
 \end{frame}
-\begin{frame}[label={sec:orgf3c3d6f}]{Cross-Validation}
+\begin{frame}[label={sec:org7fbf4af}]{Cross-Validation}
 The \alert{\alert{train}} dataset can be further split into \alert{\alert{training}} and \alert{\alert{validation}} sets to tune the
 model's hyperparameters and prevent overfitting.
 \end{frame}
-\begin{frame}[label={sec:org0d89e14}]{Cross-Validation}
-\begin{definition}[Cross-Validation]\label{sec:orgb5bad86}
+\begin{frame}[label={sec:org55f4803}]{Cross-Validation}
+\begin{definition}[Cross-Validation]\label{sec:org1d85e8c}
 \pause
 Cross-validation is a technique to assess how the results of a statistical analysis will generalize to an independent dataset.
 \pause
@@ -268,8 +268,8 @@ \section{Train-Test Splits and Cross-Validation}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org50cbab0}]{Data Leakage}
-\begin{definition}[Data Leakage]\label{sec:org3649ac6}
+\begin{frame}[label={sec:orgc06a383}]{Data Leakage}
+\begin{definition}[Data Leakage]\label{sec:orgbc1b546}
 \pause
 \alert{Data Leakage} occurs when information from outside the training set improperly influences the model, leading to overly optimistic results that fail in deployment.
 \end{definition}
@@ -278,8 +278,8 @@ \section{Train-Test Splits and Cross-Validation}
 A student who accidentally sees the answer key scores 100\%, but learns nothing and fails the next exam.
 \end{alertblock}
 \end{frame}
-\begin{frame}[label={sec:orge53ff51},fragile]{Common Sources of Leakage}
- \begin{example}[Common Sources]\label{sec:orgc8637fa}
+\begin{frame}[label={sec:org22e44b1},fragile]{Common Sources of Leakage}
+ \begin{example}[Common Sources]\label{sec:orgff591ba}
 \pause
 \begin{itemize}[<+->]
 \item \alert{Preprocessing before splitting}: Scaling or imputing using the full dataset leaks test statistics into training.
@@ -289,7 +289,7 @@ \section{Train-Test Splits and Cross-Validation}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:orgf88419f},fragile]{Preventing Leakage}
+\begin{frame}[label={sec:org8fcdedf},fragile]{Preventing Leakage}
  \begin{alertblock}{Prevention Strategies}
 \pause
 \begin{itemize}[<+->]
@@ -300,15 +300,15 @@ \section{Train-Test Splits and Cross-Validation}
 \end{alertblock}
 \end{frame}
 \section{Exploratory Data Analysis (EDA)}
-\label{sec:orgd6fb5ad}
-\begin{frame}[label={sec:org51cd569}]{Exploratory Data Analysis (EDA)}
-\begin{definition}[Exploratory Data Analysis (EDA)]\label{sec:orge0b934e}
+\label{sec:org600dc1d}
+\begin{frame}[label={sec:orgfa2a55e}]{Exploratory Data Analysis (EDA)}
+\begin{definition}[Exploratory Data Analysis (EDA)]\label{sec:org0fb991c}
 \pause
 \alert{\alert{Exploratory Data Analysis}} (EDA) is the process of analyzing datasets to summarize their main characteristics, often using visual methods.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org79be5f5}]{Exploratory Data Analysis (EDA)}
-\begin{example}[Purpose of EDA]\label{sec:org7f51a89}
+\begin{frame}[label={sec:orgb114316}]{Exploratory Data Analysis (EDA)}
+\begin{example}[Purpose of EDA]\label{sec:org31e4f88}
 \pause
 \begin{itemize}[<+->]
 \item To understand the data distribution and relationships between variables.
@@ -316,7 +316,7 @@ \section{Exploratory Data Analysis (EDA)}
 \item To generate hypotheses and inform feature engineering.
 \end{itemize}
 \end{example}
-\begin{example}[EDA Techniques]\label{sec:org691e58b}
+\begin{example}[EDA Techniques]\label{sec:org79495c7}
 \pause
 \begin{itemize}[<+->]
 \item \alert{\alert{Descriptive Statistics}}: Mean, median, mode, standard deviation, etc.
@@ -328,15 +328,15 @@ \section{Exploratory Data Analysis (EDA)}
 \end{example}
 \end{frame}
 \section{Data Preprocessing}
-\label{sec:org3ce6678}
-\begin{frame}[label={sec:org375377a}]{Data Preprocessing}
-\begin{definition}[Data Preprocessing]\label{sec:orgb81ddbb}
+\label{sec:org8ba8ffe}
+\begin{frame}[label={sec:orgbc95f39}]{Data Preprocessing}
+\begin{definition}[Data Preprocessing]\label{sec:org09f1eea}
 \pause
 \alert{\alert{Data Preprocessing}} is the process of transforming raw data into a format suitable for analysis and modeling.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgcf84454}]{Data Preprocessing}
-\begin{example}[Purpose of Data Preprocessing]\label{sec:org2282950}
+\begin{frame}[label={sec:org17fe62d}]{Data Preprocessing}
+\begin{example}[Purpose of Data Preprocessing]\label{sec:org7a3052a}
 \pause
 \begin{itemize}[<+->]
 \item To clean the data by handling missing values, outliers, and inconsistencies
@@ -344,7 +344,7 @@ \section{Data Preprocessing}
 \item To encode categorical variables into numerical formats
 \end{itemize}
 \end{example}
-\begin{example}[Common Data Preprocessing Techniques]\label{sec:orga78c06d}
+\begin{example}[Common Data Preprocessing Techniques]\label{sec:org76de8ba}
 \begin{itemize}[<+->]
 \item \alert{\alert{Handling Missing Values}}: Techniques include imputation (mean, median, mode), deletion, or using algorithms that can handle missing data.
 \item \alert{\alert{Outlier Detection and Treatment}}: Identifying outliers using statistical methods (e.g., Z-score, IQR) and deciding whether to remove or transform them.
@@ -354,15 +354,15 @@ \section{Data Preprocessing}
 \end{example}
 \end{frame}
 \section{Feature Engineering}
-\label{sec:org21c1fe8}
-\begin{frame}[label={sec:org71172f8}]{Feature Engineering}
-\begin{definition}[Feature Engineering]\label{sec:org30e3857}
+\label{sec:org4a0741d}
+\begin{frame}[label={sec:org19caaee}]{Feature Engineering}
+\begin{definition}[Feature Engineering]\label{sec:orgba57b80}
 \pause
 \alert{\alert{Feature Engineering}} is the process of using domain knowledge to select, modify, or create features (variables) that improve the performance of machine learning models.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org58a8542}]{Feature Engineering}
-\begin{example}[Purpose of Feature Engineering]\label{sec:orgd6583ec}
+\begin{frame}[label={sec:org5563749}]{Feature Engineering}
+\begin{example}[Purpose of Feature Engineering]\label{sec:org46a3963}
 \pause
 \begin{itemize}[<+->]
 \item To improve model performance by providing more relevant information.
@@ -370,7 +370,7 @@ \section{Feature Engineering}
 \item To create new features that capture important patterns in the data.
 \end{itemize}
 \end{example}
-\begin{example}[Common Feature Engineering Techniques]\label{sec:org10f219a}
+\begin{example}[Common Feature Engineering Techniques]\label{sec:org848e7f7}
 \begin{itemize}[<+->]
 \item \alert{\alert{Feature Selection}}: Techniques include filter methods (e.g., correlation), wrapper methods (e.g., recursive feature elimination), and embedded methods (e.g., LASSO).
 \item \alert{\alert{Feature Transformation}}: Techniques include logarithmic transformation, polynomial features, and interaction terms.
diff --git a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb
index 5d6cc98..e46e801 100644
--- a/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C00 Working with Jupyter.ipynb	
@@ -166,9 +166,11 @@
     "1.  Return to the **Habrok Web Portal**.\n",
     "2.  From the **Interactive Apps** menu, select the **Jupyter (basic)** entry.\n",
     "3.  **Configuration:**\n",
-    "    -   **Python Version:** Select `3.13.1` (matching the module you loaded).\n",
+    "    -   **Node Type:** Select `Regular` for now, later on we will use GPUs as well.\n",
+    "    -   **Mode:** `JupyterLab` is probably the easiest interface.\n",
+    "    -   **Python Version:** Select `3.13.1-GCCcore-14.2.0` (matching the module you loaded).\n",
     "    -   **Python Environment:** Select \"Custom virtual environment\".\n",
-    "    -   **Path:** Enter the path to your environment (e.g., `$HOME/venvs/ml_workshop`).\n",
+    "    -   **Virtual Environment Path:** Enter the path to your environment (e.g., `/scratch/$USER/venvs/ml-workshop`).\n",
     "4.  Launch the app. Once inside the interface, you can ensure you are using the correct environment by checking the top-right corner of your notebook—it should say **\"ML Workshop\"**.\n",
     "\n",
     "---"
@@ -390,9 +392,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb b/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb
index d8ab124..579d798 100644
--- a/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C01 What is Machine Learning.ipynb	
@@ -109,12 +109,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "25736e96",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Keys: dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_iris\n",
     "import pandas as pd\n",
@@ -139,12 +147,84 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "b2e552fa",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ".. _iris_dataset:\n",
+      "\n",
+      "Iris plants dataset\n",
+      "--------------------\n",
+      "\n",
+      "**Data Set Characteristics:**\n",
+      "\n",
+      ":Number of Instances: 150 (50 in each of three classes)\n",
+      ":Number of Attributes: 4 numeric, predictive attributes and the class\n",
+      ":Attribute Information:\n",
+      "    - sepal length in cm\n",
+      "    - sepal width in cm\n",
+      "    - petal length in cm\n",
+      "    - petal width in cm\n",
+      "    - class:\n",
+      "            - Iris-Setosa\n",
+      "            - Iris-Versicolour\n",
+      "            - Iris-Virginica\n",
+      "\n",
+      ":Summary Statistics:\n",
+      "\n",
+      "============== ==== ==== ======= ===== ====================\n",
+      "                Min  Max   Mean    SD   Class Correlation\n",
+      "============== ==== ==== ======= ===== ====================\n",
+      "sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
+      "sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
+      "petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
+      "petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
+      "============== ==== ==== ======= ===== ====================\n",
+      "\n",
+      ":Missing Attribute Values: None\n",
+      ":Class Distribution: 33.3% for each of 3 classes.\n",
+      ":Creator: R.A. Fisher\n",
+      ":Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
+      ":Date: July, 1988\n",
+      "\n",
+      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
+      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
+      "Machine Learning Repository, which has two wrong data points.\n",
+      "\n",
+      "This is perhaps the best known database to be found in the\n",
+      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
+      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
+      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
+      "type of iris plant.  One class is linearly separable from the other 2; the\n",
+      "latter are NOT linearly separable from each other.\n",
+      "\n",
+      ".. dropdown:: References\n",
+      "\n",
+      "  - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
+      "    Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
+      "    Mathematical Statistics\" (John Wiley, NY, 1950).\n",
+      "  - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
+      "    (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
+      "  - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
+      "    Structure and Classification Rule for Recognition in Partially Exposed\n",
+      "    Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
+      "    Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
+      "  - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
+      "    on Information Theory, May 1972, 431-433.\n",
+      "  - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
+      "    conceptual clustering system finds 3 classes in the data.\n",
+      "  - Many, many more ...\n",
+      "\n",
+      "...\n"
+     ]
+    }
+   ],
    "source": [
     "print(iris_data['DESCR'] + \"\\n...\")"
    ]
@@ -161,12 +241,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "230736ee",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
+      "0                5.1               3.5                1.4               0.2   \n",
+      "1                4.9               3.0                1.4               0.2   \n",
+      "2                4.7               3.2                1.3               0.2   \n",
+      "3                4.6               3.1                1.5               0.2   \n",
+      "4                5.0               3.6                1.4               0.2   \n",
+      "\n",
+      "  species  \n",
+      "0  setosa  \n",
+      "1  setosa  \n",
+      "2  setosa  \n",
+      "3  setosa  \n",
+      "4  setosa  \n"
+     ]
+    }
+   ],
    "source": [
     "df_iris = pd.DataFrame(data=iris_data['data'], \n",
     "                       columns=iris_data['feature_names'])\n",
@@ -192,10 +292,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "f6a1b135",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 150 entries, 0 to 149\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   sepal length (cm)  150 non-null    float64\n",
+      " 1   sepal width (cm)   150 non-null    float64\n",
+      " 2   petal length (cm)  150 non-null    float64\n",
+      " 3   petal width (cm)   150 non-null    float64\n",
+      " 4   species            150 non-null    str    \n",
+      "dtypes: float64(4), str(1)\n",
+      "memory usage: 6.0 KB\n"
+     ]
+    }
+   ],
    "source": [
     "df_iris.info()"
    ]
@@ -210,10 +329,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "bf1d24af",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
+      "count         150.000000        150.000000         150.000000   \n",
+      "mean            5.843333          3.057333           3.758000   \n",
+      "std             0.828066          0.435866           1.765298   \n",
+      "min             4.300000          2.000000           1.000000   \n",
+      "25%             5.100000          2.800000           1.600000   \n",
+      "50%             5.800000          3.000000           4.350000   \n",
+      "75%             6.400000          3.300000           5.100000   \n",
+      "max             7.900000          4.400000           6.900000   \n",
+      "\n",
+      "       petal width (cm)  \n",
+      "count        150.000000  \n",
+      "mean           1.199333  \n",
+      "std            0.762238  \n",
+      "min            0.100000  \n",
+      "25%            0.300000  \n",
+      "50%            1.300000  \n",
+      "75%            1.800000  \n",
+      "max            2.500000  \n"
+     ]
+    }
+   ],
    "source": [
     "print(df_iris.describe())"
    ]
@@ -228,12 +373,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "cdac3f4f",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iris dataset saved to 'iris.csv'\n"
+     ]
+    }
+   ],
    "source": [
     "df_iris.to_csv('../../data/iris.csv', index=False)\n",
     "print(\"Iris dataset saved to 'iris.csv'\")"
@@ -261,6 +414,36 @@
     "-   Print the shape of the dataset."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b774d7b8",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset Shape: (20640, 9)\n",
+      "Features: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude']\n",
+      "Target: ['MedHouseVal']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import fetch_california_housing\n",
+    "\n",
+    "# fetch_california_housing allows loading directly as a frame\n",
+    "housing_data = fetch_california_housing(as_frame=True)\n",
+    "df_housing = housing_data.frame\n",
+    "\n",
+    "print(f\"Dataset Shape: {df_housing.shape}\")\n",
+    "print(f\"Features: {housing_data.feature_names}\")\n",
+    "print(f\"Target: {housing_data.target_names}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "37a2ebed",
@@ -272,6 +455,48 @@
     "-   Use `.describe()` to check the distribution of the \"MedHouseVal\" (Median House Value)."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "acb9259a",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   MedInc  HouseAge  ...  Longitude  MedHouseVal\n",
+      "0  8.3252      41.0  ...    -122.23        4.526\n",
+      "1  8.3014      21.0  ...    -122.22        3.585\n",
+      "2  7.2574      52.0  ...    -122.24        3.521\n",
+      "3  5.6431      52.0  ...    -122.25        3.413\n",
+      "4  3.8462      52.0  ...    -122.25        3.422\n",
+      "\n",
+      "[5 rows x 9 columns]\n",
+      "\n",
+      "Statistics for Median House Value:\n",
+      "count    20640.000000\n",
+      "mean         2.068558\n",
+      "std          1.153956\n",
+      "min          0.149990\n",
+      "25%          1.196000\n",
+      "50%          1.797000\n",
+      "75%          2.647250\n",
+      "max          5.000010\n",
+      "Name: MedHouseVal, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "pd.options.display.max_columns = 5 # limit output width\n",
+    "print(df_housing.head())\n",
+    "\n",
+    "print(\"\\nStatistics for Median House Value:\")\n",
+    "print(df_housing['MedHouseVal'].describe())"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4760e224",
@@ -281,6 +506,25 @@
     "\n",
     "-   Save the dataset to a CSV file named `california_housing.csv`."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "987ab7ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "California housing dataset saved to 'california_housing.csv'\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_housing.to_csv('../../data/california_housing.csv', index=False)\n",
+    "print(\"California housing dataset saved to 'california_housing.csv'\")"
+   ]
   }
  ],
  "metadata": {
@@ -288,9 +532,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb b/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb
index 845aa04..8988897 100644
--- a/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C02 The Machine Learning Workflow.ipynb	
@@ -45,12 +45,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "31cb41ce",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature names: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude']\n",
+      "Target variable: MedHouseVal\n",
+      "Shape: (20640, 9)\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.datasets import fetch_california_housing\n",
     "import pandas as pd\n",
@@ -84,10 +94,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "f2555cf5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
+      "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
+      "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
+      "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
+      "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
+      "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
+      "\n",
+      "   Longitude  MedHouseVal  \n",
+      "0    -122.23        4.526  \n",
+      "1    -122.22        3.585  \n",
+      "2    -122.24        3.521  \n",
+      "3    -122.25        3.413  \n",
+      "4    -122.25        3.422  \n"
+     ]
+    }
+   ],
    "source": [
     "print(df.head())"
    ]
@@ -102,12 +132,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "d28ea4f6",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(10, 8))\n",
     "sns.heatmap(df.corr(), annot=True, fmt=\".2f\", cmap='coolwarm', square=True)\n",
@@ -127,12 +168,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "9a147acb",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training samples: 16512\n",
+      "Testing samples: 4128\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "\n",
@@ -159,12 +209,788 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "358a8b18",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model trained successfully.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Whether to calculate the intercept for this model. If set<br>to False, no intercept will be used in calculations<br>(i.e. data is expected to be centered).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy_X',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=copy_X,-bool%2C%20default%3DTrue\">\n",
+       "            copy_X\n",
+       "            <span class=\"param-doc-description\">copy_X: bool, default=True<br><br>If True, X will be copied; else, it may be overwritten.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=tol,-float%2C%20default%3D1e-6\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-6<br><br>The precision of the solution (`coef_`) is determined by `tol` which<br>specifies a different convergence criterion for the `lsqr` solver.<br>`tol` is set as `atol` and `btol` of :func:`scipy.sparse.linalg.lsqr` when<br>fitting on sparse training data. This parameter has no effect when fitting<br>on dense data.<br><br>.. versionadded:: 1.7</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1e-06</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to use for the computation. This will only provide<br>speedup in case of sufficiently large problems, that is if firstly<br>`n_targets > 1` and secondly `X` is sparse or if `positive` is set<br>to `True`. ``None`` means 1 unless in a<br>:obj:`joblib.parallel_backend` context. ``-1`` means using all<br>processors. See :term:`Glossary <n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('positive',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=positive,-bool%2C%20default%3DFalse\">\n",
+       "            positive\n",
+       "            <span class=\"param-doc-description\">positive: bool, default=False<br><br>When set to ``True``, forces the coefficients to be positive. This<br>option is only supported for dense arrays.<br><br>For a comparison between a linear regression model with positive constraints<br>on the regression coefficients and a linear regression without such constraints,<br>see :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py`.<br><br>.. versionadded:: 0.24</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-1');</script></body>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.linear_model import LinearRegression\n",
     "\n",
@@ -188,12 +1014,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "e39b94d2",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE on test set: 0.56\n",
+      "R-squared on test set: 0.58\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.metrics import mean_squared_error, r2_score\n",
     "\n",
@@ -220,10 +1055,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "fb9fc1b5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(10, 6))\n",
     "sns.regplot(x=y_test, y=y_pred, \n",
@@ -245,12 +1091,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "c6219e7e",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "California housing dataset saved.\n"
+     ]
+    }
+   ],
    "source": [
     "df.to_csv('../../data/california_housing.csv', index=False)\n",
     "print(\"California housing dataset saved.\")"
@@ -277,6 +1131,34 @@
     "-   Print the number of samples and features."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ef271545",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ames Housing Dataset Loaded\n",
+      "Samples: 1460, Features: 80\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import fetch_openml\n",
+    "\n",
+    "# 'parser=\"auto\"' creates a more efficient pandas dataframe\n",
+    "ames_data = fetch_openml('house_prices', as_frame=True, parser=\"auto\")\n",
+    "df_ames = ames_data.frame\n",
+    "\n",
+    "print(\"Ames Housing Dataset Loaded\")\n",
+    "print(f\"Samples: {df_ames.shape[0]}, Features: {df_ames.shape[1] - 1}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "24d1a856",
@@ -290,6 +1172,44 @@
     "2.  Select only the feature with the highest correlation to `SalePrice`."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f2311707",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SalePrice      1.000000\n",
+      "OverallQual    0.790982\n",
+      "GrLivArea      0.708624\n",
+      "GarageCars     0.640409\n",
+      "GarageArea     0.623431\n",
+      "TotalBsmtSF    0.613581\n",
+      "Name: SalePrice, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Select only numeric columns for correlation analysis\n",
+    "numeric_cols = df_ames.select_dtypes(include=['float64', 'int64']).columns\n",
+    "\n",
+    "# Display the top 5 features most correlated with SalePrice\n",
+    "corr_matrix = df_ames[numeric_cols].corr()\n",
+    "print(corr_matrix['SalePrice'].sort_values(ascending=False).head(6))\n",
+    "\n",
+    "# Based on the output, 'OverallQual' is usually the highest.\n",
+    "# We will subset the data to just Feature and Target to avoid NaN issues in other columns\n",
+    "df_simple = df_ames[['OverallQual', 'SalePrice']].dropna()\n",
+    "\n",
+    "X_ames = df_simple[['OverallQual']]\n",
+    "y_ames = df_simple['SalePrice']"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "c331bbf8",
@@ -304,6 +1224,42 @@
     "-   Evaluate with MSE and $R^2$."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "bb237162",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Random Forest MSE: 2,140,675,155\n",
+      "Random Forest R2:  0.72\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "# 1. Split\n",
+    "X_train_a, X_test_a, y_train_a, y_test_a = train_test_split(X_ames, y_ames, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# 2. Train\n",
+    "rf_model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
+    "rf_model.fit(X_train_a, y_train_a)\n",
+    "\n",
+    "# 3. Evaluate\n",
+    "y_pred_a = rf_model.predict(X_test_a)\n",
+    "mse_a = mean_squared_error(y_test_a, y_pred_a)\n",
+    "r2_a = r2_score(y_test_a, y_pred_a)\n",
+    "\n",
+    "print(f\"Random Forest MSE: {mse_a:,.0f}\")\n",
+    "print(f\"Random Forest R2:  {r2_a:.2f}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "8694902a",
@@ -314,6 +1270,36 @@
     "Visualize the Random Forest predictions."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5ddcd923",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.regplot(x=y_test_a, y=y_pred_a,\n",
+    "            scatter_kws={'alpha':0.5},\n",
+    "            line_kws={'color':'red'})\n",
+    "plt.xlabel(\"Actual Sale Price\")\n",
+    "plt.ylabel(\"Predicted Sale Price\")\n",
+    "plt.title(\"Random Forest: Actual vs Predicted (Ames)\")\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "272198e4",
@@ -336,9 +1322,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S01 Introduction to Machine Learning/S01C03 Exploratory Data Analysis (EDA).ipynb b/src/S01 Introduction to Machine Learning/S01C03 Exploratory Data Analysis (EDA).ipynb
index afd0c1f..79ce3ce 100644
--- a/src/S01 Introduction to Machine Learning/S01C03 Exploratory Data Analysis (EDA).ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C03 Exploratory Data Analysis (EDA).ipynb	
@@ -69,12 +69,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "f4791037",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 20640 entries, 0 to 20639\n",
+      "Data columns (total 9 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   MedInc       20640 non-null  float64\n",
+      " 1   HouseAge     20640 non-null  float64\n",
+      " 2   AveRooms     20640 non-null  float64\n",
+      " 3   AveBedrms    20640 non-null  float64\n",
+      " 4   Population   20640 non-null  float64\n",
+      " 5   AveOccup     20640 non-null  float64\n",
+      " 6   Latitude     20640 non-null  float64\n",
+      " 7   Longitude    20640 non-null  float64\n",
+      " 8   MedHouseVal  20640 non-null  float64\n",
+      "dtypes: float64(9)\n",
+      "memory usage: 1.4 MB\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.datasets import fetch_california_housing\n",
     "import pandas as pd\n",
@@ -105,12 +128,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "8259d18f",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "               count         mean          std         min         25%  \\\n",
+      "MedInc       20640.0     3.870671     1.899822    0.499900    2.563400   \n",
+      "HouseAge     20640.0    28.639486    12.585558    1.000000   18.000000   \n",
+      "AveRooms     20640.0     5.429000     2.474173    0.846154    4.440716   \n",
+      "AveBedrms    20640.0     1.096675     0.473911    0.333333    1.006079   \n",
+      "Population   20640.0  1425.476744  1132.462122    3.000000  787.000000   \n",
+      "AveOccup     20640.0     3.070655    10.386050    0.692308    2.429741   \n",
+      "Latitude     20640.0    35.631861     2.135952   32.540000   33.930000   \n",
+      "Longitude    20640.0  -119.569704     2.003532 -124.350000 -121.800000   \n",
+      "MedHouseVal  20640.0     2.068558     1.153956    0.149990    1.196000   \n",
+      "\n",
+      "                     50%          75%           max  \n",
+      "MedInc          3.534800     4.743250     15.000100  \n",
+      "HouseAge       29.000000    37.000000     52.000000  \n",
+      "AveRooms        5.229129     6.052381    141.909091  \n",
+      "AveBedrms       1.048780     1.099526     34.066667  \n",
+      "Population   1166.000000  1725.000000  35682.000000  \n",
+      "AveOccup        2.818116     3.282261   1243.333333  \n",
+      "Latitude       34.260000    37.710000     41.950000  \n",
+      "Longitude    -118.490000  -118.010000   -114.310000  \n",
+      "MedHouseVal     1.797000     2.647250      5.000010  \n"
+     ]
+    }
+   ],
    "source": [
     "print(df.describe().T) # Transpose (.T) makes it easier to read with many columns"
    ]
@@ -127,10 +178,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "03ba5c36",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Missing values per column:\n",
+      "Series([], dtype: int64)\n"
+     ]
+    }
+   ],
    "source": [
     "missing_counts = df.isnull().sum()\n",
     "print(\"Missing values per column:\")\n",
@@ -157,10 +217,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "9e5529d4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(12, 5))\n",
     "\n",
@@ -200,10 +271,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "d25c87a2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(10, 8))\n",
     "\n",
@@ -223,12 +305,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "98b950a4",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 20 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Sample 5% of the data for cleaner plotting\n",
     "df_sample = df.sample(frac=0.05, random_state=42)\n",
@@ -263,6 +356,39 @@
     "-   Identify how many columns are numerical vs. categorical (object)."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7e5dc9cc",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape: (1460, 81)\n",
+      "str        43\n",
+      "int64      35\n",
+      "float64     3\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import fetch_openml\n",
+    "\n",
+    "# Load data\n",
+    "ames_data = fetch_openml(name='house_prices', as_frame=True, parser=\"auto\")\n",
+    "df_ames = ames_data.frame\n",
+    "\n",
+    "print(f\"Shape: {df_ames.shape}\")\n",
+    "\n",
+    "# Count data types\n",
+    "print(df_ames.dtypes.value_counts())"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "e4591e35",
@@ -276,6 +402,44 @@
     "-   Display the top 10 columns with the most missing data."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1931e36d",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top 10 columns with missing values (%):\n",
+      "PoolQC          99.520548\n",
+      "MiscFeature     96.301370\n",
+      "Alley           93.767123\n",
+      "Fence           80.753425\n",
+      "FireplaceQu     47.260274\n",
+      "LotFrontage     17.739726\n",
+      "GarageCond       5.547945\n",
+      "GarageType       5.547945\n",
+      "GarageYrBlt      5.547945\n",
+      "GarageFinish     5.547945\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate percentage\n",
+    "missing_pct = df_ames.isnull().mean() * 100\n",
+    "\n",
+    "# Filter only those with missing values and sort\n",
+    "missing_pct = missing_pct[missing_pct > 0].sort_values(ascending=False)\n",
+    "\n",
+    "print(\"Top 10 columns with missing values (%):\")\n",
+    "print(missing_pct.head(10))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "411fb3c4",
@@ -289,6 +453,34 @@
     "-   This shows how the median price changes with quality rating."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c8dfc179",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.boxplot(x='OverallQual', y='SalePrice', data=df_ames, hue=\"OverallQual\", legend=False)\n",
+    "plt.title('Sale Price Distribution by Overall Quality')\n",
+    "plt.xlabel('Overall Quality (1-10)')\n",
+    "plt.ylabel('Sale Price')\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "15b0334e",
@@ -300,6 +492,42 @@
     "-   Is it skewed? (Real estate prices usually are)."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "746d732e",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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sLy+vweVH/VmPv7PrwcfHR0JCQqSoqMj8AFAXuo31PVQcr+gOXKUOvB314BqoB9dAPbgG6sE1UA+Op9dvei3o0QHXt99+K82bNzctWBWTa2jrltXgwYOla9eucvPNN8uPP/4oEyZMqPc+rRfJ+/bta0DJ0VicXQ8abCUmJkpaWpqkJafVaduikNLP0t69e906aHF2HaAU9eAaqAfXQD24BurBNVAPjqUNPR4bcOXk5MjChQtl8uTJ4ufnV+v6o0ePltDQUNm8eXODAi5/f38TdHXo0MFcbMM5NEDRE4iz68H6q0Z0dLT459Xt69Qsupm579ixo9u2cLlCHXg76sE1UA+ugXpwDdSDa6AeHG/Xrl12ree2AZe2VOXn58t5553nlAtsvcDUAA7O5Sr1oIF4Xbuq6jbK3YMVV6kDb0c9uAbqwTVQD66BenAN1IPj2NOd0K2TZmh2wnbt2pk5u+yhrWG5ubnSp08fh5cNAAAAAFyuhUubPnXiY3Xo0CEzv9bcuXNt47BiYmLM8okTJ2TZsmUyderUKl/nySefNBGnzsGlSTM2bNggr7/+uvTu3ds2zxcAAAAAeFXAlZqaKnfccUe5x6x/f/DBBzJkyBCzPGfOHDOOqrruhJ07d5ZPPvlEZs6cabodxsfHyyWXXCK33367rRsXAAAAADiaS0UfCQkJsn379lrXu+qqq8ytOppIQ28AAAAA4ExuO4YLAAAAAFwdARcAAAAAOAgBFwAAAAA4CAEXAAAAADgIARcAAAAAOAgBFwAAAAA4CAEXAAAAADgIARcAAAAAOAgBFwAAAAA4CAEXAAAAADgIARcAAAAAOAgBFwAAAAA4CAEXAAAAADgIARfwO0uJhWMBAACARuXfuC8HuC8fXx/ZMmuL5Kbk2r1NTJcY6TSuk0PLBQAAAPdFwAWUocFW9pFsu49JaPNQjh8AAACqRZdCAAAAAHAQAi4AAAAAcBACLgAAAABwEAIuAAAAAHAQAi4AAAAAcBACLgAAAABwEAIuAAAAAHAQAi4AAAAAcBACLgAAAABwEAIuAAAAAHAQAi4AAAAAcBACLgAAAABwEAIuAAAAAHAQAi4AAAAAcBACLgAAAABwEAIuAAAAAHAQAi4AAAAAcBACLgAAAABwEAIuAAAAAHAQAi4AAAAAcBACLgAAAABwEAIuAAAAAHAQf3EhSUlJ8vbbb8v69etl586d0qlTJ5k9e3a5daZMmSIrV66stO33338vnTt3tv2dlZUlTzzxhMyfP18KCwtl5MiR8sADD0iLFi2a5L0AAAAAgEsFXBpkLV68WPr16yclJSVisViqXG/gwIFyzz33lHssISGh3N/Tpk2TXbt2ySOPPCJBQUHy/PPPy9SpU2XWrFni7+9SbxsAAACAh3KpyGPs2LFyxhlnmOV7771XNm3aVOV6ERER0r9//2pfZ+3atfLLL7+Y1rIRI0aYxzp27CgTJkyQefPmmXsAAAAA8KoxXL6+jVOcJUuWmKBs+PDhtse0e2LPnj3NcwAAAADgdS1c9tIxXNrCVVxcbLof3nHHHXLqqafant+zZ49p0fLx8Sm3nQZd+hzgKCd2nZAja4+If6C/BEUFSXBUsEQkREhobCgHHQAAwAu5XcClgdUFF1wgHTp0kOPHj5tug9dff718+OGHMmDAALNOZmamNGvWrNK2kZGR1XZTtJd1XFleXl6DXgcNYz3+jVUPGpyHhIRIUVGRSbJiL11f5aTmyKaZmyR1a2oVLy7S/vT20npI63I/Ali31fdQ3XhFb6oDUA/ujO+Da6AeXAP14BqoB8fT67eKDTweEXDdfvvt5f4eM2aMTJw4UV555RV58803Hb5/60Xyvn37HL4v1K6x6kGDrcTERElLS5O05DS7twvNCpU1b6+Rn//5sxSfLDbBVWTPSPEP8ZfC7EIpSCuQ/OP5krQgSVJ2p0j86HjxC/Iz2xaFlH6W9u7d69ZBC98F10A9uAbqwTVQD66BenAN1INjBQYGel7AVVFoaKiMHj1afvjhB9tjOn7r6NGjldbNyMgwrVwNoRkONejSFja9SIdzaICiJ5DGqgfrrxPR0dHin2f/1yJnV47M//d8sxzeKlw6ndNJwluGl/vl49i6Y7J33l7JScqRQ98ckp6Te0poXKg0iy5thdXur+7awtWYdQDqwZ3xfXAN1INroB5cA/XgeJoR3R5uH3BVRcdqLVu2rFIzn7YkdOvWrUGvbX09vcDUYA/O1dj1oAF1QECAXesW5BTI2rfWmuV2I9tJhzEdxMe3crNy28FtJSohSrZ8tkXy0/Nl68ytMuimQbbpCdw9WOG74BqoB9dAPbgG6sE1UA+ugXpwHHu6E7pclsL6yM3NlUWLFkmfPn1sj40aNcq0ZmnQVTbY2rJli3kOaCgN5nfM3mECqLjEOEmcnFhlsGXVrHUzGXjTQAmJCZGTGSdl66ytYilxv1YtAAAA1I2/qzV96sTH6tChQ5KdnS1z5841fw8ePNhkGHzrrbdk/Pjx0qZNG5M0491335Xk5GR54YUXbK+jyTN0/q377rvPTJCsEx8/99xz0r17dznzzDOd9v7gOY6uOyqp21LFx99HLv7fxXJwxcFatwkICZDESxNl7dtrJW1Pmuz4doec+uc/smsCAADA87hUwJWammpSvJdl/fuDDz6Qli1bmgxyGjylp6ebJlINrqZPny59+/Ytt93zzz8vTzzxhDz00ENmzJUGYA888ICtGxdQX3kn8mTXnNI+u70v7y0t+7e0K+BS4fHh0v287rL1i63mNbZ/s126n9+dygAAAPBQLhV9JCQkyPbt22tcR9PA20PTwj/++OPmBjSmPT/tkZLCEolsH2mCp7pq0aeFZB7KlEMrDsmXU76UP2/6s0S2bVgyFwAAALgmtx/DBTSlvLQ8SdmaYpa7nNNFfPzsGyxZUafxnSSqY5SczDwp8+6a18ilBAAAgKsg4ALqQFulxCIS3TnadA+s9xfPz1f6XN3HBGxbPt8iu36wL60oAAAA3AsBF2CnovwiObq2dH63hKEJDT5uEW0iZMjtQ8zynNvmmNcHAACAZyHgAux0ZM0RKS4oNpMWawtXYxjzyBgzYfKJXSdk6X+WUhcAAAAehoALsENJcUlpd0Jt3RqWYPdEd7UJigiSM58pnargl8d/kbS9adQHAACAByHgAuygiTI0wUVAWIDE94lv1GOmqeU7nN7BdCkkgQYAAIBnIeACamGxWOTgstJ5tlqf2lp8/Rv3a6OtZRNemiA+vj6y7attds/pBQAAANdHwAXUIvtotmQdzjIZBVuf0tohxysuMU76XdPPLC+4bwF1AgAA4CEIuIBaJG9ONvex3WIlMCzQYcdr9COjxTfAV/Yu2GsmVwYAAID7I+ACaulOmLwl2dYK5UhR7aPklFtOsbVy6b4BAADg3gi4gFq6E+an5ZtxW9rC5Wgj7x9pEnMcWnnIjOcCAACAeyPgAmpgbd2K6RojfoF+Dj9W4fHhMnTaULO88IGFJh09AAAA3BcBF1AN7dKXsiWlSboTlnXa3adJcHSwCfY2f7qZ+gEAAHBjBFxANXKO5Ujeibwm605oFRwVLMP+Nsws//zYz2IpYSwXAACAuyLgAmrJThjTpWm6E5Y1+LbBEhQZZFq5tn65tUn3DQAAgMZDwAU4OTthVYIjg2XI7UPM8s//+pmMhQAAAG6KgAuoQs7x0u6EOtlxTLcYpxyjIXcMkcDwQDm67qjs/G6nU8oAAACAhiHgAmrpTugf5O+UYxQaGyqn/KV0Xq4l/1pCKxcAAIAbIuACqnBi5wlzH9ez6bsTljXsrmHiH+Ivh1Yckr0/7XVqWQAAAFB3BFxABQU5BWbCYxXdOdqpx0fn5Rp00yBbKxcAAADcCwEXUEH6nnRzHxYfZsZQOdtp/3ea+Ab4StLiJDm4/KCziwMAAIA6IOACKjixp7Q7YXQn57ZuWUW0iZC+V/c1y0ufWurs4gAAAKAOCLiACung03anuUR3woqtXGrbV9skZXuKs4sDAAAAOxFwAWXo2K2CrAKTDj6yXaTLHBtN3tH9/O4iFpFf//Ors4sDAAAAOxFwAWWkbCltPYpsHyl+AX4udWyG3zPc3G/4cINkHc5ydnEAAABgBwIuoIyUbaUBV0wn50x2XJO2p7WVdiPaSXFBsSx/frmziwMAAAA7EHABv9NAJnVHqsuN36qqlWvVa6skPyPf2cUBAABALQi4gN9pyvXik8USEBpgUsK7oq4Tukpcrzgzzmz166udXRwAAADUgoAL+N3uebttrVs+Pj4ueVx8fH1k2N+GmeWVL66U4sJiZxcJAAAANSDgAn6358c9LjX/VnX6XNnHtMBlHsyUzTM3O7s4AAAAqAEBFyAieWl5cnjVYbcIuPyD/GXwbYPN8vJnl5u5wwAAAOCaCLgAEUlakiSWEouEtQyToIgglz8mp/z5FPEP8Zcja45I0uIkZxcHAAAA1SDgAkRk/8/7zXGI7RrrFscjNDZU+l/X3ywve2aZs4sDAACAahBwARpw/VIacEV3ce3uhFbaGjd02lARH5Eds3dIyvYUu7cDAABA0/Fvwn0BLqkwt1COrD5ilmM6x0hJYYm4Os1WeGzjMYnvEy/HNhyTb6d+K32u6lPjNqHNQyVxUmKTlREAAAAEXIAcXHFQSopKJCIhQkJiQyTnaI5bHJXclFxpObClCbgOLjsoCUMTzBxiAAAAcB10KYTXs3YnbDeincvOv1WdyHaREt4y3ASMR9aWttIBAADAdRBwwetZE2a0HdHW7Y6FBohthrQxy4dXHmaMFgAAgItxqTFcSUlJ8vbbb8v69etl586d0qlTJ5k9e7bt+ezsbHn33Xdl8eLFsm/fPgkMDJS+ffvKnXfeKd27d7etd/DgQRk3blyl1+/Xr5/MnDmzyd4PXJ+2DGl3PNV+ZHs5sOyAuJsWvVuYSZtPZp6UlG0pEpcY5+wiAQAAwBUDLg2yNJjSwKikpKTShK6HDx+WTz/9VCZNmiTTpk2TkydPyjvvvCOXXXaZzJo1Szp37lxu/bvuukuGDBli+zssLKzJ3gvcg45/KsgukKDIIInrFeeWAZevv6+0GtTKtNQdWnmIgAsAAMCFuFTANXbsWDnjjDPM8r333iubNm0q93xCQoL8+OOPEhISYnts6NChZruPP/5YHnzwwXLrt2/fXvr3L52rCKhK0s+lkwa3G95OfP3ct4dt61Nay4GlByQjKUOyj2abcV0AAABwPpe6wvT1rbk4oaGh5YIta6tVu3bt5Pjx4w4uHTzRgV8OuO34rbKCIoKkeWJzs3xoxSFnFwcAAACuGHDVR2Zmpm28V0WPPPKI9OzZU4YNGyYPPPCApKenO6WMaFr2Tu6rXVatLVw6fsvdJQxJMPc6P1dBToGziwMAAABX61JYH//5z39MprYrrrjC9pgm09C/R4wYIRERESYJx2uvvWa6KH722WcSEFD/uYqs48ry8vIapfyoH+vxr1gP+lnQVtCNMzdKTnLN82np8znHcsTH30dy03PNY0VFRVJYWGh3OXT9+mxXdlt9DxXHK9bE+h4r7jO4RbCEtwqX7CPZcui3Q5IwPKFR9lfXOkDToh5cA/XgGqgH10A9uAbqwfH0esqeKYXcOuDSRBmadfDJJ5+Uli1b2h5v0aKFad2yGjx4sHTt2lVuvvlmMwZswoQJ9d6n9aJVsyTC+SrWgwYiiYmJcnDHQUnbn1bjtpk7Ms19UGyQpB5NNctZWVmSnJxs9/5Ds0LrtZ0qCin9LO3du7dOQYv1PaalpUlacvn3GNYtzARch1cflsAugeLj69Pg/dWG74JroB5cA/XgGqgH10A9uAbqwbG0ocdjAy7NZvjQQw/JX/7yF7noootqXX/06NFmDNjmzZsbFHD5+/uboKtDhw6VxpOh6WjAoCeQivVg/ZUhOjpa/PNq/nhnrMww97GdYqVZs2Zm2dzXIat6fbcz20SXbtuxY8c6t3BV9x5jo2MldWWqFGUXiX+mv8R0jWnw/upaB2ha1INroB5cA/XgGqgH10A9ON6uXbvsWs8tA65169bJHXfcIRdeeKG5b0rWi129wNQADs5VXT1oYFxb19HsQ9nmPrpDtFnf3u0q7qc+25Xdtr7BSpX7DBBp2b+lmVvs+LrjEp8Y32j7qw7fBddAPbgG6sE1UA+ugXpwDdSD49jTndAtk2ZoJKldAzUd/PTp0+3ebuHChZKbmyt9+vRxaPngHoryiyQ3pXTcVrM2pS0/nkLn5FIndp6QvDTGVwEAADiTv6s1fWpXQXXo0CHJzs6WuXPn2sZhaTeoG264QYKCguTaa68tN09XeHi4dOnSxSzrmC6NOHUOLk2asWHDBnn99deld+/etnm+4N0yD5WO3wqODpbAsNr73rqT0NhQie4ULWl70uTI6iPS6YzKGTwBAADghQFXampqpS6C1r8/+OADc3/06FFzf91115VbTwOyDz/80Cx37txZPvnkE5NQIz8/X+Lj4+WSSy6R22+/3datCt4t62CWuY9IiBBPpBMha8B1dO1R6TCmg/j6u11jNgAAgEdwqegjISFBtm/fXuM6tT2vJk+ebG5AbS1czuxOGBgeaOYMK5tJsLHEdo+VwGaBUpBVIMlbkyW+zx9juQAAAOClARfQFLRratah31u42jivhcs/2N8EW1tmbbGNJ7NHTJcY6TSu5m6C+ro6litpUZIcXnWYgAsAAMBJCLjgdfLT86Uwt1B8/HwkvGW4s4tjgi2dO8teoc3ty47ZamArSVqcJJn7M80kz5oivr4tao5qiQMAAPB0BFzwOpkHS7sTarDlyWObgpoFSWy3WEndnmrGcnUc27FeLWoa4CVOSnRoWQEAADwVARe8N2GGE7sTNpWWA1qagOvY+mNSUlhSrxY1AAAA1J/n/rwP1JYwI8Gz5t+qSmzXWJOcQ7tQHl592NnFAQAA8DoEXPAqJUUlkn0022tauLQLYXz/0gyFe+fvdXZxAAAAvA4BF7yKBluWYosEhAaYSY+9QasBrcz90fVHJWN/hrOLAwAA4FUIuOC182/5+HhH1r2QmBCJ7BApYhFZ9946ZxcHAADAqxBwwau4wvxbzmzlWvv2WpPiHQAAAE2DgAtemRLeGxJmlNW8Z3MJCAswXQpTtqU4uzgAAABeg4ALXqMgp0Dy0/K9soXLL8BP2o1sZ5YPLjvo7OIAAAB4DQIueI2sw6XdCUNiQ8Q/2PumoOswpoO5P7ruqBSdLHJ2cQAAALwCARe8LuDyttYtq+jO0aZroU6AnLw52dnFAQAA8AoEXPAa2YdL598Kbx0u3kizMva7pp9ZPrbhmLOLAwAA4BUIuOAVLBaLrYWrWWvvSphRVp+r+oj4iGQkZUheWp6ziwMAAODxCLjgFQqyCqQgu8AEG+EtvbOFS0W2jZTY7rFm+fiG484uDgAAgMcj4IJXsLZuhcWFmYx93ixhaIKtW6G2/AEAAMBxCLjgFehO+IeW/VuKb4Cv5J3Is81LBgAAAMcg4IJXyDrC+C0rTYkflxhnlo+tJ3kGAACAIxFwweNptzlvz1BYUXzfeHOv6eFLikqcXRwAAACPRcAFj3cy46QU5haKj6+PhMcTcKmoDlES2CxQivKL5MSuE86uIgAAAI9FwAXvSZgRHya+/nzklQafLXq3MMvHN5KtEAAAwFG4+oT3JMxo5b3zb1XFGnCl7kiVopNFzi4OAACARyLggscjYUbVwluFS0hsiBnDlbottYlrBQAAwDsQcMGjkTCjej4+ZboVbqJbIQAAgCMQcMGj5aflm8QQPn4+EtYizNnFcTkt+pQGXCd2n5CCnAJnFwcAAMDjEHDBK8ZvhbcMF18/Pu4VhcaGmq6FYhFJ2ZLihBoCAADwbFyBwqORMMP+Vq5jm5gEGQAAoLERcMGjkTCjdi16lQZcmfszJT8j3+F1AgAA4E0IuOCxSJhhn6CIIInsEGmWmZMLAACgcRFwwWPlpeZJcUGxmew4LI6EGTWxZitM3pzcRLUDAADgHQi44PkJM1qFi4+vj7OL49LiesaJ+IhkH82W3NRcZxcHAADAY9Q74Lrmmmtk2bJl1T6/fPlysw7gLCTMsF9AaIBEd4o2y7RyAQAAuEDAtXLlSklJqT6N9IkTJ+S3336r78sDjZcwo00zjmYdkmcQcAEAALhIl0Ifn+q7aSUlJUlYGONm4BwlxSWSfSTbLDdrTcBlj9gesabrZc7xHMlJznFwDQEAAHgH/7qs/OWXX5qb1auvviozZ86stF5WVpZs375dRo0a1TilBOoo52iOlBSWiF+gn4TEhnD87BAQEiDRXaLlxI4TkrwpWcJO5wcTAACAJg248vLyJC0tzfZ3Tk6O+PpWbiQLDQ2Vyy+/XG699dYGFxCoj/Sk9D8SZtTQEovK3Qo14Dq++bi0H9OeYwcAANCUAdeVV15pbmrs2LFy//33y7hx4xpaBqDRZSRlmHu6E9ZNbPdY8fHzMSn1c47lSHjLcD6dAAAAzhjDtWDBgkYPtnTc10MPPSQXXHCBJCYmysSJE6tc77PPPpOzzjpL+vTpI+eff74sXLiwym6N9913nwwePFgGDBggt99+uxw/frxRywvXlbGfgKs+/IP8JaZrjFnWVi4AAAA0YQtXVbKzs+Xw4cOSmZkpFoul0vOnnnqq3a+1c+dOWbx4sfTr109KSkqqfL3vvvtOHnzwQbnllltk6NCh8v3338ttt90m//vf/6R///629aZNmya7du2SRx55RIKCguT555+XqVOnyqxZs8Tfv8FvGy6suLBYMg9kmmVauOrXrTB1W6rJVthxbMdGrx8AAABvUu/IQ9O+/+tf/5J58+ZJcXFxpec1WNKxM1u3brX7NbWb4hlnnGGW7733Xtm0aVOldf773//KueeeawIqpUHXjh075OWXX5Y333zTPLZ27Vr55Zdf5O2335YRI0aYxzp27CgTJkww5dV7eC4NFEqKSsQvyE+Co4OdXRy3E9stVnz9fSU/Ld9keiRoBQAAcELApV3/tCvflClT5JRTTpGIiAhpqKoScJR14MAB2bdvn/zf//1fucc1gHrqqaekoKBAAgMDZcmSJaY8w4cPt63TqVMn6dmzp3mOgMuzHV512NxroEDCjLrTzI4adCVvSZbjm45Lq0GtGr2OAAAAvEW9A66lS5fKtddeK3//+9+lqezZs8fWWlVW586dpbCw0ARkuqzr6ToVL7Y16LK+Brwj4EL9xPWKMwGX3qrq2gsAAAAHB1zBwcHSpk0baUoZGaWJECq2pln/tj6v48maNat8sR0ZGVllN8W6sF58aop8OI/1+FesBw2yrQFXSIsQE4jbo6ioyHZv7zYN2c4Z+6zLds06NBPfQF85mXFSUnel2o512eCrujpA06IeXAP14BqoB9dAPbgG6sHxrEOoHBZwaXbA+fPny1VXXSXexHrRql0b4XwV6yHQL1CObThmlguCCiQ5Odmu1wnNCrVlt7R3m4Zs54x91nW7sLZhkrU7S/b9WnqM9+7dW2VwxXfBNVAProF6cA3Ug2ugHlwD9eBYOpzJYQGXpmX/7bff5IYbbpDLLrtMWrZsKX5+fpXW69WrlzQWbaGyXjDGxcXZHtcWrbLPa4vX0aNHK22vLWDWdepLMxxq0NWhQwcJCQlp0Guh/vTCX08gFevh6JqjUlJYIgFhAdKqYyu7x3BZW0TN/R8fLYdt54x91nU7vwF+sm33Nknfmi6WEovppluxhauqOkDToh5cA/XgGqgH10A9uAbqwfE0I7o96h1wWSdAVr/++mujZCmsjY7BUjoOy7ps/TsgIEDatm1rW2/ZsmWVmvn0F/pu3bo1qAzW19MLzNDQ0hYDOE/Fejix6YS5j2ofZdcvDlbWqQL0Xj9Ljt7OGfus63Zx3eNkZ9BOOZl+UvYv3S/tR7avcj2+C66BenAN1INroB5cA/XgGqgHx7H3h/16B1xPPPGENDUNqPTX9Llz59rSxyudi2vYsGG2C+xRo0bJK6+8YoKu0047zRZsbdmyRW688cYmLzeazqHfDpn7yA4Na8mEmNTwzXs0l2Prj8nmTzdXG3ABAADAAQHXRRddJI5o+tSJj9WhQ4fMpMoaXKnBgwdLTEyM/PWvf5W7775b2rVrJ0OGDDHB1oYNG+Sjjz6yvc6AAQPM/Fv33Xef3HPPPWbi4+eee066d+8uZ555ZqOXG67j8G+lCTMi2xFwNVa2Qg24tny+Rc5+4Wzx9at56gYAAAA0UsDlCKmpqXLHHXeUe8z69wcffGACrIkTJ5rATCc5fuONN8y4kpdeeskEWWU9//zzphVO5wvTMVcagD3wwAO2blXwPIW5hWbSYxXVIUqK8koTnKD+ojtFm/FwOcdyJGlxknQcW35KBgAAANSs3tHHP/7xD7v6NT7++ON2v2ZCQoJs37691vUmT55sbjXRxAC677rsH+7tyNojJrlDeKtwCY4Kluy8bGcXye1pi1bL/i3lwNIDsunTTQRcAAAATRVwrVixotJjJSUlJt10cXGx6f5H5jI0Jev8W21Obdr54Txd61Nam4Br66ytMuGlCeIXUDkbKQAAABo54FqwYEGVj+uEqp9++qm8//778s4779T35YF6j99qdUorjl4jiukWI6FxoZKbnCt7F+yVLmd14fgCAADYqdFHwGu66auvvlqGDx8ujz76aGO/PFAtWrgc160w8ZJEs6zZCgEAAGA/h6Uc69Gjh5kYGWgK+Rn5kro91dYFDo2r12WlE5hv+3KbFBcUc3gBAACcHXDpZMiM4UJTObLmiC07YWhzJqRubO1GtDPJSPLT82X3vN2N/voAAACeqt5juDQVe1WysrJMy5ZOMnzTTTc1pGxAncdvtT6V1i2HdSucnCgr/7vSdCvsNrEbn04AAABnBFyRkZHStm1bmT59ulx66aX1fXmgfgEX3QkdptelvUzAte3rbVKUzxxnAAAADg24tm3bVt9NAYclzKCFy3HaDmsrEQkRknkwU3bO2Sntz2rvwL0BAAB4BoeN4QKaSk5yjqTvSzfLrQaSEt5RfHx9JPFSshUCAAA0SQuX1cqVK2XRokVy+PDvLQytW8uYMWNk8ODBDX1pwC5HVpcmzIjtHivBkcEcNQfqfVlvWf7sctnx7Q45I+cMjjUAAICjAq6CggL529/+JvPnzxeLxSIRERHm8czMTHn33Xdl/Pjx8swzz5h5uQBHOvTbIXPP+C3H0y6bUR2jJH1vuuyZu0ektMELAAAAjd2l8OWXX5Yff/xRrr/+evnll19MS5feli5dKn/6059k3rx5Zh3A0Y6sKm3hYvyW4/n4+JjkGWrbLMZxAgAAOCzg+vbbb+Wiiy6Sv//979K8eXPb47GxsfJ///d/cuGFF8o333xT35cH6tzC1ebUNhy1JpwEee8Pe6Uoh2yFAAAADgm4kpOTpW/fvtU+r8/pOoAjZR/JNjcfPx9p2b8lB7sJ6HGO6RpjUsMfXXKUYw4AAOCIgKtly5amC2F1dPJjXQdoioQZLXq1kIBQxgs2WbfC31u5jswrPf4AAABo5IBLuwzOmTNHHnroIdmzZ48UFxdLSUmJWX744Ydl7ty5pssh4EhHV5e2sLQ6hXTwTZ2tUB1fdlzy0/ObdN8AAABekaXwlltukQMHDsjMmTPls88+E1/f0thNgy7NWqjBlq4DONLRNaUBF+O3mlaL3i0ktmespG5NlV3f7pLBNzMNBAAAQKMGXH5+fvLkk0/KddddJ0uWLJFDh35PXNCmjYwaNUp69OjBEYdDaWBvDbhICd/0ekzqIUv/tdRkKyTgAgAAaISA6+TJk/LYY49J165dZcqUKeYxDawqBlcffPCBzJgxQ+6//37m4YLD5B3Ok/wT+eIX6Cct+rTgSDsp4EpamCS5qbkSGhtKHQAAADRkDNenn34qX375pYwZM6bG9fT5WbNmma6GgKOkb0k39/F948U/qN6NtainmG4xEtEtQkqKSmTrF1s5jgAAAA0NuDRJxplnnilt27atcb127drJ2WefLd99911dXh6oV8DFhMfO03p8a3O/ecZmJ5YCAADAQwKuHTt2yKBBg+xad8CAAbJ9+/b6lguoVcaWDHNPwOU8rc8qDbj2LtwrGQdK6wMAAAD1DLgKCwvtHpOl6xUUFNTl5QG7WUoskrHt94DrlNKLfjS90Nah0nZkWxGLyIYPN1AFAAAADQm4WrRoITt37rRrXV1P1wcc4cTOE1KUU2QmO47rGcdBdqJeV5ZOgrz+/fUmcyQAAADqGXCddtpp8vXXX0tqamqN6+nzup6uDziCNR18i34txNe/3vN3oxF0v6i7CXxTd6TKoRWl00MAAACgVJ2uVKdOnWpSw1977bWyfv36KtfRx3VuLl3vxhtvrMvLA3Y7uro04Go5sCVHzckCmwVKz4t7muV1769zdnEAAABcSp1yaWt2wueff17uuusuufzyy83f3bp1k7CwMMnJyTHdCPfv3y/BwcHy7LPPmmyFgCNbuAi4XEO/a/vJho82mGyFZz93tvgHk6YfAABA1fmqSOfY+uabb+TNN9+URYsWyfz5823P6ZityZMnm5aw2lLHA/Wl8z4dX3/cLLccRAuXK+hwegeJSIiQzIOZsv3b7dJrcum4LgAAAG9Xr5+hExISZPr06WY5OzvbtG5pK1d4eHhjlw+o5Pjm41KUXyT+4f4S3TmaI+QCfP18pe+UvvLLE7+Y5BkEXAAAAKUanG1Ag6z4+HiCLTSZw6sOm/vInpHi4+vDkXehboVq19xdkn0029nFAQAAcAmkd4PbsWbCi0qMcnZRUEbz7s0lYWiCWIotJM8AAAD4HQEX3Dbgiu5Dd0JXM3DqQHO/5o01ZnJqAAAAb0fABbdSkF0gxzeVJsyI6k0Ll6vpdVkvCYoIkrQ9abLnpz3OLg4AAIDTEXDB7cZvactJRNsICW4e7OzioILAsECTPEOtfn01xwcAAHg9Ai64lYPLD5r7VoNbObsoqMagmweZ++1fbyd5BgAA8HoEXHDL8VutTiHgclXxfeIlYViCmS9t7TtrnV0cAAAApyLggtuwWCy2Fq7Wg1s7uzhez8fHR0JCQsx9da1ca94keQYAAPBuBFxwG5kHMk0XNV9/X2nRr4Wzi+M1AsMDq8w4qMFWYmKiua+o16W9JDgqWNL3pcvuebubqKQAAACux1/czJQpU2TlypVVPvfss8/KueeeW+0633//vXTu3LkJSglHsLZutezfUgJCAjjITcQ/2N9MML1l1hbJTcm1PV5UVCRpaWkSHR0t/v6VTyUJpyXIru93yapXV0mXs7tQXwAAwCu5XcD18MMPS3Z2drnH3n//fZk3b54MGzbM9tjAgQPlnnvuKbdeQkJCk5UTje/gitKAq82QNhxeJ9BgK/vIH9+9wsJCSUtOE/88fwkIqBwAtz61tQm4tn+7XU7sPiExnWOauMQAAADO53YBV5culX8p/9vf/ibDhw+XmJg/LugiIiKkf//+TVw6ONKh5aUJMxKGEji7g2atmknnszrL7h92y4r/rpBzXjjH2UUCAABocm4/hmvNmjVy8OBBOe+885xdFDhQcUGxHFlzxCzTwuU+ht1V2uq87p11kp+e7+ziAAAANDm3a+GqaPbs2RIaGirjxo0r97iO4dIWruLiYunXr5/ccccdcuqppzZKpjyVl5fX4NeC/Y6uOSpF+UUSHBMswa2Dbce/Yj1YM+fp+CLt8mYvXd963xTbOWOfjb2ddbm619L1O43vJM0Tm0vKlhRZ/spyGTxtsN37hX2q+y6gaVEProF6cA3Ug2ugHhxP44KqsjV7VMClF3Rz5syRsWPHmqDLSgOrCy64QDp06CDHjx+Xt99+W66//nr58MMPZcCAAQ3ep9q3b1+Dyw/77fu29Hg369FMtm3b9sfjFerBmjlPkzno+CJ7hWaVfn6ysrIkOTnZ4ds5Y5+O2i49Pb3K7YpCisxJqMPlHSTloRTTrTBsXJjJMonGxznJNVAProF6cA3Ug2ugHhwrMDDQswOupUuXyokTJ2TixInlHr/99tvL/T1mzBizziuvvCJvvvlmg/ap2dg06NJgrqp02HCMPfv3mPtuY7tJz549za82egKpWA/WXxlM5rw8+z/ezZo1++M+Thy+nTP22djbacuWBltRUVFVJs1oFl263ajbRsmW/26R3GO54rfdT3pc0sP+naNW1X0X0LSoB9dAPbgG6sE1UA+Ot2vXLrvW83f37oR6sTdixIga19PWr9GjR8sPP/zQ4H1aL+j1wqZsqxoc6+jqo+a+w4gO5Y57dfWggXFVQUB1rGnNm2o7Z+zTUdvpY1U9bt1OA69Tbz1VFk9fLGteXiMDpgywq/kddcM5yTVQD66BenAN1INroB4cx97rGbft25Ofny/z58+Xs88+u84XunC/dOQndp4wy20GkxLeHZ3y51PEL8hPDq08JAd+PeDs4gAAADQZtw24FixYILm5uXZlJ9T1Fi1aJH369GmSsqFxWS/Q4xLjJCSaLlPuKDw+XPpO6WuWf37sZ2cXBwAAoMm4bcD17bffSuvWrWXQoEHlHl+1apXccsstMmvWLFm+fLl88803ctVVV5nB/rfeeqvTyouGB1wJpzH/ljsbce8I8fHzkV1zdsmh30rnVAMAAPB0bhlwZWRkyM8//ywTJkyo1HcyLi7ODOZ/7rnn5MYbb5RHH33UPPbxxx9L376lv7DDvRxYWhpwtRveztlFQQPEdI6RvleXfgeXPLqEYwkAALyCWybNiIyMlE2bNlX5XPv27U0aeHjOhMfW1pC2p7V1dnHQQCPvGykbPtwgO77dIUfWHpFWA1pxTAEAgEdzyxYueI8ja45I8cliCW0eKjFdY5xdHDRQbLdY6X1Fb7NMKxcAAPAGBFxwi/Fb2rpFKnHPMPL+kSI+Itu+3CbHNhxzdnEAAAAcioALbjF+i4QZniOuZ5z0urSXWaaVCwAAeDoCLrgsi8Via+EiYYZnGfXAKNPKteXzLWQsBAAAHo2ACy4rfW+6ZB/NFt8AX2k1iOQKnqRF7xbSb0o/s/zj//1ogmsAAABPRMAFl2Vt3Wo9qLUEhAQ4uzhoZKf/63TxC/KTpMVJsvP7nRxfAADgkQi44LKY8NizRbaNlKHThprl+X+fLyVFJc4uEgAAQKMj4ILLYsJjzzfi3hESEhMiyVuSZd3765xdHAAAgEZHwAWXdDLzpBzbWJoyPGFYgrOLAwcJjgqWUQ+OMsuLHlokBTkFHGsAAOBRCLjgkg4uPyhiEYnqGCXNWjVzdnHgQKf8+RRTz1mHs+TX//zKsQYAAB6FgAsuaf/S/eaedPCezz/IX8Y/Nd4s//LkL5K2J83ZRQIAAGg0BFxwSft/Lg242g5v6+yioAn0nNRTOo3vJMUni2XuHXM55gAAwGMQcMHlFBcUy8FlB81y+9HtnV0cNAEfHx8558VzzJxrO2bvkO3fbue4AwAAj0DABZdzeNVhKcovktDmodK8R3NnFwdNpHn35jLsb8PM8tzb50phXiHHHgAAuD0CLricpCVJ5r79qPam5QPeY9QDoySibYSk70s347kAAADcHQEXXDbgajeqnbOLgiYWGBYoZz17llle+uRSSd6aXKftLSUWB5UMAACgfvzruR3gECXFJbL/l/22Fi54ZwKNLud0kV1zdsknEz+RYXcPEx/f2ls6tQtq4qTEJikjAACAvQi44FKOrT8mBVkFEhQRJPF9451dHDiBdiOd+NpEebHbiyZF/I5vd0ibwW2oCwAA4JboUgjX7E44op34+vHx9FaR7SKlx0U9zPKe+XskPz3f2UUCAACoF65o4VIYvwUr7VIa0S5CSgpLTKp4i4XxWQAAwP0QcMFl6AV12QyF8G46bqv7ed3Fx89H0nanme6mAAAA7oaACw5nb+a4lK0pkpeaJ/4h/tJ6UGsyzrm5wPDABtehJsLoMKaDWdYkGnQtBAAA7oakGWiSloots7ZIbkpujetZW7d0/M72b7eTcc7N+Qf72133ZcV0iZFO4zrZ/m57WltJ3ZEqmQcyZduX26Tftf3syloIAADgCgi40CT0gjv7SHaN6xzfeNzch7cMr9MFOty/7iu2apWlwZUm0Fj92mrJ2J8hB5cdlLbD2zqgpAAAAI2PLoVwmfFb6fvSzXJk+0hnFwcuJiQ6RDqf1dks712wV7KP2h/AAQAAOBMBF1xCflq+mX9LWzMiEiKcXRy4oJYDWkps91gzLky7FpYUlTi7SAAAALUi4IJLSN9b2rqlwZZfgJ9dk+OGhISYe3gHretu53WTgLAAyTmeY1q6AAAAXB0BF1xC2r40cx/VMcquDHcabCUmJpp7eI/AsEATdCkdy5W2t/RzAwAA4KpImgHXGL/1ewuXNeCqLcNdUVGRpKWlSXR0tPj7+1eb4Q6ep3n35tJqYCs5suaIbP9qu5zy51PM5wUAAMAVcZUCp8tNzpXCnELx9fetNH6rugx3hYWFkpacJv55/hIQEFBthjt4Jk2goa1bOvZv55yd0vOins4uEgAAQJXoUgins3YL0+yEvn58JFE7v0A/kypefESObzguxzeVTikAAADgari6hdPZuhN2KO1OCNgjsm2ktBvRzizvmL2DudsAAIBLIuCCU2liDOv8W9bxW4C92o9uL80SmknxyWJZ+/ZaKS4s5uABAACXQsAFp9IJbPVi2S/IT5q1akZtoE60C2ripETz+dGW0kUPL+IIAgAAl0LABZcYv6XdCTUrIVBXwVHB0v387mb5lyd/kT3z93AQAQCAyyDgglMxfguNIS4xTtqNbCdiEflyypdmYmQAAABXQMAFpykpLpGMpAyzzPgtNFTi5ESJ6xVnuql+de1XNU6cDQAA0FTcLuD64osvpHv37pVuTz/9dLn1PvvsMznrrLOkT58+cv7558vChQudVmZULfNgppQUlUhAaICEtQjjMKHBqeIvmXGJmQR519xdsuy5ZRxRAADgdG478fFbb70lzZr9kWQhPj7etvzdd9/Jgw8+KLfccosMHTpUvv/+e7ntttvkf//7n/Tv399JJUa13Qk7RomPD+O30HAtereQs547S77783fy0z9+kg6jO0jrU1pzaAEAgNO4bcDVq1cviYmJqfK5//73v3LuuefKtGnTzN8adO3YsUNefvllefPNN5u4pKhO2p4/EmYAjWXQzYNM4oyts7bK55d/LjevuVmCIoI4wAAAwCncrkthbQ4cOCD79u2Tc845p9zjEyZMkGXLlklBQYHTyoY/FOUXmS6FKqZL1YEzUB/aWnrem+dJZLtISdudJrNvni0WC+O5AACAc7htwDVx4kTp2bOnjBs3Tl5//XUpLi6d8HTPntKU0B07diy3fufOnaWwsNAEZHCRdPAWkZDYEJPWG2hMIdEhMumTSeLj5yObZmySVa+t4gADAACncLsuhXFxcfLXv/5V+vXrZ37JXrBggTz//PNy7NgxeeihhyQjozTrXURERLntrH9bn68v6y/leXl5DXodb6F1FBISIkVFRSbgtUrdkWruIztGlnvcSte33lf1vPWxis/Vtl11mno7dyprddtVVwfOLqd+N/V7Gts/Vkb9c5Qsvn+x/DDtB4ntGystB7QUT2M9F3FOoh7A98FVcF5yDdSD4+n1hj15CNwu4Bo5cqS5WY0YMUKCgoLk/fffN0kyHM16UafdFlE7DbYSExMlLS1N0pLTbB/O1F2lAZdvrK8kJydX2i40K9TcZ2VlVfm8VXp6er22q+/+Gms7dyprbdtVrANnlbMopPS7uXfvXts/MuFnhkv8vHg5tviYzLpsloz4cIQERgSKJ+Kc5BqoB9dAPbgG6sE1UA+OFRgY6HkBV1V0vNY777wjW7dulcjISNvFmraGWWVmlo4Xsj5fX/7+/ibo6tChgwkmUDNr1B8dHS3+eaUft7zUPCnKLjLdvdr2aWvSeVdkzUBp7v+oRhtt+dAL/aioKAkICLB7u+o09XbuVNbqtquuDpxWzujS7Tp16lRuzFbnjzvL+6e9b+Z82/PsHrno04uq/DXKXcd5aXCp/5hyTqIewPfBVXBecg3Ug+Pt2rXLrvU8IuAqSy+2rGO5rMvWv/WisG3btg16feuFmgZboaGlv8TDvkDVelF+POm4udekBsFhwdWuX3G7quhzZZ+3d7v67q+xtnOnsta2XcU6cFY5Q6NCzWTHwcHlP1P6Xb3080vlneHvyK7Zu2Tdq+vktL+dVm4d3c7H172nJuCc5BqoB9dAPbgG6sE1UA+OY++0Rh4RcOk8W35+fqbrmrZq6S+9c+fOlTPOOKPcOsOGDbOr2Q+OdWL3CXMf3SmaQ41GoxMea9C0ZdYWyU3JrfR8j4t7yOYZm2X+3+dL9tFsW3bM0OahkjgpkZoAAAAO4XYB1w033CBDhgyR7t27m79/+uknmTlzplxzzTW2LoSaVOPuu++Wdu3amXU12NqwYYN89NFHTi49SopKJH1f6Zgf0sHDETTYyj6SXenx2G6xEtc7TpI3Jcvq11eb+boCw/gBBgAAOJbbBVya7n3WrFly9OhRKSkpMa1Z9913n0yZMqVcynjtt6qTHL/xxhtmm5deekkGDBjg1LJDJONAhpQUlkhAWICExYdxSNCkzf7dJnYzwZiOI9z25Tbpc1UfagAAADiU2wVcDzzwgF3rTZ482dzgWtJ2lWYqjOkcY3e/V6Cx+Af5S69Le8maN9eYSZGTliRJ78t7c4ABAIDDuO3Ex3BPaXtKA67ozozfgnOEtQiTrud2NctJi5Lk2IZjVAUAAHAYAi40mYLsApOsQBFwwZla9m8prU9pbZbXvbNOUralUCEAAMAhCLjQZE7sLM1OGN4qnGQFcLrOZ3c2UxMU5RfJjAtnSH5GvrOLBAAAPBABF5pM6s5UW7Y4wNl8/Xwl8dJECY4OltTtqfLFVV+Y+bgAAAAaEwEXmkRxYbFJUqAIuOAqNC38KbecYubw2vndTln40EJnFwkAAHgYAi40WXfC4oJiCQwPNF0KAVcR2T5SznvzPLP882M/y5bPtzi7SAAAwIMQcKFJHN943NzHdCUdPFxP36v7ytC7hprlr677So5tJHMhAABoHARccDiLxWK7gKU7IVzV+H+Pl07jO0lhTqHMuGCG5KbmOrtIAADAAxBwweFStqZIXkqe+Pj5SHQn5t+Ca/L195VLZlxiPqPpe9Pl80s/N91gAQAAGoKACw63Y/YOcx/dMVr8Av044nBZITEhctlXl0lAWIDsXbBXZt8y27TQAgAA1BcBF5os4IrpFsPRhsuL7xMvk2dOFh9fH1n37jr5+fGfnV0kAADgxgi44FB5J/LkwNIDZjm2K/NvwT10ndBVznnpHLO88IGFsvHjjc4uEgAAcFMEXHConXN2mslkm7VpJsFRwRxtuI1T/3yqDLt7mFn++vqvJWlJkrOLBAAA3BABFxxqx7el3Qlb9GnBkYZbZi7sOamnSZ4x48IZkrI9xdlFAgAAboaACw5TlF8kO7/baZbj+8ZzpOF2dBzXRR9eJG2GtJH8tHz5eMLHkpOc4+xiAQAAN0LABYfZM3+PFGQXmO6EUR2iONJwSwEhAXLFN1dIVMcoSduTJjPOnyGFeYXOLhYAAHATBFxwmK2ztpr7nhf3NC0FgLsKaxEmV31/lQRHB8vB5QflyylfSklxibOLBQAA3AABFxyiuLBYtn29zSzrGBjA3TXv0Vwu+/Iy8Q3wNT8mzLl9DnN0AQCAWhFwwSGSFieZMS+hcaHSbkQ7jjI8QofRHeTijy4W8RFZ9coqWfLoEmcXCQAAuDgCLjjElllbzH2PC3uIrx8fM3iOXpf2knNeLJ2ja9HDi2TVa6ucXSQAAODCuBJGo9OxLdu+/L074cV0J4TnGXzrYBn14Ciz/N1fvrP9wAAAAFARARca3cFlByXnWI4ERQZJx7EdOcLwSGOmj5GBNw0UsYh8ceUXsnfhXmcXCQAAuCACLjQ666/93c/rLn6BfhxhuLTA8ECxlFjqvJ2Pj49MeGmCacU1EyNfMEOOrD1i17b12R8AAHBP/s4uADyLxWKRbV+QnRDuwz/Y30xboD8U5Kbk2r1dTJcY6TSuk/S8pKcc23hMTuw8Ie+PeV+G/d8wCYsLq3a70OahkjgpsZFKDwAAXB0BFxrV4VWHJWN/hgSEBkjnMztzdOE2NNjKPpJt9/oaOKmTmSdNK9e699aZrrTLn10u/a/vL0HNghxYWgAA4C7oUohGtemTTea+28RuJugCvKWVrO/Vfc3EyDodwoYPN0hhbqGziwUAAFwAARcaNTvhphmlAVefq/pwZOF1Y8H6Tukrgc0CJTc5Vzb+b6MUnSxydrEAAICTEXCh0exbtM90ydJf+buc3YUjC68TEh1igi7/EH/JOpxlfoAoLix2drEAAIATEXCh0Wz8eKO5T5ycSHZCeC1NmKHdCzVDZ8a+DNny+RbT+gsAALwTARcaRVF+kWydtdUs97mS7oTwbs1aN5PeV/YWX39fObHjhGz7ahup4AEA8FIEXGgUO7/fKSczTkpEQoS0H9meowqvF9U+ShIvTTQp55M3JcvO73aaaRMAAIB3IeBCo3Yn7H1Fb3OBCUAktmus9Li4hzkUR9YckT3z9xB0AQDgZQi40GD5GfmyY/YOs0x3QqC8Fr1aSLfzupnlg78elJ2zd3KIAADwIgRcaLBtX26T4pPF0rxnc4nvF88RBSpoNbCVdD6rdCJw7Vr48+M/c4wAAPASBFxosA0fbbDNveXjQ3dCoCoJQxOk4xkdzfKC+xfIr0//yoECAMALEHChQdL2psnen/aa5b5X9eVoAjVoN7yddDu/tHvhj//3oyx/fjnHCwAAD0fAhQZZ+85ac99pfCeJ6hDF0QRq0XVCVxn14Ciz/MOdP8jS/yzlmAEA4MEIuFBvJUUlsu7ddWZ54I0DOZKAncZMHyOjHioNuub/fT5jugAA8GD+4mbmzJkj33zzjWzevFkyMzOlffv2MmXKFJk0aZJt/JD+vXLlykrbfv/999K5c+nAdTTcrh92SdahLAmJDZHuF3TnkAJ20nPV6dNPF78AP1n44EIzpqu4oFhGPzyacZAAAHgYtwu43nvvPWnTpo3ce++9Eh0dLb/++qs8+OCDcvToUbntttts6w0cOFDuueeectsmJCQ4ocSea+1bpd0J+13TT/yD3O6jBDjdqAdGiW+Ar/x070+yePpiyU/Pl7OePYu57AAA8CBud5X86quvSkxMjO3vYcOGSXp6urz77rvyl7/8RXx9S3tJRkRESP/+/Z1YUs+WfTRbtn+73SwPuGGAs4sDuK0R94wQ/2B/+WHaD7LihRWSczxHLnzvQvEL9HN20QAAgDeO4SobbFn17NlTsrOzJTc31yll8kbr3l8nlmKLJAxLMBO7Aqi/oXcMlYs+ukh8/X1l0yeb5JPzPpGC7AIOKQAAHsDtAq6qrF69WuLj4yU8PNz2mI7h0hauPn36yNVXXy2//fabU8voSSwWi607IckygMah0ypc8e0VEhAaILvn7ZZ3R74rGfszOLwAALg5t+tSWNGqVatMMoyy47VOPfVUueCCC6RDhw5y/Phxefvtt+X666+XDz/8UAYMGNDgYEPl5eWJt9q/eL+c2HVCAsIDpOPEjjW2LGpygJCQECkqKpLCwkK796HrW++r2s76WMXnatuuvvtr7O3cqazVbVddHbhaOV11u/z8fNv5xKrN6DZy2feXyReTv5Cj647KG6e+IRd+cqFpSbaquI31XFTbOakhk5JX3Ccqs7ce4FjUg2ugHlwD9eB4+u+jPf++unXApYky7rzzThkyZIhcc801tsdvv/32cuuNGTNGJk6cKK+88oq8+eabDdqn9WJp37594q1+e6q0tbD1Wa1l94HdNa6rwVZiYqKkpaVJWnKa3fsIzQo191lZWZKcnFztejp+rz7b1Xd/jbWdO5W1tu0q1oGrltNVtvOP9xdLiUWCg4OrfL7z6M4y9bepMuOCGXJs/TGZcfYMmfjaRBnwpwFSXFQsm7dsrjLAq+mcFBAQIL0Se4mff93HhdW0T1Tmzf82uBLqwTVQD66BenCswMBAzw24NCX81KlTJSoqSl588UVbsoyqhIaGyujRo+WHH35o8H79/f1N0KWtZxpMeJu0PWlybPExszzuvnES2yO2xvWtUb9mlPTPs//j1qxZsz/u4yo/rxd/eqGv9a8Xk/ZuV9/9NfZ27lTW6rarrg5crZyutl1sfKzJQrhx5kbJSc6pdr3e1/QW+VDk2Lpj8s0N35hxk1fOvlK6du1arsVJf8HUf0xrOifp91CDrdr2WVFYXJj0ubRPpX2iMnvqAY5HPbgG6sE1UA+Ot2vXLrvWc8uAS7vi3HzzzeaX5U8//dR24dMUrAGE/oOqgZy3WfLWEhGLSJezu0jbgW3rFKhWdVFe0/r2bKfPlX3e3u3qu7/G2s6dylrbdhXrwFXL6WrbnUw7KfnJ+TWu2/387hISFSL7Fu+T/Uv2y+sDXpdJH0+SNoPbVFrXnnOSPfusqqwEEPbz1n8bXA314BqoB9dAPTiOvd313S5phrYuTZs2Tfbs2SNvvfWWSZZRGx1jtGjRIpNAA/V3MvOkrH2nNFnGkGlDOJRAE5zI249uL/2u7SchMSGStjtN3hn+jiyavkiKTpZ2bwYAAK7N7Vq4pk+fLgsXLjQTH2sq+HXr1tme07FCGzZsMIHY+PHjzQTJmjRD5+jSMRYvvPCCU8vu7jTYKsgqkOY9m0vnMzs7uziA14hqHyUjHxgph5Yfks0zN8viRxbLxv9tlAkvT5BWw1s5u3gAAMCTAq6lS5ea+yeffLLScz/99JPExcWZsSXPPfecGV+izaiamVADtb59+zqhxJ6hpLhEVvx3hVkeOm1ogzKeAag7TRc/acYk6XFRD/nhzh/kxM4T8tGZH0n3Sd2l9ZTWIj05qgAAuCK3C7gWLFhQ6zqaBh6Na/s32yV9b7rp1tT3agJXwBn0h47el/eWLud0kYUPLZTfXvpNts/aLju+2iEpV6fI2EfGSlSHKCoHAAAX4nZjuOAcy59dbu4H3TzI/NIOwHmCI4PlnBfOkZtW3yQdz+wolmKLbHx/o7zY7UX5+oav5ej6o1QPAAAugoALtdq3aJ/s/2W/+AX6yam3nsoRA1xEy/4t5ZIvL5HT3j5N2p/eXkoKS2TdO+vk9f6vy3uj35Mtn2+R4oJiZxcTAACv5nZdCtH0Fj2yyNwPuHGARLSJoAoAFxPTL0aGXz5cUtelyooXVsiWWVskaUmSuYU2D5XeV/aW/tf2Zy4tAACcgIALtbZuJS1OMq1bI+4dwdECXFjb09qaW+ahTFn12ipZ+9ZayT6aLSv/u9LcmrVuJnG94qRFnxYS1CzI2cUFAMArEHDB7tatyLaRHC3ADWhL9NhHx8qYh8fI7h93y/r318u2r7ZJ1uEsc9szf4/EdI6R+P7x0rx7c/H1p3c5AACOQsCFatG6Bbg3DaS6ntPV3PLT8+X72743LdaZBzPlxK4T5uYf7G9aveL7xUtEQgRTPgAA0MgIuFCtxdMXm/sBN9C6Bbi74KhgaTeyncR0iZHc1Fw5tv6YuZ3MPClHVh8xt5DYEInvG2+CL82ECAAAGo6AC1Xau3CvaeHyDfCVEf9g7BbgSUJjQ6Xj2I7S4fQOkr4vXY6tOybJW5MlLzVP9i3cZ26x3WKl68SuJtEGE50DAFB/BFyopKS4RObdNc8sD5w6kLFbgIfSQCq6Y7S5dZnQRVK2ppg5vDL2ZUjqjlRJfTbVdEE87e7TJHFyovj6MdYLAIC64l9PVLL2nbVydN1RCYoMkjGPjOEIAV7AP8jfzOul6eN1vr1Wg1qZFm7tajjrilnyau9XZeMnG80PMgAAwH4EXCgnPyNfFty/wCxrsBUWF8YRAryMzt3VbWI3GffEOBkzfYwERwdLyrYU+eLKL+TVPq/K1i+3MqcXAAB2IuBCOUseXSK5ybnSvEdz8ys3AO8VGB4oox8aLXfsvUPG/HOMSbyh3Q5nXjxT3h/zvhxefdjZRQQAwOURcMEmZXuKrHhhhVk+89kzxS/Aj6MDeHnAZSmxmIyFox8cLXfsu0NG3j/SpJJPWpIkb57ypnx17VcmzXxFuh0AACBpBn6nmcg0UUZJUYl0nVA6bw8A76aBlY+vj2yZtUVyU3LNYxFtI2TUQ6PMRMqHVx6W9R+sN2O7Op/ZWTqd2cmMBdMuiYmTEp1dfAAAXAJZCmFs+GiD7Px+pxkkr61bAGClwVb2kexyB0R/lNE5u3b/sFsyD2TKzu92moyGHcd1lM5ndebgAQDwOwIuSOahTJnz1znmSIx+eLQ0796cowKgVhFtIqT/9f3NuK49P+6R/PR82f71dpPlVIOxdsPbcRQBAF6PMVxeTrsSfnvjt3Iy46S0PrW1jLiHSY4B1G0ur7jEOJNkp+MZHcUv0E8ykjLk3RHvmnTyGfszOJwAAK9GwOXl1r69VnbN3SV+QX5y4fsXiq8/HwnA3QKekJAQc+9Meu7QFq3Bfx0sbYe3FfER2TRjk7zU4yVZ+PBCKcgpcGr5AABwFq6uvVj6vnT54a4fzPLYf42VuJ5xNa5P1jHA+RkDK9JgKzEx0dy7Sjn7TukrN62+SdqPai9FeUWy5J9L5OUeL5sEG0ycDADwNozh8lKFeYUy85KZUpBVYH6NHnrn0Fq3qZitzB4xXWKk07hODSwtgKoyBqqioiJJS0uT6Oho8ff3d5nvYasBreTaRdfK1llb5cf/+9H8wKMp5Jf+e6mZ06vnxT2d3ioHAEBTIODy0nFb393ynRxZfURCYkPk4o8uFl8/33pnK6uJpocG0HgqfgcLCwslLTlN/PP8JSAgwKW+hxpQJV6SKN0mdpPlLyw3wVbylmT57JLPpNXAVjLivhHS48Iedp9/AABwR/wr54VWvrjSdO3x8fORyTMnS1SHKGcXCYCHt85pQp479twhox4cZbodHllzxARe2tXwt1d/M63uAAB4IgIuL7Nv0T7buK3x/xkvHcd2dHaRAHjJeLPgqGA5/Z+ny+17bpeRD4yU4OhgObHrhHz/l+/lmVbPyHd/+U4OrTxkWuHrqr5jTBmbCgBwNLoUehHtyqPjtizFFmk3sp34h/jLqtdX2bUtY7EANHS8WVkRCRFm3r8Dvx6QvT/tlbzUPFn16ipza9G7hfS4uId0P6+76Xqor+WIMaba1TJxUiIVCwBwKAIuL6G/In9wxgfmokbn20qcnCg5R3Ps3p6xWADqyp4xn5odtXmP5pK+N11StqXIsQ3H5Pim4+am2Q3DW4VL5/GdJWFYgiQMTTDBWHXTV9R1jCkAAE2BgMsLpCelywfjPjAXIi36tJCr514tmz/b7OxiAYAtuUZ0p2iTMbX3Zb1l21fbZMfsHbL7h93mvKVjTvWmAkIDJLZ7rMR2K71FdYySZq2amcDsZOZJk3aeJBwAAFdCwOXhsg5nyYdnfCgZ+zPMRcqUH6dISIxrzNcDAFWN8+p/XX9zKzpZJElLkmT/z/vl4PKDcmjFIRNUHV171Nyq4xvga7o11nbTYG33vN3mnKgBW3jLcLuDNR37ZU9XRwAACLg82PHNx+XjCR+bYEsvLK6Zf42Ex4c7u1gAYBf/IH/TnVBvSluvtHt06vZUSd2RKinbUyRzf6ZkHcmS7KPZkptcOn6rpLBECgoLzDyDtVn535W2ZQ2ggiKDzHQZYS3CbLdmrZuZe2uAxdgvAEBdEHB5qL0L9sqnF38qJzNOmm43V829ygxSBwB3pa1Pzbs3N7eqrHxlpWTsy5Ci/KKabydL7319fc14sIwDGablTFut8tPyzS1tV1q51/YL9DMtYNoS1rxnczPuTMtR3XgyAACsCLg8kI51+ObGb8yvvO1GtJPLvrpMQmOZgBiA5wdkOsZLb/bQMa2apVCztWYdypKC7AITeOWn55sEQ7knciUvJU9yjudIcUGx6S2gN+3auP699SbTq2ZR1IQeLQa2kPyYfIe/RwCA+yHg8iAFOQUy9465svbttebvXpf1kgvfu9CMVQAAVM90J4wIMreKvQG05UszIJqui0eyTTCmXRg1QDuw9IC5Wf3W/jdpN7xdaVbFYQkS3zde/AL8OPQA4MW4EvcQR9YekVlXzDJjG8RHZNQDo2TMI2MY1A0AjRCMWcdzST8x3QoHTR0kqTtTTTKPg8sOStIvSZKyJUUykjJkY9JG2fjxRrOttrbpVBwafLU9ra20HdaWaTYAwMsQcLk5HYvw69O/mvlqtMuLDu6+6KOLpOPpHZ1dNADw6CDMOp6s/7X9JTc3Vzb8tkEisiIkeU2yCcI0GNPuiUmLk8zNKqZrjAm8rEFYXK84UtkDgAcj4HJju3/cLXNum2OydanuF3SX898+n/FaAOCEucQi4iKk4+COkjgx0dYVUSdzPrDsgBz49YAJwlK2psiJnSfMzTq3WGB4oOl6qGPK9Bbfp3Q5JJopPADAExBwuSH9R/zr67+2/WMdFh8mZz59pvS5qo/5Rx8A4DgaIFWchyskJEQSE0sDLSt9Pi4xztwG3jDQPJabmiuHVh4qbQH7vRXMjAX7tTQoK0t7LGjgpXMoxnSJkdiupfeR7SMZFwYAboSAyw1pxiwNtvQf8y7ndJGOZ3Q0CTNWv7G61m31H+tO4zo1STkBwBNpIiI9/26ZtcUk01BFRUWSlpYm0dHR4u9f9T+t1vm7up7T1dysc4tpq9exjcfk+Mbj5qbLOhZMJ67X2+4fdpd7HR8/H4nqEGXO5zrHYmS7SHOLal+6rIEa6eoBwHUQcLkhnQvmhuU3mO4mexfuNVmzTqaftGtb/QcfANyl5ciVabCl519VWFgoaclp4p/nLwEBAXa/P01l36J3C3OTK/5YV9PT6+T1xzcdt3VB1EmfT+w+IUV5RZK2O83cqqIBWUSbCBN8RbSNMMGZNSjT1jG9D2oWVON7c6d6AABXR8DlphKGJJh7DbgAwFNbjuzhLi339X1/nc/qLOOfGm+20x4OOqF9TnKOuWmK+rwTpbf8E/mSl5YnlmKLbc6wassS4i/BkcESHFV6C4oKsi1rS1nfKX0lPD6cljIAaAQeHXDt3r1b/vWvf8natWslLCxMLrjgApk2bZoEBgY6u2gAADtajjyx5b6+70+3yzmWY5a1hcq0UlWIMy0WS+kEzhknJSgyyPSI2Pn9TrM/zZiojxflF5lWsuy8bDOfWFV++sdPpenw48NMa5l2U2zWpplJiW9S5MeVpskPjQs19xqoMYYYALws4MrIyJBrr71WOnToIC+++KIcO3ZMnnzyScnPz5eHHnrI2cUDAKDRadBjDcY04YaOGdPJnMsGeDqdSEFWgem2eDLrpG3Z3GedlMLcQvO3tpTpdvYEhzpmzBp8VQzGzHLzUAmJDZGQmD9u/kEeewkCAOV47NluxowZkpOTIy+99JJERUWZx4qLi2X69Oly8803S3x8vLOLCABAk9NAR2/VtQzqPGG9Lu1luitmHsqUrEOlyTt0WVvEcpNzTddGvemyBmclRSV2B2dWAWEB5QKw0NhQCY4JLvd32eetAZu3BWr1HU/nLuPwGlJOd3mP8M7Pdlkee9ZasmSJDBs2zBZsqXPOOUcefvhhWbp0qVx88cVOLR8AAK461swvwE/2L93/x1gzH5GIhAhzq6i4sNh0Y9QWMm1h0zT21mDMdq/jzaxjzdLyzQVTYU6huWUeyKxT+QJC/wjUtNukadGLCJLAiEDxC/WT3KJcyW2fK+Gx4eZxfV4Tlui4NX1vASEB5t76t1+gn93dIbXcGlzqey4pLPljWe9PFpvWQ+2yaZa166beanis3OMni6Q4v9h0Cy2/U5GMAxlmvXJ8SpOu+Pj7mHttZdSbJk3R991qQCvz3qq6mXUDfE092+5re8zf1+zTWiZb8cqW9/fFvJw8KUgvMEG7VBiuWNX622dvL123Ar2o1vdj3qefT+nfv9eVNesn3JePHWNazedFPyc+pS347lrvHhtw7dmzRyZNmlTusYiICImLizPPAQCAxhtrphfl2jrW54o+Nf76rEFLfka+LQCzJv7QOco0GDP3J/54Xv8uF6jlFppb5sHqA7XNsrlOVWsu5H+/uNd7c4Gv78FHTDBlDa50/+5i84y6HQNHmCfzGv01bUFYgK989+fvbEGkCaKruGmA7Rdc/nlb0F329nsAXut6vye/8VQa4JjPfFHJHz8Q/D7u07pcmFdY8+NlHrMuF1Z4zvq3nmf0xwTr98saYNkCrQrM2NIWYXLV3KukZb+W4i58LJV+SvEMvXr1kjvuuENuuummco9PnDhRBgwYII8++midX3PNmjW2X2Z0nhVnDxDW/ev8W9rP3l56gtITiLtvZ9H/fm9S1v9ctZyutM/G3q66OnC1cnp7PbhSWT15O+qh/LHRC6m6BCca4PgF+dW83e8XYbaLspI/WnmKCopMXVifNxdq1vUrXsCZu6ov5urFxGWlwZn506fyst5rkGBdrvb+99cr9/I+PuaCtKqWL/M+fl8u+5iWR1ukyr33sq0FFZbLrlNpuTGPlT3Kvn8XvUItW8flPgO/L9f6eBOqqkWx3GegzHNNXtcNOP5hLcNM8OtsOiWIlmfgwNLJ7avj/JK6EevJsLo5VpwhMKx+GRfZzjWOC3XBceGzxnfC084Vqr4XQvXdLlDIPgyg6ZkfU+xogPHYgEu7D2ZlZVWZvTAyMrJer6ktYwAAAABgL19PPVSdOnWqNFZLA7Dk5GTzHAAAAAA4mscGXKNGjZJff/1VMjP/GFQ7d+5c8fX1leHDhzu1bAAAAAC8g8cmzdCug+eee6507NjRzLtlnfj4vPPOY+JjAAAAAE3CYwMutXv3bpONcO3atRIWFiYXXHCB3HnnnRIYyOBaAAAAAI7n0QEXAAAAADiTx47hAgAAAABnI+ACAAAAAAch4AIAAAAAByHgAgAAAAAHIeACAAAAAAch4AIAAAAAByHgAgAAAAAHIeCqwyTK119/vfTv31+GDx8uTz31lBQUFDiqXtxSUlKSPPTQQ2aC6cTERJk4cWKV63322Wdy1llnSZ8+feT888+XhQsXVlonKytL7rvvPhk8eLAMGDBAbr/9djl+/Hil9dasWSOXXXaZ9O3bV04//XR54403pOLUcvq3Pj5mzBiznq6/bt26Sq917Ngx+etf/2r2p/u9//77JTs7W9zNnDlz5M9//rOMGjXKfF61Pj7//PNKx4V6cJzFixfL1VdfLUOHDpXevXvLuHHj5IknnjCf67IWLFhgvgP6XdDvxKxZsyq9lp5n/v3vf5vzjtannof27NlT73NUY9a7u8nJyTHfi+7du8vGjRvLPcf3wXG++OILc8wr3p5++mnqwAm+/PJLufDCC805YMiQIXLjjTdKfn6+7XnOS441ZcqUKr8Pevvuu+9s63FO8jA68TFqlp6ebhk+fLjlqquusixZssTy2WefWQYNGmSZPn06h66MH3/80TJq1CjLX//6V8vEiRMt5557bqXjM3v2bEv37t0tzz33nGXZsmWWBx980JKYmGhZu3ZtufX+9Kc/mdf67rvvLPPnzzevd/7551sKCwtt6+zbt8/Sv39/y6233mr59ddfLe+++66lV69elrfeeqvca73++uvmcX1e19P1BwwYYNm/f79tnYKCArMPvf30009mv7r/m266ye3q+NJLL7Xceeed5j3o+3366actPXr0sLz44ou2dagHx/rqq68s//73vy1z5861LF++3PLhhx9aBg8ebLn++utt6/z222+Wnj17mu+Afhf0O6HfjTlz5pR7LX1ezzd63tHzz5VXXmkZOXKkJTMzs87nqMasd3f01FNPWU477TRLt27dLBs2bLA9zvfBsWbNmmWOuX429bNmvR0+fJg6aGKvvPKK+fdP/11csWKFOUc9/PDDluzsbPM85yXH27lzZ7nvgd6mTZtmzsWpqalmHc5JnoeAyw6vvfaaubBPS0uzPTZjxgxzsXT06FFH1o9bKS4uti3fc889VQZcZ555puWuu+4q99hll11mufHGG21/r1mzxvzj/PPPP9se2717t7lQ1AtAK71YPP300y0nT560PfbMM89YTjnlFNtj+fn5loEDB5rHrfQ53U7/kbH69ttvzevrfqx0/1qO9evXW9yJ9YRd1gMPPGCOg7WOqIem9+mnn5rPk/WcoUGNfvbL0u/GOeecY/v7yJEj5jyj5xsrPQ/p+eiNN96o8zmqMevd3ezatcsco08++aRSwMX3oWkCrqrOTdRB09HvsV7UL1q0qNp1OC85x9ixYy1Tp061/c05yfPQpdAOS5YskWHDhklUVJTtsXPOOUdKSkpk6dKljmyAdCu+vjV/nA4cOCD79u0zx66sCRMmyLJly2zdn/R4R0REmG5RVp06dZKePXua56x0WbtqBQYGlnutzMxMWbt2ra3LoXYLLLtPXX/8+PGVXkub83U/Vrp/rXPtHuZOYmJiKj2mx06PQ25uLvXgJNbzR2Fhofmsr1ixQs4+++xy6+jnV7sGHjx40Pz9yy+/mPNM2fX0dfSzWfHzW9s5qrG/f+7mX//6l1x++eXSsWPHco9zXnI+6qDpunYmJCTI6NGjq3ye85Jz6HWKnvPPO+888zffB89EwGUHHS9R9kJc6QVJXFxclWMpUP1xVBUveDp37mwuQvUkY11P1/Hx8Sm3ntaB9TU0cDhy5EiletG/dTvretb7iuvpPg8fPmzrt15VHevraDk8oY5Xr14t8fHxEh4eTj00oeLiYjl58qRs3rxZXn75ZRk7dqy54Nm/f7/5zFf1uVRlP7+xsbESGRlZab2yn0t7zlGN+f1zN3PnzpUdO3bIrbfeWuk5zktNR8f1auCuP5S9/vrr5vtBHTSd9evXS7du3eSVV14xP9Do+FL9EUIfV5yXnGP27NkSGhpqvheKc5JnIuCyg7aY6MVLRXoRlJGR4Yh68UjWY1XxWFr/tj6vx7tZs2Y1Hm9r8oGKr6WtVyEhIeVeSx8LCgqqtE/tUluXfbqrVatWyffffy9/+tOfzN/UQ9PRRC6aqOXiiy82wc8zzzzTKHWg65X9XNpzjmrMencneXl58uSTT8qdd95pfnCoiO+D4+lnXxMSafKXN99807SwPP/88/LYY49RB00oOTnZtJp//fXX8vDDD5sfgfSHFf23ITU1lfOSExQVFZlEV/pjnAZdinOSZ/J3dgEAOM7Ro0fNhaZmorrmmms41E1Ms2PqBf+uXbvk1VdflVtuuUXeffdd6qEJ6XHXVsJJkyZx3J1k5MiR5mY1YsQI8yPY+++/b74TaBr6I6P2DnnhhRekR48e5rF+/fqZi/2PPvrI1Aualnb5PnHiRLVZneE5aOGyg/4CXDGds/VXiIpdfVA967GqeCz1F/Wyz+vxriode9njbf0FvuJraR90vcAt+1r6mHbrqrhP/WWvLvt0N/oep06dasb1vPjii7YxdtRD09GLGk2rPnnyZNONR8dt/fjjjw2uA12v7OfSnnNUY9a7uzh06JC88847Jq29vm99r3rBqfRe08TzfXAOHUuoXQq3bt1KHTQR/W7rvwfWYEvp3zqNi/4oxHnJOd0JtQ7KBruckzwTAZcdqhq7oP94a/N8xXETqPk4qorHUv8OCAiQtm3b2tbbu3dvpXmj9DHra2jTe6tWrSq9lnU763rWe3284j5bt24twcHB1daxvk7ZfboTHZt28803m8/pW2+9Va6LGPXgHJqURT/nOk6iXbt2Zrmq70LZOtL7lJSUSl35Ko7Zsucc1Zj17i50ILqOT7vpppvk1FNPNTdri4q2+Oq8ZXwfnI86aBpdunSp9jn9UZLzUtP/Oz1//nyTFEnPwVZ8HzwTAZcddKLMX3/91fZLsHUQtrYYlM3khZrpBV2HDh3MsStLxxfpAF5rtkE93nqBqZnTyl7sbdmyxTxXtl5++uknc0FV9rX0VzxtVVADBw404za0j7SVrj9v3rxKr7Vt2zaTxc1K95+enl5tRidX7hM+bdo0cyGtwZYmyyiLenAOHZiunz1NmqGfde3m+cMPP5RbRz+/msRC11H6q6eeZ/TzaqXfDR2HUfHzW9s5qrHr3R1ogoYPPvig3O0f//iHeW769OlmHAvfB+fQz52fn59pXaEOmm5Mqf6bpq2KVmlpaSapT69evTgvNTGdYFpb2q3ZCa34PngoZ+eldwfWSUWvvvpqMzfN559/buZ6YuLj8nJzc82krXrTYzV69Gjb39b5V6zzXb3wwgtmQtiHHnrIzAuic/9UnAtEt//+++/NRMQ1TXysEy3rBL/vvfdetRMf9+7d2zyv6+n6NU18vGDBAjPfkO7fHSc+1jm3dM6bd955p9Lkitb5yagHx9LJtV999VXzWdLPnNaFnkPOO+88Wx1YJxjV+eD0u6DfCf1u6Ge+LJ1vTs83et7R849+t6qb+Li2c1Rj1ru70vddcR4uvg+OpZ8nPQ/r/E9608+0fg4fe+wx6qAJ6TyMkyZNspxxxhm2Sc0vvfRSMyn78ePHzTqcl5rOLbfcYhkzZoylpKSk0nOckzwPAVcdJs289tprLX379rUMGzbM8uSTT5abcBcWy4EDB8yFTFU3vcixmjlzpmX8+PEmOLIGOBXpxeQ//vEPc9GoQdVtt91W5STTq1evtkyePNkEVKNGjTL/qFc8eenfOjGsPq/r6foVLzCVvr7uR/en+9X9Z2VluV3V6qTO1dWD1pEV9eA4+jm84IILTGCvnyedBPz555+v9HnSCx79Duh3Qb8Tn332WaXX0vOMnm/0vKPnn+uuu86cj+p7jmrMeveUgEvxfXCcRx991Ezkqp9NPQfr5+7999+vdK6mDhxPf/y8++67LYMGDTL1ocHwzp07y63Decnx9EcyPQc/9dRT1a7D98Gz+Oj/nN3KBgAAAACeiDFcAAAAAOAgBFwAAAAA4CAEXAAAAADgIARcAAAAAOAgBFwAAAAA4CAEXAAAAADgIARcAAAAAOAgBFwAAAAA4CAEXAAAr3LvvffK2LFjnbLvN998U84++2wpKSmp8vmayvXJJ5/ImDFjpKCgwIElBAA0NgIuAIDb+OKLL6R79+62W58+feSss86Sf/7zn5KSkiKuLDs7W9566y2ZOnWq+PrW/Z/fiy++WAoLC2XGjBkOKR8AwDH8HfS6AAA4zO233y4JCQmmtWf16tWm9Wfx4sUye/ZsCQkJqXHbRx99VCwWS5PXzueffy5FRUUyceLEKp8vLi42LV8nT56UoKCgSs/rYxdeeKG89957MmXKFPHx8WmCUgMAGooWLgCA2xk1apRccMEFMnnyZHnyySfl2muvlYMHD8pPP/1U7Ta5ubnmPiAgQAIDA8UZrXPaZbBiMLVr1y4TQPXr10+OHDkiffv2leHDh8v06dMrvcY555wjhw4dkuXLlzdhyQEADUHABQBwe0OHDjX3GnRZx2kNGDBA9u/fb7rw6fLdd99te67iWCltWXr//fflvPPOM90U9fVuuOEG2bhxY7n1vv76a9O1T4OiwYMHy5133mmCpNocOHBAtm/fLqeddlq5x7XF6y9/+Yvs3btXHnjgAWnevLk899xzcumll5qyV9S7d2+JioqqMbAEALgWuhQCANyeNTjRYKRsMKNB06BBg+See+6R4ODgare///77TQuUtpxdcsklpnvfqlWrZP369SYAU6+++qq88MILppVJ1zlx4oR89NFHctVVV8lXX30lERER1b7+2rVrzX1iYmK5xzXQSkpKkqefftoEe2+88YZMmDChxveqr7FmzRo7jwwAwNkIuAAAbkcTUGjAo2O4NPh4+eWXTUB1+umn29bR5zQj4N/+9rcaX0u752mwpd36tJXJ6k9/+pNtrJd243vxxRdl2rRpcsstt9jWOfPMM+Wiiy6Sjz/+uNzjFe3Zs8fc67izsqzJM/Lz8+1+723btiXgAgA3QsAFAHA71113Xbm/27RpY1qJ4uPjyz1+xRVX1Ppa8+bNMwkobrvttkrPWRNT/Pjjj6bbobZuaaBnpV0A27dvLytWrKgx4EpPTxd/f38JCwsr93jHjh1Nd8fHH3/cdDnMycmRY8eOVXofZWlLmgZoeXl5tSYIAQA4HwEXAMDtPPTQQyZY8fPzM0GPLldMta4BTsuWLe3qjtiiRYty3REr2rdvn2nt0hatqui+6kPLrKniX3/9dZkzZ44JzLRbY9euXeWuu+6qcl4ua6sbWQoBwD0QcAEA3I4mrbCOraqOZiKsz3xXVdHWLQ1wdOJiDfIqCg0NrXF7DeZ0TJl2hQwPDy/3nP6t3R71pgHW3//+dxOA3XrrrWaMmI5BKyszM9O0bNU0Jg0A4DoIuAAAXq1du3byyy+/mNal6lq5dB1tWdIxWNqaVledOnWyZVHs0aNHjevquDPNgKgZDefPn18p4NLXsL4eAMD1kRYeAODVtJugBlMvvfRStd33dB1t2dJ1Kk6arH+npaXVuA8dp6U2bdpU7nFt8dKWr4o0S6KqagLkLVu2yMCBA+16bwAA56OFCwDg1XTOLZ1E+cMPPzQp2keOHGm6EK5evVqGDBkiV199tWnh0gyFzzzzjMlYeMYZZ5gEGNrapK1QOm+WpqCvKbNgt27dZNmyZSalvNXmzZvNeLRJkyZJr169TGZFTTH/7rvvmnFh2tpVlgZs2hI3btw4hx4TAEDjIeACAHi9J554Qrp37y6ff/65PPXUU9KsWTMzybC1ZUrddNNN0qFDB3nvvfdMGnqlSTmGDx9eZXKLijSo0nm8NMOgdfxVz549TbD3ww8/mOQZGRkZ8thjj5ngTMeLVex+OHfuXGndurVtomcAgOvzsVTsGwEAABpdVlaWaRm7++67ZfLkyVWuo4HbggULqnxOW7/0+alTp8q1115LDQGAm2AMFwAATUBbzbTb4dtvv226LNbVrFmzTDdDe+YWAwC4Dlq4AABwEdpdseKkzgAA90bABQAAAAAOQpdCAAAAAHAQAi4AAAAAcBACLgAAAABwEAIuAAAAAHAQAi4AAAAAcBACLgAAAABwEAIuAAAAAHAQAi4AAAAAcBACLgAAAAAQx/h/kyOfkOh4JA4AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skewness: 1.88\n"
+     ]
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 5))\n",
+    "sns.histplot(df_ames['SalePrice'], bins=50, kde=True, color='purple')\n",
+    "plt.title('Distribution of Sale Price')\n",
+    "plt.xlabel('Price ($)')\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"Skewness: {df_ames['SalePrice'].skew():.2f}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7bfa5837",
diff --git a/src/S01 Introduction to Machine Learning/S01C04 Train-Test Splits and Cross-Validation.ipynb b/src/S01 Introduction to Machine Learning/S01C04 Train-Test Splits and Cross-Validation.ipynb
index f268a4f..c8272ab 100644
--- a/src/S01 Introduction to Machine Learning/S01C04 Train-Test Splits and Cross-Validation.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C04 Train-Test Splits and Cross-Validation.ipynb	
@@ -74,12 +74,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "0dd627f9",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.datasets import fetch_california_housing\n",
     "import pandas as pd\n",
@@ -110,10 +121,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "ed4f4a79",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training samples: 16512\n",
+      "Testing samples:  4128\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "\n",
@@ -138,12 +158,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "0e31c257",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(12, 5))\n",
     "\n",
@@ -170,12 +201,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "12e98cf0",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Single Split Test R²: 0.4589\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.linear_model import LinearRegression\n",
     "\n",
@@ -200,10 +239,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "58a5ad1d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Individual CV Scores: [0.46458389 0.35374043 0.46252448 0.29716971 0.52896687]\n",
+      "Average R²: 0.4214 (+/- 0.0838)\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.model_selection import cross_val_score\n",
     "import numpy as np\n",
@@ -264,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "92460a3f",
    "metadata": {
     "lines_to_next_cell": 2
@@ -294,12 +342,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "5d08bfd2",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test R²: 0.4589\n",
+      "CV R²: 0.4214\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.pipeline import Pipeline\n",
     "from sklearn.preprocessing import StandardScaler\n",
@@ -359,6 +416,42 @@
     "The Ames dataset contains missing values (`NaN`). Linear Regression cannot handle these. We must drop rows with missing data in our selected columns."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "03993d25",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data shape after cleaning: (1460, 4)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import fetch_openml\n",
+    "\n",
+    "# Load data\n",
+    "ames = fetch_openml(name='house_prices', as_frame=True, parser=\"auto\")\n",
+    "df_ames = ames.frame\n",
+    "\n",
+    "# Define features and target\n",
+    "features = ['GrLivArea', 'OverallQual', 'YearBuilt', 'TotalBsmtSF']\n",
+    "target = 'SalePrice'\n",
+    "\n",
+    "# Create a subset and DROP missing values\n",
+    "df_clean = df_ames[features + [target]].dropna()\n",
+    "\n",
+    "X_ames = df_clean[features]\n",
+    "y_ames = df_clean[target]\n",
+    "\n",
+    "print(f\"Data shape after cleaning: {X_ames.shape}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7f866509",
@@ -369,6 +462,34 @@
     "Perform an 80/20 split and train a Linear Regression model."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d3c14e12",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ames Single Split R²: 0.7886\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Split\n",
+    "X_train_a, X_test_a, y_train_a, y_test_a = train_test_split(X_ames, y_ames, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Train\n",
+    "model = LinearRegression()\n",
+    "model.fit(X_train_a, y_train_a)\n",
+    "\n",
+    "# Score\n",
+    "print(f\"Ames Single Split R²: {model.score(X_test_a, y_test_a):.4f}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "8214ff0f",
@@ -379,6 +500,33 @@
     "Run 5-fold and 10-fold cross-validation to see if the performance holds up."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "57f12101",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5-Fold CV Mean R²: 0.7494\n",
+      "10-Fold CV Mean R²: 0.7567\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 5-Fold CV\n",
+    "cv5 = cross_val_score(model, X_ames, y_ames, cv=5, scoring=\"r2\")\n",
+    "print(f\"5-Fold CV Mean R²: {np.mean(cv5):.4f}\")\n",
+    "\n",
+    "# 10-Fold CV\n",
+    "cv10 = cross_val_score(model, X_ames, y_ames, cv=10, scoring=\"r2\")\n",
+    "print(f\"10-Fold CV Mean R²: {np.mean(cv10):.4f}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4c0708ea",
@@ -398,9 +546,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S01 Introduction to Machine Learning/S01C05 Data Preprocessing.ipynb b/src/S01 Introduction to Machine Learning/S01C05 Data Preprocessing.ipynb
index d076393..e04a362 100644
--- a/src/S01 Introduction to Machine Learning/S01C05 Data Preprocessing.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C05 Data Preprocessing.ipynb	
@@ -37,12 +37,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "e77be15a",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 1309 entries, 0 to 1308\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column    Non-Null Count  Dtype   \n",
+      "---  ------    --------------  -----   \n",
+      " 0   fare      1308 non-null   float64 \n",
+      " 1   sex       1309 non-null   category\n",
+      " 2   age       1046 non-null   float64 \n",
+      " 3   survived  1309 non-null   category\n",
+      "dtypes: category(2), float64(2)\n",
+      "memory usage: 23.2 KB\n",
+      "None\n",
+      "\n",
+      "First 5 rows:\n",
+      "       fare     sex      age survived\n",
+      "0  211.3375  female  29.0000        1\n",
+      "1  151.5500    male   0.9167        1\n",
+      "2  151.5500  female   2.0000        0\n",
+      "3  151.5500    male  30.0000        0\n",
+      "4  151.5500  female  25.0000        0\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.datasets import fetch_openml\n",
     "import pandas as pd\n",
@@ -72,12 +99,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "3adf1589",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import seaborn as sns\n",
     "import matplotlib.pyplot as plt\n",
@@ -119,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "d3d9c086",
    "metadata": {
     "lines_to_next_cell": 2
@@ -154,12 +192,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "7f4b539d",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Missing values in Train after imputation: fare    0\n",
+      "age     0\n",
+      "sex     0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.impute import SimpleImputer\n",
     "\n",
@@ -188,12 +237,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "1643a3d4",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         fare       age\n",
+      "772 -0.495582 -0.981390\n",
+      "543 -0.445269  0.506426\n",
+      "289  0.890700 -0.903084\n",
+      "10   3.747624  1.367793\n",
+      "147  0.171035  0.000000\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.preprocessing import StandardScaler\n",
     "\n",
@@ -218,12 +280,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "fe2085bd",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         fare       age  sex_male\n",
+      "772 -0.495582 -0.981390       1.0\n",
+      "543 -0.445269  0.506426       1.0\n",
+      "289  0.890700 -0.903084       0.0\n",
+      "10   3.747624  1.367793       1.0\n",
+      "147  0.171035  0.000000       1.0\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.preprocessing import OneHotEncoder\n",
     "\n",
@@ -260,12 +335,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "3d30aedc",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.7519083969465649\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.linear_model import LogisticRegression\n",
     "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
@@ -312,6 +405,45 @@
     "-   Define `X` (Sepal Length, Sepal Width, Petal Width, Species) and `y` (Petal Length)."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e32695d1",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   sepal length (cm)  sepal width (cm)  petal width (cm) species_name\n",
+      "0                5.1               3.5               0.2       setosa\n",
+      "1                4.9               3.0               0.2       setosa\n",
+      "2                4.7               3.2               0.2       setosa\n",
+      "3                4.6               3.1               0.2       setosa\n",
+      "4                5.0               3.6               0.2       setosa\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "# Load\n",
+    "iris = load_iris(as_frame=True)\n",
+    "df_iris = iris.frame\n",
+    "\n",
+    "# Define Features and Target\n",
+    "# Note: 'species' is currently an integer (0,1,2).\n",
+    "# For the sake of this exercise, let's map it back to names so you have to Encode it.\n",
+    "df_iris['species_name'] = df_iris['target'].map({0:'setosa', 1:'versicolor', 2:'virginica'})\n",
+    "\n",
+    "X_iris = df_iris[['sepal length (cm)', 'sepal width (cm)', 'petal width (cm)', 'species_name']]\n",
+    "y_iris = df_iris['petal length (cm)']\n",
+    "\n",
+    "print(X_iris.head())"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "342f0104",
@@ -324,6 +456,52 @@
     "3.  One-Hot Encode the `species_name` column."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4b66c290",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed Data Shape: (120, 5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1. Split\n",
+    "X_train_i, X_test_i, y_train_i, y_test_i = train_test_split(X_iris, y_iris, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# 2. Scale Numerics\n",
+    "num_cols = ['sepal length (cm)', 'sepal width (cm)', 'petal width (cm)']\n",
+    "scaler_i = StandardScaler()\n",
+    "\n",
+    "X_train_i[num_cols] = scaler_i.fit_transform(X_train_i[num_cols])\n",
+    "X_test_i[num_cols] = scaler_i.transform(X_test_i[num_cols])\n",
+    "\n",
+    "# 3. Encode Categorical\n",
+    "encoder_i = OneHotEncoder(drop='first', sparse_output=False)\n",
+    "\n",
+    "# Encode species\n",
+    "species_train = encoder_i.fit_transform(X_train_i[['species_name']])\n",
+    "species_test = encoder_i.transform(X_test_i[['species_name']])\n",
+    "\n",
+    "# Create DataFrames for encoded features\n",
+    "cols_enc = encoder_i.get_feature_names_out(['species_name'])\n",
+    "df_enc_train = pd.DataFrame(species_train, columns=cols_enc, index=X_train_i.index)\n",
+    "df_enc_test = pd.DataFrame(species_test, columns=cols_enc, index=X_test_i.index)\n",
+    "\n",
+    "# Join and Drop\n",
+    "X_train_ready = pd.concat([X_train_i.drop(columns=['species_name']), df_enc_train], axis=1)\n",
+    "X_test_ready = pd.concat([X_test_i.drop(columns=['species_name']), df_enc_test], axis=1)\n",
+    "\n",
+    "print(\"Processed Data Shape:\", X_train_ready.shape)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "15b4919d",
@@ -334,6 +512,36 @@
     "Train a Linear Regression model and verify the Mean Squared Error (MSE)."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "90f5a650",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 Score: 0.9732\n",
+      "MSE: 0.0879\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "\n",
+    "regressor = LinearRegression()\n",
+    "regressor.fit(X_train_ready, y_train_i)\n",
+    "\n",
+    "y_pred_i = regressor.predict(X_test_ready)\n",
+    "\n",
+    "print(f\"R2 Score: {r2_score(y_test_i, y_pred_i):.4f}\")\n",
+    "print(f\"MSE: {mean_squared_error(y_test_i, y_pred_i):.4f}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "06954b8d",
@@ -354,9 +562,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S01 Introduction to Machine Learning/S01C06 Feature Engineering.ipynb b/src/S01 Introduction to Machine Learning/S01C06 Feature Engineering.ipynb
index de3a868..9a23d27 100644
--- a/src/S01 Introduction to Machine Learning/S01C06 Feature Engineering.ipynb	
+++ b/src/S01 Introduction to Machine Learning/S01C06 Feature Engineering.ipynb	
@@ -63,12 +63,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "b043b41b",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline R² Score: 0.5758\n"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -109,10 +117,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "f28053c8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   MedInc  MedInc_Squared  AveRooms  Rooms_per_person\n",
+      "0  8.3252       69.308955  6.984127          2.732919\n",
+      "1  8.3014       68.913242  6.238137          2.956685\n",
+      "2  7.2574       52.669855  8.288136          2.957661\n",
+      "3  5.6431       31.844578  5.817352          2.283154\n",
+      "4  3.8462       14.793254  6.281853          2.879646\n"
+     ]
+    }
+   ],
    "source": [
     "# 1. Create Ratio Feature: Rooms per Person\n",
     "df['Rooms_per_person'] = df['AveRooms'] / df['AveOccup']\n",
@@ -143,10 +164,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "16f6a8c3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline R²:   0.5758\n",
+      "Engineered R²: 0.6388\n",
+      "Improvement:   6.30%\n"
+     ]
+    }
+   ],
    "source": [
     "# Prepare new X\n",
     "X_eng = df.drop(columns=[data.target.name])\n",
@@ -196,9 +227,37 @@
     "-   Logic: This acts as a proxy for \"Apartment vs. House\". High ratio might mean small apartments."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "646bf1b5",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   AveBedrms  AveRooms  Bedrooms_Ratio\n",
+      "0   1.023810  6.984127        0.146591\n",
+      "1   0.971880  6.238137        0.155797\n",
+      "2   1.073446  8.288136        0.129516\n",
+      "3   1.073059  5.817352        0.184458\n",
+      "4   1.081081  6.281853        0.172096\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create the feature\n",
+    "df['Bedrooms_Ratio'] = df['AveBedrms'] / df['AveRooms']\n",
+    "\n",
+    "print(df[['AveBedrms', 'AveRooms', 'Bedrooms_Ratio']].head())"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "id": "e4292687",
+   "id": "2bc41cee",
    "metadata": {},
    "source": [
     "### Log Transformation\n",
@@ -206,8 +265,41 @@
     "Income distributions are often \"skewed\" (a few very rich people, many average). Linear models prefer \"Normal\" (bell-curve) distributions.\n",
     "\n",
     "-   Create `Log_Income` = `log(MedInc + 1)`.\n",
-    "-   **Note**: We use `np.log1p(x)` which calculates `log(1+x)` accurately.\n",
+    "-   **Note**: We use `np.log1p(x)` which calculates `log(1+x)` accurately."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "742b04d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation (Raw Income): 0.6881\n",
+      "Correlation (Log Income): 0.6699\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create the feature\n",
+    "df['Log_Income'] = np.log1p(df['MedInc'])\n",
     "\n",
+    "# Check correlation with Target\n",
+    "corr_raw = df['MedInc'].corr(df['MedHouseVal'])\n",
+    "corr_log = df['Log_Income'].corr(df['MedHouseVal'])\n",
+    "\n",
+    "print(f\"Correlation (Raw Income): {corr_raw:.4f}\")\n",
+    "print(f\"Correlation (Log Income): {corr_log:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4292687",
+   "metadata": {},
+   "source": [
     "**Note**: If the correlation increases, the linear relationship is stronger!"
    ]
   },
@@ -221,6 +313,38 @@
     "Train a final model with all your new features combined."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9df1909a",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final Model R²: 0.6606\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define X with all new columns\n",
+    "X_final = df.drop(columns=['MedHouseVal']) \n",
+    "y_final = df['MedHouseVal']\n",
+    "\n",
+    "# Split\n",
+    "X_train_f, X_test_f, y_train_f, y_test_f = train_test_split(X_final, y_final, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Train\n",
+    "final_model = LinearRegression()\n",
+    "final_model.fit(X_train_f, y_train_f)\n",
+    "final_score = final_model.score(X_test_f, y_test_f)\n",
+    "\n",
+    "print(f\"Final Model R²: {final_score:.4f}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4514c630",
@@ -241,9 +365,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {

From 31ee5361668ce48ca24c60feac453043d8fef2f8 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Mon, 23 Feb 2026 11:29:43 +0100
Subject: [PATCH 75/81] Session 02: Supervised Learning - Regression and
 Evaluation

---
 ...d Learning - Regression and Evaluation.pdf | Bin 471543 -> 471551 bytes
 ...d Learning - Regression and Evaluation.tex | 158 ++++-----
 .../S02C01 Simple Linear Regression.ipynb     | 147 +++++++-
 .../S02C02 Regression Diagnostics.ipynb       | 285 +++++++++++++--
 .../S02C03 Advanced Evaluation Metrics.ipynb  | 160 ++++++++-
 .../S02C04 Multiple Linear Regression.ipynb   | 332 +++++++++++++++++-
 .../S02C05 Regularized Regression.ipynb       | 202 ++++++++++-
 .../S02C06 Nonlinearities in Regression.ipynb | 154 +++++++-
 ...erfitting, and Hyperparameter Tuning.ipynb | 165 ++++++++-
 9 files changed, 1423 insertions(+), 180 deletions(-)

diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02 Supervised Learning - Regression and Evaluation.pdf b/src/S02 Supervised Learning - Regression and Evaluation/S02 Supervised Learning - Regression and Evaluation.pdf
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diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02 Supervised Learning - Regression and Evaluation.tex b/src/S02 Supervised Learning - Regression and Evaluation/S02 Supervised Learning - Regression and Evaluation.tex
index f0f5b4c..3a7e3f4 100644
--- a/src/S02 Supervised Learning - Regression and Evaluation/S02 Supervised Learning - Regression and Evaluation.tex	
+++ b/src/S02 Supervised Learning - Regression and Evaluation/S02 Supervised Learning - Regression and Evaluation.tex	
@@ -1,4 +1,4 @@
-% Created 2026-02-01 Sun 10:35
+% Created 2026-02-23 Mon 10:03
 % Intended LaTeX compiler: pdflatex
 \documentclass[hyperref={pdfpagelabels=false},aspectratio=169]{beamer}
 \usepackage[utf8]{inputenc}
@@ -202,17 +202,17 @@
 \end{frame}
 
 \section{Introduction to Regression}
-\label{sec:orged6a9f7}
-\begin{frame}[label={sec:org6b1927d}]{Introduction to Regression}
-\begin{definition}[Regression]\label{sec:org762680b}
+\label{sec:org9f6e625}
+\begin{frame}[label={sec:org4f99e08}]{Introduction to Regression}
+\begin{definition}[Regression]\label{sec:orgb36f0d3}
 \pause
 \alert{Regression} is a statistical method used to model the relationship between a dependent variable and one or more independent variables.
 \end{definition}
 \end{frame}
 \section{Simple Linear Regression}
-\label{sec:orgfb00269}
-\begin{frame}[label={sec:org52fb825}]{Simple Linear Regression}
-\begin{definition}[Simple Linear Regression]\label{sec:orgca6daa0}
+\label{sec:orgdc904f4}
+\begin{frame}[label={sec:org5f48255}]{Simple Linear Regression}
+\begin{definition}[Simple Linear Regression]\label{sec:orga9f5655}
 \pause
 \alert{Simple Linear Regression} is a method to model the relationship between two variables by fitting a linear equation to observed data.
 \pause
@@ -228,10 +228,10 @@ \section{Simple Linear Regression}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org917409d}]{Simple Linear Regression}
+\begin{frame}[label={sec:org8b5c5cd}]{Simple Linear Regression}
 A Simple Linear Regression Machine Learning model will learn the coefficients \(\beta_0\) and \(\beta_1\) from the training data to minimize the difference between the predicted values and the actual values.
 \end{frame}
-\begin{frame}[label={sec:orgfb99e7d}]{Assumptions of Simple Linear Regression}
+\begin{frame}[label={sec:orgd689b2e}]{Assumptions of Simple Linear Regression}
 \begin{itemize}[<+->]
 \item Linearity: The relationship between the independent and dependent variable is linear.
 \item Independence: Observations are independent of each other.
@@ -240,9 +240,9 @@ \section{Simple Linear Regression}
 \end{itemize}
 \end{frame}
 \section{Evaluation Metrics for Regression}
-\label{sec:orga0f8934}
-\begin{frame}[label={sec:org3adfd86}]{Evaluation Metrics for Regression}
-\begin{definition}[Common Metrics for Regression]\label{sec:orge65a72a}
+\label{sec:orga019f9d}
+\begin{frame}[label={sec:org9bce8b5}]{Evaluation Metrics for Regression}
+\begin{definition}[Common Metrics for Regression]\label{sec:orgf49a57b}
 \pause
 Common metrics to evaluate regression models include:
 \begin{itemize}
@@ -254,8 +254,8 @@ \section{Evaluation Metrics for Regression}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org90fd26d}]{Mean Absolute Error (MAE)}
-\begin{definition}[Mean Absolute Error (MAE)]\label{sec:orgeb2e92d}
+\begin{frame}[label={sec:org1fa9477}]{Mean Absolute Error (MAE)}
+\begin{definition}[Mean Absolute Error (MAE)]\label{sec:org9a3c227}
 \pause
 \alert{Mean Absolute Error (MAE)} is the average of the absolute differences between the predicted and the actual values:
 $$\text{MAE} = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i|$$
@@ -270,8 +270,8 @@ \section{Evaluation Metrics for Regression}
 MAE is a linear score, which can be used when all errors are equally important; it is also less sensitive to outliers compared to MSE.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org4fb08d5}]{Mean Squared Error (MSE)}
-\begin{definition}[Mean Squared Error (MSE)]\label{sec:orge7b0e78}
+\begin{frame}[label={sec:orgbb33f2d}]{Mean Squared Error (MSE)}
+\begin{definition}[Mean Squared Error (MSE)]\label{sec:org3c26192}
 \pause
 \alert{Mean Squared Error (MSE)} is the average of the squared differences between the predicted and the actual values:
 $$\text{MSE} = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$$
@@ -286,8 +286,8 @@ \section{Evaluation Metrics for Regression}
 MSE is more sensitive to outliers than MAE because it squares the errors, which can disproportionately affect the metric if there are large errors; however, it is useful when larger errors are more significant.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgd850ab0}]{Root Mean Squared Error (RMSE)}
-\begin{definition}[Root Mean Squared Error (RMSE)]\label{sec:org7b318b8}
+\begin{frame}[label={sec:org8015413}]{Root Mean Squared Error (RMSE)}
+\begin{definition}[Root Mean Squared Error (RMSE)]\label{sec:org6e93430}
 \pause
 \alert{Root Mean Squared Error (RMSE)} is the square root of the average of the squared differences between the predicted and the actual values:
 $$\text{RMSE} = \sqrt{\text{MSE}} = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2}$$
@@ -302,8 +302,8 @@ \section{Evaluation Metrics for Regression}
 RMSE is in the same units as the dependent variable, making it interpretable; it is also sensitive to outliers, similar to MSE.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org79ba332}]{R-squared (\(R^2\))}
-\begin{definition}[R-squared (\(R^2\))]\label{sec:orgbb57536}
+\begin{frame}[label={sec:org7d438c3}]{R-squared (\(R^2\))}
+\begin{definition}[R-squared (\(R^2\))]\label{sec:org19f9f24}
 \pause
 \alert{R-squared (\(R^2\))} is a statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model:
 $$R^2 = 1 - \frac{\text{SS}_{\text{res}}}{\text{SS}_{\text{tot}}}$$
@@ -320,8 +320,8 @@ \section{Evaluation Metrics for Regression}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orga45f723}]{Adjusted R-squared}
-\begin{definition}[Adjusted R-squared]\label{sec:orgaf103c7}
+\begin{frame}[label={sec:orga9622cf}]{Adjusted R-squared}
+\begin{definition}[Adjusted R-squared]\label{sec:orgcf76cc3}
 \pause
 \alert{Adjusted R-squared} adjusts the \(R^2\) value for the number of predictors in the model, providing a more accurate measure when comparing models with different numbers of predictors:
 $$\text{Adjusted } R^2 = 1 - \left(1 - R^2\right) \frac{n - 1}{n - p - 1}$$
@@ -335,17 +335,17 @@ \section{Evaluation Metrics for Regression}
 \end{definition}
 \end{frame}
 \section{Regression Diagnostics}
-\label{sec:org91035e8}
-\begin{frame}[label={sec:orge23eb2e}]{Regression Diagnostics}
-\begin{definition}[Regression Diagnostics]\label{sec:org6960cf4}
+\label{sec:org3efe199}
+\begin{frame}[label={sec:org4b6602b}]{Regression Diagnostics}
+\begin{definition}[Regression Diagnostics]\label{sec:orge30d3ea}
 \pause
 \alert{Regression Diagnostics} is the process of verifying that a regression model's assumptions are met. A high \(R^2\) does not guarantee a valid model; if assumptions are violated, predictions and confidence intervals can be unreliable.
 \pause
 The primary tool for diagnostics is analyzing the \alert{Residuals} (the errors: \(y - \hat{y}\)).
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgacd5959}]{Key Diagnostic Checks}
-\begin{definition}[Key Diagnostic Checks]\label{sec:org165f384}
+\begin{frame}[label={sec:org454b566}]{Key Diagnostic Checks}
+\begin{definition}[Key Diagnostic Checks]\label{sec:org0441068}
 \pause
 \begin{itemize}[<+->]
 \item \alert{Linearity}: Residuals should be randomly scattered around 0. A U-shape indicates non-linearity.
@@ -355,8 +355,8 @@ \section{Regression Diagnostics}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org34568f7}]{Residual Plot}
-\begin{definition}[Residual Plot]\label{sec:org0f80518}
+\begin{frame}[label={sec:org5589815}]{Residual Plot}
+\begin{definition}[Residual Plot]\label{sec:org396cb13}
 \pause
 A \alert{Residual Plot} graphs the residuals (\(y - \hat{y}\)) against the predicted values (\(\hat{y}\)).
 \pause
@@ -368,8 +368,8 @@ \section{Regression Diagnostics}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgd70c460}]{Q-Q Plot (Normality Check)}
-\begin{definition}[Q-Q Plot]\label{sec:org90d3ead}
+\begin{frame}[label={sec:orgd9a6854}]{Q-Q Plot (Normality Check)}
+\begin{definition}[Q-Q Plot]\label{sec:org50b8b94}
 \pause
 A \alert{Q-Q Plot} (Quantile-Quantile Plot) compares the distribution of residuals against a theoretical Normal distribution.
 \pause
@@ -380,8 +380,8 @@ \section{Regression Diagnostics}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org39f4290}]{Influential Points (Cook's Distance)}
-\begin{definition}[Cook's Distance]\label{sec:orge5dbce2}
+\begin{frame}[label={sec:org8905b7e}]{Influential Points (Cook's Distance)}
+\begin{definition}[Cook's Distance]\label{sec:org39a3e95}
 \pause
 \alert{Cook's Distance} measures how much the regression coefficients change when a single observation is removed. It identifies influential outliers that disproportionately affect the model.
 \pause
@@ -389,9 +389,9 @@ \section{Regression Diagnostics}
 \end{definition}
 \end{frame}
 \section{Multiple Linear Regression}
-\label{sec:orga3c7d0d}
-\begin{frame}[label={sec:orgb60cc7d}]{Multiple Linear Regression}
-\begin{definition}[Multiple Linear Regression]\label{sec:org3c215ea}
+\label{sec:org4b98bfc}
+\begin{frame}[label={sec:org11e832e}]{Multiple Linear Regression}
+\begin{definition}[Multiple Linear Regression]\label{sec:org9b2d071}
 \pause
 \alert{Multiple Linear Regression} is an extension of simple linear regression that models the relationship between a dependent variable and multiple independent variables. It is used when there are two or more predictors.
 \pause
@@ -407,8 +407,8 @@ \section{Multiple Linear Regression}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org700e4bc}]{Assumptions of Multiple Linear Regression}
-\begin{definition}[Assumptions of Multiple Linear Regression]\label{sec:org64dc7af}
+\begin{frame}[label={sec:org1d8ba0a}]{Assumptions of Multiple Linear Regression}
+\begin{definition}[Assumptions of Multiple Linear Regression]\label{sec:org0ddecc1}
 \pause
 The assumptions of multiple linear regression are similar to those of simple linear regression:
 \begin{itemize}
@@ -421,17 +421,17 @@ \section{Multiple Linear Regression}
 \end{definition}
 \end{frame}
 \section{Regularized Regression}
-\label{sec:orgc15ca4b}
-\begin{frame}[label={sec:org087d73b}]{How do Machine Learning Models Learn?}
-\begin{definition}[How do Machine Learning Models Learn?]\label{sec:orgdebeb90}
+\label{sec:orgcd5fbaa}
+\begin{frame}[label={sec:org1836379}]{How do Machine Learning Models Learn?}
+\begin{definition}[How do Machine Learning Models Learn?]\label{sec:org0304274}
 \pause
 Machine Learning models learn by adjusting their parameters (coefficients) to minimize the difference between the predicted values and the actual values in the training data, also known as the \alert{cost function} or \alert{loss function}.
 \pause
 This process is often done using optimization algorithms that iteratively update the parameters based on the loss function, which measures the error of the predictions.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org889e11c}]{Cost Function}
-\begin{definition}[Cost Function]\label{sec:org4ddcbd2}
+\begin{frame}[label={sec:org9353970}]{Cost Function}
+\begin{definition}[Cost Function]\label{sec:org1a99b85}
 \pause
 The \alert{cost function} (or \alert{loss function}) is a mathematical function that quantifies the difference between the predicted values and the actual values in the training data. It is used to guide the learning process of the model.
 \pause
@@ -442,8 +442,8 @@ \section{Regularized Regression}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org6236599}]{Cost Function: Mean Squared Error (MSE)}
-\begin{definition}[Cost Function: Mean Squared Error (MSE)]\label{sec:org8c5f5cd}
+\begin{frame}[label={sec:org888997b}]{Cost Function: Mean Squared Error (MSE)}
+\begin{definition}[Cost Function: Mean Squared Error (MSE)]\label{sec:org0d69b4b}
 \pause
 \alert{Mean Squared Error (MSE)} is a common cost function used in regression tasks. It measures the average of the squares of the errors, which are the differences between the predicted values and the actual values:
 $$J_\text{MSE}(w) = \frac{1}{2m} \sum_{i=1}^{m} (f(x_i, w) - y_i)^2$$
@@ -457,8 +457,8 @@ \section{Regularized Regression}
 MSE is sensitive to outliers, and is, therefore, useful when larger errors are more significant.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgb2843fb}]{Cost Function: Mean Absolute Error (MAE)}
-\begin{definition}[Cost Function: Mean Absolute Error (MAE)]\label{sec:org35c2d51}
+\begin{frame}[label={sec:org4fbf3aa}]{Cost Function: Mean Absolute Error (MAE)}
+\begin{definition}[Cost Function: Mean Absolute Error (MAE)]\label{sec:orgc859ba9}
 \pause
 \alert{Mean Absolute Error (MAE)} is another common cost function used in regression tasks. It measures the average of the absolute differences between the predicted values and the actual values:
 $$J_\text{MAE}(w) = \frac{1}{m} \sum_{i=1}^{m} |f(x_i, w) - y_i|$$
@@ -472,8 +472,8 @@ \section{Regularized Regression}
 MAE is less sensitive to outliers compared to MSE; useful when all errors are equally important.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgc1a5f1c}]{Gradient Descent}
-\begin{definition}[Gradient Descent]\label{sec:orgd119251}
+\begin{frame}[label={sec:orgafab291}]{Gradient Descent}
+\begin{definition}[Gradient Descent]\label{sec:orgc3c0d73}
 \pause
 \alert{Gradient Descent} is an optimization algorithm used to minimize the cost function by iteratively updating the model parameters in the direction of the steepest descent of the cost function.
 \pause
@@ -489,8 +489,8 @@ \section{Regularized Regression}
 Gradient descent continues until convergence, which is when the change in the cost function is below a certain threshold or a maximum number of iterations is reached.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgdd8ec35}]{Gradient Descent Algorithm}
-\begin{definition}[Gradient Descent Algorithm]\label{sec:org6cbf71f}
+\begin{frame}[label={sec:orgeb557e2}]{Gradient Descent Algorithm}
+\begin{definition}[Gradient Descent Algorithm]\label{sec:org7aca36e}
 \pause
 The \alert{Gradient Descent Algorithm} consists of the following steps:
 \begin{enumerate}[<+->]
@@ -503,8 +503,8 @@ \section{Regularized Regression}
 \end{enumerate}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgd4705a1}]{Overfitting and Underfitting}
-\begin{definition}[Overfitting and Underfitting]\label{sec:orgceddfb7}
+\begin{frame}[label={sec:orgf558ebc}]{Overfitting and Underfitting}
+\begin{definition}[Overfitting and Underfitting]\label{sec:org2570ef4}
 \pause
 \alert{Overfitting} occurs when a model learns the noise in the training data instead of the underlying pattern, resulting in poor generalization to unseen data. It typically happens when the model is too complex relative to the amount of training data.
 
@@ -515,15 +515,15 @@ \section{Regularized Regression}
 To mitigate overfitting, techniques such as \alert{regularization}, \alert{cross-validation}, and \alert{pruning} can be used. To address underfitting, more complex models or additional features can be considered.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgbd60243}]{Regularization}
-\begin{definition}[Regularization]\label{sec:orgcc9440a}
+\begin{frame}[label={sec:org930ac5e}]{Regularization}
+\begin{definition}[Regularization]\label{sec:org6ced9db}
 \pause
 \alert{Regularization} is a technique used in machine learning to prevent overfitting by adding a penalty term to the cost function. This penalty discourages overly complex models by constraining the size of the model parameters (coefficients).
 \pause
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgb9a23e1}]{Types of Regularized Regression}
-\begin{definition}[Types of Regularized Regression]\label{sec:orgff4c053}
+\begin{frame}[label={sec:org6a3cc8b}]{Types of Regularized Regression}
+\begin{definition}[Types of Regularized Regression]\label{sec:orgce8d117}
 There are two common types of regularized regression:
 \begin{itemize}
 \item \alert{Ridge Regression (L2 Regularization)}
@@ -531,8 +531,8 @@ \section{Regularized Regression}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgd23f2fa}]{Lasso Regression (L1 Regularization)}
-\begin{definition}[Lasso Regression (L1 Regularization)]\label{sec:org79c3126}
+\begin{frame}[label={sec:org927410d}]{Lasso Regression (L1 Regularization)}
+\begin{definition}[Lasso Regression (L1 Regularization)]\label{sec:orga0ac71d}
 \pause
 \alert{Lasso Regression} adds a penalty equal to the absolute value of the magnitude of coefficients to the loss function. The objective function for lasso regression is:
 $$J_\text{Lasso}(w) = \frac{1}{2m} \sum_{i=1}^{m} (f(x_i, w) - y_i)^2 + \lambda \sum_{j=1}^{p} |w_j|$$
@@ -545,8 +545,8 @@ \section{Regularized Regression}
 Lasso regression can shrink some coefficients to exactly zero, effectively performing variable selection and resulting in a simpler model.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orge185f78}]{Ridge Regression (L2 Regularization)}
-\begin{definition}[Ridge Regression (L2 Regularization)]\label{sec:org74204ab}
+\begin{frame}[label={sec:orgfef541e}]{Ridge Regression (L2 Regularization)}
+\begin{definition}[Ridge Regression (L2 Regularization)]\label{sec:org1a65ad7}
 \pause
 \alert{Ridge Regression} adds a penalty equal to the square of the magnitude of coefficients to the loss function. The objective function for ridge regression is:
 $$J_\text{Ridge}(w) = \frac{1}{2m} \sum_{i=1}^{m} (f(x_i, w) - y_i)^2 + \frac{\lambda}{2m} \sum_{j=1}^{p} w_j^2$$
@@ -560,17 +560,17 @@ \section{Regularized Regression}
 \end{definition}
 \end{frame}
 \section{Nonlinearities in Regression}
-\label{sec:orgb900328}
-\begin{frame}[label={sec:orge433515}]{Nonlinearities in Regression}
-\begin{definition}[Nonlinearities in Regression]\label{sec:orgda6c12e}
+\label{sec:org1845df1}
+\begin{frame}[label={sec:org9598cec}]{Nonlinearities in Regression}
+\begin{definition}[Nonlinearities in Regression]\label{sec:orge311b6a}
 \pause
 \alert{Nonlinearities in Regression} refers to the presence of nonlinear relationships between the independent and dependent variables in a regression model. In such cases, a simple linear regression model may not adequately capture the underlying relationship, leading to poor predictions and insights.
 \pause
 Nonlinear regression models can help address this limitation by allowing for more flexible relationships between the independent and dependent variables.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgddf6a28}]{Types of Nonlinear Regression Models}
-\begin{definition}[Types of Nonlinear Regression Models]\label{sec:orgb627b15}
+\begin{frame}[label={sec:orgc9e69be}]{Types of Nonlinear Regression Models}
+\begin{definition}[Types of Nonlinear Regression Models]\label{sec:org7475ebc}
 There are several types of nonlinear regression models, including:
 \begin{itemize}[<+->]
 \item \alert{Polynomial Regression}: Fits a polynomial equation to the data, allowing for curves and bends in the relationship between the independent and dependent variables. The degree of the polynomial can be adjusted to capture more complex relationships.
@@ -580,21 +580,21 @@ \section{Nonlinearities in Regression}
 \end{definition}
 \end{frame}
 \section{Overfitting, Underfitting, and Hyperparameter Tuning}
-\label{sec:orgda7dfc8}
-\begin{frame}[label={sec:org6bb2f59}]{Overfitting, Underfitting, and Hyperparameter Tuning}
-\begin{definition}[Overfitting]\label{sec:orgdd52bf8}
+\label{sec:org1957326}
+\begin{frame}[label={sec:org9cc0ecb}]{Overfitting, Underfitting, and Hyperparameter Tuning}
+\begin{definition}[Overfitting]\label{sec:org7401f70}
 \pause
 \alert{Overfitting} occurs when a model learns the noise in the training data instead of the underlying pattern, resulting in poor generalization to unseen data. It typically happens when the model is too complex relative to the amount of training data.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org22a49c4}]{Underfitting}
-\begin{definition}[Underfitting]\label{sec:org92273ab}
+\begin{frame}[label={sec:orga2eb935}]{Underfitting}
+\begin{definition}[Underfitting]\label{sec:orgbbc4b75}
 \pause
 \alert{Underfitting} occurs when a model is too simple to capture the underlying pattern in the data, resulting in poor performance on both training and unseen data. It typically happens when the model is not complex enough relative to the data.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org7c30f75}]{Bias-Variance Tradeoff}
-\begin{definition}[Bias-Variance Tradeoff]\label{sec:org2ffffd4}
+\begin{frame}[label={sec:org7bdb4de}]{Bias-Variance Tradeoff}
+\begin{definition}[Bias-Variance Tradeoff]\label{sec:orge9e12af}
 \pause
 The \alert{Bias-Variance Tradeoff} is a fundamental concept in machine learning that describes the tradeoff between two sources of error in a model:
 \begin{itemize}[<+->]
@@ -604,20 +604,20 @@ \section{Overfitting, Underfitting, and Hyperparameter Tuning}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgbe2a44d}]{Learning Curves}
-\begin{definition}[Learning Curves]\label{sec:org8c67211}
+\begin{frame}[label={sec:org9cd0a57}]{Learning Curves}
+\begin{definition}[Learning Curves]\label{sec:org8435932}
 \pause
 \alert{Learning Curves} are graphical representations that show how the performance of a machine learning model changes with varying amounts of training data. They can help diagnose overfitting and underfitting by comparing the training and validation errors as the size of the training set increases.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org68e0231}]{Hyperparameter Tuning}
-\begin{definition}[Hyperparameter Tuning]\label{sec:org62672c3}
+\begin{frame}[label={sec:org2077f17}]{Hyperparameter Tuning}
+\begin{definition}[Hyperparameter Tuning]\label{sec:org8789ef2}
 \pause
 \alert{Hyperparameter Tuning} is the process of selecting the best hyperparameters for a machine learning model to improve its performance. Hyperparameters are parameters that are not learned from the data but are set before training the model, such as the learning rate, regularization strength, and number of hidden layers in a neural network.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org3e74da0}]{Techniques for Hyperparameter Tuning}
-\begin{definition}[Techniques for Hyperparameter Tuning]\label{sec:org9fa8330}
+\begin{frame}[label={sec:org465d02e}]{Techniques for Hyperparameter Tuning}
+\begin{definition}[Techniques for Hyperparameter Tuning]\label{sec:orgeed016d}
 There are several techniques for hyperparameter tuning, including:
 \begin{itemize}[<+->]
 \item \alert{Grid Search}: Exhaustively searches through a specified subset of hyperparameters to find the best combination.
diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C01 Simple Linear Regression.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C01 Simple Linear Regression.ipynb
index 5437d20..36a0e1c 100644
--- a/src/S02 Supervised Learning - Regression and Evaluation/S02C01 Simple Linear Regression.ipynb	
+++ b/src/S02 Supervised Learning - Regression and Evaluation/S02C01 Simple Linear Regression.ipynb	
@@ -82,12 +82,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "6c8b4fb1",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -128,12 +139,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "c1435f7d",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True Intercept: 4.0  | Learned Intercept: 3.82\n",
+      "True Slope:     3.0  | Learned Slope:     3.05\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.linear_model import LinearRegression\n",
@@ -162,12 +182,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "8896af1d-8695-4674-a0a6-0f1e4fd3a960",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 1.18\n",
+      "R²:   0.90\n",
+      "MAE:  0.90\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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4ZfkhHgdXXnfK8UQbja/G1810/mp83Uznr8bXzXT+xtb4ZgYYG7hyxsee6WnSpEmkD8Wsrr1y5UqzAvdJJ50U6cOJOhpfja+b6fzV+LqZzl+Nr5vp/I2t8V22bFlAz3PljI+IiIiIiEh+KPAREREREZGop8BHRERERESingIfERERERGJeq5sbhCo48ePm/Z2oZSampr1MT5ecaTGN3INP4oUKRKhVxcRERFxvqgMfNjObtu2bdi/f3/I2+xlZGSgaNGi2LJliwIfjW/EsHd9qVKlULlyZfN/EREREYmBwIcBz759+1ChQgUkJSWF9EKQs0qc7SlWrJjuuGt8I4LB/eHDh7Fz506UKFECpUuXjsyBiIiIiDhY0Wi8CNyxYweSk5NRvnz5kL8eAx8qXry4Ah+Nb8Qw4GEAznOfMz+a9RERERHxFnVFKQxE+I+Bj0gs4Tlvn/8iIiIiEuWBz7Fjx8xH1t2IxBL7nLd/BkREREQkigMfm1J9JNbonBcRERGJwcBHRERERETEpnwwh6pfv36ezxkzZgzatWtXoK9/xx134KSTTsJLL72Ur8+75JJLcPHFF2PIkCEIh//+97+4++67vYr4y5UrhzPOOAMdOnRAy5Yt8/01N23ahA8++AC33HILKlWqFOQjFhEREREnUuDjUO+8847X41tvvdUEK9ddd13Wtpo1axb46w8dOrRA6w5NmjQpIo0jRo0ahbp165rOZRs3bsSnn36Ke++9F507dzbfS35s3rzZfB8M4BT4iIiIiMQGBT4OddZZZ52wrUqVKn6321JSUkxb7UAwiCiIRo0aIRJOO+00nHnmmeb/5513npntee6558yM1dlnn40bbrghIsclIiIi4rmsytGjR02jITYdYqaKanCdQzU+AZ7ER44cwYEDB8xHPo60iRMnmgv+33//3cwGNWnSBG+++abZ98wzz+D66683+1u1aoVHH33UrO/iibNHXbt2PeHr/fnnn+jUqZMJMji7tHDhwhNS3Z588smsx/369TPP+/HHH3HjjTeawIxByR9//OH1eQcPHkSfPn3Ma5x//vkmaJkxY0ZAKX056dGjh1mk9q233sra9ssvv6Bbt2648MILzbG0bdsWH374YdZ+Huedd95p/s/j5Ovbx8D3lt/blVdeab5/fq9M6eOxi4iIiOSG1wurV6/G8uXLsWLFCvORj3Ud4Rya8ckDT9atW7di//79Zn2UIkWKmAUiOfty8sknI5LS09Px2GOPmRqY3r17o3Tp0mb77t27TVBTsWJF7NmzB6+++qoJdJgellubb349BicMDB566CG8/PLLJrj45ptvUKZMmRw/b+fOnRg5ciQeeOABMybPPvssunfvji+//BIJCQnmOf3798cPP/yAxx9/HNWqVcO7775rfiEUBr+XFi1aYP78+ebY+VpbtmxB06ZNTfCWmJiIpUuXYtCgQSZYvemmm3D66aebYIYBDmukateu7TVjxveYY1m2bFnzvk+dOtWMxaxZswp1rCIiIhLd14tr1qwx1xIlS5Y01yic9eE12eHDh02mTaSvG0WBT6FPYjYIiBRe7PMi/ZprrvHazgt6Gy/kOcvSunVrE3hwJiSvwOeiiy4yj0899VRceuml+P77783MSU4YFL7xxhsmHY04rcvg6bfffkPz5s3NGDIIeuqpp8ysEHEm6uqrry70GDAA5XHzGMqXL49rr702ax+DnXPOOQfbt283NVMMfPg+2ml+PF7OlNkY7AwfPjzrMd/r6tWrmzqiv//+24yHiIiIiCdeb/BmKa8XeaPYTm3jDVg+3rt3r9nPaxClvUWWZnwKeRJ7zhhEgh2kePruu+8wZcoUM7166NChrO3r16/PNfBhswOmodl40c+aIQYOueHMkh30kB1Y2J+3bNky85FBlOdrtWnTxsxGFYaddmi/PwyAmLb39ddfm9dn4Ef2bFhemBb32muv4Z9//jGpb55jp8BHREREfLGmh9cf/gIbPuZ27ufzInnDXBT4BOUkLkh3tGDgzEpSUpLXNtb8MDWLQUaXLl1M62ceL1s3syNabhjkMLDzxPSxvD7Pt8ubnd5mfx5T4bjNd4qXMyyFtW3bNvO1mX5o1xyxzufhhx82ARjfp9mzZ2PevHl5fi3OSvXt29fUTNmpgzx2fq28xkBERERiEzNEeKM1p3ICbud+Pk8iSzM+QTiJfYOFcPE3XfrVV1+Zi/3x48dnBWRs3xxJbEDAdDSmDnoGP6w/KgyOPdP3mK7G94PByYIFC0zww5omm2fzg9ywVqhhw4ZezRu4jpCIiIhITngNwhrwnK4JuZ37c6uzlvBQV7cATmJ/nHoSMzWPMyCeQdHHH38c0WNq3Lix+cj0M1tGRga+/fbbQn3dF154wczI3H777eZxWlqa+br2jBMx1Y/NGXKbkfIdO0+RHjsRERFxNmbgMPOE1xy+nX/5mNu5n8+TyHLWVbsDT2I2MvCs8fE8iZlGxuc5KQ2qZcuWmDlzJkaMGIHLL7/cpH395z//iegxsf6Hx8LOb0wNrFq1qunqxkAj0CI/1itx3BnccAHTTz75BIsXLzYzO3ZDA84mcfaH3eiYRsegdNq0aWYGzHN2qVatWiZonTNnTlaAy8+74IILzGzPiy++aBpCsFZqyZIlIRsXERERcT9ey7DZEhtfsQbcsyEWrxdZSsD9amwQeQp8ouwkZrMDdmZjl7W5c+ea1s5c5JNr00TS6NGjTVAxbtw4Mw3MDmsMiOy1h/IycOBA85HjzoCT6+ywMQKDFU9spc121Ux3Y40OAyM2KeCaQTYGRXzO9OnT8dFHH5n3lOsXdezYEZs2bTJj98orr5hGEPx6rI8SERERyQlvvrK22HcJFF6zOGEJFLHEZTphNc58sruEebYitnEWwW49zIvkUK/jw218Tb4W90ngbrvtNlOHlNsaORrfwBXk3GdQuHLlSlPbpE4zwafxDS2Nr8bXzaL1/OVlJbM7eFORN4yZGROJm8SRGl+nfP+hPtYjDjt/c4sNPGnGJw8Mbjjb45aT2Kk+//xzE0DWq1fPjCVT1X766SeTViYiIiLu5+RF38OF14dOCATycjBG3ysFPlF0EjsZx4+1RlwPhx3euP7R008/jcsuuyzShyYiIiJhWPQ9mi+oncrfrM6hQ4di9r1S4CNh0apVK/NPREREYnPRd39rI0p4Z3WSk5PN+xSr71Wh2llzdXsWibdt2xaNGjXCdddd5/d57733nimuZ97dDTfcUOg2xiIiIiLivkXfJbwzcJzFYd0v09iKFy9uFn5fu3atCYJi8b0qVODDFsNs+XvKKaegTp06fp/z6aefYvDgwbj66qtNm+GzzjoL3bt3x6+//lqYlxYRERERly36LuGfgeNsDptJJSYmmhQ2Lg3ib82hWHivChX4XHLJJSbw4UKSp59+ut/ncB/XWenVqxdatGhhWhpz5kdF7SIiIiLu59ZF32NxBq7ov3U+Bw4cMAFQrL1XhQp8GD3mhgtNspidsz2errnmGrMwpL8BFxERERH3LfrubxbBXvSd+/k8iewMXGJiole34lh7rwoV+ORl3bp15iPXFfHEtDh29mJgJCIiIiLuX/SdNSQsjueN7YyMDPORj5266HsszsDFxcWZdLdixYqZOqCc3it7rR7ODPGjC5f99Cuk81icZiN2kPBkP7b3FwTfAL4RvlJTU80byEiX/0LNPhH4MRyvF2s0voHj+cdzn3dx+DEQdvFitBYxRprGV+PrZjp/Nb75wQvtatWqYfv27eZi2bOLWKVKlcz//V23hUosn7+8dmIAs2fPHpQuXdor4Mz8t7119erVzXP8vVfcz4wtf/s4W+TE8eX3FUhg7doEPs4YccXYnCJdBkDhFO7XizUa38DGiHd37JnW/OAvOAkdjW9oaXw1vm4WbecvL0BZCmF/5N+mSGb4RNv4BoqNDRj4MBBlgMPg5fjx42Z7QkICKlasaN4f3/eKjct27NhhrrM9P2/z5s2mSxw/j9vBQCM11VHjyzS+iAY+zBEkTqVVqFAhazsjSM/9BcE3jQss+eKbtmXLFjOFZ96YEOPJwtfk6wVzCpftwfMyatQo3HTTTQV+DQaOX3/9Ne677748czk/+OADDBw40GtBUkb+7NJ366234owzzgjJ6xdkfNlwY9iwYfj8889P+CGYOXMmnnrqKbRr1w4jR47M9zF/9dVX2LlzJzp16oRQGTBgAP744w989NFH5vHHH3+Ml156ySwAy19AuWHQX7NmTTNegbDv6tSqVStq83kjSeOr8XUznb8aX7fgtQIv6O1FOnn9x8ex/veN9To5zcCV/HfmxnccefOU4+hvpmjfvn1ILlkSpy1dioQRIxC/fj0OT5yI+HvuQaQxKAtESAOf2rVrm48cRPv/9mMGLjVq1Cjw1+abwYtvX3b0yjc3r4vEYLDT23g8wXy9d955x+sxg4s77rjDa60kXuAW5jX/+usvTJ482Xxdfz8A/hpZTJ8+3eSG8g/i33//jTlz5pgg4NFHH8UDDzwQ9NfP7/jyB3PChAm4++67/f6i++STT7ICmOHDhwd0d8AT16BiUHL77bcjVPi9en6/119/PSZOnGgCoPbt2+f4eXw+3yd+3/kN+vk5/n6eJDg0vqGl8dX4upnO3+Av0skb67xwj/Xx5ffNiQe7kUHRfzu65XQjmamIDBjLli174vVRZiZOWbYMFSdMQHGPIKP4zz+j6MMPI9ICvTke0sCHgQ0j7fnz5+Oyyy7L2v7ZZ5/h/PPPz/dFZ6Tw2nvhQmDrVoD1Xq1a8SIztK/JmRRfLDbztz2c2LacPxDE97Bjx47o27cvnnvuOTRt2hTNmzeP6PH9+OOPZpr2xhtvPGEfA7Xly5fjggsuwOLFi7FgwQJcccUVcDr+EufM3qxZs3INfERERGJxkU5erPMGKi/seYHPRTuZ5qXa65wnCvLTDa7Ef/+LCs8/jxK//OK1/cA556DooEGuqpspVFc3RpAMaviPuX+cUrMf84SjRx55xNxl53o+vCgdOnQofv/9dzz00ENwg7lzgVq1gDZtgM6drY98zO2RNnfuXDMbwHWRWrVqheeff97rh5xTm4MGDTL7+JyLLroIvXv3zvrc/v37ZwUw9evXN+sy5RdnGJgCxyB29uzZWdsZVNxzzz3mazMguvnmm/H99997HXtOr8/cUu679NJLcfbZZ6Nt27bmewuk/fmHH36Ic845Jys488TzkL8AuJZU+fLlzQyKL74GX4uv3bhxY7Ru3Rr9+vUz+/iRKX8MrHi8/Gfv46xV165dT0jl43N43ttmzJhhgpdmzZqZ75ufw4AsL2wJz6+3atWqPJ8rIiISy4t08jHT5DkLFC3dyCLRDa7YihWo1qULat55p1fQc7xZM6R8/DFWT5kCVK4MNylUkMaIumfPnl7b7Mevv/46zjvvPJOaxQDp5ZdfxrRp00xr60mTJpkLWqdjcNOhg5nd87J5s7X9/feBtm0jc2yvvvoqnn76adx1113m4nvt2rVZgU+fPn3Mc8aMGYOFCxfiscceM51WWJtiBx8XX3wxHnzwQUyZMiUrfa2gM3CcTuZM0C8ePxSbNm1CmzZtcO+995pfRHxdpsKxxobnRW6vz3aK/JoMfngHh4EGzx2eb/yecsOZnJxmRRj4cEaKM5EMJJhOyLtFfG0bA/UffvjBBCScXWMA/8UXX5h9DNb5mKmazzzzjNnmL8DKzbZt20yaXNWqVc2NgrffftvMmrEeyZ6W94ct4Dl1v2jRIjRo0CBfrykiIhJLi3TycVJSEnbt2mUCI/5f8mavx3R46VKcMnMmkufP99p/pFYt7HvsMVR+8EEc3rcPR1euNO9DbulzURX4sBXen3/+mefzeLef/9yEEyeM4fzdKOA2vr+9egEeJTdhwwtmzqDdf//9praGWrZsaeqmxo4da5oF8G7HsmXLTODp2QDh2muvzbpgZ42Qb/paQTENj2lkNs8aGLZWZrDD6eh3333X/D+31+csCdPniIFcw4YNTTEei/6HDBmSY5EiZ4pYxMfP98VZRhY5chaKOC5MHWPA0YFRLGCCCs5UPfvss161VPb/ebw8TjbPKGjKIb8HG783vm+c+eFxsI4rN/y+fvvttwK9roiISKws0knczusPf2vZiH9xmzah1qhRSHjzTcR5ZBClVamCDffei4M33ojK1aphzdq15mY6Z9wY8LCOiNeBnjeSncpNaXlhxZqeTZty3s/gh90Z+bwWLcJ5ZDAzKyxAu+qqq7x+oFm7wjsbnCE599xzTWc4pmbxhGS6W7169cLWP50zG5yB4gwMfzjsqWYGOYF8Lc4MMUjizJFnK2u2xMzp++DrUE5pbgwMOWbEwIUzP0x3swOfJUuWmKDKDg5D4ddffzXNF1asWGG6o9gCaQfJYNb+HkVERGKZZ1qWv4wVbmfGSU6BkXjgtQUzaiZPRqLHNVd6mTLYfPfd2NW+PZIrVEDlk08213e81mT3WNYO8SMzcg4fPmy6LTs9+NHZkAM2MgjEtm3hn9pjKhjl1MqaETgNHjzYTFkyLW7cuHEmGme6WWcWKwUZfxBYN0O8w8I0NqaR9ejRA6eccooJKDhLZR9bbuyW05zRYr0OO5SxA9yIESNyXc/H3uf7C5DHw4YaDAb5S9Bup846HqZkcpaIrR0ZiDBIDNV0LWeKmPrH2iF2lGMvfAZjTKsLZJ0iPlfrGYmIiGSnZfGimzcGfVsv80K8IF1OPb9GoN3QXOvgQeC55wCm7x86lL09ORmZffogvWtXlC1eHBX/bRFuN5LgeLMmmuPBay6mG/LalNd4/lIPnUSBTw7YvS0QlSuHv2jOXv+ItVKV/RSVMQWRGHWz8QD/MSWRF/m84OaMSTC7r/FkZ4vnK6+80jz+559/zIzGiy++6NXNjz8sgWBzDDY6YG2SvdhWIIuf2eNiBzY21uxwpoT/GEj5YlDEFDjW2NizU/n9oeUPPhf78sTcY0+st+JMHd83pu4Rf6H6Pi8nDCRzqwMSERGJFfw7zRu6DHB4HeLZ1Y0lAbxQ5+OCXITn1CLbLelceUpJMbM7ZpZn167s7QwSH3kE6NsXceXKwbMXHK9fcqup4nbuZ7Do5PbhherqFs3YsprxQ04/L9zOZYj4vHBjYwjeeeAsC7u1+f5jJO6vPsTuosZGCPYMAgXSLS0nnE0ZPXq0uei/7bbbzDZ7VsL++sSuf57ND3J7fXtVYX/r7+SGAR8/j+lxnpjOxh/C1157zQR/nv/YKMDu7sZUQf7Azps3L9+zLgxA2Z3Ns3sMa4Z8vy/+cvCcdudrBZp/zDFkcxARERGxbvAyvapcuXLmbywvvPmRj/n3siCzPXaLbM4k8fMZ8PAjH3M797vWsWNckBE47TTgsceygx5el7AzLdfnGTcOKFeuQDVV3O/0mirN+OSA6/RMmGB1b2OQ49nkwA6Gxo+3nudzoz/kOFvAFDJ2dWPwwxQu3o3grMjXX39tFrtkYMRuYZdffjlOO+00s5+tnnnhbs/2sFMYvfnmm2Zmhj/Y/hoDeGIDA/6i4S8WewFTbnv88cezOvVxsVoGAmwSwMCIdwmY5sbULk85vT4DEAYlb7zxhmkowOPesGFDnuPCPFOmkXk2WWCQ8uWXX5r1ethEwBc7wI0aNcp0auPrsuU3GxDw9c4880yT/sbGA+P5Zv97zPyeGYgxhY9BJgMuzna9//77Jh2P38vSpUvN53lq8W8xGANQvjesxWIaoj37kxuOIY/xYQcsEiYiIuIUvCbhbINvWhofF7ZFtj2zYbfIdks61wkyMqxWxIMHc/X47O38Hjp1AoYPB+rWLXRNFfc7vaZKMz65aNfOOk+qVfPezpkgbuf+SGGtCFs7c40YBkFsI85mAJzxsWdLuH4Ogwbu43M4EzJ16tSsgIPND9i++aOPPjIX4qzLyQvrbth9jM/lDAqDKraFZic5G38gGHzxI1+bQQ+fzwDNU06vz4t7rk/Ez2O6G7+OZze03DAA+b//+7+smRd2aePdGX8Lmtod2zhe9qwPj5tr8vB76tKli+mS5zlly0YIbJDAAIf/Z9oacb0fBn/ffPONOX4GNUwr9MSgju8ZAzPW9Xz66aem0UEg0+b8nhgY8nVERETkxEU6eSORHwsalOTVItszncsVeC3ELBbe8Gbn2L88gh52rP31V959zjPo8aypYhqh79pIfMzt3J9T512niMt04cpObNNMvMj3Zc9EFHSK0x929GP3Ntbls/aH6W2c6bH2WTUofC1GuhJc+R1frrPDWRsuFOqvnsetGLhyHYLc1jEqyLnPmSQujMqW4U7OyXUrja/G1810/mp8Y+38ZY0wa5R5Ac9mSL6YxcLAhzduA8nWiCim27PEgRewnlq3BkaP5jooBU4DtEsS7OZQLHfgdUcku7rlFht4cvZ8lEPwevviiyN9FBIItrLu1KmT6QwXLYEPUxi/++67rFkpERERCb6oSOfien8DBwKffuq9neUIo0czNSbnAvYAa6qY7sdmUAwuWVKgdXxEIqhbt2546623TNMEf7+43IZ3VJ588smsBV9FREQk/C2ymc7FxgmOTOdiY4IhQ4C33/YuTOfahyNGWEXr8fFBq6niOHBM2CSKN53dUvPk4JBVpGD4A9i9e/eoGT42owhm+3EREREpWIts7nfURf7mzVZg88orVtc2z4L0YcOAu+6yurYFUVxcnAn+7H+OGo88KPAREREREfFJ5/Jcx4czHI5ax2f3buCpp9iVyVqXx8bF5NkQig2jglTrHk0U+IiIiIiI5NEi2xEzG4cOAc8/DzzzDLsxZG9nQMa1eXr35ronkTxCR1PgIyIiIhIkuXWCFfe1yHYMLp4+dSowahSwc2f29mLFuA6I1cGNsz2SKwU+IiIiIkEwdy7QsyewaVP2NpZacEH0SK79Jy6PpGfNAoYOBTwXc2c0fc89VkODGjUieYSuogVMRURERIIQ9LBxlmfQY9eeczv3iwSMndnmzOHCNFaA4xn03HILsHw58PLLCnrySYGPiIiISCFvynOmx9+S8Pa2Xr2s54nk6auvgHPPtSLmlSuzt191FfDzz8A77wD162sgC0CBj4NNnDgR9evXz/p33nnnmcU5uZhltLrkkkvMmjXh0q9fP1x33XU57v/xxx/N2NsrAouIiPhiTY/vTI9v8LNxo/U8cT8GsAsWALNnWx+DFtD+97/ApZcCl18O/PRT9vYLLgB47TdvHtC0aZBeLDapxsfh2DN+5syZ5v87duzA1KlTzQKdb775JppG4ck/adIkJDuoG8npp5+Od955B3Xq1In0oYiIiEOxkUEwnyfBw0U2jxw5ErTubCGp42La2qBBwIcfem8/4wyrmcG117LbQoGPWbIp8HG4+Ph4nHXWWVmPzzzzTFx00UX48MMPwxb4pKSkmAAsHBo1agQnYTtLz/EXERHxxe5twXyeBO/6Zd26deajvR5PqVKlCrwej13H5ZvSaNdxvf9+PoOfv/+2mha88Yb3F+XNVma/dOzIC8F8H6fkTKPpMpUqVULZsmWxZcsWr+2//PIL7rzzTnOR3qxZMzz22GPYzcWtPGzbtg1du3bNCp5ee+01jBo1yqSX2ebOnWtSu/j17rnnHvP1xo0bl/X5ffr0MSl3Z5xxBm677Tb88ccfXq/x9ddfo127djj77LPRvHlz83/P1Ly89vtLdePn3HTTTWjSpAkuvPBCjBkzBqls6+iTjrZo0SLzffNrt2nTBi+z6K+Q/KW68TG/NlMRL7jgAjMe/fv3N3eUfMc7r/ESERH3Y8tq3vXP6aY8t7PxFp8n4XHo0CGTKbNnzx5z85YBDz/y2mjNmjU4ePBg5Oq4tm0Dune36nTYsc3+AlWrWi2rWdfTubOCnhDQjI/LHD582KwkXJ2/Yf/FIOWOO+4wwczzzz9vFtwaP348HnroIZOmZU/18vGuXbswfPhwc6fjlVdeMQEUZ5V8MYC49dZbTaDEaWG+ZufOnU1P+8GDB5vPnzVrFu666y588cUXZkXjDRs2oGfPnrj22mvN52dkZGDVqlXmcymv/f588803eOKJJ3DNNdeYIIJ3bvg9ckXlF154weu5Q4cORdu2bfHiiy/iq6++wjPPPGOClNatWyPYmGrIAHPs2LFYv369CQ45BjxGCmS8REQkOrCzMFOdeNefQY7nxbEdDI0fr/V8woXXPNu3b0d6ejpKly6NxMREs50fy5Qpg71795rrCGZ1BJr2lp86rosvzuFJe/cCTz9tnSyeN0vLlmXRsRUMlSiRr+9V8id2Ap/33rN6neczws8LQ4YSmZl5/+BwSnXECOu3Yj4xL5V45+Lpp59GUlKSmd2xPfvss2jcuLGpj7GPo169eqZon7MpDIi+//57LF++3Fywc6aFWrRoYfb5q6np2LEjHnjggazHDDIOHDiA9957L+ui/fzzz8eVV15pAigGJytWrDC/ZHihz18m1Mrj9lZe+/1hEMOZHn7fnKJmEMNAbMiQIfjzzz9NYGO74oor8Mgjj2Qd24IFC/D555+HJPCpUKGCGXfi1+f3xteyAx/WZeU1XiIi4uyLZ95IDLQ2hClOTHXyV//BoEfr+IQP3zf+DeYMj+97xse8BuENSj4v0EVKC1XHdfgwO1YBTz0F7NuXvT0pCejdG+C1Q6lSgb2AFErsBD6MsFetCvqX5Y9TXH6OIZ+BD9OnWGBv48X/5MmTUbt2bfOYP7RLly41F9LMX7XVqlXL5LAyRYvBDT8ywLGDHmIAxYtxBkS+Lva5XcE0MqZscarYDsQ4U3TOOedkpYExCOHx8eL/lltuMfs8c2jz2u9vdoszQr35S8EDZ38Y+Pz8889egQ/T4Dx/sbEhAdPNQoEpbp74Wp9++mm+xktERJyJaVCcEeDFcX5qQxjctG1r3fXnBTBrenh/jzNCEj78u2u/b/4wkOV+++9zyOq40tKstXZGjrTS22ycgerWDRgwgDUMAR+DFF7sBD68wz54cNBnfDL/vSvEC+1cAyD+onz88Xx/fd6teOONN8xrMKWKswx9+/bFxx9/jIoVK5o7GvzhZd0L//niL257toi1Qb78baPy5ct7Pea08K+//uoVhNlq1qxpPp566qmm69xLL72E7t27mwt9BiMMUqpWrZrnfn9/ePh9+x4j/+hwuto3Rc73j1FCQkK+c3gD5TtLxtdK4y+4fIyXiIg4D/9usAaEBfGcGeBFMi+QWRvCG3J169bNNfjhtXaOqU4SFnzPGPR43hD2xPeT+/m8/NZxsZGBvzofTixxv0lk4eu+9ZbVuIANDGwsLWDGzrBhwCmnFOh7k8KJncCHMy0FSDPLS8bx41ldz3K6s1AYDA6Y6kUskGfwwNkSpoDZtToMuliLc9lll53w+cxlJQZJLPDz5W+bP7zTxbQ01uj4snNn7bQv/mNRIdPrGIyx8N9uyZ3Xfk/29+Z7jPyjxCCDx+RUgY6XiIg4B2+28YYh/67z76edJlWY2hAJP6Yl8gbl5s2bzXvqiY95DcI0dD4vqHVcz2eiyCcfWa2pfZsZtW9vlTw0bBiW1EuJ9cAnSjAIYnMAdl/jrAlrTdh5jUX/doCU0+dxduh///ufSbci3rlasmRJQOvmMLXro48+MildgeTD8o8CU9J+//13fPLJJ/neb6fiNWjQwHR1u//++7O2z+MCXoBpLuBU+R0vERGJPF5YMpvAX2BT0NoQCT++V+yCy5m7ffv2mcwRe+aOQQ9vVjNtMb+BQ251XG/c9y1aPzMA+OEH70/iYqSjRwMepQb5DW4KmnopJ1Lg40LszvbZZ5+ZWRLWy7C+h93CevXqZYIiBjKsbVm8eLFpF81aE86yMO2K3dQeffRR85zp06eb4CKQH/y7777bpNfdfvvtprECU9M4E/Pbb7+ZXy7c//bbb5v0Ls50MCDbtGmTufhv2bKl+Rp57ffn4YcfNg0L+D2yY9vff/9turqxSYBnfU9h8Jfg/PnzT9jOcSuoQMZLREScWRuSUwpUQWpDJDIYpDLbhdc7nuv4cKanMAGDbx1XvQM/oen7AxA37EvvJ/IaggGPx5IhuckpuOFx8pquoKmX4k2BjwuxsQFnS2bPnm1S3LiQ6VtvvWXWlWHaGDunVa5c2XRtO+XfHFIGN2yKwHoa/uMvAl6QM5BYyX7xeeAUP1tjs00220TzDgp/eXBNoMt5N+Pf5gXffvutSV/jfgY3DMTsdK+89vvDdX3YKppBGgM+tqVkqh8DuGDhLxp/x8AOeAUVyHiJiIgza0N4YekvLbkgtSESOZzZ4TUTr4GCmSJm6rgqrQQmDrJWNfXE2t5Ro4Abbsh5YacA68q4BMnatWvNMfO6TqmXhReX6Zv86AJ2Vyx/qV08aXgxz1oYnvChdjzENT6hxDoZBh7s9OavMYITuHl8w60g5z67BjLwbdiwodI2QkDjG1oaX41vsPGSaPXq1eZuumeNj72PNT68iXXaaacV+uJZ529ohWx8//nHak7w+utARkb29lNPBYYPtxYezcf1Sm7nHP+uc+kO3vRlYyTfc47XcXwOM3rCnXp5xGHXD7nFBp50yyKGcAaCi4bywpj1PpwxYuHfc889F+lDExERiTheWDINiilEDHI8774XpjZEosCOHdZMztSpVptqW+XKVtdg1iIXoHlRbnVlvGbj+ZeammqCnGLFinntV+pl/inwiSH8gZk2bZoJdoiNA9haOq/oWEREJFawXoJ1E771FoWtDRGX4tIZzzwDPP+8tRCprXRpa6mUHj2shUhDUFfG847LZbCEwV9rbqVe5p8Cnxhy4403mn8iIiKSMwY3vAOv9sEx7OhR4MUXAZYCeC6rwbQu1gVzbcZ/lwwJVV0ZH/OmNdPKuLxJMNpyxzoFPiIiIiI+mHbkhNoFCbP0dGDGDODJJ4EtW7K3JyQADzxgrdHD9LYgYdDC7m3+anzs4IcNqZh+yeBHqZeFo8BHRERERGIbGxW88w4wZAiwZk32dgYit91mNS6oXTvsdWUMitgVl53flHpZeFEb+LiwWZ1IoeicFxERyfcfT+Czz4CBA4HffvPexwV7Roxgq7CI15WxnbVSLwsv6gIfFoER8yGV8yixhOe858+AiIiI5IKrkA4YAPzf/3lvb9PGWny0RQvH1JUp9TI4oi7wYYTMfuc72HbQ1KCdFNK2k4zK2WbQfm3R+EZipodBD895nvs6D0VERHLxyy/WDM+8ed7bmze3Ap7LLgt48dFgUnATelEX+BCnA8kOfkKJPdbtyNy344ZofMOJQY997ouIiIiPv/6yanhYy+OpQQNg5EigXbuIBDwSPlEZ+NiFYhUrVjS9z0OJU5Lr1q0zK+oqtU7jGylMb9NMj4iIhCvTwFX1Jps2WV3a2K3Ncz2cmjWBYcOAO+5gX+lIHqGESVS/y7wQDPXFIGd8iH3WuaKzaHxFRESiNaBgdzHfInx2HnPi4q5F9u1DQr9+wLRpwL9lCUaFClZb6q5deQEXyUOUMIvqwEdERETEDdwQUPAY16xZg5SUFK+2y1yDhu2Y2ZnMEcd68CCKPvUUmowfjyKHD2dvT062Fh7t1QsoWTKSRygRosBHREREJILcEFBwNoqBGY/Rc6FNLrDJx1yDhvt5/BGbpUpJAaZMMQ0KEnftyt7OjJxHHgH69gXKlYvMsYkjKPARERERiRBXBBT/1jRzNsrfcfAxt3M/n8eOumF17Bgwc6a1yOjGjVmbM4sUwbG770YCt1erFt5jEkdS4CMiIiISIQx4HBtQeOAMFFPwOBvlD7dzP58XNqyznjPHqtdhxzZbXByO3XwzVnXqhNpXXIGECI6bOIv6L4uIiIhEiCMDihyOg3VHOR0Ht3N/Tt9HUGVmAp9/DpxzDnDLLd5Bz3XXAb/+irRXX0VqjRqhPxZxFQU+IiIiIhHiqIAiF+wwx2YLhw4dMul5nviY27k/5Et7LFkCtGkDXHUVsHRp9vbWrYH/+z/g44+BM84I7TGIaynwEREREYkQLoXhiIAiwDUSebysO0pLSzNLevAjH3M794esDmnZMuCGG4ALLgC++y57+9lnA/PmAQsWAC1bwun4nh45cgQHDhwwH33fcwkt1fiIiIiIRDigYPc2BhCeXd0Y9IQ8oMgHdpZjhznfttvlypULXdvttWuBoUOBt96yUtxs9eoBI0YAHToA8e64j++GluXRLiyBz9dff42pU6eaVo1JSUlo1qwZ+vTpgxrKvRQREZEYF5GAooB4LAzOQr7Q6pYtVmAzfbrVtc1WvboVCN19N/ME4RZuaFkeC0J+xvz444/o3r07brzxRvTu3Rv79u3DhAkTcO+99+Ljjz82dzJEREREYlnYAoog4DGFrMPcnj3AU08BEyeyh3b2dq6/M2AA8NBD1ro8LuKWluWxIOSBz6effoqqVati9OjRWW9m2bJlcdddd+GPP/5A8+bNQ30IIiIiIo4X0oDC6Q4dAiZMAJ5+Gti/P3t7yZLAY48Bjz4KJCfDjRy9BlKMCXngw7sWTG/zfKPtqTwVdImIiMQO/t13w4yGhFFqKjBtGjByJLBjR/b2YsWs2Z3+/YEKFVz9lrilZXksCHng065dO/znP//Bm2++iRtuuMGkuj333HNo1KgRmjZtGuqXFxEREQdQYbd4OX4cmDULGDYM+Oef7O1sVHDPPVYdT4hrwcMViHu2LGd6m1NblseCkI8wU9kmTZqExx57DE8++aTZ1rBhQ0yfPt28yYVtBxhp/IHx/CgaXzfR+avxdTOdv+4ZX3YnW7duHVJTU00WCOt7ebHHuoY9e/agdu3aJt0nlsTs+ZuZiSIffYSEJ59E/KpVXruOtWuH9MGDkcmObVSI67y8xpfn5Pbt201babuZRHJyMipVqhT0c5HXrDznea6XLl3aK7jiPk4KsAzEKde2bjx/OXaBBK1xmSHON1u6dCm6du2K9u3b4+KLLzZv7uTJk01U+9ZbbxWoucGyZctM33gRERFxNl5m7Nixw1xo+qa+cx87WvFCs2LFikp7i3In//gjqr34IpJWrPDavv+CC7D5oYdwtEGDsBwHmwzwnExPTzfXoQx6GPxwe0JCgjkXg918KxKvGWsSExPRpEmTyM74jBw5Ei1atEC/fv2ytp111lkmCGIK3K233lqgr8uThK3/Io2R7vr161GrVq2ILy4WjTS+Gl830/mr8XWzYJ2//Dqc6eGFnb80H97I5P5TTz01pv6OxtLvh/j//Q8JQ4eiiOfCo8x2a9EC6cOHI+HCC1ErTOPLYJuzj5xxzGn2hTM/nIUMdtpbOGeZYu38XbNmTUDPC3ngs3btWlx66aVe2ypXrmza923YsCFqOp/wTXfS8UQbja/G1810/mp8Y/n8tesXONsT72ehSd7I5HP4MRb/jkb174fly4FBg4APP/TefsYZwKhRKHLttSgS4uYWvuPLVDLOsjC1zF8gzu3cH4rrTH69ChUqRFWDjxIOOX8DHcOQBz5sZb3CZ0pz8+bNpmd5tWrVQv3yIiIiEkEq7I5Bf/9tNSd44w1T05OlTh2A9d4dO1pNDGKww5rTbtzHmpCfdR07dsRXX31lUt4WL16Mzz77DN26dTOrEV999dWhfnkRERGJ8B3hUqVKmTQf37JiPuZ27ndCuowU0rZtQPfuQP36Vsc2+/2uUgWYMgVYuRLo3DliQY9vIO6POqxFt5DP+Nx5551mKnH27NmYM2eOmepmjc/48eNNupuIiIhEL97hrlKlimliwGwP1jLw4pMXmAx6WNDN/W5O94l5e/daC49yAVLPrmS8zuM6PA8/zDwvRwXiu3fvNtehvjU+PCd5c16BeHQKeeDDE6pTp07mn4iIiMQeLlzOhkRsX80V6u3Cbl5gMuixFzbPjRY/daDDh4GJE4GnngL27cvenpQE9OoF9OkDlC4NJ1EgHtu0UpKIiIiEHIMbzvYUpLBbi58WXEgCRi4pMn06MGKEld5mY7OAbt2AAQOASpUQzYG4uJMCHxEREQmLghR2M+hhq1p22vJMk2OqEtPneAGrC9Wcx8734p5pXgW+uD9+HJg9GxgyxGpgYGPNzp13AsOGAaecAjcEcIUJxMW9FPiIiIiII/FilxfuDHo86zFYO8zHrBnifl7A6oI1hAEjmxR8/DEwcCDwxx/e+266iYs2Ao0aIVRCNeOnDmuxJ3JtNURERERywbvxvNj1F9jwMbdzP58nOQeMDBS5hpIdMHI79/t22fNrwQKgZUugbVvvoOeyy4AffwTmzg150MMAjgEbG2Ew4OFHPuZ27hcJlAIfERERcaRIr7kS0wHjzz8DV14JtGkDLFmSvf3cc4Gvvwa+/NL6v1sCOBEFPiIiIuJUWnMlAgHjqlXAzTcDzZsDX3yRvZ2zOh98APzwA3DJJQgHzfhJsGnGR0RERBxJi5+GMWDcsAG47z7g9NOB99/P3l6rFjBzJvD778CNN3LKCOGiGT8JNjU3EBEREUfSmithWKRzxw5g9GhgyhSrTbX9vEqVsPrWwfileRdUqpGIVgCKIHIBHNPbAgrgRHKhM0VEREQcS2uuZHeSXrgQ2LoVqFIFaNUKKFKkEAHjSSchju2nn3sOOHQo+5NLl8Yf1z6Bdt/2wOoXkrI2V68OTJgAtGsHZwZwMUyL+wZOgY+IiIg4WqyvucLGaT17Aps2IeBAJKeAsXxSEmp8/DGKM+DZsyf7Exg89OyJjxs+gbZ3lzEdrD1t3gx06GBlwRU0+MlP8Eaa8cubFvfNHwU+IiIi4nixuuYKgx4GHAUJRLwCxqNHUWz2bCSOHYs4frKNaWIPPAAMGoTjFavgoVonvhZxG+PMXr2szta5BSzBCt5yC+A401PYdXzcTov75p8CHxEREREH4gwJg4XCBCJxmZk46aOPgMGDgTVrPHbEAbfdBgwfDtSubTYtXOAdmPh7zY0brVmbiy8OT/BGsT7j548W9y0YdXUTERERcSAGGIEGIn53fvop0LQp0KmTd9DDSOm334BZs7KCHmIKWiACfV4gwRsxeOPzApnxS05ONh9jOeghtfouGAU+IiIiIg5U4ECEkVDr1sB111kBjs1ejPTDD4EmTU74Oqy7CUSgzyt08CY5UqvvglHgIyIiIuJA+Q5EfvkFuOYaK+j5v//LfoK9GOnXXwMtWuT4ddhsgHU3OU2mcHuNGtbzAhWKWSTR4r4FpcBHRERExIECDkQqrwY6drTS2ubNy35CgwZWAc1//wtcfnmei4+yTojNBuyv7ftaNH58/hobhGIWSbS4b0Ep8BERERFxoLwCkWqZm/Bd/QdQpHFD4J13snfWrAnMmAEsWwa0b59nwOOJTQYYK1Wr5r2dAVhBWlmHYhZJslt9c00mrtWUlpaGjIwM85GPzVpNVarEfC2ULwU+IiIiEpNYUL9gATB7tvUxrwL7SPAXiJTDLkwp2QfrE+ri1K9ezj7wChWsKZm//gLuucdqVV3A11y/Hvj2W+Ctt6yPf/9dsPV7QjGLJN6tvtnaOyUlxbT75kc+5vZYbvWdE7WzFhERkZhT0HVlIoHHw0Zsiz8/iFKvPo9Gnz2DogcPZj8hORno08dqjxaki10GIvlpWR1I8OZvvBn0OG283UStvvNHgY+IiIgEdX0Rp6+3Uth1ZcIuJQVFpk5Fq9GjgZ07s7cXLw507w706weUKwcns4M3dm9jIwPW9DC9zXOmxw3njhPF6uK+BaHAR0RERIK2kvzWrVtNys3x48dRpEgRlCpVytQaOCXtJhiLgobNsWPA668Dw4ZZPZ9tPLD77gOGDDmxGMfBcptFcsO5I+6nGh8REREpNF64rlmzBrt37zaF1bxo5Uc+5nbudwJXrCuTkQG89x7QuLEV4HgGPezetnIl8NJLrgp6ouHcEffTjI+IiIgUClOUeLeehdVlypTJSk9KTEw0j9llivtLliwZ8dQlR68rw6iL6+0MGAAsXeq9j+vzjBoFnHWW31ms3FLInMxN5464n2Z8REREpFBYl8EUJX8Xp3zM7dzP50WaY9eVWbIEaNMGuOoq76CHUQyjmk8/9Rv0sF6pVi3rUzt3tj7yMbe7gZvOHXE/BT4iIiJSKCxGZ10GC9L94Xbu5/MizWnrysT98Qdwww3ABRcA332XvYNBzmefWdsuvDDXJg2+qXt2kwY3BD9uOnfE/RT4iIiISKHw4pTF6DldnHI79+d0cRuMdKkjR47gwIED5iMfO31dmbh161Br8GAUb9EC+Pjj7B2nnQa8/Tbw88/A1VfnGKHl1aSB2KTBiWsTOenckdiiwEdEREQKhW2HWZB+6NChE4IOPuZ27ufzgo2F76tXr8by5cuxYsUK85GPcyuI97coKHEmKOStrLdsAR56CMXPPhvl5s1DnD1ePJhp04Dly4FbbwXi493fpMHh547EHoXPIiIiUiisxWDb4cOHD5tidNZl8A4979bzwpUdurg/2MXpdjcwFsZ7via7gfFYclu9PpB1ZYJqzx7gqaeAiRNZ2AJ7JDLLlUNc//4mGEI+Lu4d3aTBBeeOxCYFPiIiIlJoDDAYaPiuxVKuXLmQrMUSjG5gua0rk9drB7zQ5qFDVm7d008D+/dnf42SJbG1UyeUfvJJnFS5cvQ0aXDBuSOxS4GPiIiIBAUvUBloBBwUhKkbWDBXtQ94oc3UVCt1beRIYMeO7O3FipnZnaM9e2Lrrl0onZxcqCYNbGTgr86HQ8L94WrS4KZzR2KXAh8RERGXy9cMRIjxdYMZaDipG1hAqXX83t94Axg6FPjnn+xPZs3OPfdY29k27sgRYNeuAh+L3aSB3dv4VnsGP+Fs0uDGc0dilwIfERERFwt4BiLKeHYDY3pbqLuB5Zlat2cPDr7+OkpOnoy4FSu8P/nmm4ERI4D69RFMdpMGdnfzbHTAmR4GPSFt0iDiQgp8REREXKowxf1uZ3cD4/fqGYh4dgNjjUiwuoHlllqX9MMPqPnssziJa/J44mKko0YBTZsiVMLepEHExRT4iIiIuFAwivvdLNzdwPyl1hX//XeUf/55JC1Z4v3k888HxowBLroI4VDQJg0isUaBj4iIiAtFqrg/VruBeabWldywAeUnTMDJX37p9ZwjdesifuxYFOc0TBQGmyJup8BHRETEhSJR3B/L3cD4NcsdPIiSw4ahwvz5iMvIyNqXVqMG/rn3XmR27IjTWMejoEfEkRT4iIiIuFC4i/tjuhvY9u2IGzUKp06dirj09KzNx8qXx/auXbHxiitQjLNP1apFZVqhSLSI/t+GIiIiUSjcxf0xad8+a+FRtkg7cgT2CB8vVQpb7rgDW9u3R1xSEsrGQBc9kWigwEdERMSFwl3cH1O4xs7EicBTTwF792Zv56xS796If+wxlCtWDKUcsG6SiAROgY+IiIhLhbO4PyakpQGvvGKtucPe0LaEBKBbN2DgQKBSJTPzE53tIkSimwIfERERFwtXcX9UY6OC2bOBIUOAdeuyt8fHA3fcAQwbBtSqFckjFJEgUOAjIiLiciEv7o9WmZnAJ59YMznLlnnvu+kmYORIoFGjSB2diASZAh8RERGJPQsWAAMGAL6Lj152GTBqFHDuuZE6MhEJEQU+IiIiEjt+/tkKeL74wns7A53Ro4FLL43UkYlIiMWH+gVEREREIm7VKuDmm4Hmzb2DHqayzZ0L/PCDgh6RKKcZHxEREYleGzYAw4cDr71mNTGwsVkBt992G1CkSCSPUETCRIGPiIhIPnGBUHVRc7idO63UtcmTrTbVtkqVgEGDgC5dgGLFInmEIhJmCnxERETy4eDBgyesm1OqVKmsdXMUFEXYgQPAs88Czz0HHDqUvb1UKeCJJ4CePYGkpEgeoYhEe+DzwQcfYObMmVi7dq1pudmkSRNMmjTJrCwtIiLilqBnzZo1SElJMWvncM0crp2ze/duHD58GJUrVzbPySkokhA6etSa3RkzBti9O3t7iRJAjx5W0FO2rN4CkRgWlsBnypQpePnll9GtWzecddZZ2Lt3L5YsWWL+KIiIiLgBZ3I408Ogp0yZMlkLhCYmJprH27ZtM/8Y6DDI8Q2K6tatq+AnFI4dA1591arX2bw5e3vRolY62+DBQJUqIXlpEXGXkAc+69atMzM7kydPxkUXXZS1/corrwz1S4uIiAQNa3o4k8OZHjvo8ZSWlmZme6pWrWqCIc+giDf8GDTl9LlSAGxU8N57VmCzenX2do5v585WIFSnjoZWRMLXznru3LmoXr26V9AjIiLiNpy9YaYCZ3L8BT2pqalmX4Zn5zBzHR5nAh4GTQyepJAyM4HPPgOaNQM6dvQOeq6/HvjtN+CNNxT0iEj4Z3x+++031KtXz8z4zJo1y9wNa9y4Mfr3748zzzyzUCkHR44cQaTZf8T0x0zj60Y6fzW+bhbu8zc9Pd0EPkxbs2d0PI+FKXB2ehuDIE8Mhvgc/g10i3CPL/+ucww5fhxH1gD7zo7FL16MhKFDUWTxYq/tx1u1QvqwYcho0cLa4IDrg7zo96/G182OOuz6l78/AplNj8vkM0Poqquuwvbt21GxYkX07t0bJUqUwNSpU/HXX3/hiy++QLly5fL9NZctW2buromIiIQL/1zu2LEDhw4dQlJSktcfWf5NYn0Pt5ctW/aEP8C8mGfg5JkGJ9kY8NgzYgwS4+PjzfUC66UYAJX4809UmzwZpRYt8hq2ww0aYMvDD+MAAx6lEIrEtMTERNM8LaIzPvbMzIQJE9CgQQOzjTM9l1xyCd544w30ZFvJAkhISDCFopHGX9Lr169HrVq1zC9p0fi6ic5fja+bReL8rVmzpqld5YwOgxx7hofBEDu4FStWDJUqVfIKfPh3cN++fSYgql27tmtqfMI1vhw7jinHkzdJ7THlzFrSli1o8NZbKP7hh16fk1GvHtKHDEHcjTeiWlwcqsF99PtX4+tmRx12/ctum4EIeeCTnJyM0qVLZwU9xMeNGjUK+CD94R8OtsV2Cr7pTjqeaKPx1fi6mc7f6Blfvg5fz17HhzMVDHg4k8NObpz14c0+z1bXvIDn30JeIPDi3m1COb4MCjdt2mQ+egaMRbdvR53Jk1F6zhzEeXaArVEDGDYM8XfeiWJ+aq3cSL8fNL5uVsIh17+B3lAK+W8Nzsps2LDB7z7fHGgRERGnY4DDwIZ3PO16FP7xt5sY+C5uypRureMTWKe8+L17Ue7ll1H6zTcR73GNkFm+POIGDgS6dQO0/p+IFFDIA582bdqYzm4rV65Ew4YNzTa29Vy+fDnuvvvuUL+8iIhI0OWUdZBbUCQ5d8pLTE1FuenTUWbGDBQ5dChr//GkJGzp1Amlhg1DcjU3JrSJSEwFPpdddpkpNOrRo4dpbsD852nTppkCpM7ssy8iIhJFnJaK7WRFjx1D1XffRdXXXkPRvXuztmckJmLfbbdh291343CJEihXpkxEj1NEokPIAx92ZmGgM2bMGAwZMsR0tWnevDnefPNNVKhQIdQvLyIiIk5z7Bjw+usoMWwYam7cmLU5s0gR7G/fHrsfegjplSphP1PfSpVyRPG0iLhfWCoD2cnm6aefDsdLiYiIiFNxBY05c4DBg4FVq+CZALjrssuwu2dPZNSpY3XK27vXtLJmfZRSBUUkGKKjJYqIiIg4O+D58ktgwADg55+9911zDQ4PGIA9FSrgAJtC7N+vphAiEhIKfERERCR0liwB+vcHvvvOe3urVsDo0cCFF4JNvk/LzFRTCBEJKQU+IiIiLsC1blzVLW7ZMoAtqD/+2Hv7mWdaAc/VV7MTRNZmNYUQkVBT4CMiIuJwBw8ePGF9oFKlSjlzfaB164AhQ4C33rJS3GynnQaMGAHcfDM7H0XyCEUkRinwERERcXjQs2bNGqSkpJg1gjjbw1mf3bt34/Dhw2ahcEcEP1u3WoHNyy9bXdtsXH9n6FCAa/clJETyCEUkxinwERERcXB6G2d6GPSUKVMmK7WNa+HxMRcE534GRBFLe+P6O089BbzwAnD0aPb2smWt2p6HHwbUjlpEHECBj4iIiEOxpofpbf4CGz7mdu7n88K+aOrhw8CECcC4ccD+/dnbk5KARx8FHnsMKFUKkea62igRCRkFPiIiIg7Fi3XW9PCC3R9u534+L2zS0oBp04CRI4Ht27O3JyYCDz1kzfJUrAgncFVtlIiEnAIfERERP44fBxYutEpXqlSxui8XKRLeoWJgw4t1BjZMb/PF7dyfU2AU9AF5802rXmf9+uztbFTA+h1ur1kTTuGa2igRCRu1VRERkbCmHR05cgQHDhwwH/nYiebOBWrVAtq0ATp3tj7yMbeHE9OyOENx6NChE8aKj7md+/m8kOHrfvih1Yb6rru8g5727YE//gBeecVRQY9vbRSDxvj4+KzaKG7nfqeefyISGprxERGRsHBL2hGDmw4dvDsx0+bN1vb33wfatQvPsbAWhePDGQo2MvCcuWDQU7x4cbM/VDUrJ//vfyjWrRvw00/eO664wlqLp1kzOJGja6NEJGIU+IiISMi5Je2I2Vw9e54Y9BC38Rq6Vy+gbdvwpb1xXDg+vkFjuXLlQhc0MuDp2xf1vv3We3uLFsCYMcDFF8PJHFkbJSIRp8BHRERCyhUtmf/Fmp5Nm3Lez+Bn40breeG89mdww/EJeXeyFSuAQYOADz6AV1zXuDEwahRw/fVW9OdwjqqNEhHHUI2PiIg4Ju0o0tjIIJjPCyaOFdOykpOTzcegBj2s22GDgiZNTNBjS61WDanTpwO//grccIMrgh7H1EaJiOPoVoeIiISUm9KO2L0tmM9zusxt23DsySdRdPp0xKWnZ++oXBlp/fph+XnnocEZZ4S/nZ3La6NExJkU+IiISGj/0Lgo7Ygtq6tXtxoZ+Kvz4XUy9/N5rrZvH1JHjULCiy8iwWOm7XipUjj26KMo1qcPGIZmrlwJt4pIbZSIOFrk/8qIiEhUs9OO2MjAs8bHM+2IF6NOSDvixMaECVb3Nh6mZ/BjH/b48a6bAMl25AgwcSIyx45FsX37sjZnlCiBXbfdhvU334yEChVQ998gwe3CVhslIq6gGh8REQlL2hHTi5h2lJaWhoyMDPORj52WdsRW1WxZXa2a93bO9ISzlXVQMY1tyhSgbl2gXz/E/Rv0ZCYkYO/tt2PdF19gb58+SK5ZM+rWuAlpbZSIuIpmfEREJOTclnbE4IYtq9m9jY0MWNPD9DbXTYJkZACzZwNDhgDr1mVtzoyPx77rrsOeHj1wjBGdn2YTfG9ERKKJAh8REQkLt6UdMchx+HI1OeNszSefAAMHAsuWee1Kv+46rOjcGYlnnYX4+HhHN5sQEQkmBT4iIhL2tCMJoe++A/r3B5Ys8d5+6aXA6NFIb9wYacuXI94FzSZERIJJNT4iIiLRYOlS4KqrrGkqz6DnnHOAr76y/p17bsBr3LD2SkQkmijwERERcbM//wRuuQVo1gz4/PPs7Q0bAnPnAj/+aM32uLTZhIhIsGgeW0RExI02bgSGDwdeew04fjx7+ymnWNtvvz3HbgyBNJs4wtbXIiJRRIGPiIiIm+zcCYwZA0yeDKSmZm+vWBEYNAh44AGgWLGoazYhIlJYCnxERETc4MAB4LnngGefBQ4dyt5eqhTw+ONAz55AyZL5+pJqNiEisUSBj4iIiJOlpFizO6NHA7t3Z28vUQLo0QN44gmgbNlIHqGIiCso8BEREXEirqPD+h3W62zalL2dbaa7dLHS2qpWjeQRioi4igIfERERJ8nIAN57Dxg8GFi9Ons7a286d7YCoTp1InmEIiKupMBHRETECbimzvz5wMCBwC+/eO+74QZg5EigSZNIHZ2IiOsp8BEREYm0RYuA/v2BhQu9t190kdXB7fzzI3VkEsW4YK26+kksUeAjIiISKb/9Zs3wfPqp9/amTa1mBldcYaW4iQTZwYMHT1jHqVSpUlnrOIlEIwU+IuIKXJ+RN8O3bgWqVAFatcpxbUaJQa67c71mDTBkCDB7tvf2+vWtlLb27YMW8LhubCQsQc+aNWuQkpJi1nLiecHzY/fu3Th8+LBZ3FbBj0QjBT4i4nhz51pLlHg2tqpeHZgwAWjXLpJHJk5w6NAhbNq0yR13rjdvBp58EnjlFSuat9WoAQwbBtx5p9W1LUh0V1/8BcKc6WHQU6ZMmawgODEx0Tzeu3ev2c+ASAGyRBsFPiLi+KCnQwer7tv3+pHb339fwU8s48XbunXrzMWco+9cc/2dsWOBSZOsdXls5ctbqW7dugHFiwf1JXVXX/zh7B9vEvgLbPiY27mfzzvppJM0iBJV4iN9ACIiOeENcc70+AY9ZG/r1cv7xrnEDgY7vEBLTU01d6p5xzo+Pj7rzjWDIt655vMi5tAhYMQIoHZt4JlnsoMeBmNsS71unXUSFy9ujvPIkSM4cOCA+ViY4/a9q+/IsZGI4I0BzozyJoE/3M79fJ5ItNGMj4g4Fmt6PNPbfPGabeNG63kXXxzOIxMn4MU770pXrFjReXeuU1OBqVOBUaOAnTuztxcrBnTvDvTrZ832hCglTXf1JScMbHh+MbBhIOyL27k/p8BIxM10VouIY7GRQTCfJ9GFF2gZGRnOunPN15o1y6rX2bAhezs7cdx7r9XQgAVqIU5J0119yQmbWzCo5vnlWeNDnAFkzVy5cuXM80SijVLdRMSx2L0tmM8TZ8tvqhcDBKZv5RTYhPXONY91zhxrgVEGOJ5Bz623AitWANOmnRD0hColzfOuvj+6qx+7GOhwJrF48eKmkUFaWpq5gcCPfMzt3K/GBhKNNOMjIo7FltW8TmQjA3/XfbxRyf18nrhbQVK9eIHGu9KcFfEt1A7bnWuemF99BQwYAPz0k/e+q6+2Ut3OPjvsKWm6qy+54c8UZxJ9f+b48+LIbogiQaLAR0Qci9lBbFnN7m28JvQMfuxrxPHjtZ6P2xU01YuBAYMjXrDxTrXn5zLoCfmd6x9+sAKeb7/13t6yJTBmTEAReahS0uy7+hy/iIyNOB5/pnheaI0niSVKdRMRR+M6PWxZXa2a93bO9KiVtfsVNtWLF/C1a9c2d6r5XN695kc+Dlkr6z/+AG68ETj/fO+g58wzgU8+sbptBDgNGcqUNPuufljHRlyFgS9nEpOTk81HBcIS7TTjIyKuCH7atrWuJ9nIgDU9vK7kjJC4WzBSvficChUqhP7ONVtPDx0KvPmm9/Rj3bpWy+pbbgHi83c/MdQpabqrLyKSTYGPiLgCgxy1rI4+wUr1su9chwSj7ZEjgZdfBtLTs7dXrWp1aWMzg4SEAn3pcKSkhXRsRERcRIGPiIhEjKPXFNm7Fxg3zio0O3o0e3vZskD//sDDD3PKptAvo0JzEZHwUOAjIiIR48juY4cPW8EOg579+7O3JyUBjz4KPPYYUKpUUF9SKWkiIqGnwEdERCLGUd3H0tKstXaY1rZ9e/Z2zkQ9+KDVwa1ixZC9vFLSRERCS4GPiIhEVMRTvY4ftxoWsHHB+vXZ29mo4O67rTqeU04J7TGIiEj0BT68q3f11Vdj+/bteP/999GEq1yLiEhMi0iqFzuz/ec/wKBBwPLl3vu4eBQ7tTVoELrXFxGR6A58Jk+ebO7miYiIRCzV65tvrNS1H3/03n7FFcDo0UCzZnpzRESiTFgXMF27di3eeustPPLII+F8WRER12Oh/5EjR3DgwAHzMacFPSUP//sfcPnlwKWXegc9LVpYi5F+/rmCHhGRKBXWGZ+RI0eiY8eOOPXUU8P5siIirnbw4MET6l/YCS0s9S/RYuVKYPBgYM4c7+2NGwOjRgHXX88pp0gdnYiIRFPgM3/+fPz111+YOHEilvvmUhfi7mekMR/d86NofN1E56/zx5edzdatW4fU1FQkJSWZLmesgWEgtGfPHtSuXdvUxsSiQMY3bsMGJIwejSJvvom4jIys7Rm1aiF90CAcv+UWa3Vc/Q4v0PhKwWl8Q0vjG1vjm5mZGVBNaFgCHw7K2LFj0bt376D9gU5PT8dK3sFziPWenYBE4+syOn+dOb78Rb5jxw4T/DDoYXMYz31sEsPgp2LFiuFp9+yi8S26Zw8qz5iBCnPmID49PWt7erly2Hr//dh1443ITEgA/vorzEfrHjzH7L+1nGVMSEjIOs/sffYMpOc+yR/9/g0tjW/sjG+in0WwIxL4TJkyxbQlbd++fdC+Jn/Jsv1ppDGo45teq1at8C6wFyM0vhrfWD5/+fmc6WFg4+8XelpamtnP9OFY/P3jd3z370fChAkoOmkS4jwDxdKlkd67N449+CDKJyWhfOQO2xUYbG/YsAFbtmxB6dKlzUxjsWLFUKlSJbOfQTfPvYyMDBPw2PtidfaxIPT3TePrZkcddv27Zs2agJ4X8sBn8+bNmDFjBl588UWTp052iho/8g4m72S6faE3vulOOp5oo/HV+Mbi+cuUNt5N5+/IeK4p4+cGEJ/Dj7H8+8eML2cbJk0Cxo4F9uzJ3slx6dkTcY8/jsQyZZD3/UDh32r+7ebfZ55bvHHJ85DBEP/ZkpOTvRab5UfekFTdWQHO3xj++Q01jW9sjG9cgDPOIQ98Nm3aZKbDH3jggRP23XnnnTjzzDPx7rvvhvowRERchxeVvODkBaW/GR87MOLzYhbX/Jk+HXjqKWDLluztTGPr2hUYOBCoXDmSR+gqTGFj/VhKSoqZ6WHww6Cb5x8fr1692jynXr16WcE495UpUwZ79+41n8tZH6W9iYgThfyvZcOGDfH66697bWO+8JgxYzB8+HAtYCoiksudNHZv2717t7mw9LyY5MUn77LzbrwT0gzCLiMDRd55B6cPGYLETZuyt3OM7rgDGDYMUAfRAqWvsHsggxfflum8icltdn0P09uyhz3OfA4/l1/DCXeARUTCHvhwKvy8887zu+/00083/0RE5ES8mGTLat515910Xlh6phax7oL7Y+ruOi/GP/3UzOQU+/1373033sh1E/jHJVJH53o8t9iwgOcZgxtP9uLjPN/8LUTOz+F2fg0RESeK4fwIERHnY70E6yZ81/HhTE/MrePz/ffAgAHAokVem49fdBGKMNUth5tsUrD0Sl/cTpzxsf/vSamXIuJ0EQl8OAP0559/RuKlRURch8ENZ3uYQsSLS16cMr0tZmZ6fvnFCnjmz/fafLxpU6y97z5Uv/tupVaFIL3SN13Ns2U1/+8p5lMvRcQVNOMjIuICTutkGRZcY2fwYMC3AU7DhialLfXKK3Fw1ao8vwwvymM2aCxEeuW+ffvMmLFlNVunM72yQoUK5nncp9RLEXEbBT4iIuIsGzcCTz4JvPoqC0uyt9esCQwfbjUvYKrVv0sj5NWa2TdNkDMaMZcmWID0Sq7RsWvXLhw4cMAEi3Z6JSn1UkTcSIGPiIg4w86d1jo8L74IpKZmb69YERg0COCyCB6dxAIJerioHVsze85OMI2LMxqRWHPGLbNPHJfatWubWR5+5GPPY43p1EsRcS0FPiIiElkHDgDPPQc8+yzgsUAmkpOBJ54wC5CiZMkCr0fj2Qo8kmvOuG32iePC8eKx+aZZxmTqpYi4ngIfERGJjJQUYPJkYPRoYPfu7O3FiwM9egB9+wJlyxZ6PRrfwCYSa844cfZJRCTWKPAREZHwYqvk116z6nU8Fx8tWhS4/36roUHVqkFbj8afcK4548TZJxGRWBQf6QMQEZEYkZFhdWhr1Ajo0iU76OHFfufOwMqVwJQphQ568lqPJtxrzuRn9klEREJHgY+IxDTejT9y5IjpXMWPfCxBH2Rg3jygeXPg1luB1auz9113HfDrr8CbbwJ16wZ9PRoW5/u+p/aaM9zP54Wak2afRERimVLdRCRmua3Y3JUWLQL69wcWLvTe3rq1VdvTsmXI16NhKlkk15zxnH1ielskZ59ERGKZfsuKSExSsXmI/f47MHAg8Mkn3tvPPtsKeK680kpxC8N6NLmtOROO9tL27BMbGXjW+HjOPvGYwjH7JCISyxT4iEjMUbF5CK1dCwwZAsyebaW42erVA0aOBNq3B+LDl2XN4CanNWfCNePnpNknEZFYpsBHRGKO01odR8XCmFu2AE8+CbzyitW1zVa9OjBsGHDXXVbXtgjwt+ZMuGf8Apl9EhGR0FLgIyIxJ9qKzSNaq8T1d556Cpg40VqXx1a+PDBgAPDgg9a6PA4SqRm/3GafREQk9BT4iEjMiaZi84jVKh06BIwfDzz9NHDgQPZ2vtZjjwG9ewPJyXCiSM74+Zt9EhGR8HD+X3URkSCLlmLziMxcpKYCL70EjBoF7NiRvb1YMeDhh60ObpztcbBom/ETEZHAKPARkZgTLcXmYZ25OH4cmDXLqtf555/s7UWKAPfcYzU0qFEDbhBNM34iIhI4LWAqIjHJLjbnzA5nTBgg8CMfhyw9zI0zF+zMNmcO0KSJFeB4Bj1cjHTFCuDll10T9DhtcVMREQkf3c4SkZjltGJzTqpwnc+tW4EqVYBWrawJlYjNXHz1lZW69tNP3tuvvtpKdeOaPC4ULTN+4j6e3RfT09NPCLxFJLQU+IhITHNKsfncuUDPnsCmTd6doCdMANq1C3Ot0o8/Wh3ZvvnGe3vLlsCYMVZE5nJqLy2R7r7If/x/zZo1HfE7SCQWKPAREXFA0NOhg/d6n7R5s7X9/ff9Bz9Bn7n44w9g0CDgP//x3n7GGcDo0cA11/BFES2cNuMn0ctf90X+3PLndN26dea8c0N6rYjbqcZHRCSCmN7GmR5/GS/2tl69rOcVtFaJsz9HjhzBgQMHzMcT0mv+/hu4804rwPEMeurUAd56C/jlF+Daa6Mq6PGd8UtOTjYfFfRIqLsvMi01Pj7efExKSkJqaqrZr7Q3kdDTjI+ISASxpsczvc0XY5SNG63nXXxx/mcucl3clGvxsFZn2jQgPT37C1atanVpu/deICGhUHUMmkWRWJdX90UGP6FaN0pEvCnwERGJIDYyCMbz/NUq5bS46b6//0by2LEo+fbbiDt6NPsTypa1mhlwPZ4CdjTLNdBSKo/EoEC6L/JnVOtGiYSeAh8RkQhi97ZgPi+3xU3jjhxB+VmzUPaVV1DkwIHsJyclAb17A336AKVKoaByCrTYfIH1DG5pEy4STFo3SsQ5FPiIiEQQG6SxexsbGfir82FmDPfnt5GaV3pNejpKv/ceyk2diqI7d2Y9JyMhAce7dEHC0KFAxYqF+j78BVrEOgY+ZvMF7veX7iMSzfLqvsibApwR1bpRIqGnwEdEJIK4Tg9bVrN7G6+HPIMf+/po/Pjc1/PJMb0mLQ1lv/kGFSZNQqJHIVFmfDz2t22LdXfcgTqXXIKEhAQcO3CgUPU4edUxcLvqGCQW5dR9MS0tzWwrX7681o0SCRMFPiIiEcZW1WxZ7W8dHwY9Oa3jk6PMTBSbPx9n9O+Pk9at89p18IorsKtnTxyqUQOH9+3Dhg0bzCxNYetxAqlj4H7VMUgs8rduFP8xCDr11FOVAioSJgp8REQcgMFN27ZW9zY2MmBND9Pb8jvTg2+/NYuPFvvhB6/Nhy+4ADt790ZqkyYmvWb3tm1mlobBDi/KCluPozoGkdz5dl9MT0/H33//bbaJSHgo8BERKYBQtGxmkJNTy+o8/fSTCXjw5Zdemw83boz1Xbsi46KLrOAmLc00IeCx85jLli0blHqcvOoYuFAj1xZSHYPEMs/ui1xTS/VuIuGlwEckzLTGifs5qmXzypXA4MHAnDne208/3azRk9GmDRK2bfM6Vh5jRkaGOeZg1ePkVMfAwJBBT/HixVXHICIiEaXARyRWL5jF3S2b//kHGD4cmDkTyMjI3l6rFvDkk0DnzmYKiUdS8uSTvWanmGKzcuXKoNfj+Ktj4DnOmR6d4yIiEmkKfERi7YJZ3N2yeccOM5ODqVOBtLTs7ZUqWTM/XbrwgHJd3JQpNgxIeP7x2H1xO/fnFBjlp44hWGmAIiIihaXARyRWLpil0CLasnn/fuCZZ4DnnwcOH87eXro08MQTQI8e1kKkDqjH8Q20REREnECBj0gYaI2T6BCRls1HjwKTJgFjxwJ79mRvZ2DB/tePPw6UKZOvL6l6HBERiUUKfETCQGucRIewtmxOTwdmzLDqdbZs8TwI4IEHrLS2ypUL/OVVjyMiIrFGgY9ItF0wS8iEpWUzGxW88w4wZAiwZk32dr7W7bcDw4YBtWsjGFSPIyIisURXWSJhoDVOokNIU8QyM4HPPgMGDgR++817H1c2HTkSaNwYwaZ6HBERiRUKfETCQDUV0SMkKWILFwL9+wOLFnlvb9MGGD0aaNECTnP8uHXYW7cCVaoArVpZC7CKiIg4lQIfkTBRTUX0CFqK2C+/AAMGAPPne29v3twKeC67zEpxc5i5c62+Cps2ZW+rXh2YMAFo1y6SRyYiIpIzBT4iYaSaiuhRqBSxv/6yanhYy+OpQQMrpY3RgwMDHjvo6dDByszztHmztf399xX8iIiIM8VH+gBEYvWCOTk52XzUuj0xhFMkXGC0USPvoKdmTauD27JlQPv2jg16mN7GmR7foIfsbb16Wc8TERFxGgU+IiKhtmsX8NhjQN26wPTp2ZFBhQrA+PHWDNA991itqh2MNT2e6W3+gp+NG63niYiIOI2z/8qKiLjZwYOoMm0aSsyebf6fJTnZWniU0yMlS8It2MggmM8TEREJJwU+IiLBlpICTJmCEqNH4yTO9tiKFwceeQTo2xcoV851487ubcF8noiISDgp8BERCZZjx4CZM4Hhw03Ol12pk1mkCOLuvx8YPBioVs21482W1ezexkYG/up8WJrE/XyeiIiI06jGR0SksDIygHffBU4/HWCAw0KXf+258kqksG311KmuDnqI6/SwZTX59l+wH7NkSev5iIiIEynwEREpKE57cA0errtz661WkwLbtdfi6JIl+HvUKGTWqRM1Y8xO22xZ7RvDcaZHraxFRMTJlOomIlIQixcD/fsD33/vvZ15XmPGAC1bIvPIEWDlyqgbXwY/bdta3dvYyIA1Pfy2NdMjIiIxHfjMmzcPH330EZYvX44DBw7glFNOwR133IH27dtr/RIRcZ/ffwcGDgQ++cR7+9lnA6NHA1de6dh1eIKJQc7FF0f6KERERBwU+Lz22muoVq0a+vXrhzJlymDx4sUYPHgwtm3bhu7du4f65UVEgmPtWmDIEICtqT0r++vVA0aMADp0AOKVPSwiIhKzgc+UKVNQtmzZrMfnn38+9u3bh1dffRUPPfQQ4nWhICJOtmWLFdhw4VF2bfMsahk6FLj7bscvPCoiIiJhaG7gGfTYGjZsiEOHDuEI899FRJxozx5rvZ26da2ObHbQw/V3nn0WWL3a6uCmoEdERMQVInKb8ueff0alSpVQshArlmdmZjoicDp69KjXR9H4uonOXz8OHULRyZOR8PzziDtwIGtzZsmSONajB9K5AGlystXCOo/fQRrf0NL4anzdTOevxtfNjjrs+pdxQVwA9bVxmXxmGP3000+muUHfvn1xN1NECmDZsmVIS0sL+rGJSOD4qyM9PR3Hjx9HkSJFkJCQENGGJYU9nri0NJSfOxdVZsxAAmd7/pWRmIidHTpg2z334FiZMiE6ehERESmMxMRENGnSxDkzPmxo0Lt3b5x33nm48847C/W1eFFTlykoEcZId/369ahVqxZKlCgR6cOJOhpfZ44vU1W3b9+O1NRUZGRkmACjWLFihZ7JLahCHQ8DpdmzkTBqFOI3bMjanBkfj+N33IH0AQNwcvXqOLkAx6XzN7Q0vhpfN9P5q/F1s6MOu/5ds2ZNQM8LW+DDVtZdunRB6dKlMXHixEI3NeCFzUknnQSn4JvupOOJNhpf54zvwYMHsXnzZqSkpCA5ORlFixbFsWPHTPDBj7whcfLJBQkTCqbAx8PJ7g8+AAYNOnGtnZtvRtyIEShav35Qfknq/A0tja/G1810/mp83ayEQ65/A83wCEvvVV6QdO3a1VygTJ8+PawXRSIS3HSyrVu3mp9ptqfntDJvYvAjH3M794crg7bAx/PVV8B55wHt23sHPVyD5+efgXffBerXD8v3ICIiIuER8sCHd1x79eqFdevWmaCHqSci4t6p7f3795v0Md+7K3zM7dwfrmLHfB/Pf/8LXHopcPnlwP/+l/3k888HFiwA5s8HmjYNy7GLiIhIeIU81W348OH49ttvzQKmTD359ddfs/Y1atTI3JkVEXfgjQw2D2A6mT/czv18npOOJ2PZMmDsWODDD72fcMYZwKhRwLXXMlIKyzGLiIhIlAY+ixYtMh/H8qLDx9dff43qXARQRFyBgQQ7pjHg8HfTgtu5P6dAJNzHE/fPP6g7cSKS5s2zanpsdeoATz4JdOwIaBFlERGRmBDyq5Nvvvkm1C8hImEsYixVqhR2795tamg808tYR8NZ3XLlyoWtw0tOx1Nk506UmzoVpd55B/Ges09VqgBDhgD33cfWkGE5RhEREYnhBUxFxJ0YWFSpUgWHDx/G3r17TQ2NZxe14sWLm/3hWs/H93iSMzJQ6fXXUfb11xGfkpL9RK6/068f0L074IDuMyIiIhJ+CnxEJF/YlZEtotktjY0D7AVDOdPDICTcXRvN8VStipSnn0aZl19G0QMHsvZlJiUhrlcvoE8foHTpsB6XiIiIOIsCHxEpULDB2R52S+NsD2d9mHYWrpmeLGlpAFvkjxiBk7dty9qcyXqfrl0RN3AgoE6SIiIiosBHRFy5iPDx48Ds2cDQocC6ddnb2ajgzjsRN2wYcMopkTk2ERERcSTN+IiIe7Az28cfA5zJ+eMP733t2gEjRrBPfqSOTkRERBxMgY+IuAMXGB0wAFiyxHv7ZZcBo0cD55wTqSMTERERF1DgIyLO9vPPVsDzxRfe2889FxgzBrjkkkgdmYiIiLhIfKQPQETEr1WrgJtvBpo39w56mMr2wQfADz8o6BEREZGAacZHRJxlwwaAzQlmzgQyMrK316oFDB8O3HYbUKRIJI9QREREXEiBj4g4w44dVq3OlClWm2ob21EPHgx06QKwTbXErMzMTK8W6nwsIiISKAU+IhJZ+/cDzz4LPPcccPhw9vZSpYC+fYEePYCkpEgeoTjAwYMHT1g0t3jx4khJSYn0oYmIiEso8BGRyDh6FHjxRatBwZ492dtLlLCCnSeeAMqW1bsjJuhZs2aNCXK4cC5nezjrs2fPHvPv0KFDkVtTSkREXEOBj0iM800fKlGihFmcNGTS04FXXwWefBLYvDl7e9GiwAMPAIMGAVWqhO71xXXnJ2d6GPSUKVMm69xMTExE6dKlsX37dvOvQoUKoT1vRUTE9RT4iMQw3infsWMHUlNTTeoQ/5UqVQpVqlTBySefHNwXY6OCd9+16nXWrMnezotVNixg44LatYP7muJ6DMqZ3saZHt/Aho+Z7nbgwAHzPM36iIhIbhT4iMRw+tC6detM8FOxYkUkJSWZWZ/du3fj8OHDqFu3bnCCHxagf/YZMHAg8Ntv3vtuuAEYORJo0qTwryNBd/w4sHAhsHWrNQnXqlX4G+rxnGRND2cj/WGwzv18noiISG60jk+MpIocOXLE3BXlR3VCEjt9iDM9DHiYNhQfH28+Mp2IaUXcX+hzhVfNrVsD113nHfRcfDGweDHwn/8o6HGouXOtDuJt2gCdO1sf+Zjbw4kBD4ObnAIbu9FBToGRiIiITX8pYrATUshSmcR16UMMeji745s+xLQi7i9w+tCvvwIDBgDz5nlvb9YMmaNG4eiFF+IY7+IfORL6miLJNwY3HTpYk3WeWJLF7e+/D7RrF56B5fnB31mcifSs8SEG5gzSk5OTzfNERERyo8AnBjshBT2VSVzHTh9ifYQ/PFcKlD60erVVw/POO97b69c3KW0Hr7gCW7dtw/4VKxSIOzi9rWfPE4Me4jbGHb16AW3bhiftjYEOb9Twd9bevXu9fpft27cPCQkJqFSpkoJnERHJk1LdYqQTUshSmcSV8kof4vZ8pQ9t2mR1ZGvY0DvoqVkTmDED+OMPHLzySqxZu9YE3gy4eBefH/mYAToDdYk8Zify7cwJf2Vs3Gg9L1x4g4Y3asqVK2d+d3E2kh/Lli1r6tMYDImIiORFMz4x2gmp0KlM4mp2+pC/4JeP2fCAF5l5pg/t2mWtw8P1eFJTs7dXqGA1M+jWDShWLNeWxHzMO/nc7+98lfBiI4NgPi+YwQ/PD8/W6zyvVq1aFd4DERER11LgE6Xy6oRU4FQmiQp2+hAXf+QaKGlpaSZliOcDgx7OxHB/jkEIZ2eeew549lnr/7bkZKBPHysXyiONUoG4ewS6hFIkllri+eh5o4bNWkRERAKlwCcGUpl4V73QqUwSdXgHvXbt2ib4YXc3+5zgTE+OzS9SUoApU4DRo63ZHhtrhbp3B/r1A8qVO+HTFIi7B1tWV69uNTLwlwnLWJj7+TwRERE30VVvlMqrE1LAqUwS1Zg6xBqJU0891cz4MBD222WNM4MzZ1qLjLLAw8bq9vvvtxoaVKuW4+soEHcPvqUTJljd23gaeAY/9mkxfnz41/MREREpLDU3iPJUJqYssX6CqUwZGRnmIx/nmcokMYPnAIMdtgRmGpHXOZGRAbz3HtC4sRXgeAY9nToBK1cCU6fmGvR4BuIMuHOqKeJ+BeLOwFbVbFnt+7ZypiecraxFRESCSTM+UczuhOS7jk+uqUwixODkiy+stXiWLvUek2uvNa2pcdZZQWlJHFBNkYQdgxu2rGb3NjYyYE0P09s00yMiIm6lwCfK+euEpAUj3bu+SlguQpcssQKeBQu8t/MFWdtz4YUF+rIKxN2H59fFF0f6KERERIJDgU8M8O2EJO4zd661qKTn+ipMO2ItRtDSjpYts1pQf/yx93bO7DDgueqq7CKPEAXiYQvuREREJOYo8BFxQdDDQnPfDlvsusXtha25SNy0CYlPPw28+673i5x2GjBiBHDzzUB8fMgD8bAEdyIiIhKz1NxAxME4A8JgwF9bYXsbl8zh8/Jt61Yk9OqFxu3bo+g772R/QVa0T5sGLF8O3HprUIOevII7z6DHM7jjfhEREZHCUODjMOxwxUX5Dhw4YD76dsCS2MK0L99gwBNPDzZa4/MCtmePtd5OnTpIePllxNlRE9ffeeYZYPVqoEsXICEBrg/uRERERP6lVDcHOXjw4Akd2NjiVx3YYhdrXYL2vMOHrbyxceOA/fuzNh8/6SRk9OyJBAZDyclwcnAXDYX2vJmhZiMiIiLhp8DHQUHPmjVrkJKS4tXqlwuQsgUw21Kr/XTsYYF/oZ+Xmgq8/LLVgnr79uztxYohvUsXrGjbFqddcAESItQAI6jBncPp5oaIiEjkKNXNIXeAOdPDoKdMmTJITExEfHy8+cjH3M79SnuLvdRDdjVjgX9OzdS4vUYN63knYG7YzJlAgwbAI49kBz2s2bnvPpPSlv7UUzhWpgxcH9y56OYGb2Zw3SLO5vIjH3M794uIiEjoaMbHAZj2wvQ2zvT4LuDIx9zO/Xye2lLH1t15tnJmdhoL/HlqeMZd9qkyfrxPy2c+6cMPgUGDgBUrvL8gO7SxU1v9+tbjI0cins5lB3dsZOAvruSncb/f4M6lNzfssbBvbnBRV+739ztAREREgkMzPg7Ai0NeWPMC0R9u534+T2Lv7jxbObNlNZuteWIwcEIr66+/Blq0sDZ6Bj1XXgn89JPVstoOekKEY7N69WosX74cK1asMB/5OKcxs4M78r3mzzG4cxkGPIHe3BAREZHQUODjAAxsOJuQU2DD7dyfU2Ak0Z96yDhm/Xrg22+Bt96yPv79t0fQ89//ApddZv3j/23nnw8sWADMnw80a+bYgDFfwZ0L6eaGiIhI5OlK2gGYBsQLRF4ceqbBEC+uDx06hHLlypnnSeymHnLG44SuZlxrhyltTG3zdMYZwKhRwLXX5lwg5LB0LgY3bdta3dvYyIA1PUxvC8ZMD8udCvJ1g9WBzfPmBsfDl25uiIiIhJ4CHwfghRTrRti9jReHnl3dGPTwjjn3K/c/PFxxd57TP0OHArNmeRfG1KkDPPkk0LFjWBYeDXbA6De4KyQufsp1gjxbZnMmiel1uc0kBbPGy5790s0NERGRyFGqm0PwQootqzmzY9cD8CMfq5V1eDk69ZCd2dihrV494PXXs4MeTmNMmQKsXAl07hz2oMepASODHjaG8F0niI0UuJ37w1HjZd/c4NfgzY20tDRkZGSYj3ysmxsiIiKhpxkfhwU/vCuuxQ0jy5Gph/v2AU8/bVX5e3ZiYytqLjzavTsQoXV4nJrOxfQ2zvT4K8XiNr6tvXpZ6XWeaW+h6sBm39zwnUXiuaRFikVEREJPgY/D8EJKLasj/x44JvWQQc7EicBTTwF792ZvZ5DTuzfQpw9QujScwGkBI2t6fGd6fIOfjRut53mm14Wyxks3N0RERCJHgY+IE+/Op6cD06dba+6wIt+WkAB06wYMHAhUquSo985RASO8hy0/zwt1yp5uboiIiESGAh8RB92dzzx+HGkzZyJhxAjEs4GBjTU7d9wBDBsG1Krl2Pcs4gGjB5Y9FeR5TkvZExERkeDQX24RJ9ydz8zEkffeQ5HBg1Hsr7+8dqVffz0Sxo4FGjWCGzglnYstq9m9jY0M/NX58HC4n89zcsqeiIiIBIcCH5FIW7AAx/v2xUmeC48COHTeefi7Sxcca9oUdWvUQPjmSgrPCelcbFjAltXs3sbYxTP4sWMZ9orwXc/HaSl7IiIiEhxqZy0SKT//DFx5JdCmDYp4BD1HzzgDG199FZuZ8taypekuxtQxzjZI/nCdnvffB6pV897OmR5uz2kdH7WXFxERiT6a8REJt1WrgMGDrStvDyl16mB3r144dNllWVMShe0iZmPQFOnUs0hhcMOW1ezexkYGrOlhepvvTI9TU/ZEREQkOBT4iITLhg3A8OHAa68BGRlZmzNq1sS6u+5C+i23IJ5d24LcRYyLbfo2G2ANC9O1+P9YwG/Ts2W1m1L2RERExEWpbmvXrsU999yDs846Cy1btsS4cePMiuXiDJwNOHLkCA4cOGA+KqUqyHbutNbcOe00YMaM7KCnYkWzRk/Kr79i7/XX41gOqWyF6SLGoGfNmjWmUJ+1KQx4+JGPuZ01KyIiIiKxIOQzPrzLfNddd6FWrVqYOHEitm/fjrFjx5q6hSFDhoT65aUQswHhbD0clfbvB557zvrnGWCUKgU88QTQsyeQlIQSmZkh6SLGz+V7y581z6/LFs18zMJ9/jwq0BUREZFYEPLA5+233zbdkSZNmoTS/64wzwvs4cOHo2vXrqjksEUYY4k9G8ALY8/OVbwA53vG9VgU/BTA0aPAiy8CbEG9e3f2dgYuPXpYQU/ZsiHvIsbaFAa0/Hq+n2vXDnGWL55rBImIiIhEuZBf8Xz//fc4//zzs4Ieuvrqq5GRkYFFixaF+uUlwNkAzgLwAtieDQikk5hS5HykpwPTplkpbY8/nh30MEXtwQeZ82kFQx5BTyi7iDFw4k2GnFLk7Noh/hMRERGJdiGf8Vm3bh3at2/vtS05ORkVKlQw+wrKvuiONN5V9/zoFjzenTt3olixYn7rrRISEsz+nFKsOBPBNCnOGNgpcnxfOYPHmYRgHqfnR0fKyECROXOQMHIk4tesydqcGReH47fcgvRBg5BZu7a1MZdzlmNYrVo1M+Z2FzHO9nB2piDnenp6unlvOJPEgNYX33fegOB77ejxdTFXnL8upvHV+LqZzl+Nr5sdddjfN8YFgWTGhDzw4YUxL4h9saaBd7ULihd1K1euhFOsX78ebsITlTM67Fjl70SxA0t+9A18OBOxY8cO8x7wwpwX7LzA3rx5s0mdq1ixotke9eObmYnkRYtQbfJkFPvrL69d+1q3xuaHHkJK3bpAaioQgXOV7x1/xhikJiUlnVA7xICIQSr/OXJ8o4jGV+PrZjp/Nb5upvM3dsY30c9N3qhpZ8271EwBckIAwTedzRvyW3we6ePmhTBnfHKaDUhNTUWDBg28vi9eMHOmjjMSTF/0vZjet2+fCXRr165doPVOmHW1aFE8tm2LQ+XKmWja9DA2bnTe+MYvXoyEoUNRZPFir+3HW7dG+rBhSDzvPJyKyKvJVtnr1pn3ksGPXTvEoKd8+fKmdogze04b32jh1t8PbqHx1fi6mc5fja+bHXXY3zfeeA9EyAMfXgSziN4X70Rz1qegnLa+Bt90Jx1PIMfLdEM2MvAtfrdnA7i/bNmyXvs4C8QZH273FzBxO/cX5P2ZO9dqdLZpU/a2atUS0bPnPjz8sEPG99dfgYEDgc8+897erBkwejSKXH45ijhogUuOGd9ru3Mf3xvO0DHgsdfxYeDjtvPXbTS+Gl830/mr8XUznb+xMb5xAV57hTzw4Z1/31oeBkK82OI+iYyCdhILtGA+v4ttMujp0MFkj3nZsiUOTzxRG9Wrp6FTJ0TO6tUA26+//bb39vr1gZEjAdaxOSjg8cTGCHx/eXfGrh3iL6qC1g6JiIiIuFHIu7q1bt0aixcvNrU+tvnz55sOYlzMVCKnIJ3EeNHMWYKcApuCLLbJ9DbO9PhrIJeZaQUTTzyRYJ4Xdps3A127Ag0begc9NWoAr7wC/PGHFbE5NOix2TNwnIHNqa5LREREJJqFfManY8eOmDVrFh5++GGzbg87gY0bN85s1xo+zp4N8If7gr3Y5sKF3ultJ4rDpk1x5nkXX4zwYCtqtp6eNIndHLK3ly9vpbp16wYEuYGDiIiIiLg48OFF8syZMzFixAgT/LDAukOHDujdu3eoX1oClJ96nFAstrl1a3CfVyisRxs/HnjmGbYkzN7O2a8+fQCetwVYU0dEREREIissXd3q1KmD1157LRwvJWFMkbML5u11fDjTw6Anv4ttVqkS3OcVCGd1pk41DQqwc2f29mLFgO7dgX79rNkeEREREXEl17azFnelyOWmVSugenWrnMZfnQ+QierVM9GqVQhK0lirNGsWMGwYsGFD9vYiRYD77gMGD7YOTkRERERcLeTNDSR6BatgnjHGhAn21/R9DSsSGjcu3TwvaBhhvf8+0KQJcO+93kFPx47WgqMvvaSgRwp4elkLALODJdfE4mMRERGJLAU+4gjt2llxSLVq3turVcvEuHHr0LZtkFq68QL0iy+Ac84Bbr4ZWLUqe9811wC//ALMng2cdlpwXk9iDoOd1atXY/ny5fjzzz+xZcsW09Lf33pmIiIiEj5KdRNHBT9t21pd3tjIgDU9zZql4K+/9rHCp/Av8MMPQP/+wIIF3tsvvNCq7WHOnUghMLjh6tFsC89U0ISEBOzbtw979uwxKaE5tYkXERGR0FPgI47CdDbPltVBWV9z2TJg0CDgo4+8t595phXwXH2149fhEedjOhsbfjDosVu9p6ammvq30qVLm9Q37mdApHWUREREwk+pbhK91q0Dbr/dCnA8g566da10tqVLrfQ2BT151qpwAWJ+VK1Kztjog10O/QU2fMzt3M/niYiISPhpxkeiD/PkRowAXn7Z6tpmYwHRkCHAPfcACQmRPELXpG35tiznulwFaVkeC5jKxnHiDI8/3M79fJ6IiIiEnwIfiR579rD9G/DCC7z9nr29XDmrtuehh4ASJSJ5hK6tVbEXqd29e7dZvFa1KifiGDE45DglJiaesJ/buT+nwEhERERCS3+Bxf0OH7b6YTPo2b8/e3vJksCjjwKPPQYkJ0fyCF1fq0K8mOfjvXv3qlbFD65jxRkxBoee42aPKQNGLvLL54mIiEj4KfAR90pNtdLZRo4Etm/P3s677ZzdGTAAqFAhkkcY9bUqXL9JsseGaYAMcBgccpwyMjLMTA87u3G9K+5XYwMREZHIUOAj7nP8OPDGG8CwYcD69dnb4+OBu+8Ghg4FataM5BG6mmpVCo61T0wDtGujGBymp6ejbNmyqFWrlmqjREREIkiBTwQw7YUXRLzAZL4/U190FziggQM+/NBqTb1ihfe+Dh2shgYNGoToXYsdqlUpfPDD2R7+jLNWimv51K5dG0lJSUF6h0RERKQgFPiEmTplFUz8t98CTz4J/Pe/3juuuMJai6dZs2C8PRJArcqhQ4dUq5IHjpmdBsjaKN3YEBERiTwFPmGkTln5F//TTzjt8cdR3DfgadECGDPGe7VTCVmtit3VjUFP8eLFVasiIiIirqMFTCPUKYt3gePj47M6ZXE792uByH8xla1dOxS/6CIkewY9jRsD//kPsHixgp4w1KqwCxnPTdar8CMfq5W1iIiIuJFmfMJEnbICxGYFbFowaxaQkZG1OaNWLcSzhqdTJ6BIkVC9TZJDrYrq0URERMTtFPiEiTpl5YHtqEeNAqZOBdLTszZnVqqEDffcgwp9++Kk0qVD/TZJLrUqIiIiIm6mwCdcA61V3f3btw945hlg/HhrIVIbg5x+/XD0vvuw659/UIFr84iIiIiIFJACnzBRpywfR44AkyYBY8cCe/dmb+fsQq9ewOOPW8EPnyciIiIiUkgKfMJEnbL+xTS2V16xWlNv3Zo9QAkJQNeuwMCBQOXKQR9/rZ0kIiIiEtsU+ERwVffjx4+jSJEiplMW2wdzf9Rio4K33waGDAHWrs3ezjVi7rjDamhw6qkheWmtnSQiIiIiCnzCLOY6ZWVmAp98Ys3kLFvmve/GG4GRI4HTTw/Zy2vtJBEREREhBT4REDOdsr77DhgwwFpzx9OllwKjRwPnnhvWtZPs4NJeO4mLc3I/A9GoDTxFRERExFDgI8G3dKkV8Hz+ufd2BjoMeBj4RMHaScePAwsXWqVKVaoArVppiSERERERp1LgI8Hz55/A4MHAe+95b2/UyEppY2pbGGdWQrl20ty5QM+ewKZN2duqVwcmTADatSvMUYuIiIhIKMSH5KtKbNm4Ebj/fqtWxzPoOeUU4LXXgN9/B266KaxBj+/aSf5wO/fnFBjlFvR06OAd9NDmzdZ27hcRERERZ1HgUwhMdfr++3jMn1/GfOTjmLJzJ9C7N1C3rtWi2h6ASpWAiROtGaC77opY/pe9dtKhQ4dMvY8nPuZ27ufzAsVvkTM9Pl/u369pfeQyRDF3LoiIiIg4nFLdCig71ak4gNqxlep04ADw7LPAc88Bhw5lby9VCnjiCWtgkpIQjWsnsabHd6bHN/jhBBifd/HFwfk+RERERKTwFPgUgJ3q5HvX3051ev/9KA1+jh4FJk8GxowBdu/O3l68ONCjB9C3L1C2LKJ57STPNVeD8TwRERERCQ8FPvmUV6oTJw+Y6tS2bRR1+GKNzKuvAsOHW9GdjbUxrO1hQ4OqVQP+ckwzC+c6RsFcO4nd24L5vGAJ95iKiIiIuI0Cn3yKqVSnjAxr+oqBzV9/ZW/nBXWnTsCTTwJ16uR7QVHf2RfW2RRk9iUSayexZTVTGhn/+Qt+OTTcz+eFS6TGVERERMRN1Nwgn2Ii1YlX9PPmAc2bA7fe6h30XH898OuvwJtvFijoWbNmDXbv3m3qa3hxzo98zO3c73ScxWMdF/lOqNiPx48P32xfNIypiIiISDgo8ImSVKegWbQIuOgi4JprgF9+yd7eurW176OPgDPOKFAqFmclUlJSUKZMGSQmJiI+Pt585GNu537f7mtOxPotToRVq+a9nTM94azviqYxFREREQk1pbpFQapTUPz2GzBwIPDpp97bmzYFRo8GrrgiX+vwsBaK6X6c+WIQ2KzZUZOKxVob39oTPuZ27medSjBS0kKNwQ3ruDy/R77n4azr4lhF05iKiIiIhJICnwKmOrF7G681PYOfSKQ6FdqaNcCQIcDs2d7b69UDRo4E2rcH4uML2Oo7e1vVqsXxyCPJaNs2w+/nsCCf9Sk5LTbqRHyPI1nHxbHimOW0AKsbx1REREQkVJTq5uJUp0LhlFXXrkCDBt5BD7+J6dOB5cuBm28uUNDDoNC3AcTWrXHo378e5s/3P/PAi3MW5ed0ES8n4lhxzHIKbDSmIiIiItl0lVnIVKcvv0zBzz9vQbNmVXH55cWdP9PD9XfGjgUmTQJSUrK3ly8PDBgAPPigtS5P0Ft9xyEuLhNjx1bGFVf8jaJFs1OzWIPCBUW5tg7bMIebW1tB8zjZzICNDFjT43nMkR5TEREREadR4FMIDHJat85AhQp70bBhZWcHPYcOAc8/DzzzDHDgQPZ2tjvu0wfo3dv6f0hbfcdhx45iWLDguBk3BhkMNniBzk5kbL8c7oDDza2gOVY8zsOHD2Pv3r2mpscJYyoiIiLiRAp8oh1ndaZOtRoU7NyZvb1YMaB7d6BfP2u2JwgCbeGdmloWKSmbsgINzkpEItCwW0Gz+5ln0MAZFAYTdevWdXzww+PjcfoGb5EaUxERERGnUuATrVj38frrwLBh1oqqNk5L3Xuv1dCA9TxBFGgL77PProzTTy8V0dQy31bQ9uvbraA5g8L9/jqmOQ2DGx6nG9P1RERERMJFgU+0YYHNnDnA4MHAqlXe+zp2BIYPtzq2haD+JdBW361bx6FIkci2V462VtA8Zjccp4iIiEikKPCJEpkZGUj95BMkDB2KIr/+6r2Ti5GOGgWcdVZI61/c1OpbraBFREREYovaWUeBw19/jaPnn4/ibdt6BT3Hzj8f+P57a1HSAgQ9rH9hvQuL5Bnw8CMfczv3u7nVt1pBi4iIiMQWzfi42R9/IL1vXyR99pnX5qP162N9ly5IadMGdU87DSeHuf7FbvXNLm9seMDaH6bBOWGmJ1ytoN3aIltEREQkWinwcRGuk8Ng4sCv63DuZ0NR6as3keCRT5Z2yinY1aMHDl59tbnITilggX4w6l8Y5Fx8cSG+WRe3gnZzi2wRERGRaKXAxyXmzgVGdd+Ke7eOxAOYhgQcy9qXXrEidj/8MPZzqiUhwWzj5XpBC/Rjpf4lFK2go6FFtoiIiEg0UuDjAh+/vhd/3TUOCzEBJ+Fo1vbdKIsx6IdTH78Rl12fEbQAxbP+heltvrid+3MKjNwkmK2go6lFtoiIiEi0cf+VazQ7fBgZ419Aq8HjcD32ZW0+hCQ8h0fxLB7DASSj8rPpaHPN3yfU0BQ0QAl1/Uu0toKOthbZIiIiItEkpIEPZxtmzJiBBQsWmPQfXjTXr18fPXv2RPPmzUP50u6Wlga8/DIwYgTit29H6X83pyIRU/AgRmMAdqJi1tO3bUvETz+VwHnnHQ1KgBLK+pdoFispgiIiIiJuFNJ21kz5mTZtGk4//XQ89dRTeOaZZ8xMwp133oklS5aE8qXd271g1iygQQOge3dg+3ZrM+LxCu5FPfyF3hjvFfTY/v47BWlpacjIyDAfGbAUJkCx618YOPF95EwFP/Kx6lT8U4tsERERkRid8eGF91dffWWCHVvLli1x3XXXYebMmTif68yItdLnRx8BAwcCy5d7jciOizqg9Xcj8Cca5DpStWuXQEpKcAr0Q1H/EgtiLUVQRERExE1CGvjYbXx9tzHdbcOGDYhmAa/j8s03wIABwI8/em+//HJg9GiUO7s5DtcC4jZb8ZEvfkkuDtqpU3WkpZULeoASrPqXWKAUQRERERHnCntzA16Y//bbb2jWrFmhA4sjR44g0hjceH4k3tnfvn07Dhw4kDUDk5ycjEqVKpkZFIr/+WckDB2KIt9+6/X1jp97LtKHD0dG69bWhtQjGDeuCG67LdEEOZmZ2cFMXJwVCT31VBrS0o6b/9v1JZ7H42b+xtfJ+F5Xq1Ytx/ef/3fCeevW8XUbja/G1810/mp83Uznb2yNb2ZmZkA3/OMy+cwwmjp1Kl544QW8++67aNy4cYG+xrJly0wdixOxDmbHjh1IT083qX680OXFL7cnJCTglCNHcOorr6CMT8BztE4dbH7oIexnwOPnjfvmm9J45pka2LEju710pUppeOyxjbjkkuyOb+IM/LHiOWAHPnzvlSIoIiIiEhpcPqRJkybBnfHhAo28sM9LjRo1TlgDZtGiRZg4cSIeeuihAgc9Nl5Issg+0hjprl+/HrVq1TKBzrp168ysVunSpb0udItu3oyyL7yASl98gbiM7DV3MmrVQvqgQci85RZULVIEVXN4nYYNgW7djmHRogxs2xaHypUz0bJlBooUqQKA/6KT5/iqNkbj6zY6fzW+bqbzV+PrZjp/Y2t816xZE9Dz8h34zJ8/H4MGDcrzeZ999hnq1KmT9Xj58uV45JFHTGOD7uxYFmW1J/abzpmdsmXLZgV9RXbvRrmpU1Hq7bcRn56e/QmVKwODByP+/vtRzM8ioTm56irEJI6vk97vaKPx1fi6mc5fja+b6fzV+LpZCYdcnwWaVZPvwOfmm282//Ljn3/+QZcuXXD22Wdj5MiRiIV1XOIPHkSZGTNQduZMxHvUdBw7+WQce/RRFH/8cSApKaLHKyIiIiISK0Le3IBpcffee69prczaHqaoRSsGPAnp6Sg9bRoqvvoqiuzfn7Uvo0QJ7OrcGZs6dUIDtvF2QHQsIiIiIhIrQhr4MO2LMz1cTHPgwIFYvXp11j6mgjVq1AhRIz0dJd56C2cOG4aiHjVQmQkJ2HfzzdjVrRt2FS2qdVxERERERKIt8Nm1axdWrVpl/v/ggw967WPL32+4ho3bZWSgzPz5KD5jBuLWrcsa0My4OOy79lrs7tEDRytXNi2u2fyAM1/q7iUiIiIiEkWBT/Xq1fHnn38iam3fjuJXXIHav//utTn92muxuVs37KxUyWpnnJJiZnoY9Jx88skRO1wRERERkVgV9gVMo8q0aYj3DHratAFGj0ZCixY4JTMTFY8eNQ0PWPvDrhea6RERERERiQwFPoVx/fXIeOMNHC5ZEgnDhqH4dddlLT7qtHbbIiIiIiKxTIFPYZx1FlJ++QV/rVyJhlxhNMAe4iIiIiIiEl7xGnAREREREYl2CnxERERERCTqKfAREREREZGop8BHRERERESingIfERERERGJegp8REREREQk6inwERERERGRqKfAR0REREREop4CHxERERERiXoKfEREREREJOop8BERERERkahXNNIHIBIMmZmZOHr0KI4dO4aiRYuiRIkSiIuL0+CKiIiIiKHAR1zv4MGD2Lp1K/bv34/jx4+jSJEiKFWqFKpUqYKTTz450ocnIiIiIg6gwEdcH/SsWbMGKSkpKFmypJnt4azP7t27cfjwYdStW1fBj4iIiIioxkfcnd7GmR4GPWXKlEFiYiLi4+PNRz7mdu7n80REREQktqm5gbgWa3qY3saZHt96Hj7mdu7n80REREQktinwEddiShtrepje5g+3cz+fJyIiIiKxTYGPuBYDGzYyyCmw4XbuzykwEhEREZHYocBHXIstq9m97dChQyfU8fAxt3M/nyciIiIisU2Bj7gW63jYsrp48eLYu3cv0tLSkJGRYT7yMbdzv9bzERERERHlAImrcZ0etqz2XcenXLlyWsdHRERERLIo8JGoCH7YwY3d21jXw5oeprdppkdEREREbAp8JCowyDnppJMifRgiIiIi4lCq8RERERERkainGR9xDXZqUzqbiIiIiBSEAh9xBbam3rRpk1cDA7aqZtc21viIiIiIiORGgY84XkpKCtatW2dmfNjEgM0L2MRg9+7dOHz4sOnqpuBHRERERHKjGh9xNAY7nOVJTU1FmTJlkJiYiPj4ePORjxkUsZW17wKmIiIiIiKeFPiIozGwYV1PUlLSCe2p+ZgzQAyM+BwRERERkZwo8BFHY0pbRkaGSW/zh9tZ88PniYiIiIjkRIGPOBoDG6a25RTYcDsbHeQUGImIiIiIkAIfcbTixYujRIkSpomBbx0PH7PbG7u78TkiIiIiIjnRbXJxNNbxMLDhrM7evXu9urox6GFgxJbWvvU/IiIiIiKeFPiI4zG4qVGjBvbt2+e1jk+5cuW0jo+IiIiIBESBj7gCZ3oqVKhgurdxtoezPkxv00yPiIiIiARCgY+4BoOck046KdKHISIiIiIupOYGIiIiIiIS9RT4iIiIiIhI1FPgIyIiIiIiUU+Bj4iIiIiIRD0FPiIiIiIiEvUU+IiIiIiISNRT4CMiIiIiIlFPgY+IiIiIiEQ9BT4iIiIiIhL1FPiIiIiIiEjUU+AjIiIiIiJRT4GPiIiIiIhEPQU+IiIiIiIS9eIyMzMz4TJLly4FDzsxMTHSh2KOIz09HQkJCYiLi4v04UQdja/G1810/mp83Uznr8bXzXT+xtb4pqWlmeNo2rRprs8rChdywgB7HosTArBopfHV+LqZzl+Nr5vp/NX4upnO39ga37i4uIDiA1fO+IiIiIiIiOSHanxERERERCTqKfAREREREZGop8BHRERERESingIfERERERGJegp8REREREQk6inwERERERGRqKfAR0REREREop4CHxERERERiXoKfEREREREJOop8BERERERkainwEdERERERKKeAh8REREREYl6RSN9AG5y/PhxzJgxAwsWLMCaNWuQmZmJ+vXro2fPnmjevHmen3/w4EGMGTMGX331FdLT09GqVSsMGjQIFStWDMvxu8GiRYswd+5c/Pbbb9i4cSNuu+02DBkyJM/P27RpEy699NITtp955pl49913Q3S0sTO+pPM3MN988w3Gjx+Pv//+G1WrVsUDDzyA9u3b5/o5On9PtHbtWowcORK//PILkpKS0LZtW/Tq1QuJiYm5jiV/L7/88st46623sGfPHjRs2BD9+/fHWWedFeA7GBsKOr6XXHIJNm/efML233//HcWKFQvhEbvLP//8g1deecX8rl29ejVq166NTz75JM/P0/kb2vHV+Zu3efPm4aOPPsLy5ctx4MABnHLKKbjjjjvM37G4uDjXn7sKfPIhJSUF06ZNw0033YQuXbogPj7eXFTfeeed5gfw/PPPz/Xz+UeFAdOwYcPMHwheHPHrzJkzB0WL6q2ghQsXYtWqVTjnnHOwf//+fJ/Qjz76KM4777ysx/yDLsEZX52/efvpp5/QvXt3dOjQAQMGDMAPP/yAgQMHmvPwqquu0vkbIJ6bd911F2rVqoWJEydi+/btGDt2rPkdnFegzj+8L7zwAvr06WNuTL355pu499578Z///Ac1atQI9BCiWmHGl6688kozpp7yCphiDS/Gv/vuO3PzLSMjw1wUBkLnb2jHl3T+5u61115DtWrV0K9fP5QpUwaLFy/G4MGDsW3bNvP3zfXnbqYE7NixY5n79u07YdtVV12V2bVr11w/d+nSpZn16tXLXLhwYda2tWvXZtavXz/z008/1bvwr+PHj2eNRZs2bTKHDx8e0Nhs3LjRjO+8efM0liEYX52/gbn33nszb731Vq9tjz76aObVV1+d6+fp/PU2derUzLPOOitz7969WdvefvvtzIYNG2Zu27Ytx3FMSUnJbNq0aeazzz6btS01NdWc60OHDg3wXYx+BR3f/P7eiGWev2v79u2bee211+b5OTp/Qzu+pPM3b7t37z5h26BBg8zvVs9xd+u5qxqffChSpAhKlSp1wjZGtjt27Mj1c7///nskJyejZcuWWds4NcupQO4TC2fRxHnjq/M3b2lpafjxxx9PmNm55pprTFoR09kk8PONM+ilS5fO2nb11VebO7tM18zJ0qVLcejQIfNcz5mIyy+/XL9ngzC+EtrftTp/Qzu+EpiyZcuesI3XqvzdeuTIEdefuzpzCunYsWMmx5RBTG7WrVuHU0899YT8SH4e90lwMI2QP6D8o876qX379mlog0Dnb942bNhgavd8fxfUqVMnawzzovM3+3zzHUfeOKpQoUKu42jv8/cebNmyxaRyScHH1/bxxx+jcePGOPvss0269p9//qlhDQKdv+Gh8zf/fv75Z1SqVAklS5Z0/bmrwpJCmj59usmPvvvuu3N9HgvETj755BO2cwbpjz/+KOxhxDzeWejUqRMuvPBC8wecwejUqVPN2L733ntISEiI+TEqDJ2/ebNrpnj+ebIf51ZTpfP3xPPNdxzt35e5jSM/j2PpW2TPr8UaAH5u8eLFEesKOr52cfgZZ5xhGnewQQp/z3bu3Bkffvihs/L4XUjnb+jp/C1Y7epnn32Gvn37RsW5G/OBDztV5ZWmRvyF7lu8yZQAFoY+9NBD5u6XBHd884Od8Xi33HbuuefitNNOQ9euXfHll1+adKNoFK7xjVX5Gd/CiNXzV9yHM+k2djNl+jbTW9jgx/McFnEinb/5w4YGvXv3Nk2j2MgrGsR84DN//nyvH4ScMNq1U1aIbf4eeeQRXHfddbl2ufCMenkC+WIU7Fs3FE0KOr7BcNFFF+Gkk04y71W0XjiGa3x1/uY9vvbPMYMl3zthlN+f81g4f3M733zHMZDfl/w81lqlpqZ63Xnke8A042j+XRuO8c0paG/WrJk5T6Xw74vO3/DS+Zsz/t5kKitrAXmTP7e6KjeduzEf+Nx8883mX377x/NkYH4z10EIBPMelyxZYqb8POt8uNZHvXr1EK0KMr7ivPHV+Zs3/tJnSiVznblGV165z5L7+eZba8IL9Z07d+Y6jvY+/l5t0KCB13vA1CynpFq4dXwl9O8L6fyVSEtJSTEZB/y98M477/gt1XDruavmBvnEtBf2Ja9SpYrpVx5o7Ujr1q3N3TQGPzaeICtWrDD7JPi+/fZb04GkSZMmGt5C0vmbN6YSMh3g888/9zvbVr169XyNeSyfvzzfuHaEPVtmz27yjqNnZ0xfTZs2NcW3XIDPxoYTX3zxhX7PBmF8/WGNKwufY/E8DTadv+Gn89d/0y6u28eghXXsbGoQTeduzM/45DcC5kzP3r17zaKEXEDL86KnUaNGWY/5/xtvvBGjR482jzk7xMJ7LmrIAjFOBT7//POmFfYVV1wRxLfU3bgi+LJly8z/jx49ajpl8Q8yebYJ9h1fLr7HmTSuEMwpV64i/tJLL5naq8suuyxC3030jK/O38A8+OCDJg+atQ6se2B7a64mzp91Tzp/c9exY0fMmjULDz/8sLnryIuTcePGme2ef4S5CCc7BrEOivh7lc9nWgZbsnI2ffbs2aa743333Rfguxj9Cjq+PJcZkDMNkylCbG7ARb25rMM999wTwe/Iefj7lQts2r932erX/l3LGj6enzp/wzu+On8DM3z4cPNzzgVMOa6//vqr198uXu+6+dxV4JMPu3btMqve2xc4nrjK7TfffJP1+Pjx42ZNBE/jx4/HmDFjzMrYjKgZCLE+o2hRvQ02Xij2798/6/HChQvNP/Jsmeo7vryjzh+yd9991wSo/OPdoUMH9OjRQ+MbhPHV+RsYFnvzFz9/1t9//30zxc90WM+1DXT+5o354DNnzsSIESPMxXlSUpL5eWaRrSeeozxXPfHmFFOKZ8yYgT179pj29iy8V8exwo8vZy2Z9cAbIkyBYfpLixYtzO9Zja+33bt3o2fPnl7b7Mevv/66mR3W+Rve8dX5Gxh7LS/eUPb19ddfm3F087kbx1VMI30QIiIiIiIioaQaHxERERERiXoKfEREREREJOop8BERERERkainwEdERERERKKeAh8REREREYl6CnxERERERCTqKfAREREREZGop8BHRERERESingIfERERERGJegp8REREREQk6inwERERERERRLv/B6PMe16xlUAhAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
     "\n",
@@ -215,7 +255,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6ad0cc8",
+   "id": "88c2002f",
    "metadata": {},
    "source": [
     "### The Outlier Effect\n",
@@ -224,8 +264,58 @@
     "\n",
     "1.  Generate a clean small dataset.\n",
     "2.  Add **one** extreme outlier.\n",
-    "3.  Fit the model and see how the line shifts.\n",
+    "3.  Fit the model and see how the line shifts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1036458a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1. Generate small clean data\n",
+    "X_small = np.linspace(0, 10, 20).reshape(-1, 1)\n",
+    "y_small = 2 * X_small + 5 + np.random.normal(0, 1, (20, 1))\n",
+    "\n",
+    "# 2. Add an extreme outlier at X=10\n",
+    "X_outlier = np.vstack([X_small, [[10]]])\n",
+    "y_outlier = np.vstack([y_small, [[-50]]]) # Massive drop in value\n",
     "\n",
+    "# 3. Fit Model\n",
+    "model_clean = LinearRegression().fit(X_small, y_small)\n",
+    "model_outlier = LinearRegression().fit(X_outlier, y_outlier)\n",
+    "\n",
+    "# 4. Visualize\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(X_small, y_small, color='blue', label='Normal Data')\n",
+    "plt.scatter([10], [-50], color='red', s=100, marker='x', label='The Outlier')\n",
+    "\n",
+    "# Plot lines\n",
+    "plt.plot(X_small, model_clean.predict(X_small), 'b--', label='Original Line')\n",
+    "plt.plot(X_small, model_outlier.predict(X_small), 'r-', label='Line with Outlier')\n",
+    "\n",
+    "plt.title(\"Impact of a Single Outlier\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6ad0cc8",
+   "metadata": {},
+   "source": [
     "**Observation**: Notice how the red line is \"pulled\" down significantly by just one point?"
    ]
   },
@@ -245,6 +335,43 @@
     "Run this for noise levels [1, 5, 10, 20] to see how performance degrades."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9da6f40e",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Noise Level:  1 | R² Score: 0.9800\n",
+      "Noise Level:  5 | R² Score: 0.5871\n",
+      "Noise Level: 10 | R² Score: 0.2727\n",
+      "Noise Level: 20 | R² Score: 0.0834\n"
+     ]
+    }
+   ],
+   "source": [
+    "def evaluate_noise(noise_std):\n",
+    "    # Generate\n",
+    "    X = np.linspace(0, 10, 100).reshape(-1, 1)\n",
+    "    y = 2 * X + 5 + np.random.normal(0, noise_std, (100, 1))\n",
+    "\n",
+    "    # Split & Train\n",
+    "    X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "    model = LinearRegression().fit(X_tr, y_tr)\n",
+    "\n",
+    "    return model.score(X_te, y_te)\n",
+    "\n",
+    "# Run Experiment\n",
+    "for noise in [1, 5, 10, 20]:\n",
+    "    score = evaluate_noise(noise)\n",
+    "    print(f\"Noise Level: {noise:2d} | R² Score: {score:.4f}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "59280dbc",
@@ -265,9 +392,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C02 Regression Diagnostics.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C02 Regression Diagnostics.ipynb
index a86a279..3f6aa09 100644
--- a/src/S02 Supervised Learning - Regression and Evaluation/S02C02 Regression Diagnostics.ipynb	
+++ b/src/S02 Supervised Learning - Regression and Evaluation/S02C02 Regression Diagnostics.ipynb	
@@ -56,12 +56,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "58b68639",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -99,10 +110,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "5355a715",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R² Score: 0.9140\n"
+     ]
+    }
+   ],
    "source": [
     "model = LinearRegression()\n",
     "model.fit(X, y)\n",
@@ -131,10 +150,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "4e97b337",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "residuals = y - y_pred\n",
     "\n",
@@ -167,10 +197,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "cb6dcf5c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Standardize residuals (subtract mean, divide by std)\n",
     "residuals_standardized = (residuals - residuals.mean()) / residuals.std()\n",
@@ -213,10 +254,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "8560c7f4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import scipy.stats as stats\n",
     "\n",
@@ -248,7 +300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "63d8354d",
    "metadata": {
     "lines_to_next_cell": 2
@@ -294,12 +346,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "b75f2b0c",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Durbin-Watson statistic: 0.437\n",
+      "⚠️  Strong positive autocorrelation detected!\n"
+     ]
+    }
+   ],
    "source": [
     "dw_stat = durbin_watson(residuals_time)\n",
     "print(f\"Durbin-Watson statistic: {dw_stat:.3f}\")\n",
@@ -324,10 +385,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "cd8f72b7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, ax = plt.subplots(figsize=(10, 5))\n",
     "plot_acf(residuals_time, lags=20, ax=ax)\n",
@@ -365,14 +437,87 @@
     "Generate data following a parabola: $y = x^2 + \\text{noise}$."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b4ed3454",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JVq2tlp21TdLU0iZDtgZkfGmjKvCaiPpnsQ4l6Z7YiyAFVxErV67sth21YmbOnNnVXXTSSSepXhcEMuEBzOeff64eMwOuWk1ERFa3bXeTvPT3Teo2z+WQ4oJMdRu+PZkyB9sdiGJ3UFNTI21tbV0BCyrvFhUVycKFC+Xmm2+WcePGyfTp01UA8+mnn8rzzz/f9Tqo0Iv6MKjYe+utt6pidz//+c/l0EMPldNPP13MIJZVqxtavFy1moiITCkYDKkemJZ2n4wZniednZ3i9aRJTlZm14jD6rXVqsBrskYcBhXEHDhwQG644YZu27T7zz33nApasBQBgh0UtluyZImaSr148WIVuIRDhd4HH3xQLWMQCARUUHPnnXeaplovV60mIiKrBi91B9plR22zbK1ukmGF2T2GfHC/uDBbdu1tVc9NVoHXQUUIY8aMkU2bNvX7vAsuuED96wumWD/wwAPqnxlpq1ajKw1RafgO1latxppJXLWaiIjMlv9SvbdVWtp8srexXdo86InJF1dWespHHMzRzWGiVavrG92qSw0RKXao1xdQAQxXrSYiIjPZ9mWeC4aPcE5zZSFI8UhTm0+8viaZMKp7fTec7xyZ6WpkIll0Tey1G65aTUREVsx/cWVjpq1DCvKyJCNNVPmQuv3uroWftRGHcSPykzriwJ6YBI0VaitUI6Hp8rO5ajUREZkTzmvrttTLpp0NahQhTQ6mRyBNArkuHd6AeHwBaXH7JC8rXbK9AWlq86RkxIFBzCCguB1qw2CsEDOT0I2GvJhEzZUnIiJKRQ7Mpp0NKjUi25kh+xo9KnhBkIJ/OL/t3teqel4a20LicPpVzufsFJz7GMQMEJYXQHVeFLfDWCGmVnf4AuoPAHkx8796KAMZIiIyZQ5Mvsupzmvp6aLud3ib1DlNC2TGj8wXlzNDjhyfIf91zGQpHzMsJYVcmRMz0K62qnZpdfu7xgqx83CL+9jhmCuP5xEREZktB6a4MFvlwAQCQcnNzhRfICi19W0ioYP5L5iFNHFMoUwZmyOlw3JTVomeQcwAYIVqLPBYVJjV71x5IiIis1WdT/sy/8XpyFA5n1jMuNXtkwMtHjXMhN6YkypHSXocSwTogUHMALg7Dq5QjQUeo8HUauTIDHauPCLjmvo22byrUd2yZ4eIiJJVdb7gy/wXzEgKBkU6fJ2qpwb5L0iZMMKCxsyJGQBXdqaKSjHFDAs8RkrEXPnwAkNMGiYiolRUnS/IdUq+a6hK4kVPzIVnTpHKQ0rU8JHb7U75TmFPzACMLHJJSUGmNDR7u+bIaxIxVz58Ia18l0ONN+ancIEtIiKytlFfVp3H+SvyvAYeb0Amjy/qCmCMgkHMQD609DSpLD8YWGBs0N3hl85gSN1qY4UDnSsfrcAQk4aJiCgZVecLcp0JP6/piUHMAI0qcsp5s8rVeCFmKdXtPzhbSRsrHOhc+cjkqnBMGiYiIr1MDDt/JfK8pifmxAwCkpqmlI/oVrEXXXKDiVSjJVeleoEtIiKypqDJq84ziBkk7NhELjneW3JVKhfYIiIi69lmgQkkHE4yUXJVqhbYIiIia9lmkQkkDGIMxqzJVUREZA5BC00gYRBjQGZMriIiInOos9AEEubEGBQCFSRYmSW5ioiIzKHdQhNIGMTYKGmYiIgo10ITSDicREREZCOjLDSBhEEMERGRjaRbaAIJgxgiIiKbmWiRCSTMiSEiIrKhiRaYQMIghoiIyKbSTT6BhMNJREREZEoMYoiIiMiUGMQQERGRKTGIISIiIlNiEENERESmxCCGiIiITIlBDBEREZkSgxgiIiIyJQYxREREZEoMYoiIiMiUGMQQERGRKTGIISIiIlNiEENERESmxFWsUyQYDJl6+XMiIqJUYxCTAtt2N8mqtdVSvbdV/IGgODLTZeyIfDn12LEyccyQVLwlIiIi02EQk4IA5qW/b5KWdp8UF2ZLtjNTOnwBtb2+0S3zv3pon4EMe3CIiIgOYhCTRAhA0AODAGbM8DxJSzs4fOTKdkhOVqbs3tcmq9dWS1lpYdShJfbgEBER/QcTe5MIOTAYQkIPjBbAaHAf23ftbVXP660HB7f5LoeUDstVt+HbiYiI7CQpQcyqVavkggsukGnTpskJJ5wgN9xwg1RXV/d43ssvvyxnnHGGTJ06Vc4++2x54403xEqQxIscGAwhRZPlzFSP43l99eCg5wY9NbjFfWxHDw6eR0REZBe6BzEffPCBLFiwQCoqKuTxxx+X22+/Xb744gu57LLLpKOjo+t5y5cvl7vuukvOOussWbp0qVRWVqqfW7dunVgFZiEhiRc5MNF4fQH1OJ6XqB4cIiIyN1yg1tS3yeZdjeqWF6xJzIlBcFJaWioPPPBA1wm4qKhILrnkEtmwYYMce+yxatuiRYtk7ty5cuONN6r7M2bMkM2bN6vAB0GNFWAaNWYhYegHOTDhAUkoFJIDzR1SMWaIel68PTgNLd4ePThERGRuzIVMcU9MIBCQ3Nzcbifs/Pz8rhM3YGhpx44dqhcm3Jw5c2TNmjXi8/nECjAEhGnUBblOlcTr7vBLZzCkbnEf22cfO7ZHUu9Ae3CIiMi8mAtpgJ6Yc889V/7yl7/ICy+8oPJcmpqa5Gc/+5kcdthhcvTRR6vnVFVVqduysrJuPztx4kTx+/0qyMH/44Ugye12S6J5PJ5ut/EYVeSUr584Xt5cVys1+9rFFwiKMzNdJozMk5MqR6nHI99zYU6ajCrOlu01rVJa4urRg1Pf4Jby0QXqeYlo72DaZwZWb58d2mj19tmhjWxf3zBk9Nr7VdLU0tF13A8GO8WZmSYji7Kltt4tr7+/XS46c1LKCqXqtQ9xXotMnehNWkjrDtEREnS/973vSXv7wZyNKVOmyFNPPSXDhg1T91955RW55ZZb5J133pGSkpKun1u/fr2cf/758rvf/a4r4IkVftbIPTjBUEgaWgPi9Ycky5EmRfkH48nIbelf7si6Bp+8ub5F3N6gFORmiCMjTfydIWlp7xRXdrqcfESBCoCIiMj89rf4ZcXaJnFlpUuWo+egidcfVOeDOccOkWEF1uuFdzqdapJPyntiPvroI/n+978v//M//yOnnHKK6on55S9/KVdeeaW8+OKLkp2drdvvdjgcKqE40RB1YvhrwoQJkpOTk5DX3F7b8mXvjL+rd2b0cKecXFkqZaUFMkX1VGnPaRfPl8854pCDPTh4jpHbZyRWb58d2mj19tmhjWxf37bubhbneq+UFLui9rQgFWHPAbeMGj1eKsYUipX24datW2N+ru5BzP3336+SdG+77baubZh5hIAGw0zf+MY3pLDw4A5obW3t1hPT0tKibrXH44XuKJfLJXrBTkvE62Pc889v7+xRxXfnnna1Xavie3iFS6aUj0jamkuJap9RWb19dmij1dtnhzayfdEVDw1KdpZDAkERV5RJHb4Ov3q8eGh+yv8+Er0PYx1KSkpi77Zt22Ty5Mndto0cOVKGDh0qu3btUvfLy8u75cZocB+9KWPHjhWrircGDB4bXZInk8YNVbdcNJKIyHq02ayYtRqZ9aHNZh03Ir/HbFa70T2IwfTqzz//vNu2mpoaaWxslNGjR6v7CFLQHbVy5cpuz1uxYoXMnDlTjY1ZFWvAEBFRomaz2q2+jO7DSfPnz1c1YjCsNHv2bJUT88QTT0hxcXG3KdULFy6Um2++WcaNGyfTp09XAcynn34qzz//vFgZa8AQEVE0SCNAOgF661HwFPXAUE4D9cQQwEzsY7Fgu9SX0T2Iufjii1VPCmYYLVu2TNWMQU7Mo48+qoaUNPPmzVNJQihst2TJEjXdevHixWqpAisLrwGDIaRIrAFDRGRfCDiwKHA8uZDbvlxTLzLPEtvrG91deZZWoHsQgwSdb37zm+pff7C+Ev7ZyUCr+BIRkT1ouZADybNM+/KcgotknGMwFIU8SwRGVsip5CrWFhj3JCIismOeJYMYA4174rbV7Ze6/e3qFj0wVur2IyKi1OdZ+gNBy6y1p/twEuk37klERGTnPEsGMSYd9yQiIrJ7niWDGCIiIpMLBkNdPflTJw6TfQ1ulVeJHBgMIaEHBgGM1fIsGcQQERGZ2LYoNWEQrGQ5c9QspXjqy5gNgxgiIiKT2tZLTZj9TR7Jdzlkzn9NkOLCHMvmWXJ2EhERkQXX3mt1+2XDtgOqB8aqa+0xiLEgO6yXQURkd3U2qwkTDYeTLMYu62UQEdldeww1YZAPY5WaMNEwiLEQO62XQURkd7k2qwkTDYeTbDI2iu1YL4NDS0RE1qoJc6C5Q9WACafVhBk3It8yNWGiYRBjERwbJSKyl3Suvccgxirstl4GERGJ7dfeY06MRXBslIjInibaeO09BjEWYbf1MoiIKL6198KXJrBKoMMgxmJjo5iFZIf1MoiIKHZWLb/BIMZC8IeIadTaH6qV18sgIqLYWLn8BoMYi7Hz2CgRkdnoPcQTjCi/oaUaoPwGUg/Qc4/yGzhvmPE8wSDGpmOjRERk/SGeujiWJjDjeYNBDBERUZIla4in3eJLE7DYHRERkYEqrDe3eeWVt7bJpp0Ng17ENzdsaYJozL40AXtiiIiIkqivIZ5Wt18FMdX72mTXvjbJy3EMaohplMXLb7AnhoiIKIl6G+JBzwyCjfaOgKSnpUlRfpbkuxxdQ0+4jVe6xZcmYBBDRESURNGGeNArgqEjn79Tsp0Z6vEsZ0avi/jitqa+TTbvaux3yEkrv4Fb9PTU7W9Xt+iBMfP0auBwEhERURJFG+JxdwRUD012FhJ8O6Uw1ymuLEfUWUQd3kDcs5omWrT8BoMYIiKiFFdY9/o7VUAS6AxKliNDSjHdOa3nLKJNOxtlzfraAc1qSrdg+Q0OJ5lcPF2KRERkDJFDPI0tXgmGQpKb7VDbkKsSOYsoMyNNPtlS3+usppaIISc7YE+MiVl1LQwiIjsIH+Jpdfvk1fd2qHwVJPOG02YRjSxySUNLh2UL1w0Ee2JMKjxbHX/wpcNyB53FTkREyaUN8UweXyRnn1guhXm9zyKqnFTSb+E6fyBo2sJ1A8EgxoKFkuzYpUhEZHb9zSKaNL7I0oXrBoLDSSZk9bUwiIjsqq9ZRLgwtXLhuoFgEGNCVl8Lg4jIznqbRRRtVlOWM1P1wCCAMXvhuoFgEGPyQkkYQopkxy5FIqJUQi9JeO9JYU6arkNO2qSOhhavOt6jB2a2DSd1MIgxIauvhUFEZPaZoqOKs2VcoU+m6PD7rFq4biAYxJgQuxSJiIxBmxEaWXxue02rbN/VIWVlLXJ4hSvhv9eKhesGgrOTTMrKa2EQEZl9pmhpiUvc3qC8ta6OM0V1xJ4YE2OXIhGRcWeKFuRmqARczhTVD4MYk2OXIhGRMWeKOjLSxGOz4nPJxuEkIiKiQc4UjcbfGRInZ4rqikEMERHRIGaKYkYoZoaGw/3mtoAMyc+SNrePC/SaPYj505/+JF//+tdl6tSpMn36dLn88sulo6Oj6/HVq1fL2WefrR4/44wzZNmyZcl6a0RERAOeKYoic5HrHVXVtEhbR1BNunj6b5/Lk3/8VJ56ZQPXtTNjTswTTzwhS5culauvvloqKyulsbFR1qxZI52dnerxtWvXyoIFC+T888+X22+/Xd5//3254447JDc3V84888xkvEUiIqKEFJ/zBzrF4+2ULEe6lAzNkdycLDXkhOnYqLbLGaQmCmKqqqpk8eLF8stf/lJOPvnkru3obQkPco488ki577771P0ZM2ZIdXW1LFq0iEFMCqpNpktAghFdo0RE1P9M0Va3T159b4ekSUhyHWmqIKk27Rr/R48NFujF8+1YnM50Qcwf//hHGTNmTLcAJpzP55MPPvhAbr755m7b58yZI3/7299k9+7d6ucpedUmM9JCkpPhFdcQfYo0ERFZdaZoTX2bNLR0qGnXXk/3hF8u0GvCnJhPPvlEJk2apHpiZs6cKUcccYTMnz9fbYddu3aJ3++X8vLybj83ceLErp4cGnwvC75Ym3c19kgu06pN4jbf5ZDSYbmS53JIXYNflr1RxfFbIqIBTLvOcmREfRwLNuJxTrs2SU9MfX29bNiwQTZv3iz33HOP5OTkyJNPPimXXXaZvP7669Lc3KyeV1BQ0O3ntPva4wOB7HC32y2J5vF4ut0a2fbaFnlzXa3U7GsXXyCopvuNHp4rJ1eWyviR+fLa+1XS1NKhqkviKiEY7BRnRpoMK8yUprYOef397XLRmZMs1e1ppv03UFZvo9XbZ4c2WrV9GI5Hb7bb41P3OwMHcz81Hu/Bx/E8Pc5PVtiHOHdHFg9MWRCjBRK/+MUvZPLkyWrbUUcdJbNnz5bnn39eTjjhBN1+N3p4Nm7cqNvr79ixQ4ysrsEnb65vUaWvUTkyJzNN/IGQfLalTbbvqpejyl2yaXu7uLLSpbm5ezEm/AFlZQTki+175d1/e2VYgfVWxDb6/ksEq7fR6u2zQxut1j7kE2I4vq7ery4GW9tau50P9zcHpLTYIY37dkpzvTUuDnfosA+dTqcxghj0qAwZMqQrgAHcP+yww2Tr1q0yd+5cta219T87GlpaWtRtYWHhgH+3w+GQiooKSTREndhpEyZMUD1LRoQhow9WbhLJyJaK8Qd7WTTDh4Wktt4t1U0OcWblSEmxq1tPC64c8MUbWlgggWavjBo9XirGDHw/GI0Z9t9gWb2NVm+fHdpolPbhWLmnwS3ujoC4sjNlZFH34+FAIJ/w/1Ztkf0NbTKqpEBycpzi83dKQ7NXRpTkytdPKZey0u6jD2bk0WkfIjaIle5BDIII5L1E4/V6Zdy4cSrYQO7LiSee2PWYlgsTmSsTD5y4XS79ElOx0/R8/cFA7kvdgQ4pKXKpzzcSttc3eSQtLV0CQRFXlLLZnSGR7CyHFA/NN2w7rbr/EsXqbbR6++zQxlS2L3JSA6rvongdar8MZhFdTIj4H9RHW7VBPL6gtHm96rUnjS+S2YN8bTvsw7QYh5KSEsTMmjVLzVDCsM6UKVPUNtSJ+eyzz+TSSy9VXUYofvfaa6/JJZdc0vVzK1asUMm9nJmkz5oeSC5LT0uT4sIcOdDsUVP/wv9w0O2JqwZ86VCVkojISrRJDViBGjOJcKxMZC0X9LScccwQGTp8vAQlUy1RgGOplfILbTE76bTTTlNVeK+//noVmKxatUoVvUPw8q1vfUs955prrpF169bJD3/4QzXdGvVhML164cKFer89267p4fUFxOnIkBMrR/eoNonEM4zb5uc61FUDv3REZCUYQkIPDAKYMcPzVA0XrZYL7mM7armEz+QcCFwoYsbnpHFD1fRrHktNGMSkp6fLkiVLVKXeu+++W7773e9KXl6evPDCC1JSUqKec+yxx8pjjz0mH374oXznO99RAcz9998vZ511lt5vz7ZremD7uBH5Mv3wkV1XHK1uvyqR3eb2q8Sz804pt1y3JxERitJhCAk9MJFDF5G1XMjYkrLsQFFRkfzkJz/p8zmnnnqq+keJXdMD3aLoZcGXEkNI6IFBAIPeF62XJbzapFaxF5nzVkg8IyIayHA7lg9gLRfjS0oQQ8ZZ0wNDTBVjhsgpx4yR7KxMVQBPG6tFdydgSrxVpv4REfU13I4hpEi42MPjeB4ZG4MYi4vsZcGX0tMRkNUfJj4jn4jITMPtSOKNNqkBvdW42NNjUkP4OnVM9h08BjE2WtMD8KX9/T/6zsgfVRRbkSEiIqsPt5thSred6Z7YS/bLyCciMstwe/ikBtyiB2aw06ujibZOHW7Dt1P82BNjI7Fm5KN6JRGRHYfb9ajlEnkBqR1/cQGJ4Sz0BuECEu+F07DjwyDGRmLNyEf5bSIiuw23G2FKt97vxWo4nGQjsRTAw+NYP4SIiJJ3AYnHOaU7fgxibCTWAnhYAI2IiJJ7Ackp3fFjEGPDjPzIZQZwi/t6ZeQTEdlZrBeQXKcufgxibCbZGflERHbHC0j9MPnBhpKVkU9ERP1XUEcPOC8gB4ZBjE0lIyOfiIj+gxeQiccghoiIKEl4AZlYzIkhIiIiU2IQQ0RERKbE4SQyLK72SkREfWEQQ4bE1V6JiKg/DGLIcLRVXbFYGtYUQaluVLrE9vpGN+vZEBGRwpwYMpTI1V6xyiuy+XGL+9iO1V7xPCIisjcGMWQo8az2SkRE9sYghgyFq70SEVGsGMSQoXC1VyIiihWDGDIUrvZKRESxYhBDhsLVXomIKFYMYsiwq73ittXtl7r97eq2Imw7ERER68SQIXG1VyIyMlYUNwYGMWTYLztXeyUiI2JFceNgEEO64pediKyEFcWNhUEM6YZfdiKyckVxrSAnKornZGXK7n1tqqJ4WWlhqt+qbTCxl3TB5QOIyGpYUdx4GMSQLvhlJyKrYUVx42EQQ7rgl52IrIYVxY2HQQzpgl92IjLqUHdNfZts3tWobnE/VqwobjxM7CVdaF92JPci4S18RepQKCQHmjtU8To8j4jIDLMltYri9Y1ulcRbXJgtWc5M8foC6phWkOuU2ceOjamEBCUGe2JIF1w+gIiMOFsSt/kuh5QOy1W34dtjwYrixsKeGNKtkJ32ZdeufBpavOrKBz0wuFrh8gFEZLSp0bH0orCiuHEwiCFdu2b5ZSciM82WHF2SF9NrsqK4MTCIId0L2fHLTkRGny2JnmI8j8yFOTEUNxayIyIz4WxJ62IQQ3FjITsiMhNOjbYuBjEUNxayIyIz4WxJ62IQQ3Fj1ywRmU08U6MHUxCPLJ7Y297eLmeddZbs3btX/vCHP8jUqVO7Hnv55ZflqaeektraWikrK5ObbrpJZs2aley3SP1gITsiMqNYZktGm3WJadlHVgyT4sKcqD9DNgpifvnLX0pnZ2eP7cuXL5e77rpLrr76apkxY4asWLFCFixYIC+88IJUVlYm+21SH1i1kojMqq/ZktFmXe5v8sh7n9bK2+tqVBCDqrzxVPklCw0nbdu2TV588UVZuHBhj8cWLVokc+fOlRtvvFEFMffdd5/qpXn88ceT+RYpRqxaSURWn3XZ5vF3DSdhRCkQ6JT8nPir/JJFemLuv/9+mT9/vhoqClddXS07duyQW265pdv2OXPmyMMPPyw+n0+cTmcy3yrFgIXsiMiqsy6xxhsCGJ+/Uw0hdQZD0t4REEkTFeTEW+WXTN4Ts3LlStm8ebNcd911PR6rqqpSt5HBzcSJE8Xv96sgh4zdNTtp3FB1yy8zEVlh1qW7I6C2ZX+5gG1GerrqkcFzIqv8ksV7Yjwejzz00EMqUTcvr+dYZHNzs7otKCjotl27rz0eL0TSbrdb9GhP+K3VsH3mx31oftyHyZUuAclIC0m7x6vWU/L6/NLZGRRnZpoEg0HVE4MVC9IkJIFAQDLSRTq8fjnQ2CpDc9Ntt//0bCPO3ZHLQ6Q0iHniiSekuLhYzjvvPEkm9OJs3LhRt9fHEJiVsX3mx31ofmbeh8FQSBpaA+L1hyTLkSZF+ZmSHnFyMkr78F5zMryyu65NhhVmqvcc7AxIh7dTMtLTpMMXFFdWung9reLrSBOvPyg+b1DqanaKv9XR6+sapX160qONsaaQ6B7E1NTUyG9+8xuVoNva2qq2ab0juMWU68LCQnUfj5eUlHT9bEtLi7rVHo+Xw+GQiooKSTREndhpEyZMkJycHLEats/8uA/Nz+z7cHtti7y5rlZq9vnFF0CPRrqMHu6UkytLpay0wJDtcw1pkWVvVKn6MUMLnJLvSZOWdr9K6s3JdsqE0gLJdzlUT0FtvVsmlxXI8V+ZFHUY3YjtSzS92rh169aYn6t7ELN7927VI3LllVf2eOziiy+Wo446Sh555JGu3Jjy8vKux3EfgcjYsWMH9LvRHeVyuUQv2Gl6vn6qsX3mx31ofmbch5i18+e3d/ZYIHbnnna1HcXlRhXlGK59h1e4JDs7u6tOTJbTIWluDB2lyZgR+VKQly1eX0AONHfIkIJsOX1GmeTl5fb5mkZqn14S3cZYh5KSEsRMmTJFnnvuuW7bMMTz4IMPyr333qumUSNIQSSH5N/TTjut63moFTNz5kzOTCIiMulUZe2EhCnLyDXRZvV887T/XLAaedYlApb1W+ulel+bqvKL4neo8jubdWIMQfcgBsm506dPj/rY4Ycfrv4BasfcfPPNMm7cOPV8BDCffvqpPP/883q/RSIiSvICsXsaEj/pQq+CeNMPH9lnlV+yUcXe3sybN0+Nry1dulSWLFmiplsvXrxYpk2bluq3RkRECVwgtqHFq6YwW6HKL9kwiEFPy6ZNm3psv+CCC9Q/IiIy/wKxGEKKhJwSPO7KzpTmg3M9iAaMq1hTwnEFWCL70haIRS4JZvGEw31sHzciX0YW6Z/symOR9RlmOImMRzsAxDMOHG0FWC6WRmQfRlkglscie2AQQ1HVNfjkg5WbpO5AR8zBSLQVYNGljO04oGFaJVd9JbLPArHaBQ1yYCJn9Wj1wgZysdQfHovsg0EMRS9Stb5FJCNbSopcMQUjsU6r5GJpRPYQywKxA7lY6g+PRfbCIIZ6HABQZdPtDUrFeJcqNhgejOCq6pW3tsmc48skz+XsOijFOq0Sz2OWP5E1jhX9TTvua1bPQC6WYvn9PBbZC4MY6gYHgJp97VKQm9EjGEEp7uY2ryr6tGtfm+TlOLqumrA4WizTKnHAISJzG2y+SX8XS/313Pb1+3ksshcGMdQNggysc5KT2f3AgWEiHDi8/k61gFtRfpZkOTO6rpowzh3LtEpcMRGReSUi36Svi6X+em77+/08FtkLp1hTNwgysFCbPxDqNi0SiXc+f6dkOzNUMIIABsEK8l9wMFm/db+MHZ7X77RKdPkSkTlF5pvgGICekvBjAXpQ8LxYLpYcERdL4T236GGJ7LmN5ffzWGQvDGKoGwQZo4fnSkt7Z1cwgsqaOJhkZ2F5+qAaRnJlObpdNWGIaWpFiZo+ia5gd4dfdeviFveTNa2SiPQTT75JvBdLsfTcxvL7eSyyFwYx1P0PIj1NTq4sFVdWulpqHkEIhpBwVeTxBtSBpxTdu2k9r5pwANG6kpE/g8XScItplXaeXs2CW2SnJQWi9aDEcrEUS89trL+fxyL7YE4M9VBWWiAnTy2QXc1Zaupjm9svwVBICnKcMn5UgepV6e2qCePX/U2rtBMW3CI7LinQX+6bdrG0fVe9uljC7KRYCuLF8/t5LLIHBjEU1agip5wy81Bp9oSk1e2TV9/boXpW8l2OqFdN6G3RrpqSuVhaLNM8U4UFt8iqSwrgbxuziMKHdKIdC+K5WIpWEG+wv58LN1ofgxjq1cEDwMGDgSMjXc0ISFQZ8UQEH0bu5WDBLbKiRC8pEH6xFMuxwChLGpBxMIihhJUR1zv4CA98cMBa9e9dqpfIiEscsOAWWVUijwWRF0up+P1kbgxiKKFlxPUaYgkPfDDVe1+jR4KdQZk0fmjX2LiRljiIJQGRxf/IzscCM/9+Mg4GMRSXwYwxD3SIJTLw6ezMlNr6NukMiVTVNKsDmpZsbJQlDhKVAElkVKnON0n17ydj4BRrMnSNiWjFrTBTCs8vcDlUwSwENBKKf5qnnrQERBb/IyLSD4MYMnSNiWiBT2ZGuroKQzE9VBBu8/jF7fUbqpdDS0Bk8T+i+LG2EsWKw0lk6CGWaIGPKztTPaelzXuwZ+bLxScHMs1TT0xAJIqfkWcdkvEwiCFD15iIFvjg5zAW3uENSKvHJxnpaeofqgsbbZolExCJYsfaShQvBjGUNAOp8dBb4IPnlo8ulM27GiUjPV0aW73idGQYcpolExCJ+sfaSjQQDGLI0EMsfQU+SPY9ZOwQOfUr46S4MIfTLIksmvgPuIj5YmeDrNtSL5WHlBiip5VSj0EMGX6IhbklRPZN/MfFSk19m1rDDcPKL6zcKGs37mWODCkMYsgUQyzMLSGytmj5bwhgMJSMApeZmelqNiKGko1SmZtSj0EMmQZzS4isKzL/DdADgwAGAU57R0AKc51SXJAjRQUhQ1TmptRjnRgiIkq5yNpKSPbHEBJ6YBDAODPTpRS9t2m9F8ck+2EQQ0REhqDlv+EWi7tiaAmzltADE768iFEqc1PqcTiJiIgMQ8t/wywkJPEWfDmEhB6YcEaozE2px54YsiWsv1S7v13VmcG4O672iMg4Q0uYRn3o+CJxdwQkFL44WlhxzHEj8lNemZtSiz0xZDvba1vktQ+bxNPpkc5QGsuaE1mkOCbZD3tiyFYw82HZG1VS1+CXPJdDSoflSr7L0VXuHLdEZMQcGb/U7W9XtyiOyenVBOyJIduVNcdBcFhhpprGias41KTA/yOnbOL5sRbkIyJ9sEYU9YVBDBlCMgIGrax5UWGWeCNmNERO2cTiklxJl8gYWCOKesMghlIOQzjJCBi0suZZeQ7xeno+jvF2rOW0aWejrFlfq6qFIrBBGXRM9WSVUCIiY2EQQyml5aIkI2DQypp7/Z1RH0fCYGZGmnyypV69nzHD87oWouttyInISDgESnbDIIZSnqOSrIBBK2u+ZWeDuBzRp2yOLHJJQ0tH1JV0I4ec4ln7icgqPZpERsLZSZQyWo5KLAFDIqdsYjbS/uaAeLwB6QyGxN3hVwETpmxWTiqJupKuhlVCyYjCZ9fl9zPrDhcPqI3EGklkBeyJoZTRclT6ChiQo5LIsuK4Ij1vVrn8adUGtS5Lc/vBqp+YsomaE9lZmbL6w93dVtINxyqhZOYeze21zeytIUthEEMpo+WoJDtgKCstkDOOGSJDh4+XoGR2mw2FE0L4SrrhPUTakBMCHlYJJbP1aH7w2R5ZvXYXE9bJUjicRCmj5aggMECAkMyy4ulpaarLfdK4oSq3Rcu5iVxJF0NNkUNOrBJKZuvR9Pk75e11NV29Nbho0Gok4T62o7eGy2+Q2TCIodT98SUpYIg3B4BVQsmsPZrRoEcTa4Xtb/IkLf+MKFk4nEQppQUM2qwK5MCE56gMdlZFtBkbo4qzZVyhT6b0876QQ8CKvWSWHs2+hkBLhuSo28Hkn3H6NtkyiHn11VfllVdekc8++0xaWlpk/PjxctFFF8l5553X7cv28ssvy1NPPSW1tbVSVlYmN910k8yaNUvvt0cGoFfA0FsNmu01rbJ9V4eUlbXI4RWuXn+eVULJKgslnlg5Wla8t2PA+WexTt9moEOWC2KeeeYZGT16tNx2220ydOhQee+99+Suu+6SPXv2yIIFC9Rzli9frrZdffXVMmPGDFmxYoV67IUXXpDKykq93yIZQKIDhr5mbJSWuGTrTre8ta5OppSPYNE6snyPJi4S1m87MKCE9VgLUrJODVkyiHniiSekqKio6/7MmTOlqalJnn76abn22mslPT1dFi1aJHPnzpUbb7xRPQeBzObNm+Xxxx+XpUuX6v0WyYYzNgpyM9RVK4vWkV16NPvrrYmWfxbr9G3k5f/+H8mpvE2U1MTe8ABGM2XKFGlraxO32y3V1dWyY8cOOeuss7o9Z86cObJmzRrx+Xx6v0Wy4YwNR0aa+ALBhNagITJKj2bkrLuBJqzHMn17554W+es7VZz5RPZJ7P3www9lxIgRkpeXp/4PyIMJN3HiRPH7/SrIwf+JElmDxt8ZEqcONWiIrJR/Fsv07fb9bvF4W2VEkYtLdZD1g5i1a9eqnJdbb71V3W9ubla3BQUF3Z6n3dceHwiM9aK3J9E8Hk+3W6uxQvsKc9LULCQk8SIHJvwqMuAPSEt7p0wuy1bP0+NvJNWssA/t3D692zg0N12G5map/3d09P766RKQjLSQtHu8aviox3v0BkRCQQkERDLTRQL4T4SMdJEOr18ONLaq32uXfWj19unZRpy7I3v+DBHEIJkXs46mT58uF198se6/Dz05Gzdu1O31MQxmZWZvH6ZRYxYSkniRA4MhJPTAIIBxZadLWXFANm36QowKtT0aWgPi9Ycky5EmRfmZqkifnfah3duX6jbibzAnwyu769pkWGHPhGCsQTY0P0NaPUGpP+CXLEfPDAWvPyg+b1DqanaKv9Vhu31o9fbp1Uan02msIAbTq6+44goZMmSIPPbYYyqhFwoLC9Vta2urlJSUdHt++OMD4XA4pKKiQhINUSd22oQJEyQnJ0esxirtQx0YTKN+c12t1OxrF08gqIaQ0AODAGbm0ZMM277ttdr79qvcHbzv0cOdcnJlqVo2wS770K7tM1IbXUNaZNkbVSp/pqgwS5yODFUBuKHZKyNKcuW/Ty6Ttz+pU72ewwtdPQKd2nq3TC4rkOO/MqnbsJVR2qcXq7dPzzZu3bo15ucmJYjp6OiQq666SgUqv//97yU/P7/rsfLycnVbVVXV9X/tPoKQsWPHDvj34svkcvVeB2SwsNP0fP1Us0L7UAcG06jDcwAwhIQeGKO2DzM6/vz2zh4zPXbuaVfb45npYdQ2JorV25eMNvZX2wXfoezs7K7p29qiqZPGF3UVpHTl5Khp2HsaOnrMfBpSkC2nzyiTvLxcW+5Dq7dPjzbGOpSUlCAGY6SYOo2gBHVfkNAbDkEKoriVK1fKaaed1rUdeTOYjh1rlxJRrDVojJwDE8+KxIMtBkgUa22X/hKC9a68TZSyIObee++VN954QxW7w7TqdevWdT122GGHqSBl4cKFcvPNN8u4ceNUvgwCmE8//VSef/55vd8ekSlXJGZ9GxpsddxYi9jFWpCSS3WQJYOYd999V90+9NBDPR5btWqVjBkzRubNm6fG1lDYbsmSJWq69eLFi2XatGl6vz0iQ4llSmt/a9yQ9Q22Oq5ePX5cqoMsF8SsXr06puddcMEF6h+RnfVX36a/NW7I+uLtQYmGPX5kFbpX7CWi+FckRkIkZnaE09a4GTciP+oaN2R9kT0oCHTR+4Fb3Md29KDgeYPt8cPjZuvxQ7tr6ttk865Gddvf50Dml5KKvUQ08BWJo61xQ/aQqB4UK/b4cQFKe2JPDJHBDGSNG7KHRPWgWK3HTxtiw22+yyGlw3LVbfh2sib2xJClDHbGhlFwpgfp2YNipR4/liWwNwYxZBmp6E7WM2jiTA/qrQcFf+uYRRRZHRcBCHrsYulBsUptFyYp2xuDGLKERMzYMEPQRPaW6B4UK/T4sSyBvTGIIdNLRXdyKoImMh89euoS3YNi9h4/KyYpU+wYxJDpJas7WTshtbp98up7O6S5zSdjR3BpAEp+T50VelCMOMRG5sMghkwvGd3J4SekNrdf9jS0S4HLKUPys1QXvoZLA1CyeurM3oOSKFZKUqb4cYo1mV54d3I0g+1Ojpy+WVSQJelpadLe4VfbcKKKZ5orC3JZW6IK0lHsWJbAvtgTQ6anZ3dytHwblNVAUHQwcOqU2vo2KXAViaT1HzQxGdj6OFsmNTjEZk8MYsj09OxOjnZCcmVnqgClpc0rWc4MafP4xe31qyvtvoImJgPbA2fLpA6H2OyHQQxZwmBnbPQ2iyTaCQnBDHIROrwB1RODnhmvr1M91lvQZPSCXFYpEmiE9nO2DFHyMIghsXt3cl9DPL2dkBCo4PftrGuWFrdfGlq9kpfj6DVoMvIQgxGGuFIZRCW6/ZwtQ5Q8DGLI1t3J/Q3x/M9pk3rNt0GSb2FelkweXyRzji+TPJez15OvUYcYjDDElcogqrf2b61ulF17WtR7mDS+KK6gykizZezew0bWxyCGbCuWIZ5/frhbZh/T+wkJQczZJ03s92RrxCEGIwxxpTKI6q39gc6QOunv2tMq1XvbZNzI/LiDKiOU9DdCDxuR3hjEkG3taXDHNMSTk5056BOSEYcYUj3EleogKlr78V6wj3z+TrXf8R4zM9IGFFSlcraMEXrYiJKBQQzZlrsjEPMQz6RxQ2M6IfXWfa/XEMNghgtSPcSV6iAqsv0IJmvq21QAc7BHLE3cHX7JzEhXQdZAgqpUzJZJdXBIlEwMYsi2MFU6niGe/k5I/XXfJ3qIoa/fN6roP1WEjTrEleogKrL9CGrxu7K/7CkLdAbVPsdzUp18nezgkLk0ZBYMYsi2Rha5EjbEE2v3faKGGPr7fV8/cbzhh7hSHURFth9BC07eGV/uC0yfL8x1iivLkdLk62QHh8ylITPhsgNkW9oQD4Zy0MWOoYPOYEjd4n6sQzzxlpnXenQwRIXbgQwh9ff73lpXJ0EUsElC+wcbRCBYQtAUTguixo3I1y2Iimw/TvwI5Lz+TlXA0JmZLqXoqYihErNVluGIXGKjdFiuug3fTmQkDGLI1hKx5ko83feJEMvvw0m5oTX6Scwoa86kOoiKbD96YsDTEZACl0Nt0xb3TEZQlergkGs+kRlxOIlsb7BDPMnO7Yjl9/kCQfH6Q4afRWOEqcjh7d+0s1H1miG5F7OSEFSZbTXkgSaRpzrRmmggGMQQDXIWSbJzO2L5fRgKyXKkmWLNGSMs3Ke1H//Gj8xPaVCVquAw1YnWRAPBIIZokJKdIBvL75swMk+K8g+u52QGRlq4zwhBVSrakepEa6KBYBBDNEjJLjMfy+87qXKUdDTXRP15u0+fjaX9RgqqBiOedqR6thrRQDCIITJhbkd/vw91YjZGCWLsPn1W7/abOUA00ppPRLFiEENk0mGIvn6f2+3u8Xy7l6LXu/1WCBCNkGhNFA8GMUQJlOxhiFh/n91L0evdfisFiFbJCSJ7YJ0YIhtIdi0bO7XfivVVBluQkShZGMQQ2UAs02fxuDZ9FidcLIa4eVejujXTCTgR7Y+H3QNEolTicBKRDcQzfdYKuR3JnD7M+ipEqcOeGCIbiLUUPUruW3HtHD3XaRrMWkVENDgMYohsIJZ1ik45Zoys/tAYuR2JHs7Sc52mRARIVhu+I0oWDicR2UR/02ezszINsXZOrMNZWKW7dn+7BMUb0wwavaYPD7a+Sm/tPf6IkgG9HyI7YRBDZCN9TZ9FL0Cq186Jdary9toWee3DJvF0eqQzlBZz3o5e04cHGiD11d499a1y9IQ0mTKod0ZkbQxiiGymt9oyqV47J9ZaLhixWfZGlext8MuYUXmSm5MVV00WvWr5xBsg9dfeXXUt8sl2v5wyk0NLRL1hTgwR6Z78mqipyjv3tMhf36mSVrdfhhVmqpO9kWqyxFNfpb/2FhVmyb7mgOxp6Fl9mYgOYhBDRLonvyZqqnK7JyC797WqE7zZa7L0116nI0MCnfj8o896IiIGMUQUJbcDt+jtqNvfrm6R26F36fxYpiqnpyOhVyTLkZHwonXJ1l97ff5OycxALxNH/Yl6w28HEcWU2wGY/qvXejracBZyWzBMFN7Tog1njSnJk4ZWr3j9naavydJfexuavTK8MFNGFrlS+j6JjIxBDJEBIIfDSAvuRSa/JqOKbyxTleedWC5vfLhbtuxsEJcjet4Oeo30yttJpP7am5/rkKPGO7luEZEZgpht27bJ/fffLx9//LHk5ubKOeecIzfeeKM4nc5UvzUiXRm9zH8yV2iOZapyelqamn68d79bsl0BcaVnxFyTxWj6au9/HVEiHc01qX6LRIZmiCCmublZLrnkEpkwYYI89thjsnfvXnnooYeko6ND7r777lS/PSJLBAh6TnvG8FOiAof+pirj8fNmlcufVm2QNrdfmtsDCSlalyq9tbejwyMbGcQQGT+Ieemll6S9vV0WL14sQ4YcPAB1dnbKvffeK1dddZWMGDEi1W+RyBIBgp4rNCey9kp/tVzKSgvkjGOGyNDh4yUomYYYghvs0KGeVZCJrMoQQcxbb70lM2fO7Apg4KyzzpJ77rlH3n33XTn33HNT+v6IrBQgWGWFZgwrYYFKl8tlmjwmow8dEpmNIYKYqqoqOe+887ptKygokJKSEvUYkRUZOUCItYpvhxe1ZIJS3+QxVW/IYA0kGDH60CGRGRkiiGlpaVFBS6TCwkKVLzNQmK3gdie+2qXH4+l2azVsX3KkS0Ay0kLS7vGq4aMe+8F78HE8L96/40Ttw8KcNBlVnC3ba1qltMTVrccIJ+Ot1c2SkZEuf/7nFnFmpsvo4blycmWpGu6x6t8o1m3Csgeon4Oie1l5DjXlGzOmkHCMfJ3I9qPX5rX3q6SppaPrcwwGO8WZmSYji7Kltt4tr7+/XS46c1JXEGjV7yE+C1Qhbmxul4YWv7TrcIw2Aqvuv2S0EefuyN5pQwcxevH7/bJx40bdXn/Hjh1iZWyfvrAKc06GV3bXtakS+pF1QvY3B6S02CGN+3ZKc31ayvbhuEKfbN/VIVt3uqUgN0McGWnS4umU2gM+9XhpkUNyMn3iD4Tksy1tsn1XvZw8tUBGFTkt9zeKfYaFJ7FuE/aZ1+MX75fHb0z5xoypP6/eIKcffXAWlWZ/i182bW8SV1a6NDf37FnLTAvKF9v3yrv/9sqwAodlv4d1DT5ZV9Uu9S0BVY0Yxfz+vXmdVJbnJuXvJRWstP+S2cZYZyYbIohBL0xra2uP7eiFQW/MQDkcDqmoqJBEQ9SJnYbZVDk5OWI1bF/yuIZ0v6pHqXlUakWhsxElufL1U3pe1Sd7H2IV5bKyFnlzXa3U7GsXt79Tmtydkp3lkIoxhWpas2b4sJDqVahuzpZTZv6nV8Eqf6O1+9vVytlYeDJa7xmmfGPGFBKOka+j2bq7WZzrvVJS7Ir6mWB5hz0H3DJq9Hj1mVrxe4gerI8+qpLWDoeMLMmTzDSRhuYWaXCny0c7QnJe2Wjde/CSyWr7L5lt3Lp1a8zPNUQQU15e3iP3BUFNfX29emygcGWrZ9IfdlqqkgqTge3T3+EVLsnOzu7Kr9CmC08aX5SQ6cKJ2od4n1PKR6hE1h21zfKXt6pkWGG2uKJUxi0pckntAY80e0IyuiTXUn+jQfFKZyhNrZwdLRhBzRrsQ8yYCn9fxUODKugLBEVcUXKgfB1+9Xjx0Pwe7bHC9xBDSO9u2Cbt3k4ZN6pAHZsDgYBkOdJl+LB82dPQIe9tqFd/Y1bLqbLC/kt2G2MdSjJMEHPSSSfJk08+2S03ZuXKlZKeni7HH398qt8eka76q4tiFNo0YLxH/D87Sk+EURKSU5Xo3NuyB7EsqWCWSsNWnYlH5mSIVaznz5+vqvRed9118s4778iyZcvk4YcfVttZI4bsQAsQJo0bqm6NFsDEu1CjWdYvipcWjCDoQPARTgtGxo3I7xGMpHqFcDPMxDPLwp1kLIYIYpD38uyzz0pGRoYKZB555BE5//zz5bbbbkv1WyMyVJc8FmDcvKtR3eK+mU7kVviMBhOMpHKF8FSzc+BL+jLEcBJMnDhRnnnmmVS/DSJDMlKRtFgWauyrV0GvxS6T9RnFsr6T2YcOE83uw2lkgyCGiMQ0RdIGeiLXK9BI9mc0mGBkoEsMGG2l88EGvhnpIl5/UM1mG1KQbenhNNIPgxgiAzPy+krxnsj1CjRS9Rnpud4R2oTp3DUHfFK4v10kzSerPzRGT1yiAl9Ue/Z5gzK5rEBOn1FmmnaQsTCIITIwo8/qiPVErmegYfTPKF5ab9XO2iZpammTf362QVo8AXFlZarPzgg9cYkIfA80tkpdzU45/iuTJC+Pw0hk4sReIrL2rI54Ag27fkbhvVW4zXM5pCg/Q9o8fmlp80mb26eq3CLIQ/CHgAZBIYK/VCV5DybwRVE/VCfmEBINBoMYIgOzyqwOPQMNq3xGkb1V6KHyBw7mjRTmOcXfiWrIbSKhxAR/RFbAIIbIwIw8nTmekzN6E/yBTmls7eg6CScq0LDCZ9RbbxWmb+Pzy0BxQefBXhm312/KXiYiPTAnhsjABjud2Sj5Hbv2tMj+pg6p2dcmRYXZMmZ4fteaS4OdYtvfZ5TvcsgRE4tl6+6mQc3q0Xt2ULTeKgQv+B0IZjLS08Ub7FTPMVsvE5FeGMQQGdxg6pKkUuRspEPGDpGt1U0qmHF7/FIxdqg4HekJCcZ6+4xKhuSojp8V7+0Y1KyeZNSgibakQZYjTXKzM1VRvCxnhvp88BxgfRUiBjFEpmC2ImnRZiO5skUmjR8qNftaVeCypbpJxo3MT1gwFvkZ4Xes+vcuaXUPbkp3smrQRBaEA3xuo4a5pKOuTVra/TI0P0uyHJmqQrAZeuKI9MaeGCKTMFORtN5mI+Gkmz+hSJ2AEVz89ykVUnlIScLej/YZoc3//GiD+h2DmdKdzBo0kcNiQ/Ic6vdnZaRLXo5DDS3lu5zqszVDTxxRMjCIIbKw7bUt8u6GbUkvktbXbCQEAkMLsqXD16lOznoEVImqHZPsGjThw2KqTkxrQIak+WXqxGFyyjFjVPBkhp44omRhEENkUXUNPvnooypp93YmfbmCaPkd4fROSI1lSjfyZvqb1ZOo1xnIsFjV7v3y+Rdb5bDJFVI+ZhgDFqIoOMWayIIwDLGu6uAqyRgGQSCRzCJpqZ72nKjaMamqQYN9VTosV0YXO9Ute1yIomMQQ2RBexrcUt8SkKLCrIRXyI0nvwM5MMjvQCIqpgnjFvf1TkhNVBCV6mCMiPrGIIbIgtwdAVWiPsuREfXxZBRJ0/I7cIseobr9B3uGkJCq93o/iQqiUh2MEVHfmBNDZEGu7EzJzEgTr79TnM7k56QYYWp4ourrmLVOD5EdMIghsqCRRS4pKciUhmav5Lm6Dyklu0jaQKeGGymIMludHiK7YBBDZEE4uVaW58pHO0KmXK7AiEFUKoMxIoqOQQyRRY0qcsp5ZaPl3Q31HAYhIktiEENkYWWlBTKlfASHQYjIkhjEEFkch0GIyKo4xZqIiIhMiUEMERERmRKDGCIiIjIl5sQQWQjWQkItkwONrbK/xa/b2khEREbAIIbIIrA6tVZVtsPrF5/XI9sObJIzZpQnraqsFkSxIBwRJQODGCKLBDAv/X2TWp0ahe2G5jmk/oBXtte0qu16r1UUGURhXSaU5sfiiVh7iKX5iUgPzIkhMjn0fiB4QAAzZnieuLIdalp1liNdSktcavvqtdW6Di1pQRRu810OKR2Wq27DtxMRJRqDGCKTw/ANej/QAxO+RhLgPrbv2tuqnpfMIAq3uJ+MIIqI7IlBDJHJIf8EwzfZzuijw1gzCY/jeVYMoojIvhjEEJkcVlRG/kmHLxD1cSz6iMfxPCsGUURkXwxiiExuVHGuSqDF6tShUPchG9zH9nEj8tXzrBhEEZF9MYghMjnkn2AGUEGuU3bvaxN3h186gyHx+oNSW+9W22cfO1Y9z4pBFBHZF4MYIgvAFGZtGnWr2y97DrjF7Q1K+egC3adX9xZE4Rb39Q6iiMi+WCeGyCIQqJSVFnZV7K2r2SnHf2WS5OXlJi2I0urENLR41RBSxZghKoBhnRgi0gODGCILQW/H6JI8GZqbLv7Wg1OdUxFEsWIvESUDgxgiSngQpfeSBukSkGBE/g0R2Q+DGCIyvMglDTLSQpKT4RXXkBY5vMKV6rdHRCnCIIaITLUuFOrRtHu8sruuTZa9USXZ2dnMuSGyKc5OIiLD6m1Jg5ysTBlWmKlmYnFJAyL7YhBDRIbV35IGRYVZXNKAyMYYxBCRYfW3pIHTkcElDYhsTNcgprOzU5YuXSoXXnihTJ8+XY477ji56KKLZO3atT2e6/P55Mc//rEcf/zxUllZKd/+9relqqpKz7dHRAbX35IGPn8nlzQgsjFdg5iOjg5ZsmSJHH744SpA+elPfyqFhYVy8cUXy5o1a7o99/7775eXX35ZbrrpJnnsscdUUHPppZdKa2urnm+RiAysvyUNGpq9XNKAyMZ0nZ2EWQP/+Mc/VOCiQU/LvHnz5Nlnn5WZM2eqbXv27JE//OEPcs8998j555+vtk2dOlVmzZolL730klxxxRV6vk0iMihtSYP6RrdawgC5MVgV2+MNyP7mgIwoyeWSBkQ2pmtPTEZGRrcARtt26KGHyr59+7q2vfPOOxIMBuXMM8/s2jZkyBAV8Lz11lt6vkUiMtm6UHX726XN7ZfSYoecd0o5p1cT2VjS68QEAgH55JNP5JhjjunahtyX4uLiHgHPxIkTVQ8NEdlb5JIGqNjbuG+nlJUWpPqtEZGdgpinnnpK9u7dq/JdNC0tLZKfn9/juQUFBdLc3Dzg34Uxc7fbLYnm8Xi63VoN22d+Vt2HWBNqaG6WeDxBaU5Ls1z77LAPNWyf+Xl0+hvFuTuypELCghgk2oYPBfVm7Nix4nQ6u2179913VdLutddeK0cccYToze/3y8aNG3V7/R07doiVsX3mx31oftyH5mb1/adXGyPjh4QFMStXrpQ777yz3+etWLFCDQdpPvvsM1m4cKFK6l2wYEGPHpe2trYer4Eemsghpng4HA6pqKiQREPUiZ02YcIEycnJEath+8yP+9D8uA/Nzer7T882bt26Nebnxh3EXHDBBepfPHbu3KlmGE2bNk1NpY5UXl4u+/fvV0NH4UELcmXw2EChO8rl0m9xOOw0PV8/1dg+8+M+ND/uQ3Oz+v7To42xDiUlpWIvhp4uu+wyGTVqlCxatEj1jkQ64YQTJD09XV5//fWubQhoMGvppJNO0vstEhERkQll6l3sDj0wjY2Ncscdd8iWLVu6jXcddthh6v8jR45U9WEefvhhFcyMGDFCfvWrX6lk3/nz5+v5FomIiMikdA1iMET0xRdfqP9fc8013R4bPXq0rF69uus+8mxyc3PlkUcekfb2djn66KPl6aefjjpriYiIiEjXIGbMmDGyadOmmJ6Lnplbb71V/SMiIiLqD1exJiIiIlNiEENERESmlBaKXBrWIj766CNV9S/WgjnxwOuikB5mWsUzFcws2D7z4z40P+5Dc7P6/tOzjT6fT70ecmMNt+xAsuj5R4PX1iM4Mgq2z/y4D82P+9DcrL7/9GwjXjfWc7hle2KIiIjI2pgTQ0RERKbEIIaIiIhMiUEMERERmRKDGCIiIjIlBjFERERkSgxiiIiIyJQYxBAREZEpMYghIiIiU2IQQ0RERKbEIIaIiIhMiUEMERERmZJlF4AcqHfffVf++Mc/yieffCLV1dVy4YUXyt133x11lc2f//zn8sorr0h7e7tMmzZN7rrrLikvL49phe0f//jHsnHjRikuLpZvfvObcsUVV6RspdNDDz2018fefvttGT58eNTHdu/eLaeeemqP7UcddZT83//9nxjJbbfdJn/60596bF+6dKmcdNJJff5sa2urPPjgg/KPf/xDrdh64oknyp133tnr55JsnZ2d8pvf/Eb++c9/ytatW9XKstinN9xwgxx77LF9/qwR9+G2bdvk/vvvl48//lhyc3PlnHPOkRtvvLHfhebQbuzPF198URoaGmTKlCnygx/8QCorK8VIXn31VXXc+Oyzz6SlpUXGjx8vF110kZx33nl9HgNmz54tNTU1PbZ/+umnkpWVJUaB4yc+90g4xt18882m33/YV//617+iPvazn/1M5s6da6r9t3PnTvn1r3+tznlbtmxR57C//e1vPZ738ssvy1NPPSW1tbVSVlYmN910k8yaNavf19+7d6/6Pr/zzjtqteuvfvWrar/m5eUl5P0ziIly0v7iiy/kK1/5ijQ3N/f6wWGnrFixQp0cR4wYIU8++aRceumlsnz5csnPz+/zD+Y73/mOHH/88erAvGnTJvnpT38qGRkZansq/P73v++x7dZbb5WcnJyYTtTf/e53Zfr06V33ceIxorFjx6rPOtzEiRP7/TnsJwQHP/zhD9XB5tFHH1UH5GXLlklmZuq/Qh0dHbJkyRL57//+b/W+0tPTVQBy8cUXq4PTzJkzTbMP8Z275JJLZMKECfLYY4+pA+BDDz2k2hjtYiIcToCLFi1SJ0oEcS+88IJcdtll8pe//EXte6N45plnZPTo0erYMXToUHnvvffUBdCePXtkwYIFff7sGWecodoUzqgrJeOEF34sxHHSCvvvnnvukba2tm7bnn32WXn99df7/a4Zcf9t2bJF3nzzTXXhEgwGVTAZCec1/I1effXVMmPGDHXuw98q9lFfQSYu+i6//HL1/0ceeUR9j3EB/73vfU9+9atfJaYBWMWa/qOzs7Pr/7NmzQrde++9PT6eurq60JQpU0IvvfRS17bGxsZQZWVlaMmSJX1+nHfddZd6Xa/X27XtkUceCR177LHdtqVSdXV1aNKkSaGlS5fG9LxXX301ZHS33npraO7cuXH/3EcffaTa+Pbbb3dt27ZtW+jQQw8NLV++PGQEgUAg1NTU1GPbmWeeGbrqqqtMtQ+ffPJJ9T3C90mD7xm+b3v27On15zo6OkJHH320+i5p8H3Cd+2ee+4JGcmBAwd6bLvzzjvV+w8//kTq7XhkNMuWLVN/U9HaaYX9F83s2bNDV1xxRZ/PMer+6wz7m+vtOHn66aeHvvvd73bb9o1vfCN0+eWX9/naf/3rX9WxEsdMDY6l+Pv45JNPEvL+mRMTAVex/UG3GCLWM888s2vbkCFDVO/KW2+91efP4nF034dH33PmzFHdyug+NwJ0JaJbe968eWJ32F8FBQVq32rQ3Yqu7v72dbKgF6+wsLDHNlzN7tu3T8wEnymuZvF90px11lnq+4ah3r6GaHF1jOdq8B1D17VR9pOmqKioxzb8PeH9u91usSMz7b9o7x3Dsl/72tfEiue86upq2bFjR7d9o5231qxZo1IreoN9h+NQeJoFjqX4fqP3JxEYxAxAVVWVymWJPHFgaAKP9QYHqLq6uh55M7iPoKGvn00mdB1iOG3kyJExPR/DLDgI4+SDXJGmpiYxIgzlHXPMMXLEEUfIueeeq3Jc+oN9gvHfyFwF7DOj7K9oAoGAGuOOJUfLSPsQn2nke0YQWVJS0ufnrT0W+bP4TmIMH93YRvbhhx+q4Zb+8gT++te/qr9f5OBh6BDD0UaFiyD8TeGiDUMHyN2y4v7DRZ/L5YqaW2bm/Re5b3AcjNw3GC5CkBPP9xnHUrxWoo6fqR/QNyH0mkTLe8HBtq88GiSIas8LhysO5J/09bPJgnygzZs3y3333dfvc/G+kZR8wgknqDbhpIncoA0bNqgkMCRxGQUOplOnTpWKigq1H373u9/JddddJ7/4xS+69ajFuq8RwKKdRoV8BOSTIE/LTPsQn3fk90P7vPv6fuDn0JbIBEm8Fsb48bPZ2dliRGvXrlU5BshD6wsSQ4888kgpLS1VJw7sp29961vy5z//2VA5Iwg4Fy5cqHIscMJavXq1yiPD32NveU1m3X+4WECiNvYNAhkr7L9I2vcu8nup3e/ve9nb8TNR5zvLBzE4YcXSpY4/olQnWBmhzbhSwIkLCWj9QdIvruA1xx13nBxyyCFy1VVXyd///nfV3WiUNiJZNPKAMn/+fJVI2FcQY8Z9iGEXJMVee+216qrPqPuQRCXzYpYHkqqRiN0X9JBpMOsM3fLo4kfydvg+TDXM3sM/DQJkBCdIfkViqFFm9SUCvmuYSRXL0LtZ9p/ZWD6IWblyZbc/nt7gSiiWmSpaBBqZna5FnZFDTOG0iFTrkdFgTNHj8fT5s8loM654sA0HoPCchHicfPLJ6ooE00f1PAEOdr9iHPj000+Xn/zkJ6qrurerPOxrnGgi4SoiUfsrke3D546rYBxU+5vpkup92NvnHfn9iOXzxs/he+T1ertdzeM7id4APffVQOG9YUgB3zUEnbHk44VDMIDhUewno8PJGmUAUFYiWhBjxv2nDSVh/yFQi5dZ9l/hl589vpfoZQvfN+GPx3OuxPd51KhRCXl/lg9iLrjgAvUvkTDGt3///h4H1mjjf+FwYsCOixwL3L59uwogYs1f0KvNGJfH+PMtt9widtyv0WCfIHkN+yc8Lwb7bNKkSYZqH3J+cFLEeDtKAJhRtFwjHDzr6+v7/H5oj2G/TJ48uWs7Xgvd90YbikDgjN4utA0lDvoqy2AHZtt/2j5EXt3ZZ59tqKFzvfZN5PkN99HuvobC8HykJ4TDsRT7OXyyxGAwsXcAEHXjqgl1ATQIaDBrqb/CaXh81apVKiEq/GoaEStOPqmEoSQEWhhqGag33nhDJTAj/8TIMNsFvR0YOunrAIn9hX2LQEaDL+Dnn3/e775OJgw9of4EgmQMkQ3moJrKfYjPFHVTtKs8wH7C962vg97RRx+tkmKRn6DBdwzfUSPtJy2PArWHcBJA7lJ/9VN6gxwTXHgY/bumHeMwY+6www4z/f7TINcH35OBzkoyy/4bO3asqtuE72HkPsVEgL7SMLDvkGeJ2U0aHEsxcQA9volg+Z6YeKGi4vr169X/McSza9eurp2n5U5g1s75558vDz/8sDq44iCE7HtcTSHPQoOErdtvv10Vt0KuAaCgHYIFFPtBQiWiVIyJYlw8lTk5OLC+9tprctppp/V6UscB6Otf/7o88MAD6j6KkKF3AsWOEISh8iQ+B+Rh4HWMtE9RWAyVNFEdFUEJEnuRvIpu/L7aiMASQSv2IxIv0dWNSs2YNojhKKNcEaIHprGxUe644w5VvEqDv6nwE4fR9yG+P7/97W9V0jV6KnCgx/cM28NP9shxQq8h8nYA+wXPx/7EFGb0kmEf42CZqiKSvbn33ntVoIi/SXS1r1u3rtv+0XK4wtuHYQv8DA78GIZAYigKHCIw+Pa3vy1Ggs8bOT5aJXBctGnFF7XhCDPvPw2O4+glwpBQJDPtP4/H0zXdGcdK/E1q5zyct7A/MESNIoTjxo1T+xYBDI4Vzz//fNfr4GcxJR65eNpQNnIrcTzBz6OgJn4Xvs+nnHKKSnJOBAYxET744INuJbNRwRf/IHw6HPIVUNUUVQix7ACuJJ5++ulu3cK42se0wvAKiDiJImjByePKK69UfyDXX399jyqOyYZeJJwE+0pQQ1vQJg1yMXCgwQEKJ1KcZBDcoT1GqGSrwX7CVd4TTzwhBw4cUL0UOEmjQmh4AmK0NgJmVmDZAcysQLCHoAb73yhtxNAmrnbgmmuu6fYYKsPiitEs+xDDs0gA/dGPfqQCGew7vB8E+eG071Y4BHL4riH3Qitbj++a0WZ+aPVucAyIhBP+mDFjerQP29DbhuATQ1A4zqByKvaT0dqH6bOoZo1cMrQDV/G4CEC5fivsP8CFEM4LCFaiLRVhpv134MABtURJOO3+c889p4IWnBcQgOCYieAL+3jx4sXdRg+w7yLPdzjWorcRw9sIYnBMQaCDv4dESUPFu4S9GhEREVGSMCeGiIiITIlBDBEREZkSgxgiIiIyJQYxREREZEoMYoiIiMiUGMQQERGRKTGIISIiIlNiEENERESmxCCGiIiITIlBDBEREZkSgxgiIiIyJQYxREREJGb0/wH8mVCB/e0zLQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(42)\n",
+    "X_poly = np.linspace(-10, 10, 100).reshape(-1, 1)\n",
+    "y_poly = X_poly**2 + np.random.normal(0, 10, (100, 1))\n",
+    "\n",
+    "plt.scatter(X_poly, y_poly, alpha=0.6)\n",
+    "plt.title(\"Parabolic Data\")\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "id": "469e048f",
+   "id": "b20c3195",
    "metadata": {},
    "source": [
     "### Fit Linear Model & Plot Residuals\n",
     "\n",
-    "Fit a `LinearRegression` model. Then, plot the residuals vs. predictions. **Question**: What shape do you see in the residual plot?\n",
+    "Fit a `LinearRegression` model. Then, plot the residuals vs. predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "52d7d1e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Fit\n",
+    "lr_poly = LinearRegression()\n",
+    "lr_poly.fit(X_poly, y_poly)\n",
+    "y_pred_poly = lr_poly.predict(X_poly)\n",
+    "\n",
+    "# Residuals\n",
+    "res_poly = y_poly - y_pred_poly\n",
+    "\n",
+    "# Plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(y_pred_poly, res_poly, color='orange', alpha=0.7)\n",
+    "plt.axhline(0, color='black', linestyle='--')\n",
+    "plt.title(\"Residual Plot: Linear Fit on Non-Linear Data\")\n",
+    "plt.xlabel(\"Predicted\")\n",
+    "plt.ylabel(\"Residuals\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "469e048f",
+   "metadata": {},
+   "source": [
+    "**Question**: What shape do you see in the residual plot?\n",
     "\n",
     "**Answer**: You should see a clear ****U-shape**** (Parabola). This pattern screams \"You need a non-linear model\" (which sets the stage for your future module on **Nonlinearities**)."
    ]
@@ -387,9 +532,50 @@
     "Influential points can skew the model. We can detect them using **Cook's Distance**. **Note**: We use statsmodels here as it has built-in diagnostic tools."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "922c8568",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate data with one massive outlier\n",
+    "X_out = np.append(X[:20], [[50]], axis=0) # Add outlier X\n",
+    "y_out = np.append(y[:20], [[500]], axis=0) # Add outlier y\n",
+    "\n",
+    "import statsmodels.api as sm\n",
+    "\n",
+    "# Fit OLS (Ordinary Least Squares)\n",
+    "X_aug = sm.add_constant(X_out)\n",
+    "model_sm = sm.OLS(y_out, X_aug).fit()\n",
+    "\n",
+    "# Calculate Cook's Distance\n",
+    "influence = model_sm.get_influence()\n",
+    "(c, p) = influence.cooks_distance\n",
+    "\n",
+    "plt.stem(np.arange(len(c)), c, markerfmt=\",\")\n",
+    "plt.title(\"Cook's Distance (Spike = Influential Outlier)\")\n",
+    "plt.xlabel(\"Row Index\")\n",
+    "plt.ylabel(\"Cook's Distance\")\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "id": "f795a165",
+   "id": "8cd44b69",
    "metadata": {},
    "source": [
     "#### Interpreting Cook's Distance\n",
@@ -397,8 +583,63 @@
     "Common thresholds to identify influential points:\n",
     "\n",
     "-   **D > 4/n**: Point may be influential (n = number of observations)\n",
-    "-   **D > 1**: Point is highly influential (conservative rule)\n",
-    "\n",
+    "-   **D > 1**: Point is highly influential (conservative rule)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "96fb25a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Threshold (4/n): 0.1905\n",
+      "Influential points (index): [20]\n",
+      "Their Cook's Distance values: [112.39524325]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n = len(c)\n",
+    "threshold = 4 / n\n",
+    "\n",
+    "# Find influential points\n",
+    "influential_idx = np.where(c > threshold)[0]\n",
+    "\n",
+    "print(f\"Threshold (4/n): {threshold:.4f}\")\n",
+    "print(f\"Influential points (index): {influential_idx}\")\n",
+    "print(f\"Their Cook's Distance values: {c[influential_idx]}\")\n",
+    "\n",
+    "# Visualize with threshold line\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.stem(np.arange(len(c)), c, markerfmt=\",\")\n",
+    "plt.axhline(threshold, color='red', linestyle='--', label=f'Threshold (4/n = {threshold:.3f})')\n",
+    "plt.axhline(1, color='orange', linestyle=':', label='Conservative threshold (1)')\n",
+    "plt.title(\"Cook's Distance with Thresholds\")\n",
+    "plt.xlabel(\"Row Index\")\n",
+    "plt.ylabel(\"Cook's Distance\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f795a165",
+   "metadata": {},
+   "source": [
     "**What to do with influential points?**\n",
     "\n",
     "1.  **Investigate**: Is it a data entry error? Correct it.\n",
@@ -430,9 +671,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C03 Advanced Evaluation Metrics.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C03 Advanced Evaluation Metrics.ipynb
index 9039acc..7f8aef7 100644
--- a/src/S02 Supervised Learning - Regression and Evaluation/S02C03 Advanced Evaluation Metrics.ipynb	
+++ b/src/S02 Supervised Learning - Regression and Evaluation/S02C03 Advanced Evaluation Metrics.ipynb	
@@ -72,12 +72,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "91e05c29",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Over-predict by 50:  log error = 0.4022\n",
+      "Under-predict by 50: log error = -0.6833\n",
+      "Under-prediction penalty is 1.70x larger\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "\n",
@@ -105,10 +115,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "7e4c2078",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE  (normal): 7.91  → (with outlier): 200.14  (25.3x increase)\n",
+      "RMSLE (normal): 0.0786 → (with outlier): 0.8042 (10.2x increase)\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.metrics import mean_squared_error, mean_squared_log_error\n",
     "\n",
@@ -160,12 +179,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "4250afdd",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Features: 8\n",
+      "Bloated Features:  18\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.datasets import fetch_california_housing\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -203,10 +231,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "78584266",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=============================================\n",
+      "Metric          Clean (8 feat)  Bloated (18 feat)\n",
+      "=============================================\n",
+      "Standard R²     0.57579         0.57563        \n",
+      "Adjusted R²     0.57496         0.57377        \n",
+      "=============================================\n"
+     ]
+    }
+   ],
    "source": [
     "def adjusted_r2(r2, n, p):\n",
     "    \"\"\" Calculates Adjusted R-squared.\n",
@@ -268,12 +309,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "9834eb72",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE:  0.7456 (Absolute Error)\n",
+      "RMSLE: 0.2288 (Relative/Ratio Error)\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.metrics import mean_squared_log_error\n",
     "\n",
@@ -313,6 +363,58 @@
     "3.  Watch them diverge. $R^2$ will decrease much slower than Adjusted $R^2$, which will tank."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e9c54512",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "n_features_list = [10, 50, 100, 500]\n",
+    "r2_list = []\n",
+    "adj_r2_list = []\n",
+    "\n",
+    "for n_noise in n_features_list:\n",
+    "    # Create noise\n",
+    "    noise = np.random.normal(0, 1, (X.shape[0], n_noise))\n",
+    "    X_temp = np.hstack([X, noise])\n",
+    "\n",
+    "    # Split & Train\n",
+    "    X_tr, X_te, y_tr, y_te = train_test_split(X_temp, y, test_size=0.2, random_state=42)\n",
+    "    mod = LinearRegression().fit(X_tr, y_tr)\n",
+    "    pred = mod.predict(X_te)\n",
+    "\n",
+    "    # Score\n",
+    "    score = r2_score(y_te, pred)\n",
+    "    r2_list.append(score)\n",
+    "    adj_r2_list.append(adjusted_r2(score, X_te.shape[0], X_te.shape[1]))\n",
+    "\n",
+    "# Plot\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "plt.plot(n_features_list, r2_list, marker='o', label='Standard R²')\n",
+    "plt.plot(n_features_list, adj_r2_list, marker='x', label='Adjusted R²')\n",
+    "plt.xlabel(\"Number of Noise Features Added\")\n",
+    "plt.ylabel(\"Score\")\n",
+    "plt.title(\"R² vs Adjusted R² with Junk Features\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "d0c709fe",
@@ -329,6 +431,40 @@
     "Points below the line are **Underestimations**; points above are **Overestimations**."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5ff805a5",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.scatter(y_test, y_pred_clean, alpha=0.3, color='purple')\n",
+    "\n",
+    "# Identity line (Perfect prediction)\n",
+    "max_val = max(y_test.max(), y_pred_clean.max())\n",
+    "plt.plot([0, max_val], [0, max_val], 'r--')\n",
+    "\n",
+    "plt.xlabel(\"Actual Value\")\n",
+    "plt.ylabel(\"Predicted Value\")\n",
+    "plt.title(\"Prediction Error Plot\")\n",
+    "plt.axis('equal')\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4eb23b2c",
@@ -347,9 +483,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C04 Multiple Linear Regression.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C04 Multiple Linear Regression.ipynb
index 03894d3..85de62f 100644
--- a/src/S02 Supervised Learning - Regression and Evaluation/S02C04 Multiple Linear Regression.ipynb	
+++ b/src/S02 Supervised Learning - Regression and Evaluation/S02C04 Multiple Linear Regression.ipynb	
@@ -80,12 +80,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "d6a43fc7",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True Coefficients: [87.73730719 74.07686178]\n",
+      "   Feature_1  Feature_2      Target\n",
+      "0  -1.191303   0.656554  -49.265023\n",
+      "1   0.058209  -1.142970  -85.162387\n",
+      "2   0.586857   2.190456  211.225658\n",
+      "3   0.473238  -0.072829   29.196562\n",
+      "4   0.738467   0.171368   84.348096\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -117,12 +131,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "d0780a5c",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(8, 6))\n",
     "sns.heatmap(df.corr(), annot=True, cmap='coolwarm', vmin=-1, vmax=1)\n",
@@ -155,10 +180,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "5a2ec1d0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Variance Inflation Factors:\n",
+      "  Feature      VIF\n",
+      "Feature_1 1.000752\n",
+      "Feature_2 1.000752\n"
+     ]
+    }
+   ],
    "source": [
     "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
     "\n",
@@ -203,12 +239,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "ba9d9108",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intercept: 0.07\n",
+      "Coefficients: [86.31018533 73.66927906]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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MEpCnxhIE4WwXcig5npBjiBfnDrUL8kdxiF3BS5EQvolM3ihFEB48BvluUYB8cf4gsBAliBX+eRAySAlECR8+iLddVYv6TNUr+/unf/qn+t9Ulj/96U/X6mNYGDeM2ONliK8hCjGqMYUakBUIpxQjQdooSOJlgcpacvcwOYfU8QIDuM6QU8Yc1x/iiO8hYy8OqOeQRMYNJt6YQjNmUXptj8NmgXxDVF2UPsgypJA8RCqdg+b0ScE67r33Xn3+UI1RXVEUr7/++pqUBg+PuY4SfjbN3gkPj7kMIWEokx7tCxRISBYKlK949fDwaGf4nEMPDw+PlEANtkFYHoULBdgTQw8Pj3aHzzn08PDwSAmqgfHVoziEAg7CneQLEt718PDwaHd4cujh4eGREuSCUgwEGaSQhFw2jLWxkvHw8PBod/icQw8PDw8PDw8Pjwp8zqGHh4eHh4eHh0cFnhx6eHh4eHh4eHhU4Mmhh4eHh4eHh4dHBZ4cenh4eHh4eHh4VODJoYeHh4eHh4eHRwWeHHp4eHh4eHh4eFTgyaGHh4eHh4eHh0cFnhx6eHh4eHh4eHhU4Mmhh4eHh4eHh4dHBZ4cenh4eHh4eHh4VODJoYeHh4eHh4eHRwWeHHp4eHh4eHh4eFTgyaGHh4eHh4eHh0cFnhx6eHh4eHh4eHhU4Mmhh4eHh4eHh4dHBZ4cenh4eHh4eHh4VODJoYeHh4eHh4eHRwWeHHp4eHh4eHh4eFTgyaGHh4eHh4eHh0cFnhx6eHh4eHh4eHhU4Mmhh4eHh4eHh4dHBZ4cenh4eHh4eHh4VODJoYeHh4eHh4eHRwWeHHp4eHh4eHh4eFTgyaGHh4eHh4eHh0cFnhx6eHh4eHh4eHhU4Mmhh4eHh4eHh4dHBZ4cenh4eHh4eHh4VODJoYeHh4eHh4eHRwWeHHp4eHh4eHh4eFTgyaGHh4eHh4eHh0cFnhx6eHh4eHh4eHhU4Mmhh4eHh4eHh4dHBZ4cenh4eHh4eHh4VODJoYeHh4eHh4eHRwWeHHp4eHh4eHh4eFTgyaGHh4eHh4eHh0cFnhx6eHh4eHh4eHhU4Mmhh4eHh4eHh4dHBZ4cenh4eHh4eHh4VODJoYeHh4eHh4eHRwWeHHp4eHh4eHh4eFTgyaGHh4eHh4eHh0cFnhx6eHh4eHh4eHhU4Mmhh4eHh4eHh4dHBZ4cenh4eHh4eHh4VODJoYeHh4eHh4eHRwWeHHp4eHh4eHh4eFTgyaGHh4eHh4eHh0cFnhx6eHh4eHh4eHhU0FP9Xw8Pj7mEcrmsJiYm1KVLl1RPT49euru7VVdXlyqVSs3ePQ8PDw+PJqFUZobw8PCYU5ienlbj4+NqampKjY2NVcggPyGH8+bN00QRwsjvPFn08PDwmDvw5NDDYw6Bd0EIIYoh/w/pgyRCCPk3C8RR/sYiJFGURU8WPTw8PDobnhx6eMyxMDLkEEDy+J2Qw7DPJyGLYd/18PDw8GhfeHLo4TEHMDk5qUkgsHMK+f3Q0JDq6+urGzqOI4sShvZk0cPDw6P94cmhh0cHAxIHATx8+LDau3evetrTnlYhgZDC733ve/rn/Pnz1dKlS/WyZMkSTRaTrNsmi0I8bVXRk0UPDw+P9oOvVvbw6FBA2ggj81NCyPLz0KFD6tFHH1WbNm1S1113nSaIZ8+e1SSS39tkkYV/ByHKoYSVhSyyTVRK+bsnix4eHh7tBU8OPTw6DKLmCTGEvAmB43f/+Z//qYng9ddfr5YtW6aJHD9ZAErj+fPn9WcOHjyofvCDH2gl0SaLvb29icii7AeLfMYmi1IN7eHh4eHROvBhZQ+PDi86YTl58qQmeWBgYEBdffXVWg0US5u4ohLWJ2SRBZWxv7+/JgwdRhbD9k1C0HbOopBFuxraw8PDw6N58OTQw6NDICodxNAuOoGIPfzwwzpkvH37drVly5bK35KQwyDYBiTx3Llz+ufw8LAaHBzUJFHIIgUqWcgi+xEscPFk0cPDw6Ox8OTQw6NDvAsJB0sYWQgVBtff//73tdoHnve859V8Nws5DILv22RxZGRELVy4sIYsogpmVRbBggULvLLo4eHh0SB4cujh0YFhZHDq1ClNDJcvX67WrVunw8rPec5zcieHQUBIJQQNYaQ9X5AsogjWOy7AsX3rW99St912mw5dh1VDe2XRw8PDI1/4ghQPjzaFdDoJqoX8+4knnlAHDhxQO3fuVOvXr9ckrVGdMsllXLNmjV4A5FDIIpXQkNFFixZViOLixYtnkUU5Fvm9EEEhs9Lyz5NFDw8Pj/zhyaGHR5t6F7Lw/zYxJKT74IMPahKFpyG5gPb3mgFCwmvXrtUL+2CTxSNHjmiCC0G0yWJQybQNt+VYZIEo2gbfXln08PDwcIMnhx4ebQRIH6RQwsg2MTx69Ki2qSGETOGJrca1SuiV/cAWh4X9hNyNjo5WyCL+ixybkEUUxqj12OpikCzayqIUt0Aa7fPl4eHh4REOTw49PNoAtmdgsFgDski49vjx49qiZvXq1bO+z2f5fquB/cIWh4XwN8eG+ilkkdA4oNqa3EkII2polLIYRhZRKuUzQhbtvtCeLHp4eHjUwpNDD482Ljq5ePGiboFHscbTn/70yLZ37UKA2E98GFk2bNigj/mOO+7QpBCvxf379+vzIcUtQhaDx5eULIqi6Mmih4eHRxWeHHp4tKl3Id1LHnvsMe1beMUVV8RWHEvbvKi/tSpk3whBU+jCMUCIxTaHftF8xiaLEMukZJHz68mih4eHRy08OfTwaDPvQoovyC2EIN14442VtndxiCOH8vtWJIlhJI88RBb6QnNuIIsQxdOnT6unnnpKkz+bLBKyTkMWyVeEMErbQa8senh4zDV4cujh0eJhZJsYQoKoRsY38BnPeEaitnX1yGE7IGrfOTcUr7AAyN2FCxf0eaJl4JNPPqnJnVRC85PQexxZlO0JQWeJss7xOYseHh6dCE8OPTxaCOLjF1QLISqoYoRRt27dqjZv3pxK6WtXcphWzeScQQJZAMROyCIFO48//rgm1EGyGLZdIYNBsoiaa1vrBPtCt6IC6+Hh4ZEGnhx6eLQAhHhQdbxy5UqthAnJIMRJpxN+3nrrrZH2Lp1IDgVZ9x3CJuFlwDmmsEU8FsnZJJfRJov4MiYlixBFVF5PFj08PDoJnhx6eLSQd+GJEyc0+RNiyL8feughtWrVKnXDDTck6lEcBluBnMvKFmSRHE3J0+S8C1nEY/GRRx7RSqJNFiGPLmRRfBYlDO3h4eHR6vDk0MOjhbwLIQ/ye1QtCMvu3bt1tW5e27TJYTsQxSL3EbKNfyILgNxJJTQei/SjpqBF1EcIY1ieZz2yGNW9xZNFDw+PVoQnhx4eTW6BZ5MLFsLH3/nOd/Tv8S7EmqVIgtUOBLFRIXGI24oVK/QCIHa2bc7w8LC+HjZZRBlMShZZ391336127dql1+PJooeHRyvCk0MPjyZ6F9oEAlCMQtEENi3btm3LTVkSAiiFLh7JAPEjB5RFro+QRQqE6OaCCbdNFsNC/zZZhPxLa0PGgSiL/N2TRQ8Pj1aAJ4ceHi3gXcjvCGHSZxhiuGPHjly33Q7qYDuAkDL5nywAixuIIoTxiSee0MTPJosUFkWRRbv3tZ1iEEUW+emvo4eHRyPgyaGHR5O9CymIwLuQKlmUJ8hF3rALUtoNrUyIKFZZs2aNXgDkUMgiOaOQRwqMpLgFshh2DeLIImqlqI7BApdWPjceHh7tC08OPTya6F1Ir2AUp8svv1wv999/fyEErp3JYTvtNwR/7dq1egEowZBFFiqhGQfS+hD1EbIYFuZPShaDYWhPFj08PPKAJ4ceHgWHkaUaOdgCD4saWr/ddNNNFR++ovwI250ctiuwxWGh2pxzD1mk2AiFkRaIpBNAEEVZRGWsRxblGspLR1T3Fk8WPTw8ssKTQw+PBoeR6QGMqTWEgBZ4drWrJ4ez0SlqGMchfZ4pNkJlpKBFlEVsixgvdl9oUgyCZNHuCQ08WfTw8Mgbnhx6eOQMUQuDYWT+TYXrvn371Pbt29XGjRtDe/x65XA2OknxlGPhWmNnw7Jhwwb9e6xyhCyScsDvgmQxbMxEkUVURdRF4JVFDw+PpPDk0MOjIO9CmxgSTqTohL/ddtttauHChaHr4DtM6kWg3VvodTq4PpA/Fl4cuFZDQ0MVsojPIp+xySLEMo4ssojHIkuQLEpxC6Foe7x6eHjMbXhy6OGRA6RYQIid+NqB48ePq4cfflitXr1a7dy5s6bQoNEErh3J4VwlLBw3LxEs2BsxtoQskpqwZ88eTehssihh6+B6bMJok0VyH+UzQhYlZ9GTRQ+PuQtPDj08HGBXkQbDyISXsTM5cuSIuuqqqyp2J3Eokhy2s3LYrvudJ+FlbFGwwrJ582Y93ihogiyePHlSPfnkk5rY2WSRYhhPFj08PNLCk0MPjwKKTlB4vve972kFhhZ4KDpJUDQ5bPQ2PWqR53lmvFHpzLJlyxZNFvHMxGMRtZpOO5h2B8liEEmVRQk/e2XRw6Pz4cmhh4djC7ygdyFVp48++qgOBW7dujVVuzqvHIafE4/6YJwJCbzsssv02IQsoiwePXpUq9iYdttkkYrppGRRClwgjGwrWODiw9AeHp0DTw49PHJqgQdZxLvuzJkz6vrrr1crVqxIfW5Zly9I6Xw0gvBC2JYtW6YXwJgVsnj48GH9AiNdeYQsQh7D9tXeXyGL3AcsFFuROkHFNd/3ZNHDo/3hyaGHR8ow8gMPPKCLS8ghlEmTUB7VyFSP4l0YNskmAWRTwtR5o53Dx+263610HJC25cuX60XIIuMWskjHFnp7k/4gRBHSSFg6iiyKIk71M8UxklMrptwShrb7QnsV2MOjPeDJoYdHSu9CCScL2cJiBP/CK6+8Uud+uUyAPqzs0ShA2FC3ReFmXAtZxIuTvFledmyyaBu222MWiC2OKIuQT9YppDCYs+jJoodH68KTQw+PhN6Fdgs8yb+i0wldLm6++WY9ebrCk8Pwc+JRPCB3K1eu1IsogkIWeflhnOPBaJNFiJ5t6h2mLMaRRSGUkrPo4eHRGvDk0MMjAhBAJrRgNTI/sRDBOoR8LqqRwxSVLPDksLPDyu1EeAkpr1q1Si+AlyEhi0888YTONcSDEWsdwL2SJAwdRRaDfaE9WfTwaB48OfTwiPEu5P/t8JcYEeMrt3v3brV+/fpcJ3pPDjsb7UxyyaMl15YFULUMWTx16pT+97e//W1NFkVZxGInzPA9iixyv8W1+vNk0cOjcfDk0MMjxrvQJoaE1Sg6YQLDpobqzLxRZLVyu5KTdlDZ5iKodKYIBRJ44sQJ3RYSVRHC+Mgjj+j7BFVRyCL/n4UsemXRw6Px8OTQw6OOdyHAJw6bmnXr1umKzrDwWR5gu75DSmeQ2rkCUdcx2GbhHuF3hJ2FLGJ1Qxg5SBbD1MAwsij3Jot8xlYWpRraw8MjH3hy6DHnEeddyO9RQeg4cfXVV+uQGn2S29GLMG7ylAneozHopHMdNnb4Ny9RLKRe8BmUdyGLGMVzb1HUIj6LhKSjyKKtONpkUZRFvhdWDe3h4ZENnhx6zGnEtcCj6IQWeKiEFJ1I67F2zQtsV5/Ddt3vMHTKcQRRj4jxd2xxWEjH4DwMDw9XyOKBAwf074Qo8hOyGLbeNGTRrob2ZNHDIzk8OfSYs2BCYTIJqoVMNpgC024M38IrrriiRtFo1y4mYeumApUevEyiVF6TP+YT/z3SIIvqzOexxWHZuHGjXgeFXpBFFrxD+YzdvQViWY8syvgOI4vBAhdPFj08ouHJocecDSNLNbJNDJlMyC1Ezbjhhhsq3SQ6IS8wuO7Tp0/rAhtyvwipc9z8tCfkKPXGw0OQx3hljDHWWCj2ElcAiCLjlA4s3Hf22CRkHRbOBmFkkXtburd4sujhEQ9PDj3mFKK8CwETEWSJCYoWeFFFJ3y+yBZ3RauSLJgao85s375d51GK+kNeGL2hORf79+/X35PJOGpC9sh2LToFReSrcl/y0sKyefNmfU+Q5sG4xEYKj1FUQJsskvaRhixCFOOsczrpGnl4pIUnhx5qrnsX8m+UCZatW7fqyShuYihSOSxalWQyvPfee3Ul6a233qqJsH1OJC9MQn1MyJBFe0KWyZgwNHYmRcPnHLY2GlHMxH1BygMLqR7cyxcuXNBkkWIxTLntscnC2Iwji3arPxZPFj08qvDk0GNOtcADNjHEyJcWePyELEm3hzi0a84hx88kSi/d66+/vqb1WdS+iHojE/L58+f1hIy1DzmZTMD2hFyUxY9Ha6PRKpuEmFkuu+wyreQzNkkHkbHJWAySxbD9tgmjTRYhnVjwYHbP9sjLFWUxaHXl4dFp8OTQY854F9reaQDj3oceekj3kiW/kAd/ErRbzqEoo6iAeNBhyRPsg5v0uGWiFbIp7dQIQZOzGNZ71xV+Em79e6zZ1wjChpLNArjfGZsshw8fVo8++qgmh3YYmo4vQQQjCijtcr/zAimf8WTRo9PhyaHHnPMu5N8oC3itoQpAmDrVboZQGcooYWRCckyKeU3kED9USBbARCrVpiiUTKaErZmwpZ1a1kroTrOAaTaZ6nRAFikmk4IyeZFhwYngBz/4gc6flZeYMNVbnhsyZoPKok0Wgx6LXln0aHd4cugxp7wL8Vaj6ATgXUh+XVq0S1iZKk+IIRMfYWQ8G4skWUyuwd67EEVyFqVDhhBUCONcrITuNJLbLgbqwRcZng9CFkX15llgk8Uoc++wMDTPAyGLQig9WfRoZ3hy6NFRkLw4CCC9Xu2HOwSFSQATXqp0s6pYRYeVXYmnXWDDcVJckiZ8nBcI461du1YvdocMFkyPQVIfO4/WRTuQwyDIHySdhEVUb0mRoIqfLkgSdj516lRkikQUWeTFlAXC6MmiRzvCk0OPjvMuFIIoQLEijETF7bXXXqtWrVrltC0e9kUph67E0w4jBwtsilQ86yGsQ4ZYk6BwYq0TrDaVjjSdqrh1CtqRHIap3jwX5NkAWWRMonpTpc+LDUq3jE0U8DiyaPeFtsmi7bMo3VukL3S7n0OPzoInhx4dF0aWhza/x+4CFREVC+/CPKxXWjXnkImMY5UwcnDyipp8mjEp2ZXQ4mMXrIRGuSH8bFeadwo6iQh0AjkMI4sUVzHuKOCC1Inqzdjk30GyaLf0S0IWWbf8PawvdKedU4/2gieHHm0NWy2U3EJ5EJNLxFv/5Zdfrpe8HratlnMYFUZOuu5WUOXCKqEhixBeri/KL9dTilvyqoRuNFrhXBeBTiQydhU2Lypr1qzRC0CZlzD0I488opVGXnRkDPP/acgi451xbpNFuy+0b2np0Wi039PVwyPEu9AuOpHfQSZuuummCuHIC61kZRMXRnZddzMB8ZNqUyZgWqpx3oOV0PWUG4/i0YnKYb3jIuWBRfJpbbIoxVdBshhG8DxZ9GhVeHLo0bbehaLe2SEYqdAFEENCQ3mjVcLK9cLILutuNUD8KB6QnDCphGZBVbQroaUntFdbGoN2HVP1INGIeuC+whaHBVssKb4SsohlFhGOJOOzHlmMavXnx7pH3vDk0KMtW+CFeReSQL5v3z4dWoUwFKUkFVmQkiRknTSMHLbuTpnI61VC8zu7uCWqErpZaKV9ccVcVA6TFl+tX79erwcLLSGLMj6lw4uQxahUkDCyyDOQULb83ZNFj7zhyaFH23sXEtJBLeRhiX0ND1rygFrZbiZryNoOI99yyy1ajUiKdiWH9SbosErooaEhraxKJTQvCuKvGKyEbiTa8fzPZXKYhyLHuSGCwWKPT4ii+CwCIYr85LNJyaK8MIuyGCSLUg3t4ZEGnhx6tFULvGDnAfqf4kmG8fKNN95YCa22st1MVgJnh5Gvu+46nbA+V8hJmv3mHPKCwCKV0FStByuhRVWEMPqe0G7XphPJR1FtAe3xSS6tbevEPU5EQHpHC1mM8wC1IyQ2WQxTFu1qaA+POHhy6NGWLfD4PZM8yd+0wCO82MjQbyPJYdYwcpJ1zwXIRMty2WWXVfruMhlLKzXpjiFL0ZXQnTQ5dyo55LgaUeQUZuskZBFvVhwXRPkWskh+Y1ayKKbcvBBJzmInXj8PN3hy6NF2YWRCMiho/I4WeDwoG6nuFU087QmXBzrHmiWMHLXupL9vFeS9f8G+u4wzyVckBM25LqoSulPJeauPoVZSDpM8XxhzLFu2bKlRvomUUK1vG8ZDFkmTSEMWeabwPXIiwwpcOvF6eqSDJ4cebeFdKA+2w4cP63xCwjFbt26NzAlqNS/CNOsGrJ/JwDWMHFx3szqktDKp4rza3THI6yS8Jx52jEVUHclX9JXQs69NJ5KJVjmuMOU7mCaBCmgr31Fm/0IWOTYhgzwTeAm1u7d4sujhyaFHS3oXSjK4PJyZoOmLzKSNbcuKFStauqLYZd3ArrzOGkYOW3enKld5gnxEuxIaJVHywQhDc+2ZqIUstloldKMx161sGg07xAwgi9JdiJfnRx99tCanlkX6RNvHJgqhKItyHT1Z9ACeHHq0BHggQQrDwsjkh6GgET6mBV7wQReGdi1IEQNvcildw8hJSW2rE5tmklrbw05sSaTS1C4eCPaEjjunrX6+21Vhm6vHBbnjRYXF7i5k59QyfqXAhSWM+Mqx2mRRFlRF1EXglcW5AU8OPZoKO2FaHsZ2aBX1jITsK664QodUkj6si64oLoJ4SjUyuPnmm0NzKV3glcP8K02D+WCPP/54rGrTiSpbu5CoZlnZNLO7EODZKmQR2xwiMBwXIWnAGA1LWbGfxRKKtsmiHYaWVn9sO+go4dGe8OTQo2WKTuyHEQ+ehx56SJvHQpR4620lL8I8J0W7Gpk8SsJCRVRJenLY/EroJKp3u6FTyWGzClLyBsSNNBxJxeGZ+x//8R/62Pbu3autwPBVlOIWlrRkkY5F8hkhi5LT6Mlie8KTQ4+W8y48deqUNnomTEI1cpZCjEZWFLuAUA3HCgkmjEzhA+SwCIUpihzKA75V0U4TdFgltJBF8kjp5AJQwxnfndATulPJYbsqh/XA85TjQvmGCPIMEkNuGaNSrS9kMczaKSlZFEXRk8X2gieHHk0pOiG8gXE1Dyq7BR42DbSX2rlzp87xyjrpFJ1zmEfCuoSRefgGSXAjyWE7oF33m2tKT2gWwAT8wAMPaNJIJTQTs/TchSz6SujWQacoh2Gwn11UOvMsZpGojVg78TyG6AXJYtgLTRRZZFtCFsVj0ZPF1ocnhx4NDyNDDsl7QV2RzhS8rUKUUBKf9rSn6TBHq1cUZyUsdhh527Zt+g3enoSK3Pd2JVmdAnkZ4uXHroRmOXToUKUSWsLQUW3UWgleOWw/xL3YkvqwZs0avQCInYxRohq80BDhkDHK/6clixBQ1uvJYuvCk0OPhkDsEeShZBeMkBgNWVy3bp22bskjzFa0CTbIQuCCYeSwauSi9r2dlcNORFwlNAv5YGkroZuBTiWHc0U5rAc8E21rJ5ss4qrAy36QLIat2yaLQMgiggBLVIFL8HsejYEnhx6FQm5+qUaW/EJ+8juSoanyvPrqqythjTzQqJzDNOBh+r3vfS80jBxcf1HksB0xV0htWCU0bdRIP5BKaDE7Fo/FVihw6VRy2MnHlTUlhvPBCwoLL/Nh6jfPe0mViDONF9JnF/jZLVPl78GcRU8WGwNPDj2a4l0ICCMzuUGUeNjkiaLJYZrQLw88VCCSvcPCyGHr98ph5yKNHZO0UZNK6DD/OiGLUVWmRaOTSVQnFqTIcyuPYwtTv0kRErJom8ZLugRkMarVXxhZZA5BSIgii514jVoBnhx6NNy7kAcGNzxK4e7duwu5uYtWm5KuP0kYOeu602KuKHCtDJfzb5sd4/tpV0KTv8oYs3tCRxUOFIFOJIedGlbOkxwGwfnCuollw4YNerwzLoUsUogIbLIYlVebhixKGNqTxfzgyaFHYS3wgE0MJYzMhIZiSFiiqLe+IpVDkEQ5TBpGbjY5bPUJ0JPaZJXQdpUp/Xb5tx3ei8oFc0WnKmydelxFksOwexfyx0IbUM4pqRLyUkNEhc/Iy0xcO8p6ZFGOKdgXuhOvYSPgyaFHId6F9k0MeBAQRkbZoAXePffcUyh5K5ocxhWNpA0jB1FUtbInWZ0Nu8pUCgfIVyy6ErpT1ehOVw6bcWxsk5cUFjuvljF6+vRp/cyU3tEyVglZpyGLEE9SLm666SZPFh3gyaGHM+wkYkl0tsPIdvePzZs3V27oosmb5DoWgSgClyWMHISvVp59rjsJjTgeKRwgD0xywezwnlRCywRMqDprJbTPOWwvBJ/RzYSdVyv7Ju0oT548qc3iUQHtVImocWpHqVgP3xPBQpRF/m4ri1IN7TEbnhx65NoCz37ooFxAlPgZJEqNCPsWqWiEEbisYeQgfFi5c9EslS0Y3rMVG5mEGa8yCUMWk1ZCdyo57GTlsFVDrXY7SsC8ImQRyzPSJaRi3yaLNviOVDXbObd2Ljwv8UImhSza1dAenhx65OhdaN9UJ06c0L2RyYe64YYbZrVfaoRy2KicQ9cwcti6fUHK3AhdtoJis2XLlppK6MOHD2uzYyZd22Mx6mWnU8lhpx5XK5PDICTEzALCxikvMfY4lZasQSQhi3wvWOBS6sAxkAReOfTIzbsQcLPxdkeO065du3RIqxlh30blHPJQgQRjXpw1jNxI1TNqvXP1AegxuxIakCJih6ApJEN1FH9Fxrm88HUiiZL8tXYhUZ1KDpOM06C9k3TdwhuUsSr/zkoWewIFLp021qPgyaFHbmFkcpooOgGEVak6a+VqYtf1E5Zj0mSidAkjh63bK4e156OT0A7Hw2RoV0IzWUpxi1RCS1cM/kbRQCdB7r92uFZpwXOxURZHjRintGFlEbJIfjupEtjm0HmLeUhya/mZhCyWZ66/RMfs7i1zhSx6cuiRGKIWhoWRaaPEjYi3FS3w6r2ZFtneruj1C0GmKT1hZCmyyQtFViu3KzolrNyux8GEavfbtbtioNxQIcpPuytGJ4y3dlXYOlU5rAdIG+kQjL9rrrmmxgsUBRwBAwXcJothL/Ulqyf0XCWLnhx6pPYutIkhv0PK503t2muvVatWrUp0Rts151DCyPyk+pp8rbzhq5U9Wh12CzWKzvh/evAyCe/bt6/iXSdLlB1Jq6KZdi9Fo5PJoV2QEuYFynNbyCI54nRzgUjaZDGYHx9FFmWBKLJewHn99re/rdfxohe9SLUzPDn0iIXkYNgPS7lRqCKjOpdJAe9CfiZFO5JD8WokjExILc3xpoEPK88+Hx6tDe4FqqClEpocXMLQth2J5CuyFHXv5AWvHHYm+UUBR8AQEUOM4yGMRIJw1pAuQ0tmqqbDQvD2PMjfbbL4b//2bzqU7cmhR0fCTs4N8y4kn4Ob6fLLL9dL2gm8ET6EeYXw7Gpk8Wr87ne/W1iI0JPD8GvQKeg0shssSOHeFqNjqYTmRRKyKBWmkEMhi3F5YM2CVw47QzlMYxwPIIdCFh8L5NYyThEG6pFFQtdJI2itDK8ceqQqOpGwKsUYONCLxUBatItyGFWNXGTBSyPJoRBfuhOIv11enTM8Opvk2ogbL0E7ElJRJLRHCJp7izFne9eFhfYaiWBP+E5Cp4eVxQA7C3hpWbt2rV6CubX4LDIvBsli8FxKXmO7w5NDj1k3FqEgyEJQEeTNn7CqmDy7vO1zQ0kOY6uSQzuMHKxGLrLgpVHkkAcd+WIQfXLHKChgsubYbDPkoMlso9GJE/RcNotm4l6xYoVe5AVMJmAJ7ckELLY5jSYznWqAPRfIIaJGXkp0n5Vby7PTJouo4MxhjE+em4zZZz7zmTqPMSs5/PCHP6wX1gd2796t3v72t6uXvvSl+t/cG7/+67+uPv3pT2tV88UvfrH60Ic+pFavXl1Zx4EDB9TrX/969fWvf13vx2te8xr1rne9KzVh9uTQY1YLPN58IIhXXHFF5WFCSJUBSyUyeUWuD85WtrLhXHCs5ErZLf/yWn8zz42QQ4gg+aI8PJ72tKdVPN2kcwYvAviEPf744zr0It5icWbIRaJTFTcPkwfG5CYTnD0B44LAM8nuCd2ISuhO9G6cS+SwCKueUqmkC6tYpCUlRJBx+rd/+7fqH/7hH/SLDs9HCCORprAGEHHA7eOP/uiP9LzD+v/6r/9a/eiP/qh64IEHNFF805vepL7whS+of/qnf9LbeMMb3qBe8YpX6CIYOfaXvexlOkx+1113abXz1a9+td6nP/zDP0x1vJ4ceswKI0tPSnlQoy4x6G+77Tb9YG4nk2qXMPLNN99caeOU1/qbqRzKOccslrwvyRcVM+9g54zLLrusJgQoZsiMAdsMuVM80xqFTiMdeROpoFrDBCwei+Q6A+5LGYNFVEJ3qgH2XCCHjfJxLJVKuvCE5X3ve59697vfre677z71cz/3c1pcQNVjX5797Ger5z3veXrB0SPu3P/X//pfa/79B3/wB1pJ/M53vqOJ48c+9jH1qU99Sv3QD/2Q/vvHP/5xtXPnTv135ucvf/nL2j3kK1/5in7Zuu6669Q73/lO9Za3vEX9/u//fipF1ZPDOY4w70IpFkE5ggwwyG688cZc84BaMedQwsiEB+qZWrdjzqFca0J3vNGKcWzctoIhQKnuY7J+5JFH9PogiKIsFpGv2ElkqhMV0CJVNnsCJmLBtkTZtiuh7TSIPCqhfVi5fRHVPq9odHd3a0GBZ+A73vEOXa3MfEJ492tf+5r+HRE4ee4mOQ4UQiJ5RHcgnjxvX/CCF1Q+s2PHDt2u9e6779bkkJ9XX311TZgZkkqYGR/i66+/PvHxeHI4R2F7FwZb4PGT3AYUNKRsSc7NE61EDpOEkdtdOeQBwxsl64X4Zp1A7eq+oKpj5yuKqpNXvmInkqpOQSNDsGzHroTmHpf2aYTQqDBlbNsei1nyzzpdOWx2wU87hpWTQgpS2AdewlnIE0y6X8y7kEHmYNbzmc98RreiJQ2IsRyMZkEEjx07pv+fnzYxlL/L39Kgc0eIR+zDAVIYVo1MOBUSwWee9axnFdYWq1XIoV19HRdGbmT/47zXzUMBBRgjWN488/KYC6o6dr6iPVHb/nbNyFf0UB2bn2cXTwE7DULap2WphO5k5TDPgo1WRLPbAw4PD4e2jk26T+T1QwR56fnnf/5nXVByxx13qEbDk8M56l0YtGrg31RfESpEFSJ8WGS/1EYUpNQjWHYYGRPvNMSlyP3PixyyfxSUHDp0SF111VX6ep46dUoVhbT5ikzUnarOzLVCh1Y5prBKaMYgLyy2ybGMQe79sEm705XDTj22ZoaVZdxADl1y8yHuV155pf5/0rnw1P3TP/1T9ZM/+ZOV8WyLGKR/iU8jP++5556a9fF3+VsaeHI4R4tObGLIJM4bNvY1JLBCIlB+ikQzlcMsYeRGKod5nBsmQYgv15wQBW+yqHqNDM/G5SuiTttVqHH5ikWea4/OJrzBjhhicswYlEpoXmZkDEoldCcrh51MfJsdVh4bG9Pbz6twEzAWWS9EEQHjq1/9qnrlK1+p/0Z0BusanvGAnxSxnDhxojLmb7/9dv0SRGg6DTw5nAMQtVDeqOyHnliaQAhRz8gpo0KZ7xT50G9UtXLwGLKGkdupIAWSDzGElGFULg/KZpOsZuUrehSLdiLutsmxbUXCwiQLeCYwCRf9DGwWOl05bGZYeXh4WP8MCysnwW//9m9rT0OKTJijqEz+xje+ob70pS/pl5jXve516s1vfrN+RkL43vjGN2pCSDEKoAgGEviqV71KV0+TUvS2t71N/cqv/Ip+/qaBJ4dzxLswrAUeEzKhFiRswoDB5uJF3mSNIIfAfri7hJEb2f4vK4njO3v27NELVWxYH9gTW7PJYb18RVqsBQsLeAii9nRyjlS7o10JlD0GuVekEpoxyKQKccQ/zi5u6YQXlmaGXRs15zXr+IaGhip+iFmA4ocvIc9AyOA111yjieELX/hC/ff3v//9+thQDm0TbAHz9ec//3ldnSwRI3IWqZROC08O52ALPAYV6hlvORh1BtUzubHamRzKsUp4yDWM3GjlMO26pdtJsM1fcL2tQg7DxoM0urfzFVEWOSb+n0WUxWZ0zZjLRGouHJNdCc3LCGFnfEDtFxbUF1vdbseXlk5WDuW52UzlcNDBzgsfwzjwwvzBD35QL1Fgfvv3f/935QpPDjv0BiF8GlQLAQUJkAgeblFefvLggFgWVV3KzdsI5RAijOGzaxg5bP2tUq1sdzup58/YquQwLl9RcojI42GiJj9W8hVlkvb9oJuHTiGHwWMK6wnNvcYLi1RCo8wIWWyFntBJ4MlhseSwvwBT9mag9UeyR2pJXaqRbWLIA4EQMnk1YSFHG/K9VrCacVk/oHJLeiPn+ZbfCjmHfIZKZMgvrQ7t1ICkofZ2AZNuMFeMSZqFSmiOTYhiXkbIHsnQjuOpHsIKUhiDGBiLiTHPWclXJCpBrjYvMHZP6FbsHtTJ5NCOlLWSjU07wpPDOeBdyERKrh1/Iw8hSVPwVu59XA+STwnogUkoOe/Jq0jlMMm551qiXKAE291OOnEyD+trHZavaPsrkhtmKzqt5K/YTud+rh5TkopexlRYJTSL3T3I7gndCqSs08lhMFrWSAwNDennUifcD54cdrB3oW2AjOKCYpj0TbbIggtZfxHVgHY1MuuFHBZxozbTBJu3UxqxMzml6XYSdx5a/WEWdz7sfEVg5yvSrspWdJqdr9guYf25fkxZrGzqVULzO7u4pVkkotkm0Z18bCMjI4nEl3aAJ4cd0gIP2MQQYsfbKwaYGCCnNcBsVE5gnuQQQkDunfRG/uY3v9lSRSNp1h014QrZJy1g27ZtqUiOXakehPyu1UliVn9FCUEH8xVZOuVNv1loNyW6EccUVgmNqgRRxGqKlxbJaWy0dVMnezg2uxJ7aGio0OYRjYQnhx3gXciNbt8QqGaEkZkkIUlZHjpFh5VtuxzXm9k2tcaWh56rck6KOoZGF6RwHIRL6WITbKyeZr3tqPS4TmRUmNqKDsqrGCH7fEV3dCI5zDv0yvlBvWbBwy7MukkqoWVJ60uXFJ0eVm5267xBrxx6tKJ34cGDB/WDBoJEkULWh0AjwsrAlbzFmVoXqe41sn0euUwoopIzmjXhuV3JYZ77zDng4c2SJF+Rn3lXoHYakepEclj0MQWtm7i3pdUkz3A6CEkltCx5jENJQ+pUctjssPKwJ4cezYCYtDKJ8fZpE0NURMKNPGDSFig0s5rYZRvBMHKwGrmV7Gayrlu6naxcuVI737s8+NqZHBaFsHxFyROz8xWFKLrmK3bque9EcthIAsV9HVUJHcybdamElvHXbjmHZE6NjweXkpqYIG2E86XU1Vc31wAb+Gplj6Z5F5LwSgcMlEGBdP7g4UHnjzwsW4rOOZT8yCzbiAojN1rdK1KV5CHHpBDV7WQukcNGEg/UGYg4S1QvXpmgfb5i5yqHzc7LC1ZCS96sXQnNS7HdEzoJKZJnVrOVwyDRMySvSvbs3/H/9R5ZW7aUFdNeK4SVF4c0IGhH+JzDNvMuZPKScK/dLi2vzh+NCivLNtISLAgyCimhwHqm1u2ac8h5RyFGLYjqdpInOWyHib1ZhDZYgcrDXyZp8hWDRQVz0V+x3V420phgtwqCebM8G+SlBa9TnnM8C+u9tBRBDlllnKonRI/ficoXRHc381y25xCpmVu3llsirDwyMqIdMjoBnhy2WQs8Bj7/5uFArh3KRp4EolFh5SzbkDAyb8lJTK2LDv0WcX7owIAiCup1O8mKsHPSDgSx2bDzFaWogOvFJE2hEGbk5CsKUYzKE+u0c+2Vw8ZCeveyQETsSmh5aeHZGuwJbT+z4sagHcKtErxosiePwayP2p4eXDey3xM7dkwruc2aHVYemvE57AR4ctiiELUwWHQib0V33XWXDn2RX1hEy6aiw8ppyCEPP9pV0eElLoycdf2toBza3U4IJfEGmjcxlFB+uyk9rUqm7AmYHrySrxj0VxTLHMKA7Xbu66GTLJDalfDGVUJjZfaDHzyhSqUFanBwmertHVAnT/app57qqpA9tIdLl6pkL+0QdSV3WRVDsGxZWa1bZ6+ruWHlkZERTw49GuNdGGyBx6QDCCPzICgKraIcQpBRSHnY3XTTTZU+p3mtvxWUw2C3E46Zt/+i0I4EpR32OSxfUULQjGHGC2oPqRGd0kmhU8lhq1b02iFco97V5uaZsG23Gh9fNrPwfJnW6RD79w/pdJVLlxaqI0ceVwsXGhV88WKUrmwCgysxdPk+Q2737tmWX83OORz0VjYeRXkX2tK/PHQZdBSdyMNYEpWLQivkHKYNI6ddfysoh2HdTk6cONGWVdYeteBarlu3Ti+Sr0inDNSFe++9V5NJyRFjKcrXrkh0KjlslHLII7ZK6uT/TQg3mMPHzxnNwNpPk683PR29r93dXWrRooV6GRlZrF88165do19QTp48ovbtm9BhZ0gNhLG/f0B/px7Ydtx264H9diGWl11WVkEexpyVR0FmVnhy6FFYC7xgGBlQJYmyRMXq9u3b1e233164qtfMsHLWMHIrtbhLAul2gt8eSrAoFUV3X2k3dAKhlXxFVEUmkBtvvDFTvmKroh3HVRFWNrPz8mrJXpUEGrLHbe6ins2bl+6709PmuMhRZymV1qvx8QlNFFkOHjyko1YDA/1qYMCQxb6+ftXVNXsbZL0EyWpScDu73NLUfV155ewVNDPnsDzTMhExoxPQPk+fOVR0Ig9ablLMUE+ePKmuvfbailooRSmdqBy6hJGTrD8vuKy7XreTVie1Htkh5z6Yr8i4D/aDtq1K+P9WDHN2qnJo+r6XZnLx4lU9HpOjo4bsNTqsmva75TIWPV01FcJELWQscj0l7YHl9OlT+nekQPByA2Hs61ugurtLmYkh6O1l3ss+ZnbtInw8+/fNDisP+YIUj7xb4AXVQsgRIVVCTXgX2hYZjcoH5CFR9Dbs43ANI9dbfyuQrCTdTjw5nI1OJ7RM0PXyFaUfNJN4q+QrttN1CQvhouBNTpbVpUulmvy9Rx5ZrBYuHFDLlsUTchcLFrNPjQ3LGkW0FLnfjCnmHBYMuY1tziU1PGzIIgUufIY8xb6+hZow8pxOMxb5qAsxXLGirKK6hza7IGXY5xx6FNkCT0KqtFWiBV7wxmuEctjIsHJeYeQgig7Ppl130m4nnhx6BPMVxaqEMYSySMhZiGIz8xWbqRzODtXG5+uF3a4mZ497Obju+jmHrqFRF9Uwa74eYWXzfCmlsM3p0wvPLb4/MUFv8mH9Mn/48BE9Fo3F04B+sa/nsuCSaxhWhNJKYeVhTw49iggj232CyUnioR+GRoWVG6VOUpCRRxg5bP1FKRuy7iSJ67ZZ+c6dO3XuaBw8OZx9PjoJaY8nzKqEfEWURVIT6JghfXh5ZqAwNipfMS9yyGrCVL0wc2X5fwGHmjXEGZU3x/5I+LWI0KgLQTKkNNt3p6cp2AgnyknAdV6wYFCtWUMlyGpdCT0yMqxfXk6dOq1zFnlRET9QCKM9Fl2LUK64oqz6+6P/3syw8ujoqL4ffLWyR27ehYAHPaoSScL1QqqNIm5FE1DWD2FiUssjjNzosDKoRw6F8PPwvPXWW3XuWD20a0/oItGO+1zUcdj5isDOV0R9JyTNOBNlsRH5isF7ICqEG9YyjbAuf8sCF7IR912Tm1dMaJRHkguZdikEUWpaTU1157ZtqprlxWXtWpMnj3omIej9+y9VKqEhiosWkUaTbfuQQshhHJoZVh4eHtY/PTn0yORdKFVwQe9CegVv27ZNKwP1HhztHlaWMDJhMvJa8PYrQh0quiCl3mSPuoMqyuScpttJkeHwTiJaHuH5imGt1ex+0Hgtpr3fokK4Fy8qtWfPIrVkSZcaHY0P4UYhq8jpEtat912jHJYKIaXz52dXHHnsuBSClEq8zHYVtm1UQqmElhcXKW45duyQ2rdvSoeoBwdNviJjMawSOqoIpd47TjPDykNDQ3re7JQWmr5auWDwYIYUhoWReYh///vf1+pSUlVJ1tGu1cp2NTLE0NgpFBM2bIQlTBjR4ncHDx7UFcnkjJI7mi5huzh1L0qVbGVFsZX3rRWBUsMSla/Y3d2jFi5crgYGKGxZpkql3hklz3TICCp7cXVpY2Pd6uzZ+er48VLD8+5cwrr1vhsXEXDZZ3iLSzEG5E6p7OeaXMqkZCyPbUsl9PLlS/ULw9gYldAXK5XQ5DAODvZryxxjm7NA75+551mDue9575l594lFM8PKIzPdUVrRVSALPDlsgHehPGjshw2SOx532JiQX5gmR6hdlUNCX4TOeWNESSMEVqRCxk3K+S9q3SC4/2I/RLeTuLzRZpIhT7Q6y1xZQrjhvXAhd4vUxMQiNTa2RY2N0VptRD3xxEV18eKQGh09pZWOJUv6VV/fIj25pZlczfG47Hv2woSsJCvJd01YuZR7hXFXV7xhdRxcSCmPE/bbFKSkJy9pqrJ5NBoiac61nEZ+3909T/X1LVMrVy7X+zI6OqaJImPx2LETmnwSfjaG3At1qhEFQ9u3J5snmhlWHuogGxvgyWEDik5sYsjvUJQwtt69e7daS6JGG/c9ToKoauSicycbkXNor5+HAzY1dreTrOv2OYdNxPi4Kp0/r8qkASxZ0vDNz662tcme7btnlD4ZgsnIQ1elWEByxEZGhtSFC6iLR7QhsoT94gyQbRPm6emutqnWTfrdqLCyS/FLM3sQS64gpDepcsg5MIROXgBQHaUYplxTHGOH6c14LCU47pJWCllWrlwxQxZH1NDQsDp//oI6evSoVrmvuqpHnTtHKsTS2GeqCDLNUu6Gh4c9OfTI5l0IeUA54/eQB/ItsqCdwspxptZsQ3pIF4FGFaTEdTtpRXLYbmjYPpMP/NhjquvBB1Xp7FlNDsubN6upW25RKmHKR8gqa0je8ePd6tix+eqJJ2aTPVQ+brkst11W4mFa+C1WixYZEmzCfuSIXbQMkA2ZhCxSiSrXwyhw2ZRQF3LnQrKSfjdMOXSttHUhd3kQaaPiTWuCy/rM4dWGb83YM7/j7/IYEqW1OjaTE8zkimNJkyuW1atX6UpopYbU5Zcfr+kiJPmzLHYetzzrm1mQMtAi/qN5wCuHDfIuFMsJCk5cyUMjlMM8tkFBBkqahJGD1chFK4dFkywWrjnXNarbSdE2OXMlf68R+1zau1d1f+MbmhROr1mjmVvXww/jSK2mXvISLRuZvKnwrhlSrCFEj38H333Onu1Vp0+Hq3HMaVmIIUMkK/FA+bNDrPPn96r585ep5cuXVQyQIYoXL15Qx44ZJUeIIkbIQZWoaCsW0AjT6aCVjaunYdZra75bSwyFuEn4Vh4RwTy9YFU0f2M8dnV1hxDN8PPiSohdKquphL7llkG1fLlpoMzcSu4sC/2heRlnLEpVvogtzSaHnQJPDnMMI5NrxsCE/Nkt8OiLTDL4ddddV6kodEGr+xwmNbVu57Ay4JhQgoGLEhy2XuDJYWNDuFN371ETZwbV+NqNavxktxqb7FLjU+vUxB1n1KXRc2psMWqGm8ITdU1dOm1kncDr5d7ZBsi07rQ97c6ePakOHDioySQqG5GBpPmKboQh+3lKExIOWtm4ehrW2+ewPD0J30pIl1xFIYYgmphHh3RNWDkZeXIlxK6V1WvXltXy5bUqt12VTyGnVOWTqjXG25hS2vkDskixYyNDzMMdZIANPDl0BOSDQSokBNImD3+UM8gDUjgt8PLqYtCo1nZZCCgkmTc6jr2eqXU7k0MKTlg315a+13m+rcZVQs9F5TCpehoM4drVtrOtWMzvK6dialp1fb9XqfIm1aMG1KSVR1cavqCmz02o8mAxoT+XSbiReXu2p12ptFZNTEzqiZniOnKok+QruuYKNopA2zmHaYpfJE+PwhP5bu3wlVy9+Dw9m0i7VDcHz1eaghTX/sculdU8TnfsiL8piEQRqZFoDQIM7h+4gDAeEWYwhJcQtBm3xYV8h3xBiocdRpZqZIgIbza8vfBv3l5EOUtrZdIqYeW0oc16YeROIId2txPOEVY1eYcxPDk0kBDuxYtd6vz5bnXkSG17tCDRswvTg+HSuujuUmpgUPWcPq4mpxZW57QJ4nDdqlznxY5JPmsVKrdJ1km40WqjTUh53olauGPHjlk2JZKvCFHkmcDLsZsvYfZK3TTbtZ97PPoYS7wnV4ne7PCtkD3J05PrMnsclhpmexP24mCUw67C+x+79pzeurWs0tbzkX/IQqGntLKTMDTzMdfTzlfM4vcZB68cekR6F/KQRNG777779EC55ZZb9JtL3mhUQUpS36isvZEbEfbNc/3Bbidc56LUvUYqh5wj3rSZvBmvReXsEGKKytcLa5HG5MsEc/x4v7p0qUuTxCIn1WnaGp4+QbsipRYt1DtZOnNWlSnrXba0kFCp2VfVcNUwK5ENy38TwmPyFZdr/1I7X1EqT+fP71ILFiyaIYv04O1JSaAz7bJWwBhTYXl6QaJnSD5/NwobCmB095ZSoQSrCNsbI2TUX6drrmHW/QYLF5bVli3pn322ATbPOanKp0iQZxzPbZTukydPqieffHKmGMsYw/PT1bx6ZMbnsFPgw8o5ehfStopwI/k5aTpitGrOYRLfqDRh5GYoh3kRrLBuJ0WZbCfpvuICe70o3ai9jF0Z2xBEHpgsUdV3rGK2tcpssmf/Pp16wzgvNTSc1bNhtZqYukYXpuhq5e4eVd68SU1v22biXLlNpEL+s+9rM6p9w9W7cJ/DYL5iuTylhodHtGXOyZOndL4ik7EQRaNAdiUm0FHh26DNCpBK3aR5elNTKIdYbbmRnGZVZMep2Ib4lgolhi7FN2DXrmzemXFCBs9Unt0siBfMa+TJSn9yKqEZj3Z/8t6U7Vwhn7wYdQo8OczQAg/YxJBByZsIraokB63I3IZG+RyCuO2kDSO3Y1g5rttJUT2QwzwU81y37DPhFq4fD8NrrrlG/w6SyAOThYpAxhp/HxpapcbGlqpyed6sEG4Rk5ytxElCfrLvZcuT0hMiYbANG1R59RqlRoaV6u5RanAg13CnPVyy5nS5hFhdJv4wdTRp2sm8eV0zLdMW6n/zHMXPDmXx8OFDanJyXJsfL1zIskiTSjwZDekzZEEKMkAwfBtXkJH2FiX0Kseb9RZsBU/D6EKbrsIqyV3DyevXl1WGvgGpDbD5nN2fnPFIkwYJQQ8NDel5TT4DWazXqIJo4ebNm1WnwJPDlN6FYt5sS8kUnfA3qpRJzi7a56hRymGUMsaEcODAAfX4449nag/XKJLrSg6l2wmJzmHdTtqxB7LsM9cPwks/b8IuYsEkPmMSiuEFYN++c+o73zmvRkaOacNaKTjo749We1yISFHVt4lz0gh3zvSGrYes4WSXkGPWbRZBKu2wsvzbDt8CMU5m3q7+jsl5sVqyxJxnO1/x2LF9+neMw6VLB9SCBQtn/BUbp5B2dZE6RFhZZYILQXJR3upVCKMcxoWVXcL3rtXNcK/t27OvwKWvMsRvxYoVerEroc+ePavTpXhpRnUUshhWCe3DynMIcd6FtvExXU5IyIZEFE3aGqG42dsJHo+EkXnLShtGDqJocuWyfrvbydOe9rTQfJQilcMiq4oJo/Cwk+sXtR2Ob/HiperixeVq61au/WTFIPngwUNqampST+BUAUIYFyyoGiSDLEQkjMAkffHISiqzFoQU3aUj722mJZU20QtarUj4FgLGYkKw4TYrpmBD1hq+77PzFUfV8PBFdebMBTU8fGymH7TJIUuar5iVoBnlsDZlKA1cuqi4Km/18hSN0ps8fJ8GqPYuaum2bWXlYuiRZ1/l3kAlNONRyKJUQkMQiRaiKjJHuPgcvutd71L/+q//WjH6JhL3x3/8x2r79u2Vz/DM/vVf/3X16U9/WqcDvfjFL1Yf+tCHarx1eel//etfr77+9a/re+U1r3mNXnea9rwCrxwmbIFnE0MxPoYcXnXVVWoNhrkNUvQauZ0gCZUwMjcA1jxpw8j11p83spI3riuFJ/UMy9utzR1vtlzDOMIbBJ08hofN/zMhL12KNcQSvW88oOiJClnknDEuRVVcunRQTU/Py43A1DsXWVWirBNiM+xnXLYppMMmeTbRk9/Z7dDs74aT55K+VrTPi7qNsxyryVfsV4sW9amVK1dX/BUZa5KvaBTs6HxFty4q06pcrhUCGkHe3Y22k7QFjG6f51IAA1xI7aJFZbVpk9vzrsi+yn19fXpZt26dfhbxLCX15pOf/KQma4Btb9iwQT33uc/VYlGa8XPHHXeoX/mVX1E333yzJp6/8zu/o170ohfpyJUQzje96U3qC1/4gvqnf/onTUzf8IY3qFe84hXq29/+duX4X/ayl2k+ctddd+kisFe/+tX6ef+Hf/iHqY/Zk8OULfAuXryow8gwcQgSA6aRVcSNJqGci7zCyK2ec8hnCbWirJGDV6/bSZHKZ97kkEIpxi0PCvJikhDDs2eV2rOnFLl/rINF+qKaCfyiOnv2hDWBJwtBu4aTs0xM1SrVLGpjtnxB2pflYXlTVfVMPl5tQYb+xCyblSr5iM/TS1P5HTdG8/JgtP0VgSjYw8NDOl+Rfw8M9M/kNKJk9+mQcBZwDsmpzfJ4cyV3rspbknEVVZDimiMZXR0dVzikP6H/u3t3tiKUvMLKaVAqVdv8vf/971fvec97NBn7+Z//ef2MveGGG3RE5od+6IfU85//fL0gNMThi1/8Ys2/P/GJT+hCLhwxnv3sZ+uX+o997GPqU5/6lF4v+PjHP6527typvvOd76jbbrtNffnLX9Zk8itf+Yqeu2i68c53vlO95S1vUb//+7+fWszx5LCOd6FtKyKFCUyuWLYEByKEsdPCyuReoBbmEUZu5ZxDJHuOk+uXtNtJUWHlPNdt+zLu2rVLnThxItH3OG0PPZT8QYsagZKDr11391o1NpY2BB0Xhi6mujIrwXPxn0PYCF5WmUCF7AmkHZp5FlXbodV2yah/zlyMlOuHKWu3Zf8+Kwmu911bwQYo2Iw1XkxOnTqhz01/v/SDXqgnxTSpCcHWeUnhYhrN7jWiQjiqICXpy5U9Ru2XEsYmBLGe72PIGtXGjWWVh+NbnmHlNGDef9aznqXH2nvf+15N5u6++2711a9+VX30ox/ViiAv52k6aEEGgeS5QxLhJS94wQsqn0GdhHSyLcghP4MtXAk9E2amS9v111+f7rhSfXqOhpHtPDveCqLK1TtNOeScMKhIxM0jjBxEq/RWFlWNm4o3saQPmKKVQ9d1M24JjzNp4svIdcTjK8k5scPJWcLCaULQ/Ozr68mUp5WVGLoQvCT2M7P73vJfc96ZRA0JqYZvRXUKJ0Umvy+LOlq0j2JUtbJLi7y036VYhYXnMvt84cKoHmv4Kx45clRP3jLOIIxR+YpyvOaY0u1zHqbRjegcIz6HxipK/0YfN+MuzAoo2MklSPRkPVkfVVxrlyKURoWV05hgMxYJLbOg3JGvaEcY64Hn/q/92q/pOZe0NcAzk/k36JvMnMXf5DPBaJf8Wz6TBp4czgwqJtKwohMSUCEOvBXUI0jSVUTWUxREVSpqOxJGZlBTbEOItYgK7EaFlaMmMH7/1FNPacsWSCH5ImnX36o5hygp999/v35bJb9Qxm3YeoN+nXHh5CxkIiwETUjQ5JCdVIcP71fz5w9UlB5C0MG8qLBTIfYmWZDGXzBYeSvblvMYlqcX5qfHeeD8TE3xEpl8v11Cfi4tzJLt42wi5dJT14Ukme2afEWW1aur/aDNWCPd4UAl3cEo3dV0Bzleo66VWr44CYg6Z6cYAJvoVYldqaIcitoHlxofD84hyffFtcXe9u3TmiDmNY9nKbwoukNKXwpiCFAaEaO+9a1vqWZiTpPDoHdhMIxMKA7ygN0HoeR6DwwZmEXnPsjbURHk0FZJmahpcl6UNY+QqzQt+tKuH4Stn3A5fThJLBZVLS1aNedQCmoweyX9wT72eutNG07OEkaE+EkOWbm8VpXLE+r8eUMWmby5f6TYAMUnyuMwS79chgRkC+Gd70s7tJlPzAqLyb9N9WowdJee4KW9pqnDjRcvmqWvT/WsXFK4CXPw3nIx9nbN2Qvbbm2+oukHLekOdr7ikiX0gkbB7kv9PHIvQinNys+zNy+pBGGKnn2d6nk+8lLGWE7SIaURaunSpWW1caPKDc0KK8u2mUvCyGEaUGTy+c9/Xn3zm9+sESsoMmHOYl621UOs86Qglp/33HNPzfr4u/wtLXrmetGJTO62ekL+GZMryhnEgcqgtF1FiuqOAuQGyPtNKViNLP6NrdCiz9VM2ibRdrcTVLWs16rVlEOOk6Ih7BUwYiehOQxx63UNJ2crsphX8Q9j3y5dIofsorp48YI6duzoTBuzLnX27LlKWNCuvg3L0xPFxJ5IZcKNntCKyW1siKo0Mam67r9PdT3+OL4bqtQ7T5Ux5L3lVgwDG7a/LkqSy3eTktlgugMT7sgILf6G1NGjp/RnyIflXiIVol6+YlSubDWPtEr0zCOoluiJ2qn/kiA/L4g0CrSYe8vxuBahcN+5VCjTCSVPNDOsPDIyosdTVnLId9/4xjeqz3zmM+ob3/iGLvq0gc8u8xR5jK985Sv176h/4GWaOQzw8w/+4A90Xrk8+2+//XY9z5FvnhY9c7kFXlgYmTAXxJD8FRI405AvWVfR+YC2pU4eiKpGbkTBSJHkMNiGzi4qStMDuh2UQykc4icPiSi/rbj1njuXLZzsQppsMiD8fXBwvl7WriUEPa0tGc6du6BOnz6uDh3ar9UdlKCBgfAQtEEp133Nt+VcKfdtdj38kOq6/348QVR59WrVPTasph95VHVNldX0838ocflt2kISuyDFRUlyyQEFWUgK90Jv73zV19erli5dUfFXPHXqtP752GOPazLJhL9oESkP1XxFMfam+rzaam92IYZ97YOqs0v4PYsnIsqhOe7aMHojth3E5s1lhmquaFS1clRIGWQlh4SSqUT+7Gc/q59tkiOIMMXzjp+ve93r1Jvf/GZdpALhg0zyrKcYBWB9Awl81atepd797nfrdbztbW/T6yYPMi3mFDmMKzoR1QXywAnGz6gVu36APImbHUYOdgFpRE4gKGob9vpJHaC4Bm+qsG4nWddfZLVy0vPCtYMYEm6gYCruhSaKHLKp738/Wzg5TDkJmiZH9b0N7sNs4tStursXqN7eCf3SQhhwdPSCOnduWJ0+faDS1UVC0KaTRilXgpfVKse1MCPxNsfGVdejj2qFsLx4seqhu0f3IqVW9KiugwfV9OnTSs10fsh/f6vKmFu+YHaPvbTX1R6bUj0unGLevD41Pb1IjY+PqMsv3zqTGzusjh8/qfbtq9ozQQIWL+5X5XL2l1qXfNAsY1mUQ16mXHs3u7TYI1qA4XXeaGZYeXh4WCt7WUgY+PCHP6x/UsRiA7saLHIAtjnMCyiHtgm2gGMnJE11sggEmGC/4x3vyLRPc4YcxnkXcmEJoTJpclJd8ga4QJLD2OoVy/VMrYuuvi6yh7C9fnKMMC3n5sWmJusN3IrKIS8zuOonVUKj1ivh5OCLt50HZSe/S3gMQiCnICxPL2QPLHuVuodX2WfBggXdat483pyXhYagq500DFkUouxC8LKGz8LIUtJ3iVST96VRpS6NqfLggCYc0zJxkwh/+owqjY4m6kydhdxJfp6b6XSW7ZprijegbaMiRUphpt5Jc0h5fEP6eP7R55klmK9I6sbBg+O6tZ8ppCJnsS+xoOBKzrIQ6eq1crPNcWmxB3bunNb7kDeaGVYeHh7WRVBZI1FJnvUU9H3wgx/USxSojfj3f/93lQd65opayCJ9ke0LSCscjCPXr1+vW9W4ytKNUA5dVb2kptZFH4tcj6LJIVW79bqdtFvOoXTpIb8kzl4pbL1h4eSDB81EK1WMTJTSDi0qDwoyMT7eGHsVORf2pMixoOSwUDgVXplKCHpQFxzMnz+YOhk/33ByMqQmafinDQyo0vCw6hnsVRNTZoIsDY8o1bdAlRO87GYtBhGS5hJilGONyh8Va5lgVbiMy/DHR8mhXzQVvfH5inRRGR6e0DZREEbSkYAx4TaEMS5f0eV8ZSVnpq+ym+rn2mJv2bKyWrdOFYJmhpWHhoYyt85rVfTMBWIICSKHBINIuVlR92RyxaolKnk/LVq9hV5cGLkZvo1FkUPpdgKk2jxvNKt9HmMZxReghCbpdhK1Xk49q4LkQbySkqes5MelK0m9PKfZlakT+qFNGHrv3oOpQtCuk3jcBF5PXEitCs2bp6Z37lC9d9+pJk90wVBUCTXx3Hk1ffVVlIUm3N+syl+tDx7Hx7UCQaXZLhQyeXdVgsZYjPZ5zDdkH0f661UrGzLcVfFXXLGimq/IiwnP1sOHj1TyFYP+ii6qoQs5g/T29ESR6eLD/5y3vItQWimsPOhYqdxq6JkL3oWElvgpN/yFCxd0GJk3O8KpaSbXdiaHaXsjQ9yKDpEXQQ7tbidc+zy7ujQ7rHz69Gl9bFgT4M2Y9k05uF6KWxtbnZylIpUc2/ShXdIIuPbLli1Va9dWQ9Dc/3YImrAhik8wV7PR/ZrNNrOd16ndu9UkodSHH1Gl8+dwhlbTN96gphN0RQjd3xmWZ4ZXdIu+efOm1KVLYqBcJb5JKm5dSZLLoyluLNUjh2GKo/SDtv0VIQyMN9tfcfFiWkkuTNROMv/+x7RuzK6suVY3b9lSVjMdEAtBs8PKA6j3Bdm+NQM9nexdKC3wJA/QDqcSSiWkmvfFbBQ5TKPq2VW6l19+uV6SHDfHQvVrO5HDYLcT/KKKLHgp6loHSRz/v2/fPvXkk09mMuy21yvnw1Qnt35XEp1HN+02oYWFoGXyxgts//5LemKXfMVFi7L1563v8xetnLgUdMzr7VKTV12j1NbtSg0N63CyDjcnKBIyhGNm3w8cUKX7HlDq8FEdjp6+6io1fc010k4jcKyYehu1L62A7tJez7WYo16ruThymPQaQfwWLWIcVftBE4IeHa22k4QgpslXdCVnVFZ3dWUjT/EWO/VBmvfWrcWphq1QrTzgw8qtC6lKDVYjowigIuJth2qQV7Vqs5XDJKSH46ZKl04vaY+7ncLKUd1Oig79Fkk8Zd2MaeyVUH5vueWWxL6bYZDzIeHkLJN61nBy1gndtOfK135m9uRt8scIC54+vVeTOBQeoywm78+b1asv6XkVdS7YtYXfETJUPb1KDfTOkK/qd6KKhDhHkjda2rtPdX/+cyRQmdD08ROq+/CXdMuc6ec9b1Y8HPXYEIYs1yZZL+AwkBubvbd1fQXanOOwXMTs+XqElFevXqImJpaqdeuMv6KMt9n5igvV/Pmzozqu1jOow1kNsF2uF9i1i5CvKgzSCavZymEnoafTvAvlrc++uckFgRSiGJCjlXeP4FZVDiETqGgoIll6IxdtZZPXNuK6nRR5DI0oSCFnjpcaUh/yGLuy3vYIJ7v5qaX5HiFoXpxY2ObFi5e0qkh/XnwWTX9eM3GHhaDTKKPVLkxVYmd6LVeNkg3qt+erVozLd5Ifc00hCRPrd7+rSkPDqrxhY2U1pfMXVPf3H1LTV1+j1MoVs8K6Wbob8d2sj8jUHWMyFN1Etc9zyXG0xwbrtvMVKRQZHR2Zla9oj7cFC7L1HrfHj/E5zKb417te0dZVFKFMqwwNOlJBnvHNIodDQ0M+57DVvQttYihqEm3weJhjal10TkArKIdZw8hptpEXXNXJet1Oiq6GLnLdHBvXcOPGjblWWl+4UNIh5VYPJ9cqNelIeNYQnJDR/v4+vVCoVi8ELRYW0pYvqhADEMKt7c8cZx5dnFIp+1IhhyMjSh0/ptSSxTWbLS9apLoOHVRdJ0+oaYscVlXg2b2Viw0JF9+9xVyTrlxzHONyBVHzUJ2M8rS6ZrydOHFcHTw4qhYs6FcDAyYEnTZfUUgtpLeecig9muX/ZTH7Xt8eKGx9O3aowmFHC5uBkRxa57UaejrVu5CiBFQzVKXdu3frPMNGJIs2uyDFJYzcTmHlpN1O2lE5ZJ1MDhSfUEmfpS9m9LpL6tFH+9Tq1bW/r3druITUspKBrEpNVgJhT3q2rQrh2gULBtWyZRKCNiHBCxcuqgMHTqmpqbJasmRA9fUtiglBm39zO9nD0SXc7tJVZBZ55mSjhgYrU6YmVRm1uWde6HfTKocueXMuxDBN95YwK5tGGlYHUx7oPX7uHP6KQzX5iublZHa+YljqAZe2u3t65jLXkrzgeLQVdz5bPW/pj//yy8tpuzdmAvOUWKM10+ewk9CW5JAHEoOBXKywFnjY05CjxVs/BImwciMIm5A2CFrRCCNuEkbmYZGH2XOjlMO020jT7aRZdjNZwcsM1xAHfOx38iSGYN++XjUyMtUwopY1P8yFCARJbFTIq0rMDGEjIsW+1utxWyr1qkWLluuF6z82RtqKCUEfOXJ0VkgwrmONq/KXtahj1rH19qrpnTtVz7fvUuX+AaUWzFdqalqVjh9X5RUrVXnzppiwbrJ9cPFDdC1gSTOeDOHtyq0QxOWYGR9TU/PUihVL1PLlS/R4reYrXlSnTx/T+4qqSDtJ469onvvGHLx6vcbHGS+Ep5Pvj0ueIx7sV1xRbBFKK9jYCDkkba2T0NNJLfD4HUoSxtbSAs9W2bLkx6QF20K1LBo2ccsrjNwOOYe8PWPlgjqThAAXrRzmuW4JkVNwgql13rmxhJIPHaJYYbSNwslVEm7y66KsVarkjwlNCF58t5bq74N9npOCfRsc7NNhP6xSgyHoAwfGKkbckEW7F3Guyl+q74YT/embb1ZTp06rrj17VGnKfGB6+XI19cIXmnLTkLFg29fUg1M1tkO+X9rtiul2PoUg1Xy9sJBtMG0imF/Kfttjxtg6LVBLlrCsrMlXPHv2nDp0qJqvuHgxavbCysuJce9Io/K6hdKLLkJplUplmZeYdzsJbUUOmYh5awpTC7k4KC78DtJgS7zcHGZyaQw5bFRYGXUJFQ1T6zzCyK0eVqYwgGNN0+2kXXIODx8+rDv1SMcalO8895tVPfgg/xelhpVy7ZwRp2zZhRgoIbJp21qFYSdVyrbqF9eWzy2crHIhafFV0Ps0eeR5hOUSXVt6etJ7rLoocLHnaGBATb38x9T0/v2qdOYM/brU9JbLlBociCGk5ZYOJ4O0iqM9TwT32w7Z1nZzqX1JkTEe/G4S/8c052x2vuKUGh4eUcPDFFKdUKOjBysvJ8azttSQc75yZVm/LDUKzfQ4BEQnfVi5yRDvQrvohImVbick7tMNI0gaZNBwcxRZqdzoamUUyrvuuiu3MHKrhpWl2wnX+dprr03VzaZocugaVmbfGLvHjh3TBVNUL+a1bhtUJ+NQYgokyiktLKIniWB+nt7CDOkrl7sq+U12da18r1YRiW7Jl9TiJWtOpEt3kHokza6CNs+qI7rF39DQWSsETa4iYcH4ELRrr+dE56i7W5Uvv1wvQYRtM+kLt0to1eU2iFK/gnl5tZXjEPhuHdI1vy9baQgqUTcXqSLP66UjzTObFxPyYzlu6RLEy8mFC+f1ywnzoCjZ+H4Gr5/rOec8FtkJpRXDykO+Wrm5YKK3iZ/knpG4f91110XG/OU7rdq5JC14IGPNg/qAgpZXGLkVw8rSKo5jDirCreBF6ELgpJML+8exQfIFeZJD2+zaDJPw9RoVzyh3QG411Am74tYYJlcny6AiUhsuK+VAXuqbLGf1YXPrZpJuAufcQhb7+uarTZs2q8lJQtBM3BfV0aPHdFSECmlp79fX1z8rDOiiwLm1m4sjWaXCwpNJczLDQrZm/0wVefWlJNwSqPr/ZjzwYsN5zvrocMklzTO/UroEsRw5Qj78pBoY6Ndkkdx8kxZhClvEX9Fl3wF5ho2uzWh2WHl4eHimbWfnoK3CynHFF3Et8LgBpEtKu5NDCSNDiMlNIwxZFJodVg52O8nyZlikF6ELeaaYBmLICw35scFjy4scsnv/+Z8mRGuMkkUZqVUJyF3isxKynO2fp/eq0JCUS3VyViUtuzG3S5hUlCVC0Isqvpym0ICqVAoNTunPBI2RsypwLlYs9Y41jhumKwSpqne2Ek1RUzBkG7RSCb6gVMev3sNE27e/29ubnRimqYxO25e7HuJUP54nkEWiE0F/RdKSULXnz6cf9MKZ4pZkSrYNSCEVyo1Gs8PKIyMj3gS7FUAXjCeeeEITo6SqGYO83ZVD1EIIBYQYxZBQZJFoVlg5qttJXuvPC1kIHJ/fv3+/Hr/bt2/XqRBh4zcvckg42fY0nJrq0hNXkCgEN2WIWvoJzsVfMOp7cfe3SwjMRSHJquxAzMfGwhUOUl6WL1+mF649qrltjNzX1636+hZXLEzSTIZZSXC9ULSxfandD1GUZeHaAohfWJV4UIUW5azWRkUfReL9hlBmz5mbcu5G4tIz2sXsIm5Mmw4ipdh8xbExxttwxc+zWkyVzF9x927qAVTD0cywcnnGesz7HDYRDID77rtPh2FuvvlmLZW3i/+gC8Kqkbl5G0Hcii7kCZK3uG4neaw/T6QNWdsWPIzfJUuW5LbuMIT1TjbXsVzI5Ja1UMKF4GUlsW6VwtlVuKSXlOtECgXL6tXk2E6qCxdGUoWg82g3Z6xUTLFP0Nzb2P+UNYkzSl9tyDZI7mofi/VC0W4Ey60Hca2VTRq4FnK42BPV6x6DUhh3XPPnd6nu7sWqv9+057TzFZl/mNcgkhKCDuYrrl5dVjMp0w1HK4SVB70JdvPAxSfEiDFwsBNGp5LDqGrkRhyP3GxFvpWxDQn3o5CgjBIuD+t2kgVF+hymCVnz8MCmhmNKasHjcn2lOjls9+L22YWoZZ0YkxSEhO2zm0KTbRJ22SaEJ8s2TYi0JzIETfpFVG9eCXHahUP2e15Yla2t7tUzQOZcmBewUm7kG7gody4Ey5DS5PY8eRVFVbddnGIZ1yElLM/Rzlc0fp5jM2MumK+4UJvA79rl/rxu17DysCeHzQc2JlkUlUblHOaZp2eHkYOEolHFIkXfeHK+Dhw4ULfbSbsqhzxIUUMJj4dV0xdBap94QqqTg+uNJ39ZK3ezTmxZw2h52s+k26YL6TDfTTu07VxMCb8uWNCrFixYqlatInoiIWgsc86qY8cO6RA1eWOLFqEqLtT3b1orlSRCTFRUwY2guRTOuBtWZ42UuBT8yLazIslLi1EOo9Xl+LxSugQt0MvKlcF8xTOqq+sx9cAD01q8EEKZx8t9O4SVp6am9P1nwvOdg7YtSEmLRuUcsh0GqksoNompdSOUQ7nZiiahqKJJup20WlFNPeWQvz355JNq37596qqrrlJr165NvG4Xckg4+amnotcbFVY2YUDV0HBykly4sNsoa76gi0dg1oroWlWmHNu9xfysVoeb/bUrxg1qCy1Kqrd3QC1fzmImK5SMkZEL6tCh42p8/GBNCFp6QedDsmb3Vm6mp6ELwZL9zvLsdu297EKIk7608BwPCyvXC0eHwc5XHBxcrW69dZO6cOGcfo6TK060C5UbksgznWhQkWHfZiqHw8PD+qevVm5TNNJ/ELCttJVewTDyDTfcoDtlNOt4eEAWaQVDeALFkIfxM5/5zNx9GkGR+x9H4MjXQS3kwXHbbbelfnBkJYdx4eSZNUeut9Hh5HRFA+VcQpZZ9zWuIrpeyFZ89OwiDUFcNS3fnV0xntTrbpFavHiRovvi2Ni4vtfqhaDt/U5KssKIlJunoYt6lo28m+9m7xft2nvZlRAnrW6OCisn8c2UZ0NYhyI8DXt7eypV0IAQNESReYw8a+Y1cqxFWSQcnWceO3Nh0R7G9cihzzlsMrIOqEaRQyGEWchhXBg5iEaElWU7RZw36XYiRRlFEMNmWdkQ3rv//vv1wyJr7mRWchgVTq6uN/z3PFdHRiAjjQsnZ50Qs07E9fY1aIxstmUretXrUc/rURDWbi7pZXWppraPFfI3f35tFTS9oMW+hEkVRRGyiBJEYUK6a1PKRTV0sXDJamcURkrNNU2ucrkcs+ujKU1ahoSVZTxDCk0FuW0XVG0fGDSuN+swvxOsXVvWanUQPM+JlLCwPgoMIYuiLPLsFKLIzzgrulYPKw8PD+vjbWQYvRGYU2HlRuQc2n2ekyJLb2SxmSm6JWDedjas69FHH9X9r+l2QmI9/18UGm1lw7HwpkwLPKyWsl6bLPsdF04WVEOW9raK6xCSB8Gzz2Gaylsj4puJTiJaPL+Dht71JsCs1b5RRDDJmCiq77JdBS32JVKRytidmhpTCxYMWlXQfXWthOTPzbJwMf6c+Slvxp6nMaHsrJY7olabIqfq7w2pq1Wrqy8ypu2s3bfZtKgMrj3Z/qB97NxZn91ybSQEjXUXzzWEEIgiIgHzHuNMiCKCQVqi1cxq5eHh4USpGu2GOUMOG6UciuF20m0lDSPnHb5uBrkK63bChFSkAtqoghRp8cfxxHXrKUo5rB9Orqx51nqzPtOyKiZpvlfNvWNiK1e+b+flRRkj15K8oLKUnGC62MAEv5v0mmYt6EjbXYNnFflgLGBiYkxduGC6tpw8eVL/Tlqt8XN26K72/GeFSwHL/PnZFdYwUppGOXRRDSV8H9Zj3B63sk9yfsSsPqzVpLX2Wb8JCgmuBTRbt5ZVloAPz2QIoESNmANxqoAs7tmzR6uMqNhp8hWbmXM4NDTUccUoc44colI1altJCEmaMHLYNkAjKpbzINVR3U6KDo83on0e+TVcRx5yhJHzaMCelhzWCydX18t/7e4TMjmmm+DSTorVkK1ROpjYJGRrW6mEhWyNp15XBFErFRpyzBrCbkZeZB4t8pSar5YvZ1leCQdKRSq9zYMhaCEcbtt1VRzzJaVJlcPgdbKV6uq/qy8x1ZeWUkieY3KSF5fGEAfb59C1gGbRorLavDmfdB3EjaT5iiyMu6BK1+yw8mDOOZStgDmVc9iIsHKSbdlh5Kzhx0b1i3YNK9vdTmgTt379+lzX38ycQyFwd911l55Md+/endsDKg05TBJOrl1vPqG8MGNkeyI0/18bso3ONYwb/3TLmM5MINw874ogldHrlLBgFri19Jv9XTscGBaCHh+f0PcXXTd4wa0Xgm5NT8NgrqG5dqbNZG1nF/OZagV50BoqmI4QZ/rtWoSShYzbBSku5w1QhFIUFwrmK0K+xNEiKl+xmWHlkQ5sndeW5LDVrWzqER67S0aaMHIQPISb3fu4HuxuJ1EVu0Urh0Wtn4cW+TIAgr958+Zc3xyTksPk4eTKmmvWa7c4q4a34kO29UlTKVeyxT41WpVys7xpvJWLi+9j0pBwMARNFTRhwMnJCbVnz159zsQUOTwEne+5ivqu5OMBSUWwVWqxUDKfm93ZZWoK4sSzVfLH7bWXGmY9E4Wsqp8htl3OxHTDhrJK0ZzMCVWj7cHYfEVj0H1RK4xFplmFwYeV2xyNyjmM25ZLGDlqO60aVk7a7aQdySHnA4IvtiCYW+cdUkhKDiWcHLRJsV+i7dUYEli2wskm3HTixCltdQKBZzHXq371bfLjyV6djBibZTJtBkmrRyrjLqnLpO1S2Zy1SpgqaPo9L168Qi1evKTGFDksBB1U1ZNUj9sWQXbxi72OqOpxEHUtenpKkbY39Yr8XEOyLlXZLqkO3Ocohy6KIbxr+/ZiIjFZ8xUl/AxZxFOW8SbKYtH+igBl0yuHLYBWt7IJ2xYPm0OHDukqXdcq1rjtFIG0BJRjxbvw8ccfT9TtpBE5h3mGlVFBIb3sN72f77zzzkLC1knOy/nz1d7JQeUoaligiCg1bRHDabV//349qfPAPX36jDp06LBasGB+RQEaGBishKMaHfJk8s9qsN0q4dmkcOmpm2dBRprtyjizTZFnh6AP6xD0woX9ejwtWkRf3j79HchGmHVKPYugIol/vYIUl5Csy/l26+6DUspLYbVaOQu2b5/W5LZVgEpI8R/Pedrq8m8JQZP6AHmUji1R+YquGO7A1nlzLqzcyJxDIW15hZHD0KgWemkqr+VYb7rpJn1DJll/uyiHUlRDLsyOHTsqpLAIcliP1HJI3/temA1FvfXy33JNWJBJevv27TOpCiV9HaVvL7mxnD8I4tKlphVbWsXbZSLPGtp1KZAoOkcxbG7Ky8C5aBVKPCAlvYD/hyyYSF5VxevqskPQ63XYz5DFi+rYsVP6+ixYsDBVCDqP400Sfo8rSHFtz+ea65f1fjCqodl2ViFtyZKy2rhRtRx4TkpBCvmHcfmKfEaIIj9d/RWBVw7bHM1QDnkQPvDAA7mFkaO2UySSkise/Bwrx5jmWNuBHPKQgUSx2EU1Qt6K2P965DBpdXJ4OK1Lk7+9e/dotXDDBvPEl5cnXqSWLl2iF/bh0qUxNTJyXp0+fUENDx9Vvb3z9KSOAhQWLiwmd6/cMOUv6/5m/Z7LNrNWjbOYUHQ1NBu0TxElzx7e8ju+Mz5uzLInJvhpf382eB6wmJfjaTU8bHpBM2kTguZvYpljxlRXIfl6ScLvccphHu35mvF9Q3ir1cqtVoTiAlFFg8+huHxFxhyRPOZmux90lnzFYR9Wbg20Q1gZQsIApK9unmHksO20QlhZup1QlEEoOU2Oh5C3osy8Xa1saIP30EMP6YmMMDJtyex1N4McEk5OWp1sg8vCcJmenlJPPfWkJrnix4iyELUffX0LVH//gpm+vUzsRgGSitWBAcKFRgXis/Z1dAknZy/MaGWSVs5lfyUfj59STFRFtegiSPLsz+GRZ8LvwbXX35/qeUpPGKhulhA07f14KRkaGtZK9eHDh9TExKQeU0ZVrB1TLopwkvC7EI281V1XYulaRNLdPa1fCrM+YzdtKquZOqSWg8yB9eYdO1+RRhOSr8iCowY+vJKvuGzZMv2sTzKXQQ5RKjsNcyasLPYyRXcUEUNPwih5h5GbpRxGbUO6nUAO6XayatWqTOsHRV0XFysb1FDa4OFbSFFNWOgr75zGeuuVcHKWTaIeHDt2TBO87du3Jc6TsYkPig4PTSHJhKYvXryglcgTJ07o8y2TOmHoycke54k0zTl2SfZvRo4iHTI4ViF4tUUXpUDFeG3RRf3tlgpSV6skyexLyYlEo9YsWbJYL1xnXA54+WBMnTx5YkYBWqgWL4ZQLsrcpizJMcswCypsLi8O+VQ3KydMTJjna1hv5ST31LZtzStCqQd5OU9rIyb5ivKCfOnSpUoI+uGHH9bznt0POipfkTx0n3PYxhC5uEizTAkjs401a9YUSgwbWa0ctg2724mL8bOQQ7ZRRFVZ1rAyJArFkIIa1NCoCbAoH8UoQpQ1nFwuj6tHH90zM/67EhPDehOq6dtrDGxRH0dGhme6a5xQhw7tVwsWDFQqVvv6+hNNTi79mrMSw6yTt03YeKxULVLM38UWSNSoWmuV6nFGE49SSxXNhJOkZOtKkmLA+ZIQdHVMjWhV8cyZ02r/foqlFiQKQWc5Zl6gZF/zImeuqp9LJbrcT5cumUrlLNixY1ortq0K8Th0FRfC8hXPzPSDRlk06TYmX5GXY6lQdgkrf/Ob31Tvec971H333adFls985jPqx37sxyp/Zz9+7/d+T/3lX/6lFp2e8YxnqA9/+MNq69atlc+wf2984xvV5z73OX0eXvnKV6o//dM/dSascyqsXFSbnWA1ssnTuqSKRqPCyoRWbdBWC//CYLcTV3JYBNKSQz5LpTXXM4kaWlTOZNh6s4aTR0Yuqief3KsWLVqsVq1aqY+vCDWNyaea47NGjY5OVhSg06f36Ql2cBCyuCiyCMHF58+obOm/K9usdnGJJnnBdmbSd9mutgVRoVq7BVpWOzbXvLusCmkYSYoKwYYhy7U1Y2pALVnSryYn10SEoM0LSFhaQ9pjlhcyex0u5MyVWLpUotvn3HRHSbcePk4RSqBvQcuhCAPskpWvuGnTJv0sPn/+vFYWyVf81V/9VV18SX49uehZ50CIJfPMa1/7WvWKV7xi1t/f/e53qz/7sz9Tf/3Xf625xe/+7u+qF7/4xeoHP/hBpZjmZ3/2ZzWxvP322/Vc/Qu/8Avqv//3/64+9alPzS1ymBUyeHi4pKmMS1qhe/r06UoYmaqoRuQ3NtrnkAcneZR4SYV1O8m6ftCI/sf1QCoA1ciEtVBDk7wNNiqsnDWcfPLkKXX06EG1Zo3JL+QYk+5vVjVNlD/Cf5K/wzZRmy9cuFjxwQsrQgifxGtNisNgT+A2yZOcuCgT5MoWrM0mIXky8aaduCV9woVwuLbIy/pdE7IP7nO5IeqZKI5xIWjSGiQELWo1YzDNMQfDyq7kzFX1cz1vch90dU3r82AqzO2Xi+je5Pzcvbt1w8mNbJ3X1dVVKVohX/FDH/qQ+tKXvqS+9rWv6fn+TW96k/qHf/gH9YIXvEAvNH1Ikv7w0pe+VC9hYHz/yZ/8iXrb296mfvRHf1T/7pOf/KQWZf7t3/5N/dRP/ZR65JFH1Be/+EX13e9+VzuEgA984APqh3/4h9V73/tetW7durlFDrNMyHwn7y4pEkaGwSP3SoVuo4pfGulzyEMY4sQEH9XtJAu4Lo3of5zUtJscE0h+0qq1ovY9OMbThpNRCg4dOqguXDittmy5UlcWV9fbePsZtkvqgUk/WF2jAKHSTk1NqsWL+1Vf32I9sZt7yShyTJAk1FeravUaKyRPVDsmQbvoItjOLLBHTkUGLuod+5yIMJw9q7oeeEB1PfmUZnXTu3ap0g1Xq0nV33CiERWyT5Ir7Kp0RuX7xYWg8es8ePCQ6u/v1XmKSUPQwbCym01QduN32TbjxCjZeu8CZvfharb8v30PT07yMkTeffKUgC1byiqnx3yhKCIaWA+MNxQ7FtKPfuM3fkPPNah3hH2ZJ5/znOeof/mXf8nsUgLpJL0JsinAGorCyLvvvluTQ34yZwkxBHyeffmP//gP9fKXv1zNKXLYbDJVz9S6UeSwUe3zUJvoH1yv24nLNoomh3GTGF5+XMskpt1R6y+SHKYNJxNaINRRLk+pbdt2z1LK6+2vi4F0GKm0CygA/9/T0636+hapFSuMAjQ5STL4RW01cezYEdXTM6/SrcWMc2ObMhulzMUCLkUGLoUvieaxc+dVz7/+q+o6clSVBweUmppWPbffrkqH96upH3m5kQ8bGN6M8ueT/tlFKZ1prpGEoFmkCnp0dEidPz9UeQHp748PQUuBDYt7hXB4ugIIEjwJz9s9yTnfwd+FI4w4176kmbBy8tArfGbr1tZXDUEz+yqXZ3ITUROf97zn6ZAucxkFLffcc4+TfR3EEKAU2uDf8jd+BlOfEDaI1shnssKTwxzCyEE0UjkM5gPmPfCl1B+T5LTEqVXIYVTogWuELE84KmtledHKYdreycPDIzp5mgnw8ss3q3I5+NAUZSGaLEdN5nbRhdnH6r6yn6h7TGhMiihFclrkc1GTPJ/t7sZvjGXVjF2OURWPHj2mX07YxvHjJ/Rx4U0m++5meaMywa3whf2tzw67HnpIdR09qqY3bawkPvZMDKvJx/eorqf2qOkd2xsW3ownSfE5h+6G1dmfN7T26+lZohYuXBIagubZgLm7HYIWA+yobYfno8pna3uWy3GnUbLzCP+b/awl1ea4kp/LnTtNGLod0Azl0EawIIVxRbcWlnZGm1z+fPK8XLuk8GAh9MjbgB1GbiY5LKrwhfPE2w9dQXhwopC2owJqW+WEVVsDkoqzOuUXpRzKegknX7yY7Du8sKCCUm23fv2q0ElZqmQNyav+zigLVDRKSGp2mCpYdBHWmi/LqQiSUWOXY0y2SZkhEZzjonevbW2yZAmt/RZlMq4128xGKrP2tgUcZ5I5umvfPlVmTM6M3241rSZ7+pWaPq3U8WNKpSCHhswWU8AS95Lhqla6qLPBXEE7BL1y5QptxE1lPS8g586dVEePHtDPAF48eMmZN29STU93azXSPo4k+ajyEuXy8uFCDMNemIwbRJ1K8YMHVffdd6sVk8fVxtOL1SR5bm0QVy7K6SINOcwrzcoGjifg+PHjNT6K/Pu6666rfIYXneDcTQWzfH9OkcOsyEra0vZGbvewst3tBMWQYy8SRSqHYUbVkCiIITcP1dYuD5YiC1IuXuyKDSdXiyymdf9ajmvbtst0sr5Uw9rJ5RIyQ+Vg8rEPGwLAUMKgOMulyKrgJVGWIH9cI5RrO6+MF5d9+w7pCV3UH8KG9SZBl8nXRYHjHHHuk5DDct8C1TU1VdGiRM3q4j8pQ8ouLduSXddS7ucqTp0V1U5edOyXHFH7RN0LvuBUf3bpVpAs2NxJy0hyj6emyuqBBx6ZqYI25u70Gk+qvLmQWtfrFZUSUi+s3PX1b6h5f/s3qntqQl3f9W214KvDavov/kKNfvrTquxQ1NDpyuHk5KSObGS1sokDXIM56qtf/WqFDJJ2Qy7h61//ev1v0rwYs1jh3Hjjjfp3FMkw35Gb6AJPDhNcfMrGmYiShh4bqRzmTaroekHYnG4neClhW9MqLfqyrhtIFxYqram4hhRu2LChZfedSW7fvgG1c6f82/yUTcnEh5UH+YWM061bd2pCz2eiyE91gputfkEms1xqF/uZJGFDe1KWvDKKV6am1ujjhygysR84cKDSBxrVkUkdH8bZ68um/LlUrtrnKBE53LZdlZ94Ulch9S7sU+PTXap0+rQqDwyo6RQqvkthUVLT6DDSVO9cBQmewNyuEGiTUhFWSVsvDy/LeJSWkbSGHBsb0kVc0l8cpaZq7m7yFefN68k95cDsRzGeiLxARr40nT6t5n3qU6o0NaUuX3hSDfTOU+XJQdX15JOq913vUmMf+IBqZTSTHA7NVAlmVQ75PvORXYSCcEHOIBY6v/Zrv6b+9//+33ouFisbKpDFC5F57CUveYn6pV/6JfWRj3xEp5m94Q1v0MUqLpXKbUsOXbwO04SVk4aRO0E5jOp2UnTvY9lGEeobkORy8o3ILyREecstt+jimrzWX8T5eeop+h/Pi1W4UNDIL0QtQ81m3CEqxU9O1ZzD2Ua5tWpi0XlhaQoV7P014W2zTSZpsZgwdjmX9H3L2/Thw0d0MY6oipDGBQvoBVwcWcqrwGd69y5VOnpE9Tz0oJo6fdo88wYG1dSznkksKfF6shbcJLmuUkiB0mUXG8nj2c7DC+vwEkbweJwxFl18BbOqboCQMlW9hJhZCEHXmrufUgcOHNTFLEIWuf+kCtpF9QMuXVjiCHlc+L/7/vtVaXxMLRjoUlvn7Te/7OlRZWyA/u//VWM8GDKm3XR6WHl4eFj/zGo4fe+99+pCFsGb3/xm/fM1r3mN+sQnPqF+67d+S28D30Keac985jO1dY2dBvV3f/d3mhA+//nPr5hg443oirYkh1mR1MrGDiPX65ARR9qKbtWXFwmN63bSaC/FokCYnOMivzBPn8siwspUJ+/d2x273jNnzqoDB/ar1avXqDVrVs+Q4Ppkqzpx1/7eTOTpx2rWStQiOnwYu5w+vaxeTWHLlH4zRwHCV3FqakL19Q3o7gZpQ4Uuik7tOUo4Vri3X/gi1XX1TjV18Kixstm0WSmdL6dy3eewQiNIDmPCqE21BK/Wd9J8ziY1tdstNZQguVRGV3NCS5Hm7qR+GbXaqIrY5VAFTViRHFgq8NOMqzyLUOLuqdiw8vi4vuhXzXtcdZemgyXP5m2zhclhM5XDkZGRmTzVbNt/7nOfG/ucZxy94x3v0EsUUBldDa/VXCeHSchUljByM1r15UXcpNsJuQ07duyYtb+NUg6L2gYhIW4+bqDdu3fn/oaZt+op1clRVcX8G0WM8blli8kvFHDp6pNDO6xcnVDHxhodTk7zjSjiUW//urVCzFIur1NTU2Pq3DnI4kVt89Dd3TOjKkIWB2ILW7ISFicSjAq8bpNSLHUgYVpIXTUX1fRuDhoeS1GFHaa1Cy3Y5/Fx+z6JDw8HiZBbD+LmtAWUbRufzPhnhFGrl+iF+5GcM4giBu+HDx/X4y5JCDrPfa/3/biw8vSOnWp192m1ZuKQUj198gVVGhtTU+SttXhRCnNHlqK0PDA0NKRFhyJFoGZhzoWV48ihHUZ2rWBtxBuNi3KYtNtJo7wU8yaHptL3CbV//3794CB/o4jQQ95hZalOlge5TN7y4kJOyvj4hC4UIrSVXnWoDSszPLPkSLmEk7MUKgiZzEo8OD+l0gK1YgWLCRUODxtVkReIAwfGZgpbJFTYX5hdTlx1rwxRIXlRHS3CCB4/OT+iBMZ1d0m7z8nJu5unoYvtjQuqnoTGyiYpuJbME4ODC9Tk5MpUIWh73132P8m9GKscXnmZ2v5jW1Xps/fqt8Ryd7fOPywPDqrxt75VtTqYn1z8BF3J4UABxSjNAPPYxz72MfXqV79an8+2JIdZAUngLS8Io8Yc1jlpWcLIcX2cW5FUpel20qiwcp7bkOPD5ocwOXkdze7AkgS1ZtdC4tjvbn2tnnpqj55oduzYXvPSIdGfJIgKKzcqfOdiq+KiKgXzwCDfYrKt1FqdyI3yI1XQgMmcUGF//6LExu+24bEJzVYJnjHsNp1eRKUKI3j6tyVTUT4bpUJDq2mIe9Vcvvmehu79j+2XsXQvkfZxJw1B21XQWW2V0hSSxSmHV1yhVPef/ZEau2GH6vn0p3Xh09TNN6vx179eTV9/vWp1NNMEe3h4WF/rTlAOmS9/+Zd/WZNDMKfIYZjSZoeRr7/+eq0quIKB0gjFLYtyKG3iCLWhjtaT44X8FJn0myc5pOCE/ELp5sLxFdmeL691B82ubRKHCTkKKEVC+F1VHkSjo0odOqR6LpxRE/MHlJ6NEqZBcE2DxStJn28u4eQslcKmW8W0U7ixnuII+Vu+fJleODfkEg0PX1CnTp1Wly4d0uqQHYK2JwOunV1oIQjbX35niF+pofmYRVZjS0vGZnoaupArm0ynVQ7rHXdYCFqqoElt6OkxVdCyJAlB2+CRnGQKMIVDs5/fpJdfcQWSdY+a+MVf1Eu7oRG9laPAc8LO0W9HcP6wyJF+9wgR8KI5HVbOK4ycZFtFIA0B5eGA1cfjjz+uy+KxqklyHm0rmFYnhwxuiD6Vu5T9y/EVWQ2d17qDZtey78eOHZ3JL9yie2hWcOGiKt11l+o9fVRNlOdpTzw10K+m6bG5ZUvkdmS9kK3gZCx9WuPGhVt1cjbiYtuaZPMWNKSp1vTb2KUIbBWPn6g7ixcPqMnJtRUPPKMqHtT3HGqBKI9hIa2sdjku59eVoKUlltVtlQrzNEz2/extEIOel2mLCNMct4Sg7SrosbGL6uzZYZ37bULQxrOT8UV1fX3PzmTV0WwLIhrErl0811Vbo9lWNoMZK5VbBdjXIaRQX8E8TL9oxmFbksOsECubvMPIUdtqhHIo/n1x+y/dTlCgaNCN7UeabYAiQ8uu5JDvci15Ew9Tf4s22XZdd1jvZBk7KL0mv7CvdruPPaq6jx9VExvWq3LXzG188qQqPfigKq9eo5SVjzh7n5sTTs6qNpqwrLFLCTM8rq2mrVWxJAcvHMnUO/HAE/Xn0iVTgHD+/AV15MhRrfagKMqkvmBBVx3CUGp6SDiPkH01bzX7dl3tX1z6Y4dtOw05dFUsUUu7uhbpCmeT2lD17KQjkLyESL4iLyH2vgWvmZ3SYL/4mN+Rl1eqvCzxO8y/WdodzQ4rD7R5ziFzC1Y5+OVSnErUjXlnTpFDaZ/30EMP5RpGbiY5rCer291OUEfTJu7aymFRcCFv5Emg/vJ9ji9IolzXX7RyGNY7Ga8+blSACjrrmMYnlDp0WJWWLqoSQ7B8uSodOaLKp08pFWPwjYKHUpO2wM8lnGx88KpqXdUPz3Rkkd8HCR6T2dgY1w+VPGrbpVyJQ5x6x+RM3ifLypUrZ/pAmwpo+kBPTFzSRQcm/EwHjgWBavNiOre4tsjLRrDMwdRTt4rKI3XNUwzbdhpy6JpjGSTEvGQsWbJULVvGyzvj/pIaGrqgLl48r06dOjxTXS8haPLcSJmpri+uhzP3++Rkl5UbqdT27cXmks+VsPJgmyuHKIaYazOXModiqA3mFDkk3wOmj89d3mHkZoWV42T1YLeTLOqomEgXeSxZyRv9IyGGTNJUXEc9IIpqcSfrdiGemOPb4eRz587rCnJeWrhZo96I53VNqYnp4PHWrzYxOUrp37JllWZ3qnYpQvAkJC2ftUmeIaMy9pITPJfJ30VRSqPemT7Qi/QCxC6H4hZ6ngY7a8TBpWezm/qWrTMO9xTpCZznrHNzMz0Nw7Yd1fElKTGU+8Scj+p9EkxnqL1eYUVJhOr71LJlLKv1S4ipgh5SR4+eVBMT+9S8eYOJ20YGC1KuvLKsQt6j2xLNDCsPd4ByKNwIPgQxpP0e6VltSQ7Tkhw7jMzD+uabby68uqgRVb5RId+obicu2ylaOUzTuYbrSYEGVjWEXDdu3Bh7PVtVOSScLJ2TWAdhcWxVNm3arNWDkydPhK67NH+emly7QZUeeVSVqbYVAnnmjCpDQGLUcBNyomCKqll70jKhW1vdM4TBTFymYCA9wXMJvdUWCiQ/x66kMuv+okQpNV8tXz5/Jn/HtjUxOWU9Pab7xtCQscuRCdtFQXP1BsxK0Kqh/cbvt4vKarYd/n3jDmAM5e3WfvaLkPlcNZ812PklSr0T1CtIinoJkRzX7u41amxsSo+r2raRA5EhaFsRhctcdlkxL8tzLaw81AE5h4Dxghjx0Y9+VP3N3/yNdvtoS3KYBnY1Mn0IIU2NKDtP26ovr6rouG4nWVG0EXaa9XNOUUNRDSH5NUUaOaw/LbKqknY4meu3b99+NTo6orZt21a5ZlIFGqr0YGdz9rQqHd6vyWJpckp3MZi+8TpVXliVBKqTl5kQTeI/SnAwEd+0lTOTYCm3goGsZMuFpGVVlFwLOoIkq9bWxOSU7d+/T01OTumfbIvK50WLTBX0vHnZOvcU2SKv3jUySlip4T2Ek6isQZXbfK/q/ygvR7WpDKaqN6y1n8DVesbV8LqaB1ttGyl5sBcvXtBFbGLwLmSRF5WuLiMm7N7d/kUorRJWHh4e1g0W2hlSbPrtb39b/dVf/ZX61V/9VT2/djQ5lGpkCSNzEzWirV2jlMMg8anX7cRlG60QVuZGJH8S25E0+ZNFW9lkOTcohvRsHx8nv/Ap1ds7T1111faKn57k6ZlFcpTMxKSJ2qIlavKZz1Xq0EFVOn1Glfv7lKLROipxCDGyCV4U6Qy7JbLaz7iaR9uXK8296jLxFl3QQU4ZBHDhwgVq1aqV+kUO5efChbPq0KGj+jm1aJHJKUMFCpolZ91uEfYxnGda7hbV/1gMwcPUO1O93jWj1BoEPSPt3wXXG6eW2r6NRVRWu5LisH0Py4OVEDTRB5RFXlTOnz+n6HWwdClh0M5ghzKnNzPncKDNw8oibtxzzz06ykhf5/e+973tSQ7rTRbBamSS+iEgmCM36k2jETmHsh3UNEKs9bqduGyj2coh+VsQ3w0bNmh1LU0YodWsbCScLPmFhCDXreOaod5VP8cEODnJw8/8e1bv5IF+ssoTdeutvWX4R7lg+5nsilZtCNsgySl2Uf5cCjqyEFLTB7pfLVzYp6amTB9oscuhr3vVLHlRaJgw63bzIDlVpa1K2qIgfZrtz0DuyuWuirJXu87a3Lsw9a7ycsMjA4ZK7njCoph6LwCGHHYVktspHWxckGTbdghaDN6ZH8rlSTU5+ZC6884JHXFB8WJp5/ZvMm80s1p5YYu3F0wKwsoiTiCstSU5jEOcqbUYPvOZTiGH3BSQYI6pXrcTl200ixzabf6uuuoqHZ7Lc/2uSKtKmnAy+YUndE4obf2iwhJBhS9J7+QwhJldJyG0We1nXIiLC2lxCfdlnfRdQ7OyTZ4X9Mlmsc2S7TChqIqQRUKELttNogaHWaPwbwknm04vU/q8Q/LC1LsgIWJdtee5lF55G5tW3bffrrrvuEOVLlxU5aVL1eQLnq+mn/OcWKaaxOQ7Tjl0DQe7FtBkzQ9lwudZ8vSnr1DXXLNFExrChsyRTz31lP47zyBeUglTJ+0G1AqQZ68vSMkOIdbPfvaz1Ze+9CX1+c9/Xo+NtiWHYblewTBysBq5EZW3jSSHeBHB9sWmpqjm40WHlaOUSd54UQt5mLkQ31YqSHnssSn14IMHtP3J1q3b1ADqX+wYn3bqWRtFtpLscjPCyeHbLBVaoOAS6nOZ8KO2GzRLNnY5w1pVNH2gD6hFi/g7VdLY5fTV7ddsh2er/Zurxufy2XrFFfYLA+Nqaqon1blz9TRkWz3//M+q+ytf1WpheV6vKh0/ruZ96lNq8tKYmnrJi+t+Pw5xKUdZ9zuPAhpXP8e+vgm1ZYs5NsmD5cWU5zrzCGSRvu344fKcFVURz7tmqXJJIPOSVw6zQ8b785//fPXd735XffCDH9R5/W1LDoMgFBMMI4e34WpcuDesj3MesLudQAyxqimKGDYrrAzRv//++/VDjMIal7fZVmmfd+zYiPrCF/Zr1Yec0HrHJMphmt7Js9cRtd5yYYQpezg5OuQXt78ueZEu4e9sSpJRppKeX5Mj16WWLFmoli41VZETE+O6SpKWVwcOnNDrGxwUE26qWU1hS1h4Vgoyak9nNnJXLz+vEE/DM2dU9zfvVGpejyrP5HqV8ZK8cFF1f/nLagr1MMQAPum1MmR69ueyvpzlYVPkWigFNm26EJrDyrMdxZAFMGdBFFkgijzbUBOFLMa9iDSzUrlZ+zQ8PNz27fNsvPWtb9W2d1/96lfbnxym7Y3ciCpi2U4RJDTY7QSCWHThS6MLUsSfkRZ4EH3XG78Vcg5PnjytPv3p/WpgYKXOm0xmHGxIXF7hZHu9RYWTsxIAl5y/rHmRrsbeSYeUHZ6VIiP+33R+CTMBnx2qtkO2PT0UrizXi7HLGZnp1XtGjY4e1oUJknMW9L9zteoJfjfNvZmHp2HXgQMwGFWe8ZUUaII4PKxKx46q8mWXZQ79m97K3bMIsUs42bUIxaVQCqxfX1ZnzownUtcQG0jdYeHZw0sIRJFiR9J7iMoJUWyFEHQzK5XL5XLH5BzaivkrX/lKvbQtOeRAeHOOCyOHoZHKYd7bkbA5xynVuo1s01c0OWR57LHHNDm87rrrdOVdnusvAvVUOPFk/PrXD6slS7brMGGadWMynIUYmvyquH2ezj3051YgEb3NetWjjSKyUlxh75P5t6wjurgCMASFII2Ppw+dhxEFY5eDv92AdimwW7BV/e+MWfLSpUxi6TokxZG7KKuXvMOidki2PDA44+U0qdSMQqqBlVN3V0VNzBr6N9erSqLyeKd0IcVJ8iTjgOJJJ5Rvf9vYlaTbdqnykkF0KiwEjfm7kEX+v9Hh3WZWKneKCbZY2fzlX/6lvr4IbPy7p53DyKhLcWHkMBB+bUdyKGpasBd0IyxzGlGQgiJKvgM/8/JntNdP/mJDzs2pU6p04oR+Kk+uXav+c+9edeDAOTUwcIOepNOtO3s4OU5tjCNbhjBle8C75pLVQ1g+WFIiG1VcIeqdnXsH4ooroq9LktxIY8OSBUnUVexybP87WjGaPtDn1LFjh1VPz/zKhM94TGKXE9dqLut306AmlH3FFaq8bp0qHdhv1ENt3jmpSqMjavrqq42VU81306nuRjnMR5XOy7rG5fvbtlE0lE9Fb1wImpa0zQhBN9MAG6Dad4JyCCD7H/jAB/T/r169un3JIRclS2/kdlMO7W4nYWpa0SHfRmyDty8833iw7N69O/c3wYa0z5ueVqW77lJd992nyufPq8npaXXg0iU1ecNNasGyH1GlUnqDY1MVmmc4ubLXkT6H2fPvshFZaSmW9LO2NYqpfDUTeDVsWF+94/iZtGcTrVKh+WNispwVaRVdY5fTp5d161bprhrca7T2w+oLlRHVQ6qgFyyYbZcTT1Dq5xwm8TRMRa66Smrida9V8z70YVU6ebK6nU2b1cTP/pzztbKtbFy6++RB7Fy/v3hxWW3ahGIrhUj5kqi4EDTWOfy96BB0M8PK4+Pjemn3Dilyz5Om9pKXvES97GUv0//ftuSQtmlZCAvKYbvkHCbpdtKoNn1FbIPjOnjwoA4lc12uvvrqQt40GxFWLj32mCrdeae21RjeuFHt3bNHrZyYUBM/GFdHd00qNZCOHCKITE9jV5Juv2d5IUbsc5jPoUxGaS9BmuKKoHpX7bIheWGz1TvJmRNeBYGVIp3ZXKv+fuSdd5cUhISzEiXXFnl8l/u42gd6/YxdDiFo8hWP6b+jgohdDvdkXL5ekmYCLkbbkf2PN2xQ47/3dtX14IOqdOasKq9epaavuloXqbieM7vIxmWcuBaRpMuTND+5r+y0h127zLE0wguwXgiaqJddBZ1XCLrZfZVBu5NDuY9RDX/jN35D/cRP/IT+fduSw6xoF+XQ7nZC27+oG6kRx1MEuWKfeWCcPn1aG3dDEIsKQTTCyqb0yCP6iXyqXFZH9u5V69atUz3zV6mnvjGh1IrjNDNNvE4hPlGdTOKQpHglzOeQl/rR0aTVnLM7V6DeVRXJQHEFYb+jx1R5qqzKq1apaV1NatS78fHguA4hA1ZY13XidvEldFFWXXPH3Frkhf8NZYeF6AuFLdgrSVcNXtoIC9JNo69vUYxRcqk53UTmz1fTt9yS+zmTidJVtUtTRGK/NMm9I4970x1ptiperRiv3hO2p+TGjWUlnUVlfmhkRW/aEHTWNKJm91UG7Z5zKOOCOdg+Fk8OC0JW0mabPhNihWQkydcrEnZ3mbxSAlBEWS+KKPtfpPpZpJVNpZjmwgV14tw5dWp0VF1+2WWqv39Qfeux5UqVj6eeIasEL1zhyx5ONoDUscgLt0xMqDxVIpGscwXrmL3N6iRUOnBQdd95p8nFLE+r8uIlavqWm9X07t2ZCU+zfAlRZfKxvSk1VK1MQlJQZ4NdNYaHyVVEVdynPyNWOYShCRHWbzXn7mmYFdm9NiUHNU0ouvaFSe6hanu/KrmrvuhEpzxEq/+lxGN8+/byrIKDZlrQhIWgEQbofCUhaIgkRJHuLUlD0M0MK4+MjGhS28o+kEkg4+JHfuRH1Nve9jZ9PAhSbUsOsw70VraygYA9+OCD2tg6qekz28mTuEVtIy9yJYooDwm8/hiI3GRFF7wUmXPIdX5kZEQtPnVKbXvuc1Xv/PnqsaML1YULZVXiwbGw1nYjDrafWlwnk2qf2erkYhS82UpR1R5F/sb1rOYIsk0sOSE+1Qk/WXg2liydP6+6v3K7Kl24oMorV6kyxQ+nz6iuO+5QPUsH1fi6zclOSmBfGutLWP1uHmQ2vRKcfZ9d1Eom5+XLl6olS5bpMcE9iqqI8kO+IpM5OYrGZQBrntrtuIdkG5erZ5O7rq5pNX++eXnivrBzYmvthowFUdgLU3g+q/lbEfsfBNXJNrcSctgqsEPQFFjy/MSajbFFxxbSqSQEDWHk/6P2v5lh5aGhIa20tZLvowv+z//5P+p1r3ud+p3f+R09P7ctOcyKViWH3BwQQ96aKLRJamrdLmFlJpg9e/boJdj/WchbkhymVgsrSzFNefdutb6rS3Xt3avOD65VTz0+X5WGzqny5i1KrZptyWMTuiDJE4LHEMDKRp59JpxU/X+baPDZNO8IQjpFpaQLx/HjJ/Skj0JU74GbZALv2rPH5IRt2lSdF1etVL0H96pLP3hCqcTksLrPpvAlazhZZYZLUYVbSLg5BQ02oeWeZBJkWbNmtX5+Dg3Rgu20mpwkPeThma4bRlXEWszlfKXNUxRyZ/bVeEgKkQvaDIVVotvkjuKNiQmeqcnIXfBx1ewilKVLy2rjxtrftRo5DIJnDakNUlyKOCIhaF5EuO+DVdCtEFYe7gAbGwH3NIWvH/nIR9QXv/jFzuqQkhSQrqI6lwQHPIO63o0pPnhI6ziTk8ybhiA1qlrZhVwRoiLPhMT3W2+9dSYhvnb9RYYIiiKH2ClhQs6Y2vnsZ6vy9u1q8t771cN3jKnuBfPU9LarlbriSqXmMxZmkzv7Z1gXhqkpLHhQJvMJJwtkMpRuGdwPvLEz+XGeDhwY1239zNv9otAq1kTh2ZERM59aXy2pspqa36e6zp1T0xkIC+ckO+FofBi71Vrz5ZFbyXinB7Skm2zevEXb5eA7i6tCX1+36utbPBOGTvaiYYdlGZOi2tl5rPaYNWO1ur9yH3V3lzIZv1ePPfsLqiuZd/0+oAgliFYnh0Hg40s6FQvzI/MGRJHWkTxv+bsQRUhNs/sqlzpAOeTefdWrXqW7kr32ta/Vv5uTYeVGFaTUe7MJdjvh7SjLdlq5QwrSOwOO3AzyC1EVwtbfTuTQthfatm2brsbT43HNGvXEth9WpzlXhNnkuieY4MPacxGqq1etnKQ6Oere4VSfOXNR7d27Ry1dukwXP/Gn8fEJdfHiBf1Q5oHc3d2jFSFTyTqoenvJc01w/y1ebH5OTWuDYr3Nrik1dWlcTa+s9aOrd4yQA9NJRKWGS2FEHlXCQSQ5BlcFycV4Ofm2je0LHVn6+xeoNWtW6PGKqshkc/LkEXXoUPVFg5fCBQv6akhQULkz90G2kKxr/2K51rYJdhq4Vma7vAyAzZvLKvDe3ZbkMPiskgp7QtDMmVIFzQst6Q5EO3hhgSzGhaDzxnAHKYfM7zzb6TTHeWxrctjqYWUZoJz0sOTasG4nWdDKHVKwx0AxDBp3pz1XreRziMrGdROzblRRQuXgwgWltADXk47gRvdOrl+tnK21HvYwU+ro0ZM6bLNhw0a1fPkyff45T/Pn96r5802Yh3AzD0GUoaNHj6nx8TG1cGG/GhhYFKkqCqYvu0x1rVmrSocOqfKyZSZv78x5bV48vWNHqj3GyJchmOXdwcWXMHN49Mxp1fWD/1RqaESVV65U07t2z/T8LRceAs9CaMU/klvR9HM2uadBg3BRv8UOya6UFbuc/n6qmw1LGRsbn1EVGT+n9L0uVjn8xLTbZb/zKoABJue21gQ7+bbdiKErsYWYYngdhnYmh0FAAu0QNPnrgGcUFfYgKgRdVM5hJ4Dz9NKXvlS9853vVL/5m7+pz92cI4eN6pAi1WFh24rqdpJ1O63WIYXPIv8Tdr322mvVqkDXgiDk+IuuKHbF+fPn1QMPPKDzQm+88UY9llBITPqAUt/7XjVsXMF0GXnOzBxLFnMXhuxfODmM8iOMUxuTkqVTp86qS5fG9PjjjTGKPNNBA9WQhcL56WksKS7WqIpM9BDFWbmKAwNq8iUvVt13360JolJTqrxxk5rCgmR1cuXQEOBsijIWO9mLMrKd365HH1V9n/lHNXFuWOm9hmBtuFtN/PTPFGqDYkKi5vem80u1KlaIXLB3s53eADETcjgbtftjF2DEnS/zomEsTUwf6GE9dk6ePKUOHDB2OYwbxhcKYxYSL8frVt1s9j1r3rMrMXX9/s6d0/oYOp0cBsG14nm8cePGuiFoSGPSXP4kgJC2u8ehAMXwn//5n3WK23e+8x0dEWtbctjqYeUwIspN+sgjj2hFLa/ewY0qSEm6DZQ1CmvIRUJZS/JmxbUs2ovQdd1C6GnVeNlll1XGn6z7ySdRgwNfOndOle69V5WOHzczz+Cgmt6xkye5CTvXIXj1qpWzXHYUz0uXhtT0dI/avn2HnryTwswv+OLNr6iKMtkzpkNzFZcvV1P/5b+oeaPn1dilsgk1Bypb45Cmf2+e1bqZw7qjl1Tv5z6rJi+MqvLatUoRopycUKX9B1TPV25XKuDNF/S4C5I7MQiv53FnKmeNtVD4UC/lTobN9uU+qP9d0wfa5CFyaqQPNKrioUN79bXib2LEnWZs5hVGz0IO3Ymp2/eXLSvrF7codDI5tKuV64WgKRwM9oJ2EWZGRkY6hhwSufy5n/s5fU5Il+K8tS05bPWwcpBU2d1OCCPnJXe3UliZAcUx8iZ3ww03pHpLK9JuxoUc2ipoGKHn4TI01GXCyTYmJlTpO99RpaNHtY2LnnnPn9ft9aa59pdDMKMm8uq6o05JlnAyY3Dfvic1MVy1amWqydcQgdrwLKqiWFIwORFCNLmKQzWq4pIlC1V//0LV3Zde/eO0Za0fczPKVpmUu+79T6neM8fU5Kq1utJckTPaq1RpcZ+afuIRNe/a3ap7ycKaoqD6bf3M34rMectyruz7NUvoXvpAr1ixRE1ObtTjk7FDDvbhw0d0frK09uMlM6oPdFYFPexa2+3zGkFMXb/P+Nm9u9yyXoBFIy6nPxiCZnyJZU4eIejhDso5xLqGkLKNOUcOGxVWtolb0m4n7RxW5majSIMwJW9tad/IGtHiLi3EdxI1NEoFRa156qmFWgyswfHjWjHU6pGQ5GVLlTp2XJWeelKVL78sAcELDytnmQzPnTuv9u3bo61Ihocvpb4+SUKddq6idNwwuWbH1KVLB+tWQNcjLGmuYZqWfsGOL3beXZTHXZSBcffkhLo03avK9NKenrnP+WrXfFUan1BTY+ROhpN+lyIUl5y37DmORt10Ub6q55E+0P16Wb16lX5uktOFKn348CH9Qs/9Jy8jKB1FdDKx1dAkcCGmeXz/ssvKBCRiMVeUw3qA/LHYVdAYcRP1QAAwHYGWJg5BDw0NdYxyKGCuo8CSMTPnyGEjw8rckChOkMMk3U7aMaysDaAfeUQ73aMWSrukPLfhiizEkwcHVdZMRBiSRz0o9u7tViMjPbPDUaOXTL5h4HtlihIuXlQ9XdNqcrKrbhgOkuUSTma/jh07ro4fP6Yuv3yLWrRoqdqzZ28k0bIJT3U/0odnpePGsmUL1cTEukhVMTRXMQVhCSp3QDwjq6e+2pnCfCe640s0yUpWLTuxfrOaNzCoSufO6z7bspOl8+fV9BVXqKmYycQl5yxWuRsbV2rsklIDg5Wq8aw5jjbEMN1F+YraNuNh8eLFemE7pg/0kA5Dmz7QokoPqr6+hZlyycLSDtIUpDTb03DBAqWuvLL+TdLJ5DCrKmqHoEkT4uVDVEU6lF26dKkSghYj7uBLA8phvXz6dsJXv/pV9fGPf1zP5ZyPOZtzWJThsq06McCoZk3a7cSFVBV5PFFhZWR6CjTYLqFykn+zopVyDqXKmocGOYZR55Xq5L17zUN31vkfHDCxX5ypLfue0tCwUps2qGmV5GE9W/FME07mmEkw5g13585tav78fssiJ9k6XJLlbaIVpiqaopaj6uDBMa0KiV0Otiim40bJInom4Z59CXrcmWPV/9L/Pzs0W2pIYQDfnVy6VE094xmq+2tfVaWjR5TqnW+I2cKFauq5z4vclUK8FEcvqe4vf1l1f/e7So2P6YrxqR96vpq++abKiXPJyxTrmazZIEm3zX3Fs4Vl5cpqruvo6Hl15MhJNTZ2SPX391VMuFGAkjwLw8iZeWFoDyJFEUoSXtTJ5DAvE2xeLkgZkrSh0dHRihG3hKAhiuQZ8pMIGf9fdFj5gx/8oHrPe96j5ySKOz/wgQ+oW+r0FE8Dmbfuvfde9fa3v30ml3xKE+S2JYdZw4W2/2CelUs2eAOR3sGXX355YcTQPp4iyWEYuUKO5xjzCpUXHVZOsm7OIWbkBw4cqFtlLdXJ5XKVHNZg1SpV3rBelfbtU+UlSzRBRE0iia5751Y1keBQ7UKEtCEovApJwoZk0aZw/vx5gfzGZPdNUtISVO7kpyF5dkjW/E6IoFJGVZTChGPHTmoj48FBY5WDOgSjQmWFaM6bly4UnQZ5EbSpH3qeKq9Yrrruf0CVzp1V5Y0b1dQtt6rypo2q/BS2R6XiDavLZdXz93+vuh+4X5V5aeuZp/Nfez79aTWJZ+TMBOMWyp7ObErusm2poGf8cIvyIo6qiDJN1SXnROxy+EyYPVY4MZQuTa1fhLJ8eRlb1UTodHJYRD5lX1+f7uLFwvmTKuhPfOIT6qMf/aiOArLt1atXFxZe/od/+Af15je/WXctoXnEn/zJn6gXv/jF6rHHHstNsUQh5P743Oc+p3N8P/vZz6r3vve9ev1tTQ6zQAhhEeQw2O0EAlVUkUUas21XSLEINwlEa9++ffrNAlK4YcOG3LZRpHJYrz0fCi/5hbwNovTWu9mlOrnq8xa4znTIue02bedSOnBQf7i8bKnquXqnmlgT6G+V4OUnTTiZcMdTT+3RYZFNmzap3l7IVc2aA8UEVXInk6Msknsnnwu2HhPYyt1sC5kkuYpVuxOqqc+dq1ZAowqBS5dGVU/PYOxLUFbVzzU8WvNdXkauvVYvhXopzqiVYftdOnBAdT38sCrT13vm/CmsYk6eVN1f+5qaxoppfkIz84j9Tpuflyc5sskdkxo+nSyMn9HRkZl8MlSfQ9qkW1TF/n7T0SKsE4mM5yTKYd5FKNV7bvYLVnVfqzfcVVcln1c6mRw2otimq6urkuIAcaL3MC3mfvd3f1d95jOfUX/1V3+lI2cvetGL9EL72zz2iV7Hv/RLv6R+4Rd+Qf8bkviFL3xBb++tb31rDkdmeqgDnrVwFgCHgRvNOXIY5z/ogrBuJ/jiNaJYpEgDaSADnTd08gs5LqRtbpa8UDQ5BFHkULq4ECKg8KTeeayYXVsTSehLQH+/Kt96qypffbWpXl44qKYwyE58mFUSFxVODvZopt/tgQP71YYN69SqVatn8hZrw38mNFvdXzE2ZsLip1H7sk/gWXIUBUTgu7pMdbNUQOMlOTw8ol9KGItS1BLMVXRJ7m+W4ujSFUObikeQFFTC0sR4Ne9RQD7kmTOqNHRRTfUsybbhmf0OvhC0QgcXxm61D7TY5ZgUBiICPGMWLx7QeYpS2CKQbkT1+K59ve2+zua+iiZ2VfJZa0ckP+vbNpm/kWfYbzJEEkEXFxQUJWsmkrSnLQLLli1TP/MzP6M+/OEPq//5P/+n9ry9/fbb1Ze//GX17ne/W6c/QLCyNraQufa+++5Tv/3bv135Hcf5ghe8QN199905HYnS26AeAr7C/QGIdn7+859vb3KYtQo17yIOu9vJM57xjEqLuEYViyQNm7psA9xzzz1abuctKawNXquTw7AHCcURVJLT05o3p3pKSNDsuprzFjMOZ57k3RHkpepzV6vYob4Z0i8KnvlM1ci4Ormwfew/CKtddtkVmrizLdY7+7RyrmMP0zHUma0jSZh6h6qIoojP5LZt23WHFjHg5mGGqghRXLoUI+9kuWaNaJFX9HfrKo7kQvHiMjVZWxQ1Mc5JVd2DC9RkxqCGeBpmDYq4W8+UUtrlLNELc8X4+CV1/vxFdf78BT2menvnqcWLJde1T9sP0R6SaxNU7IQIMkZtKyI5D/GEt5RLEYp2wbo83YnvVOVQ5tZm2PSUy+VKziFk6pd/+Zf1gkiEgOJCDAHPcQlb2+DfuILkhfe///3qhS98ofpv/+2/qTvvvFMXz7785S/XYea2JodZwVtUXl6Hcd1OGuWpWDQJZcAAJum8rXgalXMI7PVzcxMaR426+uqrde5kEgTNrlEqeOAzqcgcbPOTap6dIWl2Bwr7Z9jlm57u1soHf4s7NVx77AfoeLJjx/ZKYRCkMkvlrUuItUiiJRXQksNbzVUk1+y4KpWqquLg4EBitSTrsWYLCVc7lbignuI4vX27Kq9epUrHjqkyDgK8mVwaVSWq6J95m5rsTlc8Ru5s9ze+obr27VfdKxarsac9S6mNG1O3mnMlR3LcYRZE9UKx3EODgxS29OmcKu4bUjCMCfdRnadLDjFt/kwfaLFbqqp80SbjqvC2iGDXrmRFKHOBHMrzvFkejkNDQ7PqCXjmMJ+0C4h2XnPNNeo1r3mNFkgQfyjKwfNwTpLDPMhUkm4nbIdctqJRlA2MFGggkfOQhAAXmdfYiLAygLCjFqJApakkN9XJtWqceeCbYokoLiJkIP3hQTxNT+EoUBFPfiFKLoUnPTN9nXleRg29YKGL+Z1MguXMEzirKIIYRhEQyVVcvXq5Gh+3K6BRFccqqiLXl7yzMFXRRcVyCQlntY8Bia5Pb6+a+LlXqXl/80lV4uVuelqV5/WqqeuuVdMv/eFU2+v6wSOq5y/+QpVGhs35OnpEzfvPx9Xgc56tRp72tMTrsdXuMPuh6k/Jaa0Wh0hfbTnuoAVRNKp/C1ax83wWOxOZ7LmXqIQ+efKE1QfaFLf09fE8z05sXa45WLWqrAtw0qJTyaHMeUW6jjTLBJuqYcYnzzIb/DupkJEE8BPmDWBzGCKgnhxmAHIyYWSxcIlyVufiMnkXjaQdTLIYQLP/5OHRb7Ho0HUjlENuaPILUdc4rqThcQknh4eFqxW5eXUzAYSVL12KfvCheOzdu0cruuvWra/kCrqkY7DNev6LRahCSRS4sH2WbQZVRVPBaiqgZaIPqorNCgmTm+ZiH5O4e8vmTWr8N39LdT32mFJDF1V57TrVs3Wzmk6z39Nl1fPZz2piiIm7no4hdhcvqCV3fkOdv3qn6unZNIvYBRU786JjjjvMfigcwaKNWr/KtKinEM+b16vvDSyspA+0jJ9Dh/apBQuqJtx9ff2J7zez79nzcAHcbufObAffyeSQua8Z5LA8E1YuygSbeYlcRrwHf+zHfqxyHfn3G97whty2w/l73/vep+d3tkl3M16WMKNva3Lo4nWYNdwr3U5oNwPjjrvpGmW4nfd2KDjBv5C8NQiUmUiLPZaiySHrJ48DGwCatJNfmOaBSQHKrN7JCchWVmXKJK1TZT37b2yLcUhKw4YNG9WKFbXG49Hh5MraQ/eXyTfrJXYNJ2cJ7cYRJVPBWq2ANqriUEVVNN0QBlV//+JIVTHLdpMgDalwVp/m96rpa66uKrtTVcXOPuQoYlc+eVr1HDmgyv0LVE9pUo3RC5CJeWCxKp8+pXr3HlSTu8Oqsku5kiN365j696FtgG33gVZqrZqeHlfnzhll2rhQYF3J342qWO8lM6qqPCmuuCJdEYqNIp0smolmtgUcGxvTHKJImzpsbAj3UuBKAShWNogbUr2cByC4nMe77rpLrxsxiIWX67Ymh41soWfnqCXtdtIocphnWPnw4cPqBz/4gTZ/5g1aJs2i2/QVuX6xsSEN4KqrrkrdqYZwMrmGUaBiOZxsZc9P4plnOnnUrheic/DgAU3gyXENvrkmb6MW3pZvbMx0CUmLrBNfEmIYRdySEqVaVXGtfvCNjFxQZ88OqWPHTlXCh7wxJ8lVNOHkrMc7nZkIMyb4LufMLoiIJHaBVn+idqYJxaqueWqq1KO6y9MVYmhWbgZ2OeHk7JprWFR1cxIDbGPl1Fvpwcs9iUkyquLZs2f0M5MCBPFVxC7H7gOd9eVHAClMW4QyV5TDZh3X8PCw/llk+7yf/Mmf1CIA5tSSvoaFTrBIxQUIJn/3d3+nySdjGsJLqJllTpLDtKTNDrGmyVFrpHLoSqzsHEp8mqRZeSPa2xVJDsViiAc6icJpiWGwOjkMZqIOC3lmUw3le8HcQI6FnCjO0/btGFvPViuSCGCQpamp2v2FD5kQdrmhqk6agg77FLvY5fT1zVO9vSvUkiUrQlXFuFxFqdTNAhknpoCpnIrYmRw9Oc/JQ7HO12jxYjV95ZWq+6HvKbVgoFJRVbpwQU0ODqpLV1zREE9Dl6KhxGH4kNZ5YdXRdh9opVbr+3JoaFibcOOrODU1OaM6Sgjazdlh927IXfbvdzI5bJZyODw8XBkHRYIQcp5h5LCxgcgQVjPR1uSwEWFl6XaCDxCkKY1fVLsoh5BejpGBEpVDWUReY9HkEMmc/EJCPixZbuS4cLLAWAllb3MXZ8Yr6+Wtjo4nqBKbN28KfSjWDyfP3obZjiG/WW4nt64i2c9R1jZ3QbIQpioGcxWZ6MXuZN48cpziK2OrhRe1XnaG5HCvQs7D9r1UGMFyUa7KP/lKNXn8uCqdOmX6hXPMfX3q3A//sCrXuadMJX5ziGE1hzbZ900BTFfqoiPmhCVLFuul2gf64kzHlsNadZQxxlhKQ2jWrCmrwHt6anQqOWxmWHl4philWcUweYEiLEFQ4GhrclhkWNnudrJt2zbdaSLtQGiHnENaAkEMeXPYtWtX5M1WdFg5b9sf5HLUXpTC7du3q29+85up979eOFlgJpTaGysPDzgpdDl37pxOZyCcQKVa2DhMHk6eXUAjRDbLPruEk9OQHfuY0xCloOWJWAlBLsOIXVfXPLVgwTKdx2kKmEYqbf0OHz6gensHYiqgW5PcuWxbK6Wr1yv1lreo7v/4D1U6clSpRYvU1C03qyHGfZ17ivGc9RGY1tPQtXo+2Dovi0ps94FevXqlmpycrqiKR48e0y8fAwP9lqoYne/KfZm1CGUukMNmhpWHhoY6ghzCb7CwAcFjmZPksJ7FjIQimZSl20nW7bRqWNkmv5AnijTiBnq7hJU5rrD2ftJCL/l66oeTa5W3bD2Q66mNqBAcDzdw3DisuXSnT6vS/gNKjY+pMrLDli0Bnx3bziO7epeVdCSd9G3LE+MjyWImcBShKMXObu9nW56YzkhRWyuFkn4pSiiVjKoIUUQVCrM6iYoq2H6I9fP9iuv57GRoDiF84Qtr/3jkSKza7Kr6UePh4gSW9gUkqBxmVaft7zNGaNvHYntzyhhiTEprP35i2i2gE8qMZakTOpkcNls5bHdQWxCFORtWjrKY4aalUjePTiCtGlaG/GLcjWp488036/L1emiHsDLnAFIfdlxp1x80u06qHOYRTgbs66lTJ/VLDOQ9LiReE05+5FHVdccdRvZELevqVuUrr1DlF73ItFcI5EhmVTjrTbxxJsVm0u2ayaOabXliEzv5PeFYtkfjgdm5hsmeA67WNVid2BXQYnVy4sQJdfDgwchcRRc/RBdy59q3Ocm2o57B7hXdjSWGwO4V7V4dHf79YB9xUl9QFU+ePKUOHDio5x1eNNauXag2b4YZupO6TiWHPqxcLNqaHOYdwozrdtIpyiFvPJBf+gdDfpO2+Sk6rOzaApCcPI6L/Qw7rjT7nzScLMiDbNlqI50ayC9kf+nmEEcMaybRCxdU1513mhZphAoYvmNjqvTY4yQv6T7PZn8NAYtSOOt3n6g1MI4idmEmxUza4+P2RJXUm7F5/Y/Dvhu0OpFcxaCquGQJdjkLM/W2dSV3Lspbki45YTl6eRk+u6h2WcPRdkGKU45mwjxLM4YG9LJ2rfSBNvmu09PfV9/61lilQpolyk93rpLDZlcrD3SAchiHOUkOgzmH/D/9CuO6nbiQNvMQLS43ISkJReHAo5FQK3mUaW6sVraykbxJcvKi2vslJZ882B98MJ0FjVjZ5BFOJpdlz5492mMSBYprFr9t6x8HDip1/pwqkSJATh2qXF+PKg3OV+qxh1X5GbfoL6Bk9fSYDbLP1RZ/xvJHyGNUCJTvjI+nP05XNYkhnoFjzXy32Hy/oK9iVVU8rkZGDmlVEbKYZoy7KI6uylsSXz5b3c3T0zCvFntpIVY2xjIo8+Yz5VmaXuo9atmypWr37iXqmmvW62cBnopU0T/++OOaHEISGWNERZKEVLmfO5kcNrN13oAnh50ZVhYylbTbSdbtALaVRTlICm58lIskHo34/GHg3WoqaNa8yQMHDugHJ6FXioaikDTnEMUQ5TANTF7clHM4mYkAD8MNG9ap1atXaS/DUomxU52Eg3YuXJJKv+ZpswNlfax6z0zUtotYrFLT4zDAkp54mfiD+4tSg+JSL48s68SdpnI0CPINs6uyja2oFlVx6dIBNTEhqiJ2ORd0Ogu+eCiMcbmKLpY5NbmCGZA8V7C2gCMvcueW5+eS1kH1a3oj+DClXV66DCdLZlUEmDJ27DAvaFLhTCSLSBfOGbwIY+TPmOIFEqIIYYwqjpBnXrNIVCeHlQcL9DhsBczpsDKqzEMPPZSo20nW7TSCHMYRK3LWUAsZzGk8GltdOeScYtaNSWiSoqEk608bTq4NwdWGXu1FECQ3MnlMTpbVoUOHNTm8/PIrtBkz6xPLk/CWfSGT4KrVqrSgzxzITL9YbT1y4bwq33CDzFRaWcnC812qR81xZg/rTk1lmwSa1SLPVs+Mqojis0y/yCxatFiPRTtXUYoSeDllknchd655ikmvcVhExJUYutgbpQ1H26QO4tbVBdkwxyCkzlg8BUldrU2RnUJh9r+6/dr7rP5+bd1a1nm1QTB/ENFiERNunheQRSINpAmJqsizkH8DeeZ1qnJY5LwaB0Qlrxx2IKQgBauTpN1OssA85EuF5x1GFaSgTuDzxxsObfDkgdHu5JBrR34hQO3FNsJ1/TzYH3rI/D8ThE307M8E/5+f5Haxavt3ScB2Rken1N69e7USgPppH4uEd6O+O2sSXb1ala+5WpXuu89U08zrVYqeuCtXqfK111nfC19vPSHeJcyZley45t01Om+tPkkyVid44lVzFY2qSAES15y2fn19i2MroOPg5guY/BoH0w6KO2fRVezSBrCq0hmFmuseNKc3RC8uL9a8hJiXMnuLyY7JtYhl0aKy2ry5/sPDNuHGZYJnP1EGyCLPEvLmecGELPJTvtNpaHZYedArh50FrEF4e0c5fOYzn1noBZaevkWTw7CQrxTX0AKPcnXXh0O90HWjyKGYktPBJc6XMe36UQzPnVOpYcgWym36mOfo6CX1xBN7tLIEMezp6U7UAznS7JqJ71nPUuWVK3URSml0VJWvu1aVd+1SatmyWF/GemhWMYhNVtKGlV1UKJdijnrnyr4VbVWRMTQ6OlQx4EZVNNWrtapioZ6GKc5XUDmsRyztdn/BYidpDyj/TlrFbhM3+wWtitpzETWGRImPKrApmpSDXbvCw/T1wDNQilbk5RlFkYW0G0D3KwlBJy1CbHU0O6y8eDEveJ2Ltg4rpyU8Qix42Erng1bs4+wSVuYnOSmQwyKKa5pJDpksKRzKYkpuFINyruHk2n6s6ZjLyMh59fjj+7TZ8vr160OPJWhWbX5XZwJnht+1SxPCcgypTOv5mDXfL60RcSuEhF2LOeLVynJsruKiRYNqYGDhTPXqxIyvYlVVFKucMFXRNaQbp+6G59VRhDWtlTo5bn4X5e9o580GVUfWMT5ub7txXpBy7rJ2CcojHL5hQ1lltNOdBZRpomEskJh77rlHq4zkuvL8JBwqZJLClnYNOTezWnlkZEQ/tzsZbU0O6036Ud1OVq1ape64447Cq4hBI5VDVFHIL6ooYeQ8+z42M6xs932+4YYb9BtwXuvPUp0cnBCCJthxYMydPn1CHTp0VIeE4o4lrGdzVh9Fm/CIUjN7e6XcbUmyEpbZhLTUwM4azcn3CxaCkAZiq4pSAR2lKoYdczCvLtj6TxQ5TMbZNvtfL69Ozg3jcHqahgIlq5Aj/bE3u7pZvm+sbEpNqa7evt29E0oYTC/vbnX55ZfrhRcOURXJ2WbeIEdRyGLRvYI7yQS7v43O1Zwkh/UAUaLohJwMMUaW7iiNSGhthNchxIdjuuuuuzTZII8y75umWR1SILzkF8b1fXZZf5bq5CBJS6ocMsFTjXzx4jm1devWugnNYpGTpXfy7HXZ/19KSSobT5RMWDcYEiwXHhJGwWo0EQ5THKv9mA1Z5TE1b96AWrKEqtQ1M6riBU0W9+8/rq/vwoWLZzq2LFLd3T2xfpOzC6qkwj055Hrkodq5FdBk3nQNCeb+TBtWdt0+2L59Wo/ZIhC0seGFA8svFq4fJIdcRQr7EE9QHW27nGYVfLRDWHnQ5xy2L6K6nTSqili2VSSp4ganjzDVa3j8ZekB3aphZQg9BTU8rLDgcXkQhPkcuoaTq+uurxwyme/Zs1d1dU2qbdt2qd7e+sVBtnJYN5wcg9mkcna4uggVLWsOlktY1+W7LgqWbU0Sllcn6zeh2Crpsz8PQRIyF5VXV0WvWrRohV5KpWl18SKdNi6q48dPqf37D6fKVXRRd5OYZRdJqF3NtmtfJNLn/LnYM4ElS8pq40ZVGOI8Dk3rPmPkTntOhBRaxqIqQhTJXbTtckwbydYpbGm2CfagJ4ftGVYmvwLZPKzbCQOKf3MzFJ2cWyQ5ZL0UnfDWB8mVBtpFoNFhZbl+XDuuYR4FNfY4cQknB8lWUOELy0+h48nChQNq06YrEz/Q7GrlPMLJ1fWGq3DGMJcHbo+ziuaiJoUR0iSX35AVd3+/eiFY/marcih+Mibi8uoA57NaDVvKRTnr7q5O8thy1eYqntL7JFY5EEb7hdiNnBmim1U5cw//u4Wjg/dGXMeXIotQikQaA2zGBYV+LPLckhA0HrlS+CJk0cX9ot3DyiPeyqb9kLTbSSMKRYokh2LezY3PcaKwFYlGhZXZBtXkFNRcf/31lQdV3vv/1FPZw8nhZGs6sgiKfNfVq9eo9etXp7RWMQqfS7J7eAeL2VY20kmB39PGC8JBgUCjc7DiSWX4RCp5dZBZLjHbr7U3qe9XZ+9zXAjWoJSbWukalgxTzmbnKkr/3pO6fy+5UqIqDgxkN/03ZDq7cuNijSTbd1Htgp6Ihhwm/75rOH3TprIquuDVpTuK2OXQUYv1iF0OzzPbLgeyKEWecyGsXJ4Jx3vlsI2QpttJK/Y9Tgoe8hhbi3k38n+Rql6jwsrg3nvv1cpHEQU1kmsKKXziidxWPROyLs96gPCCQusrlM+VK8l1Tbtes56swzQ6R7FWbTfbsM1yMQGGdOi2KzMKmlHbqaithzQmxMAOscqkW1XgzP9QFSsGxWF+dSBMRUrqV+eiALmolS5hySTKWW3/3lpV8dy5E5rciarIT1q4JYFRTLMft2sHGHez7dnfT6McuofDldq2rVjVEOTVOo91ULgizQbIBRdVkbmIc8ffRFVM4j3bzmHloaGhzA0l2gWtm22aEKJspO12Il1Sikae2+EGxA2fxTbvlpBpkT00iw4r8yYmqseNN96Yey6o5BzmUZ08e92c88mah9b+/Qd0dSn+hagzWZQl9rm7eyqTshSXoyhhU8aMIVjTM4SQvrKmYhVLSypYzWemK9fetOszJNGkZ9T61QlRqubfVUOwxodutrWJ5NXFKTHGnLhK4oLnpFk+jNm+W3LuGpP1mEVVXLlymX4hGB4ejVQV43IVTZed5hBq13A03w/bd9tIu/72lRN27MACSLUtgSIdi7mWhWcE+a6oikePHtVWaowj2y4nb4XPPMtNJXazhKhBn3PY2mCAEIZE6k7T7aSRYeU8SBVv/JBfbsJbb7214nwv2wBFk8OizhcPlIcfflj/P9ewiCIhIdAu4eQ4siXK4djYuCbvbI+XFI4la7pkb29JTUyY/U4b3q23v0zOqIPGvqP6kiVEz6Ti8kJgSDVDmHxEjlMWVEWjJJrws0nuD46/Um4kKyqvs/HkziBLMYZ9DGlavQWRh6chLzVhquLQ0EW1Z09trqKtKlZVv2ymza7hWNdwdNT3zb3QXXgRzNKlZdUoi7wi5wQB9z7zEQtNFxhH0geaFC/+DUEUVRHi6JpDLnNqM8PKA3XcJtodba8cSn9dwpBpmHwjw8qu20HCJqeQm4rjlKprextFV18XEVYWYo9n27XXXquPMY05cxrwgKSj3JEj+a9brGy4ThBDHoQbNmzUxCmr/YxNPGZXwFY/Y/+UUxfsOFHNr6usXU1NMcFxPWtDxeHqqFEIu7tRFg0RNm/uoiyaFaM2lsvJw882XFSorN91bc3XLLUSuOx31LaT5iouWzao5s3rz+wT63KtXcPRcd9PYmXjWgQDdu8uPpzcSHIYNo7wEmZhjDCOUBVZKMxj/rL7QGdrEWnm1GaElUdHR/V59WHlFgfGnlS0ph1gjSSHLm3nyFtDMQyruhbI75rhQ5gVnBN6W5Mvedttt1VsEoo6Bh76jz22QK1Zk/679Qgep59WeE8++aRWrimCMgTJ9kKskjh5nsX1bZaQo1HyJFRbP5wVJIpB8LDmXpmcnNIvVvLGjzK0YAEpEPXPhwkpwzylbSDhHUzYjRJph5857/WIYjKiVEpkHN0oBcq1GtuFILkWKCXZdlSu4ugonX2MqgiGh0dS5Sq6kmKXcHT979dXQuu9ENSzMtq4sawamarWDHJog+cgChsLNms838UuB6II0eL5I6oihCvJCwfrkfa0jcbwTAqUDyu3OHibzZLT18icwyyEh5saVe3QoUNaVeMtLD43rfiCEY4jj64y4j/JzYUSKsS+yLzGAwfmqYsXx1KRQw6TZw+7ZIomqr+vEjDyts6psbFhtW3bVm1AbBdKyOHYxK7eIXI6IKMmb69+B6Ck4WTJS+XN/aqrduuHHNfi6NFjanx8n36AY6TMw3rBgvmJrjMkgjxFChsQtIOqIqFohn9UUYtri7ysJMs1LOtK7rIK5Hl1A0kLURVLpWVq3TqjBh04sF8riyhCRlUc1JN7VNjQlRgW/31ecky3FxCseIfo2S0CbbuiYHFUmJURqRpXXtk41bAVyGEQzFMQQRYaAUAOpbCF1DD21bbLCUbJWsUAu7u7uyFFN81E24eVs6KVrWyoBENVQ12DPCXJbShaCZUb0ZUcximhRZFDwsmHDs3TBRaSBC6T8+wuErPDsFGnFfVt7969anR0XPX3MykuqnzWJZwsp6CeCphGUYK0CWHjnJs+vcb3DrVzenpMnT59UXfdOHbsqL4/MMCVz8RNMPb+BVVFU8wSXdQyPZ184gqqq1mLElwLClyUu66uqaZ1A8mrwldUxZ4eum2s0mSQlwyWU6dOh+Yquobwwwh5sN9z0EDcoDaXVtoD2j2fBWacGh/K2u1W/SSjTcnrY+fOaT125jI5DIKiJ3oUs7CvPH942SDViMiGSWMwhS08j+RYmm2A3d/f39LnNQ/MWXLYqjmHSO7Y8ZC3Rh/hpOHyRvgQujxsICWEXTFTveaaa3T7prBt5J/XqNT3vsdkTg/YdIQtjuARRiYsgsKGhyEPtTx872a35dNHkTBcOPv3UpEs51WI4WyyM1+tXMmyQn+W/EmOCeUahd1M9iYEPX9+9W0+zijbKITdlTxFjqOWpE6r8fH0Vjlmu9kJWjN6RSfzTyxuv10rfMNVN/OigaooE3htruKpSq7i0qUDqq9vUUVVNKp8bchVflb3s/bvtnF40taA1f2vXxkeZ2Xj0pIRLF9eVmvXqoaj1cmhDfaTeY/liiuu0OKIqIr4KkofaFRF46zQHOVwaGio40PKHUEOs6pYrrmAabaTlLTxtkR1V5auIEWHlV2KXshXwguLNy7yC6MSeYsgh1KdnKTFXe2+RE8GmMHu3btPG3SvX79OnT59psYEO6uwGgwJV61eypnCybaxNYgiXsFTznUQIsgb/aVLY5oo8uJC5xpsLFCFli7F7oQcofrHJlXNZp7q1sSQghj+HaYqBnMVxX7HHGux3oBFESwIbVb1zLUQgls267tjMGQqpA4VFNLIC0I1tFpSS5b068UYqk9oRfH8+Yvq2LG9My3bkvoqlhoaSo8ywXZ52QOss5FFKK0SfnUFIeU1a9bohWsDKUNVxDuWZxFjidQrXkogjY06zuE5UKncEeSwE5RD/v7II49or0bUQt6MitiOC4SopiVv3NDS35oQeVzLpbzJIeFkMbuu1+IuiLBJgu+fOHFSHT16RCdX81Ay+13tOJI1nGzWH9wHUUnKqcOcUkVsh5HDUK9FHt/r61ugF0KIhNKxOoEsogIz6RpfPEMmkxYmcD16ekSNNqqiMeIuh+YqtkJo1UU9qi1OKjVkvyXsKmQuPG+2OsaCvpNyrs1xV7ctYdfJyS69zB4/dqpIr85VXLx4eaiqyHNB2vqF5Sq6Xu80349KmXGxHAKXX15WzeISPE+b3eYuD9hpMAgndNAiR1EcLxB67D7QkLei+kCPjIzkYsfT6piz5LBVcg5JyIU8SVeXrEmuRYeVpTIszTYguyiGGzduVNu2bat7M+VJDiWcLKszBC7ZLBFG8JjYDhw4oEkRidS1b46s24RHs5KHaINtzlk5UY7ibMUwnhiaVoDpHnA9Pd067LNixWI1MbFRjYyM6nNCD1+UbzPZG6LY3x9uohwkpFVVsUoWZf85r4S1pbViV9ekThHIEilzKWhwV49EXYsfg6LQmVw6UejM76VzjB12rS18qr6k2GHXZAU0pQzHXb+61z7ntRXQVVUxmKtoClvMi4arp2Ca74dZ2biqljzOr7iiOaphu4WV04K5kiYD3FPMo6iKhKCxE5NUB7HLyZMgD/mwcueHlZtNDhnM5Bcim+/cudPpJi46rJxmG8Zs+ildrHHVVVdpK4wkyJMcBs2ujXJYf91hkyGTGA8cJg+MrXt7ax80tq9gljy4eobVcYQi+N2wwpMwuCgyYofCz4GBfr2sXbtG92SGKLLgjQcZsK1yIJbm/NZ7SZCiFmMqjkJAKFs6zZTLRlW0DbiT5CoW1dEjyr5ExgXEH+XJeEViJWSW6lCfXe1qFz6YzjKhe1V4hW+capakOC3unAdzFUdHR2ZeNChIOKT6+xdokhhXAZ1vKL6W7OZRRLNrl2n72Cx0Kjm0w+WMC+kDjRDB38QuhzmIfEXGkKiKPI9cVL/hOdBXWc115bBZVjY8VAnJUaABKaSxuSuKVg6TkjfOKdXIPOSDnVzyWH/acLIgac5h8JlBCAGiy8Ng8+bNoQ9aiCc+f1mHUxxJkx7C9YhhksKTvHr6cgrChhpKT62J8rAeB1jljI3t1yRy6dJFamBgcSKrHNSAp57ao8OOmIrPm0fHmHADbtNvObqoJcz2JljtCuxdknPf1TVdY2EixC2sqjxYHMHfjDm4+Tf7wHlPeu4b3cElzbbrFYKkqermmokfnqiKo6MX1dmz0apiPaRV/Qw578olTxOsXFlWIXV3DUWnksO4amWxw2Ehfx/3D1EViW4A+TuEkRfPNBj2OYedjUYrh/KWLeSJooZbbrlF50nktZ2ilcN6BJSbhhA5icRhnVySrN/1GILh5GAXkzThZFpAoVqh7FJdHReezUoM6xtsR4eVqyHE2sITux1e9P5mIw1JyYoJIQ7qBascFMDR0XPq9OkhdfjwMf1yJqpimFWOyWncr1atWqnPvVQni6rIYkL55tgN8Z+alQYhZuRGaaxVTJNUuxqCheqc6XRFEJRSS3saCpIQy6hhhuLoFo7tUfPmofJUVUUJP6Mq1stVzJajaVJDzP67EUPWs2tX88LJc4EcJi1AgfzxDGLhWcmzBaJI3iIFoLyQiKpIyky98zXsyWF7oB3CynKTSn4huRLkF6YlT80+njgCShgRb0aqW8kDyfJAyoMcRvVONp565UThZB4g9HsmZ3LLlsvUkiXxBL7qf5YOJuev/vgO229RZZLmF9pwDSdnQV9fr+rrW6WWLaOlFkUtQ+rixfPq2LGD6tChCTU4uEgtXmzI4vnzF9Thw0fUli2b1JIlS0Ntesz5FtJmwtWz2/qZyd6QQtMCMG1bv7xb5CUvjnCrjM6DWNa/1uFhZdcOmMFjt1VFXtRIX6CoRcgiMMUKRlXkxSPLubOtbFw7sZBn2N+vmo5OJYdZq7AZrwgyLNIHWuxy8FVEvBG7nGUzfaCD8GHlDkcjO6SI+TMVyeREUNCQ9w3brLCyHSLftWuXJod5rt81nFwbIoxet8xxnEMUK5QKSC4KRRxQtIyyVJRqO1s5FKUyCzGsV508a+tW2z8Ig5l0a0Ow1eKH2oIIE+aurqM6PLt1aJll9epyxSrn5Eksgg7rT/CALpVI/Sir3t7o0LqNMAPunp7pzG39mtlFxaUyOg9imWTfo3JhXYtI6nlYElKWsKD07rVVxYUL56v+/sWpcxWlYts1TxM+0cwilE6xsslLOYwDea9EJli4/hA/iOLJkyfVE088oVVHiCIvHGKZw4ttFkeRKPzBH/yB+sIXvqDrDxCMyJcMgmLI17/+9errX/+6jsa85jWvUe9617tqbOW+8Y1vqDe/+c06xxKe8ba3vU39/M///NzOOTQTf7kllUN5MPFWcvXVV+s33yLQqLCyvQ3OHyFyBnMeIXIXchgVTk6SuyehXUKf5BdSOEHhST0/Ry6thDrTqiVJLW/CClIM6QovPKnNo6uuw062N7501XCqELhglxj5KUUV1Qk/HbmMm2zFKgdj7eHhIf2T3tSo7IT0S6UpPdGbEPTCxFWHpq0f93j3TK5grQF3vbZ+wKUYIS7frh5Xca2MdjEIT0Msw8Lxrn6MtuVP2t69PFunpia0p2Ktqmja+tXLVWRcMAZcSL0UobSKWNepyiFzT56RN2A8OE0qzKaZPtCkFkEW//Iv/1L9xV/8hS6wvHTpknr2s5+dSytZgA3Pj//4j+tUrI997GOz/s5+vOxlL9Pj+6677tJRrVe/+tX6WfiHf/iH+jMU3vCZ//E//of6u7/7O/XVr35V/eIv/qIuBn3xi188d8lhFjDx26pLEeCiE2oF9foju4KJkMTbImETat7WCZFzHhnUaZN6o9aflRxGhZMF4nMYvN4yEfM2SEUyOScUPyQJP1ZzDeNVySSTv03kgmqdMRoWhc6QHIokzL9Ls6pdwyuRIUvZ86iyfi9JKJrQDoUnXP9t27Zrcm5gVCEm+zRWOfUMuINWOWEG3KiVWQmWq+Lo4qsnYyvrIy2d6jf72el67K7h3Pnz41XFuFxFPk/Rk0tYHCV85UrVMuhUctgIRbS7u1s3OmD5oz/6I63WofB96EMfUp/4xCfUZz/7WU28WF74whfql9os+F//63/pn6wzDF/+8pe1uPSVr3xFK5zXXXedeuc736ne8pa3qN///d/XJPkjH/mIDpO/733v09+h0PVb3/qWev/73+/JYZYLn7XjRxJQcAJ5Qk2D4edBnlolrCwWPLyVoLDl9fDJqhzGhZOr6xZlrXZC438x5D18+JAOiSe9waWSke9D3Oj4YQ8j22PORtB/LuqnYHqaTj5mwmfiHR835NBYuCTPuXUJlaUNRQe3G/ddFEJIOW/rGzduqiHlnNf+/gG9JLXKSXK8tlWObcAtfaB7eibVpUuixia3yklmEVTP0NydXGV92U1iMzR7e43Oc0x+7oKqYjVXcUidOrVvlqrIPcz3s04HnL+dO1sjnNzp5LAZvZV37typlzvvvFO9/OUv121gv/jFL6r3vve96lWvepVuYEFoN+/uKXfffbeOOtotZyGkhJkJIV9//fX6My94wQtqvsdnfu3Xfi3zdud0WBmQd5g3OaTFGEyf/pCw+TvuuKOpxSJ5nmcUnMcee0yTQvIa8l5/2mOoF0621w2Y/OWFs7t7Wh04cEiHDbZvv1yTjKhhJPlzdiVj1Z4EUptMaUrbQcWErI3KBYERYpimsMKE6rKSu+xhwnrfDVYk26Qm7Lv1rXIG9DVcsoT2jMnM5IMG3FNTqLJTMyHo5FY5eeXbFWk9k7diab9oueY5AldPwXrnrl6uImo8/7906ZKZQgR5AQu3Owp2mbnyymlVJ0W54ehkctisXMrh4WEdYXrOc56jF3L/qCkg5FtEWz3WbRNDIP/mb3Gf4fnIC3i93PmOJYdZwA2Tt9rGjUjRCRcKNo8c3ahuLEUrh6ybBymh8ptuukkn5hZxDGmLhPbsIcRtSFeQ2Mm/mdirE7rxAyyXJ9Xjj+/VIc2tW3dqZTcpaQuqgvUqod1yycyLDz6KKIhJC0+C+5sFNiHOgrjhiFp75MhhrRYuW7Y09XbDrHKqquJhVSr1xlrlxHW0mJjARiVMVZwdfpZ9MT+TEemo69fKnoZhsO8BlzzHfFRDuz1hlczJvWoTOkPmqFrt14tSq9XU1Lh66KFH9c/9+zG8r7Zrq+YqRu8fnGDzZtVy6FRy2MxCm5GRkVkkEGX6Fa94ReXfb33rW9Uf//Efx64HroDI0qqYs+Qw76IUklQJIzORY1NjM/VGhHyLLLARCx7Wj2F3EcRQjgHymSac/PjjSQlMVTkcHb2k9u17UvX29umK5DQPmfBCA0Pg6uWvZiFpZn3TmYmhy6TrooJFhaI5R9jUnD17RivrYZ0Gshh0m0KWFWrt2uU69E4OKUQRFZ8XAMLOQhb5bBiC5C66rV+1qMVWFc3Hsk3E7eBpGIRRVU0up0sBjFTB81NQq85JHmB5Vvhevmfvfz3vyipKlkH+PL2Odes26vSEsFxFscpBVQyqx7t3t04Rylwgh80IKwOpaOalIQ6//uu/XrdS+PLLL1dJAPG85557an53/Pjxyt/kp/zO/gzPuyyqYceQQxevwzzsbAhLkoNHvhp2LkGy0WwPQhdQrQUxpJiG/Mkib8g0OYdJw8kCIVYQhmPHDqilS1erdevWpho7YX57sm7Zp6jVpekWYdZlFE6Sjffv36f6+w2xwQswaZWemfSy3hvZw8lR3+XaYnuEdQ29tsPycF0MukU9s3MROY9ilUNVPWSR7VKUwN8HBgYrE339KuLZVjnyE2V3bMwQ+TirnLD0l2Z7GqYZm9VWgYTfKeCobQVYS+b0NyrfDev/HPxbPPJvBVk17K6GjMNyFYeGLqoLFy7qe5Ft2bmKmzZ1qxydTXKDFF11KjlsZlh5sE77PLhA1gKVICj4xO4G310par399tv18wu+IZ/593//95rv8Rl+nxUdQQ6zwjXcy42H3QZ+SChQ5OCFkY1GkcM8t8GxUR1KfqEcG/9f5HGkIYf1qpOjJuVDh/arTZu26LyjtIjqhGKHrMMmsLTKiqiQEI8rr9yohobIHbmozp07q8kNrecWLTL2LrSkK6Jzi8tkG/bdakVylyaGdvFIXggrfhGrHJbVq1epycmpmYn+gr53OcdM8suWDWrLnDRWOZBEzrGYbRsFLLlVTiOsZ+L6PstPvl9Nuw6qc7VkruoHKv2iS2p8PI7U1csDdCW2+Xxfng92+7zqPvboaAmLnat4+jRdNg6q5cvH1FNPGeNk7slWIWNyTK2yP50UVh7MsbcyHoYITPxkfkVoArT+YzsvetGLNAmk6OXd7363TlvDw/BXfuVXKi/YWNj8+Z//ufqt3/ot9drXvlZ97WtfU//4j/+oq6uzYk6TQxdCheJIpRAX9eabb9YJqkVsp9F9iQHroaCGN5Ubb7yxQqSKNg5PegxJqpNtsE5uPB6Wl122WS1evCxndaUasg57DqcRtu1WeCZc1q3DAixCbqi4pIvI3r179HequXV0h+huanVy2HfjKpLz2m7SwhnOD/cqC+eafbtw4Zw6fvy0Ghk5nMkqB9sbtm0I1mxV0c5VDKqHwReHJGTOfE7yHE3f59rWgLXWRlGhVhljtbdc0qpsoxyy7axztPEJLbYIJQ4mt7XqYmD2KX59taoiZtdjatGi09rBAc9X1iM9e/lZtEtFHGTcdSI5bFZYeXp6Ovf2eW9/+9vVX//1X1f+Tb0CwPD6uc99rp53P//5z+vqZJRAto2tzjve8Y7Kdyh8hQi+6U1vUn/6p3+q078++tGPZrax6Rhy2OiwsvQQRmUgv7DeA6ARlcR5EVByJ3lzYX8ZiMHcySKPI8n6eYZjHZl0N4xi9ZT+/wULmMDT519EhZPDlUOXkJ3V8YT1Dl1UU93zTZVEZX3dNSrG8PCIunDhvM4v2b//gH5wLF68UKcA9PbOT31vZLEziQsnx1Uk57VdkGVYsi/kjy1e3KcmJtaFWuWgKnIug1Y5QVJqEzraKZZKfBZy2FUpaiFFo6trcqZ4amJGtePvJgxdj8wFYayNsk+QrpXVENfmhsPdvm8ff5UcJv/+woVltXXrPFUqrdHhZ+nbC1FE4afggBciiGIzVMVOJYfynGyGcjgyMqK3Xy/nMA3wN4zyOBRs3rx5Vtg4CIgkvCQvdAQ5bCShQk37/ve/r5k54bEkN14jWvXlQQ7JyWJw8SDbvXv3rJuv6MKaJOQQnnf+fLL1QZz27HlK38j06X3oof/M1Oaufni2qhymIZVRxLDr8cfVgv/4tpo8cUb19MxT0zt3qulnPlOpwYEQR/8BvdgVuyMj57S9Cy8vRgWD3FRz62KPxE3ISVyRnOd20yqOdhcZUb8gefPmodIuVatX1xLvU6eOqIMHL+n8RAnnz5+/ILRdYOCoKsVKJ06cUseP06v7CtXT0z/jp0ihkamIrperGHYMLnDtZIINFBZO2V/Mm0sMg8efpgWlYNeuatehYN9eig0oriOyFFQVRVksWlXkWZ2liK3VIWk3zSCHw8PD+meeYeVWxZwmh2lyDhmM9A8moZ4WOhhAt1prOxfidujQIf2mS99n3lKiciebqRymCSefOXNWHTiwX7/Ro1gZNYf1p5tVkyh/VbUnvAdyksITyVXremqP6v3cZ9XU6JgqL1miShMTquuuu5Q6e0ZN/8RPmJVGgCrctWtXqMnJFWrjxmmdF0X4mZA6OXCQZAmZhuXW5RWK5jiOHDmiJ8aoiuQs260Nt5rfGXJXbQkoEOJmX5JgFxkpgqgOOXsfSmrBgkG9rFq1vkK8OZ+HDx+b6bW6sNLDN+olkfGGwTrf27r1yooSb4qFpmd6c1cNuJUyuYpSKR2Vq+iq+rmQM9cCENcCHDscnNfxS1/lpFi/HqIX/xkKx3j+iKpoqp95YTqiHn300cJVxU6uVAbNOLaRkRF97zczXaBRmNPkMKnaRmgStZC3httuuy21pNyo1nZJ7FTCHiA8qOjXiMN7XEPxZoaVk4aTOX6O5cSJk2rLlsvUkiWLKwTPmKUn338zgSf5XGnWupOYXduFJ3o92ILcd6/qGh1WExu2mM/wn75+1fXUU6q8b58qX3FF7DplOHMuRcUwFbuXdNceVIxqG7Fqbh0EJCtZsEPCXD8KPS5dGlXbt29VCxYsqCFzEnqvLYgwJDyqr3McKTEek5l2OxXBEqscFo5xZGRInTsXb5XD5/bu3acVJF66bAsdUzyD8qYirXKiDLhdc/Xcw7HGW9Qg/Xqku1D27edPjM1LQ1fi/d+xIx075trJ+GiUqtip5FDmiGYoh0NDQzp1p9PU2I4lh0XmHPK2d//99+u3PHLwklYzNqMgJW07QAgr+YWcA47NdAWI30azwspJwsnsG8ouPoZUWFOhaif8GwKXfH/SVPuaTiaynWSKoRSeyPeZMXuPH1KTg4HipgXzlZqcUqXTp2PJYVR41VTsmqIWseYI5tYtXYqh9BJt8UJuXRSZsyHqDeodJGF83FQkcx2vvHKbrvKcfTlLThY/SY63aKseQ7xp2RdtlUOonzZtnAOIYW1rv/BtB61yogy46f87PV1rwN2oIo6qkXuyAo6w77s8QlzD4VGqpVScJ8H27dOaILsgTFWEKOapKnYqOZRilGYQtKEZcjgX0BHkMCsgUXGKHjcqFclUAhEecyGhjbCyAUmVPen9TNUmFclJCGWzwspJwslcRwpPOI4dO7ZXjse+ZNKKLgnSk5ZSZcKsRyprCk+snKBST5eaHFik1IlTtV+YNJ4oZYu823lzUtVqLElmk7lqKNUQ2O5uwiLL1IoVpg3d2NiQOn36gjpy5Jjau3e/JjYojkYFm5/IPgZCTn5nvYrkvIihawGLS1jUVt7CrHIw+Ea95vpOTk6ogwcPzOQqQrznJVI6owy48VOcmOBa1xpwJ+3/7HLOw1rspX0kmurr5hWxRNkGmXuxPpFavLiscu4aWqMqMteEqYoUoAlZTKoqdjo57AQbm1bGnCaHUaSNmwpPP8jhdddd52xm2WjlsB6E9Erv56Sktxlh5SThZBQaiMnSpct0oZBMksHQbtIe3EnDybO/U44NJwuJQw1ikjVKUW2lZOmGa1U33lQXTqsyIeGJSVU6ekxNr1qlpq68vKYdWLVLhKmQTWYkXAv2oa9voVq/nsWQbJNbd153MokyjJb9RYXCgxHFlvtkzZroiuQgXBWkrHBRHOu1qeP84UPGJL527TodXiff8PTpU9o3dOHC+aq/f0kqqxxgwsndVvpIuFVOXFFLnkUglVSIFBfCNZztuv9xfqNJcw6DRShFIC9VsVPJYTM9DoeHh3WEzYeV52BYOW2otVXIoVEO4sO+3FSPP/64Lj7JQnqbEVauF06mIpaE//XrN+h8sNrwU+1nk5JDUf7svDg7by443CRnbt68KU3Q5LllkzcwNVU1STb7UqoQpAphueYm1XXqgup68HtK7T+iV1Zeu0FNvfSlSi3oD3PLccrBCqqckEFx9zf9tGlDd77GMFpUjr6+HnXsGOc/WUVyfp6G2UmCq+IYpwqfO3deF0FRrCb3Fs8PlrVrmegn1NmzpkgoqVVO2HW2Dbgl/CxEMcqAO21KRRiCpDjtc9fVk9CliCVM9UyrHG7cWFYxdraFwEVV7FRy2MzuKENDQ145nAsIkjZ8yCCGUVYueW2nKMSFfXmoPPjgg5r8ipFmnusvghzGhZOZDCG5pkfvlbqdVbWC1UzitS29jJKIBUcwgm4XQNjqQlibryiQAwb5C7vMswpPQuwlKmSnp1tNv+iFavqG61XpxAmd/V/evNnsfM65c/VCjFxvCnpYTGcIDKMv6IrLw4f3q66uXjU1NTnTbzv5rOmyz/WUuyTfL4IY8pJy9OgRtWnTpkhDfELKUnTAWBgZGZ5p53hMXbq0X9+TQgTogmOPkShSWw0/6z20ilpqVUVDxrsSW+XM3vfgsadT0FxVQ5cuMkm2X6+Qj+1v3+7IrhuoKjLGmhl+LRLNPK7hnA2wWxlzOqwsVjZ2qzi8C3nA5ykbN5Ichm1HimpQKai2Tlqw0sywMoTs4YfNhBx8DlAdumfPXv1zx44dlbflanEHZHj2+qenu7WvXNQkkyWcHCwyCPY4Dy08SaKirVihyiuqSmgUXNSgNEPSdIbo1wu5dfv27VEjI3j/DWjlEF9F6f0cZ+3ius8uBQ2uBCVsbEh1PBP05Zdj2zOQaNuMAyZyFtuj0pDFo/oeFaKIqkjbwSSoFrVUVUXyE1HM0rb1i6/uLbdEODgp5IVCUjJMvq7dqYZQJdZC5ndVldJYTVGdnKEWsaGqIs9DxiHK4sMPP1xR2CCOjfBVnCt9lQc8OZw7YWXkeW6qm266SUv0eaNR5DAs7IsqwfG5FtVErT9PsH4hU3v2dKkzZ2Z/hsIHCk8oANi27YpZD4n4SuH4sHLWXsTS6SJY7CKKYZzBbtK2b60SmmUCOnDgKU20t2/fMVORXNbhFsLPcdYu7vvsRhJcFMew/ea4KTbh2KlIxrYnq9oZtMox59NY5UxPj6m+vsWh5zMOoir2UOg0qSJVRVPUEp6rGOVpaEhWqVBPxCqRC5pNV1X+MCJXm49rirTk2sl6gvm5pr9yT8jYpJK/rDZsUC0PnDRsVZHnZJivIqoiqQztqio2O+dw0BekdD4ghlQfcVMRao16uLdjWJmHwxNPPKENkK+99lq1atWq3Naf1ksxKeRhdf78tHriia7QnC4KH2jFRl5XuFF3NIEw1crlXImhfFcp0/6sXkVyEFmHhUvunCFZ6b8LMd+//0ldwGJXJPPTFK0s1MdL6gJ5dWLtQogUUrN0KdYveIRl2m2nAhYX1TAsDE5V8r59e/VPog1xFldpxxb3gRBB1C2scvBUtK1yooqE4gh1mKpYz4AbsSlsnJn7qPr7aEVOiJwheDaRk+/JT/mbbQkVd91mj4dS5vskzsqGIpR2A9eOEDQq19VXXz1LVeTZJJ6K7aYq+rByY9AxYeWkxQYCEsKp2OV7t9xyS6FvUY1WDnkQkF8I8SWMnNebjpyjIsmhqU42XS8EbI/ewaigmzZtjix8qGc8bfrXho+RrOpGmMG2XRwQRwxdq2azoNoZJB1MRfJetXr1SrVq1ZrIY+L3vGSxiLXLxYsmXLpnzx4d3ly4UMLPi+oWYeSnOOZne0P+LscCIaTrSZyK4ap2UoVOq77Vq835NEVCF/X5rBYJSVu/hTUkNepaG7JmxiWqoiFtNkk07fz4HIpnNa+x2nXGVNtX1xmmyMW3j0xa0e72nElqnRNVkLJlS1ktWqTaEnZBSlBVtHMVSaeCRApZbHVVsdlh5ZWO7iXtgo4hh0nBjcGDneXKK6/U6lrRN4LYTxRdPcZ2GLwcEzd7VtPuuPUX+ebGOo8d69cVgWIyyzlD/eRhtnXrNp3zFoYkxtOmm0Y5twnczlE0E6wpOhH1No4YNiucnKWy2RSgHFaXXbZBLVoU3UEnDJA/UjVWrlyiibvpV3xBk/39+w/o6yn9ioNFGHkUsGQlw1HnWtIaIGIbNmysm6/npnaalx251Tg13IPLly9RK1ZQ9GKKhC5ePK/Onz+hjh7dp/r6zPmEeC9c2KcLpcI8L+X/q8SXjZjPCkHs6prSObrmhcp8UKxyktgmuVvnFFuEUs/KBjFt69b2Uw0FUfNNWK6iVEC3g6rY7LDyZZddpuYC5hQ55CYg/w6iceutt2rZnbemRpC2RsjhhMkhvVu2bNE5UHmre1VD3mKKUoaGSurwYQyYzfrpuIF/IaDwxD10N9sE2zWcXO2+gmpbzeMyCf/R5z/rKXQJJ6clWXaPZPI7+/qyKdDS7o2fFGywrFu3dlYRBte3WoQxaCnVKjNcbH6C55rnBu3wovwc7Tw4Q+SMdYr5WK05uU3STLHIbO9KkwsYVzVP6LBfLV/Osram883x40/q6mWxHkqj0kIAe3pop9ilX9KCbf0oahELMMZ8WFGLKzF0/b7Zt3RjPagc7tgx29mgnZCURHHf0X+exVYVKbRifsSKSYhiK6iKzTbBHvAFKZ0VVibBm4pdBjqKGsSw+oCbahg5zFPJE3DcTz75pL6pycUjB6oIyGRYBDnk0n3/+6wfVaKsFSZRaKgej7s+SfoYR+UcuoaTbdUBEmXy7zCmjX4oNyOcbPYz+WelR/Lo6Kh+0ejvX+BEaMMUtGARhoRLcQ7gXoEgLlu2UPX3L9b3a55k2O4wA2p/Vokc6+Cz586d1edj8+aNaunSFTVETtZnE7nZJL5UeI4kxUHLl6P4LFOlEuczqNJGW+UEz419rYNt/cbHx3R1Os/SMANu1hvlJ5gELkUsWc9fUDlctqys1q1TbY0sokcaVZGfReXpx4F9yOq44Yoh73PYWZCKXRQ1QsnyUCyatKUxqM4KCO73v/99PbGuWLGi0LcajqOo/Mk9e4zZNZMLiffHjx/TRJdCmjgFLkk4ufpZiOdkruFkSRegGwYE4tgxMxFXW9AtrqkudfMlzK6mpCGkYhXEZE8ov7+/OxH5jlKAkig4pl/xYr2YfsWXdLj0xImzamTksCan5u+QxQFLqaqyiGD3GCmAiCNy4SSkWuHKvpw4cVKPxy1brtCTZhKS7GrX497JpCuxVU7QeihObYUY8tLGd9avXz9DBGsNuGWspWnrl5fam1V1tJVDxsbu3e0bTs6TRLWiqsj806xQ98jIiB77cwFtLJrXh90RJKxit0jSFkQRpIr8B9RQ3t5QQ8k1bESbvryVQ8yuH39crF+m9UTMWysPm3pIExau5lPlE062J0VaoZE/Jy3oaD9nt6CjAMPkgpEzWWqo+XOaULT0SKYKFsWW8GIaYhjs+SzEwFbkqv+uEjVRmgypwi+yT+fMUfxCuJRJifN5/PipijorxCYsdOaizgLONdeVXEteVnipTNotyXXbLupw1DiJs8ox1kOmqGXpUia++ZETI8TQtAasugXYBtxUPJvHQ/K2fsH9dy0eykLM2Xc5nssuK6tOcCvJO10qTlWkuJO5R0zeGSNFqYrNrlbuz6FrWjugY8hhUF1igqZil8rCuI4gYS302oEcUm3N8dGdgjAyN0sjiG7e5FB6J09MTKm9e/fqf2/atDERMUyr/BlloNZCIy2M8mf81KIKTyCDvIiw2NW6Bw8+qRUdYxadrGVaHubPUWQjGFplP6lIXrlylVq3bs3Mds2xBolcmPoW/N1sklTKpJISLqVCncWkHBhiQ2hz3779WqWVc8q5N60T3TwNx8bKM2H1S1o9Teot6LptV6Nu29MviVWObT10/vw5dezYQdXT0zfLKkf6Z5NrGWWLxee6u2kskNwqJ+9cxeyqowkrw2euvLL9VUNQdC59UFXkhQOiSKQOUaYoVbFZ1crlMs+eYa8ctjN406cNHm2sbrjhhlhpXbqkFI28iJtdbU2LP0JGAm4Y3ubaieQSTj5xwoSqeNj09s5LFOKPt8mIr1ZOmqMYhmpyfnU/4sLeUq27YsVSNTm5ST9chNiMje23ws+YG8+PJCvj46ZF2ux+z8b2x+79LDAhVVMUUTUMrvWRk39LRTIVuOSsQbohGmw3C1yqseNUUulHbMKa5iVQwqVHjhzVuYnLlg2qgYEldT0Ao/Z7ZIQXlT363+RbQk6TwpXEu1i3ZDnntvXQ+vUr1djY9CyrHPITqYqmiCjOL9UmtlX7Gyliqy1qkdxfM3ZN+Nm0yHNTXLN+X8LKO3dSxKE6Ao3srcz1k/uS9K0iVcVmVysPdoKsPJeUQwHJ7LjBEwZikNar2G2UB2EeJFS6uRBew5sxqK5xLORqtYtySDj53nsvqqee2qMfGuvXb1CPPvpIqN1MEFnCwkbhwwcy2/4y+TF51vMvFNjPZRQ4aip6egbUkiUYQa9Vly6RB3Zev8wcPXpIT8Jc04ULF89Y9nTVVPqGh8xKsduvVxhQ2/7t8spbsStRcRkiaa4thJrqYRbur0uXyImi/7N4AFY7tdQjeZzb0dEx9cQTe3QHns2bN6eaXF1VP5dUB+B6W3K9TT/tJXqRsUHOJaQb8n327LnK+SSVwr4H4sZLsKilVlWsGvebMW/OeVpi76I6su3lyyfVGiOYdwSaGX4tUlX01cqNQceQQx4wVFOdOHFCq4UMulZvbZcG5PuQX8hD+ulPf3po9WY7hZWZB7785ePqySePalJILlTVqDp+/dmVv9nVynYrrmBbLvm7+WkmM/qvmsmrtptDGHGT0xQV6uvunq+WLl2lFzE3hvifOLFX/x37keXLF+rWaUnDz8FjS1ORbL/NN6uHsUu+Hvfy4CCKoSE2HBfhUlRRXhohMxJ+5liD5H5yclg99theTYyk2CINXFU/F2LomucY/D7nj+KqU6dO6xdtFG7bKoe0Fk6PEEWqymk/lwRhqmJ3d9VTkaIW8/vk/Z9diTn399atIQ3Z2xiNVA5dVUWiK0IW66mKzQorT01N6XnYK4dtBsKsTK4QpzSSNYpeq+ccMrmRX0gIefv27ZE3vN0+ryjkQUDZxy9/+Sn1+OOjMxPPYKIWd0LeOESxGAkjcsGvy7/nzy+rCxekjVft3+zTFjyFJll9Wk1NmdBTGtKQtPI0qNhg5TM8TLu0E+rSpYM6Z9YUtSxKNL7rTZbBimRbVXNtNecySbtX6UoBTEmrEyxr11LUMlEhNrxAomIZomiKWkZGhtSePfvU6tVrdHvGtMTQlZy53LYu3pdhFfSMP4r4INa8NKCiBq1yuCdGRkyKxMmTxxIbmodv3/Q15qVPiryqP+sXtZhngpsn4vr1l1Sn1Rm0CjnMW1VsVlh5eHhY//TVym0GQmJpw0CN7nucdjvcOCSB42G4c+dOXXwSh0Yoh64ElAKhu+76vnr00QXqqqu2q97e2jw7qmMhYkL+ggUPQYUl6a4YJcwkyyctRjG5URwzoWgTRk5LGrJUnrINlJqFCwfU6tXk1Znws51XJ0QxLK+uXkg4WJEc/L6LAuZi4VJkizwmJJlwIB52tS5jkjGHfyHkPO01drEnanZ1c/CacW7279+n+zlDDKMKcRgzYpXT1bVOjY4mt8qZva6qSm3GYrc+p5JeEjTg5rMmx7Yrtv9zUvT1KbVy5SXV1dV4z765SA5dVcVmhZVHRkb0T68cthl4GKXprdzq5JDPEibnRrn55pv1pJX3NhodVkbZve+++9Xeves0mUe9CYq25XKXmpwkhDv7+y6FJFI4Ui9kXd0PE+KCNBhiGF94kvekb3/X2JBU8+ouXoTYnK8UDMgkzEL4OS53TapOo7p85LXPjSxgSdsiz1jhMCGZLixnzx5Tixat1ITokUcesayHFgU8FaO3nxWuxNI1nGpfM8YWjgHc3yj6SQpxZPtJrXJM4VVvonC6nHc7VzGoKpLLOzparXzmekqhlo1qeoiptDcw/7NrV1mdPGk6vXQS2oEcZlEVGU9iRt3I4xseHtYv5kV6IrcSOibnMCta0cqGXKkHHnhAD3zC5EkNP1s5rEwoD7PuUulKtXLl5sgHsTGqLjuZXUeRyqh1ByHG1nyWySttYrxrqC+OKJnw82K9sJ9UkZo8xeO6B/XixX2qv3+JJjfB8PP/3955QElWVev/dE/OgTR5mOjAEFRgyJKjojyCAoIExUAOPjICogKCgIggCoKiBPE9VBCRJJJReH9kBBxgcp5hEtPTk7v/63fu7KpTt++turHurarzrXVXT/dUd1WdOvec73x772+7K5LDPG+a7zdu/+Mo9iV8tnPmzFYtLR+rMWMmqO7de+ifYz3U0uLkfoq1kpt8J6n6xUUclde03WHDpTAMQggxDLLp+lUHl7PKmTdvturWDUNz/p9iod6qc2d5rtIWg+7Key5IohNN4P7kuchVdO5ZDpX4K5qqYvl7t0ltuWW7ogB70aL2miNS9UgOg6iKKIq4W+DrGyZXMS5aWlp0ek+9HSL80PDksFpWNkHJIZMfGx5OToSSw9zc1VAOwxJQFm02WW7m0aN3UO+/j1rl/3g/dS9qJadDWuRv61dU8fU6YSynunjjxmiLa5z1I+jwMlbkeXFhM7J+vWPrsny59CruWtik8TBcsmRpSUVy1Of1fi356H8cBNwjqKdsNhMm4BFqKlkdcz8lTxHy7eTVOYbmPXt2y43qFwUSziVtYerUD7VKaqYZmPZI8hWlzs86CZi5f0VDc8cqZ6utioVXju/nDG2VhIJbrCgPqso0b7K+cb4WrXKC5yry/lENnddaNMGuF9Q6OXSDuUEXMIBbB+kgXrmKOF9w/yb93ls2kcNGQcOTQ8gOJ9tqPA+T2Q8sTmw+THKKTlikwyJv1coSGl+2bJmaNGlXNXly5dZjXgUpcSo5zfW+knJoEkOS5KOqMllV6/bsSchjC7XZZlvo90EIGbUGzz4n/NxHV5xyuUOG8ULC0d9v3GKKsPu5KGSQwAkTxpZdAiX3kwvyzf0LqUEFY0Pq1s2pjobUhA1xJeVp2NH3Uh7h343GfP5161brnGYUmCFDhpZYI3lV4Mtrdp4//OuWwquBA/urjRuH6ygJY8omP3v2HN0Zx88qJ1j/5+AG3OPGOfmGzu/YsHItQPY3RB0iaqaqyD7DPHr33XdDV0AHzTnsZZXD2kPUU181w8p+pIqJzITGHmLnnXfWkzrp50gKQQkofouExvlc6FAzZ0433Ts5yN83CVwUs2u/HEWHeLb5Fp5I0ruEo6I8b5xNP17f5dLfZRxZyAg3s+EOGjRIq2ClCpiTA9arV3QFLC7JiSPWhCWlzEkUbIgcXXgc26Tgz0e+EcoFF+HM5ctX6fAzNjmsIaanottqqqi8yVfmXFOAUKr8u/jVXaRlfjWesex74fnIXaViHUXPMbduqkoagDy/WVHO/CxnlYOtkxnS5zDj99kHMeDu1atdDR+OA4ETfuZn9aSy1aNyCGR/c78vVEXpSuWXqygG3FFVxVWrVlnlsJGQdVhZSBQIa8OTVbVypefA1Jn3xCZKF5fW1mbdOzkI3GHlJMLJpX+79GeyWQgh5THs61ELX+L4A8YpbHBv1qUVycMLeWDYupQqYPNVt26dXQpYOKueLPwQw3bDaGlZpRVU6Qvs/oxFVXNfm/635HkdK6Rm7UHJ5YRm16hlyzA0X6rmzp2tcxgd8k34GXlKij7cRL4pk3D04sUfq1mzZmp7LAnVhfn9pCyHSv+ut1XOwoULS6xy+vfHUxGfymDP52XAvd126zfNXWfyQu4lcuD8Tm2TKlnXav19uCGVyuXEIK9cRT9VEcLI4TkIWjYVwTQKbFg5w2plJiz5hUxSSFRc7yZRDtPMn+HGLNeib968eTphGBsMrIXY/OidHDyPrhi2Tiqc7Ec8i2FkcxOJTgyzCie7n1cqktn0IYPuuVCqgLWpZcsIPztkIUxXkWqSO/fv8twolqU5cd4qHF09qOweNmyo2nzzLTblzTV5hluDdKHx+qy6dOmhttySy1HAJK8OpVJa/jltEvuotrbo93lQ30w/sMQsWACBnatGjBipC5uq+fwgiNJsWuVAYB07J1EV56qmpq6BrXLcf3fwYKWGDHGUXe5/PHKlLZpEkZzogRS11B7BqheSm4QBtp+qyKEDVRFyKOHncqriKqscNl5YOQtyKG3+xo8fr/MLkyBzctOkaRLqp05y03Gj8b4++clPaqsUMHUqSmKYv084ty3RcLLACSMWE9BNIi3jH/VjyCqc7DadZtHDwNivIrlje71mTVq42tuHFbqKENaTriIOqenXwdi43Ps1Dcrlq0ngHB+74mOLxuT+odTie/Yj0h1/xvugBdyIEaM0EXJ8LumxrSIhSEU3hHrgwAH6MhUw+mnPmTNNde9erH4OGymIs0xI15P58xfrwqQoKkjcZSrqIUiscoYM2UytXdvuY5XjFAr5eTPKvNtmm+J4QN75G6Tz8DcclwLnMtc51j25agH1Sg7j7m1xVMXWTTmHSYDD+7XXXquee+45HfrmAHTiiSeqyy+/vCQlBYePM888U/3zn//Ue+rZZ5+tLrroopK/9cgjj6grr7xS/01EmRtuuEEdfvjhsV9jXSmHkjuSZysbJjdeakyIMG3+gkAWgjTbC3nlNTJ+dHDhZLXbbrsVNp2WFhU4nOwmcFHDleWUP4in45HmJKs7z1ckhvFat0VXOeOEk4sEyumDS7uzchXJbpSGT/H/I3TXUw0fvpVeOMmpYxOmzSGkR4hkv35spM5888qBM7vQlH6OTZt6VEdnGUFIOOOBik31/5gxYzf1qq5+JxNTARs+3DGLljEVQ3MhipVC+nGUWsZj0aI5avHiFWrcuLGBQ2lJPX8Sno6A53d8Kkutcpw0iRVq7tx5ulDBscnpaBI/bly7go/zexzOOTzgIyubfjFPUUy3HaLoWOXUjqpYr+QwaQPsSqrivHnz1EsvvaQOO+wwTSKTCisz9/iM7rrrLm0dReHm6aefrvfQm266ST+GOX3wwQerAw88UP3sZz9TkydPVqeddppWN7/+9a/rx7zyyivq+OOPV9ddd5363Oc+px544AF15JFH6la72223XazX2NQexTk6pyCXKuzb4QOH2Oy7774qTeAxR7iVhYtJ8alPfSrSAl0OvPe//vWv+r2k5flEMQML6k477aS/ZzIzEXk+FEOxouBjeOWVcKoh4KZcs2aVGjlydOQNyI9Uson8+9/vqB133FErWCYxdFS0fIeTpf8z4KvYihAqJycLhWr0aDb+bhULGsKSJOashEpbWpar9esl/OwUtQQxTBaYxDEsgrxup8sHPaNbNVGWeyHOZxz0ucPMzaKhuRMupftHcUz7dLB1iTpujMfs2TN1QRJEuZyyltbnVq3xN61ynDFlnjqq4pAhfdX++9MsoU2rRORGs44FWYdNRVHIYp5VRSIAr732mtpvv/1UPYGCOu5tCH3a2LBhg3r99dc1MYMgMl+GDx+ulTvIIspjkrjxxhvVnXfeqdMcAP9GSURIEjXxkksuUX/4wx80uQRf+tKX9B78+OOPF/4OAg17Ma87DupKOcxzWJmblQvWD6NPQ9mTk2ya78f8+5ywyJkcOnSoDo+biyPzOywxLIaVo+1A5TqoOOFj57T/4YcfdAiVxgmXoWSZBQ3O+ygNpbptRJy2fMU+0cXX6V+BWmrd4ago5LdRaAFGjZqgC6wq5UxGsY/hs5Uxa24eplpb12ilhkNPafjZCZX6pUmk3UUFE2vp8uHuGR3nM45rueN1gHAbmhdtXZwxhbRIp5a+fXtEOoCIp2N7+zodcora3SEuMY6rOgYdf3ePctMqp2fPt9Xrrzvt1/g/QslBD9Em+RNyKH8nj6piPVYqV7uvcufOndWee+6pL573hBNO0ETs4YcfVuecc46+nyCJXPvss0/szimsp4SyBa+++qr6zGc+UxJmPuSQQ3TYGFGL8DePueCCC0r+Do+BQMaFJYdVCCuT00IomZt1hx12SNVsNW1yKGSa0xuyu1fP5yjhZLO3chRy6HR78P4/CQ8xNttuO0F3xXC6ihAq7aIGDHBylQgZ+BFFtwon/yb/jU3PLGjwCqV6v67oeZU8HxXJEMMePXqqkSNHBN4MkjCshriIRQ7hZwnrccqVvrqQRdP/L04HFlBJdaJogVM3i+mYMaNLxiMuOYkzZkHyUTvaujCmKGAQ8AXQM6NTS7ACDA4Ozng0qREjxkfeVOOGg+PaHcnfCP87xTH99Ke3UuPHD9WHWfGbRVmTwizSe4Ju7jL2Zo63EMW85CrWKznMqq9yc3OznkeocuT3sd4988wz6i9/+YsOB+POEaTFrR/wGv3JT35SCCkD1tJRo0aVPI7mGPJ/kEO+ys/Mx/DzuKgrchg159AkD0mCvzllyhSdt7DtttvqUEbaLvzV8DokMZdEbi9PRoY/THWyCcgDeWyEfdydGdxmv26YH3spiXNO9lJ40rVrNzVw4Bb6cnrA0lFkhZo6dVahTzFqjdtXze/1RiUccUPRS5a0lK1ITuN5/UgCm6pU+0lfXfH/YzGHIDp5ioxpOsoVChHm1nx+HEbK3UsAALM2SURBVFbMPLP4htPxc+3Cnj+dMXVsXejvvWKFU9TCWjJz5rqKBRhClFHGR43CNSD62hY38ShOPq7z+05lelRwn44du17ndXFwoLsGa70o3yjN/B9zlKR/7qkwZsd+qqIZhpbHSSpL2gSnnslhtZRDN6SiHXDvHXXUUfoy3UEI+6LslQNC0YQJE0rEo0MPPVQde+yxmmjmBXVFDqMAlSONEwmnUzmlYgItnUXSbtOUZpic9wIp5O/vvffenrk606c7yqH7EO61wbjDp05bLhkn/99zw2mj5fX3vSuSBXwmAwbg71faKk181eiMIaFSr/7WcVS/OB1FsCIJWpHsft44m2yQz8LdVxcfTzbhFSsWFzpgQBIZ13Lh59K/WV65EuseMXN2/03IXVYm33HsmIqkmDElF5Eio6F6TN0FGBJ+pgUeubXcp/xs662HRm4BWXz+7N6/uyVfFGy99Vo1efKber4RuRFywcGWi/AghwuIIhdjZ9o9EeoLSkj8VEWzwKUa4ed6JYfVDCsH9Tk015sLL7xQnXLKKaocyIMWcNgjLxSP45///OcljyOCwF5kQr7n/8o9Rv4/DhqeHMpE4waOmzMgYOGmSIMNkIpkJw9sfeF5hJDWUliZJG/eE5s7YXgvYggpnDIlumrIJuJ0MQlLtDr+3KlK9ieGbkXI3SrN8VVb4bEB99OVr46JcnSCF+UjciqS56mFC8v3SE5DwYminjGmzJPevXuojRtLQ6UklmNMLKSmnFddOf5Iv+g5c2b7EuW4oew4xBLEuRX9SDEkhwsiTI5lS4tTgIECJobuEMkhQwbFIoZOqkY8YhY3iBFXte3Wba1atOifep7hJes3x5inFBtwsX6S0wVRJPID2YZEQhRRFsMUEpZTFdMMP9crOcwqrCwRs0rVyswPsXGrBBRDiCFFUffee2+H94WoREEK66Zwk6efflq315WIHY959tln1XnnnVf4PR7Dz+OirshhFEWO30ky7xA7EUIUbN5c8ppMEpomOUwjrMxJBL8l8h8IHaKIuhE3nCzD7zaqDkt4pBWeeUL3mheVFCHHV8250dmAV650OooQqkNN6dWrf6GqNMxiFXWz4/3MnTtTrVy5Wo0fPy50NXocBSeu8bGQfTNUCoFxvOpWaBXUbD/HoUoWQ7/xcqxZFqmFCxepUaNGb1LVOj5vnFshLrGMq7oFWc5IfZACDNIjZs6coatz161br95999+qe/di+NntU5l2ODiJ6m73Z2+mlbi72LiLwlavXquamt5UgwcP0LnRQd87a6iohswzSAFEEZcGacUm/x+mFVslVTHJopZ6JodZhpV7JeRzCDHEVYRGEeQZMrcEovpRAHPNNdeor371q+riiy/WvOLHP/6xuuWWWwqPPffcc3UxzI9+9CP12c9+Vj300EPqjTfe6KBCqkYnh1mGYk0TaKxSnF6lRQhJSbsyOknlkPcEGeLafvvt9aRFQfT6+1Grk91m105YuT1SdbJXx5Mk1Ds2YAlBOeHnFrV8Oflfc9XMmRjw0v3Cu6duEiFKCgtmzpyq35e7Ajco4kyJNPofO151TqiUavc1a/CqW6F9CefMmavVGdqkOaSmR8nGzvyYO3eOJuvlPPvitHmLSyzjFnGEPUSIgspmA2Hh91tbnUIhLrNQKEhXkaiHCSFnjm0NnpbOzx0Fv/gYN7xsl7zg3cFG/9WS75wq5XfUXnttoX3koqby8HsQAi7GFgJH5TNkEd851kIOzEIWvdJPgqqKSRpwZ6mwpQnGptwamxba9bq/KnS0xg+oexShcLkLOqVugkPyU089pU2wUReZX9/5zncKHoeAcDTehldccYW67LLLdIoElcpxPQ6BJYcJkENy8fBKJBfINIH2Uijz0Ps4CPgbLH54O+266656QwGSO5lUdbJbnXDCym2hw8nuwpNym0GcUBXrUlNTH00I29tp67VWkxTCUJAaFD0pvoC0mK8jihIjFcmEZocNGxlpwU+yNV8aiqMTfu6uLyrtIMMQmlWrlqsPPlisw89SKEROHV6b3HMshH6efV6qU5Sq7KwQ5hCBerpw4YISBZXfN/PmMH3nUIMCDrHeuHG9Vhghks6hpkuHz87MsvHzzDS/yr9FwQtSsZ/GvKOP9qxZH6rPf36gGjt260RzvCHYzFEu1hoOyxBFlCAKDiEPUtTCuMYpaoljwF3PymGWYeVeCSmH5CVWyk0E5Mi++OKLZR9DIQtX0qgrchinhV7UsDKLA2XsTBri/OVCxtUih3HDypBc8gv5W7wn8zTsLqxJKpzs1Vu5HEyiVanwJK02dzyP5H9RDCGkBgVs8eJFevwkTDpgAH1bm0PPrenTZ6jBg1EmBkea33FCo3GLMaLuySijgwYNUOvXD1TDhpWGnwmXMq6DBm0Vu5VbXlQ/L2Iq426GTQWmbRLqNarhJz4xRhNngDm6+bk5nprFQqEhQ0SppU3iMjVjBocauoo4xVf9+/dQ69ZF34CTsA2KOu+c4qTpat99N1fjxw9RaYL7UcaUFCIOLFLUwgGG/8/KKsfpMlWf5DAP1cqNgLoih1ERlbQRqkFdwyk9SOiiGuQwblgZpRBiSFgc+x33AuPu3xwnnOzFAR3lsD1wODlI4Unp648WLvMPZ5mvq7Mrp25lwdJlxgxy6voG7igiPZKHDx+qNtuM3KdsQqPVLGAxIb8r4WcUQsaSf7NA82/az4lS61TqOkpt3OcO26nGQXvh/6QDTRCfTPneHPN167xee+nPmF+EkTlAjBkzXnXrhrlzMGJbqtQ6RS0Sfp4x40PV3l4kkkEsndyI62kYtQhIci4nThyqdtstubakQYFSS49cLtZHL6scKWpJwirHVBflcbIG1qtymFW18oYNG7RoYslhgwG1Lwyh4qYkVwD7DGRftwllrSqHEhohXEd+jdfiZS5Sq1d3SiycLHAWtvaK4eSghSdJhVfDhhnN/q9sditXrtGbl3QUoeJZiKJZKOBUJC/Qj0ONGDiwd8WOJ0m95qTGKq7i6H5uLIamTZuqLUXYeBkrCT9DjpxNeJH28mO8ZFwJR8vr8VLeSsmZQ95EdROSV/z/Irxvr6ZErFuCHAKY8yjK69c7oXUzByvKIQLyN3DgAH01NQ1XK1eWWjpBZCSsTxSh3H2WhPVNlN9funSZmj17lj6o77OPkwKTJRybrFKrHA58FB6IUXuSVjluokiFaz2SxKzCyi3kTimVWM5hLaCulMNqhJW56ajcRWImvzDMZEm7e0lUAsrCgmUDShU9n1mw/CA3JjlMSYaTzbCymTvohqOMlBaeOEnwlT/7pM2fg0CMsqWjCIbVhJ8cnzoKBZwuLVL5zAbCRkJFcu/e3SMTw7ih0TjKm5fyY1aSyuWQscIjPJW3ZcscNWjo0CFqiy2cIi/53Lt2Ral1SA0HCnLqyFOcPXu+Wr8en0qpfi5fKJQvT8Mg7QGddoljx44rUfWSaXHXrNURLoi4Y+nkpEqUztW+m7rflObUZmF9s3jxR9riiZzL7bfHcF3lDtz7FB5wpW2Vw94knTNkX6umAXc9hpVbW1v1V6scNhiCEipOD+QXcvOSixfWFzGsQhkF3Phh8ichuxTTQEh4T5USbmWRmTq1LdFwcvHvFxW0jmbGJjEMFkZOYuOM0yHC6+M2VQPeC3lShPOnTZuu/x8yw+cB+Wlu7lyV12ySN6nyNcOnQt7M4Tb7RUvY1E95M/PkzNdWnAsSRi7+TDb9ESO21oUT5W4d5g15nRzWttqqvVAotHz5Mq2IS04doT0JPydt3RK3IrzS/ORQIaoTCpm5ySfhSehFjB1LJ0iLM1cdpfZjnVPX1raxYD/E1alTPJ/YKOkAqJsU5IwePUYNHNhLjR8fs51LFZCmVQ5/i/Wc3x0zZkxJUUu1DLjrMay8atUqncKSVb5jFqgr5TAqgpA2PNVQDDFJHT9+fCSVslrKIRtj0AkvxtaooEHJ7tq1XdSUKU0qhGtD4A0Y5RCgBJnrlhNOjkYM4xRlpF3py5yAuLS2rtKkkEV95coWtWTJfM8uLabyBrzImxBwcx2DvDkdaNztBUv/TtCcNz9gXxKVTMt4SWh9yRJC62P0GARBsYijtFDIzKmj+0Ux5N9PV+xyz1RD9YtzX5DvxGuHjGH47bZpimqsHub1M1edSvx+Jd1vULvnz5+lunQphp/dlfpJdw1y5sh8/dxiZ7TNNu0dOjPlHUla5bCev/nmmzpfHKNkcciotgF3PSqHLS0tofJE6wF1RQ7jhJX9SBs3FIsyScV4Bw0ePDjy68tTziGLDydMPOZkIQkCNv4ZM/qpwYPDx3+CbMBF5ZC/X1wE6C8LOQxLDEHUIa/Utq1c2BQFDUJWLF7vqLwBlJipU6frcNKgQU5FMnlzGzfSpWWt9lP069JSOgYdlbdqF7DEIUhie8OhgNwxFmNytYKafZd7bjOnTsLP0qcYNa5vX4eA9+7t3ae4EiKHU+fMVZ2efkp1nf6+Wr/ZYNW0/36qffvtOzxMci4hCaw/7rmfBDEN+/vS/UZSJSA1zFWnAnqxJwGv9BqCknPWAFJgHJ9LZ44MHKjU0KGq5lHOKoeevIQ1JfxsWuVwv0AMmR+MiVerUPf+kJYBd73mHPZKyMamVlBX5DAquGE4BbvBDcPpjQXP9PqL8zxZVyuzEMycOVN98MEHuhoZchgGVCevWtU1dNFLpXBy8XHOoiZFKbxeFKW1a4VchSOGsnF65bwJHCJXfJ1mGFT2NLO/aznlrUiwHKXOeGdlWr8N0xs/j3dsR5zf7dKlm2+XFnPzNbu0xDnYxilgiZtv5uT7kU83Xb9X1PmgSnaY52bcUN+4HPPtNWr1agpaqCqfV2Lp0pGAJ4emd99TXX74Q9W0do0+CDU3z1TN/3hdbfjyiart8MM6WLNgQO821hfErcyOkzYBuD/b20sr9SHgzFUh4BBECT+71a8wqi1/m5A2Srv4XPIRTZyY/3ByklY5RHzEKgfSQnEkUS1CyZXmrKwVaRlw12NYuXWTx6FVDhsMXmFlJHryC8nvIRcvCVf2rJVDfk41MiHyXXbZReezhIGYXTteh+EW4yDKgBSWdOpESJkNk+dwrGocntC06TH+NiHO9/KcRbLjl/Pml2uVZmWzuyLZLGry+12zS0vp5lvs0rLZZn1Uz579Is3VuOQuDsGQbh5Oa8IuOkwYZgOI89y9ejnh5wEDtupAwJkz4lPJZ+T1miLZ5rS3q0733avU2jWqc9cmtb6ph/Mm1q1XnR9+SK3bYw+l+vfTRQtUthNGRvX0QlzVMA3V0STgSjkE3CnAKirgTnccCHjvkoNXObB+QYLEAF0OD1tv3a4aoYjUyyoHRRHnDMB8YXySsMqJY8CdFuR1ZZVz2Msqh40HN2kjLGKGXJO6EbLskMKCCtnl/2i5E7Y3L3j7bVG2WFC4Sb1y3zqqa1KcIPd0Zb+3Tmr9+rZNz0E4mcUrygYWPcSZloLG4kYu4erVrXqDw2su6O96bb7SpWXlyuXa0HjVqnllu7SkQbDi2t60tJBPN803ny6t53a/bzcBR51iA4bIz5gx08j/dMLPYfPkCli0SDXPnet83mqTOupUAnGjqua331YLt5mgc+ooPPGLWCSh1sYtYgkybyT/E+WT9YdQKWSRCEZz80bVo4eTx8jn7+f/Kaoy949ZpY0IOW6cajgIoWOvYh1BWZailjStcrJUFeW5swor924gA+y6CyvHtbLhBuDkxUksSsg1yWKRJMPKLMSEIVAK6ZEc5eQ1dSon01LyFoTnSpg2TBTa6UHtEMONG6MRw7iEJY3NEl8+sSHx6pEchaBJ8UWvXoPUFlsMKtulBXXRayzjtsiLQzBaW1eqDz5w1A6n60lT1Z673BxhnMTShWXAqX5eUaJ+OUpt/2jhZ5274D3ZqLDGimTMmLH6b6eFuNXZUe4x5iPrEBcRAPIpV6xYqaMZhIupJJcCLOY144qiS84laxthU3P92nZb0k5UwwF3Aw77jMeIESP0z4JY5XBRDR0UfgbcZhi6WlY58lxZhpUbCXV3Wzkt3dojkUPUQm66SZMm6QUqj63twj4HVg9UWRO+5IpCoN29k7n5g45x2A2Iv8t7mDdvjg6l9ekTPkwapzo5DlEqt1kSWuNE36NHTzVy5IgOC2hSVdHlurSwqDvWI8UuLUH7H6dBpD/+eKmaPr2Yc1nN5w5LLCGDKF9ckJXVqz9WS5diFO2QfZOAV+wosuWWqvOwQWrD7PlKddqU4Mr9tH69auvcWc3bapBWx0xV2Y24XWDiVmcnoTpCwGn5x2X6f4oBN+sA5BzVBqI4atSokvsGO9YY9YE1i6VLl6q33npLK4bkGVbTKkf+PhAlsVpWOTxX2JzzpLDKhpUbE0w6PnxyD929hJNENcPKZpV1mC4ubrBnEU42OS2LepCcw7AViNLxBBK7atUKtXQphsZOmLR/fyf0JGpCudcblX9HDhNWIBvSI3nzzb2rTeMqYH7GzWaXFrEecXdpGTjQUb/MLi1BEZWg8FpQNRcuXKA3/CiFXuSTZtH3GUD++vbl4DJgk+kwHUWc8PPatWb4uWPxhX7u5ia15tTTVecbbtBhZEcyblJM2/n7769Gf+qTZQ9Ecc2641alJ6E6es0dt//nihWORyU5x62t69Ts2dMLJLx79y5q4kTVcMDmBhGDdKcgka0krXLCqIpe4Wf5d9xK5azIYW8bVm4scKNgEcCEo0gjzXyGalUrmypo2C4uXtXJxXCyA6ctU/n3YfrpVYJ0RBHC2atXN9W9+1Zqs822KgmTEnpiDCXvy92hAYiBcxTE8YrzW6/cFclhfjcIgiqObusRVJrW1o/VkiUfqzlzFhY6X5CnSJFApVB+VILC58yGv3LlUjVmzLhQIS7zuYMWMaShupljzrhCBrmcjiJrPYsvHPPtXnpcNbHaZhu17nvf11Y2TdOmqZXdu6uln/602vLAgyr23Y5LzOJUpTvPz71aNEkv5h2XVv2Djt/L85ZvUcj8nDdvgerXb4AaOnRYYVw/+mi5mjlznhozpk3Nm9dLpyMwto1QRYryRxSIlKeolmpRrXLiqopJFLVkVYwCbM5hA4WVTUsXkr6d5Oh0E12rQQ7peMKNyGJK4UmcKmt3OFlAz9pKYxx0A5NwhPw5R5Us/n9pmNTs0DBTk0kx3eVr166kB1Q/nOz1u2ZFMm29IAjJP290xZGCim7dCCsVu7RAwLkPzHHt04fe0J0SIShOMc5MtW5dqxo9ekIkT0G/9nxhEEd1q1QEAhkU+yEpvoAoouCD/v3JY+zvjOuwoWrNiSfqYhzmuTtsGr71ZGmBmBdpE0LGGMrjzIp/N1lz3+bOY9w2TYVXoJI40NAdiKgHhRSitBNi58LUvEuXDWrbbRerZcs+0uFVIMoXB7CwnatqARyMUfomTpyoi0/SssqR/s+mVU6UcXWriklY5WTlcQgIzZO32Uiou5zDIGCSvfPOO/pG2HnnnfWCTj5Y2kibHJKEjGIIeF9xTlle4eSgYeXwxLBobF2+SMDs0DBMtbau1hsv+UmQxT59eulQX9BeukmE6bxy9nhPs2bN1nYz7orkpJ7XeZ7Iv1ryGTGuhO25imFSJ+/Lq0tLlHw1FGD8+pg3FOM0N3epOiFOuro5aPHFgAFYRjk9b1taKBRaoGbPnqHD+oT6UWtGjx616R4Qf095vibf6n/36/GyaXLQVPK5x1mC4qqOlVIoWlpW6bUYEuiXCrP99p00QRo6dJCer6wBEBoI+L///W895kJq6sGbjvuQ90UxoZ/XZRJgzYSMc4lVDodbGVfWABlX5my1rXKy6o4CbFi5AcCplCovJiD5heSwobLJhK3VsDIdAwgLcAJEDY27IHqFk0vDym2xNlAvYhhm43dyaXrqa8iQwWrDhrVq6dIVOpTu9NINbucSJ0zn/rNCgnhf48d/omyIME4YO14BSzn1yQyT0qWFIoFilS65Xv36oXx5dWnxBn8DJQiSPGbMSNXW1pwZITbHzG2GXmrLVJzE7rfI3/Hz1vQjbRC0fv0ovuitttxyqB5PVNROnTqrZcta1OTJUwoKuF9YPy6xjRtOj5sbC8pNFzH8Hjx4iO7l7AW4kSmcMf+k+pmDGOu7GEUz5zjMSJgU5ScvZs5BgZ0R6zp547yHaoFxElsnxpVDTFpWOUFVxSzDyqtWrYqVnlWLaCjlEGUNYshNhjzvnqicYJIwu64mOeSGwa6ATgSf/vSnNSGCHMaR4P3CyaXVyt67dCVVSQpP+H13K7yoG7/z8ZVWkzp5X3j/lc9TTDKcXKkiudzvhkFcf7swU5DQr9mlZc0a1ISVavFi/y4t7nAMY8LmPXz40Iq5gmYnGyB/UloSMkfMn3X8fX/S5h0i9XslTZFzaCsRK0LNKLJbbUXXky02pUu0dAjry0VYPwliFkelBnEFuHJznkKpmTNnaJ9LUki8wBhgXVMOHASp4OViDaSyF1JDpIj1HSLDXA5bfJEFOOSytu+4446RqvmTBIftalrlSB66qSqSMpUVuV9lq5VrH15KBpOM6kwmMlVeLBzm44QcZmVQHRXcLISROS1TeEIIxeybGSX3plw4WdDURIeUttAKnLvwxCSGcVQR96bt7qUrdi74qPG6OQFCFvv376M2box2PnKHhCtVJJf73bDIwrCa5+zWrZPq2nWg6tt3oCb3LJgQmoUL56g5c2iR1keHpiGLXbt20QSdkBTjMWiQEw6TaeMmbW5SKP8vtwtza9267MLJcYtA5H2hbDMPqTSVDd8JPxfD+pIusWgR6RKz9H09YECfTb2fw1eVJ/P+41WHA7/fX7qUTjCz1MiRW+sx8MOYMe0qTP0S4yoHmwkTJuiiAnfxhRDFMMUX1QD7FYf8T37yk5rQ5glBrHKY24xtElY5EmVijeUx5EZW24C7tbXVVivXG8yWcTvttJPnjea0bKuuzUzchYiNmaRhbkTC41ShAcnZiOqnWC6cLEAx2rChLRRp8So8qYYvoWnnMmxY6cY7d+4M1aNHn5KuF0FhhoSDVCS7X3PU9+u1yXsXIcj/dgyP8jeCKm0maSu1QGlWPXtig9NHbbXV0E0m0R+rRYuWq+nT5+lwOiF2iOHAgVvq3y33OVW6HZJS7bLwBJQ5ygaKwj9ixEhfEuROl3CqylfoqvK5cxeEripPQmkGcarDy5HTxYs/UvPnzytbtAUghWPGRH9+xlW6ClH4Y/YphoCzbprFF7KeZgFeDyFxIkFhW5xWG15WOai1EMUkrXI4ZLKHE16X/a2aBtwtLS3WBLueQJiPajYmEQSKkIMfqkUOk8idYEHjfaGAjh8/vgPRjPpeVq1S6oMPKj/OK6xcbgP1yi8s/l/0cHJYA2dz4x0xYrBatcqdT9dN59NVylOUzb5SRbIXaSM0CiGTalHzcSbc1aPyONQXd4jTr7J002+VjFfYsTYJpR/BkC4tTou0LTQBYkwYZ5LplyxZvImg9/ft0pKm6hVXEIqzLDjemUp3PFm0aLHOCQ7jl0aaS7duKDBbGFXlTus5VHFRwcu1notDrNPqvwyYGwsXLlKjR4/ROa7lMHEi/dZVan2KUXQlTxFSQ5hUVMUodktRQYcuiJCkCNUaINWS3pOUVQ5znXFB3BFP1GoacLfrIj2bc1jzkMkm7YU4tZBfWImMSZeUNGGGr6OQQ9N+h/fEwha0hV7lv60Uhc7BWuIRVm4PFHIzFUMvd/s41Y9x29z16NFV9eixhdpqK2xHxE8R5YvuDMXKaJPQOASLhWejmjlztj5RfuITY1X37s7BoxJp43elQCEsGOeoRDrOBh9UeWNOoKBCYCjGoQCFn0k3Eb8uLWl24kjS0zBqOHbGjLl6PRo3bmzZA2ql5y+tKneq9Zmz0noOMi7jKqbmcd9/Eqpjx1xPDlXztVtEkDGhACXNWgzGlYgSF4dtd5iU1ydEMUyYNCzIzeVzNElQLSMJqxz2O8YFsmyOSVgD7rifWWtra1UPCXlAXRakSOUuVVZI3UFOJ5x4qmFQDaI8D5OepGoWLcy6y4UborTpCxJOLrWyaauoTEAMvApPvDYeCV+WC4+avy4qWDH609Hyw+urf2i2s+rXb6C+nDxFp0Bg2rQ52vAbXzqIIh1F1q9XhYrksWOpSO4SSJ1JqkVetTf4ICScOc3pnjzY8ePHFQq7unaFSFPZ3M+3S4ub0CRpTJ6mp2EJcDp47HHV6cknVdPy5aptxHDVdMyR6v3NB+ucYOx7wvo6lnt+UwU3W88R2l+wYL6ek/37Oz6VvXqFV2uTgnveMgdYn3mdrM+ozeUQpAglaUAC6FfM5Q6Tsu6ZYdIkChilmxXjAjGs16rYsFY5rA9CDMupqNUw4F5llcPaBycvCk+YUGEqvKoRVo6a20g+FyqohMcrLahhn6NSOFnCmELaunRxlDOImXiuSbeEYuFJW8GzzSFu3uTNfI7yVaSl4dGoIm+Q/EYnTxF1q48uHGBzZzNbunSBnl+8VnJnSKAPWvQTJ68yTlu/uAhCSiGEGDlTCERPYNM025yGXl1a3ITGzKdj343XP7h6noadf3aXav7b35xDDHNo2jTV6Yc3qp5Hf1ENO/qYigpp3Od3t54jnEcLStRt53ATXK0tvKeYqqObnHPo4v5pbV2liWEQsjxuXLuqsNxVNUzqdGlxDjbksjOeEiYN4/0n4G8SCUJJxZu2UVq0+VnlcEEIJfo1duzY0GOStAH3unXr9FWvpL1hlEM2c1S1sBJwNchhlOdhMUKC5ybabrvtAoWjw4SV2YAmT5bfK/6ssDGh/q1fx+6jtz0nR5AQvPMYWsP6F55AIpsSV9HihJPD59w16bnE1dLSS02bNr0wtziEoHY5hIb2aN55inF72WblhxhEeWNRR/WAzKG0mJ93ped2ExqzS0t7+8ZNZKafb5eW3Jhlz52rOv3tuZIfdVXr1DrVVQ179lm1/thj031+zzCpMycHD24vHG6E1DBPpQjLr6d2XNXVTc75fFGW2WQhA0EOVXCCUaNUbsA4SarJmDFj9KFdCA3vDSIpRDGI9x9rJWFrci8hhhR1NCpMqxzu/w8//FATctRU1pckrXLCGnCvQj3R87ExiHvdkkNu0Ci5Afxe2jmHYckhSeyEMsjX4Ap6Kg0TVqar15IlHv+xcaNqeuMN1fTmm0q1rFJqs81U26RdlNpuuwL59MolkssrjFx8fdE37qxCsx9/vERNnz5HHz5YoAC+fytXOsoXCxjjgpJATpjjp9gcu99znNcct0q3Eimlm8X06dO0Qu+27wn7Gbu7tKxbt0otWbLSt0tLOcQl42HzHJvffU/XhMtvdFIb1HrVxdHLly9Xav4CpYYNTe35vSDv3zzcmGotF+sL656XB2hc1dUk56wVhA5Zk9zKcjWLUJIG85D1gIv3hvcf4Wfx/oMgCqFx51Uyx//zn/8U0oTC5qLWKySUTHgdkcfLKoexEhKepFWOn6q4ahM5bDTyXnfkMCrypBwyWTk5cRrFADVsy6SgyiFz3s/suum5v6mmF19UihBU9+6qafYs1TxvrmrbsEE1jxmjOnXi9BWsIrnj+1NVJztO0UwUf792tXjxfLVgwZIOFclschIaMfMUzcKLAQP6an+6KNYYcdWbOAprJVIqfn1+3SzieTEyf3qrIUN6+3Rp6VYw3/bq0pJEi7dQSwHqm/FtUwlVxJm5e9WU4kqfnVf4GaLoeIA6c3bgwL6qZ89+kULhAlkbOEBNm+YcnFDbghbioXhm7PscCrw/DklcQmggMxxuIIsQC7NKl5x4yCSKoSWGDlg32fc+9alPFXLqq2WV4zbgbjOscrg/+IxqrbtOXNQdOYzqH1iNamV5nnLEjdfApGdCYmwdJc8hGAEtU528bJlq+r83lerVU6lNzcbbSQhesEA1v/KK6jxma12UEbTwJOtwcvlOGP5wcqRmFnKk/Hoke+UpOoUXdGhZrGbMmKMXN3LpUGmCLmBxjJfjmhaX+13eEzlSfn59cXPV3Kqfu0uLqLVOblJpl5bOnZur3nu5baedVFvXrqppHYHkdWq9cnLp2pubVdu4cVp1T/P5o9o7mb3K8QBlzjoeoIvVqlVzjPAzxULdA6+t8h6cXNSpmpBuvfXWgTdXIs7bbKNqFiah4X0zDlTpigUZazxjQT5dln6KeQLhY3IvK3k7BrXK4WLuhun/7Kcq/upXv9JKMJ9j3rvqJAk7M6usHJZT9cgNIr+QfBwKT6JWwgUJKxNO9q1OXrzYCSUPdVnlQBCXLVOdPv64cNJyQnhFj6lKPYyzCAlHUZKkRzKFN1Sahuk2I4UXfft2V+vXO0bGkBnUr3nz5usFxiGK/X3zFOMYL/Pn4pgW+401nzcehpzc/bzpwnpPhiVHbrV21aoWPbbz5s1VM2euV/369dZKLYQmyv0T5bWvWL9erfjCF9S43/9eE8P2pk2eQ716qw3f+lbqz5/EAap0zg7SGyHjunLlcrVwIebbnTX55jDQpw/hZ1EAvf0629rWqmnTPtBzBMUH03SB2+7JfXAbOzbbIpSkwdoxaBCtErcsHPw56EBoCJNCYsQqB0KZp04t1SKGjENY0+9yVjkUcAaxyvFD86b8wzvvvFM98MAD6tlnn20oYggsOZSB6NxZk7PUB9zHMkf6Pm+11VZqm222iSVhVworlwsna7AyE1Ki2sRcpdeu09Ykq7r1VG1ty0s6njiVzOUXtaihxjjVulEIqfRI7t2bJOkxkT4LMwQOSSH0ysXnIhW6Tp5iU6FCF5W4aHekIiNOvphDSjv+LkSMRPHVq1vLWpDE2dfCpg0wdtL1or0dM2MqHlfqe2nOnLn6NTrKWHlT8ziqHd1x5s6do0YcfLBqO3Af1fb086pp6VLVNnJr1bbPPo76HhCVnt90DHBbPZm9p0WM8nu7fl1xnHC280tspLSC5OI+d1ImPtYqOHMYhUZURXPT5blbWpwiJeb10KH04g0+pgRKtt5a1R0Yw7ffflvvMbvuumvh4MJaA5kRA24IiBAaDkBxmiXUEjE0Q8nVssopn/rUru655x517bXXqj//+c9qr732Uo2GuiOHccLKWSmH4stI32cqPuOinHIYyOx66FDnQl4cPNiJ86xZozqvWKLWTdpdqd599N9nocP8OYiHWlwT5qgfTVhCyibIYjJw4GZq2LDBeiONAr9pyGfjpXwxBwg3sdluthlkJlrOV5wqXb9CDkK5UlDgqKidU7M+iQrmYHNzD7XVVlxbauXXKbxYoRYvXqTHnbF1m5rHOUTQgnHBgoVqzJhRuk+3HrsTvqTTDaH4Ds0v3wlHP6IdxR1ih9WVv0en2Xfaa16z9sXJ9XQU544/l0IrLkmZYGxRaWbPnqOJNwScXMXWVqVJjleRUtAilHoTzljvIYYoW+QYmmSaAwydrrh4HKo8hIb9gMeb+XSVLMxqDaKcQgxZD6tplQORlP7P7spyiOH999+vLr/8cvXYY481JDEETe2MRJ2B/ICwICGbExxVUmmCRYLKQfJN2GxJViZUR4P1ML6M5cDfZKHZdtttPc2u33svwB8hv/APf1Rq3jzVhELYubPqNHZrte7zR6kNPbprFYl8jy5duhZCpF7FAYD7Liq5i0M4whLSpUuXqdmzZ+kNcPDgzaoaAheD6JaW5Wrp0pWaeItBNOMbdGNwt9aLO15sUHgYspiOGuWfN2YSlyxyJGXMvYzUyYdtaVmplQRIjUPCnbZzXO68r3L9pgGknnsWhZJwFvdzlNaESd0jSeQqRk1jMEn4ypUr9HtgPAYN2qpECQ+CoUPb1Y47qroC6zB5hnyFBAUNbbIecFAVQsPcjdJ6Ls/EkH0qDWJYCXwWkg/O2FI8dNttt6k99thDff7zn9d79Hnnnaf+8Ic/qAMPPFA1KupOOSyeoNtzbWVDTg+LBkSW/MIkW/OwILOphw4nmxg0SLWddhoygGpa1aI6b9FfrR26tWonr7C9XTevd0xh2XRpOTdtUw5IxxBpnHByVMIQJkTJ+0ABYrGgInnAAKcLSjVD4E5/4h6qVy9adZXmKVL8IXmKfhW6aXRRgaBCDNmI8B8rpxD7hbIrdb0BRSN174lSiayZlejeJuvNuvqWa9CgYpeW+fOXqmnT5qp+/bqrnj0HGF1a/McJYsgBAnsLx8i5WwLELPsWd9F9NDurzTYbqHr06Lwpl8454IoS7phvO4ptOWIEOa3lIhQv8P5Z41lfyKcLU3zC/S0pE6y17tZzrK1mPl0tFbZwsIIYIohUmxjKHmxWlqPWTpo0ST355JPquuuu0z878sgj9WfAuCfRBacWUZfKIR9o2LeF5QDhEE4PaQJvKwghJ0GSj7GqSfrGRjZnoebmEzAcr74avEWeCTZtNo+NG6XwpGP43gyRQhbFymXzzQmR9g9tYhxXTQn6u7LZc0qnyIKK5DgqUJzX7EcyGEtUWlG+ionYToUuG0XQ5/XLVyOsad4yVAQTSt5yy0FaBSqqcB1JmJCbqFXhcYpv5PVHXcU2bFinli93ckBRF91dWkxCjNI/fTotAtdpWxYeG1cxjKu4grivIS65XblymZo2bZYaPnyEGjhwgKGEry1YENEHulwO6HbbtasEMmpyRQzJIefeZB1OMndQ8ulM5cs0ic6zHx/EkD2QMSGcmyf88Y9/VF/96lfV2WefrYn4E088ofeFgw46SB1++OFaVUS1bRTUznEjZVQr5xBiiPksp0GUhzRCAyxI7pzDstXJZVA0CC1feGIWBwwdOkSrM2wa8+YtVqtXzzFMjLFy6ZqLcLJUJEMQx493KpKrZbLtLi5wNngn58ytrGHP0r07FY39CiQcQrNw4Ww1Z46jztBHt2/f/qpTpy5l2xR6ETj3e5bw+rBhI7QqVIm48Z6jkpO4xDAusWEu+nVpYawd1Qvfv15q5swZeu6bRs5xb984dkVJWd/E+X0KcubPn6lGjhxdYmvkVD931xdFdsXw88c6B5RqZxnbYcN6q+HD68dDjqiQuE5w+E+6qMTMp2PdEpNoLuxgoppENzIxpOjka1/7mvr1r3+tjjnmmMLeh/LL/1GcQsX5Zz/7WdUoqEvlkJszaIcQAflDTIT99tsvldfEMGNqTQIuiyKh5DTNRCGgOO9LOBk/67Dcl9fcqdNGXbRcyabGC6JmmSbGdNUQBYHNxMs/LY6aElRFcSqSp+vNC78+xxC1dLM3VbZNr8jzdYYJgfq9pyjqj6gzra3L1ZIlH2t1xvGm6x84T9F8Xv7eokWL1MKFi7Q/m2n4nRY5iaP6pZmnx1isWtWqyQz5SRzqIIRbbrmVnreEk+PnSWanmCYxhosXf6QWLpyjRowYE2iuCIqG8U7axLhxi9SIEX0Ldi61XHhB1ApiyPyAGFabmIlJtHQUYR+EiMnYZhUiJTWGIhvGJKnc+qTw9NNPqy9/+cvqF7/4hTr++OOzfjm5gVUOK1jMJAFu0HfeeUffsHh+STuetGBWKweqTvaAY2jdFpkYmhuv28TYsXJZrj74wKkiFTIjYTypTvbKV/MLg5qPLSViHcka4UOIIRXJdPiQ91a5KroplY0+KsFyzHZRZwapzTYTbzpIOK3R5hvFQv18/dPkR2zYWLLw++PGjQ3UtSFurltcYhmXGJVTeRkr1G4I4bJlS7UCQ64nc5eNjtzEfv1Qyv0LsSq99jih4LQ7qQRJw1m0aIEaM2as6tkzXM9Z0zB++PAhatSo4XptZFxRlii8EDJTS4UXEMM333xT549vv/32mSh2XibRkEQEg3fffVePpyjlRByqMbZ5JobPP/+8JoZ33HGHOu6447J+ObmCJYeusLJ0+UgKKA7knvB3UQtZBLlhqxUijxJOlo4nkMOmJscMNGhxgfkzNn832Gy7dRugtthiwKZcOvIUV6hZs2boDRNLkN69KQ6oVNDiXZBRiWyYFcnSI1l+N3r/4+jEMG5Yz5yqjjeds/AXx5Zioen6/yWsL2Mr71ly6djcsKoJEvaPS86yJpZBfh/lkNZvm222ue5LzH3ApssBZ/XqFbr38+LFHbu0BCEFcVv8xVUdQRTVk3WMzR51asKEcapLl+iFdNSnTJjAV4zLe2u12iy8wEGCsRSiCLHIq+8f6zzEkPex3ab+81nDNIkmR5bXKOFnolgQSbOoJY2xzTMxfOmll9SXvvQldeutt6qTTjqpZg4h1UJdksMoHzI3htPxIzlyCOmBGJIbwoLBc1SjTZ94KSJQwgvMIkG/Dd3JRRM1bqP+nfXri8TQ/N1KZC14qKqT6tOHjguY5bar1atX6SpSclNmzlynT7ZCaCp5/lWqEnZXJJthsDgm2yCOepNW/2PmGeFPLsg+LQCZj9JJpG/f3npcUX0gy3zO5MAGLRzKUvVjzOI8dxBiSu4h+ah49bmT0Lt166Q6dx6o+vQZ6NmlBR9Fp7Lcu0tL3AMBiBvkiPL5cQ9RhYx6+olPUJBTWV0uh222aS9Zm7yMjMVyhHQcyA1rqUQh8hJ+JkUFYshaNXHixNySDELdHIq5GFtSqSCK7rGFLCbR75nUprwSw9dee00de+yx6vrrr9dFKHn9zLJEXeYcQr7Choh5PLkH+++/fyJ5GZyYcGXnxEbxiUw+crpIGt5zzz1VWuBU//bbk1XXrvsEVg2FGLPZkUtFcUSUGyYOaZDf9ap0FM8/yfdyoxwh9apIDvq7QV9zFMRRK6Pmm8nYrlq1XH300QptVwMh3HxzJ3ndsXJpynV1bJzPizHj98u9flGXzerbIO+fsXWcCJxcOpRHrwrdrD0Nozy/02d8li5+YE3r2bNrrPeAg8nuu4ebwKTjSIUupJFUCSGKWYWfuX8ghuT10dmqVkkGYyt5iowtoXEhiszfsEooxJAw9g477FASockD+LyOOOIIdfXVV6tzzz23Zj+ztFGXymEUFNuWxTuSs0F8+OGHutqRExNhqGpXRfMcc+d2VUG7EUlFsnNOgBSiGIZ/3jiKiNnZw13p6Pb8g8A4diMoXz20+uBHsrwqkkufN144Oer7jePhGCffzPFP667a2vqq9esX6xM9Y8j40vGD8RFzaCp0vbwNq9kiL2liVCmcy+bIHONAB+EI8/yOV2V3ffl1aRk4sI/q1au/Z5eWIEhCdQx7qGBtIAzppB2MVd27k58d/fmZP3RCCQvIIBfhZ/JrJURq+v5Jx4tq+P5BlCEaPCfdrWqZZMjYkhPP2CIwcC/861//0vuChJ+5Khl5k4+aV2LI+/nCF76gLrvsMksMK8CSw01wTvTxiBuKpTRWp38mYVE3qkEOV69uVrNmdQ9EDk1iyBggmsYxgI6KckNSrjdxp05ivM2GW+pLZ1Yks+i5T79xCVrcjhhRswvimiYvW0Ye4ixtzSCHF7eVi+QpioExc5m5m2WLPBD3uctVJ8+fv0AtWfKRLrJAqY5LzMQgmssJPzvzluIA8QF18hTpT9y5KuMXllzzOqV1olj4xC2kGTkSi6B4fwOC4g4/i5ULap5ZoZtEiNRLaYMYcnjl0FnLxNBrbHlfXNwX0qMYwYPCSulRzPi6C90ghkTM8kgMeV0ohueff7767//+77r6zNJAXZLDqB96nC4pnCI5wUJkKDzxC02nTQ5RBd59t0sg4uEmhhCtqMSwWm3u3L2J16xZqZYsoaBlln4v4p3G41jM2CSGDClWJJf+repXGMf9XRCH0C5f/pGaNWue7uHtbnQPeZY8RbFyoaBl3rz5av36mYU8RScHNFgbMBNxiWUSnn5etx7ziFw6jL/Ju/TLZYvb+5new5JfK11a2HQhi5I2UezS4l1wlRY59gKFNxTkMC8IJSdxOCAjZPx4lSh4fdznXBA1CZFCVOjEAYERMgOxiUsKSE+BGLKu0Aa1nkkG7411gov3Kj2KURWlR7GQcBRHcgwhhnkzi+Z1QQy/9a1vqSuuuKKuP7OkUJfkMCqiEjckeApPOMVOmDChbH5G2uQQwWfFCqe4hk3PK3QlhSdUJAsxdK5oz8nbzSI0C/9ubsagGCNdii5W61Pu3Lnz9ELFJk9+IoTfTWbiEI24lbZZECTHw3C+WrjwI90PGJW1HMTKhYsEdjYF8hSdfLy5Wo2BREJoguQpxiW1ccfcj9RIyBQvToih36EuyRZ3TtpED31RBU24VtRwx4KoY5eWuCp32MMQ98/UqR9u6qk9SnXq5PiA4uNZatQOnPWktJ916WPEVmrbbdv1PV/NEKlUP7NGM/ZCFKO0ncNpAmI4fPhwfR81GslgTaWVJhf7GEUtjC2qHGPN4Ya8W9aLvBQMoSZ/7nOfUyeffLK65pprGu4ziwpLDmMSN079eHNBClkwglZFsyklbXcgvZOLFjDEfzr5Fp4AIYZxyFKcey1qiMq9WfIeyJ1jk2VsR4wYrpUPFq85c+ZuMod2culYtOJs9HFKuOIVsEQjSFJMsHr1Sm1V4y7ICYLevcmlEz/FDYViIRLPy7WcS4pcxS2b8yJWzA9CptwnlSq14xKzcq8fAubfpaVNjyuqY+/eKOKdPWyk9DP43o8mUevcuaPvp/w/9yJfIcrkTZNzisLMvSX3qff9GmxsKFgdPFhVFcxN0ie4pO0cyhcpKaQAmdXPlcLP/C7RIXIeIcyNDvYymbNz587VeZfsn6ZfpczrJBTbKEDdhBhiWUNlch4shmoFdUkOo07CMDYz3BDcANwIO+20U+B2QOIlxU2U5EQ1za7l7/IaTe+qYhjZ+V428XiFJNUJJ1dSQcyKZNPEuVgYsEKH8SAzPXp03uSl2C+0gXFWBSxRibTkjCm1Xo0d+4lI4WC3YbOTS+c0rmc+Sd9naTknxtvFPMVs1NJyv+8oY1O1UTjWRuXuRfP3ZarIw6Vq3Pm5N0GTx8itiBOAfvSm3zMtolDoBgwoDe23tqKGL1Jr1sw22lD2dVXtN8Wq0JbXsHr1Gj0u/H3UIbk34pJ7xitKEUpabec4DEjbObHKkQpdr/AzOY0oj6iFKJIWquC+AckmlCz5yxBn8atkfN2KLXtllHUoLFiPaHdHOPnmm2+2xDAk6tLKhg2LxT8s/vnPf+oTZiUFkIlP1RPy+ac//Wm9qAQFw/3Xv/5V7bvvvonK7tOmkVdRfI7/+7//p7bbbmJhA3HnF5oLX1RrjTjtu+KY+Lp/11GApmliMnr0qLILT1MTaqJjDo3K6BjFOqoXZKYcSYhj4RJkg06aIDFPOTl369ZZjRgxKrLJbdDnLracc7q0cH/06dNLEx1y7aJYRLnH3G3EzriWfmTeBM1Uz7Dy+eCDqZpoOcpYs2/Lw7i2M3Ftf8y/0bENZbeC+Xa5Q07Qz49CDhRDSD8pMm7z+zg7xejR7drwOq8wK3QhNEBUL+4bCBCEMkh0qNGIId1g3K4cJkzFlvGFlJPDKLmKpAAkDbxyDz74YHXAAQeou+66yxLDCKhL5TDNFnooU4QWkMx322230DkrLLhiUp10ONl8DhQICR2zaW/c6OxQbmIYRxGIaqcSt3WY+ZwQkKlTp/lWJHdEqTm0GBhTkCAVpFQ+s+m6w4xxoiJs0FHHOUq+HQoQxQS8n623Hqba2poTeW4hYyYpKxK2JtWvH2SQw9JgnXe0cuVyHdqfP3+26tGj6FXpKLulIU4goU35uVeI1Pwqv1NEU1lyxsYEYUY9oqBAVDw/xD06J1ld7G5DSQENc5f349elJWhUALLJ30Fpp0o1bOehcuAMPG6cqrkKXYgM+WrMYwQASA7zJ4wYUK9gbIIQQy/FlkOIEEUOIwgYQhR5TNyIGtGhww8/XO29997qZz/7mSWGEVGX5DBOWLkcaRPfJ9QGJnlazxMGfr2TucHa2jYWWuGhqLiJYbxCkuhkJ07rMHOzhKgTMi1Xkez3u4ANFfLENXTokEIFKSdicvSKIbx+qnfvLjFD4Mnm23XsNV38vqUFwjBdbbnlFrrgAcKEsbn8rVJFramElJnP536sPJf8vz/Bd36pa9ceasAArsElnn/z5i3ShyrJAXXnKcrfT7q6mfA3bQIdErBl6N+P+/xJVhdzcDGr9v26tJCr2NzcNWA3mCHaLqr0eeL7KtIJJadd78pW6JJiRE45az3rqZBFyKFZ/dxoeWyMw9tvv627flUihl7gYIgCy8U+KOFnbHIYc5RrUW29Gh6UA2s3OYaket1zzz25bbdYC6jLsDJviZBaWDA52bRIrHX/PSoaOeVwQxByidvsG4NsFvYkw8kmONWhpJFUXiQQpYt8nHAyiDJz4oTpzHCydLEYMmRohw0tiec1Q3itrStVt249VN++WDo4BS1e/aY7fpWin1KiFZSgcQlBCjrWqHQQWyqMZVyi3uFpdeIo5ik6ZBFyIxZEfJUFPU7aglsxJGfMGZdh2newEuI8d1J/I8q9YnZpWbVqmfr44zWeXVoEHIZmzpwRuhtMUOBosssu7TVLgGiHR6qRAPIiZIbHAMiMVD9XI5cuL8TQrTDHBXOXdUHMzTlMcnAXIs6/ywkAfC7kGELmH3roobr/LNKGJYcG8MTiJLPtttsWfsb3kEYmHvmFLLJx8eKLL+pWS3FNQgknv/hixw2Em+ydd97VRIbn8Mr1iqPKxCF4cTYbp6KyXfuXcUKkalC6WLgLBIrfE1J3XrOXvYabpHnnnrEhEMJboRcvDhCVqnOTIFhhxorPnIWbkAqHAuZpnLzOuKQg6PzidYsFEZeTp9hbbbZZH9WzZ//IrSzNOcpGQw6SjEvY34+CuKpnnM9OwNxet66o2DJ3Id5ibM4BmnEZMWKkXivS8JXce+92lUJKWaoQI+dKBMg0iObew18x7Vy6LMH7FMKcNDH0AvNTxpb9l7krY+vugsOhmMITiqh+//vfJ9ICt9FRl+QQsMmEBcog+RDkUQDCjFRagU996lOJFZC88sor2lQ27g32yivcFMrTqgZljZuK90OyuqMc9Nd5S3E2fvemZ/qbmcTMTdB4PqfNXamS5v7qV8nJ865bh4I7W4fQqBoM2vkgXlU05LLJV/VirJ3NljzF0oKWuAQrKEHhNeDtuHz5Ml15y+cdl1zEIUdxlGXu25aWFWrpUpSvVQXVy63YlkNRbW1XCxYs1PeB4+0YbLPOcuySUh295jwHq5YWp7KcLjkcfBmTgQM369ClJQnldOxYWlaqmgLuE9L6LayRs5lLR3EL65OQGUhjLYefhRginJhKarUgXXCkYIiiTkSWgw46SF90PEG5ffTRR1P1V7zuuuvU//7v/2qnEj7fPfbYQ91www0doo1uPPLII+rKK6/UEUiUTX6HvMg8w5JDA+SvMQEhguJpxYTjpJRk7sLrr7+u8y3Ik0synOyQsGJFMorWunVrNxnsLtc5euRwDBjQX5MZx++uo/m1n4ImXmjmz6oVTl6zxrFkYUMbM2Z04JBBmiTH7CICWVy/ft2mghYxh47egzao8sTnjWUDBSiMi+ToxKskz1b1ElJt5imK6lXMAfVXbEVhxnuNsCkHsTDejlmEg6sRzi+aoS/S6hghdjHglkOkdGnp3btbLG9Hzm377IMJv6oZoKKy6SfR+o3ws1n9zH0apj9xnpA1MfSL8j388MOaJL711ltapf3617+ujjzySE3Y0uqtfeihh6rjjjtO7bLLLvozpkczKjMHCj+lGDHoM5/5jCaW5EM+8MADmhzCL1Cn84q6JYcsemHfGjlJLJyQNkLJtAsidJm0eWdQy5ww4WQpPPGyqhFQ4YhnGu3m2BDEvBhVxt0j0wtZhCn5CNevX6M+/HC6tu7g8whzAo+nkgZ/v4w7Finip7h+/SrVrVvvQtFFmMTqoMob5IlCAp4bxVCUnyQKIaKuCmkRIzZXDjdO+NkxOi8S8WJluaMwOx5nEB6IIWp53OcPiiQ6qcRVHf3mvNM/er4mLQ5hLirvZpeW1atXqObmboFTJ7yw007tqgqRx8SAWwFeh5/85CcDe9YGBePO2ApRZB4zb6XynAKXvHbtIJxLESZpUHFz7ZMGkYWjjz5aCwa0xXv22WfVn//8Z21LBImDiB177LGphpgXL16si3L+/ve/awLoBQy4ea2PP/544Wc4nTDXqKbOKyw5NIDSQDUaJwIKRtLqD0mommIUiE7ccLK0wmOz9Cs88do0zE4MbLilfn9UODYlaP4c/XfXrm1R778fvCI5uW4k0V+zk+u1Ti1fLqpXyyZPOoco0q2l3PsIQhAomMGs2G3hk7VqlWQRSZA8RcYXUu6ERym46KvmzsWWqE2Hks1QaSXEHbsk/ADTynVkzGbPdvpHQwz9Qm/Oa8eXzlkbnI5DxYIh1oZynWQApBByWCugIpl1n4hREkWClUC6khBFiLpYuXDlKfycZ2LIwQ/iRxrKk08+qQ+Ksq8hvkASX3rpJfX000+nWrH84Ycf6jAxBaB+KiDuJhdccIE677zzCj+76qqr1B/+8Ac9vnlFXVrZRAGEEOWQTR1ZulLv2TiIY2VDswuTGHp1PPGDyUdYgDr6/a0w/P76buqd62wGUUNMcexyVqxYqnMMg1Yku583DsmJE1Ljuem8wWvmEk86FEUInYy9V3g0CCkVrz42EqqSTaIZh5jEtS1JglgGe1yTDoFyDRkyWBNlJ49umZo5c572+CR0R5ifuVstVSZM7+Ig+a1h4dd/WdonMm/Gjh1XVkl1bKaKa0PR2PxjHYqeOXNWmS4tztynf3KtAIWZe4liQ+6nagBi7mXlAsFgPTern7MqrMgzMYRcn3DCCVqNe+qppwrEELC27rrrrvpKG21tbZrw7bnnnmXDwxQJuusL+J6f5xl1Sw7ZEIKGlVk0if/zOyx2aRJDENUEm3DylCmlhSflwsgmypGOUr+/ofpU5rSaczaD/v17qd69yVPsV5VcGd4TG9FHHy3SrZikIjkM4vCBpFVStyedEx51rFVYYEwbl6am8qdcNmmSmrfaapD2MTQ/9zjkLK4xeRSj7qTG3DGH7qs3WKd1X389Tpzqm5s7FX5WLk8x6zxLsxgr+mvwKkJp096OEGXaSpa7f70OVMwvyCCXEHGJNlAE5e7SMnask29YCyCHmXspKReKqEIBYUkuCT8zjyGtpDZJ+Fmqn6tx0EHNhBhOmDAhd8QQ8eYrX/mKVl6feeaZzD43cOaZZ+p8QxTKekTdksMwJyQSWglZkgfIv9NGVOXw7bedkFO5Vnhx1Tv+FjkwXGwGGzeuUR999LFeMObMmVtIWkdVqJRHF4WsSI/kNWsIf40LXJEc93kFcfoA+yk3JpxOFn30NWxYMTwKEZ8zZ4bq2dOxGfEi4sxVlF0vT7q4SmmcDi5xFcsg41YOa9e26nZ4/fsPKCipeBl2JOKOIu7OU4z7/HGJtaiGccmp+/NnjcEMnVxkFMNK4eAgvKNcl5aePdvUiBGd1KJFjuqVVwNi1k1eL+HknXfeuUR5yhJOhyHn3if0j0Im1c9EHFhvxfMviU4iXmCdZw+EGMYpmEwD5BKeeuqp+l5+7rnnEs8NDYOzzjpL5xC+8MIL2j6nHOAViB0m+D4vxT0Nl3PIRJI8PD8wyah64kZA4qcikkriAw88MNXXRuIzJ6AwlUqEk999N1jhiRtJmV0zpuJHJ3l0KDJe5rpRCkGcHsnTeSa19dblVQ4/xLWPifP7cUlpS8uagkUOoTxyE6U6V2wcRo3a2nMzi5OrlnWeYpzfh/zNnDlVbbaZ0/rM737gfkERZ3yZv2y8KDEccshV7NSpWyavPylPQ/e85V6ifSIEAvW9ElEL7ktZ2pGnuEZAQJertrZFep6SC8bmLapXmvYiYcA8QFGmMpkuGmlHiZICRN+sfpZOIjK+SYSf80wMeb+nn366Dr3TRCJKZ5ak5s/ZZ5+tLXN4HeQbVgIFKUQnH3vsscLPSF2jKt4WpGQ0mfzUOUgjlgXE/M3qND5ATgKHHHJIqvI9p0A2NYpegoaTX3iBxTtY4UlS4bJymx6bj1PdCFH8eJPNiNNBhE23c+emkB1JnB7JEM7Ro0fqHsjVJjrxFcdoz8uGy+s2N3fHxsWpfGac+ayxINpsM8JLpdWNWVbIxjfLjj5uzL3Zs6erQYOGhbYekfBoSws2RK2hCoaSfP9B/4ablMnvOV+JJDiWVNhZYXzt9Kztqg8T5RQmpzsPB86iv6j5nO6f+WHw4Hb1qU/J41HEWzWR4eJzgoSJ4lip00Va4HVxMEe1gRjWqkm1dBIRosi/UcJlfKOEn8nXpVASvz7U9zxBqpEpNIGQZRnqPuOMM7QVzR//+McSb0PWDYlyEfZmDLGuESubffbZR11//fW6gwvdW37wgx9YK5u8kUMUO05HqGDkmrjtHJCrMdVMMyRCngs3I9VxQfDKK+3qo4+KhSdBiWG1bFyKxtCOqti5c5vq1WuAvmHYCCqFP1paVqnp06epAQMGqq23Hqw2bIgWLsmq60vcKl0/Usr8Za6sW7deF7aQgI3y5YSmncpyMd7OW4u8qKQ4KAixU5U8cuQI1a9f9ApTpua6deZBZ6UeT2dsUW0dIuOllvG1WERS+gFU6sQjPxdiF5WUueedU8X+oSYIpB8EsaBJYg7gaegnDro7XRARkPAoB/NqVOdCqIgS8RoghqTN1As4WAtRZHxREYUoBgk/55kYsreg1GF4/be//S2y/VtSaPLZd++99151yimn6H/vu+++2onkvvvuKzHBvuKKKwom2D/84Q+tCXaeyCELP4UnnLLoguI2yuTxlL7vv//+qVaJkeuCaomRZiVMn96uJk8OF0ZOygA6CuFAuVi5cpVWvMj1ciqf+2zqINLRBkN6AVORvNVWm8dqrQfStlFJvgOL92fE4QUllfHaeutRhXErVpY7xubt7etVr179fMc3TbPnuIpluXEr7bBjdt6hTeAiff+MHTtK9erVx1NZcxMy52el6hjEzMw1FEsoDity0JH5K6qiOb5x338SqqN5iCNU/uGHUzWxJQ8qzZQTExMmtKvRo4M9lvGV8ChXGuFRN5gz7733nn5eiGGUPOZagYSfhYzL+AoZd4+vEMPx48dXzJ2rNpgrF154oTa6RjGMav1mEQ11m3PITcKNIcDcGpd3POEwt/ZaOBkKJiJmlmmeLMl3gSBWKrdvaWlXf/97m9qwITwxTCucHGazcYyh1xSIIn509M0V1YtFzOyRnFXeXJaKoxdBYMycBP9e2iPLU/1pa1Ndpk5Rqz5uVcs221wtW7NG/57YjHBV2mjdr90rdOmlmG16dAm5LDc13VW4jmJYJGa8/yBE1WwTOH78aNW1a/R7NMjnVjp/i3mKzF0pyMrW07BIDMmnJJQMESDsFnStiPsaSNmjf3KUKDHjS3oNa4CER5OuznX6zL+jPz+IYV5yH6sBGV9RFVHHWWeFKLI/5pkYXnrppdoLEMWQPduiuujcCDcIRQ7k+aEWlqsQYiGK40EYFEGeg5vjrbfaIxFDHhrdiiV61aubZPGaOaVzDR48SIc/2GiXLaPyeY7+f1ELsjLZdrzpsqnSFRJukjJUQYjh5ptvVjD9LipnjuF58+TJqvOtt6rmpYtVV9WsBnTrrtafdJJac9BBehOEPC1cOFt1705Bi2NBxL8de6dS2xMvgucV4kzaE5HXYpLiSuMoVeyE1qm8JUcwruoW5DWa85fwqBQMLVw4V3Xu7BQMhc1TTCKUa1ZIo3QyZ7baastQ/drjKp9gu+2iEUPAeImFll91bhxzaNZQrEYgSFQlh+lSVA8wxxdDeNZfxpeLPZHxIewMYWY/ykt1Oa8Lk+j/+Z//scQwQ9Q1OWTCszggnU+aNCmQJxKh5izJoXgXTp2KOWpzaGIIWEOjvoU4YehKGw2LM8oGJ1gWJOxYWLjff/891aVL90JBi7vyudLzRrEhMcmIqYy5n1aS/Hm8V6Wm5M2ZYUy/3tTyb/kdIQfye8uWrVCzZs1UgwcP0TmGbvKj3+eiRarrtdcrtX6NWqc2qSBr25S6+9eqc/8t1YBdJ6kBA7bUBUPSQWTevEWFVomoXqiL7e3R8xTj2P1EISWOV990XaRDvk7Pnp3V+vWRnz4yKXJyuTZXgwZtrtauLdq4OMbmZoehynm2cUmZY1bdpLsc0UJR5kwYxLHvAUOGtKsk3URYE1CwuMQcGqIo5tCieLGGVHIy4PH8HoUxEMOszKTzBNZfcgpRZBlbST0g5E4ai1ldnhWRZu+jWOM3v/mNzv83iz4sqou6JYecQqluYvLvvvvugSd7lsqh+Bd+/PFGNWVKp8CFJ0l1WYijZgQhpE5F8lS9CYwZM15voIMHYz+yQS1btlIrXtOnL1SdOnXWRB61ACIjY1BuKNz5ZG7i4/45f8tNEvzJUlMq+WIm2ATpeztixEhNkP3Q6dlnVecNa9UGXc0trws5sUk1P/Yn1bbrJP0TcuPw+nP8/ooFQyRES8GQFFyEUWTievqFJfNiydLU1KxDS127dopFDON6GsoYuI3NO3YYcvIUCeO5W/jFVQ3F0xIVfubMGZ6+l9Ww39lmG1U1c2jGVhQvDvyMuxAZdwoQ8x0TZ9YbQsmWGBaBJRahZA5ZUtwBAeOQzvjSQhayyPyV8a1WdTmf84033qh+/vOfa2I4ceLE1J/TogHJIZsh9glMsDCbH4uSmatYLXLIjcHPnFAIqo4T8ku6y4JXxaXz1bGzcGp0HKWso5eZd5K/JPZLVMJLNZPQFxXJQ4cWeyTzuxs2dFZ9+0JWnI1WFJkPP5yhHyNef24iE8cGJU5bPxBnrTQJPJ87OajkX44ePUaT4bLPO3/epjE1X4D+4FXz3Lmev+NU3zrhT54b6xZyQOfNm6tmzly/qWDIGeNKvYhFsapGiznCuGJvJP2j4+5RXl1E4pIqd4chDqZCZsgtFuN4VMVevbrHen7AGCxZQmrGbDVy5NZlDxNpdLMBn/hEu6qWuMRawUGRiwMC+ZVS0II1DUqYqIqs+eSWo4RBDKvR0alWwJyEGDKGZtWvGX7GE9OsLncOk50LRBF1MY3wM+vgbbfdpi+KQvEAtMgWdUsOOXFGaaKeRVjZJIazZjWrlhZIWvlcHnfoEghJc0iid5K/n7Jm5qN5KWWVlJhyIUqzItkMfXmpOGy0QmTa24fpHDMUEiEyTqs58ug4zUafvnFIRhzVxSTwxZ63q/RJPkiyfOchW6iNzZ03yXellSNtg8sb1zohbbMd2hBdJETo+aOPlqjZs+doIiPj79U3t1pmz6tXU5QzVXc0GT7cCX8l0eIuCcUuaJ4i+c1mnuL8+fNUz55ddStKaTcXVpFhDObPX6L/1qhRo/WBKSziliDSzXLECJUZGFsKtbgggRJ+hviwjkIIKbKohkVOLRFDnDrI7axkB4PSytrAxZ7E+s344g3MfE7a3Jz9784771Q33HCDLgiF1Ftkj7qtVmZSs3CExRtvvKGTutP0U0JZoDQfs20gxHDNmmb14ovNkcJ2WYWTy1U7MrWoRMR0FuXHnfMZplLSXfm8fv1q1aOHY5GDIhMmdBSHZCRl+izdYPjcSRavpNjJc7fPX6i6nHe+Uhs3uHb5JrXh/PNV2x67R/6cTSIjHXCkVSIbclhj86jWOXSHgRhi+E0hiJlWEHW1iuOpmFxlOjYj4gcqfpVOO78geYoo+osWLVQLFizWc6aSypxGOBnsvnu7inDuThVEeyA/rKWIAihfee3SkiUxhFBHBWswh3Wpfk7C3Jy/ec8996grr7xSPfHEE2rPPfeM/PoskkXdkkPeFptdWHD6JHyBvJ4WIK3PPvus9lMsbnxN6vXXm9XSpdX1+IsTmi1HSFHFCHuRMD9mzOgO3mJxyCyvedUqiAzdLUpbzTmVud1T80NMogOLhEshtJU6WHg9d9P/e0t1uf12pT5eIX9YbfjScartC59P7HVL31zIOF+7dCH05OQpsiEEMVeOQkooViKUhR0LG07Y34/7/Gn9vnvemX6VkHHWhHLhfcffca5atGi53uSjePXFnfuAfuB5i/gxdqzbRGToeMXXvHZpqVVi6AUJP8tF1M00N68UfuYz+vWvf60uuugi3VoO82iL/MCSQxfIVyHBOU1fJTHb3m233QpeXjNnNql3362+115UJazcRiOqGO9z9OhRHVS9OJtUpVZz5JoSDvWzGMnKDxHwMlpbV2tiiGJEtWBQktXhuTdsVE3/eU+ptetU+4QJSvXqmaLqRZGUU3ABmUHt5PWjKLLJVtoEgj730qXLtF0Nm5iZEpKEl6Qm1TG4QFzD8PKG3+2F8D5zmJw6M0+R+Txv3my1fDkHrTGRFbC4PZxJ39t333b9NU/E8M0339RjRJ6a31zMQ5eWaoODFmODykzkJk1I+FnGuJJqy5x/8MEH1Xnnnadb0R1wwAGpvj6L8LDk0IV3331XLzBpldBLfiEt/FikUCn79NlKTZkySHXuHN5uISuza7+NhkVh2rTpmhBuvfVIz8U6ziZV6TUzthBE6UnMog+JcchiL9XWFm0TiKu68DktXdqiVbEtt9xCpy6EsevJspOJ+Xk5iszqQgcRPm+MzYWMexUABDmAkH5A1xO6wbjz6OKG8uObTSc3fkEJjxDxlpaVOmWA1zB0qJOaEUXxiksMwfbbt6uMu5d1IHyQH1RUiGFQgpdFl5Z6JoZ+4Wchisxlxph8wiOPPFLttdde6tFHH9V9imkrd9hhh6X6el544QVdBc144AjBc/M6/EDK13777dfh5/xuOZ/kekPdFqREDRmkaWVjFp4Q/pCemM8+26rmzXtPL3JCZIKoA3FsOZI0u/bqkWxWJCeZ31jpd/n8pLKR0B2bq1h+MP6OGtM/tIVLmCpbr7EmHIgqNmzYcG0vUy3fyjiG6F6kgs8UVYtryJDBmypzP9aqwZw5czuE9yt93nwmLLoclMaMGav/brKWK/GIXVyTdBCW2IqqhfLi+DuuVt269dKVz1x+1ftJvgY3+vdXuSKGrJ1s9oSKt9tuu1D3Mo9lfLkmTJhQ6NLC2CIOJN2lJQtiSCiZ1KhqE0PAePG5cNH9isMOrwe3imOPPVb/P+r4xRdfrLuRpQ2I6o477qhOO+00ddRRRwX+vSlTpugIiVnk2kioW+VQTpZh3x4tqMhVSbqU3iSGjiWHs+DMmIFa2bQpNEqO13JNaLp2dUKj5UyhoyoicZPzvZQsyAGLqztXLMjvBkUcBcm0cOEkW8zxcjqIlOtJHEd1cXLFKCJY5KmKVYPcVKOIBDCmzhxeoedwt25iUeRdmSt5qSi9XuHSuIphEjl2cccvKrllrUCBV4rUjDHa+5PxorJdVFvGu3fvPnqNcPwUu6SWDrHnnhysVC7AgQRiyH277bbbJhoSNru0oC7G7dJSbXAvMTaQsjz2IqYd3qmnnqoOOuggTb7YM8i9P+KII9TnPve5VAtBAWtQUOVw2bJl+jNvVFhy6AJhPybFpz71qUQGWDqeQArdxLC1Fcm7tI2YbAyyyVIMgBom3UPkJJtVONn9u5UqktN63jBwkzvJ8YIoMsaVehJHJbQ8D23sPvro41hFBHFC2XFIQZwxZw63tn6sli51PCvdHUTkXuMAR+jLK5SXFTFLavyiklsx/matGDdua9Xe3rlsniLX6tWtqnfvHmrAAOdA2bNnd11dzhgyh3gtfOV7LK+A8+/KF45GZc57VQWKE+SHnFSIYZqqntmlBcIYtktLVsSQdTjNgsqoIM/+hBNOUHfffbc6/vjj9RzGp5JilMcff1y9/PLL6qmnnvIM6WZBDkeOHKkVapTpq6++uuEqqS05dIGTDESHlktxYZJCPdiuVnivvdZUsTrZ7G7B5RjC9lV9+9I9hLBS2A4qcQoySklWpYrkcr+bJ/Vr7dp1hWIAQhAS3ndCd90iEQw+t9mzp6vWVsjPGNWtm08eEy9sxkzVtGaNauek36N7oopPFlXZ7tdudhDhYtN1+ph31vPGq3tRtfP8gqiOQrCCECou3oPTEzv4tX79OvXOO2+rHj26q4kTt1FdunQqIXhC6IToSTtHSVERxQsV1lS8ai006gUiOpAfCQdX8z2ZXVoYY9aJcl1aqo28E0MI1xe/+EV1xx13qJNOOsnzs2PeIn6k2bovCDlE0Xz++ec1B+C+gszef//96vXXX1ef/vSnVaPAkkMX6FaBMTGVxEmEkSWs7Q5HSDg5DGSTbWlZrpYscapGUWKC5tAlGU5G3aCnK1+9KpLL/W5YxAkvhiUZZuVza+sK1alT9015oP07VD6X+xvkXjLWI0eO8Q9ZT5umOv/0DtU8b64zON17qA1HH63aPvdZ1blLU8zK0vgt2uKGdL0+b5RCUjec52jW6pep2kKihQTJ66h00d3H/TOTUHn/TjjSVo1ooqhikLk44VJT8eICpuKF5UitATLG2FDIhcF11mTX7NJCpMns0hK1aKheieGLL76ojjnmGHXLLbeor371q5l+dkHIoRf22Wcf7aIASWwU1N4qEQJO14/2qndI8csvFBBO/s9/wt8gqIQDBvTWYblBg6RqdHmH7iF89SIkcVqfmWQDpY3kYgjhuHGjKlqZZBVOjlJMgL8cG+jAgZvpfK8VK5yCFnpCO23onDH28/qT/tF9+/ZQQ4Y4Ld88sXKl6vKDH6imlpYi+1izWnX+7W/Uhn59VNu++6ioCFJsVI4gMeZOW0Tz507HnmBkzZucrVnTqiZPfkvtvTfkZ4Lq3LlZrV27Wi1ZQthuhvr44+W68pnEb2mFljUJqDb54X3HVcW8+hKT+gEppy+xWIxwpanSJAUKRhgbOnZgMZaHOVGuS4sUvAgZT6PdnHtseB15JIavvfaaVgyvv/76zIlhHEyaNEm99NJLqpFQ1+Qwi2plUzH0Iobg7bc75hkGgVmdbFaNUgSCAkMxCx0UUD7d9iJx+qmaZKNYkTxA95GtdLPHqYqO067NaRWnIsMh0u7KZ0Kjy9WsWTP19w5RdIoB+KyLnT0GqMGDnbHxI2Fd/vac6toyVzV3blbNKF+Kg0Sbat6wVjU/ebdad8JnPFUwrzyxdFSwZOvUyKGdPBkz3qElG7wTwh+hxowZUeJFR5WuFANAchjnvBcDxK0u5X5KmvyYfYlR3KTDBRES2qExd4Uo5rEyV1QxPEHJ283b6wOsr1iccCEIsA5L3+c0u7SwHtHRC2JI3m7ewGujOvi73/2utq3J42cXFG+99ZbeZxsJlhwmSA4lv9BPMQQzZ5JbEe3D8rNTcfq5dlc9egzS7cZYkFC7yOFw7EV6qoEDKQQY4J/7VgYyHCx6EM9KFckCyFkS1aZBHucmSJA7ehiHCUV2vEof09SEWoiaNVS1tFBs8ZFasuQ9tX69U9Ci1Ep15JFbq7FjIYbO7/uhy5/+rjp3fs1hzyba1is17121OmddKOKAefivf/1LKxvlKijNnq7cg/weihe/C4TEpK3GVBPcU6hNjEs1lB8IIBfPBxmX0CiRgLxV5gppziv58QJjBhnkgoxLlxbsmiDjSXVpEcWQ6t48jg1k6gtf+IK6/PLL1TnnnJMpMWSsJJUFcPDk9fEZMbcuvfRSNXfuXN2tBdx66636Xpw4caIuViTn8LnnntPFMo2EuiaHUSYkmw6GnXEKT/yIIeHk996LGiINnjvHIr/VVlvqiw2gtXW5rpidM2eBPrlKDh2EMkjOHi2q2aQXLVqgRo/eWldEppWQH0UF8/+Y03JpalJbbNFPjRrVT7W3j9YbKwtO377d1ezZ09TKlUsLoVG/Ip12/MdE3jTfANXtGXiTpQWKu9555x0dKoX0hbkPZRM1Q6OixkAQGeNaNi0mFAnxHTduXOoWHl5g3FAruUwyPnnyZD3mWeYpStu3SgeKPMOJ7pSScVHGZ86cGblLixBDUVPzBlIXPv/5z6sLL7xQffvb385cMUTBNCugL7jgAv315JNPVvfdd58m7ogeAj6nCy+8UBNGhBVs7Z555plUq6jziLouSIHkhVUBSTT++9//rg455JBAk7pS4YmJINXJzt8oJUmIJO4E/dKcrnbfsGPRvgIFcINatmyJVrxWrFiqyeGWW26ur759e6tOnToqbbROmzLlPf17GHebpqCNDj5z8gupcGdsCLVz0nSI9CKtCqEUSP5XSdhu4ULVY6edWOmLH+amQ8m6W25RG089VdU6GJcPPvhAbb/99oGU5jDdF9hgGWPCjoSchYxnXTUahjSziVJ4krdwlZBxURVRv8w8xSRDo16gwANlJ41+wHlB1C4tzH3IDoQ+j2H29957T3c8+eY3v6muueaa3L0+i+Cw5NAFTg1IyJh0Bmkczk0OOfRTCwVw1BUrijzAbUlhEsJqgMUI5YINlsWJU6yQGLG+INGaXtN8hfykvSnUEvjc6aYgnpiQQDdEKWCMGWvGT0gMhKbTa6+prl/7mmqau6lauVs3teHss9X6K66I1wg4Y3BfoKZyGmds0jSShYybFi4QcMlTjBO2SxPk+7GJQpproeuCkHFphca4ClFMumiIzxBiSEgWZawRwP0iXVpYL+TA4+7SkndiiKoPMfzKV76irrvuuszTEizioa7JIaQtbIiY38GsE9f2cuGqShXJtXiKFcULoBSI8oWsXov2F2mBOUU4ENIM+QlS8Sn2IkLGmTOaKPbvr7Z4/33VvGqV2oi3Zg2QhXLgviC3iveIJ5gXaU4LUjUqmywHHiExqLp52KwgzOQ/0c4LpajWYIZG+ZpknqKE2elrDwFqVHh1aWFs+R7CnJeKbRMcBg899FBtWXPzzTfn4l6ziAdLDj02NxJP9957b98QVZDCk1oF72nOnDk6HCjvS5QYTrH1UggQZ+GmgIAFOyppZoxRHCU0CnFkbBnjWvWhk/dFvhoqCMQwSkeYNMJ2jLF0t8hqjFlXyEsl1yxtNbVakDxFURXNDiJ8DTPG/D5zZ5tttsldmD3rMUZpRpUDbpucPHRpYU5DDD/72c+q22+/3RLDOoElhx4g+XTXXXcttPlyt8KTPEZ3x5N6KiDgdEqSPBWDoihCjKQQgMUpDwtTNQHpgRiiqrKJJXE6Zj7JGLNBkvPK35cxrpViC1FT+Qr5ydPrdo+x5NDJGKft9cfzc9gi8R3S7F5X6gF+YxwkT1GKYGhThsm1RUf/SwgzoWTGOE9dWijaID//gAMOUHfddZclhnWEuiaHnGQJNYXF3/72t0KBQZTCk1oE74tet6gbXgUEUgggRBGixPhInmItmOkmkSQvthppHQrMMTaLLbiyVOIqhRqpLIUQ1kIKgjuHTrz+pGgoSXDfkF9IyBRimPTfzyvEwoVL0lNkjM08xQULFujDaK3kX1Z7DMkxxD+Rinb3mpN1lxY+OxTD3XffXf3yl79s+KhSvcGSQw+88MILuoqQGy1s4UktgvcmG1jQimQWJiExbLC1QGKiQjYwcqGqmSQvxRaMMYu/3wabJaTlG58/vmC1dmjy6klsFg3FGWPuK+YNak/WYfa85CmyxkguKCkqhCTJv0yqmr1RiKEbZpcWxjrtLi2sSYcffrjeL/AHzPuB0CI8LDn0wCuvvKIlfEIc9VR44gWpSGYBJxwYpSJZNlgWDDZYX/uWGgSbF3Y1SdqxRP2chMRIIYCMcbV7uQpQNlEM2cDy0O82qQp+GWfudyHjYXzoAGsG9xUEH2JY78p6mHHhoEOUgq+QFjMXtNFSVcoRw6h9pM0uLVxJd2nhHiG/kNf24IMP2s+sTlHX5JC3BukJi9dff13n20k7pHolhqg+5NChaEB+kjj9uUmMKDFcKJK1MobMHZLAUQ05HUPA8gKz8pkxZkyjkpi4YXbMfblq5XMNu8FKDh3z2iy2KEdiIJmMDX+DA5clPN7+lyiGrDmyXpg5dOUM5OudGKLEcy8nceBiHTND/ER54nRp4b4/4ogj9P74yCOP5Cq32CJZWHLoAU5t3DjS0aEeC09YJNjA5HSaBqGAxIjPH19RCUwvxbyGIHndGBSTV8nmnmdjZTeJETNdqS5PI9zDczE+jeJFxwaLSirquJAYCT+bSgwkEjWVcRfyY1Fq5YMS71WxDYmR9cLMU4zbaq6WDuvsPUkRw6Ah/qBdWtgz6HzC4//whz9YNbzOYcmhx0aAmsamS+EBi1JeSUwSFcnV6kBgeimyMDHOonZBZvIyxmzukGZeH4phLZ2MpWJUSAybrXRdYJyTeC9UJ06ZMkVXljZqAYEUAri74EB28HjkMIES3+i2T25IwRth9kpKPPeh6aeYR8/KNIgh74/c5moQ4TBdWjgc/dd//Zee24899lhDqrqNBksODUh+ISckTrgsStwgWed2JQXeHzl0GJZmmUPH6xC1i4uNwCSKWaktEmavl83dbDMHaYxTNCTV7FwoYqgMFkUlhvQDlBjmDBEHFHnpNGThmCSzpkIMw7bg9CIxQUP8tYAsiGGlLi1//vOf1V//+ld18MEH68KTq666Sq/L/LwaFfcUhd544406xI4F1KOPPqqOPPLIsr/z/PPP677JCB+Eva+44gp1yimnpP5a6xV1TQ4BybhB4FV44u5qYYZFOb3W0sLP+0LV4MbPU49kCdkJUWShzMJLEfIEMWRTz2qBrmblMwu8zOVKlc9m/mW9+vQl4UUHUeESEgNE7UqjYrSW+o+jOCcxd/xC/LWapyjEkHkzYcKE3Kw7EHlyCiGD//jHP3QI+Wtf+5o6+uij1V577ZX6Af4vf/mLevnll9VOO+2kjjrqqIrkEEWaaAY9nXmdzz77rDrvvPP068eH0SI86p4ccrKv9BaDdDxxt5iTIgDIRN7DHElUJFcLnF5Z+Al9V8tLEcLM+JBGMHLkyNws0GlBQnaiEkgLNLOvttuOBTWdzT3P+ZdZgEMFOYbkXpr9bkUdFxLDIdXMBa2ldIUkzL/Z5NNopejl9SdEMe8FcGIDxbzIEzE0D5THH3+8XiMuvPBC9dxzz+mQMusHPZTJP6Q4Je01gXGpRA4vvvhiTQTJhRYcd9xx+h588sknU3199YqGJodRO56YRQDS/kyIIuG2PCkEaVQkVwuy8EMUxaxYiGJSCxKKBooqvpaN2LZLWqCJOi6HHumVS+cKOVRYO5ZSsAZwb40aNUpXbPvBNJBnjE1zc8a5Hgk375ncVN4zxLAaoUgzT1FC/DKXq1XFH4Z4oRjmlRhyz5944oma2D/99NOFNBL2vn/+85/qT3/6k3r88cd16BlXj6zJ4Wc+8xl9eL311lsLP7v33nu1esjeYREeDUsOxdiaK05FMn+HycciCIkRywuIYta9iKtRkVwtuL0Uw4RF/T43yYOyOXQd/dGYy4w5YX1MeJlDtXSwSBuQD9oFMjbkN0UJ8YvxtqhdzOV6qMo1u8LsvPPOmYR6zf7lYkVkKrdZ5ikKMZQ2nHn7vBkrcvVYHwnPSjOIrBCEHLK/nXrqqerSSy8t/OyJJ57QfowU5tVaukEeUPerPRPLTQ7N/EL+Pw5p4vdRWLjYKCR/7sMPP9QStxi8VntBcvdIztsCFBaoVoTuuMywKAUS/B/kJaiXopl/yeZlc+gccB+wYbGQMjaE9FG4INBs9tXsR5xncG9xb0dVm0nr4J7kMtUuCEOtV+Wytr777ruamGVFDAHjBhnkIodY8hQpyGNdZL2Wca6mcpt3Ykixz+mnn65zjGkjmzUxtMgOdU8O3Uiz4wl/C3LCRf4RoSQ2EggMC5JZaJFWzpFZkVyvdiNsoGzKXGbRELlflbwUWfwIlbJIT5o0Kdf5l1mAPE/GkTkq4S4OPZy+GeN58+ZpYp1GiL8WIO+fHtJJVPubc9msyoV88r3ZPSTvyq3ZLhBimJd7y70um8otOZHVylPkeckxzCsxZC0944wztCJO5W8t7R2EttlrTfA9n6dVDaOh7sPKnMwldJxlKzw2VyYrGyyn2DQKLUQRY9EjRywvFcnVDiVJLqjppciCDDEkR0wMimvdAiNpEFImDQFFi+Icv/vDK8RfT2FRP6CgEhGg2j9tKx/xrJQ8RdOzMo/KLfeeGMeTY5i31xemJ7GZp5hUWpAQQw6sKM55u0f4/M4++2z14osvasUwbKpEHgpSCCNz8BeccMIJen2yBSnR0BDkEEJo5hhm3QqPQgshMOQFxvGfq8WK5GpAckGFkDMufObkJ9aauXU1IBXbYXPovMyKRR2vNbsnPzCXsMpAkQ9i4FwNz0oOfkLIq1HsUQ6sqcwd1jWIYa3eW355il6m0GHAYYpQcp6JIdXITz31lCaG5YqrqgUOGRzEAPvZzTffrPbbbz9N2GncQG4hxYS//vWvS6xszjzzTHXaaafpyupzzjnHWtnEQEOQQ1M9zFsrPBYOIYosTCgvLPjk0AUN10lFMoSQcFfew0/VhvQBZhNFPWS8zPy5Wt3MkgIVieQUsnHFqTx0Vz4DU7nNUxV/FDuWvHg8inIrBS0cKEXtqrZRP5+5HEoZn3pR48UUWgg5/2ZsZT4HXZuFGPK7EydOzNXeA9gXIVq0wyOUTNg9D+C1QAbdOPnkk9V9992nC2ZI1+Jx5u+cf/75OueV3PQrr7zSmmDHQF2TQ97aTTfdpA444ACtiOR9c2KBlcWIUIdU5EIU+bfXwiIVyTyOxOtaS2BPG9IH2FTExFbEHeJ398ltBKCGYVJMmB2VJM0uOHmpFo1SdQvxyVqh8wKHHQmLcklYVAh5musBxJC1h6+oO7XwmUaFu8IccliJkEMMCSWj8uaVGH7nO99RDz30kCZWVPxaWDQEOYQ44dWETxMnoi984Qu6PyTJwHknUW6jYkiLVORKXpdUJPPekNrztvhkjdmzZ2vVp1xhjrtPbqMUWnDbE7YhNMPGnmao1GzNJV0tIC5CYvKYn2YWVxAqrYVDg+m/KmHRtNrMQUqlBznzp5GiFX6E3MxTzDsx5HP73ve+p375y1/qUDJRAwuLhiGHAhZMnN3/93//V5t2IjkLUSQMm3eiyMkcgggZlH7PbKhsXBAfSKOFN/Ehv5BcnzjKbVQvxTyTCBQxFJAsFDGKK2ScTXNzNtc8qHMSKmWDZ3xqMe3Ar81cEgo5pJM0FkgQ91feIzLV8gblYs4wzow96w77S97WDebGD3/4Q/XTn/5U5+bxGi0sGpIcmuCmparpf/7nf3T/Rk7UEEUqobBfyDtRlMITFiQWHU7stdrvOQ2Y7d5QNKKSDVO5hShCEGScq53XlTTxoaIPxTQPhUtm/hzjjFor45xF5bMoYsyjegqVShEc48zawWFHiGKYgw/3BVZHjAupCI1MDP1SKbCCkbkkeYp5OfjwGn/84x/rdCsiaqjiFhZeaDhy6FYwKHOHKNKXEQWDfpGQxd122y13Cx8LMxs7GyondtRDii1QFFn0+ShlY81bu6hqjQ8LMwRIxicJmF6KEkYyCXmtjDPjA/EBjE/eiA+bqZlKIYbQfp6VSQPlWKyO6lkR432aFeblemu7fw9iyOMhhrUy76sFxodQMiSQiI45zqj0HMSEkGdxwGR/uOOOO9QPfvADve/tuuuuVX1+i9pCQ5NDd8IxJymIIn0jWQBpKk7oec8998w8p6ZSRTIfo+nxJ/2exUC3Xjc68/Mzxyet9+v2UuT7WhhnGR/psZ3X1ykQQ2gh5OJZyZXGOHPggvigXDI+jUJ8/CrM3ePcqOMTlRh6me+bfoogzfnsBvfP3XffrQtQiJyxp1lYlIMlhz43Okm6v//979Uf//hH/TN6NEIUafBd7RyksBXJZr9n8fiTfs+10GkhSqoAxIf3SFePam1cfuMsFbl5GWfyzdjYpTNDrW3s7nGGqCTZlpKDl2lQXGvjk3RYVPIUGWfWC9TxOXPmFIorGnV8ohJDr4OPzGfJUxRrLeZz0gVafK74AV500UU6937fffdN9O9b1CcsOawATnwvvPCCJop4QaHAQBTJUcSHKe2cLbFiiVqRLInpsrGyEbLgQxRrxVKkHFA9CCWPHDlSjRo1KrNcQK+K3Gq0S6wENiGI89ChQ3Wf7VrNlfQaZzZW/h2n0ILPiY1dDl61Pj5JjjNjQ7tAOsPwPeRZxrmeK/nTJIZ+4yx5t26Dc8Y5zpzk7z/wwAPa/w+hA1s3C4sgsOQwZAjm5Zdf1qFn2vmw8R522GGaKB544IGJLphp9Ug2CQz/5sQKUaxFM2iMiTE8RQ0bMmSIyhPcHS1kY+WqVhEIYSyIMwcLyHM9wt1tSEzkg3QO4XNBUcW9gDGyxLDj2EovYA5ekg9KWkWjtEwsB8nBlFSNJBRVt8E5a4WEn8vlg/rhkUce0V1D+MpeZWERFJYcRgShgX/84x9aUYQoUhRyyCGH6GKWQw89VFcAJtEjmcT4tDzoKMiRjZWNMo1+z2kA4ow7Pi2TkjZvTgOozTLOhO3CEJioWLBgga7aJkw6ePBg1QgwrYjMziGMM2qMubFK1xxITx7aheUNrA0QQ8bPrah6tUwUAlNLBVpxwBgwPkkSQ79COCGLYfMUiXSdfvrp6sEHH9SFlhYWYWDJYQKAzBG6gyjipYj5MkoiRPHwww/vsDFVCmOLxxrEMGqv5agEBpKbVL/ntIghxJnXitUIY1tLcHsppmHdIubfFOaQOtCIkAIAqXxmI5WQKPcrVf9h+0g3CiTUTitFxqjcnHQXDvG95IPWY35ztYhhuXxQLtZrMZLnch/mH3/8cXXqqafqXMOjjz469ddnUX+w5DCFm5gcQWR8FMX3339f7b///poofu5znyvrRUgYBzWDGz3LHskS2oAoorCggkp3liy9usSjD1UDYpgn0pqUdYu0S4xidcHcIw0BchjG/LveYRIY5jTjztgQas9zhXkWINUE4kOOathQO/OPCIQQRe5TxtePwNQyMRRXhKxUUjNthTX6uuuuU7vvvrsmgvycHsRUJx933HFVe02Yat944406akFE5yc/+YmaNGmS52Ppjwx5NcH8gPRa5AOWHFZB5ZLQM6SRamdyFCGKLJiy+FL0AqE844wzctUjmcVQiGLQfs9pKW4QZ54vjx59SVuK8D7DeClKH2BIJl094qQ11CsormCMID3MJ7MiV1TFeptXYUDhGsQHNXX06NGx72133m3eOuHUKjH0+txog4dFzeuvv64PPwcffLC65JJL1F577VUVkeHhhx9WX/nKV9TPfvYz7Z9466236v1sypQpnvnykMNzzz1X/7+A+Wa7feUHlhxWCWzeU6dO1cUshJ5JZN5jjz20oshGdc0116izzz5bXXXVVblN7mbRkcVe+j0LgQkTOg8LFAjC9hAeinPqXekxe+QG8azk/zl4sBlDDLPuepJHUHFLS0UOFoTjzEpRs0ALIi5j3UjjSCoJaxL5l+RhJg13oYXkg2ZlCB0W0hmGor28GoC/+OKLWjlENSQKRWiZtQF3DfYZcuLTOjRCCHfZZRd1++23F9YwDhnsaZBUL3J43nnn6XXOIp+w5DADsCmxWaEocjNRXIHNyNe+9jV9E3NT5X2xlH7PonTFDYlWsmKhqGL8+PG5H5e0PP5EvXV7/DEe0u4N4lNrFefVGD8Kl6j8hziXK+5iQ5XDj1k4JC3m6hW8V+4x1MJqVLWbhtDScUgIeR47O9UCMXz11Ve1D+/111+vvvWtb+l1gTUaJZGmDtjYYL1Gh5SkgbhB7jT7GVExASSVuSVewW5yyH5H+gJrF/cmnVvw0bTIByw5zAgskJyqqCi75557tKqIovjSSy/pfDpIIleW3n1Bwc1ttpeTkChEMU7bM/4WOYb1bMUS10uRsUXhqocczDTGi8IcLI/YfCB7QSGtz6RwqFoqebVBvhrEMKviHOk4JEQRIsahB7KYBx9WXg/jI72k80gM33jjDV2NTPTpnHPO8Z2bHCzTyPskXQOS98orr+i8RwGm23//+981QfUis1I0x+GXXs+kVuGwgLWURfaw5DADkH/zxS9+Uc2dO1f3dMbcWjYz1CEII0Tx+eef1ycpIYq1oJy528tJ2zOIYhhVgI4MFPPw/m0eineonU0BNYMxZU6hiolnZaMTRcnBhNjttNNOsTxIRSUXAkNYX5SuWrZuER9M1pU8bMhi2C/qLYefOAbnSRzgUQzJ2YMY5jGdhagBYePLLrtMffvb385kf4hCDr1IOH61xx9/vLr22mtTfsUWQWDJYQZATof8/O53v/O1YmGhJDdHiOIzzzyjT/eQRMIH3Eh5J4pivyBEkcW2Uh9iyc2Uils2B4tSsIGyaWE1IgcGqvzM6sW8VJhndUAhB5NxghgmSSr8emuL0pVHAuEFyC6WWbSbzJuBvNvgnHktYX5ZP9IuhqsFYsgcx9j6ggsu0OQwq/0gSljZC8cee6web3wZLbKHJYcZgBuGzTtoFZnknZE7AlF86qmndAgIosjNmKfKuSA2F6ijXn2I2WjpeMLmS5i0nvO84rYLpHCAy2tDcIdEURGT9lLMK1D5xCeUUHKaOZjmnOaCoEvlM3M6r/mfkC3GCFWeA0YtwD2nCY8KUYzSOaTWiSGqOMTwm9/8pg4nZ31PU5CCbQ32NYC1nIjYWWed5VmQ4nXfMh/xBb755pur8IotKsGSwxoEigjhaCqf//KXv+gFkpwTFEWUklogiuTOQRKl3zMhZ76yyEEMG6lSNGyfbayOCOOESf5nrE0vxTQ21azBe5XiHOZQtfPVmNOi3nKPmr2I8xLmZx4wh6j6r9V0jbidQyrNIXIM+Rt5JYak20AMUeYo4sjDeo+VDa/nrrvu0iQRKxsiY1i5Mc+wuWHNwo8RfPe731W77babLsRELMEfkSgZVkF0dbLIHpYc1jjIy3nyySc1UYQwkncGUURR5DSXx8XNSw2j8IRFn40doigEJq/qS7UhOZhx+mx7FQ7luUo0rLLEpo7aQzpC1vNeOg4xzhLmN1smZkHKpRc5kQY+93qAmbrCWJu+lWHVWyGG3Ad5mENewOSe9qyEYH/0ox/l6p7FeUNMsBm/2267Te9BYN9999WRDqqUwfnnn6+jYDyW1CFEje9973v6UGeRD1hyWEdAeXv66af1TUcIGvXtiCOO0Ioinop5bGWF2sKCzALBidHsQ0zITtSXRvOd87JiSTIH08tL0Qzz53Fj9AOEgDAgeU/VamcWxUjerHwWUl4tjz+KBlBx6rmloulbyXiLeitjXU69rQViyBoAMaSBAuHbvM1zi/qCJYd1CpSU5557TiuKyPVsQCwqEMW99947F4ocigphQPInvVp1mUQRIiMdFghT5CVMl/ZmRwcBQoFhrVji5s6ZRDFrO5FKByJCUZAADhd53zAlJCoEphoef6I6mwbgjQB3kZZ0d2K8zdxbPhMOF3kmhjhbYGJ94IEH6i4keZ/nFrUPSw4bAJyKsRSgmgyiiNICUaSghb7PWfQ8ldwn8ueC2GhIuzMuwtD1Xo2LsofnF6QNYlgtMuzVNUTC/Hnrj8vrhBjy2phHtZY/aXr8udVbQqNJKP1U/UtnmEau/Ee9FTsiyb2VHEWaEEiucx6JIaFXiOGee+6pPXHz+Bot6g+WHDYY2IBefvnlQr9nQi8kN0MUOZXG8YMLEx7BroYQYJTcJ7Pfs7TiEqIIaaw1kuBF5qkmhRCnXXEb1E6Ei4p5wqAS5s9SvYU0o/ZwsPBSnWsNpnrL3JYiLSHlUeYA9xk5apAelFWLIiln3WD9IA8TSEQiKVKeFJgPrM+sA7/61a9y9dos6huWHDb4IolBqRBFNiUatlPMkkYfTjZAwlssyGxY5VqZhSFSKAFSjYuyJUSxFjtZmIUVVEvmaTNAcRaimGWRhaQj0D2IJPd6hJk7JwbnYUg5eaooYiT6+3mpNvohmTnEVypmpfpZSLlUP2eplLOeYXCNKo73X57TOyzqD5YcWhSIIkoMRJGCFvKUUBIhipxc4xItFmHCyIQpIYZpKJSSzyVEEWJVS7YtbEx8BuRDUZWc57wiryKLarSXE/PmvHT1qAa8DM4lT9GtlHMAQy0knAwxTCtPtR6IoVgemQcwSLmMNaRc8pwZ72qmr/A5k/qDV+AjjzySixxxi8aCJYcWvh0mhCiSs0RuIqFnTrLkLoXZ/CESLMZsXOQ+VWOh8+v3nNeWZ5BmiCGbEF0r8k5kvdrLMdYmKee9hJ0rQfJUKTwZPHiwauTcORlrlC2TlJOuQWUyxNCayIcjhl5KuXgpspZwoBVSnuYBiPQN7Mh4LiI6ecrztWgcWHJoURYQOiwwhChSJLHPPvtoRZGTLQn05RZJ1DDCpGIzkkUytV+/Z0n8z5ooUonNGKESjB49uqaIoV8+l4w1EPISpxpXrFii5qnWI9xm0HwPCEPSEi/reV3LxNDPTF5IeVpV5uSAs7ZC7LEjawRXBot8wpJDi8CAVKEiYo8DUYTQUEGHoshJl1ZcJrGh8IUQ184775wbNcyr3zMEV5LRq01e2dQxAKdvNpY+9QS/sQ5bjTtr1qxCxW0jWbGEGWfaqTHGjC8HIRRGd3vKRieGtJ1kDlLcEWc8zCpzLhlr6a8dNTeQkPbRRx+tiSYNDerRhSEM+LxIIfnggw/UiSeeqNfIPOwhjQJLDi0ib0hUQwpR/Mc//qHd8CGJkMV//vOf6utf/7r69re/rS6++OJc3tR+/n4QxWpsqKKG0VO0VluZJdGHmE3Va0M1DcDZ0JMoYKpXYohaSygZpYmfoUDJWLe2tuqxzkORRa0TQzfMsYYoQvBIpZB5HdS4nwgLXU8oSKMlaqPnitLM4bTTTlMjR47U+bOM8eOPP66bOTDmedxP6g2WHFrEBjcrJq2QRK4XX3xR/4z8xOuvv15XlOb9ZpZ+zyzy5LZJ1SKkzY+8xAGVpBAfKpIbUQ2TsRYvRdlQuSAvfB4oBlS2p2kAXstgjEjzIEcNYuhHRNxFFnmxI6p1YugFiLgoiqjmzFsJP/tV9HNQOv744/Xn+Ne//rXhD0FEpHDLuPLKKzVBZNyOO+44HYVChLCoDiw5tEh0s7r22mt1z89TTjlF93HFfBtljDwaFMVaCQ2IlQhE0TSCjtvvWUgPqiGblbUZ6eilyJgwTmyau+yyS1W8N2u1aIy5CTEMqgaKHRHkBbVRuobUi0eoe4wghoR9yTGsthUMKqAUD5GvyGcEUWRNkXamPIaQKYegZ555pqpG5T/96U8LvZA5pNKSb9KkSb6Pp2oawsbBlnX8hhtuUIcffnjia8FFF12kSf2tt96qx4gw+2OPPaYjUOSM2srt6sCSQ4tEwMn8W9/6lg6JcFE4wAbPovjHP/5Rh5+fffZZbUECSYQsbrPNNjWxGQl5YVGP0++ZzQrCTL4SxLDRc4r8xpoNHXLO/MnKSzHPYB6Rp4pKBTGMulmalc/cp/wdUblqwfopCDEUI/msPQKleAhixdrH3KaNKWsKh6Dnn39eh/6rhYcfflh95Stf0a34SAeCiEH+aNfJ5+/GK6+8oj7zmc+o6667ThciPvDAA5oc4rCA7VaSQGCASEMSBaybPD/PN3ToUNslpgqw5NAiNljoSKRGEYMYevnP8RhUISrwIIpPPfWUzicRogiZrIXqSvGcY1EP0++ZzYHkan6fzarR8r6CQMYIdYsxgpyYti3V8lKspTFKSkXh70qVOfMbmNW4tdSyLW/E0IuUs/5BfkjHQR079NBD9VoI8aoGSYQQosrffvvthTGjIO7ss89Wl1xySYfHf+lLX9IHNvL+BLvttpsuEoNgJoFyuYQUpEEOCSvL/kLRDkqndS9IB5YcWiSWQMxiE7RNFwocNzdE8cknn9TkimKW//qv/9ILei0QRa9+z2aIztwMyKNh4WMxzdtmlRfl2bQZcY+RKC9CXiArpsF5LcyXpPLnmE9pkh4+Aw4+kqeYVDVuNcBrN8lzHl8rnyNRljfeeEP97W9/K0RX6HvPPbDXXnupb3zjGzrPLq11i1QN7Mk4mAtOPvlk/bnzWtzAZuuCCy5Q5513XuFnV111lX7NzMmkYBJE89/kGx5wwAF6HeV+p3iHfFu+twftdNDY/gYWieGggw4K9XiUH5KwuTiRojhCFI844gh980MUWbg4GeZVtUC14RTLZXYModAEFVGIC6EaQqJZ+TzWSstANnKIodcYmWTQ9FIkvJo338q0CAVjxHsllJxmYQXjh1rIRRoIlaLMbUKibMhmz+c8bcxCDFHnGaO8EkPUuddee02HkjFz5yI0e/nll+vOVERXOCylBVR4XofbIYHvcU/wAnmJXo/n50nCVA7Nf3P/87kyFyGp2KRBSvM0/+oNlhxaZA6I0zHHHKMvcs4IuVD1zOkQkgVhhChKEncewUaE8TCX9Hum6ASiyMLGRkrxQKOGQ/2AwvPmm28WyHMQYsdjULC4TC9FNjZRucS3Mq/zJQyYT6I8hzVvjguekznLNWbMGJ3nyFhTQMF4S1oFV5aFQ7VADHmNF154oS7SQzH0Sr/hZ2eccUYmry/PYM4TjaF4h8+ZHEQbTk4Xtb9y5gjf//73dahUKqrYtCqBzQ15/he/+IV+PKbSd955p64Ga0RABsm94UJRooIPonjSSSfpjUqIIvknedwAZCHjfRA6J68S6xCUF5KpRQGDvNR60n9ccBCAGDIOtMSLovgxflR4conKBXmhjRzVvGJHBGGsxSpHSUlg3pCSkLXyDAHEmopL2ssx3uSEQfBFwcXCpVpzWwp08k4MyeUjhQZiyPhlBe4F5hF50yb4nkYGXuDnYR6fZN6hOBdwT/OVz7pR22dWEzbnMEFA8tjoCA3cc889gcghFV9UgP3qV79So0aN0lYBTH5ORmEqYesdbJIvvPCCrqgjJwbiSPI2JHK//fbLVXiBHCJCHmPHjtW5On6t5VgE2UwhL3ns91yNXtIQCdq9pUEkxI6IC9IYxZw46zkPeWZu77DDDpkTw3IQtVyKhyBoafTX9iOGHDTyTAy/853vqIceekiHkjnEZA0KUkjZwb5GXiNr1VlnneVbkIJqjKWMgEgO8zJKQQrPx3rHXKFQkYMbRJPPj7nkpY7/8Ic/VF/84hczJdaNBEsOU8B9992nE3crkUNORIQhCTXQSQRwo0AW+BtpJSTXOsiXeemll3RCNQnRbPz4bUEUDzzwwEyNfQm3QexRwsqdbiXpXyxyGiFvToCiCjEkhEaoshoKE+RBqszFSzEP4VA/cPiBGEpP8lqaD1L5LKpiWpXPSVn6pAnu6+9973vql7/8pVYMWRfyAKxsKEC56667NEnEyuZ3v/udThVg/8HmBssYhAuxstlnn310UwOaG0B0f/CDH0SyshFiyN88//zzNUHkuZgbiCTuTkjyeIvqwpLDDMkhFVhsjoSNCBkJuAn5/sc//nEaL6+uwMJBcrcQRTYk3PUJPfO1ml6C0gOY0zShm6AQmx+Ii1SHymYqIaB6AR6PpF2gkmelALirzPNmBC15mLwWNt5a3hjNnFDuTcZe2ibGqXwWE3DU4TwTQ9SuO+64Qz333HOa5OcJ2NiICTb7zW233aYVRbDvvvvq+5O9TEDU5oorriiYYPPeoppgU9iE8sg+efrpp2u3i69+9avq5ptvLqmItsgOlhxmSA45OZFjSOGCqTIhnbNBcbqzCLdhsKlCFB999FHtIYaSCFE87LDDUutGwiZAPgzpBBQMxOkBLL1ahShKv2cJ0dVygQUKAcnkhNW8kvGzgGkEzVdCuEIU+RyrTRT5vJnDPHfUPMxaaFHJBbFDLZKDUNDUkFohhhzub7rpJp03ja2ORanRNcV6KKocGFg3GSP2Tg7DHJDylCrUiKjdnaZKIP+CvMByoPH9hAkTqvaaLLzBRorXIhfhEIgI9jgs0PiK4ZNF6JmwSFLFIGwCfP7kGfK8cZVKszqUnEU2QIii2IiI6sKVx/wqP/Ae2NArhdurDcZQ7ERML0UpAqmml6IU6JCjxzhlrWAmDd4PhSpcUvmMmohyhd2ThPohi373Ua0QQ9RCVDl6JVti6KQacD+RUsLnzH1G3i8kkM+RvOO7775bP4YxY71DSazlw3CtwyqHFcDixUQuh9GjR5csUjasnC8IgUNRpPKZnEDCJtKRAGUuykbMgicbFRtA2kUOfgUWYVSXLIAyTi4TYbVasZ+AhBACl/E2c0LT6BgCUYIYMhc5aNYbMQwa6pf1llxLmdtS+cxnwgGJub/zzjvnlhhCcihAwbuV0KmFAzpo4fFI3iL5lxQYkopDIQqRHj5P1lTy7/m8cf/IMn+80WHJYQ4KUrgZKEoBnKxYEG1BSjpgzMkLFKIoHQkgihhvk4wdZGMmHElFsl9Hj2r1e+YiX5EwJK89bL/nauVhktMEqapFSE6ojDdERkL9fI2rbkD6IYZ8foTcG40YBql8Fp9QlCYU+rwSQwoqiDZR1UvueKNDKo+5Z1hfyWOkupkDI+suqjFzf5ttttHjR94jBTxPPPGE/pwtsoMlhwlvhCS443BPSOHFF1/UPyc8KO3UUAUIedImDhCy5iRlWtmIyWeeNvl6BIsR4QtCz5xc6dtJv1AWMcgiFXReGzXEDFKJWrfjjjtmXjDChinEBbULpUWIYlaVuIwtOUXcE3HzMPOYN0eYHJULxc/sGBKWtPC32Bw5JLJONDoxdIPDF0ri+++/r+87MZSXqv6s7z1zXjzwwAO6ewdWW/vvv3/WLyk34F7B2o28bMZn4sSJ+ufscdiQ4QXLmsVnitr64IMP6hxxi2xhyWGCOOWUUzTJcwMJnTCmHvCmJnXvvffqx5om2D//+c+10shpinyVPHhhNRL4HCgoQU3koliIUDHFLBBFFjA+O8JaFAxRqccClreCAU7oYtkilbhCFM1+z2mPJSEkbH3IJ6rW82YBd6if3EQJh1Y63PF4iOHw4cN1aoolht5ziXuOiAr3IwU7Mt4ciqTnM1eWObhU8p555pk6InHooYdm9jryCAgfed4An0caGIg9DesU+x1KInmHe++9d6Fi2iJbWHJoYeGxIRHuQE2EKNLuinw5Nie8wCCMP/3pT3OjWlSqxGUBRn2Rfs9pdrAwC3Qghnn0EEwLJnHhoCdqCJe7wEK8HjEehhha+BNDQvrkGJp5taLgipci/87K5BwLLexY8P6jg1OjQ4pPCClDAlHTaYl69NFH67WTDmAcGOVxFvmEJYcWFmXAJgTRIfyPUSyLHWERafFHrkwtKD4s1LwPiCKEkQU7acsWqSRlo65GgU6eIQouxIVxN70U2RSphs7S67EW7jvCjpBsNzEsZ3IehJgniccff1ydeuqp6v7771dHHXVUas9TiwelY445Rrc9ZZ1kLSCPkJ99+ctf1jY/cnCUlnlerfMssoMlhxYWFUBOIh0DaDVFrig5pfwM41Y2d3IU+XmtGBabli1sqKZlS9RWZ/xNcmUJ9UEM81gwkIcCC8YbEs04k2OYhZdiLRFD1OewhwyTmJNagWIueYrYqCQ13liunHjiidqrj/ZyFqXYfffd9TpDTj0pOHwOKIiQaMYLgljPKSe1DksOLSzKgPZSVJKTJO0OGREaRDmAKD755JPaK0+IIkUYtUAUk+j3DPmhQIdNnarkWvJfrCYYZxRDik8YdwgM420S81qYM3kmhn6KuVQ+y0FIej5HHW/yyCE4hEghiI1O8P1CxKyFrA1EXrANQy0k71CaExCKt16G+YQlhxYWPiAES9/R3/72t7pQqBwIpZJ4TY7in//8Z13BCplkccSSoRZya9iYTW8/Fnwhin7efqg0EB4IYR4qt/MKCArWRyTdUwXv5aXI93msxK0WJF8VEk0oOem0hKTGGxcKwqOkmZx22mkNTwzNzw8/U7dPJwV8jNktt9yiCSJqIQSRvO7jjjsu0c/YIjlYcmhhUQaQn7AhUvKfCDlBFPE747SMoshJmVBLLZyUTW8/SDLFLVSGQhSl37P0ACani4KdRle9/IBCSMi9XHcYLy9F6YZT620T80AM/cZbws/kyJnj7ad+v/rqq/rAhxL2zW9+s+GJIeuCjBVdqGj5Sm4hh2pzPYAUogjjzMH4pdXK1CI5WHJoYZEi2HSeffZZTRTxP4NUoShCFLFtqIUQrPR7FqLIeyIkR1gdRbFWci2zAGM2efJkPUYQ66g9iCEuonLVWz5ntYmh1/OblkRS+UxImoMQrf7AG2+8oQ953/3ud3Wnj6xCyYwTz8/Bk/uOKuBK+XtYqeG6YOIb3/iGNqROAqjiHBDxiWVdwNeQf4sai8Ub7fB4jfjJWqu2/MOSQ4uaW4hq+ZTNuOCFhv0F30MUqeZjzPLcAs8EGyhVycwHNlBTcak34hIHhM2wYmHTZHyiAqNtIS5svGG8FGslFCnWR3lolyaVz+QTYllF4RBFVuQX06RA2rtlBYo78BAlH5o1hGppUlcw4fYD6wuEDGIrIKIRVcG7+uqr9b1PNxPGBqVQ1ndIIeNHoc6ee+6pVW9Sc3g8np7WILw2YMmhRe4XonoEC+VLL71UIIqoFYcffrhWFA844IBcbJJeEH++YcOGaUVF2vihKNZSv+dq9ZPeYYcdtPqUFFBtJRQq3XCqYdnSKMTQi+DjUnD77bfr7/GkJCwqucTVVsxRWElP+Oc//6lVVkAxHGsHJv4UO/mtyRSLkScZF6isEGeIoZiQv/zyy/rzFKWQqAhrArmZNBC49NJLdc7hySefHPv5LaoDSw4tcr0QNQIo/HjttdcKbfzYLA855BBNFA8++ODcbPqQESoP/fz5xASaTUH6PQtxyePGnxbmzp2rpkyZogt0UFXTAnmJYnIuli1pm5wnBYgEYwTRZW3J6/xgDeSATD7df//3f2srFu5RVEQOvKj+F198sSZA1QBqHO4J3IvmQRMFmS4t0pbVa01GxWbcBw0apCMWqKBRTeo5zKKIz5w5U5177rma+AEOi/JZEi0iJxnlm8KT73znO5GeyyIbWHJokfuFqJFABSW5TUIUIRoHHXSQJoq05cpKfYWEUFSBIoxqGKXfM6SFvLt6ngezZ8/WrQM5HJGPmYWXIl/JZRWiSBg6T0SxVogh/Zwhhqhd1113XckYSooI9yjkkE431cAPfvADnb/H+Jngc77mmms0ifUC7VkhsBzouY95zYSCyYUOAzGqxmqIvEKAjyHG1iIGoCzKgRayyEGRfcCitlDfJXAWsUMq7lwp8kfY9Pg/P5xwwgkdFiIWs7ALUSOCMBWLNhcbEuNH6JlezlRH4g+GWkGv0moZKKNMkWNIZ5igizwhZfKLuExT4qlTpxa6hUAU+XeeiEsczJo1S78/crAgZNUE9yWfDRdKtHhXUigQxbsyLUAuIF15J4bTpk3TFbbHH3+8JmTuOQr55l7kSgKXXHKJroCupGJGxde//vXCv1H8qJonfYX5KgU3QXwMZRyY3wgHkETuYVRB7nP6JPM9qUhEQ1irghZiWeQLlhw2IPK+EFk4YBNHgeK69tprtRUERJH8pzPPPFPtt99+evFlEyN8mQbJkhApuXMQjCigSAVvPy7p9wxxmTFjhlah2TxqIRRaDryX6dOna2IIac8SbOJ8Vlwo0WzgjDdqvnhXZuGlKMSQ15LXHENAqJTDF2r9TTfdVBUyDdE65ZRTyj6GfEfIP+PnVo05DIRR53bddVf99cMPP6y4JpsG17/+9a/18/M96zwkER9DyPLll1+uX8f555+vo0VnnHGGb3TJIv+wYeUGBKd28toqLUS/+c1vIoWV3SDMQHUz+Yrk0lnE32QJXUIUUWNRh0gAhyiyKEO2kiBZKGFsHmmFSNl0zLZybDBCFGuprRwqE2MFMcxz0RXzhoIiyQsl9E9BAePN1zRtlYQY8rwohnlNLeAwRJ4vF0UXebNokjxwUk8g2IA8SFJOyuWBu0EBCcb+rB0c/Pxg9jumhSj556wFjAtEkOfm0EfomHaiF110kVaxObhSxGhRu7Dk0CI3C5FFeLB4o1hJjiKLN1YSEEUuPqOwJIu/CeEhf442gNVQwsxQqCgTZs5c3jZpGSfUcAgFxBDls1Yg3n6QNfFSZNMXS6IkK83lMEMqSp6JIa+Pwytr1d13353bDjXkQfK5YR0jDhKMqzhIMB+J1KDykZ7CHOX/KCRELSZVBXWP3GG35ZgfyBmnhegzzzyji9HIb+SCGPIzOv+gUlOUxtqBr6dFbcOSQ4vcLUQW0TdhSDtqIheknM8KkkiIjKT5SkRRFB42Sg4E5fws04K0OWPeoSjymoQoimqRNYTwkFuV1TglCbeXYlKV5rVCDHnfrHWQfAo+8tyRhkPUWWedVeI9e9tttxXmICkOOArQ/5niQA559H8mb5hDAHnARH2uuOKKQEo3awqPpeIYYeAXv/iFrtz+0Y9+pO677z79/7QOpW2eRf3AkkOLXC1EFsmATZkNGTURVfGFF17Qqi0kEbJInpGbKJIyQPcCVDwITx42ct4HOXOicGWZM2e+JvIwJXcuL1ZDaXkpcq8T7mfcw5Bgxom0BCHQeR0nUhvIMYTccLCtha5F1Qbkj88QX0rCy+Ri4mF48803a1NwQASjWpY+FunDkkMLizoHmzQbIGbbEEWIPGEgIYpsilQakliO9cSf/vSnXHbekJw5IYq8ZjNnrhpqj7R6I2c3z9W2SYFogRBF3nNQL0UhhpiBM055JYaQXwq6IDW/+93vGr7DD6p9OWWe7li0AyXnnNQDQs2Y4mNxdfrpp1f1s7NIF5YcWlg0ENi02RAhgBBFksjJISKsyEbP90F8DLOG9B8WogipNdv4paH+8JxUjDN+ea62TQsoyxBEs4DIy0uxVoghRRRSwEUaRiN39HETQ8LFWNGg1G+zzTbqggsu0D8nAoRbAhXdRIa+9KUvaTufq666KuNXb5E0LDm0sGhgkAZAZaYQLApYUBNJBaBKOQ/5fUEAUZScOf4txRVcSfR7ZuPECgblEmKYR2W1mmA8zAIiIMScNIC8E0NaPaKco37mVSnPChA9Ko3xeGR8aJOHbQ1554SOjz32WJ1LziGStCGbS16fyG/WrYWFRapABSKHlJAaba7Y8J944gmtolBQBMH6/Oc/rzdR+sjmtXoTkAvHhQWTFFdIf2NULcmZi0ICGBfJoYXwNLrCBDg0EMrnIi1BvBQnT56s1SZ+DklnvPM2b/gcITi8NnJyLTEs4tVXX9XFheRe7r///gXiTKEOIL8cxwpy0FHOSUWxqE9Y5dDCogFBkQBt+djYf/vb33YgPBAsNgFCz/SRRQESorj77rvnbsP3g7vfM0VR4qUYJCwMMUQl4e+wQSahQtYrcCpAiWZOocwx7oxb2uH+MEAdp5CCXEqKLGrJfigN4D1LisBJJ52kv//jH/+oO8K8/vrr+v8gfzRMoC0fB4AXX3xRh+It6h9WObSwaEBA/FADsaXwKuSgUhkiyMUGTxI6RJHWiDyeDYL/wxMu6w2/HFA9sPDhwvSZjRCiiL2KVOFCXLzCnyhgeHNCJAgl5/l95oEYYmmCssq40q1j7NixWqWDJGISTr5mWl6KQcA8JlQKQfzrX//a8MSQwjQiBLhPCFB8Udo5MNLhhM5M0q/5rbfeUg8//LAm/+PGjavqZ2dRfVjl0MKiQWF2PwgKiNLzzz+vu7NQ/QyBgiiSp4iVUa0oa1KFC1EkvC79nrkgNyiGbIa8P4zALTGsrBgKMfQDpExyFFFxk/JSDAIOBlhs8XlTdEWP6UYG1lb409KzHR9aAQQfI3Aq8umXfPXVVxfGjxQUlHfxuLWob1hyaGFhEbl69aWXXtK2FhBFQtEoERBFKhhrJZeL9yF2LVj+oGhBnFFIITyWGFZuHViJGLohKi5jTmELvytEEaKeZOtEDgJ48+HJ+txzz+kwdyODvFCKzfAoPPfccws/v+OOO3QuJvnHHPgYM9rgoSTyf4wfB6ZaOQBaxENtlCJaWMTAT3/6U23XAlmh4TxGz+UA2SF0wuO33357XaRh0RGQJ9RCxheCQJI6mzv9VRnvU045RZNGQot5fx+DBw9WO+64o9pzzz01MUE5ROnCzgPDa/KtIIwWHYlhlA4xEHCqXcnj3GeffXTYnzxFct1eeeUVHfZHXYw75hD/r33ta1rdRDFsdGKIN+itt96qx4HQvwCSiIrIPKcLCmsgY3bhhRfqHETWQtqoWmLYOLDKoUVdgxwZTsDYMEAMWRhZ+NjwITJusDF95jOfUdddd502xyWEQkI2Rq+2X2gwQKzo8Sz9nqkaxi4HRZEWZXktAkBh4nNGKYQoQhJNXz++Z86Qp5jXfs/VApYmeN1BDJP8PAnjo96Kigtxl444hILDKIr8rW9+85v6MyW/ziRDjQzyaG+55RZNwCF/KIk///nP9bq4xx57FNJNsG3iYAcxJJxcK0VoFsnAkkOLugaEkMILjFuFuODNdfbZZ6tLLrmkw+MxdWVBpEJXsNtuu+kwDATTIhwYbzYjchSxyCE0RciZymdalpF3lmQIMY6iAolgI6TNoJv4Sb9nyZljA4W0QBTz0u+51olhOS9F6bFttk4sN+YQw3POOUenPZAjO3To0NReZy2C4iAOvRyGyRflKyou42ZJoAVonBXNouHAhk/+DGREwIbC9/h5eYGfm48HJGj7Pd6iPBhvCjq+//3v6w0JRRFSAVnHM40k91/96ldaocsqbEv+G/OEoggvYijvA0JCtwiUZZRFVC0S9zEBxgdRej/XMyD31SCGppfitttuWzLmeFdC+FC8KDAhdOwmlShifC7PPPNMpsSQeY8aR/U/anMQcB9QDEKqA3OS9QiVL0kwpnQ72XvvvXUKDSFjADFk/CwsLDm0qFsQlmKzRt0xwfcLFizw/B1+HubxFsGBQkhongpIEtvxD2TTv+eee7R5NUnwd999t97wq0UUsTeBGJIzR35pEAWQ90GIk/7UWPmguJBD9/7772tCwvtivrhJSz0QQy7eb7VTA9xjLn2tyYtDAeMAx4GDcScigEcfxBCD96wPqBR5iB1MEJD7h70MkQpyMCnQ4f0xV5MEdjSXX365Hst77723YGnDPWAJooUlhxYWFlUHmz0b/WWXXabVRNQgEuEffPBBNX78eJ2beOedd6q5c+emRhTZbFFMCG1DWqOEhnkf/D4bLcUspDCwmVOsAVGEBJNzST5jLQNSSDgZYkj+WZZgzHkN+Ciiyh111FFayeSQwc/w7jz11FNzUS1/zTXXaKsYDh5BwFwnLxpVjxxdlGw6ljCHKO5KGmPGjNEEkddHziGt8kAjpUlYeMPOAIu6BSEpwiQoUSb43i85nZ+HebxFMps9mxRVzuQ+oQax4dOtgTAuYTVUDcKZSRFFKpEhpeQLEmJLIu+Rv4GixnuBtJCrCnGkoheiSE4jPnKoSbUExh1iCAHLmhh6gTlC+JZ+4IT+CSkTdqYCms/hpptu0nOqFsA4o36aqS3MIXKn00ptIb3jyiuv1Crryy+/rAtRLCwsObSoW2C7wIZGdw8B4RK+pwWcF/i5+XiABYbf4y2SBQSLTf28887ThApihXkxHV1QUbA9+dGPfqQ+/PDDyEQRP0YUQ4obIBZpFcSgILLxQhJRFSGiKEAYEPP8vLekQ4VpEENU0LwSQ8A8ILSMcoiPIdYrzB1UZxRE7mc+5/vvv1/lHZK+Uu3UFor0brzxRq1S5vVztqguLDm0qGtccMEFOsxE0QPFA+T+UI3MpgGwubn00ktL/L7IV4KAEOokP46N/KyzzsrwXTQmIG1DhgxRZ555pt7gIVZf//rXdQUqeVKoQtdff73+nIISRT57FEM2W8La1aqUJj8O78dJkybpnDkqbilg4b3gu0nYFjUzT4C8QgzzEEquFIYl35ADhBm+5TM+/fTTdQ9lxpowbRIgp5F5U+5iTtYaKIDhwGRhAWxvZYu6BtY02GBQ/cfJG0sayJ+czNkAzfwaCAfehuT8kA9HLhm5PtbjMFuw4bJxQQ7Z8LGVIeyMPQ4J/IRyscchtEiY2CtnivQANm2qV3l8VhY6Zr9nQsxij4MaWqnfc7XAfUEoFmJIWDOvxBADdsLG9EqmKt4PQSuFg4CwNQbv5UCBVRRI+gpzFbIm4HvWLguLasH6HFpYWNQ06KRBdxaIIsQf8nfkkUfqC/sTiCK5VBwUUJDpKZvnfs8QRax9UBuFKEIaq0Vm8b2DqOadGBIRuOqqq7QyyKEuz7jvvvt0qgQdSCq9L9Tyb3/725qEAnIAmQP8jeOOO65Kr9ii0WHJoYWFRd2AFmy0O4Qo8pWiJPLl+DfdMqQaM+/ABsfsFEL+rBBFQrxpEUUhhqhwSaptSQICBcknvItZPXZIeQUKLEbef/rTn3RO34svvqh/TlW1tBzEZ5COTKjegPxJ0iV4j1Isgj0SPqF5qMC2aAxYcmhhYVGXoPDkxz/+sVaXUA8JSxN6JveMAqNa6QSBV6fZxg8jaEgiFwQuKaJYK8SQtA9yiUkr2H///VWeQfgZkucG7fzoSw74/PAZlFA175E5S0s7lEZyVO+44w5t8WRhUS1YcmhhYVGXoCqY/tjkJLLxYopMv2dUHJQ4/g+1hkpi+inXAqSlHDloZr9n6T0c1Z8Oix26cOSdGOLFR3EY7RjxxbSwsEgHlhxaWFjUHSCCED+Uw9NOO61Dbh/KDQQD9QnCRZ9nHo9VDsSxFsDrRlmCKJr9noP0HnYTQ7q7kGOYV2IIKAyjIOmhhx7SxN7CwiI9WHJoYWFRV6ACeOLEiTo0h0dipdw+8sAgipAPQtEQRULPFK7USo4XxJDCHCGKvC/yLclThCj6hdCFGKIYojzmFeQWYj/1m9/8ppCbZ2FhkR4sObSoSaD+1Eoo0KL6wM8wrBUMuX10aCH0/Oijj2pVjtAlVc8HHXSQ6tmzp6oVokiFq1jkYLQtRJGv5CwCTKKnTJmSe2KITc1JJ52kTa6pOLewsEgflhxa1CSoVKQLAgnpX/3qVyP7illY+IVsMacWoohH5sEHH6wVRQgjbfJqhSi2tLQUiCLKKJ1aCJ3znggl55kY0vEE+5af/exn6stf/nJm3pQWFo0G2yHFouZAIj5mxmx0KD1YQWAofPbZZ+euy0TawASYzhuEP+m/CqHxAz5p7k4OtRI2rTbI16PtHfYjhF0pbhHLEcYbBYuqWdTFpPo9pwGz3zMV2rwn3hvdZiDAdEAhtLx27VqVNzDmxx9/vO6rbYmhhUV1YcmhRc2B9meoIOecc44uLEABueaaa3QojXBzo+Dhhx/Wlh7k1v3f//2fNnw+5JBD9Nj4AY+8+fPnFy5651qUB2QKhY2evbRghIDzPaQFH7qjjz5a96TFbibPRBGQl8jrxPsRixTCzBBF8i7z1O/51VdfVV/84hd1pTm5hlYxtLCoLmxY2aLmABmCFFJAQIiMDdlv80Ad4f9rxdMuDFAKd9llF91XVt7r8OHDtYJK2D1qlwaLYGBekbNH6BnT7cmTJ6u9995b5ygeccQRunI4T6QGEojiThs27hsTEEIUeQpamB8cIsQip9q5lhz+CN9/97vf1XM5T2NoYdEosMqhRU0Bj7e33npLvf7661o1e/755ztsHmxuKCF8RfWpR2JIRe6bb76pDjzwwMLPeK98j+riB/LPRo4cqUkkG/A777xTpVdcf2DeEWq+/PLLteqGqohy+9vf/lb35D7ssMN0rhykLGtFEZUYYoi67CaGgPQC5sTOO++sO47QgpB7jbSN1157TYefmTtp4//9v/+nyTW9zS0xtLDIDpYcWtRkSPnkk0/WGxrJ6jSof/DBB/UGTG9dwmX0JuXnVJmy4bjBY6lOzXrTjgpaqvH6qUA1wfeE2b3wiU98Qv3yl7/U3n5YgqA00pOWnDOL+ESRvL6LLrpIk3M6jWC5QjELBJJ5+JOf/ESHbas95yCGEFeIIbY2lUCxCuSQ0Dm+jxwmSNngQAZZ5L3RpjDp94HySgeb//7v/9Z9ha1iaGGRHWxY2aKmQG7hU089pX7xi1+obbfdVm9aEEY2YJS0W2+9VR111FG6i8KMGTN0CBqVDfJohtDMQoxyYem8AjWKDZzNmkIDAeSEKm428kogP3ObbbbRSf/XXnttyq+4McHc4rOCJBJ+fumll3RYF9WWiyr7NOdeWGIYtt8zYWcOJHH7PdM3+PDDD1dnnHGGvmdr7X60sKg3WOXQomZAmJhNhMpkiCFgU8KsGKJEgQAVjiiFFA1QVXrxxRdrpYPwMyA8RjiavqYQTTZu90b0r3/9S5OuPINCAsLl5IiZ4PtBgwYF+hv4ROJxx/hYpAPmFnOTwwq2LKi0X/va1/Q8pSiE1n033HCDzl1MWolDQU6KGAL8EZlbO+ywg1YU6fXLwYtiKNI4CFsvW7Ys9PugGpwcTSypLDG0sMgHLDm0qBlA2NjsKMQAhFUFqBmoMoSjMEBGlWEjIx8MRVGMf1E8UNoI+VHUQo6YEEcBKo/0bUUtySNQbSAXzz77bOFnhIn53lQSy4HxI5RH+N2iOkQRle0b3/iGNnZG1aPinvnJZzZp0iT1ve99T+eB8lnGJYYcpCBySRBDNziYoBput912mihyWOM1c7CC+PLcVEVXeh9Tp07VrfBOOOEE9f3vfz8zxZDnJsWC4pugLQTp1+22hrL9ni3qBTasbFEzIOn/5z//ud5cIUZsPNI/lv9jsb755pu1WTHh5pdfflk98sgjesNCSfQqTEGxYPO86667tG/i+eefr1t1sciTl2cCoigkMy9WNuRe8tohFoTUf/e732kFBxLyla98RatWePMBqj/xuRs7dqxWYfHwo+IbciJKrEU24PN47LHHdNUzxHHYsGH6gMMhBoIXtE+yqMcQTH4PhTmLfs9ius33fv2eSfugaId7ENU/zHtMGiiWkEKUXTqxBKnoZ71hrO+9997Cz7p165ZrU3ELi6DIz05nYVEBVFJyCczNhF66ADIIOSTczMbDRW4dxJA8PMgTOYqEpsm1w8yYfDv+Fos6vnVUaUKaMA+GUGH/giqQJ2IIeO3Yj3znO9/RShG5bBTkSJEKxQ/mGBHyO/300/Vjea8QbNRYSwyzB8SEFnFcFHs88cQTOkcRZRuCB1Gkipf5X45EQVb+/e9/Z0IMAa+NamguCqDwVYQkcmAhjYN7ivfCIQV/SA5hWRNDwGsTu6cwgAwGTeOwsKglWOXQom7wl7/8RRMlchAJU7FwQ/QI2c2ePVsnvPfo0UP/H3l2PH769Ok6LEaImXA0GxVKHKoG4WU2ahQ4NmWUtv32269mi1gsag+o2RB+iOKf//xnfeiholcIlqmG/+pXv9LzHGUdtS5P4H7hIMLhDOJLSJ0DGmF03g/vKw8I4wWKcsghkhQPDlu08uT9pBHGt7CoNvIlhVhYxAAqC15st9xyi7r77rvV9ttvr/MTIYeoZYTa6AiCnxugCIANltAdQOGAMGI7AuTnKI0k3ZvqBsTw7bff1gqNCUnGt8TRIgmQA0f1PRdV9k8//bQmiqjGHH4IyTJPOeRQaHXHHXfkjhjK/UDxDf6PFK9QJIOBOx1QKETBnxMlEdJbK+QK1ZPPhWgDuZOXXXaZjlRgZVSP3qoWjQVbkGJRN4C8HXvssVqhIHfo+uuv1yoKQDFEqUB9IQ8KYojvHGEvNiTAAk9I7sQTT9TfS59mcpDI6TPDr6gfhHHJaxSFB0hiuoVF0sB+CTKIusVhh68cRvD6PPPMM/UcJZRMBXEeQTEYr58D1Z/+9CcdyuWAxT1HZ5k777xTF0fxuCRAlyB3wYj7ItwdFYw7qieHUEL+5CpzkHQXuFlY1CJsWNmiIbB27VpdsHH11VdrCw4MfgnDYWmDxQgbKsUsP/3pT3Vozgwdk0zP5kvls+QdYkeCWkMyOqSTqlM6t1AwQx4gm50Jqay2ioJFksDQnDQJLJsgjIQ5URg/+9nPasJCGoTp6ZkVyHelKhlDbULLhGK9QLg5qep57kMqpssBn0nztcRtMYlqS2hZDqUWFrUKG1a2aAgQgmMDxSQauxcIH36I2FeI6kg4CBXANMqm0pkNhnwisyCFPDA2ACn+4G+hIv74xz9Wv//973W4GvIprcosKbRIGlQ3QwypqpcUCNRw5iGhZyrvUcYJdUIUCd1Wu08yEHV+yJAhusLejxiCJG2VIGrVDLETrWCtsNZQFvUAG1a2aCigBLJJEtpC6aOHK4D4YZqNBQ4kUdQWQkWEj1EYBORMUbwCsYR0EhpjQz733HN12z6Uxx/96EdajURJZGMm/ISqY2GRBEhrIKx5//33F4ihHELojcwhhaIqscVB9WZ+Uw3N4aUafZIBBV28Pg5JEFbulzyCyn7WA76i8vNvLnOc6MJEkRrg57T5o+8048yBEwKMTRS5zxYWtQ5LDi0aGmbIjXASizuhZsLPkqtIiElyCsHPfvYzvVGQXA8Ik0EeL730Ut1fl79J9SJkkcpnrHUoguH/6YZRDtjuVGvjtqhdUATx29/+VhdE+AE1HAX7pptuUh988IFuq0hKBYbPEEXIJW0lUfbS6PfMAeqYY47R9xAHozyEt/2AywHdgvA75P7j31z4pwrIU2ashIRzKOTQx5hSVIM1FAfHvBJgC4swsDmHFhYeIH8L/zJUAaoSURqppkT9IM8LJYYQNRse/YlJtKfyUoy5UQ7xT0ThkdA1/8ZnkA4SkEgvUBVNYj75jBQYQFgJgVtYJAWIIEUgeIKihNG+jrQJlC/yAjnYxC2qopgLYohxPA4AvXv3Tuz1W1hYpA+rHFpYeABiyCaKwvLQQw/pPClUARRD1D1UAogh7fzwTBQFR+xufvGLX2jFhM0WM2DMtNlwyWmkI4kfeAwq5F577aWrrXlus01gowACDeEmT40xCRKSp0qUQiOUGxTgsIbGjQKZh8xJ1C/CpxxgSIFg7kESf/nLX2prpyiKIvm6GMzzlbQMSwwtLGoPVjm0sAiBefPmaaIC6aNKmbwjCgIgiL169dKPwYqDtnUohaiKfIXksQljyk14j2rmckbaqC1ssFRSQ3gaDbx/8jgh4RBvFC5yN/2A0kqf329+85u6kpwcMFRXrItsDlgwMB+xc0Idp40fKjakEbJI+JRCi0qKIq4AWEHRqQVPRttKzsKiNmHJoYVFDNx+++06D4kws4CCFDZUwtDmzwlVs+HSqcUPFLGgUlKFiq0Hm3Sjb7AQkkrkkEp0iCDhUgE5deSLUlluEZ4oUpwhRJHWk6Q5SHcWcmjdRBFFnW5CGM1DzmvFzNrCwqIjbFjZwiIGzjrrrBICyKbKxonPHKTk3XffLZA+QtXliCEQmw/CcYRV+/XrZz+fAKDCnCp0EyiG/NwiPCB+eBLSdYV0CnJvIdvMaULS+CfSiWjatGl6zpNbiGKL8ohiaImhhUVtw/ocWlgkCFFTII1UN9IijBZ+qC79+/fXVY1+3mvkFlIFSdiZ8ByWJGbLPgt/oMqK56SA77EmojiC/E+L6HOaNImzzz5bz2tCxqRWoCjS5YTUCR5DRT+5onls32dhYREOduexsEgBEBMsbiZPnqx7NRN6ppglyMZJX2hIJXYlFhZ5AiQQBZzcTjwUSX1AMaQbCbm1/J+FhUXtwyqHFhYpQvwPQbnKT/5PuqgQUuZ3UBotggFSgqJlgu/79u1rVcMUiSLhYxRFLgsLi/qBJYcWFlWCX6Un+YgYaxMaxVibkDK+c7blXnDsvvvuWrkyQe4bP7ewsLCwCAdLDi0sMkaXLl10GJoEfyqdMdLGc46WfhMnTtT/32igSwX2QKZVDVZAtGHDQghlde7cuerXv/61/n/CnFSOY0x+2mmnaQsgwvpUMFtYWFhYhIO1srGwyBkpgtTcdttt2mQb8kN7rkYDhtZUxLpx8skna3PrU045RVfQ8jjzd84//3xdIU4/4SuvvFI/zsLCwsIiHCw5tLDIKcTz0MLCwsLCopqw5NDCwsLCwsLCwqIAa2VjYWFhYWFhYWFRgCWHFhYWFha5BzmmmMjj/4mp+ZgxY9RVV12l0y/KYc2aNerMM8/Utju9e/fWbS3dtkcWFhalsOTQwsLCwiL3+M9//qMr+e+66y71zjvv6Op+LKAuu+yysr9HkdJjjz2mHnnkEd19aN68eeqoo46q2uu2sKhF2JxDCwsLC4uaxI033qjuvPNO3ePZC3QmoivRAw88oI455pgCyaTlH323d9tttyq/YguL2oBVDi0sLCwsahKQP7wv/fDmm2+q9evXqwMPPLDwM9pY4pUJObSwsPCGJYcWFhYWFjUHTNJ/8pOfqG984xu+j6HrEHZQ7laUmM7zfxYWFt6w5NDCwsLCIjNccsklurVkuYtQsAm64xx66KHq2GOPVaeffnpmr93Col5hyaGFhYVFBnjhhRfUEUccoYYMGaIJ0B/+8Ieyj6cDjBdxqnUF7MILL1Tvvfde2Wv06NGFx1NQQvecPfbYQ7eZLIdBgwbpaubly5eX/JxqZf7PwsLCG7a3soWFhUUGWLVqldpxxx11L+gw1bNTpkxRffv2LXy/5ZZbqloGBSNcQYBiCDHcaaed1L333quam8vrGzyO3uTPPvustrCR8Zs1a5bafffdE3n9Fhb1CEsOLSwsLDLAYYcdpq+wgAy6c+gaARDDfffdV40cOVLddNNNavHixYX/ExWQxxxwwAG6J/mkSZNUv379tDfiBRdcoAtXINVnn322Joa2UtnCwh+WHFpYWFjUED75yU+qtWvXqu22205dffXVas8991SNgKeffloXoXANGzas5P/a29v1VyqTUQZbW1sL/4cfIgojyiHjdsghh6g77rij6q/fwqKWYH0OLSwsLDIGuYOPPvqoOvLII30fA+kh73DnnXfWJOfuu+9W999/v3r99dfVpz/96aq+XgsLi/qGJYcWFhYWNUAOvbDPPvtozz5IooWFhUVSsNXKFhYWFjUK8uoIs1pYWFgkCUsOLSwsLGoUb731lho8eHDWL8PCwqLOYAtSLCwsLDJAS0tLieo3ffp0TfaoqiVUfOmll+rqWypvwa233qpGjRqlJk6cqNasWaNzDp977jn11FNP2c/PwsIiUVhyaGFhYZEB3njjDe3ZJ8BuBZx88snqvvvuU/Pnz9d+fALMnDGMhjD27NlT7bDDDuqZZ54p+RsWFhYWScAWpFhYWFhYWFhYWBRgcw4tLCwsLCwsLCwKsOTQwsLCwsLCwsKiAEsOLSwsLCwsLCwsCrDk0MLCwsLCwsLCogBLDi0sLCwsLCwsLAqw5NDCwsLCwsLCwqIASw4tLCwsLCwsLCwKsOTQwsLCwsLCwsKiAEsOLSwsLCwsLCwsCrDk0MLCwsLCwsLCogBLDi0sLCwsLCwsLAqw5NDCwsLCwsLCwqIASw4tLCwsLCwsLCwKsOTQwsLCwsLCwsKiAEsOLSwsLCwsLCwsCrDk0MLCwsLCwsLCogBLDi0sLCwsLCwsLAqw5NDCwsLCwsLCwqIASw4tLCwsLCwsLCwKsOTQwsLCwsLCwsKiAEsOLSwsLCwsLCwsCrDk0MLCwsLCwsLCogBLDi0sLCwsLCwsLAqw5NDCwsLCwsLCwkIJ/j+TlCNQPVzB8wAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.linear_model import LinearRegression\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -256,12 +311,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "02511726",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE: 154.63\n",
+      "R²:  0.9836\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.metrics import mean_squared_error, r2_score\n",
     "\n",
@@ -301,10 +375,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "1555e0e3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Raw Coefficients (original scale):\n",
+      "  Feature_1: 86.3102\n",
+      "  Feature_2: 73.6693\n",
+      "\n",
+      "Standardized Coefficients (comparable):\n",
+      "  Feature_1: 75.1140\n",
+      "  Feature_2: 71.8296\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.preprocessing import StandardScaler\n",
     "\n",
@@ -364,6 +452,31 @@
     "-   `noise` = 20"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2bbb66d1",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True Coefficients: [72.00365365 22.92513952 72.22525684]\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_3d, y_3d, coef_3d = make_regression(n_samples=200, n_features=3, noise=20, bias=5, coef=True, random_state=42)\n",
+    "\n",
+    "df_3 = pd.DataFrame(X_3d, columns=['F1', 'F2', 'F3'])\n",
+    "df_3['Target'] = y_3d\n",
+    "\n",
+    "print(f\"True Coefficients: {coef_3d}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "64e2b87b",
@@ -374,6 +487,38 @@
     "Train a model using **only** Feature 1 and Feature 3. Ignore Feature 2. Evaluate the $R^2$."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fffd5545",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model trained on F1 and F3 only.\n",
+      "R² Score: 0.9122\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Select Features\n",
+    "X_subset = df_3[['F1', 'F3']]\n",
+    "y_subset = df_3['Target']\n",
+    "\n",
+    "# Split & Train\n",
+    "X_tr, X_te, y_tr, y_te = train_test_split(X_subset, y_subset, test_size=0.2, random_state=42)\n",
+    "\n",
+    "model_sub = LinearRegression()\n",
+    "model_sub.fit(X_tr, y_tr)\n",
+    "\n",
+    "print(f\"Model trained on F1 and F3 only.\")\n",
+    "print(f\"R² Score: {model_sub.score(X_te, y_te):.4f}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "86d56bba",
@@ -384,6 +529,37 @@
     "Now train a model using **all 3 features**. Compare the $R^2$. Does it increase?"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "fed13ea9",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model trained on ALL features.\n",
+      "R² Score: 0.9420\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Use all features\n",
+    "X_all = df_3[['F1', 'F2', 'F3']]\n",
+    "\n",
+    "# Split & Train\n",
+    "X_tr_all, X_te_all, y_tr_all, y_te_all = train_test_split(X_all, df_3['Target'], test_size=0.2, random_state=42)\n",
+    "\n",
+    "model_all = LinearRegression()\n",
+    "model_all.fit(X_tr_all, y_tr_all)\n",
+    "\n",
+    "print(f\"Model trained on ALL features.\")\n",
+    "print(f\"R² Score: {model_all.score(X_te_all, y_te_all):.4f}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7ed80916",
@@ -398,7 +574,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c0ad8c1b",
+   "id": "c6fd3c49",
    "metadata": {},
    "source": [
     "### Adjusted R²: The Honest Metric\n",
@@ -412,19 +588,137 @@
     "-   $n$ = number of samples\n",
     "-   $p$ = number of features\n",
     "\n",
-    "If a new feature doesn't contribute enough explanatory power, Adjusted $R^2$ will **decrease**.\n",
-    "\n",
+    "If a new feature doesn't contribute enough explanatory power, Adjusted $R^2$ will **decrease**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6daef3ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model with F1, F3 only:\n",
+      "  R²:          0.9122\n",
+      "  Adjusted R²: 0.9074\n",
+      "\n",
+      "Model with ALL features:\n",
+      "  R²:          0.9420\n",
+      "  Adjusted R²: 0.9372\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compare R² vs Adjusted R² for both models\n",
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "def adjusted_r2(y_true, y_pred, n_features):\n",
+    "    \"\"\"Calculate Adjusted R² score.\"\"\"\n",
+    "    r2 = r2_score(y_true, y_pred)\n",
+    "    n = len(y_true)\n",
+    "    return 1 - (1 - r2) * (n - 1) / (n - n_features - 1)\n",
+    "\n",
+    "# Model with 2 features (F1, F3)\n",
+    "y_pred_sub = model_sub.predict(X_te)\n",
+    "r2_sub = r2_score(y_te, y_pred_sub)\n",
+    "adj_r2_sub = adjusted_r2(y_te, y_pred_sub, n_features=2)\n",
+    "\n",
+    "# Model with 3 features (F1, F2, F3)\n",
+    "y_pred_all = model_all.predict(X_te_all)\n",
+    "r2_all = r2_score(y_te_all, y_pred_all)\n",
+    "adj_r2_all = adjusted_r2(y_te_all, y_pred_all, n_features=3)\n",
+    "\n",
+    "print(\"Model with F1, F3 only:\")\n",
+    "print(f\"  R²:          {r2_sub:.4f}\")\n",
+    "print(f\"  Adjusted R²: {adj_r2_sub:.4f}\")\n",
+    "print()\n",
+    "print(\"Model with ALL features:\")\n",
+    "print(f\"  R²:          {r2_all:.4f}\")\n",
+    "print(f\"  Adjusted R²: {adj_r2_all:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0ad8c1b",
+   "metadata": {},
+   "source": [
     "**Key Insight**: If the Adjusted $R^2$ doesn't improve (or decreases) when adding a feature, that feature likely isn't contributing meaningful information. This is a simple form of **feature selection**."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b9fb1df5",
+   "id": "ddc8e29f",
    "metadata": {},
    "source": [
     "### Non-Linearity Challenge\n",
     "\n",
-    "Modify the target variable to be non-linear: $y = 3x_1^2 + 2x_2 + \\text{noise}$. Fit a linear model and examine the Residual Plot to see why linear models fail on non-linear data. **Observation**: You should see a distinct \"U-shape\" (parabola) in the residuals. This systematic pattern proves the linear model is insufficient—it's missing the quadratic relationship in the data."
+    "Modify the target variable to be non-linear: $y = 3x_1^2 + 2x_2 + \\text{noise}$. Fit a linear model and examine the Residual Plot to see why linear models fail on non-linear data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "91d9c2a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R² Score: 0.1694\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create non-linear target\n",
+    "y_nonlinear = 3 * df_3['F1']**2 + 2 * df_3['F2'] + np.random.normal(0, 5, 200)\n",
+    "\n",
+    "# Train/test split\n",
+    "X_nl = df_3[['F1', 'F2']]\n",
+    "X_tr_nl, X_te_nl, y_tr_nl, y_te_nl = train_test_split(\n",
+    "    X_nl, y_nonlinear, test_size=0.2, random_state=42\n",
+    ")\n",
+    "\n",
+    "# Fit Linear Model on training data\n",
+    "model_nl = LinearRegression()\n",
+    "model_nl.fit(X_tr_nl, y_tr_nl)\n",
+    "\n",
+    "# Predict on test data\n",
+    "y_pred_nl = model_nl.predict(X_te_nl)\n",
+    "\n",
+    "# Residual Plot\n",
+    "residuals_nl = y_te_nl - y_pred_nl\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "plt.scatter(y_pred_nl, residuals_nl, color='orange', alpha=0.6)\n",
+    "plt.axhline(0, color='black', linestyle='--')\n",
+    "plt.title(\"Residual Plot: Linear Model on Non-Linear Data\")\n",
+    "plt.xlabel(\"Predicted Values\")\n",
+    "plt.ylabel(\"Residuals\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Show R² to quantify the poor fit\n",
+    "print(f\"R² Score: {r2_score(y_te_nl, y_pred_nl):.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9fb1df5",
+   "metadata": {},
+   "source": [
+    "**Observation**: You should see a distinct \"U-shape\" (parabola) in the residuals. This systematic pattern proves the linear model is insufficient—it's missing the quadratic relationship in the data."
    ]
   },
   {
@@ -448,9 +742,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C05 Regularized Regression.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C05 Regularized Regression.ipynb
index ea9c5c9..707a0b4 100644
--- a/src/S02 Supervised Learning - Regression and Evaluation/S02C05 Regularized Regression.ipynb	
+++ b/src/S02 Supervised Learning - Regression and Evaluation/S02C05 Regularized Regression.ipynb	
@@ -113,12 +113,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "1ea49258",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Features: 50\n",
+      "Useful Features: 5\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -151,10 +160,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "7dae3291",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Linear Train R²: 0.99\n",
+      "Linear Test R²:  0.96\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.linear_model import LinearRegression\n",
     "\n",
@@ -185,12 +203,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "a39d5dbb",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lasso Test R²: 0.97\n",
+      "Ridge Test R²: 0.96\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.linear_model import Lasso, Ridge\n",
     "\n",
@@ -222,10 +249,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "01f84aee",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ElasticNet Test R²: 0.68\n",
+      "ElasticNet features at exactly 0: 6\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.linear_model import ElasticNet\n",
     "\n",
@@ -261,12 +297,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "4c6609d9",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lasso features set to exactly 0: 31\n",
+      "Ridge features set to exactly 0: 0\n",
+      "ElasticNet features set to exactly 0: 6\n"
+     ]
+    }
+   ],
    "source": [
     "plt.figure(figsize=(15, 4))\n",
     "\n",
@@ -321,6 +377,41 @@
     "Load the data and add some random noise features to trick the model."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "acec8443",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data with noise shape: (16512, 13)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import fetch_california_housing\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Load\n",
+    "data = fetch_california_housing(as_frame=True)\n",
+    "X = data.data\n",
+    "y = data.target\n",
+    "\n",
+    "# Add 5 columns of random noise\n",
+    "np.random.seed(42)\n",
+    "noise = np.random.normal(0, 1, (X.shape[0], 5))\n",
+    "noise_df = pd.DataFrame(noise, columns=[f\"Noise_{i}\" for i in range(5)])\n",
+    "X_noisy = pd.concat([X, noise_df], axis=1)\n",
+    "\n",
+    "X_tr, X_te, y_tr, y_te = train_test_split(X_noisy, y, test_size=0.2, random_state=42)\n",
+    "print(\"Data with noise shape:\", X_tr.shape)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "81c0382e",
@@ -331,6 +422,21 @@
     "Regularization penalizes coefficient magnitude. If features have different scales (e.g., income in thousands vs. age in years), the penalty unfairly targets high-scale features. Standardizing ensures all features compete equally:"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ac1effd6",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "X_tr_scaled = scaler.fit_transform(X_tr)\n",
+    "X_te_scaled = scaler.transform(X_te)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "317823b9",
@@ -341,14 +447,82 @@
     "We don't guess the penalty strength ($\\lambda$ or `alpha`). We search for it. **Note**: `GridSearchCV` tries every combination of parameters to find the best one."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7f37ab86",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best Alpha: 0.001\n",
+      "Best Score: 0.6112\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "# Define values to test\n",
+    "param_grid = {'alpha': [0.0001, 0.001, 0.01, 0.1, 1, 10]}\n",
+    "\n",
+    "# Setup Grid Search with Cross-Validation\n",
+    "search = GridSearchCV(Lasso(), param_grid, cv=5, scoring='r2')\n",
+    "search.fit(X_tr_scaled, y_tr)\n",
+    "\n",
+    "print(f\"Best Alpha: {search.best_params_['alpha']}\")\n",
+    "print(f\"Best Score: {search.best_score_:.4f}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "id": "aa683208",
+   "id": "7231fabe",
    "metadata": {},
    "source": [
     "### Which features survived?\n",
     "\n",
-    "Retrieve the best model and inspect which coefficients survived. Did Lasso kill the \"Noise\" columns? **Expected Result**: The `Noise_X` features should have coefficients very close to or exactly Zero."
+    "Retrieve the best model and inspect which coefficients survived. Did Lasso kill the \"Noise\" columns?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ceba9a6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "best_lasso = search.best_estimator_\n",
+    "\n",
+    "# Create a series for easy reading\n",
+    "coef_series = pd.Series(best_lasso.coef_, index=X_noisy.columns)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "coef_series.sort_values().plot(kind='barh')\n",
+    "plt.title(\"Feature Importance (Best Lasso Model)\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa683208",
+   "metadata": {},
+   "source": [
+    "**Expected Result**: The `Noise_X` features should have coefficients very close to or exactly Zero."
    ]
   },
   {
@@ -371,9 +545,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C06 Nonlinearities in Regression.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C06 Nonlinearities in Regression.ipynb
index c78065c..5db1d79 100644
--- a/src/S02 Supervised Learning - Regression and Evaluation/S02C06 Nonlinearities in Regression.ipynb	
+++ b/src/S02 Supervised Learning - Regression and Evaluation/S02C06 Nonlinearities in Regression.ipynb	
@@ -61,12 +61,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "266997d7",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -104,12 +115,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "e3bf7b62",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Linear R² Score: -0.06\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.linear_model import LinearRegression\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -143,12 +162,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "2fe47826",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Polynomial (Deg 2) R² Score: 0.99\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.preprocessing import PolynomialFeatures\n",
     "from sklearn.pipeline import make_pipeline\n",
@@ -179,12 +206,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "a2335de6",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(10, 6))\n",
     "\n",
@@ -219,12 +257,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "b286e339",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Fit a Degree 20 model\n",
     "high_degree_model = make_pipeline(PolynomialFeatures(degree=20), LinearRegression())\n",
@@ -268,6 +317,45 @@
     "4.  Compare the $R^2$ scores."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7d302a25",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Linear R²: 0.38\n",
+      "Cubic R²:  0.91\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1. Generate\n",
+    "X_cube = np.linspace(-3, 3, 100).reshape(-1, 1)\n",
+    "y_cube = X_cube**3 - 3*X_cube + np.random.normal(0, 2, (100, 1))\n",
+    "\n",
+    "# Split into train/test\n",
+    "X_train_c, X_test_c, y_train_c, y_test_c = train_test_split(\n",
+    "    X_cube, y_cube, test_size=0.2, random_state=42\n",
+    ")\n",
+    "\n",
+    "# 2. Linear\n",
+    "cubic_lin = LinearRegression().fit(X_train_c, y_train_c)\n",
+    "\n",
+    "# 3. Poly (Degree 3)\n",
+    "cubic_poly = make_pipeline(PolynomialFeatures(degree=3), LinearRegression())\n",
+    "cubic_poly.fit(X_train_c, y_train_c)\n",
+    "\n",
+    "# 4. Scores (on test data)\n",
+    "print(f\"Linear R²: {cubic_lin.score(X_test_c, y_test_c):.2f}\")\n",
+    "print(f\"Cubic R²:  {cubic_poly.score(X_test_c, y_test_c):.2f}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "ddbd8ad0",
@@ -282,6 +370,48 @@
     "3.  Observe if it is smoother than a standard Linear Regression with Degree 10."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "33bd5045",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "# Standard (Unregularized)\n",
+    "bad_model = make_pipeline(PolynomialFeatures(degree=10), LinearRegression())\n",
+    "bad_model.fit(X_train_c, y_train_c)\n",
+    "\n",
+    "# Regularized (Ridge)\n",
+    "good_model = make_pipeline(PolynomialFeatures(degree=10), Ridge(alpha=100))\n",
+    "good_model.fit(X_train_c, y_train_c)\n",
+    "\n",
+    "# Plot (using full X range for smooth visualization)\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(X_cube, y_cube, color='gray', alpha=0.3, label='All Data')\n",
+    "plt.plot(X_cube, bad_model.predict(X_cube), 'r--', label='Deg 10 (No Reg)')\n",
+    "plt.plot(X_cube, good_model.predict(X_cube), 'b-', label='Deg 10 (Ridge)')\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('y')\n",
+    "plt.title(\"Taming High Degrees with Ridge Regression\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "50fe181a",
@@ -301,9 +431,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S02 Supervised Learning - Regression and Evaluation/S02C07 Overfitting, Underfitting, and Hyperparameter Tuning.ipynb b/src/S02 Supervised Learning - Regression and Evaluation/S02C07 Overfitting, Underfitting, and Hyperparameter Tuning.ipynb
index b14a1ee..d914ffe 100644
--- a/src/S02 Supervised Learning - Regression and Evaluation/S02C07 Overfitting, Underfitting, and Hyperparameter Tuning.ipynb	
+++ b/src/S02 Supervised Learning - Regression and Evaluation/S02C07 Overfitting, Underfitting, and Hyperparameter Tuning.ipynb	
@@ -55,12 +55,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "08023d0c",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -99,10 +110,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "fecf5000",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9s+8FCa7lPC3vv+TXeMYG98y8rpIxJ50i8r6VQFs6e9JiLEQrw/pSI8+T2uvMawTHxgCdHJKxmmpaY68kEJKgLLnkLZRG2Sm4Ja2eEmSmVshLJP/75i2v9pZWr4ElC4/JhYNe5oG1Ri+J8UJRWvHTCtDl5CtjwCTLQsaPyUWJNPbI+EoJxI1j3OREKultEsTLGE3JEJAhANJ7Ien85r0ScmIXxrGNRESOQIJsGbebUcOo3C+BtDHwkEZKabCUIEyGj0lgJEPIJK3YyPhdKunHaWV5JQ8g7TXDSWoseW2S1vlY6P2cnJn/b+HChVVjgqTIS70gSWWXRh3z6u1SdE3OufK+kSFl8jvyNyUDLrVe+sxen8mYchmSJs8pDUSSnSfvPRnbntq+W+J1NT5W6h4Yi9NmdCzldeY1gmNjgE4OydgqKS2jyVtiJTiX9Ceprpnd55XecfOecWl9lfFI5nNrysleirTI42zRUpnW37DG35aTmVy8pFbhPrVtqZGgVE6g1iBj2iTFPHkrsQTEtiLpcubT86RGLhxkTKPMHGDekp5WMTsZyyY/kkIv9QqkGIy01JvPx2scF5deizsRkR7J+GIppiUzo6RWKE62S4aUFA8zJ6nr0iMqqd6SuSbf/bLNyJjhJg2glpg5JCescU42nj/kHGeezSdpzMZG2/TIdZIEoamNq7bkvmX3eiGrJBiXhmyZfUZ60o21CsyHQMhxkt5u89dDGsKzS46zvP+kmJ157QPzqVazSq4jJcCX93TywnVGxtfb2DCRGcmvV8nxcAw6OSQJiuVELAVJklfalB5HCZ4k3SmrZHySjEOS6VvMq4nLhUHylme5OJCxV3KxkZykh6cXuGWHVIcVyfcjre05YUx3k2k6zMftyclWToqZIYGmnNCsMRenjCmU/TKfRkSyKVJ7LaxFerpFeidBY8u2eWu5pMFJESNzcpySt6gbT9bJq9rLODfpiZJihEREjkR6HiVQlJ7w5GNkJX1Ytss5TXokzcn5SHqBpcFSqnVL9XTzQFWCFxkaJOd/KURnyQrlaZHe09TOu8Zzcmopzzm55pFZQOSax5xx+tWMyPWSXN9InR1Lk2wHySaUArzmwxekZoD5FHiWJD3jcpxlylIJ0mUMuXlGRGrnXqmMLllqlu71l4r7Ofl/SC+7FIpLfi1r3HfJCJEGCOn9T61GTfL3trzvpLFEiguT42IPOjkkORlLUQ2ZgkQqYUvLqXxZS+qRtKZKAJfW3JsZncQkhUnSpmS8jxQykZZImYIj+Rh0KTomFwpyQSE9olLVW4q6SPVyKSAn04aYV+TOKeOUIjIdi/z/5GQhY7HkYkeKz8m+SIVTuYgxn3omu6SFWCqqSgVc+ZGTh1Qkl+c+fvx4hr8v47TlgkIKnCTvDckpeT7ZF5nORaZZk7Qv6XE2nqAt3YMhjQGSMifkBCmZE3IhKClk6Y0/lywOeU/JfO1SLEYuXmTMurx/pQqxeQOQXHjJyVpa1OVx0tggJ+XkDU1yPKUXiuPLiMjRyDlKxg9LFWvpBZUZNWT8r/SaS3VrCdqnT5+eIh1dvkel0rXMNCJZcu+++26K55ZzsRRRk+eVomtyzpbq5xKUSVZdZuqbZPWcLD2qMhSpWLFi6v8hRbuM52qZEUauIWTf5Ts7o9lJ0iPTvcm5TsZUy/lEgjYZCiDBqZyHMnM+kKKqsk/G6viWJFlfUoVdrhWkGK80XEjjgVyHZFRzIDtk2Jf8f+R6T5intxuvPySDT64TZVmu46TjRa6Vstt5IsdMiv3KtZ1cB0jDhMysk5OsBBmjL6+nTMsr711paJAhHTIlnryn5BpH/q7MGDBixAh1bOU9JR1JMvZfhsvJtadcs5pfI0hwn9FsCaRvDNDJYUkFbUl1lpOAfLlJr7mcICX9SOaSTmvsT2aCPwm0ZcorGfMjJxhJUU5eeMbY6iljiSV4k/Fx0kgg+yBBm7F4jaXIF7c8r1ygyIWGfAFLgG4M2mWaDZlTXU4cElznNECX1naZN16OgfzfJUVOiqJJA4T8ZOaCQoJLaTiwdIAuJ2dptZb/t0yHJxc+UkFWWozl9bf02EJpkJCTo/F1lwsieT1kSpe05nk1pvlLSp00JMk0b3JM5AJGTq7m08xJz4+ckKV3SC4opYdceohkaiDzhiFJg5MeifSmqCMi0jPJPpPvxrlz56qgXHrOpWFZCn71798/3crf0sApwah5eruRBF+SnSRpzdLoKc8r37Uy7ZkEapYmY5wlMJLvd8mak9leJECX7245N0hAKBln0rgt2V45CdCFDHmSBnk5BtIwIFlqcp0igV1axUSTdyrIdGiyL5mdkjWzpENEGlakIKr8DWmIkesR6VWXucKtQaq5S4AuDfTG2XOMJJCVc6k0pEtHg7w3PvnkE9V5Yj4bQFbJ/02utaTnXq7BpBFerpMyO7tNauS9IteN0ukgDShyHZl82lxpgJCAXT4z8ppLZp1ce8gwEfm/mpP/Y7169dIsmkuOwS3BklWhiMjpSSu5jP/OzPhyGU8ojQrG3n1rk8YSuSiQXoX0AmdHJXOhyjGVjA72oBMRuTbpqZZeXcn8k06LjEjjroyZlgDTFiTIlEaSzEzXRjknmXnScy6NJZKRR46LY9CJKE3J5/iWE7sEv9LjmxnSuistzJISZu19kzHo0louDQHOGJxL6qf0NsmFGINzIiLXkvycZz7+ObPnZMmuk2wtmf7TkiRzz1g41UiG/slwsMzuG+WcvB8kA4XBueNjDzoRpUnGukvanqRZyxhBY/E8SR+0RY94eqSIkFSPlSJtMqZO0v4llU7SwpOPRyMi65MikpJ+KcWY5LMoaczGMaIZpcfK90tyhw4d0tVUWET2JJlTcu6VoWOSLi9TzRlr7sjnzp5kHParr76q0s4lFVuGwcn1ggzXkvownPKLKGs4Bp2I0iTjqmTMu6RNyRg3GfMmc8/aOzgXclEiPcpy8peaATLGzFiUh4hsT4JyKVok43Bl3G1WRtB16NBBzWFtLjPjaolchYxLluKwkpEmhdek2KgUjpOsKnvLnz+/Ko4n4+Olqrg0IAQGBqpx8wzOibKOPehERESUYxKUG4tzSgEtmdIpsz3oUmnZvBIxERGRq+IYdCIiIsr5BUU2Z84gIiKiRDybEhERkV3JUBWZ2lGmSuzbt6+a45mIiMgVudQY9P3796sxcZ6envbeFSIiIlMFZKnML8GpK5IUd5k7Woo+hoSEYPbs2WpuZ5lDWQpUZhXP9URE5MjnepcK0CU4t9S07/I8cqAl2OeURzxm1sL3GY+ZLfB9Zt9jZqnzkqMaNWpUiqkZO3bsqCpTBwUFZftcLzNOEBERpSYmPgZXH1xVyz4ePijoXRB64VIBurHnvFatWjl+rvv37+P48eOqcrRUqyQeM2vg+4zHzBb4PrPvMZN5iSmRTNNUr149HD16NNvnegnQ5bWxhAcPHuD8+fNq9orcuXPzpeLxsji+x3jMbIHvs6SO3ziOLt90Ucuv1HoFXz3+lVWP2enTpzPdoO9SAToRERE5P7kIsnTjuVycsUGex8ua+B7jMbMFvs803rm8Hy4BXh5e6X6/W+KYZSXbjkXiiIiISDeuXr2KvXv3WiTbjYiIKDXxCfGmZYObvkJi9qATERFRjkkqYHBwsFq+ePEiIiIisHbtWrXesGFDFCpUCL169cKlS5ewYcMGtV3mSf/nn38QGBioUtulSNzcuXPh7u6OV199la8KERFZBQN0IiIicmo3b97EW2+9lWSbcf3bb79Fo0aNEB8fj7i4ONP9pUuXxrVr1zBp0iSEh4cjX758aNy4MQYPHpytCu5ERESZwQCdiIhSkEBFKoHbW1RUlOnWYNBXmpdeZfaYScEy6Q12BRJsZzR/+eLFi5OsBwQEpNimt88gPx9Z46jHy5U+q0QEBuhERJRIKkxfuXIFd+/e1cUUW9Kr6eHhoVKPHemC2hGOmRSFyZ8/P0qUKMEpOR30M8jPR9Y46vHiZ5XItcRzDDoRERlJUHDnzh0ULVoUPj4+dg/cpBdReru8vb3Zg2TBYyaB371793D9+nVVAbZAgQKWfNnIRp9Bfj6yxhGPFz+rRK4nngE6EREZLwRlzK2vry+KFCmii4NiHBOcK1cuh7mgdpRjJoG5BCvymktPur0bYyjrn0F+PrLGUY8XP6tEriVexwG6vvaGiMgFLl7lR4IDcg3yWhtfd7I/fgYpLfysErmOeAboREQkYmNj1a2M0STXYHytja892Rc/g5QWflaJXEc8A3QiIjLHVGfXwddan/i6EN8TRK4rngE6JRcREY3x44Ph7z8D7u7j1K2sy3YiIiIiIiJyvQDd6jmW//33H+bPn4+DBw/i1KlTqFChAlavXp2pIi7z5s3DDz/8gFu3bqF69ep477331Jypjk6C8MDAhThw4Ari47XpXUJDwxAUFIwVK04gOLg38ub1svduEhGla+bMmZg1a5apN1KqYfv5+aFBgwZ48cUXUbFiRac7gocPH1bnpQMHDuDcuXNo2bIl5s6da+/dIhfFz+A5BAYGYs6cOSmOTZs2bXDx4sUU2w8dOqQqzBORa4t35QBdgvLg4GDUqVNHzY2Z2Tl/JTj//PPPMWzYMFStWhXff/89XnvtNaxcuRL+/v5wZDNmbE8SnBvJumyX+0ePDrTb/hERZZZUal60aJFalinFTp48iZ9++gk///wzJk6ciM6dOzvVwdy3bx/27NmDWrVq4cGDB/beHSKX/QzWrl1bzZCQng4dOqhrR3NeXuwAISK4doAuLZjt2rVTyyNHjsSRI0cy/B35wpXWUPlS7d27t9pWr149PP7446o3PigoCI5s7tx9KYJzI9ku9zNAJyJHYDAYkmQ2NWvWDC+88AL69euHDz74AHXr1rVJo6o0/sbExFj94vvll19Gr169VCVwWSayN719Bq09tZrxM2hcTo9Mo+cMmZdE5FoBusEWJ47stI5GRESgY8eOpm1y0dW+fXts2rQJju7SpfAc3U9EpGeSPjp69Gh1sb506dIk9y1fvhxPPfWU6oFu0aIFZsyYkWL6Mekd69Kli3qMPHbr1q2qF1AaeY1k+cknn1QZWk8//bR67N9//63u279/P1555RV1YS6Nu0OHDsXNmzeT/I3o6GhMnz4drVu3xiOPPKLON7/99ptVzmlErvYZlGGNr776Kj+DRKRb8ToO0HU5z8/Zs2fVrYxXNyfjGSWNKzIyUqV0ZbeF9/79+zneR2NqY3ZSHEuU8MGlSxHp3m+JfdSbnBwzV8Vj5nzHTDKEZLiPnubFNg49ktus7JNx2FJqv1O+fHkUL15cBcvG+xcuXIhp06ap4HnEiBE4c+YMPvvsMzXt1TvvvKMec/36dbz++uuoUaOGCqClsfbDDz9Ut9WqVTM9l/zdq1evYvz48RgwYABKliypfvbu3at612RsuPwteR/I33jjjTfw448/mvZv8ODBqjH4zTffVOcWafwdPnw48ubNq343s8dMZHTM5H45VrIvcpvac7GiOFlapUqVTJ9Bo2+++QaffPKJ+oxIgC2fQWOALkMKxbVr19C3b1/1Gfz0008RHh6uMhflVuoBmZPHTpgwQX2+5PMnNSikPoP03svnSJ5b3vfyPPJZk9R7o7feekt9BgcOHKg+gxLoy2dQ5iKXceWWII1ukurv6emJ+vXrm4ZNEhHFxSeeuxmgZ0JYWJjqMU9exEO+tOVC5u7du9kO0KU1+fjx4xZ7V54/fz7Lv/P0036YO/ckUrlOg3TOyP2W3Ee9yc4xc3U8Zs51zGSu3eRjJ92XL4fXhAlSRdLm++OWNy/cR49G1DPPZGs+aWk0TU2xYsXUBbzcL2NjpaCcBOf/+9//1P2SeiuBqQTikpJboEABNYxJUmTlgl6KzomiRYuiT58+Kogw/i1ZlnOFFMmSnjsjSemVwGLKlCmmoLds2bLo3r07/vzzTzRv3hy7d+/GP//8gy+++AJNmjQx7cuVK1fU8zVs2DDTx8B8n9Iir7UcK2Pjc2o4Ltb+lh5dijEbxyA8KtwujSj5vPNhfOvx6Fajm8WeU4LmGzduqGVp5JLaPtIAZmwQk3R4CV4nT56sPmMFCxZUDWnyGZShhtJgJUqXLq0KPyYn12NSM0jqDBm9//776jMof8s4r3iVKlVMve0SfO/YsUP1tsvnXT6Txn2RBjr5DFoiQJchljJOXRoNQkJCMHv2bPU9s2LFCoevZUREORcTH2Na9nT3hJ7osgfdmuREJK3KOSUtwhIAlCtXDrlz587S744dWxE7dtzFoUPXkoxFNxjcULt2MYwd29Epq7jn5Ji5Kh4z5ztmEqxdunRJNUCaNzQaPv8cbidP2m2/PD/7DB49e2YpCDFefKfVYCrPJSnhcr+kzEpm0BNPPGH6PSEptpMmTVIX0CVKlMC///6LRo0aoXDhwqbHSBCdP39+FTQY/5YsS0AvFePNX3tJrZVeOPmuN5LgQJ77xIkTqiaKBOjyfPK3zcn62LFj1e9mNI7W2INuvk8ZHasyZcqkWj369OnTGf4+Wd8n2z7Bvzf+td+hDtf2wZIBunnDgvSky2dQ6vkYG9dE06ZNVSOTFPWVximZqUA+g8bgXEjvs3zekpNt5sG5fAbl77z99ttJMkvk+1gaC+S5JfiWlHn53caNG6fYF+mtl9/N6Vj2UaNGJdl/aQCQoSzOUMuIiHIuJi4xQPdy11fcpcsAXXrKZXygXMiaX8xIb4mcaOTCKrvk9/PkyWOhPYUKALL6fPL4zZtfU9XapSCcjDn388uHfv3qYsiQJk4ZnOf0mLk6HjPnOWYSsMqPXHwmuQB9911g9Ggg3PY1KBLy5UPMW2/B080tSxfF8v9wS+d3JAVdLszlfulpE926dUvzsfI46UEz/o45CdjN/5YsSwEo88dJL71c2EtvoPyk9TdkX+RHetdSI1N7SkCfHvPgI6NjJvfLsZL3ZGrBPNPb9WFEsxEY/c9ou/agD2863KLPKVkh8nkSt2/fVrfPpJEpc/nyZXVr/AwmV6hQoRTb5DNoTq7T5LMhw0vkJ62/Ifty584d1KxZM9V9kX3I6DOYVZLRIzUpjh49atHnJSLHFB0XbVr2NLAHPUPGsecyx6yMOTSS9EBJVcpuerueSBAuldpZrZ2IFAlc0wherS1exsNHRsKSpyfpjZOA2BgMGBtWJc09tQtvSaE1prNLgJxcatuSB0z58uVT2/r372+aPcScpO8a90WCjbTmL08tECHnJz3XqfVeG4cxyLWHtSuUw0k+gzILj0xxlryoIj+DRKTHFHcv9qBnTMYCSmrVmjVrTAG6jB1fv359por3EBGR/Uj2kxRvk3HVMvZbPProo6oHWXr0ZEaOtMh4cikkJeNljSm2kh4vvW0ZkYwJqdwujbnm49KTkzTar7/+WqWymzcCEzkLe38GpYNFZkdIq0HDHp9BaayQIpLONi88EVmgB93VxqDLeCQpCiIuXryovvDXrl2r1mWsk/RUSDVRGZO5YcMGtV3S2qUHRAqFyP0yflCq78rJQYqYEBGRPkhVcqnaLGR868mTJ9XFvYwplzRzY6+cDF2SyulSQVoCBPn+l4t3edxff/2lvu8leOjdu7f6vpdzgHzfS8qsFHOTnrfMpBhLdXg5p8gYWBnvLn9X/t62bdvQtWtXNbZWxqLK9GpSLEt+pKqznKtkLPh///2HiRMnpvn80ou4a9cu9f+WNF3pWTWe02RsrR7rHpBz09tnUCqlyxRrUohOCsNZ6zNoXJahLck/g6tXr1aFIGVdUtvl/ygZM/L/lX0jIooxG4PucinuMvesTKVhzrj+7bffqi9q45RD5mSKDxnztWDBAvUFLFN7SGEPVt4kItIPCVB79Ohh6j2TYECKukkarUydZE7SXmXaJ5nq6bvvvjMVTmvVqpWpqJtcTEtVaJm6SYIJuV8qs48bN06lz2YmA+uHH35QwcZ7772nsq8knVeKUUk1dyOpMC0X7BKISOOxPHflypVVAJFR2nBa5zQJcozBEJGrfgalp16u3eTzZa/PoPzIDBJSgFKmh5Pnlr8v/x9eRxJR8h50vaW4uyWYT+bq5KR6qEgv9TGzpJVapkKThgM9FqLSIx4zHjO+z7SLaUn/lHnC9VJPQ+9jbKUqv1RflovttApc6fmYZfSaW/LcRBkfz6x+BvX++dDbZ9CRj5e9vp95fcRjxveZ7U3eMhnv/fWeWv61x6/oUq2LVT+bWTnX67KKO+lHRES0y1abJyL7kOrPkvJqTE2V+ZilcNVjjz3Gl4SIn0EiIqfuQWeATukG54GBC3HgwBXTfO2hoWEICgrGihUnEBzcm0E6EVmcpMROnToVN27cUL1YMlZWxpb7+PjwaBPZAD+DROTsYlx5DDo5Luk5Nw/OjWRdtsv9nCaOiCxt5MiR6oeI7IOfQSJydtE67kFPOkElkRlJa08enBvJdrmfiIiIiIjIUedB99TZNGsM0HWWUj5+fDD8/WfA3X2cupV12W4PMuY8J/cTERERERHpTTR70Cmz471lfLeM85YeauN4b9lujyBdCsLl5H4iSpsLTaDh8vha6xNfF+J7gsh1xeh4DDp70B1ovLetSbV2g8Et1ftku9xPRFljnGtYpu4g12B8rY2vPdkXP4OUFn5WiVwzxd1LZ2PQWSROJ9OSZWa8t60Lssk+S7X25A0HEpwHBJRQ9xNR1si8wAUKFMC1a9fUusyr6eaWekOYrci8xVFRUab9I8scM+mhlQt+ea3lNeexdczPID8fWeOIx4ufVSLXTnH31NkYdAboOpmWTI/jvWVfZZ85DzqRZZUoUULdGgMEe4uPj0dsbCw8PDxgMDCxytLHTIJB42tOjvcZ5Ocjaxz5ePGzSuQ6YtiD7hpyMi2Z9LRLMK+38d4SpMs+czo1IsuR3rqSJUuiWLFiar5he3vw4AHOnj2LMmXKIHfu3PbeHYeQ2WMm6dSO0ovoSrLyGeTnI2sc9Xjxs0rkwj3oBvagO62cpKlLGrz0tKf2+xzvTeScJHDTQ/AmPV7C29sbuXLlsvfuOAQeM9f5DPK1zhoeLyJytCJxXjobg+5YuUc6l5M0dRnPLeO6kxdl43hvIiIiIiIi1xiDzgBdJ9OSGcd7BwUFonRpXxWYy62spzd2nYiIiIiIiDKPY9BdRE7T1Dnem4iIiIiIyHXHoLMH3YKYpk5EREREROQYY9Dd4AZ3g/3rAZljgG5BTFMnIiIiIiJyjB50L50ViBOcB93CmKZORERERESk/zHonjorECfYg05EREREREQuI1rHPegM0ImIiIiIiMjlxqB76qxAnGCATkRERERERC4jmj3oRERERERERPYXwzHoRERERERERPYXzR50IiIiIiIiIvuL4Rh0IiIiIuf22Y7P0HtFbxy6esjeu0JEROlgDzoRERGREzt18xTeXvc2Fh1chPpz62PipomIjY+1924REVEyCQkJiEuIU8ucB510KyIiGuPHB8Pffwbc3cepW1mX7URERJS+o9ePJik+NOqfUWgyvwmOXkvcTkRE+ikQJzgPOumSBOGBgQsRFBSM0NAwxMcnqFtZl+0M0omIiNJ37va5FNv2XNqD+vPqM+WdiEiH6e2C86A7k/h4GCIi4AxmzNiOAweuqMDcnKzLdrmfiIiI0nb29lnT8vTHpqNakWpqOTI2ErN2zeKhIyLSWYE4wR50J+L9xBMIaNsWnsOGATGJL7Ijmjt3X4rg3Ei2y/1ERESUtnN3EnvQezzSA7v77kZuj9xqffXJ1YhPiOfhIyLSWw+6uyf0xmDvHXBIUVEw7NsHt7g4eH71FdC+PXD9OhzVpUvhObqfiIjI1RkDdG93b5TIWwJ5vfKibYW2atvliMvYd5mN3UREehDDMehOyNsb0TNnIt7zYYtLcDBQvz5w4AAckZ9fvhzdT0RE5OoVgY1j0MsXLA+Dm9b/8WTlJ02PkV50IiKyv2iOQXdOcc89h5Nz5yK+RAltw4ULQNOmwE8/wdH061cXBoNbqvfJdrmfiIgoPf/99x/GjBmDzp07o0aNGnjyycTgNKPgdu7cuWjVqhVq166NHj164ICDNXhfvXcVD2IfqOXyBcqbtrcu9ZhpeeyPczlDChGRzsagezLF3bncq1ULUVu2AI0aaRsePAB69gRGjgTitLn1HMGQIU0QEFAiRZAu67Jd7iciIkrPqVOnEBwcjLJly6JixYqZPljz5s3D559/jt69e2POnDkoWrQoXnvtNYSEhDhkBXdjgC4zoDz/5AbgckntDr/LCL0byhlSiIh01IPuZfCC3nAMeg4llCwJbNwIvPpq4saPPwaeegq4cweOIG9eLwQH90ZQUCBKl/ZVgbncyrpsl/uJiIjS06ZNGxWgS7Bds2bNTB2sqKgoFZRLQC4BepMmTTB9+nQUKFAA8+fPd8gCcZLibj5DCk5USXxglZOcIYWISEdj0D3Zg+6kcuUC5ELi888Bd3dt25o1QMOGwPHjcAQShI8eHYiQkCGIixujbmWdwTkREWWGwZD1Nv99+/YhIiICHTt2NG3z8vJC+/btsWnTJofsQa9QsELSGVJOJg3QBWdIISLSSQ+6u/46ItmDbilubsD//gds2AAULqxtO3VKS3//7TeL/RkiIiJncfasNnd4hQpaUGskKfKXLl1CZGQkHG0OdGOKu2kGFElxD8+rLVc4C3hoPTecIYWISAdj0A36m2bNw9474HRatwb27AG6dAEOHgTCw4HOnYFx44APPtACeSIiIkJYWJjqMff29k5yNHx9fVXxuLt37yKXZKllkfzu/fv3LXKEH0h9GbPb1Jy+ddq0XNy7uPrbJUr44NKlCCDBoPWi19sHeMYC5c8Bp6qo+y21j3qSmeNFPGZ8n9keP5uJwu6HmZbdEtzS/C625DGT85JbJuNABujWUK4csHWrNi596VJ5RYDRo7Vp2BYulHxyq/xZIiIiAmJiYnDcwkPMzp8/n+Z9p69rAXo+z3y4fO4y5N/TT/th7lwZc47EAF1UPQHDmSrqfkvvo56kd7yIx4zvM/vhZxM4ey0x6+nOzTsZfhdb6phJg3RmMEC3Fh8fbcq1Rx/Ves4lSF+2DDh5Eli5EiifOA2LnkkVWil0I2PpJB1P5kSXadeksjvHpxMRUU5IT3l0dLQqFmfeiy4969LTkD9//mw9r6enJypVqmSRF0d6TuTirFy5csidO3eqqZJXf79qGn9evXp1tTx2bEXs2HEXhw5dQ/zZCkCsO+ARB1Q5hVqhxTB2bEenPI9mdLyIx4zvM/vgZzPRWc/EAL1UiVKm721rHrPTpxMzrTLCAN2aJI3hvfeA2rWBF16QKw7g8GGgfn3g55+Btm2h9+A8MHChqkKrCt0ACA0NU1PErFhxItMV3o1B/uzZe3DlSgRKlNiIAQPqM8gnInJxxrHn586dQ7Vq1ZKMTffz88tWeruQ4D5PnjywJLk4S+05Zfx5XII2tWqlwpVMj5HbzZtfMzVyh56rAFQ+BfiG4Ytf6qFYsQJwZmkdL+Ix4/vMvvjZBAweiWXY8njnyfC7yhLHLLPp7YJF4mzhiSeAnTuBKg8rud66BXToAHz6qdazrlPGKWKMwbmRrMt2uT+zQb4E9TIWT1L95FbWZbvcT0RErqlu3brImzcv1sjMJ2bp6evXr0fLli3hqHOgpzZDypdvDTFt/yt0rU33kYiIErGKO2mkZ0CC9E6dtPW4OGDIEG2cuk6r1JqmiElFZqeIsUSQT0RE+iepgGvXrlU/Fy9eVNOnGddvScM0gF69eqkp1Iwkrb1///5YsGABFi1ahO3bt2Po0KG4c+cO+vTpA0edAz01T1Z50rS86sQqq+8XERE55jzoTHG3pQIFgFWrgDFjgEmTtG2LFmlzpS9fDpQqBT3JaAoY8/vTGqs+e/beDIN86V0gIiLHdvPmTbz11ltJthnXv/32WzRq1Ajx8fGIkwZqM3379lXVbSVIl0BexgLOnz8f/v7+cNQp1lLjn98fj5Z4FPuv7Mfey3txMewiSvnq57zPmjNE5IrTrHnpcB50Bui25u4OTJwI1Kmj9Z5LWf9du7Rx6VJErmlT6IUE2TLmPL37MxqrnlZwbsR5YImInEPp0qVx4sSJdB+zePHiVMflSS+6/Dgi8x50KRKXnqeqPKUCdLH65Gr0r9/fqWrOEBE5Woq7pw7nQecYdHt57jlg2zagbFlt/coVoFUr4OuvoRfSA24wpF7QQLbL/RmlsWfEGOQTERE5IvMx6GULPDynp+Hpqk+bln87+Rv0gsPRiMhVU9y9dNiDzgDdnqQXfc8eoHVrbT0mRnL9gEGDtGU7k6nUAgJKpAjSZV22y/0ZjVVPj3mQT0RE5Mg96H75/JDLI/2q83VL1kXJvH5q+ffj62DINQr+/jMwfnywXYumWqLmDBGRQ/agu7MHnZIrUgRYtw4YPDhx2xdfAFJE5/p1ux4vSWeTtLagoECULu2rAmq5lXXzdLfMpKlnFOQTERE5mojoCFy7dy3D8edG9+7FIObIw/nZPWKRUP60KZXcnjObZKXmDBGRM41B92SKO6XK0xP47DNgwQLA62GaRXCwNi59vzZWzV7Mp4iJixujbmXdfCxaRmnqcr8E9X5+eWEwyHreFEE+ERGRozl/53ymx58bU8lvbjMrflflpC5mNsnMeZyIyFlEm/WgM8Wd0idF4yQwL1lSW79wAWjWDFiyRNdHLqOx6gMG1FNB/alTb2DXrifVbfIgn4iIyFFIT7ekpbd65lPTtpBDhgx7wCVVPOFsOSDaMzFAd4u3eyp5ZmvOEBE5gxidT7PGMeh607ixNi69USNt/cED4PnngZEjtbnTdSizY9WJiIgcnbHiuaSl34y7aNoevPJuhmnqKlU81hM4U1HbkPceUOqi3VPJeR4nIlcSzR50yjI/P2DjRq1H3ejjj4GnngLu3NHdAc3sWHUiIiJHl6TiecHbpu0JtwpkmKZuShU/UTVxY9UTdk8l53mciFxJDMegU7bkygXMnw98/rk2d7pYswZo2BA4flx3BzUzY9WJiIgcXZKK5wXMGs3vFMgwTd2USn6qMpCQNEC3dyo5z+NE5Cqi2YNO2ebmBvzvf8CGDUDhwtq2U6e09Pff9DN/KhERkatIkoZu7EGPMwBhvinvTyuV/EE+ILS0trHYdbgVucUhYURENhLDMeiUYzJPuoxLr11bWw8PBzp3BiZMABKyPv84ERERZU9iGnpCYg/6nQJAgiHDNHXzVPL8lwJM2xsNusghYURENhLNHnSyiHLlgG3bgO7dtXUJzEeP1tYjIniQiYiIbMCUpp4rEvB+WBDubv5Mp6kbU8nP/LIQuT1yq22HPdcixv2e9XeeiIiQpAed86BTjvj4AD/9BEycqKW/i2XLgKZNgbNneXCJiIiszJim7uYdm7gxMleWZy4pnKcwegf0Vsv3Yu5h3r551tplIiIywx50siwJzN9/XxuD7quNd8Phw0CDBsBff/FoExERWZExTX3wO4+atuXxzJOtmUvebvw23KA1uH++8/MkF41ERGSDKu6cB50s5okngF27gKoPp2q5dQvo0AH49FOOSyciIrIiCcJf7feIaf2lHvWyNXNJlcJV8FTVp9TyxfCLWHp0qcX3lYiIkmIPOlmPBOc7d2rBuoiLk9w7bf70yEgeeSIiIit5EPvAtJzbUxtLnh1Dmww1LU/bPg0JLP5KRGRVHINO1pU/P7BypZb2brRoERAYCFy8aNOjHxERjfHjg+HvPwPu7uPUrazLdiIiImdyP+Z+khT37GpRpgXqlaynlvdf2Y/g/4Itsn9EROSYPege9t4BsgB3d61wXJ06Wu/5/fta+nv9+olF5KxMgvDAwIU4cOAK4uO1qd9CQ8MQFBSMFStOqHF5Bm0GGiIiIodnqQDdzc1N9aK/sPwFUy96q3Ktsv180gO/6+IufHfoOxy+dhh3Iu+YfqQw3VNVnkLX6l3RzL8Z3A3u2f47RESOKoZj0MlmnnsO2LoVKFtWW79yBWjVCvj6a6v/6RkzticJzo1kXbbL/URERM7iQYxZivvD6dKyq1uNbijtW1otrz65GjN3zszyc4SGhWLipomo9kU1NJ7fGLN2z1K98QevHsR/d//D3ai7OHv7LD7b+RkCFwbCb7ofhq0fhnvRnN6NiFwzxd3dzR0GN/31IOpvjyhnAgKA3bu1wFzExAB9+wIDB2rLVjJ37r4UwbmRbJf7iYiInIWletCNVYSDAoNM64PXDsY3+7/J1O9euHsBb6x+AxU+q4BR/4zCyZsnk9wv6ZvFfYqjcqHKSeb7vXbvmuqtrzu3LvZd5jmaiFwvxd1ThxXcBQN0Z1S0KLB+PfC//yVu+/JLoF074No1q/zJS5fCc3Q/ERGRKxaJM+pTtw9GtRhlWn/9t9fTrep+6uYpFZhX+rwSZu+dnaTokaTIL3h6AW6/extRo6JwZdgVnPzfSZx9IxTPun2A3OdqATHaKEcJ6Bt/3RhTt01FfEJ8jv8fRESOkuLuadZo6XJj0M+cOYMJEyZg//798PHxQefOnfH222/Dyyv9Qflt2rTBxVQKnR06dAje3t5W3GMn4OkJfP458OijwIABQHQ0sGmTNl/6ihXadgvy88unxpyndz8REZGzsGQPutG41uMQHh2u0tAlWJZx6SdunkBAiQA1JVter7xYdmwZvjv8nRpnbk7u+1/D/6F/vf4oW+DhULdktWI6d1iBAwe8EB//LFDoJvDsMqDUJRXcD98wHH+f+xvLnltmkQYHIiK996B76bBAnE0C9Lt376JXr14oV64cZs6ciatXr2Ly5MmIjIzEmDFjMvz9Dh064LXXXkuyLaPAnsxI0bjq1YGuXYHLl4ELF4BmzYAFC4CePS12qPr1q6sKwqWW5m4wuKn7iYiInIU1AnQpGDejwwxEREdg/v75iI2Pxeh/Rqf7OxKYv9XoLQxpPEQVgct0rZhbhYEFrwGtNgLNtwJuCVhzeg16/NIDy3ssh4eBdYSJyDnFPMw40muKu9W/fZcsWYJ79+5h1qxZKFCggNoWFxeHsWPHon///ihevHi6v1+kSBEEyLhqyr7GjYE9e7QgXeZNf/AAeP554MABrfq7VIHPoSFDmqhq7ckLxUlwHhBQQt0PxPJVJCIip2DJInHJg/Q5T85RF5DfHvw2zcdJr/qLtV7EqwGvphuYp1srJs4D+KsdcLYC3J7/CQleUfjt5G/os6oPvun8jS6LJxEROXsPutW/eTdt2oQmTZqYgnPRsWNHxMfHY6tUHCfb8PMDNm7UetSNPv4YeOop4M6dHD993rxeaiq1oKBAlC7tqwJzuZV12S73ExEROQtr9KAbyfRni7oswoH+B/Btl2/xQYsP0L1Gd7Qs2xLvNX8PR944gv3992NY02GZCs4zrAVzrgKwpKfpYlUaBoauG6qmbCMicjYxrj4G/ezZs3j22WeTbPP19UXRokXVfRn57bff8PPPP8PT0xP169fHsGHDULVqVSvusRPLlQuYP18bfz5kiKQyAGvWAA0bAitXaqnwOSBB+OjRgeqHiIjImVm6SFxq6pSoo35sUSumVHQdfPbs0+i+tLsa//7pzk9RzKcY3mvxnkX+PhGRXkTrvAfd6gF6WFiYCsiTy58/vxqfnlGRuNq1a8PPzw8hISGYPXs2XnjhBaxYsQL+/v7Z2h9pDb5/P7HVO7seSJq42a1D6dMHhkqV4P3yy3C7eRM4dQoJjRohesECxHXqZLU/69DHzE54zHjM+D5z/s+mnJckrZkcizV70K0hM7ViulYPxNwn56oK8uKDvz9Aw1IN0bZCWzvsMRGRdbj8GPScGDUqcboR6T1v1qyZSo+fP38+goKCsveCxMTg+PHjsJTz58/DIRUrBq9vvkHFoUOR59QpuIWHw+u553Cpf39ckaJ8BuuNfnDYY2ZHPGY8ZnyfOfdnk8VPHY+jBeiZqxWjTfd2OeKyKk6XgAS8suIVHBxwEEXyFLHj3hMRWY7L96BL73l4eMpxT9J7Lr3oWVGsWDHUq1cPR48ezfYLIqnylSpVQk5Jr4lcmEl1+ty5HXQ6Eklp37oVsW+8AY9ly+CWkIBSs2ej+JUriJ4zR3LWLfrnnOKY2RiPGY8Z32fO/9k8ffq0xfaL7JTibsEicdZirBUj1dylYJyMSZe0d+k5l+DcvFbM+y3eR/B/wfjz7J+4FH4Jr696Hb/2+JWZHkTk8OLi49QwHpceg16hQoUUY80lYL9+/bq6z9YkjTBPHsu1dMuFmSWfz+Zk35cuBSZPBj74QHIt4bFiBTzkglHGpVvhNXL4Y2YHPGY8ZnyfOe9nk+ntjsnRetCzUitGqrdLcbras2vjxv0bWHliJebsnYMB9QfYbF+JiKyZ3u7SVdxbtmyJbdu2qbHoRmvXroXBYFAp61khc6jv3bsXtWrVssKeujAZ+/jee1KRTyr4aduOHAEaNAD++svee0dERKTradZyeeSCsymZr6Saas1oyLohOHb9mF33iYjIUhXc9TwPutUD9J49e8LHxwcDBw7Eli1bsGzZMkyZMkVtN58DvVevXmjfvr1pffXq1Rg6dChWrVqFHTt2YOnSpXjppZfg7u6OV82nCiPLeeIJYNcuwFgl/9YtoEMH4NNPVc86ERERJe1Bl/R2Z8iCiIiIxvjxwfD3nwF393Hqdv9P+dA/4A11f2RsJF5c/iJi42PtvatERDkef+7SPegyznzRokUqsJYgfdq0aejWrRtGjhyZ5HEyL3qcTPv1UOnSpXHt2jVMmjQJffr0Ub9Xs2ZNLFmyJNsV3CkTJDjfuVML1oW8JjIlW+/eQGQkDyEREZFZgO4o6e0ZBeeBgQtVlXeZik2KyMmtrO+cUAM1itRUjztw5QBm7ZqVboBfufJXaNhwtbqVddlORKTHFHdPVx2DLipWrIiFCxem+5jFixcnWQ8ICEixjWxEivfJ+PMxY4BJk7Rt334LSPX7X38FSpXiS0FERC7NWCTOWnOg25IUjkte3V3I+qF9t9DnqYE4joGqqrtUd+9eoztK+ZZKEeCbP8elSxEqwJfK8VKczrwIHRGRvUSzB50clrs7MHEi8NNPWiE5sXu3zHcHbNtm770jIiKyK2fqQZeq7qnNjy5k+5p5kehbt69aj4iOQOX/PWFKg5de8o8/3pJmgC/bpQGAiEgPYjgGnRzec89pAXnZstr6lStAq1bA11/be8+IiIjsXiTOEaZYy4hMuZbR/R80HguPqHxq/UGFg4gvf8qUBv/xx1vTDfClAYCISA+i2YNOTqFOHWDPHqB1a209Jgbo2xcYOFBbtlAxGo5VIyIiR5lHNyouyml60GU+9IzuXzT7BOLWtEvc2OkPwCNGBeAxMdqcwtltACAispUYBxiDbvUiceQkihQB1q0DBg9O3Pbll4BU3r9+3WLFaGQ7C8oQEZEjjD93lgC9X7+6MBhSr0Qv2+V+6QVPOFAbOP8wo67wLaDZVos0ABAR2SXF3cAAnRydpyfw2WfAggWA18NiL8HB2rj0/fsz/TSzZu3hWDUiInKKOdCdoUjckCFNEBBQIkWQLuuyXe7XesHdgN+fAOIe9u803wLkC0v3uY0BPhGRHkQzxZ2cksxDv2kTULKktn7hAtCsGbBkSaZ+ff78gxyrRkREDl8gzll60KXCulRaDwoKROnSviqolltZN1ZgN/WCXy8G7GqoLXvGAm3+1hY9DekG+EREuktxd2cPOjmTRo20cemNG2vrDx4Azz8PvPuuNnd6Oq5cuZfu/RyrRkREeuZsAbqQIHz06ECEhAxBXNwYdSvrxunRkqTBb2oJPMilLQccgFvJqxgxoqkK6P388sJgkLT2vEkCfCIiPYhmDzo5NT8/YONG4LXXErdNmQI8+SRw506av1aihE8GT8uxakRE5Bhj0J2hinuW0+Af5NGCdOEG5O26Ee++21wF9KdOvYFdu55Ut+YBPhGRHsRwDDo5PW9vbcq1mTO1udPF2rVAw4bAsWOp/kqfPnUyLEZDRESkV87Yg57VNHi3PY3gHl5I3Rde9Di2Xv3H3rtIRJQh9qCTa3BzAwYNAjZsAAoX1radOqWlv69aleLhgwbVz7AYDRERkUMUiXORHvTkafDx0ePwXe8vTPcN3zBcTT9HRKRnMRyDTi5F5kmXcem1a2vr4eFA587A+PFAfHyWitEQERHplSv2oKemR80eaFhKKxh35NoRfHPgG3vvEhFRutiDTq6nXDlg2zbguecSt40ZA3TvLhOgZ7oYDRERkV4xQNe4ublhavuppuMStDEIkbGRdntdiIgywjHo5Jp8fLQp1yZN0tLfxfLlyNWmDbxCQ+29d0RERJYrEucE86DnRIuyLfBUlafU8sXwi5h3YJ69d4mIKE3sQSfXJYH5e+8Bq1cDvr5qk+HoUVTv1QuGv7U5U4mIiBwRe9CTGt96vGl56o6puBeb/nSqRET2EsMx6OTyOnUCdu0CqlZVh8Lj7l14d+kCfPopkJDg8oeHiIgcj6sWiUtLnRJ10PORnmr5xoMb+PHcj/beJSKiVLEHnUhIcL5zJ+Ief1ytusXFyYSqwKuvApEcq0ZERI7FlXvQIyKiMX58MPz9Z8DdfZy6lfURDT6Au5s23eriM4tx68Ete+8qEVEKHINOZJQ/P6J+/hmXJSg3WrQIaNkSuHiRx4mIiByGqwboEpwHBi5EUFAwQkPDEB+foG5l/fVntuHFmq+ox0mK+4xdM+y9u0REKbAHncicuzsuDRyIqMWLgTwPL2h27wbq1dMqvxMRETkAVy0SN2PGdhw4cEUF5uZkXbYXOfo4vNy12Vi+2vcVLodfttOeEhGljmPQiVIR17WrFpDLlGzi6lWgVStgHiu/EhGR/rlqD/rcuftSBOdGsv3neRfxesDrpkaMSZsn2XgPiYjSxx50orTUqaP1nrdura3HxAD9+gEDB2rLRETkUM6cOYNXX30VAQEBaNasGaZMmYLo6OgMf69NmzaoWrVqip+oqCg4RA+6CxWJu3QpPMP7hzUahlzuudT63H1zERrG6VWJSD9i4hLjDE+DJ/TIYO8dIBdWpAiwbh0weHDiti+/BNq1A65ds+eeERFRFty9exe9evVCTEwMZs6ciSFDhuDnn3/G5MmTM/X7HTp0wE8//ZTkx8tLS5XWI1ftQffzy5fh/cV9iqNHuR6mnir2ohORnsRwmjWiDHh6Ap99BixYABgvxjZtAho0APbv5+EjInIAS5Yswb179zBr1iy0aNEC3bp1w/Dhw9X2qzKMKQNFihRRPe/mP25ubtArVw3Q+/WrC4Mh9ddFtsv94uWKLyOvZ161/PW+r/Hfnf9sup9ERGlhijtRZkl1dwnMS5bU1i9cAJo1k6s+HkMiIp3btGkTmjRpggIFCpi2dezYEfHx8di6dSuceh50FyoSN2RIEwQElEgRpMu6bJf7RQGvAniz3pum3ir2ohORXsQ4QIq7h713gMikUSNgzx7g2WeBHTuABw+A55/XetInTVJV4ImISH/Onj2LZ+W724yvry+KFi2q7svIb7/9plLiPT09Ub9+fQwbNkyNQ8+uhIQE3L+f2MudEw/kXGR2KyKiItStzPsdExmDWLdYuAKDAViz5jnMmrUH8+cfxJUr91CihA/69KmDQYPqw2CINR2nvo/0xex9sxEWHYYFBxbgrXpvoVyBh8VhKcP3GKWPxyzreMw096MTzw1xMXHpnisseczkvJTZzDAG6KQvfn7Axo3Am29qae9iyhTg0CHghx+AggXtvYdERJRMWFiYCsiTy58/vxqfnlGRuNq1a8PPzw8hISGYPXs2XnjhBaxYsQL+/v7ZOtYyFv748eMWfZ3Onz9vWr5z74669TZ4499//4Wr6dy5IDp3bpVkW0jImSTrd6/cRY+yPTDv1DzExsfivbXvYUydMTbeU8di/h4jHjO+z6zj5u2bpuX/zv6HuKtxNvtsZra2CgN00h9vb+Drr4GAAMmnA+LigLVrtR72FSuAGjXsvYdERGQho0aNMi1L77lUgJf0+Pnz5yMoKChbzyk98ZUqVbLI/knPiVyclStXDrlza+ns8Zvj1a2Ptw+qV69ukb/jLMyPV1D5ICy9sBR3ou7g99DfMaHDBFQsWNHeu6g7qb3HiMeM7zPryH0y8TNWo2oNlMpXyiafzdOnT2f6sQzQSZ8kBeR//wMeeQTo3h24eRM4dQpo3Bj47jvg6aftvYdERPSQ9J6Hh6ecgkt6z6UXPSuKFSuGevXq4ejRo9k+vpJGmCePZYu3ycWZ8TmN06z5ePlY/O84CzlehfMUxtCmQzH6n9GIS4jD1N1TsajLInvvmm6Zv8eIx4zvM+uId9MaWIWvj2+mPnOW+GxmpfApp1kjfZN50mVcusybLuQCsHNnYPx4ID7xA0ZERPZToUKFFGPNJWC/fv26us9Zi8S50hzo2TW40WAUyl1ILX936DucvHnS3rtERC4sOi7atOzlrs/pPBmgk/6VKwdIFeDnnkvcNmaMth6hFeohIiL7admyJbZt26bGohutXbsWBoNBpaxnhUzLtnfvXtSqVQt6pArQPZxmzZWmWMsuX29fDGsyTC3HJ8RjXPA4e+8SEbmwGPMq7u76rOLOAJ0cg4+PNuXaRx9p6e9i2TKgaVMpH2zvvSMicmk9e/aEj48PBg4ciC1btmDZsmWYMmWK2l68eHHT43r16oX27dub1levXo2hQ4di1apV2LFjB5YuXYqXXnoJ7u7ueFWm39QhmTZM0rVdbYq1nBjUcBAK5y6sln84/AOOX7dsAT8iosxiDzqRJUlgPnKkXNFJaWBt2+HDQIMGwJ9/8lgTEdmJjDNftGiRCqwlSJ82bRq6deuGkfKdbUbmRY+Twp8PlS5dGteuXcOkSZPQp08f9Xs1a9bEkiVLsl3B3ZZzoLMHPXPyeefDiGYj1HICEjBuE3vRich+jaxGnAedyFI6dQJ27tTGop84Ady6BXToAEydCrz9dmIPOxER2UzFihWxcOHCdB+zePHiJOsBAQEptumdMb1dMEBPX0RENGbM2I65c/fh4vUwGN7Oi/jcEfjpyE8Y1WIUaharafXXi4gotR50D4NHlgq32RJT3MkxVa2qBelPPKGtS8G4d94BeveWORHsvXdEROSkjBXcBYvEpR+cBwYuRFBQMEJDw5AQ5Yn4TU1Nveij//rQBq8WEVHqY9D12nsuGKCT45I095UrgfffT9z27bdSrQgIDU3zgmH8+GD4+8+Au/s4dSvrsp2IiCgj7EHPnFmz9uDAgSuIj09I3LinPhDhoxZ/PbkMB68c5BuOiOzSg+6l0wruggE6OTZ3d2DiRODnnwHj/IQyLVv9+sC2bem25stFg9zKumxnkE5ERBlhgJ458+cfTBqcixgvYEtz0+qHG9mLTkT2GYPuqdMK7oIBOjmH7t21gFymZBNXrwKtWgFff216iIyDS9Gar7LjE9R2uZ+IiCizReKY4p62K1fupX6H9KKH5VOLK0+sxO6Lu/mGIyKb96B7MsWdyAbq1AF27wZat9bWY2KAvn2BgQPVshSpSdGa/5Bsl/uJiIjSwx70zClRQktlTyHWE9jU0rQ6+p/RfMMRkc3HoHsxxZ3IRooUAdatA956K3Hbl18C7doh5uLldH/10qVw6+8fERE5T5E4zoOepj596sBgSL1CstuBuiiAEmp53Zl12PzfZsu/UERE6fWgM8WdyIY8PYFPPwW++QbwelgAYtMm7DF8jUdxKc1f8/PTUu6IiIjSwh70zBk0qD4CAkqkCNJl/dHapfFRh/GmbaP+GYWEhNQz3IiIrDEGnT3oRPYgU65t2gSULKlWS8fdxhZ8g544nOKhcsHQr19dO+wkERE5EgbomZM3rxeCg3sjKCgQpUv7qvOs3Mq6bH+9YW9UKVxFPXbTf5vw17m/rPq6EZFjSrBw4x2nWSOyt0aNtKrujRur1TyIwY9YhsnYAAPi1Ta5aJBW/iFDmth5Z4mISO9YJC5rQfro0YEICRmCuLgx6lbWZbuHwQNjW401PXbU3+xFJ6KktodsR7GpxdDimxb4785/Fjk8nGaNSA/8/ICNG4HXXjNtehdb8Tt+QE0/D1NrvlwwEBERpYc96JbzXM3nUKtYLbW88+JOrDqxim8+IjKZuHkibty/gS0XtqDh1w2xI3QHciIuPg4J0HrkOQadyN68vbUp12bO1OZOB/A4TuOIzyKM7laMwTkREWUKi8RZjsHNgAltJpjW3//7fXUBTUQUHhWODWc3mA7EtXvX0GphKyw5siTHveeCY9CJ9MDNTarWAH/+qVV7F6dOaWnwq9hqT0REGWMPumU9VeUpNPVvqpaPXT+Gbw9+y7chEWHN6TWmgDq3R251GxUXheeXPY/p26fnqECc4DzoRHrSqpU2Lj0gQFsPDwc6dwbGj5cJ0e29d0REpGMM0C3Lzc0Nk9tONq2P2TgmyTh/InJNv/77q2l5eY/l6PNoH9P6yD9H4vaD21l+TvagE+lZ2bLAli3Ac88lbhszBujeHYiIsOeeERGRo6S4P+zVoZxpUbYFnqj8hFoODQvFF7u/4CElcmFRsVH4/eTvarlgroJoW74t5j01Dy/WetHUE37w6sFsV3AXHINOpEc+PsCSJcCkSVr6u1i+HGjSBDh71t57R0REOsQedOv4qO1HcIN2Lp60eRLuRN6x0l8iIr37+9zfCI8OV8tPVX1KBdOSbdOqXCvTYw5dPZTl52UPOpEjkMD8vfeA334DfH21bUeOAA0aaGPViYiI0gjQc3uyB91SahWvhZfrvKyWb0fexpStU/i+I3JRv5qlt3ep2sW0XLt47RwF6ByDTuRInngC2LULqFpVW791C+jQAZgxA0jQpmMgIiIyHx+dxzMPD4gFjWs1zlRZ+dMdn6p0dyJyLXHxcVh5YqVpGFGHSh1M99UsWtOUaZOdFHf2oBM5GgnOd+7UgnUhBePeeQfo3RuIjLT33hERkd560DkG3aLKFiiLgQ0Gmsb6v//X+5b9A0Ske9tDt6sp1YQE5+YNoT5ePqhcuLJaPnLtSJanZUwyBt3gCb0y2HsHiHQlf35g5UrgfbOLgm+/BVq2BC5etOeeERGRjorESU+vu8Hd3rvjdEa1HKWKQonFhxZj98Xd9t4lIrKhX48nprc/U+2ZFPcb09wjYyNx+tbpLD03e9CJHJW7OzBxIvDzz0Ceh612u3cD9eoB27bZe++IiEgHPehMb7eOQrkLIahVkGl9yLohSOBQMyKXIJ/1Xx+OP3d3c8eTVZ5M8ZjaxRLHoWc1zT3JGHR39qATOR6Zck0C8nLltPWrV7U51OfNs/eeERGRnQN0prdbzxv130DVwlpNmK0hW/HLsV+s+NeISC8OXzuMc3fOqWWp2C4NdsnVKVEn24XizHvQmeJO5Kjq1AH27AHatNHWY2KAfv2AgQOB6MQPORERuVaROPagW4/0bE19bKppfcSfI1Q6KxE5t7Wn16ab3p68knuWe9DNxqAbC1LqEcegE2WkcGFg3TrgrbcSt335JdCuHXBNK2JBRESugSnutvFE5SfQrkI7tXz+znl8tuMzG/1lIrIX84C7ZdmWqT6mbP6y8PX2zXkPOlPciRychwfw6afAN98AXg9b3DZvBurXB/bts/feERGRjcZHGovEcQ5063Jzc8P0x6bD4Kb1JU3YPAEXw1islciZSWV24WHwQNUiD6c+TuW7wdiLfuHuBdyJvIPsjEFnDzqRs5Ap1zZtAkqW1NZDQoDmzYEff7T3nhERkZWZp1kzxd36ahWvhb51+6rliOgIDF0/NFO/FxERjfHjg+HvPwPu7uPUrazLdiLSJ0k/P379uFqWGhTpBdDmheKy0ovOadaInFWjRtq49MaNtfUHD4AXXgBGjADisjYfIxEROeYc6AzQbWNim4kokqeIWv7p6E/YcGZDuo+XIDwwcCGCgoIRGhqG+PgEdSvrsp1BOpE+nbx50tTDXat4rXQfaz4OPSsBOqdZI3Jmfn7Axo3Aa68lbvvkE+DJJ4Hbt+25Z0REZCXG9HbBKu62UThPYUxpN8W0PvCPgYiKjUrz8TNmbMeBA1dUYG5O1mW73E9E+k1vF48UfQTpMa/kfvBK5gvFcZo1Imfn7Q18/TUwc6Y2d7pYuxZo2BA4dszee0dERBbGHnT76BXQC838m6nlU7dO4ZNtn6T52Llz96UIzo1ku9xPRPqcYs2oVgY96I8USwzgD11jDzoRmXNzAwYNAv78EyiipeDh9Gkt/X3VKh4rIiInnGJNsAfddqRQ3JdPfAl3N60xfOLmiTh7+2yqj710KTzd58rofiLSQYBeLP0APa9XXlQsWNHU8x4Xn7khphyDTuRKWrUCdu/W5k0X4eFA587A+PHSZG/vvSMiIgtgD7r9yJjTtxq9ZSrWJ6nuUlU/OT+/fOk+T0b3E5F9U9x9PH1QtkDZDB9vTHOX7+Uzt89k6m9wDDqRqylXDti6FXjuucRtY8YA3btL1Rp77hkREVkAA3T7CmoVBL98fmp57em1+Pbgtyke069fXRgMbqn+vmyX+4lIX2SWBmNWzCPFHjFNr5ie7FRy5xh0Ilfk4wMsWQJ89JGW/i6WLweaNgXOpp6OR0REDlgkzjO3XffFFeXzzofZT8w2rb+19q0Uc6MPGdIEAQElUgTpsi7b5X4i0pdj14+lOr7c0oXi2INO5KokMB85Eli9GsifX9t2+DDQoIE2Vp2IiBwSe9Dt76mqT+Gl2i+p5btRd9F/df8kqe5583ohOLg3goICUbq0rwrM5VbWZbvcT0T6cvhq5sefpzrVWiYLxXEMupkzZ87g1VdfRUBAAJo1a4YpU6YgOjo6w4MoX7hz585Fq1atULt2bfTo0QMHDhzI1AtAZHedOgE7dwJVq2rrt24BHTrIHDDy5rb33hERURaxSJw+fPb4ZyiRt4Ra/v3U7ylS3SUIHz06ECEhQxAXN0bdyjqDcyLHr+BuVK5AOVUsTrAHPYvu3r2LXr16ISYmBjNnzsSQIUPw888/Y/LkyRn+7rx58/D555+jd+/emDNnDooWLYrXXnsNISEhWd0NojRFRERj/Phg+PvPgLv7OHUr67I9xyQ4lyBd5kcXUjDunXeA3r2BB4mpkkREpH/sQdeHQrkLYc6Tc9JNdSciB50DvVjmUtxlnHr1ItXV8oW7F5Kkr6eFY9AfWrJkCe7du4dZs2ahRYsW6NatG4YPH662X716Nc0DGBUVpYJyCcglQG/SpAmmT5+OAgUKYP78+Zl64YgyCsgNhrEoVOhjfPjhRoSGhqk5UuU2KCgYgYELLROkS5r7ypXABx8kbvv2W6BlSyA0lC8SEZGDYICuH09XfRov1nrRlOreZ1UfxCdw1hQiR+5BL+ZTTP1kln9+f3WbgARcCr+U4eM5Bv2hTZs2qeBaAmujjh07Ij4+Hlul4nUa9u3bh4iICPVYIy8vL7Rv3149J1F2SMAtgbcE4BKIS6Z5TEx8ioxzCdQPHLiCGTO2W+ZAGwzAhAnA0qVAnjzatj17gPr1tcrvRESkeywSpy+fd/zclOq+7sw6TN021d67RERZdO3eNfWTlfHnRv6+WoAuQu6GOM0YdA9r/4GzZ8/i2WefTbLN19dXpavLfen9nqhQoUKS7RUrVsSiRYsQGRmJXLlyZXl/ZFz7/fv3kVMPHqYnG2/JMY7ZlCnbVOAtAXhG5DGzZ+/B0KENLLcDnTrB7e+/4d2jBwz//QdcvYqE1q0RPWMG4l59VZfHzNHwmPGYOdr7TM5LbsZZH0jX2IOuv1T3xc8sxmOLH1M9aB/8/QFalm2JxqUb23vXiMiK6e2pBuhhIVnqQfd0d+EAPSwsTAXkyeXPn1+NT0/v96TH3NvbO8l2eS65mJHfzU6ALmPhjx8/Dks5f/68xZ7LVdjzmM2ZszdTwbnRlSsRFn2/KB4ecJ8/HxVGjoTvnj1wi4mB96BBuLZxI0KHDkWCZ8ovDL7Pso7HjMfMkd5ncr4jxwrQc3twmjU9aFehHd5r/h4mbZmE2PhY9PylJ/b334+CuQvae9eIyEoV3JOnuGe6Bz0+sQfdy93LdQN0vfH09ESlSpVy/DzSayIXZuXKlUPu3DxJO8oxu3Hj9yw9vkSJvKheXStAYXF//omY99+H55dfqtViv/yCwpcvI+q774BixXRzzBwNjxmPmaO9z06fPm2x/SLbVXHP4/lwuBLZ3djWYxH8XzC2hmzFf3f/w+u/vY5fuv/CzBQiJ63gbpEedFdOcZce7/Dw8BTbpQdcetHT+z2Zik2KxZn3okvPuqQCpve76ZHfzWMcA2wBcmFmyedzBfY8Zn5++dTY88yQuVMHDKhv3X394gttfvT+/YHoaLhv3Yo8UjxuxQqgbl3Tw/g+yzoeMx4zR3mfMb3dcdyPTexBZ4CuHx4GD/zw7A8ImB2A25G3sfz4cszcNRODGw22964RURZS3GsUrZHtHvTQsFCn6UE3WPsPyBjy5GPNJWC/fv16ivHlyX9PnDt3Lsl2eS4/P79spbcT9etXVwXeGZHHBASUwJAhTax/0GTKNSl86Oenrcs0gs2aAT/+aP2/TURE2ZsH3ZNZTXpSJn8ZLOi8wLT+zrp38NfZv+y6T0SUPpl5wRigVyhYwTSveWaVzFsS7m7uTjcG3eoBesuWLbFt2zbV8220du1aGAwGNJMgJA1169ZF3rx5sWbNmiTjx9evX6+ekyg7JOCWwDt5kC71mTw9DWp76dK+CAoKRHBwb+TNa6PWtUaNtKrujR8WtomMBF54AZ6jRgFxcbbZByIXn3bR3X2cupV1i0yxSE6HReL0rUu1LhjRdIRajkuIQ/el3XHq5il77xYRpeH8nfO4F3MvWwXihLvBHX75/LJVxd2le9B79uwJHx8fDBw4EFu2bMGyZcswZcoUtb148eKmx/Xq1UtNoWYkae39+/fHggULVNX27du3Y+jQobhz5w769Olj7d0mJyUBtwTeEoBLIG4MyMeObYVbt95FXNwYhIQMwejRgbYLzo1KlgQ2bgTM3t+eM2ag0pAhwO3btt0XIhecdlEKSMqtrMt2BumUHIvE6d+ktpPwZJUn1bKkuz+95GncjUy7KDER6SO9PasF4oxK+5ZWt9fvX0dkbCQym+Ku5zHoVg/QZay4BNju7u4qSJ82bRq6deuGkSNHJnmczIsel6ynsG/fvhg0aJAK0vv164crV65g/vz58PdPHG9AlFUSeEsALoG4XQPy1Ei9hXnzgFmzVLV3kX/bNuQKDASOHbP33hE5lRkztqc67aKsy3a5nyitedBzeXConR5Jj9r3Xb9HzaI11fq/N/5Fz2U9ERfPbDQivTlx40S2x59nZxy6eYq7l6tXcZe5yxcuXJjuYxYvXpxq4RzpRZcfIpch+fYDBwI1ayKhe3e43bgBw5kzWvq7VHh/+ml77yGRU5g7d1+a0y7KdrlfGu+IkvegS4E4FvfTL19vX6x6fhUazmuImw9uYu3ptXjz9zcx+8nZfN2IdOTkzZOm5SqFq2TrOfzNK7nfDUGlQpUyleIuxSVdtgediLKpVStEbt6M+1UefmHJbAidOwPjx0v0wMNKlEOXLoXn6H5y3SJxnANd/6Tg1C/P/WK6CJ+7by7e/+t9e+8WEZk5dSuxRkTlQpWzdWz8szDVmrEHXdLb9dzIygCdSMcSypTBvwsWILZbt8SNY8YA3bvLAFp77hqRw5NpF3NyP7l2DzrpX6tyrbD4mcVwg3YhPnnrZEzZOsXeu0VEyXrQi/kUQ/5c+XOc4h6SQaE44xh0PVdwFwzQiXQuIVcuRMsQkY8+0tLfxfLlQJMmMu+gvXePyCmnXZTtcj9RagE6p1hzHD0f6Ykvn/jStP7un+9i3t55dt0nIgIioiNwOeJyjtLbs9uDrufx54IBOpEjkMBcCiuuXi2VF7VtR44ADRoAf3GeVyJLTrso67Jd7idK7eLO292bB8aBDKg/AJPaTDKt91/dHwsPpF8biYisy3wKxOymt6foQc8gQDeOQddzBXfBAJ3IkXTqBOzcCVStqq3fugV06AB8+imQkHqxKyLK2rSLsi7bdTGzA+lKbHysQ6RHUkojm4/EsCbD1HICEvDqylfx5e7EnnUicrwCccb0eGPAnVGKO3vQicg6JDiXIP2JJ7R1mZ5Q5krv3Rt4kDgFEBE5+LSLDubMmTN49dVXERAQgGbNmmHKlCmIjk6c0iYtCQlSMX8uWrVqhdq1a6NHjx44cOAA9Byg67n6L6VOCkJNaT8FgxsONm0b+MdATN02lYeMyEELxAmDm8E0F3qGPegcg05EViNp7itXAh98kLjt22+Bli2B0PTngCQisrS7d++iV69eiImJwcyZMzFkyBD8/PPPmDx5coa/O2/ePHz++efo3bs35syZg6JFi+K1115DSEj6F1q2Fp8Qr3peBQN0xxMREY0JEzZhWb+ywJYWpu3DNwzH2I1jVUMRETleD7p5mvudyDtqbHtmqrjrGZuAiWx8gTBjxnY1v7JM4SRVoqUQlYx1zXKPnbs7MGECUKeO1nt+/z6wZw9Qvz6wbBnQrJm1/htEREksWbIE9+7dw6xZs1CgQAG1LS4uDmPHjkX//v1RvHjxVI9YVFSUCsolIJcAXdSrVw+PP/445s+fj6CgIN31ngsG6I51vr14MQweHgbExsZro8FC2wJRnkDbv9Vjg4KDVLGqWZ1m8bUlskMPesVCFXP0XP7J5kKvXrR6umPQWSSOiEwXC4GBCxEUFIzQ0DDExyeoW1mX7XJ/tsiUa9u2AeXKaetXrwKtW0u3FI880cPP3vjxwfD3nwF393HqVtaz/ZmjFDZt2oQmTZqYgnPRsWNHxMfHY+vWrWkesX379iEiIkI91sjLywvt27dXz6knDNAd93wrQXlMzMPg3GhzS2DdY6bVOXvnoPOSzun2vhGR5XvQJbjO6dSV/pms5G7qQdd5HRH2oBPZqBV/6tTtCAuLSnG/BOoHDlxRj5Gxr9kivei7dwM9egB//y1XIjKHFLB/v1ZAzovjacm1L9TlMyafNWFsGFux4gSLwVnI2bNn8eyzzybZ5uvrq9LV5b70fk9UqFAhyfaKFSti0aJFiIyMRK5cubK8P5KufF+yiizgwcPaHhH3EwM3twQ3iz2/szEeL+OtPUyZsi3JZz5N25sC93yAzqsA9zj8ceoPtFjQAr90/QUl85Z0qWPmaHjMHPuY3XxwE7ce3FLLFQtUzPH3afHciVlaZ66fwf2S91M9LxjHoHu4eWTqb1rymMnfl1oYmcEAncjGwUFq5D5Jw8t2gC6KFAHWrQOGDQM++0zb9tVXwNGjwNKlQLFi2X9uIgclDV+pff4s0jBGJmFhYSogTy5//vxqfHp6vyc95t7eSactk+eSixn53ewE6DIW/vjx4xZ9hc6eT2xoiLwfafHndzbnz5+329+eM2dvxsG50aE6cIvwhc9ryxARG4EDVw+g+cLmmFZ/GqrmfzhjigscM0fFY+aYx+zw7cOm5cJuhXP8fZpwN/Hzvv/sfjT0bJhuFlRMZNbOEZY6ZnK+ywwG6ER2CA5SI2PSc8zDQ+sxDwgABgyQAZ6Se6qNS1+xAqhbN+d/g8iBSMNXWp8/izSMkS55enqiUqVKFnku6TmRizO/0n6mbQV8C6B69dTHOLo64/EqV64ccufObZd9uHHj9yw9vmRkLax8eRC6Luuq0mOvPLiCvjv6Yl7HeehStQtc4Zg5Gh4zxz5m+47sMy03qNAgx9+n0YWigd3aclSuqFSf737MfeAPbTl/vvyZ+puWPGanT5/O9GMZoBPZKThITgrGWYwUW6pRA3jmGYn8AamGLEXjFiwAnn/ecn+HSOcyaviySMMYqR7v8PCUx1J6wKUXPb3fk6nYpFiceS+69KxLKmB6v5se+d08eXI2pjE5T+/EMYvent4Wf35nIxez9jpGcj6VoSyZYTC4YcCA+qhfpj52vr4Tz/z0DHZe3Kku5l9c9SKCAoMwOnC0msrJmY+Zo+Ixc8xjdiHigmm5ZomaOd6fysUTp2m7fO9yiueTjNbpn+0wre/cdhXTQndnukizJY5ZZtPbhfW/bYhcWGYv/uUCQaq5W1TDhlpV98aNtfXISOCFF4ARI7S504lcQEYNXxZtGHNhMoY8+VhzCdivX7+eYnx58t8T586dS7JdnsvPzy9b6e3WwiJxjkPOp3JezYg8JiCghLpIFyXzlcTG3hvxcu2XTY+RCu/PLX2OxeOILOjkLctNsSYK5y6MXB65Ui0SZxxuOvmTzaZtUfcScl6k2YoYoBNZUWYu/pNfIFhUyZLAxo1Anz6J2z75BHjySeD2bcv/PSIHulC3SsOYi2rZsiW2bdumer6N1q5dC4PBgGbpTPlYt25d5M2bF2vWrEkyfnz9+vXqOfWEAbrjkPOpnFeTf/alA8vT06C2ly7ti6CgwBSFIuUif1GXRfik/Sdwg/b7y44vQ7MFzXDudtKGJCLKnlM3tSnW3N3cUb5A+RwfRjc3N1Mld5lmTWqYJB9umuCWOAYd8YYktWj0hgE6kR1b8X19vVK9QLAoSRuVKddmztTmThdr12o97MeOWedvEun8Qt2qDWMuqGfPnvDx8cHAgQOxZcsWLFu2DFOmTFHbzedA79Wrl5pCzUjS2mWe9AULFqiq7du3b8fQoUNx584d9DFvWNQBBuiOQ86ncl6V86sE4saAfOzYVrh1613ExY1BSMgQVX8itXOvXOwPazoMq19YDV9vrfjhoauH0GBeA/xz7h87/I+InIcEz8Yp1soXLG+xKc/882sB+r2Ye7gbdTflcFN3s+zROPcktWj0hgE6kZ2Cg7p1S+LixaFpXiBYlHQbDBoE/PmnVu1dSLGKRo2AlSut+7eJdHihbvWGMRcjY8UlwHZ3d1dB+rRp09CtWzeMHDkyyeNkXvS4ZENs+vbti0GDBqkgvV+/frhy5Qrmz58Pf//EeW11F6C7sYSP3slnW86vEohnFJCnpVPlTmpcujEFV6aGar+4Pb7c/aUV95zIuV2JuKKCaEult6c6F/rdkJTDTQ3xKQL0JPfrCM8wRDYIDiR9Rlro5EtA0t6lZz2zhSksqlUrbb50KR534IAMzAG6dAHGjgVGjZKWA9vuD5ENL9RZrd26ZO7yhQsXpvuYxYsXp9pbKb3o8qNn7EF3TdWKVFNB+vPLnsfa02sRlxCHgX8MxL83/sX0DtPhYeClNFFWGHvPReVCicXdLBqgh4WgVvFaSYtGptKDbrxfb3g1TuQArfgWVa4csHUr0KNH4rYPPwS6d9cCdiIiSiE2wawHnUGZSymQqwBWP78aw5sON22buWsmOi/pjPAo/fW+ETlKgG7RHvT8qfegm4abekclPjjWQ9e1aBigE7kimSrixx+Bjz7S0t/F8uVAkyZSPtnee0dEpDtx8Ym9LwzQXY+7wR1T2k/B/Kfnm17/P079oYrHXbibOGUUEaXv1C2tQJy1e9CTDzd1K5g4Lh13Cui6Fg0DdCJXJYG5jA9dvVoGkGrbjhwB6tfXxqoTEZEJU9xJvPboa9jw8gYUzFVQrR++dhhN5zfFsessukqkmx70sJAUw03bdCtg2lbI4KfrWjQM0IlcXadOwK5dQLVq2rpMv9ahAzB9upTatPfeERHpAgN0MmpVrhV2vL7D1Pt3MfwiWn7TErsu7uJBIspkD7q3u3eSoNpaReKEBOEVHk0s2Lz+p//Zd7hpBhigExFQpQqwY4c2P7qIjweGDgVeeQV48IBHiIhcHgN0Mic9f1tf24q6JeuaKry3WdQGf539CxER0Rg/Phj+/jPg7j5O3cq6bCdy9aFCp2+dVsuVClWCwc1yoWj+XPlN0yKev3M+xf3n7pwzLZcrUA56xgCdiDSS5i5Trn3wQeIR+e47oGVLIDSUR4mIXBoDdEquqE9R/NPrH9WjLmTqqE4/dEKd54YgKChYVY6WeZblVtYDAxcySCeXJqnn0XHRFk9vF9IA5hNVQi2fu3Uepct+kqRh7NxtLUDP55UPhXIXgp4xQCcis28EAzBhArB0qVZITuzZo41Ll8rvREQuilXcKTXSY7fmxTXoVFHLQJPg42y9OYgvn1gIS0igfuDAFTXtKpGrstYUaxER0aoB7PKRhynrhgRcvP+fqWHsbtgDUzHH8gXLq+k99YwBOhGl1K0bsH27NiWbuHoVaN0amDePR4uIXBKruFNaYiMNuPxpJ+CwNu8yPOKAnkuAcokptcYgfe7cfTyQ5LKsVSBuxoztqgEMNwonbix809QwNv7T1YiJj3GI9HbBAJ2IUle7NrB7N9CmjbYeEyOTSQJvvglEcxwdEbkWprhTesHBwf3XgV+7AMeqaxs9Y4EXfgBKJy1WdekS500n13XixgnTctUiVS32vHPn7lPBOG6Zpa4XvqluZPu3vwWbNpcvUB56xwCdiNJWpAiwbh3w1luJ2776CmjXDrh2jUeOiFwGA3TKMDiIdweWPQucfJi66xUDvPQdUPyK6bF+fvl4IMllnbxlnR70S8aGr5tJe9CNbsReMi2zB52IHJ+HB/Dpp8DChYC3t7Zt82ZtXPo+puoRkWtggE5pSdIrHucB/PwccPZhL12uKK0nPW84DAY39OunVX0ncuUU9/ze+VE0T1GLPa+fseHLPEAvdMu0mM//vmmZPehE5Dx69QI2bZJvQW09JARo1gz48Ud77xkRkdUxQKe0pOgVj/UEfnweCC2lrecPA55fgtp1C2HIkCY8kOSSHsQ8wH93/jOlt1uyUFu/fnVVAxgicwP38iTpQZftFeolPlaKxOkdU9yJKPMaNtSqujdurK1HRgIvvACMGAHExfFIEpHTYhV3yjA4MBfjBSzpCdzJr62Xuojy72xEHh8PHkhySWdun0ECEqwyxdqQIU0QEFBC+xwae9F9w+HmHa225y39wPRYprgTkfMpWRLYuBHo0ydx2yefAE88Ady+bc89IyKyGlZxp0wFB2YM931R7cBg5PXMq9Z/PbkMQRuDeCDJJSWp4F7IsgF63rxeCA7ujaCgQOSJ1OZCFwPeL6+2Xwg/r9Zl/nOZGlHv2INORFknY9FlyrVZs7Qx6kKKyUkP+7FjPKJE5HSY4k6ZCQ5Kl/ZVgbrcyvru30bhx24/wg1a8D5+03j8cuwXHkxyOdaaYs38czh6dCBGDXjGtK3Vs77wzu2G0LBQhxl/LhigE1H2yNihgQOBDRu0au/i9GmgUSNg5UoeVSJyKgzQKTPBQUjIEMTFjVG3si7bn6zyJKY9Ns302D6r+uDc7aRzpBM5uxM3rTPFWnLmwb80CoSEhSA+Id5h0tsFA3QiyplWrbRx6QEB2npEBNClCzBunEw+yaNLRE6BATrlxNuN30bPR3qq5bCoMPRc1hPRcdE8qOSSPeiVClWy2t+pXLhykr9p3hjGHnQich1lywJbtwI9tYsP5cMPgW7dgHCz6WeIiBwUA3TKCalYPefJOahYsKJa33VxFz746wMeVHK5AL1UvlLI66XVZbCGSmbB/6lbp3DuzjmHquAu2INORJaRJw/www/A5Mla+rv49VegSRPgzBkeZSJyaHEJiTNVeBhYiZuyTopTLem2BJ4GT7U+dftUrDm1hoeSnN6tB7dw4/4Nq6e3izyeeeDv629qFDh/RysQJ5jiTkSuRwLzd98FVq8G8j+cWuboUaBBA22sOhGRg2IPOllCfb/6mNJ+imn9lRWv4HLEZR5ccmrWrOCe3jh0aRjYe3mvaTtT3InIdXXqBOzaBVSrpq3L9GuPPw5Mnw4kaHNgEhE5EgboZClvNXoLT1V5Si1Lr+KQDUOQwHMjOTFrV3BPrnKhxHHoweeDTcvsQSci11alCrBjB/CUdhGiCsYNHQq88grw4IG9946IKEsYoJMlx6Mv6LwAxXyKqfXfTv+GPy//yQNMTuvEjRM2DdCrmP2NB7HaNWdxn+LI7ZkbjoBj0InIeiTNfcUKYNSoxG3ffQe0aAGEhPDIE5HDYIBOllQkTxHM6jjLtD7lyBRcv3+dB5mc0slbiT3o1h6DnlYjgKMUiBMM0InIyt8yBmD8eGDpUq2QnNi7F6hfH9iyhUefiBwCA3SytG41uqFr9a5q+Xb0bQz/ezgPMjl1irsU2LRFmnmVVAJ0R0lvFwzQicg2ZMq17duBcg+/IK9dA9q0AebO5StARLrHKu5kjVT3Lzp9gYK5Cqr1pceXYtWJVTzQ5FTiE+Jx6uYptSzTDNpiFoxyBcrB3c09yTZHKRAnGKATke3Urg3s3q0F5iImBujfH3jjDSA6mq8EEekWe9DJGkrkLYEpbRKrug9YPQB3I+/yYJPTCA0LNY0Dt0V6u/B090SFghVgjgE6EVFaihQB1q0D3norcdvs2UDbtsDVqzxuRKRLDNDJWp6v8TyaFm2qlmXKtbHBY3mwyWnYeoq1tNLcmeJORJQeDw/g00+Bb74BvLy0bTIeXcaly/h0IiKdYYBO1kx1f6/We8jlkUutz9w1E8euH+MBJ6dg6ynW0vpbLBJHRJQZvXsDmzYBfn7aemgo0Lw58MMPPH5EpCsM0MmaSuYpiaENh5rea4PXDObc6OQUbD3FWmp/yw1uKJO/DBwFx6ATkX01agTs2QM0bqytR0YCL74IDB8OxMXx1SEiXYhNiDUt26LIEbmeIQ2HmNJw/zr3F5YfX27vXSJyuCnWjCoXqmxaLuVbCl7uDzM2HQADdCKyv5IlgY0bgT59ErdNnQo88QRw+7Y994yISImLT2wwZIBO1pDbMzdmdJhhWn9n/Tu4H3OfB5ucIsU9n1c+FPcpbpce9PIOVMFdMEAnIn3w9gbmzQNmzdLGqAspJtegAXD0qL33johcHFPcyRY6V+2Mxyo+ppYv3L2Aj7d8zANPDisqNgrn75w3BcxSb8FWSvuWRpdqXeBp8ETfun3hSBigE5F+yBf3wIHAhg1atXdx5oyW/r5ypb33johcGAN0sgUJYD57/DNTlsbHWz9WgTqRIzp165SaB93W6e3Gz9KvPX7FnZF38HKdl+FIGKATkf60aqWNSw8I0NYjIoAuXYBx44B47YueiMiWGKCTrVQrUg1vNdKmIo2Ki8KYf8bw4JNDOnz1sGn5kaKP2GUf8njmgaNhgE5E+lS2LLB1K9CjR+K2Dz8EunUDwsPtuWdE5IIYoJMtfdDiAxTMVVAtf3vwWxy6eogvADmcI9eOmJYfKWafAN0RMUAnIv3Kkwf48Udg8mQt/V38+ivQpImW+k5EZCOs4k62VDB3Qbzf4n21nIAEjPxzJF8AcjiHryX2oNcqXsuu++JIGKATkb5JYP7uu8Dq1UD+/No2KRonxeNkrDoRkQ2wijvZ2qCGg0xzN685vQb/nPuHLwI5ZA96Xq+8DjUPub0xQCdyAhER0Rg/Phj+/jPg7j5O3cq6bHcanToBu3YB1app6zL92uOPA9OnAwkJ9t47InKhFHd3g7td94VcQy6PXBjferxpfcSfI0wFt4j0LjwqHOfunFPLNYvWhMGNYWdm8UgROTgJwgMDFyIoKBihoWGIj09Qt7Iu250qSK9SBdixA3jqKW1dCsYNHQq88grw4IG9946IXCBAl4tMXmiSrbxY60XULl5bLe+5tAc/H/2ZB58cwrHrx0zLtYoxvT0rGKATObgZM7bjwIErKjA3J+uyXe53KpLmvmIFMGpU4rbvvgNatgRCQ+25Z0TkAgG6cforIluQbI0p7aaY1j/4+wPExMXw4JPusUBc9jFAJ3Jwc+fuSxGcG8l2ud/pGAzA+PHA0qWAj4+2TaZlq18fhu1O1iBBRLrAAJ3s5bGKj6Ft+bZq+ezts1h4YCFfDNI9FojLPgboRA7u0qXwHN3v0GTKNQnIy5fX1q9ehXfHjiiyfLm994yInExcQpy6ZQ862ZqbmxsmtJlgWh+/aTyiYqP4QpCusQc9+xigEzk4P798Obrf4dWqBezeDbTVehfcYmJQdtIkeL71FhDtROPviciu2INO9tS4dGN0qtxJLYeEheDrfV/zBSGH6EEvmqcoivkUs/fuOBQG6EQOrl+/ujAYHs4Rnoxsl/udXuHCwNq1wJAhpk2eX38NtGunetWJiHKKATrZ27hW40zLEzdPxIMYFkclfbp275r6EZz/POsYoBM5uCFDmiAgoESKIF3WZbvc7xI8PNSUa1Fz5iDey0vbtnmzGpeOvXvtvXdE5OAYoJO91fOrhycrPa2WL0dchk/gM845rSo5vKPXjpqWHyn6iF33xRExQCdycHnzeiE4uDeCggJRurSvCszlVtZlu9zvSuJeegkn5s5FfMmS2gap7N68OfD99/beNSJyYAzQyd4kCD8zLwBI0BrkE5ptQejVG845rSo5NBaIyxkG6EROQILw0aMDERIyBHFxY9StrLtacG50/5FHECm9500eZg9ERgIvvQQMGwbEalMlERFlRWwCp1kj+5JpU09sdgeO1tA25L0HNNzlvNOqksNigTgHCND//vtvPP3006hVqxY6dOiAZcuWZfg7oaGhqFq1aoqf5557zha7TESOTnrQ//kH6NMncdu0acATTwC3b9tzz4jIAcXFs4o76WRa1Y2tgPiHw9qabQW8opx3WlVy+B70mkVr2nVfHJGHtf/Anj17MGjQIHTr1g3vv/8+duzYgQ8++AA+Pj54/PHHM/z9d955B40aNTKty+8REWWKtzcwbx5Qty4gVd2l93z9eqBBA2DlSqAmTxpElDlMcSd7M02beqMocLgWUOcQkOcB0GA3sLW5c0+rSg4jISHB1INerkA55PN28tmEHDFA/+qrr1C7dm2MG6dVnmzcuDFCQkLw+eefZypAL1u2LAICAqy9m0TkrNzcgDff1IJxmTf9xg3gzBn5MgK++w7o3Nnee0hEDoABOtmbTJsaGhqmrWxuAdQ+BEhHetNtwK6G8CtexN67SIT/7v6HiOgIdSQeKcYCcbpLcY+OjsbOnTtTBOKdOnXCmTNnVBo7EZFNBAZKSg9gbPCLiAC6dAGk8TA+ni8CEaWLATrpalpV6UU/8jD48bkPtwZ7XGNaVXKo8ee1itWy6744Kqv2oF+4cAExMTGoUKFCku0VK1ZUt2fPnkXp0qXTfY6goCAMGTIEBQoUQNu2bTFs2DC1nJO0i/v37yOnHjx4kOSWeMysge8zCx+zokWBDRvg9cYb8PjlF23bhx8idu9eRM+dC+RzzTQsvs/se8zkvOQmmR4OTurNfPrppzh37hz8/PzQr18/PPvss+n+jjTUy7k9uTp16uDnn3+GnjBAJ3uTaVNXrDihCsKpseibWgKPHFG96O6B29F/UB177yIRC8TpPUC/e/euuvX19U2y3bhuvD81Xl5eeP7559G8eXP1+IMHD2L27Nk4cuQIli5dCk9Pz2ztkzQYHD9+HJZy/vx5iz2Xq+Ax4zGz+/vs3XdRvEQJlPriC7glJMBj1SrEHDmC09OnIzqDRkNnZo3P5v37sfjhh7NYvvwCbtyIRJEiudC1axm88EIF5Mlj9VFWDnPM5JznyJy93kx8QjwSkKCWPQyO/74lx55WVaq1S0G4S5fc4H0uAA8qHECsdzh+OLEQbzd+2967SS4uyRRr7EHPliyfZcLDw3Ht2rUMH+fv74+cKFasmOo9N2rYsCEqV66M/v37Y8OGDSpNPjsksK9UqRJySnpN5MKsXLlyyJ07d46fzxXwmPGY6ep99vHHiGrTBt6vvgq3u3eR++xZPPLqq4hatAjxqfToOTNrfTZlTt4OHZbg0KFrWm8PgGvXIjF37ins2HEX69b1dNipAC15zE6fPg1H5+z1ZuIStAruggE66WFaVfkRh68+g9qza6vlj7d+jP71+iO3J69Lyf4p7vJdWbVIVb4UtgjQ165di1GjRmX4uD/++AP58+c3BfXmwsK0AhfG+zMrMDAQefLkwdGjR7MdoEsaoTyHpciFmSWfzxXwmPGY6eZ99swzWvE4KRT3779wu30buWRc+iefSC6hVmDOhVj6szlt2u4kwbmRrMv2OXMOmi4yXfmYOXp6u7HejAxBMyfn6dWrV6s09oyGs+kdA3TSq1rFa+HZ6s9i2fFluBJxBfP2zcPgRoPtvVvkoqJio3D8upapXKVwFXi5O2YjvMMF6N27d1c/mT1pS4+1jDVv0aKFabusi+Rj04mIbK5KFWDnTuCll4DfftMKxg0dCuzfL5POSgTGFyWnc/amwjhnr6MH6OTc9WaM2RLG8efCLcHNYs/tjFjTwvbHbFjDYSpAF5O3TMZL1V9CLo9ccGZ8n+nzmO26tAsx8TFqOaBYgMN/Vz6wU70Zqw6kkjF1MqZs3bp16NWrV5LedTlxZ7VF/Z9//lEvdK1arAhIRBYkdTFWrJAoARg/XtsmU7D9+y+wfLmM2eHhzoaM5uTlnL3OwRXqzZj3oEfej7Toczsr1pux3THzhCcCiwci+GowLkdcxicbPkG3ct3gCvg+01e9me/OfA8007aFbMuFvcUPs95MNurNWL3SyRtvvIFXXnlFtY537NhRpcFJytuMGTOSPK5GjRro0qULJk2apNYnT56sWhlkTJqctA8dOoQ5c+bgkUceQbt27ay920ROScYEJxaXCVdzqsq0LFIZ1lHHAluMwaBNuVanDiANivfuadOy1a8PLFsGNG9u7z107Dl707if9In1ZhJJz8m+E/tM6wV8C6B69ep2eV0cAevN2OeYTSw4Ec0Xa+ep7y98j3cfe9ep04v5PtNpvZlnz5m2b1nijcH/7Ge9mWzUm7F6gF6/fn3MnDlTTb3yyy+/qKlXJkyYoIJ1c3FxcYg3m4tYeth//PFHNc1KZGQkihcvrqrDDh48GB4erKBKlJ0v0MDAhYnTs6gpjsIQFBSspm2RyrAuH6QLmRZK0t5lXPq5c1LVDGjTBpg5E+jfn2+8LJDGH3l/pZbmLnP5cs5e/WK9mbR70L09vVl7JhNYb8a2x6xZhWboVLkT/jj1B0LCQvDLqV/wet3X4ez4PtNTvZl4wD9E2xjlhYQrRXHoKuvNZKfejE0iXRlPlto8p+ZOnDiR7bHuRJQx6Tk3D86NZF22y/0cD/yQDKPZvRt47jmZ3FnyZYEBA7Rx6Z9/LjlKfMtlZ85es+A8IKCEup/0ifVmkmKROHIEo1uOVgG6mLR5EnrV6QVP9+wNEyHKcr2Z/HcB34dD2y6WAhIMiE9gvZnsMGTrt4jIKQt2kZnChYF164C3zeaUnTNHWhyBq1d5qLIwZ29QUCBKl/ZVgbncyjozNpyHeb0Zc85Ub8a8SBynWSO9aly6MdpXaK+Wz905h+Lte8HdfRz8/Wdg/PhglUlHZGmmejKlQxM3hiTW7mG9maxjrjiRi2DBrmyQ4TRSL0PmZ5b09qgoYMsWbVy6FJWrV8/yL5STz9lLzsnZ682wB50cxbAG72HD2Q1q+fYj64CNlTmcjWxTb8aY3p4sQGe9maxjDzqRi8joC5JfoOmQonGbNslB0tZDQ7Wicd9/b9HXiMhRGevN7N27F3369FHBeWbrzUgw//777+P111/HTz/9pOrNLFq0SFf1ZpIE6G762S+i5HYuNQDny2krhW8BjxxJMZyNyJKknoxkyCUJ0EO1zCnWm8kenmWIXAQLduVQw4ZaVXcpIrd9OxAZqc2dfuCAdAMC7u6WeaGIHJQz15thDzo5CjVczSMQ6P1wKq2Wm4Ajj2jjgR8OZ2NGE1mS1JNZvuowDpS4om24XgSIzM16MznAHnQiF/oClcJcqpXTDAt2ZUHJkjJAFujTJ3Hb1KlAp07A7dsWe62ISF8YoJNDDWeTHvT/ymgbit4Aah5Nej+RhYeyfby4JuD+MDsq1J/1ZnKIATqRi2DBLgvx9gbmzQO++EIboy7WrwcaNACOJl4EEZHzYJE4chTacDU3YGOrxI2BwYCbFjxxOBtZw8Gbe0zL88a8iZCQISpTg9P3Zg8DdCIXLNglX5xxcWP4BZpdMpflm28Cf/4JFCmibTtzBmjcWCseR0ROhT3o5HDjgc+VT9GLzvHAZC3bQreZlpv6N+WBziEG6ERE2RUYqI1LlyrvIiICeOYZYOxYqcjD40rkJGITOM0aOdpwNkOyXvRNqBNQTN1PZEkJCQnYHqIVHyyQqwCqFanGA5xDDNCJiHKibFlg61agZ8/EbUFBWjG5cI71I3IG7EEnRxzOViqmDnDB2It+HYPn5GXKMVnc+TvncfXeVbXcqFQjGNwYXuYUjyARUU7lyQP88APw8cda+ruQVPcmTbTUdyJyaHHxZtOsGTgBDjnGcLbQkHfw5wcLTNun7JyU5L1MZAnbQpjebmkM0ImILEEC8xEjgN9/B/Ln17ZJ0TgpHrdhA48xkQNjDzo5qjbl26B5meZq+fiN4/j56M/23iVyMttDtfR20aQ0h1BYAgN0IiJL6tgR2LULqPZwDJZMv/b448D06TJQi8eayAFxDDo5Kjc3NwQFBpnWP9z4IWLiYuy6T+RctoZsVbducEOj0o3svTtOgQE6EZGlVakC7NwJPPWUti4F44YOBV55BXjwgMebyMGwB50cvRe9VTmtYNypW6ew6OAie+8SOYmQuyE4cOWAWg4oEQBfb19775JTYIBORGQNvr7aOPTRoxO3ffcd0LIlEBLCY07kQBigk6P3ok9sM9G0PjZ4LCJjI+26T+Qcfv33V9PyM9Weseu+OBMG6EREVvuGNQDjxgG//AL4+GjbZFq2+vWBLVt43IkcBIvEkaOTuamfrPKkWg4NC8XsPbPtvUvkBJYdX2Za7lq9q133xZkwQCcisjaZcm37dqB8eW392jWgTRtgzhweeyIHwB50cgYTWk8wLU/aPAnhUZwKlLLvasRVbP5vs1quWrgqahStwcNpIQzQiYhsoVYtYPduoG1bbT0mBhgwAHjjDSA6mq8BkY6xSBw5gzol6uD5R55Xy9fvX8dnOz+z9y6RA1t1YhUSkGDqPZehFGQZDNCJiGylcGFg7Vrg7bcTt82erQXtV6/ydSDSKfagk7MY22os3N3c1fIn2z7Bzfs37b1L5KCY3m49DNCJiGzJwwOYMQNYtAjw9ta2yXh0GZe+dy9fCyIdYoBOzqJy4cp47dHX1HJYVBgmbEpMeyfKrDuRd/DXub/Ucpn8ZVCvZD0ePAtigE5EZA8y5drmzUCpUtp6aCjQvDnw/fd8PYh0hkXiyJmMCRyD3B651fKs3bNw4sYJe+8SOZjfTvyG2PhYtdy1GtPbLY0BOhGRvTRooFV1b9pUW4+MBF56CRg+HIiL4+tCpBPsQSdnUtq3NEY0G6GWJcgavmG4vXeJHMzyf5ebllm93fIYoBMR2VOJEsDffwOvv564bepUoFMn4PZte+4ZET3EInHkbIY3HQ6/fH5q+beTv+HPs3/ae5fIQdyLvoe1p9eq5eI+xdUUfmRZDNCJiOxNxqLPnQt88YU2Rl2sX6/1sB89au+9I3J57EEnZ+Pj5YPJbSeb1t9Z906SoRxEaVlzeg0iYyPVcpdqXeBu0IoOkuUwQCci0gOZnuTNN4E//wSKFNG2nTkDNG4MrFhh770jcmkM0MkZvVj7RdT3q6+WD187jPn759t7l8gB/HT0J9Pys9Wfteu+OCsG6EREehIYqI1LDwjQ1iMigGeeAcaOBeLj7b13RC6JATo5I4ObATM6zDCtj/p7lKrOTZSWI9eOYNmxZWq5SJ4iaFWuFQ+WFTBAJyLSm7Jlga1bgZ49E7cFBQHPPguEh9tzz4hcEqu4k7NqXqY5utforpav37+O9/58z967RDo25p8xSECCWn632bvwdPe09y45JQboRER6lCcP8MMPwMcfa+nvQlLdmzTRUt+JyC496LwgJWcz9bGp8PH0Ucuz987Glgtb7L1LpEO7L+7Gr//+qpZL5i2JgQ0G2nuXnBYDdCIivZLAfMQI4I8/gPz5tW1SNE6Kx23YYO+9I3IZrOJOzqxM/jKY2Gaiab3fb/0QFRtl130i/Rn1z6jE5ZajkNszt133x5kxQCci0rvHHwd27waqV9fWZfo12TZtGpCgpZoRkfVwDDo5u0ENB6GBXwO1fPzGcUzekljhnVxLREQ0xo8Phr//DLi7j1O3fcZ9jvVn1qv7yxUoh9frmk0NSxbHAJ2IyBFUrgzs2AE8/bS2LgXjhg0DXnkFePDA3ntH5NQYoJOzk6my5j01D+5u2pRZk7ZMwvHrxzMM3GRdtpNzkNcyMHAhgoKCERoahvj4BISG3sWC/6aZHvNh4Ifwcvey6346OwboRESOwtcX+PVXYMyYxG3ffQe0aAGEhNhzz4icGovEkSuoU6IOhjcdrpaj46LRb3U/03s/9cAtTK3LdgbpzmHGjO04cOCKen1NqpwEylxQi0Xgj5dqv2S/HXQRDNCJiByJwaBNubZsGeCjFfXB3r1A/frAFusW9mHvCbkq9qCTqxgTOAYVC1ZUy1IszpjqnmrgppK5EtR2uZ8c39y5+5K+xn4Xga7LTavxf7WGh8HDPjvnQhigE1GOMXCzg65dge3bgQoVtPVr14A2bYA5c6zy59h7Qq6MReLIVUjhrwWdF6g50sWYjWOw+b/NKQM3M7Jd7ifHd+mS2VSuxa8ALy8Gcj0sGHi6Im5vLW+3fXMlDNCJKEcYuNlRrVrArl1A27baekwMMGCA9hNt2TGB7D0hV8YedHIlLcu2xJiW2lCq+IR4vLD8BVy8fTXzgR05LD+/fNpC0WvAK98CuSO19fNlgZ96oJTfwxllyKoYoBNRjjBws7PChYG1a4EhQxK3SS+6BO1X07+gygr2npArY4BOrkam0WpVrpVaDg0LhXeP3wAkZBzYkUN7vW8A3Goe04Jzn/vaxpDSwA8vwBDnjX796tp7F10CA3QiyhEGbjrg4QFMnw4sWgR4e2vbZDy6jEuX8ekWkFHvCHtPyJmxSBy5YlX377t+jyJ5iqj1yDLH4NZkR6qPNRjcGLg5uNj4WCw+uBg/FnwTCd1/BvJFaHdcKgl8/yIMsbkQEFACQ4Y0sfeuugQG6ESUIwzcdESmXNu8GShVSlsPDQWaNwe+/z7HT51R7wh7T8iZsQedXJFfPj982+Vb03pC+w1wq3IqRXDOwM3xyNCFw1cP46vdX+GFZS+gzIwyeGXFKzhx61/TY7yulIfb96+gdJHiCAoKRHBwb+TNy+nVbIFl+IgoRyQwk6lW0rufbKhBA2DPHuDZZ4Ft24DISOCll4ADB4DJkwF3bY7brJK0NplOJ7UiQew9IWfHAJ1cVcfKHfFus3fx8daPAUM8PF5YhoIr++PGwULq/C7nBulVtWXgJrVvpkzZhjlz9uLGjd/tth+OJCYuBrsu7cKvZ37FqeOnsOPiDtyOvJ3qY1uUaYEPWnyAxyo+Brev3Gy+r8QAnYhyiIGbDpUoAfz9NzBoEPD119q2qVOBQ4eAH38EChXK8lPKhc+KFSdSTLPD3hNyBaziTq5sYpuJOHP7DH459gtiEIn457/HiT+3o1KhSnYrTGt+LjLOxy7nKPbyJnXu9jnM2zcP8/fPx7V719I8rnk886Bt+bYY3nQ4WpRtYeVXkTLCFHciyhEJ3CS9TQK1JF8uTHuzLxmLPncu8MUX2hh1sX490LAhcPRolp9OeiXkwkfS3EqX9lWvr9wy7Y1cAXvQydXHoy9+ZjECywaq9Rv3b6DDdx1wNcJyhUgzi4VpM2fj+Y3o9H0nVPy8Ij7a8lGK4FxqC3Sp1gXTHpuGXa/vwp1372DV86sYnOsEU9yJKEeMgZucNKVgnIxJZ7qZTri5AW++CdSsCXTvDly/Dpw5AzRuDCxeDHTpkuXXevToQPVD5EoYoJOry+WRCyt6rkCLb1rgyLUjOHv7LNp+2xZrXlwD//z+uipM68rnqDuRdzBs/TDVY27Ow+CBThU7oWaumujWoBseLf0o3OQagXSJAToR5RgDN50LDNTGpUtAvn+/5AgCzzwDBAUBo0fbe++IHKqKu7tb9uo4EDm6ArkKqIC8yfwmauq1o9ePquU/XvwDtYvXtsk+sDBt2lb+uxJv/P4GLkdcNm0rm78s+tXrh9cefQ2+Bl8cP34c1QpXY3Cuc0xxJyKnIOPSxo8PRuXKX6Fhw9XqVtZlOwEoU0abeu355xMPhwToUkwuPP0p1IhcnbEHXYJz9jqRKyvtWxr/9PoHFQtWVOsXwy+qXvW/zv5lk7/PGUVSLwDXZ2UfdPmpS2JwHuWNAlufxathCzD40WEokbeETV4fsgwG6ETk8IxFY6RIzKVLEYiPl1b2CLUu2xmkP5Qnjzbl2pQpWvq7WLECuVq3hndIiB1fQSLHKBInaaJErk6Kw23rsw0NSzVU62FRYej4fUd8sesLNX2XtQvTJq9548ozikRER+CpH5/CggMLEjeeqgR88SbubKiFcWO38DrIATFAJyKHx6IxWSCB+fDhwB9/APnzq00GSXl75RUY/vzTWi8RkVP0oDNAJ9IU8ymmetKfqvKUWo+Jj8GgNYPQ7tt2qnK4tbAwbSIp0tdqYSusO7NO2xDrAfzaBfj+RSAsv2lcvlS8l+skchwM0InI4WWmaAwl8/jjwO7dQPXqatUjPBzeMi5dpmNLSP1YErkqBuhEqU/NtbzHcgxuONi07Z/z/6DWV7Uwa9esJLUbLF2Y9oMPmqJYsVwuO6PImVtn0GxBM+y9vFetu0XlBr59GTgYIGtJHsvrIMfDAJ2IHB6LxmRT5crAjh2I7dRJrbrJ2ADpXX/5ZeDBA0u+REQOzRhosAedKCn5THzW8TOsf2k9yuQvo7bdi7mH/635H6p/UR3z981HdJxla8FIED5yZFP88Uc7hIcPQ0jIEFW53dGCc2PtHH//GXB3H6duM1M7Rwr0tV7UWs1Nb6wLgAWvARfKZvs6ifSFAToROTwWjckBX19E//QTLr3+euI2GafeogXAcelECnvQidLXvmJ7HHnjCN6o/4Zp26lbp/D6b6+jwmcVMG3bNFwOT6wu7urMa+eEhoapXm65zah2zs37N/HY4scQEqbVjalRtAa2vbYNpTy1on3ZvU4ifWGATkQOj0VjcthKbzDg8oABiJLA3MdH27Z3L1C/vlb5ncjFMUAnyvjcUqPS1yi5rwfW9PgTrcu1Nj1OKr0P2zAMpaaXQvMFzTFj+ww1j3qChYZTZbcn2tFq50hBuCd+eALHbxxX61JJ/69X/lLz0PM6yLkwQCcih8eiMZZppY+TedK3bwfKl9c2XLsGtGkDzJlj/ReRSMdYxZ0o8+eWD148g1XPrsX2PtvRuWpn0+8kIAFbQ7binfXvoOLnFVF6Rmk8t/Q5fLbjM+wM3Yn7Mfdt1hPtaLVzZJjAsz8/i50Xd6p1mTZt/cvrTdOn8TrIuTBAJyKHZywaI0Vi/PzySoewunW1ojEWaaWvVUsrHte2rbYeEwMMGKD9ROvzQofI2tiDTpT1c0vj0o2xoucKHHvzGEa3HK3Ssc1dCr+EpceW4u11b6Px/MbI91E+NW79+WXP4+MtH+PPs3/i1oNb6R76WbP2ZLkn2tFq50imQZ9VfbD+zHq1nt87P9a9tA4VClZI9TpIiua5avE8Z8EJPYnIKcjJR4rEDB3aAMePH0f16tWRR+b9dnGZaaWXY5ZE4cLA2rXAiBFyFaZtk170o0eBX34Bihe3wZ4T6QcDdKKsn1vknCyqF62Oca3HqZ/j149j+fHl2PjfRuwI3aHStk2/lxCPf2/8q36WHFli2l6uQDk0Kd1Epc23Lt9apXYbzZ9/MNP7oScyJlx6+tO732jS5kn47tB3ajmXRy6sfmE1ahevneZ1kB7/v5Q1DNCJiJxYtivce3gA06cDAQEyyB+IitLGo8u49F9/1W6JXASruBNZ5twiwfoHRT+A/IuNj8WRa0ewLWQb9l3ehwNXDqj1qLioJL9z/s559fPjkR9NVcs7VuiIBrkb4PKV8BTTimVlP+1FxoxLGn5qjQvS+y33i1+O/YJR/4wy3fd91+/RvExzm+4r2R4DdCIinZExc5KWJy3/cnEhLelyspYxZllNU8tKK32qXnlFmytd5ki/eBEIDdUqvM+bB7z0Upb2hchRsQedyMLnlodTtAWUCFA/RjFxMaoHff+V/dh7aa+a51uWzcenyzRj8w7Mg/wzvJMfCYerA3vrATeKZms/7EHO5ytWnEiRni/BeUBACXX/7ou78cqvr5ju+6jtR+havaud9phsiWPQiYh0xNIFbyxS2bVBA2DPHqBpU209MlKbK33YMCA2Nkv7Q+SIWCSOKClrVQ33dPdEreK18EqdV9T86lte24I7795RU4lNbDMR7Su0V2neRvF57wJNdgCDvgBe+B4of1aVo8vpflhbRmPG78RfQ+clnfEg9oF6vByPd5u9a+/dJhthgE5EFqeHKU/0sA+2mnolPRar7FqiBPD330Dfvonbpk0DOnUCbqVfxIec39atWzF06FC0a9cOVatWxbhx4zL9u+Hh4Xj//ffRsGFDPProoxg8eDCuyQwCOiEFmtiDTpSULauGS9DexL8J3m/xvqpcfm3YNSx4YgFaFG8BT4Nn4gOrnAJ6fQv0nwO3aidRJ6C4RffD0oxjxkNChiAuboy6VePHvaLx1I9P4XKENm+8pLTPfXIu3NzSTuUn58IAnYgsSg9Tnty/H4sOHZY43LQr2Zl6JSMWrezq7a0Vi/vyS22MutiwQethP3IkS/tFzmXz5s34999/0aBBA/j6+mbpd99++20V4AcFBWHq1Kk4d+4c+vbti1idZGdI4SrzlFwisl/VcDl/fzplH0Z1voWtA9ui8ILRaBc3AL4JxRIfVPIKEnr+gHwDl+Bk+BGHq3fx4vIX1Xh8Ub5Aefza41d4e3jbe9fIhnimISKb9wBbu8LoDz+cxaFD1+y6DzYv6pYOi1Z2lRb8N94AatYEunUDrl8Hzp4FGjcGFi/WxqqTyxkxYgRGjhyplnfu1ObpzYz9+/djy5YtmD9/Ppo31woflS9fHp06dcL69evVrb1JISsjBuhE9qsabuwAML/GuHI+HtcmlkSdR4Pw1mxfzNr/KfZc2qPu2xSyEfXm1sNLtV/ClHZTUDJfSd2/fO/++S5WnVhlmk7t9xd+R5E8Rey9W2Rj7EEnIl33AGfH8uUX7L4P2ZVRQRvdFLxp2VIbl/7oo9r6vXtA165AUJAcZHvvHdmYwZC9y4lNmzapHvdmzZqZtlWoUEFNkyj36QEDdCL7Mg5ZK1VqOvbtu5xq4/vB/ddxYU1p7Hp9F5Y9twyVClUy3S9TlMn86nP3zk2SEaM38/bOw7Tt09Syu5s7fnnuF1X1nlwPA3Qi0n0PcFbduBFp933QW+EdqyhTRpt67fnnE7eNHQs8+6wMLLbnnpGDOHv2rOoxTz62UoJ0uU9PBeIEe9CJ7DdsLiws6fRrqTW+y3eJVDo/+uZRfPb4ZyiUu5C6/27UXfRf3R+tF7XGiRsnoDdrT6/Fm3+8aVr/otMXaFehnV33ieyHKe5EpLupV3KqSJFcuHYtUv+90NmcekVX8uQBvv9e60l/912pqAWsWAE0aaLdVkrsxSBKLiwsDPnypfw85s+fH0dyUNdACrvdv584LVNORNyLMC27JbhZ7Hmd1YMHD5LcEo9ZTkyZsi3VYXNpNb6bfz5fr/U6nqn0DN775z18f/R7tW3Tf5tQZ3YdjG4+GoPrD4a7wd3ub9FtodvQdWlXU7bOoHqD8HKNly3+XcPPpn2PmZyXMlvojwE6EVmU9PBKS3dqJ1Nb9QB37VoGc+eesus+5LTwjqXmQbcJOeEMHw7UqgX07AncvQscPaoVj/vpJ+Cxx+y9h5RFUlk9M5XU/f394eWlv/dkTEwMjh8/bpHnuhl107T84N4Diz2vszt//ry9d8Hh8JilNGfO3kwF56JIEe9UP59Dyg9Bk7xN8NHhj3Dx/kVExUVhVPAoLD20FEEBQfD38Ye9/Hv3X/Tf3t80nVrbkm3xcomXrfo9w/eZ/Y5ZZs+XDNCJyOl6gF94oQJ27LibolCcbnuh7Vx4x2IefxzYvRvo3BmQi4s7d4COHYGPPwaGDtUCeXIIa9euxahRozJ83B9//IGKFStm++/I+PMrV66k2H737l3Vi55dnp6eqGSh7I2z1xNT7QvmL6jGx1PapKdJLmbLlSuH3Llz81BlAo9Z2m7c+D1T7yE5v/fvXy/Nz6ds79GkB8ZvGY+Ze2YiAQk4ePsgXtzyIiYGTsTrAa/D4Gbbkb8nbp7A23+9jXux99R6u3LtsLTrUni5W6fRk+8z+x6z06dPZ/qxDNCJyOl6gPPk8cC6dT0xZ85Bx+mFdhaVKwM7dgAvvwysWqUVjJPe9QMHgHnzAF6wO4Tu3burH2uTsebbt29PkfonU61VqVIl288rz5VHhl9YgIdX4qWSt6e3xZ7X2cnFLI8Vj5m1h82ZN76PGNESefKkfX7Pgzz47InP0O2Rbui9sjfO3j6L+zH3MeTPIVh9ZjXmPz0fZQuUhS38e+NfPP3L07jx4IZab+bfDCueXwEfLx+r/21+Nu1zzLIyj73Vm4pkbtOhQ4eiXbt2qFq1KsaNG5elFLv3338fDRs2xKOPPorBgwdnKuWOiPTRAxwSMgRxcWPUrazbMjDWwz64LJkH+9dfgTFjErfJOPUWLYCQEHvuGelMy5YtVW+5BOnmwfmxY8fUfXrAKu5E+iycKvLl88ry3OstyrbAwQEH8Ub9N0zb/jr3Fx756hHM2TNHNRhaU/D5YDSZ3wShYaFqPaBEAFa/sNomwTk5BqsH6Js3b8a///6LBg0aqFS2rHj77bdVgB8UFISpU6eqk3bfvn0RG5tYUZWIyDgFi7//DOTLNxWdOv2JyZO3qe1kJzLtllR0X7YM8Hl40bF3L1C/vpwY+LI4mYsXL6q0ePmRlMALFy6Y1s3VqFFDNbwbSeO7zH8u29asWYO///5bNcZLg/5jOqldwCruRPYjWW/SO548SJf1atV8cfr0G9lqfM/rlRdfPvEl1r+0Hv6+2hj0iOgIDPh9ANovbm+1Su/fH/pePf+dyDtqvU7xOlj30joUyFXAKn+PHJPVU9xHjBiBkSNHquWdO3dm+vf279+PLVu2YP78+erkLWQqlk6dOmH9+vXqlojIOAWL+Zh3qeA+ceI2/P772Sy1qpMVyNzokvYu49LPnZMXB2jTBpg5ExgwgIfcScj5/b333kvSOC8/4sSJxAvduLg4xMuwBzOffvopPvroI4wZM0Y1wMs5X8a/e3joYxQee9CJ9Dds7tVXa6F9+3w5Pr+3r9geh984jGHrh+Hr/V+betNrfVULbzd+G6NbjkY+73wW+R6ZsGkCxgaPNW17vNLj+LnbzxZ5fnIuVj/7GaQXJRs2bdqketybNWuWZKyaFHmQ+xigE5EE5507/4h9+y6nOBgSrEvQLid1hyu25mykursUj+vRA/jrL0CyoN54QxuX/vnnUtbU3ntIOdS1a1f1kxHzYN1IplmbNGmS+tEjBuhE+iucKlOQWarSef5c+THv6Xl4tsazaq70C3cvICY+Bp9s+wTfHfoOE9pMwIu1XoS3h3e2nn/LhS148/c3cfjaYdO2/vX6Y1anWfAw6KMhkvTFtuUKs+Ds2bOqxzz5gHoJ0uU+InJtxp7zv/9Oe+oLCdKlxZ10oHBhKQ0u+YqJ2+bMAdq2Ba5eteeeEaUrLj7OtMyLaSLnJT3axwceV73m3u5aMH454jL6rOqDcp+Vw8RNE3HzfuK0ixm5FH4JvVf0RotvWpiCcze44eN2H+OrJ77i9wmlSbfNNmFhYapVPTmZduXIkSPZfl4p/CCtbnqauN5V8JjxmFnSlCnbVA95RiQdzhKfeWdm08/mhAlwr1EDXoMGwS0qCtiyBfH16iHqp5+Q8OijcMVjlryCOekLe9CJXEcezzwY13ocegf0xpB1Q7DqxCq1/UrEFYz6ZxQmbp6oAvkWZVqgeZnmqsCbp7unekx8QjzO3T6HFf+uwK///optIdvUdG5G9UrWU4F5g1IN7Pb/IycN0KWyemYqqfv7+2d6MnZbiomJsVhKjCUnrnclPGY8ZpYwZ87eJHOcp6VIEW+Lfuadmc0+m48+ijxz56Li8OHwunYNhosX4d22Lf4bNQq3ZN50FzxmejxfkoYBOpHrqVCwAlb2XIntIdsxfcd0LD++XAXgD2IfqOBbfoTMWS6ZNVGxUYhLSMy2MZffOz8mtZ2k0trdDe42/p+QSwToUpFVirdk5I8//kDFihWzu19q/PmVKyl7x2Q6FulFzy5PT09UqlQJepq43lXwmPGYWdKNG79n+Bip8tq/fz1Vu4J09tmsXh2xzZvD/cUX4b5jBwxRUSg/ejRKX7uGmAkTAJ0UCLPFMTt9+rTF9ossj1XciVxXE/8mWOq/VPWMf77zcyw6uAi3I2+b7o+Oi1Y/qalWpBq6VuuKwY0Go3je4jbca3J0Wb4C6t69u/qxNhlrLvOiJk/9k6nWqlSpku3nlefK6UTzlp643tXwmPGYWYJUcQ0NDUv3MTI1y4gRLZEnD3sndfnZrFAB2LgR+N//gHnz1CbPmTPh+e+/wJIlQKFCcIVjxvR2fWMPOhGVL1geMx6fgWkdpuHotaOq8NuWkC04eOWg+g6XMetSRE6mb2tdrjW6VOuiAnSi7NBtF0XLli3x5ZdfqiC9adOmpuD82LFjeP311+29e0RkZ/361UVQUHCaae6BgWWwevWLnGJN77y9tWJxMv588GCtwvuGDUCDBsCqVUDNmvbeQ3JxDNCJyMjgZkCt4rXUzxsN3uCBIces4n7x4kWVFi8/khJ44cIF07q5GjVq4P333zetP/roo2ouVNm2Zs0a/P333xg8eDCqVq2Kxx57zNq7TUQ6N2RIE9VDLmns5mS9WjVf/PzzMwzOHYVkScm0azIFW9Gi2jaZraNxY2DFCnvvHbk4VnEnIiKn6kHfuXMn3nvvPdP65s2b1U/y+VDj4uIQHx+f5Hc//fRTfPTRRxgzZgxiY2NVwC7j3z10PjaRiGwzL2pwcG81z7lMpSbV2iXt/dVXa6F9+3wMzh1Ry5bAnj1Aly7A/v0ylx7wzDPAhx8CY8ZI64u995BcEHvQiYjIlqwe6Xbt2lX9ZMQ8WDeSadYmTZqkfoiIUgvSR48OVD9GMqUaq7Y7sDJl1NRrkKFMP/6obRs7Fjh4EPj2Wzkx2HsPycUwQCciIltidwQREemLFF37/nuZ7F5LfxeS6i4p76x4TjbGKu5ElFUREdEYPz4Y/v4z4O4+Tt3KumwnyggDdCIi0h8JzIcPlzk7gQIFtG3HjmnF49ats/fekQthDzoRZYUE4YGBC1UhW5ltRorZyq2sy3YG6ZQRBuhERKRfjz8O7Nql5k1X7twBOnUCpk4FElKv4E9kSQzQiSgrpDbOgQNXUswyI+uyXe4nSg8DdCIi0rfKlYEdO4Cnn9bWpaCo9K6//DLw4IG9946cHKu4E1FWSOHatKaAle1yP1F6GKATEZH++foCv/6qVXM3knHqLVoAISH23DNycuxBJ6KskFllcnI/EQN0IiJyDDLNmlR0X7YM8PHRtu3dC9Svr1V+J7ICBuhElBUy5WtO7idigE5ERI5Fpu7cvh2oUEFbv3YNaNMGmDPH3ntGTohV3IkoK/r1qwuD4eEMJMnIdrmfKD0M0ImIyPHUqgXs3g20a6etx8QAAwZoP9GcxoYshz3oRJQVQ4Y0QUBAiRRBuqzLdrmfKD0M0ImIyDEVKgSsWSNXQ4nbpBe9bVvg6lV77hk5EQboRJQVefN6ITi4N4KCAlG6tK8KzOVW1mW73E+UHo907yUiItIzDw9g+nQgIEDyCoGoKG08uoxLl6JyckuUA6ziTkRZJUH46NGB6ocoq9iDTkREju+VV4DNm4FSpbT10FCtwvt339l7z8jBsQediIhsiQE6ERE5hwYNgD17gKZNtfXISG2u9GHDgNhYe+8dOSgWiSMiIltigE5ERM6jRAngn3+0dHejadOATp2AW7fsuWfkoNiDTkREtsQAnYjIRiIiojF+fDD8/WfA3X2cupV12U4W5OWlFYv76ittjLrYsEHrYT9yhIeasoQBOhER2RIDdCIiG5AgPDBwIYKCghEaGob4+AR1K+uynUG6FciUa3/9BRQtqq2fPQs0bqwVjyPKJAboRERkSwzQiYhsYMaM7Thw4IoKzM3JumyX+8kKWrbUxqU/+qi2fu8e0LUrEBQkB5+HnDLEKu5ERGRLDNCJiGxg7tx9KYJzI9ku95OVlCmjTb32/POJ28aO1QL1sDAedkoXe9CJiMiWGKATEdnApUvhObqfcihPHuD774EpUwA3N23bypVAkybA6dM8vJQmVnEnIiJbYoBORGQDfn75cnQ/WYAE5sOHA3/8ARQooG07dkwrHrduHQ8xpYo96EREZEsM0ImIbKBfv7owGNxS/yI2uKn7yUYefxzYtQuoXl1bv3NHm4Zt6lQgIfVhCOS6GKATEZEtMUAnIrKBIUOaICCgRIogXdZlu9xPNlS5MrBjB/D009q6FIyT3vWXXwYePOBLQSYM0ImIyJYYoBMR2UDevF4IDu6NoKBAlC7tqwJzuZV12S73k435+mpTro0Zk7hNxqk3bw5cuMCXgxRWcSciIlvysOlfIyJyYRKEjx4dqH5IJwwGraJ7nTrAK69o07Dt26eNS//lF6BFC3vvIdkZe9CJiMiW2INOREQkU65t3w5UqKAdi2vXgDZtgNmzeWxcHKu4ExGRLTFAJyIiErVqAbt3A+3aaccjNhZ44w2gf38gOprHyEWxB52IiGyJAToREZFRoULAmjVS1S/xmMydq/WmX7nC4+SCGKATEZEtMUAnIiIy5+EBTJ8OLFoEeHtr27ZuBerXB/bs4bFyMSwSR0REtsQAnYiIKDVSNG7zZqBUKW394kWtwvvixTxeLoQ96EREZEsM0ImIiNIi1dyl17xpU209KkoF7p4jR2pj1MmlAnR3N3e77gsRETk/BuhERETpKVEC+PtvoG9f0ybPmTNR5pNPeNxcqIq7BOdubm723h0iInJyDNCJiIgyImPR58wBvvxSG6MOIJ9UfCeX6UH3MGivOxERkTXxbENERJQZ0nsq064FBCBmwQL817Ah/HnknN7/27sT2CiqP4Djv0JBLQr+JeKBjQpKKSBtkatCOAQiCokaQYIlXolgokBE5DAEMSJBFIwYoIiQaqI19QgaA0Q0HgVi4wlKlFAPUDyqCLUchaV9//xespvuYmk7205n33w/yYbd6c6w/fXN/PY3894bCnQAgJ8o0AEAaIr8fInk5MiR774jbiGaxZ0r6AAAP9DFHQAAoB5cQQcA+IkCHQAAoBGTxAEA0NIo0AEAAOrBFXQAgJ8o0AEAAOpBgQ4A8BMFOgAAQD2YJA4A4CcKdAAAgHpwBR0A4CcKdAAAgAYK9LZtmCQOANDyKNABAAAamMU9PS2dGAEAWhwFOgAAQD3o4g4A8BOngwEAQFK2b98ub731luzcuVN++eUXKSgokIULFza43q+//iqjRo06bXlOTo6UlJS0+l/FGEOBDgDwFQU6AABISmlpqXz//fcyYMAAqaysbPL6s2bNkkGDBsVed+jQIRB/kVpTG3ue3oavTACAlke2AQAASZkzZ47MmzfPPi8rK2vy+pdffrnk5uYGtnu7okAHAPiBMegAACC5LxNt3Pw6UbdAb5vGLO4AgJbnZkYFAAApY9GiRZKdnS35+fmyYMECOXz4sAQBV9ABAH4LVRf3SCRiJ3z55ptvkt6WbkeVl5dLWlpaM3w69xEzYkY7Cyb2zdaN2cmTJ0ObR9q3by+TJ0+WoUOHSseOHe0kc4WFhfLtt9/K66+/Lu3atfOc63ft2pX056sxNbJx5Eb7PKNdRrNsMyz7xt69e0PbrpuKmBEz2pn7+2YkEmn0NkJVoDdnotBt6RcLELOWRDsjZn6gnbVuzHRbQStkqqqqpKKiosH3ZWZmJhWHLl262KvnUQMHDpSrr75apk2bJlu3bpWbbrqpyduMxrI5Yqr3Pu/2v25JbydMOJ4QM9pZMLFvpk6uD1WBnpeX19ofAQCAwNuyZYvtat6QTZs2Sffu3Zv1/x4+fLhkZGTI7t27PRXo5HoAQCoLVYEOAAAaNnHiRPsAAAD+YpI4AAAQGB9++KEcO3ZMrrnmmtb+KAAA+I4r6AAAICkHDhyITcB6/Phx2b9/v+0mr8aOHRt7X69eveSWW26RJUuW2NdLly61Y/L0Hug6SZxOwrZ27Vrp06ePjB49mr8KACB0KNABAEBSysrKZP78+bHXpaWl9qH27NkTW15TUyO1tbWx1zp+vbi4WEpKSqS6ulouuugimTBhgsyYMUPS0/mKAgAInzQTnT8eAAAAAAC0GsagAwAAAAAQABToAAAAAAAEAAU6AAAAAAABQIEOAAAAAEAAUKADAAAAABAAFOgAAAAAAAQABToAAAAAAAFAgf4ffvjhB7nnnnskNzdXhgwZIsuWLZOTJ082GEy9pfwLL7wgI0aMkL59+8qkSZPk66+/ljDwErOKigr7vptvvlny8vJk2LBh8vDDD8uBAwckDLy2s7qKiookKytLpk2bJmGQTMz+/PNPmTt3rgwePNjunzfeeKO888474jqvMTt06JAsXLjQHs903fHjx0txcbG4bt++ffb31uNSr1697O/dGGE+/qcqcr0/MSPXk+v92jcVuZ5c70KuT2/2Laa4yspKueuuu+SKK66Q559/3u7oS5culerqavuHPJN169bJypUrZfbs2bZoeuWVV+Tee++Vt99+WzIzM8VVXmO2e/du2bp1q9x2222Sk5NjC4I1a9bIxIkT5d1335ULLrhAXJVMO4v666+/ZNWqVdK5c2cJg2Ripl8Q9SB65ZVXyhNPPCHnnnuu7N27t8knRMIUs5kzZ8qPP/4os2bNkksuuUQ++eQTWbRokbRt21Zuv/12cZW2i48//tgek2pra20yboywHv9TFbnev5iR68n1fu2b5HpyvTO53iBOYWGhyc3NNYcOHYote+2110x2drb5448/6o1WdXW16devn1m+fHls2YkTJ8zIkSPNY4895nSUvcassrLSRCKRuGW///67ycrKMuvXrzcu8xqzuh555BEzZ84cM2XKFDN16lTjumRiNnv2bDNp0iRz6tQpEyZeY1ZRUWF69Ohh3nzzzbjlBQUF5s477zQuq6mpiT2fO3euGTduXIPrhPn4n6rI9f7FjFxPrvejnSlyPbnelVxPF/cEepUoPz9fzj///Ngy7QqrZ1e2b99e74mOL7/8Uo4cOWLfG9W+fXsZM2aM3abLvMasY8eOkp4e34nj4osvtlfO9Syoy7zGLOrzzz+X999/3w4JCAuvMdP9cvPmzXLHHXfYq79h4jVmp06dsv+ed955ccu150FjzzKnqjZtmp4Ww3z8T1Xkev9iRq4n1/vRzsj15HqXcj0FegLt0tmtW7fTksuFF15of1af6M8S1+3evbv89ttvtmuOq7zG7L/89NNPcvDgQRs3lyUTs5qaGttN+/7775cuXbpIWHiNmXavjEQi9mTQlClTpHfv3nZM29NPP22Xu8xrzLRL+9ChQ6WwsFDKy8ttQtq0aZP9clRQUODDJ08tYT7+pypyvX8x+y/kenJ9c7czcj253qVczxj0BP/++689ECTq1KmTHRdzpvX0LMpZZ50Vt1y3pVecdN2zzz5bXOQ1Zok0TosXL7ZF57hx48RlycTs1VdflePHj8vdd98tYeI1Zn///bf9d8GCBXbs9IMPPii7du2yY4j0DKrLvRCSaWc69u+hhx6K7Yva+0BjeMMNN7TY501VYT7+pypyvX8xS0SuJ9e3RDsj15PrXcr1FOgIDC0IPv30U3nxxRclIyOjtT9OIGnvAi0sn3rqKXuQQMO0W5y67rrrZN68efa5zuR+9OhR2bBhgzzwwAMUTwk00cyfP19+/vlnWb58ub1ysWPHDlmyZIn9kuT6CTQALYdc3zByfdOR65uOXB9cFOgJ9CxIVVXVaYHSsyL6xfRM6+mM0CdOnIg7s6JnW9LS0s64blhjVldJSYmdkfzJJ5+0Y49c5zVmzz33nJ01sn///rZtRccL60Nf64mNxHH9rkhm34wW5XVpO9Mu3HqrDY2pi7zG7KOPPpItW7bY29BFYzNo0CD7pVFn06VAPz3OYT3+pypyvX8xq4tc37iYkevJ9X7sm+T64OZ6xqAn0HEFiWNctNHrLa0SxxwkrhcdV1WXbuvSSy91+gqd15hF6a3W9PZNM2bMkAkTJkgYeI2Ztq/PPvtMBgwYEHvopBXbtm2zz/Uqp6u8xuyqq64643b1QOsqrzHTcefapb1Hjx5xy7Ozs+0EjjrEAvFxDuvxP1WR6/2LWRS5vvExI9eT6/3YN8n1wc31FOgJhg0bZouc6NVJpVeSdKyqTixVn379+tkZjnW26CidgOq9996z23SZ15ipsrIye59lvfe5djUOC68xe/TRR+Xll1+Oe/Ts2VNyc3Pt8759+4qrvMasa9euttBMPHmhr/Vg2lABH9aY6WSEe/bsOW0Sns6dO8s555zTop871YT5+J+qyPX+xUyR68n1Ld3OyPXkeqdyfbPetM0Bhw8fNkOGDLH3li4tLTVvvPGG6d+/v3n88cfj3qf3Ah49enTcsrVr15o+ffqYoqIis2PHDjN9+nSTl5dn9u/fb1zmNWbl5eXm2muvNePHjzdffPGF+eqrr2KPffv2GZcl084SheU+6MnE7IMPPjBZWVlm8eLFZtu2bWbNmjWmd+/eZsWKFcZlXmNWVVVlRowYYcaMGWM2btxoj2fLli0zPXv2NKtWrTIuO3bsmNm8ebN9aNyGDx8ee33w4EH7Ho7/qY9c71/MyPXker/2TXI9ud6VXO/mYNUk6PiBl156yd7GSq/odujQwXa71tmMEyej0CtMdd133312wgWdeOqff/6x3UHXr18vmZmZ4jKvMdu5c6ftgqOPyZMnx7331ltvtWNdXZVMOwurZGJ2/fXXy4oVK2T16tVSXFxs7xQwffp0mTp1qrjMa8z0DHFRUZE8++yz8swzz9h99LLLLrOT7Omt6lym4+xnzpwZtyz6Wnup6Fh8jv+pj1zvX8zI9eR6v/ZNcj253pVcn6ZVerNuEQAAAAAANBlj0AEAAAAACAAKdAAAAAAAAoACHQAAAACAAKBABwAAAAAgACjQAQAAAAAIAAp0AAAAAAACgAIdAAAAAIAAoEAHAAAAACAAKNABAAAAAAgACnQAAAAAAAKAAh0AAAAAAGl9/wfU5brqsZeb8AAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.pipeline import make_pipeline\n",
     "from sklearn.preprocessing import PolynomialFeatures\n",
@@ -159,10 +181,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "3511227a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.model_selection import learning_curve\n",
     "\n",
@@ -232,12 +265,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "2f0f4cdf",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best Parameters: {'polynomialfeatures__degree': 10, 'ridge__alpha': 0.01}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.model_selection import GridSearchCV\n",
     "from sklearn.linear_model import Ridge\n",
@@ -294,9 +345,50 @@
     "-   Search over `alpha` values ranging from $10^{-3}$ to $10^3$."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ca8a50b8",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best Alpha found: 0.0013\n",
+      "Best Score (R²):  0.6111\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import fetch_california_housing\n",
+    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
+    "from scipy.stats import loguniform\n",
+    "\n",
+    "# Load Data\n",
+    "data = fetch_california_housing(as_frame=True)\n",
+    "X_house, y_house = data.data, data.target\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X_house, y_house, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Define Distribution (Log Uniform is best for 'alpha' or 'C')\n",
+    "# It searches equally between 0.001 and 0.01 as it does between 100 and 1000\n",
+    "param_dist = {'alpha': loguniform(1e-3, 1e3)}\n",
+    "\n",
+    "# Random Search (n_iter=20 means we only try 20 random combos)\n",
+    "random_search = RandomizedSearchCV(\n",
+    "    Ridge(), param_distributions=param_dist, n_iter=20, cv=3, random_state=42)\n",
+    "\n",
+    "random_search.fit(X_train, y_train)\n",
+    "\n",
+    "print(f\"Best Alpha found: {random_search.best_params_['alpha']:.4f}\")\n",
+    "print(f\"Best Score (R²):  {random_search.best_score_:.4f}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "id": "e5aa1d82",
+   "id": "b1563b6a",
    "metadata": {},
    "source": [
     "### Fixing Overfitting\n",
@@ -304,8 +396,57 @@
     "Train a Decision Tree Regressor on the housing data. Decision Trees are notorious for overfitting.\n",
     "\n",
     "1.  Train an unconstrained tree (`max_depth=None`). Check Train vs Test score.\n",
-    "2.  Tune `max_depth` using `GridSearchCV` to fix the overfitting.\n",
+    "2.  Tune `max_depth` using `GridSearchCV` to fix the overfitting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a59c52b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Overfitting Tree ---\n",
+      "Train Score: 1.0000 (Perfect Memorization!)\n",
+      "Test Score:  0.6221\n",
+      "\n",
+      "--- Tuned Tree ---\n",
+      "Best Depth:  10\n",
+      "Train Score:  0.8348\n",
+      "Test Score:  0.6829\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "\n",
+    "# 1. Overfitting Tree\n",
+    "tree_overfit = DecisionTreeRegressor(max_depth=None, random_state=42)\n",
+    "tree_overfit.fit(X_train, y_train)\n",
+    "\n",
+    "print(\"--- Overfitting Tree ---\")\n",
+    "print(f\"Train Score: {tree_overfit.score(X_train, y_train):.4f} (Perfect Memorization!)\")\n",
+    "print(f\"Test Score:  {tree_overfit.score(X_test, y_test):.4f}\")\n",
+    "\n",
+    "# 2. Tuned Tree\n",
+    "tree_grid = {'max_depth': [3, 5, 10, 20]}\n",
+    "tree_search = GridSearchCV(DecisionTreeRegressor(random_state=42), tree_grid, cv=3)\n",
+    "tree_search.fit(X_train, y_train)\n",
     "\n",
+    "print(\"\\n--- Tuned Tree ---\")\n",
+    "print(f\"Best Depth:  {tree_search.best_params_['max_depth']}\")\n",
+    "print(f\"Train Score:  {tree_search.score(X_train, y_train):.4f}\")\n",
+    "print(f\"Test Score:  {tree_search.score(X_test, y_test):.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5aa1d82",
+   "metadata": {},
+   "source": [
     "**Observation**: The unconstrained tree likely had a Train Score of 1.0 (100%), but a lower Test Score. Limiting the depth (Regularization) lowered the Train Score but improved the Test Score."
    ]
   },
@@ -327,9 +468,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {

From ac0738b653ac88e603baa837b732bf6df597bc2a Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Mon, 23 Feb 2026 13:55:03 +0100
Subject: [PATCH 76/81] Session 03: Supervised Learning - Classification and
 Evaluation

---
 ...arning - Classification and Evaluation.pdf | Bin 376312 -> 376328 bytes
 ...arning - Classification and Evaluation.tex | 102 ++--
 ...03C01 Introduction to Classification.ipynb | 162 +++++-
 .../S03C02 Classification Basics.ipynb        | 184 ++++++-
 .../S03C03 Evaluation Metrics.ipynb           | 202 ++++++-
 .../S03C04 k-NNs and Decision Trees.ipynb     | 507 +++++++++++++++++-
 .../S03C05 Support Vector Machines.ipynb      | 286 +++++++++-
 7 files changed, 1335 insertions(+), 108 deletions(-)

diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03 Supervised Learning - Classification and Evaluation.pdf b/src/S03 Supervised Learning - Classification and Evaluation/S03 Supervised Learning - Classification and Evaluation.pdf
index 757f1af6206e431afb5e2ae6f475916cdafec229..6fba584c943df99913aaf537273f6e348d9142f6 100644
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diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03 Supervised Learning - Classification and Evaluation.tex b/src/S03 Supervised Learning - Classification and Evaluation/S03 Supervised Learning - Classification and Evaluation.tex
index 37439d6..01ce2e4 100644
--- a/src/S03 Supervised Learning - Classification and Evaluation/S03 Supervised Learning - Classification and Evaluation.tex	
+++ b/src/S03 Supervised Learning - Classification and Evaluation/S03 Supervised Learning - Classification and Evaluation.tex	
@@ -1,4 +1,4 @@
-% Created 2026-02-04 Wed 19:40
+% Created 2026-02-23 Mon 11:37
 % Intended LaTeX compiler: pdflatex
 \documentclass[hyperref={pdfpagelabels=false},aspectratio=169]{beamer}
 \usepackage[utf8]{inputenc}
@@ -202,9 +202,9 @@
 \end{frame}
 
 \section{Introduction to Classification}
-\label{sec:orgd9633ab}
-\begin{frame}[label={sec:orga6fc109}]{Introduction to Classification}
-\begin{definition}[Classification]\label{sec:orgc9ef722}
+\label{sec:org4c9859a}
+\begin{frame}[label={sec:org6bf8c32}]{Introduction to Classification}
+\begin{definition}[Classification]\label{sec:org0aeb98a}
 \pause
 \alert{Classification} is a type of supervised learning where the model learns from labeled data to predict the class of new observations based on past data.
 \pause
@@ -216,8 +216,8 @@ \section{Introduction to Classification}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org38505ef}]{Classification Types}
-\begin{definition}[Classification Types]\label{sec:org4a38c73}
+\begin{frame}[label={sec:org376d488}]{Classification Types}
+\begin{definition}[Classification Types]\label{sec:org366ed48}
 \pause
 \alert{Classification} can be broadly divided into two types:
 \begin{itemize}[<+->]
@@ -226,8 +226,8 @@ \section{Introduction to Classification}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgde053fd}]{Classification Algorithms}
-\begin{definition}[Classification Algorithms]\label{sec:org51b9712}
+\begin{frame}[label={sec:org4712d97}]{Classification Algorithms}
+\begin{definition}[Classification Algorithms]\label{sec:orga9d24b4}
 \pause
 \alert{Classification algorithms} are designed to learn from labeled data and make predictions about the class of new, unseen data. Some common algorithms include:
 \begin{itemize}[<+->]
@@ -240,9 +240,9 @@ \section{Introduction to Classification}
 \end{definition}
 \end{frame}
 \section{Classification Basics}
-\label{sec:org1299dfb}
-\begin{frame}[label={sec:org5e16198}]{Logistic Regression}
-\begin{definition}[Logistic Regression]\label{sec:org28f7f82}
+\label{sec:orgd8633ed}
+\begin{frame}[label={sec:orgf506433}]{Logistic Regression}
+\begin{definition}[Logistic Regression]\label{sec:org77ddf0f}
 \pause
 \alert{Logistic Regression} models the probability that the target variable \(y\) belongs to a particular class. The \alert{logistic function (sigmoid)} is used to map predicted values to probabilities between 0 and 1. The decision boundary is determined by the threshold (commonly 0.5) for classifying observations into different classes.
 \pause
@@ -253,9 +253,9 @@ \section{Classification Basics}
 \end{definition}
 \end{frame}
 \section{Evaluation Metrics}
-\label{sec:org5e195ec}
-\begin{frame}[label={sec:org79c547f}]{Evaluation Metrics for Classification}
-\begin{definition}[Confusion Matrix]\label{sec:org2ce2ee1}
+\label{sec:orga3cae8f}
+\begin{frame}[label={sec:org40dc808}]{Evaluation Metrics for Classification}
+\begin{definition}[Confusion Matrix]\label{sec:orgf3e8829}
 \pause
 The \alert{confusion matrix} summarizes the performance of a classification algorithm by comparing predicted and actual labels.
 \pause
@@ -270,39 +270,39 @@ \section{Evaluation Metrics}
 From these four numbers we obtain the core metrics summarized next.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org5a4fff9}]{Accuracy (Overall Success Rate)}
-\begin{definition}[Accuracy (Overall Success Rate)]\label{sec:org4a60261}
+\begin{frame}[label={sec:orgca6fff8}]{Accuracy (Overall Success Rate)}
+\begin{definition}[Accuracy (Overall Success Rate)]\label{sec:org4dcd815}
 \alert{Accuracy} is a measure of how often the classifier is correct across all predictions. It answers the question: "What fraction of \alert{all} predictions are correct?"
 $$\text{Accuracy} = \frac{\text{TP} + \text{TN}}{\text{TP} + \text{FP} + \text{FN} + \text{TN}}$$
 
 Despite its simplicity, accuracy can be misleading, especially in imbalanced datasets where one class dominates.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org587f3d2}]{Precision (Positive Predictive Value)}
-\begin{definition}[Precision (Positive Predictive Value)]\label{sec:orgc7da7af}
+\begin{frame}[label={sec:org9720f64}]{Precision (Positive Predictive Value)}
+\begin{definition}[Precision (Positive Predictive Value)]\label{sec:org9d07415}
 \alert{Precision} is a measure of the accuracy of positive predictions. It answers the question: "When the classifier predicts 1, how often is it correct?"
 $$\text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}}$$
 
 High precision indicates that when the model predicts a positive class, it is likely correct, but it does not account for false negatives.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgd5b10a2}]{Recall (Sensitivity, True-Positive Rate)}
-\begin{definition}[Recall (Sensitivity, True-Positive Rate)]\label{sec:org64f96d9}
+\begin{frame}[label={sec:org557a036}]{Recall (Sensitivity, True-Positive Rate)}
+\begin{definition}[Recall (Sensitivity, True-Positive Rate)]\label{sec:orgdc8c791}
 \alert{Recall} is a measure of the model's ability to capture all positive instances. It answers the question: "Of all the actual 1 cases, how many did we catch?"
 $$\text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}}$$
 High recall indicates that the model is good at identifying positive cases, but it does not consider false positives.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgd4d5710}]{F1-Score}
-\begin{definition}[F1-Score]\label{sec:orga5ba869}
+\begin{frame}[label={sec:org900b998}]{F1-Score}
+\begin{definition}[F1-Score]\label{sec:org3ee87d8}
 \alert{F1-Score} is the harmonic mean of precision and recall, providing a balance between the two metrics:
 $$F_1 = 2 \cdot \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}}$$
 It is particularly useful when the class distribution is imbalanced, as it considers both false positives and false negatives.
 High F1-Score indicates a good balance between precision and recall, while a low F1-Score suggests that either precision or recall (or both) are low.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org9b08fde}]{Precision-Recall Trade-off}
-\begin{definition}[Precision-Recall Trade-off]\label{sec:orgd8d2cee}
+\begin{frame}[label={sec:orgb3e72ce}]{Precision-Recall Trade-off}
+\begin{definition}[Precision-Recall Trade-off]\label{sec:org5b121b7}
 The \alert{Precision-Recall Trade-off} illustrates the inverse relationship between precision and recall:
 \pause
 \begin{itemize}[<+->]
@@ -310,26 +310,26 @@ \section{Evaluation Metrics}
 \item Decreasing the threshold will \textcolor{darkgreen}{increase recall} but \textcolor{darkred}{decrease precision}.
 \end{itemize}
 \end{definition}
-\begin{definition}[Precision-Recall Curve]\label{sec:orgab37166}
+\begin{definition}[Precision-Recall Curve]\label{sec:org2facafb}
 \pause
 \alert{Precision-Recall Curve} is a graphical representation of the trade-off between precision and recall for different threshold values.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org9bfe5f2}]{Receiver Operating Characteristic (ROC) Curve}
-\begin{definition}[Receiver Operating Characteristic (ROC) Curve]\label{sec:org91171ab}
+\begin{frame}[label={sec:org38bcfe5}]{Receiver Operating Characteristic (ROC) Curve}
+\begin{definition}[Receiver Operating Characteristic (ROC) Curve]\label{sec:orgbefa50e}
 \alert{ROC Curve} is a graphical representation of a classifier's performance across different thresholds. It plots the true positive rate (recall) against the false positive rate (FPR) at various threshold settings.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgad7ebc5}]{Area Under the Curve (AUC)}
-\begin{definition}[Area Under the Curve (AUC)]\label{sec:org2325939}
+\begin{frame}[label={sec:org7dae10a}]{Area Under the Curve (AUC)}
+\begin{definition}[Area Under the Curve (AUC)]\label{sec:orgab1daf5}
 \pause
 \alert{Area Under the Curve (AUC)} is a single scalar value that summarizes the performance of a classifier across all possible thresholds. It is calculated as the area under the ROC curve. AUC provides an aggregate measure of performance across all classification thresholds, making it useful for comparing different classifiers.
 \end{definition}
 \end{frame}
 \section{\(k\)-Nearest Neighbours}
-\label{sec:org7ff66de}
-\begin{frame}[label={sec:org2aa512a}]{\(k\)-Nearest Neighbours (\(k\)-NN)}
-\begin{definition}[\(k\)-Nearest Neighbours (\(k\)-NN)]\label{sec:orgcf719d6}
+\label{sec:orgf548f61}
+\begin{frame}[label={sec:org7f9071e}]{\(k\)-Nearest Neighbours (\(k\)-NN)}
+\begin{definition}[\(k\)-Nearest Neighbours (\(k\)-NN)]\label{sec:org17a2b02}
 \pause
 \alert{\(k\)-Nearest Neighbors (\(k\)-NN)} is a \alert{non-parametric}, \alert{instance-based} learning algorithm that classifies a new observation based on the classes of its \(k\) nearest neighbors in the feature space. It does not explicitly learn a model but stores the training instances.
 \pause
@@ -345,15 +345,15 @@ \section{\(k\)-Nearest Neighbours}
 The decision boundary is implicitly defined by the \(k\) nearest points, allowing the model to capture highly non-linear class boundaries without explicit training.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org8ea0056}]{Mathematical Formulation}
-\begin{definition}[Mathematical Formulation]\label{sec:orga7e09fa}
+\begin{frame}[label={sec:org0df85b7}]{Mathematical Formulation}
+\begin{definition}[Mathematical Formulation]\label{sec:org523f7b3}
 Given a training set \(\{(x_i, y_i)\}_{i = 1}^{n}\) where \(y_i \in \{1, \dots,C\}\), the prediction for a query point \(\hat{x}\) is:
 $$\hat{y} = \text{mode}\left(\{y_j | x_j \in N_k(\hat{x})\}\right),$$
 
 where \(N_k(\hat{x})\) denotes the set of \(k\) training points closest to \(\hat{x}\).
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgd2b8339}]{Considerations for \(k\)-NN}
+\begin{frame}[label={sec:orga3865fd}]{Considerations for \(k\)-NN}
 \begin{block}{Considerations for \(k\)-NN}
 \begin{itemize}[<+->]
 \item \alert{Choosing \(k\)}:
@@ -378,9 +378,9 @@ \section{\(k\)-Nearest Neighbours}
 \end{block}
 \end{frame}
 \section{Decision Trees}
-\label{sec:orgf28f0e6}
-\begin{frame}[label={sec:orga977ef4}]{Decision Trees}
-\begin{definition}[Decision Trees]\label{sec:org5df3a34}
+\label{sec:org7be8a6a}
+\begin{frame}[label={sec:orgc18382f}]{Decision Trees}
+\begin{definition}[Decision Trees]\label{sec:orge9feae0}
 \alert{Decision Trees} recursively partition the feature space into axis-aligned regions and assign a class label to each region. It consists of \alert{nodes} (features), \alert{branches} (decisions), and \alert{leaves} (outcomes).
 \pause
 
@@ -390,8 +390,8 @@ \section{Decision Trees}
 By hierarchically splitting the space, a tree can approximate complex decision boundaries while remaining interpretable-each internal node corresponds to a human-readable rule.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org687cde3}]{Information Gain}
-\begin{definition}[Information Gain]\label{sec:orgab1df6e}
+\begin{frame}[label={sec:orgf679c16}]{Information Gain}
+\begin{definition}[Information Gain]\label{sec:org567fb33}
 For a node containing a sample set \(S\), the entropy is:
 $$H(S)= -\sum_{c = 1}^{C} p_c \log_2 p_c,$$
 where \(p_c\) is the proportion of class \(c\).
@@ -402,8 +402,8 @@ \section{Decision Trees}
 \pause
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgc8f7495}]{Considerations for Decision Trees}
-\begin{definition}[Considerations for Decision Trees]\label{sec:orgc17aaa8}
+\begin{frame}[label={sec:org38e82c5}]{Considerations for Decision Trees}
+\begin{definition}[Considerations for Decision Trees]\label{sec:org54cdaa8}
 \begin{itemize}[<+->]
 \item \alert{Pruning}: Techniques like cost-complexity pruning can be applied to reduce overfitting by removing branches that have little importance.
 
@@ -423,9 +423,9 @@ \section{Decision Trees}
 \end{definition}
 \end{frame}
 \section{Support Vector Machines (SVM)}
-\label{sec:org6321666}
-\begin{frame}[label={sec:orga40162a}]{Support Vector Machines (SVM)}
-\begin{definition}[Support Vector Machines (SVM)]\label{sec:orge52c844}
+\label{sec:orgfc3fbc3}
+\begin{frame}[label={sec:org1879867}]{Support Vector Machines (SVM)}
+\begin{definition}[Support Vector Machines (SVM)]\label{sec:org3a53caf}
 \alert{Support Vector Machines (SVM)} are powerful algorithms that find the optimal hyperplane that separates different classes in the feature space. The goal is to maximize the \alert{margin} between the closest points of each class, known as \alert{support vectors}.
 \pause
 The SVM algorithm works by solving the following optimization problem:
@@ -436,15 +436,15 @@ \section{Support Vector Machines (SVM)}
 Visualization should help\ldots{}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org5ec5c47}]{Linear SVMs}
-\begin{definition}[Linear SVMs]\label{sec:orgf40ba85}
+\begin{frame}[label={sec:org463df95}]{Linear SVMs}
+\begin{definition}[Linear SVMs]\label{sec:orgf6e296c}
 \alert{Linear SVMs} are used when the data is linearly separable. The decision boundary is a hyperplane defined by the equation:
 $$\mathbf{w}^T \mathbf{x} + b = 0,$$
 where \(\mathbf{w}\) is the weight vector and \(b\) is the bias term. The SVM finds the hyperplane that maximizes the margin between the two classes.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgf3635ff}]{Non-Linear SVMs}
-\begin{definition}[Non-Linear SVMs]\label{sec:orge90af98}
+\begin{frame}[label={sec:org8b04903}]{Non-Linear SVMs}
+\begin{definition}[Non-Linear SVMs]\label{sec:orgbf896ce}
 \alert{Non-Linear SVMs} use kernel functions to transform the input space into a higher-dimensional space where a linear hyperplane can separate the classes. Common kernels include:
 \begin{itemize}
 \item \alert{Polynomial Kernel}: Maps the input features into a polynomial feature space.
@@ -453,8 +453,8 @@ \section{Support Vector Machines (SVM)}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orga97e919}]{Kernel Trick}
-\begin{definition}[Kernel Trick]\label{sec:orga5dcc43}
+\begin{frame}[label={sec:orgc513788}]{Kernel Trick}
+\begin{definition}[Kernel Trick]\label{sec:orged8a0c3}
 The \alert{Kernel Trick} allows SVMs to operate in high-dimensional feature spaces without explicitly computing the coordinates of the data in that space. Instead, it computes the inner products between the images of all pairs of data points in the feature space, which is computationally efficient.
 
 This is done using a kernel function \(K(\mathbf{x}_i, \mathbf{x}_j)\) that computes the inner product in the transformed space:
diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03C01 Introduction to Classification.ipynb b/src/S03 Supervised Learning - Classification and Evaluation/S03C01 Introduction to Classification.ipynb
index 0268dea..6e3e915 100644
--- a/src/S03 Supervised Learning - Classification and Evaluation/S03C01 Introduction to Classification.ipynb	
+++ b/src/S03 Supervised Learning - Classification and Evaluation/S03C01 Introduction to Classification.ipynb	
@@ -93,12 +93,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "343ecb9c",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Classes: [0 1 2]\n",
+      "Binary Classes:   [0 1]\n",
+      "Shape: (100, 5)\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_iris\n",
     "import pandas as pd\n",
@@ -132,10 +142,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "8eeaf214",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(8, 6))\n",
     "sns.scatterplot(\n",
@@ -170,12 +191,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "a6b27d53",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 1.00\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.linear_model import LogisticRegression\n",
@@ -211,12 +240,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "f1e711c3",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.inspection import DecisionBoundaryDisplay\n",
     "\n",
@@ -270,6 +310,34 @@
     "-   Split into Train/Test."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4b38358d",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classes in training: [0 1 2]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1. Reload full data\n",
+    "df_full = iris.frame\n",
+    "X_full = df_full[features]\n",
+    "y_full = df_full['target']\n",
+    "\n",
+    "# 2. Split\n",
+    "X_tr, X_te, y_tr, y_te = train_test_split(X_full, y_full, test_size=0.2, random_state=42)\n",
+    "\n",
+    "print(f\"Classes in training: {y_tr.unique()}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "e5d77b61",
@@ -281,6 +349,32 @@
     "-   **Note**: `scikit-learn` handles multiclass automatically (using a strategy called \"One-vs-Rest\" or \"Multinomial\")."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0b8feebe",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Multiclass Accuracy: 1.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train\n",
+    "clf_multi = LogisticRegression(max_iter=200)\n",
+    "clf_multi.fit(X_tr, y_tr)\n",
+    "\n",
+    "# Predict\n",
+    "acc = clf_multi.score(X_te, y_te)\n",
+    "print(f\"Multiclass Accuracy: {acc:.2f}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "9b60c871",
@@ -291,6 +385,54 @@
     "-   Visualize the boundaries. You should see **three** colored regions instead of two."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b8e3f0e8",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Plot Boundary\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    clf_multi, \n",
+    "    X_full, \n",
+    "    response_method=\"predict\",\n",
+    "    cmap=\"viridis\", \n",
+    "    alpha=0.8, \n",
+    "    ax=plt.gca()\n",
+    ")\n",
+    "\n",
+    "# Plot Data\n",
+    "plt.scatter(\n",
+    "    X_full['petal length (cm)'], \n",
+    "    X_full['petal width (cm)'], \n",
+    "    c=y_full, \n",
+    "    cmap=\"viridis\", \n",
+    "    edgecolor=\"k\", \n",
+    "    s=50\n",
+    ")\n",
+    "\n",
+    "plt.title(\"Multiclass Decision Boundaries\")\n",
+    "plt.xlabel(\"Petal Length\")\n",
+    "plt.ylabel(\"Petal Width\")\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "6ea3986d",
@@ -310,9 +452,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03C02 Classification Basics.ipynb b/src/S03 Supervised Learning - Classification and Evaluation/S03C02 Classification Basics.ipynb
index e12ab66..b7c8fb3 100644
--- a/src/S03 Supervised Learning - Classification and Evaluation/S03C02 Classification Basics.ipynb	
+++ b/src/S03 Supervised Learning - Classification and Evaluation/S03C02 Classification Basics.ipynb	
@@ -49,12 +49,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "4a158a5b",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -111,12 +122,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "ee8cfa96",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.datasets import make_classification\n",
     "import pandas as pd\n",
@@ -153,12 +175,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "de5e3f85",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intercept: -0.30\n",
+      "Coefficient: 3.74\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.linear_model import LogisticRegression\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -184,12 +215,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "0a893d11",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/camarocico/Work/Teaching/DataScience/ml-workshop/.venv/lib/python3.13/site-packages/sklearn/utils/validation.py:2691: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Generate a smooth range of feature values\n",
     "X_range = np.linspace(X.min(), X.max(), 300).reshape(-1, 1)\n",
@@ -240,6 +290,43 @@
     "Create a dataset with 2 informative features."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "644c8d46",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X_2d, y_2d = make_classification(\n",
+    "    n_samples=500, \n",
+    "    n_features=2, \n",
+    "    n_informative=2, \n",
+    "    n_redundant=0, \n",
+    "    n_clusters_per_class=1, \n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "df_2d = pd.DataFrame(X_2d, columns=['Feature_1', 'Feature_2'])\n",
+    "\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.scatterplot(x=X_2d[:,0], y=X_2d[:,1], hue=y_2d, palette='Set1')\n",
+    "plt.title(\"2D Classification Data\")\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "01477448",
@@ -250,6 +337,42 @@
     "Train a Logistic Regression model on this new data."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "27852e29",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.88\n",
+      "Confusion Matrix:\n",
+      " [[45  6]\n",
+      " [ 6 43]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
+    "\n",
+    "# Split\n",
+    "X_tr, X_te, y_tr, y_te = train_test_split(X_2d, y_2d, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Train\n",
+    "clf_2d = LogisticRegression()\n",
+    "clf_2d.fit(X_tr, y_tr)\n",
+    "\n",
+    "# Predict\n",
+    "y_pred = clf_2d.predict(X_te)\n",
+    "\n",
+    "print(f\"Accuracy: {accuracy_score(y_te, y_pred):.2f}\")\n",
+    "print(\"Confusion Matrix:\\n\", confusion_matrix(y_te, y_pred))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "390da85b",
@@ -262,6 +385,47 @@
     "-   Notice that even though the probability function is curved (Sigmoid), the **cut-off line** (where `prob = 0.5`) is straight. That is why Logistic Regression is a **Linear Classifier**."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7f64c3fa",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.inspection import DecisionBoundaryDisplay\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Draw Boundary\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    clf_2d, \n",
+    "    X_2d, \n",
+    "    response_method=\"predict\", \n",
+    "    cmap=\"coolwarm\", \n",
+    "    alpha=0.3, \n",
+    "    ax=plt.gca()\n",
+    ")\n",
+    "\n",
+    "# Scatter Points\n",
+    "sns.scatterplot(x=X_2d[:,0], y=X_2d[:,1], hue=y_2d, palette='Set1', edgecolor='k')\n",
+    "\n",
+    "plt.title(\"2D Decision Boundary\")\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "e73f6320",
@@ -280,9 +444,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03C03 Evaluation Metrics.ipynb b/src/S03 Supervised Learning - Classification and Evaluation/S03C03 Evaluation Metrics.ipynb
index bd0d4ae..fd08919 100644
--- a/src/S03 Supervised Learning - Classification and Evaluation/S03C03 Evaluation Metrics.ipynb	
+++ b/src/S03 Supervised Learning - Classification and Evaluation/S03C03 Evaluation Metrics.ipynb	
@@ -108,12 +108,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "0442488f",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Class 0 count: 900\n",
+      "Class 1 count: 100\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.datasets import make_classification\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -150,10 +159,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "890eafe9",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.9700\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.linear_model import LogisticRegression\n",
     "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
@@ -183,10 +200,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "170e5e33",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.97      0.99      0.98       270\n",
+      "           1       0.92      0.77      0.84        30\n",
+      "\n",
+      "    accuracy                           0.97       300\n",
+      "   macro avg       0.95      0.88      0.91       300\n",
+      "weighted avg       0.97      0.97      0.97       300\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.metrics import ConfusionMatrixDisplay\n",
     "import matplotlib.pyplot as plt\n",
@@ -230,12 +273,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "01d85e6f",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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xZjKEUqVK6TAI1PowfvD999+Xzz//3OMxgERERCa/1v0feeQRHdHvzvTp010+5+eff87gkhERUajjXKBERGRJTIBERGRJTIBERGRJTIBERGRJTIBERGRJTIBERGRJTIBERGRJTIBERGRJgTMJHhFRJouLTwrK9QPJN5gAiciyqg9b6/m2JfLK3E61mARDCJtAichSUJNDMvPWjiMX5HqC5zVGCnysARKRpaAZEzU5T5MZmkm9qSlS8GACJCLLQRIMhIVwyb/YBEpERJbEBEhERJbEBEhERJbEBEhERJbEBEhERJbEBEhERJbEBEhERJbEgTBERD5mGIZXs8YkJCSKYfBryGxMgEREPpw8G4nsxcnb5PdTl72Ka6mc4dKoEbNgZmICJCJKgX3NLCOnRDt8JUxrjZGR/DoyCxMgEVEK0joBdoXbcv3/1SNS3o5zjfoPEyARUQryxfxflezXwbGSxcMlAbl+YOBjAiQiSkGWLGFyaEQj23UKHUyARESpYOILTRwHSERElsQESERElsQESERElsQESERElsROMEREAeJ6fJJERCR6tC2HWaQfEyARUYDMNPPAqO88fl71Enn//0B7Ds1IKzaBEhEF4UwzO45cSPNz6X9YAyQiCpCZZn7uX0+iIiNS3J5Tp/kOEyARkZ8H2R8Y3ECWr1ghOaKySkQEd8uZhU2gREQBkAQ5y1rmYwIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJLYgIkIiJL8nsCnDBhgpQsWVKyZcsmNWvWlO3bt6e4/fjx46Vs2bISHR0txYoVk549e8qNGzcyrbxERBQa/JoAZ8+eLb169ZKBAwfKrl27pHLlyhIbGyv//POPy+2//vpr6du3r26/b98++eKLL/Q1/vOf/2R62YmIKLj5NQGOGzdO2rdvL23btpUKFSrI5MmTJSYmRqZOnepy+61bt0qdOnWkefPmWmts2LChNGvWLNVaIxERkbOs4ifx8fGyc+dO6devn+2+LFmySP369WXbtm0un1O7dm3573//qwmvRo0acujQIVm+fLm0bNnS7fvcvHlTL6bLly/r34SEBNvFvE3JMT6pY4wYn8z8DSUkJDo8LyHMEKvFJ8FH+2u/JcBz585JUlKSFC5c2OF+3N6/f7/L56Dmh+c9+OCDYhiGJCYmSqdOnVJsAh05cqQMHjw42f2rV6/W2qZpzZo16fo8oY7xYYz4GwqM/2c3k/5v171q1WqJChfLxScuLi64E2BabNy4UUaMGCETJ07UDjMHDx6UHj16yNChQ+Xdd991+RzUMHGe0b4GiM4zaD7NlSuXHkkgqA0aNJCIiIhM/DTBgfFhjPgbCqz/Z3HxidJ7+3q9HhvbUGIig2o37pP4mC156eW3yBUoUEDCw8PlzJkzDvfjdpEiRVw+B0kOzZ2vvfaa3q5YsaJcu3ZNOnToIO+88442oTqLiorSizME0f6H5nybUo4XJccYpYzx8U2MIowwp+1DPwE6x8dX+yK/dYKJjIyUatWqybp162z33bp1S2/XqlXL5XNQ7XVOckiigCZRIiIiT/n10AFNk61bt5bq1atrpxaM8UONDr1CoVWrVlK0aFE9jwdPP/209hy97777bE2gqBXifjMREhERBXwCbNq0qZw9e1YGDBggp0+flipVqsjKlSttHWOOHj3qUOPr37+/hIWF6d8TJ05IwYIFNfkNHz7cj5+CiIiCkd8bj7t166YXd51e7GXNmlUHweNCREQU1FOhERER+QMTIBERWRITIBERWRITIBERWRITIBERWRITIBERWRITIBERWRITIBERWRITIBERWRITIBERWZLXU6FhdfUff/xRjhw5oqszYD5OTE5dqlSpjCkhERGRPxPgli1b5MMPP5QlS5bo4oS5c+eW6Oho+ffffzUpli5dWtflwwrtOXPmzIiyEhERZW4T6DPPPKMrN5QsWVJWr14tV65ckfPnz8vx48e1Fvjnn3/qCg1Yy+/uu++2LVtPREQU1DXAJ598UubPn+92FV7U/nDB2n6///67nDp1ytflJCIiyvwE2LFjR49fsEKFCnohIiIKZOwFSkREluSzBLhnzx4JDw/31csREREFTw3QMAxfvhwREZH/h0E8//zzKT5+6dIlCQsL80WZiIiIAicBYvxfgwYNpHDhwi4fT0pK8mW5iIiIAiMBli9fXpo0aSLt2rVz+fju3btl6dKlviwbERGR/88BVqtWTXbt2uX28aioKClevLivykVERBQYNcDJkyen2MyJGuLhw4d9VS4iIqLASICo4REREYUKDoQnIiJLYgIkIiJL8no9QCIiCgxx8Z4PP4uOCOdYbSdMgEREQcR+wq3qw9Z6/LzqJfLK3E61mATtsAmUiCiIXE9I26QjO45cSPNzQ1WaaoAzZszQFeGfffZZ232LFi3S6dBatWrly/IREZEbm3o/KvlzRKbaTOpNTdFK0pQA27RpI+XKlXNIgH369NGV4ZkAiYgyR3RkuMRE8kxWWqUpcrdu3Up23/79+9NcCCIi8ky+mEiX18l7PHQgIgoiWbKEyaERjWzXKYMT4OXLlz1+wVy5cqWjOERElBomvkxMgHny5Em16ywWw8U2XBaJiIhCJgFu2LAh40tCREQUaAnw4YcfzviSEBERBfpA+E2bNskrr7witWvXlhMnTuh9X331lWzevNnX5SMiIgqMBDh//nyJjY2V6OhoXSD35s2bej8GwY8YMSIjykhEROT/BDhs2DBdHHfKlCkSERFhu79OnToprhhPREQU1AnwwIED8tBDDyW7H1OjXbx40VflIiIiCqwEWKRIETl48GCy+3H+r3Tp0r4qFxERUWAlwPbt20uPHj3kxx9/1HF/J0+elJkzZ8pbb70lnTt3zphSEhER+XsqtL59++pcoI899pjExcVpc2hUVJQmwNdff93X5SMiIgqMBIha3zvvvCNvv/22NoVevXpVKlSoIDly5MiYEhIREQXSZNiRkZGSM2dOvTD5ERFRyJ8DTExMlHfffVd7fZYsWVIvuN6/f39JSEjImFISERH5uwaI83wLFiyQ0aNHS61atfS+bdu2yaBBg+T8+fMyadIkX5eRiIjI/wnw66+/llmzZskTTzxhu69SpUpSrFgxadasGRMgERGFZhMoenyi2dNZqVKl9LwgERFRSCbAbt26ydChQ21zgAKuDx8+XB8jIiIKmSbQ559/3uH22rVr5Y477pDKlSvr7T179kh8fLyODSQiIgqZBIhenvaaNGnicBvn/4iIiEIuAU6bNi3jS0JERBToC+ISERFZciaYefPmyZw5c+To0aN67s8e1wQkIqKQrAF+9NFH0rZtWylcuLD8/PPPUqNGDcmfP78cOnTIYWwgERFRSCXAiRMnymeffSYff/yxjvvr3bu3rFmzRrp37y6XLl3KmFISERH5OwGi2bN27dp6PTo6Wq5cuaLXW7ZsKd98842vy0dERBQ4K8L/+++/er148eLyww8/6PXDhw+LYRi+LyEREVEgJMB69erJ4sWL9TrOBfbs2VMaNGggTZs2lcaNG2dEGYmIiPyfAHH+DwviQteuXWXq1KlSvnx5GTJkSJomwp4wYYLOLZotWzapWbOmbN++PcXtL168qO9722236bykd999tyxfvtzr9yUiImvzehhElixZ9GJ6+eWX9ZIWs2fPll69esnkyZM1+Y0fP15iY2PlwIEDUqhQoWTbY8gFapt4DEMxihYtKkeOHJE8efKk6f2JiMi6PEqAv/zyi8cviKWRPDVu3Dhp3769NqUCEuGyZcu0Vtm3b99k2+N+nH/cunWrRERE6H2uVqYgIiLySQKsUqWKhIWFpdrJBdskJSV58pJam9u5c6f069fPdh9qlvXr19cFdl3BuUcswosm0EWLFknBggWlefPm0qdPHwkPD/fofYmIiDxOgOjh6Wvnzp3TZIkB9fZwe//+/S6fg8H269evlxYtWuh5v4MHD0qXLl0kISFBBg4c6PI5WKrJfummy5cv6188x7yYtyk5xid1jBHjE8i/oYSERIf3SQgzgj4+CT6Kk0cJsESJEhIIbt26pef/0BEHNb5q1arJiRMnZMyYMW4T4MiRI2Xw4MHJ7l+9erXExMTYbmMwP7nH+KSOMWJ8AvE3dDPp/3b1q1atlqjw4I9PXFyc/+YC9YUCBQpoEjtz5ozD/biNsYauoOcnzv3ZN3eiB+rp06e1SdXVivRoYkVHG/saIJZvatiwoeTKlUuPJBBUdK4xzyvS/2F8UscYMT6B/BuKi0+U3tvX6/XY2IYSE+m33b7P4mO25KWX3yKBZIUa3Lp16+S5556z1fBw293K8nXq1JGvv/5atzN7ov7xxx+aGF0lP8BQCVycIYj2PzTn25RyvCg5xihljI9/YhRhhDm9fvAlQOf4+CpGfl0OCTWzKVOmyJdffin79u2Tzp07y7Vr12y9Qlu1auXQSQaPoxdojx49NPGhx+iIESO0UwwREZE3/HoogNljzp49KwMGDNBmTPQ2Xblypa1jDOYdtR9ziKbLVatW6ewzGG6BcYBIhugFSkRElOEJELOxYCD6X3/9JW+//bbky5dP1wFE4kJS8gaaO901eW7cuDHZfRgGYc4/SkRElGkJEIPiMVYvd+7c8vfff+tAdiTABQsWaI1txowZaS4MERFRZsmSlvN2bdq0kT///FPn7zQ1atRIvv/+e1+Xj4iIfCQuPkl7hXpyMSywuo/XNcCffvpJPv3002T3o+kT5/GIiChw2Oex6sPWevy86iXyytxOtXSGr1DldQ0QQwpcjcFAr0xMTUZERIHjeoJn01M623HkQpqfG7I1wGeeeUaXPpozZ47extEBzv2hJ2aTJk0yooxEROQDm3o/KvlzuB4zbd9M6k1N0VI1wPfff1+uXr2qU5Jdv35dHn74YSlTpozkzJlThg8fnjGlJCKidIuODNeZYFK+hFsm0l7XANH7E1PSbN68WXuEIhlWrVpVe4YSEVFgyRcT6fI6pSEBHjt2TAekP/jgg3ohIqLAlSVLmBwa0ch2ndLRBIoFaNHsiSnMLly44O3TiYgokyHxMfn5IAHu2LFDatSooR1hMAk1JrLGrDD2a+4RERGFXAK87777dP099PxcsWKFDn3o0KGDToP26quvZkwpiYiIfCzNq0Fg+MOjjz6qTaFr166VUqVK6aoOREREIZ0Ajx8/LqNHj9YVHNAkmiNHDpkwYYJvS0dERBQovUAxDRoWpd2yZYuUK1dOWrRoIYsWLZISJUpkTAmJiIgCIQEOGzZMmjVrJh999JFUrlyZXwoREVkjAaLzSyhPjkpERNbgUQLEjC/33nuvrs6+d+/eFLfFSu1EREQhkQDR0QVLHWH+T1xHDdB+rSjzNv4mJYX27OFERGShBHj48GHbUke4TkREZIkEaN/D88iRI1K7dm3JmtXxqYmJibJ161b2BiUiotAcB4jB7//++2+y+y9duqSPERERhWQCNM/1OTt//rxkz57dV+UiIiIKjGEQzz//vP5F8mvTpo1ERUXZHkPHF/QURdMoERFRSCVALIRr1gCx+nt0dLTtscjISHnggQekffv2GVNKIiIifyXAadOm2dYDfOutt9jcSURE1poJZuDAgRlTEiIiokBLgFWrVpV169ZJ3rx5dT3AlKZC27Vrly/LR0RE5L8E+Oyzz9o6vWAFeCIiIkskQPtmTzaBEhGRJccBHjt2TBfDNW3fvl3eeOMN+eyzz3xdNiIiosBJgM2bN5cNGzbodUyQXb9+fU2C77zzjgwZMiQjykhEROT/BPjrr79KjRo19PqcOXOkYsWKOgfozJkzZfr06b4vIRER+UVcfJLExSd6dLFfIShkh0EkJCTYOsSsXbtWnnnmGb1erlw5OXXqlO9LSEREmcawy2PVh631+HnVS+SVuZ1qBdWC6V7XAO+55x6ZPHmybNq0SdasWSOPP/643n/y5EnJnz9/RpSRiIgyyfWEtK3puuPIhTQ/N2hqgKNGjZLGjRvLmDFjpHXr1lK5cmW9f/HixbamUSIiCn6bej8q+XNEptpM6k1NMagT4COPPCLnzp2Ty5cv68B4U4cOHSQmJsbX5SMiIj+JjgyXmEiv00TQSNMnCw8P1wVwN2/erLfLli2rc4QSEVFwyxcT6fJ6KPI6AV67dk1ef/11mTFjhty6dcuWEFu1aiUff/wxa4FEREEsS5YwOTSike16KPO6E0yvXr3ku+++kyVLlsjFixf1smjRIr3vzTffzJhSEhFRpsmSJSzkk1+aaoDz58+XefPm6blAU6NGjXR9wJdeekkmTZrk6zISERH5vwYYFxcnhQsXTnZ/oUKF9DEiIqKQTIC1atXSCbFv3Lhhu+/69esyePBgfYyIiCgkm0DHjx8vsbGxcscdd9jGAO7Zs0eyZcsmq1atyogyEhER+T8BYu7PgwcPytdffy379u3T+5o1ayYtWrTQ84BEREQhlwB/+OEH7f0ZHx8v9erVk9deey3jSkZERBQICRA9P5s2baq1vIiICBk3bpxOi/bWW29lZPmIiIj82wlm5MiR0r59e7l06ZJcuHBBhg0bJiNGjMiYUhEREQVKAjxw4IDW9jDrC2DQ+5UrV+Sff/7JyPIRERH5NwFijF+uXLlstyMjI7Xn59WrVzOmZERERIHSCebzzz+XHDly2G5jQmysAl+gQAHbfd27d/dtCYmIiPyZAIsXLy5TpkxxuK9IkSLy1Vdf2W5jJWAmQCIiCqkE+Pfff2dsSYiIiAJ5KjQiIiLLJMBZs2Z5/ILHjh2TLVu2pKdMREREgZEAscRR+fLlZfTo0bbpz+xhbODy5culefPmUrVqVTl//nxGlJWIiChzzwFisdvFixfriu/9+vWT7Nmz65JIGAaBQfGnT5/WnqBt2rSRX3/91eVySUREREHZCeaZZ57Ry7lz52Tz5s1y5MgRXQYJie++++7TS5YsPKVIREQhuhoEEt5zzz2XMaUhIiLKJKyyERGRJTEBEhGRJTEBEhGRJTEBEhGRJQVEApwwYYKULFlSh1XUrFlTtm/fLp4O0Mf8o+yUQ0REGd4LNCkpSVeAWLduna4FeOvWLYfH169f79XrzZ49W3r16iWTJ0/W5Dd+/HiJjY3V9QcLFSqU4tykWJ+wbt263n4EIiIi72uAPXr00AsS4b333iuVK1d2uHhr3LhxutJ827ZtpUKFCpoIY2JiZOrUqW6fg/du0aKFDB48WEqXLs2vkYiIMr4GiGbHOXPmSKNGjSS94uPjZefOnTq7jAmD6evXry/btm1z+7whQ4Zo7bBdu3ayadOmFN/j5s2bejFdvnxZ/yYkJNgu5m1KjvFJHWPE+Fj5N5SQkGh3PUESwowMj4+v4uR1AsRK8GXKlPHJm2NWGdTmnKdOw+39+/e7fA5mofniiy9k9+7dHr3HyJEjtabobPXq1VrTNK1Zs8br8lsJ48MY8TfE/2eu3Ez6v1SyatVqiQqXDN8PxcXF+ScBvvnmm/Lhhx/KJ598oh1QMtOVK1ekZcuWujCv/Sr0KUHtEucY7WuAxYoVk4YNG0quXLn0SAJBbdCggURERGRg6YMT48MY8TfE/2cpiYtPlN7b/9f3Iza2ocREep1WvN4PmS156eV1SVED27Bhg6xYsULuueeeZEljwYIFHr8Wklh4eLicOXPG4X7cxmrzzv766y/t/PL000/b7jM74WTNmlU7ztx5550Oz4mKitKLM5TbvuzOtynleFFyjFHKGJ/QjFGEEeZUft8nQMfX912MvC5pnjx5pHHjxj55czSnVqtWTXuUmkMZkNBwu1u3bsm2L1eunOzdu9fhvv79+2vNELVS1OyIiIgyJAFOmzZNfAnNk61bt5bq1atLjRo1dBjEtWvXtFcotGrVSooWLarn8jBOED1PnRMyON9PRESUkjTXVc+ePatNjlC2bFkpWLBgml6nadOm+loDBgzQdQWrVKkiK1eutHWMOXr0KJdZIiIi/ydA1M5ef/11mTFjhu38G87joaaGBXPte1Z6Cs2drpo8YePGjSk+F4PyiYiIMnwgPJossUL8kiVL5OLFi3pZtGiR3oceokRERCFZA5w/f77MmzdPHnnkEdt9GBQfHR0tL730kkyaNMnXZSQiIvJ/DRADEJ0HrgNmZvHV4EQiIqKAS4C1atWSgQMHyo0bN2z3Xb9+XWdbwWNEREQh2QSK8XZYreGOO+6wTX69Z88eHaKwatWqjCgjERGR/xMgxtv9+eefMnPmTNt8nc2aNdPVGXAekIiIKGTHAWKoA5YwIiIiCukEuHjxYnniiSd0/jVcT8kzzzzjq7IRERH5NwFink7M0oKenuacna5gdQgsb0RERBQSCdCc8cX5OhERkWWGQbiC2WCIiIhCOgGOGjVKZs+ebbv94osvSr58+XTFBgyHICIiCskEOHnyZNu6e1ihd+3atbp6AzrJvP322xlRRiIiIv8Pg0BnGDMBLl26VOf/bNiwoZQsWVJq1qzp+xISERFlAK9rgHnz5pVjx47pddT86tevr9cNw2APUCIiCt0a4PPPPy/NmzeXu+66S86fP69Nn/Dzzz9LmTJlMqKMRERE/k+AH3zwgTZ3ohY4evRoyZEjh95/6tQp6dKli+9LSEREFAgJELPBvPXWW8nu79mzp6/KRERElOE4FRoREVkSp0IjIiJL4lRoRERkST6ZCo2IiCjkE2D37t3lo48+Snb/J598Im+88YavykVERBRYCXD+/PlSp06dZPfXrl1b5s2b56tyERERBVYCxOD33LlzJ7s/V65ccu7cOV+Vi4iIKLASIGZ7wRRozlasWCGlS5f2VbmIiIgCayB8r169pFu3bnL27FmpV6+e3rdu3Tp5//33Zfz48RlRRiIiIv8nwFdffVVu3rwpw4cPl6FDh+p9mBpt0qRJ0qpVK9+XkIiIKBASIHTu3FkvqAVGR0fb5gMlIiIK6XGAiYmJuhDuggULdBkkOHnypFy9etXX5SMiIgqMGuCRI0fk8ccfl6NHj2pTaIMGDSRnzpwyatQovY0V44mIiEKuBtijRw+pXr26XLhwQZs/TY0bN9bOMERERCFZA9y0aZNs3bpVIiMjHe5HR5gTJ074smxERESBUwO8deuWJCUlJbv/+PHj2hRKREQUkgmwYcOGDuP9wsLCtPPLwIEDpVGjRr4uHxERUWA0gY4dO1Y7wVSoUEFu3LghzZs3lz///FMKFCgg33zzTcaUkoiIyN8JsFixYrJnzx6ZPXu2/kXtr127dtKiRQuHTjFERGQtcfHJT4+5Ex0Rri2IQZMAExISpFy5crJ06VJNeLgQEZF1Gf8bCq6qD1vr8fOql8grczvV8msS9OocYEREhDZ7EhERwfUEz2t99nYcuZDm5/qtCbRr16466P3zzz+XrFnTNJMaERGFoE29H5X8ORyHyLlqJjVrip42mSYkJDrUNH3F6wz2008/6YD31atXS8WKFSV79uwOj2N6NCIisp7oyHCJicyaIU2mpXKGS6NGhn8TYJ48eaRJkyY+LQQREQWnfDGRLq+7k9Zmz8NXwvS5TnOwZG4CnDZtmu/enYiIglqWLGFyaEQj2/WMbDL1tazezAAzZswYWbx4scTHx8tjjz2mg9859IGIyNqyeJH47GuJRfNEe500/ZIAsQDuoEGDpH79+pr0PvzwQ/nnn39k6tSpGVtCIiIKGVnSUWP0eVk83XDGjBkyceJEWbVqlXz77beyZMkSmTlzptYMiYiIPIXE5+/kp+XwdEOs/2c/1ydqghjAiIVwiYiIgk0Wb1aBz5YtW7KB8ZgdhoiIKNh4fA7QMAxp06aNREVF2e7DrDCdOnVyGAvIcYBERBRSCbB169bJ7nvllVd8XR4iIqLASoAc/0dERJZeEJeIiCgUMAESEZElMQESEZElMQESEZElMQESEZElMQESEZElMQESEZElMQESEZElMQESEZElMQESEZElBUQCnDBhgpQsWVJXm6hZs6Zs377d7bZTpkyRunXrSt68efWCZZlS2p6IiCggE+Ds2bOlV69eMnDgQNm1a5dUrlxZYmNjdbV5VzZu3CjNmjWTDRs2yLZt26RYsWLSsGFDOXHiRKaXnYiIgpffE+C4ceOkffv20rZtW6lQoYJMnjxZYmJiZOrUqS63xyr0Xbp0kSpVqki5cuXk888/11Xp161bl+llJyKi4OXXBBgfHy87d+7UZkxbgbJk0duo3XkiLi5OF+XNly9fBpaUiIgsuxxSRjh37pwkJSVJ4cKFHe7H7f3793v0Gn369JHbb7/dIYnau3nzpl5Mly9f1r9ImubFvE3JMT6pY4wYn/Tib8i9hIREh+v2++2gToDp9d5778msWbP0vCA60LgycuRIGTx4cLL7V69erU2tpjVr1mRoWYMd48MY8TfE/2f+cDPp/1LV+vXrJSr8fy1/QZ8ACxQoIOHh4XLmzBmH+3G7SJEiKT537NixmgDXrl0rlSpVcrtdv379tJONfQ3Q7DiTK1cuPZLAzr1BgwYSERHhg08VWhgfxoi/If4/86e4+ETpvX29Xq9Xr57kzp7N1pIX1AkwMjJSqlWrph1YnnvuOb3P7NDSrVs3t88bPXq0DB8+XFatWiXVq1dP8T2ioqL04gzJzj7hOd+mlONFyTFGKWN8UscYuYiJEWYXn6w+jZHfm0BRO2vdurUmsho1asj48ePl2rVr2isUWrVqJUWLFtWmTBg1apQMGDBAvv76ax07ePr0ab0/R44ceiEiIgqKBNi0aVM5e/asJjUkMwxvWLlypa1jzNGjR7VnqGnSpEnae/SFF15weB2MIxw0aFCml5+IiIKT3xMgoLnTXZMnOrjY+/vvvzOpVEREFMr8PhCeiIjIH5gAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYjIkpgAiYgoYEVHhMued+vJ6BqJet2XmACJiChghYWFSUxkVokK/991X2ICJCIiS2ICJCIiS2ICJCIiS2ICJCIiS8rq7wIEIsMwJDExUZKSksTqEhISJGvWrHLjxg3GgzGy5G8oPDxcy+/rDhjkf0yATuLj4+XUqVMSFxfnn28kAA8GihQpIseOHeMOgDGy7G8oJiZGbrvtNomMjPR3UciHmADt3Lp1Sw4fPqxHfLfffrv+2IP1P6wvY3L16lXJkSOHZMnCFnPGyFq/ISRvHBSfPXtW9w133XVX0H0Gco8J0A5+6PjPWqxYMT3io//tvBCXbNmy8T++G4xRaMcnOjpaIiIi5MiRI7bPQaEh+H6NmSAY/5MSUcbhPiE0cU9PRESWxARIHitZsqSMHz8+zRGbPn265MmTx/IR37hxo55bvnjxYobH4vz581KoUCH5+++/LR93d37//Xe544475Nq1a4yRxTABhog2bdrIc889l6Hv8dNPP0mHDh3SnCybNm0qf/zxh8fv98gjj2iiwAXnXe6++24ZOXKkdkwIZrVr19aexrlz587w9xo+fLg8++yz+n04i42N1Q5f+F5d/Z7M2KMzWJkyZWTIkCE6PCijYJhE165dJX/+/NphpkmTJnLmzJkUn4PHUVZ0WsN5+8cff1z+/PNPr163QoUK8sADD8i4ceMy7LNRYGICJI8VLFgwXZ2D0JkAtRFvtG/fXpPFgQMHpF+/fjJgwACZPHmyZCR0dMhISCgYFpDRPYwxlOeLL76Qdu3aJXvs6NGjsnXrVunWrZtMnTrV5fORTBB7JJQ333xTBg0aJGPGjMmw8vbs2VOWLFkic+fOle+++05Onjwpzz//vNvtcSCEg75Dhw7JokWL5Oeff5YSJUpI/fr1HWpznrxu27ZtZdKkSRma4CkAGRZz6dIlVB/0L8THxxvffvut/r1+/brx+++/699g07p1a+PZZ591+/jGjRuN+++/34iMjDSKFCli9OnTx0hISLA9fvnyZaN58+ZGTEyMPj5u3Djj4YcfNrp3725cuHDBSEpKMkqUKGF88MEHuv2tW7eMgQMHGsWKFdPXvO2224zXX39dH8PzEGP7C0ybNs3InTu3Q7kWL15sVK9e3YiKijLy589vPPfcc7bH8Do9evRw2L5q1apG48aNbbdv3LhhvPnmm8btt9+uZa9Ro4axYcMGh+d89tlnxh133GFER0fr67///vsO5cDnqFy5sjFlyhSjZMmSRlhYmN6Pz92uXTujQIECRs6cOY1HH33U2L17t+15uP7II48YOXLk0MdRtp9++kkf+/vvv42nnnrKyJMnj5arQoUKxrJly/QxlA8xweub5s2bp9sglojz2LFjHT4D7hs+fLjRtm1bfT/E/dNPPzVSMnfuXKNgwYIuHxs0aJDx8ssvG/v27dNYxMXFpfp7atCggfHAAw8Y3sJvx/wNuXPx4kUjIiJCy2xC2RCnbdu2uXzOgQMH9PFff/3V4b3wmfFdevO6N2/e1N/g2rVrXb5XRu8b7PdDlHp8nPfjacUaYOoHCBIXn+iXi6+a+k6cOCGNGjWS+++/X/bs2aNHuqgZDBs2zLZNr169ZMuWLbJ48WJZs2aNbNq0SXbt2uX2NefPny8ffPCBfPrpp1pD+Pbbb6VixYr62IIFC/ScCprMUIPAxZVly5ZJ48aNtWw4el+3bp3UqFHD5baIBcq0f/9+h8HIqMFs27ZNZs2aJb/88ou8+OKLDs1g+EydOnWSHj16yO7du6VBgwbaLOjs4MGD+plQdmwHeK1//vlHVqxYITt37pSqVavKY489Jv/++68+3qJFC/2cP/74o2zYsEF69+6t3eUBTW43b96U77//Xvbu3SujRo3S5jdX8NovvfSSvPzyy7otalrvvvuunjO19/7770v16tU1Vl26dJHOnTtrzdgdxKtatWouYzlt2jR55ZVXpFy5ctq8OW/ePPGkBp9S7fiJJ57Qz+h8yZUrl8bJ/H24iwFmjEHtzYSyFS9eXL9fVxBfsB+WgN6aUVFRsnnzZq9eF7+pKlWqaMzIOjgOMBXXE5KkwoBV4g+/D4nVdbDSa+LEiTq28ZNPPtFmN+wA0AzUp08fbVJEc9GXX34pX3/9te7gATtInFdxB01oaMbDjgU7fexQzOSVL18+PbeUM2dO3cYdJCLs9AcPHmy7r3LlysnK/vnnn+uOFzsy7Oy6d+9uKwPKib9mWd966y1ZuXKl3j9ixAj5+OOPdceM+wHnEdH0t3TpUof3wevPmDFDm3kBO9Dt27drAsQOFcaOHauJHskC50Lxvm+//bbG8/Lly3LffffZusvjMZxrMnf6pUuXdhsHnHtC3JH0zDKiYwaaG3F+y4QDBSQ+wHeHAxAk3rJly7p8XYxbc/Udrl27VptHcQ4QkAhxQNSyZUuXr4OEiYOTVatWyeuvv+72c+B7un79utuB8Hnz5nX73NOnT2sScu4kVbhwYX3MFTORoWkcB2LZs2fXmBw/ftx20OXN6yJWiBlZB2uAFrBv3z6pVauWwzmnOnXq6E4JOwucQ0Fysa99oYOGux2rWTvCzg47dpynW7hwodfnT1DTMhOuO6hlYTvU5JDI3nnnHe1EAqgtYW5JJAz7GgfO8/z111+6DWpIzrVKV7VMnDsykx+gpoz4mB0nzAtmAzFfG7Xm1157TRo2bKg7XvN+QJJGDRtxHjhwoNZOU/p+sJ093EYt1n7uzEqVKtmu47vEwQUStDv4flwN2sY5P3RIwvyW0KxZM42vffkBBwn4zHgNxB7PQe3UnaJFi2pt0tUFvxPE2Jdw4IUaOzpW4aAL56dxQICypmXcHmq4nALRWlgDTEV0RLjWxPz13oEKNUokF9Qm0GSKmglqLEg+ZjOgJzuc1CARYwcKc+bM0evosYeaJxIUappo5sJfe+6aG91B7cEeXhtzP2LIgjOzNoFk0Lx5c00UuLz33nvaFItmXSRG1LDQzLt69WrtvYomzJRqUKlxjiuSIGpX7hQoUEAuXLjgcB+ab3GwggMeNIWbkGiRGO2bhx999FHdBjUo1I7MhOkOEk9KTYhIgL/99pvLx5DMUQvH0BD72hp6a6bUioAmXhwgXbp0SZ+Pg5iaNWtqU7G3r4vY3HnnnSl+RgotTICpwE7GF82Q/lS+fHk9v4WmLLMWiCN+NFHi3AyaprBzRXd4NCkBdig4sq5bt26KCezpp5/WC855oUkKtTKcK8NOM7WZ/1GjQdMaeuB5AkkN5/LQnInzYGhyxHugFuSunKjFOnfzd9Xt3xk+A5rIsNN3NYTAhNrnG2+8Ia+++qqea0TTKxKgeZCA+3BBM92UKVNcJkB8P/g+7OE2Xts5sXsD8fnvf//rcN/MmTP1O0dTrj0kaSRonLc13xMHBebBhyfS0wSKRIbfIH4PaDoGHGChKRmtF6kxh5Sg1rxjxw4ZOnSo16/766+/ygsvvODx56XgF9x7dnKApGV24DChCQ+1M4zJw84XnUawA0CzHJrw0FSERNi6dWs9n4WmJAxVwON4zF1XfXTQQPLB0TaanrCjRUI0m7mQNNABBOf4cA4NtRFneA80geKoG9uhCXX58uV6fsudjh076s4NCR07KzSRtmrVSnfe2OFj0mLs7JBcn3zySf3MDz30kJ5nQ6Jev369dmpJbQgCapjYQaKb/ejRozUZ4byp2XHnnnvu0XihDPjMiCl2vOZOFkkRNSI8D7UwNM0h0bmCIQbooITPhWZGdM7A+Vqc/0wP1ECRePH+ZvLBuT6U+d5773XYFska2+L8KeKWFmgCdQUJEOdI0RkmpQSG4Rr4TeI3iG3x3eE7QI3fhIMs1KbNgwwMbUCtDwduOPjCARK+MzRLe/O6mCgAncXsO8uQBRgWE8rDIJyHHuCCbvxpHQaBIQXYztUwiIULFxo1a9Y0cuXKZWTPnl27x9t3IUcX80qVKmnX8pSGQcyfP9+oUqWKlgvDDZ5//vkUh0FAx44djXvuuUfLhO9twIABOnwB3d0xHAPDJH755ReHYRBFixa1DYMYNmyYfkbnYRDOEBMM7cAQC7w2hh60aNHCOHr0qHabxzAC+2EgXbt2tf12unXrZtx55536+dEtv2XLlsa5c+dSHQaB9ylevLgxZswYh7LYx96EMqPsKcF3OHnyZL2+Y8cOfd/t27e73PaJJ56wDTFJbViNr4dBAGLXpUsXI2/evPo7RFlOnTrlsA3Kj9+R6cMPP9QhLmbc+vfvr9+Nt687YsQIIzY2NsWycRhE6A2DYAIMkQToa1evXtVkheThyc4rmLz22mvGgw8+6LPX83QH7w9Lly41ypcv79eyBXJ8AAkTyXPz5s1ut2ECDM0EyCZQUjinhjF26CGJplScCwJMoxXsMHwB4/9wTgvNnxjykd7mxWCB5kycF0PzHpo5KTmcD/zPf/6TrCcuhT4mQHJIFDiXhQ4s6DyAHn04d4fzN8EM4/lwHu/KlSvaHf+jjz7SXppWgfOR5J45VIOshwmQFDqQYDiBs5S62QcLDJ8gInLGgfBERGRJTIBERGRJTIAuBPt6c0TkW9wnhCYmQBdTTXE+QCKyZ+4TPJ3mj4JDQHSCmTBhgs4jiamnsBoAZvB3tyyOOfsDZs7H7A133XWXLjWDmfLTC1NAYb5Ac4JhzHCS0YuWBjp0gsFcilhVOy0TDFsBYxS68dHl0OLidJ+AfUN6pqajwOP3BDh79mydpgirfGNaLUzZhSmc0B3f1erhWMoGs9djOqSnnnpKl/DB1EdYu855eqe0MCfITWmWfSvBDgDzO2KaM6sfDLjDGIV+fJD8UpqUm4KTLn3tzwIg6WEeRMx9aB4tYsAu5uvr27dvsu0xVyLWr7Nfzw1z+mExSyTR1GBMG+YHxGBvzAuIWfEx/yRqkPbNG5jnEo9ZHWKAOT0xnyabfxgjK/6GUOaMrvm52w+R6/g478eDsgaIZhGMPcMkvCY0kWBCWnerQON+1BjtocboPLu9/arR5srRYA7qRkDNi3nbGZs7/ndAgkmqEQvGwzXGKLTjg/Jn9HjYlPZDJMni46s4+TUBnjt3TmtaWJ3ZHm5jWi5XcJ7Q1fbuVo1GU6n9iuP2y7/gHJ8Ja9qRe4xP6hgjxie9+BvyLD6+6qjo93OAGQ21S/saI2qAaGLFcilmEyiCirki2fSQHOOTOsaI8Ukv/oa8i4+vpmf0awLEPJNoEsHqzPZSWgUa93uzPdaiw8UZgmif8JxvU8rxouQYo5QxPqljjDyLj6/2RX5NgOaky1jAFD05AW3tuI2FW13BQpZ43H6CXxwZeLJqNJh9fuzPBaI6jdvcwSfH+KSOMWJ80ou/Ie/iY+6/092H0/CzWbNm6aKh06dP17X4OnToYOTJk8c4ffq0Po6FRPv27WvbfsuWLUbWrFmNsWPHGvv27dMFQbEY5t69ez16v2PHjrlcOJYXxoC/Af4G+BuQoIoB9ufp4fdzgBjWcPbsWRkwYIB2ZMFwhpUrV9o6umCtLvvBs7Vr19axf/3799c1vDAQHj1APR0DePvtt8uxY8ckZ86cOibJPCeI+9LTnTZUMT6MEX9D/H8WaPsh1PywvBn250E9DtDffDWeJFQxPowRf0P8fxaq+6HgmpeIiIjIR5gAiYjIkiyfADFEYuDAgS6HStD/hpEwPiljjBif9OJvyD/xsfw5QCIisibL1wCJiMiamACJiMiSmACJiMiSmACJiMiSLJEAJ0yYICVLlpRs2bLpArzbt29Pcfu5c+dKuXLldPuKFSvqQoyhzJv4TJkyRerWrSt58+bVC9ZuTC2eVvwNmWbNmqUzDplz3YYqb+Nz8eJF6dq1q9x2223as+/uu+/m/zMn48ePl7Jly0p0dLTOgtKzZ0+5ceOGhKLvv/9enn76aZ3ZBf9f3K3vam/jxo1StWpV/f2UKVNGpk+f7v0bGyEOc41GRkYaU6dONX777Tejffv2OtfomTNnXG6PuUbDw8ON0aNH69yk/fv392qu0VCPT/PmzY0JEyYYP//8s87F2qZNGyN37tzG8ePHjVDlbYxMhw8fNooWLWrUrVvXePbZZ41Q5W18bt68aVSvXt1o1KiRsXnzZo3Txo0bjd27dxuhytsYzZw5U+dIxl/EZ9WqVcZtt91m9OzZ0whFy5cvN9555x1jwYIFOsfnwoULU9z+0KFDRkxMjNGrVy/dT3/88ce63165cqVX7xvyCbBGjRpG165dbbeTkpKM22+/3Rg5cqTL7V966SXjySefdLivZs2aRseOHY1Q5G18nCUmJho5c+Y0vvzySyNUpSVGiEvt2rWNzz//3GjdunVIJ0Bv4zNp0iSjdOnSRnx8vGEV3sYI29arV8/hPuzs69SpY4Q68SAB9u7d27jnnnsc7mvatKkRGxvr1XuFdBNofHy87Ny5U5vpTJhYG7e3bdvm8jm43357iI2Ndbu91eLjDEuUYKmSfPnySShKa4yGDBkihQoVknbt2kkoS0t8Fi9erMuXoQkUk95jIvsRI0ZIUlKShKK0xAiT/uM5ZlPyoUOHtIm4UaNGmVbuQOar/bTfV4PISOfOndP/VObKEibc3r9/v8vnYEUKV9vj/lCTlvg469Onj7bbO/8YrRyjzZs3yxdffCG7d++WUJeW+GBnvn79emnRooXu1A8ePChdunTRAynM9hFq0hKj5s2b6/MefPBBXfkgMTFROnXqpCvgkLjdT2PS7OvXr+t5U0+EdA2QMtZ7772nnTwWLlyonR9IdImWli1bamehAgUKMCQuYNFr1I4/++wzXRAbS6K98847MnnyZMbLroMHasUTJ06UXbt2yYIFC2TZsmUydOhQxsiHQroGiB1QeHi4nDlzxuF+3C5SpIjL5+B+b7a3WnxMY8eO1QS4du1aqVSpkoQqb2P0119/yd9//6092ux3+JA1a1Y5cOCA3HnnnWLl3xB6fmJVbzzPVL58eT2qR3NhZGSkhJK0xOjdd9/VA6nXXntNb6M3+rVr16RDhw56sGC/RqoVFXGzn8ZSSZ7W/iCko4j/SDjCXLduncPOCLdxDsIV3G+/PaxZs8bt9sEsLfGB0aNH65EoFi6uXr26hDJvY4ThM3v37tXmT/PyzDPPyKOPPqrX0Z3d6r+hOnXqaLOneWAAf/zxhybGUEt+aY0Rzq07JznzgMHiS7j6dj9thDh0P0Z34unTp2t32Q4dOmj349OnT+vjLVu2NPr27eswDCJr1qzG2LFjtZv/wIEDQ34YhDfxee+997Q797x584xTp07ZLleuXDFClbcxchbqvUC9jc/Ro0e153C3bt2MAwcOGEuXLjUKFSpkDBs2zAhV3sYI+x3E6JtvvtEu/6tXrzbuvPNO7aUeiq5cuaJDq3BBWho3bpxeP3LkiD6O2CBGzsMg3n77bd1PY2gWh0G4gTEixYsX1x03uiP/8MMPtscefvhh3UHZmzNnjnH33Xfr9uhqu2zZMiOUeROfEiVK6A/U+YL/sKHM29+QlRJgWuKzdetWHV6EpIAhEcOHD9ehI6HMmxglJCQYgwYN0qSXLVs2o1ixYkaXLl2MCxcuGKFow4YNLvcrZkzwFzFyfk6VKlU0nvgNTZs2zev35XJIRERkSSF9DpCIiMgdJkAiIrIkJkAiIrIkJkAiIrIkJkAiIrIkJkAiIrIkJkAiIrIkJkAiIrIkJkAiF8LCwuTbb7/V65jcGrdTW94IE11jkl6sCJEZSpYsKePHj09xm0GDBkmVKlUytBxpeQ/7+KZVmzZt5LnnnhNfe+CBB2T+/Pk+f10KPEyAFFCwU8POEResGFCqVCnp3bu33LhxQwJdv3795PXXX5ecOXPalrQxPwsuWK+sSZMmuh6eL/z000+6OkBKSeWtt95KNmmwlX3//fe6UgfWsHSXhPv37y99+/Z1mKybQhMTIAWcxx9/XE6dOqWJ4oMPPpBPP/004BdKPXr0qCxdulQTuKua4cmTJ2Xu3Lny22+/6Q7YF6ufFyxYUGJiYlLcJkeOHJI/f/50v1eowJJClStXlgkTJrjd5oknntBa/IoVKzK1bJT5mAAp4ERFRWlTIpYOQhMXVpvHUicmHJmPHDlSa4dY+ws7tHnz5jm8BhLNU089peuDoUZWt25dXavPrDk1aNBA12nLnTu3PPzww7roaHrMmTNHy1G0aNFkj2HxVyz189BDD8mAAQPk999/1+WAYNKkSbo+IJbMKVu2rHz11Ve252HZGzQvFi9eXGOCWkv37t1dNoHiOjRu3FhrNuZt++bJ1atX68LFFy9edChfjx49pF69eg4r2iNeiC2+A7wnEoenPI0vDnKQbPA+pUuXTvYdHjt2TF566SXJkyeP5MuXT5599lltjk4PvN+wYcM0Tu5g2aFGjRrpYs8U2pgAKaD9+uuvsnXrVod14pD8ZsyYoSuII9H17NlTXnnlFfnuu+/08RMnTmiyQdJYv3697Ny5U1599VVJTEzUx3F037p1a93R//DDD3LXXXfpDi895+42bdrk0dqI5mKdWPh14cKFmnzefPNN/ZwdO3aUtm3byoYNG3QbnIcya8B//vmnNtdhYVR3SQemTZumicW8be+xxx7TZGJ/fgs10dmzZ0uLFi30Ng4SUANHU+0vv/yijyFO3bp18zgWnsYXi77iffbs2aPv//LLL8u+ffv0sYSEBImNjdWDF8R2y5YtWptF2RA7V6ZPn67J3xdq1Kih70shzmfrWRD5AJY9wbpe2bNn16Vy8BPNkiWLrj8IN27c0HXAsJyOvXbt2hnNmjXT6/369TNKlSplxMfHe/SeSUlJuvbakiVLbPfhfRcuXKjXDx8+rLexPpk7lStXNoYMGeJyiRdzCZuTJ08atWvXNooWLWrcvHlTr7dv397hOS+++KLRqFEjvf7+++/rslzuPgeWpvrggw9cltmEZapQNlOPHj2MevXq2W6vWrVK42yWEXHEWnX2Nm3apN/B9evXXZbD+T08jW+nTp0ctsPySJ07d9brX331lVG2bFnj1q1btscRs+joaC2zq2WmFixYoM/xlKt4mRYtWqSfGWWn0MUaIAUcc/X0H3/8UWsSqBWhpgBoOsRq2WhiQ43AvKBGaDZx4rlowkMnGlfOnDkj7du315oJmujQTHr16lU9j5dW169f1+ZFV+644w7Jnj27NmGiKRE1MNRoUdvB6uj2cNusBb344ov6umgeRHlRYzRrsWmFmhY65+CcJMycOVOefPJJrRkCamOoSdnHFjUxNDsfPnzYo/fwNL7Oq3fjtvnZUQ5816gBmuVAMyg6Q5nfszM0a+7fv198ATV1fOabN2/65PUoMGX1dwGInCFZlClTRq9PnTpVz6198cUX0q5dO92RwrJly5Kdb0OTp30zoztIqufPn5cPP/xQSpQooc/Dztdd05oncL7rwoULLh9DUxqSAM4Fmj1EPYHzb+hAs3btWj0H2qVLFxkzZow29bpL7qm5//779Zwjzm917txZkyoSngnxRVOs/blGE85FesIX8UU5qlWrpgnaVeefjPbvv//q7zC13xIFNyZACmhZsmSR//znP9KrVy9p3ry5VKhQQXeoqE2gc4UrlSpVki+//FLPI7lKFDifNHHiRD0vZXa2OHfuXLrKed9992nnFlfQWcesYdkrX768lgUJw75s+Iwm7IDRaxSXrl27Srly5WTv3r1StWrVZK+Hz+pJ71LUApFYUDNFfFEDNOF18TnMA5C08DS+OD/YqlUrh9uIo1kOnH/EQQMOHjIbzsmaZaHQxSZQCnhoCkTPPHRdRw0KY9vQ8QVJDs1h6GH48ccf621Ah43Lly9rp4odO3ZoBxL0rkRtCtA0h9tobkMzKxJCeo/00Uy4bds2r4Y3vP3221r7Qk9QlHHcuHGyYMEC/XyAx1Dzxc4YQ0L++9//ajlRq3IFPT8x5u/06dNua6OAz4uYDR8+XF544QVbzRn69OmjnY4QQzQlo1yLFi3yqhOMp/HFsBDU8P/44w8d5rJ9+3bb++A5qFWj5ydq0Gh+RdMtaqbHjx93+b6ozeIAIbWaJT6XOakBXhfXnZtn8Z4NGzb0+DNTkPL3SUgie84dG0wjR440ChYsaFy9elU7RowfP147PEREROj9sbGxxnfffWfbfs+ePUbDhg21www6YNStW9f466+/9LFdu3YZ1atXN7Jly2bcddddxty5c1PsUOJJJ5iEhATj9ttvN1auXOm2E4wrEydONEqXLq2fAx1eZsyYYXsM74+OIbly5dJOQQ888ICxdu1a2+POZV68eLFRpkwZI2vWrPpYSh1UatSooWVbv359sse2b99uNGjQwMiRI4e+b6VKlYzhw4e7/QzO7+FpfCdMmKDvg044JUuWNGbPnu3wuqdOnTJatWplFChQQLdBnNBp6NKlSy5/K9OmTdPXTYn5nThf8Fqm48eP6/dx7NixFF+Lgl8Y/vF3EiYKBaihLl68WFatWuXvolA6oBaMGvRnn33GOIY4ngMk8hF0HsEgc4x386azCwUWnHfEOWcKfawBEhGRJbETDBERWRITIBERWRITIBERWRITIBERWRITIBERWRITIBERWRITIBERWRITIBERWRITIBERiRX9P6WNXytljvy4AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.metrics import PrecisionRecallDisplay\n",
     "\n",
@@ -262,12 +325,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "975ba1b4",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AUC Score: 0.9721\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.metrics import RocCurveDisplay, roc_auc_score\n",
     "\n",
@@ -291,15 +381,60 @@
   },
   {
    "cell_type": "markdown",
-   "id": "77996463",
+   "id": "e2928e5a",
    "metadata": {},
    "source": [
     "### Threshold Moving\n",
     "\n",
     "Manually change the decision threshold to `0.2` (making the model more sensitive).\n",
     "\n",
-    "-   Calculate Recall and Precision at this new threshold.\n",
+    "-   Calculate Recall and Precision at this new threshold."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ce606975",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Threshold 0.5 (Default) ---\n",
+      "Recall:    0.767\n",
+      "Precision: 0.920\n",
+      "\n",
+      "--- Threshold 0.2 (Sensitive) ---\n",
+      "Recall:    0.833\n",
+      "Precision: 0.735\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import precision_score, recall_score\n",
+    "\n",
+    "# Get probabilities instead of labels\n",
+    "y_prob = clf.predict_proba(X_test)[:, 1]\n",
+    "\n",
+    "# Custom Threshold\n",
+    "threshold = 0.2\n",
+    "y_pred_sensitive = (y_prob >= threshold).astype(int)\n",
     "\n",
+    "print(f\"--- Threshold 0.5 (Default) ---\")\n",
+    "print(f\"Recall:    {recall_score(y_test, y_pred):.3f}\")\n",
+    "print(f\"Precision: {precision_score(y_test, y_pred):.3f}\")\n",
+    "\n",
+    "print(f\"\\n--- Threshold 0.2 (Sensitive) ---\")\n",
+    "print(f\"Recall:    {recall_score(y_test, y_pred_sensitive):.3f}\")\n",
+    "print(f\"Precision: {precision_score(y_test, y_pred_sensitive):.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77996463",
+   "metadata": {},
+   "source": [
     "**Result**: Recall should go UP, Precision should go DOWN."
    ]
   },
@@ -315,6 +450,45 @@
     "-   Note the difference between `macro avg` (treats all classes equally) and `weighted avg` (weights by class size)."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fcd300ea",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "      setosa       1.00      1.00      1.00        19\n",
+      "  versicolor       1.00      1.00      1.00        13\n",
+      "   virginica       1.00      1.00      1.00        13\n",
+      "\n",
+      "    accuracy                           1.00        45\n",
+      "   macro avg       1.00      1.00      1.00        45\n",
+      "weighted avg       1.00      1.00      1.00        45\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "iris = load_iris()\n",
+    "X_i, y_i = iris.data, iris.target\n",
+    "X_tr_i, X_te_i, y_tr_i, y_te_i = train_test_split(X_i, y_i, test_size=0.3, random_state=42)\n",
+    "\n",
+    "clf_multi = LogisticRegression(max_iter=200)\n",
+    "clf_multi.fit(X_tr_i, y_tr_i)\n",
+    "y_pred_i = clf_multi.predict(X_te_i)\n",
+    "\n",
+    "print(classification_report(y_te_i, y_pred_i, target_names=iris.target_names))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "66324a76",
@@ -333,9 +507,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03C04 k-NNs and Decision Trees.ipynb b/src/S03 Supervised Learning - Classification and Evaluation/S03C04 k-NNs and Decision Trees.ipynb
index 0ba80f0..e37a834 100644
--- a/src/S03 Supervised Learning - Classification and Evaluation/S03C04 k-NNs and Decision Trees.ipynb	
+++ b/src/S03 Supervised Learning - Classification and Evaluation/S03C04 k-NNs and Decision Trees.ipynb	
@@ -162,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "77f7b45e",
    "metadata": {
     "lines_to_next_cell": 2
@@ -203,12 +203,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "2be8c426",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "k-NN accuracy: 0.9111111111111111\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "      setosa       1.00      1.00      1.00        15\n",
+      "  versicolor       0.82      0.93      0.88        15\n",
+      "   virginica       0.92      0.80      0.86        15\n",
+      "\n",
+      "    accuracy                           0.91        45\n",
+      "   macro avg       0.92      0.91      0.91        45\n",
+      "weighted avg       0.92      0.91      0.91        45\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# Create a Pipeline for k-NN containing standardisation and classifier\n",
     "knn_pipeline = Pipeline([\n",
@@ -237,12 +255,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "dd6c8ccc",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Decision Tree accuracy: 0.9333333333333333\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "      setosa       1.00      1.00      1.00        15\n",
+      "  versicolor       0.88      0.93      0.90        15\n",
+      "   virginica       0.93      0.87      0.90        15\n",
+      "\n",
+      "    accuracy                           0.93        45\n",
+      "   macro avg       0.93      0.93      0.93        45\n",
+      "weighted avg       0.93      0.93      0.93        45\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# Create a Pipeline for DecisionTree\n",
     "dt_pipeline = Pipeline([\n",
@@ -274,12 +310,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "3638ede9",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
     "\n",
@@ -315,12 +362,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "87109c69",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(12, 8))\n",
     "plot_tree(dt_pipeline.named_steps['dt'], filled=True, feature_names=feature_names, class_names=class_names)\n",
@@ -348,6 +406,47 @@
     "-   **Hypothesis**: $k=1$ will look like a fragmented puzzle (Overfitting). $k=50$ will look like simple blobs (Underfitting)."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "aa64fd66",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "\n",
+    "# k=1 (Overfitting)\n",
+    "model_k1 = make_pipeline(StandardScaler(), KNeighborsClassifier(n_neighbors=1))\n",
+    "model_k1.fit(X_train, y_train)\n",
+    "DecisionBoundaryDisplay.from_estimator(model_k1, X, multiclass_colors='coolwarm', alpha=0.8, ax=ax[0])\n",
+    "ax[0].scatter(X[:, 0], X[:, 1], c=y, cmap=\"coolwarm\", edgecolor=\"k\", s=20)\n",
+    "ax[0].set_title(\"k=1 (Overfitting)\")\n",
+    "\n",
+    "# k=50 (Underfitting)\n",
+    "model_k50 = make_pipeline(StandardScaler(), KNeighborsClassifier(n_neighbors=50))\n",
+    "model_k50.fit(X_train, y_train)\n",
+    "DecisionBoundaryDisplay.from_estimator(model_k50, X, multiclass_colors='coolwarm', alpha=0.8, ax=ax[1])\n",
+    "ax[1].scatter(X[:, 0], X[:, 1], c=y, cmap=\"coolwarm\", edgecolor=\"k\", s=20)\n",
+    "ax[1].set_title(\"k=50 (Underfitting)\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1906e38f",
@@ -358,6 +457,51 @@
     "Re-run the demo varying $k$ from 1 to 15 and plot the metrics."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4e83792f",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import precision_score, recall_score\n",
+    "\n",
+    "accuracies = []\n",
+    "precisions = []\n",
+    "recalls = []\n",
+    "for k in range(1, 16):\n",
+    "    knn_pipeline.set_params(knn__n_neighbors=k)\n",
+    "    knn_pipeline.fit(X_train, y_train)\n",
+    "    y_pred = knn_pipeline.predict(X_test)\n",
+    "    accuracies.append(accuracy_score(y_test, y_pred))\n",
+    "    precisions.append(precision_score(y_test, y_pred, average='weighted'))\n",
+    "    recalls.append(recall_score(y_test, y_pred, average='weighted'))\n",
+    "\n",
+    "plt.plot(range(1, 16), accuracies, marker='s', label='Accuracy')\n",
+    "plt.plot(range(1, 16), precisions, marker='x', label='Precision')\n",
+    "plt.plot(range(1, 16), recalls, marker='o', label='Recall')\n",
+    "plt.title(\"k-NN Metrics vs. k\")\n",
+    "plt.xlabel(\"Number of Neighbors (k)\")\n",
+    "plt.ylabel(\"Scores\")\n",
+    "plt.xticks(range(1, 16))\n",
+    "plt.legend()\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "30983016",
@@ -368,9 +512,51 @@
     "Try Manhattan distance (`metric`'manhattan'=) and observe how the boundary changes."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "de8d7019",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "k-NN (Manhattan) accuracy: 0.9333333333333333\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "knn_pipeline.set_params(knn__n_neighbors=5, knn__metric='manhattan')\n",
+    "knn_pipeline.fit(X_train, y_train)\n",
+    "y_pred = knn_pipeline.predict(X_test)\n",
+    "print(\"k-NN (Manhattan) accuracy:\", accuracy_score(y_test, y_pred))\n",
+    "\n",
+    "display = DecisionBoundaryDisplay.from_estimator(\n",
+    "    knn_pipeline, X, response_method='predict',\n",
+    "    cmap=plt.cm.coolwarm, alpha=0.8,\n",
+    "    xlabel=feature_names[0], ylabel=feature_names[1],\n",
+    "    grid_resolution=500)\n",
+    "plt.title(\"k-NN Decision Boundary (Manhattan Distance)\")\n",
+    "plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, edgecolor='k', s=20, cmap=plt.cm.coolwarm)\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "id": "3929ce22",
+   "id": "c3ffdbd2",
    "metadata": {},
    "source": [
     "### Tree Pruning\n",
@@ -379,8 +565,58 @@
     "\n",
     "1.  Check the accuracy on Train vs Test.\n",
     "2.  Compare it to `max_depth=3`.\n",
-    "3.  **Observation**: The unlimited tree will likely have 100% Training accuracy but might perform worse on Test data.\n",
+    "3.  **Observation**: The unlimited tree will likely have 100% Training accuracy but might perform worse on Test data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "1c60d023",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Unlimited Tree ---\n",
+      "Train Acc: 0.990\n",
+      "Test Acc:  0.956\n",
+      "Depth:     5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Unpruned tree\n",
+    "dt_pipeline.set_params(dt__max_depth=None)\n",
+    "dt_pipeline.fit(X_train, y_train)\n",
+    "\n",
+    "print(\"--- Unlimited Tree ---\")\n",
+    "print(f\"Train Acc: {dt_pipeline.score(X_train, y_train):.3f}\")\n",
+    "print(f\"Test Acc:  {dt_pipeline.score(X_test, y_test):.3f}\")\n",
+    "print(f\"Depth:     {dt_pipeline.named_steps['dt'].get_depth()}\")\n",
     "\n",
+    "# Visualise the unpruned tree\n",
+    "plt.figure(figsize=(14, 10))\n",
+    "plot_tree(dt_pipeline.named_steps['dt'], filled=True, feature_names=feature_names, class_names=class_names)\n",
+    "plt.title(\"Unpruned Decision Tree Structure\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3929ce22",
+   "metadata": {},
+   "source": [
     "What, in your opinion, is the disadvantage of an unpruned tree?"
    ]
   },
@@ -394,6 +630,50 @@
     "Implement and plot confusion matrices for both classifiers to see which mis-classifications are most common."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4fdb1d39",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CsmW5WQAAgEg5lsRHVBwBAAAAAAAgJCqOAABAWLYsNwsAABApx5L4iMRRJuH1es2f+1JSYt0UxNj+ffti3QTEidSjB2PdBMRY6tFDQfeIDGXL7I/IVIiP4LNv39//HgLeY1wLtvNdA8RH7iFxlEns+ydZULlCmVg3BQAQh/eI5OTkWDcDOOuIjwAAp7pHEB+5g8RRJlGyZEnZtGmT5M2bV5x4nTErg6WkpEiZMmXMcciXL1+sm4MY4loA14H4e9I0KNJ7REaj4AjxiPiIeyL+RXwEroO/ER+5j8RRJuHxeKR06dKxbkZc0KQRiSNwLYB/E/5GTxpsRnz0L+IjcC0gkO3/JhAfuYvEEQAACMuW5WYBAAAi5VgSH3li3QAAAAAAAADEJyqOkGkkJSXJgAEDzJ+wG9cCuA5iwd3lZllWDXAH90RwLYB/E2LJsSI+crxnZY06AACQWSdb1XkC1m/b5epcCbrf8iUKyt69e62egwEAAGQ+KZbFRwxVAwAAAAAAQEgkjgAAAAAAABASiSMAAAAAAACEROIIQNxbv369OI4jK1asiMv9ITIDBw6UunXrnvHhmjNnjjl/e/bsifhn7rjjDmnTps0Zf7bNfMvNurkBAE4f8VFiID7K3BxL4iMmx0Zc3gQrVKggy5cvd+VLJjK/EydOyI4dO6Rw4cKSNeuZLwbJNRYb+/fvlyNHjkihQoXOaD9Hjx6VXbt2SbFixUwCKRI6waCuBZE/f/4z+mybJ3/c8If7kz+WKx5/kz8C8Yp7F9IiPkoMxEeZU4pl8dGZfwMDgDN07NgxyZYtW7qvZ8mSRYoXLx5Xx1mTF9mzZ491MzKVPHnymO1Mj6m+J9rrQW/scGOxWfe6wdxduhYAEg/xkR2IjzI3x5L4iKFqyDBTp06VWrVqSc6cOU2FQYsWLeTAgQPmtQkTJki1atUkR44ccu6558qLL77o/zmtNlL16tUz1QRNmzY1j1NTU2Xw4MFSunRpSUpKMtVIM2fODPrS2aVLFylRooTZb7ly5WT48OH+15999lnTnty5c0uZMmWkU6dOJsOP6Lz88stSsmRJcz4CXXvttXLnnXeav3/wwQdy3nnnmfNQsWJFGTRokBw/ftz/Xj2vY8eOldatW5vzMWzYMNm9e7e0b99eihQpYq6ZKlWqyMSJE9Mtxf7pp5/kmmuuMZn4vHnzyiWXXCK///57RNdKKHPnzpVGjRqZ9+s11KdPn6A263Wo11e3bt1M5dPll1/OpRPltZG2FNs3fEzPv/5c1apVzfMLFiww79Prp0GDBjJ9+vSg8592qNqkSZNMJdFnn31m/l3RAOyKK66Qbdu2nfRZPtrGESNGSOXKlc05L1u2rGmHT+/eveWcc86RXLlymWu4X79+JoC3mS2l2EBGIz5KTMRHxEene20QH2Vuji3xkRfIAFu3bvVmzZrV++yzz3rXrVvn/eGHH7xjxozx7tu3z/v66697S5Qo4X3vvfe8a9euNX8WLFjQO2nSJPOz3333nVcvzS+++MK7bds2786dO83zuq98+fJ533zzTe8vv/zi7dWrlzdbtmzeX3/91bz+1FNPecuUKeOdN2+ed/369d758+d7p0yZ4m/Tc8895/3yyy9Ne2bPnu2tWrWq9/777+f8R2nXrl3e7Nmzm/Pjo+fI95wefz1Pej5///137+eff+4tX768d+DAgf736/ktWrSo95VXXjHv2bBhg7dz587eunXrehcvXmzO0axZs7wffviheb8+1p9Zvny5ebx582ZzzVx//fXm/atXrzb70usikmsl1P5y5crl7dSpk3fVqlXeadOmeQsXLuwdMGCAv81NmjTx5smTx9uzZ0+zT99nIfJrQ49nnTp1/K916NDBHNPbbrvNu3LlSrPt3bvXnNtbb73V+9NPP3k/+eQT7znnnBN0vr766ivzePfu3ebxxIkTzflt0aKFuR6WLl3qrVatmveWW24J+qxrr73W/1iviQIFCpjrdM2aNebfi/Hjx/tfHzJkiPebb74x14peh8WKFfM++eSTVp5uPSd6vDf9udu799AJ1zbdn+5X9w/YgvgocREfER+d7rVBfJQ57bUsPiJxhAyhX9z0gtcETlqVKlUKSuj4vqQ1btw45Jd6n5IlS3qHDRsW9FzDhg3Nl33VtWtX72WXXeZNTU2NqI3vvvuut1ChQlH/bvCaL+B33nmn/1C89NJL5vycOHHC27x5c+/jjz8edJhee+01kyz00fPbrVu3oPe0atXK27Fjx5CHN+010bdvX2+FChW8R48eDfn+cNdK2v098sgjJpEYeO1oolOTGvo7+RJH9erV4/SfwbURKjDShMyRI0f8z40dO9b8d3no0CH/c5rQCZc40seaAAo8f7rvUImjlJQUb1JSUlCiKBxNTNevX9/qwGjzn7u9KYdOuLZtjtPACMhIxEeJjfgIp3NtEB9lTnsti48YqoYMUadOHWnevLkZGta2bVsZP368GYqkQ9V0ONFdd93lH8+r29ChQ/3DjNKbJGzr1q1y0UUXBT2vj1etWuUfiqJDWXS4ywMPPCCff/550Hu/+OIL06ZSpUqZoU233Xab7Ny5Uw4ePMhVECUdUvbee++ZiY7VG2+8Ie3atROPxyPff/+9GSYWeH7vueceM2wo8FjrEKRA999/v7z11ltmiFKvXr3McKX06HnWoWmh5kWK5FpJS59v3Lhx0ETL+n4dyrh582b/c/Xr14/o+NjsVNdGKPpvROC8RqtXr5batWubYWo+OoQwHB1SVqlSJf9jHW64ffv2dM+3tk//PUjP22+/ba4BnUtJr+HHHntMNm7cGLYdAHAqxEeJjfgIp3NthEJ8hHhD4ggZQicznjVrlnz66adSvXp1GTVqlEnorFy50ryuiST98u/b9PlFixad0WfqnDrr1q2TIUOGyKFDh+Smm26SG2+80T9Hjs6Ho19I9R/tpUuXypgxY/xzIyE6rVq1MitUffzxx7Jp0yaZP3++uSEqTbbonEaB5/fHH3+U3377LSgZoHMbBbryyitlw4YN0r17d5P40S/1Dz/8cMjP1zmQYiFtmxHdtZGRxzRtElGTgH8Xt0V//SxcuNC0+aqrrpIZM2aYFR4fffRR/q1wMmADLEN8lNiIj3A610YoxEeZiGNHfETiCBlGv7hpj70mEfSLl1YVfPPNN2ZyuLVr15pJaQM336TYvuoDXWLURydA1p/Tnw+kjzUxFfi+m2++2SSmtGJAk0S6bLcminRCumeeeUYuuOACM+mtJidwejQBdP3115vekjfffNMkBTVxp/RPrRpJe351S69XxUcnxu7QoYO8/vrrMnLkSDOZYCiaANQbbqjJiiO9VgLphMqaLAhMNOj7tTJNJ9iGO9dGJPT9mmj09cipxYsXu3oKdOJ1TR7Nnj075Ota7aaT62uySCvj9P2a1AQANxAfJS7iI5zOtREJ4iPEWtZYNwCJ6dtvvzVfylq2bClFixY1j3fs2GG+oGsiSYeS6fLYuvKRfkFcsmSJGcrWo0cP8379UqerYOmXdv2HVt/bs2dPGTBggBmOosOZdMUtrWbRf4B9q6bp8BRdjU0TFO+++64ZZqKrLWnSQpMMWvmkGX9NCowbNy7WhylT014SreLS1c1uvfVW//P9+/c3z+sqVVrx5Ru+plVlOiQxPfpzOhSsRo0a5prQSg+9XkLR1c30XGqJb9++fc31oRVrOqRJb6zhrpW0dIU9TVR17drV7FsTX/rzej2GS3Yh8msjErfccotJ2Nx7771mZTsdHvb000+b1wKHEp4J/TdFV03TIZGaqNYEt/77pO3VYbSaKNLP1aGTDRs2NL2D06ZNs/5U27LcLJCRiI8SH/ERor02IkF8FL8cW+KjWE+yhMT0888/ey+//HJvkSJFzCS0uirSqFGj/K+/8cYbZgUtXU1AVza69NJLve+//77/dZ20VldI83g8ZlJipZPH6cpcpUqVMiso6SS7n376qf9nXn75ZbPP3LlzmxW1dJLmZcuW+V/XlbZ0guacOXOatk2ePDlogl1ER8+HHk89hroyWqCZM2d6L7zwQnOs9Vw0atTInB8f/RlduSztBOm6Epb+jK6qpZMI6qp76U2Y/v3333tbtmxpVkPLmzev95JLLvG3I9y1Emp/c+bMMRNo6zVZvHhxb+/evb3Hjh3zv67X4YMPPshlcgbXRqjJHwNXOvPR1cxq165tzoVOSK2T6eu+fCvZhZocOzk5OWgfen0F3uLSfpa2cejQod5y5cqZa6Rs2bJBk7rr6nk6SbdOkH7zzTebVRnTfoZtkz9u3bHHu/9Iqmub7i8eJ38EMhLxUeIjPkK01wbxUea017L4yNH/i3XyCgCA9GilWMeOHWXv3r0xm9/KZjrhvFb1bd2xxwwFdXO/JYvkN+fVzf0CAGAD4qPYSrEsPmKoGgAgrkyePFkqVqxoVkDUYY46rEwnuydpFFtuz9cYp4XYAADEJeKj+ORYEh+ROAIAxJU//vjDzHmlf+q8ZW3btpVhw4bFulkAAAAxQ3yEWGKoGgAACFuKve0v90uxSxSOv1JsAACAcFIsi49YLggAAAAAAAAhkTgCAAARLzfr5v9Ox5gxY6R8+fKSI0cOOf/88+W7777j7AEAgJhwLImPSBwBAICwHMf9LVpvv/229OjRQwYMGCDLli2TOnXqyOWXXy7bt2/nDAIAgLPOsSQ+Yo4jAAAQdgz/b+s2uT6Gv0qFMrJpU/B+k5KSzBaK9qA1bNhQRo8ebR6npqZKmTJlpGvXrtKnTx/OIgAAOCtSLIuPWFUNAACkK3v27FK8eHETxLgtT548JrAJpL1lAwcOPOm9R48elaVLl0rfvn39z3k8HmnRooUsXLjQ9bYBAACkJ7tl8RGJIwAZ4o477pA9e/bI9OnTzeOmTZtK3bp1ZeTIkWf1iM+ZM0eaNWsmu3fvlvz584d8j+M4Mm3aNGnTpk1E+9R/tPX3WrFixWm3a/369VKhQgVZvny5OS5AvNKx8uvWrTOBidu8Xq/57y9Qer1pf/31l5w4cUKKFSsW9Lw+/uWXX1xvGwBkBOKjUyM+QmaRw7L4iMQRYFmw8uqrr5q/Z8uWTcqWLSu33367PPLII5I1a8b+c/D++++bz3Qr2QPg7AZHugFAIiI+AnA6clgUH5E4AixzxRVXyMSJE+XIkSPyySefSOfOnU1CJ7C80Ucz6FqG6YaCBQu6sh8AdipcuLBkyZJF/vzzz6Dn9bGWigPAmSA+ApAZFT5L8RGrqgGW0TJH/UekXLlycv/995vxrx9++KG/x02Haw0bNkxKliwpVatWNc/r5Gw33XSTqf7RBNC1115rSol9tDxSZ/LX1wsVKiS9evUyJZaBdKhat27d/I81cdW7d28zflfbVLlyZfnf//5n9qvVRqpAgQKmTFPb5Zvobfjw4WaIV86cOc2KAVOnTg36HE2GnXPOOeZ13U9gOyOl7dJ95MqVSypWrCj9+vWTY8eOnfS+l156ybRf36fHZ+/evUGvT5gwQapVq2Z6Is4991x58cUXo24LgL9pErt+/foye/Zs/yHRfxP0cePGjTlMAM4I8VF4xEeAvfERFUeA5TTBsnPnTv9j/UdGZ/CfNWuWeawJE13OUf/hmT9/vhnSNnToUNMz98MPP5h/rJ555hmZNGmSvPLKKyZRoo91zqDLLrss3c/VIXI6YdsLL7xgEkA6RljH6Goi5r333pMbbrhBVq9ebdqibVSaNHr99ddl3LhxUqVKFZk3b57ceuutUqRIEWnSpIlJcF1//fWmiuree++VJUuWyEMPPRT1McmbN6/5fTR59uOPP8o999xjntOEmM+aNWvknXfekY8++sisfnDXXXdJp06d5I033jCv65/9+/c3qxvUq1fPzGWk+8mdO7d06NAh6jYBEJOg1v9+GjRoII0aNTJzph04cEA6duzI4QHgKuKjkxEfARbHR14A1ujQoYP32muvNX9PTU31zpo1y5uUlOR9+OGH/a8XK1bMe+TIEf/PvPbaa96qVaua9/vo6zlz5vR+9tln5nGJEiW8I0aM8L9+7Ngxb+nSpf2fpZo0aeJ98MEHzd9Xr16t5Ujm80P56quvzOu7d+/2P3f48GFvrly5vAsWLAh671133eX973//a/7et29fb/Xq1YNe792790n7SktfnzZtWrqvP/XUU9769ev7Hw8YMMCbJUsW7+bNm/3Pffrpp16Px+Pdtm2beVypUiXvlClTgvYzZMgQb+PGjc3f161bZz53+fLl6X4ugJONGjXKW7ZsWW/27Nm9jRo18i5atIjDBOCMEB+FRnwEZB6jMjg+ouIIsMyMGTPMEo9aSaRljLfcckvQ0o61atUKmtfo+++/N9U12ssU6PDhw/L777+b4Vnbtm2T888/3/+aViVpxjvtcDUfXY1Mx+JqlVCktA0HDx6U//znPyfNw6QVPWrVqlVB7VCnU6L59ttvm0oo/f32798vx48fN5VPgXRi8VKlSgV9jh5PrZLSY6U/q1VIWmXko/tJTk6Ouj0A/tWlSxezAYCbiI/CIz4C7I2PSBwBltF5f8aOHWuSQzoUK+1qajqUKpAmTnTcrG8IViAdInY6fEPPoqHtUB9//HFQwuZUy1OeDh0+1759exk0aJAZoqeJnrfeessMv4u2rePHjz8pkaUJMwAAEF+Ij06N+AiwG4kjwDKaGNKJqCN13nnnmR6mokWLnlR141OiRAn59ttv5dJLL/VX1ixdutT8bCha1aTVOXPnzjWTc6flq3jSSbd9qlevbhJEGzduTLdSSedX8k307bNo0SKJxoIFC8zE4Y8++qj/uQ0bNpz0Pm3H1q1bTfLN9zkej8dMKF6sWDHz/Nq1a00SCgAAxDfio1MjPgLsxqpqAE5JEx+6zKOupKaTY+sk1nPmzJEHHnhANm/ebN7z4IMPyhNPPCHTp0+XX375xUwSvWfPnnT3Wb58eTOB25133ml+xrdPnWxaaeJGV1PTsvEdO3aYCh4d/vXwww9L9+7d5dVXXzVDwZYtWyajRo0yj9V9990nv/32m/Ts2dMMGZsyZYqZ5DoaOum2JoW0ykg/Q4es6UTfaelKafo76FA+PS56PHRlNd+yl1qxpJN568//+uuvZpLtiRMnyrPPPssVBwBAJkd8RHwE2ITEEYBT0qXmdfUyndNHVyzTqh6du0fnOPJVIOnKZbfddptJpOhcP5rkue666065Xx0ud+ONN5okky5Vr3MB6ez/SoeiaeKlT58+pnrHN153yJAh0q9fP5OQ0Xboym46dK1ChQrmdW2jrsimyShdqU1XX3v88cejOsOtW7c2ySn9zLp165oeNv3MtLRqS4/HVVddJS1btpTatWvLiy++6H/97rvvlgkTJphkkVZYaZWUJrF8bQUAAJkX8RHxEWATR2fIjnUjAAAAAAAAEH+oOAIAAAAAAEBIJI4AAAAAAAAQEokjAAAAAAAAhETiCAAAAAAAACGROAIAAAAAAEBIJI4AAAAAAAAQEokjAAAAAAAAhETiCAAAAAAAACGROAIAAAAAAEBIJI4AAAAAAAAQEokjAAAAAAAASCj/D9cKzjwVvUW0AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import ConfusionMatrixDisplay\n",
+    "\n",
+    "# Reset to sensible defaults\n",
+    "knn_pipeline.set_params(knn__n_neighbors=5, knn__metric='euclidean')\n",
+    "dt_pipeline.set_params(dt__max_depth=3)\n",
+    "knn_pipeline.fit(X_train, y_train)\n",
+    "dt_pipeline.fit(X_train, y_train)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "\n",
+    "# Confusion matrix for k-NN\n",
+    "ConfusionMatrixDisplay.from_estimator(\n",
+    "    knn_pipeline, X_test, y_test, display_labels=class_names, cmap='Blues', ax=ax[0])\n",
+    "ax[0].set_title(\"k-NN Confusion Matrix\")\n",
+    "\n",
+    "# Confusion matrix for Decision Tree\n",
+    "ConfusionMatrixDisplay.from_estimator(\n",
+    "    dt_pipeline, X_test, y_test, display_labels=class_names, cmap='Blues', ax=ax[1])\n",
+    "ax[1].set_title(\"Decision Tree Confusion Matrix\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "5904a55f",
@@ -404,6 +684,154 @@
     "Load the Wine dataset and compare k-NN vs Decision Tree performance."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "05f1fdd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          alcohol  malic_acid         ash  alcalinity_of_ash   magnesium  \\\n",
+      "count  178.000000  178.000000  178.000000         178.000000  178.000000   \n",
+      "mean    13.000618    2.336348    2.366517          19.494944   99.741573   \n",
+      "std      0.811827    1.117146    0.274344           3.339564   14.282484   \n",
+      "min     11.030000    0.740000    1.360000          10.600000   70.000000   \n",
+      "25%     12.362500    1.602500    2.210000          17.200000   88.000000   \n",
+      "50%     13.050000    1.865000    2.360000          19.500000   98.000000   \n",
+      "75%     13.677500    3.082500    2.557500          21.500000  107.000000   \n",
+      "max     14.830000    5.800000    3.230000          30.000000  162.000000   \n",
+      "\n",
+      "       total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
+      "count     178.000000  178.000000            178.000000       178.000000   \n",
+      "mean        2.295112    2.029270              0.361854         1.590899   \n",
+      "std         0.625851    0.998859              0.124453         0.572359   \n",
+      "min         0.980000    0.340000              0.130000         0.410000   \n",
+      "25%         1.742500    1.205000              0.270000         1.250000   \n",
+      "50%         2.355000    2.135000              0.340000         1.555000   \n",
+      "75%         2.800000    2.875000              0.437500         1.950000   \n",
+      "max         3.880000    5.080000              0.660000         3.580000   \n",
+      "\n",
+      "       color_intensity         hue  od280/od315_of_diluted_wines      proline  \\\n",
+      "count       178.000000  178.000000                    178.000000   178.000000   \n",
+      "mean          5.058090    0.957449                      2.611685   746.893258   \n",
+      "std           2.318286    0.228572                      0.709990   314.907474   \n",
+      "min           1.280000    0.480000                      1.270000   278.000000   \n",
+      "25%           3.220000    0.782500                      1.937500   500.500000   \n",
+      "50%           4.690000    0.965000                      2.780000   673.500000   \n",
+      "75%           6.200000    1.120000                      3.170000   985.000000   \n",
+      "max          13.000000    1.710000                      4.000000  1680.000000   \n",
+      "\n",
+      "           target  \n",
+      "count  178.000000  \n",
+      "mean     0.938202  \n",
+      "std      0.775035  \n",
+      "min      0.000000  \n",
+      "25%      0.000000  \n",
+      "50%      1.000000  \n",
+      "75%      2.000000  \n",
+      "max      2.000000  \n",
+      "target\n",
+      "1    71\n",
+      "0    59\n",
+      "2    48\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_wine\n",
+    "import seaborn as sns\n",
+    "\n",
+    "wine = datasets.load_wine()\n",
+    "X_wine = wine.data\n",
+    "y_wine = wine.target\n",
+    "\n",
+    "# Train-test split\n",
+    "X_train_wine, X_test_wine, y_train_wine, y_test_wine = train_test_split(\n",
+    "    X_wine, y_wine, test_size=0.3, random_state=42, stratify=y_wine)\n",
+    "\n",
+    "# Explore the data\n",
+    "wine_df = pd.DataFrame(X_wine, columns=wine.feature_names)\n",
+    "wine_df['target'] = y_wine\n",
+    "print(wine_df.describe())\n",
+    "print(wine_df['target'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a4fcbb26",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "k-NN (Wine) accuracy: 0.7777777777777778\n",
+      "Decision Tree (Wine) accuracy: 0.8333333333333334\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Select two features for visualisation\n",
+    "X_wine_2d = X_wine[:, [5, 6]]  # total_phenols and flavanoids\n",
+    "X_train_wine_2d, X_test_wine_2d, y_train_wine, y_test_wine = \\\n",
+    "    train_test_split(X_wine_2d, y_wine, test_size=0.3, random_state=42, stratify=y_wine)\n",
+    "\n",
+    "# Build pipelines for Wine dataset\n",
+    "knn_pipeline_wine = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('knn', KNeighborsClassifier(n_neighbors=5))\n",
+    "])\n",
+    "dt_pipeline_wine = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('dt', DecisionTreeClassifier(max_depth=3, random_state=42))\n",
+    "])\n",
+    "\n",
+    "knn_pipeline_wine.fit(X_train_wine_2d, y_train_wine)\n",
+    "dt_pipeline_wine.fit(X_train_wine_2d, y_train_wine)\n",
+    "\n",
+    "print(\"k-NN (Wine) accuracy:\", accuracy_score(y_test_wine, knn_pipeline_wine.predict(X_test_wine_2d)))\n",
+    "print(\"Decision Tree (Wine) accuracy:\", accuracy_score(y_test_wine, dt_pipeline_wine.predict(X_test_wine_2d)))\n",
+    "\n",
+    "# Visualise decision boundaries\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    knn_pipeline_wine, X_wine_2d, response_method='predict',\n",
+    "    cmap=plt.cm.coolwarm, alpha=0.8,\n",
+    "    xlabel=wine.feature_names[5], ylabel=wine.feature_names[6], ax=ax[0])\n",
+    "ax[0].scatter(X_test_wine_2d[:, 0], X_test_wine_2d[:, 1], c=y_test_wine,\n",
+    "              edgecolor='k', s=20, cmap=plt.cm.coolwarm)\n",
+    "ax[0].set_title(\"k-NN Decision Boundary on Wine Dataset\")\n",
+    "\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    dt_pipeline_wine, X_wine_2d, response_method='predict',\n",
+    "    cmap=plt.cm.coolwarm, alpha=0.8,\n",
+    "    xlabel=wine.feature_names[5], ylabel=wine.feature_names[6], ax=ax[1])\n",
+    "ax[1].scatter(X_test_wine_2d[:, 0], X_test_wine_2d[:, 1], c=y_test_wine,\n",
+    "              edgecolor='k', s=20, cmap=plt.cm.coolwarm)\n",
+    "ax[1].set_title(\"Decision Tree Decision Boundary on Wine Dataset\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "49440861",
@@ -414,6 +842,59 @@
     "Compare performance using 5-fold cross-validation."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "eb7baf20",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "k-NN Cross-validation scores: [0.61111111 0.80555556 0.61111111 0.88571429 0.8       ]\n",
+      "Decision Tree Cross-validation scores: [0.63888889 0.86111111 0.72222222 0.91428571 0.77142857]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "k-NN accuracy (mean ± std): 0.74 ± 0.11\n",
+      "Decision Tree accuracy (mean ± std): 0.78 ± 0.10\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "knn_scores = cross_val_score(knn_pipeline_wine, X_wine_2d, y_wine, cv=5)\n",
+    "dt_scores = cross_val_score(dt_pipeline_wine, X_wine_2d, y_wine, cv=5)\n",
+    "\n",
+    "print(\"k-NN Cross-validation scores:\", knn_scores)\n",
+    "print(\"Decision Tree Cross-validation scores:\", dt_scores)\n",
+    "\n",
+    "plt.boxplot([knn_scores, dt_scores], tick_labels=['k-NN', 'Decision Tree'])\n",
+    "plt.title(\"Cross-validation Scores for k-NN and Decision Tree on Wine Dataset\")\n",
+    "plt.ylabel(\"Accuracy\")\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"k-NN accuracy (mean ± std): {knn_scores.mean():.2f} ± {knn_scores.std():.2f}\")\n",
+    "print(f\"Decision Tree accuracy (mean ± std): {dt_scores.mean():.2f} ± {dt_scores.std():.2f}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4c9aee1b",
@@ -432,9 +913,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S03 Supervised Learning - Classification and Evaluation/S03C05 Support Vector Machines.ipynb b/src/S03 Supervised Learning - Classification and Evaluation/S03C05 Support Vector Machines.ipynb
index 2af9d79..c2316cf 100644
--- a/src/S03 Supervised Learning - Classification and Evaluation/S03C05 Support Vector Machines.ipynb	
+++ b/src/S03 Supervised Learning - Classification and Evaluation/S03C05 Support Vector Machines.ipynb	
@@ -113,12 +113,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "2ee3d1f9",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -182,12 +202,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "b1e45e43",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SVM Accuracy: 1.00\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "      setosa       1.00      1.00      1.00        17\n",
+      "  versicolor       1.00      1.00      1.00        13\n",
+      "\n",
+      "    accuracy                           1.00        30\n",
+      "   macro avg       1.00      1.00      1.00        30\n",
+      "weighted avg       1.00      1.00      1.00        30\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "y_pred = pipeline.predict(X_test)\n",
     "accuracy = accuracy_score(y_test, y_pred)\n",
@@ -213,12 +260,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "d71d7f86",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.datasets import make_moons\n",
     "\n",
@@ -256,12 +323,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "9beda078",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SVR Mean Squared Error: 0.0086\n",
+      "SVR R² Score: 0.9834\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.svm import SVR\n",
     "\n",
@@ -319,6 +405,44 @@
     "-   **Hypothesis**: `C=0.1` will be smooth (Underfitting). `C=1000` will be wiggly (Overfitting)."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "45eecba3",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "\n",
+    "# Low C (Soft Margin)\n",
+    "model_soft = svm.SVC(kernel='rbf', C=0.1).fit(X_moon, y_moon)\n",
+    "DecisionBoundaryDisplay.from_estimator(model_soft, X_moon, ax=ax[0], cmap=\"coolwarm\", alpha=0.8)\n",
+    "ax[0].scatter(X_moon[:,0], X_moon[:,1], c=y_moon, cmap=\"coolwarm\", edgecolors='k')\n",
+    "ax[0].set_title(\"C=0.1 (Soft/Smooth)\")\n",
+    "\n",
+    "# High C (Hard Margin)\n",
+    "model_hard = svm.SVC(kernel='rbf', C=1000).fit(X_moon, y_moon)\n",
+    "DecisionBoundaryDisplay.from_estimator(model_hard, X_moon, ax=ax[1], cmap=\"coolwarm\", alpha=0.8)\n",
+    "ax[1].scatter(X_moon[:,0], X_moon[:,1], c=y_moon, cmap=\"coolwarm\", edgecolors='k')\n",
+    "ax[1].set_title(\"C=1000 (Hard/Complex)\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "bd3110a2",
@@ -329,6 +453,43 @@
     "Experiment with different kernel functions on the Moons dataset and compare their performance."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7b20d2bc",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3HI1N7LInluWLZXJeXl746KOPMhzPTRz3othIyDzhpUluaOJG8VyaMWVHjCW7JFNuYiNNXCjGKxI/mvhITHJpzqM4J5rlliL5J/qNZkfcRrwfZcuWRV7QxD6vGh+Jnx/RE1UsJ9TsLixiPdEuQpxfbf1H0y+1FPGyWE4qkmei3YO288n4iEwdk1JERkDbjFpmYtZIBDLZNdXWJGVEvwDRQ0F8YIp1+CIoEEHV6dOnZT+mzDNjL0tUaCMeXzSG7Ny5s5zFE403xSySZpyC6HOQ3Qxf5oAxuzFkd17SB0Xi+USzTNFYVBsxm5obmr4UmZuOaoIuQQSamYNNzQxgbqvO8pKoXBMzrZkTQKIpvSACwRcRs36iwixzgCV6domZZzE7mPm9EzOEYpZazDKLPxCyI86nCNKJiIhelaiW1lZdnN6r/oEvJpymT58uK3VElc/y5cvlZIwmvnkZkSgTsZHohSmqs0QiLH1MkNM4Lifx2cviI00sJl5P+gRJetlt5vKi+EhbbCQm2ES/TJHgEdXjmYnzKJqy6yvxoqnq18RC6YnrRIJMWxVVeqI3l5hwvHDhgqw2E71ORQJSEEnSFxETvyKOEhN/ImZK/75qzifjIzJ1TEoRmQiRXNq1a5dsjvmiQEUkiEQJtWhOLT5ENTQ70+QVsaxr/vz5cpZIBG2iTFmMq2jRovK4aE6Z3Q4teX1eRHm4KEfPi4BHzD6K15H5fIlATwRWomoo/e52GmK3HxFoigBUcw5E6X92RPApEocvuk1uicBz3rx5sqIr/Qyj2H1Hc/xlSw8yN3kVNM1XtZWei9lDsUzzZU1gxfkUQRwREVF+KFSokEzGiCqh9Bt+iM82MWEnjr+IqFwWTbzFZ7mo1L5//76shskNMQkoPhdFrCBaE4jElGiinps4Li+I5xJEnJFXsZhokyDOjaiwSp+oE/GmSEiJKrPMyRXRrF1U7ouNTurXry/HJTbIEa0esquWErcR76PYAOdlcUtOiMp8EXNl3t1PEA3fc/ocIhGY/rbivRRycn5FMkrEkSJeSv/ea2LNzBvUEJka9pQiMhFiGZVIGIjkR2YiWaDZalYze5a+mkisZdesfc9LopRZ7NgnlvB16dJFJi/ELKBYzia2LtY2KyWWreX1eRE9qv744w+tQYCY0coNkUwTs7CZgxfNVsci6dS1a9csF7F0TVQpiZkzEfyIhKBI2omgNj3N+yJK0zW78WkLlNK/f6KUXbPD0It06NBBjj/9ey0eR+ygJ4KyunXrpl0v3hvRXyz9bj9itk/0S9D01RLEz5yY+RU9tDRBbnoiUSfK8zVBtzYigBXjT//8REREealNmzbyq4hL0tNUUot+kDmJa8Qkm3gMURkkdifOLZEI2r59u6wsFhVXou9ibuK4vCD6VInPbLEzcVxcXJ7EYnXq1JExRebd5kQVuZiME71RM8dGompeVGRpqsNErCgeQ+yml13cI2IjESOJav/M1f3pYyMRW4g4JnNbCm3E827evBnBwcFp14n35fr16xmWZ4qYSDymtvg18/kTOwdWrFgxQ1JKW8sI8b6KSnpRuZ956aY4l2JCVZxbIlPGSikiHROJCBGMZPbhhx++1uOKJXNiK2GxtbFoYCnWtYsEhJgRFM0zRS8gEQCIP/xFjwBRwTRixAj5YbdkyZJs+wC8LvEcYsZLBBii9F0EHqLng5gRE8vqxEyhCFbETOWRI0fw4MEDWdmUV0QAKZImIhgSSSExAymCPhFUiOvFjNzLSv21JXfGjRuX1iMqfd+k7IJaUZot7rNixQq51G3mzJnyHIg+AoMHD5a9FkRSSzRaFe+fIHosiOBXvLfiNmKmTARC4v0UiT4xwypothF+WbNzMbMrtlkW5foisBLbMotlCAcOHEgbv4bYQlo0MxezdIULF5bXiW2ZRe+DWrVqyfGI2TyxnbMImr7++mv585aeeN9Fc1MR7L1oGYCYTRQ/f+K8EhER5QdRjStiH9GEWtPKQFTCiM86kegQ1UsvI5paf/LJJ3JZ+rBhw7J87uWUmJwSkzwiJhHPLRIgOY3j8oJI6ojKaZFUK1eunJxQE5NTYhJPxEoithGTYrkhYhqRqBOf6ZrNZMREnHg8EQtqI5bFiVYO4vWJuEi8ByJuE/8vXrdY6igSTyJOEcc++OADmcwT8ZRI3okG7qJVhHicEydOyDYE4vwJ4j0Sr2vBggUvbXb++eefyzGI5xDxuEjUiVhJxKnpl1iK8yNiMfFztHDhwrTrxXsnEkdibI8fP5Y/Y+IxRKIrfTN2cb5FLCbiKJGAEhOTYnziPInNYzLT/IxoWkcQmSx9b/9HZC40W9NmdxHbF4vtdjNvX9uvXz+1o6NjlsebOHGivG1mc+fOVVerVk1ugezs7KyuUKGC+pNPPpHbymocOnRIXbt2bXkbf39/eVyz3bHYTlhDbHNcrly5HL9GzZjEdsyZDR8+XB4bOnSo/P7WrVvqvn37qn19fdXW1tbqgIAAuW3umjVrspwzsV1vZtmNTZwvsVVy5q2IxXa64va2trZqd3d3eY4mTZqkjo6OTruduJ+4/8s8efJEbWVlpV6yZEmGrY4bNGjwwvuJbZarVKmS9v3FixfllsZubm5qOzs7dalSpdTjx4/PcJ979+7J8+Tt7S3HXrRoUfX777+fYatnMe7Mrzk7YvvkqVOnytuLbYjFOVm6dGmW24nzoG0LaLH9tDj3Xl5e8v7i52v27Nlan0tcLx5j06ZNLxxTjx491PXr18/R+ImIiDJ7UbyQXkpKivzsF5/HIvYICgpSjx07Vp2YmJjhduJzTly0adOmjXyuw4cP5/iNELcXn92ZXblyRX6eenh4yJggp3Gc+Axv27ZtlscTMZx4rtWrV2e4Xlt8KZw5c0bduXNnGcOIGEM8bvfu3dW7d+/Ocm4zxwPajBgxQl28ePG073/44Qd53/SPl9nChQvlbTZu3Ci/T01NVU+fPl1dunRpGWeI+Kd169bqU6dOZbjf/PnzZUylievE+7Vz584s4878mrMjzn+LFi3UDg4OMi7r1auX+vHjx1rPY+ZY8aOPPpLxmRiLGO/bb78t49zMfv31VxnviPdcxJHitu3bt1fv378/y22joqLk6583b16Oxk9kzCzEf/SdGCMiMjYDBw6UZd1i9o5enZhRFFViooKMlVJERGToxHJ00dA6/S679Jxo9i16S4kqaU0VN70asUT0u+++k+0N8rvHGJG+sacUEdErmDhxoiwVF72k6PWCLlEez4QUEREZOrGEXiyzF0vMKCvRjkFM2omd5OjViRYLoteZaALPhBSZA1ZKERERERERZUP0VxSTUKIPk5iQEtUrvr6+PF9ERHmAlVJERERERETZ2Ldvn6yOEskp0RidCSkiorzDSikiIiIiIiIiItI5VkoREREREREREZHOWcHEqFQqPHz4EM7OzrCwsND3cIiIiMjIiY2KY2Nj4e/vD4XCdOfzGEMRERGRruMnk0tKiYRUUFCQvodBREREJiY4OBiBgYEwVYyhiIiISNfxk8klpUSFlDDo08OwsXXSehsrF48M3wf42uhkbERkvpxd/vt16+1mupUWRKboWXwM+rUumBZjmKqcxFCZYyljjKHE72P+Hn497nYx8quLfZ68JUSUB9ye3YGttSXP5SuyuXMelrZ2RnX+4k+fgMLJTd/DwIN/jsLSK+uOpPHJKWi2as1L4yeTS0ppluyJYMrW7r8Xb+XqmeF2QX7GF0QRkXFxdv3vV6yPOxNRRMbO1NsCZBdDaWNl72KUsZTm97KDE38nvyoPmZBygatDHr4xRPTanBVOsLNhUupV2Tjaw9LWuDLtLg0aIf7EUSic3fU6jrIdGuP+1oOw9PZ/pfjJ5JJSmaVPRhlj8ERExoWJKCIydZkn+ozt9zMnCV43IQUmpIiIKM+YbFJKlJUb6yweERlvIkrgHzxEZOqMNb7i7+dXx4QUERHlB5NNSokeB/YOxhkwEZHhYyKKiMyRsVdJ0athQoqIiPILP6GJiHKIiSgiIlZJmRsmpIjIXCiTEoyur5QpYJdHIqKXJKI0F83SD82FiIgMH6ukXh0TUkRkLpKLVYExcqxRG6rYSH0PAwXb1Icy9OEr3ZeVUkREmbAiiohIS9Do6sleUmaECSkiItIFJqWIiJiIIiIy2UkGVrbmHhNSRESkK0xKEZHZYkUUEZHpNjjnsr1Xw4QUkXGKdCwG9/hbsLOx1PdQiHKFSSkiMjvp/1DhDDoRUc4E+Rnfrsb8HZ87TEgRkbljs3PdY6deIjK7huVsVk5ElHOskjIPTEgRkbljs3P9NDtnpRQRmSxWRBER5Q1WSZk2JqSIiEhfmJQiIpPCRBQRUd5hlZTpY0KKiIj0iUkpIjJ6bFhORJR/WCVl+lwd9D0CIiIyV0xKEZFRYiKKiIi0fTawuXnuqqSYkCIiyojNznWLjc6JyCiblQtsWE5E+f0Hu7i428WY7dI9Y6qSyjxZQS/GhBQRUVZsdq77Zuf89CYig8aKKCLSV48dQVSRWCr5HhgLVknl/meciIhIn5iUIiKDw0QUEek7EWXujK3BOaukco6NzYmIyJAwKUVEBoGJKCLSNSaiXsyYlu4JrJJ6OSakiIjI0DApRUQGk4ziHxRElN+YiHo5KxcPo/pBZJVUzjAhRUREhoiNzolIrw3L2ayciHTVsDz9H+VcovdirJIyLUxIERHlrtm52IHP2DjWqA1VbKTRNTtnpRQR6QQroohIn42cmYQyTaySejkmpIiIyJAxKUVE+YaJKCLSJSaizBOXfr8ck7JERGSomJQiojzFRBQR6RITUXkvwNc4GpyzSipn/z6YkCIiIkPGpBQRvTbunEdEusREFGmwSirn/06IiIjMrtH5tGnTUKNGDTg7O8PHxwcdO3bEtWvXXnq/1atXo3Tp0rCzs0OFChWwdevW/BwmEb1ms3KBDcuJSB/Nyk2xCoTx08uxSurF2EeKiOj1sNl5HjQ7D3us/6TUvn378P777+Po0aPYuXMnUlJS0KJFC8THx2d7n8OHD6Nnz54YOHAgzpw5IxNZ4nLx4sX8HCoR5QATUUSkS+aUiEqP8VPOsEpKOyakiIjImFio1Wq1rp4sNDRUVkyJYKthw4Zab9OjRw+ZtNq8eXPadbVr10blypUxe/bsLLdPSkqSF42YmBgEBQXh26VPYe/gkk+vhMh8cGkeEelz2ZEhJKDiYmNQs0JBREdHw8VF97FFfsRPxhxDpa/QpYyYkCIi9/hbsLOx5InIAza3zsDS1t7ozmX8iaNQOLvrexi4vHEv6m3/96Xxk04/zcVgBA8Pj2xvc+TIETRv3jzDdS1btpTXZ1fi7urqmnYRwRQRvR5WRBGRPquizKUiSp/xk7HHUExIZY//boiIyJjoLCmlUqkwcuRI1KtXD+XLl8/2do8fP0aBAgUyXCe+F9drM3bsWBmsaS7BwcF5PnYic8BEFBHpGhNR+oufjDWGYi+p7HGnPSIiMkY6231P9JYSfaEOHjyYp49ra2srL0T0+gE+Z56JyNyW5hm6/IqfjDmG4mdVVtxpj4gofyiTEoxuCZ9jjdoGs4TPYJJSH3zwgexxsH//fgQGBr7wtr6+vnjy5EmG68T34noiyhtMRBGRPv9gZjIqZxg/ZcQqqRf/++K/KyKivN+BT/SVolcT2KI2sP1f/S7fEz3URUC1fv167NmzB0WKFHnpferUqYPdu3dnuE7s3CeuJ6K8WZ4nZpk1FyKi/GCuO+flBcZP2ePnVkZMSBERkbGzyu+S8+XLl2Pjxo1wdnZO62sgmmna2z8vgevbty8CAgJks03hww8/RKNGjfDDDz+gbdu2WLFiBU6ePIm5c+fm51CJTBIroohIl1gRlTcYP2XFKqnsMdFLRETGLF/LJGbNmiUbZzZu3Bh+fn5pl5UrV6bd5v79+3j06FHa93Xr1pWJLJGEqlSpEtasWYMNGza8sLknEb28YTkRUX5gRVTeY/ykHT/Lsv7bY0KKiIiMnVV+l5+/zN69e7Nc161bN3kholebQWbgTkT5iRVR+YvxU0asknr5v0EiIso/xtjsXFDFRhpFs3Od7b5HRHmLiSgi0iUmokifONnyH/aRIiLSHWNtdu74/x34jAGTUkRGhIkoItJnMopLhUjXWCWlHf8tEhGRqWBSisjAMRFFRLrGRBQZElZJ/Yd9pIiIyNQwKUVkgJiIIiJdYyKKDA2rpDJiHykiIjJFTEoRGQgmoohI15iIIkPHKqnn2EeKiIhMtdk5k1JEBpSMYvBNRPmNiSgi48Q+UkRE+m12bmw78DkaSbNzJqWI9ICJKCLSJe6cR8b4OcmJmufYR4qIcisxWQk7G0ueODIKTEoR6QgTUUSkS0xEERk/9pEiotyKdCwG9/hbPHFkNJiUIspHTEQRkS4xEUWmgA3On2MfKSIiMgdMShHlMTYsJyJdYiKKTBGX7j3HPlJERGTqmJQiygNMRBGRLjERRaaKVVLPcdkeEZHhUSYlGG2zc4UB78DHpBTRK2Iiioh0iYkoMhfmXiXFZXtERIa7Ax/lPSaliHKBiSgi0mcyikt5yJSxSuo//LdORETmgkkpopdgIoqIdI2JKDJXrJL6LwlNRERkDpiUItKCiSgi0jUmoojMG5ftERGROWJSiiibZJS5z9YSUf5jIorov89ffu5y2R4REeUPVWykwTY7Z1KKzB4TUUSkS2xYTkQv+71ARESG2+zcWHfgM1RMSpFZYiKKiHSJiSii7Jl7g3Mu2yMiInNm3lEAmRUmoohIl5iIIso5c1+6x932iIjIXDEpRSaNDcuJSJeYiCKi1/mdQUREZG6YlCKTw0QUEekSE1FEr86cG5xz2R4R5afEZCXsbCx5ksngMSlFJoGJKCLSJSaiiCgvcNkeEeWHSMdicI+/xZObT5RJCUbb7FxhgDvwMSlFRouJKCLSJSaiiPKWOTc457I9IiLj3oGP8o75RgNklJiIIiJ9/vHIqgaivGWuS/cE/j4hIiJiUoqMABNRRKRrTEQR5S9zr5JiQoqIiOg5840IyKAxEUVEusZEFJFumWOVFJftERERZcSkFBlsMsocg1Ui0i0moohI11glRURE+qKKjTS4ZudMSpHeMRFFRLrEhuVE+mWuS/dYJUVEZDqMeQc+Q2OeUQHpHRNRRKRLTEQRGRZzrYZmlRQRkfHjDnx5i0kp0hkmoohIl5iIIiJDwebmRERE2jEpRfmKDcuJSJeYiCIy/LjA3KqkuGyPiIgoe0xKUZ5jIoqIdImJKCIydFy2R0REpB2TUpQnmIgiIl1iIorI+Jhjg3NWSRERkaFRGdgOfOYXHVCeYSKKiPT5Bx4rD4iMj7kt3RP4u4qIyDRxB768waQU5QoTUUSka0xEEZExYpUUEZHp4g58eYdJKXopJqKISNeYiCIyLea4dE9glRQR6UukYzG4x9+CnY0l3wQyaOYZIVCuA0hzLLcnIt1iIorItJlTLCF+nzEhRURE9HJMSlEGTEQRkS6xYTkRmRou2yMiIso5JqWIiSgi0ikmoojMb8LLnKqkBFZJERGRIVMZ0A58TEqZKVZEEZEuMRFFROaAVVJERObX7NzS1h7GxLFGbcSfOApDwaSUGWEiioh0iYkoIjJHrJIiIiLKOSalTBx3ziMiXWIiiojMdekeq6SIiIhyj0kpE8REFBHpEhNRRETPsUqKiIgod5iUMhFMRBGRLjERRUSU/e9EIiIiypl8rafev38/2rdvD39/f1hYWGDDhg0vvP3evXvl7TJfHj9+nJ/DNOpElOYiiPJ4zYWIKL/+8NL88SUqAjQXIspbphBDmdPSPYG/C4mIiAysUio+Ph6VKlXCgAED0Llz5xzf79q1a3BxcUn73sfHJ59GaHxYEUVE+qwA4B9dRLrBGMp4sEqKiMi8KZMSjHYHPoWzu2knpVq3bi0vuSWSUG5ubjm6bVJSkrxoxMSYXvk0E1FEpGtMRBHpF2Mo48KEPRGReUouVgU2t87oexhGzSBrqitXrgw/Pz+88cYbOHTo0AtvO23aNLi6uqZdgoKCYCq4NI+I9LE0T1y4NI/IOBlKDGUuS/dYJUVERPR6DCpaEEHU7NmzsXbtWnkRwVHjxo1x+vTpbO8zduxYREdHp12Cg4N1Oub87BPFHlFEpMtElMAeUUTGiTGU/rBKioiIyER23ytVqpS8aNStWxe3bt3CjBkzsGTJEq33sbW1lRdTWZ5nDrOKRKRf3DmPyPSYawylT6ySIiIiMrGklDY1a9bEwYMHYWqYiCIiXWIiisj86CuGMpelewKrpIjIkEU6FoN7/C3Y2VjqeyhExpuUOnv2rCxJNwVsWE5EusREFJF5M6UYytCwSoqIiIx9Bz5BFRup9x348jUpFRcXh5s3b6Z9f+fOHRkgeXh4oGDBgrIfVEhICBYvXiyP//TTTyhSpAjKlSuHxMREzJs3D3v27ME///wDY8VEFBHpEhNRRKaBMZThY5UUEREZ8w58jjVqI/7EUX0PI3+TUidPnkSTJk3Svh81apT82q9fPyxcuBCPHj3C/fv3044nJydj9OjRMlHl4OCAihUrYteuXRkewxgwEUVEusREFJHpMdYYyhyW7rFKioiIKO9YqNVqNUxITEyM3Nb426VPYe/gorPnZSKKiHSJiSgi3YmLjUHNCgXlLr8uLrqLLYwxhjKXpBSrpIjIWLCnlG6ISiljXL4nKqXya/leTEIiCo2a9tL4yeB7ShkyJqKISJ/JKP5RRERERERExoxJqVxiIoqIdI2JKCIyBqySIiIiotxiUioHmIgiIl1jIoqIiIiIiEwdk1I5TEaZen8EItI/JqKIiAwXe0kREdGLKJMSjK6vlOP/d+DLr75SOcGkVCZMRBGRLrFhORGZYlU5ERGROUkuVkU2O6fcYwTBRBQR6RgTUURkiky5qjzz720iIiLKG2ablGJFFBHpEhNRRETGjTueEhER5T2zSkqxYTkR6RITUURExo9VUkRERPnH5JNSTEQRkS4xEUVE5kbEWqa8dE9glRQREVH+MNmklLOLFewdn788Uw+UiEi/mIgiIiIiIiLKPZNNSnm7KeDgxGQUEekmGcVZdCIi0/w9z9/vRESUU8qkBFja2hvVCXOsURvxJ45C4eyul+dn1oaIKJd/oGgu4g8VzYWIyBxlbpNARESGJdKxGBKTlfoehllILlZF30MwSowkiEyYSqXCueO7cfvaWdjY2qNmw3Yo4F9Y38MyOqyIIiLKnqm2SWCDc/MW+vQJ9uzciri4WBQpWhwNm7SAlRX/dCIiymv8zUpkoq5eOIbpn/fG45BbsLJ2hEqZhDnTP0TDlj0xYvwc2Nk76nuIBo2JKCIiYiWs+UlJScG3X4/DyqXzoVYDllZ2SEmOg5e3H6ZM/wUNGjfX9xCJiEwKk1JEJujB3WsYN6wFbOwKokK93+DsVhYqZSJCQ3bi0O5ZeBYXjYk/b4KFhYW+h2pQ2LCciIi0fR6Q+fh6wsdYu3IpgkoOhG+hdrCydkZ8zE3cuzoX7w3qicUrN6NKtVr6HiYRkckwzXprIjO3esE3sLBwRJma0+HiXk4mnyyt7OFb6E0Uq/gZThzcgivnj+h7mAbXI0pgnygiopz3kzLVpXsCq6TMT/D9u1izYhEKlXkPgcV7yoSU4OhSHKWrT4GDU2H88uM0fQ+TiMikmG4kQWSmlEol9u1YCe/AtrCyytqB29O3Aeyd/LBv23KYKyaiiIiIKLNtf6+DlbUDChRsk+WYQmGNAoU64+ihvQgPC+XJIyLKI1y+R2RiUpIT5cXOwU/rcQsLBWztfBETHQFzwqV5RET0Kp8XZD6ioyNha+cBS0s7rcc1sVVMdBQ8vbx1PDoiMhbKpARY2trD2KhiI6Fwdtf587JSisjE2No5wNnVC7FRV7QeVyqT8Cz2NnzNYBc+VkQREeXf0j1TxqV75ikgsCCexT1CcpL2ibvYqKuwsrKBt08BnY+NiIxDcrEqMEaONWrr7bmZlCIyMaJ/VKvOAxEWsgMJ8Q+yHH90Zw2Sk6LxRocBMEVMRBER6YYp9pNilZR5a9uhG6ysrRB8fTHUYuu9dJKTIvHk3lq0bt8JTs4uehsjEZGpMe1pLiIz1aXvxzi0az0uHRkBvyI94O5TEynJsXgSvAWhD3ag+zufwb9gcZgKLs0jIqK8wiop8+Xq6oZPxn0ld+BLSQqDb6GOsLHzQnTEOTy+swIO9hYYMXqcvodJRGRSmJQiMkHOrh6YvmA/5v0wGvt3/om7V2bJ6z19gjDs01/Qtvt7MLVkFP+IICIiotf1dt934erqjl9mfINLx8bI6ywUCjRp1hqfTZgil/gREVHeYVKKyES5efhgzJQlGPzxT3hw9ypsbO1RpGQlWFpawpgxEUVEuuQefwtWz+J40s2gnxSX7pFG2w5d0ebNLrh+9RLi4mJRsFARePv48gQREeUD04wqiCiNi5snylauZ9RnhIkoItJ1Iio9W2vjTubnB1PsJyWw6pbS9+gsVaY8TwgRUT5jUoqIDBITUUSkz0SUnc1/iajkJL4XRERERPmBSSkiMhhsWE5EhpKIIvPCpXtERJSXlEkJsLS1N6qT6lijNuJPHIXC2V2nz8ukFBHpFRNRRKRLTES9HlPtJyVw6R4REeWF5GJVYHPrDE9mDpluZEFEBouJKCLSJSai8pap9pMiIiIi3WNSiojyVHjoQ2xc9hN2b16KmOgweHoHolXnAejbrx+cnF3TbscZaSLSZTKKS/PoRRMl/EwifVOr1di1428smT8X586cgEKhQJ36jdF/0HuoWaeBvodHRJRvmJQiojwTcu86Ph7YCM/iE+Dl3wIe/oGIj7mB5XOnYN+2ZVi+Zgs8PL14xoko3zARRUTGmJCaPmU8Fs77FW5elRBYYhBUqlScOrUbe3e3xxeTp+Ptvu/qe5hERPmCSSkiyjPffd4Hqal2qNxwFmxsPdKu9y/aA5ePfYipX36K73/5k2eciPIUE1G6YYr9pNjgnAzBgb27ZEKqSLnh8C/SJe36gGJv4c7l3zBl4ieoVbchihUvpddxEhHlBzYFIKI88eTmPty8cgIFSw3JkJASHJwKwr9IL+zYuhGhT5/wjBNRniSiNBexNE9zofxliv2kuHSP9G3Zoj/g4l4KfoU7Z7jewsIChUsPho2dG1Ys5aQeEZkm04ssiEinM8yay93rZ2BhoYC7Ty3tty1QF0plKq5fvcR3iIheOxElMBFFRKbgwvkzcPWuI5NQmSksbeDiUQPnz3InLyIyTaZXh01EOlvqkH522drGGmq1CkplEqys0h34P6Uy4fntrK35DhFRjnHnPMovXLpHhkLERsrUZ9keVykTYGNjo9MxEdHrUyYlwNLW3uhOpSo2Egpnd509HyuliChXFVEiEaW5pFe3fhNYKBQIfbBD62M8Dd4OZxd3VKxSnWeciF6pIorL8yivcekeGYLGTVsg4vEeqJTJWY4lJ0UiKvSYvA0RGY/kYlVgjBxr1Nb5czIpRUSvnIhKz88/EG3f7Ir71/5AxJOjcicZQa1W4knwNjy6uxZ93hkMOzvjmy0govzHRJThNzk3xX5SRIag9ztDkJocgxtnv0Zqcmza9UkJobh+ejwcHB3RuXtvvY6RiCi/cPkeEWW7lCG3M8hfTp2BsNCnOHroMzi5FoGNXQAS42/hWdwjdOzyNoaN+IRnm4jScGke6QuX7pEhKVGyDGb8vgCjhw/EyT1d4eJRBWp1CqLDzsLF1RVzF62Gu4envodJRizSsVjaxiBEhoZJKSIz97qJqPQcHBwxb8k6HD20D3+vX4nw8FD4+bdElx59UKFSVa0NPInIvDARRYaCS/fIkDRr0Ra7Dp7HulVLcfrkUVhZWaFO/Wno0PktODm76Ht4RET5hkkpIjOUl4mozBQKBeo2aCIvr0ulUiE5OQm2tnZMaBEZMSaiiIhezsvbB4PfH5Unpyo5OVnGZCK5RURkyNgcgMgMe0QJOekTpS/37t7G+E9HoFrZAFQt7Yd6VUvgx28nISoyQt9DI6JcYLNy0+knRUSGT0zmrV25GB1b1Uflkj6oVMIb/Xt2wP5/d+p7aERE2WKUQWQiUlNScGz/Jty8fApW1jao2bAdalUtmeE2hpiAyuzyxbPo91YHqFQ28AnqDjvHAMRFXcOiP+dix9ZNWLZmm5xJJCLDr4pi7wrTYUpNzjUbeBBpBN+/ix1bNiAmOgoFCxdF63ad4OjkbFQnSGwwM+7j97Fx3Qp4FqiL4pU+hUqZhGvXdmLoO93w6RdT0G/Q+/oeJhFRFkxKEZmAS2cOYtqnbyEy7CEcnHyRmpqA5XMno0qNBvh1zkKjaY4pAqoxIwbD0toP5Wt+DytrJ3m9T2AL+BXpjEtHh+Pbrz7H9Jnz9D1UIkqHiSgiMkZiidvkL0Zj/eqlsLSyh62dG57FPca0yZ9j/OTv0LHr2zAW2zevx8a1f6FklS/gHdA87XrfQh1w78ocfDflCzRo/AaKFs84YUlEpG+mM+1FZKYe3L2G8e+3gQreqNxwPqo0XoHqzdahdPWvcPnCRQzu1w1KpRLG4MTRg7h7+zoKlh6alpDSsHcMgF+Rnti+dSPCw0L1NkYiyrg0T7Obj+ZCRGQsvp7wMTasXYEi5UagevN1qNxoOao1XQFnj3r4fMx72LNzK4zF8sV/ws27SoaElCA2mSlYagBsbN2wavkCvY2PiCg7TEoRGfkShM3LpsFCYY8y1b+Bo0tReb2FhSU8fRugRJVJuHThNA7sNY5eAteuXISllS1cPCppPe7uUwvK1BTcvnlN52MjooyJKIGJKDL2jT7IfIU8uI+1K5egUKkh8CvcCZaWdvJ6W3sfFK/0Gdy8q+HXGd/IKm5jcPXKRbh61tR6TGFpA2ePKrh88YLOx0VkzpKLVYEyKQHGxrFGbahiI00jKbV//360b98e/v7+Mku/YcOGl95n7969qFq1KmxtbVG8eHEsXLgwP4dIZNQNy0VPjN3b18MroLUsO8/M1bMinN2KYdvmdTAGNrZ2UClToVJq/+WtTIlNux0R6TcRxaqo/GUoMZRocm5K/aQE9pMiYdf2v6GwtEaBgm2ynBDxb863YEdcvXxe9psyBjY2tkj9f5ykjTI1DrZ2tjodExFRTuRrlBEfH49KlSrht99+y9Ht79y5g7Zt26JJkyY4e/YsRo4ciUGDBmHHjh35OUwio0tEaS5i9u5ZfCxs7byzva+VjTfiYrMPUgxJg8ai5FyNpw/+0Xr88f0t8PT2RbkKlXU+NiJzwkSU/jGGIspfsbExsLFxhqWV9q73NvbPY6u4WOOormvWojUiHu+CSpmc5Vjis0eICj2JZi3a6mVsRER6a3TeunVrecmp2bNno0iRIvjhhx/k92XKlMHBgwcxY8YMtGzZUut9kpKS5EUjJsY4PjiIcrvEQNvMrpjJCyxYFDER5+FbqH2W42LXlWcxV1GoSG+jOOH+AUFo26Ebtm+eDWtbd7kE0cJCIQOsR3fX4WnwVnw2fiqsrLhHA1F+NisXWAmlX4yh8h6X7lF6hYsWR8KzMCTEPYC9U2CWkyNiK0sra/gHFjSKE9d34DBsXLsC189MQtHyo2Bj93yTm2ex93Dj7GR4ehdA+47d9D1MIqIsDKoe+8iRI2jePGNzPpGMEtdnZ9q0aXB1dU27BAUF6WCkRPlfESWkr4rKTo9e/RH+eC9iI69kORZyayWSEqPQtUdfo3nLJk2bgboNGuHaqYk4u68XLh0bhdP/dsfdK7PxzuDh6DNgGEKC78ld+BrXLoeaFQqhR4fmWL96GVJTU/U9fCKjwooo08EYKme4dI80mrdsB2cXd9y/NhdqdcYNYZISw/D47mq0aNUebm7uRnHSipcojV/mLsWz6LM4tacHLh4dgQuHhuLMvn5wtE/Cn0vXwd7BEVs3rUXvrm1k/NSgemm5++CdWzf0PXwiMmMGVW7w+PFjFChQIMN14ntR/ZSQkAB7+6w9c8aOHYtRo0alfS9uy8QUGZOcVERpc+P6FbmLyuXz5+Ho6IhLxz6S/Q/cC9SBMjUeoQ+2I+zRfgwb8YlRbf9rb++AWfNX4Ozp49i8YTWiIiPgH9AAnbv3RpFiJXD+7CkM7NUJKamAp98b8HTzRMjjsxj38fvYsXWTDMisra31/TKIDBYrokwTYyiinElMTMD2zevlbr7e3j64fesAko+MgG+hTrC1L4CYiIt4cn8tnJysMXrsJKM6rQ2bvIF/j17CxrV/yXjJ0soS9RqOQsvWHWBlbY3PRg3F5g2r5C59ngHdkZIcg/Vr12P96uX4ff4K1KnXSN8vgYjMkEElpV6FaOYpLkTmkIjS+HP2z/jhm4mws/eAk3sVWNoUhTLmNB7dXYOQ2yvkbQoXLYmPpv+Gjl3fhrERyxKrVKslL+klJydj+OA+sLINQvn638LK2un/R95G5NNjOLhvHObPnYkh74/O0fPExkRj0/qVOHfmJCwVCtRp0ASt2nSEDX+nkIkno7g0j3ISQ5lik3Myb48ePsCAXp1w784NuHlVhpVNEGzsniAm4gpiIi7J21hb26JVu44Y+fF4+PlnXdZn6Fxc3WRVeWYrls7H5o2rUbLqBHj7N027vmCpd3Dt1AR8OKQv/j12CY6Omtgqe6KnqUh6/b1hJaIiIuR50kweEhEZdVLK19cXT548yXCd+N7FxUVrlRSROSWiNPbs3CoTUoHFeyOoZD8oFNZpTSyvnvwM9nbJWLh8owwMRHInN5RKJQ7s3YmD+3dDmapEhUpV0bp9Z1m9ZAh2/7MFoU8fonLDr9MlpJ5z96kF78BWWLZwHgYO+fClfacOH/gXHw7rh2fx8XDxKAO1KgUb163AjG+/wtxFq1CiVNl8fjVE+Y+JKPPBGOrFNBuFkPkSiZT3Br2Np09jUaXRAjg4F/n/9Uo8vLMWdy//juGjxqJ3/6FwdnHN9eM/fhQi44iHD+7D1c0D7Tp0RcnS5WAor33x/Nnw8m2YISElWFraoViFMTi15y1Zod6j1zsvfKykxESM+XAQdu/YDAdHX9g4+CEhdjf+nPMz+g16H5+M+zrX8ScRmTeDSkrVqVMHW7duzXDdzp075fVEhiAlJRlH/92Ao/v+RkpyIoqWqoQWHQbCw9sv3xJR8fFxWLHkT6xcvgiPHgbDAgq4eFRAwVIDM3zo2zn4oUTliTi7fwCuXb2U6yV7Ysvjof174M7ta3B0CYJCYYuVyxfguynjMXP2YtSs0wB5SSTAIsJDYW1jm+N+DefPnoSjSyAcXYpqPS4ao18+vhlPHj9EwAsak4reCe8N6gknt0ooU/tj2Np5/dcM9NxkDOjdGVt3H3uloJRI35iIMk+MocjQ3b93B2tWLMbd2zfg5OyCVm07oX6jZlAo8q8a78ypY1g473cc2LsLqSnJSE1NQbnaP6YlpAQLC0sEFO2OuMiL+HvDWgwd/kmuEz5zfvsBv/44DQpLGzg4F0TSs6eYN2sGWrfvgmnf/57nFdjxcbGIi4uFu4cXbGxsXnr7Z/FxuHv7OkpU1t7o3NbeBy7upXDuzImXJqW+mvAx9u7eiZJVxsPLv7E8f883pFmPRfN+g7ePLwYMHv7Kr42IzE++1mTHxcXh7Nmz8iLcuXNH/v/9+/fT+kH17ftfA+ahQ4fi9u3b+OSTT3D16lX8/vvvWLVqFT766KP8HCZRjjwOuYNhXSvgm8/ewumjp3Dp/D389cc09G9TGDs3LXytZuXZiYmOQu+urfHT9ClIUhVHUMnBSE1NgndAS62zUCJZ4+xWHIf27851f4UBb3fEk9B4VKz3Oyo3XIKK9eehWuNlcmng0Hd64O7tm8gLyUlJmP3LdDSpXR6NapZG3cpF8FbHN7Brx+aX3tfS8nngIwJAbTTbIL+sSmrpwjmwUDigZNXJaQkpwcG5EEpXm4qIsFC5rI/IGBuWi6V5mgsZL8ZQZEpm//o9WjeuisXz/8SpM0+xa+cRDH2nG3p2aiF7R+aHdauWonfXVjhy6Bx8CvaEo2sFWNu4w9WzitbbewW8IRM3ouIpN0Sibeb3X8O/aE9Ua7oGFerOQdWmq1G80qf4Z+vfmPJl7pJcLyLaDYiYrGaFgmhSuyzqVSmOqV9+iojwsBfeT2H5/PNArXoeJ2mjUiW/NH56+uQRNqxZjoIlB8E7oJlMSD1/fBsEFOuBAgXbY/6cX2S7BSIig0hKnTx5ElWqVJEXQTQkF/8/YcIE+f2jR4/SElRCkSJFsGXLFlkdValSJfzwww+YN2+e3IGPSJ9SU1Iw/v02iI58hsoN56FCvdkoV+t7VGu6Gl4BLfDzpIG4e2FrniSi0ps+ZTzu3L6LCvVmoWTlz+FbsF3ah392FJa2UOZyFzqxE0tIyD2UqjYVzu7/LVuzc/RHqepTAAt7LFkwG3mRkBrcvxt++2k6LO2ro3T1KShR+XPcD1FixJDeWDjv1xfev26DpkiIf4qYiHNaj4c93IkiRUvBp4D2yjWNnTu2wtOvOSwts85ciian7t41sGvHlly+OiLdYiLKtDGGyjuZq5ZJt8Qkj0jaBBTvI5M1ZWt+h4r156N8nZ9w/dpNjHyvf54/54Pgu5jw2YfwCWqLig3mI6hEbzi6loCllX22S8tEhbigVKbmqup71i/fwzugOQqVHgQra8f/P5YVCgS1RlCpd7F25VKZzHld+//diT7d2uD06RsoUm4kytSYBne/9lj1119yci8s9Gm29xVtGCpVrYWwh7u0TuzFx9xCbNQt1Knf5IVjEC0eVCoVfIJaaz1eoGBbRIQ/xcXzp1/hFRKZLmVSAoyNY43aUMVGGn9SqnHjxvIXX+bLwoXPq0rE171792a5z5kzZ5CUlIRbt26hf/+8/6Aiyq1j+zfh4f1rKFHlSzi6FE+7XvQ1KlZhNJxci2PFgpl5kojSiI6OwqYNq+BbuAccXYrJ6xSWdnBwLoyIxwe13icpQSRsrqJileq5ei5RpeTmWRkOTlmXvIleA57+LbB9y0a8rpXL5uPkscMoU/M7FK84Gp6+9eAT2AJla/4A/6I9MH3qBBlIZkfsClOydAXcvvCdXGqnIfpBhNxeJXcbHDD0g5f2MkhKTICVtXO2xy2tnZGUmPSKr5JIN4kogRVRpkvfMZRocm5K2E9KP8TP7NzffpKf94VKDUibDBKf066elVGk/GgcP7IfF8+fydPnXbV8kUxAFSn7Piwsnv+54+xWBonPHsoEjDYRTw7C06sAfP1y3tz82pULePwwWFYIaVMgqA1EDmjPzm143Um9z0YNg4tnNVSoNwd+hTvAo0AdFCr9LirUnY3QsCj8NH3yCx9j4JDhiAo7g/vXF0Cl+i/xJnqS3jz3NfwDC6F5y+eTn9lJTEyEQmEJS6vnybfMNLGV+B1ERP//91tMe3Um/YdbqhDlwPH9W+DsVgJOrll3FRHBjldAKxzcvwupOahQEkkXUWpdv1opVC0TgE6tG2LlsgVZSp1v3biKlOQkeBSom+65LOBXuDPCHx9A6MM9GW6vVCbh9oUf4ODoiPYdu+fqfY2Pj4eVjVu2x61t3JCQ8Ayv668lC+Dp11AGoumJ1yV2f7GycsCaFUuyvb+43W/zlsHT0x5n9vfHpWOjcP3M1ziz923ZoHTAkBHo3K33S8dRumx5xISd0HpMpUpBbMRplClX/hVeIZHuElFcnkf5jTvv0et6GBKM2zevwDtQe2WNiHFs7dywd/f2lz6WqND5e8Mq9OzcUsZPdSoXw/hPhuPGtctZbnvx3Gm4eFSViam05/KtDxs7b9y68CNSU+Iy3D4q9CSePtiGnn0GvHQJW3pisxTB2lZ7DGVp5QBLK1skJDy/3avavXMroiLDUKjM0LQNbtJXtfsW6oq/N6yWuwpnRyScRn48AQ9uLMaZf3vg2umvcfn4Jzj9by842CVj7sLVsLbO+NiZlSpTXia0osO1V0JFPT0mk1bFS5R6xVdKROaISSmiHLBQxWU7KyRYWTlCrVK9dNmc6AXQqVVDrF6xEnaujeBbpB/CIl0w+YvRGPpOd7mjiYa19fMleiplxnLPAgXbwcu/Ca6fnowLh0fgwc3luHt5Nk7t7oGIp8dRvGRp2NjkrqFmydJlEBd5PsPMWXoxEadQomQZvA4RTN69c10GidqIiiwnt3K4ef3KCx9HNDDfuOMgpnz3KyqU80Sgbzzatm+F1X/vxZixk3O048vbfQchKvw8nj74J8uMbvD1RUhMCH9po0+i/MREFBGZgpT/T7iJOEkb0ZNIJI5EJdDLYohPPxqCT0cOxv3gVPgW7gtnr9bYunkHurZrgn3/Zvw8t7axyRI/iSV1patNRnzMbZzc3QO3L/2CB7dW4NKxT3Dp2BgRBaBC5Wq5en2Fi5aQSZjoMO1JmrioK0hNeYYSJV9vR18xUWnv4A0Hp0Jaj7t6VpUTmQ+C/6si12bw+6OwccdhdO7WGQX9n6FMKSeM/+p7bNl9NEcb5FSrUQdFi5XG/WtzkJoSm+GYqLh6eGc5mrVoK5udExHllGnVZhPlUw8KsWRszw6x4140rG2y7sgWFXoMhYqUgK2dXbaPl5KSgg+H9oOVbRDK1/hWLv2TinaX5dQnjn6Kub//gOGjxqXNRnl4+uBJ8HY4u5fLUJllobCFhYUVUlJiZVJK9JHy8G0oG3VfODcbP377JT6bMC3Hr7V7z/5YtnAuHtxcioIlMy73iHhyGBFPjmPMx7/jdYhkka2dPVKSs1+brEyJhIOj9oArPTs7e3Tq1kteXkWL1m+iU7feWL96KiIe74V7gQaywWf4w12IDr+A0Z9NMphtnMk8d80TWAlF9PrYT0q//PwD4eTshsjQ43D1yrqE5VnsXTyLe4Qy5Sq+8HFW/7UIWzatQcmqE+Dt3zTt+qASfXH9zCSMeu8d/Hv0Elxcn1csNWzyBg7uGyvbGoid5dIvLxMV0eK68Ef7oExNgL1TQRQpNxyRT4/gwyF9sfXfE/D1C8jR6/Py9kGzlu1wcN9f8ChQL8NzKZWJMnnjH1AIdRu8uFfTy9g7OCIlJQ4qZZKM+TLTxFYOjv+PLV+gRKmy+GLy9FeO5b77eQ769+yAcwfegXdAW9g7BSEu6hpCQ7bB28cT4yZ9+0qPTflLsxEKkSFipRRROtntnFelSiWoVam4c+kX2b8ovYgnRxH2eD969Rv0wnO5d/c2PH0SgiLlP/ovIfV/bl5VZDNOsbxNs4xPbPHbf9B7eBK8GY/ubUx73qTEcISF/CP7CFRttAC1W21GzTfWonjFUfAv0gUBxXph5bKFLyzh1hagjBg9DsHXF+Ly8TGygij80X5cPzsVV0+NR7MW7dC+U4/X+lkRgcwbrdojLGRb2i556cVGXUVM5HW0aKW9L8PrCL5/F4f275GVaqIpqRjLV9/OxFff/QJPtxjcPPctbl+YgeLFXPDbvL8wcOiHeT4GIm1YEUWU/9hPSn+UKiXKVaiIR3fXyc/5DMeUibh7+Rc5AScSOy+ydOEf8PStnyEhpdn4pWj5UbKH0cZ1K9Kuf7NTD7i5e+D66QlISniSdn3I7ZWwsrJHlUYLUKP5GtRutQWV6s+S8VOpqpOgVFlg1fIFuXqNYydMhYuLDS4cGox7V+fJybyQ26tx4eC7SIi9jm9mzIJC8Xp/conqo9SUBISG7MpyTFR5P7m/CcVLlkPBQkWQl0QF28njh3H44F6EPn0srytbvhLWbP4X7d5si6fBq2QbhbiIXejzzgCs2rj7pZvNEBFlxkopMnuZZ1EzB69idu7Lz0fKCqnQkN2Ii74On6A2sLZ2RmToMdl0vFHTlnir98AXnssLZ0/DwckvQ6P09MQM26W76/Ho4QMUKlxUXid6JIU8uIeVy2bg0e2/YO9cAs9irssElU+g9l0pfQJayOTS6ZNH5bhyaujwj1GoSHG5le+ls1PldX4BBTFm7Jfo884wWP5/O+HXMWDwcOzYshHXTk9EkXIfws7BVwZT0eFncfv8NBQrURaNm2vvO/Eqbt64iq8nfCKbqKYtE7C0QqkyZTF67Jfo0r2PvIhlkxYKhUwEEuU3VkQRkTmIioxAv7felD2frGycceHQcHgFNJV9JZMTQhEashWq1GjMWbjqhZ+/4jP61o3LKFHpM63Hbew84exeBhfO/beEztnFFX8sXoN3+3bFqT095XNaWNrK3lEFgtpq3X1X7Jzn5l0P+/bswojRX+T4dYqqqlWbdmH2L99jw9oVsupcxBoi0Tb0g9EoXbYCXlfhIsXQun0X7Nz2i6yU8vJrDAuFlazgF20HxATpuPELctTCICfEcsl5s37C/Lm/ICZaVGFZyBjK0dkZb/cZgP6D3sfkb37Gl1NnIDExQe7ul1fPTUTmh0kpMksvS0RpXLtyEV+O+wgFCnZA0fLD5Szfw1srcf/qPKjVqfIDeuDQETJ4eVljTEsrK1l2rVar0naCSU8sHxOs0iV/xMzaxCkz0PWtflizYjHu3bmNkBAHiJYB967Ng7t3Lbn7ighM0u7z/0BLLBfMrdbtOsmL2PlP9McSs4yvO7uXngjMfv1jGUZ/MBCn/u0JF7diSE2J/3/pfmXMmv9XrhqMvsidWzfwdudWSFXaQqGwgYXCRvbiEn0tbt8+hAFvd5BJP7FU70XLLonyIxnFEnoyZM4uVmxyTq9t4ucf4f69B6jccB7sHPxktdST+1sQ+mAHYKGAr58/5izc89KelYr/x0ViQ5fsqFVJGeInTUXPP/tPywbgB/ftQlRUJC5GWiIm4jyCry+GT1BL2NoXyPRctq8UP4keSqI306fjpyI6KhJOzs4yUZOXvv7uF1m5tPufr3H/6u+wsfNCfOw9WECNcZO+k/Fbnj3XhI+xYumfsHXwBxAJF4/ycHavIJdD/jFrJtauXIrFKzejSLEScHDIvucqEVFOMClFZiOniaj0li+eB1s7DxQp94FMQLm4l4NL9cnPt+ZWJePs/j6IDA/PUSKlXsOmmPPr93KWzt2nZpbjIkgrWLg4/AMLZjlWrkJlJCUl4oN3e8vdV+ydCiE6/Bye3N8MO8dAlKkxFQ5Oz+8X8eSQTHqJ+7wq1//3ZMgPDRo3x97jl7Ht77W4fPG8nB1t1KwlatVpmKezbD9+NxlKlehhFQNnj/IoXf1rubufUKjMEDy6swbz58xEhUrV0LJNhzx7XqL0mIgi0h/2k9Kfx49CsGvH3yhS9kM4ujyv/g4s3kteRDuEx/e34s6lGTlKaIgd4WrWboBrN/6Bb6E3s8QKonG5WP5fv9GnWe7r6OSMLj364NyZE9izcyusbZxljBRyewXuX1+IwBK9ULDkAPmYYlwxYUfR4M02r/y6RUzj7ZMx0ZVXRJLrl7lLceXSeWzfskG2aQgq1AcdOveEh6dXnj2PmJAVCSl379qICjuB0tWnwNO3XtrxpIQhuHxsNIYP6Yu/dx5mhRQRvTYmpcikvUoiKr2Tx4/KUm6xY0t6ypQ4JCY8hpNbZRw/diRHjyV2LClfsRpuXfwe1tWmwMm1hLxeBEEP76xB2KN9mPzNTK0f7g+C78oSdFuHYqja5CfYOwbK68VSwutnpuDysY9RudECpCRGIOTmIjR9o41sLmqoRBDapUdfdHm9NlXZiowIx787t8oG5gmP96NEpbFpCSlBnGP/ot0Q8fQQFs77nUkpylNMRBEZDvaT0o/zZ0/KXYk9/RpmuF5M6iU8eyibi4v/P3/mpNxV92VEZfPQd7rh7pXZKFRqQFpVeEJ8CG6enSzbDTTPpifld1O+wN/rV6NYhTGyOkqhsIYy9Rke3l6D+9fnw9rGDX6FO+Pu1T+Q8CwUPfu8uB2Dvomm8C9rDP861q9eJidk42Kuyx2f0yekBFt7bxStMBoXj3yII4f2oW79xvk2FiIyD0xKkckno14nIBVL18RyOw1Rtnzv6lyZQFKrnpd329jaY/c/W2QTyhcRiZBf/1iKgb0649yBd+HqWRHWtp6Ij7oog6CBQz6Us3naLFv0B5QqS5SqPi1DcsXJtSTK1vwGp/b0ktsZJ8TeQmBQQUyc8iPMmWjGqVIpkZocAxf3CjKA0sbTtwnOn/lJNj/Pi55ZZL6YiCIi+o9C06YgXQz1JHgbQm4uR0J8sOZWWLNyMeo2bPrSCm2xm97YCdPwzVefI/zhDji5V4YyNRZRYadRoEAA/li0TmtfqojwMKxcugBBJd+Bb6H/mqlbWjkgqGRfOcF4/9p8PA3ejPiYO/I58jPhYwxEb1Nbe3/ERF6Uvau0cfGoCFt7d5w6fphJKSJ6bUxKkcnIq0RUevUaNMKK5cuhUn4gl4FdODwcapUShUoNgotnJSQnhuLx3Q0YPriX3MlNNM1+EbEjydote/HP9k2y9DouJhZFmrRH97f7vzAI2rFlEzx9m2VISGmIPg1u3tWRGHsRwz/6VM7wiQafeU3MaIrm6WIHO0uFQm5vLHbtM0TuHp7yq1KZIGdEs/dfsKxrIhEmdgS8dOEMrKys5bLGvGiGSrrDhuVERNpVqV4bllbWcre4gGI9EHxjCe5f+xOevg1lSwQraxdEhh7HqRNr0LtrGyxfu+2lsUufAcPQoPEbWLl8Aa5evghbW080a/ET2nbomu0ywP3//oPU1GRZ8aONWA74NHgrSpfyw4jRv6BG7fr58paKfla7d/yNyIgI+AcEoWmLNrCzs4chcvfwQnLSUfn/aqizv6H6+YSrPjx98gg7tm6U/bvEjoNvtH4zz3t4EdFzqthIKJzdkZ+YlCKjlh+JqPTe6jMIyxf/iRvnvoGFhTVUqhRUqj8nXeVNaXgUqI9bF6ZjyoRP0aLVmy8NqmxsbdGuQzd5yamEhGdwdcl+FtHG1gNB/uUw+P1RyA+iafhH7w/A9asXYGXtIHf/U349DnXrN8X0mX+kJYEMhWg4WrNOQ5w7exVJCWFyO+jMzUyF8Ed7UK1GPZ1XSV2+eBYfDu2PkAd35UyjSpmMGd9NQu16jfHDL38a3Pmk/zARRWQc2E9Kvzy9vNG+Y3ds3rgINraeshopsERfufROQ+yY5+XXCBcOD8OCP37J0Y53hYsWx6dfTMlV/CR6gookmDZi6Z4wdPiYfElIiQm933/+FnN/n4HUlBRY2zghOSkGTs5u+GLSN3iz81swNO07dceq5QtgZe2KsJDdcPOqmuU20eGnkZQYiVp1Guh8Qu/br8dh+aI/5PtqY+uChIRwfDXhE4yf/J1Bnk8iY+ZYozbiTzxPUuenvNtWi0iHgabmIhJRmkt+EFvw/vDrPEQ9PSRn+0TPgcxLwcQsUVDJAUhOTsaWTWvzZRwlSpVBTMQZrcdEgig28gxKln7x7jWvKvTpE/Tt0Q4PH8aiXK3vUbPFZnkpWWU8Tp08g0F9OsvXbmg+HDMOqcmRcmfCa6e/kpVuGmJJppi1jQ6/gP7vvqfTcYUE30P/nh0RG2+PivV+R7Wm61C9+UaUqvolzpw6h8H9uiE1NVWnY6KXJ6I0F83OeZoLkSnydjON8JD9pPRr3KRvUaVqVVw/+zUsLe0QWPztLLdxcC4ML/+WWLV8sUzg5DWxs5+Ik2Iizmk9Hh12SsZxRYuXRH6Y/ct0/PbTN/AJ6oLqzdegxhubULXJUti71MBno4bin20bYWiqVq+NRk1bQamMw5PgrQgN2Z3hvUmMf4g7F39EqTIVUL1Wxn5T+W361PGypUVQyYGo3nwdqjZdg2pNlsPBrbY8n6KZPZEhSi5WBcqkBH0Pw2CZRtRBZpWIEvIzEZXZG63exNxFq+VSL7Elrja2dl5wcPJD8P07+TKGnr0HICr0DMIe7s1yLOTWKiTEP8Fbvf+bfcxLfy35AzExcShT8we5TFDsWiOWxHkHNEOpalNx5dI57N6xGboSFRmB40cO4NSJI0hKTMz2dlWq1cKcRavh7uGG2KjLOLGrK66f+Rq3LvyE0//2lMsIPvhorGwKr0uL/vwdKSkWKFPzOzi7l5XBsGik7+XfGCWrTsalC6exb88OnY6JsmIiiojo9Tg6OuHPZRtkksPRtYRMTGkjYquI8KeyqimvVatZF4WLlsT9a3Nlc/P0khPD8fD2UjRo3CJfNocRu+PN/W0GAoq+hcJlBsuqdkFsVlOi8ufwKFAbM777Ol+ScdqI57l88ZxsTn7/XvbxqohLZvy+AG926g5YANfPfIWz+wfi9sWZuHJiHE7v6wM3Nyv8MneJTpfviUlSkZASuyWKBKeVtVNaGwuxoY27d3XM/GGqzs4nEeUdJqXI6BJR+pj51PROEo3OtVEpk5CUGJEvvZyElm07onX7Lrh+ZrK8iOTU0wc7ceXEp7h3dQ4Gvz8a5SpUzpfn3rRuNTx8m8LGLuuSMlF67+pZAX9vFEm7/BUdHYXPPhqKhjVKoX/P9ujTrTUa1SqDX2dMk+Xc2ogdYfYdu4LpP81Fw8aNYaO4BXurq2jZuin+Wr8L732Ydfvo/LZ541o5K2xl7ZzlmItHBTi7l8K2v9fpfFzERBQRUV6zsrJCydLloEwJzzZZIJbYW1nbwNZWe9LqdYikyTc/zkJK4n2cPzgIIbdWIPzxIdy/tgAXDr0Le3s1vpj8LfLDv7u2ISkpAX5Fu2odl1/hrrh35wauXDqP/LZr+99o1bgaurZrhIG9OqBVoyro271dts8t+l1N+2EW9hy+iL4DhyEo0BGK1DMoGKDE5xOnYcO2AwgMKgxdEhOgapVa9gHTdj59C3fG9asXcff2TZ2Oi4heH3tKkUH3gDCU0nvRG6F6zfq4fmM9vP2byiVh6T19sAMpyfFo1bZTvjy/2AXwu5/mokq1mlg8fw6und4jry9dtiI++/yPXPWnepVkkFegX7bHbez8EBURmW/PL8cQFYl2zWshIjwCavXzpW1i50JYBuL3mdMRfP8uvvlxttYZO9EvSjRBFRdDEBsbDQ9/n2yP29gWkOecdIM9ooiI8lfrdp2wYumfiHx6BB4F6mY4plQmIvTBFrRq2zHf+jtWrFwNKzfuwpxfvsf2rX9CmZoCOzsHdOjyFoZ8MBq+fgH5FrtYWtnKanpt7Bz8n98unz/zf5/5HX79cdrzzuSChQJOLiVx+fI99O7aGkvXbMt2sx1xbj4bL+6rf9ExUbC2cdQ6qSfY2vvKrzEx0ToeGRG9LialSO8MNRGV2QcffYoBvTri2ulJKFR6MOydgqBMTcDT4G24d3U23uz0luxBlV9EsNa7/xD06jdYLmFTWFq+cAvllJQUHDu8H+FhT+Hj64+ateu/UsAXEFQQ4VFXtB4Ts57PYq+iYM06yC+iCqp3tzYIDw+Db8H28PRrKHtCieabIhkoqov+Xr8SPXq9I5cIGLqgoCKIjboEP3TOckytSsWzmCsoVLijXsZmLpiIIjJtmp6TZBhE36FadRvh9MkpKFz2Q3j5NYbC0gZx0Tdw78rvUKZE4t1hI/N1DMVLlMb0mfPw9Xe/IjY2Bi6ubrCxscn29g+C7+Ls6ROwgAWq1azzSomrgKBCUKYmIT7mFhxdssaHsf+PrQICCyK/HD20D7/O+Ab2TgXhX7QbHJwKIT7mJh7eWYuUpEjY2LnhuynjsWC54fW2yqxgoaJISozGs7j7cHDKes5iIy/CQqGQuxsSkXFhUor0wlgSUel5eRdAg8bNcXD/vwjfewhW1nZQqVKhVqWgY9demPj1DzoZh6gGetnubJvWr8T0qRMRHvo47boCfkEYN3Eqmrdqn6vn6/F2P3w98VPERl6WPZDSCw35B/Ex99Glx++5ekzRN2L/vzsRGREOv4BA1GvQVJb4a/PP1o24deMKytSYmmGG1d27Blw9K8udEe3svbB25RKjSEr16NVPvjexRbrB2a10hmOP7m5AwrNQdOnRR2/jM5dkFJuUExHpRkpyMlq364gb16/gxrlvcfPcd7CycUBKUgy8ffwxc/GatDYJ+c3Wzk5eshMRHobxn47A3t3b0pYbKhSWaNGmAyZNnZGrNg0NGr8BT29fBF+fj1LVJsvd4jREf6uHt5fJKvyChYrk6jVcv3oJ586chMJSgdp1G2Wb1BLj//SjYXB2K4PytX+EwtJWXi8m87wDWuDC4RFQqZJx7PA+hDy4n6/JsbzQ7I02cHXzRPC1eShZdWKG85mSHI1Hd1agcdNW8PbJutsyERk2JqVIZ4wxEaWxYsmf+GrCGNjYusGjQGMolQmIenoU1tbW+O7nP/FGLhM9+WnTuhVyBxIv/6ao3PBr2MtZsVsIubkEHw7ri5lzlqJZi7Y5frzO3Xpj07pVuHR8DPwKd4OHb32oVcmyp9WT+5vQoUvPHO++IgKkpQtmY+aP3yA+Llo2TRdVT17efpjw1XdaE2Yrli2As3uFLCX/gndgS4TcXoHUlGcICQ6GMeje6x1s/Xs9rhwfjQIFO8nXJX6eRNVX6IN/0G/ge9mW0VPuMRFFRKQ/4WGhGNCrE25cuwg3ryrwCayGuKhLeBZ7H9Vr1ccfi9fC1vZ5skTfnj2LR7+33sSDB49QrMIYePo1FiXMCH24G3t2/omQ4C5YsnrrCyus0hMx4sSvv5ex16Vjo2UPKXvHAMRFXcOjuyuhTAnF518uyvH4HoYE49ORQ3HqxCExRSmX44mJyhatO2DyNz9nSZiJDWFCnz5E+dofpyWkNKysHRFUog+unf5Sfv/o4QODT0rZ2Nriy6k/YtT77+DS0Y/gW6izbHIuNrN5fHc1rK2S8MkXX+t7mET0CpiUonxlzIkojZPHDmHy+NHwK9IFhUsPkSXnQkpSFK6e+gJffDICtes2zLcm57khlux9N2WCTEiVrDI+rceSqMgpVe0rXDkxFt9+PR5NmreWfapyQswozlu6DjO+m4R1q5Yj+MbzAMrdwxvDR3+Od4d9lOPdV5bMn4VvvvocvoU6oHSNHrB18JNl5A9uLMSHw/rh9/kr0KhJiwz3uXP7Fly9mml9PPG8Lh6VEBqyEx6eL64eMxT29g6Yv3wDZn7/NdauWoYHN5fK6339gzBu0nd4u++7+h6i0WMiiojIMIwePhDB9x+hUoM/4ORaIm2CKjRkF04d/wYL/vgFQz8YA0OwYc1y3L55DZUbzoOD83/VS36FO8qxnz/0Pv7ZtjFXfTybt2yHuQvX4MdvJ+PKyS/Srq9Tvwk+GbcEpcpo39U5s6ioSPTp3hbR0SkoVfXLtAlCcR737JqLJ/27Y/GqLRmqzu/ded7w28VT+0SXi2eltP9/WQW+oWjZpgPmLlojd9m78P+EmqhkE5Otoz77EoUKF9X3EInoFTApRfmejDLGRFR6C+f9DmfXYihS9oMMyRdrWzeUrPolTu7ujj9mzcCoT59/OOrTkYN75bbKlRv2ypIoElVJAcV64uKRkTh/5iQqV6uZq22dv5g0HR99PAE3r1+F5f9309E2W5iYmCCDOjGLJ3o4iJktzQzkzB+nwbdQRxSr8F/vCBHoiYTZ5eMf44dpk9Cw8RsZxu7s7IL4hNBsxyZ27VGmJqK92LrYQIlkoVgKcOHsaXnu6jVogs8mTMOHH4+Xu8RY29igaLGS+dbk1RwwEUVE2ibDSH+uXr6A40f2o1S1SWkJKUF8xvsEvoHYyEuY++sMvPPucIOollq/+i94+NbNkJDScHYvJyu91q9enuvNZeo1bCovd27dQGRkuOxPlV3fI7GMTlSXiSVofv6BadeLRvFPnzxB5YaLYOfwvKE3FFZyJzpREX/2yIfYu2tbhopzJ+fnk6VJCaH/3Sed5P/vKB1UqKiMQQyV6O+17e/1iIqKkOetbYdusnG9uD46KkqeJw9P7c3kicg45KxUgiiHgaDmIhJRmouxO3xwLzz8mmqtBhI7qrh6VsGiP2fJpIu+hYU+kV9FI0ttHJwLZ7hdbjk6OaNS1RooX7FKloRUclISfvx2EhpXL4mu7RrLbYeb1CyN3376Bqmpqdi7azuexccioNhbWR5XJMz8i/TAzeuXcO3KxQzH2nXojPDHe5GcFJHlfgnxIYh8ehyBBQuhYaYKq/SSEhMREx0FlUoFXbtw7jTeqF8ZHw7ti+VLV2LR/AXo26Mtur/ZDLEx0ShbvhJKlCzDhNQrJqI0F02fKPaKIiJTiD1MwZFDe2FlbQ/PAtqX+HsHNEdiYjx+nv4VDEHo0yewd9QePwl2joVkYuhVFSlWQva+1JaQOnPqGPp2bYU36lfEWx2boVnd8hjwVntcunBWHt+wZgU8/ZpqTS65elaCq0c5bFi7IsP19Rs1g72DEx7dXad1PI/urpd9mcaOn5ptxbuIm0T8JOIoXROx46Rxo9CyYRX8MmM6Vq/YgGmTPkfjmqWxdOEcBAYVRrkKlZmQeoUNXogMDZNS9FpMNRGVnkqlhIXCOtvjCoWNbOK5ecNq6JtPAT/5NT72ttbjoreUvJ3v822I8zJwGD64FxbNmYmm8Vb4wTII0y2DUD9Gjdk/f4cxHwxAWNgTWFraaA2oBLGboabJaHrd334Hrq4uuHJ8DGIiL8myf3GJCjuNS0dHyeVwi1du1prUEf0Uhr7TA1XL+qN2pcJoUrs8Zs38TjZa14WQ4Huyl0ZSiptculCl8V+o2mQ1ytaajtu3Q+Qxkcyj109EMRlFRGRYVEoVLMSfGukaUqen+H9sJZIpIo7QtwK+fkiI0x4/CeKYr1/exk/C8SMH8M5b7RB15jw+VvhipmUhjFL44smJk+jTtRXOnT4hYyN7x/8qpzKzcQhEWFholir3QUNH4OHtVbh/fRFSU2Ll9SnJUbhzeRaePtiOrm/1RuPmrbI8npho/f3nb9G4VjkZP4k4atiAt2TyTFemTx2PVcsXoVCZ91C92VpUargE1Zqthqd/G0z98lO5qQ/lHOMkMmRMSlGumUMiKr3KVWog/NE+rcfEB3x02GmZaNm3Zwf0rXa9RnInmwc3lqTtGqOhVisRcms5ChctiQqVqubp8+7YsgEH9u3CF/DFIEtvlLKwRxkLewyx9MEnFgXwz/ZNiAgPh1KZjGex97Q+Rnz0Dfk187bLnl7eWLhiEzzcFbhw6H2c3tMNJ3d3kgkpf383bPznoNatmrdtXo9+PdrhzJlbKFLmA5SqOhEKu2r4feYPMhmki8TUkgWzkZIKlK7xbdrSBVEVJnYOLFVtKu7cuobtWzfk+ziMHRNRRETGR1RWp6TEIzr8tNbj4Y8PQGFpj6jIMLmjnL516dEbEU+OIi76epZj0eFnER1+Hp2798rT5xSx2pefjUAppQ2mwx+NFC4oamGLpgoXfI8AFExVYPK4j2QyTPTgzO4xEmNvIiAga9JqyAdj8O57o/Dw1hKc3NUVp/99S359cn8dPhwzHl9O/UlrQuqdnh0w+5cZsLSvLuMnEUedPn0Dfbq1kX218ptYwrh88TwEleyPgKLdYGllL6+3sXVHkXIj4OnXEL/++I1eKuCJKO8xKUW5TkQJpp6ISq/3O0Nk34OHt9dkSPSoVCm4ef4HufuJnUMQkpOSoW+iweXYiVNloHf15DjERFxASnIMosLO4PLxTxATfhZjJ2Rfpv2q1ixfiAqWjqimcMxyrK6FE4pbOeD65Qtwc/dC8I2FWRJmKmUSHt5ejopVaqJo8ax9DURvqq17jmPOwjXoN7A/Brw7CAv/+hubdx2V5duZiVLzcWPelzvnVKg3B35FOsPLvwmKVxyNcrVm4NL5c/hz9s/Ib1v/3gBPvzdgZe2U5ZijSzG4elaUCT3KiokoIiLjVq1GHXj5+OHWhRlITgzPcEzEJw/vrIGn7/OlfaLiXN/e7NwDpctVlJXZD++sRVJiGJISnuLBrRW4evJzVK9ZXzYuz0uiovvuvTvoCXdYW2T8s8zWQoG31G64cuUiGjZuhojHB9Iq3tMT18dG30KXHn2yHBOb2nz0yQTsOXIRY8ZOQK8+b8k4cf/xqxjywWit8eAfv8/AlcsXUbb2TzJuEvGTiKMq1J0DT9+GGDvqPcTF5m/vtn93bYNKqZQ9szITYxb9SR8E35F9y4jI+LHROZn0znl5Qezo4e3jhzuXf8XTkH/gUaAulKkJCHu4GynJ0ShR6VPcuTQDZSsMgSFo1bYjFIpFmD5lIi4cHp52faEiJfDt9JVo0Lh5nj9n8N3bqKuyAbRU6IvgobTSBtfu38XnX07DJx++C5UyAX5FejzfGjn6Oh7eWobE+DsYO35zts8hAisx9pyMX5R0Jycno3CZ92S/hPSc3cvCK6AlVi5biGEjPsnXXk7P4uPg45V9801rWy/Exjwvp6esPQ9etdQ8MSkJazZtw19rNyI0LBwB/r7o1a0TOrRqLrfoJiLTxCbnhkV8/o/8+Pkuxaf+7SV3BrZz8ENs5GVEPj0KF4+KsHUIgI2tney3pG92dvZYsGwDvprwMbZvnoU7l36R11tZ2ciE1edffpPnnyEP7t+VX8tY2Gk9XtrieYWQSJaVKFUGl499BP+iveDp20BOjordhx/eWYlmLdqhTv3G2T6Pt48v+g16/6XjEcsoRXzkHdBG7tycnoXCSi6lO7VnPzZvXIO3eg9AfomPi4WllS2sbbTvbG1j55V2O8pbl6/fxLylK3H02EkZezdqUAcDe3VH0UIFeaop3zApRRkwEaU9qBrz+SR8OnIw1GoVHt/dKD+Y3X3qyG2CH9/bIHd/6/52f4P5aWrRugOat2wv1/6HhT6Fr68/KlapnucVUhoubu54Gpp9YPDEIhUuru5yxxrRIP37aZNw6ehHacfLVaiKcV/+LUv988LBfbtg71QQNnbatzh2866Oq/c2IjoqMt8aZIpeUb7+AYgIPQ7/oj2ynHu1KhXx0ZdQpFFrmLO8SkRpRERGoWOfd3Hx2g1UtXRCGZUV7j4Ix7tHjmPh8tVY+edvcHR4HuQTkekx1wk0Q9WxS0/MmjkdYaFxiAk/h4gnh2Dn4I9iFUbD0bUErp74GO07doeLqxsMgRjH9J//wCfjvsK5MyflZ7doTu7u4Zlvzyc8QSoCkXVH46dIkV+9vH2wcMXfspfS1r/n4e6VWfJ60ci838ChcileXsR4D0Puy+WUfsWraz1ua+8NZ7ciuHHtMvKTON+pKQmIDjsLV6/KWY7HhJ+XrzeoUNadEunVLV61Dh998RXcLa1RU2kPJdRYemcl5i1ZgT9//g7tWjTl6TVDjjVqI/7EUSic3fPtOZiUIiaickAkU44c3IcNa5bBw6cm3HzqQqlMxK1zUxEXcxuTv/kZAYGGNYMgZjdE6bwutO3UHTO/nYSn6hT4WGScRXygTsYpVTy+6NQ9Q8Ls4vkzcmtksQuN2H0uLxuGHti7G9a2nnKZoLYgLTX5eRVgfmxBLXoxzPr5O6z6azFiYyLldaf29ETBkv3hE/RfM9GHd9chIf4JevQynGSmvpJRedl8c/hnE3H/5l38bFkQRcXM8/8f+oLlM0w+fQ5fTPseM74an2fPR0REL45Ffp61EP17dkRqqgoFgto+r5aKuoS7V35B0WIlMObzyQZ3CkVlUV4v1dOmboMmcHFyxsb4SLxvWSDL8Y2qSHh7eqNajbqySuvbGXPwybivceXSeVhaWaFipapyZ+S8IPozTfj0Q/n/YiWANmJyNiU5FrZ22iu7Xtexw/vx64xvcerEIfn9xWOj4O3fDIVKvwtbe5/nY0uKwqM7y9GwSUutPUXp1Zy9eFkmpFrBBYPVPrCyfB4/D1GrMEP1BAM//BQndm5AwUCec8p7TEqZKVZE5Y5IbHz93S+oXbcBliyYi8uXZsLK0louJev/7i+oXrMuzFnXnv3w18I5+CLsEYaoPFHFwgGia9RJdTzmWIQjqGBhtO/cI0OQWrFytXwZy3dTJsDBuRDiY+4gOuyUrIrKHFCFPtiKmnUa5lkgp5GYmICBvTvLnlU+Bd9EobJ1kJr6DE+Dt+HGuW8QFXZK9mYIDfkHYQ/34p3Bw1G2fNYZQFOVX4kojbv3H2Dbv/sx3MIHRRUZA+YKFg7opnbHX2s3YcLoEXB3074kgIiI8pb4nFu7ZS8WzfsNm9avRkhsFPz8C+L9kR+jT/8hef5ZbEzEksH3PhqLb776HHZKBTor3OFuYYUwdQpWqyLwrzoWkz+ekmHZoNgApn6jZnk+FrFhzfGjB+DgXBRP7m+BT2CrLBN7UaEn5ISaaG2R13Zt/xsj3+sPJ7dSKFH5c9ja+yI28qLcPTDy6TEUrfARkhOe4Mm9dbCzU2PsxGl5PgZzNnfxX/CxtMEQtQ8s073vorfZSBRAf/Vd/Ll8NSZ9MlKv4yTTxKSUGWEi6vWIRMqbnd+SF02j7vxaDmdsXF3dsGDVVowa2hcTL5+Ho6U11FDjmSoVlStXx4+zFsmtifPbrZvXcPniGZSq9jUe3v4L189OQckq4+HqWUW+V2Lm7+6VOYiOuIzB76/P8+f/a8k8XDh7CuXrzJS9qzREI9fg64tx//p82f+hYOHimDTtZ3R9qy9MXX4notI7cvK0/LfZUKH9D5yGFk5YnBKGk+cu4I1G9fN1LERE9B8xOfXF5Onykl0Vs7nqM2CY7IP524xp2JgSBRdLa8QoU2BnZ4exn36js1hhw5q/4OxWAoElBuHKiU9x68KPskLJ2sZFvmdiou/6mSmoUq2OXNKYl5ISE/HFpyPg7lNX7vYn2mQIYkMY78AWOLf/XVw/PUn292rZtgNGjvkCAUGF8nQM5u7g4WOoq3SA5f8rpNKzs1CghtJe3oYoPzApZWbJKPZayBsMprQHnKu27JN9rE4dPyLPUc06DfKtIkqb0KeP5VdHlyIoU30Krpz4HJeOjoKdQwCsbd0RH31dNgZt074z6r6gIeirWrF0odzxL31CSiOg2Ft4fH8t3uzYQSakTPlnSJeJqPQy7+qYmQUscnQ7IjI+bHJuPEz58+9Vz8egYSNlb9J/tm1CWOgT+BTwQ4vWb8LJ2UVn43j6+BHsHAvDo0AtFK/4MW5d/AmhD3bA0bUkUpIikPjsISwtrfHrH0vz/D3ctWMzYqIjUbXK4LSElIatnRcCir+N4GtzsPXfEwhkMipfvCw0UsCC8dNrSi5WBTa3zsDSlr1NM2NSykQxEUX6oGkImtczaLnpASE8i70DT9/6qFDvN0SHn0b4owNQKRPh4lEBIbf+QrN86BMhEh0P7t9G0fIdtB5XWNrA0bUswsOemmRAntcNy19F7epV5NdD6lg0s8i6PO+AOhY2VlaoVrG8zsdGRPmPE29kzETTc31WUPv4+uHmrYsynilQsC3cC9TF0+DtSIi7C0eX4kiID4abU0y+NH2/e+cm7Bw8Ye8UpPW4q0dF3FWmysQVmJTKdXyWk5isbu3qOLB1D/qr1VBkilOT1CocVySgX52auXtyohxS5PSGZByJKM1FBGaaC5G5KFa8FMqWr4JHt1dApUqVyR83r2ooVmEkilf6VC7fc3J2Q5Pm/zUczyviuRwcnJGUGJbtbVKTwuDopLtZT10EOpqLIIIezUUfxHbFLRrVx0KLSNxTJ2U4dkWdgFWIQveO7eDpkX+7hxARERmjjl17IjbqhuzfJNjYuiOweE+UqDwW/kW6Ii7yArq+1TtfntvZ2QUpyXFQpj7TejwpMVR+NaUYytAM7tsTj5VJmKcKhTJd2VSKWo1f1E+QCBUG9Oym1zGS6WJSysgxEUWU0Wfjv0ZczDVcOT4GUWGnkZoSj7jo67hxdgqeBm/FmLFfysai+aFVu44IC9kGZWpClmMxERdksNeqbUejfssMLRGV2W/fToZv4UCMUN3H16qHWKAMxQT1Q3ysDEb5imUxddzH+h4iEREZyefciy6mpkGj5qjfqDmun5mIBzeXISnhqZzMe3x/My4fH4nAgoXkEsP8ICrYVcoUPAneluWYqNx6cm8DSpWpgIKFiuTL8xNQrVIFTJ84Fn8jGu9a3Mdc5VPMUj7BANzDIYtnmPPjVBQppL2Sjeh1cfmeEeLSPKLsVa9VD3MXrcHX4z+R/aQ0PL19MWX6b+jUrVe+nb4Bg4djy6a1uHpyLAqXHQFHl6JQq5WIeHIUdy59jzLlKqNR05ZG9/YZwtK8nPLy9MDOdcuwcsNmrFi7EadCw+HvXxizenRGpzYtYWtro+8hEhGRAcgusZTTz7js7h/pWAzGuqHPzDlL8M3kz7F+9WLcu/pHWiV442atMWnaT3B2yZ+dawMCC6JLjz5Yt3o2LCws4RPUGpaWtkhKCEXw9QWIDD2JyVOWmWT7A0MysFd3VK9cAX8sXYGjx07Jn4kuDVphUK8eKFW8qL6HRybMQm1iHV9jYmLg6uqK1fuj4GBCJZ7cOY8od8SvtnOnT+BhSDDcPDxRo1a9DFsq55dTJ45g5HvvIDz0MRyc/GTVVFJiFKrVrI+fZy2Eh6cXjIExJaKI8ltMbBwKVa2P6OhouLiYTmxhSjGUpnUBkb4/2xKTlUafpIqMCMep44eRqlSiQsUqOtnpTuxAOGncKGxYswxW1g6wsXPDs9jHchfCcZO+Refu+bN00NTltKcU6YaxNjqPP3EUCufct7+ISUhEoVHTXho/sVLKgDERRfTqxGxa5Wo15UWXqtWogz2HL2DPzq24dOEsbGxsZHVUhUpVYeiYiCIiY8Sd98iQdoRN/xwiQZX++Y0lQSWamTdv1V6nzynipSnTf8WQD0Zjx5YNiI2JRlChImjdrpNOdyEkIt1jUsrAMBFFZPxERVbLNh3kxdAxEUVEpoBVUqTvZFRuElTGkpzSB9E36t33PtL3MIhIh5iUMgBMRBGROQbrRERE5vL5ln4sxlg9RcbHFBvyk2liUkpPmIgiIl0z1ECdiIjInD7jNONj9RTp6meNyJAxKaVDTEQRmY5HDx8gKjICBXz9Dbp5uTEF6UREROb0OZc5OWUOVVOJiQm4f/c2LK2sULhIcVhaGtd7RkR5j0kpHSej2POAyLidOHoQP30/BWdOHpHfKxSWaNK8NUZ/9iUKFy0OQ2DMAToREVFuP++M/bNOM35T7jklklG/zpiGVcsXIy42Sl5XwC8IA959D73fGSo3qCEi88SkVD5hIopM1d07t7Bl42pERobDP6Ag3uzUAy4uroiPj4OziyusrEz318q+f//BB4PehqNrSZSsMh52jgGIi7qKI4dXoUfHN/DXuh0oWrykXsbGhuVEZI648555MsbJl+TkFPz9z26cPHtexkpN6tVGo7q1EBsXD4VCARdnJ5NNTiUnJWFwv244e+oEfAp2RJEK9aFSJSP0wT+YNnmsjC3Hf/W9vodJRHpiun896gETUWTKUlJSMPmLUVi7cgmcLK3hpbDBQ2Uifpw2AYAFVGoVnBwc0bF7bwx+fzS8vH1gSlJTUzH+kw/h6lUdpat9DQvF81+fzm6l4e3fDBePvIepkz7DvCXrdDYmJqKIiFiFbm6MsTrq+Jlz6Dv0IzyJiECQtT2S1Sr8+udi2FlZITE1Vd6meoVyGD74HbzZqrl8baa0pG/d6mU4dfwwytX+Ca6eFdOud/OqBie3svhryQw5yVmpag29jpOI9EOhp+c1qUSU5iKW5mkuRKZm+pRx2LBqGYYpfLAYhfCrOgCLLQqjE9xkQqqNhStaJ9hi05IFeOvNJnjy+CFMyYG9OxEW+ghBJQemJaQ0rGyc4Vf0bRw+sAchD+7n6zhEgKq5CCJw1VyIiIhMmTEmpO4Fh6Brv6Hwik7ELMtCmKUOwjx1QXxjGQiXVMADlvjAwgepl++i3/Ax+P73P+T9NJ/t6T/zjdXKZQvhUaBuhoSUhm+hdnBw8sfqFYv0MjZTZew/M2RedJKU+u2331C4cGHY2dmhVq1aOH78eLa3XbhwoVxTnP4i7meoiSiBiajci4+LxYql8zGoT2f07toGX0/4GDeuXc7z94ryRnhYKFYsmY/eFh5oq3CDjcXzXx1OFpZ4x9IbjSyccUodj94KT/yMACQ/DcM3k8aa1OkXTTmtrO3h5FpC63EX9wrya0jwvTx/biaiiMyTqcVPlDcuXTiLiWM/lPHTkP7dsG7VUtmvx9QZY0JKmLN4ORTJqZgIPwRZ2MrrxL/P8hYOmGQZiEgoYWlhga8tAtDTwgNTZvyGKzdupt0//ZI+Y000BN+7A2f38lqPWVhYwsGlLO7duaPzcZk6Y/u3QuYr35NSK1euxKhRozBx4kScPn0alSpVQsuWLfH06dNs7+Pi4oJHjx6lXe7dy/s/8vIqEcWqqNy7ffM62jarja/Gj8GVa3G4/9AB61avQ4eWdfHH7zNy/XjJycmyOiUiPOwVRkM5sXfPdiiVqWhl4ar1eBuFG54gFbeQBG8La3RRu2DXjs0IffrEZE6w6JeVmpKIlKTnzTkzS0p8/lqdnF3y5PmYiCIyb6YSP1HeUavV+GHaRHRr3xibN21H8CMHXLgUji8+HY4OLevjYUhwrh8zMiJcxlCi54+h0nweGmtV8MYtO9BY5QgHi6xjD7KwQUULBxxSx8nvuys84WFpgwV/rclwu/Sv3RgTU04uLkhKyD4mTEl6CmfXvImfiMj45HtS6scff8S7776Ld955B2XLlsXs2bPh4OCA+fPnZ3sfMXvg6+ubdilQoEC2t01KSkJMTEyGS15hIip/+hIN7t8NzxJtULXJMpSt+R1KVf0CVZqsRGCJvpjx3SSZzMiJuNgYfD9tAhpUL4U36ldE/WrF0bNzS9mMml6NUqmUwemjhw+gUqnSrn8WFwdrhSWcsvmV4Y7ngVKC+vl9qlo4QqlS4u7tGybzVjR9ow2srW3w6O56rX8oPLq7DgULF0eZcllL03OKiSgi0lX8lN8xFOW9dauX4s85P6NwmWGo0ng5Slb5AuVq/YgqDRciPDwB7w18W34e5cThA/+id7c2qFe1mIyh6lUtgW+/+hzR0donXvTFmKqjoqJjcOdeMGJinyeYNOKeJcDdIvs2viKG0sRP1hYWqKCyxeUr17Xe1lgTU+06dEH4o11ISY7Ociwu+jqiw8+jbfsuehkbEZl4UkpUsJw6dQrNmzf/7wkVCvn9kSPPt1PXJi4uDoUKFUJQUBA6dOiAS5cuZXvbadOmwdXVNe0i7vM6mIjKX7v/2YKHD+6heKUvYOfgl3a9QmGFgiXfgZtXZcyf+2uOlv/17dEOi+fPg7PXGzK5VaLy57h3PxHD3umONSsW5/Mrgck18Z436ye8UaecDE6b1S2Pto2q4K8l82SAW7R4KSSrlLiORK33P69+Jn+ZBFhYy++joZRf7exNp8Gam7sH+g16Dw9uLkHwjSVITYmV1yclPMWtC98j4vEhfDj681fa0pg9oohI1/FTfsRQlH/EZ/H82b/C07cBAor1kEueNBycC6Fo+Y9x/eoFHD28/6WPtWn9SrzbtzNu3YpGicpjZQzl5tsWy5csRu8urQ0mMWUsCakLV67h7SEfoliNRqjavD2K12iEQSM/w627zysVSxUrggsW2uMnpVqNS+oEWTGlEW2hgoODfbbPZ4zL+Xr3HwJ7e2tcOT4GMREX5c+zWq1E+OODuHbqc5QoVR5vtGqv72ESkSkmpcLCwmTlReaZOvH948ePtd6nVKlSchZw48aNWLp0qazWqFu3Lh48eKD19mPHjkV0dHTaJTg496XLTETpzqH9u+HsVgyOLll3EhF/zHv5v4Gzp44iIeHZCx9n3uyfcfPGDZSvMxNFyr4Hd5+a8AlsgbK1foJvwfaYPH6M1uV8ItC/evmCvBhyqbouiX+jYz4YgJ++m4SKoQn4UhGA8Qp/FA6JlEssxaV2vUYI9A/CQkTIHWPSi1SnYrUqEtUtHOH1/6TUNlU0/Ar4oWz5Snp6Vflj5Mfj0f/d9xFycxFO7uqC0/92x6k9byEmdC8mTfsZrdt3fqWqKDYrJyJdx095FUMZyq7Hpk5sHnLn9jV4B7TQetzFszLsHX1weP+eFz5OTHQUJo4dCa+AN1Cuzi/wCWwpY6jCZYagfJ1fcf/+fcz+ZXq2Y7h4/gwePwpBfjOWhJTYVa9lt764uO8Yhlp4Y4oiEP3UHji0/V8079wLV2/cwju9uuOUMg4nVfFZ7r9BHYlQpKKV4nl7hBB1Ms6p4tGuZbMXPq+xLefz9QvAwr82wdVFiQuHP8DpPZ1xcldHXD35BcqWLYk/l66Dje3zflv0+ozhZ4IovexrSfWkTp068qIhAqoyZcpgzpw5+Oqrr7Lc3tbWVl5eN5BhbyjdEEG2QvHfbFBmFv8/plIqX/gYq5YvgndAKzi6FM94fwsLFCw9CKEhO7Bh7V8YMHh42rLBub/9gGWL5iEq8nmyysHRGd7ePrCytoGHpxfe7Nwdbd/sCju77GenTNE/2zbhn+2bME7hjzoKp7Tra8EJFWCHX5f+iTbtO2PKj7MwuG8XjFSFoK3KGX6wxk0k4m/V8xnVwQpvWX6+ThWBf9UxGP/BBFhaGnYwmVuiUuHjz79C/0HvY/uWDYiKDId/YEG0atMRjk7OuQoSDD3QJiLjktv46XViKENiLvGbiH0EhaX2GErEPyK+SlWmvvBxNm9cjZTkFBQuPQQW/9+0RMPBuTB8AttjzYql+OiTibCxsUlrrP7DN1/i6KG9abf1DygEWzs72NjYonLV6ujZZyBKli5nVgkpUe0z/JMJKJxqia/gDzvF8/NZCQ5ornbBJ89C8PHEqVi/eA42bd+Fr/ceRFOlM+pYOCIJauxRxeA44tHVwh3FLexwU52I7/EUgb4F0KVd6xyNQZyjxGSlPGeRjlknfA1JqTLlsX3vSRzavwfnz56EpZUV6jVoigqVqup7aCbJ0P/9EOksKeXl5SX/KH3yJGNjO/G96HWQE9bW1qhSpQpu3vxvF4pXxUSU/lWsXB0b166QS55s7X2yHI98cgBFipaCg+N/yZHMYmOiERkRitJFqmg9bm3jCie3Yrh985r8XswWjxkxCLt3bEGBgu0RVKapCN8Q9uhf3L+7ATZ2PgiPcsT4T0dg0bzZWLB8Azy9vGEuVi35ExUsnVDHIus5b2nhig1WsVi5bAGmz5yHZet24PcZ32LOnh1QqVWwVCighAquljb43iIU99XJSEQqho34BG/1HghT5e3jiz7vDM3RbZmIIiJjj59I/wr4+sPL208ud3L3qZXleHzMbcTHPkClKjVe+Di3b12Ho0sh2Nh5aj3u6lUFIbdXIDzsKfz8A3Hu9An07/kmrO38UaLSZ3BwKYZnsXcQcmsFEh7ehJt3TWxcvwmrli/AF5Ono2efQWbzB/WRk2dw/e49TFUEpiWkNMTuxD1Ubph+4hTuPQjB4t9+wK/zl2Deor/wT+hDeRtrcR8VcEaRiOMIxn1lAooFBWLl/N/g+ILle8acmBKTew0aN5cXIjIejjVqI+Hq87+tjW75nphhqVatGnbv3p12nUgQiO/Tz+a9bGbowoUL8PP7r/9QbnHXPMPRvmM3ODo6yx48SmXG9fVhD/9F2OMD6PPO4Bf25REzc+J4clK41uNqtQopSZFwcHCU3+/dvR07t21EySoTUbT8h3DxqAAXj/IoWm44Slf/GkkJj1AgqB0qN5iHB8GP8fGH78Kc3Ll5DeVV2c+8llfa4M71q/L7suUr49c//8LR83ex69AFHL/0AGu37EeXQUNRtnMnDBgxBjsOnMPwUa/WW8lUcGkeEZlC/ESGw8rKCm/3G4jQB9sQ+fRYhmOpKXG4c+lHePn4oVmLti98HAcHJxkjiX4+2iQnRciv9g6OshLoy3GjYOtYBOXr/g6foFZwci0h2yVUqj8Lzu5lZQxVudFf8C3cWS73P33y6Gu9Ts2SdmNw885d+bW8hfYEUgWL52V8N27fk0nij4YMwPkD23Fx/w5cPbwLt08fxM9TJqBel1Zo1q0dlv7+I47+swHFChfK9Vg0y/mMqc8UERkXVWyk8S7fE9sZ9+vXD9WrV0fNmjXx008/IT4+Xu4mI/Tt2xcBAQGy2aYwefJk1K5dG8WLF0dUVBSmT58utzQeNCh3My/udjFwtDOv0m5jIJY4/TxnMd4b8BbO7u0FD7/msLZxQXT4SUSFnkHbDt3Qvdfzn43s2Ns7oH6jN3DmzGbZP8pCkfHHWARrz+Ieo0WbDvL71X8thotHGXj6NcjyWB4F6sgk1ZPgLShXazoKln4PRw99hRvXr6BEyTIwl/ckPCJr/y2NcAslHJ0zbtPr5OwiL4LYbe51dpwzFZmDQGMJqonIMOkrfiLDNWDwCJw9dQL7//0UHgVqwtm9spygE7uaWVuqMXvpOpn8eJEWrd/EvFkzEP7oALz8G2c4JhJVT4P/Rs3aDeDm5o7LF8/i2pULKFPjG1haZlzmqbC0RVDJ/rh0dDTiY66jSNn3ERN+Eov+nIWq1Wu/0usztmSKk+Pzyc8IpMILWc+7uF5wdvzvDxFRARng91+vuL7dO8tLXjGmqinKH8b274go3yulhB49euD777/HhAkTULlyZZw9exbbt29Pa94pGio+evQo7faRkZFyC2TRB6FNmzZye+LDhw/L7ZBzw8X+eTKKCSnDU6deI6zfdgCdunZCUsw+hIesQtFC9vh+5p/4dsYcWdr7MkM+GIWEuHu4fuYrJCU8X97wfBePA7h1fhqq16yPajWezybfu3sHjq7Z//w4u5dHYvzzpp2efg2hsLTGsRzsXmMqWr7ZGQcVzxCtZdb0sToZp9TxaPVm3gVMploRJbBhOREZe/xEhl1B9+sfyzBl+m8I8FUh7MFKpD47ip69emHD9gOoVKX6Sx+jfMUqqNewOW5fnI6wh3uhVj1PnCQlhOLG2WmIjbyKoSPGyOvu33teCeTiUS7b+EkQMZToT+Xh2xRHD+0z6T5S6TVrWBcOtrbYoorWenyLKgoFPDxQs6puN30xpgbolD+M6d8RkWChFrW5JkQEYWJb4+MX7qdVcpBp2rX9b3w2+j0kPIuHk2shpCTHIPFZOGrVbYSfZi2Cq6ubvF3Pzi3x4KG1XKqnzbXTk5AY/xCVGsyRwdmxHW3wyReTc9wzyNiFPn2MDm/UhldcMj6Al2y2KX4tXEYiZiIUah9PbNx5NEeNvM0BK6KIzE9MbBwKVa0vd6hzcTHd2EITQ63eHwUHJ8N/naI9Aycfcy8uNgajPhiIg/t2wtbeHTa2boiLvg87Ozt89e1MtHmzi7zd4YN7Mah3R1RuOC/LxjLCs7h7OLO3H8rUmCYrz4NvLEHEwzU4fuGeySekNL7+8VfMmP0n+ll4oo2FG+wtFIhTK7FOFYlV6gh8O+EzDO7zll7GJiqmBFZMmRdjWgJrjmxunYGlrXFuqhV/4igUzu65uk9MQiIKjZr20vjJ4HbfI8qp5q3aY1+DJti6aS1uXr8CO3sH2UuhYuVqGW7X9s3O+GbyOCTEh8DeMSDDscRnj2XT0EKlni9viHh6BEplMqrVeLXSc2Nt2v3n8k0YMehtjHx0H77W9lBCjdDURBQrWgK/LVhl9gkpU0hEiX40kVHRcqctp3RLCYiIyLyISdu5i1bLXfV2bv8bz+LjULR4KbTr0DXDhG71mnXh7uGNh3fWokSlT7M8zqM7a2Fl7QI3r6pyMivyyb60KnVzSEgJn498D/EJCZi7aDlWWETB28Iaj1XJUCuAccPfx7u9e+htbFzKl3cSEp4hMSEBLq5uJrezNJEhYFKKjJqjoxO69ez3wtt07NJT9ji4euJjFC77Idy8n+9MEx12GrcuzICNrQcKBLVB4rNHuH/1d1SqWks29DYnoifUtgNnZVP4M6eOQWGhQK26DVG3QZMcLac0RaaQiBISEhPx67zFmLd8DZ6GPpXXNaxTC6OGDkCjull3cCLDTy6a679JovRYJfX6ylWoLC8vWi44/KPPMHn8aFhaOSKwWE+5a59ohh5yayUe39ske0lZKGwQfGMRYqNuos+A73M9DmP9fBXE7+Np4z7GsP69sObvbQgLj4C/bwF0e7MNCnh76Xt4TEy9JhETz/n1RxzY+49MvLq6eaL72/3w7rCRBrkih0s2Xxw/iU2YzHkjJkPG5XtkFkIe3MfIYf1x6cJp2Ng6Q6lUQZkaD0trJ3j7N0NqSiwinhyAn18AFq38G/4BQfoeMumBqSSi0iekOvUbhlPnL8HTvyXcvKsjNTkGoSFbZd+QX6d9ibe7PN8QwFzduReMRavW4drN27KC7M2WzdG6WSO505WhePw0FL/NX4IVazciLCoaPu7ueLt7J7z3Tm94e3roe3hmgcv3DA+TUrqz4I9fMPP7qUhOToaNrSuSkqLk9c6uZeDsUQHRYYcRH3MfH348HkPeH53jx+UyI93hUr7c2/3PFowc1g/2zoXgE9gONrZeiA4/i7CQbShSrBiWrNoMZxdXGBJd/ptKTk7B5p17sGXnv3iW8AxlS5VAv+6dUTAw46oUfdv8zx7Mmr8Ex86ck9/Xq1EVwwb0RaumDfUyHi7f045JKTJYhw/8KyucTp84ClhYoFad+ug3cBhq1K7/So8nZjjOnT6BY0cPIDU1FSnJSTh+5LBMWLl7eKJDl+7o0r2PLM0l801GGVIiSvzMBoc8RKpSiSB/v5fuqpTZD7PmYdrPc1G29gy4uP/XqFatVuHW+e8R8XgXLh/YDi8zTWz8PHcBJn0/E84KK5RS2SBKocYN5TOULV4MaxbOgl8BH30PEXfvP0CbHv0QFxmDpipHFLKwxR11Ev5VxMHNyxPbVi9CoJ+vvodp8piUMjxMSmUvKjICyxbNxfrVfyEiPBRePr7o0qMXevYe+MoxTkx0FP7ZtglPHj+UlQaPH4bgyKEDUCpTUblqdfTqPzhXS/eMfdmeMSenzKXHVGxMNMJCn8LVzR0enrmrWnv2LB6NapaBvXNllKwyIcNO3/Ext3Hp6HC83bc/Pv1iCswxKRX88BG69huK63fvoZSlA1xUFriiSMIzlRJTx32MIf3ehiGY/MNMzJg9H+UtHVFP7QDRSPugxTNcVsbj8w/fw8cfDNb5mJiU0o5JKTJIc377AT9P/wrObiXg4dtY/hEd8XgP4qLvYOyEaegzYJi+h0hGTheJKJFUSkpOhq2NTa7KhcX9Fq9ah5/nLsad+88bxnq4e2BQr64YNXQQbG1tcvQYZRu0gtqmGopX+iTL8ZTkaJza3Q0TRr+HEe/2h7nZuG0n+o/4GN0sPPCWwgO2Fs+XxF1XJ2IqHiOoVDHs3rBc72Xe7XoOwJ2zl/GN2h8eFv8FxaHqFHyGh6hQpxpWL/hdr2M0B0xKGR4mpbR79PAB+nRvi6ePn8DTrxnsnQvjWewthD/6F4FBBbFk1RZ4ees/4c4qKf0xlsRUSkqK/AzObeXyg+C7+Pn7KdixZSNSU5PldXXqN8Hwj8aicrWaOXqMtauWYPynI1CtyXLYOfhlOX738mxEPd2GQ6euw8bWFoZCF/+ulEolGrbrjog7D/A5CqCohZ28PlGtwhJVGDaqo7Bi7ky0bKKfSiSNg8dOoH3vdzFA4YXOCo8M8fEKdQSWqcKxc/USVK9cQafjYlJKO8NZn0D0f6dPHpUJqaAS/RBUsn/aH4WBxXvj7pXZmDZ5LKrXqif7IBHpKxGVlJSM5JQUueQrc+LiXnAIfv5jIVZu2CJLmt3d3NG3e0cMH9gXnh4v37Viwrcz8Oufi+Hl3xhlagyBQmGDiCeH8MOshTh2+gJWz5v50qqp2Lh4PH7yBCWrZGz8r2Ft4wpn91K4cuMmzNHPs+ejqqUj+sIzw/tX0sIOI1U++OLyFRw8dhINaj/vQZc+mDl2+ixWbdyK8IhIBPgVkEsgy5cumedjvHrjFg6dPI1PFX7wSDdLK4hmuj1Vbvjp4GFZTVW4YGCePz8RGZ/Px7yPyMhEVGq4IMMf04HFeuHysZGYNG4Ufpm7VK9jZN8b/RLxj3gP9JWYEp+jorm+ja1dllhGHNu0bgUW/TkbVy8/X25Vo1Z9DBgyHI2atnzpY9+/dwdvdXwDScmWCCwxAE5uZZAYH4KLF9ejb492mL1gpeyX+jI3r12Bk0uQ1oSUINohhNxegadPHyEwqDAMga4SvbsPHMblm7cw3TIoLSEl2FkoMEjhjVtIwc9z5mtNSkVFx+CvdZtw4ux52Y+tcd3a6NyuJRzs8343unlLV6KQlT06qTPG3SLm6wEP7LaKw/zlq3SelCLt2C2VDM7yRX/A0TkIQSX7ZfhjUfx/4dLvws7BG38t+VOvYyTjIT6kNRfxYa25vKpDx06i64D34VehFgpWqYdKTdrhl3mL5Np64dK1G2jU8W38tWE3PAK7oETlsbBzb4rfF65C0y59ZH+gFzl78bJMSBUu+z5KVf0SHgXqyuCnaPkPUbrGN9h/5BiWr9v00nGK6izxgS8qorQRgV9qchTs7YxzW9rXIRrRnrl8Bc3UzloroSpZ2MPHyhb/7D2QpUdXr6Ej0fqtd7B99d94sPMIVi1djQbtu2P0xCmyiWZeOnf5qvxaw8JR6/EaFk7y64Ur1/L0eYnION2+eR3HDu9DUMl3s/wxbe8UhIDi/bBn51ZZTaUvXLZnWIkpXYqPi8UvP05FwxqlUaN8EKqW9sOHw/rh8sWzaXHJl5+PxNjRw/A03B7FKo5B0fIjcf1mFIYN6IE/58x86XNM/fIzJKfYoELdOQgo9hZcPSuhQME2KF/3dzh7VMLnYz6QlT4vI3b0TkmKhVqt/baa2MrOzvx2MxaxUaCVHUrjv4SUhoipmqqdcOTUWTk5mjmZVaFBS0yY9iNu7TiEK9v2Y8TnX6JK4zY4f+l5vJOXzp27hGpKO61xnsLCQh47e+5inj8vvRompcjgnD1zCq7edWHx/+U06Yk13a5etXH29Em9jI2MLxElvG4iSmPF+r/Rvs+7OHExFEXKDkfJKl8gyaI8vpz+C7q9O1xWTw0e/QVUCm9UarAIhUoNhE9gSxQtNxwV689DaEQiPp70zQufY+GKtXBwLAD/Ip2zHBPbbnv41ML85WtfOlaxxK95w/oIC9mqNaiKCT+LuJhgtHvj5TOGpkZUuAn22XwEigDGwcJSLr1M75NJ32DP3oP4TOGHOeogTFYE4E91IQxV+GDB8tWYMWd+no7T1ub5DPIzaE92PcPz99Xm/7cjIvN28cIZ+dWjQD2tx8X1oh3CpQvPkwD6wj5ShpWY0kVyKi42Ri4r/WPWTFg71kbJKuNl8vTwoTPo2aklDuzdJRuLr/5rEYpX/ARlanwD34Lt4Fe4I8rV/kWulvhh2gRcv3op2+d4/ChE7pLnV/RtWNtm7J2mUFijYMlBePokBAf37XrpeJu3bIekxEhEPD6U5ZhInj0N3oyKlWsYxFJYfSyrFPFTdu0NHP4fW4nbady8cw+9h45EmSRLLFAUxlSLAHxnEYi5loXhGp2ELv2HIDJK+yTqq7Kxtck2fhLEsZy0wyDdYFKKDI6lpRVUyqRsj6tUyQa1MxYZdiIqr4Lf0PAIjBj3FbwDW6F83dnwK9IZ3gHNZSVUmZrTcfDoSYz/9kdcvnYNQaWGwMrGOcP9xay1X9Fe2LprLx49eZrt89y4fQ8OruVhYaF93GKm79bd532mXmbkkHcQH3MHN85Ok1t4a4KpqLDTuHnuK1QqVw6N69WGuRHbdBfw8MAJdcZZPI1H6mTcS3mGyuXK/Hfdk6dYsW4T+sID9RXOcpZNsLKwQDuFG9pauOH3eYtlYjKvNKxdE7bW1til1h6o7VLFwNHODnVraF+iSUTmxcryeWyUXQwl4id5Oz3FUFy2Z3g0MVJ+vze//fQNbt28hfJ1fkWxCh/BO6AZAor1QMV68+DsUQWfjBwsm/O7epaXlU3pieSHaOdhZ++JlcsWZPscwffuyBjH1aOS1uNObqVgZW2Pu7df3ragXIXKqFu/KW5f/B4RTw7LZK6mQur2xRmICjuDocNzvtOkKf3bqli2DG4rExCm/i/plN4JxCPItwDcXF3SrpuzeDnslcBnFr5wT9cf08/CBuPhi6jo2BytAsiNVm80wUFFPBL+/96lF6dW4qjFM3kbMgxMSpHBadSkGSKf7INSS1CVmhKPqKcH0ajpG3oZG5lXIiq9ZWs2Qq22QJEy72Wp4nPzqgIvvyZY+/c/8teqm5f2JIG7T22oVErZKyg7zk4OSE0Oy/Z4UmIYnByfL9t6mTrVq+CPH6ciNuwgTu3ujouHh+Lc/l64dHQUyhT3x6p5M+USP3NjaWmJAb17YLdFLC6on2U4lqxWYTbC4ObijE5t/+tfsefAYaSqVGhu8V+Qld4bChdExMTg5LkLeTZOD3c39O3RGX+pI7FPFQOVWuwbAyjVauxWxWANIjGwz1twdtK+vI+IzIvYndjSyhpPH+zQelxcb2trj6rVdT8ZwWV75puYSkpMxJoVS+ET1AGOLsUzHFNY2qBwmQ8QHRWBC2dPw9VTeyNyhcIKzp7VcenC+Wyfx9HJKS1O0kYklJSpSXByzjhpmJ0ff5uPylUr48qJz3Fmb09cPDJMbhATFrIdX079CY2btYIh0VUFYrc328geULPVoUj5f1yicUYVj33qOAzq2zNDfLntnz1oqHJI21QmPbGJSzULB2zftTdPxzmwV3eorCwxVf0IkerUtOtFMm0KHsPazhb9enTJ0+ekV8dyEzI4b/cbjJXLFuLGma9QvNKnsLJ2TvswuXF2CiwVanR/2/x2CyNoDZp09SF86dp1OLmVzlIBpeHqXQM3QnbK/1emPoOVddbEUWpKrPxq+4KdWjq2aYEd/34htxx2dCma6f5xiHi0EwN7ts/xuDu3bYmm9etg5YbNuHz9Buxs7dCmeWM0rFNT7zvL6dOIwf1x4MhxjDtxGnUsHFHZwgGRUGKHKhpRFirMn/Id7O3+65eQlJwiitWzXfLnhOc/h0lJ2Vd5voqvPhuNJ0/DMP2f3VhqFYUApSWCLVPxRJmEbu1bY/yoD/L0+Uj7Tjk28Qk8NQa28x5l5e1TAB06v4VN6xbA3qkQ3H1qyd/zonok4vEBhNxahl59B8LFNePSJl3hsj3DJd4bsStffjRAf/QoBPHxMSjsnXHjEA17p0A4OgdApYxOi5O0UabEwi7d53JmpctWRGBQETy+tx6unpWzxDiP7/0tV2M0btY6R+MW/04WLN+IM6eO4Z+tGxEfH4cixd5Gxy494eHpBXPl4uyEP376Br2HfYQhqrtoYeECV1jilCoexxAvN34Z2q9XhvuIvquO2awAEJzUCkQn5m38VDDAH3/98Qv6DB2J/gl3UN7CAWqocUmdAGcnJ6z8YyZ8fbzz9Dnp1TEpRQancJFi+Hn2Iox8r7+ckXDxFFUnakSHnZTrg3/78y/4+XOnKXOir0RUeiJBoUzJ/g+h1OQYWUElYqCnwdvgX7Rblts8ub9V7sRXrWL5bB+nU+sW+HHWfFw79RmKlv8Erl7VZGAVH3MTdy7OgI2VCkP69szV2EUJ9ZB+b+fqPqbOxtoadna28v0SAcphdRxsYYGCsEGSBfDtz7PQtEFdubuiUKFMKaigxhn1M1TT0nhcLAW0VChQpmTGWeDXJfodLPz1e7lTzV/r/saT0DBULOCNXl06oOoLfo4ob5JRGpa22f8hRPrhan79hXNk3KRvZW+dwwc+g7NbMdg6FERi/B3ERd9F0zfaYvTYyTofE5ftmXdiSpNISs0mhlKrUuWkW4mSJXDzxm4ULD0IlpYZJ++SEkIRFXocTQdn//MrKnM++OhTfDZqKO5c+gWBJfrAxtYdytQEPLm/GQ9uLETvdwbD0yvniQgRf4nKQn1UFxryvy+R1BGSoMIqVQRSoIYvrFEItrhw9TrWbdmOtzr9N4FaoVxpnD52Htoi0VS1GmcUiehUsWyej1NMwJ4/sB0r1m/GoeOn5PvZu3Z19OjQTibXyHAwKUUGSZTE7jxwDmtWLMLJY4flL5Fa/caiS48+Zj07oWtPHj9EdFQkfHz94eaWcUtVc0hEpde6WWMsWb0esZGX4OxeLsMx0Ug87OE2tGzSAJ4e7li5YR5s7Lzg6ddIJqpUqhQ8vrcJT+5vxPjRw1/YWFEc27BoFnoNG42zx8bA3sEDCoUN4uMew7dAASz7YxYKBgbo4BWbtl37D2HXgcMYr/BHLUsnWUmgmVW9q07CyFt3sHjVWrz3Th95ndgyuGLpklh4Ixgl1HZwSTfj91idgtUWUWjTrDH8CuR901MxrppVKskL6TIRlW5nytSMyzyJDJW9vQPmLlqDwwf+xYa1yxH69Cl8fWuiU/efUatOQ71VyOr7M1zXEpOScPf+A9m/q2ihIKNZKp8fiakCvv4oVbYingZvgadv1p/B8CeHkJQYjcHvjcaYDwfh+umJKFbhY9jYecrjCXEPcOPcZLi6uaND1xdPsL3Z+S3ExETj+6kT8OT+Jjg4+SExIVwmpnr0GoAxekjKmuK/ry++no7isME0RSCs/1+NqanK/F71GOO+no5ObVqmxbui1UCvQ0exQxGNlgrXtMcRt1+mCkeEOhnv9Mw6mZsXXF2eT8xyctawWajFT4MJiYmJgaur6//YuwuoNrIuDuD/meDu1lKou7ts3d3d3d23rl9dtr51d3d3d/dSKO4uIZnvvKGwUEILLSHC/Z0zh90EkiFQcue+++7F/RfuMDFV3PuDEPJzD+/dwspFc/Dw4R3x/yW8RJxEMnLCdLi4ptxSps2JqOTYCOFqzTriq1co8pWcJiam2BuwNC4Ebq/XIMDzIk7u3oTSxYug94iJOHPpCoxMHKBvmAPREV8QEx2E/t06Yv6U8ekKTtmf5jsPnyT0MpLJUK5kcTSsXZ2a/GeSnsPG4eXFm1gu5FR4kbZA7o3APLa4deZw0m1vPnxE0469IUTGoK7cBDk4XXwSYnGZj4Ctgx3O7N9GpeDalIhKJiwyCnZNeyM0NBRmZtobWyTGUAeuh8DIxEytt+9RpZRmyG69pKJjYrDgn/XYtvsAQiIixNtcnBwxpF8P9O3SQWO2zbPEFJNZialzp49h1OAecMrdTmxarqNrnDB4xf8+Pj6bi3Lly2HjzsO4df0yRgzqgZjoaJhZFYUglyI06DVs7ByxYet+FCpSPF3PFxISjNPHD8LzmzssLa3RqGkr5HB2gbZRxb8v1he1cuM2mMI7oRKfutrIQ4jDIJkbdqxeiqb1a4u3sZ/12OnzsHnPAZSVmKCyYCRWV13jIvFWFoVZE0ZhWN8eyA5YzJFWrKHuIh/cBW+asSKFsOgYuIye/8v4iSqlCCEpXLtyHkP7dkJe6GMM7wBHThfvhRgcO38OnW9fw64jF+GaJ5/SklHqGriy5tiHNq9C2z7D8OL2UJiYu0BHxwzhIW8hkfBYv2Su2Fic2bV2qbjdav+xUwgMCkZOp6Lo3KY5CudP/+vGAtcq5cuIB8l8Xl4+cJXpgpMovkDIzUrQfVJOSWQ/vyvH92LVpu3Yd+g4wqKCYGthgQGdemBwz65ilRzRnkQUISRzqOv7emZj01fb9x6CB4+eopFghkqSnIgTBFz2CcP4mf/DJzd3/G/KeGTHiqkGjVtg4tR5WDh3Kvy+nYSJeX7ExQYgKtwLZctXxZLVm8XPq1q9Ni7ffoFjh/fi2eMH4CUSVK46BI2atYaBQfr/VrPq/s7d+yE7yOp/X4kTpHNzivujOnN60OV4ePn4pohpF8+cjIplS2HD1l1Y9fK1eFv1CuUxq2931KtRLcvOn6gnSkoRQpJIpVJMGzsEpQVDTOEcocMnXLAX4gxRUzDD2AhPLJg1CWu3HsgWiagfOTnY48bxPeLWrzOXriImNg5FC9ZF59bNUyQkaLuV+rO3s8EHPu0+DGylz9baSmHjzIXTJmLB1AmQSuOhp6er5DMlykhEMZSMIkS5slsvqZ0Hj+L2g8eYJ8mBYvz3pmccUAbGKAADrN+2Gx1aNEHp4ilbAKh7YiqzdO8zGA2atMTh/Tvh9uUjTEzMxGQVmxqZvIKMNRjv1mugeBD1Y2tjnRQn2XOpYyAfIQ5SQQ5bm5QxFPsZt2/RRDzi4+PF/2cLvoQwlJQiKsXKOZ8/eYjTJw4hNDQYOZ1d0aptZ60ssdUEN65egH+gP6ZKXKDzQ4k566HTRm6OVdcuik1UHRxzZItE1I/YG2iDWtXFg2iuDq2aodvFK3jOR6FE4sVDsoDqFheBie3SnvLJgilKSKk/SkQRbRYeForjR/bh1fMn0NXTQ7UadVCrbmO12uatqe/1v4Nt2avIG4tTvn7UhLPAUZ0wbNt3SGOSUok/v8zuLzVouGZUi6k7VSV9ixbMjyL58uLQZ2+UFowg+eF64aA8GGbGxj+Nk9XpbxRRD/QbQVQmOjoKo4f0xrXLZ2FobA99Q3tEhp3E2pULMWTkRPFNS1P23muLr26fYCTRQZ40SnKLcoYQ5AK+ubulOymlDYkoon1Yf64qZUtjzpMX6Ca3RC3ODDrgcEeIwHYuCDmcHNGrY1tVnyb5DZSIItllqz2LoRJ67xSCPD4aB/ZsQy7XfNiw7QByueRW6flltyop5rO7BzrCVOF97MK9kEwPnz5/habJ7MQUyTyqiKvZtdmsyWPQoe9QzBa80AlWyAcDeCEOh+XBuCCEYeHYiTAypK3xJP0oKUVUZuqE4bh14xoKlpkBa0c2jYMXp2N4ftqLVcvmw87eEW07dqefUBYyNTVHjEyGMIksxXSxRAGCVPz4qyEC6tywXJuFhoUhNDxC3HZm+H0EM0l7lW7fptUYM3UONp46i/Uy/6T7alWpiFULZsHCXH0bPZOUKBFFspN3b15ieP9uMLMuj6JVRkLfIGEqcUToe3x8Ohu9u7TEqYv3oK/i94Hs9t5vamwM/9iEOEmRAF6G3Br6vqLtiSm2nczfzwe6unqwtrFV+0VxVSd96/xVBTvXLsf46XMxxscj6XZrc3MsGf03endWziQ9or0oKUVUwsPdDWdOHEKeYqNh41Qz6XaJjiFyFeyF6EgPrF+1FK3bd9WYMbraoHa9xpg9ZTROyUPQiUvYM558q+UJhCK3Sx4ULFws1ddSIkp17j95hgUr1+Pyzdvi/xsaGKJjq6aYOHwg7L7v/SepmRgbYf3SeZg+fgRu3nsoTlgsV6oE8udxpZdLA1AiimRXWzeuhq6+FQqUngZekjBynTExL4ACZebgybUeOHvqCFq06ZQtL5hVpU2LxtixbS86CzKY/LCw91GIwWtZFEY3aQBNpY2JqdiYGGxctxx7dmxGUGBCA+9CRUqi/5BRaNikJdSZqpO+rOK8Xo2quHH3Ab55+Yi9VWtXqwx9/f/+JpGfxy3kP5SUIipx9eIZMZCyzVlP4f32uZri1d3R+PDutcIECFEOK2sbdO01ENtYwCvn0JgzhxEnESuk9siDcE+IwJKxK5NWkCgRpXrnr95Al0GjYGjiirzFx4jbYMODX2H3kaO4cO02LhzYCgc7W1Wfpto3sGeNN4n6o0QUIcDFc6dh7dgyRUIqkZGpC8ytS+DS+VMqS0qpwwWzKgzo0Rm7DhzB1ChvDBSsUZAzhEwQ8FCIxBouQOzD06x+HWgybUpMxcXGYkDPdnj04B5sczZC4TyVIYuPhK/nOYwe0hPf3Geg76CRqj5Nte+zWrNqJVWfhkahISuKUVKKqERMTAwkEn1IJIpLy3X1zJI+j2St0RNnIl4mw46t67FHCIY5r4sAWSz09fUxY9pydK5dEqA+UWozfnrQ+Okwsy6HgmXngOcT/qRb2lWAnXMjvLozGNMXLsf6xXNVfaqEZFoyigI6kt3FxcZA53ucpIhExwzR0dFQhexaJcU4Ozni6I5/0WPwaIzx8oC1jj6kkCNMLkWl0iWxdfUSrRiQoS2JqX27NuPh/TsoUnEJzK1LJt1u41Qb7u82YdnCmajXqDlcXPNAnbDXPjsmfYl2o6QUUYkChYogLjYM4SFvYWpRKNX9wX4PoKurD5fcmv2Gp6mrHpOmzUevfkPF8v+Q4GDktzVAi0b1YWpiIn4OvRmqh9OXriAoOAilawxKSkglMjBygL1rexw5tQkLpk6g/khE41AiihDF8hUoDN+Ah3DK3SbVfTJZLCJCnqNQ4V4qe/myc4xQsmhhPLp8Ehev38KjZy+hI5GgTvUqKFuyOLSJNiSm9uzcAiuHv1IkpBi2GyBn/m7w8ziOg3u2YcykmSo7R0LUCW9qqbTHpqQUUYlqNerCwckZ7m/XolD5BSkqpqIjv8Hn6340atYKFhbK++UnP1fYLAaFOzVK+v/sHGSqqw+f3GBoZCVu11DE3LoUvr6R4us3T0pKEY1AiSjyK1YGYTA3yt6vU+cefcRhMYE+t2DtUDVF70dW4SGNC0fbTj1Ueo7ZfXGvQa3q4qHNNDkxxf6tfP3yAXmKNVV4P9vNYWxeBJ8+voM6oSopoiqRD+5SUopo5xv24pX/om/X1nh+ozdsczaFgZEjwoNfw9/zDBwc7TFu8mxVn2a2lLz0nhJR6s3Y2AjSuEjIZDEKt8JKY4MSPs8om1/BEbVGiShCMqZlm864duk8Ll2YBmuH6rCyrypOLw7wOofQwJditbMqthxl56172ZWmJqZYNZSBoTHivsdJisikwTA2zpml50VIdkVjzYjKlClXCfuOXUStOlXh+Wkr3j2eiYigi+jWqzf2Hb0gjmQlWYMFFIkHCzASD6LemtStBZksDn4e5xSuAvq6H0fBfPmQ1zWXSs6PkJ8lohKPxD5R1CuKkPQv7C1dvQWTps2Doa473j+Zg08vlqBAPgus3bIf3XoPUtlLSbFD9k1MaZoGjZsjwPOMuOX1R+HBbxAW/A71GzeHuqAqKaLNaPseUan8BYtg0Yp/MW/xGkRHRcLE1Aw8r5pcKbuIf3jvFo4f2YegwAA4OOZA6/ZdUbR4KWgjqojSfK65cqJ98yY4eGp1wjTLHHXB87qQxobA/f1WBPnexdK/FyVNSyRElWhyHiGZR0dHB117DkCXHv0RGREOHV1dGBgYquwlDgkOwt5d2/H42QtIJDyqV66Ids0bw9TEWGXnRLKOJlZM9eo/DKdPHMa7R1OQu8gIGJrkFK8FQgMe4dOL/yF/wWKoVbcx1IEmJv0IyQhKShG1oKurC11zC5U9f0xMNEYN7oVrl8/C2NQZeoY5EHPnMfbs2IjW7bth5vzl4sqkpqNElPL5+gdg+77DuHj9DqTx8ahYpjh6dWqHAnlzK+X5ls+dijipFEdOL4DHu3XQN7BCZPg38DyHRTMmoUWjekp5XkLSgxJRhCgXW3RgC3qqdOPqRYwc1B1xcVKYWpUAhHgcPzcfc5atwYGNK7WuyTdRTmJKLpfj1vXLOLhvBzw93GFpZYVmrdqjYeOW0NPXz/SXPX+Bwli7eR9GDe6Jx1e7wsTcFbL4KERH+qFo8TJYs2m3mPxVF1SFSLQZJ7CUsBYJCwuDubk57r9wV/mbNNEcf48biuNHDiB/yb/FSRwsyBPk8fD1OIvPL5di4LCxGDZ6MrQxEcWCgP3HT2Pjtt148e4D9HV00KBOTQzt212cIkPS79a9h2jffwTi4mQwt60MiUQPoQH3II0Lw7LZU9C9fWulvZxvPnzEkVPnERoWjtysgqpFE1hZqi7RS7IvbUxEhUVGwa5pb4SGhsLMTHtji8QY6sD1EBiZqOf3SY3O1cuXTx/QqlE1mFiWQd4S46GnnzCgJjbaFx+ezALiv+Hh+SOwsbaCNnJz/4bVW3bg8PEzCI+MRG7nnOjZuR16dW4HAyUkUjRBTJxM/JiR5JRUKsWYYX1w8exxmFrkg5FpQcRGeyEk4AkKFSmBTTuPwNLKWjnnGxONc6eO4tWLp2Lyq0at+ihXsaraVJknxvGUlNKO+EhTY6LI32x0HhYdA5fR838ZP1FSiqilzx/fI8DfF3b2jnDNk0+pz+Xv54PalYvBueAA5MjTLtX9X16tRljABVx/8EalpfF/Uuab1hsZS0gNGjsF+0+cRlmJCcoIhoiCHFf4SPjJ47Bx+QKqtEmngMAglKrTDHpGBVGg9Ezo6JkmvMayOHx5vVrs73Ru3zaUL13iT3+8hKgdbUxEJUdJKfVBSamfY1v53r55KV5QFypSHEZGyt0+N2faOBzafxCla+0VJ5YlFxcbjMeXO2DKqEEYOaAXtM3j5y/Rqlt/SGLjUVtuDBtOB28Qg1tChFgddnjbehgbadffQmUlppYvmo2Na1cgf+mpYvP+xIRQRMg7vH04EeUrlsOGbQeQHVEvKe1BSSmzNF8banRO1Mq929fRtmktNK1bAT07NUPj2uXQsWU9PH54V2nPyUqFZbJ42Ds3VHi/nXMjRISH4Onj+9CUZuVMehqW7z50HAdOnMZ43hEzOSe04C3RibfGGsEZVWCMAWMmi8kW8ms7Dh5FTKwU+UtPS0pIMazXU55iI2BkkhNrtu6kl5JofbNybUtIEaIJWLXH/JkT8Vf5QujWrhG6tm2IGuULY9G8qYiLTd3IObNcvnAWVo61UyWkGFY1ZWFbCWcvX4e2kclk6DlkDJxigfVwRi+JLZrxlmI8tYDPiRfPX+F/K9ciu0qMPdPTCyk6Ogq7t2+Cg2sb2DjWSFGhZGJRELkKDcbNaxfw6eM7ZDfUS4pkF5SUImqDJYf6dmsNT+94FCo3B2Vq7kTBsjPh9jUCPTs2F5uQKwMrGQY4SCSKL6R0dBNWGeNi46ANiajk/t22G+V5E1Tn/0uiMDoch4GcHQSZDDsPHlPKuWubq7fuwdy6PHT1Um+Z4zgeVg61cfWWeic2CfkVSkQRon5YHDOwV0fs3rEFNjlbo1T1TShVfSMsHJpi26Z1GDagm5hEUdZzS3TSrsaS6BojJo7FWdrl/NWb8PDxxUDYwIhLGXMV4gzRSDDD9r2HEB0Tg+wqvYmpN6+ei4u/bFiLIixRxfM64sJ1dkLb9kh2oj7d20i2xraRzZwyFqZWJVG4/ALxzYdhkzCs7Crj9f0xmDllHI6fv5Xpe7yLFCvJZu8h2P8+rOyrpLo/yPcOOJ5HwcJFoUlb836FBajP373HUN5O4f1mnASFBUM8efHqtx4/u5HJ5OC+/94qwu6TyeVZek7k94WFR+DEuYvw9vOHg60tmjWsA3PTlMnb7Lo9jyqhCFEvZ08dwf0711C00jJY2JROut3YLB/MrErgxtWJuHzhFOo1zPzx9qWK5MeD53cgFOiVKj6Ty+MRFngfTarVhrZhsZGNjj7ywUDh/RV5ExyO9MAX928oUkC5bSjUGYtR2Va+nzVAl39PmCbG/j/ieIm4uKesxKo608Q+UqxdNdvaevPeQ7DO1VUrlEW5UsXVpkcXUU+UlCJq4fGDO/jm/hnFKq9M9abEtj/lyNsNr++PF5sQFivxX8CVGYoWLyVO2fj6bgNMLAonNelkoiM94fV5J2rXawJ7BydoeiIqOfbmoCORIOYnsw5iOAG6uvRnIj0qlS2J+092i5NbJDpGqd6gQ3yvo3LZUn/8cyPKt3bLTsxZ8g9iYuNgpqOLsHgpxs+cj0kjB2Non+7ZJrCiRBQhmuHAnu2wsC2TIiGVyMq+EsytiuLg3p1KSUqxht6Xrg+Hj9sROOZuneJ9z/3dJsREBaJ359T9OjWdnq4uYgU5ZBAgUfCeECPIkz4vu/tVYqpg4WLQ1zdEoPcNGJmmnlQc5HsXMlkcypSrhOxCU7fteXh5o8+wcXjw/CWMJDpszR8z5fEoU7QwtqxajFw5c6j6FImaoqtNohY8v7mLH00tCim839SicNLnJSalWMDz6MEdvHz+GLq6eqj6V+3fboq+cPl6dG3XGM+u94BNjoYwNM6FyLAPCPC6AAdHB0ybvRjakIhKjud51K5WGVduPUJzwQL8D0GVhxCHd7IojKr5V6Y+r7bq2aktVv67DZ9eLEG+kpOSkqvs99Tz026EhXzAgO6jkJ2FhIZh75ETePn2PQwM9NGwdg3xd5D9LqqLLXsOYvK8xWjGWaCNxAk20EWgJB6H4oIwbcEy8bz7de0IbUWJKEI0zzcPdxibVUvzfiOzQvjm/jLFbSHBQbh04RRCQ0KQ0zkXatZpBD09vQw/d+1qVTCoZxes3boSwX7XYWlfQ6yQCvK+iLDgd5gzaQyKFSoAbVOnehXMXb4aD/hIVOJMUt1/CWHInSMH8rg4q+T8NCkxZWpmjlbtOuPgvj2wsC0LU8v/diawKY7u79ageKny4iJydt22x2LJu4+e4tiZ8wiPiETe3C7o3Lo5HOxsoS7YebXo0hdR3gGYzjuhDIzBLi0e81FY9/Yzmnfpi2sn92frqnOSNkpKkT/28vkTnDp+EGGhIXDO5YqWbTvDwTFjmXALy4RRwTFRngpXSaKjPMWPlt8/78P7NxgztC8+vn8FHR0DyAUZ5DIpatVrgv8tWSO+wWVE7rz5cfj0NWz9dxWOHtoLr89BsLF1RL9BQ9Ct92BYWGR8BGZmJKOUXbY7rF8PNL9+C+vhh168LQy4hOSAlxCH/8EXOe3t0aKh4j3+JKWcjg7YsHQu+o6ahKfBz2HpUAu8RB+hfjcRHvoFE4cPRO2/Um8PzS6OnbmAQeOmQBonRV6JASIgx6Zd+1GiUAHs3bgKjvaKt5Fmpbg4KeYvXYW6nBkGSP47H2tOB/0ldoiRybFg+Vp0b9ca+voZv3hTV5SIIkR1k/JOHD2AVy+eiItrf9Wsi+q16kMiydh7P4uN/IMS4iRFYqM94ZTLJqldwqpl87Bp/T+Il0qho2sIaVwkLCxtMH3uEjRo3CJj8QrHYe7ksahQpiTWbt2Dh0//EatJq1euiKG912jt+17p4kVRrXwZrH78EhaCROwjxcQLAo4LwbguD8fSASPUatFFnRNTYybNxJvXL/Hs9lBY2VWCsXkhxEZ5IdDnKmxt7bD0n43Irgkp1k6gx+DRuHr3Pux09GEFHRyWx2D+8jWY+/c49O+mHgtlew4fh7unF9byLnDi/4uRynHGmCPoYqC3O3YfPIZBvbqq9DyJeuIElnrVImFhYTA3N8f9F+4wMU177CD5c2xaxtjh/XDlwikYGtlCz9AWkWFukMtiMWLsFPQbnP6qEDYZpkbFItA3rYD8JSekuI/9ir5/Mgu87D0u3nqGAH9ftG5cA/Fyc7gUHgxz6zIQ5HHw97oM97drUKRYUezYfyrDQV1yLGjLykAiKxNRP9q27xDGTJsLQ45HMbk+IjkBL+VRcLKzw6Ft61AwX54sPR9N9/r9R2zYvhvnrt6GLF6GCmWKiwFD9coVkF3df/IMjTv2Eic69udsYcnpiP+uXyEai+EHh7wuuHqcjRNXbe+Eyzduo03vwVgpcUEeLvUkqa9CLIbIvmL/xlWoVyPtqgRNS0Qx1Cfq58Iio2DXtDdCQ0Nh9pORxtoSQx24HgIjE/X8Pq0MwmCecoe0Rrp+5QLGDOuDqMgImFrmhzw+GhFh7siTtxDWb92HHM4u6X6s7ZvWYOHcaShVYysMjXOmuC8q/AueXOuNmfOXo12nHli+aDY2rFmKnPm6wtG1tdiyICrcDR7vNyPQ5ybWbdkvJsd+d1R94mVFdtjmzKYTt+05CM/evkN+iRFs5Dze83EIlMVhWN8emDl+ZLZ4HTKKJaaYHxNT7Frg2OG94nbUb9/cYWlpjRat26N9l15ZujisSor+TbXvMwR3bt3DKMEO5TljcWdDhCDDLnkgTggh2L5qCZo1qANVq9+mK3ReuuFvXnG7k/lyb0QXzolLR/cgO2JxlybHWpEP7oI3zfi/w7DoGLiMnv/L+IkqpchvmzphOG5cuYQCpafCxqkmOE6CeGkkvn3chWULZ8LWzl6smkoPPX19jBj7N2ZNGQ2e10WOvJ1hYOQg9nT69nEnAryuYP6StdDR0cGOLesQFRWHUjWWJE064yT6sHduBH1DBzy9Owo3r11EjdoNfvt7y4qElCoTUcn16NAGtapWwrb9h/H85VvYGOijf63qaN20AYwMNfePp6qwhqbL50xT9WmolRXrtyAnr4+xgkNS7w0WqBeDESYI9hj3/gMuXLuFhrWrq/Q8Q8LCxY/2abw12iOhN0hwSCg0ESWiCFEP79++wrD+XWFqVQaFK46CvmFCZWZ48Gt8fDYHvbu2xonzt8XYKD1at++Knds24s39MXApNEQc2sJSQ0E+N/D17WrkyVsQTVu2Q3BQILZsWAXnfN2Rq2CvpK83MnVFgTIz8PreaCxfNCfdSSlFslMSxsbaCheP7MLZy9dx+PQ5hIWGo3XuXOjevrVWbllU1lS+xOQU+31niVN2ZEeK+ki9eP0WF67fwjjeQWyen8iEk6A/b4tvghSLV61H0/q1Vf5vj8VGRYW0Uwv2gg4eB2tm/ESUj5JS5Ld4uLvhzIlDyFNsNGxz/Jed19E1hmvh/oiJ+oa1/yxBizad0v1HsmPX3pDJ4rF84Rw8+npcLCmPl0bDxNQCM+YtFx+LOXZ4P6yd6iclpJJjTT5NLfLj5NEDf5SU0vZE1I9Y48Gpo4ep+jSIFmLTcs5duY5esIaET/23oDBnCBcdQ5y+eEXlSSlX54Rtx2+FGJTlUo84Z7eLn5crZSWCOqNEFCHqZ+vG1dDVt0LBMjPFYS6JTC2LoECZOXh6vTcunD2BJi3apuvx2M6A7ftOYOywfnj8cBokEj0IEMS2BhUqV8eiFf/C0NAIJ47sR3x8PBxzt0r1GGy6mYNrO7x5+DfcPn/8ZY9OTW3EnNnYYilLCLCDZP5kvuxC0bY95vSlqzCT6KIaUvdhYtdXDQQz/O/te3j5+CGHoz1UKbdLLrzzTFmFndw7Pg65XSlZSxSjpBT5LVcunBYDKduc9RTe75CrOV7dG4uPH94if4GEJuXp0aVHf7Rq1wWXL5wWt+nZ2zuhZt2GYjCVKCwkGM72aU/C0zNwFFcD1YWyG5YT8ifbVK/cuounL1+LE4LYlrRC+TM3MIyTSiGTy2HGp/17by7wiIqOhjr0CCmSLy/2fvFGccEQet97rEUJckQLMuxGIArmdkX5UiWgzigRRYh6u3DmJKydWqVISCUyNssDc+tiuHAu/UkphvXy3HnwNF6/fCYOgWEXrBUqVUOBQkVTNDfX1TdRuKjHGBg5Jn1eelA8Q7JzYsrfz0dMHif01M2Nug2aQt/AINMSUkxUdAyMeQkkguIFfnMu4WuiYxIWzVSpe8c26HbjFu7xEUlVXTJBQARkeCWPxishEmM7tlH1aRI1RUkp8luioqOgo2MIiUTxH18dvYRG4zG/caFpZGSMpi3SHh/slDMXIkLeKLxPEGSICn+PHM4NoUqUiMq4yKhoeHh6wcjQAM45nFRehqztWCKq5/AJ+OrhAX19M3HcMpsuV79mdWxYMgfmmdQ3x0BfHy5OjnjiE4naSP2YrC/COyEGzQv83uTMzMR+55bMmYKW3fpjnNwTJWT6eCFE4yNixftZ6NetYllIpfHQ01OvMd+UiCJEc8TEREFXX3FiiJHomCM6Kuq3HrtIsZLioUhOZxfExYQiOvJbqt5TTETIW/HvoGMOzakGJQk8vLwRGRmFnE6OMDHWrKZrmpaYYtWGC+dOwZ7tGyGAg66eCWKjg2FmbokZ85ahYZOWmZKQYgoXyAdvaQy8JHFw4lInsZ/Io2BqaKjyKimmcd2aaFS7BuZfuYF68SaI4gQ8ECIRBbl4v721FZyd0i4qINkbjYQgv6VAwSKIjQlBROgHhfeH+D+Erq4+crlmfpPsdp26i9M4IsNSl477up9GdKQP2nTohqzG3lgSj8Q3mMSD/HwP+pjpc1GwYi1UbtwGJWs1wV9N2uHomfP0simJm/s3NO82AMERxihedTXK1j2G8vVPIH+pv3H1zhN06DdCrKLKDOwCp0+3jrghROCFkPIiSy4I2CIPgJwDurZNfxCnTJXKlsbJ3ZuBnLY4IoRABxyG8/aYxDuiFmeGnfuOoFP/YeKkPnVIRCUeDGugmXgQQtRX3gKFERrwSOF9MlksIkJeiHFWZqtdvzFMzSzh8X4LBCHl3/h4aQS8v+xF1ep1YO9AF46agm19r9W8I0rUaCTGUAUq1MTwyTPFJuyaJDFeTh5HqyuWkNq17V/kzN8L5eseQbk6R1Cm5k7oGZfEmKG9cfPapUxJSDFsAralmSk2CgGQ/jCbjA1fOc2HoVPbljD8jQqtzMb68W79ZzH69+yCi1wE7gkRaMCZi43P+/G20A+JQuOOPXHl5h1VnypRQ5SUIr+FjSy2tXPC17drxWl7ycVEesHn6340atYK5uZprwT+LtZ7Kn+BQnh9byQ8PuxEZNhnhAe/wqcXy/Dp5VKxQWKJUmWRFSgR9WdCw8LQuENPHNh3FM1jjbBA4ozJvCMMPnmj1/DxWL9td4YqfwaO/RtFKtdB4Up10HvEeNx7/PQPz1A7/bNpG6QyXRSusBhmlkXFxBEbMGCXsx7yl56Be4+f4HImBg39u3VClQplME3wwnKZD27Kw3FGHoJx8MR5hGHp7Kmwt00YV64O7Gyt8cXdA805CyySOKM+b46qvClGShwwg3PEtdv3sHXvQZWdHyWiSHalLZP3OnfrgyDf2wj2u5dqcp3Huy2QxoWjXeeemf68BgaGmDJrAfw9L+HNgwni80dFfIWvxxm8vDMEnBCKcX/P/uXjqHvSILvYvv8wugwaBbzzwETeUYyhWseb4vjhk2jQthsCg4LTXam+Ycde1GjaHgXK10TVRq2xatN2hIVHQNVN0NWNr48Xdm/7F7kK9EHOfF2go5uwTc3QJCcKlJ4GM6viWLlkXqYkpMTHNTDAuiXz8JSPwQjuG47Jg3FLHo71Mj+MhSdy5XbB5JGDoC5YFXl4RAQMeQlWSFzQR2KLyrwJWvCWWIGcKC4YYOCYyWqxsEfUCyckzm7VsnHG91+4i40fifLcv3MD/Xu2g66eLWydm8HA0AFhwa8Q4Hkadva22H3oLGxsEybKZDa2f3vRvGk4fmQfpHEJSTFLK1v07DsYfQaOUOr0PNqal3lmLFqBfzftwBLkgDP335Qh9mdpo9wfp/hwvLxx7pcJix0HjmDk37NgJ9FDNZkReHC4LYnCt/gYzJs8FoN6dc3Es9Z8LmWqw8y+GVwK9Ut1H3vtX9zsjSa1i2LtwjmZ9pyxsXFYvWUHNu/YB08/P/G2GpUqYOTA3qhZtRLUydxlq7F2/RZshSsMv/eV+nGssX8uK9y7cEwl2/OoEirrhUVGwa5p71+ONNaWGOrA9RAYmajf96ktSSm2/YdN37tx9SKsHWvByr4qZPFRCPA6h5CAZ5gwZS569B2itOe/dP4Uli+ai08fXifdVuWv2pgwdW66+oAqGltPsr7KvHCVuqgZb4ShvF2KlgfeQhzGwBMdOrfBwmkTf/k4Lbr2xev3H1GJM4Er9PANUtwWIsShHif2bFbJohHbzpd0jmq0pY9NAF84ZxrK1T2clJBKLsD7Gt49mo7zN54ip7PrHyWkknv8/CWWrt2EM5euilXm1ubm6N6pDUb07wVz09RN0FWFJTILVqqNtvGm6Mhbp7qfVXcNkX3FlpUL0bJRfWQnLI7T5Pgt8sFd8KaWGf66sOgYuIye/8v4KUsqpVavXg1XV1cYGBigYsWKuH///k8//8CBAyhUqJD4+cWLF8fp06ez4jRJBlWo/Bf2HD6Pan+Vg8e7DXj7aBrC/M+hU9du2Hf0gtISUoyZuQVmL1iJ6w/eYceBM9h79BKu3H2FfoNHKSUhRRVRypnKtn3PQdSTm6RISDEsuOrEW0MiALsPH//p47z98ElMSNWHGdYLudBTYovuEhusEZzRhrPE5HmL8fDpCyV8B5qJJZ3CI8Khb+ig8H722usYOCAkNCxTn1dfXw+jB/bB8xtn8eXhdXg+v4OjOzaoXUKKefPhIwrJ9RUmpJjSMMR7t6+ZtsUxPdvzaGte9kTxk/ZObFu5fgfGTJoOXbzHu8cz8PH5QuR2McLqjXuUmpBi6tRvguPnb+HYudvYvu8ULt56gY07DmdoMA1RrX1HT0IeL0M33jpVD05HTg8N5abYe/CYuCD0M+NmzMPXj25YzucSt6mz2Gsc74BVfC4EffPB0AnToQrJ21+oU9UUWxTX0zdTmJBi2AJ94udlVkKKKVOiGHauXQbPF3fx+cE1vLt3GdPGDFerhBTz9ZsnYuLiUJpTvHrgwunDRkdfjN0JydJG5/v27cPo0aOxbt06MSG1fPlyNGjQAO/evYOdXeqkxe3bt9GpUyfMnz8fTZs2xe7du9GyZUs8fvwYxYoVU/bpkgwqXLQElq3ZgtiYGERHR4nVaSzYyipse2DZ8pWV9vjJ3whpVTBzsaRHcHg4ivIJ035+ZMJJkJszwGc3958+zqbd+2HO62IA7CBJFpjxHIcevA1uc9H4d8celCtVPJO/A83EgtecTk4ID34JB5dmqe6Xy6WIDnsHVyUNC2BJYwtz9avA+LFcPoxPO+EUBhkM9PSU0oyfKqJIIoqftJuuri569RuGnn2HIjQ0RPx/Y2PFF7rKwP5+5VdC3yqSNT59dUdOHQNYCIpj7qKcIfZHB8E/KAg5HRUvQvn4+ePYmQvoA2vk5lMuDubg9NBdboVlN27hk9tX5HV1gaqboKtD1ZRzLldERweKrUoMjFP3XmPtRHheAgfHnJmWkPpxeAw71BUbVsSECjJAQYgUJ8gRKcjUogcWUS9Kr5RaunQp+vXrh169eqFIkSJicsrIyAibN29W+PkrVqxAw4YNMW7cOBQuXBizZ89GmTJlsGrVKoWfHxsbK5abJz9I1mMjUC0srbI0IaUsyauiqFm58hgZGYqJowDEK7yflScHIh6mJsY/fZx79x+hvNwAugoSBOzxK8sMcfe+4oay2VXPjq0R6H0FkWEfU93n/eUwYqKD0K19a2RXTevVxgdZND4KqUcss0ajF/lINK1fJ9OSUlQRRVQRPzEUQ6ke+ztiYWGZpQmpP6FOVSvZmZmpCYKFeMSn0YUlMbYy/ckkvscvXiFeLkdlTvHvXtXvtz948hyqpE5VU/UaNYeJsRnc329ONSxAGhcCn68HxEpEK+v/tjz+eE2hzfK45ELB3K44izCxMv9H14RwRMvj0bhuLZWcH8mmSam4uDg8evQIdevW/e8JeV78/zt3FDfRZbcn/3yGVVal9fmsoor1P0g8nJ2dM/m7INkBJaKyHlslaVi7Bs7y4akmijB3hQj4x8eiVeMGP30ciUSC/zoPpCaFIK5akZSNxwsXyIPXd0fC/d0WRIS8Q0jAE7x/Og9ub9ZiaJ/uKFIgX7Z9ydhY44J5XDEPvnglRCcFVgGCFAsFbzHYH9a3h1Im5xGSVfETQzEU+R3afmGtCVg/npD4ONwQwlPdJxMEnOHCULtqJZj/pIeL5Hu7i7RiqHgkvPcps0+rpk3oMzQ0wtQ5ixDgdQmv741FoM9NcRK5t9tRvLw9GHq6UoyZNDNTq6M0Lck+dtgA3JdHYJM8ABGsYur77yQbcrMBgWjRoC7y51Hcb0tbZdd+Uhmh1LKWgIAAsW+Mvb19itvZ/799+1bh1/j4+Cj8fHa7IpMmTRK3ByZilVKUmCLpQVvz0of1zWHTxlipuJmJCRrU+uunQU5GjBncF42u3cRcwRt9YYOcnJ6YoLophGMdF4A6VSv/cttdjb+qYNP7HYgW5Kl6ALHHus1HoXmNeplyvtrCxNgIJ3f9i5mLVmLvkf3w+LBNvN3JwRELpk1Ev64dsvR82OSfQyfP4PKN24iXycTeCV3btoSdTeommRnF+kNt23sIr99+EKvzWIVT66YNYGSYdnDAttEc2roeHfsOxYT3H5BDxxBG4PBZHi1+3bZlS1GiaKE/2prHaHKAQpQrK+InhmIoQpTL29dPfG+LjZOieOGCYkyTGVW27LGa1q2F1ZevI14uoCZnCl2Oh5cQhy1CINwQi5VDB/z0MSqULgl9XV1clYWhA5f6/faqEC4mrqpWKAd1ouotfc1atoepqRlWLJ6Ltw+niLexxc9adRuJ0ytzueTO0muMF2/eYeeBo3D39IKVhTnaNmuEGlUq/nEyMTomBkdPn8eJc5cQERGJggXyokeHNihWqMBPv449v19AAKYvWI6zQhhcOAMEcPEIlMWiYc2/sHrhryd8kuxH4/da6evriwch6UGJqIxhyagRk2bgq5e32K+JrXQY6ulhaL+emDh84B+/4bHkw671KzBw9GQMDHWDva4BIoV4RMjj0ah2daxfMv+XwVvvTm2xbstOLJb5YAwcYPQ9MRUjyLFK8EUoZOjXLWuTLJqANcdcOutvzBg3QuwXoaenh0L58oiVZ1mJBVPtew2Gb2AgikqMoScHFl66joUr12H9knlo0ej3E4pL123C7CX/wFKih2Jyffhwcgy/egOLV63H4e3rxTLztORwtMe1E/tw+eYdnLtyXaxcGVCkMNq3aPLLLaXJUSKKqDOKoQhRDnZBP37m/7D38HFxixybCiyHgBKFCmDd0nkonP/Pq5HXL52HIeOnYcXZC9goCYQppwOf+BhYmJpi64LFqFyu9E+/3tLCHF3atcSuvYeRT26Asvx/720vhSjs4ILQqmF98f1Q3SQmelSVnKpZpyFq1G6Ar26fER4WCkennOKAJ/Fcsqg6ii0aT5y9AP/u3AdrHT3kkenitUQuDgj6q0I5Mb7OSLySHEtwte7WH588vqGExBjmch5HHj/Hxp37MH5of0waMfinXz+4Vze0btJQPBc392/idtPWTRqIcT8hWZ6UsrGxES9wfH19U9zO/t/BQXHTPXZ7Rj6fkF/5scQ3u5TQ/inWQ6B9nyEoLNfHMIkzCsIAwZDhhDQEi1dvQExsLGZNGPXLx2Fbn27ee4iL129BKpWidPGiaN6grjiNjalbvSpe3bqAE+cviVUtbFsf22ue3u1juXLmwLbVS9Bz6Fj0jHdDebmhuC/5AR+NWE4QExuZEfxpKxYosJ+JqkYHt+05EGahMZgjcYUDpwdIIJZ7r5H5oe/ICcidy/m3qpLYyh5LSHXkrNAB1tCVJCQ3Pbk4zPb1QYfeQ3Dn3JGf9sETt0tVryoeGUGJKPKnKH4i6kjV/Xw0BYt7+oyYgMtXb6IXrFFXYgYWmTwRorD1gweaduyNK8f3IleO1I2yfxQYFIz9x06JU81YEold6CdufWKVu1v+WYRJn76IMVRkVBQK5M2DFg3rpruR9JxJY+D21QPTb91FAc4IrnIdfOPj8VoWhYqlS2LJ7IRKIHWVPKZP/vuZFQkqtmjqmjvheRKSUeFZep2xevMOMSE1gLdFY8ECEgkn/u495qOw8NFTDJ0wTYyPfyfZ1anvMER6+WONxAW52IRsCcT+ZYf5ICxctUFsfM8W6X7Gwc5WnLpMSHpwgqIuZJmITdyrUKEC/vnnn6Rf9Fy5cmHo0KGYOHFiqs/v0KEDoqKicOLEiaTbqlSpghIlSohNPn+Fbd9jvaXuv3AXJ8GR7IkSUX+uZbf++PbgBRYjZ6om4vvkgdiDYLy4cU5800mLl48vuvQfgadv3sJWRx/6HI9v0mjYWlpg6+qlqFK+DDLLN28fbN1zENdv3RPflCtXLIvendrBNVfqCShEPWzctQ8TZ/4P//KusON0U9zHgp+BnAdqNauHNb9R6l2/TVfEvfqCOXBKVW33QYjBKJk7dq1dlmnNNikRpd3CIqNg17Q3QkNDYZZJ25fVLX5KHkMduB4CIxP1i6GsDMJgnnbfZqJkic2ayc/dffQEjTr2wkTeEdV40xT3hQkyDIEH2nZujYXTUv87Tm79tt2Y9r9lEGQy5NAxQIBcigiZFJ1aNsPyudOgp5fyffN3sa3C567ewK4DR+Hl5QN7O1t0atNc7K/ItrNrGlY9lZyyElSqvNZgi7zFqzVA6WAZhkpSV7Kdl4fiH8EPjy4cR26XjPVbZttN2/QejP9JcqIYl/oP7ky5F6LzOeL6yf1KmUKsjbJzT6mw6Bi4jJ7/y/hJ6dv3WL+nHj16oFy5cmJwtXz5ckRGRorTZJju3bsjR44cYrNNZsSIEahRowaWLFmCJk2aYO/evXj48CE2bNig7FMlGo4SUZnH1z8A1+7exyjeAbp86jecppwF9gkhOHLqHAb16qrwMeLipGjdYwCCvnpjDp8TJQVDcODgIYnD2jB/tO89GFeP70O+3JkzZpiNPJ4yeijADqIRzl26jpKcUaqEFKPDcagpM8LZi1cz/LjhEZF48PwlRvL24BT8/ubnDJBLxxAXr9/+o6QUJaKIMlH8RIhm2n/sNBx1DFBFSD3VzoyToK7MBPsOHceCqRPSvKg/cPw0Js5ZiGacBTry1jAXJJBCjkt8GNYfOyUmpFhiKjOwXS2N69QUD22QPDmUfHtfZiSo1OVa4/nrt/ANCkIdieKEUw3OFGsFf1y4fgv9u3XM0GOztgUOOgYoKihOotSGKRa8/wD/wKBM6f1JSJYkpdjKnb+/P6ZNmyY22yxVqhTOnj2b1IzT3d09RV8atqq3e/duTJkyBZMnT0b+/Plx9OhRFCtGe1CJ+r45aJvA4BDxo5OCZAFjzElgyevCPygozcdgpeTvPrthpSQX8vD/lZE7c3qYJjhigNQda7bsFPsakeyHVX34BwbCREh7lY31B5NKE8ZaZ3TVl9H/yYBZPXCQyTL+2D82LNfklS+i3ih+IkQzsS13jnIJeAWLIkwOThdhUUHi+5uiaidW7b1wxVpU4k3Qn7NNSlyxRuYNOQvEyAVsOXgU44YOUMt+T+rkx+uCzNiCqg7XGr4BgeJHozTiHBbjsMU9VlGVUfHx8dDj2DIyl+ZjJ34eIRrV6JyVmrNDkatXU6+Ct2vXTjwIUYQSUcrnYGsjTlz5LMSiMJf6ojtYiEeALBY5HR3TfIxjZy+gsMQIebjUfQ0MOB61ZMY4evLsbyel2Ha9M5euIioqGoXy5xX7/mR1k27ye1h/sQnT5+HzN0+xz0asIIcuONwVInBOCIWXIBVvj+TkKFKwcIYf39zMFLlz5MBd7zD8hZRbJxhfQYpPsigMK/nzyY7JUSKKqALFT4RoHpYousVLxW3oLDHwo09CLGwtLNLcfvfmwyd8dPdADz6Hwkqq+pwZtsoDcerilQxXwSQu3LD34bcfPolTaRvVqSlWm2cH6pBQ+tNpxeNnzsf+oyfF1NADIRIunD78BSlOy0NxX4hAHARYQQfRkKFkkYz35GTNyNdv3wNPSRxysF6fP7gnRMLJ1hb2tjaZ9F0Rgp8sIxOiZomoxCPxTSXxIJnPytICjerUwDEuVGw6/eMK3n55kNhnoE3TBmk+RmREFCzlaf+JseJ0EBkTk+Fzi42Nw7BJM1CyRiNMnrUQi5euRsf+w1GqRmPcuvcww49HstaVm3fQsd8wmHuFYBzvgFjIsVHmh0Vyb8yTeyNSkKMKZ4JcnB4CBSk+fP6M95++ZOg5WBDfv2dn3BQicFcekeI+NpVxDfxhZmKCNk0b/TIRlXiwiqjEgxBCshvqJ5V+ndu0QGB8HM4Koanu8xGkuMxHoFunNml+fWRkVMJrzimuHTDiJDDkJWJj84xicRKLl1jcxOInFkexeIrFVSy+IuqLJRM79RuGI0dPoTus8BdngkPyIFyRh2KI7CtOCSEoyBmgMmeCUCRUMZ25fE2M2zOiecO6Yu/X1fBHtCBPcd8jeSQuIxx9e3SihWCieZVShGRGVRQloLLW1DHDUf/OA4yP9kQ7uQWKcoYIQDxOyENwQwjH/HHjYP6ThnUF8uXGvruPIBWEVI3SmReIRn7XXBk+r8Hjp+Dk2Yvoy9mgLmcubvH6IInBloBAtO09GOf2b/+taW3KFhUdLfbguv3gsZg0qVapPFo2qgcDfX1kFywwmjJ3MYrCADM4J0g4DtGQY5XcD5yAVE1hewm2mBblja4DRuDu+aMptnr/St+uHXD7/iPMuXAZZTgTlBIMxAazV/hIREk47Fm9EsZGqRNMVBFFCCHkTxQrVAC9O7fD+t0H8E2IQz3eDCaQiFUtB7gQ2DrYYXBPxf04GTagRYfn8UKIgiubfPYDVsUeLpOiwPcpfOn1/NVbMU4qINPFWEku5OMMEMXLcVEIxdbDxxEdHY2NyxdAHb169wF7j5yAX0AgHO3t0KlVMxTMlwfZydnL13Dj/kPMZX1aeSOECzJMkLljudxXnJA9XZJDbK/B9BJscEIIEdtksAnLbZv9fBEuORaXbluzDO16DUJ/qbu4s8GCk+AFF4OH8gjUr1ENQ3t3U+J3ql00vcl5VqFKKaLWVVFUEaU6BfLmxpn9W+FSthiWyH3QW/YF42Ue+GJniNX/m4mBPbv89Ou7d2iNYFkcDgqp+049k0eJ5b+9u2as7Pzl2/c4fPo8BsMWzXlLMSGV2Lh6OhxhI+OxeI36DUW4/+QZitdogqGTZuLU1Xc4cfkNBo2bgpI1m+LZqzfILtjP7/XHT2gDSzEhxdTjzGEOCepyZqmmFFlzOhgKG3z46i423swIHR0dcVT22oWzISnqiv16kbhmLqB5+xa4dmIfalSpqLAiiqGKKEIIIX9i0fRJ+Hv0UNw2lWOEzB19ZF/wLwJQpU41nNm/DdZWaU+ysrW2QpP6tXGYC0WgkLJvD1vo24ZAONpYo0Gt6hk7p9XrxThpBhzFhBTD4igWT7G46tCpc2LyR52wvkVDJkxHtabtsHH3SZy/5YH124+gUqPWGDt9ntifMrvYfeg4CkqMxIQUY8pJxB5j7BUYK3FMSkgxbPGT/VxLS4yxbvOODD9X5XKlxel6bErkTQtgn14E4gs6Y+W8Gdi5brlGTmUk6o0qpYhaoIoo9VQ4fz4c37URn7+644v7N3HLU5kSRdNVssu+duLwgfjfynX4gFhxWocBeNwTInCJC0eNyhXQtW3LDJ3PoZNnYSHRQ02krtDS53g0kpti88Wr4p57RVUwquDp7Yu2vYdCou+KsrVWwcDYSbw9OsIDH5/NRcseg/Dg3GHYWFshO0x1ZFyS9SjwhRShkOEvLnXvJ6YQDGCjo4/bDx6JfcMygv2edmzVTDx+RJPzCCGEKAur7B0zqC+G9O6GR89eIjY2FoUL5BOrfNJjzuSxqP/oKUYGfUNjuan4XsjeL09x4fDkpNizcKG4+JJeLC46c+ka+sAaegqqjmuyPlWSYBw6eQZFC+aHupi15B/sPXoSeYuPhZ1zQ/C8DuSyOPi4n8TmPatga2OFCcMGIjvw8fGDi1wHSBaCv0eMWCWlaIoxU00wwT8vX4sTsdPqYZaWPC65sHDaRPEgRNkoKUVUhhJR6ottNfvm5QNDQwOx+SV7Y2JHRrFAIXcuZ6xcvwX/+/BRvM3eygpjug7AiP69MvwGGRwSClteFzppTGxzgC5kcjnCIyLUJim1ec9+xMTJUabqPOjo/pd4MTRxRsFy8/H4SgdsP3AEowf2gbZLbIrpJsTB5nsAlfiT/FnHA3ZfWmOzM4ISUYQQQrJiMYr1e8rh6CDGIlUrlM3wY7DY6+LhXeLC3oFjpxATFyi+D9atWgUbhg9EuVLpH9TBsLiIxUcOvOK4izVkt4EOgoJT98FSldCwMGzYsRc58naFg0vTpNt5iR6ccrdGbLQPVm3aiaF9eqhNzKdMDg52+PTWLcVt3C/jp4z1kyJEVSgpRbIUTc5TbyGhYZi7fDX2HDyW1IS8RKECGDt0AJo1qPNbj9m+RRO0a95YrJKJk0rhZG+XodW95Fycc2CfLAaRnCxFmXKi90IMTAwNYWVhAXVx4txVWNrXSJGQSqSnbwlLu2o4ce5KtkhKsT4bRfPnw6FPXigtGIlb+OygC1vo4LoQhjKCkbjqd0kehiDEwxI6yM0anstiUa1COY1NRPkGheDVFw/o6+miXMG84kdCCNHU2I0oxiqRFq1chyevE7blG+nro32rZpgyashPt+ulxcnBHivnTcf/po6Hf0CQOFnWwjztXp4/w+IiFh+9i4lBBZikuj9SkOGbECvGWeri6q17YoWZfa4mCu+3z9UUXp/3i5XU9WpUg7br3KY5ul66iqd8JErxxuJtxTkjXBV84SPEwQwSXBbCxV5kTDHOEDcRifIli2d4EVhdsO2ZT1++RmhYuLg4rk6/n9lF5IO7WfI8lJQiSkeJKM3AVqQate8Bz6/f0ExuilISG7Ex9Nn3nug+dAwWTJv4W6OH2ePuOXwCF6/dglQqRZlSxdGrYxvkypnxN5aOLZti3tLVOCAPQk+JbYr72Djcs3w4OrRupVZvvtExMdA1NE/zfh1dM0TFuCM7YKu8c6eMQ9tegzBN8EIHwVLsa8Gm7R0TQuAlk+INYmAHHeTi9PFIiMQZIRRmxsaoXK6MRiWiEpNRY/7ZiqPX7yP+e98LG1MTDO/QFGM7Nc9Q43ZCCFElGjbzc9v3H8aIv2eJyQI2tENsDC2NxuEDx3Dz9j2cPbA9w4kpNhyE9aTceeAo3L56wNLKAu2aNRanI2d0cY/FRR1aN8PBvUfQUDCH7Q/bvVhcFc8lxFnqIjo2Vvyoo6c4htLVS0jQxcQkfJ62a1i7BqpXLI+5Dx6jo9xS3HJZCkYwBI+ZMi8EIx5RkIuDidjwmH8Ff/HrxmWw9YG62HfsFOYvXYWvXt5Jt9WoVAH/mzYBhfLnVem5ZTe8acaT6hlFSSmiEYkothd+/7FTOHTyHIJDw5E/tzN6dGiNmlUrZcq2HgIsXbcZ7l89sAQ54Cz5Pu2FAyoLJtjA+WPK3EXitDg7G+t0v1xPXrxC256DEBoejlKcEfQFDhsfPsGqjdvE1b9OrZtn6KVnvRhY49CZi1fAVx6PhjAXA7+nQpTYENTM1grjhvRXqx9nscL5cevRAwhC/1S/q4IgR3jQQ1StkbEyfE3GGozv3bgKE2fMx2R3j6TbdSQSvJPFYBTvgJqcqVhFJRcEXBfCsSLKDzMWr8D/poz/rURUbJwUujqSLE0CBYVFoO6w6QjwDUJvWKOcxFgMFi9EhGL6xr3w9A/EipHaXx1HCFG91y+fYs+OzXj14hn09PVRq24DtO3QHdY2KRd3yO8JCg7B+Bnz0ZA3xxDOLum9vhhnhBqCKcZ4emLR6g3439QJGaoQYUmunQePwlHHAPllunjPy9D93CWULVYEB7ashaVF2gteirD46Pylaxjr74nWcnMxLgthi48IFacqTx8zIt09r7JC0QL5xI8h/vdh7fBXqvuD/R6IH1mvruyA9cncvWElJs1ZiF1HTmBLfEKfTiYGcWLfsfESx6T2CKxJPhtUtOrfrejStgWcnRwz/Jzs91AqjYe+/n+9QLPCxl37MG7GfHHRcpAkJ2ygizdCNPY/eI6G7Xvg/MEd4kAmoj04gaXhtUhYWBjMzc1x/4U7TEx/r8SVqFdF1DdvHzTvOgBuHh6wtKsAXX1bRIW+RHjoF7GB8ar5M9LVeJv8fLpJgQq1UD1Cgn6S1AFJhCBDD8ENk8YMFXtBpQdLRJWt1RQ2EVJMhoM4SY2JEeTYIPfHRYTh3P7tGe6LwOw6eBSL/9kANy8v8f/Z6OTmDethzuQxahVQMZdu3BZHMOctPgYOLikbbnt+3g+312twes8WcdJJdpK4Aiz2LjMwQL+RE9AszhjdJAl9p5LbIw/EQUkY3t65lGLrwq8SUeuPncf6I+fwydtP/B1pUqUsxnRqjgpFlN/EdcbmfVix6zhWcM5wStbYnTkpD8E6uR8ebFyA4nldlH4u5M+FRUbBrmlvhIaGwsxMe2OLxBjqwPUQGJmo3/dpZRAG84TBUySdNq5djqULZsDQ2B5mVuUgk0Uh2O82jIyM8O/2gyhR6tf9jhKnIRPF1m3dhWnzlmAL7wqL77FOcltl/jhvGIv396+m++J+2frNmLPkHwzl7MTptPz3RNdrIRpz4INKVSti36ZVGf6RePn4Ysr8JThx9mJSBW/uHE4YO2wAOrdpoXY/4jptuuH91wgUqbgcuskqpuJig/D67jCUKZoDx3eo39RlZQsIDMKdh08gk8tw9+ETbN+5H1s4V5j80NoiSpCjN9zQt293TB87PN2P//zVWyzfsBmnzl9GXHw8nB3sxYnZA3p0EmM2ZWLXD4Ur10WtOAMM4v9L8iZej4zGN5SpXQU71iyDuks+1VmTt+/xf1ApFRYdA5fR838ZP1GlFFHrrXnswrXroDHwDYxFqRpbYWSSK+l2f88L2Hf0fyicPy+G9+uZqc+bHXtJBYeHoyiveBWFvcm5cgb45Jb+bWb7jpxESFgYlvCuSQkpxoDjMYS3w2suFmu27MDmFQszfL5d2rYUq6zY6GLWlD2vSy61nV5Xu1pl9O3SARt3LUGI321YOdYUK6SCvC8jyO+B+Lub3RJSDAsyKpYphYplEvpwRMXGop4kYTLhj+pxZtglDcT1O/fRvGHdFMkoRW/0LCHVauICXH/6ClU5EzTn7cUJf5fuvEDt24+wY9oItKpRUanf39YTl1FbMIETn/oCpCFnjgOSEGw7cxWLh/ZQ6nkQQrKva1fOiwmpnPm7IVf+HuD4hPdiaWwI3j36GwN7tceFm89gbJy6xxBJv49uX+GsYwALQfFlFauYOhgVjICgYORwtP/l47FJaWs3bkdDmKE+n7IaqghniH5yayy5fhPvPn5GwXx5MtynisVdLKnx6as7jAwNxWl76rqdfM2CmWjYsTee3+wN2xxNYWTqisiwTwjwPAVTIwlWzJmK7IjFvIm9Xpet3ojKgjFM+NTXYEYcjyoyI5w9fzndSakL126i68CRYr/PznILWPISvPCNxvyl/+DMxSs4sn29+HujLEdPn0dcXBw68DlS7TBg1yMt5ObYcOkaAoOCf6tXW1bT5IRUVqKkFFHrHlH3Hj/Fs1evUKTCwqSEFMP+SNnlrI+woKdYs3UPBvfq+tvNswlgZGQorsIFIl7hyyETBARx8TA1SWismB6Xrt9CSc4oqYw4ObY1q4bcGCeu3vztl58FUMULF1T7Hx/7XV04fSLKlCiK1Vt249XT+eLtpYoVw4KJ89GmaUOoo9fvP2LTrn24d/+xWIlY46/K6NO5vVKaTLLggzGG4qDY6Pv8Y9m399D7ZP3LN/h/Dp3GjaevMYvLgRL8f2UVLQRLLIYP+sxbjVpli8EiA7/PGREvk8EnJBT5ePs0pxy5ynTh7vtf6T0hhGS2rf+uhrlVUeQq0DvFxZ2uvgXylZqKx1c64+TRA+jQJX0V0EQxM1MTBAvxYqzE4psfBUAqfjQxTt/F6at37+EfEoJaEmeF91fjTPAPz+PyjdsZTkolT2qo62Jecuz7u3Z0F5at34J9R/cjOiYaRkbG6NqmKUb2752uJJ8qeokeOnEW+46cQEBAIHLmcEKX9i3RtF5tpVyrsGbwacVPiTFU7Pc461fYQm+/kRNRSm6ASZwDdL8nK+vAHA2FaEx5/gpL127ClNFDoSwent6w0dGHdRpJ3nycPmQyObx8/TQiKUXSRz3T4kQtE1GJR2IiKvFQpht3HkDfwBwWtoonb9k41YWvn2+GKnhIamzFo0Gt6jjLRUCqYEfvPSEC/vGxaNmofrpfvvh4GfR+sjlYH5x48Z4dsIsBVtl188RefHt2B57P7+DKkZ1o26yRWvZE27bvEP5q2g5H9x2D86cA2L33wZYtu1CxQUucvXw905+veJGE5OIDIVLh/Q+ECPFjmcIFf5mQYlWU6w+fQw2YpEhIMexioT9ni1ipFLvOZf73kfQ8PA9zI0N4CdI0z9GHj4eNeeqJjIQQkhlYL5gHd2/CyrGOwvcZAyMHmFkVw73byvtbmF20alQfwfFxuCmEp7qPJarOcOGoXbUSzNO59ZfFT4x+GpdpOuAgAZ9tYig2GGfZ7CnweHoTbo9vwP3xdSyaPkktE1KseqdB224YPnkGoh6/RX63EHjdeYpew8ejXe8hYsIqs5UsURSPJDFiH05F8cZDSTRKlSiWrsc6cuocwiIixFhJl0v5+1eIM0Q9wRRbdx8QBxcpi42VJYJlUnEipCKJsZW1pfpM2iZ/jpJS5KdUkYhKTi7IwYl/FBVfuHPf906zzyN/ZszgvvBCHOYL3vAUElZU4gUBV+VhWMH5o07Vyhnq/1S2VHE852IRncbP5h4XjTLpfJPUJsZGhkote/5Tj5+/xKipc9AIZtgMFwyV2GOkxAFb4YoyMn30HDpG7POWmdiY31pVKmIXF4yAHxI5rFEnu716icIo5PLrKq3QyCh4BAShLKe4CsqS00F+iSGeffwKZWEXgJ0bVMdFPlycYPmju0IkPGWx6Fw/deNWQgjJLEJSDIU0Y6hfxU8/VseT1IoXKYRGtWtgFfxxSR6WtLjnLcRhgeCDr0IsxmRgCAurDjLU0xMXBBV5JkQjWh6f7WIoVrVtbmqq1n1kh0yYBo+PblguySVWaw+Q2GEhlxNz+By4c+8hpi9cnunP2adrB3jFx+CAEJTqvsNCML7Fx4ifkx4v376Hs64hHBTscmDKccYIDA2Fj7/yKr1bNq4POQScFEJS3ceuS45zofirQjlxKyrRHpSUIj+tilJFIiq5CqVLIiY6GGFBzxXeH+h9DZYWlsjrQs2C/1TZksWxc91yfDThMUDmhn6cO7rCDYvlPvirehVsWbU4Q1U93Tu0hpQTsE7wE1cKkzstD8ErWSQG9OwCbcKmRF65eUfskeSRbIStJlm/bTccJProz9uJ28yS9wIbxTlAIhOwbe+hTH1O1iNq7cB20LUwwlDBAxtkfjgvD8W/Mj8MhTsEM0OsnzgofY/1vTQ+EopX2NiqYQTkMNBTHHBllpHtm4I31MPf8MQjeaS4ghklyHBCHowl8EWDCiVRtXghpZ4DIST7Ere4lyqPIN9rCu+PiwlEWNALlCn76/561OT81/5d9j/Uq1sTy+Q+6IYvYgzVX+aGV0YCtq5ajCrly2RoO2Cnti1wlAvFRyFlZU2IEI+NfCCK5MubocfUBG8/fMLpS1dx6/4jcQCPpvnk9hXnrt5AL8EKebmUzcBL8cZoAwvs3HdEbOSd2ddKE4cPxA55IMYL38Q4gw1UmSh4Yos8AKMH9UHVCr8eaMDo6ekhUpArrLpiIr7HVgb636d0K4GDnS0G9e6GnfJA7JAFiFtjmfdCDGYJXnBDHCaPHqK05yeqQU14SKqVMHUKPtj4+Px58sDt9XIULr8YegbWSfcF+92Dn8cJjBnUC3pKvsDMLurX/Auvbl/AsbMXxeDAyMAAjevVEptgZlRORwesXjgbg8ZNwRsuDjVkRtDneNznovFaHon+3TuhSb1a0AYymQz/W7kOG7buQlhUlHgb69HFtkQumfW32k0E/Jkbt++hqswQEgmnsGFmebkhrt26i79H/VlA8OPkPFfnnLi1YT7+OXga205dxvGwENiYmqBf40YY1q4xHK3T1zfAyEAfNUsVwcUXbqgvmCdNLEr0GjHiqmGTKsoN5l0cbHF+5Qz0nLUS0908xAQfS86y8+lYtxr+Gd1XLbduEkK0R48+gzBmaC94fTkMR9dWSX9zZPHR+Ph8AQwMDdGybWdVn6bWVEFvW71EjJ1Onr+MiKgoFMybGy0a1fut6ugZ40bi6bNXGPP6DapwJigAA/gIcbjGR8LQ1AR7/1mkNe8hbNLb2Olz8eDZi6TbHGysMWnUEHRv3xqa4vaDx+LHapzirfk1OFPsjgvE42cvUata5Ux97gnDBooVe2s37cC/DxPOo1LpUtjRuxua1q+d7sdpWLsGVv67FY/5KLEq6sdFvQtcOMoULQJbJfcjmzFuBPR0dbF603bsiwsStxJKBTly2Nlh7/8WoVLZ7DcgSNtRUiobU9dE1I8rfTvXLEGzrv3x5GoXWDnUgJ6hPSJDXiDY/wnq16yOMYP6qfo0tQpb/ejQokmmPFa75o2RL7cL1m7ZibNXb4orX6VLFMXOHp3RuG4trQmohk+eKTa0bAEL1JPYwQg8HgqR2HPtDhq174FLR3ZrTDNGtjjGp7FdlhHvSWMFLaOJqB/7Q9lbWWBO/87iwZpYSiS/V8w7tktLNBs3D2t4P/SCDYy/b/N9K0RjMXxRMk8u1CtfEspWNLcz7m9eiLuv3uPZBzcxeV6/QknktP0vuU4IIcrSsElLPH/yENs2rUSA52mY21RCvDQCQb5XwAlxWL1pD8zMqS9LZiqUP694/Ck2WObEnk1iZfL2vYfwyNMLluZm6NuqDfp366hRi10/w6YoN+nUC/ZxwGTeEYU5Q/gjHicCQzDi71mIjIzCoF5doQlY0obFSGlFLlyyz1OGxnVqigfrJ8f8zkTFSmVLoWKpkljx4g3GCxyKwVCM1aMEOXbLA/FUiMSOwX2hbOzcWTP1oX264+yV6wgLj0AeF2fUqlpJrbdv/hjzavrkvcgHd7PsuThBWf8yVCQsLAzm5ua4/8IdJqbpayiYnWTl5LzMxEbXbt17CPuPn034w+TqjF4dW6NV4/o0dY+o1JMXr1C7dRcM5+1TjW72FaQYBg8M7N8DU0cPgyboPWI8Hpy/jjWCc6oqoxhBjp5wQ58+3cRVrMxIRCnL5lOXMXzpRrEhbEEYIIyT4YssBsVcc+LYwsnIYav+U4eI+giLjIJd094IDQ2FWTqbFWtyDHXgegiMTNTv+7QyCIN5yvkF5BdYmH/7xhXs2vYvXr14Bj19fdSp1xCde/RHLpfcv3z9Els5EKIMHfsOxeubD7EUOWH4Q/+z9TI/XNCNwts7F9PdJF6V3n/6gooNW2Es74CafOrz3S8Pwj5JKN7cvghLi5Txorpdc3XoMwSPX72Bi44hLAUe74VYxEKOOZPGYKCWtd5QFm1JSvGmf7aoHhYdA5fR838ZP1GlVDagqYmo5NjY2rFD+okHIepk9+HjsNXRRx0h9R9ae04XtWTG2LXviMYkpQb06Iwjp89jKxeAHrxN0nhr1rj1H8EXcTzQu1NbtUxEJde7SW00rFgK285cxesvHjDQ18OiauXRsFJp6GjIKhshhPwpVuVQtXpt8SBEnfgFBOL89VsYwtnCUEFVTzveCqeloWJM0rPjz+MOdVAgb27UqlwRW+4/QV7BAM6cXtJ9b4RoHEAwOrZuqdYJqcRrrguHd+HSjds4fvYiIqOi0CBvbnRt10pszUGIMlBSSktpQyKKaCfWDPzEuYtw/+YFS0tztGhYD3Y2mrudycvHFy4yHYU9mBjW7PJUkK9YTv07pdRZrWKZUpj/9zhMmrsIt7koVJIZIR4CbvFRiOBk2LB0vjieWR0TUT9ysrHCpG6a04+CEEII+ZkXr9/i0o07iJfFo2yJYmLvVU2ILRTx9fMXK/ny8Cmbgiey4nRgpaMHLx8/aIq1S+aieec+GPr1KypyJnASdPCZl+KxLAIVS5cUK400AfudqlejmngQkhUoKaVFKBFF1N3uQ8cwadYChEdFwVJHH2EyKf6eswgDe3UVt4NpYmBla22NpxKZOKnkx+1ujIcQByszM4363lhpdoUyJfHvjr24e/+ReO6ta9RHv64dxVHVaSWj1CERRQghhGgbtqWqz4jxuH7vIYx4HXF4Bouh8uVyxpbVS1CsUAFomsRemyxOKvDDtDomTJAhRCaFrY3mbLe3t7XBpaN7xCr6fYeO4V5AIHI45cKajm3QuklD6Ov/Vz1FCPkPJaU0HCWiiKZgJcBDJk5HHc4MnSW5YQ9dRPAynBRCsGrjNujoSDBtzHBomg4tm2LbvkO4zUekmrjCxthe4iPQtU0HaJoyJYph7aI5Cu+jRBQhhGS/GJOohlQqRZueA+Hx4Qsm8Y6oxJmIzbRfS2KwwTMALbr0xfWTB5DD0V6jfkRODvaoXrEcjj98heqCiThhLblj8mBwEh6tGtWHJjExNhKb0bODEJI+mrN0T1IECYlH4ta8xIMQdcTKs+ct+QfleGOM5O3FXkuMCSdBR94aHTkrrNm0A0HBIdA0bFIJm3ayFH44JA9CqCAT+y/dkUdgErxgYGaCIb27QdOxRFTiwSqiEg9CCCHai2JL1Tt18Qqev32PKXBAVd5U7PXIeoUV5QwxG06QRkZjw4490ERTx46AByfFNMELL4UoyARBHBLzr8wP+4QgjBzYW+xxRIgm0YYm51mNklIahBJRRFO9fv8R79y+ojksxEDqR015C0il8Th98So0Dft+Nq1YgM7tWmInH4Iusk9oJfuAuXIvOBbLj9P7t4qrgdqSiIqQCrj5/A3uvHyH6Ng4VZ8iIYQQotUOnTyLwhIjFORSX+SacRLUlBvj4NFT0ETlShXHoa1rEZXDChNl39BC9gF9ZF9w2SAWU8cMw8Thg6BNC7RvPnzEzXsP8Pmru6pPhxC1Qtv3NKh0mlariKYKCQkVP9p9r5D6kTmnAwOJBCFhYdBEBvr6WDZnKiaPGoIrN+8gLk6KkkULoXiRQtA0aTUsj4iOwd/rd2P7mSuIjpOKt1kYGaJ/qwaY2rMtdHXo7YQQolxBMWzKaRjMjeiVJtlHcFAIbOUSII0NESy2Cg0Ph6aqVrE8Hlw6gdv3H+GzuwfMTU1Rp3pVGBtpT6UJm2Q3c8EyvHj3Iem2SmVKYc7kMShbsrhKz40QdUBXEWqIElFE27jkyil+fCtEI2eyEbmJ3IRYRMnikfv752kqW2srtG/RBJrmV5PzYuOkaDZuHp6++YTWsEBliQlkEHA1OhxLdh/DB3cv7J45SmEVHCGEEEJ+X948Ljj/9JW4tY1t3fvRW8TA1Tn1VFxNwuKHqhXLiYe2OX3pKroNGoVinCGm8U7IwenhkxCLQ8/eoWnnPjixa5NYMUaIOol8cDdLn4+276lhnyjqEUW0TU5HB9SpVhkHuVBECLIU97Ega6cQCDtLS9Sv+ZfKzjG7Sb41j/lZn6id56/j7qv3mMU5oRNvDVdOH3k5A/SR2GIc54AjN+7jwoNnKvguCCGEEO3WrV1r+MTH4pyQUHWeHFvsuytEoHundio5N/Jz8fHxGDd1LspxxpjN5UAF3kRMSlXnTbEQOeAsk2DizPn0MhK1xJtaZt1zZdkzkVQoEUWyk7l/j0OEkQ5GwxOn5SH4KMTgpjwck+CJB4jCsnnToKureHsfyfpEVHJbTlxCed4EhRX0s6jKmSC3xABbT12hHxMhhBCSyVgVTe/O7bBG7oflMh88l0fhnRCNbbIATBW8UK5UCXRv34pedzV09fY9ePn7ozNnlarKTZ/j0U6wwKOXr/H2A0261BY/7j4g6UPb91Q8Xpf6RJHsomC+PDh3YAemL1iGdVdvQC4XxNvLlyiGQ2OGoUaViqo+RY3m7umFzbsP4MKla4iTSlG6ZDH07dYR1czkKT7vd6aBfPXxR33op1lyn1+mBzcv398+d0IIIYSkbdH0ScjjkgtrNm7HRf9v4m2mhobo2aEj/h41VOxtSX6PTCbDqQtXsH3vIXz56g5LC3O0ad4Yndu2EPtb/Qn3b17gwSFvGjFUAc4g4fM8vVAof94/ei6iPmjyXsZRUioLUCKKkAQF8ubGng0r4RcQCE9vH1hZWMBFA/sgvPv4GWu37sLpc5cQExuLIoUKoE/XDmjTtCF4PusLUK/dvofO/YeDi5ehqtwIBuBx2/MyDpw4g2ndW2Fyrw5/9PjWZibwDo9M834fPh4OlqwBMSGEEEIyG4sthvTuhgHdO+Hdpy+QxcuQN7eLxjUDl0ql2Hv0JLbs3I+PX9xgbGSElk0bYGCPLiqJB9lgmh5Dx+DslesoIjFCSbk+fL3CMPXVEqzfugvHd29CrhxOv/34VpbmkEOAL+LhgNS7AbyRMDjG2tLij74PQjQdJaWUhBJRhKTNzsZaPDTR5Ru30XnACJgIHGrJjGHCGePp84/oP2Yyzl+5jvVL5qWZmAoNC8PZKzcQFh6OvC65xOowiSSNcTrpFBQcIjbQLCTVwUTOGUaShOeWCwL28UGYtf0IyhYpgAYVS//2c3RqUB1zNx9AV0GaaoIi24b5XBaFUfWq/9H3QQghhJCf09HRQdGC+TXyZWIJILaAdvnWHbElQDsYIzAmHnt2HsDuA0dxaNv6NBt+C4KAB0+f48Xrd9DX00PtvyrDycH+j89p0eoNuHT1ptiAvAJnkjTh0EeIwxRfb/QaMgYXj+z+7UEu9Wr8BTMjIxyJDsIgiX2q7+mIEII8OXOgdPGif/y9EKLJKCmViSgRRYh2+fLVA0fPXkBYeISYRKpTvQp6DR2L4jJ9TOYcoPc9AdQWwE0+HAtOnkXl8mXF3g/JyeVyzF+xFqs2bkNMXBx0OA7xgoBcjg5YMX8Galat9NvnuO/ftYiJjsFo3hVG3H/JMJ7j0BFWeCiJxsr9p/8oKdWveT1sPHYBU4K90EduLTbsZCt/N4UIbOICUSqvC1rXoO2XhBBCCElIQJ2+dAUv3ryDvq4eGtSujnOXr+P6nXuYxedAad446WXqIsgwI9Yb3QePwrNrZ1L1F339/iMGjJqEl+8/iH2Z2KIbW/zr0LIpFs+cDEODhC1wGcUq3Tft2IvGMBMbkCfnwOlhoNwGM169EZNhFUqX/K3nYJVs40cMwpT5S8DLOLThLWHD6cJTiMMeeSDuCxHYMn6aSqrsCVEnlJT6Q5SIIkT7xMbGYdTU2dhz5AQMeR1YSHTgK42Fnq4OYqVSDJXkhl6yBBBTjTfFDURgw9ZdqZJS0xcux+pN29Ges0JTiQUsOR2xSekO3yB06DsUx3dtRMUypX6rieLtVx9RHIaw4HQUj1iWG2H38zf4E1ZmJji/cgZ6zFqB2W8/id87S0qxxFr9ciWwecpQ6OtlrEk9WyGMiomFgZ4eJN+Te4QQQtQzviUkvW7df4Q+w8bBNygIdjoGiBZkmL9yLfR1dFBDbozSOv8lpBgjToLBgi2G+n/F6YtX0aJRvaT73L95olmn3jCPlIrJrFKcEWIgx0UhDNuOnkJQUDB2b1j5W5VM7z9+QXB4OP6SOCu8vwxnBGOJDm7ff/zbSSlmcK+u4uLkghVrcSLmCwx5CaLlMliammLt1Nlo2aj+b22DjJfJxF5iv1vFRTIfNTn/fZSU+g2UiCJEuVjC4ou7B0LDwsW9/NZWWTeSlGEJqUPHTmEwb4c6nBn0BR5+Eim2xwfgKqRwE+LEla4fVYIxlnxxQ2RUdFKfB29fP6zdshNdeWt04P/bsliQM8R0OGGc8A3zlq7GsZ3/pvuNThUNFHM72uH62rl49O4T7rx4JyaSapUpjkIuGesBERIRieX7T2Lz8UvwCw0Tg9Q2tSphbKcWKJJbcWBICCEk69EwHs0UHhGJz1/doaeni4J582RpFc6bDx/Rrvdg5I/XwVSJC1yhDxkE3OMjsCLeF+8Rm1Dp9EMixZXTh5OuAR4+e5EiKbVy4zYIkTGYB2eY8gl764wgQXPOEtZyHcy/egN3Hz1F5XK/Xw0uZPD2jGJJo2F9e6Bnx7Y4fekqAgKDkMPRHg1r18hwg3rWQ3TF+s24eue+GCvnd3VBvx6d0KtjW3FrJ1E9bWhyHvngLnjTrL32ot/e30xG0Rs1Ienz+PlLHD1zARGRUciXOxc6tmwGq580dDx7+Tr+t2w1nr19l/BHiufRpH5tzBw/KkuaYLIte6wJ50DOFo35/86T9VIazTvATyYVS67LJSs9TxSLhEl3ySt/jp4+Dwk4NOVSf8+6HIdmcnMsu/cAPn7+cLCzzXAiqlrJwphy5zGChXixAis5FrDc5CLFz8ksZQvmFY/fERQWgbrDpuPLNx/UFkxQiHeAv1yK85cf4Oi1+zi5+G9UKV4w086VEEII0eTk0oHjp/Hy7XsYGuijUZ0aqFqhXJqVMaxv5awl/2DvoeOIio0Vb3NxcsTwAb3Rq1PbLKmo+effbTCTAdPgCIPvFeVsy10VzhTGkOBv+Tc8EaJQljNOFa/EQUiRWGG3HThyEg3kJjBV0H+zMmcCRx0D7D926reSUgXy5RarlW5EhKOwJHWM9ViIRKQ8HlUrlEVmMDUxRocWTX7767fsOYjR0+Ygv8QI/TkbscLsgXsQJs5agBu372PLP4v+uE8pIapCSalfoEQUIb8fTPUZMR4Xrt+CtY6emDDZGR+DWYtWYsH0iejRoU2qr9l37BQGjv0bJXlj/M07wZbTwRshGkcv3ED9e49w4fBO5Mqp3MTU8XMXxUCKVUj9iK3sNect8T+5N/x+aPrNgqerXCSqlC2dYuUrIDgYFhJdGAuKA4Uc3x8jMDhETEr9WPr7qxWX7g1rYu6Wg1ga54tJcEzqK8VWIvcKQXgvj8bi9j8Pgtx8/ODpHwRbczMUyPX7U2Z+ZcqG3fjm6YulXE448/+9Rs0FS8yI90L3WSvwdu8/0KGgihBCSDZ2+uIVDBzzN6Kio+GiY4gIQYY1W3aifIli2LVhJWytrVLFXM0698GXj25oKZihnMQeUYIcF3xCMWb6XHh4eWP62OFKPWexcfepc2gjN4WBguqsEpwhckAXN4RwlEXKpNRzIRoBsljU+aty0m3x8fEIi4qCE2+q8PlYTOYolyAwKPi3zpfFan26dcTytZtQUm6Eisn6SnkLcVjPB6JskSJpNl9P7Ev16u17yORyFM6fT0w8KQP7+Y2bMQ9NOHMMhB04PiHBWAdmuCePwJwLl7Hr0DF0b99aKc9PiLJRUkoBSkQR8ucGjJmEWzfvYSLviMqCiVgtFMrHY4csECOnzIadjY246peIbXkbP20uavFmGMXZJ5V25+MM8JdgilFhnpi5aCU2rVig1B9PWEQETCW6MBAUl7tbf69G8hBik5JSUkEQq6deCpHY3a9nis/P6eiIwPhYBPOpK5mYD0KMuIqYK8oHep8iMlz6y/o97Z87Fm0mLUTveDdUlhvBADwe8tHwlsdiRp8OqF9Bcb+qJ++/YOKaHbj27HXSbWXyuWL2wC6oUzbtIOx3hEZEYff5G2grmKdISDEsCdgXNhgZ4I6zd5+gadVymfrchBBCiKZ49OwFegwZgwowRn/eFawyLmYAAQAASURBVDaCrpjwecpHYdmr9+jQewguHtmVYlve+u278e7DZyzhciJ34nssB5SCEXJDH8vXb0anVs1QIG9upZ03SyKxYS42vOLLS1apZQsdcWqvTBDE2IdxE2KxkvNHyUIFxUqwRKzhuZ2lJT6FxKKeoucTBLhxUlR0cvjtcx43pL+YVJp9+RoKc0YoKNeHHxePe0IEnJ0csXnVYoUVZux7XbzmX/y7bQ+CwsLE24wNDNC1fStMGzscRoaZu4Vr295DYqzUk7NNdT4smVYeJti4fQ8lpYjGou6yyRJRiQfbmpd4EEIyjpWan7l8HYNhIzYATww8zDkdDOHsUEJijCWr1qf4muNnLyA8KgpdOetUvQZYE2+28nfi3EUEh4Qq9UeSz9UF/tIY+AhShfe/EqLFP5wz5F6YKvfEQpk3euMr9gtBmDl+ZIpEG9O6SX0xsNorDxSDyuTYyucJLgxNKpeGna2tmIz6nb3otcoUw9PtSzGoYxN45zLDW0d91KpdHtdWz8bErq0Ufg3rDVVn2HS4v/yMMbwD1khcxOq02M9+aD5+Hk7feYTM9MnTBzFSaaqS/UQs+cgqyl58ds/U5yWEEEI0CUsgOfF6GM85JPWvZIkINrFuHOzw5PUbXL55J8XXbN9zEDUEE+TmUvcoasFZwEKihx0Hjij1vFms4+xgL8ZJisQIcnzg4uCGOPTl3LFI5o3JgieGyr7CLKc9dm5YkSrh0qVDK1zmI+AjxKV6vDNCCIJkcejStuVvnzPru7VjzVJsX7UEOSqXwoucxogtmgtz/x6Hayf2i31Nf8RiuQFj/sbiVRtQNZzHEokzVkpyoVmsEbbv3I+2vQaLA3MyO64uIteH4Q9DdhKVhRFef/iUKs4kWYeanP+ZbF0pRQ3LCVGOUxcui9VG1ZC65JoFHA3lZlj48jW8fHzh5GAv3v7hsxusJHqwV9BAnCnEGUAqk+Gbtw8sLcyV9qNjDTbZ/vzt0QEYC4cUCbIAQSomkVo2aYC/KpXH6QtXEB0dg/aFC4gNLAvlT91rydzMDDMnjMKE2QsQKsjRDBbiSiEL2g5wIYjS5zFnQNc/Pu9c9jaY3a+TeKTHmBVb4CjjsQA5k8rsc3H6qCAYY7bghWFLNqLBvtJJ/bGuP32NlftP4crjF5DJBVQuVgBD2zZGkyrp67VgqK+XlIhjq7c/ihPkYsBqkMEpfoQQQoi2YBU4Zy5dQ09YQ+f7Fq3kisEQzjqG4iJd3epVxdtkMhk8fHzRik+Ip36ky/HIK9cVB8goW88u7bFg2Wo0FWLExabkDsqDEAU5dq1djgvXbuLDx89wMjXBoMb10bJRPYVNv4f27o5jJ89hgrcX2srNUZ4zRiTkOC8PxWkhFH06t0fRgvn/6JxZH6ZmDeqIR3pcvXUXh0+fwzjeATX4/1o95JEYoIxgjAmPnmDv0RNJbSpCw8Px74692LnvMLz9/GFlYY6ObVpgUM8usLP5bwDOz7CeYhF82gmncMigr6tLk/hUTBuanKtKtktKUSKKEOVjW/HMeB3oCIqbalogoQoxKjpG/Hjo5Fms37YbUpkU0RK5wpUgfyE+4WvNU/d6ykys5HrZ3GnoN2oiAjhPNJGbwZbTFZNILCFlYG2O2RNHi8k0lohKz8rJ0KqFYDGmD+ZtP4IJ/v8FhbVKF8XS4b2U2sdJkbdfPXH3zUdM4h1T9X1gVW1dYY2Rge649Oi5uPVv7ZGzGLVyK3JLDNBWMBN/eneef0GbJ4swrnOLdCXCCuZyQh4HW5z3C0PpH3pJMNeFcMTIZWhSOXMaihJCCCGaJk4qFfsTWXyfNKdoYc9C4MU4iwkJDcOAMZPFCm7W61IRVj0TwMtRyEy58RMzoHtnnDp7EZPffkBjmak4FCaS9bZCGO4KEfh75BA0rltTPNKDDcY5vX8bJsz6Hzaev4z1Mn/xdmtzc0zpOwwj+/dCVtu+/zBcdQxRXUi98FqEM0R53gQ79h4Sk1L+gUFo2rEXvrp/Q3XBBA05S3gGSvHvxu3Yd+gYTu3ditwuv5483LBOTXFo0FdJLFx+qIZj2xgv85FoVLdWpn6fhGSlbJGUokQUIVmrUL488JJGw0cihYOCyqdnQhRMDA3h5GAnjsftN3oSysMIDyDgnBCKllzKMaSs98AJLlRs8Mn2+Ctb6yYNYGFmigUr12HR0+fibXo6OmjTrBGmjB6aVN2VFkXNyns0rYdujevg4dtPCI2MQt4cDsjj9PPHUZavPn7ixwI/rGImygt9McB18/bD6y8eGL1yq1j+3xe2Sc01W8MKh/kgLNp9DDVKF0XdciV++pys98W4rq0waPEG5BR00Ya3EvsjsIbsD4RIbEAAWlQtl+UJOkIIIURdGBoYIKe9HZ75RaEWUieRIgWZ2Iuyeb68YlVV254D8eHNBxSFIS4IYWgtWCUNPEkec32VRWNpkwZKP39jI0Mc3bkR/1u5VqwMOhiV0IS8oKsL1g6egI6tmmX4Me1tbbD1n8XilOI37z9CX18fZUsUg/73Cuys9vXrN+SX6YKTKF54zQ99nPPwFP97wsz/wd/DCys4Z+Tk/zvfdoIVJod4icN9zh3Y/svnbNGwrjiZep6PLyYIdsjzPX4LEeKxTvCHP6QY2rd7pn2PJPuKfHAXvGnK67CsoLVJKYuoLzBNNkWB+kMRknVaNq6PybMXYnN0ACbAIamnFPNNiMNpPhwd27QWg69ZC5ajNGeEKZwT1sn9sEXuDzkENOTMxXG3nkIcdgiB4hS+AyMHZ9n3UPuvKuLh6e2L8IgIMRFlZvrf35RfJaMUlfCyxEyFIn9WZp4ZLL9/H76Qwhapk4b+iIdcbKJuig3HL8BSooueLCH1Q6+HVpwlrkoisO7wuV8mpZiejWvBKyAYc7YewAmEIY+gLz4Xa8heu3QxbJw8JBO/S0IIIUSzsPfZXl07iFvgGgjRKMwZpqh42i4PQDwPdGnbAqcuXMGjl6+xSOIMM4kEo2TumC77hn4SO3HRiQ1huSWEYz0XiMplS6Fm1UpZ8j2wCXRzJ48VF/Hcv3mJ2/Jy5XT6461lbEIxO1TNysoCvnza/S99BSksLSzEJBqb6NwX1ikSUolDc3rILTHv6XO8eP0WxYsU+ulzstfw8Lb1aNdrEIZ7uCOPjiGMBB5v5dHQ1dPFpiULUapYkUz7HknGUD+pP6e1SSmGElGEqAbbArd60Wz0HDoWo/ENDeWmrDsCXgrROM+HI0eunJg8YjBevfuAd1/cMJPPIfZu6s/bgZMDW+UB2IFAmIBHMGTiRJNNCxeISaKslsORVTPZ/3YiSh2VK5QXue1tcdQvBEV5w1SB4hF5MEwN9NGwUmks23McZeSG0FWwIsi+rrzMCNfefEjX87LP/7tHG3SqVw3bzlzFx2/esAoNRy1bKxTL7YyA0HCYGv3+a5jY4PNPA19CCCFEVVivoQtXbmDK0xeoIzcRB4SwnkEXuHC8EqKwZNrfcLS3w8ETp1FIYpSUuJotyYGFMh+MlrnDHBLEQI5YCKhfoxo2LJ2XYlpfVmALjwXz5YG2aduiCQbdvIOPktR9s1jv0RtcJMa26oYXb96JWzErShQvaFbgTMQWm49fvPplUoph2/zunDuC0xev4vzVG+LWQLZ3oFD+hNdYKpWKzeb/JIai+On3aco1gLrS2ul7+ro0OY8QVWpSrzZO7N6E3FVKY43gj1lyL1wylqJHj044e2C72Kw8cZKe4/ctfjoch0ESe2yW5EYv3gaNeQvochzGDR+Alo3qq80PlCWiEo/EiXma9GbEAtNZ/TuJ/R2WyX3h/X2qTZAQj00yf5wQQjCxexuYGBpAT1cXMUi7uSYLetnWxoxg2xZL5nPBpfvPcO3JK1y4fA+z/t2HIp2Ho///1iI2TnFfjLScv/8UTcbMhVm9rjCp20WcKnjk2j2aQkMIIUTjsGTO4W3rMHJIXzy04DFb7oXlcl+YlSqAff/+g96d24mfFxgUDAf5f9c7BTlDbJC4YjrvhGa8BUpwhjDS18e+javEoSskc7RqVB/FCuTHTPjgujxc7OnEWhE8lEfib3jD2toKvTq1FeOnxDhJkVhxXwCSPi89WNKpZrVK8A8IFJvF3755F3u27kP3oWNQsnpD3LqfscnJQcEhmLN0FQpXqg2rAqWRv3wNTP3fUnj7JrR5ICSraHWlFCFEtSqVLY0Dm9cgPCISkVFRsLa0SLGKk9gf6r0QAyfuv9JmNgK5OWcJdyEWu+SByOviAlXTxIqon2lXuwqi4+Iw/p9tuBzlBhOJDqJk8dDX08Ws7h0xumNC34cGlUtj/tuPCBNkMONSJvulghw3+Ei0rZK+hqWJLj16ga4zV6AqZ4IeElc4QA8xnByX+DBsOn9DDNM2TkzfVs3Fe45hyoY9KCgxQk/BSlxpufXGA51mLMOYjs0wd0CXDJ0bIYQQog6JqYnDB2HckP4ICAqGgb5eqsSSi3NO3Hz2NkWFC2uXUJ4zQXmY4H9yb7g6O6joO9BerJfVke3r0W/kRCy8ex96PA8JOETLZShVuBA2/7MI1laWKFeqBMyMjHApOgy9JKm3HV4WwiDhedSoUjHdz81+1l36j8CzJ8/F6X9VYQodcPgiicW/wQFo13swLh3ZhcL58/3ysVjiqXGHnvDz8UMtuTFy83bwCI3Dtq17cPDoKZzcuxl5XVUff5PsQamVUkFBQejSpQvMzMxgYWGBPn36ICIi4qdfU7NmTfEPa/Jj4MCByjxNQkgW9BdgfQB+LCt2zZUT1cqXxUEuFNFCypUktuq0SwiCrYUF6tf8SyU/I02viPqV7g1r4vOhddg2ZRgm9WmPteMH4suhdRjfpWVSgNu7SW0YGOhjvuCN4O8TEJkIQYbFgi8iIMfg1g0z9LxzNh9AQc4AYzkHOHxPRrKm5014C/TlbLDz3HV88vT55eM8/fBFTEh14KywGDnQgrdEM94S/+Nyoi9viyV7T4gJMEI0DcVPP3ltYswQGpWFPwxCVEgikYiNvhVVOnVt1wrf4mNwWQhPdd9HIQZ3hAh0T8eUYJJxNtZWOLJjA26ePIAZE0dj8vgROLd/Gy4f25M0TY81fe/boxOOIgRX5GFiXJuYWGJVVdsRhHbNGv1yeE5y1+/cx62HjzFOsEcN3kzcYcDk5vQxDY4wlwErN2xN12ONmTYXET4BWAlncZdCQ95C7Ee2Bs7QCY3E4HFT6VcjHaiflAZUSrGElLe3Ny5cuCDuc+3Vqxf69++P3bt3//Tr+vXrh1mzZiX9v5GRkTJPkxCiQvOmjEPjjr0wLu4b2sgtkJ8zELeTHUUongtR2DxrAfT0fn+PfEYpmpynzYwM9NGhTtU077ezNMeR/01E64kL0DP6C0rCCKxe6jkXDU4iwe7po1DIJUe6n88rIAh3Xr/HWD5lA/xEdTgzbOUDcejqXTE59jPrj56HnUQPnWGdqg8CmxZ4SRKOdYfPok7Z4uk+P0LUAcVPhJBfqVK+DDq0aIKVx0/joywGtXgz6IETt+Yf5UJRskghdGvfil5IJSpaML94pGXS8EHw+OaFJSfOYI9OCFxlOvCSyOAmi0atKhWxeNaUDD3fkdPnkUPHAGWE1NfGbHGvntwE+06dw+oFs37aQ8zDyxtnr1zHEM4WDnzKGNuS00H3xCbsb96heOGCGTrH7EhbrhUiH9zVvqTUmzdvcPbsWTx48ADlypUTb/vnn3/QuHFjLF68GE5OaY/9ZkkoB4f0lZvGxsaKR6KwsLBMOHtCSFZhzR1P792CKfMWY8m9h0m3F82fD3vGDUeDWtVVnohy9w1ARFQ0ctpZw8w4a5PkYZFR8A8Jg425GYwN9XHq9iPsv3wbQaHhyO1kj15NaqN84V+Xaf+JKsUL4s3elWIF05VHLxAvl2NS8ULiND0HK4sMPVZYZLT40YZT/Pajz/Gw4HTF7/tXHr/5hNJyQ0jSaMJeTmaEO28/Z+j8CFG1rIqfGIqhCNFc7H2OJR/YFqv1W3fhRGjCRDhDPT10aN0CM8ePEgfPqFJkVDTcPT1hqG8AF+ccWdpIWy6Xw8vXT6xMyuFgj2/ePti+7zAeP3spLnbWqVEVHVo0/eVk5T+ho6OD9UvmoU/XDth18Bi+eXqjgq01lrRoIk5DzGjz+bDwcNgIkjRfRzZROU4qFQ82sS8tL9+8F1+X8skm1SfHtoAyz1+9oaRUNsObWmpXUurOnTvilr3EgIqpW7eu+I/v3r17aNUq7cz9rl27sHPnTjGwatasGaZOnZpmtdT8+fMxc+ZMpXwPhJCsS0wd27kR7t884eHpLY7bLZQvr1KDl/RURJ258xhztx7Ew/cJiQ0DHR20r1sVM/t2hKO1cv9ov3f3wqwt+3H0+n0xCcSqilhCLDgiEvklhrCTS3Dq+QdsPnUZ/ZrVxYqRvZU6WcfS1ATD2jYWjz/hZGMpvo5v5DEoxqX+u+4vSOEji0GeHL++sGZBZVQaDUSZKMigr5d2UEaIOsqq+ImhGIoQzd/eN25ofwzv1xPPX79FvEyGIgXyqryxeWhYGOYuX4M9B48hIjphMapQntwYPaQf2jX/szgiPcmojTv3Ye2mHXDz8hJvszY3R3BYmFhNVEIwQAgHTLp2E4tWrsPBLetQouivp9/9LhbLVixTSjz+VB6XXLiAy4gR5OL38qPXQjQcbKyhr/dfn1ZFEncgsBjKWsH9ibGV3i8ehxC1T0r5+PjAzs4u5ZPp6MDKykq8Ly2dO3eGi4uLuBL4/PlzTJgwAe/evcPhw4cVfv6kSZMwevToFJVSzs4Je3kJIZolV84c4qEOW/N2nL2GfgvWogRvhIm8I6w5HbyQR+PEhdu49vgVrq2dk+EqofR6+dldnCBnGCtHL1jDldeHO2JxNCIE+uAwGLbILzGATBBwjg/F2hMXkSeHPUZ1SGhOrs5YYq1dnao4cfE2agmmYlP7RKzfwg4hUNxS2K5W5V8+VuOqZfG/DwcVNmFnAdtNPhLdq1VRyvdBiKbHTwzFUIRoT/Pt8qVLQB2EhUegaac+cPv0BU3kZigjsRH7UJ5z80f/MZPFBtssiaYMrPpn5JTZ2HngCGrwpujGO4kNlG+EheOKIKAMDDGedwTPcfATpJgf5oN2vQfh4aWTYv9Tdde1bUssXbcJBxGErhKbFPe5CbG4wkVgdOf+v1zUZQkyU0NDXIwJVdiE/aIQCl2JRKzmImmjflKZJ8PL6hMnTkzViPzH4+3bt799QqznVIMGDVC8eHGxp8L27dtx5MgRfPr0SeHn6+vri43Ukx+EEKKoWTmTnobloRFRGLFsE+pyZpjD5UA13hSFOUO0562wBDkRGhiKWZv3K+1FHrp4A6xiBaxATrF5d0neSGzgvUriglzQwz8yXzHwYtVTjXkL1OPMsGLvSUjj/2tErs5m9OkAI0tTjME3HJIH4Z0QjVvycEwRvMRmoCtG9YGp0a+3HLAm7IYG+pgLbwQI0qTbQ4R4LBB8ECfhMLBlAyV/N4RoZvzEUAxFCMlsqzZtx6ePn/E/5EA3iQ2KcoaoyJtgKueINpwlZi5aIfY0Uoart+5ix4EjGM7bYyzvKD4v26I2WuKIcbwjbgoRuC9Eip9rx+liEhwQEBSCA8dPQxOwAUGTRwzGXiEIc+XeeCCPwBshGjtkAZgIT+TPlweDenX95eOYGBuhf88uYhP2C/JQcZEzcXHwpjwcuxCMzm1bwNbaKgu+K82mLf2kNK5SasyYMejZ8+fZ7Tx58oil435+filuj4+PFyfKZKTfQcWKCWMyP378iLx582b0dAkhGo4lXx4/fyk2dwwNCxenmnRu3Vyc5qesZuX7L99CrFSKbnxOcTUtORbEsJW/PRduYNGQ7jA2NEBmevXFA3fffMRk3hHGfMrqH0OORxfeBjPknviAWBRAwnPX4c1xPsRD/NpS+XND3eWwtcLVNbMx7d892HX5DuJkAeLtZfPnxuFe7dCocpl0PY69lQWOLZqMVhP+hz6RbmLgy4PDSyFKHJ+9f9ZY5E3HNkBCsgLFT4QQVVQt7T9+Ci9evxW3YrFpxnX+qqK07f4sZtu2+wBqCyZilXdyLPHegbfGGSEcuw8dw4RhmT9dfcueA8itY4i6Quoiheq8KY7Kg3FWCEUlmCTFdMV5I1y8fhO9O7eDJhg7pB9yONpj2ZqNmPk1oY+YsYEBOrdri79HDUl3xdfE4QPh5e2LFUdPiE3YXWQ68JTI4CWLQaPaNfC/qROU/J0Q8gdJKVtbW/H4lcqVKyMkJASPHj1C2bJlxdsuX74s7vNNTDSlx9OnT8WPjo6OGT1VQoiGYw0y+wwfh3PXbsJGRx/W0MFBeQzmL1uDmRNHYnCvbmkmo/5k5eLjNx84ShKeT5GiMMSuuED4BIVketKD9ZJiiivot5Rwe8L39U2IQwEuISllgITEmTReBk2R09YamycPxdLhveDhGwBTY0O4OqTcspQeFYvkx7t9q7D7/A1cffJSXOXrVKwQujWsASsz5TUvJSSjKH4ihGSls5evo9/ICYiOiUEeiaHYJ4j1WmKDZPZtWi0mNjJbRGQU/IKDUYRXHBsZcTzyQB+f3BKSKZntw4fPKCbTB6dgAEpiDHVLiEhxm4EASKWaUWmeqFPr5ujYqhk+f3VHTGwcXHLmEKufMoJtC1+zaDb6duuAXYeOwcvHD4VtrNChZTNULlc6S5vSE/WYvKeqJudK7SlVuHBhNGzYEP369cO6desglUoxdOhQdOzYMWlyjKenJ+rUqSOWmFeoUEEsMd+9e7c4Ycba2lrsiTBq1ChUr14dJUqoxz5pQkjWGTJ+Kq7fuCv2dKosmIjb1SI5GfbIg/D3vCWwt7VFp8L2mV5Cy/oesS1gUsihq6CRpD8Sgpf0bDHLKBOjhERTIOJhipSVUkzw9+dmNUGJ7sojYKyvh0IuyuvHpSwWJsbi8SfYz2FAy/riQYimo/iJEPKnnr16gx5DRqOs3AADeFfYQFesYnoticGSzx5o23Mgrp/cD13d//o6Zga2pZ71IvIXFCd52DkEcjKl9W8yMTFBIBKqrxUJQnyK+ClKkOE5F4vhJYtB07CkEZu8+KfKlCgmHiRjqJ9U5lLeqKbvU2AKFSokJp5YoqlatWrYsGFD0v0sUcWacEZFJYz+ZmWlFy9eRP369cWvY6Xubdq0wYkTJ5R5moQQNfT+0xccO3cR/WEt9nRiCSnGmJOgD2+DCrwxlixdAV7P4Jc9ojKqTc1KiJDF44oQnuo+tu/+NBeKv0oUgp2lOTLbXyULw8rEGKflIQrvPykPhRF4lP5eSfVRiMFxLhRdG9ZUSpKMEJL1KH4ihPyJf/7dBhvoYDznmDRQhCUx2Db3ybDH289fcObStUx/kVn1TdMGdXCej0CckHo67kMhEt7xMWjdWDn9Hls0qY/7iBIn+f4oWIjHbSECVXmTpHhuveCPeE5A9/atlXI+RLtRPykNqJRi2KQYVvmUFldXVzFjnohNzbt2LfP/QBJCNM/pi1dhyOugJmea6j4WWDUUzDHLwwufvXwzfQsdqzhqX6sy1l29B7lcQC3ODPocDx8hDlvkAXgvRGNC0zpQBgM9PYzr2hKT1u2CuUyClrylmIiLEuQ4KYTgqBCMgjDAOSEU74QYMcBifaTm9O+klPMhJDuXsif9d0xslj43xU+EkN/Frq1Onr+EDnIz6PKpt2Dl4wyQX2KEkxcuo3nDupn+Qo8a2Af1LlzBbLk3+sIGLpw+4gUBt4RwrIE/ihXIr7RJgWw63ZpN2zE9yBvDBVsxXmIxI1vAWyrzgfA9GbVbHoirfCR8hTisXTgHTg6Zv5WREKImSSlCCPld0THRMOZ56KZR0GnOJWxti1LSxeKGiYMg4XmsunQLm/hAGMt5ccIbOxsW1PSZtxqHLt/BugkDYWuRuVM/R7ZvirDIaCzadRRHhFDYcXrwF+IQK8hRtmAefPMJwI7IYLg62GJus5bo17wujAxSNhQlhPxZIopJ7K/A68TQy0kI0ZikFBvWYvbDsJTkzAQO0dHK+btWvHBB7N+0Gn1HjMeQ4K+wleghXBaPGMjFDpgv339Aib8aYsbEUWJfpMxkYW6GYzs3okv/4Rj71R32OgZgeTlWnWVnZYnCDvY4+vELdHV1UK9mTQzu3ZW2rhGiBigpRQhRyz3axc10EBAfh6+SWHGV7UePhSgY6unCxeHXgxd+t2Jpy5RhGNe1FVqMnw8f/yDU4kzRk7MRp+CxscI7HrxAgxEzcX3dXJhk4hQ+tqo3vXd7Mdm09+IteAUEwcHaAh3qVIWznU2mPQ8hJO1EFCGEaCI2Wa+AqwueugehISxS3R8tyPGWi0Wt/Mqbal69cgW8vHkBc5atwj8btyEHp4eOnCWqcWbwQBwOBQdh0PipYgKNNe3OTPnzuOLu+aO4eP0Wbtx9IN5WuXwZNKj5FySStBN1hGTXflKRP8RBqkBJKUKI2v1xZ3u0W9SsAvvVO7E5PABT4Jii4binEIeTXCg61asuNiVXpucf3eDhH4hFEmcU/j75jqnPmaOQYIBhHu7YcfYqBrVqmOnP7WRjhdEdM3cVkRBCiShCiHbr060jJs9ZiKfySJTi/2sqzpJA2+UBiOHk6N5BuX2UdHQkOHX2IsryxpjOOYH/3huUTd8by6bzyYEZ/1uGNk0bQU9PN9MTc/Vr/iUehCiDtvWT4lW8IKfURueEEPKzRFTiwSQ2K0/8I6+nq4NNU4biJR+L4fDAMXkwbsvDsUnmjzHCN9g72GBWv45Kf4F3nr2G4rxRioRUolycPipyxth++io0HQtUWUWWh18AZLLUzUkJ0YaVwMQjMQBLPIhmCYoxQ2jCjBxCiAI9O7ZFzSqVMEPwxnKZD27Kw3FeHooJ8MQJIQTzp46Hs5OjUl+7R89e4vM3T7SBZVJCKnlFeDveCn7Bwbhy647G/wwjo6Lh/s0ToeGpB+QQQn6NKqUIISqtiPqZuuVK4MqqWViw8wg2334EmVwOS2Mj9G3SAOO7tIKVWcIEFWXyDQyBs5D2Cp6zoIcbgYon5WlKMmrHuWtYtvs43nh4ibc5WVlgYOuGGNWhKXR16G2CaE9ZOiWgCCHZAas82r1hJdZs2YlNO/bgoq+3eHuVsqWxb2DvLKkg8g8MFD/m4vQU3u+MhNv9AhI+TxN5eHlj/oq1OHzijNjHiyXf2Gs7ccQglCxaWNWnR4jGoKsNQohaJaJ+VKZgHuybPQbRsXGIjImBhYkxdLKwJ4CTnRXcPNIOmNy4ODjZ2kFTTf13DxbvOY5KvAkm8Y7QA4c7wZGYtWkf7r18j31zxmTp601IZqBEFCEku2OJqZEDemF4vx4ICQ2Drq4uTE3+28qnbA52CT0/3YRYlOJSX3K6IWFQjaO9ZsZQbu7f0KBdN8hCI9FBMEN+3gDekOLk9ftoeOsuDm5Zi6oVyqr6NIkSrmu0beueOqCkFCFE7RJRihjq64lHVuveqBa63H+GZ3wUSvIp+1d9EmLwQB6BFU2Uv41QGR6/+ywmpHrzNmjNWyXdXh4mqCI3wYy7j8VG610bVFfpeRKSHpSIIoQQxf2VrCxTNzxXttLFi6Jgblfs/+qP4oIRJMm28LEq7X1CEBxsrFGzSkVookmzF4APjcIS5IQln3BJXZpV+QtmmC7zxpBxU/D4yinx9SdEXUWqQZNzhv6VEEIyzc96RGmqFn+VR42SRTAH3jgoD4KfIEWgEI/j8mBMEbxQMq+LxiZtNp64CDuJHlpwqXvqlOONUUZijH+PnlfJuRGS0T5R1COKEELUB+sbNW/qeLxCNKYJXngmj0KYIMNrIRqzBW/clkeI9+toYJuAb94+OHftJtoJFrD8oQpMj+PRHVb46uWNa7fvqewcCUkvdWhtoHl/BQghalsVpekJKEXY1rXD88dj/Ort2HnuOrbGByTczvNoW6sylo/srZIKrszw1u0bisj1IZGkbECaqIRgiKPunll+XoRkZFVPHYIpQgghqdX+qwr2b1yFibMW4G+3r0m3uzo5YdukGWjesK5Gvmyf3dzFaq/ivOK4txAMoMfzeP/pC2pVq5zl50eyZicIyTyUlCKEZJi2J6J+ZGxogNVj+2Nmv464+/I9BAEoVzgvHK01+2LYxMgQflzak/aChHgYGxhk6TkRogglogghRHMTU/fOHxWn8Xn5+MLWxhoVy5TU6G1txkYJsW8wZHBScH845JDK5TA2Stn2gWi+7HDdowqUlCKEpEt2S0QpYmNuhqZVy0FbtK5ZCYMePIMHFwfnH6bjRAkyXOMj0KN2fZWdH8neKBFFCCHas5WvXKniANih+UoVK4Kc9vY47ReCopLUMfFZIVScXtygtma2dyAkq2luipoQkmU9on7sE0W0Q/vaVZDH0Q6z4I2XQpRYis58FWIxA94Q9HQwuE1DVZ8myaY9ohjqE0UIIUTdSCQSjB8xENeEcGyW+SNckIm3xwpynJAHY5cQiF5d2sPW+r8hMkSzaePWvUg1aXLOUKUUIUTpk/OIejIy0MeppVPQduJCTPz6DfY6+tAFh2+yGDhamuPEnLFwddDMUc1Ec1BFFCGEEE3TrV0rBAWHYO7SVTgphMGJ10eAEIcIeTy6t2+FORNHq/oUSSbTxmsiXk36clJSihBCiahsjCWd7m9eiMuPX+LSw+eIl8lQoXB+tPirAvR06S2CKAclogghhGi6Ef17oVPr5th/7BQ8PL1hbWmBNs0aIq+ri6pPjRCNQlcchGRTVBFFErFmo3XLlRAPQrIqGaUuq3NEc4VGAebUR5gQokJ2NtYY2qc7/Qy0mDZu3VM3lJQiJBuhRBQhJKtRIoooQ1CMGawMwujFJYQQonTauHVPnVBSihAtR4koQkhWo0QUIYQQQoj6xmm8GlWsU1KKkGyQjKLsPiFE2SgRRQghhBBtQlv3sgYlpQjRIpSIIoRkJWpYTgghhBBtRov7ykdJKUI0HCWiCCFZiRJRhBBCCCEks1BSihANRIkoQkhWokQUIYQQQrITbd26F5lsErK6oKQUIRqCElGEkKxEiShCCCGEZGfaunWPV6Mm5wwlpQhRYzQ5jxCSlSgRRQghhBBCshIlpQhRM5SIIoRkJUpEEUIIIYRo/9Y9dUVJKULUACWiCCFZiRJRRBsExZgBCIO5karPhBBCiLbRxq17kWrYT4qhpBQhKkKJKEKIKoMRdesnQAghhBBClEsd4z9KShGShSgRRQjJapSIIoQQQghJH9q6l/UoKUWIklEiihCS1SgRRQghhBDye7Rx6546o6QUIVmQjKI/bIQQZaNEFCGEEEII0aR+UgwlpQjJRJSIIoRkJWpYTgghhBCSeddy2lxMwKthPymGklKE/CFKRBFCshIlogghhBBCiLagpBQhv4ESUYSQrESJKEIIIYQQ5aEG56pDSSlC0okalhNCshIloghJn9AowNyIXi1CCCF/Rlu37kWqcT8phpJShPwEJaIIIVmJElGEZExQjBmsDMLoZSOEEEI0sJ8UQ0kpQn5AiShCSFaiRBQhhBBCiOpoe4NzdUdJKUIoEUUIUXEySp1XrwghhBBCCFEWSkqRbIsqogghWY0SUYQQQggh6kPbG5xHqnk/KYaSUiRboUQUISSrUSKKEEIIIUR9afvWPV7NK/IpKUW0HiWiCCFZjRJRhBBNFxMng4GeRNWnQQghRMtRUopki2SUtme/CSGqR4koQoi2CDbOC8vIT6o+DUIIUSpqcK4eKClFtAologghWYkm5xGiHkKjAHMjVZ8FIYQQol5xKq/mW/cYSkoRjUeJKEJIVqJEFNF2/iFyuJhAYwTFmMHKIEzVp0EIIUSDaHuDc01CSSmikSgRRQjJSpSIIoQQQgjRLtTiRT1QUopoDGpYTgjJSpSIIoQQQgjRPtmhSirywV1oCl5ZDzx37lxUqVIFRkZGsLCwSNfXCIKAadOmwdHREYaGhqhbty4+fPigrFMkGvIHI/FIzGYnHoQQoow38MSDYfvwEw9CsgrFUIQQQohyZYfrSV5D4lelJaXi4uLQrl07DBo0KN1fs3DhQqxcuRLr1q3DvXv3YGxsjAYNGiAmJkZZp0nUECWiCCFZiRJRRN1QDEUIIYSQ7EJp2/dmzpwpfty6dWu6q6SWL1+OKVOmoEWLFuJt27dvh729PY4ePYqOHTsq/LrY2FjxSBQWRo0uNRFtzSOEqLKsWVNWkkj2QDEUIYQQorzrzuxQJaVJlFYplVFfvnyBj4+PuGUvkbm5OSpWrIg7d+6k+XXz588XPy/xcHZ2zqIzJn+KKqIIIaqsiqKteURbUAxFCCGEkESJca6mUJukFEtIMawyKjn2/4n3KTJp0iSEhoYmHR4eHko/V/L7KBFFCMlqlIgi2k4ZMZRfsByaJjRK1WdACCFEnWWHBueaKENJqYkTJ4LjuJ8eb9++RVbS19eHmZlZioOoH2pWTgjJSpSIIupGk2Ko8LB4aJqgGIr/CCGE/Bpt3dPwnlJjxoxBz549f/o5efLk+a0TcXBwED/6+vqK0/cSsf8vVarUbz0mUZ9MNP3jJ4Rk9ehbTSpbJtqPYihCCCFEdbJLlVTkD/Gw1iWlbG1txUMZcufOLSamLl26lJSEYk3L2RS+jEzwI6pFiShCSFaiRBTRFBRDEUIIIaqVXQoleA1bmFXa9D13d3cEBQWJH2UyGZ4+fSreni9fPpiYmIj/XahQIbFReatWrcSy9ZEjR2LOnDnInz+/mKSaOnUqnJyc0LJlS2WdJskElIgihGQlSkQRbUcxFCGEEJJ5skuVlKZSWlJq2rRp2LZtW9L/ly5dWvx45coV1KxZU/zvd+/eiY01E40fPx6RkZHo378/QkJCUK1aNZw9exYGBgbKOk2SSf+ws0vWmRCiGpSIItkJxVCEEEJI5soO16uRGrh1j+EEQRCgRdiWP3Nzc3x9fBNmpgkVWSRzUCKKEJKVKBFF1EVYdAxcRs8XF9K0eaBKYgy1YKcf7BytYGepNkOa08XKIAzmRqo+C+1hGfkJBnoSVZ8GIYRkyjVsdklK8Wq0dS+98ZPSKqWIdqBEFCEkK1EiihD14Rcs17jEFMlcMXEySkwRQjRedkhIaTJKSpFUKBFFCMlKlIgiRP2Eh8bD1FyzwsSgGLYKS9VSmSXYOK9YLUUIIZoqO/WSitTQrXuMZkUbRGkoEUUIUeWbpzqVGhNCCCGEEO2QnaqkeA2NpykplY1RIooQktUoEUUIIYQQQpQtO1VJaTpKSmUzlIgihGQ1SkQRQgghhJCsll2qpCI1eOseQ0mpbJiMyi7/OAkhqkOJKEIIIYQQogrZsUqK19CtewwlpbQYJaIIIVmJGpYTQgghhBB1QIUYmoOSUlqGElGEkKxEiShCtJtfsBx2ljw0SWgUYG6k6rMghBCiCtmtSipSw7fuMZSU0gKUiCKEZCVKRBGSPYSHxsPUXLNCxaAYM1gZhKn6NAghhKhQdquS4tV069638+lLmGlWpEGSUMNyQkhWokQUIYQQQghRZ9mtSkpbUFJKg1AiihCSlSgRRQghhBBCNEl2qpKK1IKtewwlpdQcJaIIIVmJElGEEEIIIUTTZNcqKV5Nt+65n76Z7s+lpJQa+j979wHeVPm2Afxu0733ogXK3nsPAUGmCqIIooC4/oC4AAeKeyKiOEEcIC4QEFAEmQLK3nvPAgVK6d5t8l3P4UvpSHfGSXL/risimScnIXlzn+d9Xpci/6DsKe0lIsuHUWr9ciMiIiIiKgl/N6uLJiisXNdjKKUSDKKIyNwYRBERERGRLfyWtrdAKs1Gpu4JhlIWxCCKiMyNQRQRVdS1BC1C/B2tasclpQO+HpbeCiIiItNxtJHZDQylLBxG2VuiS0TmxyCKiCorJSkX3r7WNVy8kemDALdkS2+GTUjwrA3/tNNwc9FYelOIiAyyxyoptZN+UprgCCA7u1zXt65RhhVjEEVE5sSG5URERERky+y1uXmaDU3dEwylTIhBFBGZE4MoIiIiIrIn9lol5WgjU/cEQykjYxBFRObEIIqIiIiI7I29VklZw9S9imIoZQQMoojInBhEEREREZG9s8cqqbSd21RfJaX0k6oAhlKVxJXziMicGEQRkSVxBT4iIlILNje3LQylKoBBFBGZE4MoIlIDrsBHRERqYc/T9tJU3uC8MlP3BEOpMjCIIiJzYhBFRERERFQye5y2p2drU/cEQykDGEQRkSXDKLV/2RARERERmZs9V0nZMoZS/49BFBGZG4MoIiIiIqLys9cqqTQraHBeWXYdSjGIIiJzYxBFRERERFQxbG4O1feTqszUPbsNpQqGUfaatBKR+TCIIiIiIiKqHHuftpem8gbnVWU3oRSDKCIyJzYsJyJbW4FPho0h/o6wFjcyfQAkw9fD0ltCRERVZe/FJI42OnXP5kMpBlFEZE4MooiIiIiIjMfeq6SsZepeVdhsKOVy9gA0AQGW3gwisnEMooiIyJYleNaGf9ppuLloLL0pRGSngZQ9V0mlWcnUvcr2k7LpUErj6mbpTSAiG8UgioiIiIjI9Ow5kLKHqXs2HUoRERkTgygiIiIiIvPgtD1YRZVUVafuCYZSREQlYBBFRERERGQZrJKCVVRJVWXqnmAoRURUShhlDV8ERETmwBX4iIjIXFVSDKTsB0MpIiIGUUREREREFsdpe7cOkqv94Lgxpu4JhlJEZLdYEUVEREREpA5cbc/6VHXqnmAoRUR2hUEUEREREZE6cdoerKLBuTExlCIim8cgioiIiIhIvThtrzB7mbonGEoRkU3iynlEREREROrHaXvWWSVljKl7gqEUEdkMBlFERKZljSvwiaR0wNfD0ltBREQl4bQ966mSMjaGUkRk1RhEERFRaW5k+iDALZk7iYhIhThtz/oYc+qeYChFRFaHQRQRERERkXXjtL3C5DeOtVRJGWvqnmAoRURWgUEUEVnqSGBqjkxZIyIiImPjtD1iKEVEqsUgiogsWZKuPwqoyc7mC0F2LcGzNvzTTsPNRWPpTSEiG6qSYiB1i7U0OJdxkjGrpITJulS+++676NSpEzw8PODn51eu2zz88MNwcHAodOrbt6+pNpGIVPyhrP9glhJW/YmIyFQDLP1JBlr6k6VYwxjqWoLWZPdNRES2jX2kDHO00987JquUys7OxpAhQ9CxY0d899135b6dDKDmzJmT/3dXV1cTbSERqfXogL1+IBOR5Zp0WjKEsrYxlKzA5+3rZHXNzoFkrsBHRGRh7CNl3VVSpmCyEcWbb76p/Dl37twK3U4GUGFhYeW+flZWlnLSS07m6ipE1oJBFBGZk5qDqII4hiIiIlvGaXvFOVrJQXlTjJ1Ud5hrw4YNCAkJgb+/P26//Xa88847CAwMLPH677//fv7gjYjUj0EUEZmTtQRRxsAxFBERqRn7SFlvlZQpqSqUkrLzwYMHIzo6GqdPn8bLL7+Mfv36YevWrdBoDDdWnDx5MiZMmFCoUioqKsqMW01EZWHDciIyJ3sKovQ4hiIiIjVjHynrrpK6YKKpexUOpV566SVMnTq11OscPXoUDRo0qNTGDBs2LP//mzZtimbNmqF27drKkb+ePXuWON2PfaeI1IdBFBGZk9qDKI6hiIjIXrGPlG1USWlMNLaqUCg1ceJEZXWX0tSqVauq21TovoKCgnDq1KkSQykiUg8GUURkTmoPomx9DCUr8IX4m2whZ5NISgebnRMRmREDKeuvkjK1CoVSwcHByslcLl68iPj4eISHh5vtMYmoYhhEEZE5WVMQZctjKGtdgS/AjQviEBGZGxubW3eV1AUTTt0TJju8deHCBezbt0/5My8vT/l/OaWmpuZfR6b5LVmyRPl/Of/555/Htm3bcO7cOaxbtw4DBw5EnTp10KdPH1NtJhFV8kNUf9In/PoTEZGpBkT6QZEEUfqTLeIYioiIbAEbm5fOmn47aUw45jLZIa7XXnsNP/zwQ/7fW7Zsqfz5zz//oHv37sr/Hz9+HElJScr/SyPzAwcOKLdJTExEREQEevfujbfffps9o4hUgBVRRGTJI3O2GkAZwjEUERFZOzY2p/Jy0Ol0OtgQWX3P19cX15Z/Dx9PD0tvDpFVYxBFROamxiAqNTsbHX76VTmQ5uPjA1ulH0NN/eka3D3Kfp4yfc/aekrJ9D1fDg8rxT/tNNxcDK+GTURUEPtIlf0by1qqpC6s+K/S47Hyjp+sqxkAEZk9jLKWD0wisl5qDKKIiIio4hhIUUUxlCIiBYMoIjIna21YToWbnctQ0tqqpbgCHxGRabGxue1USZkDQykiO8YgiojMiUEUWRpX4Ku8BM/anMJHRKViY3PbozHDQUOGUkR2hkEUEZkTgygiIiLbx8bmtlUlZU4MpYjsABuWE5E5MYgiIiKyH+wjZXsuVKHBeUUxlCKyUQyiiMicGEQRERHZHwZS5cMqqZIxlCKyIQyiiMicGESRuJagtbpm50REVHUMpGzTBTM1ONdjKEVk5RhEEZElBytcNc++yQp83r5OVtfsHEiGr4elt4SIyHoxkLLtKimNGVdFtq5RRDnodDrlz5T0DEtvCpFJpe3Zmf//jl5+ty7IyOSeJyKju7h6W/7/a4LCbl2QnW3zezstO6fQGMNW6Z9fZnpKhW7n5OSEdGfrqpRyzU2GJs/SW2F9nNJTke2ssfRmEJEKuKRlQOPqBuSmW3pTVC8tMwuOTtbzGy01JxcaI4zvyjt+ctDZ2Ajr4sWLiIqKsvRmEBERkY2JiYlBZGQkbBXHUERERGTu8ZPNhVJarRaXL1+Gt7c3HBwcTPY4ycnJSvglO9jHR8rAyRy43y2D+5373Z7w/c79XpQMlVJSUhAREQFHR+uqCFLbGIr/viyD+5373Z7w/c79bm+SVZpNlHf8ZHPT9+TJmvMoprzoanrh7QX3O/e7PeH7nfvdnqj1/e7r6wtbZ84xlFpfZ1vH/c79bk/4fud+tzc+KvxuLc/4yXYP9xERERERERERkWoxlCIiIiIiIiIiIrNjKFVJrq6ueP3115U/yXy43y2D+5373Z7w/c79Tvz3ZWv4ucb9bk/4fud+tzeuVp5N2FyjcyIiIiIiIiIiUj9WShERERERERERkdkxlCIiIiIiIiIiIrNjKEVERERERERERGbHUIqIiIiIiIiIiMyOoVQVnTt3Do8++iiio6Ph7u6O2rVrK53vs7OzjfMKUYneffdddOrUCR4eHvDz8+OeMpEvv/wSNWvWhJubG9q3b48dO3ZwX5vYpk2bcNdddyEiIgIODg5YunQp97kZvP/++2jbti28vb0REhKCQYMG4fjx49z3JjZz5kw0a9YMPj4+yqljx45YuXIl97sd4BjKcjiGMg+OocyPYyjz4/jJMmba0PiJoVQVHTt2DFqtFl9//TUOHz6MTz75BLNmzcLLL79snFeISiTB35AhQzB27FjuJRNZsGABJkyYoASte/bsQfPmzdGnTx9cu3aN+9yE0tLSlH0tg1kyn40bN+LJJ5/Etm3bsGbNGuTk5KB3797K60GmExkZiQ8++AC7d+/Grl27cPvtt2PgwIHKdyrZNo6hLIdjKNPjGMoyOIYyP46fLCPShsZPDjqdTmfpjbA106ZNU5LLM2fOWHpT7MLcuXPx7LPPIjEx0dKbYnOkMkoqR7744gvl7xLARkVF4amnnsJLL71k6c2zC1IptWTJEqVqh8wrLi5OqZiSwdZtt93G3W9GAQEBynepVCKTfeEYyrw4hjIdjqEsj2Moy+D4yXICrHT8xEopE0hKSlLeEETWfhRVkvdevXrln+fo6Kj8fevWrRbdNiJzfZYLfp6bT15eHubPn68c6ZYydLI/HEORLeAYiuwZx0/ml2fl4ycnS2+ArTl16hQ+//xzfPTRR5beFKIquX79uvIBFxoaWuh8+btMuSCyZVIVKBWYnTt3RpMmTSy9OTbv4MGDyiAqMzMTXl5eSnVgo0aNLL1ZZGYcQ5Gt4BiK7BXHT+Z10EbGT6yUKoFMTZKSz9JORX+YX7p0CX379lX6HD3++OPmeP1sTmX2OxGRsUlvqUOHDilHncj06tevj3379mH79u1Kn8BRo0bhyJEj3PVWimMo69nvRETGxPGTedW3kfETK6VKMHHiRDz88MOl7rxatWrl///ly5fRo0cPZTW42bNnG/dVsiMV3e9kOkFBQdBoNLh69Wqh8+XvYWFh3PVks8aPH4/ly5crK/hIE0kyPRcXF9SpU0f5/9atW2Pnzp349NNPlUVEyPpwDGUd+51Mh2MoskccP5mfi42MnxhKlSA4OFg5lYdUSEkgJW+EOXPmKH13yPT7nUz/ISfv6XXr1uU32ZaSXPm7fOkQ2RpZ90Oa+Evp84YNGxAdHW3pTbJb8lmTlZVl6c2gSuIYyjI4hlIPjqHInnD8pB5aKx0/MZSqIgmkunfvjho1aih9pGS1AT1Wk5jWhQsXcOPGDeVP6X0kpYtC0mKZU0tVN2HCBKUMtE2bNmjXrh1mzJihNNAbPXo0d68JpaamKr1V9M6ePau8v6XhdvXq1bnvTVhy/ssvv2DZsmXw9vbGlStXlPN9fX3h7u7O/W4ikydPRr9+/ZT3dkpKivIaSCi4atUq7nMbxzGU5XAMZXocQ1kGx1Dmx/GTZUy2pfGTjqpkzpw5OtmNhk5kWqNGjTK43//55x/ueiP6/PPPddWrV9e5uLjo2rVrp9u2bRv3r4nJe9jQe1ve82Q6JX2Wy+c8mc4jjzyiq1GjhvIZExwcrOvZs6du9erV3OV2gGMoy+EYyjw4hjI/jqHMj+Mny3jEhsZPDvIfSwdjRERERERERERkX9j8iIiIiIiIiIiIzI6hFBERERERERERmR1DKSIiIiIiIiIiMjuGUkREREREREREZHYMpYiIiIiIiIiIyOwYShERERERERERkdkxlCIiIiIiIiIiIrNjKEVERERERERERGbHUIqIiIiIiIiIiMyOoRQREREREREREZkdQykiIiIiIiIiIjI7hlJERERERERERGR2DKWIiIiIiIiIiMjsGEoREREREREREZHZMZQiIiIiIiIiIiKzYyhFRERERERERERmx1CKiIiIiIiIiIjMjqEUkY2oWbMm7rzzznJd9+TJk+jduzd8fX3h4OCApUuXwha98cYbyvMrr/79++Pxxx836TbZuiNHjsDJyQmHDh2y9KYQEZGNjncefvhhqNncuXOV8ce5c+ds4vmYc3+IcePG4Y477jD5dtmy+Ph4eHp6YsWKFZbeFKIyMZQiUvkXuP4kP/SrVaumDFwuXbpUpfseNWoUDh48iHfffRc//vgj2rRpY/B6MniQx/7oo48Kna/T6fC///1PuUyCH1uwefNmrF69Gi+++KLBy+VLXZ5vREQEtFot1GTLli3o0qULPDw8EBYWhqeffhqpqanluu3Vq1cxevRohISEwN3dHa1atcLChQsNDpoLvh8LnurWrZt/vUaNGmHAgAF47bXXjPociYjItsm45L777kONGjXg5uamjHkkmPj8888tvWmqIN+348ePL3b+e++9p1z2yCOPqG58Uhlnz57Ft99+i5dfftng5UePHlWer7xHEhMToSaybX379oWXlxcCAgIwYsQIxMXFlfv2KSkpeOGFFxAdHQ1XV1fl34D8m0hPT8+/Tvfu3Uscjzk7O+dfLzAwEI899hheffVVoz9PImNzMvo9EpFRvfXWW8qXU2ZmJrZt26aEVf/9959SiSJfyBWVkZGBrVu34pVXXjE4uCmLBFJyBGv27NnKF52thFLTpk1Dz549UadOHYOX//zzz0owI0Hd+vXr0atXL6jBvn37lO1u2LAhPv74Y1y8eFEJEaUabuXKlaXeNjk5WQmzJJh65plnlEDrt99+w/3336883+HDh+dfd8aMGcWCrvPnz2PKlClK1V1BY8aMUarOTp8+jdq1axv5GRMRka2Rgys9evRA9erVlYpl+T6KiYlRxj2ffvopnnrqqfzrHj9+HI6O6j6uLmHEsGHDlGDBlD744ANlPCcHGyXIUft+KQ95vWXcK+8HQ3766Sfl/ZGQkIBFixYpwYsayPjrtttuU2YhSFAoYyYZj0nYumPHDri4uJR6+6SkJHTr1k25nyeeeEIZj0qg9e+//yIrK0s58Cjk9S76nNPS0pSxl6Hx2GeffaaMW2+//XYTPGsiI9ERkSrNmTNHJ/9Ed+7cWej8F198UTl/wYIFhc6vUaOGbsCAAWXe7/nz55XbT5s2rczrnj17tth1n3zySeW8V155RWcMeXl5uoyMDJ0pvP7668q2luXq1as6Jycn3bfffmvw8tTUVJ2np6fus88+07Vs2VL38MMP69SiX79+uvDwcF1SUlL+ed98843yvFetWlXqbT/88EPleuvWrSv0erRt21YXFhamy8rKKvX2b7/9tnL7zZs3Fzo/Oztb5+/vr3v11Vcr/byIiMh+9O/fXxccHKxLSEgw+B1ty2T8NmrUqDKvJ9+3MgYr+h0+cuRI5bu7qrRarS49PV1nyjGtjCtLI+OHoKAg3ZQpU0rcxpo1a+omTJigu+eee3Tdu3fXqcXYsWN17u7uyjhbb82aNcrz/vrrr8t1ez8/P92ZM2cq/Ng//vij8jg///xzscuaNGmiGzFiRIXvk8icrD9OJ7IzXbt2Vf6UKhRDZApaixYtlCoqmUr1+++/518mVU1SFi+ef/55pdRXqn/KS6ppvvzyS0yePBnvvPNOocvkKM7rr7+uHNmRI4NRUVFKCbKcb6j8XCpxGjdurFz377//zp+uKNPoJkyYgODgYGUu/D333GOw9FmqgGRfyHW8vb2VKWOHDx9GZfz111/Izc0tsfppyZIlSoXZkCFDlCOfsk+lcq0oOU/2cb169ZT9Hx4ejsGDBxd6raS0Xo4CNm3aVLmOPE8p9d61a1f+da5fv45jx44VKtcuqdJpzZo1eOihh+Dj45N//siRI5XScal6Ko0cfZPHL3j0TI6ySqXUlStXsHHjxlJv/8svvyhHMzt16lTofCkfl/LyZcuWlXp7IiIiId+TMibw8/MrtkNkenlZPZgOHDigVJnINPTIyEhljDJnzpxifYz0/Tc3bNigtC6Q68v3sfxdyPe7/vu5devW2Lt3b7HtkaoT/fhDtnfgwIHKtK2yeihJriTbJdsnVS9SCVTZcYtURssYS77/5XkWrJCScYZUN8v+lOcRGhqqtFyQyqKi+1H2xapVq/L3xddff63sC9l2GUNImwfZXrkfqco+depUsW3Zvn27Mo6RCiF5XvI6yFiuMmQmgIyBShqPyf3KPpWxmJw2bdqkVBYVVZ6xlr7qql27dsp2+/v7K5VOMo4uWL0k4zH5syyLFy9W9qdU++nJ85AxYVnjMZmGKK+jVEjJuCo7O7vY+Lms8Zi8H+W9WJRMgf3zzz+V9x+RWjGUIrIy+gGOfHkWJVO2hg4din79+uH9999X+lBJkCLBhZCA5JNPPlH+/4EHHlD6ScnApTyee+45pQRYei5JWXLRL/+7775bKVO+6667lP4PgwYNUh5LtsfQgE7uTy6TQUPBYExK9Pfv368EXGPHjlW+SItOM5TtlhBKgpepU6cq0wilwbZMRStvE82i0wZk7r0+sCtKAjQZPEq5uAyCZM6/bFdBeXl5ymDkzTffVAay06dPV0I8GcgUbPr96KOP4tlnn1VCO9n2l156SRkwyRQFvS+++EKZjifl3qWRknAJ04r2BJMScQkmDQ2mC5IBjwxCi9KXiO/evbvE28p9yyC84BS/gmQfyPOW4IyIiKg08v0r3zmVWSRD+mzqAx45aCbjC/nelvGFIRKsyHeXjFdkrCRhjfy/3EZuK0GPfJdLUCYHaQr2aVq7di369OmDa9euKQeh5CCajCE6d+5c5vhDei3KeKV58+ZKy4BatWop061k6lVFyPOaOHGi8hwk/Co6ZU8CKDnwKNsk15W+kfLcZLtzcnIKXVemQsp4UIILua6MHQpODZSDcpMmTVL2q4xTHnzwwWLjOQly5Ltexm0yPpSARQ52lTWGMUT2pQRiLVu2NHi5PA9pC9C2bVvlNZPxyq+//lrseuUZa8lrLNMs5UCatMqQv8v15TnpyfOX8Zj8WdZ7UN4Thnq0SuhV1nhMwjg5sCkHdqWHlDwvGZ/JayhtGkojB25lnC/jbgmmDI3H5DWpbABKZBZmrcsiogqXOq9du1YXFxeni4mJ0S1atEgpb3d1dVX+XrT8W66/ePHi/PNkSpdM7ZIpZ6VNySuJ/rr6+37++edLLBt2dHTU/fvvv4XOnzVrVrHpXfJ3ue7hw4cNPt9evXop5dl6zz33nE6j0egSExOVv6ekpCjlzY8//nih21+5ckXn6+tb6PzyTt/r0qWLrnXr1qVO7ZMpcXqdOnXSDRw4sND1vv/+e+WxPv7442L3oX8+69evV67z9NNPl3idgtv9zz//lLrdCxcuVK63adOmYpcNGTJEmYJXmqeeekp5Lc6dO1fo/GHDhin3O378+BJvO3HiROU6R44cMXj5L7/8oly+ffv2UreBiIho9erVyne9nDp27Kh74YUXlCnoMp2rrOlu8l3m4OCg27t3b/558fHxuoCAgGJTxvTjmS1btuSfJ48j5xWdeiVTrop+F7do0UIXEhKi3L/e/v37le9SmUZX0nS1a9eu6VxcXJQ2CwW/719++WXleuWdvqff/gceeECXm5tb7DoyDjM0jevvv/8udr7+vuSyguT5yvkNGzYsNI3/008/Vc4/ePCg8nd5HnXr1tX16dOn0HOSKYDR0dG6O+64o8T9UZKHHnpIFxgYaPAyeS/IZQXbRwwfPlzXvHnzQtcrz1jr5MmTymsmUwCLTn0s+Fz02y1/lkZabcj15s2bV+wyGTvLZZmZmSXeXsaOch15fu3atVNep6+++koXGhqqtEO4fPlyibf9/PPPlduuWLHC4OXyXjfU9oNITVgpRaRyUvorZcdy9EaOnshRkD/++EMppy5KVoaT6W56MqVLpnLJERqZjlVZ0ghbSAmyIbJamxxJatCggVJ2rT/pp4X9888/ha4vpd0ytdAQKV2Wo2R6UiIvVUjSVFvI0SA54iNH9go+lkajQfv27Ys9VnmXzTVUeSbmz5+vHIW8995788+Tx5bpgwVL4aVsOygoqFAzVj3985HryP/L0cSSriPk6KuMP2UKXGlkSqEw1EhVjgjqLy+JNMqU/SZHguXopBwVlqPG+iOCJd1ejhrLfpEjmfK6G6Lfn/LaEBERlUYqdWQRFqm6lmrpDz/8UKnskdXHZMxTGmkB0LFjx0JVPrLyWdGqHj0Zf8j19WTsIGTMUnDqlf78M2fOKH/GxsYqVSsydVDuX69Zs2bK9ssqvSWRCiuZkiVjhILf91LNU5nxmEzxku9vQ+MxmUYn21NwjCTVMlJdXnSMJPcj+9kQqbAq2Jxb3z5Cvz9kX0iFvlRsyThK/1hS+SVT/WRqXUVXAyxtPCbjLrlcxmB68v/yfilYBVSesdbSpUuVbZPqtaKVZgVfH3mtZTxWdLpoRcdjBa9jiH4hGXnsdevWKftUZgvIdspYU1pnlDZ1T34nyGtuCMdjZA24+h6RyskXkYRBMg3s+++/V77kS1rNRcp+C36ZFgySpKxcpp9VhkzZk8GWlIRL/wQJxwqSQYlM5ZIvRUOkpLnoIKgkBQeEBb9M9QGQPJYoaRWRgr2VKqKkufb6fgMyEJKTkDBGBpcy+JMQTUigU79+fWXKZEnkOhIcFhzMVoV+6p2hvgNSBm5oal5BMpCWwYysziIl4kLeIzKlUwZDMoA1RHpNSam6THMoa38WfT8SEREZIlOypKeTfL9K0CAHSKQNgIw5JAAp6WCWHLQqGDLplbSabtFxhoQ4Qg7+GTpfP/7QHxyT7/qi5ACN9GaSQMbQFCr9bevWrVvofBk3lRTCGCKr7F2+fFmZJicHwop+D8sYScaLRftwmXI8JttUEtmWijy/ssZjsr0yBtb3tpKpfDLVTab16VtLlGesJdeRMKqk95Sxx2MFr1Pa7WVKYsGxV4cOHZTnLAcODZGAUMJcaXNR0viT4zGyBgyliFROAhH9HHWZLy59k+QIivQBKCk0MDZ5HDlCJX0D5MijBD8Fl52Vo03STFIabxpSdKBX2hezoSN/Bb9U9UfdpK+UoZCttFCoJNJPqmgDUP2Aa+fOnQYHkkIGQfpQyhKkkbr+6G1Rcp4Mysoig339kWmpSGvVqlV+w9eSKuPkectgruDRyqL0+1MGzUREROUl1TkSUMlJvoekYkcOAhmqfKmMksYZZY0/1EDGONI0W5p2S18pOVAo+0dPxkgSSMn3tCFFDx4aYzwm/bEKVqkVVNFxaknjMelZJb08JeAxNB6TA2zSlN1SB8LKGo9JQFbSAWWhH69JU/qi5PU0tE/0z1uUVBUoOB4ja8BQisiKyABBpldJQ09phi2NGwuSI0cyWCj4pXzixAnlz4qsslfSQEFWJJGKGmmYLtPo9Ecm5UiVhBpSrm3qAYE8lv5LuqTVWSpKph1KuXdRMqiTBpgSgBUdnElTSmn8fuHCBeVoomyXrEAjTUTlNiVtuxxJvXHjhlGqpZo0aaIMUGU1GZmCpydHmeWocsHzyvMDoOA0A2Fo/8pRQNlXMrWwtNDr7NmzSnBVUrBFRERUFv1BOUM/9gs2STe0Kpyh86pCvxiKHBQsSlZok4MwhqqkCt5WDnZJg/OCTapLChxKItPBZEqjjAUff/xxJZjSt26QcYZ8h8tYraxqaWONx+RApTHHYzL2kgorfaWa0K96PHPmzGIHu+T1mDJlirIynxy4Lc9YS64joZosklNSoFYRMs1UAr+iq/sJafhe1mPI9EohVehFSWWc7JeSQil5LlJRVdp4TJTUboFIDdhTisjKSBgg1VMyxUpfElzwi6vgCiFyZGnevHnKl2Flp+4V/dKVMEoGXbL6naz+JiT8kC/Sb775pthtZA59RVeWKY30PpABkJRpF11FRj/AqygJ12RQqO+ToCcDI+mhIKsESkVRwZOsbCP0q75IzynppSBhYUlHFeU68v+ywktJ1xFyPzLATU9PL3W7ZcAmA0EpaZcVAfUkRJP+BLLyop7cl9xnWT2eZMA8a9YsZSVBQ4GSTOOUnl6lHZUTsoqSLEddcFBJRERkiPQ6MlSRpO/TZGjKXMFxgUxhKrhKmQQSJVULVaUaRsZTP/zwg/I9qCcrBspBu/79+5d4W/mulgNWsjpxwedZ3hWQi5JxkPTSkimKUrUsfYj04zGpen777beL3UZW6y243VUlQYoEIrLysr4nkjHGY7J/iq7+K+McCfOk3UDR8ZisDigVWfrXuzxjLZl5IAfOZNW9on2vCr4+Eo7J2En+LIs87vLlyxETE5N/nrwucnC44HhMxq5ynwWDVnl/y6qMy5YtKzROk/eV3J+hflFlrYKsJ/tSxmIyJiNSK1ZKEVkhCUTkC06WApYvaD0JEWQZXJlyJiXA0oNKmmLOmTPHaI8tZdNyBErCMRkISsWQLKkr5eSyLTKwlCN0MiiSL105X65vaJncyg7E5EiZPKZMNRs2bJhydEoqlv766y/lsQ0FQ6WRgE0qjuToon46nlQ9yVFWmadfUkAnjy+DIOm5JQ3lJQCU5aHlqJiEWRLGyX2OGzcOAwcOVI5qynZLhZWEP1J+L4Ohf//9V7lM/1iy/TKYkn1ZVrNzKVfv1KmT0jxetv3ixYuYPn26Mr1S7l9PtkkeQ6Y/SCN1PemnIO8lqfaSo2myb+XIogRThsjzlRL0go3fi5IBl/SdkudNRERUFmkALgdPpOJHqkKk4lf66CxYsECp9C44Ra2oF154QQkt5Ie73I8cOPv222+V7zUJp4xZwS1T1fr166eEJzLekgNvEjTJj/6C361FyThFwhOpdpeDPhJgSaggrREqO81d7lMOFMq4R0IWCUBkLCD9P+VxJKSTsYCEYTLmkCmQn376abG+oJUloY7sZ9kfEnjIayRjIzlIKeMXGa/JlLuKkEonqcyXsZO+d6gccJX7e/rppw3eRsYkMh6V5yfjq/KMtSTMe+WVV5TwTsZrMgNA7kfGz1IFLvtPyIFeeV4yji6r2fnLL7+sbIM8xjPPPKMEdfJ+kfYWBd+/sn+kakl6cck4Xk/6p8l7WPaBvIYShElbDBnbS5/PovQhXFkHCeU9Ir2q2OOTVM3Sy/8RkWH6ZWhlmdmiZPna2rVrKyf9ksCytK8sNSxLGzdr1kzn6uqqa9CggW7hwoWFbivL8cr9Tps2rcxdX9p1ZdlhWT5Zlv29dOmSslTv1KlTdY0bN1YeW5awbd26te7NN9/UJSUl5d9O7u/JJ58s9/PVL01ccElm/fmyDLGvr6/Ozc1N2RcPP/ywbteuXfnXef3115Xblsfdd9+t69mzZ6ElpuW2p0+fLvE2b7zxhnIdWQ5avwyyLFUs+8TZ2VkXFhamu++++wrdh7xesj/ltZHloYODg3X9+vXT7d69u9h2F33OJZHXolOnTsp+kPuT/ZucnFxsf8l9yn0XNGzYMF1UVJSyLREREboxY8borl69avBx5HWUxxg8eHCp27Ny5UrlsWTJZSIiorLI98YjjzyifDd6eXkp30l16tRRvouLfifJeGfUqFGFztu7d6+ua9euyvgjMjJS9/777+s+++wz5bvoypUrhW4rY6WiDI1NShoDrV27Vte5c2dlDOTj46O76667dEeOHDE4ppH7KDh2kzFReHi4ctvu3bvrDh06ZPD5GFLS+Ono0aO6oKAgXUBAgHJ/Yvbs2coYTB7H29tb17RpU90LL7ygu3z5cpn7Qj9eKGn8KM+t6L6XcUFgYKCy/+V+77//ft26detK3R8lefrpp5XXXm/69OnKbQveX1Fz585VrrNs2bJyj7XE999/r2vZsmX+uLVbt266NWvWFNvuos+5JLL/e/furfPw8ND5+fnpHnzwwULvv4L70dBrLo/doUMHZawlr+eIESN0sbGxxa4n76Vq1arpWrVqVer2yHtDHkves0Rq5iD/sXQwRkRkaXIETaqSpLrLUBNNKj85YitH5ApOJSUiIjKnZ599Fl9//bVSsVJS025SH2mlINVyUkUmvUqpav8GZNVumcLHSilSM4ZSRET/T0rQIyMjDfbGovKR/gZSqi7TBqQROxERkanJNLqCjb3j4+OVaU8yzV6mL5F1kelq0kKBr13lyb8BabAvbTRK63dGpAYMpYiIiIiIyGpJA3KpdpZePdJL87vvvlN6EUmfpdtuu83Sm0dERKVgo3MiIiIiIrJaUgmyaNEizJ49W5mmJBVSEkwxkCIiUj9WShERERERERERkdk5mv8hiYiIiIiIiIjI3jGUIiIiIiIiIiIis7O5nlJarVZpbOjt7c2lL4mIiKjKdDodUlJSEBERAUdH2z2exzEUERERmXv8ZHOhlARSUVFRlt4MIiIisjExMTGIjIyEreIYioiIiMw9frK5UEoqpMShTavg7eVp8sfLyslDoke0yR+HiMia+KWfhauzxtKbQWQUKalpaHJbn/wxhq3SP78nXtoGFzcvS28OmZFHUAAiQl3N8lgB/kCQj+kfJ9jpCjzdHKBGPpcOwNnJ2dKbQURkUilp6ahz54gyx082F0rJMrBCAikfb9MOqDKz85AWUBscthERFebtePOT0c2FwRTZ3hjD1p+fBFKubrYdwFFhru4+cPcwTyjl4Ql4mmHw7OWUBi93df6b9fH0gLOzi6U3g4hIFeMn222MYGISSCV41rb0ZhARqRI/H4mIyFJCnGJVG0gREVFhDKUqgYEUEVH5Py+JiIjoJt+YvaySIiIqgKFUBTGQIiIqH1ZLERERERFRaRhKVQCP+BMRERERERERGQdDqQoGUjzyT0RUfvKZyUCfiIiIiIgMYShVDgykiIiIiIiIiIiMy8nI92dzGEiRtUtKL/s6vh7m2BKy989SNxeNpTeDiIiIiIhUhKFUKRhIkTUESmW5kelTjmslV/lxGGxRaVP4/NNOcwcREREREVEhDKVKwECK1BI6lS9UqhrjPEbZwRaDKyIiIiIiItJjKGUAAykyZ/hkjtDJHKpakcXAyvZxCh8RERERERXEUKoIBlJkqgDKVsKnqihpHwS4JZe43xhW2QZO4SMiIiIioqIYSpXw44movAyFKQygTBtWMagiIiIiIiKyfgylilRJMZCishQNSRhAmU7J+7b4NEAGVdaBU/iIiIiIiEiPodT/YyBFJWEllPrDKkMVVQyp1IdT+IiIiIiIqCCGUgX6SBHpsRrKujCkIiIiIiIisj52H0qxsTnpMYiyn5CKVVSWxSl8REREREQEew+lGEgRgyh77U1VuCcVQyrz4RQ+IiIiIiKCvYdSDKTsF4MoKhhSsYqKiIiIiIjIMuw2lBJcac9+wyiumEeG3gsMqMyHU/iIiIiIiMguQymutGc/GESRMQIqTu8zLk7hIyIiIiIiuwyluNKefWAYRVVVuJruVg8qBlRERERERETGYVehFPtI2TYGUWTqgIrT+4iIiIiIiIzHbkIpBlK2i2EUmQun9xkX+0oREZE98Y3ZC2dnF0tvBhGRqjia8s7ff/99tG3bFt7e3ggJCcGgQYNw/PjxMm+3cOFCNGjQAG5ubmjatClWrFhhlO1hY3PbC6P0gZSEBWxeTuZU8D1X8L1I5cPPYyLrGT8RERERWWUotXHjRjz55JPYtm0b1qxZg5ycHPTu3RtpaWkl3mbLli144IEH8Oijj2Lv3r3KQExOhw4dqvR2sLG5bWEYRWrCcIqIbHX8RERERGRqDjqdTgcziYuLU474yWDrtttuM3idoUOHKoOu5cuX55/XoUMHtGjRArNmzSrzMZKTk+Hr64vze/6Dj7cXp+3ZEE7TI2shvacEm6KXzj/tNNxcNGZ5TYiqIjklFTVadUFSUhJ8fAougmA746eCY6gXvriMvNQco20/qZ9HcCAiw1zN8liBAUCwr2kfI8QpFl7uDlAbTt8jInuSnJqG0B73ljl+MmmlVFGyMSIgIKDE62zduhW9evUqdF6fPn2U8w3JyspSBlEFT3rsI2UbWBlF1lo9xWl9ZeOKqESWGT+VNYYiIiIiMgezhVJarRbPPvssOnfujCZNmpR4vStXriA0NLTQefJ3Ob+kvgtyVE9/ioqKUs7PyslT/mTfEuvFMIqsHcOp0vHzmchy46fSxlD6yhkiIiIimwmlpDeC9DWYP3++Ue938uTJyhFE/SkmJib/Mv7gsU4Mo8jWMJwiIrWNn0obQ0WEmmcaFxEREZGTOXbB+PHjlR4HmzZtQmRkZKnXDQsLw9WrVwudJ3+X8w1xdXVVTkUlekTDq4rbTeZXcDU9Ilsj72vpN6V/n7PnFBFZavxU2hiqYLVUelw8XyQiIiKyzkop6aEuA6olS5Zg/fr1iI6OLvM2HTt2xLp16wqdJyvPyPlk+9VRBVcyI7JFXK2vOPaVIlLf+MlcTa+JiIjIvjmauuT8p59+wi+//AJvb2+lr4GcMjIy8q8zcuRIpXxc75lnnsHff/+N6dOn49ixY3jjjTewa9cuZXBGtj9Vj8hewyl7xWnWROoeP7G3FBEREVltKDVz5kylR0H37t0RHh6ef1qwYEH+dS5cuIDY2Nj8v3fq1EkZhM2ePRvNmzfHokWLsHTp0lKbe5J1KhhGMZAie8V+U0Sk1vETq6WIiIjIqntKSfl5WTZs2FDsvCFDhignsk2sjCIqvd8Ue00R2TeOn4iIiMhemKXROVHRKUqsjCIq7ta/i2TlvwyniMjSpFrqItjwnIiIiKxw+h6RHqfqEZWfvfWakr5SbHZORERERGR/WClFJsXqKKLKYdUUEakFq6WIiIjIVFgpRSbD6iiiqrO3qikiIiIiIrIfDKXI6OTHM5uZExkPgykiUkO1lEdwoKU3g4iIiGwMp++RUTGMIjINTucjIiIiIiJbw0opMhoGUkSmZ6tVU2x2TqR+rJYiIiIiY2MoRUabric/lm9VcxCRqdhqMEVERERERPaFoRRVCaujiCyDwRQRWQKrpYgqxzdmL5ydXbj7iIiKYChFlcZAisiyGEwREREREZE1YyhFFcbpekTqwWCKiMyN1VJERERkLFx9jyqE1VFE6l6Zz9cDVi0zOw9uLhpLbwYREREREZkBQykqNwZS6nQtQWu0+wrxZ/Gk9YdT1htMyQp8/mmnLb0ZRFTOaqmLCER6XDz3lw1QXsewCEtvBhER2SGGUlQuDKTUGy6lJOUa5fG9fZ2MEnAx2LIsaw+miIjIMi5eyVLCRiIiInNiKEWlYhhlXGWFPsYKmCrDeI9d/o8VBlimwWCKiMyB1VKkRiFOsfByd7D0ZhARUTkxlKISMZAyfvhkydDJXMr7HMtTmcXQqvIYTBERERERkdoxlCKDGEiVnz0HUFVR1v4pK7RiYFU2BlNEZGqsliIiIqKqYChFxTCQKp2hoIQBlPGVtk9LCqwYVBXHYIqIiIiIiNSKoRQVwkDKsKIBCEMoyyp5/xf/SGNQdevftjU0P9evwOfmorH0phBRObFaioiIiCqLoRTlYyB1C0MoWwqrGFRJtVSAW7LVBFNERERERGQfGEqRQn6s3pzmY78YRNlHUFV06p+9VFIxmCIiU2K1FBEREVUGQyk7x+qowmEUp+XZvuKvceGPQVsOqRhMERERERGRmjCUsmP2HEgxiCJDIZU9VFHpgykiImNjtRQRERFVFEMpO2WvgRTDKKpsFZUtBVRckY+IiIiIiNSAoZQdsrdAikEUVTWkstUKKjY+JyJjY7UUERERVYRt/LKicrOnQEpCBH2QIOEC+0VRZenfP/r3UMH3lrXSfwboPxPUJMGzNjKz8yy9GUREREREZGKslLIj9hJIsTKKTKlwuGnd0/vYX4qITIHVUkRERFReDKXshD0EUgyjSA3T+6wtnGJ/KSIi0oeJZBq+MXvh7OzC3UtEZABDKTtg64EUwyiyNFsIp9hfioiMidVSREREVB4MpWycLQdSDKNIbaw1nNJP42MwRURknzyCAy29CUREZKfU/2uJKs1WAyk2MCe1s8am6Lb2OUFE6qiWYthBREREpWEoZaNsMZBiGEXWxhrDKTWuxkdE1o3BFBEREZWEoZQNs7VAquiPfCJrYS3hlC19ZhCROrB5NhEREZWGoZQNkkoHW/lxqf8BzzCKbDGcUiP57FBLtVRmdp6lN4GIjITVUkRERGQIQykbYyuBVNGpekS2xBqqpiwdTCV41rbsBhCR0bBaioiIiErCUMqGWPpHpLFwqh7ZAzVXTdlCsE1E6sNqKSIiIiqKoZSNsIXG5qyOInuk5qopWwm6icjyWC1FREREhjCUsgG2EkgJ9o4ie6TGqilr/jwhIvVitRQREREVxFDKyll7IMXqKKJb1BZMCVZLEZGxsFqKiIiIimIoZcVsIZASrI4iUud0Pmv9bCEidWO1FBEREekxlLJy1vqjkSvrEVnHdD75jGG1FBEZC6uliIiIqCCGUlZKfiRaYyClr/5gdRRR2dQSTBERGRurpYiIiEgwlLJC1lq1wOooIusMplgtRUTGxGopIiIi0mMoZWWstY8UAyki6w6miIiMjdVSREREZNJQatOmTbjrrrsQEREBBwcHLF26tNTrb9iwQble0dOVK1f4SjGQIrJragimLFGlmZmdZ/4HJVIBWx9DsVqKiIiIhJMpd0NaWhqaN2+ORx55BIMHDy737Y4fPw4fn1uVQCEhISbaQutjTRVSrI4iMn4w5e3rpPzbCvF3NPtnT4BbslkfM8GzNvzTTsNeuJzeW+Jl2bVbmnVbyPLsZQwl1VLpcfGW3gwik/GN2QtnZxfuYSIiS4RS/fr1U04VJQMoPz8/k2yTtbK2xuYMpIhsL5jSfxb5epj9Ye0mgNK4uhc7Ly8ro8TrM6yyXfYwhpJqqYtXsiy9GURERGRBquwp1aJFC4SHh+OOO+7A5s2bS71uVlYWkpOTC51sjbU1NmcgRWSbU/msKRhXIwmW9CcJnwydDCntugXvk8hax1DsLUVERGS/VBVKySBq1qxZWLx4sXKKiopC9+7dsWfPnhJv8/7778PX1zf/JLexJdbW2JyBFJHt95iytqDckoqGRqWFTxXFgIpsYQzF3lJERET2zUGn0+nM8kAODliyZAkGDRpUodt169YN1atXx48//ljiUT456clRPhlU7Th4AV7e1hHk2Mq0PQZSROYnU/mEOafySW8pc03hk55Sbi4aWKOCQZQ5yXQ/PU7vM47klFTUaNUFSUlJhfo1mYu5x1AzFsbD3cO8z1Om8bG3lOVItZq5AsLAACDY13T3H+IUCy93B6gFe0oRkb1KTk1DaI97yxw/mbSnlDG0a9cO//33X4mXu7q6KidbZG2BlL5yg4jsp8cUFVdwKp25A6mCj1mwFxXDKftkz2MoIiIisg6q/wWzb98+pSTd3ljT9BgGUkT2NZVPwnJr+owyJ1NM06ssQ72nyL5Y0xhKqnTYW4qIiMj+mLRSKjU1FadOncr/+9mzZ5UBUkBAgFJOPnnyZFy6dAnz5s1TLp8xYwaio6PRuHFjZGZm4ttvv8X69euxevVq2BNr6SPF6XpE6sGKKfucqlcerJyyTvY6hpJgitP4iIiI7IdJQ6ldu3ahR48e+X+fMGGC8ueoUaMwd+5cxMbG4sKFC/mXZ2dnY+LEicogy8PDA82aNcPatWsL3Ye9YCBFRJUNpswZoJurt5SaqTmQKi2c4pQ+dbPHMZRUS0lvKSIiIrIfZmt0bi7SpFNWkLHWRufWUCXFCikidZNgyhz9pczR8Fztjc6tJZAqis3QravRubnHUJZodK6nD6VYLWVebHRuOmx0TkT2Krmcjc5V31PKnjCQIiJjMVd/KXtmrYGUoX5TRGphrhXgyDapbeU9IiIqm+pX37M3rJCyLjGxFZ9mEBXOAbcaXgdbfm3M2V/KXqfwWXMgVZBsP1fpIzVibykiIiL7wFBKJeSHHQMp6ws8chLiK35/CLT5UMRaXouCnP0DS7x/a3xdzNFfSj6zZAqfvbL2QEqPvaZIbdhbioiIyH4wlFIBa1laXb/svC0zFEpUNewo7/0ZCkWsMQyxttfDll+Xm/9mzdNfyt6qpGwlkCqpaopN0EkNWC1FRERk+xhKqYTaq6RsNZAyZ+hRFkOPW7CqylqCEGO/JpZ6PUp6/KIhlTW8Lqacxnfzs8v0Dc/Vwtb7LzGYIrVgtRQREZF9YChlYdYwbc8WA6mCoYKlQ4/S6LfNGoMQaw6iSlNw2wq+Lmp9Tcwxjc+UEjxrq2YFPlvpI1UWBlOkJqyWImsmK+8REVHprPeXig1Q+7Q9W1u9y1qCKFsIQuzhNSm6zWqvajNH03N7YOuBlF7Rlfk4nY8sgdVStinYF3bF2dnF0ptARKRqDKUsTK1VUvpAytqrpKypAqcq1VNqDEFsNYiyxtfFHKvx2foqfLY+ba8krJoiVQRTCER6nO18X9izwABLbwEREakNQykLUXOVlC0EUrYafJRVpaOWEKSs18VWXxM1h1OmnMZn66vw2cu0vZIwmCI14DQ+IiIi28S5HBYMpNRYJWXtgZSEAAWDD1sOPwrSP09DjdvV9LrY22uixtfF1qblmou9BlIlTecjMne1FBEREdkmhlIWwkDKuOw1jCpIjQGIvVRHlUb/fiz4HrUUU4fNaq4ArSyGMLcwmCI1VEsRERGRbWEoZWZq/9FmbRVSDKNKDqYsHYDYY3WUtbw2pqiWUmPQbiz2XiVVEIMpshRWS5k27OP+JSIiS2EoZQFq/PEmP1KtMZASDD0KU8P+sPfqKDVP6bO2f+eWxCopwxhMkSWxWoqIiMi2sNG5Gam1Ssraesww8CgfnVaLv5fNw5Gt3+PSuf1wcnZHs/Z3ocddTyOiRmMTv0oMpMraN/pG6JZogn4zmHIy2Up8toRVUiXvl7ysDDO/GmTvlJX4rqhrijrZpvPnz+O7777DH38sR2paKmpF18KIEQ9i6NChcHFxsfTmERHZFP4isfPm5tbU2LzoVD0qmVabhz9mj8SKH55AUmIeqtUZjcDw/ti/bRWmPd8Jh3etNOnrxNfHuqbz2UP4XlGskipfMMX9RJbAaikypb1796JPn774Zf5SuPrejohaj+BaYgBeeWUKRowYiczMTL4ARERGxFDKjBhIVR6n6lXMoV0LcOLQX6jf6g00ajcN1WrdjxoNHkPLbvPgF9QWc6Y/hPS0RJO9TqT+6XwSRBu7SlJtn3FVxSqp8mEwRebE3kdkSjk5OXjs8f/ByS0aLbr9jOhGYxFRawgatH4bjTt8jO07duGLL77gi0BEZEQMpey8csBaKqQEq2/Kb9+2eQgM7YKgiO6FznfUuKBWk+eQk5OFnRt+hinwdar8PmOopw4MWcqP/aXIUsEUq6XIFNasWYNrV2MR3fg5ODl5FLrMN7AFQiL7Y968n5Cdnc0XgIjISBhKmYnaKgisoY8UV2+rnNycLNy4dgIBYZ0NXu7iFghv/4Y4f3JnlV4fsv5gyhTVUraCVVIV31cM88jcGEyRse3fvx+e3hHw9Kll8PKA0M5ISIjHpUuXuPOJiIyEoZQdVklZQx8pVkdVnoPjzX/WeXkl9zzQ5mVBo3GGKUgDb7Lviik1fu6VF4OVymEwRebGaXxkChqNBnl5WdDpDB+wkcuEs3PZYyjfmL1G3z4iIlvEUMrOqqQYSNk+CZsia3XC9UtroNPpil2elnwGqYkn0LBlb6M/tiVWkrM1lgimjFktpabPu8pilRT3G1kPVkuRMXXv3h2Z6fFIvL7b4OXXL61CdHRtVKtWrVz35+zMlfqIiMrCUMoOqwXUWiHF6XrG06bL40i+cQjnjs5UqqL0MtIu4uS+txAQUhPN2g804iOStQZTav08sARWSVUdV+Qjc2K1FBlb27Zt0bx5S5w9NA2pSSfzz9dpc3Hx9Hxcj92EcePGwMHBgTufiMhInIx1R6T+qgE1947hdD3jqtt+MG5Pv4r1iycj7tIq+AS0QG5OCpKu74VfYDWMfXUFnEx49E6m8LHhedXI/pP9KP82zFGBJp8PIf48TsEqKeMFfNm1Wxrp3ojKrpZKj7sZ5hNVhYRN3347Gw8Mfwj7/30cvoFN4eQSiLSkQ8hMv45x48Zh6NCh3MlEREbEUMqEVVJqDKTUWBXBQMo0PZ0GDX8WnbrfiS2rv8XFswfg7BqO/vc/jlZd7oermydMRQIUa++JZG/BlHwuePvy64CMF+zlZWUwmCKzVUtdvJLFYIqMJiwsDKv+XoGVK1fizz+XIyUlBXXq9MeDDz6IRo0acU8TERkZf4XYEQZS9kMfYIRE1MGghz+wyOPHgNVSxgymrDGY9y28mraqceqeaYIpInMGU0TG4uLigoEDByonIiIyLc7VsKMqKbVhhZTxqS28UNv2WHMwZY7qM2N9Vqjp868iOHXP+Bj2kTmx6TkREZH1YShl49Q6bY+BlOkCILWsgKeW7bAlpgym1PYZQdZPH/IxmCJzYNNzIiIi68RQysarpNT4Y1N+WEvlBxth224gpSfbw2op61uRz94wNDEdVp+RubFaquL7i4EeERFZEkMpG6bGaXv6QIpsP5AqiMGUcZjj344aPzfMgeGJaTH4I3NguEJERGR92OjciFglVToGUvYTSKWlxGPf1iVITb4O/8BIBNbsBxd/Nj43FlOtxsdV+MgUuBofmb3pOQKRHscDYGR9dDod/ttzENsOHoVG44jb27VEi/p1LL1ZREQmxVDKRkm1g5qm7TGQso9ASgZTKxe8gzW/fwStNg/OLt7IzkyEq5sXug1+B807P8xKOSOtxmeqYMreVuBjBY95cDU+ssS0NAZT9iXEKRZe7g6wVifOxeCBF9/DkTNn4eLiBZ0uD6989h06t2yGn9+fjNBAf0tvIhGRSTCUssEqKbVNv2EPHPsIpMTqxVPx92/vIrLOQ4iIvg/Orn7IyriKCyd+wOpfnoGLqxcatrmPwZSRgilTfoaE+Fdtdrd8Hga4JcMacOqeeUPA7NotzfiIZLfVUlfYf48sxzdmL5ydXcp9/Ws3EnHHmJeQnuOFxh0+gW9gC0CXhxtXt2LPkRnoN24ytsz7DG6u5b9PIiJrwZ5SNkotVVJcZc9+AqmM9GSsWTwNEbXuR40GjymBlHB1D0WdZs8jMKwrtvz1DnRadYWm1swUga9aPjvI9jD8I3Nj03OyFrMX/YkbyWlo2O5j+AW1hIODAxwcnRAY3hX1207F0TPn8Pu6TZbeTCIik2AoZaQqKbVQU5UUAyn7CaTEkd0rkZ2VplRIFSWDq/Do+3Dj2lno0g+x8bkRcMGAquPUPcsEU9zvZA5sek7W5JeVGxEQdjtc3IpXQXv61IF/UEvM/3ujRbaNiMjUGEoZiVqm7qml0oGBlH0FUiI9NVE5qufiFmzwcjePsPzrCa7Ip+7psWoKuE2J1TuWwWCKzBVMsVqKrEFicgrcPMJLvNzZPQw3klLMuk1ERObCUMrGqqQYSNkmtQdSIjA0GjptLlKTjhu8PDnh8P9fr2b+82Awpc5qKTV8jpDtYhBI5sZgitQuOjIcqYmHSlxEJj3pCGpHlRxaERFZM4ZSNlIlpbaqBk4tsq9ASjRo3hN+gZGIOfG9Ek4VlJebjkunf0athl0QElFXOY/BlPFwMYGKY6WO5fE1IHPgND6yBo/d0wcJV3cgKf5AscviLq1BavJ5PDKor0W2jYjI1BhK2RA1VDfIj2MGUsan9kBKOGo0GDb2SyRd34ND257B9csbkJZ8CldjVuLglnHIybqK+x6bXug2DKaqjv/eKo8VO5bDfa9uATa48jyrpUjNHhzQC11aNcOxnS/g3NHZSEk8huQbB3H64Cc4tf8DDO/fC7e1bmbpzSQiMgmGUlWcuscqqVtYrWGaKilrCKT0GrXqg3GvL0dAsA+O73kD+zY9hlMHPkT12vXw3Hv/IDK6ebHbMJgyDv77I2vEain1CgyAzVVLMZgqTPYHK8nUwcXZGcs+fQtPDh2ApNhlOPDfGBzc8hRyk//Fm2NHYvZrzymLxhAR2SInS28A2UaVFBubG5+1BVJ69Zp2x4T3u+P6lTNITb4Ov8Bqyqk08jwZqlStWsrY/blufqY4IcS/8scubob2yfD1MOqmkQ1VS+VlZVh6M8iAIB8gPc+2do2ELxevmGZhCCJjcHdzxQfPPo7XxozA0TMX4KTRoFHtGnB24s81IrJt/JSz8gbnauglxUDK+Kw1kCooKKyWcqro89ZPR0tOuIQTh5YjIz0BPn7VUL/Z3XBz9zXR1toG+bdo7e8bc1XncPqYul6P7NotLb0ZVEK1VPwN26sOSo8zzSIRRMbg4eaG1o3qVfr2ubl5WLl5B3YeOqYEWz07tEKn5o1ZaUVEqsVQqgrUMHVPDVVSgn1tjKeqFS+5OdlwdNQoPZ6sib5aSuPrh7U/PYMDO36Go8YVLq4ByEy/gk0r30fXvi+hRYeRlt5Uu6mWIjI1VkupV7AvEJdkW8EUq6WoNFqtFtnZ2XB1da1SgOMbs9diO3rP0ZO4//l3cOnqVXh4hkCrzcH73/2C1o0aYMG0KagWEmSxbSMiKglDqUpgldQtnHKljpX2tHl52LL2e2z6ayauXDwCBwdHNGzZGz0HPYe6TbrBWsjznvfFJBzY8QtqNPgfwmrcBY2TB7KzbiDmxDxsWP4GXN180LDFIEtvKpmJf9ppuLlYV8BKFcdqKXUHU7ZECabAailLkIBT3lNqc/LkScycORN//PEnsrIyERwShhEPDcdjjz0Gb2/vSt2ns7MLzO187FX0GzcZcI5E865vw8u3HnQ6LRLjduHIoWkY8OQr2P7z53B1Mf+2ERGVho3OK4lVUpy2p6ZAas70h7Bw9jPIyQtGneYvombDcbhw5gw+f70vtq6bC2uRkhSHvRtnI6reKFSrPVQJpIRUS9Vq8gwCw27DtvWfQae1/LRVtWJQTNaGUynVz5aanuux6TmJnTt3on//O7F8xSaE1hyOui0mw9GtLT77/Cvcc8+9SEqynlT2q/nLkJXjiIbtpimBlJCDlP4h7VC/zQc4fu48fl/3n6U3k4ioGIZSVkoNvaQEp+1ZNpAS29bPxf5tS1G/9Vto0PpNhEb1Q0St+9Cs8yyERt2JBTPHI+H6RViDQ7v+glabi7AaA4tdJqX0YTXvQdKNc4i7csQi26d2xv73KFOD1fJZYyzsJ6VeXIlPnfSVLbYUTHHFORK5ubkYO+5JuHnVQfOucxFVdyRCIvugTrOJaNLxK5w9dxEffPCB1eyshWv+RUBEbzg5F6/u8vSpDb/AZli0ZqNFto2IyGKh1KZNm3DXXXchIiJC+UG5dOnSMm+zYcMGtGrVSpnPXadOHcydq64qD7VM3bN0LylWY6gjkBKbVs5GQFgnBIZ1KXS+HB2r2XAMHDUu2Lp2DqxBZnqy0kfK2cVwfb+rW7DyZ1Zmqpm3jIhMidVS6h5DqXHKlTHYc7WUPHd7D+fWr1+Pq1diUaPheGic3Atd5ulTC6HVB2PRot+RmmodY47ktDS4upXcM8rJNQRJqSr6IUNEZI5QKi0tDc2bN8eXX35ZruufPXsWAwYMQI8ePbBv3z48++yzynzuVatWQU0sPXXP0pULXG1PPYGUTqfD5fMH4BfczuDlTs6e8PZvikvnDsAahITXRV5uBlKTThi8PPnGfonb4BdYw+zbZk0YGpO1YrWUusdQtlgtZc/BlL07cuQI3NwD8qe6FeUf0gGZmek4f/48rEGdqGpISTA83tPp8pCedAD1a0SafbuIiCza6Lxfv37KqbxmzZqF6OhoTJ8+Xfl7w4YN8d9//+GTTz5Bnz59TLil1sfSK+5x2p7lAyk9JydX5OWUfBQvNycVzs7VYQ0atLwDvgGROH/sGzRs+x4cHZ3zL8vJTsTF07+gZr3u8PYNt+h2qhlX4SNrxZX41D2Gstmm51eyLL0ZZCEuLi7Iy81UVqgrON7Qy81JUf6UykNr8MS9/TH+/c+ReH0v/IJaFros9uwSpKddwyP3lP8zhYjILntKbd26Fb169Sp0ngyk5PySZGVlITk5udDJlFP3LF0lZWmswDCuqgZSMqWjSZv+iLu0Cjpt8aAyPeUsUhIOo3Gb/rAGGo0Tho+fiZQb+3Fw8zhcjVmJpPgDuHTmN+z/73+ALgPd+k9RrpueFo/Duxdi79a5OHdyE7TaPEtvPqkc+0lZB1ZLqXcMJcGULVVL6YMpVkvZp549eyInJx3XL28wePm1iytQvXpN1KpVC9bgoTt7oXubFji28wWcOfQZEq/vwY2rW3F8z5s4e+QLPDX8HrRuJCvy6bBx1358OX8pvl+yEpfjjNuPkohIVZVSFXXlyhWEhoYWOk/+LoOkjIwMuLsXnu8t3n//fbz55puwFzJ1j1VStlElVdVASiQnXkXdpt2wf9synNz/AaIbP5Xfjykt+TRO7H0DgSHRaNFpMKxFwxZ34Km3V2HF/LdxYv9U5TxHjTPqtRiIrndPgTs8sOGvt3Fgx0/Q5uUql2nzsuHtF4negz9EVK0Oln4KRFRJrJayjjGUBFPxN2BTJJhK549zu6HVapVQtnXrNjhw8FM4u/gorRDkYF9eXhYunZ6vhFXTpk2Do6OqjuGXyMXZGUtmvIGp38/H14v+wuFzvyvnR1erhtcnP4VH7+mP3UdO4OFXp+HUhRhoZPykzYODAzDyrt745PlxcHN1sfTTICI7pKpQqjImT56MCRMm5P9dBl9RUVE23eDcUlglZdxpe1WRdCMWi7+fhAPblimr1Ym4y+sRH7sR3gFNoM1LR0rCcQSG1sK41/6Es7N1lJ7r1WrQEePfWKGEbhmpifD2D4WHp5/yHlz9y7M4sG0eouo9jLAad8PJ2QepiUdx/thsLJ03Gvc//htCqzUt92Pl5eUgJfESHBw08PGrBgcrGXwSEVliDMVpfNbP3ivDFi5ciOnTP8GlSzE3z3BwxJEdL8LTOwoubiFISz6J7KxkPPfccxg6dCisiauLC14bMxIvPfoAzl++CieNBjUiQpVg7cS5GPQZ+xI0rtXRpOMM+AQ0R15umlKV/uPyb5CUmoZfPnilQo8Xl5CIhKQUhAYFwNfL02TPi4hsm6pCqbCwMFy9erXQefJ3Hx8fg0f49PO8zTXX294bnAv2krJ8H6mUpDh88nIPpKWkokbDcfAPboucnGTEnv0d1y+vg5trOqrXaY3GbV5Fs3YS2lTsqJeUdaen3kBeXi68fIIteoTQxy9UOem5OVzE/s1zEd14PCKi780/39u/ERq1m4r9/43BtvWfYeCIb8q877zcbOzcNAsHtv+M9LQ45TzfgJpo3eVRNG07XDlaaq3k32kMjFONp//sCfG37rCOU8Ksq1pKXq/s2oV7opB6xlD61fhsqVpK6S8F+6mWsrWV90KcYst1vW+++QZvvfUWgiJ6oEnHiXBxDUJi/F5cOjkXudnX0L5tXTRsOALDhg1DzZo1K7wdUn0VGxePAD8/eHkY/ndnrqqpukWamk+dswA6By80bP8xnJw8lPOcnL1QrdYQOLv4Ycm6d7Hn6Em0ali3zPvfefg43pr1I9Zu23XzfjROuKdnF7wxdiRqRUaY6FkRka1SVSjVsWNHrFixotB5a9asUc6nmyw1dU8qVBhI3Qptkm6cR25uFnz9o+DscvOL3ZSBVHZWBg5sX4Yb187jxKGNSE6MR/Mus+HmcbPhtwx7fPwbK0sYS5PwJ17+HcHhtSv8OLv/+w3rlnyCi2f3Kn/3C4xCtwFj0f3Op6BxKt4E1Nz2/PsbnJw9EFr9zmKXOWpcEVZzEM4cnIHMjCS4uZe8hrlM+/vz5zG4cHozQqIGILpJF2i12Yi7tAbr/3gVN+JOo/uA10z8bKznM8fbV1VfFVUKO4hslSXGUJzGRxWVmpKEq5fPw9PLF2HVjLuSrpe74YNJx44dw8aNG5GSkoIvv/wKEdFDEN34yfzL3b0iERTWFQe3PAE/Pz+89NJLlZo+++mnn2LxwkXIyMpUDujdeVtHvPzYcDSvX/HxmLHl5OZi0ZqNCK/9cH4gVVBwRA9cPDEL8/9eX2YoJf2o7n56Clw9olCn2fNw84xEatJx/LV5EdZuew4bv59eLBAjIiqNSX9ppKam4tSpU4WWK5ZligMCAlC9enWlbPzSpUuYN2+ecvmYMWPwxRdf4IUXXsAjjzyC9evX47fffsNff/0FS+LUPdI7svd37Nw4EwnXTyt/d3bxRKNW96JTrwlwdfMxSSC1c+OvWPjNc8hMT1T6ReXkpCAi+r78QKqg8Oj7cPnMAmz/Zx7uHF6xPiErf3sXK+e/Df+QdqjXcgocHV1x4+pm/PHTazh9dAsefWG+0ojcGDIzUrB703zEnNkHJ2dXNG7TD/Wb9SyzKis1OR6u7iHQaAzvR3dPGQTpkFVGKHV0/1KcO7kBjdpPUyrN9AJCO8Hbvyn2bf0M9ZvdhfAoVmoQWYq9V0upfQzFaXxUEQnXL+GXL6Zg89qFyM25ueJh/cZtMHr8q+jYzTSLsSQkJGD8+KewadNGODm5QafTKr2kIus+WOy6zq5+CIm6B3/+OQfvvvsuvLy8yv04ly9fxt0DByExMQMh1YfCy68+MtNjsX73UqzaMgHLP38HXVqVv61AWaSaSQKmxORU1K5eDQ8N6IXQQP9Sb5OWkYnsnBy4eVQzeLmDoxNc3SMQn1j6Ygd5eXl4/M1P4OHbBA3bToWj5mY1vm9gc4RE9sHhrU/iuWkzsfyLd6vwDInI3pg0lNq1axd69OiR/3d934JRo0Zh7ty5iI2NxYULF/Ivl6WMZfAkc7jlaENkZCS+/fZboyxlbAtT91glZVk7NnyJLWunIzCsKxq2fQxOzt5IiNuBw7t/x6VzO3H/4wvg4lryIKYygdSBHX/ix09HI7haLzRqP1opr96+agC8/BoYvL6ENR7e0UpFVUVcPn9ICaSq13sEUfVG5p8fGN4VgeHdcGjny9i18Re0v/3WZZV1eNdKzP1kFLIzU+HlWwe5uWnYtOIrVKvZHE+8vBj+QSUfXQsIqY6MtMvIyU5WmpIWlZp4DBqNC9w9S++XcXDHr0r4VjCQ0guvORCxZxfi4M5fGUqR2aTt3Fbq5Z5t7auBPxueW8cYitP4qDwS4y/jg4ldkZGWgWp1RsM3oCmyMq/j6vmlmDxuEF567zv0HTjCqDszJycHDz44AidOnke9lq8iMPw2nDv6NRKubVfGUobI2ConJxtxcXEVCqVef+MNJCfnoWnnb+DqHpx/vlR1y0p4j7w+HUeXfgeNRlOl5yTB0qgpU/HXpq1wdw+Ei3sQ0pL/wRtf/YD3nn5UWV2vJN4e7vD29FIqmoIiuhe7PC8vU1mxuXpYk1K34Z+d+xBz5QqadX4tP5DSkwOn4bUexLrtU3H+8hXUiAirwrMlInti0lCqe/fuylSnksigytBt9u69OXWISC2SbsRgy9qPEVl3BGrUfzT/fJ+AJggK746Dm8dhz+bv0OH2Z4y20p7821n+0+vwC26Lui1eUXoc6bS5cHR0QWbapRJuk4esjFh4enet0GNtWf0dXN2DUK3O8GKXBYR2VAKcf//+psqh1MWz+/Ht1KHwDW6Lpp2eUaqe5Hkm3ziIU/vfxcy37saLH28vcapgm67D8Me8Kbh46idENxpX6LLsrARcOb8U9ZrdCRfX0pttJsSfRViNIQYvk4bn3gHNkBB3tgrPlIzBP+1mRaIt9pMyFEI5ehs+0q1NSSh0fXsKqOy5WsqaxlCcxmc9LNHkfNm815CRlo6mnWYq3/vCW943YV1xav9UfPLWU7it1yB4eMq5xiFTVw8e3I+mnb5QxmpCqTbPilcCGI3GrdhtZGwlYy2Zwlde165dw+pVq1Gz0VOFAin9gcLq9cfgwOaxWLd9L3p3alOl5/TEW59g1da9qNfqNQSFd1PGK7nZKbhw8ge88MnXCA3yx/29iwdON7dFg1F334HZvy9HWI2BcPMoHBjFnl2MnJw0jLjrjlK34eSFS3B0dIaXX0ODl/sGtlD+PB1zmaEUEZWbdXeutZOpe5ZscM5eUjcd3rNQaQYZWad4ybf0cQqqdgcO7Pi12A+Iklbak1XlNq/+FuuXzcDBnX8pTcWLir1wBFcuHlGm6umbbkt5tTTnvHL+T+TmpBW7jSxfnJl+DW1uG1ah1/nyhcPKtDVHR8M5tW9gK1yJOYKqWrfsE7i4B6N+qzfyB6by3HwDm6F+q7eU53toV8lTTbz9QnDng2/i8pnfcHzPW0iKP4CMtIu4cmE5Dm55EhpHoOPtz5W5HS4unsjKuNnc3JDszDi4uJX/KKla2cKKmW4uVTuyrMZ+UvqASUKogqeSFL2O3F5/smVqes2o7GopW2wCbqur1JmzyXlmegp2bFyAsBqD87/39RwcHFG9/qPIyszEuhULynV/Mv1u15Z1WDDnEyz5dSauXDJcGb506VL4BDTMD6REcERP5OVm4Or55cXvNy8bVy/8jm7dusPfv/SpcAWdOXMGWm0efINallh95ezsjiNnKlbBXtTJ8xfx+9qNqNnwaQRH3K4EUsLJxRvRjZ5UDiB+8N38UoPs5x8eilB/TxzZNl5ZHCcj9SJSEg7j5P4PlX6kk0bdX2aTcqm40mpzkJOdaPBy/djKmyvxEVEF2Eb3WhufuicsNXWPbkqMPwdP37oGj6wJn4CmuHrhT+TlZsHJ2a3EPlISPi2d+yL+/Xs2dFotNE5uSrjk6x+B4U99jYYtbh2hykhLUP50LXI0q1qdBxF/5T8c3jYRNRuNhU9AM+TlpuPaxb9x7ugs1G58G2rUrdjROFd3b+RcvVzi5TlZCVUOaWSgdGDbHwiPfkA5ylaU9GDw9quPfVuXoHmHQSXeT89Bz8HD2x9/L3gPh7Y+/f/nOqBmvR7oPuBV+Pgb7pdQUL2mA7B/+69K1ZsM6ApKTzmHpOt70K7rVFgzWZigpFCULB9GVVbB2xasoLKn6ilSH5udxnfF+oN9S0uMv4TcnEzlwJchElR5eIXj0oWyK2OPHtyJt58fhUsXTiqLnkiQ9Ok7z6DngAfw4buvw8v9VgPvxMQkuLgVHj+5eUYgrMZdOHv0K6VaSv5fqqdSEo/h/NFZSE+9gKef/qhCz8/T82Zldk5WIuBVvHF7Xm6asjCOl4fh8WN5/blpqzK+DK7Ws9hlcnAvtPpdOLrzZaVCqU51w+OgkAA//PPdR5g0fRb+2PAFtIdvHvQOCQzERxPHYNzQgWVuR78u7ZWV/a6cW4rq9UcXu/zK+SWIDA1FqwZ1KvU8icg+MZSiErFK6hYXV2+lekaCFX3VUkFymfQyklNpjc0XffMctqydg+r1RiOs+t1KIJKadBIXjn+Dr98djKffXo1aDW6ulBQQcnNwk5JwBB4FBjoeXtVRo/5jOHvkCxza+gwcHF2g0+VKf284ufjg7LFtOH10M2o37Fzud3eLDgNxeNf/kJ56vtBjCTmqeD12Ddp1Nzzdrbxk3+Vkp8PZpeQf5E4ufsjJyijzvjr2fBjtu49AzJm9yMpMQY6mOnz8I8u9QmSLDiNxaNcCHNnxPGo1nQAv33o3V1W8vhunD34Ev8BopdE5kVrCKEP096cPp2w1mLLnKXzWhtP4qCh3z5uJZXbmNYM7R5uXheysRGU1vtJcOHscz43uA2fXSDTt9JkScmnzMnHt4mps+Hsmnk25ip9+nJM/RqtZswb2H9ygtDXQVxUp5zd8EjeubsOF498pJ+mLJNugcfKSgQq++momvvuudZkLr+g1atQI1apF4eqFP5Rm30VdjVkBR0cH9O9atc/n9IxMuDh7KisNG6IfW6Vnlh6kVgsJwq9TpyD2ejxOnLsIN1cXZbU9Z6fy/SQM9PPBk0MHYsbPP0Lj5I6wGndD4+ShhHIxp37C9cv/4OtXJ1S5fxYR2RdO31M5S07do1vqNx2AjNQYJMbtLLZb8vKycPXCctRt0h8Ojo4lBlLxV89i85pvUbPBGGUaoL5Cx8u3Lhq0eRceXjWxYv7b+df3D4pCgxZ34PKZX5Gbk5J/vqwec/nsImVA1qjdNNRs8ARqN3kObXrOR9uevyn3t3D2c6WWcBfVsvMQBIZG49iul5F841D+bWVq3LFdr0CrzUC3AeOr9JaQAV5ItQZIit9j8HIJv1ISDyOsesPy3Z9Go1SE1WvaA40b3VxuubyVQd5+Ebj3kR8BXSr2//sEdq8fhl3r7sPh7ZPg7RuEe0f/mF/xRtbN0v2kTBVIFVR0Wp8t4RQ+68FpfGSIb0A4ajXshCvnligBUVHXLq5CTnYqevS5t9Qd+PM3HwIOHmjYfrpSIS7hk4QisjhJ7WYvYdPGddi9e3f+9R944AGkp8bi6oXCLQHir2xUArL6rd9EnWaTlIN8jdpNRfs+y1C/9dtYu3aN0o+qvCR8eeaZpxB3aR3OHZ2N3JxU5XyZ4iaPHXP8G4y6uw8iqjgVtGGtGkhPj0d6iuFpgInXd8PVxRU1I0LLdX/hQYHo1qY52jdtWO5ASu/t8aMx9v67cOHYbOxeNxgHNj2E3euHIP7iMkx97gmMvLt3he6PiIiVUmX0k7LXqXu20IvGWDLSbmD3ljlKhnt8zxuo3XSisiKd9F9KSz6jVCzl5iSizW3/y7+NocbmezYvgpOTO0Jr3FnsMpnOFlZzME7sn4qUxGtK7yRxz+gP8cnkHkoj9fBaQ+Ht1xA3rm5GZvol1Gn+otKLyT+k8ApykXVG4siOF3HxzD5ElbO6wMXVHePf+Auz3h2Mg1vGw92rmjJVMTXpNDy8AjFmylKERFS9FLtr38fx+5wXkXh9D/yCWuWfLyHYhRNzkJeTjo69ipeDl4fsc3nfSjClr5iS+718fhcO7ZqP+KunoNXmIjSqGZq1HY7Qak0xesJ6nD3xD2Iv7FFez+p1uqBazXYGq+HIelkq2DBHIGVvVVOkbpzGp26W6JG1/Z9flB6ZGWmJOL7nbeVAmkyjk+lz12JW4fzRL9H77ocQWbNuifeRl5eH9SsWIqzmA3ByujVFT09W1vPwDFP6SLVpc7N9QcuWLfHggw/i518+QWricQRH9lEaj184/j18AlsgKPy2YvcTENpJ6UP166/zK7RqpQRg8fHxmDbtI1w9/zs8vaOQmXENWZlJGNb3dnzy/FhU1V3dOiLI3x/nj36F+m3eKdQGISPtEq6dX4RhfbvDx4i9nBKSU/Dj8jVYvnE7ricmITTQD3d164CHB/bF9Elj8cxD92Lh6o24npCIqLAQDO3TQ6mkIiKqKIZSVKLyToWyZdIHYfGcEUhKiEV0k6eRcHULTux9G5qD0+Ho6Iqc7AR4eAVj0MjvERRav9SV9tJSbsDFLbDEvlRuHuHKn+lpifmhVHhUQ0z4YAOW/jAZR/ZMV0rLC17f0HRCaaop4q+dK3coJaRSavKM3Ti2bw2O7l2t9L+qUfcltOx0nxJaGUPn3o/j0K4VOLrjRaU5fEBIR+TmpiLu4iokxe/D4Ec+QmBIzUrfvz6YEtKza/2fr+Hgzl+gcfaCNjdDOUp7/epRHN61AKHVmqPf/Z+gdsM7lBORtQZSBcnj2WIwxSl81sXWpvEp/aUQiPQ46x8XmbPJ+c6NC/D9R6OUBVpCqzdQVs7d/c+DcHENRG5OslJN1OfuhzDprZml3k92ViayszOUMMsQaZju4h6GhISbvTj13nvvPdSsWROzZ3+LQ1tvVkw5ODgpTcFlyp6hqXCePg1w7vzhCj/X8ePH45FODfDryn9w8dp1BPi2xtA+3dGoduXHNAVJH6fv35yIeye8iYP/PYbgqIFwdQ9FSsIhxMX8icgQX7z1ZOUO6hmy//hp3Dl+CuKTkuGocVN6Yx09A2zYuQ8vfzYHU554EBNHDlFORERVxVBK5VP3WCVlWcf2L8P1K8fQvOtsZVpcRM1BSnXUjatbkJVxVZm21677eERGty9z6pj0iMpMi0V21g24uAYUu1wabUpPKh//wo05wyIbYMwrS5BwPUZZrW/npgVIT7mOXeuGwNOnNiJq3Y/gar3zw6nM9FjlT0/v4o9RlLdv8Y+A9j0GKKfSVPZ96eTsgide/h3//PEp/l35NY7tXqGcX6tBJ9z/xGI0bVv645Y7mEIgdvz1kRJIefnWR1rKGUTVexihUf2hcfZEwrXtOH/0a/z2zVAMH7cMXj6F9zlRVUggZO4wypaDKal0yytHrzlST7VUXBJsklQa2UIwZQ7avDws+u4lBIbdhnotX1PGKGE1ByI+dpPSDuHGtS1w1qTghXdmw6mM6WNu7h7w8QtCSsJRBFfrZbCNQrp8z0d1LtY2YMyYMXj00UexatUq/PTTT9i8eQtizy3B1QsrlPuKqjeq0KqAWRmxiKpR9vjJkPDgQLz46HCYyh0d22D9tx9h6pwFWLHpC2h1Wnh7euF/992BF0c/YLQqpfTMTNz99KvIyPOGg0MaPLyjlabmPv5NkZV5DbFnl+DVL75HZlYWpjwxwiiPSUT2jaFUKVP37BmrpG46svd3+Ie0UwIpPU+fWspJZGddV4Krtv2eVf5eUpWUaN3lfiyd+xJiTsxDrSbPFKpwys5KUEq+W3QeDHcPH4NLIC+Z+xL2b12KoGo9Ub1eV2h1OYi7tBYn972vrBhXs+H/bk5XO/MbfAOqoVaRRueGAigR4u9Y4bC06H1VJKRydnZF73tfQK97JiEtJR5Ozq4Gn3NVyPLMe7bOhV9weyTGbUfdFq8gJPJWNVRQeDdlxcS9Gx/G7v++Rbf+U4z6+GSf/aQsVR1lD8EUWRdbnsbHYKp8Th76F4nxF9Gs8yv54x2pFA+JvNlvKDCsC/b/9z/s3bEBbTsVD5oKktvfed9oLJw3E+HR98DdM7LQ5bFnFyE7KxlDhw41ePuDBw/iuecmQuMcgOhG4+DmGYm0pJOIPb8UN65tQ7NOnytVWDKWunFtO+6d8AHUqk3j+lj40WtKcJSangl/H68K94Qqi0zJu3YjAT4BkXDz1KBxh+nK1Ech+75Wk6egcfbAh3Pm44n77lJW9SMiqgqGUqVQQz8psqz01Hh4+LQudJ4c4ZPmlRlpMchMu4zM1HQlBKlRrXifg4KkcmnQwx9g0TfPKmFWeI1BcHEPRnL8flw+Mx9Ozg64c/gbBm+7d8si7NuyGA1av43A8K755wdH3I5Lp+fj3NFZ8PZvjISrW3H98no8+NQ30GicioVHFQ2gDCl6H0VDqvIGVHIE09s3GKbghotISYiBf0iUUt4eXO32YteRarWQyH44smcxQykV8k87DTcXjdX1k1JDIGWoAbotBFOcwmd9bHIan5UGU+buJ5WcdHO1PXev6vnnafOyEX/lX6U3pixuIs6fPlpmKCWGPTIRG1cvxeGtTyM8eij8Q9orUwBlPCbN0h9/Yhyio6OL3U4O1j377AS4ekSjYbuPlOboQqbwSY/PA5vH4/ShTxEa1Q/nj32B2rXq4J577oHaebi5KSdTWLd9D7z96iH5xgHUafZCfiBVUET0EMSemY9FazZi3NCBJtkOIrIfXH1PpSy16h4bnBfm7RuOtJRTt/bPyXnYs2GEssTvzWWGHZGRFocFn/RCSlJcmfv3tn5jMPLZuXDEFWWlt70bRuHMoRmo3agVJrz/T4n9lP77+1v4BbUsFEjpRdQaAhe3IBzbNQWJ1//F0DFfoNeg0flBkYRI+pMpFL1/eVz9yVIkJBR5uanKgLjgctAFeXjXVBqR5uXlwFJkwFyRlRJJndS86p2EU2revvLgKnzWxxZX4zN3TyZr3nb/oGrKn2nJp/JbC+zd9IjSl1P+X1YSlp5OX059Hn8u/LbM+/P1C8SXP2/Abb36IubEt0ql88EtT0ObfRjPvDIDz0180eDttm3bhrNnTyOq/hP5gVTBg1NRdR5SKqplIZsWzevht9/mw93dMotjqEWe9tZvEA/vGgav4+ziAzc3f1yNL9zHy9w4fiKyDayUUjFL9JMSnLp3S+PW9+Hvhc8hKX6/0kNKVm2R3kSRtR/Ib5CZfOMgju95Hd99OAzPvLO2zJXb2tw2DK263I/Y84eQmZGMoLBa8A0w3LxTLzbmCIIi7jZ4mQQufkFtoM09hXe+2wNXt5sVW8YMoeRLf9uGZfjj1y9w7NB2pcqpdcfeGPTQc2jUvFP+9Qo+ZsEKKnO/l2WfyqqBublpyJXBrzYXDo7FP+7Skk/DzSMAGs2tVWzMQZqwy7TPfdvm4drlg3DUOKNG3dvQuvNjqFaz8GqKpH5qmrZXGlupmCLrYYvT+Gyt8bmp1GrQEcHhdXHx1I/KAixHtr+gLNbS4rbv81sg5OamK/0dP3p9LCIia6F1x+JVzQX5B4Zgyoc/YPzk6bhw5hhcXN1Qp0ELpSeVg8PNfppFnThxAo6OGvgENDN4ue//rwT87rvvYuTIkTCF2Ovx+PLXZfh5xXrcSEpGZFgIHhnUB0/cOwDenqVX2VtC+6YNsWTdFhlhKn1UpRK/KGk7kZFxAxEWWNHx7MVYfPrzYvz69wYkp6YiKiwMjw3ui7H3363K/UlEZWOllAH23k+KbqnbpD+q1WyHIztewrmjX8M/tBOq13u40Iot0puodtNJOHN0M86f3Fmu3SehTrXoZqjdqEuZgZRwcfVATlZiiZfLKoDB4dURFe5l9KooCaS+/XgS3pk4GOfPxiEiegRCooZg387deOGRrli19DuDtzNUPWUu0reqS9/HkJF6HtmZcUppf1FZGdeUVf8keDR3ILV6yYtYtXgisrNdEd34aUTWHY2rl05h4bfDcHj3QrNuj60yVz8pawmkCk7ls2bm7hNGxpvGZ4vMPSXOmrZTxjnDxnyMpPi9OPDfGKXlQf3Wb+QHUsLJyUPpsenj3wC/fj+93Pft5x+EZq27oEGTNmU2Sffw8FCqp3NzDHffz8m6WenTsGFDmMKJczFo/+BT+Hz+X3Dw6opq9f6HVF1DvP7VPPR4dBISklOgNiPuvAPuri5wdvXD5TMLkZdb/IeRtI5w0jjivju6mXXb9h47ifYPPYUf/toMn7CBqN10IrKcW+Dtr3/B7Y89j8SUVLNuDxEZB0MpFfaT4tQ99ZAKmkEjv0edxncgJyte6TlgiPQ2cHELwOFdN1eTM7ZWnQbjeuw65OakFbtM+lolxu3E7f1ME67s/PcvLP35EyU8adLxc1SrPQxRdUeiedfvEVr9LnzxzhhcvnBriqNawqk+Q15GvabdlSN9pw5Mx/lj3yAj7aIS7l2L+RsHtzwFN3dvtOr8KMzp+ME/cXTvYqX5eqP2HyK85iBE1h6G5l2/Q2j1O7F22StITrwEWxDglgxfD9uf8qX2QMpWgilO4bNOtj6Nz1qCKUtMO2zSpi+eeuMP6LQ34O5Vs9CiMXrSBiEoojd2bVmDnOzsSj1OiFMsvNwNV6n37NkTzs4uuHJ+ucHLr1z4A8HBoWjZsiWMTQ7qjZjyITJyvdG8249KABcRfa/y/d+089c4dfE6nv/4a6iNv483fvngZejyUpCZfkmZJhl/5T/kZCcr0zFP7v8Ql88swKv/e8hoK/6Vhyz689DkD+DgUg3Nb5uHGg0eR1iNu1C3+Uto0nkWTsRcwyufGz5QSkTqxlBKpTh1Tz2cXTzQqefN1fU0Tp4lTqGTXgW5uZUbUJWla/8xcHTQ4uiuyUhPPZ9/viyPfGzXSwgMiUT3fg+a5LH/XPAlfAIaIiJ6cLGBZM1GT8LJ2QsrF5c9qCoaTlVGXOxpLP1hMj59pRc+f70f1i39RFnBr6RqqTGvLMED476Cj3+YclRvzz8PYceaQTi5/wOERTbA/U8sgKeXaZqtl2Tfth/hF9ym0GqA+v0Z3Wis0lD00K4FZt0mqhwJd6wlkLKVYIqsN5iyxWopawumLKFx695o2rY/XFxLDi80zl5KgJOXZ/yp/gEBARgx4iFcPDkHseeWKc3WhRzku3B8Dq7FrMTTT48vs+KqLL4xxas4dx4+jgPHT6J6g3FK/6qCpGIsrNZw/LZqI+ITk2Fq2Tk5yqp69054A90fnYjRr32If3cfKLEnU5/ObbHr15kY1KMjsjMuKH1Ld6y+G/s2PYa8lK345PlxmDhyCMzpn537cObiJdRo+BScnL2L78+aQ/DLivVISi1+AJeI1I09pYjKwcs3HK7ufki4tg1+/99/oCBZRjgj9RIia7Uwyf6UBujjXl+Ob6ferzRH9/SJhk6Xg/SUi4ioXh9vfPYHPDwLf0Eby/FDOxAYUTiQ0pMAxSewDY4fKt+0RXFramHF+k1tWjkTi76dAAcHJ2VwJwOSU4dexerFH2LMlKWIrt+++PY5OaNjr9HKKSsjFds2b0BuTiYCvCPhG3BrRSBzirt8CNXrP2HwMo2TB3wCWuDqpYNVfhxnf/5IMiVrDnUkmNKmWLY5LdknW1uNzxpW5JPtsnRz9up1WmLXv4uUSmWZElZUwrWtiKxRD65upqlunTJlCtLS0rFgwSe4dPI7uHqEIj31ohJQPffccxg1apRRHkcqsgradfi40jPSL9hwr8jA0M44f3QWDp8+i9taN4epXE9IQs/HJ+HE+RilQbmTsx8OnLqO+SvX44F+PfHN6xOg0RRfEKZ+zSj8MnWK8v8HT57B6ZjL8PXyROeWTeDibN5enGLP0ZNwcfEy2ONK+Id0VHq/njh/EW0b1zf79hFR5TGUKsJe+0lx1b3SOTm5ommbodi79QcEhXcr9IWYl5eJs4c/g5dvKJq3H2Sy16hZu86Y/usZnNyzBEf3b4GjRoMW7XqhTZf+BgcTxuLk5AJtXmaJl8tlTs4VX5ZYwqnyNkPftHIWFn0zUZmKJw1TAS1SEo7AycUXjk6BmPXOILw+8wg8vEquWnF190K3Xnfmv9ct1dBfBqiG+jPoyWVOTkFGeayo8Kr/ELHEKor+aafh5qJRfd8ha6uSsoXG5zKFT17f7NrGn2pDpq+WijPc1sfqqT2YsrSOPUfijx/fwJnDnypT1xwLLDxy4+pWxF/ZhKde+qjMhWIqy9nZGR99NA1jx47BkiVLEB8fj2rVBuPee+9FeHg4TMXF2Qk6bR602mxoNMXHSTJ+FFWt0ipNVnY22jwwFlfjb8DFLQTuXlHKgVRpSeET2AK/rlyPutUjMPmx0qvtm9atpZwsSfanVpsDnTYHDprCAaDQj61cLRCYEVHVMJQywB77SQmuule6LoPfQMz5nUovIp/AlggI7Yzc7CTEXfobebkpGPPqUjgVOUpmLPpgoFqoO6r1G47u/YaXev3MjDSsWz4Pa/+Yh4T4qwgKqYY7Bj2MHv0eVFarqYi2Xfpi8/pViKo3utBAUmRn3UDi9R24Z/h7lXhWt6qm9OGUoWAq/upZ/P7d8/ANbIG6LSbD1f3mdDtZUvrEnreV5qkyENn+z4/ocdfT5QpqJJiSSiJLvOej69+OmLOrEVlneLEVAaU/WFL8PrTtWrn9aSrGbJxvC/2HrHHaXknVUtYYTJH1stXV+NQaTKllWqGXTyCeevU7fPzaQ0j55xACw7vD3TMKiXE7cOPqFnTqcScGDhtj8u2oXbs2Jk2aVOb1Dh48iLlz52Lrth1KUHZb185KNVWDBnJQrPx6tpPKeh3iLq1FWPU7i11+7eLf8PP2QasGxXttGcvjb36MazeSlDAwuNrtSrsJrTYX12JW4PTBGfDyrY8v5v+BCSOHwNXFNGNYY+nXuR1emvENrsduQEhk72KXy1TMiJAQNK5dwyLbR0SVZ32/NOyApfpJUcky0m7g75+eQtxFmX+vRdL13Th75AtcOvMLmrXvjec/2oK6jW8z+i7UNwavyIp6SQlxmDCyE2Z+8DTi453h6tUZV6/m4bO3nsCLj/VAWkrFDlXfPfwZZGVex6n9HxRqtC7nHd/zmjJt8I67R5f7/lKSbuC379/H/wY3wQO3h+HpB9th17rZyMnONFiV8+/Kr+Hg6IKGbd/JD6SEm0c4GrZ9D9q8LLh5RODYvrUVriCyxBS3Vp0fQWZaLE7un4rcnFur7kgT9mO7X4WndyjqN7vL7NtFtj9tryhrD9bIetlifylh6Wlyat2mnRsX4McvX1GqhrIy4pQV3SQQcXW+ggmvfY63ZiwwabVQRfz4448YMGAAlq/YhDxNW+Q4tMLi3/9G3779lCqrioiODMeg27sg5thMJF7fnd+/ScaRV2NW4sq5JXjqgYFwcy1/GLRx134Mff5t1OjzIKL7j8D/3voYB06cMXjd64lJ+H3tv6he/xGlj6UEUkIOMIbVuBsRte5DRtoF3EhKwoGTZ6F29WpGYcBtHXH+yGdIjNuVvz8lZLt0ZqES8k0YMdiksweIyDTU8Q1ApGJZmclYNOchJCdcRY0G/1MqpPLyMnDt4irEnl0MnVaL0GrGn7uuD2gqWqUy443HcOXSJTTv+k2hpZdTEo/h6I5JmPXh05j49g/lvr/a9VvghXd/wkdTRmL3tc3wCWillE4nxu+Bp5cP3vz8L3j7lu8XxrXL5/HC4z1w41osAsK7wS+kAxITjuOr98ej3h/z8M7MVYCvZ6Fw9sCO5Qiu1kvpt1SU9KbwD+2MpPg9yMvLQ0VYqmIqLLI5+g75GKsXP48bV/6Fd0AzaPMykHzjkBJI3fPwHKW5PqmXrYU51lgtxSl81suWp/HlV0zh5gEPS1ZMqaVKavPqOZj36RMICOuCJh2fVg4oyfT7S6d/xo3rsWjUvL1qAqkDBw7glVdeQViNexDd+Mn8EEerHYvTB6bhuecmoHnz5qhVq/zT2GZNeRaDJ7yBzdsmwtuvDlzcqyEz5QTSUmPx4IBeePGRYeW+r/e//Rlvff0jvH1rwjekP3R5OVi0bj1+XrEO37/1PO7vLSsO37Ju+x7kafMQEtXf4P2FRg3A5TO/Kf+fm1uxMZSlfPfmJAx+7g1s2T5J2Q9OruHK/szIiMdTw+/BuKEDLb2JRFQJ6vgWUAl77ifFqXslO7R/MRLizqJ519nw8LpVEhzdaBw8vWth58YP0KXv44iu38HigVTsxTPY+d9y1G76fKFASrlPvwaoVmckNv79DR59bhr8AkLKfb9de9+Phi06Y9Xv3+DogW3QODmhVYdp6HnXKHh5F29aWpIPXxmB1JRstOg2VxmYFlxF8OjO5/HdJ5Pw1JSvC03nkwoqN6+Sm7jL6n8yfS+6fjtUlLmDqdycLGUqgFRCRUZ3wOHdv+HKxf3QaALRvvsI1G96Z6X6cxVlz03OTdlPypaqpKy56blMzczLyrD0ZlAV2PI0PjVN5bN0lVRWZhp+mz0JIVH9UKfZC/k9o1zdQ+Ef0h4H/huLGe88iy9++gdqMGfOHLh7hhUKpPSVRbWbTkDS9e2YN28e3njjjXLfp4+XJ1bN/ACrt+5S+jfFJSSjZkQzjLr7ebRv2rDcfbT+2bFXCaSq13sEkXVH5N+ueoPHcPrAVDz62kdo36QBakSE5d8mKysnf5xkiP58Fydn1U9502q1yMjKho+nB9Z8PVUJ3Bas2oCEpBRER3bF6IF90bhOTUtvJhFVEkOpIuy1nxSV7MCWHxEUcXuhQEovOLI3zh//HvO/GofJn+6xWBild3TfZqWcOTCi8NEyvaDw7jh35CscP7gd7btVbIqY9KV6cEzpA7GkhOtYs+x7HN77HxwcHdCsTQ8luPL28cfp4/twdP9/aNDm7UKBlPD2b4jw6GFYt/wnPPz0Bwjx988PpqJqNcfZk9tQvf6jxQZvOl2esiIidHnodMcjqAx9MGUqUkl3aPcC7Ns6D/HXjivnSSDVusvjaNf9SZM9rjGanFdFgFsyfD0q1+Rczf2kbK1KSv+crLFaiqyfLa7Gp4ZgSi1VUnu3LEVmRgoa1x1Z7Ptbqp+r1X4AB/d8gP07N6F5W+O3QKiorVt3wC+ka6FASs9R4wrf4E7YsnV7he9XppP169JeOZVExm6bdh/Az3+txdX4BESEBGLEXb3RsVkjZd99ueAP+PjVLhRIKdvl6IRaTSYg8doWfLdkJd568lY7hWb1bx6cTLi2A4FhnYs9ZsK1m8/lvju6KuGZGp08fxHTfvgNv63aiKzsLPj7+GL0oN6YMGII7ujYxtKbR0RGwp5SKsN+UuoiFSepSZfh5WO4CaWDg6NSgRQbcwQxZ/ZZNJD6/w26+afOcMApfQxuXs34K9zs2boajw6oiR8/fxl5//2D7E3rMefjSXh0QDQO7d6krBgojb0DQjoavH1g+G3Iyc7AmWM3K130fbR63zcOaUmncOX8H8Vuc/Hkz8jOjMOAB16Hf1CUwfu9cHoPlv/8GhZ/Nwlb1nyPrIxUgwGOKaqLJJBatXgS1i2bAp1jMOo2fwm1mj6H5MQULPvxUezZ/K3RH9MWVGXlPVOxhebm9lgJRuqvlrLV/lIFK5UsERJZukpKJFy/CBdXn2IHovQ8fespf34z4zWogQyN9OMkg3Rag+Mn35i9cK7CQjeZWdkYMuEN9B37Ijas/BfJ245i9fIN6PnYRIx8+X3k5OZiy/4j8A25zeDja5zc4RPUHv/tPVzo/Bb166B14waIOf41crITC12WlXENF45/D19vb3z60lMGtyslLR3fL1mJSdNn4fWv5mLvsZMwp91HTqDTyGewaN1ehNQcjnotp8AtsBe+mL8CXR9+Dleu22iiTWSHWClFVAJ9SOHpHYiMtEslHtmS1d/kCNrWNd8j6n+fWS6QAtCkpRzhc8T1y+uVJpZFyflOzq6o39S4FRGxMafxznMD0TTXGc851oSvw83nk6DLxbTMa3jz6Tsx5LGXZYcp1U0OBj56dNqbPaQcizSovP2OvjiwfTzWLvkEN67+i8Cw7sp9xF1ajZSEw+jc53H0vu/FYveXkZaEOdMfwrF9a+DiFgBnFx+kp3yFJXNexINPzUaLjvcUr5iCcafxHT+4HMf2L0W9lq8iuFrP/PPDqt+N80e/xqaV76FmvR4ICK5ttMck47OHsMYap/Gxr5T1s/X+UpaomFJLlZTw9g1CTnYKsrMS4OJaPNSX8ZM4uHcL4uNiERhsOLwyly5dOmHZn+uhazim2Oq4eXmZSIzbjHsGlL76cWVMmj4Ta7bsxMuO4egILzhoHKCVyinHFMxY9y8iw4Lh6OAIna7khZCk16fGsfgY8rs3JqLn489j/6aHERw5AO5e1ZGWfBpXLyyHl7sT/p3zCbw8ilcXL167Cf97awbSMzPh5ROFnKwkfDhnPnp1bIuf33vJ5JVVMl1v1JQP4egahcbtpsPJ+ebjSY/R8JqDcWTbeLzwyWzMe/clk24HEZkHK6XsnCmnLdlCICVBRbvuDypL52ZnFh9MynLG6Sln4elTF3FXDK9+Yq5ASrmPiBro2OMexJz4TmlsXlBS/H5cOv0Tbh/wEHz9g2BMy3/7Ei55WrzkEJYfSAl/Bye8jFBoszIRf+2SEibJUr6GyJLJ7h4+qNuocDm2HBV8dsqn+N8rP8E/wBGnD36EM4c+QXCYPx57aSGG/u9zg2Hhdx8Ow+kj21C/9Zto03MhWtw2F61v/xXe/q2VsOr00c0mr5g6sP1n+Aa1LBRI6Z+TrIYjjdoP7vwFxmTM7dev/GhNIYWppu7ZepWUtQVwppyiSeZny9VS5qyY0t+/GqqkRMtO98DBUYPYs4sMHoiSVfg8vOWgjA6xl87B0kaPHo2sjHicPvQJtNqb/ZhEXl4WTu2fCq02Ew899JBRH/PajUT8+MdqDEcAOjl651dCOTo4oLujD+6FP2Yv/BPd2jRBQuw6ZRxVVE52MpKu70CvDi2LXVa/ZhS2/PgZRt99GxIvL8HJfe8jPe5vPDWsH/Yv/Bp1a0QWu82/uw9g5MsfwN2/PVrfPh/Nuv6AVrcvQv1Wb2DT7iMYPvl9mJpMZTwdcxHVG4zND6T03D2rISx6OJas+0/Zf0Rk/VgpRWxyXkZPnm4DnsS6ZZ/i4NZnlObm0pxTBihxF1fj/LHZ8A1qhbycNHh4lr/htykCKb2nX5uNKWP74MB/Y+AX3AbuntWRnnoaSdf3o3HL2/C/52fA2HZtXI4uWg+4aYo/Dy8HDTpq3XF872a07Xon9u+cqWyT9JHSB0g3rv6H2HMLMXjEc3BzL370TQZpd987HB1uH4bc3BykJudBoyn54+vs8W04cfAfNGjzbqE+CtJcVcq/D2wZh9WLPsTYV5cVu60xK6birhxFRG3DA1hHjQt8A1siLvYobK2flBrk5Wnx38GjSlPZasEB6NC4XqWmrVpLSGOv1VJkO9VSttxfqmjFlDB21ZTaAinh6R2Axq174+COn5WQJyL6Pri4BSMt6QQunJiDlMQjSuPuC8dPV2jRFFNp1KgRPvpoGiZNeh6JcVvgF9xZmc6XeO0/aLUZ+OrLL1GzpnGbaW/ctR/ZeXnoqTHc07aXow8WZN1A+yYNsXTdZpw5+AmiGz+lVOiL3JwUnNz7FlydNXh4YF+D91E9LASfPD8OH08ai6zsHLi6OJf6ffj+d/Ph5VdHGS/p+2tJ5VhQRHelGn/dttew6/BxtGls/JWn9Q6cPAMnJzf4BDQzeLl/SDucO/IlTpyLQUiA5d87tubStevYeegYHB0d0bF5IwT7cx+TaTGUKrDyHpuck77SpOCPet+AcLS57X7s2vQbju58Wb6Z/79nkyOCq92O4Mh+OLJ9Ilp1ecvigZRyvz7++GjOv/h39W9Y88cPuHH9GGrViUTvib+i0+2D4eTsbJJV5dxLKbyUy3JysjDhzTmYMq4vDmweC9+gFnB1j0BGykmkJJ5Eh24D8dDY0veh7KtrCc7wC3Autf/a3s2L4eoehIDQ4v2rZGAVEjUAR/fNUFYGcnXzNFkwJSvp5WYnl3h5bnYKNJ7GK4FXy6p70uS8MozR5FwsXL8FL8/8CTEF+k3UjQjF9GdGo3e7FhW+P3upktJj03MyN3uYxlcwMDL2dD41BlJ6I56ahRdH1kDsuaW4fOY3JeSQah8XtxA0aP0Orp5fgug6TVCjVgOowZAhQ9CiRQtllT1pai4VSwNHDsXIkSONHkiJnNybFVkljaH059eICMXMKc9i7LszkHB1E3yCOihBX1LcVrg4OWDxx68jNLD07yoJotxcS+99lZSahn927kHtZpMMNnwPCOsMd/dALFn/n0lDKXdXV+X55eVlwMnJw+D4SbiW8XyoYuITk/HMB59j6T+bkae92V/NRaPBAwN6YvqkcfB0r/oK0USGMJRSETY5t7ySftT3H/YqDu74C46OvvAP6QA3zyj4BbVCatIJnNr3DiJrtUKTtgMsHkjpObu44vY7RygnYwYJJQW3dZq0w44NKzBapyt29C1Pp8NOxyy0aNYBPn6BmP7DZmxeuwjrV/yMxPhrqFuvGXoPmoFWHfsoR2TKcmufORn8N5OdlYF9W3+HxtlXOaJniItroNLfKruEUKqqwVRG2g0c2bsYLq4eiD23GJ7e0QiM6KGskqOXmR6LxPg9aNWpYmGmtVRJVWblvao0OZepe2L+2v/w8LtfoKODF57VRKEaXHAWWVhwJQH3TJ6Kpe+/hDvaNS/XfdpTlZQeq6XIkmy9WsoUfabUHEgJ34AwDBj6NJbP/wxBET3g6Vtf+U5084xEzMkfkBC3ExNfXVTpBVhCnGLh5W7cxVvq1q2Lt99+G+bQssHNhXR26NJwm4N3scu369KUfdOifm3UiAhTqlZmL/4LW/cfhZOTBr3vGoLR9/RFeJBxDkr9uWHLrXGSARJUubj5IzUtA6Yg1fP/7jmAzfsOQavNw/Hdr6N2k+fg5hlR6HpXY5YjNCgILevXMcl22CPpH9ZvzAu4cO4SnkAQOmm8kAcdNulS8OvydTgTcxkrvpqqvO+IjI2hFJGBPlJFBYbUxFNvrsR304bj0pkFyhfy+WMzkZuThnrNbsfDE+aVOp3MnIFUVQKosoMEw7e7c9h4vLRuMRY6JuB+h4BCg4uftfGI02ViwJBxynmyQk33fsOVU1Xc3H8392XBcOrPn15FcuI1GTopq804uxQvOU6K3wcPr0B4eJfeyEQfTInyhlMnD63A34smKSvvefk3hJuHE07sexeuJ75H4/YfKb0QpA/Z8T1vwdMrBA2aD4Qa6d+n1kSrccaLX85DV0dvvOAQlv8jpyk80AjueE13Sbm8V9uPyv0DyN6qpKytWorNzm2HvUzjK1Y19f/fMaKiAZXaAym9EeM/gJuzFr//8hUSrm6Gs6sPMtPj4eHpjcnvf4+uvdT5PVgesvJeVTSsVQNdWzTBjwdPopHODUEOt6rZr+hy8KtDAvp1bKsEUkJ6QE2b8D+YwuW4eDz53udwcHRRxkmGqs2laX1K0jnUq9nL6I+fnJqGIZPexqbd++DpXQ2+gS2UBW12//MgouqORFS9h6HTZuPy2cW4FvM3Ppo4hgGJEc37YzWOnDmPGY7VEe146zNlsEMA6mjd8PLeQ/hz01bcc3sXYz4skcL6fnUQmVBpVSZRtVvita8OK6u5nT+1C05OLmjYsjcio8tXdWHJQKpoCFXZKhZDt5Opr906t8QjY1/A9zM/xHaHDHTReUAnR1cc0nFKl45HnpmKuo1aw9huTufTKvtVgqnMjBRsXTsHYTUH48q5JTh/7FvUbjqxUACRlnwGcRdXoPtd48oVJCrBVGyWElqWFUxdubgfK357FoFhXVGryTP5gVha8ikc3fUq9m16FO6ekcrfvX0jcc+oeXBx9TTJtFNjsKYm52LdroO4mpiMKZoaxUInjYMD7tP549ULl7DnxBm0rl/6iof2WCVlbdVS0uw8L8s0R+vJMuxlGl9pU/rKCqeKNktXeyAlNBoNnpo8HQ88OhEbVi1GSlICwqOi0e2OwXD3MO0qbuYgB9uq4ps3J6HnoxPx5I0YdNd6orqDK87qsrDRIRXhocH4/JVnYA5zlq6EFo4Iq95fWZ0vNKofPLxvTVmU/lrnj86Gs5MGD/QrvICLMTz6+nRsO3gSDdu+r8xKkO9x6eF66fSviDkxF/Gx65GXk4SsrGRMHHk/xg213jBTjaThfjsHL0Q7FP9MaebogUYOHpi37G+GUmQSDKWIKtCLR6aXNWrVRzlVlLkDKWMFUWXR3++kF19GmzatMGf2V/hx91ap8UbTNj3wzogJaNnhDtM8eJFg6tThfcjOSkNoZF94etXAqQPTkJEag9DqdyrLUSde343Yc0uUZarvuOf5cj9GeYOpPf99CzePcNRr8Uqh5aQ9feqgUdv3sHfjaPj6+6Nrnxmo27gvNE4uNtVLqiqM0U/qcvzNIKUGDO/Xmv8/0Iq9ngCUoxWGvVZJEVmavVRLlRVOlXVda1tZMSgkAveNeMqSm6NKUgW15ecv8eX8pfhx2Sr8nXgd4YH+mHDPMIy9fyAC/Qy3TjC2jbsOwjewHarXH61USh3Y/CTCatwNv6DWyM66gSvn/0BKwiHMfm0CAnyLTzWsimNnL2D5pi2o2+LlQhVaGo0rqtd7WKkwz0rahbFD+ysN3etUr2bUxycg9tp1dNeV3He2utYZl69e564ik2AoZcfkR7YxVhiz5Wl7xmLOQKpgGGWqIKok3Xv2VU76Ciq9G5mmfVx9MOXpdesjLbT6ADi7BuLS6Z9xct+7ynkaZ29o87LQ/a4n4e7pW6HHKE8wdeb4ekTUerBQIKXn4R0Nb/9G8PSWKXt3V/g5lmf7rLnJeVX7SUnVTFjAzdf0ArJRE8X3x3ldtvJnWBnNYO25SqpgIGctU/jIttjbND5rDpwq8ppS6WQFuTfHPaycLEVfYezk7I2mnT5FzIl5uHLhT6VSSbi6h8HTwwMj7upt9Mde8d92ZXGYoPDuBi8PjRqAI7Eb8dCddzCQMpHQoABcSIor8fIYxxxEhgSZ6uHJzlnX3AwT4cp7ZAuBlIQB+kBAwihzB1JF6bdBTgW3zVRk/0bVaQEXN09cj/1HOS8gtAOadvoc7e5Yita3z0etxk9LAToatuhdpfeJocok6Z+Vl5ulDOZK4uTso1zHmExRJSXv2aq8Xy313uvVpjlCfLyxSHtDeT2KNtxfjATUrxaO1vVrlXlfrJIishyGGETmd1vrJkiK34HcnFRlLBPd+Em06/U7Wt/+K9r2WgJnZ1f0at/KJI+dlZUNJyd3OGoMVzo7udysFsvMvnlwiYzvobt7Y5suFRd0xceph3UZOJyXjodMEEgSCYZSKsGV9yzDFH14zB1IqS2MMsRc4VSNar7oPuBRxJ75DYlxu/LPd3b1UyqkLhyfjbpNuiOiRuNKP0bBYKpgICRHGANDGyAxbofB2+XmpiP5xgEEhzWENVX5WRMXZye8O/YhbNClYJr2Ck7rMpGl0+KoLgNv6y5jvy4dHzw5otQm56ySsr79oV95kWwvmCo49YuITGv0oH5wctTh1P73kJd3s8RdQiJX9xBcOjMfqcnnMf6BQSZ57KZ1ayEzI0FZ1dqQhGvb4ebqilrVwk3y+ASMursPGtSMwiu4jFXaJKTq8pCky8Mf2gS8pYtFx6YNMbBHJ+4qMglO3yO7Zeo+POYKpIQagyhDbm3nze2+kWn8PgljJ32Ai+eO4PD2SfANbA5Pn3rITL+EhGvbEBxRDyOfnVPlx9CHQEWn8zVv/xDWLXsF8Vf+Q2BYlyLNQWdBq81GkzZDYQy2FEhVpZ9U0UBiRN9ucHR0wCszf8YziRfyz68ZEoSFz4xDv45lH+VllZT1NDxns3PbZ6/T+IjMLSI4EL9OfQVDn38He9YPgV9IV6WnU1LcZqSnXcO0CWPQpWUTkzx2387tEB4cjPNHvkCDtlOhcXLPvyw95Ryunl+EkQN6wsfL+hvjq5WXhztWfv0hnnxnBr74dzs+115VzndydMSQ3t3x6Uvj4ezE6IBMw0FXdI6DlUtOToavry92HLwAL28fq5m+Z4lKKXvuKWXqH/SmDqQs2TfKWAr3nDLuv7+83Fz8vfx3bFj+La5dPg8v3yC06z4cbbsNh6ubp9H/HQn5t6TV5uGv+U/h9NHVSigVENJJqZCKu7QKqYkn0GvQe0YJpUz5/q3K1D15X1bm/SihVFX7SRWVk5uLDXsPIy4xGdWCA9C1WUNloYLyVAUxlLpFQim195WSFfiya7c06WMkp6SiRqsuSEpKgo+P5cYL5hpDrdgRD08vdTxP6S/FUMq6Q0VTTscMcYqFl3vJ1a+m5Buz1yir76nNuUtX8PWiP7Fqyx7k5uWhY7MGGHP/XWjZoK5JH3fL/sO4c/wrcND4IijyTri6hyIl4TDiL69C3erhWDv7Q/j7GLfBOhl2/vIVbD90DI4OjujcsjHCg6x/QR2yjOTUNIT2uLfM8RNDKYZSsEfmmLZn6kDKWsMoc4ZTEvgKc4S++nAqK/4aDu78Bfu3/YQbcSfh4OCIGnVuQ+uujyOq1q0VZdQcqNpCKFUZEkoxkCpMXyml5mCKoZRth1KCwZT1svVQytYCKUs7euY8ps9biIWrNyE7JxshgYF47J6+eObBwaySIrLhUIo1eGR3zDFtzxSBlK2FUXr65yPhlDxHYwZT+lX55DUxdTClX53PNTAEbfo+i+btRyA3NwuODho4apysqsLPWhizl5A19E6yBGuYwkf2gdP4iGxfw1o18O0bkzD7tQnIzsmFq4tzqX0gicg2sNE52RVr/VFvq4FUQfrnZuxG6PqA0ByBi7yvCjZCdw+OMEogVbCpuql7SFWlSsrc/aSEsaqkBKukiNRJX2nDxudUtEqKbJNMt3dzdWEgRWQnGEqR3bG2PlL2EEiZepU+cwZThsKpoiv1VTaMUntT88q+Rys7dY/MQ8I6NVeSSTDJFfhsnymngJF1Tt0Tlpq6R0RExmNdczVMwNJNzsk2+kgxkDJ+uGHs6Xy3wkLTT+XTK/h+06/UV1TBxQYMXW6uIIpT94jIGnAaHxERkW2x+1CK7IOp+0gZu0LKnqqjyhNMCeOGUzc/+ioTTsmCpScPbcCuTfORlnIDAcHV0eH2UagW3azU2xkKlwwFVZashuLUPSJSM6m6kabnDKaIrFNcQiLm/bkGOw8dg5NGg14dWuH+Pt3h4eZm6U0jIgtiKGVh0oTZXFUb9krNq5VZayCVnZ2N9LRUeHn7wMnJyeqaoFe2AXpWRiq+/XAYju9fCw/v6nB1j8CJg1ux8a8v0bXfGNz76MdKH4TyUst0PGNUSVnr1D01T0tTG9lXal6Fj+wrmCKypMquvCcHtpJS06BxdIS3p4oHeibw58atGPHyB8jN08Lbvxl0umwsXvsp3pj5I/747G00q1fL0ptIRBbCUIrsgrU0Nld7IHX61HHM/mI6/l6+BDm5OfDy8MLtfQbAPyAQOTk5iIisjrvvGYqg4BCrmM5X0WDq15njcPrIFjRs+x78QzoqDTi12lxcOf8H/l35mVI11XPQBFgjU6wYaSrG7h/EBufl20dchY/UQt+nKP6GpbeEqHzy8vLwzeK/MPPXpThx8bJyXtuG9dCueSMlqNJXDfVs36pCB7esxaFTZ/HgS+/BN7gjajWdAGcXP+X8jLRLOLX3Ddw5fgoO/v4NfL08Lb2pRGQBDKXIpllTHym1BFIlrYa258BBDBs9Ft45Wjyo80WEozNOZGRi5ZKFyIIO4U5uuKbNxoypb+LpSVPw2NhnbSqYir92Dns2L0StJs8iILRT/vmOjk6IiB6M9JSzWLfsU3QbMB5OlTh6ailVDVatedU9VkkRWTdO4yNroNVq8fCrU/H7mn/RxdELAx3DkAMd1h2LwZdHT8DX0Qlujhp89svvaBJdA4tmvIkaEWGwJZ//sgTOLv6o2/JVODo655/v7lkN9Vq/i93rh+GXFesw9v67LbqdRGQZZoniv/zyS9SsWRNubm5o3749duzYUeJ1586dq1QfFDzJ7cj2aPNyERuzFzFntiItJc6q+kjZQiAloYChk0ynKnpycXLA0y9MQVSOAz5HFO5zDEAnR288rAnGF5oa8IMGUXka/OBQEwO13vh46htYvGCeVazOV96V+Y7sWQ0HBw1CqvU2eHloVD+kJl1FzBnjVvGYQ1Xfx9Y6dU+wSorUjOOnsqulJJiyRwnXL+L4/g04d2IntHl5lt4cKsXCNRuxaM0mvOgYhhccw9HN0Qe9HH3xviYKDzgEIEmbiynaUHygiUT8hSsYMPYlZGRm2dQ+Xb5pBwIi7igUSOm5uofAL7gN/tq03SLbRkR2UCm1YMECTJgwAbNmzVICqRkzZqBPnz44fvw4QkIMT/Hx8fFRLteTYIpsh5Qp79v2A3ZunIX01GvKefJjv3aj3ug+4DV4+YSqvo+UtQZSRatTyhsKbN6xG6djLioDJneHws872MFZCalma69hDEIwWhOMOG0uZs34EPcMecgkZejGrpoqT8VUXm628j511BgOyTXON0vOc3OtZyBZ1X5oxgoGLTF1j1VSlQvw1NxXSt4b2bVbwlZw/FQ2e+wvFRd7BvO/fg6Hdq2UAZVynn9wDdz14BR0vuNhqIW9hoWGfLPwTzTXeKKzg3exy4Y6BmJVXhL+1iVhrGMoXtOFYezl81i8dhMeuvMO2Iqc3FxonEqemieXZWXfWo2YiOyLySulPv74Yzz++OMYPXo0GjVqpIRTHh4e+P7770u8jYRQYWFh+afQ0KqHFGS5VemK2rLmI2z86y14+bdBs85folX3nxDdeDxizuzCgtlDkJZa/qqpjLQb2P3ft1i9+Hms//M1XDj1H5z8Aqymj5Q5AqnSqqDK68jxk3BxdERjGJ4y1dLBA1oAF3XZyt/7O/jiUuxFxOz8C6ai32fmqpiKjG4ObV4WkuINhyIJV7dBo3FBeFQjWANjvY8r895Vw9Q9wSop22Gs94SacPxUfvYSgMRfO4+pE2/D6SP7ULvpRLTq8ROadPwMGufamDfjcaxe/HGFDhAeP7ARv858BnM/eRSrFn2E5MSbBwqNXc1m7w6fPIeWOsOfUU4ODmjq4IHz/z9+inJwRTNHTyxeswm2pEX92ki6bnhhERlbpcTvRquGdcy+XURkB5VSskLX7t27MXny5PzzpGqiV69e2Lp1a4m3S01NRY0aNZQ52K1atcJ7772Hxo0bG7xuVlaWctJLTjb/kXtrJaGNLElvTonx57Fz0yxUr/8Youo+lH++u1ek0qdn37+PYdemr9Gt/5Qy7+vo3iVYu+xl6LRaePrWQ252Ig5s/wlh/7TGU68vkZhB1dP2TB1IFfzhX54AKi09A4v+XIEde/Yr/067dmiLgX3vgKurC9zd3ZCr1SFdo4Unit9XMm5OHXD9/6pG////aElPT8/fjgTP2rDmiqnajbogLLIRzh+dCc/20+Hscuvx0lMv4PLZ+WjZ+V54+QTBWliyubklp+6xSorUzhzjJ1sZQ+mrpeyhv9QfP72J7GwtmnWZBRdXf+U8d89I+AY2g4tbEJbOexUdej4EH7/Sxz9pKTfw5VuDcfrIZrh7RSi9frb/swDLfnwdw8d9hi59HoXahTjFqmrlvf3HTys9keISElEtJAgj7rwD9WpGKZe5u7kiOaPkKZYyhtKPn4S/zhGp6RmwJWOH3oXhL76DqzErlXYHBcPRc0dnIyc7FY8N7m/RbSQiyzHpL5Lr168rq00UrXSSv1+5csXgberXr69UUS1btgw//fSTMrDq1KkTLl68aPD677//Pnx9ffNPUVE3vwBInY7sXQwnZy9E1Lqv2GWu7qEIjRyAw7sXQastvT+C9KFatXgSAsO6o02vhUrFVcvuP6Fxh49x4+pZzH7/PuWLzhTUHkjpq6JEeSuituzcg6Zde2PClHew+4+12LZ0Nf436RW07NEfB48exx3dusDR0QFrdIZ/sKzSJiEITqiDm1Pb9uvS4ejggIZ1o/Mfv+B2qb1iSk4STBWsJpIKzocnzoM2Lx77Nj2sDKKuXvgLpw5Mx4H/noBfYCgGPzIN9lIlZampe6ySIntgjvGTLY2h7KG/VGZGKnZu/A1hNQblB1IFRdZ5UJnNt+OfX0q9HxkbzXrnflw4fQiN2k1Fy24/o2mnL9Gm50IERfTBj5+NwaFdf8MaeLlbvr1Hdk4ORr86FR0eehI/L1iOg6u345uflqD5kMcx8aOZyr/Du3t2wT+OqcjUSU15YVd02cqYqaODl/L3XJ0Ohxyz0Kh2TdiSQT0645F7+uHU/qk4sn0SYs8tw6Uzv+HQ5icQe24xPn5+LOrWiLT0ZhKRhahu9b2OHTsqJz0ZUDVs2BBff/013n777WLXl6OI0rOq4FE+ax1U2YOUxEvw8K4JTQl9ebz8GuDSmfnIzkqDm3vJlS87N86El1891Gn+Ihz+v8eRhAZ+Qa1Qt/nLOLx9Ek4e2oB6TXvk3yY9LRE7/vkJpw7dLImu1agzomq1REZaIjy9AxBdvyMcNRqT9d8xdSBV0coovQsXL2Hoo0+iVrYjpmtqIgQ3m1DGaLIx/cZV3Dvqf9i+ehmG3zsQ8xYvg7dWg+4O3tA4OCgDrKW6BKzVJeN/jsHKeTd0uVjskIS+PbohPDSk2PaYonLKXCvzRdRoguc/2oJ/ln2KHRt/RWZ6InwDquGOwRPR/a6n4OF5c4ljNTNmtV9lp+6pocE5VZ6a+0rZs4qOn2xtDGXr/aVSEq8hLzdLGScZ4uziC3evaoiPu1Dq/Zw5tg0nDm1Ew7bvwT+kfYHb+6B20wnISDuPFQs+QJM2fQsFWWeObsXWdT8iMf4yfAPC0LLTPcr5chCxeu2W8A+qBns0ecY3WLRqA551DEUP+CjjoBydFisckzBzwTKEBvrjyaEDMW/ZKrybG4unEIIQh5vjrDO6TEzLu4JAOKGbw82xyxJdAuK12UqAY0tkjP7F5KfRpUUTfLngT+w5PONm9Wf71nj2oQ/QvW0LS28iEdlqKBUUFASNRoOrV68WOl/+Lr2iysPZ2RktW7bEqVOnDF7u6uqqnMg6uHn4Iys9FjpdntI0uqiMtItKXx5nl5L7g+Rkp+PC6f+UwZM+kCrIN6i1MjA7sO2P/FDq1OF/leqprMw0+AY0l69HHNz58v9XU908cuUXGIW7HnoLbbs9YNI+UqYMpCrzY/+bnxbAMTsXU1ADHgX2Z5SDC17ThePRxHP4efEyfPj6ZKSmpeGTFasxz+kGgnVOOJeXgUzo0AoeCNc541dtPFY6pMDFzxvvT3ne4OPJNmZm5xk9nLq1T2+GfqaazhcYUhP3Pf6JcpIjoKZo5K72QMraG5yzl1Tlyb7TpiQY5bUgy46fShtDBTtdQTqME/Cbm61O4/Pw8pdf9shMuwQEtS52eV5eJrLS48qcQr5v6x9wdQ+Cf0gHg8FBaNQAnNz3PlKT4+HlE4i83Bx899Eo7P53oTK2cvesiZOH9+G/Vbd6wzo4atCy4yAMf/JzePsGw15cT0zCd7+vwHCHAGU1PT1nB0cMdPDHFV0OPp23EE8PH4wln76NB55/C4+lnkMdJ3ek5+UqfTg94YhRDkHYpUvDP7oU7NCm4sVHhqF5feO3PLA0eX890L+ncpLxk36VdSIik/6acnFxQevWrbFu3br88+RDSP5e8GheaaR8/eDBgwgPDzfhlpK5NGg+EFmZ1xF3aX2xy3Jz03E15k/Ua3YnNJriS8beut7N/hdOLoY7aMoXnJOzD7Kzb87HT4y/hFnv3gM3j9poc/sCNO4wHY07fKRM+/MLbq2spla/9VvQuETjx09HY+u6uSbtI2XMQKroVL3K+Ovvteiq9SgUSOkFODihDTzw16p1Sm+p7z79EJuWzccDo4ah9aA7MHToYHTv1B77HDLwhu4yFjuloP89/bF2yS+oHlnyUdOC0wqNPa3PlA3Qi4aT9hhI2UKDcyK1U8P4yZI9eyrLlqfxeXr7o0mbfrhyfonSGLoomU6em5uGdt2Glno/2VnpSlWUoYN6wsn55k7M+f8x1O9zX8bezUtQr+UUtOz2Ixq2fVdpsF6/1RtwcHRGSGQ/RDd6Cod2/4OPXuyJjHTr60tWWWu27kZWbi76OBgej/Z29MWN1DRsO3AE3do0x8kVP+OLV55GpwFd0Xvg7RjWtwd8A33xle4apmpjkVEzEN+9OQmvjxkFWyfjJwZSRGS26XtSFj5q1Ci0adMG7dq1w4wZM5CWlqasxidGjhyJatWqKX0NxFtvvYUOHTqgTp06SExMxLRp03D+/Hk89thjpt5U+212jkDkJJhnGdbQak1Rp3F/nD74kdKYPCSqn7IMbPKN/Th/9Gtoc9PQ9raxpd6Hm5svPLxCkBi3C0Hh3Ypdnp11A2lJJxFe/RHl75tXfQNtnk4JnqSflZ6LawAatH4TO9fdj9TEo6jf6k2c2v8Bls6djNZdhsKlwA9gY07bM5aqhlF6GZlZ8DLQvFzPBxpczcjM/3vTRg2UU0HJKalITk1FoL8f3N0MT800RL/t+sopY1ZNGbsBuiipCbq1UMP7mA3OidQ/fvJ0s97qBVuexnfXg69i2vM9cHTnS6he/3F4+TVEbk4yrpz/AzEnf0DXPo8hOLz079FqNZtg41+zkJVxDa7uxRuiJ17fCU/vQPj4hSIjLQkb/5qNarUfRHC1XvnXkUArKKK7Ut1+8eQ81Gw0Fr5BLbH/38exaeU3GP74RNgqaXKul/n/iwSUNIaS8ZPI+P/rebq7YfSgfsqpYHh8JT4BThoNQgL8GNQQkV0y+WH+oUOH4qOPPsJrr72GFi1aYN++ffj777/zm3deuHABsbG3jsYlJCTg8ccfV/og9O/fX+lvsGXLFjRqZB3LrFPZ+t43HQ1bDMS5o7OwffXd2PZ3Xxza+iw0Tlm495EfERBc+oDKwdERzdoNR9yl1UhJOFzoMpkWKPfrqHFG+x43V/eThp3+oV0KBVJ6GicPBIV3R8K1rcpAILLuCGSkJeDQrr+MOm3P2H2kjBVIiSaN6mOf5lboVFCeTqdc1qix4R4Wej7eXogMD6tQIFWQKaqmjF0xVbRqypoYqx+anqlWjDT11D3BqXtkLSw9fpIm0tZYLaUPpmyxWqpm3TZ4+q3lcHRMwIHN45Tx047Vg3Dp9I/ocdc4PDDuszLvo123YXBx9cDZI19Cpy18gCU16QSuxaxA176PQuPkjBOH/lVaJoRE3eovVZCcr9VmI+n6Hnh41UBgWDdsXjWnUNWaLdKvvNekbi3lz726dIPX261LgwMc0KhWjRLvS6bpymp90nuKlUNEZK/M8stq/PjxysmQDRs2FPr7J598opzIdjk5u+KOez5Ax57P4uzxDcjNzURQaH1ERnco9xdym65P4MK5bTi0bQKCq90Bv6A2yMlOwrWLfyEt6RRGPPP9zf4LynS/bDg5lfwrWoIpbV5O/tLKTs4eypQ/Y/ffMcYP+co2My/NIw8OxfB/t2CNYxLuKNATQfyuS8DVvCw8OnwITM0UVVOmaoB+0833h9qrpowZSFUl4Ktqg/OqTt2TXlJkPGx2bh5qGD9JMHUt1zpbKNhif6n6zbrh3W+P4ei+dbhy8Rjc3L3RtN0A+PgVr3oyxM3DGw9P+B7ffDAcBzaPQUhkfzi7BiApfi/iLq1CtZqN0e/+l5Tr5uVm54+TDNGPrbTam2MoT586iD27FfaiTaN6aF63Fn44cxkNdG7wKtArNV6Xi98cEtG7Y2vUiChfHzgiIntlXYf7yWSc/c03hU/Pyyfs/9g7C/AorjYKn5ndjbu7QCAQHIK7u7sUKF6kUAoU5wcKlBaXFnd3d3fX4Bp33+jK/M+dsCGyIbaeefvMQzO7OzM7O8neOfd850Olmn2K9FpDO2f0GncU7+//h1vnNyE84BQbAFq+aku07LYWXhUaZj7Xzasa/B5eBcOQUMXsN+ck6Dwm/C5MLLzZn9NSwiEWJcPU3E7t+TvKdEdlpU2zRhjUuxtW7j+Cu0hCfZhAAgY3KCGeSZMwecwIVK9cEapC0UHoyhCm8gpB1+UMKRmcS4qDCzsvORC3lDCF0UphSlbGp4vCFOkUXKFGK3YpCtXrd8WkxZdx9uDf8Hu4hh0fmZrbo03PSWjV/XcYGGY4y11LZXREiw2/K9ctRcZPBNINmZAs9IepRYaTryRAJlI3zZuMVsMnYUxKIFpLTeECPbar3kVaCGMLU6ycNk7dh8nBwcGh8XCiFEdGrlRo7tBMTRbQCKXcLVHKfTpa9ZiK1JQE8Pn62XKgZDRsMxIPr+1G4IcdcC0zKJsbK/jTHqQmBcKrckanuKCPe6FnYIJ6Lbsq5FgVFWyuLEGKQM7H8vmz4FulMtZt3YXlHzI6NflWqoCtwwehS9uiDXo1yTWlTGFKE11TOQWp5KREXD+3F18/vGB/R+o27YLyVeoVqlSgONeyugPOOZdUyYBcJ6TUM710NXUfik6hC8IUR25K+9TD2DnHIEpPRXpaCgyNzXM177B1LAWfGq3x+c02NjNK3/C74JSWEomAd5thbl2dLd1LTQ5DdOgVtO+T4bJSJppUVlrRyxO3d67BP9v3Y9+ZK0hJT4epoQF+6tQOvw/qBSfbjDErh/by5M0HHL18E4nJKSjr7oK+bZvB0sxU3YfFwaFTUAyxiugQJEPB3NwcD14GwMQ0/5tPcqOqyJvUwkKcFppwI0tEKVU7pYojSBEhrTCcP/QXTu/5H0wtfWDt2JSt8Y8MuQJh3Gu4eA2ArUsrhHw+wDqu+o5eilbdx2tM2Z4yBSl5JCWnENMZjAw1o9MZEaYIigpBJ7/zBEX/3pPfZRnq/J3OKUjdvXoMS2cNRmqKEMbmHhCnC5GaHAGfqg0xc+lhmFv+uH24Iq7l4pTuEZFBEaLUj7Kk4pNT8DUqFiYG+ihla6WRuR7RwiQce/wK4QlCOFqYomv1irAwVt/vqDQxFsY1c7eUVzeStBSliFKkmYN79QaIj4+HmZn6xgyqGkO9fv0apqbZb7qIMEXQNmGKQIQpXXNLqYqYyEA2XD0hLgo2Ti1hbFYKyYlfERF0ji3r86m9FMkJHxHwfiOMjA0wY+U9uHtYKzVTiohSRCxVV8i5LFMqJ2KxBEmpqTAxNGCzoji0m8SkZPw0/S+cv/MABoaW0NO3hDAhAHoCHlZPHYsBHVqipENkhA/+Qex1X8rFCeYmxtDEY7z55AVuP/Vjx3cNa1RGvSoVNHKsp4skCJNg37R7vuMnTpRSsyhF4ISpvAkPfolg/4esiORRpTXsXCoVWpCS8erxOVw7tQYf/K6Tv1Dg8fXYAE8ZhsZWaN93Ftr1GQt7K16JFKQ0FZkwpShxSlnClDrFKXnlem9f3MPkoY1gaVcfnj5j2E5LpEwjNuI+Pr38G6XKlseSrTfz/WJWhEuqKNewogQpgjxRKiw+EXOPXsSRR35Il2RcY+UcbDGlQxN0raG6ktX8BlNLzl7HkjM3IJVKYUkLECMVQcDjYVrHphjXsr5aBlacKFXyRCmZMKWtohShJAlTqcmJeHbvOBJiI2Bh7YiqdTpDz6Bof8gT4yNx+fhq3L6wHQlxoRAIjL6Nn77Pa3tXaYbBv21CGW9XpYecq1OUykuQ4tA9uv42B5cf+KFUxcmwdmwIiuIhPTUa/m83svlrx1bMR6t6viip7D1zGYs37cG7wGD2ZwOBAL3aNMWf44bA1tICmsDHgGD0mzwPLz/7w5wnYNfFS0SoWqYU9i2ZzeW9aZAoxZXvcWgkCXHBOLN/PMICn4DmZYhQ18/Mh1fFxhg0YRvMrQo/KK5Qow27yMyB5N+Pr64jOsIfJqY2KFe1BaxsTTQmR4oTpLSvnI8gu35keVNFFadEojS8uHcMb55dhFQihnuZmqjZpD+MjLN/0WftBJjz2j2w5S8YmbihbLVZoOmM55FcNSv7uqDpGXh1fxJePLyKKrWa5XkciuheqG5RVZ4gFZEgRJu/NyIxLhn9YYnKPCPEMWKciYjHkE0HEZmYhBFNakPdrL18BwtPXkUPyhJdaSuYUzzE0mIclsRiztGLMNLXw7DGtdR9mBwlCG0u49PFfKmckLHN5eOrcHzHHKSnJYMvMIZYJISBoRm6D/0LjdoOL/Q2Tc1t0WXgPHYh2ydCeFx0CN69uMZ+P3mUrQlHt/JKeT8cHEWBuHd2nLwA/9BwWJmboU+bpqhdqXyBJ3GevfuIc7fuo2z12bBxapK5Xs/AGl5VpiA9JRh/bdlXYkWp5TsPYfqqTahLm+B/tDM7NnkqScbxM1dx58lLXNu2EtYW6jV9xMQnos3IKaBjk7CQdkElGLIy+nM6Gf9+DmYfu7/3P5hpoLurJMKJUhxqDzzPCcmHOrS5H9LTxCjnuwBW9nUyAsnDbuHrm7VYPactJv9zG/oGRfsjIvtCIv+WrdRU7s29unOkVC1IEUdKcVFFjkvWEHRNFqYIdHoIblzYj/jYSBiauaJW094wMbMqkEBFOir9N68TYqMCYGpRFhRPH49vHcLJ3XMwZNJu+FRv/UMxiiASpePBzVPwKD86U5DKirlNDRiZuuDW5UN5ilKKKNtT5zX5I/4+fQ1xcUlYTrnCjsqYPQMF+DLGWE9FYtah8+juWxHWeQxWwuMTES1MhoO5KaxMFBAcJ4eUdBGWnL6O9pQ5BvNsM9dbUnwM49lCKJHgn1NXMbB+dejxVf91znXgK3lw+VKaz7XT/+HgxklwcO8CF69+rEM2NTkUgR92Yvea0RAI9FG3xcAib182hrKwdkLtpv0UeOQcHBlIJBKcv/MI91++AY+m0axWNdSvVrFAghK5X5i5ZguW7TgIfX0zGJmVRlryW6w/eBLtGtbBroXTYGiQf8XFiat3oG9gDhuHRrkeI5N7tq4dcffZQkTExMHOSjNcQaoiJDIas9ZsQTfKEkPo72OTMpQBGjAmmBgWhH+27cdfE+QL4OkiET4HhbKfbSkXR6WVum49fg6R0bHYQLvDhs4Y55ErqBpljHmMAKPC/bH7zGX80quTUvbPUTg4UYpD4wLPXz0+gMT4UFRvvAMGxk7sOvI9RGYqjEzd8fT6EDy6sRf1Ww1T+L4VlSOlDYJU1pv+4pZIkRyXrNtTpkClCcLUO78HuH/9BBsO61m2Chq06Al9g4xzSEqstqyYjON7VoGiBdA3sGIznPb9Nxk9hi9g88qyklOkSksRYu3/2kMs1ke1xlthZOrJrieW8U9+S7FpcW/8b90DlPb4cXmZKC2VLdUTGFjJfZwM7gR6lkhNTvrhdoorsBbnOlZWllSqSIR9d5+hI2MGu28DlaznpS9tjQvSBOy//xyjm9fL9vjDz4FYcPwyrr//knGMFIW2lcthVpfmKOvwfXCmCK69/Yz41DR04sl3pXSiLXBJGIC7HwPQuFwpqBKuA1/JRduFKYKuuqVIaPmJnXNh79oepStNyFxvYOTINnSRSlJwdPts1GraDzwedwvAoZ5SngMXruGDfzBMjQ3RrXlD+JT2yHz8xfvP6DV5PvxDQmFkZAMpI8GizXtQtVxZHPhnFlzz+Z79d/9xVpByLzcCTp7d2WoLMhaKDr2BC3f/wphFq7Fl7qR8j1OYkgI9PXNQcib1CHr6Vpk5rChhotSuUxehBwp96NzjSydKD60kpth+7Cz+HDsEfP73MWBaejoWb9mHjQdPIiohkV3namuDcQO6Y0yfzrkaLRSXg2evoC5lDBvZxGMWHCk91KSMcfDcVU6U0hC4byQOjXNLvX1+Alb2DTMFqayQG3RLu9p4dGO/QkUp4jrRhGBzZQtSihSispJzW8oWqGTnR3a+iiNOFUaYSoyPwcLJvfDi0RXoG1qxZRFJCcuxYclETF6wC77122Dnv7NwbPdKuHkPZWeqyXPS02IR9GEX9v77O+xtLdCy8895/hkm13ZCbCiqN93N3khktYx7V5uDp9cH4Nbp1ahWbf0Pj9XQ2BSWNk6Ij3oKW6fcTihxeiKE8R/g6tlHKY4/TXZJRSUmI0kkQnla/u8AsaG70Hr4Ehmbbf31t5/Ra/VOuEIPv9EOcKIE+Myk4fjLT2j17jPOTh6G8k52CjtOEsBOsM3jq9oO3/IRklMVtk8ODl0Xpgi6Wsb35tllJAtj4F2jV67HiODuVKo3XtwahY+vbsO7cmOlnFcOjh9lEI1dtAYpaWkwMXVGelocFmzcja7NG2LT/35HXGIS2vwyDRLaDpUbrIOpRTlWUIqLeoz3fv+g7ehpeLBnDYwMDPIMmv9n2yHYubZjXYJZnU1kYluUHov9Z1dj7i+D8hW3ynm4QZh4HGkpEazbMCfx0U9hbGQExxLYXfFzUAjcaX0YQf69SjnKAEeSYhEnFMLGImMmQCQWo+fE/+H6w2dow5ihLs8FYobBtahE/LF8Pd5+CcCa6b8qNCMzLkGIMkzeUoctw8fHb+IYh/pRrCTJofUUNURckaQmx8JQjiAlg9yoCxMUJ5opqmxPkwUpcpMvu9EnApIiBSl5ZN1H1n0rW5wqzudGFiLE5OV2Iw6o/43vhLcvH6Oc75+o0ewgqjbaiepNd0HPsCzmT+yKF4+u4eiuFXAu3Q8uXv1ZQYpAOraUqjgONk5NsXv9PNaaLoOIoVmXVw9OwtymejZBSgaZ8bN2bIE7V47n+57IF3u7HiMQFXIRiXFvc9nb/d9tAhgJWnTKKpApzvGnqS4pgqmhPmvhjoBI7uNkoBTNiGFuaJDt8x+/4xjKwwBLKFc0p81QnjJEe9oCyyhXmKcDf+w7DUVSyi5jsPuakS86vWYyRCtP27w7C3JwKAt1BE0r0i2liwJK0rexkbxJPXb9t++VpETlTTwqO+RcXXAh58Xj4t1HGPq/JTCxaYAazfajMhk/NT+CMlWn4eT1hxj+v2VYd+AEhKlilKu1hBWkZIKSpW1NePv+jc+BwTh04Uae+3j67iPCo6Ng79pO7uO2Lq3ZTKFztx/ke7w9WzVmxS//N+vAMN/HbIRkoT8iAo5jUMeWMNAvecH3FqamiGLEkHzL6M1JJMRsaZ5xFvFw/7mruHj/CWbDESN4dqhEGaEabYzfeA4YQ9thy7GzuP3slUKP08vdBW9o+RVAZBz8lk5DaTcXhe6To+hwohSHXGGKuKXUhbmtJxLj3uT5uDD+Lazt3XWubE8ZgpSqxaicyPZJFtmxKFqgUpQwlVVUlPd5Pn94BW9f3EGZqrNg7dCA7cJCMDR2gXf1edAzsMe2VdMgSk+Bo0dXudt39OiGyDB/fHj1MM9jSEtNAV+Qu+uVDL7AjC3RKAjdfvodpcpWxuv7v+Gz3yrEhN9DRNB5vLo/AWH+x/HLH6tgZeOgFMdfUa9lZbukCERsauHjhTNIYAWonFxnEhAvFaOb7/cSyevvvsA/Nh4/UdYQ5JjJM6F46AlL3PzwFZ8jfnyzF3DmVrblR9T0dIG3vQ32IBrpzPeujoRURoq9iEFVF0dUctU+pwqH7ghTxC2lbeiqMGVtn1EGlRgrfwwl/DZBYW2n2DGUKlFX5z2O4vHnxj0wt6oEryrToG+Y4VKiaQHsXFrDs8IEHLl8A7tOXYa1Y3MI9HIrm0amHrCwrY4DF67nuY/U1AwBgq8nfwzF4xmCx9NDalp6vsdramyEdbMmIDr0GvzujEF4wGm2c/HX1+vw6s5oeDpZY8bwASiJ9GjZCFGSdNxlhLkeI2OVc1QiOjaumy27a9OhU6jBM0FVOndOZyvKHM48A2w5otiJvSHd2+GVJBkPpLmP8zYjxAdJCoZ0a6vQfXIUHU6U4sgTdQhTZJ+V6w9EfNQTxEU9yfV4TPgddrBVr0Vud4e2l+0pUpBStxhVEPeUtglTty8dgrGpGxsQnhOapwc7l3b48PoRQPEg+JY1kBM9g4yBWHJS3iJmae8qSIx5DqlE/qApPvoBPMtULtB7MTA0xqINl9BtwHgIY2/gzcOp+PBsEZycTTBn5Um07TFSaSWoxUGZLikZk9s3QRDSsYAJQQCTlin0nJHG4V8mEl2qV4CPs33m8z9FRLNGdW/ILxuoQGUc85eo2B+KUDxbp8wl6+N5ud2WD+iET1Q6JjFBuCyNx3smFRek8fidCUQwT4yl/TsW8uxwcCgebRamdAmvCg1g41AaQR+2QyrNnldIvlOCPu6As0dluHlVV9sxcpQ8QqOi8eDla9i5dWadTzmxcW4OPT0TxCYmZo6T5CHQt0W8MDnPx7093cDn8REXIX/iLyHmBcTiVFQqk5HVWRDx5ex/i1DdyxAfX/yD1w/+QEL4KYzs0QpXNy+BlXneE4i6jG8Fb7RvUBsrEcGOR2STZl+ZNMxjQhBBizFlSPZoiI8BISjPyK/GoSkK5aV6+OgfrNDj7NykHjo0rIOFTBjWSSLgxyTjJZOMtZJw/MOEoUeLRmhdr6ZC98lRdLhMKY4fhp6rKl9KJoCR/Tq174uPT/bi7aPpcPToAWtHknvAIDL4MsK+HkFF3/aoWLODzpXtKYqsYpQmklOYUlTmlLIC0Akkayo5KRECfes86931DGwglWZYvIVxr2FqWUHugIjg6OqV577b9hiFE/tWI+D9VjaoM+v+IkOuIC7yKYb/trvA78XQyASDxi1E/1FzERcbAT09A5hZWCst2JygyS4pGTVLubLC1OKTV/AQSTAHD8mQQgSGLe2r65XdSWBmaADy6cZCAis5X51RyLgBNPs2M5hVaJIJUDnJuj7r893aNcj8f3IcZyYNxZ/HL2H528/sOnJ8xOm1o0sLVOZcUhxqRpvzpXQt+JwEBfcfuwar53TCq3vj4eTZG4am7khO+Izgz/uQmuSPkVPPKjS3hYMjP4RJKZnZmPKvW9IUxgLGeikQxmaMk3JCsqWS4l6gbGWfPPdDuuB1bd4AJ2/ugZV9vWxlrGJREgLe/ofSri5o7FulwB9aoxpV2CUuUQhhcgq7Dz1B7uDsksaG//0O394jsSo6HOsQAUPQiIeEdbu42zvAxT67uGhuYoQoYd4u/yhKAhtTE4UeI+nqt+fvmVi8ZS/W7z+JUwlB7Hp7SwvM6jMQvw/spfBwdY6iw4lSGkCGU4dfoFbxuihMZRWkCDy+ACOmH8HpPf/DnYtbEPRxF7vewMgczbqMR7vesxX2R0TdZXuKzpHSdEEqK+QYs3btU4Q4ldUxpQhhiiATp1w8vHH78gmIxcng83MrN/HRz2Bl48xevwHvN6O871+sgyprsHjI592o7NsMji55d0pz9SyHob/9g03LJkEY9xLWTi3Bo/URE34L0eG30bTdADRq1bvQ74cvEMDGzjnPx4sbbK5NLilCaFwClp+9gSqUERpTZgiHCAagUAsmOMnEYuqBM6jm7sSKV4TWFcvAkM/HSUkcBvFscmUTnJTGwcXCDDZvviLgrf8PxSh5yJ4riQzJ9Vh1D2ccGT+IPebIxCTYm5nAvoTO0HJoJtosTOla8LlPtRb4bcE5HN48FW8fz85cX9qnPnoM3YRS5eoo7RzqovuMo/g42dnAUN8A8dHPYW5dNdfjaSnhECaGoGuHFthx8gLiIh/BwtY323PCAk4iSRiCIV0n/nBff/82Ao9eTcbL28Nh7dyWzaZKTQpBVNAp0EjC9hWLiiTKWpiasAtHBn9t2YvomHiMpeyQTEmRBgbu0IMN+JgXEYbhs5fg2Oo/M09Xz7ZNsXrbQfzEWMOcyi4/ELf6c2kS/mut+OYLAj4fM0f8hCk/98F7/yBQoFDWw4Vdz6FZUAwZTesQCQkJMDc3x4OXATAxzb/FO7nhLEwreGURESvVOFFKBhGmCIoWprKWB+YVsJ6WIkSw/0v2C4RYzvX0jXSmbE+RgpSiu+qRPws3nr/G/ku3EZMghLuDLQa1bQIfz4wbdEVDxClFd+ojrilCccUp2d+JiLBgdG1RGQ4e3eFR/pdsgxph/Hv43f0VfYdPg0+V+pg9rj0MjN3g4N6NzZwij4f5HwZNpWDJ1ltwK1U+330+vHUGh3csxctHV9mf3UpVRKe+Y9G66zCFzuwoomRPkVlSqhKlFp+6ihVnbmAb7clmQmVFyjAYBX/UrVYWm4b2zFy/8OQVLDlzHQNoa3SgLGBM8RDLiHFAGoOTTBzmVa2Azq7fS/OKChGmsrqlNBVpYiyMayrnBrc4f0uU0fEzIVEI9+oNEB8fDzMz9Y8ZlD2Gev36NUxNCy98EmFKG0WpyPiMf3VFmJIRHvwe8bHhsLByhJ1T3g5dbRKl1JEpVdiQ86i4eOw8eRGPXr8Dn8dDyzo10L1Fo2wZOyWNcYtWYeeZ26hY979sDiYSIv7h2QKkxN7Dh5M7MHDmYlx58Ay2Lu1h7dgIjFSEqOBLiAi+iOHdO2DlH2PyFZWiyaTTzkPYevw8YuLjoa+nj96tG2PSoF4o484FWxeXxKRkeLbuiw7pxvgpxyQd4Yo0AcukYXh+cCPKerhmlnDW7v0LzJJE+AU2bBQCESCeknI6KhIWTja4u+ffPDsrcmgvCcIk2Dftnu/4iROlOFFK5cJUQcQoZSIr21OEKKUJOVKKdkcJU1LRZ9ZSXHr8Es48fdgzfHyh0hErEeHXHu2wePRPSrP+K1qcUqQwRVi/7l+s/Gs6LG19Yefang0kj418gIjAU/Dw8sFfG6+w5XJvX97Hzn9n49n9i+zraB4fdZt2waAxf8LZvWyh9ikSpYORSqGnr/gvak0RpGTXsaoEKUL7JZtBfY7BNJ58AWmnJApXjFLxfskf2TswHruE/y7fAc1QsKT5iJKKIODx8Kt3afQv5VZsQSqrW0rThSlOlNI9iitKEThhqmTCiVIZnLp+FwOnL4JYJIYPZYh0isEbSTKcrK1wYs1CVPDKCKMvaUTExKHxkIkIjRLCzq0rzKyrID01iu1iR7Jit/45Bb1aNUG6SITFW/Zh/aHTiI6LY1/r6uCACQO64pdenQo1/iQTrMmpaTDU1+PKtBTIjcfP0XrUH1jLc4c7lfs+jmRM9ZB8xIqp4zC8e/vM9X4fv6DvpHn4GBwKa74e22gmXiJCjXJlsG/J7FwlfxwlS5TivGschS7lK444lbNUT11oStmeJpbrjf5nPW4/fY1ZtBNqwRgUTUHEMDhFx2HVoTNwsbPGrz2/f8koq6RPUeV8isqZIowcNRr2Ds7Ytn4Z3j2Zy64zNrVEpz6/oN/IOawgRShXqTYW/HcesdHhSIyPhqWNI0zNCiaW5KQws7PqFKSKgyqzpGQQjzCPTWeSD/kLkdNITBxq87q1wujmdXH0kR9bSudiZY5asUKYCQQKEaQIZDtEmCI5U5ouTBEhUNPcUhzqRxvL+IjLR+aY4uAoCi8/fEb/P/6EL2OIMbRLZplSMC8di+PC0WHMNLw4spnt7FbSIFlMN7Yuw/z1O7H79AEEftjOrq9frRKmz/sTzWpnhO+TvKZZI3/CH0P64GtwGJsL5OnsUCRRiQhYxoac80bRyIZGdB5jKLKe/JdzDFXRyxPPj2zGpXtPcPf5K/D5PDStVQ11K/twOXccnCjFUXCyCkmB+O52+pFAlbODX15iVFJiDFKS4mBqYQ99g9ztQhWFIsLNNaVsTxmC1JfQCBy8ehejaTvUpr/XzgsoCl0pSwQyaVi25wR+6dpaafXYig5CV7Qw1aVLZzRt1RmREaFIT0sD39wbAj3517WltT27aBqKFKRkFPd6VqVLilCnjDs2fA1GCiOFYY5uQGQgdZtOQp0y8rO/HMxN8Uvzut8DyhUoSGmTMEXON3FLcXDoSr6UNgafS8QixEYHg8/Xg7mVo1pu7nTZJVUYVu89CgvwMZlyZMdNMpwpPcxkHDAs5iv2nr2CET2K36xHG7G1tMCqqePw14ThCIuKgYmREStWyYOIU7LSLw7Noop3aRgIBLglSURfKnd4/T1GCAkY1Kuau+EPERdb1fNlFw6OrHCR8xqEIrvBKRsiLskEJiI85bVkfa48QerLu3tYO7cDpg1ywrzRPpg2yAW714xEXLRi24IqsmxPVwUpwoX7z9jWrE0p+fbKlrQ5wuLi8fxjRpCzMskpTikqAF0RkM/fy8MRzq4esDdLK7ZzTpsFKUWU7RUXIkgVlsENfSGiGKxmwlknYNY8qV3SaARI0jCq2Y8dQEQwIuKRogUpGcraLgeHspEJB0RE0EaIyKLpiERpOLFrLqYM9MCMIWXwx0B3zB1dDfeuFLw7K4diOXPtHppIjbMJUjLsKAGq0kY4c7Pw31e6BskNKuXilKcgxaHZkMD3AR1b4gji8IbJiN2QEcKkYwsVjYZVK7LOKA6OgqI9KoiOQ4QSEnaubRSnDO/1kwvYuKgHDE3cUbryJBgYOSIx9jWe3TmGt88u4bdFV2Fl66ZTZXuaLEgR0kQidjCln4cl1/ibjp2ang5VoMhyvpzClCJcUzk79GlC04SCXLuKFKQUgSKu5cK4pAhu1hbYOKQnhm0+CD/mK+qTGwlQuE8nI5hJw9yuLVG/rEe+gpQq0GS3FAdHfo4pbUNWxqfJHfnEonSsntMJH/xuwc6lHdy860EiSUFk8AVsXToYkaGf0LH/9857HKoJOSd5SMZyMnZkGDEUUlNVM37i4FAmf00Ygdcfv2LKi9eoTpvAi9FDCCXCXUYId0d7bF0wlfsAOAoFJ0pxqM1uvnv1CJhZV0M53wWg6YxL0cKmBuxc28Lvzmgc2z4NQybt1jgXWnFdUpoqSBEqe3kgVSqFHy8FlZD7jT5ikqDH46GcuzNUhaaX88muCZkwRVC1OEXKzd75PcDNC/shTIiDo2sptOj0M2zsnJVWrqcI15+iws2LSqfqPrjpOBobrt3HVb8PEEslqONVBsOb1Ebt0m4aIUjJyvg4OLTWMZWinWV8mixM3Tq/Ge9fXEeFOkthbl01c72NY2MEftiBU3vmo0bDHnBy81HrcZY0KnuXwmM/f3SX81gaI8ULOg0/ly+jhiPjyC+ImZRVPn37gS0bbFO/FlrX82XzrDjkQ7K6zq5bjP3nrmLr0bO4FRIOG0sbLOjYF4M7tYaZifKiWDh0E06UguY7HHQRv0dnkBgfhqqNFmYKUjL0DWzg6NkbL+79h8T4SJia22p9tz1F3cArU5AiNK7qA28XR2wOjcafjD5MqO/HG8Sk4wgVjx7N6sLGXPW/L4p2TSlDmCKoWpxKTUnCX3/0wcNbp2FgZAd9Q1skJezH7nVz8ctvczBg6K/Zjk+RqLtsr6guqax4O9piad+C53uwGVJqgHNLcWgz2povpanC1PUzG2Dl0CCbICXDuXRfhPkfw61zm9FrxFKdyZPSBkb26oyBzxfhCp2AZrRZtomj7dIoCCHGsG7t1HqMHNk5c/M+Bs5YjOTUVJhZloVEnISNh0+jnKcHjq+aBzcHO+6U5QER8H7q2IpdODiKS4kXpWQOB02BCCiJ8WLoOhEh76Gnbw5jM/lBwmSgJZWKERX2udiilC6U7alCkCKQgNTts39FmwnzMDotAC0lpnCgBPjApOIqJYSbkx3+HjMQ6kLTham8xCllClTL5wzB0/tX4F39f7B2bAiK4kEsSkLQx11Yu3Q2XF3s0alrb4Xus7g5UjLU6ZIqCjJBStVZT5xbikOb0fbgc03syBce/AHu3s3kPkbTAphYVEBo4FuVH1dJp0fLRrh07zGWnbyAGxCiDoyQBgbXKCE+MClYPnk0vNxU5zTn+DHP331Cnyl/wsymFsrXm8BO6hESY1/h47P5aD9mBh7v+5cVXzg4OJQLF3SuQSgigFtb0Dc0hViUDLFYviKYnprR0c/A0BQlvWxPVYKUjKplPHF5XB90qlkOp/mJWCkNx2NjEcY288XZ4Z1h+P41KwbkXFSFJgeg57xeZAuBCFSKDkQP9n+PW5cOwqP8WNg4NWEFKQJfYAyP8iNh7dAA61YvzdWWtzgosgxV3S4pbRCkODh0AW0OPifClKYFn+sbmCA9Le/Ox+K0mGKPnzQNdXTeI3lShZ3Y+2/mBGyc8zskXvZYI43AFkTDs6YPTq9ZiFG9OintWDkKz/Jdh6BnYIuy1f+XKUgRTC0roEz1+fgYEIjjV+9wp5aDQwWUeKcUh3qoVLMDDm/+HREBp+FUqme2x8gNdJj/cdg5l4ODa3mNKNsrLsV1lShbkMopKpXy8MByDw8sG8hALJVCUIC6+pzbMK75485lmpIzpYwA9B8Lmtmvp+I4qF7cPgg+3wC2zi3kPm7v1hGvH/yBr58/wrN08XMsNKkMVZVCqKYIUlwJH4c2o82OKYImlfH5NuyG+9eOwcVrAHj87H9LhfHvkRD7CjUazlD6cWiaWKcMChpynrXl/YAOLdlFLJaApil2HYfmceLaXdi692PdhTkxMS8DM8tyOHn9Dnq2aqyW4+PgKElwohSHWrC0cUGd5oNx//IG0Dx92Lm0Zv9NT4tF0IcdiAm/jUETd7CzTprgPiuOS0pTcnfyu7GX5zgh578gglTO10sTY7NtW1kClTaU8/3oWspZ4ldYUlOTwRcYsb878uDrZQR9pKQka4QgpWiRVRUuKU0RpLgSPg5dgOvIpxhadJ2Ae1f24M2j6ShVYQKMTN3ZCb34qMf49PJvOLr6oGqdzlAFXJ5U3vD5XFC2pkJ+X9LS0jPHSfLgCyyQnJqm0uPi4CipcKKUBlJScqV6DlsOiTgdD64uQ8C7jdAzsEaKMBg0j4cew5ajRoNeai/bK45goMllezLBSFk39XkJVMoQp7RRmCqo2ElEqx89p1LF8khNiUFSwicYm+U+3rjIRxDo6cPVzUMjBClFiazkelJV2Z4mCFIcHLoE15Gv+Ng7l8Wv805h/cI+eHp9EIxMXSERpyItJRLuZXwxetZh8Avp8OHgKEmQSVdvTzdERj2Co3tuAVciToEw7iUqlO6oluPj4ChpcKKUhkGcPRGxUpQEyIBpwLiNaNV9Ch7fOoDkxFjYOHjCt1EfGJtaq61sLyo8CPGxkbCydYKVs2GxupYV9SZeWYJUfs4oZSDbjzLFKW0Wpn5Eftdek+ZtYW3rAP83/6Kc78JsjqkUYRDC/Q+hU5deMDUz1xhBShvL9jQJroSPQ1fQxjI+TerIV6ZiAyza/glPbx/F1w+PwOfroWLNtihToUGxXeZF7bqXlpaKoK/vQdM8uHp6g8/X3tuMwuZJcWgfo3p2wIS//0VsxANY2tXK5qLyf7cZYlEKhnRpo9Zj5OAoKWjvtwWHzmDnVAZteyk2+6AogtTr53ewffUM+D25zv5MBnUNm7TC71PnoIy3j8rDoJUlSKnSYaJKcUrRwlRxcqaSkoR48ewxJGIxyvlUgo2tcloKCwQCLFm5ASMH98SLW8Ng69oR+ob2bOeYyKAzcHJ2xMSp/9OIkj0CV7ZXzPNn6wRJZIhCPgsODnWizflSmiRMCQT6qNWkD7uok/T0NGxbOx/H92+EMCHjpFjZOKHnoHHo8/NErc1UKmyeFIdieP3pKwLCImBlbgZfn7JKu35+7tIGZ289xIW702Dj2AyW9vUgFichOvgc4qL9sGzyaLg7OShl3xwcHNnhRKlMN0KC0lq2F4WSUsKnjPNWFEHq6f1LmDOuA4xMPVGm6gwYmrgiKf4jHj86gL7dWmPXwTOsuKBKl5QqxSipVIorbz7hZVAY9Pk8tKpYFl72NlCFOKVoYUpRAehFcU2JRCKsWvIn9uzcjJRkYcYx8QVo074LZsz9GxYWihcEa9drhD1HLmDDv8tx+fwGSCRimJpZYsDgnzF89MQi71ORgpQiy/ZUCVe2x8GhPDhhSnvJGnAuFokwbXRXPL1/HfZuneFZsREYqRiRIZexYdkMfP34GtMWbi6We0sdnfcKQ2hUNI5fuY2EpGSUdnVCh0Z1oK/HCVqF5YHfW0z8Zx0ev36buc7D2Ql/jh2M7i0aKfhTAwR8Pg4smYU1e4/hvwOn8O7JRXZ9g2qV8fuseWhT/7t7ioODQ7lQjCJ7hWsACQkJMDc3x4OXATAxLbjIRLJbNEmUIiV8nCilmrI9IsgM7VgG6SJLlK+1OFsXDrE4Ga/ujoWXlx32HD6n9HBzRZftFUSQevI1GMM2HcCX6DiY0DyISPgjI0WHKuWwdlBXmBkaQJkQcUoZJX3ENaUIYYpAhCkZeYlT5E/phNGDcfk86SjZG7bOrUDz9BATdgvBn3fB3d0Ve46cg7GxCZRFeloaklOSYWpqBl4BA+pVJUgp4ppWVZYUKZHTVEGKOKXc2jWApkB+f5XZabMov/eK+J3PSUKiEO7VGyA+Ph5mZpozVlDWGOr169cwNTVVyT6JY0rb3FIyiGNK3W4pdZC1dO/csR1YNH0oKtReAgtb32zPiwg8hw/P/8KyzedRo24zrRKlSPlefk4p0l1vyvL12HjoFBkEwJjmI14igq25GdbMnIBOTeqp7Hi1nYev3qHliCnQN/aAU+kBMDEvh9TkYIR8PoCY8DvYPHcS+rWT32lYEZAxXFyiEHoCAYyVPO7l4ChJJAiTYN+0e77jJ+3003LoLEQgevX4HLYuHYCVM1pg27KBePPsIru+IBTFJfXi4VVEhH6Ba9khudrC8vlGcC79E549vofPH99DFahSkPoUEY2uK7ZBLzYNS3iu2EuVwl66FCbQ9rj68gN+WreP/aJWJrLjI8erSCdMTtdUcSDiTM6Svpzcv3sDF88eZ5127uWGs92QDIwc4VSqJ3xqLcenD29xcO82KBM9fX3WGaUpgpQMRQlSJTVHioNDlyFiAxEdtBEizGR1DambuOgQnNwzH8umtcLy6a1xZt8iJMSGK3WfJw5shqWdby5BimDr0homZh44dWgzdDFPatLS/7Dh4EkMgBV206Wwm/LEfzx3eCVK0e+PP3Ht4TOlH6uuMGX5RugZucGnzipYOzSEvqEtzK2ropzvAtg6t8CkpRuRmpautP0TJ5+lmSknSHFwqAlOlNJQiLiiqA5y2kJ6WjL+m98J6xd0wYdXzxEfb4B3Lx7hv3kdsXFRD4hEaQov2yOEBn4kX0cwtawgf9uWFdl/AwO/qsQlpcr8qNUXb0MgYjCfckI5ypD9UtajaLSgzTEZDrjx/gtuvvsCZUOOM6s4pYnCFCGrMJVTnDqyfxdMzD1g7dgk1+uMzUrB2rExDu7dBU1G0YIUOe+KFKSU7ZKSCVKa6pKSwQlnHLoIJ0wVj+f3T2HmsHI4u/8fREaIERGWjlN7F2LGUG+8/laWpAhyinDBAZ9gYiE/3oCMKYzNKyDQv/g5m6omP5cUyTzaePg0BlM26EFbwZjK+N50pfQxlXJEWcoA89ftUNHRajcfA4Jx77kfnEr1Ay9LwxbZNeRaZhBiE+Jx5tZ9tR0jBweHcuFEKQ6N4dCmifj46hZ8ai1G5fob4F19Jqo03IxyvvPx5tklHNv2h9zXFVe8Mza1IMZdpKdGyn08LSVjltEsnw5mxQk3V3SJU0Fu4IkD6vCDF2jJmMLo22AqKzUoI7jw9HHo4UuoCm0RpuS5pkKCg2Fg7JVnboaxWRmEhwZDE5GJbFnfW3FRdDaaqgL6NV2Q0vTj4+AoCrLSLE6YKhrhwR+wfmEfmFn5wrf5QZT3nY/yNf9EjWYHYGxeEf/O74GYiACFXZxZu+6ZmJpnjpPkkZ4aDlMzMs7SLY5evgkBRaMNlXtsyKModGDMcOfFawSFyx9bcnwnOCIqc5wkD5LzyhcYcOeSg0OH4USpLFgZJEDTKCluqYS4cDy4thuuZX6GpV3tzBt78i+x8bqUHoC7l7YjWZiRP1TcHKms+NZvC30DE4RA3Kw1AACgQUlEQVR8OSRXuAn9ehj2Di6oXDW3NT0nxbmhV7WjRCKVQpgugj2VvWRRBjn3dlI+YpMzsplUhcw1pchyPkULUwSZgCMTdKxtbJCeEpTn81OEgbC0Vk54fHFQRrmeMkRWVeZIxaWmYs2TZ2ix7yCqbN2BFnsPYvXjp4hJSVXJsXBwlEQ4YaroXDv1L3g8Q5SpNhN8wffcQoGeGcpWnwOGoXD97IZif0byShVbtO+NmLCrSE+NzvVYcuIXxEY+Rov2vYq8T00VKmMTEmFB82FIyR9/ysZW5HkcP8bGIkPYS0kKlPt4WkoExKJU2Frqnripi1y8+widx82AZb2O7NJxzDScv/1Q3YfFoeFwolS2DnyaRXGEFm3jo98NSEk4pEsruY/burSEWJSCT2/uKPw8GZmYodeQKWyYov+7LRClx7Pr01Kj8NlvJaJCruLX36f9MKdHES4pVZc48Xk8OJqZ4B0j/0ZbxEjxmUqDh41qHCrKdk0RgYQs5HwrWpwi9GvfGAmxbxEX9VTugCo67Aq69lBv225lu6MKIkiJxGIEhEchMq7gkwCqKtsjhCUloc/xU9j+/CWqpQgwgrJF9VQBdr7wY9eHCpOUeiwcmhdyzqE6tF2YIqgjY8rv0QVYOTTOVfoky8a0sm8Av4fnFe6SInTpOwqmZuZ483AS4qOfsZN5DCNBTPhdvH00Fa4e3mjernjffZrYec/DyQGR4nREMiK5j5OxlYDHg7OdrcqPTdvwKe2Oil6lEfplP3vt5CT4034YGxqxXQ1LOjHxifAPDVdqvlZx+GfrPnT6dSa+PHyNfhJzdgl8/A5dJszCX5v3qPvwODSYkmHD0XKIG0jTO/GlJMUjJTkepuZ2EOgVvmsFaWNPoOUMqLKul357Hhn0vH95CpePrsXX94/A4+uhZoM26NJ/Asr45O9oyknvoTPAZxKxc+NKhHzeC30DC6Qmx0BfXx8z5v6Nrj37a7RLqqiZO4Ma+mLFmRvoxFjAjcp+7o8zcYiTivFT/epQF+T9kO5e5P0pqsMXOdfk5pWIJ4q6gSWffceWTVC7RnU8eTITrmVHsEIqTeshJvw2At+th42NDfr+NAzqJquAqkgxKr/rOSklFYt3H8OWE5cQlShk1/l6l8IfA7qhYwNftXbbI8hcUnNv3UFacjrW0O7ZXIQ9GCtMTQnCnJu3saGtfPGcg4Oj+BABgnTk00aIYEM68hFhqqBd+aQSCeJjQ9lxjKm5bZ4l4PmNofgG8sdPsjGUbJxFiI0KxpUTa3D/2j6kCGNgbeeBhm2HomGb4dAr5HjE0toOK7dfxMxfe8Hv7gR2/CRlJBClJcKnSh3MW7EPBoYaOPNbjK57hO4tGmHyknXYnRaN8bR9ts8tnhHjOBWPzk3rw8pcNV0stRly7hb++jO6jJ+Nt49mwrXMYBibl0VaShgrSIX5H8Oi8cNhaqw915Giufn4BRZt2o2rj56zP5saGmBAp1aYPqx/ptNMEzoozv53G/pSVugHa1B0xu9EV8YS++gYzF23A419q6JuFR91HyqHBsKJUhoOcQFFxBas85w6+Pr+Ac4dWIjXT8+z7XAF+kao1aQ/2vaaATNLhwJvx71Mxk1pTNgt2MlxS5H1FEXDzas6K0id2PE7Lh//F+bWlWHv3h9SSSoe3LyEG+f34/c/d6BJm76Feh/WhomY/McMDBk6AufPHEN0VCQcnVzQul1ndgbwR6jbJVWcEOhfmtfF8cevMDUiGJ0Zc9SkjCFkpLjIxOMqk4hfW9VHWQf1zvLldEwpQpxShjBFnHQHN63GhJnzcfTMSnz2W84G6JO8suq+9bB4+X+wtLKGLotReQWbJ6emof3vf+LZuy9shlkN2hlCSHDhYzh6zlqCZeMGY3S3Nmov2wtMSMTN4BD8RtvnKmu1owT4ibLG0tBQfI2Ph4e5ZgwCOTh0EdYZkxKKCLEjtI2sTqIfCVOkecuFQ0tw7fR6JMRmOMNcS1dDm56T4duwZ6H2WapcLbx6fAtM+VHsWCkrjFSM2Mi7qNmwE/tziP8rLJ3aEmmpqbBxaglLe0cI417j4KY/8ODafvy24BwMjHILKURoy+mSkuFeqhx2nHyOJ/eu4uXTO+DRPFSv2xQ+lb/HMegaRCBZOnk0Rs5fhlhGgg6MOWwoAV4zKThKxYMxMcD8sUPUfZhaQ8u6vtj/zyz8uvhfPL81MnP8ZGpsgr8mDMev/bqhpHLsyi0MmLYQpSh9/Erbwxp8vEpNwa5DZ3Dp9iNc2bpcI4Qp0onSgaePvkSQyvJ7T/6/N6xwjS9kn8OJUhzy4EQpjiLz5tlFbFjQHQYmrihVcQIMDB2QGPsaD68fwqvH5zBx0TVYWDsXaFt2TmXgXaUF/N9vhplVJRgYOWbL4gn8sA0Va3aApY0r3j47zgpSpStNhIN7xiCL4FrmJ3x48TeWzRqMitUbwcbOudBZYtY2tug3cDgKi7pcUsXtSmZmaIDTk4ZgztGLOHj/BXaKMzIhXCzM8HfrdhjWuBY0BUW7ppQhTJmaGGPzir/wvynjcePOfaSkiVC9ciX4eJPwTjGyJ6LpjhiVn8C6+tAZPHn7GYtoZ3jT36/3xowpNlKRmLx2Ozo1rAkXW2u1le0RXkdnXP81qe+ZLFmpTRlnPC8qmhOliMMjUR1XNEdJgpTxaaMwRZAJOPKEKbEoHWvmdMb7lzdg69IGzl51IRGnIDL4Ajb+1Q8RIZ/QrvfUAu+racfReHh9HwLeb4Vb2SGZN4RkEu/r2w1IS45Ek/aj2J83LOoHUGao1ngjBPrfM3oS497i9f3fcXT7DPT9ZVWhSxLJPmvUbcYuJYWBnVrB3NQY8/7bjrlfMoLkeTSN9g1q468JI+DhXPDJWQ6gQ+O6aFO/Fq48eMqWqFlbmKFN/ZowMih8BYauQFzmo+YuQx0YYwocwPvmPqoBY7RgzDA5NBjz1u3Aqqnj1H2oePjiDXylhqB5uYVomqLgKzHEw5dv1HJsHJoPJ0rJEShiUs2gSWRkJmlWCZ9ELMKulcNhZl0V5XwXgKYzXAUkpNzerT1e3hmD49unY9DE7QXeZv+x67FyRgs8u/EzrB2bwsjEHUmJnxEdeg3W9h7oM2oN+7xLR9fAwqZqNkGKQNF8lKowHrHht3D+yEb0H/U/pWeKFdclpYgg6OLeuFsaG2HVgM6Y360VPkZEQ5/PR3knO3ZgpWlogzBFcHVyRP8eXbKtS02XZF4vscaloUxyXpfKFKPyy5EiN0Ebj11AY5jCmzLMdRMzgLbGRSYR289cw4xB3VVWticTpLJ2sxN8u+ZTIYUZcp+zZGS4VklOCEcGiiqr5eDIq4xPF4WpW+c3492La/CpvQQWNt+/e2ydm7PZlsd3zEKNBt1g71y2QPspXb4uug5eiKPbpiMu4i6sHBqxf3tJAHlSwlf0HrkcrqWr4t2L6wgNfI2KdZZnE6QIphbl4OjRA3cu7kDXQQtyuaXyckkpE23IFiMlep2a1MN7/yDEC5Pg7mgPe2v1ZHHqAnw+D63qFT6GQ1c5fPEGEpKTMYTnyXZ1zIoTpYd2ElPsPnWRLW80NlSveKcnELDjp7xIgZR9DgeHPDTvrlONaGLYuaZ24vN7dAaJ8WFwLzcqU5CSoW9oB0fPnnh29yiSEnN3Y8kL4qqavOQO2vSaCkn6e4R82QVIvqJ939mYtPgmTC3s2HPw5d0jWNjVk7sNvsAYZlbV8M7vgco6Lir7hj8vFF3eZG5kiBoeLqjoQmZiNPdPg7YEoOdEFiietWOfbFEEObeXdX/KJL9g89R0EYKiY1EphyAlw4jioSxlgLcBwSot28spSBFqONhDn+bhMiP/78IVJgH6NI2aDvYqOkIOjpKNLgSfEzGHOI2yuo2un9kAK4cG2QQpGa5e/aGnb4GbZzcVaj+k7O+3hRdQyrscIgOPICr4GMpUqIpJi6+iWaex7HO+vn/IducjE4ryIMeUnpaE0MA3ag1uV1fIOcmTKgpkgsXbwxW1KpbjBCkOhfLOPxAOfIM8O2VXoY2QnJaO4PBItZ/5No1q4y6VjGQmtzCVwkhxh05G28bcRBaHfDRH5eDQqmypsKA37KDJ2KyU3MfNravhqyQdUWFfYGxa8BwdIxNLtO4xlV3yEuV4PD6kkrQ8tyGVpoHHs9Rpl1Rxy/a0HW0JQM+LnEKRooSpwghQUqkUB0+exYYd+/Di9RsI+AK0atoQ44b+hBpVKilEkGKfw+eDT9OIhXynJzubDwlMDA1Udl3nLNuTYa6vj57lymL/67dwgR7qwxh+SMUDRohAJh3PkIyuZcvAogSXEnBwqBpdcUxlDUAPC3wLD58xeYaSm1hUQGjg20Lvp1yVpuySFzSPB4YRswsl5yZXNrYi46ycx19SKEjIOQfH1+AwrNh9GAfO30CCMBHuTo4Y1q0tRvbsoNByQzI2SmTEEIGBQE4+W8y3boXGRsWvvCguw7t3wL97j2GRKBS/wx4UKFxlEvCVSYMfkwIRHxjRvb26D5NDQ9FcOwSHRrul9A1MIBYnsxkI8hClZXjV9Q3lZ7MU9X0Tca5G/daIDr0kt21sWkoE4qOeokb97KHJuuaS0jVBKiQuAX+fvoafNx7A6O1HcfrZG4gluT/fnO+fLETIUKRriqBMx5Q8srqairMURpAaM3UORk2aga9hBnD1Hg1b9364fPstWvUajEMnzypEkGIf59Ho1KAmLlFCiJjcHbVeMikIlKSiW6Pa7M/qKNvLyu81a6CxuysWS0PRU/oJ06VBuMEkIgwi1pR+4Ys/bgdnuLo4ODh0zy2jLGTCDhGm9AyMkZ6WdyabKD1WYeOnrFSo3goScSqiQ2/IfTwi6DxMLRzg7FEp33BzDs1AIpHg7K37GDlvGfpPW4D563ciICxC3Yel0zx58wG1+o/F9pO3YGTbDu4+vyKRKY9Za7ej1cipSExKVmh5qFAiZsch8ib1ziIeNX3KwtnOBurG1cEWh5bPxUd9CQZKvuAnySdsk0biHZPKNrdJE4sxbcVGpKTmbSzgKLlwopSSxArlZUtpBpVrdYRUKkJE0Dm5fyTD/E/Azskb9s7eChWkCF37/4bkxEB8erk8myiWlhKJ90/mwNTcCs3aDdB5l5SusO3mI1SZsQwrTl/H12dfcO/hWwxYvw+NF6xjxarClPN9Cg7D1tNXsOX0Fbz1Dy62MKVqcUpV7D92CvuOnkTZajNRvtY/cPLsxjYKqNxwK2ycmmP0lNkIi4gstiAlY1K/zgiHCIuYUIQxInadlGHwUCrEP1Q4fL1LoQ6juEFcfuQlSMnyov5p0ghuJiYwAo0/aWds55XCer4nNvI8UEYswK8Xr+BdTAH7vXNwcChMmNLmMj6CTOCp17wbooLPQyLH9S2M/8A2jalev6vC9+/kXgE+1Vvj6+vVSIjxy1zPMFKE+Z9CeMAptOw6Hjy+QO1le9r+WauCiJg4NBz4K7r9NgfXz9zA5ytPsGLLPpTvNAhr9h4r1LbSRSKcvnEPGw+fxvGrt5Galq6049Z2EbDf1IWg9VxQpfEOeJQfCUePLux4qmLdNXjxIQD/+6/gebr54VPaA12a1MM6ROKqNAHib5N7sYwYq6QReClNxrTh/aEpNKlZFUsmj4YUDNpS5tjOK43/+B7YxSuNibQDzly/h1HzSXdqDo7saIbtRoMgIkW86u6NiizSqDv03MrOHbWaDMCjG+tA8wxg69yCzZYSpccj8MMOxITfxqDftiusFXBWQa58lboYP2cTVs0fgZiwazCzqg6pJBVx0U9gamaJ+WvPwsjETCddUuoq28ur3Ekebu0aFPi5l19/xG97TqIdZY7BtA2bL0R4x0vBX+Fh6LNmF65NJ7llPxZk4yh9jN1yAGfffMm2vnn1itg0fQwcCxk6KhNbVFXOp2rW79gPK/ta7O9tViiKB88K4xATdgM7DxzF5LEjii1IEap7l8KBPydh0PxVGJ78BW58QyRBgihpOupVKIut3ZuApim1le3l5HpgEAKEQizluWYLZ3eg9DCDdsRoxh/bXr7CosYNlXi0HBwc8tDmMj6ZMNWx7wTcurAfbx/NYLsXGxq7sBN68dHP8OnFX3BwLY+qdTorZf9DJ2/Hqtkd8PLOWJhaloe+oQOS4t8hJSkEDVoPRctuE7MdqzrRhjwpdUGul14T58D/UyD+4rmgAgxB0RSb3bObicbkZevg5mjHhrDnx/7zVzFlyTpExMWDnHEie1ibmmDeuKEY0rWtSt6PtnDx3mP4h4Sicv1Z4AuyNwMwsfCGvXs3bD9xBHNHD4aJgkrqNs2bgiEzF2PpjbvYxIuGJcVHkCQVfD4f66dORNsGGU5zTbkuV+88jBq0MUZRdpn3gXyKQjPKDOlSKdZcuIbZowaitGveE4QcJQ+VWG/Wrl0LDw8PGBgYoHbt2njw4Mch1AcPHkS5cuXY51eqVAlnzpxRxWFqBTJxRhPK+HqPXI1qdbvg4/PFeHS5B57fHIpHl3siIuAkug9dihoNexd7H+R9ynOItew0GJuOv0eX/mPg7MpHqTJWGDFpKbvOq3x1jQ22V0THPVUJUuQGXrYQZ0lBl6yvy08EWHHuJsrThviFtssUpAhECPidssfL4HBcffv5h9tIFYnQbeV23HkXiPG0PQ7xvHCU54VJtAOePX+P1uPnIjFZfpmpppbzKXvA8PLNG1jY5tUswBSmVpXx1O9VtvUy55gsGL6wtKlTDZ8P/Yf/Jo9Eh06NMahna1xZNRcnBraFjYmR2sv2snLRPwClaYNc3QIJehSN5jDDxa/+7Lnk4FAm3PhJ94LPCdWr+mDxuuMQp33Bk6sD2K7DT671xat7v8HWwQkT/jwLvpKyjUzMrPHH0psYOf0AvMqXg5W1FNXrt2bX/fTrOnYSSN0uKXWhTXlSt5/64f6rdxjP2KIiZZR5829I0RhK26AKbYwlW/flu53Dl25g8MzF8E6QYg3PHSd4ZbCe54FqQhpjFq7EtuO5KyJKMk/ffICBgQVMLMrLfdzKvh6SUpLxMVBxZf6kq97+pXNwf/e/GDWoO9p2a4m/J/2Cz+f2YGCnVtAkPgeFwu+LP9rBXK4xoSllBkOah+PXbqvl+Dg0F6UrG/v378fEiROxbt06VpBasWIFWrdujXfv3sHOzi7X8+/cuYO+ffti0aJF6NChA/bs2YMuXbrgyZMnqFixorIPVyvQlNBzgZ4BBk3cjlY9/sCT2weRIoyDtYMnfBv1ham5bbG3n5/wZu/kgcHjFhV6u8V1SZHSPXW6pJRNYW7e5ZH1dZLIkMzt5XRQCVPTcOvDV4yj7eV+cZFZPyeePs69eIfmPl557u/oo1d4HhSGFTw3eFHfwyWbUGbwYgwwJsQfO85ew5jubRUiTOmCa4rPF0AiztsSKpWkQE9gVix3VF4Dq8Htmua6rlUltBb0mk4RiWDB5D1nYwkeUiUSdjZZ+5NudAPiatSF382scOMn3Q0+J1Sv3QRHrn3BiaOH8OntY4jFeqhYsy28KzdRmMs8L0iQOSkPlFciKBOk1O2S4vgxp2/egy1fH9WZ3DOs5PppwZhi6ev3iIqLh42FeZ75kjNWbEQd2gRTKIfM684Zeuwkn1QKzF69Bf3aNYeeQH73t5KGQCCARJLO5tpSVO77FIkkJbPJi6KpXLYUu2gySSmp7L8WWSaas6JP0TCmeEhKzngeB4fKnFLLli3D8OHD8fPPP8PHx4cVp4yMjLBlyxa5z1+5ciXatGmDyZMno3z58pg/fz6qV6+ONWvWQFUQB42m5kplRRPcUgRHNx+07zsHPYYvR9OOvypUkFJWjpa6XFLFRZk37zldUYpAnoNKhuhbkLlxHn+GyODIBDTSRD8uVd137xmq0sbZBCkZLpQe6sAEu85dL/570RHXFDmvLRvXR3TIBbnNAlKEQYiPfomWjRsoVJCSh6oEKdl1XVBKWVjgHdKQKqetMeEFkwIPU1PQSr5x5CjZaOP4SVXoimNK38AQPfv+hKETV2DklL/ZznnKFqR+BCdIaQ9pIhE7fsrreiHjJ8KPsqHuvngN//BIdKMsc22H/NydtkRkfAIu33+i4KPXXlrV9YVIlIzoMPnNAiIDz8HF3h7eHq4oibg72UNfIGDHSfIIZNIQJU6Ht2fJPD8cahKl0tPT8fjxY7Ro8T23hNiCyc93796V+xqyPuvzCcRZldfz09LSkJCQkG0pCWhSGZ+iUaYgpQiXVHEo6o29Ml1SWcUiRYlR8sgpTpkbGsDZ3BSPmSS5z49hxPgkSUVFV4cfbjcqQQhnJu/fA2cIEBkTD0UgK13T9hD0sUN/QnJiAD69WMp20ZSRkhSED09nw9HeHn18HJUuSGlaHpqM7mXLIImRYI80OleJnh+TjNuMEL19yinwKDk4VD9+0vYxlC505JNBXElkIaKQukrnNEmQImKjqvOktKl0j1ClbGn4i1MQ/q15SE4eMUmwNTeDww8uqKjY+ExnlDxk6xU1htIFiFOpWe0a8H+1AvHRzzPXS6ViBH8+gIjgi/h9YHfweOrrzq1OzE2M0bN1E5yg4jMb28ggIe1bmWjYmJmicwGyzjhKFkoVpaKiotguBfb29tnWk5/DwsLkvoasL8zzSZmfubl55uLqWnKUV10UppTtkFKES6oopXua6pLKKkYpU5DKimw/QefuoLujPa4hES+kybm+uDYwkTAQ8NG7dpUfbs/F2gKfqbxnAj9TaXAyye2iKg7a3qGvTo1qWPPX/xAVch5PLnfH64fT4Hd3PJ5c/QnGBkk4OW8cDPX1ipwfVZTAfolUijchEXgZFIbk9HSFXt9xJpZ4HBaOV1HR7H7yw9XMFJNq+eIIE4uZ0mC2480DqRBrJeGYJQ1GDQd79C5X/M6i2ow0MRbGNeuo+zB0FlWMn3RhDKULHfmyIhOEVC1OaZIgxVEwerRsDHNjI6xnIiHK4ep9w6TgEiXEsB4dwOfnPWZ1sc+obPjEyC+l+oSM9S72NtzHkoWdC6aikpcTO256cWsY3jycgWdXe+Pr638xvn93jOzZUS2dGJ++/QD/0HCFbpeUeD579xG3nvohLKpgXYf/HDsElnbWmIgg7JRE4ZE0CWelcfgNgXhKpWDjvMnQ19MuEZhD+Wi9mjFt2jQ2s0oGmeVT1KCKuGpiUn/cxU3daEq+lDYIUpxLKjsFcUeJpFKc+fQZB9++g398Ikz0BGhb2hN9y5eDrVHR1T3ZPn+SSHE/MgazYoLRQGqCajBCPCS4RCUiFOnYOqQ366j6Ef3rVcegVx/wBEmoThtne+wtk4LH0iSsrF2PFUIUeROt7R36+nbrhIZ1a2HngSN45vcG+no2aFthMHo2rg0jA32liFHyBCniRFp35R7WXryN4PhEdp2Zvh4GNKiB6R2bwVi/6AOXiNRULPkQgEv+AZB8czw5GhlheNXK6Old9odlMoMqVoCziQk2P3+JpdEZN/U2BgYYUb4KhlSqCL0SOgvKoVsocwylSrQ9XyorMmEoMj5DLIou2H1gkeEEKeXxwT8Ia/cfx+mrd9hyuyrepTGyVye0b1Sn2GWaJKNx24Jp6PX7/zAGgWgpNYUVeHiGFNxihKhZqRwmD/5xw6Hq5cvAx8MVBwKiUZkxYjukySDfmfuYWLja2qCx748nB0saVuamuLZ5Kc7dfoiDF64jLlGIUq718XPnNqhURrWZT++/BmLm6i1sxpj02zindoVymDN6EJrWKt64dMeJC1i0cRe+hkWwP/NoGh0a1cHfv4+Cm0PuXGgZ9taWuLptBRZt3oNdJy5gf2oMe723ql0DW4b1Q53KPsU6Lg7dRKmilI2NDWtfDA/PrtqSnx0c5JfkkPWFeb6+vj67KMNNE593DrBGkSHi8JEY/+PsnZLukFKXS0oRKNolVRBBKl0iwbiLV3A7JATVKWO0p0wQJRJj14tXOPT2PTa1bY2yVgU/rhSxGGKpFCYCwfcuMQ4u+K+DI/Y8fICDXwNxPTkcAppG28rlsLVVfdTwcMl3u+2qeKNpuVJY8O4rukst0ZgyZS2gZFB2ELHw9XBBr4Z1gNREhQtT2h6E7uLogGnjR7P/r8xSvR85pKbsP41N1x+iBWWGMbQL9CgK99OTsPXKfTz+HIRjvw2CQRECVp8evYxBNx8iPU2CYZQNmzsWDzHOp8Rj3p17iE5JxS/VfjzQbuHhzi6xqans74O1oSH4tEqa1nKUcFQxflLmGEqV6ErweU5yupYULU5ldWKVZIcUKd1TBhfvPkKv3+fCUAo0khrDmBLgyeN36PlgLkZ0b48Vf4wtsDBFXJMJScmsEJU1cLx1/Zq4vn0llu84iL1XbiFdLIGHgx3m9OyJ0b06wdDgx7/bZP9Lp4xBp3EzMIMJRg/GAp6UPgKZdBxGHF4wydj3x+8lthTtR5BzQsRFsqiLd18D0fTnCTBMEWMUZYuytAFbMnfibQA6jpuBvX/PQsfGdYu07WU7D2LGqs1oSJtiJM8FFuDjJZOMQzcfocmL8bixY1Wm004edlYWWD55NP4aPwyRsfEwNTZiS/s4OPKCYpTc05p03KtVqxZWr16daQN0c3PD2LFjMXXq1FzP7927N5KTk3Hy5MnMdfXq1UPlypXZkM/8ILN8xIL+4GUATEyL53IiopSmO6VkyNxS2ihMqUKQIi6p4ghSsiypopbuFTdLSpGiVEHzo9Y8ecq6ROZQTuwNvYx4RoxZTAgkxgKc7NE137DnawGB2PrCD48jMmZa3ExM0bdCOdZtlfUGn3TqI84sz3YN2OyUwpCSLsL/jl7EzluPWfGLoM/joVedKljYow1Mvg3MSMkRQVllR8Q1JUNbxKms5YeqFqSefA1G88Ub8Atth/a0RS6X2x+SICzs3RYjmtQu9DW+2O8tTn0NwQraDXZUdlFrtyQK+5kYnO/VHY4mJtA2yO9Kzm6WJb18T9nd9xIShXCv3gDx8fEwM1PNuEDV46esY6jXr1/D1NQU2gQRpgi6JEzJIK4pRQlUmi5G6UKeVGxCIsq2/wnl0/mYSjmwHcdknJfGY7U0HFvmTUHfts1+uB3SOW/JtgPYcfwcYoVJEPB46Nq8Aab83BcVvDyyPZf8fRBLJEXqknf90XNMXvIfXn76mrmuvLsrFv42HG3q1yr09jhUQ5dfZ+LVg5dYAheYZOl2R1xui5hQfDbj4ePZ3RAUshMgKdMr2+EndJSaYQgvu/AUzYgxAYHo2rEF1s4Yr7D3wqG7JAiTYN+0e77jJ6WX7xFb+KBBg+Dr68sOrlasWIGkpCS2mwxh4MCBcHZ2ZnMNCOPHj0fjxo2xdOlStG/fHvv27cOjR4+wYcMGqANtKOHLWsZHBB5tEqZU5ZBSBLrikiqIIEU64+1//Q5tYZ5NkCKYU3yMhi0mJQbiTnAIGrg457mdHX6v8PeDR6hAG2IcbQ8DUHiQlIR/7j/E49BwLG3WmLUDZx5TZAibNUUozE23oZ4Ai3u3w/ROzVihg2jtVd2cYGVilOtckptpZTim5JX0abI4pSox6kfX8o7bj2FP66ENlfvOqBxliDqUMXbceFRoUYo48k4EhqENzHIJUoRutBWOS+Nw7MOnfN1SHBzqQtvHT6pGVx1TOcUjWVlfYQUqTRejdIldpy4hNS0Nv9JO2QQpQmvaHHeoJPy79+gPRanw6Fg0H/IbwsOj2NI8b9qMDTU/d/keTl2/ixNrFqJ+1YqZzyeTeXpFdPKS8rz7e//Dyw9fEBIZBTsrS1Qr56XWTpAcPyYoPBIX7j3GOMoOJnT2+xMeRWEArDE2zh9nbz1Ap0KGiu89ewU0A/SicwfbWVN8tJOaYe+Zy1jy+6h83XgcHAVF6aIUmbmLjIzE7Nmz2bDNqlWr4ty5c5lhnAEBAdlcEWRWb8+ePZg5cyamT5+OMmXK4NixY6hY8fsfXlWhTSV82ihMqUqQKq5LqjhoWgg2cZAUJNA8IDERselpqJtjhkSGNwxgRfPxNDwiT1HKPz4B/zx4xLYa/pmyyRzcNIYZGklN8WdAAE58/ISuZctkvkZ2bMQJQo61sG4Qkj/VtHzpHz5H2cJUXuKUpghU6hCjyLmWJ65+Co9GWUYfPFr+wLc8ZYg9BQzWlEGum2RzGwjFItbKLg9DioYrpY9gobBQ2+bQTJTtklIX6hw/mQW/AFOuPrQNXRamCiJQFea1HMrj/ss38KEMYUnJv82qyxhhzZsPEIsleQaRT1+5ETHh0VgBFzjwvju52jMW+J84BENmLMbrE9sUVlpHxmiksxxZODSfr8Fh7ARseVr+OM6D0ocxj49PgSGF3jYJS3fm6cOEkX9tlYUBUtKjERkX/8NsKQ4OjQs6J1Zzssjj2rVrudb17NmTXTh0U5hSpTuquOHmWUv3ikpxSveUUbZXEMgsi6wLHuToBaRIQsKQ0MO8Z9EOvnsPU4qHAbR1rtm22rQJasAY+16/zSZKZe7f1qnIwlRBkJ1XZQpTOT97dbqn1CFE/eg6Jk68o49fsd32rLN0DSIDrPdIRQgjgiFohEjTYV6Izomya9yIL2ADW8Mgv1U2sbZHQoS6Wp6lw6H7qHP8RMqa4l2raa0wVRLQNZFJ1Z0UlVG6R+DxaIh/YDISgWGjD+g8xlDRcQk4dOE6BjCWcKCzH58BRWMIY4OJEQG4cPcR2jYonJOYQ/v5GBCMzUfPsP8fyYjgSmVcI0JGgudMMtLBwAZ8pEglsDQrfESBtbkZIqXpEEEKQQ6nHyEUIvb6teAyojgUiNZ331MF2lLCJ+O72JPx8WqSOKWOcj1FuKTUVbqnaArikiK4mpqyXcqupSSiGnIHEz5lktlsqTpOeW/vQ0wsKsIQenK+0Aik096OuOgfHmtewtT7sEj8e/kuTj5+hWSRGOUcbDCkSS30rVMV/ELMGhKxRJZ1pOx8nKxiUE4HnTJEqpz7ULUYRZCd26wkp6ej1+pduP3RH+6UHt4jHe+YjCyuNZIIfEFa5nPJJ1nLzo0Vq/IrI8ialUZe19LDHWe/hqAdY8EO4rNynUlEjFSMDqW1b0aY/E6oG03Lk+JQPAK+QOuFKaTorltKl1FlnpSyaFarGg6ev4YwXjocvgkGMsj32XUqCY2rV84zP/NjYDDbXKM6T34wdFnKACY8Pl598i+0KEXcWbtOX8SGAyfx5ksAjA300aVFQ4zr1w3eHtrXebOkceLaHfw0dQEMGAqkrc9xaSwqwRDbmWicZeKQxk4bZ8ADBZ/S7oXeR6/WTbBw025cphPQhsqe95nOSHGWSkC7+rVhxolSHApE84N81Iy6yr4UgUz4kQlBJU2QUrdLqjile/Ju5lVRtieD5DwNrFQBV5gEnJPGZbaZZbfFpGEtE4GK1taoZpd35w0DPh8JkOT5eCIjYcPIf3gc3445q8vrxtvPaLJgHU7deYHmqUb4SWoJvZBEjN91Aj+t28e6cIrqmlI2JIjUPywSwXFJoAT62Tr35VwKirzXysL1sy6qRuaQyumSmnXoPJ58CsRfPBespN1RGvr4nyQY0yRBILfBc2lnHOZ5YQPPA+0pC9z9GIAFJ64UaJ9Zr/ERVSsjjpZgjjQYb5gU9kYgmZGwA7g1TDhae7ijXGHqXjQIdYecc5QMZA4SZXUn00XnDQcHoWerxrC1MMdihCOG+T4xTNznO6TReCtJxvgBPfI8WUbfcnoSGPnjmVRGinSpFIb6hXN5icRi9J40F6P/XAHBx3D8JLFAM6Eejp24jLr9x7CB55pMvDAJn4NC2ODkkkhAWAQGTluIWowRtlEeGEvb4zGS8Yv0K04ysWxcxjaeJw7xvDCNdoQ9LUDPCXMQGBZZqP0QcXJAuxZYz0ThiDQGSd+uw/dMKv6HUITREkwf0V9J75KjpKIZaoUWoG1uKU0q51NnmLm6XVLFEQMUVbpXmLK9rPT3KY9PsXFY8/4DjtJxKC81QBTErDXYw8wMK1o0/aF7pZm7K2YEBLCthWXWYhkiRoorVCKaebjlexxZhakUsQSDr91BOak+ZlCOMPg2y9gZlniEJMx/9QH/Xb6LX1sV7qZd2TlTEokUqw+fwZrD5xEUkTE4cHewx/iebTGqSyu5s6WFEabUIToVpfQ0PjkFe+4+Qw/KEhWpjF/OeTxnjJb4wwY8LOS5ZrqanKCHETw7GEtorLxwE8Oa1IKDuWmBr/EylpZY36YVZly/icnCQDZHKv1bOWpnr9KYUZdz+nBwFESYEonStdIxVRLypTiKjjLFViMDAxxfswCdxkzHkIQvbFyBCWg8o1MRzaRjwa9D0bp+zTxfX6G0B9ztbXE+Mh6VkXsgSyYMSQlg+4aF+x5btecozt95iDm0M3yzNLHpzVhhgTgU/SbPx4ezu9jj1yReffyKeet34tSNu+zkHsnR6tK0AWaPHICyJcjdtfnIGfClwHjKnq1CqE+Zoi+Thr1MDMbT9mhJf6/nJY9VYAwxVhiIZTsPYvnk0YXa19qZ42FoqI+tR89iuzSa3R8pB3S1s8GJebNRrVzu6A0OjuLAiVI6GHiuKeV8WR1aqhakFOGS0iUK45KSQerF59Svi45epdl8qIC4eJjom2BuqapoW8oThvm0mG3t6YF1T5/jz+QQTIYDvKiMQU4UI8JaaQTiKAkGVaxQqPdw/vFDxKWkYgzPI1dJFhlgNZGYYuPV+xjbol6etnhVC1NkAPXzorU4eOUubJ1bonzNJmwqV1TIZfy+ejtefArAf5OG5xL4NE1oUkQW2lP/EKSKxWjI+y4uUaCQCAn609a5PlNCZ9oSh6VxOPzwJca0qPfDsr2cVLe3w+me3XA3JAQfY+NY915jVxc4GMsvieDg4MhbmNJGOGFKeyDioapL95SRJyWjqrcXnh3ejJ2nLuDM9XtITEtHD58yGNa9PSp6ef7wtWT88sewfhi9YCUcGQG601bsxArJQ7zNCLEF0ejdugk8nB0KNRZZv+84msI0myBFIN+9pKvyCOFXHLpwAwM7tYKm8Pj1e7Qa+QcogTU8yo+FoakHkhM+4ey9I7hw9zdc2fRPvudTV7jx8Bl8GUMYZhnfJkEKa/DRjMptnLCg+GgpMcXukxewbNIvheqmqCcQYNXUcZg2rB9O37gHYXIqynm6omWdGgoL1+fgyAonSpUgsrqmlClOqVOMUqRLqrile8UJOFcURXVJySBfYDUc7NmlsBABYH2blhh9/hImJAbAjdaHASh8lKaygtbKpk1ZN0theJ0mgQetnyujQUZtyhiX40IRLUyGbRHCHXOW8ilCnDp95wkOXL4N7+pzYOPUNHO9lX09mNv4YtuZxejZrC6a16gEbUZ2zn7k8JM3IIqHBCTq3C2Pz9SE4sGWFiA8QVgoQSqruFrf2ZldOHQPXe28p2mQm3dtdEsROGGKQ11YmZtifP/u7FJYBndug7CoGPy5YRdOIgHujD4iKBGiJOno0LA21s4YX6jtRcUlIDAyCj/R8l2DjpQePPmGrAikKaIUKbsfNX8l+Ibu8Km9DDx+xsDewqY67Fzb4vW9XzFm4Wpc37IMJQF5Y6hYSOBMCTIbFOWEjK0SU2KQkpZWJAeco401hnVrX6Tj5eAoDJwoVQJK+LIiE4mUIU5pihilSJeUugLOFdl1r7AuqYjkZBx9/wEfvjlLWri7oaGLM5szVVjczMxwrHsXXA8Mwq2gYIikEvSysWHdV8aCjCDdwkC6/aWCyTP4WhbwKCjmLI4iXVMbTlyCuVX5bIKUDDuXNgj/egAbT1zSalGqIIIUoaqbI3tN3ZAmoi9lza6zAI8NN/zKpGeW9MUyYsRCDAvwIQDFdoFxzKN0ryguQI6iQ34vOEomnDDFwfFjJBIJLt57jONXb2c6SwZ1ag0X+7zzN/OCjHGmDeuPfu1bYNepi/gSHAZLM1PWIeVbwbvQ25M1gckahJ0VMq5KpRjw+Zrjgnn0+j38Pn6CT63FmYKUDL7ABE5eg/Dg8Ry8/vQVPqU9oOs09K2Cta8+IIWRss45ghV4eMUks3llpOsw+TcY6ZkxCF+ZNJgbGcGQ6zbMoeFwolQJKeHLSXbRKPtlUFCRKmeAujqFKF0KqFenS+rQu/eYf+ceKwR4Uwasi+XYh4/wtrTEutYtYGtU+BPLp2lW1LI1NIRYKoWXpWWRBClCA2dn7Hn9Fm95qSiP3E60K0hEdTcnWBgXv/RNUcLU24BQmFg2y3PQaWJZFW/8H0HXBSmCuZEh+terht03H6OC1BCVaSOYUjzUpkxwQhqLUtDHfiYaT5jkzGGzAwSQUkCPmpUU6gJUFSSXbf+7d3gTGQ09Hg+N3VzQuYwXzIswQNSEznsErvNeyYUTpjh0IZCeuP4UXboXEROHrr/OxJN3H+HBN4S5lMYZ3MKiTXvwz++j8EuvTkXarrujPQZ0aInA0AhWlCpKNzWZa6u6txeufAhFU+SeYH+LVISIU9Gqri80hQ/+gey/5tZV5T4uW//OP6hEiFJDu7XDql2HsUISjomwhz5FowVtjuOSOJyXxkFISXFaGoeYb02GzMFjhcYhHTsWqnRPU0hNS8ehi9dx6MJ1xCUkooyHK37u2hZ1K/to5fvh+DGcKFUC3VI5yS0mFfyyUKUQJRGL8eb5HQiFcXBy9YJbKR+luaRI6V5RXVKaUrpXWBfJ7aBg/O/2XbSjzDGItoExlfH+31ApWBwXhjEXLmNf5w5sOVRBIV37Nr14iV1+rxGTlsau06d56ODliUm1asJUr3CDwvrOTvAyN8fSxDDMghPcKf3M4PT90hg8ZZKwtZXibMZZhami3oybGhkgKi06z8fT06JhZqG9+VGFdfbN694K70MjMf3DV1SCEUoz+mymVChEmCYNZGf2xtH28KD02U6Ph6Wx7ODjc2RMZklmQcr2NIGdr17j7/sPYU7xURWGbPbDsrDH2PT8JVvaWt46wy1WGLjOexyagLaX8pVEwkMC8On9Swj09FGxal0YGmlWtp6q86QUCXEZ9Zo4B18/BrCdZUnANEVTSGak2CWNwsR//oWrvS06NK5b6IDvKcvW4crDZ5nrKni64X9jfi70tggTB/fCgGkLsZuJQi/aGoJv4zl/Jg3LEAEfD1e0rFsDmoLJt4nQ9LQYGBjlLjtMT80YW5kaaecYqrC4Odhhx6Lp+GnqAgxh/FFHYsTesemDwjomEhQDtKHM0ZA2JT1dcFOaiLNMPD4HBLOZYoXNWlUnIZHR6PDLVLzxD0Rl2hg2DA9X333FrtOXMLRrO6yaOlar3g9H/nCiVAl2S+WFJjmeZJw7shG71s1FbNR3p0C5yvUwetoalPauqlMuKUWU7hXFRUJulMvRhviFsss2A1GeMsTvtD2mxQThXkgo6jk7FXiQNvf2HRx9/xFtKXM049lBHzQeMEIc+fAFb6JisK19GxgVwjVFSgjXtmqBEecuYEyiPypQhmz51yukII6RYFbn5uhSo+Dh6YX5PIrqmurRpBYW7jyB9LRfoKef/bNNS4lAbPgd9OzaFyVBkCIY6enh8PiBOPHkNXbdfoKX0bGwNrGGXXQsTIVi/EV/78BXljJAQ8oUM5lg/LbrBG7PHoPAs7cLLEiliSW45O+P97GxbI5ZMzc3lLVSXGnsj7gdHIzF9x+yLZp/om0yB//RjBjzRSH45fwlnO3VPd+GARwcmoY2d+TLFD9SSk5HvsjwYCybOxZ3r59mv5cJRsZm6DFwHAaPnsWFFiuA20/9cP/VO8ylnVGR/j4INaJoDKdt8RUiLNm6v1BCEhGkmg39DRZpDH6jHVj3ehgjwnH/aPSaNBdb//wDvVvnjgX4Ed1bNMKnwBDM+XcbziERPhJ9xFFSvJImw8vZEUdW/alRN/rNalWDsaERQr8eg6fPL7keD/M/BkszczSsrr3xB4WlY+O6eHxgA9YfOonLdx6x3Z3rOZbC1QdP8QftiAb096iDijwjVJMa48+7j3D82h10bVa4ztRvvwR8K0VNgbeHK7q1aKiSzozk71S/yfMQFRSONTx3dpJSNtF9gY7H2qNn2OMZ16+r0o+FQ3Vozl8eLYLr7KZaju5ajtV/jgRfvwIqN1iHmi0Ow7vGXAT5h2PK0Cb4+tFPoZ9LcQLONYnCuEiSRSI8DA9HS5jJtcRWhCEcaT1cC8iwUheEF5FROPz+I8bS9viFZw9vypD9YiGzcwtpZ7yPicWBt+9RWJxNTXCka2csbNQA9q42kDqYooOHM442qYuJbRpBWeQMQS8owzq2YMsJ3z6YBGHcu8wv3MTYV3j7cBLsLM0xsC3pyKcdkPcv67BXVAGVZH51r1kJRycMwqP5E/B33/YIT0xCfyp3Bz5iT+8HK7wJi8Tjr8EFvrZJhlmLfQfxx/WbOOX3HtuevkS3Yycw5sIlCNOV30ls+8tXKEsb4OcsghTBmuJjKuWA6NRUnPn8RenHoesh5xzqQVb6RIQpbUXVJWPqIC42CmP7N8WTB/dRquLv8G1+ENUab4eFfVvsXLcI/8weVSJL9xTN6Zv3YMvXQ/VvuYhZIWOqlowp7r96i+i4go9Tpy5fzwpSS+CM5rQZXCg9tmveXMqJdcJM/GstW95UWKb83AdPD2xAv57toe/rhVINq2Dz3El4uH89WyqoSZgYGWLiT90R+vkAgj7uhlic4QwQi4QIeLcVYf4nMOXnXtAvpOte2ynt6oS/fxuJxwc34tmRzTAUCFCGZ5hNkJJRhzaBD88IW4+cKfD2k1NTMWDqAlTrNQL/rN+NXbuPY8TcpSjdph8rUimbB35vWZF3NGOTKUgRSKVGG9qC7TS4etdhNsONQ3fgpmgLSUlxS2kKiQmx2L5mJhw9uqNUxXGZ620cG8PCtiZe3h6F7WtmYM6K4wp1SRWndE8bSf/2h90kD52azT5iaKQV4gvgyPsPcKAFaCGnTW0pygD1KRMcfvsegysV3tmkz+ehk1dpdsmat0McYsosbypKdz4iOp1fPgPdpi/B81sjYWziQFQpJCWFw8vFGUcXzoCFiWaVUcgIiYrBo7efWIdanQplYfD+lcKD+AmkNI/gQ8m34BO3HuHhxbvoVC3/0gK/qCiMu3gFVWCI4TxHOFN6bPjnbSYR/waHYeLla2z5nLIyCchs3v3QMAyhbOTug3SPJO/pbnAIupctA21BE0POuc576kObHVMlpSPfgW0rEB0VicoNNmYrfyKuE0NjV5w9ugTd+v+Csj7VS1TpnqLzpFLTRTAGL8/vFNnYKrWAEyKBYZG49OApJtD2MKKzj0fJjXl/WGOk8CtOXr+Lnq0aF/p4y3m6Ycnv6hckC8LUoX0hTEnByl2bEfJpFwyM7JCSFA6GEWHa0H4Y378bNBEimNx9/hqRcfFs0L2vT1mljTk+kkwtqT6Qx61LOak+nvpnTOoVhKGz/sb5G/fZ668xZQoBaITx0rE1JRr9py7Auf8Wo4ES3WlX7j+BGU+AGpA/Nm5KmeJyZDA+BoawjikO3YATpYqILmZLaSK3LhyAWCyCi1e/XI/x+UZw9OiBhzdXAEmfYG5V+O4mykBT8qQKg5m+PhwMjfA4NQn1kXumhZQcfZKmomchSp+CExPhxeiDpuV/CZPSrHtJeWctFRbinlGFMFWUrKkKnq54vWs5zt57gtsv34IChcbVfNCyZhWNssrLiI5PxK/LN+HYjQeQfCv30Ofx0K9eNSzo0UZOxHzxMDfMsINHQAxPOaOqSGQ0XzAR8AtciupACTCDcsp0KZGuNI0pM+hLafwZGoJnEZGoZm8HZUCccESYyuqQygkfVOa5LQhcyDmHJsIJU5rN6cPbYOPUSm4ej71rG4R83oEzR7arXZTSdiqX8cR6cQrCeSLYU7kjCR4xSbA1N4N9AcdQgeERmeMkeZCJFhMeH/6h4dB1yBhp4a/D8Euvzth//irCo2PhZGuN3m2asv9qIgcvXMeMFRsRGBmVua68uyuWThmNprUUL+BbmJkiIiRvF14kJYZ5AScTX374jGPX7uB32gFNabNsk2lT4IBJCMKiTbtx+t+/oCwkUik7ZiMZWfIgjxHEYs4ppUto3t2QFqDtmUXaRFREEAwMraFnIP+Lx9jMCwwjRXRkmE6U7ikqT6qwAdBk5q2XjzeuMonwY7JbAYnDZIM0AgY8PjpmcSblh4WBAcK+iQnyINkI5nqF70D2I2TvWxWd2bKWrxVEUOTxaHSo74tFowZg4aj+aF27mkYKUsKUVLSZMA+Xbj3BcMoWO3ilsIXnid6MJfbeeoJ+/+5hBwyKpH4ZD9gYG+GkVL4Th6w35/NRv1zFAuVIXQ0IRBuYyRWFalHGsKMFOPflK5QFcZZVtLbGPSZJ7uNxjBhvmBRUtSuckM6FnHNoItpcyidz6OhiKR9xasTFhLPjJHlQNB8Gxh5s5pS60IXSPUKv1k1hZmSEDUwkRDkmG8jf+kuUEMN6dACfXzAXvrV5hhgQyojy/A5JkUpgbZ57ElFXcXWwxaRBvfDPxJH47aceGitI7T1zGQNnLIJLVCr+4bliL680/qSdIQiMRqdxM3AtS2i9oujZpikeMEnsuDonEYyIHYv0aiu/C3ROjly6CXOegM3zzAmPotCWMWOD92PiE6EsalUsjxhxOt4hVe7jd6VCWJuawMtNs5vdcBQOzbsj0iK4bCnlY25lh7TUGIjS5c8ApAgD2H/dnG0Uts+ilu5pO4MqVEA1B3vMlARjiSQUl6UJOCKNwTgmAPeQhIWNGxSqW1770p74yKTiZQ6RSzaguopEdChTcJFLE4WprOKULGtJ29l+5ipe+wdhAZzQgbaAFcWHHSVAL9oK0ylHXHv3GedfFj4L7EfoC/iY3KEJLjAJ2CKJZK8PQjwjwTZJJM4w8RhRvSpbtpkfqRIx60CyzsMITARY8liSSLm5Uv0r+uAJk4QL0vhs69MZKdZII8Dn0ehSRv7NIgeHtsEJU5oHj8eDiall5jgpJ2RCLy05GFbWynGMlpTSPYKxoQG2LvgDT6kUjEMgDklj2DHUcmkYpkuDUaOiNyYN7lXg7ZV1d0FlL0+cQBzrus3JSWkc+Hw+OjdVriuco3Cki0T4Y9l6NKJNMZ12ZMv0TSkeqtLGmEc5wxsGmLJ0XWbDAUXxU8eWcLGzwWyE4Jk0id0+WV5IkzEbobC3scTgzq0LtK34pCRYUvxMN1JObL6NrRKTlZdl06JOdTZ4/z9EsePArJD3dI5KwJDu7Utclpiuw4lSRYRzS6mGhi17sW1NQ74cyvWYVJKOUP/D8K3TBHb26s+EIHlSRS3d0wTIDf+61i0woWYNfDJm2MHUdiYa5VztsatDW7TwcC/U9hq5uKCarS0WSonAFc/ejJPB1VNpEmYwwdDX42NAhfJKeS+qFqYI0YwAy648QMOBE1Cz71j88s96PPugfUHWWw+dRl3KJFu4pIzqtDHK0obYfUfxs83DG9fCnC4tcJpOwGDpF/zMfMUgyWd2UD6uelUMrOBToO2YCASw1NPHK0Z+CLaQkeALkwY3M+WWX7cv5Yne5cpilTQck6SB2C+NZgW3YdKveEIlY0mzJjDXV6xTsCTlSXEh55oHJ0xpHm26DEBkyFmkp+X+/Y0OvY5kYTBadx6glmPTNdo2qI2rW1egdpOa2E3FsmOoDzb6mD1mME6tXVSormUke2je2CF4waRgMROGQCaNXR/LiLFDEoX9TAwmDuwJKx1xShEB5fL9J+j/x5+o3WcUWo+YjE1HTiMpRb5TRlO5cOcRIuMT0IeyypUfRUSeHrDAy09f8VLBY0NzE2OcW/8PbEs5Y6Y0GAPwlV2mS4Ng6W7PPmZpVrBrpYybC4Ikqey1Jg8ytjIxMChwKWpRIFUEe5fMRrwxHyMYf6yVhOOANAZzpSGYIQ1C/RqVMX1Yf6Xtn0M9UIyi5Vo1k5CQAHNzczx4GQATU+XedMgCz7lsKeWy87/Z2LfxTzh59oSjZzfoG9ohIcYPge+3ICnhLXYeOI0q1XwVUrpXHJdUcUQpmcOmuOV7MhGmsOV7eZVB8WmKLUUqKglp6Zhx4yauBgZBAIq1/qYyUnhbWODvZo1R2sICykSWw6PssqdHX4LQY9UOpKaJUAvGMALNCg+RUhH+HNEXk/p2hqYjuwa9/7cObVKN0YeWb41fJQlDhIsxrk5XTkhqbFIyjjzyQ1h8IvSCItCuUhW2FLQwLH/0GHtevsZS2hVuWcQ18nW3SRqJU4jHpd49YGuk3Fpssr8rAYHY9/ot3kRHQ4/mobG7Kwb4lEdpS4tCXcfqLt0jolRBgv1VKUqpMuQ8IVEI9+oNEB8fDzMlC5qaMIYKv3oYZkVswEDCzwnaFn5OIOHnBF0JP48IC8LwHnUgEhvAtewwWNrVhUQsRHjAWQR92Iq6Tdriz1UHlRbAnF/pnipdUrLSPWU4pXIilUqRLhLDQL94+zp86QYmLFqNqIRENvA8VSqFQMDH74N6Ycbw/hoZAVCUMlPS2W3P2Svw5BminFSPzUB6Ik2Gp5M9zq7/hy3d0wY2Hj6N3/5ag+N8+Q1MohgRBku+4OiKeWhTv5ZSxhy3n/rhxuMXYMCgYfVKaFi9cqF+v2MTElGqTT80EhthHG2X7bWhTDp+RzD6dW+L5ZNHQ9mEREZj3YETOHj2KuvgKu3ihKE92qN/+xYQ8LlYbG0hQZgE+6bd8x0/cZ9oMeA68amGAaPmQk/PAAe2LkbIl4OZ653dSmPhssMKEaQ0AUV1NFOEIEUoSKlUfpjp62F1y+b4Gh+PO8EhEEulqGhjwwZMq2IQrIrw88TUNPReswvO6TRm0J4wpzLOGykh20NFY+aGvfASJaPLwL7QRLKWHJJr0M7MFIE/mJ0MpERwV+LsrKWxEYY2rpWRjVazdpG2MaxyJdzwD8KUhCC0hzmq0UZIYCQ4zyTgMZOEqbVrKV2QIpBrvLm7G7twcJQUuPBzzcHOwQVrdl3Bn3/8jLePZmWu5/EEaNdtMMZNX6YWQUpdqEKQIhCxqLiCFKF7i0bo0KgOztx6gK/BYWzWVIfGdXXGIUVYvusw9p29ygZrN4EpKF7G9RhEpWNOeCj6Tp6HmztWacV1amdlAQkYBDPpbBh9TgKYDMHe3lo5LiNyjkhXvOJ0xiOOqhVTx2LU/OUIY8Rox5ix5XwvmGScohJga2+N6cNyN59SBiQ3bN6Yn9mFQ/fhRCkFwHXiUy7kj2zvodPRqe+veHznHJKE8XBy9UKjelVhYaz5X1IcgIe5ObuoA2ULUwcfvEBccgqW8L4LUux+KQoDaGs8Y1Kw7s5LtCzvmfmYJjhOcopRMvrWr4Z5Ry7KHVSRfLA30hRMratcB0Rxyy5J9tm2Dm2w5slTnPjwCfvFMex6b0tLLKvqi1aeHgo6Ug4Ojh8JU9oIce8QxxRx8uiCY8rVoyzW77+N96+f4sObZ9DT04dvvRawVGOWlC4GyysLkpvTtZluZkeR7mlrdx9BS8osW6c3ggulh9FSG8x5+wH3XrxB3SoFK+FXJ63r1YSViQkOJsVgPG2fTUgjE5WHEQsfD1dU9dbsPMlBnVrDxsIcizbuxl9vP7DrDPX00K99C8z5ZRBsC+H05uAoKJwoVUw4t5TqMDQyQYMWPTKFQEXmeqmzdK+kQmzGqpr5UqYwdfPdF/jQRmwYeE7I+2vEmGDL+y+gTCzYn0kZVFZBSFUCVc4Q9ryceQPr18DOm48xPSoYPzFWqEeZQgwG15gE7EYM6pZ2Q7sq3ko7TkWVoJK8phl162BiTV+ECpNgwOfB0dhYK2Zb5ZWgqhNNy5Pi0B5hipRMaWMZn0yY0iXK+lRjF01BHaV7uoIseUXbvs9y8iEgCGGxcfiVdpH7eDXKCGY8Aa4/eqYVohRxx/05fihGL1gJiRToTlvCGQJ8RBqbA/aSScGRCSO04nNr36gOuwSGRSIpJQUu9rYwMeLucziUBydKKQjOLaU6uK6H2oswPR27X7/FkXfvEZKUBHM9PbT3Ko1BFX3gZGKilcIUCW//kZzJAwUmiwiXVQxStkBVUCEqK2aGBjgx8Wf8uvM4Vrz6gBUIZ9eTfLHuvpXwT98O4POU06FSkZloMgz5fJSyUI9LT1GoO09KU9x96sqT4ii5whRSdMMtxaG60j1lcuvJS6zcdRiX7j2GWCJBjfJl8EvfLujVqolWCB05kUozxDU6j0Mnq+ksz9MGfu7Sli3fnLVqM67G+2eud7e3xYEpk9G6fk1oE9qS58Wh/XCilALdUpwwpTq47ofaR3xaGn4+fQ7+8QloBBN0oe0QKhLh1Jv3OP3xE7a0a4OySuzmoSxhqnZpV5x59obtVELq7nNyB0LU8nSRG0iaUyTKKSLlJwzk9/yi5pTZm5ti/9gB+BIZgwefA9mw+/pl3OFoobyAZ2UIUhwcHOqHE6Y41BlwritsOXoWYxeuhAfPEH0Yc+iDwv23QRg8czErVq2aOk7rhCkvNydYm5rgtlCIirzc5Q9+SEGcRIR6VStAmyDlb33bNsPVB88QGRvHuowa1aisE8H0HBzKghOlFARXxqe9Lqnilu4VFyIsKCLknA2G1uAb+n8ePGQ7qi2jXeGepSNaT8YKM8RBmHL1Oo5266z0QZWihal+daph0YkrWCkOx1Q4woDKGHQQZ9RJJg4vpMnY1rxDgbaV33UgT4RSVEC+PDxtrdhF2XCClOaW7nFwKArOMcWhDnShdO9LUCjGLVqFdpQ5RsIO9DdrUUdY4gIdj1VHzqBZ7epalz1F8rJG9OqEJVv2wVdqjBr0946fMYwY6xCF8u6uaOxbBdqGnkCgda4oDg51wkm2CoYrLSt5LimSJ8WRv0vqzKcv6EZZZhOkCKYUD0Nhi4/x8XgcnlEqpmxk4l1xA7UJFsaG2DGqL17RaRjKfMV/knDskERhPAKxQRqJsS3qoVM1xWQhEAEq56LtcIKUZpfukRJTTSrd49Du0iltFgi4cG7tPYfaXrq36egZGNM8DKFtQeeYuGtFm6Mizxjr9h2HNjJ1aF80r1sdc6TBmMEEY5ckCsslYRjOfEWauSH2LZmtdQ4wDg6OwsM5pRQI55ZSLooON1ckXMj5j/kYG4d0qRS1eN9nwbJSmTJkHUavoqLh6+AAVaBIx1TT8qVxe/YYbLr+AGefvkGaWISqHu5Y0qQ2+5iuEBQTj+23HuH+xwC2pK9x+VIYUK86bEzlf675wQlSHIWFy5PS/o582uiY0rWOfOqEK90rPI/93qGK1AD6PPlegpqMIQ6+yeiSpo2OooNL5+LQpRvYcvg0bgQEw9zUHNPadcWQrm11ptObRCLB2VsPsOfMZURGx8LVyR4DO7ZiXWCc6MbBwYlSCidDNElATKrysldKIspyoKm7dK+koPdtIJUCqdzH08BAzDDQo1X7WShSmCJlbgt6tGEXXeTY41cYufUQ+AyFqowhSLP3Re+/YNnZG9g7uj/ql/Uo1PY4QerHcKV7HLoIJ0yVXNThktJmZ15W9PX1EM22TJFPMiOFvkB7fQZ8Pg992jRlF10kMSkZ3SbMxq1nfijDM4STlI/bfp+w9+wVdGvWANsWTIWAr72fHweHIuDK95QEV8an+HOpqS4pjvwpZ2UFG30DXJTKFxdvMImQgEF9F9VnYimylE9XeR0cjhFbDqEuY4xtlCem85wwi+eErbQnSon46Lt2N8LjEwu8PU6Q0p7SPQ4ORaPNpXwylw9Xyle886dKtL10j9CmQS08QzIiGVGux8iE3nU6CW0bcWXWmsqYBSvx9MVbLKBdsJxyxWSeI9bCFVNoR5y4ehtz/9uh7kPk4FA7nCilBGTiCSdMKf6ccmgnAh4PAytVwHkmHqekcZAwTGYY+GNpEjYxkWjp7g43M/U4DDlh6sesu3oP5uDhN8oBRt+C3AnmFB9TKUeki8TYeftJgc41J0hpl0uKy5PiUAacMFWy4ES84tG/XXNYm5liIcIQlkWYEjISLGPCEMWIMLZv12J/ThyKJyAsAocv3cAgWKEK/f1mhpTsNaJN0QUW2HDwBITJKdzp5yjRcKKUkuBEFMWgTGFPEaV7XMh5wRlcqQJ6lfPGOmkEGwg+TxKMMUwAG25Zwc4W8xvVhzrRBWEqNikZp569wZFHfvgUEa2w7V7x+4CGjAn4csJGSVB9TRjhkl/+eRacIKU9LilNhMuT0i04YapkoWqXlDY68fLCzMQYJ9YuQoKZPoZLvmCaNAhzJMEYxHzBfToF2xdOQxVv3cmvVAdkkvSB31vsP38VF+48Qlo6CSkoPtcePIOUYdCEkj/p2ow2Q2JKKh76vVXI/jg4tBWugFUFogqXL1X0c6cNAh8Xcl4wSMeYWfXqoId3GRx5/wHBiUJUNTDAjNKeqOvklKujjDooTMYUCf2+4PceqSIRKjg7oGFZD9C0enT+NJEYs4+cx/abj5EmkWSub1auNFb+1BkuVubF2r5YIoUe8v589EAjJct+5cEJUtoFV7rHoQq4jCndR50uKV0o3ZNBRCe/Y1tx4Pw1XLj7CGKJBB0reGNQp9ZwsLGCtoV+X7z3GO/9g2BqZIh2DevA3lp93YRvPfXD+IUr8fprYOY6GzMzzBz1E0b27FisbZPPiYye8hpDkfGT7HkcHCUZTpRSQTc+Tpgq3jnUZZIe3gNtqr4vYnVQ3toaM+paQ1PJT5hKSRdh0t5T2Hf/OSgGEFAUUhkpvGyssG5od9TwcFH57N6wzQdx4cV79KIs0YJnBgPQeMgkYdf7QLT7ZzOuTB9Z5A55hBqlXPHg1VcMYJhcXWJEjBSPqWT0Le2T5+s5QargcKV7HCUNTpjSfbiOewo6j0aGbEc6smgrVx88xcj/LUVgZBQMaR7SpBLweGswvEcHLJ4wgg09VyXEHdVhzDSUlgown3ZGOcoQ4RDhRGIsJvy9Filp6ZgwoHuRt+9bwZuNqH/ACFGPMs31+D1GyEZcVPH2KuY74eDQbrjyPSXD5UsVDWXncXFd9zjyE6bIQsQUIvoQJ5KM4ZsP4fD9FxhO2WAvrzQO0qWxmOcKXkwKui7fjg9hUXK3GRqXgPnHL6HajOUoPXERWv21AbvvPIWomLNjdz7649Tzt5hI2aMPbQ0bSgATioemtBkWUc6IThBi/dV7xdrHyKa18UWSioNMLHs+ZBBL+kZpJBIZCYY0qin3tZwgVXi40j35pXscuos2O1q48HPNc0npUumetiMSiyGVZnRefvjqHbqOnwXr6BQs57mx46fdvNLoy1hi/YETmPD3GrnbIOOOk9fvosPoaXBt3hNebfph/F9r8C6Ls6mozFq1GS5SPv6EE6rRxjCkaHhQ+viV54BOlAXmr9uOeGFSkbdfuWwp1Kvsg61UTK6g+q9MGg5ScejeohHsrCyK/V44OLQZzimlQscUh26V7XHoNh9jY7Hl9SecP30ZaVIpnMxN0apSWZx+8RaTaQc0pr/nA1SAIebDGWPFAVh+/ib+HZQ9cNQvKAxdlm9Damo6GjEmsKWM8TowDuN2HsORhy+xZ3S/Irdz3nfvGZx5+qgPk1yP2VECNJGaYPftJ5jRqTmKSqNypfBH+yZYfPoa7vCEqCc1hohhcJNOQjCThuX9O6KMg02u13GClHa6pDS1dC+9dDV1HwKHkoUpIibEu1bTXidQSigixI7qPhyNQl0uKW0WOrUdsViCTUdOY/3+E3gbEAQeTaN1XV9ExsbBieFjDuUIwbemKSSXshdlBUMphQ1Hz+H3gb3g6eKYTZAav3gNNh4+jfI8I7RmDJHMSHHo2AVsP3EeB5bMQat6vkUOIb/x9CV+px2gJyd+oQdthdPp8Th6+SYGd25T5POxZf4UtBj2O0ZHB6CR1ATOlACfkIbbjBDlPN2wdPLoIm+bg0NX4EQpFZEhsHD5UoU7Xxwc6uFhaBh+uXAJ5gyNnrCELS3A64QU7Ln1hM0FqIbcFyjpSteGMcX+hy+xakAn8HkZFnSJVIpB6/bBIpXBPMoD5vR3azpp8Tzv3Rf8c+Y6ZnYummgUFpcIVykfFE/+wN+d0sf5xEgUl6kdmqJmKVdsuHIPxz4GgEdTaOLjhc3N6qJWKddCCVKJ6ek48/kLAhMSYaavhzaeHmrrvKhpaIpLiuu6x6EOtFmYyhRgOGGKheu4V3IFqX5/zMeZm/dRhzLBeNoeSZDiyr2X+CxJQQvKFAJebgGoJWWOXbxYHLx4HVN+7pO5ft/ZK6wg9Sttj1aUOWTRTD8x1vhLEsbu6/2pXbAyz10alx/h0bGZ4yR5WFF8mPL4CIuKQXFwd3LA3T3/Yv3Bk9h94gJuxyXAxc4a87v2wdCubWFqXPCbHiLS3X/5BudvP0S6WIxq5cqgU5O60BMIinWMHBzqhhOlVAyXL5X/+VG2IEVK9xQB6bzHhZwXjGSRCMliMSz09cHPJwyciDg3g4Jx9stXJKalwd3cDN3LloGXpWqyt0RSKaZcvYGyjD5mU04w+Dab1xxmaM9YYIokAHuZGIyEXa7XOkCPDRpPEYlh+k2UuvL6Ez5Hx2IJzxXmVPashKq0EVpLzLD1xkNMbte4SG4pBwtTXKUD2YFKzrwngj+TBnvT3C6qotDcx4td8uNHgtTh9x/w1937SJdIYU8LEMeIserxU3TxKo059euy2QolEU1xSWkiXOleyUJXhClCSXdNqcMlRa4dXXJJkVDwqLgEGOjrwdwk/2zIoPBIbDt+Dk/ffoS+ngDtGtRmy8MMDeQLL4pm89EzOH3jPmbRjqhJfx97dGQssIoKx1UmAQMZW1bwyQoZa1lTAkTHZY/vWLv3GGrwTDIEqSzoUTTGwQ4/p33BrtMX8Wu/boU+Vlm4OhknlZIjTEUzYiRIxHC0LX4Oqo2FOWYMH8AuRSUiJg59J8/DnRevYcETwJDiYZk4FfaWFti1eCYaVKtY7OPk4FAXXKaUCuHypdSbI5UVA72SeeOrap6GR+CX85dQe+ceNNl7AI1378eSB4+QkCa/1W5caioGnDqDsZeu4M2XYKQGx+HU6w/ocvQElj96nC3PSFlcCwhEZGoKRlC2mYKUDDJo6URZ4jKTwIab5+QDkwoLQwMY632fsXr4JRBWPAG8YSB3f/VoE8Qkp+BLEWfi+tSpimBJGm4xwlyPRTAiXKOE6N+gOlTFjwSpS1/9MefWHTSUGmMLzxMbaA/spEthNG2H0x8/48+791GS0QSXFCnd00SXFFe6V/LQ5lygkp4zRd43F25ePFJS07Bw4254te0PjzZ94dC0O1oOn4QLdx7l+ZqdJy+gfKdBWLZ5HyJu+eH9tScYPncpqnQfivcKyF8qCBsOnEBd2iSbIEXgURSG07bggcJFJj7X6+IZCcLEaXBztMsmyD1++wF1GPmz1ZYUHz6UIescKgpuDnZoVK0SjlFxSJczpjssjYGBvgBdmjXQCAdal3Ez8PbVR8ymnbADHtgIN6zlucMuIZ19TFWfMQeHMuCcUiqG68gnHy5HSve4GhCACZevwY3Swy+0HazBh584BQdevcGtwCBs79AW5vrZZ6YmX72BgOg4/MVzQUUqYxBCsouOM7HY/MIPLiam6FmurFKP+210DGxoARt0KQ9f2hgHJDFsdxZ3fH9OOCPCRSoBg+vXBp3FDUYGYmKGAUNlus6zQd4fIT8HWV7U83JHx6rlsez5WwRJ09GCMoMhaNxnkrAbMbA2M2GDyuVBRL67HwOw++4TBEXHw8bMGL1qVUGLCl5sBkRhyC8/iuzr3yfPUJ0ywjjaPtPVpU/RaEdZQAQGm99/wKiqleFoohhnl7bAuaQ4OHSnI58MIsoIUxhWoClJjil1CnHaLGRmJTUtHR3HTsejl2/QlDFFDdoJQkhw8eVndB4/E2um/Yqh3dple82tp34YOX85WsIMw2hbGH37Dg/ipWNRdBg6jpmO50c2s44rZQonr78GsqV28iBNWMrDAO+kqblsEQek0aB4NHq3aZq5jowTaDKGYvvXyUdEITMuoSj8OX4YWg2fhJmSEPRhLNnuexEQ4bg0FheZBPz1y/A8HWox8YnYeeoCrtx/ygpodar44OcubeFslztjs7icvnkPT99/wj88V5SnDTPXk9LDOYwTRokDsHL3EaydMV7h++bgUAWcU0oNcI4p9QlSiird4/gxKWIxZly/hZowxnLKDe1oC9SmTTCUZ4ultCvCEoRY++RZLjHobmgofqHsMgUpgoCi2LDJRpQptrx4yXZ8UyZ6PJp1QREhSR5CJqNb3m5JNN4wKQhi0nFCGospTBBsLEwxvlX2GbUm5UsjQSrGE0Z+t4NrTCJcLczgaWtVpOMlg7aNQ3rg58a1cJiOx8+SL+gj+YTl0jBU8nbF2SlDYS1nQCWWSDBiy2G0X7YF1++/gfRDJJ49+Yg+/+5G1xXbIUxNK/AxFCTQ3D8hAe/j4tCespBbZkjyJPgUhUv+ASiJaIpLioNDU5CVYGmz0FBSHVPqdEnpQune6r1H8eDFG8ynnDGWZ886j1rS5lgMZ3Y89dvfaxEaFZ3tNSt3HoIHbYCxtB2bcSnDhdLDVDggICKSDexWJjRNsRNsREDLi0RI8YJKwTlpHIKZdLxkkvGXNBTHmTjMGzeELXP7vj0aTXyr4DqVJNcpH8qk460kGc1qFV24rlnBG6f+XQS422C2NBi9JB8xVuKPZ2bAiiljML5/d7mvu/P8FSp0HoSZKzch+t4bJD18zzrUKnQejGNXMsZEiuTI5Zvw4hmiPPVdkJJBXP3NpSY4fOG6wvfLwaEqOKeUmuAcU+pzSHGle8rnAsmDEokwjOfMCg1ZcaX00R7mOP7hI36rWQOG/Iw/Q9cDg2BM8VCXku+SaU6ZYY4wmBU3PM2zZwsoksaurlj95BnuMkI0pHIHZ15gEmBjYICPEGNyaoZVmk9T6FjVBwt7tYWNaXYBiISA+7o7Y21gBP7HOGUGahJx7QITjytMAha1bFNoZ1JWSBbV4t7tMK1DU9z68BXpYgmqujmilF3eOQiLTl3F0cd+bNeZxpQpO5gkg75ndDIWfQrEb7tPYuPQHtle8zIwFNfffYZEyqB2aTfULuWKwLO38xWkCEkiMfuvdY4cCRlkEE0+/yRR9pbJuo6muaQ0rXSP5ElxpXslF84xpV1wZXvFh3wPbzp4Co1hwpamZYVM6AyENa4widhx4gL+GNI38zXn7zzET4wl+12eE1dKD+V4Rjh7+wH6tit6J978ICJS2wa1cfn2U3RmLFmneFY+M2n4jDRUK+uFf99/glSaITR5Othj48gRGNChZa5tjh/QA50fzsR2Kgr9aRt2olKW9/Q3wmFnaYGerRoX67jrV62IR/vX49Hr9/gSHApLUxM09q2SZ3g4yXbq9ussuKUBU2hPtoyQkMRIsEYagYHTF+HurrWo4OWR+ZqklFQcv3oLweFRsLWyQOem9WFpVvBw9sSkZFhJaSAPUxgZWyWmpOSZL8rBoelwopQaKenCFFeyp7t8jIuDI60HB0r+jGUVygj7xTEIT0qCh7k5G4T+OCwMfCaj3E0espk/kSR33b8iKWdthQbOTvg3JBzGUhrVKCP2C564pw5JY3CPEWJBzQZoX9oTb6JjkBIdDg8TY1Tr2kzu9shrt4/qg67Lt2FMhD8bbm7D8PGGTkOwNA2DG9TAiCbyy+sKi4WxITpULZ/v85LS0rHp6n10hgWa0mbZjrUaZYxBUmusf+yHOV1bwsXKHOHxiRi26SBuffSHIUWzz0uWSlDOzBT/+FaGp0f+4efOJsasQPmKSYEXlTtfK5BJQ6xUDPcS2IVPE1xSHByaCidMaQfqdoNps6MuKylpaayrqSftkGcJXBkYwO/hI6RXdmHXPfgcCJFEklmyJw8jhkL6t8khZfLbwJ5oees+ViIcw2EL028NXj4zqfgL4fBydsS1rcsRGRuPz4EhMDE2RJWypbPFHmSlVT1f/DVhOKau2IgrSEIViT6SKQaPmSRW1Dm5egGMDORndhYGMq4hrimy5AcJkk9JTcU0yhNmWRrYkIm1iXDAcPjjvwPHsWZ6RindlqNnMW35BlY0MuUJkCQV47fFazFteH9MHty7QCJSGTcX3L37FCJGCkGOvFOCH1LYc8sJUhzaCidKqZmSKkxxgpRybmxJGVV+jhVVQNxPiYyELYHL6ZQixH+zdrPPS0/H0DPn8TYmBkRu+sikyhUtHkiTYMznw7UQM0tFZXGTRvj14hXMjgiGC60HGykfn5DGlu6Nq14VncuUZp9XydYGsLVh3S6yEjZ5AoOThRmuzfgFxx6/wuGHLxCXnIqGDp4Y1NCXdRupehDx+EsQGzbfnCc/56QpZYb/pBG4/vYzutesyJbzRUTEYTrtiNqUCZuNRRxV64WRGHb3CQ45uMAyn0GhhYEBWnq44+jXYDRgTLM5piQMg+3SaFjq6aO5uxtKCprkktLUgHMODl0Splh0sDOfTJBSd7i5LpTu6fEFrHM6Lo8SOOKEiYcYxsZGoI3MsOfWY/yy6SD0QeG+NAltaItcryFjl1dIQefyZZR+/HWr+GDT3MkYNXcpbkm/oBwMkQwpPkoyRJOTaxexDiSSu1TQ7CVSQteyTg1sPHIGT1+9g4G+PhY2rosBHVoUym2kKM7feoCajBHM6Ny2JeLkaigxxvmbD9if9569gjELV7IRBX14DrCnBIilxTgqjsWcf7eBx+Ph94E9893nkK5tsWrPERyiY9GXyu6CJ1EStxkhFvbMcM5xcGgjnCilAZQ0YUpdghTJk+JK91RDMzc3/Pv0OW4ziWhMmeUaUJ1j4lHR2hr2xsaYc+s2AmLj2aypRdJQrJdEYC7PJVsmAhGqTiMO3cp6Z5b7KRMSwL6tfRvcCwnFuS9fIUxPR00zM3QrW0auKCYTAmXilDxhylBPgL51q7KLuhFLM9xm+nnECpLBLbm1IDOvRx754U1YJFbz3OGZJfy9OmWMBYweRqb648Db9xhZtXK++51Y0xcDwsLxW2oAOlAWKE8ZIIIR4zQTx4p+yxs2gV4xAku1Ec4l9ePSPQ4OXRKmdDkAXZ2ClK64pAh8Pg9tK3nhop8/OjIWuSb2iLgUIElDpxoV8TUyBmM2H0JzmMKbMsBaJgK3pIloQJtmm/RZL40AQwN93cyR/ugm9HwbKvU99GnTlM152n7iPJ6/+8SGq/+vQS10alIPgiKO4XxKe2D55NHQBMRiMUx+EMtsAAoisZgNP5+7divqUSb4lbbLnIAk5X5DeLYQSRgs3rQbo3p2hLHhjyf2vD1cMWN4fyzYuJsdLzWHGYxA4wEjxDkqEbUrlceI7h0U/l45OFQFJ0ppCN8FGt0WpnTFIaX36Sl4+rnDBjm+l8A1dnHG2uBQCKQU664hZXkJjAS7pFF4ziRjVdXaiE9Lw6mPn9GbskIZ2hBTKEfMlgRjlOQLWlDmsKX4bLnXLUaI8jZWGFdDdTciZPBQ19mJXQoKEafyc01pAhVdHNgcrPuMEJ0py1yPP2CS2F431T2c8b8jF9iSw6yClAwbSoAGMMHpj58KJEo5mhhjd6f2WPX4KfZ//oK0b6WYNe3tsbF6FdRy1J0bNG1zSWkqXJ4UR1Y4YUrz0JQcKV1wSRHBiDCxc0u0evkf/kEoWwJHvmvJhN5zJgXLEY6qbk5oUaks/nfoPCuAjKTtoAcKL6UpWCwNxSUmATUpYyRBiktMPMIhxsbhfeDo6ARpckLmfpQpTtlZWbClabqIb6Xy2Pvui9xSOvI53aNT4FuxMh6+egf/8EiM5sl3xHehLXEy5QvO3X6A7i0a5bvfmSN+gqezI5Zu3Y8F/hmZplYmxhjbrRumD++v1M6KHBzKhhOlNIwMsSZDuNE1cUpXBCmOgvF308b4/co1LAwOgTXNhxXDx1dpGiiawuy6ddDM3Q2PwsKQJpWiDi8jHJy04l3Bc8MxaSzOMHFIYqRspmNDV1f83bSRSlxSxaUgril1Y2dmgi7VK+LAkzeoxhjBLYvgRMJDtyIKNT1cUNnVEeEhkXBh8j7vxIr+LC2pwPt2MDbGwkYNMLFmDWx76Yfzn7/iYXg4/C5cRitPDwypVBGlLXOXH/wIdrAeGYkHoWHs/1ezt0NNBweNz1bQpGuDK93j0BZ0UZgiaKNrSt05UrqATCAikHI8Qs3SZtgxdgCGr9uLoelf4ckzYDvahUnTUcPDBQcmDmYzmB59DEBVxhAG3/KYSOMS8p1+WhqH9UwEaOJ6pilcnDkaNUu7ZdtHzn0r2z2lSwzv3h7rDpzAZkRhBG0LOstY4ygTiy/SFKzu0xmxCYnsOvs8brftwGdd6bLnFYT+7VugX7vmOHHtDjYdPo3rj55h6Y6DOHvjPkb27oQhXdqybrvCEJcoxJFLNxESGQU7K0t0a9EwWxdEDg5VoPl3eCUQXSznU7cgxZXuqR5jgQD/tWoBv6gonP/iz3ZV62xmhk5epWH1zabM+zbDlM76cjJwovQwmmeP0bBHqlSC3tLPqO/ipBWClDa5pki3vldBYZgQHoj6MIEXpc+2Z75GCWFuYoT/lXJnj93Z2BDvhfF5dnR5x6TC1VR+x8S8IMH2JLPrTXQ0GsEUPWl7Vgy7+DmA7dz4X+sW8HWQH/Kak1BhEn6/chUvoqLZQFVyhKsZCcqYm2N5i6ZskL6mQa4LTbkeNNUlxZXucZQUYYqgjeV8mpIjpc2lezJRKKtQJKNjjQp4v2om9t95Cr/AUBgI+GhfvQIaliuV+V3M59EQZhk/EXGEuMxb0Obsd/YuaTQuGaRkClI5ke1XVe4pXaGcpxtWTh2LXxetwQukopHUmJWX7lLJeCdNxpSf+6B57ep489mfff57JpV1u+XkPVLZT4+4nwrDoYs38POsxWzmaX/GCiY0jScBsWx4+qW7j7Bn8awCC1Nr9h7D7DVbkC4SwZInQJxEhClL12HykD6YPqy/xk/ucegORe9BXgBiYmLQv39/mJmZwcLCAkOHDoVQKPzha5o0acL+AmRdRo0ahZKGTLwhYo5M0NFW1C1IlTQ0qSyI/P5WsrXFpFq+mFO/LgZXqpApSBF8rK1hrqeHK1L51/gtdm6QQb1ClNBpmjAlc04RgUcmUGkCViZGODdlGKZ3aY4Aax520rF4bibFL63qY3edanAxNmKPvUflyqzD7SaT+2+3H5OMJ0wSupUrW6h9r336DB+iY/A37YrfeA5oRZujL22Nfyl3lGH0MenKdTbPqiDi1rCz5xEWnYA5tBN206XYZSHtgtTEFAw5cx6xqanQJDTp91PTXVIluXSPGz+VjHItGTJhR1ucR5oiSGnrtUAEoB8JUjLMDA0wvHldrBzcDYv7d0Kj8qWziQQtKnvjGZIRy+TuqkeK42/QQrSsWi7f4yHHIDuOrMfGkTfDurXHxY3/oHqjGjihn4yDgkS4VPfGkeVzMXf0YPY55Uu5o1YFb+yn4tgOzlkhWV+7mRi42dmiiW+VAp/qiJg4jPjfEjSEMVYxruhBW7Hh9qQRzSzaEWdu3semI6cLtK2tx85i8rJ1aC4ywlbaE9vgge10KXSWmOHPDbuwZPsB7hLg0A1RighSr169wsWLF3Hq1CncuHEDI0aMyPd1w4cPR2hoaOby999/oyRCRJys4pQ2wglSqkVT3BcFRZ/PQ/8K5XGKicNlaQKkDJNN8NjMRKGZqyvczLTbMZhTnNIUyIB3fKsGeDR/AkJXz8KZRnUwUKAPOxfPzOOt5+SE/7d3J+BRlWf7wO+ZyQ4hIRCWsATCqgKCIAiyKQi4ILTqp3+/WlE/bK3aqliFurR8anGrImhFqxVx/6wb1aoIIi4FBBShCsi+B2TLSraZ87/eE0+cDJN9zjnv+577d10jEEJycjI5vuee53nec7t2wcM/DqHfaBw3B88/HzyEP4X2YVDbtpjYLafen7O0Iog3N23GuUhDj4hdFpN8flzry8ShkhIs+XFeQm3e3boNuwsKMNPfAaf7m5uvEosFez9/Cu7xdUBeSak5hF02qv2ckvO4fqp/GKFypUw4EfCIhwh8ZA6nZAqkVPzeh4dRtQVS9fGLEYPQPCkRs4zcasHUcSOEuaEDOBgqx2/G1f//NwynGubM/n3wyoN34cCnb+HQ5+/g3b/OwrnDh1R7n0duux7744L4PfaYL8DuMErNYfQzjL1Yh+OY84ffmjvw1dcL736EUEUQ1/ramLNaw4l10Bm+5pj36jt1fpyKiiDueXIBRvtS8etAG2T8uCNymi+AXwZaY6IvHQ/9/RUUHZfrhT3Sl239MBs2bMAHH3yAVatWYdCgQebb5s6di/POOw8PP/wwsrJqrnxISUlBu3q2bniBiu18MoVRonVPZqJ9xp964rBpr/jVqf2wJ78Aj27dhv/zH0G3UCJyfeX4PlSC/pmtce9IfW7gowVTDQ0oREn+97mHcLT4ODplpKFDy6a1p0WGZNYxWkTIM2vUCOSkp+Plbzfgn2XHzLc3i4vDpb16m8Pn4xuwoNpXVIiC8nKc/uMcsUhdfIlo64/HhiOHMSGnS60f6/2t281dADv5TnyVXJTKD0MzfLB1W72GsHutbc+69shYJSVa97xcJcX1U+OCKZXb+FSZNSVTIKVilVR9qqMaWvH8j1uvxsUP/x1XlWxHf6SYA8+/8R1Hmc/AU1MvxYCuHRv8cXVu68srLDLb6hLi49G3R9dG7wZYXwNP7oklzz6COx57Bo+sWlv19sEn98J7v70aIwfWv0pK+GbTFvT2J6OFL/q663SkYPbuvSgtK0NiQs0/G8u/+Rb7jxzF7wPRWzsn+dPxz+M78NHy1Zh8tjzrFtKXbT+Jy5cvN1v2rEBKGDt2rDmYb+XKlfjZz35W47996aWX8OKLL5rB1MSJE3HXXXeZQVU0paWl5sOSn69mRVFDducTZA6nZAqkLEkJcm4zL24Ii1atgJcF/H7cN3I4Lu7VE298vxn7CgqRnZyE67vlYFSnjoj7cYCnG0Tl1hd79uLDHTvNVrEuaS3w85490DH1p+2WYzEMXYgWVmza/wNeWbEW+4/lm8PJO2akY8Fnq/Hd/oPm34vbgjEnd8e9F09Ar/aZjQ6jIoOoSOJ7cN2AU3FNvz7YfPQYQkYI3dLTkRJ/4oyEuiT4K38Wi0Upe5T7GlHSLl7ltd6vNvmlpcip5X9jbRCP78qOQwYytu2RnJxaP+m2htItmJJt1pSMgZRXw6hwg7tn45u/3I6XPluDxes3oSIYwnXdO+Oq0UPQuXXTXvCMDKcaGkztP3QYz7/zIdZt3obkxERcMGooLhh5hu1BkFBSWoY3l3yKT9esgyjCH3BSD3z13fd4/cNPUFJebr5P25bp+O0VF+Gm/77IvL7apX+v7njvr/djV+5B7DsohomnI6dj48ZSJMbHo9hXvRUwnNhxUVSNx9XxYuGxwsoNajJrWENlIr4qxCNygm1XhdzcXLRp06b6J4uLQ0ZGhvl3Nbn88suRnZ1tVlKtW7cOt99+OzZt2oQ333wz6vvPmjULM2fOhFfIHE7JGEZ5kQg3ROBQV9AgE1GNc1q7tuZDFmIW0fWLlmDdoUPI9iciwwjgC+zB375Zj5sHDcTV/fo0+XOEf4/CQ6IOE4bhtlffw98/W410fxw6+hKwKFSGY0YFMhDAXb72aOdPMIeMv7lxN8Y/+Dd8eNvUOoOphgRR0SQEAjildSs0RVbzZuiWloaP8vMxBCcOSF9pFCLfCGJ05051fqzOaWnYkF+54160YZwbfCVStX7KViUlIw44d279pNMaSpfB55GqAqDj7lVNyRpGie+1ClVSdgZSlpbNUnDDhBHmww7h86aE+oRTCxYuwg1/fgwBA+iNJBT6Q3j5X0vQq3MHLHxiFjq3q36Ni6WvN27Gz393N3KPHEX3uBTz9a8X/rnInFkz1tcC5weyUIoQlhzLx51znsWOvbmYM/1G2E18zU39uicMH4wX31uMrYESdIsYgSBeSP3YX4jxQwbV2RLYrWPltWSDcRzDfCe+0LoBlS/o5TRwCDuRY6HU9OnT8cADD9RZet5Y4TOn+vbti/bt22PMmDHYunUrunXrdsL7z5gxA7fccku1V/k6dar7Zkanlj63w6nweVcMpEgHIuS4Zckn2Hn4GGYFOqIPks1tlcWgyldDh/HI6jVo37wZzs3pGrPPGR4S3fnYC5j//Tb8yp+JCb50xPt8qPAbWGzk4a+hg/gWJRjiS0W2LxHDjOa4tWwP7n7jQ7x2wy8a1JrnBhEeXXNqX/zh08/xUvCQOaQz0ec3z/l64zieMA5iSLt29Qq/Lu7dA7/auRPL/AUY7at+DVwTKsK6UDHu7z0QbpOtbc8iY+ueoGvrnmzrJ93WULoGU2619IXPtZItkFKFE4GUk8TXUZ+WvmWrv8Gv73kU43wtcLW/NZr92Gq2JVCC+/cewOQb7sCXr86r9w5xDSEGgU/8zQy0Kg5iZqALOqAyuNwfKMMjwVx8bhTi/6EVWvsScVIgGTmhRPz1jfdw5YXjzVY72V04ehh6dMzCA/sPYIbRDl19iebbi4wgng0dwjajBE9ecUmdH+fkbl3MFsLXNu3CAKMZkn/cDVsoN0LmEPbuHdrjzAFNfwGWyJZQatq0aZgypXJXgZrk5OSYpeMHD1a2mFgqKirMHWUaMi9qyJDKgXFbtmyJuqhKTEw0H15UPQD6KRhyKqBSIYwS86Rkbd2jhikoK0NhWTlaJSeZFTt2Wv/DIaw6cAB3+bPQ15dSbRD3lf7W2B4qwzPfrMeErl1ivl2uaBN8YdsuTPK1xET/T6X3cT6fGVAdMirwlnEUlxoZ5kKvuS+AnxnpePzbzVj95mK0SUqSKoSK5sLu3bCvsBCPf7UW74by0BNJOOyrMHf569e6Nf5y9qh6fRwxhP38nK54ZNt2/Mc4jlH+VATgwxehAvzLyMOIDh3M75GbZGzbk7VKSneyrZ90XEPpHkxVsblyStbqKJUGnMsUSFUEgziYV4iE+Di0To0+zzGWLX1/mf8augeScD3amK1klu6+JPzeaINpO3fjgy++NNv5Yk3sKFdQVIw5vi5I+3F4t9Del4A/BjrgquB2vB/KwxWB1ubbx/vS8I9AHp5f+KESoZRofVz4+J8x8foZuHHvTvQMpKC54cMGlKDCDzx1x80YMbB+czRnz7gB5/zPrZhWsQeTQ2lmwLXHKMM7vjzs9pdj4V032drWSNSkUCozM9N81GXo0KE4duwY1qxZg4EDK1+p/vjjjxEKhaoWSvWxdm3lUDjxih/VzAqFwqun7AioIncBlDWMosqbYVlDiYZYe/Ag5n39Db7Yuw/GjwO2L+zRDb/ufypaJSfb8jk/2b3bbJsb5Dtx8SZCqHN8LXD/0f04WFyMts2atsCLtDr3AIoqKjA+EH2A+Xh/Gl4NHsFaoxhn/lhy3cuXZJ6bA4nN0T7TvpL4WBLfv/NyuuKNTZuxIz8fHePjcVvXLhjesUO1RWxtxPfizyOHo0fLlnjx2+/wQUme+fb0hARcdVIfXNf/VFdnklmBFKuk6nm+SuWY/2UXrp+coXMwVVNbn6UpIZVqlVEyt+7JEkiVlJXj0fc+wTNLluNgQeVsoNO6dMAtE8/GpEF9bKmaKisvx+Ivv8avfJnw+098HvXyJaNLXDL+9dlKW0KphR9/gTOMZkjzn3iLK17EG+5rjhVGIa5AZSgldrDrHozHtj3y7ngZqUuHdljz+tN4Z+m/8d6nK1BSVoYLeubgykkTkJVZ/xELA3r3wNLnHsVdc/+OuSvWwAhV7oA96rR++Nv1U3BGv5Nt/CqIHJopddJJJ2HChAmYOnUq5s2bh/Lyctxwww247LLLqnbe27t3r1lavmDBAgwePNgsMX/55ZfNHfpatWplzkS4+eabMXLkSPTrJ8fuSbILD4liEVBFhlCRn4PkniulumW79+B3iz9GZ18CfuNvYw5e3BA8jnc3bsHnu/fihYnnobUNwVRZMIQU+E/YbtfSHJWVWqXBYMw/t/UxRc1PNNbby8wYqlKuUTm0M12xigcx7+nm0wc2eVD+/5zaF1f2PQU78vLMmQpd09Jsr6arL9kCKdmrpHRt3WsIrp+azgvBVGR4FN7eV5+QKvJ9Iz+erGSvkpIlkCotr8DPH34WK7/fibFIxSB/FooRwuJdR/GLuS/gz//vfNw4YWTMq6bKK4JmO36zsHawSM0Mvxmk2OF4SQnam9OjUOMaKnz9JBzwBzEw3f2KtoYQuwdeMm6U+WiKvj1y8Pace82h9LmHjiCzZTo6tm3YxjlEsWDr9gdiFxgRRIngSZT/XXTRRZgzZ07V34ugSgzhLC4uNv+ckJCAxYsXY/bs2SgqKjLnGoh/c+edd9p5mNo6MTxq3K46qoZQonWP1FUWDOLOZZ9jAFJwhy/LbF8TBqEZzjHScGvRbsxevQb3joj9TX/vVhmYHyrDHl+ZOWQ80iqjEGnxCWgX4yopoUfLdPPXr42iE+YkCV8Zla92inlSgghhFhrHcEpGBrqkRa+u8oJ4v9+smJKFjG17Ms+S0r1KqqG4fmo6rwRTNQVK0UKq2t5fJbJWSckSSAl/W7Ic//5+B+7zd0CfsDEEo4xUPOc7hDtf/RcmDuyDLpkZTf5c4VVTcYaBnPZtsfpAEc7CiedBbNjyfeg4ftEzektxU/Xt1Q1f7Flhro0iq65FWCbWUNlh6zoxe3Jr6DgemnAWvKx961bmg0jLUErsFCMqn2rSpUsX8wJhESHUsmXL7DwkT1M1XGoKVeZJieoFf2psb6hV3IUv3OKdu3C0rBTXBNpXBVKWdr54XOhLx2tbt+P2IYORmhDbBeo52dl4IPFLPF12EHf6s5AQ9orfJuM4PkQ+Lu99si3VOCJYGtyuHV4+eASnGiloGTYTQexKtyB0GN2RiBxfInYbZXgxdAjrjWL8dWDsy+BJr7Y9Vkmpg+un2PBaMKVL6KRilZRMgZTw7JLlZqtaeCBltb1f7m+FRUY+5n/yJf50yYSYfL7wqqn/ObMv7npjCc4OFWGg/6cX74KGgb8ZhxCIC+CKC86BHaZePBH/t2gZFvqPYbKv+rr6fSMPO1CGq3ytUWaE8JlRiGd8h3BGn94YN3SQLcdDRBKEUkRUv6qFolUreKoibDuWh9b++KiVSsKpvmQsCIawt6DQrGyKpcS4AB4cPRI3fPQxfmPsxDlGC7RCHNYZx/GpkW/OKcotKsJ/Dh1Cn9aVcwli6U/Dh+KX776PG0t3YTxaoIsvEbuMUryPPOQjaBaeXxbcikIjaM5PevjMURjRqWPMj4P0CaRkrpIispOXgykdyVglJVsgFQyFsOXgYZzrbxv178WmLb1CSdi0r/qGCrEgzsG1owdh6Xfb8b8btmFoqDkGIgUFCOJDIx/7jDJ0bdcOry/6BFdMHIdmyT9tzhILwwf0wc1XXIxHX/gHvvYdxwijGXzw4XMUYlWo0Hyf+41cBA2YwdT5wwfjmf+9DQFJWv6JvIqhFJHmVB14nhIXZ4YupUYIiVFmExw1KmcvpcTbcxkb2iELr1x4Pv6+/j94ddt2lBsG4uFDdyShWygRq7bvwfvbtuOOoUNw2Um9Yz5r6dVJF+DZdeuxcPNWc/B5ciCAC7p3wyW9euK7w4eRV1qKjqmpOKtzJ2nmJxGkDaRElZSsgRRb98huDKbUJ2uVlGyBlCDa1pLj43A0WFHj+xzzB9EjyZ6ALzG1JV6bdg2e+fATPP3xKjx26ID59pYI4Cyk4nhuIW59+En87fV/4v2nHkKbjMqxBbFy343X4NRe3TDnhTcwe9MW8239unfF05dfi87t22DNhs1IiIvHuKED0bNLp5h+biJqHIZSpCUxT0qV1j07qTzwfEx2Zzyyeg2WGvmY4Ku+YBFtv6IMu1d6OjqlVu5AZ4eeGS3NHdz+tXUbRiMVNwXaVbUSXmsYeDb0A+5bvhKntG6NvpmxrZgS86ruGHoGpg8ZjKLyCjN8s3aSO5l9/9IGwDIGUirggHO5lH29HBgxFjphMKU+GaukZAukrBa9Cwf1xeIvv8XPjZbVRhAIG43j2BoswT2D+tp2DPFxAVx3/hh8vXM/9h/Ow598WTjJ/1Mr4U6jFHfu3odfz/wL3nzsnph//ZeOP8t8FB0vMdeMzVN+2hRn1KD+Mf18RNR07u2VTUSOkXnock2y01rgvJwu5vyBj0P5qPhx/lyeEcS80EGsMYrw69P6m4sPO726caO5jfBvA22rzbYSO/Nd489EW388Xv5ug22fX+wu1yIxoSqQIjnJ/DPGKilqSgWIjqGGrFU3FJ2s3y/xMyJbIGW56bxROOoL4s/GfuQalTvdiXBmbagIs4xc9O3YDuf2j22Vd6Qf8gvxj1Xf4lJfRrVAytqs5UojA+//exW27rbv/5+iPTA8kCIiObFSikjjYeeqV0v97/AzURE08MjOnXjWdwgZvjjsCZXC5/fhzjOG4Jwu2bYfw1f7D2Awmp3wSqMVTA0LNceK/blQWXkohKU7d+Hfe/eZg0j7ZWbi/G5dkRIf7/ahKUHmOVKyDzcXWCUlH39KKmCUVW3zrmPFFKlFtiop2UPbPp3b47WbpuCqJ17C1OM7kB1IQjFCOBgqw2ldOuC1m6cgzubW/zXbdpvri+GB6BXtw32pmI0DWP7Nt+jWSb0xE5bte/bjxfcWY9f+A2iVnoZLJ4zGgN493D4sIqUwlCItW/dU48SwcxVnSyXFxeGRMaOx6cgRfLBtBwrLy8x2vYndu6FlUmyHY9bmpz1CTxQSpeJQd5ej7Xl5+M2Hi7G7sBBd/Inm3Kx3Nm/Bo6tW49ExZ2FIVnu3D1FqMgdSFllnSZHcwrd51zGY4uBz0mmOVDRj+vbExsfuwBsrv8E3O/chIS6Ac/ufhOG9c2yvMhesz2HUsn4Kfz/ViMqzmfOex4PPvYZm/gA6+xJxwCjHYy+9gYvHjsQzM29FYox3hybSFUMp0hLnSelTLSX0ysgwH24Y3KE9Xj+6ESVGyNyxJpyoKvoChRjewf6KLTsUl5dj6vuLEF8SxJxAZ+T4KoO+g75yzK04gBs+WoJ/TL7QbKUk9QIp2aukxIBzVknJjcEUuU2EhzJVSakSSFlSEhNwxcjTcYULn3tQt8qNWJYZBfgv34lrOLGbsQikxI55Knry/xbigb+/il/4W2EyWiIJfnN/5GX+Ajy+5HPc0rwZnrjjd24fJpESOKSEyCPEjbPMc29kdWnvXijzGXg0lGsGUxaxG98ToQM4bJTjv08+CSr659ZtOFhcjD/62lcFUkIbXzzu8GchKeTDSzbOy1KZ7IGUhVVS1FTWzbfs7UpNqZgiOcn6vVElkHJbq+bN8N8jBuJ1HMG6UHG1v9tslGABDmNiv57IzmoH1ZRXVOChZ1/BWF8LXOZvVfWipRjrcLa/Ba7wtcKChR9i/6HDbh8qkRIYShF5rLKBwVTDdExNxUNnjcIqXzGuCm3H7GAu5gYPmL9fggLcM+JM9G7lThVXU328Yxf6+1LQznfiq9BigSV2HFy8facrxyYzFQIpmYebW1VSpA4GU+QW2aqkGEg1zP2XT8SgHtn4Q2gPpof2YF7wIO4M7cXNwV3o1rEt5lxxvpKB91cbNiP36DFM8KdF/ftxvhYIhkL44PMvHT82IhUxlCLt5kmp2rrnxA2kzDfRMhuT3Rnv/HwyLjqlN/akB7AjzYfzenfHmz+7EJN6dIeqjleUowVq/nlp4QugNFjh6DHJTpVASgVs3VMLgynycpWUisGJLO2DC2+fiud/cznandwBW9vGI61nWzw19b/w0d3Xo1VmWyXP7/GSUvPXmtZQKfAj3ufH8VJuqkBUH5wpReRBKg49d1unFqm4dfAg6KRnRgYW/bAVFYaBuCiDRr9GMbq3jP2OkKpSIZCysEqK7KDzjCmBw8/lIkuVlGpzpGQjdvn7+ZBTzYcu15VeXTsh4Pfja6MYWVGqzb/FcZQZIZzSrYsrx0ekGlZKEXmsysG6oWYbH/1X7144YlTgtdCJMw8+DxWYMyAuPakXT5RCgRSrpMhuulZMWQGIbBU6XiTj94CBlP3nVzzEdUWFa0v71q0wcdRQvO47hh+M8mp/V2yEMB9H0LNjFkYO7OfaMRKphKEUadW6pzqnqhsYTJHQM6MlbjxtAF4xjpjzHhaF8rAslI/7g/vxYGg/zsvpigk5XT1/slQLpFglRXZjMEVeq5IiZ6h0bXn41uuQ3KoFfoc9mB/8wXwx7/XQEfwWu7EnPoRn773d3F2QiOrGUIq0ouo8KTfIfoNNzvhV/3549OzR8LdKwZzQATwUysXeVB9mnDEEs0YOh9/jCypVAimLzIGUhbOk9KDSzWNDsGLKXTJVSbFtzx2qXFs6tGmNzxbMxRWXnI9FSSW4P7Qfr/iP4uxxZ+KzBXMw6BRWmhPVF2dKEUla8eBPdWaWD+dL0Tldss1HcXk5goaB5vHxnn91L7y9VYVASoW2Pe64px8VZ8HUN5gqLy/jjCkXz78s2Lbn3nlX4drStlVLPDzt13jgpqnIKyxG85QkJMTHu31YRMphpRSRhysd2MZH4VLi45GakMBAKqw6SqVAilVS5AZVqhoaihVT7lRJyRJIieczAyl3qXRtCQQCyEhLZSBF1EgMpUibeVJs3WscBlNE6rbrWWQPpFglpTeVbh4bgsGUt9v2yH2qDUAnosZhKEUk6U2Wk+04DKaI1AykVGjbs3CWlN50rSphMOX8uZaBrs9nVekafBNRJYZSRBLeXLlR9cBgirwcRqkcSLFKimRhVTTohsGUt6qkGEjJicEUkb4YSpEWrXu6croKgsEUeY1q86MiyR5IWVgl5R0MpkjVKikdA1XdMJgi0hNDKdKCjvOk3LrZZDBFXqFidZRqbXucJeVNDKZIxeHmAquk5Mdgikg/DKWI6AQMpsgL7XqqVkep0rZnYZWUd+lYeSICFPGQqeVMVTKdQx2fqzrjAHQivTCUIpKYuOl0qyKCwRTpRtXZUaoGUqyS8jbdqxkYTMXuPLrNeo6ySko9ul9niLyCoRQpTed5UjJgMEW6UH12VDgVAikLq6S8zSvBlEwVP6pg2x7Fiu7XGSIvYChFynNznpQTlQBuVksJDKZIZTpUR1nEdUCVQIpVUuSVG0buzNdwMoV4uj4vvUb36wyR7hhKESlSAeB2MCUe4Tf4RCqFUToEUqphlRR55YaRwVTDAym27VGs6X6dIdIZQylSlpda92SpjmDVFMlOtzBKtTlSVpUUAyny2g0jg6mGnysZcI6UXjgAnUhNDKVIaW627nm1WiI8mGLVFMlCxzBK1UCKqCYMpryNbXvkFN2vNUS6YShFpAiZbkrDb/oZTJGbdA2jZP3Zrw9WSZGXbxZZMSV/256FVVJ60/1aQ6QThlJEilUGyFAtZeGsKXKLF8IolQabC6ySovrS/WaRwZTcgZR43jGQ8gbdrzVEumAoRcrOk5Khdc/pigBZb1DZ0kdO8UIYJVv43BCskqL60v1mkcFU9PPhNl2fb+Tdaw2RDuLcPgAiangwVbRqBfypLaU6dVY4sOtfn1eFBoHMLJePinQQ3iKqawil8hwpgVVS1NibxVBxvnmzmDBohPbBVF4nZ1/IkoH42mUJpCyskvJ2MKXjtYZIdQyliBS+cZUtmBIYTlEsRM4q80IYpXogxSopakowpTMRypSXl5kBjZeCKZkGmwuslCFxvbGeBwyniOTB9j1SsnXP61UCKtywRg5D50B0qov1PIlsz2MgJT8GUhSrG0Vdea2dT7Y5UhZWSRHb+Yjkw1CKlCTDPCm3b8ZEMKXC3BmGU1SfEMqrQZTKFVIC2/YoVhhM6UPGQIrDzSkcgykiubB9j0hxsrbxRQoPGcTcKQvnTnlHtGo5r4VPOgVSFlZJUUyfT5rPfNF9zpSsgRSR12baEamEoRQpRcbWPRmGnqsSTNU2d0pgQKWPmto1GULpE0iJKikGUhRLXrpJ1HHOlIyBlIVte+T1aw6RzBhKkXJkat2LvEELJCa7FkypKLJ6KjLIYEglt9rmhDF80j+QIrKDV4MpQeVwStZAilVS1NBWPt2vO0QyYihFFAOiWiBhq3vDS61gSqVqqbpCDIZUzmvoMHoGT94MpCyskiK7eC2YElSumpI1kLKwSorq+zzxynWHSDYMpYg0olobX1NDKq9WU9m1kyFDJmeoHkixbY+c4LUbRFXb+WQOpDjcnBrKa9cdIlkwlCKl5knJ2rrndgufyvOlmhKY1BRUyRxYxSJQYnikLh0CKSKneO0GUaUh6DKHUQLb9qixvHbdIZIBQykiTVr4vBBMNSScqSuwcgsDJe9SPZCysG2PnOTFG0TZZ03JHkhZ2LZHTXnueO26Q+QmhlJEmvFaMFUThj8kEx0CKbbtkVu8eIMYOWtKhnDKOg7ZAylWSVEsePG6Q+QWv2ufmUiz1j2Z2lusG1/rRpiI3KNLIEXkJq9WvYjwJ7ytLzwYcqs6SoVAyqvPF4ot8TwSD/G8YthJpGAodd9992HYsGFISUlBenp6vf6NYRi4++670b59eyQnJ2Ps2LHYvHmzXYdIpHVbC4MpIvfpFEjJdH3THddQ0Vk3h17kRjhlfR7xkD2MCsdAiux6Tnn1+kOkbChVVlaGSy65BNddd129/82DDz6IOXPmYN68eVi5ciWaNWuG8ePHo6SkxK7DJEWqpKhxGEwRuUeHQMrCQMrh8801VO3nx8M3htHCqVgHVOEfU6UwysvPC7IfgykiBWdKzZw50/x1/vz59a6Smj17Nu68805MmjTJfNuCBQvQtm1bvP3227jsssvsOlRSgCqte+EDz93ahS8SZ0wROUunMIpte+7gGqpmnPNSKTwoCp871djZU5HBlipBVCRWSZHdzy/OmSLSeND59u3bkZuba7bsWdLS0jBkyBAsX768xlCqtLTUfFjy8/MdOV4ilTCYInKGjoEUq6Tk57U1FG8MUWuA1JjKKVVDKAurpMgpvP4QaTzoXCymBFEZFU782fq7aGbNmmUuvKxHp06dbD9Wco7KrXuyVRiwlY/IXjoFUhYGUmrw4hqKrTQ1s1ruGvJQGYebk9N4/SFyMZSaPn06fD5frY+NGzfCSTNmzEBeXl7VY/fu3Y5+frKfSq17st/IMZgisodugZQI1WW9jqmKa6jY440hRT4XiJzCnfmIXGrfmzZtGqZMmVLr++Tk5DTqQNq1a2f+euDAAXP3PYv4c//+/Wv8d4mJieaDSNYbO1lmS1nYykcU+zDK+tnSgWxVnrrgGsoebKXxNrbtkdt4DSJyOJTKzMw0H3bo2rWrGUwtWbKkKoQSsw3ELnwN2cGP9KFy6174wHMZWTfPRatWmL/6U1u6fERE6tGtOkrgHCn7cA1l/00heROrpMhtDKaIJJ0ptWvXLqxdu9b8NRgMmr8Xj8LCwqr36d27N9566y3z96L176abbsK9996LhQsXYv369fjlL3+JrKwsTJ482a7DJMmp2LqnUtUB2/mIGoeBFNmJa6jG3RSyasZbxPebgRTJgu3ERBLuvnf33Xfj+eefr/rzgAGVsymWLl2K0aNHm7/ftGmTOQfKctttt6GoqAjXXnstjh07huHDh+ODDz5AUlKSXYdJ5NlqKQvb+Yi83a4XjnOk5MA1VNOCqYRBI2L8HSHZMIAkGbFiiqhxfIZhGNCIaPkTO8h8uX4Xmqdy6KHqrXs6VEpZoZRss6UiWa18Atv5iLxRHWXhYPPa5RcUIvu04eYLaS1a6Lu2sNZQe+fNRItkNV8QtNr4GEzpjVVSJDtei4iA/MIitD3rojrXT7a17xE1lQ6BlEqVB+JGm+18RN4MpIh0wRYa/bFKilTAaxFR/TGUIumoPuBc9Rs/BlNE1cMo8QgPbXXCweakI94M6o+zpEgFvBYR1Q9DKZKSLlVSqlVLWawbcOuGnMhrwp/7OoZRAgMp0hlvBvXEtj1SDa9FRHVjKEXkIFWqpaJVTTGcIq8ID6MYSBGpizeDemHbHqmK1yIil3bfI2ps655uVVKRO/GJYEr2oefhrJtyMQhd3KxzCDrpSved9VSv4CRqDO6GpRfd2vZ2vrusyR8j+4JRMTkWshevRUQ1YyhF5EIwpSJxk24FUwLDKdKJ7q164bjTHnn1ZpDUpVPbXmQQFWjVrtEfK3g4t9rHY0ClVsUUdwklqsRQiqSh64DzaFSrlqqpakpgOEUq81IYpWILMVEsbwbFjSBvAtWjQ9teLIOo2j4OAyo1sGqKqDqGUiQVXVv3dKmWsjCcItV5rVVP4GBzosqAg8GUelStkgoPiWIVRNUm/HMwoJIbgymin3DQOUnBS1VSVjClQ8VC+CBoDkMnFXfVYyBF5B0cNqweVaukRCBkhUIiKHIikIoU/nnDj4fkwWsSUSWGUiQNL1RJRdIhmBIYTpHsvBpGCayQIvoJbwLVC6RUq5JyO4yKZB2HeDCckg+vSUQMpUgCXquSitz5SpdgSmA4RbLxchglMJAiOhFvAtWhciAlI4ZT8j7Prbl3qlYHEjWFtpVS6cXb3T4EagAvVknpvCU7wylym9fDKIGBFFHNGEzJTbUbc6sCSZbqqLqwrU9OvC6RV2kbSnm5Akcl/B5Bu2qpcFYYIB5WSBA+YJoo1hhGVWIgRVQ33gDKSbW2Pdmro2rCmVNy4nWJvEjbUCoxvrLyhqGH/LxaJaVzG180rJ4iOzGM+gkDKaL64w2gnBhIOYfhlHx4XSKviYPmYUdJWdAMpo426+b24VAEBobVg6mErV+bN5OBxGStnyvhbVRFq1ZU/d6f2tKlIyJVhVfdebE9LxoGUkSN35qd3KdS257VrqcL62sJHs41v7bsC0a5fUieZl2XxM9EwqARbh8Oka20DqUEBlNy83qVVLRgykusIEGEU+EBAwMqqg3DqOgYSBE1njVkmDd/7lGlbU/Vdr3GzJsSGE7JUzHF6xPpStv2vWjBBytz5MHvRc3BlO5tfHXNnhI4f4oihT8nIp8vxECKyIuVOjpiICVvOEXuYTsf6U77Sqmagim287mPVVI180IbX33b+1hB5V2RQ/EZQkXHCimi2GC7jLthIAMp+bBqSh68PpHOPFEpFY5VU+4TwSADqZp5ZfB5fYRXxETu4Mdd/PQU+f1lVVTtGEgRxRYrEpynQnWa7i17dWHVlBx4fSJdeaZSKhznTLmHbXv146XB57GqohI4i0pNrIhqHAZSRPZgRYLzZK+S8nIgZWHVlBx4fSIdeTKUigymBLbzOXvuqW4MpmoX2crFkEotDKKahoEUkb144+cMVdr2vB5IhRPngjv0uYsD0Ek3ng2lwsMRK5xiMGUvVkk1HIOp+mNIJTeGULHDQIrIGQym7KVK2x4DqdqDKYE79LmD1yjShadDKQvb+ZwLpFgl1XAMphon2lBsUU0VDdv+YivavC8OKY8NBlJEzuJNn72BlMxVUtx1rnZWWMeqKXfxGkU6YCj1I7bz2YeBVNMxmLI3qKppaDrDqrrVdO4YQsUeAykid/Cmz77zKiuvDzZvCLbzuY/XKFIdQ6kwbOezDyukmo7BlD1qCk9qC6u8GFjVdi4YQNmLYRSR+3jT5505UgykGo5D0N3HaxSpjKFUFKyaim2VFAOp2GEw5Zzagpa6AisVg6u6vh4GT+5gIEUkD9706T9HioFU07Bqyl0cgE6qYihVA1ZNNR0Hm9uDwZT76hvQ1DTDqimsoKs+oVhDMHSSDwMpInmDKdJzjpTAlr2mnz/OmXIXA3RSDUOpOrBqqnE4R8peDKbUYFfQI8Iuhkh6YyBFJPcNnwhYEgaNcPtQlKFCIMXB5rHD3fncx2CKVOJ3+wBUCaasyilW/9SNgZRzwVT4zSt5BwMpvTGQIlInmKKGnTNZsW3PnmAqctYUudvORyQrhlKNCKdE6MJwKjoGUs4HU+IhbmIZThGpzfo5tn6uiUhuDKbqh4PNvY3BlLsYTJEKGEo1QnjVFMOpnzCQcg+rpojUxuooIjUxmKqdKhUanCPlzPllxZR71ynrWqXKzyR5C0OpGLX0eT2cYiDlPgZTRGpiIEWkNgZT0XGOFEVr5xPBFMMpd7BqimTFUKqJGE4xkJIJgykidYS33bJdj0htDKbUDaRYJeUsVk25i8EUyYihVIx4MZwK/zqtr53cxzlTRPILD6MYSBHpgcGUOoGUhYGUu+edFVPuYDBFsmEoFWNeCafCwygGUnJi1RSRnFgdRaQvr89tUSWQEmEIAyl3MZhyF4MpkglDKZvoGk6xOkotDKaI5MHd9Yi8was3eyoFUiQHBlPu4gB0kgVDKYfCKfGwAh1VAypWR6mJ7XxE7mN1FJG3eC2YUiWQsrBKSh4cgO4+r12vSD4MpVyunpI9oAo/TrbqqY1VU0TO4zBzIu/yyo2eSoEU2/bkxaopd3nlekVyYijlcvWUrAFVZJse50bpgVVTRM7hMHMi0v1GT7VAiuTGYMpdul+vSF4MpSQKqCJb/JwOqSI/L8Mob1RNWTfORBQbrI4iIi/c6KkUSFnYtic/BlPu0vV6RXJjKKVQFVWsg6poHzfy85O+wreiZzBFFBusjiIiL9zoqRZIsW1PLQym3KXb9YrkF+f2AVDNogVDJWXBmAZTDJ9IBFMJW7+uupkOJCbzpBA1EAeZE1FDb/QSBo1Q8qSpGEiRmsFU8HCu+f3LvmCU24fjObpcr0gNDKUUwxCJ7GBVTDGcImqY8CpD6+eIiKium71Qcb55s6fajZ5qgZSFbXt6VEwxnHKeytcrUgfb94ioClv6iOqPrXqkq90ffO72IWhPxfYYFQMpVknpge187lLxekVqsS2Uuu+++zBs2DCkpKQgPT29Xv9mypQp8Pl81R4TJkyw6xCJqAbcpY+oZhxkTnbjGso7N3riIW70ZL7ZCz8+lQIpC6uk9MBgyl0MpkjJUKqsrAyXXHIJrrvuugb9OxFC7d+/v+rxyiuv2HWIRFQHhlNENYdRbNcjnddQrDBxjsw3e+FhlGqBFIeb64fBlLtkvlaR2mybKTVz5kzz1/nz5zfo3yUmJqJdu8r+YSKSA4ehk5dxbhQ5ze01VCCjLVBwpMkfhxo3t0Vwe3ZL+E2namGUwFBVXxyA7i4OQCdPzJT65JNP0KZNG/Tq1ct8hfDw4cO1vn9paSny8/OrPYjI/nlT4TfqRDpiZRSpJpZrKHHjxxt7Z4VXI7nZ0qdydVQ4tu3pixVT7mPVFGkbSomy8wULFmDJkiV44IEHsGzZMpx77rkIBoM1/ptZs2YhLS2t6tGpUydHj5nIaxhOkRewTY9UY9caisGUd8Ip1WdHWfic9QYGU+5jMEWuhFLTp08/YRB55GPjxo2NPpjLLrsMF154Ifr27YvJkyfj3XffxapVq8xX/moyY8YM5OXlVT12797d6M9PRA0Pp8TDqihh9RSpznoec2YUxZqKayhWmngjnLI+tvX5VA6kLHzuegODKfcxmCLHZ0pNmzbN3CGvNjk5OU09pmofq3Xr1tiyZQvGjBlT4/wE8SAi91htfQlbv64KpgKJyfyWkBI4M4qcoPIaSlSeZF8wKmbHRo276QufOdXUuVOqz4yqCYebezuY4nXK/Zl4bs/DIw+EUpmZmebDKXv27DHnIbRv396xz0lEjcdwilTCMIqcpOoayhoqTO4LD48iAypLtBvCaO+nUxBlYduet4XPwWM45TwOQCcpZ0rt2rULa9euNX8V8wzE78WjsLCw6n169+6Nt956y/y9ePvvf/97rFixAjt27DBnIkyaNAndu3fH+PHj7TpMInJo7hRb+0gWHGBOspNxDcUbfrmEt9pFa/MLf9T0vjpi2563sZ3PfWznI9srpRri7rvvxvPPP1/15wEDKm9Qly5ditGjR5u/37RpkznDQAgEAli3bp35b44dO4asrCyMGzcO99xzD9vziBRlBVMCW/vIbayMIlXItoZitZQadA6b6sLQlCKvV2zncw/b+aihfIZhGNCI2M5Y7CCz86vP0SK1uduHQ0QRRDgVjrOnyG4Mo6ip8gsKkX3acDMEatFC3xt/aw216upfoHlCQrW/s1r42BZDMuIsKYrEa5b7RJuxwDlT3pVfWIS2Z11U5/rJtvY9IqK6du0T2N5Hdgh/XkU+54io4dgWRbJilRRFw1Y+97GVj+qLoRQRuYYBFcVSZMDJIIoo9hgAkIwYmlJtzwtet9xjzbGzZtwRRcNQioikwICKGitaEMWqKKLY440/yYZhA9WFwZQcWDVFtWEoRUTSYUBFdWF7HpF7GASQTBiWUn2fI7x2uYvBFNWEoRQRSY0BFVnYnkfkPgYAJAsON6eGYDAlBwZTFE1c1LcSEUkosiWLO/npjzvnEcmJ260TkYrBlNiVj9cv94MpsTOfmDHFnflIYChFRFqEVCKgCg8whEBisgtHRU0R+T3kbCgieW/siNzCKimKRcVU9gWjeCJdwmCKwjGUIiJtq6gYUskv8nskMIgiUgNv6sit5x1RLMIpXsPkauVj1ZR3aRdKGYZh/lpQWOT2oRCRm9r0qPbHhO3rgKLISqokhw+KgqUlJ5yEsq79qr+hoJAniqRirSmsNYaurK+vsKys7ndOzUDwyAHkHz/xZ5rIToXl5QhktAXq8zwlqk1qBr59a7H5204ThvNcucWXgFBxAfDZYiQMGMrvg0YKiorrtX7yGZqtsPbs2YNOnTq5fRhERESkmd27d6Njx47QFddQRERE5PT6SbtQKhQKYd++fUhNTYXP57Pt8+Tn55vhlzjBLVpUlh6S/Xje3cHzzvPuJXy+87xHEkulgoICZGVlwe/Xd+NiJ9ZQ/PlyB887z7uX8PnO8+41+ZJmE/VdP2nXvie+WCdfxRTfdJm+8V7B887z7iV8vvO8e4msz/e0tDTozsk1lKzfZ93xvPO8ewmf7zzvXtNCwv+31mf9pO/LfUREREREREREJC2GUkRERERERERE5DiGUo2UmJiIP/7xj+av5Byed3fwvPO8ewmf7zzvxJ8v3fC6xvPuJXy+87x7TaLi2YR2g86JiIiIiIiIiEh+rJQiIiIiIiIiIiLHMZQiIiIiIiIiIiLHMZQiIiIiIiIiIiLHMZQiIiIiIiIiIiLHMZQiIiIiIiIiIiLHMZRqoh07duCaa65B165dkZycjG7dupnbMZaVlcXmO0Q1uu+++zBs2DCkpKQgPT2dZ8omTzzxBLp06YKkpCQMGTIEX375Jc+1zT799FNMnDgRWVlZ8Pl8ePvtt3nOHTBr1iycfvrpSE1NRZs2bTB58mRs2rSJ595mTz75JPr164cWLVqYj6FDh+L999/nefcArqHcwzWUM7iGch7XUM7j+skdT2q0fmIo1UQbN25EKBTCU089hW+//RaPPvoo5s2bhz/84Q+x+Q5RjUTwd8kll+C6667jWbLJa6+9hltuucUMWr/66iuceuqpGD9+PA4ePMhzbqOioiLzXIvFLDln2bJluP7667FixQp89NFHKC8vx7hx48zvB9mnY8eOuP/++7FmzRqsXr0aZ599NiZNmmT+P5X0xjWUe7iGsh/XUO7gGsp5XD+5o6NG6yefYRiG2wehm4ceeshMLrdt2+b2oXjC/PnzcdNNN+HYsWNuH4p2RGWUqBx5/PHHzT+LALZTp0648cYbMX36dLcPzxNEpdRbb71lVu2Qs3744QezYkostkaOHMnT76CMjAzz/6WiEpm8hWsoZ3ENZR+uodzHNZQ7uH5yT4ai6ydWStkgLy/PfEIQqf4qqkjex44dW/U2v99v/nn58uWuHhuRU9dygddz5wSDQbz66qvmK92iDJ28h2so0gHXUORlXD85L6j4+inO7QPQzZYtWzB37lw8/PDDbh8KUZMcOnTIvMC1bdu22tvFn0XLBZHORFWgqMA888wz0adPH7cPR3vr1683F1ElJSVo3ry5WR148sknu31Y5DCuoUgXXEORV3H95Kz1mqyfWClVA9GaJEo+a3tE3pjv3bsXEyZMMOccTZ061Ynvn3Yac96JiGJNzJb6z3/+Y77qRPbr1asX1q5di5UrV5pzAq+88kp89913PPWK4hpKnfNORBRLXD85q5cm6ydWStVg2rRpmDJlSq0nLycnp+r3+/btw1lnnWXuBvf000/H9rvkIQ0972Sf1q1bIxAI4MCBA9XeLv7crl07nnrS1g033IB3333X3MFHDJEk+yUkJKB79+7m7wcOHIhVq1bhscceMzcRIfVwDaXGeSf7cA1FXsT1k/MSNFk/MZSqQWZmpvmoD1EhJQIp8UR47rnnzLk7ZP95J/svcuI5vWTJkqoh26IkV/xZ/E+HSDdi3w8xxF+UPn/yySfo2rWr24fkWeJaU1pa6vZhUCNxDeUOrqHkwTUUeQnXT/IIKbp+YijVRCKQGj16NLKzs805UmK3AQurSey1a9cuHDlyxPxVzD4SpYuCSItFTy013S233GKWgQ4aNAiDBw/G7NmzzQF6V111FU+vjQoLC83ZKpbt27ebz28xcLtz58489zaWnL/88st45513kJqaitzcXPPtaWlpSE5O5nm3yYwZM3Duueeaz+2CggLzeyBCwQ8//JDnXHNcQ7mHayj7cQ3lDq6hnMf1kztm6LR+MqhJnnvuOUOcxmgPsteVV14Z9bwvXbqUpz6G5s6da3Tu3NlISEgwBg8ebKxYsYLn12biORztuS2e82Sfmq7l4jpP9rn66quN7Oxs8xqTmZlpjBkzxli0aBFPuQdwDeUerqGcwTWU87iGch7XT+64WqP1k0/8x+1gjIiIiIiIiIiIvIXDj4iIiIiIiIiIyHEMpYiIiIiIiIiIyHEMpYiIiIiIiIiIyHEMpYiIiIiIiIiIyHEMpYiIiIiIiIiIyHEMpYiIiIiIiIiIyHEMpYiIiIiIiIiIyHEMpYiIiIiIiIiIyHEMpYiIiIiIiIiIyHEMpYiIiIiIiIiIyHEMpYiIiIiIiIiICE77/3L6seh/XWLAAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "kernels = ['linear', 'poly', 'rbf', 'sigmoid']\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
+    "\n",
+    "for ax, kernel in zip(axes.ravel(), kernels):\n",
+    "    model = svm.SVC(kernel=kernel, gamma='scale', C=1.0, degree=3, coef0=1)\n",
+    "    model.fit(X_train_moons, y_train_moons)\n",
+    "    y_pred = model.predict(X_test_moons)\n",
+    "    accuracy = accuracy_score(y_test_moons, y_pred)\n",
+    "\n",
+    "    DecisionBoundaryDisplay.from_estimator(model, X_moon, ax=ax, cmap=\"coolwarm\", alpha=0.8)\n",
+    "    ax.scatter(X_moon[:,0], X_moon[:,1], c=y_moon, cmap=\"coolwarm\", edgecolors='k')\n",
+    "    ax.set_title(f\"{kernel.capitalize()} Kernel (Acc: {accuracy:.2f})\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "6e61a5f4",
@@ -339,6 +500,51 @@
     "Use grid search to find optimal hyperparameters for the SVM model."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c273ea34",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best parameters: {'svm__C': 10.0, 'svm__gamma': 'scale', 'svm__kernel': 'rbf'}\n",
+      "Best cross-validation score: 0.97\n",
+      "Test Accuracy: 0.97\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "# Create pipeline for tuning\n",
+    "svm_pipeline = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('svm', svm.SVC())\n",
+    "])\n",
+    "\n",
+    "# Define parameter grid\n",
+    "param_grid = {\n",
+    "    'svm__C': [0.1, 1.0, 10.0],\n",
+    "    'svm__kernel': ['linear', 'rbf', 'poly'],\n",
+    "    'svm__gamma': ['scale', 'auto']\n",
+    "}\n",
+    "\n",
+    "grid_search = GridSearchCV(svm_pipeline, param_grid, cv=5, scoring='accuracy')\n",
+    "grid_search.fit(X_train_moons, y_train_moons)\n",
+    "\n",
+    "print(f\"Best parameters: {grid_search.best_params_}\")\n",
+    "print(f\"Best cross-validation score: {grid_search.best_score_:.2f}\")\n",
+    "\n",
+    "best_model = grid_search.best_estimator_\n",
+    "y_pred_best = best_model.predict(X_test_moons)\n",
+    "print(f\"Test Accuracy: {accuracy_score(y_test_moons, y_pred_best):.2f}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7e2046d8",
@@ -349,6 +555,66 @@
     "Implement SVR on the following synthetic dataset: $$y = x^2 + \\cos(2\\pi x) + \\epsilon$$ where $\\epsilon$ is Gaussian noise."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "43cfb8a2",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SVR MSE: 0.5352\n",
+      "SVR R² Score: 0.9884\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate synthetic data\n",
+    "n_samples = 100\n",
+    "X_adv = np.sort(5 * np.random.rand(n_samples, 1), axis=0)\n",
+    "y_adv = (X_adv**2 + np.cos(2 * np.pi * X_adv) + np.random.randn(n_samples, 1) * 0.5).ravel()\n",
+    "\n",
+    "X_train_adv, X_test_adv, y_train_adv, y_test_adv = \\\n",
+    "    train_test_split(X_adv, y_adv, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Fit SVR with RBF kernel\n",
+    "svr_pipeline = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('svr', SVR(kernel='rbf', C=10.0, epsilon=0.1))\n",
+    "])\n",
+    "svr_pipeline.fit(X_train_adv, y_train_adv)\n",
+    "\n",
+    "# Evaluate\n",
+    "y_pred_adv = svr_pipeline.predict(X_test_adv)\n",
+    "print(f\"SVR MSE: {mean_squared_error(y_test_adv, y_pred_adv):.4f}\")\n",
+    "print(f\"SVR R² Score: {r2_score(y_test_adv, y_pred_adv):.4f}\")\n",
+    "\n",
+    "# Visualize\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(X_adv, y_adv, color='darkorange', alpha=0.6, label='Data')\n",
+    "X_plot = np.linspace(0, 5, 200)[:, np.newaxis]\n",
+    "plt.plot(X_plot, svr_pipeline.predict(X_plot), color='navy', lw=2, label='SVR Prediction')\n",
+    "plt.title(r'SVR on $y = x^2 + \\cos(2\\pi x) + \\epsilon$')\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('y')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "9003cf0b",
@@ -368,9 +634,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {

From e32b109c1bce5258ec070a507ff5f41c7000f721 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Mon, 23 Feb 2026 14:34:04 +0100
Subject: [PATCH 77/81] Session 04: Unsupervised Learning - Clustering and
 Dimensionality Reduction

---
 ...lustering and Dimensionality Reduction.pdf | Bin 430634 -> 430640 bytes
 ...lustering and Dimensionality Reduction.tex |  78 ++---
 ...01 Overview of Unsupervised Learning.ipynb | 225 +++++++++++++-
 ... Dimensionality Reduction Techniques.ipynb | 277 ++++++++++++++++--
 .../S04C03 Clustering Algorithms.ipynb        | 258 ++++++++++++++--
 .../S04C04 Anomaly Detection.ipynb            | 199 ++++++++++++-
 6 files changed, 934 insertions(+), 103 deletions(-)

diff --git a/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04 Unsupervised Learning - Clustering and Dimensionality Reduction.pdf b/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04 Unsupervised Learning - Clustering and Dimensionality Reduction.pdf
index d8dea9af4582f41a7ced2f2c77e47661b27045c8..bd048a4ea27413e027e96725cd5fbbf3b4c60ed5 100644
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diff --git a/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04 Unsupervised Learning - Clustering and Dimensionality Reduction.tex b/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04 Unsupervised Learning - Clustering and Dimensionality Reduction.tex
index b029cc2..ad137c9 100644
--- a/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04 Unsupervised Learning - Clustering and Dimensionality Reduction.tex	
+++ b/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04 Unsupervised Learning - Clustering and Dimensionality Reduction.tex	
@@ -1,4 +1,4 @@
-% Created 2026-02-04 Wed 17:15
+% Created 2026-02-23 Mon 13:55
 % Intended LaTeX compiler: pdflatex
 \documentclass[hyperref={pdfpagelabels=false},aspectratio=169]{beamer}
 \usepackage[utf8]{inputenc}
@@ -202,14 +202,14 @@
 \end{frame}
 
 \section{Introduction to Unsupervised Learning}
-\label{sec:org2f2069c}
-\begin{frame}[label={sec:orgb56e413}]{Introduction to Unsupervised Learning}
-\begin{definition}[What is Unsupervised Learning?]\label{sec:org9202d9f}
+\label{sec:org3275423}
+\begin{frame}[label={sec:org3e16b30}]{Introduction to Unsupervised Learning}
+\begin{definition}[What is Unsupervised Learning?]\label{sec:orgcc039e7}
 \pause
 In contrast to supervised learning, \alert{unsupervised learning} does not use labeled data. Instead, it identifies patterns and structures in the data without predefined labels. This is particularly useful for clustering similar data points or reducing the dimensionality of the data.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgdb32e40}]{Types of Unsupervised Learning}
+\begin{frame}[label={sec:org137f12f}]{Types of Unsupervised Learning}
 \begin{block}{Types of Unsupervised Learning}
 \pause
 Unsupervised learning can be broadly categorized into a few types:
@@ -221,8 +221,8 @@ \section{Introduction to Unsupervised Learning}
 \end{block}
 \end{frame}
 \section{Dimensionality Reduction}
-\label{sec:org47c7319}
-\begin{frame}[label={sec:orgf913ae3}]{Dimensionality Reduction}
+\label{sec:org8b93320}
+\begin{frame}[label={sec:org3e990a7}]{Dimensionality Reduction}
 \begin{block}{Dimensionality Reduction}
 \pause
 \alert{Dimensionality reduction} techniques aim to reduce the number of features in a dataset while retaining its essential characteristics. Useful for visualizing high-dimensional data or improving the performance of machine learning models by reducing noise and computational complexity. Common dimensionality reduction techniques include:
@@ -234,14 +234,14 @@ \section{Dimensionality Reduction}
 \end{block}
 \end{frame}
 \section[PCA]{Principal Component Analysis (PCA)}
-\label{sec:orgf9e2e6b}
-\begin{frame}[label={sec:orgdaac264}]{Principal Component Analysis (PCA)}
+\label{sec:org06bf07c}
+\begin{frame}[label={sec:orgea98a4b}]{Principal Component Analysis (PCA)}
 \begin{block}{Principal Component Analysis (PCA)}
 \pause
 \alert{Principal Component Analysis (PCA)} is a widely used technique for dimensionality reduction. It transforms the data into a new coordinate system where the greatest variance by any projection lies on the first coordinate (principal component), the second greatest variance on the second coordinate, and so on. This allows us to reduce the number of dimensions while retaining most of the information in the data.
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:orgfaab9df}]{Steps in PCA}
+\begin{frame}[label={sec:orgc2e68b2}]{Steps in PCA}
 \begin{block}{Steps in PCA}
 \pause
 The PCA process involves several key steps:
@@ -254,7 +254,7 @@ \section{Dimensionality Reduction}
 \end{enumerate}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org46f652c}]{Applications of PCA}
+\begin{frame}[label={sec:org8d52197}]{Applications of PCA}
 \begin{block}{Applications of PCA}
 \pause
 PCA is widely used in various fields for:
@@ -266,7 +266,7 @@ \section{Dimensionality Reduction}
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:orgd8319bf}]{Limitations of PCA}
+\begin{frame}[label={sec:org0beef3e}]{Limitations of PCA}
 \begin{block}{Limitations of PCA}
 \pause
 While PCA is a powerful technique, it has some limitations:
@@ -278,7 +278,7 @@ \section{Dimensionality Reduction}
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org1eb5ee7}]{Evaluating PCA Results}
+\begin{frame}[label={sec:org57b4efa}]{Evaluating PCA Results}
 \begin{block}{Evaluating PCA Results}
 \pause
 To evaluate the effectiveness of PCA, we can use several methods:
@@ -290,14 +290,14 @@ \section{Dimensionality Reduction}
 \end{block}
 \end{frame}
 \section{Autoencoders}
-\label{sec:org9242cbb}
-\begin{frame}[label={sec:org9c98984}]{Autoencoders}
+\label{sec:org5dfe9da}
+\begin{frame}[label={sec:orgb3ce7ae}]{Autoencoders}
 \begin{block}{Autoencoders}
 \pause
 \alert{Autoencoders} are a type of neural network used for unsupervised learning, particularly for dimensionality reduction and feature learning. They consist of two main parts: an encoder that compresses the input data into a lower-dimensional representation (latent space) and a decoder that reconstructs the original data from this representation.
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org85f82aa}]{Structure of Autoencoders}
+\begin{frame}[label={sec:org6538089}]{Structure of Autoencoders}
 \begin{block}{Structure of Autoencoders}
 \pause
 Autoencoders typically have the following structure:
@@ -309,7 +309,7 @@ \section{Autoencoders}
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:orgf12c362}]{Applications of Autoencoders}
+\begin{frame}[label={sec:org8cf3ca3}]{Applications of Autoencoders}
 \begin{block}{Applications of Autoencoders}
 \pause
 Autoencoders have various applications in unsupervised learning:
@@ -322,7 +322,7 @@ \section{Autoencoders}
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org2d4a1f0}]{Limitations of Autoencoders}
+\begin{frame}[label={sec:orga87f901}]{Limitations of Autoencoders}
 \begin{block}{Limitations of Autoencoders}
 \pause
 While autoencoders are powerful tools, they have some limitations:
@@ -335,15 +335,15 @@ \section{Autoencoders}
 \end{block}
 \end{frame}
 \section{Clustering}
-\label{sec:orgd09f944}
-\begin{frame}[label={sec:org3462e54}]{Clustering}
-\begin{definition}[Clustering]\label{sec:org78cea45}
+\label{sec:orgede2b04}
+\begin{frame}[label={sec:orgbaa9e59}]{Clustering}
+\begin{definition}[Clustering]\label{sec:org495ffe6}
 \pause
 \alert{Clustering} is an unsupervised learning technique that groups similar data points together based on their features. The goal is to identify inherent structures in the data without prior knowledge of labels.
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org2d45aa5}]{Types of Clustering Algorithms}
-\begin{definition}[Types of Clustering Algorithms]\label{sec:org57f7871}
+\begin{frame}[label={sec:org28d7d4d}]{Types of Clustering Algorithms}
+\begin{definition}[Types of Clustering Algorithms]\label{sec:orgc21c7da}
 \pause
 Based on their approach, clustering algorithms can be categorized into several types:
 \begin{itemize}[<+->]
@@ -355,14 +355,14 @@ \section{Clustering}
 \end{definition}
 \end{frame}
 \section[k-Means]{k-Means Clustering}
-\label{sec:orga774d9e}
-\begin{frame}[label={sec:org396ab67}]{k-Means Clustering}
+\label{sec:org4b1558e}
+\begin{frame}[label={sec:orgbcce641}]{k-Means Clustering}
 \begin{block}{k-Means Clustering}
 \pause
 \alert{k-Means clustering} is a widely used partitioning method that aims to divide the data into k clusters, where k is a user-defined parameter. The algorithm iteratively assigns data points to the nearest cluster centroid and updates the centroids based on the assigned points.
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org9eba3d8}]{Steps in k-Means Clustering}
+\begin{frame}[label={sec:orge3cb80b}]{Steps in k-Means Clustering}
 \begin{block}{Steps in k-Means Clustering}
 \pause
 The k-means clustering algorithm follows these steps:
@@ -374,7 +374,7 @@ \section{Clustering}
 \end{enumerate}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:orgf57c0d8}]{Applications of k-Means Clustering}
+\begin{frame}[label={sec:org4739bd4}]{Applications of k-Means Clustering}
 \begin{block}{Applications of k-Means Clustering}
 \pause
 k-Means clustering is widely used in various applications:
@@ -386,7 +386,7 @@ \section{Clustering}
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:orgd20a1af}]{Limitations of k-Means Clustering}
+\begin{frame}[label={sec:orgb9b237e}]{Limitations of k-Means Clustering}
 \begin{block}{Limitations of k-Means Clustering}
 \pause
 While k-means clustering is a powerful technique, it has some limitations:
@@ -399,14 +399,14 @@ \section{Clustering}
 \end{block}
 \end{frame}
 \section{Hierarchical Clustering}
-\label{sec:org1c3a6a2}
-\begin{frame}[label={sec:org9ef3320}]{Hierarchical Clustering}
+\label{sec:org73aeef3}
+\begin{frame}[label={sec:org4bd7be7}]{Hierarchical Clustering}
 \begin{block}{Hierarchical Clustering}
 \pause
 \alert{Hierarchical clustering} is a method that builds a hierarchy of clusters, either by merging smaller clusters into larger ones (agglomerative) or by splitting larger clusters into smaller ones (divisive).
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org1648d65}]{Types of Hierarchical Clustering}
+\begin{frame}[label={sec:org5f94fa2}]{Types of Hierarchical Clustering}
 \begin{block}{Types of Hierarchical Clustering}
 \pause
 Hierarchical clustering can be categorized into two main types:
@@ -416,7 +416,7 @@ \section{Hierarchical Clustering}
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org7fd81c9}]{Steps in Agglomerative Clustering}
+\begin{frame}[label={sec:org5f397ee}]{Steps in Agglomerative Clustering}
 \begin{block}{Steps in Agglomerative Clustering}
 \pause
 The agglomerative clustering algorithm follows these steps:
@@ -429,7 +429,7 @@ \section{Hierarchical Clustering}
 \end{enumerate}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org2b5c3ff}]{Applications of Hierarchical Clustering}
+\begin{frame}[label={sec:orgf1bec32}]{Applications of Hierarchical Clustering}
 \begin{block}{Applications of Hierarchical Clustering}
 \pause
 Hierarchical clustering is used in various applications:
@@ -441,7 +441,7 @@ \section{Hierarchical Clustering}
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:orged94729}]{Limitations of Hierarchical Clustering}
+\begin{frame}[label={sec:org9fcfa6d}]{Limitations of Hierarchical Clustering}
 \begin{block}{Limitations of Hierarchical Clustering}
 \pause
 Hierarchical clustering has some limitations:
@@ -454,14 +454,14 @@ \section{Hierarchical Clustering}
 \end{block}
 \end{frame}
 \section{Anomaly Detection}
-\label{sec:orgb2761af}
-\begin{frame}[label={sec:org086416d}]{Anomaly Detection}
+\label{sec:orgbbd97b2}
+\begin{frame}[label={sec:orgf55f911}]{Anomaly Detection}
 \begin{block}{Anomaly Detection}
 \pause
 \alert{Anomaly detection} is the process of identifying rare items, events, or observations that raise suspicions by differing significantly from the majority of the data.
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org2d38fa3}]{Types of Anomaly Detection Techniques}
+\begin{frame}[label={sec:orgc3829ed}]{Types of Anomaly Detection Techniques}
 \begin{block}{Types of Anomaly Detection Techniques}
 \pause
 Anomaly detection techniques can be broadly categorized into three main types:
@@ -472,7 +472,7 @@ \section{Anomaly Detection}
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:orgc21167a}]{Steps in Anomaly Detection}
+\begin{frame}[label={sec:org8fe6eca}]{Steps in Anomaly Detection}
 \begin{block}{Steps in Anomaly Detection}
 \pause
 The anomaly detection process typically involves the following steps:
@@ -486,7 +486,7 @@ \section{Anomaly Detection}
 \end{enumerate}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:orga93eb69}]{Applications of Anomaly Detection}
+\begin{frame}[label={sec:org342d721}]{Applications of Anomaly Detection}
 \begin{block}{Applications of Anomaly Detection}
 \pause
 Anomaly detection is widely used in various domains:
diff --git a/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C01 Overview of Unsupervised Learning.ipynb b/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C01 Overview of Unsupervised Learning.ipynb
index fe3d6e7..95fea3a 100644
--- a/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C01 Overview of Unsupervised Learning.ipynb	
+++ b/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C01 Overview of Unsupervised Learning.ipynb	
@@ -102,12 +102,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "ba31e479",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Shape: (150, 4) (4 Dimensions)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1074.47x1000 with 20 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
@@ -143,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "96d9d960",
    "metadata": {
     "lines_to_next_cell": 2
@@ -168,12 +186,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "d99a0fc8",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New Shape: (150, 2) (2 Dimensions)\n",
+      "Variance Explained: [0.72962445 0.22850762]\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.decomposition import PCA\n",
     "\n",
@@ -197,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "2e584405",
    "metadata": {
     "lines_to_next_cell": 2
@@ -226,12 +253,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "2ef74dc7",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n",
     "\n",
@@ -267,12 +305,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "f6764d98",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adjusted Rand Index (Needs Truth): 0.43\n",
+      "Silhouette Score (No Truth needed): 0.52\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.metrics import adjusted_rand_score, silhouette_score\n",
     "\n",
@@ -292,12 +339,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "945a012c",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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1w44Zu5Gz48XOSfFNvKqw+ChW24ddY4sWLeJig22bicfS22TXHSuox8b47rvv8mPIRBWznFVHnLDj/8cff/AaMyyGiMV7MbHJ5sa2X1PXLtF8EbBs6voeBEEQBEEQRDEUc0IQBEEQRIOCxAlBEARBEA0KEicEQRAEQTQoSJwQBEEQBNGgIHFCEARBEESDgsQJQRAEQRANikZZ54RVsGQZ0KwQEkEQBEEQjQNWGZgVgKyoHlKjFSdMmDicTpisRaW0GxNOOGGz2iCWiCFA5RqZNQWa47xpztU/zxabDTabAwK7E2KhEFJJzTf1qwlYtShW2p8Vuqtkf8JGD80ZzQZnTV7fQtbm4+qrNUpxwiwmTJjoVO47yDZkzGYzktKTERkZ0ayaXjXHedOcq3eeT8WlIyk5F/JMK6K9NejQJhi2Bnyuk5NSEREZ2ayub5pz88Bcg+da7GOBRCZpmuKEIIimCxMlDGm+w0WYEATRfCBxQhBEg6GstYSECUE0T0icEATRoIhR+UBqNpO1hCCaMZRKTBAEQRBEg4LECUEQBEEQDQpy6xAEQRBEjeNsMo//AjEglYv5Twidnld08LVrZJ8kTgiCIAiixnBC6OWARAFebKwpIHOKofAPg0gkgkDgrLAGmbUQcBiZKqve3EmcEARBEEQNwYSJTC1EgH8A5DI5UyiN/9g6nbBYrZCyquye5sMKo5pNyM7JhhkOOIzVK5hI4oQgCIIgagKBk1tMmDDx1vg0mWPqZCViIYBUKq3QGiRjYgxApj0TZmPRZ66VJuIRIwiCIIh6RlDkyuEWk2aKXCYvEjDVVBckTgiCIAiiJmkKrpx6njuJE4IgCIIgGhQkTgiCIAiCKEf8pXg88dRjGHzdQNw0YSw+XPoBrFYr6gIKiCUIgiAIwgWtVovHnngELSJb4L0F7yMtLQ0fffwh71D86svTUduQOCEIgiAIwoU///oDBoMB7y9cDI1GA4vFwt9f8N58PHDfAwgMDEJtQm4dgiAIgmiAGAwGLJy/EDeNugljBt+IcaNu4r+z92ub3bt3oW+ffvD29i55b/So6+FwOLBn395a3z9ZTgiCIAiigWEwGHDvnfdCt98OL5MfJBDACSc2n96D/Xv2438//w9KpbLW9n8p4RLG3zzB5T21Wo2AgABcuhSP2oYsJwRBNAhOxaUjKTkXKXFZ9T0Ugqh3Pln6CRcmCpMGgsvFzNhPL6MG+gN2vrw20Wp1XIyURaPW8HiU2obECUEQ9S5K1m0/DWm+A/JMK6K9NejQJpjOCtGs2b5xO7xM5cUBQ25U8+VNGXLrEARR79YSJkqkZjPG9m5DZ4MgANhMdu7KcQezoNhMvAVwraHRqKHX68u9r9VpeYBsbUOWE4Ig6l2YkLWEIFwRy0U8xsQd7H2xvHZv3y2jWuLSpUsu7+n1OmRnZ6Nly2jUNiROCIKoN2JUPiRMCMINQ0cNhclL5/bYsPfZ8tpk4MBB2H9gH3S6K2PYuGkjhEIhBvTrj9qGxAlBEARBNDCefPpJqPqIYPTSllhQ2E/2O3ufLa9Nbp00BQqFAi++/Dz27t2DVatX8QqxkyfdWus1ThgkTgiCIAiigaFUKnm68IhnBsDWKxfGTln8J/u9ttOIGSyu5PNPvoBIJMKLr7yIz774BBPHT8QLz72IuoACYgmCIAiiAaJUKvHK9Ff4qz6Ijm6Fzz75Ak6nk1eIlUqlENRRx2WynBAEQRAE0aAgcUIQBEEQRIOi1t0627Ztw1dffYULFy7wnOng4GCMGjUKTz31lNvqcwRBEARBNG9qXZzk5+eja9euuOeee+Dj44PY2FgsXbqU//z2229re/cEQRAEQTQyal2cTJjg2jioX79+PKhm5syZyMjI4JYUgiAIgiCIeo05YRYUhtVqrY/dEwRBEATRgKmzVGK73Q6bzcZjTz755BOMGDECERERdbV7giAIgiAaCXUmToYPH87dOIwhQ4Zg0aJF1doeq5RnNpvR2GC54qV/Nhea47xpzu45cymT/0xJzYM8w4oojbpR/i2XxmIxu/xsDtCcyyMQAzKnGHA6eW2QpoLz8lwqNafLc7dYrXDayi8WVfKwCJx1dATPnj0Lo9HILSefffYZt5osW7aMV5+rKidOnICu0IiTOfm1MlaCIGqHS5laZGUZECH2gjTHxN9rEUZZe0TTQCoXI7pzGMLDIyCVSNEcsVgtSElJRvzJVFhM5dVJq25hUGkU6NKlS8OwnLRv357/7NGjBx8UC5TdsGEDxowZc03bE0vEiIyMaJRP0xkZmQgODuKBwc2F5jhvmrO0nMXEbBYhyOiFUI0a7fq1QlOyImRmZCKIX98yNAdozjK3lhP2wC2VSJrU95zT6eQxohKJpBIVYp38GASHhrq1nIjFDbh8fbt27fgkExMTr3kbAgggkzXeLwF24Tbm8V8rzXHeNGfgVFw60jN0UOY4Ee3vhw5tmmaWHhMmze/6pjmXIHRCIHACAkGdlXmvTZKSEvHDj9/jxMkTuBh3EVFRLfHHr39W/KHLc+eWI1H5YyAQWBquODl27BhXYRQQSxDNhxiVD6Rmc5MVJgTR1LgYdxE7d+1Ep06deVJLXUbR1Lo4YZVgO3fuzK0lcrmcx5588803/HdWKZYgCIIgiPIYDAYs/foLbN63BxZmjXA4MKL/QDz90KO13pWYMXTIdRh23XDu1nnzrRk4e/5c0xEnrDrsmjVr8OWXX/IJhoeHY8qUKXjwwQeblE+OIAiCIGpSmNz1xMOIi4mC8/rh3FXC7qG/JCZj7xMP46dPv6p1gSIU1l/7vVoXJ4888gh/EQRBEARROZZ+/QUXJohqgeLIDS5QoiIRf3n59GdfaLKHk7oSEwRBEEQDY/O+PXC2iHS7zNEigi9vypA4IQiCIIgGhqWCjB/2vrUJZANVBIkTgiAIgmhgSCuoMsvelzShCrTuIHFCEARBEA2MEf0GQJiY7HYZe58tb8rUS50TonFgNpmw+q8/sW/fXtiEAkgADB48FGPGT4RYwn4jCIIgaoOnH3qUZ+XEX44xKc7WYcIk+uIlPP3pG036wJM4IdxiMhrxzhvTkdYqChgzsugPw+HAP+cv4Mibb+C1t+eSQCEIgqgllEolTxfmdU7+28JjTJgrh1lMmDCpizonRpMRu3btZL38kJ6RztObN27awJf16tkLvr5+tbZvEieEW37/4X9IbxcDQVSLkvcEQiGc7dsiEQKsWfEXxk+ZSkePIAiillAqlTxdeHo9HeG83Dy8+torLu8V//7FZ1+hdy8SJ0QdwkyHx04cg3Ps9SX59aVxtI3B7v+2kDghrgrrqcOQ5juQEpeFaG8NHTWi5Htm646N+O+/tbAUWiEQAT1698LEm6dAqah9qwBxdcLCwnBo/xF+rlgjU1Y4ta56BpHlhCiHzWqFvYLuk8yCYm3aWWxEDQmTpORcyDOtXJSwF/XVIRjsZrdk6QIk7kuFMscXCojghBOHY0/g8OGDeGvGXGjU3nSwmjGUrUOUgwW7Cq3WCr9YRI6mncZGVI8zlzJdhAkTJSRMiGL2HtiFxP0pUOcEQAhRSad5hckbotNKfPXNp3SwmjlkOSHKwSwm7WLaYF96BoQhbjrIJiTyYCiCcMelTC3CRX4uwoQgSrNmzb9Q5LiPV5BZlUiKS4TJbILRaMDBIwdgt1nRpXN3hIe6r5hKND1InBBuufP+B3DxjdeQ3acHhMFBJe87U1IRcvYCJsxbQEeOqBASJoQnTIUmyJ2e40qEJjE+/PQ9pF1IA1KlgEOA9SHr4dPCGy8+Nx0qpZoObhOH3DqEW9TePpjxzjz0ydFCs/o/qDZuhffq9RhosmHG3PmQeynoyBEEcU2IpSI44PC4vMCSj/RdOVBfDIHa6Ae12ReqhCAY9trw7oJZHiunEk0HspwQHtH4+ODxF16Cw+GAlUVqy2R1FqlNEETTZdjwUVh3/j+otOVdO1ahGVaYodL7uXX56BOycfLscXTp0K2ORkvUB2Q5Ia5+kQiFkMnlJEwIgqgRRlw3GqpOUhQqCniWTjEWkQm5LZKg0QV4/Kw0W4XtWzfTmWjikDghCIIg6hSJWIKZr7+DLre0h75DOvStMqBtmwa/4XKMvnEMxDapx88KnAJYbbY6HS9R95BbhyAIgqhzpBIp7rvnYTjucvAy6TKpDGKxGKnpKdi1ZheQ4P5zFh89+vS5oa6HS9QxJE4IgiCIenUbl64IGxYSDv8oX2jTDJBbXDN6bEILnOFW9O8zqB5G2rzYsHED1q5bjTNnz0Cr1SIyIhK3T70DE8ZPrBMXP7l1CIIgiAbFC89Nh6yHA/qQLJhFRh4kq/fLgalTLl59eQZEoqLCbUTt8dPPP0Aul+P5Z1/AkkUfoH+/AZg77x189fWXqAvIckIQBEE0KJQKFebMWojT505i27bNvKVG7z43oF/vQSRM6ogliz+Er48v/z9L3e7WtTv0eh1+/PlHPPTgw9ziVZuQOCEIgiAaHMx10Kl9F/5qrhgMBnz//Uc4dnQzZDIrTGYJuncfgWnTnuEdi2uTYmFSmnbt2uPvf/6G0Wis9f2TOCEIgiCIBihMXnzxTjx0+yW8cG+RWGMWjB37f8OLL+7FokU/17pAKMvRY0cRFBRUJ/ulmBOCIAiCaGB8//1HXJgM7ScoCUBlP4f2Ax66PYEvr0uOHT+K/zasxz13TauT/ZE4IQiCIIgGxrGjmzGkr/tlQ/o6+fK6IiMjAzPfmonevXrzjJ26gMQJQRAEQTQwZDKrx5Rd9j5bXhfodDo88/zT8NZosHD+e7UeCFsMiROCIGqMM5cykZVloCNKENXEbJZ4bHDI3mfLaxuTyYRnX3gGer0ei99fApWq7rpBkzghCKJGOBWXjpTUPMgzLJDkmOmoEkQ16NZ9BHbsd285Ye+z5bWJzWbD9NdfxaX4eCz94GMEBgahLqFsHYIgakSYJCXnQp5hRYBchnYxgZDJZHRkCeIamTbtGZ6Vw+r4sxiTK9k6Anz9axQWLXqmVo/t/IXzsGPndl6EzWDQ4+Spk5CIxXwcLKVYKvXc/6gmIHFCEES1RUmMygfyTCsiNGoopbVvbiaIpo5SqeTpwiwr5+tfiuqcMFcOs5gwYVLb6bx79+3hP5d8uLjcsn9XrEZYWFit7p/ECUEQ1YIJE2mOGWN7t4HZbEZyUhIdUYKoAZRKJR5//DUA7FW3rPpnTcn/mcXGYrFwa0ld9NVhUMwJQRAEQRANCrKcNDPSk5Pw6w/fIyktFU6hAEqJFDePn4i+Q4bW99AIgiAIgkPipBkRd+4cPly6BPpB/SHo0Ym/V2Cx4rtN63Eh9jzufOCh+h4iQRAEQZBbp7nAfIZfffYx9KOHQ+DrU/K+QCqBpX8f7Dl7GqmJCfU6RoIgCIJgUMxJMyHhQiy03moIPKR3Grp0xKoVf9f5uAiCIAiiLCROmgnZGRkwaTxX9xP4+yE9LbVOx0QQBNEk8VDZtVngrJm5kzhpJvgFBECu91xW3JmXj8Cg4DodE0EQRJPCWeRCN5lNaK6YzKaisvuOBh4Qu3btWqxcuRKnTp2CVqtFVFQU7rnnHkyePLnO8qUJILpde6hy82CyWiGQlC+SpThxGjc9+gQdKoIgiGvFKYC1EMjOyea/ymVy1qWv8R9PpxMWK2s06PQ8n8uijM2dHQNA0LDFyXfffYfw8HBMnz4dvr6+2L17N2bOnIn09HQ89dRTtb174jJMCN7/0KP49KvPYRgyEAK1ir/vtNkgPXYSPVu0RGR0KzpeBEEQ1cBhFMIMBzLtmU3mAdzpdMJut0MkElU4J7YeEybsGFSXWhcnn332Gfz8/Ep+HzBgAPLz87Fs2TI88cQTddZ+mQDad+mKl559Ab/8+D3Ss7PhFAnhBQFuGDsO142+gQ4RQRBEtRHAYRTBbHQ2mcAJi9WKjLQ0BIeGQiqpoKeOgwmXmhFktS5OSguTYjp06IDff/8dhYWFUKmKnuCJuqFFTGu8+tbbXOGyF4lDoib66hAEURZBteMuGgpOG2Ax2fhPiARNtwjboUOHEBwcTMKkHmGmuaZiciTqsQtxphVSsxkd2lAwNUEQjVicHDx4EGvWrMGrr75are044eRNxhobrHlS6Z/NheY476Y45zOXMvnPlNQ8yDOKuhC3auFT8rdosbj+bC40x3lXZ84p6ck4ffoEpFIJenTrA43aG42B5niea3reokpmGgucPOenbmBBsFOmTEFMTAy+/fbba3YpnDhxArpCI07m5Nf4GAmCcM+lTC2ysgyIEHtBmlOUKtkizHPtHIIoi6FQj59/+x8M6UaIsr0AoQO2YCOi2kRh4s1TyM3cDGjVLQwqjQJdunRpGJYTlkb88MMPw8fHB0uXLq32RSiWiBEZGYHGBnuKzsjIRHBwEG8/3VxojvNuanPW2zMRLvKDJMeMdv3cZ3axJ6vMjEwE8Tm7r0bcFGmO867qnB0OB96aMx047gUfm++VBZeA9LwcbFCvxYP3Pla7g64mzfE81/S8xZVUHXUiTkwmEx599FHodDr89ttvUKur/7QlgAAyD6XYGwPsZtUYx++w23H2+DHotVqEtmhR5fTjxjrv6tBU5iyRSCCWOCCROK46H/YF1hTmXFWa47wrO+dDR/fBmgSobIpyyxQFPjh/4iysNgtUyoZvjWuO57mm5i0QWBqGOLHZbHjuuecQFxeHn376iQfCEo2T3Vu34K8/f4chLBhmuRzKjevgU2jCUy+8hODwxmfFIgii7ti6dTNkOZ6zM50pEhw7eQSD+g2l00LUvjiZPXs2tmzZwouw6fV6HD16tGRZx44dm4S5uzlweN8e/Lx6JYxjR0MgFPL0fSOAQoMBC999B2/NnQ+ND6WUEgTh+UFV4KzAne8AbHZWhZQg6kCc7Nq1i/+cP39+uWWbNm1CRAQ9cTcG/vz1FxhHDuXCpDQCpRL53Ttj9V9/4o4HHqq38REE0bDp3acfVh1YA1VB+dpXDEGoHR3bVRwkSTQfal2cbN68ubZ3QTCXy5bNWLNqJQx29nTiRHhgMO6cdh+PC6ku+bk5MMgkEHiKZIoIx/ENW3AHnQmCIDwwdOAw/LvyL9i0Voidrv29TFI9gqODEOgfRMeP4DSR4rrNm++//Bw/7NiCtGGDoBszCtobR+N0myjMX/guLpw5Xe3tWy0WON00CyyGF3MT0KVEEIRnJBIpXnzhNRg7ZsPgmwur0AyzyAh9aDbkvQV47umX6fAR9Vshlqg5kuPjceBiLKzDh7h0NBB4e0M/egS++eIzvLvko2pVg/ULDIQkv4CXu3e3HWdBAQLctCkgCIIoTWR4FN5fsBS79m3HkcMHeZ+W4SNGoX2bTlSxmnCBxEkjZ9WKv2Do2smtCUwgk0LnrUHChVi0bNP2mvchEonRt3dfbD4fC3s71+0wweJ14DBufYw6TBMEcXXEYjGuGzSCvwjCE2SLb+RkZqRD4FeqoFEZjBoVcjOLSo5Xhyl3T0N7gxmy3fvgzM+H02aDMzkFyrUbccvIGxDVuk2190EQBEEQDLKcNHICg0NwKTcPAn/3bhUvnZ67ZaqLUCTC86/PRNzZMzzwtiD/HFpGt8KNM2fVyPYJgiAIohgSJ42cmybeglNffArTsMHlljktFqjyChBVDZdOWVq174Cn2neose0RjasLcYyKatkQBFH7kFunkcPKx/du2QqSg0e4q6UYp1YH1YYtePDRJyjQjKgRYSLPtEKaY0aHNlTlmSCI2oUsJ02Aex99HDGbN2Hd6n9R6LBD4HAgLCgYd7z0KsKjWtb38IhGLEoY0nwHFybR3hoSJgRB1AkkTpoALL13yMhR/OUp3ZcgrtVawkQJCROCIOoSEidNDBImRE3B4kukZnLjEARR91DMCUEQBEEQDQoSJwRBEARBNChInBAEQRAE0aAgcUIQBEEQRIOCAmKJKpGScAmxZ07DS+6Frn36wkupLFlmMhZiy7p1vKGXSCzG0KHD0GfIUN5LgyAIgiAqC901iEpRkJuLDxfOQ7ZIAF1IEIQWKzQrlqNvtx64/b4HkHDxAj5a/D507dvA0bcHYLcj7sAerPx7OabPmgN5KRFDEARBEBVB4oS4KjarFfNmzUTWgN4Q+PpCdPl9XacO2H7sJAT/+w4H9u9FwQ0jIJDJwKusSCSwd+2EzMgwfLBwHqbPfoeONEEQBFEpKOaEuCp7t29DXotwLkzKYu3WGdt37YA2ugUXJmVhn8kWC5FyKZ6ONEEQBFEpSJwQV2X71s2wtW3tcbmpRThsomJ7Snn0ocE4d/q0y3usku2F06ewevkf2LpuLQw6HZ0JgiAIgkNuHeKq2FhDwYqCWiUS3gHZE0KrDfJSVpXMtFR8uHA+8r01MIYEQZBswor1a9Cnc1fc+cBDVOWWIAiimUOWE+KqdOveA4L4BI/LFanp8NJ6tnyoE5LRtW9f/n+zyYj35r6N9EF9Ye7fG8KWLSBo1xb6G0ZiZ3YG/vrlJzojBEEQzRwSJ8RVGTXuJqhPnYXTYi2/MDkFbcIiECEQASmp5RaLT51B93btoVJr+O/bN/yH/LatIVCp3Mav7NqziwfgEgRBEM0XEifEVVGq1HjiqWegWbsBgnOxcBYa4czLh2zfQbS8mIDHnnsBL82chc6ZuVBt2ALnqTMQHj8F9doNGOTtj2mPPFayrQP798ERE+1xX6aQYMSdPUtnhSAIohlDMSdEpWjTsRPmL/kI2zesx4kTJyCXyzFq0lS06dy5JEbkuddm8HooLNCVFWFr360b5F6KcoGwEHrWxA6hAA6Hnc4KQRBEM4bECVFpZHI5Rt88gb884e3nh16Dh3hc3rFTF6QmJgFRLdwu90rPQsu2bems1COn4tKRlJwLeaYV0d5F7jiCIIi6hNw6RJ0yetxNUB0/5TZ+RRh3CZ3atC1nbSHqTpSs234a0nxHiTDp0CaYDj9BEHUOiROiTlFpNHjyyWfgvW4jhKfPwVmghSMjE14796B9rhb3PfYEnZF6tpZIc8wkTAiCqFfIrUPUS/zKgg+WYteWTTh9+hQUCiVGPvgoIqNb0dloAG4cspYQBFHfkDgh6gWJVIphN9zIX0T9E6PygdRsJmFCEESDgNw6BEEQBEE0KEicEARBEATRoCC3DlEtWN2S9ORk2GxWBIeHQyot35mYIAiCIKoCiROCCwzWFdhqseDE4UNIS09DeFg4+g4dWqHY2LFpA/5d8TdMvt5wiMSQZuegW5euuPvBhyAS0aVFEARBXBt0B2nmbF67BuvXrYZWABgNhXCaTBC0bQ1JdjqW/70cU6fegf5Dryv3uQ2r/8WK3TtgunEUBJcrvpoA7LoQh6y5c/DSm7PrYTYEQRBEU4DESTPml2XfYEdyAsw3jOQCg10MTqsV9m07YZPLoR13PX5asRz+AYFo07FjyedYY761a9fANO76ktL1xThat0L87n2IO3sGrdp3qIdZEQRBEI0dCohtpuRmZWHvyeOw9OlZYvlgCCQSiEYOg+N8LGC3o3BQP/zx608unz195DD0EWHlhEkxpg5tsW7NqlqfA0EQrlyIO4dffv8ev/35E5JTE+nwEI0Wspw0UzatXwt9u9ZwJy+Y6BC2awvHhTiIOrRDTkGBy3I9i0/xkkHkaeMKJXS6+NoYNkEQbijQ5mPhorkwJJkgypIzGyj2/rcH/q188dLzb0AuY+/VDA6HA0dOHMLRw4dgsVow9sbxiIpsSeeFqFFInDRTcnKygWB/j8sF3hpeVp7hLKNgLGYLBPGJQDsPDfrS0tC6dZsaHS9BEJ4D2ue/9zbsh2VQ2wOvLEjUoCBDjyXC+XjtlVk1cvhS01OwaPE8ONJEEOfJ4RDYsfjQe2jRIQLPPvUyxGK6pRA1Q51cSQkJCfjmm29w7NgxxMbGolWrVli1isz+9UlUi5Y4mBIP+Hi7Xe7MzILA1xdOoxG+CmXJ+2dPnMDy9WtgM+gh0ekhUKtcP+dwQHXyDK6fO7/W50AQTZmsnEysXbsSmZmZCIuIwI3Xj4OvT/kHipNnj8OUaIPK7lNumdysQkZcBtIzU1FoLMSKFX/yBxOlUolx4yaga6ceHt2zZcnMysCst1+H/6VIiB3SKwsSNEjLycGXio/xxCPPVW/SBFGX4oQJkm3btqFbt27cJMiUPlG/DLv+Bqx75UXoW0W7xJygOCj2QhzEt06E19admHDbHfjnt1+wZ89uZObmwCaVQBgWBsvqdRD36Qkh24ZAAEdWFsQ79+KOu6ZB7V3+i5JoeD11GKX76hANg59+/Q77t+2DOFkFqV2ONMlx7N22G6PHXo+bx01yWXf3rh2QZHvu5C1IleGzLz9C3iUtvNJ8ILVroBNasez0MoR2XcXdPiKhRyct7A47Pv9qKY4ePAR5lo+rMLmMl16DcyfOwVCoh1Lh+sBCEA02IHbEiBFcnHz00Ufo1KlTXeySuApeSiWmTpkKxYYtvDNwMY7sHFhXrII4KhKadRsx4brh+PO3X/BvZiqyrh8O4Z23QTJpAgQ+3hBKpXBk58K24l9Y//4X9uOn4COWuE09Jhpmsz9pvoMa/jUwtu/cjANrD0ETHwqFVQ2xQwKFWQPNxVBs+Hsjjp064rJ+ZR72kmNT4J0cwoUOg21TnRGIjP15+GvFbxV+9utvP0Xc5kTYdE5oTH4e1xNkSHA29lSl50kQ9W45EZZ5MicaBgOHDUd4RCT+/PVnZORmw2l3QCoUIrRdB7Ru1w7Xjboef/36M1JjWgLRUSXBs8zSIurYnkXOwmkwQHzL+JJtijZsrbf5EFXvQsya/Y3tTfFBDYlVq/+BMqO8+0YAAVQp/vhr+W/o1qlHyfsDBgzCuZ3fQZbh3nqiU+fANyfU7TJFgQ92796JW2+5w617R6fX4cyx01AXhCLPJ5MNgsXauqdy3iGCqBSNNnrJCSfMZjMaGxaLxeVnfRMSGYmnXn7V4/LDRw7DyeqZuFkmbNcGtr9WAr178t+dOj00KpXb89LQ5l0XNMQ5n7mUiZTUPMgzrIjQqNGqhU+N/h1ZLGaXn82Fmpq3zW6DWWuGzINRW+SUoCAnx+WctWvdEZJwASzZRkjtXi7rG6V6WGUmeNncu1qY4HEagPyCPCi8rsSWFXPk+AE4UyX8/0qzD7TyXPgYSwXdlsIZZEGrqDaN8nu5stD1ba72MRQ5m7g4sVltSEpPRmMl43ImTEPGYbfDLChKLXYHs6A4nY6iX5xOSA8eRr/h1yMpKblRz7umaUhzzs7RQlPogNRiglIqQXJSUq3sJ7MBzbkuqe68WUyezWGrcB273VbuvN1527343vYNjOlaSHOVcAqcsATooYrwgrqg4lgiK6zISM+ARFwkQkqTlZUFXP4T1xgDkOR3Fiqzd7m4E61XDrx8FMjKyIJIlIumDl3f104rv7CmLU7EEjEiIyPQ2GBP0exmFRwcBKm0fGBZQ8Jv717s3LIV7/t649eB/cst575uowm3/fQrXj56HP+bMgUdhg9v9POuKRrinPX2TMhEDkgEZkREun8Cru6TJfviDuJzbj5NIGty3n7BfrBfsnIrSbn9iEyIaBmJiMjIcsvefXsRzl88i0MH90EoEmHggCFoEdES3/3wFWLjE+BlVZf7jE1oRWBYAKKjW7kdi0KlwPZ124B4FqAoRGhBK6R6X4DCooHS4g2HwIE8r3QIIITggi8++WIJHn3kKcREN01XIV3fQdW+viubbd5oxQkzR8pkjffLj92sGvL4fffsQbenHofAasW7v/3J3ysrUJwJSbgHwLz9B7kb+rk//sCx0WOQN2BAo513bdCQ5iyRSCCWOCCROGp1TOwLrKHMuS4pPW+W5ZKblwOpRApvTeWz1+688z58mv4hvC+F8pt+MQ7YUdgyB0/f+YbHY9u1U3f+Ks3UKXfhrVOvQXxOBkkpiwerUaKPzsDD014t2Z5Or8WW7RuRnZWFltHRGNT/OoS3DkNWqpanJbOA2si8DiiUaqGT5kErz4G/IZxJE4jsMkjP+ODzz5Zi7tz3oFY13ewvur6vHYHA0rTFCVHLwuSRByGw2Uri38oKFEdKKqauXIV5F+P4cu74sdn45459+U2FAoUgqktmdgaysjPg4+2L8NDyVoT6hImS3//8Cfv27oGwUAyH0AG5rxS33XYXenbrfdXPd2jbEXc/dB9++fl/QLoUMIgg0NiBYCsee/BJRIS1qNJ42DF6/fVZ+PiTxdCl50GgEwMKByQBQjz5wHNoFRXD11u+4lds37gNohQviKwSnFCcxYq/lmPqHXdhg3Ed8uOyIMlUQugUwiQphF6eD7FTCofIxoWJQZaPbFUS1El+WLNuJabeejcaKhlZrLHpr0iIu8S/vDp07IQJ4yfD19tzNhJRt5A4ITwLE0eRs7m0QLGfOIWf/f1xt1brKkzYT7Y+CRSiFknPTMMnny6BLr0QAr2o6CbrL8RDDz6Otq3b1/uxZ67OJR8uQOrebKgKQriFt9jq8V3W1zA9aMTAfkOuup1+vQegd4++OHryMLKzMxEaGobO7btdc+ZjcGAI5sxaWCLqvL19EVFK1G3buRk7/tkJddqVMUOrhFPrjV+/+wkvz3gNFrMZm7dsRHZOFrJOaCGzyBFWwFpgFI/JH3aBDSk+sTh4eH+DFSeHjx3Esi+/gjzBB3KbH0+uOHnqHA4ffA3PvfBKiVgj6pc6yfE1Go1Yt24df6WkpECv15f8npvb9IOnGrMwKaZYoCw4fQZLCw2Yd+y4izApWc/h4J9n22HbI4iaIq8gF+/OmwXrfinUl4KgyvaHKjEQ4qM++GjJYsQnxtX7wb54KRbJJ1KhLPC5cpPnX7QiaBJD8fvvP3PLSmUQiUTo1a0Pbhg5Dl079qiRkgxBAcHo1L6rizBhrFq1Aqq0AJcxM5jwUCT6448/fkHrVu3wyINPYsqtd8AGK0K0rVzcTnzMTjGCdVHIa6Df62azCcu+/RLeF0MhtxVlJ7E5e1k0UJ0PxscfL+ZByUQzESc5OTl49tln+Wv//v1IS0sr+Z1VjyUaAE4nOr3wLI8xKStMygqUiYeOuBUmLgLFauXbY9sliJrg9z9+hjTW2yVugiFyiqCOD8KPP35b7wd63bpVkGW4j7VgN0FBFitUdhoNCb1BB6vWXk5oFMPiTFKTU0t+DwsJh8Ap4kLEHTKbgouxhsaF+Fi8OvM5CBJlbufK5uPMEOHE6aP1Mj6iHtw6EREROHfuXF3sirhWBALEPfcC2r85o2LhUeanO/jnnU6+PbZdgqgJzp89C4UtyO0yltqanZYGq83qNiXWE+wp+fCxA9i5cxv/f79+A9G/zyButbgWtNoCSC5XYXW7Pz2g11+pyNwQqGxvnWKEAiHk0oq7HCu9GlYJ+/2H9uKHr5bBmGuGv6WCKrf5EsTFXUS3zkW1m4j6g2JOiBJSp97OLR3t35pZoUCpiOLPnX37naLtEUQNUVxSxxMChxA2a+XFCSs89u6CWbAmArJcdjMV4M99y7E84jdMf2UmggJCqjzGyMgonJHE8bLzbsfo70RoSDgaEqwXjlQjggMOni5cFovIiPDIK2OWy72gClDCmeos5wYqTk/28W84vbVsNht++nEZz36yKlP5+IrL+JfFKbHBx6fhjL05Q3XlCRdSb78DZ2fPqbBKtSdImBC1iULtBbvAfbwGC2oUeDn5jbOyvLd4LnBMwWNXJI6iNFtVrj+kJ32x8L13rin2YOyYm2GJ1PLxlMUmtMArWIIW4S3R0Bg/fhL0odnlxu2EA4VRuTzTqLSlZfSoG2AIyCm3HfZ5Q1gObp08FQ2FQ0f3QZgu564cb1MA8hUZbtdjY3eEm7nljKh/SJwQNSJQSJgQdXEDNQSVvyEyCn3yMXjw0Eq7KFjgqiHZDFmZcu8MJlLsqSIcOXGwymP09wvE2Ak3QdsiDVahueSmVyjTwtAuC888/RIaIoMHDsOIycOha5MGvSIXRrEeBp9c6Nqn4f5HHy4XQHvD6JvQ9rrWyAtPgUls4KLRKNVC2yoNo28ZhU4duqKhkJqWCoGhyEnALCYih5gXjistxNj/9SFZuG7kCHh5ee7wTNQd5NYhPAoUnwMHELJqZaWOELslpN88nlw5DbjZH4N1IU6Jy0K0d+MrkNW31wCcGnkCR7cfhTzNh99omDXCGFiAsG7BuGX8bZXe1tGjhyHM9FwkTpLnhcMHD6BXt75VHueNN9yMNm3b4c8/f0VOVg4LwEK3rt0w4ebJlS7GxjJ6WCzMgf37ePzL0KHD0L5NpyrHh1SFCTdNxqgRY7Bj12ZkZ2cjKqolWrZshTNnT2PL9g3o0a03r5nCYON4YNqjONzlAI4cO4T8/DxERHTAuLET4O8bgIZESEgonAobcLn2V5AuCrmKNCT5noHMpuTF6IT+DkyZcgdGj7ixvodLXIbECeGWsF9/4cKksrEnbL2Qf1civ09fEigNtAtxjMoH0hwzFyYd2gSjMXL/tEdwcWgsVqz4E7k5OVCrlLjj5gfRpUO3Kt24JRIxnALPbhsWfyGpRsuB1tFtMf3lN6/ps1k5mViw8G3YU0SQ5il4n5yzu76EsoUMr736Fo8RqeizK/9dzoM6WTXg4cNHY2D/IZWOw7FaLcjKzsapU8exect/kFuVkLJ4HKET/4T9jZjOrfHEI8+WBAyzuJwH73usQVcD7t2jH34N+RHO/KIYGfbPvzAMfoWhMIuMMLTIwrvzl5QIL6JhQOKEQF52NjJTU6Dy9kZ4VEsuTKoaFFvsAmLZPudOn8Q8nQ5GpwNChwORoWGYPPVOOtL1KEzkmVZIzeZGK0pKE9OyDV587rVqbaN/v8HYvGoz7xnjDluIEdddNwJ1DbOYzF/4NiQnvOHluHLDl6UpYMo24P0l8/DWG3PdfnbXnu349cefIEvUwMuqgRV2rDi9EqvX/oM3X38HKqX7IN1ijp44jG++/BySRDVyFOkI1EW6djO+CFzKTMFn+BBPPf4CGgtMmN1+5934+ZsfoEoMgvhyzyImTi0hWtww9kYSJg0QEifNXJR8+sEiZFrMsPj5QGwoxD2nzmD6qVPXlK1TLFAm/Por9t16C34dUhRYlp+Xh/j35nOzaWNs1tiYhQlz4zBh0pitJbUBK0YW2S4CqZk5UBhcXVwmmR5+rTSIblH3lUKZK8eRIuIBumVhVoy8hEwkpyWViwHJycvmwsT7YlhJBg2rNaLK84P5uAFLP1mE116Z5XG/hkI9vvnqc2hiw2CU6niTQBdhchmFzhsXTl7gmU5e8sYTmzGg72D4+vjh199/RF5mFv+2UvkocffEe9GnZ7/6Hh7hBhInzRSDTod3Z81A7uABEPh48/cm7957zcKkmJJS93/+zUpc8l48Al9f6K8fjn+W/4Y+A6nnTl1DwsQ9zzz1Ej4Vf4D4M/FwpkqYLweCMBtCY0Lw7NMv13qZe1bsa/Xqf6DXGxAaGoqJE6fgwL69kOYVVS51hyBdiqPHDpcTJ/+u+huyJI3b1F6ZVYmMhHTk5efA18ff7Xb/27gWkmQVTyXWy/LgW1iBkE2VYv+h3cz0gH379sDH2wejr78R7Vp3qNWYmOrSvm1HzJrxblE39Wuo70LULSROmjiZaanYuWUzCo1GdOrUGd369uNlsNf8/Rdyu3QsESasvsmLq9ey7+cKU7iKhcvVCrWx7bDt/TqgHy/EJpDLYVApkZ6cjKgY6l1B1D8ioQhDhg6H0VSIHN9sREW2xKQJt/MKqLWJ3W7HgkVvI/NULryyvCF2qJEozsTCo3OhDPSCExXEbwgdEIvK/4VeuBgLuaWCwme5EiSmJHgUJ2fOnITcWOT2YTEuAmcF3wIOAX7/8xeocwIg06mgE2Tj84OfwbuVkltn5LKKC7TVNyRKGgeUStxEsdttWPrefLzz4SKsMuRjs9iJL7dswPRnn0JaYiIOHz0EtCj19CUQ4Jl774ZVLILdwxOFs1RWTkVpxuzzbDtse6UrxFp9NMjLyqzpqRJEldHpdXjjrZfww3s/IH+zBeJDPojbnMQDUROTL9XqEf3l9++Rc0AHdUYgr2xbXPLdOyEMuakFMPnpPH7WGWpFn15FncFLI5NKedaJJwRSZ4WigaXPOgQ2/n+FRQO9PM/jujpFDnwSQ6Eq8ONp1ywdW50WiMKDDiz95H2PnyOIqkDipIny3eef4YRcisJhQyCMjIAwOAjWHl2RM3wI3p8/F3aHs9wTxJ62bfDAow/BJhLCXkEdk9PvL/ZYB4UJE/Z5th22vdJI8rXwDQispRkTROVZ8uF82I9KoMoqKsDGgiSVBX5QngnGosXzedZKbVlNDu7fDy/dZYtlGXzTQ6HzyoVZYii3zKjQ8jgZVkulLKNGjYHRr8DtNlkND6e/lTfu88T114+FOaiorL7a5AetPJenaZeF1TThxeRs5YNr5WYVUi6mcvdRacwWM37940e88PKTeO7ZJ/DCi0/ih1++hdFk9DgegiBx0gQxGgw4GXsOjjatyi0TKLygbdcGDqMRTkNhueVMUNz/yENcZDgvd0F1V2AtZert2PP0sy4CxcGECat/4EaYOE0mKPUGhES6+soJoq5JTU9BbmI+j8Vw1/xNmCLHzj3bamXf+do8CI1it7EhDCaUfNV+UPYTQdciE3ppPnSyPOiiMxA61B/PPPmixxowXjFimKT6csJEF56Bm26eyN1YnujYrjM0MUoYpPl8bCEF0UjxiUWuIp0Xk7OITNAFZSE7NAGheZ7dss40Mc5eOOMiTN6e+wb2/3oYihNBUJ8LgeJUEI7+dgpvvT0dRmP57yCCYFDMSRPkwulTKAzz3BfE0ToakrhLkB86AvPQ8qWa94vFWHLrFLz8zwpW+IA38SstTHZv3YIVy/+A0dcHt/bvi9l793OB4hRLsGDMGBzQF8Jpt0NwuRaCM78Aqh27MeFW6rVD1D9nzp0E0j3X/ZDrVDh06ACGDx1d4gI6f/EMj9VihdC83JTIt9lt2LpzI06fOgmHxQGBWIDuPXti8i23Q6m4IoKkEhkcQs/1VZiYEElEeGvGu0hOTcTRY4cgFInRt1d/BPh7tjqyuiMzX5uDjz9fgpSLKRDkSQCpE/CzYfzNEzFy2PUVHhNmRe3WrSdWnlmBPEcGd+0ozBrkyzOgl+ZC5JSgZacoBAsCoE0ucv+4HwggFl+5rfzz73IYTzqgNFwpPsfEj8LkDeMpLX75/Qc8cO+jFY6NaJ6QOGmmKJRK9OzYCds2b4exR1cIfH24dUN6+iwiC83o8dbbODbuZnR64VneXbhYmOzYtBG//LcG5htHQSAU4oeBfWGNaoEXV67GnJ49MW7Be7D8tx4b/1sHo5MF1jkQGRyCyS+9Cou9qt16CKLmYcXVnCLP16JD4IBEIuWunc+/WoqLZy4CmeIi82GQFd1698C9dz/ExUpxY7mFi+ZAe8wIpT6Q33yZyDh24TSOn5iOWTPfLakxolapoQzwgj3eDpGzvCXDKNOhZ/ce/P8RYS34q7KwuJGXn38DWl0BklISIJPJ0Sqqdck4r8be3bsQkduOx66YJAYeFBtgiCix8uSmpWHKHXfgz2PLocpxH1grCLWic7srpev37d0NhcG9qJKb1Th+9Chwb6WnSDQjSJw0QVp36gzFj99D17Wz2+XCC3Ho138grh8/Af0uDsI/y/9A1pETkMvlGHPjTejeryijJ617N3z//AvYu28v7Pv3QimV4VJCPHDPHS7xKixdmGXlSI4cR+SB/Rhx41j+Yil7xesZjUZs++8/HNq1HT4+vhg4bBjU3tT9k6h7enTpjb/CfgcuuF9uCdBjxMipeP+DecjZq4PaUMoKmQ+cyDyLL82f4LGHn+Zvrd+0GgUnTFDr/V2tA2ZvmE7p8L8fvsaTjz1fsuyuO+/FZ1kfQxMf6tIFmLlP7DF6jL9pcrXmp1F7o1P7qve2sVvsfNzMtaW0lI+JEViF6NKxO1a0+BPmfGO5vkSFPnno2qu7S28aO7MieXBhcRFnLYrDKa44SxDFkDhp4Bj0Ovz370ocOXyIu05YOvCNE26Bt6/nUsteCgW6tu+Avecvwt7W1T/M4ky8z13AdY8WfbG2iInB069ML7cNvVaLuTNeQ1abVnCOHMqzblJ++h2imGiI3WXzCASwdGyH/9atQY/+RbVMioVJUlwcPlq0ENrQIFhDQyBIysPa2ZvRq0Mn3P3QI5TaR9QpzHrRsUdnnMu6AEWB69+RSWqAsqWMr5NxPgtqQ1C5zyvzfXD26BleiIyVPN+2dTNUWvd/j3KrGufPnne5AXds3wUPPv4IfvxxGezZQjiNAsDbBk2oCi8+9TbUqvrpeySSioq6O3sQEw6JA0qlEjNeexuLPpiHgqQMCDKlgMgBhFvRqWdn3HfPwy6fEUqLrEgeBYqkyCVFEGUhcdKASb4Uj8XvzYe2Uwc4Rwzh76UkJWPfjNfwxJNPo03HTh4/e+8jj6Hwg8U4v3k7dDEtAZkM8pQ0qDMy8fzrMyCTV1yLYMncOUi1mICTp4pe7IvESwaBqoJaCjIZt5CUFTlLFi1AwejhvNZJ8XOivnUr7Dp6HOrff8PEyy4jBmtTz0QN1SIgapMH738M3wq+wKkjJ4oKsNkFEITYENgyAM898wp+//MniNM8V0AVpMiw98AujBl1E+xmZh3w7DoRWkQwmY0uPXG6d+2Fbgt6Ij7xInR6LUKDw3ifmvpk0OCh2HphJ1T68kLLJNGjdbs2PKiWNS98+80FuBgfiz37diI4JBT9ew/igq4sffr0w4ELR6E0+rh1YXXs4t66SxAkThoozCWydPF7KBg1DAIvr5LnDkGLSGhDQvD5J0ux4IOlEEvcB/YJRSI8+eLLyM7IwM6tm7lo6HzDOHTq2euqPuit69fhYm4WxKNHQqC5XJgpvwC2zVthj70AUbcu7seclo7omNYu761buQJaVuzNjRiyduuCLStWY9wtk3Bw9y6sWrkCBocdAocTgT4+uP2uaWjVznP6I0FcK+wm+/ADT0Bv0OHYqSOw2azo0LZTiUAwmUxuY0KKETiEMBYWCXGRVHgVi4Mdcln5IFomwFlMSFkOHt2PFX//iUKtgce5REa3wB1TpyEkKLRWT/jYG27GgUP7YDieD4XBu2Q+BokW+haZmDrFNVOIxcMM6D0EEZGRHhv/3TLhNhw7fgSFJ7XwMqlLtsmEibO9Ea1axWDmrFdhMZohU8hwww3jeKn5ysbJ1AYX4s9jz56dPA1xwIBBFaZgE7UHiZMGyqnDh6ELCuTCpCwCqQS66BbYs20rhowqyijwREBwMCZOvaPS+zUZC7H8rz8gumU8D3gt2aePN8QTboL1h19gz8yCKCiwnJiS7juIm+a86/L+sWNHudXH3dc2+3LW+nrjibvvANrEwDF8MASXxZbWYMCST5fi4Wn3omuvPpUeP0FUBRY8evL4cWRkpOHcmbO4adxEXiG2S9duOL8lDtK88n9/DIe/BR07Fj31Dxw0GNsu7HZvcRAbEN06utKui1//+AF71uyFMt0fSmeRpSXtXD7mnn8Lz73wMmKiXVP0axIWBMwaBC75aAGOHzsKoY2N2cldU4p0H96QkLl0/HzdB8O6gxV+mzVzHn7740ccPXIYDosTAokAHTp3REZGOlZ9tQbKXF9InUrYBHb8cf5P7OixBS+/MKPC1OfagAnVhe+/A11iIUSZRef98KajUEXJ8cqLM91ahojag+qcNFDOnz8LU0h5f3cxtrBQnDt3tsb3u2PTJpg6tncRJsWw1GBh966wrVkP++mzPF2Y4cjMgv3PFRg7cjT8g6rWXM4pEcOsUcE5ZGCJMOH7UipROHoYvl/2LXf1EERN88PP32LBW3NxYUUSzDskiP07EQveegc//LKMuymcYWbYL1dNLQ0LXJWHidE2pj3/feyYCfDqKITBK59bUIoxivWwtdfh/nsfqdR40jJSsXvTbmjSgnlQajG8AuuFEHz+xdKSvjC1KdbOnzkLkVXCGw0yYWIWF/JuyeJTGnz4cdUrwDKBwrKbliz6FEs+/AQfLP4UQYHByD+q5yXwhZctVMxSxX7POJCHtetXoq5ZuGguTAcBVUogvKwq/lKlBsB8UMizsYi6hcRJA0WtUkFgMnleodDI16lpkpISYA/w/GQkDAqEiBVYs9lg+3cNrH//C/vho4jUeGP8beXrmHTs0BFITvG4PUd8AkT9+7pdJhCLYQgNxtljR69xNgThnp17tuLQ+sPQJIRCblPyrBn2k/1+cP0h7Du4G88/9woM7TNhUOXBCQccsEPvnQtLpzy8+MJrJXFRUokUr738FqJHR0DfMR3a1mnQtU9D9M1hmPXmuzxGozKs/PcvyJLdB8MysWLLciI+wUOKUQ3x/uK5vPFgi7wOCNFFI0TXkv/fLrRBJ8+BNkWLjKz0a95+sbtm5/Zt5YKRi1EW+GLr1s2oS+IuxUKXYHBbmE9mVUCfaOTuHqLuILdOA6X/0OuwevYm6Fu7r8aounARw59xXy2yOgQGBEKQnQb4+7ld7sjNB8xmCAf0hbBDO4jPXYBvYjJemj3b7fpjJ07CvpmvQxcczN1RpbGfPM2+rSCsQAyZNCpkZ1I/nqpyKi4dScm5iFFRurY7Vq36B8oM99edKt0f//77N+bPXYL585Zg09b1OHhgH7cmjhg8FkMHDuMukNJIpTKMvX48Ih6M5C4c9qpqUHdaegpkNvduJIYjT4CsnEy0alk7rh2WfZSRmIUIg2uMBYsTCTCEI9nnPMQ53khMuYTgwGsP3mXWH5upKG3ZHex9q7GCQm+1wL79eyDM9JwkIMqUY8/eXYicFFWn42rOkDhpoLAaIL06dsbuYydg6drZ5YtOdO482geGICgsrMb3O+z6G7DxrRnQt4wq9+XKvlTEJ09h+OChuLB1F0RiCbp17QpHREvs2LQBvfoNQGiZ8vTefn548smn8enSD5HfIpwH9MJo5MKEuXEELSLgzM2DIMx9sJ+8QIfA4PrNYmiswkSeaYXUbK7v4TRITDoz1KycqRuEEMGgKzpurBrsTWMm8ldlKV0htSqwCrCJoqxy9UOKcShs8PVx/9BQE2zdvhE++Z5dyd7GAOi981yyjq4Fno13lXCSqy2vD4RV05pENSG3TgPm7gcfxuioGPis+Q+yA4chO3gEmtX/YajaD489X/NWk2JRNKz/QMj27IfTduXpxWm1Qr59N26bPAV3PfQIXnlzNoRCAf47dgT/FGThr+x0zPv0I8x/awYPqi0NS3l+573FaJlbAPu+A3CkpEI8oC/EI4dB3LUz7IeOuB0L26cqIwvtula9oFRzpbQwifbWoEObYP4iXHEKrhK7cbXltcD4myfBGOK+GzBzKuUJM2GxWGtt//n5+SVdkt0htkvh9LKhXesO1d5Xq7YxPFjYHez9th3qNkOmf/9BsAd7dqPbgowY0H9wnY6puUOWkwYMe8KYdPudGH/rbUi6eBEOhx2RMTHchFyb3HL7nQjeugWr/vkbhSIhM5lAJRBi4uRb0XtAUS+e99+ZjbggPwh8fSHw9+NWkMKWLRCbmoYP5r2L6W+/47JNlvI8fupdWPbdVzD06VUScCvQaCAICYZ101aIBw+A4HJKolOrhXLnXtz/4CP1mlbYGN04pYUJ4Z6Q8GAUxJp5oz13Aa8hEXVvrWsR0RICHwey81PgbwgtqZ1iE1qRpolDQH4EfvvtR3SetdDlc6wLMHP3sIJw1amVEhPTBkeVJyEvKB93wSiU6TBg4KAayaK5+477MTv2DQjPiyC1X3GnsAaD1rYFuGPqq6hLolvEwKelCoZMA+QW1/mzDtGaKBV3p5nJEllnkDhpBDAzcXQd1/sYOGw4fzEriEAgdCna9sf/luFiwiX+dMnqnzh37+UCRTR4IHfPpF6MR8qlSwhv2dJlm2ofH9w2+Tb88def0HfvDDBXjs0GmZcCikuJUG3fAwNrGOh0IjQwELc//zIioqPrdN6NGSZMpDlmEiaV4M477sX7CfOguchKyF+52bK+MoboLDxxx+sl7xVo83kwJPs77NC2Mw+ArS3EkMDpECDJ9xzPYnEKHBCyHjf6cHjZVNBmpyE7Nws7dm7BxbgLSLgUD6FJDKFRCnjZIQ+U4uGHnkSrKM+dgz3Rs1svfCH/GMoCH5djUnxc9Joc3HdX5TKPrgZLR54x42188eVSZKekQ2gWwyGzITAiAI8+PAe+3kXuq+S0JKxY8QfSUlMhkxfVQends1+tpBmz9OX3Fr+D/IRMiNLZ950A9mAjvKPUeOXFN2p8f0TFkDghKkReqk8GY+2Kv7H+9AmIyvTXsV+Mg23dBojH3gB9dBTenTUDKv8AyMVi3DBmLHoOGMjX6zNwEDp27oJVfy/HxS07IRZLMHzkKPR/9iWPBeWIqkEWk6sTFRmNx55+Gt988zmcWSI4CgChtxOCICcef/AZbsUwm034+LMlSIpNArIlEIgBZ6AF/QcNxNRb766VKsYCpwA+xiD+Yq4cFhxaOnC00GTA7JlvQJgmR648HeH5bVysP/YEG5bkLcArr72ByPCqBW/u2LMNGpM/kn3PI1AXycUQwyjRI0uVBKVcBaOpsMbK6wf6B2HGa3NgKNRzAciymkrHs/y7+i9s+Pc/yJN9ILNpYBLY8Ovp37C63T/8czUtEln36Fkz5uFSYhz27tvF3+vXbyC3qhB1D4kT4qpo8/Px92+/4OSpk8jOyYbw7tvLfTGLYlrBmZLGq8SyPjuGqBYwD+gLp8WC7/fs4B1Pb76tqBicX2Agpj3yGB15ol7p1L4LFi38GOcvnkFOXjYC/ALRplV7fm2z4O93F86C4ZADalMpV0kOsC/7IO+Vc9ft99X4mDR+3jAJrRA7JC5NARl6aT4cBsA7ORxZ6kQE66LKuaV40764IHz/47d441X3GXSe2L9vD3wLQqARBiBXmY5scTJ/n2UQhRa0gsmmw9kLp9Gne3/UJEyQlA2yjUu4gA3/bIAmkbm3BCVzY92QC4/q8PWyz/DEI8+iNmjZohV/EfULOfOJCmHl72e/MR3bxU5ksyaCXTp5fGIUdekEx5lzcFy4CGF00VObQCqFrWc3nBPYcXz/PjratcDR3FQ6rtcIu5bbte6IgX2Gom1Mh5Jr+9TZE9BeNMDLVD4zRZXtj/2798Focu0jVRPceuvtMIRluxRzKyZHk4Lg/Jb8Zm0SF8LL6r5iqcQhRWZyBhdQVYHPXcA+L+PCJzKvPX8FFYsgIctYqZtbxvLlv8ErxddturFXoRrnTp3lli2i6ULihKiQzz9agvzrBkEQHgZYLICbcvolKLzg0OnhzM2HMMQ1GNPWpRP27dtNR7uGRQl7pduuvSgW4Z5Nm9ZDmlVBufI0CY6dPFQr1pzRk65HQatUGKVa2AV2nr2ibZEOiVRSUjmWuX8qgvX+sdqqltkzeMh1KNQUeFzuDC7qP1QXZGdluQTKlkWklyAzO6NOxkLUDyROCI/kZWcjy2opaf4nCAyAM8XzU7qDVYItKIB4zKhyy1g2j6WWS283J4pFiVBzBmJJEi6ac2DxEcLiL8OZWPrSri688Z+jgqBLi4CvUxvcPPYWzJo7F12ndoBqmACtJ0bg5dmvQa1WlwtSdQdvQugFyKqY1Te4/3UQRFl5tlJZCr0L0Ll7Fyi83Gfy1DQsQ8+d9agYp8QBubyCByWi0UMxJ4QLrI/NOdYILS0FJoMBFu+i4Den0QjHpUTYE5Mg0upKBEsxrM+OY9deiG+b5LZZIfPhC6lHTo0KkzC/PHRRpyBDpMdRayQu5uSgA9yXBCeqRpcuXbFx71Yo3TTzYwiCbWgTU3sZdP6+AZh214Ou7wX7ozDWwmuReBsDkaNIQ6AhotxnC73zMXDQkCoH7LKqt6+/OgvvL34XulQtRNkyOIR2OMMtaN21NR6471HUFf0HDsK2szuhNJQ//ixQWOon5gG1RNOFxAlRwpkTx/HtF59BHxIEk48G0gIdzKdOAwY9nFk5EPXoBuGYUbCuWgNRrx4QtmnN65U40jOgOnwMmqBgpHly/cRdQqeOdWMSbsrxIUyUtPT3RZizSJhEKIu6QzOhciknBBf1+egAeqKsLiOG3YC1a1bDcV5T0piuGLPICO8INcJDXash1za3334PPkpcDO9LoVCb/WCUJiJDfQl+hjAeZ2IXWFEYkI/gbgGYNOG2a65SO++dxTh34QxOnDwGL7kcA/sPrVIn4prg+pFjsX37FphPGl0q5rIeR7qodNx/+8N1Oh6i7iFxQnBSExPw2ZefwXDDCO6CYV/HzGgsCA2Gfe9+SCaNL3kSk0y+BY5Tp2H9+XcoxRL07tMHE15/Ew6nA/PmzIZ22GAXywrL4Ak8dwEDHnmiWR/tYosHExfXChMlEWpA6SwSJuFebfn7J3RAi2BfpGfl1+CImy+sk+4TTzyLTz/9EJIkFRSF3rzmSKFPPkRRDrzwXN13qW3Tqh3uevBe/PLT9xBmyKAy+MCk0CE15DwvwBYYGIjJ46ahR5de1SpcyP7O27fpyF/1BWsb8OYb72Dpp4uRnZQBQYEYkDkg8ndi2h33o0fXXvU2NqJuIHFCcH798XsYBvfnwqQ0jmMnIBk1wsVELJBJIerZHcLuXaFauwEPPPF0yfLXZryFZV9+hrSCfDgVCoj0esS0aIk733obOXmeb5wOux3rV/6DbVs3wywARA4n2rSKwdRp98LHr26f2mrbFcPExbXCRYnCgnCvokq9RO3RoV0nzJ+3GOs3rMbRY0cgEYsxZtgtGNB38DX3z6ku/fsMRK8efXDg8F6kp6UiOCQUfXv2L9eIsCnA6p7MmP42r4Cbmp4CpVKFqIjoWqkvQzQ8SJw0U6wWCyxmM7yUSv6UlZKRDkHPLuVXNJnLxZcUw1w6FpWS10Hx9i2yBrBmhK/OmsMryxbqDVB7e0MilfKyz57ECYtzWTx3Ds4rZbAzy83lp759mVk4P/MNvP7WbPgHNc5S7LHmApzNzUHroMArrhiFpRpbvGItIWofVn9j0oSp/OUJlnb8xx+/IDczB2KJGC1jonHblLsQEhTKWz+wuj9XpbLrMculWIKBfYegueDr489fRPOCxEkzIyXhEn749mtkFOTDIZNBbChEr569WHy/h09cJcPGZnf7FMkqy5atLuuJAzt34IJYAEenDi6jEAYFIn/oQHz9ycd4dfacRlcvxGq1Il+ch/a+NkSoxWjHRQmJi8aM1WrB2Qtn+M/oqBjs2rMD/y1fB2VKALwd4Xyd5PPZmHv2Lbx74/UY9t1SJN33NDLH3upxm0Fr/kTkd0sR+/pCaLv3q8PZEEQzFycXL17EO++8gyNHjkCpVGLChAl47rnnIJU2PVNkQyYp7iLeX7QQ+mFDIFCrSrJotsZdYoUF4DSZICjVQ4ch8PGGIzOLC4Xi9aHT8yc9p5ccKrsDyjIpjlVl/drVsPbv7VYeCbw1SNNpYdDpqr2fq4mS6saDlEVktSJMkIsuGh2iFA6yeNQDNpsN6zb8i+3btsJmtkMoEaBvv/4Yf9NkHldSWdh1//fKP/Dff2tgKbCyPF6IVUJYzTaEZ7RzKRYmsynQJ7YA18XOgdjpRKsP3+bvuxMoQav/QKuP5vBHgPYznsDZdz4lgdIMSMtIxX8b1iAvLw8xMa0xcvj1dZam3ViodXFSUFCAe++9Fy1btsTSpUuRkZGB+fPn8xoBb775Zm3vnijFNywTZ9Qwl1Rf5r91xETDmZAA8a59sI0Y6uLTFfXsAdvqdRBMngDHxXgeCAsfnyITdEoqovoN5F/c1fEDG1ktFZlnoWrXqFCQl1sr4qR0kGpxsGlNWk7CkYowr2gSJvUkTOYueBP5xwqh1PpBBgGvnbH3wiEcOnwAs2bO44GXleHn37/H2lWroCr0RYA5hIsJrSULNqkJdoENYueVWK3uzkzMxR6ILtsj2bruBEppYcL/emx2EihNHPZd+flXH+HsoXOQpCohtkuR6LUD/61di7um3Yv+fSiWrM7Eya+//gqDwYCPP/4YPuymxm42djtmz56NRx99FMHBjTOWoLGRn5ONPKfDbQ0SzuCBUPy7Fvb1m2Do3AHw9+Mdh1WnzqJ9l244uXwlCkODIZ48sSQmhP2hHTl6Aj989UW1euXIxRLeg4eVuneHSKeHt29Rl9LaDFItcrtk1Kg4ySlUI1TWusa2SVQeZjFhwkSlvXLtMAuHstAHxpNa/PTLd3jo/sevuh2zxYx16/5FeG4bSC+ntVqFFl7S3cviRLLPWUTldebbLhImOyGCo6SvrzuBUk6Y8KqvDsBGFpSmDLO+xW6LhybnSr8miUEGZ6w3flr2PSIjWtR5inqzFSfbt2/HgAEDSoQJ48Ybb8Rbb72FXbt2YdKkSbU9BII37yuAXeXZbMiEgcLHBy++8jrWr1qJ1DMXEBgYhDEvvgKVWoPX33gVZuYOKp21IxDA2qMrDm3cipuysnhDv2uBdS1etmc77D26lVvm1GqhlEkRa9UBuboaPZelC5kVZcDUbKCpWWiGzVF510F1CBKdw0mBHhcVcuRkaIBY6k68bdsWKLXuAym9zBqcOH68Ula/tRtXQqH34cKEWV5YbRG70AaNKQAya1FcVYLfKdyU54u5zn0uwgRuBIrm+EEEbFnjIkxQSYGSlJKA9MxUeGt8eWoxZa40HuwOO3Zs3wplTvkHciZsvRJ88efyX/HsUy/Xy/ianTiJi4vD5MmTXd7TaDQ8J58tI+oGJhzEBVqPy52FhVAplHy9O+53rUy5ec1q6Fu19PhFqGsbg83r1+LWu6dd09j6Dr0O27duxoXT52Bv36bEMuPIyoZi1170ePp2yL3Ll9SuLoPd1AtpjPCxBwLBiixEyC7igKY3dh5LR/xBLcb2boOmCEsv3bZjM7RaLdq164DePftBJHSVBHaz3W3juGLMWjO++/ErtG3THv16D/SYHnz2zBlojEUiJ0uVxBvueZsCSpYrrBpYBSbMcK6FGA6PPUGKBYonYeIiUKwOtHn3FRz6bSt3oSanJeGTT5bAmGEF8oQQqJ0QBNhx9933o2e33lc9XkT9k52TBehEHq9JmV2BlKSiTtBEHYgT9uXBxEhZvL29eTzKtcKeYFh6amPDwiqolvpZV0hkMoSo1DifmwehX/mgT+nRExgzdrzbY5qdkw2HUuG5EZNKheysnArPx9Xm/fQrr2H9yhXYvW4TLAIBL3XvGxKEdk9PRUyMHa29tqAmCfMqvrn4IEAYVSvXktlscflZm7A5BKijYLfvhVp2DLtlAYi9FIV/955BlEaNdjHXZtWqKhaL2eVnTcMsHf/78WucPnwKwhQviOxiHPE+jl+Cv8djjz3DM2hKEF3uM+PhZlBoNODkb7E4rj6F34J/wgMPPIIO7TqXW0+j9kY29Dy2xCwuRJC+Rbl1JE45vpC0wuuW2IqFR5mfbud4eXn8XY/BbLFwIbZg/hyozgdDXRzbogccaQ58+9mXED8lQrt6KJhW2+e6IVKdOdttNjgFFfQLuvyvId7XLDV4rkXOJp5KbLPakJTeeFVmRkZmne9zzPhJSP/yE+h6dQciI7glhMV6iI8cR4zECyr/QCS5Ue5KpRripEtwRBSlSpZFkJEBi9obmy+cuuoYYhM9C1JZl/YY3qV9iamdpeEG++YiCqmQGdTwdpTvI3Kt2Ep5iJJQu9dRZmbdnesgREAmTIbRLxEnhQ6cKgzGhbQc5GRnoUVY7WQ7uSOzlq7vjVvX4cym89DkhV55M0cFe64NH324CI8+8hRUyqJ5tm3XHucvxkNpvOJSLoZ1+mVZNQqbGsgDHHl2fPnZp7j/wUfgV6amRqcOXXF0y1EoCn2gMpffVjGbbJ1h1GgxR5tRoUCpiOLP7bvjMVzo1AdISsLf//4O2UUfl6BbhhBCaBJC8MOPy/DYQ8+gvqitc92QuZY5s+81h8LCS/AL3DzqGaV6RIRHIDkpCQ2VmjjXrfzCGoY4YVYTna58rACzmjDrybXCih1FRtbczaquYJYDJkyCg4PqPpU6MgIz587Hmr//wol1G+EQCuEllvCYj96DBnt024SHhWLzxvXI79yhXNAqa/gnOXsWgxe8CInccxdUm82K/PwC+Ph4Qyx2/ZL1RH+1FWpBAs92aYxBpcxiwoRJUFAQZBVkI9U0kYiAv/kCdBIZwnu2QebpAvgb5YiIrH3rCXuyYl9gQfz6rlpX3Kths9tw+uRJqEsLk8uInGJ4Jfvg+KkjuG3yXfy9O6ZOw9z4N1F4SguFWeMiTDI0CQjPv3JNCSGCMikA+w/uxkP3u7ZZiIiMxH8bVyP3hI7vxyNOAfZ07I7zPXui7afzqixQitd/1ycGgX0Hom1kUWBkWkoqFDb3Te7YeAw6KwKCAquUGt3Qz3VDpbpznjD+VqzMXwF1WrCLRY9Z5SwtC3DH7S/A17vmg/8b0rmubHHlWhcnrVq1KhdbwsRKVlYWX3atsBMrkzXePwgmTOpj/DJZIO55uOrdRR998ml8+tnH0PbuAWFIUUCXIzsHkr37cMN9A9A2zMpyGCrMXMm25yPAVwRJmRL57lA6T15uahfaqONBGEyY1PW5ljgkEInFRX2SRGJ+zOtyDOwLrKb3l5KQBKGWJQQLPAa5nj51ErI7i/bL9j975jz88MsynD5xEg6TE/n6XMjMSi5MWHff0shtSiTEX3I77jemz8b8999GwtkE+BrdZxgWqvIxot91yB17C+LE4nLZOBVRvN4S9MR/unB4r1qB117pfnlhxVtgjQlZlef6+j6sjXPd0LnWObN6JkZTITasWwdBqgwCswjwtUEYbMfzT75aVFW4ASOtgXMtEFgahjgZOnQoPv/8c5fYk3Xr1vE/pkGDKKe7sdC2U2fMfHM2Vvz+G06u3cC/SYMjAzHg5dEY2t6ICEXFKbhMnAQosuGvsFVKnFAlVeKaEBSZz3fu2YZ161bBYrRAKBZi2MiR6NKlO5bO/QDqLPdWiIpgBbLenrkACxbNQcbOvHKuIpvACnOoDtcNGcl/zxw3BZoTh3jwayWHjY1ogTWCVpA4gPzcvJJlGl8NTAJrObcOg8UoQOGASllUVJFo+Nx040SMHjEG+w/vRUF+HlpGt0Kndl0p86quxcntt9+OH374AU8++SSva8KKsC1cuJC/TzVO6odrLtMuBnrdeSvCL6fgttL4llg4rmbdKE6rDZVFNLunrOYMq2m098AuHDt2GDKpHMNHjEKrqGtz0bUIbwmntwXOVPdBrkaZDj06d8Hij+Yj6UAalDm+kDs1/Aa+/fwu7O2wC1DbPQbJMndPdEx0hWN44dnpWOSch/TzmRCneUHoEMPqUwhniAV33XofZJdN3qyOydWyckrD1huFRJxwBmC1oCUXVMVMnnwbvoz/ApoUV1cAo9A3H0OGDKvWjY3VcbmUeBECgZAHFKekJWHlyr+QlZUJ/4AATBg/GdEtSgUaE9VGJpNjyIBhdCTrU5ywuJL//e9/mDNnDhcorHz9rbfeiueff762d01cpSLqtaDLzoPp4n5IfAsQ078TxJLGnYZL1B6sJseiJfMhSJVCWqCEQ2DHiZ0fIKC1L15+cUbJjbwi4hMvYvny35CdlQ2RWIig8CBkZuRCnesatGoX2GFtqUNAQCAO/nsE6pwA18JrBl8YT+ig7KGCPigH6swryxkOOJDpl4B7h9551aZ70196k89ty9aNMBlN6NqtO7p17oHUlCLR767A2tUoTjN+HodhkRZCP6QobobRuUM3DLv5OmxdtwWyZG8eyGsVmWEO0SKqeyQm3uy5b8/V6m58/+M3OHboCAR5UpbDjHxpFmRmBTQ5QZDalUgT5eODo4vRsX8HPPLgk/R0T9QZdZKtExMTg++++64udkXUUpn2Ql0h/v3gB6iRgUHdjdBlizDv1wMYef0ITLmdxAlRPoBuwcJ3oD4f4uKOkKcokZ+rwyefLeFWiIpYsfJPbF61CV4pvpDafXiWQ5a6AHr/HNi9Tbz8t8gugc3bxC0Xzzz1EpYt+4JbTNzhZVajIDcFPu28cdFxDP76MH6jN0n0KPDKhm9+CL764jO8PXs+vDWes3IYkeFRmHbXlXpAxemf1q8Xo9XKX64pW6dYoLxqPovzpkLkllo2aeJUDBw4BP+s/AspKUkI8fXDzTfdi9at2uFa+fjTxUjckQ51QVGcg1aeA5lYhUD9lQqlUrsc0mQ5zm47j82tN2DksOuveX8EURUabSoxce1l2nnhMV6qvXLYbQ7Mfvs7zHi8AJ3askJXzOTsxKN36fHe52vxx2/AlKk30CkhSvjw0/cgSJS5jZPwMqqRcD4R+QV58PF2LyQSky9h86qN0CSGlrgyWPqlSucLiUkKzSAZOo7pBJ1Wh3btO6BPz/4QiUQwG8zwQvm6SsXoswthSXPCxx6MfK8sfh17G4PQIpd1xBbCeFGLv//5A/fd83CVz6bXH99i1NZV15xGXFqgsGyfOInEpRdPSFAYHn3oKdQEqekpiD9xCeqCK8G9BV5ZCM9zX7RPkeWH9etWkTgh6gwSJ00lHqRKZdqrFoi8Yf1+3DjIcFmYXIH5uV9+zIppz2/GxEkjIZHQ5UQAJ88ex/lT5xFhau/5cKSJcTb2FPr3Hux28fK/fuMWE3exITKrEtnJ6Rj91FioVa4mQKFEWGHhNYfDAX9dKE8d9isMgU6WC70sDz6mojRruUWNkyeOV/k0Oh0ODNuxDo7L0t3jeqUESEWF2tjyyO+WIvPGyUVNNmuYTVvWQ5SqKDc4dlzcwWqqmA1WfvxYMgNB1DZ0lTUgUbIu8zAv017TrxJhco1l2v9btwWTbmRfu+VhAmVgLxOOHaVWBEQR27dvhtjM0nTdXzMcESC8XG6eBc0mpybyEu0sDoKRmZFR0mTPHUKdBGkZKeXe7z9gIAqV7gv9WUQmiFjabakbsNrMakoIYBYZi65n9u8qqbvuiE+KwwxNB9ggRNEMylMsSLJGjCsRIG7XEwjhlEgQ+/rCWhEmDL1OD5HD9WGiouqlHGH1uo8TRFWgR90G6HapaarbP8ZitkJZ5iGrNBqlA0Zjwyu5TNQPJr2Z3/RZDIOv8Ur31dIIQqzo2K4Llq/4FTu2b4NQK+E3a6e3DcOHj4RYIoEDdo9P8k6ZA0pF+UaWY8dMwN59u2E8pYeX+Up6rVVoRpomDqHa8rWVvI0B0LF4C0MEbEILvP2qXhySlcI/DD+8gcG8KzETZiI3wuTs49ORP/FO6Dr3dBs0y4WJWOS26V9N0rFjZ8RujoMs/4oAlNilXMCxOJOysOMXGhFK4oSoM0icNAAXTlm3S01TVTdOWTp0aou9h1MxoJf7p6b9x2V44aby/UaIZooAUJv8kOh7BkqLT7mbXYFXJjp374xff/8BpzaegyonpMQN40x2YmvmdgR28UWWTx5U+eW7CrPMGrGfAGEh5StEsyqps2a+i2//9yUunDkPoVkMnVULi8HChYm7G6/QKYZD4ODuIENYLu6Z5FohtjKEBofDrrHgaH44XncOuixQnFygFAuQ+eJ2WLbqH4QcOIDQ0FC8dM8T6P3Dp1dcPXUkTBgD+w/BX8t/g6PAzou4MfwNYUjXxCM8v41LJVyWCWVolYUn73yjVsdEEKUhcVLHsSC8Uqq5AHn5YuQIckqyZ6rjdqkJMjLysGnDPpiMRnTp2h69+xbFC+zdfQoXLyRg+2Yb/vhCAonEVaCcOOuASBaBwMBrb0VA1A7smtqk0+OiQo6cDE2ddSn2D/KDJd6CsILWSPeOg9TmxXvSsFTiAnk2ZEFi3DJxCt6ZOQuaHFfLChMpqix/ZCSkQdZKAtMpPeSlLCBMmGhbpuGBOx/x+BTPCqY99djzsNqs0Ou1iEuMw4+LfoA03315d4M0n2f9sO0OHzsSHd00/7vqnH0DoApSwpJixDapAM/aO+Ej82kILncpZpVfN9lbISBDi9yCXDiPyvFEeBxeHXojJm9fW2Q1qiNhUpwS/eSTz+PjpUsgSVDBy6SBxC6H2uqDxODT8BMGQaiXQKB2QBwowNMPvcAzlAiiriBxUkW3S7UPuM0GhUALsSYLg/1YEF4e2nFrSf0IExbg9v78ZcjPPoPxowzwVgO7dm/DF5+pIJVI0btzDmY9bcPJs0Lc+5wJj0+TYlAfIfQGYPkaEbYeCMR7i6teDp+oXdi1VHQ97UKE7CIOaHrj6Lkc4CBqXaBcP3ocfs34Ed5xoYjMaw+jWA+jVMfLsHv5yfHyS69h247NEKVUEFOSIseAMQOREBqP+Nh4iPRSOCV2SPxEePDOR9G9S89K3YB9ffzR09sPv4b+AFuWpVzJelbZVe+TiwH9B2PihFsRGly5pmTumHrr3fif+WsUpGYjNqcj3uBunn1Yhk688itDYdUgW5UCqV0GWWIYFjjTEH7nI+i1+g8eY1IXwqSYtq3b4513FmLNun9w/PhxHt4ysvcIXD9qLLJzMpGdm8VFV1RkNLlziDpH4GS1nhsZJ06cgMlqg07lOWWw1twuvN9L9SwnOdnZvPJicRn3+ixi9vGHP0Ej2o0+3RyIihAiwK/oafT1eWbcMEyM6wZc8Zxn5zrxyworVm0AWkRHYcItY3Dd8B4Qia4eV83qQLCOx6xZY3OpENtQ5pxiPI9DWfHYleyHgkvRkKU7MNgnBB3auO8RU905s66q2sICfP/9N0CmGI4CAYTeTiDIhvvue5gXLPv2uy9w5s843s+G1S8plOrgFDh4Jo7EIYVRokPPuzvjzqn3wmgsREZ2OhReCgQFuI9huRrpmWmYv+BtCBNl8NL5cDdKoaoA9shCvPTS69W2ChTPOyAwAK+++jwCL12uNMu+XstYeNI0FxGoa8HTrFksh89wGaa/NLPWgl9ri+I5s8aIzelvurnNuabnLfaxQOYlRZcuXSpeD02Y6rpiiouWDS6uDVIDbpeGVMZ9147j2LBuK4b0BQ4cFeLHP238/ecekSAnz+kiTBhMuDz9gBQjBtmxfHMoRozqVU8jJ6oCv2YDgWBFFjap0pGIEOxMTwdiUSsChcFcI4vf+wRnYk8hKzsDAf5B6NCmU0kaapu2bXFCfQb5Jj0PnFVavHkp+AJ1Nl+uFnujdeuivzUvLwVaRl57k1AGa6i2YN4H2LpjE/bs2QGnAxjZbxhGDruhRrv5yuVe0PgUlczncTRuBIdNZC2J85A4ZMjLyW10woQgapsmK06qW6adwWJB2qlOIkLBrCVNq0z70cPn8cv/vsbKZVJ4eV35YoxLcODF2WYM7O3ZGtKpnQgfLEuso5ESNUHxtRukliKgUyTSnflALSdYsZiQjm07A+xVhv59B+N7728hsEkRmccKoBVdg6zjL3MDpQfGoVslXDcJSfFYuepvZGdnITQkDONvvqVcoKzZbMKW7Ruxe/cOOJxOdO/WA2Ouvwkqpbp2gk0HDcb22N28ZH5ZWMqy2C7hdUMYXMQIa1aY2Gw2HDq6D0lJSQgMCkL/PoMq1SqAIBoSTU6clHXFVCc1t6asJQ2Rb7/6FR+9bXcRJoxWUUI8Nk2CH5dbPX7WanXyzAKCYMQlXMDGjethNBrRvkNHtAyvuHkeQygQQiKWwk/XolzBNC+bCv7GMBw8vA+D+g91+3nmjf7ym09wet8ZyFI1PAvnojgZCw7ORb8R/bk7iJGZnY5589+GIEEGhY4FbQuw59hBbN+6FU8/8wLaxFx7+XdP3HjDeOzbvweFJ7TwMqlL5mcWFSJdcwlhBVea6LGaLNcNrFo2HXNzHTiyDzqdFq1j2qBtTIeSmJAjxw7iu+++hjBdBoFeAqeXDctDf8OEiZMwcjhVcSYaD+Kmai0pccVUKzW3aQqT3BwtvJVaqJTun9iGDxThvU8tsNudEInKr7NuKzBwcD/o9UYolXI4HE7s3HEcJ4+dgpfCC6NvGIzIFlVvS080LqxWCxYunous8zmQZCh5Ua9LO/6DOUiHp558Hh3be856iY07B5lW6bGSq6rAFxs3rfcoTjZsXoczW89Dk33FLcXiV+QJSuxbtx9tWrdF7579sWjxfMhO+3L3STEKozfs51X4+JMleH/BR5BIXINkqwuzUrw141389Mt3OHHsGKwGO7SF+ZBavHgGE4upKbaiCFpbcP2ocZXe9h/Lf8bObTt4M0WBRYQN/hshDRHiuWdfgc1uxbdffAVNHKuAe/nhwQo4tU788/MK+Pr5o2e33jU6V4KoLRqtODE7bOViSoqtJU3VFVMVdKxR3z/bkJyQhICgANw0YQSCgoqamRkMJviyAEUPMEHi5yPAq3OtWDhDAmEps/OlJAc++saByIj/cOLgRlxKdsJqsWDiGOCmQTZo9U58++kuCKUxeO3NRyEWuy+iRTR+PvvyI+TtM0BdeEWISgpk8NJ647PPPsKC+Ut4Wq87zBYTYPXszhA4hTx43BMb/lsDVXb5GigMZbo/Vqz8CxpvH1jSHFCVEibFsEqxolQ5du7dhuFDRqOmYXEsD973GM+GY26lM+dP4+ef/wdzbgEsZiEcaisCIwPw1BPvwEvumrWUlZOJQ0f2c9Hfo1uvkgyiVev+wa6Ve6DJKGrUx0kDbBkWLFg4B8EhIVAk+F8RJpdhAlCdHIQ///yFxAnRaGi04kQgdPLS7KVpCPVCGgJr1+zCyj//xl23GHHDJCeS04BF7+xCTIf+eOTxKQgO9sXFBHbq3QcdZGY7kJ0nwYDrbsC05/dicB8j/H3s2HdEgmOnCvHRHDE6tDHDUOjEQy+a8O17cvj6sBtN0Zdivx5mrN18Bh8t+QEvvHxfHc+eqAt0eh3izl6EurDUjbLUjV+SpMJ/G9dg4s1T3H6eBbg6/awsk94tRpkO3dp08BhTYTHYIPfQfYMVENMXFOLs2dMQ5JRvPFiMRKfAqZMnakWcFMMCgFlAL7NY9OjaCylpSdAbdLyJX9mmhxarBR8ufQ8psalAKhu3AP+FrkNgS388+/TL2LhhPVQZ5QOYWXq06JIC8YUXEGJ3/73HAnC1eYXc2lXTliKCqA0arTgRCx2IUOc1mxiRynLyRDx2bPgTyxZZLls8BIgMAwb0MmPJVzux+t8QjLt5CGLadcT2fQcw1E1ZhSVfC/HGrKfRt18H3PfgeBw+FAudzoiktD+w4ltWyl7A406eet2MO28RXxYmrtw4wom/1p3mrh+VynM9C6JxciH+HARZnm9yikJvHD58yKM48db4ILRVKLKT9JBbXK0rLLXYEqnFzeMmuv0sz/i5ah8YQKFUwiG2Ax48u6wonMJNCfzagsWFRIR5rqS85KMFyNqlhbqwlAC5BOSn6jHX8CYE+WKPbjB2vLPNyRUPwCngAcEE0RhotFGNEoEd7RQZLq/mLkwYP33/F159wuziiinmyfts+HfF+qL/P3MnfvgnDMt+YwXVir6wEpIdeG2eBOGthnNhwmBumb792qNldAg6tjFxYcKYudACk9mJMcM961tmcaGGgE0TLhAq8NgxgSF0E69UmqefeAHiblbo/XNgF9h45kqhTIuC1qm45/77eQE1T/sOCA3gfXDcwWI5WrRsgf59BgJhnlOSrKEGjBrZMIJE0zNTkXY+HV6F5SP45RYVDMlmWBwVp1eJRSKPx4RV1pVpJJS1QzQaGq/lRCBp9kLEHXptNkKD3GtOqUQAP28T9DojVGovLP5oOrZsPoRXFmzkcSN+Af64bdp4dOhY/ukuO6sAkWFFdVBi4x2QSgAfb4HHzqoe6k8RTYT2rTvCEWABctwvL9QU4KYhYyvcBotHeWf2ezwrZ8OGtTBbCtGjbSfcNHZiOZdHWe66634sSV4ITVyIS3NAJnKMrXJwx+1PQ63SoFvfHjiRdxrKHNZ9+ApGVQFC2gWhRURLNAT27N0FYZrneivSLBVyQz1bRkxSPTp17YYE0SVoEkLLWVgModm4dbx7KxZBNEQarTghPHAVq63dBggvV3RllV1Hje7DX1cjIjIA6/9hfnAz1myyYdI4Mc6cd+C/rXbcNNr9ZbTzoBfm3XklbZJoOshkcvTp3xeHs45DmesqJFjVU0RZMHTgsKtuRyQUoV/vgfxVFVpFxeDRp57At999BWeWEA4tIPRx8j4wzz36Mo/pYNx3z8P4RfYD9u/ZUxR/4hDA4W9B+84d8PADVW/wV4xWV4AVK//E8WNHmVkCCrWSl8BnFTSvBbvdDoGzggBhCBEQEIDCgnwotEWB7aWtIuYoLe67+xWcOHUMy3//A5JUJSQmOawSM+zhhRg8YggGD7jumsZGEPUBiZMmRmBIBOITTyG6RXnrSaHRCYNZBYWi4oJMrIbEkcMXsGH9VphNJnTp1hk3jhuEnAJfZGSlorCQxQwIMHGMGA++aEL/XqKSsvfF/LXWgdbtuvBUY6Jpctft98Fi+RLHDx4FUmQQ2IRwBlog8gdefuGNKgVesmvu7IXT2LObdfMFBg0a4lK/g4mBVWtWFIkBJ2uf0BKTJt6GRQuW4kL8eeTm5yDQPwjRLWJc+sCw/985dRpunXQ7T19m2TMxLVt7zCKqDCybZu7cNyG6oIKXJYBbKawCK1bHreLVcKfd9WCVt9mzRy/sDNoJpLhvyWHzL8S0O6dh8+YNSDqVwsUHS922KAphCy/EQw89Cn+/QAwbMgp9eg3Alm3/ISkxEYHBwRg9YgyP8SGIxgSJkybGPfdPxtx5cVg6xwQv+ZUvaZaW+M5HUtx+l/sgw2KMRjPeeGUJWkem476bLbwWys795/Dkw2txx7SpeP7t3zC4dz72HbLj9okSzJ0uw/NvFVWUHdBbBJ0e+PkvGwTydliw6K5y28/P16OgIAuBgT7w9b3SbZZofLAb/wP3PgrdZB32H97DO1rHtGoDiUDKG8ZVFiY8Frw3B4VJFoizioKnT2w9CUULOaa//CbyC3Lx/nvvQhyvgpeZ9cURIP5EKuYeewv3PvQgeve4erM81sSyU7uKe3lUFlYfRX7W36V2CuuT45sRhoNbD2LokOFo2aJq5fZbtWwDVaQXzOmFkNkVLsssIhMkYUD3zr3Qo0tvJKUkYP1/a6DT6dC+fX8Mv260Swl+pUKJm268pQZmShD1R6Nt/Od0GtGqTT4aG3XRDO7g/tP4/OP/YdRgIzq0tiIpVYTVW7xw483jMH5ixab22TM/xqSRZ9Cvh6slhAXNPjJdgTdmvYg/f1+Dndv24K+vpVCrBPypd+d+B46ftsNuB3YeDsCyH+e6PMHGx6di0YKvoJTp0DLCgcQUEWyCADz74oOIiKj8jawx0VAa/5VuAJhsyMKmVG+cuyiHT7wG4QJFjXYprmqDMHbtzJj1MqwHxeVuymZJIWS9HDCajJAc8+ECwOWzcKCgbSoWLviAp+vWBdk5WZg7YxZU8e57EjEhkdsiCQF+gQgIDMSkSbdxa05lRdq7C2bBnGSHJLuoQJ3FzwBRuAOvvfpWlQRfXdAcm+A1xzkzqPEfUSP07tsRXyybh53bj+FEYir8A/2w5JNekMsrNrPn5GhRqL1UTpgwmAXl3smF2L3zEF6e/gDG3TQcj73+Ge6fosfwgQL07ylEvlaIn/9RY86851yESUZGHt55cwkWzTAgLOSKuyk1PQEvvb4Qcxe+huCQa++BRFQOlslWlM22C519UrBB0xlHz+UAB4Fob02tNQGsiPMXz8KQaIbaXt6dIbMqkH8pDXaBHf5lhElxHIYkRYlNW9fXmaUgIysN0Hk2OLMy+tY8O8TxPsgU6bDk1PsYPGYwbptc3opYFo3aG/PmLMaJ00exc+d2OBx2DBg4kVtLihsmEkRzgdw6TRQW7Hrd8B4A2KtyHD8Wh8G9jfxr3x3DBwHPvn0cd00bh46do7Fo6Sz8vXwjfptxnKcuR8e0RlhEOt6b/zkUCgUm3DIG/Qd2wjdf/I63nnMVJgz2+5vP6vHtV7/jtZmPVnvOROXoFzCIW1GAkzgZGI64k0qkpBfWapdiT+zfvwfibM91cKQ5KuR7ZXpeblQiNjYWuBF1Ao/d8HJ4XG4TXOk4zISKNCkUO9fsRNeuPdC+Tcerbp+J+q6devAXQTRnSJwQVy4GsQhms+eMAYuFrXPlktFoFLj3/vH89cmHP6Mgcy+em2ZGZJgQOXlO/PDXV/j3n2jk56ShfWv3T37s/aSEODoLdQy3oAQCwYosbAKQiBDsTE+vc4FSZGGrMCGdlXz1uJTV9fD2Zg396gZWRE3iL4Ajye6SwlxMviID3kbWOuMKijR//PjzMi7YdVodOnXpgqmT7uKNDwmCcA+JE6KE3n3a4pUfFZg2pdDtUfnnPyGGjxpc7v1tW4/AadyHlx6z8WDYQ8cdkIiZkDEjIuwcMlMr7q8jlXh+EiVqj+KChUFqKQI6RSLdme+po0GtMWjQUBzcdBhIdt8+3BZshEwugTPP6bY6qi3MgBuur7ieSk1z330P49OPlkITH8JL5TNYATmdPAdmsRH+hnCXNN9U71gIzgrgawiF2CHB7tP7sG71ajzy6JMYOnA4GhusV9C6jauxa+d2WIwWqH01mDhhEnp26+viziWI6kDihCjBy0uGzt1744flO3HPZFfBsHmXDd/9LkBE+N9Yu/Jv2J0K3DJlHEaO6oO//1iNVx+x4snXLHhsmgSP3yvhX1J5+U58+j8LktMssNnkEIvd3FxsTpgs9ATZXGHBoj5RKhgyDeXK2LPCYv7RPujWtQe2GLZClRZYIlB4NVmffER3bYnw0GurLXKtdGzfBc+++CJ++GkZtBnZcNqBXEMWVIW+vOtwaRGV4nMeKrMv/AuL6q4wvGwq+BQG4cvPP0V0yxhEVlDSvqFhKDRg9juvw3ZOCIXBDzIIeOG77+N+wIHB+/HoQ0+RQCFqBBInhAusMeCnH9nwyPQjuPE6A9RKB/5eJ0FmjgPffyBGaHCRVaXQaMAn//sRF87HAw4DPvzagrnTpWgZecV9w3ruvPGsDFk5rK+PAy8/Xt6CsnyNEMNGDqGz0Ix55aUZeG/xu8i7lAlhRlEGhCPEDP+WPnjp+dd5Jo5CqcKaNSsh0Ip50TPW1bdv//64fco99TLmNjHt8Pab82E0FvKGfe/OnwXxEW8XYeKADVaRGX5umyOKEZgfiS+/Woo5b72HxsLX334K50kZlBaVy1w0GUE4s/McjvY7hB5de9frGImmAYkTwgVm8Xjy2Tuh1U7k2T5pRhOy89fi+w8c8PK68sWr8BLg5cfseOHtvcjKEaJlmMBFmJTm5ccleGS6BEqFHfdMdvL0Y63OiV/+EeH0pWi8M/96OgvNGC+5ArdPvQtr161CRkY6wiMiMH7cJJfS8qyQ2KjhNyAtIxV2uw2hweEu8U/1BRNO7HXHHdPwZfYn8E2MKIlF0crzoDS7CpbSKC3eSEw4jcbkzok/Hwe1pbzYYigzfbFy5V8kTogaof7/uokGCQt2HXvTABw+HIuBvSwuwqQ0D9xmxPT5arRu6dnXHB4qRGiID6R+ffHSvKNw2M0QS+QYN/4G3PV4D0qTbEbk5uXgn3+X49zZM/z3Nm3b4kJcLExJNl6ATeCU4mJCAj6+tBivvjyTVz0tLZzDQq7EczQkOrTthAlTJmPjpnWw5ToBsxAmeT4EuU3HZZmbnwuhqXxKdzEipwQGrbZOx0Q0XUicEBWSlpKD1lEmj5dKqyghfP2UiI33XBCPWUkkUhn69m+PyVNGNaviRcQVzsaexidLP4TskgZyS1E59W3x2xCkawGV7Up5dVmmApYcE+a/NwcL5n7QaMRr6+i2GDZ7JHLysqE36OCt9sFLLz0Np859MK9BWgCplxSZ2RkICqj7GjNVRaVUwSEpav7pDlYUTySpOPidICpL4/irJ+qNkDB/XEz03B8nPtGB9h1aIjM/EIZC9ymfv/0rwvVjR1x5o7JFiRtf8WLCAzabDZ999hE050PgZVEXVT8VGyGze/EA0bKwGiG2FODYycON7pgy607bmPYIDgpBdJsY5CnSy63DgkhzlalQJQZizuw3cerMcTR0WJdn72ANr+XiDoM6H0OGXr3ZI0FUBhInRIX07Nka+48rYDS5Fwrf/u6FSbeOwdPP3Ydn3pQhr8DpUpp8w3bg0OlwDBnajb8n3n4MiqipEH+zpsL9suVsPdHWo3SGapku6hRkCw4gVaDDzvx0nInNqPF97D2wE6I0uUttEINUC5XJtTIwy8LRynKQrolHgSkfq9b+w5v1NVZmvfEuZK0FSPI/C700DyaxAbmKNCT7nkMgsxhZfaCJDcGXX34Kq839Tb8h8cD9j0IfkwGb0HWsJpkOsnYCjBoxpt7GRjQtSJwQFcL8/I88MQ3PvCVDetaVm4TR6MTiL8UIj+6HqJbB6NajDR579mlMXxiGp2bKMfN9Oe59QYUjcQMxf9GLvGKtat85aKa8DUFOAeRPf+hRoIi/Xs2X8/VumUECpZZrnUQoAzEyrAADe+2Co5uVC5Q1B2NrdD/nz5+DSOfqzuOODoHTpS9Not8Z2ERW+OvDEGiIQPqRLLw+80Xed6YxwlxSixd+igl33oICZTZ08lxI7DK0yO1YYjFigk2YJsPe/UUdmRsyUZHReHn665ANsEEbk4aC8HRo26Yi6sYwvDXjXd5gkSBqAoo5acawnjdmkxUhoX6QSj1fCr37tIda/TzmffE7dPkZEImcsDmUmDj5Roy+4UpH2E6do7Hk4zeg1xlRaDTDz0/Nq84y7BsOo9UTn4B1BmT3I3ZLYgKERbPYHhzrKkye+Ygv5+tZbVygmP5+B/Zh3Wv5iDRPSvfc8fE6iV1efoi7FM0FSk313GFVXO0iK1AqZEFp9kG2MoXXAWEWkzRNHMLyW0PiuHKD88sPg/lYIU81nvPWAjRWDFo9/HShbl1YDLFejgsXYjGkGkXZWCbTlq0bYNDr0bFzF/TvPQgiUc3HgLAsqlkz5/Hg5ri4i2jXrgPUKvdF9AjiWiFx0gzZvfMYfvrfXwjyZ3VMnDgfL0bnbt3w6BO3QcJKu7qhXftIzHvvxRJ3TUWVIFVqL/4qhrlmlFPeLhImjqIn5eKi5aUFioswufxZtj4JlLruuROPkyovxCGkxnruDLtuFHb8tx2Ic40rYQqUBYY6BA6ozD4uwqQYmU0BXXImLiXFoWVkKzRGfHx9YRe7irPSsGU+PtfW/NJut+OjT99HwslEiFIVEDlEOL3xPP4I+xnPPPMyWkVVrityVVEqVPD19iNrCVErkFunmbFl0wGs+fs7fDY3GwteM2HGM2Z8v8SArlG7MWP6h1x4XI2qlKhmwoRZPmCzlQiTku2UEiiy++eXEyYl67HPXbagUAxK7cIsKL0Co9E/PBJh7STwD66ZvjX+vgHo1KczDP653EpSTHBBNHK8U5CrKbKgeIIVZzt69BAaK0MHDYcjgslw9xQG5PNS/tfCt99/geQdmVAnBENhVUNmV0CV5w/F6SB8sGQhDIX6aoycIOoHEidNhMzMfOzdcwanTl7yKDBYYOHPP/yFha9bIJe5SoAxw4HokCQcOnCu5gbldEI27V3AYi0nTMoKFMlvW9wKk5L12Oct1qLtURZPo+Sh+x7HoCn9oe+YDkN4NvQR2SjsmIlJt92KPgP7wSnwHPjKLCviRhzPoFF7o+/g/tAHZbuIM/Z/5tqyFFoxb95snlZcFYwmI04dPQGFtryIZJVbxYlKrN+wukbmQBBNyq2za9cu/PXXXzh27BiSkpJw11134c0336zt3TYbcnO0WDD3S0gE6eja3oLsPBE+XOSFO+6+BcNH9nFZ99jROPTrZnTb44Zx1y0WvPfNevTu275mBicQwPLWfdwyUqHwKPPTHcUxKOa37uPbJRofzOI2eeLtmHjzFKSkJ/F04rDQCIiEIlyIO4ePD3wMeaprf50Swszo32cgGjN33X4vj7357dcfIS9kMRosnboQapM/wnJbw5ZvwaLF8zB/7pJKWyfjLsVCkONZtCkKvXHo8EFMmjC1BmdCEE1AnOzYsQNnz55Fnz59UFDQOCPuGypGoxmvvrgQs5/LRevoYiOYAzabBdPn/wyhSITrhvUsWT8/X4+QAJYC6P6LLyhAAK22Zk3ALJbE5HR6dNlUhuLPmZY+6xI8SzROWJBmi/ArpekZrVu1g3e0CoXZeshL9W1h6GS5KLDm4udf/4ept92N4MAQNFZ8/fzgbw2DrLBojixzp7hAm8Qhgy5Ni7MXTqNDm06V2p6AFagrlfHkDiGJeaIRUutunVdeeQWrV6/GvHnzoFZTRHdNsmrldkwdl1dKmBTBLCPvvmrBz9//5eLiadUqFMfPeS6odvyMAy2ja77Dq+2hcdAvfqLEhVMVSJg0H6a/NBO+gxTQRWcUCRJZNpK9z0Mvy0d4cnskr83GO7PfROzFs2isnDx+HNICJQ8GZq+ylWOF2TKcOnmi0tuLadkGTn/P9VEKVQXo129AtcZMEE1SnDSW0tONkR1bd+NGD5mHUokAMS0KkZiQWfIeq0eSnuuLtIzyvn0mYj7/0Qu33l47lgnz/WOQNPOOKgkUEiaNC1ZEbOeerfjxl2VY+skiHDiyF3aHvdKfZw30Xn91Fp58+RnogrLgFDoQrGuJUG0rCCDkWTvqi6H47IullQrcbojIveRwCD0fE6fIDi/FlUy3qyGTytCrX18YfPPKLbMJLXC0KMTI4VQYjWh8kHJoxAjggETi2VHio3GisNDs8t70GY/jxXc02L7PAcflINXkNAdeekeKQcPHIiIioNbGmzNlCMy3Xldp1w5bz3r7CHLlNHCSUhLw9+o/8OTzD2Ll56tg2iNA9sZC/LzoF7w24wUUaD33XXLHoUP74JMZCh9jULnUYpFTBEeGCGdjT6ExMmL4aNhDCz0ud4abMbD/kCpt866p96L9qBhoY9Khl+ehUKKDPjgb1i4FeOWVmfCSV17sEERDodHWOWFPTmaz6423MWA2W1x+Vge/gEDEJWTx5nvuOHlWjCn3ebscJ39/Fea9/wr+Xr4J3/5xDKxGmsbbH7fefRM6dGhRa8eUzdf/jx2Q/bmt0rEnPIvn180w92sP832N7+mvJs91XWK1WvnLZhdAYLfx/7u7LvLyc7D0k8UwpBrhzBJDKlSjQJ4Dq5cV/oYwSLL9Yc4zYuH77+DNN+ZWev+XLl2C1FLBDTVfiITES2gV1QYNBYvF7PLTE0EBIQhqE4jsHC0UBo3LskLvPLTp0gYKubLKf4f33v0wCsbnY8/+nSg0GHhhtI7tuvDA2tr6m67snJsSzXHONT1vkbOWxIlOp0Nm5hVXgSciIyMhlUprtZFYUlIyGiuVOYZXY/B1vbH4q1gsnWMrF92/97ADKp8Q5ORmIye3/GdHjO7JX6WpzePJhEnknF+qFBRb7AJSPf8p8vLykXPrYDTXc12XFAizkV0ohk4rA7QW5BTokCw1lXPhfPL5EihiA6C2X7nJ+hqDka1MRq4iHX6FIbyxX0FSAfYf2IOwkIhK7V8qlcEq0kNsk7hd7vSywW6zIzkpCQ2NzIyrn+tJ46fib/yOpLgkiHPk3H5t8zOhTfs2uH7kuGrNq3O7oh5WjJTk5AYz56ZGc5xzTc27lV9Y7YiTdevWYcaMGVddb82aNYiJqZ3KhAyxWIyIyMp92TUk2FM0u1kFBQVBJqueeIuMjEBeTgGemrkeT04zomNbEbQ6J/5YLcLOw8GYu+AReHlJq2SNOnniEvbsPMD/329AL3TrEVOlomvukC1bB1UVhUlZgRL59s/w9fVpVBaUmjzXdYnYbEJAoT/UhQIINHL4S+SIiAx0WWfTtvUQpyiKqryWwd8QjiTfs/AtDOKxIuIcL16/o2+fqwdmsuuub//++PnYj/BKLV/qndcICbFh+NDRtVKa/VphT5TsizsoOIiLq6vx/DOv8uJoF+LPQygQok2rdpA3MvdLVefcFGiOc67peYsrqTqqLE6mTJnCX/UNu2HKZI334mA3q5oY/61Tb0Df/j3w+y+rkLgsmW9z7E2j8NFDPXizvcpSUGDAzOlL0DYqB+OGm3gpkfXbj+D7Zb6YM/953ifnWmDN/eQvfHrNacRlLShisaTRxaDU1LmuKyQOCSQSCXf5QSTm/y87/r17dkGh83H7eZaBorCoYZToobBq4BSyIE/FVY/B4WMH8dNP38GRLYTRXIgsVTIC9OElGS12gR36yAzcNvVOKBQKNETYF3dlzzVbr69v48+kqcqcmwrNcc41NW+BwNK0Y06IK7SICsJL0x+o1iF5640P8cKDqejYhgmaoifSTu2suBCfjjdfW4Kln8+sugXF6YR09ndwCiouxVAsXK5WqI1th23P9sCNVIitllE6T0Is8cY5hRw5GZpy/XVYP5eyabClETpFcF4+6c4wC/pdxWpy4vQxLPvsK3hfCuXWFiX8UeCVhUTf0xAJxVB7q6EOUOGh2x5F18490JQoNBqQX5DHq8iqlFRugSDqRJykpKTgxImivH2j0YjExETuGmKMGdN4TPRNmdOnEhAVknVZmLjCaqh0jMnFsSMX0b1n66ptmAXjff8674nDmve5K2HvLJWVw4JfPQkUp1AASMR8e1Qhtu66FEfILuKApjf+PheP+INajO1dFITasWNnHD52Egqza1BnMYVSLbyNgbzORnTHaN5bpyJ+/vl/0CSEcGHCYMKHZeuwbRjkeehxU1dMu6N6AryhkVeQiy++/BjpiWkQmiRwSG3wC/PFIw8/hZCg0PoeHkE0bXGyb98+vPbaay4VY9mLce5cDfZxIa6ZvbsP44ahLODRvQ9/zHVmrN91oOrihD1hD+sO09/vQD5xBpxlmv+VrWNiH9jZbSXZYmHCtsO2R9Rdl2ImUHy8TmKXlx/iLimx5mAsor01uGnsROzdtQfyWBWEZSoS6KX5vE9OYUw2WndqjccefqbC/Wh1BTDmmqF2lncTMZGiNPni5LFjwB1oMuj0Wrw9ZwYkZ7yhtl8RIoXxZrybNQszZryNoIDqdYImiMZMrYuTSZMm8RfRcGHuGofnnmtgeqI6QbFMUGj/eBPqW2ez37hAcVdgjVWSZRKptEAhYVL/AiXFeB5APE6qvBCHEKSkF2IwQvDwY0/gqy8/gSRZyXvF2IU2mAIL4PC14O4J09Cn5wB4a9zHpZTGbDFDaPccH8UEitND48jGyh/Lf4HovIJnM5WGlbD3ivXD9z9+g5eee73exkcQ9Q3FnBAYNLQ3Vvy0HX17uA9UWr1JjpET+lfrSNmGdkPcp08i5slP4bRaeQyKu145LgKFqROymNQ73MUTCKiUwbCxSulOOWAGunbqhoXzP8CGzetw+vRJKBQaDB8+FV4SBSJbtKh04Jyvtx8cChvPxHEXx2IVWuDr74umBCtjr7AEuV0mtXshKT6RdxGnCttEc4UqxBJo2zYCmQUhOHKy/NPpyXMOxKUGonNn10Zt14K+XztuQXH6e1fYxI8LlKXPFq1HrpwGDSs5P37cJEx/+U088+RLaBvTvspWNlYWoGfv3jCqteWWMcFSGJ6LyZObVlddp73IIuQJgU0Im91Wp2MiiIYEWU4Izqx3nsbsmUvhvz4dY4cbeczpuq1eSMsJxJx5z9bYUWIWlMKE364a1MqEC2XlNB/unHovklPmIPNUFryyvCF2SGESG2COKMCIsaPQrnVHNCWkXhI44CgXr8PgFiQ5Mxq6L0JHEM0BEicER6XywntLXsGF2BTs3HGQpwGPv6MX2rWr+S7Flc62oVbvTRaL1QKjsZCnzrJiasx68trLs3Dy9DGsWvMP9HodosPCMXHCMwgLCUdTY9ToG7D60jqo8/zLLWMZTn379a928UOCaMyQOCFcaN0mnL8IojZIz0zFt8u+QEZyJoQ2EZwyOzp17YJpdz0AmUyOLp2681dTZ+SwG3Ds+BEkH0yDIscXIqcYDthh8M2Hb2clpky6s76HSBD1CokTgiDqhNT0FMx/dzYUFwKhcVxJnz2XEIfZl97A7JnzkJKejL/++h3ZWVmQe3nhxhvHoVf3fk0uMJTN58VnX8O+g7uxdu2/MBm0kMgkmDh6PIYMHNagSvMTRH1A4oQgiDrhm28/g+JCECQO1z5DXkYN9Gdy8d6Sd5ERmwl5qg+kdm+YBDb8fOZXrOm4Cq+/MqvJxWAwt03/PoP4iyAIV5rW4whBEA0SQ6EBOam55YRJMQKTEPFHLkGTFFLSTJC5OlTZ/tAdNuHHn5ehMcDSf1k8DWtgSBDEtUOWE4Igah29QQehxbOrIl+RheCCKLfLvPTeOHbkCGx32njgbEOEdV3+4ZdvkZuVywvKsWyb/gMGYvLE2xvsmAmiIUN/NQRB1Do+Gh845J7rdjgEdp4+7AlhoZg3xwvwD0RDjKVZuOAdKC4GwNseVpIOvC/hMM6dP4M3pr8NkZBiSAiiKpBbhyCIWodl4rRsEw2TxOB2uV1cVCHWEw6xnW+jIfLlVx9DeSGoxB1V0hNI74Pckzrs3lvUS4wgiMpD4oQgiEqhdJ6EWJKEi4ok7MxP500Aq8JDDzwOdDLC4JVfIkSYxUQXkI3A8EAY5eUrxDLsAhtUAUqoVeoGd6ZY08L8dK3HWBplni/++29tnY+LIBo7JE4IgqhUfx3WBHBkWAFGdjwFRzcrjspyuEA5E5tRqSPICq69/db/27sT6KiqdG3Ab1VqTGWeIQMJIWAYBJFBRO1GlIuICCqNNELTKDIIqNg22kRoGm4jXvW/4lW8gIqorSiNtGJAkUlJs9SIoMEQAoSQBBKSkHmo+V/75AYMJIJQSZ06533WqpVKVSXn7DpVp77a+9vffhZDJg+AtW856nuVAgPrcO/se/C3Rc/CndIAm59YWek8UfujJqkEDzzwR1keparqSmitbY+Oa91+sDW2vmYVEbWNOSdEdNlEgBJnOYIQcxYyzGE4fsIirVKMXCA1Jfqy1uIRBcZaKzKW9pel+J9XX8TZohJoanSAyQV9uAYzpszGNSnyLF8fFhoOl0mshog2e338A1quPExEl8bghIiuaJXiaP9S7AgoRo7ZhL15kAKUrgkhV/VBv2jhf6KishzFZ04jMDAYcZ3aYfkED7L4ByAqPhJVeY0tck6a1UdWYNydk7yyb0S+jMEJEV1ZgALghthoOETHQb0JsHrmiQwNCZcuvmLmw/Pwt5KFcBwNhNkWICXDikX96sLPImFALAb0G+ztXSTyOQxOiBTqi+3f4uNNW6FFPex2DeISkzB12r3o1Nl7H/yiOJm4/Npy9OJvrDarVCVWbqXdRY/PwoVL8c67b+Bkfj40Lg0MZgPuGnEnbv3NCC7gR3QFGJwQKdDK//cODI5v8dIiByz+TavbZucewOKnc7Fg0XwkJ59f28aTHE4HDmR9hz17v0BYeDh+c8twhAaHScM0b7/zBgrzCwEnoPP3w7Bht2HUf4z5xUBFVFzd/MlG7P1qD1wNgFvrRGTnSEyZ/CAS4hIhF0EBQRgz6l7ExcfDaDR6e3eIfB6DEyKFyf7pJGrLvsOyPzulihvNUlO0eGlJPZ5cvhr/87+LPb7dEydzsOrVd6E9ZYS+zgyn7gh2bduFbr2TkfPTYViORyLI2RQUianEOwp2SUXK5s97qtXeBdFb8sJLy3H661L4V0ZJwyVC7TEb/qvo75g3/wmkdO3h8XYQkfdxKjGRwmx8/xNM/33rCSDhoRrERlUj/8TlTf+9XPVVFfh443oEZscgsCoCJocFlsYQBB2LQfbOI3AWaS8qUhZQEY6T+4vww6HvW/2fP/50AEUHTsNSGXYuMBFETZHAYzFY+/oqj7aBiOSDPSdEClNWdhZd4tr+3pGSZMOpU+Xoknjpqb+XK3v3dlgKQ6HFxfkgYTWdURCaDXd95xZBhuBfGootWzajb+/+F/3dli3/grm09dk/YlHAhlInThUXonNMHHyJKNz23ob1yP4pG267G35GLYYMvQlj77pPcSsvE10p9pwQKUxwcBCKS11t3n+ySI/IyGCPbvNsfj7M9tYruIqAxOiwXFRgTdC59KitqW3172pra35xvR3Ua1FRVQFfIoq2Lf7b08j910kE/hSDoNxO8M+Kwr53v8Gy5c/A4Wh7/SEiNWFwQqQwd997B9Z92PqHem2dG7knLOiWEuvx7f7S2jji3tY4tDYEBgW1el9YWDjs2rbnJ7sDnIgMj4IveWPd/0KXHdgikGtahycU1T824otd27y6f0RyweCESGH6X5+CyobueGeTBk7n+aCgpNSFuYtMmDVvqse3GZaYiAZDbZtBS6OurvUiZdGVGDPmnlb/buzY+9DQubLtoCbGH1ERnhuaam92uw35x05I+TitsVSHYPeuHR2+X0RyxJwTIoURM1/S/joTH7z/Of7wxJcICbSjoRGwBEbh0SfvR/cenq+6mvrb25Bx6BWYTvhL68n8XG1EGfQWHaw1DTA5/KXbmoqUVaDbwCT0uqZPq/+zW1J3XHtTH/y46xAsZ8LO/d8GXS3sKVV4erbnZxy1p7r6OmhsLU+5Yqirwr8ENr+GpuGvKj3sDjtzT0j1GJwQKZCoHXL/70dKF5vVDj+dH/z82q+j1D8oFHePn4r0T96G9rQRuloTXDo7nLGNuP7GARg18i68t+FtHD96DG4nYLSIImWjLlmk7MGpM7GvVwa2fPIR6moapL7eHqnX4Hf3LfCpKrKCxd8Ct+F8Tkm1qQzVpnKE18bC7AiQFjmsspdi4eI/YfHC/5RK4xOpFYMTIoUzGNtvBojFnYXEgDJ8GzMA2pJADB8/F+6yw6isqmgqwnbTrQgOappxM3f2E1e0jSEDh0qX5qGRrMM/4HDuT0hMSEan6M7wFXq9AV2SE3Hq2FmpF6jKVI64yu7nZjCJmU6hdTGw/lCHl195AU896Vs9Q0SexOCEiK54fZ2mNXYypFWKt6M3srLrkFgbh/7dbsC1PT07xffjTzdh5/btQIke7kZAE+VCcGwAHnv0z1IV2mY5R7Px0UcfoqK8Alo/DW4cejNuH34HTMaLc1462rSpM7CkYCHO5tcgojb2oqnVgtFuwekTxdLMnubAjkhtmBBLRFdlcMRQXB+ZhNtTstDzpmoUxLuwr/oMsnM9V+ht62efYMfGnQg4HIPAiggENUQgMD8KjV9rsOzvi2Cz26THfbjpH3jl2ZdRucsKw4FQ+H0XjF1vfIm0xU+iprYa3iaCjSWLl8MvAtJQTlu0lXoUnMrv0H0jkhMGJ0R01UQPighQhneqRELvEjj7OrC3stgjAYrT5cRnn6Uj8HTkRT0NBqcZrjw99uzdgbyTx/DV1q8QXBgDvcvYYpqu9pAFr7z235CDwIAgxHaOh0sjlhdog8ENk9HckbtFJCsMTojIYwFKZ3MEegWFIiLEH+HRnin0lnfiKDQV+laHQAT/6mB89eVubNr0AcynQlt9jNHuj+ITxbLoPRFuu+0/UB9S2Xa9mDAHunbp1uH7RSQXDE6IFKax0YbPtn6DDe9tR+Y3OdICer7M5rBD42h7Ro8IWpxOF8pKS1utpXLucTV6aXVkObhh4FCYuvnBekFtGBGYVMeVYOy4+35xtWYipeOrn0hBPtzwOebPSYOt7C0khfwTBzJWYca0NBzOPglf1SUuEc6QppyS1jQaapCS0h16g+GXh0qMTgRYWi+x39H8/PzwzF+WInJYMKq7nUZtZDlqO5ehrncJ7pt6nzTLiUjNOFuHSCE+2/pv5B9Ox5vP2/+vdogOv73RgaqaSjyS9jKWPbcQUVG+N/tD1Pvo2qMrCgrPwNzQMrhwwwVrQjXGjL4Hh7J/wKbDm6XVji8kaojow/1kNfXY32zBk48vlIaaCk+dhMlkRpe4JPaYELHnhEgZxNDNpg8/xdOPNAcm5wUHavDYg3XY8I9P4atmPjwPlv5+qI0uldbbEcFGnbkKVSmnMPXBhxAWGo4hg26GpYcBjeaaFn8relOqk4rxwAPTIEciQTa1e28kJSQzMCH6P+w5IVKAkuIKdOlshV7fem7G9X00WPVuDnyV0WDEX9OWSwXYPv8sHQ0N9ejX43qMHHEXAgOaelN0Oh2eeXop1ry5Ckezj0BbZ4BL74Q53IAZk+agd2rrZfKJSH4YnBApgN3hhMHQduKr6E3Ranw7MVa0oU9qX+nSFqPRhDkzH4fV2ojSs6XwN/lLvSpE5FsYnBApQOfO4TiSp4fb3djqWjVH81yIlFG+RXsTQUpcJ88vcEhECpit43Q6sWbNGkyaNAmDBw/GoEGDMHnyZGRmZrbnZolURyzqd9MtQ/CPzS1XBBYcDjdeWGPCxMljvbJvRESyCk4aGxuxevVq9OrVCytWrMDzzz+P4OBgTJkyBfv27WvPTROpzuSpY5BTdC2eetaIgz85UVzqwudfujHtT2aMvmcikpM7tUig9fX6J0SkXO06rGMymfDFF19IAUmzoUOHYvTo0XjrrbcwZMiQ9tw8kaqI4ZynFj6EEyeK8cnmL1BVUYWkbkn4r5W/RWCgv/SYzG8P4923/gmnvak6qcYvGPc/cDeG3MhkUSJSSXAiCg39PDBpvq1Hjx44edJ3i0IRyVliYgzmPvbARbdvTc/Avl0bsWKBDSFBTXkp1TX1WLHqdRSfugvj7hvuke2HG3PRVXcI38UMRvlBPfIyqzFqQIpH/jcRqUOHV4h1OBw4ePAgunbt2tGbJlKthgYr/rVxM577y/nARAgK1GDZk3bs+Gwramrqr3o7nYzd0E2Tiu6h4dIqxaYb8nE8uhHpmbkeXaWYiJStw2frrF27FiUlJZg6depV/R8xXm61WuFrrFZbi59qocZ2y6nNWz/NwNjb66HValodDpowuh4fb96N+353db0nzW3tbe6Ps+aTcLrykeVvwI8mMwqO1cBut6NHciSUxmaztvipBmyzetg8+Pr2c7dTcFJTU4MzZ85c8nHx8fEwGAwtbsvIyMDLL7+M2bNno3fv3rjaHpiCgkL4qst5DpVIje2WQ5uP5h7D2N+4xKmh1fuTEoDdm4+joKCHB9tsQoLWArOlAA0xETjl58aOA/UoLytFQmd5rHHjaWdKvH+sOxrbrB5nPPD67hrWuX2Ck23btiEtLe2Sj0tPT0dycvK53w8dOoS5c+dKybBz5szB1RLVIOPi4+BrxDdLceKOioqC0dgyeFMyNbZbTm1O6ZGCoycOoF+v1u8/dkKDbinJiL/K99SFbY5HHE5bj0ITGI0MnQaaKhPCG0yIi1dW74n4RilO3FHRUTAYjFADtlkdx9nTx1p3mVHHrw5Oxo8fL11+jfz8fEyfPh3XXXcdli1bBk8QXdFGo+++OMSJ25f3/0qpsd1yaPMdo27Eo7PSMW5kHfz8Wg7tuFxubNhixnP//VuP7efP26x36aHX66ETnTZ+Oum6t5+P9iJO3EptW1vYZvUweOD1rdHY5JEQK75FTZs2DZ06dcLKlSulExMRdSyTyYB7J4zDE0sNKK84P+hbUeXGU88aMPKu0QgINPOwEJHyE2JFETbRY1JRUYGFCxciNzf33H0iH6Vnz57tuXki+pkRI4cgKioCi17aBGt9uXSb3hiKiQ+MxYBBqXyuiEgdwUlZWRkOHz4sXZ81a1aL+2JjY7Fz58723DwRXaBf/xT067+AzwsRqTc4iYuLQ06O7y7TTkRERCoowkZEREQkqyJsRKQ+FncWdPpg5PibUF4SBOQCqSnR3t4tIpIp9pwQUbuKNXfH4IihGN65CsN7HoKrrx0fNeZJJe2JiFrDnhMi6hAiQImzHEGIOQsZ5jAcP2HBmm8P4qaQGPaiEFELDE6IqEN7USAVh81DVoAZOWYT9uaBwzxE1AKDE/KqwsIyvLn6AxSfyode74bNbsKIO2/F3WN/I1UBJuUGKAGWaDjsAOpNgHrWyyOiy8DghLzm2LHTWLH0RSx5vA7JiU3pT1ZbHdZv3ITlS4/g6WemM0AhIlIhJsSS17z0/Bqs/Gv9ucBEMBo0mP57FwJ0P+G7zCM8OkREKsTghLw2nBMVWoWw0NaHbv74Oxs2/zO9w/eLiIi8j8EJeUXx6bPomuBo8/7oSC2qq6s7dJ+IiEgeGJyQV0THhCKvoO2UpzNlLgQGBHboPhERkTwwOCGviI+PRHF5ECoq3a3ev+5DA8bdN6rD94uIiLyPwQl5zaPzH8S8xWacKHCdu81ud2PdB1qcbeiB6wf24NEhIlIhTiUmr+nWPQ5pf/szXl29AaXFBVKdk0abEbePHIZnHrqV04iJiFSKwQl5VXxCFP66bK503e12MyAhIiIO65B8sCIsEREJzDkhIq+wuLOg0xfgmH8B9lYWc5ViIjqHwzpE5JX1daQ1dpCB3iFF2B7UGwdyyoFMICk4iKsUE6kcgxMi8prBEUNR1CCWKchChjkMx09YUFRcz1WKiVSOwQkRyWKV4mj/UuwIKEaO2YS9eWCAQqRiDE6I6FcRs6qsVjuMRr3HkpibhniAG2Kj4bADqDcBVh4YIrVicEJEl6Wqqg6rX30fx3JzEBTgQlWNH3r0TMXDMycgINDMZ5GIPIbBCRFdVmDyxLy/48mHK3Dd7POT/L458A3mzzuGF1f+hQEKEXkMpxIT0SW9vvpDzH+oAtf1bnnKGNRPixm/L8P6dZv5LBKRxzA4IaJLOpKdjQHXtn66uGmgFj98/wOfRSLyGAYnRPSLXC4XzKbzizNeSCTFmoxOPotE5DEMTojol08SWi3qGvykWTqtcbncaLQyfY2IPIfBCRFdUr/+fbFnX+v3fb4HGDRkIJ9FIvIYBidEdEl/fOgevLkpCp/tcUs9JYLT6canO9z4YGsnTJoyms8iEXkM+2KJ6JLMZiNeXPkU3nsnHf/4UyYMegdsdh2G3HwDXlw5Egajns8iEXkMgxMiuuwAZdr0cdKFiKg9cViHiGTD4s5CYsBulMaUYW9lMdIzc729S0TkBew5ISJZEOvrNK2xk4EQc/MqxUlSgJIUHITUlGhv7yIRdRAGJ0QkK4MjhqKo4QiAPGQFmHEcMSgqrucqxUQqwuCEiGRH6kGJBKL9S7EDwOnoUOw9WMwAhUglGJwQkSw1DfEAN8RGY68dQLQJsHp7r4ioIzAhloiIiNTVc7J27Vps2bIFhYWFcDgciI+Px4QJEzBp0iRpTQ4iIiKiDg1OampqMGrUKKSkpMBoNGLfvn1YtmwZamtrMXPmzPbePBEREfmYdg9OHn/88Ra/33jjjTh16hQ++ugjBidEREQkj5yT0NBQ2O0iw42IiIjIS7N1RL5JY2MjMjMzsXnzZsyZM6ejNk1EREQ+pEOCk/z8fIwYMeLc77NmzcLUqVOv6n+63W5Yrb43r9BqtbX4qRZqbDfbfPVED6u4OJwaaJwO6boc3/c2m7XFTzVgm9XD5sHXt1/TouaeD05EguuZM2cu+TgxK8dgMEjXO3XqhI0bN6K+vl7qOVmzZg20Wi3mzZuHq+mJKSgohK+6nOdQidTYbrb5ylVpy1BWr0NNtRGotqG8qgaFhkbI1ZkSFb6+2WbVOOOBY901rHP7BCfbtm1DWlraJR+Xnp6O5ORk6boIUvr06SNdHzx4MAICArBixQpMnDgRkZGRuBI6nQ5x8XHwxW/T4sMqKioKRmNT8KYGamw323z1x1lnbUREfTgC6zXQBJkQrjchLv7KzhntSXyjFCfuqOgoGAxGqAHbrI7j7OljrbvMqONXByfjx4+XLlejV69ecDqdKCoquuLgRNRIEVOTfZX4gPbl/b9Samw323zl9C499Ho9dH6iP1gnXZfz60ecuOW8f+2BbVYPgwde3xqNTb6zdfbv3y8FF3FxvtfzQUQdW8Le4s5CYsBulMaU4aPGPGmVYiJStnZNiBX5KdOnT8eYMWPQpUsXKU/k66+/xvr166UqsREREe25eSJSyCrFQAZCzFnIMIfh+AmLFKAkBQchNSXa27tHRL4WnIjun6SkJKxbtw4lJSUwmUxISEjAkiVLMHbs2PbcNBEpLEApajgCIA9ZAWYcMptQlFfPVYqJFKpdgxORCLt8+fL23AQRqWiIJzahO6L9M6TfTweZsfdgMQMUIgXqsCJsRESeEGeJxA2x0dgrikxHmwD1lBYhUg2vJMQSERERtYXBCREREckKgxMiIiKSFQYnREREJCsMToiIiEhWGJwQERGRrDA4ISIiIllhcEJERESywuCEiIiIZIXBCRH5tLyqam/vAhF5GMvXE5HPrbFTWJeBxIAyfBszAOVVDiATXKWYSEEYnBCRT65SHGc5ghBzFjLMYTh+woKiYq5STKQUDE6IyGd7UBAJRPuXYkdAMXLMJuzNA/IyqzFqQIq3d4+IrgKDEyLy6QBFClKQgajAABwMsqPoYD2yc0uQmhLt7d0joivE4ISIfF6cJRJ1mlCcCgEQbQKs3t4jIroanK1DREREssLghIiIiGSFwQkRERHJCoMTIiIikhUGJ0RERCQrDE6IiIhIVhicEJEiWNxZ0OkLUBpThr2VxUjPzPX2LhHRFWKdEyJSVDG2kpAibEdvHMgp55o7RD6KwQkRKWrNnaKGIwCykBUZi+NZXHOHyBdp3G63Gz5m//79cLtd0Otd3t6VX0083Q6HAzqdDhqNBmqhxnazzd47zg63HQ6XE41OLRxOLWDTwKT1g0Hv147H2gmdzk9lr2+2WQ3cnjzWWkCr1aB///7K6zlpenK00GiM8DVi1w0GqI4a2802e49eY4ZeC5ibz3D+XtwZIjrHbrdfVoDjkz0nREREpFycrUNERESywuCEiIiIZIXBCREREckKgxMiIiKSFQYnREREJCsMToiIiEhWGJwQERGRrDA4ISIiIllhcEJERESywuCEiIiIZIXBCREREcmKTy78pyRr167Fli1bUFhYKK3aGx8fjwkTJmDSpEmKXN3U6XTijTfewO7du3H06FFptcsePXrg0UcfxYABA6BUGRkZ2LRpEw4ePIiCggLp+C5atAhKcezYMSxbtgzff/89LBYL7r77bjz22GMwKHi1x/z8fLz++uvSMc3NzUXXrl2l97KSbd26FR9//DEOHTqE6upqdOnSBZMnT8a9996ryPNVsz179mDNmjXSOau2thbR0dG47bbbMGfOHAQGBkIN6urqcMcdd6CkpAQbN25Enz592nV7DE68rKamBqNGjUJKSgqMRiP27dsnneTFG2DmzJlQmsbGRqxevRrjxo3D9OnTodVq8cEHH2DKlCnSiX7IkCFQoq+++gqHDx/GwIEDUVVVBSUR7fnDH/6AxMREvPzyy9LJ69lnn5WOtZICsAuJgER8aPXt2xcul0sKtJVu3bp1iI2NxVNPPYXQ0FD8+9//xjPPPIPi4mLpg1qpKisrce2110qBWEhIiHTsxWtd/BRfttTg1Vdflb5cdhixKjHJy/z5890jRoxwK5HD4XBXVlZedNvIkSPdM2bMcCuV0+k8d33YsGHuJUuWuJXitddec/fr189dUVFx7rb333/fnZqa6i4uLnar4ZguWLDAfeedd7qVrry8/KLb0tLS3P3792/xfKjBhg0b3N27d1f0a7zZ0aNHpff4e++9J7X5hx9+cLc35pzIkPhGYrfboUR+fn4IDg6+6DYxtHPmzBkoleghUqovv/xS6vES3yibie5f0ZsghrOUSsnHtC1hYWEX3Zaamir19NbX10NNml/vSj1X/5zozb///vuRlJSEjqK+d5dMiXwT8QYXuRibN2+WhjnU1HYxbi/G7Mn3HD9+/KJjFxQUhMjISOk+UrbvvvtOysEICAiA0olhDavVKuXcvPLKK7j11lsRFxcHJdu2bRuOHDmCRx55pEO3y5wTmSTWjRgx4tzvs2bNwtSpU6GmpGCRp6CmNiuJSIwUwciFRA+Z0vJrqKXMzEykp6djwYIFqnhqhg0bJp2rhJtvvhkvvPAClKyhoUHKH3v88cc7PPhkcNIOCa6XMzwhZuU0z2To1KmTlP0sukXFm11khYsu43nz5kGpbW4muv1FYtns2bPRu3dv+IqraTOREogkWPGhNXjwYNX09IpkfvGBLWbtrFq1Spq08Oabb0pD00q0atUqhIeHS7OxOhqDk3boAktLS7vk48S3jeTkZOm6+PBqnpYl3ugiQl2xYgUmTpwodY0rsc2C6BqdO3cuRo8e7XOZ/lfaZiUSvSYiWLuQ6DW5ML+IlNNbJmbbibwL8eVCLfk311xzjfTzuuuuk87ZYsr89u3bMXLkSChNUVGRNBNJDF81v7+b84rETzG1WJQNaC8MTjxs/Pjx0uVq9OrVSxrbFC8OXwhOrqTNYihLnNzEm1wkW6nxOCuFyDe5MLdEnMxKS0uZR6RAYor4jBkzpGO8YcMG1dT5uJBI4tfr9Th58iSUqLCwUEr2ffjhhy+6T/SUiSn0ogxEe2FwIkP79++XChopNdFKDIdMmzZNGs5auXKl9AYn33XLLbfgtddea5F7InqWxLfpoUOHenv3yMPJ66K4nghG3333XSkRVq1EEr/48FbqeTo1NRXr169vcVt2djaWL1+OJUuWsAibkolvHqL3YMyYMVKlRfHG//rrr6UXhKgSGxERASV+6xJtrqiowMKFC6UiRs3E8FbPnj2hRKIX7Mcff5SuizFr8W1LfIALvt4lLKYYvv3221I2v/hGLRIGn3vuOel2JX94ieMoirA1H18x2675mA4aNKjVabe+Tnwo7dq1SyrCJtp74MCBc/eJ965S86vEsLPIiRO9JSaTSSqoKIpGit9FpVglCgoKktIM2urdF5f2pBHFTtp1C9Qmm82GxYsXS1PxxAldvOgTEhKkk/rYsWMVmWQlugqHDx/e6n2i8uTOnTuhRKJ0/dNPP93qfTk5OVBC+fqlS5e2KF8vkiWV+mF1qdey+ILR1ondl4mpsyIQa82OHTsU24sgEmFF/pj4UiE+MsW56vbbb8eDDz6oiinUzcSXZzGk0xHl6xmcEBERkayoI8WaiIiIfAaDEyIiIpIVBidEREQkKwxOiIiISFYYnBAREZGsMDghIiIiWWFwQkRERLLC4ISIiIhkhcEJERERyQqDEyIiIpIVBidEREQkKwxOiIiICHLy/wEYWNYNUqynGQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.inspection import DecisionBoundaryDisplay\n",
     "\n",
@@ -333,6 +400,36 @@
     "## Load and Scale"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2d322da2",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Shape: (569, 30)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_breast_cancer\n",
+    "\n",
+    "data_cancer = load_breast_cancer()\n",
+    "X_cancer = data_cancer.data\n",
+    "y_cancer = data_cancer.target  # 0 = Malignant, 1 = Benign\n",
+    "\n",
+    "# Scale the data\n",
+    "scaler_c = StandardScaler()\n",
+    "X_cancer_scaled = scaler_c.fit_transform(X_cancer)\n",
+    "\n",
+    "print(f\"Original Shape: {X_cancer.shape}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "03d7bfe4",
@@ -345,6 +442,38 @@
     "-   **Question**: Do the classes look separable in 2D?"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "00112062",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pca_c = PCA(n_components=2, random_state=42)\n",
+    "X_cancer_pca = pca_c.fit_transform(X_cancer_scaled)\n",
+    "\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(X_cancer_pca[:, 0], X_cancer_pca[:, 1], c=y_cancer, cmap='coolwarm', alpha=0.7, s=20)\n",
+    "plt.title('PCA of Breast Cancer (30D -> 2D)')\n",
+    "plt.xlabel('PC 1')\n",
+    "plt.ylabel('PC 2')\n",
+    "plt.colorbar(label='Diagnosis (0=Malignant, 1=Benign)')\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "67558ddb",
@@ -356,6 +485,34 @@
     "-   Check the Adjusted Rand Index against the real labels."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "bb06d483",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "K-Means Performance (ARI): 0.66\n",
+      "Silhouette Score: 0.51\n"
+     ]
+    }
+   ],
+   "source": [
+    "kmeans_c = KMeans(n_clusters=2, random_state=42, n_init='auto')\n",
+    "y_pred_c = kmeans_c.fit_predict(X_cancer_pca)\n",
+    "\n",
+    "ari_c = adjusted_rand_score(y_cancer, y_pred_c)\n",
+    "sil_c = silhouette_score(X_cancer_pca, y_pred_c)\n",
+    "\n",
+    "print(f\"K-Means Performance (ARI): {ari_c:.2f}\")\n",
+    "print(f\"Silhouette Score: {sil_c:.2f}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "0979b31d",
@@ -364,6 +521,46 @@
     "## Visualize Clustering Results"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c039bb55",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "\n",
+    "# Ground Truth\n",
+    "axes[0].scatter(X_cancer_pca[:, 0], X_cancer_pca[:, 1], c=y_cancer, cmap='coolwarm', alpha=0.7, s=20)\n",
+    "axes[0].set_title('Ground Truth (Diagnosis)')\n",
+    "axes[0].set_xlabel('PC 1')\n",
+    "axes[0].set_ylabel('PC 2')\n",
+    "\n",
+    "# K-Means Clusters\n",
+    "axes[1].scatter(X_cancer_pca[:, 0], X_cancer_pca[:, 1], c=y_pred_c, cmap='coolwarm', alpha=0.7, s=20)\n",
+    "axes[1].scatter(kmeans_c.cluster_centers_[:, 0], kmeans_c.cluster_centers_[:, 1],\n",
+    "                s=200, c='black', marker='X', label='Centroids')\n",
+    "axes[1].set_title('K-Means Clusters (Unsupervised)')\n",
+    "axes[1].set_xlabel('PC 1')\n",
+    "axes[1].legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "9fc37854",
@@ -384,9 +581,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C02 Dimensionality Reduction Techniques.ipynb b/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C02 Dimensionality Reduction Techniques.ipynb
index 001cb0d..c09c653 100644
--- a/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C02 Dimensionality Reduction Techniques.ipynb	
+++ b/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C02 Dimensionality Reduction Techniques.ipynb	
@@ -112,12 +112,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "b2c5fe97",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -153,10 +164,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "16e6adde",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Principal Axes (Eigenvectors):\n",
+      " [[ 0.70221828  0.71196172]\n",
+      " [ 0.71196172 -0.70221828]]\n",
+      "\n",
+      "Explained Variance Ratio: [0.94933309 0.05066691]\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.decomposition import PCA\n",
     "\n",
@@ -190,12 +213,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "075d03a0",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(6, 6))\n",
     "plt.scatter(X_pca[:, 0], X_pca[:, 1], alpha=0.6, color='purple')\n",
@@ -219,10 +253,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "f6a8215f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Drop PC2 by setting it to 0\n",
     "X_pca_1d = X_pca.copy()\n",
@@ -302,22 +347,84 @@
     "### Load and Inspect"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "659b5b5e",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape: (1797, 64) (64 Dimensions)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_digits\n",
+    "\n",
+    "digits = load_digits()\n",
+    "X_dig = digits.data\n",
+    "y_dig = digits.target\n",
+    "\n",
+    "print(f\"Shape: {X_dig.shape} (64 Dimensions)\")"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "id": "30f354ff",
+   "id": "21a05a0e",
    "metadata": {},
    "source": [
     "### PCA Reduction\n",
     "\n",
     "-   Reduce from 64D to 2D using PCA.\n",
-    "-   Plot the 2D result. Color the points by the digit label (0-9).\n",
-    "\n",
+    "-   Plot the 2D result. Color the points by the digit label (0-9)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "23d9e558",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pca_dig = PCA(n_components=2)\n",
+    "X_dig_pca = pca_dig.fit_transform(X_dig)\n",
+    "\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "scatter = plt.scatter(X_dig_pca[:, 0], X_dig_pca[:, 1], c=y_dig, cmap='tab10', alpha=0.6, s=20)\n",
+    "plt.colorbar(scatter, label='Digit Label')\n",
+    "plt.title(\"Digits Dataset Projected to 2D (PCA)\")\n",
+    "plt.xlabel(\"PC1\")\n",
+    "plt.ylabel(\"PC2\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03cf1fa0",
+   "metadata": {},
+   "source": [
     "**Observation**: You should see that visually similar digits (like 4 and 9, or 3 and 8) tend to cluster together, while very different ones separate."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "03ee6478",
+   "id": "acc2acc1",
    "metadata": {},
    "source": [
     "### The Scree Plot (How many components?)\n",
@@ -326,19 +433,155 @@
     "\n",
     "-   Fit PCA with `n_components=64` (Full).\n",
     "-   Plot `cumsum(explained_variance_ratio_)`.\n",
-    "-   Find how many components are needed for 95% variance.\n",
+    "-   Find how many components are needed for 95% variance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "483c4db8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Components needed for 95% variance: 29\n"
+     ]
+    }
+   ],
+   "source": [
+    "pca_full = PCA(n_components=64)\n",
+    "pca_full.fit(X_dig)\n",
+    "\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.plot(np.cumsum(pca_full.explained_variance_ratio_), marker='.')\n",
+    "plt.axhline(0.95, color='red', linestyle='--', label='95% Threshold')\n",
+    "plt.title(\"Cumulative Explained Variance\")\n",
+    "plt.xlabel(\"Number of Components\")\n",
+    "plt.ylabel(\"Variance Explained\")\n",
+    "plt.legend()\n",
+    "plt.grid()\n",
+    "plt.show()\n",
     "\n",
+    "# Calculate exact number\n",
+    "n_95 = np.argmax(np.cumsum(pca_full.explained_variance_ratio_) >= 0.95) + 1\n",
+    "print(f\"Components needed for 95% variance: {n_95}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a19bcb69",
+   "metadata": {},
+   "source": [
     "**Result**: Usually around 20-30 components are enough to capture 95% of the image data, reducing the size by half without losing quality!"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "abfabb17",
+   "id": "6485ab87",
    "metadata": {},
    "source": [
     "### Correlated Features Exercise\n",
     "\n",
-    "Create a synthetic dataset with 4 highly correlated features and apply PCA. **Question**: Given the high correlations, how many components capture most of the variance?"
+    "Create a synthetic dataset with 4 highly correlated features and apply PCA."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "eb9ad181",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create synthetic dataset with 4 correlated features\n",
+    "np.random.seed(42)\n",
+    "X_corr = np.random.rand(100, 4)\n",
+    "X_corr[:, 1] = X_corr[:, 0] + np.random.normal(0, 0.1, 100)\n",
+    "X_corr[:, 2] = X_corr[:, 1] + np.random.normal(0, 0.1, 100)\n",
+    "X_corr[:, 3] = X_corr[:, 2] + np.random.normal(0, 0.1, 100)\n",
+    "\n",
+    "# Visualize correlations\n",
+    "import pandas as pd\n",
+    "df_corr = pd.DataFrame(X_corr, columns=[f'Feature {i+1}' for i in range(4)])\n",
+    "\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.heatmap(df_corr.corr(), annot=True, cmap='coolwarm', fmt='.2f', square=True)\n",
+    "plt.title('Feature Correlation Matrix')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0314f7ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Explained Variance Ratio: [0.95355226 0.03281419 0.00854854 0.00508501]\n",
+      "Cumulative Explained Variance: [0.95355226 0.98636645 0.99491499 1.        ]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Apply PCA and check variance\n",
+    "pca_corr = PCA(n_components=4)\n",
+    "X_corr_pca = pca_corr.fit_transform(X_corr)\n",
+    "\n",
+    "print(\"Explained Variance Ratio:\", pca_corr.explained_variance_ratio_)\n",
+    "print(\"Cumulative Explained Variance:\", np.cumsum(pca_corr.explained_variance_ratio_))\n",
+    "\n",
+    "# Scree plot\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "plt.plot(range(1, 5), pca_corr.explained_variance_ratio_, marker='o', linestyle='--')\n",
+    "plt.title('Scree Plot: Correlated Features')\n",
+    "plt.xlabel('Principal Component')\n",
+    "plt.ylabel('Explained Variance Ratio')\n",
+    "plt.xticks(range(1, 5))\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b40e692",
+   "metadata": {},
+   "source": [
+    "**Question**: Given the high correlations, how many components capture most of the variance?"
    ]
   },
   {
@@ -361,9 +604,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C03 Clustering Algorithms.ipynb b/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C03 Clustering Algorithms.ipynb
index aa536fb..d4a03ed 100644
--- a/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C03 Clustering Algorithms.ipynb	
+++ b/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C03 Clustering Algorithms.ipynb	
@@ -92,10 +92,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "3031aa26",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GYRiGYeIOFiwYhmEYhmEYhmEYhok7WLBgGIZhGIZhGIZhGCbuYMGCYRiGYRiGYRiGYZi4gwULhmEYhmEYhmEYhmHiDhYsGIZhGIZhGIZhGIaJO1iwYBiGYRiGYRiGYRgm7mDBgmEYhmEYhmEYhmGYuIMFC4ZhGIZhGIZhGIZh4g4WLBiGYRiGYRiGYRiGiTtYsGAYhmEYhmEYhmEYJu5gwYJhGIZhGIZhGIZhmLiDBQuGYRiGYRiGYRiGYeIOFiwYhmEYhmEYhmEYhok7WLBgGIZhGIZhGIZhGCbuYMGCYRiGYRiGYRiGYZi4gwULhmEYhmEYhmEYhmHiDhYsGIZhGIZhGIZhGIaJO1iwYBiGYRiGYRiGYRgm7mDBgmEYhmEYhmEYhmGYuIMFC4ZhGIZhGIZhGIZh4g4WLBiGYRiGYRiGYRiGiTtYsGAYhmEYhmEYhmEYJu5gwYJhGIZhGIZhGIZhmLiDBQuGYRiGYRiGYRiGYeIOFiwYhmEYhmEYhmEYhok7WLBgGIZhGIZhGIZhGCbuYMGCYRiGYRiGYRiGYZi4gwULhmEYhmEYhmEYhmHiDhYsGIZhGIZhGIZhGIaJO1iwYBiGYRiGYRiGYRgm7mgzgsVPP/2Es88+G0OGDMEhhxyCG264AZs2bWrpy2IYhmEYhmEYhmEYphEIKaVEK2fu3Lm45JJLMHz4cJx66qkoLi7Gc889B4/Hgy+++AIWi6XB51yxYoX63adPn2a4YoZhWhpu4wzTtuE2zjBtH27nDNP2aRMRFl999RU6dOiARx99FAcddBBOOukkPPDAA9i4cSMWL17cqHM6HA7101Tsdjvmz5+vfjN8L+MF/lxGr41Hk0R8XxLxNSfq6471a47HNt4QEukzkkivNVFfbzy180S7/w2B7w3fl8Z+ZpqTNiFYuFwuJCcnQwjh25aamqp+t3QAidvtDvjN8L2MB/hzGZ8k4vuSiK85UV93Ir7mppBI9yuRXmsiv954IdHuf0Pge8P3paHEoh21CcHizDPPxJo1azB9+nSUlZUp74oJEyagf//+2GeffVr68hiGYZg2jpQ88GUYhvsYhmmL0JK41Wpu6ctIWAxoAwwdOhQTJ07ELbfcggcffFBt69evH15//XXo9fpGn5eiMyoqKpp0bZWVlQG/Gb6X8UBr+lwmJSU127mj0cYT9X2JFm3hNRv1Nug9c+HW7w+HKyVhXndDifSam6udx1sbbwiJ9BlJpNfa0NdL0cNmw1bAUwa7pwAe2frWGul1xtN3eaJ93hoC35vQYoVJvwkFXZ2ocFS1wLsS38SiHbUJ081//vkHV111Fc466ywcccQRynTzxRdfhMFgwDvvvNMo081Fixa16txXhmkr7Lvvvs1yXm7jTDTo0ikNGeZPoK96HR7j8bDpbsLqtUV8c+OgnXMbZ1o7ZrMZvbobYLSNBjw2uFLfwNpNSbDZWt+kib/LmdbbBgWM5aPJMQWulLewZqMZFRUsdsWqjbeZCIuHH34YBx54IG6//Xbftr333luJF5999hnOPffcRp3XaDSioKCgyarT+vXr0a1bN1it1iadK9Hhe8n3MtpEo41Hk0T8jLfm16wiK+yTgYrX1d8653dIMQsMHnR3nZEWrfl1N5aWeM3x1sYbQiJ9RhLptdb39foiK4ouADx71DZD2WXo3X0KHJ4+rSrSorlXYBvazhPt89YQ+N4ERlaYDZu1NiiL1TZD+Wj06TEVDk9feGSNd2IiU1ndnpqTNiFYkH/F0UcfHbAtPz8fmZmZqlJIY6Evi2iFsFGH2JzhcIkE30u+l9Eimm08miTiZ7y1vWbpKYK0TQEqXg3YLuzfqs+VNfUeCH1Om3vd0SCWrzle23hDSKTPSCK91rper3SthSysESs0qoCii2DOmgIYB0KINjGMb7F2nmift4aQ6PdGJSC4V0PuqRErtB0VQPGFMGdNAwz9IETjrQeY+tN65NkIUEnTpUuXBmzbsmULioqK0LFjxxa7LoZhGKbtoYkVbwK2V0IfUPUNZNlDkO7dsb40hmHaAJpYMSpIrPBSBVl4EeBcDCldLXB1DJMoYsWoQLHCd0CFJia6lrHhdoxoE4LFyJEj8eOPP6rUkD///BNff/01rr76amRnZ+PEE09s6ctjGIZh2gjSUwZZMSO8WBEgWjwB6S6M1aUxDNMGkO5tEcSKINHCtS6GV8YwCYJna3ixIli0cG+I5ZUlLG1CsLjoootw//3346+//sKYMWPw6KOPomvXrpgyZYpKC2Hq5pVxU3CS9Ty8fPPbfLsYhmHCIVIgLMcAIqOOe2SCSBoJ6FL5XjIM0wAsgOW0ug8zHQTo6uqHGIZpOFbAclLdh5kPB0Qa3+AYYGgruWvnnXee+mEaztyv5uPDCV+g7/4F+OjZrzDk6EE44OTmc3plGIZpzd83Ul8AkT09wgqMCULlmA+CEMYWuEqGYVorQp8JJF8NVcKv4q3QB5mOgkh/CEKfG+OrY5i2j9BnASnXa22w8p3QB1lOhKinVxXTdNpEhAXTNN578jN0H9QFlz02Ct0GdsaHz3zJt5RhGCaCaIFq0aJ2pAWLFQzDNF20EMlXA0lUSjG4i2GxgmFiIVqIlOsB6/m1d7JYEXNYsEhwdmzYhUUzl2HYqUPVIPyAk/bBgp8XY8+2opa+NIZhEqjOedsQLVisYBimGUULFisYpkVFC2k+gSMrWgAWLBKcPz+bB4NRj/7Dequ/+w/rA6ETmPvVPy19aQzDxIFTtnKrd++s/2PcOyAbYARnNFSgT4ERep0DrVa0oPJmujyIrMmcBsIwTIPQ+sz1dYsWdYgV0rUhZN8rXZvVD8MwTREtzlNiBVLublAaSKQxkXRtgXSuhMfD1X7qggWLBOfv7xei+15dYUm2qL+T05PQpW9HLPhlUUtfGsMwLV3Wy7UScs85kMXj6iVaqC/m4rGQhedButbUfbynGHr7O9AXnwajnAvpqUCrg5zCXVsgMl+BdG0EWuNrYBgm5uh0OpiNJdV95siwfaYmWlxVt1hRdBVk0aVKZK7ZvhmyaDRk0SUsWjBMo6kCjIOhsxwPgcp6P0obE90IuYfa9+qgNrsFsvQONV4S7mUsWtQBCxYJjNvlxn8zl6JgSPeA7SRgLPx1qTZhYRgmccUKKq0nSwHnnDpFC69YAecCwFOoHhtJtCCxQtqmAraJ9BdQcj3g+KNViRbSYwPsvwIlV0PuGQ6U3gZZOQPSXdLSl8YwTJzTuyADKL2hXn2mWuWtQ6yAey3g3gJZdJla0ZWurUqsUGUX3RtZtGCYRiDdWyELLwdKb4csuQkoulhbnKinWAHnfEAWKZNur2jhFSvgmAPIMsjCS1i0qAMWLBKYNQvXo6q8Cj326hqwvfuAzijcVoRdm3a32LUxDBMnYoWXCKJFgFjhJcIAvEaseMF/q3aOViJaeMUKWXKzJrh4KX+aRQuGYSJCkRUW+60Q9ewzw/ZD/mKFFyVaXKpECki/1WAWLRimcWKF2y86wkPbLoooWgSIFb6NJFpcBenaVCNW+PaxaFEXLFgkMEtnr4TeoEen3u0Dtnfu21H9XjGv/l+aDMO0YbEigmgRUqyIMAAPLVa0LtEirFjhhUULhmHC9R/uHSqyQrgW1t7ZANEipFjhL1qU3gGR/higa+e3nSMtGKbRYkU9RIuQYoUiBSLrRcjSuwLFCt8DWbSIBAsWCczyv1ahY698GE3GgO1p2anIaJeGlX+zYMEwCYVrVXixIoRoEVGsCDEAl+5IYkWwaDEL0lP/XNFYoYQU+8zwYkUt0aI4lpfHMEwc4+0zAyIrwvaZa8Ofx7UxvFgRIFrcC5E+HhCZIUSLLY19GQyTuGJFLdFiE6R7l/YYGheFFCt0ENnvQpY9ElqsqCVaLIfH44na62kLsGCRwKyavxYdewVGV3hp3yMPa//bEPNrYhimBREmQFjrPk7lUuu1rxBddj3Om6xKfkLoIfT59bgQAyDS1fFxhzAAuvTq11/Hofo87XiGYRiFDhD17TMDF5MCob43tR7nSSF1A0BQFaZ47V8ZJi7QAzo/kS8c1W1QltyrhAZIB6ALVUHEA0h7Pds+jcEsypSXqYHvRoJSaavCphVb0al3h5D7WbBgmMRDGLr5SnSGxXIqROqdEPpsZQIn0h4EzEeHP17fGSLzbQhDZwj6crecCJH2SISrMEJkvgWY9oYgASXOUNdkGgqR+bomrIQ7Lv0JwHwMhC4lptfHMEz8ovrM9AchTfXrM8OfyAiRNl5VLgiLoQ9Eyi2QJbcA0ua3fRBE5qR6iscMk3jQYoPImAAY94vcvjJehCx7DHD8BLjXQxZdDpF6G2A+vtbhVAlIJF8KmE8Of05dO63tGwui9EraDixYJCjrF2+C9Eh0LMgPK1js3lKI0sKymF8bwzAthzB0DS9a+IkVvuMjiRYhBt40gZfmoyBS7w0jVrwKaRwYl2JFfUULFisYhgnbf1CEWur9kMYjGyVW+EolFo6CSHs0tGhh6AuRele1WOE3jmOxgmGaLlr4ixX2H2u2u9dGEC1cKpVUpI4LLVqwWBERFiwSlLUL10PoBPK6hS6TlddV275xGec4MkyiEVK0CCFWRBQtwgy8pacMwv4DpGsVRMo4vz1GiPQnIW1vQDgXQVJoZRwTTrRgsYJhmLqwO1Nh098WGGnRALFCK5VIXhcX1hYt1GRqEmDoDuj9ytazWMEwTRYtZDixoi7RQt8RIvMNCENHiNRbA0ULFivqhAWLBIX8Kdp1zqlluOklt3O2EjRYsGCYxCRAtIggVoQULSKIFaj6ShnBofJdSPeWatGiWqyofE+rEEIl+Rz/tjLRgl4Dp4EwDFM/Vq0pUpEWINGioWKFb2OQaEGTqfRntZQRmmxlTgQMe7FYwTBNFC2kcSikvg90GZPCixXhRAslVkyGMHTRzmloXyNasFhRL9gNLEFZt3hj2OgKgoSMnA5Z2Lh0U0yvi2GY+BItkD1DGUBFEisCQp1JtIATQh/CH0e6Id2FNX+TaGE9DyL7o+o80NnVO9xaGLN0AwJxL1pIEi1yf1ZGeexZwTBMQyItrOkR+sxaxn0h0nS9okXWVOqRIEvvhEh/SuufyKeCRAvv/xmGaTDKQDvtadjtlTBLIwSVJq4L5Rvjhki7j1o6hL5j4DkN7QESLajtVwsZTHg4wiJB2bBkM/K7+dXmDkFOpyxsXrktZtfEMEz8QV+y9RErao7PDTvwFvoMiKTzgeQbA0WLwgv8xAodRMZEwHQwhK4eFUviRLRQqzBssMkwTEP7jwh9ZuBx7SEyXwMMvUOLFnvOhiy+RUWrBfgG6fNZrGCYJmJ3pmPFqhL1m0xrYRgY/mBdnhIQVaSqPqeWWOFrmxRpwWJFvWDBIgEp2lmC0j1lESMsiNxO2di0cmvMrothmLZPSNFClrZasYJhGCZWRBQt9JkQmS/wBIhhmgmPx1PdDvMhMl8MLVr4xIpu/D5EERYsEpCNyzYHGGuGI7dzDrav2wmnwxmjK2MYJmFFCxYrGIZhGidaBOXIMwzTvIQULVisaDZYsEhANi3fCp1eh5yOWRGPo/0etwc7NuyO2bUxDNO6kZ4iSPeuBooWOiD9BY6sYBgm4ZCe4nr1mWFFCxYrGCY6bdG9G9K9p3GiBYsVbct0c/Xq1eqnqKgIQghkZmaiZ8+eKCgoiPWlJHSEBYkRBmPktz+7gyZobF29HZ16tY/R1TEM06oH3rbJgGM+kPE0hL5dnaKFy3QOYDwGHrSDmdNAGIZJIIz6CkjbVMDxT736zGDRAiRaSDbtY5ioiBVk/g09kHpbvb27NGPbFwFp14zKmdYrWMydOxeffPIJfvnlF5SWlkJKGbCfhIvU1FQceeSROPPMM3HAAQfE4rISlk0rtip/irrIyE2DwahXggXDMEzdYsXbgO1F7e/iW+o1AHe6k7BmzTb07GniG8wwTMLQpVMa9PapQMVLDeoza4kWDMNER6yo+kL7m/5pqGjBtF7BYubMmXjuueewZMkS9OrVC2eccQYGDBiAzp07Iy0tTQkXJGBs3rxZHTNr1ix89tln6N+/P2666SYceuihzXl5CcumFVvQ74BedR5HaSNZ7TOxZTVXCmEYpv5ihcI5t94DcIfDwbeXYZiEiqzIMH8GVLzaqD6TYZjmESsUVZ82WLRgWrFgccMNN2DEiBF44oknVNpHOIYMGYJTTz1V/X/NmjWYMWOGeuw///zTnJeXkDiqHNi5YTcOGzGsXseTYEHGmwzDMPUWK7zwAJxhGKZWn6kiK6r8xAruMxkmPsQKLyxaJI5gQSkgGRkZDXoMCRt33XUXxowZ02zXlchsXbNDRbbUZbjpJbt9JjZzaVOGYRoqVtQSLZ6kdUVerWAYJsH7zLd8aSCR+8zHAZgg9DmxvESGadNoppp6AK7wYkUI0QLgUutttkpIQ8WKaD2WCY9XfGjXOafegsW2dTtr+Y4wDMNAOoDKT+q+Ec5/Afc2yLJHId3sicMwTIIi7Q3rM21vNahqAcMwEZqfuxCy/FnIyk8BWQ7Yf637dtl/BmQF39YWhsuaJhhbVm2HJdmMlMzkeh2f1T4DjkoHinYUN/u1MQzTuqA8a5E1BdBFMn4zQWS+qgYJtJIhi8awaMEwTEIi9HkQWVMBXX4dfeZLkOXPABWvqVVgFi0YJhpixTNA5XtA+aOQVXO0tihSIzTYNIis6YC+E9/+RCtrSqaaH330kTLaLCkpCVkxZP78+bG+rISKsMjpmK3uc309LAjyscjK1/7PMAzjRZXxypoCWXgR4NkWWqywvQI4ZmubXIuUaIHMSeyszTBMQvaZMnMKUER95vbQYoXqM//SNlV9Xh2Wfgen1DFMU8UKL+X3QuJBJVrIwgsBWRZarDD0rveciWkjgsWTTz6JN998E3l5eRg4cKAqZcrEli2rttXbv4LIzNNSc7av34X+w/o045UxDNO2RIsQYoUXP9ECSGuJS2YYhmkx7K48iOTXYbJd7idahBArvLBowTDREyvqEi1YrEhsweKDDz7AEUccgUmTJkGn42yUlmDL6u3Y5+hB9T7emmJBUpqVK4UwDNMA0WJPeLEiSLQwp7+I3gW5DR6AQJcGIZrnK0xKF+AphdDXX9xlGIap1Zd4KgFZWasv0Qk7bJVGmHyRFoXhxQovLFowTOA4QFghdNaGiRXJ16j25tsWLFpAxH1khUzAMUrMVYPDDz+cxYoWotJWhcJtRcjp1LAPOKWCcGlThmHqI1qQp4XImgxZ8Wl4scKLLINO7kSS6y6YjYX1usHSvROy5A7AtVT70o4y6pzOxZAld6nnYhiGabRY4ZgDWT4hwIOCthvlX0jTvw0pkyEyX9HECo8tvFjhpeorwMP9EpPYUHtSvliOP7V2FvZAEiY+9v0pUq5XBppC3wWwjqw5zvY4pMhWQkWrECuciTdGialgQdEV7E/Rcmxbs0P9zunQMMEiMy8dOzbsaqarYhimzUVaGPeGSL0eoEFBOPTdINKfgCy6HMI5Fyi5CtKtVTGKKFZQuT/HL9pKSJRFC99AgKJEHD9BltyWUAMChmGiLFYUXwNUvg9Z9rQ2yVLb/wBKxkJnfxei8nnAuRSyjCZff0Gk3hfhrHoVuQZ9d36bmITFJ1ZUzoAsHhNZtNB3gch8QyupnnI9pKcEqJgMWf4khL6zJlqIZE2ooPLBhj6tQ6woTLwxSkwFi3vuuQdbt27Fgw8+iCVLlqCwsBDFxcW1fpjmSwchcjplN+hxmfkZ2LEhMRoEwzBNRwg9hKETRObboUULn1hxFSCLtMe4VkEWXRFWtPCJFSRuqA2VURUtAgYCqNI2OmYl1ICAYZgoixXwaBurPoQsewpwr4csHktHadsr34N0zIWwDgcqp0G61oQRLarFCuN+EDoLv01MgosV3hQPT0TRQggTYNoHIvsjn1jhO5cSLboAWR8qoUKNW4RoHWIFEm+MElPBwmq1YsiQIXj33XcxYsQIHHzwwRg2bFitH6Z52LpaK2manJ7U4AiLnRt3w+Op/uJlGIapByFFixBihY8wokUtscK3IzqiRciBgJcEGhAwDNMMYoWXqo8gba9CpJEg4TcpqvoU0rkIIvWeMKIFixUMU1usQL1EC0oBkVXfB4gVvl3lT0A4/1FjiXhG8hgltqabFFlBxpuDBw9WP1wlJLZsXa1VCGmoepiVlwGn3YWiHSXIri5zyjAMU1/RAplvQxZdojTysGJFkGiBzNcg9B3CixVBooUyzDL0b7ARZ8SBQJBogfTHIfTtGnR+hmESg4hihZeqr1RshUi7H7L0/ppICxItaHvqPZBlD0FaL1CihSx7mCMrmIQnvFgRKFqIjEmQpoN8RpzSUwxpmwLYJoZvt6V3KflQWk6E0KU0su3T2MEBoYt+1TMeo7SAYPHNN9/g9NNPx/jx42P5tIxfSggZaDYUSgkhyMeCBQuGYRorWkDa1AA8rFjhL1rYpkEmXw1R9UN4scJftCj+H0T2u4BooGu2pxSy5JbwYoUXxyzAPhPSOrzZqpMwDNOaqYQs+V94scLfONNyAmDcF3D+7bf90+rte6lIC2TNgMh6FzD05TQQJrFxr40gVnjxQJbcCpH7A8X0Vz9uN2Cj8umRIfFQmA8D0HDBQnrsmlkupXvR+CDaooXkMUrMU0IMBoOKrGBahi0UYdHACiHelBBiJxtvMgzTFNFCpYM8Dhh6RT7YfCxE8qXQ6dMAy4mAlUqNRUCXr1Yhha7h/RuVBVOmXLo6IieSLgUsR7NYwTBMmM4kQ0V6kYlfxD6HzP8cfweKFWr7OEj7TMD5H0TaeEjqJ439WaxgGBLtUh+o48s8ubr9aYusCn0HiIznA1OwamGEyHwLaMT4QRMr5kIWX6UWY2TlJ5Ce0qi+XzSuETxGia1gcfLJJ+OXX36J5VMy1TjsTuzZUoTsBlYIIawpVliSLdixYTffT4ZhGo3QmVWah8h8LbxoYToGIu0BzbHbKyikjAkvWpBYkTVVq07S2OsydIPImhZetEi6FCLlKggdp8QxDBOmHxE6zbwv652wooVIuaGW+Z+2fZzm3VP5jhIrYDkOOl2qZhrIMAmO0KUC1lPCixbVlT6UsEHt0Pe4JMB0aATRolqsMA2BEMZGixWAW9tY9kjziBYGHqPEVLA48cQTsWvXLlx55ZX47rvv8N9//6lqIcE/TPTZvm4npJTI7tC4AXdmPpc2ZRgmSkg3ROqdgKEgcLvpcIiks2u+/KsJK1pEQayoc0DAYgXDMFEQLUTKTZCmQ4DKD+oUKxqbS88wCSdahBEr6hYtoixWeIm1aJGUGAsqMU3EHTVqlPq9bNky/P7777X204SaDCFpPxP9CiFEbseGlTT1kpGbxikhDMM0GenaCFl0MeApg8h4GrLsccC1WhMrrKdobt/GIUDGBAh9XoBogZQxmkVd5dSoihW+5zB0A7KmQRZeAHh2JsxAgGGY6EGTJo++p6qQpMyGpU1LA/EUAmUPQaQ/rfnmyAqI1PshXetYrGCY+rQtXSokiRY0lii7r06xouZxSZDVooUsvl5NfxsrVuh1LsDxd2ixwl+0oN/WM6LqaSESeIwSU8Hisccea5bzXnjhhfjrr79C7pswYYJKRUl0tq7ZDqPZgNTsxqn2mXkZ6hwMwzCNRXqKIEtuB9xbtL+Lb9FEC/tMCONekCV3kOMm4JwHaXsDSLkBQpdcW7TQZUBYT4uqWFFrQFD1NUTSeQkxEGAYJroI2CBLH4VIn6CZCHt2+dJApO0lJVrAvQFSlkFYjwdMAwDzMRxZwTD1FS2EHjAOrFOsqC1avKD5XDRCrNDpdDAZ7ZC7rg0vVngpewQw7Q9E2YRTJOgYJaaCxRlnnNEs573vvvtQXl4esG3y5Mn4/vvvMWzYsGZ5ztYYYUH+FdTYGgMZby74eXHUr4thmMRBfbGmj9dKiHq2au7XVLI0aVSNWEEYh0IkXxYgVuyy2VBUpdVKz7KMRGmZHi5Pja9OpsWK3OTk6A0Iki7iyQPDtDL8+4lQRLOfiIhIgUgdB1n2GGDsA1R+VLPP+R9k+YuAoSeg7wipL4Aw9Avo7ximLdPUdqpEC8tJgEiql1gRLFpAGBosVhAejwcOpxmmjBcjR1gQlPaq74jmQCTgGCWmgoXL5UJVVRVSUkLfYBIdLBaLqibSEAoKgvKgAdxyyy04+OCDkZXVcJPJtlrStCklSSnCoqKsEuXFNqRk8JcqwzCNQxi6AFlTAkQL2F6qOYDEioxnAtJBCBrcnDA90KjOn29HXRzViUgiDQQYpq0Q634iHDQZkqa9IVLv0NJCgnEtgttwCGAaBaPer6oBwyQA0Winjf2OFrrqkqeNxO0xAJb9ITJeCS9apN4JYT0z+iVOE3iMElPTzYcffhgjR44Mu/+8887D+PHjm/w8//zzDzZv3oxTTz21yedqK1A6R3bHxos33tKmO7i0KcMwURAtRNYUQNchcEcYsYJhGKa1oVZwSbTIfFsZ/AWQPAZFVSfD6U5qqctjGKaRCJ1FpXuQaAHoYy5WJCIxFSzIaPP4448Pu5/2zZw5s8nP8+WXXyIpKQlHH310k8/VFnC73NixfhdyGlHS1D/CgmDBgmGY5hAtZAixwuOpCPnYEf16IcNi4TeCYZioIqWjHsfY631MSNEi+Tq4TaOwaUtZ0y+YYZiWFy0MvQHTsEaJFR5PoKUBEwcpITt37kReXviVs3bt2mHHjh1NTjv55ptvcNRRRynRoilQ1ZKKitAD5vpSWVkZ8LulSpqSaJGWm6JSchqDwWqA3qjH5lVbm3xPWvO9bCu0pnvZ1Hbc3G08Ud+XaCBELozpb0FX8QI8SeNgt6cC0N4Pi7EIwv4NpOVEVDoyVe4ocftBgzGyYAcuH3wYRn46E8XVfRrtj6f3si4S7b2u6zU3VzuPtzbeEBLpMxKt1+rtJyLtD/d5sBj3AM75cOoPhctdO2ycqtiZDTsB5yI4dQfD5TGHOWabqnzk0O0Pt9uktutEP5gy3lKGwm7TuSgtFwnz3npfZzx9lydS22oosbg3TWmn8XZfDLohMKY9Anh2w6XbG84qmlrXfe3kKWjWb4ZwrYBTtz+c7tabbl8Zg3YUU8EiIyMD69atC7t/zZo1Yf0t6susWbNQWFiIU045BU3F6XRGrcTq+vXr0VKsnKPd80pZgQ0bNzT6PClZSVi+YCV6LuuElqQl72VbozXcy3333bfZzh3NNp5o70u0MBqN6NhhLDYs2Q4pt6ltg/rnArZnIau+hHTMhCnlYbjsQokV53RfipSqieih74kZw5/wiRYOux3LNm5CayOR3utIr7m52nm8tvGGkEifkaa+VmNeu4j7Q/UT5JvWuyAFoHx090aIpDtQXHoQtu0oD+ineve0AkVXaN47SQ9iT/He2LnL5jvGZDKhVw8TUHSpmryI5Cewa09v7N6jTV5SU1OQmnIMtm7bkJDvbXZ2drOdu7HtPJHufzzdm8a003i8L+S7WNDNA1lyKSArIVMmYev2fJSURBYskpOT0bunHrJotCpPqk8bj9KKwVi/oTgGr6B1ElPB4tBDD8WMGTOUt0T//v0D9i1ZsgTvv/8+TjjhhCang5AwcsghhzTxarUvqFCGng1VnejD3a1bN1itTTN6aSzrft8KvUGP/nv3g17f+Cyg7PZZcNsk+vXrh5YgHu5lW4HvZfTaeDRJxPfF/zU7jUZkW8qgr5gAVH2p9gvHHKD8bvTPfxS90jbCWDFRbde516CH/lafaGEym1HQQn1TY0j09zpWrzne2nhDSKTPSLRe60Zb6PDqp449AWaDAcJoRHKXzr7teiHQJbUCxtLLlFihtlU8hvzMu5GTe7KKtPBFVhRdrIkVNHiuuBcdsx9FXrujVaSFL7Ki6EKASpjSMbZb0bndBOS3P9gXaUGkZyTWexuLFdiGtvNEu/8NIRb3Jlw79bbV4HbqT4bZgrQGFmdojvtCMVJmwwag6CJAauldhvIx6NHpFTg6DYaHzDkjRFYoM17PTm1j6e3IShuPtIGHtspIi8rqe9OcxPQdv+GGG5SPxdlnn61SNrydy6pVq/DLL7+oih50TGOhdIcff/wRp512muq8mgp9AUUrhI0+3M0ZDheJ3Rv3ILtDJpKTm/b8Oe2zsHvznhZ7HfFwL9saiX4vo9nGo0kivi/0mtMMRdBXPO0TK7yQaCFL74Qp9S7ICorCK68lWuj0+lZ5zxL1vY7Va47XNt4QEukz0tTXqqsMvbJJYsXYbwL7lRxrEt494xAYS2/yiRU+yh6GMVXAaD0d8BRDFtaIFTXH3Alj2qMwWk5QIoUsrBErNCRQejNM6RMAyxEhy5Ym0nsbj+2c73/L3Jtw7dTbVk9/b3rECiL5aWktel+UkONaAVl4gU+s0HCpSC1z5quAdT8IUTttTDpXB4oV3u2lt8OQNh5685HQ6Rtf1bGtElPTTfKv+Oijj1S6xuzZs/HSSy+pnzlz5qioiw8//BD5+fmNPv/PP/+scp64OkjtkqZZ7ZteNisjLx07Nuxu8nkYhmH8SU9PVZ4V/pEVwSjRouwRiOx3AdPRgLDWiBbiVnROrgnNZhiGiYRXrOguQ4gV1ciyh4DKTyArP6stVniPKb0TqPoasuL9ILHCdwRkyc2A/VdITwWkDFECkWGYVoNyn1FixaggscKLC7LoSsAxr5ZBby2xQmQCxn0DRAth/wUed1Ezv4rWR8xjashY8/HHH1cGOeQ1QVBkBamjTeWLL75Ahw4dmjXnvTWyedU2dBsQOrSqoZVCSnaVwl5ph9laWzVkGIZpKCZDGXp0MQKVbwNVX0Q+mEQLx1yIpBEAzoIsuUXljZJoIcqugzS8BaFvvjxphmHim0yLVa3ABuNwBwoF755xLLrLMWHFCi9KJE17CNJ8DGD/MfQxpXdDpD8JaToYcMwKdQRk2WMQVElAlkAa92rgq2KYxGinodpqPKHSOUylkHvCiRWBooXI/hgw9lVbpHuXz7NCO1kWRPoTgLRBVqYqUdMnWmS+CY84SD0fo9Fid4IECjLgoZ9oiBUlJSUq3eSkk06KyvnaCm63W1UJaUpJUy+ZeenqN0dZMAwTDaR7N4TtYaDsDgjr2YAushEXrBdACAtk8TWQtlcg0p+ujrQwQJd6B6CjKiMMwyQquWRml51T68ek1wcc9/y8Bdijv6HuYbBhL0CXAzhmhz/GOFTrhxzzQ+/X5UJkPAtZfJWWWuL4FzoRv5MyhmmpdhqqrcYTVL3E5TYAKdfWfbDlZNX2vUjoIVJuCBArZOnDkMU3Q9Cx5iO0faYDAX13FitiGWHxyiuv4IILLlBuqA2hvLwc06dPx1VXXVXvx6Snp2Px4sWNuMq2za5Ne+ByuJDTqemCRVa+llayY8MudOnbMQpXxzBMW2CXzYaiqsqIqyk0QAkWK2TpAxD277S/S+9VqwpSOewH5nb6xApjX7WaqXAu9BMtUgHTEAhRY2zHMExi498v0artCycGVo9bZ9MDyROQ7biZpiK1T2DYCyLjecC5BJBhcu6N+0Ck3gy4KFKjKoxY8Rxkya2Ae7PaRH2cKeMNpKW2XB4+w8TrmIHaKhlvjvvhW8QjLncSjNYR5FADlD0e+iDL6RCptwdEfOr0WfCYj4JIexrQpyuxAm7NqFKW3A6RPh5S3xsi6TwIA8+xYipYUMWO119/HSeffDJOPPFEDB06FPowyhmVJJo3bx6++eYb9dO+ffsGCRZMaLas0soE5nRsumCRnpsGnV6HnRtC5WkyDJOo0MDjhOmTw+6n0E9/wcIrVqBarFA4/wkvWgSLFb7HkGjxulq9ZLGCYZiG9EskYExfXIG7DwghWpBYkTkRQp8PqcuASCfRgVZH1TSlRqxIvhSy8FLAegpE2iOQpXdFFCuqOy6g+DJ07fgKPKLp6boM0xbbZjwjdOmQ5mMBTylgeylwp/l4iJRrAJ0Wle4PiRbSNBiy6HKfWKHh1kSLjFcAHae1xlyw+Pzzz5WvxJtvvqnKmaoa1b16oVOnTioignwsKJVj8+bNqlKIy+VC7969cc8996hKH0zT2bJqO3QGHTKroyOaAolNJFpsX8+CBcMwjSOkWEE53c7/QosW1gtUDmgtscILPab4RiDjOQh9Hr8tDMPUmy9WbQDQFU8d8SL0JRTm7QkQKwihS4I0HwaR/gxkyU2aaOEVK4rHaZEVZLypa6eJFuXPAqaDIJLODSFW+DouGMquAjLegJT7sODKMK0Esh2QrvUAmW5ahwPJVwO2l2vECvNhkLvPhMh6G9I4EELUTLWla0ugWCGSAV0+4F6jiRbFV0FkvgZpHAqhs7TQK0xAwYLeVBIe6Gfp0qWq5OiCBQuwcOFCFBcXq2MyMjLQo0cPXHHFFTj66KMxYMCA5rykhIywyG6fGTaypaFkqUohIcK1GYZptUhPpXK9hr4ThD6n+Z7HXahM7PzFCpF6N6DvDEmGU5Xv+okWb0HapkOY9q2eJESAHlP2PJB0HqBLgzB0abbXwDBM26BreobP+K8CBqRlvAxpe0lFRZC3hXQuAgx9NDFBVkA6FkCk3QdZ+QVE8ugasUIkVaemGSEd6yGypgOeMsjyV8KIFYGRFsiiykdsxMkwodpmqBTTpiBdq0lZgDB0bpzpppHKHI/SqgLZXgOSxwIptwDOlRCWozQzcHggCy+CyP4AMPapWawh000/sULrNwyQ5ZMA57+aaFF0BUTWVHh03SHkNsDQu5agSQaecFfvSxBhI2ZVQvr3769+mNgLFtFIB/GSkZehTDwZhmlDYoXjT8jiMQA54afd33yihS4FwjoSsup7NVgnsUJSfnjZwxAp4yCt52mihXF/le4hkk4nBypA3xFwbwl/XuNeEEnDIYtGAfquQMZEFi0YhokImfuRyZ8X6TkQwjioeqVzHOCcD5H5OqRpX0CY1ARHOuZBJF9SS6yQ5S+qPkpkvaNFfLnWQmS+DCl3VU9EQiONgzgyjGHqaJvRgsQKVeFDlwxkTm6waEGmm26PAXrLKUDFW5p5JqV4OJdCmIepMQ4MPQASRUxDAV1mzYNJdEgaCZSN94kVsnyiOlZkPAVpe0PrKwz9tKiL0tshHX9qizemvX2ihao2UnIn4PgDIvNVSON+CSFaxLysKRNbNq/aih57dYva+ch4c+1/FELJMEybEisoFNr+PWQpmk20oC9caRoCmfEGdO7V2gpm1SfatZQ/pYkW6c+qVBBZeqf2IHLZzngRKLkxtGhBYkXqbVqYpaRIkeWQxdexaMEwCWbuSyQbTbA5HWGNNu0ul/ptNhjU/pV7dvv2UX25jil2mMvvgHDOUduoXyHRAiRaWE9Xx8jisdUP8Bcr1mr/LyEDzqXVj70aIvMlyPLnQ4oW0rAPkPYMCxZMwuH2hDC5rYYMN6md+rfNuky86+ofjDo9Oifvgq7oQmqYgLsIsujiRokWTlcyDMlXQ1L7p/FM6UOAe4PmbiPSIDKehqz8HiL1Bgh9TeUzoUsDlFmnAULfqVqs0IpFkACqRIvKnyFSr9PSZh2/afuKRvtEC3hKqsUK774rE0a0YMGiDeN2UUnTXTjgpH2jKlgUbS+Go8oBk4Ud+RmmzYgVXmIgWtjRFWbn1xDVYoXvmsqfAlLvAVzrajZS2GXxtZDpkyBKbwoULYLFCi8sWjBMwhn1EZ+PvACnzZgWdr9XwBj7zZe1VnSnnX44uniehnD/5bfHGVq0KH+6tlhBwkS1WKFRBVl0TUjRQhr3QZXxMQhnOpKMDb4VDNOqcUs/89ogSEw8/b3p9Tbxrqt/6J6Ricmn7A1d0TWaWOG7iM2NFi2EPhOwngFZdJkSK3zIUsjiW1RKh39J0wDRwnIMZNFYn1ihUaWJFlmUCjvFJ0jU9EFXQmR/DFn2WNA+d8KIFnUUoGZaM9vX71SiRW7n6DnOes07d24MrXwyDNOKxYoA0eJ+lXMZ9ed274Gp6hWIqhmhDyh7CIJSQCg9xItnF0TJGMi0Z7T0kEhiRZBoIVW5QYZhmNB4xYpB5qdhDBArAkULOOYDwqKJFpT6EVGsCBItUq4HjEOq+y4tsmLlGs3LjWGY5sErVnRwBokVQaKFdG1q0Hk188wgscK3sxSy8ELAtVIVlwjY5d4OWXQ94FoU4qxVkIWjIcxDa/oKhREi/VHIsgeDxIpA0QLOeZCeEKWV2wgcYdGG2bwyeiVNvWTla/lY29buQKfeHaJ2XoZhYoOUDsDxV3ixohGRFhSiGc4gy7vfZ7pZPgm6qnciXyOlh6TeDmk5Haj6LEC0QMZLQPmzECnXaQZWocSKWpEWL0MYuL9imESHzPyCmXraERhkfiqMWBEUaUHO/7ruQMl9aoVUOfqXTQgjVgRHWrwGWfE+hPVkQBaqynkMk4ikmExhS5e2T0mJynPkJadgyqlD0d5xRWixIjjSImsahL5DncUkLGS6WXRJaLEiQLQYBZE1AzAWaJvcOyGLrgNc/0V4hupIi4xntWpDruUQ6Y9DVr6vFpkivIjqSIs3IE37QYi2F7bFgkUbZvOKrTBajKoUabTIyE1TZVLZeJNhWifKR8LQERDpkb/ElUfUwYAw13lOCs8MFaJZ+4RWCPOBkJUU7hk+JJRyw6HvAlR+FbjdswduVykM6U+SqlFdDswvfSQUVDKVgwkZhqmOpghm4c496NVtaB2CBXUjWQCVLtUZ1KQAroWauZ5xQB2CBdWFzwP0uRDWsyDJdC/jRTidHKnKJCbkHxOcluUlnJDRUErsVdhdKZFr7AqDO/JYBwYqClG3gEgREx6ph47MeSMJFgSNYciE04dRM+KMKFjQ49qpyiGqrDs9JxmTGwfXIVhQ/5QJ6Nu3SbGCYMGiDbNpxVa065StyvBEC51ehyyuFMIwrRphKACyp2tu2WFEC5H6AGA9BUKX2ujnCWWClWbcG9lpz0FfekNo0UIZ2ZFj9stBYZM67DFNQKWjF7okUZpbNkB5mxQKGU60sJ4DkXwdhCG/0a+BYZj4ERvqmsyQv0RDeXTWAggMwVndrkWa88XQB+nyIGgF1tBV+zvlCtV7kfggUu+AtJ4NVH4Q+rH6rhAZL0IWaVFtIvNNVDoy4XbvaMTVMkzrILIJpq7ZIyyqXC6c/+mveGf4XehnfAQG94LQB5qPg2iAZ5fDlQpr6p3a6KUqtOgCw0CIzBch9PmBvhfJV2mPq3gz9OP0XSDS7oUsuRXwFGrbKt4Akq8AkscAtkmhH6fLUeWUhaE72iosWLRhNq3YgpxO0fOv8Pex2LaeS5syTFsVLaIhVkQywfr5wvOQYnocWY7bAkULkQQPVRApfxJw/ldLrHhobiWu9PMQVpOHcKIFixUM06YodzjCrsp6+fTc8xt17kdmkSFmGNEiWKxQ5nmZfqLFY+FFC69YUXy9T6wQFOHmqGjUdTJMWzDJ/fDskc0eYUFU+okWA82PQgRX62mgWOFFHR9OtAghVtRLtCCxglI6qn6oESt8L+RLlbICGpNRhFaCiRUxN92kUJoZM2ZgxIgROOCAA9CvX79aP/37U1gOE60Ii2gabnrJbp+Jrau3R/28DMPEXrQQ2dMBkRl1sSIShVUSj/8tUWh6XFsTFWlKrLCnvYUH/tiBnbrraCRRS6z4ctX6EK+hq3LIht7vy5rFCoZhGiFafLFxCDxJN1Z3LqkhxQpf36PLhEi5Aki6XBMtSAS2nlO3WMEwTMzwihaOlAma4a0gTy1jo8QK/4h1epxIvROwnFIvsaLmcZkQyVcBSZcGiRVva+OZpBFA8li/J22vqo7oDJ0hrCOA1NsTTqyIeYTFE088gbffflsJE6eddhrS09Nj+fQJha3EpsqPtusS/bKEWe0z8d/MpUqAIgMahmHaRqSFSLmx2cUKwmIw4oju/bHFbkJW1jvq+agu+zZbDs7o3w4lbj2yM96GvuQyuFMpDWSAiqy4ct9D0M6aBOnepvlh6DJqR1qQ4RSngTBMm0Nfj/FGsrG2mR8ZbXq9K7wGwKFMgvOsLiQZXXB6BsBirp7Y0IQgjMhAFY8AuxItVKRFxTQ16ZA0tHbM0kw2K79msYJhgvBEsLCyu1z47NxRqrxpKLxtuKHm3+UuKywZzwPu9bSCDhh6NkissJoKsdfALNidYSItXOtDihVatTVHgKFnQKSF/cdqsaKTto/GNckXavsqP1RihTB0qd6XBlhHaPtsryeMWBFzweLTTz/Fcccdh+eeey6WT5uw0RVEu87RFyyyO2SiorQSZUXlSMtq3okNwzAxEi1yvlIGm80tVhDriotUOOh3550EQaW/yh6GLvM1/LO1FLf+/Ls65teLLkHnnJ9hEEnokqTVFpfSA7hWQO4eBSRdAiRfFCRaUIilnj0rGKYNoq+HH9fyPbtrhZrTJKZ3duBYKNgkWHqKIMvfAMo+hCH7A0jbj0DVFxDZ08KKFbL8GcD+szZpoEiL5EsgyDAv+Wog5UqSZiGMvQHzw5ErMjFMgrHDVh5237gfvg3ZZqNh/i3dJZClDwAiCyLj6XqfW7o2w1MyDpA2WDPIR0ITEPxFC6rUIchcN+D5dmvP51oMZE6BMHSuJVogeXStx3lFCySdq/UpfnhFC1hOhdDnIlGIqWBRVVWFgw46KJZPmbBsWq4JFs2VEkJsW7ODBQuGaSM0NIezPuZa5AQeDhIreib9A1n2gLah6Aqc2etVAIcq0aLKLSH0Nf2XT6woJM+NcsA2UVtlCBAtONyaYZhAKHorEppY8aoyt1NeFLbXfF4Ucs8FQDalhPSqLVZQqUH6W5UvrFnpJJNfNVEpe7g6v90I6LIBtVJaO7WEYZjaY4eVe3aHjaSoV1Wy4Hbu3gpZdAXgWqX9XXwLkPF0LUGg1uNcmyFLxkE4/1F/e4rHQJcxyRf1EG785BMr7N9pfxddFFq0CIN3XBN6XxoSjZgKFsOGDcOiRYtw7rnnxvJpE5KNyzYrc0yzte6ShA0lu0OW+r11zQ702U+rL8wwTOISzlwrnHnWoZ2SkOKZCXjFCoUTKLrSJ1r4U0us8BJCtGAYhvHHTeHf9RUraDJT+aHfAUUBokWwWKHw7Nb6pmrRQhMrHvUz43MCnu2QhReyaMEw9WBDSXFYU06KvmioYBEsVmjNcm6dooVXrEC1WEEIGosUjwGCRItIYoXCvSWkaMHEoenmfffdh4ULF+Lll19GUVEdNXGZJrFh2eZmia4grCkWpGQks/EmwzAhMUUI3abIitRaYkWwaOFErjWSWKHlo/tEC9sUSE8xvxsMw9SbOsWKINFCOldAVkwNFCu8fZESLUZDOldrJZlDlTusFi2kawP7fzFMjKgtVuhqiRbSvbNeYoWPatFCujbWT6zw9hPVooV0bWr6C0swmjXCYsiQIbU6ZbfbrTws6MdsNgc4rhJ0/Pz585vzshKCTcu3oPvA0MpfNMjumIUta7Y16RwbS4rx5J9/4I+N62HU63FEt+4Yu98wdGYzVoaJe7QveF1AKCT15r9ddCqyrWYcPf1bZZ5FURZe07suKQ6YXb/UpIGERBMtMjJfg8etAzxFgL9Yoe8MkfE8ZOVHQEV1fjlHWjBMmyecqZ7T7Ybd7fIZ+gZHdqWYTHWIFbeFFyt8DyiCLLwIIuNJyKqugHsDRMoNgOkAyOKxEOlPqb5JFt8OkXo1pP0nNTkJJ1qYM6fAaDQ26j4wTGsgkgkmjQe+Ou/CkL40kdJBmi5WWDTvClkOWaL5TvhHWmgIQNrDixVe/CItIFJogxoFBYsVIvVuwNADsvgGqoEcEGkBXQognXWmpTDNLFgcf/zxrCK3AA67U6VrHHjKvs32HNntM7BlVeNLmy7Yvg0Xf/qhcgE+pkeBGnD8vG4tvlq5Ak8eeyJO6tU7qtfLMEx0xQpZcivFWwHpDwWIFe3xBmTZMvx0/nM4+p3vsa283Geg5XFthXTUR+h0aUJF6cMQ1lMgTQcqJ20lVqTdpwYgIuliyKQLakQL91ZaEuG3mWHaKOFM9Whyc+qM0OaYRMhJk3Qr8UChy1QREnUiKwHpAESyqqgkPYVAye0QmVMgq74Gqh6AoMmLu7C6dGK481SoCVHwgh3DtCXqY4IZCmrPZLwZFWhM4CkLECuk7XVAlwWR/miNaCELNZGi9FE1NRZp/wM8JXWfX53bBVn+AuBcDJHxDOApDRArpGuJ8sQRGRMgi2/WRAtPhTLilbY3garvlWE4e3C1oGAxfvz45jw9E4YtK7fC4/Ygr2vzucfmdMzG3K8jKI8R2FFejsu/+ATtU1Nx60GHIal6lWF433547Z+/MfabL1DhPB4j+g+M8lUzDBM1scLxp/Z3CdAx+R7MvOhU5OMNoOoTWp+AsfwG/HbhRPy22em3YmJCj7SRtAYBkLFdSARE+gRI+2+A/UtI+zdqEiBFDoT1GMiS27Xw6/KnIVJuUaKFx1MBV9LNsDTBOJRhmPgjnKEvGWl6vSmSGxGpoDn736WVIy25CyL9seoSg7+GeYQFIut1yMofISzHaWJFxRS1RxZfowmplTPUiqvIfEkrmxjyidOUQWeVszPs9mUNvm6Gae1tty7zzLpMcuva74/ymMiaCll4OUTarZpY4fxX7aMWqkQL25sQGS9Clj0BOH7S9pUZIDJfhiy6GnCvCX1yXQeIrDchbe8AldO1xxXfpEQL1adYjq4WKz6pPudTmmhR8qD2OIrosr2i7Su6lEWLeDLdnDhxoipr2rt36NXzVatW4bvvvsN1110Xy8tqc2xYuln9btelGQWLTlko2VUKW2kFktOSGvTY+379CVJK3HTAwT6xwhvKOWa/A2E1GHH7T98jOykJR3br0QxXzzBMNMQKheMXJKWMhRWTlVjhRbiWAyXX4eD2FGnxqYq08PL3pWchg8YptUQLP7Gi6tPqbW5tEkADioovAlZCSbRAyu1YUzUMwq1D7+ax7WEYJs4Mff358OyRjTp3oGhxRwTRonpltuwZCDqe/CmqxQqFe6MKAxfpT6j+URZdo/VXxWMDJzvVYgUMvSGd4SdyDJMIbTeceWYkk9z67A9GVebJegOy5H8+sUJh/wmSxhwZL0GWPRnoOWH/ShM0wokWAWKFX1/gWlgjWthe9IkV2r4VmmiR9RZk5ec+sUJ7UetYtKgDXawFixUrVoTdT4LFpElU35ZpCuuXbEJadiqS0xsmJDQ0woLYsqphPhazN23E92tX44K99ka6xVJrv04IXDpkHwzJb48bvv0Ka4sKo3bNDMNEWawgD4usdyArAsUKf9GCIi1+Ov84tE+hHE+NoW9+gmJxFpB8RR1iBfxEi2shLAcD5iMDd5WPR65hLjLNnA7CMEzDRQsSIWA5QRMtLCcB5mP9jvCGkb8GYT4Ukvqmijdrn8hPtKCQb9VfZbwA6HvWEiuCvd0Yhmk+pHuHatsBYoUX+4+QZY9BWI4INPNW+76CLHtWiRa+dhxJrKhGWE+FtE0K7YlT7XshTIMoXyRwX7VoIV0hvG+Y2EZY1EVxcTGbEEWB9Ys3NWs6CJHbSSttunnlNvTe168h18Ezc/9Ez8wsHNgxfEkfndDhmqEH4N5ffsR1X3+BT84dpbwuGIZpPlSIM6wQOms9xAodRNpDgHEfSNurgZEVKWO1FUQysYLDJ1r8esHz2FwuMeLDb9TKC4kWK685T1PNba9HECu8uP1WQWXAKmiG41F4TGZ4XEeq6xe69KDrp6gMAaHnEAyGaWt4ZOgSyl6zXwo990dS/risVP2B9Ni0voFyzY37qD5N6LtCkvmvfY6fWHF4QBpISIIjLZRoQZEWt0FkPBogVhj1FehTwP0RwwRDJrnUng06HQ5sb8V2mxOrix0B+xskVpBvhHNe+IPsP0DCo9JDyMeCkkVk6f1a0ohfpIWn6GoI6jciiRU+z4raCzi1Ii38PS1CRFqAxjGyKsDY3Ivqt9S+xOlDmn0WOG/ePMydO9f39w8//IANGzbUOq6srAxff/112HQRpv6sX7IR3Qd1bdZbZk2xIjUrBVtW1j/CYuH2bfh76xbcdODBda4wUKrI2P2H4Z5ff8RTs//AXYeS+skwTHNAJbZk0SVaaT/TwbVFCzKboxQPn1jxMKR9FoRpf8C50HeYSLkekgynbJMhMp6CLB6niRbuTTCgDF1172LG8JEY+ekfSrRYW2pBQfr5EJbTIN2bI4gVXtyQjvkQxr0hg8K2heMXCEN7ze0/6RyfaCHdeyDLyE/JBKTeklBf8AyTCOywlWPsN7XLiHrNfmuJFfaZkOUTIDPfgnAugCx/HiLzdQiRA1l0FUTa/apPQtKl6jg4FwApY4Cy5+q+GPc2bdKj66AmJpDFQMZEwJDvG/dIdzH0jvdhcc6CSKLKBM0XDcswrQ3yvLrjp+/x7vDDkWS7Fbm6U/DVro6Y+PcStT9c5ZGQSBvgWln3cc4VAHlilT9HYVcQaQ9Alt6niRbOv9R0WUfpIdIFKZNUKmxtDIChAKh4t+7nc1NZUwHocgF3WdC+LZCyAqJqtha9mvkqhL59oFhh/0271gQy62x2wYLECkoFIaiz/v7779VPKAoKCnDPPfc09yW1aeyVdmxbswMHnbZfsz9XbqdsbFpZ/9ClyQv/RV5yCvZtX9PwItE1IwPnDhiEN/6dj2N7FGBgphbVwTBMww2uQplc7amoQIp+J4yllwLuzZDF18GT9gJ2ufZGmVP4jtdyQKercnwi5WZI+y/aqoRzCUTWS5BFY7RqHiRWVLztGyco0aLkPojMVyBtkwH75+ipn48Zw59UogVR5cjEjh0OdOywNwypdwBlj4V/AdYREFQeLOgYaToCImmUWtGksqiU41qqOw1GnURS1dMQ9s+14+gfFi0YplXy1LEnhIy2pHQzb4QFRVVsKCmuVR6RIi0Mwo484z/Ql94EWE6DoEG/KrEsIYsug0i7G/AUqomKNmF5DHAvVY+XxTeq0qWyfBLgWhzmCo1a7rpIUxMSEkGkYQB0fgIwiRWy4h3A9qyKLpOl4yAzJnBZQ6bNQW0uVOST//5QmPV6/H7RqUix3QzhXoIM9xKMGXQ3Tu51JsqcJhh1urBlT2sZeeq7a2mrhedrDuGh0HXSvCrKn/f5WKioCuoDyidCZE1VokBFRQXWrVuH7t3bw5o5GbLo4mrhwYtLlS5V457S8eHNOkVSdV/yPOBeG3xXgOwPIZwLIUvvrDbkvNInWvjEipKbqvut0QAJrwkgWjS7YHH55Zdj1KhRymTxoIMOwgMPPKCMN/0hIcNqtcJsNjf35SSE4abHI5HfIy82gsXy+gkWpXY7vl29Cqf37adSPurLiQW9VFTGrT9+i4/POKcJV8swbZv6mNMFm1z5ixUaHuhKx8JofgKv/O3GVUMP9R0vDD2BrBmQZY8rsUI7fD1k4TUQWa9B0qpCtVihcP6tiRbZ9JhXlFihcK9DT/3/lGjhXXEsLCxEXl4ejNYzNVEhlGhhHaWEE1lGZcdqixWoFisInW0CUtL3ASrfgXD4rbxWfciiBcO0UkisCBVJEWzAGeqYT885Ax1MC6CvvEMTK4yDfWKFwr0eksooZzwJWfy/GtGi+HqaMSgFVpaMg0h/GrL8RcD1X9AzGCHSn4SkMsueMoisdyH1HcKKFV6E8y8tfS7jaRYtmDZFucMRsb1+PvKCkNvbWcthKL1JiRVejLaHkWu8DV+t74g+ud3Dnjd4jENjDGkogMiaAll4UW3RgsSKrMkqRQN2v1KqVV9rokXWuxCGmhT2qqoq7byGTkAo0UKWQJbcr6WNFI0B3KtrixWZb0PapgPO/8KIFYt9YoUvhaToSiDjZcC5xCdW+PqtBBEtmt1002KxIDMzE1lZWfjpp59w2mmnqb/9fzIyMlisiBLrFm1Uv5vbw4LI7ZKjPCw89Sgx9M3qlbC7XTi0S8NSVUjcuHyfodhaVoaX//27CVfLMExwGkigWOHFgyz7rbhjfz1SjTLAB0KWP1sjVlQjkk6rFitCGNGRaFFyD4SVUrr88k5JtBD/Q+dkW+C5dOkQ1jMBirTwJ+liVJmvxB5HdsDXljQeXkus8PprGKrehsFfrPAXLcqeVqkiDMO0fc7p3xN9UpYhy+EvVjxUM+j34idawFOkiRYZz1MCu7ZfOgBBJpzPA4a9aosVlTMAxxzARZOK/0H4VzQKIVb4cM5VogV5BTFMIiPd22EquwF6P7HCS4bzcVzSZwsKMurvYaFwb4O0zVCRDxDptSMryiYA9m9qP45Ei/IXw44VSLQQmZMBvZ8nn0jVyhrru6oyyNAX+O1LUtEaMA6CSLsFMAyoW6wIMOu8GtCZQ5h1aqJFWzfrjGmVkI4dO6pICqb5WPffBlVy1JLU/NEq7brkoMpmx+4tdVfy+GLlcvTPbYcsa8NzNTumpmF4n354678F2FQROMFhGKaxnhUXhxArAkWLXMMCSE+lJlZQZAOV8/PD51kRSqzwQiuItinaYCFItDCWXgGLsRB6v9DQWqJF0sUQydfA5k6DU38gPOkvqK8uFVmRfEFIsUKZd9prUg8pjYWu1QeLFgzTKqAQb1o1pR9K96gLo05fS6wYt48HRscHQOZ7mv9NKLGiTtEiV5vcGPpX9zMPAoaBtcUKL67FKjpD9bWRxAovLFowbQx9HV51wftJrJBF10KETbnSRItult9x3VD/yX54pGurFklV9a5K0/CJFr40kInKWDMsVR+p6It6iRYkVvgqAekg9B1qRAuvWGHoD0EeGfp8TdhIuloZjkcUKwLMOp9URsBUdSjRRIuYll446qijIpot0j5KC8nPz8cBBxyAc889F+npgW7vTGTWLtqI/O7tYnKb2nXWzKw2Ltvi+38oCisrMGfzJowevE+jn+vU3n3xx8YNeG3NShy7z76NPg/DJDqSDOHIRC6sWOHFA1H5JmDeB5DlAPlWBGAETIcAxWPrflIy5hRJgKFboAGWex3gWoaU5MCqQcowk0QL4wD1ZS/0WchVUZ7JkJ5DAArT1GVA7h7uJ1bQPCIbMHQH/NJGRMr/IClkUxghUm6CLH9G20G5qinXAGATToaJVyi82xviHS5v3Z/gMeb5A3ohQ3wIYTlb9RVSlRoMI1Z4ca8HKNrBNFirRiRLgaw3IHX5EBQ1VjENImm0VhEJApIEW3+xwotrmeYNpO8PkKBRF85/AM8OQB+bMRzDNCd6na7e+6X0AK4NgEvzjImEsWoqzujzos+EMyI0dvG2TfcaSFexJhQoo1s34NC8tCJCix8ptDASesHdmx6icmANvZRY4dun7wCQaOEpBgx9lFhRszMVwtgbsvJzZfRLCzt14lqlUs7U2Mgxu3a/RdFd+nYQwoi2RkwFi/333x9LlizBqlWrVDWQrl219ACqGrJy5Uq1rVu3bti4cSMmTJiAadOmYfr06ejcOXwJTKYG8glZu3A99j+p8cJAQ8jKz4DBZMDGZZsx9LjBYY/7ed1adW1DOzQ+v8qo12NU/4F4et4cfLN2NUYMCv98dUEdo3+HwjBtwVyTDObqgxBmSPMRaoVQlt4b9jiXfm+4U56ClUr/yQy1ciALR2lfygqnZjCV/ihkyZ3aQDsk1SuQtjdruXXLtCfhEkNRUroWwd2DEi2oCknw9VNOuGmIEl5E5ouQRVcosyuFZxdk6UNaidSSmyGSr9XEiurJgkwi09CbIKmMatY7gL5Tve4ZwzAt399RHxfKxM/ucmHcD1r+OY01vMfQ9pfnL8NzRx8DWXIlIEwQGZMgy56IODFS1ZJIPLD/qszxYDoIggKSHbOUMTGJuWockXyZZpznWhTiLDoVmaFMN/VpkFlTlWkxPNvDPKsRIvMNNalhmEQz3aQxuTTuBZHxEmQxLSSESTXXd4U99TWcM/Xrel2DpNSMTPKTuBQiZRykcxZkxXxVEhRJV2pRFkVXa8JGKLxRE3oaoGj+FWFFi3D7SLSgH//r8pln3uIro6qNZ64F3MvDnQlK2LD/VFusoL3pT2h9VRsUK2IuWBxzzDH45ZdflBAxdOjQgH1//fUXxo4di5tvvhlHHHGEqi5y1VVXKeHimWeqV8SYiBRuL0bJ7jJ06Jkfkzul0+uQ1yVHRVhE4vs1q9ErOxvpFkuTnm9Abjv0S0vH03Nn46S+/VXp0/oiPeWQtleBys8BzzZIKiVkOQEi+eqQNY4ZprWZa0YaGAQjdKmQlpOV/4yunOqN1xYrljvvgrXcjF6UMin0kLQ6ECxaUFsquRsi4wVIirSoJVrQIPxlSNsbgOPPwNdheggu94FIRePS15TwYhoKd/or0JdcVSNauJYprw3lDF4xI3Bls2KqJlpkf6TlmNYRssowTPybCfv3fRtLS3yGfDPOOhP3H5wFPYkVFIklSWQdE1G0UGIFrWJWfqiJFeajtexpP7FC4ZyjmQqnXKf1fXDUFiuMByqxQp1XVVoKJ1pUixWmfSBEA/PzGaaNmG7SYoQ0HQh32iToS8fUFi30XbEWE1BRYsaeysgV0bzodGZ4DAMhsj7S5gD+pdPLH4FMuVuldKgU2WDRIijFI1oEV/pQOH5VgVzhRQsBkf6sqsyGqq9CixXmYyB0KWirxHSZ+bnnnsMFF1xQS6zwRl9QNRESKAhvSsiffwYOcpnwrFmwXv3uUBAbwcLrY7F+iWb0GQpa4Zi1aQP2yQ9UFxvLsfntUVhViVfmU13k+iGdKyF3nwLY3gKM/YCkiwAjVRD4GHL3iZB2/owxiQeJFkU4CsXGO0KKFed9+mtA4LQKZawWLSD8yoZZTgRc61SkBXR5tSIrPPYFgHVkwNcNiRUTFyahxKFv2msQZux09MMe83MB+ruwngNPxWdAZYh66CRaVH4SvsQZwzCtniO6dsJemduQYx8bmDZG1T5ItEi9VeWTN0qs8KJEi7eUYFvjz1NbrPCd30BGfFMBnf8YjcUKhvG1EZ0V2x0DUWh+OnCKWi1WnPfp73DVw+g/oN1Rm6+YGihWeCl/GNK5AiJzKqmPLSNWeFGixYNKtIC+r/8rgEh/HjAfrvl2BZh1JoZYEfMIC0r9SEsLMgrxg/wq6BgvPXv2RGU9VTQGWP3vOlhTLCpVI1ZQNZJZn/6lwjBDrVbO27oFlS4XBue1j8rzZZnMOKZbD7w2/2+cN3Av5KcEueUGIV2rtfrLukwg7TEIfU31FGk5Fah4BbLociBjEoTlyKhcI8O0BCQO0kojGdOFq29OBnYBiFS4zSfCYzarSAtpHAJ3ygQVWfHJuaPU8dJdqAQKoTPXjrSwnguh7wxZehuga++XHlJYbUT3PoTjT3jMJ0KX/ixQciOKTA8osWLyopU4b69hqt8o6Nn4KCeTwQqHa4gv0kKk3qXSQJT/RjhsL2lDheTRyguDYZjWS/uUlIA0EBIrxh+WC1PpFYFihRcq0ezeAZF6GzxlT0C4lgBp46vTx+opVnjxRlqoKLMblKlfKLHCS2CkxR4g43WOrGAS2nRTjTF0qb5UBjdM2FTVDylpE2Equ04ZWtpSXsW6bW7cf8TRPiPe+oxx6Nwqbct/8SL5SjVGobauKLsTMvVRJVrIogs1gSDWYoWXWpEWK6rFikMhdOS7kQRkvgRZdI3yq0gUsSLmggV5UXz66acqcoLKnfpDwsTHH3+MTp1q8oB27typyqEy9WP1gnUquiKWYc5k8FlWZFPpKNntq0t/+TFzwzrVgXSJonnqyT17YdaWTZgwexaeOPaEsMdJT4mW30658Cl3QOiSg5TcNMjkmwDbRDXQQPYMCGPgigvDtBa8Odz0Rd47u34CgGZmR0aWpwKGXAjjXrDq81QaCEHO2PRlLyzHQBr3DxIt3lFCoNx9amB6CEVayAqt1Gl1GojO/i08SRejMvkNTPy7UIkVhFGng1m/BSb3w4CRjDIbXkXI9xrk/oBuGqDPBLzXFAny1LCOAFiwYJhWzbby8oDQ869GnoIc9y2hxQrKk0++GrLyAwjLWdClPwa4t2p+OZLM7AYDpv3UeEFNdkqpokgdK7pOMvW7CKBceX2XsGKFv2ghM6fA5dgKDwbCwmkgTIKabkr3Lsiyx1R5cuVhIYzKq+asDz7B6b274+4DJ6HI2Q7nTfvGlwbSkDEOJAkT79f8nXydGp8IQwGk9ZyafRRpkf29JlRARF2s0K7FDln2SN2mv45fAfclQOYbEO5lgHFotVihQRVGSLSAc6nqtxJBrIi5YHHdddcpj4oTTzwRw4cPR5cuXdR2iqr47LPPsGPHDjz9NIUBAW63G59//jn22Sc2BpJtgdX/rEXBkB4xfc68bpqb9frFG0MKFlTZY2C7dlEVUci74oy+/TFl4b+4dMi+6JtTEzXhjyy5T3PmTX2olljhRQgDZPK1QNlDWh5q9mcJ0/gZJsDTwkxVnGoiMzSxYgJQ+YEWKZH5ai3RQkr4+VqUVIsWdwH0hUqVSLSzo9D0JJ77Yws2l1fhtw1aChlFgnRI2g0UXwThKYQsuRpVqS9is80Kj5RK6PRWB6i/p8VeajAgst6GLLwkKK/cHzNE1hRAH53IL4ZhmmaoSX46xrx22Ggrh66ywneMux7h33nJNREW9H+HRwd72iSYy67UKhEFixVkdCcrNePetPGAuVd135cCqWvn6wepOhGypkAWXhDBVJgC1e6BdCwAnP8qcz6ZehuEPnL1IbsrDxs2VaBLl6alxTFMvBmAe6Fop3AYdDq0T7LDU/IIhONHyKrv4c54C5squsLu8uCpY09QizA7Kzpi5Z4V9fasqIW+C0Tm61okdTKZa1aoMuwkGYjU2zXRoupLLVVLtVlNCGkOY36tP5mmjZc84SseUXQq9VU6XQqkPiuwsoifaEFefKH2tVViKliQUGG1WpUo8dJLVFamhl69euHee+/FkUdqYfmUYvDWW29xWdN6YiuxYdvanTjinIMRS0ikMFqMWL94E/Y9NrByx56KCizfsxtHdo++iHJ09x74bvUqPPnnH3jjtDNq7ZdVPwH2r4HkawPSQEJBJleSVNfSuyHLxkOkPxz162WYeCecWKHhhiy6Mki00IF0SCl6wZUxBYbii6pFi+1+pnKaWDH+bzc+Xr7Wd34SK6acug+MJRdpoZl0pGs5DKXXwOV5FOd+8hs+OHtkgwQL7TVoX2nSODiCaFEtVhgH+Y5nGCY+DTWDjflCoROoZe5HgueMM55CT/04TbQIEisUjj8hS++ESB/vKyUaPAHQUjimhRUtlFhB5QQpR55wztPWT+sQLWiMa7NxyjPTdttvOCNwEivePOUwWCo1sULDAX3xaCSbX8Q9szbj/EH7qq2zN0c29a8LNb43DQWyPgKqPlNihRc13ifRIusDwNAjJpN/QWXXvYs8IUQLJVaYj/YtnEa6JpFAYgUR89qOVAHkiy++wMyZMzFjxgz1Q/+nbV6xgjAYDOjYsSNSUni1uz6srjbc7NgrtiuGVCkkv2su1i2qbbw5Z/Mm9Xtgrr8RX3Qw6PQY0X8gflm/FvO3BXZoUlZpYZzGvQDjgfU6n9DnAUkjVXiYtIeop84wCUJtsQIBogWcf0F67L6tFD21oSwX24wv0bet3/ECjtTnwooVHV3X+MQKLzr3ChTo7sR7ZxwOXROislQubLVoUWOGR7BYwTCJMpka+cnvWAMqS3pCbbHCi+MPyJLbId07w55LM8ucFmQqHEKs8FL1KWTZ46ovZRimtlixb8pr0PvECl9jRK79Wkw4shOyLFEszempAOw/B4gVAaKFc5FW5jRGkGihUk90ORHFCiaQFlteateunfphosOq+WtVpENul8hhiM2VFrJ2UY1Zqpc5WzahQ0oqMq1BRn9R4sBOnfHFyuV46s8/8M6Z59SkndimaishKTc1LBXFdGT1isuDQM5nbbaWMdO68TecMul0SDc7sKvSEN5YMypiBcJGWhB2jxvXfbsIb570Eto7rwG5RlFkxfqiAhzZ3Y2LBh+ITaWlKp3rgDwnLGUX14gVFNaYfAVkGaUD2jXRQn8nXMmBUXgNhdpvYKQFGWlxZAXDtCZSTKaQBnuUKuKmnDRKSw9T4pxEi0dnLccbJ42GLL0PInmM1r8Fe1JUixZIHw9RHWkRTHCkRVixwl+0oN/1SA9hmLYIpYR8ePZI5TFD0Gh8v3wLMpyPQ+/4OcyjNNEiK/ct/HbRJUg2OlHuNKLKLRs1xpHuYsiK6YCNKolVYz1LSxe3/6QdU3q7Fi1qPj5s+nhzR1qwWBGHggV5U/zxxx/YtGkTSkpKVFicPzTBHDOG6u82nE8++QSTJ0/GmjVrkJSUhEGDBmHixIm1DD7bIqv+WYuOBfnQh6kO0Jx06JGH/2YuVe+t//NThEU4f4loQCuwI/oNwNNzZuHPzRtxcOeukJ4ySNvLgPkIzZimAVCIu7ReCJTdqxnxJI1qtmtnmMZCaRL0o/pO1wpVojMj4yotP7KpuFZEECu8uCGLb4bI/V5FLHhZVViIS7/WRAuz2IHx8xz4ePlnah8NWihke9rpx8Bc8WSgWJH+GGTlZ5q7fvE4n2hhtE+DNI5t0gCiRrSgsFUdp4EwTCuDDPjqMthbuSd8Pvg9h+6jDPVE6jjIqm8g0h6BLL0rpGhBExhpHRF2scIrWpBXBZVLDytWeKEyipaTIXWHxdQMnWHiAfKgoLQQb7rWiQU9cUg7d4jIimAc0JfejE7Zn0JWzEBm8lGqBHGjUjjlbsD2fM3f1vMg9J0AYyYkjQnsP2iHldwDkUsp9bERLPxFCziXAebDOLIingSLRYsW4frrr8f27dtrCRVNFSzIE+O1117D1Vdfjb333htFRUWYPXu2mkQnAivmrUb3QV1b5Lnb98yDo9KBrau3o3Ofjmrb7ooKrCkqxAkFvZr1ufdp3wE9MjPxzOxZOKhTF20AQaFdluGNOp8wdIM0HQJZ9gJgOZ07ECYuja5sDjs6Jm2HvvhC5Wzv9thQaR6DbRXGBptVBmAcAKTeAZQ9Fv4YkazMsbaWSNirczCpP6eBCa2ojP76L/TMSMF3azcq4yyzwaDCQGl/uUuHCutDSPKMgZAlmlhRej/g3gzp3uoTLRz6YXCbLkJyE8QKf0Mwi576RoEqd7Fvf5PuE8MwTaY+hprhjtlSWupr3+HGk8Tdv87HtFNvgyy6GJBFqnpYSNHCeg6chsOxvrAk4PHB/QSJFh4y5jT0hax8T6ssEo6U/wHGvVmsYFqVeWYoA9xofF9+s3oNDu60H07pMBoprrfCHygyITNeh6fiI+hsL0JWvAV3+mRsq+pM+dsoqaoK26boOjum+VXp0XWASH8WsuRGwDoSgkxxy59UCxgi7WEtCsr+K0Tmm6rqWawh0UJSSXhd219Yb1WCxQMPPICqqipMmjQJQ4cORZr/h6oJrF27VkVSvPjiizj88MN9248//ngkAuXFNmxZtR2HnlU/v4Zo06Gnlte5duEGn2Axb+tm9btvdvNFWBDUaZ3VbyCe/PN3zNq4EgdZ3tKiK5pSqpDCxRyzNfEj5ZpoXi7DNBkSK0xYA33x1b7Bsq7qfQiXC6v3nI6CnF6NHlgIKgFsPVP7Eg8lWohk7DC9gqs+/geLd31XazeJEqsLC9UPQWJFsBleutmC3y98GUn6Ldqkwa31FXDOh7QBMuNVLNhpRpbOhN6W5jMEozBzFiwYpuXwpnQ05hhq36fNmBbR3K97RiaeOLIXUC1WKOw/ahUC/EUL65kQhl5wlk7A2sKzMebbP3yFB0P1EzqdFVL09quQFEK0SPkfhPUcCH30SrozTEuZ30br+/LuX+cBRxyMEV0MMFS9VvsAkYmK1DdhqvoVhgqtaiT5zuhLLobJ9BKKXT1w+ntUejS8Sa+/YEHlQKX5cCBzOoTzH8jyp6r3eCBL79b6geRrACOVMfX3u4odLFbEoenmihUrcMUVV+Coo46KmlhBfPzxx+jUqVOAWJFIrJyvGdp5xYJYk5yejPTcNKxZqBl/EvO2bkG75GRkJ9XUDm4u9s7LR8/MLDw351uVEgLzSU06n9BlA+YjIW1vaOdjmDiBVhIpsiLfUSNWeElyfYzDsj9D+yRnk56DRAthPVOLtKgVWTEVV327HIt37Wr0+U16PYRwAv5ihRcSLconwqTXqXxXhmGYxkBixeRThqCD8+oascILiRb2n9RkhRYohKEfZNkjsLo+x6FZH2DSCYfU2f+osocGTbSASA3cyWIFw0QULbbJs1FuGB1SrEDVbzVihRdZiXaOa9AjZTv2yW+okb8Hwr3WT6yo2U6ipfDsIrMLfsfinJgKFvn5+RFD9xrLwoUL0bt3bxVhMWzYMAwcOBAjR45U2xOBFX+thiXZjNzOLWfsRFEWa6orlRDztmyuM+80mlEWw/v0xfwdLswtOgpCH4XntZyquYlXaCs4DNPSeD0rvGkgoSDRwmqfBOkOrL7RZNFCJKuB+daKDk0SK3KTkvHu8INgLb+iRqygwX7ylVQwXf2pc83DANMT6JhSU4mEYRgmKmJFgGjxK4R1JKQ3mkzfEVZTatNECxYrGKZOFu+248utB9eIFn5iRZLj2dAPkpUwlFyMicf2qLdoIT3lQNU3KprCh/kowDi0+g8PZPE1gGMOpIfLDMczMU0JoeiKN954A+eee25Uy5Xu2rULixcvxsqVK3HffffBarXi5ZdfxqWXXorvv/8e2dnZjZ4gVFRo+VuNpbKyMuB3c7B0zgp06JUPh8OBlqJd1xz899sydb8qnE4s370LowYMUilA0cLhsAf89mdg2mZ0SynGC4v6YJ9O0XhOK/SGg6GzvQW7OAcQzVPppKWIxecyWpCBbnMRjTYeq/fFYqoEqKxopJzp6vQQadoHDt0JcNedIh4BIwyG02BIMQKmIahydUOlS3P7DkeXtHR8dm54s9pnjx2GbmI84KoRK0T605COOZqfRQkJJG4Y3X/DaJ8MB8bA5W5ctR7Kw61rf0u+962pDcbiNTdXO4+3Nt4Q2vpnJMVoCpvO4X9MqPfPf/GLvHP8z3N453QklV0UXqzwPfA7wDhQpYRQGqhIexDS8S+SLLk4JG8Vzujbp179hEAXmDOnAY65cBtPgdNOfVZFQr+3wdDrjKfv8kS7/035rvQeE3x/04wmfH3eheEfI6Eqg4U6xmwwosKZAaelAB6ZAWk6Gha5G7pwYkVApMVNeOmkj3HAmzMifg5oMdNs2K5Fc/qe+AQI86GALhWyQgc4/qoWLcYAOb+gsir8ojp/ZsITi3YUU8HCZrMhOTkZxx57LE4++WQVcRFc1YI+YJdcQiXo6o/3A/rcc8+hb9++atvgwYNV6sm0adNwww03NOp6nU4nli1bhmiwfn1N9EFzCBbd9+mMDRtrlxaNFYY0HYq2F2PerL+xXudQeafJVVXY2AzXRKatwXRI+wondTLhxeUZ+Pa/NeiX3vSSpAbdIHTLmoldm17GbtsJaIs05+cyWuy7777Ndu5otvHmfl+sVgsKuj4PQ/loKLOHMHjMp6O8ajDWrIvO62qXOxRl5W5UVi5TRlyRkE4nnDt2+v4OPn7cT3Pxzum3obNurHoNSqwof1qrdmI+yidaSMNecJouxLKla32DqYwOHWBzu8I+d7LegOKtW8M+dzAOux3LNm5CS9Ma2mAsXnNztfN4beMNoa1+RqiNBnvcBPPpOeej1FZbKBV+pUypGoE/vbKy8OZJj6K9h0osB5poBmA6HKBqYpUfaGIFCaaenZBJo1HsOQafrvgSF/UfXK9+wmo1Iy31QOzY2bAxT1t9b0PR2MXD5mzniXT/w1HXd2VTvi/DTWO9Ixi6+/l5R6Oo2IH2ee2Qbj4fOvs7Ec5oxi7TU7jx21/Dzgf9Pwft81KQm3Yn9BWP+sQKWXqPmv4qk2/14ubBkzoBmzcVYk9hUJpqCPgz0zLEVLB4/PHHff8nISEUjREsyA8jIyPDJ1YQ9Hf//v2xevXqRl+v0WhEQUEBmqo60Ye7W7duKvIj2uzeUojSXeUYsF9fdO3SMlVCiBRDGn56bRb0lWYUZgskGYzYt1cfVXo0WlBkBYkVJHSZTDXlFIVnB4xV67BfxxPRYZMJn+/04IS9ukThGbvAY98PnTK/Q27n62iEhLZCc38uWwvRaOOxfF/cQkJkTIG++KKQokWl/mR4LDfBJDPQr1/7qF2XN7uLXMMjYTKbUdCvn+/v4OO3lZfh/M/m4t3hk9A5uQSy7HGtlCph/1kNHmT6RCwuyoGlzIw+ffoEnGv4B+EHMrSK0y/Cc9d1rbEmEdtgS7zmeGvjDaGtf0bqaqPEprLSkKJGpEgub4nlL86eAoPqK0OIFqbDIaynQJa/AJF2n0+sUFS8hVSjDvcdemCD+4mses7J2/p7G+sV2Ia280S7/01th839fZnprcpuGAOPTgddZag5ohmu9Ldw41crMHdr7YVL7xzSfxxASH0OpLETdFQlSIkVtAjiUBXJSLQo1V8Bm6sP2uWloV2ETBP+zNR9b9qMYPHTTz81y3mpk9q4cWPIfXZ74/Og6YMfrRA26hCbIxxuw3//qd8Fg3vAYmm5sjjtu+UpH41NS7ZiUX+JXtnZSGqmLwESK/xfq6ditkrZMFoH4dReZXhlwRast7nRJ6vpjsZSfypQdjesut/V4Kat0Vyfy9ZCNNt4rN6XDUUdYDK9jDxlvGkLECtml5yHLlRdI7t5XpO3xFnY/TpdwHWHOr7c4YDDrYOn7AkIr1jhhUQLaUSZ4xJYzXWfq6HPHen4liIR22AsX3O8tvGG0FY/I3W10aZAosX6shwkG19Ce2dgpIXDcBSQ/D+YSm+uLVZUk+p8A6d1AmRSn2a99231vY01jW3nfP/r1w5j932ZhCLX5TA4nUh2vee33Yxd5hdRbOuEuVt/a9DnQHoEPHbpJ1Z40UQLJL8Ct8dY79fHn5kEMN3s2LFjvX4aypFHHoni4uKAMKCioiIsWbIEAwYMQFtm2ZyVyMxLR1p2kEt1jKHOrENBPlb9uxb/bt+KgvouMzQRKZ2A/TfAMAhCGHBQxwzkWI14+d+6w7rqA9Vch2GgVjGkGQxjGaah2D0SV3+3HDtMLyszTH+x4upv/ojZDT1/QF+c0adXyH1ShvbTSTWZMeOMw9ANt0O4loc8Ruf4Dvskv4mOyU2rdsIwTGJjd7tVpMU240uA0MqL2g1H4e/yK3DddwuAjCdDihVeUl1vIMX1LqSnOMZXzjCJy5ZyPX7aeSJshnMDxIobf9oIm7Nh1TyUkabjT4iSsUFihRcHUm1XId+8Vs0nPJ4qeDzxY/gdbiyViMQ0wsLLjh07MG/ePOzZswfHH3+8CvF3u90oKytDampqLV+LujjmmGMwaNAgXH/99bjppptgNpvx6quvwmQy4fzzz0dbZsnslejSrxPigY4F7bFs3mqUHFKAXjESLOBcoJkQGgerPw06gZN65mD6km24eb+u6JwWhagTywkAlUNy/g2Y9mv6+RimCWRarHjy2JPhgIA7YwpE1QfwmK5BF50RX5/fQ+1vLujcVI893aRDO9MqSGHCuGGHoMypDQRyrEmQrtWAexukcT/f8V46JrthqfgfhCMosiIIi/sHSEdXSNMYCF3jIqWCnzvUfoZhWg7/NkpeNZQnT6HnLimxoaTYZ6gZinSLBZ+PvCDi+dPMZl96yJsnvYRs+R7ml43EpV/OxA+jhgPFl4UVK7zoKl4CjD0gLSerRRGGaWsEf1f6t0VajPQeEytSTCYYjdkoNVwGi8EKl+lYrN2TgwsGt0OmJUmlg1EkRbjXEoB7i2aoGVKs8OKAvng0kPsjhGsrICQ8hoHQ6WpSz1sC6doEOBdBmg+D0EWvUEVrJaa9L61Qjx8/HtOnT4fL5VIfOCpHSoIFmWaSSSaJDg31sKAGRQLFY489hnvvvVeZ7wwdOlQ9T25uLtoqTocTq/9ZixNGH4V4oFOv9vj9oznQVXZDQZY3Ia15kVW/AbqOEPqa9/mIzpn4dOVOvL5wCx44tGfTn8QwCNB1gLRNgWDBgmlhcpOT1Q8hZQZgvBmpugykxmA8Qc+bbTVAOBdCFl0OCD3yM99CXgp9uRuVWCH3jAJkKUTGS8ixHojc5JoywypKyXBP9THhVy09+p5wm0fC3EixIvg+xRtFO0tQXlSOTEs2HJVOcER466NoRzFspZUwGPRIy0lFUiwaYBvDv43SGJBM/ShPfnNlRZ1mnFUuFwZGSjgHsHLPbvXbK1pcNeQM3P7LTLg8Htz761y8fuJ4baKCCNFcpoMB0zAWKxhUlleiZFcZXC43ktOsyMzLaBN3Jfi70r8ttkS6ksPt9rX/+w89CN+sWYm5W2f69pO40ttrrFUX+mytbLrt5YiHydS7AHcRUHQJ5ZVAZL7ZoqIFiRWSrsW9CSLtMUjL8QkvWsRUsHj99dcxZcoUVd502LBhGD16dE3oXWoqjjvuOFWGtKGCBZGVlYUnn3wSicTahRvgqHKi68DOiAc69tZM/joVCSQZTc3+fNJTBLj+A8zHBWw3G3Q4rns2PlixHWP37YycpKZdC9Val5bjgIopkK7NEIb4iGhhGCH0gIj+oGmXzYaiqtomaakGgTzLak2sgB3kkimLRgOZb8ElM6ArHuUrJUi1zUm0kKYDIXRapBOJ1FJfAFfGVBiKLwwpWpBYsVY+AZ0tGQUtu8ARdeyVdqz8ey2ev/Y1rF+ySYntB5w8BFc/fYlKqWPinypbFZbNXYWJ172Bjcu3qPfwoNP3w5VPXoj2PSJPoJnAvsWk1ytPG6+YaezUEWvKSpVZ94dnj1TbLQYj1hUX+aItgiuC1BcSLcb9NNv39++btmC7/Rh0ynxbmxiEEi1MB0OkPw6hr7uKAtO22bZuB167dRpmffoXPG4POvfpgOteuAz9DuwNa0rL+cc1x/c9RVhQ5RAy4/T6W1DkQvACwJbS0pDjBH/ocR3T0hp9bff/Ph9NQegygeTRcEsPdBWvhjym0noPrMa9gUJKQamqHtdc2mKihb9Yof4uvQMUT5LookVMBYsPPvgAw4cPx80336w8JoIhR/iZM2tUNCYyS2evhMFkQMc4Gei265wDGHXI2Rmj3HPHn5oNi6F/rV3HdcvGV2t24+1FWzHugG5Nfy5aZan8ALLyHYjUW5t+PoaJY2gQcsL0yQHbRg8eiDsPyAC8YoUXWQEUjYY+fQKkvhPg8vbt7rCixdaKXBh9ZnjFtcSKkZ/OxIyzvPmrbYcNSzdj3JH3+0q10u/ZX8zHyvnr8NyfDyOvS9uNCGwrrPlvA2479iGfpxG9h398Mher/lmLCTMf1L4HmXr1LS+ceEqdkRT+x9D/o4mUekh9PgT1XSU3B4oWpgMgUm6GlB41WWASl12b9+DWox/E9vU16UObVmzF7cc/jGd+fxADDqqpUNhWvu+DoaiGYMGCHnfajNAVH71Q2lZTBItoQKJFuWEUhMGJVNdbtcQKi3VojVjhRVa2iGgRLFb4tpeyaBFT081t27ZhyJAhEZ1Xy8vrLq/DaCydsxKdereHwRgfeZV2jxuOHDMsm2qXW4w2NFiU9t8BQy/fRMifZJMeR3bJxNQl21BmD50D2xCEsGg12yveg/Q0n7M5w7QUNDAPh1esEMVBYoXvwRVqwC9SrtFSqHxoogUccyA9NYMBh8eDS75aWG2Gl1FLrCiu8hs4tBHKi2144453fGKFP3u2FuK/35a2yHUx9ad0Txle+9/UkAbMOzbswop5jS+jzsSWoe07oHNKCVB0GWTldIh0itA11ogVSedDFp4HUfUNPK7QJRSZxGDl32sCxAov1A+8Mm6q6heY+GZnpRFfbTkIZYbRtcQKESxWBIsWrsXKiJMWWywWU8zFCt/+0juAqu8gPYk5T47pTDc7O1uJFuGgqh7t22tpBUzdLP5jOQYc1CdubhWFbTpyLKhctaf5n8y9EXBvBiznhD2EzDe/X1+I6Uu34eohUUibMR8N2L8Bqj4HkrRwVYZpC0iPDXDMhzT2rRX+PDgvH3ce1BNizxmhxYoA0WIcROZkyOKrAE9hYKRF1vuAaZAvTHTSSafBKXQqPURf9gBcqeNVGog3siLYPKu1m2hWlldh6Z/hzUbnfDkfx1xwWFgzMablsVfYsWzOqrD75327AIeeeWBMrymR6Jqe4esD6tPeI/UZvTI8kIUXAe716ockKBItZOVHEEkjIIv/p5U9LH8MQp8DjzgGOn189zFM80DtOhzL565S/QJauFJfW6I5vuvpMUM7DYC09obHZQL0HWA1HwTsPjW0WBEsWmR/BrNeoqCbB55m+IqWnj0RxYqASAtdlmbESSnBCURMBYtjjz0WM2bMwJlnnomUFC0Pxzs4++OPP/DJJ5/gsssui+UlteoQtd2b96Bb//jxU1hVuBsyPxmlizajqrQSlrTm+3KX9j+1ko6G7mGPybQYcWinDLzx31ZcMqgDLIamNW6ayEnjEMiKqYD1XJ5YMG1HrLB/B1lyO2A8AMh4GsIv+G7hju3YbvOgveVUoOrDyCeznA44/vITK6oxHwXo80OafFVWWGA3jofenYOCrKRWaaJZuL0YLocLeoMOWe0zQ/YNtC8jLx3b14WuSpDXNTdqfQqt/BVuK4LL6YbRbERWftswh2tphF6H9Nw0ZbgZivyugSk9pYVlqCq3Q+iEeu+N1dGQtCJbZbNDV709XqIk4x3yvKi32V4dfYbHtQci5UbIkhu1CgIUBUbvsfVMn1ihMPRTVchYrEhc8rqFT9VLz0lV/YLL6ULxjhJ4PBKWZDPS/AQMt8uNou3Fap85yYT0nJZNkYh3muO7PsCs3HOpNv312CCtpwOV70V+sPVMQAqgeCSMVGY0k6Ls+il/u+hhhUi5FbLkerrC8IcZ9wGM/RNOrCBi+i1JFUDmzp2L008/XVXxoMHZa6+9hueeew4LFixAv379cPXVV8fyklotS2YtV7/jxXCTWF1YiMzuOXBhM7Yt2YLuwwqa6ZlocDFLDSTqarSnFOTgt01FeH/5Dlw0sEPTn5oMPsvHa5My8wFNPx/DxItYQTjnQhbfgo7J42HWG2B3a+lUh075Ar9fdAnaU/ZVONHCeh6EvhNkeaD5sTQdA0fS3dhUTJNxzbXf7fHALaUqX1bldMJhFzCZawy+wpl8xRs0IaVUjtdvn44tq7Yhu0MWRt19Jg4580BktksPOJYc5c/532l4/trXQ57ruIuPiFoFklmfzMW0hz5SqSYde7XH5Y+dj72OGIC0LF4FbAok/Iy45VS8duvUWvtoPHPoiGE+c9V1izfh5ZvexpI/VyhTvtOuPR6nX3eiWmyg7WTcmZSWhOHXnYBTrz0eOR1iU1krVga9/tSnLT917AkwG7QhaZe0dEjfoF3g03O18vRGnQ6Ld+5Q/88wW1Dhcjb6eYVwqtBqkf4MZMlNNaKFY07NQTTGyHgBwtAl4vMwbZtDzzgAb989I2Q63zm3DVcC8eR738PnL36HirJKZcR5zTOXoMdeXVBeZMNXr/2IT577WqUF9t63B65+5hIU7N09bsw6SQisyx+Gjgkm2WiM+Lj+Oe1Q7rBj0Y7tYdtnp/TA78lYoIw4CV0qkDxG62nCiRZJoyCsl0AWnVuzEFN0IZA1HdLQJ2qihdAlQZoPhUh/PrxoYdwHIuM5CH1imjvHVLCgSiDvv/8+3nzzTXz33Xcwm82YN28eunTpgjFjxuDyyy+HxRIfDTjeoUFQdsesuBmAUoe9qnAPBnTPwxazAVsWbmw2wULnXqEZ9RkH1HlsfrIZwzpk4JUFmzGyXz5M+iZ2LmTwSZOyiqkQLFgwbUms8OKcC7PtNkw/fRxGffZb/USLpNHwmI6Cjip/+GE3HIX5pZehdE8prgthrhfJdC+UyVc8QStqv733J54fUyNAkEBAgsSmZVtwyUMj1YTUf0J78PADsPCXJfjtg5pqBVRl4ubXr0a7Lk03a7SVVuDdRz/GJ89/7dtGQsoDI57G2ImX46QrjubV/CZA79UxFxyKRTOXqhQe33a9DndMux65nTTRYe1/G3HjIXeragLedKDf3p+tJjIPjnjaN/GpKK3AO49+jIW/LsG9H41DVissk9hYw75gSKxoiAHnZ+eOwunvTW/U80r3dsji6wHnAsjkWwJFCy8sVjDV5HTOxl0zbsSj5z+noiW87H/iEBx65gGqTVNqiJdlc1bixoPvxtO/PYDPJn2LX2fM8u1bOX8tbjn8Pjz+/T0YcrS/31PLQZV66mp7ZJ7Z0MdRpZ8RH8wIu5/acEsIFv4IQ3540SKUWEHIcsjCURCxFC2MiS1WEDGPQyRB4tprr1U/TONZPGt5XKWD7K6oQElVlep8qrrnYsuizc32XMI9FxBZgK5jvY4/rVcObvt1NT5ZuRPn9mtaRRVVlpG8LCqmQrq3QOjrdw0M0yrEimqE8y8MMD+B6affWqdoIa0XodJ4Ad5csBQX9hqHdNcrgC4TdlGA+eVX4NIvZ+KZ409CW2PP1iK8fsc7Ifd9Nuk7nD72pADBwrtCP3bS5Tj/rrPw329LYEwyYvBhA1RkhjW56WJ98c4SfDYxdNnHN+58BwecvI9KPWEaT1Z+Jm5541qVkrnoj+VIzUxG/2G9kd0+E+Yks0r3eOmmt31ihZeTrjwGUx/8IOQqLS1AbFuzo1UKFq0NTay4SeWmK2xP1xYtWKxg/LAkmVXf+dby57Bk9gqUFdow6JC+yO2cg43LNgWIFV6onVM/MOy0obU/g1Ji4vVv4qmf71ORdwziT7QIJVboSJA2AZ7tsRUtWKxQcOJkK6TSVoW1CzfgjOvjZxKwulAz2uyYmobiHrnYsjCycUzjcUHvmg+Y9kV90707pVqwf/s0vPjvJpzZux2MTY2yMB2ilTitoBKnlOvKMK0HKe2AfWZYscKL0f039rI+jT8ufhRztmmpGnnJKdhRCbTLvg4qQFQkwWkZjXXFejz712KY9Xvjsr0mQ4cKLN+dhEu//AGuEBO0tkBZYblaIQ8FDVa3r9sRsuQ05S/TT/te7bBmzRpkdcyANSk6kYU71u8KOSEm6FppMs2CRdPJyE1TPwVDansoUTQFrbAG07FnPtYsWB/2nPN/WBhXJtqxMNSjMHPvym2oyivRRrp3K7FCJJ0H6DIgK6YB9l8DRQvbaxDpjwOujZD69hCiunIIk9CYrWa075Gnfvz5+7uFEauLnHvr8JD7Ni7brNJH4kGw0NdjMB3qGH1UPRziSLSAASLpMsjCEX5iRS5E+mMqRU2W3q+ZY3pFi+yPIvrpNUm0qHgbIuPZhI6siIlgcccddzRqBfvRRx9tlutpK5CaS6s33eLMvyLLalU56Tk92mHJ1/+hotiGpIzohnQnm9bQkFBLzWgAZ/Ruhzt+W41PV+3E2X2bGmVhgVQlTt+HTBmrlTxlmFaCEGZIY39Al6+tFIQ/EsViOCbM+QczlgaWa6QJR6/06+ibFRZ9PoTYgTSzGYd3yYUovR3wbEN+0svolZWFZbs134q2htEc+evTmhrZdJgmaHZ7hKorjaCunGiTmSdfzQ0ZbNKKbBVVDvCDDPfIANVpD+27QGaeiWyot3xXaDPaqCIsEGn3QJY9DTj+hEgfr01Q/EWLjAmQJbdCpD/NYgVTJ5HardlqCkgh8Udv0KufeECv0zXqGENTF//iUrSg6H8BkFBpOhKo+sgnViihQlYpQdMnWpBRuUiJ/rV4RQtanNU3PV20LdCsggUZbDYULulWN0tmrVCD4XhaKaMKIR1StI47p6d2XVv/24yCw6K7YpRqWgwp8qFrYAPukqZFWbwwfxNO79Wu6V4W5mMA+7dAJZU4DV9alWHiEWHoCmRNhSy8MIxoIVBoehQT/jEEiBVeYzyH241VJd565Lvp6x0zhh+GAt0dEG4tPDbPcTVeO/FlXPHN0kZdoysopD4eB6o9B3fDmoW1V80pgiK3Y+xNFHM6ZqnnLtldWmtfj726JsykuCUhs9UTLjsKn77wTcD2uV/Nx+FnD8OP02aGHPcMPXYw2irUX6zcowmXyUYTbM7qChx+kAlvQ2mwUSCVKCx7AXD8rv1Zcntt0aLqfYjMtyEM8bMgxMQv5GPx8s2TQ0YIkZHy7M/nhXzc4ecMU1FarbEN+29rCSPf5jTkFob2NX+kjoMUqRCWQ6oFCi3VXZbcpokWld9DpF4PoW+euRiJFkD4ymmJRrMKFj///HNznj6h/Su69u+kDMDiAeq01pcU46huWkhUWn4GTEkmbPlvU5QFiyokm1fCoz/Yr+hi/TmzOsriwxU7cH5/v06pSSVOJwPWs1loY1qlaOFMnwxDycVBooWAI+UpTJjjqRVZEcoYjyIrZl54MpJsd0BXLVYoZBnyq0WL1aUNX9l3y/gWLDJy03HH9OtxyxH3BwgEtKr2wKe3KlPkWEPPSc9927EPwl5ZMykkEePO6TcgI6hyCRN9KIrinHGnq+oxa//b4NtORqsvzHkUq/5Ziw1LNweIFbdOuQ5ZHaqd69sgG0qKff0GpYCcNmNaSIO+htIQo0Dp3gVZei/g+MnvCHegaOFaw2IF0yDIf2jcG9fgqcteChAtug3ojHNvG44V81Yrw13/VD1KFbz0kfNhiYJvUUu04foKhs1l5BsrQ26hzwaSz4csutQnVig8uzXRInNKs4kVTG3Yw6KVQZ3estkrcdjZWvm0eGBjSTGcbjc6pqb7QmKzu0ffx0LnWgAhXJC6xokgndMsGNYxHS/M36jEC0tTw/F8JU5nA+aDmnYuhmkBNlekQ6d/AV0wtlq00CIrtpYPxIylH9f5eBIrKLIi2TbWF1kRQLVokdluMvrl5LS59JCu/Ttj0t/jsfTPFVjy50r0GNRFOb/ndsluEUGZnrPP/j3x2qIJ+PenRVi7aCMGHNQb/Yf1iauIvLZObudsPPL1ndi4dDPmfDVf5akfcsb+qnTp+O/vwfpFG/HXN/+qiJiDTttPCU3RMF1lQuMTK+z+YkWwaPEEJJnbGdhIm2lYGt4hIw5UFYD+/Gwe9mwrwv4n7YPuAzsrMYOi2t5Y+gxmf/E3dm3ag6HHD0aPvbqpts/EN9K1qbZY4S9aFF2kypuKKPpXMOFhwaKVsWHJJmXUQ+ptvEBhYka9HvkpNXlcOT3bYcNfa6P6PDrXXDhc7aAzNz6M7qw+7XDrL6swZfE2XLl3pyiUOO0CaXsbggULphVChpijP5uL6aeTaHE9Cg3XqzSQoR0r1OpJ1/SMgLBq/xBQo06H9844Ej3FraHFCi+yDObSi/DhGdMwZ4cFFU6nOq/D7YLFEL6OO/nhtAbyuuSqnyNHHoJ4wGAwhDSHY2ILiRP0s88xewVsp3RO2j70+L3bnIkm9Q+0EhuM3aVVGYqEf1/QLT0jZIQVpZN4IzHSzZZ6GQUqo82wYoW/aHErRObrkPpcCNE6+h4mPkhKsSKpb0ec27e22EV+Np16d8DZt5yG1tCGvQujDrsdJrMZLilVmw7VhmlbqHGCF4vegE/OOT9stGR92nBLQVUAZdHFocUKf9Gi8Hwga4aWZss0KyxYtDIWz1qhar53DtExthSrCvcoscLflIcEi0Wf/4uynaVIbdf0PD3pKYPOsxQVjn2R0oRIsPxkM47skqUqhpzTNw8ZFmMTS5weD1S8BulaC2Ho0fgLY5gWYlt5GUZ9Nhfvnfk2Xvz7P5UG4k0FoUFM7+wavxj/HFanx4MNJeXolNkXVkQQLOh7XZePjWVu3PTd1yi12+uszx6u7nu0KS0sUwbGqVkp0IcYcMUTrelaEw2X04XyIhv0Rr1acaX/G4x6pGRG34wtnk00qX+oK00jHOuKi3yPpUlQQ9I9wqHGJCQ+mParQ7CgFZF0QBkIs1jBhGfPtkK4HG5k5KXDbDG1SSPciooKLNu4CQX9+mFzZUXYtjjuh29DjhO8LNqxHWe8H7r0N/HZuaMQv5gAfUFkwYLQd1Vmvkzzw4JFK2Pp7BXo2Ku9ypOOF2iQ0js7MNw4t6Cd+k1pIX2PHdD0J3GQcZFEpbMbmjoEPLNPO/yxuViJFncOa6LIYBqmlTilKIv0B5t4ZQwTfaMqi8GAkqqqkMbGyUYjPj33fAgILNxVgoO79lU/3TMyUeVyqlQvf5Ei2GTrmm/+wEsnnoODMwGr64uQ1+XRF2CNfBwjP5mpxIp4gMJ2F/yyGJ8+/zWqKhw4bMSByiAtHtMmCrcVYeFvS/HJc1+h0mbHoWcdoK41v5vWxzItB+Wsb1+3E1+/8RPmfP43ktKsOOmKY2AwGfDVKz/gzBtPVqVK46F0YazxGvR6yyHrRM0980ZJZFqSUFRV4SuR6I2waO8XrRkOOk+o6Cxa9fVOpAihSwOsIzSPirLxoU+my4JQod09G/5CmYRgx8ZdWDRzGb546TtUlFZi3+MH4+QrjkXHXvlx4ycXrbEDRVgY89pho61cmeFSe6UIKBIV/Qk3TqgvunqUU20plDdF+iOQJXcBjl9DH0QpZBnPccnRGMGCRStjyazlKNg7fvKl9lRUoLCyEp3TAqMokrNTYM1IwuaFG6MiWEjHbEjRBR7ZdCUz3WzAKQU5mLx4mzLf7JYeufxgJKhGuzQfDVR+Apl6I4SO8xKZliGcUVWkaIZwK5mRtvsj6xAtpKEX1njGK7GixF4jmrQkhduL8dToSfj7+4W+besXb8SXL3+PZ2c9gvbd40cIKNpRrMzc5n37b9C1/oDnZj3MaR8tzJbV23H9gXeirKjct23p7JUYdupQHHDyPnjgrKdw5MiDMeb5S5XxaSIRyqC3vn1Tfcz8NpaW1KuPqlO0YLGCqYOdm3Zj0vVvYvbnf/u2rV+yCd+//SuemfmQMsFvzdRlchluTFBXJFRd7djTiMpAcSNasFgRc1q3LJhg0OB129qd6Dqwc1yVMyU6BQkWtIqb0yMXWxZsbPJzSE8h4FoOj74vosVJPXKQYTZg/Jx1TT8ZCRZ0nbbazucM09bxihazis6Bx3JGzQ5DL7hSX44rsYLYsHRTgFjhL2R89MyXcNidiBeomoS/WOH/XfDBU5/DUVW7PCQTGyptVZh874wAscILGey1756HpLQk/DJjFrav38VvSwtDooWwjgBSb6/ZyGIFUw+2r90ZIFZ4KSssx9QHP1C/mbYJiRYi/RHAdIRvmzLn5ciKmMMRFq2IJX+uUL/jy3BzD7KsVqSYzLX25RbkYem3i1Topn8YeoNx/KW0NanvBaAE0cBs0GFkvzxM/Gcz/thchEM6Nb6knNClQpoPAyqmQSZfXl07mWHaHnWZbLmTboYOAnAtgsh8DWVV6XhvxLkh01T881e9fYR/P0FGYNGGQl2/eSN8ue2f3/kdI28bHjcO7t+9/UvYfTQRPu/OM5HbKTum18RolBfa8MfH9N0Umn9/XoR+BxRg/g//4fcPZ6PP0LafbhBswNnUfobokpbu6xeon6DICu8xoaC+yXsNwX1IQKSF7VVOA2HqxW8f/Bl236xP/sKlj5ynvIWY2u2Yoqi2lZc3yFg72AQ01P6WiLTwUKSFpxRIe4bTQNqaYHHRRRc1+DH0xTR5cuTQpESFSudl5qUjI1crHxoPLN+zGx3TQl8PGW9WlVaicP1uVea0KekgMBSQzIBocmCHdPy4vhAP/LEWX509BCZ9EwKOzCdqpl6VHwDJ4TtahmnN1GWyRUj9/8iSE0Kfj5xkICdcvfR0P4OvZcvQr18/JCU1j9hnK7ap30npScq0OBxaLrKMaK7otDthsprqZXxJAgkZMJqsRliSGp7Opo9wrVQ+Oo5TgBOCSPef3h9vxHOwYF/zuTCpKgJt1YCzsfh7UJC55sB2efU29SQhNVzf5C9awHKqNhFhmDoQETwqqJ1H+MrwUVFWAXuFA2k5qQljmkztOFLaSDjj3GAT0HhA9RWpD6KqshLCmY6kxvv1M/EoWJAaHovHJApLZq9El37xkytX6XRiY3ERji/oHXK/13hz84KNjRYspHsH4FoDWIbX60uhIdAg8uJBHXDXzNV4e9HWJpU5pc5Mmg6CtL0GJJ3HTuNMwiL02Q0y+fI3+NJVagZ83lWUpg5atq/bgaVzVuGHKb+pyeXJVx2L4y85Aj9Nmxny+GMvOhzp7WoLsFW2KmxbtxOfT/oWm1ZsRf9hfdR58rrnqjKiwdBr2rp6O377YDYW/LwYGe3ScNq1J6BDQR6y29c/euP40Ufhu7dDG34dc+FhCeeLEE+kZafgiJEHq89WKIYcNQg/TNE+Z4OPHIgFvy5Gp17tUWVz4I9P5mD+9/+pyctp1x6PjgX5yOnIkTLRgCI7vGJJuD5EiRYMU0+OOPcgfDbxm5D7Djv7QKTnpkb0vyBjXjLrLN5Zir2PHIjDzxmmSp0yDTMQj+bYoDHYnWlYvmoL+vXjkuFtTrCYOnVqc54+oXA6nFg1fw1OuvwYxAuri/YoB2EK2QyFOcWCtPbp2PzvBgw+Y9/GPYljjlZeiCIsmiG1vEuaBcd1y8Zz8zfilJ456JDaBFNPyylA6R3KgBNJtcPgGYapv8kXRXE0ZVBCAsNjo57HsjkrfdvmfbsAt0+7HgcP3w+zPqXKQzW065KD4WNPhNFoqNX30uMeOmeCT1Bf+OsS5Xfx1M/3od+BvUN6T4w78n6U7inzbfv1vT9x8QPn4OSrjkNmCFEkFJ36dMChZx2I3z+ifrCG3M45OOvGU2A08zJPS2FOMuO828/A398tVJ4iwRMc+gyQ0HXU+YdgyZ/LMe3BDzHi5lNgSbZg2kMf+o6d+cFsnHvbcJx5/UnIat/41ERGY0NJsW9Ft6l9CMMQ2R2zcOR5h+CXd/8IuCFU/ef8O89Ccnroz9iuLXvw7Zu/YOoD7/u2UXWqj5/7Sn13dB/UlW9wI8YH3K4TEzbdbCWs/nc9nHZXXLkRr9i9G0lGI7IjhHGTj8Wmfzc0MR2kV7NGLJzVpx2sBh0e/HNtk84j9B1VvXdZ/jKkjB/jPoZJROb/sDBArPDy1OgXMfL2M/Dg57dhyFEDleBw7XOjldt7qLKmhduK8cTFE2tF/5Hh5eMXvYDC7YGl3mjy+vItkwPECi+T73sfJbvq78NDwsbYiZfhoS9ux5CjB2nX+swlePb3B+OyBGsiYa904Iepv2HcG9fgvDvOQL8DemHo8YNxz/u3YP8Th2DpnJX435tjUDCkO6Y/9JF6zIcTvkTvoT1hNAWKYu89/imKdkbHn4lhmOhStL0Yp1x1LO77aBz2PXYv1dZJfH78h3sw+/N5Kt0jnM+Nv1jhhb4bXr11KvZsLeS3imHi3XSzvLxc/VDobDAdOnCoVDDLZq+E0WxAh4J8xAvLdu9C57T0iLWUSbD4a+qfcFY5YbQ0bDVQujYC7s2AZRiakySjHqP6t8fEfzbh5w2FOKprEwz3KHWl9E6g8mOOsmBiSjijqmCDS/98+mSjUe3TCx0Mfn4JlAdO+aV6IaAPkb8ba9OrhrJnWxG+e/PnsD4UL98yBQ98/D8M/mQA3C43UjKSwxoDb1+/E1UV9rBlLUt3lyErv2ZlvKzIhn9/XBT22v75cRG6DehS79dCq3gHnrwv9jqsf53XysQOmnR89MxXePexT7DX4f0x8NB+sFfYkZWfjtlfrEZ+t1y8edc76rPoz3+/LUGf/Quw+I/lAdvnfPE3eg7u1ib7I7fHo6Ixg0kzmX19E40j6LhI5rvBpp4UTRFMODNOhmksv743C5889zV679sDZ9xwMsxWE/77bSmu3OsW9X+KgktKrb1w98+PtatReaGUsPKSCmR3yIq7sQPNyxx2O0xms8rEprZLBpnB44u6xgkutwdWoyGsV0W8jyOYBBcs3nnnHbz99tvYtGlT2GPIgI0JuidzV6Jjr/YwBIUrtxROjxurC/fgsK7dIx7XrlcePC43ti/dgs77NGwwJikdRFgBQw80Nwd2SMPMTSm47481GNYhHVZj40yRhL4zpOkAyPJJgPUM9rJgYkZEo6r48emNCR6XBy5n+CoFbqdLCRfpuXXnsrurz0OTqP7DeiMzPwPb1uzAmoXrtf3uQNHd4/ZE9GIi8bYxJKXy4C6eoPeYPkMETV7ohxh8+ABVcpb253dvh0PO2B+V5VVqv9NBnzt3SONX2teWaKhxXn3Md4NNPesy4GSYaOCya21z3eJN+Gn677AkmbByvhaRS+05XHdPUdHhoP5BhliwjYe2qtrixk0oaEYjbIaJ65SQd999Fw8++CC6dOmCG2+8UTXYiy++GFdeeSVycnLQt29fPPLII7G8pFbD0tkr0TWODDfXFBaqFY6u6ZFnQplds6E3GbDpn4alhagBP6WD6PtAiOZ3VNYMONtjV4UDk/4JL6bVC8uZgGcnUPFOtC6PYZgGkN0xE4efHT4y64iRh9R7Zat9jzwMPX5vPPzlHRh0WD8Vzn/Y2cPw4Ge3YfARA5CeE2i4lpKRhL77U1Wj0Ox73OAGvBImXqFIl4NO3y/k4sJhIw7EHdNuwDn/Ox1Gi0ktNtzzwS0qrJwiZVZVT3b8oSgahmHij0NHDFP+RnfPuEmJkFTd5/y7zsJtk8fiyJEHIzUztDC3z7Hh+3pKK7GyCM0w9Samy/XTpk3DIYccgtdffx1FRUV45plncPjhh2PYsGG4/PLLcdZZZ6G4uHaIX6JDIaU7N+7GCZcehXhh6a6dsBqMyE+JXHtab9Ajp0cuNs5fj4MuP7z+T0CVQTy7APNxiBX5yWac1isXr/23BcN7t0NBZuOUZaHvAGk6FLL8RcB6FoQuvIM0wzDRh8qTkqjw9es/YdvaHQH7aPI47NT6Tw4z8qjCx/G4b/gTvhV1YJaasD727d21hA+q9nDNs6OV6SaVQA2uQpKRl2DhLm0Uini59OHz8M8P/6GirMbR/qfpfyhDvfvOeBKbV271bf/8xe8w+uHzoDfqVMSFPyRwkMjGMEz80b5HOySlWXHfGU/4tv38zh/KU+6BT25FWnboMR5VDznmgsPwY1BVKjJLvvqZS5DXhX2IGCYuIyw2btyII488Uv3faNT8DJxObUCXmpqKESNGqJQRJpDlc1ep3136dYybW7Nk1050Tif/iro/QpQWsmm+Fj5dX6TjT0CkAvr653pHA6oUkmM1qtSQJpXYtZ4JyApI2yvRvDyGYepJh575GP/d3Rh111mqbCQJFRfedzYe/fpOta++lO2xKdPNGrFCo7zYhglXvITiXaW1HtNjr66Y+NdjqkIEVR8ho8Xbp16PSx4ciZw4yFlmokPH3u3x4vwnlKBFJqj0vt8+bSw+fv7rALHCy1t3v4v8bnk49uLD1eeCDDnHvXktrnzqIuR2yuG3hWHikMqyKrzzyMe1tlMloC9f+V5VkgoFCRIXP3gubp18nfK/oDZP3wkT5z6GbgM7x+DKGabtENMICxIl3G4tHzglJQVWqxXbt2/37U9OTsbu3Vr9bKYGcrqnXOuM3PhYmSNTK8ofPap7/bwl2vXJx6IvFqB4SxEy6rGKJKVbK2dq6BdzczmTXocLB7THk39twNdrd+Pkno1TwIUuC9JyEmB7G9J6LoSBv5wYJpTJl7/BF0VG+B/TVEiYGHXvCBx/6VGgroTK0wWXLa2LnRt3KXEiFOsWbUTp7tJaZUotSWb0GNQVYydejrLCcmWYTJEXTNuCPq8khl319MUYdfdZ0On1qnrMD5N/C/uYed/8qz4XxTtK+HPRDCbDwccwTDRMN8Px9Ws/qRLTOZ1C9+/53dqpH0ondDvcSMlKRnp23d5JiUxdbZvbdWISU8GiV69eWL68xhl78ODByteC0kJo0Pree++hW7e245IdLZbNXYXOfeOncsqKPbvh8njQI6N+K4XtemmrmZQWUh/BAq4lgCwFjAPREuydl4p981Lx6Ox1OKpLVqMNOGE5GbDPhCwbD5E5KdqXyTCtFn+Tr+Y2+CKBon33do1+fFWFI+L+SGaJlDZCP0zbxmQ2+irF7NiwS4kW4SgrKoc12QJrD0sMr7Dt0VBTT4ZpLOVFoQVrospmh6ce0bj5XRv/HZRocNtmWlywOO200zBjxgw4HA6YTCaMHTsWo0ePxhFHHKFdjMGAF154IZaXFPdQRAq5ER913iGIFxbv3I5Ukwk59ZxcWDOSkNY+HRv/Xoe9ThtS5/HSTukgOYCu5Uq4jhqQj1t/XY3X/9uCsfs2Li1FCAtk0kjA9iKk/TcIcwM8PBgmDtlls6GoqiZfP9TKRzxNIkoLy7B7cyH++vofCJ3A/ifug5yOWdAbdNizrRjzvv0XFaWVylSTylBmBEVKEB165KmV9FAluCl3OVz+cmOudc+WIsz9ar76+4CT91W+BmlZ7IHTWijcUaz8LAYd2g+Lfg9d7ezAUxLbXDNcH0Lty5jXDhtt5ciWMq76ESYxoLa7Z2sR5n23ABUlFep7gfwrDjnrAHz+0ndhTZST07iSRlsfSzAtT0wFCzLVpB8v++67L7766iv89NNPSqw4+OCD0b175DKZicbGZVtQVV4VV/4VC3ZsR4/MrAala+T1bo8Nc2s7owcjPVWA82/AeIAK4W4p8pLNOL57Nl5esBnn9s1Hu2RT405kPBAwzIQsuR/I+QpCx19sTOuFBhgnTJ8cdj+FccbLIKN4Vwmm3P8+vnjpe9+212+frtzeDznzAGWK6WXyfe9h/5OG4JbXrkFW+8xappsjbjkV7z/5Wa3nuGbCJcju0HSzxJLdpZj6wAf4bNK3vm1v3PmO8ka48L5zkFGP8qtMy7J7ayEePmcCtq/fiXvev0V9voJ9TwYe0hftuia2V0VdfUi89SNMYmArrcCPU3/DxLFvBnwvkGhx/aTL0Wf/Aqz4a3Ut88xLHzkPyek8rmvLYwkmAU03Q9G5c2dccskluOCCC1isCAF1kLQy2LlPfKSEFFZWYlNJiRIsGkJe33xs/z975wEdRfWF8W+2pfeekAQIvfdepQsIooCVpgIKgih/UVGKooA0UVFUQAFBkA5Kkd57r6FDCCEhvSdb5n/eWxKy2d2QspvsZu/vnD3JzszOzszOm3nzvXu/ey0KmcnGFVWO8iwgZgKy2ihr+lT1gVwi4LtTRSvJmhcu6jgO4RVPxNTvTLp9BEEY5/qp2zpiRQ4bf9iG6LuP4envrjP9xNazOLDumJ7ZrqOLI/qPfwGfLH+fG3eysqZVG1XGzP++QIvejSGVlqzsMrtG3DhzW0esyFtZ4ubZZwu9RNnChInNC3bg8pFwVG9aBdeOX8eU9f9Dk671+UMNO9cGftwH/cb2REZKFv1cBGFhxNyL1RErcji14xyPemMVgV7+sBePwmNtulmPhryNszLGxkw3CYKw0giLHK5fv479+/cjMjKSv69QoQLatm2L6tWrl8XmWHyFEGbYY+dgB0vgXHQUJIKASkUVLGoGMjdN7mNRrWNNo8uJWYcBaTAEqe7DRFngJJeiTxUf/HX1Ed6qF4SwYpc59YPo8DKQvhSiXScIds1Nvq0EQeiOlhmKiMhhz8qDPMqCCQJ5WTf3H7R7qYV+lIWPKzq93g6NOteDKlsNhYMcbt6miXrITldizewtRuf/PWszarWoBkcKO7ZYEqKTcgWndi+3xPx3f4VMLkPXIR3Q6Y12yEzPwv7VR7D6200Y9s1rvBwiQRCWw/Ylu43OWzNnCxetmcnykK9egZ2DAuEnb+Kb176DRCpBky4NEBDmV6rbSxC2RqkKFsy7YtKkSdi0aRMfxcpxhGe5i3PmzEHv3r0xbdo07m9BaLl64gYqVAuwmMNxNuohglxd4fSkLG1hcfV3g6OHI+4eu2VUsBA1SYDqImDXBZZC54qe2H4njkdZ/NDFuNDyTOy68egRMeljwHszBIllVHwhiPKIMkuF5LiU3AgGn2Ctg3vMfW0VquS4VFRppF9BgH1Grdb3qsjBw8/dLKPzLCXEGGyblNna6lqEZaJRa5CenM7/Z6OvGamZ/P9NP27n5x57n5aYhoDKfjzFkyAIy4E9g8Q+TDA6n90vHJ0dcHrnBS5OOrk7IvzELW6uy+4vhTHdJAjCigSLWbNmYePGjXjttdd4CkhISAhv7Pfu3cPy5ct5xRA3NzdMnDixNDfLYsnKyMLdSxFo+FxdWALZajUuRj9Cq5DQIn+W/c4syuLOsZsFfMEx7V9ZCYQBEyOXSvBiNR/8dv4h3otLRU0v52KtRxAkEB2HAylfQEz6BHD/qdRLthJEeTW+YoaVCY+ScP30LTi6OKBakzA06d6Ah+e3fakFIsK10XzB1QKx7+8jcPZwwu3zd/XWU79jbTi5Gi6F+PhBHCJvRPEqECE1guAX6sMjMaJuRyMmIhb3LkfwUbbAyv48daSwOLjYo2mPhrh1Tn97GGyekxuVZ7RkHJztUbN5VV7R68H1h/z/Zj0bIax+Rf6+erOqkMuluHL0OnxCvPn5yM4lQSJBxdrB3AeFlc6NjYzn54GHnxtCawXD2dvRIq4D1nQtIIiiwgZPWcTdgTVHDc6v174m7J0V+OXcbNy7EsGrhoyYNQiJMUlYM/cfKOzleHA9Crcv3EVSXAqqNw6Dh787fCp4QZmlRPR97f0hPioBVRtX5vcN32BvrVDyIB73r0Ui7mE8KtUN5QJn/jLZZQldGwibFCw2b96MPn368CiLvFSuXBmTJ09GamoqX4YECy03z97lIzeW4l9xMeYRstRqVPcsnmlYQK1AHP39ELJSM2HnrF/STcw6BMjCIEgsq3PepoIHNt+IxQ+nIvBTt+KLKYLUG6LjO0DaPCDtF8B5pEm3kyBs0fgqIToRiz9dgR1/7MudxkJ2fz4zC9uX7MHnvabn+lKwjukrn/TlIsZ7TSborIeF8A+aPMBg6sXdyxH4pOtXiIt6OgrXuGs9jP7+LUx+cRbuX32QO90r0BNf//spf1gtFALQY1gnbPlpB9KStKP0OTAzNzaPbRthubBKMe98+yY+6jAZW3/bhS83TeCmqcsm/41PV4zFX9+s57nwOdg72mHcryOxbfFuXgXsh2Nf8/z5s7sv5i7DhLdp/34KBw/z3w/JCJOwdeq0qs5FaCZI50Uqk3JzZZYO8n6Lz3RKFrd+sRk+/HUEHt2JwWc9vuapXzk07dEAH/72Lh6EP8Tkvt/yCiQ51O9QG//7fRQ3hv6k6zQuVuZQq1V1fL5qHBc7LAG6NhA2abqpUqlQv359o/MbNmzIy3gSWliOnEwh42GklsCph5HwdnAsdDnT/PjXCoKo1vDypvkR1ZGA+g4gs4xokrzIJAJeqOqDHXfjEB5vvB53YRAUjQD7PhBT50HM3GmybSQIW+XI5lM6YgXDwcUBty/c4z4WeU002YjWym/WIyUhDV0Ht+edUUatltXwxd8fIiZfZzWn+sPEnt/oiBWMPqN7YN6IX3TECgYbKZvy4ixE3owq9D74V/LB/CNfcyM3FnnFXs2eb4Tvj3zN5xGWjVKpQvT9x5j41wc8smfvqsO8lG7T7g34fTyvWMFgDzazhy3AS+N6oUnXelj/3b86YgWDPeB81v1rOEgsWzAkiPKAT7A3Zu+Zgs5vtMsViGu2qIb5h6dBrVJj2ivzdMQKxuENJ7B/zVHsX3NER6xgnNx2jkdifNF7ho5YwTi/7zLWztnCU8byihWMK0fCsfSLVcjKtz6CsHVKddimTZs2OHToEE8JMcTBgwd5adOisn79enz66ad609955x2MHz8e1sr1U7cQVMU/t1Ndlqg0ai5Y1PfzL3Yqg1ugOxw9nXDr8A1U7VBT32xTcABkVWCJtKnghg3XY7DwbATmdapRspXZ9wPUURATPwQ8l2pFDIKwAlhYOIu6KGh+aRL/KAGrZmzQm96+f0tsX2zcRG3dvC1o82JzNOvRiFdhunPpPr4ftYiH9lduUJEbHecQFxmf632RF68AT1zYf8Xg+llpy8SYZARVKVxqCIv8CK1ZAZ+uHMs7uQxnD2c4U7k8q+BxRBwWjlvKz59vtk3E2FbatNaOr7TBj2MWG/yMMluFOxfvodvQ5/BV/zkGl2EPQTfP3NU5H8vrNYSJidlZWVDY2ZX6dYQgGP6VfDF24XBurClqRDi6OvDoqRXT1vFoZ0Ns/H4rJq//Hxcf8lK3TU0uVuYXMnJg0X8fLX4PO5ft15u3e+UhvDGpP9+e8oql9SUIy6dUBYuxY8figw8+wOjRo/H6669zDwsG87BYsWIFHj58iHnz5iExMVHnc+7uhTM6W7RoEVxcXHLf+/lZRmRCcbl24gYq1tYeo7LmUkwMUrOzUcun+BdQJnQE1A7CrYPXdaaLohpg6SDSmhCEshdnDCGTSNAzzBvLL0dhXNNMhLjqp7QUyc/CaQSQOgtiwjta0UJex6TbSxDmgKWHmCNFJMeAuaioVRrE5TFLY6H2KqUaLl7OiN1v3ESNfYZFQLBOJitTmhyfyqezB06NSrdjmmPemZ+s9ILNEwsy0jSGs5sTfxHWBXuYYSOiLKWH/c/y1tlAg8JBoZfmkxcWtVOrZXWjDzWMx/djy5XfkbFrSHp6Oq7ej0CVmjXhWMwoToIoKeweYh/qoydA58CiL2RyaW6bjX+UCDt7baEA1uaZ6W5mWib8KnobFLpzYJ9nFUaMGTHnj+Yob5irL0GUX0pVsHj++edzy5ru3q07+pUTttuzZ0+9z129erVQ669duzY8PYtWbtNSYWFikTce8VxrS+BIxH2eCuLnVDzTyRyC6gXjwILdSItLhVOOgaXqCiDGA4resGTaB3vwKIslFyIxpU1YidYlCAqIzuOAlFkQ4wcDHosgKBqabFsJ6yGvqRUbZZT7+eJ+WiokGelFMruzRnMsFiHBHtqyotSIUcTyKhwFlQtl18XE6CRER8TCxd0Jrt4uaP1iU95hbPtSSy4uMP8KJw8n/pB399J9g+vpM7o7qjUOQ6NO9ZCVkQ3fEG9cPHgFB9cf551ONjKWmpQOv1BvHipsCJZ2orBXGO1YlqdRcUIXFuLNDPdYvru7nxukEgnPSWf+JYzxS97jhpreFTzRoldjHPvntMFDGNagEmIexPFz7HGE4Yebak0q8+tCWaPWaHA9LtbgdPWT/puzQsHNuS39ukMQRaFeu1q4efYOXv6wNxckmDjp5u2Cw5tO4sbp20hLTsPHf4yGvZMdrwjEymBfO34DNZoZjxhmbT4lQSuU54d9nvkXsXsjM+pkgrpPkCfcfd14xIepiUxO1uk7sOcxeVAgbqUUXXQniHIhWIwaNapcjRSYE2bExQiuHlTWm4JMlQonIiPQMlhb1aUkBNbV1p+/deg66vVp9NRsU/ACJGW/rwVhJ5PwMqdrwqPxQZMQuNsXrbRrfgTBEaLL/4DUuVrRwn0uBPvOJttewraML63NHItFOEx6YSZ3SM+hUed6/GHPkOEYG8n6ZfxS7Fl5KHcaW27Gjs/x72+7MKnvzNywXRcPZ3y2ciyadKuPUzvO66zntYn9eMePGSTm5A8zkaL/+N74YOFwfNFnJg/VZ7Dr3WcrxqJl78Y4ukX3ofPC/qvo/V43rJu7RW9bW/RuzEvfEeUPZpS3ZvYWnoM+ct5g3Pv7CLYt2oNR3w/Fqf/OYcfv+3IFBvbgMXHVOCTFpuDqMd3IQhbuHVwtAD+P+wOvTOiLH0Yv0vuukJoV4BXsAUuAiRI9n3F9+aFHL7y/7R+Lvu4QRFGp045VCbHHdyN+yY24Y1GBPd5+jt+v2LTfJvzJDaBz7hud32yHTm+2Q2itCrh3RdfniDHsm1dxZucFg983cu4QLop+8cJMXk0oh+bPN8IHv46A9xNh1FSwvsMLq/402qYJwuYEi/fff9+s6+/VqxcSEhIQGBiIAQMG4O2334ZUapkpBoXxr2BqLStxVNYcj4zg1UHq+vqXeF2OHk7wDPXCzf3hXLAQNRlA9klA0QrWoGV1ruiFLTdj8dfVR3i3YXCJ18dFC+ePgbSFEBNHAc5jAKd3edoIQZRX2KjR571m8JKPeTmz6wJ+Gb8MH/42kldJyGtquOnHbTpiBYONQp3dc4mbFuaFjVxN6jMTPxybzsud3npSwrRqk8roOLA1Rjb6mBup5cD+XzVjIy8rx0pM5ggWbKTp26ELMHf/VHgHeWH773t5uD+rJMLCfvuM6gZ7RwU2zN/KO5hs9K3LoPZ49ZMX4RdCZpnlkRNbz3IzV1Y6NzsjG//+uoub87HUj22L9+gsm/g4mVepmXvgS0zq8y03ZGUPM8yMs98HPXl0hncFL15RgI3Q5jz0sIeh1n2bYvjsN/EoMQp+oGgdgigrVFkqzHjje51oOiZKsrZftXEYdv95IFesyLlvMG+K6k3DMPbn4VgzZzOO/3OGf4ZFEQ74uA+/v701/XWolWpu3MnuQSy6cMhXA1G7dXWM7zhFr2LJ8a1n8MfEvzD6x7dg71T8tGSCsEbKtFZaSkoKz1Usqajg4+PDxRBWgYR1Bvbs2YPvvvsO0dHReiVUiwK76LC8ypKQkZGh87ewXDkWjoAwP2Rnl30e257bN1HRzQ2OEolJtse/diCu77uKjPR0yDSHIYUKKlQHnrFu9tCS929Z4CgALQNcsOzSQ7xZwwvyYube6yF7G1LRH5LU76HJOA6lw1eAxHwPPMU9L8sCc+Yzm6KNl5RnhXuz+YXZxsKEjedfV85nJIKASW0b4/fzN3EvKbHI311UHj+I0xMrcji49hjvtEH6tLpHUnQKNny/VW/ZLoM7YPMCXbOzvKaG+/4+wjuHzKeC3Rsc3Ryxe+VBHbEiL8zAc8TsQTpVHZhAMbHndCw8+y1e/qg3TyFhYrKrjwuYrvjy+Bfw3GttkZWWBTtHBVx9XKFwlD3zuFlTGzQVBe2zudq5Kdt4anw6ln+5hv/fbWhHLnIxug7pgD+/XGvwM+x8ObfnEkb/OAwapQYSmRQXDlzGl/3nYsTsN/HxslEQpAJav9wUddvXREZKJuR2Mu7DohKVUMepzX6OFObakbfaTnHWb872wNq2TJoBpcp6HuJsrf2z/bSke3lhj79cLsfRLaeMpv6xksX9x7+Aiwf1U9fZNWH47EE8EpBVoWIRgMy7Yuui3bxM6i/nZmHUj0MxaOoAZGcquYeSq68Lbp+7pydW5LBrxUG8OrEfPALdYCpK0rbN2U8oCTJJBjSiAhrRdIPWttZmi0JpHJNSFywuXrzIxYRTp05BqVRi8eLFaNmyJeLj4zFx4kQMGTIEzZs3L9I627Zty195q5HY2dlh6dKlGDlyJHx9izc6wbavsP4Zz+LuXe0IX1EEi9B6Qbh3XzvSV1Y8zszE9fh4tPPx4wKQKXAIcUba1lSc23MG9ZruhgB/xCWyk71wJzw7V8qSRs4iDkQqsfLUTXTwM2UHqQEc5S7wc9kMmbIvHiQOQUIGq5pjvtCTop6XZUHjxo3Ntm5TtvHiwjwrCoI55zMzupKux9C63AMDsbHfAIR6CHBSLsHAGq/iToIzstVaUVCuUpv8+DCBOuVBRoGdn6T4ZEQnPw2FdZG689zg/Hj6uyPqzlNDtPxE3Y7mIbUH1x3j70fNH4YH4YaFEsajOzE8eiI/LOQ3MT4JyconYk4W8NDQZSgdeHwvuty1QVNjaJ/N1c5N2cY9nbwRfVf7IOHu45brO+Hq4cyjJ4zx4EYUjx46nS8EnKVDpaan4sGDfCHjWUB0cumdIznXgYJwkhc/BbKw17Di7KtCoUDVynYQMpdDiaG4edu42a4lYkvt38vLfBHDxW3nzzr+NWvWRESetMX8MGHBw9eweMAirOwc7LD5px38lZ+U5BRExuVpF1lATLIcMXd1Cw/khYntyQkpeJRk/D5WVJhfhTGyVCqeFhLs6sYOsuHPm6GfUBIC/Jzh43ocEmllRER6IjlfWdmSYktt1pIoVcHizJkzGDx4MK/e8cILL2DNGu1IBYOZZaampmL16tVFFiwM0aNHDyxZsoQ3ouIKFkxZrVKlSolVJ3ZyV6xYEQ4OhSvTw8rhsRHFmo2rIzQkFGXJwcsXuJFWs0qVubGYKfD28saFZaeRejUSDq0eQC3rDT/nZ/9GLLKCiRXsXJE/qZNdFrDaMzUfq7HjsRqDm5m6iksINGITSLNXoaLnAgRLz0Dl8BlEScnTT0p6XpZHTNHGSwoz2CwIVuaPOeeXdD0MQS6HU8jTc4l1Pyq5i3DMnAMhawsk2dtQzftPZKkCc0ddAtxMN5KTQ5RgXGRwcLKDp68HstKduMeEk6sjFA5yHi6bv/IGc2+vWDsYN85oPX/yU7FOMI5sPJn7/vbFezyE99CGE0aWD+HRGFM3fMyrjTA3eBalwUbIXd1dEORduDKlhcEW22BZ7LMp23h6YgbPSb97OQIx9x8jpEYQFx1iH8bzEuSRNx8Z/Bw7Rw2VwGXGr25ubjrVzSz1HLmb+uzrS0muYcXZVxZZYSd7BCQMAjTRcLaLR/26k5CtMr0xoamxpN+2PIzAFrWdGzr+LBUrPjIRKfGp/PrPjDUFUUD1ZlV4GVJDsOvBIyPREH6hPtzzxhAsNcTFzQVBPvr3FLts42IEM5V283JFqL/WD84UFGSuOX6nNoJx08DXEeZpXHAyRz+hOLBIK5lqK5AyjZ0VCAtZjGyxtkkiLWytzRbn2JiTUn3qYyVLw8LC8Pfff3NxIq9gwWBCxYYNG2Ap8BBiE4WwsZO7sOu6eFmrVFauWxH29mUX4pimzMahiPtoFhQMB1Nuh0JbLeTm3ovo9LYDpPa1ilTOlIkVcoW2jFRZ0a2yN747dR83klWo41Oyyin62AP270PMPgtpxjJIU1+C4DwCcBoOQbAz6TcV5bwsj5iyjReXnGogRudLJIXaxmeth3E/OSnXFI+lgfzyfBuECCsgqLZpF9DEAQlvwN5zBQRZZZgL5hNRs2U1XD2qa0TISsZ9s/1zLP50JQ6sOZormrw8vjde/exFLPxQ1/Rvx5K9GDrtFUx/43u972CRElUbVcbSSatzp21btBu/np/NUz8MRWy8Obk/9yfYu+owf8/Ei17vdsXk9ePhU8G72OVXC8IW22Bp7rMp2zhbD8s7/+KFGfxc6jeuFzfiY94VL33YG9+/95veZ5jQ5hvio2Oel/PAUq1x5ULd4y3hHNGkGi7vWxgKew0r6r6KqnsQ47ViBUPI2gkBAhxcp0CQGq7uY2lYwm9bHihuO885/iz1j5ndM6+KnDKmLEVj2NevcfNmZuRsqKrHkC9fwdp5+kaz2nkDsXfVEYPz3pj0MnyDDd9T2PSwBhVx65z+A2DfMc9zPyWFXclM33UoZDUQSz9PRU0ykLENYspXT6YogcS3YOexBFA05NX5TAG12bJBUtrpIP369eMhfIaqTbDIi9hY43WLi8LWrVt56HGtWrVgbYSfvMXN5Fi4c1my+/YtqEQRTQKNh4sVl+BGIXh4KR0pCTWKJFZYCo38XODtIMeKK7qdUFPCy5y6Tgfsu0FM/Rli7PMQs7QPUgRREnLEipZuK2CfI1bkoImDGP86RJXhqAVTwGSIYdNeRZNuDXTEinG/jsA/C3dg/99HdPJq187eAndvVwya3J+PMOXgGeCOGi2q8ZKSrEOZAxv5nr13Muyc7OAd9NRRneUSq7JVmLlzEipUe3pdc/F0xoeLRiI1IS1XrMjxwWCGmuEnbprtWBDWRZ3W1THulxG85GDUrUcY9vWriLkfi9gHcRg67VWdlKLK9ULx5aYJPKWIPfzkUKVhJcz87wudc5AoGlqx4o1csSKXrP8gJk+BqDZNX5KwDZhI8UnXr3LFCgYTtReMXcLbOmuvLAIvB9ZHf3feEATXDMJHv43kbToH1tZHzhmMZs83wnvzh6Bm86q58+wd7bjI3r5/S6MCOBMzWZRf4y71c6cx8bzf2J546YOephUryglasWIzxJQv881RQkwYBmSfhSiWvScgYSURFjKZrEBzJ+aRUBwF76233uLRGdWrV+fvd+/ezaM4Bg0axA05rY3wkzcRXD2wTEvAslrqW29cRz1ffzgrTDuqz6hQVwVBIiL8gBeavg6rQyoR0DHEA5tvPMYnLSrBzc48TUkQ7AGHARAVbYD0PyAmDIVo3w+C60QIEssPeyWsTKzIJ1rATJEW8ZEJvIpHr5Fd8cJ73aDMUvEOGSv1OOetnw1+ZsagH7Dk6nfcaJMJC8zgko1es7r0fqHeaNCxDn8wlClkcPV2gaefO7/fzNn3JVLiU1hjgqunMwIqs6QuYPr2ibmhv2wdmemZGNngf0aN1dq+1MJguVXCtnD2cOaGm0xsY+ebo6sDPzfuX30AFy9XNOxUB/FRifz+zbwrvhowB+/NH4rPVn7AjfV8Q7z4YASLuiBMLFboiBbMXMR6Ii2IsuXQ+uPcENMQv45fhuff6YzR3w+DnaMdlNlKODjZc2F9ar9ZmPnfJMzY/jkSY5OhzFRyAdwz0INHA7N7y1dbPuGljVlVIWcPJ3gFePCKUgXB0kkmrvoASY+TkZmaCSdWYc/fnXtiEIUVK3RFC8FjCUQTRloQ5ViwYFU8duzYwY0188McZtevX4+mTZsWeb2VKlXCunXr8OjRI95BZflFn332Gd58801YG2xUkQkWTXs0LNPt2Hv3NlKys9Ay2LTeCTnY2V2BXzUBV3fHW6VgwegQ4oH112OwPjwaQ+sFmfW7BGkgROdPgez9QMZKiLFHAfe5EBTmM6QkSgcPewdsf30w/59dv5hBHcv5zhl9YfOLup784mNO5Q9moPVanZpo4XUe9plGxIq8okXCe4DXSggST5NXCWGjV2tmb+avHObsm1qgqP3wVjSvRY+KutNZNJ1viDd/5YUdw8AwP4C98uFf0Ze/clg1U1vxwZh5GutsEgQ/32S65xsTJia/OMvowTm5/Rxi7j3mppt93+/BzV+tDeZlxcz38uPn5AyJUPA1qLDXsMIgquMhJr5vXKzIK1pk1AYch0GQ0EMeYRy1Wo0bZ+4YnX//aiQXGMY/N8XgfCZg+AR7wc3H1eB8JqyzV1FhUYN5IwfNhZu9Pdb2f+WZy1gs6nsFiBV5RYu3IfjsA6Q08GCNlKpgMWbMGLzxxhsYPnw4evbsyaeFh4dzh2xWLYQZKr733ntFXu/nn3+O8gILLWVKbHB18z4AFwSrELAp/Crq+PrBy8H0OWuiJglQ30JI46o4/fdDZKZkw97F+hRPd3s5mga4YeWVRxhS1/wRMXz9dh0gyuoA6T9rR5hcPgUc3yzTaByiZPg4OfFXjnDL3PSZQV1Ro83yricv1+Nic30rGKwUb4/K7dHQsR0U6gPGVyi4QnCfDwgeMIeHBQudf++7IQirX5GXjJPbKeDoYs/PZWNl1ljURFFhIb7MvJNV23HxcOIjV8bM04zBOo1yewrDJQwjlUngE+wN32Av9BzehY/CsmnXT9/CPwt3ckPOHL8WFjrOjPhYlZH8D01xkQlIS07nId/uvq4Q5JZzXWfCZ97riCGYWFHNy7wRDYLUk4v1YtzrgFhARRB5SwgOL5FYQTwTJniH1q6Ag+sMzw+s4m+0ChCL2GOlipm/Beu7My8Mli7C7nFsvdZApkqFl9esKnAZQ0KkxcBM6Z3GAmnzC1oIgvv3gGDZPhyEBUVY/Prrr5gyZQomTJjAp82YMYP/DQkJ4fNq1KgBW+bak1zp4Bpll9v6362bSM7MRNs69cyyfjH7HD/1QprWwIkVB3F9733Ue6FsKzUUl+dCPfDN0bs4HpWEFoGl4znCQlx5tEXGaojMCZl5Dbh+YZVeIETpo9RoMGTLfvzR+z00dIRh0YKJFZ4rAFk1s4hhXkGe+P7INKyYtg5z31nI69Oz3N6+Y3pg6saPMbnvt3qiBXvQMzaCZYiMtAzuBzR/5G94cP1hrrfF2IXDUa1JGP++vDDhxFAlEsaA/73AO6AEYYiASn74aNG7iAiPxK8fL0dijLYyQN22NfHZyrE80mfRJyvg6OIAV08XTBs4D2N+eoefjwyWWrJ/zVH8PvEv/uDD2hxLORn94zCeSkvoIsiqAF4rjIsWTKxwnwVBWrwKcYTt0XFga6yeuYkLDoYMMv/8cq3Bzw375jVoVGp8+fJsnNt7mU9j95F3vn0Drfo0LZUICVtHkLoDjq9zbyzDogUTK34GFC0gSKi6h7VSqqabjJYtW/K0EFYNhFUNmTt3Lq8WwqY1a9YMts71kzfh4e/OOzVlQWp2NjaGX0V9/0B4miO6QlQDynOAtCJcfFzgXdkNV7Zbb03jWl5OCHBS4K8rhsvZmQtBkEFwfB1wfAvIWAUxcRxE0XCNbIIwJlqcTX8P2dJ2pSpWMNRKFX54fzE3uGRiBYPlD6+asRGXj1zDoKkDdJZnAsOo74fBwaXwYamPbsfg025f54oVDFaC8pNu0/Dojn5ZVRbSO2v3ZJ00Efag2XtkV+5ZYC2jZUTZwFKGFoxZkitWMC4evIpZQ3/i5q3M/PWT5WOwdMpqnN93GZ92n4aHt6O5MHd86xleYSSnCgGbdnL7WXzW/Wu42llGuUBLFC0ErxX6EWAkVhDFgHlSfLL8fbjnEcWZr9Irn/RFlQaVMPjLgTpG+Mwk+sWxz3Nj3fHPTc0VKxhM9J497CfezonSEy14n5hFWuhAYkV5ocyk+5o1a/IXocvVEze44WZZseHaFSjVarQPDTXPF6iuA2IqINe6Joc288f5jTeRna6EwtH6Qq7ZA13HUE/8fS0acRlKeDmU7j4ILEVEcAHSfoSY+AHgPp+LGQRRlEiLZi4SCNn7SkWsYCTGpOB8ng5eXjbM34Zvd01C1YaVkZ6SwR3X712J4Cad3x2aVqgRq/SUdKyb9w9USpXePDaCtvGHrRg5dzDsHZ8KIGx/K9UNwXeHpyEhOhGZaVncrd3Dz42PjBOEMaLvP8biz1YYnMdCyVMTUjFi7mD8NmE5IsIf5qZ/3jp3B/aOCiz5bKXBz0befITH9+LhF0KRAoWKtCCxgigmx7edwT+/7MSI2YNh72zPq0mxilQ3z9/B+f1XsG7uFl7ilJlmMpNoeyc7nNl1HlePXcfjCMMVaRZN+BO1WlYv84p/thtpQWJFeaJUn2yuXr2KW7duoVevp8ZNBw8exMKFC5Gdnc2nDx5swXlSpWH8c/o2Or3etky+Pyo1BTtu3UDbkFCzVAZhiNlnAIk3BIl2VKRiM3+cXhWO6/siUOd501cjKA3aBbtzwWJdeDSGNzCeB28umPGmiDH8Ai0mTQTcZpCnBZGLMSO8vKidv4QsbRYE5+FmFysYUbeNRyQxP4u4yHh8NXAuJFJJbgQGg03PCaMviNSEdISfMl6WlaWKpMSn6QgWOTAHd/YiiMLCHmDuXXlgdP65fZe56WaOWJHD5SPhqNGsKmIjDefHM26euYN67WtZxXXElOaaRRYtUn7QVs+iNBCiiDCj5zO7LuLWubuYOfiH3Og6Nv3NSf1x+fA13L0cgdlv/aSd9+S+VK9dLWg0hv2WcgRHazBrzt+2S2L+bTGihSCBIKtBaSDliFIVLGbNmgV7e/tcwSIiIgKjR4+Gu7s7fH19uZ8Fmz9w4EDYIvevPOCjeiE1S/+hl7H8/Dm4KOzQooJ5KoOImlhAfRdQtMqd5urnBK+Krrj0722rFSxcFDI0C3DFX1cf4Z36QWUiFgiKhhAxAkj7CaI0EIJL/rA4wlYxZsaZH1H6BSBxK5Xz17MAQYB9PytNysgrVjBYubioO9HISsviy3gGuMPRRT91jY1++QR74u6l+wa/g1V3YKNoBGEKWJNx93XTSQfROd+CvXH91C296YFh/vyBgEURsao5hmClfo2Z0FridaQs4KKF61TtwwpBFBHWBvP3u3OqVcVExPJ2qjPvyX2JVY8qqDwxu19J5ZafSpi/bZfE/NsS4NcBh1dZCDJ5VpQjStXD4tq1a2jc+GkZxk2bNvELBfOzYD4W3bp1w6pVBTvVlmeuHr8JQSKgQrWAUv/us48e8lenypUhl0jNF10h2APSEJ3pFZsH8AgLlhZirTwX6on7yZk4+tBwh7U0EBQtAYcBQNoCiBkFu7kThMEc0FIS21gEQ16viLw07dEAFw5c0ZteoVog1Co1Pu70Jd6p9xGG1x+Pnz9cygWM/Lh6uaD/Ry8Y/f6XxvWCi7tlPnwR1kNmWibO7r2EvauP8Hx2Q7A8+GqNK/PR27wo7BVo+FwduHo7o/e73Qx+lgkZFeuFWIRgYemQWEGUhE6vt8mNJsjLjt/3cvNM5lmRn/tXH6B++1q8LRvi5XG9uKhOlFF/hgw2yxWlKlikpKTwaIoc9u/fj9atW8PT05O/Z//fu3cPtgrLhWMlkuwc7Eq9XNnS82dR0d0DNb2Mq8UlQdRkAcoLADPKylfNomJzf6gy1bi+NwLWSg1PRwQ52+GvK1FluyF2vXgEi5j0KURleNluC0EYgV3nWDUQVgoyL+zB7r3vhuaWgMyBiRus2sKUfrN4mdIcL4rti/fg61e+Q/S9x3rfwUbMmIM7C9/NQSqTYvisQVz8IIiScuv8PUzo/CWWfrEKTbo2QIdXnkYP5ggOX23+hKd+5IWV9P10xRg4ujpBrpCj39jn0aLX08EcBvNqmbHjc2SKrCQvQRDmhEXdTV4/nvtW5KXP6B48Yu/jP0bB3kk3Kq/jK625p8X07RN5m85LhwGt0P2tTmTWTBDWmBLi4+PDPSwYMTExuHz5Mvr165c7Py0tzaDCaStcORpeqPxsU/PvjXDEpqXhnUZNzTfCqroAsCoWsmp6s1haCKsWcpGlhfS0zrQQrfmmB1ZdjUZsRja88930SnM7RMdhgDoSYuJowGsDBAmV1SIsD+auPnvPZETfj+WmZUxEYKki/qE+mLz+f4iNjEPU7Wguarh5u+DbwT8azPUPP3mTmx76hfroRXH0fKczWvdthtvn7/K2wb7Tzde1zKowEeUHVor0l/HLcqMfxraeiM9XfYD+H/XhqUiuXs78nF7xzTqeEsKECxYN5OzmxB+KWEWcYd+8Cq8Ad3gFeuJ/v49C/KME3L8aySOEWBi6k5cjrl27Wta7ShDlHjZQ2LR7Ayy6PI97zWSmZnITZla179SOc9j88w4uWmSkZCItJR2Blf1xfv9lfDtkASb9/SF+PT8bD289Qkp8KkJrVYCnvwdPCSEIwgoFi06dOuHPP//kBpvnz5+HQqFAly5dcueHh4cjONg8/gmWTmpiGu+otHyhaal+7+P0NGy8dgVNAyuYLT9VFDUQs08A0mAIgmHjHpYWcm79DWSlZsPOuWwe9ktK2wru+PtqNNZei8bIhmV3HguCHUSn0UDyFxCTp/J69ARhibCHMvcAV9j5SVCxYoXcfFnmqs5e1RqH8fd3Lt7DlXxRF3m5ciQc9drqGxOyBz/2KgshmCjfML8pFhWZA6sqMKXfbMhkUlSsE4zmPRujYu0Q7Fp2gM9nkT4evm68fG9aUjqfxirlNOpUT+dcZZ/Jm0tOEETpwKKdWDRf/nTFUzvO49Kha/zl7K4VHBOik7jPBRPCszKy+b0sv2hOEISVChYffPAB4uPjuXeFi4sLpk+fDm9vbUhwamoqtm/fjtdffx22yNXjN/jfirVK13BzxYVzsJNK0Ta0ovm+RBUOaBIB+5ZGF6nUPACn/rqGa7vvo36fKrBGnBUyNA90w8orzHyzAqSS0jffzEGQ+kN0HAyk/wIxoz0Eh6eVeQjC0sjIyNCbxkqLKrNVPI1DbifnXgDM2LDH2524AJGemom9fx3C2d0X4RWgTSs05jPAKoIAIly8XGDvaPfMak0Jj5K4sZq9sx1FY9g4aUlpSEvO4A8m7r6u/KEmR4BgAgOLtMghtFYwhn39CnxDfbgpLDPcc3J14J9n51NcVILOur0rGD9vCYKwDFg7Ze29ec9GaNuvBRT2ctw4cxvbl+zVLiAI3F+J3bNY1RCWClaY8tsEQVioYOHk5IQ5c+YYnMdG1g4cOMCrhNgibITQyc0R3hW8Su07L8Y8wvHIB+hbvRbsZeY5FVi4rJh9HJD6QpAY75w5+zjAt5oHLm65ZbWCBaNzRU9MPnQbBx4koGNIGXdGFa0B1XmIyZMBRRMuYhCEpZMcn4JLB69h8acrcP9aJM8t/mjRu9x7gqWMrJm9Ccum/A03b1d0f+s59BzeBWENQo2WlVs2ZTUOrDnGKzm0H9iKl6nL7/qeQ9zDBGxfsgfr5//LH0RrtayGEbMHoVK9UDjky18myjcqpYqff4smrMDp/87DzlGB59/pjH4f9OQpHh7+bug3tif+mKQ1Ch84oS9a9m6CJRNX4uKBq3B0dcDzb3fG9B2f45Ou05CeoivKsQegxl3ql9HeEQRRWDoOaMWj/VhqyI9jliAjJYOXGh6/5D0kPk6Cg5Mdln+5Bpt+3M6jp+q0qY4RswfzSKvS9qQjiPKKxRhGMO8KFnUhl2tHL2yNS4evIbR2hVJz6Vdp1Fh67iyC3dxQx9ewW79JUEdwPwVIn11HnkVZ3DwYiYykLFgrYe4OqOTmgD8vl7H55hM/Cziw2toyiEkTyWmesIqHxP2rj2Dyi9/yh0VGzP1Y7hUQWNUf0wbOxbUTN/n0pNhkrJ65ETt+3wMHZ/1Us0d3YjCm5WfYs/IQXy+L1ti1/ADGtv4817gzL6wk5ayhP/IH0JxRc5aG8kGbL3D9pH5JSqJ88yD8IUY3+xQnt5/lod+s7Oi6ef/g0+5fc38VqVTKBbPGXeqhYp0Qbpr5UYfJuLD/Cr/WsgeXNXM248f3F2P876N01s2ihiavGw+vIIqwIAhLR2Ynx9LJq7H5px1IT07n7fv8vsuY+tJsVKobip/G/YEV09bx1G427+LBaxjTaiJun7fdIgIEUW4FC1uGhZJdO35DJ3fV3Oy8fQtRqSnoHlbVrCKJmHUEkHgA0mfnkLNqIaJGgys7dMu/WRPsWHau6IH99xN4mdOyhhtuOg0Dsg8CGWvLenMIokDioxKw6JMVetO7D3sOP3/wh0HR7eT2c0h4lKh3Td3xx16dcP28wgQTMVjqR16Y+efpnRf0lmffyUbVEmLKrmQxUbqwaIg/vljFK9EYKmV4/dTtXGPXT5aPwZT143lEEDvv8sOWZSOwX235BK9++iI+WjQSv1+bjyZd68POSDlEgiAsh3tXHuiVJWYwIXzxp3+i/cv66c4sBezncX9wYZ0giJJDgoUFcPPsHW7gxRzsS4OkzEysvXIJDfwD4O9sPrd8Uf0QUN8CZLV4OPazcPSwh39NL1zYrB1BtVZaBbnDWSHFn5cfwhIQ5A0ARVuIKdMhqh+V9eYQhFGSYlP0QudzHgwfXDfeni4euqbzno10Hdl00ujyhzcczzU+zIGNmBmDVX1IT9bfLqJ8ws4NQ+JVDvtWH879n/mqQAQ35DPG0S2ncO34TS6uJcen8rK+ChIrCMIqOLbltNF55/degX9lX6PedCwyiyCIkkOChQVw4cBVyO1kqFAtoFS+b82VS2ADlR0qmreEqJh1CBBcAWnhDT0rtwzE3eOPkBJjve7oCqkE7YM9sPpaNNKU+iNuZYLD609SQyZRaogNwEZ1WAnQ1HwP5ZZOjqFhfgSJwHP+jeGar3ycTC7lHgLGYH5BMrmub4+Lh/EqSSyEn1V/IGwDdr4VdP5wkSLf8vYFeJyw8oZJj5P44ARQdmbMBEEUHRdP4/cGBxd7aFQaVG1UGW/PeB3vL3gbPd56jps7M1GSpbsTBFFyqCVZABf2X0bF2sF6HWhzcC8xAXvv3kbb0FA4mdEvhEdXqG4A8jqFiq7IIbSZPwQJcPFfbcitNZtvpivV2HBdP1e+LBAkTgCrGpK9D8j8t6w3hzBjhMKRzSfxWY+v8W6j/+HrgXMRfvKm1YzyuPm48mthfs7uucg9AoyJCcwcMy9Obk7o/1Fvo9/z0oe94eii+0Bav0Nto6JIh4Gt4OZjvmg0wrLw8HNDn1Hdjc7vNqQD/8tSQCLCH+L4v6fR+Y22Rpdv1acZtv+urSjAjDkJgrAeOgxsbXRe92EdoVKp0fblFvjvj31YPnUNUhPTuUfNoCn9eWUhgiBKDgkWZQzLo75w4Ap3oTc3LBd76fmz8HJwRJOAIPN+V+Z+QHADpJWK9Dk7Jzkq1PfFhU3WnRbi46hAU383LLkQCY2BvPuyQFA0ARTNICZ/CWh0y+sR1g9Lpdj4/b+Y3PdbXD99G4mPk3Hqv/N4v8VnOL+PRVVZxnn4rAfFz1Z+oFcS7sCaoxj2zWvwr6QbestGr9jyngHueuuq2aIaOryi39HsMqg9qjUJ05vu+cSPIL+nD6soMvSrVwocQSfKF8xQs+uQjqjZoqrevKHTXoVfRd9cY873Gn+MX8YvR/OejVGprr4PFasyc/3UTaiyVfhg4XB4BXqUyj4QBGEa3P1cefREfsLqV0SfUT3w1/QNWPLZSm4UzTySDq47hkl9v0WjTvUo9YsgrLGsKaHPzTN3eG40CyczNycfRuJq7GO8WqcepGYMUxNVtwH1bUDRrkjRFTlUbhWIfT+cReydJHhX0g29tSZ6hHlhyqHb2HU3Hl0rlV652gJxGAQoP4E8cxaAN8t6awgTkhidhJXfbNCbzoSK+e/+hh+OVYa3FVQlYKXgfj7zLRdyLx8JR1i9UDTp1gA+IV6Yu/9L3Dh9m5eX8wv1QesXm/FS0IZKx3n4uWPU/KF46YNe2LfqMI/cYiNl/hV94Oajf11hIbwtezfGkqvf8Q7n44g4NH2+Iao0qASfUiw3TVgGrK1MWfc/RIRH4sDaY3D2cEKHAa3hE+wFZ3cnpCWnY/HElchM11a1mvHm93hv3lD+/4WDV/jn2/RrwVNBWLWZxZfn8XM1f2QPQRCWjXegFzq90RaNu9bHvtVHkBKfyiP+WDRgSkIajm05pfcZZtj724Tl+GLNR3oCPEEQRYcEizLm7O6LsHNQILh6oFm/J1utxp8Xz6GKpxd/mQtR1EDM2g1IvAGpfmh3YQhu5AuFowznN95Ep3GGw8Ctgaoejqjh6Yhfzz1Al4qepVaytiAEiRtEh9cgTf8Vrna12Th0WW8SYSLYgxUrv2gI5meRHJ9iFYIFaydMjOjyZnv+ygsTDtirVZ+mhVqXu48bf9VoVqVQy7MoigrVAvHqp/2Kte1E+YJF3bBX/Q51DBpzHv/3jM77WcMWcIPYyvVDuYl2lQZa/6bGXeqX6nYTBGF60YK9mICdl/+m/G30M2f3XOLXBRIsCKLkUEpIGXN653mENahodv+KrTfCEZ+ejs6V9UOhTYryHKCOARSNixVdwZAppNzL4vzGG1YRxl4Qvar44GxMCk5EWVBpK0UbaCR1EOy+GBD1yz4S1oncTv7MMPeyhI1IszQVpVKlM12VpYKrgxvUSsNiS36U2Uq+nvRk6zIUJcoXTFiTK2T8b9PuDfD65y/h5Q978XbIqoHE5yu1SxCE9RMbGYeoO9HIzszm7+0djJcmZv16SxioIojyAEVYlCEZqRm4dDgcvYZ3Mev3xGekY+O1K2gaWAE+jsbdjkuKqEmDmLUPkFaGwCIsSkCVNhVwY98D3DsVjYpN/WGtNPB1RqirPX48cx/NA+vCEmA3UJXiDUhVkyDJnAs4zSzrTSJMQGCVAB6tlZWh7UjlpWKdELh5l41pJBMXbpy5jTWzNvH/m3Srj14junLn9YjwKKyasRFRt6NRvWkYXv6wN/eMUNjriy/M4PDRnRhs/mk7H7liKR8DP+7DBV83bzI2I0oX1p4G/K8P90o5ueMsjm4+xavPDJzQBxq1BoFV/HHsn1Oo3rQq92YhCMJ6YfceVqaU3X9YGje7j3Uf1gltXmqBRZ+uKMCsme5NBGEKSLAoQ87uvsSNuGo0L1y4cnFZcfE85FIprwxiTngqCDSAomGJ1+VX3QMufo44u/a6VQsWTBzoW9UH809H4FRUEpoEWEjHVeKF2LQu8JNsgJjVE4Jdu7LeIqKEeAW6Y8LyMZg2YK5OaoiDsz0mLB2tV4qxNGC5viumrcXGH7blTrtz8T7iIhNQu3V1/DB6ce70u5fuY9fyA5j53+cGQ/DvXX6Asa0n5noGsPWc2XUBAyf0xSsT+nJfAYIoLVjJwvYDWuHD9pOQHPc0Uu3iwato91ILLmS812QCOr/ZDiPnDCZRjSCsWKxYOH4pDm84kTvt9oV72PH7PszZP5UbQjPTzbyw9MtBUwZwbySCIEoOpYSUIUe3nIJviDe8g8znKXHlcQyORNxHx4qVYS8zYxlT5XVAeRGQN4IglNxNn9W1r9I2CJf+uYXMFP0RY2uiSYArQlztMffkPYtKcUnObAiNpDbEpE8haih82dqRK+Ro2r0+fr0wG/3G9kSzHg15R+qXc7NRqZ5+9YLSIO5hvI5YkQOr3rHww6UGoyhmD/uZfy4v7IHw+1G/5YoVeVk9cyOF3xOlDvOEWfbl3zpiRQ4H1h3jo7CsIggT4aLvPaZfiCCslJiIWB2xIoek2GT8+eUadB3cAT+emIFuQzqi2fON8OFvIzH/8NcIqOxXJttLEOUREizKsJzpsX9Oo1bLamb7DqVGjSVnTyPY1Q31/MwXpSBqUiBm/gtIg3g6iKmo0q4CVNlqXNxyC9aMRBDQv7ovjkcl4+ADSxIGWGrIYEBMh5g0yaLEFKJ42DvaI7RWMEbMGcTrwLPIA9ZpKiv/ClZWNT+sSkJKXCqU2bpeFjk8uhuj9xCYmpDGK4YY48L+yybYWoIoPOycPLLhpNH5B9ceyzWNZWV5CYKwTvb/bbz9MiEjMzUT1ZuEYdxvI/h9t8dbnfhgJEEQpoMEizLi8uFwXq+5ThvzVWn493o4olJT0L1KNf7QbLaqIBmb2H+AokWxjTYN4ezlgAoNfHFy5VWrf5hu6OfCK4Z8c/QOVBoL2heJB+A4FMjaDmSsK+utIUyERCLhIetWa/iVf7ufsRtWu5+E1ZGVkcVTORkFnnYS3YhBgiCsA2bszNp5DgW28zwz2cCA4hnm1wRBFA8SLMqI/X8f4TnlITWDzLL+hynJWH/tClpUCIa/s3lqQDMRQczaCagjeOUJU6SC5KdG51A8uhqPiDMxsGbYA9Vrtf1xIyEdq64+giUhKJoDdu0hJk/VpvYQhIlo0q2B3rT0lAy4eDobrWriX8kXrl66BqGsLFydNjWMfk+99rVMsLUEUXBY+H9L92HKi7MwY9APEDUatH6xmdHl273cki+f8z9BEJYNM4U+v+8yZrzxPab0m41dfx7g6YnMq8YYbV9qDmdP8k8iCHNDgkUZoFKqsG/1EdTvUJuPhJoatUaDX06dgItCgbYh2jrwZiH7GJB9ClA0gSD1NctXBNX1hmuAE47+fgnWTpi7IzoEe2D2ibuITbcwXw6HNwGpH8TE0TzFhyBMAcvhZ6Ue83Pr/B28O2+wwTJwH/46El4BHjrTmcAxZsE73EA0P69PfIlXDCEIc8E8KMZ3nIJZQxfwNCc24DCm5UReytTdQBUAVh0gOyObe6t0G9qRwsMJwsJhfhRLPluB8c9NwYG1x3BqxznMHPQDPuo4Bd6BHgZFCzboyO4/bl5UCYQgzA1VCSkDjv97hudoN+5Szyzr/+fGNdyIj8Og+g2hMEPuOk/PyD4GMWsPIK8LQVYV5oKF0tbuXhHHll5Bh/sNACs3XH6lph/ORCdj6uFb+KGL+dKBioog2EF0eh9ImQwx8UPAYyEEoWx8D4jyA4uMeOWTF7kR2ZrZm5EYk4ym3Rugw8DW2PTjdkxeO56PYrEHwsr1Qvn03SsPomKdYD0RIqRWEBaemYWti3bhzK6L8PB342UlWclWqhBCmAtllhLr5//LS+/mJTk+FQvG/I65B7/C7j8P4uS2M7ysae93u8G7gheWTf0b32z9DFUbVaYKIQRh4UTefIRti/foT78Rhe1L9+LtGa+jXf+W2PLTDqQnp6Npj4bo/EY7VKgWWCbbSxC2BgkWZcA/v+xEcI0gBIaZ3gjzZnwc1ly+jFbBIQhxM/2ooyiqIGbtArJPc7ECMvOILvnNN8+tv4kjiy6h6agwWDMudjK8WScAC848QLebj9Grig8sBUHqD9FpFJA6B2LKdMBlInkDECXGzdsVDZ+rixrNqnCjTSdXR176ceOP2/Dfsv1o93ILnhrHOoyT+87ky7z8YW89wYLlBwdW8ceQr17BgI/78JQSByfTp6ERRP4w8e1L9ho8KOf2XuLCGzO57TWyM6/U4+zhhLTEdHyx+kNuMEsQhOWzbdFuo/O2/rILvUd04+WK67SuwT0uPPzdoVCQXwVBlBYkWJQyEeGRPNSMjQyamuSsTHx37AgCXFzQPrSSydcvqmMgZv4DqKMBRXMIsiooDWQKKWr3rIQzq8NR/cUA+Fl5paiWgW44/SgFEw/eRH1fFwS7Ws5DlyCvB9FxEJD+ByDxBpxHlvUmEeUEB2cH5Dy+5VQIYSNV25foj2pp1Bqj62FpI66euh4XBGFOckw2DZGZlgW5XAbvwKflyfN7sBAEYbmwqOHsTONputlZKojMWB6Apz+lHxJEWUAeFqXM399u4p2ZRp3qmnS92Wo15h07gky1Cv1q1ILUhN4YoiYemsytENMWA5p0wL5bqYkVOdTsHAo7VwVOLrJ+U0hmwDmsXiCcZFKM3HEF6Uo1LAnBrhNg/yLE1Lna35wgTExQFX9IZYZTjrwCPeHiZR6jYIIoKixiok0B5ppd3mxLB5UgrLxP1mVQB6PzWRSgqyfdkwiiLCHBohR5cCMKO5cfQPsBLSFTyExqsrnw1AnuW/Fyzdpws7cvealSdTTE7OPQpC2DmPozoLwKKBoC9s9DkHiitJHZSdGgXxXc2vMId09YVpWN4uAkl+KDpiG4l5yJMbuuQVnAiHKZYP8iYP8CxJSZ0KTMs/qysoRl4eHnhsFTBxjsOH7wy3B4B5b+NYYgDMHSjt6c3J/7U+SnQcfaCK5RgQ4cQVg5leqG8HQPYz5Mdg5WbqBGEFYOpYSUEuyB75ePlsLVyxmtXmhq2oogp0/ieGQE+tWsXSjfCuZDATEF0CQDYir/n1eG4O/jAXU8C4JlWeO8cgQUrQBpSJmbMIa1CUT43nvY8vkRjPo3AHZO1p0/GOJqjzGNgzHnxH2M2xOOec9Vh1xqGRoie3CEQ3+IggOQ9jNE1T3A7RsIEv1OO0EUJz2k5/Au3JBw2dQ1iLkfi7AGFTF4ygDu78PPP4KwEJh3yoKTM7Fu3j84tuUUr1bz4tjn0bJ3UwoRJ4hyAKtM9fnqcTi04QQ2/biNp3q16tsM/cY+j4DKVp6HTBDlABIsSon9fx/FsX9O8w45M4szBenKbPx44jjOR0ehT/WaqOntoxcpAc1jQP0IoiYGUD8GNHFaseJJPp4WOSA4Avxh1A1QhACCJyDxLHORIn/FkHoDK+HQvCvY8vkhvDS3g9U/2NTzdcH7jYPxw+kIvL39Cn7oXAOudpbTLAX7XhAlvkDabxDjXgTcZ0NgZqsEUUJYalyTbg1QuWEo4h/Hw8ffG25ebnRcCYuDlR9naUwj5wzC65/34++plC5BlC9YOuIL73XjKSDMR8nFywUKE/XXCYIoGZbzZFSOeRgejV/eX4lGneuibrtaJlnnzYR4LDhxFImZmRhYuy6qeHpB1GQBmgcQVRGA+gGgfsjs7dhjJyC4ABI3QBoMSJwBgb0c+UsQrOc0cPa1R4shtXBo4UV4BLug04dNYO00CXDFxy1CMf9UBF5YdxZznquOxv6WU9dbUDSDKK0ApC2EGNcfouPrEJxHlUlqEFH+sHe2Q1JEIgIdAsp6UwiiQBT2CngF0HWPIMorbBCMxEiCsDys50nVSrlx+jYWjVoF7yAPvDSud4nXF5+RgQ3XrmDPnVvwd3LE0Doe8FSchSbtvrZ6BzRsWByQ+ACKOoDg/SRSovz81JVaBiAjMRv7F5xDZko2un3anFcSsWZqeztjWtsw/HQ2AgM3XcBL1XzxfpMQVHCxjAoigjQQosskIGsHkLEWYsY6iA6vQHB8DYIspKw3jyAIgiAIgiCIckj5eYq1MNKS07F+3r9YOX09vCq4Y/BXA2HnoCjWurJVKlyKuYXD92/jRFQy5IKI5wKj0cT7DqQstUPpBLCwfUVT7V/BFVaeKfFM6vUOg8JBhuPLruDu8Sh0/aQ5qrS17tx3XycFJrWujF1347HhxmNsuBGDTqGe6FPVF20quMPZhEatxYGLXvY9ISraAVlbgYzVENOXQJQ3hGDfBVC0BGTVy5U4RhAEQRAEQRBE2SGIZP9vkDNnznCjTIVCUaga7YnRyfwVfTsWt0/fw+X916FWqlGvS000fL427OwVXEQQoAYENf8rCCoIUEIlZiFbk4FsdSYyNZlIU2UhMTsLjzPVeJAuw70UB2RrpPCxz0A9z8eo45EBmeAMlcYdKo0HNKIDbAOR5xVKuDGlVphIjEjDpbV3EH8nFS4BDght5Qvfmu5wq+AEJ287OHjY8Qoj1kaWWsSpuCyciM1EZLqal/Op7CJHVVc5gp1k8LWXwtNOAhe5BE4yCeylAn/JJYUUbEQRKpUaMlZaspgij4BsOMqvwklxGQ6y25AISmhEBTJVIchSV4BS7QelxpOfp2qNCzSiIzSiPV9GhAKiKIPI/FOeAWuD1atXR1m28dKCbY9SqYRcLrdq8a0o2OI+2+p+F7TP5mjnltjGi4ItnSO2tK+2ur92dnYWcy+3teNfFOjY0HEp7jnj7OxsljbOoKFQIxTlAjbvtUWIuhFjcN7lfdf5SyJkGpyvEQVkqgv+Gbzy/H/lyQtIffJ6UOjtLM+wB/qUqAxcWncPAHtpUbvIEP1NfVg3cpbog5spSv4qiBN9VsJdkY3SRuBbCEgEJmLc5K/CcCfxS6Qra6MssMROCtsma324Ki62uM+2ut+lvc+W2MaLgi2dI7a0r7a6v5a0bls7/kWBjg0dF0s8ZyjCgiAIgiAIgiAIgiAIixyYJgiCIAiCIAiCIAiCsChIsCAIgiAIgiAIgiAIwuIgwYIgCIIgCIIgCIIgCIuDBAuCIAiCIAiCIAiCICwOEiwIgiAIgiAIgiAIgrA4SLAgCIIgCIIgCIIgCMLiIMGCIAiCIAiCIAiCIAiLgwQLgiAIgiAIgiAIgiAsDhIsCIIgCIIgCIIgCIKwOEiwIAiCIAiCIAiCIAjC4iDBgiAIgiAIgiAIgiAIi4MEC4IgCIIgCIIgCIIgLA4SLAiCIAiCIAiCIAiCsDhIsCAIgiAIgiAIgiAIwuIgwYIgCIIgCIIgCIIgCIuDBAuCIAiCIAiCIAiCICwOGSyUtLQ09OjRA9HR0Vi7di3q1q1rdNnnnnsOkZGRetMvXLgAOzs7M28pQRAEQRAEQRAEQRA2I1j89NNPUKvVhV6+W7duGDZsmM40hUJhhi0jCIIgCIIgCIIgCMImBYtbt25h5cqVmDBhAiZPnlyoz3h7e6NBgwZm3zaCIAiCIAiCIAiCIGzUw2LatGl45ZVXUKlSpbLeFIIgCIIgCIIgCIIgygCLEyy2b9+O69evY9SoUUX63JYtW1CnTh00bNgQ77zzDsLDw0u0HezzJV0HQRCWC7VxgijfUBsniPIPtXOCKP9YVEpIRkYGZsyYgXHjxsHZ2bnQn2Omm/Xq1UNgYCAiIiKwcOFCvPbaa9i4cSOCg4OLtS3Z2dkQRRHp6enF+nze9bAUl7CwMPLUKCF0LG3zWDo6OpplvaZq47b6u5gKW9xnW93vgvbZHO3cEtt4UbClc8SW9tVW99fd3d1s6y5qO7e1418U6NjQcSnuOdO4cWOYC0FkrdxCmDt3Lg4fPsyrggiCgOPHj2PQoEHPrBKSn5iYGF5hpHfv3pgyZUqxtuXixYv8ByAIomwx1wWQ2jhBlO92Tm2cICwHupcTRPmmsRkFC4uJsGBlSZcsWYIFCxYgJSWFT8tRS9lfVubUycmpUOvy9fXlB+3y5csl2ia5XI4qVaqUOGrk7t27qFixIhwcHEq0LluHjiUdS1NjijZuSmzxHLfFfbbV/S6Lfba0Nl4UbOkcsaV9tdX9taR2bmvHvyjQsaHjUtxzxpxYjGDx4MEDKJVKDB8+XG8ei7KoX78+/v7771LdJhblYaowVXZBNFdou61Bx5KOpSW2cVNii+e4Le6zre53ae6zpbbxomBL54gt7ast7q+ltXM6/nRs6JyxDixGsKhZsyaWLVumM+3q1auYPn06pk6dWqSUkOjoaJw+fRp9+vQxw5YSpiDuYTyUWSrIFDJ4BrhDIrE4/1eCIAiCKDZJscnISM2ERCqBu68bFHZyOpoEYWMolSokPkqEWqWBnZMdPHzdynqTCMLqsBjBwtXVFc2bNzc4r3bt2vzFGDx4MB4+fIidO3fy9//88w/27t2L9u3b81QQZrr566+/QiqVYujQoaW6D0ThOnAntp7B71+sxuOIWHj4ueO1z/qhw8BWvENHEARBENZMZnoWbp29gwVjf8eNM7dh56BAj7c7YcD/+sCngldZbx5BEKVEbGQ8Nv6wFZt/2sHFy4p1QvDed0NQvWkVOLpQKgpBWJ1gUVg0Gg3UanXu+woVKnCTzW+++YZ7X7i4uKBFixYYM2ZMsSuEEOYhO0uJbYt2Y/FnK3OnJUQnYsHYJYi6E40hXw6EgzNdwAmCIAjr5da5uxjXbhKvXMDIysjGxh+24cL+K/hm60R4BXqU9SYSBGFmWP/261e/w6VDV3On3b10Hx93/hLfbJuIpt0a0G9AEOVBsGARF6y+cl6WL1+u875BgwZ60wjLJD4qAX9+tdbgPNaZ6zOqOwkWBEEQhFVHEf70we+5YkVebl+4h4jwSBIsCMIGiL73WEesyMvPH/yOsL1T4elvnlKvBFHeIOMAotRIjkvhI02G0Kg1iIuMp1+DIAiCsFoy07Jw/dQto/NPbj9bqttDEETZcPGgYbGCERH+EBmp5q2cQhDlCRIsiFJDYacocL69s32pbQtBEGWLqEmDJukL2CW3QQ2fCRBUZ+gnIaweiUSAvZPxexmNqBKEbVCQuaZMLoNMZtFB7gRhUZBgQZQa7r6uqFjbsK+Id5AnN+AkCKL8I4pKiInvAhmboJG1hygAivQREJUXy3rTCKJEuPu5oeeIzkZLLzbv2ZiOMEHYALVb14BUJjU477nX2vA+MUEQhYMEC6LUYFVAPl89Dm7euhdp5pT85aYJlNdLELZC2iIg+wTg/CHUir54mPg6REkFiInjIYqqst46gig2coUcL4/rhWpNKuuJFR8vG83FeYIgyj+egR6YtPYjXtY4L6G1KmDw1IGwc7Qrs20jCGuD4pGIUiW0VjB+OjUD107ewo3Tt1CpbihqtawG3xBv3qEjCKJ8I6ofQUxdANj1gCCvCWRlQoQMasUbkGR+BWSsBxwHlPVmEkSx8Q7ywlebPsGDG1E4teMc3P3c0axHQ3gHehSYLkIQRPnBzl6Bxl3qYcnV+Tj933nERsahwXN1EFqzArwCSbgkiKJAggVR6viG+PBXu5da0NEnCBtDTPsFEBSAQx/d6ZIQQN4UYtoSwKE/CZiEVeMZ4MFf9drVKutNIQiijLBzsENQFX/+Igii+FBKCEEQBGFW4jPSMWrrFtT7+XsM3pqMKFUPCIKD/oJ2XQD1bSD7OP0iBEEQBEEQBAkWBEEQhPnIUqkwbPMGHI64hy6hQHiyOwbt8ke6Uq2/sKwGIPGDmLmZfhKCIAiCIAiCBAuCIAjCfCw8fQKXY6Lxv5Zt8FLIEXzcMA4PU5X48UyE3rLcx0bRHMjcAVHMpp+FIAiCIAjCxqGUEIIgCMIsxKan45fTJ/F81eqo7JIAaKIQ6F4D3St5Y+mlh4jLUOp/SN4MEFOA7JP0qxAEQRAEQdg4JFgQBEEQZuGPc2cgQEDvatUhZh8FBFdAWhE9wryg1ohYFx6t/yFpCCDxgpi1l34VgiAIgiAIG4cEC8IsZKVnITUpDRqNho4wQdiod8Vfl86jfWhFOMllWiNNWQ2e9uGikKFZgBtWX3sEURT100Jk9YGsfWW27QRRXNg9j9372D2QIIjyReaTvm3++xZBEOaFypoSJiUpNhl3L0dg3bx/kBybgpYvNEX7AS3hX9GXjjRB2BC7bt9CQmYmOlcOA1Q3ATERkNfInd+6ghu+PZ6I6wkZsM//YXkdIHsPRPVDCNLA0t50gigW0fce4+C6Yzi0/jhcPJ3w0rjeqFQnBG4+rnRECcKKSXychNvn72Hdd/8iLSkdbV9qjjYvNodfqE9ZbxpB2AQkWBAmIzk+BSu+XocN87fmTrt8JBxr52zGd4emIahqAB1tgrAR1l+7jKqeXgh0cYUm/V9AcAYkQbnza3s7wUkuxX/3EvCCZ74Py2qyWAsg6yjg+FKpbztBFJWHt6Ixru3niH+UmDvt2D9n8MJ73TB46kC4ernQQSUIKx2I+33iKmxdtCt32uXD17Bm1mbMO/gVAir7len2EYQtQCkhhMmIfRCvI1bkkPg4GUs+/wsZqZl0tAnCBkjKzMSBe/fQKjhEOyH7NCCtok33eIJMIkE9X2fsf5Ck93lB4sy9LrjvBUFYQZj48ql/64gVOWz+aQdiImLLZLsIgjBN5FResSKHuKgE/DV9PbIyqKIVQZgbEiwIk3Fkk3FXfxYimxKfQkebIGyAXXduQSNq0CywAkT1I0DzCJBV0Vuuvo8zrsSlIzHbgNeNrBqgPF06G0wQJSA5LgX7Vh8xOv/AmmN0fAnCStn71yGj83avOMTbP0EQ5oUEC8JkqFVqo/NEjQjyKCII22DHzRuo6uUFDwcHQHmOlf4AZBX1lqvr48z/nkswMEIlqw6oI7WCB0FYMiKgUWuKdW8kCMKyUSmNt1/e7qlzSxBmhwQLwmS06tPU6LzmPRvB2cOJjjZB2EB1kEMR99DIX2uWKWZf4KVKBUGht6y7vRyBTgpcTDQkWFTV/s0+Y/ZtJoiSwO5tBd3/2r3cgg4wQVgpHV9pbXRem37N4OypFd4JgjAfJFgQJsM3xBvPvdpGb7qjiwPenvkGnFwd6WgTRDnn6IMIZKpUaBQQCFHMBlTXAFklo8tX83DAhQSl3nRB4g5IfCDyCA2CsFzYPW7YN6/CyU3/Htfu5ZbwoypZBGG1BIT5o3VffUHS2d2JG+o6OOnVuSIIwsRQlRDimcQ9jEd6SgZkchkvz8Y6Z4Zw83bFyLmD0falFvh71iYkx6eiWY+G6DO6BwIqU1lTgrAFDty7Ax9HRwS5uAKqyyxEApBWNrp8dQ8H7HuQhMRMFfzs8s1kn1OeN/s2E0RJYVWwfjo1E1t+/g9Ht5yCi4cT+n3QC9WbhiEjJQMKe7nReydBEJZBWlIakmJToFZpuADp6e8OD183jPlpODoMbI21c//hZU1bvtAYvUd2g38l6tsSRGlAggVhlLTkdJzfexk/ffA7d0mWSCRo/WIzDJ/1JvyNjBh5+LmjTb/mqNehFtRKNVeg5XZyOsoEYSPsv3cXdX39eUUQjfLyk3Kmxjt1Ye7a0anzj1PR1S1faK0sDMhYB1FUQhDoOkJYLuz+GBjmzyMtXhz7PK8YsviTFfjmte/4PJYyMmLOIKP3ToIgypbIG1H48f3FOL3zAkRRRHD1QLy/4G3UbFGNCxdMsGjUuR4XM5w9nSCX0yMUQZQWlBJCGOXqseuY/OK3XKxgaDQaHFx3DBO6fIXYyPgCj5yrpwsXL0isIAjbITIlGXcSE1DPz187gQkW3L/C+Gd8HORwkQk4/zhNfyYTLJAFqG6Yb6MJwoTIFXJEXHuI95t/inN7L+XeOw9tOI6PO3+J2Mg4Ot4EYWFE33+Mce0m4dR/57lYwYgIf8j7u/cuR+Qu5+rF+rZuJFYQRClDggVhkISYJCz8cKnBeQ9vPcKdS/fpyBEEocORiPtg2kQtHx+ImnRAfReQGvevYLBIjAqOUpx7nKo/UxqqvU0ptQ9+BGHpJLJ750eG751Rt6Nx+8K9Ut8mgiAK5tyeS0iITtSbzsSLxZ+tRGqiAUGdIIhSgwQLwiBZaVm4d+WB0aNzYR/LTScIgnjK4Yh7qOThAWeFHaAKZ2PLPMLiWVRwlOFKXHruyFYOgmAHSIMgskgNgrACMtOzcLcAQf883TsJwqJgEVAnthqvRnX12A1kpGaW6jYRBKELCRaEQaRyqUHH8xz8KvrQkSMIIhcmNhyNiEBtH22Ovqi8BghugMTjmUcpyEGK5Gw1HqRkGbgYhQLKi3SkCatAKpNy7yZj+IWShwVBWBLMYyagsp/R+cy/QiqjxyWCKEuK3QLT0tJw8eJFHDhwAAcPHsSlS5eQmmogpLcE62/Xrh2qV6/Ov+dZHeVff/0VHTp0QL169TBw4ECcO0el8EoCy9Hr+34Pox0yZjxEEASRA/OueJyehpreTx7IVFcAaXCB/hU5sJQQxqVYQ2khFQHVdYiiig42YfGwh5sXxzxv9N7ZpFv9Ut8mgiAKpsug9jw90RADPu4DT/9nC+8EQZiPIlncRkREYOPGjdi9ezdu3LjBw6jyq5RVqlRB586d0bdvXwQHBxd7w3766Seo1epCLfvbb7/h+++/x/jx47nAsWLFCgwbNgybNm0q0TbYMqyEae93uyH85C2c2vFU/GEmmlPW/w/eFTzLdPsIgrAsjkc+gEQQUN3LG6ImA1DfA+y6F+qzLnIJ3O2kuBKbhh6VvXVn8pSSbEB1B5BXNc/GE4SJYKJEzxFdcO3ETZzcflbn3jl53Xh4B9G9kyAsDd8QH0xYNhqzhv4Eterps8dzr7VBqxealOm2EQRRSMHi5s2bXBDYuXMnXF1d0axZM3Tv3p2LAew9i3BITk7GgwcPcPnyZfz5559ccOjSpQvGjh2LsDDm9F54bt26hZUrV2LChAmYPHlygctmZWXhl19+4QLFkCFD+LTGjRvz7Vu8eDGmTJlCv3Mx8QrwwIRl7yP2QRyunbgBNx9XVG1YGV5BHtwJ3VQwo6Poe7G4cfoWvII8EVa/Iv8OmYxKRhGEtXA8MgKV3T3gIJdr00G4f0XhBeMQF3tcjTMUYfHEA0N1lQQLwiJIik1GXGQ8rhy7DmcPZ1RvEgbPQA/Y2Sue3juXjuYVQa4evwFXb1dUa2T6eydBEKbBwdkebfo1R80W1RF+8iYyUjJQq2U13q5Z1buszGzEP0xA+KlbSE1IRc3m1fjAnZu3K/0EBFEKFOqJsE+fPmjfvj0XBlq1avXMB0mVSoUjR45g1apV/LMsXaQoTJs2Da+88goqVSrYXZ5x5swZnorSo8fT9AWFQsHFEiawECXD3ceVv6o0fPZvURweP4jDVwPmcFOjHOyd7DF920TUaFGFRAuCsAKYaH38QQSaBFbQvleGA4ITIPEq9DoquChwJkbfiV2QOEGU+EBUXoXg8IJJt5sgikr8owR8/94iHN54Qici8bOVY9G0R0PYO9rxaUzgZ6+wBua5dxIEYVrsHOwQGObHX3nJysjCye3n8PUr30GlfJqa2KpPU4z96R14BlC6CEFYhIfF5s2becQE85QozKg3W4Ytyz7D0jKKwvbt23H9+nWMGjWqUMvfvn2b/61cubLOdBbV8fDhQ2RmkrOvpcJuAsu/XKMjVjAy0zLxaY+vEfsgocy2jSCIwhORnIToNOZf8cSMV3UNkFYolH9FDiEudniUlo3ETKXhKAsWYUEQZQhLg92z8pCOWMFgDzHTBs5DbERcmW0bQRAw38Ba/7k6YgXjyKaT2LXioF56PEEQZRRhUdSUjuJ+NiMjAzNmzMC4cePg7OxcqM+wVBQWUWFnpx3VyCEnVSUpKQn29vYoDuzz6enpKAlsn/L+JZ6S+CgZu5btN3hImGhx8+xtuPk555Y6pGNpOqzpWDo6Gq9WU1JM0cZt9XfJy+E7t8G0iVBnZ2RmpkKhug21rBXE7Oxnflb5pBPob6+9HV2OSUQTPxedZaRiACSqIxb1W9nqb22ufTZXOzdlG0+JS8Oa2ZsNzmMPLQfWHUW/D3ua7AHGls4RW9pXW91fS7qXF/b4M2++Q+uPG23T7HrQYWBLOHsZrwxkbdjauVlY6LgYpzTOFYsyCfj555/h5eWFl156CZaAUqnE1aumGdW7e/euSdZTnnCRukGZbdz5P/peDI+gyR8lQ8fSdFjDsWSeNNbQxm3td8nL7hvX4Gtvj7ioKKRKIxHskY34BAdkq2MKvQ55ZgokAnD8ViR8MnU7ts4KOwS4xeNG+HGoNOUrZ9jafmtz7bO52rkp27inkzcSopOMzo+6HcO9vNhAiSmxpXPElvbV1vaX9e/NRXHb+bOOPxv8ZO3aGEmPk5GWlo6ImPsob9jSuVkU6LiUDcUWLE6dOoV169bl3pxzRsFzYOWBWCpJYYmMjMSSJUuwYMECpKSk8Gk5ain7y8qcOjk5GbyYZGdnc/PNvFEWLPKCbYObm1txdxFyuZxXPSmp6sRO7ooVK8LBwaFE6ypvpMSmccf02Mh4g/NrNK+GSpWemvbRsTQddCxN18ZNibX+LrcunkUtX3+EhIRCyvwrlDJ4eNdgsRGFirCIj4+Hr7cXgpwyESc4IDT0idFmDhoFkLkO1cMEaGQ1UR6w1t/a2vbZlG08MyULNZpXxdVj1w3OZyVLg4KCEBgYaJLvs6VzxJb21Vb315LaeWGPP3uOYO1662+7DM6v1iQMru4u8At5kg5ZDrC1c7Ow0HF59rGxOMHi999/x7fffssFAmaMWRJRIAcmfDCFdPjw4XrzBg0ahPr16+Pvv//Wm5fjXXHnzh3UqME6yFrYyDzrNBQ3HSTnQmWqEDbW6M0ZDmeNOAQ74O2Zb2DGG9/rzavWpDICKvkZPGZ0LE34G9j4eWnKNm6rv0tcejruJSehd/Wa/HqrUd4FpIGQK4rW0ZHLZajgao+bSUx81r1ui2IIkKmAnfQuBMcOKE9Y029tjftsyjbO1jN81pv4sN0kvUEan2Bv1GpRzSwdfFs6R2xpX+zs3t4AAQAASURBVG1xfy2tnRfm+NdoWgW+Id6IuR+rN2/E7EHw8i+fpYrp3KTjYkkUS7Bg5UIbNWqEhQsXwsVFN9e4uNSsWRPLli3TmcbCu6ZPn46pU6eibt26Bj/HtoP5XWzbti1XsGDCx3///ceNP4nSgV3IWY6fV2Dhy7axG0yz7g3xyfIx+G3Cn4h7GM/d1lnd68FfvgJPf3ezbzdBECXjdFQk/1vdy1s7QXUDkFUv1rqCXOyw8248fxhk14ccBEECURoIUXWDe2UQRFlRuX4ovtn2GX4cvRiRNx/xe9abk15GuwGtYPekQsizyM5SIi0hDVKFlJdMJAjCcmFi5Oy9U/DzuD9w/J8zvK8bGOaP0T8MQ5UGFQu1jrioBCgzlXD1doajCwlUBFEqggUL/ejdu7fJxIqc1I7mzZsbnFe7dm3+YgwePJhX/8gpWcqiPEaMGIEffvgBnp6eqFatGv766y8kJibirbfeMtn2EYaJuhONI5tO4b8/9kKVreJ1rLsO6YigKv6FOmQuns5coKjXvhYy07IgV8jg7ueWWxqOIAjL5tTDSHg7OsLL0RGiJg4QEwBpULHWFeRsh6QsFeIylfB2UOjOZOtUGQ7FL4jM9Cxe2eHG6Vvw9PdApzfa8s4mQRQHR2cHNOnaAHMPfIms9Gzuw7T11534vNd0OLk54qVxvdCgYx2DgrtapcajOzFY992/OLPzPFy9XDDgf31Qu00NePiWPFKVIAjzwCJ+P1n2PpJiU6BSquHk6lCocqYxEbG4cvQ6Nsz/l/td1GlTAy9/2BsBVf30igUQBGFiwYIJC6z0aFnAlE21Wq0z7Z133uEjcswDg+VCs2gNFgUSHPzU/4AwPVG3ozFt4FxcP60tLctY+c167Fy2H7N2T0ZQ1YBCrYeNpPpUMJ8ZE0EQ5uPkw0hU88yJrrip/VtcwcJF24G7lZBhQLCoAGT+oxd9URDXTtzAl/3ncJ8c/0q+SIxJxsrp6/Hed0PRe2TXYm0jQTCY+PUg/CHGtpqI1MS03IMy/fX5aN23KT5YOALu+USIiGuReL/lRF4Fi8EiNKa+PBtdB3fgqSZu3uXLUJYgyhOOro78VVhY1PDyqWuwfcme3Gmsze9bfQRz9k1F9aaW459FEJaOpDgf+uKLL3D06FEuCrBIBnPBhJHw8HCddJDly5djz56njZ/BOq8symL//v24ePEi97po2LCh2baL0MJU47xiRd6a1VsX74YyS0mHiiDKMRlKJS7FxOSmg4jKm4DgDkFSuLLU+fFzsoNUAG4kGChPxwQLMQ3QPCzUui4duorxz02Bo6sDJiwdjY9+exdfrB6HZj0a4vv3fsPWRbuLtY0EwchIzcTvk1bpiBU5HN54Eg9vR+tMY8v9NO6PXLEiL/8t3WfUfJogCOuEVRTKK1bkkJWRzdNLYh9SmycIs0ZYBAQEYODAgdx4c/bs2TysidUqzi8inD59ujirJ6yAtOQ07F5xwOj8A2uOoteIrgio5Fuq20UQROlxIfoR1KIG1XL8K9S3uOFmcZFJBAQ42+FWogHBQhL0NIrjGREcLFXtixdmokK1QLz1zWtQ2GujNdjfF8c8D41agx/fX4TqTcMQVr9wOcgEkZeUhFQc3nDC6EHZt/owN+HMITUhDWd3XzS6/ImtZ+hcJIhyxOn/zhudd/lIONKS0uEdWD4NOwnCIgSL+fPnc8NNPz8/1KlTx6ReFoR1wEzwJFLjATpSqQSFjNomCMJKORUVCUe5HMFurhBFFaC6C9i1L9E6/Z3scDvRQAk8CUsbs9cKFgV8h0qpwlcD5nIDxMFTBuSKFXnF9D6ju+Pu5QjMf/dXfHdomp7gThDPgt3e2D2Q+VIYQiaT6p137Dxjaa2GkOZbniAI64aZ6hqDXQ8Km9pIEEQxBYtVq1ahffv2+Omnn6ijZ6M4ujig+9DncPzfMwbnd3qjHXyCyZeitBA16RAkjgXOhyCHIBSuggtBFNZws6qnFySsiofqHqt/UKIIC0aAswKnopL1pj+tFHKrwEohq2duwq1zdzH6+2FG841ZJSMWacHCcvf+dRidXm9bom0mbA9XbxduGL3j970G53d8tY3OexdPJ7Ts3RiHN500uHzzno3Msp1E+UAUlSznruD7vJgNiGoIEtOX1iWKTuPO9Y3Oa9CpDpzdneiwWhESQQ0Pd92S64VBFNWAmAlBQr93SSjWsBIrG9qhQwcSK2ycKo0qoVGXenrTQ2pWQOc32kEqpRGj0kBUx0JM+wmiKtK4WJF1AFBe0HZ6CMIEaEQRZ6KiuGDBUd1iY0qAxK9E6w10tkNUWjbSlQZGrpkYwsqmGiHyZhRWfL0WHQa04tehgmCpIDVbVMOfX63RM3ImiGdh52CH1ye+BC8DlQJ6Du8C31AfnWlMPHt75hu8Mkh+Bk7oW6iKA4Rtwu/byov8Ps7v5waXyQayzwDZRyFqDESoEaWOu68rXv30Rb3pTKgYOXuwwUpChGXC2pdCuIwKfg8glSiLJlaorgKZ2yFqUsy6jeWdYkVYMLHi1KlTeOWVV0y/RUSZkpqYivhHSUhPSoe9sz1cvJzh5W+4I+Vf0RfjfhmB8JM3sW0RM9lUoeOrrdG4S30EVDb+0JKVnoX4R4lIT8mAg7M9PPzc+V+imGJFyjfa6gmZOwGPJRBkQfnEiv0Qkz5g48oQPJdClOuLTARRVK7HxSIlOws1vLUPZizygYkVglCs20ouzMOCcScpA7W985l3Mu+KAiqF/DT2d7h4OKPzm+0K9V1dBrXnBpyH1h1H+wGtSrTdhO3B7nPzj3yNIxtP4MC6Y3DxcMKLY3uiUt1QOLs7IuZ+LPe64JE+osjN9lh1gJwKAkzUd/NxgXeQF78HRt97zM057RwUcPNxhdSOUpVsnRyxQowfxIYLIbh9B9GuvU6kRY5YISa8xYyEILj/BFHRkiItTExSbDKSY1OQnaWEq6czPAM9cgfmmGluSnwKsjOUcPZwgk+IF+/b9hndg99bUuJTed/Xyd0JPkGe8Al54vtEWDza9nUWSHwLMqgBtx8halo/s33liBVi/JvcMFyABqJ9dwgSslEoDsXqWY4ePRrjxo3DlClT8PLLLyMwMNBgtIW7O6mH1gSrD79xwXZs+WkHsjOz+QMBC1MdOXcIgqr4GxUt2KtR53rQqNRw8ym4lnx8VAL+/Hodtv22m+easxzgjq+0xtsz3oB3EJkPFVes4KjvQEwYlita6IoVIg/XF+MHc9FCIlQv0ncRhKF0EKkgIMzjSbvlgoXh60RRCHDSek7cSTQgWEgCn1QKiQakut916r/zOLHtLAYZ8K0wRkiNIIQ1qIj1328lwYIoFn6hPug75nl0HdqR+1Yw7xT2cLJjyV4s+mQFeo3swsWIv2dtzq0oUqtlNbw5qT9mvbMAPsHe+PiPUbxSyLIpf3Mhn1G/Qy2M+3Uk5HJK47NVdMWKbO20pA90RAtdsUI78ismvkeihYm5f/UBZg76IbcyHouUGjF7EFr1acrFirnvLMTVY9dzIyjenNIf7fu35ALGz+N+x/l9V3LTqd+c3B9dBnWAmzc9uFqLWMH71k/aF5JGA+4LICpaGRUt8osVfFryRJ7OSqJF8SiWfN+9e3dcvXqVe1n0798frVu3RsuWLfVehPWQlpyOzT/vwLq5W7hYwWCjmMf+Oc3ryjMxoyDYqOazxApWBu6PSau4IMLECgZz69+94iC+f/dXPhJFFFOsyOGJaKFRPcwnVuSgFS0UkmtwcaY8V6JkhpuV3D1gJ5NpQ5A1USX2r2A4K2RwVUh5hIUeOdVBePrJU5iR4W8TlqNS3RDUbVuzSN/Xpm8zXDkSjhtn9Es0E0RhYOK+k6sjFysY5/ZdwrwRv8A3xBtegZ5YMvEvnfKnrCT47Ld+xjsz34So0eDwxhNY+NHSXLGCwR5wPuk2DS4KV/oRbBBDYsWTOdr7etZ+aDTJemKFFg0XLSg9xDSwyKcP20/OFSsYyXEpmDV0AS+f/d3IX3PFCgZr6z9/8AfiHibg4y5f5ooVDNbGfxm/DEc2neB9bMLKxIrc9jUKyD5iMP3KkFiROy95IqWHlGaExahRo8jdtpzBLqxMSDAES/lIiEmCfwlLlCZEJ2LHH/sMzjv6z2kkxiRx4YMopliRg7wOhOwDEJMn5xMrcsgGEoagYoVF0AiGHesJ4lmcjIxEo4AA7Rv1He25Jn3yvoSwtBAWYaGHhKWfyLWChV3r3Mn7/z6K2+fvYdT3w4p8b6rVujo3UNy2eA+qNqpsis0nbJj4RwlY/MkK/n+Ptzth7dwtBpdjKSGpSWno+34P/PLRMoPLsIGCmDtx8Auh8uC2hHGxIneJJ5EWsyBmHc73MKUrWlB6SMm5dPgaTwcxxOLP/kLnN9ri8uFrOtPdvF3xIPyh0cG+pZNWo0m3BvCpQOb01iVW6IoWQr5Ii4LEitx1U6RF6QkW77//fvG+jbBYmOqbmZ5ldP6D6w9Rs3nVEn0Hq0PPIiqMkRCdhODqT/0XCH2YmiumrzAuVkgrQ3Ac8KSjU5B6nw1ZytuA9x42pk2HmigSkSnJiEpNQTWvenkECwUgMU1err+TArcS041UCgnQqRTCDDOXTVmNmi2qolKdkCJ/F8tBbtKlPvasPIiRcwYVOp2EIAyhzFQh8uYj/j/LYS8oOvHupfto268F4qISjC7DKt7U71ibDrYtoUnkkZCGxYq8osX/IHgshai8AKhvGRctvLcBkkpm3ODyzcUDVwtswwGV9D3b/EK9cefSfaOfY20+O6Og35coUzTxBYgV+UQL762A5Mlgh5ik7X8bESt0RAtZZUDR2LTbXY4hRyeC4+Bkx/0kjOEbXPIHEQeXgo01mWEZUTBMxWWCBGRGwt7Vt7XmQI6vPfNQqh0nQami/GiieNEVjOpe2tEhUXWbe0qYqq68v5Md7iZlGg6ZlQQAqpu5b/etOoIH16PQdXCHYn9f0+4NkJaUjqNbThd7HQTBkMqlcPfRpnFkpmYarAqSg2+ID0+VZHntxvCv7Eeh47aG4AjBTWvOWiCOQwHleSNixROcxwICVaApCaG1jFecYh40iQaiLxJiknn7NgZr83I76n9ZLIITBNcvnr2c0/uAkNd/zwGC20y2goI/5/AqICUR0eyCxbx589CnTx+j8/v27Ysff/yxOKsmygjWqWr7UguD81gerikMMd193dDwuToG57HcczafeDaCNACCx0KjooWYtgiC42DAaYTxlbhOx6PEelBrtDnXBFEUTj58gAournC1eyJCMsHCBIabeSMsUpVqxGdqvW50YD4ZTJh74l2xYtpaXp60JNFZrNMZXCMIe/86VJLNJgh4BrjzMqWMXX/uR8/hnQ0eFXtHOwRXC8TW33ah29COBpdh1UUq1QsmwcLGECROgF03CG4zjC/kNAKC0zBAKMC40XkcBIdXIUjJAL8kMPN5ucJwQPorE/pin4H7xuOIWFRpUFFbIcgAfUZ3h0cA/S6WCq/kYd8TguuXxhdyGgvB8XWd9sVTQxRtILj/aFy0YG3SeSwEKRUaMLtgsWPHDrRrZ7xsXPv27bF169birJooI1jo6rBvXkXddjX1HNC/2jQBQVVLnpvO/Ck+WvweqjXWzRMPrh6IKes/5ttAlFC0EFwheK4ApKHazowB0UJwnQGl5DnEPC44ZI0gjHEi8gGqeWmjrnhtcc1jk/lXMPzzlDbVg32PJg6iJgmH1h9HRPhDnkNcUhp0rI0T284gLYnaBVF8WMW0Tq+3Rfdhz/HqAc2fb4RXP31RJ/rIxdMZE5a9j79mbOBmfb3f7YZ2L+salbMojW93fYF0DZlR2yIFihZPxApB6gs49ILgMkV/GRIrTAYTtKdvn8ijgKs3rYJ67WrxQb4X3uuGNi82w+CvXuFtOi8dX23DS5vO2j0pN+IqB9bW+47uAbm8ZCXAiTIULQyIFYUSLUisKDbFai1RUVEICTGeK1yhQgU8fPiw+FtFlAmBlf3x8e+jkByfioc3o/lIEXuxsqU5JD5OQvTdxzi6+RQUDnK06NUECnsZDqw9xss3tXyhCfwq+updoPMKINP+/QzxD+MRExEHr0APXoPe05/EiuKIFvBYCDFhJDf5yRUrZNW0nWMWBuo0TOtkkfaL9jOuMwD7blBnmiZ0n7A94jPScSshHt3CnnjaqO8+TdUwEX6OWh+Ju0kZaOLvql/alAklylv4a/oGVG1UCaG1gkv8nQ061MGWn//Dkc2n0OXN9iVeH2G7MPGdiRTMy+Lg+mPw9PfAr+dn8+oBGrUID393XuJ7+Kw3cyMYP1g4HIOnDsDDW9Fw8XTi4eROng68Ihthu6KFyEULlhr/ia5YIdGmeQgSV4hMtGDXxJQnwgWJFSaFRVeE1g7BnP1f8sp5rGzxG5NeRkBlP3gGeMDF2xk/HPuGVxNhXm0sWo+VLGXtng3UzTs0DY/uRCMpNoWX0maGnOxzhHWIFiITLbjvxCTtRKcxRsWKp59zgPhEtBATR2s95UisKH3BwtHREZFPcpgN8eDBA9jZUai5tRF5Mwr/e24qrwjChAVmgqnKVmHGjomo1bI6v9j+OHoRDq4/nvuZ3z9fxV3OWT7eym/WY8XX69C2X3O8/+PbvFNmCA9fN/4Ka0D5WyYTLZImQHCZ+FSsyJkvyREtBAjSilys4CM30Dc0JIjCcOLJtb+m95P8XNUdQLAHnnSgTYGdTAIvBzkXLPSQstQTAWd3HcXNs3cw/Ns3TfKdbj6uCK1dgUdtkGBBlISo29H4asBcfn7msOiTFRj3ywg079WYC/QssjAvbISWvUJqPs2XT0+n67StoyNaqO7oiBVPl8kjWohJlAZiYhIfJ2P/6sP4ccyS3GlrZm9GjeZVMPGvcXxQL6hKAH/lJSszG6d2nMeX/edAJpfB3smOCxoVqgdi+raJXKwkrEe0gChCrXwMKF6BvBBpVjqiRdYRCM7vUxpIaaeENGvWDKtXr0Z0dLTB6As2r3nz5iXZLqKUSUlIxbzhC/H4QRwXKSJvRCE9OR3Zmdn44oWZ/IJ9eud5HbEih40/bEPleqFwctPm6rFlTu+6QL9haaaHuH2nJ1bkzpd4QHAaCth3fSJWEETxOREZAT8nJ3g5ats760RD4mfyUtfMx+JeUqbedEGw4+VN/553hqeqVc2XYlYS6rapiVM7ziEj1YBQQhCFID0lHevm/aMjVjCYgey8Eb8g2Uh5RIIoOD2kq0Gx4ukyrtr0EPKsMDksGiqvWJHDteM3sWXhf1BmGa4kEf8wgQuXrDoe60snx6Vw36X7Vx/gj0mrkFVAZT7C8kQLlbQr4tK7Qal2LMLnnqSHkFhRNoLF2LFjkZ2djZ49e2LGjBlYu3Ytf02fPh29e/eGUqnkyxDWQ9LjZJzfd8XgPOaenxidhLVzDNeTZxxYd4wbE+VVnw05JxPmgZn3FPTAyEULCZUvJUrOsQcRqO6Vx/2cG26aLh0kB19HheEICwC3r/njzJ5ktO/f0qRCSd22NZGdqcSp/0hwJYpHfFQidvyxz+A8JlqwlCOCKCrs/m1MrHi6jCsZbJqBfauPGJ237bfdePwg3uC8S4evQa1SG5y396/DSIyhPrI1odI4IDIqtXjV/chgs2xSQipXrowVK1Zg2rRp+OOPP3TmNW3aFBMnTkRYWFjJt44oNYwpxLnzlUqet2cMFuYWUPmp1wVbVq00fKEmCMI6SczMQHhcLDpUrPzUcFOMA6QlN73Mj5+TAiejkvlDXn5RYsOvjnDzSkP9DrVN+p1egZ48L/no5pM8tY0gigo7XzPT9CODckiMSaKDShBWREFtlvnSGCy//YzPqZQqqFQGqmARBGGQYlvU1qhRA3/++Sfi4+O5Z0WO2aanJ5VpsUac3J246zELWTMEMwlq3LU+ti/ZY3A+qy7CjDhzaNKtPpzdKf2AIMpbdRDWNavp45PPcNN0JU3zlzaNy1TC20FrwslIfJyOvWsz0bl/IiRSDTO1MOn31mpZjRursZExqcy06ybKP3I7Oao1CcP1U7cMzm/SrUGpbxNBEMWn2fMNsW3xbqNRecybwtg8YzAPGwfnJ2XBCYIwT0pIXphAUa9ePf4iscJ6YdU6mGO5IZ57tQ33pxjwvz68drzeZwM8EFavYm4HjS3Tf3wf2OV5yCC0iKIaovIqRHUUHRLC6jj6gPlXOMPH0clshps5+D3pBN7N52OxdQlL1xDQrHMioIkx+ffWalWdR4ixcpMEUVSYAR8zgmXlTfPDvJ4qVDN9+hRh3YjqhxCV4RBFJsASlkZY/Yo6Zrg5SKQSvDX9Nd4HNgQzrzcmWrw3fxivIkIQxUFUP4aYfQGixng0n00KFv/884/RkKeCYJ9hnyXMR2Z6FmIj4xD3MB5qdeFSMOKjE7m5ZmqS1oE8KTaZ59027lIPU9aPR1AV7Wgpi7hgF+MRcwbD1dMFgWF+vHRTk671eYg2G31kYsaktR9h4UdL+TQ2jy3DliX0xQpWflSMfxViwggSLQir43DEfdTKia7ghpt3zWK4meNhwbif/NTHQq3S4J/fzqFBxyA4uWgAtenLZ7ORL2cPJxz/94zJ102U/3ssi1L0DfXCrD2TeaRFjojfa2RXTF43HoFhpo9GIqwTqVQKO3k8xPi3eb8AKhItyhrWxllbZ31k1vYZLE3wq80T8Pw7naGw196Xaraoijl7pyC0dnCB5Y0/++sDvDKhL6+klyNazto9mUfyEUSxxYqkzyDGDwSUJ21GtChUSsg333yD77//Hv3790f37t0RHFxwzft79+5h27Zt3IgzIyMDvXr1MtX2Enkuqg9vRmPl1+t4+DILLev9bld0GdSB13Q3REJ0Il929bebeMnSOq2r4/XPX8LeVYfx39L9CK0ZhHe/G4pvd0+GRqWBTCHlpUnZTZXBBIqKdUIwcdU4pCWmc7nLzcuFz/t29yRAw1JLHCkVpECx4g1ATAdU17hoAY9ftKVJCcLCeZyWhpvxcegeVvXpRPUdQGqe8sT2Mgk87GW4nyfC4vi224iNTMVrn7QEhKOAGSKV2Mh49aZVcPSf03hr+usmXz9RPu+xWRlZeHA9CsunrsG5vZcQWqsC3p7+Ol9WkAj8XurkWnh3eaL8U72KC5A4HFDf5O/F+NcheK6AKKsOQShxADRRRGIj4/Hf0n34Z+F/yEzLQvNejfD6xJcQEOYHqVTCB/WaspQuAbzCBxvUk9kVnDboHeiJQV8OxAvvdefXFCZeuvu60W9DlEisQPZ+7fuE4RA8foUobwpBUr5TjAolWOzatQtLly7F77//jrlz5yIoKAi1atXinhVubm48kiIpKQmRkZG4dOkSL23q7u6ON998E0OGDDH/XtggD288wntNP8k192LGP0sm/oVDG0/gyw0fc/O4vCTFpfAoiD0rD+VOO7HtLE79dx6frRiLE1vP4urxGxjT8jOMX/IeOr/Rzmj+NvOmyO9PYe9UvhuKScWKHEi0IKyIwxH3+N/aPlpzXVGTCmgeA/KWZvtOP0cF7iU/FSz+XXQeITU8EVzNA2KaF0TNI9Z3NDls9OzPL9ci5v5j+IbkqYhC2AwPbz7CqKafICO1cPfY+1cj8UGbL3j5QsaVo9fxcZcveUj4x0tHk1hB6MAiK5D4PoQnYgVHTCXRooxgURWT+szAjTNPyxHvWn4AhzecwPdHv8GCsYtxbs9lnc+wtOfvDk9DlQYFi/ZyuQw+wV5m23bCNsUKLWqbES0KJeE6Ojri3XffxYEDB3ikRZMmTRAeHs4FjHnz5uG7777j1ULYNFYlZP78+XzZ9957j3+WMC2sA7Vs6t8Gncivn7yF2xe0Dxb560HnFStyYPWh/5y2jo8c5fDzuD8Q+zCBfjZzihX5RAvytCAsnUP37yHUzR1u9k9uiOr72r9S84W4s0ohOaVNo+8l4dTOO2j+vLZCCSSeZkkJYVRvEsbzk09uP2eW9ROWTUZaJpZN+TtXrHjWPTb+USJ+++TPXLEiLxcPXsXDW9Fm3V7C+jwrWGSFjliRT7Sg9JDS5ebZOzpiRQ7sGvDX9PUIqKSf5pyVkY3fP1+FxMdU+YcoC7FCV7Qo7+khRYo5k8lk6NKlC2bMmIEdO3bwaIojR47wF/ufTZs5cya6du0KuVxuvq22cdhIz7Etp43O3/OXvjBx8dBVo8vfvXRfZxQxLSkdybFUH9oksJxUY2KFnmhhegNBgjAFLIru4P17qOPrp5sOAgUgMd/IkW+eCIsdyy/BzkGOBu1DtDPZ96qjiuWv9CwcnB14SD+LQiNsD1am+9g/xj1Mdq84qPM+IyUD53ZfMrr8kY0nTLp9hPUiqh9xzwqDYoWeaHGjNDfNZmH3kN1/HjA6n/W3a7eqYXDeqR3nkJ7HZ4kgTI2oji1ArMgvWpyBKCrL5Y9QoiQ55m3AKoOwV47PAWF+mMGdnYFqHTkYypN1KCBlg61PItENrKZyfiZC4l64ko/yRiWpMkwQZuVaXCwep6ehnt/Tc1lU3QOkvmYx3MxbKSQpS4WEDCX+W3YJ9dpVgMJe9lSwENMA0TziKvOxOLP7IpTZ5fPmTxiHndP2T0xfDcGqZuVfnpUzNYaDS/kN0yWKihyQ13/2YtIgQOJKh7cUYO3XMV+bzgvrbxu7D1A1PMLsCHLArhCpt6xamzQAAlu+HEKuPlaIu68rnn+nk9H5XYd00JtWu3V1g2XWGMxI6NLha7nv/Sv5ws2HbpSmQJAGQvBcAkifhLEbwuE1CM5jIEgNm6USRFlz8N5d2EmlqO7l/XQiK2kqMW81IJYSwtiz4xZiIlLQpGueXGGWEsIwU4ngGs2qIDM1k3sRELZ4j+1sdH63IR11l/dzxXOvtTG6fPv+rUy6fYT1Iki9ILiMB+z7GV+ImW56/EqG3KVIj7eM96l7vP0cDm88aXBepzfbcUNdgjAXgsQNgsPLgMsnxheSeHPDXkFmHhN0S8CiBIv9+/fjjTfeQIsWLVCnTh106tQJ06dPR0pKSoGfY+ae1atX13vdunUL5RGZXIbeI7uhooFySi9/2JvXgc8Pu6CO+22E3nRPf3f0+6AXdvy+l79nDsYT//rAaF1pwsSiBYkVhBWw7+4d1PLxheJJJB3Pk9Q8Klz0UAlNNxkHVl2Fd6AzKtby0h1NYLcwMwkWgVX8eXnTU+RjYXOweywrQ2r4HtuLi/p5cXRxxCufvGiwZOlrn/Xj91mCyCtaaBw/hEbxooGTj8SKsoCVLu039nm96ZXqhqD7sOdQuf6TVMQ8BFUNQP8PexcYwUwQpkCQuBoXLWxArIClxaAnJiaiXr16XIBgVUZu3LiBH374gf9dsmRJgZ9t1KgRJkyYoDONVTEprzDH4enbJyL85E3s+vMgXDyc+YhQYJgfL7WUH3ZBbfdyS9RoWgXbluxBzL3HaNu/JWo2r4r0pAxM2fA/uHg4wdHVCcqsLG4U5uhiDzdfNyizVEh6nMTrT7ORJ0//gsWM+EcJSIxJ5gZk7j5uXCyx9bA5JlrAcwnE+GGA+rZ2IokVhBWQkpWFkw8jMaheg6cTueGmaFbDTYaTQgp2Nbux8x469qumk34iCDKIgjs3rDVHUgqLSKvWOAwntp+l8qY2iE8Fdo/9/Mk99gCvjNVzeBe9e2xaUhriHyVx473P/vqAJcTjv6V7oVGL6DqkIx8ESE/J4C/mP1XY+yhRvslWOSNLHA43ewHIXK+dSGJFmcHa9Gufv4yOr7bFv7/u5F5und9oi2pNqvDSxAPG90H3oc9xf5vsLCUvTero4sCvE4xHd2KQHJeCrJx+r5+bXjW9opIUm4zE6CRuAuzi6cLXyb6TsF3RAg4vs54XkDLDpsQKixMs+vTpo/O+efPmUCgU+OKLLxAdHQ0/P+Phx66urmjQIE+H2gbwDvLir5a9WSkb4Zm55OxCV7FOCN6dOwRqlRrR9x5j9rCfcGH/ldxlWvRujLe+fh2T+szEaxP7ISE6ERu/3wZltorPD6rij0lrx3PVOf/3MeOiOxfv48uXZyPy5iM+Ta6Q4dVPX8QLo7rDzdu200x0RAtFC0oDIayCg/fvQi1q0MA/4OlE9V2WZM1vlubG90YKVOkqNOwYqj+TpVGZKcKCUb1pGP6avoGLsPSAaXuwBxXvoGZo2buJwXvs4wdx2L5kD1bN2JhbIYRFOE5c9QHSUzLxWY+v8frnL/Hltvy0Q+c+Onnd/1CxTrBZPWAIy+b23UTUr/shJExyVV2iNJAyxs3Lhb/YdV/UiLxSFEOpVPIyx9+8Nh+P7mrN0Znw+MonfdH97eeQ+CgJX78yT6ff2++Dnugzqkexy5lG3Y7G169+xwXTHAG985vt8Nb01+heZMMIeUWLtEU2I1ZYXEqIIVikRc4FgzAMu6gWtdPDOuDfDlmgI1bkuCH/MWkVPls5hg0UYc3sLbmdLAa7II/vOAUx92P11smmsXk5F23+u2WrsGzqGhzZfMosbv7WmR6ylMQKwmrYfec2Qtzc4OP0dLRIVN8DJD4QBPObLdudjoUkwBE+FfQjxyB4ABrzCRYswoJxeucFs30HYb332LO7L/Lyp3nLmbIHmgldp8HdxxXBNYL44MD67/7Vu49+1HGywfsoYXuRFszTQvBYRJ4VFgI3on8iVjAe3Y7BJ92m5YoVDNbmWdtnERATun6l1+9d/e0mHNl0EhqNpsjfHx+VgM97Tc8VKxhsPf8t3ccFdFZOlbBdhCfpIYLXJpsRKyxWsFCr1cjKysLly5exYMECPPfcc89M7zhx4gSPsKhbty73wTh50rBBDqElOS4Vl/MYbeaFXWRZ5MaG+f8anJ+SkIqrx/SN6Ng0Ns8Qy6f8zS/CBBMt/Mhgk7AKVBoN9t69jUb+gflm3DW74SYjO12J7DOPkVnFSHQWi/DQPDZbGS8XT2eep3x653mzrJ+wXtjDy4pp6wzOS09Ox4UDV/gI7IbvtxlcJiU+FddOFFDakrAtI06p+a+nRPE4/d8FntJlyPfi6rEbvC0bYtXMDXh093GRvy8mIg73r0UanPfvr7uQ8CixyOskyqFoIfWBLVHslJCDBw9i7dq1iIiIQHJyst7oOVMod+3aVax1d+zYkaeAMNq2bYs5c+YUuHzTpk15OknFihURExODxYsXY+jQoVi+fDkaNmyI4sL2KT09HSUhIyND56+lkBCdVOB+pyVnIDYy3ugyN87cQYs+jXPVYxaudv30E28GA7CQWJbbV5LjaanH0hqxpmPp6Gi83FhJMUUbL8+/y4mHkUjMzERdLx9kZmY+maqCQv0AallViNklH+lRKlU6f/NyddddiNkaxIc6IS0jCwppvlFujStkUCM74wFESZ6UFRNSuUEoN95MTU01WmmpPPzWpUFB+2yudm6uNs4iJx7eejqqmp/b5++hasNKiHto/D568+xtNO/d0OgorC2dI7a0r7a6v5Z0Ly/s8ZfL5bh57o7BeSzd4/6VB0Y/y/rQqmxVkbaLPTtF3dE+/xhCmaVEWkq6WfsttnZuFhY6LsYpjXOlWILFokWLuIjg5eXFTTJZRQ5T8uuvv/Kdv3nzJn7++WeMHDkSv//+O6RPHOrzM2bMGJ33HTp0QK9evfDTTz/ht99+K/Z2sDSUq1evwhTcvctyvi0HNx+PgmtSuzrAK9DTaGcrtE4F/vuwSBiGnZ0dKtbVd1TPmwuckZWBqKuGVWNrPpbWjDUcy8aNG5tt3aZs4+Xxd1lz+wbc5HJIkhJxP1krciqkjxDioUZ8ggzZ6qchsiUlPl7/WnNuy3U4BDhA7abA1QeP4Gevew+QStQIcAViYy4i1QTiiSFcghyQFJuCvZsPILC6X7n9rUsTQ/tsrnZurjbu4+zHR1hZrrkhKtULQXJ8Kq+4FWckujCktu591Bi2dI7Y0r7a2v6yZwZzUdx2/qzj7+vri7D6FQ3Oi30Qz83sjcGr7QlikbaL9b89A41XFWL+GCwTszT6LbZ0bhYFOi5lQ7EEi2XLlvHSo0xYYOqjqalRowb/y6IjWIoHi57YuXMnunfvXqjPMxW3ffv22LFjR4m2g+1blSpVSrQOJrywk5tFfzg4WI67b8rjNNRsUc1gakeLXo0R9zABfd/vgcWfrtCb7+TmiLptasLNTzen3MvRm89j7sr5ef2LlxFY0R8Bop/ZjqVMkgmZTI0spTNX2yUSFRRyFbKzFdCIFpn9VGZY6nlZ2piijZfX30Ujijh15gSaBgUjNPRph02qigCyBXh4V2PyRYm/h0VWMLHC09MTcvnTW5I6W42I43Go/FwFsMB5tYML/H2d9T4vZtnBx0sNL7kBU04TEBQQhF0LDyHpTho69a1ZLn/r0qIs9tlcbZxF2zBj6jlv/aw3z8HZHvXb18b8d39Fn9HdsWTiX3rLsAoCdVrV0LuP2uo5Yi37Kpelc6E0M1v3d5NL0yGVqpCZ7Vqu9tdaRmCL2s6Lcvwbd63P23RGak6UoRYWYVWrZTXellkFoPwM+LgPfEO94a3xLMKeAKlxaahQLRAPrj/Um9f9refgE+QFf4VuaWVTYmvnZlkeF7k0DVKp2uB1Q3tNYc80rhbvAZhzbCxOsGApIN26dTOLWJEfFr3Bvuf+fVZGr3ThkQYmCmFjJ7c5Q16fZbrJQk7zhjM7hjpiwrL3MWvoAh0vi6bdG3AX4r/nbEHz5xvhpQ964uSO89zNPDM1E1G3ojFx1TjuhJ7/O+1C7TBn31RMeXFWrjkRq2fff/wLaPNic5M1cEPHUtSkAJk7IcZ/B3vPFYA0CMg+BzF2FOw8fgfkdXgZROLZx9KWMGUbL2+/y6mHkXickY5WIaGwt39aZ16TFgkIXpAr9MWDksDECrniqQBy7/gDZKcpUa1FEA5GxyMuS6MzPwdR6Q2Z5DEkebbRpNgDYQ0q4fzeK3hjYv9y+VuXNqW5z+Zs44271OeixZpZm3NNNX2CvfH5Xx/wClusMladNjXw0oe9sOmH7VA9SXvyr+SLKev/Z/A+auh+bkvniCXvq6hJgpi2mpchdfD4A4JMG1UqahIgpi0DMrfDweN3CLKgcrG/1kRx23lhjr9fJTlmbP8c3w5dgArVAmDnoMCdixFo178FLzU6878v8NXAuby0aU6/t8/73dGmX3Pde2e+frgx2PZ8/e+n+GrAXNw8eyd3/zq+2hqvf/4yXNyNi5ymhM5N8x6Xp9eN/+DgsUTnuiGqEyFmrAWSV8PeY1nutcaWKdYTHIt6uHPHcE6XqTl//jwP9XqW6WZeWG7Xvn37+HaWZ1h+3I2zt7F35SE4ezih+7DneEfI1dMl1zeCuZD/98depKdmoG2/FqjaqDIPY2V4BXrg4z9GIT05A6lJafxzDi72OLfnEjJTMnD73F2u5jbr2QjbFu1GYNUADJn2CvwqPe1kxUTE8pDYncv289w6tg0zd37Blejs3HrU7rB3sivWPj5OS0NCZkbuxV7u54v7aamQZGijODzsHeDtoAEyt0JM/oJPE+Nfh+CxBGLyl4CYBjH+TQieyyGSaEEQhebfG+HwdHBANa98pUtZhRCp+UZ3cgjfcx9O3g7wDHWBe2IKYtKNpHxIPMxa2pRRrUllbnaWkZYJByczCSOE1eFTwQv9P+qNzm+0475QCns5HJwdcPnINT7y2q5/S2xasIOP0P585lskxSbzconuvq64ee4uv292G9IRfqHeSE1Mx8VDV3F08yn4hnij66D2cPd3K+tdtFjy9g2MwfoHeasblVys+BNIm699nzAE8PgDkLhATPsdSFv4ZPpQoIiiBWHZ2NnJ4VXBC58sfx//Ld2PlPgUDJ46gPenmUG9o5sjpm+biJSENGRlZMHD1w2OrqyylRdv848j4vDfsn08crlJ1/qo1742L21cEIFh/pi+fSISY5KRkZIBV1Zy1dcVzm6mOZ/La7s3ZZs3J1qxYgmQ9ov2fcIw4IlooRUrVgGpc5/MGwSQaFE8wWLKlCl45513UKdOHfTu3dtkP+Do0aP5OllUBVMlr127xg002fvOnTvzZT777DNs3LgRV65oy3GeOnWKe2p06dIFQUFB3HST+V08fvwY8+drbyzlESZGsLJHty/cy5225ef/MHBCXwz43wtcLFgzZ4tOpY99q46gcr1QTN3wMbwqePDIii96z+AjQyzX7sNF72LGm9/rOB7/NWMDxv06gnfG9q0+go3fb8XIOYPQ/a1OPPVjyWcrsXvFwdzl96w8hNqtq+OTZWOM5v0VBXZh6r5iqdH5x4e9AWTuyxUrOJo43vgFt28hJk8F1PdJtCCIIlYH+ed6OJoHBUOSZ7RXFDW8PUHRyqzHk40yX98bgeAGrHSqAHd7GaLTjOT5SzwB5Rmzbk/1JlWw6cftuHjgKpr1KL6RM1H+cHZ35i+pTIqJPadz36cfjn2DyX2/zS11uP/vI/zvkC9f4fnp/+s0FW9O7s/voWNbT+SRiWx5dl/PYc3szfzeW7kljawVp2/A2P76YJM8vOQXKzjqCC5aCG7zgIw81WLUd0m0KGc8uhONrYv34K9v1udOY/3hkBpB+HLzJzi77yLmD/9V5zNsYHDW7kk4vvUsfhi1KHf6gTVHuZAxc+ckBFfPV30rH2zAj72Iwrd7U7X50hQrOOo7T0SLRRAzd+eKFdp5kSRaFDbCwpAooVKp8PHHH3Pxwt/fXy/MiXUyN2/eXKQfkRl4bt26lXtjsA4rEyD69++Pt956C4onocBslJ2VPc3Bx8eHR2DMmzcPiYmJPFSHeV9MnTqVr688wtzJ//1lp45YkcPqmRt52FhWerbBsqTsM//88h96Du+C6a9/nxvG+sKo7lg6ebVeeSaNWoMF7y/BhOVjcH7fZT5t4UfL0LxnYx7+llesyOHy4XAc3nSSp5OYkxGNasIDeyEmT9KfyUSLpI9JtCCIYnA04j7iMtLROjgkX7uKBsRMs5c0jb2dhISIFDQeqDV09rCX415ShvHSpmIqRE0yL/VlDpgbvIe/O07tOEeCBaFHemomVn+7ieecj/p+GP6evTlXrMjLH5NWYcHJmVDYK7B86hpM2/IpFA5yLP9yjY5YkcN3I37Fz+e/pSNehhgUK/KKFknjILhNh5j0KS+xrJ1OokV5gpnn5hUrcmClR9d/9w9eHPu83jwWecyil1n/OT+srS/65E98+NtInjZG2A4GxQod0eJtCK4TIQoeLLQiz7xImxctCiVYuLu7G5wWGmpak7Phw4fzV0HMmDGDv3Jg28CiMGyJhJgk/PvrTqPz4x7E48jmU0bn//fHPrTs05SHquUQUrMCfv9c3xiMkZmehfSkdB1DTXah3rl0n9Hv2PrbLrR5sRn8Qs1TJ3hEw5p4p9ZjSFK/Nr6QjmgxRdu5oPQQgngmG65dQaCLCyq556smpHoikkoKDmctKTcOPIBULkFALa2rvIe9DKceKaHWsMog+RaWPHGeZ2khZhIsmABfrVFlnNxxzizrJ6ybxEeJ2P3nAf5/7VY18Ov/lhtd9ujmk+j8Zjt+j2SpI8zf6ZNu0wwuywZuLh24hsAw8wqERDHEihxYvyJ5KgS3GRCTJgCa2CfTSbQoL+xbddjoPJbW9eIYfcGiea/GOLfvstGSxcf+OY3kuBQSLGyIAsWKvKJF8tcQ3GdBTPwfiRZFFSyWLzd+8yVKH5YDm9+tWK8edYrxGs3ZmUqosrSRFTloVE+jVgyRmZ4Jud1Tk1XmV1HQNmSmZfLoDHPRsoIvHNUbnr2gJh5gpRellXjHAsiCmH0GgqwyIJCyTRD5ScnKwvabN/BC9Rr6hoDMv0JwgyAxr3P4zYMP4FfDEzI7aW6EhVojIj4zGz6OCn0PCwhawUJu2hLbeanWNAzHt55BzP3H8A0xjxBLWCfsnpuVofVYESTa+6Mx0lMycj2dMtOyIEgkBd4rWf46UUaI2UD2/mcvp34EiOmANPCpYMGnP+RRF6IY+ExjdMJySUs23gZZNDO7/eTHwcmOR1gYg7V5jcp8fWTCAhGVQNahZy+njtQuK/UHVPlKYqtjAM2jJ9cU3TLv5Z1i1XpkHhIPHjwwOj8yMpIvQ5gHVkKJqbdG53s4o+1LxmtDN+xcB54BHtzJOAcmPvCa0QZgN1r/Sn5Ievw0IiOoWiDa9Gth9DuYUae7n/ly79759yDOpI+EqOhQwFICBNepELMPAtna0S84j4fg8FKRQsc1Gl1xhyDKM1tvXkeWWoW2IQY8aFR3zW64qcxS4e7xKATWeWr26WmvvVZFp+kbb/LqP4I7RI15jTeZwZogEXDqvwtm/R7C+mBm1Q2eq8P/Z0bXddsaL3/brEcjHN5wgv9fu1V13D5/t0C/p7odall8SbvyiiD1geD+AyBvUMBScm0UZ/pfgPLJtYGFc8ubQPBcCshrGxQrtFUtqGSkNdCqT1Oj8xp3rcf9a/JzZucFNOhQ2+jnWJt3cKXfvzzBPb4KQJD6QvD4GZAZPy9YqXjBfTbEtMWA6mq+eXIIHosBeX0uVmg0BQ80lzeKJVh8+umnOHv2bIGVPdgyhHlg9aAHTxkAe0f9yhtVG1WCf0UfVGlY0WAniH1m0OQB3KV84IQ+udO3LPwPb04ZYPD7ug3tiBPbzuR2mho+Vwde/u5o1LmuQadjFw9n9BvzvFnd9JUaDYZuOYBMxymAomMBYsUxXkHkqVgxEIK08EKKqLwJQXUBGo0Rwz+CKGf8ffki6vn5wyt/6WDW/lmEhZn9KyJOx0CVqdYRLFzt5JAIwCMDggVH6gWo9GvWmxJHFweE1AziPhYEkRdPfw+8M+MNyBUyLPnsL7w94w2dAYEc6ratwUsiRt97jHrtayH+USI2zN+KIV8OhEQv1wk8ddPe1fzl4wnjCFK/AkQLOQT3uRAz1gDZR3JNgNkDh+A8GqK0KgTB8O9nJ4tBWEgqJAKNsls6zByzRvMqetNZ1PHQaa9iz1/6Xm7pKZlw93VD856N9Oaxtj5y3mD4BuerwEVYLSIbzFFegigWPMApSP0LEC0UEDx/h5h5FFCeMixWKBpBEBQQVQ8gZB+ARp0vAqMcU6wqIc9S+1lZUanUtkJVSpugqgFYcGomlk/9G8f+OQNHF3v0GtmVlxX1CvTky0xeP57nye74fR+PoGjavQHenPQyKlQL5BfaXiO68BrxyXGpUNjJudr7/ZGv8cv4Zbz2M6sn/8onfXlH/dvBP3JnY5ar99xrbXmpUg8/4JttE7FpwXbs/vMgD4NlHazXPn2Rb5+5YaJFZJoCVd2nQUz6HMje+2SOAMFtJsSsfXnEio+KJVbw0mViCi+TqpHVgURSvPKsBGENXI+LxdlHURjTzECEFjOAEpPNLljcPvoQ9q4KeIY8rTUvFQB3e7nBCIvcEU2e8mVeqjUOw5FNJ7nxsaFRNcJ2CalVgRtqXj99i4d7Lzw3C4fWHcPfszbD0dUBPYd3Rqu+zfDNq/Mx7JvX0KJnI0TciMKIOYNQpXFlLDgxA4s/XYFLh67BzccVL3/UG61fbIaI6HsIgHk9Y4hnixZw/wFi4vuA8lyeB4glEGUVAc0vT8UKt1kQk78C1A8guP8EjdAEEomzzvpE1X0gcQhkLLzb4w+IYgOjwgZR9rASo5+t+ACXj4bzvjITHFgZ4upNq8AzyAPdh3aCnb0d1n/3LzeuZ9FWTMjwCfHC+z++jXrtj2Lzgu08Srlmq2oY+tWrCK6hrRDCiggkPEri9xQ7RwVVBbFSsUKMfwNgxt+eyyHyqCpZgaIFPH6GmPAuoLqcR6xYBsjrQnAJgagOz1P9TF+sEBPHAqqLEFxnQGPXERKp4Qh5mxQsWIlR9sqBlRPNW60jh+TkZKxatQqVKlUy3VYSerDOMiup9OFv7yI1MY2HKnv4uekIRQGV/DBoygD0eLszU5l4qoir59MbJxsBir77GH9+tRaxkfHwr+SLQZP7Y9DUAYiLTOB5e8zNvGaLalhybT4k7Dv83XUqwrAL+bCvX0WfUd35ezdvFziZsE40q6nMyhQxmHlRdlYWFHZ2udvA5gtSJ8AtR7TYB9FtPiDxAYQnDzxOwyFKKyM5KwMxGcpC1WnOFSs0Mdr3rEwqiRZEOWfVpQtws7NHk0AD5dZKyXCTCRb+Nb34NS0vHnYFlTb15jd3UVSatePPOqjMZC385E3Uamk+vwzC+mBR/1mZ2di2aDcuHwmHvZM9Fyl+Pv0tj2V18XRCSmwaJv41Fht+2Ibh9cfzwYAXRnVDjWZVUaVhJXy+ehwfmWUPRJ7+7sjIyID4iNJBntU3yIuaVZJ7MqimkEq5CJv/c8Upe6grWlzm/QEoGkLCHiA8foKY+CkE52FPxIq7/DNi4nta0ULxVLRgYoWYMFibp85g/QzPpRB5mDeJFpaKVC7F9ZO3ePtmRvR12tZA5bohcHBQQMGiij/oiY6vtuFiJRMonVy1EYrMeP7lD3uhbb/mfLCXRUizAT9GXFQCdizZg7Xz/uFCR6W6IRg5ZzCqN6uS+3lCFye5AptfeaPA+WUjVjx5Vsg19i+KaHHjqVjBrgH8WjNfK0ooLxoVKxhi8ic2I1oUWrDYtWsXfvzxx9zcu9WrV/OXIVxdXTFz5kzTbSVhFHbxYy9jyBVyBFbWHxFlERd/z96ENbO35E5jZUq/HbIAr3/+Ei9/evRJpZEug9rj3XlDeKqHIewc7LhwYQ5YxyKnc8Eid67ej0CVmjXhmC9cneWactFCdQ/KrMtQpI+B4DIBcF8AkTX4pFGQyAbhbFR7NAysWWCHJb9YoZ2YQaIFUa5JVyqx7upldKxYGTKJgegBbrjpAEjM502TlZqNyAuP0WJQLb15BUZY8Eoham2blQaZbfvYqBjrjJ7cfo4EC0KHu5ciMLbVxFwDTWY8vW7ePzi/9xKm/fsZXNxdcPfiA3zUYXJulCoz4Fw1YyMu7L+KyevGc5HClIJ/eSZv3yAvTKDouWKp0c8xkaM4gkVe0QLqaEDOTIkVTx8+3CZDTBieK1ZoUemIFoImXles4Cghxg/mfhckWlgmcQ/j8XnP6bhz6X7utEsHr2FMq4n44dg33N+IDaIZ84Fj8wLy9cNZlb75I3/B0S2nc6fduXgfE7p+hakbPi7QN8OWSVNm44VVfxqdb0jELC2xQktmkUULqB8/uZ7I811r5msNfeU1DYoVudthI6JFoT0sBgwYgLVr12LNmjX8ZjtmzBj+Pu9r3bp12Lp1K44cOYKOHQ35ChCWQkJ0ItbN+9fgvHVz/0HXQU/NLNmIYmJ0EiwfCTTZx6FI15Y6FVNmao1r0hby986qZegRsB++DqqiiRX5RQvVJfK0IModW8KvIjU7G50qhRmcz8RAlg5iTrP7+6ejIarF3HKmeWGlTaPTs1mwmD4SbRocrxRiRlgEGxsJP7nduIcTYXskx6fg5w+XGqz2cfPcXdy/+oA/oCwYs8RgSu2Vo+GIuh1dSltLlNjTQlEvV6zIfXDREyvyiRbZpyBmbM8nVuiKFlCe51FihGXBBvDyihU5sDSO3z5ejpSE1CKvMy4yXkesyMuCsUu4SEJYJmzQ3rBYoStasEisQnlaKJ5EVhi81tQvUKzQES2y9pZrT4tCCxa+vr6oU6cO6tati2XLlmHgwIH8fd5X7dq1UblyZchkxbLGIEqRuIcJRkupsXA3jUa3U3X3ivnzw0tc3zj1B0jy10vPzQFDrmjhrFoNUZOsvw52QTAmVuQXLdS3TbbtBFHWsIeopefPoqF/oPHRR9YZN7N/xd2Tj+DgbgfXAP1t8LSXI1utQWKmgQ6A4AwI9toygmamRrMquH7qNpLjUsz+XYR1kJmaicuHn6bM5ufollNQKdW4dd7QA62W0zvPm2nrCHMiqh8VIFbkEy0U9QC7vkaWeSJaqMLNtKVEcTmy+aTReef2XuYRy0Ul/NQto/NYpaGCSqkSZQeLlrGTJxQgVuQTLVS3TXONSfrAqFihI1ooj0OjKfr5WG6rhDRr1gxeXvojYIT1YGegwkheZHKpXilVi0ZwgeDAqp48wxRTcIJo11n7gKOHDLArRGQQcwsXCl8WlSAsnZMPI3EtLhZdwvSd0BmiJg3QPDa/YHE8Cn7VPQ2WAWQRFozodH0fC7644AVR8wjmpnqTKlzgOb2TypsST84/iaTA1EyWs87OaWZ2bQxWUYCwPkRRAti/8OwFZVW1qWvKJ9VEDCGvq/XfIiwKD1+t54QhnNwcub9bUXH1emoqnR92rcjfBycsA+alp9HIAfsez15Y0RiQGD93CovIDDkdBj57QWkwIKsOicR8FRrLkkKFQgwaNKjIK2YNbulS43mERNni6e/G8+2Y6U9+QmpWwKO7T5VDR1dHBFUxf9WPksDyxER5Xajdf4c0cSjLhtdfSOKLbLc/EZfpjiA7rVYnquMBiQsPxxJk/oDTKPDYkgzD/ixQtILgOg2C7GmevKhOgFCEvDH+nYIjhHJ6USGsj2XnzyLQxQV1fY0IEsy/gsHyKs2EMlOFhxdj0eS1GgbnMw8L1i1kPhY1vJwMp4WUQoQFq+AQEOaHkzvOouMrrc3+fYTl4+7nxqturZnz1BMqL236NoOzuyO6DmqPf3/bZXDUrnGX+qWwpURB8PBtMRlCTopZ7nQloEmBINWdzpDIfKFxeFISPu17wyuW1YTg/iPE7EsQHAdBTJ0N2HWFIK8LMXWOdhl5Iwju87W564RF0X5AKyz/co3BeS+8161YYmNYg4pcwGTV9fLTolcjfp8hzIOoTgQEGYR81XuehVSSicAAZ2SrnODg9K72WSHdyHOuojWvVihIfQtcp0adBEFMgyAzYHT+BInUExq7TnycVEyeaGTj/s/eWYA3db1h/D3xtGmTOlbch+sYDJjC3I0NGRtzd2UwN6bMfYwNtv3ng/nGjAkOw11KKfWmbRo7/+c7t0mTNknTkLRpe37PE0pzb+69SXPvPfe93/e+2WApr4NpWm7gRVgVFnQ3qfZj//79+Oeff7BhwwZYrVbxoBQRei43N7fe6FNJ00LRp7M/u004mfuSlGrC5U9MFmZhBB1Q7//sNqS2O3SVsDFEi1xbFxzUv1i30kKVCaflXWiss5FhUHqFuSsfvOwRv75REi1Y4tVAIDWTxIqkmaL1hF4rluHcAl5ysyjZCles4Na5gP0v8BZatiVpXuSUleLbbVtwXNfuUAUzqBCChbba3DJG27G2AC6HG1k9614UEFoVQ7Jeg9ygSSFpoj+8Mc49lBby7+JV4m6LRKLVakRKQI8hdQeLN75yOdLap4rErQvvPRvdB3Wuc3PntnevQVrb+D/HtnixwrEOvGQ2OJngeZ93KGOEsoe95/1AogVItEi8LqhYQUlGdJOCu/aAmV8A048Dd+0GS7pLihVxTkZ2Kq6de0md5ynN45QrJ4jEvYaS1i4Fsz65pU48NqWKXPnMxTIlJIZiBa94H6j6TqkcDfd1biu0rh+RnvgTtOoKIV6yxCuBhKmHJlZQupn1GXBnIG8bf9GCC9FC8ehrbWIFEdZeNm/ePL/fKdL0qquuwgMPPIAzzjjD61nhdDrxySef4Mknn8QjjzwSmy1uRRTnlaAwt1iY71CcaGqbFOEi7qGirALFeaXI2XYAhgSdGBSR5Ld3k3KXke4C0usCxSPRIKn7kC54acXjWP3Lf9i5bjd6DuuGvof3xIE9BzHmjJHCEZ/u+qR3SGs0X5KD5eUosgXu3aOLA0uguEUfKl0ct/+yB08f/SIyqq5SKi2qxQpWcjNUrv+gKp4Cd8o74JUfg9k+B7ct8nPoDlhp4RErCs6i2yjgvAJIugW8YCrA8+EuuhKOpOext9wId4ALJopSSzdUgVufAyrfB698H8zyMrjucFlpIWlS5q1ZBb1Gg7Ed/S+k6hpuZoKxiLoIw2LPyjxojRqkdAxeKptSX1IIrwR4CcBie/HXe3h3/LLgD2xbtVM4xEtaF+Wlyrl3//ZccY6l8+OBXQdx5/zrcWBXPv5ZtALmzGSMPm2EuNghv6i9m3Owf0cern5uuqha/PfbVaKUfNSpw5DeLrXOzQNJZGOFKmeNx82Tx00Ux7ba0z1Rp56IU69YUUjVxDbwkirA/IBSzk1iReE0AGT4aweSZ4Kp04NUWpzjX2khxIrnldhl+9/gxVfSSAYcDIxaRCo/AjeeC2Z+SlZWxDEJSQkYd94R6HdkH/z5+b8ozS/DyJOGol33LKTTuDsCKMFv0FH98eb6Z/DP4pXI3XEAA4/qJ0ydMzq0/Jb7UGN9iiOudDigVtUdb9hdrojXp0Ypkl2fQFXxrBjfu5I4itxjUGxXh4w7JrECtsVA2d0Q8pJKBa45T6m4ql1p0VCxovhqxeMGTsB0s1/1dliVFurWIVYQEV2FPv744zjzzDNxzjnn+C9MoxFpItu3b8ejjz4qEkUkkUGmOw+e9xQ2/L3F+1znfh1FtQPFI5UcLBHlpxRL6rnLR710171wKb575xfRX013dK54agrGnHk4UgKUrOXtysfss54Ed7tFxQVF9SWnmXDPgpsw5OkBTfKnowPYxBCRZJ+dWV16GQQ66MwafzzsjCntIWW3g1vegLr4JjDXf8pMvAgomgqV5Qlw+++Aa3udWDE/0YLuiPiIFYKqb8FRBZZ8G3jJnWDO/6AuuQZV7vtxwWdLRNqCB7pnvXLGJHDrK0KsUHCDF18hRQtJk0KDggXr1mJcpy4waoP31zeG4eaeFXnI6G4J2Q9MPha5QQWL6osIaguJQt9oKDr3y4Y+QScGmlKwaH03EhY89hk+fXYRrn7uYvz60VJ88+bPuO7FS/HrR3/in8WrxPfD9k8VPnt2EWZ+fAvW/bEBb929wFv9Y8lIxgNf3iG+O7XvsEoObaxAIsXzJ5ws/p+dnIzTF3rOuYEjENPJz8tHrBDYfwK3dgczjBUm2yRWCKq+oY6RgKIF51Vgri3g2j5A4rVA1Q9g5ifBbUvBNBnCdJPO+4LKBeCGM8CS7gUvewAcBsB0VcCWE0nTU1Zcjm0rd+LhSc+ifY82wgPul4V/YvTpw3HebaejTZfQF6fB0Bm0aNe9DU6/Ngw/hBZGqLE+7b/XLlaqvAPt31+cf5EQNYJdAwSCxApV5QKonC/VPFd2JzS6+/DhOgvO7TcyoGDhESv8WjHK54BTNSqJjb6ihXNrRGKFwPaVsoyGiBblL7casYKI6HbZpk2bkJ2dHXR6hw4dsHnz5kPZrlZNWaEVT17ygp9YQVAVxOyzn0RJQZkQJBY+/rlfSXJ5SQWeuPhFnHn9SaKCwm6z47mrXse+LfsDDrpIrKBl7lq/Fyt+WCPm2/DXFjw14+WIYpriATrg9ExLR7YlDRr9ICDlXbiLbwRzrfOfkReBF98KlnwXoO4aMFbM0x5SR6zwUPWLUp1hpmoiNdTu9eipmokPTh8Hk646nx3AM8cfgYSql33ECviJFrI9RNJUfL5pA0qrbDg+iNkmIe4qUlxoDHuruZtj76qDyOwR2gvGU2ERONqURAp1zKNNCSoBpotNEiwkrYu/vl4uWiZ7DO0qnPwXv/ET+hzeAyX5Zfj27V9EZPjKH9diw1+bUXywFHdOfBBd+nXya1Wi52895n4c3FPQpO+lJXLL99+Iix16OOtp2TKoWV2xgtCNANP19RcrPAjR4n6/9hASK2D/V0kLoSoKbV/hR8Gtb4CpNP5ihQfbp+COdUK0QOW74NYXFX8rSdxRtL8I95zyqIgmXr90s9i/aT//6pXv8ffXgaNJJbHbv0msoHF+oEdA0cFVLCorkn3ECg8W+2xc1b8YFp0rPLHCg/VJ8MoPxbI97SHM/HhkYoUHEi2sc8JvD0n9oNWIFRELFhRxumjRItECUht6jqbRPJLIKD5YgpU/1rrAroZKkKkc7b0HPg443elwYs1vG3DY6F7e5z6e86VQiH0pOlASNGKN4tWo3LXZ484HL74Wak9lRW3qES0Uo6194AXkAB6k1y2EaJGk0wmxYnz6l1DZPgi2kVK0kDQJdAH11qoVGNq2PbISQ5hPuSjS2AWo28RsW0r2lMNWYkdmz9CVEakGDSqdLpTa6557GFMDLBW8EYw3id4jeoiL0uYq7koaTuH+Isx/4H/i/xMuPgpfvvSd+P/x047Cly9+G/A1VZV27Fi7C10HdPJ73lZuw7rfN8g/QxMxsl0btDXsCixWGCeBl9xWV6wIIFr4iRWeC5DiK8HLngDTD6++2AkinEjRotmIlIHMMYkPn/wi4E1BSXx5VlAbSDBItEhR/e7naRFSrAgmWqgzIhcrIhAtWD3iSEsjIsHi0ksvxfLly0X7B7V9/P333+Lx4YcfiudWrlyJSy6pa1AjCY+KstBmjC6nC7k7a0yhanNgZ56IUfOQuyMPtlrLrG+QXVnWvDOgubsCvOIDMOe6emYsEiaYLOnW6icc4MU3ANwqHuL/wQYtHqp+AWjQUh2JSqJFN8M3uGfMKBzZzoFEZ2BxqQY3eMlN9Kk34B1KJIfG0r17sKWwABNCVFcIyL+CThUxjNs7sJ5cu4GMbqEFixSj0rYS1MeC0npcoU/00aL3yO6iMmT5d6sbZX2SpofOveRVQZjTk3Fwj3KXPcmSGDBxywN5V6Rk1W3L3LWxcb6rkro8fsxoqEvpvOs/NiITTGHGXe95/xvA/g/AK8CLb6x1AaIGS7oJvIwM8uox5rV9CqjbAtrBotICrh3yzxVn7NmYE7J9W0YMxDE8HygPLlZ4UJfNVMb93tdZwUtn1b98SvmhNvMwYORVUXJ7cLHCg+0rwLkJ7gj9OloqEQkW5513njDcPHDgAO69915MmzZNPGbOnCkSQmbPno3zzz8/+lvbSqDBD8WcBUOr16BLv+AtOeR1sX+7koThiU9KtPj3dYWKYVKpVTBZApvPNBeYKgEsYTK4fkKtCbUM/dSdwUzX1KiozAKW+pbyU5UClvomwOqJLE2YArgLRc8qYVMfg3+LjsZdP/+Kd/6rRIn2lno2NgEs9V2IpjSJpBGjTLOTzeibEVql58K/Il2k8MSKvPUlsLQzQZegDV1hoVem51qDJYWkN0q0KWHJMKNdtzayLaQVQalZ5E9BHNiVJyLAiYP7CtChZ3BD6C79OoobB7XpPaIesVASM6Z88SOc5teUOGQfeMk9YOaHQp/3mQlIvArQHwEws/C/Es8xz7jJBV58mygRp/N7KJjpOnD734BjJVjy/YCmZzTeniSK9BnZI+i0zodlh/RdkjQxqnZg5meqG7Q9kMGx71hDC5fldUDls8+rUsFSXqvH6pFVm+qG1y7LkSw8J8SxIhSJ14Fr+kAVxKejtRKx5TsZbi5ZsgQLFizAnDlzxIP+/+uvv4oqC8mhZbofO3lswGkjThws7uxc8siFAaeT0NBtYGdsXamo9GToRXFr5HLst47MZIw4YXDAZRw3eSxS2jQ8VzreIFMsm/FOISAItEPBUucDCRfViBXJ9yilnyQ4kEiRNh9QdxceIMpCUqvFhCCDl4RLwRIuBncqAhGta7n1Ekz/6le4OMez/67Du5s7wW26O4RY8Z7iJk4l7RJJI0WZ/rBjG47r2q3mux4MZ+wNN6nCIq1b/cccrZrBrAthvMkoKaQQ3N041Up0wUmJEDLetHVAQv+ljyrn3sWv/4izbzq55v83nxL4NRnJyMhOx76t/tHXaW1T0FMmzDQZu0qKsa8iTRkTVIsWzEQtohRd/i6Y5YmA533ynBC944nTqZQTcOcCmt5gaZ8oFziqauHKuRbc+pIw3gwmWgixwl0iEgaEWGE4CUwVPCVJ0jQMGNcXyWmB/y5TZp8nhGtJ/N68hH4cXElPKaIFI9HgeTDLHOr/EmJFvv555FX1AGO6mtfR/3XDFIEhoGhRLVboxoh1UBQyr8c/S6XWgqv7gKW8HVy0oGhk41lQadoKT5vGanFtDhxSRh2lggwaNAgnnniieND/Gyv+siWTkGTE9Icn4YRLj/E6iFPFxfjzjhCZ7iRKUATp7fOu9TuIUmXFXR/cgDfvVswdMzum49Hv7kVmp3RUWmsG8LYKG9xujhteuRzjzj3CW81B6zpxxjG4+KFJMJoCO+02N/aV67DMegns+kvBEqeBF5wDpsoEEq+pV6zgrjwxnZc9WX0HpdbgJWEGmH4seP5EMONEOBJm+okVHki0KMYExVzLFylWSJqID9atgV6twZiO/n31teHcBbj2xtS/wl7hQNGOMmSEIVh42kJyy4NUWHic+92N01Pce6Ritrh52bZGWZ+k6elzeE/c8uZVwudp98Z9uPSxi1CQUySqGulGgm91IkUUPv7DfUhOTcTo00fgtKsn4rgp4zD4mP548udZQsiQNB0OtxtM002IFizpHnDXXuFFwUxTwJ25dW5WiFQPxybwomkAjQ8q3gEvOFe0ofHy10RriKjO8BUt3GVgKe/WES2kWBG/VNnsfmNmqp56/IeZ4magB9rPr517CboPaT2mh80VEhQK3MNRpH9CSe4pexy8nETJOcjXv4A7fz2I8gBdGsFFiwBiRcld4IUXh+E9EUK0SLzeX6wgI96CM8GdcnxBhKUu/Pvvv+Ln8OHD/X6vD8/8koZDd1+uenoaLrjjDJH+QSIGVV7QT8/BcvDR/fHwortQlFcCnV6LtHYpMCTqcfPrV9KehuRUE3J35uHJaS8IX4wL7j4TeoMOnz+/CAW5xRhy7ABc8sgkXPzA+ai02pCQbERqG0uLy4N/ZcVmjDxxHHjxpXQqArc+BSTdhyqHDboAYsW+0lKYdWUwVswEs/8mluEuo2KLdwAy5aR+tYTLwfRjwIsmi+m8aAZ0Ka8jLSETz0w8SWS9k5uxh8IqDdJSThNFaRRjJsUKSVNBOeYUZUpihUETugVDaa+wx7TCInc9VUQAaV3DEywsBg32B402TavZbg0Z6caWTod1EMfNv79eIUw4JS2fpBSTqIAk0aG0oAwJJiNGnzYc1uJymMyJOPKskagss4GpmBCz5t2vxLufOONYFOwrxLLvV2HyvWcHvWMrCc7B8nIRh+g5jnniS33pZLaI6glCo1LVmcf33EzL2FyQjzYJiTDR3dFKxRybF98CljpPtGewtPfACy5SWkcdmwDbh8o8RZNFhCE0XcCtTyt956Kl5A4w86PgJXcDiZPBDEeLCxNaHi+cLDwvhE+GK09UViBptqysiCPD++2rd+GzuYths9pw1AVjMPT4gcjMThceNPd+eBMqyirhsDuRlJIoxuG+fnGS8KGkj0D7L9E3PQOfn3chHO7AHg6GCG6MWx1qJKp7wm29BcypJDC6y9Uocd+JP/auCvo6Ei14tWjBi+gawgWYn6sjVsC+RMzPiy4BUt6oJ5pUCzfvAGZ5UUkQIu+MxCsAw8l+YoXwtKFlFl4IkKiq6YbWTFh/9cmTJ4sLudWrV0On03l/D+U+T9M3bJAO2IcCCQdtuwYWD8j465ajZglBwpcJ08ZjxmPKRfSrt83Dd+/84n1+zS//Yf6DisM5sWbJepEgQnd66E5QPEAZypSNHggqu9Y6G2ZCk2HU4+2TekJdfLEQK7yUzYY2aRa4+WVA3QHMp7KCxAqt9R4w5x/e2Zl9iRAt3Ja3oHb8CqYd5BUrFKrEwayX5XX8b2MRzugz2O990PtiqkRw42mKF4C2v2wDkTQJP2zfhoLKChzbJYyTn2tXdeVC7ASLnHUFUOtUsLQPzzcnzaDFxvxyEW1a+zQkBhfMLMooG6OrWE3xasO6iWi7qbPPa4Q1SuIB+rvTRQw9AkGVF7cePQuFucqFM/Hb//7CSZcdi6xOmbhp/H04//bTce5tpwkBRBIeJFZMnP9OyHnoQociTYPx8Tnney+USKxwOAtxQtslgFO5OBDwYiEueG9kpH0Ibn3NK1YI6GYHXaw4/vWKFcrzBxTRIuUlQN0RTKV4U3FNX6X9k2JU9ROFmbEL2eCaCdDJNpC4ECteueVd/DDvV+9zK39ah7Zds/DYd/fi/Yc/wTdv/uT3mowOaXjs+5nI7hXcv0YSmEqHI+h+SvtnqH2Y9nE0sGu9U1IlVCUkVmz2Psccf6Or9lGsuPQRlDuN9YoWsLwGp6MEnI2EPoBYIXBtr1e04O4CwPoEuHM9mHkO4PxPSRsqugg89R3wiq+8YoXAXShFi3AFi3ffVT44Eit8f5c0DQ67A588+3UdsYKgHPhTrpwAl8vtFSvoQnzsOUfgnpPJ+dofuiv04g1v4a73bxBVHU0NZSgHylEmKioqhAjW1hzekYpzJyyaraJMy0+sqIaVzQJLng2uHubXBiIqK3zECg8q+xKo9OMATX+giKLQalMFVnwp7hr5OvIdamQl1R3M0uCFG04BmFF6VkiahA/WrUbP1DRkh7EfcfKvIB8Xpo/Z9uxbkw9zhwRh9hsOqUYtbC43im1OpBg1gassGikpxGPI9sEjn4qUiHg4hkqaluKDpXjnvoV+YoWHr1/9Afd/djs+e24RFjz2GY658EgpWESZ+jx59lut3ouhR44aIcQKk69Y4StaFFyoVEZUfu8vVlBJePIscPvvgO3ruq8l0aLoSqVKo1qwII8qMtIj7yyVygSbXYu8Ih3SMwyik17StFA0qa9Y4aGitFLsy7XFCuLg3gIsfOxTXD5nqtyPG4g6RLBAfdTru1ULuoGhLrkC8BErvMty/AWD9U4YLU9ST0aIdepQxfsj5+BetGmjCyxWhCFakFjBS58AbJ8ov1N1lnYQULlA+b1wKpj5YXBbW//WVrcULcL6xowYMUI8av9e30MSG0oOluH7dwLsJNVsWrYV3771s/f39j3aYvuaXaLyJRBUaUHCRUuCxAo41lZnrFcFn6/0PrCqRaLPVPGsuNPbBlIH4wVgVLolqjWCoYgWmbptcAcx/mMqkxQrJE0CtTv9uWc3xncOs12CEkJiWF1B5KwlwSL8VKJUgyJSBPWxaGTBgow3aQD17+KVjbZOSfxiLbLij0//CTp9za//oVd1OsjSL5c14pZJfHl4/PDgYkXtSgv9AEBdU4XKku8Dp0jTQGKFr2hROFkRfT2vY2px/heTOcPB/Ar5R4kTFr9RV5AgJk4/Cks+/DPo635Z+CdK88tiuGWSQ4HECvKlCSRWeHH8JdrARJtWCNxcg6KiSui1pcHFilqiha+nBXf5ixUC50avWFFToXUXmPnBuq24bkW0aK2eFhFJXL/99hvKy1vWBW6zgnM4HcFzfF1ONxxVDu/vdOcy1Pxika56ssKbG9wBuCkb2VH/rM7d1bnI5Ph9MOh8TJ1ZrwuwggOcykVpGySSOOLjDetE/+fhHZQ4xlBwMpaglpAYGm5WFNtQvNcKS8fwBYsUg1a0ewQXLNLFfswbaf9LNCeiU98O+Our5Y2yPkl8Q/cF3CHOp06Hy1tN5KgKfV6WxI6sRCM0PIzzOa8EuA3wbdug+MMQY4Wa11rF64PdLJLEB/T38R0z+0Jm9E67M+T+LIn3a4GS+udz51dfB4RT3UHXCnlhrLtU8QDzQibm4RxzyumizCcm2XcaHY8qFEP0VkZEgsWMGTNEBcWZZ56Jhx9+GN999x0KCwujv3WSgCSlmXDkWYcH/XR6DO4inMh9S916DA5+R7Xn0K5I9HE2jxc49zfWUzE3EhIMIefxwFRGQDcKzPJC6K950sNgpsvAVClg6jZgKa+Aa3rXTFd3A7SKeSy3PquY3iRcEmKrVeDJT6HEPRQqdXLAbQy2zQ2h9sVYNJYpadm4OcfH6//DyPbZ9ZttihfkKSfHGBpu5qyjQQJgzg7/+KNRMSFa5FiDGW+mVw8M/GMkY0mfw3tg+Q9rYA8y6JW0HhKSDRh6/ICg0weM7Ysty7eL/x9+yrBG3LLWTbJejzN6Kca4Zr0BX23bj3+tF8OmPq5mJkoE0U9Q/q/uDiTeBpb6FnjVSsCxxjsbL74VLOFCQBeikpglKpGpml7gdByt51wd6TmchOXGEmdbKnQRevzU8QGn/fzBHxhz5sigrx11ylAkmgPH1kpiSzj7DNN0UrxjfMcxhvMB3fE1v5NPTerrYOrQXiSkVSQm6FHlsIClvCx86IKiSlfShzRdvNtKNz1FXLJ2lM98bQG9z3ePDHrNc5RwAJdynqiZZlTei6ZPq6zSjkiw+PDDD3HTTTehTZs2+Pzzz3Hddddh9OjROOGEE3Dvvffis88+w969exu83CVLluCiiy7C4Ycfjn79+uGYY47BI488grKy+sutPvroI0yYMAH9+/fHqaeeip9/rmmJaGnojXpMuvvMgD1z5Fzernsbccdv0FGHiedcThdWL/lPGG/WRqvT4KpnLxbmQfEEp3aKqj/BnTuU37kTOtU2dOtYCRVT7laIaVV/KPMGFS2OCC5aJM8BU+kV8yzPa9RtYU96AW51L0DTAyz5DrCEScIRGKoMODQjAeO5gHFKULFi4ZZkHLQp6xMZ65VfeSszat7X9sg/G9dBoGKhKC9TllkEVH4C7joQ8TIlLZ9/9+3FvrJSjO1UE80WEk8pc0wFiwJojRokpjfMIyPFoAldYUG4cxo16pJc5df+ur7R1imJT9LapuLSRy8KmLY18qQh2Lc1V6RyjT9vNLI6yVjTxhIrFp4xDveNrEDfVD0WnHEkZo6oxLoDRTWiBUsRFxNMf7iIF2SWZ8A06eDqTmCm6YDOd/xkrxYtpogbI8HFit6AcxuY/Xe4XSXKeZ/O/z5jFrpY5s6tgP0fcLetQe9LVMFRSbljtRQtDpEu/Tti4HhlzOxLeWkF2nTOxLAJA+tMS0hOwJTZ58GS2UAHSMkh05CxtJ9oYbwYzHg8mOlSQHdC2GIF7Wt69XZ0yS4TN0/pWiGoaFFbrKAWDvtfYpv9RAtVW9H6wfTHA4ZTFbEiZR647Xdlvw4qVmjQGonoXQ8YMEA8LrlEudO8detWLFu2TDyoXeTjjz8WB+H16xs2eCsuLhbLpRQSi8WCLVu24Pnnnxc/33zzzaCv+/rrr4VQcsUVVwixY9GiRbjmmmswf/58DBo0CM3JTLNwf7EoTdMn6EWMEpn92Cvt0Bl0SG1rgUar/MnadWuDF/59FF+8+C1+//RvEXd6xnUnYvgJg5HaRjF+u/O967Fr417YKx1CtMjsmI6x5x6B92Z/hKIDJehzRE9ccPsZMFkSsHdzDnRGnYhGJQf0Jj8Q2f8AL75aufgg4yq3HSieonxhLe/C7UwDCi8SZVzMMhdcRAzVdfml53i1aCGWR6VcHrECFeAl9wLqbCDlNbDqGMQdpXoY2JPokpQPXny9uMvMzI/Dqe4DTdm9gONfuFO/UiQQr5NvjVhxz5K/8c2FvYVYwSs/AsoeF/Fo3PIaGLkBF18DqFKB1Pe86wz7sxFGPzMB+4/KHR/TTeAV7wIVrwPaIYDlWbAYew5ImiefblyPzMRE9EwL7yKJk38FM4t0m1iRs+Yg0joniQjIhpBq1GFfWeDBvdjnKd+8EX0syEmejtd/fbkcQ4+rO7CVtAyKDhQLEz6VRgVzerI3ZpzO0xWlFeK8TeXjKpUKc/95BJv+2YIPn/gSWr0GJ112HIwmPT59bjHunH89Bh/dTyxD0jhiRTd2B1S2LUhMvIUOEmDWxzGpx014f0s3aFUzMKrtJSLdg3x7WNJMcNtioPwFsKR7wQ0ngJkfUqJK7YqZOcRFgwYs8RJwqugiT4sAYgUvmqaMU8xPgbuKAesDYjyiVg2DXq+HXr1XxKZS+TizvAKuGwGmMoQtVojIQ+4AS30bXDsQjIVRPSepA42b737/ehQcKBF9XdRarVarxHE9tW0Krn/pMuRszRX7N7WBqDUqZPduj4yOys0+W7kNxXmlov2ajgv0GklsMJNLrbhGCH8sTaIFT10IJrwlLgeYHizlLXDWVogIYQmDRZOhoWqmlLfB+UAhWsDyMngxGXpuqBErUub5iRViH3UXg1leAtcdrqzPQukgO8BLbhLVrOKYQ9Gmmq5gSVeA0w0XGueLjZdiBXHIMk1VVRUKCgq8j5KSEtEP1rFjxwYv67TTTvP7feTIkSKZhMSIAwcOICsr8IXYc889h5NOOgk33HCD+J1Ei82bN+OFF17Aa6+9huZAQU4hPnryC+Eibquowo2vXoGSgyX4aM6XKCu0CiX3zBtOxClXTEBqG4sQhGiQPP3hC3DOLadCpWawZPirvNbiCvzv6a/w76JVIhK082HZuOLpqbj2xRkoLyoXrSVLPl6KT5/5WmRLJ6WacMGdZ+C4yePCUox9M9EDkaDRoriq5qIiUauF1e5fwqVmKmh80gHSjSokYxlUpdfSrq70opPplflRRbxw7RHChTj5q9sr04uvgTv5eeQ7B0GtrpsyUke0SH5CEStK71VmcO0BL5ohRIu91hSk6PRIM7irxYrS6mz126ChMq3qtARV4clA2iLl9ZXvAeanUOoehmEdVEKsaG9CjVgh3mhXMPuf4KV3V/e/5YOT4NIA0cJPrBAH4B7g5a8q6yccK5RtlqKFpBZVTicWbd0sokxV4TpsU4VFjMWvfevy0b5/w6u7yHhzxQE7yCogYLiIKg3cta9Rok0JOh73HtkDS79aJirWGupiLolv6GJk4z9b8fw1b2D3hr1CkBh16jBcPmcK9m87IJ7vNbwbhh0/CO89+LFowySfijFnjMCsT24R53QyZaXz7IgTh2Dryu047IheTf224opSpxParEzsLrfCUFWFMnvdCio6dolIw+q2jpLq8YVJp/OOLWjP88xD83c1V0FXdhWYc4syvfxJgC4MEi+DufwpXNZvFpiuJ3jJDMVkmM61ZfeDmW4ET5gMXvaAWKafaOH4ByzldfDKrwDbYiVJpOwhcROhrliheF3QhYko9TbdKsYhWsub6NUtRUQZghdVt5pcHpZo4S9WKH5yvHCaFC0OkbKicrx19wdY9k3NmPnKp6fBaDIIoeKLl77F0i+WCY8aijK9/MkpMJqMsNvsePOu97Hko6Xi5iDdULzqmYvR78jeSExuue0i9V0DuNxuGLVa2F11/Ra0KjW+PP8i0apaG9pvf5x8cZ3rBcKiA9rq1oAXX1tnLF1obyMi2wPRNgEwqXYoYgV523EHeNHFYKnvwO20QKXRhb2vgfZrep26HxgvBjPdDG59slqYfEz8dLsywPjBarFCqeLmxVdWixajhIChiBUHao45yY+Dq9KgUmcA5vvByXrD8Werr6w4JMGC2i2ommL58uVYt26d2LF79OiB4cOH49xzzxU/09OjU+pIlRaEwxG4R2/Pnj3YuXMnbr31Vr/nTzzxRDz++OOw2+3eONZ4paSgFE9d9jL+WaS4zFPU2fY1O/H53G+889Ddm/fu/xgF+4rEQdLTM6fVaYWAURsqO739+PuRv6+m3WHnf3tw18SH8eQvs7B+6SbsXL8XP3/wu3c6CSOv3joP1qJyTLr7LOiNukPKRKdBw2kL54edrXxm7654ZExijVjhgUQJyjU3PwJeOlsRGDwnf7pgd6wSr9HonoAT5O1R946wR7QgkYE5/q0RKzxUixbtLa/B7nJAUzzZK1YoOMBLbhbCidgyijMrOBGMRIuEs8FVbZGiNiMlUWkD8RMr9BPB9GNqxArv+wpftKgjVtCgh8QbX3dhsZlStJDU5ZddO8TJf3R2p7A+HmESR4N3qtqJERVFNpTss6L/KTXu++GSbtTC5eY4WGFHG5MucFKIs+FtiYdC31G9xGCWLmg79c1u1HVLYgudO2879n6veSKNeTYv24ZtK3fi/nPmiOrFw08ehocueNr7Grqo+fXjv7B15U5MnX0e3rz7A79l/v3VCjz+w0yktUuVfz6qsK2y4fRPPgxrrEB8fM75OPujBUHn91RWaEvvAHMpYoWX8pcV0cJ0PTTaDn5ihQeKGwwoWiTfC1DyQNkTYAlTAcME8JJ7wEwXA+pbwVkHsFpiRfUSq8cQc8CTHgRjTqhLpnnFCgVXvaJFwAsogV2KFocAjZlvO+4BcfPQd7+/c+JDeGPD0+Jn7s4ao8U9m3Jw76mP4ZXVT+KBc+aI3z3kbMvFPac8gse+uxdDjg3uZ9Pcqe8aINS+XN8+Hmj6Gb264s4RaqhKbws4lk4yv4szv/wFe0r9TTbfOWUCephKlLQQXyN+blViRIVo0auOaBF6X5sKlvIGePmbouqa9mswrUgeJGNNIUw41vm1nNM2c+vLYOYO4EXTvWJF9drAS28DS34Mbv04r2ghDEM1nVttG8ghe1hceeWVePvtt9G2bVtRxfD3338LL4t77rlH+FgcqljhcrlE5cZ///0nln/00UejQxBX++3blf6lLl38B73dunUTIgcJGvEOtYF4xAqCDDUXvfpDwHkpC7o4r37HW4pO8xUrPNBA652ZCzHqtOH4ZcEfAV9LVR1Fub4n0dhDYsUdw9RQ1xYr/ESLO0WcmGjhIOdtOvknXqZkGIMj1X4r0jWrgnpacHcpmOPvumKFB1G9MQN6dlCUi9WFRIs7wIxnKJ4WtMyCEwHXQa8BTnCxYqb/AbbWgTZUH17YYkUt0UJ6Wkg8fLlpIzpbLGifHGYJOi9QHO5j6F+xf73iwZLaueFl8WlGpex5vzWYj0UG4M5V4o0bie6DO0Nn0GLplzItpCVRVmTFa7e/Vyfp4YRLj8GCxz4Tz58441h8+MTnAV9PFy+2Clsdn6jdG/eJmwaSGLeB1BYrPFQsBNONBS99sI5Y4SdaUGWnR7SwLQZ3rAcvvRNwrAQvuVW5EeFcJTwteOlDYO7dAcQK7xLBrXPBtL3E/P5ihb9oEcjTIvgFlL9oIT0tGs6qn9b6iRUeqBKKxElfscIDVSLvWLPLT6zw5aUb30ZRGON1Sf14xIrUqlpihQd3PjQlUzDv1GHITjb7iRVj2gUQK2qLFnwT3E57w/a1okvAEs4AtH3FTVSlpYyuOZ1KNYX2MCCBKjqq0QwEo8qJOmKFd63gpbeDVS2B21UEps4A01JsuhQrxMcXyY4yfvx4rFy5EosXL8aqVaswdOhQUVUxbNgwIRQcKkcddZRoASGOPPJIzJkzJ+i81IJCJNcaiHt+90yPBBqIVFQcWk52ZWWl38/aUOkw3ZHzxeVwwREkRom2qWB/EVLaB2/Z0Gq1WP5djaN1bTb8tUXEmAaL2iIPjZKCMiRn+cR4BYDEj2gwMKsN7hqZAUsleaKEiP/yVFqkvAZecLYiWhTfpJRBFl8n+sCo0oKr56OK9/PbPrL+0LhXK+pnKKjSovQ+xRej8LzgogWVg7pylKzl4ksA84twqg6H2vVDjVihHQBmPM3fOyPg+6oWLVI/h83uXx2i01RAVfF4TS9bwkXgvCK4WOHdzBVwl9wGnvQk7I7EBn8v44mEhNiVVEZjH48msfi7VDgc+HHHNpzcvSdstvBM3WiQTycHpzsNCFCWGQ32rj4ArUGNhDQ9rFY7nM7wY7qMKg6dmmFvaTn6pQYQF90WaOCCvXI3uCq0mVY06Ta4M/74/G+ceq2PA3kQmtM+GC1CvedY7eeHuo+T6d5/f2yq83ynPh3w7iylIqBDj7bYsiK46Lzp323o2Kc9Du5VRDoPq35eh8PG9Ax6Lm1N35FoRn/OP/2Y0GKFCASZKwSHYGKFn2iR/CC4/hhFtDA/A244TfhbiAsgl2f85gRLvke5MAoaeaoDs8wBL5weRKyoJVqkLoTN1cP72Rh0RUDQCyj/u79I+xw2Z/u4iVSl73A8nct99y3ybVvxQ+Ax86Cj+2Hlj+sCTqO2kPV/bQ66DqrQqCyrhN7UvHxFwj3uROsaoD4GZLXB3aMyYam4pN6xdAfnVVh45vs48p1PMGXgIIzpoAEvCCJW1BYt0r5ARUVqw/a14luUJKGS232OBagWLa4CS10I7twE2H8FS3kevOCcIGJFLdEi5Q1UOYaJtprmQGUjnKMiEixefvll8ZN8IjytIS+++CLy8vJgNpsxZMgQIV5Mnz49oo169dVXxZsnM8+XXnpJmGm+9dZbjW4GSRUaGzZUG6kcItS2EgjqhU2w+JtFavWhD24agzrkdpHXR3qI1A9LZnK9JndMw+p979RvGopwT5Mb8g9iS0lfDDSOhs71e+jtSpgKXvmxEltIGE8WdxM8mchcOwYVtlRs2bbe70RNbUH9evcBJxHBJ54swBoUE62KecFn0R+lGPp5+l01A2FzdsW2HdvRvesg6NU9lBJUxwYlv113OGD/M+T7chmuwoH9hTiQt9vveaNRj26dJkNb9RM1uQG275TWGFv7ekwF9XDpr8L2bXkoL7c1+HsZT5Ag2hz28WgSzb/L0vw8VLlcaM85du/eFdZrUo1rYDYacSCPTtaxEXR2rsxBUrsEWK1KClRZqW8LVv2YNSpsO1iCXGNdcZeShNqZgfy81bDaGy/yL717Cn6f/y+WLV2OREt4g/PmsA9Gm0DvOVb7+aHu45aEVGG8V7tiscJaKUwzS/JLhUcFpXZRNUbAZWQmY9uquu+ZjLSpSrS+wV5r+I7UN55oCM//uw4PjL4Gaa7rxYVDIHj5O6JCk5feFfoiiFIAqMrC/heg6ScMtEF+FXVwg1d8pMSeWx8NsjA7eOWXAKWLlD8b8j24tUehotyELdtrzOuzOyQjRX8R1LZXQr9WfypKSxl27Iyv1KK0tNil0UW6n9O+RYmHwVqzyGg3WIqetbjca3IfCDLftNlt2LCh8Qygo0l9x51o7rOh2HAwD5uL+2CQuEb4LeS8xeoZmLdmPVycY96qlbhrVE8wwwTAFrrFDAkXwM3h/Q517JAMi34S1LZ6fBBp2eSPE2g8rj9GcdWhFnI6PlT9Cma6urriOjhcOxxu1hHr129oNFGoOXBIdSY9e/YUj0mTJgmviK+++kqYXP7000/C5yJSwaJ3797i5+DBg0VMKZlxfv/995g4cWKdeUkgISj6NCMjw/t8afXg1zM9EqhSoXv37jgUaCBCO33nzp1hNNZNsSBKU6xITktCaYEycN+9cS+6D+6CrSuVSE9fyGgzvW0qTGmhe6SPu2gsPnn6q4Dq+unXTBQH4TZdMpG7o26ZW8+hXZGWlYIO5rYh10HmWKEI13aODHmmfrkE75xyLYaZVGD2XwMvj4xt3LlARbUvhvFsxXiy7BFlOerRcCXeD7U7Bb17txHr1+kqRIUBfQ6VDsBofhrukhuDiBZM9KG5uQos2MFNfzyYfjx46T3KAYjaUZKfBXOY4f2qUP9pyeXC5IvMOilhRPG9CCJaJM2CSns0UjXJSA1wXhSHq5T3FHMud57SGiP8PGYGES30QMo7cLt7omNHVcTfy9ZANPbxaBKLv8vrP+5BZ7MZg7r3DPs1GlsxwNsGNTqOBkXbypHZMwVJyclCrKCfGk34onRGoRvFbo42QQZNvCoRmal2pOnC8+2IBqmmNPw27x+U7qjEsEmhL8Bb4z7YFO85Gvv4OTefgpdu8u/V/un93zFx+lFY+Pjn+HnBH5gw/Sh8POfLgDclDjuiNz54+FO/59UaNYZPGIzUENWSrek7sqtauIwG321XhP8HRj+LtKogogVVR2ouAUt+qK6/lAeKEDRdL9pPuboLVJZnAfuaoBUUdCeVJc4AY3rwstmBN47SBJJmKTddyucGXo7+OLDEmTBwDQb2N8Hu9ImwV0+lLxVQ8VLg5RvOBhJugMFpQp8+ocdwLekObEP389r71vFTx+PT56qN1H345o2f8MKyxzD/wf/VuXjcvmYXbnzlclFpRWabtTnlyuORlZ2BNqrGubCPFuEed+q7BogWDrcbU79YgndOvRqDjAgqWrhNs/DGKgteWqGIDrTX93lpITZceZNyPRJsXE/7rHESbPYU9OnjI1ypLwZUaqBCuUlfB8OZYLoBirde7Vu0+glgiZeDF55bc/wpvQc8+UGw5PuDihZcOwIq82OotKegV6/0ZvediUvBory8HCtWrPDGma5du1YonFQFQVGi0bpb0qtXL3Eg2r3b/86zh65dFbNCukvh+b/nd3pddnbk5mfUrhGtEjba6YMty9DRgEcW341bj71fmGv+7+mvcdvbV+Opy17BwT35fv1y9392GzKza4SZYKR3SMUNL1+GZ696TZh/eRh1yjAMOrq/MAq7du6leGLaXBQfrLmzmZGdjrvev0FkydeHKogbbyR4RItll8yGxnVvnUqLcMSK1ZXXIVWlR4+0hGrTwK2iZcRgmSsijRQSoDI/DV5yoxILWrMGkR6yoqATUowmpGqvRrKDSj6DixXknaGyPAemboMEv6KYBHBqW6HkkXpEC5Y8G5zy36vmw5g4HUwVWK3nvBeQOr/acTiUaKEHS30X0PaHIYy+t1Dfy9ZANPfxaBKtvwulg/y6ZxdO6dkbBkP9UXkE7Tu8cjeg7Qd1jAyLq6x2FO4uQ98JXbwiBf3UaMI/JaWb9FiZWwptkG3kzgyoGbWdhPe+o4GhnQEd+3TA8m/W4ORL628Laa37YGO+52js4+PPG401v67HH5/9631u9S//CQPs7Wt2Y9m3q3DC9KMx6Kh+os3DV5SgCNO1v/lX/Gl1Gtz3v1uQmZ0ufE/qozV8R1iUL35Cihaa7mBJt4v2DbfhNLiTn4O69Dp/0cJHrIAQK54D03SAm5nAkunce6f/CrVDoUp+EGA6cMMEMCrtLrvffx7dKLCku8ALzgBo/aY7wGpXY+iPhyp5lliOqvIL4ZlhTLoZTO25m5EArr0YnJKIyl/0f63xHDDTTdCo0xAk9KDFEul+7tm3MrLTcP1LM/D81W/4CRMjThoiquVufftqPDn9RT9hYujxA5DSNgWzPr0V95/1pF8r98Dxh+H0605Eoil2seCxpr7jTjSvARoiWgwXNzaX+E0nEaDIPR4vragx+g9LtKgWK5imPRLqDD88+1q1Ua8vxvPAEq8Ar/wksFiRdDtQ9XtdsdT2M3jyTLDkB+r66VWLFYG3RRLRR3LmmWdi06ZNwhyTvsxUCXH55ZeLNpCBAweGPTAOh9WrVwshJJjpJgkSpAJ+8803OPbYY73PL1q0CKNGjYr7hBDPHZjuQ7rg1dVPYtvqnaLqgQ6QT/w4E/u25GLH2l3I7tUenQ7rgPbdw1PMqVT1yLNGililtb9uQHlJhTiAJqcnYduqXTjz+hNFOeqzfz6EvZtzsHfzfnTuly3WE6z87VDpYkkRzr+hOFhpRF7ltRgoVFRFtHDT3QhmgsqqeJnYdRcCupOgK5mkTNceiXL9TBSWVCFB78TOokK0SzhYnfRRLPwhHOZ3sLfCAqfbDTWM6OpXaaGIFSsLu+CcT74T27i/tB/O7lwjWji0JwHGS6EtPtsrVrBqsSIQTN1OxKTWiBZ3gaX9D5z8Lapz3D1iBUpooFR9yAsiWjCmAtf0FJFpXtGi7DHFpbjokmrRokaskCY9EuL33btQ6XRieLvAx8+AUG815WmpAn+3o0HuxkLxhU+LwHDTNymkzO4SjyRdgMoMTwxyI9N3VE/88uGfsFc5oKunvU/SPEhtmyKixifPPBdrf98gogoPG91LPH/bO1cjf28B/lu6BRc/eD6YSoW1v64XMeFk2GfOSMbAcX0x5szDRToXxY/3HtEdae1TWtz3o76YQyLFYKwTPR5qrED/tznrtnXp1Bq/eUKJFs8f8wrUJWR+5wTX9IAq6TbRe+5WZWGL7VTM+3cnbhz6JNKqblHO75q+YKbrvGKFONdrlGOoSp0Ct/5of9FCO1S5a1pwAaAbpMSfGiYqySIe0cIrVpwH6EaA645GXl4xMs13gVkfrrkpUi1WgMSK6teKsYGPaMFUFiBxmvK8R7SoFitqhA1JQ6Ax87hzRmHA2L5Ys2S9iCAmATKljUWMiXXH68QYfd0fG1FaYBX7tCXLjKyO6UjJTMYb658R0cfFB0tEYhQ9TzcZWzNfiNhSt0j0qY1GpRLTT13wXoNFixWXzoIRs33G0vcDhpNQVBTYhNsjWmy66mZln/GIFj5iRTDEeDzx4up9rVq0MJwLZrpB7GvceJbyXPnzPmLFHWKZXHUiGFVil82qPgYcI0w3yUzTzY4DS0aNaKEdISJRQ21LayciwaJ9+/Y49dRThUDRt29fccEdDa655hr069dPVFWQ6LFx40a88cYb4nePGHHXXXfhs88+w/r1Nb151157LW655RZ07NgRI0eOFGLFmjVr8N574e8ITQ19hlmdMsSD2L8jD3dOeADW4gqktUtB0YESqDUqPPb9THTsHd4XOik1STw69va/WGnbxb/MmzKjR5zQ8G2mgcc3F04NOt2o0fgdqChvub6osq/On4zUxGy4Ex8Er5gJt+4YPL3Sgu4pSTgu6wLoNRrkYzLKrBxdE2dD4/gRy61XY+qCT0WVRreUFLx90kBoiq8UYoWy4gPQlEyFWj0XU774GzllZWhnMmHJ5KfAS2+GyjgZhe4hyK0sFwMgGvy0NZlQpesNl1YH5toKh+EWWO1qpCU/BVXl26I0NJhYUVe0uEJRWzXZ3hx3ZjjKT6wQlL8SvmhRfB0YbQPFoaa8K+4UMfPDUqyQ+PHd9q1ol5QUfjoI4axuRavn+30o5K4vgEqjgqW9Ce5Q/eNhJIXkWKvQKzUhcFIIOflzOxhdADSiYEFpTnQHfvgESjCStJQLGnp0G9TZ73lDgl6IEFSd+M59H+LXj5ai/9g+IiZ87rVvikoKuvDpNaybeLRkwok5pDFDIMHCojfgszPPhU6vF/3nnrFCOBGnFKEe6KKIRI1dJcVYnq9GN9OzSHE+D578AuDeA6jbw5n0PLTlCZg2uB2cag6XeS5UdFFieQbM8R+g6SZMNj1ihQc/0aLyf8rNh4ILqYNeXESJc7yvaFH1PVjyw4B9qRAraJqtKhE5uXuRln4KNEnU5/5vQLFCYPs4tGjhPijFiijgHTP38f97V1VWYePfW/DAuU8JEdKUkoiPnvgcg4/pj6uenY7UNhbRrk2P1kR91wBU4Xn6wvcj2m9VjAW9wZlXmYDO3rH00UKsYKokpBhUQbenc1IVePlrSlQxPaFuC2Y8G7zyU3DjuVBpMusXLTjFVecDCdeJKiZCpWkHt0e0cG72ihXK65LBjSdXHwN+84oV4nXqVLj11aKF7Suw5EelWBELweL556uVpCgzYMAAITaQ6SaVT5Iwcs455+CSSy7xVkpQqRZVdvhy8skni/4Z8s+g11LE6dy5c0XlR3OkJL8MD1/wtMiFVn6vadm47/THMefnWeLOTlNDg45Qd0o2F+R7c9KJ+qorCI1ahR5pSt8W1z6KwkoXXl6hCE+PH00GNsBtP/1P/Lx8SB9cOXQ2pn7wkZ9Y0dbhI1Z4cB9ANq7B/FPn4sJq0WLcvMVYMvlZlLtUGP66fxmZhzuOGIgLDjsNSca2MBkptnQcoB8Opg6vt0wRLd4CVCbR14rqbGVuX+UvVjRYtFgAplbadpgmG0ibTzKzrKyQeCF36R+2b8OYjg3zcOAkWLBEgJljGmmakp0kRAu3MzLBItWgFbncOWW2wIKFGBi4RCY6vC1hsYcGrTSAXfr5v1KwaCXQmGTxGz/iy5e+Fb///snf3mkzT3sMb254RtwYkAQnWaPBvgN56N6nD/Y2sNR8d2lJQFHDV+w4vmtHXDf8ceisCehmGQqkvAGDOg3dfUKGuHs0oBssRAE3SwKjagl14ItQ4VNBlRjJg5TKChIrPNQWLfTHgGnagqsmiLZSZfygvEenywgtRaUbTgosVtQrWkylDZeVFTEkf28hZp35hGiv9m35WvLRUvQe2QNn3nBS1G7cNifquwZYe0C5hgkGfZ99rxE81CdSkighLvzpJh3TC7Ei1PZwZy54+UtA5YfglR+CpS9Wqq3yJ3jncYchWjj1k1FWViL8YXxbrjyiBVVT1K6Q8IoWhuPrXDd4RQv9WDB1/PjNxCtxtYdddtllonqCvDEoNpVMPK+//nqYTDWGQ48++qhoR6kNCRvfffcd1q1bhy+//FJEozZXivNKRGlZIKh9o7XkOtPOXVRVU+p920//ioeHV1ZswFa6oVGfWOErWrhItBgp7jrnWK3o8dIH2Fka3B700T9XY39FzZGJqRLCFitq3keaIlaIAVExeMUHgcUKX9Gi/E1wd+DYMxItPGJFzXalSrFC4sfK3P3ijufQtg2M9XTuFNUV1CIdK3LW5SO1Y+jY5PrQqhgsRi32WQOXgYoKC8IvaqxxeqmpymLpl8viJlJQEluKckvw0ZN1DTcJ6nn/++sV8k/QxFB7yMkLvxaVnkylD3iBz1RG7/MqtTmoWMHdFUDVb0Dh6eAFVJ4aYMzhES3AhVihLD8p4PiBLmpCihW+okXZHHBXgd+FlGwDiS1/fPaPnxecLx89+QWKcoOMOSUxhfYlj1gRDEWsmCvECgUSKo4Dz/cpKy9/Tkx3O+uGEPjicCVix67A118kWtSuxPJupyo56HUDiRZSrGiGgoVEgSLSQlFR2vIz2RvKDSMOQwZ/O7hY4cF9AFnsY1w+uE9jbVqt9ZcB5W/UP1/5W8q8EkmE/LhjG8x6Pbqn1m+g64drR0z9K1wONw5uLUZqp8j9K3zbQvaVBT5eCpGQmcGbwsfiiF4iBjNQlKWk5UGihCflKxA520LfaZQ0M7gNvOyB+sPbqcfeuTk84ZKWWe3TFRLbJ94Yd0njkLPtQNBphbnFcAURMyRxAPlxCWPMeqh4DYw1XgS6pOFIwSIOSU41QaPVBL17RyZAzRHytAhFO1Pkd1xv/mEp1lReAieZY/qi8r+7bNdfhr+LT8P9v9VUajQqdOc6lXp8Q/XUJwqfCuqxk0gi5ccd2zEwqy1ULPzDPHcXxtxw8+C2IiFaHIrhpq/xZjDBQkBlo87GFyy6DewMo8mAPz9vouOMpFHRJ+jQdUDwtiPqdZc0PYcyxvCFKhxZ6jzF2NeLQSSL+M2X/AigHSjGbfWiSlfO+9SOF3wmMMtLgNrfR0USW4YcNyDotJ5Du0JvjH9z/+ZKZmKiMOiMFK5uD2YhY1qf6w9mAZhPGykzgqW8Bc7qj59tDkEOLRUpWMQhJEicdnVNb5UvR08aIxyJmxv3HTkEo9sBJ3QLPKgb1aEtPjt7NNokRKZwUltITlkFmOkquPSTlScTJgt3bxhOE786TY9DbzoDmYlGYerVFDCmBbQDQogWiSJNBCqzMm8YcF4F7twp2k0kEmJvaQm2FhZgcNu2DW8HIWKZELK+UPxM7RgNwUKH/Eo7bMF8MKgtpAkqLCjOsteI7vjzCylYtAbIdPPyOVMCTsvsmI4eQ2oi1yVNw7PHjxJjDKOagbsKhVcP5/6Rg9y5H9yxFW53/eMQpumiCAxCtEgAS1sIZn4C0B1VI1ZQvKmqpqU5GNx1QGld0/QOIVpUixW6w0XrSuDl7Ad3Nm4LXGugz4juSG8fuFLxsienCDNeSfTpmZqGT88ajbdOHhuxaKFSmcB1w2pEC1UGmGUOmPlpkT7oFSs0/aFSacGd+8BdOQGXZdTl47Degfdn7soFd+4OMi0P3LmrwdvOXfnK2J7LCh7xt2zwJyiJOeQ6ft7tp2PyzHOQkKScmEjBPevGkzHj8clINCc2O7Hi9I4roC06GbOOSKojWpBY8dRR2UitOA8J9lfFYKKhPHXsKByVsQjqojPADceDJ80BU6WDF54Dph0It/ltaDTJ4AUno6dxMb4+L7Ag1LSihSJW8LIHRfQZdwb2MaktVpC7OPXj8fJ3pWghEfy8cwfUjKF/ZsNcy7lzuzJYVsXWcDO5TSK0xkMPGs9IUPafoFUWqkyAF4K7rWhsyE2eWkLydh9s9HVLGp9ew7tj1ie3irQQgu6qjzhxCJ74cVbMosIl4YsV49MXiTFGe+N28Ir/geefBjjWeUULIVZYnwAvPAvMubZBogWJFcJbovBCMNM1QPKbYYkV9B3Ra0vAi28CL7wAcO0KIlqEKVYUzQAvmiZFiyhD+/ScX2Zj2IRB3moZSvS7//Pb0WNwl2ivTlItVrx5Uj9kVV2MIYmvREm0eF1Eh/LSmeDWZ8DMT4GlvOMvVhRNBy+8pI5oQWKEu/hqoPhSGHUH6ooVRdeCF15UR5gQYkXxLeCF5yvjq4aIFWUPgRecATg3StEi0pQQSexJybLggrvPxISLj0JVRRX0Rj1S2lqaXW67R6xIcrwifk+rugFzxr+I6YOHIrfcLpz+B6XlQ186neokoKqcr3SFmq6tYywZCI1KLcSKYzIXIdGpmOpQ1CnUHcCtT4vfqddUTQcp258iNYBVvIqeCcDX503ESQu/rbe/VKdWi8STQ8mVDyRa8GrRghdSDJPWK1bA/ruy3RSTljYfTNM9tFhRNENJQyifW50wMkVxD5e0Wn7ZuR0909KRoNXFneHm/vWHbrjpIb1a9NhbZkO3FGNgwYKgu5eq3mhMeo/oIVJQ/vxiGU6/JoLcaEmzIjE5AaNPH4HeI7qjvKQCGp0G5oxk8bwkMAfLy4UxMKFTqaFt3w7bykr9UsUoYtzzf4pJvOX7b8L+ODsmm7HskvOR7HgVKlu16V7RxWCWJ8G1/cALp4ClvguuyhJiBWxKMgGneVLegrv6QiY0enFhAfuv1a+dDpb6NjhVXdTzyp7dLUDpzYBjmfLawovAUt/zihYkgIBXhi1WkF+Gsg3TgJS3g5oAShoOpfzcs+AGkeLndLiQmGxEWrsG+kO14P03ECadLmQ6YFKQ6V0sZiFWtLFfAfBS6F2/YEgi8NbJl+Pir34VCWgRiRaa9sq+4dpHtznENQKzvOwnVggPL7EvXgKkviGS/kis4MVXgzmVwAdedCmQ8roQLD1iBZyrq183GUidB6bp5BUr4PiretqFAAmcmq5hiRWwfe19HaPjAR0XGtDi2yoFiylTApc6hoJUyHfeCZ3HLQmNVqsRKm5zpYOJ48Ju66CqUMQKBRd0ZVeho/EZ/L5bjfN76aEvvVKIFV58RAvfnGeKtHW43SLCzEN2kht9dN9BXfVhTRuIKkW58PfCwUtuB0u6l/4H2D4XokWvRIaVl56DPVb/vOdOZosQKbyb43DgpA/mNThXPnzR4l3RU+crViibXRRUtKgjVniQokWrhwb1S/fswRl9+jbosxDCHZ2stbHrt6d1UEtInwnR6cHWqVVC9NwbtMKCBpRqwLUb0DauYEEeFuRlQT4WUrBoPdBFTGu9kPE9X4eaxwNd7Eyc/05YUYbE5+ddKB6EijG4qkulk3R6fHzO+eL/WpXaO0bomOSGquIlqKpvZijYxUWEEC3K5wHOreD2+V6xQsArwxItuDNH3K31iBXKkyXghdOEaOFGn6Bxl6KyouJuMOfymifdB+uIFnAdBHQjwhYrBK7dUrSIAVTd3NwqnGOJ7/4biC/PvyjkPk37cu3pVFnxyVnDYChVxAoPJFqMSFbhj6l3otzV8Bu33LnHR6yoxrkBvOQ2wPwgeNFlXrFC4NqmiBYpL4OX3AlUixXKNFrWNUDKS+DFN3vFCoE7t1rseEukBHnECmVaQb2iRW2xQnmyXIoW4QoWkUSzyTi31g35KRjsC4GK5wNMdYlKi2sGvgBmW+AvVtQSLdJN1yIjUSmxraiowFafvHWqrNBUzIPa6StWpHsrK2ptkai0UEQLctr+XMSHmhI41Hwirl38rXdOGnDR3WkPoaorDhUhWqjbKwdEX7EihGgRVKzwIEWLVs0/OXthczkxMKuBPhS8qNpws2FtJA2hJMcKW5k9KoabHtISSLAIfJeHMTW4KgPctbveu52xoN/o3vjixW9hLS6HySIHupKWDQn3kYj34aLXaLznZjovnzL/vTrzeIQPagMxVr0PjZ9Y4Sta3Ka0cpS/6n9xEKZoEVCsCFO0EJ4VpTdD5StWBBEtoO4KpjKEL1Z4kKKFpInZWRLaV8335qNvG4ihdJqfWOFBbf8JJpcbCUmzSPpsoFgxxV+sIFgKmGlGXbHCV7QougIs+U6lUsKTQsgSwJJuBC++EXCurfUiPVjyreImqadyyo8QokVAscI7sbzVixZhCRbz5gW/uyyR1NmvXMXgFe8p2cZBcYGVXA1mfgSc2wD7b3Vn8VZaXFMnZ/yJY0b4tYHAeEEIsSKQaOEUBwVPe8jn5xyP0z76vtH/mKJkLJhYEUC0gDo7tFhRW7QgEUcd/oFd0vz5dddOpBqNyE5uoA+Fp79S7Z+sE032eww3oxBp6iHDqMXGworgM5DxZgSGV9Hysfj0uUX4Z9EKHD3pyCbZBomktTHn2JHCs0LjqbwMgLgIKX8ZsC0OvqAgokVIsaIe0YLECvKsYIEuZgKIFlR23mCxwoMULSTNhC4Wi18bSDCo0oJXPACuvR+MUsAiFitMYJYnwEsfCixW+IoWpY8oFVnF1wPcDmaeA259MYBYoa6u3HonsFgRQrQIKVZ430x5qxYtWte7lTQO1ACvSgzz66cLXGHhnYVcfOt+TW1OFzh81kGiBzOEs3EA0wNUpeB9KhEVDn+38MZD5R+vFAxG2iI9WPVnFsauS3+DVnZAkwBLdu0UZpthRenVMdyk/S06/hKByN1QAEOSDgkp+qgtMzNBh8JKB8rtQQQ8dZYYrHAeQuCLEZZMMzr0aoc/Pvun0dctkbRWKh1OuFHPeTXsMQO1h5JQUet4ygK3aPjPQ6/V1KqwYGGul9apDrVwJUq1XnRyHCCJe5xuDjdXV49160Hse+GObT3XGbXhVAoOsDB8vmgeMX6gFjS6FehQriMCLjPYtPr2bzouhON3pAl8PGoFHPLVjNVqRW5uLnJycuo8JK0TRpGcxrOApDtDzKWF2/IqeOVngP1v5amEycrdUA+Jl4MlTheeFLW5d8lyfJ1zBKyai5UnbJ+CO7eAhVynCiz5AfCqJUDVD+IZnngjVpYcgQs++xlNAVOngyXPAvTHB5+JYpiEEtsZjA6cukHijo9y0ApC0u1gxrPF30LSesi1lok40wENbQchnDsAddsYG24WIKVTUoPFlHCSQsh4MyDCeNMuekvRRG0h/3yzCnZbCGFWIpFEjXuWLMc3+8fAabg06Dzc+ixYwlSAxioeVO1EtabfXViRItAHDDU3OZimHVjSXYA+RNqYKlW8lml7+T3N1JlKZakuRMWVqp3wtmKajkFnYWSOnDIX0AwIvhx1d7DU14VxoEQSz+wpLcGUL1cgR/NStfdUYGyaE1BpuLVO1XUwmKY9WMqbgLpL3WqFkpvBTDcDGp99VHcEoBtV8zvt+6YbxLxUcQU4wYtvBUucCmiH1VqbW/HLo2OKbkzwjVK1Aas25vRupzoNzHQTYDwnxJsxg6W+D2i6R3UM1eIFi/fffx/HH388hg8fjqOOOgrHHHNMnYek9cJUySFECy3y9XPx6N9lyGMXV/eD3aEMDsyPKqJFCLHCw92/LPMXLSo/DiFa+IoV3/mJFed+oogXTUVI0cJHrPDOX59oIcWKVstvu3cJ3b1fRlZkhpuqCISOBlZYpGZHN7M+zaiFmtGAJ5jxZvVnESQjvTEEC5vVhpU/1i4flUgkseLOn/9FLj8XZZppdSfSmMPyErh9I5imnyJSkEhgfkCUmTPTdf5ihXs3UPmpaHf1LkLTNrhoEUSs8L5WnQkkPQSuHRORWBGWaCHFCkkz9LwIJVqQWLG05ELkVDTMdDO4aFEGXvEumOVFxS9GNw7MeCYYiQYkOGj6glmeBy9/kxROnxdWgZe9KCJSoR1Za20OcOvzYMmzAd1RYYkVYYkWrHWLFRHHmn7wwQe4//77MWbMGJx11ll4+umnMW3aNOj1enzyySdIT0/H5MmTo7+1kriKLKovzjO/Ug0VToA50Q1V+WN+YsXtSw5gye59WHEgEwtP/xjqyneAyoXgtg5iIFHizMLefDss+hJUOB3elBC6/eub6EGJCCRanNQOMDnfUkQL8rNIfgS81CNcqMDMz4BTr2q1WAHTjVhbOhrnftL4vhXBRAskz1Ja9zzbGECs8M7PdODVogX12IoSNUKKFa2a33btRNeUVCTpG9hy4c5TTsjqtrHaNFSWVqF4rxUDTukW1eVqVAxpRh12BzPeVBnBmRncRWKOz52TRiKrcwYystPw+6f/YORJQxt9/RJJPLGvtNQ7riCh1De+tD7ofO8xwfZN8grG2vwqWG1jMLEtkOR8u0asMD8Nbp0DaPqDJ1wEl/ZYaIyngxdfB7gPAAnkPfER8irTkGbYBVXxZMCdD/AKwHgumNriFS2QdJfiF1X1bVhihfe9OJLh0tyDBPYQmMfDqwFiha9ogZS5SmqBc43ypBQrJIcw1qextjYrE7vLrVBVVgQd79M+GCq2lGKFQ5GVaAoqWiw+711oS6cA7kI/saLEnogslyuoGX6w6xISLSiKVESSejwrtEPAzA+LfYhbXgFzrAAvuVW0WzDLHHDNQGUfNz8MXnyD17OCq7tBlfIcmCYboPkoKcRRXSmuzgYTkafZSvpIyT2A/ed6xQrvdlLliOkm5ZhS+VH1k1KsiFiweO+994RY8frrr6OoqEgIFuPGjcOoUaNw6aWXChGjuDi0O6yk+UcW1Rfnqbx+Pm4+fAAmdbsRZsdc2JNfx87CLJx9WDucfdhgHNPRBHXlm0KsELj2gpfcgITkV3Hb93/isWOPx2kL54eMRWIsHU5jD7hcGqgr3xMKKXdsFJUWvOwxpbICWrCEc8GpFcR0NaA5DH3SUvDdRdPgrpWC4xu7Fk5UW+35oyJaOFYGFSuCihZJN8k2kFYMfY//2LMLYzsFNmkLy3CTSqJjxIGN0Tfc9JCRqMPu0iAtIR4fC+dONAV0N4SqLCje9IZXLoM6jAstiaSlQuOCUxfUTfd48riJ3osfMgymEvHabCkswC3ffyP+//UFk73nZZfbDVf1eTxRq/VGn5p0OljtZjgN3eF2aaGyzVfEivIXAMca8eCsDVTGY8CLqsUKouJtcGZAiv6MGrGCsD6pXEgEEy0c/4YlVnjfz7ZiDOz/EFB2N+Dc1mCxIqBowStkG4gkqmP9YOP9SocjZGzpVxdMDilokDl4sOl5VZnIMr8LTckUcO0RcBtuRTbTCrEi0PEj1HYS3F0pxgAs+W7FaJOqwBNnAK5cuGEAc26qFisUPzsSIVjKy3CzRKiEMPEM3CRa8AqoUl72ig6iWsojWrhzwFLeVsQKMS2jRrRwbqhXrAgoWti+a/WVFYckWOzevRuTJk0S/9dqldIch0O5w5uUlISzzz5btIxMnz49ksVLWhhz/iLVfwAu6PsFbv7uXyzZ/ad4/qcLT4bO9rpIA/HDtRea0svw2dmvYpdVE3bMGXdfCiScB24l52+qtDgbLO0zcOtcUbXATTPB0r8RYgaKL4VW0wPdUl6rt78z1lFtgUQL6pXzHPRCzu8RLTK+F+ah0rOi9bL+YB6KbDZhuNlQFMNNC5gqHNOnyMhdXwC1VgVz2+jvS5lGHf7KKYGbAyoWpC3EsVLc0W2Kcsp+R/bBzwv+wLrfN2LguMMaff0SSbzjESKIj885P+SFEKFWqfzix+uDO88HjBPBy2YrYgWhageVvq8i+HvECg/lL0PDbWAJk8CtPolnIUQLKhUPdZOhzjZxLiotjOZHlPQBTQdEike0oD566VkhaQxoHwzFjuKikPuxJ4I4mPCg0/cET/0IjCXApE5DT6MSZ9xQhFhhXwpefJVSrWB5QuxvvOQmQDsKLOF8ZVq1WKHgUCJNU16CG4OEaKGyPCPMuyvtGUjwuTTxiBb0GqZu77duj2hBRv8N2b89ogUSrwDUHVptG8ghe1iQKOFyKY7rJpMJRqNRGG96SExMRH5+w79UkpYtWkz44FvRBuIRK7I1C+qKFbVEi27JpeidVn90kYAOQKUPCrFCQO0hRdNqWiys94soIiZSSbSAc4uIBeOu+DKIFZ4WYYgV3vmZTgxQpFjRuqHqCr1ajR6p4ZlR+eHaHtN2EGL/hkKkdEyCShP95JqsRB0qnS4crAhibEneHNTywpUqj8Ymu1c7WDKS8fv/qstGJRJJo8Gdu8ELJ4MXz/ATK8izgpfcVVes8ECVFu4SxdPCFxItKj+s62nRALGijhHnIYgVNctpI8UKSYuCxsLhGmzWK1ZQygcvEvGkvPhGQDsCzHhCALHCV7S4Esy5Cm5XKSrtaVi7viT4PlxLrKiZlhHR/i08Lej9S7FCENHIsUePHti4caP394EDBwpfiwMHDmD//v1YuHAhOneO7MAtabkUVCo9cnOOHY+O+r/AgokVHlx7geKr8ek5IRI0quHuYvDSBwD7L/4TqvvfvNj+B27/Dcx0vfI7iRbFN4O7muZCRiKJFr/v3o0+6ZnQNrDlQMR9VieExJL9/+VH3XDTV7AgdpcG8dyhu49EE7WFUKxhvzG98dsnfylePBKJpFGgczvdmKBybd/xALM8C146K7hY4aHiHYClAPqJ/s9bnwTsfzVJXLJEIgkTijT3iBUeeDmgsoCZrgUvuTOIWFFLtOCKOOl0hppXEneCxamnnootW7bAblfuZl177bXYtm0bxo8fj6OPPho7duzADTfcEO1tlbQQHvr9L1Sqx4JrB9YzpwFIvh8P/7Gi3mUyOvgk3QSo6ikRVbcHM5wEToMQ8cIksOT7xMFLImmukBndspy9OCyTIjwbiIuqnuwx9a9w2l3I21KM1E5JMVm+SauGSafGrpIgPhYsSbj+cxJmmoj+Y/uiIKcIG//Z2mTbIJG0OlRJIkYUUERND9z6lBJpGCoenNCNANSpQNVPtZ4fD+iGgjHpSSORxC1UnZEYIN7YfQCc9mmKNq6PxCuFD56kGQoWZKr50UcfQadTTgBDhw7F119/jdtvvx133303vvjiCyFeSCSBKLTZMG7eItgSHg8hWhjgtryOnMrOmLd2Q8A5bhrZH9kmh0/pWFew1PeCixYkViTfD15yB+A+qIgVqfMBTU8wFv0y9Ua/i8SDlMNLWjzL9u9DlcsVkX8Fmb2JU4GnCiEGHNxaDLfTjdTOoV3DI4UqJsnHYmcQ401RUUnvrwkFiy79OiIp1YTfPv6rybZBImltMKYFtP2FoaWfaEFl4pULwcxPBBctdCPAEi4ELyYzPp/zq248mPkhpT+9Gu4uA3fFZys0dxWIh0QSbyTp9Fh0/kmY0r9nTJbPVClgiZcAiZfVnVj+IsD0QOLVwReQeB1gPAsq8qmJAdxdGrfHjXgjaldp2dnZItr0oosuQpcuEbjUS1qlaIHkpwKIFopY8cDSfBRVBS63vHlkf1zYfRt01lvAXQfqFy1asljh3AleeBZgJ1NBKVq0Rv7YvRtmvUG46zcU7tqmROgy/zuQ0WT/+gJKCkNqx9hUWBBZJh12lgSPYRY+Fq7twuiuKVCpVSIt5NePlzbZNkgkrZFQooXbvkLEHdYWLbjucCBpFnjxnWGJFbB9LUrPuavGzy1uxArrU+Blc6RoIYkrNCoVFpwxFt3Zrbh2YEWTiBaMDLmN5wCJ1DbS+GIFKr8CL74m7o4bLSYlxMPmzZuxZMkS7NunGCl26NABRx55JHr1Ci/SSdKys5zt1casoUSLzUU69EiZA5TeDOZYrbSBpLyBDcVZGNHBLGLJAokVk7pvg9nxtPhdxAlZ5oBRdGG1aIHU98ALL1LiyEisSHkTvOTeFipWUOzaAfCi6cr71A2O6cWnJP74fc8u0Q4SkTmTcyugiq1/BSWEUDqI1nBIp5yQtEnUYem+EpTZXUjSBSjTppYX/jvA8wEWppFvlBkwri+WfrkMm/7dio79Aht0SSTN+bwfLPo7UNIWRZlS0legC5n6oPFFoMSAYOsi0YJXixa8cIoiQmhHwG64GK+u2IhrBr8CVny50rOuG4kKw2zc9f1KPHXUXKhL6ELHGVqsKJ2p/F58HWB5TkntiBOxApUfKb/TP0k3H5KJoaTloVOrQ8aPeuapjUGjEYk+wQi1H1Nlxai2WiSU3wiVazNSXPfi2oEPAOiJd9duRixECyReouwD5a8qzyXNBownQaVKhtt4bk3VRWOKFWWzlN8pMtXyTFwcN+KViEaP5F0xc+ZMfP755+JOERmKEWQmNmfOHJxyyil48MEHvS0jkuYJnfg9OefBpofKcvbNVSc6Jpuxu1a2uk6jweFvfYwlkx+HofI+8IRr8MCfBzFv7a9i+pfnX+TdBvp+pWirkKr+DupyRawQOP4JLlqU3A5mfkpJ3bA8rmQrk2dFCxMrasyBpGjR2ii2VeK/vAOYMWRYZA7alJKjr89P5tDIIcPNjrEx3PSQlagXP6nKon+GKYTx5g5A1zSCRdeBnURbyJIPl2Jyv7ObZBskkmhS+7wfCDqHe0QEGjd8cf5F4v80fjxtYV3zbd+xA40bPEIsze8ZQ5BY4RuFGmhdIUUL61zhbWG1mXBCz4EodAJpKa+Cl78hvLMOlBhw7cixKIcO5pTXwcvfAzPPDilWCByr4kK0qC1WCCjunX5K0ULiQ6XDUW+M8NcXTK7zXInNhrM/WhD0NbQff37ehXVESRVj6JBYAW3ZVWCuGnEixX4v7hnxBK4cNhkqlf/1RaTXJcFEC6ZqL8QKplLGJSpNuxrRgi6NG1GsEDhWSNEiFoLFE088gc8++wyTJk0SLSAdO3YUJ5Rdu3Zh3rx5IjHEbDYLPwtJ84VO+sFO/OFQezBBg5TaB0UalHjaQ3648EE8vXSdn2cFJR54stbtVQVgtm+gLqee01oEEy0sr4CRYZaICGoHWF5Q3IFbnFjhQYoWrY2le/eIE3C/SPwrXOTp4AZo34gRbjdH7oZC9D+5K2JJqkELvVqFHcWBBQumMoEzM7hjKxgZ6TUBarVapIX88uGfuHDWmU2yDRJJU9I+OVk8iEAVErXHDnRx4hkD0Pz1XVjVh1e0MD8pxgYZicpYh3C7E6EydxdjiO4+RQicDwPT9PaOJYKKFXEiWgQUKzxI0UJSC3UYFU2B5qmvopP2Yxr3e/bfmu/nfvCiqwDnpjqvUZXdikzzk4D+mJhcl3hEC0DtFSu86/aKFrxxxQoPUrQISURXbWSqedppp4kqi65du0Kj0YiBGP3/vvvuExUWNI9EEi4kWgx546OgBpuUha52fA51ZQCxopZo4edp4TPA8PzecsUKf9FCelq0Dv7YsxttTUlIT0iMrB2ETKfqS9c5BIp2lcJe7kBa59hWWKgY0Makx45QPhYkzLiaNqVj0FH9kL+3AJv+IbNTiUTS2JBoUXtsQNBdXc8ND//5deGLFbVEi8buTQ8pVviKFtLTQtIEKGLFZQHFCu88JbcAVT+Cu8tj52lRS6zwFS1UmvaNL1bUEi2kp0VdIrpyoxzagQODlxAPHjwYrnr8CySSBsHtYJ4o0lA4lisl7i0U7q4AqpbUnx1PokX56wANrCQtmt9370S/SOJMhfhF/hXtIvO+aEA7CBFrwcLjY7G9KJRg0RYgwY833fmpS/+OsGQk47ePZFqIRNIs4VXg5a/VP59jDeDc1bgmu7wcsC2qf76qxZTt2hhbJJEIxHnXsSmkWOGBW18GeJCY8uYKt4NXvFH/fI5VgHO3NOeOhmAxZswY/P7770Gn//bbbxg9enQki5a0IvpmhH+RJfpGU96pxxxQDWZ5BdD0RUuFqRIAw2lA4vWhZ9SNrjYHk+ZaLZm9pSXYXVKCfhkNbwcRg2gSLGLYDuJJCDGlG2FIVjwmYklbkw4HK+0oDZIuBHUHxWzPtQdNBXk+DRx/GH77+G+4nO4m2w6JpKVAcmvv9MbzpWHqdDAaj6izQ8ylAqMWVO2AmArCdVB3UEzFWYiKO5aozBNy+yWS6MKYGtANBzPPCT2jujtY6utxOX7VaoNEIId93Hg7jOPGS0qqUWMeN1qqYHH99ddj7969uOaaa7B06VKREkKPP//8E1dffTVycnLEPMXFxX6P+li8eDGuvPJKjB07FoMGDRJtJx9//HG9KtPRRx8tkklqP6qqqiJ5ey0eyvzljnXg7qZTL68b3g/zTu6Fw9LCv4ipcmbBnvhaENGiWqzQjQRTKcvk7mJwxxqlDCtCuHMfuDO+SreZ2iKy4YOKFkKseAxMHdldd0nz4Y/du4SBVUPEPy/uPICXihSdWJKzLh8pnWJfXUG0Myn7PvlYBI02hRpwbkFTMviY/igtKMPWf3Y26XZIJM0dGtK/dMIYvHdyH5zZu+E+OdyZA25fA3cDy8+ZpkMI0aJarNAdQfXt4I5NjVbVJVpeyW8jmGjhEStonmbeHitpfjBVIqA/Orho4RUrIr+RQteM5FXFg1Rbc+d2cOfeINN2gTt3B5xm0B5Er276QxISKAAguGhRLVboDgcLYjramonIdPPEE0/0xpr++OOPftM84sJJJ51U53UbNgT2J/Dw9ttvo3379rjjjjuQkpIiBJB7770Xubm5QhwJxYQJEzB9+nS/52RKSRCxouwBwPaduMDnuhFgKgMaW6yY0nMPzOXXItF4FWYdORSzflte7+vou7Vlmw39+rwDFE0F3PtDixXl7wLlc8GSZoIbTw3asxZSrCAvCHcxkPYemKYH4km0QMKF1RFNz9ZMkGJFq+K33bvQLSUViZEkMnku2sktO0bQPrt/XT56HdMJjUGKXgujRo1txRUYmBXAeJNpwFVtwZ1bwHAcmor2PdoiIzsNK7/5D6dfUvdcKZFIwhcrRqd8CGPFV7hj2BNwatwNEyvIh8K+VFwouHVDoaILqgaIFlT5yWk84q3aqiVWFF0NuLaDUWqZEAkCRC5HGRIixLpS54MXXqi0iYgJUqyQxIdowatFC15yc9TFChrbiO893bRLec1veUKsKJwEMBOQ8rayD3un7QIvpAQjFZA6D0zT0WfaHqB4OrTUppIyjzK/It5GkVqY8jZ40bRaxw0pVkRdsKAqiliUqrz00ktITa0xNho1apSozHjrrbdw1VVXeeNTA5Geni6qMiThiBWLld+LL/eKFvmVroiz1D0kanXeuDKx/FqVMWa9HksvnoRU9j3U1sfFc5rKFzGp29U4qccF2F+hhpoxPzfi2hFFDodDVFoYUudVG0/m+YkVlAmvYaVIcn4IVcULynaU3Q83d6MYx6OwSlPv+/ATK0SKAsALLop/0UKKFa0Kl9uNP/fuxvhOXSL3r2AZMVXyi/dZUVliR3pXMxoDOi21M+mEYBEUqiihPtomhM6fA486DEs+/BOVVhsSEhKadHskrRs6b0Z6/q8vZtAzT6B1GTQaEXvoQcQNVv/fM8akcUWwdbVNsMNY9SxUti/F76n2W8GNz4O7j6xzXKv9Hrsm28DKZoHZlQh1XnxlFESLfXXFCudaZfmFFzWtaEHPycoKSQBq71dutxv2qiro9HoYNFqU2avgcLmwLs/fO432UNp/TTodyh2OOsula4BkvT5oGlDbxDEweUQLH7GivuNRsGORn1jBSwAn7YMzvKKFV6xwF5LVvyIYVIsWXrGi2h9OXF9UixYkVviJC0WTwVPpeiCysVdd0YKOG1KsiIlgce211yIW+IoVHvr06YMPP/wQFRUVMJnq3jGTRCZWKLi8ooXD1QcT578bchmh8s2Jcocdpy54L+j0xReciUTVj1A7FLHCAwkLKYkMqelTwFSW+t8L54rymTpP7OjQDvFWVpBYoap4DyqnvyGWyvog1NoqLNrcESf2HBryfdQWK5Qni+JatAB9HiTayDaQVsN/B/NQbLNhQFaEsXlkfBVj/wpqByHSujSOYOFpC1mVVwYauwTU1cnHwvE3uDsfLIbpKPVBgsUP7/6KpV8sw8mXHt9k2yGR0MXBxPnvRHT+b2jMoO+6KNa8vphS3wsp33WJNIyy5wDbpz5zc7CSawHLXHDdGD/RwrNeEkn+nHoKWNnDXrFCwXHIooUYM2iH1RErlE0r9xMtGgNf0UIg20AkAai9D9P11obde9C9Tx9st5bhtIXV358g0I3KQLHqJFSMf+eNkPt2j5SjwSwvA1qKDW4X1vEo0LGojljhgZ4rmQkk3QZeNKVarKjGtRu85DYg+QFFOPA1s3fvrxYt3gQvucvf98p9UBE3oiRawLUL0A6VbSD1EJUGtrKyspilgixfvhxZWVn1ihVffvkl+vXrJxJKZsyYgU2bmvYOWvyLFf6iRYb2P5zYvVNM20A6G36FpZZY4aV8rmjhoFaOcBGiBRn4+LSBUGVFUi2xwoPZ8QSm9tyNVL2zYWJFLdGCysnjCSFaGE6QYkUrbAcxajTonpoWWdqMa2/MTddy1uYjMc2ABEvsDTc9tEsyoMzuQl6FPfAM6uoyzyauskjJMqNdryz8OO+3Jt0OiaS5oYgVTwC2TwJNBS++BrD/Du72v0vrESuSqmqLFbVEC/vyiDwtoBsVWKyoJVrAuREqxhvV00KKFZL49bQYG702EF+xglB3BEucUlesIFRtwUxX1hUr/ESL6WCmawBVLUGmWrTgzgDXCg0VLaRnRewqLIi1a9fimWeewbJly0SZ/htvvCFaOAoLC3H33Xdj2rRpGDlyJA4FWvaiRYtw++2312u6OWDAALRr1w579uzByy+/jEmTJuGzzz5Ddnb2Ie0EpDQeCpWVlX4/Gxudpgys/CGwqm9CzOWCuuRK3DfqWVQ42uOXXfsCzkVlYqE+D5oeiEsH9cLUXvugLX809MaSaEGCqO4COFyJDfgs7dCqy6G2L/S2gQSDRAu33gi77UQ43f7eHQZtkehRCyhW1BYtUuejytk+zmKHglygxeH3siHEslw+Gvt4NGnI32XJjm3olZoOp92O4BJcYFSu9dCAw8kzAXv435uGsndNHlI6Joko7GA4nS6/n4dKG4NSbr0pvxSp2kC+NdR6lg5X1X9w8aFoKqrsVegxqguWvP0XdqzfhazOjZdyEI/f71jt5/G2jzeExjpOBzt3154nGp9jOOsKtV4a06gqnqpVWRFEtDC/ADsbAZdbI5bz+5STQ4gVvqLFFWApr6CKD4LLHX77hl5bCpRcDRZIrKglWmgt82AwGJrFOTga0PuMp3N5cxoDNTa+n004Y9xgn319+7r/vm2P6HXUOqZX7wWKAogVqkyw5NlKy0ltsYKlgJkfAS+5PbBY4V3ZfvCSe4SRPS++3n8dQrS4EDxlPmyOhie1+RO7cVhj0Bj7UUSCxYoVKzB16lRR+XDqqafio48+8mvrsFqtWLhw4SEJFmS0eeONN4plTJkyJeS899xzj/f/w4YNE5GqJ5xwghBRZs2aFfE2kBBTn1FouOzc2TRu8G3bmJBhGgg1QgkWtPMmwc7TsLs0sHMuQT1tVCYWDG1W4KSCTQVWOBgZ1FAcUN0+txpUcLI+2Lv3IIqKA7v0BvssUyxGdMjqDY0oGgp1sNPCre6OnbtzUVbmv4N175qCRFV3qEIJFnRwVrVBVZUKm7ZsbPDgK95oqu9lQxg6NHYXldHcxxvz71LpdGLlgVxMaNMOu3fvavDyU43LYDYacSCP9sc8xAIaxOxbcxCdj8xCUWFRvfOXlUae5lMbi06FNTmF6KIKnIRkMaZBp1mHXbkN/+yiSZfB2fhzwXIsePoTHH/FWLQWAn2/Y7Wfx+s+Hk/H6WDn7oac/6O5rlDrzcpMRJaZxjShBAvFYNLhzsS2XbtRWVkl1rvbUY7+Sf2BkIIFDUWSwVkGdu7MEePZcOnSyYxkde/QgoVYfhrsTiOcTmuzOAdHi7S02MVURrqft6bPP5LPRtu+XVjn+kCffX37erBjSkNep1ar0bN7MvSqLDBXLcGCWwFeBaja1RUsyIiWplOLaCjBQqykPZnhAbyuKMNV7WGzcXE9EF83MFseEQkWTz/9NLp16ya8Jehg7itYECQyfPppPSeTEJSWloq2DovFgueffz6k2WYgMjMzxeDnv//+w6Hm7Xbv3v2QVSfa6Tt37gyjsWliarj6dHC1Gsz6cOAZmAVOyzxM+/APbC8KfmFBBjzU0xaM3eWBT+y/7dmH25YALx//OnSllwYRLVSA+UVwNhRt2mrRpm3DP0s6cLiSX4S69KogooUWB/VzYbd1Q4cOQfpTNTPBy1VgVd8GnMw1fcV2wmFBr14RegfEAfHwvYwHorGPN8Xf5eddO+DiHEf27IWsxIZ7+2htB8DRUYjOsaJwVynsVic69M1CSmpK0PmosoLEiqTkZGg00TGjyy7m2GNzok2wgY+rFzTOTeiUnUROdGiqCgsS5vuP641Vi9bjmqdmQK1u2TGDTXHcibd9PB4/r2Dn7oac/6O5rvrWy8mrSa0Gyu4N/CKRAPAe3K7O6Ny5Zr0nfvA9Pj37OPSj3b48SDWmKlXElVa5OiE7O4IbEprrqc4dsC0M3pJmeQuVZYlwOotbzTk41ndgG7qfyzFQeJ9NjjPUTUZ4qxzIb7Ch+3qwY0pEr6MAgZLLwZyba57jFaK6gtJI3NYXagmJdvDiW8AsT8JdzsAcywKui2uHQ0UtJcWUZuL/WXDtICD5WTCHGb17N9/rgWh+Z+JOsKB2kJtuuknEhgZKC6FBcH5+YFfY+rDZbLj88suFLwZVaSQlJaGpoPcWrRI2OiE1nRN8Arj2THD6W5U95D+JWcDS5iOnLA3bQogVBAlHod6DqjJ4Od6vu/dhTeFIdE58AelVV9fa8WvifPRhJBaE+ixzSvtBq3sKafabaokWilhx+5IDuOvI/iHeRwK4+j5wutlbW7TQ9IUq5WUwdRskULFIC6Bpv5dNTzT38cb8u/yVux9tTCZ0Smu4aSTndvCKnWD68VBHEocaJgc2KH40WT1SodHUf6ohsSKc+cIh22zEf9vz4WRqGLV1RRDu7gbqo9GrtoLpm7ayYeQJQ7Di27X4b8kmHH5y07WotNTjTrzu4/H0eYU6d4d7/o/muupfL41pThQxBby0lmjBTGCp7wOanjDqVHXWe8bHIUSLarGCaXvBEPE5nsYQNyrjrcoFdfvpq1MJDAZle1r7Obip93P5+Yf+bJjVGfFnX9++HuyYEtnrEsBTXlNSQfxEi3Jw23dQWZ5TWjqca3xeYwOv+BIq8xPgJXcIM24/tIdDRW0jpQ/WbdnQDoLK8jyYOqvFXA/EOxHdzqFBZahS+AMHDkR04KA+5xtuuAHbt2/H66+/HvHdP1o/mXX2798/ote3RPIr1SjCBLgS76xTWbGrLE2UmB8qTlfouxFGrRG3LclFvp4GCdqYZA9bHSo8+HclCnRP+Xy9a8QKEk7qg6nTwZLvA/QTap7U9AWrFiskkqaEyg5/2bkDAzIjTQfZrgiGMTbc3LcmH0lZCTAkxU4UCUbHJAPcHNhaHPiunjjWqNqCOw6tCi8atOvRBh16tsOXLwWu6pJI4iVGmVz/Qz0ojtAD/d/z/IaDeSISkR5VTic+Pud88eiYHHl6EFMlAYYTwZIfCChWCLPJIJBosa7sKPBEunlSV6w4VJg6Fcx0A2A8P6BYIZHEE/tKS737Jz22lZWKVhD6GU6bQ6AwrqaAqduCpbwm9n8vhpPAkm4B07QHS5kLaAbUTNMdDWaeqUyzPAlofWwMtIeL58Q08/2AbpxfZQWrFitaOpMnTxaPeCCi21kDBw7Et99+K4w1a0NGKJ988gmGDx/e4OXOnj0bP//8M+644w7RarJq1SrvtL59+4qKDvLOyMnJwffffy+e/+qrr8Rrxo0bJ1pByHTz1VdfFX1NF198cSRvr0WixATNx00j+uPCHjfD7HwD+7UviTYQqqz4acr0BmWpB8Ko1YiYsmBQVvNdRx6LKhXgsrwJdfEMcPMzYFF0yKVtvHrEODg0briML0JdcgNcltdgr+omKivuCuN9eEQLJFdXWrj2SLFCEjfsKC7CvrJSTOrnc+JtCM4NAKML9tiebPeuykN6I8aZ+pJm1CJBq8bGgnL0zwjSMkNxZI414Nwd8uIm1tDdqVGnDsPHc77E/h0H0LZLyx8ESeILOifWd/7XqdU4+t03Q87jGzcYTlTqVxdM9o4ZSLwQkeWMQaNSQV3dChzqfE2iBSfRgi4iyh4LKVYEeo923TnQ06sr34+aWOHdNnUqYLpBGInD/qcUKyRxC+2rpy54L+C0J4+bKPbRtiYT9gfxc0nU6iI6rgTbtyN9nUe0gKfSQtMDLOluZTwvprUBUuaCF1HqRzqY+QEwtWJ2LcQHy5OiTYQkGCFWUOuZmJYBmB+Gm+JNKcmQ2kBagVgRb0QkWFx33XW46KKLcNlll+Gkk04Sz1GM6N69e4XRJSWFXHUV+Qg0jD/++EP8fPTRumkSP/74Izp06CAqO3wjVOm5vLw8PPzww6KNhFpIDj/8cLGNh5IQ0lJ56h/q4eqPcw/7ENM++8nbBmJ3udAzgvJyX8odjpCZ6r5ZzZybgYyfwVhCVLOH/XLa3aPEOjSqZGRHUN/pES2odlxWVkjihZ92bBcXD30zGmZe54E7Nih3+wK080ULp92F/esLMOQcnzsdjQi9tewkgxAsgqLuKi4k4NoJaMgUuOkYfHQ/fP3q9/jihW9x+ZOhTaYlkmjje94MBlVKxEJ89YwZFl0wGeW794he+IZU6HpFC/0xSpVEEPEx2Ht0q88DSzg3Jud4j2gBXCXHEJJmyS3fK4b9JFoEG98HExfCOa5E83X+osXr4hLXI1bUTCPRgqq8VV6xomaaIloIwaJarKiZlgEkPQCbrVJ4VrSWNpA33ngD8ULEFRZUxUAJHJ7IUY/I0LFjRzGtd+/eDV7uTz/9VO888+bN8/t90KBBdZ6T1C9avLB8A6pch94GEimMaQF1WmzXIYSQQxNDah/sJJKm5ued29EnPQP6CPweyL+C8sqhrylvjAUHNhbCWeVCRncLmoqOZj1+2VUEh4tDq2aBnb+ZAXCsanLBQmfQYcQJg7Ho9R8xZdY5MJpavgmfRBItRHsIIvM7U2lie6dUiBYSiaRRCSVAhqqOCDWtypGMTVty0Lt366mu0MXQ56yhROxwNmrUKNEWQlE25AxKpXxU0dCvX7+Y3rmTRIemFCskEklklFZV4Z99+zBlwKDIFiDMqMi/okvM20FUGhXSOiejqeiUbITDXYitRRXok173bg1janB1V3D7SjDjmWhqRp8+Ar9+/Be+ffsXnH7NCU29ORKJRCKRSHyIVXQp2SA8++yzopuAugaoW4Bu/N9yyy047LDDhI9EUVGRKA548MEHsX79eqSnp4tEzQsuuMBvWXa7HS+//DK+/PJL7N+/X8QJUzcEeUTWFiA+//xzcdN/y5YtYlrPnj1x5ZVXYsyYMWK6x7/CtzAg0PInTJiAo48+uk7XxNy5c8WyqTOCbBtoPgrtiIRDtmSn8r1AcTYSiUQiiS6/7doJF3djUNsAub9hwB3rlcg/lX8pZLTZszJPiBXqAAkdjUVmgg5GjRrrC8oDChYCTQ/A9jm4Ox9M1bTVVClZFgwc1xcfP/UlTrnieKijFPEqkUgkEokkfrnvvvtEEQDZLXTr1g3FxcUiPGLbtm1CsCBKSkqEFcMJJ5wgBIjFixeLTgeK9D377LPFPGSbQIIDvfbcc88Vy9q8eTPeeecdUVzw4osvwgOJCc8//zwGDx4sbBRoOatXr8Zff/3lFSxqE2z58+fPx7p164QlA0EiBSV+9urVSyybxJBdu3ZhxYoViJSIBAuqqqAP8eSTawwWf/vtN6G4kPJCz5M5pkQikUiixw87tqGT2YKMhAj7Ox1rq/0rYvtX2bX8ANr3b1oBQMWoysKA/w5acVavIH4fmu7UGwLYlwGGiWhqxp17BJ654lUs+Wgpjr4g8IBBIpFIJBJJy2HJkiVCAKDQCQ9UPeFLXl6emO4JlDjvvPPEa5566imcdtppQnCgqoc///xTVEQMGzbM+9oePXoIUYQEgyFDhgjx4IUXXsBxxx2H5557TkTFhlNFEmz5nTp1wkMPPeRdPlVXOBwOvPbaa0hNjU5bXETW6E888QQWLVrk/Z2SOa655hphuklQycrChQujsoESiUQiARwulzDcHNK2XUQfB3eXKgaTZDYZQ0oPlKNknxVZvVJiup5w6GQ2YEtxBWzOwJHLjOlFWgi3/4V4gOJNew3vjvcf/iRkdLhEIpFIJJKWQXJysqhuOHDgQNB5NBqNECk8UNUC/V5QUID//lMi2r/55htR9dC1a1cRgOF5eCof/v77b/Hzhx9+EGOMq6++2k+sIELZOgRbvicZ1LN8ej8EtbhEaywTUYXFxo0bcckll/j1wNAb/vTTT4WSQn0yCxYs8PtgJYdO3p58rP9zE/5etEJE340/fzQys9NgSDQ0eFkUVeRr2kcpIbVdwCk6qCFOvfX1dsWq98sXyn6niKZQNPR9SSTxwD85e1Fmr8LQCAULOOiExmNuMLl7uXLCzezZ9GZzXcxGuNxcpIUMygpiyqc5TGkLceXVcQZvCo658Ei8eMNb+OOzf3HkmT658JK4JH9fIbat2iH8R8wZyTj2orHI7JgOk6VlnWMomShUbLlnnkhxuN3QZmVid7kVqsqKgOdreX6XxCPlpRXI252Pn9//Hfk5hRh9xgj0GtoN6R1iaywfLRK12nr37S6Wht+A2FdaGnI8Tvt2++oL29YOeVVQ9cT48eNFC8i4ceNw+umn+6VdZmZm1klQ6ty5s/i5b98+EUJBlRPUAUE+k4EgcYPYvXu3uG4n8aEhhLv8E088ER999BHuuecezJkzR8xP1RwTJ06sI5DEVLCg+FCLxeJXyjJ69Ghv2Qf9/9dff41ogySBydl2ADePnykGRx7ee+Bj3Dn/ehxx6jDoE/QhP7raucYkUATLXQ6UqR4OZoMBH59zfsjpsSac7PeGvi+JJB74ZusWZCYmRjRwILhjNaDKAlOZEEt2LzuApKwEJFhCH5MagzSjFma9BusOWkMIFj3oUguw/wEYz0BT03VAJ/QY2hXvzlqI0acPj/jkLmmcmwh3n/gwdv63x/vcR09+gUsfvQgnXX4cTObw4znjncp6YsuJry9QDNpqjzlcFEcf4IaF74XSlsICb4RisPO1PL9L4o2Kskr8+N6veP6amvjH799dgvbd2+Cx72ciq1Ns/aKiQaXDWe++/eX5FwWNL6V9PRC0v4a6zvji/IukYFENXeBTi8X3338v2ikoTpTaKchjgsSLcKFqBjLOvPPOOwNOb9Pm0OKbgy3fZrMJEWTs2LHid4PBIHwtqOLil19+EbYR1JlB3Rdvvvkm1BGI2xEJFhkZGUJh8fTUUCnKmWfWuKyXl5fLQVaU1duXb3rLT6zwVCw8NuV5vLnhWbTrltWgXONYZKrbnE6c/dGCoNODHewkEkloaMD/7bYtGN6uQ0QpTJy7lPhO7YCYf9Q7/9mPzJ5N3w5C0EfV2WzA6rwyXITARqWM6cA1fcBtSwDDaWCs6QWCCVPHY+51b+KXBX/g6ElHNvXmSALgsDvw6bOL/MQKD6/f8R5GnjgYJnPHFvPZqcMQznznqT3mCASNQ+q7UJJI4pmCnCI/scLDvq25eP+h/+GqZ6dDb4yfaMhAaNT179tatRo905rWl6qlQxUUF154oXhQpcIZZ5whvCE9gkVeXh4qKir8qizISJNo3769+NmxY0fRBUEVDaHGijQfiQ90Ld+Q4Ixgy6ftMpvNolXEA91sofnoQQIHvZenn35aiBhHHHFE43hYHHPMMXjvvfdEtAr1v1AfDZV6eNi0aZNfGYvk0CjNL8NfXwV2VnU5Xdj4N0UVSiSSlsqynH3Ir6jA4R0iPK46twPcWm0yGTsqS6uQu7EQbfs0fTuIh+4pCdhnrUJ+hT34TLpBAM8HqAolDujcryMOG90Lb979AexVjqbeHEkAivNK8fWr3wf9bH758E/5uUkkLZy/v14edNr3835FycHSRt0eSfODIj+pc8EXigolAYOCLDw4nU4/f0iaRr9Td4MnSYQSRMgH48MPP6yzHqqCIGGBOPbYY4WgQMabtT0mQrXvh1o+bY9n+ZRyUhuPMOL7nmJeYUEeFWSyQd4VlBX7yCOPiDxYT5YsmXKQQiSJDiRKhPoClZfU9HtKJJKWx9dbNiE9IQHdUyITArhjOcASAZWiwseKXf/mCpuMNn3ip3eXfCxImV+dZ8UxnYN8fvS5qNqB2xaD6QYjHjjx0mMx59KX8Nlzi3Durac19eZIasM5bOVVQT8Xa1G5/MwkkhZOqP3cUeWQ5smSeqGuBKqimDBhAnr37i0qKCiJY+3atX6pIZmZmaJNhPwqyLuCWiwotfOBBx4QCSEEpYVQ3CklglAlAyV2kCCyfft2cW3++uuvo3///iLV44orrhAxp5MmTcLxxx8vig9onbSem2++OeC2Bls+RZvS8t9++22xfBJCli1bJt4XVX9Qxcj7778vWlKGDh3aeIJFYmKiMNEIBH3Q5F9B/SuS6JCQnIDsXu2wZ1NOwOmHje7dKj5qKj/S65u+L14iaex0EBIsRmd3irAdhCuxneruEb2+Iez8OxemdCNMGYF7WpsCg0aFDskGrMwrCypY0MfCdSMA22fgjq1g2thWooQD9T6POmUY3nvwYxw7eSxS28RHm41EwZhkxJBjB2D594Grcsh4T9I8oGNkrI+NkpbJ8BMGY/5D/ws47bAjeiEhOX7Ohc2R1rBv0vXyBRdcILwrvvvuO/GeqfWCRAESEzyYzWaRwkndDVThQIUCM2fOFNGmHjxVEyQcUFEBeWIYjUZ06NABkydPRpcuXbzzXn/99eJ56pigVg2ar1evXkKUCEaw5bdr104YanqWf/TRRwth5X//+x+KioqQkpKCESNG4NprrxWFDo0mWISC3kykGyMJTGobC6594VLcftwDdSotxpwxEmntWsdAVq/egx5dGNwt/OAlkfjyx57dKLLZcER2hP3wrr2AOxfQKWZIsWT7n/uQ1Ts17gYY1Bby+95i2F1u6IL162r6AOx38Mr/gWlvRzwwYdp4rP7lP7xyy7u4873rm3pzJD5QCsiMxy/CmpH/wWF3+n02vUZ0R8c+HeTn1QzgrgOAcwe4dgCYquWYpEoaB/KPGziuL1YvWe/3vFqjxpVPT0NyqrweihTOqwDHOnB1OzB1YA+qlgBVNtx2221hzduvXz+RwhkKqraYMWOGeNTHWWedJR7BmDdvXljLp1YQqvYwmRRTd493RTRpencxSVj0GdkDz/7xIPqN6S0OhOntU3HV09OEkGFOb/mxQNy5BSiaDK11uhAuJJLWwicb/kN2shmdzDXJTA2B2/8GmAHQ1CjrsaC8oBK5GwrRrl/8GXP1TEkQYsW6g8HLd4XIoh8HONeCk0FpnFTXnTjjWPz0/u9B7+RLmg4SJeb++yhGnjQEGq1GnIsn33cOZv3vVqS1bR03Epq7WMGLrwcvmgpU/Qbulu21koaRkmURaX3TH7pA/J+OA1R5NfefR9Clf8sx3W0SscK+HLxwMnjRDHDX/qbeJEkTE/UKC0lsMCQa0Ofwnrj/s9thq6gSlSypbS0R38msHXMabJ5oLrOhyyMKKypgUu+BpngKwIuUJ4smw255F/sqMkXue7mjxsCFfqeoJA9qxuq4m0eyHRJJU1BaZcP327fizD6HHUI7yF+AuicYa3iMVEPY/qfSstauX/z4V3hIT9Ai3ajDstxSDGkT4o6Xpheg7gJufRswPwKmavpjxfCJg7DyxzV4asbLeG3tU0hIavptkihodRp07d9JXLBUlFaKfTSljTmiyLZ4JxZjhkStznu+pmOVp/zb91hH8zQkKpXGAJ4UNJo/WFKJR6yAQzE05yXXgZmfA9cfKSstJA0irV0qzr3tNBw3dTy4i8OYZBAVWM0F3/0w2L7oux+GC+1/vssNND0QJZUlSMBaqEsuI6tJwLkZ7qIZcCa9hD3lCTDrDfUmEElaHlKwaGYkpZrE41AJJ3IsHpZZR6wgeJF4Tqd9EeWOTpg4/52gr6fBjYxikjRXPt+0EQ63G0d27BTZApzblHYQ/VGINdv+2IeU7CQkpMSnf1HPVCOW5ZbgEnd7BOsKEV4W+hOAitfBy98ATFc3eXsLrf/sm04RgsUrN7+DG1+9okm3R1KXxOQE8WjJxOL8TjcbTl3wXsh5fEUS320gUeKkEOd+z2sDbXNtsaL6WSlaSCKGRMr0dvGTjhXL/TBc2icni0dDKyv8xIpqmHMz1KVXwOl+FCVoJwWLVohsCZHEdRtIHbHCO7EIbR1XobOpAF0jTE6QSOIZusOxYN0aDG7TFpYIq4K4/XeAJQHqzlHfPr/1cI7Nv+xBu/7x1w7ioXeaCVa7CxsKrCHnY2oLYDgJcPwFXrkgZEJTY97BO+XKCVj0+o/4/dO/m3pzJJJmS2Cxwl+0kO0hEknTtYHUFis8qFxb0F11B7ITW1/r1rx58/DVV1+hNSMFC0lcwp27wQsuCixW1Kq0ePeUEUg3tuy7W5LWx+oDudiQfxDHdOkW0eu52wbY/wDITC7GVQK56wtgPViJ7EGZiFfaJuqQYtDir5ySeudl2j6A/jjA9jV4+Utx0dtOPgn9j+yDJ6e/iP07DjT15kgkzQ7uygcvviGIWFFLtLD/A84djbh1EknrhXM34FgLXnRpQLHCV7TQlF0J7jrYqNsnaXqkYCGJT1gCoBtZ72xcNxJbiypRZKtslM2SSBqLd1evRGZiIgZkZUW2ADLb5JWAZiBizaaf90Br1CCzV/waDZJm0yctAf/sL4HTVX/VBNMNBwyni0hYXnIzeOXnTTpIItHpnFtOhSFRj/vPnoOqyqom2xaJpFnCDGDG4I74XtTtAE03MKZtjK2SSFo9jKmq97v6zcG5/hRyq2n1n1lrQwoWkriEqdPBkmeC6ycGncemPhYVhttxyVe/BjTfkkiaKwcryvHVlk04rmt3qOhE3kCEaVbVt4Cmu9LiEGM2fr8L7funQ62J71PKYelKW8iag6HbQjwwbV8g8XJA3Q288jPwkhvhLr4Zbusr4LafwF25aEzIcHPKrHOxa8NezLn05bhoV5FImgtMZQIME8GSHw4+k7o9WMq7YJrsxtw0iaTVwyi+NOU1cE2PoJ9FqfZalKpOA1ObW/3n1dqI79GlBK1dtKg03iGEidrQc8us07GrTCPFCkmL4/3/1kGrUmF8pwijSJ3/Aa7dgHY4Yk1xjhU56/LRaUQbxDtZiTrx+H1viFazWjBVEpjxRMB0PWA4C1BnA86t4BVvg5fcAnfx7eC2b5QWnEagffe2OO+20/DzB79j3uyPGmWdEkmrEC2kWCGRNLlo4Ux6GW5194BixcLtfZBvk3kRrREpWEjimpxyHf4tu9hPtPCIFVRZIZG0NCqcTnywfh3Gd+6KRF1kZY+88gtA1UZEdMaa9d/uhEqjQoeBGWgO9M8wiXhTqrRoCIzpwLS9wAzHgyVeDJhuAgznACozeMX7Qrzg9n/QGAwa3w8nXHI05t3/kTDilEgkhyhaSLFCIokLKLp0G3/UT7TwiBWPLV3dpNsmaTqkTCWJa5Sc5i5wJ94DXqkRJdBu451oo9Lh60mdRTZ0qLilhubCSyRNzbe5+2BzOXFSj54RvZ47NgLO9aIaoDESOdd+uQ3tB6RDl6BtNoLFz7sK8fveYkzsmhbxckjAgLaHeHBXMWD/Edz6HLj+KLCEKTHvfz960pEoyS/DM1e8gkRzAsadMyqm65NIog2dnz3nb7fbDXtVFXR6PVQqVb3ncN/Xhlp+KNGCk2hBx8zyF8FS3pFtIJJWSe19KdC+2JhjabPegBK0hyvxFbCyK8D1p8KtOg1HddXgqK6D5Li+lSIFC0lc45u77lTdjfJyK7Tcgp5pMhVE0vKw2u34fN8eHNmhI1IjSL4R3hWVC5TqCk1kgkdDKNhZgn2rD2LcNYPQXEjUqtEjJRE/7irAhC5pURF1yCeEU7uIYzVQ9Y2IToTpRjCVMaYmnKdfewJs5TY8cuGz0GjVGH36iJitTyKJ5fm9oqICG3bvQfc+fZCQkNCg10aKV7QgkVEduXgpkTRnau9LDd0XY7c9aeCat4XBZqrajNRD290lzRzZEiJpNtidSdi6vbipN0MiiRlvr12FKpcLJ3ULbjoVEvtS4a8A/TExjzIlVn++VaSDdBwSYZJJEzG0TRL2lVVhY0F51JZJHzfTDQSMFwCubeDWJ5Rc+RhCd7/Ou/109BvTGw+c+xR++99fMV2fRNLSINFCihUSSXzC1BnSYLMZsGPHDjz88MMYNGgQRo8ejccffxx2uz2q65CChUQikcQBB6xWvL1mNUakpSPV2PA789xtBa+YD2j6gGk6Ida4XW6s+HAzuoxsC41ejeZEJ7MRGUYdvtlREPVlM01HwHg+4NwJXvYcOG+YV0ZDUavVmHT3meg/tg8ePP9pfP/ukpiuTyKRSCQSiYQoKSnBZZddBqfTieeffx433ngjPvzwQzz66KOIJrIlRCKRSOKAx//8DVq1CkdmZEXWClL+DpVYAPrj0Bhs+XUvSnPLMe7qgWhuqBgwrG0yvt2RjwNWO7JM0c10Z+r24MazgMqF4BXzwBKnIeaixZ1nQm/U4fFpc1GSX4qzb6KseolEIpFIJC2ZsiIrig6UoLykAiZLAiyZZiSlmBpl3QsWLEB5ebmosDjyyCPFcy6XC7Nnz8bll1+OrKyslldhsXjxYlx55ZUYO3asKCs57bTT8PHHH9ebNU/TX331VYwfPx4DBgzAeeedh1WrVjXadkskEsmh8G/OXny6cT3O7NkHRnUE1QpVSwDHUkA/QXHAbwSWvrkO6V3NSOvSPPPQB2SaYNSq8fX2gzFZPtN0AfQTgaofwG2xT/JQqVVCpDj6gjF45ZZ38fLN7wjzNIlEIpFIJC2Tg3vy8dD5z+CSvjfgulF3YXqfG/DwBc+I5xuDX3/9FSNHjoTJVDP2POGEE8T4448//ojaeuJKsHj77bdhNBpxxx134KWXXhLCxb333osXXngh5Otee+01PPfcc5g2bRpeeeUVZGRkYPr06dizZ0+jbbtEIpFEQpXTibt+/B49UtNwZHbHBr+eO7aCV7wNaAeDafs2yh9h/3/52P5nDg47oUujeGXEAq2KYUSbZPyyuwgFlY6YrIPpBgHaoeAV74I7NsdkHX7rYwwnzjhWmHF+8szXwteiqjK2PhoSiUQikUiaprJizqUvY/n3/nGvy75bjadmvCymx5rt27ejc+fOfs8lJyeLa3Ga1iJbQkikSE1N9f4+atQoFBcX46233sJVV13lF3XloaqqSogUJFCQYEEMHToUEydOxBtvvIFZs2Y16ntoLhwsL0eRrTLkPBRjdKgu3BKJJDTP/r0Uu0qK8dBRx0HVwIt/7soBtz4JqNsB+uMb7aP+5YVVMGUmoPPINmjODG2TjL9ySvH5ljxMH9A+NivRHwu4D4rIU5gfBFNZEGvGnDFSlIS+/9D/cNO4+3D/57cjrW1KzNcraX3IsYRE0rL2Ybozrs3KxO5yK1SVFeI5eT0QnxQdKKkjVviKFjQ91q0hpaWlSEpKqvO82WwW/hYtUrDwFSs89OnTR5h3UMyOb7mJhxUrVsBqtYryEw86nQ7HHXccvv/++5hvc3OFDk4T51PPe3Aol1kKFhJJ7Phn3168svwfnHtYf2SbzbDZbGG/lrtywUsfAZgRMJwFxhrH+HLfmoPY8O1OjLl8gGhDaM4YNCoc0d6Mn3cV4YSuGWgbZS8Lgv4u3HA6UPGmIlok3QXGYn/q7Te6N6565mK8de8CXD38dsz+7Hb0GtYt5uuVtC7kWEIiad7Ifbj5Ul6iCErBqCgNPb05EfejzeXLlwvDjkBiBeEpN+natavf8926dUNOTk6DLgAkEomkscivqMD133yF3ukZOKVnrwa9ljv3gpc+SHaLIkaTqRqeKhIJbjfH17P/REp2ErqNiVFFQiND5ptJOjXm/ZcTs3UIXxHjGYBzG3jFAjQWHXq2w3UvXIpEcwJuGnsvvp8nE0QkEolEImkJJJoTQk5PSA49PRpQ+wcVDtSGqiuoyqJFVljUZtmyZVi0aBFuv/32kKUoVFGh1+vrfIBkxkkfmMFgiGj99Hqq7DgUKisr/X7GC+GYsdE8h/r+o0m8fpbNkeb0WSYkxO6AG419PBIcbheuWvQVKp1OXDpgEOxVSl613V7l9zMQKtdGaKpeAGdJcOnOApxUFRDdvOtg/Dt/I/auOogJdw+H2+1CNDwdnU6X38/Ghppwjso249OtBfhrTyGGZsWqfDILTDMO6qpvYOcdUOUeLJ6tCvG3jgZ6kw4XP3IBvnzhWzw+dS7W/bEB0x+dBK1OE1fHnVjt5021j7eW43S0xhLN4b1Gk9b4fuPpXN7aPv+Wdj3QFMTjdyYpNRHDjh8o2j9qQ8/T9Fj/3Tp16oRt27aJzgYPZWVlOHjwYJ1ighYpWOTm5oosV3IenTJlSpNsg8PhwIYNG6KyrJ07dyKeoP60+rBXVWHD7vgzLo23z7I50xw+S/KkaQ77eEMGVi9v3YQVeQcwpUtXlOXloSzA8S8QyfqVyDAths2ZicKKceC8nIoCG2W787eU4rvHlqHzmExo04GiwqKoLr+stBRNRQaddBPVeGvdfqQ6k2DUxKr4sD1SE7rByN9CcTENEtsH/VtHmyFnHgZjhh7fvPEzVv/2Hy589HSktou9n0a4x51Y7edNsY+3puN0tMcS8fxeY0Frer9paWkxW3ak+3lr+vxb4vVAUxBP3xmVSoUrnp2Kl657x8/LYuhxA3HFc1Oxc9+OmKeF9ezZE59//jkmT57sfe6bb74R2zZ69OiWLVhQ1cSMGTNgsVjw/PPPBzTb9K2ksNvtwnzTt8qClkGO6YdSjqLVatG9e3ccCqTE0ZebHFQpASVeIDOd+tDp9ejepw/ihXj9LJsj8rOM3j7eUJ5b9jd+ysvFJQMGYXQH/1QQqqygC9g2bdpAp/OtGnNCbV8AtfMXuNWDoDEchUxTcM8Kt9ONqnIHwAFdggZq3aH5WxTtLcN3dy5BSrYJR0ztf8jL84UqK0isSEpOhkbTOD4cgTjZlIQ31x3Al3kuXDu4jai8iA2nAvaP0CHlQ+wqnIa0zD7Q+/2tY0fnTp3Rf8RhWPjo55g7+R1c/fzFGHPWSLTk405T7OOt6TgdrbFEc3iv0aQ1vt942s9b2+ff0q4HmoJ4/s7cPu8alBZYUVFSgQRzApLTTKK6MgOxEwk9tG/fHj/++COeeuopcb1+4MABPP744zj//POFpUOLFSzIc+Lyyy8X5SQLFy4M6Dzqi6fcZMeOHejdu7eft0W7du0ibgchSPCIVgkbfbljWQ7XUDzOvyHnUaniapvj9bNszrT2zzKa+3g4lRWUCPLqqhW4oN8AHNO9Z9B5SazwHLuEuab1BcC1G9CfCLVuEDlXeHG73Ni7+iC2/bEPe1bk4cCmQpTlVQixwoMxRY+Mrha0PSwNHYdkofPItkjKDO99715xAAuu/AEqjQrH3TIc+oTYXFyTWKHRNN0pKUWjwcndMvDRpgP4ZlcJTu1BdRexgWvOAa94B+3N74Fr74bBEL0+z/roPqALbnr1cnz81Fd4YuqLWP7tGlz93HQkp4Y+1zbX405j7uOt8Tgd7bFEPL/XWNDa3m+87efy82/e1wNNQTx+ZxISEpCSaWmydVNa58yZM3H11VcjMTERZ599tuiSiCZxJVg4nU7ccMMNQmyYP39+WMrMkCFDhCHn4sWLvYIFlYZ99913GDt2bCNstUQikYTG6XbjwV9/xrtrVuH8w/rjlJ414moogQNVv4JXzqNTApAwFUzdxjtt/7oCrPxkM9Yt2oHy/EroEjXI7JGCLiPbwpRhhD5RC6gYHBUOlBfaULLPig3f78Lf764Xy8jobhHGmTR/h8GZMKUbxaDPs/zcDYX45731WPHRZjHv0TcMgdHcOJUATUWvtASM6WDBgg25SDNqMbpDbAYAZJLq1J4N5poPje0JcP3dYKrY3wnxYDQZcdG9Z6PvqJ74bO43WPHDWlz97MUYd+4R3u+ARCKRSCQSSX1Q8cDdd98d0xbuuBIsZs+ejZ9//hl33HGHcBxdtWqVd1rfvn2FuebUqVNF+ocnspTaQKgig1pHKBaVemk++OADFBcX45JLLmnCdxPfUKYyxZbWN49EIjk0CioqcMO3i7B0725cMmgojulaf7Qk4/ngZe8DzjWAZiBgOA6M6WArs2PNF1vx7/sbcWBjIRJS9OhyeFt0Gt4GGT1SoFLVf7FZUVyF3PUFyFmXj3Vfb8dfb/8nnjck6xTRQsVQdqBCrIsEiuGTeqPP8Z2afYRpuIzNTkFZlRMvr9wDNweOzI7RXQuWjHzrccgy/wxeMhtIugVM498iFEtImKA+1+6Du+DT5xfhoQuewdevfo8rnpqGbgM7N9p2SJo3ciwhkbSsfZg8D8izgtpAPC358npA0tTElWDxxx9/iJ+PPvponWnUH9OhQwexI7lc/m7y5HdBdwTffPNNFBYWok+fPnjjjTeQnZ3daNve3MhITBQPiUQSG+iY9O22rZj5yw+wu1y4Y/RY9Musr2qsEqnGJdBWLgVYAmA8F1B3E8kcyz/ciLVfbIPT7kb2kEwce+swtB+QEZZI4UuCRY+uR7QTD8JaUIn87SUozbGistQOKuyg5ad3NaNtnzTRCtKaoI/zpO4Z4oL+pZV7kGO14exebRALvcbpToZLewE0zs/AS2eJKhroxzZqlYM5PRnTZp+PDX9txpcvf4crh9yGseeMwqS7zkTXAZ0abTskzRM5lpBIWtY+TKkSZLBJnhXx1vogab3ElWDx008/1TvPvHlUHu0PDe6oyoIeEolE0tSs3J+DOX/9gT/37MaQNu1wyeChSAlh0sTdJUDVT9BVfoOUBBvc6mEoyBmA9Yv3Ys0XHwtBgaof+p3UFT3GZyMxNXJvntqY0oziIaklWnRLR6pRiy+3HsTqPCum9W+HnqkxGLwxE5AwGaj6DrziNcC+FEiY1KjVFkSfw3ui57Bu+Peblfjx/d+x5MM/Mejofjj5suNw+ClDoTe27HYgiUQikUgk8UlcCRYSiUTSXNlfVobvt2/FpxvXY/WBXLRPSsbNh4/GkLbtAt4xFyKFYy24/V/AsQqleWrs+a8rNvxuwN5/81G0+3NojRp0HJqFIef2RNvD0htcTSGJHPqTHdHejM7JBizakY9Zv2/DYekmHN0pFYOzkmCIYuUJY1rAcBK4phdg+wG89C5wzUAwwzhAOxCMNY5YoNaocfjJwzB84mCsXvIf/vz8Xzx4/tMwmAwYMXGQeL7/2D5o162N9LqQSCQSiUTSKDAunN0ktVmxYoUo6SbfjEOBlkEmoBS5JM3MDg35WbbOz5L2wV69ejXpPk7zlTudsLlcqHA5UWq3o5AiSCsrsafCis0lJci1VYIuYbsnJ2NEWhp6JevBHJVw2SvBHWVwV5bAXVkMh7UQ9uJCVBRUoDhHjYM7DTi4TY2KIiUrOyFNj4xeZmQdZkFGLwvUupbeksFF0onikRGf30UOjm1lDqwtsiO30gU1A7ITNchO1CLToEaKToUkrQpGDYNBzZBAM9S3X3EOl8sNNb1vv3ndMGq2IVG7EVp1PjhXo8rZAVWudnC60+F0W+DiiXC4U8F59CptglGcW4qty3Zi5+q9yNuZL9JnjEkGtO2ZiczO6Uhtb4E5MwlJqYlIMBthMOlFkozWoIFGp4HOoA3ruBOL/Txa5/Gmojkdpw+V1vReW+v7Jc+5pj6Xt9bPvyHIz0Z+LpF+ZygEIxb7OCErLIIQrQMYLae5DpbiDflZys8y2t+ncHlz62Z8sXdXvfOR5LDLasWe8gKY5+9E4l8FQeaki3NTrVcqWMscsC7Lx45l+WFvn6RxoG9M2+r/VwHYWv2ojVblFo9DIxmAb9RobvVDgYOB80Zu06i+vVFZZsP25bvFoz5Ovek4jJ86qkmO4c39QqQ1nfNa03ttre83npbd2j7/hiA/G/m5xON3RlZYSCQSiUQikUgkEolEIok7WnqtsUQikUgkEolEIpFIJJJmiBQsJBKJRCKRSCQSiUQikcQd0sNCIpFIJBKJRCKRSCQSSdjs2rULb7zxBlavXo0tW7aga9eu+OqrrxBtpGAhkUgkEolEIpFIJBKJJGxIpFiyZAkGDhwIt9stEkNigTTdlEgkEolEIpFIJBKJRBI2JFKoVIrDxB133IF169bJCguJRCKRSCQSiUQikUhaM3nlVhTbbEGnWwwGZCaaYroNHrEi1siWEIlEIpFIJBKJRCKRSJoJxTYbJs5/J+j0by6cGnPBorGQKSESiUQikUgkEolEIpFI4g4pWEgkEolEIpFIJBKJRCKJO6RgIZFIJBKJRCKRSCQSiSTukIKFRCKRSCQSiUQikUgkkrhDChYSiUQikUgkEolEIpFI4g6ZEiKRSCQSiUQikUgkEokkbCorK7FkyRLx/3379sFqteKbb74Rv48YMQKpqamIBoxzzqOyJIlEIpFIJBKJRCKRSCQxJa/cKqJNg2ExGGIea7p3714cc8wxAae9++67GDlyZFTWIwULiUQikUgkEolEIpFIJHGH9LCQSCQSiUQikUgkEolEEndIwUIikUgkEolEIpFIJBJJ3CEFC4lEIpFIJBKJRCKRSCRxhxQsJBKJRCKRSCQSiUQikcQdUrCQSCQSiUQikUgkEolEEndIwSIImzZtEg+JRNIykfu4RNKykfu4RNLykfu5RNLykYJFEOx2u3gcKlVVVVi+fLn4KZGfZbwgv5fR28ejSWv8u7TG99xa33djv+d43McbQmv6jrSm99pa32887eet7fNvCPKzkZ9LpN+ZWCIFixjjcrn8fkrkZxkPyO9lfNIa/y6t8T231vfdGt/zodCaPq/W9F5b8/uNF1rb598Q5GcjP5eG0hj7kRQsJJIWAOfypCuJ1XfLKT9aiUQikUgkEjnGahKkYCGRNHO4qxiwfQfuymvqTZG0MLjbBtiXgzv3NvWmSCQSiUQikbQYxNjK/q8y1pKERBN6skQiiXexgld+CFifBHRHAuZHwNSZTb1ZkhaAOIE6loEXzQDU7YCUd8A0HZp6syQSiUQikUiavVjBi6YBrn1gllfAdSPAVAY0NxYvXoxPP/0Ua9euRWVlJTp16oTJkyfjrLPOAmMsauuRFRYSSUsQKwj7b+Ald8pKC0l0xQq4ANce8KKpstJCIpFIJBKJJCpixW4xxuLFlwP2fw650oK7Chr97/L222/DYDDgwgsvxEsvvYSxY8fi3nvvxQsvvBDV9UjBQiJpCWKFBylaSKItVniQooVEIpFIJBJJlMQK7wDrkEUL7twFXnyV+NmYkEjx6KOPYtSoUeJx88034+yzz8Zbb70Ft9sdtfVIwUIiaSlihQcpWkiiLVZ4kKKFRCKRSCQSSZTEikMXLTiJFYWTAcdK8bMxRYvU1NQ6z/Xp0wdWqxUVFRVRW48ULCSSZgR3VwC2r+uKFaq2AUSLe8FdhY26fZJmjnN9cLGitmjhOtiYWyaRSCQSiUTSZKjV6ohfy135IcSKWqKFcx045w0TK9y5yhPu3EYXLWqzfPlyZGVlwWQyRW2ZUrCQSJoRTJUA6I8AVBk1T+rHg1meBTNd5zOnBixxKqCK3sFC0gpQtwe0/eufL2ESwPQBJ3HXAXBnqBNyy4O7K5VBg7u0qTdFEidUlFViet8b8Mad85t6UyQSiURyiOi1RejVo+FjakrwE+IB0wEJk+t/geYwQN0hLMNKXlus8NCEosWyZcuwaNEiTJ8+ParLlYKFRNLMYJouYKnvKaIFiRWGk8ALJ4mLJUW00IClvA7ohoHRAVIiCfe7pc4CszwPaAcFnynpdjDjOWCq5MBiRfEt4IUXgDt3tBqxAvbfwfOPB6/8RIoWEsGyb1dhz8Z9WPDYZ2HfKZNIJBJJ/MFd+4GSK6GvuBwGbX4DXpfnHRPBXQBmPANIujv4CzQDwFLmgqnbRC5WNKFokZubixtvvBEjR47ElClTorpsKVhIJM1UtEDqB2CGk0UyCOAEKt5WRIu0/0mxQhIb0SIMsQKOvwH3QfDCi8IWLVQqVfMWK4qvod+AsoelaCERbFm+3ftJ5O7Mk5+KRCKRNFOxghddDubcKFpiUXxxWIlpHrECjr8Adz544YWhRYuGiBWuAvCS24KLFb6iRcltjZIeUl5ejhkzZsBiseD555+P+riueY4SJZJWDF0kuV35YM5t4CV3KGKFBxItbIsBd1FTbqKkJYoW4YoVHsIULdSqKvTr2xYqFj036SYRKzxI0UICYM/mHGRkp4nPYu+mHPmZSCQSSTMVK0BiRQjzcfL04q7cwGKFh1CiRQPECoKp08DMjwOqeuZXtRHz0fyxxGaz4YknnkBZWRlef/11JCUlRX0dUrCQSJpdr/wOMMdq8OKr/cUKD+Uvg1fMExeREklURIuGihVhihbcXQ6t+zeoi46DTrUZnAf4PjcnscKDFC1aPfu3H0DX/p2g0qiwf7ussJBIJJJmL1YEEC2EmWbpTPCiS8BdOYHFilCiRQPFCg9M0wksdV5w0YLEitR5Yr5Y4nQ6cfvttyMnJ0eIFWS2GQukYCGRNCuxYhuYez948bWBxQoP5a8K0cLtlKKF5FBFixdCiBV5wcWKekQLEitQ9SNQeguAKqBoKuBYG/eihSJW/BFcrKgjWpQ05uZJ4oT8vYVIaWOBJSMZB/fGvhxXIpFIJI0gVtQSLeDaBdiXA85tgGNLcLEiiGjBUl5usFhRr2jRSGIFMXv2bPz666847bTTRJTpqlWrvA+73R619WiitiSJRBLji6Q/gaqlgGEcHabqfxFLAMJwGZZIQn6N1Bn1zKAL81SjriNW8BISKzxUgRdOAUt9F1zbH4zF6+mJ9ildtd7vCj0nM8r7Aq0Qe5UDpQVlSE5LEo+CHBkvLZFIJM0HOs8HTkLzRwfw8pqxAC8jFSGM19F4SBXwRlBDYSRK/J+98wBzourC8HdTN9sLvfci0lXE3hUVe0ERURDFgr1gwYoVG0VRUQQr8FtAsYtgxY6iiNJ7Z3t20+//nDub3fRsyWazm/M+T1iSmcxMJrl37nz3nO/kvl5lwBlHsYL4/vvv1d8333xTPXxZsmQJ2rVrF5P9JOqIkGGYALFCSwHxQEKvFFlZMB6AM/R5Sr8RsJwHnb4Fn0em3hD0+8p6VDN+dXwbeiVd64qLZ4cIYkXjES2ELgXSNAQi+0WtXnoY0UJkPgSknAqhi30uJ5PYFO7Romoy89KRkZOO/F2FDX1IDMMwTDVREQ850yELJgCuP0OvpO8KkXmnFlFBQgWNb4rugMh6XIu9dHwX+n26lqrSXywFBeEVLYpu1zwr4iRWEF999RXKysqwevVqDB48uN72wykhDNOIxApF+auQtu+UaAEYg9/EYgUTZ9FCZD0KmI6so1jhL1okcnqI0JmBCtHCN3KkcjmLFUmNV7BIz05DRi4LFgzDMI1RtBA50wFD/yhiha8g7dREC8s5gOmIuIgVfukh2c/HVayIJyxYMExC41C+FZVihZdwokUYsUKZArl5lo+Jo2gRIFZU/Z7XVGOLdsC9A5Cxy3+sV9FC3x4i923A0IfFCgaFe4orBYu0rFQU7dWeMwzDMI1TtBDpN0BkTAL03SAy7wshVgSIFqkXAKZj4iJWeKnvaiANCQsWDJPACF0WhOVCIP2W4IWBokVYsWI/ZMnjkGVvsWjBxEe0CClW0O85ByJtLJA2LvK2sp4CzMdA6FIT+hurEi2eU2kxIuMuSDOngSQ75F9BkFhBD3ouZQSDVoZhGCZxRYvsqZCeAkjnnxA5LwEkOkTy96JUUEN3iKyHNdEiDmJFUyfxEoQZhvFD6LMAywgtJ670qWDRwnwcRLPPIIUpjFjxGGBbpD2nf1IvrjANZJj68bSgyAhhCG20RKIF0q7QfovWWWHEiuMgdGmN4+uhqj0FVwAeqphypRqUSF0vCBGcKsIkB8X7SmBKMcJoNqooC5fDhbKScqRlJrYAxzAMw/ijhIryeUDZa9pzpEBk3ASRMwuyYBzgWuv/Bl0uRO6bEIau2nMSLaQ97JiIqR4cYcEwDYz0FEctfUiihbCMCIq0EJkPQpgOUB1hNLFCYX1WRVoY9WWx/RAMU/lbbRHywiylE9KT7xNpcUVQpEVjEyuka5Mq2UpihfaCVXvu+hdSRq4gwjRdivNLkFohTqRmUKUYoLSAnOQZhmGYhoTSo6WntPpihfVVwEqRzBXYFkCWPquqiJBoQZEUYcWKikprLFbUHRYsGKahxYrydyHL31H/r4loQWIFUk4LWYUgpFjhxfos9I4FaNeGqxcw8YHECmWiWXQPpHuPv2iReoW2UuaTjVusqFzAokWyQ+KEJSNF/T810+KXJsIwDMM0DNJTCFk2F7B9ElW0CClWeCmfHyxahBArmNjBKSEM08BiBUoe1Z7TP5bzI9Zl9qaHwHwUoG9Xc7HCi/VZ5FrcgP4iGlLH5PMwTESxgip/kOlmkV2ljahIDF0OXObLAeMweEQ7mBu7WBEgWqj0EAOnhyQbJQWlsKRrgoWlIsKihCMsGIZhGlassM4FrM+p8bag11KGQejSayZW+IoWtJ30GzXRQtogDF3q90MkMRxhwTAJIFYoyBiz/H/Vi7Qw9g4pVig8+wHbx1GPQW+fA73eWeNjZ5jaihUKx7fKoNIbaeF0p2HNBh3cHnOjOLHSUwJZ/l54saJyRStk6UxAcoWIpIywqBAsUisiLUoLqheCzDAMw9SfWFH5WvHd4SMtZDlQ9nr0DZe/D8giCH0bFivqGRYsGCYRxIoaihYRMXSCyHnFv9xpICIDzvQ5sDuyar8fhqmpWOElQLSw2WyN5lySUChSL9MinSJhOhwi817NZJRJKkqLyioFC3OqGUII9rBgGIZJELEiqmiha6XSOyAiRX2aIHLnqrLmTP3DggXDJIpYESPRQggTYBoUXrQQGUDOG1i/WaeloTBMPMWKANHCbCxWN3SNCaHPVWGgYUULEiuyHteqpjBJGWGRkqYJFjqdDilpZpQWsukmwzBMoogVkUQLIXQApXOGFS0qxApjPwgRYXKQiRksWDBMPCERwvk3YOgRfh0y73GuBqJUDvFFesqrJ1qIDNUB210dUV7eeGa1mcaDlB5tkFB0a3ixwovjWwjnz+jcMfFu7KWnrHaiBYsVSU9ZcZkSKbykpKfAWsSVmRiGYeKKex9gfT7qarL4fi2NUzrUI7JoETuxQkoZdawR9r2e5LqmsGDBMHFEijQI01CI9FsBQ8/gFQzdITJuhzANgaRIiOpsk0w2rS9DundEFi0qxAraL0dWMPUlVlBJT+H4GciZA+iaR35D2nWAcRAyjO/AZEicKgrStQUon6eElxqJFixWMORpXFRWGWFBUHoIR1gwDMPEGfKWyJ5eYbEZDiNEzhxtzO34FXD8HkG0MGljmxiJFXCvq4juqFlEtXRtrtYYpSnBVUIYJk5QuJmwfwVZfJcmHmQ9BVn6FOD6z0+skIW3KKM+kRnewdi/IsgUwPYepP1TIGeWMv/xFS2kEi1mA7osJVY0tvB7pnGJFTJ/pJqpEFlPQua+DeRfBHj2Br8h7ToIy7nwFF4LnesfSPdmSP3dEPpmaGixQuaPAjw7tSintMshdNkRRQuQaEECZMqJnAaS5Hg8HpSX2mDxjbBINcNanFyzYQzDMA2N0KVCmo5QooUsnOCtxxcsVhj7QDj/gCygMutCTfSpsbMwKdGCqn0h53W4nKXweHohJUZihdxP46VCCIrssJwWsUqgr1ih0m2rOUZpKjRIhEV+fj7Wr1+PDRs2oKCgoCEOgUkSpHQhEVC5cbbPIIvvBEyHUh0kyKJbINJv0W50AsSKqA7GAWKFwrUWsmBc2EgLFiuYeIkV6rWiW9UAACRaBEZaVIgVsvBaCNc/2u/U/hFkycOQFMIZdX/OGr1eK7GCsM5Upc2qE2mB1AtYrGBQXqKl55l9BAsy3uSUEIZhmIYRLVAhWqhIC2M/QN81jFhB9wxOyIKxQZEWdncXbNyaAY/UxVSsUK+V3AeUfxQ10kL6ihU1GKM0BeISYVFWVoZPP/0US5YswYoVK4JEipycHAwYMAAnnHACTjnlFKSmptZqP++//z7mzp2rxBDaRt++fTFjxgykpFSFZjLJg7rxsS+FNB+v3VAkglhBx5JyKqDLhiyapEQLZM2EEE7IwhuCSiCSaBGqVnSQWOGlQrQIjrRgUyAmfmJF5TISLQIjLXzEClSIFZXYPtLmPzLCR1qoC7r9W0jTYAh9K5/XywHnb5D69hCGjnUXK7zQgID+Rou04DbG0HinxFYZVeGF/CxYsGAYhmnYSAtK5xCyBBBmSORCGrsGiBVeNNHCN9KCdIZSqy3mYkXlspL7tPF+mEgLGShW1HCM0tipV8GChImXXnoJ8+bNg8PhQM+ePXH88cejffv2yMzMVF9ccXExtm3bhlWrVmHSpEl46KGHMGLECIwbNw65udW/yZw5cyZmzZqF8ePHK/GD9r18+XK43e76/IhMAosVsvhBgNIkLH+pkO2GEC2CxYoTIYtuB3TNILIegSx5BULYIAtvDhIrwokWYcWKKKIFw8RTrAgpWtiWQKScFFqsqIZoocSK8g8gSx4EDAcAOS8o0UKJFY6fIAvHa9EcuW/USLQIK1Yk2YCAiY3hpjeqwlew2LMlcTxaGIZhkg5hhCChouBKJVggd0EYsSK0aFFXIokV0UQLGU6sSKIxSr0KFscddxw6duyI22+/HSeffHJUAYJSRT777DMsWLAA8+fPx++//16t/VBqCUVSPP/88zj66KMrX6d9MkkuVhBkTEN/G0K0cK0OECvuJttiwLMbsugeiLwFkPuGhxUr/EQLfQdIYz/IsrfCixWV+12riSDZzzdodAnTxPEURBQr/ESL3P8BluFaVFE4scJXtNC1BtKvhdClBYsVxgGAcxVkwXj1G4drvSZWwKO1rfxLqi1aSHe+Fv4ZbiDgxToToDZoORNCsP0TU4MIC04JYRiGaeBS639D5o8GYAdENgRVBQkrVgSIFs0+A1DHsbSk8dIof7GCjD717dS9gp9oYWgPSSbeQlSMUa5I+jFKvXpYTJs2DQsXLsRFF11UrWgJWofWpdSOqVOnVns/7733Htq1a+cnVjDJSZBY4YVEi9JnVcOv2/Z3Q6q8Nnv13qDvCKTf5i9WePHshCx5DiLzvigOxjTiHQ4YukHoLBCp52qlTyMh0rXtNlGllUkQRApE5gPR17Ocr12UdXkQGXcDIivy+vrOEKkjQ4sVKadBpI2DyJpSIcxdrbUr4ZNKWCFaKCftaOjIAPdRzf07EsYhgPkIFiuYakZYmPwiLLzeFgzDMExDiBWXamKFd4xg/wpIJQEjCuk3qDF1xH24tkI6V2tRp2GxQGQ+UmUfWWG+L2j7pkOqVksZplUm8RrkV3uMcghgPrLJjlHqVbA48sgj4/LeP//8Ez169FARFkOHDsWBBx6o0krodSZ5CCtWxEi0UGJF4QRt9tbxS1TRgmokS9dGNcsbJFZ4sc2DtP8EkfVseNEiZThExl0Q+jz1lNI8RM6s8KIFiRUV5UtVSSaGqSeUoEDRQ1lPhl/Jcj5E+s3q96suwGQwq0qEZYUXK3JmQxjahhYrTIdAFl4HWT6vQrRYB1n6DETW0/6DimqKFsp7wtgfIndO+AGBcQhE9pMQ+pbVOCtMMkMVQgizxcd002JGGQsWDMMwDS9WeLE+pyZdkDo2/AbSb4WwkKF2dmSxouBSyPyLtRTZMKIFTTjCNBSCokJFVmWlQFl4PYTlYk20oNTvjEl+6bA0RpH6dhDZz0QYowyGyKTJIAuaKk1Chtm7dy/+/vtvrFmzBvfddx8sFgteeOEFjBkzBp9//jny8rQbvdrkG5FhaF0oLy/3+8vUz7k0GcqgK5sSXqyo3Mg8SJEGp/kKuNwWLdyK8sqiYDYWAcU3aFUPVGTXlUD2i7CjPzye4Gak1zlgxCoI2DUzTV+xQtdKhdJXdp4kWlCnlDUVsojWrToeaR4OmXoHHHbqhHx/i9kwZ70IFF0F4Vpb9TLdsOW8AZurI6TT1iR+l7U14Y1XG48lDfm9VLcthHgn9LojYcx8Eii+1X9RyvnwWG4I+v0KtENK7mvaIEIWVa2v7wxkz0K5IwdwlEGvc8PoWQr4ihXF92ttxPFjRbuZAll0G2TpVIjsZytCPANEi9z3YHNo0Rrh0IleMOW8ChRcDkBzBickzVpkPgGbPSOgDdaNxtQG4/GZ66udx7uNF+7Xfs9SJ2GzaX2wzqSD0+5CUWExjKbqD7uS6TeSTJ81WT9vIl3Lk+3814TGcm6ijVloudmwHQglVviKFmnXaqJF2Sv+y9Jugdt4Dpx2EgnKQp6XFONeoPAywL1dPVcpsjQGd3dWJp2hjlWvGwxT7luQRTcDrv+095G3XfZUSF1f2OzUTqp+y2aTFSi8EtBlQWRPgSy8zW+MosSKtMsh918AZD0Ohzgabk+UqO0YE4/fipC1G6HWmu3bt6uUDzLaLCoqCvqx0ZdKBpo1gbwqNm3ahEWLFqFXr17qtcLCQuWhMXr0aNxwA90E1oy//vpLGYUyjYPU1BR06eCAsXRM5Hx6kQNn+mys3Sih1wt07aSDzW7G2vXB5XV1Oh16dstCSopey8F3BUbsGOBKfw5bdrVCUVFV55KXm4q2zdZDb70TIvsJSOsbgPMXbaG+K0TmJECWQhbSzZ02oHWnTkSp8xikmf6FwXqTuiHzmE5HKa7D+o2hS//q9Xr06JYBc/n1EO61Sqxwpr+K9Zv1KC+vZspKI2Dw4MH1sl1u41VkZljQqb0OJWVGbNxUu/JYzZuloXXuP9BbJ6rnHvO5KPZcEXJ7XTplI0P/PvSWg7V2QKIFRVZk3g2XYxv2Fg3Czt1W9RvvSb9x99sQxq5VYoUvpkMhLBcB0gZp/xKwf+G32J16F/YWD8XO3aHLA/uSkW5Bp3ZFMJSMUwMCj+Fg2EwPYc26wlqKOUxDtvOGaOPLF/yGD59egrHPXVj52sYVW/Hli99h0hc3IC276c6AMUwk+FrOxIqUFDO6dTbA6TJFvD537ZyDdEyHzvFRxO3JTPLC+heibJp67rbchP3W47B9Z+hxg8FgQM9uqTCVXVkpVlQi0uBKn4N1mw2w2x3o0TULBoMTazc44XK50KNbJszlN0K4/w3YqgmujFnYtC0LJaVVAkBuTirattgEQ+mNKgpDpF5cJVp4xYrCWyD1HWC3TMOadSUNVnCivtp43CMsFi9ejIkTJ6ovjKqEpKcH5wRV5uzUANpWdnZ2pVhB0PMDDjgA69atq/XxGo1GdOvWDXVVnUhM6dSpk4r8YOrvXHqEBLJfAwovDS1aiBwg53V43O3RrasbJt0GoPBipBkPw4B+98PupBnUKszGQug8GyGLn4FwrQxxRC4YSq9Fl3YvwtFOi7RQkRWe5UDxLerGijoVpYjS4XjyITInahEXhi4qxFzdrKXfAmk8HWaDBVKXC2RNg7R9CaTdBpMrHb17V5VuDEn2i5DFt0Jk3AuPuxM6dar7uUwWYtHGY0lDfC864YZJrAIKxyI75Sz073sDHK7I+Zrh8OhaQk+RFo6fgdTrkaJ+v6391jEZSqEre1oZx0rLMRA5MyFLHofIfAjS/jv01vvRKuceNGt+moqC0ukcEPrBkEU3BosVhIq00EOkXQnYl/kvy5gEaTgV2bkWZFfTL8sjPEDOq5DWmRAZD0PnzEKvXlHaYC1IxjbYEJ853m38n4wNyrOiY4cqw1fnPi1EuH2b9mjZsXm1t5VMv5Fk+qzJ+nkTqZ0n2/mvCYl+bugu0WzYChRcAoOhO/r3fQp2ZwRfLMNESKuAsC8OvTz1Snj0/QH9AOhVGrUeMJ2LTGMqMrODz0vnzp2RnVHiF1nhh7TCUHoZenV7E1K0AopvgijfiAN60HMLUHR1CLGCcKjJkm6dXoXD0xseWZXSLcmEPGs6UDRBiyyl+4qyecrrS4kVhg4ARV07c9Gjh/+YKx54z019ElfB4umnn1ZfNJlx0t9YQZ3Uli1bQi6z22s/00ziSaxC2KjR12c4XDIR6VxKeYCqDqB8JnxFC5EDkUdVA7ojRbq0vHfl1lsO4VgClACWrAch9NpgUrp3AlRGqHS6Wjc8LqDwKpjpBielPwQZchb73lg5NCWUqnVQHlrh9dpssnMFpPUVCCr1aGgPky6zIjMtFdJzJITpIJXzb4jisaORCkk5cbpsWEw186xI9t9lLNt4LInX9yKlA3D8pVXJgBOw/Q86AVjSb6lldRn6/R4PmA+HIcTvV7ldl05TYoUqFeZYCmlfCkGiXf5lEJl3QabfCZRMhjHTBIN5GITz9/BihRfH95BwKy8YLa3KpQQ8WM7waVs1OS8DIAxTVBtMNaJeScY2GM/PHO827ra7YbaYkJKSUvlaRnaFAOiqXepLMv1GkumzJuPnTbR2zue/cZ0bFUlBZttqjF8E4fxVpaJasp+O4DGVCqm/C7JEqgpkfpCBd9pYGHU52vYNF6u/Rn02jGF+Z9kZthBihUErlVp53+GB8OwCrA8CTqp4KSDkPqD4ERXJER6HSks1576heWtVTuKnqnsDZM/QPLxoi5ZLlFgBQwfocmYpf7v6Hq80JHF15CsoKFBmmLEUK4hjjz1WpYCsXr3ab1+rVq1Cnz59YrovJrERQq+561JjF2lBYoVUYsXqis7OR/F3LIEsulcz7nTvVOUSpeNnzewye7oWnREOfWv10OlSAENnQNcsYHl7CCGrxAovJFqUPOx/HMqYJ7XSYLPan1ufywabTC3EihWQBWM0scJL+f80I6hamtOSEWfY369IhTCfUCFW/AhYZ2niIVX6kPnKi0K9N/1e7WKtywjdpkJtmirpGLtD5P0PIuN+JVb41jGv0Wegeu01bIMM4zXdNPuUNPU14PSWPGUYhmFqK1aMDBhL/wJZeLMyxg//ZiuE+TjAfGLVa5aLIfQd/MbgZK4Z0WBTSnikATAO9XnVqIzHK+8VhEUz1KRS6EqsUO/U/u9bDSQc+vaAvkVQxoFQpp1HQGTPgDAdCehbAaYjlQk/iRVNnbgKFv369cPOnVFq3deCE044AX379sX111+Pjz/+GEuWLMH48eNhMplw8cWaWsYkqWihbxtdrPDi/AlwbVRiBVxbIVJO0W7ciier8KuQogWJEZn3KxNNciMWhs7afnXNfTwrJlbl6Qft89foHS3DxEusiJFoEQ6hRL2eVWJF8JFViBZZKgSSqtxoberNqjYVartZzwDGQcrEU5Y+D6QcXWuxgmHqKliYUvxjerwlTrlSCMMwTAzFimqIFlQpTJlv09jCfKwmWiixog1k8STIgnGQ7h3VPhZKmRUZtwAp51WIFVNU1TKqUiiyn4KgtG4rpWf4+95RlUKhywNSyQQ0DHTPkPtKWAFCkGhhHAjpyVfjN5FxLUDpIklAXAWLu+66Cx988AE+/TRKJYcaQuaIL730EgYMGIB7770XN998s/LHePPNN9G8efXzRZkmKFrkvVshVrgrxAoy5QyVS2lWZRFlyUOaWJH3thb94FgOuMnH4uFg0UKJFfdBFk3UOlHnSn/RwjgAIvPO8GJFTdRhhollma9IYkWQaLE/Zufe49oFWf5GGLGi8gg1k1vHt/C4NcNZYejkLwT6QDMZ1NYlVfegEEz7F5DFj9RoAMIwsaLcaoPZ4h+X6424sFWUPGUYhmFqgErjDiNW+I2lb/IbS0vXFm2S0kOveSCL74EwHwWhbw1ZWlGOnYSQGooWqkx7xi0QlA5ePk95acG9RYkWqrSoK9A1yxxPAAEAAElEQVSmQK8dD5VgV6IFGXvXTKwgJE2Ols0BymYDskSrtOb6LylMwePqYdGzZ0/cdNNNSlC4++670apVKyU2+EIhMCRq1JTc3FxMmTIlhkfLJDpS3cw4IfQtQi+XNpAPp8djh05nhgd5EM0+hixfDJQ+GrC2G5BlgK4NRN6UCrHiR5/FVaKF8qTQpVeIFXcCnr3a/vJHQ+TOhTT2U6IFsp4E3Nu01JRInSyhPoPWoTFM/aIHKNVCmAASLyKh66LaGF0kRUWOZzSkexddVSEM/hdd6SnW8jp1bSO5UVRggNS1hyDxQpIPkZFGHhBZT2jlvyranMh4EDD0rhIrvCjRghyZ79ZmUdy0vvCrbc4w9QGJEkZzgGBhMVVGXzAMwzBVqOuzMEHoIhhnkj9EtcbSLf3H0sKgyoFqggUqRItJIbafV4tbYjek9QXA8RNE5oOAroUqVapEEe+9giyAyLhbGe0rw311n9EMQtcBMnUUUPa6TzT2PWqc48Xj2q3SyQWlfqBCrLC+AlhfqjoEJVqMVFGo0tCzVoUrGgtxjbCgiAeKsqBUjQ4dOiiRgap5+D6ysiL8YBnGR6ygkCu6UQmlino8VgjHb5D7ToBwroTHZYNAPuS+U7WblvQ7At7hgix6QDP9CxQrgkSLZyEyH/ETKzQcSrSoirToAOjbQWQ9ooSQsKScpjo0vpli4gGlWWgpU29W+byEIu0OCMtJKipJWl9WF8vqiBWyZJrypJCurf5iRflCIH8EpPlIzWMiLEYg502tbvm+EwDHL5CUqpV/gTYQyHpMDQyUWGEarEWK+IoVfqLFw5Cu7ZBFd0MWTaoQLhim/qC0D69A4UVv0MNgMnBKCMMwjA9aBMQFkBTR6QkvRtB4WuS+VuOxNE1YiJyXAEOP8O8zHqqq9oWb/AyFdO9R0dVk+C0yH1BprtL6vPKyIHHEm0ouMh6AdP0LWTJFRYOKzEch7d9BUuUQ8uYi0UKJFRSNfRNk/sWQrk1KrED5AiVGqPGPJ4RYESBaNPVIi7hGWLz44osYOHCg+puR4V9CkmGqi9Fg1RouhURRWy0YB/iYzlSKFWTkR7PDBWNVmJUseU6psyrcPOtJSBItSh+v2GoKRLO31Q1RSLHCT7S4HyL7OfWeYBwBkRYdIEWG9pzEDE+AuMJiBdNAooWsEC20MEtraLGi4CrAvQ6wrtOiItKuCBtp4RUrYHtHe154nXK0VrMb5QshSyZrK9IFOPdNCNwPWXJ/eLGigMoTl0MWXAmR9Sik6WBVtlQW36vCMNU+qLpJKLHCV7SAhLCcoqVu0XjIpxoQw8Qam9WGnBbBpm0kYpSz6SbDMEyVWFEwWqu2UfKENsawnB820kJNAua+pqVB1GAsre4Ncl5SYwm41tRZrDAbiiGL7vITK2D7WPtM1hc0w82iWwASNWh/5e9WpYNk3guUfFv1PP0mgKK1lSl/ofLmpPOi/C+s07Ud2r/XKheWRUilTYJIi7hGWJSUlGD48OEsVjC1pmP7TOhtL1eKFYH5Z4FihYYNMn8sRNoYiLxFgOlQTbSgDir3Q8ByLmC5SLtZiqDwVqJu7lyALtzstAugGeWK0C6hz4EwdAxWh1msYBIx0iJQrPBinRU20iJQrFCQZwyJFmQO5TsrQAIDiRZBkRbBYkXFhlQ0k0g5DTAfowY3svghQNrC+NEEQMcrqKykzqcaEEdaMPWDzWqHKSW4thwZb5KYwTAMk+z4iRVeSLSoTaRFtcbSRq3UuaE3RO47EDlzAeNBEOnX1ehWWAkBwgN4CoPECgVFWJNokfs2JFUFKX+zahlFWpAxJ/luqTGJJlrIgss0sYLIpsoif1SJFdqHBvljRIXSZ9WYyI2mSFwFi0MOOQRr1gSoWwxTg8iKLOMCoEybXfWDRIuS5yGoVKifWFGBLpfsddUNlEi9pEq0cK8BLBdoM8eG9qpcEHVoYaFQ9Jw5kMWPhqmlrIPIfl5tX7n5hutoWaxgElG0CCdWRBAtQooVfqLFDarNUNsJFi2O0VI7lFjxVgixAqFFC+ePkCXTIXJmk+tm+A9oHAyRdplWqxwe7TUWLZj6rhISkBJCUOUQ9rBgGCbZCSlW1Fa0qMZYWqVvkLF94Y2q9ChNoEjblxCZk1WUJqWBS/e+6h27lLA7s7XtOH7yFyu8x5cyHLLsTaA81JjoXy2ywke0qBybKLFiFWCd4b9Piuo2HwWYT45wZCbtnBgPhCCBowkSV8Hi/vvvxy+//IJZs2ahoCB6PjTD+HpWUGSF3j439Emh2sTmIdqNVpBY0QYi6yHNc8K9Veu0/ESLbYBnp3IWlqUvayFaoUQLEisoNJ1KE2XcGCIlxCtWDA0SK/w72rfYs4JJPNGi2SeRxYoQokVEscJXtCi6JVi0MB0HQa7arv+AvA8hUBxGrAghWpiOBpzLKkSLV0OLFpVixa0UU+m/jEULJu4RFmaUl1QjIohhGCYZxYoaixZvVVOsuAVwroDInQVZ8gRg/xwof12JCiLnZcD2UY1EC5OhFLJ0unpf0HFl3KU8K6jKWlhCiRZhxApfo1ARVrTwihV9IUTwtaepEFfB4tRTT8W2bdvw9NNP47DDDlNlSAcNGuT3GDx4cDwPiWk0OCEd34VdKkyDIMlRN/tpQKSGECvu8nEJdmqiheVCTbSwfQWV7eUpAZzfKpffINHCK1YU36uVOqWmk3aFdqOX/QIgcqOKFZXHamjDBptMwokWyonaU6hEvajYv9EqjFC5YOdv0dd3b9bcsQ09tecpF2u+EuQ/QyGTZfO0nM2oKR4uSMcvEKaK64S+GaRopzxi/ESLQLEi7Wog7cqATa3U0koYJobYy0iwCBVhYUQZVwlhGCZJkZQmTV4MPmVHw2JbErWKWbXG0rIUcG2AyJsPWTpTEyu8kGhh+xwi53XA8bvyoIsGRYHqhEuVUPXDMgIiY6I2xnGsqN6YSKQAKedAZD4MYegCOL6J8AZPhWhxDGA+PenECiKucSMnn3xykzQCYeof5TeR9SJk4VUQ7rVBy6X9e5WLJks1l16KnIDIDiFWeHFCFt0GkT0NksoL5Z8J6HKU2ioLr9VEi6ynlQoKz/4qscJTrBnqUFkkyyVK3ZWlUyByX9cqgkQRKxgmoTFSekiFqVVgVIIXMnTKeanKpCqHqvVcCbg3hV5fpELkvKKFSDq+9RcrvPsonwOZfhuQfjNQ+nT447NcDJE6GnL/6UDKeVoddH02PJ7MKmNbQ3etvVZ8BpF+vValhC7maeMBKkOmaw6R+4ZKA2OYWOHxeOCwOUNGWNBrVPKUYRgmGaFUBWkaoMYPasxAfm+hoAkHqsYXizLk+s6aWFHymL9Y4Sta0LGREXiFcX+0lBCbMxsWGtOQ6TdN8NC4hMqnu/6DgEWrNlj8CODeEHojwqLGHxItIUy7NNHEdAhE1rOQRTcqH4zQ6LSxS+YdkCU6wPZp0ogVcRcsHnvssXjujmli2J25gGUazLbrIVxrA2ZTL4csGK9uUGSpHSJrGqkcWtmhILECPpEW12sCB1UgcCyHLLgiSLQgwxslUHjFioobKqo0oPwyZKm2ndxX6I4qPieDYeoBuuhJyoEMJ1pUihWtq95j6FTlwB0oWlSKFQsA2/uhxQpC3wnC2BPS8acWDWGdGXxwlvMgDN2UGzYofUWkQeipdjqg0+ngQZUXhzC0BXJnA7avIKn0cNkcbRtp45QoIlJOhjB0juWpYxgVXUGEirBQVULYdJNhmCRGCLMab4cVLSrFipax2aFnV3ixwk+0EED6FVqkaRSM+jJI2xcQVN3D8Zsai8jSJ9UyadGEGeVxUTghWLTwihW6LhDkxUWp6hQ9AYPaXnjRwqCiuaVpMHRk+J9xJ5B+NaDvkBRiRdxTQhimrqxZV6IiLWgW1U+sUKZ6FTdArpWQpSRYZGo3NxFxQpYvgKBKIYRnr+biS2Z+umzIopsgi24PFivS6fVbtHAzwr1eVSKhSiUM05hRF78K0QIwhxUrpKcE0mOtFC1UrXN9pwCx4lVIXSegol2I1PMglXgQIISknKRqk6N8HmA+SRMtfLGcC5F6KWQZ5Z2+BkGhlGSk6wOJFsLYQxMr1At5kJSG4hUrCCuVBZOACF2alWHq6l9BhIywIMGCIywYhklySLRAhWjhN28ea7GCoOhK+9fR17MtUiko0lOqHuGgcYZe7wTIx8v+E4SuRaVYoSh/H9L5F0DRFpl3Afou4cWKwmurDDftn0EWP6DSREi0gLFfeLECNGGaB2HomjRiRdwFi9deew1jx44Nu/yKK67AW2+9Fc9DYhoZbrdbRVqInFmA+YxgscILiRbFD1QY8vl4WgRiPBgi9UItbcQ705v1ZMUN2FyVJkKlgiKKFZUHx6IF00RFixBihXLHtn8VWrTwihWGA6Ez5EFkPwUYh6ioDUEzA6bD/XdofUm7+FKVnqIJWlkuSt8gLOdAGHqpdA9K8RLptynjW6qnTuGZoZCeQkjr64D1ueCFVEasfD6ku6KMGMPEWrAIVyWkhFNCGIZhgkSL+hAraCxAY/qcV/wnXwIRWVpKKfJUmgXsS8KKFpT2R1VCkDcfQpcFWfJA8Erl8yAp/VXXVts3iRaRxAovIUWLYLEiWYmrYPHOO++ga9euYZd369YNCxYsiOchMY0VMsFMGxFarPATLR6GyHkttGhBYkXaqApjPocmVuS8WjlDq8qc5szVwuPJFDCSWOGFRQumyYkWb4UUKyhNSrWFEKIFtTklVug09Z/8Lki0ELkvA7qWqpyYn2ihaw7oW2vbo6o9VhrEeFRYpTD0Vg7ekAWQBRMAz2bA8Rtk/siQokWVWOFTxzyQ0qdYtGBijq0yJSRElRCOsGAYhgkWLcg/oh7ECkKnM0Ma+4cXLbxihegE4aBxzV3K3y6SaKEX5RCOn/0jKwKhSIuyNyqEitlqHBVRrAgjWqj7DxYr4i9YbN26NaJg0aVLF2zZsiWeh8Q0VjzFmglmOLHCtxIA5YJlBKigxkM1scLxG6BvGyRWVEL5bGTakzkJIv0WVb85rFjhK1qUvQPpKavlh2OYxBEthKlvSLHCS0jRwtSvUqyo3BaJFqZBELoU1c4qRQtdeyB7NqRtqSZWeKFa6da5mlhRubMCVVJVUMUPWRxatKAQ0FAeGIFQ2li0tswwtUoJCR1hYWMPC4ZhGD/RQpgG1otYEVW0CCFWeAklWlB1kNzcVBgMLsiSR6LvuPwt5aFHhpzC2AdCOCCL7w4vVnixf6ZSSujeRJgOSvrIigYRLIxGI/bu3Rt2+Z49e1R+EMNEQ+ipjOiLgD6Ky3/qGC083bMNSL20KrIi/TpVipRqIIvMe1VZo0CxQlJJJRI77EuVn4WqPpA+oao0YzioTFHqSAhdhFQUhmlkhBIrwokW1UETLR7RQkJt76j0LpiH+a/k/NX/ubEfhOUCyJIHK3YcQrTQt9Jqq0f0lDZq6WLeSicME0PTTaM5lIeFEfay6GXzGIZhmHoWLSKIFaFECykdMIm/0bb5JrjcRq0yoIhksi9UhKivr5dQlQhfj+qhJTIf0MY6XFXTj7iqA/3798f777+P0tLgWa2SkhK89957ah2GqQ5aysac8KJF6hgIQw/I4omQpVO1ziLjPhVZAedvkPZlKsddFt5Mv8CQYgXlzZPxpiZaZFV4XDwNGHqFFysybqusXsAwTV2sqItooZUXngCUzYWn+CHVdmA+NfSqdAFPG1+RklXus2N/0UIIE2A6KIJoUSFWUHk1WpdhYu5hETolxO1yw+lw8vlmGIZpQNEimljhK1pIxwrA8StQOA6G0hth8PwCSZHXuVrKR1ixwnQkhM7if19BnnjZT4YVLUT6zZBIUwagTAMKFtddd52KojjrrLPw+uuvY/ny5epBZpz0GkVf0DpMcuKhmyIfSNGstWiRegWE6TBI52+VP3MSLaTjJ8C1EdJTAJS9XBVmvn8kpGtdkFihvC1Up3VzlWih8vSpUkkvrbOynKNth8UKpqniKQgWK4wDAEOf4BkJaYOnGqKFdG2BLBwPuKiO+XkQjq81o9yMWytECwFYzlcCQ1ixonJjxVoJMWrXKsw0nGjBYgUTh7KmISIszBVpIlwphGEYpnpIGSXtuzaiheFASF0PCOyvSNHwgYwuDX2rnpsOh5CFFaXYSUSQyhhcOJaHES1CixXah7FCFlwOWUa+YK8EiRZkSC7pXqb4VsD1l5qAifXnb8zEPcLihRdeUF/Cww8/jDFjxqjHI488okJfZs6ciYEDB8bzkJgEwePaAVG+GNK1WT2XnnLA/h2kK6CGcXVECxIr0scBVHVA3xEi86HKnzqVPdTECp9Sh76ihXMt4PzPT6yoXMUrWkiHyulX5oLZz0Hou0BkPc+RFUzTRZenqudUYjwIIm0MRPq1PuW3hDKWgmcPhONHeNzF1RIrlBknRS5l3AP4iBaU8iX07SGyZ0JkTAwvVqhdU6jlTJUqVvlSkGjBYgVTv5RXRFgYw5Q19Y3CYBiGYcKj/CPsSyvvC2IBTYQK198QnjWQIhci6xltckR13IOVP5ZIH69NyJBYYTkHsmhihVhRuRXIwutCiBYRxApCpAOUfm4ZAVl8H0T2lErRQkVWuLeoSFFkPQ8Y+2ueXrYvI5ZZTSYiJfnWC4cffji++OIL/PPPP5UGmx06dECfPmRIUvGjYZJOrED5u5Dk6l/eDTL7JQjXP9qMqS4XIAdhg08t4zCiBXLmQNo+g0g9l+6INKddxzLAfIomWnj2QHqKgsUKX9Ei/xKtEyEjTvfG4FWKblYpIdJ8rOqcZOkMwPk7kDoawjQ4VqeEYRIKoUuDNB+vRAuqmCPSLtWq6wi9VvLX+gJExl3qoi3zL6OGApE1DR7TodDpMyOKFdL6qkrRQsrZSrSQJZMrRIs7IAuv10QHMug0nwLY3g9xcDkQeW+psqhBi4QJ0le04DQQpp4jLAxGPfR6fVjBgiMsGIZhqiNWfAVZdKuqLIbcNyAMHet02lTUtuMPFeUAYVLVSaTpaCVayLLXlY+WVnlQADmzIDz52v79xIoA0SJ7BqRpqCZauHcBpiNCixUKHYSQ2uSnLIEsfkS733Ct18SKsje11dLvgPT0AgouBdzbITIfhkwZBqFLT+qfTdwFC4KMNQ888ED1YJIXKT2Q1MDL360qQWjooamWxfdooVee/So/3Zn1Ovbac5HWtjW2WEuhK9cqcOSkWNA8La1KtEi9WAu7KrpbzdaS0gn7p5CGPtry0qlRDqoAsuguiJxZkPvPDL0KiRZ5CyGtr2hiBVFGNZwFkHY1hD6yoQ7DxLodCVH3YDmPxw2dLvhGy1e0sOkOhzm9GWTBlVoEktS8K0TObEiRBeRT+9PSMmTR9UGihXTv0fJFA8UKwvY+ba5KtCjvBGRPAwqvVP0BiY5SugD7hz4HlaPMr0KJFYGihff/DFNfUPSEV5gIxFvqlCMsGIZhqilWEJ7dajKRRIt8R3Psrxj/h8L3niCsWEEChHSqewslWhiPhEjP1dI+qCIamfHrsiELxoQRKwJEi2afAIbegL5bBLFCDYoqJnQq0t/dG5RoAWMfwOYzril9XEVxq/QQ93aVtiLo7UkuWtSrYLFixYpap3jU5b1M4kNpGdK9H7B9UiVWpJwKYTq0Sqzw4tkPQ9EotM16FfuEE5cu/hXr8vPVok9HjvbvnHzECqr+AZGm6jyTfwWoGoH5BMD+ZYQjM6qZYlnyWPhV0sYBzn/8OxiibI521CxaMHFCCX6uTZqRVKQLZRQ87nwI55+QJBgGlvb1ruMphxnrqsSKyoMoVxd2NUtBoqBTEyyodFegaEGlTZFxL+DeqsqTVooVXryiRebjgLFvRd4o7e477aKdcTsk3ID9Y02syJ4CWTIFyHpAlQ4LBwsVTLwiLLzCRCjTTcJWauMvg2EYpjpihZcK0SIjay7OXPQldpT6e955CbonCCVWVC6waqJFzquQJU8ClF6eQekfZMX1GUT69ZClT0X+ntJvVBUHhTDQrGmU79SiTdSocY1be8m9QXv4Yj5NK7nu+LnqUItZtKhXD4vRo0dj1KhR+Pjjj1FeHib32Aer1YoPP/wQI0eOxGWXXVafh8Y0tFhBRnu2j0OIFff5ixVePPuhK7ocLS1FeO30g9AtNzd4u+69fmIFOfvKotshqQ4ydTwFV0JQBIb5pDBHZtTCu8ih17E89Cpp4yD0ncO7CpNoYZ0J6fbetDFM/YkVsuA6yILR2g09+b7UVqywfwlZeJXWXlzbg9fxlEOQEW3B2CBvF+1gylU1HTLGrPK08BEtfD0taObCN7IiEBItnH8AzhWAx+dYHD8oIVGkXwlYLtLEiuLJmu9F/hWQ7h21+vwME0vBIlRJU8LEppsMwzA1Fyu8eHbDUDQab545BG3SM6p1JsOKFQFGmCLjeiDjQZUSIgtvgzANUIaXIp1SRMKQOhYwHwfogiM6QiF0KZDkq5f1ONVfD70SpbCnjYakqiTw+B9q8d1qkjdZPS3qVbD47LPP0LFjR9x+++047LDDlIDx+OOP480338TixYuVOEH/p9dI2KB17rzzTnTq1AmffvppfR4a05BihWMNhHMVYJ2hvUgVAFJODy9WeKH0kKLb0DLNgFdP7YcDW7SAUVdRAcRNVQwe8xMrYPtAe1/ZmwGixSjAfHzAxo0QWVMgyd/C+SOQOSV4/6kULtYtYgkkbX9zIMvn17C8I8PUXKyAa2VlWGJtRItKsUJFNdH1/JeQooXw7AgvVviJFrdAZEzSvGcCRQs35WlSTug9gLNq5iAk5W9rhrs0IPDF8aPyjRGWMzSxwr1Je91NkR/jtagthmkgbCrCIkxKSKXpJkdYMAzDBKa2gqI8w4kVXjy70d49AfPPCT3x6HC7sWb/PvXYWJAPt3NTeLGicudWyMIbKqJUKc+1ILpokTZBSx/Zfw5A/hPVQI1P8i+GtC2GyHosWLQgscJ8BGT+RRApxylz86BtFNOk7M+QsiJCI4mo15SQ1q1bY/Lkybj55pvxwQcfYMmSJXj77bdhs/lfsFNSUpSfxY033ogzzzwTuSFmz5nGDd2owLMT0rUeKHlGS88wdAdcVJVjtapNDNNhgOP7iNsRqVQSaB5au5bhnbNfhU7shZTZatZWzbyah0BSGJVXrPBCokXqSFXJg266hOXMitDyZVViRfl8dUPkTrkIH2/OwhEtH0KOQyvlWGq4HD/tPhp9mznRAik07Ax/kPq2ECmnqZx/hqlfsSKUAVQk06cIYoWXCtECWU/4pIekAZazgPIFkTdKJX6pdLBHS9mqhExqyThLlwWRMQEy/2dAhs9Dha41hPkwLWLKF8ottZwPWfxglVihMCiTTiRxfieTGIJF2AiLitfZw4JhGMYf8uGSdE9gPFSbNIxAgWECXvzdd/xTxeaiQkz4ZLG2TQAzTjkcR+aeilTXosin3HJBRRXAikhQr2hBkZwUYVrhrVU5eUkGmvoOkKnXBJUnDYswqwpoNPGqUl+zHquoQOKuEiuK71VG40o4cYb4jMZByvNCiPB+Y02VuJhukgBBKR70cLlc2LlzJwoKtJD5nJwcJWwYDA3i/8nEATWrWvocUP6GFv1gGqxFOuS8BFl0oxIt6AZJZD2hxVeEES1E+q2Q7u2AbZGqVmAouU3L/aJSioaegK4ZJDXwQLHCT7QYByFaQBbfBpH1KCRMECmnVooVInsa9jsG4qYvXsfovj1wXf+HYPRswOLth+Ger7/Fwa1b4o3hc6EvHB1atCCxIuc1zeCTYeIiVtRctAgrVoQRLYShFZB2ndY+w4kWJAjqWkOW+pQ/Jai6SMYkCIPmMSENB8CdNRf6otGhRQsSK7Ima2KFZ5e/WEEVSUqfBVyrfd5gUCa5qpqIMIf9zAwTn5SQ0GMZnV6nyp1ylRCGYZhQnWQWROZ9kMWTACelRIQg4z44Xf3x4dovop5CGq9c9+n3mHHKuTgyF+FFi9TLIXQZkKXTAjZQIVrkvAhp6ACRfpuKEkfKaUDRtZBlr2jVQXTZ1fo6yTBTq7Y2xV+0sC+DMA2tECuGQKSOUPsNimg1DoLIngqhb4lkpF5TQkJBwkT79u3Rr18/9aD/s1iRDGLF69oNVfGDEKaBgFe0yHpWi7SAUxMtLOep2sehxYo9gP1zrbRi6UzA9VeFac7FgHszZOnz0WeAy2ZBUrlSKplYdKemaPqIFVRVpNipNYu5f63B9D8teH/rENzztdZ5/rJzN7aXtYM7ey7FBvlvW98WrsxXsaE4DXutnA7CxBbp3hdBrKhcS0sPcf6qVdQIgcdjhXB8H16s8OL8BZ6iOzRjT1WFpxXcNJtguTh4XRICzcNCiBUnQmTcWylWqO0IPXba2sGW8QqFTPmvr2sNT/YrkCVPB4kVyJkLT/l7LFYwCYu9zFEZSREKc4qJIywYhmFCpoT8Bbn/POXhECodgqIcKOqgmeNGLDjnhGqdQ69osarsEsBybvXFisoNFEAWXAVBKa+e/aQ6qMplIvNBJbCo6iXuzdX+PlWVjwrRgiK8pe0jCOPACrFiaIW/hTE4hcWY3GJFgwgWTLKJFc9XiBWVr0YRLe5SVTpgOiZArNgHYT5KRS9I61xNrPCrDDIZIoVy2qKULRRpcJuOBsynKu8M1Un4iBVC538D9dpfa/HAtyv8Xit1eXDJh6uwxzyzSrTQt8VmMRVHvfEpTnzjVRTYameAyDDhf7tmiNTzo58gKoWl76y5VodAR6lKhgO0FI2ICAjLuZCoilqQHhuEsTdgOds/skLfBpIiIMgx24v5WIiUYVq9cenvTZNpcsBj/12Jj5WiRUVkRWn5RhSaJwO65hWHkY7dphdw8Qd/Y61jnKpkosGRFUxiQf4U4VJCCJOFIiz42sAwDOOLKs1Olb4MbSELbw0SLVRKBnnf2d5Hie5ULNm4tdon8OA27TCopRmCtpdyetUCquhn7BVerKjsuAcDro1A2VzA+hLg2aNNeJJoYT5Fm1CpAUGiBaWaVIgVQt9KpceL7OkVSS0sVnjhPAymnsWK10It1UQLSg9RAialh8zSDPvSb4bUtYTIelgLCTcPhXTt1CIh1Oyt1HLACv+pqmVMOL+FtAptO6pkUAhzQJEGmf0iSl2tkGNuDpk9FbKQKhuMDilWRIIiLW74Eph6wky0cE3GZvE4Llr0A/ZwZAVTT6hZAKqmo4yXNG+VIPQdIHLmQBjaRd6WsSuQMwey4DJlYhViDcjMx2AVhyFTr+VnStdm5dCtDGwz7oakjBORqqWBlDwA6LtAZr8AAR2kc4UmNFIJVFkCkfuWyk8VQqiQynTnXOjssyGNEyGyX4AsfkAr92Wdi0zb+yhKeQrOrDkwFk/AbsODuPLTf7Fq716MWLgfP142E6aSCZofDaeBMAmErcyBzLzwg1cy5GQPC4ZhmGBUafKcl+ChcQOJFtlPauP6lJMrxQqn5WY4dWfguK4mHNd1QEjDTV8OadMOrw/vD33RGLrzUOMjSaMous8wnwBQxDV5bJF/RSjMJ0CYj6+ISPUxuvSKFrlzIfQVkys1wC89pHxhhVjRomJZqpbWmz0d0joHIvuZpI6s8MKCBRNnsSJQtLhfC12X5crTwuYyYmshiQ0CbdMegF7ngdm4S93QwPWv9s6SJyGyn4YsvNlftHB8o+WEhRItRJo2m1vyKDJSLoD0nK51ABRZoW66qi9W0Gzx9GGaSruh1AhLs9m46K1FLFYwDStaVFOsqNyWsStkzqsAOWj7iRaaWLF4cyv0bqFHpoXEii1a6GPFerLkYa1eubRXCIlGrQpPyb0qAkINMFT01INKeKS0LZH7NqQ+D7L0ZejKZwOpl0GgTL1fZD2g0l1Exg2QsgBZtlvgNM7APvOLuPKjr5VY8eSJp8BsMGBDsRldsl7E/nI9rKoWu9YH5KRYguqvM0y8PSxMbXIiChbsYcEwTLJDadOhIpF1woy2Gc/DVHxNhWjxBKRtmRIriozXY/6abjiiox4HtmgW9F4SK279oqrCZK+85nj9jEEVnnMezSev8HqI1EsgPflqokVQlEXKaZo/V6BoEU6s8BUt8kcDuW9CGDrWXrQwHQ6h9/88laIFpYIELEtWWLBgYo8wQZgOIA0iynopgK4FkDYOMPaAECRW7MMpb5I/BJBhMmP+2Ueju+4BCLcmVihc/4UXLdwbISl03ruMRIsKsYLKIcK1BpI6lgpzvtp0BFuKiypdiAkSLziygmlQ0aKGYoWXrdZmaJM1W80+aGJElVhx0xc/4NOR3SvbtCrh5SNsyBIqywWfKjvzVHqVKj3qXafobi1aqvTFitQPCsvsDUlihS6zMhRTiSFq/TsrzHANkCIHEz77VokVBIkVvu0ukE9HjmbBgknYKiGE2WJU6zAMwyQzJFZ4x/qBZJrN+GbUDKRar4Wu8Ab1mhIrNvTCE8v/xBEd+1ZrH3vLrHB5DNDpu0GXfkVlKXRZ8lDlOtI6U4kWsIzUvCPsn2sLUoZBpJwJWXhtaLHCi65V5f1EbVDpIQgdladNpFZ/MrWpw4IFE3M01fBkiCxUlOwJtZIFIusZLf2jQqzwhcSKeWcfhW66uyDc/wW/P5RooW+vOQwXXgdp6KKFmBfdq5UlolKqrjXYZ54Op70H2qaE97qgmVq6+fENM6NSSV7srtBmhgzTIKKFdVatxArC7nZhxMKfMe8sEi3GQqbfVClW+O2P8iopPLFwAuD8w2dJgFgRiGeXltqV82pliVQPlS+mmYkSMpcKxKVEC2Q/j/VFzfHLzm9q/JkYpiFNN6kSSDhIzLCVRiiJzTAMk+QU2+1YvtOJThmPoat+Ikp0wyrFikgEjt2JvTY92lOkJ6WZ+JVCr8C5Eh7rTOiyngEo4rtYkMO4SnulqAyYDg1buRCGfhA5M7TxEVPvsGDB1AtClxZetCCxIucNSH07JW4EihWpRqMSK7rr7oYulFgRSrQomQKRcbu2L88+wLFPSw/JfRPSTTO0BiVW3PnNXtx6mAfarVP9EZhLx+HqTL2JFhS2qM+r9XZW7N6lRIv557yDj9atDxIrKvenUqh8RQtdZLHCC1X7KLgcjqw5KHGlIkt+Bl1pKLHCiwsovAZdM2Zi/rlnY0+Z5pbdOj1dRTORYOgb9umFXg/MXyW47TFxLWtqihRhwSkhDMM07bSO6lx/XW5P2PdQ+mfz1FRsKgHatHkJ+60OtM92YPqw9mp5mtEY8lofan/StSm8WFGBINGi6Cbosp5Vk55+0ddZj0MW3REsWrBY0fQFi/Xr1+Pdd9/Ftm3bUFRUFOQeT6Zsc+eGDhNimoBoQWIF1S029IYuXBUDIaBT5rjViGRQpRuNEBl3qrrGSqzw4vgZcv/ZyrinwHgb7ly2EUs3b8NNQ/1/czUJVSO8/hWRoIgM3/B1Dldn6ku0iAUkWvSY+Xr0/fmJFn/RCxVtMBoe7LIWY8mmzTirYwmyoq4vUWK34tU/fsOn67dUq/0Fpmp54bbHxAt7uUNVAonkYVGwt4i/EIZhGj3RxsqRrr9uKmMaBkr/PO9/88Iuf+f8EWGXB+9PRk7pqFyNxjEy2EuCjDADRAtp6AcdR1Y0bcFi4cKFuOuuu2AwGNC5c2dkZmYGrRMoYDBNSLQgk83c15VYEa7kIlHqcGDE+99i3tmPo6v+Dujc60OuJ/VdocvUcviDxAoFeVk8CYdsifWlWTjnAHoMQLopSunTGtIhMyvoJorTRpjGQKgQysDlkUQLaV2goiw8RRMhnD+F3oiuhar4MfKtpdheUgy7qz8u7Ho9spzhSonpsd/8NB76sSxIrGCYRMXtcsPlcMEUsaypiVNCGIZh6oCnBreJwtAZyHkZsuAKwB26FKo0HACRNR3C0D70NipECw+JFp4SIGsap4E0dcFixowZ6N27N2bNmoXc3Nx47ppJBNGi2ZGALieiWOGlyG6LKFpIfTd4smbCQz/hsnnQhxArStJehM7TGTd+8Y2KrPDy0UWj/MLJTHq9EklqK5pRVFAkM0CGSVRoJqI2lTW8ogWZdNLF3Jb2KAwld8Do/sV/RV0LbDPMQHFRuhIriCd+/BPHdj4LqalGGMueCtiyHo6MGdha1AXHdCrD4rXhwzgZJtGiK4hIHhamFCOXNWUYJmkIVWqUoInDcNGSlP5JaSGhUj+J3dbSGh0DiRYyexZQOC5ItCCxwpH+LHaUpKFrhNtSJVqkPwqbvQzCmY3U8N080xQEiz179mDMmDEsVjQwUjqUSliXvPfaiBZAzW6MwokWHn1XbJCP46I5i1HucmHyMUNwYosipLm8IWJm7DU/j5s+34Q/d/+i1vHFLSVO8wljo04zsOoHwyQb0r0f0GUGecqEw7cu+MYiA+zO23CgeUqVaFEhVoxc9AumnnKq33vXFbqwtKBrQKRFRWTFN4VYvPY9bodMo/OvICJFWLCHBcMwyURgerSXhRdeHHGir67jcOnep1Us1GmR/FtKs9Em80UYiq+qFC28YsVxb36KuWedF3Wbdlcm/lu7A716sclmkxcsevbsqUQLpoHFCscfkKVT1AxpornbhgpRJ08Ld9qLEMXj1XMSK0Ys/AaFNs1t/dYvf8KTJ5yEE6lCqut9Taz4ait+3L6zXo6xY1a23zFShAbDNGakeydkwQSIjDsgTQOqLVp4STOZsLUkFdacR5BZfg90no0oz3gV63bpMPGIo5BiCN4eRVoA3vSQ5yrTQKJFVXjNN32f7ywt5TQspkHxliuNVNaUPCy8wgbDMEyyIlSNs/qbfJElT0AYB0BaTleiRZrJjB1luehQEWkhRYZKA6HIChIrQqW/htw22xYkh2AxceJE3HDDDTjqqKMwaNCgeO6a8RUrCi6nWj6QBeOBnBdqLVpEcwhOM5pgdValWrg9HhXdEBgWRiFjXqjT6JFXZXqzvbhY7WNDcQo6Zb6gurgRcz+sFCu8eEWL4d0ux4bdwMh+LTCyn+YlERhWVtcOhwQK32MM51bMMPVBtHZX06oYmlgxHnCthiy4TJVIraloUeZ04tqPP4RZb8AbZ96CFqkGXDxvGW469DBloGVzOf1EBhL9/ESLAxbhoW//qnUKSKiZGgoppX2HC0nl6iFMrEuaRhUsLCY4bE7ld6E3sNDNMExy4vRUwwizBr5xXtqmuSBLngZsCyFtC7XS75bT0Twts2JclAuZ8zIEmfUb2kVMA2GSSLAYP16bEfclIyMDI0eORLdu3dC6dWvodLogP4CZM2fW52ElJYFihcL1T51Ei2gOwR+MuARnzHsj4jYC0zECHX5pH95tkLgx5cRTgsQKX9Gie97FGPn+W37bj2VHyTANTbR2V5OqGL5ihYazVqKFp0IEtLtdGLlomWqr+eXlSjAIJSaQy7cXEi1e+3s9dpVWLy+Voimqk8IVbt9euHoIUy8pIZE8LCoqiNisNqRl1dw7hmEYJtkJVRGMIrFnDjsC5vKXAfuiytdlyf2VooU3PUQZcTKNjnoVLNasWRPydRIqrFYr1q1bV5+7ZyKJFV7qKFrEEzLGdHnCl0Ii7AG1nUMpsaHC0/224XIFzQb7pn0Eho5Fq7QQ6j0M09AEixWotWgRzkCL2l91CCVW0Ht92xW1SxqoBFbg8bZXWp9ECi++kVsMkwgpIeYUrUJVeSkLFgzDNG6ijX3p2rw2f3+tKn0Epn76vr6psDCkWHFY1jzo7MGTFKFEC6bxUa+CxVdffVWfm2fqKlbEWLTwhmBX7Vv6dTih0jPqg5qa9XTOzvF7T8u0dOh80uvSjEY4Iwglta20wDCxJFoKhG8aRHixwl+0cGfPxi57V5S7IqdTWJ3OqJEUdYWi70Lh7VNo4MSpWkxDp4REK2tK2KzsY8EwTNOGhIRezZqHHJObo3i/BUZSeqFt+d5H+IoVFnf4iEoWLRo/cfWw+OWXX9C1a9ewVULy8/Oxfv16HHzwwfE8rKaNpwSy5KHwYoUX1z+A/WtIy9kQQhtU1ZRoIdjxqL5BQkO0EqOBN1GUXx/tuCOlrTBMIhCt/Xl/u82oHpf96whihRcn9KWTUeKegtPnfxS0nfpuAzQBEyr1hav4MImcEmI0GyJWCSFYsGAYpqmnp4YaP/u+Hgt65OZhcAsdLGUfR11Xlj4FkXJiTPbLNHHB4tJLL8UTTzyB4cOHh1z+448/4pZbbsHq1dEG0kx1UaVLs1+ALBgDuDeGXzFtApBycq3FingRmK5BYeAUJl7ddA9vKJrvNsgokGGSBWrjMuUUwL0bsD4XfkV9F6zzPIJRi6JHyunDRD+EC/ukdhptwEJpJgzTtKqEGCtTQhiGYZIVGsvT5CFFUgTSPjMT6/Lzq7Wdf/fvw0M/bMFdQ55Env1WGnWEXpGqguS+Cejy6nroTAMR1zu1aNUZHA4H9FwiMuYIQ1sgZ3Z40SJtAkTaKAid5tyfyASmlCwZRakuVQRWIwiVhuKb7sEwyYhq62mjVRRDSNGCxAo5BRct/DZiRZLK1QPMk73kpKSEFCaonUZLE6M67QzTmFJCKG3JYDJETQlhwYJhmGRIUw3nReGduAiXSlqT9PFFa+i+pnN40cIrVhh6QIjQYxUm8al3wWLHjh3Yvn175fMNGzao1JBAiouLMW/ePLRp06a+DykpCStaNCKxIpwRZ03TUAJz4xZdOLLejo9hGp1ooe8CZ+ZLuGju4mqJFZGgssYNnSbGMPFKCaEIinBeK4TJx3STYRimqaepnve/eWGXx9LjyitaPHH0DOiLrqsSLVisaDLUu2Dx3nvvYcaMGeoiTo8XXnhBPUJFX1B0xQMPPFDfh5S0BIkWjVysYBgmxqKFvgtE7mxsLTLXWaxgmGRLCTFGKGnqmxJCZU0ZhmEYxFS0uP2wS9Eq+3nIwmsAkcaRFU2Iehcshg0bhu7duytB4sYbb8SoUaNw0EEH+a1DQobFYkHv3r3RrFmz+j6kpMYrWsC+DLCcViexIrCkUWAZwcCqIX2btwwZzeD7msVg8KtwkGk2h3yPdxaLszuYZCNcKbHalvH0ihYqtzPlOAh9G3jkvhpVInF7PPhgxCXKd8L3OJxRjolKBoebZckyp4R9T6jPX9Nyw1xqmKmPCItIGIwGGIx6jrBgGKbRY9LrI0ZJ0rU6EuH840x6Q9jIZxpjfDjiEhjD2AcY9BbANBQi+wWAqh5yGkiTod4FC6oKQg/i0UcfVWJF+/bt63u3TBTRQurOhdCFviGoLoHlPANLKQZWLaAbk0jhYQR1UmfOfzPs8oXnXIBuuXlITU0Nuc/aECmEl2ESjXBldOvSFki0kJbzovYJ0SqRBJYW/Wv3rojbc7hdYfuEcEIGDZJ89xEOLjfMxNvDwmiKbvpsTjXDxikhDMM0cqKlZEdL+XCH8TU8Y94bEe8XAscZoZCmQwFhYs+KJkRcTTfPPvvseO6OiUBdxQqGYZoW3CcwTP1GWBC0Dpc1ZRiGqT94PNP0iKtgceedd0ad6TabzWjVqhUOOeQQDBw4MG7HxtSdaCkiDQGVPQ0MWaPKBIEhZr7HTeHtvspv4HIOJWcSkWgpEN516rKteLZpChetTuoHwyQCdpsjYklT30ohZSXsD8MwTOMmXDnz6kLvplTSQMhCgCOfmQYVLH766SfYbDbkV9TXzcrKUn+LiorU39zcXHg8HhQWFqof6xFHHIFp06Ypfwsm8QkMwV69d09QTnq0qgAkDkQrS1oT6HcUKWTNexNWnRBzhklkYpkCEW5bgW060NuC2mtgagq159q2Y26XTGOKsDCaow+pzBYTR1gwDJMw7LVa/Uy26T7M2LIFtlhLoSsvq5woCBwThCtnXt0xP433O+fkBr1OY4hT3pxbi0/CNGXiKljMmjULY8eOxXXXXafMN72CBQkUb7zxhqoo8sorryjjzTlz5uC5557D1KlTMXHixBrtx2q1KrPP3bt345133kHfvn3r6RMxNclPi1bikFh44cV+63DZQ4ZJHMLlnEbztqhNO/aE2RfDJCI2azUjLFJMKOcqIQzDJAgkVkQTCCjasaYTItHG/OGMNRmmwQWLBx98EEcddZQSLHzJzs5Wr+3ZswcPPfQQZs+ejQkTJmDTpk347LPPaixYPP/883AnQDoC0/Bh8GlG/3SOcNtgGKb+aJ2eHlK0SDFEv8FjmMaArcxWTcHCyKabDMM0eUSUOn46NrxnElWw+PPPP3HyySeHXd6rVy98+OGHlc8HDx6Mzz//vEb7WL9+Pd566y3ccccduO++++p0vEzN2F5c7BdWRnloNUf43dj0ymvm9zw9LQ3rS4ohSksqc+h8w9JCha0xTFMO3wz0XgksLepLYPsIty2qxEEO4IHtq3ZtGthZWhpypoUjqJimlBKS2tJSrZQQ9rBgGCZRoLFDbdaJ9j6nJ/LEMUdRMgkrWGRkZOD777/HxRdfHHL5t99+i/T09MrnZWVlfs+rw+TJkzFixAh07ty5zsfL1Ay68aFyRHW7GZF+NzahUkSilVVkwYJJ5vDNSG0ksH2E21a4bbDAwDDhU0JM1TTdLNhdyKeRYZiETvWMtk513scwjVKwuOCCC5QvxfXXX4+LLroIHTp0UK9v2bIFb7/9NpYtW4Zrrrmmcv2vv/4avXv3rvb2P/30U6xZswbTp0/HqlWr6uUzMNWHjPZ8b3D0IrJBT6gQsmghZQzDxL9NU4oHRU14aZ+ZGbVaj29qltPtht3tUikh4USQDJM5hkfOMPWLvdwOYzXKmlKERXmpjb8OhmEaNYEm+YGY9IZapYRES/XmNO7kJK6CBflUUJWQuXPn4osvvvBbptfrcdlll1X6W9jtdpxzzjno2bNntbZdXl6Oxx57DDfddFONozLCQeHPFOVRF+i4fP82JopdLhTaww+sss0pyDQYwoaLB1YFeOf8EVH3uaVYqxhT2xw3cjeu63eWDDSm32Vqamq9bTsWbTze3wv9xmtLYPtwuWu2LW+brk5bDqSdpep7JPfx8/43L+L6H180KqG+m2Rug/H4zPXVzuPVxm1WO3QGnRrjREJv0quUkOocUzL9RpLpsybr502ka3mynf9IREr19FYAo3X+DagSRhEWdTHb9oTYZrj7i0AaYmzAv5nwxKMdxVWwoBKTt912G8aMGYPly5djx44d6vU2bdpg6NChyMvLq1zXbDbj7LPPrva2Z86cqd5/7rnnxux4nU4nVq9eHZNtkYFoY4PKGp313oKwyxeecwG2767qbIxt28T8GGoacuaw27F6y9aYH0dTpTH8LsnLpr6IZRuP1/dC7bK2BLaP2rbZQE+KaAMTGuz4nufqfIZkacuNoQ3G4zPXVzuPVxunyh+lZSXYvGVzxPWsNiusRWU1OqZk+o0k02dNts/rO8ZPlHaeTOc/HJHGAZEqgNVm4sIXGt2f+vbr1bq/SCT4N9MwxFWw8O20Tj+9Nv4Godm+fbuqLELpJiUlJX7qG/2lMqdptTBiNBqN6NatW51VJ/pxd+rUCRZL46pGQbOgkTCZzejmk7JDZpjRajIHdnBGnd4vqoJCzn2paUJI4DExTe93GUti0cbj/b1Ea5exbLN1TRXxkmlOQQuf/VL0FkVQeMUMp8MBo8mkRG2/GZbc+hvgNjTJ2AYb4jPHo42r37DNiRYtW6Bjh44R193Vej+ctpXKZNz3957sv5Fk+qzJ+nkTqZ0n2/mPxB67PeykA13T6zIuoDF/qDEBkWEyNaqxPP9mop+bJidYlJaWquiK4uLikKFIBx98cI22t23bNqWuXnnllUHLLr30UvTv3x8LFoSPFAgHDSZiFcJGHWJ9hsPVB7ryyCFXOp3O7zPpKip3hKPAZsMF78wLqsMcKaSspgQeE9P0fpexJJZtPF7fS7R2GS6kk3BJiW01fH9NUkXCmXVSPqrv56H/tUJmpahMM2M0OEnE76K+ScY2GM/PHI827rA7IT0SaRmpSElJibhuekYa3C4PjAZTtUw6k+03kkyfNRk/b6K1cz7/gKO8LOw4fPFFo8KKGQafCmLhxgWRTMAjRWgk8liefzMNQ1wFi4KCAjz00EOqVKm7ouweCRbeWQbv/2sa1kXGnK+99prfa7SNRx99FA888AD69u0bw0/B1LZEkY79Mxkm7kQK6eSqHwwTm5KmRHVMN6lKCFFeUl5twYJhGKYhsLmc9ZYSwjAJK1hMmjQJS5cuxahRo3DQQQchM4qzfHWh7QwZMiTksj59+qgHU/+Qq3+kGyCqCFBTl2HaptctmAwDTXq9isrwilx6IaD3UXnZPZhp6oRz0Kaa6OT5Qm3Kd7mjQhyuSSpH5+wcNRgx6w0w6vV+61MKV2DqVrhqIL7HzDBNXbAwVadKSGqFYFFqQ1az2IyBGIZhYjWmoLE2eUhRWoYzgsl3pCpfRMu0dORZLGrMHopoKXEM02CCxffff4/Ro0fj9ttvj+dumXhRi77H6gyv3hLUifbIa+YXOk4RNYkaKsYw9U3ztDT1qC6bCguiRlLQwCNcKoe3/RF/79kdsb3SAMR3fYZJBqhCCGE0V6+sqTfCgmEYJtGh8QNNUgRW/ttYWBA1pZvGEH1btgp6fc3+fTjlzbkxP1am6RJXwYJyO9u2bRuXfVHExX///ReXfTEapQ5HxM6Lw8cYJvHaJcFtk2Fqj62s+oJFZUpIaeTypwzDMPGgwFYeVTzg9FEmqQSLM844A19++SVGjgwdHsQ0jtBz3+U1gSqCBG4vzWiK6T4Yhqk5odpmqPZHzz8YcUnY7XB7ZZIRe5mj+ikhFrP6y4IFwzCNGW9KKaWCeo29qzsmiPX9BdP0iatgcfLJJ+OXX37B2LFjceGFF6JVq1bQ++RHe2HPicYZel4byJOibSaHkDNMQ1LdVI62mZnqUV32Wq1q9ibSoCSN81iZRo7NqkVLmFLCl+kLTAkpK+EIC4ZhGi/eFJHA1NHA63+4MQBd/+v7HoNpOsRVsLj44osr///DDz8ELa9tlRCmceD0uHHmm2/6vUYdHXdYDJOcoabU/tMs7EfDJFGEhdd0kz0sGIZJ8us/j/+ZhBQsqMwo03Shih2R8ESuesowTAO0y+quwzBMtAiL6IKFwWiAwajnlBCGYRoNHbOyw6ZwcPoG0+QEi7PPPjueu2PqEKpdm3At3/Ki1TXuoZKL5BZc230yTFNqi1ROzNiyBbZYS6ErL4tJm6hOuwxcpzqpHNxOGUbDVhFhYTBVb0hlTjVzhAXDMAmByx2+dKkXHbgCGJNEgoUve/bsQX5+Pjp06MAlKhPUFTjW4Vo6gWqVQOIbISaZSMSwyUQ8JoZJ5AgLiq7QVUMc9KaFlHFKCMMwCYBbemKyDsM0KcGCqoQ8+eST2Lx5s3o+e/ZsDB06VIkXY8aMwbXXXosTTzwx3ofFxIBA11+KnthcVFj5PMUQPVyWYZjYEs2N27sOwzC1w2a1V5YrrQ5UKaScTTcZhkkA0k2mqGVLaR2GSRrB4quvvsKECRMwYMAAnH766ZgxY0blstzcXLRs2RLvvfceCxZNpKoIpXr4RlRwHWeGafh2yTBM/URYVBcSN7isKcMwiYDV6Ywa/RypnDnDxIPqxS/GiOeeew4HHXQQ3n77bYwcOTJoOQkZXCGEYRiGYZjGVCWkOiVNfUublpVG9pBiGIZhGKYBIizWrl2LiRMnhl3erFkz7N+/P56HxMQ5RYRhmOQhWjoKp6IwTSXCwmg21kiwKC9mwYJhmKYLX/+ZRitYWCwWlJeHv0hv3boV2dnZ8TykpCHQ9d+o00VN0TDp9TFPEWEYpn4IV9nD7fHALWVlHmoo4bC+qn5UJx2lrEyrhsIwjRVbmb1GKSHKdJMFC4ZhEoA0ozHq/QCtU1M4HZVptILFkCFDsHDhQoweHTzjtnfvXixYsADHHntsPA8paQh0/X/n/BGcs8YwSVb5hwYloXJVueoHw9TNdLNmERZm7Nuez6ecYZgGp9ThiHo/sOjC4DR+hmmyHhY33ngjdu3ahfPOOw/z58+HEALfffcdnnnmGQwfPhxSSlUlhGEYhmEYpjFABpo1jrDgsqYMwzAMk3gRFl26dMFbb72Fhx9+GFOnTlUCxSuvvKKWHXLIIbjvvvvQrl27eB5S0uLRIsQjohcipvvk8ooME7ldeDweOOx2mMxm6HS6BvN54NxThqmZh0VaVvVTqsypXNaUYZjEQFeNsX511mGYJiNYEN27d8ecOXNQVFSEzZs3K9Giffv2qqwpEz92W0ujrqOvuGGKFZzPxjCR2wX5OazeshXdevdGampqg50ubqsMU7OUkJyW2TUz3eQIC4ZhEgAZo3UYpkkJFl6ysrLQr1+/hto9wzAMwzBMTCIsSISoSYSFy+mGw+6EqQbeFwzDMAyTjNSrYEEGm7XhrLPOivmxMAzDMAzDxJryUjtMNRAsUlLN2vtKylmwYBiGYZiGFCwmTpxY4/eQEScLFgzDMAzDNNWypgQZb2Y1y6zHI2MYhmGYxk+9ChZLliypz80zDMMwDMM0GOTDZbeSYFGzlBCivMRWj0fGMAzDME2DehUs2rZtW5+bZ+rg+m/S6/HBiEv8KoIEmmw2RHUChmFiW9nD7fHALTXLrHSTKeQ63NYZpnbYyx1KtDDXIiWES5syDNPQ0PXf936A+jN6UMQ7PbzrMExSmm4yieX6v9dqRYGt3O81eu77GnVYkbbBMAwaVWUPb7sPbOteuM0zTHTDTaImKSGVgkVxcJtjGIaJJ20zM9XDi6pWtno1evfuDauUamxgdTqwZv++kO/ncQITD1iwYBTUIZ3y5tyIZ4NmZlmwYJjkaffc5hkmMuWlFYJFjaqEVHhYFJfx6WUYJmHhewMmUfDPAWAYhmEYhmGqhc1qV39rkhLiFTc4woJhGIZhosOCBcMwDMMwTF0iLGpguqnT6ZCSZmYPC4ZhGIapBixYMAzDMAzD1AJbhWDhTfOoLkqwYA8LhmEYhokKCxYMwzAMwzB1iLCoSUqItj4JFuxhwTAMwzANaro5Y8aMGr+HSuhce+219XI8DMMwDMMwDWm6SZhTzbByhAXDMAzDRIUFC4ZhGIZhmFoKFjq9DgZjzYZTlEJSVsJlTRmGYRimQQWLf//9tz43z8QQqqNMJQyjrcMwTPK0e27zDBOZ8pJyFS1B0aE19bCwFnFKCMMwiQvfGzBJIVgwjYfmaWnqwTBM8sDtnmHqBkVJ1NS/gkhJNaO00Mqnn2GYhIXHCEyiwKabDMMwDMMwtaC8xFbjCiFewYIjLOpOmdOJH7ZuwY/btsLucsVgiwzDMAySPcKC0kTeeOMN/PPPPygpKYHH4/FbTmGVX375ZbwPi2EYhmEYpsYeFlTxo6aYuaxpnXB7PJj9x2+Y/vOPKHU41Gst0tLw1EnDcHj7jnXbOMMwDJO8ERY//fQTzj//fCxbtgwtWrTA1q1b0b59e/X/HTt2IDU1FQcffHA8D4lhGIZhGKZWlJfWMiUkLQVlXCWkVpBAMe7DhXjsu29wePsOeOKEk/HIcSeiZVo6Ll/0Hn7atrV2G2YYhmESkrgKFtOmTVMCxaeffopHHnlEvXbVVVfh7bffxrx587B7926ccsop8TwkhmEYhmGYWmEtqr2HBUVnBEaZMpEptttx6fvv4Kft23DH4UdhdP9BaJeZhU7ZObjtsCPRM68ZJny6GEU2rdwswzAM0/iJq2BBaSDnnXce0tPTodfr1Wvei3X//v1x4YUXYurUqfE8JIZhGIZhmFpRVlKm0jtqClUJkVIq0YKpHlaHA5cvehdr8/fj7iOPRr+WrfyWG3Q6XHPQEFgdTjz94/d8WhmGYZoIcRUsSKRIq6hEkZmZCYPBgP3791cup+iL9evXx/OQGIZhGIZhagWlddQuJUQTOdh4s3qQoeZVHy3Cv/v24Y7Dj0SXnNyQ6+VYLDirV2+89ddKbC0qqvH3wjAMwyS5YNGhQwds2rSp0lyzS5cufgab5G3RrFmzeB4SwzAMwzBMrcuaesWHmnpYECxYVM9g8+bPP8Yv27fhlqGHo1tuXsT1T+rSDekmI2b9/kuNvxeGYRgmyQWLo48+Gh999BFcFaWnLr/8cnz++ec46aST1OOrr75SaSEMwzAMwzCNIcKC/ChqCkdYVA9Km3nom6X4bP06XHfIUBzQvEXU95gNBhzfuSveWb0KxXZOuWEYhmnsxLWs6TXXXINLL7200r/i7LPPhk6nU6IFvTZ+/Hicc8458TwkhmEYhmGYGqM8KErKa+VhYeEIi2rx6h+/47WVf2DsgME4uE3bap9fEiwW/rcaC/9djUv7D6zx98MwDMMkqWBhNBqRk5Pj99qZZ56pHgzDMAzDMI0Fp90Jl9ONFEstIizS2cMiGl9v2ohHvvsap3fvieO7dK3R+SUvi0Gt2mDBqr9YsGAYhmnkxDUl5Pjjj8eSJUvCLl+6dKlah2EYhmEYJpHx+k/UxsPClGKCTq+DtdBaD0fW+NleUowbPvsI/Vu2wogD+9VqG0d17IR/9u3Fmv37Yn58DMMwTBMVLLZv346yMu0CHwpatmPHjngeEsMwDMMwTO0Fi3TNQLMmkPG4JT2FTTfDmGze9NnHMOn1uPqgIdAJUatf54BWrZBmNOGD//6t1fsZhmGYJBQsvBfpcPz111+q3CnDMAzDMExTjbAgWLAIzdw/V+C3HduVWJFuqnnJWC8GnV75Xixe+6/yG2EYhmEaJ/XuYTF37ly89tprlWLFI488gmeeeSZovdLSUhQXF+P000+v70NiGIZhGIaJkWBR8wgL7X1mjrAIYLe1FE8t/w4ndOmG3s2a1/kXOqRtOyzbvBH/7tuL3tWoMBIOKd2AtAKyBPAUAp4C7a+0AcII6NsAhj4QutQ6HzPDMAwTZ8EiLy8P3bt3r0wJadmypXoEkpqaij59+uDiiy+u70NiGIZhGIapE6UV/hMUKVEbSOgoLWIPC1+e+flHmPQGXNjnwJj8Ovu0aIE0oxGfb1hXbcFCRWM4f4Es/whw/gG4twOyuBrvNEOmnAaRMQFCX/2KJgzDMEwDCxYUMeGNmhg1apQqbTp06ND63i3DMAzDMEz9R1ikmmsdYVFaEN7XK9nYZC3F4vVrMWbAIKQaa58KEpgWMqBVa3y+fh1uGHJY1PWleydk0R2A40dA1xIw9AKMfQGRAQgLIFIBkaY916UrkQJwAp49mrhh+wLS/gmQ+TCEhSOGGYZhGl1Z09dffz2eu2MYhmEYhqkXSgvLlOhA1T5qA0VmlBaWxvy4Givzt2xEy7Q0HNOpS0y3O6h1G0z/+UdVeaRtRnifNOlcC1lwqfYk7SbAODCi71oVZkDfXj2k+QSgbA5k0c0qbUSkjYrdB2EYhklS4m66SV4VL730EsaOHYuzzjoLK1euVK8XFhbi1VdfxebNm+N9SAzDMAzDMDWitKAUlgxLrc8aCRYlBZwSQqzJ349f8/fjtK7dYdDFdmhKpVH1QmDpxg1h15HuvZAFl2vRExkPQJgGVVOs8EdQFEbqeMA8DLLkIcjyxXU8eoZhGCaugsWuXbuUSDFt2jT1///++w9Wq3axzs7Oxrx58zgKg2EYhmGYRuFhkVpL/wpvOdRSFiwUr678A9lGEw5t0w6xhtJLejZrjq/CCBZSeiCLbqEQCyD9NghdVp32p4QOy0WA6XDIojshnVxWlWEYptEIFk888YQSKBYuXKiEicAyUyeccAKWL19e4+1+8sknuPrqq3HUUUdhwIABOPPMM/HOO+9wGSuGYRiGYepNsCDRobakpltgZdNN7C4txcfr1+KQvGYxj67wMqBlKyzftgU2lzN4Yfn/NM+KtCshdNkx2Z8SLVLHAPoWkIU3QUp7TLbLMAyTjMRVsPj++++V8Wa3bt1Chtq1b98eO3furPF258yZA4vFgokTJ2LmzJlKuJg0aRKee+65GB05wzAMwzBMFSX5VqTWJSUkIwVOuwu2suS+mX3r7z9h1OkwKCe33vZBxpt2txs/btvm97r0lECWPAWYjoAwxqYyiRchTEDa1YB7M2TpjJhum2EYJpmIq+mmzWZDbm74C5I3PaSmkEjhu12qQuL1xKCqJLp6UuwZhmEYhklOivNLkN0svIljNLxiB3lh1LbSSGPH6Xbj7b9XYmjb9kjR6+ttP2S22Tw1Fd9s3ohjOnWufF1aZwOyDLBcUC/7FWTEmXIGYH0ZMmU4hLFHveyHYRimKRPXO/muXbvil19+Cbv8yy+/xAEHHFDj7YYSQXr37q0MPsvKuGQYwzAMwzCxpXh/iYqSqC1ew85kNt78YsN67Csrw7EdOtXrfiiqt2+LVli2eWPla9JTrCp6wHwChC6n/naecjqgawFZ/CCnKjMMwyS6YDF69Gh8/PHHqkoIiQkE+VhQZZDbbrsNf/zxBy677LKY7Ou3335Dy5YtkZ5OdbKTG4/HA7fLXaP3uJyuejsehmGYeOJxe9SjPuE+M/ko2V+K1MzUOkdYkPCRrLz995/okZeHdpm1j1SpLv1btcamwkJsKSrUXih7C5AOIGVYve5XCCOQOhJw/gzYv6jXfTH1P552uWo+PqZ7Hb5GMEwjSQkhM8wdO3Zg6tSpePbZZ9VrV1xxhWrIlLZx0003KePNuvLrr78qYeSOO+6o03bouOoaoVFeXu73N56UF9uwe+NeLH7pS5QVleHYiw5HryHdkdEsLex7CncVY/miX/Dnsn/QrmcbnHTZMchtnQ2Duf5CNRvDuWxqNKZzmZpa+xuCeLTxZP1eGsNntuaXYfM/2/DJK19BJwROGXscOhzQDmk5tfcd8IV8owt3FuG7937Cqu//Q6cD2+OEUUchp3U29MbI8wH8XcennddXG6dJAGtRGUwWo0p3rQ16s/Yb2bdzf8hjbOq/ke0lxfh+6xaM6TcADofm4+H9Wx90y8hU5U2/XLsGI3r3hNn6Gjz6IXA7KUqmdt9h9ekJg64PUPQ4HO4hKLe5mvR3Gwh9zkS6lte0bdlK7NizeR8Wv/glSvJLcMyFh6H30B7IbB55UtRe5sD+bQX45OUl2LttPw49fTAGHt8X2a0yEzbapqn3O7WFz0t44vFbEbIBWgyJFp9//rmKrCC1skOHDjjppJOU6WZdoXKp559/vko/mT17dq39K/766y84HA40VjJTs/HZzGVYNONTv9c7HtAOk967GftK9vi9bjQaYXam4o4THlKDMC90/u58+3q07J2LMnvi3NgxycPgwYPrZbuNvY0zkclNb4YXrn8Nv3+x0u/1Q04diLFPXowC6/46nUKTyQSd1YiJJz4Mm7XqZkdv0OPe925Bduc0lNt4wNeQ7bw+23hpQRkmnzQNJ1x1BDoPrN3YhaJ+Xrl2Ps69ZxgOPrM/ko35Wzbiw+1bcUuvA2DSxWdSZM6Gdcgzp+CxwaXonDsNm/OvhMPdMi77Nul3oUPOy9haOAb7y+o+OdfYaKzX8gxLJr5+7ScseGKR3+ttu7XCAx/ehn2le0O+Ly0lHauXrsfz17/q93pe6xw88vndKHLkq3sghmkqDK6nNh73CAsvbdq0iVnqhy/FxcUYN24csrOzMX369DqbbdJNPFU0qavqtGnTJnTq1ElVMokXW/7eHiRWEDTbuGTud7jwzjMhfE5PeZEN95z+mJ9YQVBn+uRlz2PWX0+hY5eOaEga6lw2Rfhcxq6Nx5Jk/F7q4zNTvvryhb8FiRXEzx+vwCljjsNBw/rXaYbLWlCG289/yE+s8M68PzFqBl5Y8QQyO2eEfT9/15ZG3ca3rN6u/nbq1hEdO9R+soXKoqaZMpTvVjL9RtweD7794xcMadse3Tp1UZEVNOHUqlUrmEz1Z0A62OHAJxvWolXuz/DoeqB1u4MRPzrAY1+J9rmLkNH8EmzavKtJfrcNMQNb03Zek7a1Y83uILGC2L5uFz58/ktc9tAF0BmC7zf2byvEzBvmBL++swBz75mPm1++CkaLEYlGU+536gKfl+jnpj5pEMFizZo1+Prrr7F9u3bBb9euHY488kj07Nmz1tukkMyrrroKJSUlmD9/PjIywg8UazLojVUIGzX6+gyHC+TzOcvCLvt41pc445qT0axtlVnpvi0FWLeiyozKF3u5A9vX7UbrLq2QCMT7XDZlkv1cxrKNx5Jk/F5i+Zmp6sLC6Z+EXb5w2icYdEI/pNXBf2DPhn3YtmZHyGUk/O7bXoBWnaLP3PJ33TjbuL1Um9HNbZ6DlJTaG2+mZ6fCVmyLeIxN8Tfy7eZN2G214tqDD/U7fyRW1OV8RmNwu3Z4d81q/LlnGw7tdCbM5vrbVyik4Xyg+Hak6z8EcHCT/G4bgtq28+qc/6/n/xB22Wezl+LC285E83Z5QcuWfv19WFH8xw9/Q3mxHVl5WUhU+LfJ5yWRiKtgQSFb9957LxYtWlTpW+GdxX/qqacwfPhwTJ48WYXa1gQywLnxxhuxYcMGvPnmm8psM9kpKwmvZpMAEdiJRjPlDJxFZBiGSVTcbg8c5eFDhG1l9hobEQfiivJ+h43TjZoyRXuLKwWHukCiWTKabi74529ltNk1J3yp+/qgQ1Y2skzAt7u74NAe8Yyu0BD6lpCmo2BwzIZO9I37/pnYjqepnw8nSthKw/ux0H1PfRtBM0xTIq5VQqZMmYKFCxfioosuUqaYK1euVLln9P8RI0bggw8+UOvUlAceeABLly7F+PHjVfURqjbifSRrjvrxI48Mu+ywMw5CRo6/8WZGTrpfxEWgct25b4eYHyPDMEx9kJGbjmNGHBZ2+fEjj1B9Xl3IystAZl7oSD7ysWjdmYXzpkzB7iIVBu4tTVpbUjMtKNyniR/JQkF5OT5fvxZHd+ysxhfxRMCDvjl7sGxndwhRs8mxmGE5E5ClaJ72WcPsn6kRR50/NOyyIacNQnpWaNFywLF9wr6v+6AuSKuj2MkwyURcIyxIkKBKIRRl4UuXLl1w3333KbGB1rn77rtrtN3vv/9e/X3ssceCli1ZskSlnCQbXfp1UhVB/v1pbVAZtVH3nY+UNP8wyLw2ObjhhStx7xmPB6nF590yHNnNQ5ccK84vQeGeIqxevhYOuxMHHt4LWc0ykNs6fE3z/TvysWPDbmz9dzvadG2Ftt1bhwynYxiGqQ40Q52/qxD//rxW9XHdB3fBMRcejg+e+0w5s/vSokMzHHH2oXW+Ucptk4MJM8bi4Yu0ile+XHLveTBbTFj7+wb1yG2Vo0TfvLY5MBgaJBOTiTEFuwuV6FXX31FaVhoK9ySXYLHov9WgUcYR7RvAF8u1Gv1yt+L51S2wx+pAi7T4ixZC1wxuwxFokfEhXHICjczifgxM9Wnfsy36H30A/vz6H7/XU1LNGPPwxTCmmLBr0x5sWLkZRfuK0WNQVzRrl4tm7fJw9AVD8fWC5UGC9oTnxiI9Jw27N+/F5n+2Yt/2fHQd0Bkt2jdDTsvETRNhmIYiriMnSt3o3z+8E/bAgQNVpERN+eqrr+p4ZE0PEiDue+dWLHnzW3zw/KcoL7Vh6PCDcNGdZ6N115ahoygO7IAHFt6Od59ZjPV/bELz9nk4ffxJGHhc35C15gt2F2DZ/OV44Za5fqFtw68+CRdNPBvN2zcLes+O9btx5ymTsWP9rsrXKLLj8S/uRYdebWN6DhiGSY4bx5duex1fvvFN5WsGowH3vnMzJi++Ex/O/AzfL/wFdF95xDlDcPLlx6pZ7bqi1+vR65Duqp9999nF2PT3VrTq3EL5A/U9ojdm3vIals37rnJ9S3oKHv7oLvQe2p1FiyZAwa5CZObWLUqHoJuW7WtDe6E0RWhC5O2/V2JQqzbIqkevirD7t3+PA3NdIJnpm20FOK9nw0RCuQ3DYHB+C73jLSDthgY5BqZ6pGVZcO30sfh6wQ9Y8sa3sBaXYeBxB+KsCaciMy8dK79ehfvOekKlW3s5ZNhA3DRrPEbdez66DeqivC4ojYxKoZ570+lKOF/72wZMPHmyn9l970O7Y9KCW3gSj2EaMiXkiCOOwHffVQ3gAvn2229x+OGHx/OQmjQkBJx/63BMW/4IXlr5JK5//gq069FGDbQDoQ74hZtn4+krZqLHQV1x1VOX4tgRh2PhtI9xyzH3KfU3kF2b9uH5G18NysP7cObn+PuH/4LWJ+X50ZHP+okVBG373jMeU87JDMMkH3Wp6ERihK9YQbicLtx31hTs275fRYBd+sAFKrJs79b9mHDoXdj095Y6H3NJfimeHPscpl/3Mg48oheuemo0jjj7EMx7bCEmnjIZg44/0G99Eo3vGvYw9m0L7kuZxgddr0hsqCvp2WkoSKIIixW7dmJt/n4c27lL3PctpR1w/oLMtJ7omm3B11sasC3qclFsGwSD/TVIT/J5mDQmqM8eP/A2/LF0Fc67fTiumTYGaVmpqj//8o1v8faj7/uJFcTPn6zA4pmfYfbdbyvTzlPHHY9xT4xC6y4t8OB5T2LHut1BYgWx+se1ePWet5XPEsMwDRRhccMNNyhzzOuuuw4jR45Ehw6aL8LmzZuVWeaOHTvwzDPPoLCw0O99VKaUiY7D5lRh0Xu37lPPW3ZqDpfDib3b8pUBXatOLZDVnPKuq9I7dm7YjeL8UjVLecmkC2BKNWPP5r3Yv6MAHQ9oj7NvOA1vPfyuCnfzTdtw2B0qciMc7z69GH2P6IVmbaveU7S3BP/+vC7k+lQeim4sqD41wzDJAfVX+TsL4NjlwR7zfuS2dCtfCBI3C3cXYf+uAmQ3z1Ihsjkts1FaaFX9xJ6t+9SNHoXkL5uvpQSGmsndvXkfhl99smaQKISKrnA6XErkoHBd2r/T5kCLDs2R3TILloBUuUgU7i3Gf79sUFFrXft3UsdMqXhnXZ+Ktx5+Tx2bTq/zE3RpEPrfL+tUX8w0bkhoj0UqI/lJ2Upt6rdBIeZNnTdW/oGWaeno26IBIhscKwBpAwx90K+FE19s2g+XR8Kgi6+Phpf8ssORZfkDKJsDpFNqCJOIfL/wZ1w5ZRQOGTYIhbsLUW614dybh6tryz2nP4rRD16IP5etCnrfwhmf4vZXr0NJQSnSstNQVlymtpGenY7ta3cGiRVelr79PS57cASsxjIVyUWp1zSWzm6RiczculdAZJjGSFwFi1NPPbWyrCl5S/ji9U047bTTgt63evXqOB1h44UiJH5Y+AumXv2SUnopXG3YFSdg+rUvq87Smzd37s2n4+wJw1TnRyX5Jl/4DNb/uQlGkwF3z7sJi1/8Ar9+9kfldiki45aXr0ZZQMdqszqUqBHJp8Jpd9ao0ki4zpthmKbHjnW7MOnMx7Fl9bbK1w49/SBc8+xleHLsTBVm64X8H+5951a89+xiFcHlhdLObnxhnDJA9N0OcdqVJ6hKIPee+Xjl7BfdEN4251rVF1496HYlgBD0/JJJ5+KMa04Ja6QZiL3MjrvfvhH/e+oDNSPmpdOBHTDxtQkoyi9RPhYUWeHLni2aoMw0brSc804xMYgl6MakdZembdS6v6wMH639D+cdcCB0cTbbJKRjOaBrC6HPwYAWZXhvzR6s2F2Mg1s3jGeA25MBj+FoCOurQOooCB1PziUilMbncnhw05GTlDDtjQocfs1JeOabB7FuxaaQ7yNBk4Tw6RNeroxSpvRr8rWglBH6f6gKIzmtspW4cf+5Typhw8uhpw3GDS9eiWZt4ltZh2GSTrC49tpr4+4InSyQ+PDEZTPU/+kcn3fLGbj/7CfUbKIXGrwveGIROvVpj35H9VZmcSRWECeOPkbl5/mKFd7tTr1mFh75+C6/11MzU1QY9Iolf4U8nl5DuilFOXBgRrnlFK4dCB1zbiu+WDNMMkBRFXef/qjqX3xp36sNZlw/20+sIDb+tUWF0Z4y5ji/1yma7PFLZ+D6567A5BHPVL5O0RcHnTQAD5z3ZFAUGhkOU76xbz9EfePc+xYoseGIs4dU6zNk5KVj7n3zsTLAiI3STcjX547XJgSJFQT5XjCNGyplSFE7OS3qfqPrnTGlFJOmLljMW7VSXeuP6dg57vuWnlLA+Qdg1vqQztkWZJoNWLqloMEEC8JtHAa961tI6ysQGbc02HEw4clqlqUEbt9S1VSWdNGMT9Ghdzv0OCh0etMVj41UERjeSUOCBIpl839Am26tcfCwgfj549+D3jfm4Ysw6YzHVWSzLz9+9Buy7pmH66aPCTLOZ5imTlwFiwkTOOQtltCAmwY5lO4x//GFla8fcFhPrPzmHz+xgqBKH5TiQX+L9pVg3YqNlcsOPmUgJl/4dMj97Nq4R81gOsqdyG6RBbfbjeK9xTj09MEqbYQ0KJ1er0KfXQ4XFr/0OUbefV5Q6BoJEmdOOEWliwRy3MVHsDMywyQJ+3bkB4kVBJlVvj/1Y2Xce/DJA1WUljHFiL+/W40Pnv9clQoNnJWiCiEUYUb9C6V4eMvQfT53mSrvfPb1p8Jpd0HohBJNP5+zNKRoSrx2/wIceETvkFWRqNpIaYFV7YvSPUwpRvzyqb/A62XTqq0qYoxm4Whg6xsp0rZbq5Dvoc9Es3DeGbzsZpnIa5tbK5G/tLBUVZ4oKylX4g2l1FjS6240ymjs2apVnqHrYV3JbFYhWITwiWpK2F0uzP1zhaoMkmFugNQXx8/UygBDb/WUIjz6N0/Hks37cfuQukfK1BqRCaScBFjnQlKUhZ7TxRoSGu/SNYWuEVnNMtGiYzP89NFvqg/1mmW6nW6VYvjZq1+pScBHP71HpYy07dZajYEpFfDnT36HzWqH3qjHmEcuRsfe7bRlBr1KY1w04xPcMfe6IMGCogDpWhYoVnghv6aR95yL1l1YsGCSC66v1kghxfa7937CC7e8hrOvH4bta3f5VQihTjfQgPPWV67BnHvn4Y0H/4dbZ1/rt9zjcqtZxnBsXLkZW9fsQJd+HfHKnW+pDp1o2bE5xj99Gd6f9pGaaaRw6munjVH55oGYU804e8KpMJqM+OC5T9Vgmjrnk8cci/NvHq7KuzEM0/TJ3+nvU+SFBnQTX5+gcobvP3dKpf/DwacMwD3zblIpHCQUBBqckZDQfXBXNbAkeh3cDU6XG3mtslVKiFfIoJzjgl3hU9mo3wxMZfN67Ey/bhZ++3ylek4D0hMvPVoNOB+7dHqQ8bA6pkIrWnVurioj0fpHnXcoxj0+KmTJZ3u5HX9/9y+mXPZcpfkw+fnc+uo16Htkb5gt1b/BI3+PqeNfUqZvlcc66mhc/vBF7BEUI8jniQh1naspVD2GftOB5XebGu//+49KCTm1e48G2b90/AjoO0Loqiq7DGqZgam/FWJrsQ3tMxvwBtB8GmBfCln6HETWAw13HEmM0+nCut834vFR01R/T5Cx5j0LblaRchTF9+Jtr1emHlK7vfCOs9Clf0c1affl69+osqYEjWuvnT4GxftL1TWCUgZn/7ZBLTOlmHDGNScpE+g23VrhxFFH4au3v1fjbxLKr37mssrxdShovWjp1QzTFGHBopHy38/r8PS4FyoH2R37tK/sLHdt2I2hZxyMr33WH/vISDw7/qVK1ZY6Yl9o9pE6Ut+QN1+6DuyiZvseONc/xJpqSD9y8bN44P3b8fe3/6qO9tGRU1VeHwknvpBAMfvut1BeYsONL1yp8sZpVvGbd3/EM+Nfwl1v3lDt/HGGYRovNEsVimbt8/D+tI+xdJ6/kSZFMtBsFQ3mAsUKosfgrkpAKMm3wmDUIzXLogw3rz/0Lr8IB4oqG3RCX3z3Ps22BkMREOZUk99ruzbuxuOXTsfqH9dUvkYCxWevLoX0SJx82TH45JXg0toU3v/k0gdQXlIOo9moZuNpkBuKnet3465TH/ETPki4uPu0R/HCiimq5HR1oDQF6n9J/PA71jlLodMLXP3s5TUyFmVC4xWhaNa1rlAEDf02qIJNU8XpduP5X3/CIW3boU1GcPRSfSM9+YBrtSYM+NC3Rboy3Pxy835c3rfhyqoLXRpkyulA+QLItEshDF0b7FiSlV0b9uCOEx/0S+OjKLkXbpqDia9fj9uOf6DS84ig9eZMmqc83siryDv+JshA95WJb+KxL+7FpNMf9RMjaYz9ztOLcdlDI1TE3/XPj8Oo+y+A0+ZUUXB5bXP8op8DoX1ZMjhajkk+WLBIYJwOp5qJpIE6DaIpTIxEBQoZfuWut3DEOUNw/i3DVdixyWJCq84t8NGLX2DNbxsw6r4LVPQDpVr0HtIdWc2zlJt9wZ4ilcZB1UIOOqm/6jApZzs106J8Kuhm4YdFv/iFXJOxGNWafuPBBaGP0+5U4W+DTuyHf5avwbCxx8Fuc2LjX5vV9vPaaGHNlFaybN4P6gZi+Ye/Bm2H3P9ZsGCYpgcZiFFVDeorUjNTkdMqS3ngUJ912BkHg7obt9OlZprJSycUf327GjqDDrfPuU5Fa9EN4+9f/omVy/5Rgq3HTRFiUvVdbpcHH8/6wk+sIEh0uOSec5VwEMpf4rybh8PtcKtZNPK7oIEhHbuvWOHLV299h3vm3xQkWPQ5vBdMFmO1qki4nR6889SHIaM06LWPX/wCI+89D9bCMjW7Rt5A4aop5e8u9BMrfPl87tcYMfFsWLqGTklhqs+OdTuVWEGieyygSI2dG3c32a/gndWrsL24GNcdfGjDHACZbUIPGHv6vWwx6NEnLw1fbsxvUMFCYT4RsC+BLH4cIvelhj2WJOT7hT+p8TWlEFK0HvW9NNa2Flqx9nctOuKC287AAUN7qusLpd2RAfT/nvwA/Y4+AOfceBr6HnmAel9ZSRl+/exP5bEULnJq4bSPccyFhyO3VQ5ad/YXkem6QZWnvB5zvpw1YVjIKD2GaeqwYJGgUJnRRc99hnefWazCv6gjPf2qE3HB7WeqDvGky45RndqjI6dVRk30P6YP7p53I6aOn4WPZn2B+9+/DTNvnKNqRBN0c0C5dN++uxw/f/Q7nvnuIbWPx0fPUKovDeKHX3Mybp41XkVv0MCfqo2QgSd13Jv/8Xfh94WWdR/UGefeeBrem/oR3nv2I/V+Eiuufno0Bp88QHX8gTcQvtANTQctvZRhmCbC7k17MPPmOVj+wW+q/ZPwet2MK5Tw8Mbkd1REAPntkJBx/q3DVRTFc9fPDtrOxXedg9XL12DuvfNVX0GCxWFnHIQHP5yIdSs2YNo1L1f1hcf2wSX3nIc/vlqFHeur0uUIMsq8Z/7NmHnTnEofDRJWR917vor8IJPh5R/8qo61bffWGPvIxWE/G+U504wX3biSmEDCLKWvUOrJni370a57m6jnhzyI1v0R2mWe/C4OP2eI8hf6c9k/lf04zcodeHivoIgNMjMNBx1fWXF51ONhorP1vx0xKWnqhaIRA3+nTYVShwPP/Pg9DmvfAR2yGsZYW9q/BwzdIERwatXgVpmY+/cOFNicyEkxoqEQwgRpuRCwzoC0fwNhPqrBjiXZIF+2fdvyVTriW4+8px7evvahRXfgi9e/VhWhFjz5ARZM+aCyQtWoe8/Dqh/+U2Pyrf9uV/5L3nHv6AcuUOJ3OOga5ii3hxUwH1h4O54eNxO/f6kZ21Mlv9OvPkn5aJjMDfc7ZZiGggWLBITCyd5+7H3V+XkhQYGEAIpCuOqpUSpEmELUfKE60FtWb8eTX92nnt9y7P1qfS+UOvLoxVMxacHNaiBOA3/f0GiacSQDIVJwn/7mQZTkl+Kf5f/h4RHP4NFP71Yhzt7STIHQskNOGYQnxz7vZxZE5U3JvX/y4juV6VAkMivKuzEM0zSg9j/xlIf9DDbJT4JyfKddO0vNQnmhSAbqk867ZTiOPPdQfPvuj5XLtCixTEy9elblazRIXP3jWlWV4/6zp/hFhf25dBW2/LMdN8wch/vPmeJ3TBSBRhFht86+WhlTes2CKcrsidEzsOXf7ZXr7tyghf6Hg6Lb6Gbz3v/dApfTDaPZoAyPqc989vvJ1TpHFFXSrkfrkLNpV065VG2LBre+/fjdpz6CqT88rM6LL9nNw6cokJgSLiWFqRmb/9mqZmFjBXlMUYUuunHS62MTtZEozPh5OYrtdlzQp2+D7F+6tgLuLUDKeSGXD26VgVf/ApZsysd5vRq4SovxEMBwAGTxg0Czj0IKLEzsoTZ3/CVH4d4zHgvqa2nCj6In6DriO/6l6Ama2CNvpa3/bfczYKbrHi2j60KHXm39rileKC3bEEF4oAhpEtbpGkWTlmSeTGI/RRcyTDISfiTGNBhUj/3D5z/HSaOPwdTvJ2Pmr49j2vJH1Axg98FdUF5qV7OEId9bERK8ceUWP7HCCw3qSfigkn/h8rgXv/AFiveVKLO6eY8tVPm1JEKcOu6EkOvTgP7Ic4Zg56bdKvyNHPrJj2LSgltUeSbqeF+69TXoDTpVezoUPQZ3UfWqGYapG3TTQ+2QBlv5EQwm4wEN1AKrgRgMepVi5itW+LJ45uc4dsThfq8Nu+L4ypktX06+/Fj1eqha9tQXknkalXEO5PiRRymBlvomElvTc9NUlEfgwJLEDBqkhtoGcdhZB2Pj31tx39lP4KELnlJ9JqV3tO7aUg0uqQ+m74EGt2TqFgq9Sa8i5wIhg2MaCPsOoL3Q533lzjf8yuURua2zVWnWUBx+9sExqWqR7JSXlmPXxr1o1Sl21Rzod0gpSIFm2Y2dv/fsxisrfsNZPXujeWrDmGpL+3eASAXC+EJkpxjRMzcVn2zch4ZGVQRKvRRw71AGnEz8WP/HxpB9LQnsFP0UbrJuwZRFSM8JPdlGZptnThgWctkZ156CFu0jR2lRNar2Pdug+6Au6jrFYgWTzLBgkYAU55fi4Y/uRK9DumHyhc/g6oPuwH1nPaFmAGnA+e9P6/DfL+vDvv+PpX9j747wJdI2rNwCa1GVeVCoMGeaLSSoxjS5I8+6/Q01A0hGQWQg56sSUwoJ5ZdvXrUV9717K9IyLXjqiplqAP/tuz/hyidGqRxzt9uDG164Ev2O7uO3P+qMJ/3vloizgwzDRIcGV5RTe9WAWzGq67W48YhJ+OZ/P6A4P7zreH1CnjaBNO/YDDsj3JhRhBlFHQSGyNKsVSDte7XFml/D94Vrf1uvXNy9UO36u9+6EenZqfjita8x4dC7cPXg2/Hmw++pMswDjj0waBtvPPQOxj81Whly+jLwhL6qb/vmnapIEK/QQP0ghQhTFBx9D2P73IS5k+aFHfRShAUZu9HxVW6nf0es+S38Z/vv5/XK3yjwPD248HaV/+zLoBP64ZpnxwSZLTM1Z/0fm5Rg1LZ77LxAqFoAQVUKmgplTidu+uxjtM/Kwuk9/L0j4oWUbsDxnYpaECJ85MrBrbPw3bZCFNtDi4rxROjbAilnANZZkE4tHYCpf5F/xZLQ55r8KcIt80bspYYpGU2pY/2O6u1nMk2i1AmjjsKpVxxfo+pPDJPscEpIAkKRBj9++KsaKPvOFr5462sqymHo8INCli71Qnl3lrTwHSHNbpJxWyTadGuJyR9OVJ0rRVBc8dglSt1NSTXhwUW3K3f81AyLurkgmrXNQ0q6GdOunuXnlkxmRZQSQoIECR1kFkdhcoV7CjWTzWaZSoTJ4Zk/hqkTNNs+647XVXk135SGhy58Bje+eBVOGXNs3MPNqS8KpHB3kYo+iETLTi3wwu9TVIQIpYKkZqSGrGJEaWuR+kKauaY+86TLjoUA1LYoF/iuYY/4pa6t+u5fTDz5Idz//u1qAErpKZXHu6cIz45/EY99PkkZX5KfD5mepeekoWWH5rj5patw+eQRKNxdqEyDqT/b9M823O6TskfRHPOfWKQEnEn/uxk5Lfw/P32+o84/FH2O6IV9FSZt5F/x/vRPwp4jOoZQpo80E0cpfHTcVLUpu2W26l/Z0Dg2rP5pHYwpxphGWNBMKnli/Pn1Pzj6gsPQ2CFB5+6vPse24iI8dOwJMOgaKM3FuRKQRYCxX8TVhrTJxBurduLzjfsbPi2ESBkOOFdAFt4C5L2vqogw9QddF1t0aB5yGUUr0vUoHHQts4eprkdpHDq9Xhkzk1EneQjR9Yoi+UjMiGUfwjBNHRYsEhBHmUPNkobi09lf4cRRR6tB+Oy73gqZU33keYeSWb76fyiTy9OuPFEpwu16tAkK1yZ6H9pDvZciNaj8EkGDfIq6oIEI/f/lf57Br5/+ienXvayW57TKxq2vXOMnVnih98x79D08/NFd6nlWswz1oGolXkqLylT5P71Rj9wY1LZnmGSDblB9xQpfqMTawScPCFtOtL7oM7SnMqX0LUVKUQFUb55u0EI5qA8+sZ8SRlt1bomu0CIFaD2qeET9ny9fvvENTht3gqqaFAj1Yf2OOgCX97oBBqN2qSMT4cPPPsRPrPBCKSEfzvwMJ446Coue+9Rv2ZDTBiuBoG231kHvIyGAHl6PHopymTo+tMs/RaLt3rRPla8r2VeKbAu5vYuKPtaIVh2bq4eXEy45CvMefT9kygtVfQon/FCkBT2Y2PPXN/+ovPRYVQjx0vOQbvj+/Z9w9TOj1W+hMUMlTBf996+qCtIus+EiJ6V9GaBrBaGPHA1DZpu989Lwwbq9CSFYCGGATLsaKL4XsuhuIPsZLV2EqTeoLDaZ3Af2tZv+2opzbjgVr92/IGQlp7OuPxVfvfVtyG1SxTy3043rh96trkcp6SmVYjiNv59a9kBU8Z5hGA1OCUlAivcX+w3wfaEOk/KhacAfOBNDQsLtc69VueG/fbESt716rXrNF3oPiQU71u/ENc9eHuR0Tmacl953PnZt2oseB3XDQScPqBzMU0dONx8PfXgnsvIy8P3CKg8MGsCRaVg4aNbSG43hi73MrqIwHrnoWVzR5ybcfOQkfPLyEhVRwjBM9dn2X7D46Bt9URrgdxAPmrXLxSOf3O1n9kizTnSsFPUReFNNXhHn3jQc5SX+JUephNyhpw9WpVB92bZmJw46ZaAyTPNFRVG8fYNKhaFoMIrMoAdVK/r5kxVhj3fl1/+gx0Fd/F6j6ktkBGrxSdeIBAmv29fuDL+Pb1bh7Uffw/gBt+Oekx/He09/jH3bQ5e+oxxnqvwU2I+TdweJKEx8oXRJMrfuPrBzzLdNkZOUQ0+VvShEvbHy+so/8NTy73Fe7z6qMkhDIT35gPMPwKiNYaIxtG0Wlu8oxO6ANKuGQuhbA2njAPvHqnIIU780b5+H2+deF9TXXnD7GcrAmaqE+KZDEyR+H3raIDVOJiNpXw46qb8aQ1O5boImD30j92iykPxwGIapHhxhkYBQ6HPE5RYTHhs1XXWkkz+8Uw12qSQg+U0snf895j/2fuUM3PSfHsWmVVthK7Gh99AeSEkzq+d000CO+JdPvkilfKiwt47NlRvxE5c9p9I+KNx6woyxSmig3FqaRex0YHvlaE4RHCR8+A7SI+VHU252KLd9EjJuPe7+SuW6rKQcT1/5gppNvWbqGCWMMAwTnWhpXsYo/Up9QJENBxzWA7P+elpVViDH824DO6O00Kpc1K968lIlKOzbkY82XVoqgeHRS6Zi6nf+FTZoxpn6q0vuPU8JGtvX7qhMFXn2KtrOaNXfkZ8Fef106dcJea2zVX9C+/73p7UQOuCQUwer4wgH9XEHDO2J+9+7Dfm7C9FjUBcVPdaiffUjUyhKjD433dyGgvrBr+f/oM4BPebeOw8/LPwJD34wEc3a5Ab1m4cOPwizV09VHkLUz/Y4qKsSeqgUKxNfSNCi31SvgOossYCuv+fccBrefXYxXV5xw/Pj0Nh47c8VuP/rrzCsW3ec3euABj0WaVtKrREw+ntmhWNImyy89vdOLFq7F1cOiFzRLF4I0yGQ7vMhS6cDIh0i7fKGPqQmC6VlDT3jILz455NYt2IjymjMPKQ7WnZqjvmPL4LDZsf05Y9gy7/bVL/dY3BXFdV4x0mTcfNL43HHaxPU9YvGzR16tcOGvzZj6vgX8cQSrWpfIHSNMBj4Foxhqgu3lgSDBkOUG93xgHbY/M+2oOUUEUGmnKTWUgWPlNRP8MrqZ7F7816MH3ir37pUjokez3z7IA48vHfl6226tsKOdbvgdrnxxGUzlNCQ1SxTRTVQrjXNcmY1y0KvQ6oGZV36VhnXeaHyp0vnfa/+v/b3jRj9wIUqbDFU+PJpV54Q5FNB+5t2zayQYXZfvfUdRtxxNgsWDFNNKB+WBl2BlSMIikzIat4w4h8NyuhmjB5e9mzdpwZ2j42apsRTEgoolYIE0wOG9lBihC8USnvQKQPw0m2vK0GXRFNrkeYnQTNi3pSMwNLJtC6Z+VJ/6uWUy4/FRy9+EfJYqc49GSBSuG5tIQ+LYy86XJl6Bp0LowHte7RV1Ut8of6TzBwDBQvCnELpMS1C+oEw8eWb/y3XUoO6B6cGxQKKIqLrIVXyOmXMcWh/QP3sJ9bQNf/5X3/GU8u/w6ndemBk3/4NmsIgpROwfwUYDqx2adA0ox6DW2Xif//uxrj+bRMnBYP8LFAOWfIopGc/RPpNEQ1EmdqTlpmqHtSHS49HTQKQv8XR5w/F+EG34d1nPlKmyiQWv3Dzaypqb+Q95+KDmZ+qsqb0PoomJB8iiko+ffxJWPFVaMNOmpTLbuF/nWMYJjycEpIg0E3Gym/+weQLn8bLd76l1FpK+/CFoigmPHcF/jdlkXpOObRk4EadXvuebZU3RSDDx58YcvBNIdbXPzdObZMG/lS2icQK2ueVUy6FzRo9VI32OfLucysHLJ/NWYqrn71M5eoFliw97+bTg8LpaL8U7RGOv75bHfUYGIbRyGubg4c+nKjStnyhm3vyl6EqGIkCGY89uOgOFUZLs1XU/5BYkdcmF7fOvjbIINJRZsfJlx2rUs9okEjrk1hBfeANM68MKe6Gg3w8KLIskIHHH4hjLjgsqP+qKfSZLntwhJ9I4o2suGHmOCycEdpIc8kb34QUe5nEgH53yxb8gAHH9qnXm1kSLUgUWTzzMzQGPFLioW+WKrGC0kAaWqxQOH7SzDZNB9Xobcd2yMGGonL8trthqiqFQp3LlAsAy0Va5ZCCyyFd1e/vmOpDJs9fvf0d7j71Edxy7AN4c/K7amzusDtw3fSxah3yaaO0MOoPeh/aXV2XjrnwcHXdoIgLMromsaLrgE644LYzVLRFYHQxXRtogo/LlDJM9eEIiwSg3GpTZnI0e+ilrKhMudJv/GsL1v2xEZ37tMcBh/dC0d4SHHHOEDXoplJ1dDNiMptgam7CmIcvwvCrT8LyD36hqxyGDj8YzdvlhnSHp3Hx3Pvn4/Y51yoDOupk6WaAQo2fu2G2quQRDVKZKbf7mBGHY/mHv6o68gOP64tZfz+N37/4EwW7izD4xP5o26O1qg4SCN1shIvIIHzz3hmGiQzNBPU8pCte/vsZJX5uXbMDBxzaA90GdFb5uYmE0+5Ss1B3vXWjOs49W/aqyC6qZ08ePoC/yCp0OpUScuWUUSodbc3v69VsFqVskABQk8oKua1yMOyK43Do8MFYvugXWIvL1U1iYBRIXaD++fHPJ2Hz6m3446u/1fmnkqlU+Smc1w9FujX4jR4Tlu/e/1mJ7GReW5/QzQ2Zw9L+rnxmVEJ/Iw63G7d98QkWr/kPlw8YhBO7dGvoQ1LjCWlbDBi6QehrZjJ8QLM0tEoz4c1VO3FQq8wEEy1OhdR3BMpmQe4bBpk2GiJtDIQuOCqLqTkU8fvkmJn45dMqj6NNf2+BgFCRz32P7K3SRX5a/JuKDiTfChrX3jP8UWUo/9LKJ/HTx7+rKliDTuyvSlVT1COZar66eip+++JPVda6/7F9VCQgifMMw1QfFiwSgIJdhXh54pt+r9Gglh7HjjhczSB6/SHa9wAOPNy/prm93K5uVrxu9V37a876kaC87L1b9+P+c6ao8FbqeH9a/LsSL6gsXmBIdjgonJsedLPhC4kf0aBjPfiU0CZ4NGjrc1jD1G5nmMYKpV80hvQBGtSRDw/5PFBfQeLDD4t+VYbCdLM/7YdHVBSGl8xmGWo2a8rlzylRtX3PNhX+D/PVzJZ39qu6UFlReoRKdSNcLhdcDreKVqmtiEADUnoMOl4rqUgpeJSOFw5KAWASl09eWaLCwZvXwM+kttC1b8mb3+K/n9dDBGv9CUGpw4GrP1qEn7Zvww1DDsMhbRPD9wHO3wH3NsBySY3fqhMCx3XMxYJ/d+OuoZ3RPDX+vj+REMY+kJmPAiTIWOdC0sMyHCJ1BGDoy4JnHaAoPV+xwss7zyzG45/fgwmH3gWTyYAL7jgTzdrmYdbtb6jJREojpPEymTL7Vr7zYraYVZohPRiGqT2cEpIAbF61LaSPA0EeEeEc5ElwoDzp+8+eoozqqNwauYxXB7oZmLTgZpVTTY72NCNLYgWFM9/19o0hIyJiDYkwVKkkVFmnm2eN53JPDNNE2b5uZ6UpJdWkp/6HxApiz5Z9FVEWVVBe8finR6t+iWbCaH1vCeUbX7wyZn0Fhf/+9+t6PH3FC7jvrCdU9MbuECVQawNFlFE5arrpDYTMFkkoZhIT8oiiSBkS2OMBpXGSeeyfS1chEdlfVoaL31uAFbt2YuLhRyWMWCGlB7L8PUDfAcJQuwolx7TPgUEIvL4qfKWfhkQIC4TlfCDrGSDlDMC+DHL/eZD7z4Qsmw8p/SssMdHxpjSHgip7kCk9mTo7HC688dC7mHnzHCVWUDs9/+bqV5BiGKb2cIRFAkDu9RGXh5jho0H9bcc/oHK5vXzzzo/Kx+LyySOUiWYkKCKjz+E98fLfT6ucPTJ8O+CwnirdpGWH+p9B8kLRHdN+fAQrlvylQu1admqhTPFadGyuHPIZhml6RI1aCLG8bbfWmLb8YaxY8jd+XPyr6iOGjTlORWTEoq+wFpepksqz7nij8rXfv1yJtya/i2e+fahORpy+pskUPrz2t/X44vVvkJZlUSJGm26tQ6buMYnBl298A1OKEf2Ojk/lC4ow7Ny3A/7+7l8MOMe/lG9Ds624CKPefwdFdhvuOfIYdMpOoBAQ8q5wbwYstU+lSTPpcUyHHLz+9w5c2b8t0gPKXCYKQpcBWM6ATDkdcK0E7Eshi+8FSp4B0scBqZdU23CUiXxN2rxqq/KkuO/d29S1h1LDKMWPUrINRjZAZZh4kJg9cZJBYWQ0+0Yhw4FQqdKMgIGs0+HE+9M+9hMrvHz00hcqtDiaYOEtFUiCwah7z1cRHqHKjsaDlh2a45TLj8NJlx7TYMfAMEz8oPBYMuF12p1ByyjSINzNe4sOzXHy5cfixEuPjnlfkb+z0E+s8EJRay/d9hrueP16FelRV2iQS48BJ/bF9u3b0KZNG6Sm1n27TP2x9O3v0OfwXiq8O15QJM5nry6Fyxk8Lmgo/tm7B5cveg96IXDfUcehZXrilNaV0q4iDGDoAWEIDs2vCad2zcOXm/LxxqqdGD+wbtuqbwTNeBkHqId071bpIrLkScD6OpA5CSLl+IY+xEYhVgwbe1zIyk6UFkht//YTHlTCOJkzUyTy+1M/UtWeho8/SUX/UUUqhmHqD747TADIT+LaaWOCXqcB/a2zrw4qB1q0t1gNZMLx2atf1fgYEkEoSIRjYBgmPn0eVcwIhEqUkhFwtJS0+ugrVixZGXbZj4t/R/H+4HKxdQ1fLyoqiuk2mdhDlawov51mVONJ5wM7KCPrHf8GT0w0BN9s3oQL3pmHDLMJ9x2dWGIFIcsXAbIAMNXdCybPYlJRFi/+sQ3Fdi11rTEg9C0h0sYC5HOhbwZZeDU8BTdBegob+tASnnY92+LIcw8Nev2wMw/BL59oRslUyWr5B7+qVG1vaerP5y5D0b7EqSrDME0VjrBIAEitPfaiI1T5z3ee+hA7N+5Bn8N6YPjVJ4c0z6OiGlQ2KRz2ckc9HzHDMEztMaeYVPpZ574d8c7TH2LHul3odWh3nHnNKWjVOTaVOmoK3RxGrDzgCe0zxDRtvl7wg6pY1fOgrnHdL0U/UhrKxj+2ARegwaDf/msrV2DyN8vQr2UrTDjkUKQY/EuUNzTStQGwfQSYDofQx6b6wlk9muPbbYWY8ftWZcDZmBD61pBptwLO5UDZ65D7hgPZT0OYDm7oQ0tYaGJwwoyxOOmyY/Desx/BZrXj2AsPU2PzNya/G/Z9FAHF5agZpv5hwSJBSM9KRc+Du+HWV6+Bo9yJlIwUGI2GsOVEjzjnEHz5+jchl58w6uh6PlqGYZi6QekVJNJOeH4sdm7fibbt2yAtI63BTiuViQ5H70N7qGpITHJBNyJf/2+58ncyxNnLgNJE2/dqi42/b0FDUe504t5lS/Du6lUY1q0HRvbtB1000604Iz1lkKXPAboWgGlozLabk2LEGd2aYc5fO3BezxbokZvW+DwZTIdBGnoB1pmQ+ZcC6TcBaVdoaSRMEFSB6tDTBqPfUQeoFG3q8+k8Hn3BUCya8UnIM3bYmQfztYFh4gD3WgkG5ciSIBFOrPBGZFxyz3mVpU59oY620wGJ4djNMAwTDb1Rh5LyYgh97cqHxoq8trk4YdRRIdNUrps+hk0xkzQdZOu/29E/TmabgXQ6sD02rtga0t+qvlmzfx/Omv8mFq/5F1cfdAhG9RuQeGKFdGtihSwCUs6CELE1QDytazO0SjPh9mVr4QxTyS3REbpcIH0ikHIqZOmTkIXXQHo4FS0SqRkWZOSkVxpxtuveGged1D9oPYq8uuzBEWp9hmHql8S6+jDVpnXXlnj+18dx5rWnKAO3Dr3a4vrnrsBdb92A3DiUJGUYhmlKZOVl4MonLlV9aNf+nZDXJhfHXXwEZq6YolJXmOTjq7e+gyXDgh5xTgfx0nVAR9itDqz7fWPc9un2ePDy77/ijHlvoNzlxAPHHI8jO3RCoqHECusswPU3kHI2hD724x6jXocrB7TFqn2lePoXrYxyY4SEHGG5AEi7WVVSkfvOgnSG9+xhgiMvbnv1Wtz00lXoeEA7NeY+7coTMPO3J9C2Rys+XQwTBzglpJGi0+nQpmsrXPnkpbjorrOh0+uDzDkZhmGY6pPTMgvHjjgCA4/vB7fTjbTsVBXRxiQfbrdblTOl6ApDhIjH+qRtzzYwp5nw66d/YuAx4VOWYsXfe3bjnqVf4q/du3BKt+64sE9fmPSJN0yUHhuk9UXA+RuQcgaEof48JrrlpGJE71Z46c/tKi3k7B7BvmKNBWEaCKl/ELA+D7n/QiD9eiBtHIRIvO840aCJwFOvOAFDzzgYHrdbRWBwZRCGiR/cSzVyTGYj8lrHxmSKYRiGAbKbRy8LzTRtfv54BfZt24+L7zy7wY5Br9eh/YFt8P37P+OKRy+pDFGPNTtLSvDsT9/jnX9WoV1mlqoC0iOvGRIR6doEWToT8OwFUs6FMHav932e2iUPO0rsuGPZGuh1Amd0axhj4Fgg9C0gM+4BbO9Clk4FbF8AWQ9AGPs29KE1CnhikGEaBhYsGIZhGIZhfMw25z32Pjr2aaeMLxuS7kM64ZNpy7Dqh/9w4OG9YrrtzYWFeGXFr1jwz99IMRhwaf+BOKFzV+h1iZctLD35kOUfAvavAF0zIHU0hD4+wgEJRWP7t4FHSty05D+sybfi+sEdYGqkpdhVRIXlQkjjYKDsVcj950GmnA6Rfg2EoWHSnxiGYSLBggXDMAzDMEwFn81Zhn+Wr8GVT4yqt6iG6tK2Vyu06NAMcybNw5Ql99X5eApt5fhq4wa8/+9qfL91MzLNZpzZszdO7todqcYEK1fqsQGuVZD2HwDnr4AwAuajAePBMTfYjIZOCOVn0TrdjJf+2IbPNuzHhIM64JTOeY1XuDB0g8x4EHAsA8oXQdo+hDQdAWE5CzAfA6HjSDOGYRIDIbmAcEh+//13NctiMpnqdIJpG06nE0ajscEHPo0dPpfJeS6pDfbs2TNh23iyfi+xIhk/c7J+7kifuT7aeU3beMn+Uvz+8d9Y/OyX6H5oFxx32WFoSOjYXW4Xdv67B4ufXYIh5wzEsGuOQWbzjLDru6WE3eNGmcuNEqcD+Q47dpeXY4u1FGuKi7CxtASSKpCkpWNgbjP0y8mFMe4RFXQEHuiEC4ATemGDQDkEimA2FMOo3w+TfgfM+l2AcMPlyUWZsxvKnJT+0fD99Y4yFxZvK8OaYifSDQIH5ZnRO9uE9mkGNE/RI8uog8UgYNIJJXSEhL5blxsGg55CHtDwuJBu+gsZpt+RYtgCKQVsrs4od3WD3d0WTndLOD05cHvS4ZGp8EgzpJrzrJ5wRL9Ns9mcMNfyZOx/qwufGz4vtf3NpKen10sbJ1iwCMOKFSvUF0CdGcMwTU+w4DbOME27ndekja/4dBVev+PdyucGox46Q+LMnDvKnZX/f3LFJOh0/jdZ9/3xG/4o2F/t7Zl18Y1QIIRwQ6DqczR2yt3Rf1dje/yNO/r/isaETthrtH6h7ShsL7kh6np8LWeYpo2pnsbrBAsWDMMwDMMwDMMwDMMkHIkzfcAwDMMwDMMwDMMwDFMBCxYMwzAMwzAMwzAMwyQcLFgwDMMwDMMwDMMwDJNwsGDBMAzDMAzDMAzDMEzCwYIFwzAMwzAMwzAMwzAJBwsWDMMwDMMwDMMwDMMkHCxYMAzDMAzDMAzDMAyTcLBgwTAMwzAMwzAMwzBMwsGCBcMwDMMwDMMwDMMwCQcLFgzDMAzDMAzDMAzDJBwsWDAMwzAMwzAMwzAMk3CwYMEwDMMwDMMwDMMwTMLBggXDMAzDMAzDMAzDMAkHCxYMwzAMwzAMwzAMwyQcLFgwDMMwDMMwDMMwDJNwsGDBMAzDMAzDMAzDMEzCwYIFwzAMwzAMwzAMwzAJBwsWDMMwDMMwDMMwDMMkHCxYMAzDMAzDMAzDMAyTcLBgwTAMwzAMwzAMwzBMwsGCRRj+++8/9WAYpmnCbZxhmjbcxhmm6cPtnGGaPixYhMHhcKhHXbHb7fjtt9/UX4bPZaLAv8vYtfFYkozfSzJ+5mT93PH+zInYxmtCMv1GkumzJuvnTaR2nmznvybwueHzUtvfTH3CgkU943a7/f4yfC4TAf5dJibJ+L0k42dO1s+djJ+5LiTT+Uqmz5rMnzdRSLbzXxP43PB5qSnxaEcsWDBNGindCbEN/+25Yro9hmE0PJ7YzZbFut0zDJM8RLvOCyGQlmap9voMwzDJjAEJxCeffIIPPvgAq1atQnFxMTp27IhRo0bh3HPPVZ17OI477jhs37496PWVK1fCbDbX81EziYp07wccyyFNh0Poc2q3DddGwFMEaTwQQtS9uUjpAJx/QOpaQRg61Hl7DMNoeDylENS29G0hDJ3rdFqkexfgXA1pOhhCl86nmGGY6vcfrm2AexukcSCELvQY1GzYjS7ti+DReSBdmwHPPkhjPwhh5DPNMAyTyILFnDlz0LZtW0ycOBE5OTn44YcfMGnSJOzatQvXXXddxPeefPLJGDNmjN9rJpOpno+YSWSxQpY8BtgWAamjgbRraixakFgh80cCdCOU+1qdRQslVjh+gywYC+hbAjlzWbRgmFiJFY5fIQuvqWhbs2stWpBYIQuuAVx/Q2RNgTQfz6IFwzDV6z9c2yALRgPuHRDZL0KahgSJFkqgKBgNA4wQOc9B5l8BePIhcuZAmgawaMEwDJPIgsXMmTORm5tb+Xzo0KEoLCzEq6++imuuuQY6XfgMlmbNmmHAgAFxOlKm0YgVRNlcSPpbA9GiSqzYpz3Pv7ROooWfWAEX4N6uDWpYtGCY2IkVlW1rTK1EC1+xQj0vuo1FC4ZhaihWbNWeF14VJFqQWEHjCUJk3QdZQGLFbm1ZwWUsWjAMwyS6h4WvWOGld+/eKC0tRVlZWYMcE9PIxQovJFpYn4d0F9RYrNCwaYMM5981zjUNEiu8VIgW0rWlRttjGCaMWOHXtsZoKV21FCsqXy+6DbAvgfSU8mlnGKZaYkVFR6RECzh+gvTYA8SKyZBFd1WKFRpOJVrA8QekdPKZZhiGSUTBIhRUJqVly5ZIT4+cR/zhhx/iwAMPxMCBAzFu3DhVl5lJLsKKFTUQLUKLFbUXLcKKFV5YtGCY2IoVtRAtwokVlctZtGAYpkZiRaBo8QNkyfMRxAovLFowDMMkdEpIIL/++is+/vhj3HHHHRHXI9PNfv36oU2bNti6dSteeOEFXHzxxVi4cCHat29f6/1LKesc2VFeXu73l6mfc2k0WKEvmxZerPAVLUQqnKZL4XJXOXQTKaYiIKxYESBa5P4PNncH9RsJBxnFmnX/AeHEikDRImc+bI74GPw1pt9lampqvW07Fm08Wb+XhvzMlB5o1v0bXqzw4k0PyX0b5fbMkKuYjaVA0bUQYcQKP9Eiawbs4jB4PHXX+vm7jk87T7Q2XhOS6TfSWD9riqlU+VGEFit8RYtrILKnQaZdDlk4LoxYESBa5M6Hzd014jijsUDfayJdyxvr7y0e8Lnh81JT4tGOhEzQnpCMNs8//3x07doVs2fPjuhfEciePXswbNgwDB8+HPfff3+t9v/XX3/B4XDU6r1M/KHyYF3al8FQQsartvArigw40+dg/WYdysv91+vYPhNZxv9Bb58TcV/SeDSs+juxbkNhVMGiR7dsWBx3Q7h+i7hNt+VG7C87Adt3lERcLxkZPHhwvWyX23jjxWAw4MDeOUDJLRDOPyOvnHY9yuRp+G9tfsjFzfJS0TpvDQzW26l1h9+OviPslhewZn0pXC4uQdgY2jm3caa+adMqHc3Sv4G+fErE9aShL3TpFMW1HtKzByh7LeL6HuMQlBsewNr1kccZjYmmcC132Jx46ao3MeTcgTj4jP5x2SfDJHsbT9gICyppSmkd2dnZmD59eo3ECqJFixbqpFF51LpgNBrRrVu3OqtOmzZtQqdOnWCx+M/oM/6YjGVwuSzwSFGrc+kREsh5DSigHFEbQOUNM+6ELL5fi5oQGUDOG/C4OqJTpzBn3zAO0OuAsv+zdx7QUVRfGP9m+2aTbAoh9N67gopiV+xdVFQQAXvBAvaKXbH38gepih0RK6JiBRVEkS69BtLr9vc/9012sz1tN9kk93dOTpKZ2dk3u/PevPnm3u/OCLtaGI4FUh6CzpmKPn3a1OwrND8LUXwrFGcE0cJyK2AYhVR9ElKtaBD4vIxdH48lzeF70Woc0GiccLos9T5mo74CDldS2Am73QWYrc/BU3RLZNHCMgnCOAoGocPAAakhUVVehLYtoH0WKL41vGih7QykzYBwZqJnT8SE5vBdN4VjTrQ+Xhta0jmSCMeqUTzQ6SrgcNZs7PIitOcBWi1Q+kT49bpBUNKeh6j4HCh7BkryTRBJl0UULYR+OJTUp6CtzTyjhT+BrW0/r+v5tvLb1di1dh9yt3+Hy+4YjeZIIvTFRIQ/l+o/m3iScIKFzWbD1VdfjZKSErz33ntISUlptLbQE/JYhbBRp49nOFxTR7hzIIruhTHlRkDXL2oljmifpRADgIw5EEV3QEm9B6L0VSjWJyCKH5HhmND1htkQXhBRSYLQXQ0BBSifHrjKcCw01kehaLOQVKtS6UkQNFkpvBkIFi2SJ0MxXwS9Ng2NUX29pZ+XsezjsaSpfi/CUw7Yl0FULIcu+UYo2lAj5Zoes3DthiiaAhP1OV23CK9Kgsb6HETRLUCwaGGZBJgvhEYxQql4H1D00JvPg6IJlxqSBOE5FlCehSgKEi20naVzv6JrX8t+37y/66ZyzInax2tDSzpHGutYpcml8x+Ikv/BbJ0KRdu6xq/1uD2AMgyw3ASUvRC4UjcImvSXoWjbQFhGQ8AFUfpCZNFCPxyatKfl+8djvGmu1LWf1/Z827Nhn/ztdrlhNBqhJaGqmdKSxp3awJ9L45BQppsUZnvzzTdjy5Yt+N///ifNNutCTk6ONOscOHBgzNvIxEmsKJwMOJZC5I8FXGtrXYnDixQ69P3VXNGiu6RAIIqnQkl/XYoVdFGrdh+adCiWCUDSuKqFhqOgyElMVt3apc2WT1igHxoiVijatDrtk2EST6z4EaLoJqBiHkTpSxDu/Lrti8QKyuGm/ps/BsK1JeK2iq4DFOtzgH5wiFihKEYIEitKpwElj0FUfAzhKQ6/H40FMB4Lxfos/RciVjAM0zzxiRX54wDHEjl3EO79NXqtx10Kxfk7kH8RgDJVtPBCkRWVYgWhaKxQLGPkNlK00KQDSeOrtqfIikqxgklMcrYfkL+ddhcK94e/ljAM08wFi6lTp+L777/HNddcI0uZrlq1yvfjzU8bN24cRo4c6XvNokWLMHnyZCxcuBDLli3DBx98gDFjxkjVc/x4vwsBk9hiBV3w5YKKeosWcJNj9wSA8kTl/zshCq6UBnw1apOnDHD8ok4yzBcBhhFQzOdClL8D4am+LGqNRAsWK5jmKlZ4oxPqKFr4xAr3dnWBJ7d2okU4scJLbUQLbRcWKximJYkVqPRBcPxUI9HCK1aIwuulsSbK/lclWgSJFV6qRItJcNt+VR+MWK5nsaKJcGBnHlIzVXP03F15jd0chmkxJFRKyC+//CJ/P/FEaB7gkiVL0KFDB3g8Hrjdbt9yWkYmm4899phMI6EUkuHDh2PSpEn1qhDCNIJY4VuhihYKpXZUkx4Ssk/XNnlz4xMrvEjRYhyQPkve3EQVK+yLIYrIgA9Qkm8DDIfKFBPp3A0PYLlCfTJSR9EClJoCPUdWMM1XrPBCogX9rmF6SIhY4aVStEDG3IjpIbJfU2SV0IYXK/xFC/odIT2ERAtBooVhOBRtZrVtZhimGYkVXipFC1gfDxvxECJWeCHRImkilNS76Q3Cvi+JFi7DaNjFGdA40mC2jAOSLqlzBCfTcOTtzUeHXu2w9reNOLArD70PaZr+OAzT1EgoweK7776rdps5c+YE/D9kyJCQZUwTFivqIVpEFCtqKFoEixVyWfANT9lb6s1OvUQLnpQwLUCsqIVoQVU/TPo8NTIqWKyojWhB5pnuoshiRS1EC6B2xnsMwzQTsaIa0SKiWOGlfDoEeWAZTwZS7oKiaxeyidOdhA2btqNv39ZQNJwW2lQoyi1B3+G9sG7ZJhTnclU3hmmRKSFMy0AIB2D7NrJY4duwAqLwDkBUnycoPIUQJU9FFiv8RYuy1yA8YS40ohyi6P5q30s+QXFHq6HOMC0E6qPFd0UvB0pUzAPcOyKubpOdolbmiSRW+IsWxY9HT82i8aKUfCiqgcYLwRNOhmmRiBKIotsiixVeHD8Bjl8DUlQV2CGK7w4vVvhj/xpwrZeRwUzzESxS0i1ISjWj8AB7WDBMQ8GCBdPgKIoBMJ0KmKn8aBQ07aCkvwFFU30oOT2hUFLvB/Qjom9IfhTJN0HRhKk+Q2abGbOomKLfQj2g+D9pVaBYXwa0nG7EMKB+lzEbUKI7iVOlHugih84eyC2HSLpaVuIJfGHQk0fdACjWh6NHN2myoKS9QY6ZfgtNQf1aByX9LUDTir9EhmmB0LxCSZ8ux4vAFdbAvy3XAMZjAqI8hZKuvtZ/23Ak3wqhHwiNhqfazQGHzQFbqQ0WaxKS0ywoYsGCYRoMHkWZRoFCw5Xk6yKLFiRWZMyGoutUi52a5M0MjOdGFitSH5HbRa4wMlC9AZM3NwYoac+o5VAVukHSQMn4GND3rQwZp9DQAxDuPfB44ltjPBr03rINbtW9mmFiiXDvhnDnhl2nKFq1DHHGnIiihRQrjCdB0ahGZcEYdKXo3ycDbo8ZivXRKtGCfCRov+ZRVWJF+qsQyIRw7YRw7Qn/fhqT9J3xiRZKMpS0F+SPKj5WihWGYdLrgmGYlomi6wolY65PtFAofSP9TUDXE0rKPeq6pAnSi4LGQUK486B49kJo+0LJmBlZtEi+FTCfD41fCiiNo+TTE4yonEfEC+HeJ3+Y+lFaWCZ/J6WYYU4xo6SwlD9ShmkgWLBgEk+0qINYIVNCymZB5J2pOnAHixYkVqTcB5F7BlDxaeQqAT7RYo4UK0T5XIjih9VSYxkLZG68yDsfwrUJHnc+lIqFEAdOguJc1SiiBb2n4vxbbUPFAhYtmJgifWHyRkOUPFon0aI6scLjzoXW+RVE7kjosRpCsaiiRdL1UEynQ+SdC0XXE7Dc6BMrFPcaiLyzIMpn10C0+B8U6zMQpc9DlL4sq38o6TNYrGAYJkC0UFIehHD9JyuKyTHDtVl65iieHIiyeRC5dN3foY4leedB8WyNLFqQWKHtBUWUBYoVxVMh8i+BcG2XJdbpx6gvhii6t7IS0s74iBUF10EUXMuiRT0pKVC/TxIrzMlGlFb+zzBM/GHBgkks0aIeYgXKXgFEGUTBZYGihVesyDtflhwTJQ9XL1po28kypnD8Dri3SdECsAG2rwBRAJE3Bop7O0TFBzIHVk5yGli08IkVBVeobSidxqIFEzNUE9uxgCcHsH1ea9GiJmKFYvsCouQRtQJPwdVQnCshYIRiPAii6E4ALogSMrxrDyEsqlhRMF72c/K8qFa00HWVNxhwrQNc/0KUvgLoOnNkBcMwfoNFGoR7C1DxAZTkGyHKZwEV8wFRpIoWxsFQUu6GKH0RqHivcg5wSXjRQooV3YCiaypFiG0+sUJ6WlB0Rv5lMOpy0LN7JlDyAOD4vrIc+7iYihZesYLGPrho7GTRoj6UVUZYmC0mmJNNLFgwTAPCggWTOKKFZVL9xArfQj/RIvmuALHCt0kU0YJqr8tKIY7fqhaSaFF0O5S0aWp6CE1YCq5RS5fR5KSBRYtgscLXdhYtmFiLFV5qIVoo1qdqIVZ4qRQtXH+rfdrP0I6ECcWzsUqs8BJFtBDuvRCFNFlfV7XQ9Q9EwY38pJFhmMpxokAacYPGmJR7IGi8qPjIbyAplibbwrkMsC30Wx4qWlBKiSpW3FA50OUBzrUQxQ+qYoVvANwLFIxDsukAFP/xKYaiRYBY4Rv/WLSISYRFMgkWZpQUcEoIwzQULFgwiSNaWMbXX6wIFi0MQ2W4pb9YEU20qBIrfg3dp4y0eCRQtCi8rcFFi0hihe8YWLRgYi1W1FK0gHFkLcUKf9HiGijm82RklIREkORJ6vnuL1ZEES2kWFFwLeBaG7q9FC1uYNGCYVo4VWLFTD+x4mO/LRQoqQ9AOJYBFR+G2UGlaOH+D8K+Rqa0+cQKaGWUmbAtAOzfhL6WIi2KJqspcJo2MRUtwooVXli0qDNlReXytynZqEZYVEZcMAwTf1iwYBIGr5FlvcWKANFiHJTkKwBd3/Cb+IkWUcWKsKKFNUi06BJ30aI6scJ3XCxaMLEWK2ohWkTqy9HFCi9ONZqJzDZNF6hiRdHk8GJFiGixN7pY4YVFC4Zp0Qh3vp9YcVcYsYJMOO+HcKwAbJ9F2VGBHDMVfRug7A3vKyvFis8B+w+RXytFi3srRYvWMREtoooVXli0qBPlxRXSd8RgMsCYZJT/MwzTMLBgwTRNhFsNt6x2OwcgbFHLLgpPbmX4uQfwFNVgn3SRUgAqzyqxA8LpV33EJWu8w69ue8wQlfuurv575XEpNdiOYapOGqfaX6rDU6j2l9oi3BCe/Bps6FYFCk1qVf+qtk20X486NogahOrKPsr9g2FaJp6qOYSSEv7aT1FiNZkT0MMDujb75gA0PzADHrpWV4Mcb4XffMJ/nlGDcS9kfzUd/0p5/Ksl5cXlMFmMUrQg082KEhuEELX/jhiGqTUsWDBNEkWbCSX5JsA8OspWelmiTJBJlnOFush8QaB4YblWTUXRpEPRtoGS/hqg6x95l5psKNbHIIruAzwH5L7UKgSvAK71aunTtBch9MOh0abE7oC9b69NkfuWpVapZGMkksZDsUyQx8QwNUbXA0rGvMil+rwmttYnoWj9ngj6QRM4Iezhz19dNmC+GLBcF6URGiipD0E4fgHKp0OUz1MjmhA0offHdI4cDxRdeyi6DlDSZwLaKOll2q6yWghtHyuEJ/wxMwyTeCjaVqrnhOlMiOL7oJhGAsYTArYRRXdBMZ8JGI6OsiMLlPTZEI5VlVGWhAeicDIUy+WAfmiU16bLsU0UPyqjKnxoMuU4rOgo1bTmCBJNtK2hpL/t15YwaDvLMTKW419LoLykQgoWBEVY0LXOVlYDgZ9hmHrDggXTTEULEitmQuiGQEkar94EUY11bUco1qdV0cJyDRTjcYCfh0VU0SJArNgTJFb8GyRW0JPh+FCtaOETK7Lj1gameUJPjqDrGVm0qIFYAfd/gP03iAgpUdFFC40sPSrFCtuX6iLH76poQf0ynGjhJ1b4jiOaaBEPscKdB1S8I9PKGIZpaqLFaRBF94QRLVyVosU5gOH4MDuwqGOlvj+UpLPV+YSZDL4JO0ThFCiWiYD+0AhixfOVYsXW2IgVlAYnhZMoogWLFXWGUkBIqCBMlb/LOC2EYRoEFiyYZihaqGIFDEOg0VoAw3AomZ9AuDZClD4LUfoalPRZUAzHQuRfKEuMUX31qKIFiRU0ufAXK9Jeb3CxolrRgsUKJl6iRQ3FCpF3KUThNbLKTvWixbWhYoX+MCjGk9Swai/G4yHIfDP9rUDRIoxYEVW0iJNYIUqeluVX6QaFRQuGaUaihaKX6R1K0oWBkRZesULXB3DvgMgfB1F8NxTdgCrRQjGqr7VcCegP8XttOpA+BxXODhCa9JiJFeSlIQoujyxasFhRLypK/ASLykgLrxEnwzDxhQULppmJFlVihUITDUKUQ5R/VFWqzLUaovhhQOSrkRaePdFFCxIrMubKmx3oB6piRcZcCG13KYY0tFjhayM9/RH03s+rogWJFfJGj3MqmRiLFrUQKyBUfwtZUjSKaEFvISuBWK6pFCuegVBM6nsbj5XihRQtUu6UBpwabSaEbmCVaBFFrAgrWsRRrICtcmxxLmPRgmGatGjxMJTk2+XfUmygdA/7SoiiO6Ck3gsYTgwjVowBPGp0lSiZWilaXAol7TnVoLjwBiiWy1TRgiIrMufC7u6IDZtygdTnAP3BMRErvL4XYUULFivqTXmpDUazKph7hQtbKaeEMExDoGuQd2GYBhAtQKIF5YxS2kelWKE6gb8KVMwOrRJAkRbWpyGKpvhEC2TM9pVWlf4PJFoIOxRdZ/V1qQ+oddm1naFRtBCWcYD5XAhNdoOKFWo5tleB8lkQlklQMj+Vnhqi4GJANwhIf5n9K5h6QcKBINEi8311gl5jscKLKlooaa9CGA6HojH7nb85EIW3As4/gLQ3oWQuhLD/CJQ+BWEeDSX5ZlW0aPUloGkFRZpvUmRRMjwYCCXzY7VNNRAfSLQAiRbk3B9PscJLpWiBtKcjfmYMwySeaAESLZKvV+cQKfcCyZOk2Klo2wOW89Xl1qmqYS+JAEFihRcpWqQ+DuH4HXD9py6j9JC0p1XxVNNBjpsejwd2pxXmtBek+aZvnlFnscKLKlr4Ht6QaCHHQvasqA9ksmkwqXNLqhTi9bVgGCb+cIQF07wiLXTdQsWK8iCxwgtFWlSKFlEjLfwmEfJJjHwPNQ2DfCIUfc9GEyskZS9CFD8EUUBeHVyykYmtaKHoutZBrPASGmkRIFYQhVfJ85fECknFfIjS5yuFwm4+scILiRaKvletJt8y0qIhxAovHGnBME2Oquu7LsLfWijaLDUKIoJY4UUU3wVFkxnkaTEZouAqWepUq6kqSy7nETETK7wERVqwWFFvKkor/CIsDD4Rg2GY+MOCBdMsEZ5yiIr3I4sV/qJF2dtQrI+r/5NoUXC5FDsS+7gqxQovzt9DI0gKb6thCUmGqSOevChiRZBoQR4y7iKIonurxIpI5y+JFuVzIDxlCffVCE8JRNmMyGKFv2hR/CCEp6ChmsYwTAMgH4aQUBBBrPBtV/IYFMNRfv4VDsCTA1F0M/TKRlgslro3gvYTUawIEi38K5Awdaai1AaDV7Co/E3LGIaJPyxYMM0SRZMExXS6DOGMviEZYo1Xb0DUBVBS7lZrqDfl44IJSsoUQGm4yA+mBaIYoCTfUv12ppMBCqvWJENJuUmmc0RF0w6K+VwomnpM6OOEoklRDfg01VThUZKhJN8YvUQswzBND8UIJeXOQGPgcBgOU387VwUtPxZC0wnl5fUwbKQxNGlC9duZL+IxKJaCRWUqiN6oh6JROCWEYRoIFiyYZoui61hpuNcxslhBRn9lbwDOv1WxIu0V1WCwMt9eeIog3Lm+l1BYuyxh6P1fuKqNxpDh46Iq/LPeaNtL88DIooUJSsYctdSawjY1TPyQ6Rrm06GkTI28kelUKCn3qWHVlEql66uen17RwngWlFbfVVXlIbGi0kuGIoSE8NS4PdRXhacU8YbCt6URbyTRgsQKacrXG4rCl1mGaS5QlKOMapDGwM9EFi0MR0AxXwJRdJuMdKhaTq97BHZnqppSV0cUTRoU8tAKWx66EvMYKMk3QNFm1Pl9mEDBwhtZQemSRrNRVg5hGCb+1HsmZbfb4XDE8GaMYRpCtKipWFHxgVqykEQHysF3/AJROEnNw6ccUudaiIKxEK7wIZfCvRei4ErAsTImooWc4FBofeGdarWEENGCxQomgUQLP7HCt72/aGG8AIrlYojCq6BYn1An816xwrVNTTeh870GooVwH1DTTezfNK5owWIFwzRfscL+E0ThLdJ8m8y2ldSpoaIFiRVUyjRk+TFQki6LaZsU/VAgaWLoChYrYo69zO4z3fT6WNjK7LF/I4Zh6i9YLF++HI899hhGjRqFgw46CEOGDMHgwYPl37Ts0UcfldswTGKJFv+rurmXpcpmQDg2VytWoOQpwPYxRMk0wL0NovBGmX8vSl4CnP9KsQKuTRAF40JEC1WsuBpw0XYT6y1a+MSKfLqJWwFRcGOQaMFiBdN4ooUwnQgl+e6qhUb6/44AscK3PVXYUXpAsYyCKLhCOulTX1FS76gSKyg/27258nyPLlqoYsV9gOM7iKI7G0+0YLGCYZq3WFE0CXAuV6t+iALQZZkqgvjECcNwWSJVFNwg/aYU6zRZbl2KFeazIAqvhii6H0Z9ST3bUwhRNheicCIUTXKgaMFiRcyh+ZetnAQLNcKCoGgLWxl7WDBMQ1CjeHGn04n33nsPb7/9Nnbv3g2r1Yr+/fvjzDPPlH9TRy4uLsauXbuwcOFCzJkzB+3atcOECRNw0UUXQa+vUiQZpqERrp3SgFJJfQCi5ClZMlGUPAclZTKE5xIopiMjixVeSLRABZTUByEqPoRiOlaKFD7DK/dO9f/0WbIiQZVYsd7P/GoilPTpEIaDoSiGuosVVFZVvucmn2hBUR9K6qOcBsI0CsJTDMX2LYRnp/S0EM6/oZhGQpROA1LuCREtPG47FM86iIIJgCirMrwtvBuwPqpWvPHkVO68RJ73lGIhdL1CUiz8xQrfsqI7pXWEMJ6kTubjiHT3z5gLUXANlLRnOQ2EYZqzWIHKNA56cFEmoKTcA1E+T0aSCfs3UMyjIUoeh0LjWNHtcmsl/U2ABIai29X0EMcSoATo2f22eokVVCFM/l/6ApTkmyBItKDqSpwGEnOcDhc8bo8vJYSgaAs23WSYBBIsTjrpJClanHPOOTj11FOlWBGNf//9F1999RVef/11zJgxA999VzWRZJgGFyukS/bOypuYZyBKn5WRFWqd8lkQ2p7QRBMrvNi+lEZZSvIUGcIe4s7tEy2mQxQ94CdWoF6iRVixwveelaJFxnRAQx4B7FnBNLxYgYrPIUoeUP9PGgPFeAJEEUVbuCHgAfzSQqRY4V4bKFZ4vStSbggUK3xvEl60CCdWNJpokTkPUNLYs4JhmrtY4cX5J0TJI6pxt+0HKOaLfJ4VAi4o1qeqRAvzBQEvVRxLYNF7AP1DAJLqLFb4lkvR4kZA561IwsQSbySFf0oIRVtQ1AXDMPGnRnc3V199Nc477zwYDDW7wRowYID8mTRpEj7++OP6tpFh6i1WSDy5qqDg26BC/q+kz4HQ9JM3TxHFCkI/GIphcHixIkC0mKhGYhT9B3gO1Eu0iCpW+N5zE0TeeWpoOtWHZ5hGEisk5XMDp/Uk9NFvegKJlMhihfVhVeQIFisiiBaypGoEsaJRRAsNG9sxTIsRK7w4V8ihTIoWhZOrDDYdv6tChU+0cMv0EH8TTsX5PUTJgxDWh6Bos+osVvjWl76k/mG+GEi+ic02Y4jXq8Jb1lT9myMsGCahPCxGjx5dY7HCH3oNvZZhGmOiIWyfV4kVETesgCh9WhUDhA0ofS3ipkrSeIjyWdXUPa8ULRx/AKbTImzglPXZ4alBDitFfJS+GVms8G2XK8NS5Q0kwzQUwgFR+lz129m+Btx7oKAEovTlQLGCMJ8LYf8+sljhez96/SukRADu7YDj++qbSP4zgp+CMQxTS4QNovSJyGKFF+cKdV5gGBa43PG7+ls/GHD8BpDhMP3th0KCa3XzFF97KoDy6dVvV/EhIApqtk+mVoIFlTP1YjAaYCtlDwuGaQi43hrTLFE0SVDMFwJJfhEV4SBjQOs09emoJkstRaikhN1UFN8FJenykAlHCOaL1f2RuBEObQ8o6a9D0WZWfxzaNCipd8rqCVFJGgfFcq1asYFhGgpNJpTKVIgoG0FJexXQ9VRLm6Y+COgPDtzE9hkUXW9ZVSQq5DWTeh8UTTqg6wfF+kLksoLe9mXMq1FfYxiG8YfKgVLaKDRto34wSvJtEI4VsopYwHLyt3D8JP0upMeFbYlMI/HbArC+COj61OyD17QOLAkd2hBAyYKS/naUsudMXbBXpn4EpIRQhAVXCWGYBqHOCe/l5eX45ptvsHPnThQVFYXd5t57763VPr/88ktp2rlmzRpp4tm5c2eMHTsW559/vqx5HC1s/q233sI777yD/Px89O3bF3fddZesYMIkBhTKCE++vPCTuaV08PfsV30X4nSTLWuPW65Vn42EEw9M50KxXEGJ8Or2ikbNj6eQc5mCERQBIZzyCYf0sCAzQec/ofs0XybLmQnbJ5HFioz/QdG2q8VxZKlGhEX3AI4fQjdIvg2K8Tj16U0EhGsH1T+Doo0+8WJaJrJ6DT3lIw+GWtzc07gs6JzOnKeWIBWF4cUKw+E+U1uq2gNvaLRzJWC5AYpxBET5bCiGIyCgBWyLwosV1icBTTqEa7MqkhiPlqKFKLop9CmoV6xogDQpGTru2QcomVC01ri/H8MwdYfKlMuxStsJoKhE+XdnaYwpo7d8fxfLbUgkkFWLPHtD9kWVkARFe1XMDxUryMeq4iMoKfdD0HzBtsB/C7gsz0Eoh8Goqd7Dgoy8KaLNWxJatsc/Uk2xQkl7WqbXgaoX1dLYm6lhSohflRD6O39f8DWPYZiEibD47bffcOyxx+LOO+/EK6+8grlz54b8zJs3r9b7nTlzJsxms9zva6+9hqOPPhr33XeffI9okFjx4osv4vLLL8cbb7yBrKwsWaGExBSm8VHzLt+GyD0NcCyHx10I2L+DyD0FouKTuKYxyCcklmtDIy1IrEi6BCL/QoiSqRCuPer2ZOZXKVoERlro1FxU20LpYaFYrgmNtCCxIvk6KLps6RSO5FvrLVZUHUeWdB0PibQgsULXHSLvTIiyGRCe0DBQ4douBRiqYiAnPQzjf37QJNixCiL3LGncJifztTk3SUyuFC0CIy1CxYqAUsNU7i9lKiDKIfIvUcUKjwMK9RvTeRHFCmmYm3u2ap5LIdKVokVApEVDixX2pRC5p8oyhsIdXsBnGKbxofGN0sRoDIFzFUT5fIjcc9RxpewdiNxRAPlflTwCkXcu4FoLaNupkQ1BkRZSrDAcAlA6WySxwvoshPEowPFjSGTFntyucHuqFxbUqmPXQuSPAdy7fKKFL9KiUqwQxU/KsqlwV5Nax9QaW5gIC0oP8QoZDMMkoGAxdepUKSxMnz4df/75J9avXx/ys27dulrvl0SKZ599FqeddhoOP/xwTJ48GaNGjZLlVD0eT9jX2O12KVKQQEGCBb2O9pGWlibbxySGWIEy8obwQBReA4VqmFNOO1HyaMOLFl6xggw56QmF/ftqRAudWjpUfxDg2qTeJHkK1Jsk/ZBAsYKiOrypHP6iRT3EioiihVesKLxeVmNA2RshooUqVoxRvQFcVEaSRQsmjFhBlTnICK5ifp1FCyGSoaS9UClaaGSZXaHtEiJWVL0oRXqvoHyGjI4QxfdD0bVWxYaU26pEi2CxIv9ySg4HKj4IL1o0hlhRdLMa4VE6jUULhklwsYLKlNMYIvLHQ9F3B/QD5biiGPpCSZsGUfoUYPtC9biiSIYwooWS+jCEJw8oukmmeEKTXbn8SQjXLp9YAeNx0FDEA80npDGvAiXtJTiVw5CXX15jsUK2wZMTKlpQ1GqlWAH3f3IdmYmT6TgT+5SQAA8LEwsWDJPQKSF79+7FlClTMGLEiJg2JiMj1GWd0jvef/99mYKSnBzq8r5y5UqUlpbKcqv+Zp8jR47E4sWLY9o+pj5ihRcSLW5Sb2boX/tiVbSgv83nxj89RDcIiq5TlVjhhUQL+p3yABRdu4D0EJnKYhgKjWKEoHxW5yo5CVE0KUDaS4DtG8B0eogjN4kWINGCJinGI+slVoSkhzhWyJBPn1jhhUQL+m2ZqBp2esUKL5WihdH6KjQatrBpyYSIFV5ItKDf0mW+ZukhcoJefAuEp0QNS6YKHpSvTTfw6bOg6HsHbu8ulCaxKHvJfylE4SQoaS8DhiNV0YJeZzo9VKzwtfUD1YmfhEESLdJekZVyGkWs8ELHTL/NF3J6CMMkpFjhxQFROEW94afqG1QpzPYdYF/i90JVtJDiAPnm0G9K7zAeC4UEC/0AQNu1cvl69VpvGAIYD1HFVo0aBUEPF0DzCdc2GXXmroFXY4BY4aVStFCrgpFo8TZEwQ2qWOGlUrQAjb2UgsfUG3u5I3xZ08pypwzDxJc63bH07t0bJSU1qHAQA1asWIHs7OywYgWxZcsW+btbt8AJavfu3bFnzx7YbDyYJI5Y4cUDUXwPFOPxgHFkg0VaCCUlvFjhJUKkBTl/K4pRXabrBBhPVsUKKSBkV96YhC9pKEUL09kxESuqdpoCRTGFihVeKhYC/pEVwbjWAUXXoXcPzrVvqUQUK7zUItKCxApRdIsUFODeAlFMfXkRYP9Gin3yaZ9zQxix4oVwe4MovAFw/ExleQDzJZHFCl9b/SItSOhoTLHCC0daMEyCixX+osUdUFLvgaCHD/avwuwgMNKC5i1UJlnRdQaMJ6gm37oulQ8yaHlXwHCMT6zwIkULEjRq6FkRIlZ48UVabIMomhooVnjhSIuYR1hQJKHOoAsw3eSUEIZJ4AgLiq649dZbcdRRR2HgwIGIF5Ru8sUXX+COO+6IuA2Zc1JEhdGo3lB6SU1NlWacZAhqMpnq9P70eorsqA8VFRUBv1sCem0ZtPa5QHnkEqE+0cL6GATdMNl/UEULATj1Z8DlNsf0syRlzqjbqN6ghRMrvNi/ByUfKcn3w+ZM91vhCtow+Lyo7jyp33nkRaNxwoC/gMJrw4sVZGJqfUS96YtSIlJxrYNR3AyR+irqeYrHnaSk6id3dSUWfTyWNMR4oVHcMCjrgMIIYoWvMfPl5+NOuhEOV3jB2GzIh6foFigkVnhxb1V/vFSKFvS0zyVaQWt/Fyh7MUoLK0UL68twaYZBh/8AKaw4orT1Azl2eJImweEKnz4YK7QaO/SeZUBxBLHCX7QQAi7DuXC6Q139W+K1Idoxx6ufJ1ofrw0t6RyJ17EadCXQlD8XQaxQocpDovTFwMiKSKJF2hzYPd0hKs26Vcpr8Lc/5VGP16gvlA8VlHBiRYBocbk6hyrapKbXRRIt0mbC5myFxoSOM5Gu5bU930oKS6E36mQauhdFq8Dj9qCosBh6PyGjqdOSxp3awJ9LZBriXKlTDzv00ENx9913Y/To0TKyoW3btiHh5aREkidFXdm3bx9uueUWHHbYYbjsssvQGDidzjp5cYRj27ZtaCm0aW1Ba2s6ef1Xgw5QzABVDKnEJVKxd+9+5OaVx/SzTElJQY8uJGqRwVUUwUKeu+lwulwx++5jSbLFhK4dTdDJTzeMYEFPzukmip5OV4PQWFFhc2DT5vXyYp+oDB06tEn08VgSz/HCbDaie2c99OQiT5VvouBGBvLzCrFrT/h86MEDsqAoNYjUUcwQ0CMvtwCZyTUbG1yeJOzNOYC2WXroFH3luR2trekoyC/Czt27EE9SU03o3Dapsg+6ordJpGHfvlzsz90RcZuWdG2Idszx6ueJ2sdrQ0s6R2J9rB07pCLdmBF9zKGKYJqaRBwa4HRpsHnrFtjt0cejuh4vzZ17dk+DmSIpq3uxvM6LqOO4gAV2uwsbNm2I6AXXUGRmxq+8dF37eU3Pt53bd0Gr12L7ju2+ZUUlaoWQ1atWw5xStwejiUxLGndqA38ujUOdBIuvv/4at99+O9xuN3JyclBWFnoDGK0MaXVQ1MSVV14pjTNfeumlqLn2FEnhcDik6ukfZUH7oDZYrXUPe9fr9ejRowfqqzrRyd2lSxdpVNpSEOS1oNUCJQ9H2MKgmltRaLi3Lnk6lQXrjqzWemS1js1nqdM64XKrOYcOjwbGjNkQ+ZcBIrSahoSM/iyT4HamoW/fLDQG1HU0ih1uT2DUkBeP4gHSZwKU2hL81FkUQxTeBiVjBkThrYFPuv030w9Hhe5BOFxW9OnTBi2VWPTxWNJQ44VQBJA2GyikvlA5fidNAGyfV0XmWK4HjJcixZCEvmGGUb2uHEIo0KROhSh+AIrXBV83EKDwaNtn6v/a9lDSZ6DCkY3UNBobTq0cG+6P0Do9kDYdHtEfWa21cCuALn0uUDAmcnRU0rWAcSySqa3xscEJwKPpCKS9BRReGVm0SH0cHs3xyMwyIjPMUNISrw2NccyJ1sdrQ0s6R+J6rNoJFJ4IlL8ZZl17mTKiJI1TSypXfBB+HyTMps+Fx9URQRnI9T5eS5IRiuIMuOYrSdMgim+H4lwW4Zi6Qkl/E57iR6BQKdYwCF1vwPo64ExH795tmvUT2Nr289qebyuT18JkMaJzp86+ZbYcdezv3KEzMtuFTwtuirSkcac28OdS/WeTcILFM888g65du8pSovQ7lpDnxNVXXy09Mt577z35ZDwaXu+KrVu3ok+fPgHeFu3atatzOghBgkesQtio08czHC7xSILQny0dsUXJQ5HFCsfvcomS8RGEfQn0ZisMxu4x+SyFex9E2UzoLROgaFtDuHMhKn6GEkm0MJ0HJflmKLo2SKryVWpQhPAArg2qqZfp1IgmpEIMBjJmQeSPCxEtlOTr1drx6TMgCiaEihb64UDqU9i0eo8UK1rWeRm/Ph5LGmK8EKIfkDFXNXBLvkXmTKvhxXcD5gugWC6DXpMGfZiqex5XPhTnCoC8VJSDoEl9SFb5gChUS/66d0LQ0z/Hz/I8pJzuJJ3/2HC6VOZE8X1Be9bL7WE4CCaKAPG1ta80rKPyvCGiheVaKJbxalvRcAgxDKDqQQWhooWS+gRgOhnGoBz2cLS8a0PDHnOi9vHa0JLOkfgcK405EyHoaUDZG1WLSUxNfQTCsQpwbYaSdFmlaEEPT/ygsqHpb0Jo28JsiG3bLEkmGHWbAdcWwETeGN4xIwmCzEALpwDBooUUK2hcbQ+NTAG9CXCuDNxG1xua9DehaNs22nymIalrP6/p+eZ2emAwGgLuKZJTKw1VhbZZ9s+WNO7UBv5cmpDp5v79+2U6SKzFCpfLhZtvvlmKDf/73/+k2WZ1HHzwwdKQ88svvwwIDfvmm29w9NFHx7R9TO2QN9vms6Ck+D9JNagX/vJ3A8WKig+Bslch8idAuDbX+6OWYgU5Z5fPgCi6A8K1G6LkSaD0SWm+RaIFlPSwYkVj4RUr6KZM3shVfBbRhFShEHn9ICgZsyrTXCqXp9wLmM+RpqA0mZE3f1q/fqofLh3R7U7V44VpuSiKFtD1gZL5MYRzDVA+HaLoPllpQ7GMg6Kh8qSRxIo/IIpugii8ForzLwglGUh9FEryTRBFkyFKHlfNL6VLfeh1QprWmk6TpQHDiRVUASewrWSA26ey1LAlRKyI1NZ4Io14yZA3/a0A7d8rVgQb7jEM03gomnQolgmA5epAsaLoDmkALJyr5ZxEGmOaL/J7oRVK2rOqkFs+G8ITPpqhLiQlmWDQ/Ce9MUTR7YDtawhPlSBLD1pk1SV6yBBGrFC3yVbLSesPrtpG11vOs0isYGJnuqn3qxDiX+KUK4UwTIIKFmS0SaVNY83UqVPx/fff45prrpGlSletWuX7obQPYty4cbJkqRdKA6GIjBkzZmDWrFn47bffMHnyZBQWFmLixIkxbyNTH9HCIG+whW4wlCT6bnRVYkXFu+oLPHvrLVr4xArXP+oCxy8QxXdBMR6n3tw7fgwULRJMrIBQPT2oYkltRIsqsaIqKiNAtKgUK2gSxDASKn1bNhuwfVL5/x5ZWpSWVydWkHEuRRZ4RQtQZSAyy6RqHfL8fRyK408IP4+acKIFUh7ypYGEEysiihaNKFZEEi1YrGCYJiBaJN9WJVZ49qsry9+GcG2SXjmKrocqWnjFiuLHAPdmaRYsyubERLTQKALdO9kq0/Iqx8ziO6OLFkFiRdU2fqIFixVxwV7hkKab/nhLnHpLnjIMk2ApIffeey+uvfZa9OvXD6eddlrMGvPLL7/I30888UTIuiVLlqBDhw7SNIi8M/whvwt6WkyiRX5+Pvr27Yvp06ejY0euP50I0A20INHCdCKgyYRG0UMoh0Fp9S0EhWd6xQovlaIFMujCHD09pFqxwotjGQQ8UKxPqU8ypGgBKJmUr2pIOLHCt65kqjTeEuYzw6aHkGghSLTI+lYacIXdhiY36W/THyxWMFXnljsfovQloGJe4KdCogWlTGXMVsv4RhQrvFSKFtYnIfTDAMdPVe9RfKd6/ppOkuX+Qs5NTQqcupOgpB8Jt8cakAYSSbQQJFq0+rzyfG88saKqTUYIEi2yvlNLDnNkBcMktGgB0wnqGOcVK/wrI5lHQ9F2VEWLjDkQDirX7PcAhUQL+m0ZC6VGRp2hCOGSkRUonuATK0LHzKooLfmQgUQL4YKiC18iXZZYJ9GC5jkcWRFzbBRhURlRESxYcIQFwyRwWVNK36BIhvvuuw9t2rQJWyVk4cKFtdrvd999V+02c+bMCVlG70VRFvTDJCbqjbTfzTQ9jS17Dah4L/wL6iBaRBQrvDh+lxONANGi8FYo6dFKLDaeWFEb0QLa6IJLpEkO0zKJKFZEEC0iixVeXPJppWJ9Qp3M10K0cLlN2LhxB3r1qplpmYy00CbW+SwjLarpgwzDND7CtS28WBFGtJBlTM1nQEmeDFH6TExECxIrQOVK/SIrQraJJFpUgxQtmLjgKHeElC41mFSBnSMsGCZBU0Koekfnzp0xbNgwGWWRkZEhl/n/1Kc6B9O88XjKAftXkcUK34Z7pQAh3Aeq3acgAaT4ochihb9o4Vgqc+0lrn8gCm+WN3CNgihUJ0URxArfZpQe4oxSk51hanrKUXlQqgYSSazwFy0Krobw5ENBURSxwl+0uBNKymRAEyg+yFBn138RXxkcNccwDBNraCwTBRMjixVeyHSTor2MRwLllSloplMDtyl7EbD/CiHctU/DI7PsCGJFTcdMpuEjLAzGCB4W5Xb+OhgmESMswkU5MExN0WiSIIwnArZvqkqahkMxQ7E+CqGkVluPXIaGp9wOQdU1ok1GKNTTdLqauypfaJW5rKAw0UaBjnGaDKmPejNoOh/Q9WzIhjHNFPKIoGgH2L4CnH9E2TAJStpTso8I2ADLdUDZy9H3nXwdRMUCwBMkAJovlZVrGIZhGg2aS1ifViMsEOUm03AYQBENJYsBw+GqAGv7NnAbmsNQKhiZF9eqDWYoqVSN6WaSJSJvZx4j5ytMYkCihDUzsGqh19PCVsaCBcMkZIQFw9QXRddBpmaAct7DbpABJXMhhLYzNBq1NrlBV4JuXapy1oW7KDD6QtNGNaDUtI4sVsgKJZ8AnjxA0wFK5qcQmrYyrSgcwlNQowiP2kD7E+5C9TA1ZgjdAFmZIWJ3NJ0PJWUKFG1mTNvBtFxUk7ZnAf0hETZIgpIxB9D1kxNyDaUUmUcBlhsi7zN5kmoQWz4zcIX5UijJN0JQiWN3qJho0Nlw8JDO0OkC9XPyJZL9j0KogxDuPAiK1GIYhqkBHrrmegoA/UAoGTRGqfOKwLHqfGkEriSNgyi8DTAMhWK+CKLoNqo/V7Wd8UQoqVMrx7R96pjkKZal00PHqsB5iqJJAozHANbnZNn3sJip1PQNaunnGM8/mLrhkKabgREWlApPlUOoggjDMAkoWCxatAh33nlnxPV33XUXvvjii/q0i2nJooUUK95RoyAqPpI393SDopQ9gxTlNRh0peokoOJ9iMIbIdw5EJ4K1ZOCyjKmzwwVLUisSHsNouBWKKbjAeM5UDJmQBReDcX+VdhKBvJmqfRNiKJ7YjZpkG0tnARR8Z7vuFD6DIRre3jRgsUKpqFFiyCxwks00UJJuQtCfzhQThVrwosVin2JfLJJ57oXj7sIWrEOIu9c6JUtvuWy5K5rE0TumYBzdYBoIVw7IfIvAuw/sWjBMEyNxArF8TNE/mjAvRPC7YCS9lygaEFihX6gKk7IdJDjZHUzWc40glihUBUvSptz7QQqFqt/VwoYcqwKmqdUQfsTgCzrrEQWK8r+B1F0F4sWCVolhDCaDBxhwTCJKljMnDkTBkNkN3cqNUolRhmm1qJFpVjhoUmCcyVQ+jQ85e/BY/8Zin0BNI6FUMqmqa7dZILlXAlReJM6CSEjTeefEMUkWrxdJVrIyIq3IApvAdzrVHPApLMgSqerhpfF9wC2LwNEC69YgfLpgOOHmIgWqlhxM+BcoYoUZO5l/xWwfQyUPhEqWrBYwTSEaGF9AtAfVLkgCUr6/yA03cOGOquixfmypKhvH8lTIIwnA7rOallP7wTcPAqK5coqsaL4bsC9BaLgKnmuk1ihuP6V/8OTI/O6hXN9lVhBRrSe/Wr4tnM1CitKYbdvgyi4HHDvgCiaBJdtKfYV78fGvFwcKKsqBcgwDENjQmF5DkBiRdFkdZ5QcAUUjROi/MMq0cIrVhQ/qI5RxY/KqEboaO7whlp2OZxYUfo44FqnlnM2DAY8ByAKrlMNwCvFCpRO881T1IcrVEp6LlB0i/TUChAtgsQKlL1V+SCGRYvGhqIogiMsCI6wYJgEFiy2bt0qS4dGok+fPtiypeppGcPUSLQwnCDFCnfhXVBIrPCuL3sGinsTYLlG/d/+KUTZTCjyQq9RJwPF98jcVMoPJUFAFN8vb7xICFEjK24EaB8Sl7xJUoyHAoaj5BJ/0SJArPBST9EiQKzwUvos4F7vO64A0cJ8AaeBMHFHRisUXAnFMhEwjFDzu0uegOL8RY1aCjmPc6WIKJ8OWq6sTAPJhULeMeXzIBw/QUl9ADCfB0XXRzXh9OyDKJ5atRP3VgjHtiqxwvv0UhSoZnQkIpa8CIiiyhfYpWiRqlkHfflz8qaj8gXQFt8MnetXTF/5Mwps0U3sGIZpWeiVUqTgD4DECq9fBIkWxVOhWC6RogXSZ1WJFf5jFI1NdL03HAQlfYY03QwRK7yQaFE0Wd3OkwtR8bV8ICHFCi80p7F/D1E2Byij8qNkfrBQihZyLhNOrPDCokVCpoQQZMTJZU0ZJkEFC3oCVlJSEnF9cXGxLHvKMLUSLVLvkZEVGtdfoRuUvamaUnpv7imNw/6Tn2jxN0TZG1Csz1SJFqWvVxpa3uInVgSVYTSfEyRafA5h/yVQrKinaBFWrPAdF01K3KGiheUG9qxgGkCsuEx9olg4RfYFUfY64KTKOTeoTyX9RAsSK0TJo2qFEdoOqscEldoVdG6XvQLYFkE4/oCi7Q5R8gjgXAZR/DCUtKerwq9TX4Si1QWKFb43UUULJeVaKaBUYQcKJkAxnxaUQiaQ4bgDtw0FUvXRKpgwDNPS0kBSlT+gFPuJFV4qRQskXwvFvT5QrAgQLSZUiRapD4QXK4JFC/LREjY1AtQPmRrn2qZWF/HHthDCsUyKJoBNPowJECu8sGjR6CkhwVVCCAN5WFQ4GqVNDNOSqJNgQaVMycfC4QjtpLTss88+ixqBwTBhb+qLpgREVtRLtND1hJJ0PkT+BWHEimiixX1Q3HsA8yXhX1JL0SKqWBEiWlxbJVqUPqv6WzBMXMWK3ZVLbGruNkVKqFsEiBYBYoXvvP0fFE26DHEO8K4g0S/gyeKfEGWzVNEi9SUo2pTwYkWAaDFBDckOEi2ksGIZF1a0yNStYE8LhmF8nhUBkRXBGA6VkWFhxYowogU9qIsoVniR6SE3QzGdIE3AA8QKTxlQPiP860joLb5DHd/0/SkfNPx2LFo0CvTdO2zOsB4WFHXBZU0ZJkEFiyuvvBKbNm3CZZddhu+++w47d+6UP0uWLMHYsWPx33//4aqraELKMDXEvdfvZikKts+hGI8lYwr1f8cvgK6rWnaMcK5R80GNpwDOf2V4ZnRcEBULoZjP8C0RtgVQTKdEfoljmeo2XuPj+rv67WxfQCHncO9x2b8DBOfkM7FHCDvgXOUnVkTcEqLsbfVpoSgF7D8ErdergoLt6+rflPoAlfMz9IcofyeyWOF76wII+4+A+dygFXYI21dQTCeHtFVTQU82OS2EYVo6CgmwUkSNXDZUljev+LD6nbm3yjQ1OR5Smkd10LaevYDhOL9x8kiZclqzcdIo/YAi4lgeWjaaiSsOm/pwNqyHhVHHVUIYJlEFi2OOOQaPPvqoFC2uv/56nHTSSfKH/iax4uGHH8axx9JNJcPUEH0/1XMCoQq2D217KKkPScdtGZGgpMhoChnaKYUJnTQQlDdEZS9B0M2LhbaNguEINRS+6F71f4rMSLm78j3CYYKSMRvQdYvDcU2qOq6MeYC2Q83eg2FqgUITYuOxlZFJUdAfLE3pFG06oO2knpOKxW8DpzSSU6yPBjxNDLMjNTWrbDpE7nlQUu4EDNVcHyxXQdEPAIqmBC43joRiOBKiJPApp1s7CI7kZziNimEYKNo2UKzPQegHRfw06HqrpD4G6PpE/cQU67OA4TBotK1kWXRou0bZWgPF+oJaCcn2rt84OQmK9RFA0zbKa/WqOXj5R5UCSTiMlfOP7vwtNyD28miCBXlYcEoIw8SbKHdR0TnvvPOkSPHLL79gx44dclmnTp0wYsQIJCcnx7KNTIyR6QyU9qDrBUVjTojPV1EMEIZhcFvfhLaIonNc4W/qi+6UTtw+sYLyROXFvVKsqPgUcPxUuc9kCG1HaQ4YNidUihUXqNVF6IkviRXpbwHCASj6MA9nKsUK/QAoiq5WxyUrLxRcEeW47pIVEXxiBX03Sp30RIap/rzUpECYTpPe9JQGFV6seEFWEVHPYw2Ero88N2X1Dm/0DxlqFpHh7eOV5/C+4B1J0UOUzwMcv8klIvd8KK0+gihWU6zCihWGwyAKJgYsFsZToTEerXrNUGqYn1ixznkfzKVG9PSrUsgwTMtAmgG7d1XOaZJ8vlhIfQ6i+BbVFDgIxXItRMUCKMm3QJQ+B7jWhxcrqLypRhVqFYp8SH+z0pBzaxix4nlZIUTRd4OgBw7UJoKqIBXdXTlO3qNGYISIFTNUrwxtW9UTiOY54cQK/cAazz+Y2OD1qAiXEkIeFrZyG3/UDBNn6nVHRMLEySefLFNE6If+ZrEi8cUKytkU+RfJ1IZwlQAaC7q53+/og1wjOWjrgm7qHwkUK9KeDxIrngkUKyhKgkSZopvUNAvL9YFvZjg6rFihaNtB0XWBkjEX0LSql1jhf1yoFC3CHxeLFUzjiBYg0SI40iJIrPBtTwJapWgREGlBkUyadlAy5gRFWtAkfCaEbgDgKfRbXgg4VkJJvT800oLECvPF0oQuANMZcFluR4HNFlasuOTTpVGCvxmGaa6o/jqPq3Ma+48BPjZbi82wJ00LibRQkm+DoGpDlEbmyYeS9iqg6xtVrPAtp/LNIZEWlWIFRVaUPgEIrRz7AqIkPXlVokNApIWfWKEYoOi6Vs4/svy2YbGisUuaEmGrhJgMsJWp6xmGaWTBYu/eYDW45tTntUx8xArYF8tJvyi8NuFEC4PODKfmIBlpIW/ute3htr4NF7LUXHolBUifC6HprubVk1hB0QuGg31PfZXURyGMJ1XdJHmKZZlQJE+ufBMKh38EICGBIh78xApv7fZtJWlwWWdXihYmuNNmYmd5R2zKL5Tra0uIaEFiRfosQJOt3vBxZAXTKOggdL2hJFemXugHQ0m+tcpLJYgQ0UKxQsl4ByCRjybyPtFCFStgGAKNrh2UtJd9NwRK6uOwaUdgU5FJihbCK1pYrgLMF2NTkRHF2jEQSVf4xAoSIIudaXAbToQ75VG5WOiHwJX6AszG9vj4wkuQbkqMaDGGYRpWrIDtM9Vjgh5Q+IkWVpMZe8pT1EiLStFCRjQKJ0B+FKkPoVQ5ApuLzXCkvAxROUaJ1GfCihXhRYvKNBDylSh7XpZRh+FgKLpO6jVeihY0T3kLMAyqHCe9ooUeSJvuEyuq9u8vWrBYkTgRFpFSQjjCgmHiTY0eFY8cORJnnnkmLr74YgwaFDkn0J+VK1di/vz5+PLLL7F69er6tpOJqVjhRRUt6AIrDMMTIj0ky0ITBAuEOBTQzpQXdb2uI4TwQKTPhdPphMfVGeakJIj02WpopX4wNIoRIu0F1XTTcAg0FPKecgdgOg7QHwZF1wbCfBGg7QwYhkLRZkFoUlUBQdfdJ1YQBbYKnDJvFjpZrZhz5qswKOW4adE6/L7ne7n+q0vHVbazdvjSQzLouNrICY10HqebP4LTQJgGRAqVlKZReB2EeTQU65NSOBOFV6pVc1IfhKJtFeY8rkoPkRNuXQ8oilIVMk2iBXnK6AdBodQqubwjQKKFawOEfih2FrplH0szmfDnhPuhOM+EXTMY/V6tMrVbecVYWK39Zf44tSNLdjkLhOdUQJsp3fTN2mxOA2EYtHSxwrdUihZSQDAeLa/T3mu1sD4PONdCGIYColyOW9/uSsM1X5IJMJBiMODdcx5GW9NeONxDkB1BrPDiTQ+BaxuE/iAookQ1AKe/NaaqcY9Ei8p5ivQPqnytSJ8Fl2MfPKI/TH5iRdX+uwIkWlAVpjpEdjINlxLCZU0ZJv7UaAR855138Pzzz+PCCy9Eu3btMHz4cPTv3x8dOnRAamqqvOkqLi7Grl278O+//2LZsmXIycnBYYcdhnnzKm/GmAQTKxJXtPDd3FNoeuVFmm6SbK5O2LJtC7pWRmLSZECItlXbaLMhNK2gKOrTYUXXFh7NadBoKm+atGkQppFV6zVJEORjUfl/MDuKinDpwj+QbjJi9f4DcTouRT7hpokWe1YwDS1WiMLr1BSLincgKN/a+Yca8WP/RvWYqEa0ID9+r1jhW0eTcdEhpF9Rf/VosqHR0ORcrd5TaLNh2IxP8b8zTseoDz8I2H5/hR7WjFNC96NJhjAeE7HfMgzTEsWK8KKFv6eFR9Najj//5QELNxjw8p9qCilR4nDg4gVLcVCbbNx9lBaBCXHhobHOo2kDjYaEiFQIbXaIsBA8T/Fid2Vj+84ydOoUeRwj0UIIN491iZISYgiXEqKHnVNCGCYxBAuKqpgxYwbWrVuHjz76SJYy/fjjj+U672SVRAuibdu2OPHEE3H++eejb9/AnECmkS7sEcWKINGCzKRItAij9jcGwRd4QcXKKstLRdom+CbGK1ZEWl/dTc8uEuLoxi2GhLaZ+lDgTR/DxAtBprKO36vECi+OHwM3rIFoEYlI/UoVKwIh0WLUhx/Vaj8sVjBMy0S486KIFUGiRdpLEIajfA9ivOOPBwIv/7ki5FUkWvy4YyfurkV7VLFCJVIURLjlNGcuq0EqAY91jY+jMsKCxIlwKSEcYcEw8adWMWYkQNx7773yhyIotmzZgsJC1ScgLS0N3bp1Q3Z2TXRppsEgEyfDCIiogoV0mFJ9FRJErGAYJj7IKB9de7XPi4Lo2xpGVHq9MAzDJACKEYpxBERUwUI6b6spoIqansEwdcVWHi0lxCDLnsr03qBoQ4ZhYkedk+JImGBxIvFRNKkQ5jPU8oUlD0bYKB1K5jtQuLZ3gyAoJ1VJ8uX3M0xDo+h6AJnzIPIujShaKClTAfPpcgyJJ1lJFtx++GDct/R32FxBZX8ZhmFCUsJOUvVWqhwWDiXVrzx4495ESoNPUQ5FY23UdjD1j7CIlBJCYoXD5oDRzHW1GSYhy5oyTQN5w0GiRUoYwYLFigZFeAohyt8BnP+oExmGaUTRQsmkah/pjSpWzD/nCJzZ9iPMPfsYmHRsLMcwTPWiBaRo8UTiixXO1RDlc+W1n2maUMqHolGg1YemKHrTRLi0KcPEF54dtuRIiyYgVuwuLpZVOwhSsfXt22FzSTGU0hK5jEoZtk+tuqmikqPe7SNBr6lLlY+YiBVlc4CylyBggJIxS5Za40gLJpEiLaKJFdX1r9r0La9Y0VncBDh2o79hD+aefTvGfLq0VpEWkdpk0GpR6nBAqyjQajQJMw4wDFN/cisUCM8IpKc8Dm3JXepCJRWutDnYXpQJu2c/kg0GONzugNcFlz9+euQpMAYJpfSajXm59RonfGJF/mW0RwjhAizjoGjSan+wTKObbhqM+rACmLfUKQkW1lCrJ4ZhYgQLFi1VtCh9IeHFCoJuRM6aPzfi+oWjxwQIFt6SpNGoriwpTVBom2jr6yNWqDgg8sexaMEklGihJN8cNbKiuv5V05K/meYk/DT2FOiKxwPu3XKZ3v0nBpuexoqJT2JXqRFpNexnkdr00qln4MYvF9W7rQzDJB5qv5+LiUP64NoBDyDN+Rz26V/HhA+WY2N+XsQxgPq9/zWexIloc4y6jBPBYoWk7BVIa3oWLZpkhIVXmAjGYFY9nmyVlUQYhokPLFi0VNGCyntqsxq7OQmJf+32WBAqVnhh0YJJINGi1eeqSW+c00CIDEMeREGVWOFrh/N3GEvvRI+0p6FoWUxgGCY601etB9AHVx38MSYs+M4nVtT0Gu+NpIgVYcUKL36iBcBmxk0pwkIfpkIIQZEX3m0Yhokf7GHRAqEbEhYrGobIYkWgaMGeFkxjQ6VLG0KsEK6dEAWXhYgVPpzLIQqnQLhz4t4WhmGah2hx5MyPaiRWxJOoYoW/aFE2C3pteUM3j6lPhIUh/PNd9rBgmIaBBQuGiSeuDVHECi8OiIIrKE+HvwumWSM8BRBFd0QWK/xFi7LpEJ6yhmoawzBNGKfH09hNkNdweS2PJFZ4KXsFWrERycnJDdUypp5VQiKlhHgjL9h0k2ESNCWkqKgIixYtwq5du+TfZIjoD5nTPPbYY7FoI8Oo51Tl5xB4pkWHDPfcQedmg6LrDViuBcpei7KRHkraG4DCIfBM80bRpAPWJyDyxwKevZE31B8MxTIRiob7BMMw8cV/nlCvOYOSLK/louByklAib2e5Gm6lJ0pLd9SxxUxDYquwR/awMKmpPZwSwjAJKFj89NNPmDRpEioqKqRCnOpneuilLuWktm/fjunTp+Pvv//Gpk2b0K1bNymKVMfxxx+P3btDn9j9888/MBq5LnJzQK/RYNZZx0CBgnGfLQ1x/g7HyK4d8fjxR+L6L7/H8j370BhIR3DLeFVkCSta6KGkvw0YhkBROKeVqR3CUwQIR0KleAk3hWW7oGizw65XdJ2AjDmRRQsSK9JeiPh6hmGYWJFhdGHpZefiwo8X4+L+XXFOr+646ONvsaeyElltoIpfgq7l6TMjixaWq6FYJsBp47lpk4qw4JQQhml6gsWTTz6JrKwsvPTSS+jdu3fMGkMixdKlSzF48GB4PJ6QqI1onHzyyZgwYULAMoOBbwCbk1gxxPyy/H/mmTdg3MKlUUNASax4+Mh0pJWejudOeAm3LEECihYsVjD1EytExYdA2SwpACi6zo36cZJE3SbJCVH6GuD8G0h/HYq2be1ECxYrGIZpIISnGGn4Chr7dCy6cAYU+xKkOB7CvLNfxqWfLo+9aFEpVshIM7CHRdOqEhL+dkmr00Kj1cBWZmvwdjFMS6JOggVFQtx+++0xFSu8kRInnnii/PvOO+/Ev//+W+PXtmrVCkOGDIlpe5joHCgrk6XFImHRG1DmjJ7LGVzjPGSfQvjECoP7Z7loiBmYdZYqWpCo5e/yTeLG4jGXoKN5M3RFV8knva3t12LGaW9jVd4RyLc55Db/7q8y9KMQUK1GU+N21+W4QkULFiuYukF9RINiWD2fQ1P2lFxGN/5O60zsKk9DisFY4yo31fXh4PM4mljxwkmHI8n+CmB7Xy7zFFwDV+or2FxkkOv9+5heo4XNZUI369vQF42XooXQHwxn8jP4L5fSvvZBoygB6V9eAdtqMsHmcvmW1yTaimGYpkN14xLhjNDvnx55Cow6dWrbKdUqS5sS2ZZkaBT4rv9ZZg8yla+hKX0MsFyBVM+XgEN9KNIRN/hEi7oQVrQIECuYpgT5U0RKCaFociptyh4WDJOAgkWXLl1QVhZ7MzSN34SWaSp10GdFXL9w9Jio9c3D1Tj33ydNLFZdeREMpa9CcapiBUHCxSEWDf6c+CAK7CYcO3u6b91755+LzkkkVtwkxQoVO4zF49E1+TW8uHw77j7qBJz93rw6t7suxxUoWuigGA7jNBCmTpBYobF9DI3zhaqFnn3QFV0OrfYllDg61liwqK4PB5/HJGDQsmBam+1IcrwGTaVYQSiuddAVXQ+4H8boBUtR4qgS+egm4sYvF6FNcjLeOfsltNdNx1rbBOwtKMN1X3wWtc0fXjAaoz6YH3CDQvvrbE2DQav1Lae/qa8GC5L+x8IwTOJR3bhELBk73idG+NM2OTlgfAiGXrM5bxvG9twOjfNpKVbIEqNlqlgh8eRI0eK7S2ehxJ1Uf9HC8SsUy2UsVjThlJCk1MjngZEFC4ZJTMHipptuwkMPPYQzzjgDHTp0QCLw2Wef4f3334der8ewYcMwZcqUmEeAMA2fBqIvvR+K88eQ9bSM1mWkPCy3pfQQSgMZmL4bhmJ/scKLXUZaPHfCayhqxAeyUrRIGgtoktizgqlTGoiMrPAXK7x49qEjboQreSZlZsfl0yXxIlgMIc8Kd0mgWOFFca9DT829ePecR3BxkGhB7CstxSWf/oY7D78Ed33/A54aeUqt2zRl8VfyNwkpvTJb1fr1DMM0PUodDil6BhNOxPCnZ7oBR2RshzVArHg1dENPDnRF45CRQQ8nOtdPtND1VK/9TJPEVm6HNStyyW8qbcopIQyTAILFI488ErIsIyMDp512Go444gi0bdsWWr8nW17uvfdeNASUSjJo0CC0a9cOO3fuxOuvv45LLrkECxYsQMeOHeu8XwpBLi+vX54hGZP6/25OkM9INGriQUL78P+M6X9/zwqNX2RFMBRpoS9/ALPOug6z/tmMqSPSYCi+OoxYEShaZKTNxKHt2uD3CJ4W1bW7LscV1PLKNkZqZ/xpSudlUlLdnnA1VB9vqO9Fpy2H1rnAlwYSlspIC0/6bNhd2dWeq9X14ejnMWDQlUJT/lJYscKL1rPeJ1pQpAXdaASLFjcvjtzPa0p1bU00mlIfbIhjjlc/T7Q+Xhta0jnif6zVjUt15aZDBqCz8Qfoy54GkiZGFiu8eHIg8sdApM+p0XgaGbrml7fY79Z7nIl0La/N50/pHhqtApstvE8F+VuUFJU12XEmmJZ2btYU/lwi0xDnSo0Ei7lzI4e///DDDxHzuhpKsPB/H4quGDFiBE499VRZceTBBx+s836dTifWrVsXkzZu27YNzQ19duuo62tycXfY7Vi3Y2fAPpMNRrRPMcDgXF/t6xXXWrntMZ2zYfb8XAMRwA7F+RcOads+roJF8HElKk3hvBw6dGjc9h3LPh7v76V3j0yYnD/4yvtGxHMALsdO7NhVjtLSinr14WjnMY3xfXtnwuj8o7oWQSt2ItPkRFaSJUSwiBVNpc81xT7YEMccr36eqH28NrSkc4SOtbpxiaiLdDC8fTa0jnfl34phCAQZFleHJxcux44ajad1oSV9t5mZmXHbd137eU0+/7LiMpTby7F9x/aw6z3wIGdPTpMfZ1ryuVkb+HNpHGokWKxfX/2NYyLRunVrOflZs2ZNvfZD6SU9evSot+pEJzf5fpjNzStnekdZadT1NSltazAa0aNv34B9Uv7qmE+XYc5Zr6KD6zo5YQiLtidcqa9izLuLsbO4CNrjj8bZHRTobG9HfL9i/Q2wi1Pxyor5dW53XY4r0WjO52VD9/GG/F6UpKchiqdAcf4eYQ9auK1vwKMMRseOunr3YTqPe/XrC0+EuwO5OO1/QOFEwL01/EaKBTmG13Dt12uwtbAA8SLR+1wwLbEPNsYxJ1ofrw0t6RzxP9YDnurzNqu/Cocy7rMf8Ou4qbB47oO+cAqUtKchyI7N+WeEV2iBtDcgMKRG4ymZekYaK1vyd9sQT2Br289r8/l7XAKtslqhc6fwqUEpqckw6kzo24SuP9FoaedmTeHPpfrPJuE8LPbs2SNTQkwmU9j1FDaVn58vUzSaMnRjGqsQNur08QyHaww0FeX1vrEno1X/z8W7TxIgxi78E19cNBPmkstDRQsSK6wvQPHkoEd6itz+ju+WY/hlF8OqcyHZNSesWPH+lv4YVo3tSiwEi+DjSlSa43nZWH28Yb6XJJQnPwlt8W3Qu4Mn2VrkGV+A09EP7ayR821r04dTDBoYtWsBTVsouvYRtkqCPfUt6IuvDBUtvGLFNxvxT84BxJOm0ueCaYl9sCGPOVH7eG1oSecIHWt141JdoYpCy/fZ0cp4CwaZnqtGtNBCSXsDMBwGo8ZY7b6Faw/g2QXoBkHRhJ8bt/TvNhH7eU0+f4fNgaRkc8R7HpPFBKfN1ey+Rz43+XNJJOpUluOEE07A4sWLI67/7rvv5DaNRU5ODlasWIGBAwc2WhuY+kMixPJ9OuzSvQpoWoWIFbqiq6EtHI/Hj87CcZ1VFeKfA3Z8ve84lOrGhhUrnvhtFX81TJNmd6kR/9pvh1M7LESsuP+XIpTGyBrlhK4d0dqwASL/MlmaT7h2R9x2Z5kFm8U0QNu1aqFigT317QYRKxiGYWqCRwiMXbgU/9hugVN7EASJFpZxgH5YWLFCqaFYIQomQOSPk8KH8IT3OmCaHpQCbC93SJ+KSLDpJsPEnzpFWFSXw0+5ZHUpUUohJUuXLpV/7969G6WlpfjqK9UB/tBDD5VRHePGjZMRHl7BZNGiRfj+++9xzDHHyFQQMt188803pQno+PHj63J4TA1roVv0hrAlDmu6PlxpweCyiVSasMKVCqd1NvRFlwFKOpD2EnSF9DRXzVdvZb8Rr5/0FnZXHAuNxohiRxpc5m7wuPTQVMyA2zIZTpyJIzprsbDzAKQajbLcoZdkvQGlTkdAuz+96FLf/xpFkZMc//XRXu9t98a83IDjqmmpSYaJRprsMx3gTp4GXekdUJwr4LG+ISMrbjm8duU6I5UpteggxQpt0ZWqL4x7uxQt3GkzsL0kCe6ga4BBo0U5suFIfQsGirTw7IfLOgv59o54+Ng2sg/5vyLVYAzoY7SO4pZoO+/yZIMhrN9FmskU0P98bdaTsR3DME1xXiENt7NbyzS1FKMp7Lhk0ulQVGl8aDWawo4V/mOIF//19PcHo0bL8UakPgFRdg9QeAeQ+QFQ/DDg/KNOYgXcW9T/C66Ekv4WhH5YrSItmMTEaXfK33qjPuI2BrMBhQeKG7BVDNPyqLFgQeJBcXFVhywsLJTCQTC0zRdffIGsrKxaNyYvL0+WTPXH+//s2bNx2GGHyYua212V30hlVffv34/HHnsMJSUlSElJwfDhwzFp0qR6VQhhqq+FHo8yguHKJqq0giftHUDxAH5ihYpT3lglGV9CuRiAAa2z5VLhuRowHwedtg+ytFZkpYR/TxIWzpof2Vj2wwtGR63rTjdP0V7v/axYsGBi3UeE7lnAvQdafV+0MxvrtS8vQjgAx59y4h1gYuveDk3hBAg8g0sX/Iw8v5xkKiVIJQZJAJl/zjSkGO245tNV+CdnccD6mvapaNsE78tLdeIowzCJP6+INrf4d38Ozn5vXo3HhOrWf33pOPRIexJw7wa0HYG0p+V4Cl3fOokVKm4WLZpZSVPCEE2wMBm4rCnDJIpgMXPmTLzyyiu+XDESCOgnUgTGzTffXOvGkPiwYcOGqNvMmRPoTTBkyJCQZUwzRdFUmvuFqwTglJEWbutbECINimKAokmXT0kYprmiaFsD9BMjqsSKK8JX3HFvRzftZLx7zjO4OEi08N6MXLTgJ2Sak7C5ID9m7WIYhok1ImQMNdV4PA0vVnhh0aK5QOkghC6KYGE0G1BRqgobDMM0smBBpULJUIbEiGnTpuH0009H//79A7YhIYNMWmg5+0cwsUS4DwCF4yOIFYGRFtC+AxgG8xfAMLXFuSayWBEkWrx/3isY+c6igHQpotBmkz8MwzDNEeHOjSJWBIkWGXMg9ENrZNbNJB52b4SFKVqEhR62Ur7mMUxCCBYHHXSQ/PF6TYwcORK9e/eOZ9sYxg89YDofKHs+6qciKJRTq6aEMAxTS7RtAV0fwPVv1M2KNWdgybadIWIFwzBMs0cxAEkXAiVPRN9O1wvQdmCxohmkhETzsDAmGWVKCD3QZWGKYRKoSsgNN9zAYgXToCjaNLgNF8KdFOhx4o9bOxD2lOehaNs0aNsYprlAfUdJfxXQDYi4TbH+eny4bQAe+4Ur7jAM0/JQNKlQzKOAlDsjb0QPT9Lf4PlIM0kJMVSTEuLxCFn+lGGYRoywePnll2u9Y1IZr7/++rq0iWHC4nQnoUw5B3qdA6mu10LEivWuB6AtNqBf7f1eGYapRAp+6a9CFFwXEmnhsdyCDzf2YrGCYRi0dNEC5lFq9aPgSAsWK5pfSog5epUQoqLUBmMdzK8ZhomjYOENewoucUrLvWFRLFi0LHYXFweUK7Po9QFlCakMWZE9ep4fVRnoYLVGXJ9TocMfuwbj3E7X+kQLj24QyszPYPdeO9qneKSTeLSSo1TWzOFXacbl9tTySBkm8ftfpP7VPjW12hKDCvTomPIi9CWToHhFi+TJKNOcg7aphXjp1A4B2/dtlSWrengEkFNW6lveKdUKAYEUgxEL/EoN0tWDtjdodREr7Og12pofPMMwCV8K3Vvy27/ClsPt8o1NJZXzBafbjdU5+0LKitO12zuOaBUF9srX0twiYUQLFitaXkqIV7AosSEtK/L8lWGYOAsW69evD/g/JycHV111FXr27Ilx48aha9eucvmWLVswa9YsbN68GW+88UY9msUkAjSBiFYqkNb7QxMV/5uPupQypPrp0QQLEkFap7RHgdIVZrMOOudSrHPej4s/+BTlTrVednXlzIKXLRk7Xi7zkm1JhsbPH6uV2SInVl5ooqTVVGVTWfSGaksqBn9WDBNrgvtfOOg89hcsopUYpL72x4QXYSiZBJhOhmIejT359rDlAetaVnDBRZcE9D1/6ObEv1+5qaS1ECHLvXAfY5jEL1n6+cVjff3X7nL55gTVjSHRtnl/1GjfOEIiqdPj9omeXsGU5haEVtFAp9XEbMwIEC0qPuU0kGaGo8JRremmT7Bg402GaXzTTX+mTp2Kzp074+mnnw5YPmjQIDzzzDOYNGkSHnroIV8ZVKZpkmWxyJ9EoqSszDdhuXfEQTi/77m4eNbHYcWKmlLmdEadKNHkakBrNvJkWhbUL3aWmtEjncRnAxStFVpNbkzfw+XxROx71O96ZbaK6fsxDNO4kNjv7dfeKIr6QlFd3nEknKhBY8nA7Ph5W3lFC5jOhKLlnNTmhK2sMsLCUH1KSHlJ9OgihmEa2HRz2bJlGD58eMT1tO63336rR7MYpnoe+eUv/Ly7rF5iBcMwkaGnkzQBJ7GCYRiGiWLEyWJFs0wJ0Rl00FRG5YTDlKT6VlSwYMEwiSVYGI1GrFoV2SH+r7/+ktswDMMwDMMwDMM0xQiLaOkghDHJG2ER3aONYZgGTgk588wzMWfOHKSmpmLMmDHo1KmTXL5jxw65fNGiRRg7dmw9msUwDMMwDMMwDNN4VUIMJlWQqLZKCEdYMExiCRZTpkxBQUEB5s6di3nz5kFTaUDo8XhkhZDTTz9dbsMwDMMwDMMwDNMcIyy0Wi30Jj17WDBMogkWBoMB06ZNw8SJE/Hjjz9i9+7dcnn79u1x9NFHo0+fPrFuJ8MwDMMwDMMwTINFWEQraervY0FlTRmGSSDBwgsJEyxOMP5lEP1LFLZNTg74cKicWE34d3+O728qO+ZfhjE5KSnqezAMU3+o/KnD7ZJlB/37I0XQxRKdRhOxrKlBq43pezEM0/hQv96Yp1YbohLF3v5fn2s5vda7nz6ZrULGFIfb7XtPmlMkWvUzJrFNN6uLsPAKFlwlhGESVLBgGH8qnK6AcmJPjzwlYOJAE4UPLxgd9UNLNhhxwpwZATdO/oIFVQSJ9h5drGlqPXTv/vQGuY/A9zD46sATFn3g/8HEolY7w8QbOk+Dz/Vw2wT/H+7cJ6Fi1AfzQ5Z7+1vH1FTsLC72Le+ali77tl6jxY7iooD+6BYCKQYDPr3o0oB9OT1uaBRNxLKmn1/MPkgM05SINJ74U+F04vR358i/3x812tf//a/lna1pUjAlaEyhakXea7V3DmHQ6rC9qFD+va2wEFMWfyX//mz0mGrLlLNgwdRGsKhJhIUxyYiyonL+YBmmMQULiqIgnwqqDELpIPS/oihRX0Pr165dG6t2Mk0AXVDZJ+8EwgvdTIW7CfIn0tNWL56gEqbB70GTEW+Nd4ZpSZCw5y/u1QSauIebvPtHVYTrb3TTEO6mgPpvuOXULwdmt/H9T087z543L2rbtJXeSAzDNA0ijSf+eCMdiJyy0rDX8kjjiD+RtoltDBjT0rGV2mGoSUqIhSIsWLBgmEYVLK6//nopQOh0uoD/GYZhGIZhGIZhmhu2MhsM5ppEWBhQXlTRIG1imJZIjQSLG2+8Mer/DMMwDMMwDMMwzYWKMhss1owaeViUFpU1SJsYpiVSp5jbigpWERmGYRiGYRiGabllTQmTxYTyYr43YpiEMt0cNmwY+vXrJ38PHTpU/qSnp8e+dQzDMAzDMAzDMI2REmIy1MjDorSQIywYJqEEi5tuugkrV67ERx99hLffflv6WXTr1k0KGN6ftm3bxr61DMMwDMMwDMMwccZeiwgLrhLCMAkmWFx11VXytxACGzZswIoVK+TP999/j/fff1+uI8Hiu+++i21rmbhxoKwMBbbo4WzV1S+nkmP+ZRXp/PDHajSGlF0k61aP33ZUctR/m+AyjBatDl9cPFZWrYnURoZhata3DRotShx2aBQlwF2f+iWVIaX+WOZXmcfbp60mU0D5QrfHI8uXBpcMjtQv/csfejweOOzkxG4M6Nfclxmm6Vz/a7p/GnO8JY6Tg8odi8qxh8qbe+cBWkXxVQzyjjPe14Yba7hMORPrsqY1ibAwJ5tQUWKT17NI81OGYRpYsPBCkRVU4pSiK3r37o2ePXvi008/xbZt27B379767JppYGgyccq8WVG3qa5+eZnTgbPmz61VGbLaliEt3LMHffv2RVJSUo1fwzAtmWh9u7rygXTTMKB1dlzLH5aXl2Pdjp3owf2aYZrs9b+++/cfi+i94jHuMExtcDldcNpdsgJITQQLEvQrSipgsdatnzAME2PBorS01BdV8eeff+Lff/+Fy+VCjx49cPjhh2PSpEnS14JhGIZhGIZhGKapGW4SBnPNBAuitLCcBQuGSRTB4rDDDpO/BwwYIIWJK664Qv62Wq2xbh/DMAzDMAzDMEyDGm4SNTLdrBQsSgpKkd05K+5tY5iWRp0SrcxmM9xuN3Jzc5GXlyd/8vPzY986hmEYhmEYhmGYBqSiVBUsjDWIsEhKqYywKOBKIQyTMBEWf/zxhzTbpHQQ+nnhhRekeJGRkeErc0o/AwcOrNV+t2/fjunTp+Pvv//Gpk2bpDfGokWR86u9UN7YW2+9hXfeeUcKJ+RxcNddd2HIkCF1OTyGYRiGYRiGYVootREszMmqsXQJCxYMkziChddsk37GjFGdnHfs2IFff/0Vs2bNwrfffiu3Wbt2ba32SyLF0qVLMXjwYOm0G1xlIhIkVrz44ouYMmWKNP+cN28eJkyYIA1AO3bsWJdDZBiGYRiGYRimBQsWNSprmmyUv0sLSuPeLoZpidSrSgi5u//111++SIt//vkHdrsdOp1ORjnUluOPPx4nnnii/PvOO++UZp7VQe/3xhtvSIHi8ssvl8souuOUU06R0RoPPvhgHY6s+ZctM+l0KLKpg3GsMGi10uk7En0yW4Wst7tc+Hd/ju9/KlXmcLsDSpRR9RGCRCx9dmvsKCuFpqK83qXWGCbR+mZNzvFIJQip/5U6HAFlAAmn240PLxgt/zbp9NhaWOBb1zY5OWrbSDTemJcbtnypfz/1wv2RYZonLrcn4FodrjS5fxlki14vxyMv4eYGdP2fsvirsMu97+U/nvH4wjQkNm+ERZIqRkRDq9XCnGJGcR4LFgyTMILFk08+KdNC1q9fL6uDkKfFoEGDpPnmsGHDZCoGLastdaldvHLlSlm15NRTT/UtMxgMGDlyJBYvXlzr/TVXgsuK0Q3MqA/m+/5/euQpIROKTqlWGHVVpwhNFqJR4XRGLZH42egxUdeHK7NIZRWjlUqtT6k1hkkEqiv5F3yOR9q+uhKl4bbx9nvq6xQV5y9U7Cguwqb8vLA3FJHei/sjwzQ96NpOfTcaeo0GJ8x5O+o2/uNCTcajTy+61CemWo0m3xwk0rjD4wvTkJSXeAWL6lNCCEuqGSX5JXFuFcO0TOokWHzyySc4+OCDcdppp0mBol+/fjKqojHYsmWL/E1+F/50795dpqfYbDaYTKoZDhOZcJMDEgt6Zbaq8cfm/1Q3HDVL8GEYpqH7PfX1Aa2zfcspqqK6mw2GYZoHJIhWJ/yHi66oLySSeh+c1ETgYJjGSAnRG6tPCSGSUjnCgmHiRZ1UhmXLliFRKC4ulhEVRmNgyFZqaqp8SlhUVFRnwYJeT2kv9aGioiLgd2NBoebxPv7q3qOmniS1eQ29Z32/o5ZIopyXNSEpKSlu+45FH68v1fWb4HO8Ln25tp9DXd+jLv2xKZ2LsaQlHne0Y45XP0+EPt4czpG6XL9jvc/mdL1PpO+2IaDjTKRreU0+/6L8IhjMBjj8UpuiYbQYUbC/sMmfoy3t3Kwp/LlEpiHOlcYJi2giOJ1OrFu3Lib72rZtGxoTyouvywWgNsdf3XvEQ7Bw2O1Yt2NnrffLJMZ5WRPIk6Yp9PG6Ul2/CT7H69KXa9vX6/oe9emPTeFcjAct8bjDHXO8+nki9PHmcI7o27eL+T5rOydojtf7RPhuG4rMzMy47buu/Tza579z6y7oDBps37G9RvsSWg/27cxp8uNNSzw3awN/Lo1DkxcsKJKC1E8y3/SPsqDICwo3tFqtdd63Xq9Hjx496q060cndpUuXOvl6xAoy8ast9PnVxjy1uvfwz5GvTRuiYTAa0aMOBq8tnUQ5LxubWPTx+lJdvwk+x+vSl2vb1+v6HnXpjy31XGyJx90Yx5wIfbw5nCObS4pjvs/azgma0/U+kb7b5vAEtrb9vCaf/3LzP0hKSULnTp1rtM/Wbf/D9jU761R0IJFoaedmTeHPpfrPJp40ecHC612xdetWWWbV39uiXbt29fKvoItprELYqNPHMxyuOrwVB+J5/NW9RzwECzJqbczPtanT2OdlYxPLPl5Xqus3wed4XfpybT+Hur5HffpjSz0XW+JxN+QxJ0Ifbw7niFIaezPB2s4JmuP1PhG+2+ZAXft5tM/fUe6EyWKq8X2EtVWqrBLSXL5PPjf5c0kkal+WI8Eg88/k5GR8+eWXAaFh33zzDY4++uhGbRvDMAzDMAzDME2LitIKGM01qxBCJKdZUJJfCrcrtOQ3wzDNKMKCQkqWLl0q/969e7csV/rVV6qL/aGHHoqMjAyMGzcOe/bs8ZUspTSQq6++Gi+99JJc36tXL7z77rsoLCzExIkTG/V4GIZhGIZhGIZpWpQX116wIIpyi5HRJj2OLWOYlkdCCRZ5eXm46aabApZ5/589ezYOO+ww6RLtdgeql1deeaU0b5oxYwby8/Nl/tj06dPRsWPHBm1/U6qzbtLpZA30aOGZ9Jr6vEcwFr0hZL2bvk8/461kQ+A2/q+h755MtyiPlUJD69JGhkk0/PtNTc7xSP3MoNXK8qRaRQkoMexyUx/z+PoXbROuDTV5D29/De6nkfbDMEzzgPp2uLGDZg2eymu4//hi0et9cwytooFOGxrQ6399N2i0WHDhJXIe4j8X8R/PeHxhGpKy4nKYLIEVCKORnK4KFgU5RSxYMExjCBYvv/xyrXdMF5zrr7++Vq/p0KEDNmzYEHWbOXPmhH0virKgH6YWddatDfAeMYRKRZFDOJluNZccQYbx7zc1Ocfj3c8a6j0Yhmk6tE9NlT/xgsa+vC1b5AMnvr4ziRJh0bpjqxpvn5qRLH/n7y1A98Fd4tiypguJm7uKi5BmMiHVWHePQablkVCCBcMwDMMwDMMwTFOKsEipFCzy9hbGsVVNlz/27MJt33yFHcVF0CgKzujZG/cefRxa8QNIJlaCxfr162uyGcMwDMMwDMMwTJOmvKiiVoKFTq+DxZqEvD35cW1XU2T1/hyM++QjdE5Lw+1HHIWcslIsWL8Wv8/fhdnnjEKPjMzGbiKT4DT5KiEMwzAMwzAMwzCxoryEBIvapS2kZaUibzcLFv7YXS7c9NUitEtNwV1HHo0hbdri5O498ejxI6HXaHHxR+9ja2EBn7hMVFiwYBiGYRiGYRiGAeCwOeByuGBKqnmEBZHaKgUHduXxZ+jHnH9WYUdREa4ZeigM2qrA/gxzEu456hgYdVqMX/ARCioq+HNjYl8lhNJE5s6di7Vr16KkpES62wd7WHz77bd13T3DMAzDMAzDMEyDUlZULn8ba5ESQqRlWZGz40CcWtX0sLmceP3P33Fs567okBrq9E/Gm7cdcRQe+GEJbvzyM8w8ZxR0flXOGMZLnc6K5cuX44ILLsAPP/yA1q1bY+fOnbKEKP29Z88e6fB8yCGH1GXXDMMwDMMwDMMwjSpYmJNrmRKSbcX+7blxalXT49P161Bgq8CZvfpE3CbbkoxJhx6OZbt34YXlvzZo+5hmLli8+OKLUqD46quv8Nhjj8llVFL03Xffxfz585GTk4NTTjkl1m1lGIZhGIZhGIaJG6WFZXUSLDKy0+RrqcIIA8xd/TcOatMO2clqBZVI9MtqjVF9++PVP5Zj2a6d/NExsREsKA1k1KhRSE5Ohlarlcu8KSGDBw/GRRddhBdeeKEuu2YYhmEYhmEYhmkUSgtVwaG2ppsZ7dLl771bctDSWXdgP9Yc2I/junSt0fZn9e6DPq2yMGXxlyhzOOLePqYFCBYkUlgsFvl3amoqdDod8vKqTGYo+mLz5s2xayXDMAzDMAzDMEyDpYTUzsOiVaVgsee/fWjpLNiwDqlGIwa3aVuj7TWKBlcNPQR55eV4dtkvcW8f0wIEi06dOmHbtm0+c81u3boFGGySt0WrVq1i10qGYRiGYRiGYZg4U1pQKu9vjLWsEpKUmoSkVDN2bdyLloxHCHy2YT0Oa9+hViaa5Gdxbp9+mP33X9iYx14gTD0Fi2OOOQaff/45XC6X/H/8+PH45ptvcNJJJ8mf7777TqaFMAzDMAzDMAzDNBVKCspgTjFBU8uKFSRytO7YCjvW70JLZtW+vdhXVorh7TvW+rWn9uiFrCQLnvzlp7i0jWlBZU2vu+46XHbZZT7/inPPPVd2ahItaNk111yD8847L9ZtZRiGYRiGYRiGiWuERW0NN71kd8nC1tU70JL5evMmWI0m9K5DtL1eq8WofgPw8h/L8NfePTiobbu4tJFpAYKFXq9Herqap+Xl7LPPlj8MwzAMwzAMwzBN1XTTnGyu02vbdsvGisX/wOlwQm/Qo6UhhMDX/23CwW3bSV+KujC8Q0d8sn6tFC2mn8UPwJk6poSccMIJWLJkScT133//vdyGYRiGYRiGYRimqVBCERYpdYuw6NCrHVwOV4uNsvgvPx87ioswrB6RERpFwZm9+uD7bVvxX35VUQem5VInwWL37t0oL49cY5jW7dmzpz7tYhiGYRiGYRiGaVCK80qQlFK3CIv2PdtAp9di7a8b0RL5dut/MGq16N86u177OaJjR6SbTNKAk2HqFqtTaSwTidWrV8typwzDMAzDMAzDMC1BsKA0kE79OmDlkn/QElmyZTMGtm4DQ6XPYV3RabQ4tks3fLx+Lcocjpi1j2nmHhazZs3C7NmzfWLFY489hueeey5ku9LSUhQXF+OMM86IbUsZhmEYhmEYhmHiSEl+KboO6FTn1/c5tCeWzPsRFaUVdfbCaIrklZfjr317ceXBw2Kyv2O7dMWC9WvxxX8bcUG/ATHZJ9PMBYvMzEz07NnTlxKSnZ0tf4JJSkpC//79cckll8S2pQzDMAzDMAzDMHEWLCypSXV+/ZDjBuDL/y3B4tk/4qzrTkZL4YftWyEAHNQmNpU9qLxp/6xsfLxuDQsWLZwaCxYUMeGNmhg7dqwsbXr44YfHs20MwzAMwzAMwzANgsPmgK3MDou17oJFRps0HHziQMy45x0YzAYcdPwAZHfOQnPnu62b0SM9A1ZT3QxLw3Fkp854Y8Xv2FtSgrYpKTHbL9MCPCzmzJnDYkUzy9XL3Z0vXZEZhmFaAh6PB3l7C+TYRxNUhoknFWU2ea7l5xTyB80wCT4nJpJS65fKcc6Np6Jj73Z4ZuKruKz79fhqxndozjjcbvy4fRsOqkd1kHAMa9ceOo1GpoUwLZcaR1iE86p45513sHz5cuTl5eGhhx7CoEGDUFhYiE8++QTHH388OnfuHNvWMjGlOL8EG//YjFkPvIc9m3PQuV8HjH9kNLoN6gyL1cKfNsMwzRK6cfzu3Z/w2avfwFZux4izD8GFt52NNt1aQ6Opsxc1w4Tgcrqwe9NezH34Q/y15F+kZCRj1K1n4PCzDpFPYRmGSSyKclXBoj4RFgR5V1zxxBiUl1Rg0evf4KUbp+PQ0w5CRpt0NEeW796JMqcTQ2MsWCTp9Ric3Qafb9yAiQcNjem+maZDnWZm+/btwznnnIMXX3xR/r1hwwaUlZXJdWlpaZg/f76MwmASF3uFHd/M/AF3nfoo1v/+n1SUV/+0Drce8wB+W7QCbpe7sZvIMAwTc/L25OPB857CW7fPxb5t+1G4vwifv/UtrjvkDuzdnMOfOBNTtq/ZhWuH3oEf3vsVRbnF2LVxD56/5k08d9XrKDxQxJ82wyQYRQeK5W9LWmwe3FG1kTOuOQkQAl+//QOaK99u2Sw9JzqmWmO+70Pad8CqnL3YV6qKSUzLo06CxVNPPSUFigULFkhhQgiyWKnixBNPxG+//RarNjJxIH9fEWbc827Yda/cOAN5ewr4c2cYptnx319bseGPzSHLy4rK8e4Tn0gxl2FiQUlBGV679W047c6QdcsWrcC+rQf4g2aYkGpFAAAAYkhJREFUBKPQK1jUM8IiWLToO7wXfvp4GZojHiGwePN/OLhtO1lJMtaQiadWUbBk65aY75tpxoLFL7/8Io03e/ToEfbE7NixI/bu3RuL9jFxfMoYbhJFlBaWySdBDMMwzc234pvZSyOu/+WT36U7PMPEgrKiMvz9w9qI65d/voI/aIZJwAgLnUEHo9kQ0/32PqQHNq3YItOxmxurc/ZhX1kpDmnXPi77TzYY0KdVlhRF4oFw50KUvwdP8WPwFD8CUTYTwrk25IE808Q8LGw2GzIyMiKu96aHMImLTq+Nul6ri76eYRimqUECu9lijLjeYNLTRg3aJqb5omgU6PQ66WMRDlOUc5FhmMaB0gRT0i0xjxToPlj19Vv320Ycdnrz8mL48r+NsBqN6J3ZKm7vQdEb761ZjTKHAxZDbMQkIewQpS8CZTMBuAFNGxq5ITwU/eYAtF2BpEsB86iYvB/TwBEW3bt3xx9//BFx/bfffot+/frVo1lMvCHTn5T05LDr2nRpDWsrLh3EMEzzgiagp101MuJ6WpeeHfv8W6ZlYm2VimMvOiLi+uFnDmvQ9jAMUzPBIjnC/Lg+ZLRNR3K6BeuWb2p26SCLNm2Q1Ty0cTStHtKmraxE8tuuHTHZn/AUQOSPUcUK05mA9RUo1iehWJ8A0t4Akm8HtNkQJY9DHDgOWvscKCRiMI1Cnc6scePG4YsvvsCbb74pq4UQFDazfft23HbbbVi1ahUuv/zyOjVo8+bNGD9+PIYMGYIRI0ZIvwyHo/oThKqS9O7dO+THbud85HBktkvHPfNvDomkoBC4u9+5CZntIkfQMAzDNFXa9WiDM64OFS269O+I0yaeAK2Wo8uY2GBKMuKyqRciq0NmyLrxj1zM11mGSUAK9hfF1L/CXzDv0LOt9FFqTqzYuxt7SkpwRMf4VoZsm5wif77fVv/PT3jKIfInAq6tQMq9UMznQtFUiVSKooOiHwjFcj2QOg3QD4bO/hz6ZU+GxvE5hPDUuw1MA6SEnH322dizZw9eeOEFPP/883LZFVdcIUULKgl3yy23SOPN2lJUVCTFkC5duuCll15CTk4OnnjiCZmCcv/991f7+pNPPhkTJkwIWGaIUdhQc4OEioFH9cNbq5/F4lk/YPM/29F3eE8ce9EI6A06bPjjP1mSKa11KlIzOdqCYZjExel0IX9PgS/32JqVisy24UvHpbVKxeUPXYQTxx6Dz177GuXFFRh52TFy/GvVPvTGkmHqQ9uu2Xj+50fw15LV0nCPSpmeduWJMCebkLszF06blaN6GCaByN9XiNSM2EdYEG27ZcuKfM2JT9atRaukpLimg3gZlN0GP2zbKu8365qyQ68VRfcArv+AlHug6LpE3V7RZgGWiXBoToC9aB5SbPdAuOYCKVMAw5FxMRllYiRYENdee60ULr755hsZWUFmZp06dcJJJ50kTTfrApVDJf+Ll19+WZZHJdxuN6ZOnYqrr74a2dnZUV/fqlUrGZnB1AzK1+7Yux3GP3oxXA4XKspsmH73PHw1/Xt43Kp6OODIPrhj9o0yTYRhGCbRKCkolSUj37p9DipKbXJZm66tcd/7t6L7kC5hIyasWVb50/ewnnKsI5GDYeJF606tcPL443D4WcOwcsk/uPu0x3zmrhTZc+/7t6Jz3w78BTBMAlCwr1BGQsRLsPh+/i/S3D45RmVTG5NShwOfbVyPk7v3hKYBbtyHZLfB15s3YXNBPnpk1PEBQ8UngP1zwHJ9tWJFAJo22Fd8AUwdHNC7P4EomAjohwLJNwKGw1m4iDP1SjZq166dTP144IEHpKgwceLEOosVxI8//ojDDz/cJ1YQp556qhRDqDIJEx9IHfR4BOY+/CG+eHOJT6wg/v15Pe4760mpODMMwyQa65ZtxIvXveUTK4h9W/djynEPYv+O3Kiv1Wg1LFYwDQI91fvpo2V4dPTzAZVotq3ZWaNzlWGYhumnBTmFET3e6kt25yz5e8e6XWgOLFi/FhUuF47t0rVB3o8qheg1Gizdvq1OrxfuAxAlj6iREYbhdduHtgeQfC9guRXw5EMUXA6Rdx5ExQIIUTUPYRJIsNi4cSPeeustPPjgg/Lnf//7HzZs2FDn/W3ZsgXdunULWJaamoqsrCy5rjo+++wzDBgwAAcddBCuvPLKerWlJSrKi15fHHbdtn93IHd3XoO3iWEYJhqUAjLjnnfDriMBY9kiLhvJJAZ5ewowZ+oHEU3+Nv1V/RyHYZj4QpEPTrsLKXFKCcnqmCkfEu5YtxtNHbfHgxmrVmBY23ZoldQw0SJGnQ59W7XGT3UVLEqmySogMF9ar3bQd6gYDgJSpgLJt6mVRYpuh9h/BDxFd0HYvoXwcIn0WFKnOFgywSRPiU8//dTnW0FQJMQzzzyDM888E4888kit/SOKi4ulQBGM1WqV/hbVmW4OGjRIRn3s3LkTr7/+Oi655BIsWLCgzlEfdGzl5eWoDxUVFQG/E5Wy4nI47c6I6/duyUGHvvEJkWtun2VToCl9lklJsTe/imUfb6nfSyIcs63cFnXit+7XDTjtqhNkamGiwd91w/TzROnj9go78vYWRFz/38otGHryIDmPaonnSEs61pZ6vIl0LY/0+e/eslf+NqYYpH9ePEjLTsXWNdsTYlyqz7m56L+N2FZYiLH9BsbtswpHn4wMfLppAwqKi6WAUVMU9xoYbQvg0o+Bx0mvq12b7XZHwO8qegGGXoBuP7SuX6HYfoWm4iMIaCA0veDRDoDQ9oFH21v+D6X5lbOuaIBxrE6CxbRp06QQQILAmDFjpHcFqU3kZTFnzhy8++67UmS455570FDce++9vr+HDRsmK4xQOsn06dNl9EddcDqdWLcuNuY427bVTQ1sKNJMmVHrxae3TcP69evlRaGxSfTPsinRFD7LoUPjV688ln28pX0viXDMVnMa2nXPxva14cNruw7pgq1bt0pvpESFv+v49vNE6eNp5gykZaWi8EBx2PUd+rTDpk2bwlZFa0nnSEs61pZ2vJmZ8TM1rms/D/78//td/b/EVoTtO+JTCcKSYcb6FZsSYlyq67lpc7sxbeXv6JOSCm1xMXYUhx/X4kGmywW7240FfyzHoLSaVzTskfkYoMvCjgNUzaTupVH37dsXZe3B8kevyYfZsA1m/S4YdT/CoP0YiuKBEBqUO7uh2DYYBRWHw+5qX+d2tDTqJFgsXLhQGm4GV+6gdA7ys6BSp7RNbQULiq4oKSkJWU7RFSSA1IbWrVvLCdCaNWtQV/R6PXr06IH6qk7U6anyidlsRqLicrgx8rKj8eX070LWte/ZVppuJmfETx1vTp9lU4A/y9j18VjSEr+X+hwzCeVUNvLhC54NWWcwGXDUecOR0b52146Ggr9rc4vq43Sujr7zXLw+eVbIOjLf6ze8N6zZKS32HGlJx9pSjzeR+nmkz3/fqnz5u3f/3tKYPh6079YOuzftRd++fdFUz82Hf/kRJW4Xbjv4SLS2NKx5aEchkL5zB3ZqFFxUw89Q4/oDhvI1cBquRefUWhht+kGRFSRWtGnTBkZjdRkEncgi1PefUziheHZB8WyDSbcRSYYv0Db1I7i1h8FlvAFCNxBNmYrKcybhBAuXy4XBgwdHXE8eEt9//32t90uCR7BXBQkYBw4cCPG2aKgJRqxC2KjTxzMcrt4kAeMfvQQlBWXYtWGPFCnoSRCV/Hvgo8lo3SH+5YqazWfZhGjpn2Us+3gsaYnfS12PefAx/TH+kYsx96EP4HSoEWJpra24/8PJaNMlS0aOJTL8XbecPk6VQvRGHd6YMhsOm9NXQeShBXfIhwKRyuO1pHOkJR1rSzzeROvnwZ9/SW4ZzClmpKYFioexhPr6ysX/wGQy+VLqm9K5OXPVSry3bg0mDDkYneIYNRONgdlt8Nue3TX+zj15bwLaLtAn1b+aB4kVRqOplq+i7ftW/pwKIZyA809obYugLR8LmC+GknonFKX5i5d1pU4zuSOPPBI///yzTAkJx08//SRTMmrL0UcfLb0n/L0svvrqK9mha7u/nJwcrFixQkaCMJGFp/3bc7Fs0Z/4b+VWHHLqQZj42CWyRvQ/P65Fz4O7YcS5hyI9Oy3y57z9AA7szMWSeT/BYXfimAuOQIfe7dCuW/gStJSfS+/55zersPbXDehxcFcMP2MYWnduBV0tctEYhmEIa6tUnHfz6Tj+4iORtzcfeqMB6dlWZLZLR+6uPOz+Lwffv/uTNMU6/tIj5djUupPq1F5TnA4nDuzIwy8LfsfW1dsx4Kh+OPjEgfJmk6o70OTz35/Xoeugzjji7EPlchoXaYz785u/0ap9Bo6/5CjZJmpvPCHR5sCOXPzy6e/Y+s92DDiyLw4eOUi603O9+NiTuzsfm1dtlRVAUlul4sQxR6N1l1Yo2l+MTSu2YPnnK5HRJg1Hnj8cm//Zjv078/D8z49Izyi6Zma2zUCrDuk4sCsP65dvwrLP/kTrzlk47uIjkRYUccEwTHzJ250Pa6v49rtW7TKksSddn2p7LWpMCm0VmPbrz3j3339wRs/eOKFr90Zry8DsbPz4xzbsLytFa0t0g1Th+BNw/gFYbkqYa6Ci6GUpVKE/DLAvASrmQzhXAOlvQtG2a+zmJSR1ukO86aabcPPNN+OGG27ApZdeKj0sCPKwmDdvHvbs2YPnnnsOhYWBpTD9y5WGY/To0dID4/rrr8fVV18tRYennnpKLs/OrroBHjdunHyPxYvVqhaLFi2SER3HHHOMTAUh080333wTWq0W48ePr8shNntIOFi//D/cMfJhOGwOKUocd/FRuPnI+1CUW5WLNuehD+STyoNPHARTUqBRzL5t+2Up1K/froqm+WbmDxh0TD9MmXEd2nYNFS22/L0dk499AOUlanjg4jk/Yvpd7+LJxfeh3+G9ElptZhgmMaGxqU3X1vLHX0x9ZdIM/PbZn75lX05fghHnHYprn7ncV16uOtwuN9b8sgF3n/qoL4KDxq1uAztjytvX4fYTH5LO8l7evuddPPr53fj4hc8DqpS8P20hbnnjahx53qFIyYjPhJjMRdf+tgF3nfxIQFsp5eCZpVNlm5nYsX9nLu457TFZmtTLZ69+jddWPoX7z34SOzfs8S1//+mFGDf1IlkR5Lphd8iooF0b9+Cnj5bjuR8fwoPnTZPnrJd5j3yE296+Hp2GteGvjGEaiNw9+XEXlVt1UKMSdm3a1yQEi13FRZixaiXeX7Na+thNHDIUx3ft1qg3/wOy1PuLn3dsx3l9+0fdVpS+AWg7AHryl0gsFEUDmEZC6PsApc9B5F0IZMyFoqtb2kpzpk53h6eddposafrtt99iwoQJOPHEE+XPxIkTsWTJEllO9PTTT8fhhx8e8FMd5FMxa9YsKTSQaEEVR0aNGoU777wz5Gbb3/W9Q4cO2L9/Px577DHZBnpd//79MX/+/DpXCGkJJdamnjdNihXEWdefjNkPvhcgVngn649f+qJ8ihQMTbb8xQov/yxdi2WfhZYTzN9XgEcuetYnVnihNlBbqE0MwzCxgEQGf7HCyy8f/44Nf/xXu7Hy/Kd9AoCXkZcfi6fGvRwgVhC03cMXPisjPvyhid7z17yJgpzoFa/qQ95uGtdD20ptfOzi51GQE/gQgak7FHXzyQtfBIgVxPhHL8Z7T30aIFZ4mfXAe9JXRaPV4O1738WxFx6BE8cejXmPfBggVnh5ZuJrMMhQYoZhGkqEtGbFN8KCIgBpDNi7OZp5Y+Pj9Ljx9K8/4/jZM/DR2n8xslsPPHvSaTihW/dGj1SwmkzompaOn3Zsj7qdcK4HHEsB4+mqOJCgKNqOQMp9Mo5A5I+FcKvVaph6RliQmBCvk7V79+6YOXNm1G0oCsOfIUOGhCxjopO/tyDArbxL/06Ydf97Ybe1ldmkr0WHnm0DyqB+/qYa4RIOWjf8jKFo65caUnSgBLv/Cz9AU1uoTVmVyjPDMExdyd9XiIWvfR1x/cJXv8bgY/rBmmWt0QQ2WJQg2nZtHXKz6qWkoFROSIMrL5HYvur7f9GpbwfEg9zdefK9w0GVVIpyS6Km+DE1p3B/cdhr4MCj+mL6Xe9EfN3qn9eh9yE9sG7ZRqz5dQMOP3Mo7jvrybDb0vny70/rA66jDMPEjwM789A5TuOzF7oukGixJ8J8OBGgKhzXffUFft+7G+f26YfTevaGKcHStge2zsaP27fCIwQ0Ee5JRdl0QNMKMAxHoqNo0iFS7gRKHoYouALIeA+KJnq6S0uiTmffjTfeGPuWMA0K5c/643FHL99UXhxYL9rtdKGiJHIN4/ISW8g+I5VM9UL5vAzDMPVFHZ8iu9LTOmc145EXW5k9/Hu4oo+Z9nIHdIbQUtFlxfFzy6f3jIYrKPKCqQdChD03FI3ii1yMdO4ZzarDfEWpDYpGE/X6W14U//r2DMOoUVOFOUXStDneZLbLiPgAr7GhaMBXN63HisJ83DniaPRvnZiC6eA2bbFw43r8uz8Hg7JDU+dklIJtEWAeDUVJLLElqmiRPBkomQpRdDuQ9kqjR7MkCokbH8PEfbDU6rQBURRkDBaJboMD86lSM1NxxFmHRNz+kFOHwBo06KdmpsCcbIqoOGe15+gKhmHqT1q2FYedHjlfdfiZw2ocaUBPt8N561C6HHlDhIOiK9Jap8pxNZghx0XPt60P2V2yIvoAUVtpDGZiA1USIG+nYMhIr/+IPhFfN2BEH2z+Wy3/1u/w3ti5YTe6DlR9wMIx8Ji+8gaCYZj4Qul/1NfSsuLrYUGQAfOuTaFpY4nAxxvX49e8A7hi0EEJK1YQPTMykaTXY+n2rWHXi7JZgGICjMegKaFo2wNJ1wD2b4HyGY3dnISBBYsWCk3WL7n7PN//i17/BmMfuCDstqdMOD7sRHfoSYMDTO68WKxJOP+m05FsDSw3lNEuHVc+OSbse1x897kNomozDNP80Rv0OPny48KOKSTMkr8EeSXVBNrH+beeHrL8y/99i4mPh6+URVVLfvn0j5Dlw04ZIsXieEFtvWDKmWHXXf30ZXKSzMQGEoCufGoM9IbAJ3fT734XVz5xacADAS99h/dCSWEZSvJLpahBnlELXvwSlz80OqzQdOhpB8FkVaMxGIaJL1TxiaB0jXhDD+j2btmfcGJkfkU5nl7+KwanpWNY28SuVqHTaKT55ndbt4SsE55ioOI9wHh8kywVqhgOBoynQpQ8A+Fc09jNSQiaRowM44MmOPYKh3yCR4OqrdQmQ4wpZIie6NFEPTj1g/whaFC0pJphsapPBE0WI86+4RT0PbwXCvYVysgHEhoe+/IezLxvPjav2oasjpkYdeuZOPzMYWGjL9r3bIsnvroXHz63CN+987MMNz78rGEYc98otO0Rqsrq9Toce9EImb+9d2sOLKlJMiQ2vU0aeg3tJtvEMEz9oL5OfjAUZq436hNaCKS2luaVIcPSCuWFFTWuqV4TaHx6dulUvPv4J/jpw2Vkx41jLhiO0XeeK9dVlNnk2Od9Wp4e4XOicfPC285B14GdMfch1Ryxy4COuPD2c9BtYCdkd2mN6XfOk34WbbpkSeF38LH9sW/bAexcvxtrf9sov4OzrzsZx196FFp3bIV4kZRixqjJZ8kn9nOmfiDb0KV/Ryms9DmsZ9ibaKbu0LXs9VVPS8+U5LQkeV02mPTyevzGqqex5N2f8NHTnyHJmoRTJhyH3of0xBuTZ8lz5Kjzh2P7ml24/OHR6DWsO1787VG8dcccrPlloyyrOOrWM3DM6BHYmbMdbcGVQhgm3niNb2vibVRfMttnwFHhQN6efLRKoOjiV/5YDpfHg5FtqjzrEpkhbdrirZV/IK+8HJn+84fy+YCwA8aT0GQxXwi41kEUTgZaLYBC0SItGBYsmgjlpRVSRHhj8mzpcE8ha+fedDp6Du2GB895Clq9FqddeaJ8suedEJNZ3MfPf44v3vpW5jZTRMQVT16KTn3ayxQMEnZpwq5ObPejQ692uGraWNz61jXyZofycEsLA70rgqGJ/xVPXIrzbzlD/p+cbkFalJJQdINSkl+C9578VFYZadOltZy8JZjIzDBNEqpA8fPHy/DOYx/Lyj5dBnSSUU1UMjhS+kJjQeUdf/5kOd559GMc2JUnb6yvqGxrSnpsjKY0ei2OufBwnHPjqfL//JxC6I067Nm8Dx88vRDfzv1JThqHnjQIEx69BB37tYPRGCqc0ng7cuwxGDpysPTHMJgMsGalIm9vPoTHg9OuOhEpaRZZAUmj1cLjETL0/575t8Bebpc3sFmdMqFrANMyausJlx6Ng04YJNuqNxkaJMS5pea8lxdXYO7DH0hB/uwbT8UHzyzE6qXrkJRqxulXjcQb/zyDipJyKZhZUpLwyKI7seiNxbh68BTZJ8+ZdKo0gCUjzgc+uk36YpAPBj0ksNlsEPv44sgwDcH+7bnywZ3XYyaetGqvRtqRj0WiCBYHysrwzuq/cWq3HkjWBT78TFQOatMWNEL+sH0rzq8sbyqEHaJ8JmA4Eoqm6ZpMk++GsFwNFN8HUfoilJTb0ZJhwaKJsPbXDbjrlEd9/1PUBJVFG3bSYIx54ALMuPsdfPTcIvz59So88fW9cpu7Tn4EO9bv9r3mj6/+kg71r/75JLI7t8JHzy/C/Mc/8a0nAYFqx1OteBJFli1SS5OOHHs0Jj4xBpltw4cTU6QE/dRkcrdk3o949eaqKjAklEwb/wouvus8XHzXOTAnN73QLYZJBKiSBZVN9K9csO3fHbjn9Mdwx6wbcdwlI2qcBhFvyorKMeehD2S1Di8UoXDvGY9jyozrceLYo+rd1n1b92PSYXejOK8kYPnrf03DIxc9J8c7L398tQp//7AWLy17DN0GdY64T/9IM4fdgZ8+Wo5XJoXmmFKZ6DH3XxhxzGwIonkSMbFhx9pduPnI+2TK5C1vXI0Hz50mvU285/j70z7FX0tW48xrT8KzV74uo1xOGX8cFrz0pdyGKrrQA4NV3/2L+96/VaZqxkqsYxim9hEWDTVu0rWBhEmqwDf4mPj5GtWGmX+vhEbR4MQu3ZC7NzH9NcKVN+2RkYFvt/znEyxQ8QngyQNMp6Gpo2g7QJjPA8pmQJhOgaIP9U1qKbCHRROAwk1fvmF62HV/fvO3LMFET/y8pet2rNuFjX9uCRAr/FNEZt77LorzSvHBtIVh90mTrJPGHev7f/GcH0Mm/XU6jr2FmHHP/IjvWZBTVWaVYZjaRyxEKjX8+uRZyN+jpj8kSls/e+2bsOvemDJLmp/VB7pp/Hrm9yHj1sEnDMSWv7cHiBVeKKJszkPv13isO7AjT6bPhWPRa4tRksvjWXMmf18B/nfnPHmunXrF8Xj/6U99YoU/m1ZukamalBa0fvkmKcoHe0Kt/mkd9m7d34CtZxgmGHqA1lAplFRBikSL3Zv2JsQXUeF04p3V/+DYLl2lkWVTYljb9li6fRtsLieEcEGUvQkYDoGibRppLdViPA3QdoIouhtCtNxqiixYNAGopGi08kfkON6ue5VnxLY1u7D0/V8jbk8iB5VWCze5IshXIjhFY+s/2xEL/41wrvkEtYXCqxmGqRs7N+yJ2vfoaW6iQJO0SGZjZEhIP/WNNvk1jOnlYWcMxW+f/RnxdSu//Ve+tiaQsEFP0cPh8Xiwd0tOLVrMNDUqSu34Z+la+Xf3QV3w78/rI2777y/rfJE7a35dHzaKZ+U3f8extQzDVMe+rTkNGpnWqkNG2AeLjcGiTRtQbLfhpG490NQY1q49bC4Xftq+HbB9Brh3Acaz0FxQFC2QNAFw/QeUVUWotzRYsGgCkFFapFJ1BOXc2cqr6sEbk9T86mjbU+33aOj0geHYXrPO+kAGgNFoiLxBhmmuJKVEN2QKrmbQmJDJZTzbSuMXeQgEQ4IpmSNGHRtrWPO8uvGsumNkmjZ0mni/Y6fDBZPFFPW8Ii+T4L/9SWnFJWcZprFwu904sDMPGQ2Yxkd+c4kiWJB3xaDsNshObnopae1SUtEx1Yov/9sAUfoqoD8Yii5yamdTRNF1lQai5GUhXLvQEmHBoglA4gNV3wgHmWe2695G5mt76XtYL5x8eVVKRzBn33AqUjOTZb5sODr2boecHapbMkEVRDr0qn9olbVVqtx3OKgtkdrDMEz1tO2WjaQIXjJ9Du2B1AS6IcrunBXRBJQqBkUTXGsCCawXTA4t7/nx818EpLsFc/pVJ0pzzJpAYf1UjSPSuniWL2UaHyoRS34UBJnHnnDpkRG3HXR0f6xbvkn+TWasG/7YHLCeRLJhIwfHucUMw0Qib3c+XE53wwoWnbKQs3W/TEdsTDbk5eLvnH04rks3NFUObd8Bi7dsgM2+CzCdh2aJ+TxAsUAUP4yWCAsWTQAqVXf1M5fJSb4/FHVx/UsT8M3spT4PiwmPXozWnVrJUnvjH7k4ZF99h/fESZcfK28Ipn5yG0xJRjlZot8E3URc/fQ4fPP293L/JIjc+94tMnStvlCo3b3v3Rpyo0LvPXXBbT7XZIZh6tC/2qXLPh0cnUA5ubfNvEEKhol0s/cgtTUoSoHaePvsG2PS1r7De+HY0SPk3217tEF2p0yZGuP2eDD6jnNCtu93RC+ccOlRNa7kQePsHbNuQEqG+kTKO5bSWHzfe7eidefEcH5n4oPRbJQVPii947eFf+Lwsw7xCVh0jaMcdYKq9Pz2+Z+y8tYds2/A6t/Wy5QhiiikMqh0zkyZcV2D3igxDBPIns1qCl9DGiVnd8mSFaWipXM2BB+u/RepRiMObtt0PR8Ob98GZU6B7w+c2OyiK7woihlIGgM4voewfYuWRuLECDNhDYBKCsqQv7cA6dlWPPXt/XJgW75ohRQkRpx3KDxOtwwrO/v6k+VAS5OkTSu2yNcfd/GROOSUIfjxg2UoLSzFkecdJp/C5u7Kw5a/t8nypq+vmobCnCJp7EmCAd3cUHoJ1YZPbZUqnxQqGshcXQp/pW0o95xC5yhVhd6TMtH3bz8Al9OFjr3by2ogVF6RfDJad86CNTPFZ2REE7rXVj6Fv39YI5849RjSRZbf02gVWcGEJnoUacHiBcPUDrrR7j+iD/635jks/3wltq/bhUFH90P/Eb2R3SlQ7GxIaLzI3ZOPgr2FKCsul+MVjQf9Du8tK3ZQ9aId63bLtvc/vJcslbxv+wGU5pcib2+B3DY1I1mOXeTiTuNKzo5cpKRbpLBB21eUVqBwfzH27zgAg9kgoxtIFLn2mcsw9v4L5JgHBcjqkIlkqwXte7TF0RccjqUf/Iryogo5NlKkGpVZ3rMlB4U5hdLLgkRiGgNpTMrZvl9WZyo6UCz3b22Vgq6DOuPl5Y+jKLdEvocaKWZFq04Z0EcwLqsos8kxl6LYdFoTiveXwtjBKMfTiP4+ewqkx4/D5lQ/v2wrzFFSEJjYQN9Vwb4iHNgZeF5Rue+i/cXIzymQwhudQ/t35eLBT26Hs8Ihz9Mka5I8R+icoXP7/JtOl+dOmy7ZOHnssfI6SecsnZMZ7dPl97xr014c2KmWVsxokw5LBqcVMUxDCRbecsINRZsu6nV527870X1wFzQGLo8HC9avw4iOnaDTJEYVsbrQVv8juqcU4JPt/XBaPzRf9MMA/RCI4ocAwxFQNNVXaGwusGCRoJDJJpX7XPNLlZFX9yFdcO/8WzDp1Suxf2cuPn35S3z03Oc+80x60jfplSvw+Zuq8nbalSfitVtnSmGifY82cjC+7YSp2L8jV07yH/xoCp647CVsXrXN9x79j+yDi+84F4+Mfl7me1MKxz3zb8GsB97HoGP6yWiPdx79SObsEhSCfu2z47D88xUwJhnlez46+jkpgBAUpXHKxONx6T3nyyeS9DSJbgraXN4aJ19+nJzov3bL21gy7ydfG2i7Rz67K2K4NcMw4aHoCrrxPndSYpTzoifJW/7ZjvvOfAK5u1VTXRoDxj5wgRQMHr7gGfmkOS07DfMf/xhZHVvhpteuxAvXvoWV3/7j20/PYd1k1MKMe97BD/N/DRgrHv/yHnw//xe8+/gnAWMhRXOVl1TgiUtfgL3C4YuCuOHliejUrwMeOPspdO7XAXqjDk+OexmnTDgOJ112rBwjD5DAUdlWKlF61nWnyLZS6VUvBx0/ADe/eTVevnGGFF28dBnQCVM/uT3ACNkLjXcLX/0K8x75qKqt6cm4/8PJUliiahL+0E3tml824KELnvEZkZKwcem958s20Q0xEx8oGmfBy1/h3cc+Djivnv7uQSye/QM+fuELvLFqGj5+/nNZnvfZH6biw2c+w+dvLJbnPUFi202vXimrYO3bdkBeu999/GMpbtF3TlW9nhr3Cp5acj/ee2IBvpn1g8+MloSMhxbeAaNRjX5kGCZ+7PlvrxSbvZFRDQFVDEpvkyYfIFJ0X2Pw4/ZtyKsox1GdGkcwiQXCcwCi4jMc2X4E5m4ox4FyB7KSmqcnnqIoEOaxQPFdEGUvQ0m5HS0FTglJQPbvysOL178VIFYQJCw8fukLcrJDddvfn7YwoNIHTWifGveyzN2mn2mXv4yCfYUymuHgEwfh8UtekGIFcfMbV8lJur9YQaz5eT0+efFzGbFBUETH1POfljcRFPo664H3fGKFt4LJc1e9gVMmnIALbz8b957xuE+sIGji9sVb3+KH936By1X1Ou9k/KNnPwsQK+Tx78jF7SeqwgrDME0XisS67fipPrGCoBuyg0cOwtTzpskbto0rtuD3L1bKsYZECioVGeyZM+qWM2QZVH+xwuuvQ5FZcx/+MGQsvPf0x2Aw6mVUgheKHnt6wqtyXCRBll77x1erZBTbO49+jA1/bkbHvu0D2kqCL42B/mIF8dd3/+LNKXPQvmebgOXb/t2BB855MmAc9PLP0jWY/eD7gW0tKMVdpzyK/dtDxztaRuv8q6bQa2kff//wb5RPnqkvdI2d+9AHAd8VCWQrl6zGh88uwiX3nI+/l67Fgpe+xEmXHSPPnc9e+9onVnjL9z552Uty24KcQvn3mHtHyXODvtehJw5Ct0Gd8OVbS2QZXv/KOSSa3X7CQ7Bom54JHsM0xYeEjeE71K5bNv4Lmoc3JB+vW4NOViu6pDXNlDQaM0XpLEAx4YjOQ6DTKPhgffOu0KVoWwOms4CytyGcG9BSYMEiASktKMXKxVVPF/2hyX1ZcQXee3JB2PUkJqz9baMMmfYKC/SUp7zUJkNTvZCj+X9/bQ27jxWL/5EhrF6oPB9FSnz+5uKw29ME7bfP/kDuzjxZEjUcNMHbv119auklf28hFrz8Zdjtqa3b1wbeIDAM07TYuGJzSJlQinKgfP5Iebu/fPI7hgYZEHYb1EU+uQ7mpMuPw6cvfxV2PzT+rf5pLfod3itk3bdzfsRR5w8PWU7j6gWTq8qhkccATWJ3rAvvyk3eBQcdPzBkOYkbBfsKApbRzSsJDeGgdLpwpahpGa0Lx5ypH8h9MrGHxIXZUz8IWX7y+OOw8BX1fDt61HC8/9Sn8u9TJp6AD55eGHZfJJJt/XeHjLwhkWznxj3o1LeDvFau/nkdLn/4Inzxv/D5yCRm7Vizp8aVaxiGqRs71++WUU0NTftebbFpxeaIZb7jCZUx/XbrZozo2IQ9H+w/Aq5VgPFkJBvMGN7Oinlr98LlafjPs0ExnQ5o2kAU3w8hqkTy5gwLFgkI5cNGgyb7e/2qggSz+7+9vhBogsLcyGPC/6lkSV5J1Pdw2l0hky7/SiTh8v9Kgm5MgieA7qAIC3ryGUngIHZt2he1jQzDJDbkTRFMets06ckQCRJAXX5RXATdtFN6RzCUbxxtLNy7ZX9YM0MSYTPapoVdTv4B/mNlcZSxMlxbvRTsrxKIvQJKtDF089/bAp7O0wSWlkWC9uUf7cbEDqoWEO67yshO8y2n0rneKEBKa/KPIgp3XmVWnm/+f1NJQ2vrNJQVlUd87e5Ne1mwYJg4lzTds3kfsjo2vGBBvm/kVUfv39B8vmmj9LA4sokKFsK9G6J8FqAbDEXfUy47qWsm9pU58OWW5h2hrSg6IGk84PwLqJiPlgALFglIcrpFRjREQmfQytzrSHQd2Bnm5Kq8V5pIkcmcFxIJrFmqCWY46L0pr9sfmrhHf89OUZ39ybyOKo74Y0wy+Bz2w+5zQMeI6xiGSXx6Htw1bJoIGUdG8+HQBBlQ0thB5pfhjImjjUtd+ncMe+MZbXnRgaqohYoSG9KijJXULq0+vFEZmTP6Qze15J0RCSp36T/u01P1AUf2jbg97Yv2ycQeg0kf9rwiM9hOlSlDDrvTV6abjGDJJyoStC/ysAj+u+dB3XBgR67PlDocXQZ2ChCyGIaJLTnbDsiHdNGuS/GCxgMa68mrqKH5aN0aDGydjXRz0zP3FZ4SiJLnAI0VMI30Le9iNWNgVjJe+2snPI0QtdKQKPregOFYiJJpEO7m/4CXBYsEJDUzGUeNCg1XJg46YQBMySZc9uCFYdeTCSZ5TWS2zZAGmQTlP9PTOjK79FK4vxBDjh8Qdh/03iu++TvA7NNebscZ16i+FsFQacJhJw2RkRxpWeFFizH3j5Iu/8ET+kvuDl8vmdraoZc6GWQYpmnSdUDnkLxgikgg/5o+h/YI+5qR447Fbwv/CFi2+se1uGBKVaqGl69nfI9Rt54ZcSzsObQbNq1Uqyb5p3kcf+lR+GVB4Ht4x6m5D3/k+59uFHds2B02rYQ4cezRWLZoRcjyAUf1lU/j/SHBZeLjl4TdDwnCw88cFrJ8+BlD5bpwTHzskrAiDlN/SHynzzeYL/+3BOffop5vi179RprHEh+98DkuvW9U2H3Rd5TdubWMlKD9UmUtirIgkaL3Id0x/a55EU1yKUS9bY+q6zbDMPGLBKQyow0NzdNpbvz30jUN+r5bCvKxcu+eJmm2KTwVEKXPAKIEMI2CogQK9+f0zMKG/HJ8vTUwDb1ZYh5NEjtE0X2NklbUkLBgkWCQwRdNtK944lJZltT7xI0U2MPPHIZb3rgGWe0zZTlQqgjiH75MN/j3vHMTZt4/X5pj3v3uzb6b/ul3v4Nb3rwag4/rL935V367Wr6eJsTe/Fg5kb/kSBw96nBfpZEhxw3A3fNuwq+f/YkDu3Ix+X/XBkRSkLBwzzs3Y/4Tn+DNKbPx+Ff3SsHE3ytjwqMXY9hJgTnphFarlRP+S+8dBYOpasDpO7wXnlx8H5c2ZZgmDoXYPv3dA+h5cLcADwvK6b9r3k0YetIg33KqfnHKhOMx+s5zISDkeETQ+LTs8xU45sLDMfrOcwLGirTWqeh9aA9Mnn5tyFg47dv7ZeUiukH0QuWSH154pxRLyUDRC1XqoPGRXmcw6XDMhUfIikdUFpYEljtm34hhJw8OaOtJlx8rqx9pdBpfW4nDTh+Ku+ZOgjWMeNtraHfc9vb1snyzF6rY9OzSh9C6cyvfk3v6IWgZraNtvNBraR+9hnWvwzfC1BQ6r6YEnVd0LvY7oheuf2E8vpv/C9p0y8Y1z4zDX9+ulmV2r3xqbIDARL4VdO68fc87Mgrx9lk3yGsxlTB88pv7sOiNxTLV6diLjsC4qRcFRMzQ9/vkt/ejxME+JQwTT8gomeaq0aKE40mPg7viz6//btAbzg/XroFFb8Cwdk3rwaDwFEOUPgm4dwLmC6FoQ1M++2RaMDgrGU8t3wa7u3lHpykaC5B0OeBYClR8jOaMIpq7JFNHVq9eLX8PHBhqqFYbysvLsW7dOvTt2xdJSZHr5VaUVmDf1gPSZZzcioeeMADHXDwCtlK7rPlusZqRnJYccBNvq7Ahd2e+zLGmUkyUXkETaTKzJAmCIh4cDpc0vaNJd3p2mjTx+uzVr2WayKGnHYQjzj4U9jKbNPJMtibBkm5BeVG53I6UX1OKGf/8sAbfv/uzjJ4YNeUsOYErKyyXk3SalHvcbuTvK5LeGnRzQDcbFNXhqHAgtVUqWrVPhykp/FNCwmFzyDbTe9KEjfYZ7sJR08+Sid152ZyJVR+PJc31e6ESkYX7i+WYkJKZLMeJijIbSvPL5NhHaWoWq0WOLamtklGcW4Kyogo5dllSzTCnmqVfha3MLkuDkjExiRGW1CS06dpaCr3ki1GcWwytQSfHD9re6XQiZ+sBlBaUgQbF5PRkZHdpBb1eL3OGvWMjPQUnAYO8f3Zt3CPH4bw9heoYedYh8glY/r4C+d6UJkKiQVp2KlIzUuRxUNUR8iGgMnUkovgLEsF421p4oAgeoY6ZWe1bIW9vATat2IJFb3wjtzv96pFS4KD1VFWCPkM3tZWe0rdLl2N9U6Ohz+/69nH/8woaBU6bE5+9/g0OPmEgeh3SQ54/WR0yYCtzoCS/BOlt0uFyOFF0oBhGiwlJKSZ5bdUoihQyykvJh0WR5y0gZBi6NStFph2RSEXVaujaSaIcnUf6JF2zHA9a0tgXiZZ4vPE6zrr0c//P//kr38TW1Ttww4sT0RiQAf7rk2fh5eWPo/ch4SMPY4nT7caIt9/EwW3a4fIhB4est9ls2LFjOzp16gyTKfLcvaERrm0QpS8AorxSrAisJubPrhIb7l76H24c2kn+xAK73Ybt23egc+dOMBoT53MhRNmbgHMllMzPoOgip57GC29/Gjp0aNzeo+EKDjMRsdscWPbZCjw+5kWfwkrl/eY++jGe/fEh9D+id9jXmcymsGkTlIdHk9u373kXn7+lRkqcOOZo+ZSOIi+80HtQKb9p3z0Q+B6VIdwUtnrLkfchb0+VmdiSd36WTxVHTT4zYFKe1SEw9y+7U81D62hyRjcd9MMwTPODBAR/EbKirEI+UXrCb8zzRl888c29mPPQhwFpaRRpNnXBbRh28pCwYibdvJPg4B81QZAwESm1rF33QM+BsuJyGfL/1h1zA8bI+Y9/gud+eljuJ6NN6NMcs8UEc9C+ouFta3KrJHmB75TeXo6xT4x9Eau+rwoLXv75Sgw6pj/unjdJptWQAMM0LN7vis7R206YKq+JxOLZS+XvM689SUZG0PWPhK7Jxz6AAzsDzd5oG3p6S1VERt16hrx2UspmMFSClyIW/VM3aRLIMEx82bxqW6OmIHcd1Ek+cPzunZ8bRLBYvGUzcsvLcXzXqsjHREYIF2D7AoIiCDStAPM4KNro18MOKSac0T0LL6/ciRM6Z6Bfq2ZeHjppDFC8HqJoCpAxVzXlbGZwSkgCULC3ENMmvBoSDkZPHZ8e/6p8EldbqGqHV6ygMNbjRo8IW1KPojNev3WmfHroD5Vfo9QSf7HCy7xHP0LurhaQG8YwTFzI212A5658PWTMo2pEz17xOi69J9DbhrwkHh/zUtTqIvWForz8xQr/Estv3jZbChrxgMbnv5euDRArvPyzdI1cxzQeTrsTH7/wuU+s8Oez176RBpplRWXyuhgsVni3GXhkX+n1ROW96TxjGCYxoKi9XRv2hAjYDQmlRw8dOQhfz/w+bDWsWDP777/QJ7MVOlkTWwSn+YFw/g1RdA9ExYeA/hAgqXqxwsu5vbLQIcWIGxavR1FQ5cPmhqIkAZZrAOcqNQqlGcKCRQKwY/0uOSkKB5W1K84trfU+v5rxne/vdj3aYMvqHRHz4/5a8q8sq+RPUV4Jlr7/W8T9/7rwz1q3iWEYhti7OUeKE+GgUo/BFYWI8uJy5IYRUGPFX0v+ibhu2aKVKM6r/ThcEyjt79OXv4y4ntZR6gHTOJBg9dWM7yOup2gLOjeWvvdrxG3++n41+h6mlt37+ePlcWknwzC1Z+s/2+HxCF/Fn8biyPMOkymJ7z25IK7vs2Z/Dn7fswsndVfHo0RFOP+DKHlcVsCAopUlPBXTcVDo7xqi12owaWhH5FU4cd3X62B3NXM/C10vwDwKKHsDwlZ1D9hcYMEiAXDYwosVXupS0ox8IbxQvXjK0476HsHGNELN342EvSL8zQbDMEx1OGs7HlVS3TgWr3FYPumJU2lJ4RFRj4vWRfo8mIYh2vdDT2jp/HA5I18vyavCW/6WUkAZhkkM1v/+H/QmPdp0a9yUZPKxoUjo+U8uCFt5Kla8vuIPtE6y4JB2annmRIN8KjzFT0OUPAh4cgHTBYB5DBRtYJXBmpJtMeLWQzphZU4xrv56LSqijNPNAuPpgH4YRNFkCOdGNCdYsEgAug7s7KsGEkx25yzpPl5byLPCy+5N+9DjoK4Rt6Wypf5O6AT9f/CJVQ7+wZARHcMwTF3o0LtdQGUNf8ivQRsmwkJv0NXKG6e2HHRCZMM2qlwUzUizPphTTThhzFFRx3LKb2Yah+R0C44899CI608ccxTMKWYccsqQiNsMObY/Nv65Wf494uzI+2IYpmFZ++tGdOrTXqZlNDZUNY9KaN9/9pN48LxpsvoepYls+Wd7TCqIbMrLwxebNuCMXr2hjXDP0VgI9z54Sl6GKL4XcO8ATGcDSROh6HuispBhnemdacHkQzvjj73FuPiz1dhX1nwfuCqKBrBcJb0+RMEV8nNtLiTWGUspEJs3Y/z48RgyZAhGjBiBp556Cg5H9U8kqDO/+eabOPbYYzFo0CBcdNFFWLVqFZoC6dmpuPiuc0OWk4hx8xtXywl8bek6oBMGHtVX/u1yuvDvL+txwqWhk2IKvb7+xQnSqNMfmpxf8+y4gDJrXkacc2ij1KtmGKZ5kNYqBZcE+VR4/Ryuf3E8Frz0Rcg6KvWclm2NW5tadciUE8ZwQskNL02QlUTiAV27jjxvuKxEEgwZEY849zBf6Wnm/+2dB3wU1fbHzyaUQBJKEIIgUg0daVIVKRYQfDaiPHoRgiAg4h/kSRUFRPSpgBQBBUSKICIqPBBEEJCHIqAIKEWqQOgthJDM//O7OHmzk02ySbbM7vy+n89+kp2dnbn37px7zz33nHPF5yCpaqeRsWmM+qBms6pyZ+U7pHCxgtJ9bHuVXNMMYtPjj59Tu8jUa1VLov/evpYQ4l/gubb7+31qm2ErAKNJ55FPyxMDHpFjf5yQBeOXycTu70tczZdkYJPhcnz/Xzm6/hubN0jR/OHStEz6C5i+Rku5KClXPxLt4hCRm7/d8hAI7yWO3FU9Ou5VKxohwxqVlRNXEuXRJTtkw1Hv5cPyNw5HPpGIFzH7E+1cF9GS0+ZWCkQslUb04sWL0qVLFylTpoxMmjRJTp06JePHj1db7IwYMSLD737wwQfy3nvvyUsvvSQVK1aU+fPnS/fu3WX58uVSqlQpsTL5I/PLky+0lkoN7pL5Y5ZI/NGzEnNPedVxYSUyO0TdXlheWfCCbPr8v/L5pJWybv5GGTgjTuo9UltlKz9/6qKy5LZ/5SkpUcG1qxWszlO3vymLJnyuMvpjlS920KMqUz/c1wghJDtga87WPR+Qu2qXk4XjP5fTh+Ol3N2lpePwtmqSXvSOImprSGw1d3v5aOk0IlbuqlNO7SLiLQoWiZReEzqrSeWiN5ar3AV3N61yq4/0ckI2GIwnrhulVtNWf7ReHXuoa1N5uGvTNDufEN+DPFDv//iGSpq55YttastvTCoaPnpP6u4tGC8nbR0rn775hfy0ZpfyjGz1bAu1s8yiN5bJgKk95Z6WtaSYF72ECCHuc+TXE8qQGFO3vGWaDZ6H8GDWvZiR3w7eWSumr5F+Df4lb64dmS0Dy/o/D8m6QwelX70GkivE/94kWkqCSOJK0a5jccIhkvd+Fcrgzd0tyhbKJ2PuKy/Tfj4m3b7eLR2qFJf/q19GIvNYairsERwhUaJFDBG5PFa0cx1EomaLI9SaYUDuYqlfaeHChXL16lWZPHmyFCp0SwlITk6W0aNHS1xcnERHu55YJyYmyvTp05WBomvXruoY9oJt2bKlzJo1S0aNGiVWB6t3DVrXkSoNYtR+7FCI8MoJ8Mx49LmH5b62DSUlOVkioyLV1mnwvEAMeUSh/FIgKjLDLd2QiKjf5B5y5fxV9b5QMRoqCCE557aSRdTrrtpl5dqVBIksFC5RxW95k2GSN2zxi2qnJHh5RRb2TUhE4WjEEd8rtVrUkOSkZAkvlN+rRhIjMEzA6wSGHFCwWAFLuCmTW96OMFr1fquzdBj2pHpfONo5Uz12ASlTpZT0ebebMrZhvCxQJEItDoxaNliFdxJCrMOv6/ZJZFS4MjZaFfQrVRtXkrLV75QZQz6Wlx9+TSb9MNZp++PMuHA9QYau/Y/UKFZcGpT07wKupt0QSVwnWsJyES1BJHcdkTyNxBGSs/mOuxTMm0v+r35p+ebPc7JwzylZe/icjGhUTh4qWyToPBkdocVFi3xF5MoE0c7GihSaIo48tSRQsVRIyIYNG6Rhw4apxgrQqlUrlXRy06ZN6X5v+/btcuXKFXWuTp48eeTBBx9U1wwkYLi4rURUjo0VOhBAuKti33cYKwBWL0uUi87QWGEkb768yvhBYwUhxNOER+WXM1dPS1gBZ3f68AL5VV/oK2OFkUJFC0iREoV9ZqzQgYEC3nF40VhhPfKE5VFjqdlYYX5uYdyAgSJfRL7U/wkh1gGeCz+v3C3V7qucbj4lK5G/QH7pMba9hOYKkWFtxqmtlN0hKTlZ+q/6Sq4lJUnP2nX9NimHR4V2/SvRLrwo2rVPRELLiYTHiSOshc+MFTohDocyULzRtILcHp5X+qzZK52/+lX2ng2+3bgcSFYaOUIEHhfn2ot2ZapoWsYbPVgVS0npwYMHpVy5ck7HChQoIEWLFlWfZfQ9YP5u+fLl5cSJEyqkhBBCCCGEEGJv1n78vVy9cE3qPRI4K84w3nd/vb2cPnpWJeXMbLe+hKQk6bfyS/nh2FHpX6+hFMmfNg+PN1G7a2HXj6tzRLvYX7Rri0VCS4vkjxNHvtbiCPGvx3bR/HlkUL07ZdA9d8qhCwnSZsnPMuCbvbIvyAwXDrRzxFCRsNaiXXlXtDP/EO36WtG0wNp9zFIhIZcuXVIGCjMFCxZU+S0y+h48KvLmdV4Nw7UgMPhuWFjaZFiZge9eu3ZNckJCQoLTX8K2tAKB9Fzm9+Ig6wkZt+vv4insWGe71jujOntLzq0m41nBTs+Inepq1/paZSw/dThe5o5cLBXqlZEC0REBtahZMDpSOgx/UuYMXyyDHxojQ+b2dZmMettfJ+S1TRvk2OVL0qdWXSlfoKDb9bxxI9Hpb9a4ISHJB8WR/JuEJG8Xh3ZSxBEhKSE1JCW0pogjUgQ7iyZbZ3vn6lFhMqZRafn++EX58tA5+fLAGbknOkKeuOs2aVaqkAojAYmJN5z+Bhwhj4oj7G4JvfGphFx4TlJCykhy7ickJXcL0ULuyNGlfdGPWcpgYSWSkpJUB7hnzx6PXO/PP//0yHUI29KTBMJzGRERoRLpWl3G7fa7eBo71tmu9XZVZ2/IuZVlPCvY6RmxU13tVt8zZ874dSxPSdFk56rdsnLyehVa0TC2tpw8GXjbPjoKiLTsd798M+N7iav5f9Ioto7c2aC0nIvKJYe1RPnp/Fk5fO2qlMyXX7qXLS9RiYly5MjhLN/HuW2SxSE3JcSRJA7HDQlxXJfQkGuSK+Sq5A65KLlDz0ne0NOSJ1e8iCNFklPCJOFmSbmW1FwSk0r87dCPSa11DXSV84jExITLLxeSZOvZBPnX93+qUt8VmUsqFcwtpcNzSfGwUIm6ekwiczkkX6hD8oQ6JDTgcl/ESljuI1IobJuE550kuRP/LYk3b5NrNypIws1ScuNmtCQlF5abKZGSnJJfUrQwSdaw6J95Tq19+/Z5RcYtZ7CAR8Tly5fTHIeHBLwsMvoetj5F8k2jlwU8LxCvldF300OP88qd+1beB0JIcEEZJyS4oYwTEvy4K+dHd5+QRSO/TH2/aPgKCWRuJNzKRbBu9mYRvDDvuec2ie9QQf0fn3hdZh88kO3rhzjgYaG5cWb+v1/prdKflcAktyBoYt/lm+qVEWtaLZXSEWnnr1YnxHGrXnlznVGvwvKDy/OSU/LJ3rPzbu3o4icsZbBADgpzrgoYMOLj49PkpzB/Dxw6dEgqVaqUehzXKlGiRLbCQWrVCpy4NkJI1qGMExLcUMYJCX7clfPq1avLI+0e9np5iB0ZJMFMCOSnhP/LYBmaNGkimzdvVp4ROqtWrVJbiDVu3Djd79WuXVu5k65cudLJRWz16tXqmoQQQgghhBBCCAksLOVh0a5dO5k3b5707dtX4uLi5NSpUzJhwgR1PDo6OvW8Ll26qN0/1qxZo94jDATnT5o0SaKioiQmJkYWLFggFy5ckB49evixRoQQQgghhBBCCAl4gwVyTcyZM0fGjBmjjBbh4eHStm1bGThwoNN5KSkpkpyMNLP/o2fPnirpzuzZs+XcuXNSuXJlmTVrlpQqVcrHtSCEEEIIIYQQQkhOcWiY5RNCCCGEEEIIIYRYCEvlsCCEEEIIIYQQQggBNFgQQgghhBBCCCHEctBgQQghhBBCCCGEEMtBgwUhhBBCCCGEEEIsBw0WhBBCCCGEEEIIsRw0WBBCCCGEEEIIIcRy0GBBCCGEEEIIIYQQy0GDhQ+4evWqNGnSRCpWrCi//PKLL24ZdCxbtkwef/xxqV69utSvX1+effZZuX79ur+LFXCsXbtWYmNjpVatWnLvvffKgAED5OjRo/4ulq1ZuXKlPPfcc6qPqFmzpjz22GOyZMkS0TRNgpnvvvtOOnbsKA0aNJBq1apJixYtZNy4cXL58mWxC3YZGz777DNVR/Nr4sSJ/i6a5bBrf2AnebCTPmM1nePAgQPSrVs3JVuNGzeWCRMmyI0bN8TuHD58WEaMGKH6mypVqkibNm38XSRLYOf+2Go6XC6vXJU48f7770tycjJbJZtMnTpVPvjgA+ndu7fqMM6fPy9btmxhm2aRrVu3yvPPP68UpYEDB8qFCxfk3Xffle7du8uKFSskLCyMz6gf+Oijj6RkyZLy8ssvS+HChWXz5s0yfPhwOXnypPq9ghU8fzVq1JBOnTpJoUKF5I8//pBJkyapv7NnzxY7YLexYebMmRIZGZn6Pjo62q/lsSJ27Q/sIg920mespnNcvHhRunTpImXKlFFjzalTp2T8+PHKWITJup3BuIsJ6N133y0pKSmckP+Nnftjy+lwGvEq+/fv12rWrKktWLBAi4mJ0Xbt2sUWzwIHDhzQqlSpoq1fv57tlkOGDx+uNW/eXEtJSUk9tmXLFvVcbtu2je3rJ86ePZvm2LBhw7TatWtrycnJmp1YtGiReh5PnjypBTt2GhuWLl2q6ujqWSfO2LU/sIM82E2fsZrOMW3aNPWMnT9/PvXYwoULtcqVK9tizMkIY98yZMgQrXXr1n4tj1Wwa39sRR2OISFe5rXXXpN27dpJ2bJlvX2roHUlvuOOO+T+++/3d1ECnps3b0p4eLg4HI7UY/pqJ93b/EdUVFSaY5UrV5YrV67ItWvXxE7ASg+SkpIk2OHYQFxh1/7ADvJgN33GajrHhg0bpGHDhqnjDGjVqpXyKNi0aZPYmZAQTgddYdf+2Io6HJ9QL7Jq1Sr5/fffpW/fvt68TVCzc+dOiYmJUa6iGGgQJwWlBsdJ1njyySdV/Ob8+fNVjBniSN9++20Vr1i7dm02p4X46aeflLt8RESEBDtwhU5MTJTdu3fLlClTpHnz5kqpD2bsOjYgLhrKHmJdp0+fHpRu8N4g2PsDu8iD3fQZq+kcBw8elHLlyjkdK1CggBQtWlR9Rog7BHt/bFUdjjksvERCQoKKjUPcHh/q7BMfHy+//vqrUmZGjhwp+fLlk2nTpqkYyNWrV0uRIkU8+KsFN3Xr1pXJkyfLoEGD5NVXX1XHMHlAXHloaKi/i0f+5scff5Svv/5ahgwZYos2adasmYolBvfdd5+89dZbEszYcWzAhKBfv34qPhqrrevWrZN33nlH/e52jx23e39gJ3mwmz5jNZ3j0qVLykBhpmDBgiq/BSF274+trMPRYOHFxEoYfJ566ilv3cIWwG0QbldI1FSpUiV1DEovLHgff/yxyjhN3GP79u0yePBgefrpp6Vp06YqYQ5Wenr16iWffPIJk25aACRyguKOzPGdO3cWOzBjxgw1adm/f7/qN5GM7sMPPwxaI5odxwYoMXjpYLeAvHnzypw5c9TvXaxYMb+Wz6rYoT+wkzzYTZ+hzkGCCTv0x1bW4Wiw8ALHjx9XGVLhGqNv76LHOuEvtu5CXB/JHFjDEROlD+4A7+FSCOEgWYsRxvZDyHasgyzlMF4sX75cnnnmGTanH8HqT8+ePdXzjUzLdokp1WUb295hmz9sG7ZmzRpp2bKlBBscG8Qpdhzj5J49e2iwsGl/YDd5sJs+YzWdA+3vastFeFfAy4IQO/fHVtfhaLDwAseOHVMJR7BybQZWOVjUFy9e7I1bBx0VKlSQI0eOuPwMMVPEfRBLithxI8WLF1dbNaXXxsQ3YFu1uLg4pUwtWrTIaetHO1GxYkXJnTt30D6PHBuIO9ilP7CbPNhNn7GazoH8FeZcFZAxhOqYc1sQYrf+2Oo6HA0WXgAxenPnznU6hlWkcePGyejRo5UFirgfG4XM2mg/tCvAvuVI7tK1a1c2YxYoUaKE/Pbbb2lWuNCe2Gea+C+T+gsvvKAUKSQnQzInu4Lkc5jABGvSTY4N/wNxwHAZxeoysWd/YDd5sJs+YzWdo0mTJipniDGXBRK+YrW8cePGPi8PsT526o+trsPRYOEF0BEixskVVatWVS/iHg888IBSWvr3769ixxD3jHipPHnySPv27dmMWQDZyMeOHavcNBEzixwWevww3LOJf4Bi/u233yq3WWyVtWPHjtTPMJnDsx6MPP/88ypLPizyYWFhsnfvXpk1a5Z6D7kPRuw6NvTo0UPVG78tWLt2rVo5xyo6EnISe/YHdpMHu+kzVtM5UJ558+ap3WiwYo5EgRMmTFDH7T4RRQ6C7777LtWohL4HxhxQr149l9t72gE79cdW1+Ecmj82Q7YhW7duVcrZkiVLgm7VwNucO3dOrbig04DlDpmnhw4dqtwriftA1BcuXCgLFixQ24shNhjxpFCcypcvz6b0E1DkoCC4AhO7YPU2gKKOVXa4DuLZxIrbgw8+qCa3wb5bgN3GBkxYNm7cqJKWpaSkSJkyZSQ2NlY6deqkdg0h/8Ou/YFd5MFO+owVdQ6EqYwZM0Z+/vlnVR7E26M8dp546uFZ5vAdHXhBpWdYDHbs3h9bSYejwYIQQgghhBBCCCGWg2lOCSGEEEIIIYQQYjlosCCEEEIIIYQQQojloMGCEEIIIYQQQgghloMGC0IIIYQQQgghhFgOGiwIIYQQQgghhBBiOWiwIIQQQgghhBBCiOWgwYIQQgghhBBCCCGWgwYLQgghhBBCCCGEWA4aLIhX6dSpk3plRvPmzeXll1+2/K+BMtaqVStH10hJSZE2bdrI1KlTxZecP39eatasKd99951P70vshbsynx22bt0qFStWVH89WQ6cB5nMCVevXpWGDRvKF198Ib5k//79UqVKFfn99999el9iHyBzkyZNyvCcY8eOqfM+++wzsTrQN+Li4nJ0jb/++kuqV68uP/30k/iSDRs2KB3k3LlzPr0vsRfuyHx2wXVxfU+XA+e++uqrOSrbrl27pFq1anL8+HHxJQsWLJCmTZvKjRs3fHrfQIIGC0JMJCQkqA7SnUlRdvjyyy+VstOxY0eftn3hwoWlbdu28u677/r0voRYgVOnTim53rNnj1euP3fuXAkPD5fWrVuLL6lQoYLcf//98t577/n0viQwWLFihXz00UdiN2DIg7zDkOINpkyZInfffbfUqVNHfEmTJk3kzjvvlOnTp/v0viRwCGaZ3759u5LrS5cueeX6//73v9UYXrJkSfElTz75pCQlJcnChQt9et9AggYLQlwYLCZPniz//e9/vdI2s2bNUh1iZGSkz9v+n//8p+zevVu2bNni83sTklPuuecetQKCv1nl9OnTSq69YbCAogGDRWxsrISGhoqvadeunaxZs0aOHDni83sTawMDOZ5NOxosIO/eWCmFd8Pnn3+u5M4fPPPMM7Jo0SK5cuWKX+5PrI3VZf65555T43h2+Pnnn5Vce8NgAd1g8+bNfpHrvHnzyuOPP64MTZqm+fz+gQANFoT4kN9++0327t0rrVq18ku7ly9fXmJiYmTZsmV+uT8hOSEkJEQN7PhrJdavX68mMf6S60aNGknBggUp14T4AIR9wTDZrFkzv7T3ww8/rFzHV61a5Zf7E5ITcuXKpcZxq7F06VIpUaKECp32B9AfYGD94Ycf/HJ/q2MtrY9kGVjYX3/9dRWTibgrxFB369ZNraIb2blzp/To0UO5L8KNEeEI5thLPa7swIEDMmDAAKldu7bUr19fXnvtNUlMTEwj2J07d1b3w30feeQR+eSTTzz6C8KCirrB3Rn3ePDBB2XGjBkqB4Q5bhZeC1hxeOCBB9S5Tz31lEsL7sqVK1VZEXuKmHWsSiIvBdpPvx7qBGDFxbVdxdDBvbxPnz4qlrRBgwbyxhtvSHJycqZ1+uabbyR37txSt27dNJ/hmv/617/k3nvvVXVAmUaOHJka04bYYJTlxx9/VL8J7ovrjBgxQp2D9ho8eLBafcZrwoQJLi21mNx8++23tOIGKMEg888//7w88cQTTsd69+6tyrJ27VqnOuCYnnclvRwWuuzXqFFDhT1BRozgfBwHQ4cOTZVrc7w9VmWRzwLtdd9998kHH3zgVn0g13Ahhau2Gb1tIa8oHyYbcDs1/waHDh2Sl156Sf1eOPedd95RMorwMaxI4bdp3LixzJ49O8090KfUq1fPqe1I4JEVeQTLly9XrsR4rvD7Dxw4UD0vOniWYUyDEqw/8/pYhzED4YH4Pp45KOnt27f3uLKMuvTv31+VD+Mu7md+TvWxDf3TuHHj1POP8vTt2zdNrgaM/2gnjJOQU9QRcmvMg4Xrof0A+iy97uZ+A/0E+gWUq0WLFsprwl15R5sjBMwM+qyePXuqMRh1ePTRR2XOnDlp8mCdOHFC5dHA/+hr5s+frz7ft2+fKjO+C4MI3PvNFClSJE1fSQKTQJV5jE0oJ+TVKJvQSStXruzkAQG9HXmWkOfJWGcjKNvYsWOV7EMmoA+cPHkyTVtBrwWQV71+5rAvyCf0e+gp8GZG3hd3gDzh/g6HI81n0EGgQ6Fs+I0wxzDKpp4HCwuSOA99E+YsulERHtvwwNR1AHhymEF5CxUqRLlOh1zpfUACA0xo//Of/ygBwer5hQsX1KCPzq9q1arqHLj/YwCFMGCiAGHEgN6lSxc14YAAGXnhhReU8j1o0CDZsWOHzJs3T3U+ekehJ4i56667VEcIaykmwKNHj1adWIcOHTwSloE6YRIP96zbb79duYK9/fbbEh8fL6+88koaFzh0hnCVRP1mzpwp/fr1SzUQAHTi6NzhYYC6Xbx4UV0nOjo69TpRUVEyatQo9UJngxcwdq4wTGAiiHaDgQDti0lEqVKlVOefEagD7q+XSQf1hOJ0+fJlefrpp6VcuXLqGH7b69evS548eVLPxUB22223qfpBOcJkDeEluDbaCXVEBw0jDu4FNzMjeC7gdvbHH3+oz0lgEQwyD6UGygGMLxEREeoaiE2F5wQmEVBGAP7HsYzixD/99FNltIMigfodPXpUTfDhcQB5AGgnTJqQ5wF9hH49KB466A+effZZJfNY6UAbT5w4UckIjKYZAdnT294IlBe0DdoL90UbI2xj3bp1Sk6N4D3Kid8AyhGS8kJ5QUwrlCgYM6AgwTiKCZY5LAb3N7YpCVzckUc8H5h84FnF2IGJ/ccff6yeN0y8CxQooJR+jClQ/GGoA/okG88JZAdKNhRpjJ9LlixRMoDjmHTkFIwxCEPEGIv+KH/+/GrRAIYITD708dU4tqHc6LMw4cJEH0n0YLzTeeutt9T4jsk8JvqQMYzHxskdZAMTCLQb2gDjKYB86Rw+fFhNENF2MJ7CIAtjAuQI/VxG4V+//PKLqpeZTZs2KSNEsWLFlNEB4zT6Zege6JuMOgTaA/2gLteoZ758+ZQxE0aOhx56SMn+kCFD1MQS+oURlBP6DQkOAk3moVNg/Ny2bVvqMRjbcG+M2RjPkUQSQD/BtV0Z+HSgi8NzCWXDdWFE6dWrl9M56C/+/PNPpe+jbsjLpuvtOrjX6tWrlS6O+6EdMfZDX9HPdwX0bRgRYVgxA90Ji4noFyDf0LcRPrJx40Ylq0YdAu2PxZyWLVsqnenFF19UhhwYYzCXQf2gm6NM6BfMYzXuj7YjLtBIQFOnTh1t9OjR6X6ekpKiPfTQQ1r37t3V/zoJCQla8+bNtW7duqUee++997SYmBitd+/eTtcYNWqUOr5nzx6n75vBPVq0aOF0rGPHjuqVGc2aNdOGDBmS+n7KlClazZo1tUOHDjmdN3HiRK1y5craiRMn1PujR4+qstWrV0+7cOFC6nnffPONOr5u3brUY23atNGaNGmiXblyJfXY1q1b1Xm4v87Zs2fVMbSHGZQRn02ePNnp+OOPP6498cQTmdYT9+/Xr1+a44MHD9YqVaqk7dq1K81n+u+2dOlSdW/zb/nMM89oFStW1EaMGJF67ObNm+pertp++/bt6jpfffVVpuUl1iMYZB7POa6/fv169X7v3r3qff/+/bXY2NjU81AuyJbODz/8oM7DX3Djxg2tYcOG2mOPPaYlJiamnrdo0SJ1nrEc+j0hR2ZwHj5btmxZ6jFcr3Hjxi7l1UhSUpKSv/Hjx6f5rEOHDlqtWrW048ePOx03/i76bzB8+PA08ovrTp8+PfX4xYsXtRo1ajj1lTorVqxQ19m5c2eG5SXWxV15PHbsmBoHp06d6nTevn37tCpVqjgd79Wrl9P4ZnzGjDKjP1+NGjXShg4d6nQ8vfHQiD4WG+WrS5cuatw13gfPPsYs9FE6+tjWtWtXJ9kYO3asquelS5fU+/j4eFW/Pn36ON170qRJ6vtGuVi5cqVTX2EE7YHPtm3b5jTuV6tWzaUcGzl8+LD67rx589K0J/pXXBvtaMRYJ12HmDZtWhq5hrwbx+UDBw6k2/b4Pj47c+ZMhuUl1iaQZX7mzJmqTJcvX1bv586dq+7btm1b7c0331THkpOTtbp16ypZNtdZB3XEe9TZyIsvvpimHLgnjqG/MYPjVatWVTJqvrZZXs1s3rw5zZwBoO/BGA695Pr16+nKta5DYBw2yy90+x07dqQe37hxY7q6CPQA9AUkLQwJCXBgUcUqO6yDroAVEBZJWAGxrSUssnhdu3ZNuXbDOmoMsQDm1VJ9NwujW1VYWFjq/7Co4ppwT8PqJt7nFLhRYRUU9dPLjBfCGbA6YbTqAlg0saKqo4dcoDwA7YNt/+BtYLTyoszZ8TIwr66grO5kI8dqOOpkBO2PlRKsGGHl1IzZPQ2WdeMxrJZjhVp3eQeIr8Xqul5/I/r98TyQwCMYZB6rCFht1UM38Ld48eJKPpHnBR5WutdFRt4Vv/76q5w9e1atXBi9kLBimtWktijPY489lvoe14M8upIhI1hVQVnNco32QVvDdRRxsUZcuZy6kl+zXOMeZcuWpVwHOZnJI0IZIcNYaTWOj1jRL126tFs7XOEZ02UG18LYdPPmTfXcQQZzCq6HVVKUESu7ehnRJyGcA32UuQ+Dd6FRNjCOY7zXE2fCcwxlNHsyZmfHLeyuYwzNxCpterJlrhcwyzvaDDoAPCvMn7mSd6xwm+UaHhbGPDjwDMFnHMeDn0CUeV0+4WGoj+MYr3FcH9uhd8NTxFUYtI4e8mnehtzoleQumCMYQzMrVaqkvBgyk2tdHzbLLrym4IkCbw9z3g2zXEOHMO4SpssvPLsQIqKj/5+eXMOrGjoQcYYhIQEO3AnhxgjXK7gIwnUZSr/uPgilAMCtMD0w2TBO9tH5GYHww8XLOCGH2xVcOuG6ZhYsXC+nO2DAXRPuZXo+CTPmuFbd9VtHr48eRwdXL70uZlDfrHTW6LSMLmj6/TBxcQdzXgnUBQpdRm6oRsyTH72tzW2A4xmVyZUSRaxPMMg8FCeEcOhKDa4NhQbKDhQg3AOKGBSqjBQdXa7N5UfIldmFOjNgMDHLBNoI/VB25FpXRtw1iLqSa1d9DY7rk6aM7k8Cl8zkETKO3xthA65ACJI7IPkywhmRPwWhDjp33HGH5BSEPqGMcGFPbyttGBuNIZlmGdAnD5mN4widMvZn7mAeL3M6jmdF3tOTa1d9EI672hFBvz/H8eAgEGUeCw8wsmEcR3gWxnGEKmPsRigGwrT0vFkZLTzAIIm6muVaD+XyhFy7u6uIWa71nbfc0c/Tk18cNx8DlOusQYNFgAPPAij0sL7CEojYKCSKw8QCExld+JBrIb34NFgFM8IsgBDgrl27qs4EEyd0EJggwEqK3Ajm1dvsgGsgwRxi61xRpkwZp/fpbSXoDSU+J9sWQrHK6XZM6e2Q4O7OCbpCllE8H7EuwSLziFOdNm2aUmqg8CD2ExMUKAZQcpBYDmRksLCCXEMZQnt5Q66z0q/p96dcBx9meYS84Rjk3tUzkpl868n7IMtIVoscEJA3XGv69OmZrka6g94ndO/eXU1mXGGeoKQ3hllpHMcYDnIi7+ndm/JOAknmoQPAwxfjNxYZkV8O4zWuC88NeILiM+gNZgOdt8juXEAfN60g1zACGT1ayS1osAgCkOAJ7mR4YcUC7tCYCGDyoq8ywiUKrlLugI7HuDqJ9+gsdQssEsYhoy8SABlXRNxxSXMXKDJwYXe3zJmhl1O3lhpB/Yx4c8UCHbc5dAQdOX4fJCjzBfr9jQnISGARDDIPxQYrPEigBddw3TCBhHlQcqD0wDCJ1Zr00MuC8hq9sXBdPOdwB/W2XGNlC/2VWa719oRLrC/A/THhg2s5CWwyk0c8b1B28T6z3zu95x5JZXEP7IZlPAeJaT2BXn5MarwxjhvbB+7cZs8Ib8k7jLWYTGQk756qb0bg/phk+WoiSLxLoMo8xm0YUbDrBZ5H6Li4NhYeMI7jldn2v0g2irpCro1eFQcPHnS7bjlFv69ZrnWjKvRzsxeMN8D9s+NZYgeYwyKAgeu0OXYcSj4mM/pWmIhNg8DBBUzfUiij0Aqgb6+lgyzEoEmTJk7WQqN1EOVAlm1PgTg9xMUhC68rCySst1kBbqdw1UQmZWM7YKsh84QC1k39Pp4G2b7R8em/D8AkAxZvZDFG9nFvry5h+0u4pLkbgkKsQzDJPOI4MZmBsoNVS/15hOsoVmaQ/yEz7wrUFQo7sukbZQpur2b59bZcI5+GEZQLxhe0ke7K7s0VY8g14vJzGo5H/E9m8gi3cMgkJh7mZwnvjfmJ8Ny7yjHjSqYhdwjH8gTol5DjBrtYnT592q1+KDNglISBENn3M2ovo7x7IqeWEfRZ6HfM8o7wPEwm586dm6aP8Za8o98hwUGgyjzGaIy92NEHY7duUMD/8OiA7GcUDmKsI8JIjBi3A/a2XGOOAGOkWa6Rbwd57+CFYt5m1htyjfB04+5l5H/QwyKAwWQEK6rY0xcriXAJg5UTk159P3JMhrFVGLbQwnY62H8ZgokVTayOYhUWK7NmCx/cs+HGiY5M32pIX61EqAYGbZyDZHcoB7ZEgoIClzBPAHc1rOriHlg9hjKAuHkYF2AlxvZ9WV1ZwLaBffr0UQkz0Q5QKjBIwJBhnNhh9QSKP7ZfwwqvPpnyxBag2K7x/fffV4YSdIQ62PoI7v1IOoTEY/B+QFsi+Si2oTQnAsoJeEZg8Wbsa+ARTDIPxQNyjfsZn0dM8uFdhVdmBguUCdvBYVtTJOhCuAzqgm3IzDksYMSBHMG4AQUEbQd31qzmukhPrqGcIS7YuPo1bNgw1d+gD8O2ppjQIF4X25nhfE8BjxIYeFxttUgCj8zkEc8ynnts8YnnCQZvPNP4HhI4YwzBGAogY19//bWMGzdOJZHFc4+tiZEDB9v/YYtR/I/vQjYw9kH2PLUFMxJkIgEwygRZO3PmjKoTtl1EvbICvK2Q1BLGWL19kGMGiQmxumsc0xAOhwkaDKKY3CDZILYH1kPNcirv2H7UuIUw+l1sh44tlZFTCP1u0aJF1Srx/v37Veiep4BXHeqd2TbqJHAIVJmH0QxGRIx9GON0MI7rhsXMxnHIKuoKXReyivxWSNhr9n7W6wYgfxjvoQNAf3AnJMYduUaoLQwRel8C+cYWqhjLkQAb5YQege2UkRwT24x7ChhLkJ9K39adOEODRQCDiTUUVEx00QlByNCp6UqCTv369dUqBybKsNqiY8JACmXd2MHoYM9zJMlCx4iOCNmKEQ+vA3cluJDhPAgrlAiUAwYE7FXsCTCZgbUVVk1M2uEZgY4DBgQk9cnOKiI67LffflvF+qNuuBY6dFzbHI6BCd+YMWPU55gMYF94TxgssDJTsWJFZQwxGiwwoVy8eLFqd+zJDkUIx2B59mQsG/aEh9HHU78T8S3BJvNYeYFyZlyBQTnhegllxZ38FagPPE8wIcCe9ZBTfb96I1Bsxo8fr/oATCzgpQX59oTBAgoTJkyQaxhFdaBs6nIN5Q0rNHBrN+4E4AmwewIUHRhGSOCTmTwCZK3HGIYcMlOmTFHHkNwNxkWMdTroF7BzEIx4OBfu1/gcE2oYD9BPfP/992rS8uabb6rxFgZ1T4BrwsMIq8LwesIzij4Dyfowacpu0mH0gzCY4rnHhAmyj3oadwpCPzJ69GilQ7zyyiuqj4D3gycMFthNCL8NFk6MOwthsolVYfweMKqgf0b/gsmkJ0Hfj7p6uh8h/iNQZR6GAhgcsGhiHMf1sRteC7h/ZowdO1aNodB/IVfQYWbMmKEWaIxAhxkwYIAytMADG6EkON8TBgvs6AV9SU8CbtzRB/0GygOdCr8PdCLk9fIk+B2gH8CwStLiwN6mLo4TG4KJPBQLKAF2iouEwoH6fvjhhz65Hwwkr776qlpl9aTnhDu8/vrrKqYQAxk9LIhdZd4bQIGEXGEykZPEvNkBRhLIs67EksCE8pg94C2JFV2sQMPDwRfAUIudG7Aq7GvgwYFwGy48BD6UeWsBT02E2MKQ40sQVgOjEjxjs7Odqx1gDgtiG+ApYc59ARd5uHZh8PcV//jHP5QV1VXcrTdBjOOSJUuUUkdjBSGeBast8GT56quvfNq08JqC8ROrToQEO3DDNqPHuvtyHIfXJVaV9W0bfQXCX+B9FhcX59P7EmIHEJ4NT0mE3fgSeKLBc4NhnenDkBBiGxDD361bN2UwgAUVsaVwK4PrKOLyfQViXbEzgq+Bux0SmRJCPA/iieGp4muQ7waJugixA4jNR3gJwiXhBr59+3Y1niLEMrPkfp4Eiw6ukmR7G9Sb4zgh3gHJwM2JN30BDBU0VmQMDRbENhQsWFAl7EHsKzKUQ9lBfBxiYvU9mAkhhBBiTZADCiFXM2fOVMl/EVuORJzwHCSEEBKcMIcFIYQQQgghhBBCLAdzWBBCCCGEEEIIIcRy0GBBCCGEEEIIIYQQy0GDBSGEEEIIIYQQQiwHDRaEEEIIIYQQQgixHDRYEEIIIYQQQgghxHLQYEEIIYQQQgghhBDLQYMFIYQQQgghhBBCLAcNFoQQQgghhBBCCLEcNFgQQgghhBBCCCFErMb/A9VTzLbcQKWQAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1074.47x1000 with 20 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import seaborn as sns\n",
     "import pandas as pd\n",
@@ -127,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "019c30d6",
    "metadata": {},
    "outputs": [],
@@ -154,10 +165,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "05a3c1bc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.cluster import KMeans\n",
     "\n",
@@ -186,10 +208,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "3b3f41ab",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Apply K-Means clustering\n",
     "kmeans = KMeans(n_clusters=3, random_state=42, n_init='auto')\n",
@@ -221,12 +254,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "81d0c71b",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Silhouette Score: 0.48\n",
+      "Adjusted Rand Index: 0.43\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.metrics import silhouette_score, adjusted_rand_score\n",
     "\n",
@@ -274,10 +316,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "e54079e4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.cluster import AgglomerativeClustering\n",
     "from scipy.cluster.hierarchy import dendrogram, linkage\n",
@@ -305,12 +358,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "e407a838",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Silhouette Score: 0.45\n",
+      "Adjusted Rand Index: 0.62\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Perform hierarchical clustering\n",
     "hc = AgglomerativeClustering(n_clusters=3, linkage='ward')\n",
@@ -363,12 +435,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "ec443e73",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.cluster import DBSCAN\n",
     "\n",
@@ -407,12 +490,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "97c36d16",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QMgzDMAxT7nBktAphNBrx1TdfQ1k7GKqnn8gVogSl5pV1QiB1fQa7d+3C0aNHy3VfGYZhGIZhCBajVYiDBw8i/OYtKFq3tBj1VNQOgcrVVaTyGYZhGKY8yMzMRFJSkgiiMAyL0SrE9evXKQQKyc/X4jokUo2+3rgaFlam+8YwDMNUb0h4rlmzBt2790BISAgaNGiAhg0a4osvvkBERER57x5TjrAYrUJYWVlRcSig0xW9olYH6zwpfIZhGIYpTQwGA17/3+t49dVXceXfMPgkBcM/sQ6UEXaY+dssPP3U0zh79iy/CdUUFqNViHbt2uVEPi9csriOnJUFXL+Bxx59tEz3jWEYhqm+/PLLLyIq6pdYBwGx9eGS6QXHLHd4p9ZCrcgm0CUaMGzoMGRkZJT3rjLlAIvRKkRAQACeePJJ4NARyGlphZZTR71hzz5Isiw8RxmGYRimtMnOzsaff/wJ53QvOGW5F1quktXwiauN2LhYrF27lt+QagiL0SrG1199BRdraxgXLYPh5GnI2dmQjUYYb4XDsGqteO6br7+Gp6dnee8qwzAMUw3Yv38/kpKT4JLhbXEdK4MNNFoXrFq5qkz3jakYsM9oFcPPzw8b16/HpA8/xPa/t0O/dXvushq1amHSH3+gW7du5bqPDMMwTPUhISFB/LTS2xS5nkpnjbjYuHzPUR3p+vXrkZycDFdXV/Tq1Qt16tQp1f1lyh4Wo1VUkM6dMwe3b9/GoUOHoNVqERQUhDZt2rDRPcMwDFOmODs7i586ZRasDXYW19OrtXB1cxX/jomJwcsvvYxDhw/BSmENtdEaOkU2fv75Zzze8XFMmz4NLi4uZXYMTOnCYrQK4+/vj379+pX3bjAMwzDVmPbt28NR44jEtGjRsGQOEpppVono1buXiIL27dMXd25EwD+xLjRZrpAgQYYRKTbx2P/PQQzoPwBr162FnZ1lcctUHrhmlGEYhmGYUsPW1hZjnh+DJE00Uq1zUvZ5MUh6RLhfhZOTM/r06YPZs2fj5o1b8I8OhWOWmxCihAQFnLI8EBBTDxcvXsTixYv5XasisBhlGIZhGKZUmTBhAp559hmEu15CuNtFJNvEIdU6ETEOt3Dd5zQkRwMWLloghOvcOfOgSXODtcHW7GvZ6O2hyXTF7FmzhUsMU/lhMcowDMMwTKmiUqnwxx9/4Pvv/w/+jb1wx+Uywl0vIMMzAUNHDca2v7ehWbNmolY0Lj4Wmuyi60EdMl1w89ZN9iWtInDNKMMwDMMwpY5SqcTgwYMxaNAgJCYmCv9RNze3nOmBTLWGxSjDMAzDMGUGTQokmyZzkAe2h7snUtMTock2vw6RZpuIGoE1uIGpisBpeoZhGIZhKkz0dOSoEUh1iEe2MtPsOlmqdKTaJoimKBK2TOWHxSjDMAzDMBWGMWPGoEbNQNz2uiCsnGTkNCmRtVOyTSzCPS8iNDQUQ4YMKe9dZUoITtMzDMMwDFNhcHJywuq/VgvT+4OHDgrTeyujNbSKbGiN2ejUsRN+mfYLp+irECxGGYZhGIapUHh4eGDlqpViHOiGDRuQlJTE40CrMCxGGYZhGIapkDRs2FA8mKoN14wyDMMwDMMw5QaLUYZhGIZhGKbcYDHKMAzDMAzDlBssRhmGYRiGYZhyg8UowzAMwzAMU26wGGUYhmEYhmHKDRajDMMwDMMwTLnBYpRhGIZhGIYpN1iMMgzDMAzDMOUGi1GGYRiGYRim3GAxyjAMwzAMw5QbLEYZhmEYhmGYcoPFKMMwDMMwDFNusBhlGIZhGIZhyg0WowzDMAzDMEy5wWKUYRiGYRiGKTdYjDIMwzAMwzDlBotRhmEYhmEYptxgMcowDMMwDMOUG6ry2zRjiYyMDPz11184dOgQdDodateujUGDBsHPz49PGsMwDMMwVQoWoxWMDRs2YOKbbyI9PR0qP19ApcSmbdsw5aefMGb0aHzyySdQKpXlvZsMwzAMwzAlAovRCsT27dvx0ssvQ1EnBFaPPwbJyUk8L2u1wIlTmD1nDvR6Pb766qvy3lWGYRiGYZgSgWtGKwiyLOOjTz6BokYglD265QpRQrKygqpNKyg6dcC8efMQFhZWrvvKMAzDMAxTUrAYrSAcOHAAt27cgKJda0iSZHYdZdPGUNrZYeHChWW+fwzDMAzDMKUBi9EKwoULF6BQqyH5W25SklQqyP5+OH/hQpnuG8MwDMMwTGnBYrSCQE1JsmykfH3RKxqN3MDEMAzDMEyVgcVoBaFFixaQ9QYYw65bXEfOzgZuhaNVy5Zlum8MwzAMwzClBYvRCkLjxo3RqEkT4OBhyDqd2XUMBw5DMhgwePDgMt8/hmEYhmGY0oDFaAXi26+/hjIxCcZlq2C8cUt02BPGhEToNm+D4d+j+OCDD+Dt7V3eu8owDMMwDFMisM9oBaJJkyZYtXIl3pgwAWHLVkJlbwdJpYYuORmOzs5476uvMHLkyPLeTYZhGIZhmBKDxWgFo1mzZtizaxcOHz6MgwcPCpP7kJAQdOnSBTY2NuW9ewzDMAzDMJVLjG7evBnr1q3DuXPnkJKSgho1amD48OHo27evRT/N6g6dl7Zt24oHwzAMwzBMVabUxejcuXPh5+eH9957Dy4uLsLc/aOPPkJUVBRee+210t48wzAMwzAMU53F6IwZM+Dq6pr7/3bt2iEpKQlz5szBK6+8AoWCe6gYhmEYhmGqK6WuBPMKUROhoaFIS0tDRkZGaW+eYRiGYRiGqcCUS1jy2LFj8PLygoODQ3lsnmEYhmEYhqmu3fRHjx7Fpk2b8O677z7U65AHp7nIamZmZr6fTOnC57vs4XPO57yqw59xPufVgerwOZdluVjN6pJsclYvA6hpqX///ggODsbs2bMfuF70zJkz0Gq1Jb5/DMMwDMOUHNnZ2di/fz8uX74Mo9GIoKAgdOjQAXZ2dnyaqwlWVlZo1KhRxRCjZOs0dOhQ8e/FixdDo9E88GuRGKXdJv/NgtAdxo0bN1CzZk3Y2to+1D4z94bPd9nD55zPeVWHP+NV45xv2LABH3/0MVLTUmEHDSRIyECq8Mx+9713czVBdaU6fM6vXr0qIqP3EqNlkqbPysrCiy++iNTUVCxbtuyhhKgJOrii7qzojeU7r7KDz3fZw+ecz3lVhz/jlfeck7/4hAkT4JTljuCUEFgbcsSWTpGNOIfbmDx5MtRqNUaNGoXqjm0V1ivF9ZMv9QYmmiA0fvx4XLt2DTNnzhSNSwzDMAzDVE2ojG7SB5PgmOUG38TauUKUUBut4ZMSDJd0b3z+2ecia8owpS5GP/30U+zatQsvvfSSsHM6efJk7oPrPhmGYRimarF161YkJCbAPTVApObN4Z7mLzTAqlWrynz/mIpHqafpqXCZ+Oabbwot27FjB/z9/Ut7F5gC9Rvz58/H4SNHRDF5owYNxHjWZs2a8XliGIZhHhrq67BV2MNGbzn1rDZawc6gEesyTKmL0Z07d/JZrgBQwxfdEEybNg0qe3sYa9UEFBIub90q6nh79+mDH3/4QXS9MQzDMAzDVFmfUaZ8mD59uhCiyo6PQtGyOZSqnLdeNhqhOncBa9auha2NDf7v//6P3yKGYRjmgWncuDEyjenIUqXDRm9vdh1qZEpXpIh1GYYHw1cD0tPT8dPUn6Fs0Qyqtq0h/SdECUmhgLJRAyg7dcCSJUtw69atct1XhmEYpnLz7LPPwt3NHXGOtyGjsHskPRfrcFtYPPXt27dc9pGpWLAYrQaQ11tWRiaUrVtYXEfRuCEUNjZYunRpme4bwzAMU7Ugy6avvv4KqTYJuONyGdnKu9MStcosRDqFIck+Gh9/8nGJWD0ylR9O05dio9DatWuRkJAAFxcXdO/eHXXr1kV5cP36daicnSA5OlpcR1KrIXl6CANehmEYhnkYunXrhhkzZuC9d99DmM1J2EkOorM+XU6FvZ09vpv8XbU3vWfuwmK0hElMTMTrb7yBnTt2QGlnC6WjI4ypqZgyZQoe69AB06dNg5ubG8oSa2tryFrtPWfESlotNzAxDMMwJQIFYZ555hls2rQJJ06cEN9BDRo0QI8ePaqsyTvzYLAYLUEyMjIwYOBAXKJI5HNdoKhbW9RnKgwGqC5dwcFde9C3f3+sX7u2TFMTjz32GL7//nuor9+AFFTL7DrG+AToIqPEugzDMAxTUsGQ3r17iwfDWIJrRkuQBQsW4MLFi1AM7Atlg9DcRiFJqYSyfj1IA/sh7No1zJkzB2VJixYtUK9+fcj/7IecnV1ouWwwwLhzD5xdXPDcc8+V6b4xDMMwDFO9YTFaQlD6Yc7cuSIaqvD0MH+y3d0g1auDufPnC8P5soJS89OmToVNRiaMC5fCcOacEKWyTgfD5aswLFkOKfw2fp0+XdzFMgzDMAzDlBUsRkuI1NRUhN+6BSk4qMj1pJAgREdGIj4+HmVJaGgo1q9bh3b160O/aSu0P02H9sdfoP9rHRp5eWPF8uXo2LFjme4TwzAMwzAM14yWNEU0CBVreSlSp04dLF2yVHTXUzE5RWdJpFJBOcMwDMMwTHnAYrSEoIYkv4AARIVdB0ItWzjJV6/B08sLrq6uKC9q1aolHgzDMAzDMOUNp+lLsC5z9MiRkC9dhjE2zmLHunzhEkaOGAGlUllSm2YYhmEYhqm0sBgtQYYPH46QkBDIy1fBcPGy6FI3zX83XL4Cedkq1AwMxOjRo0tyswzDMAzDMJUWTtOXIA4ODli1YgVeevll7F+7ASoHB0iOGsgpqdCnpaF127b447ff4OTkVJKbZRiGYRiGqbSwGC1hqBZ0+bJlOH/+fL5xoDRxomHDhiW9OYZhGIZhmEoNi9FSon79+uLBMAzDMAzDWIZrRhmGYRiGYZhyg8UowzAMwzAMU26wGGUYhmEYhmHKDRajDMMwDMMwTLnBYpRhGIZhGIYpN1iMMgzDMAzDMOUGi1GGYRiGYRim3GAxyjAMwzAMw5QbLEYZhmEYhmGYcoPFKMMwDMMwDFNusBhlGIZhGIZhyg2eTV/NMBgMCAsLQ3Z2Nnx9feHm5mZxXVmWceTIEaxfvx5JSUlwdXVF79690bRp0zLdZ4ZhGIZhqi4sRqsJWq0Wf/75J2bNmYPoyEjxnEKhwNPPPIPxb7yBxo0b51v/zp07eH7sWJw5dQoqZ2fAUQMkJWPmzJlo1aYN/vz9d3h4eJTT0TAMwzAMU1VgMVoNoCjosBEjcPDgQShC60HdoT1gYw05IhI7jh7Bzp49MXvWLDzxxBNi/YSEBPTp1w9RKSlQD+gDqWYNSJIE2WiEKuw6Tvy9A/0GDMDG9euFoGUYhmEYhnlQWElUA77//nscOnQIqgF9oOr2LBQ1A6Hw9oKyeVMoRg6FMTAAY8eNQ3x8vFifIqiR0dFQDOoLRa2aQogSkkIBZe1gSAP64tq1a1i4cGE5HxnDMAzDMJUdFqNVnMzMTMxbsABSi6ZQBAYUWi6pVFB2fQZanQ7Lli2DXq/HgkWLIDcIheTkZPY1Fe5ukOrWxpx5c0VdKcMwDMMwzIPCYrSKQ6n59NRUKBo1tLiOZGsLqXYw1m/YgJiYGCTGx0MRVLPI15WCauL2rXBkZGSUwl4zDMMwDFNdYDFaxUlNTRU/JQeHole0t0cKiVZTDajxHhHP/5abUvj3giKuRqOxWOsyDMMwDFN9YDFaxfH09BQ/5bi4IteTEhLg7e0t1vfy8YF85WqR68tXwxBcuzbs7OyKFMK///472rVvjxo1aiCwRg306t0ba9euFRZTDMMwDMMwLEarOK1bt4a3ry8Mx09aXMcYnwD9tRsY0K+fiIyOGjEC8sXLMMbGml8/IhLGK2EYM2qUxdeMiIhA5y5d8MVXX+GOvS1UnZ+G8snHcSIqEq+88grGvfgidDpdiRwjwzAMwzCVFxajVRylUonXX3sNxvMXoT9yrFDDkZySCnntBvj6+6NHjx7iuTFjxqBOSAiMy1bDcOYcZL0+Z12tDoaTp2Fc8ReaNmuGQYMGmd0mpeNHjByJ24kJUI4eDtVzXaBs0kh07ysH94eqT09s3bYNX3/9dRmcAYZhGIZhKjLsM1oNGDFiBG7fvo1ff/0VEonLenUh/eczarx0BZ4eHliyaBFsbW3F+g4ODli5YgXGTxiP7Zu2QrljN5QaB+hJuOp06Pbcc/jh++9hY2NjtoFp7969uHD+PNRDBkDh6lJoOdlDyW1aYe68eRg/fjwcHR3L5DwwDMMwDFPxYDFaDaAmo0mTJglT+zlz52Lnrp3QZmvh5++PEe+9JyKcLi75RSP9f97cecJPdMOGDWIcKI0OpehpQEBhi6i8rFq1CipPD0j+fhbXUTZrjOyDh7FlyxYMGDCgxI6VYRiGYZjKBYvRakS7du3E434ICgrC66+/fl+/ExsXB6OzE5RFdNpTd7/SxgZx92isYhiGYRimasM1o0yJ4+ToCEVaepHryFlZMGZnc4qeYRiGYao5LEaZEqdbt27QUz2qhW58wnD2PBSShGeeeYbfAYZhGIapxrAYZUqczp07CzspedM2yJmZhZYbo6KB/YfQs1evXB9UhmEYhmGqJ1wzypQ4arUa8+fORf+BA5A+ZwHkxg2hqBEI6PQwXroM+cIlNAgNxVdffslnn2EYhmGqORwZZUqFBg0aYOvmLRjcqzfUx09Bt3g5dCtWwy0mDm+OH4/Vq1ZBo9Hw2WcYhmGYag5HRplSgyygvvnmG3zyySe4c+cOVCqVeI6M+BmGYRiGYViMMmUCmemHhITw2WYYhmEYphCcpmcYhmEYhmHKDRajDMMwDMMwTLnBYpRhGIZhGIYpN1iMMgzDMAzDMOUGi1GGYRiGYRim3GAxyjAMwzAMw5QbLEYZhmEYhmGYcoNN7xmBLMs4deoUbty4AWtra7Rp0waurq58dhiGYRiGKVVYjFZyaLJRZGQk7O3tUbduXSgU9x/s3rZtG77+9ltcvngx9zm1lRV6dO8upie5ubmV8F4zDMMwDMPkwGK0EhEVFYXly5cjLCwM8fHxuH37Nq5cuZK73NffHy+MGYPnn39ejN4sDkuXLsWbb74JZc0aUPfvA8nPF9BqYTh/AWu3bMGRo0exYf16FqQMwzAMw5QKLEYrAQaDAV9//TX++OMPyEolJE8PGLOzIcfGAVZWUHZsD4WrG6LPnsfnX3yBQ4cP488//rinIKWI6jvvvgtlk0ZQPvsUJEnKWWBtBVWbVpDr1EbE4mUiOjpt2rSyOViGYRiGYaoV3MBUCZg8eTJ+++034JG2UL0yFqqhA2E1ZgTUL4yCItAfhh17qOoTquc6Q9mnB/7evh2///77PV938eLFkBUKKDt1uCtE8yC5OENu1RLr1q9HXFxcKR0dwzAMwzDVGRajFZyrV69i9uzZUHTqCNUjbSBZW+cuU7i5QtW7ByR/X+h37hFNSMrgICgahGLm7NnQ6/VFvvbOXbuA4Fr5XrMgygb1YNDrceDAgRI9LoZhGIZhGILT9BWcRYsWQWVvD0WzxmaXSwoFVG3bQLd8FeQ7EZD8/aBo1AAxi5bh7NmzaNq0qcXXzsjKAooQooL/lmdnZz/cgTAMw1RTEhIS8NdffyE8PBw2Njbo1KkTWrdubTYjxTDVERajFZzTZ87AGOAHZRH1n1LNQEChgBwTC/j7QXKwF8+npqYW+dpBNWvi2onjIqJq6aIoR0SKnzVq1Hio42AYhqmO9f5fffUVZs2cJf5tC3voJR1++eUX1K1TFzN+myFcUCoCFHCgfWSY8oDT9BUcYdVklIteSZZzHv8JSiM1NgHw9vYu8tf69+sHfVQ05FvhFl5WhvHIcdSoVQutWrV60ENgGIapdtD189133xX1+86J3giJbIEaEY0QdKcZAuPrI/xSBHr17CXcUcozYvvTTz+hebPmaNiwIfr07oOhQ4Zi48aNMBqN5bZfTPWDxWgFp0Xz5pBuhUPW6iyuYwy7LsSo5OMtLoA4cQoNGzdG7dq1Lf4OXWwmvPmmELC6NethLCBIaXuGHbthuBqG9955h9NJDMMw98GxY8ewZMkSeCcGwSMtACpZLZ6XIMFB64yA6FBoU/X48osvy+W8kgh+6smn8OP3U5B9A/BNCoFXck2cP3QJ48aNw+uvv86RUqbM4DR9BWfYsGGYNn06cPgIVI89Umi5rNPBcOCQEKJk+WTYsxeG6zcxfuZMi6+5ZcsWvPjii1DUrQ3Vc11g2LUHuiUrIHl5QuHvCzlbC1wJE6/95ZdfokePHqV8lAzDMBUPurnPzMyEUqkUk+nuhwULFsAGdnDO9DS7XCmr4JzsjW1/b0NERAR8fX3zpczPnDmDrKwsBAQElHiZlFarxdAhw5ASk4ag6CZQG+8em2uGD5Jt4rDmrzUICgrCxIkTS3TbDGMOjoxWcPz9/fHmxIlCcOq374ScnHI3hR5+G7qlKyHHxkJyc4XxjzkwHD4qfEG7dOlS6LXod+gi9P6kD6AICYKyRzco/XygHjIAqn69IDlqYLwZDuP1m4BOh82bNmHUqFHlcNQMwzDlR1JSkqjrbNWylcgwkSh79plnsWzZsnu6lJg4+u9R2KU7iUioJTRZruK6TM2mBAnfb7/9VqTNe/bsiYEDB+KRRx5B3z59sX///oc+LtoWvc6AAQMQfvsWfGJD8glRE05Z7nBJ88aff/wp9olhKn1k9ObNm5g1a5aYe07TguiPesOGDaW92SrF+PHjYWtrix9+/BGZx09B7eoCo1YHfWoqVFZWkJQq2N2JROfOnTFmzBhR+5P34rN7927Mmj0b+/btg06rBRQSFL7eAHXT29qKjnyyhKKH+J2MDOhmzBQXrUaNGpXjkTMMw5T9iOW+ffoh4s4daNLd4JddG0bJiJsn74go4bq16zBr9izRFV8UomTqHuX+UoEI7IABA3HqxCk4pXmgVmYglEYVMtWpOHPgPAYdGSSGj5BIfRCSk5MxZvQYHDp8CApZCVudBjb6nGZXc7hkeCMs9QT27NkjvlsYplKLURKg9GFu0qSJKIgWf6DMfUGd7i+99BKGDx+O9evXC+9RtVqNdu3a4bHHHrPcCS/LmDRpEubNmweVtxfk9m2FeDXeiYDx9Flor1yDemBf4Veab3t2dlD6+uTerTMMw1QH6DtqxPARiL0dh1oxTWBluCs4XTK9kGaVhL3/7MWnn34qpuIVRZNmTfB3+E7IqbLF6GiqdaK4ftevX19ERE+fPIWA2FDY6TS569A+OGa5I9L5qqjjJEsoHx+f+zou6pIfOXIkTh49hYCEUMQ6hMNab1vk75iOPTY29r62xTAVMk3/xBNPCDE6depUNGjQoLQ3V6Wxt7fHoEGD8OGHH4ouzQ4dzE9OMkHjQ4UQffYpKEYMgap1SyibNoa6W2dYjR0DyUoN3Yq/IJtLO/FNA8Mw1QzKHl28dBFe8bXyCVET1HjkluyHJYuXiE70oqDgQSbSkGITb3a5UTIgySkKHTt0hKurKxYtXAynFK98QtQEiVmv5FqAQRLe0/fLrl27cOTIEfjEhUCT7SIirjpl0d7ReoVW/HRycrrv7TFMhROjwpqIKXN0Oh2mz5gBBc2db9q4kGil+lBVr+6Uu4Hx0pV8y+T0dBgiItG4sXmjfYZhmKoIGdPbwgF2WkeL6zhneom60c2bNxf5WpS5eu655xDpehXxdpFCfJqg1Hu4x0XA1ohJH07C4cOHkZGZDucM881OpoYn+3RnbNm05b6PiwSsvdER9tocYemY5YZ0q2RolVkWfyfRLho21jkG/QxT2lTabnpKQWdkZBR63lRsXd2Lrvfu3Yv42Fiouz1rcR2FuxukwAAYzp2HskFo7vOGg4ehUirRvXt3s+c4L3y+yx4+53zOqzrl9RmPjo6GMktdZNORyqiGWmGFqKioe14fKfVua2uHlStXIN75NmwMdjAoDCJi6uPti1+mzULNmjVx4cKF3NcuCqVRLYaZ3Gu7Bbl88TKsM+1zj8sp0x0xmlu443wFgQmhQujmhYRqomMkhg4cKpwE7nd7TPGoDtdyuYihOlVCjFLkz/QHbI4bN26gOnP69Gnxk7rsi4KWy7fviH9Tp77+4GEYT53BmLFjhd0IPYpDdT/f5QGfcz7nVZ2y/ozTl6bRyrKnM2GQ9NAZtUKgFfUdZGL48GF49tlnsHPnTiF2ySKqRYsWaN68uRB69Bpk4URkqdNzo5fm0FpnwNfFv1jbzYtwX8kTmVVAicDEerjpeh5hHifhkuElosG0ToptHFJs49GwQUNh63e/22LunxtV/PvTysqq6opRauAJCQkp9DzdYdAbS3eb1IFenTtCBWlpgLOzxfXklFTIiUkwzlkAXWwcbO3s8O7kyRg6dGixtsPnu+zhc87nvKpTXp9xqskn95FMdRpsdQ5m10myjRHlZ1QT6ulpOa2el9DQUHTs2NHi8nr16mH6tF+RmB0FuwRHs5HZLFUG0tRJGDX6Q/F698OTTz+JBbcXwZhshOK/6jzqpg+Ka4w4hwjE2d+BrMkZfOLj5YPnh04QHfTu7u7QaArXsDIlQ3W4ll+9erVY61VaMUp3sHZ2dhaX0xtb1PKqzpNPPgk7e3tknz4LVYdHza4jp6VBvnZd/Puxho2EN2mvXr0e6LxV9/NdHvA553Ne1Snrz3jXrl1RI7AGouRrCIipB5Uxf0SHaj3jne+gZ6+eQkCUJG+/8xZee+01WDmEi4lNeQVptjITER6Xxb717t37nrZSBRk9ejTmzJmDWE04PFMDc1/bymAL3+RguKZ5I9zzPILrBcPVxRU//vijeEiSAk8+8QReevklUQPLlA62Vfj7szgpeoK7i6pw5/2woUMhHz1RaNQnQdOVdBu2UPwcitrB2Ltvn7hzr6p/EAzDMPeC0ubzF8yHvbsNrnufRrTmBtKsEpFqnSDqK296nEejJg3vaev0IJDIJCu+OM1tXPM9KbYda38bt90u4ZrnSXj4u2PpsqX3LUQJ8vcmF5Z4hzu443wZGepUyJBFyUGCXSTueF2CrcYWFy9exKn9Z+GTFITA+PrwSqqBA9sPo3///pg7d26JHzPDmGAxWoUh+6fWrVqJKU26DZthvHZdeIzq/z0K7ez5kCMioe7dHapunWGUJCxcuLC8d5lhGKZcofKvrdu2YvTYUdB6p+KW2wWEu16EQ7A1Jn34AVasXAEHB/Mp/IfllVdewbZt29BvSB8oaxmg9U9GjWa++Pqbr7Fj53YEBgY+8GuTV/WUKVPgEGSNG+5ncMHnIC55/4sYl5to0rIxUlJT4J7qj8DoBnDJ9BY2VjQatEZUQzGNicQs2UMxTGmgKouaCPIZNdUxpqWlidnoBJn3kr8aUzrQHfTE8eMx4MCBnDGf5/4rRFcqxVx6ZdvWUHi4i6fkurWxfuNGIWAZhmEqujk92SFRvR01BFEK+X6N4IvC29tbjFV+//33ERMTIyKmXl5eZWJVSH7c3333HfBd8X+Hvlvnz5+PFctXID4+Xojl7j26i3HOVI9qgsaA9u2bM1r01q1b4tw9+uijeOvNt2Bv1MA9zV+UIiTZxQjbJ4WsgEO2ixCpmfapmDlzJlq1alU6B85Ua0pdjNIfxhtvvJHvOdP/6Y+nTZs2pb0L1RpTl6Z6+CBIBgNkvQGSxgFSgVSP5OCA1KiYctpLhmEeFOqUJt9LlUpV7Pqsysy6devwzdff4Oatm7nPkUikhpvPP/9cCMmS7AL29/dHRebAgQMYOWIktFk6aNLc4K4PgC5Zi+ULVwh/0e+//17MuDdBwpoGppggq6g9/+yBZ2oNhLtcRLpNEtR6G9HARWn8KMcbiNHchCbDTXirkpMNNRBXt5sfGhwwd+48nDl1WozQbta8qRD7RU1BZCqQGKU/5EuXLpX2ZhgL0N28ID4BUq2alt3z4uPvrsswTIXn3LlzmDVrlpiVnpmVCTs7e/Tp0xtjxoxB3bp1URWhiXIffPABNNmuqJHaUEwrIjuiZNtY7Ni8EyeOn8CGjRtKVJBWZG7fvi2EqDLFGsHxDfP5hXqmBiDK6TrefPNNkd631ICUkpIibmgoGkpTmQIS6oloqKnJSafQIsrxmjjHMADp6elwLsKhpapB2d1x417Ezp07YC87wjbNEbIkY2/MfmzdulXYX9GEyeom0Esarhmt4lDKJ7h2bRhPnCrS3sl4JQyDBgwo031jGObBWLFihYgErl2+DvaxbvBNCoFdtAtWLFqJZ55+BuvXr69yp5ZS0VS36JLuDf+EurDX5VggkQCj2sbAqAZIiE7Exx9/jOoCNRXpsvTwja9TyLheggLeyUGwNThg+rTpFl8jZ9ynhGx1BvwT6wqhn7eTX220gn9SXdjoHMTzpVUvW1F55513sGfXHiHS6TPmmRYoosg1ohrBL7EONqzfgE8//bS8d7PSw2K0ikPpg/Gvvw7DlTDo9x8Sd8AFR38a16yHq5sb+vXrV277yTBM8Th27BgmTpwIxzR31IpoKr4cnTM9xc+giKawT3PGq6++JiKnVQlKOStkpRAC5nw41UZrOCf5YMvmLWI6UnVg2dJl0KS6QSkrzS6n8+SU4onde3YjLi7O7DokLj3c3WGts7NouE+v457uJzrww8LCUF2gmmQaEeuRFFhIpItzm+UO92R/zJ+/wOL5ZYoHi9FqQJ8+ffDWW2/BsO8AjLPmQb//IAzHT0K/aSv0v8+Go06PJYsWwdHR8jxmhmEqBr///jusjbbwSQ4uJMooGkZRUopmUbNJVWL3rt2wy3CyKLxMYy4NRoNobqrqGAwGJCQmwFpftB0fLacgRGxsrMV1FAol7LOdihyDapfteF8m5pUdarb+7LPPxA2Qc4aHxfVoehWMMlavXl2m+1fVYDFaTZgwYQLWrFmD5x7rAOsTpyHv3APvpBS8NWECdu3YIdL5DMNUbGgEJUX+HJM9LAoHEqSaFHes+WuNaGyqKmi1WiiNloUoQcLBtG5VhxqRbKxtoFcWfaym5UVNUtI4OECWjEW+jml5Va+NJJFPPrJNmzQVNaFqrbUYn2oJpayGNezuTj1kHohKO4GJuX/IkoNtORim8pKYmCgifzQ5pyis9bbQ6rSiU9rFxaXE9+PatWu4cuWK6OBv2rQp3NzcUNoEhwTj5oVwyCmyRSGeYZUiftaqVQvVga7dumLT6i3CksnSOUmxj0VovVD4+flZfJ1HOzyKxdeXQk4xipsZcyTbxon3u0WLFqiqUAR5/PjxIjXvmuoLK4UWmVY5AwIsnd+c4QE6HhjzkHBklGEYppJgKqXRK4qOhukU2cLuiCaxlXS9KvlUkp0Nde2PGDECzZs3x//+9z9ERkaiNBk6dCgypDSkWSdZFAWJmkiEBIdUacGUF3oPspAhrJfo+AuSZBuNFKsEjHtxXJH2Q/Q+auUsxDncsfh5SnaKFp3jZXHjUV7s3LlTpNt9E0LglVpD1IRqVVnIVKdZ/B36PGYbs/D000+X6b5WNViMMgzDVBIo1frYo48hxTHWrPgg6PlUxzg888wzwiezpKDhJX379MXpA+fgl1gbdaJbIiSmOVwT/LDxr03o1rVbqaYqSQC3a9sOUW5hSLVOzHf8wg/T6RpSrRLx/gfvVxvfx2bNmolO7niHCDFbPtkmFlmqdDG+9LbrJUQ4hwkRT+M8i4KswF5//XUxu/6O0xXxGgTZZiXaRuOW93m4erkIN4OqzNw5c4V9k2NWzjAY+2xnWOltxGeLPmMF0St0iHO9hYYNGor3gnlwOE3PMAxTiaAo1/B9wxHncLtQepYEWozmloggvvDCCyXqtfjSiy/BJkMDv/g6UOSJY1CXNTUOhcvn8ebEN8X89NKABObsObPx/JjnceDgAdjBAVYZdkIwpdslQ1IAP3z3g7C8qk7Q+0xlCb9M/QVHjt4d11k7pDY+ful9DBo0qFji/LXXXhO1tjTF6VrCKSglJYyykYqQ8cQTT+Lbb7+p8l7Uhw4dgkOqe+7fFP0ku6sbbmdxzf0U3NJ9hQeruOGzSUCyUwzsnW3x64xfq80NUGnBYpRhGKYS8cQTTwh3DJqsk2mXCsdUD1jprUU6MVkTi3RlMj766COLJucPAjU/0uzykKTm+YRoXlsl1yQ/7N23V9STBgUFobTKFJavWC6mDpHV07Wwa7CytkanTo9jyJAhVV4sWeLJJ58UD4pMU9c8RdDpPbgfgUTrdu3aVURIyY2AxoXSSOmOHTsiICAA1QFqXpLk/J9vG709asU1RqzmlphGBel6bgMZlS3QCO3qcn5KExajDMMwldAdgxwwZsyYgX///Tf3+faPtMfLr7yMTp06lXgtnYPeCVaG/GOE8+KY6Y4ol+tibGJpiVGTaGrfvr14MPmhJqWiGpWKA3XLU4lHdaR27dq4lRYBZPjme97aYCuM/6lWO8bhFlI0ceJzHhwcXG77WtVgMcowDFMJIcFAj5iYGCQnJ4uueXf3nFq3kobS9JL+HrZKZIAjKZGVlYWqgmlICKdgqwcjR43E2+feFjWzFBEtCEVNs+zT0KtXLxaiJQw3MDEMw1RiPD09RUSntIQoQWlInW2mxaYpIluZCZ1RC39/f1T2VO3atWvRq2cv1KhRA4EBgej0eCcxepNEeWXiwoULmDRpkhh8MnDgQEyZMgXR0dHlvVsVlt69eyO0Xn3c9ryEtAJNctmqDNz2uASlnYQ33nijXPezKsKRUYZhGKZIqAlm/vz5SLWJz+00LkiCfSQcNY549tlnK+3ZzM7Oxrhx47B9+3Y46J3hnh4omliizsbjw0kfirGPK1YsfyB7I4pgL1myBKdOnRL/b9SoEQYPHgxvb+8S2XeKSK9fv16kj9PT03Hzxk1cuXoF1pINrDPsIUsyDu4/hCk/TsEnkz/B888/XyLbrUrY2tpi6bIlGDN6DI4dPwZb2EOVZQ2jWo90ZQo83D0xd95ihISElPeuVjlYjDLFTledOHFC1I7RFBiKflCqggrcGYap2jRp0gRPdHoC/+zeC0WcSswwN3UcU/QowT4CifZR+PCNDyv1NYFsknbt2IWAhFBosu8OC3DN8Bap2+vSJdHN/9eav4qduqdr5/Tp0/Hdt9/RGCPYZuVMQtqxbQd+/PFHTJw4URitP0wpwL59+/DiuJeQlJwIe4MTtHImdEqtGBnrnEnTunKSoIYkPWIdwvHxxx+LmfQULWXyQxmGtevWilrsFStWICoyCg4aB3Tp0kU8StIujbkLi9FKRFJSEv755x8xM5fupsl3ryxGs9Es4ldeew3nzpyBysEeChsb6JOS8dnnn2P4sGHo3r17qe8DwzDly4zfZmD06NGik93e6AibjJwRkukOyciWM/Hyyy/jpZdeqrRvU0JCgujQd0v2yydETVANoVd8TWGfROb/LVu2LNbr/vHHH2K8pFuaH9zT/KCUc752ybcy3j5CuCLQdZyslR4EChIMHzZcRD+DE3O8LsM8T8A7OQgumfndBWjbXqk1YVDq8dUXX4n0fVUf7/kg0I1BmzZtxIMpG1iMVgJIfH722WdYsXIltNnZuc+7eXjgf6++KnzmSqvAnuw9evXujVSlAqp+vaCoVROSQgFVRiYMJ05hzty5uH7jBn7/7bdS2T7DMBUDiqQtXboUu3fvxrx583HxwkWoVVbo/OgTGDlyJBo2bIjKDKW4jQYjnDMs20ORx6SNZCem9BRHjNI41v/77v/gmu4tJvoUFIaeaYEwSkb88MOPGD58OJycnO57v7/+6muosq3hF1dXNJFFa25AaVDBOcPT7PoU0XZL8xVeojt27Kh2vqxMxYTFaAWHUuL9BwzAuYsXgTYtYdWoAWBvDzk2DonHTmDy5MnCW45+lgZfffUVUo0GKIYOhGR3dx42/VvVvi0Mjhrs2rQVR44cweOPP14q+8AwTMWAvBVNnpZVDfLntJKsoZItRwpJyJHwo/rP4vqzUi2nf1p9i+u4p/siUROFVatWifGe98P169ex/8B++KbUzvV/zVZlwlanMesHmzfKa6WwFlkvhqkIcDd9BWfatGk4e+ECFIP6QdWuDSQHBxEFVXh6QN3lGaie6oQ///wTBw8eLJWL86bNmyE3b5ZPiOZF0bA+lK6uWLR4cYlvn2EYpiQg8Uj1mS2bt0SAfwBqh9TBq6++iqNHj+auQ0bxOlkHIwwWX4fqY41qnTDfLw6XL1+GneQghgJYQmW0gp3sgEuXLt3nUQFXrlwRPx2ynfIJZiqfKAo6DoNsyE3RUwnYnDlz8N677+G5bt1FpHvTpk3Q6wuPwGSY0oDFaDlCd8xRUVEiDW8OGs02b8ECoGF9KLzNp44UzZtC5e4m0uUlzblz52DQ66GsY9nYV5QHhATh+IkTJb59hmEqnu3R8ePHRSMjdYUbjUWLnorA6dOn8XjHTvj5x5+Rec0Iz6SasI9xxZY129CzZ09xw09QupqEaLJtnMXXylKniVGr1MhS3EiyEfc+R9TprlLdf6JSocj5Cs9rQWSf7YR0qxToFFqLv0e2RQZZj0ceeUR037dq2QrffP0Nwk9GIfpEIg5uPYKxY8fiqSefEpk3hiltWIyWA+fPnxc+ZfVCQ9GiRQvUrVtXpOK3bduWbz26U05KSIAytF6RYlCuWwf/7N1b4vuZ+0VDQ5+LQiHlmkMzDFP1oGvBrFmz0K5NO9GwSPWNNDry0faPiqafivr3n5iYiCGDh0CfaERQVDP4pgSLzniPtADUimwC91R/0Vy0bt064Sn69NNPI97ltvCULIheoUO02w0EBtQQI1mLQ6tWrZApp4tOfEvQtjLkVLRu3fq+j69p06ZQKlVIsY3Pfc6JuudlCTGam2Z9YUXjlMttNGncRNxcjB41GqpkG4RENUdAQj1xjmrENECtuEYIv3obAwcMFOViDFOasBgtY7Zu3YouXbtizY7tMLZrDVXfXlB1fhr/3rwhOlW/+OKL3As7RUYF1vfodrSygk6nK/F9rVevnmhWMl7LmcVrDtpX6doNNGzQoMS3zzBM+UN/42+99ZawA0q/pkXNuIaoHd0CNeIbIPFyOt555x1Rs14RBemyZcvEdCrfuDpQGfNfRymdTaJUo3XB1J+niv3/4YcfUCM4EDe9ziHS8RrSrZKRoU4RIyBveJ+BtYsK8+bPFRHP4kATssibMtbplllhSM/FON6Cq4trsaOtBW2Iund/DklO0bmRUGqM8k0ORrJtLMJdLiBdnSK2I8OIZJs43PI6D8ke+OHHH0TjlFpnDd94Oj/5LYuo7tQvpi5u3Lgh6lkZpjRhMVqG3Lx5Ey++/BLkoJpQjBkBVdvWUIYEQdmkEZRDB0L5xONi1jR1ahKBgYFCDMq3I4p+4YgIcVdf0vj6+uY0Khw9DjnbfMrHeDUM+phYDB0ypMS3zzBM+UPXIxJ1vkkh8E2qDTudo6iBJK9R/8Q68E6uhZkzZwqj+IrGsqXLoMlwhbqA0MorSJ3TvHDh4gVR3+nq6op169fhlddeBvyycdPtHG64n0WaexwGDuuPrdu2ok6dOsXePtVk/vTzFGTYpCDc/YIQtznCUBYiN9ztAtJtk4QwfFD/Spqw5OimQbjXeSTbxArR6ZTlIaydMtVpuOl+Fhd9DuGiz2HccbmMhq1CsW79WjE+dteunXBK9rLY7GRtsING64oF8xcUa18opf9///d/GDFihHiQbVVExD2+vxiGxWjZMm/ePBiVKii7dYZkpj5I1aq5EKe/zpgh7tI9PDxE2ggnTkG2UEhujE+A8UoYRgwbVir7/MH778MqKxvGFathjIzKfV7W6WA4eRrG9ZvRqnVrtG/fvlS2zzBM+TJr5ixodC5wzjRvFeSa4SN8R2fPmo2KRkx0DKz0dkWuY/3fctOYTGpOevfdd3H8xHHs2bNH1MeePnMa33zzDfz8/O57H8hlZOGihXAPcRbi9qrfUVz1OyZErmuwo5hsRRHUhwkarN+wDsH1a+GOyxVc9D6MS17/IsrpGgwKfe46H0z6QFg5rVm7RmS9wsPDxfeMnS7HhN8SNlkOIjpaFKaocps2bTHt5+k4vPG4ePzy0zTh1UljSCti5JypOLC1Uxmyes0aILQupCJMhqXGjXBx9Vpcu3YNwcHBmDhhAnb16AHjmg1QdH5KdNObIHEor9skoqL9+/cvlX2metaVK1bghXHjEDF/MdTu7pBtbSDHx8OYmYXuPXpgxPDhuYX0DMNUHcgI/tTpU/BLq13keppUN+zdt1eUFlWkCTU0OSe9iEYeQv/fcuqmzwsdR0mNfezQoQP27d+H/fv3i4YqgnxZH3300RK5dpKfaVhYGOz0GthmOopIp9pgA02Wi4iORkvXsHrV6nzWUaZOeoNk2T2AMEp3u+4t8csvvwi3AqrBdUsnY39l7sQnk7E/nU9yMGAYc7AYLUOSk5KAhqFFriM5O+YW3pvmF8+fNw8vjB2LjBkzoQiqBdnOFor4BOjvRCCkTh0sXrgQ9vb297UvVJBOo87mzp+P69euiRoouoMdM3q0SM3nNdGnUYCHDhwQEQK6s87MzBQRggEDBsDT0xMXLlx4oPPBMEzFhv7WCaWx6K8KpVEtIl/kEFLSYpSE1l9//SVsjKjjvG3btuIaVZzu8+49umPmb7NgTKkBBczXeSbZxcDbyxuNGzdGaULXVBKf9ChpKCopZSsRGFu/0HE6aJ2hiqmLC/IprF27NncEaGhoKJydnJGSHmcxOkrlBOmaRDzb6WmL2yZbqClTfhJG+mTib97Y3yD2kRrfimuLxVQvWIyWIc4uLognQVoEclKy+Em1S3nvqo8eOYKVK1cK38+UtFT4NWyEgZ99jqeeeuq+LUEoHTVg0CBcvXIFytohkDq0h06vx/5LF7Fn5Ej07dcPU378MV+RPv2bSgZE2UAeuMuSYaoudB2yUlshS50OB23hEZkmxHJ7BzGlqSApKSlCBFG2h4QqlfQUJyJI4pY8lKm5KDs7W/h1kk0Sjdf08vTCTz//JK6NRUHi588//kSk8zVR80o1ovn2zToeyXaxeG3spGI3JVVED1VyYvFMtiy4yeSeSi3mzZ2XK0atra0xfMRwzJg2A04ZHrDVF37vEuwjkYl0jBo1qsiaYr1OB7d0X4vrULQ0SRMt1i3qtZjqC4vRMqRfnz74fc4cyB0eg2RlPu0hnzyD+g0bolatWvmep7tJSrHc74QOcxYtI0aNwo2oSKhHD4fCw/3uwjatgPMXsHrVKvj7+YkuWYZhqi+2trbo2asn1q/cCNd0H7Nih6yCUjVxGDE4f7kOiUlK3/78089CTNpK9iJCRr6eNWvUxC/TfkHz5s0tbnv58uXCNso1zQcB6b65xvGZqjTE6m6JeexLli4RXpmWoBKmqb9MFXPfb9pkwSnZEzZ6O+gVeiFCU23i0bVbV4wbNw6VuTGWruvUUFYUlL6nVH5eXn/9dezZvQfnz16AS4oPnDM8RFc9WVEl2keJqPErr7wiLKqKNPaXHQp14+eFGshs4ZBr0s8wBeFCvzKEuguVRiMMGzabbUjSHz4Cw7XrePXll0tk1jxNUNq7d6+oU6LaL4LS7GdPn4bU9dn8QvQ/lPVDoWjdAn/OnIn0dMveeAzDVA9eeuklGNUG3HG7IoRnXvSSDnfcL0Nlq8Lzzz+fbxk1/Hz77bewi3dFSHRz1IxojFp3mqJGXEPEXktAv379c+snCxIZGYkli5eIGkTv1Fr5JhhRBC8grh6ss+3x/nvv37MxhoztKSLX9snWiHK5huvuZxDuegGudR3w+RefCweTyhoVJUz7TjPui4JuBFTK/PEnOzs7rFi5AoOGDkSyaxSueB3DBZ+DuO5xGupACKvBDz74oMjXpXrSe22boC7/BzH2Z6oH/MkoQwICAvDnH3+I+k/jzLmQGzWE5OEGpGcAZ8/DEBEp7lR79er1UNuhecPf/d//YcvmzcLUmFBbWeGZp5/G7j17ILm4QAoMsPj7yuZNkXH4qEj99O7du1jbpG5LSsXRT0r/dOzYEd26dYONjc1DHQvDMOULdV7PnTsHz495Hletj8Mh3QVWBhtoVVlItUuAvb0dFi1YKKzo8s5MpwioZ0og3NP9c5+nNLm9zhG2MaHC73LSpA+xfv26QtukenYJCoupX7Es2Q9Xw87j33//FfXuRUGRvQUL5osbdCpToohvUFBQidz0lzdU+6lx0CAlLQ62usKp9ru1n0no9GjhsgYqraCbhvHjx4totLOzs4goUylFccQjmfXPnj1bRFOpHMActCxDTnsgY3+mesBitAgomkiF85QGoYsXCax27do91AWMajy3btkiaqFWrV4NbXa2eD2qfXrh2++KPdnDEmfPnhU1n1kqFaROHWAVVBOyUYbhylVs3LqVcmdQ+PkWeQySoyOU1ta5VidFQem3n376STQ3Ke3sAE8PSFqtMEn++JNPMOPXX+9Z18UwTMWGrn37D+zH4sWL8dfqv0TTiqerK14dMBaDBg2Cm5tbvvUXLFgAtWQFVwtiktL9rsk+OH78mGiAJEGVl9Onz8AuUyMaYCxBaWmlpBJjSe8lRk2QXR49qhL03TR4yGDMmTlH2G+ZrKrykmQbg0ykicEqlnBychIClN4LipgWFxqj6u7mjlhtOPzj6xaqy6WIKJn+k/n/s88+e59Hx1QXWIyagaKJlGKiVLXBaITK1QXGrGxxpx9cuzZmTJ+OBg8xcYjsksjqgsbQUacoXUzoURL7/cK4sci0tRFNSRJ1tdraQkHRyWRPGPV6SP5+kNPSinwdOTsbBq22kNWJ2cksb7+NXf/8A9UzT0LRsH6ubZUUn4DUnXswfMQIYQ1VVM0RwzAVHy8vL0yYMEE87sWxo8dgm5FjMWQJTVZOk+bJkycLiVGSMMWBZA/7V0JENXft3IWb8gW4JvmKkaAk5LXKTCTYR4lGJGrmomBKSZNj7P8TRo4YKYz93VL8YKfN6ZjPsEpBvNNtZFql4fepCzhNz1iExagZqEaGCucVj7SFqnlTSGSlJMtQ3wrHzd170btPH2xYv/6+JnFY+iPO2zX/sCxZsgThN2+Rhwiwck3OkyoVFPXrQc7MhOThDkWTRjBs3CLM8hVu5rdtPHseSoVCRHGL4ujRo6IUQNW9K5T16+VbRq8t9ekBw+Ll+PyLL7Bu7doSO06GYSo2YkzwPdfKWcOcmKSb/f3/HIAxyQDFf56VBSGho5f1pW7JVBmgqOZfa/4S310bNmxAtNMNKCQlDLIejhpHvPvKu6KJq7TKEjp16iSM/Sd9MAnXrp+DSlKJ2wnafnBQML76+o9SsbRiqg4sRgtAKZ+FCxdC9exTUDa9e5GjP2KpRiDkQf2QvWAJPvv8cyxcULwRaWUBpecnffQR4KiBslULKGrVpNZ5MZ3JcPwEkK2FVDMQynp1YNizF/rN26Du3xuS9d3GAMIYGwvjvoN47rnnRCSkKCgVR1FjRWhds8slpRJSqxY4tnYDLl68KGrPTJw7d05MpDp85IjoBG1Yv75o8CIPwapQx8Uw1ZnGTRrjzIlzkJOMor7THGnWOV7K5rJMZD9EjUVkmE7z483VQMY73kFQrSBxzSgpqOxo06ZNQtBRKQJdA/v06SPEVkVvcqLxnnTOPvnkE1E2Rb0DVCNLvQr0EI4GJZCBswSVY/2z9x8cPHgQZ86cyfWophKKvNd02o8tW7bkrkNe2l26dKlQwxIeFjpGylTS+ebvs+LBYrQAJJBUzs5QNG5o9oQJ8daqBXZv3S7GqdEfeXlDH/oxL7wAo4szrAb3zycwqWNe2ag+tAuXiRn3NIZU3bsHdMtXQzt7AZTNm0BRIxDQ6WC8dBmGU2fg7emFb77++p7bPX3uHIyBAVAWIR6FKAYwefJkvP3222jWrBk+++wzUTOrctTAGBwESaHArQP7sW7dOnTu0gXTp03jxieGqcRQSnjOnDlItIsW40ILQnWECY4RaNigoRAsBaHxlTRUY9myZaJT2y3dJ9c6iJphYp3CkWmdii+/mlFiX/Z0Qz982AjExEbDXu8EpU6N09bnRGNm7ZA6WLhoAfz97zZjVVTS0tJEluz48eNQSWooJSWyjVkiQjph4gSMHTu21AQSvS5ZbVmy26JzSdHTxKRE2Cpymp0yjelwcXbBV19/hR49eqCyotfrhWvDnNlzxPhYgoYpjBg5QgRa6GaBsQyL0QIcPX4cxqAaIk1tCUXtYOi3/C1sSSqCGCW7pjvh4VCPGFIo0klIGg1UT3SAfs0GGK6GQRkSDPXwwTAcPAzD3gMw7N6bs6KVFSSDESuWLRNpn3shIgXGe1h6GHO6+fcfO4a9PXrAz98fd27fhvKJx6Fo0TT3PFOqTnXpCrZt2oq333kHv0yd+kDngmGY0iMuLk4InRMnToiMRv369TFkyJBCIo3q4keOHIn58+aLcZOuGd65zUhCTDrfQrZNBj77/DOL26LX9fHxwa/Tf0WiJhJ2skYI0ww5FW6ubpj+05wSa46kJtX+/frDkAwExzfN1wSUoU5FOMLQr29/bN22pVjXxvKCXAx69uiJ7GQd/BPrirpcaijSKrMQnx6BTz/9FMnJySIwUNasWbNGjAN1zHJDcOrdc5ylykBcVjhefvll8T1AVlyVMRL6wvMvYOeundDoXOGTHgyFrEB6RjJ++L8fsHjRYqxctbJC6IWKCovRAuTUL93jrvG/u8qKUji/fft2qD08oPDxtriOIiQYsFJD//dOKAIDRE2n4rkukJ/qJKY+0cO4dTt69u4tLE+KQ9tWrXB15UrIBoNIyZvDePmq+KkcPghSZDTurN8EZesWULVqXuiOmkoIkJUlTPfHv/EGgoOD7+s8MAxTesycOROff/a5cOewzXaEJAO7tu/G1KlThTH6e++9l8/0/vPPPxcZjpl/zkSCUwRsDfYwKozIQKrovv5j2oIiu+DpmkDi5YUXXhDuHGSuTnX2ZA9U0mnd6dOnIztNi5oxjQt18NOoTP+YergunRJuAiSaKiofffQx0pMzoMq2RoTTVcAJwu7JJcMb3im1RHSZ3E/Isi8kJKRMx8qSJ6xTljt8E2vn67inIQR+iXUAlyt4/70PRMd9ZbMEpM86DQ8IjA/NN6nMKcsD2jR/3JYviqj7zl077jl5rLrCZ6UAzZo0geLGzSKFpjHsuvj5MB31JZ2WoXn1RSHEIv2Bp6RCP2cB9EeOwxgdAzkuQTQsyZu3oW5QEL784otib5dSD/q0NBiOHje7XM7KguHwESiCa0Hh7AxJpxPPK1tYnrpCHfkqOzuRnmMYpmIwf/58UYvomOyBkMjm4ks3ICFU/Ns9OQDTp/+K7777rlDm5OOPP8aRo0fw9rtvoeugZ9FnWE9R13j02NFiRzUpvUmClF7/yy+/FJGzkhSiNNKYRi07puR0oJuDfFU1Ga5inGZFhWpEd+3aCR20Quy5p/mJB0WTb7tcQrjLBbikecNKshbvZ1lCNbgpqSnwSAkoZP1E0HO0LDklCRs3bkRlgmqLFy1cBNcUX7Mjc+mz4x0XhCtXL2PPnj3lso+VAY6Mmql1ortw1bkLUDasX+iEyVodcOQYHuvwWKGRneUF1Vdhx44iI5RyRiaQlo7atWuLFNrmLVvEPHrC2cUFw8aNw//+9z+zs6UtQa9D9UdU/0mvrWzVXHiUytQ4de2GaJSSM7OgeuLxnH1ISgY0DpAcLVtGUU2r7Okh0mYMw5Q/FNX6+quv4ZzhKaYh5YU63T3I1F6S8euvM8S4Yk9Pz3zrUBMQXVsqKhERESLNarIjsgRFg8Nvh4nawIo4SWjihImQZAUCEurBQeuc+zwNHaBmsXCXS4h2ug67dCccPHCwzBuD7SUNrAyWgybWBlvYSRph9dW3b19UFrZu3QqdTgeXDMsNv7Y6jSgzoZpSaoZjClPx/qLKmZYtWwrrpjVr10LOyICySWNI1jl34cbIKMg790CVmoYPJ32IikL//v1FtEF14ZJZAU0YTp4W9ZkktMmgmgz9SfBR2osEKk1NehCo9igrKwsrV69G5rETAEVo9QYqooHk4wX10IFQuP53t0gX8GytEKvUtGQJRXZ2pUvTMExVhbrLKaoVkma5bIbm1ic4RoqMRkUWnuagayAh32OkJS1XSIoKmWalyXfHjh+DT3JwPiFqwiHbBV4pNRDleB1Ome4wGO49vrMkofri4ljHVkbf2Pj4eKgU6twGO3NQ5FeZpUZ8XHyZ7ltlgsWomTqlKT/+KAzfybrJcOAwFO5uQlzp4uLh4+eH35ctQ8OG5rvtywOKUD7buTO2b98Fo4MDFDXvjuWjP2zjhUswHjiI50ePyZ2UQv6mJeFxSueLxPCbb76JpUuXimEB5ESgbNIICt/8XbSKWjVg2LsfxmvXRROVOYxx8dBFRvHdI8NUEKhW01ZhV2RUi9LbNDOe1q1sUPOVj7cPUjLihWizRJpDohjeURHFqMjmSWohNC1B05liNDeRbpeM+g0eK9P9o+/L+ZgvGqkobW0OWpYup1ao79biQA1teqMOBkkHpZxzY2MOo7UOzi6FbxSYHCreX1UFuVP++quvcPjwYbz5xhvo+1gHDH2uO+bOnYvDBw+iRYsWqGhQ93nrFi2gW7YShkXLoN9/CPq9B2Cctwj69ZvQ47nu+Ih8SIuAoqUmb7r7xd7eHi+++CKcXV0hqVWFhChBDVaSjzf0O/8RJvwFkfV6yDt3w9XNTTQoMAxT/lBK2gij8Pa8V+SwIqav7wXVto4cNRKp9vGic94cKTbxSFMmoW+/ipk+joqKgo3RToxZtQSVVKgN1tBDJ+r9yxKq87W1s0Ocwx2zy+mzFedwG/Z29pWum/6ZZ56BQqlEkp3l781MVRrSFano3r17me5bZYLF6D1qMd944w388MMPonD+6aefrrDGxyQGly5ZIuo329QKgub8RThevoInmjYTHaDULWrpi2Lv3r3CRoXMh2kGddOmTdGrd29hTHw/UFPB8KFDgbMXRITTHMK/NSkJhvmLYThxCnJ6uhCmhvMXYVy0DNKdSPw2Y8YDlw0wDFOyUPc6+VRmWhBquVEtRYpYtzJCDVKNGjfCLbdziLO/A72U02ypU2QjWnMTt50vidGm06dNFw0rFfH6r1doi7xhoGV6hU70OpT1eGbav48//ghJdtGIdAwT59UE/TvS6RqS7GLwyeRPStWYvzTw8PBA3759xNhTczcz9L5Eu19DgH+A0BCMeSrfbSxjERKbXbt2FY/iQtHeSZMmQeXjDVXnpyG5ukBOScGJ0+fw/PPPiznUb731VrFfjyxeNm/diutLV0Lu0F5MZ6J59UJwnjoDef8hMR+ZGqXIkkq/bUfu77Zt1w6T/vwAzZtb7rZnGKZseeyxxxAYEIg4fTj8Y0MLzZsnkRPreAsO9g7o1atXpXx7SAD16t1LNM/EaG6JdDY1A8kKIxRGJdzS/eCU4YFbinP4/fff8e6776Ii0blzZxGISLdKNlszSqRZJ8Gg1OP7778vl6lA1BxMtaNkD3bV/jjs5P/m10spokfgm8nfYCgFMyohX3zxBa5eDcPJEyegyXCDY6ab+PykWychVRMPjYsD5i+YXykzB2UFn5lqzLFjx/Dhhx9C2bI5FE90zH+BalAf8qF/MWXKFDH7mVIRxcHR0RF/rVqFiW++ie1b/oZxx24obW2FBRQ1UNHFZvInn4jI5507d8TgAJogRaNCy9L3jmGY4kE1klN+moJBAwch3OMC3JL9YK/NMX7PVKchXnMHaTaJ+PX7XytdVCsvZEzuqHWDd1KQ6D43KPRQGdRwyHaFUs7JiGlS3bBwwUJMnDgxt/GpIkB+rbVDauOW4Tps4hoUaqbRKbSIcb6Bxo0aF+ntWtrQIAQar0pd5XnHgVL3/P04uVQ0KPK7fPkyMcFxzuy5CL9zUTxPN2gjh4zAuHHjclxvGIuwGK3G/Pzzz4C1NeTUNOg3bYWiZg0o6tYW9kqEqm1rSNdu4Pc//ii2GCWoMWrunDmiw5NS/ampqSKVQfUypgYqws/PTzwYhqnY0Pz35SuW452338GVq+dFswzdvOqMWvj5+uHnL74XZuWVFa1Wi8tXLsMnMxgqo1o0+5hDk+WCW4kXEBkZicDAu42ieaHpVORbGh0dLUQKRS0pPVuaUTFqVKWm22xFFsLcT4mJVzR9iaLWdKOQYB8pJmG99PJL5T4rnfaTRGlVg27EXnrpJSE86b2nIAt973HJWfFgMVoNISsmGrlJY0RhYy2mHiE5G/qz54Fde6Du3jVnXj3RqAEObdoqmpvut/u+Zs2a4o+TYZjKD9WD7tq9C0eOHBFzz03jQMm8/l4d5uHh4SITQl/Y9DsVKaqYF+k+10hPT8dff/0lfDTpunryxElcu34NNpKdmIIkq/VCmPr6+GLO3Dml1ilOZurHTxyHb3IIMqxSRKNQrCY8Z4+NCtFlT+NXaW56ZWsQohGnZNK/acMmpKWnw9vbC4MGD8KAAQMq5GhW+lugMbbM/cFitJpBd9CvvfYatm7fDtWzT4mJR6ZIqDE+Afrtu6Bb+RfUg/pD4eeba1B/69YtkVqhn/SF8vjjj4toCcMw1QeKqpEoLW6j0v79+zHlxyk4eOiuybq7mwdGjR4p6ssrStSImi+Dg0IQeyERzpmWzcspyujs5Axvb29hZUfjNzMzMmArOyALGcJL0z+pLjTZOTPhTZ3UMcYb6Ne3HzZv2Vwqw1JIrNnLGjhleoiorldKTWhVOY4lVnpbYb2VrI0T07CuXLkivKUrAytWrMCbE98UTgAOaa5QGx0QER2Hzy5/hum/TMeSZUsQGhpa3rvJlADcTV+NRGhKSoq4g968eTMUXZ6BsmnjXCFK0Lx6dd+ekNzcoN+zL+f3EhLpGwg9e/XC5M8/x4L16/Db/Hno168fOj35JC5dulSOR8UwTEWFbl4HDhyI0/vPwS+xNoJjmqFmXEMYbinw4w9TMGzoMDH5qKIw5vnRSLVOEOLRkmNAqiYOw4YPE9FQ8la2irNHSHRzOCd7wSgZUCOhPhyz3fKNvCT/VZptr03Xi7nwpcGlC5dgne6Qu13h+6rTiIdpxKl9dk4UkcRoZWDfvn2igVaT5obgiObwSQmCR1oA/BPrIDiqGdJjs0QdM2XtmMoPi9EqDtmQUG1o85YtxR0kNRBJzk5Q1Ktjdn0Sp8o2rSCH34YxLg7GE6fE83KbVlC/PBbS6OFQvPQC1AP74XpSIgYNHozbt2+X8VExDFORoWvChPETRAd6YEwDOGV55Ix71DnCJyUYAXH1cPjQ4VITZw8CCWdKo9/2vIhkm1jIyJlSRHWXJFJve12At4+3qHec/MlkEYWkiUdqozUS7aJgn+UsxJ85SBA6JXti7Zq1pWINRfWoxZkgZVq3MvDzTz/DTq8R57iggwOdc7/YukhISBRTv5jKD4vRKgzVaHXu2hXfT5mCeE93qHp0Axw1kIJqFlnETpOSCP2OPTDGxomZ86r2bSHRqM//UnU05UkxqB+yrNSYM3dumR0TwzAVnwULFkAySvBOrpUvSmiCuvGd0jwxb+78ChMdpfKjpcuWon2HR3DH5QrC/E7glvc5XPM7gXDXiwhtEorVa1bjwIEDYjyqR6p/7rFlqzIsWiqZcMh2hk6vEzWQJU37x9ojwyE5V0CbI8U2HkqlCs2aNUNl+O46cPAAnFK9zH5+CLXRCpoMVyxauLjM948peSrHLRLzQGn5UWPGIDI5GaoxwyE551woDYf+BYz3mP1Lc4TpNW7chMLODsrHO5hdTaL58S2b4+i2HaK7NDjY8uxqhmGqD5s2boJ9uouo9bOEc4YnrqWcwtGjR9G+fXtUBJydnbFo8SJRfkSpeJo7Tk0yzz33nBgGQpw9exZ2kkOB8ahSkUIwb2SyNAan0EQlqhuNt4+Ae7p/oeVk7ZTkFIXu3Z8THd4VnYiICPHTRmdf5Hq0PCoqsoz2iilNWIxWUeju/fzZs1AP6pcrRAnJ1wfGq2GQn+oEycJF0Xj5qqgT9fT0RLy/T9FR1JBg6LduFxdoFqMMwxBpaelQGfJ7XRZEaVTndqRXNOrWrYv33nvP7DK6HhacdGSn1YiRoeaEoAlaTr6TpdE8RCVYr7/+OqZOnQqtMhtu6T6wNtjBCIOIiCY434GTu5MYcFIZsLOzEz8NipxJWJagiVI2NpXX25a5C6fpqyhr166F2t0NUmBAvueVzZoAaekwHDtp9vfEzPh/j+Gpp56Crb3dvc1OytmzjmGYigcZfGutM4pcJ1uVI0Irmw0ORUgz5XSRmjfhkuGNLKt0pNjEWWx+SnGMw+Ahg0ttMMA777yDyZMnA77ZCPM8iSt+R3DZ9wginK+iTYfW2LBxfaUxXqchKN5e3ki2tTzvnW4I0jUJeLZz8T2wmYoLi9EqCqWXDI6OhaKaCk8PKFu1gGHXHuh37oGckjNLVzYaYbgaBuOSFaDEyCcff4ymjRpDunFTpPwtYQy7JgQpXTwYhmGIQYMHItUqUYgwS0IiURON2rXrlJr3ZmnRpUsXuLq4Ch9PU4TUIdtFjIC87XwFsQ7hYh45QZHJJNsYhHudh6+/j4helhZ0rR87dqzwG/3jjz/w7gfv4rPPP8PevXuxeMniSjVghEoZnn/heaTYx4lpWAWh804jW7PkTIwePbpc9pEpWThNX0Wh2idlaqoQkiKtZDBAvhMBOSsbUu0QKG2sYTh8BIYjx6AkL1GdHobMTDRu2gS//DwVQUFBomuUIqyqCxehrF/Yy03WaoGjx0VBfEBA/ggswzDVFxr5SN3Qd+RLouvZymCTf5a95hZSrRLw3cQvy30i0IN4kn79zddioMcd6TLcU/xho7eHX1JtSI5hiHW4LQSplWQtph4ZZD06Pd5JjFa+38EhDwJ5t3br1g2VHRLWBw8exO5du+GYluOfStOxKCKdpIlGqjoRn3zySaW7mWHMw2K0knPo0CHMnjMHO3fthDYrGz5+fhg2ZAg6deokTJlVt8Ih34mE4fhJKs66+4vOTpBq1IB85SpG9R8gxnSSkX2TJk1yVyFj6x49emDDpk2Q0zOhbNwQknVOHZgxMgryzj1QpaVjVBUc7cYwzINDYzCpM33ggIEIU5yEJtMVNlp7UQOYpklEtpyJjz76SFxfKiPU0PTbb7/h/ffexzXrU7BV2Iuu7wxjGhwdHUX0lCKRdB5oTGppGN1XdWhK1+zZs/Hrr79i9qw5uBGfM8ueaNigId4Y/x26du1arvvIlBwsRispFPH85ptvMG3aNKjc3SE3bwbJ1gaREVH47ocf4OrigoAaNXB79TrIOh0UTRvniElHjTCyNxw/BeOFiwgOCcFnn31mdhsUsSCPUo2jIxYvXgzsPwiFuxuQnQ1dXDx8/f0xdcECESlgGIbJS0hIiBgfSlN0Fi5YKOx6bGxs0OfZXhg1alSlj2iRIH3mmWewZcsWMQ6UoGMigVRRJktVBUH6xhtviGldp0+fFs1uNP2qTh3zPtlM5YXFaCWFop4kRMl2SdG6xd1UV7MmkDu0R9LKNciMjxepdFWfnlDWvmu7JNnZQeHvB4OfD8K278Lu3btFVNQcJDS/+/ZbvPH66+JLhWZM0xcKrf/EE08Ij8ALFy6U1WEzDFOJoCjh888/Lx5VEbo+UnS3skZ4K5MobdGiRXnvBlOKsBithBiNRvw8dSqU9epA1aZloeWSxgFSz25InzUPijoh+YRoXhTNm0J19rxI81sSoyYo5TR+/HiUNFqtFufOnUNWVhb8/f259pRhGIZhqhksRishx44dQ/itW1APGWBxHUmlFOb1iob1La9DjU2h9bB7167cRqeygsQnRXbnzp+PxPj43Ocfad8eEydMQLt27cpsXxiGYRiGKT9YjFZCYmJixE+J6jctIOv0d6ckFQHVmeoNBuh0ujKr/czMzMSQoUNx9PhxoFF9qLs8TbP4IEdG4t/jpzBgwAAhVHv27Fkm+8MwTMWCbo5pkMb69evFtYmyJtSh7+XlVd67xjBMKcBitIJCF2BTwTYZFVMzgAmNRiN+ymnpkCwYKEsO9sL/k7reFQGWp4LQcjcPjzJtQvrhhx+EEFUO6CNqV3NxdRGRWnnTVrz+xhto2bJlpfLGYxjm4aFRnOPGjsPVsKuwlmyhktXIkjLw9VdfY+iwoaLhkmoIGYapOrDpfQUUoT/99BNatGoliuIHDx6Mjh07onPXrti2bVuu5ZKjszOMp89afiEyqqc0/IlTkPV686tkZEI6f1FYQZUVFBVdsGgRpGZN8gvR/5AUCiiffgKyQpHTwc8wTJWtfU9LSxPXPBPXrl1D7169EXE1CjXiGyAooilqRDZESGRzuCX5i658Mo4vahAHwzCVDxajFQi6KI8aPRrf//gjEv18oB42CFYvPg9V7x44n5QoJk3MmTNHdLOPGjEC8snTMN64Weh1yODesPlvqFUqKDMyYVizIWfMZ951UlJhXPUXNHZ2wty+rDhy5AjSUlKgaNTA4jqStTXk2iHYtGVLme0Xw1RnkpKSsGTJEmHlNnfuXERERJTatm7duoWPP/4YofVCxQx48uAcNnQYdu3aJezqslN0CIgOhb3WSXh3EkpZBfd0P/gkhGDdunXYv39/qe0fwzBlD6fpKxBkorznn3+g6tsTilo1c59XOjtBpo74Xf8Io+i2bdtiwoQJOHnqFPauXANFvbpQNAgFbKwhR0YBJ09DSkoWI+Eo/f7C2LHInjETqB2c4zMaFQ3jzXBhu7Jk6dIyrcOiSIjJXqooJHs7pEdEldFeMUz1hNwsvvjiCyxYsBA6rRZqhRX0sk5cZ7p17YZvv/sWTk5OJba9w4cPY8iQodBmaSEZFFBCDYWswP6dB4UnKTVReiXXhFI2n4Z3zHJDAhwwb948PProoyW2XwxTlblx4wbmz5+PDes3iO9g+s4fOGggBg4cCBcXF1QEODJaQdDr9cJiiURlXiFqgi7Syo6PQumQcyEmkTl/3jx8+MEH8EpOgW75KujmL4Zhx2482aIl1q5ZIwyZybLp0MGDeOfNN+GTkgaJzO5v3BJp/JTkZIx98UXMmjVLpMzKAh8fH/FTjoktcj0pNk7UyjIMUzoYDAYx0nLO7LlwivdE7egWCLnTArUjW8EzsQa2bNyKvn365d5APiyxsbEYMngIsjIzIRtlOGQ7ixGP1no7aKUsKIwKkX6nOe+WoEipXZojThw7USL7xDBVnb/++gsdO3TE7D/mIOOKAVa3nRB9OgFffvElHnv0MZw5c3eyVZWPjIaFhYm77xMnTojxaNQlTZ6VPLnnLufPn0dMdDTUT1n2+5SUSsihdbB561aRzqIifvoyGTdunKi1onpMEnA02jMv7u7uUKlUuHP7NlS1akDVtDEkWictDZFnzuHjTz7BiZMnMfXnn6FQlO79SdOmTVErOBi3jp+AVCPArJ2UMT4B+mvXMfi1/5XqvjBMdWbDhg3YunUrAhLqQZN9d2a6UlbCNcMHdlpHXMY5/P7773jzzTcfenvff/+9sHTTZLnCNzlEpN5NaJVZCHe5iGwpAwbJfI37XeiawTWjDFOcceFUY+2Y7g7vpFqgXIQJXYoWEYbLGDRwEPb8s0fohCodGU1OThY1iVQP+csvv4j08vLly4WYYu6SkZGR8w8L3fG52NkJ0ZkXEpDUbd+oUaNCQtTkS0o3A8p2raHo3wfKOrWhcHOFokYgVM91gap7V3H3tGjRolJ/S0h8vjlhAgxXwmDYdwBygYisnJQE+a/18A8M5KkmDFOKzJk9Bw4G53xCNC82ens4prlh/rz5+ZqMHpTVq1bDSm8L/6Q6+YSoTpGNZNs4WOtySncina5BtiA26flM+2Q0adbkofeHYao6U3+eKv6OfZKC8wlRQm20gl9sHaSmpGHhwoUobxRlMbaS7InIN/Kxxx5Dv3798Pbbb4vno6OjS3vzlYbc9HV0joeoRWJic9ctLtT0pHJ1gfKx9mYjkcrQulDWCcGfs2aVSZdq79698cEHH8Bw4DCMf8yBfvde6A/9C/2a9dD9OReetrZYungxbO8lzBmGeSDo7/zY8WNwSCu6XkyT5Ya4+DjRdPQwxMXFISMzA24ZPpD++9qRYUSEUxiueB5DvP0daFVZUBuskWWVhqseJ5ClSi/0Oqk28ciQ0sq06ZJhKiPR0dEi4umU4pnbCFgQldEKmnRXLF60uOqL0X/++UdM03F2ds59rkuXLqJGkTsi71KjRg20at0aICsmC4JQdMBfuoIhgwbd13uwZds2yPVDi5ywJDWsj7ArV3DzZuHu/NLg1VdfFSnCfl26wPnaDdieOI3aSjW++Pxz7NqxQ3TYMgxTOtD1lx6SXPTUNWouMtWXPgyRkZHip43WITfCecf5CpJtY+CVUhN1YloiKL4xQmKbo2ZcQ7HdG25ncgWpQTIg3i4CEa5XxfcHBTbKi6tXr+KTTz7B0089jcc7dsIrr7yCAwcOsN0UU6GIisppALbR2Re5Hi2vCIHBUq8ZpVrGvn375nuOurg9PDzEsgeFBFtuajsPphR2wVR2ZeDll17C888/D3nbDiiffBySSpVfiK5eC1dXV5G+Nnfsls5TVkYGlPb36F7/r7s9Pj4enp6exd7nhznfQUFB+Pzzz8WjIMU9vupIZf6MV1Yq8jknG6aNGzeKv13qfO/cuXOxbuZq1ayFhIspcMn0trhOmnUSrK2sRVCBsllUp05uHuR1/CDjgw2KnHrQdKtkpNjGwz+xDhyz3PM3KOkcUTO+Ia65n8INj7Owle2RrciAAQb069cfn3zycbm8D3QtpQzf1KlTYSVZwy7dCZKswLar27F27Vo83vFxTP1laqXN6FTkz3hVpTTPufTf36fpb84SeoUO1tY2pfadW9xR46UuRlNSUoT4LAhdNKme9EGhGqYLFy4UaWVQ2aAC4tdeew3Tpk8HLl8FQuuIGlHExMF45SocnZww+bPPxJfP/fgAOru4IDkmtkDFSH7k2Dhhkk9+g0Wd16p0vis7fM6r9zmnL7Dp06Zj7769UEABK9hAh2z8+OOPaNG8BSZMnGD22mviqaefwswbM+GuyhAd7QXRSzokOcRA1hsw+ZPJYhoSCUISZP5+AXjzrYkIDg4u1r5SFNbVxQ3JGTFw0Doj0S4K1jpbUQZgDqopdUv3Q5TTNbR8vBkCAgLQqVOnhw5iENevXxcNo+RgQhmpxo0bF6txkxq+yC7PIzUAbml+4pwTcoqMVOsE7P1nL8aNexEffPA+KjMV6TNeXbhRCuec/ua8PL2QnJXzN2cOylCkauLxSJu2D/S9X1yK06xeaX1G6Q4974jMvBdoemNr1qxZKe9QQ0ND0b17d2FATV3zdLfi7e2NAR98IGYzm0aB3g+DBg7EH7NnQ37sEbPjQ0UT0YlT6NihgyipuB8q+/mujPA553OenZ2NkSNG4tSJU/BKqgXnTA8oZCWMMCLFNg5nTpwVAnL5iuUWrxmBgYHYsX0HbksX4RUXlM9kPlOVhkjXMOihhVOaBzzSAmBlsBFfXhTVjDPexgfvf4Bly5eJa1ZxGPfiWHz7zbdwykxEljpdeIZaqmUjNFkuiHIChg8fLqbQPSwXL17Eh5M+wqnTJ6GQFGLbBtkAP18/vP/B+3j22WeLPN/Lli6Hc4anOBd5oddxzHaDnGDEoUMHRSSofv36qGzwdaXqnfMXxr6Ar778Co7WCYUaFelvOdYhHNnIFGVzxf07fpCyluJQ6mKU7sxTU1MLPU9R0YcxU6awr10Rxun0xha1vCLToEED0f1Oj5KAUv80gjNr9TooeveAZHf3Q0+jQg3bdojI6Bsz3njgc1aZz3dlhc959T3n5H5BDUhUX0lpbRMUrSPvTludA25IZ4VDxsSJE82+Bh3HylUrMXrUaJw6fQq2sIcyywqylQHpihThnuSS4QWflOB8wouiLHYxGtzyPIfPP/sca9auKdY+kw3dv4f/xe7du6kI9J7ry1JO7by1tfVDn/Nz585h4MBBQJoE/+S6wl6KyFSnIT77tshIUfq9YEmZiZ07dyI5JQnB6U0tboPKDeKk2+K9admyJSorFeUzXp2wLaVz/uKLL+L4sePYsnULnNI9xM0UNS1lqzKQpIlGqjoRH3744X0Hoe6H4pbzlHoDE9UFFkyrkDglA2RaxpQ+1H2/eOFC2KamQf/bTOg3boX+8BHot++C8bdZwPmLYgxgmzZt+O1gmErA7Fmz4ah1zSdE80Jpd4dUN8ybO1+koy1Bk1g2btqIFStWoNfgHmjTrTme7feUqDtVSWrRXGQOisK6JPviyNEjuHTpUrGzWbPnzMb4CeOhtFYgxSbBooUTkWadKNLndHP+sLz15ltAmgIBMQ1yI7I59aka+CfUE5Hlt99+x2LpGLkJ0HQqc+UMJuj1rDJtcdPMiGaGKQ+USiV+/+N34V5jXVPCDfezuOp5HOGuFxHQ2Ed4CL/88ssV4s0p9chohw4dxJjLvLWjW7ZsEReZ9u3bl/bmmf9o0aIF9v3zDxYvXoyly5cj9sZNkb7rPniwsEnhGwOGqTwjPC9eugifjKLrNSktFx5/QXTK+vn5FRm5eOSRR8TDRM8ePWGX6VTImzAvjlmuuAOIYSY0Y764gpQM9Fu1aoXBgwcjxSYOTlkehdYj4/tExyg89dRTDz2u+NSpUzh95jQCkuoJQ39zItIjpQau2h3HypUrRSapIBSdpZQ+2VGZrKnMISuNsLG1eaj9ZZiSFqTk+EBRUpq2RFqMSv9q1679QE2IlVaMDho0CAsWLBA1CXQy6ML43XffiefLciY6k9MgRdMY6MEwTOXENLq3qHpLsfw/26YHGfVbHNunnElID/b6ZM1EfsNr16yFLjkbLhneomkptybV9RZU9grhSf2wHDx4ECpJVeSYUTIAt892FBNrzIlR2l+jbBDRXKc83f95IfP+dHVyudpOMUxRopQmIFZUSl2MUl0ozVIn+x4SpDQOlIzvaRITwzAMc3/Y2NigZo2aSMxKEvWhRaW5nRydzd70U1CAIprkSkJRzTp16uRb3qhxI5w7eR5ysuVIINk+EQ/S+EARmSlTpgj/6fnzFyDeKQI2sIUeemTLmQgJDhHpfGrseFioTEH6r2GpSIySiDqbg85Pu7btcOLQKdhHO4q6u7yQiI52viHq/izVnTIMU87d9GT/MXfu3LLYFMMwTJVn1OhR+OzTz5CVmi7G/ZmL0qU6xmPc8LH5bFXu3LmDTz/9FFs2b4HBeNfIvkWLlvjww0nCP5QYNmyYCCIk2sXANaOwDymlqxMcIxBaL/SBoy2UsqcmTcrUrF69Grdv3xaNHE8++aQYbUzd7yUBpSN1Rq0w0Dd3rghyIdDaZYh1LfHDjz+g+3PdcQvn4JzsDcdMd9EwRpHcBMdIMTlq5vSZcHDIMfZnGKb4VFprJ4ZhmIoA2a9RLRZF1chs3t/fv9S3OWTIECxZvATXcQme8TVEfShF/kxp7hi3G/Dwcse4cePyNeGQmEpNSIdHUqDw+FTIEtKtk3Hx38vo368/5sydgyeeeEJYE4ltLFkCo2T4L42eU2+ZrcxEjPNNIb4+/ezTh647oyEb1Gmfl5I04KbjcXfzQHzmHfgm1TYbIU2yi4bWmC2O2RLkSUrNXjR9adu2bYhyup67rEnjJpj04aQK0Qdx5coV4ViQlZUl/FnJsopt95iKDotRhmGYB4A6r3/44QcsXbIU6Rl356g/9uhjeOvtt0rU3ictLU2IXSp7otovKndasXIFxo0dh0OHD8FGsoVKbw2DSotMOQMN6jfArNmzRJ24iYkTJiItPgM1ohvkSzOTJRFZHd1xu4xXXn4Fx08cF+nmr7/+WjTuUFYrwSkC1lpbMZaTIoxKhQrDRwyv0DVoeSOwkz78QJSGKY0qeKQGivpUU4SXor8krocMHnLPRk4Sd7NnzxZDR44dOybGpFI0tSQ6/osLudGsWrUKmzZtQkpyCnz9fDFgwABRbvHO2+/gwMEDUEpKKCWVENgaBw3eGP+GEPwVqWGFYfLCYrSaQRcyuojSFxvd6Re3C5ZhmLvQpLJevXrjRth1OKZ4wiszRNgdZVil4Ng/J9H3UF/MmjVLdIM/TK0jdXeTjdO58+fEc85Ozhg2fBjGjBkjakFXrV4lusXJ29I0DvS5554TNm15hcfly5dx8NBB+CXWLlTvSFBdqGdSTVy1Oo41a9aICKFKpRJp9Hr16gmD+wx1qhC8DlkukBQS5s2dh82bNmPpsqWFak5Lk4SEBCHO3dzchCgvDiTWKNr6ycefINkhDraZGjHKM9suDVo5G4MHDcZXX31V7H3w9fUVj7Jm//79GDN6DNLT0+GQ7QylQY1rNjexdetWqBQqqIxq8R6LqDcU0CozEZ8eKd7HxMREYfHDMBURFqPVKIrzzTffYPmKFcjKMwe3SbNmePftt0tkwgnDVBcoVXvj6g3hW2mTx3vSKtMGTpnuuON2BS+9+JIwpn+Q4R50s/jCC2OxY8d2OOpc4Zv+n9hNT8Hvv/4horFkWE9RuSZNmohHUezZs0dEyyyN3xT7brCBg94Ju3btyk1XHzlyRAhRhwwXeCcF5UYUxT4qsxAhX8aA/gOxa/dOuLhY7lZ/WGiq0bp16/DnH3/ixMkT4jmVUoXnuj8nfBIbNmx4z9cYNWoUunXrJuztyHxfp9ejXr26oj62LMX0wxj3Dx82HFbpdghJaA610TpnQTJw0+UcstQZCIhrIJwBTFgZbOGTEgS1wRrTp09Hz549yzSKyzDFhcVodYni9OmDa7duAc2bQF0/FJK1FYx3InH26HEMHTYMv0ydKqxWGIYpGhrisXrVangkB+QTonmjjN6JtXDF+rgwk3/hhRfu+5R+++232LVzFwISQqHJY0lEhu3uaX64bbiIoUOGYt/+fcWa+0zjLClta5qnbglJrxRRR0rN06jQY8eOi2lJNL2FxHBB8eoXUxdhipNYunRpqZlnkxB9//33hUWgRucCv7TaUBrVYqToljXbcmfGFzXO0wTNtn/jjTdQGfnpp5+g0KrgF1cnn/+rXqFFunUKvFJq5BOieXFL90GyU7RoSiNrRYapaJT6BCam/Pnss8+EEFUMGQDVo49A4eoCyd4eyjohUA7qB0X9UIyfMAExMTHlvasMU+Fv7Pr16w+jbIRThmVbJUqFO2Q5iQEf9wuJQUqBu6b45BOieV/bOy4YdyLuFPv1qdaR6gep+cgS1PyUbZOBAwcO4sNJH+LfrcchR6ugNKhwy+0Cbrqeg17S5fsdis5pMlyxcP5ClBY00pSEqE9SMALiQoVJPo0kdU/3Q63IxrBLdxJR6PDwcFRVqASD3munZM9CgwhopCkkWdyoWIJukOzSnHFw/8Ey2FuGuX9YjFZxqL5q9V9/Aa1bQOFe+GIlKRRQPtkRRkkSF32GYYq+sYuLjhNz2/OmrM1B9XzpaXcbm4rLjh07kJmVKebCW4IsiuwNTqK+szhQ1NBR44gE+wiL69A0JC2yxEjLkOjmCIyvD/+kOgiObYbA+FAxzzrc9YJo+sm3L1oH3ImkWUylExX9bcZvcMx2h0uml1mR5ZsYAtkAIVirKiS0abiAnbbw+FdZyhmpWtRkKLFclqA3WB4NyzDlCYvRKg5NH9FptVA2sGxMLdnYAMG18PeO7WW6bwxTmaAGEOpg1qS7iuFDJM6KijLqbDPhH+D/QNtRSIq7NYEWUGVbIS42rthG+RMmTkCifTTi7G/nE5S0r6nWCYh0DhMCu0Zc/XzbJiskB60LAhJDkWmVJqYQ5cWg0MHGunRGYFLj1fUb1+GcZjkKTeUDDqmuWL3qL1RVyBGAIDeDgljrckpFyNLLEvQeZ9mnIbT+/Q8oYJiygGtGqzjkNSewtS16RRsbZGRYTuExTHWHJhaRlY9nagDSbJIQbx8Bv2TzJunUeZ4upYqxx/cLTSWiMgAyri9KkBqstHAzk+2wxNixY4XQnTp1KpKdYmCX5iSiaVl2qciQ0kS01zu5psUIm51OA7tsR+HJaRqJKXxNNYno/PQzKK2yCIIacIqClqekFE+YV0aowcrN1Q0p6XGw1+WPjlobbMX7Em9/B45ZrmbfP5qWlSGlYsSIEWW41wxTfDgyWsUxGXDLkVFFrqeIjkGNwMAy2iuGqXxkZmaKL3qlbCWaiJLtYhFnf0cIsnzrqdJwx/USGjZoiE6dOuVbFhsbi59//hmdHu+Epk2a4cknnsSMGTNEOU1ek3aKNCbaRVvcF4rKpqmSRXd0cSGrp3fffVeUAQweOQjuTZzgVN8Gj3frIBqEKNpLtZhFQWnibNXdm9Y4h9vIRDpGjx6N0sDV1VX81ObZZsGILkV6U2zihc0TpfWramR0xMgRSHGIy6kRLYBnaqDopr/lclG4HOQ9R8k2sYh0u4oOj3VAhw4dynjPGaZ4cGS0itOqVSsE1qiBO0ePQ/L3M2t6bLwdAX1EJIZ+WXyfPYapbpCBPKW3SYzRRCK9UosYx5tItIsSzSMmn1GaaOTo4IgFCxdAobh7v793717hEZmdpRVWSWq9NSLD4/DV5a/w808/i/Xp79XR0VF4ic6eNUeIv4ICUa/QIdItDN5e3ujSpct9Hwf5hn755Zf5ntu3b5/4KUv560ELYqTlck5KONEhCinW8XjrrbfQokULlAYhISGoW6cuIs7GiClTJkh8RmuuQ6fSQmFUCtEVE5uGxzt2wvc//J84j1WNV199FTt37MS5M+fhkuwDl0xP4SpANybJtrGiiUmnycBVm+Ow1ztBoVdAa5uFbDkTzzz9DKZNm5bv88gwFQkWo1Ucuvi8/dZb+N///gfs2Qflo+0gqe6+7cbIKMjrNqJBw4ZiJjTDMOZp3rw5nJ1ckJAeCd+UYHim1hCTixLsokTzj1GShek48fMvP4sxl3lHNI4cORJWqeQR2QBKOWc9Qp+iRYTbVWHVtH3HdgQGBgpz8iuXr+Cff/6BRusKTTqJXZqDnoJUTRwcnOyxcNFCMSGpJCCfUorGksjzSCtsV0WQ4EuxjRVi+KbbOQTVCsLn4z9Gv379Su0jQzfP/3v9f3jttddEFNot3Vec6zsuV8S590/yg61OkzsGNVJ/G/369kO357qJSVXk8dq1a1e0a9eu0k8fopGeNHXr008/xYrlKxCru5W7zNXFFR+99pFIw5Mf686dO0WJFn2WqFSkOD6sDFOesBitBvTp00fYNtEUDunseRjrhAifUSkiCvpb4QitXx+LFi4UF2+GYSynSl9+5aWcMZl6W7hm+Agh5JesEctpTOYdz0to3LBxoclLlIqXtAr4xef3iDRZNZF35HX1KcycOVN07JPInDd/nvDvnDVrNq5cuSzWpdGOY4aOFt6lJTkBSKPRoP+A/li6cJkw7Sez9IJQJ75eqcNHH32Etm3bCgFbFgKP/I+pkYlqXVM18chCBhwz3eGXZ858TpOVM+xiHYVQXrdmPeyMGhhVOsyZMwf16tbD7DmzxdS5ygyVIpBPKJVV0I0KTdSjSVw0tMTkN0vi80FqlRmmPGExWkltPshM+9atW6JLlurSKKpJ4/tMBtdr167F3PnzcfnSJWHf1LpVKzHujiI0O3fvFuvUqlULw957X6T6imOczTDVHaqNJM9HMllPcYqFfZozJKMSWbapSFUnol6dUMxfMD9fOpRqTf/6aw2cUrwKCVET1MWuSXXH0iXLxHQnujEk8Tt8+HAxIYgaeXQ6nZhyZOqsLmneeecd7P1nL27hAlyTfOCU6SH2i9LA8XaRSLKPxiuvvCJmnJc1VOtK0c3Jkz/FpYsXhcG7SYjmhUz9qX6SBKlbkh/stU4iYnrz0k306tkbW7ZuFuKtskOfg/upF2aYig6L0UoEjQh8/4MPsGzpUiisraHwcKd2eeGv5+3riz9++w1BQUEYMnQoTp86BVVQLchtWgIGI/ZdvIDdu3ahR8+e2LNrV65wZRim+FAkkMQipX5pStG+vfug1ekQEhSMESM/Ro8ePcQNYl6oOUmrzYatrug56rQ8PuOOML3PO0KUtlmaozbzNgutXbdWRN3IYD3K6ToUkhJG2QAXZxdMHj/5gaZJlRTUfNOgQX3cPh9ZpMsA1dmSUX+mVaqIltLDOtoWN6QzIkI9efLkMt1vhmHuDSuSSgJ1iVLd56YtW6B88nEoGjWEZJUTIVFHRSNuxx70HzAAdevWxbkrV6AeMQQKH++7v9+2FVQXL2P9hvXw8fbGxx9/XI5HwzCVG2qQKW6TDNX6EXpF0YbjpuUFxWxZN2n9+eefiIiIEPPsMzIy4OfnJzr8K0L2RK/XC/P2oqCIKbke5HU5IPHqmOqOJYuX4L333ivXc8wwTGG4ta6ScPjwYTGDWer4KBQhwYDqbrpP4e0FRf/e0NvYiIio9OxT+YSoKbqiDK0LRdvWmDN3LpKTLRskMwxTshHHJo2bINX+rg+mjuaJWyULP1IjcozMqR6y/SPtS6wp6WGgetTBgwfj+eefR+fOnSuEECXoZjtLnQaDZFnYU1kBOR1Y6/M3YjlkuyAtPQ23b98ugz1lGOZ+YDFaCSBvQqrngkIBw47d0P42E9oZf0K/7yDk7GyxDkVJjY4awMEeipAgi6+lbNpE1J5t3LixDI+AYao3z7/wvKgpjbUPxy2XC7jieVTUNd5wP4PLXsfE3Pd0RbJYj7EMNeaQvVSCfaTZ5RQNpa57StNTt705KntXPcNURViMVnCoSalzly4ICw+HskUzqAf0hbp/bxEdNfx7BLpFyyCbJicplZA8PETDkiUkB3uoHOwRFVW0CT7DMCXraEE1j7GacGFK7pMcjOCYpqgV2xguGZ4iQkp1mW3atOHTXgTe3t6iiYrOY6z9bRjzjMfUSzpEOV4XwwjIdouamfKSahMvznFAQACfY4apYHDNaAWvEx39/POIy86C1fMjIVHk8z8UQbVgbNEMuiXLod+8Feq+vUR0VE5ILPo1dToYMrOERQjDMGUDNSUdP3YcDjoX+MfXzSeUbFMdROd6uPKCaI6iCU0PyrVr17BkyRJcvXpVNCk+8sgj6Nu3rzDSLwqyCFq1apVoXEpNSYWfvx8GDBggnDoqmuUbddYbjUbRjJToFAmbLAdh1p+uThH99d7JteCSmb9jPluZiRRNPF4e/lK5lxxQpuvff/8VGSpqOG3UqBFHa5lqD4vRCsyhQ4dw8fx5qAf2yydETSjc3aDq1BH6jVtgJBFqYwM5Ng7GmFgoPD3Mvqbx4mUYdTo880zpzJJmmKp0M1hSKV0Seunp6QhJbF4oYkfY6O3hmuyDNWvWCB9PaiS6H0jYTJo0CYsWLYJasoJ1lj2gMGLzps348osv8f0P36NXr15mf5f8Kse+MFbsH3WeK/QqXLYNE6U89UPrC3P9imSHRLZZdKyjRo0Sx3vx4kXxPP28fTOnHtQgGaCUaTKTURj5x7mGIyDQv1xsqUxQNooM6zdu3ASD4W7NK53j9z94XzSJMUx1hcVoBYYaltSuLpBqWE4rKerVAf7eCeO5C5BuhcPa1haGrdsh9+8NqUDHqJyYCOw9gE5PPCE8RhmGyU9kZCTmzZsn/D7j4mNha2OHLl07Y8iQIQ/l77l161Yh9IqyJHLK8ES0403s3r37vqcakR3TsqXLRFTQOcMz189Up8hGjNMtMcGIuvqfffbZfL93+vRpjBwxEtYZ9ghJaJ67f3KyjEx1KsIuhmHgwEHYvHlTritARYG6/EUtfR4/V4qarl69GrHO4bCGDXSSFjqjFo89+hh++eUXODvnH636sDcrFDBYtmyZaIpycHAQN/lk0l/wXNHnqvtz3ZEQnQj3JH84ZblD+m987K3TEWJyEo3rtHTDwDBVHRajFRjqeJcdHKAoIjojRnva2sJ48jQc1Gr8OHUqJkyciKx5iyE3bQRFrZrkhwLj5SvA6XPw9/LClB9/vGdKkdJIdHH39/dH48aNOY3EVHmOHDmCYUOHISsjG5o0N3jra4nRlxtXbxaRTeosDw0NfaDXptQ3RRyLggzmyZSIIpT3w6VLl0Rq3js5CK4Z+V00SFz6JoaI7vPJn0zG008/nc+Q/4cffoBSSxOg8pcO0H7Y6RzhF1MHV+RTImJL3fUVGRKANKWJBCmNxKR0OJUndOvWDbVr1y7RbZF37OhRo3H02FHYwh7qTBsYVQb8/fff+OLzL/DnzD/Rvn373PVpvGtCdBICoxrkuyHRZLvAIdsZkc5XMWH8BFFXTO4LDFPdYDFagXFzcwOSUyAbjRabkuRsLalHuLu6ijt0sj6hB9WdrV23Drrde8V69hoNBg8dijfeeEMYaO/fvx+bNm3KHSdHDRZU2P/tt99i8dKlyMrIyN1G7bp18fabb4qLOsNURWhc7vBhw4FkJYLjmglhaMI9zR8xmluYNWuW8Bbt3r37fb8+1WBesr0qIo7mJgcR2apM0Q3u4+NzX69NqWoryUY0QpmDtueW6oeb4Wdx4MABPProo7lp4x07dohoqrnSAVP5gEbrivlz51d4MZo3Yvryyy+X6vCRIYOH4NK5ywhMCIV9tnPue0rNadG66xg2bDjWrVsr6kHv3LkjRKp3Ui2zkXH6Xc/kmrhqd1yMf6UGLYapbrAYLUOuX78upiXt27cbOq0WtYJCMHjwUFErZK5JgNI9NKtaFXYNytohZl/TeO48JFkW9V10ESaCg4NFhIDqk2ib9Np16tQRkQNqcOjbvz8uX7woSgBkamRKTMKvv/4KB0dHZJJVVIumUDeoD8nWBnJUNK4dPYFx48aJ2fY0DpFhqhok6DLSMwsJ0VyxkBqIbOs0zPj1twcSo9QMRGU31DVvrzPfTJRgFwlXF1c8/vjj9/Xa58+fh02GvTB6t4SdTiOmKVEU1SRGb968KVLNFAEtCtssB1y7fg1VHaq73bZtm6ihJcFJc+zpfSPP1bzQ+3jm7BnUimsEW13+Wn4rgw384urgpuoc/u//vsf8+fOwb98+cZ4dM83X8RMqWQ37TCfs2rWLxShTLWExWkb8/vvv+Pzzz+HkqETnxwF7O+Dfk7cxatTfaNO6JWbPmVeonqlp06Zo3bYtjm/bCaOzc874zzwYb9+B/M9+9OrZM1eI5oUioHnHCFLdUu++fZFkNEA9qB+kwABRAiAbDJBWrUVa+G2ohw2CwutuhEWqVRNSzRrAzj2isYLSSCR2GaYqQfWWmnRXIQrMQYLUOc0Lp06fxI0bN1CzZs37en0SmA3qN8DVC1fhF1NXRBxNUDQ00S5KzH7/+H8f33e3N3XNk/dm0chCEOW96TXVwN7rd2m5WvXg9bKVAar9fOnFlxEbFwM7aKAwKpGlTBdlDFTPSSNETedr/rz50OhdCglRE1Sv65zshZ07d4hJVtnZ2TkzoeSinRTFNjOzSuX4GKaiw2K0DFixYgU+++wzvDRCjYkvqmFrk5PSoS+HA0eUeOm9k3jhhTFYsWJVodrMP3//Hf0HDsSVuQuhqFtbiEMYjZCvXoMh7Bpat26N7777rlj7QQX8SenpUIwaJvxGTciZmZDDb0PZsnk+IZpvelPHR4HzF0Vkl2c7M1UNqi900uevtyyIaaIPrXu/YpRE4IKFCzBo4CBclk9Bo3OFbaaDEHrpmkRkIh1jx44VGYj7hbxJD+w7CEOSvlBU10SqTYLoLKfrhYn69evDUeOI5LRY2OoczP4eCWXav6c7PomqyvHjxzF40GBYZdohKLFJ7o0CdeTTTQKJTxqLOmXKFPH8lStXYZNp/nyZsNc6ius7ZaYowkrnkSZHWRKwtFxrl4FaQdxYylRP2PS+lDEYDPjxh+/w3FMqfPD6XSFqEnntWyvxyxdKHDx4WKRzCkIWLxvWrcPkTz6Bf5YW+k1bod/yN2pbWeP/vvsOy5YuhZ1d/rF35qCL6bIVKyA3aZhPiBojIqH7c65oclI0CC2yUUquWxvbdux4oPPAMBUZ6oSmEZ1FYVpO6z4IVJu9afMm/PjjjwhqEYBMn0QYAjLwTM+nRIMQ3eQ9iJUUdfpDISPWITzfPHYTJKoSnCLQvHkLIUBN0Hz2ocOGIsUxDlkq801TJMZIKFfl8pxPJ38KVbY1/GPr5YtYkzWUe7ofvBJrYfny5Th16pR4XqVSCl/TojBFmylqTWURPt4+iLePNPv+EGnWOTckQ4cOLdFjY5jKAovRUoYaBm6FR2DsMJXFL5qO7ZSoE6zGkiWLzS4ng/oXXngBB/btEzWflCbc8fff4kuouCk9qg+jpiQyyzchJyVDt3w1fbuK/xe0giqIZGMtOuwZpqrRvUd3pGniRfTQEsn2MQgMCBQNgg8K1W0PHDhQiNKLly/i7Lkzol6bGqMeFBK5ZJaf4BCJCOerucKShE+qdQLCPc8DDjK+/fabQr87fvx4hNQOQbjnBTFGk6YYiSieKgORjmGIcrourj15I6pVCaqhpY548ni11MTlnOkJG8kO8+fPF/9v/2h7pDskWRSWBHmb2tnaoWHDhiIq/s677yDFNg4xmpsw4u7UKHqNNKskRLldw6PtH62y55lh7gWL0TIY50k0bVDEiE5JQrOGMm7dunHP17O2tn4gv8NcOxf57gVUf+QYoFRA1TXHAN8YaX7ec+5+RkWjRmDgfW+bYSo6ZKBOQizS6ZpZkZFsE4tkmziMHjM6nzVSRYFsp6i+UeVnxDWPU7jqdwxXfI8i3PUighrVwJo1f+WLipqgKO/qv1ahZ98eSHC9g8veR3DR5xCueZyE5K/Dxx9/XKXLcs6dOyd+OmTfra0vCNV72qRrcPrU6dzPCkUxKWpsDpr2lOwUg4GDBuZOuqNGKDqXCZpIhPmdwB2nK4h0vIab3mdxy+08WrZpIeygSmrIAsNUNrhmtJShVBiRkgY4F9G0mpIqw9q66Mjkw0B1S6Jb/spVKPz9RNOS8ex5KJs3hdLPFwYfbxiOHhcz781dEGmqk/76TQyf8Gap7SPDlBchISFCzE2cOBFam0w4pXjCRm8nfEaT7WORYh2PTo93ykmJV1AGDRokRn+SjRCNA6Wb1nbt2olGyKJwcnISVnDUoEiG++RzSvZS1HRV3qMzSxvTta6oKGcOd6dxUfSSyhbmzJkDrSoTLuk+sDbYinKIZNtYJDhHwC/AV3yW8vLiiy+KoQNUd//Pnr2iY792nWYYPnw4HnvssQp5k8MwZQWL0VKGjI+VSgXWbNZj1EDzEc3EJBk798uYOLH0mgREfdjgwZg5fz7kZk2olZYM8yD55ngaKtu3hX7lGui3bofqicfFnPu8QlRevQ5BISHsNcpUWSh6RV67036Zhj3/7BENKERIcAjeHjVeeEZWdMFAArRr164P9LtUn36/k59KCjrXJ0+exPbt20V9O+1LvXr1Sn27TZo0ET9TbRLhbMF6iUo3Mu1T0KJl59znyBnF29sb06dNR1jqCagkFQyygcKo6Ny5s/BrNmdeT41vJPrxUSkeFMNUQliMljJ0werSpQt+mb0VTzxqRKBf/i8zg0HGZz9qAVkpIhulCY0E3LRlMyKXrITcvm3Ok+QrSmI0OAjo8owQo9oLl3I6921tYIyMFp32tYKDsXTx4txIL8NURSiSSA/qmI+OjhZpbMoqUK30hQsXynv3qiTUcf7Ky6/g9JnTsFJYC3utLGSCApFDhg4Rwu9hRrEWRVBQENo/0h7H95+CJstVNC0VJMEuCtlylrB4MkFRUrqeUnkECWiyzaNG0k6dOpm12WMYpmhYjJYBn3/+BXr1OoWeo6Lw4nAJPZ9Vwd5Owr8nDPhjoQGHTxjwyy/TRDTgYceHrl27VsxJptpSSrM1b948N71Ed+prVv+F115/HQc3byO/GRjOnoOyYU4tmbJxQyhqBMJw8jSMYdcg6/VAVjZq1KolGqboNRmmOuDh4SEepQ1NQNu5c6cYL0mewDQAg0ZYmoPSups3b8aihYtEQyI1Qz319FMizUuCuTJC16qePXoiIyELAQn1RO0m1WgaJB0S7aKxcOEicV2jJq/Sqqf89LNP0aNHT9z2vAD3xADYaR3FPugVWtEBH+9wRzRxmRsFS+/BgwxBYBgmPyxGywBPT0+sXbtB3OH/34y1+PLnux3pTRo3wPz574s76gfFaDSKercZv/0GrU4LtbMzjFnZwkKmfsOG+HXatNzZzBSpXbl8OS5evCh+h0aCGi5ehrJeHbFccnKEijxFOz4Kw4VL0K/bKEaBshBlmJKDhOU333wjPCwzszKhkBQwykbYWNtg2PBhYpZ53r85Gt05ePAQXL58CQ56Z1hn2SFNkYyZYbPEQI2vv/4aw4YNq3RvEXkkpyVmoEZ0A6iMd+tTlbIa7un+UBtsxJx5GkVKAzdKAxKZq1evwquvvIqwa+dgrbAVEdJMZIiI7IRXJxSq/2QYpmRhMVpGUJSFRnSSBcvRo0fFVA5KEZH1x8Py4YcfYt78+VC2bQV182bCR1Qhy1Bdu4HL/+xDj169sHH9erE9E1SPRV9i/3v9daxdswbGW+FQNm0MyckJcnIyDCdOQT51Br379EHPnj3vyz2AhC5FMagei4Q4wzB30ev1eOGFsdi1cxdcU3zgn+ElZpaTjyl1aM+ZNQdXr1zF3HlzhRgi4UpC9OaVW6gV2xi2+rs+p8ZkA6Idb+Ldd98V1xhqkKksUDSYMjmuyX75hGheHLPckAgHzJs3r9TEKEH1wFQnTFZ8e/fmNBcFBgaKkczU4MUwTOnCYrSMcXNzK9EvjDNnzogLterpJ0RnfL6pScG1IPv5IGPBEnz2+eeYO2dOvt+lZoypP/+MunXq4I+ZM5F4IsfUmXB1d8e4d9/Fq6++WqymDbJI+WHKj9i755/c58hfr3OXLvhw0iRxYWcYJmci244d2xGYEJrPUkhttIJnWqBIE+/ZswfLli0T0U5KzVNEtKAQFX/DUMI7pRZ0Vtn47tvv8Mwzz1QaeyCqwSVhTrWalqB0uV2qM47+exS//fYbTp8+LZqdSDySXytdT0sKMYSkfXvxYBimbGExWsmZO3cuVE5OUDRtbHa5MLJv2VwU2d+5c6dQcT0Jxtdffx0vvfSSmM+cmJgoateoiaO4TQPnz5/HJ59+CoODA1Rdn4Xiv5GlxitXsXXfXhzo1g3r1q7NF5llmOrK7FmzodG6WvS2dNA6i+WzZs4WE3moRpRS8wWFaF7B5pLqjYuXLoibwpLItpQFVF5EFEc6x8fH48svvoSdLqeedsP6jaJj/b333hPXrsoiwBmGMQ+L0UrOv0ePwhhcE8oiopeKOiHQb9shogqWOj3JT/BB0mA6nQ7ffPcdDB7uUPTrBSmPgFW2aAY5tB5SlyzHq//7HzZv3Hjfr88wVQlqWDp/4Tx8M3JquC2hSXfF5SuXkJSUJJqVqEa0KGy1DrllMpVFjNIkK6VCiTTrJLhm2FpcL9UmASqDFWrFNIbKmHN9oQEF1Fj0xRdfiMwNeXgyDFN5qdimeUyxoguSdI+38T+haopEFBeDwSAMtKdMmYKffvpJGGIXfA2KuCYlJEB68vF8QtSEZGcLdGiP0ydP5s52ZpjqCt28EQq56L9ZxX8WQ7Q+dWwbFPoi1zctp3UrC1RP3rlLZyQ5RcMgmT++VOtEZKszRCmCSYgSZP/klVoTruk+IkKakpJShnvOMExJw2K0ktOkUSNIN27lGnSbw3jtuvhpzprEElu3bkWrNm3E6LuffpuBKb/+KlKG7dq3F6LUxD///AOVlycUnpZtcBTBQVDa2QoLG4apzlAzjLOTMzKsihZP6VbJcNQ4Cjs2sm9Kt0/KN9O8IDT5x9bG9qFm3JcH77zzDlT2CmGrlKFOzZ2ERMeaaBuN2y4XYZftaLGkwS3NDzqtDqtXry7jPWcYpiRhMVrJISNmfXw8jBcvm10uUyTmyHG0f/TRYtdsbty4UZg5x9nbQj1iCJSvvgjFq+OgHjYIkUoJw0cMx44dO8S6ZAYu2xTtPyopFFBY2yArK+sBjpBhqg5Uo03WTamaeOFjaQ56PtUxHkOHDYVKpRI+ohQ5pK55c2Mrs1TpSHaKFrPQyaS/so1hXbV6FVwDXXDD/Qxu+p7GLa/zCPM9gUjnMCgkpWj0orpYc1DTl63sIJqhKDpKziLdunUTj8mTJyMtLa3Mj4lhmPuHa0YrOW3atBHj/7Zs2krKEIpGDXLT5WKM547dUCaniI724kCC8a133hETmJQ9upEPDQxnzsEYFUVz8SDVqS1+vvn2Wzj67xH4+/sDu3YKg3xJZf7jJKelQZ+SnLMuw1RzxowZg2VLluG24SK844Jho7fPXZalykCU+1U4OmvEDSFBhvbkI0r2TdQ1T81KVCNKqXmKiJIQDalTWzTzVEaoxvXAwf0i40JlQTQOlFL4ly5dwoGdh4RjQNHIOHLkCOqH1hdiXW2wFj9pvOisP2dh7Itj8fHHH5fR0aDSDh9Yvnw5Ll++LEbikp1fZak9ZqoGLEYrOdRFOm3aNLz73ntYuWIF5H0HIXm4Q8rOhi46Bh5eXvhjyRI0btxY2KhcuXJFeJxSI5O5CTPr169HSlIS1P17w3juAvQ7dokpTJK7G20MRrJ/slIjVqsTXxx9+/YVfqXSuQtQNmlkdh8Nx07CSm2FHj16lMEZYZiKjZeXF1asWoFhQ4bhmnQK9gYnqLKtoLfSIl2VDB9vHyxctBA+Pj65v0MWT/T3SvZN1DVvglLzQwcNFUJUo9GgskJNSDR9ih4ECdI///xTZGAo8ptXsOclW5mJdKQK4eqY6QaP1ABYG3Kavej3YjXh+P2338X/WZAWhjJbb7/9NtasWQMlVLCSraFX6MTEq5YtWmLGbzPg6+tbiu88w+TAYrQKQJNafpoyBePfeEPc3VJHLc2Qp6lO5DtITUc///wz5s6bh5jo6NzU+VNPPSV+p2nTu/6kZMiv9vKCHBkJ/cYtUDQIhap9O0guzmI5GeLr9x+C8cw5zJ8/H7NmzUKHjh2xb8du+maEonZIrs2KTPZOp87AcPiIGEHK5tEMkwNNRNt3YB+2bNkihEBsTCzc3N3Qq1cvdOnSRbhbFIT8ienvmeyb6G+cmpWoRrSypeaLS9u2beHm6oZYbTj84+sWStVT9DPa6YbI1DhmusMvuXa+dUjA+ifWxW3nS/jz9z/FFKWqeq4eBGqOGzliJA4fPAyvpFpwzvQQjXN0XlOtE3D2+Dn06tkLmzZveuhR1QxzL1iMViFq1qwpGgIK3vkOHTYMR44ehVS/HtRPdCD1iv9v707AbCzfP4B/zzr7vjCrfScila1UKGUNiQoRZYvSv1X8RKVfyy+kZImiSLJUhiKkKNlDtmEYY8bs+5yZs/6v59FMhpkxas55Z875fq7rXMM5r5nX85455z7Pcz/3bUtMwvaDB7Cjf38sWbwY99xzjzxebISyqVUwb90BdfOm0D5wX6kafqJDk7ZnD4i9r7t/+03OYEycMEHmwm1f9y20oSGw1YmWdUZVZ+NgzsySea3PPfecw8eDqDoTdXxFX/Mb6W0ufhfF8qkrLKGKfNnX33gdY8eORULQCQTnRMLd5C0DToM2D2m+Cchzy5TBqGgWUFZeqbhPPHbGI0PuuhctmenvVbBdu3ehTnoLeBn9So2Zb1EQ3JO9cF51VK68ifxbInviBiYn9/bbb2Pv/v3QDB4gg0h1dBTUtUKhubk11MOGysBxzJNPyqLSQuPGjWG+lCySR6ERM6JlFJMW94nZUrPRKF/QxMzsxwsWYNWqVeje7haEpaQhIiML/bt1x3fffSfz3SrTxYmI6EriQ7Jo7OEd7Y644CM4G3EQZyIOIC7kD3hG6WQqg5vZA3qLe7kD52b2hM7sJvNK6W9Lly6FjzmgVCB6JTGmvjnBWPnFSjmpQWRPnBl1YmLWcvmKFVC1awN15LXF7sWGI3XP7jB+uEi2Hhw3bhwGDhyIGa+9BquvD9SBZZdTkf/Wzxe62rXkkqHIRxUBapcuXeSNiK4vLi5OBkgijUYUgBfpMuwkdC2RTrTn9z3Yvn27bNwhiJlhEaiKHNMMZFfq6VZR+TtXJMYyKD+qwmNEq9b0/EScO3fuhkoDEt0oBqNObNeuXSjIz4euVflLeioPD6ga1sd3MRtlMOrv7492bdti76myS0URESod/MTGxiI7O1vm3Ik0GkHcN/WVqfj5l59LHd+0STNM/8+0f9QJzdmJNCARlIrblZo3b47Y07EwqYugs5ZdYs6oMcCkLZIfmukKojrKdQfkr/x/BvJkZ1w7dWLFNfZU3mXvRC3h5YWcnNxSO3eRnQ1rRma5/0RsZBK79V0hd43oRog37i+++AJ33nEnunbtKsvkdOrUCff3vB+LFy9G7169se+XAwjPaoiml25Ds6TbZS3NC0cTZWMJ0XCCKkfukFepkOadUPa1gA2p3gkyD/Lll1/msF4VyOd7VjyrLHJyPT08Sz5IEdkLg1EnLyEj2NIu54OWR5WeUaqMTK9eveAX4A/rL7vL/EQs7rP88hu8vLzksUT09++GKLMkyuWkHs9CVEYz1E9tLXd1nz0Qj+nTpsOYZUb0pRbwN4TK3csqqGWHoei05vA2BGDixKdlig1dn3jd6t27FzK9kpHkexamKxoJiNnSRL9YZHumonZYbVmuSKRG0GUjHh+BXF2G7HxVFjGWOb6pspmCp+flcllE9sJg1MkL4tcOD4flwKFyj7GmpcN87jwGDxpUcp8oC/X6zFmwHD8Jy3ebSs2Q2jKzYI75HpajxzDztdf4IkV0BdGWcsWKFQjLaoDIjCbwKQqQJYZ8C4MQlBkpVz1DsqOhsWnL3vmdXQf5+Xlsb3kDFixYID8UZ3pewunQfTgbfBhngg/hdOh+2RRAb/JAXlwhFi1YjM6dO8sZUlFz2dWJMmKilujFkBPI8kiBFdaS2eQ8fRYSah2HX5AfJkyYoPSpkgtgMOrkeVaTJk68XLz+9/3XzHKKpXbbhu8QGR19zQxn//79ZUkPr8RLMC1aCusny2FduhzGRUvhGZ+A9957D4MHD3bw/4ioelu0cBF8TAEIMFxelbhSgVsO1NbLs6DlETuYvcx++OWXX+x8ps5FNN74fe/v6N2nN3zCPVGkK4CnyQcNU9uiYdrNiMpshgYXb0at7LqyPvL06dPh6kQVFNFc4a5udyHRP1ZWKjhf6yjiwg8jPuhP1G0ajfUb1qF27dpKnyq5AG5gcnKir7Vo9TZ//nyojhyFrWljWWcUSZdgPXlavtCs/PxzORt6NRGQigLcolf94cOHZTDbqlUrWRdRFNwmor8lJyfjyNEjiMhrXOaw2FRWqOSyfNnbRsSMlFFTKHfXZ2VlcWhvkOgqN2/ePLRv1152Y4rIalxqrEVb0aCCcDk7/emnn2LMmDGy1aorE127li5bKjvziYoq4qto2yzynEVDBVZ3IEdhMOrkxIuJWJYSZVBEXbltO3bAWFSEqOhoPDZ1qpzdrKgzkghSRctPcSOi8uXmXs6901p1ZT6uN3vAojHJFpZuFo9SQahYTk73SpQzesLPP/+Mnvf1xISJE/DAAw9w2Ctp27ZtSE1PRf281uUG/QH5tZDhlyg3mb300ksc2786gokOVcePH5clnJgjSo7GYNSF8kfFjYjsQ/SOF80dirQFZRYSFzUbNRYt0r0TEZ7d4O+Wlr7nkOGVBO/CAITmRkNncYNRW4i4Axfk7J3YDDV58mRetko4deoU9Gq3cnvZF8+QuhV6yWOJqHpgzigRURUQKwzdu3eXO5BFkHnti60awfkRyPJMRop3PKywINc9XQaitbPrIzqzGXyKAks2PEWmNUVIbpTsoiZqBlPlWohabdYyx7/0xbDJYx2JtTqJysdgtBozmUzYunWrTLhfs2YNUlNTlT4lIqrA+PHjUagpQJJ/bMnu5GIWlRn5HtkyCEr3uYgz4QeR5BsHzyJfBBZcu0lELDMH50XC0+aDJYuXcNwr4dZbb4XZZkKeW/k5t2a1EQX6HHmsvSUlJWH27Nlo07qNzMVs2KCh3J2+f/9+u/9sopqEy/TVkPgELfI7P5j3PpJT0iHaulutgE6nQe/effDaazMREFD+jlwiUka7du1kFYqnJz6Ns54H4Z0b+NeyuwG53hnQu+vwxadfyMBk0aJF8vfcP7f8guIiIPXJDcKWrVtkOSJHz+bVNG3btkWzps1w/thFeKX4yiX5K4kZ0xSfeOh0Ogy6opydPezbtw+PPvIoDPmF8MkLQpipPswaEzav+wHr1q3D1KlTMXbsWLueA1FNwZnRakh8kn711VfRtUM2fljlgXO/e+KPbZ54+WkNtm/7FgMG9JMtBomo+hE7kbdt34bHRj4KdR0TskKS4N5Ag/FPj8NPO3+S3ZjELu7Ro0fL47WWsjc8FdNa9HKHfWFhoYP+BzV7w+Z7/3sPVk8j4mv9iVy3jJIle4MuDwkBp5DlmYJZr8+SrY/tRaxiiUDUlqVGg6SbEZZTHwGG2gjJi0LdpJsQnBeBWbNmYdOmTXY7B6KahMFoNXPw4EE5s/LKJB3emeaGZo3U8gU2wE+FJ4bq8PViHZIS4/Duu+8qfapEVI4GDRrgtddew+E/DuNs3Fns278Xzz//PMLDw0uOCQoKgkatkRueKlKky5ctGbnDuXJED/q169aiXstoXAg8gVPhe3Ey7HfEBf+BXLfL3ehmvzlb5uJmZGTY5TksduoX5BsQntb4mgYHYrY7JDca3iZ/zJs7zy4/n6imYTBazYj6d5FhWox+pOzZkkb11HhsoBqrV69Efn6+w8+PiKqGt7c3et7fEzl+YsNT6fzSYnKTk086Hhr8kNypT5XTunVrfP/D91i5ciWCggPlOHoV+sm2rFEZTWG9oMMHc+bj/p734+LFi1U+rKtXrYZPfiC0trJfx0VA6p9b6/KHlbNnq/znE9U0fHWrQkajERs2bMCgQQ+ibdubcPtt7fDMM8/g0KHy23Febffun9C7hwoaTdk18oS+92qRm1uAo0ePVtGZE5ESRM6gUV2IxIAzZWx4suBi0GlAb8PIkSN5gW6QWFESG78y07JRL/Um1MlsAd+iIFmxQCyb101uhdSL6XjiidFVvtM9JTVF1pWtiJv5cr/3lJSUKv3ZRDURg9EqIpZ7+vXrg3HjxsFmPIAhfXJw/13p2P3zOlm0+o033qjUC56xyATPa5shleL512tcUVFRFZ09ESmhTZs2mP/hfOR7Z+Js+EFc8olDumciknzP4kzYAZh8CuQmJ7HsTzdGzDhu/XErQjKjyqw7KlqvhqbXwR9/HJabjaqSj7cPzBpjhccUPy66IBG5OgajVUAEmaNGjkBC/HF8s8wdqz/WY8pTekydrMcvG3R4ZZJetuNcsuT65VnqN2iI368zkfr7QcvlY+vXr4rTJyIFifa627dvx7BRj8kNT5nBiXCrBzw4qD/uve9evDX7LfTp3Ud+oI2Pj+e1qiTRxlir1sHXEFzuMV5F/nBXeeLbb7+t0nHt3bc38rwzrpntvlKWVwqiIqNkxyMiV2f3YFQUa54yZQq6deuGJk2ayKR+ZyP+j7/v3Y+5s7S4uVXpUiJiuf2pYToM6aeVpZrEUn5FHnnkMfy8x4Q/jl8OOK9mNNmw+Asb7ryziywPQ0Q1n/hgOWPGDJlDePLUSXTo2AGrV6/G99/8gHO/JeL0rvNY+OEidOzYEXPnzlX6dGuErKws6G1ustlAeUTuptasR2ZmZpX+7GHDhsm6spf8z5ZZgD/bPQ3Z7qkY8+QY5gITOSIYFT2WT5w4gfbt28PX19cpB/3LL79E4/o6dLmt/OEcNVSH1LRM7Nixo8Lv1atXL7Rs2QyPTzbj132WUkv7yalWPPWCEbHnbJgy5f+q9P9ARNXDCy+8gA0bvkF4VkPUS2yDiOxGiMxqLEsEBeVE4K233sKyZcuUPs1qLzAwEEYUwqoq+4O9IAJFk7ZIHluVRFrF+3PeR45XGs7XPopMj2QU6HJlqamEwJO4GHAaDz74IEaMGFGlP5eoprJ7MCrKmYjlkjfffNNpc2MuXoxHiyZWmTBfniYN1NDrVNfduenm5obPP1+FiKjmeOjJQtw31IRnphdhxKQi3N6rEL/u12HJkqWyuDYROYaYOTtz5gxOnz4ti8/bS2xsrJwRDc2qA39DqJy5K6a2aRCaFw3/glD89623mTNeifQHCyxyFrI8eW6ZKLIZ0L9/f1Q1EWx+/fXXuP3u9rgUcBbngo/IUlOhzQPw1luzMWfOHM6KEv3F7u08XKEciaenNzKzyw9EhZxcm1xir0ytwODgYHzzzUbs3LkTX365CucSL8ifMW1aD9k1xFlnmImqm2PHjsll8U0xm2CxXp5hCw0JxYjHR+DJJ5+Eu/t1dhveoFWrVkGvdpMBZ3mC8iJwJucgNm/eLAvsU9mio6Nx//334/uYH+Bu9oSHqfRkSJGmAClB59C+XXu5kcweRMvR5SuWIy0tDcnJybKclziviiYuiFwRe8tVgW7dumPatJ+QlGJFWGjZwffaGDM0GjXuvPPOSgfxXbt2lTcicmxlDDED+scff+D119+A1qRDcHYUPE2+csk3uyAN7779LrZv246Vq1bCw6PiEj43Ii4uDnqDZ4V5jm4WD7ip3eWxVDHRHCTx4hAcOnwIPoWB8DYEQmUD8t2zkOOZLnN1Fy1eZPdhFBMM4kZEThaMilzKgoJrO5cYDIZSXx2hZ8+eePPN1/HSG0YsfFsvl+OvFBdvxfuLrLi3Rw85q1nWeddUSoy3q+OY20dCQgLef/99xGyMgclsunynDfAw+MralKIUkOBl9ENBQQgO7D8oNx1Nmzatys5BfAi1acrfgX35lGyw2C7P0jrTa4k9nuMajUbOTIrUh+WfLUfcudPy/vCwcIx57Dk8/PDD8PLyctpxvBF8XeGY2ytWq8xKwA0Ho7m5uZUq0hsVFQW9Xg97MZlMOH78eLmPnzt3Do4kNhS98cYs9B5mxOhH1Li9nQYFBuDbH8xY+qUVPr4hGDL0kQrPuSZz9HgTx7yqA9EXnn8RRXlGBOSEw6coQN6f65aJDK8kxLkdQd30lnJWUhCzpAE5tbH6y9WyjnBVteqsW7cuNmk3wagpLAl+y8pzNNtMCAsLc9rXk6p+Xbnllltknn1hYaF8cxSz2eINUlx3ss+YU+Wdc/Ixr0wseMPBqMhTmjp16nWPi4mJsWuhZp1Oh4YNG5b56U5cWPGiXpXLZ9cjasW1aNECc+e+j2em7y6538vTHX37DcLkyZMREHD5Dc6ZKDXeroxjXrVEcDLl2edgzrWibkoraK26Ul1yxEai84FHcTHgFOql3VSyqUjcn2ZMkPmA9957b5Wci8gn/HTZp0jxO4+IjMalNjAJolxQWkACWrVsJTfoOCs+xznmrsAVnuexsbGVOu6Gg1GxgUbclCY+1VY0GyEubFXNVlRWp06d5E0Uphb5XFqtFjfddJPTVhFQerxdHce8avz22284HXsK0RnNSwWixcR9tXLqIT7oT1mex8t0eQOhzuImv4rawVX13BffZ+68uRg9ejTig/9EUG64LMwuetfneKQj0y8JWh815syd4xK/b3yOc8xdgYcTv39WdrNejc0Zrc7E7Ia42ZvIc1q3bh2++WY9srLSERQUir59+6NPnz5O+ymLqKpt2bIF7moPmQtaHvGY1qJHnntGSTBapL2cZxgaWv7O9xv5XRbnkZqaKvPKP/roI7z37ns4ear0Mnynjp3w+huvo1GjRv/6ZxIRVRd2D0ZFXc0jR46UTEmLWUOx1C/cd9999v7xTuvw4cMYPvxRpKVl4I7btWjTBDifcAbPPvsz3n57Nj777HM0b95c6dMkqvby8/Pl7OfVS+LXdOqx6koVUBe5pMFBwejcufM//tmiZqnY8b140RIUGPKhUWlhsZmh0+rw4IAHMev1WTh//rzc2CTyHtmjnoickd2D0T179uCll14q1ZFJ3ISTJ0/a+8c7JRHQDxnyEOpHFWH9Eg9ER/xdBiYuXodxL2XKxzdv3iI3ORBR+cTvSKHaIANNUVi+LOIxsanIxxokd7NneiYjyzMF08ZPk/nr/4TVasWkpydhw4YNCMwLQ3hBE7lpyawyye/99eq1OHXyFL5a8xVXOojIqdm9Ir3oQiGCzrJu9M8sXLgQOk0hln+gKxWICvWi1VjxgQ7GohwsXbqUQ0x0HQMGDJCzkVke5VcJEY+JgNQKs2zveMnvLB5//HGMGTPmH4+vWCFav2E9wjMboVZu3ZLd81qbDsH5EYhKbYo/Dv+BRYvsXwfT1YiqMGLVjiWdiKoH52+P5GTEZomvvlqFh/up4OdT9rJiUIAKg3qpsHLlCjn7QkTli4yMxMCBA5HqHy97h19N3Jfse04m4ucHZ6Bzjw5YuXIlZs6c+a866Sz9ZCm8LX7wKyy7GLroGOSTH4RlS5fBYim/vzpV3k8//YShQ4aiadOmsjtSkyZN8MQTT2Dv3r0cRiIFcQNTDSPKyOTlGdC+9eWdvOVp30aDJSuz5QyAn1/5GzOICJg9ezbS0zOwbduP8LL6wjP/8u9MgVc28tU56HpnV0yYOAGtW7eukl2vopzUb3t+Q0henQqP8zUEIz7lT5maU69ePV6qf2HevHnyOovrG5ZbH1qLG4waA37a9LOcpRa5u4MHD+YYEymAwWgNU9wLOze/4uNy823yqz0bDxA50+/Vp58uw48//ihnIg8dOizvb9+mrexD36FDhypNLRIrFuKmtlW8OFX8uGjyQf+cuK4iEA3OjURIXtQVm9UCEFgQJtMupkyZIutFi3J8RORYDEZrGFE4v3nzJtjw/Rn0vbf8y7d+kw3t2rXhxgeiShI71rt37y5vV6vq3ELRpjI6KhrZhhwEGGqVe1y+Wzb0ejeEh4dX6c93NQsWLICXxe+qQPQy8ffa2fVh8MrFkiVLMGfOHMXOk8hVMWe0hhE5aiNGjMLWnWZs+8Vc5jExP5qxa69JHkdE1dPwEcOR65kud+mXRXRbyvFNw4ABD8Lb29vh5+csMjIysHv3bvjlBpdbvkvc75MThG82fMM8eyIFMBitgUReU48e3fHEFCNen2NE3AUrzGYbYuOs+M+7RRj3khF9+/ZBv379lD5VIirH0KFDERERiYTQE7Kz05WKNAYkhJyAzlOD8ePHcwz/hezsbPlV91e1gvLoze4wmoyyfz0RORaX6Wsg0Wb0448X4Z133sHyz5ZhwWd5JY8FBPhi8uTRmDRpklx2JKLqSXRaWvP1V3j0kcdwGkfgafWBtsgNVp0JedpsWVD/s+WfcePSv+Tv7y+/ihloL5S/mdOoLYSbmztTm4gUwGC0hhKFtkUzgcmTJ2Pnzp3IysqS+aR33HFHySYnIqr+ZaV+3LYV27Ztw9q1a3Ep6RL8A/zRq1cvPPDAA/xdrgLidfHOO+7Evh0H4W8ILXOp3gYrcn3T0L9/v39VrouI/hkGozWc6EF/7733Kn0aRPQvNjOVt3GKqsZTY5/CkJ1DkOJzHqG5dUoFpKKjVpL/GZhURowaxTx7IiVwHZeIiJyaWDGaMWMG0r0TcT7sCNK9EpHjno40rwTEhR9GrlcG5s6bi+bNmyt9qkQuiTOjRER2Iorbi8YTgo+PD5eAFSQ6LbVq1Uq2V/3+++/lrnmR7tS3b1+MHj0aLVu2VPL0iFwag1Eioiom6pIuX75ctvy8kHBB3hcZEYXHR47AY489Bi8vL465Am677TZ5E22V8/PzZcksEZASkbIYjBIRVaHMzEwMGvgQTp48AR9DECIMjeX9uYZMvD7rDXy1eg2+WrMagYGBHHeFiM507E5HVH0wZ5SIqApNGD8BZ06eQd2UVojIbAS/wmB5i8hqhLopLXH21FmMH/d37VDR6lPspO/Tuw/q16svb/379ceGDRtgNpfd2IKIyJlwZpSIqIqcOnUKO37aIYNQd/O1S/HivpCMOtj5806cOHECUVFRGDZsGH777Tf4mAPgXxAmj/vz11MYt3ccunTugqXLlrL2JRE5NQajRERVRMxm6tVu8C0MKvcY38JApKrd5LFnzpzBvj37USe9BbyMfxdkD8oPR54+C7t3/Yrnn38e8+bN4zUiIqfFZXoioirsg663ukFVwUureEwcExcXh40bNyIkM7pUIFrM2+iPkKworFu3DgkJCbxGROS0GIwSEVVh60mjukh29CmPeEwcc+nSJejUevgZQsr/foZQaFRamVPqisSudzGDLIrR9+vbT5ZgEgG8yLMlIufBZXoioirSp08fzJ07FznuGXLTUlly3TNgtBYhODgYbhYPqCuYE1DbNHCzeiA5OdnlrtH58+cxZvQYXEq+BC+LH7RGHf7Un0RMTAyiIqOx4vPlaNiwodKnSURVgMEoEVEVadasGTp16oS9u/fDPcUTbmbPUo8XaQqQGhiPjrd3lJuXzGqjbEdZVr90QTwmjhH1MF1JUlISXn7pFVhybKif3rrUZrBCbT6SbGcwcMAgbNn6A0JCyp9ZJqKagcv0RERV6MMPP0R0/UicCz2CRL9Y5LilI9ctQ/75XK2jiKwXgQ8/+hA9e/ZEoc2AfH12ud8rzy0TRdZCeawrWbZsGQrzCxGZ2vSaqgTi75EpTZGZkSmPI6Kaj8EoEVEVEsvv3238DlP+bwrc6qqQEHgSFwJPyD8/+9wz8jExm9e+fXs0b9YcqUHnYVIbr/k+JnWRnEVt3boN2rRp4zLXSOSDrv5yNXxzg6G1lt0dSWfVwyc3CMs/Wy7behJRzcZleiKiKib60E+aNAkTJ05Eenq6vC8oKAhq9d+f/1UqFRYtXiQ35pxXHYFvTgh8igLkY7lumcjxTUVgaAAWLvzYpa6PGK+8/DwEGCMrPM7L6IuEjFPIycmRG8eIqObizCgRkb1eYNVqOQsqblcGosXq1q2LTZs34eHHBiM/JB1xwUfkrSA0A0OHD5GPRUZWHJQ5m+Je8TZVxTOe1r8eZ1tPopqPM6NERAoKCwvDm2++iVdeeQXnzp2T99WvXx+enqU3P7mKwMBA1KtbD+mF6fAtpyKBkOuVjpYtWrrsOBE5E86MEhFVA2LHfMuWLeXNlQMskb4wbPgwWQKrvM1dIo0hV5eJkaNGOvz8iKjqMRglIqJq5aGHHkKrm25CQvAJpHpfKNngJTZ1pXjH42LQSdxzTzcMGDBA6VMloirAZXoiIqpWRB7otGmvyu5LYmd9qs8FaFQaWGwWeLh74IlhT+Cll16CVsu3MCJnwN9kIiKqpgHpNBl0bt26FVlZWTKftFu3brJaARE5DwajRKQYEWAUFhYiICAAbm5uvBJ0DT8/Py7HEzk55owSkUOJIuVr1qxBz/t6okWLFmjXrh1aNG+BF198EXFxcbwaREQuhjOjROQwFosFk56ehHXr18HHFIDw/EbQWLUw6HKxesVX+Prrtfj88xW49dZba+RVSUxMxJdffokzZ87IfMYOHTqgT58+8PDw+Fff9/Dhw1i1ahUSEhLg7u6Ou+++G/369fvX35eIqDpgMEpEDvPRRx9h/fr1iMhsDL8rakiKzkNB+RG4GHwSw4cNx6+//VptuurYbDbs2rULH3/8MS4mJEKn1eL2jrdj+PDhcma3OMieNWsWFi9eDLVNI/uni6LtX331Ff4z/T94f877uPfee2/4Z+fl5WHsU2Oxbfs2uKs8oDN4wKa1ICYmBq/NeA0LPl6AO++80w7/ayIix+EyPRE5rOf4oo8XwT8/tFQgWkxj0yA8vaEMwEQQVx0YDAYMe2wYRowYgUO7jiDvuBEZR/Ox5ouv0aNHD8ycOVMGq9OnT8eihYsQnBWJBkk3IzqlOeokt0TDlLZAmg5PPPEEduzYccPpDI8//jh27vgZkZmNUS+xDaIymyI6tYX8vrYMrQzc9+3bZ7f/PxGRIzAYJSKH+P3335GWkQb/glrlHqO16uFtCMDar9dVi6siUgp+2rETURlNUTe5FWrn1kNYTn3UT2yDWjl1sGDBAhmQLl26FKHZdRCcHwmN7e8FJ73FHREZjeFZ5Itpr06TgWtlbd++Hbt370Z4WiPZiUgFVenvm9YYOqM7Zr85u8r/30REjsRglIgcIjMzsySQqojO7IaMjAzFr8rx48exMWYjamXWhU9RYKlgUAW1TCsIzA/DJ0s+gQ56BBTULvP7iH8XlBOBM2fPYM+ePZX++Z999hm8rL7wMvqV+bgaagRk15YpDWfPnv0H/0MiouqBwSgROYSoESkYtYUVHmfSFSE4OEjxqyI2DLmp3OFnKL8/ughGTWYTNEa9DA7L42n0hVqllgFuZR3/8wTcC7xLBcFXKw5UY2NjK/19XY1IY5gwYQJatmiFJo2b4r4e9+Hzzz+XKRhEVD0wGCUihxA75EOCQ5HpeancY0Tbx1z3TAwYqHybxwsXLkBX6CFnQcsjZnm1Nh2sKst1v59YoRd91ytLbJQSm6AqYv3rcY1GU+nv6ypESsQbb7yBvn37YvO6H6C54AnPpACcP3gRzz//PHp07yGrHxCR8hiMEpFDiFJHT419ElmeKcjySLnmcYvKjMTg0/Dz9cXAgQMVvyqihJJNW3EwaINVBoRmXREsFQSkeW6Z8lhRU7WyOnbuiALvbNhQfp5pjnu6HNc2bdpU+vu6ik8//RTz58+Xub11k25CaF60zOmNTG+KBqltkHguCY8MfRRms1npUyVyeQxGichhxowZg8GDByPRPxbxoX8i0yNZBlTJPucQF3YYaj8rlq9YDl9fX8WvSteuXZGvyYZRU/5ybq57BqywwAob0r0TyjxGPJ7udxE3tboJrVq1qvTPF6WjCm0GZHiVPXtnUhch2y8ZvXv3RlCQ8mkN1YkIMN//3xz4FYTI3N6rUx3czJ6ondoQp06flK1GiUhZDEaJyHEvOGo13n33XbkLvdltjZDkfwYJASdRVCsHw58Yhi1bt6Bt27bV4oqIIM/fzx/JAefE/Oc1j5vVRqQHXET7W9rjxRdfQJr3RST5noFRczknVsxo5uuzcSH0OCyeRsx+68Z2vbds2RJjx45Fsu95JPmeLQmKxbmImeULtf6Ef4gfXnnllSr6HzuPX375BalpKTKntzyeJh+5QWz1l6sdem5EdC0WvScihxJ5kyLQEzexiUTcRP/x6pb3KLobfbTgI1lnND70GAJywuBV5AebyoZc93Rk+V+CV4An5sydg+joaHh6euLt/76N2LwD8FB7yqCxyFqI+vXqy2Nat259w+cgAk0x6zl3zlzE5h6ETq2HxWaB1WZBl85d8O577yIsrPyAy1UlJSXJr6L5QEVEEwHR1YqIlMVglIgUDfiqc0vLO+64A2u+XoNZM2dh7769Jfdr1Brc1/M+TJs2DZGRkfK+UaNGYejQofjuu+/k7na9Xo/bb78dHTt2vKGNS1cS/07Mjoqi+z/88ENJO1CRQtCgQYMq+386Gy+vy0GoWW2Czqq/5vEijUFWdSjSFVTr5x+Rq2AwSkRUgVtuuQVfrPwCW7ZskV2RxAyo2DBUq9a1xftFYDNo0KAqH0/xfcWucKqcLl26QK/TI9sjRW5aKibSJlK9L6DALafkvsOHD2Pq1Klyh311yFUmckUMRomIKkHMgDZr1kwGo1S9BQQEyPJgX61aA++iALlcLzbKJfifhIfJGxGZjeFp9IFVbUG2expWfPo5dv2yG+s3rJMpI0TkWNzARERETmf69Olo2rwJ4msdQ6JPLC76n4JvYRDqpreCX2EwdFY3uatelHyKTm6OuNg4zJgxQ+nTJnJJDEaJiGqYuLg4zJs3DzNnzsSHH37ITThl8PHxwdp1a/Hk2CdhCMyR1VprZ9cvs6OVCEpFa9W1X6+tFq1oiVwNl+mJiGqIzMxMPDP5GVkCS6vWQQ83GG2FstPQAw88gHfeeUcGYeU5evQovvjiC9nL3s3NTeZWihxXZ12aFhuZXn75Zeze9SvO/HZOdssqj58hFCnmeFkWqk+fPg49TyJXx2CUiKgGyM/Px8ABg3Dm1FmEZzWEryEIamhkK9Jsj1Rs3vg9Ei8m4qs1X8kd91cS5bMmTZqEjRs3wl3lAV2BB2waK378cRvefONN/O/9/zl1AFZYaIDGUn4gKmitl98O2bOeyPG4TE9EVAN88sknOH3qFKJSmsLfECoDUUFt0yCgoDYiU5vi4KGDWLVq1TU92sePH4/NMd8jPKsR6iW2QVRWU0SnN0ejS23hlu2DcePGYdu2bXBWog6s0aP8TlqCQZcvv0ZERDjorIioGINRIqJqTpSUWrZ0GXzyg8ot5C46CvkUBuKTJUtL3b9v3z58//33qJ1RH/6GkFI5k1qrHuGZDeFl9MPM12bJwNUZPTzkYeSrc1Cg+7uk09UyvZMQERaBDh06OPTciIjBKBFRtZeamopLyZdksFkRb0MAzpyNRUFBQcl9K1asgAe85E7ysojgNDAnXPZpP3jwIJzRPffcgxbNWyApJBYGbV6px2ywIsU7XpZ4eva5Z6tdJzAiV8CZUSKiGjAzWhkqm+qa408cPwm3Aq8yd5EX8zJeLvZ++vRpOCMRYK74fAXqNa6HuJA/cCHkTxmAXvKNw9nwQ0jzSZBF7x9++GGlT5XIJXEDExFRNRcSEoLAgEDk5WfBp6j82dF89yxEhEeWtMMUdDotrKqKg9nix7Va531LCA0NRcymjYiJicHyz5bj7Nk4eOr1uP/ugRg+fDiaN2+u9CkSuSznfeUhompD7FD+5ptv8NXqr5B8KRm+fn7o3acXHnroIQQGVrz0TJeDxEcfexQffvARgvLDobeU3i0vFGkKkOOZjgkjX5Y97Yt16twJRw9/DGu2RW52KovoTiT+za233urUwy3KWfXv31/eiKj64DI9EdmVWPrt0rkLnn32WRz9+QSyjhlwdk88Xp/1Om679Tbs3LmTV6ASRo8ejbCw2kgIPY48fSZssoy7yHm0IdctAwm1TqB+/fp49NFHS/078XczLLIne1nMahMy/ZNw1113IyoqiteCiByOwSgR2Y3oZjNo4EPISspFg5SbEZXWDLVz6iEyswkaXmoLTbY7hg8fgWPHjvEqXIeYQf563ddo1LIh4oOOIy78MOJD/0Rc+CFcCDyBm9q1wpqv11xT9F4EmFOnvoJ070Rc9DuNQu3lEkZWWJHtnirbZXr4u2HWrJm8BkSkCC7TE5HdiJ3cGenpqJ/SRvYCL/XiY9UjIr0RzuuP4oMPPsBHH33EK3Edogbmps2bsHfvXnz77bfIyspCQEAAHnzwQbRp06bcf/fUU0/JLkuz33wLZ9MPQ6vSwQoLrDYrOtzeAe+8+w7q1KnD8SciRTAYJSK7WfHZClkb8+pAtJgo3O6bHYKNG2NkYOXv78+rcR3FuZ03mt85ZMgQDBw4ENu3b5e97UX+ZKdOndCoUSOOOREpisEoEdmFKKCeeCkRtY31KjzOw+QDi8WMpKQkBqN2ptPp0KNHD3v/GCKiG8KcUSKy2wyeTquDRW2p8Dir2iy/ipk6IiJyPQxGichu7rjzDuT5ZJTs/C5LtkearI1Zt25dXgkiIhfEYJSI7GbUqFEoUOUi0/NSmY/n67OR65mOkaMeh1rNlyMiIlfEV38ispsuXbrIgPSSXxwS/WNh0OXCqrLIAu3JPueQEHxCbqIZOXIkrwIRkYviBiYismve6IwZM+QS/AfzPkBcypGSx3y8ffDU8KcwZcoU6PV6XgUiIhfFYJSI7B6QipnPYcOGYc+ePUhJSYGvry86duwIDw8Pjj4RkYuzazBqsVjwySefYMeOHYiNjZWlXpo0aYJJkybhlltuseePJqJq2F9dLMkTERE5LBgtLCzEwoUL0b9/f9lXWWxQWL16tZwhWbJkCTp06GDPH09ENVROTg7WrFmDXbt2wWg0yp7rjzzyCBo3bqz0qRERUU0KRt3d3bF161bZhq6YmBnp1asXPv30UwajRHSNDRs24LnnnoXRWITb22rg4WHDhnU7sXjxYgwY8CDefvsd1iQlInIidg1GNRpNqUC0+D6xVB8fH2/PH01ENdCWLVswfvx49L1Xg1cmu6N2yOWCH0aTDV9/p8ar/10v03/mz/9Q6VMlIqKauoHJbDbj8OHDaNeu3b/6PiL/tKCg4Jr7DQZDqa9kXxxvx3PWMRe/0zNn/gedb9Vgzkw91GpVyWN6nQpD+uug1QLP/mcDRox4HC1atHDYuTnrmFdXHG+OuStwhee5zWaTm1ivR2UTRzrQggULMHfuXJk72rJly3/0PY4cOSLzyIjIefz555948cUX8eUCd3RsrynzGLPZho59jLipzT1yBpWIiKo3UbqvVatWVTszmpubK0uzXE9UVNQ1tQPFZoR58+Zh3Lhx/zgQLabT6dCwYcNr7hefMM6dOyfrGrJsjP1xvB3PWcf80KFDEB+gb29Xfi8OrVaFjrcA5y8lo1mzZg47N2cd8+qK480xdwWu8DyPjY2t1HE3HIxu3rwZU6dOve5xMTExaNCgQcnfjx07hokTJ8rNSxMmTMC/JaZ9PT09y31cXNiKHqeqxfF2PGcbcy8vL4h1GqMJcHcr/7iiIht0ejdF/u/ONubVHcebY+4KPJz4daUyS/T/KBgdNGiQvN2I8+fPy9JON998M2bNmnWjP5KIXEBx7eFNP1rQ//6yX5ry8m3YvhsYPYZl4YiInIXde9OLJX3RfSUsLEzmiorldSKiq4m0m86dO2LOEguycspOZX9/kQmFRTZZc5SIiJyD2t5F78WMaGZmptxscPr0aZkXJm5iswIR0ZVef/1NpGd5YMAoE2J+NMNkuhyUHj9txeRpRfh4uQlTp76K8PBwDhwRkZOwa2mntLQ0nDhxQv557NixpR6LiIjAtm3b7PnjiagGzo6uX/8t/u//nsWTzx+Am5sa7no1snPNqBUahHfeeQlDhgxR+jSJiKimBKORkZE4efKkPX8EETmZRo0ayYBUbHrcvXs3TCYT6tWrh27dujHNh4jICTm86D0RUWWIovaOLGxPREROuoGJiIiIiKg8DEaJiIiISDEMRomIiIhIMQxGiYiIiEgxDEaJiIiISDEMRomIiIhIMQxGiYiIiEgxDEaJiIiISDEMRomIiIhIMQxGiYiIiEgxDEaJiIiISDEMRomIiIhIMVrlfjS5GpvNhv3792PNmjVITk6Gt7c3evbsiR49ekCr5VORiIjIFTECIIfIyMjAmDGj8OuvvyMqXIdG9aw4Fa/G2rVrERUZhsVLlqFly5a8GkRERC6GwSjZncFgwNChg5GYcApL/+eGuztroFar5GPHTmrwfzPTMHjwQHz33SbUq1ePV4SIiMiFMGeU7G7dunU4evRPrPhAi253aEsCUaFFEw2++FAHT3cD5s6dy6tBRETkYhiMkt2tWL4M93TWoWVTTZmP+/uqMGygChs2rEN2djavCBERkQthMEp2d+LkKXS+9e/Z0LJ0uV2DoiITzp8/zytCRETkQhiMkt1pNBqYzLYKjzEa/3pCqvmUJCIiciV85ye7a9++PWJ+rPiYmB/N8Pf3QcOGDXlFiIiIXAiDUbK74cMfx8GjJhlwluXMOStWbbBhyJBH4e7uzitCRETkQhiMkt11794dvXv3wviXjfjfQiNS0y8v2RsMNqzaYMLA0SbUrh2NCRMm8GoQERG5GNYZJbsTeaDz5n2AN94Ix4efLsOcxQYEBWiRnWuB0WhD9+734J133oO/vz+vBhERkYthMEoOodPpMH36dEyaNAkxMTEl7UBFK9A6derwKhAREbkoBqPkUGL2c+jQoRx1IiIikpgzSkRERESKYTBKRERERIphMEpEREREimEwSkRERESKYTBKRERERIphMEpEREREimEwSkRERESKYTBKRERERIphMEpEREREimEwSkRERESKYTBKRERERIphMEpEREREitEq96OJiByvsLAQGzduxPr165CRkQp//yD06dMXffr0gYeHBy8JEZGDcWaUiFzGyZMnceednfD000/DkPMLmtY5AXPBLkyZ8iw6dbodR44cUfoUiYhcDmdGicglJCcn4+HBAxHkn4vtazzQsN7fn8XjLugw8ZVsDBnyEDZv3oLIyEhFz5WIyJVwZpSIXMInn3wCgyEbn8/XlQpEhXpRaiyfp4MKBVi4cKFi50hE5IoYjBKR07NarVi5cgUGPqBCSJCqzGMC/FQY0leFr1avgtFodPg5EhG5KgajROT0cnNzkZ6ehVtv1lR43K03q5GTm4/09HSHnRsRkatjMEpETk+v18uveQW2Co/LLyh9PBER2R+DUSJyeqJkU9u2rfHNDxUHo+s3W9CsWWMEBgY67NyIiFwdg1EicgkjRozCz7+ZsHm7uczHd+w2Y+vPFgwfPhIqVdl5pUREVPUYjBKRS+jfvz969XoAY180Ytb7RsRdsMJms+FCohWzPzBi1LMm3HVXVwwZMkTpUyUicimsM0pELkGtVmP+/A/x3nvvYdnSJfh4eV7JYz4+nhj1xBN4/vnnodXyZZGIyJH4qktELkMEmiLgnDhxInbs2CF3zQcEBKBr167w8vJS+vSIiFwSg1EicskNTT179lT6NIiIiDmjRERERKQkbmAiIiIiIsUwGCUiIiIixTAYJSIiIiLFMBglIiIiIsUwGCUiIiIixTAYJSIiIiLFMBglIiIiIsUwGCUiIiIixTAYJSIiIiLFMBglIiIiIsUwGCUiIiIixTAYJSIiIiLFqGw2mw01zIEDByBOW6/XX/OYuN9kMkGn00GlUilyfq6E480xdwV8nnO8nR2f4xxzezAajTIWa9u2bYXHaVEDVRRkisfKClLJfteC4+1YHHPH45hzvJ0dn+Mcc3s9ryozMVgjZ0aJiIiIyDkwZ5SIiIiIFMNglIiIiIgUw2CUiIiIiBTDYJSIiIiIFMNglIiIiIgUw2CUiIiIiBTDYJSIiIiIFMNglIiIiIgUw2CUiIiIiBTDYJSIiIiIFMNglIiIiIgUw2CUiIiIiBSjhRNbvHgxvvvuOyQkJMBsNiMqKgqDBw/GI488ApVKpfTpOR2LxYJPPvkEO3bsQGxsLGw2G5o0aYJJkybhlltuUfr0nNauXbuwdu1aHD58GBcuXJDP72nTpil9Wk7hzJkzmDVrFg4ePAgvLy/07dsXkydPhl6vV/rUnNL58+exZMkS+Vw+ffo06tevL1/DyT42bdqEb775BseOHUNOTg7q1KmDxx57DAMGDOB7pJ389NNPWLRokXyPzMvLQ61atdCtWzdMmDABPj4+cFVOHYzm5ubi/vvvR6NGjeDm5oZff/1VvrGIJ8BTTz2l9Ok5ncLCQixcuBD9+/fH6NGjoVarsXr1agwbNky+wXTo0EHpU3RKP//8M06cOIH27dsjOztb6dNxGmIshw8fjrp162LevHlITk7G7Nmz5fOcwb59iABUvFm3bt0aVqtVfqAl+1m2bBkiIiLw4osvIiAgALt378arr76KS5cuyeCIql5WVhZuuukmGfT7+/vL5/y8efPkVzGZ46pUNhf7bZ8yZQqOHj2K77//XulTccqZURHo+/n5lbqvV69e8hP3ggULFD0/ZyXetEXgL9x9993o2rUrg6Uq8PHHH8vn7Pbt2+WbhvDll19ixowZ8j4xo0H2ey6LAEm8VnNm1H4yMjIQGBhY6j4RjMbExGDv3r0l14Lsa/Xq1XLcd+7c6bKvKy73TBOf/kwmk9Kn4ZQ0Gk2pQLT4PrFUn5KSoth5OTu+YdiHeGMQs/nFgajQs2dPGTCJ1AiqenwuO9bVgajQrFkzOalQUFDg4LNxXf5/vca4cmziEsGoyBcVv1wil3H9+vVy2ZgcN/Yi/0vkfhHVJGfPnr3meevr64uQkBD5GJEz2r9/v5yd8/b2VvpUnJpYNSwqKpL5uvPnz5erWpGRkXBVTp0zWpwQ36NHj5K/jx07FiNGjFD0nFyJ2EQmcu045lTTiA0dIvi8mpj9Z24uOaN9+/bJJfoXXnhB6VNxenfddZd8bxS6dOmCd999F65MW9M2JFVmuVfsmi/e7RoWFoY1a9bIJQfxiyZ2sYmloKefftoBZ1zz/ZMxLyaWMkVi9rhx49CyZUs7nqVz+TdjTkT0T4hNS8888wxuu+02rh46gNjsazAY5K76jz76SG6qXrp0qUxtc0U1KhjdvHkzpk6det3jxCe7Bg0ayD+LN+tWrVrJP4tfMrH08NZbb2HIkCFyuY2qfswFsfQwceJEuXmJuzIdM+ZUtcSsqPhgcDUxK3p1bjRRTV8FEBVQRO6imEBg7q79NW3aVH69+eabZYwiysZt2bIF9913H1xRjQpGBw0aJG//RosWLWSuxsWLFxmM2mnMRWqEeGETv2SilBY5/nlO/57IF706N1QEp6mpqcyBJqchSpU9+eST8rktqkW4cq1LpTRp0gQ6nQ7x8fFwVS6xgelKBw4ckMV8XTlR2J7E8vLIkSNlesTcuXPlLxhRTXTHHXfIuoti1ujKWWsxa9SpUydFz42oqjaYiiYO4kOXyO931bJCSjt8+LDcSe/KcUmNmhm9EeJTnpid69Onj6xxKX7p9uzZg88++0x2YQoODlb6FJ3yE7YY88zMTLzyyiuyiG8xkS7RvHlzRc/PWYlZ/iNHjsg/ixwk8elaBE2Cqy75VIWHH34Yy5cvx/jx4+XMkdhs8N///lfezzdt+xDPX1H0vvh5LaqgFD+Xb7311jJLEdE/V1wzV9R0FWN96NChksfE6zVz0queSFsTeyjEbKi7u7tsWLJkyRL5d9GJyVU5bdF7o9GI6dOnyzIV4k1EXPTo6Gj5RtKvXz+XTRK2J9F29Z577inzMdHlY9u2bQ4/J1cgWoG+9NJLZT528uRJh5+Ps7UDnTlzZql2oGKTB9+kHf8aIiYSRN4/VR1RTkgE/WX58ccfXXqmzp4bl0S+v5g0EOGXeG/s3r07Ro0a5dLltJw2GCUiIiKi6s/lckaJiIiIqPpgMEpEREREimEwSkRERESKYTBKRERERIphMEpEREREimEwSkRERESKYTBKRERERIphMEpEREREimEwSkRERESKYTBKRERERIphMEpEREREUMr/A8htLoECQxbaAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cluster Probabilities for first 5 samples:\n",
+      " [[2.74503381e-011 1.00000000e+000 0.00000000e+000]\n",
+      " [1.16033052e-007 9.99999884e-001 0.00000000e+000]\n",
+      " [9.72921434e-009 9.99999990e-001 5.88168010e-278]\n",
+      " [2.02193473e-007 9.99999798e-001 3.33173699e-141]\n",
+      " [9.67775891e-012 1.00000000e+000 0.00000000e+000]]\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.mixture import GaussianMixture\n",
     "\n",
@@ -450,6 +556,31 @@
     "### Load and Preprocess"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2766fa95",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_breast_cancer\n",
+    "\n",
+    "# Load the Breast Cancer Wisconsin dataset\n",
+    "data = load_breast_cancer()\n",
+    "X_bc = data.data\n",
+    "y_bc = data.target\n",
+    "\n",
+    "# Standardize the data\n",
+    "scaler_bc = StandardScaler()\n",
+    "X_bc_scaled = scaler_bc.fit_transform(X_bc)\n",
+    "\n",
+    "# Apply PCA to reduce to 2 dimensions for visualization\n",
+    "pca_bc = PCA(n_components=2, random_state=42)\n",
+    "X_bc_pca = pca_bc.fit_transform(X_bc_scaled)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "245cb377",
@@ -458,6 +589,33 @@
     "### K-Means"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "bce87ebb",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "K-Means -- Silhouette: 0.34, ARI: 0.68\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Apply K-Means clustering\n",
+    "kmeans_bc = KMeans(n_clusters=2, random_state=42, n_init='auto')\n",
+    "y_kmeans_bc = kmeans_bc.fit_predict(X_bc_scaled)\n",
+    "\n",
+    "# Evaluate\n",
+    "sil_bc = silhouette_score(X_bc_scaled, y_kmeans_bc)\n",
+    "ari_bc = adjusted_rand_score(y_bc, y_kmeans_bc)\n",
+    "print(f'K-Means -- Silhouette: {sil_bc:.2f}, ARI: {ari_bc:.2f}')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7ca563fe",
@@ -466,6 +624,33 @@
     "### Hierarchical Clustering"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ee2956a3",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hierarchical -- Silhouette: 0.34, ARI: 0.58\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Perform hierarchical clustering\n",
+    "cls_bc = AgglomerativeClustering(linkage='ward', n_clusters=2)\n",
+    "y_hc_bc = cls_bc.fit_predict(X_bc_scaled)\n",
+    "\n",
+    "# Evaluate\n",
+    "sil_hc = silhouette_score(X_bc_scaled, y_hc_bc)\n",
+    "ari_hc = adjusted_rand_score(y_bc, y_hc_bc)\n",
+    "print(f'Hierarchical -- Silhouette: {sil_hc:.2f}, ARI: {ari_hc:.2f}')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "ddc1f229",
@@ -473,6 +658,41 @@
    "source": [
     "### Comparison Visualization"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "113d58e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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5kJycnOHf0l9YKl+k2kWW6hERERERFQWMa4kKF7Y1ICIiygXpVyyBrPQMliFm0iNNBnq88sorcHJy4jEkIiIioiKBcS1R4cK2BkRERLmQ2tP38uXLqi+b9DSTfrMySI2IiIiIqKhgXEtUuDA5S0RERERERERERGQH7DlLREREREREREREZAdMzhIRERERERERERHZAQeC5SEZDGOz2WAwGPLy2xIRERFRLphMJmg0GjW4jxibEhERERWF2JSVs3lIErPyUdjJPsqk8aKwr5S3eO5LLp77kovnvuQqiee+qMRiBaWoHI+S+LNKt/Dcl1w89yUXz33JVRLPvS2XsRgrZ/NQasVsvXr1UJglJibi5MmTqFq1KlxcXOy9O1SAeO5LLp77kovnvuQqief+2LFj9t6FQoWxKRV2JfF1im7huS+5eO5LrpJ47o/lMjZl5SwRERERERERERGRHTA5S0RERERERERERGQHTM4SERERERERERER2QGTs0RERERERERERER2wOQsERERERERERERkR0wOUtERERERERERERkB0zOEhEREREREREREdkBk7NEREREREREREREdsDkLBEREREREREREZEdMDlLREREREREREREZAdMzhIRERERERERERHZAZOzRERERERERERERHbA5CwRERERERERERGRHTA5S0RERERERERERGQHTM4SERERERERERER2QGTs0REVGRER0fj22+/xSPNHkG5cuVQuXJlDBw4EL/88gtsNpu9d4+IiIiIShCTyYQ1a9agXdt2qFChgvpo+2xbrF69Wt1HRJQbehRSly9fxty5c3HkyBGcPXtWvQHfsmVL2v3x8fGYP38+9u7di0uXLsHBwQH169fH2LFjUaNGjRy/94EDB9C/f/9Mtz/77LOYNm1avjwfIiK6PxcvXkT3bt1x8+ZNuCf6IsBYEVaNGb/+8Ad27dqFQYMG4YMPPoBGo+GhJqJ8wfiUiIhSJSQkqLzCH3/8AXeTN3wTy6nbz/95GWOOjMHy5cuxePFiuLq68qARUdFMzkpCVhKvDzzwAKxWa6aKqOvXr2PlypXo2rUrxowZg5SUFMybNw89evTA2rVrUaVKlTs+xieffKKSvqm8vb3z5bkQEdH9kdf4Xj17I+pGDCqHPggHi1Pafb4JQYhyCVF/A8qXL4+hQ4fycBNRvmB8SkREqV555RX8eeAvVIioA1ej53+xaWJpJBhi8dfBw2qbWbNm8aARUdFMzrZq1QpPPfWU+v+33noLx48fz3B/2bJlVaWUs7Nz2m0PP/yw+rply5Zh/Pjxd3yMatWqoV69evmw90RElJe2b9+Oq9euoHLYAxkSs0IDDXwSSyHZEI8ZX89UbQ4MBgNPABHlOcanREQkZPWurOwtHV0lQ2I2lavJA/5R5bF161a1bcWKFXngiKjo9ZzVanPeNRcXlwyJWSHLBaRqKjQ0NJ/3joiICtKqlavgZvaCkzn7ZWHeCaUQHhGG3377rUD3jYhKDsanREQk1q9fD4PWAZ5JftkeELlPrzGobYmIimRy9l7Exsam9afNjWHDhqFWrVp4/PHHMXnyZCQnJ+f7PhIR0d27ceMmDCmOOW7jaHZRn3mBjogKE8anRETFT0hICBytTtBCl+02cp+D1UltS0RUJNsa3IvPP/9cDYLp1atXjtu5u7tjyJAhaNy4MRwdHVUDb+lVeOHChfvuByO9cRMTE1GYJSUlZfhMJQfPfclV1M+9u7sbbujDctzGrDOqzzIgsrC/Dhekon7u6d6VxHMvcVhhGwpo7/iUsSkVZiXxdYqKx7mXVbwmjRE22FSLrazIfWaNEU5OToxNi9G5p3tXEs+9LZexabFJzsoQsFWrVuHTTz9FqVKlcty2du3a6iNVs2bNEBAQoKZ8Hz16FPXr17/n/TCZTDh58iSKAul9QyUTz33JVVTPfYOGDXD48GGYtUborQ5ZbhPtHApHRyf4+PgUmdfhglRUzz3dv5J27uUCTWFRGOJTxqZUFJS01ykq+ude5tek2JIR7xgF9xSfLLeR+2SbGjVqMDYtRuee7l9JO/cOuYhNi0Vydu/evZgwYQJGjhyJzp0739P3aNOmjQp+ZfDY/SRnZQhN1apVUZjJVYrUpuS39+2l4o3nvuQq6uc+KCgIy5ctxw2f8wgKr55pCZlMxI3yuIF+vfqhUaNGdtvPwqion3u6dyXx3J87dw6FRWGJTxmbUmFWEl+nqHic+5o1a2LRwkU4e/w8nEJcYbBmbL9l0qYgzOcK6tWthw4dOhS6VR32VNTPPd27knjuz+UyNi3yydl//vkHL7/8Mjp16qQ+25u86MqwsqJAfhmKyr5S3uK5L7mK6rmXfZ47by769x+Ay4bj8Ijxh4vJHRaNBbHO4Yh1jUDjxg9h3LhxJeYPfUk593T/StK5LyxvfgtTfMrYlIqCkvQ6RcXn3H8/93t07NARl7XH4R7rB/dkb3V7nFMU4jzC4ePvrbaRweVUvM493Z+SdO41uYxNtUU9Az18+HA8/PDDeP/99+/re23dulV9rlevXh7tHRER5aXHHnsMW7ZsxlPtWyHc+you+h3DFd8TcKqkxZtvvYHlK5YzMUtEdsf4lIioZChbtiy279iOfs/3RXJANC75HVcf8v9ym9wn2xARFdnKWSl3luVgIjg4GPHx8dixY4f6d5MmTVRT3cGDB6uBCQMGDFDLvVK5ubmltRaQr23durVaUvbSSy+p21577TVUqFBB9fVKHbiwYMECPPXUU0zOEhEVYnXq1MF3332HyMhIXL9+XQ1YqFSpEnS67CflEhHlFcanRESUXmpv8Lfffjutj2ZJWrJNRMU8ORsREZFpGVjqvxctWqQ+37x5U30eOHBghu0kebt48WL1/5LEtVgs6nP65t2bN29WE3BlSIL0MhwxYgSGDRuW78+LiIjunwz9kg8iooLE+JSIiLIiydhatWrx4BBR8UrOSvn/6dOnc9zmTvdn932kFYJ8EBERERExPiUiIiIieynSPWeJiIiIiIiIiIiIiiomZ4mIiIiIiIiIiIjsgMlZIiIiIiIiIiIiIjtgcpaIiIiIiIiIiIjIDpicJSIiIiIiIiIiIrIDJmeJiIiIiIiIiIiI7IDJWSIiIiIiIiIiIiI7YHKWiIiIiIiIiIiIyA6YnCUiIiIiIiIiIiKyAyZniYiIiIiIiIiIiOyAyVkiIiIiIiIiIiIiO2ByloiIiIiIiIiIiMgOmJwlIiIiIiIiIiIisgMmZ4mIiIiIiIiIiIjsgMlZIiIiIiIiIiIiIjtgcpaIiIiIiIiIiIjIDpicJSIiIiIiIiIiIrIDJmeJiIiIiIiIiIiI7IDJWSIiIiIiIiIiIiI7YHKWiIiIiIiIiIiIyA6YnCUiIiIiIiIiIiKyAyZniYiIiIiIiIiIiOyAyVkiIiIiIiIiIiIiO2ByloiIiIiIiIiIiMgOmJwlIiIiIiIiIiIisgMmZ4mIiIiIiIiIiIjsgMlZIiIiIiIiIiIiIjtgcpaIiIiIiIiIiIjIDpicJSIiIiIiIiIiIrIDJmeJiIiIiIiIiIiI7IDJWSIiIiIiIiIiIiI7YHKWiIiIiIiIiIiIyA6YnCUiIiIiIiIiIiKyAyZniYiIiIiIiIiIiOyAyVkiIiIiIiIiIiIiO2ByloiIiIiIiIiIiMgO9PZ4UCIiontltVqxf/9+rF+/HhEREfDy8kL79u3RqlUr6HQ6HlgiIiIiKlAnTpzAihUrcPnyZTg5OaFFixbo3LkznJ2deSaI6I6YnCUioiIjODgY/fv1x6nTp+AMN+iSDbA6mrF27VpUrFARixYvQpUqVey9m0RERERUAiQlJWH0qNHYtn0bHDVOMCQ5w6a3YsuWLfjg/Q/wzbffqAICIqKcMDlLRERFQnR0NLp2eQ5h18JRIaIOXIwe0ECj7ksyxOEmLqr7d/6wA4GBgfbeXSIiIiIqxmw2G4YNG469e/aiTHRVeCb5QfP/nSONumSEGi/h+YHPY9XqVWjatKm9d5eICjH2nCUioiJh4cKFuB58HWVDa8LV6JmWmBXOJneUDamJ6MhozJkzx677SURERETF36+//ordu39CmYiq8EoKSEvMCgeLE4IiqsPR6IJJkz62634SUeHH5CwRERWJyoSFCxbCPcFHBbtZMVgd4B7ri6VLlsJoNBb4PhIRERFRybF48WK4wA1uKd5Z3i/JWu/YUvjrrz9x+vTpAt8/Iio6mJwlIqJCLyEhASGhIXBN8cxxO1ejF2LjYhEWFlZg+0ZEREREJc+Jf0/CKcEtw2qu27mmeKnPTM4SUZFMzsqUwwkTJqBjx46oXbs22rVrl+V2q1evxjPPPIN69eqhQ4cO2LNnT66+f0hICEaNGoUGDRqgSZMmePfddxEfH5/Hz4KIiPKCTqdTn20aW47b2WBVn/V6tlQnorzH+JSIiFJJvGnFHWJTDWNTIirCydmzZ89i7969qFChQraTt7du3Yrx48ejTZs2qsfggw8+iJdeegn//PNPjt/bZDJhyJAhuHTpEqZMmYKJEydi//79ePXVV/Pp2RAR0f1wdnZGrZq1EOcSmeN2cc6RKBtUDv7+/jzgRJTnGJ8SEVGqZo88jCS3mLTigKzEOkVAq9WqojAiouwU2tKiVq1a4amnnlL//9Zbb+H48eOZtpk+fTratm2LMWPGqH8//PDDOHPmDGbOnJnjQJidO3eq4Hrbtm2oXLmyus3DwwODBw/G0aNHUb9+/Xx7XkREdG8GDR6E119/HQkOMWog2O2SDHGIc4nA6EHvqiCYiCivMT4lIqJUAwYMUANrI1yvwy+hbKYDY9YaEeV5U630LV26NA8cEWWr0L57vdMb66tXr6rKV6maTe/ZZ5/F77//nuMwmF9++QU1atRIS8yK5s2bw8vLS1XrEhFR4dOtWzc8/tjjuOZ3GuGu12DWmNTtFo0ZES7XcdX/lFpBIYEyEVF+YHxKRESpJKcwevRohHpcwQ2PCzDqktTtUkkb4xSOq4En4ebjgvfee48HjYiKZnL2Ti5cuKA+V6pUKcPt0gJB2hZI8janr02fmBUajUZ9r9TvS0REhYvBYMD8BfPRq09PRPpcx9lSf+J80GGcLf0nwr2vomOXDlixcoVqgUBEZA+MT4mISpY33nhDzcqxlErEuYC/cS7oL5wp8yeCvc+gfpO62LxlM8qVK2fv3SSiQq7QtjW4k5iYmLR2BOml/jv1/qzExsbC3d090+2enp45fl1u2Gw2JCYmojBLSkrK8JlKDp77kqs4nXupPpD+4rt27UJkZKR67W7dujUCAgLU/YX9NbigFadzT3enJJ57icPkgru9FMb4lLEpFWYl8XWKit+579evn1rh9eOPP6oiMUdHRzz66KOoXr26up+xafE993R3SuK5t+UyNi2yydnCSqp2T548iaJA2kJQycRzX3IVp3P/wAMPpP1/RESE+qCSce7p7pS0c+/g4GDvXShUGJtSUVDSXqeoeJ57WcWbOszcYrEUmbyAvRSnc093p6Sde4dcxKZFNjkrVQQiLi4uw1RuqTpIf39WpHohPj4+0+1SlXC/jbpl2W3VqlVRmMlVCvllqFixIpf/ljA89yUXz33JxXNfcpXEc3/u3Dm7Pn5hjE8Zm1JhVhJfp+gWnvuSi+e+5CqJ5/5cLmPTIpucTe0Ze3v/WPm3BKE59XWR7c+cOZOp1PjixYtqMNj9kHJlFxcXFAXyy1BU9pXyFs99ycVzX3Lx3JdcJenc27OlQWGNTxmbUlFQkl6nKCOe+5KL577kKknnXpPL2LTIDgST4Fay7Tt27Mhw+7Zt29CsWbMcy4Yff/xxnDp1KkMp9e+//47o6Gi0aNEiX/ebiIiIiIonxqdEREREdLf0hbncee/ever/g4OD1TKv1ERskyZN4OPjg1GjRuG1115D+fLl0bRpU5WYPXr0KJYsWZL2feRrZVDMyJEj1QAZ8cwzz2DWrFnq61955RX1WJ999hlatmyJ+vXr2+kZExEREVFhxviUiIiIiEpMclYGu7z88ssZbkv996JFi1Qytl27dipInjNnDmbPno1KlSphxowZaNCgQYblYNKMWz6nkmVl33//PT766COVnNXr9SqB+8477xTgMyQiIiKiooTxKRERERGVmORs2bJlcfr06Ttu161bN/Vxt98nMDAQX3/99X3vJxERERGVDIxPiYiIiCivFdmes0RERERERERERERFGZOzRERERERERERERHbA5CwRERERERERERGRHTA5S0RERERERERERGQHhXYgGBERAcHBwQgJCYGbmxuqVasGjUbDw0JEREREdhEZGYkrV65Ar9er2NTR0ZFngojoPjE5S0RUCO3duxdfffkVDhw8kHZbhfIVMGz4MPTv3x9aLRc+EBEREVHBOHPmDKZNm4atW7fBYjGr2zw9vNCvf1+MGjVKFRIQEdG9YXKWiKiQWbx4Md566y24WjwRFFsNjmYXmLUmRCeH4t1338Wfh/7E9K+nM0FLRERERPnu0KFD6N2rN6xJgF9MWbgaPWHVWBCbEIHvvpmFH3f9hHXr18LT05Nng4joHrD0ioioEDl58iTefvtteCeUQvnQ2vBM9oeT2RVuRi8ERVVH2ajqWL9hPRYtWmTvXSUiIiKiYi4pKQmDnh8EbZwDKt6sB9/EMio2dTF5oFRsJZQPqY3zZ86rAgIiIro3TM4SERUi8+fPhwMcUSq2IjTI3F/WI9kPnsl+mD1rNmw2m132kYiIiIhKhk2bNiEyKhKlIitBa9Nlul8StT7RZbBp4yaEhobaZR+JiIo6JmeJiAqRbVu3wS3OF5ocXp49EwJw+cplnD17tkD3jYiIiIhKlh07dsDN7AkHi3O223glBcBiteCnn34q0H0jIioumJwlIipEEhISoLcYctxGb711f1xcXAHtFRERERGVRLGxsdCach5Vo7PpodPoER8fX2D7RURUnDA5S0RUiAQEBCDFkJjjNin6W/eXKlWqgPaKiIiIiEqiMmXKwOyUAhuyb6dl1CXDYjMjMDCwQPeNiKi4YHKWiKgQ6dmrJ+JcI2HWmLK8XwLjaI9QNG3SFEFBQQW+f0RERERUcnTr1g2JmngkOsRmu02kyw24ubqhdevWBbpvRETFBZOzRESFSJ8+feDq5oLr/mcyJWglMRvicREJuhiMfnm03faRiIiIiEqGRx99FPXq1sNNv/NI1idkLhpwDkWk2w0MHzEczs7Z96UlIqLs5dw8hoiICrytwdJlS9Gndx9c0P8NtwQfOJpcYdYZEe8eCSOS8cnHn6Bly5Y8M0RERESUr7RaLRYuWoge3XvgnO0o3I0+cE5yh1VjRYJ7FBI1cejZsyfGjBnDM0FEdI+YnCUiKmQaNmyIvb/sxdKlS7F82XKEhQXDxcUFz7XrgoEDB6J27dr23kUiIiIiKiGkl+z2Hduxdu1aLFywEOfPn4der8cjjzTDoEGD8Pjjj0Oj0dh7N4mIiiwmZ4mICmkF7dixY9UHEREREZE9ScuCvn37qg8iIspb7DlLREREREREREREZAdMzhIRERERERERERHZAZOzRERERERERERERHbA5CwRERERERERERGRHTA5S0RERERERERERGQHTM4SERERERERERER2QGTs0RERERERERERER2oLfHgxJR8Xb16lUcOXIENpsNtWvXRpUqVey9S0RERERUQkVHR+PAgQNITk5GuXLl0KBBA2g0GnvvFhERkcLkLBHlmbNnz+KDD97Hnj0/q8RsqubNm2HcuAmoX78+jzYRERERFVhS9sMPP8S6tetgNBnTbq9apSreePMNtG3blmeCiIjsjm0NiChPnDhxAh07tMOl8/vx2XgDjv7kgmN7XDD9I0dEhh5Cly6dcOjQIR5tIiIiIiqQxGzHDh2xduU6eEaUQrWQRqhxsynKR9RGyMkIDBs2DAsXLuSZICIiu2Nylojum1TJjho1EuXKpGDzQgN6djTA20sDLw8NOrfRY+N8B9SracWLL46A2WzmESciIiKifDVp0iRcvnAF5UNrwz+hLAxWR+hsOrgZvVA2oiZ8Ekrh3XfH4cqVKzwTRERkV0zOEtF9++OPP3Dq1FmMe1kHD/fM/bucnTWY+KoOwcE38eOPP/KIExEREVG+Vs2uWbMWXrGl4Gh2yXS/BhoExFaAHnosXryYZ4KIiOyKyVkium979+5FgJ8BjzTO/iWlXi0dqlUy4Oeff+YRJyIiIqJ8I620jMYUeCb5ZbuNFjq4Jnjhp10/8UwQEZFdcSAYEd23lJQUuLtp7jj1VqpqZVui1HYYoaGhSEpKgr+/P1xdXXlgiIiIKE9iU6Gz5vx2V2vVITk5mUecMlRdR0VFwdPTEz4+PjwyRFQgWDlLRPetfPnyuHLNhPBIW7bbxCfYcPq8RW1LJZvVasXKlSvR+qnWaNiwIZo3b47atWvjpZdewr///mvv3SMiIqIiLjXeTHSIy3E7o3MiKlauWEB7RYXZvn370Kd3H9StWxePPvoo6tWrh27duuGnn1hZTUT5j8lZIrpvnTp1glanx4KVpmy3Wb7BjMQkK7p3784jXsITs6NHj8Yrr7yC4KMhKBtVHRUi6sAnMgjb1+9Eu7btsHv3bnvvJhERERVhklirWaMmotxvwoasiwcSDXGI18Wgb9++Bb5/VLjMmzcPPXv2xKE9h1EqqrKKTctEV8GR/f+if//++Prrr+29i0RUzDE5S0T3zdvbG8OGjcD0uSYsWGWC2fxfEGy12rBmiwmfTDehT58+CAoK4hEvwebMmYP169cjKKq6mpTskewHV6Mn/BKCUPFGfTgluGHokKG4efOmvXeViIiIiihptfXmW28i3hCFm54XYNFYMiVmb/ifRZ3addC6dWu77ScVjv7E48ePh098aZQPqQPvpEAVm3olBaJ8SG34xZXFp59+yrkZRJSv2HOWiPLEG2+8gdjYWIyfvBDfLLDiyUcBnQ7Y85tGtTzo3LkzPvzwIx7tEsxisWD2rNnwSgyAZ3LmAR1aaFE6sirOOx7GsmXLVHUtERER0b14+umn8cUXX+DNN99EnGukGv6ls+mR4pyIBG0M6tauiyVLl8BgMPAAl2CzZ8+GM9wQGFcRGmScnyH/9o8vhyTXWMz6bhZatmxpt/0kouKNlbNElDcvJlotPv74Y+zYsQMtW3XDXycq4cDRimjycEds2rRJLQdi8Fuy/f3337gZclMlZ7Mjb5rcEnywYf2GAt03IiIiKn569eqF33//HS+OHomgRgHwquOMpk81wty5c7F121Y1kJRKLpPJhJ07d8Ij1jdTYjaV3O4R64df9v2iClGIiPIDK2eJKM97fEmVAtHtYmJi1GeDxTHnP0wWB8RE39qWiIiI6H5ISy1Z4SUfROklJCSolV13ik1T74+Li4OHhwcPIhHlOVbOElG+CwsLw7Rp09Ds4caoUKE8atWsjlGjRuHw4cM8+iWABL3bt2/H1ClT1b+N+uQctzfpk+Hnn7ntAREREVFeSEpKwpIlS9D6qdaoUL4CKleqjO7duqt4RYaXUvH3559/4p133lG1sSl3iE0ldpVVgl5eXgW2f0RUsrBylojy1dGjR9G3by8kJsai0zNa1KmhQ2S0EWu2bsa6detUUPTiiy/yLBRTiYmJeH7g89j/6364WDyggx6RLjfVoIWsmLVGxLlEYnT3Fwp8X4mIiKj4i4iIQPduPXD69Cm4G33gl1QONo0V/+w/jiG/DUHbtm0xc+ZMtuMqpmw2Gz788EPMmjULznCFo8kZ0c434RdfBposatdssCHWI0z1MHZ1dbXLPhNR8cfkLBHlm6ioKPTr1xvlSidgwZdO8PX+r5fTy0NsmDoLqk9t+fLl0b59e56JYujll8fgj98OoHxEbbgZvRDlHIIbXucRYbwOn8TSGfp7WTRmBPuehbu7O7p3727X/SYiIqLimZgb9PwgXDhzEZXC6sPJ/F+yzTchCLGOEdi+bTsmTZqEiRMn2nVfKX/MmTNHJWYDYyqqWDRFn4gLfkdx3fM8ysRUyZCglcTsTY8LSNImYMSIETwlRJRv2NaAiPLNypUrERsTg7lT9BkSs+rFR6vBqyMMaPmIHjNmfKWCZSpezp49i23btiIgqoJKzAqvpAD4xpdBiOclXPQ7igiX64hxCsNN94u4UOYfaDwtWLxkMXx8fOy9+0RERFQMl7L/+defCIyolCExm8ojxRe+sUFYuHARoqOj7bKPlH+MRiOmfzUdXgmB8E2USlmN+jkIiq6GGOdwnA04jDC3qyo2DXO9hotljiDaLVTN02jcuDFPDRHlGyZniSjfrFmzAm1aaRHgl/VLjUajwYBuOhw/fhJnzpzhmShm1qxZAwetIzyS/usfK0FwYFxFlI+spQZ/hXhcQrD3WST5RWPI8MHYvWc3GjVqZNf9JiIiouIbmzhpXOCWkn3vUO/EQJiMRmzbtq1A943y388//4yo6ChVMZueZ7IfKofXVz8X4a7BKjaN8LyGtl3aYOvWrejZsydPDxHlqyLf1qBfv344ePBglvdNnTpV9QzKSqtWrRAcHJxlf0xHx5ynNRJR7oSGhqLdExkrZm9XvcqtxG1ISAhq1KjBQ1uMXL9+HQ4mZ2izuA7oluKtPmS52KUyR9F/QF+MGzfOLvtJRJSXGJsSFe7YVJ/kmKGt0u30Vgd1cVliUyp+sam0LXA0O2e6Typoy8RURemYKgj2PIuqzctj+vTpdtlPIip5inxy9r333kN8fHyG2xYuXIgffvgBzZo1y/Frn3nmGQwaNCjDbQ4ODvmyn0QlkZubG0LCEnLcJiTsVjsD6TNKxYsMTbDqzCoBm9ObIIvGpH5WiIiKA8amRIWXxBtWB3OO21g1FphsJsamxTQ2tcGqzrHOlnUqRGJWm94CN3fGpkRUcIp8crZq1aqZbnv11VfRvHnzO/Ys9PPzw4MPPpiPe0dUMEwmE3bt2oXTp09Dr9ejSZMm6kPaBthTmzbtsXzZHIwbY4OzU9b7snKTGUFBgahfv36B7x/lr9atW2Px4sVIMsTDxZR18j3OMRJGq1FtS0RUHDA2Jbo1eOvAgQM4dOgQzGazWh0lf+sNBoNdD0+bNm2wbt06JOsTsuw5K2KcwlUCTwp5qHhp0aIFdDo9op1DVc/ZrJi0KYh3iOb5J6ICVex6zh4+fBjXrl3j5HcqMVavXo0mjRti6NChWDDvK3w78wt06dIFTz7ZUv0+2FP//v2RmAS887ERZnPmgV87fzZjzRYLnn9+KHQ6nV32kfJPy5YtUb5ceYT6XIJFk7lKxaQ1ItznKho2aIh69erxVBBRscTYlEqav/76Cy1bPIGuXbtiymdT8fXUGSpOfajhQ1i7dq1d9+3pp59GmdJlEOJ7McvYJEWXiAjva3i69dMoV66cXfaR8k9AQAA6dGiPSK/rSNEnZrrfCituel+Ei6ur+vklIiooxS45u2XLFri4uODJJ5+847abN29G3bp10aBBAxUwSNUhUVEiVYljxoxBs4ax2LXSGX/vcsDR3Q5Y9o0TXAyX0K1bV7smaCtUqIAvv5yO9TusaNvPhOXrTTh60oI9v5kx8u0UDHvdiP/9738YNmyY3faR8o8k3L+f+z10HsDlUscR4XodKbokJOsTEeZ2DVdKHYe7nxtmfjOTp4GIii3GplTSErPdnuuGG2dCUD6iNqoGN0KV4IaoHPYAjMHA6NGjsXTpUrvtn6wwm79gPvReGlwqfQxhrteQZIhDoiEWNz0u4nKpf1G+cjl8MeULu+0j5a8PP/wQFatWxJXAfxHifglJ+ngVn0Y5h6h4NcU1HnPmzGZbCyIqUEW+rUF6smRm+/btatiXJGhzItvIMuoyZcrg6tWr+O6779C7d29s2LDhvq6SyhKexMTMV+EKk6SkpAyfqWiKiorCe++NR9+uenz8tkNaCwOtVoPHmurw0ANadB9mxBtvvIqNG7eo++1x7qVCYcmSpZg161u8OWmf+h0RlSqWw7vvDkSfPn2QkpJSYPtTUtnr975SpUpYt34dvvzyK+zYsR0hlkvqdgeDAzp07ICXX35ZtZgp7K+bRRlf80uuknju5W+MvVv6pMfYNHdK4s9qcf39e/WVV9XArXKhtTMMBJUWAkHR1aCz6TDu3XF44okn4OXlZZdzX7lyZaxdt1a9/9u4cRPCTFfU7Z4enhjU83kMHz4cTk5OjE2K6e+9DP9esWI5ZsyYgVUrVyMi8bq6Xf52PP7Y4xj98miVJ2Bsmn/4ml9ylcRzb8tlbKqxpWZKioG9e/eqCjz5Qyt/8O92cqf0IGrfvj0mTpx4T49/7NgxGI3Ge/paoru1fv16LFmyAIe2O8PXO+tfdqlQ7T8qBZMnT0atWrUKRUI5MjJSBUVBQUGF6g005b+YmBh1MUzOu1RVcwgYEeUHGe5aWFqlMDalkuTEiRN46623VMWsm9Ery23MWiPOBh7GwOcHoHPnzrC3hIQEhISEQKvVqtjU3j1xqWBJgciFCxfUhbTSpUurggEiInvEpvritmxMrsA++uij99R/plGjRvj333/vax/kD3pWgyAKE7lKcenSJVSsWBHOzs723h26R19//TUebqjLNjErWjysg6uLTiVEJTkr5/7XX3+FxWJR00ofeOABLtkpIQrL7/3DDz9st8cuqQrLuaeCVxLP/blz51CYMDbNnZL4s1oc7du3D3qNAa5Gz2y30Vsd4Gr0QHBwcFps+vfff6skqbx5rV69ukqSUfFXWH7vOSC85J57Kngl8dyfy2VsWmySs8nJyfjxxx/RoUMHu17xlIqwO7VUKCzkl6Go7CtlzcGQc+G7tDjQ6zWq9+fRo0cxefLHOHjwr7T7XVyc0KXLc3jzzTfh4+PDw1wC8Pe+5OK5L7lK0rkvTCsyGJvevZL0s1ocSbyp1WihwR1+D20a9bsaFham+n/u3LkTVqtV3SW3P/XUU3j77bdRo0aNgtlxsiv+3pdcPPclV0k695pcxqbFZiDY7t27VV8YaUtwL2Q5izSwLyzL4IjuRALWP4/I1afsE7RHTlgQE2tGfHw8evToDmPCEcz8xBGHdjhj7zpnvNDPgi2bV6Bz5w6qupaIiIjyBmNTKomxqdGaogYsZceqsSDFOQH+/v54ts2z2LP9Z/hHVUDV0IaoFtIIpaIrYf+u39CuXXtVWEBERFQSFJvK2c2bN6vhXtKa4HYDBgzA9evXsWvXrrQlZnv27EGLFi1UOwPpgTh79mx1tff555+3w94T3T0ZpDVz5kwsXG3GiP6Zq8WlnfSMedI/yR/ffz8bz7TQYMbHDqqSNtWYYQ7o8IwVXQZfxbhx4/DNN9/keR+nAwcOIC4uTvVweuihh9TvGRERUXHH2JRKGhm4HBgQiIjkYARFVc+ygjbS5SZMViMO/HEAKdFmlA+pC73tvzjWO7EUPJL8cC3gFIYOGYrffv8tT2NHiY8PHz6MmzdvqhZfTZs2LTFLa4mIqPDSF5chM9LjSJKwWZUMyzIZ6bGZqmzZsmoA2Mcff6ySRu7u7qoP4ujRo1GuXLkC3nuie1O+fHkMHjwYn3w9VwWa/bsZ4Opy6+f/ZpgVn800YcceM7p2fRybNq7DR285Z0jMpqpcQYtRg7SYNH0LQkMnqgsW98tkMmH69OlYuHAeIiKi024vV7Y0RrzwUra/q0RERMUBY1MqifR6Pd6b+B5GjhwJred5+MeVh8HqkFYxG+lyA2EeV1Ubuk2bNqF8ZK0MidlUOpseAZEVcFF7TBXUSJuDvLBmzRpMmzoNly5fSrvNzdUN/fr3w6uvvsokLRER2U2xSM56enri+PHj2d6/ePHiTE2/b7+NqCiaMGGCmi776Yw5+HqeFQ3qamA0AYf+scDR0RHTpn2OtWtXo2VzHfx8sk+Gdmmrx8QpRvzyyy947rnn7mufZNrpsGFDsHv3T+jXVYdenZ1RKkCDC5etWLQ6DO+++65qAv7ee+8xQUtERMUSY1MqqTp27Kj6Lb/99js453oYriZ3ycwi2TEBFkiMOEwN/nLUOsHV6JXt93EyucEZrvjpp5/yJDn71Vdf4bPPPoNHsi8qJNSBk8kFZp0J0fGhmP3dHNXebvny5XBycrrvxyIiIiqRyVmikkqWeUmSc8iQIVi2bBnOnDmjbpvYqYlKsnp4eGDx4vkoUzbn7+PlIY2qb01PvF9y4UMC6XlTHdDq0f9eYhrV16mPBnW1GP/ZHDzxxBOqtQgRERERFR89evRAmzZtVKXqwYMH1QrG6tWro3fv3ggKCsLEiROhsxlyHBwm9+ms+jyJTY8cOaISs/5x5eAf/98qSZ3ZgMC4inBP9sFfh/5S7b1eeeWV+348IiKiu8XkLFExIIHu66+/nuV9ZctWwNGTJ1Xrg+xaCRw7ZYXNdqvlx/2Qx1iw4Hu0aaXPkJhNb0B3PVZslO3mMTlLREREVAxJgcCgQYPUx+0k3kxBIswaU5ZtDYQVFqTok+47NhULFiyAk8YFfvFZfy8Xkwc84v2wcP5CjBo1CgZD1vtERESUX7T59p2JqFDo2bMXTp014ddD1my3mbvMjDJlAvDYY4/d12Ndu3YN585dQpdns39pkQRx5zYa1UNMkrlEREREVHJ07twZGp0GUa4h2W4T7RIKs82E7t273/fj/bjrJ7jGeedYqeuZ5I/wyHCcPHnyvh+PiIjobjE5S1TMScL1oYcaYNS7Zvx19L/BeMJksuHLOUas22bGyy+/qgY53A/pMSY83XMe9uXpIY9tyTCoj4iIiIiKP19fX1VRG+FxDdHOobDhv4v18v+xjhEI87qiWnTJANz7lZKSrFok5ESGkIm8aKNARER0t9jWgKiYk4Fh8+cvQr9+fdDp+aNo0kCPJg00SEy0YctPQGiYGa+99hr69u17348VGBgIg0GHf/61okkDXbbbHfnXitKlA+47GUxERERERY8MiI2MjFR9aSM9r8M1QYaDaZDkGotETRyebv00Pv300zx5LGmNcCMqAkjIfpskQ3zatkRERAWNlbNEJYCPjw82bNiE6dOnw2SrjfU7vLHnQGk8/UxP7Nq1C2PHjs2z/mLPPtsOi9bYkJScdcuC8Egb1m2zoXfvfnnymERERERUtMgF+i+//BJLly5F48cawaEq4FjVhsf+9wiWL1+OuXPnwsnJKU8eq2+/voh3ioRRd2uF1+2kWjfaIwSPP/a4muNARERU0Fi2RlRCyHADmZxbsWJF1KpVCy4uLvnyOC+++CI6dNiOEW8Y8eUHDvD2+q/FwbUbVgx9zQxXVw/068fkLBEREVFJJXMImjRpAnd393yNTbt164bvvv0OwbbTKBNaDY6W/x7HojHjptdFpOgTMWbsmHx5fCIiojthcpaI8lSdOnUwb94CDB06GI3bJOOZJ7QoE6jBuUs27N5vgb+/L5YtXw5/f38eeSIiIiLKV5L8XblqJXr16IXz+AfuJm84pDjDrDUhwTUKGr0GM7+eiaZNm/JMEBGRXTA5S0R5rkWLFvj99wNYsWIFtm7ZiKOno+HvH4hJk7qhS5cucHNzy/Q1Z8+exY0bN1QAXa9ePfajJSIiIqI8UalSJezZuwebNm3CiuUrcP36Dbi7eeLZdv3Ru3dvlCpVKtPXXL9+HRcuXFCrz+rWrQtXV1eeDSIiyhdMzhJRvk3ilRYH8pGTHTt24MtpX+LY8WNptwX4B2DwkMEYMWIEk7REREREdN+cnZ3Ro0cP9ZGTY8eO4bPPPseePbths92aoeDs7IIePbrj1VdfVbMciIiI8hKTs0RkN3PmzMHEiRPhZvJCubiacDS7wKw1IiYxDJM/nYw///wT33//faYEbXx8vErqhoSEqCrc1q1bo0yZMnZ7HkRERERU9P3666/o27cfdCkGlIqpDFejB6waK2KdIrB00TL8vOdnbNy0EX5+fhm+zmw2Y/fu3Th//ryqtG3WrJlq9UVERJQbTM4SkV0cP35cJWZ948sgIK4CNLg1OMzB4gSXGA+4Jfvgxx9/VMlZqaBNDXw///xzzP1+LpKSk2DQOsBsM2PcuHF4ts2z+HTyp/D29uYZJSIiIqK7kpiYiKFDhsIxwQVB4dWhhS7tPqd4V3gm+eOq7STeeustFZ+mWrVqFT6e9AnCwkNh0BpgtVlhsVnQsEFDfDHlC9SoUYNngoiIcqTN+W4iovwxf/58OGmcMyRm03NP8YZnoh/mfT8PFotFLSsbM2YMZs6YCZdwH1QLaYRqwQ+h+o3GCIiqgB+27UKXLl0RGxvLU0ZEREREd2XDhg2IiY1BYFSlDInZVI4WZ/hEl1Grt4KDg9Pi2bFjxyLlqhWVwx5AteDGqH69McpG1cDJw2fQsUNHnDlzhmeCiIhyxOQsEdnFzu074Rbnk2ViNpVnYgCCbwTj9OnTqop2/fr1KBNVDYFxFWCwOqptdDYdfBJLo2xoLVw4dwEzZ84swGdBRERERMXBDz/8oFptySqu7Ej1LGxQLQykvdZ7770Hn4TSCIquBifzrYFhGmjhkeyL8iG1YY6z4q033yrAZ0FEREURk7NEZBeJSYnQWQw5bqOz6tOWmS2YvwCuVg94Jmfs8ZXKyewCjzg/LFm8BCkpKfmyz0RERERUPCUkJEBrzlwxm54UBeg0OhWbLlu2DLBq4B9XLstiA51ND5/oIBw4eIDVs0RElCMmZ4nILkqXKo1kh4Qct0k23Lq/dOnS+OOPP+Aan3M/WY8kX0THRKthDEREREREuVW2bFmYnJNhk9LYbKToktS8AxlE+/vvv8MlyUMlYbONTZNvrRI7cOAATwQREWWLyVkisotefXohziUSJq0xy/slMI5xD0XzR5ojKChI9Z3V2nJ+ydL8//2yLRERERFRbnXv3h1JSECCY3S220S63oCHuwdat24Ns8mcFnvmEJ1Co9EwNiUiohwxOUtEdtG7d294e3sh2P80TNqMbQissOKG53kk6uMw+uXR6raqVasi0TnnYV8JjjEw6A0oV65cvu47ERERERUvDz/8MBo/1Bg3fS8g0RCXqWgg0uUGolxv4sWXXoSTkxNq1KyBFJd42GDN9nvK97HarKhWrVoBPAMiIiqqmJwlIrvw8fHB8hXL4eLviPOl/kaw9xmEuV7DDY8LuBD0N+LcIjBt2jQ8+uijavv+A/ojziESyfqsWyFYNGbEeoaiY6eO8PLyKuBnQ0RERERFmVS4zps/DzXr1sAlv2O44n8CYW5XEeJ+GZdKH8VNz4sYNGgQXnzxRbV9nz59kGJLRrRzWJbfTyV03YNRoXwFNGvWrICfDRERFSVMzhKR3dSpUwd7f9mL8RPGo1R9XxjLxsClmg4DhwzAz3t/Rrdu3dK2lf+vVr06ggNOI94hOkM/sBR9Iq75n4bOWYvRo29V2hIRERER3W3xwMZNGzFz5kzUblYd5rLx0FYw4plOrbFhwwZ8+OGHKokr6tatiy5duiDE+xKinG9mqKA1a4244XUO8U7RmPDeBGi1fNtNRETZy757ORFRAZAq12HDhqmPnDg7O2PVqpV4fuDz+Pufv+EMV+iTHGF1MCNBF4sA/wAsWLgcVapUKVTnLTIyEitWrMAPO39QU4DLlS+Hnj174sknn4ROl/NEYCIiIiIqWA4ODujUqZP6uJMpU6aoeG716tWI8A6GY5IrbForEh1jYTAY8NUXX+F///sfCpPk5GRs3rwZ69auQ1hYmEpIy8qzzp07w8XFxd67R0RUIjE5S0RFhr+/PzZv2aym465ZswY3b9yEu4c7nn32WbRp00YF04XJ9u3b8eLIF2EymeGW5AWtRY/L/17Dzp07Uad2HSxZugQBAQH23k0iIiIiugcSe3755ZcYOXIkli1bhnNnz8HgYMAjjzyiVn0VtlZbp06dQu9efRASehNuJi/ojY64ZriJX3/7FZ9+8ikWL1mMBx980N67SURU4jA5S0RFiiwlk4BXPgqzP/74A8OHDYdbojcqRFeC3ma4dUesDIeIxbmT59CrZ29s274Vjo6O9t5dIiIiIrpH1atXx8SJEwv18QsJCUH3bt2RFGFElbAGcLQ4p91n1CXjhuUcevboiR92/YDy5cvbdV+JiEoaNr8hIsoHn03+DI4mV5SJqvZfYvb/uZg8UCa0Ok6dPoktW7bw+BMRERFRvpo3bx5iomJRNrRGhsSscLA4oWxYTRgTTJg1axbPBBFRAWNylogoj128eBEHDh6Ad2wgNLg1NOJ2zmY3uJu9sXjRYh5/IiIiIso3NpsNS5cshUe8L/TWrNuA6Wx6uMf6YdXKVUhJSeHZICIqQEzOEhHlscuXL6vPLkaPHLeToREXL17i8SciIiKifBMfH4+o6Cg43yE2dTG6IzEpEeHh4TwbREQFiMlZIqI8JtN5hUVrznE7q8YCB4eMLQ+IiIiIiPIjNpXYMydW7a37C9uQXSKi4o7JWSKiPCZTbl2cXRDrnH3VgQ1WJLhH4YlWT/D4ExEREVG+cXJyQqNGDyHOLSLH7WJdI1CtanX4+fnxbBARFSAmZ4kKmNVqxYEDB7B+/Xrs3LkTMTExPAfFjKurK3r17oUYjzAk6xOz3CbcLRgptmQMGDCgwPePiIiIKL1Tp05h48aN2Lx5M4KDg3lwiqHBgwchXh+NWKesiwfiHaMQ5xCJQYOfh0aT9cwEIiLKH/p8+r5ElIXVq1fjyy+n4NKlq2m3OTs74rnnuuPdd9+Fu7s7j1sx8dprr2Hfvv24aDsB75hS8EoKgM5qQLIhHpGuNxDjHK62qVOnjr13lYiIiEqoQ4cO4YMPPsDhw4fTbpPE3JNPPomJEyeiUqVKdt0/yjvt27fHzh07sWnTJiQmxME7IRAOFmeYdCmIcrmJKPebaNXqSfTu3ZuHnYiogDE5S1RAZsyYgU8++QTPPqnHlHFOqFtTi8hoG9ZsMWP2kmU48s9hrF6zDm5ubjwnxYCHhwc2bFiv3tisX7ceoeYrafeVKV0G41/9Ar169bLrPhIREVHJtXfvXvTv3x+OKS4oG1sDrimesMGGOKcI7P/xN7Q71A4bN21E1apV7b2rlAe0Wi2+nvE1qlarijmzv8f5uH/S7nN1ccULz7+A119/HXo9UwRERAWNr7xEBeDEiRMqMTt6sAGvj/yvwb6LswZjhjqg9eMWdB1yClOnTsWECRN4TooJT09PTJs2DePHj8f+/fuRlJSEMmXK4JFHHoFOp7P37hEREVEJJTHJyBdGwjnRA2UjqkOTrtudd1IpuCf74qrtJMa8PBZbtm62675S3pH485VXXsHIkSPxyy+/IDIyEl5eXmjRogWcnZ15qImI7ITJWaICsHDhQgT66zFm6K1JqberU0OHfs9ZsHz5UnXFmsFR8eLj44MOHTrYezeIiIiIFOktGx0TjarRDTIkZlPpbQb4RgXh738O4/jx46hbty6PXDEbEPb000/bezeIiOj/cSAYUQH4Ze9PaP80YDBk31y/cxsdYmLjceTIEZ4TIiIiIsq/2PSXX+Bq8VQ9R7PjnuIDvcag2h8QERFR/mFylqgApKSkwMMt56mnHu6atG2JiIiIiPKLxJsaS85vBTXQQKfRMzYlIiLKZ0zOEhWA8hUq4q+jOW/z11HrrW3Ll+c5ISIiIqJ8I/Gm0SkRVtyKP7Ni1CUjxZrE2JSIiCifMTlLVAB69eqLvb+bcPJs1gGw2WzD3GUWNGvWFJUqVeI5ISIiIqJ8jE17wWhNQYxzWLbbRLpeh5urG9q2bcszQURElI+YnCUqADIMqkb1Khg4xoQjJywZ7ouJs2Hse0YcPWnF2LGv8nwQERERUb6qWrUqOnfujFDvS4hxCocNtrT7bLAi3PUaIl1vYszYMRxUS0RElM/0+f0ARAQV1C5ZugL9+vVGu35n0fhBA+rWsCEyGti51wqbVYcZM2aiefPmPFxERERElO+++OILJCcnY/v27YiEK5wS3GHTWJHkGosUWzJeeukljBgxgmeCiIgonzE5S1RAypQpgx07duGHH37AihXL8Ns/V+Hi4opRo55B7969ERAQwHNBxcaVK1ewdu1a3LhxA66urnjmmWfQtGlTaDQ5D8YjIiKiguHk5IQ5c+bg0KFDWLRoEY4f+xc6nQ7NHmmL/v37o3r16jwVVGxER0er2PTs2bPQ6/Vo0qQJ/ve//8HBwcHeu0ZExOQsUUEyGAyqbxd7d1FxlZSUhNdffx0bNmyAXqOHo9UFZo0Js2fPRo3qNTB7zmy1lJKIiIjsTy6aSpJKPoiKI5vNhq+//hrTpn0Js8kEF5sbrBor5s+fDz9fP0z7chpatWpl790kohKuSPecXbduHWrUqJHpQ5bo3OkFWhIFLVu2RP369dGjRw/8888/BbbfRETFkcViweBBg7Fpw2YERldCleCGKH+jDipdfwDlI2rjyulgdO7UGdeuXbP3rhIR5RvGp0REhcfUqVMxefJkuEf6oupNiU3rouL1+qgc9gBSblgxcMBA/PLLL/beTSIq4YpFW4Pvv/8e7u7uaf8ODAzMcXtZvjN9+nS89tprKpm7dOlSDBo0CBs3bkS5cuUKYI+ppCWs9u3bh9OnT6vKWalMqFu3rr13iyjPScuOvb/sRfnIWnBL8U67XQMN3IxecAp1xWXtMRUkywcRUXHG+JQKs2PHjuHgwYMwm82oWbMmHnvsMWi1RbpuhyiT69evq4pZv7iyCIgvn+E+J7MrykbUwFW/k3jn7Xewb/8+tt8iIrspFsnZOnXqwMfHJ1fbpqSkYNasWSoZO3DgQHVbo0aNVL+ZuXPnYuLEifm8t1SSbN26FR99OBFXrl6Hs7MWVguQYrSiYcMH8Omnn6ufXaLiYsH8BXC1eGZIzKantxrgGROA9evWY8KECfDy8irwfSQiKiiMT6kwOn78ON5663X8/fdRODpoodVJSyIrKpQPwrjxE/Hss8/aexeJ8syyZcuggw6+CUFZ3i8FBL6xQbh46V/8/vvveOSRR3j0icguStzl0cOHDyM+Ph5t2rRJu02agLdu3ZrLGShPrV69GsOGDUONyqHYtMAJp/c54dQ+J8z5whHJ8SfQpUtHFSAT5SQhIQH//vsvTp48qSYqF2ZHjhyFS4JnjttI4tZoMqphDEREdAvjUyoIEnd27doJxsQT+H6Ko4pLJT7duMAJ1SqGYOjQoWpgElFOTCYTzpw5o36eYmJiCvXBOnr0KJyS3KCz6bLdxsXoAb3GoLYlIrKXYlE5265dO0RFRaFMmTLo3r07hgwZoiaNZuXChQvqc+XKlTPcXqVKFSxcuFAlP2RyKdH9TgN9++030a29HlPec0hbIqPXA/97Qo/HmtrQdYgRb7z+CrZt/4EHO49JX2l5o7tmzRqEhITA1dVVVcc//fTTqrVEURAcHKzar6xZvQbJKbeSsh7uHujdpzdefPHFXK8WKHC3ftSJiEo8xqdU2GKj114bi0plzVg9xwGuLv/9wW5YT4d5U7V45T3gzTdfV0UrHh4edt3f4ujy5cuqkvP8+fPqvWqzZs3QtWvXDO35CrPExER8++23WLRgEcIjw9Vter0eHTp0wOjRo1GtWjUUXTZ77wARlXBFOjnr7++PUaNG4YEHHlDJr927d+PLL79UyRhZMpuV2NhYVSnr6OiY4XYJQCRokat/95Ocle8hf7gKM5mmnv4z5T3pY2w2GfHWS05Z9i6SgPjVEToMGvsv/vjjDzWYrrife+m9+/PPP6sKdXn8smXLonPnznne51ku1Lz04ks4eOggnDQu0Cc7wmowqwEtgQGBmDV7VqFvJyFBe6+evZAQkwSPWD+UUm0CbIiNj8TcWXOxbes2rFi5Qr0GFqZzX69+XRyJPQ7El812mzinKDgYHNR5L+yvlcUFX/NLrpJ47iUOy+rvbkmOTxmbkpDhx8eOncDCrxwzJGZTyc/qW6MM2LAzWSUQ+/fvXyJep6RaUtqQSfwo7Zbatm2rfnfzkvT1/fDDD7F8+XLoYYBjiitsWiu2bpH2Zx9h0seT0L59exRmcXFx6N+vP06eOAWPeF9USK4DrU2LBIdYbFu3Azu278CChQvQoEGDQnXua9eujb2798ISbYbOlnXqI9EhFmbbrd7LjE1RIn7vyX5K4rm35TI2LdLJWWlcLx+pHn30URXUSgXsiBEjEBAQYJdlHrL8uCi4dOmSvXeh2JI3Yk0aahHgl33nkFbNdaoP7ZYtWwq8mrOgz70sYf/0k08RFh4GZ5srtFY9UvSJ+PrrGXiyVSuMfHFknhwDo9GIN994E1cuXkW5yJpqCb30khLJ+gTctFxAr569MXXaFAQFZd17yt6sViteHPkikiONqBhWF3qrQ9p9LiYP+CSWwhXbv3hhxAv48KMPC9W5b9myJX777TfEOUbBPYu+s2atETHuIXi8xeOqMlg+qOAU1td8Gdaxc+dOXLx4ERqNFrVq1VRV7oW2OrwIKqznPr9IktOeClt8ytiUhCQgXVy0aNEs++Xdgf5aNH5Qh59++gmNGzcu1q9TkZGRmPzpZJw8dRIOcILB4giTLgXz589HrZq18OZbb+bZ36GZM2fih50/ICCmArwTA6HFrXNg0qYg1PMKXnnlVYSHh+Phhx9GYfXVV1/h1IlTKB9aG85mt7TbnU3u8E4shWt+pzB40BDMmz8300Ume577hg0bwgorIlyvZxoIJmywItzjGoLKBMHNza3IvI8vLgprfCIXTH/88Uc1ONFsMqNc+XJ45plnUKFCBXvvWrFRWM+9PWPTIp2czYr0kp03b556Yc0q+JUKBEngyGCw9H845BdQstmenjn3S7wTSTBVrVoVhZlcpZBfhooVK8LZ2dneu1MsyXHVmnPeRqfTqEEMvr6+qFWrVrE996dPn8a4d8dBm+CASlH1VBAnrBoLop1DsWfPz9DpdZgxY8Z9VztJG4PzF86jUnh9OJv+CxxTJ7KWC6uFS7pj2LFjB7744gsURvv27UPw9WBUjMyYmE3lYHGCf1R5HDl65K5ebwri3FevXh2/7v8V+/f9Cv/ocvBKCoDWpoMNNiQ4RiPM5wrcfdxU5Vjp0qXzZR+o6LzmSzX9J598ohJWBo2D6gknPyvH/jmKlStW4rXXX1Ntiqj4nfv8dO7cORRG9oxPGZuSkESjxJ0Sf+bE2QnQODoX69hUqkBHj3oZN67cyHAxX/4GxTtG4cLpi5j43kSsXbf2vts7yGuSXIAsFVMJPokZYx+D1RFloqrC6mPG4kWLMWDAAGi1hW8kTEREBPb+vBc+MUEZErOppJ9r6cgqOKc/rFZ/SauGwnTuR40epZLL8t5DBoMZ/j++lsKNMK8rSHaMxyeffq2qbKlgFOb4RN5PvjfhPVXx7pLsCY1Vg5PHTquCqo4dOqpK97u9AEFF49zbOzYtdsnZO0ntNSsVOrJ0IX0vWulZe7/9ZiWAdnFxQVEgvwxFZV+LGvnZWr5sH5KSbXB2yjoI/ve0BdExZhUIFPR5KMhzP3XKVCBJi3JhNVWiLpX8vwSpeqsBP/zwA/7++29VXXQ/VixfAXeTd6bEbCpZzuQZG6CqRyZNmgRv78zVnYWh6toZrmlJ7Ky4J/vCoL2k2kTcbUuM/D738+bPw1tvvaUCm3Dva3C0OquK2RRrMurUrqPaSlSqVCnfHp+Kzmu+LPFctHARAmMrwjvhv0oiWXoY7nYNkydPVm+KBw4caO9dLfIK27nPT/ZuaVAY41PGpiQk3oyKNuPkWQNqVcs6AShx619HbejXv1axjk3nzJmDq1euomJIPTha/ksOSILWPcUHDiHOuIRjWLVqleqlej+krZaDxhFeiYFZ3i+P6RsXhEvXjqtYOH3VfWEhSSmzxQyvxOyr/qV4wM3kiV27dqFfv36F6ty//vrrqip2yhdTEOV2Ey4ad1hhQZItAf5+AZg1fTFatGiRb49PRSc+kZ/1t99+W/2sB8RVUO9ThS3aimjnMGzevBlanVYVFVHxOveFITYtfJfm7tO2bdtUg/XsrnzJ0gZ5cd6+fXuG5V6SHHr88ccLcE+pOOvdu7dKvK7YYM6278jMBWYEBvrhySefRHF17do17Pl5D7xiSmVIzN6eaHSBm6qeu18yOdYlKefqIrcUL3UltLAupYiPj4fObEhrx5AVLbTQw0FtW9hIAkF6Kx44cACvvv4KnhvQGYNHDMLGjRux84edTMxSWiuD2bNnwz+2HHwTyqQlZlMvogTGVVSBsSw5LUk9qaj4YnxK9vbUU08hMMAXM+ebVByalWXrzIiJtaBXr14orqR91MIFC+Ge4JMhMZue3C73L5i/QG1/P06cOAHHRDcVu2VHLsjrNDq12qwwknhTq9FBb8u5BZnEr7ExsSiMiZGRI0fi73/+VlWP3Qd2RZ/BvVSS/s+/DjExS4r8rn/4/ofqAk3pmCppiVn1MwQtvJMCERhVCevXr1e/10R5rUhXzg4ePBhNmzZFjRo11L+lP5Jc4ZQG9qmDcmR5iLwJlKt4QkrQhw8fjq+//lot75FluNKcPTo6Wn0/oryqgOnbty8+mLYUGi3Qq5Mejg63km1R0TZMmWXE5h/M+Oqr8WrKaXElyzflDYAsF8uOJCGd4z1w9Mix+348WQomvaNyYtPcul8u4hRG8tplMiSrpXXZJWgtGjNSbMl26audW9LT936rTaj4WrlypbpgI33qsuMXH4RzLn+rpFZul0gSFQaMT6kwkvYWb709DmPHjoW3F/DKcAd4e/5/X/4Umyoo+Ogrk3ofJctNiyt5zxcSGoKyKbfeP2ZHYtdroafV9vfTe1av0wOarJPh6UncV5hjU6vNAqMuWVXIZrf/JscUlCqd/d91e5PVOJIbIMrK/v37ce36NVSMq5ftezCvJH9EegdjyZIl+Pjjj3kgKU8V6ayQLI1du3Ytbt68qa50SCDxzjvvZFhKIbdLX7v0hg4dqhJG0vtLmsFLT6W5c+fm+dR4Ktlk2bz8nI2fvBRfzraiSQMbjEZg/yEbbDatekF/7rnnUDJK+HMOSlUiMg9WojZp0gQHd/8Fv4Sy2W4T6xQBdzd3VKtWDYWR/EzIlfw4pwh4JPtluU20Swg0Wg06dOhQ4PtHlFe9l5xMrtlOThYOFmc4a10KbQ9RouwwPqXCqnv37moa/fvvv4flG5LxaGMNZEbJgb81iIwyq/dQ0nKmOMttbHrn+3OnSdMmqhe/tOzJ7m9enGMkrDZrgQ9hu5uqa4mdI+NvoFRc1q2pkgxxSNTGoVu3bgW+f0R5QeJNqRDPrj1eagWtY6KrWq1JlNeKdHJ23Lhxd9xm8eLFWf5RlupZ+SDKL1IR+9lnn2HYsGHq6tqZM6egdzTglVeaquViMgisuKtbt66qZo1zisw0BCF9YjbJLRYtGj993483YOAA1UYhxikcnlkkNlN0SYj1CMfAXgMKbQNyOWaPP/Y4/th/AIYwx0y9Z2VQRbjnNfTo0aNQV84S3en10aa9Q5U7bLDAUqxXF1DxxPiUCjPp492uXTu1cvDgwYNItJjwXLda6NOnT6EfapwXvLy8UC6oHGKTorK9CC7inKLUdvc7n0BanU2dOhVhbldVy57bK/JkSFWk13U8+MCDKgYsjCRmHjZ8mJojIQN2PZP8MzwPia9v+p1Hjeo10LJlS7vuK9G9ksp1m836/xdmNDmuwmRsSvmB73ioQEnvQOn3KX1/i+rQjrslge7EiRNREpUqVQrPPPMM9mzfC48kvyx7VUU7hyIJCXmyzEiu7MsUTWnWnhKbCJ/EUtBbHVTT/1jncER4ByOoXBm8/PLLKMy+/e5b9OrZC0ePHYWHyQcuiZ4qUZXgFo14XTRatWxV7CtbqHh7+OGH1dC4nJZIJjrEwmhNUdsSEeWXlJQUJCcnq9i0sC4rz2t+fn4YNWoUSiJ5/zFw0EBM+mgSkuPLqGTj7ZL1CYhzicToQe/c9/sVuZD+3nvvYcKECbBozfBNCIKT2UXFdfGO0YjwugabixmTP5uMwkxi5ytXrmD16tWI8QiFa7w3tDYtkhzjEOccifLly2PxksUl5neIip9mzZqp38tYp8gsi3yERWNConMsHnnkkQLfPyr+it1AMCp8ZODa0qVL8fTTT6pEpUwhbtKkkRoaFBUVZe/do3z27rvvwtHDAdcCTyLeIUr90RNmjUlVEdz0vqCqQB966KH7fqzY2Fg82OBB+Pj4IsL9Os4E/IXTZQ7iTOlDuOF9AY+2ao6NmzbedxVEQVR1rN+wHlOmTEH5B8sg3OcqIv2CUbtZdXzzzTdYsHDBfU3uJrK3Tp06wc3VDaGeV9JeE9KTCyrhXtdQqWIlBsBElC9kVkWfPr1RpUoVNUi4du0aKma5ePEij3gxJ3115ZxfDTipigTkb46wQiayh6rba9Wqqba7X0ajUSXDq1apijjXSFzwO4LTpQ/gbJlDuOpzEhVql1UxX2Gtmk0lSddp06apAb5NWjVCjN9NhPlcgX8dL0x8f6Ia+irzBoiKKplFJC3yIr2C1XyP20m8Gup+FRqdRlXEE+U1Vs5SvlfKDho0APv2/YonHzNg8EQHODho8OvBCHz99RSsWrUcq1atRdmy2fcIpaLfe2/jpg14YcQLOHnqJBy1TqrnVjISVaA3bPAw9WbofisT/v77b/Tt0xcxsbFwT/KGj7k0TLoUdTXfYNDjiylfFKmhQpJ87dmzp/ogKm5kieS0L6epti9Xfc3wjQuCi9EjrXVHhGcwLM5GfDX9qxKzyoKICobMA5C5AN9++y0eqGPAB28Y4Oetwb9nzFixYQlWr16B+fMXoXnz5jwlxZSLiwtWrlqJsWPG4oddPyDM5zIMNieYNMkwWU14+qmn1d8o2e5+hIaGolfP3jh1+iTczF7wTg5UbQwkSWuxmdXfQKmoLSp/52Q/ZZWafBAVR9KSsH279riCf+ETHQSPZB/VZzbJEI8It2A1u+TTjz5VF1yI8hqTs5SvZBnPwYO/Y8W3Tnik8X/LXDo+o8eowVb0HBGikrc7duxSvUmpeJLhW7t+3IU///wTu3fvVssHJSHfuXPn+5qAmyo4OFi1AbDGaFE1vCEMVoe0+8yxJtzwOYc3Xn8D9evXv6dBYDExMaovmyx9lMGB8n2KSiBNVFg9++yzqgJn/LjxuHzlX+g0elWVIBOh69Suo5Z4NmjQwN67SUTFzKpVq1RiduKrDhjc+792S+1aAy89b8PQ14wYPHggfvnlV/Z2L8ZkldL8BfNx4cIF1Q5LVvPJyqr27dujcuXKebJysHevPrh45iIqhdfPMGQoMLYSQt0vY/bs2WjYsKF6zHupyD1w4ACio6PVfjdt2hQGQ+b2YUSUe/I+cfOWzXjt1dfw519/4oZGC/nPbDOjVGApfDx+hnr/SpQfmJylfBMREYHVq1fitRG6DInZVOXKaDFlog7dhp7C/v378fjjj/NsFGOSzJQptPkxiXbevHlITkhB5bAHM03C1VsNCIqojosOR/Hdd9+pVgF3k5T9+OOPsXrVaqQYU9Jur16tBt5483W0adMmT58HUUnz5JNPolWrVvj1119x8uRJdZFO3qgyKUtE+VU1O+u7mXimpSFDYjaVq4sG33zqgCbPJquWXGPHjuWJKOYkEZsfswh++OEHnDx1AhXD62Wa/i7JHhkOZnJIweeffaEGtOX2or/FYlEtrubMnoOIyIi02/18/THihVsDr1nwQnR/CVppgydx6R9//KEutEj7Gxl2x57KlJ+YnKV8s23bNlitFvTs6JztNk0baFGtkgHr169ncpbuidVqxbKly+Ae55cpMZtKa9PBI9Yf69etx0cffaSWVOcmMdu5U2dcOHcRXjGBajKtzqpHkiEON4w3MWTIEHzyySd50o+MqCSTN6SPPvqo+iAiyk/yZvv0mfMYN8ox2228PDRo31qL9etXMzlL92z5suVws3jCxeSe5f0aaOATVxrnL/yrWnPJhcncxLyjR43Gxo0b4ZUQgEqJ9dVQTRmuGZUYomLcM2fOYOrUqVzhRXSfatWqpT6ICgrXkVO+iYyMhKe7Dt5emhzflFcoZ1XbEt2LhIQExMbFZqpKuJ2z0VVVv0pFd258+umnuHD2AsqF1IJ/fDkV/Ery183ojbIRNeGdUEr1yr18+TJPHBERURGQGgNULJfzW6BK5TSIjOTQWrp3ly5dhkNyzj1rnUyu6vPVq1dz9T3XrVuHDRs3ICiqGkrHVoGz2U3FpvK5TEwVdbu07di0aRNPHRFREcPkLOUbT09PxMZZEBOXeRJ3+uVl125oVd+n4kr6q65cuRKdOrXHgw/URePGDfDKK6/g6NGj9t61YsHR8Vb1i0WbeapmehbtrUm8uamajY2NxaqVq+AVWwpO5luB8+3VDoGxFVRAvGTJknvedyIiIio40ptTBN/IPjYV127Y4OXlieJKKjD37NmD558fgIYN66Nhg3ro378vfvzxR3Uf3T+JN7Oa+J5eauyam9hUzP1+LtxN3vBIznoYkWeyvxo8Nm/e/HvYYyIisicmZynfSD9OG7RYuyX7wOSf41acOmu6p0b4RcHNmzfRtu3/VDLWRX8M/Z+LR/sno7Bv71p1fL744guVoKZ75+DggOaPNEe8W84VsXFu4WrIkK+v7x2/56FDh5CckqxaGWRHCx1cE7zw4w8/3tN+ExERUcGqXbs2KlUqj2Ubso9N4xNs2LjThvbti+fQFxkkNXz4MPTt2xfBl/eiZ/tY9OoYh9Dg/RgwYAAGDRqoCgvo/rR++ikkuEbDorlVHJCVGOcwODo4qmFedyKDv44eOwr3hJynxLvH++LPPw8hMTHxnvabiIjsg8lZyjeBgYHo1KkTPv/Wgr+PZw5MboZZ8cr7FlStWhFPPPFEsTsT0rB/wIC+iI68iB9WOGPJDAeMGeqAcWMc8NsmB7zxogHTpk1TAyfo/gweMhjxuhhEOYdkeX+sUzhiDZFqu5xcv35dTY/fsGGD+rfOmnmQXXrSgzY8l20SiIiIyL5kUNKwYS9g004zVmw0Zbo/OcWGl8cbYbHqVfKyOBo/fhx+3LUTsz93xPalBrz2ggNeHeGArUsMmDfNEb/8sgdvv/2WvXezyOvTpw9sWitCPC7BhsyFGMn6RER7hOC5bs+p1YbZiYuLw+rVq9VQ21zFpv8/f0HafhERUdHBgWCUryZN+hiXLl1A18H/oO1TOjz7pA6ODsC+gxas3mSDs6sPVq1aVCwnH8rSsOPHT2LjAifUqpbxOohOp8GoQQ44d9GG6dOnolevXsXyGBSUp59+Wg3mWrRoERIdY1U/WAezE0z6FJWwjXENRccOHdGtW7csvz4qKgpvvfkWtm3fBilk1v3/datEh3i4p9xaApmVREMckiLjMHPmTLz44ov59vyIiIgob/Tr1w///vsvXv9gCTZst6Fbew18vDU4ecaKJWuB0AgNZs+ejaCgoGJ3yG/cuIHly5fjndF6tGmlzzQHovXjeowbY8OEz9bglVdeRbly5ey2r0Wd/Px8/vnnePXVV2E2pMA7rhScTe6q1YFUzMZ4hKBStcpqfkFWzGYzPvvsM8ybOw9JyUnQawyQHG+SQWJTn2wfVwbXynZvvvkm5syZw/cXRERFBJOzlK/c3NywYsVqlTRbtGg+Nuy41fDe29sDffr1xdChQxEQEFAsz8LKlSvwQB0DGtbLPuk6qJce67aF4Ndff8Xjjz9eoPtXnMgbio8//hhVq1bFzBkzcSn02H932m7dL4HtqVOn1JLG2/vLdu7cBZfOXUZAdAV4JgVAa9Pigt8/iHS9DrcUL9Vj9nYSHCc5xsE9yUc99lNPPYUaNWrg9OnTqhr69OkzMOj1aPpwU5V89/PLeRmavZhMJlXl7eTkZO9dISIiyncSE8jQz0ceeQTz53+PMRMOq9sdHQzo0LEjhg0bnilWKC7Wr18PBwegV6fs3wJ2b6/HZzMtWLNmDcaOHVug+1fc9OjRQ7XT+mzyZ/j3xL//3SGFtDb5mXPAwYMH0bp16wxfJ31/R48arQZ7+cYFoWxiIAxWR1z3PIcolxD4JgRBZ8v8/kISv9EuoXBL8cbOnT+oituePXsiNDQUy5Ytw6GDh2CxWlCzZk1VGS5xc2Ekcam035DYVH5fiYhKAiZnKd9Jk/vhw4erRGx4eLi6Euzv7w+DwVCsj/6N61dRr0bO/WTrVNemVTLQ/ZHgrWPHjmpYgk6jh0uip0qsOhvdVTXtvh9+xS97f8HyFcvRpEmTtK+bMWMGLp6/iPKhdeBk/m+qbkBcBVz1OYUbHhdQKq4itOmCYKlKuOp9Wk3ZDYquigsuRzF//nwVSMrwN0eNExwSXWHTWNVjSm/hDz/8UFX3FgbyOyitG+bPm49/jvyjbgsqHYQBzw9QwXpOy+uIiIiKS8wgH7J6Rvpz+vj45HowU1El7ZsqlNXD3S37hJeLswaVyusYm+aRli1bYvHixThx4gScUzzgluwFV6OnWt118Z+rGDhwICZNmqQ+p9q1axc2btqIoKjq8Ew3/MsvPgixThG44n0CZaNrwGB1SLvPpE3BNe8zKu9bKrYSQrSX8P2c7xEfH48PP/gQNivgkuQO2DQ4sP+gqqqVKvKPPvoIen3hSAns378fc+fOTRtM5+7mjh49e2Dw4MEoX768vXePiChfFY5XYioxfb6Ka5VsVpxd3BARlfM2EVG3krfF/c1AQXn99ddx82oIKoXUg4Plv2PqlOgCryR/XPM7jUHPD8Kff/2prsanpKRgyeIl8Ij1y5CYFbJkzCe+NCJdbyDWORweyb7QWnVIcohHkkOcSsyWi6wFLfRwjffCurXrkJSUhNIxleGVGADN/7dGsESbEOp+FW+//TZcXV3RtWtX2JM85xEjXsBPP/2oJv6WTqis9jUhKRaffjIZSxYvxZq1q4vlck4iIqLbeXt7q4+SwMXFBZHRNlitNmi1WSdoZVBtVLSNsWkekRYZu3/ajbKRNTO0ynI2u8E92Uf1pB03bhwefvhhVdEq5OK5q9UzQ2JWSGxbNqoGrvicwNmAP9XXGyzSxisZcY6RahZC+chacLA4wSPBFydPncR7772n4ln/+HJp/Wit0VZEu4RgyZIl6v2ZrACzt6lTp2LKlClwsbnDP6489BYDkuMTsGjuYixbugyLFi9Cs2bN7L2bRET5hgPBiPLJ00//D7v3W9MSsFlZs8UMR0cDHn30UZ6H+3T16lVVaeATFZQhMZtKKl8DIyshKjoKW7ZsUbedO3cOMbExKvGaFalskI4Gnkn+qo1BvFOUStCWjaqOSuH10ioWZNBDQmICAqIrwjuxVFpiVuhsBlXB4JHki48+/EhVrdqT9C/bs3u3SiyXC68F76RS8EoKQJnoqqgc8gBCr4ahf7/+qmKBiIiIig9ZPh8aZsK+A5kH9ab64y8rrl43ZVpqT/e2PF9WdHkk+GU5w0DaZgXGVoADHNVA2lQHDh6EW7xXlt/T2eT6/7FpAMw6I+KdImHWmlSsWTWsIVxM7re+t02rWid4JvojMK5iWmJWaKGFT2JpBMRUUK3nrly5YtfTK3G5JGYDYsujws268E0oA8/kW/td6caD0MY6YED/AQgJyXrwLxFRccDkLFE+6d69O3R6A975xASTKXOC9uRZK75daEWnTl3UUrrCIjg4GLt378bPP/+MiIgIFBU//fSTCnJvrzJIz9HiDFezJ3bu3Kn+nZooVQFsFmSomHA1eqBK+IMq6K0QVRseyX4ZErAJztEq6JWK2azIfvnGByE0LFQdW3uRpW3Ll6+Ab2xQlm8SpNIiMLwyTp0+hX379tllH4mIiCh/PPTQQ6hbtxben5J18UBUjA3vfWFB9WqV0bx580JzGmJiYvDLL7+oWO/SpUsoKk6ePImbITfVRf7sSDzpFueDbVu3pd1msZizjU21Nr2qkNVZdagUUV/FppUi6qlka/oErAwdkySuX0JQlrMThHdCoBo0Jv1o7UUqtb/95ju4G33gl1A2075Kb92giOpITkpRMx2IiIortjUgyoIk7RISEtQy9HvtwyQJ15kzv8Xw4UPRcaARg3tr0fhBHeITbNiww4LFa6yoULEaJk6cmOXXy2ApuZp96NDv6sp7jRp1VG+ohg0bYseOHVi5chmuXbsCF2dXtHryafTp0+e+lqJLADl58if48cfdKlASBoMObdu2V0vyy5Yti8JM+sVJr9n0vWGzojXpkBCfoP6/QoUK6vwmOMao5WW3c7S4qJ61Ea7X4Z7sm/VgMH08TJoU1QZBKhGyI9/foHXA+fPnYS+///47zCYTvBMDs91GKi5cbG5Yt24dWrRoUaD7R0RERFmTFS1ykdXR0VF93Guv3Zkzv0PXrp3Qpk8cBvXUoPXjemi1wK5fLJi3worEZBesXft9loOYpHJREnk//rgTSYkJKF2mLHr06IVnnnkGhw8fxuLFi3DyxDFotFo0atQUAwYMQN26de/5lEqRgAxvW79+LZKSUtJub968GV5//U00btwYhZm8lxCyRD8nOqsBCUkxaf+uUrkKbiZGqITr7SQWlWIAGQzmF18Wepshy8Fgic4x0MMAx9vadqWnhQ5Oya5qJZm9XLt2DWfOnka5+FstHbIiSWf3BB+sXrkar7zySoHuHxFRQWHlLFE6f/75J154YQSqVq2sJvVWr14NL7/8Mo4fP35Px0mC1dWr18I7oCnGTDCieYckPNMrGcvWO6JX70FYu3YDPDw8MnyNJEY/+eQTtGrVCls3L8ED1c+jaf1LOH5kG5577jk0eLAeRo4cieS4A2j18HXUqHgWc7+foQJVmcp6LySg7tSpPS6c3YtP3jHgjy3O2L/RGW+9pMOB37egfbs2uHjxYqH+WSldujRMViOMuuRst5H2AybnZJQJKqP+7eXlhQ4dOiDWI1QFslnxSrzV0uC611lYNKYM9yUaYnE94Azc3Nxg0+bcBkAe22qz2HXoglSe6LUG6NMNkMgq6NelOCA8LLxA942IiIgyu3z5MiZMmIA6tWuiVq1aqFKlCnr37qmGJt2LqlWrYsuW7WjWvAM+/9aGll2T8HjnJHw6w4KHmrRV99WoUSPT123evBnNHm6KmTOnokLgCTRveAWJMb/jhRdewAMP1FMx6r9Ht+PRRlfRtP5l7PlplYqDpefpvbRKCgsLQ8eO7bB92yqMHGDF3nXOOLjNGdM/ckRsxCF07/6cqqQt7LGpSDYk5rhdiiERgYGl0v49YOAA1UM2WZ/113kmBqihs1f9TmaKe+Xf1/xPQeegU5WzEn/mROJXe8emwsGc8/wNuT8isuis6CMiulusnCX6f9Lr6d1330XlCnq8MVKLoFJ6XLpmxfL1G7Fhw3p89dV0dOrU6a6Pl1zVX758lernJAG2g4MD6tevn+2ghVmzZmHGjBl46yUDhvY1wMFwq3IhKdmKJ54zw2RKxNYlTqhf678K0Q/fsGHiFBPGjh0LPz8/PPHEE3dVJTxixFDUrGLGkhkOcHX5r1JiWF8tujxrQ9chcXj55ZewadPWQvvzIm8AXF1cEelyA6XiKmW5TbxjFJKRiB49eqTdJsn3nTt24pr/aZSKrJRWYaD6yDrEINL7OkqXKoOI8HCcczkM10QvtZzM6JyEBG0s6tSqg2f+9wy+nPYlzFpjtolPCbItNosa+GAvkkQ2W00qEZ1+6dvtrA4meHln3euMiIiICsaBAwcwYEBfOOiN6NtFg7o1HRERbcParX9gwIB9GDZsmErcZlXlmpNy5crh66+/xvvvv69WTgkZRuXrm3UP/t9++w0vvjgS7Vtr8dFbTvB0/+/xBo6x4Jc/EvDtp45o+5QubV/ef82Ghau0mDjle3UxXGLUu/HOO28jIe46Ni0woGK5/+qJOrfRqscZ8aYRL744AocOHYa7+60+q4VN+fLl0fihxjjxxxk13yCrFVgmrRFxLhEY3XdEhtZoC+YvwBXNKdVuSmYgpH6tJGxDfC/A2cUZTk4OOKc/DDeTNwxGB5gNRsQ7RKsBd++/+zFee+01JDhGwy2LVla3HjsFCfpYu8amskpR7YsuRbUfy47cf3tBCxFRccLkLOXIZDKpSk5JKBZnEnS+8847GNRTj/deNWSYYPtCfxte/8CIl18eraoNcrs8S47boUOHVJP7qKgolTTt3LmzSsxmJykpCdO/moaBPfR48fmMx3zbTxYE37Bh53In1K6ecem+m6sGn40z4NJVG6ZO/fyukrMyRCs4+Cbmfu6EuAQbrofY4OetgbfXrWPg56PBO6O1GPLqPzh69GiO+2/vCcQvjHwBX3zxhUqwypCr9EFwkiEOIb4X0eShJqrnWio5p6tWr8LAAQNxXvcPXC2e0Bp1MDunIAkJaPBAA8xfMF9tu2LFCvyw8wckJCSifIX66N27N5588knExsZi5oyZCHW/gtIxVTIF35IMjfQKxoMPPIh69erBXpo2bYpZ381CtHMofBNvVQ9n1aYhQRuHdu3aFfj+ERER3YlUYRqNRrW0/26TkkVJeHg4nh/YH/VqmDB3qoOK9VIN7A7MX6HBhM9no1q1aioeyS1pryQrra5fv65iJxn81bJlS+h02beFmjr1C9SrpcW09x2g1/+3HxevWPHTPis+eccB7VpnfFsp2w3ubcDNMBu+/XYGhg4dqi4S54bsm7Tw+vANAzw9NDh70Qo3V6B0wK0krRQuTHrLgGbtkrB27VoMHDgQhdWYsWPQt29f3PS4iFKxFTPMLJDEbLD/aZVMTV84IOclNTY9cvQInOEGfZIDrA5mJOhiEeAfiEWLF6oYduPGjVi3dh3CwsLh6+uDzl06q/cbUgTy/ZzvceXkNbiEemRq+yVFCKGeV+Dk5ISuXbvCXipWrIjy5SogOjkUbsasCwOssCDePRI9Ow8u8P0jIiooTM5SlglZCXQWLZyHI0f/VbdVrVoR/fo9j169eqVd4SxOZs/+DrWrGzIlZlODy8/GO+CPwybMnTsX06ZNy1X/pGHDBuPIkeMoW9qA0oHAr/vkcWajefOH8e23s7OsTti+fTti4+IxpHfmK8crN5rxWFNtpsRsKtnvIb11GPraEZw5cwbVq1fP1XOXZXGlA3V45xMjDh+7texM3uu0aq7D8P4GNGukw5OP6uDpoVfLxwprcja1CvbmzZtYsmQJor1C4BrnBa1NiySXOMTpo1C/bn3MnTc305u5Bx98EAcOHlDHXz7iYuNQqnQpdOvWTVUTpG4/atQo9XE7Cao/+fQT1QfLojPDNy4IziZ5A2JDnFOUSsxqXG34YsoXsCfZz3bt22Prpq2qt6zz/0/0TSWVvyF+F1EuqNxdTWmW6ms5Rjm9sSMiIrpXcsFbBpXOnz9PfbZYrPDz80bPnn1UYi51+Xhxsnz5ciSnJOLbT50yJGZTPd/TgAOHrfjuuxkqPr9ToloKAF59dSw2btwML089qlXSIDIaWLx4MSpWLIdZs77PsgBBBnD9/vsBfD3JMUNiNjU29fYEnmuX/VtKKXyYvSRZtUWQ/cyNvXv3wmq14ad9FoybbMT/j0JAnepaDO2rR5dn9SpR+2gTHX78cVehTs5K4nvy5Ml48823kOAWpYZ/6a0G1cogziVSxWbLVyxXn9MLCAjA1m1bVQHJmjVrEHIzBB6eHmjTpo36SC2c6dmzp/rIyrQvp6FL5y64EnACPtFl1HyE1IKFCPdgxDtFY8aUGXatPJaf28FDBqn2F9HOXqq4Ij0brLjufV4moanZG3dzEUdmdhgMOff7JSIqLJicpUyB24AB/fDbb7/jiUcM+GycAyTf8vPv1/DhhxOxYsVSrFixWlWBFlWRkZGq8b0EA5LAlM8yBOujNzMnZlMZDBr07AR8PW+DqszMKQklwwu6PdcZsIZh8dcO8HDXYMUGi7ru6+Olwd+HD+K55zqrnl63J7ql7YGfjwEVymZuB30l2IYubXP+lX2wrjbt++QmOZta3XsjxIKqFbWY8bEjSgVocO6iFYvXmNFjeLL6GejZyQBPdy2Sk7Pv51oYaLVaNTiiY8eOWLBgAX7/7XcYzWbUqF5d9e+SatD0VeCnTp3CqlWrcOPGDVXNIQnJb7755p6SjFLxIJUOH77/IS7dOKaGk0lAabVZ0bBhI3z22WTVK87eJk58D5cvXcLRo8fgkegD9yRfNRFYhqLFuYfDzcsVCxcvvGP/MRnAJm8cZdndhYsX1G3169XH84OeVxUbDIaJiCgvSKwiy+/nzJmDejUNGD9GDy9P4OiJOCxa+C2WLl2EpUtX4IEHHijS8feJEydUgYQMK5Vk8/r1q9HuKS18vLNPuvZ9To9eL1xWsxFyWpkjSaohQwbj4IH9+Hy8Axo9oMXaLRYcPmaBs6MGN0KD0bVLZ2zdtl1VY6YnbblEw3pZxKbXrahTQwsnx+z3sXSgFqUDDSo2vZt2DtIq9Y/9DgiMLQVHkyssOiOu/huKMROicfBvKz591wHenjbcjM65n2thIEN7mzRpogb9bt2yDYkJUQgsFYjRvV9Q8WP6xKwMXZOVWlJoIbGYrHqaNGmSijHvlhRUbNi4AW+8/oaqwNVqtGp1l7TZKle2HL76YIpqC2ZvkrSX57t06VLEuUbAI95fJbCT9QmI9QyDUZeC7779Vv1u3Ckhu3XrVsybN0+9v5HXjqAyZTFgYH9Vvezp6Vlgz4mI6G4xOUsZvP32Wyp5uHKWk6qYTNW9A3D6vBW9XriA4cOHYu3a9UXuyElQOGXKFGzevBFG463hT9KrqXXrZ9Qf7/T9rLJSqbwWKSkpKimV0xVmqY6NigrB5oUGTJ5pwvbdFpQro0HjB7VITNLg4hULzp49j1dffRXfffddhq+VpUWJSVaYTDaVEM5wnyMQG5dzU//YuP++T27IUihZ3vbhGw4Y2OO/K8tNG+jQq5NeVSu8OckIueh89boRW7dsxO+/70PNmnXV1Wt7LtHPjiTbH3nkEfWRnbi4OIx6aRR2/bgLjlonGIxOsOrNagJx2aCyqro2q+oRCfrkZ0COb1YVKu3bt0fbtm2xb9++tKBagvE6deqgsJAk9Jq1azB//nzMnzcfV2+cUrdLv95+vfpi+PDhCAoKuuNSy+7duuPs2bNwT/JB6eQqgMaGi4evYuyxsVi7Zi0WLFyQbV9lIiKi3JLVMJKYlVhlQHd92t/frm2BMcNsGDA6Cf369ca+fb8VueRLfHw8vvzySyxfvgTR0beCOHl+rVo9gZCboWjXMudq2IrlNGmFATmRlU8//7wXC6c74Mi/NjzZLRkebsBjD+sQ4KfBzTAL4hMSMWBAf/zyy74MF6lTY8q4+MwxqKODBjGxOcemFosN8fGWXMemEpfKCj7PJH+Uian6X6soE+CR7Ido5xAsW38eVStpsO+ADVrdRbT5XxuULVdWVZDeqUWDvUj7iQ8//FB9ZEViTKmw/fabbwGbBs5mN9g0NlU1O3Hi+/jii8/VENvbyXsYKZ7ILjaVeHbb9m04duyYSlhKol4Gvj366KOqqKEwkP2W5y6JaGnFcPTYUXW7TqvDs22f/f+hcw/ccRXXiyNfxJatW+Bm8UJgfEW1ei4hKQ6ffjIZixcuxuq1q1W/ZSKiwojJWUoj1YPr1q3DhLH6DInZVDWqaPHpuzoMfuUg/vnnH7UUvKg4ffq0qlZ1ckjA6y9o8ERzZ8jg2F2/mLFg1XZVHXz0hAWPP5x9MHftuhUODvocr1xLxcOyZYvRvb1GJWZ//s2iqlHbPaWDTncrYIqOtWHyDCOWrN2MZ599NkOg9dhjj+Gjjz7Cj/ssaNMq469ni2Y6bNxpxvixDioYzsq6bWa4u7ugUaNGuTous2d/ixbNDBkSs6mkiviD1x2w6xcLxkwwwtUFeKDmdej1N7DnpxPq6nbXrl3wxRdTilRPYgneBgwYgL8OHkap6MqwaE2IdY6AxWKCweaIkGuhagnY9h3b1URkIUso586VpZR7VPDs6eGF3n164fnnn8+UyJRAt0WLFuqjsJKk6ciRIzFixAj1ey/HJDAwMFdvnORNwNAhQ3Hx7GVUDK0HJ/N/1d/eiaXUELXff/tDDdebOnVqPj8TIiIqzuRvrvQr7fiMPstYxdtTg9lf6NGsXYxKYg0eXHR6Ukq/+m7duuDihTPo00WDDs84wc1Fg4N/WzB3xS+IjTPj0NGck2fXbtxKjMrArZwsWrQAD9Y14Mx5YOosE14ZbsCIfgY4O9+KJ40mG5atM2PC55fxzttvY/Jnn6V9rVyI9/R0w/rtKahTI2Oc3PIRHdZsMePMBSuqV856X3f/akFMnCXX8xBkQK/eZsiyh796rkmBiHeKwodTI1W7A3ejBVeu3MS5oxexbds21KtbD4uXLIa/vz+Kko8//hjffvst/OLKQm8xIMYlXA3B0psdYIoxqwSl9FlOrXSVQW7ff/89NqzfgOSUZDgYHNChYwf1O5BVCzI5j4WxqCJ9glZ638pHaGgoEhIS1ErN3LZc+Pzzz9X5LxtVQw1fS//zIsf0mu0U+vXth592/1Qok/dERIXjchkVCtILSlYyd2uffc5eeo+WKWVQSdyiFNgPHzYYAb6J2LHUASP6O6hEc61qWowe7IDtSwwIKqXFNwvMKvGU9fewYfkG4Nln2+X4B136nUZGxqB8kEZVzH423lG9oUhNzAovDw0+ftsBrR/X4ZNPPlL7l/7qduPGDTF5phWRURn3pd9zBtUb7LOZxiz389/TFixcZUX37r1ytfQpODhY9cTt1Sn7lwHpLdazox6OjsDfP7jg60lOmPa+I37daMAX7zlg08b1apBaUSKBmyyX840ui1CPKwh3vwYns4tKLMo0XLPNrALCN998Ux1nSZbLcrQDPx2Ef1R5lImuBt11Z3z/3Vw82epJHD58GIWBVPTK0kMZopH+ZyonkkiW5LIsE8ttRYtcmDl46CACIypmSMymkmPoF1MWa1avUUvziIiI7tXff/+Ny5evoV+37GPTUv5aPNNCh7VrVxWpAy1thq5ePoP18xwwfqwjHqitQ5WKWvTqbMDWxQ5o+Yge+w9YVXFAdiShWqF80B3nAfx7/BiaN7bhq++Nqgfs2GEOaYnZ1AFbkvx+80UDli1fpi7cpr+g26tXXyxZa8Wxk9Km6z9tWukQ4KvBuMkpSE7JHJtGRdvwyXQrHnywXq7bTmzcsFH1ZdXm8DbVKzFQuh6gQkQdlIusqSpsK9ysq/595t+z6NWzt4qLioqrV6+q1XS+cWUQ5xSBm14XobXq4JUYoBKNVs2t4z7m5TEqxpOVb8888z9sWLkRbuF+Kjb1jAzElrXbVOGHtEUoDKRKV95vyPOTIX65Jf12K1WqlOvErMTt8+bOg3dc6QyJ2VQOFieUCq+Cs+fOYvfu3Xf1HIiICgqTs5RGlkQF+unh7pb9EipJMlYsC4SFhRWZI7d//36cPXcRH72pg7dX5ucW6K/F+68ZEJdgw+sfpmRKfEpi9r0vjLgSbL5jRUbq8iCpEihbWoP2rXXZXh0eOdCAK1eC8euvv2a4b8qULxEd54r2A4xYvMaEiCibWkomVQllArWYvcSM58case+AVCLYcOmqFVNnGdFtmAmVK9fAG2+8kavjEhMToz6XDsx5yVyZQA0kvpUEbfqkbY8OBkx41aD6jl68eBFFxcIFC+FicUeY+xU4mVxQLfQhBEVXh398ORXcVw99CG4p3qpfrSRmpYohMKYiyt+sC9/EMvBK8kepuEqofONBWKO16Nunr+pjbC8S9E6YMAF169ZDs2bN0LhxYzzctJkK8vOjR7BcmHHSOKtjlB15MyHvmuSCT0l29OhRfPLJJ3j77bdVS5ULF2715iUiotxJXa5fufwdWk9V0CA8PLTIHFaJGzasX48Xn79VLHA7WSH12TiDqhsd8HLWic81W0xYv92MIUNH3HF5ukarwamzNiQmAcP6Zj8gqX83AxwdNVi5cmWG22XgadVqtdF9uEnFnFeCrUhItOGf41ZUrqDFgb+s6DAgRe2PJGRDw61YtNqEdv2NiIhxwVdfzcj1sZHWUwZLuqAzC3rLrRVb6Str5f/lAnGZ0Oo4eeqE6j1aVMhqNJ1NpyqCLVoLKoc9gApRtREQX17FnNVCG6nqz9i4WIwdO1a15nKL90al6w+obSQ2lThW/u2VEIDXXntNtTCwF2kB9/XXX6NJ4yaqvZcM132g/gOqpYNUxea1Xbt2ITEpET4JpbLdRgbhuljdsX590WvNl5dkaPVXX32lYlN5n5Pam5eI7I9tDSiNh4cHIqIsSErWw9kp64SdvHhfDwGq1Co6Pb127tyJcmX0aPJg9oHrE8118PKQqbMWnDxrRJ/OWgSV1uDiVRuWrrPh9DmLGjTVsGHDHB+rVKlSKFXKH6fOheGRh/5rZZCVRvW1KskpvbWknUEqWUq/adNWfPDBRIyb/CPe+eS/K81SVduzzxPYvGk9eo/8L9Hj6uqE557rrapYpadobqQOdTt/yYqG9bKvBj53yQpf71ttDm7Xo4MeU76zqiv08ke+KJCBGzYzoDFoUC6qJnS2jC+D8m9ZEnUu4LAadiVX4CUpezvZLiiiGs4ZDquhYtIioKDJQLPnuj6HhJhEuMf5wS+lAmwaK2KTIjDpo0nYtm07VqxYfk9DJLIjF2YMJscslxqmPzYOGqcidREnL0nF0YjhI/DnX3/e6mlsdUCKNlm1efjf/9rgyy+n2XUyMhFRUYpNxfUQK/x9s49Vrt+0wdMz56X9hcnevXuRYjShW/vs/z5L8UCLR7T4+TcrWnYxot9zGjV8KyoaWL3Fin0HTKrHqrRYupMGDR7CwQO7VOFAUOns42Ep0KhbQ6di0/RkgO2qVWvU0vtZS1Zi2uyktPsqViyH0S93xaFDBzB63O9pt0vcKINWx4+foKogc8vX1w9JUf99/6wY9bcuPuutmdtqSa9Wd7MXFi9ajC5duqAokBYFeqMjkh0SUCm8fqaVSRpoVfI1RZ+ITRs3qeddJlraPmQ8lxKblYqpjBSXBMyaNUtdsC9oUvzR7bluOHXyFNwTfFEuuaYaPBsfH425s+dh3dr1WLd+7V39TNyJxJsyiNdgvUNSP8URN2/cREkkBRtvvfWWav8ix8rJ6gyT1qSKUGrXqo3Zc2bn6TkhorvH5CyladOmjQq6Nv9gRvcOWV9V/+MvKy5dNWFyu3ZF4sjJMpdt27ailK81yyb5qSSJ6uujQYrRhlPnbGoIVvrA8uPJI1ST+juRlgd9+w7AtGlfqOqEnCSnSP9TW5b9WitWrIh58xaoqkhZ0pfavL9mzZrqfrlqLrfL1U9ZbiZXpO822SNLhh57rDkWrzmI59rZsjw+SUk2rN5sRtd2Wb9USBK/fi0UqYpACVyTDfHwSSyVKTGbSpbS+SSURqj2MvwTsg9UJDiWgVirV60u8OSs9Dfu368/UqLMqBhaX021TeWe4oPEhFI4cvgIxo8fr6o28/KNsllvgg22bBO0VlhgsqUUucEseVUN1blTZ4QFh6ulllJhLMfJCitincPx084f0bt3H6xZs1r1jiMiouxJD/3AQD+s2BCtlv1nRXr5b9ttxejRnYrEoZRCB1l1JHzukE8O8NXCxdmK6yFmTJ2lh9F0a6l+o0YPYsaMIejUqVOO8W2qAQMGYseOnZDr7PL4OX1NUrImy9hULv7L+wRJ8Pzxxx+qOrJMmTJ46KGH0ip3JakrF47l39LGQO6/W917dMOMr2YiIK5ClnGaxB+RLjfgbHRTy9Wz4pjkhgvni05sKsfLpDPC2egOZ1PWRRYSS/gklMFl5+NqhdLtidn027nH+KviFHkfJIn1gvT6a6/jzMmzqBBaN0OS2c3oBd+EMrhmPYmBAwZiz8978jQ2tdjMMGtMql9xdiwGE7x9sl/5VVzJ+0iZF7H3518QGF0RXkkB0Np06ndJZkVcPHkZHTt0VPM27jQUmIjyD9saUJrKlSvj6aefwqTpVpw+n7m/1Y1QK96cZEHt2jXQvHnzInHkXnllDKIiw3DpmhVJydkv2YiNsyH4hg0vDDCgUnktateurgLPkydPYd68+blKzKYaMmQIvL19sedXi3rDkJ0tu8zqsyxFz663p1TaffPNdHz//XeqOjW1kkGCaqnilWFikjy+1yq8F154EX8fN+GDaUbVvuH2xOyIN5NVEnlAt+wDncSkrIP4wqpBowawaa1wMuUcrKr7NYDOmvPQAIPJCeFh4ShoEnQHXw9GqfDKGRKz6Zdv+cSUUVfI7zTF+W60a9cOSUhAokNstttIElJ690rfs5JGKhBuXg9B2ZBaKkmemsCWhL8Ew0FhNXH48F9qEjUREeXMYDBg0KChWL7Bgu27b8VN6cly/zHjjdBqHdC7d+8icTjnzJmT1tLq1Lmce8T/e8aKRxvr0K61AVqdVi3fPnbsmFph1blz51wlZoWs0HrkkWYIjwT+OJz9Y567aMXxU0Y88sgjWd4vvUNnzpyJGTO+wuxZ32DJkiX4888/05ZFy+qvtm3bqoKPe0nMir59+8LB2QHBvmdg+f9eq6kkmRTmdhWJjrHwjc8+iWTVWGFwyD52LWwkprdqzXC+Q2zq/P/JTq0157fwjmYn1Zs2OjoaBUmKRrZt3wa/6LJZziWQlUSB4ZVw7vw57Nu3L88eV94LGfQGRLtk3zJBqo4T9DEqji1p5HVj957dKBNRDT6JpVViVkiMKknzciG1EB+VkKfFHER095icpQymTJmGwFKVVb/TtyalYM9vZuw/aMFHXxrRuocRKWYfzJkzL9fBoD3t2bMHW7Zsw9jhBsTGARu2Zw7qU63YaIbZDPTurMe7L2tx4sQZNcwoty0C0pNE6erVa2C2aPDp11kP75I+sl/NtaJFi8cyLSGRhvkvvfSSCm73712LWhVPo1LpE1izej4ef/xxNY00r3oDtWjRQvUbmrvMgse7mDB9rhGrN5vwyXQjHm6fgp9/t6qEdcVyWb9USM+xv46a8Oijj6KoUMv/bIBFm/3Pg0i9/07bmXVGeHp5FviEZ3lz52h1Uvsnb1ayIpUVFrMFP/zwQ549tpzralWrIdT3EkxaY5bBb7j3NTz11FOqArwkkeEjSxYvhXusb7bVPJI0dzf6YP68+QW+f0RERZFMqZeLfcPfSMGgV1Kw7SczfjtkwXeLTHiyuwm//qnFnDlz4e/vj8IuPDwcn3/2CXp21CHQX4N5K7KPMf48YsGxk1Z076jHB68bYDGbVELLx8fnrh9X4valS5fBx9tDxfRyAf52JpMNk74ywdfXK8sElvSylwTvooXfonzAMdSufBp/HdykksSDBg1EUtIdlozlUunSpbFw4QJYPYy4UPpv3HS/iGjnUIS7XsOl0kfVIFeXFA94pGQe/CRUNaB7FFq0bIGiQtpTyLVcs9aU43ap90tf2pxIFa4oyBZKsqJL+sxKSKqz6tUqqqw4S+9XuOXpXAL5nXiu23OI9LyORENcpvstGjNu+l5AgH9AiSwckDZtrlZPuGczL0JWAnrEBmD9+g1pM0mIqOAxOUuZ/ritX78JI0eOwY+/eqH/qBT0eiEZKzc7oWevIdi6dUeRSLjIgKohQwbD3Q0Y2seADs/oMHGKEXt/zxgES5Jzxx4zJs8womcn/a3+Xg/r4Outx88//3zPj1+9enV89tnnWLrOjMGvpODg3xb1WFK9u2qTCR2fNyIx2QMff/xppq+V3q1bt2zE1IkO2L/RgM8nOOLLDxxxcJsDXh9pwJdffqn6SOVlslICpEaN22P6XA1emWjE0g2O6NR5AKpXq4rtu29VFt9OWjJ8MNUMDw93tayuqHjyySfhHxCAKJeQHLeLdQuDQWdAnHP2w74k2It3jUSnzgXz/OPj41Vf4QYPNlCVKtLH9LLvvzjnfxjRzpmfjyzt0mv1eVo5IUvv5s2fBzc/V1wufQwh7pfVkqh4h2jc8DiPSwHHUaFqBUybNg0ljVSMxMbFqIrZnLgleuHEyRNqmRkREd25ZdQ333yHyZM/w7XQyipJ22NEMj7/1oqHmrTH5s1b0bJly0J/GKUNQKdOHZCYZMSgXg4YOcCAlRvNmLPUlGn10smzVox4IwV1a2jRqrkOfj4atGimw88/3/ukeVnltGjxMpy9aEDH528luSUha7HYVDFGzxeM+Pl3G6ZNm56p7Y60YZBhTsP76XFouyOmf+SEz8Y7Yu86A76b7Ih9+3Zj9OiXkFekclcq/QYNex6WMkm47nUOUb438FT7Viq5ZnJOVheDsxLuGowUWxIGDhyIokIuLLR8oiXinCJVbJmdGOcwFYeluCRme2Febo/1CMPjjz2e1rM5P0mFrqwaatTwIVVJLY9/zfsMzgT+hVD3y7AhY6W2VGtqjYY8r+p9//33Ub9BfVzxP6F+XuIco1R8KpXWktTXeQKLFi8qkS2l/vnnCFzicy4kcU/2htGYgrNnzxbYfhFRRuw5S5nIVVaZyjp69Gg12Eb+6Mqgq6L0x+z999+DTmtE1YpaODlq8Pl4Rwx9LQV9X0pBo/omtHpUB6sV+GHvraqECmU1mPiaQ1qfWXc37X1Puu/Vq5eqvP3000noOuSq6msrAbBo2fJxTJr0SaZEtySVpX3BR286oFv7jMux5HmMHuyAsAgbpn81FQMGDFD9ZvNCgwYN1NXu6dOnqyvfqW0KZEBB1y6d0On5FLw0SIs2rXQw6IF9ByyYucCCP49Y8f330/NsPwqCVI989tlklZSOcLkB38TSmbaJcQpDnCEKHdp2wOZNm+Ga4gXP5FsD1FJJD9Eb3uegc9AXyFJK6RsmAxZOHD8J79hSKJvkD73FgGRDAiJcb+C613n1fOTqt/Ro80jyVWG7yWrCTz/9pKYf16lTB7Vq1cqTFig7dm5XwfjyZcsRkRisbvfz8cPoAaMwfPhwDrwiIqI8IwmpPn36qL+3N2/eVFWa0jv/XlY42cvcuXNx9coV9f81qmhQs6oeN0LlQrcRC1eZ0OEZPVydgYN/W7HnNwucnYAtS5zShst6egBXQ5PuO95bv34jxo9/B8PfOAxZCCcfEhPXqVMTS5e+n2k1lMSFn3/+Kbo8q8M7ozO2sZKYue1T0gsXGD1uh2q5UK9ePeSFcuXKYcKECepDVpVJiwuJ4aSyr+OZjrhsOwnvmFLwTApQlZrJ+gREut1QCUyZzZBX+1FQZEl544ca44bnBQRFV8vU11+eX5THTbWEX1pbSeJT+vKm304So+Fu15CgjcWw4cPyfZ+l8OSNN95QyXuvhEBUTnwADmYnmHQpiHKRaufrKqY2mJ2hgw5uyV5wMrkhySEO586dw2effZZhpsb9uDW0biXmzZunViddvXlS3e7k6IRu3Z7DyJEjUaFChTx41kRE+YPJWcr+h0OvV4FRUSNDtH78cTceeUiDy9dsKnBwcdZg0XRHlYxdvMaEuctuLQt6oI4W7q5A+6d1cHS4FdxI8jP4hglly5a9731p3769ak/w22+/qX6xkvSU4V3ZTcOUxKynhw7dO2T/qymVwAtXJWDLli3o1q0b8pIEven7x0oib8PGzZgwYRxeHv8rXh7/37Z169bC0qXvqT5mRc3TTz+tEohSgZzkGAvvhFJpwWS0awiincPw3HPPqZ6/Oq0O6zdsQJwxHB4JftBZJSEajxiPMFj0JsydM1ddvLgX8rO5f/9+LFiwAL/u+xUms0klPvsP6I+uXbvCxeW/Kc5SMX3i3xMoF1pbTSJO5WLygEu0B8LMVxHmfhW65FtvUGK9w2/1zLUYcPT3f3Hojz9VH9j27dph2pfT7juhLs9ZqhSk0vv69evqjbMMEZA3TyWVvGZ4uHsiLj4Srv/H3lVAN3m20RtP3UuLu+uQIUM2ZLgOd3d3GDB+3N29uOvw4Rvu7k6pe+PJf54nS6kkpR1a+O45PWxNGv2S7773vc+9WtsOhRj7cOTLm4/dYAIECBAgIHU8hcbe0xpoUmLt2lUoUVSEc5dNzDVpWmtkXzmq/yxhcXbzLj20ehOyZRIjbw4RqFPTx0scxxeu3RahyA8fLi6RaLlr117cuXOHy2XJhJE/f37OPbUWW0YxYQEBweja2npcD6FOVQkmz5dh/fr1mDw56VTYhyI+N6Wy0V27d2HMmDHYtWsXAvTP4y5L550Ow/pPQuvWrZHWkC5dOsybP4+jzZ7LbsEtyhf2WieOryLBmXhn7jy5MGfOHGzevJmfv9o+Gs6RXhylRBw2wikIMZIIDB06FD///PN/fiz3799nbrpv75+IiYmGp6cnmjVvxnnAtCliAYnEJMymD8/JufoWKAz28InKCketC1643YXYJIXJaGThmbip2CjB6wdv4fdoLTQmNfzW+GH5iuUfXEZF3LZnz55c0kvrQRL1Kfs4Pp/+HlGkSGFcjbgJxNhe25LTWC6TI1euXJ/1sQkQIOAdBHFWwDcHSzEBjYxRpMA/F40oV0rCzoMav0j5x4LDJ/U4/rcmwe9Wb9ZBIpV9tFF9Eq3IhZCSXNZnz56hcD4R7JS2M30zZxDDx1vG1/0coIiGTZu28A735cuXmcTTDnfRokXTRPawLYwaNYoJyPx58/Hs+e2436f3TY9+Xf9Ax44d+b2bO28ufir/E5YtXYZ79+/FbVyQ8E45eORG/S+g13HkyJHw8/ODvckRDtFusDdJ8CoqAMOHDcfyZcuxectmFkHJxU1ZpkTA4wuz8eEZnZGjGijKQCtWMVH3icjOIiG5Kij7K9w+CPv/PMCjZOvWr+Pn8aFQKpUsKAsATxe0bNUCy5es4MIFa7mzlIUWKQ/F7x2GCS+ZAAECBHwnILfv69cBGNFTjmu3tNiyV4/eHcyCY/HCEv6Ju26QEaVrqfDHwHeCJJXMPn6mw5TpH29ShwRZ+nkfiG/a20mQP7ftDUWpVIQfChrx/PlTfA64urqySElcjnJ4KTKChD3i2h+D23wp1KtXDx4eHpg+bTouXroY93tHB0d0aNkeAwcOZLc4lQ8TP1+8aDFOnjoZd72yZcqia7eunPv/X0FGkUGDBkMOORyj3OFqcEJMeCzmzKQSuKVYu24tSpYsyddduWIlHA0uCYTZ+HDUuMFJ7W6OoBBJuIjKJyIrnFVeXJRKTt9oRRge3HyE+vXq48DBAywEfyho8ztz5swffDvfCtq1b4czf3fi15rek8TQi7WIdAlEg4YNePNDgAABXwapOntR4zdlyZAD0M3NjR2BpUqVSnK9o0ePYtKkSTxKK0DA54alLKtsCTHy5RJj1FQNNi+xg5dHQiHRP9CIsTO0KFFEjML5JBw5QMVg81fp0aNHTyZ+nxvkDIiKSV7wpMcZG2tM4CL4HMiZMyf/fCsgYZmiJ6iEgcbwqKSD3vMiRYokcDSSQEvXadq0KZfE0SglZYN96CjlwoULWZj1Dc8OV1W6d2NpMeZSrZei+2jdqjUOHT7Ej4+yTLOpbDtm6O9dVJ4ItX8LiUmCrCEFOeIg7nlAAvdYs0P49JnTOHjw4HfZWPupQYI9RWG8Mt2FV0gWJsFmcdyISLtgBLk9xw9Ff2BntAABAgQI+D5AG7IEVxcxGtWSYuFqHSqUlqBI/oSCp1pjwuD/aTjeoGFN8zKNys/6jjagbNnSPH31uUF8U6szQqM1xU2ZWQPxV5n9541AIyGPCsm+JVgMHU+ePMGLFy9445cMEYknnqgkmH5CQ0N505047H8pi4sPmuYaNGgQXGO84RORjdJh4y4zROrw2vMBc9MTJ0/wa//P2X+QLjr5LhIXlRdeud8HTGrmpjTxZQHxI8rpVwY44Jn4JhYsWMCOYAEff2KwUsVKOH3qDLzCM8OVxHGTxFyeR7m8Hs/h6ObA4r8AAQLSgDgbFBTEJz8SMGhHhXYot2zZwqHsFBAff1yALqMxVwECvgQou4hw+ryRSwoad1ajenMVWjeWojJlzZqAIycNWL1FB5UaKFNCjNFTNThySoRX/joehaJxoC8BigjYsWMHnr6U8libNZz4x4CIKEOajBP4WkXawoULp+h6/zW+IDHICbtwwUKOU3BTmW+TCiCohZfIkkJvD9+gHLhjusXFdBYhnrJkk4VJDJPICLeYDAmE2fhw1Lqyy4GaWwVx9uODHC87d+1E1y5dceXqFSjESsiMci5v0xm1+LXqr5g9ZzY7jgUIECBAwPcBGgV3cXbE6fMajOovx50HRvzWSc0xVvV+lcLRAbhwzcixWy9em/BzWTHmLNfi/FURrt/W4cdSJbBs2YovMrFEQiGVwO4/akCDfwXjxAgMNuLviwb8/rvATT8WaCopJZNJJMh+qChrAU2T2RucePLKMnWll2hZRpUZFMgQnAePZVexdu1ajl8gQwxFFiQH5q4mcH9DfGE2PmRGBZwjPbnHgNZgAkf6uCDTCcVGUD7wzp07EeL2EnKjHQwiHdQmFfLmyYuly5Z+cKyEAAECPpM4S6Mj5Bgj52yJEiVYgKUsGnJ/UYnR0qVLP8oYggABHwrKSS1evCgWrbmFbcsl2OunxJzlOnYpzFhszppVKsidABQuVBAXb0TwSats+VJo06YNlyV8KdCo/P/+Nwb/mxGLpdPkkMkSkvDIKBOmLDCicKH8X/Rxfg5ER0dzltiDBw94RI1c+jSmlZbH1eLnt0VERiBHTHaoZFHcLEwNvRbzrFLrCPcYH9iZHHkTjOIPCLHyKMhVtkW9WHkE34ajOunIUnzYx7jgxo0bH/dJCYgD5Zvt3bcX165dw/79+9nRQgtzikr5ltznAgQIECAgZSD3Y5OmzbFpyyp0amHCpsVK5qUbdurht1XP15GIWcNCtmw58CLAiKdv9MiePRdWDWqNypUrf7GccjpvlS9fFjOWXkD50hJ4uouSTHSNm6WDTCZHkyZN8C1Dr9fj8OHDuHjxIucIkyGEzu1URpXWQdNhNFmVPjIn59eGUJmXXRCMYgNfLtMr4BbrA8doN2zeuJkdth7uHlDFRLET0xZi5JH8r5OVcfr4oEmj59Fv8PLlSyH39BOAnNdU/kwC7bZt2zhqhY7b6tWrc0xFWo6qEyDgW0GKVY5z585xCDgJswRyylLrIX2YaeeM2lOphTQtFkgJ+PYwcuRoNG3aGF0G6fDHICmm/K7A7/3kuPfIwM7ThWv03E574+YtODgo0ahREz6ev/TxSyfOuXMXoEOHdmjSVYse7SQ89qbXAweO6bFgtRFBIUrs2Dnvmz2J0i48bfZMmzqNHaZ2IkeYYOTyLip6INchjXGlZRAhEovEUMti8Nr1IUcN+ERmY8csuWfD7QLxxu0R5DolXr96zcdl2bJlce3MTY4uSNzgS6AoBGq/FfD1gMYQ6UeAAAECBAjg2Ju9u9Csezimj5ZgYDcZ+nSS4cFjIy5eM2LuCi3CIsDxcRKJGFWrVuWYreLFi3/xF2/y5GmoX78O6rWLRM92YtSuKoWdEvjnkgGL/Qz87/z5c7/pvEqaZOrfrz8CgwJhJ3LgkX+VKRp/jPkDw4YPQ4cOHdI0NydxliAyivDUgzbwRXCP8eXuAqPIgEhlCIKcXkBqkCMySMfPtVXrVlgwdyG8ojNandiivwuzf/sFno0AW6A1Rf/+/YUXSICArxDW56atgOIMrAlXJM5u2LCB2xApv/Hu3bsf+zEK+A5zuah4ijIx//nnHz62Uosff/wRq1atweVbSpSvr0L99lr0GK5Fh/5azF2hRwYfEZbNkGPLUiU6tzDgwJ8bULPmr9xc+6Xxyy+/YOPGzdAhLzr01yBnmVjkLR+L/mO0SJ+pNHbv2ceFXN8qKG/qf//7H+xCXJAz4AdkfVMI2d4UQbagwoh9rUWrlq04Eystg/JqjSYj3rg+hIvaAzmCi3KBFBFgF7UnsoTlh294DmhlasTExvDfUA4UCbBv3B5yBEJ8qKTR3IYrMyghMooRrQxL9v5jHSJSFOUgwCykz5gxA2XLlEP+fPlRruxPmDlzZtwiRoAAAQIEfB94+PAhuyZ5+iUiItV/ny5dOmzZsh0iSUbUb69GlaY6dBqoRdchWoyepoVeL8L4IXJsW65kY8GTB8fQsGED7N69G18aWbNmxZ49fyJP/ooYNlGHApVikb10LFr10iAkKjvWrPHjMqtvFadOnUKb1m2geq1D9qAizEuJnxJPVQQ7Y/To0WwiSMuwdCm8dXnGMQM5gorCOzozc1PKhc0QkQtZQgqaI7jEZgmhXbt2cHZ1wiuv+9BK1AluTyfWMjeluC0yH1AZVXKgy50cnb64USYtICYmhnsrfq1WHQXyFUDxH4pj2LBhuHfPXFwsQICAb9w5S4Ti+fPnVi/LkSMHNm7cyDuGlNdJ9ngBAv6LY5KE/oUL5+HZs5dxv/fydEPbdh3Ru3fvVI20//zzz7h06Sr27NnDpOrChQuIUflj0WQ5aleVxV2vTHEJOjQzoUXPWLRv3wZnzpyFTPbu8i+BMmXKYP9+cxEUbXjQKBvFGKQkeyotgwSvqVOmwiM6A9JFJSy/stM7ImNwHrz0vIfhw4bj1OlTqXIo0PFFQj/lt35pZwMdm/QYxEYJfMNzWnXCuqnSMVENCQ7lx04lIIuXLEbPHj3xyO4KHGJdITFKoZZFQyWPhkJnh8yh+RHk9IpLwdxi01l1MUTLwxEtiUD7Du0/07NNuzh58iQ6dugInUYHxxh3yPUuCH8bw43FlBm8ctXKNO/iFiBAgAAByYM2hKdPn4KLF6/E/c7OToGGDX9jQSQ1eZ+0Zjp2/BSfX/bu3cs878XrO+jRTophvd7xkx+LSdCqoQkD/tCiX78+vKGaLVu2L/pWZc6cGatX+/HYOY3163Q6Hj8nfvqledWnNo0MGzoMdhonZAzJm4CzkYjpG5kdYpMYkyZOwm+//ZaqmL+viZvS8UUTagGBAcgSmh9SU9K1kL3OCV7RmRDk8pKNWxTZtGXrFrRo3hKPxFfgpHODTKuAVqJhDkt5tJlC80EnVeGNy2PEyiKt5s6SsBvpFIwOLdsLebPvwbNnz9C0SVO8fv2aRXOF1hlakQ5b1m3l+EnaKOjSpcvHOzAECBDw2ZBipYtGasjJaKvFz9fXlwXazp07c47Jlz7BCPi6ERkZycfJvn17EBkZDk9Pb0ilcnYj1KkmxbSRSuTMKoZ/gBGbdkdh9uwZuHnzBpYtW56qzC2KCWjatCkLKD/+WAqF8oqw5zCNXxm5Bbd4YTEfq26uIsz8Q4pqzfxx6NChr6YoqVChQvzzvYC+Q2ASwTPaeiA9jZB5RGbAk6e3cfbsWR71fx8uXbrEkSsHDhzghYSzkzMaN2nMm0nkBPkQEDGiMTfK4KYQfcqEo1y594GIu0KugH2IG8TJDDCQQPsi5C7n7lKuGRUwXrh4gV+ngwcOIiY6BiGhGmjCxXDmuAMxvKIyIkoRiqcet+AbmY0LGLjUQWRAuF0QglxfoMJPFYRNtBQ4pNq3aw95tAMyhxZKUMZmiNTjjbv58kOHDwk5sgIECBDwDYA4Aq11tmzZDH//V3BwcESmTOQY3Y1iBcVcMluqmATRsSbsOaTH8g2bcf7839i5c2+qBFpyHdImbaVKlfDjjyWQNaMIT1+YhdhqFSWoWkECqVTEP1NHyXHirJZdcl9Liz05G78nd+Pff/+N5y+eI0tEQaub6QTireFOAdi0aRPH/aVEYFu1ahW2bN6CyKhINoWQualjx448lfqha6wjR44gJCQErq6uHI/h5pZ83iuB1kPe6bwR8SoaSr3tDF23WG8EOb/g+6CpWer6+Ofs3+zw3rZ1GwIDgxAbK0L0W8A51g1Sowz2KkeE2QXihftdpIvMyhFdYkhggolF3CD35/D29eKIOQG2Qf0/zZo2Q8jrMOQILAq5wS7uMlNUFgQ6vcDYsWNZl6EeEwECBHyj4ix9wO/fv89ChyV3NjHoBEDkgQRcwVYvwBbOnz+PDh3acuHTz+UkyJsFuHzjCW7dM2D0ADk6t3y3U+vpLkGhfBL8Uk6PjgOP8PHVvn3qHH8kbDVt2ggGgxEqtQixKhHnzq7dpkepYmIsmarkcoN8ucQoWlCKffv2JRBn37x5w6U+oaGhCAsL4xMe7RRTe23GjBnfe/+Um0ruCHLuEvHPnTs3FyYIBXpJcf36ddipnRIIYYlhr3WGVCzj675PnKURM45IgAPcIn3ZSaqJjsXaleuwft16rFq96j85H8ktQG2yJORTe4dYJIHBpIeLsyv69O2Nrl27vneDyvSv4yI5UDOuhWhb4OXlhT59+vCP5fii57hh/QYE61/xa2Mw6mCEnkmwQqSE1CSHRqTix0jf5TSW/6WKRdIKeDxRJ0aGkFy8gIgPOj4zhOTGU8V1LFu2DFOmTPlij1OAAAECBHw4aLO1desWuH//EUoWlaFUIRP835qwe/dV1KoswdzxchZLCV4eIvTtJEfdakY06PAKo0aN4kim1ICKItu2aYXXr9/Cx5u4KfD8lRHb9umRKb0IS6cpUDCvBEqFCA1rAPv27UogzkZFReHPP//Eq1evmJvSGszHx4dzzlOyqU9uUJoqo5gG4uPEbRs3bixsNloB8U2ZWM6uUVuQmGTsrKUi0PeBXnfa3DVqTXCK8kB6vTf0Yi2O7j3G6wU6nrp164bUghy4kyZNgt8aP6g1akhEUuZ9cpkcTZs15eOHTCvJwdnZmSOykgM9V6lYmiDag263WbNm/GNxBC9fvhyzZs7G48ir/PrpjTr+vb/rYwS5vYDSZAedSAuNSY2iRYpi6TKhXPx9IAH85auXyBFYLIEwSyCDhndUFmjlKsycMYvXsoJZToCAb1ScpTHr7du3v/d69OW8cOFCfA6QE45G1m/fvs3iRZYsWThWoVGjRsl+GVGmJ5GwxKD28pS43gT8dzx69AitW7dE4Xx6zB2vhI+X2TXYaaAaOr0InVpYPyQrl5eixi9GrFq1jPONUnKyoRbVrVu34vffR0Ah16Jneyl6tZfD0UEEo9GE438bMGScFi17qrFzpRL2diKkT2diJ6+F+A4dOoTFWhFMEItN0OrMbboGo3mHuWrVypgwYRK3s1sDOTAGDeqPsLBIFMgjh1IB7Nmjx9Spk9GzZ2/eyLDkNllaYMmFSe2ZaV08CwwMZIfngf0HER0VhYyZMqJZ82aoUaOGzc8ZkbaUICW+/KNHj7JoSREJ3lGZE7gdaCTr9b/Ox5OnTqZIZLeARPq6derC/2UA0oVng4vKCxKTBBpJLEJj/DFu3Dh+7jRWlBy44TZUlex1NBLz5R4eHjavo1QqMXHiRG7NJbGYHh8t0qpVq8ZRNPQ7WnjRbeTPn5+dOu8j5987aIGzY/sOOEd6JxFmLaDfO0V6sktk/PjxXzwKRYAAAV8PBH6a9txoLVo0hTrmJfavU7IpgLBojRYnzhkwfqgiTpiNj2yZxejZXoxJ8/bijz/+4M3TlIA267t374rQ0CA0qiXBkB4ypPcx3ycZFYZO0KJZdzX2rLZD9ixi+KYTIzIyKk5UpRz0ZcsWIzZWDbkM0GiJk5q5kdEEFClSEOPGTbRZJEYGmi5dOuDx4+fImknGYvOxv4yYP38+atWqgVmz5jAPtYDukzIuibvR+H1aBnHsXbt2YceOrQgOCoSLqxtq1arLpgniTh/CTVPCTklM79C+A2RRdsgQmhti0zuO4RGTgZ2PxCMp/oIcrykFrXloevXYX8fhHumLTLHp2ABAoi+VzG5YtxEP7j/Axk0bk13rkvnEoLzOjlZbLmHKktUZdclyU1oj0eOhdTk5bCkKg/gqGSJofUPvAUWZUcE4mVbq1q3L/y0geWzcsBFOWjcoEgmzca87RHCL9sGDh3dZHylYsKDwkgoQkIaQ8gDPrxCrV6/mUWLKe6JxDSqPot1GKnB530jJr7/+ymPN8ZHWCUdaAAn3Lk46rJolh4O9KI70kJN1UHdZsqJro5pitO//HC9evGAh/n35YAMH9sWrV2/h4yUCccxFa/RYs0WPAV3lLAKT4LtugRg1Wqqwda8ebRpL8fi5GAUKe8UR9UcPb6FKeREOnTCiXhUJurSWoVBeMTscdh/UY/byE9xeu3v3PnYdxMdff/2Fzp078YjaiD52TOIJYRFSrNigw+zZs1mMHT58OGd3rVixHAcP0ui9AUqlHHXr1kenTp1QoEABpDWQm6NXz14w6I1w5GxUGYIe38TpM6eRLWs2JofWRuLI7XH86AkYRAYWPK1BJYtiUvi+12X+vPlwNLgkEWYJRIYzhObCY8VVrFmzBiNHjkzxc5s6dSr8X71F5oACkMdzFygM9vCNzAG53o5dl+RQpRw2W2jStDHmz1nAu9zWnMJEjCOcAlG4UOEUZQ3TSCWNl8UHLRQtkw60IBEKG1PuaNJoNVDqbI/1EejyYM0r3hxMbpEiQICA7wsCP01b2LFjBx4/foqjW+yQO/u7DXPaxP+lnATubra56W+1pPjfTC1Onz6Nhg0bJns/tGHaq1d3XLlyHS5OQOYMIuz/y4BdBw1oVk+KsYPl7JbdsFCJ6s1VmLNciznjlHjy3AgPDy/myyNGjMC6dWtRp6oEB44DBfOK0aejDBXLSEAa4vF/DJi7/B4aN27E5bJUkJt4nP633xrAx1PFxgRLvJdGa8Lug2KMnnYYHTq0w/r1G1lIpFiobVs3IzIqhq9XqVIFdOjQiY0uaQ3Egcgd/fZtICqVlaJCScA/8BUmTriGWTOnY/mKVShXrpxVbqozaqGSRdt0z1JRq0oR9V4xjDinXqNH5kTCLIG4KnFWjTIG8+cvSJU4S8cwmRIo39VJ8y7CgCbGPGMywk7rzGuNtWvX8trCFugY3rlzJ3Nta9mwhDD7t1AqlLyWfh9IkLU2Xt+/f3/+V+CmqcPrV68h1yYvYlu4K01+CuKsAAFpC2lanF20aFGCjCdy99KimjJ8KLMmviMxMWiknEZ/BHw4aFSfig3IjUy7nrQrSgVy1nerd6BvJ1GcMEswGs27/q7Oye84u7qI4sa4kwNlkZI7t2RRERZNVKJoATP5efPWiEV+OibRao0JvTvIOcqAxNN123XIm0uM+490GD22EZOnWzdvYv4kKboO1qJnOxmG9X4n3jvYAy0ayvBzOSNqtQ5iskzCs8WRSC6D0aNHovyPEiyeIodE8u65ubmIMKi7HLQXMGPhAv6b6dOnI3sWKYb0kCCDjxRPXhixcddO7Ny5HXPmzEtTDbjsCOnWHY6xbvAJz55AeFRLY/AGD9GkcVMc/etIAmcGgcRFEq1DHF5zQ6w1wTLY+RUyZ8rMsRK2QMfixUsXkSEyl82dfyLFTtEe2LJ5a4rFWXJTb92yFS4R3gmE2fhwj/VFhGsgL86TE2dbtWqFpUuW4rXHA2QMyZOApPPzdHyFKFkYevaajA9Fyl0fAgiWzzE1EicHg1if4PoCBAgQQBD46ZcHnfcuX77MLlFaD5CLlLLbrWHDhrWo/JMsgTBLUKmBLBmT56Yu/+pX7+Om/v7+aNiwHpSycKyapeBYL+KGkVEmbN6jx5T5WgSFmLBsugIuTiK0bSLDtIVaDOxmxK6DJnTr3pSj5UhcmzBMhrkr9Cj9gxir5yghl717jFUrSJl7tuypZSH4yJFjCRyh5Lq1U8Ri8xI581ELFHIRmtSVIYOPGM26/4O5c+diyZKFUMh0aN1IhPx5FAgNM2HL3n/QuvVJXmcR900rY9NBQUFo0bwJvNyjsGWRHbJmevde0+ved5QW7dq1wZ9/HmAnZ3zQuiZD+owI0b6CXaJCMAtCHN4AIlOSTfLEIA5J3NOWAYFu2yXKG5cuXWQuSyaklGDVylVcxhVfmI0PB50znFTuWLliJefa2nrfaLoqd648eIEnyBiYNwnXpU6DUOc36NyuM0cgfAgEbpp62DvYQyU2u+htwcJdBW4qQEDag231Mg3AWvg+hZLTCC8JgQI+LTQaDQYMGIB8+fKibdu2GDlyBPr164dSpUqiR4/uHEQfH+Ro1mh0KFE4UX6jhOIERLh+x5js/V2/bYRUKrEq/MY/0Y8cOQxF8gN+c2VxwiwhvY8Y44Yo0LO9DDMW6/A2yHx/5Da4/9iEvr+TGzMvi35+a1aidlUxLl0zMvHu1yXpyPLxf/QYMp7ItIEzu/LmzcNklbKpyLn77NlL9O0kTSDMxkfH5jImw9OmTUOH5hIc2ypDtzYyLkSjLLPTO2WoXUWEvn17486dO0gLIMcxNdqK9RIotA7QS7QJLqeCgQyBefDy5Qve5U8Miofo27cvgp1eIcDpGY9jWaCRxuK1+33EKiMxcdLEBJsvJIaTC9kCy7FHLtbkINcrERqW8DhNDhR9QjlezupkRrkggkOUK86cPpPsbZHTevWa1TA6a/HE9xo/Xxo9C3Z4jee+NxHk9JJzbf9rOd2TJ084WqFA/gIc21CsaDHeQKAMZgHJw8nJCaVKlkKUU/LHRpRTMMqULiOM4gkQICABBH765UA8cPHixShUqCBvbNN5dPDgwez0bNiwvlU+RY7W4oWT3lbG9GZumpyIZOGu74tHoqx3gy4M25ZJUaXCO27o7CTiroVFUxQ4fNKAo6cNcdyUorS6D9VCKlXyhi5t+lIMATl5A4JMGNVfkUCYJdx9aMTwiVpcvaXHmzcBPGVELlqK2SJuREW87ZuKEwiz8VGulARFC0gxd+4sFMmnw9975GxOqFtNinZNZdi/To4xA+XMJ7Zs2YK0AOKIFHMVFh6GKuUB/wBTgveUYh1WzJDB3UWPhQuTZgcT35w4aQKiFeF45X4/LnKKQDyV+Bvx1v4D+idYo9B9kIElPigfOCXc1CIopwS0MXD9xnU4xiRfSucU68HFZhS9ZQv0XNeu84NXRk88SXcdb1wfMTcNtX+Ll1538NL9HqpWq8pTf/8FFL01b948/FjqR56go+Nz/LjxXLom4P2oUbMGYhzCuOTXFqj8l8qPbXUECRAg4OtFmhZnrYF2yenE6OjomOz1KHCdrP7kbKNMHCo7E5ByHD9+HIULF8DmzZuRwUeDwvnFnHtFsYvlSppw+uSfaNiwLp+ELbBkHEVGJyW5TetJsWO/HsGh1gkwjVv5bTNxXqmtTCjCtm3buMyhe1sJ9h0xYO4KLZau0+HBk3fCb4+2Mn6sm3aaxTzmTSZAbpcBq1b58WN+/uI1qv8swdlLRlSrKOVChviYvUyLNr01CIswYdIIOVbPVmBYLwluXN2PunXrMGF1dJCgRBHbHzFyDzs6GJE/txhjBsghFie8D5lMhBlj5PDyEPNYWVpYDP3wQxHcf3Af9i46hHs8x2Ova3jhdRNayTtHCeUkOWndsdZvXZLboSKFy5cux7kQHnhfwmPPa3jifY1vS5LOhN9//51H02bNmsUxJrVr10HmzJk56qJsmbIcKWDJ600sDieGTqKFk6PtgofEsAjAIlPyX510uV73Tiy2gFwQFL9C7hcSsml87uhfR9G2UxvofKOZBIe6vUbFGuX5WLaUfqUWtFlQ+ZfKXHwmeW0P3/DsUAa44NjBE5yXm5L88O8dnTp3QrQkHGF2b61eTguVaEkEX09A2oWlFIcc+/SdQhnN8Td6BAj4WBD46afH48ePUbZMKc7sVMoiULSAGK4u5st+KCRGWNAVNGhQF7du3UrwdwqFHFHRSW+vaV0pi53/XLRtHli+QY+MGX2sjsNb8PDhQ2zfvgWtGlGmrBHzV2qxcLUWZy8Z4kRCcrwWKSDmslqCVmf+/aPnUqxa7cdrm8uXzqH6z8C5y0bkzGouso2Po6f0qNNGhXOXDRjQRcbcdNooOXSxl9ktSZxJq9Wzazc5ODkaIYIRi6a8iyCLj04tZKj+swxLFi/46t2PlHf6U7nSbAhQyE1YtUmHJl3V+OU3Ff65+E7gsrMTofVvIuzZvZtNPoldz7RuFIvEiJKH4rH3VTzyvIInXtfxKN0VRLmHoHv37rz2pPMICcFt2rRB1qzZkDVrVhQsUBATJkxgDkjXIe6ZHCzc1cXl34P3PbCcs8Tv4aaWyxOf40gwprJm+qE1EG00HDx0AMOGD4VDDjlz07cuT5D7x5xcfEdFqP8lZ596Ryr/UgXTpkxD9EMtfMKzwTXYFzfP3+E+ESos+9qPpy8N2qQxio146/yUp+wSI1YWyZForVq3EpyzaRy0kUifN5p2IL0l8feSgG8TaTrWIDFI8Ni/fz/vkicH2j0vXLgwu/QooJxEpRYtWnA4ubUczNSATipfu2uXRKH4/6YWFBvQvn07/FQKGNXfDnly/JulGm7Cio06zFmuQ/P6Uhw4/pKLioikEEhUzZ49C3YdfINfKyW8zVaNpFi7TYc2fdRYPl3BLlcLIqJMGDBGi1f+wMw5nay+vuRWnTx5Ai5dugrS5fqN0iAqBvBwA6JjgHGzgJ9KiTFttAIZfcUoW0KCa7fNZHvvYT0yZcqA7dt3sWOOAuoJJOASObZPtMF96ISenbeUkUs5X5bRoMrlyQ1rwqD/abFz9y7+e+IYtia+wiNNCAoBBnaT2hwvIoG2WT1gkd9Ofh0TX492v4lwkkuSMpNLlSrFGVAkhBPBoi9zykGjgqO8efOiffv2H/Te2wKdOOhz1LaJFJ1amMfF6LU7eNyASXNj8FJ2E5kCCsWNRynVDnjx4nmC95J2zTt37gKpRo70kTmh0NkjUhmMGEUEtBIVj09Rhiq9DlKRFCajCAaRDiKTCE5qdzhoXBGujuQd+EyZMiN7thwI1gbCSWPdSWCCEdFOIWhUu2GKP7OW4rdYeSTkKttttmr7aOTPmSfudq9evYo5s+fg73/eOQNIFKb23J49e2LIkCHs7KH3iQivxRX8X75LiPx26dwFdjHOSB+aM0GhlVdUJvi7PGGHO7U6Jxe78L2DRvtatmyJ9evXI0YZCdcYc5QFbTREOAQiQhnMEwPly5f/5r/zv1XQuWzkiJHcfCwXK9j1rjGq4enhhdFjRvFmYFrH9/jeEw/72katvzQ//R64qSU2wMk+HJsWKVG2pDlLlWKsdh/S44/pWhTII4ZMpkXfvr2wZ8+fccdJ2bIVsOfwHgzpaUow7VShtIQzWXuOUGPFTMpnfXc+JY4zb4UOew7pMXFiH54mSwzikxMmjOdNHyqi3bATmLVUz4Kx0UBmBR1yZxdhwnAFSv8g4ZitZevNTssDfxmgUEixbdtO5MqVi98/g9HAJgjqBiMxMT5evjGi+zANC6/zJyp4MsuCZvWBVZtEGD1td1ycWHK499CIer9KbbprCa0aidGq12NcuXKFpxbjg8rDqEyXNtxpA4wub9CgAYuN9DpRLwG1zkdHR8HHx5eFTOJ4H/t7ijaqe/fuxS7kOWOVLNATzl0xYtYSLVr1UmPNXCXHQBCKFZJAo1Wzkzpbtmz8O8rdpSiuiJBIeERkgKPajXlplDIUGnksRBIxuz/psyoixmWSQGcyi6t2Wid4qTJDExOLZYuWY83qNShbrixOHT0N76hMfH1rCHcM5GgBKudKyeeWjmM6b8XERMJFbbuULkYRCQd7B572odulTFLamNy3dx90evNxJ5VIUbNWTeaJtGagH3rPpFJye5tfJ2vHekrcvc2aNkNUYAyyBxblsjILPGLSc47t8uXLeaqMjgcB1kH9BpMmTeJziU6hgWtkOtjpHDlmK8IuEBGOQSharChvFnzr3/nfKqhbZ/Cgwbhy9QqkIhmveYmbEl/t3KUzrxuTi+5MC/ge33tTCrmpyPSNbFHRyHzjxo253XLlypWpOmhJ4KJFGAWWU9vqf8XNmzdZXPmWQYdLnz494ePuj42LFCweJsaCVVpMWaBDu6ZSbNwlxqpVa+KyRYmsrVixDGvmyFGxTMK9gdv3DWjaTc3uBRr5ypVNxKNH+/8iFinB4CHDULJkyST3R22Uf/wxGrmymaBWG/DomQkdW0g5NiBTejG7bg8cM3B2F42I7VmtxIjJWpiMYDGxTR8NBg4ciIoVK8Y1nrZv1xqNaqrhH2jC0xcmHNqojPtANeqkYsF169J3v4sPIuxlamsQGGzgUgcL6UuMm3cNqNlKzYsIGiOzhZ0H9Ojzu4bduBSsb3mMfn5+2LNnN4vA+XNLuKTs7kMdXF2d8OOPZXH06BGIRUb8+IPZ1UxklK5D5RA0jvSxvtipXIIcniP6yNC9bdJSPcryqtFCBdUrd2QMM+e9BTg9hz59DI9OWUhb+3btIQ6XIwPlryYirFHyMB6lItetd2RmOGrczEKKJBYhjm8Qbh/Iv6fSAxo3e+l9F65ezggMCoRPRHa4x/okuD3abfZ3eYxIh2DMnTeXnbcpxZjRY3DvykNkCSiY5HESYmVReOZ5kwVXisigAgbapJDr7OAW6cNkncaRIuyCeXc7c5bMmDR54kcbjadRQ3LIZvcvavXx0XN/5nMDRcsWwfDhwz7KfX7L33e0qN6xfQfeBrxz0Pr6+KJho4aoVq3aVycCCUgZaEPvjzF/wE7jCM+ITLDTOfF3ikoajRCnV4hUhnJsD4n0AtIeaLOSSny+Bnxpfvo9cFPC/PnzcOXSXzi8Sclj6olBLlVyTPZoJ8XC1XpMmTIlTlQkZyvxwME9aNM9IY+hnNUmXVUcf/VDYQlKFxMjRmXCn0dNCA41cBs9vb+JQRECQ4cOgsgUjgK5TTj2txFVK0jQp5M5covOLzSdNWOJFtduGbFuvhKXbxiwZJ0O+/zsUKuVFuUr1kCXLl3ibvN///sDOtUN1KkqZk578YA93FzNz3XiXC027NDhwgF72CcSbi3o0F/DkVx9OkrRv8s7cSw+6HHlLBuLvp2Svhbx8eK1EeXqqnjDPH6Hx7Fjx7B06WLmdQXyyCCVADfv6SGRSPHzz5Vx6uQxxKq0KFZQDE8PEUeWBQab2CQxZcq0904+phR0zHfo0BblimuweGrS6TSdzoS2fdXM8c/stmNRnswXnQZqeFqNzACE4cOG49GdJ1wCKzMmfD10Yg1PdXGRV2QWuKg9uT+Aog7C7AMQ5PiKM2CJ+xLve+V5HwYHDTRaLZxjPJA+ImeS/FoSKWkTnSK+KleunOLnu2nTJmzeuBnZ3yYUPuPnkD71uY4adapzIRgJs0OHDIUqUs0Cn8XIEKUIQ4RzAOSOMkyeMvmDTUvxS5LnzJmDHIHFmMtbw2vXh5CkN2DFyhVxQrAA6yDTx6aNm3D33t0Epg+KPWjSpIlQcp5GQVEmA/oPhCZCC8/wjGw+ok0c+q4JdfDnNW+tWrXQtWvXL/1QBXwibppi5ywV4dAuGolj3bp1S7YEgUa36As4cdnPpwI1ZVM0ATkzKccmtaIT7UxSUQCJfB8Kcr3lzJkTXzNol4IENRq1SW1YOLk/nj9/hUlDlVaFWUKHZjIu3qJuBLVaywTJkntDr839+3fRof/f6NDcyA7b9D4iJkfrtusREQl28wWE6XDnYRAcnZzRrXstPtFYiFJ80GhO166dUKygicu0GnbUY+wgOTo0fzduQ+6B+tWlKF1cjNqt1Rg3S4tzlwwcxdBpkA4//1yRj5/4RKBps5bYsH45xg+VcEnAybMGbnalnNoLV42YO05hU5Sh/K+m9cRYtMaImUv0/4qjSa+7da95rOjVW7P4bAuv/I2Qy6UoUqRI3LFNI3u7d+9i1y1FQpAL9+hJA7JmIkdwNAtKVcpTLIJ9XMtwrMqE1Zt1mDzvPCZPnsylEh8DGzduhLcnOWatjzjRIolyfsdMC4FOrIXUKEOMUyhqVa0ZtzAih29MbAxyhicVZgkhjq8h1yuQNaRggoIxhcGeya3EKEOg0ws4qz2Z9PmEZMcL3OG4ChrFilKGwDU2Hd+3RqpCmL0///tTuZ9S1DYbHyN/H4nmzZpzkZdPWLY4EkyiJ+WRvXV/jKxZsnLWHX0JT582HQ6xrsgQSuVk756bXZQjXFVeeIk7HDNAAu6HghwqJ46fgHOUp9XXkUALAedIb5w/f45J98daCH2ryJ8/P5/7aLyICifpPEO/S0ui7Id853+LoM9Jt67dYKdxQqbgvAk/l3pHZAjLA5HrIyxetJgdPJ+Ly3wKfI/vPU0PfC34Wvjpt85NaY1y+vRJ9GgrsSrMEsqUkKBMcTEuXzfC1UX6r9O2IV9GXITue9q8eXjw2IQOzaUcORUSamKu9spfhCxZMkLhkB57j72AXCZD9ZoV2NVMrlZr6NWrJ8SIwKbFMtRpq0bdahLMm6CIEwnpHFK2pAQliirRqqcaQ8ZreNrL21OERp10cHX35RiC+PnFnTp14TVY19YKnsxatVmHAV3lcVNgDWpKbQqzhJYNJThySo+Vm0xo3sAIH6+kx+OhEwZ29b72T9678+qN+XJyelu43J49e9iJ2bCWFP07KyERAyfPGZEtsxi37uk59zajL3Bwgx3/jmAwmKesBvzxGn369MLJk6c/irC0c+dOREbGYHgfuyTCLIHWMEN7yXldcOxvA8dK7DxgRI4cWXkiht4firy7fec2MoblTiLMEsIc3jL3yxZSCAr9uw12qVEOr+hMUOoc2FhAHJT4aYbg3HgkvQypTIQI+yBoldFwifKFQm/H4mmEQwCi5RHw8vLk7w1LHFxKQGLusb+O4ZXpHnyCc7CbMn4Zr7/7Y8jtZFxYRpN0JMxqI/XIGlCIH68F9DxcY73xMt1dzJwxEwcOHvgofGfChIlw1LnZFGYJ7jE+eBp2k8e3S5cu/cH3+S2DPnP0/UPfWyS0k3mHHNypOWa+NL5HfvI+LFmyFOooDbIEFkjwuaS1ZrqorJAZFDx5QI520gXSKr7H9/5RCrlpisXZdevW8S4NlRclBxLRaCyBRkHj7/Z+KtDOLO0eEDEjgYfG0r8k6AT2sRxwnxr0YUjtY6UDi4TGciVtLzBo1KpcSQmePDfPTdFiJP79rF7tx2Pwa/1WY7Hfu/wUby93jBzZnYlnShcwBw4cgL9/IFZOV2LVZj0y+FDDrfXDmkgoCYjUiKs3ABevi9CiRUuMGfNHkpMZFXvt27cbM5cEo3A+MXoO12DmWCBzejNByfye9t7MGUTQ6024fseEVr10GNpTwuNUdHyQ22DRGh2L0aQHr9+uR5M61qMNiLTS9fLnLxwnotF7QK7Zzi2luPfIgFI1dCxCisTkoBXD1ZnKDogQm3Dmoh51q5lFUyLsPdrJYTAC0xae41E0cnbGd0uQ+E5flkSMiRglV75mwdWrF1Gtopno2kLNyhKMnga8dL0HjSwWJqMRV69c5UiGRo0acfSCo841SSuspQgsVhGJDGG5Egiz8eEVnZHdBuH2AfCOygJ7rTPnvtK/LrFeCHPwx2u3f4uwTGDnLV1GUQrkTKKTQ0pRpkwZ+K31Q+dOnfFIfgUOWheIDRKo5THmbF0T8Oz5M1SqWAn58ufj8a9M4QWsjq9RQZpbhC9279rNWVtubtZbdlMK+h6kwjKPeIsEa6CFAAlU9P1Ji38B74c1135aw3/5zv8WQc6uN/5vkDWikNXPJW1gUATIY7urvNFFGW9pHd/Te/+1bJx8Tfz0W+em5HxVqbSo/JPtuCFC5fJSzFqqhbOTeUM8/v0MGzaMp2jmzpmJ3e384z0eBRo3bo6RI0emeDOTxJKjR49i7CApzl8xcrzW8D5J3ZsE4tSDe8jRsKMaz16af1epUlnMmjUnyfm5Zs2aKF++HIaMO4dKZSSYvUzHmbDEe6kDgabFkkOmDObLJVInNOoUg1H9xKhSXgKpVMSb/Jt26TBtoQESqRy7Dmoxqr8cjg7WP0/rd+jg7GzPmwfE2cmIMXnSeFQuL4GbM1CtmZqn1QwGIFd2EXNQmcyE12+J9+owoq/59SDHaq0q5giFpt1CMX36dIwfPz7BfRHvpUJWAvWF5M6dO0XTEflzy+NEYGsokl/C64YZi7QY8IeG4yLc3cOxdOlS/t6njgCpWMbuNWvRWGF2gXBTeScQZuOD3Kg0LUUZ9STOSk0ySA0yeKTTYurvCvht1eLo6ScstBMK55Pgl59kmL0sGCdPnkT9+vWRUtCxvG37NrRu1RpPcQMORidINAropGqoZNHMTRELFmdJ1CHHZeaQfAkEIAvocXqHZsFj0W2OpkguTzmlCAoIhFyb/OfT8jpGRESkme+rLw0yC9BPWsb3xE+SQ3BwMG9geUZktPq5JLjF+iDcNYCnaWk9mtbxPb33ohRyU2lqAtXJRm2tgTZxFgpdj3J+PrU4S65JcjRR3iaJwSkRkqyBMqHI7UtONwEf58CiPCvKiiVYcpssIOGPxurpvSNRjk7C5Iqlk39qA+bp73Nmk6NgXgkuXtNwiVf8vLDEqFVFgglzzORuw4YNfLxSthT90OMiBwB9SXh6emLr1h08EnXj7kM42ot41CnDv9Pxz1+ZrDb7WkCXuzg7YvmKVRg0sB/qt38N33QydvE+f0UlVA5o2rQRL9iu3jIywe7X+V1+rfk1NGH0NC1HO3h6vwsII8ero4MYKzfp4exofq27tpahWxsZPN3Nf//spRGT52vRa4QWMqkINX5591Fv10SGuct1nFlEu28EysKbMH4snj1/FXc9qVTCC4GxY/+XrIBn0OuheI/JQf5v9plOooFSb8+uVfrcDh0yDJMmTkKmzJkg1lt3D1NMAMFJ7WHz9mmMzFHjGnddyoQ1iYwssNjrnOCq9mLXLo2VSY1SSEwyGGFAtFMYHwcjRoxAalChQgXOAiJxmTaubt28xWTWKzIzOw4gMnFswc3rt+CgcbF5kiW4qLwRqHvBx3LdunXxoSc5coDrJZoUlU0IrlkB3yNoE0ohtkvgLEoM2ihyMDrzdb8FcVbA54XATz8vLNzJ+J6gNk5yM9EGtg7Zs2dPcjk50Zo2bYpz586xs5Zc88RNKQ81tXnWBoMR9WtI8cc0LYrkF3PfgS1QeaybKz00F+zYsRN58uThWIQzZ87w5eR0JF5K5/cVK1ZxjuqhQ4fZDDF+tpbLb+kleP4q+TDZF/9evmDBYsyYMRWdB12BmysJo2K8fquH0ShC3boNeJqHeF2fURosnKRIUoq7ZY8Oew4bIJVq4l57EhWCgsNw+Tpw/goV+QKVf5JgRB85cmZL2k1BXJ0EawvIRUyv07atW3g6jG6XittGjhiGS5evJrj/0qVLYcyYsczZbYHiv1JiwCUvyKPnJr7vx8+MCA0Nx6xZ0zF37mz89FMFSCGzuomnlaphkOiS5aYEZ5UHAp2fs8PWINJDJ9Wgc0s5T+PRD4niwSEmODkC6f51Ml+4Cvj5rU6VOGtZb504eYIjBNasWYMzp8/AZADzUq+ojPw8aMLr9rU7kBilcNDaLlgmA4NSZM/r+Y8hzjq7uCBYEpHsdYinE7600UqAgC8B2lAyGPT8nWELZB6wj3bBubPnPutjE/D5kGJx9unTp0xYUgKy1VOr5afG2LFjcfz4cd7tphEI2t2zgHaRSGyjwhbawSZx2ZJ5Sn9D+aIkOFHhAu2QEuGxlCUJsA3abaVMVRrz/7mc9cMnJtaEvy8YzA7acmWSiLPxhSTKbPwQkCvR8d+JU9qdt4iAtkDZqwTKFqOd+O7du+Lvv8/GXe7s5IAmTZtj0KBByJIlC44cOcbkmIoL6DMQFBQIR8e3WLNViwY1JFbFasq43bLHhHr1G6Js2bI48/c5nDhxgltQacFGO/6UH0e3SeIsFYtNX6TDsTMGtGjwb8zDSxPWbdPhwRMTShYVQ0NP7l9cvHgesSojav4i4fEzigwY1ishA6VCLiLUJChTCQaVTFhEaydHEYoVEuPabXNO0e+//45Vq1ahUlkxJg9TokRRMbs8dh/UY8HqA6hX9zJ27/nTpkCbO09+nLn4Otmg6zPnzY/fINFDKzLBLTYdZP+WK4WbAnAz4iZkdnKYwk1J8rdI6GRncOLfJwI5ZU10XXoPZDEQGcUJxJfEI2lUlKWMdcTdu+/ymlIDWrTRRhRl0LpovJA+LF52mAnwjMnA4fxSQ/KrA4pa+Fih6FTYQJ+pkwdOwz0mvc3XLNIpGD+W+vG9m20fc7SXPkfkIKMiMiL69FgFCPgSoEW7mD8d79lsNIn4ugIEpBYCP/28oDxfBwcljpw0sBvSFg6fMMDZSQSp3IE3n62B1gMfKkZZCpMc7QG9wfRekZC4k0IuRv2GzXk8maa39u/fB53O/P0jk9FmeS0MGTKUJ31WrlzFUTvEIR88eMDrnNDQEGz/Mxwj+pjYTWsN67YbUaBAXp6aotF9yiMm8Y3WUFTERKVdYWFhLM4yN12sQ+XGKi7tpcms0DBg6z4dTp83ovQPYpy7YmBeS8YKEhbIX5Epg4i7IzL6irB0moJduRZQPu6g7nIoFcDUhTq0+k2awO1boYwEtx/EsGmGBO7+/fogW2YTx0FQXi9RzKOnDViw+goaNqyPTZu2xMWmJQZx7R3b9QgNk8bFeyUGlai9fGOCixNw/Y65BI2m5ciFTHEWNGVhEQ0Tc0gSW/m9MyXvViZB1HJdrVTFLlkqmrPA1VnEP/FRoTSwaO3dD+KBUyZP4eivTAH5Ekyduaq82chAJobkzoF0GfHTj1XYU6dubUy4MQH6CIo3s/6BoOk3RwfHjyIGpwQ6nY4n6Oh4ow0Y+kwIpgUBXwrv+Ob71rvETd/T6iggzSLFq+PU9oZ9jp4x+kIlUH5mYtCuYcaMGXl0N/7iin5HY8yU70hCAe3O0fg2FRp9rNDzbxmUB1uwYD7MXvaQd7njt8FasGStDtGxgEojwqBBQz7p4yGSunu3HpFRUuTOIWYRMDmR8PS/IiEVdHTu3An5c4s4P/adIKmF36ZVOHv2DDfk0smaXJL0YwEJ/e3atcPMJZT1ldDtSgUDA//QIjwScWI/jXtRAzP9xIdF7Py1kpkMLt+gx5Dx2ridfCKiE4ZTDIMB3ul84/7u7dsAeLoBeXOJmaSSY9YaaFysXxcZarV6l6dlAXWD0OdiwYIFWL16FRPvicPlcc+FHBOU21utkhF12wVizJjRWLRosdX7adWqNZo334+jp8UJ7iPuvnQmzFyiZcHSWe2O9OG5EuShUmMtlQBQ822ovT88YtMn+HuFzp7PUzHyCDja2OUn4kvtuU5qt3e77/8KtcmBxNwPKUajhZFep0e68KxWSa7cYAc1jZMlA8oCI5Bo+TFARQ8U9xHk+JIzzxI/rhCHN4iWhKNL108fO0NNsfRdu3HDRo5bsMDL0xt9+vbmz0hqR5BpoUCOZWo9pkUpvX/FSxTn26pevXqaajCljQHaGNm7Zx+iY6Lg4e6BJk2b8KZihgwZvvTD+6az2lTGWC4PtJV/pxfpoJJGJWkhFyAgJRD46ecFbZY2atQEa7dvQMuGRvimS3oeoCKsi9fNC9qxYwd/0pw7S1TS5RtG5MkhxsHjOnZIJhbhLKAYsLeBRp7mqlOnJpTyaAzrJUHVCnLmP3+dNmDFhv2oXfsEtm/fxc5aMqHQJoAFr1+/RsWK5XliauFkOeyUCe9r6Tod/jqjw9y5PeLOu1RMkrichMRputzZUYS9a+yY05OBgCIKCMULizF3vAK37hnw4JlT3MQbGR70enB8GHU1TB2VUJiND+KXFO+1foc+gbnA0ltHZob+/Xojfx4RtiyxS5CjW7eaFFXLS9Cylxa9enbD3/+ct1oeRSVtUyZPwtL1uiQGBgtmL9OC9okz+IrgN1cZ51wl9Okow4LVOkxdoOMC2cxhCc8FZDAgE0CMIpwntGyBLqdxfeJiFIVgWSskB53evHb4r6Ai2vsP7iNzaH6rcWBygwIREg07eW3FhdF0GZ0jPxY3pdjDGdNnwN/tCTKE5E7Si0AcP9wpAF3bdv3kGZS0TiRj1sL5CxEcGhz3ezulHVq0bMHTnal9DHSbtD5cvWo1zp0/x2usHNlzoE3bNvzc09LoNonVNKW5acMmBAQFwN7OHjVr1WSenZxbXcCHgb7XLd8ZtIlia72rcohC8YJpP9JAgHWITClUUaktljL3qJXzfRg9ejSfGEgg+J5AO9CEr6UhODnBhEQBWnT+l5MFvbdNmzTGD4VMGN5HiqIFzFmq/gFGbpldsYF20aVYunTZBztj3wcSWX/8sRQGdxczAW7XT4O18xQ8KpQYao0J9drp4OL+A65dv4bK5YyYN0GWhDzefWhE485a1KzdhLOvrGH+/PkcC5A/twzN6ong4y3Co2dGbNwJLg2bP38hateunexjp3yuUiV/wK8VozBphDnzlqIgIqNMnL1FOV+37xtQvYWaC/Z+++03vk727FnRtZWZzNPu/qbFyROIYlVj0LaxDP26mMnpm7dGlKmtgoenB4KCQmCnBC4fsmdHrTWs2KDD+DkGXLhw0Wp0CG2AtG3bGufOnsakEVLUqSqJy5+lEbvfp2hw4h8jZHoFcgYVszoeZoQRj7yvsLM2S3B+2OucE112GXK9HbKEUnZr0scZZhcAf9fH8A3PDpFJgiDXl9CJ1MgUmo+bcq3BINLhke8VDB46GL1798Z/Ab0nt47fR6awvFYvj1SE4JX7fWQNLmSTvJMwrcwixoWLFz6am5SKGSmzzdHgApdIbx7RpkiJcMdARMvC0atXLyafnxIkojZt2gzXr1yDa6QPu6XJhaGWxnKJRrh9IEffUNZuShEaGopmTZtxQYez1gP2KmeOr4hxCEe0JILFWXruH6NM5FODmpUHDRoMOeRwjPKAzCBnV02EQzDEMjGGDh3CeZUfQ2z+0O/8bw2UBfpDseJAgAzpw5O2ZRMCnZ5zrhdFHtE4cVrF9/jepxUu9rmQVl6PDz1WSVAgYVOMYPwxUMIj9TQxFB1jdkHS+D/1DfTvPxD9+/f/pNnExIsqVCiHXJnfYOJwGUrXUqF3R1lceVd80DJs4Fgtjp6xZ45l1D3DtqUydpnGB/G9pl11MIoy469jJ60+fpoM7NSpA1ydjWjRQIS8OcUICjFh6z4Trt3Spfjc36ZNa7x9dRr718l4o1+lMiEk3AR7pYhdqFQwW6qmGrXrNMfUqVP5b6r/Wg1OyruoUkGKaQu1uH/GPtnXuOMANYuQJIpaOhbK1lHhTYAJLi5OiIiIws6VSpQoYt0JffWWAXXbqrF69WpUrVrV6nVmzZrFPL5/Fxk6t5TF8Vx6Leev1GLpOj2Xlp3ebWczr5ce519nDPAKyQ43VboE54vnbnd4Az5HUDGOtrK2+f7E8zpPMtlrnbj/IFYZycIvOYitgY6HGi11SJ+5And0/BdMmTIFS+YvRfbXxLmTvgfU/P7Q+zLSRWZNYoiwgHJyA1yfcu4uZTF/DJw6dQpt27aDRCuFc4QX7HROMIr0iLAPRpR9CE8bUqfDpyy1oteXJgbpuKG4B/cYcyGbTqJFuF0gwpzfokTJ4ti4aWOKHwcJsVTIRiV0FIfkEO0GsUnM73WUIpTLGDdv2fyf4xc/J4jztGzREqoYNZyi3XljgcrqIh2DoRWpWWim9S85/D8U3yM/eR9ojXP51FVkDijAE56JQccTlQxSnGelSpWQVvE9vvc3U8jFUrzqowOAWjipLCg50OV0vbR8wAhIHiTSr9+wEa8DvZkYlamtRYUGapSurYLfFiOTpGvXrn9yYZZAO7p0op+6UI83AUb8VEqM7sM02HVQz4VcFjx9YUSH/jo8fg7kyZsPRoMOE4YlFWYJ+XKJ0a2NGDt2bOMvDoof2Lp1KxdoWfYyiOCSazJj1or4Y4YOXQZrMGcZULpcPezbt/+9wiyBBKRu3Xtx4deGHTq+bRcnERNEEmapOKzzYA2Xho0dO5rvnwiARqPjQjIqAKO82eRAt8nXsUzbm0ycRUv/ny1bDnbIVq0osSnMEhrUkEKvN3AGmzWQeLRkyTJU+rkq+o7SoHQdLdr3U6NhRw3K11fh1FkjjweTOGdNmOXbgJgvp8f1zPMWXnjfRqDjCwQ4PcOz9Degl+gQq4jCG7eHTCrjC7fB9q/Z1UBjHv6uT8zXEamhkCsR5PKcd/+TvC4wIcjpFURiEZcj/FeoVWqIjbZHKKkMgpy/r9zusSiZ+DEEO7xGhF0Q+vbr+1HH/Lt3787EM3/pPHjt9hBPPW/ildsD+ObzYqH/UwuzhMWLF+PalavIGJQP3tGZuWmU3n87vSPSR+SET0Q2di4Q8U8J6Njt0rkLHt59hGxBhZExJA/cY33hEZMBmQMLIFNoXhw+dJiz6r520HOm6BTXGC9kf1MU6aKy8HPxicyOnG+LQx5th/HjxnP0BI2cCvi4oEXF6DGj+LP31vkJ9P/m3BHISUTfPcGOrzkXPS0LswIEfE8g4WPHjt1I51sIHQdo8MOvGlRqpEaxqrEYM12LHDnz4vjxkxgwYMAnL40jXjR48DAcPqnHwtU6dG5FRWQ6dmqSWGxBaJgJY6ZpWTxu1qwl7t17iLGDJEmEWQJt2tNl9x885rIxMsAQD6XYLBrPJvz88884ePAwKldtikV+YuamIydr4eJRmotkU3ru79GjJ+4+1PPfksuTYsooM5eEWYou6zJIjahoIzZv3sjFNASR2MSRWtR5xtG+77H+UD5w/Ge5djvxeBPn65IwS7EI5NK1hWIFJciSScZ5/bZA3+EkxM9dYUCJ6hq07q1Gy54alKyuxrL1eqTzAqpUkCRbpNa+mYxj0966PsEz3xsIcHrO54jn6W4hRhkOkRx45X0PKml0An4XqQjFU4+bLI6GOr7hjfoYRSRz02XrDXj5xnpkzt4jBty+r0ObNu3wIbEa5Ii1FVtAfIyceZSFS2KPJXbBgihFGIJcX3DMxccSZgk0hbh37x5UqfMLgt1e4pnnTbzwuAt5RmDwkMFYu27tJxVmCXS8ED/2icjOXJSKeYmbkomBuGqmoLy4ePES89OUgjYAdu3ahQxhuZEloCDHmhGnyxiaB9mDiuDF45do17bdZ5kq/hAEBQWhVctWMIaLkcO/KHwjc/DzoNclx9ti8IhOz5/3QgULM8f/2p9PWsSIkSNgUOrwyus+F2JbQK572jzw93jEhdPxJ3oFfFuQpmZUlnaEqBiDynNIeIsvJlDmEC0iKWKAFj4dO3b8VI9ZwFcAagikLFXapSfRjogh7QxSeP3nDnInpzbtwIyYtBHpvCQ8NtZ7pAb/mwEUyi9GWLiId9g9PFzh57cEM6ZPReXyZpJpCz/9KMbUhRpUq1aVi7ksyJcvN4YNG4kqVapwZhf9kAsrJiaGn3dqHXvkjHvx4gWGTliDFRv1aFRLwsLshasG7P/LAN90ImxdosDEebFo1bI5Dh46wrm4z15SwYQEUxdoERJmgoeN50IjdaHhYHfztdsGzFuhw+GTBri6OsPf/yWcnQAnG028FlBJQfwMNWugXa9ly1ZwBho5AqlgLZ2LEk3zOvL/U8SArTZbC0jENJmMTHJ27tyFB/fuQyaV4pcKtdCmTRt2Sffr2w+PlFfgoHcGDGKoFFEwmPSc+eUR6wsXlRfERilU8iiEOfkjRhKJp+luwCcsO5cbEFHVSGJZeImwD8LY0WM/SHzJkjUL7l17CFOElazcfzO7vKOy4KXbXTzxugYnrTvs1I4wiA2IcQqD2hTLrl16fh8btElCP1RqQsUi9L1M5XufY0ybvg9Wr1oD52hPm45hahyNcAnisX5yS7wPV69exdlzZ1mEJYHXmhDuEZkB69au48XY58rT/S9YMH8B7AyOvDhIfNxITBJ2Yj/wvoSgVyHo0KEDu4Epp1rAx0OzZs14euGPMX/gkWOQ2a1vAlTyaP6+GtBvAC/sBQgQkHZA0WW7d+/j/NNDhw5xliqJtiQypU9v3SH4qUAlw5TfOnr0KM5YzZ1dxHFYJNaWKiaG3iDCxWvEHSS8qUi5sT7eMpQraVsoLFJABAc7Ebp07gjtv3m0hHTpPNG9ey9eq+XKlYvdrBMmTOD4NhrPTu2INsW9TZ8+A4MHD8LB4yo0qy9F+nQiLszatk/PDmTKk6W4BRK76Xzr7u6Fpy8foHEdMdQac4xYxTLWl5lR0Sb8c9HAZbZkRFi5yTx1JxaJ+X0KCngARwfbEWUWONiZkuWm9Pe0EdqyZUts3LiRC8bod23aZsKyZctYdKVItORA7xuBhG3iuOfPnee8x5IFf4iLIOrQvgOeim/AweQEsVYKjVwFLcxRTsRLie/IDAqejgl3CECEMhhVmmgwaYQMtapIOSIuONTcNTFnhR716tVlof2/gmL61IjljUdb+a7ekVmYB5MLj9ye9jEu/PtYhwjEiCPxc8Wf41zRHxNUyEzCHn02iJ+SwEdrOOqr+RxTTytWrIC9yclsCLEC4gLOMR4cT0DZz9YiM+KDvmOWL1sO96j0cFEnXU/Q2iddcHbcuHmD425o3fi1ggqOY6JjkSO4GBcnW1vPqGTR0BpU/J1FYu6oUaO+2OP9FkGxERs2bmAzymPpNf5sivQS6BQqaExq1KxeE3PmzklTEW4CPlGsAeHGjRvsGKQPIy30KVOJcp5ImCLHLIlUJHRQjuX3mEnyvYyOfQoEBwdzORudmCnE35JhlRwob5IKA2hjgIRhV1dXPrHcvnUNKnI0SuRwc3ODl5cX572SuEHH7a+//oKieR7HRQkkxmt/I+q0VTGBo9Gjur9KefT//BUDlqw14MwFA+bNm8dk/0NAwgAtIOjEvmTJEpw/dwYSiQlkgMiaSYTmDWSoVVnCOV807vVbZx3q1m/Dz/fQwY08blaurgot6kvxvyFJnws5h1v2UuPCVSOUcnAOsFQC2Ds4YuvW7WjVshncXcL5ukc229kkwWcvGdCkq5o3Z0qVKpWq50j5zsWLl4DRaODIATeV7ewqypsNcH2Ghw8f2lxI0HcN5Y0SwSEBkITtB3cfInNgft79jg9yArxxfYQIO8qTMkEuVrBDV21UwdXFlXcnibB/CKjkiooSM4fmg6ON+ITXrg+gzCLBoMGDWDikYjk6ziv9XImziynH+Vv73NMChoThLCEF4KA1E35roFxclU8YZ6O9D7QpuNlvK7K9KWLTDUKjVw99LmPixAmfRPD+WN91RYsW5Q0DGpG0hbdOT/nYddS5QO+uwtVrV/l8+618538tCA8PZycIif80ikw5juSmt1WAmNbwPb73aYWLfS6kldfjazxWiXM8fvyY/zt79uwpKgsi3kNFUiSMkjBMohNNX/399ynEREfDBDFcXFx5vVS8eHHmECRu0jnu4tmNOLTROv+laK5m3dS4dc/IXQNN60mRzkuEB4+NWLNFj0279byZR/FzH+IMpmXh7du3+VxF/R0rV67kTfqYWMDTXYRGtaRoUlcCqUQEkciEIeN0iNbkQYeOXXhD68R2JboP08LBHti8RAn5vzFX8TFlgRYLVungYGe+XdIZ6HWhjUjimo/uH8bL10Zc2G9v00hBGb4lflVh8JCRPC2U2udYsWI5hAQ9R7VKUswYY9utSdm6NVqqsW3bNjamWAPxcnqt/vzzT37f6YfWJ+SktCbYEd996/KU/9vBXgJnJ4qf0EMikaJt2/YYOXLkB01TUQRU8R+KwznUm12PVq/z72OYMWMG9u3dh6tXr7HduUjRImjXvh0qV678XmEyLX7u6XPsEpQOnjEZbV4nWh6OFx53cPLkSV5fJgcqz6PemlwBxdmRbA20HnnmexO1G9fA7Nmz8bWizI9lEPlAzY5iW4hUBvMknkdUBoQ4vcb+/fu5LPxb+c7/WkCbTjQdQd8rpK/RxiOZCiy5tGkd3+N7fzOFXCxV3/wkuNKJh3YfiXiQyEDCEpEVOlhIAKMDh0qUBAhICWgXe/bsWey6tjQPpvP2QMtWbXnH0ppIR6UDw4YNwdmzVAIg4mgCjcaIzJnSY9jw3zFt2rRk7zN9+ky4df+J9cdzz4BWvdScmbrPL2E5ADkAyv8o4cKvgQMHoGLFiv/JnUeiLG1g+K1ZicCg0Ljfe3sC44cqUOMXKT+ORX46TJ6n5UwuS+vvurWr4OLqhtBQPcbNBIb1lGHsTB27FHq2lyFLRvPjvXnXgCkLdDh7yQhHR3uYjCZky+aN1q3bsvhA5ITcCTJRJC5dN+DkWYPVnF4isIv8tMiSOQPHWaQWJHCQA2DXjt0Isw+0Kc7yCJhTMI9qJOfwIHGqdevW/EMu0KJFi8Et0ieJMEsgAY9G56Ptw/Bbk0bsJKBYCHKVUDbpxxidokZZGj2/cvEq0gfnSiBEcuSC4ysW2EYPncHfjS1atMD3AIuTRWJM/hRDLmftv+OYKVnwSjUUjWB70UmZtgqRgq/7tYIWvPS5eq+TXG8Pg0QHz5BMeKK8Fje5IuDjgjb1KPtYgAABAuLn15KIsn37FsTEmB2Q9vZKNGjQiAVIay5c4iS//z6Sx7YpCkqpFEOlMsLFxRGdO3djR29ybie6zWcvDYiKliaJmqLc2Na91Lj/xIitSxPmsBbMK8G00RIUzCvG71NW8mQjtc7/F5AAuXDBXI5OsIDMCX07ydG5pRQBwSYs8dOhTmsNomPN3h57JaDR3sGE8X9ALpegx3AdhvSQoesQDT9mKuMqWtDcTUHmByoYW7VZDwd7BxiMRnh6OqLRb43w448/squQysCuXBLzGO/qLVS8a91N6bdVx+5jSx9DakCPpUOHLiyC7juixx8D5TbjvUj09vJ0YyHdFkhI/fXXX/mHzu/VqlbjSSlrwiyBRsWjnEKQv1QeVPu1Gq+lKaaNTCRkKvlQ0NqESl9pSofy7F3jZeUy31aGIND1OedbEjeln+8B9N7otDqIbZSgxZ9gIiTnyraA+KZMLLMpzBLotZeqFPD3f4uvGUHBQXDWJ5+La+GujhpXxDiHcUQEZTsL+LigNSpNI9OPgO8Lqd6Wo9FtWsgIixkBHyMYvl27NsjoC4wdJEXJomJExQC7D0ZgwYLZOHXqODZu3JJgR4WE2Xr1asPTTY35E0nIlEAmBa7dMmLB6kAWdMnpkJwI1rRpc3TseBRXbkrwQ6F3BPfyDQO7EkjonDZalkCYtYCKEX7vL8eew2rO+qLdemrIteTBkuuXxE9bILdnhw7tcOb0KTSrL0HjOkp4eYi4hGzVJnN2bfP6emz/04D0PiIM7yPn5zdlvhZqLfj5ZssUjXNXxNhzWI8LV0WoWtH830QgM6UXcdvt2yATfH29sXbtzASjUZadKkKTpi0watRIFCskRq+RGiyYCFQobW7pJVAuGuXTHv/biCVLRv9nJwaN1l28cBGvXr/ijFXKYkoM+n2sOAqdOndK8e1SnIZWq+GRMVugzC2HWFfcuX0XM2fOxMcGvSYrV61E2zZtcenyJR4/UcQ6wCgyItYxHFqTht0w3wvxtYCEcFqExsojrQrnFqgVUcicKVOKzz1G2b87FTZA+cI6ky5F7qYvBUvsi16iBZLRpelyaoJWGOxgb3Dm410QZwUIECDg04KmuBo2rAe1KgSdmhPHUoLoD43w+23djKNHD2H79l3Ili1b3N+QuNakSSO8fPEAI/oQt1NwhwCVoq7eosaMGdPx6tUrjm2yxaUaNmyIqVOnYPMePTq1eOeeDQw2okEHFfwDgd9qS2wWZLVpLIXfNhNWr17F4mxkZCTnmxPv8/X1ZfEzOXGYRtgpk75aRSlG9VEgR1Yx/ANM2LhLh4lztTxFRRFh0RESOEWlh7POHqFuzxGr1qJsCTGKF4nBs5fAgWMG9BppQJUKYly8ZkTddmp2+MplNJlmgr29HUaPHsHrSMtrkYCbNmnCbt1qFSWYvUwHd1cRWjaUxhXN0lTYxl16TF+kQ7t27Xk67r+ApmtOnz6Fw4cOYfA4DeaNV8TdhwXH/6FOCAN692mf4pF7ipG6c/cOMsTkTvZ6TlEezBu3bd/2UTsHLBgyZAhPhtBEYZjbW9hHk3FKBJVDJFSIQe1atbnY6XsCHW/ET8Njo4BY25N8sbIodjGnJAqFOJ3eqOe8elpz2IJRroczZcl9xSADjC5eBr81WC6n52of5Yozp858pkcnQMD3gVSdDWgHiezVRDBoZ4+cg9/K6J+AzwtyGHTt0gllS5iwdJocSsU7QvRjMSK2BjTvfoMb5ydOnBh3GTlmSZjduVLG2bIWFCskwbLpYoyYBIwcOZydA7ayRCkvtkCBvOg65BHWzAHy55Yw2es1QsME8sVrE+pVS/jRoNxZcqES0SZ+VqoYcPjwIVy8eB5HjhxNkEubI0c2pE+fERKJGG5u7qhbt27ceBCNbJEwu2augl24FlDRQpXyEkyYo2NXQblSYqyZo8TZywZ06K9hUuvuImKiXKG0CL3aK3HtthG9f9fgr1NGc7GCCBBJM6FU8aK800b3mRzhI7fBokXzEBERjBxZgFa9NMibU8TEn4TZQycMLFRTplZKCs5swcXFBUf/OsoRAtQCGqMIN+dv6RXQSdUIdwxEtCwcAwcOTFWRIJF5i1syOUiNUhbsP6XzbsfOHfzduHbtOjx++IhJfIVKtZn8v28k6lsEffboM3jywGm4xqbjOInE0ErUiLQLxcC2fVJ0mzVq1ODxc8q7stNZF18j7YI5g5ic0V8riOznz5cfr268hbPaw+p1yNlCwf+Wy0VGEY9NfirQphd9NslVQlMwFLvwqQtzBAgQIOBrRI8eXSEVh+LgBhl8vd+duwrnk6BlQyMad4lAt26dcfDgkbjvSYq6evL4AXatknOprAU0zTRmgAL5cooxcOwmdkba4jl0bqCN3ElzN3G+K23G0+1TYVhUNG3uAw2qJ+U7dx4YcOOuefKsXAkTtuw7xZvCW7duRmys2fVL8PHxQvbsuaBQyKFU2vHjoHguEmQoKorLQnvL0KPdOxGSzBMli0rwSzk9egzXMJ/KFliUXa3PvG5ALNfDwwEIDTexg/Z/gxUY2tOIniM0OHTCyJmuBJPIEzlyF0CvvjXi7tMWaOSzcuWfcfb8SRZoR03VYt5KHX4pR68HcOyMgR28BQsWwNixY/FfQUL1smXLWcSkYrNqzVTo2EKGwvnFCI8AZ+tSOVelSj/z2Hpquen7Jofocjrn0tr6U4iztOaYMmUKH1NUBnfpwiW+v/JFSzGvp5iy7/E836ZtG0wYPwHaqExcAmZtkz/SJQi1atVMkYuZ1pRULkwFo+SItgYqdoqRRKBmzZr4mlG7Tm1sXLMJpqjMNgucw+0DIdcr2UErNlF2dsqm3/7rpBkVuNFnirKdyVn/KT4rAgR8TUjxEU47gfQFT8KsJaaWxo9pPDslZS4CBMQHiSwxsTGYNsougTAbv4W1W2sDFvptwrBhwzgqw5wxe54dsxIxjRrpeBeeRq5+LidlQjykhxxb96k5eoOKlqwe9FIp/PzWo2XLZvi1+UMWSd1dgVf+JnRvK8Xy9XooleQ+NSIsHDh3xYDl63Us2hKPocOf/hWLL3IB2YRhMtT8hca9jGjfX4PHj59CbHqGnFnFeHhXjPY7dyJHjixYtmwV1qxZyc6H+MKsBUSShvaUYfufOmT0EcNvqx7jZmnh6gw0qCGFqwsVmxkxZJyWx8rWzVfi9E47VGioQemydTg3ijJ1UwpyF65fvxktWzbFtdtvWZSNjDbi0Ak9IqNoTI3E8GE2X8fUgHaW9+zZw9lM8+bOw8NH7zJGixUthu49uqNWrVqpuk3K3yGQWJdcrqlWqUKWLJ+2BItIMImR9CPADBr9PPbXMbz2eADfsOwJSinU0hi88XqIjBkysEsmJaDNhkwZMiHA+BSZAvMmKSvQSFQIdnuNXyr9wnnoXyvoc07jhvT6hNkFJMmdJWGW2qB1Eg3cY31gFBmgUcR8EpGfvlNHjhiJf87+k+D3+fLmw5g/xvzn0VgBAgQISIug7OkrV65j1SxFAmHWAm9PMcYNkaBlz7u4ePEiC1wkrm3cuA7N6omQK5sIB4/reRqKeGLxwhIu+GpcR4rlG0xYs2ZVspvQ48dP4LzQrkMOIm9OKcqWAPYfo+IsKRat0YOGQihrlTbqn7wwMBe8eithdYhEoseWzWvRva0Ev9W2g50CHDFw4VoQjPpg/FBIjPBAEYYPP4iJE8Zh/oJFWL9+HfLklKF7W+ub3bWrSrF9vx5nTkmgovO35z1IZSbUqSZBlgxiPHtlwuzlOixao8PymUrsXWOH9v00ePYmI/YfOJzqaZYFCxbxZN2hExeQP7cUUqkRJ84amJvGqky8Abt06dIPzkQlgZbczLS+nTJlEkZOPh9ntqA4rxEjOnLBdWoEIdqclkllUMui4ah1tXk9lSwGzk7OnzxvkXoNPle3QVoAvdcrlq/Aa9N9+AblTDDdRa7Qt+6P2eWa0rUPx1HUroP9ew9w8XDiaTFy1L51fwovT6+vXpylHgwS8imL2FphLRXZUeasT2Q2vkxtH4UiuT9+ljmVxY0ZMwa7du2GwfDOmODt5Y0BAwfwFNn3uLEg4PtAis82Cxcu5PFt+uBSiye1sdPvRo8ejaNHj37aRyngmwKVrsydMws/l5VYjQ6wgMoOZixRcfwBOTcpXJ8msh48NmDoeA1UaiLKInZ5Tpqnw48/iDHzD3KkinD27D/JnljpZPrnnwexd+9eTJo0HsFBwVzCVaaEhAlwrVYq3Lz3jvCSeDu4hwy92su4vGDXQT1mLdXCZDSiSgWz6EQOV4og2LVKyeTXcuK4dluC3iNeo1atGlCpNLj/WIyZS7Ro3kCahPzTSNVvtWXYvFuHzXvARHlQd1mCQoUHT4xo11eN1r3VOLDBDq0aiTF/1X4mmKkFCT5//XWSc8a2bN6AsIg3sLNXom79mp/E9dmoUSP+oSIvOvlSLhaNGP0XUP5XlsxZEKrxh32os9UsUpUsCtGSCDRv0fyDHjeNB1IRIrk9qOBDIAXvB7lfVq9Zjc6dOuOR4goc1K6Q6mXQKdX8nmTLmo0bSS1j/u8DLcJWrl6J3xo1xjPJTThHesNR4xKXnxblHIxMWTJh9pyvt2whvmudnKpr165FlDKUW4OlBjk3OYc6vIVKHsV5yXY6J4780Jv0XFL1MUEjpPXr1Ych2oQM4bngpHZnpwQ521/cfIOWLVpi+YrlwoaDAAECvhtQmZabC2342xb9fiolRnofGZe1kDhLJaYhIeFwdZGhdG0VAoJMHFdFE1kzFuuQPYsIk4YrULcasNAv4UZYYtDUDbk5qYho0qQJWL3lDoxGoEUDGZau1WPoeC3uPTRC/68jlcwJjWtLMG4YRWCJ2FU6Z5kOD54aUbakHBl8RGjdW4M7D41YMEmBmr9IuKeB8PKNEcMnqtC2bRs23WTPLMLoaSY0ry/libLEaFJHiqOnNHjtcRc//Sjh24s/wTZmgBzdh6nRob8a+9fboVNLGZp3f86bgD/88EOq3gfiBZs3b+U+irV+q3H//l2IJRJUrvIj2rY1r0M/Jg8rUaIEF+USz/P392fzUY4cOf5TIzqJrfXq18O+7fvhHuMLMZK+liTYRTkFo0OL9h/0PFQqFd6+fcviMTmvP0eBV1oHTbtt2boFLZq3xBNch6PeFTKNAnqpDtHKcH7//Fb6JRtRlxgTJ03E/fsP8Eh0G05RnnBWefLEWIw8ApEugZDYibF6zYaP0nXxKUGdHLSWpElGMr7Q8avQ2UMv0fE0V5QyhHOUafoxRhbJXJ6+Pz72ZG2D+g3w/PELeEZkhEusF0cokKkjVOXPpiHKBB80aNBHvV8BAtKcOEut5PXq1cPQoUMT7A7SB/jJkydcMCRAwPtAJK1Fi6aIjIxAOq/kDz8SXi1ZXgTLWO+8lXpuqm3fVArfdGLodCYcPmnApHlaNOyoRqF8RIrfP2ZBLtPGjRuzG3z27BnwcBPhzn3zaJhcLsLccXIWbF/6m7B+hw7TFpqLt8id2/o3GccQUJbW2Ola5M4h5rGuY1vtkMH3HZkjwnvwuAHPX+thp9SjakUJTEZg6Tod5q6gogMZeneQJSBnJARHxwCVy4t5xCwxccudXYwVM5U8grX/LwOPYKnVGnZb0NhHakGOBtp0oUI/Kvwj0ZRGeT4licicOTP/WAM9D3LZEuEkMZTcqNbaKYk0Dx4yGL169UKg0wt4R2VKMIZjdmc+Qp5cefg2KO+Xnt+qlasSNCa279CeXbsyWVLHyI0bN3g64MD+AzAYzashGkmnbFxyfAoibfKg6JsLFy9wU/Wf+/5EZGQU0qf3RZOmTdj5ktIMNwuILB88dIDHL3fs2Ikg7Qv+vbOTCzq37oyePXsy8f7aQccNZb2Rm2XK5Cl4GXgv7jJ7jTMyheaDg8YFIfZvEOT8gp07/+WzbQv0vdSvbz8Yo4HMAQUS5KQ5atzgoHHFa/cH6NO7D65eu5psSZ8AAQIEpHVQNmfHDu1w4cJF5Mwq4rJZW6DuAXdXY1xckoWbzlyiQ83KEvTpKEOBPBL+nr1wzYgZi7RcNEsb8jpLw+t7zg/kriWOQ5tyRAGfvTQ7cSMiTRjZT84GgIgoE3bu12PnAQOCQrVYMVOB6j9LUamshDfv+43SYOJwGU6dM2DNHAV++Skh56ZJrIvXDLytXbakmAuxKC929WY9O2JnjFHAThmPm7qZ/5umuJZPV8DOLuFr5OYqYm76U71YrNyow+Du5vM7cezUirMEEhzJaUjTmcQJySBEvJ3OR5+Ke1F+ra0MW2pMJ0GexHh6bJThS4/N2mOhPoo9e/bitcdDpA/NmeAcqxdr8cbjIRT2cnTo0IF/R7ESq1atwskTJ6HVaZE5Uxa0btPKZsn2mzdvOCZt08bNiFWZj8N03unQtl1bdO7c+btpP/+voLzoEyeP8/u5dctWLupycXFGnbp12EBCcWypAfHOXbt3smltrd9aPA+/xb8nBzUJ9RSLQWJ/WkDTpk15MnHihIm4dv1a3O/lOjv4RGaHa6w3ohVheOvxBMWLFecSvI8JEoefPXmOzIH5ExTn2ukd2UhAj4MKyGgNUbBgwY963wIEpClxlnYSEzdV0v8T+aDIA0Gc/bZAQta1a9cQFRXFTkESRT6UDD179gyNGtWHh2sUfPOLce+RWQi1BcvlFgGHjjVyEEwYJkebxrIEbtNaVcyFYrVaq3HmvAlNmuZKlYuNTgY375pw+YYRvTvKMLj7O1G0WCGgbjUaKdNi4lwdihUUo2oFszDco60M/5up5eiDhjWlccLsw6dGrN+u44iAV/5m1y0VPNj/S2ajok1YuMYs+FKsQ5dW757PzXtGjk7o1EJu8zWnCAcqYNiyR4cmdd79LTnx1q1bjQcPHjF5JFdH27btUbVqVZu3Ra7QwYMH4s8/D8BOKYaXpwRBwQYu8iK377RpM6ySw09xzI0bNw5+a/y4XE0hUkIHHSZPnoyyZcpiztw5ScL5KbuMdlDp78iF4BDtxjleGrsYREnDkDtnbnZn0m1TxhaVY9Auueu/uVD3zj9Cz8s9sX79eqxZsyYBoSXS1rVrN8gNCnhGZOJRJYNYh5fX32LAgAHs5KaSsf/irPieQKSVFgv08zFAoj5FePzxxx88wUGvP51/UhPp8TWAPo9Egknkp1w4alWGScQtweF2AQjweMKFcrRxQhMqH3t099btW8gUls9qgQW50L0jsrDjeffu3d9doZ0AAQK+ThAPvH37Njekk7uS8rGtbaym1n3YonlTPHt2B7WrSHh8nkbnLXwtyfXVJjx7aUCufOZsUQ8PD57qalBDglljFXFci/6lDoV1C5Ro3UuNHfv1yJEj6UazLZQrVw4eHq7syu04QMNdBMtnKBNEgf1cVsodDTRNReLwsF7mDoeRfeWo00aN2cv1yJ9bHOcEDgoxF3zt/0uPOw9MqFtNgtED5BzXQCCzA02GjZikRf/RGiya8u753L5nFohbNJIkEWYtcLAXoVl9GVZv1qFdU/O5hTb6Dx48yLFeFy9eYn6XN09utGrdjkvQbG3+0fWopGz58qVsuCC3cmSUkfl60aKFMHfugs8meBG3njhhEiKjIqAU23Mu6QzjDGTPlh2zZs9i52185M2bl4vZOnboyOdRx1g3c8+CTI0ou1A+dteu28CbrsRlli1bBns4woFcl0YJQqIjMO5/47Bi2Qps2bYFWbJkSWByadSwEaLDY+Ac5QkvTVYuoY2KDcGMaTPYTEDO0M/B29My6Likfg76+Rig95QMbP3798fjx4/5+KXJwNQKvV8D6Lvnz/1/YteuXRg8aDBiVbGQQY5YeQQiXAK4UK7Mj2V4uupjZsDShtemjZvgEuGdQJiNDyqVpkzg1atX/6eJUQECvnak+BOl1WqTOOksotmnLCoR8HlBIhY5Bf3WrERAYEjc7/Ply43evfuxe/q/gkiWnSIG25cp8c8lA7oN1eDmXQMK5bM+hkO79/SdT85swo0b17kkoVUj64ctkUsSQCfM0bIYeejQIQ4TJ4JCLgRb49N08iQH5a1bN9kxEV+YjY/ubeXs0CVHAImzhHq/SjF6mhbBoUC5khIYDCYuL1i7Tc8O2IhIsNjbp2NChyA5FIb2lCMmxsTxCC0aSOHoIMKbt0Z2w1KJQsG8yYt+dPmRUwZs+9OIggXzoW3bVrh//yE7ehv3EUOjNeDPv86ifftTqF+/HubMmWt1UdK8eVM8fXwbk0fIUL+GlBcksSopdh7QY9LcQ2jR4hW2bt3xSd1zFHVBMRTkrvSIzGge8zbKuHQiUhmKy+eu8gg2kYXEroZu3bpxHinlJB09chQatQb5sxHxb8WOWPreoutcOHsRWUIKJMin9YhJz2NHdBlNAZATweL06NG9BxxjXJE+LGcCR66z2hMRyiBs3boNRYoUQfv27T/Z6yLANujz/C3smtN3DY1p0XG0YcMGXL50mV3atCFGJXqfYuOTXDoysRyOGtsuYyrKcDC48NSMIM4KECDgS2Pnzp2YN2827t9/FPe7dOk80a5dR/To0eM/iwTUUXD7zm3sXaNgV+j+Yyps3atH2ybWRd/dB/U83fTmzWv+/2PHjvG/xOmscUeKpRrcQ44GHdQoVaosbxKTWYHWUGXKlLE5FUEj6o0bN26oZJ4AAQAASURBVMOSJYu59HXOuITCrAXUYUBCKE149e0kY7dr0QISnvx6/NSIxnWl/Ljo8tFTtSyw0nKO3LJzxyvYCRzf7NC4joz/7T1Sg2u3jFy4q9WZsHKTeSKtZJHkR+cL5RXz67Nplx52dgps2bIJBw8exg+FZBjQRQS5TIzTFx5g6NAhWLt2NTZs2Jxkc5VE+OHDh2Pjxg3o1V6Ktk2UzPGJYx//W4IJc++hYcN62Lt3v80prI8FEk5JQCXHYI7oYlAY7DhOKVYeiQDDKzRp3ARbt21NYmCiyaGz587y8bVzx06EhYUjg6c3fmvSgzdlaUJtxYoVfPsUY0Sj4nHxXLG+0EaqORe1ebPmOHHyBB8vJPi1bdMW6lAtsgYWSpDl76RxgyrGB/dwD8OHDceChQs+6esiwDrofcqX79P2XHwukHBN62kSaSnCksRTWjMTJ6QNiY/tYL9+/ToLwT7q3DavQ58RhyhXnDx+8qPetwABXwtSxWRopIR2rC0gVyWB3EvWdugKFCjwMR6jgM8EEtk7d+6I48ePoVk9CZrWUyKdpwj3HhuxetMTJr8vX77kMfLUgkTS/fv3YXhvCY8+UQMrFSeQQEuugmyZEkYBrNmix6bdehTKK8JfRw9xtszly+dRq4okAZFMjF8riTF+NtC5U3uoNe82DRQKKZo0ac4EKzEJvHDhAo+50822bGRdmI2fgzv4f1oeJ3NxEiH+5JBaY+ICrw079Rg3RI5YtQkzF+vQqbltVwfFM6zZqseeQ3pULCNBu356Js6EsHBTgjyvxAgNp/cMOHVOh+zZYhAd9QYHNygTNAV3bwvsPSxG79/3IEOGjFxClNgNcPvWTexareAmYgtIoG3ZUIZC+cSo3+4mX69Lly74VDh+/Djn/2YMy83ipwUkilK+kV2AI17gNo+yTJw40WpOErln6ScxaBG0b98++IRlt1ocRr/zCsvE908iGTkUSOg16U3wDc9htbHURe2FaFUYFi9awo7cT+meJeGa8ufoMd2/d5/HpMqVL8f3ay3uQUDaBE0okOPic4A2WyWQWM1pjg+RUcxlNwIECBDwJUHnfnJJVakgxajeCo53ehtkwubd4Zg+fSquX7+GpUuXpTpzk/gmmRGqV5KiYF7z3zasIeVN/myZxahQOuHtnb1kwB8ztMyNKAKBvh8vXbqEogXN01S2ULywGA52VIa7kcfXLSC+V6lSRZ5Q8vX1TRLxtGnTejg6gPkhRW/ZArlVl67T8xQXuWktLtbwCBPoK3z3IT2GTdCyueHXShR7oEHPdnKbfLpOVQmmLhBh3Q49x3YN/EOLl29MPNVFEV7JwXL5uu1GZMuWG0ePHsHSaQrU+OXdkrNDc+D2fSla9XrA6w4/v3UJbuPKlSs80TR5pJy5qAUUN0HHwA+FTKjZOgoTJozHkiVL8alADu3x48bDPdoXPlHZEgpEWhfYBTripfddDB0yFEeOHkmyfiAzAY2004+1Nde8ufNZ9HX/d5or8QYpFVZRLipNcpE5hgSyFy9fIFtw4QTCbPzRb4/wDNizdw9+H/V7kmPqY+PWrVs8dXbu7HkYDQYUKFSA+yrIeSnEfn0boHg5MgrQz+fgpgSJMfnvcbFJAq1W/ckfjwABXwKpUhQo549GwC0/FsfY2LFjE/ye8lroXwFpC8uXL2dhduUsOSaNUPDOO5FNInqr58jRr7OMcxJpJPa/lM/odAZ2dRJoV37NXCWPgv3SSIXuQ9VYsUGH2cu0qNhQxe7Tji2kXPIVG2vOUzIajFx4kBzJnrJAZx67agCc2G6HF5fscf5PO3RvI8KmTWvx228Nk4gNixcvQs5sUnYm+HglL1ZYLo+ONpPPi9fM0QvZMouw/U89Vm3WY1gvGdo1leHpcxPy5hLH5XRZQ3ofMbJkFGGRnw4/1VcjIsYNkyZNA60vtu2z7UinEjQaSyOyTMT+ydMXmDVWmkCYtaBONSl6tJNi9eoVcRsqltfLz28VZ6TFF2bjg35fo7KEr0fX/1RYvWo1HIzOcFJ7WL2cSKpzpBe2bNkal/OWGreNVCSDi+qd6JsYLiovvs6OHTv4/3fv2g3HaHcmALbgGpsOr16/TLBh9bFBecvNmjbjZtK/D51F1B0tQm5GYaPfZs4InjZt2id9XwR8u3lraqMKWonK5nVobFMjjxEiiwQIEPBFcfHiRRZmqRx11SwFKpYxC6HFCkoweaQCy6fLceTIYR5zTS0oR/Tho6eoXP4dT5s4Qo5SxSRo2VONRp1UWOynxZK1Ov7vJl3VKFqAprSkfO6lv6d/45e2WsO67XrEqIAyP+iwfbkSzy/a4/YJe47punXjFKr/WoXj4+Jj06ZNUMXGwNNdBB/vlHHTKHNFA0LDTByvlSOrGAeP6zFlvhbVf5Zg4nA53gaaOUOZ4raXgCSCUkHuib/1KFFdg6NnxJg6dTrsyQm7J/lpSXIdU9JE5iy58OTJI/TtKE0gzFpAubzTRklw7tyFJOsKEvwyZ5BxOZk1EK/u0lLMcQkkoH4qkOuVeiK8oq0X11LZl3tEBty9dzfVayNyUAcFB8ItxsfmdShOi6K4Nm/azP9PRgN7kxOLsMnxWZjA04OfCnTMk9mF8ka3b9yJsFsxiLyjwfE/T3JcE8Ux0WSeAAGpQdasWflfcqUnB41dNLLnELqOBHznzlkS5QR8uyB33prVK1DvV0ncrnt80A4oibM79oMJMBXZfCgypRfjwHo7JnIbdupw9LSBtqNBvUtTRxEpk6N5dy18fM05o3nzFcCp8ycxzGSyuiN76IQB+44YeEyrQY13zyG9jwgDu8lRrpQEzbpd59F1i4OUxMojR45gzEApJs8DHj1NPgf38TMTpBJzIYLRaOIcWmcnoGsrKYZP0nFzbqtG5l1+mrDTaEzJkpuwcLPQaoQbxowZwAVlNK5Ni5FlG7bi15+TCqd0v6OnaaBSg58HEfrsWWQo/6Ntot36Nynmr1Txc7WM21Dx19OnLzC4S/LFX9UrSbDn0AsuzKAxrI8BIm00Wk2tnO7u7vx87WNck3XyUZt8sOoV7t+/n6pyCSoWUxiVVhtzLaDL6DqUX2vJ4VUaki+WkhnMrxu9Lp8CdHxQHMP5sxeQmQui3r0+pkgjgh3fYPbs2Zx3ZymVECAgJahRowacHJ0QEvMGvpHWM/vC7QOhM2m5kEaAAAECvhRWrVqJbJnN5anWQE7KWlUMWLVqOZtGPnSShWIBVs9WcDnW2m1kGtDBYCRXFzg+4Pd+MsxdroeDgx0XqlK+6IEDuxEeaX3ayT/AyIYD+tv/DX4XfUDc0VIuW6t1OPr27cMb0Bbs2LEFNSuL8TbQiEdPk9+EffTMzF29PMy3vWqzjier+neVoVVPMiSYsGCieTLMYi7W6sw81Roio0wIj6QcUxk6d+7OG8QUv0B/TxFQ3HdQN+n7sW67DlduGlGlShWOExszZhRaNrIdifXLTxIWYWkTvUWLFnG/v3LlPKpWNCU7KUdi85jpWi5upfv7WGshmqajSVHqIDh//jzs1E5Ws9ktoHggiUiCy5cvp5qbEmxla1og0yi5sMrCTcWa5Jfu9FhlIvkn46aEuXPnchxDuois7PqN46ZRJkQpQnH8rxPczWCJChMgIKXibJnSZXD979twVntYnVxUSaMRJQ1Hm7ZthBdVwPctzlLxjoBvF9Q++uLlG0wZoUx2J71BDWDNttTvxpIgKJNJWIDt3PLdly3lrLZvJuMfEqMqN1ExCR0yTof5K83OUEeHa8iXNxfS+fjg4UMq2RKh+s9JSSFlwVJZV3xhNj5K/yDhUa2VK5byiBEReCIvJHbmzCpB/eombNilR/d2MijkSQmhXm/C2u06JoRUuPvHeA1OnzcT4hP/GCCXkRNAzM+JQM4LckuQeyFXtnfPmYom/LbqmcQ+f2Um3O7uBhYqKU+KMH78eDx8eA+NO99G69+obEzCMQrXbhsxb4UOdx4aUa3ar9yoefnSJeTKRkUNtkmsr7cYLs5SBAUFxYmzFsfl+2LaLJd/DIcmZRrTeOKqlasQGfVuZ5SInVEm5hwvWwKt5fdEnlMDErt1Il2yt02XaaBh4Xfbtm1wc3NHRIC57MMWNFKzK8BWs++Hggr5KO6Boh4cNQlFcSIsXtEZoZOoMXPGTF44WTLABQh4Hyg/etDgQRgzZgwkRhk8ozPEbV7QZyHCLhCBrs85687iZBAgQICAL4G//jqCbq2RrFD3Wy3zJvLTp09TVRJFMVe5cmbDX6df4bfa734vlYp46oh+CMMnarBxl567EMiJGhJKcVkS5M+fB16entBqTZi3QotR/ZNudhPXU8htZ9KSC7hPJzlGT/2HN9sto+gUB5a9gghlS0gxZLwWT18YOWrBGqiAK6OvCEXyi7B8gw5zlhHnAaYv1MHXGwgKBYoWNP9tyaLm7/q9R/RoGk9gJY6366ABa7bouByXIJeLmZsSdyOQK5JEyIFjN+DMBSNaNJQic3oRc1m6X+ploA6Hn376CS9evIC7qzROMLYGek+JvxI3jQ/i5VJJ8m5hi8icWk5oC1u2bMGM6TN5Iiru8YnEkEhkMIj0yQq0xMn+Czcl6CVang6zBbo8OiaajTE0Ym5QapPls3qxDlqj5pNxU5pemz9vPkc9eMQmLOmlx+Ss8YAhTI89e/awQEuxYwIEpBRDhg7Bb781xmv3h/AJzxYX32HJeX7r+Rj58uRHzZo1hRdVwDcJoWZcACM21ixEUYlVcnB3FUEVm/qcF09PT9SsWRsrNxo5S9UaDh434OETE1bMkLML9MVrE7JnptElPXq11yGDl5kwdR+mxZotWi4osODeIwPOXTHaFGYtqF9dipDQCN4VJ1jG/KmIq2MLGY+CUQkC5cfGBzXYDvqfBk+emxASZkLJ6rHYvt8Ql896+ZYbjCYp4k/xUFyApzswcY6WhV0COSsad1Fj2kItfigkxuIpCs7iql4xFgsWzEKdOjV5N50I2JYt29G2XRds3qvEr83VKF1bhR7DNbj9wAg7pRjXrx7DkCGD8c/Zf/A2Ia9NAnLnxsQY2OVhAblgfXy8WFhODsf/NvD1XF3fc3C8ByQ8U27tvLnzIPV3QI7Aosjr/yOyBxWBa0w6xCgi8MblEZ+ArT4HRTgXfqS2IKl69erQmFT897ZAl+mgwdWL19C3b1/4+79BhH0wdCJz/pE1RDgGIE/uPJ8s95XG6ZQie5tRD5ZCs7DwMM4h+xKgkc7t27dzDnWnTp14zO3evXtf5LEISB06duzIWd4hzq/xOP1VvHJ9gNcuD/E0/TW8cX2M+g3qW813FiBAgIDPidhYNXPP5EBdBoTUjlKTWNqmbQccPKHHrXvWuRCJojv269G7gxTd2kgREESlrkDLhgb07WhA8QL+PFG1YqMeQydo2HVqQWCwkfsTqlaQxG3cWwOVy9L+919//RX3PNRqFV6/NfFlVIbbeZAab4MSCoAkqJIwu2O/gfl7uboqjJ2hBVG99D7Ak5d2iFY5csksOWUJWTOJUamshDf6LXycxNABY7To87sG9nbAzD/kWDlLgW6tRdi/bz2qV6/KE070elG5LxkIrtxOh8ad1fixlgpNu6m5oJYiy4IC7mHSpP9h2bKliIjUsyEhOQQEixNwU0L+/IVw4mzypoDj/xj48XyM8qV58+Zx5nvk41hkDS7I3DRXQHEuqDWIdXjmcYsFWmtQyaKgN+lS/ThIwLZT2iHcznYsg16sZSdqgH8gRv0+irsRVKYYRMtt89kwuwDIZDKekPkUoGgFOj6JfyYXrSAXK7B5szmO4XODhPJTp04xx7FwndOnTwsxYGkApUqV4u8OrUsMHvlcwUu3e7w2fO5zC889biN3gVzYuGlDkpJ6AQK+Ffy3alMB3xxop55Izo27RuTPbXv8++Y9I9JnsB0wT3mud+7c4Z12yuIkx0NQ0Fs4O7uibNnyOKW2R5NuKvwxQMJNsXSfJBxu3q3HpHnmTKyb90y8Iz9ttBxN/22ZtZRb3bwrQcteGoyZpsOc5UbkyyVCeKQIN+6YWadD8tNBsLcXxT1OcgL07NGVR9ioxIvKvqb+Lkf/P7QoWSMWzevLkDWjCK/emrBltx5BoeYyBHLz9u4oQ8XSEtRuo0Z6HwmKFv8Zhw4dwK37MXj20sjkl9y300Yp0GmQBq17q9Gno5zbbp+/MmL3amVc+QSB8ri6tTWiadfX6Nq1E3bt2ssjVb///juPkBFhHzFiKOwV0VyqRnEHlHHmHyjF75O17Fa4/9iIPDms77fQwsJoEnFOqWXUiZzDrVq1xYIFM9G9rfkxJwY9l12HjOjZ88NLr0hsPHL4CDKG5oGTxj1BppZvZHbYa53w2u0hN87GLwUjECmOcA5EnTp1OAYhNaAG3UIFC+HRrSdQBjhAlqhEQSfW4K3zEyi1DsgWUhg6iQah9v4IdfTHM++byB5YJIFjgsTjEIfXiJSHYkLvsR+99IAKFomAnzhxAkYt+PHYclXQOJxULGOHyucGxVJ07dKVxWEHowtEOjF0ShWPutWsURNz581lh6aArxN03NJitGHDhli3bh3OnT3HGygFCpoLPcj9JECAAAFfGhnS++Dm3eR3oG/dM7IL08fHen4niXzUfUBxTjQpdvz4X3j29DFkcjlKl/4JOXPmRPMeT/C/QSbUrCJh/kab6iQ4UiQBiaN5ckrQfagGbZtIMWaAnIVIC4b3MaJdXy1zWRJKSxSWQm8ALl/X83ZzcsJsfO5qyU8dNmwIi9J7DgOjB8g5ZqFeezXK1VGhQQ0JihWSICraxF0H9x5R1BfY0FC/ugStG8vQvJsG6TwBRYwbMmTIijNnzmD/X4Y4A8P/hsjRoL0KDTqoMKCbHC9eG7F9vx7zJijYxGBB1QpUXGtChwE6tGvXGmfOnOWNfYqPoFJSKkMbMWIYnj97iME9pGhcR8pTXsTryUk7Y7EOuw/q0byB9UgKEsRv3dOhe+9qCX7fpk1bNG9+EIdOiFH956RLVRJ8l60zolKlCtwe/yGgNcvkyZPhGZUR3tGZ434vNkp4QslJ7YZnHjcR5PgyQSlYHB90fo1MGTOx2Jpa52yLli2weuUajkaw1yUs1jbCiNcuj7j7IKd/sbi4oSCnF3jlfh/ZggpBaXBI8DfR8jCEuLxG6xatUs2V3wf67OzevZu7GWjSRifRsqvRmoNXDDHkajvms58bxIfbtW2H+w/uwx6OEGtkMCi0vAbJmycv1vit4alDAV8vKMuYvlvIzX748GHERMcgU+ZMHLNFcSmfsoRZgIAvDUGcFcAgQluxYgWs3vwPfqtl4pGuxAgIMmLvESMGDGhl1XlLhXEbN65DSMi7HV0qMihXUgyjyR8rlt+GRCpDjNoLzbq/ga+3FE6OBrzyN0GjBQuxw3rL8FNdFTq3lKJZvaRkrlA+CeaMk6NNbw0Udr4Ij3VHtlzZUKdBQUyePAEXrhqs5mBZcPGagUvISIymIoEHD59g7GDZv2KvjosSiCRX/kmCTbt0CIsAXJzMpVpU8kXjbNMX6VC1ghR/ndaDNu7UGvCuv8lkZMfB+NladsTSa0hZaGvmAH9M13KRBIFE5/jCrAXZMokxabgE7fpd5aZaEhUJJHBR261EHI0dK2Xw8RIniCtYNEWB4r/GYsAYDTYuUsLZKeF79+CJEdMXG3kXnZ53/BwqItjbt29B025vMGWkhJuJaYFDLoqT5wwYNsEAX99MceV//xW0MFqxfCWctO4JhNn4cFF7IVT7FoGOLzlf1pI1pJJFI9D9GeSOUh6Rig8a+6NMMGr4pPHrokWLJhFL6f+XLluKenXr47noJheLkQBMiFKEIcz+Ld9XxvA8TDJJCCUCTpmyAS7P8NjnCpyjvVhEJgdFlFMIYkXRccLWxwJlifXvPwCHDh2EBFKIDWby+8j7Cr8e6cNzJhmro9Img9HAo5mfE1R80aplKyhiHZAjrBgUBrMIS+nJEXbBOHzwMDp36gy/tX4CifrKkSVLFowcOfJLPwwBAgQIsIqmzVryJvKQnibmlIlBk01rtppQpUplntJKzD3Wrl2L5csX4/Hjd0IR8bxSxcTMoXbtfIqwcD1y586BPqMeY/R0CdJ5GhEUYkJoOFC2hJhFS5pcKllEzLmxiSMWvD3FXJxLE05OTp4IifI0f7f+Xhrjx/8P564Y+LHY2sy9eNXsiKXJoJcvX2L79p0Y1E2GeSt16D9Gw5w0VgWOuDp72Ygtew2gJKNKZSQYPUAGg8GEtn01yJtLwpELgSEm5Mkphh5OkEjEPMVFhbt0fXIZE9/cudIOQ8dr0HO4hp2/zepJEwizFjg5irBgohSla0Vj69atPAFFIIGEoqDu33+ArUsVHOVlAYnR/TrL8c8lAybO0/JlVE4WHzRJNmS8AZky+rJxgERzC8qXL49q1aqg9+/H8MdAExrVkkKpML92t++TYG7Aq7dSzFs04oM/NVQ+phApWYi1BuJ+brG+vGlPblGZ0ezY00rUCHJ+wZNXc8fNTMB1LL0KxOvomCxTpgy7WRNj+PDhuH79Bq5evgKnGE8uriVRWCWPQqiDP3PATKF547gf3b+d1pGdvE+9b8BZ5QkHjQtMIiOi7cMQJQvDL5V+4ciijwXatCW39NIlS6HX6/n5k1HlmedNKHUOyBCey2purklq/OzcNDQ0FI0aNkKofziyhBSAvdaZeb1lJP7Z/ad8+YGDBz66eC3g44I2gbp27co/AgR8TxDEWQFx6NOnLxo3Po2BY02YNELO4/oW0Nh/hwF6uDi7Jgjtt+QPNW36G+7du40W9UWoV10JJwcRLt0wYOFqHfYeMbAjdnAPCU6e1eP81UBu3qX8pBfP7vCoGBEvyt3adVCPyGgqXbAtsBK5zJJRhKjo13j16hVevHiKevXqwcHBEbsORmNgNyPfVmKQy2DNFj0yZ87CsQFbt25ByaIydGgmR2QUeIefCGrfTjL062J2V5JIGZ+EZ88sw9ptesxfqWXxslxJCY6d0fMu3/nzf8NR8Zhzdanlt1cHOQvTdJ3BPWQYOkELjcY8vmYLP5eTIL2PjHenLeIskY09u3djYDdRAmHWAnLQrp2rQIOOGlRuokbbxpQvK8LbQODOAwP+uUStuTkwefKUJH9LUQUUn9ClS0e07n0DGdPLkNHHxA6MNwEGzlPz81ufokgDEkgPHDjAojfFRZDg/9tvv+HHH39ESEgIHjy8jwwxuZO9DddYL/i7PsFj36uQ6+xglOkRi2hkzJARK1auiIs0oHyysWPHYs+evTAY3o2a5cqVGyNGDEe1agldGJkzZ8aBg/u5xGDL5i0IVr/i34tMYrjGesMzOmMSRy2VHIS7BCBb3izwf/MWbyL9IZFIUKVyFXTo2CHFLgkSw+n1IKKReHQvPpFv2qQp7t66B5+w7GaCDgmLr5F2wQhwfo7n7reRNbQguygsiLQLYUGUdpI/J2jcXaqRI0NwHnZIWEAit6vKGxKjFMdPHGe3ToUKFT7rYxMgQIAAAd8OKFN99eoVaNMnGitmSllQtSAmlgQ+LXcVTJneO8HfkRg6ZMgQbNiwAbWrSvG/AUpk8BHhyQsjlq7T4fjfRpQsCnRpJcEbfynWbn+MYcOG4fr168xlOraQonFtKQrkkbCz9PwVIxZMUtjMviWBtm41CQ6dCMKTxyF4/PghbxiXKFES586dx7EzBlQun5T/0eNc7KeFUinl6aD58+fDwV6MTi1lyJ9bjG5DNTh1zoCKZcSYM05plZsSKpfXY9UmHfcE5MgqwsXrJvTqVYujsu7flSI8Qo967VUsmlLsVtZMIgzpKcfIyRrceWBCy0a2uSk9t2oVJdi9e0ecOEtYs2YlmxXiC7PxsWiyEpUaxqJmKxXn0+bNIcbbIBMePjHi7GVAq7fH5s2rObIqPkjEXrhwMUd3DZuwE5Pn02QYOIbhwRMDPD3dsHHjahQsWNDmY47/+p47d477BKj01dnZmeOuyLBAgumxo8fgEOVmtXzIAuJkIY6v8STdNdjpnWASmxAjjoCDvSMWzlgYxzmJB8+YMQNr165GRER03N+nS+eJLl26s9AUX6An88XmzZuwePFirF61Bs+Db//7oM0luBnCcsNOn5A3ksPWWesBmS/9vQKvXj/i3xcrWgztO4zl9VDi19MayFRD3JzWQ8kJlSNGjMD6devhEZUB7rE+7JYlsTNGEY4Ap2csFGcLLgT5v5v0BI0kFjHiSBbdPyeWL1+OwLdByBZQOE5EJ5BA66B1QcbAvHgqusGCPBksBAgQIOBrgyDOfkOgHNXHjx8z2aCRVFtCkC2QiDZv3nz069cHR09rUKeqCN6eItx/bOKxeWqFX79hY5KTOJdXPbiNrUtlKJLfTNAePzNyGdbTFyY4OwJXbxpx+ATlUVETrhinTp00uxwND9ArXgMvtdI6OQCZ0tsmSURsqHiLxNYJw5UYP0uFzp07sUvh+fNodqgunaZEvlzvbuOVv5GzZINDTVg0wbzT/vbta/yQz5xnRWSV6NL0xTrUjSeeJia/NMZWq7IE63fo+bm8eC3iMgkS6po1a4Vx4/7grC4SpVv0ULPYazQRkabHDS5soBgFW6D7y+hrHh+y4NatW9BoqYjM9oh40YJS/FZbj+1/ijBjiZ5bei1QKKT48ceyvINtLb8rffr0HKNArtRdu3bitb8501apEOPOnfvo1683Zs6cw029tkBtuR07tsObNwEoWlAGbw8jzp4R8xhRiRLFMG6cObtSYrQdmcHP/193QPO2zViUpmOYSG/lypVZGLUIs3Vq10HA6yB4RmRk54DEJEGsLAr+Wn92+c6cOZOLK+KDxGISFckluGnTJowePRrZgwpDYbCehcFkLsYVqlgVbt+5xVEYVLqV0nEaGsVZsngJzp0/Z35uYjEvCLp16xYnvFuwfv16fp+zBBZMQMRJoHVVpYNS54innjcQav8WnjEZ4uIYQt1eo2KFip+1tIkKV/755x9kiMiVQJiNDyowszc5MQEWxFkBAgQI+H5BfIZGxykHkjLavb29U/X3VGy0fv0mtG7VHGXrhKF6JQlyZacNaBP2HjFBpxNjwYIFKFGiRIK/ozx0EmaJkzWuY+aZlAc7baGehVY7JXFDE+Yu1/H0Fgm369auRv8Bg1mcHdFHzpvfBLovQt6cyZ//8+UicdaAiweUWOyn43F5elxEG8h5O38iUKW8JE6gi4gycS/BibNGNGvWhHkOialZMkrYIEHTV5sWAw06aBJs7FsTiOnyXiM0fF9kCnjlL2EzBa0NKLpm3BAZc/m+ozToNxrMT6nc1lKslTlD8s8tc0YRbj0KTSDu3b37AF2a2S4j9XATYexgOfqO0sJvixFaukPLcxABFSsV541rayDhktYk+fLlx6yZM3D+iprds/b2YgQHh2HQwH6YOm0mZ1TaAvFF4qaXL19DtswyLh979kiM7t13I336dFi5cg00Wg3EpuQjmGjDmVC/UX12j9L7VLp0aS7LpggyAkWldezYHmdOn0L7ZhI0r2/HcRiPnxuxdls4xo0bx/yJjon4Ai1x8379+nF2P03N0W36RGSHu8p6RAfBKdYDr0MfcHwc3T/xy5QIsoSbN29i0aJF+HPfn9D/a24oXrwEr6Nq166d4LHRlBQdO77h2eEW7/EQPyaeRy7eJ5432ECQKSxvXBxDoNtzuLm6fdbSJnL4rlu7Dk7RHgmE2figyTi6nIRw6pcQxuMFCBDwtUEQZ78BUKv79OlTceLEqTjxzdHRDr/91pRD0G0RH2ugHVcSjmgM7MCBvYiOjoZPOh+MGdOMXZC04xwfNLKzdetm9GgjjhNmKae0UScVFzismqVgN6hEIuJYhFWb9SxcUqD8oEGDsXWLHmHh0rgyBwd7EahvjPKqksvoonEzGm+j0awlU+Vo1k2DyzeechwC5WpVa6ZCyaJi5Mgixuu3Rs6wJSLYvHlLJgsnT55EdFQM344FBfOaiem/PCvZ3FqDETBpzeLsL7/k4Vwccs/Onz8H0xZEYOb/ZJBKxNj/lx5+W/Vw9QDy5RDjwjUjVCoT7OK5kuODRtNevgEK/fDuPbO8p1YmohKAytR0OgMa1JSifVOzQ4FG2zbv0WP5hrW4d+8OVqxYleTvLO6SnTt3oGd7Kdo0VrBDlwrXDhwzYPK8i2jQoC727t2PdOnSJfn7J0+eoFnTxsiWWYM1s+ziFi90uyfPijHgj5to3drsto6VR8FRa/t4pFEuVxdXFvxtkSYSVUmYzRyQP0EWq6PWFQ4hLvB3eYIhg4ewm9Ta4yWXgCWjLLn2XYLIJILBYGSymprxLCLfVDDhaHBB+qgckBoU0EhjcXz/KXYWz549G40aNYp7nVatXAUnlXsSh0T8sTonlQdHMDirPBBlF4pwl7dw9XLBtGnT8Dnx4MED/pdcCLZAxF0Z44Tbt/51gQgQkIZATnfacKJFOLn1PzTTUICA7xFv3rzh89Pu3Tuh0Zh7AWjEnrjS4MFDkDt38pM08UEOyRMnz7D7cdvWTbh4MwBOjk7o0LEuO2utbR6vXLkMlcrK4oRZ4l4teqrx7IURk0fIOX+VuBjll1Iu/4Q5WsSq3sZFP124asRPpcy81vHfaM+QUBOQTCcpGQCIxxKnHd5Hzlxx2bpLqFtNjDeBQIf+GuTMKkLxIubM2KOnDCyQFiiQH1OmTOFN2kePHiEw2ACjUcoibI6s5sdAt5scHP7VF0mjI877448l+fUi7l6ixA+YNO8a/jdYiv8NFrNIu2StjmO5fi4rwp9/mbgPwc3F9gb681cmuLu/KyglsZ0QP3vXGsglS3pfsUJidGstQ5kSEr7fvUdoPXAGdevWwqZNW63+LW1cT5gwwSw8dzDzS3bCXjZi2qKXaNa0CTZt3mJVoKWJpObNmyAk6AnWzVdwbJdFeLz7UIrB40LRsEE9zhaWya2XfVlA3JVAIqqtUlo/Pz9eW/jNNd+XBRRjNuV3CYoVFGPwuHWoUqUKqlatmuTv4xfeSo2y93JTy3tApoGUYv/+/ejWrTvkBgU8IjNAoXOAXqLFg/OP0O1yNy7Poqk0y+tEE45UTEsmAWuQmGQctfDW+SlUkhhoZbEIc3kLvUKDFUvXfdbSJtoECgkNQSaNWSS2BVorvAq5z59zIdpAQFoCbQCR5kPaDG1y5s+f/6P3ngj48hASldM4qI2yYYP6ePvqb0wfLcPfe+xwdIsdOjbTY+eOtahfvw47EFMDCkqnHKRTp/7BlSs3sP/AYXTo0CGJMEugTCWVSsMlAARqku04QM1lCtuW2fGuPwmzhHReYgzrJcf4oXIeyaKToglirNqsezeW9ZOEs4x2HrBNlJ48N+LiNSN+rWQmP5Tt2quDuXyBirWOb7fDwkkKHu2/+8jI5NjFGchfoCCTop/KlWY3ATlniRiHhJnFz2yZxXGEPDmcvUSk2ezgLVoAePPiLwwaNBA/lSuDoKBQvHprQJMuWnQaqOYIhOKFxdi10g5vAkxQqYHdh2w/t+N/G+AfoEOtWrXifkdFFXRfp85abxImnL9qwOUbRozqL8fccQoUKyjhRUeWjGIM6SHHhgVyXLt6GUuXLk36fM6e5UbVqaNkfF1LdAI5RogQ71ghg0YVzJlT1kBZww72KqyfL0vgKqHHXKmsFOsXyBASEgapXo4w+wCbjbd6kQ5RjsFo1bqVTWGWyjJot98t3MdqSRaJgukis8BoMLFr0xZy5crF/0YrImxeh8a21A5RyJc/eaKXGLTxQMKsd2QWZA4swKSWyKBHbHpk9S8E52hP9O/Xn8tJLBscz54/g6Mq+fwrZ40HdFINHqW7gmC3V6hRrzr+3P9nso7m/wq1Ws2FZHv27OHjgxwJFljeG3p9koXICIk4eae0AAFfE+hcSWPNRYsURZMmTfg8Qe6oZk2bsaNJgAABKS/lqVOnJk4c24H+nYET2+1wepcdxg6S4s7NI6hXtzbHB6QGLi4uLB4dOHiEuenJU39j6NChVs+BNL5+/fotNKkjjos/6DdGg9v3jZzN36KhLG6TnByqrRrJ+PcEelx5cufAYj89c1UCla1mziDCpj3v+Kq17FsSeWn834LubWXsTC1cQIpty5TYslSJQvnEePDYyEJvxvQiODrYYfr0GWjYsN6/RThnERRiwOnz5vMu9R54uBE3tc0BCbT5LxHTaD1QOL8EBvVlzJg+iYXZS5euIFZlxKD/abnIloRZ4ukHNyhZAKbHuG677edG5opDJyjCq2aCje6MGX04csEWyBm8ZJ2eYxQ2L5bzmoBEZnLUtmsiw941MsjE4Rg96vckf0vi2ejRv6NFAynmTZDH8UviliTwblwkR6F8JgwbOsjqVBjl41Ie7rr5MlQs865cmECTdcRNHR20MKiNiFFEQC2NsfociOuEO79lAdiWMEv3v2bNCn6e8YXZ+GhWX8ZC7dKli22+XmSm8XD34MeTHChSgK5Hn4mU4tmzZ+jRvQccY1yZi3rEZGBuSlFUmYLys1t3xYoVbDax4NKFS7CPdrFa+mWBk9qDyDeeel/nUt8iZQtix84dKFeuHD42SIy+ePEic1MqSib3dhJuKkp+DUdRYATLNJ4AAWlBlKW1doniJVC/fn3ekOSp0l8qcwyigG8LgjibhkE7J926dkbZkibs9ZNzERaNJRGJHNRdjj2r5QgPfckZnJ/yMRA83EXYtk+HMrVUePjUhO7tZHB3s34yb9lQCh8vEQvLTZo0w6ylOgwZp2FhN72PGIXziTBlAWWIJT3BmvPFNPDyEKFWlXeuR3ORFe2GG1lUpAKvVbOU2Odnh82L7TCwm5zHfzp0aI9sGd9i1yolLuxXcqHXoP9p2CWaPYsYpYuLsWydjkm2NVy5aRZBa/4iweXDdti+XI4/18pxZrcdfq0kZnfAxGFyzBgjZ9GYHBGzx8k5UiE8EviplJgLw27eTUpmn74w8nMjct20SSN0796dX19aeFSp8guWbTCyw8Mapi/Ucg4vOYetoUQRCX6rLcbmTevZDRYffn5rkDObDE3+FdgTg/J72zUVYdeuHYiIiIjL1bp37x7neO3etRPtmoiTFJHFJ8GVy0kgk4iZNL1wv8sj+fFBxQqvvO/B0cUR7dq1gy2wUGg0wEXlZfM65IZ1VLli2dJlcY83MbJly4ZyZcsh3NmfR7CsgUoeYkRR3EicGixetBhOere4+IH4IILrG5EdUpMcq1atSuA+ed/ep8UpQa7bK1cu8xgnFbx9TND7SiJ8saI/oGXLlnwMkuumVMlS/HhpAUJxJCS6RipDbN4O55E5RsAnvQ/q1qmLPLnzokC+ArzJQ597awspAQK+JKhcsHat2ti0bjOcgr2QI6gocgb+wEV8l09dQ8MGDXnDQoAAAe9H3z69oJCFY/86GXq2l3MZVNZMYrRtIsP+dXLkyKJF1y4dk/CRjwXqQiDQhNXVWwaUqxvL0Vq1q0i4WNYaihaQoMYvElw4/w+6duvJHQmteqlx856BTQZ1qkqwa78Bh08mfcx0Ths3S8vO2TaN37keSYQkMZa4KYuKxSWYO16JPWvssG25HeZPUCAqWoVGjeojKuwmlk5T4N4ps4A7aqqWp7tIPKWC3K179QmmveIjNMyE9dt1yJJJhH/22mGfnwI7Vshx8aAdev8bHdbmN+LFChY5qehs3FA51mzV48AxI+r+KsGWPQYWlxODoiAo95b2Y6dOncyj7yS+0/Np1aoddh8y4ulL6zxq5QYdjAZwiZrFqBEfZNro11mEU6fPsNM6sbhq0Ot4PWPNHUbi8sBuUtx/8JiLYS3vA8W70eTD0qULOUIifsRZfLg4idChmRSQGrjjgLipSmZ2yFpAZoI3bo+4mJYmEZOPlXuOBjWSF/wa1ZLg7NlzcZvziUGCYes2rRHlGJKEJ1tAv49yDEWbtm1SJTCyYcEggm9YDqv5upQnS1m2xGEtHM1oojHB5G/X8s706NGD+d227dtQrFgxfGyQiaRsmbIsThE3bdOmDW+kktOXXNIkbGfNkpUny5JDtH0ofH3SswuaeCnx03p162HHjh3MgQUI+JpA58hOnTph+rTpMLyQcL5zroDiyByaD29uBfHnjnLKBXw7EMTZNAzK04qOicGUkTImKYlBYmPfzmIeY/H39/8kj8EiDvlt1WHAH1oWN+mcbnG1WgMRNNpdPnXyL84yImzcpcePNVVMoGnUizZD67VT8ZjZ9TsG3H9s5KKD6i1UuHXPyATW0txKINcsaVw2ehpQviQd6iYm3mvmKFC8sATubmKM7i/HsdMGVG+uwoYdOjStK8XNe0bO7YpPgomokIuhbV810vuIMHe8gomdBSSKL5ysQKUyYixdr8NvtaXInlmEn8uK8eQ5WNClv1k8VckO3Xrt1Og5Qo29h/UcfTBwrBqVm6i4FZceX5UKYt4ZrlSpAru5hgwZhjcBUrTurcO9R+9IMInI5DK+dN3IpWrJjTdQ3EFAYAg3AcfHpUvnUf1nsxvBFmpWlkKt1vKONY3elSxZjHNgaTRfpzegbMnkCWL50hJoxBpkDs0PrUSFh96X8cLtLvydn3DR1SPvK3DwVmLzls3JCo7k6CRQxmxyEBulUMWq2aFqC8NHDIdeocVrz/vQSFQJdtUjlEHw93iE8j+VR8WKFZFS0OLi6rWrcI6yLR4TKXaK9MCunbv4/6lsjaJDSAxODlGKMHh5eqNhw4ZJGqk/FgEgV9K8ufMg9bdH9qCiyPO2FLIGF4LqmQG///47NwDTfdesVRPhrgHQi60TWWo11phULN4/OP8E9v5ukL12wumDf6N58+YYOHBgAjeuAAFfGuSY9X8ZgCwBBeEVnYnbp8mdT66izAEFoIx1QtcuXeM2JAUIEGAdNJp/4eJljOwjZvEtMYjnTBgmwctX/ux++xSgnFqpVIIzFwxo3UuNDL4i5onVf04+yoimr94GBGPwYLMQd/q8ETVbqvFDtRj8c9nAOlWXwRr0H63hKaqHT43M4xp1UnNsF02GJc6lpfu1FVVPQrFCDqTz1GLnSgXfv0wm5pzc129NqNIkFnOWa1GuFGWKAs27q/DgSUIhlDoemnVXQasD1s9XJOhscHMRsbg5oKsM63fqUTAfmRAkcHcFihcSY902Pfp1kWH2WAVPwFEebZMuKmzeo8PB43pMnq9F2bqxuPfQiMVT5OjSSoZr166iYsUKLICSQJYhQyY0765jjmwR9ehfmuhavFaPcqUkXChmC2SmoHVBYic18c2SRSVsxrAFKt11cZLiwoULLD5WqvQT59xT2dezZy/jYils/n0pCQwmE9JFZuVc2aeeN/HU4yZz01eu9/HI9zJincJ5Mzw5J6iFm9KxnRyog4NeorZtW3KPgTXQJrZXOk+8THcXsbLIuCkl3vSWRfLvvX28+Hqpwa4duzhvlboMbMEl2gsPHj5ggZtQpGgRqByjkp2UsnDX1q1bI0eOHPgUmDVrFvdiRDxSIUtwQeamOQKLQRnoihXLVqJ5s+b8erbv0J7FWeqgsAZ6PSMUofB/+wZnDv3DvJT46f3zj9G7d280qN8gLtZEgICvAVRyd+yvY8gQkge+kTlgp3PiTGXKfM4YkgeeURkxadIkzocW8G1AyJxNwzh69AjKlpCws9EWGtWUYtQULTt+SBT52ChTpgwyZEiH+SsDmCS1aSzFmQtaSK3skMcHbfbqdBrMGy9HuVJSqNQm7Dui5zGy6FgJjEYDi40bd+q4VIFA5QXVf5Ggb6ek5JccEYRihayTjiOniDQCI/tSodO7x3bxmgE0FeTrLcKwiVq+DoEKHQ6fjEXFskRiRbjBArGJc1/3rlEw4aeYAnulCBXKSNgdQbfbu6McDTuq+f6iYoCc2cTYuleH3NlF/PqQALpliRLrduh5hGzPITM5S+clgk5HJFWC0iUkfJuD/6fB1n0B6NatK7Zs2YqNGzejS5eOqNo0GIXyyuDmasK9R0BgsB5SqTgFpNB8eeKdYaPBANl7vgksl48aNRLBQf5oWleEmlWU/P7s2G/gMbrkoNFSgIUI9jon5Az6ARF2Qey8jFaEQSfRMKmjLFlLsYItWIqvKP/LVuYpkUjKrlXo7HDo8CEe5bJWmEU7+2vXrUXXLt3wWHYVDgZniPQS6JRqFhZ/rfor5s2fl6rCAAupkxmSz9miy4NjY3hU+ocffkC79u0wbco0aKIzQBGv8Ta+s5icFP3b9ftko1i0sDl+/DgyBedNkAv8f/auAkyquu+e6W2WXbobJERSVFQQle7uRkFAkE5BQLq7u6VTRECRlC7pbrZreuZ7zm+cZWNmgVdA+Jzzffvwujtx7524557/+Z3jY/aHT4S/FD9w5I3tuxRqDx08hNv2C0gTnkVahSk6czspzIb5PZALgGwhBeX+TqSJySKv/ZrVa0SE79Wr1yvZFw88eBFwUYVZ0OkjcrqMS2HxXYbwnLiqPSHuGgoSHnjggWvs3r1bBLMvPnF/rqIomT+PBr/88ouM8r9s+Pv7SwHnojU7RPycOtwLn9bRx5dfuQN5JtG1Ld2q3nJ7ju3PXGzBmfM24Yic/tp30Iqftj11mb5fXClZo+U/Skym7ty34exfNjSr45pkUWhlGRkF1ITTR8dOO3hViTJKTFvgyIZ1ulgr1NejZFGlGDBu37Xh8AmbbOfEoRo8DuX2mqFSA6WKquQ2RLsmGul7mLmYk2FAhnTMmXVEdDWvqxH+Om6wVqbQFq8xo+dQB6ljQS/F5Q9LKvFBKTUqfcauBwX6jDChSeOG+PPYCaxZs05Kt5p0OoccWTXInsWOuw8UuHbTLP0XAf4pE0SW5HLiLSk3pcD7rK4FcmqK1ps3b5JprqoVNBjURYfQCLvsA49tSnD+XWPTIlfIu4j2CkeE92PEaiNhUutRpHARyZJ9VokdC2d1Wg1OnLGiTHH3b7ITZ21IF6zAvXuPxDjD8q+kYPkyYwFaNG+JKzgHb/hBZdDA6mWGHjHIlzc/Fi9Z9MJ5qZFRkQi0pjxtpfn7/LdhwwbhZzzXbdmyBdFeoQgwJDcF2BRWyZhlMW22bNnw6npVxiFtdFZZOHVCZVUjfXR2+BtS48TxEyKgf/PNN9i0aTPOnDyD4IgsCNSnlWk6OqD5uj5JdUfU8YyRuWXhNT6uIZbCbTQunPkLHdp3EKOIJ8vTg38bNLEsmLcAAXHB8Dcm72vh+5efiZiAMMmHfhWOdQ9ePzzi7FuMuLhYZEid8rwJBTudVpkol+dlgkJR7dr1xVLPFXVnbivHwepVc82qSLiYrUpS1GWgCVkymtG4thrN6mhQo6Ia1VsYERLmaMX9c4cPfj9iRbseRrRupMbg73Quow4mzDFJAZi7Jt3te6wy2uRsoz34J1f0zbId8t/HbKjwsQpffKwSZ0FEpA1NvzHhwFGrxAxodUCm9I5SiHrtjXj0xC4RBiTqWg1Q40s1vu+pFcLMBuDhE00yXnb6vBVPQoHSxR3CLMGcs/ZNNWjXRI3IaMD+dyZuvo/iMHmuRZwMjWtrZBTtp21WHDhwUEqY2Ph7+PCfIiLw4odjPDVqZULDhg3RuXNHHD9zE+0cvVsucfyMVQh4UpJZ4J2C2H/kCL77yv19eXHCzY8Iv49NixxjccS0BSaJhtj2q0UcDu6w+WcrvI0OkY5EKSguo/xQzKNr9sqVK88UZglmfmXPlh2hhvvwMQW4zMEiqTZq4pA1tAAepr0mDuSuXbu6fLyyZcvi+IljkhO7Z88eOaYs/6lRo4Y0JpOgclSqfPnyz7V9JNWESW1IJEomBUk/YwoaNmiILVu3SHTCqpWrcMt+Dl4GXykQ8zb7IcAQLEL04+CbyJI1M1q3bo3/Jato165d4rS/c/sOMmfJLBcEdJY4iyT4mZw/b76UjbkrbEsdlwHRASFSXrZ4yWJs3rIZ3b7thqN/HoVaoYZKoYHRpoeXzgswApnC8iQ7Bny9SIiNaj3mzJ4j4zjMrfPAg38TXLzkZyCluBQ6FbggRDHJI8564IF78DyaKoBOz5QXjOnefFXclGjfvgO2b9+G1g0pGCqQLo0Cvx2ypuie3XfQ4hA651ixYKX+74JVDbYtVaNZFyNOnrOBJsnfN3hLfm2LrgaJbFg10yvZ/vI7ZewME/x8gJqVXD/nxh1m4VaVyjv4E+O85i43S5Er8cvvNpQqqsRnZVX4oKQS6YKBCg2NOHPBhr8uO0TZXNlZuGvHzMVW/HUlcW7sx+8rMbSXDnlzKoUjr1hvQauGGty+a8e6bWYxFjgjyMhR2TPAH/JqCsLMu23cyYBf/7ChZMU44ejd26uRM5sCN+9ESd5ho0aNsG3bTokW4OIVi5nezxmI4SOri7D3+941UnbrKtaAuHDZCr3BlmxqqkCBApgz++cUC4LPXWRvhAVhERela6LaF47jPGkuTSKQybSvW7hXeLfvtkCrUENr8ZYFZnIu/hCXMxzF7Vs3n2tSyc/PD9Vr1MTitRvQqqFdcoyT4nGITSbdOrbQ4I8/IVFhrsRZgkLnnr2/Yv/+/XKMOUFHMbZ69eryvmJ8gLe3Nz7++ONnCsdOpE4dBGO4w+HrDpxsc8ZnsbCPXJiLHLt27kKY8SG0Fi9ZwEylTyeTU0+CbsHuZUH/Af3xouB+HDt2TAqoL/51EUHBQdILUr9+/URZuozUYikZF/ddwcccIF0OSxYtEa6/atVKmYTZtHETngTegkahg9lulPc3X5WgqIxI7aLgjHw1fWhOHDh4QFyINE544MG/CZZD3ntwD9niCrq9Dd/VflFB+Hnnrte6bR68OnjE2bcYmTJlwdmTJ+UE526FjyNXRpPtlZQGOVGoUCH5l1EBXPkvU1yJ6YvMqFrB0YSbFDv2WHH1ph0B/kCh/Epkz6zE1PlmLF9nwYoZXujZUY1+P5owchpjElSoWE6Nnh1tGDfT4R74qjmJtlKKGjhGNWqaFddu2tGns9pliQFHzUhk3yvsEBRZhMDc13fyKiRzK3MGBW7ctmP5ejP6H7Cie3uNCLc8pHQL5MyuxP2HdtluokENFdo2VovYGx4BrN1qwbSFJpy/bMPq2Q7x+OZdu4iWdNt66eAyJ4yvWWDAUzcER9JYbMZrlXnLzTh83IoSRRQ4eZ6ugM2Sd0UxjWSJPwnRpElzDBs2RNp2eWxcOVcXrrLjs/LlRWxMiGbNWqJ9+z9w4KhKRrySgiR9/gqbkJpendSJBHA6MLJmVGD1Jgua1NYgX67kz82Rv3OXbMgak3zF3vz3WPzRo3/Gk8+EYAQDc8fu3r0rRJSuzd59esvq+KOAm0gXnQ3KvyMO6JiN00bhXurL8DEGiMiosXshNNR9Nirh5eUluar8oXOD7dIUTQ3GpyTW18dXxqX4GmhSsHKkT58e75d+H+cPXUQqfRqX4jEzbiO8nyBVXDoYEYMffxyJ2rVrISQkFBaYRdg1qYyI8HmEB6muM2wW771bDPPmz5UIhBcBR9OaNW2O23duiTNYZdLi1rl7IjBlyphJnMO8AOJxvnX7FrLGFUiZAEQHxQtZ2bNnx4aNGyQ7jb9zCtvMBd60Zgv8je5dHUGxGXDF7544R0jEPfDg3wQFIhUXGJ4Vl2JVx2dZeuCBB65BkY3FpiFhasl8dQVyksvX7ChWOvMr3Q46Q0sUVYow2KS2SqazKNa54knMTd240ypCLk2cFCk53s/YrdmjdRJZ8GUjvThmGZHFcquJP+jQupsRLb81oufXGuGZ5HYUHCfNs2DHr1Z8WkaZTKwjr9qw3YIFqywSx8XpJPJZFuqyzLZbOw0KFVBKYS2zZkdPN6NhTTWOnLBArweKFlKi5LtKmdBiTixTglKnApZP95IpLfLJnXusmDLfhNpt9FLQy3JcumDpyo2NA85dsou5gPFYGk3i7WNpl+/f69HhEXYRj4sWZIGvRQRuuqIfPbFg06YNIs5yn1meyJ+kDuYVK1Zgxx5VvHCaFHOWWZA+XTBKlCiR6Pd83EmTJkppcJc2joXkhCAPmbrAAq1WicrlFYken+YPmikopnMqz9Vz07W8coMV/tGZZDoiIawKq7hCI6Oi8ccff0hUQkKwz4AL3uQ/3HduO2OhtmzZiFbdDJIlnDDKgZ0SX/U2isjcrJ4Gdx6YcOVOCFICp7YYq+WM1lq+fDl69OiOR4+exN+G0R1Vq1bD8OHDn+mkrVe/LmZNn4100dndnutY2utl8oXW5oXBAwcLpzt39rzwVnJTq9KCSPUTPPa/Hc9558xdgsKFC+NFwCJcOlT3/7Ef3vCFRu+NW6r7+OOPAzKiTRcsRWHi1917RHxKqZSMC6s3w87J6/Huu+9KKe+AAQOwY8eO+GsLRncNHTIUwbGZ3D4OR8UpBLMUzSPOevBvw7l4qbalPEKgsmlgMD6N5/Pg7YZHnH2LQcckV6n3H3HfDrpgpRlp0gSK8+9VQUcVkrlDsRRcFZI7O2W+Bc27GDCsjy4+jJ/RBeu2WTBkvEkyadMGM1bAhjWzdfjuaw2adjKIC2HbUi/0+xHiWK3aTI/m9TWo/JkSMbEaGbdats4ihN9socPVinfeyYeyZdNj/MwDUKvMaFxLLY7hRatNGDLeLKvnuXIoJKt2x16LCLPftNagzzeaRKI2Bde+PxoxfrYZObICP6/0jnfaslCC4uyAbzX4usVTkhiU2iEWl/tQhdqt9ej1gwl6A0R0ZTHaqAFacdf2H2mS8baEWWAJwePC/WVZRoa0StSqpEajjgYRsEmeSS6e9V5YMH8Omnd5jNljHMKxEyT3vYaZcOuuAiPHfpPsvmx8/PDDMmjf60/82M+OqhVU8USdJRYDRllw/7ECCqUjJiMheIFz665FBO767fXo1UmL2pUdbbwPHtuw9CcLpi8wI5UhWEhPUlCAVFu1sMAko8VOcsnsqL59+mLtT2vFlcn8R6vKImMbWbNkFfJ15vQZGVPiSD0FWkYZGDSxIsxmDS8gYq1ZYUgmRrsDiVvduvVkPIqCqBNKmwrWCAWmT5suDt+5c+emGC3Q6ZtOaHm0JZ7430ba6GyJCCUJ7r3Ay7ApLUIQ48xR+PXX3fJDMTdPdB5o/441oKs4xO8uInweo0HD+i9c/sWCo3p16yP6cQxyhrwLb4tf/N/YSvzQfg3169XHrl92Pc3ytamfmeVrsfJC0Ao1ZwlZ+PbOO/LjBBcS1HpdikSaLkStUifuZA88+LfBxUur3QKjOk6+a1xBvk+8HQsQHnjggXtw8fiHH4Zg2U9mdOuQXFQjNv9sEcdjgwYNXj03/TsmunIFtYh5DToYMGaQFh+/zyJZhSz0U3DkqD65DLlg/1FmdG2nwbftNVLo+lUfI3at9MY7eSFlWm17GFGrkgUNa2hEtB0/y4QarawikGo0KjwOsSJ9+jSoV68cfvrpJ/QfaUTn1hopvWVWbbueBtmugvkUIpIySqvbYKPEI8wZ45XI2MAyMJoH+o4wwccL+GmeF94vpooXeSk+fvqJCjNH6eLdqd4qR8/AZx+rULetHt8NMeLSVS7eK6SjgEVYtSqrUaOlATv3WiX31RUcnQ8O88MXn6jRoIYa9dobsHu/RbhtWOhTodAVWCD6xRcV0GvYPjEssKDLybt5TTB1gVn47/Dh3ZPxKn4vf/11R4ydMUOeiw5mZ3wXC4THzjCLM5ZoXCtxHA33MzwSIiLz9WNXQ7N6LCPmtYSjq2HUVDMUei+kiUm+QMD4JbvCDrUSuH79erw4S0F49uzZGDN6DExmM3xt/nI7Cqf+fv6oUOFL7Ny5XTo0OI2XKb0C1245jCSMT6MBhdcvd+7Ryepw6D4PmPc/b+48MY04oVHx/WPHb/u2onbtM9iwYXOKAi0nPvgYNDBkCcuXKHuW57dQ33uI9YpA5vB80Fm8cT3stHBijV6HHOFFZJqLvI4RARRxnwTcxkdlP0omqj8LLMHlFNjxI8eRNayAXBs4+SING48Cb4pwu3LVSsn5NRoN8LO7n0RzTuQRTi7rjJpIOG1GNzB5J/mnO3A7uL8eburBmwDndZ9BEwMvi/spQ6M2FhkzvNyCaA/+PXjE2bcYPGmVLlUCXQeexsKJifNWOULEFW4KmYMHd40nqa8CpUqVkqyljTstaNNYgzVbLOJWuHnHLi4DCoUc2yc5iohkW6laREsWDTg7gTKmU2LmaC983kCPfYdskhHWtrEGYZF2LFnDnCzH7ZitWqxYURQsWEhOvDwGfH6Obg8ePAg/TlmB8bOsSB0I3HtgReuGdN1qYTDahSyNnGzCu+8okwmzBEntqP46/H5IL45epzBLLFlrQZaMjjgCV8ifW4nm9TSYu8KM3NmB67eBfl00aFpHgzi9HWOmm9B7mBELJnpJvlZCcIyNI1hVPlcJcST4WrZtosH0hWY5llu3bsStW7dgt3OMTS2Et0mTJvFf3HQnrFy1Fs2aNcaXjW7hg5IaFMhtx+MQx1icRqvD3HmzRdRM2hJLkW3hwsXo0uUbdBmwG8MnaZA/tx3hEQqcvWiWC4x3382FUyf/RJNOBmRMr5DiiM8+UokQTlfJuMEaiYgYMMqEwWNM4lhgZIM8vtkLGcPzJBPrIr1ChABzXD7c92G8I5Xkl+Puu37+BekjcsgYPMVXyZLVxOCJ7RaePHkiFbG+hlQiqJAcc9SKYqifMVCei8Kt2WaWMbDnAVfZTxw/Di+zrwinDF0nCeU2kohqzF74+eef5UKLYrg7cCxr0KBBGDZsGKJ0YTI+RQGa20mh1ao0I0t4fke2LHPk7ECAMRiZIvImOkbcn0yReST+YNgPw6QIjK/z82LBggUICw1DzsdFJU8tIXiSz/K4AG4oz4jYzNZatUotAjdHxNyBxz99uvTxwqy78T67JuWyLxussNgtnkgDD94I0JEfmCoQYbEPpHDBFWJ1EdAjVtxcHnjggXtQIGrVqg0mzZuDrJkVqFMlcVkpY68GjbGiatXKyJs37ys7lIwZ4uL9xp3XRXycMtcsnJPRU806G5Ets0LE2Dv3mZFqR4l3yUN1OHbKUbhlsUAMB1NH6PBh9TgsWm1G6lTMegWKFVZj3gpm7j8Vg955Jz/eeaeQjKRzouyLL74QXlO0aFGMGjUCy9cbkD2LCrfvWfB+MSXGD9GJeFe+nqP8llxv2o+JhVknyCX/OGLFiXOOmAMnKDJyomxID63L2AAW13LRnNFgpFgs3P3yUxWG9NTKa8LpMD43953CcUJERtvFWMDjRL5H0A36/XdatOrmCMG9evUamjZtKhlfLFPNlSuP9Fs4J+r4HNOnz0SHDu3RpvtvyJdLgw9K2sXEsOs35qBa0a9fP+FUSbkpwb/xMSbMmokZi2woXkQBo0mB42cscl1TsWIF7Nr1M4ZOMCJtsFLMBRSdq32uxtDx3HagfTONREVQCGZMQ0ysw0FM92j2kPzxwl7CxevQwFuo+KkKO/dZ4+OfiFmzZjlcqjEZkT02M9R/cysupj8x3JZpIJ1Og9LvWREWYRc3dtogBcYO1kr8Gfn/9Vs2HDpuweTJtZ7rfczHpKiqtWuROjoT/AypxUAQrQvDxb8ewNvPgkcPb2PUqFEYM2aM28eh2L1g4QI0b9ZcSnnJvbkYyXgCclPGgTHXNdXf2bIUbxV6FbI8eSeR05bHK43su0YMQozlYuza84LdBiyLzRZaEH6mxNNg5KqZw/LidtrzGDVylMR+8fN0PyJEcmFT4qZEliyuow+c3NRsNwv/TKkUzaa1eripB28EqHMwz/nPfSckSsSV6YWf32ifUHRs1vtf2UYPXj4Udme1pgf/GGfPnpV/ixQp8tqOJh2VzZo1wenTZ/FhSQ0+KAHEGYBtu4Hb98z46quvRCgiueEYCfOLOOZNZyIdCwlzff4Junfvht2/bMBXzRUYOcWRecWyL61WIaIdV8izZlbKqnvOrEoR4L5oqEeeHErMGvN0xbtee72skB89acO88TqJNHj8xIa67Q2IjEmFDh2+EpHK3Qn4wYMHMgY/ffoUlP/AhOkjdfEXBR37GrD1F6sIh0SOLEo0qqVGvWoOp6cTHAObMt+My3/4xJeHvV8lTjLD+nd17QIhOMZWsbEBn3+ixIGjNhz/2Sd+lZ9OiZbfGuRCgAL2ByVUcky2/GLF8nVmETzXzvWWZl0nSOo+qaVH0ULMrmWGFaR51mbn40EE5549e0nDqHMf+bqSyK1duxoPH96Hv38AKleuJq81L5g4IkECTKejqwzV8+fPyzgPXayMERAyt2Au9HoDPinDzDYlLlx2FFxQwF44UYt+P5qllXfqcB0K5FVg934bYmLssi97Dlqwa69NhMbUsRlEFKQ4ybH+GK9wpIpLC9gV0GYHjh3/U0Q/ZmmR3FPAdGZ/JQQF01sZzkMXoIYh2oSsjwrEu00Tkus76S7ik/JlsWTpksR/MxikIZrvFZI1usq5kl+qZCn464OQOSJfshOgXhONW0HnobJrUKBoPuz8eSeehfeKvoeIB9Ewqw0iJCtsSqQypEVQbEZ4/e3OC/N+gIeBN5D7yXtuHXtmpRFX05/A8BHD0apVKzwPuD98ftsdDTJG5XJ7u0f+N2HKEI2z586KQLtz4y7keFDEJXElAbiW4RS69+iG7t27u31M5vR27tw5xX0K936Eh6mv4+DBg6+sROJtyJKi2B8TEyOjgXSbvWjBx/PiWZ97DyBuqB9++EFau/kZTfgdwKKQ+2kvo2iJdyXK420qCvkvvvb/Bhd7k/FvHA9Oonz3XXesW7ceeXNqULG8Y3R/70Hg1DkzPvmkLObNWyAiCBfXOTrOeB2Oi5Ov8DvxZWDZsmXo168vRvRlXJZZ+F/DmipU+UyDzbssCI+0IzhQIfyueBFHJEHv4QbsO2jDkW3e8Z/1kVNM4l6luZNO1n5dtZKT2qGXEfuPAG3atBVuygVwV+D3PM+NM2fOgNJ+Fz+vfLpQv2StCQNHmyVGgPyXObCcQHI6PZ1gBwInqrYv85JCNaJjHwMeh9qxbp53Cq+FHe98EociBZT487QN6+d7xXcEPHhkQ912BjERtKivkfgCZufSSbxwlRlRMXasnuWFQvkTmz9KVIwTw8KFy3bhpCwn9vdjtwHzby2oWbM6JkyYJLFRBDn/gQMHsGzZUly9egkatQYffPixlMHmzJnzmd9T5KSrVq2S0i86bHmfjRvX4datu/K65c2lxINHDocqs4znT/DCgT+t4q4d2E2LetVU+HW/FY9D7HKc+e+0BRaooUKq2AyyqG9X2KSoNtr3CfLnUaBxHRUGj7HIe5OlsrzeKl6sOPwj0iJDdPKSWcrTdKWaA/WwWPRYMFGLch8mFn4ZEdH0GzNCIlPjjz8OxR8fJ28jJ2LHBLkwOxa4eFG4YGEYw83IEVpYOGii11Zpwp105+CfxoioWA1OnjyNgAD3C+wE3aR7d+6DFVbYlFYxCNC9yvOeUyyVPoi0J5BJirNcfxa5vzcynkaVOpUkRuB5QR57cNcRZHtYyO2EFV+Hu6kvCVdnVNbgQYOR69F7Lgs7uShwK/15lCpXHCtWrnD7vCwHpqEnU0QeMX24gkEdh+tpT2HGjBmoWbMm/oug+YVZ0fyX7yUWNubK5f464p/gv8hPXhTM8mbsXkBsMDJE5oqP8nNeH95LexneabTY99u+V3YN8SrwX3ztzz4nF/M4Z99y8MRev35DhIWF4MiJhzhy0i6rvMWLl8K0mX1k3IRZkCNGjMDq1SsRR+X2b8yYPgWdvumKdu3a/eOLzUGDBuP48aOYMOuWFBAw72ruCgs2LPBClQrJ32a79ztGpb7vkZhoUKzduc+CtMEKeRyu6DPKgPlR/r4RmDhhjKwMV6z4JcaOHZfsi4hO0oIFCyImRo+OLb1kv0gKf5xiFmE2ayaFOHeZ+8Tcre/HmbBglVlyurJkdJBgOmaNRo5cMXfL8bgms4PQpQRnppjZBOTJqYwXZokPSqrkWEycY8bA0Q6HBME8WhLwbu214nBICK62E2cu2MXpS1HX+Rx0P8xYbMbo0aPlPdChQwf5PZ0ELBhwVzLwLNDtMHTo0PiG1Dq1a6FsaWD0QC+kT3CRwGPXqZ9RCjJI3LsNNqF1d6MUU3BUkMdu9jI7IqNsIkxyHP5RwK34uAC6UzNG5JYShrtp/0L3Vt3j3ZiLFy+Gj91P4gpcgav2gZHp8UhxA5kzZcYNnIV/bLA0tpIsxniHIdonDPny58PkKZPj78f3AXOspk+bgajoSKgUKljtViGHvB//nw2ursgiXbRBsZkQ6ntfhExeaFHYTQkc9/p54y/I/aCMkH6eUJM+dpR3mLhq3YmYBEewfOwBOHfuHJ4X0dHRCA0LRRZTvhRvx1K10Jj7EoHAHN/t23fgXvBlZAzLA3WCiwAS9ftpriAwdSq5mEoJVapUQZrgNHhkvYEsTwokIhLOxwpLfQ8VKlT4n4VZXszzIsZJHlmKwcWEtwEcl/v2227444/9UCs00LBIzW7AkO+HoHmL5hg8eHCKucYevBrwO5Tvp5kzZyIy1WP4RgdCYVfC4BODGFUEihYpKq6jt0mY9cCDfwvMy6xUqTIuX76Eixf/Etcjx/1z5MiN2bN7SCElb8NFkdmzpuPR46fZ8FOmTESjRk3Rv3//f/y9Tqf7nj270X/kLxI31aCmGqs2MHdWi+F9kk+UMX5qw3arRF8l/KznzqGInwaiyWDVJjPGTjeLMEpRctnS+TKFUrRoYYwbN1F4aEKQLzBDc8CA/hja0+GgJCgQDxlnRoAfUK+6GpnSK8VZSVcuY8nmT/SSxXwiWxbHfTiu7wS5tl8Cc4ErUGzlYQwKdNyOLlknMqZXYuNCBzdlHwP/JZRKoPoXKnz3lRa5sid21NKhy2G8M3/ZUbuyCgO76YSzE8yv3bhTiX4jt8FqtWH27Dnyex5LFrDy539BpkyZ8N1338n/pkhaqdIX8NKEYtcqRk0oE71+3w4yonlnA7Ys8UJUNCTKbMYiiNmDNPP3IwrcumOBUgXkyGbD/Yf3EWq4J/dPF6xE9wZqiRZr0smCcuU+EWGWoHGB++QqBkH2EQqkic6C616nUaRIYbT89jwqfGxFzYoq+HoDR0/ZsGqTHUqVH1auXJZImGU26g9Df8DtO7ehVDgMLPw/Jz/NHplcmJXX1qZFmvCcuKOg69gkvD1pPm5S8O+7f9mNXA+LiSNWaVdKGVpCRHmFCHf3S6E7gNvmExuAUydO4UVw5dIV6GJ9U4y+8jUGxC9iU5iaNWMW7tkvIdOTvIn4Ms0aDwOvy2Ra9+/cmwYIvo6ckjmw96Bw36RCL3OGHwfdQNo0aeX76X8BXzeWiVEIJo9jBnPatO6LRt8k0LhC/rlq1WrYbTboFF4w203iFP+s/GeYNHlSfOmxB68P77//vpSud+3SFdd8TsAvJkg+90atHtFeYQgOCpIIkLdJmPUgZXjE2bcYjx49QuPGDXDlyjXJgmrXkIHQdGM6VqiXLl2C/Pnzo3mzJjh37hS+aq5EwxreSJ9WIUH4C1dFYciQIeJWoCD3Ty46+aWwYcMWlC5VDKWLKcXh2fArA9p2p5NUjTN/2WRlnplLdAVMnGtGscJKKS5IiHsPbVKyNaibCh37mPD7YQdJbl5PLeIgH2PjDgvGzPgV9erWwoaNW5K5f3fv3g1yHqezYPEaC2YtMWPwd1rJEnO6YZkVSxLMbNyWXQ3YucJbslZv37MJ8eTomxN0+9LNmxL+POUY5Y6OtSP674wzZykbBWYWQ9CFkDGdQ6zd9ZsVDWuoMfg715ETdNvyJenaVoPOScoQKPz2+UYrIu2ECWNlrOxlt95PnDgeObIBs8dqoNMmfm+ULKrC0qle4n7+5TcrBnbT4PQFO8Kj/HH0bFpo1Fo0aFhWhDxesCxZsgSBsWnhZwyGl5nESoFIn8d4kvoWir5XFF9//XX8Y585dQbesQEpEjcKsQ/t1zFw0EAhb4sWLsad0IvyNxZddWzVW9wBzmNCwjRw4EDJrKWDN3dsLokVcMYWOMoNUn7/c6U9xP+u/G8Whz0LHPXauHEjoryfuHQeWBRmceQmHalzhRf9ZDrFPZsi5fcsRWOCCzq8mFy8eBHatmmLa7oT8NUHQm3Rwqw1IlobhnRp02PFyuXPbC7mAgFFrEYNG+Gm6qwI6Wy5Z95ulHcoogKeIFOWjBg/fvwL7tXTvLeZ02ciJOxpmYafrx9atmoZX5r3poJTC7Vq1sLjeyHIHJ4X/oZgKSKxKM0SnbFo4SIRb7mPFC48eH3g+Y/fEdWqVZMFov2/7YfZbECRfO+gRcsWcrHmEc098ODZ4PmxU6eO2LFjJ0q8q0HvTipoOBVz2I69By9jxoxp+PDDDzFhwgRpYmc8UquGXlIo+uiJHas3WzBr6SKcP38WK1asSiRivSi46Dt79lxZwMud5R6+aqbBll1WNOygR/tmWsnV5/h5YIACeXIqMHuJWbhfy/qJhbD7j+wiWFb7XCkRXpPmmkWY7NRKKyWpdJPuOUCn5kXUrVML6zdsSpTF7nRAUdx7v7iDm544a8W3A42o/qUKYwbp4KV7eqanM/fr3ga06W6I7z+4fdexuM1cWydyZlNi7RYaGOzJeFrC6CzyaueaH8f62WfAolq6gZn5+vCxw4DAxfX7j23SgcCIBVfgovuTEE5yKTFxqC6eUxPk0PWra6BWKdB10DYRCxnD9TJBN3RIyGP8vl6XLIqBvQ6LJzsi0mYusWDAt1r8ccSOKzcVOHkxu5xXS5YugslTm0uWLAVfdnbUqaxC8XeVMvG1/VcrGnc0w2RJhZEjR8c/NhfIfSz+iRauk4LTYV5Kb5Qv/xmaNWuORYvmo3P/y/K3VAF+aNCwMdq3b5+opJnFYl27dpUS1RzRhcUMQLdAlFcYnvjdkSKupIvcCUHXr1ahhom56XSWPAN169bF8GHDEep/V5x4Sbk2xWDGjjn/62WD51G7IuXyIid3JZ9jnNfqtavRqGFjXMMp+JkDoTV6w6oyI8YnAiq1UsRbRtw9C+SdNWvUwi3FOaSKSgt/vYODxWgjEZnqEZQ+CixYuOx/4pGchBr54yhcuep4vQnG0FWvXk0yg9Olc+3WfRPASYf27drjt32/ITgyC1LHpZdrE0ZAkLf/se8AateqLTETL2vi1oPnB13cLKjjd9/mTVsQGxuDLOkzoFHjrlKq/Cy3vAdvFzzi7FsKR6B6C0SE3cSuVV6SeepEx5YUMJXo9v063LlzF6dPn8Tq2VqUePfpyb1gPhXGDlahcAElBo6eL6VQ/+uKthNcUUsdFITYuAgR7/p31WDEZDP2HTIhf26HKPvHUavkzpLLfdPmqVBK3H9ow2+HbMiZTYFpC1kWAcwYqUtUVEDnaJM6GpQuZkP1ljdlNYlZoU78+eefWLFiuYiaXMHnv3SY1q+ucpkXS0fArNE6VGlmwC+/WyWLizm91b9IvG1N6qjR/XsTzl+yJhrvcoLOWjodcmRV4OQ5u4ym8bbnL9nQe7hJHAvMXcuQTiFE2ZkTlkKvFMbOMMkqP7Nn3YGtw0t/0ksJE6MAXhY48v/rr3sxqv9TYZbH8+AxK0LCHMVvZUup8NlHSoycytE3IE+e3Fi1am0yAvLjjz+KQ5KFWncjHAIqodPq0KRRE4ndSOSOkUWClAkhM2YJiq8csSexpZuCAgsXCpIKW8y3ojBLIhoUlyH+9yQfQXEZRTy8EXxGiHCG6Jwun9NZluXt7fNc5IREkeOZjNkwq0wiCpPUi7tXF4FHATdkPy0qU4pFRBxbiVVEuR2XdAWOiBQuVBi3Tt13O75FMKcod67c8SuudFQcOXoEq1evxuaNmxERGYH06bOgQcN+qFWr1nOPntCxv3XbViHCO3fsxEObY+HCx9sXLZo0lwiFF13lpTDbt29fISeBcemRK7YotBYvObYslps5Y6ZcPFFYe1NFtMmTJ+Ph/UfI/qhwItcGs9vSxmSREg7GknCBid/JHrx+UEh42WKCBx78l8B4kF9374qPpnKibRMWTKnQvMtfaNKkIc6evYARfbUyTu9E5owKcWt+UsaKRl8fl4Wqb7/99h9tD88H/Ezfv3VPopkYd9XsGwP6/WiSSKmcWRU4dd6GZevsUiDbqJYKqf92mRIUXldssIigefk6DRBmtGuqxuDujtxWp5uUJokyxVWo3daIAf37ikCb0EwxYEA/+d+MtCJoGsiZXYEJQ3Tibk0ITlLNHeeFD6rHyeL+oO46LF1nFn5M3u4EhW06XtdstkjvgSvw7/6+wL5DjgV/istF3lGKKYE8tGZFNfLnUeJJiB3rtltEIH8nr/sl4XGzzDBbgM6ttYl4ckLUqKjCmBlqLF269KV/n65YvgQ1Kz7NyCU3OHXOJj0XPPVT/OZE2oTZZmzZZYNW54t161aIuJGUo5HLjRo5HN2+Jx9zgK/p559/hmHDRiQqgHS81s8WK3kLctBmzZqJcSI8PFymfch5knKTiIgI9OrVG4H6tMgYkbCbQSHZrxRebwSfxcNU15Ez1PUoLO8j/FRhkbiHZ4FCzg/DfkDv3r1FBKXbVzoQ+N5Ux4hZgfmz3JFoXbj0JrjeTzvi/KLwWckXu3Ys+0lZLL++AvYoWzLHrhNR3iGysOJ8zeh63ffbXmzduhWrVq7C/Xv34ecfjCpVW0v/xvMKn7zdtu1bMXHiRHmcEL3DMa1SqlC5SmX06NED+fKlPHHmCuTMFPr9TamRLbogfEz+EhkR6R2C7Zt24tifx4QTv6ku2m3btmHP3j3IFvoO/ExPS5QZccZrCG+TvwjajHtgDrQHrx/8LuKx9xz////wiLNvKZiBdPr0ORkpTyjMOgkEW1ovXLFh3vLDqFtVlUiYTYgW9dVY8pMdCxcu+MfiLFGsWCls2LEDtSqpMHamGe8WVOLHfloRg50iJlt6SYoXrbLg87JqIbVsT+0y0CiZZHQHcETq3YIKtw2yjA1oVEsho0EU5kjOKNR9//1A5M2pwMWrkPZbiojMoWLLqzvQYVuyqBJrNpux94DDQZAlU+LbVPtCjbnLLWjexYgZo3RS5uAk5Y+e2KRF968rDmcFR/vvP7SjdTcDHj5hPpkaw/tqodU8JbF9u2jFVTxvuQUVyqpR5u+xNeeFAIsL/rpqx3uFHCv57sAohhxZNbhy5Qpedi4KjylLMlasN+H6bQdpD3k6eSjFChwni46FkFiu3rkS73icOnbsKOIeV5ZjY2MlM7hcuXIuRc4SJYtj94M9sEc7RrpcgYSRZKpw4cLy38wgS4n0UJj1hq+sBrsChVEpJvN5jHTR2VxmrjKLiqhUqWKyVmFX4H6PGzcOqVOnxoL5CxAacA9e8IHJboIFJmjN3sgZ8i5uBV/AY787yOIi65bkl25dOoeYZ/ciaNO2jZBFHit/41Oy5USsNlKcGb3bfpfINc8LCL5e/PknoGt/zpw5Mip+48YNOWb/JFuIgiWFWcZhJLxY4EVF+ugc8DUGSl4xxVlGtbyJ+UorV6xCQFRal7lpBDOWfW0BWLxosUec9cADD946cJF0xfJl6NZBlUiYdaJoQRVGD7CjQ68LyJ5VJVNRrsAF/tqVlVi2bJFE7qRUQvk8oFO3/9Yt+OuKFb1/MCJVgAKzx1AEVonASL5z6JgN3w0xSolqz452MRTYbHYplnr4SMKPhPcwjqp7+6fCbNKppm7tVOjY95gsFnJhmovPjFSymCMlWoFRBnS8cnqKxVxJhVknGL/VoLoaa7c4br9ttxWl3+PI+99r2BK34OhyYEQXi3RpAnA+HifN2J+wapNF/jtTBoW4Y0dMMYkzlk7lBRO8EJT66fP37KjBDxNNWLTaIv0LXdsmdhD+vM+CpT9Z5PnLlHA/3UFeX6YEx9KfLsi/DDCy6c7dByhRWIUla0zSsUHufuWptirXEe8XV0qhW9OmzUXcd5dhzKgJ5pCSm9KUwNt98MEHiURZJ1jstmHDRsl5dRaBJQWnoYw2fbwg7TQMuIMs3ptMyB6V3SXfpYGAnPRu0CURTr0tyeO0OAFmgBHZsmZBnjx58DygaMzP1NAhQ3HN+yR8lL6w2mwwQg+VVY2s4e8g3OcBQv3uCy9xNeEV6f0YBnscWrRogRcBb0/HfIjfPaSNyerSkBCR6pGU+Sac1CIPZsQBf/4J+HqwsJeL/cwwtlqtkqn6rKkwd+C0U+9eveX6IqETWWlTSbkwI9ru4IIYUVgo9yaC1yh+lsBEwmxCkGcHRAdj2dJlImC/yRNqHnjwtsMjzr6lYGO8o/XUPTlq2UAtK/MJs0KTgsShViW6S39/KdvFkydX3bsMMCJDWoVkuSYs26JAWa+aBsFBCrToYsSspWbYbRAiGBLuWJFePs0LbXsY0LpRym/P7FmUiIiIwjsFCghpJhmksDmst0aiHcbPMqFOVcdjcOwsJfDvHO2ii4DEd9IcCx4+AprXVyNXNiXuPbSjaEEF1myxoX57AwrkoXisQkiYDb8dtMnz834+XpDoBo7InThrE2css3MTCrME/75wkhdKVY5D/Q4GfFlOheKFlRKHQFctR+gIxlSkBF5Q0IXxTy9cEhLfwYMHYf36dfLfMxebpdWW4L5UKKtEtw6OfNyVGy2Yt5wFGWpZuXbnVmQ2KF2zv/3+m2wvUahgIXEScLwq6QUOywIYRs+4AVeuT6vCLONHlSpXeu7V8gP7D8A3OnWKUQkB+jRCRA2aOPjIWFlihPnel5xa5uA9LyhIMsOJBVmMOLh165aQG++IIGSJyC/bkzEqJ+6mvox7uIy00dniHQzMZg3xuystuqOHjk6UccvjyEwrCt1s83RFKnlst23bjr179iAoKpMQR+bX8sKCI/RhAffx0YcfyWv3KkHR/GW4BRYuWCjCpTsXB4ssAvTBQjLbtm37xmWDcgElTh+L9IbcKd7ONyYQx44de23b5YEHHnjwssBzt81mQZPa7rNiOaGULo0CwYH2FL+nWYq1atNjGT//X9xsCcHzJ5+qc38j7jyw45dV3iJ4OsHt+LCUCuvmOUbif5hgFBfsojUWKUElWCz12yGL8EUu/LsDDQJE5coVpV+Az8ufal+okDmDBkvWmvH+e4xCeD5uyoxZGhro6mVuadNvDOjQXIOS76ok2ip7Zkf0UY+hJoyebhbBmUaI3b9bEad3PDfFWBbSkqYdP22H3uzInk3t6H9KFEswtKdWnKjjZ5kltuqLT1WyH/zfjChzghP0KUUCGwx2qFSalzYtyMmTuXMd4hajKTbutD41WRRQ4tt2GhQrosSmn62YscgknJWmAXfC7LVr10Q037hxPYxGR9ZuliwZ0Lx5a+EQSfOO+Vg/jvjRMWEV5ToOICTgLjJmyCjZps8Dll35GAPcir0E4w4UdgXitFEuxVlODvG5Bw4ajBdBw4YNZVyarkkW82zbug2Pbj1Brsfvyb5prDrcDD4rZbhcAGdOK39PMTjM5yFCAu7IdFhSRzJFbkY40XzhSuTmZ7lXr14YO3as8FwKmIyDsCmsEqcQlvo+gtKnFjHzVYKGHk55/VOsXLkSsAHp3AjsXIwPjMiA7du24/Hjx29cvIHk5J46ibSxKXdAMIbrVuR53L59+7kXATzwwIMXhyfU7i3F48cPkS+XLUViS1cls7O4ep4S/H0VMJscK+v/FPfv35Wc0is37GjfTJNImE2I8h+qUSi/EmOmmTFpHlCwSDn5fZtGanxUWiWCYFJBMyFmLTFh0GiTjHgN+k4r8QfftlMjTRAwbJIZNb5Ui9g7faGDcDFbKyWwuZXH6sO/M3CzZnKMgFVtZpCWW5J1OnErlXf8ncedub33HtihUpM4AgO+1eDPnT5YOdMbGxd64+AWb1Qur8I3/U345ffkx5fkvn51tTgx6O6lUP3TNgvKvq/ClsU6cUhcvGrHtZvuc0NPnrPhwSOzhM6/DGdfw4b1sWP7OhTM6/hd2fcdI4ArZ3pJKRlf13rtDbh514b+XbVYPMVLsoroVnQFtuuSvB3dexwZwnMhR0hhZAnPh9unH4ibgZEUTsE2Yfg5XaIPU1/HE9+7IsYSEgegjcCd9H9BF6CV0Q6ueB8/flxclQzhJ4F3BbPFIqUHKYG5U4RJlTgLi8/72O82or3D0KNXDymm+F9W6tu0aSM5uAajQVy6ThIXYEiDzOH5xMl6Ld1JXE17ElfT8N8TsKSNEwLL8TiC+8dj/cnHn4jTnS2qdGg0b95CIj0SgoL9vHlz0a59O8SkCcGV9MdxMeMRXE5/DJHBj9G8ZXMsXrL4rVgB537v/2M//KJTjkIIiEuDW7dv4e5dRzbwmwS+VwleZKUMhThYPPDAAw/eNnB8P02wWlyn7sCF9Hy5Fc9cfHauRz5PxvuzcOnSJaQKYNmWHTW+UCUSZhMic0al8DIKfIykSp32fXh5aVG0kFJ6Cvg1ntIpc99BC6q3NCAokGP/GuGmQ3pokSeHApt/topIyg6DLoMc+8QogZTAGClOZFWpoBRe7OMNPAmzi7mh4KdxeL+qHlMWWPBZWRX8fBkXBdy4bcOVazZo/9ZFG9RQ4dA2b6yf743Vs7xxYpcPurXXYM4yiwiwSUEncZtGGuG13Nf5K8xYtNqMNEEKLJiow7ftHWaAbb+6v27gNNzeg8AHH3yEfwpyRIp548ePQ8G8jgmmAnkVGNlfK9x07CC6j4F2PY3iMmaE2ebF3g539GzXTsUTJ06gWtXK2P/bOnRrBxHll07V4cPiIZgwfjSaNG4onDghKDYO/n4wwn0f4UGqayIsOmFQx+Ju8CWJrBo1epQszPM99+uvv4pBgcXMrkCTgsKWMjcVrmhXiHEg0XGRXNpQPPK/jU/LfYqqVavifzHUcCGfeetRkVEIiE0Tz029LD7IHloINqUNt4LP42ra47iW5hQup/8TYYH3hFuSnzqvQ7mvdWrXQcmSJYWb8rqk4pcVsWHDhmQ8n9FWI0eOhCYrcD3taVzMeBgXMxwR3l/2s48k29SdqP6mYc+ve+ATlyrF/ohU+rSw2qwixr+JsNv4+qTMTZ3c1cllPfDAg1cDj3P2LYWvrz8ePUz5izQq2i5OUAqPKeHP0zZkz/6/taa7Qvo0Cty8bZfV+5RAMhkSHojf9x8SgW3v3r1oWNPBJjlu9cefVpd5q7t/t0iWbZe2GvTqmLhRt1Mrjbh2h00yYd44HTr0ckQlrNliQbf2rhk1hdu9B6xSwNCuiVruQ9frH5u8cOKMXUReCqiMHmBhQ/fvjThywoody71x7IwV9doZ8X0P3leTTByf9qMOUTFGDJtoQoWyjvG5hGAswOI1wLalXsmE9qkjlPiwhh7DJzv2hRc0CcECiFHTrMieLbNEBPxTMEvo0qXzaFFPKREOU0fopLHWCZa8tW6kQce+Rnzd24jfNiilPKJqBbWUHlB8TJj1euHCBfTs2QuBsekSlw6YHYJkuPcjERopLlLAdYLHgXlQFDQZRxCayhEHQJHWaDOgQP4CmDFzhhDB2bNm4/6D+0+Peeas6NjpaynjStS0nCc3rkfcBmLd73+sNkoytu4HXkWUMVRKGWwKC6J8QmBWmmSU559m3znF46Sr68wW4+hTtFco9NoYiVAwKfQ4dvyYlCE4CVHnbzpj85bNCDCmQdbYApJVytse/vUo9u2rIxnMdEI4QeGVRQSMN3C6IwoUKIAqVaq8VaH+3HceOzYLpwTl37nAz1OK8brBsTmNWiMXb3SJuIPeO0re4x544IEHbxt4vmJhFIXXhAVXScHJJFNyXTAR/jxpg1qt+p8WRF3BS6dEeIQNn3yQ8qXPpx+osHC1RRZ9GcHEc2bT2g7+mC+3Ett3WyWDny7ThLj3wIavehtlom3WKC94ez/9OyfBpi00Y8x0M8YP0WLSHDNCw+1S5uUuvouCFsvRqnymwszRXjIJN2KyCXPG6CRO6tZdloABpd9zZOQuW2fGgFEmrJ3jBdKG96voxa07dpAuER+iMYC5vsTU+eb4wt2EKJDX8d8s1CpeJPF59/OPVRLJxVI0xnIlzOd1bvfEOSZ5D3B8/p9i3759stDfuY0a0xda0LaJGt9/lzBWQoWGNdWYOMeMUdPMYvwo96Ea37bVYNikHbJgkFDoo1Dapk1L5MtlwpIpWomicIL3a1qHhWCnpCh59OinhWDOyS4ufLNQ66rPCfgo/EQk1dtjkTY4LaaOXyj7X6VKRYmecyIw0B9NmjQXLpbQkZs7d2784X0AtnBbvEEgKRhnYFfapESX/QSMqRKzgneY8L8KFT6Xhfh/ChvHGJNwUzp1cz95T8wD5C7SkaCJw7z58xJFLzHCisfL15oKmaPyiluUfQC3Tt2TyTFGpdEJm/B9yHgDdmUwUoLTQtmzZ8fnn3/u0m37JsNgMMRzT3dwclfe9k0DX5O8efPiwdmQRJ0cScHX39vL+617fTzw4G2Dxzn7loLN0X+esuD6LfcOK47p80v31HlFfAFBUty6a8OOX61o0vTFMoPc4d1338P5S46Tr3Mc3h2YB2U0mWQcnuP0RPq0jvs2q6OWkaxtu804fcEqJNaJ2cvMKPWeMpkwS7C8aspwHfx8IM25egOkjGzyXDM+bxCHmq30GDrBGH/cKHD2Hm4Ud0C9amp5PJJWCtp7/rCJi5dlCSRszgsNksA79+3Yvd+Kll2NCPADmtV1fWKmGEsR+cZtR55ZUvB56HZw5YBm2QFLGX7db0WTTgYcOmYV0scMNLozGn1twokzwPgJk/9Rs3tISAgmTZqEGTOmIsDPhpUbLJJTnFCYdYLHYNIPOolw4O0INhbfuHEb9+45gvWdoLCqhc5lGyzB8fQAU7AIrElX1SULa+hQEe0Hfz8ITdo2RPtO7SQa4Jfdv0hJCEXH2OtmcePmfVQS2UMKI/qqXty4SR25LVu2QLSGRNa1OstG0jDfB/AzpkbGyNySefXE7zbMGWNQv1k9IY8k1f8UbOj19/NHjC482d9IzFMZ0iJDVE6ooUbePHnjhVli/vz5MjKaJTw/soTlk1E3Csg8jtkfFkZAbDC6dukqcQdJwcepUaOG/FC8fZuEWYLfEenTpUecNibF2+m1USKAMurhTUNgYCBq1qqJyFSPZSzQFeI00YjWhKN1m1avffs88MADD/4p6JiL01ux9Rf3rkpyuqs3HZNHt++55rDkrIvW2IXrvmh5pCsUKVIEDx45tonxVymBZVcEOURMTEwibtqktgaPQ+0S+XTqvBV3HzzdfmaxMnpg+o+JhVmCHK9LG0fG7ZK1Fol1YPzB3oM2fFwrDtVa6NFtsBFHTzp4nkPgNOPaTTtaNXIs/LdqqEbqVJDSWmb3ckKMub5OcZQxEHx+OlprtDQgJhaSGetuwo7mB8YcUABOischjv1yNf3Gx2tYQy2vHzk1M3QZo0CwCLfrQJO4cgcNGvyPhHWKqBRlu3frKvm767ZapbwtYRFbwm3q3kEjPRfzVzq5qRoWS3K3Igt0Q0LCMHGoOpEw6wTF6C5tlFi7drWUeSUFJ5k4Bk4TQbP2TdDyq+aYO3cu/jz+pwjBFHB9NRcxf4IOR3d4Y9cqbzSopsfChbPRuHGDRC5aitcmu1GEV1egCMu4LbVViyxh+aX4i3FX0cEh+Kjih5LDv3jxopcyAcXPiN4nMtnvyd8ZG5UhOge8zL7Cz+mOdYJTW+TrwTGZke1xQeGx5KbMqs0a8g4yROYUzs4yL1fcjhEQzJdlzMLbKPzlyp0LJp84ea3cIU4bHV9s9iaiVetWiNa5v0ZiHFp0QAgaNGzwP/dGeOCBB88Hjzj7lqJatWpIny4YPX+wyPhQUvx1xYbxs6349NNP8ThUifY9zVJclRAkUc06W5Apc2Y5Kb5ofiJXQb/88jN89tkn+OqrDti/f78QjZhYuh2An/e6J+cUGLf/akF0VAxatmwmpUnE5WsOx8WDx3bodMDXfUyo1tyAEhXj8FVvg+R9HT5uQ/O6yYXZhAJi/eoabNzhIMrnLtllHOu9wirJj/1pqwWf1tGjZVc9KjeNw/7DVswcpYsv3qKbldlcZy+6vmhwkvQfp5jASRAKuCk5RBhPwAKJi1dtyY4Bt6WSi9IMJ3JkcZQ/nDynQoOvDMhf1oB8ZQ1STBZnzo1Vq9dIecH/CjaMlipVAlOmjEe5D+wolE8ljgxegLgD82bp9HBefPn/nb2W1K24ZfMW+EcHpZjzmiomLS5euigZRq7A0TAKiST4XNllVhWJNbc7U0QeZI7ICx9zADQ2LXzNAcgckU8Ko+jI3blzZ/zjUJAskP8d3Et3SRwACUkUR9PuBF0UQZbFCySUapVGLghPnDwhY1vO4rF/Cp1OhyZNmyAqgG5co1uXRLRXGFq3aZ3IOTp3zlyk0qeR7UsKHuMMEblkPI7tyP8f0bxFc0T7hoqL2RWYVxaVKkQE0IT5vG8SOMqn89PgbrqL0GtikownhuBe2ksoXqy4XKh44IEHHrxt4ITAZ5+Vw8ipNty4k5xDhUfY0We4FdmyZkKmTBnR9BtmuiZeySdXbdfDhEchSnTp0vWFnp8FlFOmTEHVqpVQvlxZEcPWr18v00Xp0gbJFNTOvSk7B3butUgkQOtWzUVI02hUEmNFwZS8mZEFLLyt3sKAD6rpUaetHrt+s0hpKst4KSS6AxfyT5+3yT6e+csuRoJihZUomFeJ42esqNvOIMJqo6/1Is4yTur9Yg7XHXkmY6/OueGmFFK52M/isFt37CIAk8+mxOX43Em5KbFms0XKbZ35uUmRJZMSVhvw4LEK3/Qz4p2P9SjwsQGVmhhw+FRqTJgwAe3bt8f/Ci7MlylTCj179kCe7BH48hMlHoXY0bi2JtkEmhO8JmhaW419B62IjHYYH1xx023btuKDEmrkyOr+2HCKjzm0nOhzBRoiyDMyZswogtu7774rcUr9+vWVouVVs7T48lM1MqajyUKJQd11WD1Tg3PnTmP8+PHxj0Ney8mxR4G3ZJrMzvDSv8FF3EcBNxHlHSLclAvyWpsOUCqwbftWLFmyGOXLl39p+fotW7VEjCpS4sNcwSJ9D4+FTydcMKFxgIW73EZXfD8oLiP8LYGYM+efu3vfRPC6N04RLdcWrkB+F+b3ANmzZUfp0qXxJoJ5yizsJTelSOu8RuK/NA3w936pfaWc0QMPPHi18MQavKVgTtDceQvRtGkjfNnIjBb1FfiolFKcohTN1myxI0fOvJg2bbqMk3Ro3xZlqsWh/EcqpE8DXLoGcd7mzp0Dy5atREBAwHM9r6zmT5wo5CI4tRpffuporj1w7DYaNdqG/PnzyYoqR1Q4gsWVa1eFZGu3WmQki1lcI6cexuFSh5E9exbMW/kAYeF2KSOoW1Ut7k0SrONnbJi/0iw5W0SOrCmTEa6us0SB4uz477XyWM5YAINRi7nLHeNleXMp8NM8Hd4rlPK4dEJQQCaYXcZVeqfLwh04yc6fhOZWHkeOX92+Z8f0ka4/hhaLHcfPKkQ4HzduHA4fPiztvyRiXOGmC5Ph8lw559gXV6DZJsuCp+d5PXfs2CFu0EY11ej/rZeI03sPWLD3oFXE6ZSQJaMC+w46/vfx01botJpk+VB0nKSzptx+SjcAERUVlej3vCD64YcfsGrlKpjMJqgVGljsZui0Otk3f3Nql2VhBJ2k0f4hmD9vvrhunJ+XVatXomXLVjh9+hR87H5QG7xkNZglC0q7SohltC4cUameQO2txOIly5IVQrwMdOrUSYTrO7a/kC40O3xNgUJoScqjvMLwJOiWFKYljHo4f/68xDdkjyvk9nGVUMEvJkge+1UXKfwbaN68ORYvXIx7tovI+CRvfHEaQcH2YfA1KHT2N5o85syZEz+t+wltW7fFDeUZ+MAfSrMKZq0RRrsen3z8KWbNmvlW5AB74IEHHrjChAmTULduLVRrfgeNaylR+TOVODR/O2TFkrV2mCy+WL16sRTyNGvWGBUb30LpYhrky2XHw8eOvH8fbx8sWrQAhQq5P+clBSdcOnX6Gna7BRXLKZE2iEaFu+jS5QDSpUuDou+VwC+//CLi7LHTVpQsmpz3UXzd+osV7ZqqsXFnCAYO7I+qVatj2bqtuPfAJAVhHOtvXFuNTOkVuH7bjsVrzGj7nVE4XkqCH+HMur37APi6hUbcnj5/u2y5YM9t6zrIKOWys8foUKXC81+mPXxiQ0QURLwt+Z4SV/4WlFMS78hfk2qdNE7wGAzpqXUrhB49aUO+fLnxyy97cfXqVclVpQhKcb5gwYLC6TjpxPgpFpfmz59fuOnzOCNZ1NWsaWPkz23CxvneUv5LsXX9jrhnctPMGR1/Z1HaX1ccAhPH5RMiKioC2Z4RZ5o2WAGNWpGMm/J4coR/2rTJCAuLhI+3CgYjxX6F7FuAP4VY127lYkVUaFHPihUrlkpMlpNfMjqBsU0seg5LfQ+6OJZj2RCrixCJLCgmo4hkt9Kfg14VI9cDFNJeheu97EdlcfjgYaQNzy45qZzocgp0T4JvwctfJ9vuBLd7x46dCIrKmHLhbnRanDhxXBZPXkZB7JsE9j9QdD355ylkDMkDX1Oq+GNB08Bj/9sieI7uO/yNK6p1gu/F1WtWo3279jh85DC8FN5Qm3SwaSyIQwxy5siJhYsWynWnBx548GrhEWffYrBlcuvWHdJgOnbmZsmiIoKDA9GmbXMRKTjO/PHHH+PwkT/lxL9jxzbc+yscPj7+mDy5hYw5v4gQwFF1CrM9vtagY0uNxAjQ6frDBBNu3KKj9rK4BtiSGhYBcaj+2E+L6l+oJZ+Lo1KL11gke4vxAG0aq3Htlg3Lli5C9+96SckTxd5Vs7wSEeci76jQqJYazTrrceSEHXcf2FGsiPvtPHfJIaAO/k6LBjUSu0DpPuB4GYsYGP3AfNuEoIvj3kM7ihZMTrJJzBasNIvArVAqhKRzvI35vu7aexmv4MhWs4tDgYSZZP7ISZtkYzmfh2Nh23+1Yvl6s9yGroSISIjYLU3CH34ohIzEsF271oiIiBJXbUJs27oZP/44DBUqfIlZs2aJU9MVRBweNRzlP1JjzKCnRDI4yPEvYxgypTAZzsIJFn6wbG7ZOqB6jZqJRvCJoKBgmGJclyA4YdQ4/h4c/NQNyvKPFs1b4MjhowiKzITU+nTSZEsBLsznAZ6Y7iFTbMpNof7RwTh0+JCIvE4CTEK4desW/PHHHyJoX7t6DVHRUVBF2yVW41Gqm9BqtKhdp7Z8dpgF9iqQJk0abNi4Ae3atsPZc2cdJMiqg0ltgMlmwCdlP5FM3YTCsPMCge25KUFj1SI62rXr4W0Hj9uan9agSeMmuKY4KQI9yaNVbUaULgx+vr5YunjpP271ftWg2HDg0AG5aKWYwEUMxjDQufCyHNoeeOCBB/8WeK7dtGkrpk6dilWrlmP2UseUgE6nQa1adSW73SmY/frrPlkoXrfuJ5y8eBdKpQb9+tV57kXmhKPVX33VHl9+osCogV4IDFAIz5m3woLzl1iiG4LDh3YLZ2WcVf0OBnRpo5HyKI62k8ts2GHByKkm5M+jxLfttMiXy4Jug/fLyPrmzZskZoElVM3qPuWUhQsA1b9QYcwMM2YtNieKOXCFW3eswo+rfq5C/66JJ8AohFKMJV9kd0KWTIpkUQ8UuBvXcj3ZtIyxCmpyKE4/qSVe4M9TNpT+23nrSsyl8SF7FbVwU8ZsrdlsxuZdVollYJEYwePIWK4la83SUWG22EFKUr9BKRHnKLxSlF2+fDmGDxuCa9dviVCdsNdy9+6fMW3qFOQv8I7wr5Sih6ZPnw4/XyMWT36aB+vrzfcPXLqxE+LGHUeBGh3Sg8dakCtXcrdi2rTpcfVmyiLZzTuO/STvSIgff/xR+hnogG7b2Bt5cioRG2fHpp0WDB53G/WrPY1AcwUaRfh5YCHZRx85ytJ4DcbruK+//lqmvvg3lnN56VUICQ0R1yXfJ+XLlUenbzr9o2m5lMC4Agpw3bt3lwiC0KC70Fq8YVWZJVM3V85ckjWbUOymIG+xmKGmozelx7Y5rjPJtf+/ibN0US9cuBCtW7XG0T+PwtfuD60I7FbE+ITLvyOGj0jUB/Emgm7odevX4dSpU1i3bp2Yf/gdzI4KTuH+k/g8Dzzw4PmhsCcNe/TgfwYdqgRdja8bzEW6c+eOnFwpKrkT5Qg2kLIYiELfi2TH8CRcunRxfP5RDMYOdjw+SxFadzfi8AkrurfXoFEtjYh2EVGOkf0x000wmgBvL0cJAQVRkse2jTXo1UkjbtYTZ62o2cogY1A9e34nhLlnR/flXaUqx6FMCSVWzXTtauQ2FS4fJwTtxM8+ybK/nLhz34aPaujFWcsCCGbccnWe+VmXr9txYLM3MiRw/TKnjKNs0xeaUbY0V/KBhRN1KFNVLyIzx5aSQq+3o047g2TcxiXQKemUSJ8hO27cuIn2TSlSa2Q0jEVkH5ZU4sNSXI0HtuyySi4wg/O7dOmCBg3q4uGDu1AobHLx0bmNRsg8S8/+OGrFjMVmnP3LJqQ4f/68+PnnX8RRm/S1pyDEi581s73wQcmnxJ1fB+Xr6mUMiwUU7tqDy1SNQ4fmahw7bcPp8xps274zmSg2YsQIzJ05D7keFHNZ5MTV+NvpLqDIR+8IEXCCJIfOz2whBWUFOtFrqzTiSvrjyBr2jpQiuAMbbO+mviROY2dkBsGxM8YUbNq4CWaLo41ErVKjwucVJCesePHir20knseaDnOS4MjISCGstWvXFteJqxgRjmVmDSsgo23ucD/wCoILBeD3/b+/tM/9mwYK7szeXbtmrThs+F6O0ztajPPnyy/ZWfXq1Xslrue3Ff9fXnsPXhz/xdf+3+RibyL+zePB7+vr16+LiEdRJyXB9Z+8V5s1a4JH9w5i6xLmqDo439gZJkyZbxYhjSJsruxKWQTfsceKUVNNEp9FYyhdkuGRjgJdlm+NHqSTkX8aDwqXN6BXr/4YM2YkSr5rx+rZ3m7P50U+i5NS0T93+sDbyzXvrN5Cj1Pnbdi+zEtMB65Arkluykk3ujB/3mfFoyd2HDttwc/7bFg4SYfPP07sreHE3Df9jXivEDm1HVcPeuOLRgaJ4Vo+3QvaJOVl3N7ew4z4aZtV+h+cCPDnLvgK3/+ghAKjB6owcY4FqzdZJOKg8mfqeBc0+d/7pUtiztz56N79W5ngoihKzv9Vc430OPB6gGaJ+SvMctwJPz8f/P77H4mmrZyvPd2nZcqURrd2QOc2ia8DnGW8v633TlbG5nQef9lIj6yZlMiWWYEFqywipCYVxRh51bZtW2xd6iXZva4wbKIRq7d44fjxU/Fc4vTp0yJUDeymlf1LipKV4iSLt1cn94YXCuKlKulFhGX5lRN0F1Ogpas2PPypW/f990ujadNmwv8SmhheNfiZpaHnwYMH4nBn+RcdokkFOr6PyLu8HgcifXRih3JChPk8xKPAmzh//lyyzoP/L+cofsf99ttvsvhw7uw5REZFIS42FlabFRnSZ0DTZk1lAuz/mzj9T/D/5bX34MXxX3ztzz4nF3vrnbO8OB8+fDhOnjwpJxCehJnr9yw3KE8oXA1fsWIFwsLC5M1B1yab499GUIBKKEK9bHCsetiwYQgJiUCH5k/JKTO2fj9sxYoZXihb+inJoXDYrokGJYooUaetAaWLKVEov0pGkphXSuIbv+1/lxnwhEZRkY5aipksjGAsASMHglI7bkMSXTi/EgeO2iQ2gaIqxVTmmPE5OT63aI1Z4h0+KKF0K8wSJHAZ0ykwZ5kZvYc58mO9dUCsHlApgQ+q6qUYq/A7KoSGOVy2LALLmU2BUu+pxEFLwblfVy2GTTTJOFmnlhrkzqEUknjgT5uI09du2rBmjg4+3koZtWKe2awlFowfPwFnzpzBDz8MlVE5jrFtXOiFEu8+PY69O9mxYoMF/X5cgj2/7obV8hh5cwFR0QpsWugdf1wIirSff6JC+x4GHDxuw6VLV8SBwM9DUjhLo1islhBcnSfp7D3chFIrzSIaJwQdwh16OdpGZyy0yMhVu3YtXboVW7ZsiYULFuJ+0GVkDssn0QEJhdlH/rcQq4pMNIYuzuT5CxCgD04mzBIqmwYKuxIGTUyK4izzPH18fBNdCHL0rnat2oiN0CN1ZKb4+zPKYN+u33H0yFFs3LQRefKk7Mp9WeCxLlWqlPw8C9wmRh3cPn1fSstcjY8xoiHKJwydGv3vOW9vA3ihxEzW9evW49HjR+KgTR/niLi4d/Yx+vXth2VLl2HlqpUvpUjGAw888OB/gYefOr6vXySa4EVB8WjmzJnYu/c3jB2sjRftLl2ziTDb+xuNTEo5QZGSJa8fllShSjO95MdygoixTlUqqIQXOkEHpK+PEkePHoXJZEWTOjoxCJy5YBW+yKkrjtw7z+eMiGIRVo8hRnHF0oFKzqjTAp9+qBI3JoVZeifcCbMETQvF31Xi9yNWlKoUh6gY5snSOcvnAdp2N6JsGTMqfESXrR1bd1slx5aXPR1bqNG+lxlXb9oxeoAOzTob0PQbgxge2H/A7WQnxdT5Jmz5xSrHjNyd4u+lqzb0H2VC377d5OKRcWgfVHMUBNHEUL+6ozSX6NYeOHLSirbdT6JmjWq4f/+e8O89f1ixbl5i4ZmZufwZP8uESXPNiImJQ8eOHSUL2NXryazX0sWSmwPaNVFLl0SvYSaMHfT0tXYK2oPHmnD5GkvmrPj1D6BYsWIu3YoURfPmyYlv+t/FiukKZMucmAdv+tmCeSus6NatQ6JFXgqqmTNqZDtcIVsmhbw3UgLNE0TC8XAuMDdsWA+XL11AkzoK1K7sJY7hE2esmL/iJHr0OI5Zs+agUqVKeF2gE7p3797PvB3fD/Ub1MfKxauQNjqLxGslhWTqBzzBF198/taV0b4IKFxTRGfEBxcAGJ8WHJ1FStziYqMxafwkuSZatXrVK/1O9MADD95uvNXiLN1mFIAYxs7xKbZkjho1CgaDAYMHD07xvhRmWRrQs2dPGcnhOE6bNm2wadOmt7It8lViwYIFcjz9fEhUgbw5nxIZts6W/1CVSJhNmrHE8a3TF2xYNMl1kP+5v8nKtWuXE6yOPx1dIrFlLELfLhrJr2XOK12vLOQaMRlSqJAhnQJhEXZMX2SWkbHsmRVCaFMCBVSKpU/CICvh9ao7ROMHj23SujtjkVlyt9Zvt8Ln7/0m0WYm7tFTdNk6SFyHZswMc7g0WKLAbYmLs8vzk/gP76NFhnTM/2L7sBm79pH0dcP7778vP1myZEG7du0wcYwukTAbX3BQRyMC74KV99HrGzVGTbVJFllCYTbhhcfQXjp8XEsvRH3+vLkyQpg058jppiXZ909iFGV8BIXx78eZsGaLBQ2qO9qAz120YtVGC+IMPBa8ENFI8QQd267A/eKIVKuWrXDd6xT8ooKgs/jAojIh2j8UBsRJriybWp0IDQ3F9RvXkUXvejSd+VcsxQr3eYTgmEwuiaBVYZXM2WaNmkqhmFP0/arDV9CHmZD9cWGobU9FZ25TYFw63LX9hfbtOmDP3l/fuFwobk/Xb7viq6++whO/O0gbkzWRQMuihntprki0ROPGjfH/Hf379cfBA4eQLbSguLLpyqBb2q60QWlT4cL5v8QJzfI4DzzwwIPXDQ8/ffU4cOAA2rRpCYvZ0UVQvMhTPrD0J7Ms5rtyOBL82zetNBgy3oTFU9SJpqScYERBeIRFJm6IFevN6DbICEsC/e2TMir06azBu++oZOKIs4jkjRQ+OdHEaAJOc81faRFumjMbcOsuuw/sKY6/R0fbceeeXfhlywZqEY1Z/rt+uwXjZppw7JQNh4+ZZBKNz8t/rRZg+QarTKoxPmzMIB2WTfNCnxFG1GtnED7KbXgS6phm+7qFGuU+VEmcASMdlq2zSOYoi7zIEffs/Q0ffVQG7Zow4iD5caTgOmqAHR373kGdyir8esCKFvU1boXnb9tpsHKjRbj38WNHhO8ldYM6jTWMmUgKGjwmDdPJa3DwmFWiG5jxe/e+DcvXWySOjJmzNSupER1LI8Qtl9vBCcMlS1egUcN6+KzeA1T7Qon3iyllum3zLjtOnDWjbt06yYwNhw/tR5XP7PH9FUnRoKZajB5Xb9gk7sB1LJoVRd8tlCgzllNmV69cwE9zEx+7nFmVqPGlHV0GmPDNNx1x9Oix1+qefV7w2nnlipW4H3wFmcLyJjFi2PAg1Q3olTHSt/D/HTR80a2dPioH/AypEeHzCKG+92BVWaCwKxAZGoX69epL/MGbWlzrgQce/Lt4qwNEVq1aJaMg06ZNk1xVjrL26tVLfk+h1h04rjN79mw5ofACnvk9HKkPDAyU1kkPnmLXrl0yYt6uiQo9OqpgNnNkyR6ff8VVYIqvKaHq52rcvGMXx0FSkKQtXO0YjTIYjOIK0OuBaT/qcGKXDw5u8ZZ8W7pza7U2iHB6/pIFMXEOkknwf9MlkDG9QshmYCrg1j27lCIwG9UdOJYVHQuM6q9F2yaaeDcv21V7d9Ji5iiduGj7dnFk5jKbi7Ar/HH2L2/4+njj+7FmiXRgBtmR7T6YOkIn7okvy6lk+yh+9hhqkjGmJp0MOH3OGl9Q5cSePXuQLYsGFcq6P46tGmokg/bCJbsIwV9+6v62JKsl3lUiQ1oFoqIjJPIiKejWpOC3ZVfyNjP+nhENs0brxH1CkbbrQKMIzxRu92/0xq5VPuKqTR0IGAzuc2X5udy7by9at28FW2Y9HgReQ1SaJ6hSp5KM83O0LCEsf8/X0R3rDhRlrUoz7gRdhFVhTnx/JUXKS1B7qRM9Np0vFy9dRNqwbImEWSfUdg3ShGXD5SuXcOTIETwLZrNZcmCt1pRdEi8T1apVQ//+/RHifxc3Mp4WkTbc5yEeBFzD9YynoE4NrFi5/F9xi964cUOE9sqVKuPzCl/IggAzAF9Fag6dNSzVShORBSa1HjeCz0qpW5rYzMgUkRup49JDYVPg+LHjEpHhgQceePC64eGnrxa3bt1C69YtUayQGUum/J1nGfP0fMOsVfKkpOP8CUGnLE/hJ8+65okLV5llBJ98jXyO5V9cyD+81RvHf/bGxKFa6VGo29YgPPCvK448WSc3ZdkW8/ttNgVaNVCjYF4Fbt5xZLGyX8AdyJX3H7Wh2hcqeT6nm5d9DhQ/1833lukuxgZ0bKERJ6zdBvj6+uH4WW+o1L4igrLbgBFg+9Z5y3Rb64YaVPtCLQvyPDVzgovctGozgxgNVEoFNFo6Uh0ciSPaZrNVxGF3qFReJUJ3aIQdkVFA7cruualarUCNL1Wy/xS4mf/valE/S5YMIm67Al3P25Z5wWi0Y+Ics2TzTphjlimwzYu9cGirD/p21iJLBoVc67lDtmzZsH3HLvTq3R/Hz2eQabHhk63wDSwt14GTJ0+JX9x3wmK1wMtNXIVs25cU0RVo3sUgAm1CUIyns/ePoxZ0694z/vfkkWvXrsZXzZQuRW26g5lzbLdZ5DvlWSAn5WOSo74uMEqPWbRGv1hcz3QKD/1vCDd97H9L/jvK74lcYz/PlNjLBidj6ayvWaMmPitfAS1atMT27dvjrzVedqzBlMlTkcqQBl5mX9xIcwbhPo+lWI3cNG10VnHRRkZFomvXri/9+T3wwIP/H3irxdnff/9dhFWKqk6wnZ1fkFxRdweGrXOMxNnk7lyt/eKLL+QxPXiKqVMn4cOSzFPVotwHjqKC7bsdpMmpS2m1KbsMWfBFMDs1ISjuDhhlkhyt9h2+lpMlM7Y2LPQSAkbCR1LasaUWW5Z4iTj5TT8DTp13EEsSxp0rvPDnTm8snqxDcGqlkM0mtTXiWGAkwvfjjOJaSAqO57NALTg1ZFTLFZit9V5hpdyOjlE+Z1CgEhU/1aNqBSMC/E1CuOq3N6B+e724bUmquaL/01arxCpw2zjitWiSTggls8haN1Rj1qzpksVG3Lt3DwXz2ty24hIcu6JDmLlodDyQ5KYExi2QvBMfl/0AefPmxmflPxGxiu99jlR98UUFTF3AEojkFyYU1ZjtRcJ/cIsXLu73wZk9PiLaZsnoeGCLxY4zfymQPXuOFLeFJJjO6/MXzuP27du4fv2aON05cpYUdAX4+/mL2OYOOqsPfA2BiNNF4WrGk7gXeAWP/W7jXuBlXM1wAkhlwbLlS8VR7wQbmr2U3i6jEpzg31jOxQUJd+D3Chd0OPJF50O+vPlk9IuZsK8DjICgG7Ry7YqITReKh4E3oM0JdOnWWUTw/zWW5f79+5LF+/lnn+ODMh+gQf0G2LBhQ4oXN873yaRJk0SEXzBnIW4deYAHJ0Kx7acdqFWrljjCOcnwMsH9p3jPcrSHqW4gKDYj8jwpLm7iQH16pI/OgbyPSsDPGIjvB38fH+HhgQceePC64OGnrxbkMlqNGXPHaVGiqFL4It2fTlisdmhdm2bjofvbuXrhsi2ZaYDO27nLLfjqq064efMaMqVXYPsyb1nIz5xRiXRplKhXTYPNi71RvIgSnfoaxBVJXlyxvEry/Mn/Ni3ywsdlVFjykwXZsyqRIwuEnzLyivn9ScHn/mGiUbJrR/RxHc/GKa7GtdVYvdmCiXPN4vikjvhJGQNqfmlEjiyO8/bA0SaUq6uXiTJOX12+7pgKY4TDL6u9sGO5g5uuneuF4z/7YPwQLXbv3oMLFy7E84I0QWoxLLgDuWihfArEONIP4gu8UuKmTudx//69kSdPLpQqVQxjxowRUw0F0ebNW2PTTpaVuRZo2QnBwuGZo7T463cfXD3ogynDvSQCzYkTZx3cMyXw2pHxCgcOHBFuevPmLaxatUbiA1xNT+XJkw+Hj7t/PMao8bqEWcaf1dejRVeTuJwHjDKidBUTlqy1YuTIkZLfmpBT6vVGt9chBCfXvvhEiV27dri9DWO7+vTpI5yU3JTlXZwsdSWAvwpwAs5pxEBWo3BTY4Yo1GtSVzh1gwYN/qfHZYEYi6irV6su3LRy5SpSihwR8eziWz5vqZKl8OOIH3H5wA08PhmOw7v+FGf4559/Ie/vlwlqC3fv3YFfXDDupL4IL7Mf8j4ugQzROYWbponNgtxPiiFNdBYpg2URogceeODB/6tYAwaW161bN9HvmDHJsG3+LaX7ERRYkq7+MVOIYkJCZ+N/FTzZnzhxGnPG6oSo5M6hwMfvKzFulgkflXaQYRLWA0etqFXJ/VuJRVUatQL1OxjxxSc2FMwHhIazeItOBxvGjRuHixcvCikd2U/r0umQKYMS332lQa8fHGNcdAGUKf6UiHEkjcLu8EkmTFtgluyrnXut+O2gTVp5O7XSSD4tifOOPRZMnmfGrbt2/NhPm+IIe4WPVDh1zgaF0iH2lv9IgVYN1TLCxowrNtv2GW7G0ZMkkibJYNVpgKnDteJQIHEtnUSDbN1Yg4WrHYSDxJBZyU/up0xoOeKlNwJpghQIDQcGjzHKcaVTge5VHn+WsfHvzCA7+5dVLky4zfWrGeDjo8DVGzcxdsxIpA4KwrJlKzFs2AjUqFEVtVpHomtbBWpUVIvwy1IyitzMxqUDN2sm104IOhsePLK80Bh9UidCUtCx0aRpExH6gmMzxTe8Ji0FM/jECMFizvKG9RsQHhaOjGnSoHO9r9CwYcNko1902NMx6yqr1Qn+jQ5a3tYVGIMyevRo+Nj9kTaKDlwtjOo4/LRinZRTzZ03N1HBw6tCiRIl5IdwOlOZccVxKromGNNSsWLFZ+ZuO8Hct+7dugM2BXxjAyXX9/SN8zhwsDNy58ot2a0J89ESgt+XFHVJNtPEZI6PmbBH2iVm4Jefd0vzL50LLwshISHQKXQI930Ib5OfiLFJX1duR5bw/LiiOy6fsyFDhry05/fAAw88eBY8/PTVgee9tWtXoWF1Rm05vvs54j5rqRl1qqgl3iBfTiUO/GmV27rjeOSuBHNQ9x8BypaGw4CwB7h1xyziFjMkyVFHjdUJ500KFn8N66NDhfqOxfahPbWJsvozpHXELZT7QIVO/SjCqXDjjhUPn9ilHIwFuOSK5F5c2J+5xCydCtU+VyF1oHtRtMLHKsm3JcejzYZGgrpVVPisrEoW+g+fUKHtdyZx7k6aYxa3Kk+TjBZo00gjIilRuMDTx6xaQYUh45SYOHGiTBeypCU61vrMCAZGg6X6O95/yjyzTFzdf2gH41o5Eda8niZ+zJ8uZUak0eVc+dM4pEurQkhYKFavWoClSxZh6rQZsqj76+5daNLpFDq3sUqEVpog4OZdO5asNWP+Cot0THz+scqlOYQTc7t+M2P48OZ4WdyUaNq0BTp0+ANHTjoydJOCU4Xrt9tRpkwZ1KlTH6tWLceabXfg4+ONevUrShkUrzMTwsk3g4NSvgYIDgKu3XOd1fbrr7+iXbv2UJgV8I9Kg2DGhylNOPjLEezevVsMBJxmetVg4R+NGPxxfu4uX74s28AS10yZMskEWEJDVUpgAVvTJk1FiGURrtqsQ4T6LoadHYYJ4ydg8ZLFEg3nCpzc4jHxjUuFbBFFnk7MRbGTIhp3FFfRsEFD7Ppl10srj33y5In8q9dGS6BD1vD8ycqQyVVpJKABZdrUaYlMYh544IEHb704y9ENV82vDBxn3ldK96NwoWMqfwLwsXhC4X3/V3GW92cD3ZsMp2PT+a8TzNVauXIldu7ciuioaBENCWaoOjF6oA612xhQvYUBHVtqRBRcsMqMru00iYoUnKArc/VmOxo3aS5i+Pr1a3Fq4wN4+3ijbr1KIuzxhF6hwmfiNKUI6w6Vy6vQdwTQtI46kTDrBIlAn85abNxhFVeukFFm2l60oe13iR2AhfIr5O8sdHAFugyYIbtj798uYZvDmcC22XXbrOjXRYO8uZTY9LNVBFnRyKyQbNqihZSoVdm9ZYM5UnQTs8juyOGD+PiTchg8eDtu3NYgZzbX27P5Z4sIyxRcWZa6apNFxFSObpGwTppnloK0+RO9pHQiJMxxv3TBEPcHQRKfNpijdmFo3ao5dv68G6tWrcXQod+j9/D9UrLALC2KzlmzZkKtWqWxceNGTJprktda9zcJ5nt8129W9B1hloB/vq4v8z1PAkux867tIjKE5IaXxfE+JPTqGDxMcw1BaVJLLAlFWBL5pEi6PVywMSj0kkeblCw5YVNY5TZp0qRJdn86bynMUoRMmveaJiYL7gddQft27bF9x3Z5P78uHD9+HH1698Wt2zehVeqggBJGmx6BqVKjb78+iRavXH3u6argeFWquLRIH5nz6bGJBgzqWNzDFTSo3xBbtm5O9p1IV+2Y0WMlrzddTGKHCo8PR7vs4VZxujIr11Vp3P8CbofRZoRVF4tMEXncCu4UaFPFpsOa1Wufq9ji/zPcfed78P8f/8XXPiVB7r/KT99mbspjsm7dOuGPjx4+FLErIiIaQamf8qxvWmtE3Gzc0SDuVoqXLMoiV6lYLvmlDieqZi2xonjxomjbtgNWrlyOVVsuQa1SoUTJ9zFmXDMUL14c3333nURJUQh0B/JIRgVkyahA60auL6sowLJUlv0LBPncoxA7+owwyY8TeXIqhOcVTeACTYjIaDvGzzSJa5ZwulAZbdC6u1FEXebULl9nkUV9clMKzhQ3+S/FWXefDY7QF8gDGfv+4vPy6NtvIPR6m0QwUPR2hYtXGTVmQ6n3FHKc2FPwxScqEWXZA8G4BBbesnuhdDEV9h60So9E2iBg5SYeC5vEmaVOBaROY5Ns1Y0bN2PuvAXiMp08bx3GTI8TowOntVKl8kPdupWwccN6dBloxqgBWnECO8Hj0L6nGVmzZkGVKlVe6nueE0J8v7TrcQ4Th9jjhXDi3gOWqVlw5QawbNh3MhlWo0aNZI+RdHucRgK6txNmJifF+UsKpEuXIdn92flADuwV7YdMLN5NMBAbFJcRIX53xZVMFzEX7V8XGD/Vs2cvHD16BGqFBmqFGka7EYMGDkKLli2k78UpiLv63PP+jRo2gjUCyBNSHBrb0+9DS6QJ94OvinC7afMm5MyZM9nzjx0zFjqzNzKH5RVenBDeZn9kfpwf1+ynsHr16v/Z1ZsUzu/sKO8QiTJQ2V1/ZshZ+dqcOn1K2upf5zXDm4b/Ij/x4L/72tufk5u+1eLsmwjm/PDL9m1AwnFfOu/Gjx8HL50NtSopxRF77ZYemx5Dml6XTvWSsioKsJsWemHYRJMUKZBg8vzKsoEpI3TxbbDEqfNWfDvICKtVJ+4Dil4kuwlBosHj9eTxI7ybL+U37LVbdnm+um5IYnwTbyVV/HhbT+bVHrGKs/WdvAp88r4KB45Zce6iXUROkvlS7yUmRBcuW9GggwGpAhTigqAjlwT3l9+tmLfcDIXCjpFTHXlOdJay7Zb5rsy9XbTajIN/2uT5a1d2vZ10DJAkM5t2yy/78OjRYyGcvYfrsWSyTkajEoJxEGNnmoW8rt1qRbkPlJg6wive+UAM7q5Fx74GNO9skGNEdzHF5xJFVSKgs8iNx2DZOjMio0jYH2Hy5MlCHr/7rieaNGmGs2fPSrRExowZ8e6778rryBXl8bNWSi5wxU8VIir/cRS4csOCkiWLo337Dq/k/T70h6EYNGAQruO0g1jZHWVgVqUFGdJnwJChQ/D48WP5eR4ULFgQVlgQ4f0YwXEZXd6Gf+NtChcunGyfJk+aDD9zqmTCLMHtyhSWB9e8TkpcQ9Ic3VcFbuOAAQOhM3gje2Qh+JgCZNvo5g3R30Xfvn3lM84LFHef+1EjR8HH7I+MEbmT7RdF8UxP8uE6TkmBYlJXMIXdyKgI5I5xH6VAghqS+i5mzZolTueXgTx58sjrRGgtKTsedBZvPIi6j/Pnz0uT7n8dnoiH/y7+a6/9804O/FfwtnJTZssOGTJIRGku0NeooEBomB4/bQNGTzMjKJVCpoboYF0+zQujp5skK5Zj9tTNvulvxNiBQPUvVfFxUCyUHTTGJELpkCF1pQS4d+++ybaDx+vcuXPizqVw6Q5c0ObzNazhXvgkmBH7dR9jvNOX8Vrb91jj47U4tUSexodgNunXLRIv8jPWihFaDx/b0a6JRhboGXfF+8xbacaFSw4hlaYCCsV9vtEifx4FQkLtkkHLHN4h40wY0tP9xBgFVXLe67duonfvHihSpBBGTr2I0sWU8ZFWTsTG2dH/RyP8fDlxZUe2zAosnuIlBgQnBnTTYsQkE/qPNCEotcMkYDACBfMppeCWkQk0QzAfl5m+SoVF8uuZr9+kSRNUr15dFqF5rUDHJaeGKIJxsXf8+LGSmctsYRoPLlwBDh+3SGbt9wO/l6iCl42ePftgyJDBaN39KtRKhVxH8KsmVm+Hj48X+vfvK4snz/tZowkmQ/o0mL8iAsVHuhZnz/xlxbHTZvTp82Gyx2W8h81sR6bwvImEWYKcjuYBvVc0pkye8syYh5cF9lx8170HYsJikDk8HwIMQcLj6eZleev8efNx7eo1dOveLdH7MOHnnvsVF6NH7ifvQWVP/Dng1FqWkPy4rj6N8ePHS9xXUgfrgYMHkCmKi/eueR+LgP1NQVgwfyGKFCnyUvabTnPGssVEx0BrTZmbOrmr8739X8d/jZ948N997bXPwU3fanGWTgLm0SQFSRzdCSndz2QyifMroTuBq/M8UaR032eBY9kUD95kcJWCHwZmclJ8O3PmDMaPG4tKnykwbrAXfBKIg4O6adG2hwEtuxqweZEXjp6y4ed9LNKyo0JZpYwqccSIba8UaPPmUggxu/vALivBLOeKiTVg586d8lw8abKhkvm+XIV2rpwqlEqcPM/RK4qmrkmjM2Cf+UspgX+PjYMUhHVuo8G37bXYd9Ai7tm/rliQOYMC9aqp8DiUuWIWtGygkREpgs/fsa9RMl5XzvKKLwkj2jfl2JgaFZvEyeP376qRPFwnyn0IKX3o+YMR331vFPKZP3dyYsC2XfIRxjRU/dyG5l3OoUePHpLv+2VjvbQLf1RaBUZ1btttweK1FGYVyJ9bhdPnbZg91ivZiFlQagXmT/BCmWpxEnXATDMK6mwSduLTD4AOzTVo1tkgbuJVq5bJyDm/KJhRVb58+WTbSpLM0T6OzB87dhgWixl5C+TH4B+ayjjRq3An8SKSI3URURHwtvvCNza1/D7WO0IaX/n5+vDDD12OIjFPlyvu3CcWSzjfX9y/OnXqYOP6jdBadTIilRBRuhA8TnVLclI//fTTRH9jLtWly5eQOSZfii5N/+hg7P99v4xAvo7VN0YR6IzeyPqkYCJSTtKZKSIvlDa1kGA6jBn/kPRzz7Fb7leWaPf75UUCa06N33/7HV26dEn0N5aFaJVayQB2BxJjnd5XXpeE7cT/BHyckiVL4tixY7CojIDZ3+1tzSojvL28UahQIfyXkfS19+C/g//ia89Ipn8bbxo/fRu5KY9V+3atkT44FjuWeiFj+qfnuX5dtSI0ssgpfVoKpAqs2+7I/S/5nhK5symx94AFt+4p0HWQEcMnK2SqiYIoxUwuNHNSaOuWzXj48KGIwDxG5DVVq1aN/6z4+/vj5k07HjyyJXr+pCIlF/CfyU0TODzp9CXP5Ag+3b6MKKDQStcpIwLWbrHi/CUrCuV/yuGGjjeJ23bDQm/JnXWidkallIe17m7A/sM2KemaMlyXKCKsfnUNFq0xY9BoEwoXUMp/JwXjsP66Ysd3XzG6S4nPG0bgvfeqYNfPD1GpSSjaNtZIFwOdryzpnb/SLMe7WV015iy1JBNmCU5cDe2lxV9XbSIO0zww4FsNvm7xlDuTp7aor0bfEUas3mzF0aOHRehyOgpLly7tkgdQuP3pp5+we/fP+OtGDDJkyIQJE+pJpmtS5/nLAsvCLl++Ci108IsKEnekXhsDuy4cqQPTCn8k90wKfqY5nchOFP49ofu9S9fuGDBggAjp37TSyPsyoVmkQ08z8uTJKVzcWdbmxO+/7UdATBoo3UyEkdsFxqTD5SuX5b3satteNrgv0eHRyP6ocCLHK0VVTllRuGQ+bavWrYTLJ/3c8xj9uvtXBESnSSbMOsH9TRWdFvv27hOBNul3JeFtcs8N5e8GPzF4vCxuSrRu01pKwRi/lhKEuwIiDOfNmxf/VfwX+YkH/93X/upzctO3WpzlOHXSbFmSYQqASfNkk97P2TBeoMDTwCU+FjNx/kneLMkzScXbAH4YuK3z58+TooLJP2iSuQMo+i2a7IXSleNQuZkBegPwYUklMqZTyDjTrt8s4pzNnUOF3p3UkvPKPNn8uRXo2VGHYoWVEoOwZs0aZEinRp4cClz5C/LfuXJlx7x5CyUjM0f2HLjw10UZQatU3vXb8rdDjvmts3/ZkCMJAUyIsxdsYBEn4w+cJIfbzZQGU4RjLGzbbkYf8IIFaNLJgEk/6GQ0jYTz+i07NizQJRJmEx4Pf18FcpZQJBJmnaAzY+wgHX47FIf5K8wYMygxQbx+yyYxAZXKqYTo8+fdgjacOnUCAwYMxpAh30tJmjOSgY7XulXVQtg+qaNHt3Yat9lfbPJtWlsjuWsdmqsTCbNOcJ+mDtfhk9p6xMQYpLTE1ehVQpA8cLzsdWHQoEHYsX3H3yvuwU+Fw5hsiNaF49DBwxg4cGCiHFN+dqdNm4YNGzbCZHKQHjpsW7ZqKY5Nvtc52sWLPeZf+doC4BXrD4MmBnpdNGwKxwH/68JfEuVQr169+NUt56q21pLy9wIJ5+PIR//T558nKY4S8juJz/vRRx+Jy9yd+H3kyBFcu34N2SISC7NJc60i/R5L1tfXX3+d7HPvzMd6FoHVGXxx987dZPvFRRar3QY7xxJT6Ja0q6zxz/myMG/ePJQu9T7CvR8hwJDG9fPCjpiAUNSsWeOt+U5+1XjZr4MHbw/+S6/9vx1p8Cby07eRmy5ZskRceFsW65KVUlH0G9FXi9PnrejUzyTO1cL5aRhQSJ7r/JWORXBmuU4bocP+I1bpGQhOrcCo/uwEUKH79ybs+u0YLlw4IT0C5ISbNm3EqFE/YuLEyTIGTmHw/PkzWLjagv5dXTte2GNAHnz2ovvxf+LMXyx+BcqWUoowSzCCizyPnJWO0kPHrIiK4Yi0Y2pt8jCdcDl2DTDeitFdCYVZJ8jdOdnm62vDxKGJhVknWjXQYN8BK2YuNouLN+HnhBNd/UaaxHHLCAdy2XpVFdi8fQuWL18lJVnTF5oxYbZjaoz7W7GcCnPGatF7mFFiJJIKs07weej0PXzciHffUSQSZp0gV/+xnw77DunlGoJ59qNGjUJKoBOUsRP8eR3YtGmTbFNwTGaki+YU1dP9NaricA+X0aZ1G+z+dXf855R5qeSqy5YuQ0Sko8TKx9sXjRo3FMdnhgwZpGQ2LCwMY8ePx4oNQPXP7bjzwI4/DtsQGePoFUgTHCNu0hYtWiAo6Km5IDIyAmksWZ/LpUku+6LfARRKuRB/6tQpMQVwsozlX2qO57kAhdFNGzchVVS6RMJsQqTSp0FE4AOJ0Us4keX83PMxoqKjkNmcIcVtY+fAE9Md2S8aEJxwxskwqiwlWJVW6HTal/q9yPciy3Tv3XqAdNHZXfJzIsL3iZS2OacU/+v4L/ETD/67r73iOT/rb7U4+8knn8i4bMJsLzo0Ob5KccMdKHpQWGBTopP80q3HZkc+5n8JJA5sjRzSQy3E8LdDFiGomTMqRFjlG4nB/iSOLBwg6XOSSp6of9pmwXffm/BtO7XkeiXM9mKJQP32BhmjZ6kYHQEkfLzfyXPMj72P+vXrYNu2nWjbrj169eqBHkM5IqVA2dJPhUXmTM1dbsbmXVZxO3C1vurnT7OekkYA/PybVfK1Wjd0rLhu2G4R5wSfv0tbDd4rpBSXA8VexjPQ4csyB+4vR7pIThlX4Ar3H9pw5YYd3Tq4t6WTJHPMbvYSM94tqMSHJUn67VKgxdgDuidIQp34pAyw8ecLmD9/IWbMmIpiBcNl28l96L7l8WCeldHI8oaUR7P5dwq7KRW0UdguW1opFwpz5syUzwAdNW/C2DfLnnhBFhyZRTJLk8LfmBrpwrNLjmmvXr3kQvbkyZMS7G/R25AqMj18TQEitkbFhWDcmHH4ZdcvWLV6lYyPkdyyPGHO7DkicFqtNqQyBMPXGChi3v2zj+VxmW+3bNkyOWk4ywvMaiO8LX5ut92sMsDfL3nGYErgZ4FC4/hx4xEdEw0vpTessEq+bcF3CmLylMkSyZAUHIXSKDXwNbl3UbEAwccYIA5TV3BePDAqwh2JJmxKK3xcCAJ0vlvtFkR7hbkVSOkeiNVEyW1fJpghPHzEMMmSDfN5IPldCcHX8mHADRhhEOewBx544MHrhIef/nOsWb0CVSookTbIUTwbGmGXKacyJZQiPnKC6XGoY2F62TSdRG85cem6FVWaGtC4lgpffKqWn4To96MRv/5hxcBuWnF+OovFbt1VY/gkA9q3b4elS5dJBj7P0cz0z5RBgeZ1ny768/z9634rBo52FG6t2WzBt+208ZNYCUE+TOeqSgkM7qGLd0Vy4ixrZk4+6SSnlY9NB+uEOSbs/cOGZp2NyJlNgQB/9gWw4NU9t+P+NKjOcjH3F4DcV2bTDh5rQpPaGpmyoilhzjKziKLr5nvFxz9QFF64OgLp0qXDl19+jquX9mF4HxXUKgVyZVcgfVoHZ7z/yI5yH6ZcqEXhnCj7vjpF7sztn73UjA3rf5JOCvLTV+WCfRHwtSZP49RVuuhsyaaNOEGU6UleieLatm2b5P2Tz9auVRu3b90RF2h2Q2bArkCsVziWLVqOLZu3YP2G9cJjKepVqFBBOOrCNZtgNJqE32WMo0FBhbi4KEwYNxHLly3HT+t+incVB/gHwByZskvTpDLIv89bxOXE3r17pdPg3v27f3caKGC0GZAubXqM+HF4ssgsgrELRpMR/obE02kJwcfxiQ7En0f/dPl35+ttUzriq1ISV4mkC1aclArwT4XImCfwjnbN2ckRY/3DUa188n34J6CzmQsLn1f4HA9SXUOmyOS9CIxQ47TegK97eYRZDzzwIBn+fTXmH6BRo0YiuHD1kfmHFFTojuPv06dPH387joJwjD7hFz8LahYsWCBfoocOHZKxcgqVrysv8p+ALrs9e/bIKi63nQ3t/yvo4qBAJdmrleOEtLFRtmYrgwiWG3daMGWeSSICOCbvFGYJCrfOp+YKOke7GMZPRy2J6MYdjvKDZdO9ZBTKSfh4Pwbfr5qpgUoZgylTpqBmzZrIli0rzGaFjHhVba7Hj1NMGDTGiDLV+L8dq/UdWzpyufqMMEKvd6woO3HjDomsQQTcdfO8JZPV4QYwok4VFeaNp5NXJc9PYbf8R2psXuyFrJkg97l83Ya79+0IClS4PWFG/j2lmDVjyqsf/DuJNHO2Pq2jR6UmBixebZaLiYY1VNix14JVG80S1WA2O5pieVLv3bufuI9ZmlAov0OYJThGRoQ5Ft7dguIykSNrytuXOYMS3joFzp09I3EGn376kSO7ymnZ/ZdA16rdakfquKefX1er7iSKa9eulc9CyxYtgWg1cjwogrSxWeBjDoCfKVBIEUf+z5w6iyFDhsh9KUBzpZ4jo2q7BrlDiiJzRD4E6tMhtT49soQWQPbQQjh25JhktjrdGYULFRYXqjvQPRrjH4badWq90P5OmjRJtk39yAd5HhdHrnvFkOdeCWQNewc3z99BrZq1cPHixWT34+tE14a7OIKnG6Zw+5q+9957ko8V6f0k5f3yC8fnXybOmyU4ClaqZCmEpr4Hi8Ls4r52PAq8KSuiCYvJXhaYR0dX9MNUN3A7zQVEeoUgThMlbtpb6c8hwu+RiNx0enjggQcevE78F/kpzzVc9CQ35SLoP81SZKRQdIxNOGCjjgZ8088obtL3q8Rh8jwTVmwwIyzcjjVzHJ0ICaFUKGAyARXLq6X868oNm4ihnJy6dI35+xbpFGCMlFOYJbJnUWLWaA3KFFdi6NDByJ07N6pXryqOV0YClK2px5BxRoyYbELFxnrhzOwQaFKHhgGg1bcGKcJNCD5nh14GPHxkx6QftPFxV31HmJA1s1L46pefPhV9i7yjwoIJXqjwMQ0SkMzYC5cc3I5RYe4QEWlHFhfFvAnh/PvKDRZ82UiP8vX0YlIgd2R2K80Ky9ebcfSkFSaz4znJT7t374F7DxWYu9yaSJglmCPr5J7uwLxcgsaPlJA1kwJGE+PQ9CL+lSxZTCa3XEWEvE7QOcpppdQxGVKIgfKFvyU1Vq5YKf/NstV7N+8j+8PCyBCVU8RWX3OAOCqzPyiCmJA4tGndVoRfomjRotLNYTZZkDXMwUVT6zMgUJ8WmSJzI9fDogh/EIlmTZvFX/fVqlML0f6hsME9d4/0f4JCBQu9UPEUrzHp0o28GYscIUWQ+15x+cn55F0Y7lrQoUMHMUkkhXO7FPaU34c8hrz2dAV+B5Z5vwyi/UJTfIxo3xAxMSR0EjvF2mbNmyIq4ImUCLtCqO89GOxx4lp+2eA06JSpU2T7b6Y/Kzm75KZRXqG4E3wR9wOvCn9t2rTpS39uDzzw4O3HW+2cZfYWyeuwYcOEAJMIcxyZOZpJCWNSAZMX9TwhkgBznIRCA7OEWAzwpoKC0oQJE7B06SJERj494TD8vmPHLkLy/5fxCK7kc+W8fVONjNBTnGQmKQsGugwwSqkCCwQSZtE6Yf/73Pr9WJOUYMX9XbpHAknn58ellTIu5gqMCGheV4EZi9ehWrVqyJfvHdy/f09kHTpFV7HJ1U6S5rj9yP50OGiklKDPcBO2/xqHGl86tpfjZPsO2hDgB6ya6RUfe8B8VzqC+3bWJnPaRkTZ0aabETfvOFb1PyipxKHjViknoPCbtJjLuV/EzTt2FEshQ/7mXbsUeO1a7Y0bt+1CXn/eZ8G8FRaMn+0Yt/ubj4nL4aOyDnckL9zoBB827AcsX29D+Y8oHCtw+LhCXic24aY0Nrd2i0VerwePgJwpZP/fvGuT14dEeVhvLdZuuY/Bgwfi0KGDmDlzVnxW6+sGL8Z08Bbh1B2Y78oxLd6WI/uhYaHIE1rMZTMqy66CIjNi7Zq16Nevn5A4uk6Pnzgu5Jf5rElBAh0ckRXr16+X+3DsrGOnjvIdE+pzH8FxmZKJkA8Cr4kD9UWIHjPumJWVJjqL5HAlJK10CPs89sdtxQUMHDBQnBJJyZ/JZoReEwNvs2tnAEe6DF4xiUZjE4Ku4KbNmmLe7HkI0KdJ5grmfj32vwOT3SAk3RXGTxiPGtVr4jbOI3VERkcMhV2JWF0EwgIeQK+Nxrxp8+S7+WWD33Xff/+9tCLTCc3mW+fvy31aTl6zlCYoPPDAAw9eFf5L/JTbylz66dMm49ZtcjgHAvx90aRpc5lGedE4Bj6mxWrDngM2KU9tVk+NHFmUuPfQLqLsxDlm+HgBlT9Tuhynd65JrttqQZcBVjx6Yo9f6OZ0VOpAoHFt11yKIuk3rdVo0umqCFBp06aX7Y+NNSAqxi5c12Znjqjj9lxwH9FHh4bVbWjVzYD3q+ol9zV3dsf2btllAXWoH/uzxMvBbeiOPXnOJqYB5yJ8woIxxlvt+s0mEWIVy6tkaov/fe6SzT2nDlTg5p2UF9idf9+wwEum5LgfoWE2iYbgdBp/nPyU2bfp0qVBcHCwiIaLFi3BVx3aSbfBp2VUSBMMXLyqkH3cuJMuZLvb2K2ftjqiH7TqlEVc8mXGPNDIsGK6Dr8eiMWCBTOwa9cObNiw+YXdny8L5JuEtzllLqM1eEu2LLMFGQfA/H+di3IoDfNXQ3PgCs7L4g2niyhAL1q4CEFRmZL1Ijjuo0OGkFy4jrOy+MFsXV73LVm8BA8CryJTRPICrFCfB4jWhOHrjkOf+/qQxcA9vusJX0MqMSwkFKPJEzOH5cP91FfQu1dvWVhKmBfpnMIjB9TFuc+R1PtE4d2C7rsA2rZri/ZH2ovLlOaJpKDQGaUNR9t2A1zuV7du3aQr4aL9L6SOyoDAuHQyTWbQxCLM96GYEr799lvhj68CtWvXlnzf6dOnY/fuX2H/+2KZYnL7DoNQv359j2vWAw88+P8nzhJc1V60aFGKt1m6dGmy3/HLnO4E/rwNYJh869YtcfjQAbRqqEKjmt5In1YhboDFa0IkgJ3NpIMHD36hxyX5Z+7q2jleslrvxIelVCJWjppqxozFZilccAV/f0f2FIVHts+S6HH1nmSN5VV00tJFkC6N61XUTz9QYfwsg6wi5siqQeNaCiz9yUF6eb718WYWFh2zGhFmiQY1NDh2xoqVG6xC+Ei2UwUo8F4hisp2dOhtxLJpXuJOOHrKiuJFHNmuSYl/+54GXLlpw5rZXvigpGPfb9+ziTOCIiiLwpKCuWfSSLvWjFqVHS7cpKBreO0Ws0QLZEirRIa0wNZfzFL4wHG8Lm20kttLZy1dshNmm3D48AEZB+JFGFekWUjB9+3RI4dgtpjxQdkCcqKnmPfTVuaFJd82jszRqcyMtaU/mTH4O9ejYHQIHz5ug6830Ki2GuU+dPwwKuLrPtulOIuE4nUUByQFXZZ0YVIYdOdO4N847sTbcnzMz5IqxWbUQH16PLbcFjJLQkR3rpfCB37G1CncJy1CAm+L+MsLZTq7z58/jxkzZiDWL0JG1NRWLYyaOFmdNykNmDp16gsF+/P1VSs0SBOT2eXfKTYHRWTEocOHhOgnLHNh5hczdUOMd5ElLL/LY8Vxfyss8tlyB47SHTxwEBfO/4XAyPRIpU8rBJaib7jfQ0TrwuQ7hWKwu+/frdu2YPDg77F37x48sF+L/xudGoMGD3rpkQYJwc8fXxv+sAiOFze8iEzqpPg3wezIR48eSZQOIyrehPgQDzzw4NXjv8JP6QjmBFSNimpMGuLgXiyKWr3JhAWL5uDMmVNYtmzFC42n8zyt1xvFaVq36lO+w4moYb11Ehf1VS9jsp4EJyi+cmSf0181K6kkaoDuTZbX0nwQG+voL0jquHXio1JKWRCnsJ4qQIU6lRlN5ciFpWmAi9vkpyzOGj1QJ69ZsSIqDP5O68iy3cc8Syv8fBQSN3Xlug2Dx5gRFKiUXgWWY5G7Msog2fGcbsbKjRaMGqAVYZpiMeO9Pqyux7zlZkwZ7nqbWYTGBfp+XbSJ3MAJQccw47acfJ/XEM27mJAtkwLdv9Ki8mcq2a5Dx2yYutCEg3+GiEDNcyyjOo4cPSYlXD//vAOXbkUjY9ZMaNSsHAYM6C8O3OF9tMl4MV24y9ZbhAsvX29Fw5p2t9x5zRazRDiwA+LjMmr5aVzLhjptruHzzz/D7NlzUaJECbxuODMRLUoLVFb35gGL0gxfnwCJzSO/C9AHu39MUwC8Fb7CY8mT2IegN+iRJYXJMW+zP3xs/mIeoDhLXjh12lR0/qYzbnqfRUBUWujMPrCoTIjyD0GMKkI6BygWPi9++eUXPH7yCLkii7rkltJpEJUNV71OyHujYcOG8X9jBAbzifds3yd80pVpIlYbiRhVpHRCuEPlypUl1oK5tHHaKJmmoymDJa/hPo9kMqp61Wpo0KCBy/tzMWzd+nWyOLZmzVo88b8T/zdy537dR6FZs2Z4lShVqpR8/3OBjREX3Cbmhr8pGbMsQ2MhE/ODee35Xyll8sCDNx1vvTj7XwFF1AMH/sDSqbpEeayMB+BP0YJKfD9utoyoP68YcuLECaxYvgTtm2kSCbNO8ATSo6MGKzeasWmnFVUqJCYkNptdygGyZgTeycdsKosQVuaDMbuKo0kE3berZ7v+0meWrdymrRo1K6rQrodFHA8WqyNq4OETR2nX8dNPnSV0w1KY7fG1Bl3bahI5Yh88tqFlVwNadDHg17Xe8vh8nKQ48KdDoOTxdAqzBGMb6B4eNskR5cCcWueJlIIuXQV3H9hx+54d42aa0bOjJtGJ1miy49tBRhGUW/2deXv3vg3fDjKJk2LmKF386BpFbQq4n32kQr32ZvTo0Q3bt/8sf8ucOXP8WL0TfP6jR4+g+/d/iNO5SR2NxCfcuGMXMXb7r1YpQWPe7tQFFpQsqkKVCok/4hTKO/U1IJW/I6KhRb2nrylvW7OiBdt/PYbKlb/EmjXrXmqL6fOAkQMTJ05EjC7cpXOAoHAYhxhZsZ88aTJUZvdEmaDYqFSo4sfiSJLUJm2KkQAklFqFTm5L8DXmAgjzqufOmYsjR484bqdUoUrVKkJ+GRPwIjh65Ci8YwPECewOztwuun0TirN0Ng/9YahcvNMxQaLszI21KiziDHjifxsdv+6YoshOskhXLksuVq5chRD93fi/5c6VG6N7DpeLspSQM2dOLF26BHfu3JHtZH43xVwWHbxOZMyYUX7eFDDDfMrkKTh56mT87zJnyoL2HdpJDu6/5U73wAMPPHhZ+PPPP0WY7d9Vk6gk1d9PgX5dtfjsYyuadDwqpUh0sz0PQkND0a9vL5QsSj7m+vzOqKxyH5px8JhNuFFSwYMCJ7lj7cpqEWgpqtKFGh1rR2QUtw/icj263cdlRivNzDQblH5PgekjNegx1CT349c2HbsRUYxJYratTcb/WVDGuIReP5jwxacqTBuuSzR9xd6B7t8bJZphyxJH7BX5sjPuy4nwCJaZmfFtOw2a1nm677xd13Ya9PvRhLy5TOjYQpPovmf+4vSYVfazYx8DZo/1SrRfPEYzFpmla2H6SF28GEq+zOOyebF3oqzcj0rToOEl3LVnz+/k2oLdHvzh+StplntsbCyGDx8ui/9tGvGawtHhQOfy8vUWvJNPic6t1WjXw4TR08zo0zk5d+42yCjHmMemb+en+06xv9c3Wgwe8wi1a9fC9OkzUL16dbxOsBjOx9sHkT6PJZbAFawKK2J9I1CpSlOZgNMotG4LoQhyUPLXhNxUpVCn2AFAqI06EdacYKkvIwv4Gdu+bTusNmv8NtPsQaHzRUAe5630kZgGd9BaveCLALltQnGWoFN+377fcDftJaQPyxH/OIzJouP1UdBNKXSliOsOfG9w0YeGh1kzZ+HGk7PxfwtKHYRe7Xuhc+fOKS52c0Gc0VacgKM7mTEr5Igffvjha+VfNAu8SYaBc+fOyRQuOaozUoMRZ02aNpHvaGeHjwceePDvwCPOvgXg2NviRfNFvEwozBIcv2dJAp0ByzcwiHzRc4mzbMDt3LmTiKAkr+7A0gX+nYJoUvxx1IYr1+14J69CmnAZfVCvqlpGtOL0jtEv5nIdOWET4uhqFGvnXos4Pf19ITm3dAMnLA/jqj7LAVZvsmDibBO6ddBg6nyT/J3klaNPSqU9PnKBztZ5473wcU29ZJJxPIpxB8zDTegkYN5rvtwKce4mBeMTmBHW9jsj8udW4MtyahGM2ch7/ZZdog3oupgy3yyO4YY1NUJuua1Lf7IgNIy5XyrZ7vBIYNdvVrn/999p44XZhKATpFcnJdp0Pye5Vu5EPpKVevXqY//+P3DstA0bdjhC/gknz+Bxqvq5EpkyAF/1NuL94mbUqayGj48Cf55yOI15LuY42/c9tMiVPTGx4b5wPC11QCzatmmJ3/cfdNvK+irAfX/33aK4cuYqvB/5QW1LXLxG4fFJ0C1kzZIN5cqVw+rVq/GX7tIzyxBsdqs4Kp3jplZNyu5cG6ww202JGmAJklz+MP8vJiZG/v6/juxLbqw95RV054iaq9xYRoHworhP7z646n0CvtYAZhnAoI2VSINOX3cSUvoscPvpLujTp4/kBZLAcnGA414vssLPkds3dez2dYMjyHQc+1kCkSU6P3Rmb1hUZkToH2PokKE4fuw4ps+YLm7so0ePyhghBW06kzyirQceePC2gFn1ObNpJLs1IZjzGhJmR/YsCtSposCyZYtFTHkWn+C57quv2iE6Ro961dwXrxJ1q2nQub9RsviDE5yqmUPLItiihZQiDHLyqnUjtfBDRgbsOWCVToMbt+xYuMqMTq2SP88vv1uFK1Uoq0St1gZERdslAqru3xyXzmBms06ea0azb4xYNctLXK2M65oxUgfqY+w88PN1cDcKpVOG6/BJLT1GTDKh/EfsaQBOnbfivUJPeSiFZD6vq8ktLr7T+UvheeEqC2pVUgmvPXzcisMnbCI4kypQsOYEWONaahTIo5T7rNhoxqWrdvj7KmRxf88fVuz63Y7oaDtmj9G5LDGj8WFANw227jZIxn9KmcfMh6U4S2NGh17GRNyUQvepczYsWGnBpx8oMX2RGdv3WER8pgmC3QsrNji4M+MfeK3DfUv0WldRY8g4EwrnZ5ZrZ1n8fZEM1X8K8iSKV4vmL5JFczpYk2Xsp7oBKO0yrUQ3LCOhyFlduUed9zFrjIm5qd0i7luaCtzBqjUlE/uYV8uCbIrkvL7j9iblr88Lxqw8KzOWIH91xU3z5cuHtWvXSJ7uddVp+Nr9obCoYdbqYbQbUKF8BeE/z/ouoPBKAwLfd+SmXLRhrEWZMmWg1ab83ZAQvA/5sgfAwYMHJbNYaVQjveQg/12gHBOCBXMXYM+evdi4cYNc33DakO8nun0rVqzocdZ64MFrgkecfQvAsdjbd+5jRO+nq6lcoaf4t3mXBca/eRCbZDkW48pFkBQ///wzjAZHWUPgMxbJKB6SRDKi4J28T0/YFCuDgzgmb8e6eYkLGSiWknjR0Vu9hQFjppuwbJp3sgIvjlhxpX/UVAuyZFJg06LEq/d5cyoxbrAOaYMVmDDHjDy5FLh6047i7ypkxIsuVqJgPiVa1FejfnW1uF8/K6vC3OWW+MKyJWvNiVwdvB8JsavjxMyseeN04kJo1c2IR08cDozcOZQoWlCBDTtsIC9g6y0vPihA83koMhcr7Bjny5rjfZy/FgEvb1/kyxcDf68ryJzRPdmhe9bPVyUnzpQcmE4XZO0qKkyZZ0HBfAo0rKFGgxpqeY3o7J252OHwYE7Y5Rtm9BkRlYgkZ88C9OyoE9duUqRL4zgeHVso0fOHe/J+Sml1+1VgxozpqFmjJm4pziNVZDohwhRR6aaNSPUIal8l5s2fK8SNRVNbt26FXhOdjCw7Ee7zEL4+vvGlKyRpy5Ytk1Ep5su6QqR3CKywumyjdZK9f5p9VrBQQVw4fRH2SPcicYzO0QDnLjeW+8/RNo640cHkFPmYXfyiLlK6DNgW7ME/w9mzZ0WYDY7JJA4b52urszryjPl+5nuWjpP7D+6LU4bfQxabGZkyZsLg7we/dleQBx544MH/gt9+24M2DR1CHkFONHeZGas2meMLTMkrHoc8xunTp585kn7mzBmcPn1O/ncq/5R5LMffiU0/W8St6cSvf1ikpIvRASz9atP46d+4QP7FJ2qUfFeFyk31mL8iuThLcwHjpsiZfpxqEb63dYl3Iv5LTtqtvVb6ClgKtniNo3fh0w9VqN3GIJEJRIZ0CjStrRaxNXWgAs3rqTF6uhm/H7HJ40+db8a88Sz+cuzrnfs2ic4KTu1inFyhwHdfaaW4q3kXA1ZtcsR6yXPUUWH1Zqu4eRmVEJhKgXkrHCW0/B1jyi4r7Mj/TnFcumWURcBChbxx8uRRMTu4A6O5yhR3cNOUxFkKjFqtGp+UAe4/tMh0Vu0qarRuqIGXlwJ7D1gxY5FJDB18bqMlHUZMfiBCNLePGh8zbts10aBLm8QTcU4nNh3LlcqppDOBHI7TTK8TXMDmwuqZ02eRKjpt/Ng+p7kiAh4iTh2NKZOmyOI23axc9GZmatKeAieivcJgtOsli9o5OaZRaxDh/QhpYl1PPBnUcYhVRbnlCBRl/2nGP/lmnD1GjA10yLqCRWlCnDLaLTfldcyRo4clIoE/FPnISRlDUKiQ+6xZV6CI6+kP+Oeg8aJd23bQxvogc0i+RFN77K5gtu9N+wW5DmAUFwuF1Uo1zDYT/Hz98HXHryWn1xPN5YEHrxae8Lu3AAaDwyHJXFWCq97VW+hx6JgV3dppsGyaTlbk8+ZUwGy2SOGEu4Z2J/bu+QWff+x4+U+ctSWLK0iIk+fYfumN+h1MmDjHJC5YOgIePLKJMEyi6C63q3ABFWp8qRKSzNIB2R+jXZy4ddsa4OcDvFfIMT7WvYPG5eo90bWNRvK9vunnIMybdlpk7GrajzpMHKqVYqv+I00SZ0A3MUeo+Njku3TPjpzKAgkTnoQ4RuBIaCOj3BcTkBiWKKoS4kjnwSdl1CJOb9xpQ4dmajStrRJhmWT9ygEfnN3rg79+98Hy6V7IkE6DAgXewfYdv2D9+o2yuu/jnXIJAi8YvHRKyRZOCRxTypIlI6YvtIhLecdyb7RprIWfr1Lac1nqtmWxt1y0hIeHIjw8CrlzZZfcNK6iUwTev9HHpTBLXLzqeI0Y9VCkgEZW/183OCa/Y+cOVKtTBeFB93Et3UlcTXcCT1LfRoWq5bFt+zYULlxYbksSkSd3HjxIc02IZFJEeoUgzO8B2ndoH7/qW7ZsWeTLmx+Pg2/CrHzq8HDCoI5FaOq7+PKLL5EtWwqtav8QzZs3F2JO8u7OVRHmfx/vFHgnRcHe399fSiGmTZsmzgl+/v+t8X5+tq5duyZC8ZUrV+JHpv5rTjIvhXciYTYhWJrmrw/Cg/sPJS843/1SyHevFHKGFEH0TYNEZKxZs+Zf2XYPPPDAgxeBwWBEgJ8iPrOf3HTZOrMIc0um6DB/gg6flFHKCD+jmzjunRLo1sqbSy0TSifOWpNx04TnFLoxNRqlOCoHjjLg4J8WcW6y/IvaHl2ZXLR3BQqlnVpp8CTUkYnqfHzy63rtDbh1145yHyikzLVBdXUiYTYhPv9EjVLvKTFkvAVmM/DLb1aJ9hr/vVbiAyiUsrehWgu9HB/ySecucLGck1XdBhtx9aZNnl+nU8jkVlIenpRX0zHL56HQHBXtyHIlv+vXRSPO4AeP7Ti92xvn9vngykEfrJrpjYrlNDCbjdi27Wds3rxd+BPjGNzl9jpB/vosbkpBsEqVali+zoKcWRXYvcYHfTvrhJdSZCfnpPnivcJKKGDF/fsPEBwUKAvJxYuXRMZ0Khzb6S2Ct6sJs7sPbIiOZTSQErUqKbBt60b8G7mza9auwdedvoIlYyxupD0j3PRe6sso+H5+KcVjZwPBItl6deshJPAuYrR/r1IkgF4dg8dBN/HRhx+hSBFHuzDdsA0aNkBoqvuI0yT/nLCP4WHwNclMfdGoghcBhWWKcSF+T2OukiLU9z7UGk28sOwKGo1GDA6MKpszZw6GDh36wsLsy8TDhw9x7NgxGetn/NZ/DRs2bEBkVCTSh+d0GafGguSgiEx4+OAh0kZkQ/6HpZD3XknkeVwc2kf+GDduHPr37/+f5PUeePA64XHOvgXgSIFarcLJszZkyahEh14GlCmhklGkhJlSjB/gyv23g9ZK8QyzhtzhSUgYPiilEpcnnQN5ciqwZK1FRNOYWEejrWMcTY3fDpnx8cdl8OefR8RNwJzZhGC5QUqo9Jka63dYUaKSHmmCFCLsxumBch+qMGGIDr8dsuDkOVOyfNSEYHbXF58osW23DcFBCvw01ws5EjT0siDr8AmriLODxjpIJPVpHh0WgvHYcbv5Q+GSRRF0l3L8jWNorsCxOIq7LJEwmdkYzFIydfzzBgWaxM1br5rDretEgTz2+GZXIleuXFi5QiGitLsmW451hYSZRZhMCVyxLFq0OB492ibFE9w+XoxwX4MCHSIvHbokuN+PM4l4vXzdPcyaNRO1atXGtm0bko0AOsET7uI1ZpQuxtIzJTKktyEyMhL/Bug84Mj+kCFDpCiN28ZRKZYNJAQdGMuWL0P9eg1wXXEK/nHB8DFyTMeKGP9wxCojpQiBxVcJ3SeLFi9E7Vq1cVN5Fv5RwVIOZlfYxMkQ7RuGvPnyYsLECa90H0nI6XzdsH6DPDfbZJ2EiULz41S3YNDF4Psh378xBQIpYdOmTZg+bTrOXzgf/7u8efKiY6eO4pZ4G/bhZWDH9h3wiwpOMdOYDoUo71DoLN7xt6Pzm2Ltg1TX0LdPX3FE/1vN1B544IEHz4Ps2bLixLlbaCMlq3RkArvXeCcqYf3yUzXaNraiUcdb6NWrh5Q6uUNoyGMUzm9HujQacd82ravB1l8sWPaTBQ+f0HVJN6dCClnXbLahRInSuHjxLyxeG4nFax0iK081vJ0zHssd2AMwYBRQt51BnLCMYmCWbIE8Cvw0z0tE509q61PkpkSNL1nwZRIBetFkL3xSRpXob3SCNupoQOvuBrRpqI7fRkaCMfpq/XYr1m/Xy/RV6lSMTHD0Inz8vmvTw+kLVhGPs2YGzl+2oXQxFaYMVwvX5XmWQnKzzkbs3GdDzYpPt71QPuDE+nuJuGlUtDXZVFxCME/35DkFatRKmZs6F75Z5Dawm06croyCiDPYZZ8oAvN65YdeWlRpZhBH8/nLMVi1apUUtVI0O3bajrKlXT/24jUWiT+rWE6Few9siKQi/S+AAi0FKi6Cc0qG5hlOtPFYJsWPI3+UotL9f+yHvzk1fOMC5Xwf6x2JaG0YCr5TELNmz0p0HwqYly9fFoeuf1wQAvRpJGKAk14sn/X298aSpUteaKz/RUEjw4CBAyQWi8+dNiZLfMSYVWFGiN99hPrdR/+e/d8KjsIppYkTJmLfb/vihcU0QWnQsnVLdOrUCV5ert3B/9+wc+dO+JkD3bqhndz0UaqbUNvVUNod3z+8fYbonMJXWWBZq1YtiZbwwAMPXg084uxbADoeOVq+eO0uhEeaZbWdTllXJQYUaLnyP3/+bBlBcpdf6OWlRVi4Bd06aNGsswEVGxnADHAWROXIqsCte3bJZeX4FR0I+/fvl9gEkqxrN+3iEGDxLkXWZ+WqUwgluN0U/jgGVrGcWmICCObeEtqUe52kwIq3nfSDLpEw6wRHr3p10mLkFAdJZq4qyS6dwRSZPyqlAhf/t/xiwe79ViHHA0YZMO1HLxE1KRpT3D7zlw0xMXbsPWjFuwUV2LrE0dKaFMxYm7vcLE4BFl84ERquQN4MTyMc6AyYMWMG1mxm9ILrnWRERVBQqhQjBJi3xPGgc+fOIl8uBZZvMEszL3NwCRaBNaqlkbEwvg8GjjbJxcaKGVq072XCnj2/wMvLF627xWHRJF0iUZpNwCOmmHD0pA2LJ+scDsibSrz/YWIx9HWDToJnjTMx5/TnXTul1XXJ4iW4dfuavO/LvF8Gbdq2kaykpMIg3cw/7/oZs2fPxvJly3E7+oH8Pn269GjfsjvatWsnY/6vGuPHj5cGa257aOp70Bl9YFfZRVRmQP+8qfOSZUjTlUp35u/7fofZ4hD0W7RsISL0v9W2yhV1uiN4AZI1ugA0Vi9p1X10/rEI43Qq/PDDD/8JgTZOHwcfm/uGZsLZ9mxTWoEE5jBeuKWLzoarvick4699+/avenM98MADD/5nNGzUFCN/HIaNH1tw4bINa+Z4JRJmE7o9+3a2YcCoHbh7967bokqdl7dwqL6d1Vi5wYyKjfSyys7s/PcKKRERZcfarRaZHuLp5Mnhw0idisKjAvce2RERyTgEiMmArteUoPlbuKX5IWdWOz5+Xy2mhdLvOUROllsRz9LByJ/Ik79tr0kkzDrBBXPyVmbXMm6L/FTFMrFrdhGQ+3VRC//946ijF4B/HzDSgC1LfcR1ysdnBi65fUysXXocmGW7f4OPS5fppx+oUaaEWYpiE4qz7Kfw9nnKZ+mcTZc2CLOXRsn2ucL6bRY8CbWgadOmbvdfr9dLBBaFWTp6r9y0Yvxsk0zMESzlpXPWWT7MKT92NIz/XoesmRQYP2stCuTPi64Dr2P5dIe47AS56IYdVsxZZhaRm5FpvAZJlzYt/k2Qa3GS7Vm3oXmAC9cLFyzC6dOnZCIqX9586N+ml7hOk3I2/jcF68WLF2PhgoW4c/ei/J5lZE0bNZHJmpRKXl8WWrRoITFZw34Yhki/x/AxB3CcC3ptNBRKBfr07CPCZkJcv35dtnvLpi2IjolG2jRp0aBRA3nvpP2XXi+WXrVr1x46ixcyROaS8X2aNyJjn2DShEn4/bffsXLVyv9EnmpMdAyUlpRlH8Z0MEvYqkg+fRuoT4+IwEdy/eERZz3w4NXBI86+JejSpStq1NiFBavMqPq5yu34v7M04KetD3Hy5EmULFnS5W1KlXofP237A19+6sh7qlxehUnDdLK67cR3X2nQc6gRm3ZaMW6wVnJNSVg5crV8nRljZ5jlvvsOJiaASbHvkBW+PjqkTmXFtBHaZFlS+XM5iBiJabkPkz8Ox7solO4/YhOxlSNzEZF2WUFPOo7FzFkWPVCYJcFlwdjqWV7IlOEp2eO43aHjVhGlt+624eETg2TWkhQbTY78WoJZuCxQmL/ChLZNtC7dvJ98oMLJc9ZEsQBn/zKjU1dHvimRO3duaTMdMm6NkHzGETjdHCwqm7qA+WwWjBjRW4Q6VzlBzLBct26txFaQ6N42MgPYLEI6jym3haVsi1absXGHBStn6OTChfvD5xrQVYPy9aLQtWtXLFw4D6WrxIlgzRG7J2F2rNtmwcPHdgzro8VnZdXyWNdvmTFmfF28DWCRAkkrf1hmQIfxs4RAksWBAweid+/ekq/E+3AU7XUWMnHsa+zYsRI7QULOfGk6IihI16xZMxFh5EUKM8woKHvDF77RqaGxq3Ar/B56neuF2bNmY/Wa1bIPrxO///67CLPporIlyknzsvjA35gaYT4PsWDBArmQ+bezVJ0XDxvWb0RUVKQsfNWtV1diIV5WmVm6tOkRFxWb4m2MGkfet9qa/HuFDhW6v1mA4RFnPfDAgzcZXHxesGAOBo56LHykTHH3aWlcNB46wSJxSSz6cYVSpUpjzpwT0BvU0Ggd0QQrZnghXZqnj8uCr/krLRg63iR8d3gfrfAcujx37LFi8Ji/Hbz7LVJU644LsFeAsFismDbSWwrDEiJLRoXEaR04asX7xVzzgvOXrJi2kAWjjoxbFoyxNIxdDQlBVysduRevshPCYVpYPcMr0eNW/VyNb1prJFbhzgM7KjWOk9+xpJVRDRQ1/fwcHJwct9+PJgzvq5Xi3qSgAWLU1KdRBJzc2rwLqFO3ciL+0btPf/Ts2VOm2rq110jZmeOY2KWcbOBoC+rWrSNZ9klBTjJ9+nTMnDkNERHRwkONRjsGjTbLdNaQnhrkyKLE2Ys2rNxgkWxgFvby2JCbEoyWWLzGjsJFiuL4MQMqNr4jURAsP9Yb7Ni624rzl2zC7Rl9xkmxLbtt+O67hngbwLxUTkjxh8eLP8/K7KSTk58Pnv85is8R/PTp0792h2ebNm1k0Z8xS8yL5rYzloCfeWeJWcKR+W7fdoPSpoJfTBD8rGkRGarHhHETMWvmLCxdthSlSpV6rdsfEhKCr7/uCN+4VMgclje+YJeg2MzM4BPHT2L06NEypfdvIiwsTGIxVixfIW5rnc4LFSt9iVatWkk578tApsyZcM7rQoplyEaVHnaFHZokZcwE78PrjkMHD72U7fHAAw9cw5M5+5aAGZsLFiwSQsZog5TAYi2CjfLuULVqNdy5Z0H3IUYRMCcPTyzMEiR8XNnOlkUhwqmT4HIlnwUKowZqZZyeBVk3brvOuGVO1E9bmS+gRnikBR/X0qNjX4Pk5TrHS5hBlT4NMG0BXcGJs2zuP7TJCFSrb42SY8WG23sP7OjY1yjjZhzHSgiK1nRNsFjAYgEmDtUlEmad+KCECj2/1oiAe+2WTSIdmI17aIs3ti9z/Bzd7o2GNdUYMt6Mj2rECVFNun10ZjijwSi09h9pQfr0aZI5YEeNGoUaNWuj1w8mfFDdJBljPA6lKhsxc7FFxod4Ek4Kjkw1alQfGzesQs+vFTi92weXD/ji6A5vdG2rwZ17JNxWIbPDeuvw6xpvuShp3tUouWbOuIU8OZUomE8rIuS0aTPxTsH3xH0yfJIJK9abUe4DFXYs90KrBhpxNHw3xIJixd59K1dHKa6+iEOTYiiFOUYpvE5hNiFy5MiBvn37ivA6depUIb9JV/LnzZsnf08fmQM5HryLdDEUQzMjS2gB5HryHu5cvyctrBSnXyfmzZsvbbzBsZld/j0oLgP8LIGYN3ce/k1QEChfvjyWzF8K+20NgkKzwHJTiXmz5uPTTz/Fnj17XsrzNG7SSKIxmA/nNkuYJXXGVNDYXLuV6Fyga8UDDzzw4E1fGF25ci0sVq1MV6V07qWrMji1OkVuWq5cOfj5+aJDbxPCI4AFExMLswSfgxNCNSuqZCHZqXWRs9IosGqWN6w2ZuAC2391fT6kWDlziQWpUvmJa7V2awMad9RLyS5FXoIuTXJFxj0xAishKBx+3duASk0MIrYyIoFGgh8mmGTxe9tuS7JtzpVdKc/F2/f4WutS8CW/nzxMJ/yVHI5OW7pLd67wwp6ffLB5kTdO7PLB999psWaLBUUrxGHOMpM4apNx07/pMbk2uV5EpFWy7hOicePGsvg/b4VV+GjHPgbhpx/VNKP79yZUqlwNY8eOc3kMBw4YgJEjR6J2RT1+3+iNy3/44MJvPhjZ32HCIK8uXkQlMVv71ntLHFv7ngZcuGKT2DTna1apPIvgjmPM2AmoX7+BmDE4yTVjkRkZ0ykku5gZvsycbd/TDG9vX9nutw18D7xImRJvy1g7Tnr9W6P3XMCmUMzpv5kzZ6Jz587JhFlOc9H44ReTGrnuF0OGqJzCTTNF5kHuB8Vgj1AKN00Y9/Y6wIk0i8mMjOG5EgmzCQXawOj0WL5shZhg/o+9qwCTqn67Z3q7d4Glu0RQwFYEAwRE6W4ESaVERcIgFSSUVLq7EekQQenuhoXtnp75nvMOs2zMLKn+/ZzjMw+4U/femeWee37nPeefAqPbqr9eHaNHjkbi2XQEx+aHLioA65ZvkAJj9kk8CTBeLF2RKhEZ7hDvGwWVTQ0/Q7Abbqr0cFMPPPiL4RFn/0UgaS1evAQuX889jPvyNcf9oaHuR2uZ38lxYzpDmaPqauWdoDO1ZQMN1m+xChnNDDpASRo5ntWokwHb91oySgxIBinoNupkhMFoQ94wPdo21ogL4Mx5G5p0MeCjQUYZu2errt7ocB2QGJ4+ZxXizLyqph8aJPN1+U8kpt6YOtoLa+d449dF3iLENvtQj4PHLFi+3iwjcFt3WyRL1t9XIS6Fyk+7/4ozQ5Y6FvspGtVRYfQgXZZxPBYZfDNAJ+26UXfs6DnQiC6fOLaZMJrs4gqmk3XOUjPqtDbj1HkVpk37OUceFP9/woSJ2LRpE958uxmuRT+NmORn0a59N/z++z4hPK7AUfAjR45g4WQdurbVSomFs0GX5H76dzoplFizyXEBQiF6zFAdrly3IzwUeKnKvf2h65gr8CRb8+fPx5tv1oTZApQsphG38NGTNnw4wIi3muphsflj0KCh/4kx9EcBC/codHOF++8Q0VjGMXHCRASmOxqCKfBlBl2q+WKK4/SZ0zJy9HeB36ft27bBLzks14zVgNQwHDh4AAkJCfgnwAbwrl27wTc1yHHxkFIUoen5kDe5mFw8aJN90bFjJ1y4cOGx36tVq1bw8/fFzfCzsCizCrR22BAVcBEGTSrCUl2PJtpghcErTf6N9sADDzz4XwfzNus3aIhrNxS5FlmR08XGWXLlpsz0HDduosQ1vf6iI//eHdo20eDaTTsOHsu6SM+x+No1VOJ67THQKNNW5JROnL1oQ5teRpy9aIUC6cKBG9RRS2xW98+MEj8QG+94fLreLhEJzbum4/cDFlmEJ7/t8bkRW3+zipD650YfTPvWCwsmeeP39d6o8YoK3T4zYuM2i+TlcgH8l+2MoLI5ugGUkIV/d2CsQtFCCkRFQ9yydA6XL63KInJ3bKERwZLbNnyCGe+10+NOzL3jsGWXRXJ0uQ1Nupgks3XEiJEoUaJEjvej+EYe2rFTD8SmVhZ++nqNxti4cSMmTZrscqJrw4YNmDV7tkxbffWJDkXvRo1x21o11GD5z14iaDPegGAM28RhOhGM2XHcuK46Cze1mC3yPsOGDUPfvp9IdFpEuAZVKipxJ9aOwd+a8EIdPY6fAT79dKBEXnng3oV58+ZNiZv4O8BrG53VB+FJhYXlZIbarkH+2NLQp+ll8ut+ZdVPEhvWbYBvejBUdveZeex6SNenYe/evfgnwILE5s2aIz3GiGK3n0H+xJLCTWm+KHqrIsJSCsgCCKMxHheMSHu6wtO4HXZRio8zg9cUCT63keB7G6GpkVC6kYcMPskebuqBB38xPLEG/zI0atQMo0cNF+LIMSRXmLfcgiJFCqJixYq5vhZH7ceOHYsSd7Nf3YHkkCIes74y59wy64okdMNWi2RZtelpRMFIDQoXAK5HAVevm8WZ+vUnWiG/TqGPbbKrNlrRZ6gRwYEmvPGqo22WeWKbdtqwcbtBHufr7RjL377cO0fGLMk3CevrDdNRv4PDJXpvu+jYdcQe5CYuUuiksMr4go8+cD/6xpyrBSstItIuWmXB8IkmDOmjw4IVFsk3273Phl37TJJtOnZ8N7lQoXvRlQuTDmiO0DwIKBQtmD9HHL2Vn3bt6OSxe7GyGV+PM4pzg2NlJPYcL6Qj2plHxs/u5DkLqr9dLEMsnjZtOtatW4dp0yZj8LfH5RiSg+cJVyE1LVlaZ+vUeQdjx477W/JX/w0g2Z01a5bkh9285WiyDQoMQqvWrWQELbujIDNIljkaRnJatmxZibt4UNA1y8xheANJPjGAXYEAQyhC0/JJkZTTBaAz+2DI4CFSSsWiib8654vHw2a3QW3LPTDaOb6flpYmiwN/N6ZMmQKtVYfIhBI5HBQsPeDI22Wvo3Kc6XJ/HLC0bsHCBWjRvCUuqg9JSZ3W7A2L0oRU/3iY7EZ4mfzgYwpw+fwEnzsw201o0aLFY22HBx544MHfBeZnctGXuaiu4qkIRkdRAKUb7H4iQkR4GEoVd++wdU4EESy2zY43X1XLVBHB8f/Rk2woV5KRAMCJM2bhiY3rqDBioC5LPBZjqjr2MaJDbwPmTtTh94M2VKmowNGTdjTpYpTHMO+VouiUUToxHGQGF84nfqNDTKxBhF5yZycoylKbCgx0THm5A7lonnAaLYAeHbQu+yWcMREsuS1d3BEd0Lm/EatmeuHISRt27bcJp+PPKleuhOnTu+P555+XhV5XZVKcHGLE04OAE119+nws5ow2jVx/1hRr2bEwebZZuHPp4iqZuqtXUy3CsXOijUL33gMKFCtWMuO5H330kUxtTZ8+Dd9O3iQTa4yBCA5iXJVdpozmz5+NKVN+kqkjD+5m865cKRNKR48dlUOi1WhRv0F9Ed9dxVJkFggZo0Q+xymySpUqPbAxg+aRbdu2iqPyfN4D8jPGMoWk54O/IUQW7ZljGpAWjjWr1+DI4SOYOm0qnn766b/8Y0tJSYH6br6/Ozi5K7npP4Fly5YJty8e80yOKAEeu/DUgjDq0jHu+/GoV6/eYxlm6MSePWc2mjVrjrP2o/A3h8Bb7y8ZvGn+idAjVT6r4DTX0WjpmhSkqBPRtl3bR94GDzzw4P7wOGf/ZaCg6u3jiw8HmKXAKjvo4FyzyYIuXbrdd3zG19dX/qFnllVuiIp23E83qivQfUrSSXHyjbdbIjhPLTxbxUG+B/fWoHUjTZYTCv/O3Nd+XTWYu8wijgLG0JJgM1Nq2rc6EXRpwHj3bZXL8i+nuMrXJs888Is3rv7pg5njdKha0fF4d1ELTtCRS2GWcQnu3oOgm7ZcSaWQ6p4dNVIA9t1kI4aOMaHZe8yPpau5Om7evCY5oRUqVEClShVktZPuykfF+PHjYbbY8e7bua+hvFdLLe2+rXvpZRyPx5fiLImwE7MWMTJCIblXTlA8Zg4oM60KF1BjzgStjKXtX++Fg5t0+G6IFjt3/Iq2bVuLQ/JJ4tSpU0Kwa1R/A69Xqy5knG29zqiLvxMkpidPnsTZs2dz3U8+rkH9Bhg+bATSLhpRIL40CsaXhfKWN6ZOmopaNWvh6tWrWZ5Dwjdu3DjJ2uKxpoBLovzaa6+hUcNGItbeDxSD6SZhY2qelCLyviyNovvycuhxJHpHZzyWZQd83OE/jsi2cpufJHhRxhE25swyH5eiPYsqjOrcR8KMmjSoVep/xO3CC47169YjQNy9rn/PlVDBPzkMS5csfSKxELy42b1nF/oP6I/gMr5IzRMDdRErWrVviYEDB8KgTcXtwMtZog9ssCHeJwrRQVeljMNz0emBBx78W8Bz3LPPVsQn31hdcq8/Dlsx6kerZFg+SC56aGgYou7kzuGYk0/4++UuWBQtWggtWnZBaL53ULbC+wgODkSdN1T4bqhXjt6CZ55S4ccROhw+YROjA0Vc5sRSYKQYO3aoFr4+EGHynRquF825KM4sVQqzM8bqcO2Aj8RG1X1bJb7ClFQW3LrnOuRBV2847meUlzswOoD3X79lw7eDtFJ+S7G2dU+DxIVREOVCcEBAIDp37iyCWJkypdG7d2/hYI+KNWvWQK9PE6E1e4dEZtR9Sy3HgJNyUdGOz5LHLfOlCcX8k2fNaNY862IkczYTEuJFmP52kAandvng4CbGjWkx7wcdUpMuoGHD94W/PklwGopFreRzr71aDa1btxEH8T8RM2Q0GnHmzBn5rFJTU90+jgv+nITs2bMnLh28hvwJJVEoriwC4/Ji9ZI1eKfWO8LZMoP7s2LFCol6Yo4sY9W6du0qCyevV3sda9euve/27du3Dw0bNJQx+NC0SOGm+RKLS2bpjeCzuB1wOWPKy8vsK8V+sVcT5DncrycJHoPjx4/LfrKAlr9DzFg1eznMPu7g5K5/d1eDE4sWLpYIAbcRV1AgKDUPzp0/+1i/s5nNA+vXrxNjVtHKBZGeLw6WAqmoUaea9F+odErcDD8n2bNO8DNM0cXjVvg5cd7WqVPnsbfDAw88cA+Pc/ZfBoobs2fPRZvWLfHye0Y0radAhbJKJCSy1MmOwyfM6NixY45cKVfgCFGN6q9j3oqdaNvknrM1M3iCW7zagmovqjKKApzgavbWPVZxIjzzlBJHDh/AjBnH5D5pbvdToel77lctWzbUCJGcv8KCt19X4Yfh93JvGR0waLQJLz+Xewboy1VVGP+TGUdOWrFigxWbdlhFLCbOXbLj8HErnqng+jWWrHG0/T6IHsj8Mj62RX01vp9mxoSfLejUQo3+3TTYuc+KHTu2o8bLGhlx44jW/kNpmDN7MpYuWYjFS5ajZMl7roAHJWUbNqyTv3vpcr/wcLoq/jhsF2cvLyI46vf8M0qJouA429hpZnTv7siKiomJyXjuhAkTYDElYNksjcQ4OMH3bFpPI6J1o05/CFGjk/Zxwe/Tl19+ienTp0On8IJ3aqCQj/UXNsoK8nv13sO48eNcOjueNC5evCj7v3rVapgtDpEsNCQUrdu0lhZaLl5kRv/+/XH6xBkUji4Pb8s9JzFLrzgGdMN2Bu3atse27Vvld2nx4sX4/LPPRdDUWXzgZfeFQZMueaJ0vR7dexz1368vTbF0tLgjvxTzQtLyIU9ykXvRAUYIGY4KuIRbgRegs3iLg5buTI1Vh7zxxXAFxyXigMI38TiiIwVOisxz58xDUvI9N1PVKlXx8isvY9eW3QhLzS8iZ3ZwlD85IBZ1360rI6v/xIifxWqRzyA3MBoi1miQiyDmKD4uOLrLiyXessPf31/K6JJ8Y+BjCJDvhNE7TVy1rVu1lvE/DzzwwIN/C3jOmz59Bpo2bYS3ml7Fu28r8doLKimW2rjdhq27rXj++edktP5BULtOPXw/dpQsogcHuuZAi1ebJSbguWdyLrpt3mWRWIOihVjadU0WvNhyT/Fm6dKl+KCV+wxPlpqVLQnhekUKKLBkWtbc25mLLahQRpmrMPlSVce58NotG74YZZXS1bS7a5hc6F+6lhzSNT9mTEPU3XV9231O2zyt89jzWOfPp8C46WbpIGCPRK+BBmz77TTUuICvPyHHU+DcJZZzrcSqVSswefLUHP0ID4JFi+aLKYGTZ7mBXNi5D6N+MGPcVzpcvWFDSLAj/oKRXH2/tODll18UtzQXyDMLwPv2/SGRZs9lyublMa/2ohpLp9lQs3mCZPRz8fpJYPny5ejTuw9gU0iJFEXH2xcOiDO0XNlymL9gvohbfzWYycyitXlz5yM5JUl+ptPq0KhxI8l25fc4M1i4ytKuyMSSCNLfm5byMwXLdNXN0PPo0L4D9v6+V7afRojOH3TGneg70Fq8oLNxgV0vDkpfYxBun46Tcl1eK7AUzB2vatumLdSpXigcVxoq+73PKFifBwnedxAVdFG4KV20FpUJCpsSBaPL4VqeExj2zTApCXNy00c1ZvB58+bNw6QfJ+Ha9WsZPy9SuAheePEF/Kb+DQZ1uvA7V0jwu438kQX+9rIyJ+7cvg2tKWu/RHZ4mR3bTrMPhfTHBfssaPTiLTs40dehQ0dcVB+GryUQSrMKFm8j9EjDc1Wew88zcsb2eeCBB08WHnH2XwieRH7dvBU///wzFi5eiClzHCuq1aq9ipkzO+Ctt9564NGHTh90RvPm24XQsak1+/N+nEnh0yaB/NlBcnnjlh0+XhAR9eeF99xzN27cQImizPxyvx0c6+IYPbXFCdkKybiyzk1hNlVucOaIdfvMhIL5FBjaVysZVXQldB1gRM8vjFg63StHC++fR6wYO9UkJHzvAZtkkHE0zBWuXLdJ8RjLzbjKz6KFRu+qMLiPDl9/b0RsnF2OT+ZRvndqsHXXjubdktGmdQvs2r1XmnEfFCQ+BoNJyO/eA1a8+rx7kZr358+rwFNllOKcLlFEIXltYSE2KXhISraKa2LAgAEiFGZ2dS5fvgQftOAInet9Z2HFK89pMGfOzCciznK1lsKsryEQXhZfeJn9EGAIAZIVSPaKxdq16+Dt4y3Ohb8Shw8fRtMmTWHV2xCcFAlfUwBsChuS0+Lww4QfsPnXzVi2fBkCAgIyIglYKJUnsWgWYdYJrnpHxBXBOZzEnj17xM1BJwMzYgumlM9YFTcrTYj1uyG5ThFJhZFuS0KXzl3w54E/XX4/OI7vY/fPKszeBf8/X3IxpOuSEOcbhTzJOqTqEqWMQWf1hn9aKH6a9pPEY2xYvwEGowH+vv5o3LSxOHgLFSr0wMJskyZNcfTwUQSmhKOovpCMPuk1qTi1/xzSVEnQqDVyARAZXzILSWd+6q3gizCrjUL2/wk4Rfbs+a/ZwfuVCmWOIra/Alw8q127tgj4HCe0WqwoXaa0RBk8TNyFBx544MH/Cug+W7t2g4glc+fMxLJ1jgKgcuVKY/jw9iIGPOiFPQufxo8fi95DjJg6Omdh7Y69FimcokM1+30nz1qxYZtjob7JuxoMOWvOGFsmNyWeLut+Woo8ODhQCaPJhsmjchaSccSekVu5gVNZxLhpZuGzHZtr8OZrjNoCBnxjxKgfTWJqyB5ZxRJcRn5RVL5yHdi00yL9CK5A4fvXHRakpgMv1tXLpFW5UgqZIGMR2rbfbBjQXYPu7e9xe5ZvfdjGjl4DTejW9UNs3bYdRYsWxcPg+vWrCAl0cM+PP3D/OBb/8m0Za8Zis16dNFj7qxVFCinwyvsmKSXmdcvUqdNzTPqRc776giaLMJsZ5KwtGygwg4vgn3+eYzH9YcFCUAqfjIZiWafW6o1AfRhUyWoZ57545jxaNG+BDRs3/KXiVGxsrCzaX796XaZ5ChsKCtdL0yVi2YLlwuVWrFyRkftJYXPqlKnSRZBZmHWCC+aR8SVwQXcICxYswJtvvincV5mqRdHEpzP4LIXZBO9oRAdckTiC4NS8UhTHslRXnIRlW+npepSIfzYL58ss0Kbd5aZB6XmQ5B0Df2OIPDYoKS+2b98uhgdGcCUkJkgEQ81aNeVa5dlnn31gYZbmhdmzZyPQEIbCaeXFnGBSGRBvjMaiq4vg7+ePKJxH/ujSMlWW8VzYEed7E0lesfii97cPVdT2JOHr54ckZe7ZwBS2ib8jXo5GkQMH/pTIu19//VXMCow8YZkYtQdPD4kHHvz18Iiz/1IwF2jo0KFy8uQ/nmzyfBTCwPFq/oM7duqfIjwyI4pOgWu37Ji/3Cx5WxFhCgn554mQ/zAzv3TuMjO+m2wWkjpzvA5L11iRN2+ejNelQ45uXudzXIEr59QK2zdT58jU4lhY1UpKabxl6YM7rN1skW14sbISP43xyuIyXT1LiYad9Hi9gR5N66nxyvMqGI2O53DFnq/PCIXqjQz4brJJLgCyOyG4/WOmmEDdrE1jNfQGYNVGCxautCJfhElcv13balxmrLGMYeI3arzd7BZ++eUXvPvuuw/8uTibWbm985aZ8UELjbgNsoPjbKt+seCjThrkz6uUkrWPhxjh70snrQ1vv/22fEdckW+OpaelGVDjldxbYN94VYGRPxzH44Lu2+/Hfu/YL40eJrUBcX63cNuqQd7kIgg0hMOaaBHBim7PBxUPHxYUqNu1bQekqFE4hiv+9z47X1MggtPz4Jz9tJA+ujKc5RcMyHdFfp1ghigdCKtWrRLCSbLMttrMoiozpSioEjH+11E4vhyuqE+4/H7wYnLLlq3Ik1zYbdmWjDyl50G0/zVYFWbJT+X7Ev6GYFxPPIP1KzYiICUcwVYtjKl6zJ05DwsXLMTMWTPFrXI/jB49WoTZgjFl4XM335Yg0aUDmO6IZN9YmPzScFF3CP6pIXJhY1YZkeIXB6js0vLLuI9/AkFBQRIpcer3cwjSu3a9yNhWQCxq1Kjxt7kC6KylQ5s3DzzwwIP/D+CCJv9N44g0G9ApejzKghfz0hnPNXHieLzRWI8OzTQyqp+UzCkxC9Zutgr3k04Es13iCVjSumGLFV+Mdiijg3pr5Ofkdc78def0RkISEJpL/PmFK3Y8U4EO2pyiDQVDFtDq9VrJ+XcFFoHJ+3kDq2Z5ZzEIrPjZG827GtCwowG1aqhQ9021xHPt3m8V0wOdwoumeGPwaJNkttauoUZAppgqJxi7EB3Hcls1wkIV+GWbBafO2aWM7MYtG16uqpTM2uxgAfD3X2nxfB2TCFu8lngYKBRKRMcDt+7Y3E6ncWrrp4UOFy8n6H6YaUG3Tw0SV3blmh1FihbH6tWjUblyZblG4HclM06cOInPeuRuMnnzVRUm/KwXLss+h0fF+fPn0fVDh8rsFayHj58jhiEm6DKCk/NL7mdkTCmcth+TiAPGl/1V+Pjjj3Hzyi0UuvOULLI7Qe7FLNAbttPCX3fv2S3RZMx8vRV1C0XS3O8/Oa5fWjBWrliJ3/f+DoVejUKxZbNMOpE7sohKbVPjZvB5FIotjzT/BMydO9fl92P5suXwTw/OtW+A25scdkJKUMn36ex1TptFwY7FC5YgMDUc+SzFYVGZsXntVrlOGDJkiIi09wMNE/z+MkqBYnBmbuoXH4R4H3/cxmWEBIfiouIIAvQhkvdvVVhk3+gG5bUGF4L+Kbxbry6mXp8KW0oR+QxcgdFlnOpj1MffAf573bhxY7l54IEHfz88mbP/cpD4kgw/qqBAR+DBgwdQ+w2l5IT1+sKIeu0M0kT7xxGbrPiTDDfsZMDztfWo0Sgdld9Ox7eTzLJCv3WplxSKrd1sQ61adTF16lTJWqXb7sp1s2RguSJtS9aY0fYjg+RR/XnEhm/GGTFuuknIqbPtl0IxxWGOp7kCowxW/+JwR4wZossx/l+0kBKbFvqgfGmlZNuy5IGklcIsixTm/eCFwAAlhn2qlTiED/oZxSHrBMe/un5qxKpfrPhukA4DuuswtK+jmZfZs4xk4JgaXQHuQGJf+Wm1CHYPChY2kBBR0679JkfngMZd9Fm2jaLxoeNWIfh5whztuNq7HIn5v78sdAiudFHf1xVxn2kijuA97mrp5s2b0fXDrvAxBKJozNMoFV0FJWMqo3hMJXGtkgySgLA5Va3QYNGiRfirQEIXGxeLvHHFsgizTtDRG5IYidWr18jvB8kinUB2mx03Ay8g2SsuI0cru1CqNmskkzUpOUlaVt2JqmGpBWBX2GDQpMHX7o8dO3a4HG2z221ZVvtdQe5X2JGmS0aBhHtis3MbC90pJxcXJK95U4qgeNQzUCd7oX279hkuInfgBdP8efMRlJInizCbeZ/zJBUVUtmyVUt07fEhvIqrkBgeJRmrHTq3x46dO8Ql+k+CTuFUVaJkurpCrN9NpClS0LFTx7992zzwwAMP/r+BnIFuxkedRKAjcMXyJaj8tFJyX5nx/147A9r0MmL9VvJER/lrj4EmPPN2Ot5onI5n3kxHr0FGyYTlNBMF3dlL7ahe/XVZACU3ZR6lWq0UgTc7yD23/2YR52p0rB1xcTYM/c4oC/SrN1kyJrXI+ZJTgfE/u57GiEuwy9QZadOwT3U5JrdodmBUQvP6Kvyy3So8k/yUYisnn9bO8UbhAkoM6q2V8l+aDGiecHJj9jPQeTv4WxPaNVHj28Hkp1psW+aNSSN02LTdiiMnmZPrXjijIaJRHWD1quUP/JmQd27atAmxMbEoXUIh0Q7Mt922x5JlLP3aTRs69jHg2g07+nShs/me4D1vohfKldaK0FSlSpVcueX9Bt3vHo7H4qcUdt+rVxd5wozynTm23Ru/r/fGHxu90a2DGnH+N3A74JLk+ftZg4QP/VW4dOmSOEpDEwpkEWadUNs1iIgriqvXrsrjOHXDnFAeqGi/68JvKDy6Ah2lN2/cxJ7f9iA4Oa/LCCoiwBAmUQdJvnfgmxqELZu3uHxcfFw8NJYH4KaAxDdFJBfOwSHzJBRF3pSiwk3DUwugaFRFiQhj9BmvGe4Hlp/5WYKyCLOZwTgFX2sgSpQsjq+++hJ5KoQgOeIOzPlTUKv+21i9erWU4P2TbtBWrVrBrrLjduAll9cVqboEJPnFoH2H9g81femBBx78e+Fxzv7HwTEXH28lxg7VCWE8eMyKjdssYPY9xU0G+l+6akWDjkZoNHZUfUaFBu8o0eQ9tYx6Rcfa0LqXGQqlCpMm/QitVoHwEDVi4h1tuF+MMmPxFGXGqv+GrRYMGGZEYhKLtpijxHZcm+RQkWyPmWJGscIKjByoQ903VVi3WSVtsz3a29CyoVqacOncpbuAsQQ0mDLGwN1YvsO9qsMLdfUYPUiLWq+r8UF/Pc5dtImISdSqrkLZkgrJz/11px75IhSy7Tei7AgKBCYO0+H9Wvd+VejE6N9Vg/OXbNiyyyo5XrmhcAE7bmTKeXUHkluuAo8fNwaxsfEoU0KBH4d7iShLwfztZnoZg2OEweXrdpw8a0Op4grMGuclTotd+ywI8APWzfFG4t0eKGbMukOxYsXg5+eNzbstbnN5iS277ahYsRIeFSwe6N/vE/gYA8V5Gh1wVfKtmLNJtynzVNn0yvIAOjE5Vnbt2r3sqCcNumCZpeSK/DpBh+Ud6xXUfLumjFz5WPzhZw6RcSkWHZC8sgws82uQWPF+5kL5KHxzfX06aFmQQHFWYVNliZtwgrmnJI18zdwg99uBInFPZSG/KV7xUFu0UGdrgKWQmj++FC7qDsv3jQ5hd/jjjz+Qlp6GvOnuM5M5puabFoTfdv+GbTu2SdHb/xreeecdKWLj4lGadxKCUiMc429qA5L8opGiSUDfvn1lksADDzzwwIN/Fjt37sT1G1H4cZiXlHSRIy5bZxbuGJlXgberkYMqUOnNdMk0rVReiTdfU6BBbbUUYVFIpchKrnf1xm+yABqZR4OUNBssFpt0FdDVWbyIgwiSZ304wCCRUHkjKKAyL9bRTcD4rdh4SL5t/25aWQz/tIcGIyaapVC3cyuNLMTTubtxmxXfTjIhPtEuo//uCr1oJqBw++vOdNSqrpbpp5/mmzF3+T1xrURRpRTCskugZXcDggMhXJrclPy1ZwcN+n54T7AhX+Djb962Y/gEk0xQ5QYKwHHx9zLkcwOzeocO+QJnz10U3j5/oo+Iz683TEfbj4zC21mcS2F63yEbggKA2eO9UKGsCovXOIRqip8s7L12w4KXq7nnpkTFp5/Gll3H0ClrT1gWbN5lhb+/j3DZR8U333wNP990DPxYi8VrLOj/pVlEX5pP2jRR46tPNBg0+g6C9Hmg0/vi8qXL+KtA4VutUCNA7/7YUCT2hi/69+uP6JhoeNl9RFC1Ks3CoTlFlT+xlLhTM4Ncx2hyOMpZQOUOXHD3MwYhTZsEP2MIDBwXdIHgkGDcuZGQ6/6Y73LX8ORCCEvLn/HzFF2CcNbsYi3fOyKlMIxe6fjhhx/FXOIOLLtlHFhkaolctyEgJVR4LK913eXn/tNTsOy+6NGjB0w6A4KSIyTujTFbyT4xSPaJw+vVX5f7PfDAg/8GPOLsfxxsqX+ukmMln2D+VfYMrJAgFfz9lDJCv3mnAu/XcoyVscF23WarCLNqlRVf9tegYW0VYhOA6zcV2LzTgrnLrajVwoAP26glGmDANyZpuH26nBIjJ5pR9y0VenXUCrGlOEm3LiMGWnU3iLOVLoBX30/HDzPMQqZJNpmvxUX6iFAgOBiIzJO7AdwpntKBGhykQL+uOjTtYhAXLcfBRkw0yb44ERXNKAaK0wpsWeItI2DZQRLcqaVGXA+bdljwTg33K5pXrtsRli9ERMHZs2fi0KFD0izK1tw2bdrj/fffl3I25rHyVq+mCms2AR2aayTe4akyKnRsocasRRaEBDliJeiu6N+VObcqeQw+rY1iAAEAAElEQVSdCnT4Mn8tNESJCT8bERISmKvYxPG+xo2bYd7yuWjVwIbIvDmP454/rPj9gBk//tgOj4otW7YgJjYaOqWPOGR9jAHikKVzlC7UJJ8YBKVFSOYV85/sSqscj78KJHUqc+7/9DF/FnYF9HEmFIuvKG5aJ/TqVNwKOo+roSdRLPbpDPGTZJbZquZUI7wVD5B/xjI62GHUpLuMcGC+VPXqNbB/6x8ITs/r0oXL5yf63BFROzPRZQNtsncsglPz4VbgRRjVaSKA+xmDEZKeV9y1/qmhWLxoSa7irHPUUJXL6BrB0ba0bGOJ/0vg7+ugQYOkTGHSj5Nx5uzpjPsqPl0R3bqPlJZiDzzwwAMP/je4aWCAWoRZp5DY90Ody+KtPX8Aa7fY8e5bCmz/zYoFKyxY+QunvmzCK9s3taN9My8RNC9eVeDoCWDsNAvqttFLKRfduZwWY6HWmKFaDBplQqliSnzSXSvRAIxFuHjFhkmzzfhsuAnpeqBbO61ERzFWatk6i3BT5tCazA7TwZvVVLh81S78zB34ujQ5cPqLf3ZsocHC1RY07aLH1wN0ItZu2c2yJGREMbAgjVx692pvt9y3eX218OjlGyx47UX3XIe8MSjIXwqiWB66fdtm6A1G5M+fD82atZIMdJYQ08XYsWMHKZnlpFaNV1QZMVvfDtaiYx8TCuRTSNeDv58Co7/Qol5NtXRO6PV2TJ9nFq76UhW1OJDjEiyoX79+rp9/m7btRZD67Q+Vy2JgZvMuWGkXDvuo7uxbt26JIErTQ4feRnjDCz6poWIcOBCbgJ370vBKVRUiQhVISI8SDqXz8vlLualaoZUIrdxgshkRF21EwYQywumc3NCsNCIq8BJuBJ9B4bjy8DE7OhMo9KVw4sv5RVLYc7UlO+8yeqWhVNGyLh9Tv0F9fHfhO1iSzOLodYUEn9vQWHRZhFk6ezmpxOIxloaxM4Hv5232lYky9jMEprC07E+Z7MpefuYEpzMJlrblBid35eP/jj6BRwFjMpjXPX7ceOzctTPj55H5ItGz00B06tQJajbweeCBB/8JeH7b/+NgLIIpZ/JADtjtCnTu3AVGoxHrNq1HWmqanEzeeLM0fvllI6pXU+LnBWZ8870poyiB7lPmbTH7auBIk/x/tReVGPWFViISmr6nxreDtBkjJfyTBVTzf/QSl8Cnwx3i6c3bjC3g4xzjYgF+CkTH2TBmigUqNaTMKzc47yepJF54VoWar6tk9Iu3cqWVmDpaKw4HuncZcfDZcCNef1HlUph1giNdxMqNVrfiLN+b0Q6ljJdltLpKRQ16f6CQpttd+86gb98+mDt3NoYM+VKE2f7dmF9LcdaKcqXuEbSm9TQizp69ZJPG2+cqKeV4kWzt/dOK3kONCA9TSD4vLxZmLbGiT58PMrJr3YEFCJs3b0TjznEY0lcpThJeTKTr7XLB8c14C1577ZXHEq4OHjwoZNOiNKFIbIUsIiJXyeN9o3An4Iq4PDnCw/Hyv9LByO/tEa9jImy6ix2I8bsGpV0huVzZnacsUCgUXx4Xww8j3uc2IlILwaTSi2DLNlq70ga9PQ0GdVoWUTe725X3a83esNjNLltTiS5dOktTcLT/VTlWmbeX20+nBF+LmVuOn1HwjpcRKYrL8X6OQha6dCmGx/hfk6zbiJRC0Fq8EZ1wtxLaDSIjI+VPOnz9TEFuH2fS6VGyQOkcP+f3kxd+HB2k4P7CCy8gTx7XI2h/Nfj70rBhQym24/YwNoIXnkWKFPlHtscDDzzwwAP33JTnj9x6CwirVSGlRSwRWr58MTZuj0VAgD+q13gZK1aswGvPK7B7HzkRM2Kdr83cVUem/4+zrDCbzTIxtXiqN1r1MIibdslUryx5svwZ47PoCB0xwYS8EQpZvG7dyCE6Xr1hl9fMnw/o3N+EQ0dtSEmzC5dyV4zLiC8KpG9Xc4iPnADr20WDId+Z0aKbAaHBCnz1iRbvidDp6BHoM8QgwmhupoRAfwUKRCrw+0Gr28fQWUynaEBggIhDRQpq0LE5pAjt6KkojB07CjN+noaZs+aiT5+PUOMVJaaN1qLUy+lZuCl5Mg0QjCcb9YVj4o3TZcT5yzZ8PtyIqzftGPeVBsdPWzFotBU1aryOMmXK5PrVZwb/4sUL0aHPPgz8yIZGdR1ir9Vqlym3od/Z4OsXJhz2UXH8+HERvg8ftyEysYTk9WdwrNRCSNUmYN+Bs8gfCaT6pMCitKPhmw/eHfGwIDcy2Q2wKNwLnsm6OFiVFhFf2ZGQGRQ2CyaUxuXQ44j2v44i8eVFDL0efCbLyDydq+6iAPi4VF08dGZfpHonoFXrVi4fR+H+h4k/ICr0AvLHlcqRl8qYsiSfWEQkF5JjytdN1yYLZ6Wjlp0EaV6JwoE5fZXoHYMEGg30oQi9K+bGxMS4FWfZJaDT6oSbsmjMHXi/t5d3RrlvZrAs9+jRo/J3diI4S9b+CbCIa8HCBVImHBUVJUJyyZIlJVfYAw88+G/BI87+x8Hcp++/3yyr8RyNd4Xf/rQhNc0q5VIUV4YNGyY/p7jxwvNVhNzs+N0Gsxmo/YYKdd5Ui/hI4rp4tQVKlSO3lO6A3p21WLXRCpMJ6Nf1XoNsZlAQ7ddVi0adDBg+wYwW9dVo/K46y2O5vRRnb912rKAfP21B6RKuxdRZiy1CHl+5u/pOsn/2At2ZwLMVlJg9wStL2y/H4ig08z1yA8fWnKNVzAN77YWsJ1FmhfUcaBBR+tLFC5jxvQ41XlYJsWS22MkzNhmTO3nyKDp2bI/wUBW6ttHg+i3H68bF33v/waONUhihVkGOC52zBfMrZdSPY3j8eYVyCrT7yChZtI0aNXwg0hoREYHly1eje/cP0bHPYYSFsFhCiRu3rEjX28TVO3r0t4+1asvcVhtsKJhU2uUYU2hapIibiT7RSFemICw0TMbQ/ypQoONFG4lidnJLkEQm0XWanieHMJs5loBEnsIyxdEU73iorRrxO1jtNgQHhyDWeEPiA7ILwHz9GL/r4sJI9YlHo0aN3AqEr7zyipQx8Kb3TUFAclhGG21yQCz0ilR5XFTEBSHmdE7QVSE8XMFChjwZbgTCrDQh1u+6REsw//d+DccVK1ZE8WIlEG28Dd/4QJditkGdLrEAzVtkLVWgU3zE8BG4dPlSxs9UShVq16mNr776Sr57/wT478jjjEF64IEHHnjw13PT5BQr9h204cUqrgUKls5SsOzb73l0795dypScebVvvfWm/P33g3bpNnjuGaUUw3L66MRZG+YspYPTLlNMpJYftNIInzp60iZczV3RV69OWsxeahGnLfkjYw78fbMKpeEhJok74Hg8oxhYiEXhNjvfXf2LBckpwPvv3ONXx07bxXkbFKjAmtleEuXlxKvPq1D7DbUYCO5XtpuYZEdcAjBljgkftsnKY0xmR+RDSirdxdclGqFXRw2OnnIcl9/+tMLX2460tHg0atgA6XoDvvjIG2o1c4Qd3NaJCT9bJJ+XZgV2VnwzTiHiLfkzjyVL20KCgXHTTNiyx46nypfHhAk/4H4g55w5czY++aQ/vhi1CiMmWlEgUoXYOBti4y2oUuUZTJo0NaPo7VGQkpIiXCk0tYDLslA/UzAiEorhqv0CNAoTLHY7Wrdujb8KNEGwDIvTUGFprkXJOL+bUmrlirsSCiiFU3NK7VbABYm34sI8F+NNaj3KlC2Dq2euy7SVq84FCqRmtQlQK1CqZCm3XJyRaTNmzkDbNm1xWXtMuClNABSOU/xikaJOFL4YG3ADaQGJ8nODLd3BIRlpYPJH3uSiGQYGTs7RSUujhknliF/w98/Zc+AEF/vp3l25eLXk1LrK0OVrsui1aZMmWa5hmDs9eNBg7P9jf5bHV61SFV9+9aXw3n8KNI/w5oEHHvx34RFn/+NgS+XYsd9hws8mDOmTc2SMJG7sVAtKlyouK3tO0EHbokVTKKDHC5WVOH7KhiUzvLJkl9Z8XY0PW2ukTOzWHY47QcaHZiw0SwtuZtKZHXSGBvhDiOuClRb8ecQqJQxs6+XJPTXdniFe7dv3G2q3cpzMOVrFka5WDdS4cduO+SssWLTagoEfaYRYEsyrvXQ30nRIX10WYdaJt6qpMGmWWca06EJwhWVrLSJCv1jZUYrwxqsq1Hvb4XDYd9CKJbz/rjDNfDKKwxyd2vabVfLRWjVS33XqWnD6fIJk8HKvGKdQvLBCnl/9ZbUUn+3eb8O0b3Xi+N3zhw1rfrXIhQmdxsM/VePAMQvGTqEQbkXhQgXwzTfDHnjFlSvTq1evkxVkNtGmpqaKcEaHobtV64cBV4I52sQcK3dg7myC7x2JNJg8ZfIjF9w9COjKLVe2HC6euYQCd0pDZ806pkaCSPerO/LrBO9P8L0tIm94SgGJHjCrTLgUfgTvvV9P8lxvhpxDWHKBDAJqVKWLczXZO07+/53atTFq1Khc34eO63LlymHatGnYunWblITRVVSzZk107NgRV65ckbFDFsnR6UrBddrUaRIdkTe5WBZBlaJyvuTi8j3jBUCT2k1yfW9e/PXt10fat5llFpFSUMi/E0Y6hsPPoXChwlnc1fPnz5eiBeaeFU4tD2+TvyO2wjsWv67bjEMHD2Hd+nUZAi1zejnGSnBfPeTUAw888OC/ixdffBElSxTFd1OuY+FkpcuF9/E/mWCHEs2aNcvyc0bYnD9/TvoEGD3FCa1m799zIr75GtCllQYd+xqET9FgUP0llRTSUkTlIro7kA9SJGXfwMFjNlSrb0CTeiq88QoNBBC3K+MHihUvgZs3r+OLUUYMHGlGoL9j4b99M5oSgHVbLBg33Yy3qylRtKDjnHryLEtuLSImf9xJ45Ijv/maGjMWWSQCjPzPFbhPFGYb1lFh2HgzNmy1yqQap8dYdEteHHXHIQKTlzLvdvQks8SHFYxUoH4tNXx8WNZrxc7fDfI4ZvASb72mlliz3p01MBghfL5Law0GfqTFqXNWyehlJm6xQgp0aqFDkD/QupcR2/ba4OXljW+/G4tg5pE9AOgenDjxB3zyyQCsXLkS0dHREvdEwfBJCGjOQlRGPblDoD5MBEOz0iKL5CVK5J5x+jig4ElH6vy586Gz+ORwhDJSizFYIWmOiSZ38LnLXcVkoM8j/Jrj/5z2ohGAxWDX85xCWHwh4bHkiJxs4yRYrN8NuRApVrqIODlzixjj9demXzcJN122bBliaEUHxMVObkpxlUaIhIQEEdHpTB0+bLgIxYXiy2WJb6DzNjQ9Uly0t4IuomCBgihe3DER5g4ffvghVq5YiZuh5xEZXyKL2EzH8K3Q81BoIbEATjBWrnGjxlDq1cifdC+bl27ik3+ckbiNxYsXo2rVqvJzXg/xObzm5bGjm9UDDzzw4K+ER5z9j4Nk4LPPBko7Zloac7Q0KFLQMU5G4jl6khVHTwELF47OskrPlsujR09g5vc6dOxrxNC+WpelUvnzKTHuax0af2AQIZKgm4AEODfwvViY8H4tlQi6LAr7cqw54zUoerJxd8+ePahaSY16NZXw8QL+OGLFhJ/MGD/dLNlfTlBovXXbDkYOTZljkfICbgOzbl2h+ftqIaoDRxox/itdjtwwkugpcym+KjDtOx1W/2LF7CUW9BzoICcsEmv+vkZGsGYussjfPx1mxN4DVswer0ONV+796rHNluS5x0Ajhn5nwvDPdELgB402Yf0WC/YfsiIyDwswVJJRRodudpdumZJKjJ9ukWzfmYujpMGV7sSHAcnuX7FibLfZ4W32dxshQFAgZc5Xy9Yt8dJLL0lrLQsoSIiYx/rGG288kmDLsSWKpKtWrkJySjKCAoPRqHFDjBg5Ar0/7o1L9qPwN4bCx0Dx0IY0vwSkKZPv5c7mAuf9xWIrZoygqSxq+JtCcOjAIfz0008Y8MmnuKQ7Ci+Fjzh0TAoDlAoVnn3mWcl65YLHgzTFvvzyy3JjBmxSUpKUhVEA7fphN8nzFdx1ywoUjtzZNF2iZJJlB5txKc4+SMQARx45ZvX1118j1T8OvinBQoKNujSkaBNQsGAhLFq8KCNCg4/97LPPRKzOm1Q043NX2pUITc8nZPg6TskFNAVcNmgz943HRx6nVEoRxOeff/6XXgh54IEHHnjwvwmeF0eM/BYtWjRD6x4mfNJdLU5V/vzydRt+nGmWySxy19DQ0Bzn/CF9NZix0IJ6b6uyCLNO0Bk7aaQXnn0rXcRQ8lKeghh5wFtuIHdkTi1jqFi8NXWOBT/OdBR5yenc7tiOooU0EknFya3zl+xYuMqMOcssMkXmBKfOegw0oHwpJX6cZZZ4qptRdryXqYQ2M5iBy8kpctNl070RFJCVP3DRfugYi0xskc+/+5ZDzP10mClj21ka9spzSvQeYhKuSbcs+e7nvTRSbpaZ71JwZcxY+95GrJ3thXZN1WJwGPmDWUqzUtKAtk0c21qulApD+2XlpryWKFFUgbIllDhzwYJPPumL9et/wcOWJj1OfIE7sIhVY9e5nZAiuBjNktoCFfLKQnl8fDx+/fXXjFgkLpKTjz0s4uLihMMtmL8AUbejZPT+rbffEmcus3DZ1eBnDYRvWrBw43TvJCRr46FVa8UJmxvsCscXrEBCafib7vG/4PR82LZtO1asWI6+ffri7LlT0Cq8oLApYIRBvrs81uRvtWvXhobhxvcBOdro0aOFH1KEpaDOAq7hw0bg3PmzLrmpTWmVSTlOp2W/LgjUR0gkA+M27seNKZTOnDUTHTt0xEXdIfilhUBj9ZLYhBTfeOi8tJg9a3aGyEuO2a1rd6jStSgYUzZLFEOgIQz+hmDcCD+D7t26i+j83XffYfHiJdDr7/UpPFPpGXwy4BNPeawHHnjwl8EjznqAzp07y+roqFHDsWh1Kgrm18JotONOjBmFC+XHggXjJc4gM+bOnYVqLzpG8EkCmQflDlIiEA7ciaGj1IYyJZRCBHNzpbI5l6NSJYooMWKCWU7ojEFgJipdDl+OMUkjLAvDSDSdaFJPgz6dbWjSxSDi7MoZOiSn0IVqlngDonMrtbz29ZvuYwuY/TX2S52Mrl29YcAHLTWoXEGJ5FRmzFowZ6kV+fIVwYWLl7HuVytaNtBI/EJiMmA222V0jk7dOq3SERriKEpYscGKEZ9rswizBAkIoyB4LEf9YBIXQ+tGahGaWVrG5mC29uZWLEG3RlioQsTbtk0UmLNkIT799FMp/coN165dw5o1a4QokmDS/fikBTH/AH9YVfcaiF3BqrDCrrALMWzerDl27d4lpJhj8BabGSHBIejbry/atm37QGImwf3q2aMnlDYV/FNCEWYNhCnJgJ+nzpDyix9++EEcm7NmzsLlK5flvfg9b9e+nRDL6FNxQtjcIdkrBhqLl2RaeVl8Mgi+t8EPly5fRq1atURUJpE/cuSIXKQ89dRT4vp41MIzfp4kv1988QVmzZqFwPRw+CtCJFaBIqxEMVi1Mr4W73sb10JOi0Aakp4vy+sw5oDuBbpuHwR0KDDXjxe9m3/dDIMhFUUKFkTL1gPEYZ35e8YLDmbvOrPGskNr9UJQYj6JPdixfQcsaTZEJBWGv8HhEknxSsDOTbux97e6WL5iuZR4eeCBBx548N9zz86btwD9+n6M99vfQt4IDbRaBa7dMEmR1ciRA3OMmc+bNw8hwWqZTiKnGjPUvcBEYbN2DSXWbrHh1x0WVHpKJW5Q5qc+58aVyqzW3w9Y8X4tNUb9YBbRlXyNYqqfrwLzV5hlQf7DNhp81lMjnMyJjz/Q4MMBBokKWzRVh/AQJTZstWDKHLOU677+olIKzhjnxQksV+DrUVSu11aPWs31IqZyaoviMt280+dboTf6wM8PmD5Pj28H6/DGq2qkpduRmmZHYIDD+PD19w4jQbHCQJ+hZuHwXdvmFCkpuE4d7SUTcDv2WuW1hvTVCgcvXEAhjtoC+dyr2eRrZYorpcj2s14s3joufKhSpUrIDRQ/V61aJRyVfIn840EXsx8U5C12lU2y+jNPBGWHTW2RTFIuJi9ZvARmixlqpUa4qVarQ6tWLYWTPSiv45RQs6bNkJSYDP+0EISZC0pp14YVv4jTdMCAARJ3RW568NBBMTjQddqu/afS4bB6yRrYk913JiR5x4mgS5GWEVjkXLK/Jn/EmK6Jo3Xrtq34888/sX37dhGpCxcuLAvxD+pqzg7uOyeeli9fjo8++kgcuSHGfBL95WX2E3cyRW6LyoxE7zu4HXAJek0KIpNKZNkP/p0iaWyMY7rsfuD3Ys9ve4R3rli+EokJCQgOCUGnRm3EhZzZgLBz505cv3ENRRIq5MjIJRiNEJ5YCJfVx/H22zURczsGgUkRiNQzU1cNvTYF5w5ccuTt/vCDxL554IEHHjxpeMRZDwQUvZo0aYL169fj3LlzMhL/3HPPyYmPTrbsOHPmLPp2UeBOjA0BfpCGVncgmSpdXCE5VWOmmsSJ+v1Us+S69umSkwxSxJo4w4TwUIjTgPEGS6d7IyzE8R5sfz113oZOLdRZhFknIvMqMWWUDu+0NODoSbvk4DKy4e1qanEA+Poo5LU57kaySkLtCswoIw88fc6G7p85iCzBxl+rDSLAhYeH4fORsdKeG+CvkIwtjoIdP2PLKJ8g7/h0mAF+Phwzc/8rxziGMVNMWPWLVcbEJnytwzNPWaR1l7EQuYEXDMwY476QqP84M12KmNwVazFrq0+fj7Fu3Qb4eCuRN0KFmDirOG5Z1jBu3IQsbpTMoHuTpJm5TfxukGRT1HVXPvbmm29i29ZtkofqzD7NjiTvGPmeTJ0yDWnx6VLOEKAPk7EnZprGp98SpylX5nv37o37gRENPbr3kJV0lmVlHp9iGRbzuLp374HNm39F+/btc+S3MYph4OcDkaZNchlvkKpNRKouUT7ba6GnpHyLGV58bTpqtXet4XQe1KlTR25PCiSYFGbzJRUTAs4RMB6vzJlpLC0LMIThjv8VKWBgrAB/lgUKR97eg6Js2bIYOXKk3HIDBVef9ECXeWZZRgUDL8OUakHRO09neSxJPO+/HnEaXTp3we49u5/oBZkHHnjggQf/DnBi5Le9+7Fjxw4Rk5gnS6GK51RX7etnzpzCi89SiHT8f6HI3M8dJYpywd8m7tIN81QSKzVxhhmzxyuzCKtOzF9ukdgC5tWy8GrZT154uuw9oefsBZu4aulCzX7eolt38igvPF8nHRu3WjGot0MQZeHX++0Nwis5uUZn7ZETNpfTaAT5dGiwo/dg6BgThnzn+DnfjuYFIAUREWFYtDoF+SKAZyuqZLv2H7JJTFZC4j0n47BxJtyOtqNjc/fn66qVlChf2hG1RXG2UwuNxB98NdaEdD3jx+zCf93hTqxd+DvjIgID1CIIuhNnyUnoxJw6dTKsFjMK5tcgNc2GCRMmoEyZkpgw4Ue3C7b8bmzdulUm6hjzxGx5ipx0uLpCjRo1JFaKC8Lkb67A4laDKl045ZWLVxCSlF8iozgtxSgAZrRSRL1y+Yq4OO/Xz8DJpxbNW8AQZ0bxmEpZXLvhqQWlj4DbNGnSJFmcJjLzU063cew+zvemy1xairFxvrek2+BGyDn5mY8xEOEpBSVayimk8vV4jcfbk0JsbCz69u0rpoHg1Hy4HHZUDANZ4rUskOmpJK9Y3Aw6B28RbrOaB2jUuGe1vT8oCvN9ecsNu3fvhrfCR97THTjlp7V54fatKBSJeVqMF05oDKFiIogKuoCPPvpYFo/+qYJbDzzw4P8v7jO848F/CSS6JDIcJ+bKbfXq1V0Ks4RKpRSHqLeXAknJDnLmDiQW1286CsEOHbOh71dGNKyrwvfTzBg71SSuUico9n7ytQlrf7XKKNrp83Z8PUCXIcwSO363ynu2buzeEfFUGZWMwK3YcM+x+WJllRRCMOdr6lyLkMp5yzNlH2QDR+IIjdZbSO87NZSY8I0Wp3Z5489fvNH7Aw2SEmPl1+jjwSbJkx3/kxk3b9vFzUB3wbtvq2SkZ+8BO4oVVsrxcge6iNkYzLxaFjrQKUsSPLSfBhcu23H0lPvm3XWbLdAbmPOrErGc4Gq4K5C0ftCpA3Zs/wUjP9fg0K867Fju+JOC8LEje9C4cQNHWUI20JXy7LMVMWDAJ9i/dyF+2zVfVslZzkDB1hXorGQO6u3gy1IMlmN7VAYkBN1Cnog8SE1IQ8E75URodAqqJEdcXSe55JjR+fPncT9MnjxZCFZkNmGW4Ip5/viSUJgV+Pnnn+Vn2S+iuDLOPK0bYWeELJOEOwu1+P90pOos3ige/QxKRD+LPCmFka5JwqXQY0jyj8Err76CvwozZ8yEj90fgekRiPONgp8h2GWZBclwnpQiUiBG90JmMCvWpDbIhe+TBr93dCvnBuaKEf5prksp+LPw+MLiaCah9sADDzzw4L8J8lAKaeSl5Kfkqa6EWcdjVTBZWELlOKdfvZH7wvbVmzZQg7VY7Wjc2SicbefvjpipG1H3+AqdpyzX+nqcSWKvft1pFXdsZmH2dowNew/YxEnrbkHRx1shLtWVG61ZDAWMSNix14aOfYwyjTZ5jkm4syvwvW/e5kneS8RYiqdf9tfg4CZvnN7lg5EDtYA1Dhq1AuN+sqB1D6Nkz3Iaq0FtDYb000o8AXsg1m1x7CNLvNyB+0JOfeyUTYwAzk6JuT/oREJjBq07XLxCw4INtaqrhdP6+yndclPim2++wYQJ4/FBSzv2b/DGzhUaHPhFi4WTvaDGFTRqWF8MJNmxf/9+vPTSc7LYvvXXuTj852KMHPE1qlR+VhaUXS1Ec5KpSuUqiA2+LtwuOyhm3gm5An8/f1y8cBEFYsogLC1/RowVhVUKqgViS2Pb9m3iGr0fli5dirj4eOSPKZUjToGcja/HaKwJ4ydkfP6Zv0vc5p49eyI64BpuBV6U+CrntiZ438bl0ONcd0fhuPIoeacy8ieUhFVhxtXQE8Jd80fmR/78+fFXYOHChbBZbMiTVBiJPrehtmmk9MuVw5dTaRTEOeFFIdkJuphTdPGo8PRTT3z7GJNG/p9bxBqhsCqhNflkEWaz8OqkorBb7ViwYMET30YPPPDAA48468Ej4dlnK+OXHUC9mmpZ7WcGlTv8fsCGy9ftkhPLFtxrN+xYvNoqY1sUSiu/nY7GnfV4v70ez9fRY/UmC74drIXeYJesVWZsZUZMnF0ywYoUyP0EW7yIMkurLEHnKkXiCmU52gUhrOWqpWHod0ZcuW7LKEGbOteMybPNqFr1eaSm6iU+Ydq33qj/jsbhNA1X4uPOWsyf5AWr1YaQu11XLB7bvcobH39A8qvBhG+88Nsab3H/xiXa3ZLtjIbdZDu27Lbi5Xp6dP/cgFY9DBjwjVkabwd8bZQG3Oxg/hqjH+hKKFpIiSMnHfvBMSVXoLPg8JGjmPeDBi0acH8cx5HFaPVrq7FkmgbXrl2WzNTMmDt3rlwc1alhwm9rvLB5kQZbl2iwa6U3Xn0uXdqSmUWcHSxwmDZ9Ggw+KbiW5yQSvaNFkCWpjPa7hmt5TyI8MlzKHoIS80hplSuEpuaHVqGT7cgNaWlp2LB+AwKSw92OqnF8yT85DEuXLBW3RXbQ8Tpr9iy0btsaSaHROJfnAM5G/oHzeQ4IwQ1Oj0DR2IrQWb3FvWpS6WFVWmFTWWCGUZzCw4cPl9G8Jw1m8fqlhMCiMkk5BF0c7kAiGagPl8ZeJ0iEY/yvSYxD06ZNn/j2FS9RHCaf9CyEOzvStQ7h38/gviTOx+wvWb38vnrggQceeODB/VClSlX89ofDMevtBczNZQGefIpxAuSwowZqJY5qwk8W4YZ0tr70rh7vttWjaRc9KtdMx4iJZrRr4shrZfxBk3ezLizG3eWbXIjPDSXuctPMfLDuW2qZyipeGPD3BTZus6HEi+kSg3DwmIOj8PFbdlnEDFCmdEno9QZ0aqnG8p+80KGZFuGhSpmeYszWurleCAq0S8YszZzvvqXCgV988MXHWrRropFpsoO/+KBCGQf/oxs4N3B7OcFFvt51gAEf9DOgTiuDnOU58XXmQk7xk5Np/b8yIiJUIRNsUdE2RN0xu+Wm7Bogv/uynxafdOP+KDLESRaXLZmqQXioEV9+OTTL81jY1Lx5UxTIEyv7vXuVBhvna/DHBi90bQv88MNEySZ2hYk/TIR/mB+u5T2BWN8bjoVrlUHKsa7mPQG7n1nMBRQSfcwBLl/DzxQMf3OILJzfD4sXLYa/PsTtFBk5W1BqHpw5ewanT592+RjycHZKKCJNuBh+RLjpmbz7ERV4SQTF4rGV4Gt2TC9xEd6qckTDGbRpiLp9G3369MHFixfxpEF3u48+CCq7Bsle8cI9c4uLIHdlBBdvTvC4MwaNERJPGiz0MijSYVG4/zeBRWLcHj+j+xxhHlff9CCJ+PLAAw88eNLwiLMe3HdMhdk6DMJn4+X3338vOZ3t2nXAkRNmIWQUUEf9aJJx/uy4dtOGfl8ZhezWfUuJ119Si3j50xidkFuKrEaTY3X96CkbXqqiFALZ7D2NFJQxRzW7A4HjU1wE56hUboi6Y8sxasVyLb4cx7g+aKURhwFdEEvXWVC9kR4tujlI+DfjTOjSpQtu374lI2okzq7wwrMq1HnTkUXL4okP22hzbC9dER+21kjJAx0E7sA8LxLkl59TSibu+s1WcRKTqC6Z5iXE+O1meol8OHaaTcFW2c66rfTigPhuiE4E3p8WWFGpUgWULl3a5fv8snEd3nqNzmLX7saSRZWoX0uBefNmZwiXdNF+/fWXaNlAjdGDtFkyxigI/zBMi7pvqjF48EBx5mYH4zFWrFyBKtWewa2gC7gQcUhIZWpYLJq2bIK+/frAZrfJKL470AHrkxaE3bv2IDewsMFqs0q+VW5gG67eoBcx1xUY0zBs2DAcPnwI48ePR4NG9eXnheLLIl+yw5FrVKXjctgxIaKhaZEoEvuUOBa0d/wxbfI01K1TFzExMXhS4MUZ885UNlXGiJo7MdsJjVUrBQwUSzmidyPoLJK94+Dn7+c2iuJx0LJlS6QrUpCmdS1Mczvi/G5JQZi7ix3nRQpJsD4jH8QDDzzwwAMPGG+ll/Hurl27okOHDhgyZAjOnDkjUy/sG/hhpgXVXlSK+Dp3WU4xhk7Y7p85imoLRjqcoKtneWPtHC8EBTg6jKidXrhkE57LklcutA/tp0P6XeOnUzx0wsk3o+4TQ8VyWk44ZeaKZy/aREC7ecdR2MV+gl6dNMKLGXnwbhs9qtXXSzFXlaqvoFTpsmJw6N81J+ck8uVRomcHrRyL/HkVGPeVTrJms8csjP9GJzx86Vr3glV0rE3cxJxGI9ZvZVGwDe2bqrF5kRcKRCrxXjs9Bo824o/DVhw/bRWDA/kqjx35Pt971iKLjNS/++67Lt/nl19+Qd4ItTiPXYHxaV3bKrFz5y5cvXo14+dDhnyB0sXtmDtRi4rl7vFauqf7fqjFoN5aMRu4mrpi4eyGjevxbsO6SAiJwsWIw8JP7wRdxqtvv4zZc2bj9p3b8Ne7jj3I2La0YBw/cfy+fIWRWTqzm0Dhu/C6y115reUK/Lw7duyIQ4cPyfRX774fZxgYCseXF0cuRcYrIScR63dTRvHJS4vEVkBYYn6sXbYO79R6B4cPH8aThEHPqSnHd4Sck/0HucHpHGbnBJ3Ld/yv4k6gowfhr4gLaNiwIZQqZY5JsszgfeSoQem5v7/KpkY6xy898MADD54wPOKsB1kabklgtm3bJqLctGnTULVqZYwdOwopcb8iLWEzJv34PZ57rqrkjdatWxvdPzPhhcoKEVgbfWBA534GKcxav4XtsEa82UQvsQUkiJ1aOk7EHG0iEaZgS6cAm2f3rvVB64ZqnDpnk1V+Il8eBa5cs4mDNjNerKwUF+6ile7dunTBsnShzpv3iBrF49E/mtGkngr71/vIyjwdBoN663Bgow9ef1ElzylYqIKE8jPj9OrV62hQO/cMqfKlVLBYIE5Zd9iy2+HGGDTKtfuVbuDB35pQoYwSCyd54dQuH8mgPXvRLhloVSqqsHaON6q9qJLoBDoWSNhZPtHsfTVWzvAWos5GXgrQffr0dzvWc/nKdbxdLfdffcYj3L4dI0SS4PHgKBrLylxdCPBnvbtoEBubgI0bN+a4nyNlzItVazTw8/WXZtqyZcpi2PBh0vLqFAkp2OUG3m8xu7+IcDp1CTpLcwOjCjgu6W480omgoCAZozx44CBUFg2SvGNxIfwQzoUfwOWw41Da1CgeWxERqYVEbGRGbd6Uoih0+yncuholsQ9PCjzOHEvTa1NlZIxwjrW5g1GjF3fv+YiDuBR+VFy03A99uv6hMmcfFIyDeOH5FxAVdhGp2oQsDlpeMNwOvIxUXYKjtfeuwOwKfKxRqUeBAjlz1TzwwAMPPPhvGAQWLVqE6dOny2QOM++Zu1616rPo27cPbl3dCEvaFqxZNUsKOIcOHYx+/fqLOKjVQqaOPh9hksmsBSvM2LjNkeP/2vt6/HHEJtyUi+rOfNlK5VUoWczBMem8/XWRF77sr8PNKCAl1bFNjJ8isrtFC0Yqpc+AvMwdLBa7ZLeyBNaJ+AQ7Wvcw4KnSSvyx3gfDP9OhVUMNPuqkxW+rOYmlkYmodFO4ZK/On78Q+/f9hndqqDMmn1zhrWoO/svX0mhcP27hSosYFljS68r9ykmyAcNMchxnjvPCiR0+GPm5VgwOLGcrXUKFpdO80KG5Bmt+tUhxWO1WBoycaJK4hTWzvVGhrBKzFpsxeY4ZH37YTQqpXOHcuVN44xU6fd3vE68duEjNDFiC1yKHDh1Fzw6qHOKzE20aqxEarHY7dcVCXPZskDt66bxQIH8BcZdyUd7p8lXch5sq7kY1uZrEygw/P/+MmCx3cHJXJ5d1B61WK8WzzmJXCqIXww7jfPhB4ah0gNIwQDMBeSmnkZhTWziqApCiRts2bXONmHhYFC1WNGNqSm3VCPfMDca7jlkaBs5HHJAcXaXVcRxv3bqFJw1mD3f5sAti/W8g3oci7L3vO7eZrl3eR25qvs/1g8lbj4KFCj7xbfTAAw888Iiz/3FwjKhp08bibBw0aCCGDRsq7bclShSXMaCny9qwfq4WCybpMP9HHf7cqEO3tkqMGTMGZcqUQ9t2HbFuCwsVKMAB2/da0esLIz4cYMTazRYhxyxm+G5w1hXtE2eskvlKYnzpmh0tu5ukgItu2g/6GSXDtv47aqSkASsz5cYSm3Y4xtAmzTZj/+GcRIjP/XiwUXJq62VyvLIRt1gRBUYN1OUgqnQQTBqpQ6A/cPz4cTkm3bp1lfvcteY64eQ2pYrn/HViTMFXY404cpKB/sD5K3a8WDcdw8YbcemqDRcu2zBplgnvtNTjRpRdGn8pwDFiYNinWnHt/jDTQfQLF1Diu8E6HNrkg1njHCNR+SKUcuyZg1allhFL19nls+FFiis4x+jcRAlnQJHtfh6TcqU04sZwh1LFlChcUItjx45l+bnZbBZ3S5s2bbB/6x/wvh0E/+hwXD8RJQH+LKKLjIyUx7KAyx3E+emTitJlXTuCnWDbbNUqVZHsF5vra6UExMlxYoTB/RAVFSW/K1a1GenaZMl5ZXEAyXBkUvEc2WEEIw9CEwrIheSTHCFr3aY1UnzjZB98jUFCKN1FCFDgZDMu7+cFgcqqga8h2DHuZjahVs1asm9PEhS8Z8ycgcrPP4troadlNDAq4CJuBJ3DhXyHkOhzB74+fpI/zDINd0j0iYYNVhHGPfDAAw88+O+A0zoff8w8+2fRr19fjBz5Jbp164ayZcuIO9bPJwUzv9dh5QwtZo33wr51Wnz/pRZbtmzEwYN/YsSIEdjzpz/MFgffOX7aJiJj5/5GTJlrlnOi0chF9axOTRbOHjpuE8OB2aJA4y4WqFXsDFDIKP/laza8XFWFPGEKzFyclZuePGsFS+Z37bNh1pKcAq3VascXo0yyGJ95MX/RajPS9A7xMzgoKzelaNyniwavvaBEbEw0evXqhcaNG8rUD8Xj3MBeCFI+Foi5El2XrDZj1mKLRB/ojUDtlnp0+9SAE2csYnCgm7ZeGz227bHixSpKBAU4JtkYh8VC3unzzEjX28XROqC7Fvs3+GDTQi/h0b6+CgQHKuQ4vPyeGYNGm9CuXfvcC12l+Cr3fXJ2tDm5LLkpUeMV9zn35NPVXrTj+LGcTlEWb1HgXL1kDZQ3fBAQkwcpFw0YO/Z7VH+9OpKTk+Hn64f0XLipk7uGh0VIBEJueLdeXaT6xee6MJ3gHY2w0HA888wzuB+4wL5xw0axerNY19scIIVbVqUF4akFchbB3h3LzxtXDHHxcVi7di2eFPh7qUeaLL4z0iDJK0bybl1BxFA6WO0OMZrit48hEDqrwzXM0rSDBw/iSYOREO3atROTwKXII7gVeEFuF/Mdxu3AS2LW4FRcvJ+jVM0V0jUpSFMmoWXLFk98+zzwwAMPPOLsfxgcC3r//Xdx89ofmPCNDrPGaxF8N2anYKQFT5dV4sQZG2o1N+DLsUYhlhzb6t9NK0UIEyeOx8cff4wDBw5h+PARIrIVLVYmw4HAaXHmoK6e5YXG794joiRVXEHPG6HAN59qoFIqcPq8Bl0/NcHbS4W9f1olWmDCT0Zx1g7+zoTd++8RGY6ovfmqEhXLKdG0iwE9Bxokh+v3A1aMm25CtQbpOHfJhp/H6kR0dUYcbN5lRdsmGnHuugLLulo21MDXB/ispxIH/9go7gmOaeV6HG86Vl8ZRZAZdCHQOUwCX7uGCr07a9ChmUYI9ZQ5FlRroJcohbFTzXj1ORVKF1MIuXeCx5EEft9Bm8RDOMHPIDbBQWJDIp7F1t/z4dSl4ujQsTv27v091xxROlTz58+L7Xtzd0xu32NFaGgQIiLc55k+KJiNxQzYfInFkSe2mORMcfW+UEw5GbU6/OdhTJwwEc9UegYJAY6RIldI1SXKuDxF3vuhc5fOSFUnyup4dvD1Y/1uyGsxqsMdLBaLuIApLL/99tvys7xJRVEi5llxxpLgai1eItK6Q4A+DGqFRsb1SPLPnj2Ly5cv39ddcT8CHBYehhsRZxGgD4VBk4bbAZdyHDcKs9eDz8KmsEnEA3PISkdXReHEsvJ3HvsLpy+haZOmSE29awl6QggMDJTiCxZE1Hi3GvI8EwxFHgusdgt0dh94RwfCy+SLaP9rQuCzFkLYpck3JvA6mjdvniHce+CBBx548P8fdMdygXzTLyvxaQ8V1s/zQsmiDt4WHmIT7sc803YfG9G2l0EWwbng3qiuBuO/0mDz5q0oXrw4Dhw4LOIbY7gqPF0Z3t6ORVSKsqWLKzH9Ox2+6p81FmDhKgvS0hkTpUVQgBJJySrhpnEJStyJseP1hnoxH+iNdixZY8FPC8wSJ0XMXWZB3gigY3M1Bo0yodmHeqz6xYJ9hxzRChzzp1N19BdalC3puPwir56xyIK6b6pyxCQ4we1r31QjpoTPP9IgLekAUlLSpTw2tx4DpxM2ewQYj1eTzgb0/cokUQW9OmrQo70GRQoqsHazFe+0NOLV9/XoM9SEsFCl5MWylDYzyE0Z58VysswiKD+HpBSgRMlnsevPAth3pBBee72hcCCWfbkrGSaKFC2BnfsYW+Z+n7b95ni/8uXL42HgOExZjy+d2IyuCkmJRGR0KQSn55F4qvwJpVD8TiUk3U5Fm9Zt0KhxIyT7x7p1vJqVRqT4xaFtuzZui+CcoPnFrrRLPqwrrpuiS0CyXww6dGwPtXOMMMe+2HHgwAH0798f1avXkGgulsKWulNFzAJeZl/ZVZbGugPNA36WQGzYsEEm6jg9ydvjOGlffPFFvPLyK7gdelG4MaOpyEHJRbNsP+y4438Fem2KlMcWii+HMneeR5HE8igaV0GKds1xNjRr2vyBCoAfBvz+8TPfsmULmrZuggJVI6AtrIAJBmighVdMIHz0gTJhRn6a2V1L6NWpiAo/j7JlyuHNN998otvmgQceeEDkPq/twf9rfPnlEPj7pGLVDC0uXbOh2YcGGUOaO9ErgzjShUpS+e1kszhER3zucGx2aa3BjIUGLFu2TPJoKZg5RTNmjtLxsGH9WrxURYXymVpgGXEwdooJazZZxeVAMh3gp0SvL/QYPHiwjLCxROnGjRtYum6XPIdlWy26Obat+ktKnLlgR6eWGtSqpsJTNfSShbXqFwdhk6gEFfPEgAk/mVGmpFVKGhi1QHJWqmju6xG8n89t1UCDhrU1eL1hOlZssKJ3Z5tL1yj3Z9c+q7zn4tUWKVtwHrfWPQ1SMLF2tleW5w7orsHwiSb8NN+CT7pp0KqRRhwGLbsbxK2RGYXzO4je1t1W1H1LIeSdMQ/T59vw+uvVMG/ew7eF1qpVB3NmzxDiXqZEzn1iQ/Gy9Xa0a986w1X69NNPY/HihYi6o3LrnqUgfvW6SR6beVxs1qxZUJu0iAq8mMGNvUx+CE3LJxmzEfFFsGXrFnz33XcYcGwAboacQ57EIhmFCeJy9YrD7ZDLePXlV2Vs/n545513JDN46tSpSPNOQmBqOLRWLxnzSvKPRoo6URwczD/77bffULRo0SwiIElq61ZtcO36VXjb/KBXpAlpD0nPl/EYm8ICtZVtxe7JOFfg1dBIi+/o0aNF8CXy58uPps2bombNmggNDUW+fPde90FGs5YuW4oWzVrghuKikGw6UEkmSdCZ88VxMboo7AqbxC4UiX9KxGQnuM0ccytwpzQu4xiWLFkiuX1PmgS/9tprcqND+vzipSiQUFryz6SVGAVxM+g8bgafR7TlOgIMIfK8dL9k6JGKOrXryMWcBx544IEH/x0wR/Ps2ZNY8ZMWecIVqNfWIHFX83/0kjIuLlzTFbphmxVfjDJKJABz+bnAXqu6CqWKaTB37hy8/PLLeO+99+RGcFGU0V1cMC5fWiUOWKeYRicpOdzX35skW79pPQ0qV1Dh7WYG4bh0RJLPsLh069bNEmUVEQZ8OcaEOUvNeK+mGtt/s6LGy2oM6avFL9utwol6DjTK61OTZJYtdcc5yyy4dNUOg8nRLcCyLU4e5YYSd7nr02VU6NRcI3ydsQzynq/kvJSjwDlnqSWDm7LHwSns0R178aoNq2Z6ofLT9xynjE9Yvt6Cvl+apENhQA9Hv8DYqeyUyCpS0VxBI8O+gxZULK9E0YKc4rLjx5lmhIUFyeIs+dXD4J13amPbtu1YscEhtGcHue+UOVa89NILKFGihON43OWb5MiMeXAFo4k8XYH3G1TK+Bm3deSIkVBbtEjwi0K8v2OMXmPRITg9L0LS8iF/TElcUBxG/vz5ERDsjxu2M8gbWxxelnvuWL0mBbfDLiFP3gi0bdv2vvtYsGBBTJg4AT169MBVnQGByREiptLpmuQTg2SfWJnoevXVV7Fnzx7JXi1ZsmTG85OSkvDBB53x22974AUfmKxGeFv9kS+peAYXZYYrnahqe+5TYUqzBsePHUelipWQnJIsP6NLmGI0f2f43oyVYuTDg4C/S9N/mo6OHTpi7+97obN7SwTXuYgDCNKHS8+DRWmWqSgRuu0Qbpr5eBLktPljyuCq6rj0nHCB5UmjbNmy4q6ngaBfv34ITymIsNQCGccw1nwT0QFXZdKLJguVXQWDV5qYPkqXKoP5C+a5Fc898MADDx4Hnn9Z/qNgns/u3b9h7FCthOa3720SQZbjYVz9zuzS7N5eK2NLA0ea0KaxRlb66WTl87ga3qpVqyy5ncxBGjduvAh7/b9ejpE/mKRplZEFFDIZGfr1AIcwS7xXU4Xvpmhw6tQpyXgiSErYSv9ZTw06t9JIlAFFYjpOCW5hQIACYSGOZlsWYs2ZcE8EXbXRgmXrLPKnjw9QoawCfx6xi/MhN9yOsUm2FjklXbeMFuj5hUkculNG61Cu1D2SwrEvRjgkJgGVnlJi5iKzOIVfqqrC0rUWxMbZsXpmVmGWoLNgcG8tTp11uHl7dtQKeaRr1nlMWAQ2ZooZK+5GOjCP9quxJrz6vApJKQpcvaHEuImfPtJnTxfob7/tQvOulzH8M5WUgzHji2R+5z4rBo6wISQkLzp37pzxnAYNGuCbb77CuOlmKVHL7g4g0f1+Kkl5sAijTlBwt1ltrHYS8qizeEuWE8kZRTl9aioiUgrJijXLCX76+Sd079YdF3SHRDxUWFUw++hhsKejRvUamDx5cq7OCye4fYMGDRJ3xeRJk3H6zL3W2wpPVUCevFUwfdp0IX7Ox79e7XX0699PhNJGDRohNVaPorFPw6hJhz7wgpD1zGCUQbo2RcRjdwItHQMGmx7nzpyTQgk6M+xKG5L0sRg7Zqzc+FRuE92+9evXv6/zgqAraOfunVi/fj2WLlmKq1evIV2fhtSUOImR0Gi0sBmtQn7D0yKzCLOZwREyNgfPmjn7iYuzTty8eVOKWyISi0jjsRNs8c2fSLdKMm77X5YRN14MVK/ymoydvfTSSw90LDzwwAMPPPj/AS5gLl40H43qKFGhrApDxxiRprfj14XeWbgUeRQFUbo9Kd6SK7E3gKKor48V+/f9Lov9YWH3SkYpMnHRlnxl+PBhmL3EjNdfUkn81u8HbBI3wIJYummdgih7C7Zs2YQ9e37PEMcqVCgnIvDkkTqJQJi91CwCLSO8eMrieatcaSVOnrVJJMLiqTqULKaSEjA6P+cvt0jsFzWv8qWVYjC4fR9u6nS/Mq6V+z55lA7PvcMYAiMmDAPefFWVMbWWkGjH1+OM2POHTRzHB47aMH2+GR+0dGTX7t5vw7RvdVmEWYLbTQ564Ypd+Ozwzxyvx30sFOn4O7kqBVjycRoZ5q+wYv4KvThwycd/3WnDmDGDHlqYJUqVKoX36tXDJ1+vlXLcFvXV8PVxvC/58dAxVly8qsSK7wdnPIccr3LlSpg44wSqv+w6d3b2EgviEixZpq64IH3t2jWoFVoR5rxNAbArrTK1Q8ckR/NZ/upnDMayZcuxbPkytG7VGpcUR+FrDYDKpIFFZ0K6MgXFihbDvPnzZOH8QUDhM2/evJgwfgJ27tqZ4X7OH1kAz5V/E8ePnchSmla+XHn0+qiXxC9QAD5y4CgKxJeG1uKNSxFHEJocmYWDkptyYd6kMogpwRUk1kOdhuQoPXxMASiYVgZKuwqpqQmYM2MuZs2cJdw0Ml8k2rVvJwVkD1IgGxAQgMVLFss13Lx583D61BmkpqaIuzfZECvXhRajI8bA1xiYQ5h1gmIohet169bjm2/iH/jYPgy4WEMOHmgIQ3hq1vzYsLT8Eg8R7XddzA/BQUGoULYsWrVuhdq1a8t1rgceeODBXwGPOPsfBYVQou6baiE9JF8c8coszGYGCSudqC276yVTy88XklO1f/8+IUa9evUW0usUU/hn/foNoNcbRECiG5PFBd3aaeS18oTfI9kklK89b8eRM47sKGL+/HkoXIDNrI4CKpYnOAsU3miSjq17rGhSTyPuBpLOTi00KF7kHtFkJhZvmYVUxggsXGVGfTcFXyRIi1ZbUKvGPZLL9xw4yoTrUXbUbG4QAlq8iBI3o2zYe8Am5Do0BJg8QosW3Y1o0d0gBWIHjlhRs7oKkXmV7sfUmmkkX5e5sweOWhGXADR7Ty2vzXI1g9GOHh004gZgvAKFbZZckMQPGzYyi0P1YUAhfdasuejXrzc69/8dEeEacejeumPHzSgzKlWqgGnTfs5ChlhMMHjwUMlr4mgdx+AK5Xfs28UrNoyZasa6LRZMnjw8g7ScPHkSq1atFkEyb3Kxe+TRDCFDzEqVjCezHzRGHebPny/u1YOHDkoB2fbt22EwGFG4cCEZ5a9U6Z7r4UHAY8x2VgrLLEygI5sr3T179sLObTsRkByBCH1xIYnMkd23/U+8v+d91KxVE4kJSSga/bSQ3FSvBCnfcjp5nQhMDxdBMUUXjwCj6yZfR6aqXZzCdERYVWYUjC8jhD9IkwfXQk/BxxCAK4dvoGfPnpKxRbfog4iSJMrcP95cfZfHjRsnbmQfkyOrhPmtyd5x4vQgdBZfBOrD4G0MwOUrl+Q5T0oMZY4tL36YtcuxNJL+YH3OETung5fuCebRdujUAd27d38i2+CBBx544MG/Czx33ImOQ903vYQDLVtrES7nbmKHXQZvvKISE8DnIxwFYFysT0iKk7zaJk2a4ssvv8owEPA8V7VqVeEBPEdevm5DaJBCYgUYa8W4g8xg1uuqX67J6DcFRzpCuejJhXsuaj/3jEpuxCdfG8XJSgMDuVzHPkbpUnjumXuc863X1HJzgttT6uV06VYY2EubEcWVHYtWWVAwUpExiRYRpkS1F5UitPJ9yJerVlIhJdUu28CsXXL0cV/pMHOxWRb3d/7uWOyPzKvA23fLwlyhbRM1Js82Y8M2ixxfisdjhmjl82j3kUHEXhbRNqytloxc5vmSh1OYZUZ8s2bN8Cgg/xgxciQCgwLxzbh5GDvVijIlVDKJdu6SGfnyRWDBgimoWLFilud9+eU3aNSoAVr1MOOLj1VS6kbEJdgxY6EZE2eYxf3sdNvq9XoMHjRYuCdH6jMvXpObkbNeDTmFOwFXJRLq7JmzwqU44UXTCHNa4+PjRfjngnqNGjUe2F3qxPPPP4/5C+bjzp078p3nd4vfx/Xr1ktea+G0p6CxcQoqHdeO3pLrK3K9P//8UyKpyJtStYnyWhJjkAkUFRkXkOBzG3lSirh8f/JAFnZ5G/3FZGBWXUHh+HLyeBoRroSekCLZtEtmjBwxCr/+uhkLFy6AD90uDzE15QoUbps3ay774BSKGVuWpkuUGC66l+m0pXh7x3pFIvielDjLCK+VK1fi0KFDUnh8K+oWiqRWcPlYOn0LJpbG1TwnUPW5qpg5a+YT2QYPPPDAg9zgEWf/w+4E6jBc3Obq/v0C9blS/9qLKmzYahERl6v0JKZXb9jw0wIDvv76a3EUfPLJJ5g2bRqmTvlRCLYTZy/SkaCSrFpXK9skkirlva/jju3b8EFLRzkWQXGXWbQc+6/xihLT51lx/LQVbZuqMX2BGcr78CJOn7Cw7PeDNiFrbJbNDBlxmmiWUbPvBt+7jyJteIgCb7yiQHKKAjv2WnH0lE1ei5vWocMHmDdvDl6tb0KZElyJBfbstyI6zo5G7+bu8Cx3l2Sv3mTBpFlmKTqIirbjhxmObIMN87I6RSgKN6qrRsvuZvzwwzjJ43zUsRqO0i9evEzKFEhUOK73dOVAWa2vUqWKS5GODmmSrq+/Goola9JQsphWoiLOXzIhJCQQkyaNQL169TIeP2PGDCGXWYTZTAhJzyvCZpzvLUcuqskX3377rYiOH374obgnnwS4L4wtIGSU7MJVFIouL8TLCZ3eWwjxzdBzQo6DUiMySr4o3nJMjNlTdHs6waIFkkfGNWjidTmKF1K1CYjxv46g9DyITC4uRPp68BnJGsufVBK+5gCEpkYi3vcWSkZXRbJ3jERA8MKD+c2Pu89169aVCwo6KJjrGhV4WaIYuN/8PCgcR/tfkVIxXlg8CWGWTgSKyxwdVdqV8LL4waBMk9FBCrTuwIsjL7vPQzf0Mn6CjmuWYpQrVw4VKrgm2R544IEHHvzvwxn9Qy31ZpRd8kvpiMwNb7yqkikkdhg0qsPeAAUSkhwxBWOmLsKNG9cxe/ZcWfAdNWo4zpy5l2PJLNWSNZTo1l6DvJlMA05w0otwTuxs374Nr76gEnHUmd+6c69F3q9cKWbWOhb5Kc4yxsBZYOUOPO8yHiAxGfh0uBFjhuiEW2fG+i0WcQYP/EibpTOB21C6uF0csMs3WDLiu3hjRAALsOq0jkLVig6eePWGHVeu24VDu+tecBbNhgYrcOykY4KLuuPN2zaM/8mMA8dsmD/JC8/fFaSJIgUdDmMWri1btRJ9+vRB4cKF8Sigs3LEiJHo0aOnLPBSmKNw2f+zajL15Yrzsjhr4cLF6NWzG95tE4VCBTTw9Vbg4hULFEoVevX6SEbXnVi1ahVS01JRIvFZl1NFPuYAcU7G+t6UuCW1VYNfNmwSnkFuU6dOHTwpcFqIN3I/ck/m3WaeMKLzlYJxjN91icfytvlmiJrkWET2TFdyLQqs7FagcBtoCM9yv1Gll0gpCs9cGDerjCJGXws5jeIxFcWIQN5+PeQ0IpNKIjgtL44cPCxxICNHjnzsfWY0GRdLbEk2yXC9GXwOJrUBGosXVDY1kr1iEON/TSKwiAcp7X0QcILri4FfiDjvYwuABSZmj8E7m7idHVq9N65fv/5Q70XXPkVoZvgyFoOTYA8r4HvggQf/TXjE2f8omCNEAkdhkHlXBAXH3MD7SxRRolb1e1+bwgWU+PoTnTTXjpowAdeuXRW3JMeR2jT2krGw29EOssqV9dPnbZg9wQtazT1iyDGpLbuA9xu8IHm18jOjSZphz1+xosenRpy9ZBfhk+D5jTy54QcGjBqoQ6VySikEY36rO4GJTgLexSyxId+ZsHWPBS3FjaHA5WuOXF26AQb30Yr7wAkS76s37eLSZc4uRVyOcm3fa0G3T03SnEvHI0/6a9eugkp1Ci0bqmTUi1m3ucF53BkVUO0FJc5etEkbMF9/0gidS6cI4yWGfapC7Va3JdCeY06PA4pZDyNo0cH6/vvvY82aNThx4oQc754fVxKymn3kacXyFQhMi8g1kzVYnwc3gs/K3zmmxZX+4cOGY/my5bJSXvfduuJ8vV8D7oOAWXFr1qxFWGKBLMJs5nzYvAlFcT4iHvZMm+xnDJLsqWSveHH8ZgaJ9LWQU7gcdkwINB/ryMiNR7ouGX6GYORNdgjDfqYgcSWwvIsjVCTdzIiN9b8hjgHmnKV5J2LqlKlo3Lhxlu8yR8LomGBRCokeXdP3E1OLFCmCsJAwxBpuIM0rSXKzGCHhHHNjiQXJe4LvHRQrWAxPAoySmDN7DsKTC8r+8MKHkQXJXnG5xj/wPqvSnCUeJTecPn0ag74YhN/3OUZNnWA8xNAvh+KFF154IvvjgQceeODB3weWkGq1avyy3SKdBYT9PtyU3JCcsHVDTcbUE3P8aQaoUEaJZl1347PPPsOiRYskGmrQj17iaCXXWrPJgh9nmfF+OwNWMoYqIivv2rjdjooVn5LzLUUdgyEdeYMgDtUeAw3Y9bsNlrvclG9N5+6nw0yIT7Sj8btqzF5qER5JPusKnFzj1BTNCCwGO3VOL2VbXLyPT7BLRBazdd99WyVFY06Qi7Kwt1ABJb75VCdRYeyFuBNnw6vvGWSBd8KECVi3bh2WLl0MH++DKFbIipJFcxaEucp2TUq2SzYuYxEiyyswbrpF9q1VI3UWYdYJHvev+mmxcatRxtkHDhyIxwF5DnsBHhTPPfccftu7H9u2bZMeAV5LNGtVTPhjdtfl7Fmz4WMMdDvyT3BRnYvr5C4haZFQ6IGNGzai2muvIygoEK9Xf134MIXVxwU/S8ZsUZTNLMw6IRn9qQUlk9aa6ULNy+wHlVUt/QLeKVnNAXw8RVfGh8WZbolA65gSSxIuy31nbANfm38vmFAal8KPCndlFwS5LPsLuP95U4ogOCkSixctxqeffoqgoKAsC/LkpjR48Of8HB5ETH3hxRexd8texPndlHiGIrEVxMnM7aHYzKzXO/7XoFFrJMbrcUFhm4sGLCTOn1JWBGhGWFAYtigtYiRxB6vKAp8HvAaho5pxbrzWsFrvieb58uaT2LRHdZV74IEH/x14xNn/IFJSUnDkyBFoNSzicoihKiXQub8R3w5iAUNOUZAC6rbfLGjwjuuvzAetNJg02yrCLDNJKXw6wXGrAd21eO0FFVp2M4hz9cM2906Ek2ezCdeC7ds2SxEE4e2lwbxlJnw9zuFceP0lJeq9rZbCh/2HrFi8xiIuWma+Us/R64Fte6woXUIpmWMcg+LKP2MJWBLGRl2OcQ3/TCukfMA3Juza5yhqIF6qosTs8bocxQoLV5phswL133GQURJ0RjrsO2hDRHiIkBE6Krp16yY3EpdJsxaIM4EtvZ9/pHUbFUHS7eMN1K6hxIqNNtnOvOEKiS1gJII7MIetVHGVkO7HFWcfBRxruh/B2L17N0xmEzTW3HOZnPerLRrcCbgCs9oIb4Mfbh2MxXV1lJBsljbMmj1LRhEfBzt37hSyREHU7fbYdOKGdY7+E8zE8jEGINr/qmRzZSZxLFwoFFcOV8JPIM0rUXLKSKV9TP4ZDojMgiRHtfg6JIXhaQWgseqyOB8CUyNw5uwZyUKj84Ri7KhRo7Bg/kLJlHWiRPGS6NuvTxanco590WjQolULTBg/UfJu8yeWzLIt3Nd8ySS9Cly/dh3JycmSF/aoOHfuHGbPno08SUURmqk4jaJ1vF+UREc4HR/ZkaZNkmzeB2m/5aIAI1PsaQrZJ2fBGMfiLh25iqZNmmL2nNl4/fXXH3lfPPDAAw88+HtBoYdj4z7eXpg8OxWTZzsmvD4fYcSkkVkz/zPjlx0WPF3WURSWHS8/p8JLVdRSaNqknhqjv9BmPI6ci/FSzI+t186AwaNNmP7dPcFuz34Lduw1o1Qpgyx2UkTz9dHhpNKK1+qnIzaeE1AKNK+vEYMCF9jnLbcgOtaO0T+aRcxkDNS0eWZZ3N+wxSLTUYz44vRZ8SIKTPjZLKVnn/fSSoRV988MIu46QXH0mwFatGqozrJ/LAM7cdaG/t10GdyUXPjgUYd4xxIpLnZyoZc3xjF8/PHHaF6fLmObbGv2CAcn1v5qgckMNH1PJYVlF68CpYspcPqCHY3rur9sZCTDu28rsH7d6scWZx8FdCa+9dZbcsvN0cjIrQBr1oX27GCUFeMr7Ao70jUp0Hsli7Mz9ngSohXxOHzoCMZ9Pw4jRo4QkfZxcPnyZVy5egUF08u6fQw5DoVFLqhnNhQE6fNIvBYFVR+zf5bHs+eB3JLRXAaNgz9SCM2TXFh4cGbXMHmut8kfid4x8lp8PsVZm9LBTRlLFWO+JuI3BW/+LtBFPGXyFNy+czvjdWgI6NS5k1wP5eYUbd++HbZu2QKtzQuF48tLxqwT3K7QtPzy/hSXOeFH0fdRwUgSxlgwxitzcRq5PqMbkryjEZZWwOVzLQozUr0TUbPm2w8kzL5X7z3cuHITYUkFZBqPTmCDJhVx+igpxo2JiRFDjwceeOCBO3jE2f8YePJo1qwxrl+/jKbv0QWrlTzTHb9bMW+5GXXbGLB0mpeMKWUGywESEoFWDV2viFKA9PezS7MrXbOu8GJlFerVVEk4P4sJbt5msYAJi1ZbxfVQumgUut0VbTfvtOHXHYBKDRFNq7987zUpuHZvr5GSris37CLMhoYGo1PfBCHCjDylS4Gug6Fj6MClEwCYOtpRZJWS6vx/HZatNWP77zYh6JlH50g81vxqxbeTzGjZUJ0xwkZE3bFh+Xo7PujcJkc5FVdMz509jU3bD4qbY+h3Jskmy37RwPxYjty9UFmJZett+KiTw5k7abZZsscyO4tdITTYjm3btoqj8km4Sp8EOL7DsUHmOC1YsEBIj1Gtz/U5zvvpnFRCgWIxFbMUBLDQ4LblElo0b4GNv2zMyAx7FPBYMZYgt/F6gnEGBk16lp9FJpWQDC46ZDkuJqKrXSmOV46+mVSO/aAjt1hsRbcOUb43BVmL6q5DXJ1+70KAf94VqxkRQmG2caPGOHHsBIJS8iBfeqm7RC8Nd0y30bVrV8krY5aaOxQrVoy5DOKicLdNbKhN9IsWZ0H79u3xqKBbRqtg03FWJwkFWZ3ZG7cDLqNI3FM5xghJfmNDrqFUyVKSw5Yb+HvZs0dPIFWJQtHMirv3WfobQ+AXE4QboefQvVsPHDp88JFKSTzwwAMPPPj74ww++qgXNm/+FTWrqdGgjk7yTI+etAn/ZOnX9DE6VH8p6/lj3yErdu+zSSaqOwT42YXnfvFxTi5GcEqpZwcNBo024dZtG3x8FFi82owREx2ZBv7eV/H1JxqZXPrziBULVtI5C3zSXYMe7e9NbL1TA9KT0ONzI37d4ch9pRN44s/RmDLHLNNnYSHkoHYMG39vMf7HEY4oA24jBd8+XTSwWO2Y8JNFODM5debtPnbaim4DjKhYnrmzqiwmiunzbXjttZdFTM4MZsHSlMHxeW8vYMA3Rsz7wQt+vlmPx7WbNoyeZEal8kosW2dFzddVGNJHi1PnbGjf24jQkNy5aUiQAlFRtyRv/kk4Hp8EGEewd+9eiUGicYD/7+Re7kDuScqkNmth1KZldAU4eZQ1ySITVf3790dgYOBjRR2Q62Xmge6gsmmEK2eeQgpPKSAL31dDTkpUGAVBvo5ek4Z4n1syNeVEmagXRNB1B8YcUEgkbLDBrDaIg9axbVooFUrhpuRhNKKQ81EwLpJeAVqLlzh1E9PuYNTIUdJr8uOPP7ot8BUurwBCU/Nn4XGZQZE4Hrdk0f9xxFkWVycmJaJ4SqWsxWl2jRyvWL9b8tlmLyfjcb4TdBkajfqBHK80Uly/cgOF7pSHznpvCszb7I8Cif6ItnhJLATjOUqXLv3I++OBBx78/4ZHnP2PoXfvjxAfexXr5+pQsui9k+ZLVVXo0FyNRp0MaNXDgF0rveSkeifGhp8XWqQcgCNVjClwBZ6s78TY0b6p+2gB4v1aaixfb0SVWg63rEatgr+vEgsma/F02Xsn6OKFrdi8y4Lu7Sia5vya0t1LJwVLugiLxSyE+rOeGtR/R50lc+zbSSaUL6OETmvHV2ON+GmBRQoPar+hlpzdNxrrpZiLY2T8f47QbdxukfzZV59XYnBvbcY+Hjlhw8dDrAgKDnfZbk9X6YKFi8UBPHnyD5i3PFlIbccWGilyYEHBsnUWuT1fWYk/DttElO3X1fEekXkUchzZGhwe6vo4ms12KRFLTU2WfN+HGf36K0CiSxI2deokJCQkQ6NWwGyxC/FK9IlGRGohl4IoiU+8T5TkkXJsqFh0+RylWxy3yh9bGlc1x+Q9vv/++0feTpaNMTeWgrCXi1gD5zYZdelS3EU3p9Ppye0oGltBCiLofCUpd8LXECQOTroXKNbmBr6+RWnOIKOMcVBZNRkE2HRXrGbRxNixY3Hi+AkUjCkr5M4JxiP4xgXKdnz55ZeoVq2atBy7Asu4vBQ+bveXoBPY1+qPY8eO4XFw/NhxeKX75yD/JMP5E0vhauhJXAo7KiOCLKwgmDmcFHgHugAdpk6bet+oBo7PnTt/DoUSsgqz995LiYjEwrioPSzOcldlaR48Olisx9I6OtzKlCkjkShPqkTOAw88+O9i0qRJ2LJlM6Z9q0PN1+9xPo7Qt2msRpf+BuFpu1YopWg1Ld2O5est+Pp7k3C3991MdREXr9qE27mLFiDIGweONOHt5ibo9XYRRxn9Nf5rnZS8OvFODRWWrLGgamUKulqXRoUJ3+hQpVY6klM41ZUm5oOubTSyH+SuFFE3bLXim3EmhAYD5UsrZbprzBSTCK6MY+CU2LmLdoyZYpGYLGa6+vko8Mdhq3QnFMqvwE/f6TKyYymqDhxpwYXLCoz87tMc28V/p5kHX7lyZYwf/z0On7iEt5rqxSjBDF2TyY71W60S8cXjxGiDqpWU+HG4QzhmNANx6qzNZTavEyfPWaFW2zF48CDMn78A/zRYSPz1V1/j+o3rwg/sJPgKQK9NlbzT7F0BTiT43obCpoRFYxJhlou/mcFF5rxJxWBRmSWKq3bt2o98LmQ0Ap9rUKfJaL87GNUO9yu5ZmhapPxdCRUKx5WTCAaW7Mb53cvtZ95sgYTSsgB+O+iSREcps3HszKBpwMnVk71jYVVaxG3qFKttdhvCw8NF7KQwmy+xuESTOcEJOO9kP+HNHOvn9FLTpk1zLaV2ckFXIHf0Tg3A4UOH8Tjge3krfaGz5uTBeZKLQK9JFfMFo7i4vzRBUPBOCLgDoyYNU36cIj0duYGTZ0uXLEVQcp4swmxmhKcWQEpArIjNw4cPf6x98iCn8YwZv4yeyZcvH15++WVPxq8H/1p4xNn/ELiSvWXLNnz/pTaLMOsECde3g3Ro0sWASm9yhVyNK9fNUKsdq7mMA3AHttPSKcqW3NwQ4H/XYVCnuWRKcRVx9KCswiwxe4nDZUDXqjtwxI1E9uwFG9LTU7FujleWsTdn5thTZZRo3tWA1+obEOAH9P1QIy4JguVkFGCXr7eKMLpivWOMrkRRBQwGuzTh1m6lFyfxtZtKnDpnRqmSxTB3wVy3J2tmr7JxnqVWHCWbM3smun92IuN+5tzSGaHTAvsPOsRZJ+q+pcaXY03iYu7d2bUThAQ6Js5xkTBv3mwZkXnUYrDHBVf8uZ908bZroka7pt4oVliJhEQ7psw1S9HZ9aCz0niaWaCVFWn/KzBo0yQzK0AfmkOYdYIiXEBSOFatXCXFc35+9/mSuQGJYnBQMOLToqSgy+X+aJNhUKWhbJmyOHf2LEKTImVsjE4EEkUpYOBqf0qklEbQKevMLaNjlJlgFHUpoLoCYw8o/DJmINE7Wkq5mAMrFw2wIykgGs8/9zyCg4Mxb+48BCZHZBFmneC28Hkp/nFyUduyZUsZYaRglvm7QML/oBcMjyuyKZmNwjlAF6AjgZliFJTvBFzGHcVl+blKpUbt2u+ICyO708cVSL60Sp3beASCxNjXHiCP9YizT+7cwYzfnbt2Zvl5+XLlMWToECHCHnjggQePArPZjJkzpktpbGZh1gnytPHfeKHy2+l4rYEBhfJrcOu2FXqDDWq1Ci9WVuQ6bcSFerpecwNLuXgKrFL1dbz66quYMOF71H0jLYswS2zb4xj5b93IvcuRwiojFGYusiAlJU04d6O6miwCLsXg5yopUauFHjUa6kXApXnhm0+18nyCrlXGdFV5WonNO63yvoUiFRKlcOqcHe+1N0imbkISHb0WBAb6Y9bsaXj22WfdnuM5ks4bHaSTJ0/CV9/vhtXqmORhZFfDOmq89ZoKrXoYMWeCNqOcrGxJpYjIzMXllJkrvnD5ug3b9tjQsI4Ky9fvksW8Bzmv/xXgAiILZidOnCgCYJGUp4RLCc/yjpXsf/YdcKQ+e/YsC1RpHGDslsKmElelK5CHhaTmw5WrJ/HHH3/cd/LHHSh4vl7tdezfdkDiBlxNOVFgTfGLQ5XKVXDgwAGZ1uJCt1MIpMmB5a9++mARTDmh5Sx/ZbTBHdsVMUtwisoVzEqT8FN2JDDGgZNOjIxyCpoUfv18/fDGG2+gdavW8LMGZhFmszteA8yx0p/A6S3GG9Ap+khTfgrHcX4c8LvKz90VKLJzokvEbd9bkoHrBLl4/0/648UXX7zvexw6dAhGk1G6HdxuB5TwSQ3Cju07HnFPPHAlitOkwo4TRuk5kTdPXvTp20eujTzw4N8Gjzj7H8LGjRvh56sSAdAdOGZfqIAaAUFlpF2SxKp+/fpo2bIZps49ibdes0PjggRv3GaTkSyWarVs4H4beL9Go5JyhqlTpyIoUO1yezj2xUz57OUM2VG0IFfygYZ1VW7zyF55ToXXXlDi2g07Ni7wgt1OZycJMh23dmzdo4DJTKetAt8N0eGV55RyMj922oKh35rxJzO81CVQ7qnyGDDwPVSvXv2BVuT4GI7C8MacptatW6NRHZWI0RqNEp8NN0rmV2aHLAXldk000opbpADdIFlJ8O8HrDKOVqqYQiImNm6LkRbRokUdpVN/F7g6OXr0aMybOwfpeoPkuDF3LWM/guhi1iJ/XmDgiEScjzgo4086izfMKpMIkxyBIl+SsH1T7lmnvD/afA1RUVGSpfYo0Gq16PVRLzmRcztC0vNlIX10UdwOu4inn3oay5YvEyF40cJFiLZcg0apgdlmlrEuEvmI1MIZzk8KjnQYcLVdY9HhVuBFFI2rkKNggAVcJLx8TFTgRRGmWTrBbC0SR75OqioJ3Xt0x9GjR5GSmoJi+mK5Ej2/1BAsXbJMFgGI8LAItGvfViIPONLP4jC9LU0cGdlHtjJvV5oyWR77OKAj5+D+g7AlWV06pXkRwVKKFO94WZRhpm65cuXEJfww2WEqqO9L1hVWpVzwe/D4oPuaOWqmZAsiE0vAXyI9FOISv3LsBpo3a46fZ/yca86fBx544IE7UGyKjolH8/q5FDQFODoE9h4KQrUa74ugxXJS5l4uXTITXdvaXPYlXLpqQ3wiuSedsHa3i5CHjtvEKdu5c2c5z8THJ6F1o5wOOJZ3EUUK5n4OYlkuDQYliylE8HSF/PmU6NBcgx9nmrFpoQ55I1RSKubErzvZRaDC4ZMKDOimRe03VSLsMlpr1I8mMRVAGYGKFSvh22/fkOPxoKWaFKB5u3HjBt566w0ULagXMTYkSIkla8wZE3VO8Lj16qhBl0+MGDbejAHdNVmuBa7esKFtL4P0KJD7LVunl8/17xZn+RnPnDkTkyZNRNStaATqIxCZKWeUfzI/lVFLV8NO4EL4YXFLkmPaFFZxjNJVK1qeXSk5rrnxDSd3ZW7so4qzBLlpw10NERV0AXkTi4kj1gmL0oSbYefg6+8r102rVq3C+HETcDHlMNRKDaw2q0yFce/YI+CINUgRnkmeTT5GR26M7w34GoOyZNMSNlhxK+i8RHWR05KnUsjm+Z5gR0KCXxQ++uAjmahkEWve1NxLZP1Tw3D23Fn5ThLeXt5o3KSxlHLxd/eppxwle3y/YH1el69BXpzum4TXqzxet8YzzzwDgy3drVNaMm5TI8WRzJgwGjnITx/muspZZu0uosEJfhYmrrJ48ET6cxrUb4ALZy8iOImu5wj57jOaI15/G5988onE7DHr1wMP/k3wiLP/sRWmkGCVuBDcgSfL/HkViMhfHEOGDMn4+cCBg9GsWRN80M+Mof3UGZm0HM+i23TQtxYUL1EKazdfQP+uNhk7yw6Ois1eakedOu9KRhPbPQtGMm4g5/YUiKS4YpNYBVeE24lzl+witNaukftXuc4bagwYZsLzdfQyakZQhCWBvh1tkRFyjuW36HYBAf4qGc1n7ELevOH48cehGQTjUVGjRg1psqfgl5hsRscWalgsjuOXHZ/20CAmzoZeg4z4cZYCtaqrQTPkrt+tIhRTVGa5GbN1nQ6Bv1uYZW7xqZPHkDfcBo1WgWbvuz7+bRprcfC4DavWW2XVniImR8UCDCFI1drhY/ZDileCkOLcwDZVYuXKlTICRqfeo+TPknjxZE2CmxQYDd/UYCFLBq8UpGgSUKZUWcyZO0dW+Ckg8uS+efNmJCQkiJt14viJSDylF9fC9ZAzMKkN8DL5imDLv7PQjMIVCX9oWj5xbPATZgMuIwyc+2lV0z0bAm+Tn2TWctTJgHQRjulM2LHDsbJOwTc3KG0q2f6isU85Gm7TYjDmu7HY+9tezJ03V7KtwkLDEGu8gfzxpeQig2IsHbvcXoUdIpZTyH1cl2mrVq3ExRvne8ulO4MXAPGBt/BMpWdkoeJRQLJMks19cOe05jFmLu8/5dj5/4beH/eGKckqOWqZ84IdGb/BuBlyDj2698DhI4cl1sUDDzzw4GG5KRGZJ/fF+Mi8ConC4nnSCU7urFmzEs26JuG7wSo8W8GxuE6RjpNP/b6yIF++vLh87Ta27LbirddynlP52KlzLShcuICYEpYtWyY/L1Y4JzctXcIZI2BH+VxiI6/ftIFr+OSmuU2lsARs7FQzmnQxIjrW8bMyJRTiTt20w4K+ffvjj/2/o9egPRgwXAk/H5VwU29vHT7+uKcID+5yPR8EBQoUwLRpP6Ft29Zo2d2Mzq1UUrZLUG8i33SCUWBD+9plumvlRgveq6mShfjjp234dadVCtYYMUGTwT/BTfk5fv75Z5gzZ65MzN2JUkj5lStx1cfij7xJRREVdAlpuiSZeiJZ41SOt9FfRE1mkt6PmzrLXH///XfpNaDoyHzUh51E4nPo8v2o10e46HNYFt7p3DVq9EjxiUdAgD/mL5iPvHnzyne+Xbt22LJli4jrnNbbv38/tq3eIdNd10JOyT7RCOBl9pM4A06F8ThcCT0u02AUpMkf07Up4hgVs4QCSNcli5AbmB6GJO8YpPjFI02VJCYdCqvOfNz7cVOVzSFSFoorK1m55MCL5i7Gls1bsHrNapmcfKPGG/ht6+/itCW34LHke+q1KQ5tHDbhxW3atMHjgJw6T0QexJpuoEBc6RzfB4rAdM76ePugX79+jzSd5xRyeZy5P+5g9E5DmeJPPcJeeJAdvG4/f+6C9E9kNp9wYSF/oj+0Zm95DI0Dj2s+8cCDvxOPfkb34F8HjuHfiTGLKzW3eAI6DbK72V544QXMnDkbB0944dX39Xi/vQmtexrxfG0TPvnGhDp13sOCBQsRGhqOlj0sOHfJ0RjrBEXWjn3NiIlTZjRVUqC9HW2T98wOrtBzSnrBSgfxcYUzF2wy2kW4cvNmBl24RNN6akwaoZOSrrR0YP4Ki4zbNGnSBFu37pBSpO49PkHHD/pgxowZ2L//wGMLs06QUFG8una7kMQsLFptwfnL9hzHimNk477SYcEkLyG+dFWw7Zdi7IjPtTLaVrSQEnv2W+Hr6yUk5+8ET3YnTx7DoikaxMbb8V7N3C8+WjXQwAY7CiSUQoH40pJTZVVYYVWZkOpNGwrHzGLcPp8uW67qEz9M+AEDP/9CclYbNWyES5cuPdS2cztZ2rZmzRrUblALisJGmPInoeRzRTFu3Dis37BOVvWdCAkJkcwsfnb8U60hWbfgaugpEWGLxjyNYnEVUSCxtBSBMZeW42R2hRWxfjdxOew4roQdF2HW3xAs5PeHH39Axw86QpnHiqigi0gOu4N36teUberUqVPGBRMhDo5cwKwsuoA5vsaYBcY1FIwtg32/75N8Xo1GI23CJMY3Qs7iZuA5cTHTIUA3BvN3SSYJFmY8DgoVKiTknSSXI4MUUJ3El1EP1yNOw+ptxvARj5619e6774oziAKwOyR434EVlgcqcPAgd5w4cUJE19AElnaoXcdrJBVGalqqLJx44IEHHjwsnBFRzIbNDRev2BAWHpHlZxSrli5dAYWqAN5vb8CbTc1o08uI1+qb0bK7AXnylcWqVWulJOvjwRZs/80iIp4TzK796nsTftluQf/+n4rQSW5KsLQ2O55/Ro0Af0g2qzsYjHbhd5wmc3JPd9Devb9MCaVk1TICgaaBKXMs8PP1kUVP9hjs3LkT/ft/gbbtP8J3343BoUNHpYzqcYRZJ+igXbZsBQJDq6LXF0Z8PsKhzq7fmpN/sz/h10XeeOMVJWYtsWDiz2bJux3SRyPlu6WKKbHnD4eg6S4L/6/Cpk2bRJj9dpAWoUEKcYlSYHUHOu2oAtI1WTCuDCITS0opll6XAovaJPyL5gGnAJsdHP9njj7BseqhQ76UyIhqr1XDrl27Hnr7ea3B6KCOXTrAu4QShvyJCCsfgM8+/xS79+xGpUqVMh5LQbZu3boZQm1AAJ2/dlwPPiNlYMzJLRHzrESKFYl/Sv7uYwzMiG1gB8Dl8GMSM0UBV6VQ46OPPsLnX3yO0KJBuB10GdFBV1HxlfKYPn06JkyYINOANC4E+AcI98wNPHZ04tJZTLcquycK334K8bcTHKWuAAYNHgSVr0K44W3/KzgXcQC3A67ApDIKf0z1ShDO/ttvv2X5nX1YMOrr2+++RaouETdCz4iD1gkWw/H6guaRr7959Ng0TvQxTiTB/7bbCAV+X1JViWjdutUj74sH90xCC+YvQGByuNupwLC0/PBSeEsJogce/Jvgcc7+h1CrVi18/fVXUkbVvplrwrJ5lxV3Yqzy2OzgOP+BA4dFRCLx4OhX46aF0bx58wwX46JFS9G6VXO80fgmXqqiQYmidkRFA9t/s8LXxxezZ8+QXEyCxIIlT5t3qcQdmhkRYUpUqajExBlmPFdJhZefyzoqQlGw+2cGcZRyjGr3PqvEF7jDzt+tKFxAgcF97rntWjZQY8i3JsxZdhbPP18ZkZH5UaPGWyLUUpTYsGGDjItTrCPhqlKlymPncr733nuoV6+etOZeu3YNn3/2CUZMNOCn77QZxQ7E7Wg7Nu+04NAxGyxWRzPwjSg7Vm+yyP6+WFmFkT+Y0ahR0wceY3tiJ8QFc9G6oQLPPKUSodiZj+YO3F7iasgpESedyB+ZH3Xq1pGVeJYLxPregE1plewrkjq6TknS7gRelozWsNT8QvLYIpviFYcj+47h3br1sG792oeOdeAIPm8Pi+eer4pFFxbDbrOjWPzT0mCbGVyxLRxX/u6oXLg4hK+FnEZEchH4mQKQ5BMr36ehQ4fKjaP3JI7Zv1f8fXr2mWdx9s+LkvvlyvlBUsnR8nxJWfNz6fwITInA7Flz8PHHH0tRBZ3CdDdy9CoipbC0+jqjB1j0EB14VYThuXPnykjXo4LldHRP0r17wfcQvJU+sLKEzaZH0SJFMWHihMdaweaFwce9P5YyBboxWIrhLCAjIaaQHxN0TXKmnAK3B48O5hKqFZocZSiZQde4nyVQHuvJ9/LAAw8eFhQ1fH11mLPELAVgrsBRfrozO3Z8Icd9xYsXx7btu2TihEWQiYmJePm1MIxp0EBGzXl+nTbtZ3Tq1AFteu1F6RIaVK1oR7rejs27IH9yqonuQKdYGeDvi0UrTfisV87s/47NNfh+mmPRvHOrrFzaZLaj9xCjcLaK5RTYvd+Kjxxrri6xa59VjAjff6kT3kswBmHRKgs+H6lHjeqvomChQqhc+XmZOCEHIy/lRA/PhzVr1pQJmcftHeBnsGTJMskXP3fuHCZOGIcfZpxBzWp2MQk4wWO1dbcFG7ZZweQg3ihi06V88za3XYXRk6x46qmyWcTEvwMzZ/6MKhU1aPa+BivWW2VKKzco7v4XE3QFtkx6WlCQH9566x0ZbacZgYIhF8HJuaSkyuQvi/DXQ06LmJsvvnhGqSsXu++Yb8q5kAaPh4374cQPp+x4e9jPjzwaOggHzZ7Lz/N0wYQyuBzmKH6NjCuJ68En4WsMQf7kErjge1AmqLp16yY3i8UiYmx2bsrFgOYtmmPGtJlScOVq0ZZuY05niTs3UzwDp53C4gti3/59OH36NMqWLYsVK1egZYuWiIm9JdFn4SmFMiLBrAozYv1uSXYwQT77OO7ZWbNm4pP+n+Cy9hi8lI48XsZ+BQUGYfxX49GoUSM8DgYMGCDXw7eCLyBPYpGMhQGJZ9AmIyrsIp4q95TL62sPHg7Hjx8XU0CE3nV/CMHP1zclGDu2Z+1K8MCD/3X8a8VZjsvwxEcyduHCBVlVowOSK38U0XIDx0D4D3V2VKxYEUuWLMH/V1AQJEZMNMkq/YtVspLg0+dt+HSYUUaxSM443pUdFALpIHTXwEmSvGLlGslE2rZtCw6cjIO/fyAGD34XjRs3ltVdJyjSPP9cFQwafVi2xxmV4MQ3n2lQu4URzbsZpKDgvVpq+HgBvx+0iqPWaGTLLVClohrzVzqiApzkNrvbgs24n/a8R7Jj4uxo97EBx07ZUKGsEhXLxSExOQ7jxh3Bt9+OkqywUsU1yBdhx45jCsyZMwfPVa2MadN/zuKsfBSQ7DADiTcKWR07dkDrniZ89IGjIIJO2kYfGMBSWTbpvliF7b7Aus0WrPnVirAQoEMfM2zwExL1d4JZqImJKWhYx5ENV7yIEvsPWdGphXt3wr6DNinaCEspKOSLwitHrhKsUfI7TOFw185duHb9moxZkewy/iDR9464GkLS8kmjqlOgpBjH7FLf20G4hpP4/LPPsXDRwvs2ef76669y0UY3LC9onO6Yh0GLFi0wf958yavNLsxmJqBB+nBxq+ZLLiYiqE1pllV7EtvMmbl0trpDv/79hOAzN4wlDZlzXJkhS4eEljmuLgoIWCpxKeWoZL698sor8p4My+frcNuzk3ZGHlwPO40hg4dgx84dj7wIwefRycGLyLVr18qFHveR/5YwiuJxFzcIfueZNcURwKTAO/BOC3CQbN8UGO16cVSzldqDx4fBYIBKobp/IYcn49cDDxy/Ch5u+tAgJ09LM2LNr+RzZrRrmnXBMj7Bjg/6ObgpY4lcgedWxkfx5gr+/v746acZ0r2wYcN6HDlzHRqtDm3bvSbnq8yLeeRlrVq3xfTpk/FSVSWqvZj1UomC7KwlZnz9vQlrNllkoT9PuAJnL9rETRp1xy6u2c6ttOj2mRH7D1tdis6paXb8tMCMWtVVGdyV4m6/L41YudEqEWPVXkyH2XIGq1acwc8//yyPyRuhRuniwLVLColgKFAgL6ZPn/lERnfJ4XmjiaJevTqo3zEd/T5USjmZ3gA07KTHxat2vF9TJTEH1IT3/GHD/BVmiTUYP92MP48Ac+cOeiLn+4c5V+3Zs1emy4gSxRQ4dDgZ9kS72/MXsyntCjtqv6FCvw+1CA1R4PQ5G2YsMogATrGN4uqmXzbJa9BhylF7LgJT+uH4P4VQ5wIxQVHUJzYAN0PP4uOPeuPQYYfomdt2U2i/efOmfO9ogilY0HVpV26g6aN/v/7QGrzdFqZyO7mgfSvogmSjetHsoLKIk9RkM4pY6kRuYn/Hjh2xcMFC3Ag/i8jYElkipjiRdSPovEyY0ZGcHTQbaJRa4eN8PxoRmMFKHkujQebPSmXXIE+Ko9+Bk2D8PXVXxPwgePPNN/HHn39IB8jBgwdFNyhfvryIpbl9Rg8Kcm1OR/bq2QsXvA/BTx8k1zQmbz3SFSmoWKGixI3lxvs9eDDw94Zw1W+RM+NX7zmsHvyroP43/2JOmzZNVrqZI0liRmGV2TQkMA/SrsgR3MwB7o/UJPkvW2kqEKlB4fxWNOliQLUX6VhVCYncsdeKX3ZYRSRlKQGdow8LnmynTZuC33/fL/9PYla9+uv48MOubtu8v/p6GOrUromazfXSbvvGK45/aJkNtnStBQUiIRlhyzdYxDVBkABy27fssuK9mloUzq/A7MUWETTpPnBmjtlsdnEl9P3SiKKFFGh+NxeV5LdNTwOiY+1Y8bMXqlZSZWn1HTTKiHWbrRjaV4lXn1fL6+zYq0T/r49K1uqaNeuf2HeFxG/27DkYNOhzNOp0DX6+ShiMNhQtqMCSad4IC7lHVN6upka7plY07WJAml6DVauW/e3uQLo2nOUcRIv6agz+1oTzl20oWTSnME6Xxc8LzdIgmzmHNJBtroYQXA89i84fdBERlnEHdJs6ie6twAvSqhueUtAlueaqdEhiJHbt3iVlDK7cs9xexhgsXbIUZotZyhMsNjN0Wh1atW6FL774QorCHhT58uUT96878usE70/wvSNE1aawiSDNjFs6XJiZ+yBgdAMJab++/XDBNwF+acGS88VxMuaCaS3eKBRfNoszIePY2BzkjxloBB0VWoUOwel53LcOJ0fiwsVT+PPPPyX/7HHA34+/KlaAv9uffvqpLPbQ6fvnH3/CZrPhqQpvyr//FSpU+Eve978INi0bbQYYVfqMVujsoJPd5J0uLiMPPPivw8NNHx4nT56UP1s3cvCJxWssaFBbLWPyR0/ZsGKDRcb/36muwpkzD89NyWcnTfpRRFmz2cEjn65QDm3atJfziCsRkdmTc2bPRJteetSqbsH7tdQI8FPgz6NWiTSwmIGPOqllwZzRXgQdsFUqKaFU2FGpvAqVnlKIoYBFWd8N1jn4tlqRYYZguSuF5z4f3uMgX4w0Cf8cO1Qrx8A5UcV+gp/mK2Riqk1jBXp2cDzn5FkVPh0eh2ZNG2PN2vWPlMXvCuRTjIPo368Pun56AF5eStisLPQFVs/0QoWy93jH6y8BnVqq0aiTAXsP2jFp0hThL38nnIVMTm5KwXzuMoNEZnGxOjvoZmTef1iwAj8O12UcZ5ag8TZmigKjRo2Sn7G0lUKj0wmZ4B0txV3Ms80szGbmU+GJhXFRexjr16+Xybsc72+3Y/LkyZg4YSKSU5LvFntZhF9SRGThbkREzu12Bxpn/P38oU7O/drE9260ASOtLCqz5HLG+d+UTFZ3CxvZwSg1GiJatmiFi+rD8DMEQ2PWSW5tsle85N4WTCgLndXHZZGtGpoMbvrLL78gKTkRxVMquRXReewZF8BrfJbdPg4oOpOH8/ZXgNFbNCMsXrwYv276Va5BChUuJI5aTqU9iRgSDxwOc4KOZK3efZGkwSsVJf/mwmwPPPjPirPM22EYemb3GwVAjsrPnj37gcRZXkz+3WM3/zRIQudM8JIw/7nLLPhsuIPQlCqmwNC+WhFIG3d2n/PqDhz9GTNmDKpW0gipzJ9XicvXbZi3fA+aNt2BYcOGoW3btjmex89QrVGgVUOVEPBZix3v7X3339qJ33ihYnkVvvhYi4REwGyxw98P6D3EJCTx4DELhk+wyUgSowCYOVayqAL58ylw/pJdxq3y5VFIfqu/n+PEv3GbFSfO2rBurhcqlssqbLHIYPzXOtyIMsjYGsVZpVKBGq+osWCSErVaXJCTbocOHfCkwJXyXbt+k1wluiB4mzhMl0WYdeLZCir066qVpt7HdfA+Cpwr+rxgYulbo7pqzFpsRqvuBvwwXCdRFM4Lneu3bOgz1IhbUUDhlEIuSZp/OovBEiQfK/voNMftSfpcjU05EaAPFRfAH3/8kUOc5UUyCdHhA4cRkpRfXLsk1xRMOXI1c8ZMXL58BTNnznjgkUDn4+yK3LPxKMgSybo4uQxI8YuDwtsuGXEPA144kuhRXP1l4y9IT9fDmGiHV5IvisRUcHlhIPuuTs/I43Ne/Hql+8kxz01Q5v2nTp16bHH27wDdPYyG8OCvAx3mzJeLT7slLdCuwItfum7oKvfAg/86PNz00dGni1YcmnSfjphgkkiniDAFOjTToHVjNSbNNAOXH9400KXLB+JC/bSHCk+V1iAuwY5l685LDM/vv+/FmDFjc4gm5BSpaXp0aK6W2KzO/R0Z6uSdLHMd8ZkGLRtq0a8rY6fsSE23izBIPkROWukp4J0WBol+ouO366dG5I1QoHRxhRgDTp+3C89dPNVLclqJqzdsklX7VX8tGr+b1V3H4tzu7bUy9TV5llmOia+PAuVLqzD/B3JTg/BwOveeFByTcKuFE1BkHD9+HIb01WYRZp3IG64Uc0SDjoZHzu18HPA9AwP9cPSUEXXf4lSdCnXfVGHj1otyPxf+neIfM2Sj/a8hxTsew/reE2Yzg8aDcdPNCEspIHmpmWFS66G2aiXGyh24mOmLAOzbt8+lOMtSO2a5BqflRfG04vJ4xgEkecVi15bdqPfue9KB8DBOUR6DdFgfiJsy05XFqVIKpkvGyKGTHioag9fOv+/b67hmWboMsbFxUBiVUMWrUCyqIjR2105Ui8IsE05ObsrvFiMGXAm5TpC3s8CNj/03gJ+ZMx7Cg78G7Lh4+aWXcXjPMXFdu7q24YQhi55btX64iBAPPPin8a9dwmEWTvaxZP6M0QbR0Rw58SA7OPJ07YYJl67Z0aSeBmvneOPKHz64vN8HW5f6oF1TDRKT7DhxxvRQ41EkvxRmP+muwfKfNEIqufLcsoEG6+dq0L6pCgMHDsShQ4dyPHfTpg2oWU2JQb11+GODD/au9ZbbrlXeiMyjQKueBoybZsLla3aYLHbsP2xD4w+MIrAajMDBow6iUbWSAke3emPORB2eqaCSJt07sXYULsACBwXyhN/7qi9cacaLlRll4HocgkSNcQJ/HrHhwuV7Ilzp4krUel2NuXNmPvEvFy8MmHPGDNKny2mEWLoDBXQ6BVl+8HdDslCfrYifF1rFUezjrcD8SV4ICVIIKX+npQEfDzai6Yd6vFxPjz8P25A/tqzbwHaO/jPGwM8YnOM+jptxBT43OE/IPG7ZQVflwQMHUSCmjATDO10PjCOgi7dAbGls27ZVSuAeFIxEKFa0mJD63JDsFSeOhNiAG/L/fnm9sXjJ4oy85YcBXQocr9u+Yzv2/7EPg4cMEtJhYbuuC0j2qt9tFC9WIuP3mN8vdyUF2Z/tWdn3ILPQ1LdfX3GBx/hdhy3ThR+/TywWiQ6+IhefT8qx5YEH/2Z4uOnDgw33XNTdsssiEQIzv/fCpf0+uLjPBwc3+aB/Ny0iQhXYukeBpys+eFY8R8W7du2CN14BtizRSBwBuem7b6sxe4IWE77Wyfi6q8IYjprnz6sS08LWpd74Y6M3dq/yxpHN3hKz9cUoMz4fYcSx01boDXZcumKXMq3hExxchFNbjMfiZcru1d7YMM9LnL/MoqUwW+kppURUVSp/j+PQMcyyMRbXugO5aZoeWLflnokiwF+Bdk2V2LBhnUQ4PWmUK1dOuI9WoxQ3rztwcb5UMY30UvzdIG9p0qQ5Fq+xIynFwXW+/0qHd95QygL+5cgDuBl0TuKgzuU9ICWtH7ZRo/47rvdn2XoLlFBIDEBOOLjp/eJ+mHnriptypJ7CbJ6kohJ95ZxK4Qh2sD4PCt4phzs374h79mHw8qsvI90/MVeuR27KMlsaFGBXwOpvlAkt9mE8LBhTR6PKho0b8Mef+7Fo8UJY7RYRe90h0ecOlCplxvvx30tu7/34Ka8H+FgPPMgc/WbU6HEr5DwsSnOO8rWbEedQsmQpifzwwIN/E/614qwrMMCcmZgcxXwQ0HXFzBu6bDnezDzK/89grk54WDDGTDWLsOYUIp1jVhyz4Uqxj4+3y5Ved5g+faqE8Pdor3ERHq+QlfYiBdUZeVlOxMbG4sKFcyLsERqNAgUjlXLjKvzKGd6o84Yak2abUa2BHlVr6dH9MyNOnbPBWdx5O8bhYhj2qRe8vZSo/pIaY4boJAerUR01mr2nwcbtNiQk3jvxX7luR+WKuZ/kSTKdTobMeO0FBc6dvyS5cn8FLl26iPx5c3dl0t3r56NCUlIS/gn07t0Pfxy2iuuasQX5IpTiQp493uH2ZVHEkZNavPXW21DatfAzOYoSXIEr9+4Kr1jAwJGV3Egb73euVJNgvvzSKyhbpiyer/o8xo75XorEfMz3co4zw88UDH9zsDhoHxT8frfv0B7J3nFI07gmoKnaRKTqEmBWG+Dt7y0tscy5YmHDkwCjXCLzReJm+DlxF2cGjxVFtGRdPHr3+Tjj95E53HqfFHFmuAOFcj7/UYrSPPj/C+bLccQ3xv86LkYexs3A8xI5cjnyKG4Gn8c7dd7Bd999909vpgce/M/Cw03vP5FTo0Z1TJlrExcqwXOXVnOPFyxZa8G1m2aJrnlQcIFWo3ZEBGR+LSfq13aIcz9NnyIL3pk/Ly5+BwbYZTt4I89hL0KAvxJTRunwcWcNNm23ok4rAyrX1KNZVwO27bnHVZJTgJQ04NPuWnku3aZffaITlywn1T7rocH1W8DeP++975XrNpQvpYS3t3vRL38+JfJFKHD1RlZexKgvRjYw5/2vwKVLl+Dna4efr/tt43HKn8/+j3FTlpra7F5o/7EZd2Js8NIpMHmUl/DTOrXtsIfHIc0nEe/UriXxAc3fd5/9yesMFn+5mtxipBS5F92n7kB3rl6VKg6/+fPn452a76Bc2XKoVPEZ9OzZEzp4SzGrK7AHIDA5Qhypycnuhc7s4HSiwa5HvG+Uy/u5zfG+txyirLcRXbt9iCNHj0gJ8pMAr6fffONNRIdcQZo2yaUwHBt4A61atcpwBJMXszCW+b/uwOOcrkx+Yhzag/8f4ITf9OnTYAhIw4W8B3Ej+AxuBVzE1TwncCXsOIqWKoJFixb+raXZHnjwn441cIWffvoJd+7cQbt27XJ9HDMmOe7M8G6u/FHQnTJliuRScRX9ccK6KXCyff5/FYMGf4levXqh66dA/64alCh6T4Qc/5NZcl6/+WaolC7Q6UoRko4ohqa7QkJCAvbu3YcxQ7Ruw/8p0DZ7DxgzZR1SR43KcOb17NENVqsFJ866fh4bYkcO1MmYW5teRhkJZxYT/6Hl57V/32+4FXUbhSLN4mrNjNRUxzgcW1snzTKj5xdGTP9WJ6SXue8sYsgNqY44JGi1WbeNI2rcH+YIPWmHIWMN+B00FlXI98jd8eRYW3KqxTHC9JDfNWdmrPPPR8ELL7yAESNG4IsvBmLdZiNqv6EQgf34GUgzcbGihfDzjNnye0VXdW55lVzB54q4KzAfNdEnGslesVIAlh0UEuP8bklWFgP4jQYT/NNC4GUJQoo6Hck+SSiQXjrXffFLC8HxE8fle/yghQBcuFi7Zi0OHjiE0KRIBOnzSMarIy4hGrF+12WFv2WrlkLCg4KCxD3hykHxqJg5aybatG6DS8oj8NeHQGfyhU1pQap/vJBzimkcSXd+Pxo2bCgFWrF+NxBxt2AhMyjaxgfewtNPV5R4iP/lf8M8eHg87u99ly5dZHFv4cKF2L9vv5wXypZ7Rc6jLNLk/3u+M/+beBL/5v/bkNv585+Ah5veH3379kPTJvvQ9EMTBvVW44VnHRFJ7AGYt9yMMVMsaNiwgUySrFy5Uv694Wg0L9DdOerWrl2Jem8rchUUm72vxooNN6U8kw5egufKW7duINHbkZvPCaHMoJHgo05atGqoxnO1DahT530RnBgDQBPCtq1bcOPGdSSnpKJezayXWYw/IDdlIe9T/9fefUA3WXZxAP8nadK9C2XvvYfiQAFBUQFBNshGRWSo4EAQB6io4ERAkb1kg2xUhsgQZe+9oUDp3tnfuU+/lhbaUqA0bfP/ndNTSNL0bd6M+973PvdW1uK9z4xYNNlNJV2ldYFcnxUprIhPtKsevGmZzck/J3FGTr8XSwXy4sULkZRkV/tDCgQye92du6jBI4/7OiQ2lereqVNnoF+/l/Foq1g801iHciU1uHTVjvWbbXB1dcesWZPVyYC1a9di/xEbypXOOI6X8D6zwgCZmXDd5zzCvK6oyteMqASpxo4F8xeox0+KAdySfFWcdtHzEgLii2RZeeuTFIgbxkvqeSkxd3bI4Fc5mSrPQZMuEQHxxZLbJcCq5jdIKwer1opmTzfDu+++o56vIiefL2PHjcWrr7yKPXv3wMviB48EH/UoJnhFIV4bg+bPNFfHcim/U+a+FClcBDdMl1AyvOptj4kqOPC5pFbxSAzCOKNgud/Xvaw43br1b7UC8s8//kR8fAKKl6iBjh07qB6/0qqDz5m8ibFpPknOygTu7LQkkA/WW4f4SGJLAirp8ZISYGVGmqyn7VUowZ18qMkBqCxlatGixT3/DRIYHTt2DHm5ifZ7772HyZMnYW2HOJQrLdWuwNkLZnh5eqBnz5ewauUKfPTRh6nVtaJChbLo1auvOhBP6+rV5DO00mM2K0WDtTCZjdi/f79Krl68eBF/b92OPl1cMGOBRS0Nq5VBHysx/zcLihUrrHoKnz59GqM++RA3wiLQ5HEXaO1WtaTrVimTc6WS85ev3dB3SBKatE9Et/YuajjYqj8s+HBIxtUUQnryenoAdWuk/7vWbrShSuVKOHHiBHKSBLUfjhyOyhW0OH7Khj0HbXgok+pemYprMOhVz+R7fa6dP38+y8nJ0qT/9KnjsNntKFu2Ap5//vnUBuwpy9x++ulndbudu3ciyZiEoKDCeOutZ9VJD3kty0GUp4enClIzC2BdrHp1Nr1wbKnbgjKZjCuVr7IkzR5tT9czTJawhPpcUBWqSRF6uCV6okJ4dTXdNeVMe7RHWKY9WVOkTPqUpLhMys2ud997Vx1wb9ywEaE+F+Gi0cNiN0Onc8Hjjz6OgQMHquS5vD7+/fdfXLhwQR1oVqpU6a6GPGTlu++/w8aNG9XQgdDQ63A1GNDwkcfV+5e8n9363JCDR6kkkooOGXAh1RkS+EqFQ5jfJVjcjOjTp3eefv+i+5PV6z47ZCnircsfjx8/fp9bRflh3+c3dzPo8U4Ym+aOTz8bg2++GYtO/UJQNNhFxXbnL1phtcnKr+fVgXfDxx9FkvHmic7ChQPRoUNndTLy1oR8VGQUihXJOkkvvWjFwYMHVZJXYvjZs6ajXQsdlq6xYtFKi2r5lZElq62wWjVq2azRaET3bl1x+Mgx1KrmghqVbfh37835CSmCgzSqZZYsvvrla1c14PWZzomqf3+AH3DwqA1nzttQvkzGscuWf6yIigaefCR9fLhmoxUeHq6qAjinP8N/+OEHuLsZYTRCPR6v9cj48dj2nw3nL5nRr3btBxKbSkWuHKPt3bsbZrMRhQsXxdNPP6OOS1KKJeQk+8SJP2PTpk34e8sm7D0crQZlde3aWA3glYIcqUatXbsmpv16DK2b3xzSllap4lq1NFpOuksrrFvjRmmNJQlaGdIq8ZTu/7GkJEIjPK+plSZeHl64cSUc5UJrp+upKn3aZUhsVlKul4rlW1v4ZUWei/IcWLRwEc547oOLxgVWu1XFelUqV8HAQQPVsYMMUNu2bZs6lpFKcRkunN1Vp3fywcgP1DH5urXr1LBejUaLylUqo2XLFmoV163V3a8PfF314L0UeAxBMSXhbpb5CBrVvivM+7I6RnjztTdx6dKlHNk+KnjxieQHbh08furUqfvcKsoNjE3zeHJWEj3SXuBO5Ixnyhm/lGE3UqEmw8AGDRp0T79bJotKckbu636Ss1J1m9d770mSTc5WyhuXBFCSGHy1WjU15bxXr+5wN8Ti8/f1aPm0C9xcge3/WTFp1iV88snH+PHHCSrASSEJOKkkPXvRhoYNMm8VcO6iTQWO0kRegihJKvl461Sf2h27rOj3jhEzf3BDlQo3AxaLxa6qeaW/7FdfvY1ixYrhtddegb9vDJZMcUfZklp8/ZMJMxaYb6tuaN/SBR9/bVJDqZ5ooMOaue6YPMeM8dPMKsAU0sLhvQG3H8CdPGtTQx3kPtJWXPy1w4Iduy345pt+avlOTpIE3qXLV7HgJ1d88o0Jb35oxIKf3VSLh7Q277Bg/FQLur7UAw8//PA9namSN0J5Dty61EOeB9I7ePLkyQjwc0GTx+3QajT4+99zWLdunaqS+/jjj1OrVOQxkDOTWRn8xmB8+eWX0FtdERhfNLVHrASKca5RMLom/v+s/u0TdSU4k2m48e5RKkEb7n8FrgkesGltSHCLVmdEH3voMfz3zy6VGD1VeK+qxJXBVjL8SwYdSOIxo362KRJcoxEUWEgtl7rbSqsJEyaoilsZaicHDf7+/qqyOyWQ3rdvH7784kvs3Xez17L8jsaNGmPEByNuG2B2L+Q58P7772d6vbzGd+zYoQ445W+UhMXCBQtxOn6vGsIgj70McypfrrxqvyDvAVTwZPW6p4LNGfe9nMTNSYxNc4c8R+VzUYo0ZJCSJDzbdSypJqu/9dZgnD51DAN7a9HxBXcEBWrU0vMZC6LVECyJX4YOHZru/goVLoSzF7JO6Jy9kNxWQD4fpSe8JJWiouPQr7s7NBozPvvBpIbKNm+sS40R5Het3WjFlxNM6NSpi0oK9OrZHZcuncSCn9xULLx5uwXbdxlx6LgtXeGBxJXT5lvw+19WFWPL7Iepv5rVLITwyOThYR+NM2LG9263FQ9IL9UvfjSritt6NW/GhhevyPBdG9q375LjS78lttm27W+89YrMckiOuWtW0arevbfG+EM+NqJWrero0KHDXcdTd3qfkoFkw4a9C7vdimZPaODrrcG+Ixfx8cfb8PDD9TFp0s9qlVLa2Eh69Wfm3XeHoVevnhj6iVkd76QMDBanztmw8g+b+hukTVSRmHK3FQ/IIK9Iz2sqeRjlcx3uid4S2CLJIw4WmFWct2XLFrgbvXE+6LCqHnUze8A/oQgMFo/UllyZSTBEJ8eLjRur46y7IcUTb7/9tkpQh4SEqKrTRo0apQ70laKBMWPGqJVtadt5VK9WXRUe3JrkuhcSS/bv3z/T62/cuKGOAyVRLglz2VczZ8zE+WuH4Kp1S07O2hIR4B+Ab0Z+w76hBZQzxifkvPv+dDZj0zyVnJXJ5PJ1N6Qi7dVXX0XdunXx2WefwdHkw/RuKvAcRZLIstQ57bZ27Nge3h5xWD7dgED/m4HI040kUafDwBFmDBv2Dpo125c6jVV+vlmzppi79G90ayfDhG4PyExmO+b/ZseLL7ZP/Tl5URYK1MHLQ4s5P7qh+6AkNO+SqPpm1a+pRXScHav+sOL6DbtqqSBVf5IMi4qKwKqZrqqHV8pwLEm4LlxhQZ8u+nQB8IQZZrw81KiGhFUsq8XXH7ni0/cMCIuwYejHJvw4zYyzF+x45SUX1KqmVX1pl66xqJ+TpOzgvsn3J5fP/82Mbydb0bRpExV43s1U0+yQs9ce7joV8E7/zg1d+iehaYdEtHnOBQ0f0iHJaMfqDVY1ZKJQUKA6y3w/lUHyRnjr81SqIyQxO3ywHn276lW/rpTlcvN/0+KjcQvU/ktbdX4ncrIkLi5O7bto3+vwiPNTCdQkzzgkaGLVtM2gQkFYuWIlzDqjClylRUBy8jYSYQGX4B/gp6pEJQkqb2yuBlc88eQTKmiVJVI2uw3uNm/4xAWp5fnSD/Zi4DG4mj1VWwQZ6HBr5YP6u7RGxHiGY0ift+DpmfHAsjuRx1Cm1MsBjDymKfezdetW9OjeA3qjG0rEVIKn0U/9TdLXdeeW/9BhbwesWLlCVdI+CFJt8c7b7+Df//6FVqOFTqOD2WaGr4+vGvAk/b7ksZTnkPTclpM1eWkZMD0YGb3uyTk4077P6fcyxqa5u+/kpG/aIgk5wXvy5DEs/kWfbpBr3Ro61P1Mh6oVNRgz/if1M2mTk+3bd8Z3343FR0PsCEgT06Y1Z4kVlStXUMcQ8rslISwkITtmuAFRMXa88rZRJUSbPamDLCjb8LcVx07Z4OnphjFjvlADnv7Z+R9mfu+aWqQgla0y1PaXOWZMGHNzm6X3rMTT748xqutlgO2wgQYM7adHWIRdJWqnzLOg3ctGDOydHHvLXIX1my2qWEEG3c7+wU1tq8SFazZY8OUEG3z9iqrl4jn9GpeCFaPRjGZPuqNMSQ0uX7Wp/rpPPa7D8810KoG8dacVK/+wwA4N1qybdc/xVGbvU5LkHDp0CNo864JR77iplmcpSfK/d2oxeOQBDBjwGpYu/S3bQ6OeeuopTJgwEW++ORi//2VEi6aa/yf8ZZibGaVLl8Db73RR/dTtWjsCY4urFgESx0l/1Bu+l2HVmzFh/AQVS8lzQGZh1KxZA507d0aH9h2St1FjU+0FpHuXFCRc8T+phsWa9IlI0MdkOBPBBhuifK+rBK+sgroX8hjKfAKZpSIFMVI0IM8ZabHQoX1HRIfFoHBUadU+QWPXqWTw+YOX1YAvaRvXsmVLPAiyCmHEiBFY8dsKlRh20ephtplg0BvQvUd3lUSWKna5TpLMcmLmfloNUv7gTPEJOe++12QzNs1Tydm7JWfX5YOkaNGiGD9+/H29gW/evFn1JXHWyjFJDkrv2IlfuKZLzKaQZT+fvO2Cx15IxJIlS9L19X3ttdfRseMmfDTOjlHvGNSQsbSJ2bc+MiEiKnmwTIqgoCBcC7UiIdFFtTxYPcddtRqQFgZzllhUX9imDXVY/5dd9ZQR8+fPwQvPyDAEbbqlR11fdMFn35vg7QU13EF+vyRXZ3znis79k/B4q0Q1nbe2JGCj7Vi2Drh42aYC+cNHDqJt38up92cwuKBQoWK4ciUET7Y1ws9Hh/AICzRaHTp3fgmjRo3O8cTsraRaVh6PeUvNmLfMohLPQlosVK+kQeHitXJ0yaaQs9cTJoxXy9UG9Dbc1l+tZ0e96tM7dtI0dTZcer1l941o+PDhain0rFmzsGPbDlisFjxU9TE1vEACMQnC5P6mT5uOcJ8QuGncVeWBVHRKD9RJkyaqappmzZql3q/8zLPNn4XGokO5iJpws9w8GJDlZjKJ95rvOVU9eyHgKIpHVUx3G1mudj3oLIKLFFbbca8JUJm4u3jRYiQmJaa2SJH7+3Dkh3BL9ELxsMrpWivIJF4Jhi/aj6o+uevWr1MVttJWQR4rOUC8U1uWO5EzkS+0ag1jtAnFIyvBRw1c06olYuEJISqxLysUsqoqISKie8PYNOdIsnTevNl46UVNusRsWhK3zF0KzJgxI11yVlb7/PTTRLw2LAnTvtGna4Elib3Jcyz4Y4sF338/KPWgSWJTce6CTSVOp37tqloJzF5iwcKVybFYrapauLlpoXOtqmIx6cVdvoweTZ/QpYuZh/bX451RJhQvYsJbr+pTB32N/8yAdn2T0Lp3kkriPtVQErDSFxXYe8iCRx5pALPJiH7vHkj3d5YqWRw2Wyi6DjAiKMAFMXFWJCba0KRJI3z77fepQ5YeFHc3DWb94Ialay2YvdiCd0ebkreruPTQ1WL/UQ91PJbTxo79Ag/X0eG7Ufp0xxeqsvQxF0weq0GnfntVJaYk8+6mBYBU2P7666/4/fc1iD8Sh6JFi+O777rihRdeUEkDiU0/Hf0pzrjvg7vWMzk5a0tA0SJFMeW7SSqGvZUkH8PDwlEiqrKK91IExZdQq7ku+h+DzuqCSwHHUSyqglrdlVKZK0O7rvufg8VgwnvD3runx0tWdMlrYfasObgRltwqsFzZcmqYrVTLxtyIQanr1aFPU7TgbQyA1w1/hPifUnNJpD1Z8kyRHaoFgrQ8kIra7Ca/MyLH2J06dsKxw8cQFFVSrZaTgWtmNa/hmqqclareX375JcdnehAR5Rf5NjmblJSkKmblw+ODDz5I11tEgiU545ZC/v3iiy+qZRwpZ+HlQ12W2MtyCjlLJxWDkhSRZcnOSCr9XA1aPPdU5h+8kkR9pK5O9SlKm5yVyjsZECVJuM07zGr4V4kiWtXqQCpmI6NlCfgktWQshSTr5GeWrbWge3u9Cvo6tdarrxRSETD/N6M6+ysuX76KV7vcvn2fDjMgMcmOIR+b8O1ks6q+lX5ev/9lURNzJfjaf/w61my8Cnd3NzRt+gwm/tRHBfGS5JPlc9JnVQIxCT5kqID0xJWljJK0lGBdArUHGfhKz6yERCv+2W1T1bMycGFQXwMG9tEjKQnQuchBCvDw80Y0b1knx3//ihUr1EHQK91uaZCWhuynH6basHDhQrz55pt3df/yGvzqq68yvE6CsI8++khV2f72229qGZbsC0nGyms0I3/99ReOHjuK0lHV0yVdhQS5AQlFYXRJQLRbmOpPe7bQAXhYfeBi0sPiakKCNlYt5Z8zd47a33dLAlYZyGUz2uEdE4Qgs5f6PUf/OYmB/w1UtykfWSfDnrcSjAZFllCDyJ5o+ATOXzifGphL4F+7Vm18PuZzlai9FyM/GImkaCNKXasOl//34BXyOBWPrqiqiD//fIxqA5OyzI2IiO4fY9OcdfToUURERKPNc5nHJrJiq/WzwKJVf6W7XGK3WbPmoGePbni8dRI6vaBBjSo6hEfasXiVHcdOmVVLtLQr9iReLF48GLOXhKvkrNz3Uw1d1FcKqR5t2DoRn3+ePOX+8qXzqFP99iF0nVvrERkFjBlvwtxlZjRv7AIP9+RWYWcu2FGhfDnEJnli3E8nVdJLYtKpU/uo/rkSF0nVqvT0ln9LPCDLP6UScvXq1Wpor6xkkmOWB9lKTVb3uLkZsGGrBVUrGtTJ+i5t9OrLaLLDZpXKJw1a9zKhTp17i1nutP/37z+Eqd+4pkvMpvVYfR1qV9dj3rw5d5WcFZJMlhYA8pURSfDLMYi000vZF/Xr10fTpk0zTFTKcv25c+ehUGzJdInZFNJ2S2YwSKsuV5OHStC62T2gT3KH3cWCOH00fLx9MHvK7NtmfGSHVMa2b9seISFX4R0XiBKmysnDc5Mi8NGHH6l/S0I4bWI2hcShhaPL4JTbHpW4TlmCKyf37bCheNHiGP7B8NRjsrslSdcjh4+gVGh1uFuSV1EK2ZbCcaVUr1lpoSYtLOSYi4jIGeXb5GxYWFjqIJLXX3893XXSn0d67aSQSdJp++pIv1o5071o0SIVSAcHB6ul6nK28EFXReZVcmbUzVWb6YCsFN5edvWY3apHjx6q6limhP4wZRWMJiM8PNzQtm17VTFbuXLldLeX5vOtW7+Az39Yg8rlteqseFpHT1ox4ksrGj3ZMLWSUALEqJjkyoW0ZJt/+NQVfTrbMO4nExassKBcKY2qbvjrH5tK4me2REcCrccff/y2y0uVKoV+/foht8g01koVy+G7KRfxcB2tCoCFBPsprVh+mGJSy9hkGX1Ok6b9pUvoUaRQ5merpeqkakXdA2veLUlSqYTPDnnteti94WHyyfz+Eooi0vM6XC2uKFm6OGrWqomIiAgUKlRIBZeyrO1eqgDkvad3rz5wiXVD8fBKqUPFUipjzwUeUu0V0g6AuI1do/qThZ6OQKnYqqrtgZBWDqf2nUX7du2xcNHCu+4rfPLkSWz+azOKRpVPl5hNq1BcCcR4h2Lu3LnqhAoREeUMxqY5H5sK7zT9/zPi45VcZXsr+Qz9c8MmVUm4cOGvmDIvBjqdVrXj+mj0y7dVPkpM0L//IHz44YeoX0urTkqnFRFpR793Laq9VLt27dRlrm7uakhXRvr31KuihxkLzZi9yKKG1VYqp8XVUDuaNnta9fHPjLT0kq+0pK+qtPnKLbIcvk2btpi5aCm6tLGjUODN/eBqSP73pm0W7DtsxrRpN4s2cjI2FQ3qZh2rNahjx6adOdtrOoX0bE3Z19kpdJDeF/4JwZnexicxCNe9z6sWX1JM1PKF53H9eig8PT3UTA8pXrmXZb5SDd6ndx81hKxsaC019DWFb1IQXLzPI9wzBD6JmReaSBsG2a4LJy+hWEwFdVtJzibq4xCRFKKKKKSNV9oCney+jqf8MgXe8YHpErNpqepdqx+mT5/O5CwROa18m4mU5J4sxc+OW293L/3DCjqZ3hkda8HJs3oVOGZEeo/uP6JBi1ZlMrxeqhx//PFH1btUgmQJaLLqrzFu3Nfo0eMq2r+yC82e0KN5Yw20OmDzNhvW/2VFtWpVMXHSz6m3b9KkKZav+wOD+95eoaCWhNfUIThIi2LBdvyxwF2dZW/3sgkzZ05/YP2Tcops/6effYFu3V5CnyEmDB+cPIxs+VoLzl2yqQEFh47Z1dl9GYyW06QlSHyCXQV3We2z+MTk2zralctXoE9MHhqQGYPFXSVApYJW+qxmN7i+E1kCl5SYiPLhVdMlZlPorQZVRZsZqUC46ncankZflIqsmjokLSU49Qz1xeVCxzF44GDs2LkjW8u7ZL9JVcLYr8aq//skJS/NzIhss3uCL7Zv2w5nJAl6qYKRChdZOfHcc8/d9cANIqKMMDbNWbK6Q2KS3QetqFA288/C3QfsKo7NbJ9IslXa+UhxgSTEsjox26dPHzVRfviYmWr1V/sWGvh4AweO2rBktR1u7j6YN+/X1BkKEpt+MWY7boSnT16mKFNSiwpltKpf7bJpbihWRKtacS1Y+Cvee++9PD8MReLOvzZvQMd+Mfjkba0qfPhtvRXHT9tUfLrnoB1PPdUk3bDgnJISb0p8KivKMpOgYtOcbfd1L2RZvqvGHbpMTo4LWVEl8WmSaxx6vdQLn3/+eY78blnRdeToEZQOr54uMZvCxWpQMbMWmT/3r/mcUy0XyobXTDerwcPsDffISnDxOacqcKW6O7stLKTdxLvvvIeo6CiUSry5qjUjXnH+2L1rtyqocrbWBvLe9Pvvv6sTEvIeJS39nLXVIpEzc653PsqULJfS67WYMjfzpJI0/L9+w6KW+WRFPlAl2LxT42MZGrBgwSKMHTsO16Mq4L3PTKo/16mLpTBq1KdYvHgZLBYLwsPDk88I9+mL0+fMqk9YRrb+a8Vv6y3o0eFmX6qWzTT4779d6ufvh1QkTp06FRMnTsTKlSszrB6+X9LjafbsOTh2xg/Pd0vCE20S8fMcM46csCHkWvJtNmz4XQ3BexC/+/oNM/7dd7PC/FYSiB8/ZcqRSa73y9vHG1aXzJ+rwqaxyHosVZV8r8uwMrJs6TJ4JQRkWpmqt7ohSR8PG6wZXh/jFgGLzozg2LLpErMpJHCWflyXrlxSgzCy45tvvsHo0aOhjUkOprVSmZsFrV2rXlvORE4YSQucenXr4f33h+PHbyfgk49HqWFo/V7tp5aLEhFR3iFxYoC/L6bOM6sZBhk5c96GDVuteKlbzyzvK3klkpy4193xdjJgWPrkBwY/jlHfmlXbrHWbfdH35YFYv/5PdZL8+vXr6nNUBkDpDa74aKxZ9Y691aUQmxrm9WwTnUrMipZP6xAdHZeuJdu9kJOMc+bMUUNX5cSxnHzMaXLycumyFXD3Ko8eg42o2zwRo78zqYT55at2Vbhx/PhRlRzMaQ899BBcDXqs/D3zeEVWlK3dJHFsEziaJOzNMKmT8JmR1gIWnUmtFpP+tDlFqnbd4ZnpijJJ2No1djV/ICPS71ZWb8nchoyG6Epit1BsKVVZK8+17Pjzzz/Rq1dvxF5OSL4Pe9ZpB7lehvymXe1a0MnxqVQL161TDwMGDMD4b37EV2PGqsKBFi1aquNPInIeTM6SOtPbvVsX+PtCtQT45meT6uGawmazY+1GC94fY0GLFs/ftszqfsjZQVmmv379BrVcXs4YShAoSwMbPfm46rNVq1YtNGz4KPbv34/XXnsNn/9gwoD3jfhvn1UN+DpxxoZR3xrR+80kPPGIDi93vVkQLoPFrFbbPSdnJRHaqVN7tQR+zOejMHHCWNVG4+GHpTfY1PtO+t5KEp8yndXdTYuvPjBg3x8e2LDIA3v/cMfsH10RHXEC7dq1UfssJ8l+8PbywEuvJ6HcI/Fo3C4BP80yqcdXyEHRp9+ZEVw4EM8//zwcTYIWGaxg1t6+jDFFlMcNaDVaNXk2J6d3R0ZEwmDJvP+dX0Jh2LRW9fszIlNxZVqvmyXzZWvuZm+4aT3wzz//3HF7pMLnu+++Uz3OCscmVw7FG2Iyvb0aaOERhypVb/aALuiktc2rr/bD7Fmz4RdRFBWv1Uf5K/VQ6epDCI4qiz/W/Yn27TogLi7O0ZtKRERqtZYZvXp1R1JSjOrROnC4UbUVSOvQMSu6DTShVKkSaN++fY49bhIzSD/XuXN/xYULF9Xn7M5/d6s2aN1e6qwq2qRHbO3aNVRiVAoK1m22omM/E9ZtsiAyyo4rV20qjmrdKxGuBuDT924mvNxcNal/472Ij4/HkCFv4eGH6+ODD4bjp0njMGzYu3iofj2V8MvpAgIZytq4cfJQ1td76rF7vQc2LfbAP6s8sG6eGyqUikTPnt3x77//5ujvlbkipUqXxtc/mVGmQTzqN09Q8b5U7AqJwb+fYkZUtBU9e2adnM8NUj1stpkQ6xaZ6W2kRYDZxYix48aqkw85+VjpTMnVsRnxMvrBxapXrQ0ykiBxoybrlVcyM8EjwRdb/96arVYGbw99W/3ekhFVoLFpkeCaSf+PlG1wi0GZ0mWcqsWgDDSXyn7tdTeUD62rYtOKIQ+px+zk/tNo07pNav9fIir4nOfdjzIlySuzOQYbFrqq5KwEQTMXmfH0ky5wcwW277Li3EVZ7g78++8OlZSUPrI5mfBKWb4kg7k6dWyHsLBraN9Sg0aPuqrhXus3X8OXX3yGGjVrqgrBqVN/RvtXbgYYklju10OPIf30qf1axfb/7KhYoew9LY+RbWn74gtwc43BhDGueL6pTvW3PXNej6m/xqteYRIMvfvuuzn2GMigh+3b/8HCn93UYLAUaijF4y5YPMWG57pGqg9zGah2vySwlUFd0o6ibCkXtGuhh5+vBvsPW/H1z2ZMX2DBgN4uWL7WjiMngdmzJ6pErqNJi4Ixn4/BNfNZFA+rdNsyLakMiPANwYttX1Q9ZnOSn78fblzJPMB0tbrDM8kveXmYTacC3ZsDv2xI1MdnOCgsLbX0zK5L7beXFamaMWhcERhXXP2cq9kDYV5X4Bnhm2GQHuMWjiQkqD7RzmLVqlXYuHEDSkZUhbfRP/VyeYwDEorAw+SN05ojajBkZoNBiIgo98hwoF279mLpVDdEx9oxaIQRDz+fgGZP6hAUoMGxUzbsPmCDFMK6uFxTy8MlySEttXJSSvwoyUepDn22iQve7OMK+TX/7E7Er/OmwNXVR60Cmz9/Lvq9uy/1ZyUp+8IzLhjxpiFdy4Nt/1mh1+tU0vNuJSYmolu3Ljh29ACGD9ah4wuu8PPRIDzSgPnLzfh+6hxcuHAeM2fOyrE2VFKoMGnSJLw3UI/BfdPHgDJkbeYPWnTpb8KHH47A779vyJHjAxkMNWjQAHh7An26uKBUcS3OX7Zh6WoL5iyx4J3+ehw4asfqPy2qZYXME3E0mZHx8EMP49DuI3C/7gW9zTXd9VaNGaGB51G6VOm7Hl52J/7+/rAapGrXnmHsJyu1JE687nseeqsrguJKpMai8jOSNE5ZWZWV7MamskQ/PCIc5aJrq6SuX2IhRHpcQ0B8kQzbPhh1iYh1D8dbvQfAWUhR0rhx4xAUW0INRUsh+0/anHlc98FFzRF8OPJDzF8w36HbSkS5g5WzTk6W+i5etABd22hQOEiLN142YMtyd3Rs5aL6nO4/bEOd6lo0eVyHwkFA8yfjVFLyyy+/zPFtkUThK6/0ht0aij8XGvD5+64qCG7RzAXjP3PFbzNdceb0Eeze/R+2b/9XnaH29dFi8lhX/LfOA8MGGtINNDt9TnrXWtCjZ5972p5Roz6Biy4ay6fp0eZZl9T7Ll9Giy9GuOLdAXp8//33ObrkZPasGXj8IX26xGxaMrCrRwcNli5dlCNVfgsWLFCJ2RFv6LFlmQFvvWpA7056fD/aDdtWuMPLAxj1jRlWbRUsXLhY9UDKC2Tp2NRpU2H0TMCFIocR4XFN9ZZN1Mfimvc5XAw+inIVy6qliTmtXft2iPOIVEF2ZiQQdXd3wxX/Uzhf9CCu+pxFiO8ZnC22H0mGOJj0ibBK24VMSEVwoj0O5cqVu+P2/LvzX7jH+6ggWwK64NjSqjr3it8pmLU3A2hJDEe5h+JawFlVeSxVP85i+vQZ8LL4pUvMpuVm8YR3bCBmz5rjdO0eiIjyorlzZ+GReno1DOqZRi6qSlPirrBwO/YctMHXW4Nu7VwgK6B7d7Jj/vzZ6NOnZ7YSR3dLigJ27foHv050xeSxBrzQ3EVt00dDXbF5iQH+vrEY/8O3WLJkueojKz55W4//1nrgu9Gu6RKzcfF2zFxkR4sWrdTS9rsl7Rb279uHXyfq8Wo3vUrMikB/DQb1NWDGdwbVEmnZsmU59vfLAFEfby1e6Zpxslfi48Ev63DkyHHs3bv3vn/f4cOHMXDg63i2CbBzjQEfDnFFr056fDzUFf+u9UCTx3QYM96M3Qf91cqhWwdDO9LESRPhF+yjYtNQr4uqWEC+wjyv4HzRw9D7ajF9xvQc76naunVrJCI+uQI2ExIjSuI8zPsyzhbbhxDf0yo+vVDkECI9k/unZfXzksQ1esSjUuVKd9ye3bt3w0PjpeIrERhfXLVVuBB4NF1rBblPWQl3ufBxNYz5Tq3zChJ5XblAj6C44pkeS/hHFcXfW/9OHY5HRAUbk7NOLjQ0FNExcXj84ZtPhbIltSrgXD3bHet+dcf4z9zQ9nkXXL8BjHrHVSXyZBmXBE85SZZwHzp0DF+N1KFksdufmrWq6lSVwOrVaxASEoJRo0ZBo/XEhBk2VdmbNsm7/T8rXhpoRrmyZdGpU6e73pZr166ps779e2ozHPAgXuuhR1CAi6pczCmHDh9G0yeyrjiQqpH4+KT7/qCWnk4//vi9Osh4vZfhtkqHooW1mPG9mzrw6dGjFxo0aIC8RPrkrly5Ao2efQLX/c7hTKH9OBd0CJai8Xj1tVewYuUKNWk4p0kbDld3V4QEnoZNc3tf2Uj364hxD8OID0bgt99+w3NtmyOotg+K1PVH5x6dsHjxYmh0WkR4Zt6aItzzKlxdXbPVK1eW7KetkvAy+qNEVCXEukbgVOE9uBBwBJf8juNk4T0I8TuN5s89o16/OV35nlfJ83zf3r3wis84MZvCOykQYeE3cPHixVzbNiIiytipkyfQ8OGb/w/w16B/TwOWTXfH7/PdMfMHN/TvqYd0l2ryuAtm/2DA9u07st0PM7ukH/mCBb9iUB9dhifOJUYc/6kOFy5ewR9//KHab9WsWQ0TZthx4JhVtQZLWzTQ8w0zIqP1eOutt+7p82z27Blo9YxODcHNyJOP6NDkcT1mzZyGnHLw4H61L9zdM48bGj+qg95Fg0OHDuXIij7pz/vDaENqC4gU7m4aTPzCFUWDXdC48VP3FOM/SNKjd+26tejUrSPigsJwttAB9RUZEIIX2rdU11WpkvNtpaQtWtUqVXE98JzqH3srKV4I87uskrh///03er3SE0XrBSGwljeeatUY8+fPR9kyZRHuFaISphmJdQtXCeBu3bpl67maNjaVnrcyrEwKE9RjEnhQxaYSu18IPIIylUpj8ZLF8Pb2hrPY+c9OeCRIcUXmfbB9kgJTk91EVPCxrYGTSxmMYLpD2yujMfmDWucCdaZ+xkI7Zs6cia+//jrHtkUGbZUuqUfDNIniW0mSeNS3FrXcSQLghQuXoG/fXmje5RpqV9ejaGEbTp/XqsFh0gts+vRZqRN174ac+ZdetS2bZf4ScTVo0LyxtHq4v6n3kmjetGmTqoSVRJtq+pQL9uzZgwsXLuObDzNfAihThqWdwpLFC1VSMq+Rvm9SgSCDOWTZnSzhk6D3QU4/ljYJM2fOQM8ePXHOcADeMUFwM3vCqrUg1isccS5R6N69u5r4LAnQhx9Oc3T5f/37v6aGy8nyMP/4IqlLy2SImATGEV4heH/I+9kKUmvUrIEzx1bBHn1zKZu0UvA0+iHK/QbiXaNg1hlh1ZnxzjvvqNdNXp8OnZPkZI0MmMjOIArhTIMoiIjyKp2LC4x3KIJNuV5aVD5aT4fmjV0we9Z09OrVK8dOQMpQI6PRjJfaZt4nvlolHerU0KuhTC1btsS8eQvwyst90HPwHpQpqUfl8jaERWiw56BF9e6fN28GKlW6c/VhRgUVEreNHJx+ufytWjTT4N3RR1Tv2Xtt8yAxqRQpyNAxibF8bq66fqCkbcOaNavwTn9NujZlt8bfL7XVYMKM3/DV2LE51r4hp0hvYmkZJu0WZHWdxBXSduFeKqWzS57vM2fNRLu27XBecxDecYHwMPqqatV490jEukegTp06GDt2rDou+uSTT267j5EfjlRt6676nkHhmNKpg28lWSstsa4HnMXTzZ5Wg9ruRGLxBHucWgmW0t5Bqmgr3KiLWLcIdX8WrRkml0T1mvn222/v6XgtP5OVWneOTZNfA4xNiZwDk7NO9iEgQab0NZWBUhIoyBnnUiWLYe2GULVEKzNrNlpVe4OUpf0tmgEbd9y5IfxdN/4vZs8yoPb00KhqVbltSn+n7dt3Yv369Vi7di1iYqJR7+FgfP5le3UW+V6D85QhDdIvLCty/b0ug5apuiNGvI+1a9ep0EeGgJnNVvyxRauqcjOz4W8rPD3dsrXk/U5BvqhULuvAoFJ5DdZtydkBZA8iEJav3CLPLVm6NmbMGJw5fVaiJ9mFKFy4MN4Z+AleeeWVLJ9777//vmopIv2bI32vwi1BkrB2JHrEwgIz3nzjTQwaNChb2yKJ4IULFyLaPUz19Eq7HCowoSgCEoJxsdBR1KheA40aNYIznoAqV7Y8wo9Fwz8x8+eIJLHd3T1U1QsREeXeCTQ5WSxtls6ePYtixYqhVatWePihR7B24+94b4Bd9d3PiAyr9XAHqlVMjmNeeEaLAcPPqBgxpxJhcl8e7rpMV1GlKFXchrDIcPXvwMBALFu+Av/9959aLXP1agiKlvLGhH4ygb2FWhlzL5JP4CcPu82KJC/T3v5uSEwrCbyZM6epVVpenjrExVsRckUSp/ZMq2e3/GOF2WJXJ83vR3R0NEwmCyqVy/qPlNg1KcmI2NjYB5r0vB9ygr1+/fq59vtKlCiBWbNn4YMPPsCu/3Yh0vO6ik09PDzQs3NPlSzO6uS8tLySNhHSmuOMZzg8En1VD1qjRwKS7Al45ulnVO/h7BxbtWnTBp98/ImagVA0ply63rdSQCBfMpvB5mNScWxOt3nID6rXqI5TR86kK664Vdz/h6hVrlw5l7eOiByByVknIS0IXn2lDy5eCkH1ynoUDrTh77/2quVfpUqVxMo/bOjVWRKwty+t+HunVQVd335iSLesyGzO2b5eElwd3KdRS8AyC8SlV1dYhCVdICZnzF944QX1lVNSPgRlGNrzTTN+maj2Cbs0qFy96j0Fnx06tMWN0PMY9Y4M4nKBt5cG0xeY8PE4s2rL0LDB7fvi2g0b5i61o0OHznc95VW2V4LYmJgYFbCnVGWGhtnh75t5oHX9hh0+3j53/TcWZIsWLVKDowx2GapQXA1XMLkkISosHJ9/9rkKkJ9//vlMf16CUGnLIUO5pOfUwQMHodFqVBAvy8VKly6d7W2pW7euWqa2ZtUa2DVW+CYUTq3ElaVtoX7nkeSagPeHvw9n1at3T4z6ZBSMsQlwtdxe/STVGzE+N9C1U5ccqSqW15e8xmV4nrNVghARZVd4eDj69XsFO3f+hxLF9KhYxoYTR7RYvnw5ggsH4XqoGfN/k96yt5+wvhRiw8yFZhU/+XgnxzDu/y8Szcm+sxJvJiRacf2GDcGFMk8gXbisRcmyNyfdSwLrkUceUV85uXLH19cLO3YZ8dTjmd9OYtfixYNVUu5uSHXe4MGDsHbtavTv6YKeHdxRNFiLQ8etaNk9CVPnm28bCCZMZjvGT7OiRo2q99TPXqplpX2EFEakxLYSm2blelhyMcfd/o0F2b59+/BS15eQEJcI37jC/1/VZUacJkKtdvTz87vjEGMp2mnWrJlqc7B161YYk4yoULGCik2l8ja7RS8S+0jc+dFHH6nqTxlAllKJKzMbJGkr7b2GDx1+18czBYUkpeWkVLT7DfglFr7teqlYjvQJQbWq1VC7du37/n1yHCjHgPI6l+eCs7Q3I8pPmJx1kmmQnTt3QMmiSVg3z01NVhWSBN24TYt3RoXA4OqKrq+b8HZ/Gzq+4KIGLYRF2DH/NzN+mGLGUw11qqVAip17gAoV7n5JVlZefPFFzJ49G1v/1aLxYxk/NZeusUCKWqWq4kGS5Tj169fBz7MP4+kndRkurfpjixUnz5rx6Rd3P/V+/PjxuHL5HFbOMqBi2ZvBfs8Oevy5xYJebybh1e4u6NJaj9IltWpfSYL8o3E2GNwC8MYbb2T7d0mwKx/+s2ZNx7FjycPLAgP90KlTV/j4eGLBCqMaspCRqBg71m224Y03ci7xnd/t2rULQ4cOhW98YRSNLpfubHeh2JII8T+N/v37q+WAd+orVqFChQyXlt0NCa5kMJ1U4kiFTrj/FRiSPGDX2RCvi1aJ9RkTp6sDxGPHjsEZyYCJuXPm4iKOIzisLDxNfqn7TSYUS482Lz9PDBw48L5+j/SqnjZtGubNnYfomORqB6lY7vtyX3To0CG1jQwRkbOThNxLL3XGtZBTmPGdK5o+oUs9MX/omA7vfhqFyCg9ho8x4ewFG3p31qt5BFK9ufJPC77+yQwvTw3e7n8zWfjPHiv8/LxV5WpOkeGz7u6u+HW5BUP6Zbyc6tAxKw4cMWPwkBfxIMkJv86dX8KChdPxajebGuSbUdJ6xe82vPlm77tOvsgqtJUrV+Hnr1zR8umbcXjNKjoM6K3H2IlmnLtgR/cOLqhbQ6vuX/72MeOtOHhMhsx+fle/c8OGDZg+fSq2bEleiefmZkCbNm1Rr14dLFhxBF3bZp5kWrTCjqZNn7rntg0FjazG696tO6xRGpS/UQe6/ydCU4ZxhXteUbFi2bJlVTySFXn9yOqt7K7gykzfvn1VJbasMovyDoWHKbkoJNEQC41Ogw+GfaBakBw/fhzOSJLdbVq3wepVq2HX2G4vrvC9gARDLD76+KP7SqTKe60cX8+YPgOXLl9SlxUJLoLefXqjd+/eTtXnlyiv09jlE45yREoT/Ptd0pPTZOjAtr+X448FhtSprmlJYNWiexIeql8f+w/sh91uhac7EJ+Y3MerSxsXfDjEkLpM6t99VnR4JQlTpkxRy7NyijwVW7V6HqHXjmHhz3rV7zStfYeSh3w9/UwrTJw4CQ+aDCjr2rUzmjyuwafv6lG8aPL2WCx2rPrDivfHWPDY440xa9bsu/rQlA/Jhx6qi06tEtUE2hSSgJ3/mwXTfjXj1LmbL0upqNXptIiKtqo+uj//PEVNNM0O6TfWt29vbN26TSWZWz2tg8Sx/+y2YskaqTpwTf7Q/tEVT9xSqSuVEINGmLF5hwb//POvWrLvSAkJCdi2bZs66yvb8thjjzmkz5j04/p73TaUvlYzw2VINthwrtgBtO/aFuPGjcvVbTtz5oxKxMsJGTmIe/LJJ9XSMqkGlcdPkrNVq1Z1ykoTaePRt09f7Nu/D+4aT7gkGWA1WJCgiUXJEqUwe86se+oBmEIe204dOyE2Kg7esYHwMPnAprUi1jMCsfpIPPtsc0yePNkhz1ln3/fOzBn3fV6NxRwlrz4eMkx1xIjhWDfPVfVsvVVktB1PdzKicJGquHjhHGLj4uHlASSZJA4DmjbU4auRhtRqVikoaNLeiK4vvYoPP/wwR7dVqv/mzpmB6d8Z0OhR3W0rml563QILiuKvv7bCRQLnB0hOArZ4vjl8vWPw3SidGpabEkPv2m/DkI+t0OgKY83a3+Hvn/UgzFt17twRSbG7sHx6+iT0ln8smDTLjB27bvZk93DXqHZc4ZFWFCsWjO++G68GtWbXF198oYaT1q2hR6fW0rJMgxNnbPh1efK+lNYGI98yZNjm68fpJpUoltV/jRs3hiPJSpmdO3eqdnFSLSptrxyR7JIharJyq8K1enCxZXwS4XLACRSuFoC/tmzO1apJqZCX9lsHDhxIfS/q0qULgoKCnPIzKi1pcSZtJJYsWQKD1hWGxP8XV7hEw9PTCxMm/IjmzZvf8/1LRXrnTp1x9OhReCcGwivRXx25xLlGIdYzHGXKlsHSZUtVVX5uc/Z978yccd8fymYsxuSsAx703CRLa+vWqY0h/YCBfTJvoNr7TRPCYiph5qy5KoEwZcovqFQO+OFTA6pWvBn4bd5uxZsfWVChYi0sXfpbjgehMhyrU6f2uHr1Ml58VipodTBbgN83W7H+Lyvq1aurBi3k1hIYOas/eNAAxMUnoEFdHXy87Dh4TINroRY8//xzGD/+x7t+U5HgRJLaK2e5oW6Nm1XMb48yYclqC557SofOrV1QKEiDE6dtmLHQiiMnrBg0aDCGDRt2VwGV9J2aP382Zn5vuC35KgcUXfubce2Gi+rb1eY5HTq0kqppYP8RG6YvsOHiZWDyL1PuKzDIicBFkpyzZ81GfEJ86uVBgYUwYODr6NevX64FmdIWolq1aigcWRoBCUUzvd0Nr0uICbqB06dPpfbRkl56Ke8R0itZej474wehvI/I1Nnt27erqm7pnSyvh9wYUia/W3oALl26VA058fHxUYMoZAnf/VS1ykmQho83RMy1eJQIrQIXW/qDyVjXCFwJPIkBAwdg+PDhyG15Zd9T7nPGfZ8XYzFHyquPx7PPPo1igacw7dvMe4uOn2bCj9M12LFjJ9atW4cvvhgDd0MivhzpguaNb77PHjtlwxsfWhAR5Y216/5A0aKZfz7faxzy8st98NdfW9DsCT1aPSOJSQ127LZi6Ro7vH0C1YDa3PpcP336NHr37oFz5y6qVmUlitpw7pIWJ8+YUbVqJcycOUe1V7rbz8eyZUvjgzd0ePmlm4/trEVmjPzKhHo1tejZ0QUVympxLdSOBb9ZsGGrFY0bN1JFCndz4lHaVkhVZkbJ1ySjHQOHm7B5h13NYWj0qB7d2mlRqrgGFy7bMWeJFdt3WVRrKVnF5CjyeElbqgkTvsfly9dSL/fwcEPnzl0xYsSIXH3PbfpUU1w7EI7iUZmfZI4zROFi4FG1skvi0JTEqSSXpRWItNSSVlm5mbjNS59REqfLYyNFIDLHQtrl5WQV/p1e09JKQoor5LUkxRVt27a978ekd6/e+GvjFhWbulvSt9ky6hJxOfgYaj9UC8t/W47clpf2PeUuZ9z3h7IZi7GtQQF38eJFGE1mPPlo1st+nnxEgy8mnFQViVJxIGd+Bwx4Dc93S8TjD+ng72fH4eNanL1gxmOPPYKpU6c/kOoAGQSxevU6zJo1C3PnzsKCFdfV5RUrlMWoUS+r5cm5uYTp6aefxu49+1QguWXLFpWEeb5laXXGNyWwuVspA8TcXG8GP3OXWlTLhvGfuaZrH1G7WnLC9IMvTZg0aaL6oM5uU3g5Wzp//jwM7qu7LTErihTSYvxnLslV0w89hJV/7FHbIFLiMl8f73seeJYTJHnXq1dv7Ni2HX6xRVAkoRL0VgOMLomITLiG0aNHq0BGlkzlRjApj6n0ajJYsk4kyvVGY5Kadnzp0iXV73T7ju3pbvP444/j448/vufnUX7tff3G4Ddw4uQJ6LUG6KBDki0RH4z4AO++965aAvcg9+OD6AEo1qxZg2vXr6mlhLcmZoW3MQD+sUXUkrI333zTaQIRIqLMnDx5Cp3fynoI0JOP6DBuUpI6mSbLb6UyU5KSLw+9iDo17Chd3IZLIRrsPWRRw20XLJyb44lZIW2DZs6crVoHyaCstz5KbhFUKMgfL7/SE3369MnVyjNpibRlyzZs3LgRK1euVLFJrbqB+Hh0WzX4816GK0my0WKxwc3tZrx48JgVH4414eWuLvj4bUPq53PtasCzTVywYIUZ747+G8uWLUPnzp2z/Xt++mkCmjZ0ybAqVmLjHz83oMHzRlSq8zD+278ff+80pl6v08qqPhe1LXJfjuqb+dVXX+HHH39UMfukz9xQpaJWVfwuXmXB5DmzcejQfixYsDhXTjwLeY0YLFkXrrj+P3aV20pSVmYfrFixIl2cX7FCRdUrVoaDOYuwsDC1ynTz5s1w0bjARWOAyZ6kYvSXXnpJtR+71yF+d/OazumKf0k2/7nhTxSLqnBbYla4Wt1RKLw0/tv1nyocyom+tkR0f5icLeBSghbp05oVWcKetnKsadOm2L17n6ow27hxA27ExKN+g1IY920Xldh4kMGQNCmXBMbgwYPV2UvZLlkq5KgATKp0pWm7fOWEMmXKwMVFp1oLVK2oVcHl9PlmtHw6fV/fFNKDbfS7BvyxxaSS1pKIzI4//vgDJpMZL7XNPBFUs6pOfUklY7sWOrz4nAsCAzQoFixL1ez4+qdEVZn6yy+/5GgLi+yaPn06tm/bhpJhVeFp8k29XIIM9+gKcDd5qz5K0hNOnrMPWspwJ4vu5oFCRuR6ed6eOnVKLSdCog7FoivCOyl5iWGcayT2bTuIF9u8iMVLFqtKhYJOllS1fbEtEK9Dqchqan9KWwiTLhHh8SFq2Wh8fPxd9VNOWVL4119/qYqDixcuwtPLUz0f5ARKbk1w/u233+Bl8YOrNfPXmn9CEZxOCFHBv1TrEhE5M4npTHeMTZO/p8SnksCQ1gES38j77vXwGwguEYifB7ZRyaQH2TZGEoJSICBfcuJVTh77+vo6bMq8PCayqimnVjbJ31G+fGns2H0Z3dolXzZzoQXFi2hUa7OMYvAubfT4fbMd06b9ogZJZSdOl4TRkSPHMTSLimlpmdCuhQZzlu6Cq9EXxaODobe5qpOfWrsW4Z4h+Prrr1X1lawQy20SM0tidsQberze6+aqxJLFNBj6mgFPNbSi02sHVNuGOw3gyine3j6I1iVmeRvz/2NXKTJo1fIFXLt8DQHRxeGbWAg6uw4J+lhcM11V7btkxZokJgs6ObEhsXjIhasqiemTGAgtdLBozIjyuK7mFciqTjkeudsVVgcPHlTtWw4fOqxeXw0eaaAGAcuKsdywatUq6LV6+CTeHFSYUfGAq8ZdJemZnCVyPMdEFJRrJJCV4QhrN2Zd/bh2E/Dwww/floiSRu2zZ8/BkiXLVCP5Rx99NNeSpPJBJola6d1UkCZKyhKZ559/HjMW2hGfYFf9Zc9csKtWBpmRgWQSqK5dszLbvycyMhKeHjoUCsz6sStdQqpoNfh+tCueauii+pdJ76/K5bWYPNaAZ5vo8MGIYepAJDdJ8Dh92gx4JwSkS8ymJdNNPW0+mD5teq5sk/Rvk9dAjHdYpreR6aoxPuHqgGnQwEHQxhtQ+np1+Kng10V9+SYVQunQ6uq61197Xf2tBd3w94cD8VqUCq0GrzQDuQxWdxSNKY+g2BIYO3YcLl++fFfVDq1atkLPnj3x99ptuLI7DMf/Po0vxnyJh+o/pCpac0PYjTC4mDJvGyP0VtfUoR0FgbT4kOnPPbr3QLu27VTViSyNZBt7IsoOiTnXbcp67MW6jRYEBPimS2ZIklROFstJ46VLl2Pq1Klq+XFu9vOW+FjiAUclZh+U7t17Y91GK85dTI5J1m+yqCHBOl3mcWSn1lqVbJVVQtmR8hlYukTWj12pEjIMFygZWgW+xiB4mL1hsLqpfqrBsWVQOKYUJk2apHrt5zb57CtdUp9h5a+QlmWdW2sxb+4s1S4gN7Ru8wLiPCNg01gzvU2UR6gaBCWrASUxW+p6dQTFF4feZoDWrlOxWYnwKupk8vvvv6+SkgXdxIkTcfniZZS4XlUdU0hiVrjY9QiKL4Hi4ZVUi7u1a9dm+z7leEmKjORYb9n85bj47zWc++cKZk6dpdoVSOI7N2Ilea3p7W6pQ8YyIrG4rEgsKLGpPPYS+7/66qsqNn3llVfU6oLcPoYlulcFK6qg20gLgC5dumH+b3Y17TYj6zZZsP+wLB/vw0cwlwwZMhQ3InTo9aYZJ88k75eUoRaZkQSqDMTILklsxydYcSM86wDg3EU7alRJnrqbUdXu0Nf0CL0Roab45iYJ9C9fuQSfxEJZBhVecQHYunXrbYGOVFRIlaJ8SO/bty/HAqHXXnsNcbpohHleyTAxG+p9EYmIUweeFy9dRKHIkirovZVcViiyFC5duaQqPwsymcS7e89uBEQXy/CxEHKAIG0OpIdbdsgBz0tdX8KJwydROrw6Sl2rgWLR5VEisgoqXK0Htxhv9O/fHzt27MCDFhgUCIvBlK2Kldyq5n2QZClt/Xr1MfKDkfjv9704tuUMVi9ei/bt26ND+w6qEoWIKCsSc+4+YMbvf2VcPHD6nA0LV9rRtWv3B76kmJLJipNSpUuj6wAzdu61IDZeYtOsT/AXKaxJPWGXHSlDyi5eyfqk9MXLNrhAl5osu1VAfDEYNK6qMjG3bd26GS8+mxwjZ0ZWwt0Ii1TxT0YDxFavXq2G3OZU8lYqMrUuGlz1OwM7bn9sY9zCEe1xAy91e0lVVPpHF1HJ7ozi6sIxpaGxaTFv3jwUZNJLeu6cefCOCVJL/DOrLPWy+mLmjJnZvl+p5l62dBmKRpVH2ZA6KBZdAcWjK6LclTooFFtSFTvJfJcHTV5rZo1RDSrOjBy3WHSmAhGbSt/eRk82Uis+t6zapmLTv1dvx+uvv67mQpw8edLRm0h0R0zOOgFpD1CkSCl0eNWs+kMlJiYnqSRp9/0vJgwYbkKrVi3VUmDKHdI3Vgabnb3kjdffT07anDqXdaAq1wcXznxpyq2kctNg0GP+b5mfLTx8XIaN2dApi6pdab1QqrghdcpqbgZNQmfLehmR1qaDxWpJrT6VpKz0h6pdq45qRSEf0q1atULjRo3Vsp37JY+rVAmG+lzAxUJHEe12Awn6GES5h+Ji4aMI97qi+lTJRGV3jQfczZlP7XU3e6nbSD/jgmzPnj3qu3dS5sGfJG09EnzUwK7skOEwR44eQbEbFVNbJKSQiodikRXV4zv2q7F40F588UXEuUSp4QqZifS4Dg93Tzz11FPIz3bt2qV6A+uiXFHhej2UCq+GElGVUOZqLZSMqIo9/+1Fjx49HdqrmojyPmlD0KLF83h9mAnfTzGpfp1CYtQFv5lVzFqsWGkMHDjQ0ZvqNGSlmvRJ9Q+siI6vGuFqAE6dvUNs+v/rs9tzV4amVa9eBfOWZl7hKc+Bxast8IopnOltpBrQLcEbe/fsRW4zGk3w8sw6ae3tpUkXy0qBgFR5N3i4gTqRKSf6pU+vnOiUZN39fmYWL14ck36ahDjPKJwveggRHldVbCoDSS8HnMBl/5OqwrxixYrqd0krg8xIiwOPBF9s2rgJBdm5c+cQExsNn6Ssh355xvtjz97kODY7s15+/fVXFIoqDf/E4HSxqZxoKBRXEgHxRfDtN9+q45UHSfa32WZCjHvmq/2kzVqSPRFt2rRBfiZ9lDu074gbFyNQ7kZtlA6toWJT+S7/j7wSrYoH5NiMKC9jctYJSAXl0mUrULd+E7z3qRm1nk5CvWeNePj5REyYCfTp8womTJhY4JZn5XVSWblz5y4VlAUG+mHGAkum1Z2R0XYsX2dHh45d7+qMaZcuL2H8NCt27Lo9CL5+w4ZBH5jh7gY80yjrBKhGmxxY5qYiRYqo5YsJhqyrMRINsShapJjqBZWYmKh6vM6YOgPuN/xQPrQuKl97WCWQQo9FYsCAAfj555/ve9ukh9iUKVNQ+ZEKuOJ/CueDDiPE7zQqP1xe9UyVbQ8NDYXW7pIuMLuVXCdtDlKC94JKEufJj8Od2pNoYLNmr8XDnNlzVJ/XzJLf8vv8Yopg1+5d6mz6gyTJ/8KFgnE16LTqU3arWNdIRHpfRa/ePfP9MLCvvvwKBpO7WuonPQDTPt7eRn8UC6uIvXv34M8//3TodhJR3iYx56RJP6FX75cxYQZUTCqxac1mSXjvMzMeavCUil2lryvlHhnMu27dHyrBVLpMJSxcaUVcfMbxn81mx+wldjRu/KQaKJwdskrrtdcGYOM2C6b+evvnpdFkx+CRJkjeSpbXZ3lfdse0PCtVqhT2Hso6Jt5zwKr+1hIlSqj4ecSIEerEfdJ5G8qE1VSxqSSN7JcNqn+uxKdSVXs/ZBn9ypUr0OT5Rgj1v6Bi00sBxxFY2Qc9enRH82ebq1kIKYUNWdHZXJCUVPBj02SaOz7P7DZ7to6DZP6Bi0YP/4TMXw9S9R2fEK+qpx8kORHSrGkzhPlfQpLL7SsvpaAgNPCCagOW3/vNSk/gyPBIlAitArdbBuPJ/+XymKhYdYKEKC/jQDAnIX1OZ8yYhQsXLqgPAzlbWL16dbRt21Ylb8lxbSc6duyolpNI38zPvjdhxBuGdP29omPtePUdM/R6d3Tr1u2u7l+GLJ07dxpdB+xA88YuaPWMFm6uUMPIFq+WKbduMJnjERZpV20TMqvYvXDJhJo1ayI3+fj4oHXr1li7bL06y5zR0jaz1oRYzwj06zlU/V8S3Qf3H0TJG1XTJe2kj5ZnhK9qOfDpp5+icePGqFq16n1tn/S8ky85C3vo0CFMnTIV23dsx969e9MFdFaNGTp7xn3JrBoLEjUJKF26NAqyKlWqqKVT8YZotS8yIsvwjB5xqF6jerbuU4aKuCXePn02LQ+Tj/ou73vSf/tBvo7nzpuDTh074ZzuAHxig9Tvlt5vsZ7hiDFEoFnTp/Hee+8hP5PPjX92/qOW52kyObcrVcyeVl/MmjVbHSgSEWVG+sTKxHhZjSJ9MGVwZNmyZVXFlyTAyHGJc4mTpGVAs2ZN0O9dk5pBkFINKiwWOz75xoSDR62YP//uqpvbtWunlvuP+mYSVv9pR6cXNCgUpMHx0zb8ugwIDQNsdsDkkpjpcnOJGRI9Y1C7Tu4nlV56qQc++mgkzl10QdlS2gyHLMtciaZNn0LRokXVADsZXivL3KWaMoXOokfRmHLwNPpizZq1WLhw4X0P4apTp45KQMncCYmTpk2bhnXr1qt9mbYFRKIhLtN5DhKvmTziUbZc/k7Y3Ym8x7gaXBHvGqV6GmcmwT0GlSpVytb8k/Pnz8PN5JFpOw4h7STctR7qtg/aD+N/QMcOnXACh+GTEAAvWcFmB+LcolR8Wqp0Sfwy5Rfk9yS7DG7zjgtU/ZMzIr2qvWMCMW/uPNVPWYp/iPIiPjOdjCSB+vTpg2PHjqnkVH6v4ioomjVrhtGjR6tk6tqNGnRoBRT+f6C6bK0dOp07Zs2ei+Dgm0FddpNGs2bNVWdyZ82ajkEjkisI/f190KNnDzVd97nnmmPCdDM+G3Z7Tzc5S/zDFLOq7HVEomXQoEFYu2YtrgSdRJGI8uk+dI26BIQUOo2AwADVayspKQmzZ82BT2yhDKspVR+t2JKI8w5XU3ZlCY/0tY2Pj1ePkwRejRo1ytY0VumtJgeSJ06cQHR0NNavWw8kaVEkqpxaui+/S9odXPM9hwjP6ygUVyLD+4nwuKbWL3To0AEF2UMPPYSKFSohxHwFnmHpWxCkHVRhtCepfZkder0BZm3WywBtmuTrc2NQjJzs+uPPP9SB0K/zfkV4XPIgjSqVq+DDV4ar11p+DwZTBq94ZHJAl0KS5qdOsLcXEd3NSp8ujE3zGKn6nD59Fvr27YVHWxnRvoUGFctpcS1U2g7Ycf2GFV9++aUacnQ3JMklfTllBdn06VMx7PPt6nJXVz3atGmrhvkMHTIUZw9cgGeoX4YDjSI8rsNkN2Y7ZshJUlQxdepkdB98Fb+M1aF65ZtxY0SkHcM+N6tBv2O/HaIuk5P3njbfdInZtHyMgfAx+ePnn35W7SFkibac+JdjNEnuSjstd/eMk9RpydAjSQRLeyiJbTdu2IjIsCi1isgvoZBazSWtDq74n0S45xV1EjmjeCzBEIN4TaxqDVaQyWC/Dh07YMmvS+EfX0S1xLpVokscYgzh6NP3/Wzdp8SbNm3WK8Ak+W2FNVdiU3lvXbHyN8yaNQszp8/E5asn1OWFCxXG0N5DVE4gv69OkOOwiMgIlDBVzvJ2cjLiUuxVhIeH3/XxNFFuyd9HikQFyMsvv6wC1RkzZmDGwjWIjUtAcHAQ+r7cVVXVSoB2LwwGA3r16qXu4+rVq+rgp0GDBqq3mBgxYiQ+/PBDNRV38Mt6FC2sTR3W8O1kM1b8bsGPP37qkIEc0pt3ztw56NO7D04b9sI7yV9VGlgMRsTqI1GsaDHMXzBfVR5LMCq9o8omZl6FmqRPgNVsU71nV/y2Inklk10DrUajGubL/Y38cGSmvZckWS1N/MeNHadaEbjDE0n2BLhYXFE2vLpqUZAiILEoTC5JuOF1ES42F/gl3Ow9JYGZ9KgN872EV19+Ndu92vIrORAb/ekoVfl9JeAkCkeXTh1EIdWl0o/1hu9FdO3SVVXZZsdTTZtgUchi2GVwRSZVnNHuYXB3c0fdunWRG6Tnm5xgGT58uKpakdeeBL3ZqbbID1IOJOxZTINOvt4GvSHj6gUiIso/GjZsiE2btqjKz8WLF+DG4nB4e3uiRYsX0Lt3b9SoUeOe71uSjvIlyUiZa1C/fv3UgWGffvapOnF9JegECkWVSl2qLCuOIjyvIsz7svr9cmI9t3l6emL+/EXo0b0rnnvpHBrU1aNqRTvCwoENW23QuRjwyy8/o169eiphuuOfHQiOLZPp/ckqMIvdrE6A9u7VO/Vyqb6UGMnL0wv9X++PN998M9MWdDIAd8ibQ3Aj/AY8NJ4w2U2w2awoE14LbpabhTjeJn8UjS6vErTXfM4hOLZ06qBWiU0lMSstmurVrY8mTZqgoJNWZFIEctl2DIXDy6pZBRKry2Mh/XpDA8+jetXklabZIScqli5dqloGZFb1LZW6JpsRTzzxBHLr+SptM2RIrsSmciwjx00FpZ1hSmwqr5Ws2DTJSfPcSIoT3SsmZ4nykFq1auG7774D5CuHSYJIWljIV9rqUBnuI9eNGfMZfl2ehGqV9CpRe/SkCd5envj++6/VEjRHefzxx/Hfrv+wZMkSrFyxElFR0QgOLo+OnTqqXp8p1QRSOSvSJkjTkiX1FwOOwcViUFNoJTkojflTKl0T9bEIS7qiAhjpXStVPLeaOHEivvjiCwTEF0XJuGIw64yqp1eRmLIZ/l4JxuU2V33PItw3BJ7xyWenE71iVVJXkpEjR47M8u+PiIhQ/WulgqJkyZL5NtEnVclSVfrG4Ddw2nWvqiLRWDUwuiaogxI5eSDV49klJxxkiV6Y1xW1H28lldXRvqHo2qlL6omI3CKBX3b77+UnslzSYHBFtHt4ptXg6uDOKwrPNcregQwREeVtcuJRTjrK14NKHklSNm0RgBQrzJs3D4MGDsJZ/QF4wBtauxaJ2ni14qh/v/4PbHuyQ+KxP/7cpIaTLl68EP8evAIvL2+8+15LNehLkl9CkrOSDJMerhkxa404H3gIVo0NOpskjewoFFdKDeySwVwmXZIa7iV9aUNCQjB27Njb4sC///4bvXr2gkeSL8pF14be6oqThXcjKL54usRsCt+kIFiiTbjucx4xnjfglRCgErQm9wTEa2NQr049zJo1M8vknQyzunz5sjqekPYA+TXhJdXhy5YvU0nx85pD8IAXtGYpAklSg7Ief/RxTP5lcrYql4W0ZPnk408QarqAEuGVbisekJMLYX6XUbVKVbWqLDfJ/pQ2hwWxArpa1Wq4dOAa/BIzj71jPcJRoXzF1BNARHkRk7NEpJa1SIXCsmXLcOTIEfWI9Hm1rqogzQutL6T6UCqL5SurQFkk6uNSqzJTSFXsZb+TcDd5q6o/rVWLMuE10iVUpRVCicjKuOZ7VvUjkmqOlOBaSIJUKmYD44qrSgMhFZ86qz7Tvl2S9C0ZVQVn3fejePmicNElB6916jZRFR+SjM/Mnj17VPuFDRs2wm5PPtsrQcWr/V5RPcny4xlveUz37d+nKpe3b9+uDlrKlSuHrl27pu6/7JK2LDKYbdy4caovnSTM3cxesGrNiHK/gSjfayhdthSGDRv2wP4eZyOvw3bt2mLZwuXwTQy67XUmZKmkHNBI8pyIiOheSWWhDPWUpfoSM5hMJhUzSLyaF06AyuoYiZOzmnQvST1fHz8kxsfBN+n2VVJXfc/IAi74JAUixj0MZcNrwjVNQlU+Z4vEllWXyZA2mcWQto2EJH6Hvz8c7kZvlAivrOJOGUJq19pUgjczgQnFVN9ZXVErilcvogocypWvpVoZSMVsZi2+JCErsemSxUuQZEwuiggKCEKPXj1UZaYkyvIbqb7eum0r/vrrL5Vsj4mJUcve27dvr05K3w1pk/bjhB/Ru3cfXCx0FAExxeBl9INdY0eMWwQifUOg9dRg/I/j822xRV7U9+W+eOeddxBniMpwtoUU6Egl9Psvv83HnfI0JmeJSJHqwvycUJEhIg8/9DCO/ntCBblp+2jFuoXDqjMjIDoYlwNOokRE5QwrXeVnCsWWQrTnDSxatEgFmimkb6/kSIPiiqdbvi2VDRn17ErLxWpQS+t/+OGHbP0tq1atwsABA9WSqOCYMnAze6qkY6jxBoYNex87/9mpArusErTSb0yS7X/++SdiY2JRpGgRVYmS3bYBD4ocqEhVckaVyXdLhshIAP3N19/g3LVDqZdLb1c5WJJBMxx4mLOkUmn7tu24hKMIiCqmDv6k4ibJJQERniGqd7D0ir6fpa5ERERCKjJbtmypvvIjScB179ENv0z6BYViS6QbECtVsTKYqWhUOVz3uQj/hOB0idm0pCIw2vc6Zs6cmS45K0nr8xfOo3RM9Zuts/6/fFtry3qGgt5qgIenDr//sT5bf4u0RevQvgMSY5LgE1MIRYy+aql4bHw4fvz+RzV/YcnSJVnGXRaLBRs3blTxaej1UPj4+qhCBUm6O7IYRJLRMv9Dvu5X06ZNsXDhAoz6ZBQOHb4Zm4qGjzdULTukbRvlbB9oaU8hCXZJiPsnFFFzSixakyqkifAJwRMNn7jvgXtEDxqTs0RUYLz9ztuqCvOq3xkER0urgeTANM41Cm4mT5h1ZmjsGngbb1bE3srFpldLw/755590ydmDBw/C3eiVLqmrt7rBpDOqD3+ZBJoR6YFk1Mdne/L0xYsXVXJLlpkVi6yQLvEr2x3tFoblvy1XE4plaEZGtm3bhldefgVxccnTeLVWFxxyO4qVK1di8aLFqo9vUFBQhj8rlSnr169PV9kqQU9ebZ4v+1uGbcn2XrlyRQX3Um3zIJduSaWK9DiWAQu7/tsNu82GGrVqqJMbMuE6P1Y1Z5c8b1auWqmqy6WaSVp26DQ6WO1WBPgHYPSQ0apVChEREUGtlJJp8pctJ1AsrCL0tuT2DXGukWrugcHiDpvWogoLMiOxoGecP3Zs35HucolNXTR6NdwrhcGSvKol0RCbZbxrdE9AtXKZr+C6NakqS/9NUVaUvl4z3fAsqVSUZNhpHMOw94apNgAZuXDhArp3646z587C0+YDndEAq96MDRs2YM7suZg7b06mJ3Yl7tq5c6eqbJWhvFI5LS3X8mqS87HHHsP639er/SNJbYkLpQdx+fLlH+jvPX36tOoPveHPjUhKTETJUiXRrXs3VXEtVb0FlRRlTJ02FWPGjFGvtTDjZeg0LrDaLaodV4+Xeqj5Kvm1/QY5DyZniajAkGoCWW4lFZVxnpHwjPNTydZ4tyi4mj0AjU0lZ+9U6aqxaWA2mTO6Jt3/ZGm39OyS4RSF/9/q4FaR7qGw2C2qB1l2SB9VjVWLopHlMtxO6RUWnxiJKZOnqDYPtyYCJRDs0b0HXBM8USGiXupBgPQClWU9xw+fUL1uV69ZfduQN+lbNnjQYISFh8ET3qo3b6IuHmO/GotX+72KESNGZLrUzZFkm6SnbW6wWq1q6ZRUVstAOI84aWmhxfZrO1WVcrNmT2Py5J+z3Z8sP5KDounTp6tkuFQpSI9m6dsm1SKyzJOIiIiSFStWDAsWLkC3l7rhtG6vSpjqza6qcCBtTKqxZ31iV66XJOltl98SKkr1ray4Cve8Ci+jf4axpMxZiNNFoXv3btnaTZJAvXzlMsqG1UqXmE0hA9sCo0pgzdo1qjeu/M1pySAqqbqNuBqFsmE1VSuxtDMCrtnOolPHTiqheWsxw/nz5/Fy35dx/MRxuGk84GLVq4G7EyZMQLOmzTBh4gT4+NxMTuclUhWcVQuznCRV1SNHfgg99CqRLz2MT4ScwZA9Q/Dj+B/Vc1D6RxdUckwjK+befvtt9XwNDw9X/WWfeeYZ1ZaLKD9gcpaIChSZqNqgQQPVm2v92vWIT4iHwRKEsGvhMMQXgU1rU0Fp2sDw1v60Ro94VKqcfgJwzZo18efvG1Qz/5TqWfkeGFcMYV6XVesCqRxIXVYGO6Ldw3DD74Kq7pTkVXas/G0lvOIC1JTezPgmFMaFq0dw+PDh24K+r7/+BjqTAcXDKkObZhCBbJdUNxQPrYyj9oOqdYL0bUshFQk9evSAe6I3ykXVvmUy8jVM/nmy6kn2+eefw5nJWfnFixejWFQFtaQ/dX/H2lUVzF+b/lK9cOWgoaCTIL9bt+wd2BERETkridV2/LMDS5cuxeJFSxAWFgZPbRAuXrqoqkKlgjbBEJMae2Uk0T0WFStWvC02NdvMSNDHwtPsk6ZFV0lc8j+uVrdIe6yUlWRCbnu10ClUrVwNzz77bLa2f82aNfCwecPdknlPWYmJQv3O4/fff1ezLNKaO3curl+7jrKhtW/rV+9q9UCJ0Mo4pzuEn376SQ3eTXHt2jW82KYt4sLiUSq8mloNJn+fHTbEuIXj781b8VLXl7B02dLbCg6cydq1a/HBBx8gIK6oKhZJjf/jgCSXeITglCrM+HPDnwX+cZJEvSMHWRPdj4K79pKICgSpVJQlQfv27cP169eznTSSBNnGzRux89+dmPfrPJhhgkVrgd5iQJjnFZU8zYj0zDTZjbclnSTBCo0dYV5X0l1eKK4kAhKK4JrvOZwqvBtXfc7gqs9ZnC96ECF+p/BC6xfuKqEZExuj+oBlRSbxiujo6HSXX716FZs2bYRfdHC6xGxaElh7m/0xe9bs1MvkwODDkR/CNckDJcIqpzs4kAR0obgSKBxdRp2VP3nyJJxVREQEpk+bjqCYEqr/W9pqFPm3VMMUjiyF5cuX49y5cw7dViIiInpw5HN+79696rtKsGZjtoO0OFizdjX+/W8n/tryF/x8/RDtEQafpAC1CktaYWUk0SUOsfoI9OqdfjZEw4YNUbpUaYT7Xk4X10o8Uiy6PKLcQ3Gy8C5c8T2Faz7ncLHwEZwPOoTyVcrh1/nzsr3MW4ZkaU1Z13RJAlivMdwWm4rZM2fDOz4ww0GiyT+rh09MkGq9JatxUshquOjwaJS4XlUVGKRWGUOrBqwVv1FZDZr97bff4KzkuTdu7NfwNvkjOLbMbfG/xPTFblTEmbNnVJKdiPIuJmeJKE+SfqcTJ07EIw0ewdNPP41WrVqhfv36ql/Vrl277uq+pCeVDJMI9b8A76QgxLpHqASqRXOzdYGchY/wuKbO+ksLglurE6TnqvS0Dfe6gmve52DWmtTlEij6JRRR/b4sOjM8KrggsJY3WrR/TgWL8jfczVLvQoUKwai/GZhmxOiSoL7fOq045QAhbe+xjLglequ+VGlbIRw9ljxVVgLejMigCoPGVVU/OCvp2StLCuWxyKpyxKB1xcKFC3N124iIiOjBW7FiBVo831z1t3/hhRfUd/n/3SYIpYLxrSFvIcrzulqqL/MLLvofg1F3MwaUhKtMoL9S+CSqVqmmhp2mJa2txnwxBgmuMbgSeEJVSaaQlgay0squtcG1tAZ+1d3R4On6mDp1KtatX3dbDHmn2NTqZsq0sEHI9ptsxtvu12g0IuRayB1jU6mKTUxKVNWyQpK0ixYuUsPHZLhTRjzM3vA2B2DG9BlwVrKK7uSpE/CPu7l671aSoPW2+GHe3Hm5vn1ElH1sa0BEeY4MpZLBA1u3boVPfBBKJ1aHzuaCRH0c/t28G+3/bo+ffv7prqb3/vDDD+iX0A+bNm+Cwe6qKmSjPULhYfSF1q5FolusStZ27tQZX331VYb38cYbb6hE67ix4xDlfR0edi81qTbBHqeGIU36bqbqbXQ/OnXuhLFfjoUlxqz65d5KAuMor1BUrVIVlSqlb72QUgGRMqk3M3atVTXPTxvYpQTymZEz8e4J3jiw/wCc1eXLl5P7nWVykCCkHYXB7K76sRIREVHBIfHh+PHj0fgxPaZ87YrSJbS4eMWGectOYeDAgWql1/Dhw7N9f6+88orqjSkVonoYVJx7pvA+uBu9VaxhNMTDpEtCvTr1MGPmjAyHOjVp0gQzZszA0CFDcdZwAB4aLxXXxmviYNDrMeKdERgwYAA0tzanvQvt27fH/Pnz1ewCqWDNSKTHdegNBrRo0SLd5SmzCiRezkpK1XBKQYMMEEtITEBhY7ksf84zwVc97lKccD9/Y36OTYWbKfOWE8KQ5IFLl5JvS0R5E5OzRJTnSCJ169ZtKBFWJV0QKGd+/RILIcT/tAqCZfJp0aJFs3WfMqBp1uxZ2LZtm1qev+vfXYiLj4PFLRE+3j5o2rCFGiR2a8IzLQn6Xn/9dbz00ktYtmwZTpw4oZKcDz/8MJ5//vkcGYYk7RMmTpiIkKCTarlWSn/blMSs9LeNNURg8Buf3RaEVq9eHZ4enqrXrVtsxn3L1GAwryg0f/LpdH9XyrW3Dj1L/7Npb+t85DlkgVk9hlkNlbPpLAV6IBgREZGz2bhxo0rMfvCmAf173jx5XrWiFs82AX6Zq8Gn301QMaGs+MoOianef/99tG7dGrNnz8aff2xAZGQErAYjXN11eLhGfVUYIO0Lsoq/5Pft3rMb69atU6vLZJWPrBqT3ps5MQzp0UcfRe3adXD84HHoQ6uooWNpxbpGINznCvr06AM/v/TJW4mTH6r/EI7/e1q1ActMjHs4ihcrkRrXZzfevHNDiYItJd60as2qAjszVq0FHh4cjEWUlzE5S0R5iix/mjF9JvziCmd4dl6W3ReJKocz7vswb948vPPOO9m+b1n+JUHjwQMHEREVAU+7NzQmF8Sa4tUytbAbYZj8y2Q13TMrEujeOuwgpwQGBmLuvLlqqu8Zl33wjgtUU3cl6Ir1CUci4lU/3VuXtgkPDw906doFs2fMgV9CITVk4VaRHtfUfaTd/jp16qjvsW6R8EkKzHC7bLAi0SMG9R+qD2fVtGlTfPvtt2rwl/Rzy6wvXALi0KxZs1zfPiIiInowpk2bgtrV9ekSs2n1667Hmg12TJv2S7aTs2nbBhw9chTXrl9VK3T0Sa4wGS3Yvn07Ll64hFmzZ6pka1akQEBiw4ziw/slidIZM6ajU8dOOIuD8E4MgEeSD+waO+K8IhGni0LzZ5pj5MiRGf5835f7YsCeAYhxDYeP8fY4M0Efg1iPcLzx8gcqVhelS5eGt5c3YuMi4PH/YWcZSfCMRK3atZy2eEBOBni4eyDa44YaBpZZVXK8ZyS6Pdsx17ePiLKPPWeJcqA36o0bNxAXF8fHMgfs3r0b0TFR8EvIvBeWVJN6xftj9arVd3XfZ86cQccOHRETEo9yN2qj9LWaKBVRFWVDaqNERGXs+me3SopKgtiRpLfu5r82Y8Dg16ErZcVVvzOIDrqOZq2eUsOmpMI3M0OHDkXpsqVwKfgYIjyuwqqxqMulh5n02ZXBZS+//LIK5lJUq1YN9erWQ4RviErCZiTc8yrMdhO6d+8OZ1W3bl3UqlkLYQGXYNHe7FecwqqxIjTgAooVLXbXB2ZEREQ5xWazqSGWkZGR2RpWRVmT/qdbtmxF59ZZJwA7tdbg77+3Iz7+Zu/XO0lISECnTp1xeN8RlPx/TCqxaZlrNVE2rCbCL0Wifbv2qcvXHUVmL6xdtxajRo9CYFVfXPM/p2Y5VHu0En766SfVyzazFWTSm1dakYUEnsJ17wswa5PjbImlwjwv43Kh4yr2lYFpKaSFQ9eXuiLGJ0y1dsiItFmIdYl6YAUT+YGnp6d6nKK8Q1WBwK1ktdd1n/OqrUSPHj0cso1ElD1MzhLdI0n0DRs2DNWqVVGVh8nLh17E6tWrGQjfh5Qkd0b9VtPS2fSIi727hPh3330HS7wVJUKrqBYJKWSJupzJL36jEg4cPIBVq1bB0WRZlzy/Dhzcj4sXL+LsubOYPHkyGjRokOXPSWXw8t+W47kXnsUN/4s4UeQ/nCj2r+phZi4ch/feew+jRo267ec++/wzWN1NuFT4uKpgSBn6IIPPrnufxw2fixg0aBDKli0LZzZh4gS4+RlwochhhHuGqAMGOciIdL+Gi8GHYfcy45cpv6T2WCMiIsotkpD95ptv8FD9OqhZsyZq1KiBxo0bqsRZUlLGCS66s5Rka+GgrJOzKdffTXJ20aJFOHXyJIqHVoa30T9d2yR3s7eKWeOjE9WA2byQCOzbty+2bvtbxabyJTGntGXIKu6RathJkyZhwMABSCwUiVPBe1RsejJ4FyICrqJD5w6Yv2D+bT11Bw8ejCJFg1XBQYxbuBrem3IyXAoQLgedUC0fJPnrzNTxaI2q6nGS5HeSS4KK36XdxKWgY4jyDMW4r8ehZMmSjt5UIsoC2xoQ3YMdO3agd+8e8PSwoF83DWpWdUVElB1L1+zHa6+9hh49uuOLL7502iU293tmXhhdEuFiyryHq9mQiFJFM16+k9kBiyRdA2OKp+vjmpYEwd5mf8ycMRMdOnRAXnG3ib6AgABVxXDt42v466+/1EGCXCYJ31q1Ml76Vbt2bXWAMHjQYJzXHYab1h1auw6J9ng1UXjYW8NUkOzsypcvrypHvvjiC6xZsxbXrefV5fKYSrWs9I6rUqWKozeTiIicjAyi7NixHW6EXkW7Fho0etQVZguwblMIPv30E6xevQJz586Hl1fWg4Podj4+PnB1NeDEGZvqL5uZ46dtcDXo76rPq7TyklZJaYsG0pJiBZ+YICxetBgffvihamGVF6S0H8gu6T0rw9Kkh+6GDRvUqkPpl1q8eHFVeJBRr36JXZevWI6BAwbi3//+hUHrCr3dACMSYYUVbdu2xdixY1MH4jorSZovWboEX3/9NebNnYfwhJtDaWvXqo2333mb7baI8gEmZ4nuUlhYGF7u2xv1algx9RsDPNxvJrq6tAEWrNDg3dFzUaVK1XTLcyh7JElYtkxZhBuvwTMi4+BWqhVjDZHo0nVYth/W8+fPqwEJnsaMp8ymcE/0wfETJxy+u6QaQSbjnj17Vi0Tk8oA6SN2N4OmihQpgi5duqQum5Nptll56KGHsH3HdmzdulWdgDCZTChXrhxefPFFeHt73/ffVFCUKlVKJb/lveDw4cNq+ahUzssBBhERUW6T1gWvvNwbNvN1bFhkQMliNxNnrZsD+w7p8NLAA3j//WGYMMHxFZj5jcRhL77YFr8uX4bXetjharj9JLfRZMevy+xo82I7dVI7OyR+OHP2NIokZb0qycvoh7Cky6q1QVaDax+0qKgoLF68GPv371fbLoNoJc4MCgq6q0RiSl/c7MSmUliwbPkyHDlyBOvXr0dMTAwKFy6sYlPGXekf148//ljNpdi7d69qxSGVsiwYIMo/mJwlukuSMDOaEjFxjFu6xGyKLm302LHLhl8mT0LPnj3v+syys5MKxLeGvIU333wTN7wuISiuRLolXrKEPKTQSRWYyRnz7ErZDzK8ICtyvc6B+0x6GMtABRl25qLRw9XoAbvOjmXLluGTjz/B+B/Ho3nz5g/s98vj1LhxY/VFWZODkSZNsiihISIiygX//PMPDh46ivk/uaVLzKaoW1OHYQNt+OSblRgx4gMUK1aM++Uuvfrqq/ht+TIMGmHC+E8NcE9zDJCYaMdbH5twI1yjbnc3Ma/EXSmtpDKTErs68phi5syZGD1qtIpTPcxywl6DNavWYNzYcRj69lBVEfsgVwxKIli+KGtSWf3EE0/wYSLKh5g1IrpLK1YsRctmWvj7ZR6AdGvvggsXr6gzy3T3pKXAO++8gxvel3Cu6AGEel1EuEcIrvidwpki++BZyAMLFi5QZ4mzSyoNPD08Vc+qzEhwnOAVece+rg+6b9Sv835F4ajSKH+lLkqFVUPp69VRIbQeNOEGNczr77//dtj2ERERUd6yYsUKlCmpR8OHMz+0a9/SBS4uwJo1a3J12wqKqlWr4ufJv2DzDg0ebmHEJ18bMX2+WX1v0NKEjds0+OnnyWrIanZJMrNu3XqI84zM8nYSuwb4B6B06ey388pJUjDwwQcfwCPSHxWu1UOpG9VR6kY1VLhaD77Rwaq1QF7oiUtElJ8xOUt0l6R3aekSWZ8ZLlU8+XqZkkv3ZsiQIapHbIt2z8FYNAaRQSEIqOKFD0Z+gM1/bbrrZV1yJrlL1y6I8bmR6dRXCX7jNbHo3ccx7ShkifzChQsRHFkWgQnFoMXNXrMGqxuKR1SCu9EbH3/0CYfOERERUWpsWqq4PcvKRW8vDQL9dYxN74OsXNq8eQu6vvQq1mz2w5jxdvW9c5eXsWnTX3j22Wfv+j779u2DOJcoxLpmfMwgw51ivcPQo2cPh/RWleXxn47+FH4JhVEkpixcbDfnQejsegTHlkZgXDF88/U36nlIRET3hm0NiO6Sv78/Ll6JzvI2F6/YU29L965evXrqK6e89dZb+POPDbhkP4rAyBLwSQxUCVCL1oQIj2sI976CNq3boGnTpg6rTHDVuMEvsXCG10t7h8CYYjh56pjqJ1W/fv1c30YiIiLKWyTePHpIo07cZpagjY2zIzzSytj0Pkn1qgzmkq+c0KpVK9W6atPGTQiKLgG/hGC42PWwaayIdgtDuP9llC1fDq+//jocQSqtY+NiUSGuYro2Y2kFxhdDpPc1LFmyBP369cv1bSQiKghYOUt0l9q0aY/VG2yIjMq8P9SvyywoXao46tSpw8c3D5GprytW/obHGj+KEL/TOFVsD84U34tTwXsQE3gD/V7rp3q6PsieWVk5cvgI3OK9Mw1+hacpeaDZ8ePHc3HLiIiIKK+SAUvnL5nVzIPMLF1rgcUCtGzZMle3jbKm0+nwyy+/oGevnogMuIpTRSU23YdTRXfjmv9ZNG3+FJYtW+qwwawSb3povdQKrsxINa2H1ZuxKRHRfWDlLNFdeumllzBp0o8YOMKMqd/obxsKtmCFGcvWWvD55wM4DCwPkkFiUqF67tw5/PHHH4iPj0eRIkXUwYqvr69Dt00NhdBkfmAlUoZGcNAcERERiccffxw1a1bFu5+exsLJmtuGgu07ZMXYiVa0bt2aw8DyIFdXV3z++ed4++23VaVqaGgofHx88Nxzz6FkyZIO3TaJN23IOjZNGVrG2JSIyEmTs++//z6WL19+2+VTpkxBo0aNsvzZ2NhYfPHFF9iwYYOaOvnkk0+qCemSuCG604T26dNnoXfvHniyrQld22hQs6pWVdIuWWPHv3vN6N69O3r16sUHMg8rW7YsXnvtNeQlDR5pgP179sMWbYXWfrPfbFqxbsn9vNjSgIgo72FsSo4gK36mTZuFjh3b4elOV9GuhQaNHtXBbAHWbbJi/WarWs315ZdfcQfl8RVePXr0QF4i8eZE20QkusTB3eKV4W1klkO8JgYPPfRQrm8fEVFBka+Ts0LOJn799dfpLitfvny2ek+ePn0an3zyiTpb+f333+PVV1/F0qVL4SKjTInuUKGwbt0f6kTAlF8XIyEhecDUo482wOTJL6sqTEctjaf8S5L6kyZNQpjnFRSOK3Xb9VaNFRF+IWjQoMFdD0QjIqLcwdiUHKF48eJYvXodZs6ciblzZ2Hu0jB1efnypfHRRy+jW7ducHPLfGk6UUaaNWuG4MLBCDNdQonwKre13pIVXTd8LsHL00u11yAionuT77OQEmTcbV/Pffv2Ydu2bZg2bRqeeOKJ1Cq6Fi1aqGXO8p3oTuQkwJdffolPP/0UUVFRcHd3h5dXxmeUibKjVKlSeOeddzBu3DhYtWYExhdXPb4k8I13jUKY32VoPO1q6RsREeVNjE3JkZWXQ4cOVUUoEptKoYCfnx8LBuieSdHSN99+g549e+JS0HE1tMzd7KWStFJNK8N0Y9zCMXHsRHUsRERE98YpB4L9/fffqo9Pw4YNUy8rV64cqlatqq4juht6vR6FChViYpZyRNu2bVWVQrxfJE4X3oszxfbhdPE9uBhwDKWqFsfy5ctQrVo1PtpERAUIY1PKSdL7UxK1/v7+TMzSfatbt65KzsLPgvNBh3Cm2F41UPdcoYNwK6nD5MmT8eKLL/KRJiJy5srZCxcuqF44RqNRLfMdMGAAnn766Sx/5uzZs6pS9tZl55KgleuIiHKb9MGWQRBr166FTuMCvc0VOo0VRnsiypQug9GjR6Np06Y8yCIiyuMYmxJRQWCz2VT7wJ8m/QSLxQJXuzv0GgNMdiMC/ALw/hvD0LdvX7YEJCJy9uSsVLrWrFkTFSpUUImN+fPnY+DAgfjhhx/UdMvMxMTEwNvb+7bLZVL74cOH72ub7HY7EhISkJclJiam+07Og/s+b5KTS9279cDhg4dRJLIcfBODoIVOTceNdQtHiOYiPvn4E9SoUSPD967scKZ9L0Mek5KS4OnpycnBTrbvKT1n3PcShzmy5ztj03vjjM9VSsZ9n3eNGjUKc+fORVBsCQQkFIGLzaBabSUYYnDDdhFfj/sajzzyCCpWrHhP9+9M+14S3fHx8artjay6dHbOtO8pPWfc9/ZsxqZ5KjkrCdbQ0NBsDVowGAzo1atXusulqqxLly4YP358lsnZB50UOHbsGPKD8+fPO3oTyEG47/OWdevW4cCB/SgdVgMe5pvJVy208E0qBLdQT5zHIVW9IO9x96Mg7/sDBw5g1cpV2L17N2x2G9xc3fBU06fwwgsvoESJEnB2BXnfU9acbd9LjJhTGJvmLmd7rtJN3Pd5i6wmlcRskeiyCEgomnq59Jr1NPnCLbQqLuAIPhjxAUaNHnVfv6sg7/tLly5h1apV2LRpM0wmI7QaLRo80gCtW7dWRRfOriDve8qas+17QzZi0zyVnF2/fj1Gjhx5x9vJsl8ZxpRRf6XmzZurYTpSNZXZRFLpN3vt2rXbLo+OjlbVs/dDzoRJJW9eJmcp5MVQpkwZNm53Mtz3edPQIUPhbQxIl5hNy9XiAe+4IPzx+5/48MMP72n5WEHf9xMnTsT3338PD7s3CsWUUtUdRpcEbFq/GZs2bsJPP/+EJ598Es6ooO97ypwz7vvTp0/n6P0xNs0dzvhcpWTc93nTvHnz4Kpxh39CkQyv19ldEBBTFPv371crlWSo7d0q6Pt+06ZNeOutIdBatPCJKaTieYvOhIP/HMbOnTvx7rvvol+/fnBGBX3fU+accd+fzmZsmqeSsx07dlRfD5r0lv3nn39uKy8+d+6c6lt7P+T+PDw8kB/IiyG/bCvlLO77vENOJJ0+cxrFErM+qSPJ20vhx1QVV/Hixe/59xXEfb9mzRqVmC0UWxJBcSVUVUeKoLjiCAk8hddff10N3HHmCtqCuO8pe5xp3+d0SwPGprnLmZ6rlB73fd5yYP8BuMf5pIupbuWdFIAQnMaJEydQpUqVe/5dBXHfS/Jp8KDBcI/zRrGIimo1XIqA+KK44XVJFZTJkF8pLnNWBXHfU/Y4077XZDM2vfkuUUB6uUiFg/S9yaxqVjRq1EhVyUqCNm1i9ujRo+o6IqLcIieJskNzl7d3JhMnTISX2e+2xKyQ3r0SFNtMdsyaNcth20hEzomxKRHlRzabPYu0LN3JjBkzAIsGxSIqpEvMColVC8WVhJfVD5MmTuKDSUT5Ozl75coV9OjRAwsWLFBJVknK9unTRw30evPNN9PdVs5IjRgxIvX/devWxRNPPKEuk16PsuTgjTfeQOXKlZ36zBUROeasYZnSZRDvFpXl7eJcI+Hn64fg4OBc27b8MhX9wMED8IsLzrS6Q2vXwTsuAEsWL8n17SMi58HYlIgKijp1ayPRK1YNAMsqNhXVq1fPxS3LH5YuWQqv2EBVJJARiVl9Ywph1+5dCAkJyfXtI6K8J0+1Nbgb0tvGy8sLP/30E8LDw1WvV2mqPWXKlNv6ClqtVlW5kJYsgf3iiy/w0UcfwWKxqGSt9Lu9l16ORET3o3ef3hg9ajSSYuPhZvG87XqTLgmx3uHo37M/J7zeQt7/hVlrRKjXRfVvd7MXvIz+6ZK1Bos7wiMv84lKRA8MY1MiKiikCGrRokWIdr8Bv8TCt11vgxWRvtfw6COP5vl5K7lNVrlFRkUiAO644XUZdthgsLrBJylQFQykMFiT+21GRESgWLFiDtxiIsoL8m0m0s/PTyVms0P64NzK29sbY8aMUV9ERI7UrVs3LJi/EGdxHIUjyqgeXpJYlGqFeNcohAacR3CRYKcdGpCVQ4cOQYo6rvueh4tVry4L012G3uKKIjFlVa9eYdIZ4ePt4+CtJaKCjLEpERUUstK0ffv2WL5sOaxaC/wSgqH7f2Ix0SUON/wvwupmwkcff+ToTc1zJPeg0+oQ4RUCrc0FWrsWFq0J13zOqRZcgfHFVJxv1hnV7e93IDkRFQz5NjlLRFRQSDP0xUsWof9r/bF9x3a4at2gt7jBojMiyZ6IWrVqY+rUKQgISE40UrK1a9figw8+UFWyMgzM3eKlLk/Ux6pBC5f8j6NkZBV4Gv0Q5x2OHu265+uHTlaAyLJpo9GIokWLqio9IiIiogcxwOabb75RscacOXMQ7nsFblZP2LQWJCAOwYWD8fPkGahduzYf/DROnTqFF9u8CL3ZFcWiS8PL6KcSsbIKLsIzBKE+F2DTWFE4rhSivUJRq2YtlCxZMl8/htevX1cDi+U4hccqRPeOyVkiojxAgplFixepvtnLly9HWFiYqsJ64YUXUL9+/RyfQJ7fSYLy3XfeVVXGxSMrpWth4G72RsnIqio5G+J7Bp5mX9h0VvTq1Qv5kclkUoMlpk+bgctXLqnLDHoD2rZri4EDB6J8+fKO3kQiIiIqYKRtoLQBHDRokGpxcPHiRbi6uqJx48Z45pln2A4wAx+O/BCWWBtK36gBnf1mqkXaGhSJKQed1YAbPhdh19gRq49E/9c/R35t3bBq1Sr8MvkX7Nu/T10mxypNGjfBgIED8Pjjjzt6E4nyHSZniYjyEOmdLV+UtTVr1iAqOgrlo+tmOAhMLiscWwpnCx1ArEs4fprwU75MYiYlJaFH9x7YuXMnvBMCUDKxiupXlqCPxW+LVmLVqtWYP/9XPPTQQ47eVCIiIiqAihcvjiFDhjh6M/K8c+fOYeu2rSgWXSFdYjYtaWkQ7hWCcK8r6gR7mzZtkB8Ts5999hl+/vlneFv8UDy2IlysrjC5JOLfTbvx15ZO+Oqrr1TbNiLKPiZniYgo39mzZw884A3X/w9TyIgMV3O1ueP5ds+qCuT8SCpW/t35H0qGVYWn6WZPMvl3QEIRXAk6gV49e2PX7v9UewwiIiIiyn379iVXkHonBWZ6Gy208E7yh38VT4wYMQL5kVTMSmI2OLosAhOKpl7uafZRw+Okt+6wYcNQq1Yt1KxZ06HbSpSfaB29AURERHfLarVmWDF7KxedS77tfyX9u+bNnQf/mCLpErMppCqjSER5REVHqlYYREREROS42FRo7FnHpxK/enknz0nIjyb/PBneZv90idm0f5sM5HWFu2rJRUTZx+QsERHlO1WrVkUCYmHWmjK9jQxfSLDHqdvmR1u2bEFiUiL8E4IzvY30MPM2+WPlipW5um1EREREdFNKvBnvGpXpw2KHHUmecaheo3q+fOiuXr2K/Qf2wyeuUKa3kQStd0wgY1Oiu8TkLBER5Tvt2rVTQynCvS5neptwzytw9/DIl/28RExMjPruYjNkeTudRY/IyMhc2ioiIiIiupXMjKhVsxYifEJghy3DByja/QaS7Ano2bNnvo5N9dasY1MXq0EVGFgsllzaMqL8j8lZIiLKd7y9vfH+8PcR4XkN17zPwaIxp15n0ZpUv6tIz+v44IMR8PT0RH4UFBSkvpt0iVnezmIwIrhI5tW1RERERPTgffzJxzC6JuBy4EkY08RvNlgR4XEV1/zPoW3btqhTp06+3B2Bgcn9dGX4V1bkeh9vH7i4cMQRUXbx1UJERPnSK6+8ovp7ffnFl4j2DoW72VtdnqCPgc5Fh1EjR6F3797Irxo1agRfHz9Exl9X/bsykuQSjziXaHTo0CHXt4+IiIiIbnr00Ucxa/YsDBwwEGcM++Bp84HGqlUJW4vdjM6dO6thr/mVFA480fAJ7Nt6EL6JhTOc/yCJ6FjvcPTo1N0h20iUXzE5S0REDiUJ1u3bt+PChQuqVcETTzyBYsWK3fHnNBoN+vfvj06dOmHRokU4dOiQulymw3bs2DHfDgJL4ebmhtf698O4sePgbvKCb1L6/l5mrRFXg06jZImSeP755x22nUREREQFzcGDB3H48GH179q1a6N69ez1iW3SpAn27N2DVatWYevWrTCZTChTpgy6dOmivud3AwcNRNftXXHd+zyCY8ukS9BKYjYk4DSgR74ukCByBCZniYjIYSSpOvarsbh67aoK7mRQglarRfPmzTFmzBgEB995ub4kYSVJWxANHjwY586dw+LFixFlC4VXbAB0di0SDLGI9YxAUKFAzPt1oMAtiAAAHVpJREFUHgyGrHt/EREREdGd7dq1Cx+O/BCHDief9E9Rt05dfPb5Z9lqSSAn2KVQQL4KGlnZ9fnnn2PkyJFI8IqGd2yQ6kFrdElErHeYSsxOnToF5cqVc/SmEuUr7DlLREQO8fPPP2PIkCGIP2dC2bCaqHL1UVS+1gCFI8tg8+9/4YVWLyA0NNSp944kqr/77jtMmzYNdZ6ogRt+FxDidwbu5XR4+92h2LBxA8qXL+/ozSQiIiLK97Zt24aOHTri7IELKBlRBVWvPoaqVx9FiYjKOLH3NNq1baeSt85OqmKlMrhFu+cQHXANIX6nYQyORvc+3bBhw59o1qyZozeRKN9h5SwREeU6qQb97LPPEBhXHIVjS6UuidLZXRCQUATeSf64iCMYPXo0JkyY4NR7SNo3PPfcc+rLZrOpybeslCUiIiLKOWazGYMGDoZroidKhlWBJrWOTQMfYyC8rvvhUqHjGDxwMHbs3KFOoDuzunXr4scff8T48eNV6wZpTUZE986531GIiMgh5syZAxfoUSi2RIbDBPQ2V/hFB2PlylUICwtzyDbmRXIgwMQsERERUc76448/cCMsFIWjSqdJzKaJwaBDoaiSuHTlErZs2cKHP00RAROzRPePyVkiIsp1W/76G57xfirQzYxPYiFYrRYuHyMiIiKiB0qG03rAC24Wz0xv4272hpvWQ92WiCgnMTlLRES5TpY/ae1ZfwSlXC+3JSIiIiJ6kG0NtPbMiwaErPbS2XWMTYkoxzE5S0REua5ipQpI8oiHHfZMb5NgiFHfOe2ViIiIiB4kiTcTtXGwaiyZ3saiNSEB8YxNiSjHMTlLRES5rnv37kjQxCLeEJ3h9ZK0jfS+iurVqqNGjRq5vn1ERERE5Dw6dOgAaDWI8Lya6W3CPa/CoNejbdu2ubptRFTwMTlLRES5rkmTJmjQoAGuBp1GrGtEugpai8aMEP/TiDfEYPiI4WrQABERERHRg1KoUCG89lo/hHlfRrhHCGywpV4n/w7zvIxwrysY/MZg+Pr6ckcQUY5yydm7IyIiujOtVouZM2filZdfwY5/dsAdnjAkeMCmtSLePRouLjpM/GEinnrqKT6cRERERPTADR8+HImJiZgxYwYi/a7CPcFblQ8kecTCZDdiwIABeOutt7gniCjHMTlLREQOIVUHixYvws6dO/Hrr7/i3NlzcHN3Q9OmTdGlSxcEBARwzxARERFRrhUPfPbZZ+jVqxfmzJmD/fv2qxVc9erXQ48ePdhrlogeGCZniYjIYSTgfeyxx9QXEREREZGjVaxYEaNHj3b0ZhCRE2HPWSIiIiIiIiIiIiIHYHKWiIiIiIiIiIiIyAGYnCUiIiIiIiIiIiJyAPacJSJyUna7HTt27MCaNWsQExODwMBAtG/fHrVq1XL0phERERGREzp58iQWL16Mq1evwsPDA82bN8dTTz0FnU7n6E0jInpgmJwlInJCFy5cQN8+fXH8xHG4azyhs+hhcTFi6tSpePyxxzH5l8kICAhw9GYSERERkROIi4vDG4PfwO9//A6D1hUGsztsOgvmzZuHkiVKYfqMaahWrZqjN5OI6IFgcpaIyMmEhoai7YttERMah9Lh1eFh8oEGGthhR6xrBHbv3INOHTth5aqVqmKBiIiIiOhBsVgs6NWrF3b/uwfFoirANzEIGmhVbJqoj0Oo7Tzat2uPNWvXoFy5ctwRRFTgsOcsEZGTmTRpEiLCIlHielV4mnxVYlbIdx9jIEqEVsHx48exaNEiR28qERERERVw69atw86dO1E8rBL8EgurxGxKbOph9kbJ0KowxZrx7bffOnpTiYgeCCZniYiciNFoxPxf58MnJgh6myHD27hZPOFtDMD0aTNyffuIiIiIyLnMmjkLXlY/VTSQEZ3dBb4xhbFq1SpERETk+vYRET1oTM4SETkRGa4QFx8HL6N/lrfzTPTDmbOn1TIzIiIiIqIH5ciRI3CP98nyNhK7Slx6+vRp7ggiKnCYnCUiciIaTXILA+nhlfUN7eluT0RERET0IGg12tTYMzMpsStjUyIqiJicJSJyIsWKFUOAfwBi3bJeEhbnEYmaNWpCp9Pl2rYRERERkfOp91B9JHhFZVk8ILGrq8EVlStXztVtIyLKDUzOEhE5Eb1ejx49eyDWKwxGXUKGt4k3RCNWH4k+ffvk+vYRERERkXPp3bsX4jWxiHULz/B6s9aIaJ9QdOjYAT4+Wbc/ICLKj5icJSJyMv3790eZcmVwKfgYotxDYYNNXW7VWBDhcRWXg07gscceQ7t27Ry9qURERERUwDVt2hQtW7ZESMBphHlehkVjVpfbYUOMW7iKWf0L+eHtt9929KYSET0QTM4SETkZqThYtnwZGjZ5HCF+p3G6+B6cL34Ap4vuRaj/BbzYrg3mzJmjqmyJiIiIiB4k6SM7ceJE9OrdCxEBIThd9P+xafG9uOx/AjUfqo5Vq1chODiYO4KICiQXR28AERHlvsDAQMydOxdnzpzB2rVrER0djaCgILRu3Vr1pSUiIiIiyi1SFPDZZ59hyJAhWLFiBa5evQoPDw80b94c1atX544gogKNyVkiIidWvnx5DB482NGbQURERESkCgj69u3LR4KInArbGhARERERERERERE5AJOzRERERERERERERA7A5CwRERERERERERGRAzA5S0REREREREREROQA+XogWOXKlTO9buvWrShcuHCG112+fBnNmjW77fLatWtj0aJFObqNREREROQcGJsSERERkVMlZxcuXHjbZcOGDYO7u3umidm0hg4dikceeST1/56enjm+jURERPRg2O127Ny5E8uWLUNoaCh8fHzQsmVLPP3003BxydchDuVTjE2JiIic2+nTp/Hrr7/i3Llz0Ov1eOKJJ9C+fXvmmyhL+frIpU6dOrdVxJ4/fx7vvvtutn6+dOnSt90HERER5X3Xr19Hnz59ceDAfrjDE7okA2wGi0rUliheAjNnzUTVqlUdvZnkZBibEhEROSej0ahyUUuXLoVB4wrXJA/YdTasXbMWn336GX4Y/wOef/55R28m5VH5Ojl7q9WrV0Oj0aBVq1aO3hQiIiJ6QOLj49GpYydcOnsZpcKrwdPkCw006rpElzhct59Dh/Yd8Psfv6NEiRLcD+QwjE2JiIicw5C3hmD1qtUoGlUevomFoP3/iCez1ojrfhfQr18/zJ07F40bN3b0plIeVKAGgq1ZswYPP/wwihQpkq3bf/LJJ6qq5rHHHsPIkSMRFRX1wLeRiIiI7s+CBQtw9uxZlAitAi+TX2piVrhbvFDiehUkxiRhwoQJfKjJoRibEhERFXz79+/HipUrEBxZFv6JwamJWaG3uaJ4REV4GH3wycejVFsuogJbOXv8+HGcPHkSo0ePvuNtDQYDunbtqnp/SH+6AwcO4Oeff8bhw4exePFi1RfkXskLLSEhAXlZYmJiuu/kPLjvnRf3vfMqiPt+xvQZ8EoMgKvFI8PrXex6eMcEYfGixXjnnXfg4ZHx7Qq6grjvsxOHySqqvICxafY543OVknHfOy/ue+dVEPf9jBkz4KZxVxWzGZFCgoCYojh56hh27NiBunXrwhkVxH2fU7FpnkrOxsbGqoEed1KyZEmVYE1r1apVKqn67LPP3vHnZViYVM2maNCgASpWrIjXXnsNf/75J1q0aHGPfwFgNptx7Ngx5AfSn5ecE/e98+K+d14FZd/bbDacO38ORZPKZXk7L6MfwoyXsXXrVpQqVQrOzBH7XoLuLVu2qApnUb58eTRq1EgNbX3Qbo0R7wdj09xVUN6n6O5x3zsv7nvnVZD2/f59++Ea75VuNdetPE1+6vvff/8NNzc3ODNH7Hur1Ypdu3apKmeLxaJW3Ddt2hQBAQF5IjbNU8nZ9evXq/YCd7J27VoV5KfNRMtlTz75JPz8kp/wd0v6fkhlzZEjR+4rOSsJ4goVKiAvkwMmeTGUKVMmVw6SKO/gvnde3PfOq6Dte/nM12q1sGuyXhJm09jU90qVKqFs2bJwRo7a97IKSQZfyO/3gLe67Hf8riqeR344Eh07dnygE5JzEmPT3FHQ3qco+7jvnRf3vfMqiPvey8sLoZqs22TaYUstNnTWobWO2ve7d+/GW2++heuh1+EOL+jsOiRq4zFv7jz06NkDw4YNg4uLi0Nj0zyVnJVg/V4C9j179iAkJERNxnM0KVfOL8sn5cWQX7aVchb3vfPivndeBWnf169XH8d2nkJAQtFMbxPrFo6gwCCVnL2fdkUFQW7u+/nz52PEiBHwSyiM4rFVVZ81YdIlIczrsrrO1dUVXbp0eSC/P6dbGjA2zV0F6X2K7g73vfPivndeBWnfN3yiIY4c/AW2aCu0dl2Gt4lxD1ffH3/88QLzd+eHfX/gwAH07tUb+gQ3lI2speZTCKvGgkiPa5g1axZMJhPGjRvn0Ni0QAwEk5YGsmOlJPlebd68WfWKrVmzZo5uGxEREeWsPn37IM4lCrGuERlen+QSj1jvcPTs1dPpE7O5SeKojz/6WCVmi0aXT03MCoPVTV0m18ltCnqvMcamREREzqN79+6wwIIwzysZXq8Sgb5X8eQTT6Jcuaxbc1HOkrhTl2RAiRtVUhOzQmd3QVB8CRSJKotff/1VzaBypHyfnJVeEb///juefvrpTPt2VKtWTVVqpPjyyy/x1VdfqZ/7559/MHnyZDUwpEaNGup+iIiIKO9q1aoVmjdvjiuBJ3HD6xIsWpO63KqxIsLjGi4VPoYK/+8lT7ln5cqViE+IR1BciQx7rsllcl1cfJy6bUHF2JSIiMi5yHwDWRof5n0ZIb6nYdQlD4m3w44Y13BcDD4KvY8On33+maM31amcPHkSu3bvgn90UWiRcUWzX0IwXDXuqoLWkfJUW4N7sW3bNkRGRqoDtawa/8oAkRTSr1aW3S1atAhJSUkIDg5Ghw4d8MYbbzywPhNERESUM3Q6nTqx+umnn2LunLkqEDZoXWG2m1QQ3LJFS3w19ivV/4tyz6FDh+AJb1Ulmxm5zlPjrW7buXPnArl7GJsSERE5n8GDB8Pb2xvjxo7Dmej9Kja12a2w2C2oW6cuvvv+uzw/n6igOfz/alhvo3+mt5HiAfd4bzXUzZHyfSaySZMmOHHiRJa3ufX6e+0fRkRERHmDTD2V5OzQoUPVUNAbN27Ax8cHzz77LIoXL+7ozXNKqqeWxjG9YfMSxqZERETOqXfv3ujatatapS2DryRebdiwIdtn5gMaB8em+T45S0RERM7L398f3bp1c/RmEIBatWphhn2GGv6VWfWsUZeIeHssateuzceMiIiIChwZfNq6dWtHbwYhOTYVMqfCN6lQho+JHTYkesag/kPPOfQxy/c9Z4mIiIjI8V544QV4e3njhvdF1V7iVnJZmPcl+Hj7ZNmOioiIiIjofkkbiccefQwRfldh01gzvE2kx3UY7Uno2bMnHInJWSIiIiK6b+7u7hjzxRhEu4chxO+UqpJNIf8O8T+lrpPbZDbElYiIiIgop4waPQpwt+JSoWOI18ekFhDIQOFQr4u45nsOffr0QdWqVeFIbGtARERERDmiXbt26vvIDz7EGfd98NAkD2VLsMfB18cPE8ZMQNu2bfloExEREdEDV716dSxZugQDXh+AC7rDcNN4QAcdEhEPFxcXDO4/GO+9957D9wSTs0RERESUownaFi1aqEFtBw8eTO351bJlS9WHjYiIiIgot9SpUwfbtm/Dtm3bsHXrVhiNRpQuXVoVDAQEBOSJHcHkLBERERHlKGlbIEnalEpaIiIiIiJH0Wq1aNSokfrKi9hzloiIiIiIiIiIiMgBmJwlIiIiIiIiIiIicgAmZ4mIiIiIiIiIiIgcgMlZIiIiIiIiIiIiIgdgcpaIiIiIiIiIiIjIAZicJSIiIiIiIiIiInIAJmeJiIiIiIiIiIiIHIDJWSIiIiIiIiIiIiIHYHKWiIiIiIiIiIiIyAGYnCUiIiIiIiIiIiJyACZniYiIiIiIiIiIiByAyVkiIiIiIiIiIiIiB2ByloiIiIiIiIiIiMgBmJwlIiIiIiIiIiIicgCN3W63O+IXF0R79+6FPJwGgwF5mWyj2WyGXq+HRqNx9OZQLuK+d17c986L+955OeO+N5lM6m+tV6+eozclT2BsSnmdM75PUTLue+fFfe+8nHHfm7IZm7rk2hY5gfzy5JLtzOsJZHowuO+dF/e98+K+d17OuO/lb84v8VhuyC+PhTM+VykZ973z4r53Xtz3zssZ970mm7EpK2eJiIiIiIiIiIiIHIA9Z4mIiIiIiIiIiIgcgMlZIiIiIiIiIiIiIgdgcpaIiIiIiIiIiIjIAZicJSIiIiIiIiIiInIAJmeJiIiIiIiIiIiIHIDJWSIiIiIiIiIiIiIHYHKWiIiIiIiIiIiIyAGYnCUiIiIiIiIiIiJyACZniYiIiIiIiIiIiByAyVkiIiIiIiIiIiIiB2ByloiIiIiIiIiIiMgBmJx1cu+//z4qV65829fff//t6E2jHHTmzBn06dMHderUQcOGDTF27FiYTCY+xgXcsmXLMnx9f/31147eNMphFy5cwEcffYQ2bdqgWrVqaNWqVYa3W7x4MZ599lnUrFkTrVu3xubNm7kvnGDf9+jRI8P3AvlsIMprGJs6B8amzomxqfNgbOq8GJveG5d7/DkqQEqWLHlbsqZ8+fIO2x7KWdHR0ejVqxfKlCmDH3/8EdevX8eXX36JpKQkdUBPBd/UqVPh7e2d+v/g4GCHbg/lvFOnTmHLli2oXbs2bDYb7Hb7bbdZs2YNPvzwQ/Tv3x+PPvoo1q5di0GDBmHevHnqxA0V3H0v6tWrh2HDhqW7rESJErm0lUR3h7FpwcbYlBibFnyMTZ0XY9N7w+Qswc3NjQfmBdiCBQsQHx+PCRMmwM/PT11mtVoxatQovPbaa0zUOYHq1asjICDA0ZtBD1DTpk3x9NNPp1adHT58+LbbjB8/Hi1btsRbb72l/i8J2pMnT2LixImYMmUK908B3vfCx8eHn/WUbzA2LdgYmxJj04KPsanzYmx6b9jWgKiAkxYVjz32WGpiVjz//POqwmr79u0O3TYiyhlabdYf55cuXcL58+fVaz+tFi1a4J9//mGbkwK874mI8hrGpkQFH2NT58XY9N4woifVE6R+/fqoUaMG2rVrhw0bNvBRKUDOnj2LcuXK3VZBVahQIXUdFXzSg7Jq1apo1qwZJk+erCqnybmkvNbLli17Wwsbs9mskrdUsP3333+qclb6DXfv3h27du1y9CYRZYqxacHG2JQYmxJjU2Jsmh7bGjg5SdjIgVqFChUQGxuL+fPnY+DAgfjhhx/w3HPPOXrzKAfExMSoZOytfH19Vc8vKrgkAT948GDVi1Kj0WDTpk34/vvvVd9h9ht2Limv9VvfC1L+z/eCgu3hhx9WA8Ok93hoaCimTZumhkTOmTMHdevWdfTmEaXD2LTgY2zqvBibUgrGps6NsentmJwtYCTBKgde2Rm0YDAY1KCoW/uDdOnSRfUmZHKWKH978skn1VeKJ554Aq6urpg1a5YaClW4cGGHbh8R5Y433ngj3f+bNGmiqpYmTZrEfsP0wDE2JaIUjE2JiLFpxpicLWDWr1+PkSNH3vF2MqVblrNm1B+kefPmGDduHJKSktRABsrfpDJODowyOlsp1bPkXKTn6PTp03Hs2DEmZ51Iymtd3gukaiVt9VLa68k5eHh4oHHjxvj9998dvSnkBBib0q0Ym1JajE2dE2NTSsuDsSmTswVNx44d1RdRCuk3e2tvWUnQ3Lhx47ZetERUMKW81m/t8yf/1+v1ajUFEdGDwNiUMvpMYmxK5NwYmxKlx4FglI7NZlMVDhUrVmTVbAHRqFEj7NixI7VCTsg+lirphg0bOnTbKPdJ1bxOp0O1atX48DsRSb5Kv1F57d/6fHjsscdUmxtyHgkJCfjrr79Uz3mivI6xacHD2JTSYmzqnBibUloJjE1ZOevMrly5gvfffx8tW7ZE6dKl1TJ3GQh2+PBh/Pjjj47ePMoh0kNYhr7IoLfXXntNDYMaO3asujw4OJiPcwH28ssv45FHHkHlypXV/zdu3IhFixahZ8+e6Za2U/6XmJiILVu2pL63x8XFpSZiGzRogICAADUc7p133kGpUqXU80IOhg4ePIi5c+c6eOvpQe57qU6bOnUqnnnmGRQvXlz1pZ8xY4ZaPSHDP4nyEsamzoGxqfNibOo8GJs6L8am90Zjt9vt9/izlM9FRUVh+PDhOHr0KMLDw9XS1ho1aqBfv37phghR/nfmzBl8+umn2LdvHzw9PdXU7iFDhrBaroD77LPPsHXrVly7dk1VHknlpCwv7dGjBzQajaM3j3LQ5cuX0axZswyvmz17tkrGisWLF6sBUCEhIShbtiyGDh2Kp556ivuiAO/7IkWKYPTo0Thx4oT63Hd3d0fdunUxaNAg1KpVK9e3lygrjE2dB2NT58TY1HkwNnVejE3vDZOzRERERERERERERA7AnrNEREREREREREREDsDkLBEREREREREREZEDMDlLRERERERERERE5ABMzhIRERERERERERE5AJOzRERERERERERERA7A5CwRERERERERERGRAzA5S0REREREREREROQATM4SEREREREREREROQCTs0REREREREREREQO4OKIX0pERDlr2bJlGD58eOr/DQYDihUrhoYNG2LAgAEICgpKd/uwsDBMmzYNmzdvxtWrV6HRaFCuXDk8/fTT6N69O3x8fDL9XaGhoZg9ezYOHDiAw4cPIyEhQf3/kUce4W4lIiIiIsanRER3gclZIqIC5I033kCJEiVgMpmwZ88ezJ8/H1u2bMHq1avh7u6ubnPw4EH069dPJVVbt26N6tWrq8sl0TplyhTs3r0b06dPz/R3nDt3Tt2uTJkyqFy5Mvbt25drfx8RERER5S+MT4mIssbkLBFRAdKoUSPUrFlT/btjx47w8/PDjBkzsHHjRrRq1QoxMTEYNGgQdDodli9fjvLly6f7+SFDhmDRokVZ/g5J5v7777/qvtevX8/kLBERERExPiUiukfsOUtEVIA9+uij6vvly5fV9wULFuD69et4//33b0vMCml/IG0QsuLl5aUSs0REREREjE+JiO4Pk7NERAXYxYsX1feUZOqmTZvg5uaGZ5991sFbRkRERETOiPEpEVF6bGtARFSAxMXFISIiQvWc3bt3LyZOnKiSsU899ZS6/uzZs6pXrAwMIyIiIiJifEpE5FhMzhIRFSC9e/dO9//ixYvj66+/RnBwcGry1tPT00FbR0RERETOhvEpEVHWmJwlIipAPvroI5QtW1YN/JL+sfJvrVabrl9sfHy8Q7eRiIiIiJwH41MioqwxOUtEVIDUqlULNWvWzPT6cuXK4dixY6rtAVsbEBEREdGDxviUiChrHAhGROREpPdsUlIS/vjjD0dvChERERER41MicnpMzhIROZEuXbqgUKFC+PLLL3Hu3Lnbrg8PD8ekSZMcsm1ERERE5HwYnxKRs2NbAyIiJ+Lr64uJEyeiX79+ePHFF9G6dWtUr15dXXf06FGsXr0adevWveP9pCRwT58+rb6vWLECe/bsUf8eMGDAA/0biIiIiKjgYHxKRM5OY7fb7Y7eCCIiuj/Lli3D8OHDsWTJkix7zqYIDQ3FtGnT8Ndff+Hq1atqaJj0o23evDm6d++uBodlpXLlypled+LEiXv6G4iIiIio4GB8SkSUPUzOEhERERERERERETkAe84SEREREREREREROQCTs0REREREREREREQOwOQsERERERERERERkQMwOUtERERERERERETkAEzOEhERERERERERETkAk7NEREREREREREREDsDkLBEREREREREREZEDMDlLRERERERERERE5ABMzhIRERERERERERE5AJOzRERERERERERERA7A5CwRERERERERERGRAzA5S0REREREREREROQATM4SERERERERERERIff9D8wH1FG3G0e+AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "\n",
+    "# K-Means Plot\n",
+    "axes[0].scatter(X_bc_pca[:, 0], X_bc_pca[:, 1], c=y_kmeans_bc, cmap='viridis', edgecolor='k', s=50)\n",
+    "axes[0].set_title('K-Means Clustering')\n",
+    "axes[0].set_xlabel('PC 1')\n",
+    "axes[0].set_ylabel('PC 2')\n",
+    "\n",
+    "# Hierarchical Plot\n",
+    "axes[1].scatter(X_bc_pca[:, 0], X_bc_pca[:, 1], c=y_hc_bc, cmap='viridis', edgecolor='k', s=50)\n",
+    "axes[1].set_title('Hierarchical Clustering')\n",
+    "axes[1].set_xlabel('PC 1')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {
@@ -480,9 +700,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C04 Anomaly Detection.ipynb b/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C04 Anomaly Detection.ipynb
index f2e1ae3..8bab66e 100644
--- a/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C04 Anomaly Detection.ipynb	
+++ b/src/S04 Unsupervised Learning - Clustering and Dimensionality Reduction/S04C04 Anomaly Detection.ipynb	
@@ -86,12 +86,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "b97ddbc8",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -137,12 +148,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "d33f230e",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Threshold Distance: 3.28\n",
+      "Anomalies Detected: 16\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.cluster import KMeans\n",
     "\n",
@@ -174,12 +194,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "b438b708",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(8, 6))\n",
     "\n",
@@ -209,12 +240,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "934bf17f",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Anomalies Detected by DBSCAN: 7\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.cluster import DBSCAN\n",
     "from sklearn.preprocessing import StandardScaler\n",
@@ -242,12 +281,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "411045ec",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(8, 6))\n",
     "\n",
@@ -274,12 +324,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "30de3830",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- DBSCAN Performance ---\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "      Normal       0.96      1.00      0.98       300\n",
+      "     Anomaly       1.00      0.35      0.52        20\n",
+      "\n",
+      "    accuracy                           0.96       320\n",
+      "   macro avg       0.98      0.68      0.75       320\n",
+      "weighted avg       0.96      0.96      0.95       320\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.metrics import classification_report, confusion_matrix\n",
     "\n",
@@ -320,6 +397,36 @@
     "-   **Note**: Isolation Forest returns -1 for anomalies and 1 for normal."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "025ee4db",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Isolation Forest detected 20 anomalies.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import IsolationForest\n",
+    "\n",
+    "# contamination='auto' lets the model guess the % of anomalies\n",
+    "# You can set contamination=0.06 (since we have ~6% anomalies) if you know it\n",
+    "iso_forest = IsolationForest(contamination=0.06, random_state=42)\n",
+    "preds_iso = iso_forest.fit_predict(X)\n",
+    "\n",
+    "# Convert to boolean mask (True if anomaly)\n",
+    "mask_iso = preds_iso == -1\n",
+    "\n",
+    "print(f\"Isolation Forest detected {sum(mask_iso)} anomalies.\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1db638fa",
@@ -330,6 +437,34 @@
     "-   Plot the results of the Isolation Forest. Compare it mentally to DBSCAN."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7bcd3c68",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(X[~mask_iso, 0], X[~mask_iso, 1], c='blue', alpha=0.6, label='Normal')\n",
+    "plt.scatter(X[mask_iso, 0], X[mask_iso, 1], c='red', marker='x', s=100, label='Anomaly')\n",
+    "plt.title(\"Isolation Forest Detection\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "a42ef7d6",
@@ -342,6 +477,42 @@
     "-   **Hint**: k-Means might just think the dense anomaly cluster is a valid new cluster! This is a common failure mode."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ac427971",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Add a dense cluster of 50 points far away\n",
+    "X_dense_anomaly, _ = make_blobs(n_samples=50, centers=[[10, 10]], cluster_std=0.5)\n",
+    "X_new = np.vstack([X_normal, X_dense_anomaly])\n",
+    "\n",
+    "# Fit k-Means with k=3 (assuming we still think there are only 3 normal groups)\n",
+    "km_fail = KMeans(n_clusters=3, random_state=42, n_init='auto').fit(X_new)\n",
+    "\n",
+    "# Plot\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(X_new[:, 0], X_new[:, 1], c=km_fail.labels_, cmap='viridis')\n",
+    "plt.scatter(km_fail.cluster_centers_[:, 0], km_fail.cluster_centers_[:, 1], c='red', marker='*', s=200, label='Centroids')\n",
+    "plt.title(\"k-Means Failure: Absorbing the Anomaly Cluster\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "ce272ed7",
@@ -360,9 +531,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ML Workshop",
    "language": "python",
-   "name": "python3"
+   "name": "ml_workshop"
   },
   "language_info": {
    "codemirror_mode": {

From e1d5b64a89532506e3458715dbefaaab8f531f9c Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Mon, 23 Feb 2026 21:29:11 +0100
Subject: [PATCH 78/81] Session 05: Neural Networks I - Foundations and
 Optimization

---
 ...tworks I: Foundations and Optimization.pdf | Bin 771656 -> 771653 bytes
 ...tworks I: Foundations and Optimization.tex | 122 +++++-----
 ...uction to Artificial Neural Networks.ipynb | 213 +++++++++++++++++-
 ...5C02 The Multilayer Perceptron (MLP).ipynb | 190 +++++++++++++++-
 .../S05C03 Training Neural Networks.ipynb     | 144 +++++++++++-
 .../S05C04 Optimizing Neural Networks.ipynb   | 193 +++++++++++++++-
 ...S05C05 Building a Network in PyTorch.ipynb | 182 ++++++++++++++-
 7 files changed, 938 insertions(+), 106 deletions(-)

diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05 Neural Networks I: Foundations and Optimization.pdf b/src/S05 Neural Networks I: Foundations and Optimization/S05 Neural Networks I: Foundations and Optimization.pdf
index a894f0313086949f503558d6d8e412be9269d97f..0fe8461e18f9940cca29ab660a76c79a825ab955 100644
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diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05 Neural Networks I: Foundations and Optimization.tex b/src/S05 Neural Networks I: Foundations and Optimization/S05 Neural Networks I: Foundations and Optimization.tex
index 45a681b..36d4825 100644
--- a/src/S05 Neural Networks I: Foundations and Optimization/S05 Neural Networks I: Foundations and Optimization.tex	
+++ b/src/S05 Neural Networks I: Foundations and Optimization/S05 Neural Networks I: Foundations and Optimization.tex	
@@ -1,4 +1,4 @@
-% Created 2026-02-15 Sun 20:39
+% Created 2026-02-23 Mon 14:35
 % Intended LaTeX compiler: pdflatex
 \documentclass[hyperref={pdfpagelabels=false},aspectratio=169]{beamer}
 \usepackage[utf8]{inputenc}
@@ -202,14 +202,14 @@
 \end{frame}
 
 \section{Introduction to Artificial Neural Networks}
-\label{sec:orgfa4b015}
-\begin{frame}[label={sec:orgbdcaabd}]{What is an Artificial Neural Network?}
-\begin{definition}[Definition]\label{sec:org1ab2f4a}
+\label{sec:org2796f27}
+\begin{frame}[label={sec:org16118e4}]{What is an Artificial Neural Network?}
+\begin{definition}[Definition]\label{sec:orgd8fdf29}
 \pause
 An \alert{Artificial Neural Network (ANN)} is a computational model composed of layers of interconnected nodes ("neurons") that process information by transmitting signals to one another.
 \pause
 \end{definition}
-\begin{example}[Key Properties]\label{sec:orgb971635}
+\begin{example}[Key Properties]\label{sec:org3a497c6}
 \begin{itemize}[<+->]
 \item Foundation of \alert{Deep Learning}
 \item Can learn complex, non-linear relationships
@@ -217,7 +217,7 @@ \section{Introduction to Artificial Neural Networks}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:orgd95f9b3}]{Key Components of a Neural Network}
+\begin{frame}[label={sec:orgde11673}]{Key Components of a Neural Network}
 \begin{block}{The Building Blocks}
 \pause
 \begin{enumerate}[<+->]
@@ -233,7 +233,7 @@ \section{Introduction to Artificial Neural Networks}
 \end{enumerate}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org089ab53}]{Biological Inspiration}
+\begin{frame}[label={sec:org517b2ac}]{Biological Inspiration}
 \begin{block}{From Biology to Mathematics}
 \pause
 ANNs are loosely inspired by biological neural networks in the brain:
@@ -250,8 +250,8 @@ \section{Introduction to Artificial Neural Networks}
 \end{center}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:orgca28863}]{The Perceptron (1958)}
-\begin{definition}[The Simplest Neural Network]\label{sec:org310cae6}
+\begin{frame}[label={sec:orgace929b}]{The Perceptron (1958)}
+\begin{definition}[The Simplest Neural Network]\label{sec:org153a587}
 \pause
 The \alert{Perceptron} is a single-layer network that takes inputs, weighs them, and makes a binary decision.
 \end{definition}
@@ -266,7 +266,7 @@ \section{Introduction to Artificial Neural Networks}
 This can solve \alert{linearly separable} problems (drawing a straight line between classes).
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:orgc0c1cda}]{The XOR Problem}
+\begin{frame}[label={sec:orgec6db79}]{The XOR Problem}
 \begin{block}{The Fatal Flaw of Single Perceptrons}
 \pause
 \begin{center}
@@ -281,7 +281,7 @@ \section{Introduction to Artificial Neural Networks}
 \end{center}
 \pause
 \end{block}
-\begin{example}[The Limitation]\label{sec:org59d29f4}
+\begin{example}[The Limitation]\label{sec:org6943fa9}
 \begin{itemize}[<+->]
 \item \alert{AND Gate}: Linearly separable (one line can separate classes)
 \item \alert{XOR Gate}: NOT linearly separable (needs a curve or multiple lines)
@@ -290,9 +290,9 @@ \section{Introduction to Artificial Neural Networks}
 \end{example}
 \end{frame}
 \section{The Multilayer Perceptron (MLP)}
-\label{sec:org5d3f846}
-\begin{frame}[label={sec:org880307e}]{From One to Many}
-\begin{definition}[Stacking Neurons]\label{sec:orgec20826}
+\label{sec:org6f21c04}
+\begin{frame}[label={sec:org00ca316}]{From One to Many}
+\begin{definition}[Stacking Neurons]\label{sec:orgb1a6b37}
 \pause
 A \alert{Multilayer Perceptron (MLP)} or \alert{Feedforward Neural Network} connects many neurons in layers to solve complex, non-linear problems.
 \pause
@@ -305,13 +305,13 @@ \section{The Multilayer Perceptron (MLP)}
 \end{enumerate}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org79ba5a5}]{Activation Functions}
+\begin{frame}[label={sec:org3b297f3}]{Activation Functions}
 \begin{block}{Why Activation Functions?}
 \pause
 Without activation functions, an MLP is just a giant Linear Regression (linear + linear = linear).
 \alert{Activation functions} introduce \alert{non-linearity}, allowing networks to learn curves.
 \end{block}
-\begin{example}[Common Activation Functions]\label{sec:orgd817691}
+\begin{example}[Common Activation Functions]\label{sec:org5c6a566}
 \pause
 \begin{center}
 \begin{tabular}{llll}
@@ -325,7 +325,7 @@ \section{The Multilayer Perceptron (MLP)}
 \end{center}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org41cf858},fragile]{Forward Propagation}
+\begin{frame}[label={sec:orgf0d59ea},fragile]{Forward Propagation}
  \begin{block}{How Data Flows Through the Network}
 \pause
 For a single layer \(l\):
@@ -340,7 +340,7 @@ \section{The Multilayer Perceptron (MLP)}
 This process repeats until the output layer. The magic: \texttt{np.dot} handles all connections automatically.
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org212c40f}]{Network Capacity: Width vs Depth}
+\begin{frame}[label={sec:org3910c1e}]{Network Capacity: Width vs Depth}
 \begin{block}{Architectural Choices}
 \pause
 \begin{center}
@@ -353,7 +353,7 @@ \section{The Multilayer Perceptron (MLP)}
 \end{center}
 \pause
 \end{block}
-\begin{example}[Trade-offs]\label{sec:org1499594}
+\begin{example}[Trade-offs]\label{sec:orgbade7d0}
 \begin{itemize}[<+->]
 \item Deeper networks learn hierarchical representations (edges \(\rightarrow\) parts \(\rightarrow\) objects)
 \item Wider networks can approximate functions but may need more parameters
@@ -362,15 +362,15 @@ \section{The Multilayer Perceptron (MLP)}
 \end{example}
 \end{frame}
 \section{Training Neural Networks}
-\label{sec:orge394614}
-\begin{frame}[label={sec:orgcee9169}]{The Goal of Training}
+\label{sec:orge6b015f}
+\begin{frame}[label={sec:org04238e3}]{The Goal of Training}
 \begin{block}{From Random to Learned}
 \pause
 A neural network starts with \alert{random weights}. It knows nothing.
 \alert{Training} adjusts these weights to minimize prediction error.
 \pause
 \end{block}
-\begin{example}[Three Components]\label{sec:org83d1a93}
+\begin{example}[Three Components]\label{sec:orgbe39d7b}
 \begin{enumerate}[<+->]
 \item \alert{Loss Function}: How we measure error
 \item \alert{Optimizer}: How we update weights
@@ -378,8 +378,8 @@ \section{Training Neural Networks}
 \end{enumerate}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org0c0fba7}]{Loss Functions}
-\begin{definition}[The Scorecard]\label{sec:orgefaf011}
+\begin{frame}[label={sec:orga7649a0}]{Loss Functions}
+\begin{definition}[The Scorecard]\label{sec:org8f8f264}
 \pause
 The \alert{Loss Function} (Cost Function) calculates a single number representing "how bad" the model is. Goal: reduce it enough to generalize well on unseen data.
 \end{definition}
@@ -400,13 +400,13 @@ \section{Training Neural Networks}
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org3478b90}]{Stochastic Gradient Descent (SGD)}
+\begin{frame}[label={sec:org686dbec}]{Stochastic Gradient Descent (SGD)}
 \begin{block}{Optimization Strategy}
 \pause
 \alert{Gradient Descent} minimizes loss by following the slope downhill.
 \alert{Batch GD} calculates gradient for entire dataset - slow and memory-intensive.
 \end{block}
-\begin{definition}[Stochastic Gradient Descent]\label{sec:orgd23dda0}
+\begin{definition}[Stochastic Gradient Descent]\label{sec:orgd795eb9}
 \pause
 \alert{SGD} updates weights after seeing just \alert{one example} (or a small "mini-batch").
 \begin{itemize}[<+->]
@@ -415,14 +415,14 @@ \section{Training Neural Networks}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orge5ded9d}]{Backpropagation}
+\begin{frame}[label={sec:org7f11a30}]{Backpropagation}
 \begin{block}{The Chain Rule in Action}
 \pause
 How do we know which weight in Layer 1 caused the error in Layer 5?
 We use the \alert{Chain Rule} of calculus.
 \pause
 \end{block}
-\begin{example}[The Process]\label{sec:orgee60b7b}
+\begin{example}[The Process]\label{sec:org10c73cb}
 \begin{enumerate}[<+->]
 \item \alert{Forward Pass}: Data flows through network, loss is calculated
 \item \alert{Backward Pass}: Gradient propagates backwards layer by layer
@@ -433,7 +433,7 @@ \section{Training Neural Networks}
 \end{enumerate}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:orgced0470}]{The Vanishing Gradient Problem}
+\begin{frame}[label={sec:orgb33d70b}]{The Vanishing Gradient Problem}
 \begin{block}{Why Deep Networks Failed (Initially)}
 \pause
 In deep networks, gradients are multiplied repeatedly during backpropagation.
@@ -441,7 +441,7 @@ \section{Training Neural Networks}
 $$0.25 \times 0.25 \times 0.25 \times \dots \approx 0$$
 \pause
 \end{block}
-\begin{example}[The Consequence]\label{sec:orgfe3c32f}
+\begin{example}[The Consequence]\label{sec:org268e1ab}
 \begin{itemize}[<+->]
 \item Gradient "vanishes" before reaching early layers
 \item Early layers never learn; deep network fails
@@ -450,12 +450,12 @@ \section{Training Neural Networks}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org3f82fc1}]{The Learning Rate}
+\begin{frame}[label={sec:orgbdb45b6}]{The Learning Rate}
 \begin{block}{The Most Critical Hyperparameter}
 \pause
 The \alert{Learning Rate} (\(\alpha\)) controls step size during gradient descent.
 \end{block}
-\begin{example}[The Goldilocks Zone]\label{sec:org841dc56}
+\begin{example}[The Goldilocks Zone]\label{sec:org1a9da0a}
 \pause
 \begin{center}
 \begin{tabular}{ll}
@@ -469,8 +469,8 @@ \section{Training Neural Networks}
 \end{example}
 \end{frame}
 \section{Optimizing Neural Networks}
-\label{sec:orge829fdd}
-\begin{frame}[label={sec:org8ba08a6}]{Weight Initialization}
+\label{sec:orgaa0c86b}
+\begin{frame}[label={sec:org6b38122}]{Weight Initialization}
 \begin{block}{Starting Right}
 \pause
 \begin{center}
@@ -484,7 +484,7 @@ \section{Optimizing Neural Networks}
 \end{center}
 \pause
 \end{block}
-\begin{example}[Strategies]\label{sec:org4b01a4a}
+\begin{example}[Strategies]\label{sec:org4eee8d7}
 \begin{itemize}[<+->]
 \item \alert{He Initialization}: Best for ReLU networks
 \item \alert{Xavier (Glorot)}: Best for Sigmoid/Tanh networks
@@ -492,7 +492,7 @@ \section{Optimizing Neural Networks}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org87cc935}]{Advanced Optimizers}
+\begin{frame}[label={sec:orgd47190d}]{Advanced Optimizers}
 \begin{block}{Smarter Descent}
 \pause
 Standard SGD is like walking down a mountain blindfolded. Advanced optimizers add intelligence.
@@ -510,13 +510,13 @@ \section{Optimizing Neural Networks}
 \alert{Adam} is the default choice for most tasks.
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org5d89534}]{Regularization: Dropout}
-\begin{definition}[Fighting Overfitting]\label{sec:org4d63258}
+\begin{frame}[label={sec:org44c7eb7}]{Regularization: Dropout}
+\begin{definition}[Fighting Overfitting]\label{sec:org2709ba1}
 \pause
 \alert{Dropout} randomly "kills" (sets to zero) a percentage of neurons during training.
 \pause
 \end{definition}
-\begin{example}[Why It Works]\label{sec:org2af5a66}
+\begin{example}[Why It Works]\label{sec:orgd94c28f}
 \begin{itemize}[<+->]
 \item Prevents neurons from co-adapting
 \item Forces network to be redundant and robust
@@ -525,13 +525,13 @@ \section{Optimizing Neural Networks}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org82a17ab}]{Regularization: Batch Normalization}
-\begin{definition}[Stabilizing Training]\label{sec:orgb5b57f6}
+\begin{frame}[label={sec:orgdf050af}]{Regularization: Batch Normalization}
+\begin{definition}[Stabilizing Training]\label{sec:org5b515be}
 \pause
 \alert{Batch Normalization} normalizes layer activations per mini-batch during training (with learnable scale and shift).
 \pause
 \end{definition}
-\begin{example}[Benefits]\label{sec:orgcb9133c}
+\begin{example}[Benefits]\label{sec:org10bbf8c}
 \begin{itemize}[<+->]
 \item Stabilizes training (allows higher learning rates)
 \item Reduces sensitivity to initialization
@@ -540,13 +540,13 @@ \section{Optimizing Neural Networks}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org30f3510},fragile]{Early Stopping}
+\begin{frame}[label={sec:org7270455},fragile]{Early Stopping}
  \begin{block}{The Simplest Regularization}
 \pause
 Monitor the \alert{Validation Loss}. If it stops improving (or starts getting worse), stop training immediately.
 \pause
 \end{block}
-\begin{example}[How It Works]\label{sec:orgba5a44a}
+\begin{example}[How It Works]\label{sec:org2a71bf2}
 \begin{itemize}[<+->]
 \item Training loss keeps decreasing (memorization)
 \item Validation loss starts increasing (overfitting)
@@ -556,8 +556,8 @@ \section{Optimizing Neural Networks}
 \end{example}
 \end{frame}
 \section{Building Networks in PyTorch}
-\label{sec:orge369199}
-\begin{frame}[label={sec:org25ab4e9}]{Why PyTorch?}
+\label{sec:org1d0a986}
+\begin{frame}[label={sec:orgc1c8968}]{Why PyTorch?}
 \begin{block}{Beyond Scikit-learn}
 \pause
 Scikit-learn is great for standard ML, but Deep Learning needs:
@@ -567,7 +567,7 @@ \section{Building Networks in PyTorch}
 \item \alert{Automatic Differentiation}: Backpropagation is hard to code by hand
 \end{enumerate}
 \end{block}
-\begin{example}[PyTorch Advantages]\label{sec:orgcc557c1}
+\begin{example}[PyTorch Advantages]\label{sec:org46bbeff}
 \pause
 \begin{itemize}
 \item Readable, "Pythonic" style
@@ -576,8 +576,8 @@ \section{Building Networks in PyTorch}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:orgabf51eb},fragile]{Tensors: The Foundation}
- \begin{definition}[NumPy Arrays on Steroids]\label{sec:org4ad692c}
+\begin{frame}[label={sec:org77e077d},fragile]{Tensors: The Foundation}
+ \begin{definition}[NumPy Arrays on Steroids]\label{sec:orgbf28607}
 \pause
 A \alert{Tensor} is like a NumPy array that:
 \begin{itemize}
@@ -586,7 +586,7 @@ \section{Building Networks in PyTorch}
 \end{itemize}
 \pause
 \end{definition}
-\begin{example}[Key Operations]\label{sec:org837f6ac}
+\begin{example}[Key Operations]\label{sec:orge635a04}
 \begin{itemize}[<+->]
 \item \texttt{torch.tensor([1, 2, 3])} — Create from list
 \item \texttt{torch.ones(2, 3)} — Shape (2, 3) of ones
@@ -595,13 +595,13 @@ \section{Building Networks in PyTorch}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:orgfb3215d},fragile]{Defining a Network (\texttt{nn.Module})}
+\begin{frame}[label={sec:orgc59b612},fragile]{Defining a Network (\texttt{nn.Module})}
  \begin{block}{The Blueprint}
 \pause
 In PyTorch, a network is a Python class inheriting from \texttt{nn.Module}:
 \pause
 \end{block}
-\begin{example}[Two Required Methods]\label{sec:orgbd04975}
+\begin{example}[Two Required Methods]\label{sec:org0019515}
 \begin{enumerate}[<+->]
 \item \texttt{\_\_init\_\_}: Define the layers
 \begin{itemize}
@@ -617,7 +617,7 @@ \section{Building Networks in PyTorch}
 \end{enumerate}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org2fd7327},fragile]{The Training Loop}
+\begin{frame}[label={sec:org906f93f},fragile]{The Training Loop}
  \begin{block}{The 5-Step Boilerplate}
 \pause
 Unlike Scikit-learn's \texttt{model.fit()}, PyTorch requires manual control:
@@ -633,7 +633,7 @@ \section{Building Networks in PyTorch}
 Wrap in a \texttt{for epoch in range(epochs):} loop.
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org4a3b823},fragile]{PyTorch Outputs and Loss Pairings}
+\begin{frame}[label={sec:org80928fe},fragile]{PyTorch Outputs and Loss Pairings}
  \begin{block}{Match Model Output to Loss}
 \pause
 \begin{itemize}[<+->]
@@ -642,7 +642,7 @@ \section{Building Networks in PyTorch}
 \item \alert{Regression}: model outputs raw values, use \texttt{nn.MSELoss()}
 \end{itemize}
 \end{block}
-\begin{example}[Inference Rule]\label{sec:orgc81bec3}
+\begin{example}[Inference Rule]\label{sec:org2dd71e3}
 \pause
 \begin{itemize}[<+->]
 \item \alert{Training}: keep the final layer linear (no sigmoid/softmax) and use the matching PyTorch loss
@@ -651,8 +651,8 @@ \section{Building Networks in PyTorch}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org3a48938},fragile]{Loss Functions and Optimizers}
- \begin{example}[Loss Functions]\label{sec:orgc448c4a}
+\begin{frame}[label={sec:org7a8e591},fragile]{Loss Functions and Optimizers}
+ \begin{example}[Loss Functions]\label{sec:org26f0266}
 \begin{center}
 \begin{tabular}{ll}
 Task & Loss Class\\
@@ -669,7 +669,7 @@ \section{Building Networks in PyTorch}
 \end{verbatim}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org8bdca76},fragile]{Evaluation Mode}
+\begin{frame}[label={sec:org4b1eacc},fragile]{Evaluation Mode}
  \begin{block}{No Gradients Needed}
 \pause
 When evaluating, set \texttt{model.eval()} and use \texttt{torch.no\_grad()} to disable gradient tracking:
@@ -685,8 +685,8 @@ \section{Building Networks in PyTorch}
 \end{block}
 \end{frame}
 \section{Summary}
-\label{sec:org90ac091}
-\begin{frame}[label={sec:orgdf0017d}]{Key Takeaways}
+\label{sec:orgf785473}
+\begin{frame}[label={sec:orgafebd5e}]{Key Takeaways}
 \begin{block}{What We Learned}
 \pause
 \begin{enumerate}[<+->]
@@ -700,7 +700,7 @@ \section{Summary}
 \end{enumerate}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:orgecb1a8c}]{Next Section Preview}
+\begin{frame}[label={sec:orgb93ef77}]{Next Section Preview}
 \begin{block}{Neural Networks II: Deep Learning \& Transformers}
 \pause
 \begin{itemize}[<+->]
diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05C01 Introduction to Artificial Neural Networks.ipynb b/src/S05 Neural Networks I: Foundations and Optimization/S05C01 Introduction to Artificial Neural Networks.ipynb
index 001735a..3c6b247 100644
--- a/src/S05 Neural Networks I: Foundations and Optimization/S05C01 Introduction to Artificial Neural Networks.ipynb	
+++ b/src/S05 Neural Networks I: Foundations and Optimization/S05C01 Introduction to Artificial Neural Networks.ipynb	
@@ -108,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "32b78c0c",
    "metadata": {
     "lines_to_next_cell": 2
@@ -142,12 +142,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "8dfd2c87",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Perceptron AND Accuracy: 1.00\n",
+      "Perceptron XOR Accuracy: 0.50\n"
+     ]
+    }
+   ],
    "source": [
     "# Train on AND\n",
     "clf_and = Perceptron(random_state=0)\n",
@@ -172,12 +181,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "d76c6082",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from matplotlib.colors import ListedColormap\n",
     "\n",
@@ -246,12 +266,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "3127bc88",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MLP XOR Accuracy: 1.00\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# For XOR, use a solver/activation combo that converges reliably on tiny datasets\n",
     "mlp = MLPClassifier(\n",
@@ -315,6 +353,88 @@
     "-   Compare their boundaries."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f48e8f7b",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rGYZhGCafOOikWB0WFub+vWHDhmLUEKkgDx48+L7jmkhJukaNGjm9RIZhGIZhHgG29QzDMAyTT0TiPJ1zz7nMWq1WjDViGIZhGCZ/w7aeYRiGYfLxHPSDBw+iUKFCCAgIuO/taO7wwoULoVAoxAxJGi3hOS86qxw+fBg0VY4ej2EYhmHyAhaLRWiy1KxZEwUJtvUMwzAMk3Vb/9gd9AMHDmD16tX46KOP7ns7EomrVq0aihYtiuvXr2Py5Ml49dVXsXTpUpQoUeKhnpucc/ox2fLQeAoHYLPZIJPJgIw1855c+PzwOeLPEP+dFfTvIYejwM08ZVufBrZl94fPz4Phc8TnJ5s/Q0qH9pEfskDhyFlb73CQjpos741ZI2G4V155BWXLlsX06dPv23+eltjYWLRt2xYdOnTAmDFjHur5jx8/Dr3Jgjj4PtT9GYZhGCa7iYQBfioFqlatWiBOLtt6hmGYvM3m1ctRWXkQVSsURqGIwPtO1mKyh0RVK8h9QjNl6x9bBp1GrA0YMAAhISGYOHFilpxzIioqCrVr1xbj2h4FuVyGwhFFkFewWMxISEhAeHg4FApWqOfzw58h/hvj76G8Ro5/T8dfRUGBbb132NbfHz4/D4bPEZ+f7PoMkWbIrF9/RGJcLEKD/XDrwHfsoAOwmD1sfQ5MDVOnZC57/tgcdKPRiEGDBkGj0WDBggUIDAxE7iHJk6PaaNOXF9eVV+Dzw+eIP0P8d1ZQv4csBaS/iW39g2FbxufnUeHPEJ+fRyUpIV4450SdatHwUake+TELEgqlEqocsPWSLNj6HG97s1qtGDZsGC5duoQ///xTiMM9DHfu3BGCMwWlBJBhGIZhCgps6xmGYfIHZ44ddR/Xrhqdq2thcimD/sUXX2DLli0YOXKkGK125MgR93WVK1cW2YjevXvj1q1b2LBhg7h85cqV4j5NmjQRpe0kEvfHH3+Ipv2+ffvm9JIZhmEYhskCbOsZhmHyB6eOHXYfs4P+hDrou3btEv+PGzcu3XWbNm1C8eLFYbfbhWqeC7qMROHGjh0ryuKpJL5BgwZ49913H1rBnWEYhmGYnIFtPcMwTP7gzPF7ydK61Uvl6lqYXHLQN2/e/MDbzJkzJ9XvNWrUSHcZwzAMwzB5E7b1DMMweR8a3nX6mNNBjwgLQMliYbm9JMYLBW30KsMwDMMwDMMwDJOGuDu3hUicSyCOx6vlTdhBZxiGYRiGYRiGKeBcOHVvXDX3n+dd2EFnGIZhGIZhGIYp4Jw/ddx9zP3neRd20BmGYRiGYRiGYQo450+dcB9TiTuTN2EHnWEYhmEYhmEYpoALxF047SxxjyxUGMUKh+b2kpgMYAedYRiGYRiGYRimAHP9yiXotBpxXLl6rdxeDnMf2EFnGIZhGIZhGIYpwJw8ctB9XKVmnVxdC3N/2EFnGIZhGIZhGIYpwNw5+Jf7uEMVda6uhbk/7KAzDMMwDMMwDMMUYA4cveI+ZgX3vA076AzDMAzDMAzDMAUUi9mMI6duiOOy0ZEIC/HP7SUx94EddIZhGIZhGIZhmALK+dMnYTJbxTFnz/M+7KAzDMMwDMMwDMMUUE54CMTVqVYyV9fCPBh20BmGYRiGYRiGYQooJw4dcB/XqRadq2thHgw76AzDMAzDMAzDMAV8xJpMJkH1ysVzeznMA2AHnWEYhmEYhmEYpgCi1+lw6dwZcVyhTBR8fZS5vSTmAbCDzjAMwzAMwzAMUwA5c/wI7Ha7OK5esUhuL4fJBOygMwzDMAzDMAzDFHCBuGoVi+bqWpjMwQ46wzAMwzAMwzBMAeTk4XsOelXOoOcL2EFnGIZhGIZhGIYpgJw8ckj876NSoEKZyNxeDpMJ2EFnGIZhGIZhGIYpYKQkJ+H6lUviuHqlYlDIZbm9JCYTsIPOMAzDMAzDMAxTwDh51Jk9J2rz/PN8AzvoDMMwDMMwDMMwBYwTh+71n9dhBz3fwA46wzAMwzAMwzBMAeOkh4I7O+j5B3bQGYZhGIZhGIZhChAOhwPHD+0Xx4FBwShXigXi8gvsoDMMwzAMwzAMwxQgYm/fQnzsHXFcuXpNSKXs9uUX+J1iGIZhGIZhGIYpQBw/fMB9XLVW3VxdC5M12EFnGIZhGIZhGIYpQNzZN8t93L6qLlfXwmQNdtAZhmEYhmEYhmEKEHuPXHYf169ROlfXwmQNdtAZhmEYhmEYhmEKCDabDQePXxXHJYqGonBUcG4vickC7KAzDMMwDMMwDMMUEC6dOwOtziSOOXue/2AHnWEYhmEYhmEYpoBwwkMgrh6Xt+c72EFnGIZhGIZhGIYpIJw4fNB9XK9GqVxdC5MHHfQ1a9bgrbfeQuPGjVGjRg107NgRixYtgsPhuO/96Po//vgDTZs2RbVq1dCtWzccOXIkp5fLMAzDMEwWYVvPMAyTdzh5xOmgSyQS1KoSndvLYfKagz5z5kz4+vpi5MiR+P3334Wj/tlnn+G333677/2mTp2KCRMmoE+fPpgyZQoiIyPRr18/XL9+PaeXzDAMwzBMFmBbzzAMkzcwGY04f+qEOK5UrjACA3xye0lMFpEjhyGnPCwszP17w4YNkZycjBkzZmDw4MGQStPHCEwmk3DKySEnB52oXbs22rRpg2nTpmHMmDE5vWyGYRiGYTIJ23qGYZi8wdkTx2C1WsVx3epc3p4fyfEMuqdz7qJSpUrQarXQ6/Ve73Po0CFxfdu2bd2XKZVKtGzZEtu3b8/R9TIMwzAMkzXY1jMMw+QNTtwtbyfYQc+f5HgG3RsHDx5EoUKFEBAQ4PX6S5cuif/LlCmT6vKyZcti1qxZMBqN8PF52HINB8xmM/IKFos51f8Mnx/+DPHfGH8P5S1y/nuaNFkkKGiwrb8H2/r7w+fnwfA54vOTWY4f2u8+rl65GEx3/R5Lmv+Z1OT0+XHAkWlL/9gd9AMHDmD16tX46KOPMrxNSkqKyJirVKpUlwcFBQnxOLVa/dAOutVqQ1zMbeQ1EhIScnsJeRo+P3yO+DPEf2cF9XsoEjZAVrCGqrCt9w7bsvvD5+fB8Dni8/Mgjh10OugKuRRRITLE3E7t9/Bn6P7k1PmxqapCLs+DDnpMTAzee+891K9fH7169UJuIJfLUDiiCPJSRJQ+COHh4VAolLm9nDwHnx8+R/wZ4r+zAv89FH8VBQm29elhW3Z/+Pw8GD5HfH4yg1aTgptXL4vjqhWLITq6xL3PkNnDlinZ50hLTp8fdYos07d9bA46ZcUHDBiAkJAQTJw40as4nGemnMrQSSzOM4tOj0HjAoKDgx9hJRKRnc9r0KYvL64rr8Dnh88Rf4b476ygfg9ZClB5O9v6+8O2jM/Po8KfIT4/9+PimVPuUdb1apSByovNIufT2+VMzp4fSRZs/WOpqaOe8UGDBkGj0eDPP/9EYGDgfW/v6j2/fNkZAfLsTS9atOgj9J8zDMMwDJMTsK1nGIbJG/PPibrVef55fiXHHXSS+R82bJhwrsk5J3G4B1GrVi0hILdmzRr3ZRaLBevXrxdz1BmGYRiGyTuwrWcYhsl9Thy+56DXq1E6V9fCPDw5XuL+xRdfYMuWLRg5cqQYnXbkyBH3dZUrVxblgr1798atW7ewYcMGcTmVtVPGnUrhaXRL+fLl8ddff4n56f3798/pJTMMwzAMkwXY1jMMw+SdDLqvnz8qlcs7mltMHnPQd+3aJf4fN25cuus2bdqE4sWLw263w2azpbqO+tWph2L69OlITEwUs9OnTZuGEiXuiR0wDMMwDJP7sK1nGIbJXRLj43Dr+jVxXKlaDcgK2HSQJ4kcd9A3b978wNvMmTMn3WUkBkdZdPphGIZhGCbvwraeYRgm75S3P12jVq6uhXk0OLTCMAzDMAzDMAyTjzlx+ID7uGqturm6FubRYAedYRiGYRiGYRgmn1LNNA9XDix3/961ys1cXQ/zaLCDzjAMwzAMwzAMk08h3a59R5zjqaMiAlGyWFhuL4l5BNhBZxiGYRiGYRiGyadcuBKLJLVeHNevUVpoeTH5F3bQGYZhGIZhGIZh8in7jlxxH/P88/wPO+gMwzAMwzAMwzD5lL2HneXtRL0apXJ1Lcyjww46wzAMwzAMwzBMPmXf0XsOet1q7KDnd9hBZxiGYRiGYRiGyYdYzGYcOXVdHD9VOgqhIf65vSTmEWEHnWEYhmEYhmEYJh9y4cxJmExWcVy3OmfPCwLsoDMMwzAMwzAMw+RDjh8+6D7m8vaCATvoDMMwDMMwDMMw+ZCTng46Z9ALBOygMwzDMAzDMAzD5ENOHT0k/pfJpKhZpURuL4fJBthBZxiGYRiGYRiGyWcY9HpcPHtaHD9dvij8fFW5vSQmG2AHnWEYhmEYhmEYJp9x5sRR2Gw2cczl7QUHdtAZhmEYhmEYhmHyc/95tehcXQuTfbCDzjAMwzAMwzAMk884eYQF4goi7KAzDMMwDMMwDMPkM04ccQrEqVRyVK1YLLeXw2QT7KAzDMMwDMMwDMPkI1LUybh26YI4rlG5BBQKeW4vickm2EFnGIZhGIZhGIbJR5y6mz0nWCCuYMEOOsMwDMMwDMMwTD7ihIdAXP0apXN1LUz2wg46wzAMwzAMwzBMPuL4of3u43rsoBco2EFnGIZhGIZhGIbJJzgcDpw4fEAcBwaHoFypyNxeEpONsIPOMAzDMAzDMAyTT4i5eQPxsXfE8dM1akEqZZeuIMHvJsMwDMMwDMMwTD4sb69aq26uroXJfthBZxiGYRiGYRiGySfcOTDHfdyhijZX18JkP+ygMwzDMAzDMAzD5BP2HbnsPq5fkxXcCxrsoDMMwzAMwzAMw+QDrFYrDhy7Ko6ji4cjKiIot5fEZDPsoDMMwzAMwzAMw+QDLp87A73BLI7rVovO7eUwOQA76AzDMAzDMAzDMPmAE0cOuo95/nnBhB10hmEYhmEYhmGYfMCJw/cc9LrVS+XqWpicgR10hmEYhmEYhmGYfMCpo4fE/xKJBLWrcol7QYQddIZhGIZhGIZhmDyO2WTCuVMnxHHFsoURGOCT20ticgA5cpirV69i2rRpOHr0KM6fP48yZcpg5cqVD7xfs2bNcPPmzXSXHzt2DCqVKodWyzAMwzDMw8D2nmEYJmc5e/I4rBaLOOby9oJLjjvo5JRv27YN1atXh91uh8PhyPR9W7dujX79+qW6TKlU5sAqGaZgoFEnY9u6VYi9fQvBoWFo0qYdIqIK5/ayGIZ5AmB7zzCPB3LQ/tu6ERfPnIJCqUS9Rs/jqcpV+PQ/AZz0EIirW53L2wsqOe6gUya8RYsW4njkyJE4ccJZlpEZIiIiUKNGjRxcHcMUHJb9NQuzf/tZzMeUKFWAxYxpv3yPl3v2Rf9hH0Iq5Y4WhmFyDrb3DJPznDx8EN+Neh/qxHhIlL6A3Ya5kyegSu16+HT8RBGcZ54UB50F4goqOe6gs1PAMDnPro1rMXviD9D5hkMXHAW7VA6J3QY/QwKWzJkGpUqJ3m+/z28FwzA5Btt7hslZYm5cw9fvD4YBCiSHPQWr3AdwOKAyp+Dk0SP47J038NPsfyCTyfitKKCcOOIUiJPLpaheqURuL4fJrw76o7BixQosXLgQCoUCderUwYgRI1ChQoVHfFQHzGYz8goWiznV/wyfn6xiNBqwbP5MGFXB0AQWdV/ukMqg84+CxGHHolnT0KF7LwQEBj1xHzH+G+NzlPc/Q9T6JcGTTPbbe7b1+Qn+ns7cOVr/70JY7A4khJSEQ3K3Kk4igUkVjHiJHI5Tx7F3+xbUebYxnjSehM+QXqfF5XNnxHHVCsUgkTpgyoJPY7l7W9f/zOM9Pw44Mm3p5Xm5VK5atWooWrQorl+/jsmTJ+PVV1/F0qVLUaLEw0eMrFYb4mJuI6+RkJCQ20vI0/D5yZhLZ09BnRAPXUgZr9frfcMRoI/DxlXL0KCps93kSYQ/Q3yO8upnKBI2QPbktqDkhL1nW58/4e/pjCENp/07tkKrCL7nnHtgUfjBrvTDhhVLULzsU3hSKcifoeMH97u1vCqVi0DM7YfzZwryOcoOcur82FRVIZfncwf9008/dR9TNP3ZZ59F27ZthSL8mDFjHvpx5XIZCkcUQV6BIn30QQgPD4dCwQJ4fH6yzvULZ8X/NpnC6/VU7k4RdqVcjsKF885n/3HBf2N8jvL8Zyj+Kp5kcsLes63PX/D39IMxmYwwm4ywBYZ7v4FEAotEBofNxra+gO6nN1y/4j5u3KAyChfJ2p6OMsNuW8ai24/9/KhTMt96kmcd9LRERUWhdu3aOHny5CM+kiRPKsHTl0leXFdegc9PxpQoXVb8r7ToYJSl/wwprHrRo1ayTNkn+jPGnyE+R3n1M2R5wsvbc8bes63Pj/D39P0JiyoMQ4oeBl8vQnAOO3xsJpQoXYZtfQHd65w5ftR9/EztslA95Osk5/Nh7/skoMih8yPJgq1/cmvqGKaAULhYCZSvWh3BxgQhDJcKhx1B+jhEFi6KGvWeya0lMgzDMAzziDRt2wF+ZjXkFn266/z1cXBYzWj9clc+zwWUE4cPiP99/fxR+al7mkNMwSPfOOh37tzBwYMHUbVq1dxeCsPkOV4d9C78lXIUUl+Cnz4eCrMOvoZERKkvw8dmxPtfjGOFZYZh8gVs7xnGO8+364iyFSojMuUqArS3ha1XmVIQqr6GQF0seg4aguLRpfn0FUASYu/g1vVr4rhStRqQPcG6JU8COV7ibjAYsG3bNnF88+ZNaLVarF27Vvxer149hIWFoXfv3rh16xY2bNggLl+5ciW2bNmCJk2aiFI3Eo35448/xNiIvn375vSSGSbfUaRENH6Y8TfmTZmI/zZvgP1uJr1mg2fRa/AwVKxaI7eXyDBMAYftPcPkLEqVD76eNB0Lp0/BmiULYEiOF5cXjS6N7v1GoeWLnfgtKKAcP+TMnhNVa9XJ1bUwBcBBp2b7oUOHprrM9fvs2bNRv3592O122Gz3SnOLFy+O2NhYjB07FhqNBoGBgWjQoAHefffdR1JwZ5iCTLGSpfDp+InQpKiRGBeL4NAwhIRlICbDMAyTzbC9Z5ich8qbB7w/Er3ffg93bt0Q/bKFihaHRMJaFgWZO/tnuI/bVzXm6lqYAuCgk7N99qxTZToj5syZk+r3GjVqpLuMYfIj50+dwPK/5+DYwX2QSKWo3eBZvNj9dUTn4AiUwKBg8cMwDPM4YXvPPMnlx6sW/YUdG9bCaDSgTPkK6ND1NdR+plGOOc5KlcotEssUfPYduafgXr8mtzEUdPKNijvD5DdWLpyH38Z9AchV0Mn9xWWxy5Zg7ZKF+OCbH9C0TfvcXiLDMAzDMI/A2ZPH8PGg3jAajdApAmGXyhC7dx/2bd+Ctp27YcgnX3F2m3kkqNJ4/zGng144MgjFi4TyGS3gsIPOMDlksMk51/mEQRNQRMwnJTQOO4I1tzD+0xEoX7kqipaM5vPPMAzDMPkQs8mEz98dCK0ViA99Cg6pc1utdTjga0zCmsULUL5yNbTpxMrqzMNz7fJFqFMM4rhejdIc8HkCYAlAhskBlv81W2TOPZ1zgUQKdWBROCQykWFnGIZhGCZ/snPjWqgTE5DoX9TtnAskEjGr3OQTjCXzZsDhcOTmMpkCMl6NqFejVK6uhXk8sIPOMDnA0f17oVMEpHbOXUikouT96IE9fO4ZhmEYJp9y4vB+OFT+sMlVXq83KINw/dIFaDUpj31tTMHh5OGD7uO61dlBfxLgEneGyQnIMb9PwFxCV+ay4ipF9M+dPIZ9O7bCYjajbMXKeKZZSygUylxdF8MwDMPkByR4kB13bgRyW2E9LuY2tq1bCXVSIiIKFREaODTphckfnDxyyH1cpxo76E8C7KAzTA5Qq/4zSFizChoqa0trmB12+Fl14ja5BRnpr0e8gxOH9kMiV8IhlQFmA4JCw/DJ9xNQrU79bAkA5PamhGEYhmFyiqp16mP14r8hsxphk/uku97PnILocuUREBiUa+Ji0376Dv/OmyGq9xxyJSRWE6b+OA59hwxH5179H/k52NbnLBaLBWdOHBXH5UpFISzEKTrMFGy4xJ1hcoAXe7wujGCQ9pZwyN047AjR3BB/eO1eeTVXzr3NZsNn77yBE0ePICk4GrdDyyMmpBziwp5Cgs6MT9/uj6sXzz/UY9NM1knjvkTnRrXwQq3yeL1NY/z15yToddpsfx0MwzAMk5s827wVwiKjEK69Bandcu8KhwN++ngojSno/PqjO8EPy5zff8aSudOR4lcIMWEVhK2n/1MUwfjzp3FiqszDYDTo8c/MP9C73fN4oXYFdHquJiZ+/RluXbua7a/hSefi2VMwGZ1zz7m8/cmBHXSGyQHKVXwawz4fC39jEookX0BQyk0EpdxAkaRz8Lfq8fF3P6NwsRK5cu4P7t6O86eOIyGwBEyqIHeGn6L/CcHRMNnswvA+jBEZ3O1FrFy8ALF2H6gDi+G62og5v0/Ae727QZOizoFXwzAMwzC5A7WEfTXxTwT7KlAo8RxC1NcQqLmFQuqLCNLexss9+6JFh5dzZW3U9/7PzD+h9YuEzj/SWSlHsQOpHJrAIjCogjHztx9F0D4r6LQafNC/J2ZM/BFXE3VQBxRFnMMXq5f/i8HdX8SZ40dy6BU9mZw8fK+8vW51nvzzpMAOOsPkEK06dsHv/6xC+06voELRcFQqHoWXu7+OP5aswbPNWuXaeV/77z+wylSwKPy8C9j5hGLr2pVZUp2l2479cCi0ZruI0GsDCgsFW3VQccSFlMH1q5dFtJ5hGIZhChJlKlTC1H/Xoc8776NK2RKoUCgETZo+j++mzsXAEaNyrdVr77bNsFkt0PuGe71e7xcuFOjPHneWT2eWOZN+xsVzZxEXXFrYeLL1ZPPvBJeF3i7FNx+8m2Wnn8mYk0c8BOK4//yJgXvQGSYHiS77FAaP/Nz9O5V6nzh0AJfPn0WZ8hVRpHjJx37+r148B5tUkaFInV2qgM1qFb1rMpkz4v4gju7fg1vXriAppIw7Su/CKvdBiioMm1ctwxvvjURgUHC2vA6GYRiGyQuQ4FrXvoPED0EO6qkjB8UYtohChVGhSvXH7qhTpRyF2e2e4988EPsAALdvXkPlGrUyXdpOQX6NKgRWhW+q68j2J/kVhuzOBRzYtQ31GzfLhlfBnLjroEulEtSs8vj3jEzuwA46wzwGyOGd8/svWDp/FkxGg/vymg2ew7DR3yCqSNEcfX6LxYyDu3YgPjYGeq0WCovO2RsvSV9EozTrhFHPrHNOXDhzEhKZ3HtWHhCl9FbdHVy/dDHTGwGGYRiGyW9sW7cKU3/6Dgl3brsvKxZdGm+P/Bw1Gzybo89N1WxUYn7p7GlcuXBOaMwrLHpYlOmFxZS0D8git69fE3sYU6j3PQs57RKFCudPnWAHPRswGgy4cPqkOK78VBH4+3kf58cUPNhBZ5jHwE9ffIxNq5ZB6xsBQ1gJ2KUyqEwaHD54AO/17opf/1qK0PAIEXU36vXw8fWFTJ49f55Urj7puy+hSU66O/7NIYx2aPJVJIWWTnVbMuQ+JjUUCmdkPbMo5Iq7Ynjk2qfPEkjsznI3hZJHuDEMwzAFk82rl2H8JyNgVAVDF1pGtJMprEZYYuKFAOvXk6ajZv1nhCNt0OsglyugVGWP00UO+Xej3seV82fdtp4scoj6KuLCywMemXSyyQG6WHF98ejU+4D7Ib+7N3DZ9HRQa5zdzuNas4lTxw672wVYIO7Jgh10hskG4u7cxppFf2P/7u3iy7Rarbpo37Unipcqg3Mnj2PTyqVCNI16tVwYfUMRqwyAJOki5v8xEXKFEuuWLoJBpxXHNKe0W/83s2Q807Jr0zp89/F7MPoEQxv2lCg3l9nM8NfFwc+YiPCEc9AGFIJD4gwY+BoShWFv0qpdlp6nzrNNMHn81/A1Jqd6jS58jUkICY8QZf0MwzAMkx+hEu/Nq5djy5oV0KjVKFG6DF7o1A016j8Dq9WCyeO/gdEnBMmBxd1tZGZlABIU/ghXXxF2ss1Lr2DpX7MRe+uGuJ7GmlJpfO1nGj30umiCyoj+r0JjtCA5uJR4TonDLmxvoDYGUfFnoQkoLPYACqtBKMzL7FaEhIajXKUqmX4eqgSIKlIMxqRkmFWB6a6nAL/DZkG9Rk0f+rUw9zi8Z5f7+Nk65fjUPEGwg84wj8jB3TvwxXtvwWqzQS8PgEMiwZWLf2P5grmifP3C6VOAwgcGn9B097XLFNDJ/bHyn78hkcmgUQbDGhQqnOiNa1dj56Z1GP/nPJSr9HSW10U95FRmZ1YFITmwxD21dpkSKUHFRLabjHeo+prz9jQjVSaHQurAK30HZum5ikWXQsPnW2HP9i2ir402B64Ivp8hAX7GJLwy+ONsqwpgGIZhmMdJXMxtfDjgNcTcvA6zMhBWiRxXrl3Hzg1r0KxdRzzbvLWoVKNgeDqNF4kEGp9wMcL0jx+/FRl2Y1AJSBw2HD5+Esfe7oe3Px4jAvsPw6JZf0JvMCIumHRgnHaWAu96vwhhk0NTromxr7QqyppTZl8CB3q9PSxL7WxSqRTd+g3CxG9Gw1/mA51fuLNVzuEQJfOh+juoXv9ZlOZgfLZw0MNBb1z/qex5UCZfwLtlhnkEqKebnHOdRIWk0OJugbQUh12MWPn5i1GoWrsezPcRZZNbjbBJpEgILgO77F4JOCmvRmiuYtyo9zF1ydosC8ycO3kMd25ehyaktNfn1vlFCscZMjkgVUBqMcDPV4VPx08U4nZZZfiX4zBm6CCcOLQfDqUfzJDBx2GGw2LCiz164eXX+mb5MRmGYRgmt6GS9C/eH4yYO7GIC30KNrmzLD3F4YCPKVlk1Sm7jrvOrzfkNucs68Sg6FTZZ4NPGAK1t0UrWt3nmqJQ0WJZXtvGFf9CowhyO+dpNWDISZc5bHDIVZDYTFA5rHjt7ffQtlM3ZJW2nbvjzq2bWDhjCoLMSTBKlFDABqlZj6eqVMOo737O8mMy6aFqzCP79ojj8MgolCsVxafpCYIddIZ5BNYuWSgy557OuUAiRUpAUfjZDEiIuwOl3eLszUrjKEttFtGflhJYNJVzTtDjqX2jcPPKJZw8fABVatXN0trUSYnif1sGmwXKpBM1atcTZXqUpW/cqp3of38Y/AMCxViZw3t3YeuaFUhRJ6Nw0eJo/dIrYgwNwzAMw+RHTh89jIunTyAxuJTbORdIJDD6hEJv0ePo/r3iIpnNBJvcJ91j+BmSYFIGpi8Nl0ig9S+EALMa6/5diF5vv5eltVFpPQUHrIHp28tcj29T+KBU0ShRTk+itM3bvYTwqEJZep57DydB33dHiKqBtf8uFMJxAYFBaNy6nSjTz0pGnsmYYwf3iZnzRO2Gz+XauD4md2AHnWEegUN7dkEv9083WsxtdOUBUCclAxajiLKTIfeE1NTpK5fK5bxhVvgLZ5/EX7LqoEcWKuJ8DqsBJpnCa+ae6NyrP+o82xjZAZW/1W7YSPwwDMMwTEHg6P7/IJErne1bXhCicMmX4esfgAB9vNCcSRWQp7GldjN0qgiv96c9hFHuK2x9ViGhucDgEOjMBhiRvpWOBFxVdjPqPNNYzGXPLqjSbtCIT7Lt8ZjUkM6Bi0YtWvPpecJIP2OJYZgs9Xk/8I9MJkWjli8gVHsL/rpYkTUXBtOoRoj+jriNxG71el8SeaHbPozKK/WA0U+gIf6uwroHDgcC9bEIjYgUirIMwzAMw9zH1t8ngemcjQJ0fr2f0HYJ1tyA3GoQtpemo4SlOLVepBmpn4uM2cPZesqsUql6gDlF6Nekxc+QKFrNWr3UJcuPzeQO1Lawc9V8cSyTSfFWk6yPxGPyN5xBZ5hHoGqtOjh3+iTU3maKOxzwt+pQtWEjfPDNeIRGRGD1or8QqHM65UTFajVx48ol6A2JSPEyQ9zXmAipVCZU0h/GaA8e+TlGDuyFCPUVaHwjYJH7QG4zIcCQAKVZi7c//tarcBvNUN27YwssZjPKVqgk5pm6xqswDMMwzJNE5eq14LCYM5wpTurlAUHBQmA1olBhTJ/wA1ISL7ivjyhcBIWKVMDJkyehc0Sma3cjZ15m0qFB0xYPtb5Or/fD1nWr4Ii7ghRVOEyqQDEKjYIF/oYEIT5Xqlz5dPdLjI/D9vWrREtcRFRhUaYeGBT8UGtgsg+afX7xapxbHC481HvlBlNwYQedYR6BF7r0wJK5MxCsuZW6pM3hQIDuDiRmPTr26CVmgr714WfoOfAdHN2/B2aTSfRll36qApbMmY6pP34resU9FVGpJD5YHyei3mERkQ+1vio162DcH7PF6Bfqn3NRrFQZDHz/f6jX6PlUt9ekqDFu5Hs49N8OSGi2uUQGh8UoMu2jvp8gHo9hGIZhniSq12so7KbjVgziZCXFBBYXSpMGAaYkdHjtLSiVKqG7Qv3ZR/b+h+TEBEQWLiLEYi+fO4Ohr3dGqOY61P5F3I8ht+gRob2JIiWi8WzzVg+1vuDQMPw0cwF+++5L/LdlAxzaW+JyChp06T883WQWqgiYPeln/DPzD5GthVwpsuxTfvhG9Je/3JNFXXOTzWtXuo9fbl0zV9fC5A7soDPMI1CkeEl88PUPGP/JCPgm66GTB4oxa/5WLSRmA/oP+1AYZhdBIaFo1LJtqscgdfOkhDgxJiXIlAirXAW5zal+/myLNnjro88e6T0ip/rX+f+K8S40JiYkPBxlK1ROJzhCBnv0kAE4e+oEkoNKiJ46CjjILQZYNXfwyeC+mDjvX5Qsw7M4GYZhmCcH0lf5/MdJ+OCN1yBNOg+90qmM7mMzQG7SoNYzjdFjwGD37SkoX/e51JVvJMT66fhfMe7j96BKPAub0h9S2CEx6VG0ZCmM/X2GuN/DEhYZhc9++BUJsXdw5eI5ESyoULW6+D8tf039DQum/Q6NfxT0vhGiB57a7/z1cfjjh7Hw8w8QgQYm9/vPO7aqzm/DEwg76AzziDRt016IpSz/ew7279oOu82GqrWeF6PFnq5R+4H3J0e5/7CP0LZTd6xfvhhxMbcQHBImIvAPM/88I2iN9xufdnjPLpw5dhiJIaVTCeFYFb5ICCoJefJFzP/jNzRp3Q52uw3lq1RzC9ExDMMwTEGmROmymPzPSqxZ/Dc2rVoOnTYFRUtUQLtXuqNJq3Ze28XS0vD5Fpi3YRc2rliCC6dPQaFUot5zTVGvUdNM3T8zkDr7/RTaSRl84cw/oPWLgM7/3u0oo68JLAqpw4aZv/0kHH6jnoIH0SKozzweYm7ewOljR8RxzSolULJYOJ/6JxB20BkmG6BS9aGfff1Ij0FGsM877+fa+7Ft/Srn/HJSjk+DgxTlHcDWdSuxbZ2z9EoileKZZq3w7idfisoAhmEYhinIhISFo8eAt8XPw0IjyV56tQ9yi/07t8FsNEIfXir9lQ4H7BI5kuLjMPqdN9wXl6lQWexxyj9d9fEu9gmEtARcdGxZI1fXwuQerOLOMIxAr9HAIpGlE68hgx2afBUKqwla/8KIDa+A2IiKSPYrjN1bN+OD/j1h0LPCKMMwDMPkdfR3Z2vbpelzdP76ePgb4mHwCUV8WDnciaiMpOBonL98FR+80VMIyDKPs7ydHfQnFXbQGeYhIfGXrWtWYMPyJUL8Jb9DPXAqmzHdSDaVOQUqixZJIdHQ+UfCLlPCLlXA4BeOuKBoXLt8AeuXLc61dTMMwzBMTmE2m7B3+2asX7YIh/fuhs2W8ai0/GLrCYVVn+pyGvdK4rY6vwikBBWHVe4retNNqiDEB5eCyQ7M/PXHXFr1k0GKOhkHdu8Qx9HFw1G9cvHcXhKTS3CJO8M8hLGeMv4brFv6D2xWa6qRaR98NV6UqudHSBCGFF1JJMazL83XkAyz3BdmZWC6+1jlPjCpgrFmyUKhVp+bxN6+hTVL/saZ48cgl8tR59nGaNH+ZSgeYq4swzAMw6xe/DdmTPgB2hS1+2SERxXGO6PGoEGT5vnyBFWrUx9RRYvDkhCLhKBo94hYH6PzNWr90k+NcUikSFGFYf+ubUhKiEdoeARyC6rY27xqmQiaWMwWPFW5Ctp27ibel/zOrk3rYb27r+zYsno6MV/myYEz6AyTBWgcCY0hW7NkAZJV4YiNqISYyKeRFFQSp8+cwft9uwkF1fxIsehSeO3NdxGoi0VoyjWoTClQWHQiyk6OeEaYZUokxsUiNysZvv9kOPq0a4q/p/+BPUeOY/eBQ5g8/mv07dBMzBNlGIZhmKyw6p/5mPj1Z4g1SxEX9hRiIqsgIbQsbqv1+PK9t0Qvd35VpH9/zLfwsZkQpb4MH0OSmO9ONt8ulcHhpfSdEPsAhwPJCfHIreTIwhlT0L15Q/w69nPs2ncQe46ewKK5M9C/Y0us+3ch8jtb1nr0n3N5+xMNO+gMkwVOHT0kZowmBRSDzj/K2cMlkcLkE4y4wGikpKSIuehZdfrPnjiKFQvmimh9zM3rufae9Bw0BO9/MQ6lIkMQqr6K8KRLkMMBhc2U4X0UNjNCI3Inmk4Z/56tnsXm1cuhUwXjTlgFJAdHIzG4FGLp2GjB50MHwqDT5sr6GIZhmPyHyWjE9Ak/QO8TKsq9beScSiSwKPyQGFRSTDqZ+tM45wzxLECjTinAT/b+9NHDWb5/dlG9bgOMnzYPNapXQ4jmBsKTLsLHooXMbhOl7t6QW43i/+Cwx68qfuzAXvRs+Zx4T3QWO+LCK4iJM2Tvb4eWh1YVgknffYlzJ44iv2I2mbBz07p7I3nr8UjbJxkucWeYLLBp5b+A0tc5IzwNNKJEqwjGumWLMOD9kZl6vBtXL4uM/MUzJ51lZmSsJcBzzVvjvTHfilmkj5uWL3ZGiw6dcPv6NRGxpv56ylArzdpU49cImdUEH1MK2rx0b/7r42LtkoWY/st4WGRKSGQypAQWTyVwR+9HQkAJyBLPYs/WjSh9nxFzDMMwDONi/86tQkxNF1Y+/UmRSKDxCcf1Sxdw8ewplKv44HGoRoMev3z1KbatXSnMvLBVDjuiy5XHx+N+vu8I1JyiUrWa+O6POaICTp2UKMa8De7WQQjFaQPSlIs77Ag0JaFWw+cQFpG+BD4nuXrxPD59uz8MdglkVDUXHA2bzGNevEQKTUBR+NpMIoveuEUb5Ef27dwqRuARTVq1hULBLtqTDGfQGSYLJCckwCShrLn3viCrXAVdihp2e2qhNW+QURzRtwcuXLqExOBoxERUxp3IylAHFMWuLRsxesiAXBOjob4n6qUvVa48Grd6AU/XrINwzXX46eNFdF1it8HXkIhIzVUUKxmN1i+/8ljXR+dlzuQJMPiEQGa3iv+9vSfkpJsVAThxcN9jXR/DMAyTf6HWKbIpNrkqQ1svbpeQ8MDHoiz5l++/jW3r1yDZvwjuUGtcRGVR6XX52g0M79dDaKjkFjTvvHT5iihZphx6vDEYAfo4BGpuigA8OeZKswYR6itQwYo+7wx/7OtbMmc6LA6JCMZTBYPX90QigVYZiFOHD+RaVcKjUM00D0dW/OT+vV/L9Ekg5smCHXSGyQLhUYWgspmdme4MSsB8/PxFj9eDWPbXbFEST6XxZlWQMDAkxGLwDUNCYAmcPHwAB3dvTxWBJ8X4uZMnYNn8WY+t152i6l/9+ieeb9MOIfpYFIo/jULxpxCsvYW6DRpi/LT5jz3Tf/7UcSTG3YHeJ0z8TuctI+wSiVt0hWEYhmEeRHhkIWHnZXfLutOiuHs5ObcP4uj+PTi8ZycSA4qJ6SekjE723qwKFJNQ9HoDlsydfs9m2e04uHsH5v/xKxZMm/xYdVReHfgO+r/3ESJlFkQmnkPhuJMIS76C0sWLYNyUOUKQ7XGzbf1qaBVBVFwIx33cFtoH0LnLjw66zWbHsg1HxLGvjwKtGlfO7SUxuUyO109cvXoV06ZNw9GjR3H+/HmUKVMGK1eufOD96A9s6tSpmD9/PhITE1GpUiV8/PHHqFGDZwIyuUeLFzth5cJ58DUmCUfaE6nNLC63KeSil0j5APXw9cuXQKsMElnetJgV/rAr/bBl9XLUa/Q8Nq74F799O0Y46RKlD2A1Y8r/xqJj917o9c772fb6KKuvSVEjIqow/APvqbb7+vmjz5DhaNi0BW5eu4rIQoVRqXpNFCleErmBQeecu07nziL3FWX2ei/KsyLTb9WL7ADDMDkL23umoFD72UZQqHwQoIuFOqhE6gothx3+ujiQG2gyGB74WGTHHUpfmLxMQiFBNo0yCBtW/Is3P/hUzBn/cvjbuHPzOiQKpQgSzPz1f6hSux4+/OZ/2fb6dBoN4mNjEBAYJBIPntVzL3TujrLlK+H86RMICglDdNlyqFi1Rq4oipMvYDIaYAsIhUMiEe8HVfF5E7LzM2tRonS5TCVI8hp7Dl1CbLyzvL11k6fh58vTZ550ctxBJ6d827ZtqF69epYiW+ScT5gwASNGjECFChUwb9489OvXD8uWLUOJEiVyetkM4xVfXz9hlIOo/MtmhsGHjIYUKrNGGA6HRAaL2Yx9O7bguQf0QdHYFpsqA7EViQQmiQJJiQnYvWUD/jf6Q1HGrQ0vIXqvyPH0MyRg6V+zYHc40K77648swDJ70s8ia+/Kmjdq2VaUs9EoOVJEp/EqrsqBqCLF0GvwsFxz0F1zXJVmHfS+4UJ13teQAIOvx/l0OBCovQWHzYqrF86JLEadZxrx2BKGySHY3jMFBaVSBYVCAV+tGhK1Q8wGp7J2hcWAAH0sFFYDJHIlNqxYLILV9yMlOQlm2m5n4ODaZCroNfGIuXkDHw54DRqTDUmhZWCR+4nrSV395NEj+Oyd/vhw3C+P9LqolJ4c/u3rV7vHxFauUVvY8wpVqmPGhPFYu/QfmI3OCgGqCGzf9VWUq/Q0FBQweMxQUID2GcY4NVICioh9VpDmVrqgiVCiN6XAaAgUCY0WHV4S72F+4d91h93HL7e+/+eJeTLIcQe9WbNmaNGihTgeOXIkTpw48cD7mEwmTJkyRTjkffr0EZfVrl0bbdq0Edn4MWPG5PSyGcYrVFZOJkHnGyYcZOrVIkQkXRmIlMBiKJR0PlPl5xS11iR4VxeXmfXwNWtw7uRxjP1oGGxSBXQ+4W5hFCqRIxV5YuXCuWjUpj0KFy7yUO/anq2b8OXwwbDKfaEhtVqpUoxW27pxvSizs9tt0BotSAkoKjL7MrsFxsRE/PDZByLI0PHV3o/901KoaDHUbPAcDh08iNigaOh8wxGsuQVfYzKMqiBIHHb4GpKca1UG4MTJUxj9Tn+81LMPBg4fxU46w+QAbO+ZggIlk/Q6rVBxJ4HU8ORL7ussch8khZQSmizxdx5s6yMLF4XKcbc1Lo2TLrFZxeNIZTK83aOjyGzTPkI453dvS1Ni4mUKOC6cw5G9u1C8xMMFxu/cuomhr3eBmp5DFQFLgB9kdjOOnz6Dj9/sjeKlyuDG1SvQqMJgDCsh1utjUmPx7Gm4evECxvw8OVey0x269sTUH8eJfVdyUAmEpFyHIuEsjD6hsEulooJOadHDLPPBzYQUTPz6U6xbuhBjf58J/4D0VQt58bO2dJ2zvF0mk6J9i2q5vSQmD5Djf2kP88d86NAhaLVatG3b1n2ZUqlEy5YtsX37vZ5chnnchIQ7x4mZlYGIC68oRF6SgqMRH14BySGlIIEDDrsNoWlUTukL+PjBffh37gwxXuX2jWto16U7/Mwp7tElBGXGQ5MuISL5IhwOO5IsUmhk/iIEQJf561LPG9f7honKlI3LFiPuzu0svx5Saf9xzEgY6fUElxYGz6L0F+XiscGlodbqoNXqxDGV9JM4Cym5k5Ekp/jPn7+HRp2M3ODtkZ8j0FeFQilXYJPIofGLEqVvgdoYEWW3yRRIDC2L5JDSiA0uI6LvS+fNFCq6DMNkP2zvmYICZW5p1BVVyJF9Twgpg6TgkogPLYeE0HIiWO0Dq1dF85tXrwg7T/b+xOEDaNmxE2AxCWFVNw4H/LW3EZVwWowxNUpUiLfIRZaexp6Fqq+IUnoXJI5mVfpj25oVuHbpwkP1WU/7+TuoNVrEBpWG3j9S2Hqy+XFBpWFQBIjHjQ8oAW1AITHz3KrwFWruiYElsH/HFuzbvgW5QbtXXhVVChHqq1BadEgJKCySFf4kZqeNEdWMtCdJDCuHxJBSYlb9+dOnMGX818gPXDhzChevOpM9TeqXR1gI7fmYJ508qeF/6ZIzUkn96p6ULVsWs2bNgtFohI+PTy6tjnmSIVXzUk9VgOXqTSQoA4XIiyfkQPv6B4i+cRfnTh7DF+8PRiJl1d1iZg40aNIcxaJLA9evQu0TBpMiACHqa5DbzcI4JgWXutefTsZcH4tA3R1xnYlE5e72r9khwbolf4uf6rXroe97H6HC05mLwO7dtlk42ClhT6WL7NulCmh9IxCovX1XnMUDiURk8P0TkrB17Up06PYaHjfFokthwrzFQjRv27pV7nI9WicFTYTwnsd69X4R8LVosWTeTDRt2+Gxr5dhmPSwvWfyKq06dsbiOTOhszudWU9URjVgNqB5+5fcl2lTUjBm2CBnqxjZU/qx21Gy7FNo1OoF7Fi/GnKbEQYfZwUeadZQAICcSovi3uMrTRqEqq8K26sJLOa+3CKR4+yJoxjSoyOKFSuB7oOGoEWHlzP1WmiM2q5N65HiF5Ve94bGxvkXFplomcMKS5r70n7DrvLHmn8XoEHT5njckJ4PZcP/njYJKxfOhzYlwW3r6VymBN07R65ghto3AptXL0e/oR8iJBfmtmeFrWtXuY87tqqeq2th8g550kEnZWvKmKvSiGwFBQWJqKFarX4EB90Bs9mMvILFYk71P5P3z0/fdz/AmKEDEZZyFRq/SCFSJreZRZmanzEJvT74VJSr0edsw7LF+PXbz2GRKqEJKS2i7vQZpFLsvTu2omKVamjcojV2blwDmzbG/RzJQSVTG1FyiP2ioDJrxYxSl4NOCrNShx391WqE2u1Ysm8PPuzbHV9OmiFmnD6Ia5cvij46m9z735NZ6S+cc4pQ29OIstDvJFhH1QAZ/U3RODQStvPx8RV97dlNeFRhDB09FgOGfwJ1Yjxm//4zduzcndo590CvDML5k8eg02lzpZ8ur5IX/86erPMjBiPjSSTn7D3b+vxEXvwOat+1JzauWAp7yhWofSNF+5TUQSNGkxFkiEOdRs+jfJXqwv7dvHoZw3q9AqPJJJxqMfoTEigtWtiv30BifBw6vd4f65Ytgi7pgjvgrQkonMo5Jyjwr/WPEpVgWv/CTtV3Kjc36/CswYAX9HqsNhqFNg0JvdHjPogb166IdjXnHiQ9VB1nk8ggs5m8Xm+QqnDr+vUMbT39rZJ4q0KpFD/ZjUQqRY8B7+CVvoNEC+HebZswjSr4Ar239hlVwWJPderIIdR5rgnyMpvXrHAft3n+aZhy0EchjSTP/5nHe34ccGTa0udJBz0nsVptiIvJeilwTpOQiVmaTzJ56fwULlkK73z6NeZN/gXK2Ht9aX6BQXi57zDUeq4pYmJu4/zJ4/h17GjYJTIkhpbxUB2VOMvFpQqcPnoIrTp1w4s9++Dnz0fi6pXLsEoVGc75JKNPvdYusbYgKue22/EiGUYAzxkM+CgqCr999Sk+mTD1gb3WVptdiKhRab3YBKRBanPG0u1exphRn7fDYgKkMvF6PUmKj8PaxX9j96Z1MJuMUChVaPB8C7Tp3AMRhQojJyDFWxpVkibX75U7MTGQK9Kr5z/p5KW/syfp/ETCBsjyn/JwXoZtff4kr30HffDtj5jx8/e4cOq4+zIKwD/Xqi26vjEYd+7EQKfV4NNBvWE2GkRLFbWBuRDtcHJfSJMuQKvV4Lvpf2HR9MnYSk6ZwwGjihz59Bh9QkTFnIJ6q1WB8DMkQuKw4hWtFhUsFtQymTA9MBBzfvsJlWvVc7ffZYT+rto86bJY4ZvuetoDULCfhG69obBb4evvn87WU0Bl84p/sXnVUiTHx4l9ConOte3SQwQvcgSpDDaXmRd9/elv4rooKTkp3ZrzEglxsTh19JA4rlQuCiqpCTG3bz9xf2d5jYQcOj82VVVkNleVJx10ipxTlI7E4jyj6hRpJ4cjODj4oR9bLpehcMTDiWnlBPTlRh+E8PBwzujlo/NTuF1HNGvbQZSyxcXcQlBwKKrXa5gqcjzxy1EiVqb3DfU6EoSMOI1eOb5vN5q1aS8i0MIRvq9TLXGX1wUY4oUwyrDkZOGcE/TsPdVqjL52BdrkRDxV6f4zS1u92AkL/vxN9MZRT1oqqKzekCBK8CiYkBa6DznpL3TqhigPgTrKqI8d8TY0Gi00ymBYgyIht5qwY/NGHNq9A99NnYsSpcsiJ6j7bGPs3bpRZPxdgnqe+JtTxKbhYUV2Cip59e/siTk/8VfxpJJT9p5tff4ir34Hkfjq/2b8LfqzL545JQK71es2EP3pLpbMWQ29NkWIx3k65y7I/uuUQUKQlbRTQqnkmirkrGYxOswbLv+T1OKp4s7XlIz2d51zF920WqwMCsKJ/XvQpc+A+76OQoUKi1J7840Y57i3NM9L5fb0rFQRmBa51QClKQVtOnZJJUZLWUZXSb9eGQxTUAlI7TacOHkap4+MENVtz7/wInKCZ5o2x8wJ44WIXdqRt4SPMVm8V/UbNUVQsPcgSF5g14Y17uOX29RG4SI565/Qe+b+O8uBSof8jiWHz486xXsALN846K7e88uXL6NixYqpetWKFi36iP3nElFOl9cgg5QX15VXyKvnp3bD57xenpyYgBMH9wl3mkaoZDhKDXJoNc4Sz0JFiuLa9euQWfQic+1tPjoZIzLcoZrrKGe2oG+KGjXTlOJUuvt7wp0YPF29lji+fP4sDu/ZBZvNKkapVK1dzzm+pFhxtOvSAyv/+cuZofcNEw45PT9F76mknoSfwjXXofYrJERjSIiNovmB+li80KUHikc7R565mDTuC6i1esQFl0n1GnT2cESqr2DC15/ilzmLkRM0b/cSZv36E2zam4gPLHEvMCJ6+OMhN2nQpVf/PPlZygvk1b+zgn5+LE9oeXvO2nu29fmRvPodVK5iZfGTUQ+xTSr3GhR2QdfpNHHitUUVKQrYrG6bTkJtaaF+cLL1ZIepSq6nRiMq5TzxdzhQ0mZD7O2b7nNGe49dm9aJfvjCxUugYdMWooeb6PP2+/jy/bcQrLkJjX8hp3122IU9D9LdgY+vP8J1t5DkiIJJSW1ipOKeghBDLEqUKSf67T3fm+V/zcLJQwcQHxwNi0dggsRr6Tl+/WY06jd+Pkd6wEuUKoOGTVpgz85tIqhAexMXCrMOQcYEtOzYGRGRzmk3eZXtHg56lxdqQ/WYPvvkfD6u58qPKHLo/EiyYOvzpINeq1YtBAQEYM2aNW6DbbFYsH79ejRu3Di3l8cwD4RGpRBktKlEzVuEl5xGlc2IQkWKi19bv9QFB3dvF1n0YM0NJAdHC2fZM2NNDvPzbV/EljXLMSI5CSXvCqN5ckfmjNAFBAUJYZhxH78vRrO4BOpIHd7Pzx+dXu+Hzr36Y9CIT2C32bB6yQIEG+IAuQIOs1HMEH3rs2/ESLMfRn8ERdwFSGRyOGw2yOQyUZbff+iH6dRrj+3fA3VQ8XQBBnKW1X6ROHfiGC6ePYWyFbxvdh4FH19ffPXrVIx6qy/kieegVwaKFgM/qw4SixHd33hLzHdnGCZvwPaeye9oUtRCk0VuMXgdpebKhNO4NaJpmw7486fvYLLLhAo5jVTzbGujx6EAeJ2GjXBoz05012jQMY1zTtio+EYmQ9WgIKH3MnPiD1gyd4aw51QGDipbl8vRsElzMV604fMt8N6Yb/HbuC/hm3hWaMjAahGTZ1q/9Aq69XsT338yHGeOHaaRDM4nsdtRo/4z+Gjsj1B5BMuo4m/Z33OgVwWncs4FEolQWvdNOIP1yxaha99ByAneGzMWIwf1guPsaVh8gmCWKKCymyA3afF0rboYOGIU8jKUnNm3c5s4LlksDDWeLpHbS2LyEDnuoBsMBmzb5vwA3rx5U4xPW7t2rfi9Xr16CAsLQ+/evXHr1i1s2LBBXE5lboMGDcLEiRPF9eXLl8dff/2F5ORk9O//YDEMhsltwiIjRXmVCUpRnqbzi0gnxEYqrhKbBVKJRGS4n3m+JarVbYBjB/ZBadYhMv6M6DmnnjCVOQUKqxFtXu6KNz/8DAd3bsEynQ5D1Op0z700IAChIaFCJG5Evx64fPHi3bntSaLknnrizBY75k6ZiOV/z8G3U2ZhyKdfoWu/Qdi+fjU0arWIvDdp3c49Q3T2mm1iJvrNa5fh5x+A+o2beY2KX7lwVvwvSui84Lp8/85tOeKgE1Qh8OfS9Vi7ZAF2bl4Pk9GIYtE10Om1vqhep36OPCfDMGzvmSeTYiWjEZuUArndKErF0wbkqUTcx6hGkeJP48Cu7ajV8Dm8/uZQzPz1fyKAHJF4Toia0Yg1hcUo7D21gY387mf8POZjrN20Du11OtHC5skOX18kOhzC4Z8xYTwWz5kOnU+oyHpL7VahZk6Pv2PTeuzesgHDRn+DVh274NnmrYWtj7lxHf6BgWjc6gUULuZ0Dn+atRAXzpzEycMHRZUdlfNHl30q3Wsm8df4mNuirN0bFJCnueQ7N67NMQc9MDgEP836R0xx2bB8MeLjYhEcWgrtX+mBJq1eyPM6Mzs3rYf1bstCx5Y1HqgZxDxZ5LiDTrX8Q4cOTXWZ6/fZs2ejfv36Yo4zRf88GTBggIjQTZ8+HYmJiahUqRKmTZuGEiU4wsTkfXz9/NHshY5Yv2o5LDIlwpMuQedHKrCBQojF15AEP2OiyJAv/2c+lv09G7UaNsKIr8bj7z8nYd3Sf8TYMHLiqSgmPDIKA4ePE4aUvsRffWsYJn//FXwcDnTWahFmtyNBKsU/AQHY6OeHdwYPw74dW3Hp7GkkBxYT5WYkOpMSUNQtBielPu2UayLbPGPFJhQqWhyv9Bno9fXIZDLUa9SUYv/3fd1KlTMIQX1oVD2QFrqcOLp/D7r3fws5RWh4BHoMeFv8UH8ricR49s4xTG5y/vRJzJ48AW06dsazzVoVmDeD7T3zJEJtYscP7IVREYAgzU3hkFPZOtl3lSlFzOsmDh/YJ1rNoooWx6fjJ8IvIABzJk+AJjlJlLrDRFVgfnj5jcEim00VYd0HDMbwHVswJjIS/ZOSUNZqhVEiwRZfX/wRGopnGjdDWGQU/p03ExrfSDFJRgjThpVxZ+VJAI5Gtv30xSgULRGNKrXqom2nbhm+nnIVnxY/90M4vxKJCAR4xeEQivfXL18Se3xqlcsJqHy/5YudxI+nrc/rzjmxxUO9ncerMY/dQS9evDjOnnVm1TJizpw56S4jJ4Sy6PTDMPmR1wcPxf7d25GUlAyzTIkA3R0E6pyj1Ki3zKjwF4qvBBnnw/v34psP3sH/ZixAn3fex+ljR8Qtyz9dLV22+sXur8NiNmHupJ+xLCAAgTTH1OEQZekDhwxHu1dexZhhb8KqChBl8dT/pg4snqr0zi5TIjGopCgF37xqmRgp86hUrV1XlNJTT5t7/InD7lR8p1JzQ6LI4t+8fu2Rn4th8hMUcN67fQtm/f4Ldm/ZKC67ff1agXLQ2d4zTyLPNW+NOs82wYHd22GW+8LXqIa/IdFt62l8WUJYeThkclHqbkuIwUcDX8fvC1cIR5lUvPVaLYpFl0onoEqO8pe/Tcf4kcMwRKFAkEQCI7V9Uua8dTu8O3osNq5YArvdIcrsSaU9Max8qpJ5CsqnBBaDj92ExXOmCQc9O7QCChUpBsudeOh9w517C4cDEodNBCYUFoMYP2s0mMVYtEgOkKfCbDJhx8Z14jg4NAyN66evUmCebPJkDzrDFAQiogrjl9mLMO3n77Bj41rY7+qy0r/qgGIw+t0rg6PsdoJUjrPHj4pScspWOzPW3qEAVpfeA9CmUzdR3nXtyiVElyrrLEsPdJaRpyQnwQI5VGYNdH53DWgayEkn1dl9O7Zki4NO0f+g4GA4kuPF65TZzaLcjp7ZLuQxHLDIVCIjnxaqoqEe/KP79sDusKNy9VpC4CY/RMIZ5n6qsKcWvY8f/tiAY6dvpLru/PEDKHzzd9xBQyrY5JPIMPkQmVyO0T9Nwvw/fsWKBfOg0zhHmpENNKiCkUJl4HftL5WdxwdGQ558QWS93/zgU1R7QOsVlZnPWrdTBPZOHjuMiIhIPNu8FYoUd04j0aiTIZEroLToYFak7md3I5FAqwwS7WUULMyOcuoKVaoh5tZqBKdcg10ih69JLbLm9LopEG+VyCF3eM+wXz53Bts3rIZOoxUtAs3adRQl608KsZs/F+P5iJeaV4BCwe4Ykxr+RDBMDkJR45HjfsZgmsV58wY+HPAaEuCXyjl3YVb4w670w/Z1q+7rnKelRr2GqFizDkqUiE6lsFq0REmcPn0adocjldhcWshxJhHG7IKCBLRJ8TfEi/FsmoDCIoNPYnmUQVfYzHi6Wo104nKj3x2AW9euQKL0FaNnls6bKUr3xvw8BU9Vvv+4OIbJa6Sok4Vg07ypkxB7+1aq60qXiMB7b7RA367PIMDfB3fic22ZDMNkA5RR7v32++jxxtu4dvkCZvzyA/YfOoSU4PQjPSmjrVUEYfPq5cJBzwxWqxVlKlREVImSKF2mXCpbX6hYcTgsJkgV8vvaejEy1WbLNgednGrqZReK8xKp6L2nAIHMZhHteTTulOazh0cVct/HaDBg/KcjsHvzekjkSjhITNZswLRfvsfgjz5Hm05d8STw79rD7uOX29TM1bUweRN20JknGhI6OXfyuDBaZcpXFKVGOQHNTKXosMlogC0ggyixRAILZELZ80GcOHxAROupn41Q+viKHqzXBg1xl8OToNymlUtFllxl0kDvF5n+Kakf3qoXwmrZBY1eW75grgg4JIWUcqvHm1TBMPiGIzzxAjQp916jXqcV5X7xyWokhpZ1zmGVSCC3GmFLuSVUWqcsXi0qEhgmr3Pz2lXMn/oblsybJT7bntSrUQojBrYSGzKa080wzOOB+qDPnz4BbYoaRYqVRNGS0TnWE01l6eQEWyQZb7FtMgX02vQir2mJu3Mb86ZMFG1oVI1DjnXdRk2FyFy5Ss4+8WeebwVf/wBYTFbILSbRc+7SmvHEz6JFmfKVsq0fnMr6qWpObzQhIbRsqsw9jVoLTb4iRFpJVd71nD9+/hH+27YJyYHFReWgs4/dggBdLH756hOxByO1+YIM7TeXbzwqjn19FGjZqFJuL4nJg7CDzhRoyLit+/cfXDxzSsw1rNfoeSG0RsZi7uQJYkyIUe8cXyKTK/B82w4Y9MEnCAikGaDZCxnWqMJFYUiisWtebuCww8dhRpES6SPunuzZuglfDX8bVrkPNIHFRJaaSttWL1koFGJ/nv2PcNKfrllHOO0bli8RvWA0pi2VuqzDIYRjJA7HfQVjskrMzeuipF0TUMTtnLugTLrWLxIH/9shyvIoaLFxxb9IiLuDeOqb85gjS68vPrAk5EnnsXLhfNGXzzB5lZNHDmHWpF+wceW/qURP6e/+xZbVMHxAKzxXrxwr9TJMDgXbSXSLWsRsNqsIOpNieVhEJLauWYEZE/8n5oW7oDFcb380GqXLO0f5ZjcUAPA5dDDDsWtKq0Fkvu/HnVs3MfT1LlBrNNAoQ2Dx9RO2fO+ePTi0Zxe++W26KI8nMbkhn3yB70cNF+XlJFSn9iirJ3yMyVAYU/DSq72y7TUa9DpYLGYhgJuurF4iFXsAVdIF7Nu5VUypuX75InZsWAN1YDEYfe/NfrdLFULAVmG3iOkyBd1BP35oP2LjneXtrZs8DT9fLy0JzBMPO+hMgYXGbvz8xSeiXNoo94UMDmEcZv76I0qVKy+cRK1vOIxhRUW/FKmtblqzQszo/nHmAhEZzm7avdIDM3/9CTqLAVZFai+dSsKoTI0y3xlB0ejxn30AgzIASYH3DLBZFQiDNRS4c0WMbhk2eqxwBIaOHouiJUqJjB4pufsYk0QmmzLnAZYUSC1GDP18rJh1nl1cvXBelK6lfX0uqOfdpovBretXUSE4BNs3rBGj3zydc89RLTpFELauXckOOpMns3Ik9LP4t1HYtudcqut8VAr0eeUZUcpevsy9Ek+GYbIXmlYyanBfqBMThDCq3SHB3u1bMW/yRLTs2BlrFv8NkyoI2pDSws4oLXocP3ES7/fphh9nLUTppypk+1tCpdqr/pkv7LreLyLVdXKLHr6mFLTvMvi+j0GTWtQaLWKDSsNOpeBkP0V2OhThKdcw/rMPMXPlZqHp8nzbF8X0mN++HYO4OzFQJFAiIEyUnvtatFCYNHj+hY5o3v7lbHuNJHJJ02ZMgWnmoN+F9gC0F7h64Zxw0Hdv3gCJTC7Gx3rtkVeF4tLZUyIwkZ17krys3v5Sq9Ttfgzjgh10Jk+RGBeLzWuWC9XP0LAIPNuitRgzllVOHNqPH8d8DL1PKDT+hd3lXjKrEY7Eq0jYvR3JQSWcJVZ30csjhfPoOH8Oa/9diJde7YPshtTXt69fA5w/B40qRGwayFmm0SgU4e7SZ4DXmaMudm1aB71Wg5Sw8umi8hTBTlGFYPOq5Rg4fJSYV06Gu/sbb6FTr374d84MbFm7EjeuXIRMJkfdRk3Q6bV+qFyjVra+RpWvD2C33lVuT19KRyIyxMqF8/BU5aqiDNgqybjcl5RpDXp9tq6RYR4FCpSt/OcvMSrtyoXUjnlkeCDe6f083nq9iThmGMb73xAFzEksTKFSoXbDRgiKSN+G9SBIaOvjt/ogWWdCQngFd6CXyrwpKL168d9i5BllbV020yhTwqQMRFTKZUz7+Xt8/du0bH+LqMz9pZ59hJaKwmoUTqlr7FqgKQlPPV1FtINlBO2B9m7fDHVAEbdz7kYiRYpfFBQxF0WbW51nG4uLGzRpjvqNm2HnhjVYs2SBqOqxWi0oW7EyOnbvhedfeDFbx52pfFxjVTMatWaH3WrBhhX/iskyBoMeoNeSQZ882XpXNURBZsvaVeJ/qVSCds2r5vZymDwKO+hMnoD6tainev4fvzkVQBU+kFhNIhvc/MXOeHvk51l6vEWz/oRd4SvKpjwdWZvcByapCtT+aVQFe4340qzy1YsX5IiDTln576bOwaxff8L6ZYtgSnLOR40sXBRd+w4TRux+XLt8UYioeVVpFUJzAbDoYhEXczuVo0/j17r1f1P85DS0SZjyw1iRraee87T46RPE2BkqbY8oVBilyj6Fy5evICWDUkAfmwHRZSvn+LqZ+/990qaJ5tx7U+B/UkhOTMCSeTPx97TJSEpIrexWoWwhvP9GS7zeuQF8fdJXgzAM44TK0L8dOQw60ltR+QlnesG031GuclV8/tPviPAQFXsQW1YvFxNLqEWK9FZcUFDeqAyAyqSGxj8qnW0Ro8dUYaKSjhIDJEia3VCgnJTWF86cioQ7l8VlKl8/tO3+GnoNHuZ2cL1x89oV8b1LNt0bpNUikcpE2bjLQSeocq5RqxfET05DI+GKlIiGKS5JVPGlxdeYLPTcb9+6hU/f7o8O3V6Dw2yAzGryuoehVj25UlmgR7JdPn8WVy+eF8fP1S2HiLDAvGfrTRbIZFIoWVk+V2EHnckTLP9rtugJp/5k6mci40lGm1S/Ny5bhPCIyEyXONMXzP5d26D1jfTq8FEG1yL383odYZH5Cgc3p/APCMTgkaPR993hosyb1F+LlyqTqci2L5Xd2ywiMu0tCu2KZOdEeX5mKVysBJq2aY9t61aL2eciECKRiPfTXx8HH3OKmMkus5mwaPY0jP5xkthk0Xutp3FwHqiMashNGnTIhhFwTNZRJyVi0ew/sX7xAqRoUqBSKNHkhRfRte8gMbP3SeHqpQuYNuEHbF65DCYTTSG+R+2Gz2F0/2po36JatmanGKYgcuHMSXw+dBAMMl+oXfO6HQ4xDvTCubP48r238POcRZn+W9qzbbOofPN0zl3QTHCyQd6uI4QgqcOBuNiYHHHQhf5E99dF4P3GlUsim12sZKlM2WcqVxePYbPAoUl09rKHFnFrWIgKNbsdPn65Z+tpLT0HvoMfPvsAAdIY996N1upjUguNG7L/Yk76qePw9fODX0AggnR3kJSmR54U34NMSWjeoaOo/iuobPYsb2+dd8rbbTY75vx7ADP+3oMLNxLFW/Nc7dIY/PpzaFy/bG4v74mEHXQm1yGRkXlTfxPl6NqAe0rd9EWv84+ExGHDv3Nn4JU+A4Rzm5m+UFINJcPsDalwcJ0zyb1BjmNQ2D0Bk5yCDHDZClnLDD/TrKWoKqDIdCrBN8LhQIApCaUrVEJUkaJZKjWkXrmV/8wXAm+0LnKwO7/eP53SLb1XJFJ3/tQJMZ+87nNNULFqjXTCV9QDHx97BycO7oNNelsI2cltJrGp0PgXgsE31KncGh8HdWIiXuzRSwRpVFYd9EqnQ0+jW3xNyXiuRRs825xaHZjHCWWVRvR+BSkxt9FSo0F5iwW3ZDKsXbYYu9avxrhp890qwgURCvQd3vcflv32AZatPyp+d0HZhS4v1MLwga1Qt/qTE6hgmEflnxl/wCqVI1E4aHedcIlEtHslSqQ4f+o4juzbjVoNnsvU45FNolGhGdl62j9I7FahZ5IWsklESA5Nb3FBVUf3a13zRpkKlRBeqAjUNy7DHHd3TKNRA0lRZ788BbSlMqkoac8s9B22e8sGUXZ/+tgRUWJdo94z6PR6PzGuNe1tTx89jAO7t8NqsYhRpw2bthB235Pm7V9CYnwspv0yHv76eFjkPiIwIrNbYVQGQR1UXLzPdqW/eO7hX36Hb0YMgVx9GVpViFvkNtCsRlShQujzznAUZLbeLW8nOraskWec87c++Qdrtp7GM0YDOhqMMEgk2LT3PHocuIyxH7RD7851c3uZTxzsoDO5zqkjh6BJToI+tJzX62lcR0BCHA79txONWrbNnDEsVx7nb9xJ58RKbWZhPOR2CxRmHSxK/zTXW4RT2OrF15EXoZIyclh3bd7gFL9TucaUWBGguyOEYGjUWmZnnFLZ8shBvXHuxDEYFX6wKYKgN9uxeulibFq1DOOmzBIOuGu029cjhkCdGC/K7OGw4a+pv6FS9VoiC+4a70ZQ6d6rbwzGqIP7RK8fQRswg0+ou5+OlFslUqnoIaRZsKXLVcA/s6aKWegEbU5efvUj0cfnyqbQRuHYwb1ISUpCVJFiqFS9Jqti5xBTxn8NU8xtTIyJQWEPVfKOOh1GRUXh+4+GYsqyDQXu/JMCO80nJkV2Utv1JMBfhTe6P4eh/ZqjVInUwk8Mwzw4eL5r83poVRFeK8BoNKdD6Ycd69dk2kEv/3RVHD98EMlpq8ocDijNzgkt5Dh6Bv9d1/vp41G24tMoVPT+auq5Ae1jXhv4Dj59d+C9C2OvwOEXAj+VHEH6WLzwSg+hUp9Zpk8Yj0Uzp8Km9IdJRnsfB/bu24v9O7eK6TWutj5q3/nivbdw9vgRIfIGqUyUpoeER+KzH35Np1vTpfcATP9lvGgpoD5yi8QfBp9gWBX3svsWSKHTaIRYHLX5zfvjVxzZu1tcp/LxRavOXfHqwHfc+wgKEFw4fRI3r14WWXcKINAIu/xMfGyM26ZUqVAUZaKzrrmQEyxcdQSrt57Gp4mJaGi8VyHWTq/HlKAgfPrDKjzfsBxKFs35xBVzD3bQmccKZV6p9zr29i0Eh4YK5VGj0SCus8u8fxzJkSPSzhS+Hy/16C1maqqUaph87vWaU1aWMMt9Eaq+IsaAOBVFJVCZUxCoiRGGu2aDZ5FXGf7l97BZh+O/rRsgUcTCQQ6vySDmKr/16VfCAGaWOb9PwNmTx2GRKqAy3zu/lJEwOBz48v23MWfNNty+cQ2fDO4HvUOO5LCnxAg0V1nimZMnMOqtPpg471/I5PfeQ9e4ONp0eY5U8VSypRK9oiWihZNHqretX34FSfFxsDvsCIuISlXmSJ+b6RP+JwIE7ucoWQpDRn2BmvWfeahzyXiHNmgkSDggOTmVc074Oxzon5SEkbKrOH5wnxjzUxCg75elf83B3Cm/iv5PT4oWCkavl2tj2MA2KBThRYGYYZh0zgiNOCUni7Ku9Ro1RYMmLYTqt80vg62nRCL0SfQ6p2OdGV7o3B2LZ08T5dQaD80ZqoRT2M0wqIIRoCetFwf0vhEiQCy3GBCoi4HCakDl6jXz7DtXuERJWCyWVJdJrh9HYGQkmnfsjEEjPsn0Y1GvPTnnZrmPSE64XGeqC7LKlJgy/huRTS8eXRofv9kHV69eQVJwtDPALpFAbjXCqr0tbP1vfy9P1eJE9rtQsRK4kqBFCmXMvY2QtZtEeT9RpVZdfDt5FjQpahh0OuGUezrf504ex89fjhICgi78A4PQ443BItufX4PCJA7nqsR6qXXe+dzNWrgXdc2mVM45QWe5j0aDTYEBmLfsID5+q2CPv8trsIPOPLbI+cRvRmPtkgWAwgdGiQIq2MSM7orVnF9UFO32VFV3QeVPRIlSme+DodEqh/buwo71q2E2BcGgCBC95wHGBKGkmhRSGsGaG2JeKP24vowsMh/xf14WwqKZp6N/miRGy9CIMjFPPCQMHbq+ivAs9NGZTSasWjRfvDeQS+4a4wBI7/b+++tjRena3h1bcGDXNphtdiSElLynzH63LDGeouvnzmDfjq2p5pdSL3r1eg1x5MgRcTuXkr7AbkewLkZE5D2DIWR4vfUCkhLvxK8/E8EUXWg5odRLmyvLnXh8+nY/YewLiqOYHWg1Kdi04l+cOnpYlEFSAKNJ6/b3FSXy5NqlC7DZ7aiVxmC7qGo2QymRiA1Ufj/vcXdu468/J2PhrD/F35In5StXwSdv1MXLbaojMSEOIUG51+/JMPmFzauW4X+ffyTGlxplzhGnOzeuRWhEFILDwsX8bFJWTwvplND3eonSZTL9XCTCNuSTLzHh68/gazOKsZxUXeZnUovrdb4RsMpVIoseoI8Xa5LAIUqraeRX6EMoxz8u5v0xKd1l1L5XvcFzIlCfFZbOnyVsN81RJ/Fc116LesWp+o7O2bL5s1D7mUa4euEs4kPLpsqAU1A+IbAkFOqLWDp/Jt7+eEyqx2/f9VWRRddZwlLdj/DXxcFhNaPFi51SXR4YFCx+0oqoffBGTxjsUqiDo2FR+It2OJ0hAX/+NE5U/fUcNAT5EarMcvFyNvWfW602rN9xFut2nBXibpXKFkKPF2uhUETmxedOX4rFAL0zUZYWH4cDVQxGnD5/J1vWy2QedtCZx8LCGVPE6DJ1YFEYfMKcUe67GVjK4AaFhMGuTxDRWk9HjnqWqZSrWKkyopw5s5CDPfLbn4SA07K/5uDKhbOQy+UoU7mKKNuiL/zk4GghTKKkzLHDAYvCTwQDVIZY4Vw+apks9dHF3LiOoJBQobLqEn3JLqhHjX7MZjNiYm4jMDhrmT3KihsNBtGblxhSxn3e7TIptAGFYJPKEay9hQO7tou+Ka3Y+KQvSyQDSv1l29evSuWgE4M/Go1hvV5BlPqSUMwlI09zYUkoTuJwIDlBh8HdOuCV3m+gRYdOXiPjtMY/f/pOaBSkeIzKIWGgBIU/ItSX8cf/vsWvfy3N4hksmBzasxPfvD8YJoMRlawWWCUS/LxmBWb9Mh5fTJouxv88CFc2Q0sVDGky6AT1p1kdDjEeKb9y/vRJMSZtzeK/YLGkfo2tmzyN4QNaokWjSuIzaTLT9GGGYR4E9S2TaJjeJwQp/kU8RpyaYE+5AX+5BH5mI3QWfWpHjjRUdLHkgaL1S69k6US37dRNZH4pk04K8Ta7DWXKV8Slc2egtOqh8y8ksudUJSe120XGmOxbZNIFcb9HhYLlZ08eE3sMykJnhwp5zM0b2LTKadOiIgKxY9GHqPXCV9DpzVi3dBG69Rkg9jeZ5eThQ2I/lRhaRthsF9QGaFb4ISLxvBDXpUoim8o/nZNN0HupUQRhy5qV6R30V3pi+7rVwNnT0KhCYVIFQmnSimC/zOEUr/1o4Oto/0oPdOv3ZoZCeaSxY7ID8cGl3PsNGz1vYDGhKzR/6m9iPF1oeP5qMYqOm4r9O7eI45LFwlCzirPC8FG4eUeN19+djbNXE1DGbkOgzYaNSiV+nrYV40Z2QPcOmRuhq1LIoLmPKKNGLkO4it3Fxw2fcSbHMZtNWDRrGnQ+YanHbt3NwCaTSmnyLfj7B0AqHLkQWOW+kFtNCDQnQQk7hn76dZbLmqg8mgw9/VBZEfU6qxMT8F6fbjDr7iAxqKTIxLr61Kn/PMiUiOeat0ZwBqIxNGZp/dJFOLR3tyjPfrpmbbTt3A2Rhe4ZZMokUzQ/ITbmbl7eIUar9Bz4tujVyivlWZRHIEhhNVV2+y4k5EZlgJRNpai1zT8ow8cyS2TQadO3IJQsUw4T5i7G9Ak/4L+tG93lXRS9N6qCRH2d6VYcfvx8pBg98sZ7I9M9xr4dW2DQaaHzMvudftf4RuDimZPi/lkV4ilo3Lx6BV++OwhVdVoMS0xEGFVHAELcbZzNjs8G9cYfyzc+MJjzVKUqCA8NwzqdDhXVzkyUJxtIOVgiFSKB+Qn6/O3dvgWzfv8Fu7dsTHWdQiFDz5fq4/0BLVC1Yt7rSWWY/MDiOdPEiFN1wL1gKkFq7QkBxSFNPCfKoRFzFVplsHDkqGrL35QsNFT6DvvwoRzcqrXriR9XUFedlIApP3yL/3ZuE0riVNruzto7HAjTXENgSCgaNG2e4b6Fsv7kdGo0KShZqgzadO6GCk9Xc9+GRFW/GzUcZ44ddl9G9r1xq3Z497OvHlqNnGagv/t6FxHoJ97s2QTlyxTCl8NfxPCvFonLRr3dH/PX7ch01RyJ6ZEj7umcpxo/qwyCJiUFWq0GFmRcQUgth0ZDktfKPuotnz3pZ6z99x8Y746QpefU+BR2iv6atVgwYyoO/rcL30+dk85Jp3F5+3dsRUpAEa/JAFKJDzQmYOvaFXi5Z1/kJ1ZvOeEOBJM43KPuA0nYrdfQOUi+Go+f4+KFiCuhk0gwLTgYI75ZjuJFQvBcnQdXo7RqUgmb1x9DV602nVN4TS7HKbkCgxpXfKT1MlmHHXQmx6F+Ip1GDUMGInDkrAVrb6NL7zdw7eJ57Ni4VpRx0RdYvUbPo1Xn7qhQtfpDP//ZE0cxd/JEoUZKhpn6pJU2GyLVl6BVhTrLpS16BJqTERoc5NVJJI7u34PPhw6E2WSG4a6RO374ABbO+AMjv/1RiLfRbb4Y9qYQXEuhEjG5r8jWU3kdlX+Rwe3e/y3kBQJDQoSLTuV/XpFIYZWpEBAYJCoKDHFqr3PNqb+MSgszykTQCDkSkaMKil++/ARJQSVT6QLQsZ88XmQ/6By6ROlcUJk9zXvNaPY7rdF5uzixTsoABAQFifF1TxrL/poNX6sFoxISRGmai6I2G8bExaKPXCbaSqiP735Qz2jn/m/ijx/Givt21GpBZ5O2Fzt9fDAzJATN2nVMFZjKy1jMZqxdtsitueAJla2/9XoTvNP7eRQtzP3lDPMoUEBVqwj1OsaUvsOtqgCUeaoCGrVsg9WLF0Cf7NR7KF2+Elq9/Ape6NTtoZ+bbMC8KROxceW/MN9tz5ErlKKCi7K6VCVHVXMBFAywGjHi+9+92glykEe+2Rs3Ll8U6yWH9dSJE8KGdezRWwiq0QjK9/t2R5Jag6TgksLBpQy1jzEZ2zeuRUJ8LMZNmZ3ldrk7t29hQOcX3LOyC0UG4e3eTcXxm681xvSVl3Hy8EGRYR83ajjGT52TqcclBzrFnHGWlKrbSPG+RHRp+O7fl1507y4qqx5Fiqee7uKCqgSpL57OUf+OLUQAhsrpXZ8FIRRrCQFOn8SSOdOFKFzaBAgFUa0y761YIpGg8BHvM2kZUA87va7cHCubWZauO+I+frnNo5e3b9p9HmeuxONHD+fcpRHzTnIyLqqUmDRrZ6Yc9Dd7PoMVm07gu7AwDE5ORujdwP4luRzjIiNQslAQ2jfL2sQh5tFhB53JcUh5m/AWEXVfLpEIR3DkuJ8xhDLdSYmiNFyp8hHl2w/L4T278NmQN2CRKoUgnE1Kvct6BBqToHJYILvbf05G/Pm2HdDr7WGIiCrsdeTU6HcHQueQIzGslHtki9puE2Xg344chkkLVmDaz98LAbrEoGi3UaIZrJpAp5Ei5dL2XXuK15rbUO8XBSsUFiNMNKs8LQ47FDazKBWsXrcBpv44DjqLLl0EnoIP1F9GlQT3Y9PKpbCqAlM55y4oix9kTsaqf/5K56CTWJzDbhMlkt6cdNeonCVzpol+dAru0OemRYeX0WPAYNE+kd8gXQAKUGU1yr538zo00WhSOecuKJtex2DA3i0bHuigE6ToS5/7GbP+xOLgYJSyWHBboUCcw4EGjZ7HO598ibwObeAWz5mOeVMnCWFKT0ic8LVB7+CzzioE+GeuN59hmIwRzpXVCrsyY6fUBon4fus/9EP0GjxMOMNKpQoBwSGPZOvpu2por1eQEB8PjTIE5uDCYmJLoDERUpsRIcYE2HTOPlrSRnntzXdRpWYdr6/hy+GDcePGDcSHloNV4SsuV5PquyEBy/6aheKlSgsnMTkxEbEh5dyTSagE2+AXDptcKUaMHty9XSQZMgudl4/f6ut2zgsXK45t8wYhKiLIPd7xw7E/YmjPTs5qvuVL0Pm1vmjQ5MGj1kqVfQqJx05keL3cakCR6BJo06mbCPSSXdf5p87Ok7icypSC9l3v3wO+fR2JoQEa/8LpAjVUOq9TBWPFwnnoMeDtVDaOxOLod7nNCAv8vWoUwGLE+ZPH0a1Zfeg0KeLx6zzTWAjIlatcBXkRGmW7eoszMBwW4o9G9Z5K9XmjH09B3MywcedZlHTYUTGNiCBBj9RSo8Wk/ZdgMluhUt7f1atSoQimjO2KIZ8tRm9fH1SwWGCUSXFRIkOpIiGYO+F1+KhSj9djch520Jkcp1S58pDKZKLfXO/FwXL2gNtFWS1x/MA+LJr9p+hlc8CBsMhC6PR6X3TKYkkTRVjHf/YBjDI/JHjMXDWrAkUffFTKZdRr3AyDRowSo0ruF4UlkTJSU00MLZ2qHJyOkwOLwSf5Av6aOknMcNUElfSaPSCxGv+EeGxbuwrtXumB3IaypK1e7IzVy5ZA7xcuxqN4QpsRiqg3b/+yKDmkyoYzJ47dLUt0Zgv8TMlQGdUiEk4j4O7H1UsXYJA7NzvpkEigl/oIgZi00AbH1z8A/vq4VD3oAocDgYZ48d4e2LcPKb6RsMlUUFgMWLN0sShj/v7PedTvgLwObc42rliCFfNn4eL5s5BJZaKEvFPvN7xuJDPKFAfcjX57g65LzkD4LS20Ueo/7CO0frkrNixbjDu3bqBUaBief+FFVKhSPc+0anjj1vVrmDf1N/w7b5ZobfGkXo1SGDGwFV5uU1NMPmAYJnug74Qy5SvhzJXrMCDcq4PlYzWImdrEjcuXhK0n20LfXf4BQXi2eSsM/ugzMXorK1CFWnxCAmKDS4uguAsa7Unl7GFKCX6Y8RdCQsMzbGEjaN9BY0cTg0u5nfO7Lw56vwgorQb8Q6PKbDYhSOdyzj0xKwJERnrVP/Oz5KD/PX0KDv63092nvGPR2yhZLPV5bBKwHuM+aIM3P54nfh/3yQj8s3kPFMr7V4y92P11nDj0rthvkX6LJ6Rq72PWol2X7mK/1v2Nt/D3n79DaTNCrwoRSRRyzKnygEasPSgYf40qDxR+XlvnXJNdkhOuQ6/Vwj/wnpgZJWXqNmqKvXv2iHaEtEkd2gNQa+HRg/uhU1EQJlq0Ju7bvx+H/nsVI7/7GdHlKyGvsW/nVmh1zkRChxbVhN3Ze+QqJs/dhS3/XYDVbkfVcoXQp1sDvPJC9Uw56yaL7f62/m6QnsrqH+SgE22aVML+lcPxz+ojOHb6lmj5+vCZp9C6cUUo2E7mCnl/18rkeygqSvPLg40JIgvqicRuRYghFqWeqoCK1Wpg1m8/4ov33sTh4yeR7BcFtV8UYhKSMPWHsejXoXk6leX7sX/nNjGyix4jbakWGVUSLTuwc5voE6P5nBfOnBTR/IweSy/3925wJFLo5AE4vO8/8WtGpdj0nHaJFItmTRX9YHmB7m8MRlBgACLVl0VpHr0fMqsRgZpbCNLGiFI1GqdComFjf5+JV3r1R5TcgrDkywhVX0WZQmF4/4vv8PpbQ706nK7qCYICIDK7UyzGG3KHLVXPHkWVSaDu+0+Gw8fHF37GJASnXBebCdro0UYjPOUq5GadaFO4E1wGer9IETwgkTv6PTlFgz/+NxZ5HTpXP3w6Aj+P+RihJ45hcFISeiUm4PbWjfiwXw9sWL44U49TptLTOJBB3yOd+cP+/ijzdNUsrY1aF/q+O0JUt7z10WhR4ZBXnfOTRw7ho0G90b5+FTEuzeWc03o7tqqOHYs+wJ5lH+OV9nXYOWeYHKBjj15QGFOEPUmFw4Eg3W2hoN7m5a5CzG1Iz5exYc1qJMmCofYvggSTA+uW/oMuTeri7PGjWZpasXXdKqQoQ1M55wKJBGq/QtCmqHH1wnkRmL549hRuXL3s1kRJK7JJs7/TOrEuyGGNvX1T9Etn3B4mgUWmEs42BTYzw81rVzHhm8/dv8/8X590zrmLXp3ro0EtZ+kyBbV/+Wb0Ax+fxq9Wq9MA4SnX4K+LhdRmFs4tzYKP1FxFmQoVhVCrePzB7+G9Md+idFSosPNk76PkVnTtMwDfTJohKh48ofNIexrX+aSycyEM5+X8ElK7VXwnK1T33iuqGvjt2zG4fvkSpBYTwpMviaAA2Xrak9C0HTEuTyJFXEgZUZUoSub9whEbXAYGZQB+GvMxzKbMBaAfJySq5+Kl1jWwYOVhdH5zBs5vP41eiUkYnJQM1clreP/rZXjvy6XOyToP4OmnCuO8XI7kDJz5/T4qUZru75f5Vr+QIF8M6N4QE7/ojB8/fQntmz3Nznkuwhl05rHw5gef4vypk8DNiyLqbFGQCJwZAZYUBPj7CcX1Ywf2iqhtin9h6P3vjT4h9VUaA0Kq4x8OeB2TFizPlINw/cpFSOSK1FFwD8gA27W3MXrIAJH5dlGj/jPo9+4H7ig/YbVZMyzRJ5zX2dylYmJOeBqo903qsAthGRpDk1Wl2pwgqkhR/DRzIX756lMcP7jXfTllrF8ZPAzdPPrlyeiSo9bzzSGiXFihUCCqSLF078WZ40dEhmHPts2w26woWrKUiN43at4a8X/PgcZuSxfocKrpa9C4VVvxO2Unxn86AtvWrhQK8QaZCiq5jxgJ43t3fA4RHlUYCbEaJJOoTJrHpICI2iccB3dtQ5e+g1A4G5R1cwr6PGxZswIfJSaiiUeGu5NOh19DQvDLF6MypQ7cvtvr+GLPLqzz80Nrvd59OW2T5gUGIsHhQLsuuV+9kZ3QZmb7hrWYM3kCDuzekeo6GivXt0t9vPdGCyGyxDBMztK8/Us4vHc3tqxeBotJDb1rxKlFA6nFgOFfjYdfQAC+/mAI9FJfJIVSxZnTtpKzpTRpEKq+IkZtTVu+IVM6F2RTbVYLzIHeJ6WQCBrkKjFNhqrqrHenMhSLLo2eg97B821fvHdbm9W5noz2GHcvp8RDSvK979hU0IQL2gfY7Zj643f49IeJ910/ObZff/iuGD9HkCbG889kLMpFGdbJY3ui3ovfwmy2Ys7kiajfqCkatWiT4X2one2LCX9gyg/fCB2SwLvl/lKpDI1atRWq7GTjnS9RglYdu6Dli51FMIKqB2mvkNYxp1n3pB1DqvIk5OofFIw2L3URozepeoCm4qQLdJBav1ktKgtcj7dmyQIxRhVyJXQyP0gVflCZdSI44MIvIBAmuQJqRbDz/fREIhH7RlXCWRzctR0ls0GZPzvt07b1q8Wxr48CVSoUQ/NXf0cbnQ5vq9XuLGk7vR5bfX3x/dpjaFS/LLq0vb/uUpcXquO7SRsxhQThkpJSyfodViqx3c8Po7o2yLPBdObBsIPOPBbImP0ydxGW/zUbqxYvQGLcbdGH3bJTT7z8Wl/heJD6J33xUhlZKiQSaP0LwdeYjMvnzwhHnnqiHwQJljhIbI4cQolUZFzJwaPSbCqDpvJ5clzOnr8ATWBxWOQqKKwmHDp8BCf7dsN3f8x1j3Z7unptXL64ECnehFMcDvhbdajasBE0ajUOibnfwakderqNLlZk0C0yX/w6doyY35oXZkhThvz7P+eKjAJFsSlbXaVW3QxnZpNRzUgQbseGNaIf3y73Eerq1JNnik3G5PFfo2qtOlAplYjQXEOifxG3kSWBvjDdLdFrTsJjxKKZU7Ft3SokB5UQCrxCrZ02GRYjIlOuIKpQIXz4zQ+4eOYUfv/+K6/KtARtDmjzE3PjGipVqZZq9vWh3TtF1L9cpadzvWSbytprm82pnHOCPkFvqNXY5u8vBIq8VSrQ6zt+cJ+oNqCKhZr1nsEv+3Zjp58fntXrQTUMWwMDcVomEwEWGs1XEKC+vpX//CVGpV25cC7VdTSCp3v/N9G19xtoGrg+19bIME8a5DyO+Op71Kz/jOhlvnTuNGQyOeo3fh6dX+8vbCq1jBn1OqjDK6Szp9SCZlSFACY1Vvw9F/2GfvDA5/T1dX7/U4UWVQpRxtXPkCQEWu1SGUyKADisJlw8fw4an3CY/QNEtZgpJgHfjxqOhLhYdOn1hngMsgUOixHytGPg7kJZXXJEX+jSHbN++xlaqzGdw0j7DJo3rvMNw85Na0X7G5WNZ2RjSCzPNVWiWOEQjBuZel64N6pXLoHxozpj6JgF4vfP3h2EhZv3IOo+QVxywId+9jX6DhmOU0cPwWF3oEKVagjLQAme1luoqPeJFreuXcX7fbohRaOBRhkEa2AIdBYjlsyfjZCQUGFnJBcvIsG/qNNJl0hExj5Yd1tM6KFRay4RX5p6o/MJFTpBrs+DxGZFaMo1+NpNGDZmLMpVeBpvdW0Hc4D3+d5URSdR+eHWNafooAuaQEOTO6jigfaZ1K9OVRSPixOHDiD+bmVmy0aVsWTdCSgdDryRkpKuhLmpwYANAf6YuWBPhg76pWsJWLbxBJLUenRoWQVL1hzDFR8lWqRoEWS346CvD3b5+qJR3TLo19U51YDJn7CDzjw2yCGnXmX6Iacifeb1KAzKQO+RaxrJpgyErykZ29evzpSDXr9JM0z67kvRS60yacQ8VFL8pvmn1A9Pjjr1XVO5lMsokEE2+AQjUn0FP3/1CSb/s0qsk3rGVyyci0BtzF0jIrnneOvjIDHrRZaYMgNDXn0ZYUkXofWLcqvG+hvi4WNKgTqwmNg0KHVxGPVWX/wwfX46UbTcgpzuR5kJSwaQst4GZRCSA4u7zxGNsVOaQnD80EF07P66KEWUJZ4HVH7OEjizAUVKROOriX+KEndyMpfMmwm9KlQo/HtiIwVXGt9zy2mEaQQcPQb1yjskcq+ldASJxrnG70z45jNsXb3cWY5Ha3Q4hILwx+N+emAffU5x8dxpDPTIeHvi53CgqsGAC6dPpruOMhhfvTsI586eQrhEAspH3CIxIz8/xJYshQnXrojPb7VadfFF7zey1A+ZV0lKiMeCmVOxYPoUIdTkSYWyhTB8QCu81qk+fH2otI+dc4bJDSe95YudxI83W3/h9AlRZZauHP0uNIKTbP3m1csz5aAXLRmNkmWegvlGjKjE8jckwiaRixJ0CgDT7xSMjwsqBZtHRZ1ZGShGiVL/etM27YVAbN3nmiKicFHYkmIQHxidqjKLgvzUh93+1YHC3i+ZOwP2pEvQ+keJcmsx0cSYLPYEBlWwCDTQc8+e9JMo53YFATyh77Dxoz9y/z7p61cRFJi5/vshfZth487TWLHxmPheHDe4PdbPG4YTvq/f937U692giffxcpnlh9EfIlmnF/snGr1GUHhZZ4uAQ31F7CUqPl0Fp48egkTh4zyPJj1Uvj74YPwEd/Jj6byZQpld46H2TjhkciQFR0OVdA63r19DERrN52HT00H23GYR7XjOXx34d+4MzP79F5gMeucez2FHcGg43v74c9F2+TjYsvZeeTu1WW3ZcwVVDUb4ZlD+X09vwPTz6VstLVYbRo5bib9XHkaABAijxAMJyUol8C9TBLMux8Fqd6BMsVCM7lofvTvX5fL0fA476Eyu4C2STFFNiUOf8X3ICYNEjNHKDFQa17pjF6z59x+RzU4MKS3EScgIkHPur7uDAL3TcU7lCEqkUPtGipFv504dF3NPab72kFFfYOLYz+Fn00OroCiuBP5WLaQmnchsurLh5StXxZlTJ0X014VVqnRmg31CEJRyAzaZQijBz/rtJ3w7eRYeB1Q+R+c9p0aSbFzxr1DwTQnzCGCkyooE4b9tmzB7zVbs3rwBp44cglQmRY16DcWmyDWOhjL5KUkJMIR4DxZQNJ56BA/s3I71yxaLjRdlS3QebREufA2JCA4LR6nyFZ3qvO+/hSP794p+R4OPU/yGNl2OK1cxvG93/LZgea6MDqN2Af19hGF0MhlC0ogAUfb/04G9oL96BWPj41HdbBZj82hu6a9hYbhot2PyojVCcTiro37yIlcunhd95aT+S4EWTxrXf0oIv7VrXjXLargMwzxmWy9XChssnCov11MrGGEgxyqTz/Ham0Mw9sN3Qa4izdHW+4bddcocIqMdnHID/oYEpCiKp67O84sSTjeVfZMSOH1Xfv7jJHw08HXIk89DqwgS9trHqhfl91Xr1MerA94WjmDH7r0wd/IEBGpvI0jrVKCnKjma101Vf/S8rr71Ob//ghc6d083G33syPeEc020erETXmyV+YA9ve4Z/+uD6q2/xM2YZGzadQZfT1iNF97rCovJLBIGOVEZRhVL5HgnB5V0O+eerWXJPhGiZe7PZRvEa9uzdSPMJpMQoGvatkOqc3Bo725o5c4Me1rIqSftH2qZOHFov7DXZNNdVXWe0Ll2WC2o0eA58TuNcfvzp3HQ+YZDF15CBIPkViNMuliM/WgoPlcqHzlIkRkoIUHQ+9C+eTXsOnQDcbKMbZReIoFSnv76z39cg0WrDosxaK30ejH2VC2V4u+AACy76MBPo1/CSy2rQKlgt66gwO8kk2d4pmkLxPw1J1WZkwsqU6fSMqlEkqUsb8uOnURpcHJQ8VS9UPRFr/UvLGahUrQ77Rc+Zb4JitySg0680KUHSpWrgH/nz8Sh/3bB4bCjSr16eOnVXqjV4DmhGk+le7Ext0XPHc1GJSeesvQWUi+/W+JF4jnkTNLItyN7d4vxMBmVmD0q5JSSwNjiOTNw7aKzDLhsxafFzPkmrdtlq/G+dPa0qEBIqwbvwqgMQOytG+I8UbaCfjJas5N7VQoqc4rITNDngDYEdO6PH9oLi9kEu0SOAF2MKGvUBFIvulzczs8QL4Tlurz5odh0UWsEjd2jqLzIdngED2IVvpAmX8TSuTMxYPjHeNzUa9wcm9avRhetNlUvGXFTJsMphQLvNU6d/d61cR2uXr2MibGxKGu9l1UoabXii7g4vKFQYOXCuaK3ML9CnwUSX5z9+wRsXbsylagTvaddXqgpHPM61Uvl6joZhsk8NJ1ixYI5IrttUaZpTyKH2pgEu0SGklmoaKKKOZWvHxKRpk1OIhGBccq8kiNNjrOn8jo5gVSiTho3Lqjt6feFK7H879nYvHoF9LoU0QpGlXRUFUCz00nkjgTlqFFO6xfpbLMicThSL78bGKDqPbPCD9qAwvBLOIP/tmwUPfouNqz4V/y42gA/+mY8DSTN0kcpPDQAs3/qh5Y9f4Ld7sDnPy7HL7Oego9KhdCISHTo9ho6vdYvw5a1h7X1hEnlvdxcXK4Brpw/i2ebt77vFBLnd/q9fQiJwPoZEiGzmUSLHFUckgjg9UsXYJMqRF97eNIFUaUnqiHu7g9IRK5itZqiCo6SERQQIedcjLi9C1VtJAWVQFjKVUz7ZTzqN26Wo61tFMi4dO6MOH62TlkxLq/Fs+XxwbYzuCWToajNqVvkgn7bHBSAFo1T6w/ciddg3tKD6JWsRnuPSrtgux0DU1IQJ5fjl6lb0aXNvTY+Jv/DDjqTLdAXaNztW0LIo1DRYg/1GB26v4blC+YgRH0V6qAS7lnj5NSGpFy7+xXuQKuXumT6MQ/s2gGJQinK49NBY1N8wkSmm4y3p9Gm+alE2nnlNGKEftJCTueX7w8W4zToueRSOYJSbiElqKjTOb9bHhekueWMCvuGix41gma+54SDTobv129Gi6CB2ScI+sDi4hzSCJzvPn5PGM8+Q4Zn2/PRmBcpMlYflZIyKc04fUD/F22EAoKCoTOphUElwSClRS/my1OfGc2xh82KE4cPwg4ZzEp/8f5RSSQJyFkoUm63ODM0FPhp3kaI9ZDgnEPp5/WzQJ81ypSsX744Vxz0Tr364f2Na/FjaCjeTE5G4F1H9IZMhm8ioxARGYXGrdqlug+NJqpktaZyzl1Q+VzzlBRsXLsyXzroVIlB5a2LJo3G3sOXU13n76fCG92fw7D+zVGqRBq9CoZhchSq3KH+Y3JsipUsJcTHskrtZxqhULESsN2+hiTPcWYOuxCEVVmcYmntX3k1049Jo9GolNkQes8h84TGrVGWm9rbqO3KjcMhetcDAlLbehJFe+O9keInLUvnz8SU8d/ApgoQQWlSF9f4y8TjknNO02qodJ4CEEkhNPZNAYlMLmaXu6Dj70feE2H9/cuX0Sw4a865i2bPVsSrLzXA3CXOSTKJScnwKV0Vep0Vc36fgH07topKPZcI3KPiGukmpYC5l2wwXe68XQYq9x5Uq10PKTt2QGuPFMJ11A5IjjgFNmRUsm41OFvZhIPtC6vEV7QsRiRdEJWJUtD7ZxGVdE3btndP3TEZDdCFU5IkDVQ14ROOG5cvCn2EshUq43GUt5N6u/i/VRX8b8pmfG23YVRcPIrfddI1Egkmh4QgRiLDwB4NUz3O+h1nhV5AGy9tcLSna6/VYtTtZJy+GCvU3ZmCATvozCNBPbAzJvyAbetWCxVVV4a21+ChWe53LVoiGp+Mn4ivh78DVfxpMUuUHHKKmNLXEI1nGTh8VJZKkCnDCnL0M4iSuvrLXM6cCxo9QkIw1epmTsRt5T/zhXOeGFQSZlWQCCqEJV9CqPqaKHlzle1ZKIIbXEY4hHISrJNIRJQ7JyAjRc459b17bkgMvqHwk8VhwfTJ4j3yFnB4GOixqFpBZEXSiuuQcqtFLYSDKPtwP0iEjqL+f0/7XUTT5TYjEkLK3Mu0iAxLMoI1N2DwCxPZCYI2ReTMy+1WETmnjVKQ7o7YlGh1OhEIMVF+OoPPglWuhFaT4LVnMqchYaIPxv4PP372AXb7+eFpoxEGmRyn5TJERkbh68mz0mVADFoNwj3G2KUlzG7PdIloXoHaV5b+NUeUst9MI/ZTtFAIhvZrhoGvNkZIcM60aTAMk7FjvmDaZCz7ew60d8edku3q9FpfvPxavyy10VAbyvdT5+Ktbh0gS7oggq9UeUW23mmLJajbqEmGVVbeMJOt97DpaSF7IMKeaXp/qTKPROEat34hU89DzuKUH8ZC5xcBjb/T9lDbGjnk5GCSvZc5bKICgErAqXKPbJPDZkVEoXvO0/effYjYeI27N7lbh7p4WNQaA3YeugmFXwAsei1gt8F4+zJM5eqJwAROHMPi2X+i56AhyA6oLY0C7b5Gai1Ln1ygy2mOfdXaD35NL73aG7s2rROJGR+L9m57QrjbTtMegOw6Oe3JVJVILYp2G4I0N0R7okEVIiogKSseFBzq1sOBVJqhxoFrFC7tCR7XeLWOd1sXSBtl/q+90HPIbAyUylDZaoGPzY6TPio4ZFJM/LwTalROneTS6U3wkdybbe7N1ovb3Z21zhQM2EFnHhqaGT70tS5ITE6GRhUuRpyQY3rm8jV8PnQQhn/xHVp0eDlLj0mzOqev2Ij/jf5IqIzSuC36mi5Rphx6vfUunrvPGJG00H39A4PgMOmFgfQ2n5wisXZy/e8aA8rEUsk7lUa/PuTTdGNFMoLUaqnHmpxzgiLmCaHlEBV/ClaZjyj5ov534bi6DIwpSZRYUWlbTrBy4TzYVf5OA50GKgEMsiRj1aL52eigN0XxUmWAmzcRH1DcnRWh10qbF6lZj1f6DLzvY1CZ4dolC3Hu9AkEBAbDoU4SAYZUZZBUsugbKsbYUCkcCfRQSwS9v9TnH5F0UUTag0yJQo2eqiDIQScFVx+HJcO+R4XFIJTkc0vNnTajpGNAc4DPnzqBAIUCLzzXFI1bveAWvvGkeOly2Ll/D6xJSV6/yI/5+DyS6N/jhFT1//pzMhbO+hOau5t/F9UqFcfwAS3R/cW6UCrZZDHM44Zs6VfD38GBXdugJfHOkNLCLut1alEqTLO4R3w1PkvfnZSh/mvjbkz69gsxscNg0IjHDAgOQaeefYStyEp2nqa20PNTnziNa0sLVbDR47unq9wVdAvR30HtZ5uIIGlmWL3oL0hkCmj8C7ntSEpwCcgTTSKTS1V5ZItoP+DqgacMO1UXunqet61fg1WL/hbHIUF++P2bno9kdxavOQaLxQZ7dE3g4gEhvAq9Go5bZ2ApXhk6ZTBWLJiH7nd77B+VwOAQ0fK3YsFc0Z8vVPfvCq6Scx5giMdLfQeJ9yQjdBoNNq78V7QJ0Lg70p4hYb20U3xoH0F7gDD1VVE9R60EFIShKktl/FlxTFl2ytZXr9fQaespiWO3i55zbyNvyeknogp7r7bILpt29IBzdC0p2pcrdS+QUb50FLYvGooVm05i8+7zQgCuRcWi6PFiTUSFp6/wK1MyAjoHcEGhQDkvQfljSiVkEglKlfCoDGHyPbzbYR4aEjhLTEpCbHCZe+XhCuo/CkKw5qYQVHumWct0oigPonCxEhg/bb6IiN++cR3JajUqPV0VyjQiWRlBGVByTknlOSE2RkTNqbQ8KSQ6VW87fUn7GhJEZj4y4ayI4LsUQtu83BUv9uiVqecT67x+FUZSLvdch1Qm+umprI6MGEXS6fEpwxxsjIevXJqtJeZpuXj2NPQyZ0DAa3m/zA8XzpzKtuejzdQ3k2Zg1Ft9gKsXRHDABqkwnnSO3x39jcigZwS9Z5PGfUEPBIPUV+gD0LtFYm7eoMADif6QY+1y4Knc0CpTikyGzG5GjwGD3bdv3uFlrFn8t9hApCpxvJt997ek4IXObyM3CYuIFEJFmYHG/FAf5+KAAHTTphZOPKJUYp9KhcFdeyIvc/70STEmjcYMkXq/Jw2bNscXb1RFi0aVeJYrw+QiND5z/44tSAyOdgehCbJpVIq8edUy0VtNWixZgQLgwz4fi3c/+1oEZ2NibqNylerw9ct8hQxpi0yfMB5njx8Vtp5K5ElXhNqhXLiCxCQyG6K5AZsuBhK7XbRkFY0ujU/GT8j0d8y50yehl5GmTOrSbup1Dku6JErodaRpYrdBZjMIG0UCZoPGfCuqoFLUyfjqg3uZ7J8+74oihbzbuMxy5uIdOJS+cKj8IClVHY7z+0QAAvHX4fALgck/AOqkayL4mV0JgTfe+whJ8fHYsWE1oIyHWaKAikrNLUa06NDJ60hQFzRa7ZO3+0OnSRGVkqQpo7qPrSe1fapIUJm090aqSqQw+gTDx6iGDDa81HuAOxhf65nnEBgSCqM+FsmBJVLvgRx2BBkTUKFqDWdCIYfYstYpDkc0f+HFdNfTTPSu7WqInwfRrGE5FAnzxwyzGWPi44UQootEqRSLQoLRslF5r849k39hB5156FLULWuWQ6MKS9W7LaCZ1TS3PPGsiIy37dTtoZ6DjDfNCpfIneqod27dEBHbHRvXwWwyCiGXDl1fE4IznsZ15sT/YeGMKeLLnpTAyfkiJzky4ZxQdnUKjWjha1SLknMqmyKjKrNZxQi2YO0tlK1YOdMGm2a80m1JGC7defKjWeBSBGlvif5oF5Wq18I7H48R6vA5BW0GpBpnCZ03aL008/xBmRMSw9m2bqUoG6P3gzQAylV8OsOsCI2m27t9M/7bukm8T6Tc2vqlVxAeVSjD5zn43w789u0Yp6gLlaxLpKLNgMbaeQrIeOKqeqA2iFSXQwKlw4L3xowTG0az2dnr/1SlKmId65YtEpF1cvApiEIljsHGBBEY6vhqb+QX6LySMz/rz0k4q1KhuU4HH4cDe3x8sD4gANXr1M+SXsPjggJoNJd2yW8jsW5b6tFxCoUMPV6sh+EDW4rMOcMwuc+qRX/BSlVgHs65C8qeOpSJIsiWVQfds+Q9kkabQSICvSTytX7pIqxbtliMIaPS8DYvdRGOn2cfNdmm0UMGwCz3gSaoJBxSKYLVNxCeeF58v5P+i9xmEkFZicOBhNCyYuwptU05IIWPWSOE6LIy2YT2JbI0Noeg6rjE0DKiVDtUfdV9Ob2ufu9+LtTLq5nmYcDo2YiLce5p2jR9Gr27pO43fhh8VDQBx+YcOeoXDBSvBMd153er+D+6knPtXiqxPKFKiHX/LsTNa1cREBSEJq3aoW6jexNWPKFWtY+/+xmde/XDppVLne9TVCHxHtHeLCNoH/HJ4H5IMduREFZB7B9pjxaZeC5d0MONxFXlmMbWi32XFa1f7ore77wv9iuutb098nOMGzkMYQ47tL4RInBPwXxyzn1gwZsffIKcZP/KKe7jN1s8mqsll8vw45hO6PXePAwrXAgdUjQobLXirFKJlcGBkIcEYMywzFeXMvkDdtCZhy5vp4yX2d97CZMQRVH64tb1e4bqUTh5+ADGDHsTFqsVOoUzmpq4/wAO7NyGdq+8KoSwyEm+fvmicM6pN8w9dksZIIwnRdbph77myRGn0miXA22gnicRabcKBz0rBpuMV+1nGmPf/gOpeqdc0CaBNge9Bg8TpU4krJOTkVsXzzVvjZg5M6Cx29L15ZEyqq9Zg2ebt7pvCdpnQ94Q41TsSj+YIYfPvn0iSNK+a0+89dForyOtqDeNlFvpJ7P8M3MqbCr/VDPmrVKFyLxTaSJlRNJC/WfkjHuWsJGhV9hMePOj0UJtNy1DPv1KBBFofq0uySk8I5XK8FyL1uL1pBUFzOu8PngYipYshfmTf8E3t26Ky1RKlWgtefPDTx/Y7/84sZjNWLtskRAtOnvyeKrrqMzzzdcaY0ifZiha+NGySQzDZC9UfmyUZaACLpHAIPPB9cuXsuW5SDztk7f6iOekajyLVIXYy9dxcdwXQlPlu6lzERgULJyxn7/6BCaFHxKCot12Iz78KQTo4oSCOtkPshGku0KjzyirTiXTJgSL2wZYNfDJQraeaNikGY7u/0+086VNTlDAn/YDjZq1EBVb1BNdoWp1t51cv/0k/vx7pzgODPDBlG9fy5bqoFaNK2Dawr0i8UDZZkl4cTh0yUDiTZExNt84h0ot22RYzUhB0+m/fI9Fs/6ERKGCQaKCUmLD1jUr8FTlqvj6t2lidnpaaO3UGpDZ9gCChFgpwZMQVt49oo32Y/Q+UbDcc9qOZ7UjCfml0rYhlXyzRiRoho3+xvk4HqroNKWGxOym/fw9bl27JzRK78ebH3yKilUzP84uq2hS1Ni826neHl08HDWreBGryyKN65XFkin98O1vGzDhsHNfTVONGtaKxvhPOqJE0fTvD5O/YQedeSj8A5wOE5URW+DFSafyKqvFfbtHgWYefz3iHegccjHL3OVsUlEvzcRc9c98YSDIIVu/bJGYka1L04NGRjk5pBSC1DfgZ04WRsybuAkZdZlcIfqps8IrfQbgwO7torSfRE5cayTDEqG9gSIlS6Fr30EPpXj7sJATTTOjwzXXkBRQzF3yR05smPameG+iihQT5Ys0moQysp78MPpDnDl5PNX8eOfomERRjk4Z5869+j/yOinQc3T/Huj8U89PJ+V2MtpU/ZAo90m1GaLzSloB1rvz5AkSFwrR3RYK8JQp9wZtnl4d+A669B4gyuxI+IhG51FpeX6Epgcc2r0Dt2/dBHXulTSZcU0iEaX8JoMB74359oGq+TkNlXQunjMd8//8HbG3b6W6rlSJcAzr1wL9uz+LAP/sGwPEMEz2QQrnMm3GglokzBkYlD3BzQlff4obN28iLrScGH/mfg6LAZJLF0Wl1chvfxJOcnzMbaSElk1lN8ge0LhNEp4L1VwX6ulkk9NmZ6nVTGrSZUnXhqAM8fw/J8GuuY4Esqt3tW2kd4P7MocdPd98N111HDmlA0fOdf8+flRnlCyWPeXmz9YujeqViuHY+ZtICCgu7LWEsuiGFMCgEQK+N29cExWNoeEReLpmnVRZcdpDkXOeElAYet8I9/lUmHWQnDuLsR8Nw7gps7Jlrft3bINREZBqfjrpyVAwhfYW1Ltv8XDSnYJwN8VewCSEg++KzupiIbWa0KP/4PtqGjVs2gIXz5wSgnAUnKe9Tk6zc+M6WK1O4baXWtXIthatE+duY+/Ra/CVABVMZqTIZdh18Ar6vT8Pcyf0QpGo/JVgYO4PO+jMQ0FjwahM+/iZs/cEQjygkjKHzYJGLdo+8hnet30T9FotksLLp8sEUy8xZYL/nTdDOOjUs26SqTIslRK9yqYksT6K2lJUXTymwy6MQ6A+Di/27OM1Wnw/SNzr/THj8POXo+CXSH1VfpDBLjYARUpE49vfZzxW55wgY/TNb9OFYJ+ceuxVAcIISkw6Id5iN5vFuDUXlatWx5DRY4WjTtmLPVs3ilmjqSLa1LvuFw651YB/Zv2Jjj16PbIDaCcFUofDrXbv6aCbFP5Q2IyISDwnZtm6ytQoe26XysQYFn9dnNgcBVhSoJBL8dn//hTl/RfPnsLyv+bg0N7dwpGt1eAZvNSzjyjPp1K/qrXrIb9DvZc71q3E+0lJeN5gEDPUKYewydcXE9csR1hkJPoP+yhX1nbr+jXMm/oblsydKTanntBc3M/eqIVObWuK8j2GYfIuzV7ogJtTJkLjJWtMAV8SZnu+bYdHfp64mFuibF0dUDSVc+4WC/OJwI71a8Q0l5gb18XlrjGmaXHpklCfeXjKdST7F3Y60w6aDKNFmO42ostVQP0sTpvxDwzEuMmzMGpwP0gTzolRayQ0S84s6eR88uNvXlvXfhv3Ja7ecI5Ze/6ZChjYszGyC3IAZ/+vB15/fz6OnbkMidIHNokMjmB/xBq1IkN+5vhRfPrOGwgICEBUZCH0HzFKCJCS/V044w9hX/V+kenOYaJfYRzdt1vMPqcKwEeFguJ0vjxRmXWwQQarwgdhyZfFKFQan0r2nYT8nI1r5JTfFgGYAKtO7GP6DhkhRG6pvJ7E+2isGVX+0R6mQ7eewjmn6oX7ldznBJvXpB+v9siPufs8Pv5+FdrrdOiTkgK/u4ruZxQKfAsHer83F2vnvOm1qpHJn7CDzjw0JAJCpWghmuvQ+BW6a/zs8DUkCWXU59t1FDOtH5WLp0+K8ueMRmYYlEG4fO4MTEajKH1TOKwZKnVT/5lUJhPjRuZNnohAYyIcSh9IrGY4rBa07dwN/Yd++FDrpABB7YbPCRXuS+fOCCeQVFvJSORWFpOM15y120UP+ckjB8WwOioh3LdjC17UatFOr0eIzYZjKhXmHj2MD/p0xc/zluLgru2QSJ0iLN6g/j514iXRs/ZU5SqPtEY6T8VLlYUpJl6os9/DAYdMjoTgcvDTJ4jRaj4Om9AQoD51KjEMtqgR6dBA5euDJh27oWOP3ihaMlq8B798+QkcchV0ctqoSZC0bi02rVqGdz/96qF1EfISWk0KVi+Yi64pKWhhcKrSEuTutjIYEJOSIkaWkXJvdlSyZJZTRw9j9u+/iFJGm82eahP5YstqGD6gFZ6rV46F3xgmn9C2c3cs+3su7JprwmFzlRpT+1GYPgaFihZDs3YdH/l5KNNJtpvGZnmDLrdrbwtBOFISd7VredsbkK0n+g4Zjr+nTYaCepxV/iIjS0Jm5SpXxRe/THmowHnp8hUxc+VmYVcP79klSqupZJr2AK51eXLs4H7MmzrJ3S/+x7jXs/37LyIsAKumv4Gd+y9jzbbTMBgtkMmkmL1oD+ITnIEBbUoKhup0OG004tuPhsJuswmnmwIj+hDvezVqMyDVehrbmh0OeqWqNXDm5AmoqcrSMygvlSApOBq+pmSRLKEMuWg/JC0BhQ9CUm4gQmKCVGJBtQYNhF4MjXujwMFHA3tBp9M62x+lciQePSaCCk3atMcHX/+QLcr1mcVsMmHX5vXiOCzEH8/VLZctjztp1g4xku0ttTpVeKOixYIP4hPwISTYvu8SmjbInudjnhAH/eLFi/j6669x+PBh+Pv7o2PHjhg2bNgDVbmbNWuGmzedfZWeHDt2DKoHiF0wOQ8pcn/83S/46YtR8Ek8J6K2sFnhsNvQrP1LwhF6FMiRXLNkAc4ePwKpxSCcNBJ+Sz/n1BlJJINHX8g0i1tp1qQTtCHD/H/2zgK66asN40/cU28p7u7u7u7uvjEYDBiw4exjbAzGcB/uw50hw93dnXob9+Q7720bKmlpoQUK+Z2TQ7wx/ve+9jwSYwRsdhuq12uEhi3b4cjeHQh58wYeXl6o1ag5C+4+tLOgU79PqwSeEBLVoZZvOgW/foXeTWuhh1odT/m7itGIEsHB+I7LxdpFs5G7YOGYxTMp/3iuc644LWjZuTvm/joRIqMKppikAG0CKXtOFivkdR7rdx79AhzIpHqEKrXrsd9gXJ4+vM+Cc53YC2p5ZmeiRuNwQKF9jTm/jEOBIsXTZLPxKbl2/gxMFgsa6HQub6+v12ODwoQbF8+jYs1oe5/0gqowJN64asFsNuoRF9qQ9mxXGcP61kX+3EkLBbrJ+LjX+i8TUv6evnQNJg37Bpxnj9mcMh2DHVYzchcojPEz5ydrqZUSJxRqvz62ZztbzWW6EGaVFleJPd5az+WgbJXqEIhEkOnDmIJ6/Ls52PV0b75AiLUHT+H4wb14dPcWm0suX60Ws+D8kCCZurTqt2jLTsm+N5MJE4d9w6rYxKQfmsWz3EpLqHpavUIedqK/V6v9HJTncECfzlI6TgOYa7NhQ0QEuF5eWDL9F0ycQ7fEsZ9LCH1GXC6s1sT2Xu9Do7YdWccjCcDGas5Qx6FcH8KcW0gPKFYTKBaF5hVTZV938FS8Ygd1xlGHoNpoQahXPue4G+1sKKFPvyla61t17YWPxYVT/0GnjRbnbVqneJp0iJEP+plrz/G9VudyR1bEbEYWOPDvyfvuAP0LIt0DdJVKhR49eiBnzpyYM2cOgoODMW3aNBiNRowfP/6dj2/QoAF69+4d77qU2m25SX+q1WuEclVr4tThA3j17AmkcjkTByO17w+BKry/jBzM5pONfCnAE7PgikTeIj1zxhMLkZnVzDIjtm2ZKuSe6hfs4B9t28Fh/pkKzRs2p0zZ4IM7/kHv70eyWeQvBRoDuHD1IkxGA3Lmye8yAKWEBKW2mrsI6uQOB5qqVFh1cB9qNW4Oh40s4XTx5sHi+sfzhUJkz5M22dqGrTuwOXSah7eYlDDQzDsp0jps8FC/gkqZ9W1HBJs/C2Jer1QxTwiJ2Dn4wnjBudNdQJ4ZMquOzeZ/P+4XZGRikyOymI1fQmQ0OhDTUpheUNcK+fmSVRp1U8TFz0eBQd1r4tvuNdl5N1827rX+y4Zmdxdv3c8Sg6wbi8NBiXIV2TzzhwS6Qa9eYPSAHgh+9QJWoQxWoQJSYwRkhjB2vKaRqljIeYU0YgqXKM0SAlmy5YTl4T2m5k2CrzTXzLWZWfVVbFbDIpBg/7bNaNm5B6tuuxIOTW8WzfiVWZ4SpYpmww/96n3wc1Jn0okLjxESpoG/rwKVS+dk1fK4XLvzGg9eRGCqWo1i1NkE4DSASAA09PSTWo2hEglCgt5AJJGyNd1pYRYHPmnBWEwf3CkXS9YcufDdT5Mw53/jIbUZoBUoWCKFEgSk4UOaN3HHKIQmNeTGKLTo8V2iTsSzx48gLPgNIrzyOoPzWKhl32DWYuvav1m1/WO1fse1V2tRP+XieclhstjirekJ4cTcZjJH2wS7+TJI9wB9w4YN0Ol0mDt3Ljw9o1t/qB1o0qRJGDBgAAICkq+o+Pr6omTJ9FNbdJM2FVryQE0rSIl98g+DoOdLEeWd23ngpZZmT9VzeEU9RSjNo3N4TCiMb9KgbYxYGdl6UduWlS9hB3vyP6cDP1mK0fwyHfw99SEIeZO4MyOjQv+ftq1ehmM7t8JoNjmvL1i4GIZO/i3ePFxkWCgyORyQJBHU5bRaYbVZkT13PmTJkQv2oBCE8sXxFj+yKFOaIlCnWcs0Uz2nFrRRv/7Jkj07NqzG0wf3WKUjd8nSuHPtKiRRD6Hjy+HgkPKujgXnNIdILfwJuXbxHPTU1p6E/zs9z9ULZ5HRiU3AXBCJUMNoTHT7RbHY2Y6Z1kSGh2HTyqWsdZTm/+JCv7duAwfjpxZcSMTuZOrXgnut//KhIKdUxSrslBZQBZQst4JDwxDmnc/pyEGJdLk2iNmT0rpNLh7UTq80hqFeizZOjRhKEpNyukwfHl0x5/BYUpfWfOq8Im2SWEuzT8Gtq5fx99yZ0a+Vz8eKGb0+uKJ65MwTzF+9Ga/D33bAZfaVY/ywRmhW5+2sNQXvsWs6/cWpADpSQgTAVQA7YhTPyRu9Yat22LlxLRNos8YpflDXoZc+GL6ZMjOnmrSicdtOyJ47LxMPvXz2JOw2O3IWKsKKPILIB9AJlSzZIrYZ2P6uXLVa6NB7YKLnuX3lEiCSMn0CV9D7CQt6xlyH/DIFIr2hTrJjB6IDdJGIj/rVC6fJ83oqxAj0keOiXo+qLtb6MC4Xj7g89Mgfp8vQTYYn3QP048ePo1KlSs7gnGjUqBEmTJiAU6dOoXXrj5/RdPN5s2P9KiYYFqmgqunbrCdlVaM8c8Av7C4L0oVcB2A2MlXuWCVWoUgMgVAEjUABlTIb8zenxZ6EZGLFzgR2Czy8vPGlsOj3KTi4fTPaaTRopNfDw27HZZEIq29ex489O2DW+u3OjgZScA3icGDkcJhndkKe8/ng83hQenri599nY2TfLuBFPYJWqISVK4TQaoDUrGaLa99ho9P0fVCQ7qrKQfoCOzaswsXTJ+Gw21GsdG2WES9UvJTL52HVHNf5hxhIcCZt5/8+BfQdFC9VFquvXESxoCB4x8muh3O5WO3lhZJlK7CKRVrx7PFDrFk0F7s2rGAzjnGpXiEfRvSvjyZ1irmFar5C3Gu9m9Ry7sRRvH7+FGFeeeLZZVKATR1wpLTuoXlJ5VDwTFoULVMBA0a89a8m9w3uoycI8czFhEMpICeNEpqbplE4D/VLePh9Gvspam3/5bv2TuuvcUMao3ihrB/0nHuP3cHYWcdQyWjEjxoNclgseCYQYKPRiIE/b2Y56aa1o4N0P5/o/c4zPh+eZjNoBz6dZvJJcA/AWtoPGAxMSLR6g8a4ffUycPcW0/Qx8aXg2S1QWNQQCfgY+8ecNJ/jphEDOiUcayRF+SP7dkGv1SBrjoLMRpc6NV3+fQ6HWdi+E87H01+JTQjVrVIozZxJKDHWo30FzFh4GLX0epSIM1pI3+UiT0+IRXy0blg8Tf6em68kQH/8+DHatGkT7zqlUgk/Pz9227vYtWsXNm3aBIFAgLJly2LEiBEoUKBAOr5iN5+aM8cOQ8uXu1RiJwEQk0gBJceCGvWboEn7zmzGKBY6iNdu0hwHdu9kdiHU9hYXmksigRiakf8SeP74IRNE+yYqCs30euf1lY1GFAsOwrc8LjYuX4ih4yl/Dibks3r+X9glk6FdnBl0QsfhYLeHB6rUbcjaB6nyOm/DTmxftwL/7toGnSYcfoFZ0KRNHzTt0CVJT9W0hl5H7OtPCcXLlMfLbVugTihCQ1BlxqpD2cqN8SXw/aRpLAnzDYeD+hoNssds2A4qFBB7++D7ib9+8N+gWUbqOFg5/y8c27/bOUtJcLkctGtSBsP710e5Eh8uCOkm4+Je692kFhIec7AKqNS1v7rECx6a1yhTuhQatm7PBFfjirrVbdoS548fAV9qYo4ucaFWd0om12ve85N8MYtnTsPtB9HBWumi2TFmUOrs3Fy1tf86519UNBoxNiLCGXPmt1jY5V+8vTHlz/1oVKMQa3cvWTgL8mT1wmazCUXDwlkVnVrdRwCYFmcsxcvPn63lvy9dy4Lj3ZvXIfj1S0hkctRp1o45n2TO9mHaPKnROiABXzqlhGKly2Lr6mUskRPPIz0GiUmFTFmzw9f/41SWaX2MpUX9tO387d+pEk5deISxl4CqBgNKGYxQ8Xj4VyHHGz4Pi6e0hVLutir9kkj3AF2tVrOAPCEeHh7s4PAukbjixYsjc+bMePHiBRYuXIjOnTtj+/btyJYt23u+IgfMaSRslRbEzoem55xoRsNiscDBSfqnaefwkDVnNnz382R2OeH32aprb/x3YC98VE+glgXALJCzKjoJxCn1IahYsy5y5Sv4Wf0O3pdDO7dCyeGgYZzgPBaFw4FGKhU27d6OfsN/gkAghJevP5p16oYV61dBxeWiiU4HL7sd14RCVnHVSKRo33ug87Px8vVDryEj2Skhn9vnR7P3K+f9iYM7tjCLP6qeRM+uxwTpDjsbe+DYrGjQqv1n9/rfB9+AQExftQU71q1gugoanRYKuQJ1m7dBiy49WYUpJe/T1XGIKj+04aCK+c0rF+PdXyYVolf7yhjUoyZyZYtOgpm+gM/zXfP+aSWKmBAH6+rI2LjX+uRxr/WJYSNpSHo2mFrWiRG//MH0bWx2O2xx/g+WqVIdeQsVBe7fhUoa4NScEZnUzEmGjn/1WrT96Mf6h3duYXlMa7tAwMPi37rA7qAZ4fd/HWevPMOrUA2GazSJjhV0mTrofggV4+TFR6hYKjqgHjO4HvqP3oTJPt7ooNYgj8WCMnw+snM4eG6xsJbskf26Y8XOQyxIb9axGzsl5HNbKylJfGDbZqxfOo81y3moX8TMrseMVNEYnzGSFWRadh7M9pQfg3/37nR28dWvUSjN18Qlv3XAmm2XsHbrRfz3OgoCHhf1quXHnM6VULxgYIr+XnqvZRkdy2e01n/WNmtjx451nqfqeZUqVVh7/LJlyzBx4sT3ek6r1fZJZ5KSIjzGBuNrhcTgDHodxFIpm2FV3bgBrcM/8Rwx+ZVbdUywJijoDd68eIZje3fi1pWLsDscrJpeqU59NqNmeP0K3lFP2QGcnoXV/TgclKtemz32S+D1y+fIbLUiKRO3HFYrzFRVffIYcmW0Onqj9l2Zmuu+rZuwVf62Cp4jVx4MGzISfLEkw30+NMs4a+Jo3L91A1qxD2wiLpTaIIjCtGwOjZBateA67Og9bDQEEmmGe4/J0bhDN/a90udAFSbaIJit1lS/RzoO0f9D6pggD3kScEroUtCsQ1f80BjwUNDcnwVBb76cz/FTHadtomJ4D7enLwb3Wv/1QEEhtS+Turpf5qzgmXVMXyahvzohNmvgExAIlUaN169f4uShfaxirtfpEJg1G6o3aAr/zFnx4M5NKJnmTLS2TOx6X7JiFegNBnb6WFBic+IP37JjMfFNl0rwVXI/+Dh5/9FL55ruitjr6X45M0UHqoVzyvDriDqYtewMRojeVlc9FEr4RakQGvyGjZCN+34gRkz5PcNYX+7dvA471ixnCRmjMitb6/3C78EkVMLG40NiNTDnn+oNm6Jk5eofZa1/9ewp+ywJstyDVYegN64dVj6ExtWysZPVagePx3F+Z6n9fX3tMUdGWOvTfUtA1XONJlqsIi5UPacqemrw9/dHmTJlcOvWrfd+PSTQkck3/cUiUpNVpx+Cj48Pq3B+bZDt1+a/F+Ho/l2wms1MpbNoqbLgmvWQGsLjt6iTTZYuGLCa0aZbbzy4cRUzx49is2ZaAQWaHIQfP4bTh/cDXB7CPXODAwcTNaPA3CSUwUv7BitmT8ffu44wEbKMDrWe3eDzQbk+V+/mKZ8PkVCInLnzgM9/uwHqN3QUuvb/DtcvnIPBoEe2nLmRh6zVMijkR3v/xlWWRY/VGjALlcxPVWRWM0/c0pWqod8PYz7YSu9LhI5DD+7dxeFdW7F97UqoVVHxbi9WMAuG9qmDdk1KQyj8+iJJyqY7j9PpcNxQqT+eT2964V7rk+drX+vJ8WH72r+Z64MqIoxdV7hkGSae5qF7k0hzhqrgYpMKrToPhM1swpTvB0AVFQE9BWFcPsJu38XtmM4etSILO+6TkByF5ha+lOnPHN75Dxq1bo98VGX/SGxYvgj3bl5n5wvkCcCUkW0gEiWVQk85BfKanWt6IRcVYbo++n5ZkSnw7R63XbNAtGlSHuevPUdQqAY+XjJUKpUDj5+Hokqr6dDojDh+YA8qVK2O9j3743MnPDQEu9athFbq57ReDRMqnRVzkUWDgEyZMXjsFObq87GSDtc2jXGe7944R7zv4GtayzI6ls9orU/3nVbu3LkTzZpTwB4aGspu+/hwPkubNlqwP8fXlZ68fPoYP/ToAK3BAI3QE1aJGHyrCVevXGbqrErtG0gtWuhZ8O2A3KIB16TDgJE/QyaXY+aEUdALFYhSZHEu7Bq7DQFhd6ATe8MqjJ5JsgjfWodEyTJBEPEAF04eQ82GTZHRadCiDbasWIy9MhlaJrBOoxb23XI5cuQrCIvJBGkCn1r6vVWr92FzcZ8Lh3b8A6tI4QzOCRtfBI0iEBoEwlf1hFU2cuZ9q2jvJpoHd26x+fJ92zaxTpa4kAotCb/VrVYow1RX0hNasCnhldZ8CaKF7rU+ZXyNaz0F5xOG9MPdG9egE3rApMzOnFVu3LkLnsUCsU2DTFGPoCH1bg5VQHXMXqtCtVpo3rEbBrZrgkitHqFe+Z2VdgrFvSMesP85BrFXzMz62zl0Ep1TmFU4uG0zipRI7PaRHrx4+hjzpkWP3hFLf+sOpeL9/eHjUrVsHmT1U2CDyYgJ4RHxBgOoI26DQgGFRAA/b6XLY1SNCvHXvmIFs+HvGT3QduAidvnPyWNRrFQ5lChXAZ8zJw7uYW4uOqmf8zoq0lAxh04yXQgE4aEoVrocRCIylf047D58w3m+beOy6bJOZIS17EtB8Bms9eluDFi9enWcPn2azafFsn//fqZKSC3rqYE81C9duoRixUjqwk1G58/JP0NjNCHYIzd0sgCYRB7QyfzZZTP4CMyWA8UK5oOnIQRexnCUK1MGUxeuRMvOPbFn83qm9Bo3OKf2d7Jdo2CeKqc+4fch04Uyq5BYbHwxOEIJs/GKC4090Dw3Ca49e/QAGYWsOXOjSdvOWOLhgaVKJV7yeNBwOPhPLMZwX1+QIceTW9fxTeuGePrwvvNxep2WvU/qYIgr+pVRCXr9CkZe0ouxiStC0KvoFkE30TN8544fxbedWqJtzfLMFz42OKculh5tK+HagfE4sGYo6lUv7A7O3bwT91rvJim2rf2bBedhHjmgVmaBSezBgulQZS4WXHM5XFSsWhXeVhU89UHIlzUAg3+ahHEz5uH6xXNM6T1CmultG7zDwQJ4SuhTd5Rv+D0oNK/As761GaWAXc+T4P6dm/FeC43wnDi0D/u2bsTV82dYy31aQM8zYeg3MBqi9WDa9+yHquXTLiFMe+ZxQxvgokiMST7euCUUMmFX+neitzez23QYLGjQbSG27LsWb6zz4bMwPHwaCov17V6IaNOYBD6jfdnp+D+iX1dWof6cob2aQyBhQbkryLGHujHVUeT4/nEgL/mzl6MLkUULZEbenP4f7W+7+XJJ9wp6x44dsXr1agwaNIj5nlOQ/fvvv7Pr43qg9+jRA69fv8ahQ4fY5d27d+Po0aOoUaMGa20nkbjFixczle5evcgowk1GVx+n9jSyQovrsU3QZZXEF9wXz/Dj1BmQKDwQGJg5XtXh6oUzzM86Njhnfp1RTyCwGmASKmARSMC3miHXBTNxOKeACAWjdquzdUWn1eCvKWPZbFvcQJVao0b+8sdH8c78UPoOHwOOgI89W9bHmykvZjJhYng4pA4HxtntmDykH6av3ITVC/7Csd3bYYoJyPLmK4BO33yPyrWiF+qMiKeXNwQRz5O8naz1PL2/HGu994XEcg7s2IJVC2Y72zBjkSmUaNOtF7oNGIy6Xkc+2Wt0kzFxr/VuXEHr6s6Na1hrukWQoJrM4UAtzwRphBoFipRAr6GjERCQKV7l88blC+AIxG9VumnUTfsGMkM480CnuWPyPqd2eKkhEpEeOZhvOkGaI6KY2Wt6HZuWL8L6pfOZoGgs/oFZMHTCVJSqUPmDvsBNK5bg0pmTztGzYeN/IdPYNP1RkCDYryNq46+/z2Fk+NuZ8kCrFWMjI1HOaMRcT0/8MHk7CucNwOHTD/D3hjMIjoxOGvh7StGrY0V827WK04992ujWOHbDwF57yJvXGD2wJxZs3MlGDz5HSF+IQ4kYVy4tFNTYTOBwuZApon8DH4NY73OiVUPX9q9u3KSWdP8fSHPmK1euxJQpU1iQLpPJ0LZtWwwbNixR9jHWL5LImjUrQkJCMHXqVNYSr1AoULFiRQwZMuQDFNzdfC7EVqlNcVqS40JBNvH88SMULFnG5X1ovjwWWrDpwBxOfqpx7Da0tgB4Rz5mit6RXrnZIu6wWlCxZh0m4jLuu764e/M6omSBMIo94SB/cJMaN65fx/DeHTFv/Q4oPEgZ9vOFMutN2nfFhcMH4ffiGZrrdMhutbJTLEPDw/E9l4vBHVrAHhWB9ioVSphMiOTxsPfWDUz54VsM/nkyGrfthIxI3Wat8OD3KeBZjaxLIi6kQSA0aVC7cQt8rWjUKvyzejnWLV3AuibikjObDwb3rIWGVXMhT54cEAndwbmb1ONe6924Qq/VIjI0BCal630bJeRpzX7++AEqo1Gibp3oi2/XelrDKThXyTPDIPV5e4yTZ4Kn6jk81c8R6lOA+WNLLBpUrVOf3b520Rx20kl8oPfJzvzSmT1XRCjGfdcHvy1ZgyJJ7DXexavnzzBryjjn5dW/t0FFftoG57FUL5cD1x9EYveOyxgYEQlfux2FzWZnO+x3UVG4JJOi3+iNePEqEvV1OlSLEck7oZPgj0VHcOveGyz4Xzu2d6BAffecFijV6DqCQtU4f/I/1qb//di3rfqfE7UaNcO6xXPZzLlB8vb7d9qomiJRvlqtj2YDSxyJUW8nWjVwB+hu0oaPkiLLkycPVqxYkex9qMoel5IlSya6zs2Xg0hMCtAA125jgi8J4dqjg0uR2LWvY+kKlfHk4UqoHPYYC7UoaGUBifxUbTwh1PJAeKmfQ6IPh6cxFCUrVkWeAoVx6vAB3Ll2GeGeuWCJkyigQD1UIAUn+CH2btmADn0G4nNHHRmB4JAg9NfpUMlIje3xyWexIMBuR0RkOBYGByMwNhlmsTDP9HkeHlj0+xRUrduQZagzGvWat8b29auAN88RJfGHHRy2iRNaqHLggEAkhqdPgsX8K+D1i+dYu2Qetq1dybpF4kK+5cP71UObxqVhs9u+KjV2N+mDe613kxCBSMiC7tg1PREOB3gOm3NPkBDqZtuwdEF0MC2UMfFYk0AWLzhncLhQK7PCL+wuZLowSOx6yORK1G/RFqrICGxYtiCesBhBzxcukMBP9RR/z5mBP5atS/UXSJX5ycMHsdZ5YkCX6qhdpWC6/hDOX3yKajo9arpY62kIoJJWh/0vHfgxMhLV4tynpNmM0iYT/nf0DlqfvI8G1aNfZyZ/D2yaPwC1Os5gfuvL58xAibIVULNhE3yOY331mrfBoV3b2P6RfgtSEogzqdhekH5rJHpLLfs0rvUxkt8XTh1n57Nn8UbJIu4Copu0Id1n0N1kLGixuXfrOo7u24nzJ47BbIoz05WGFC9bAWIpLbYRLm+n64ViMUqUrejy9ibtOoMLBzw1L9nCTdX0WDuthJhEymivTO1rFCpWAj/9/he7/t/d22ETyeIF53EDe2rJo3bgLwWyoctlNr8NzmOgAkVXjQZ2qxVH9qZP1j+9kUhl+GPpWhQrURKe6hfwVj9nIkQaeQCrrBgdPPz64xDW4vg1cOvqZYwa0ANNKxRlPuaxwTltXprXK4Hjm0fi3M4x6NC8nLPV0Y0bN18X1E3z34E9bCY7KiJ9bIWEQhHKVqkBuVkdPWKW8HaLDjDrUalmXZePL1m+MrLmygNv/RtwrSa23htFrh2A7Fw+zAIp5IZQeMsk+HXRCpZwpvdHgacuritMLBwuNGJv3Lp84b0seCkxfPb4UXY+W2Yv/P5TG3wOCB2OeMF5LFWMRhS0WbFm64V411erkA/Tf3772scO7sdE7z5HhoydguYdusDDEAqfqMdMsV8v8YFGHshGHrasWorxQ/oz54T05uThg079lpb1S7r1WtykGe4A3Y2TO9euMLXUoV3b4PefhjPV1c71qjCBl7QWEhNLJGjbvQ9khjBI9WHR80SxPuf6MMgNYWjVpRekcWaq45IpSzaMmTYLMqsO3jHep3Hb4OLjYEFo6259WIZcrogO5CPCQmHmJJ1htfKEiIp0nUD4nHj9/Bke3rkJby9vHJe4rkI8EAgQyuOhjIsFm/C025HN4WDPlVEhj+4eg6JHZzSyAIR75YVe6sdOoR65oJX64+85f7AE1JcIjQn9d3AfhrQohc4NqmH/9i3OsSGxSMAqO3eOTMKOZYPYZsytyu7GzdcJBeMThw5Ez6a1MG30UEz9cQi61K+CmRNGO0XO0pL2vQeAZzWwhDp5njuF3sxaeOteI0/BIiheznUyntqwJ81aBB8PJfwjHiQab0sIrfX5ixTDyj1HkbdgEXYdVdA5fEEivZtYrDECo6lNUvg+m4c/Jwx3Xl48rRuUCtdrcFoQpTbgvwvPEJjZE6dlUrjqSaBP94REgkxJ+KUTRQxGPHkWbXUXl6F96qJt49LOyvDw3p1h/Ige8imFKuO9hoxkRR6LUMFGGqgzglTcIz2yI8IjJ66eP41/Vi1L99dybP9u5/kW9Uum+99z8/XweapAuPnoPLxzC6P6d4MRfKg8cjJBFq7dAr0+HIv/mMpsUjr2+SZN/2anfoOY3/LO9avgYQxjtlg8qxkOq5nNQnf75vt4ugQJqVKnARZt2Yft61Zi9+Z10W3ucdrXYqHraZCtSbtO8YKSgMDMED14GJ3Vd2EhJbAZ4Zv5rZDh50bw65f4a+IYXLlw1nkdqbeXkUhQx2BwmjlEcLmY6eUFrsMBnyQUa2kpD+dwIJV/PGGV9GDXhtWAUBJtwRL3O+VwoJX5Q25VY9eGNSgw5Xd8KdD/TfIWXrVwNp4kcCfw9Zbjux618G33mvDzydjfrRs3bj4ccvAY0aczXr18yea4qfOM5rXFpij8u2cHc7v4ddFKJsibVhQtVRajps7EjAmjII64B7tQxjqcYDYgb+FimDx7MQvEkyJz9hxY9M9eZqe5euFsWI1RrGKacN0mRXehVY8GLdvFG4/z9c/E9hWUHHAqwceBxGXpuXz8Uq6+TUWLAWPWQKWODmC7t6mEhjXTx2/daLJg8uyD2LDjEkzW2GIGMMfDA9+pVKytPTY4J5E4cnLJk4w6PSXrXSUSaH+0bHoPXLyvZa4v927dwNTRwzD5r4X43DhxaC/0Wg1U3vkTicWR3apO5ImdG1ajXc/+afpbTuiZTRV0wlMpRbXyedPl77j5OnEH6G4Yy2f/ARN4CPXI6TzY2bg8Zoli53KZuErjNh3TdD6ZFuRvfhyH5h264fDubczew9vPD3WatGRzRuw1JBOgE1ly5MSgMRMgFIlYpZ8SC0xgLmbhpnY4smmrUKMOU1aNC82mUesbtUdRG3y8/xhWIxOLa9zmu8/yF0LV/5E92oMXFooRMeqtGi4XU728WDC+VaFASaMRETweTovFrN4gtNuxWyZDfb0+0X98yrirHQ5UqR0tqJMc1M7loLluQfp5aOqo5d5ug1zpkapK7x3y2eVJXSZc6DodT4Y7N67iSyAyPAybVi7FhmUL2e8hLvlzB+CHvnXRvW0lSMRur1M3btxEc2DbZrx69hShXnmcgpq0PlCnkZUvwY1L53D++FFUquW65fx9qdGgCUpXqooju7ez4I/W7Io166Jk+UrsGG82J9+OTJ1vrbr2QvbceTF2UG/m0EK6M7HHeiooeGteQqH0RK3GzeM9lrRV5v82GTJ9CDTyzPHWB3KAUZoiUKZSNdaFlVL2bd2E3Yeju7EC/JT4c0J7pAfRiYBNOH7mATqq1Kij14OO6CsUChyUSnFeIkHVWBE4Wsc5HMjtdlwTiRDM4yEgwR4qlMvFKbEYvUvndPn3KHCfsWwtujSswbopdmxYjZLlK6J1l57plmCm+X2F0gO8VCjHP7h9ExDJWGHH5fMKFYgMe4ao8DD4+KdPoeXimRPQaqItpJvUKQaBwB1SuUk73L8mN2xzf+XsSWjieorHgaqR8vAIHD+4F03bd0nzT4yC7O4xrcnvS8/BP+Dls8dsY2EXyWDkCCF0WMA3aZGnUFH8MGlaosfQZqF89Vq4cPI4NBYfGOKouFNFP3ue/GjYKn0W3Q9l25rlMISHYX5QEFNxJRQ2G+aEhWGpQoHtcjleymRs40WLNVXI9VwunnG5+NXLC73VamSx2UBbomMSCRZ4eLAqypuXz5GvsOsqwLnjR7B1xRJcv3LRac/WvGsv1G3WOs3apWkecvOKJXh09xa7nClrdrTs3IP97lKSBRcIBOA4osV6XEFVm/RMLHwMnj1+yObKybooYfthmUpVMaFvCbZZSK4i5caNm6+T/ds3s6p5QreL2Moj6bIc3LklzQN0goKwFp17fNBzlKlcDf1+GIMlM3+F3KKGnidldmqk2E4dYL/MW8Y0SeJCllt9hv6IBb9NBs9uY7PosSrutNaLuUCf70emKjn6+7gfnZcXTu0Cb88EFnJpxIkLj/Hv6QcYFxERTwD2e5WKBeYTfH1xUCZjGjM0d+5jsyGczwff4cBYHx8MjopCsZjkx02hkFXdaWUIj9Qlu46WqVgJp44eZpcnD/8OT+7fxcCRP0OWRl121LW5bsk8nP3vMBx2OyQyORq1as9EeVNSCOLzBdGjkUl0QJJgHLtfOgrFHd33tr2d5s/duElL3AG6G5ZhJKwuFmyC5rY4AiEiQkM+20+Lgq4Jfy7ExVPHcWD7Ztam5+Xjy+y3qtSp7zIoowDm5+lzsGL2DOYhbo6Ifn9cHg/V6zfGt6PGQyyJrwr/uXBo2ybU1WicwXkstEwVtliwjcNBHZ0OPTQaNl8ezuWiW6ZMaKPR4JBUin4BAfCzWqHlcmHgclHdYMBjiQQ3L19g7z0hm/5ehL9n/4HCVhsGaTXswHH61g02s0jaBYPHTvngIJ382ck+xSJWQqfIyhZdY5gaC6f/gtvXLrMWyXcFnRVr1Mbrdaugsdvg4MYP6KlSIrVoUalmxrOSoyrK1QtnsX3uCOw4eC2eJgSXy0G7JmUwvH99pszuxo0bN0kRERoKS8zMtStMHCFCg4I+6w+wdbfezLec1m2ySSXf9Eo167CuuKSCu+Ydu0EilWLF3D8REfrIeX2BYiXx3U8TkSt/ypXXp48fxYJ0gma2W6ajtdamPVeRw2FDRRf6MTmsVvDtdmapOiwqCrmsVpaU7+/nh1wWC14JBBjt6wtPm43tDchWNafFgup6PS5fe+7y7527+gzDhkyBh9mK0gIBLlPHnMOBdUvm4/7VS5ix5h+njs/7cuXcaWZxa+XyoZEGwMYTQGfRY9v61Tjz32HMXLERnt7Ju66UrVqddU0KLLrEQr8OB2SmKKZt4OHljfSAPpOjMfPnAqEQDWpE6x24cZNWuAN0N/D08XW2dVOLeEI4discFnOq2r8+BRS8la9Wk51SozDbf8RP6DJwMO7euAqb1cYqyBTcf65Q279KrWYLbUJocd4olzOP8yEqlXMOnR/Hbq1rcDBOSSR4wedDTG3tBgOy2mwYkITAHGXOKTjvoNGgu0bjfM4Gej0OSiSYtXUjylWt+UEVl4d3b7HgnMTddDL/eJZ3IqMSxw/sQeVa9VibZHI069AVOzesgZfmJSIVWZyiQNT+6KV5xSoDjdtknACdvmvyWF21YDauXzof7zaZVIR+nari+951kDPb5/t7dePGzecDjZGFvYo/EhMXkcMMv0yJtVw+Nyig/u6nSal6DNlz1W7SEneuX4FOo2Ziszny5EvVc5w8fIBpfhBeHlLMmZy+60lwsBo5DSbnuhsXGlmjNPT/wsOhiEna0v1oXZc6HJgbGoprQiFuiqITMkVNJpQwm7FY6TrAtlIC/qdNyKs3YlJoGNOt6U2Vd7rNZsONq5exYs6MVH/u8f6GxYLffvoBRp4Y4crszq5Nk8gDerE38OYp228Mm/hrss9D6v458xUAnj5DGCcrrALJWy90XQgEJg069B6A9OL2tSsIefOana9YvRYUctcFLjdu3hd3gO4G3r5+KFWxKi5dvszavBO2ucv0ocyKyVVl9XOG5qcO796Ow3t3QhMVhSzZc6BRmw4smExYiaW2LZpBywhQq7dSocRzrRZI0OIcwuPhoVCInyIi4i3oSsqyWyxM5b260YjaCR73hM/HCwDdypRP9PeoSuHD4aBLnOA8lvoGA/Yrldi9YdUHBeh7t6wHBOJocbcEmMQesJoU2LVx7TsD9IDMWTHxr4WY/MO3EEXcg4lPbYcO5odOokGT/loMv0yByAhCTtvXr8baxfPw8tmTeLdlDvDEkF610b9zNXilU1ulGzduvkwatmyH5zOngWc1JmpzJ1V1nkmH+s3bIqM5WFw89R/2/bMRL589hcLTE7UbNUOdpi0TtbvT+kmide8DidpOGv5Wl2bGuHbMQzw98fdX4ppYxJLvCddfSrTXNBicwXksRc1mHJVI8K1KxbzP6RQLTaSfkstQr3y0zk9c/j11H28idPgpIpIF+cR0EvQlBXkadzQasWXlMvT+fiSkMtcOO++CWtpVEeFQeedLtNekeXK1yIvZvfYf/hMbTUgK2sNNnrMEYwb0AJ49jLbMdfAgthsoC4Beg0egWr1GSC8ocR5LzQZN0+3vuPl6cQfobhi9vx+Bmz06wE/1FCqJH6uk8+wWSPXhkBoj0PW74WkqEJfehAa/wah+3dhMtVmogIUrwPM3QWyOunKdBhjz65/pOpuU3tRt2Q6H1ixHa60W3nY7tBwOTCSCFtNm7uWi9b2lTofZnp7YZzKhoZ6846OJJJV3X1/4+wW49KJ9fPsGCur1OCsWR7fQm83xnr+sTofdMTPj78uje3dh4Ildi7sBMPCleProfoqeq3TFqli19z8c3LEFNy5dYK1oOfIXRMuO3VKl0vupfrfrly7E5lXLoI6KjHdbvkJFMLZfeXRsXg5CofvQ7caNm9TToFU77N26EZwXzxAl8Yun4u5hCEPRMhWYNktGwWa14tcxw3Dq3/1Mf8bAFUPwJhR3rk7CP6uX4/ela+AXkDZJ2T/Gj3ZWTamlvme7ykhv2jcpge0Hb+C8SIQKJhOM5EhCQnAOB/QcTqK1nmii02GPTIZZnp6s9T12p0NaNPM9PRHB4aB3uwqJHnf7QTBolxfO4+EVn89a6Ok0FcCgmA69yKhI7N+6Ga279Xqv9/Ps0QNwBKIkRypNQjmsumAEvX6BPAUKJ/tc9L0u2Lwbp48cwqnDB6DVauAbEMh0a3JTdT0dObJvF/uXRvtqNaTCwb/p+vfcfH24d3luGOQX+tuS1fhrylg8e/g2ECIV7a6Dx6J5p+4Z6pP638gheBMUjFCvfE6VTw217xlV7GC+YdkCdB04BBmVNt1647892zHE4YDcYsHzmGSD2G5nm60bQiGKmM0sYD8ikeCxQMAW6WJGI+Z4emK7TIYyJhObSTsjlUKqUGLq3KWJkhYaVRTevHqJu2Ixy9YTJD5DWfuBKhVro6M5doEw6ZnGlEBVDl6MqIsruHYrRLKUt5BRMqltj37sROrAQUFvoPDwxOfKgzu3sHrhHOzbuh5mc3z/2vrVC2N4/3qoV63wR/Uuf/E6Ess3n8e+f2/CYLKgQN4AdGtTHk1qFUqRAF2kSg+jyQo/bxnrwHHjxs2nhyqf05euZWv9ORLo0rxi15OCNlWcvxk1Pt1sqdIDWstPHT6ISGV21m0VC89qAoKf45fh32HW6i0ffOx8uW80UzQnqJ15w691P8rxuFq53KhVIQ9+PQdkM5vxTCCAlcMBL6bCfVUkQleNhlXGz4vFuCwSsfOk9n5AKsUlsZiJydER+6xcikhw8MfPLVA4X6ZEXQjnrz1nbi4Tfd7OfxeOGZf71mrFvJjrZk/5ERVrkjNO9lS/H2Z/Z7dFC7y5ECWmtT76finzkyd9Ieqso1PsWp8pnbvkRLdn4PH9u+x85TK5UcsjYwfnOr0J63ZexsYdlxESpoGfjxztm5dG5xaloUjBvktvMCNKY4CXUuJ2jUlD3AG6GyeFipfCgk27mbrm6xfPWNt38XIV2Jx2RuLereu4d+MqIj1yJLLgoAVcZ9Fhx/pVaN97QIZ7b7GQHkDL7n2wbNbv8HU4MDQyEh52O66IRGwubatczi4vVSpZZT2PxQIdl4vXfD5b2F8IBDAGBMLDxwedGjRhavUJRVlIIfynfl1hjYrENyoVqhmNbOGnNvk1CgXe8HiYEBGBYwoFqtVp+EHvp2qd+rh24QzzsbXxhInE3eQWDarX64z0ggR/rp0/A4vFgvxFiqV6LvF9oMr++RPHsHLBXzh15FC82wQCHjq3KI8f+tVD8UJZ8bG5cO05un6/GlyTBTU1WjYicS1Cg4GXn6F1g2L4a0KrJIN0apOcs/w/XLwVvfH3UYrRtU15DO5R1b14u3HzGUDH+gl/LmDV4Hs3r7H/y0VKlX2nMNfnhsVixvb1q6ATe8ULzgla+yOlAbhP+4Gb11Cw2PurbGvUKvQbFR2cEzPHtUP2LB/ns6LvZuTAOjhz6QkibTx002iY/sxTgQBb5DLcFgqxSyrFLpkMLwUCZLNYIHI48EggYEE52a+ez+QDH08pGpfNjZ5ty6FgnsS2YxNm7cfJC4/RSqdDI52OictS8L9aocCPPj6YERqKrTwe3lgsLPk6om9XrNh5iNnlpYZKNeuxfYvEGAWDxDuxuJsxElly5EKW7OkjeGo2mXD5zEmoVZFsJK5YmfKpdjzZtv+K83yrhuknEPgxIDX/9t/8jYfPwlDZYEA5swXPQiLx67MwrN92EVsW9Yavt+txhgdPQvHnsmPYe/Q2LDYHhHwumtUtih/61kTOrOkjzvc14Q7Q3cSDMsIkkpaU1VZG4PqFs+Dw+NF+6C4wij2gjXyM548eIm+hjKm8qYqMwKq5M5mn+ZCoKLYQE9QCV8FoxDgfH8z18GDKrxRck9o75dtvCYX4zdsb4qzZsWj7wWR9R8mb/vGDe5gdEoLc1rdVXWqVJ7G5kb6+GO3nBwNfgBYf2GFRu0kLrFs6Hw7Vc4TLsjgFX6gK4ql7AyGfx1R408ODdf60Sfh39zbY4/jFFilVDiN/+Z0t4GkNJQEO7NjChN/u3Yz20Y3FQynBwC41MLhXLWTJ9GlGSgxGC3qPWIdcWj0mhIaxLgmis1aL42Ixfj9wA2WKZUPPton1ClZvu4jRv+1GUasFwzVaKOx2XNKKsHDFcZw89wgb5/dwB+lu3Hwm+AdmZqeMyvPHD6FVRcHomXiemqA9AIcnwPUL5z4oQJ81ZRxeB9MUNtCwZhH06VgVH5Nx0/cgwGzB7yEhznnzciYTGuh06B0QgMUeHvC32TArNBT5Y8RjyQP9L09P3JFJsHVJH+TNmVjfJZYnL8KxfNN59FWp0Fr31n6titGI4iYTBvn741dvbzj4fPhb9QgJU+PW1UtMzf7n32al2laXZsNPHjkEB4cLo8iDjbaxRLwuBCKTGl0GTEjz7gRKiO/csBqr5s+CXkv9lNH4B2bBkLFTmH1fStl+8KrzfKt0VPD/GIyetgvBz8IwNziEuQHE8lKtxmiHAyP+twMrZiS2V7557w3aDlwOmcGMHio1slmteCIQYM/+azhy8h62LemLfLmS/s25eTfuAN3NF0e0BVVyB/fo26hFKaME6FQpoDa+o3u2Qx0eDrPNCrvVip5qtTM4j6WU2cws1GjRGx0Z6Zw/48SIx4wLD8dQLhdnjx9Bldr1k/yb/27fjPImU7zgPBZqn6fWt/skvDZ7EVt0P7Tt8vfFq/HzoD7gvH4IiKRwgAOOScfGLCb8uQyBWVPfTpcc1NI3fkg/XLt4HhqpH6w8EXh2K/g2E25cu4ofenbAvA0706yqRFUYmolct3QBgl9HV5djoVbBLv0GYVxbySdXg935701EaoyYHh7hDM5jIYHB00YDlq0/gx5tysXbRAWHaTB2+h42/0jiRLG30G+olsGA0QAWrz+L73tV/8jvyI0bN18iTrvJZII5u92GR/fvsFn15BLSSXH57ClsWbWMnZfLRFj0a9d0b22/cP051m6/hIePQ8Dj83Dp9iv8GKVKJAandDhQS69n8+aTwsOZG0ssATYbxkdEoJ84E5ZuPIdpo5IWMvtn/3UoONGz6wmhv0nXr1IoWGW0XuW8qNRyGowmCzatWILiZcujWbvUdbf9MGkaS46fP3EUHEEI86TnWQxszeg9bDRqNWqOtGbr6uVY+uc0phRv9MjB1nquzQxzWCTGD+6LqQtXokS5iu98npCgNzh3JVq4tVjBLMidI+MGoa+CVdh//C4GRUbFC84J+i11j4zCX6ce4PnrSGTP7BXv/90Pk7chQGvAtJBQ5z6BkkbUfTEyUwBG/7oT/yzu89Hf05eEO0B388VBrXoOGwnchUFgNUBgMbAFnMRH9BIfiI0qFvz9OXE0a3Nq270vPmdoDnzsNz1x/84tFLFa2RzaPaEINj4P8z088GNkZLz/yHSoDOfz2VyaKxk8yrDnsdtx5sihZAP0iJBgZsmSFBS4a7NlZ6JsKVElP7JnB84cOQiTwYDchYqiSbtO8VrJs+bMjeU7DjGVV/JJJYuxQsVLonr9JhAnYQH3IdDmgLottDJ/SI2R4Nvevlc7h4vw0FBsX7sCPQcP/6C/8+blC6xdMg9b16yALk7mniDf8hH966N1o1JpOqf98k0UVFojMvsrmRVQarh08yVyO+wIjLPZi0tlvQHTXkUiUm2Ad5zn3rDrCtMn6KFWJ0qPFbRYUFOnx+ot5zGkZ7WPOkvvxo2bL5PsufJCKldAr4+AmKuCyKxhbdJWgZit9XSe47Azm06DTodxM+exmeWUQkHk5BGDnZf/N7Jlura2U+Dz8x97sfKfC8gMOwrrjQindUEgxBqlkiXG/RKIwgXx+ShmNscLzmMhJfbaai32Hb6VbIAeHKpBZpsNSTWr5yAvdA4HPVuXY/PJ8//XGb1HrGS3/W/Et2iW7wVQchS7TOv2hZPHcHjXNkSGBMMnMDOztytdqaqzlVwskWLS7MVsHPH4gb3M8o4S8HWbtYKPf+L2+w+F9h/LZ/8BvcgTPJsJ3qqIePslC4eH+b9NxqIte9/5XP8dfHuflg3evysjrYhQ6fE6WMXW+SwBqXMUuHLrJewOoLLR6PJ2un5WzP3iBujX7rzGrUchmBQVrUOUMKHTKTIKv13nshZ4dxX9/XEH6G6+OAqXKB3t7R4eBCtPCKNYCa7DzmaepAY6MDugk/iyoH3Zn7+hYNESKFq6HD5XZo4diaC7tzEzNJQFOgQdEk9Ru7GXF5sH76l5G/jR8k0iMjSDnhQeViuMxvhWawnx8vPHs1cvk7z9uUgE30xZUqTa+nP/7oiMCHNW90/euIpdG1ej15ARaN/rrVcpVTiq1GnATunN+sXzYOXyIdeFwiyQQqXIGse9IAwyQzi2fUCATj6pqxb8hYM7t7JNSywUnDarW5wF5lXL540XrKo0BkSqDPD2lEL5HpX0/849xB8Lj+DynWilYT6Pgya1CmPMt3WRLc4Cmxw8LgfWZOJn+m1FP3f83o2HT0PZ6AOpC7uihMmEg+FaJihDHu5u3Lhx8yHQ/HO5KjVw7MBuODg8Nr5GyVWxSQPvqCfsvIUvhkYagIunj2P9kvno/u3QFD//stl/4MmDe+x8hVK5MKhH+qrbU2BOp0FRUWik1zu7457z+Rjv7Y1fvL0xKywsXgKUhGD9k1vr7XYm8pkc/r5yvObxYCIBNBe3kzCdiE/2rtFrUq/2VXD64iMs3XCSjUS17r8AK/8dCIFAgElD+uPqxXPIS9auRiOeiMUYd3AvylWpjrEz5sebWS9QpDg7pTe7N66FzWaF0KFjn12UMpvTvYD2hXJdENunPH/0ANnfoT9zbP9u5/kW9d4/QKcOBOo6k4qFLOmRWp6+jMCv8w5h/393YaUomxx1imTBiIF1mLBgSuDFJEws71jrE87oP3gayv4taaJfTGJibf3ofu4A/f1xB+huvjgunPwPUeFh0MgCon21Yw4yankgPFXPIDJroZf6wM4VQGbVYfu6lZ9tgP7q2VOcPXkMP0RGOoNzgt5RVaMR93Q67JXJ0FGrdfqWEnSe1Fwb6PWJnpOsWe6IRGj1joWIrNwW3LnFPNJzJWh/ui0Q4Dqfj1Et276zNX/Ct70hDwvBH6GhrO2OXR8VhXUKBf6e/Qey586LijXq4GPz7MkjtkCT3UukZ06noiyJ1GkUmdl8nEwfyuzOUmoxSG3zJ/49wObLaUOYUL22Z5vyGNa3Lgrkia+ge+9xCKYvOoKDJ+7BZneAz+WgUc1CGNG/VrKzgwlb0weN24LCZgvGaDRsJvGOUIht/95EswtPsOvvfikK0mlxX7X1Ih7x+cjjYrzhmEyG4vkyJUogSCRCRPJ5Lv16iSgulwX/bos4N27cpFV3GXVcmYVyRHnkYMdsQivLBJk+DApdEDRib5jFSmgtXti1cQ069v0mReKwL58+YQE6wefzseS3buAlSEqmJbR2LF5zijmkNEmwblP78fdRUfjZ1xc3hUJWMY/FwuEwMTfaHbjqmLsslSD/O2aB2zQsgVnLj2OfTMY0ZuKi4XCwV6lAiwbFIIpj7zlncidcufUCl248w6NnoRg3ZACyZsuGe5fO45fwcJSOCd5oPSCLuF9Pn2At5t+OnoCPDa3F1DVJbe1hPvmdQrQODthe0MoXsYTOqaOHkg3QqQPu3Ilj7HzWQC+ULpb6sTtKwv+57D9s2HEJGkP0nq5M4cwY0qcm6lbJn6LnIM2A5r2XQKgxoK9KjQKkXs/jYdc1C7oMWY1Fv7Zn+4d3UaFkdibsdlQqRTutNtHtRyUSluSvWDJHvOspqUBE8HjOPV3CtZ6QSVPereImMel3tHHj5hOxbe3fsIlk8YJzBocLlTIbCx8oa0q36fhyXLtwDndvXMWxfbtw8dRxmM2us4KfgqvnT7P/pNUNrqvdNH9GNmf3Y+zRwrlc/OHlBROHi9MSCa4K4x8gabFcqVAwZfeGrdsn+7frNmuNnHnyYUxAAPZKpVBzOFBxudghk2GCvz8KFS2BKnWTr3TT3HxwSBBGh4XFO5DTq+2u0bCW/a0rliT7HKQm/++ubVj21+9Yu2gOnsaxAfwQLGYzeA4bdFLqpkh8KGTXAzj57/4UtUJSC3vr6mUxpFvbeMG5l48vBo74Cfsu3sHCX7smCs6v332NZr2X4Pqx2+gXGYWpYWHoFEV2gDfRuOciJsbyLqiKMXrqTlQxGDAtNJQp7hewWNhma1ZQMDgqHf43N75SfFLUr1YAOTJ54A9fHyY0FAt9e5vlclwUCjGgW5VEjyP7tZfg4lqC3xxBYf5BpZw9t8BtuebGjZs04OCOf2Aymdi6HhucM2htl/nBzJdCbFKxq0iMTKtW4dqFszh+cC/zzSax1aRYPncmrDFJ8e7ffI9iBdPXTeP56yg8C1KxNT2pqqSHzYaLMRVo8kPfKpPhkVAINZeLDQoFW9/jcoZs1wRC9Gif2PM8Lrmz+zB196UeHlge49Bi4HBwWizGqAB/WGTiRNohYrEAWxYOcI5QHd23Czs3rkXXqChncE5wYsRr26tUOLh1I9NiSS5JceXsKayYOxMr5sxgxRa67kOxWszgwAGDyCORSwxBCR7qtKDfRnKQ2wrtG4gW9UukelRLrTWibf/lWLfxLBqGROB/YWHMgSfq+nP0GL4OK7acS9HzTP7rAEQaPf4MCkZznY6t9TWMRvwWEoryRgNGTd0JsyVxcj0h3p4ytG1cEhs8lKygExdax9d4erDkTcIKf/UKuSEV8dm+0BWkieAlF6FCgsDeTepwV9DdfHHcunIJOqGXS+EYB5fU3eUQWKIXQRIJ0Rk1GNa9nfM+cg9P9Ph2KJq2T6xc+TGgdujrF88h5PUrPLx7mwXoSU0nx2bMp/r6wscBPOMAIpEYI8f/D//u2ILxF86ipk6H8kYjs1k7qFDgDo+HQaPGwy8gea9Qmvv+delazJkyFvOPHsJcT09nW1T1Bk0w6KdJ75znu3zmBPLYHcjhohJL305trRZzrl5iSRFXVY2zxw5j+riRTHWVI5IANivWLJyN8tVrY9TUGUxc7n2RK5RQR0UwcThX0G/FweUxC7akoNs2rVyKDcsWIiIsuu0rlvy5A/BD37ro3rZSjHL5IZczhyOmbEdmXbTYipbDwRIPD7YxstMnZLSiZZ8lmPZzC7RtVCLJ17Hv2B2o9NFqqglTDV52O1qp1Fh27A4ionRsUU4OmoVfNasrOn67An25PJQ2GeFps+OaVIIQcNgMeYt6RV1W3ksXyozpd4FhYdEVFHotITweFnt64BWfj3ndP676sRs3br5cSEncLJDBznW9laWWd4X2TfQsut3KAtjx3/WLSVXTSJUA9Vu0xoARY6P9uWMIevUSOzdEz1hTW/eM/pTYTx/uPgpmM72RUdF7EkESI0K0XtLxdJtCgStyGd5wudA7gL4dKsDHS4bfFh7BHbEItbU6CB0OnJVIcEIiQeMaBdG6wbvbyCcPa8SC7SVrT2OL8a0DTplCmbFsbAuXtlk5s/li9azeaNprLrusUqngk0TQWsdgwBqLBbevXkKF6rUT3U7WvhOHDsSLxw/BEUR/F47lCxGYLQcmzlrIOu3el9wFCuP29auw8ZMeG6NOOioGJMfRD2xvn7vyBJ48DcWM4BBksVrZeCJ1LVCRhRg3fS8ePQ/H2O/qx+tWiEtIuIZZmX4TpU40xkj7RNoDDBSJWTde09rvFkGe/ENDvHgVgbGXgAI2K3IYTXguFuEuj49KJbLjlxGNEz2GvNH7da6MOX8fZ6+hsV7PujYpqbNTJmNWvz/3qAaxyFVPh5uU4g7Q3XxxUFaT40jmdjhYu5PArIPEFMXmjzUKf+f8sV4fhnm/TmRV0Tbd46tQvnz6mLXJ3Th7ii3xxSpUQbMOXZAtV540ee2UMZ7/y1gEBQfFv14kQkUX8z4UyAn4fJSu3wQSWozzF2QKqDKFglW3SeRs97pV+DcshN2/eKmymNx7IMpVrZGi10Ot3T/PmIfQoDe4fe0y+2yLlirLfNhTgt1mh9CRdAacNhKx90vI7auXMWX4IBiEcqi980d72jscEJuicOHUcUwd9T1+mRutrvs+1G/ZBltWLGFCgrG2bnHh2izg2m3wDYhf8SaeP3mENYvmYseG1YkW9Wrl82HkgPpoUqfYO/1V44qt0CzhCF9fJsbTX61m9jaRXC5b7L6ftA0RUXr071QpyXk0Hw6Y0I8rCpvNbE7tVZDqnQE6QW31RzcPxpa917DvyG2EGcyonS8TurcuixKFXOsO0HtdMbML+oxcj/E3AT+Og82jPwMXUrEAiye3QakiH9/T3Y0bN18mLBZMbrGPvhez8PLUvGJJV7UsIN788f5tW5irxpS5y5zH641/L4bFEn0s9fPyYEnSvHkC0L11OVQrnztNRC5JGXvYxK04e/2F8zqew8E632JneONCXXKRPB7qV80NXx8PZPb3QOuGxZEjS3TgnDeHLxauOomZMfojOTN5YHynSujZplyKWvPpPiP61cI3XSrj5MUnrCuLWuML50u8/sWlSZ3i+Om7Rpg6dx+7PMnhwAb63BLcLzbxEFePJRadRoMf+3ZFWJQKEZ652V6MPcaqhz04iN22cMue93ZUqd+iLXZvXge+NYkA3OGAwGpEQDKWg9TefvzgPmfSpkbFlLWjx2K12rBu2yU00GiZJdlkb29cEYnQQqdDtZg9BCVUVm06j2cvIvD3H51dfm/UaUEj5yQY6Aoah1ByovcEKYGKB2tnd2dB/8ZdVxAcrEIOfyVGNC2FelXzJyleO6JfTdYRsGzzeaz39ECA3Y4gLhckNzeoaxV80zVxl52b1OEO0N18cRQvWwHnL16EzhEtBBcXrt0KoVkHjcwfHurnsPLECPfM5WxxtnJ5UCuzsna5lfP+RMNW7VmwS1AL/B9jRzCVykox8zonnj3Bns1rMXzK76jduMUHvW5SLp/4fX+UMhrxo0rFBLdormiUry8Wenggd3g4myuOu2Bv9vBA7aat0LrXAGTKFAhhnPZiqkiTAFvbHv1Ya59AKIRE+u7gzBV+mQJRI1OTVD+uQLESzBoujMtlXuwJOSOVImeuPC5V2tctmcdmwyIV2d5+jxwOjGIvRICLS6eOMxXYlIjMUKWa2tf2b93IuhKoWlK+ag3wBUI2q2gUe8ZvkXQ4INOHQCgWo2rdhs6rr144i5Xz/2LtfE6Ln5jgtF2T0hjevz5TZk8pj55FV+cpGF/k4cFEWf4KDXV+VvRMJKy32GbD1LmH0LpBMfh6J+4aoA0DyQTS53xRLGYtihQc08KfyWZDaEyremos3ChL3qtdBXZKKVTJ2bakD85fe44Dx+/CZLaiUN4AtKxXjFkUuXHjxk1aUbxMBZw++i9Lptp5Cap1lMw1UgJeBqX2NVNzD/PKF53ojamhUxu8hS/C5TMncen0CZa4puP6vq2bnE9TVGVEYNQL3Hgehk7/3UWnZqXw+5hm70y+JkdYhBat+y0DJ1yNnyKiUMFoZC3rv3l5YZ9Uyi6XiZOQp/Gyed5eyOavxLhB1ZAlSxaIEowSNa5VmJ00OiOsVjs8lZL3SiSQgGeD6gVT9ZjJw1tgz9kIXLt4DuEASM99UYKZeGq353I4yFc4cffVoV3/IDw0GGGUiI/Tgm4RyBCmyA5e5APs37YJHft8k6LX8+r5UxzYupEl8m12O4qVKoss2XOy63VWI6uWx4XU//lWI+ono6lDI2xajZqdb9u4TKq1VKLUBkRqjUw/gD6LC2RRGx7OrMniOuzQXmDCGWDvsTtoVidxBdwjZg2nvSGNoN0VCllnBXWsUSKeuvB0juj1O6VQIoC+89R87/T7/2V4Y/TtUBHbDlxHSLgWmfyUaNuoOLJkiu62dPNhuAN0N18MURHhMBkNaN6pGy6e+g9yXTC0sgBncEdZdA/Vc3ZeaYwA7FZEKTMlMX/sB1lEBJs/btCqHV48ecSCc2oXHxLHW3ygSoU5np6YOe5H5ClQOJ5tWGr5+89pKGQyYWJYmLOlPYvNhhlhYfjOzw/9/P1Rw2BgrVH3RSKcE4mQr2AR9BwyEuqYhSOpA2lKRc7SmtpNWmDlX9Mx29sbP4eFxVOI/U8sxhmRCN916enSFuXSmRPQyANdjiqYqAIiEOHkoX3vDNBpw7Vp6Xwc2b0NDqEUep4UHIcOL9euhEDAh8BqhnfEI2jl/rDw36q4S0wq9Bk1nlnC/Lt7OwvMr186H++5KeHRpmtPdO43CI0CTqb684kNWmmxJUEWEmpxlcjopNFgr0LO/GoHdK6c6PbGtQph4p/70ScggM2KUyKH9AJWKBSop9fjjVCA4vkCXLYppjW0KaTZM/f8mRs3btIDqmaSQFylWvWYU4a39hXCFdnYSBLD4YBcFwKh1QC7QASOScuSsLHBeVzMQgXsIhkO7fyHBeh3b1zDm1fRVe1SVBFWRc9MO9RqHJJIMGvXFRQtEIiebcu/9+snX3J1hBYLgkKcx3uqME+IiGBdVON8fFDabEJRowlhPB7+k8sglImxZlo78LjJz2SnJjBLKyjA+3PFBjQuV5h1lF0mETnyO4+5/QWfj7VeXuz7cjVad2z/HpiESpfz4ZR40QsVLCmekgD9ypmTWPLHL7CDC51Azrolg/fvBSxGiMQSeEc+Ztaq8VTcjWEoX70WSlVwXfWlufM1i6Pb+Inh/eohtUjEAjamEMHl4qxYjMImU7zgPBa6rrDVgvXbL7oM0PPm9EXOQE9MJ3V+KnzYbEwgcL1CgfxmMwvwOTwSmU1dkuV9oT3FsD41P8rf+tpwB+huMjyk4rp+6Xzcv3mdXSZf1GJlyuPGpfOQWzTQ86Xg2O2QWNSsG84/cxbkLVSEicRQBd0VtChw+EJExLSGU1u70uGIF5wTdH5wVBQu0dzNprUYNGbie70HSgA8uHcH4zSaRPPmJK72e2goBgUE4ErWbDhvNsM/MDO+aduJ+YtyuNxkA/RPiUyuwE8z52HykP7oIxKhlkYDhd2OS1IpbvD5qNOkBetSSIiRhHIcDqa07xIOJ3rhdqE8mpD/9u9mwblanhl6ibcz4NdQ+7rmORQyAbL6B7DZt7gWc537ToRGo0HzSiXw8tmTeM8Z6O+B73vXQf/O1eDFWsZTH5zHzm3LxQJsl8th4nKZ2IsrqGsji8OOF6+jXN5+4+4bVhGiBE53tZpt+qgiQxvKxR4ezHpvxcCPr5Tvxo0bN2nF88cPWUB+5sgh2O02cLk8FC1dFvduXocg8j70fDkcXC4kFi04VjM8vH2Y7erF0yegSWKtp/XABAHCQoLZxdULZztvahT3btQqbTDgikSMJWtOsXGf962ib9l9BTW1ukTJWFrtKCHfKzATnvt54qnNAYVMiF4NizMHEG9PMYLevFs09FNQS3kIm+f3RfPec2jpxipS0xeL2ejdCamUVbC/+3myy8dSZZosT5PCxhVAl4K1PuTNaxac6/gyRCmyOosvGocjWovAEI7ipcsxnSIlXWZWfWI06dQDPYcMT/L7PLVsINMlIJrWKY7C+ZNuhU+uM6FmhTzYf+Y+jHYH0wVKigJGE64m0aL+JlSNsEgdAq1WDFGpWNWdfkXULj/L0xPb5HJ0a1UW/j5vdQTcZEzcAbqbDM2ezeswd+oEWEQK6JTZYOfyoDNrYbh6GZlz5EShoiVx7/ZNCIUCVKjeBY3bdoSvfyZ2IKcAnW8zJjl/7LCY4e0bPWt9/ewp1tbuKlyk6yprtbh+5v2CNCJWiCxbEgFaLpsNEg4HrXr0SzQXb05iFulzoXTFqpi7aTd2rF+FY4f2MUG4XPkLYXSHrqhWr5HLRZEq/hKZHFqLjlXLE0KCP1yzHlly5Hrn3ycvc7NIyexU4kLVlghZZvAi7mPgyJ+Rv0hxvHn5HBaLBZfPnsZv40Yxi7W4FCuYhWXPO7UonyZ2YVKJEN90r4rpi4+yhARV0l1Bv4pwDheeHhKXHQJ/LDzMWuGHRUXFszijxbuZTscSANRq7saNGzcZkYd3b2Fkny4wWu1QS/1ZmzK1JV+/dg1iPheNWrbF3VvXYTaZUahYLSbymr9IMfbY3s3rQh2aRBLb4YDIYYGvfwA7lp46+u9bEVMXd6+uN2BKkArBYVoE+idem1JCWJQe2ZNY6/kxARo/f16sn9M93m2mz3ytb1qnGH77qS1+/N8Wdnm7yYTiBQuhR5eeaNS6A0vYuyJ7rjx48foUC6RddcxJbAZky5m0SGosB7Ztgs0BqOIE5wwOh3XjyWx6+Pj5Y83Bk3h45yZ4PD4KFi+Z5OuKtdsb+8cO5+WJw5rhfRnSqzraXXgMqcWa5FpPBPP58IxRx0/Iis3nAZMFv4aHs8R9LN42G3qpVPjD2xvFCyQvAOwmY+AO0N1kWCionf/bZOgk3tDIMzsP7NSyZhB50pEVNeo3wYhfpid6LFWgi5Ypj2s3bjL7lYRt7uR/TTPbzvljUoFN5rXQbXFnklMLJQ2IxwIBsroQUWG2Jw6HS8GyjEDWnLlZd0FKOwz4AgFb0LetXwWD1Tt+ayJlw3XBLLCnFvrkMBr0ePrgLvS0YLuAntcukrNuC0oa7Nu2BXv+2eC0UomlUs06mNS3GOpWK5QmAkFxIVV0nd6MBWtOMgXU+np9okTQYbK5cwAt60dvOONCYjC3H4divEbDfoe07UuoDkviQ8N/2YFVMztDKHAf9t24cZOxmDXpZ+itQKgyl7OVneyxDGIv+Kmf4v6dW5i3/m0gFZeGLdthxbyZ0Fr9ErW5C80acM061o129+Y1NipHlKZ12cVzUVs0QVKz70smHzkeabSAC0s1qoY+lYhQPZMHMiIj+tfD1VvPsW77edgdDqgNRjRu0zFZx5UmbTvhzNFDTDPAKIk/jicyqcAzadGkbcd3/m2agTfwZfH1ZGLhcKDly3H1/Fl4+/qhfLVa73w+StaP+bY3NNroanfvDlVQpvj7W4eVL5kDC6e2x6Bxm3GOy8UrHo+NMcaFrjtH8+lNXKvE79h/nXVfxAbnhyUS1t7+ms93/j6nLTiCahXyMCFBNxkXtw+6mwwLzYzRMUorozny+EETVcW1QiX2bFmfpI9m36E/QuSwwlf1DEKThs2oU0ZeqX4JmSEcPQYNcwrEFa1QGaflchb8JIQMxOi2YhUSzwanlMzZc6BI8ZLY4uGBhDlyOgzTAVguk6Fija+nTblj32+QOUtW+KufQK4NZqr7IqMKPupnkBoi8O2o8SlQdY3RH0jiVkqqmAw6/Lt3F9rUKIft61c5g3NKEjRr3xmbj5zFwo07Ua964TQPzglKNPz8XT2m2vpGJMQEHx88jPG113M42CaTYYGXJ9o2LM6UehOi0UXPsVG7JC31v3h7s4p5A70es0JD2YmUYs9ceIz+ozfC5kIx340bN24+5+r5o7u3oJL4vp0zj4EuqyR+eHj7Bh7fu+Py8U3adUamLNngp3kGiSEiugPLZoFMFwofzUuUrlQVZSpXw/5tm52PKZNEuzO1a+fI5IFMvu/fQtyhZRkcl0lZMJaQ/yQSvAYXHZII0D53aI1cPK0b8hYszC7TdzJ5+HfJFjDo86/brBU8NS/hoX7J9mOUOFFqXsFL/QJV6jZE5dr1U/S3U3CnFL+XxTOnOXVn8uTww6wJHfChNKpZCGe3D4OPpxRj/PyYYByt23Si8z8H+CN7Zk+0a+z6+6f1nubOia0yGWZ4eSGnxcI81eeFhDAHGEuYGi16L0FwGEnHusmouAN0NxkWsjyzCaWJFuxYzAI51JERTFDGFQWKlsC0xauQK2smeKueIiDsNnwjHsBf5GCV3rit5M06dIWKlFQ9PVlAHgudn+/piSgOB806dvug99N3xM94LhZjlH8AE4CL4nJxWyDA/7y98a9Uir7Df47n1ZoaqK388tmTrK3/2aMHyAgolB74c+UmNGndHt52NXyiHsNL/RwFc+fA+JkL0LhtJ5ePo40AeZKHhwRDKBKxyrjEHL+90eGwwxHxGo57pxEZ9Aqvnj9z3uahlGDUNw3x9NQv2PlHTbTNex3FTWvT/f3Wq1oAa/7qhuBAbwzx80P7LJnRMTAQyz090b55aUz/ubnLx2UN9ASfy8FNoRCnYtRhx0VEoI9azVrc6dRXrcbP4eE4dOoBU1d348aNm4zCyyeP2b+kyO6K2OtfPI2+X0Io0f7H8vUoV7EyPLSvERB2B/7hd+FpCkfDVm3ZekLsixOgX/P1YTaXsVB4eUQiwTGJBH06VfogFfdebcsjc2YvjMrkjz1SKXPfeMnjYaVCgZleXmhepzDKlcj+Xs9N69+VWy+x58htnL3y9JMkZGnees/CTk7XEPpc/1sYfzQvYWA9bOI09B/+E7IqRWw/5h31FFlkPPT6bjjG/Ppnkp+3Rq1iI4u0xyEHH4lVx4otiSDhQKsWpcpXTNF7uHzuNJbO+t0pgrd2dp9UuaAkB82H7109EDmLZcMUb2+0y5KZneh89iJZsWlhryRdT3Jn98EtsYj9NlcolWil1WJsZCQbcctltbJk/F8hIdCHazFz2bE0eb1uPg3uXkc3GRaxRAau3cIOvK6yoqTGTQd+kSjpg2qRkmWwaMte3L91HW9evmBBYfFyFSAQxFcTJXV2WkD+nDgaF2UyVIlpJz6lUICmlIdOmIqceVPni5mQgsVK4tfFa7Dg1wmYdP9tEBXgH4Afh/7I/M3fZ7HetuZvbFwyL56QXOFiJTB4fOpfM3mYXjp9HKePHGLt4/S5kMeoj3/6zDcrPDxZsqTP0FEIC34DkUTiUgWWiLbH2Yh/Vi3D6+dP2XW+mQJRpGRpPLl/hymz64RKIOIVHKHPmaprXHJk9cHQ3nXQp2PVNFuI30c07uzOH3DkzEM8fBrKZtTJ+iS5WUdvDymz19l2+CYCTGYUMZlQ1oU6bAWTCYVsVqzfcYnd340bN24yAmKp1GmTanORkGf7gBhXjaSgtubJsxcj6NUL3L91AzweCcyVg4dXtLPF+ZP/MT90okylqjBEvUFvgQCV9QY233tdKsYDLh/tGpVAr3bvr+BOkAXa1sV98NPvu7Hg+F3Miykuy0R8DGhXAT8OrP1e3VonLjzG+Ol7cP95dJs+kdVPgZ+GNECLeontzd7FrQdB2LznKqvE+vnI2XsvVjBlAmn5cwdg5cxeaN0/Ovkx/JfNWF52AEqUc23bSQF4q6690LxTdwS/fskyIgGZs4AX07qdEBpLW7t4Lq6dP8MuiyRSVKldHzwO4Kl9hci4c+ixInEWI1p07vHO107aMz9929vZfUlz5xVK5UZaQu3nO5b1w7U7r3D2SnSBoGKpHChRKEuyj+vaphxG3H3Dxtjo3XXUJC5AUTddY7UGW/dcw6ShDSEWJSG26+azxh2gu8mwVK3bALs2robQrIVZpEicLTWr2JwRVVGTgxZCqqbTKTmoBYvU33dtWI2rZ0+x68pXrIxm7bsiV/60sbQoXLI05mzcxdrCaLOg8PREoeKl2GbifVizcDbWLZ6Lxjodmuh08KGNhkiEtdeuYGSP9vhz7VY2H54SqCo9YVBvPLx/F9kcDnhZrTgnFGLtwtn4ZvQE1kaYXpBPenKvk4Lzhb9Pwc4Nq2ESe0CvzM6SNoYoNcL274GXrx9Uj+6ymb+ErXZ5ChTC1CHV0LpRKfD57/c5pyX0GupXK8BOKWXs4HpofuUp7odrmShcUhQwGHEjzubNFVqdCU9fRbBFPU92n3Rp63fjxo2blFKyfGUWfEsN4dAoEgeINPJEoqIlyr27Okqt7nRKyK5Nb7uk2vfshz4VI7Bm+yXsPngDj/Um5MsTgLFtyqFO5XxpckykgHfJbx3xOkSFm/eCIBTwULZYtiQrp+/i1KUn6Dp0NQoZTfifSo08Fgte8fn4x2jEt+O2wGq1oU2jdwutEVR1HzVtF9bvugJvOJh47WmhAMs2nkPrBsUwc1xLCFKwVrZqWAojB9TH9EUHmTf7yP7dsOHQKZYsSQra62TOlvyc96kjB/G/kYNh5UugVWRhKu9Ciw5HD+yBXK4EtGqII+9DR6r+HA5kVh1gNeO7MRNZISQ5aH8wZeT3rGBDlK5YBWMGxdXzT1soIH9XUB4X+g53HryJAxces+8lrlBcXAqYzdCbrQiP0iNLgOtZdPpNPHoezr5vqsy7A/nPC3eA7uaTQu1J5Gfq5eObbPbbFWSlVrhUWdy5cQ0RCGQ+mhSU8WxmKHVB4NtM6NBnYJq+Xqo4Dx47BelN7gKF2OlDoBbvDUvno7NGg65xsqxVjUaUDA7GEC4Xq+f/hTG///XO56JFa/w3PRH+4B6mh4ejsNnMOgh0HA5rsyIlff/ALMxH9lNw8/IFFpyr5JlhiKPWbrQ5oNYG4fXra4keU6RAVvxvZHM0r1ciwwehWTJ5Yuff/dGo6wK8Sca+JVjAhyezhUuMWmvCnOl7sX3/DRgs0S2CubN4YXDvGmifQech3bhx83lgMhqZbSmJhcVWrVOToG3fewBWzp0JO5cPvcSHjbZRKzMF7aQZ03HwiPceAdPrdDi8a4tzxGlILS3EYhm+71WdndITqqR+qJgXrc8TZ+xFAaMZ/wsNc27slRYLfo6IwO9eXpg8az+a1S2SIpHQ3xYdxsZdV/BdVBQTLaVH2GJa/OccvAEfLxkmDo0R0H0HU0e1wrmrT3D83ANWdBjzTS/M37DjvYsO5LM+Y/woGIUKRCqyvRUHFilgsHrBoX6CarXqwy8gEy6cPs66/oqXqYdm7bukaE+1Y8NqHNz5DzvvqZRi+6xmrMX9c4ESI3/P6IyOg1fh1pWnTBfJVX08mMcDlwMo5YkTPna7A0vWn8XfG84hKDI6oe8pE6FL67L4oW9Nd6D+meAO0N18EqjFjKq7F079x6rdPL4ANRs0Qbdvv0dAZteK2wmhoGrirIX4ZcR3uH7hLDgCERzkpWnSQSyV4cfpc1n1+Wvl6L6dEDgcbEYpIXKHA81UKiw/vJ/N6CdnM0L8t38PHj28j6nh4SgSR+Fc5nDgW5UKj0UibF624JMF6Hv/2QCHUAKDxDu6Qq4OhSP0KaCNZBuLWChD3KNtJQzrWxcF8mRMRfykoCz54D41MGXWAbxRqxHoQh32vEiMyY0TV1HUGqqy7EbYGzXaqNQoYzJBw+XigNGIYVO2IyhUjSE903ej6saNmy8PUkWntf7Qrq0wxyQPqbW868AhKap4x9Kh90Dotfb81+YAAKxFSURBVFpsWbkESmM4HHwROFYT4LCjXa8BaNer/3u/xiP7dkKnjx4L6tC0LMTijNUSfPthMHPymKRWJ9rUc2LaoL+h+fkzD1G/evLdflFqAxavPo02Gg0ax1GZp3C6nsGAEBUPq/+5gKG9a7BW/ZR0hG2Y2w+lGv+C4FA1zh4/igXT/4fvRo9/r/d68t/9MOi0UPsUSDTaSAr9GpEXzh4/jI1HzqHf8DGpeu6njx5g2k8jnJeX/NYN2TKnLpn0MRAJ+Zg2qilqd56PoxIJ6hsM8W6noH2PUoE6lfNDIYuftKL90bTFp7Dn6APU0+kw1GAA3+HAGa0ES9acwvXbr7BqVle328tngDtAd/PRuX7xHH7+thfMHAGzR7PxhBBYDDh8cB/On/oPs1ZtfmeLUyw0M/7b4tW4d+s6zhw5BJPJiJx58qNGwyYQS1z7SH4thIeGwN8RHUS7IpfFApvdDlVkxDsD9NULZjHl0BIufFhpiWyg0WDW1UspCvbTgycP70MPERw0Xx7ylCVp4r1GLg9+Ph64ceAn+Pu+n3dtRqBjs1L4e8NZ/AwHBoRHOGfRL4hEWOjjzdRh27poc1y84SzevFHjz+AQZLe+lUGkx5Nw0e8Lj6BV/WLIljm+BY4bN27cJBecf9+tLUJDQqAResLsGcjU06/dvI1bA7pjzG9/oVq9RilOyPf+fiQTbD26dyerxnv7+aNW4+ZJ6pKklP3boqvnRLfWKU8afC4Eh2qca7or6JhONeA3MfdLjhlLjsLicLDKuSvIHWStUomTFx+jae0iKXp9gQGe2DivH+p0msUq2kv+/A3Fy5RH9Xopq8LH5cWTR+AIJWzfmJQ4sEUXgtCgN8ieO2+Kn5fcW6i6b9BH7x36dqyKtk3K4HOlQG5/tKpfFPMP3YQ+isu+L6nDgUd8PpZ7eeKNUID5fRIXTM5fe47dRx9gaGRkvMC+sMWCckYjxlwCtu6/jo7NyGjQzafEHaC7+ajQwXn6uB9h4IoRzuaEuW/9TMn/UvUEC36bgilzl6bqeQsUKc5Obt7i5e2LUE60VRcduBPyXCBgwixKT693brLevHiGnHZ7knZlVJEnEvqHfwwiw8MQ/Po11M8fwpFQvVUkBccvJ5R8B3L7cb/o4JygbPnmhb0wcMwmTLrDgSTmCzM4gLJFsmDBr+0TzThSRn3j9suoo9Uhk9WKIB4PEocDHjECOe21WuxWKrBxz1WM6Pdu71g3bty4IaglnYLzEI9c8QIqo9gTnpoXmDlxDOu6Sk0y3S9TIGt3T8sxuzP/HXaOClUumyfDfXl+3jLnmu7rQiCUuqfoaO7vk7QXeSyHT96Lt6YnJPZ6kymun827qVGxAIb8PAl/Th7LLv80qA+bR8+aI2fqBQPtVtY54RSBiwMJCRKpHZmc+9tk3L52xSlwN2vih1uqpTczxrZkXYFLd1/Bcg8Ptt5rHECgtwyrJrVBycKJZ9s37rqKLA476hgMCI1Rxvex21kCp7jZjDJmE9b8c8EdoH8tAfqjR4/wyy+/4MqVK5DJZGjRogWGDh0KodB1BizuxnHJkiVYt24dIiIiUKhQIYwZMwYlS7rnITMa9F0+uncbF04cQ1jQa6g9cyc6uNJsmUrsg4unj7PsJy3Ebt6fmo2aYeXcGdglk6FDgjZ3A4eDnQolcuXNj7PHDqNslepJeoq/efGcWcw84/OZHQwphCbkkkgEmUTCVNc/Fs+fPMKaRXOxc/3fMBjjVw74Yik4vtlh9c4GLuyQR95H8zoZrzLyPtAmc9ff/XH1NqnDkpo9B5VKJ60Oq9ObEa4x4jWPh86ZMkEfs2iTGnwnrRalTSbktljw7GXER34nbjIa7rXeDUGzxuRdfmjXNqiFnomrnRwONLJMEIffw4lD+1CveZtP9sH9d3AvrDGV5zaNSn+QfdqnomiBQOTP7oPNZjNKmEysHT0WWrs3KRSQi/hMjf3e4xBWfU2K4DCtc02vnaB1mrgYI7pbOF/qR8Rm9PHDswulsHXfFaY9NLxPZ6zafSRV2gGk1L5q3p/wUL0Ah94dhwOTUMGSPg5wIDdFIk+hIqnaP4YeHosVc/9k5wUCHtbP6cus4j53qNX9j59bYFjfmjh04h5by/Pl9EXtyvmSFLx98iwMcosV/fz9ERSjkE+J+eY6HTsVNpqw+yV5E7n54gN0lUqFHj16IGfOnJgzZw6Cg4Mxbdo0GI1GjB+f/AwKBeezZ8/GiBEjUKBAAaxduxa9e/fGjh07kC1bYhVON58nl86cwKI/puLF44fO65Sa19AoAlnlPJGfqcOBV8+fugP0D8Q/MDNadeuNVauWsXnipjEq7tdEIqxUKhHEAez372LG+B/B5/HQoHUHDBj5cyKLuViLG5pnX+ThgVGRkfEOHLeEQhyUSlG6TIX3Fn5JDVcvnMXK+X/h6L5diRTZyWpF5OkLEZ8HgU0Nc9Rj8DlkX8NDl5ZfV8sWZc9dZdATYjRZwIMD94RCpgBf3GRCBI+HfVIpxnl7MweA+zw+7v97CyfPPkL92oXQr0NF5M2ZtBKvm68P91rvhuyx5k6dyJLszP6UdEpsEUzQTS/xjTczTEE7tSq/iPE4/1T8u2u783zbxp9vS/O72v/HDW2IHsPXYpKPDzqp1chrseAln48tcjmO0hpusuLnP/ay+1cumR2zJrVxqe4tlwjhYdAwGy8K9qm6GktUjPe2l0KMQnkD3ut1Lp/eA9fvvMLDpyG4e+Mafh3zAyb+OT/Fz/H04X32PGKzBmahjAkFKjWvINcFwyyQQmDSoGOfb1LVgddt6HLn5V9HtULpYikbsfxcoO+xZ9t3W//RfikoXIsQgQC1DAYMUKnY9cclEixRKnFVKMRzPh8anRFF6kxF6WLZ0KdjJdSsmPJRATcZKEDfsGEDdDod5s6dC09PT2eb86RJkzBgwAAEBLj+T24ymbBo0SIWkPfs2ZNdV6ZMGTRs2BDLli3DxIkT0/ulu0kDzp84holDB8DMl0LjkRNWvhh8qxEyfSi8op4g0iNnPIs0Z3uSLHXtSW5c0/v7H1mr19YVS7BV/jYZInY40F+lQlO9HloOBwdkMqzesh5aVRRG/xZf1Z28zrNkyQb5k0c4KxZjkJ8fm0OjFuirIhH+k0hY5nrgqOjWtfSAjhkUkFNgfv3S+URZZD4JBPrnglGZGRYOB1qHg9nveaqfQyzgYM64xgjw/fiz8RmB2StPQOQA/gwNRdY4wnK0gM/09MRemYxZtlTUmKBRcbFvuw6bd13F3zM6oUYF98LtJhr3Wv91ExYShKHd2yNKrWFuGiahHFymsh4BpTaIndfK41RdqUXZZv2kaz21t586eoidz+SnzJDt7bFQ1XTZ752YD/rwkLfVX57DgTp6PQapVOw8reHLrzrQtv8y7Fv9TSKht6b1i2Lrlgvg22xsrafZ5pxWKwvcDkilLNn/U4+q7/06PZRS/LNoICq2+JV1vm1bt5J5o7dKgT/5g9s38duYH2AQebDfGCV+2Hu0meEZ9RRiCs77D2b2uimBqd8P+xZvQqID1XrVCjEB2S+VI2ceIDhch5FRUWx9j6WCycSEYf/w8oKnzYa2ag34KgfOaAzocvYRhvaujpH9a3/S1/41ku69PMePH0elSpWcwTnRqFEj2O12nDoV7SXtisuXL0Or1bL7xkIt8fXq1WPP6ebzh77jedMmwSSQITwmELfzBOzfSM9cTMxDqX3jzLQTMmMEfAICkbdgysRH3CQPtet1GTAYaw6fwdgZ81C0TDnQFPaKoCA01+vZAUDpcKCdVovvIyJYux8tggmfo/M337MKa1WDAYFWK/5WKjHDy4u1u9k5HNRt0QaZs6Z91pnsb9YvW4jmlUpgeJ8u8YJzslH5bvQE5MmdHYKAnDB6ZIm22bOaIDZFgeuwQSv1h9lsg0ya/DjN14rJbMWmnZfRVKuNF5wT9NvopVazf2lejWbR+6jVWPbqDUro9eg/aiPU2qQt3dx8XbjX+q+bDUsXsC6KEGVO5qZh5wlhFUigVmaBRhbAkvIUSMUiMUbBYbOgap0Gn+w1H9u/G+aYme12Tcp+VnZa70P9agVwattQbJzbHT3alGPX/RIejuFRUSwpT9r01YxG/BocgqAQNdZsv5joOfp2qAibiA9/hx3lTCbsl8lY4LZTJoOZx0W2QE/0alfhg15n8UJZsXhaN+flqaOH4c716Pnv5Ni6ZjnsfCGiFFmdNnsiowoCsw4aeSamkZOaEYXNq5bh2IE97LyvtxwrZ/bKkCMOKYXU9/PZrKjpYnSBAnYSGSxoNqObRsPG2/4KCmZ7gFnLj+P4uUef5DV/zaT7L/Hx48fInTt3vOuUSiX8/PzYbck9jkj42Dx58uD169esRd7N582tKxcR8volNBK/RHYYdFkn82Ne5QKrHhy7FQrNG4iNUeg2cPBHaZX+mqAqesUadfDgxjU0U6tZUJ4QOmj7cDg4sndHottqN2mB/iN+wimFElekUmThcCADByoeD7WbtsR3YyeneTVm7q+T0KB0AUz7aThePnvivK1YwSxYMaMnXp4ajz41yRbGyAQGuXYLy6L7RdyHp/olPNUvoNAFscccv/AM6QVl4bU6E6zWBAJ1nyn0OmljVq/zPOSt8Qs0BgurkJ8Si/GvRIK7AgGbWySovTGr1YoXMbNqBKU6BkdEsurHP/sS+8u7+Tpxr/VfLzTDTfPmpNROSfiE6KW+cHC4kBgiWeVcbIiEpz6IKbhny/XpqtYHdkT7XRMdmpXFlwAlGaqWzY1Xb6JQ2GZx6bySyWZDVZ0eW3YlDopzZfPB2tndEe7jgcNSKTy5HPjBASOXi9x5ArB5Ue808cnu2roivu1ek52nJMno3i2Yo0xynPvvCLT86E44ufYN/MLuwEv9HJ6al/BWPYOdw8WFk0dT9Pcf03jfhNHOy3//0ZOpzb8LWvdoLCyjcOLCY3Qftga5q07B0ZP3UdRgxDWhEIckEpwXiZglG0E7dBppeBVnrafr2mq1yG23YdnGs5/sPXytpHuLu5qCAWVi5WQPDw+WbU3ucVQxF8UIUsRCz0UbYnqsOBXCEm9xwPwJlKaTwmIxx/v3S+LNq5fsX8qiu8LCj77eR/MKHLuFZS67fjcctRq3cH5HX/Lnk1ak9DOiarTJbEaWBJXSWCglEmixICIs1OX/kSbtuqBq3UY4tn8XEwEii7tq9Rsja45crAkiLf5fPbp3B+uWzMe+bZsSKcLXqVIQQ/vWQd2qBdkMmgN26AyxiToOvCKfsKq5SpEVRhEdcxysSiPXBmPLgbv4plva+nhr9SYs2XAOG7ZdQqjKAD6PgwbVC+KbrpVROF/q5/M+BharDd/+/A+OnnnI2to6GY1Y6+GB/3l7M5Xf2EQaZdKHREWxOUaaOyRV97hQ4F7AasXFGy/QuUUpfOnE/hbTy6XAwYZEMjbutT55vuS1jAIrs9EAi4drXQoKzmnmXG6MgMIcCYfVgoq16mPw2CmfbK2PiozA6WPR6u0BmbOgdLGsbH38Uo5DYeEaZEkmkKTE65WI6D1BQooXyoTj/3yHA8fv4dqd1xDweahWPhcql87J1t60+pymjW6Ji9ee4vy1p3j6Ihyjv+mFP1dsTLKKbbVaYRdy2cy5xBgJndQfeokXExgWmbVQaIPw6M5tpmGUJXvS6vAmoxE/DugBY0wleUCXaqhXvWCS74tijq37b+DvjeeY3zxRulAg+nSqhEY1k/eV/5Qs33Qev8z9F3ntNnTWaLFBLmedENtorCRmraeW9u4aDRrq9Yh0sdbTvSppddh/48Vn///jS1vrvzqbNaoekUL450Z4eDi+NDSaaM9Najl2FaTzbNGtZaXKlUfewkVRoUYdpgIe5OL7+RI/n3dhMhpw7thhXDj2L3TqKPgGZkWVBk1QrGwFlwvYuz4jGjmQiiV4zNcgOm8dH0uMTUsWmdzldxBLhVr1411O7r4pgRa/6xfPYduav3Hp9Il4t/H5fDSvS6JkFZyiNMFB0VVxQim2gc/nQqYLBt9mRph3Ptj4oniVG0oEcUMfY/uBK6hVIXWWLkmh1Zvx3fg9ePYyCnW1OhQxm5nC/YEjt9D6xD1MH1UP5YpnxufGhj23cOzMQ0wID4eX3Y4ffXzAcTiYvYrYbmcCgmYOh6n8/+Tjg44aDeuSoNGGhJi5VPkwIujN53c8TS/S6zhkExVDnMKFmzTAvdZ/PKKDag74VjNMrsSvHXbwbBbkyJ0HRctWQNmqNZA5e05ERkVRqPxJ1vpdG9fAZo3WvKlcuz5CgoPxqaA18MzVl9h56B5evVZBoRCjTtXcaFQjL6TixNXqlHxG3p4iPBKLWDeUq4DgkVAIP29Jssfv8kW82SmWuGtvWjFrXDM07bMMESo9Th/9F7OnTkTHvq5F3rLlzgv9oycQWPRQKTLDIHnrPGMSKZlInH/kQ6xfOh9dvx2W5N9cMnOac5SPOjiG9aqU5OdA3830pWew/d97KGc2YZhODxuHg/+umzHozhv0blsSfdt9fknqh88iWHDeRqtFO40GP/r6wsDjseQ7jSk+EAgQyudDZrdjtqcnNBwOTksk6Bqzb48L7QmoJOJe6z/uWp/uWwKqeMcGanGhCjhV0ZN7HGVWSSwubhWdsvSUwUvusclB1gOZfAM/q4WNDrY+Pj6J1LMzOma9ji0ONHumUmaL3+bucLDr6brR0/6EWCz56j6f5AgPCcaU4YPw+uVzlDGbWQvy3efPMf/iWVSqURsjps4Eny9I9WdUp2VbHNi4htlpJLRL2y2TQe1woHnHbsj0ESzuqC1y88ol2LJ6OZ4/jj/fJFcq0bprL3To2R91vJNuWSPJoVb1i2LT3husah43OI/FIpTBJpTi0Kln6NSyUpq89sl/HcSrl1GYGRyCXDGbPII+18m+vpg85z+c3DqECdh9LtBGY/uBf1DdYEBhsxl9/P2RxWrF+IgI+MX8FvQcDuZ7eOCYRAJvmw2rlEqUNxqRJ857JEgw6CGXj+9qFEGmwM/neJpeUDbd+X/sHfag74NKnfFHetxrffJ8yWvZ0wf3WGVIagiHXuLtFO+KhTqZqLupdbdeqNmo+Wfx+RyPmT0mOvTsh0yBl/EpsFrtGDZlO/YcvcsqnYX1BqayPet+CP7ZdwerZ3d1qq2n5jjUtU1F9D73jInCVUowEvqIz2fXj2td/qMdv8MidXj6MgJSsRAF8/iDy43eD9LfXz27N5r2nMfWqHWL56JS9VqoUD2x0BvtCf4YOwI2Dg8G8dvEQSwOLh9asRfOHv0Xw8ZPBc9FJESigDvXr2LnhSIRflu0AjlzJj2qdeTMQxacU0cZVZljofMb5XIs33IVzeqVRIlCn1dCfu66q/CBAz3Uavzi7Y1wHg+/hYWhWExlmPoo90ulbL3PZzZjtVIJud2OBjpdvOeh+x1XyFGjSn73Wv+R1/p03z3SDHnCWXMK2ENDQxPNlyd8HPHkyRMULPi2hYSeK3PmzO/Z3k5w3um//imgBelzfF0fQljwG0AggsSkAjQcaKV+sPFErHIu14VCYlKz+1lNJgiVHl/d55McM34eDtPLF1gQEoJsscGRWo3TYjGmHT+KLSuWoPu3Q1P9GZH9yNnDBzDc4UCnqCiUMRqhpsqvTMYC9BadeyB3vgLprpw7b9pkbF2zAiZT/I1DoL8HfhzYAH06VoVCTv/HSUgy+fc04fuG+Gf/TVhdBOexmLgipl4qSoPfkMFoxpY919BUrYkXnBP07P2jojBQKMTRM4/Qol4xfC6oNAY8D1ajk8HAZgu1XC7mhobGS9RIHQ4Mi4rCHaEQCpsNYTwe/CwW1l0RW8cJpoXe1wdZ/BRoXrfYZ5WESG9oU5wWv6GEUH0io+Ne61PGl7iWhbx5xX7BHIcN3lFPmGAXWabSZVJxl+tCWLKey+W9871/jM/nwZ1bzOKLKFs8Bzrkp2rqp/lOFqz5D/uP3sWYiAhUNRqdR4JXPB7GwYEh47Zi19/9WWEqNcehelULoF6V/Jh2+j7aqDVMBIysUmkPsdHTA4XzBqBrq7LpcjyLy5sQNSbN2o99x+7Aao9un84eoMSQPjXRqXm09WnjWsUxZURzjJ2+gwXpE77rhst7xyIq95B4z1W7cXOsWzQHT9+EJNY1isHKE8OiC4GNHAJiLGLjFj6m/tDHefmPn1qhU8E7yX7367ddRn6bNV5wHgvNZ+9XKrBh51WUL5E23Xlpxa3br1FGp8cbPh/nxWKMiIx0BucEhYlN9Ho8EghYssbK4UBit8NBn2tMm7uREvaeHgjjcNG/U+V0/618Tgg+g7U+3XdW1atXx8KFC+PNp+3fv5+16FapUiXJx5UuXRpyuRz79u1zBugWiwUHDx5kz+nm80emUDIVwih5Fsh1QfAj1daYdisbhw+DyBNSs8ptqZaAh3du4db1KxgXEfE2OI+hstGIphoN9qxfxYJtygCnBm9fP8xYtQXzp07AnBPHWNWD8FAo0atXf7Tr2R/pxZuXL7B2yTxsXrkMRkP8xS47ic4pFdCL5KhUNl9McJ4yvDykyJvTF9dfJS0cKbKbkDnAN9Wv+c7DYKzaegGXb71igWi9qvlRsWQOaI0WlI5R/01IdqsVARzg9sPgzypApzlCwsDl4pJIhFImU6IuCoLuRZu57TFzansUCpyUy1DSYISWz8NVoQh+3jKsn93tqwrO3SSPe63/epHHJNjJ+kquD2VBeuxaTxOXZIslNUVBrkisR/Qp2L1lvfN8j7Zp01X1PpgtVvy98Swa6nRMXT0upBXzTXgEJtzl4NLNlyhbLFuqnpv22It+bY9pCw5jzdYL2GCKFlcT8Lho2aAYJg1rCIk4fQOukHANWvRZAlOoGn1VapQ0mZimyT6DASOm7mRV9cE9qrH7jhnUCGcuPcaeIzcQHqlDu28WYd7OAfH2OJSkqFKnPl6vXhFt08dJPOonsBohFIuZMG7CEb9x3w9ASFh0R2+T2sXwXc+3VfqwCC3W7byMf089YO4mZYtlRffW5XDr3hvU1Cce8YpdK0vp9Lh99zU+NwRCPvQxaz0lZqq5GFNDjEMLKfXTqFswn49uAQEoazKxx1yWSmDmcjB7QmsULfDld8p9bqT77qpjx45YvXo1Bg0axHzPg4OD8fvvv7Pr43qg9+jRg6mzHzoU7UlJbe10/zlz5sDb2xv58+fH+vXrERUVhT593mbA3Hy+VK/fGJtXLIZDwkWob0GIzBpwbVam8mqiWSH1M5SrVjPRgfRr59qFsxBzOKy12BU1KHjSavDk4T0UKFI81c/vlykQE2YvZloMzx49YAtgweIlIRSmLthPKWSfQv7lB3duZX7msdDmrQYAMlsp7XDAqlJjjEiEn3/bjUPrvo1XMXgX3VqWwfg/90NrNcLKjx/cC80a8Mx6tG9cItHjSORs7fZLePg4GHK5BM3qFkGrBsXYxmXJhjOYOOsAOAIBdDw5a9G8fOsoJOLowyb5wbqCUiqUfhAnEbzefhCE63djhXdyw99H8V5V/J3/3sKtB0Hs71C1pGzxbIk+M/pb5H1qsdhQrGBmVCiWFYevWCCw2dnsWVLQPDpl1LkOB7PRq1yrCCIjtJCLBZhSoxDaNCwOuSx9fi9uMibutf7rpWipslB6eUNv0CHcOx+bEebbjHCAC5NIwSroEpkcJStU/tQvlVVoD+7Y6lQ879As2o7sU/DwaRjCVAa2pruCvKnlHOD0pSepDtAJSqBO+L4Bhveticu3XsJud6BYgUD4eH2cPdfsv09AG6bB7KBg5xhVdhKhM5uRyWrF9EVH0LZRCQT6K1lCYfWs3ijT5H948iIM568+xR8Tx+CnX2fGe866zVpj84olrDODNGbiwrVbITdHoU6L1omcgBbPnIZTR2LiC6EAZUvmRVCohv3tC9efo8vQtdAbLTAIFEwN/tajq1j5zwV4ycVJrvWEmseFyIVOABEcpsHJC4+ZOGvxgplROB8N5aUOSiyQEvt/5x7BZnegdJGsTJhOKIi/vwgN12L3kVuIiNIjSyYPVK2QB/Nvv2JFHgq2kwr2qGpOUOWc3kXZ0jnB40SPXvQqmR1dW5RBtsxeqX7dbjJAgE6z4itXrsSUKVNYkC6TydC2bVsMGzYs0Y8w7uad6NevHzuYLl++HBEREShUqBCWLVuGbNlSf6By8/HJW6gIKtasi3PHjyLKYYdB7MkynjyrEd6aVxDYzejcb5D7q0lC5TGp8NR5vQurNFe8fvEMJw7ug1ajQmDW7KjRoClkCgUL1OmUHtD/55OHD2LVgr9w4dTxRLfX5nIxxG5H3KYwWhw6qdQY90SAG/fesAUtpXRsVgprdlwGnj2BSuwfT8VdaQhF6SKBqF05r/P+rI3uz/1YtukcAuFAEb0BEXwefrzwGPNWHMcP/Wuz4Fwr9YVWlsnZTqeyW+GreQ4B14J9chmb7Uv4PZFdmcYB1KsWf1SA5u+GTtyKCzej3Q0IPpeDNo1L4H8jGqe4mnH49H0MHrsFar0Z2eCAjsPBvNWnUL5oViyd3oltviJVenw3bguOnX8MKQegUDrSAfh5SBDKFyC/3Yz7IhFrYSN/3IScE4tZRl3A4SB/vgAs+bV9qhImbr4+3Gv91wtfIED3b77H3KkTmGI7WaiS9gfZp8r0YZAZwtF56I8QS1xrzXxMbl+7wtZEonblAvB7jwRpWhF75E1ureemfKlnY0w7Dt3E81eR8FBK0KxOEeTM6s2SqdXL5/no3QGbdl9ho2CxwXlc2mm12KlUYMu+a84qupenDFsWDkDl1r/BZLJi4/JFKFm2Ahq36eB8XPbcedGkXWfs2byOCQ+S5oGdy2Mq7h6GMMilUrTp0Tfe39qwbCEWTP+f83IFoQhrNp7D6n8u4s8JrfDjtN1Q2fgI987N5tgJtcPOVOEdmnAckcmYHziNgMUlgsvFBYkEo2sXTmTFNvaPvdiy96qzrZ8oVzQrZk1szb6TlPDidSR6/rAWd5+GIYDjgMABLAUHAV4yLJneEWWKZmN7rd8WHsHCtafAsTtAvSwRjugCAZfPwxmphFXSbwqF8VrcY6H2dr4jes/JFfIwa3xLZMn0brs5N+nPR+lPJO/yFStWJHsfqrInhDaEVEWnk5uMyaipMzFz4micOLgXnvpggMeHw2yEwtMLP/6yCAWKJq5qfu0UKVkGBoeDtSaVc9FGfVIsZotQjjz5k30eEt2Z+8t4HNz5DyQcDigHGuwAFk//BQNHTUDD1u3T/LWTfcmefzZi9cLZzGc0Lr7ectSqXAjHLzzHtOBgl1Nf+WMWkGevIlMVoEslQmyZ3wM//b4He47ehl372ikK2a5pCfRrW5RVS2JZs/0SC86/iYpic1ixt7zk8TAedvz8+244BJJ4wTlBi3e4PBv8w+/iklCEvxUKdNRq2cJNy/BFkQjzfbxRq3xuFM0fGK/Vr03/ZeCGa/BzZBTrjqDg+F+pFKv3XEVomAar/uz6ziCYKu99Rm5Aab0BA6KimJ8tbX3otzLrFtBj2BpsW9IH3YeuwcO7rzE6IoKNRdCB/r5AgEU2T2jEYtyHkAXgi5RKDFapnO8fMcIxt0QidnvWTB5YPqOzOzh3kyLca/3XCwVNBoMeK+f9CVl4BDgCERwWM6tkdho4BG26xw+aPhUHd0VXz4m2jct80teSN4cvq9Ce1IqZE0hCyK9a7QAqlKS6c/JQJ9iEmXthNtuQieNAJDj4bcFhdGhaCtNGN3WOOH0sotQG6IwWJnDrClozs9lsLAiNS+liOTBnUif0Hx0dE0wa/h3yFymGvAXfBsHfjp4ALx9fbFm1DLKI+87ri5Ypjw79BsEv4O3ay7n2O/4Y/zY4p4694Vots52d4e2FweP/YUF0hE9BZ3Ae/UAuNPJASG16mK1G/OLrg6ERkfCPKSTSXmG6rw+UCgnaNy0VL/k/YPRGnDz3ED2jVKin17MkOM2B/33DzvYB+1YPfGfnHHXJdfh2BSxBkZgeFsGEXTkxAq2zrV7oMngVDq39Fut3XcG8VSfRRa1GM50OCoeDOcqsUyhY63qIVASe0YoFHh6YFhYGZZwkA4kFbpXLWbeckMfF3390cQfnnxHuAUI36QplzH/67S+8+nYYzhw7BINejxx58qFSrbpfnJJtWlGwWEnkL1gEC+/eQvbgYATE6Sy5IhRip1KJ5u06v7MaseDXSTiyayu+jYpii4QoJuO7RqnEX1N+ZkrpVes2TJPXHBURjk0rlmL9sgXMRz0u+XL544e+9dCjXSWcuvQUpy6vQxCfz2a1E0LXEwaTBb8vOsLmxfx9FWjTqDjyZE9+htzbQ4qF/2vHRGmu3XkFLoeDsiWyQybhx7MHoQV00eqTqGY0oFkC4ZesNhuGhkVgtK8v9GK5SyEaGtGwCOUomlWKrU+AvUoF8lgsCOfz8RocVC6RHfN+aRfvMUs3nIUmUoeFwSHMQ5ygtrNWOh2zPJl8Fjh16Qmqln0rnKnTm7B62yVs2H4Rb0I18PGUQiwRIsBqxU/h4U7RNgquKZEzKjQMox3AnFUncfnO63iKrUR+iwVTQsLQP3MmNGpQHGqNEQdPP8ANkQh16ffhcLDq/22aWeNyMKxfLQzoXBlikev2PTdu3LiJS9vufdGgZTucOLQPYcFBTPOkWr1G8PBKWcUwvaFj/7+7trPzlLBt1fDT2mNRC3r3duUxf8VxpmkSNyFPQRYlewvn9kOFkjmSfR5qbf5x2i400unQRaOBt90OeqaDUimW7LkCgYCLaaOa4WOikInA43Ki13QXhQZa/UN5PNY5tmzjWdx9FAKJWIAGNQqiT8cqOH3pEVZsPs30aob36YK1+/9zahhQO3yXAYPRpnsf3Lh0gdl95sibH/6BWRLZvvb4YSUsMXsN6mkbHCdBMDIiEl0ziWDiCdm6nggOB1qBB7wdZjzzUqC3QMgE48hm7QGXh0zeMqyb1ZXtPWKhdfzw2YdMQyiuen4VoxEFg0MwkMtl+4GfBtWL97vcfeQ2Vm46h1v3gyAU8JA7lx+eB6mwKDiU7UtioX3T5NAw9BYEYsHa09i48zLaazTorNU670PaMpR4J4vUVwH+aFqvGJauP40+AQFsL0iJ/bsCAU7QHpIDNKxRANN+bAY/H/n7fdlu0gV3gO7mo5AlR0607dEvzZ/XbDJBp9Wwlu30mqH+2FAV9acZczGqVyf0I1EUvZ4FZXfFElwX8FGmfCV0+ya+gntCaL78wPbN6BsVhaZxglBauAdHRTH/y3UL/kKVOg0+qDr6/MkjrFk0Fzs2rIYxwRxd6YpVMKFvCTStW9zp216tXG5WMdimk+F7lSre/Smvu00uh0zEx7DJ28Hl85ltGs9qwl9/H0frBsWgkImx7dBN6HQmZMvsie6tyzIhF1rYY6GZMjrFYkqQwX8drMKT11HopkusykpQUOtps8Fkc535j32tObJ4YfWfXbBx1xU8eRGOUjIRmtctioqlciT6TLfsuoI6Gq0zOI9LBZMJ2R12bNl7zRmgkyds+4F/4+HTUFTRG1DNbMaLcD6ruPdSa5zBecLXTc/zz77ryG23uWxno01JXbUWB0/ex6q/uuL4uUfQ2jnMLoZmzX1sNmS3WPBcIEDmAA93cO7GjZtUoVB6oHGbjmn+qZFnOTmAiFwIgKWUO9ev4uWzJ+x8zYr5P2l7eyxDe1fHrbuvMeEMUMxqQSGDESF8Pk5LJfDxlmPxbx2TXaMpuJu56AgL7r9TqZzt8rQbogQ0VUeXb7+M73vViLcupjcUeDesXhB7j95iiYOEuzOy8qSxK5rzttrssAslTOeFOtuKFQxEtbK5IJZIWYD+9OF9dGtUEws37UJA5izO56Dby1UlJZtoyJo5Lkf27sKVm0+j7wtgWgK9dqpsV2auJknPmDtiPtBzO3/AtoM3cOH6C5b8/65sLjStXTjRGknreDaHHRVdaAjR+k/7gM27rjgDdPr+RvxvJzbsvsK+/9Z6Axtb26HSs1n9uMF53HW8lkaLHQeuw2ixscp5QuhlN9dqMSZYjJoV82L3oZsICY7CIamUde8p7XbksFjwRChk1ne+3m4tqM8Nd4DuJkPy8uljrF08l2XqaeEWCEWo06QFOvUbBP/Az8OPkg68pw4fwO4Nq/Hk3m2WQKhYtyGzMsuaI1eyjw3InBVzt+zB/m2bcGzXNtyJikSm7DnwY9tOqF6vsUt/z7icPnKQKYzWd2ENQgduUoKf9PghXj17gqw5c6f6fV29cBarFszG0X272OVYKBCv27QVun8zBMVKl0Vx09pEFYMRA2vj5z/2QuhwsDk0yvaSddcmuZwt2jBZoZYHstkyptLqsEOuDcI/B26Aw+NDK/KETeoFXZgeU+Ycwq7Dt7FpbnfW5p4SSGiFSKouTJ+PgAOmkeDydrsVIqse5YpnY/60P/St+c6/GaY2JFLkj/v3shhNCIt4u8iO+2MvXj8NxezgEOSIeRy9mkMyGbySEHej51FabXgcpkFRMxmjuYZa9NR6EybN3IecZjOmB4fE27TQp/OHlxcmzdzPZhjdFXQ3btx8KigBv/nvxdizZT206uikbqmKVdG537coWjp1Am8Hdv7jPJ/W4nA37r7Gkg1ncfzMg2gxr2LZ0KdjxXfOfpPY1/I/OrMK6tptF3H6eRhrmx7RqAQ6tyjNXEreJTR371k4Jmm1LmfZG+j1WOHpgf3/3UGvdhXwMfm+Tw00P3Uf4/390CcyinVx6TkcFiT+7eHBXq+Gr4DaMxPsXAEbtheZ1Lh+9wWu3w2C0DcLjK8eA3YbHj+4h871q2LZ9gPImTf58T4iJOgNJg1/q3E0AoCrXRd1snEddnDsNji4iccAZBYNE+iTSUXo2rIsOyUHreNZjaYkdQVoH7BH/baYsXnvNRacD4+MZIrqsdwSCuHtIjiPxdNuh05vZvPjVHhxRWw7/qY9VxESosLc4BBkTvCc/0okmHngBnq2K89m2t18PrgDdDcZjod3b2Fkny4sc6gR+cIqE4FvNWD/7h04fexf/LliEzJnT74lLL0h4Y5ZE8fg0K6tKGK1oaVex3ynj25eh0PbNmP87EUoXbFqss9B7VzUMkin1KLXaSHjcPCGx8MZiQQmDge5LBZUNRhYMOYbc5DW6d62Rb0LEnGkgJwU2a9fOh/vNmoXrF6hAGaMa4tSReggfw8w3XP5PD3blmeqptMXHMZuqphzAJ0DkIr44Fnt0AiU0Et84rSXcyA2qWHhSxDpmcu5iBrgA53FB9fuPmXt8BOHpqxdP7O/Epm8ZDit0zGV3ITQXFYolweuzQKpPiz+a3HY4aV9BSGfy2b7Ukombzkea1x/1rS0PpWIUTtA+VaN9fAt9I5SOYPz2ISCv9WK60JhvIU8llAuF3f5fDiMZtwTCOJ5l8fltkiIzL4KXLr9Gj+p1Im0AOiddlKrcVQiwb+n7qNp7SJILaReSzOImfwU8FB8emEoN27cZMzgfGSfznj66CG0Qg+YPXKAa7fg0uUruNqvK8ZMm8Va6FOv3s5Dq4Yl0+x1ktDZsMnb4O9woLZGy4KmMyodOp1+gB/61MDwfm/tvFxB62eLekXZKbVo9dFrGP3NrTIZInk8FtjVNBhYMpeqrbTGanSubUFTAgmckt3oqXOP2OdYvnQu9GhTDvly+SX7uCL5MmHt7G74fvw/GMoXsNdhdEQngT09pQjXWBElzwzEBsYcDnP7IUXxCK/csPIl4PDlcDy5wm4OCw3BqAHdseHfM8l2FTBLtcH92Ogd4SkWoZXRdZv9OZkUDi4XnpqXiFRme2vd5nBAaggDz6RDv44pHw/IHKDEEYkY9sjo8bOEkO847Qdi+XvDGZQzmxKt6dTKTtoytFNLmDagz2+PTAYLBebUbi8QIJ8lcVL+doyP94mzD1FTq0sUnBO1DQas8/JknYDvE6CrtUY2VqiUiz9qh8bXQNJ9HW7cfIbQ4jB97EjorUCwR26mFmsSKaGTBSDEIzdUOgNm/2/cp36ZOLTjHxacU1Z0ekgwExLrq1Zj+atXKKbTYuoPg9jmI73wDcgEtc2Gwf7+2C2Vstliqop2DwjABZGIzR3zeTxkypz1nc9FYirrly1E80ol2CxY3OBcwOWinFiMZnIFnjxTodcP65h3+Lvo17ESLu8diVkTWqFzx4oolNsfOpMVNpsdUmMU/CPus0w6QQs2326BWpElUYbbIpBCLfLG2h2XmahKSiDhuO7tK+CQXMY+i7hoOBzM8fFGFj8F+neqBKX2DQLUjyDXBkOheY3AyPuQ2XRYMq19qqxqOrYsg2NyGV4nsH4hKBB+A45TaObEhUdMtCbu/BpB25FGej2OSaUsAE+4YE/x9mabtDHhEWz2bAf5mCfgIc2dSaWoXjkfu5zbxaJOUFudmAO28KaGs1eeou2A5SjddAZqd56P4g1/x7fjtuB5AiEgN27cuHkX65fMY8F5qDInNIrMbK03SHwQ4pELBqESMyaMYsnolHD94nmneju1Rft6p017+7NXERg+ZTvq6vRY8voNemg06KLVYk5QMHqo1Zi57D8cP/8I6UW2QE+2Nozz8cFqhQJnxGKsVCrRIyAAa+VyPOPzEWUHcmf3ea/n33fsDmp2mIv1688g8MZTZL3xFNv/OY86XeZj896r73x8xVI5cXr7MKye2QWD+tdCmRLZ2Yw12YFxbGYEht+FTBfCAmKqYpPzik7qx4JzguPhDwS87fK7f/sW65xMDuquPHv8KDtPYwxiTy+sVCicqvmxifElSiUT0yNPeFrXA6Pus3WeOvYCVI+g1Abhu+5V0aB6wRR/XrSO03pO63pCaP2nfQDtB2JH2a4/CEYlFz7rtNbTKKKrdZxG0sJ4PAyJjIKf1YpVCgVLNsSF2uQ3eypRtXROhKn0Sa71FATmNBjxOjh1a/2rYBWGTNyKEg1/Z2t92eYz0arfUmYJ5yZtcFfQ3WQo7t64iuePHiDKI2eiYM3O5UMl8sG182fYQpw526erou9csxwVTYmzohQODomIQE+hEEf27ECzDl3T/G+zSsHWTWy+6vvISKbiTZ/UKx4Piz08WCBH1fXKtRskK94TGvwGG5YtwqaVS6GOih9g8fl8NONyMcZshpACSaMRERoNJtj8mJL4qW1D36kaS9YvxQsGYvz0PVDojBgapUJBi4W1u5Oy6DWbGVHKbOBbTbBx+bAKXFdiyWdXHxmKpy8jUShvQIo+o2+7VcGVmy8w4RRQymJGMYMR4TGLp0AqwobpnZhveI0KebB88zlcuf0aQj4PDWuUZG2CpL6bGnq3r4Bte69iFBzoGKlCBaMRBi6XtZdtVSjQqn4xVqQn1daTl6Nn5lwtpy10OmaL8qOvL5vrK2syMY/Wg3IZHvMFTHSussmEthoNlnt44LFAgLoGA6R2O7NO26mQo2CeAMhixgFe8vkus+r0HVClwzcVSQiqtvcZuZ5tBEaoNUzckBIJOw7dQLPzj7BzeT/kyPJ5iEW5cePm84ZcSPZt3QSt0DPxsZ/DgVqWCeKIezi2bxcat+30zufbsXGN83zjNHQwWb31IiQOB3MEibviUdDcXqvFCbkMyzeeSzebM2qPpsCzs0aDljodZA4HSzSTnstapZIJgfl6SFA/ge1nSpMP347djIo6HX6IiHTOkVtUKsz39MTwKTuYtzdVyt/VIVCpdA7MXHwE9++9QVuVmiWgqbPvsFSKvXY7+DYT9GIvcOBgiZi4cDLlhUOvAjThrDr++/hRqFSrHgQJEtXEwzu3MP/3X6Ifx+Fg/Zy+ePQiCpP+OoBzchmqaHXs8zqhkOENuPh1VFN0a1UW1crmxt9bzuPA8XswW20oUzo7ercrj+oVUve9lSmala3nsw7eYIrrtP5KYtbf9Z4eyBzoiWZ1i+CHKduxbf81lpgwu+gGoIo4reNLPTxwUyRCbb2ejQWelkhwSCph3XUNDAY21z7Z2xsjfH3Z95/ZamWJ+H8UCuikEvSuXgCXbrxga70r6LN4JRahYipm0Ck4b95rMSwRWnRRa5jCPAn+7b5iQechq7Dgf+3eq/POTXzcAbqbDMXzRw/Zv2aha7XJ2OtfPHn0yQJ0Eq57/PghWriY/yZo5rqAzYY716+kS4B+49J53Lx2GRMjI1E+Tgt3FpsNYyMi8I2/P1NW7TlkuMvHP7x7G6sXzsGefzbAkkB0pWKN2tCEhSDfm9eYEBER7zaag/ohLByDuDyWdSfBtHfxy+yDLDifGRQMecwsO7V2Uev5756eOIFXUEt9wXHYo81gXSxknFhVdEHKbWQoebD0t47Yfugm1my5gN3PyD9ViJ4NiqFH2/JstpwgcRU6fSieSgn+WdKH2cAtOH4X82JS+XKxAN+0r4Dq5XOj7cC/kc1kxmC1Bks8PNjGhaoxcaGkS3W9Hvc8PFiL20559O+9cG5/2B+HoFjM991LEx0g/0Nz/dLoGUYe2aZl8caLN5G4tSEYMrsdW+Ry9lkn/OTocfTaUrqpI8/bEZO3o7TBiHHh4c7nI+sgaqH7AQGYNOsAlk9/90bajRs3bqLCw6HXamDycF35ZarbQimePXrwzg+LBEwPbN/CzpPAXL1mrShkT5MP+dK15yiti3ZJSQitVpW0Ouy7/hzpAXWNzV72HxMDo6p9LGS11V2jgZrLZbaZE3pVZ7Pu75N8ENnsGBYZFe/9UVj8XVQULkslWL7pHGb83OKdz0VdbtfvvsHM0NB47dgFVSrkM5sxiwOY+NFrFccRP2nM2tlzFIfj3hnAYsSLJ48xffwojJk6I16rO3VTTB87AtaY5+85aBjqVC2IOgBKFsrM1OIPX3rKthGVy+dBnw4VUbpodBchtetPHdmEnT4Eej3MSzzQAys2nsNmY/Rr4XLA1tORA2uj57A1iApSoZNKjXMiEQ5LJEzoLeHuhjSEtigUrNOPEvNEgJcUvjYHSrwOZfcnccCp4eGsiv67l1fM5+dgPujZ/ZWYOOsAG3UggVlKGCWcV6e//4LDxR+NU255/L+5h2CN0OKvoOB4z1fNYMBv3t4Y9b+dqFM5fzzxXjepxx2gu8lQiGLahrh2q0tbDLqe3U/86eZeOXQkpixzMjNSdBvNwaUHxw/uZTPmrjzU6RNrrNNhqVKJR3duIzBrdmfV/fzJ/7BqwV84efhg/McIeOjUvDx+6FcXObL6okj931DDxQw0kctqRU7YcfrS03cG6EGhahw58xBDVGpncB637Yo2GMcpuORwmIgLtbybxNGBc1ykpkjm3Zk7W+qqs9Tq3rZRCXb6GJDvKSUFXoeonFYqJD5D4nbV285GAYMJv4SGsu/oNWXA5XJm30b2LLG/pKtCIauK0HzhQJUK22QybFYqkTunL24/DmHzhwTdn/zdqU2OMuf0S5hIiZkQNfIYjJgbFo6nAgHGe3uz7DtZ89CmiTQL6O/uk8kwYUBtJoyTEg6feoBQlR4TVapEwT6J2bSJUmHByXtsNj3A99MrJ7tx4+bzhtTa467piWAt0dYUrfWPdo2GVhPdwtu+cXFU5KdNcE7w+Nx3rvX8mD1BWnPi/GM2GuZKxZtoqtNhr0yGExeeoG/HSql+fpo5r6CL9vFOCB3nqRp9+lzK2vfXbb2AykaDy1lpqjKvUyqht+hZt5zEEAWLIH5Fl8MXQhSYG8bnt9nljcsXQaS6jmFzDzpdYmZMGIPXz2PGGErkxKJhb5Pr5UvmYKePAe0txnxTF0N6VMPFGy9gtthQJH8mZPb3wLgZexERpMKfQcEItNnY5zHWx4ftyWgkIlYTJoTHw6/e3mwvtyA4GKckEqz08mSdh5EqAyLi7B/JsWV6eDh7DCVlNspkuC5RIvh5GGaEhrJk/fd+fhjl64teajXKG41MyZ2C9lWeHqhVPg+qlEleuDiWSJUee47cQi+VOlGwT6+oh0qFfmIxEyVs1aB4mn6uXxvuAN1NhqJM5Wrgky2EIQJaeeJ2ZqkxAnKlBwqXLI1PBfm7FytZBkcvnmMBUsKlmdqeHnK5aFWxSrr8/cjwUGaXldSWgAnEcTi4efUiKtSsg4M7/sGqhbNx98a1ePfzUEowsEsNDO5VC1ky/b+9+wBr8uriAP5PCAkQ9nTvvXDvPXBvbavWPautVWttbf3stssuO9Ta4d6j7oF77733rOxNCCSQ77kXSAFRAUEC/H/Pk/qWQAgvIfc99557jkviY4cnZgUo0xmwkylkEZin35/soV+4/KwK6bQDE0TqtUjNjo2LlkVjnCIfIVSpgkGtTVXExUYfjjcHdTYP0pZODNLiluzY2Xu4/SgUX4WHm4u6DYqIgJ+VFWa4uqK4wSADdfG6ua1Wy1Vy0U5HFP8ZHBUlL54W77okVzi22NnJnubJQ7cyKSNB1CAQmxSsDPF4NygYx21s5Iy8SJMThWQmePxX7MfJTo1PRrfG8FcyXvH39v1g2CsSJ2jSUy0uDqJ4vtiLzgCdiJ7H0dkFVWrWwYUr16C3cX4ie0oTFwGTIRZN2vg897GWrDtmPh76SuNsPfnNG5bDD+fuIyIsDI5pxkWxDrzfwd5c8yO7RekSx8702nciRTHYqzf8svT4Yhx/1lgvxpeUXVye5YFfOJrFxj31cSrHxsLPWid/z7b6EBhUNoiRnVwUcqxXG6LhZopEqXqVcPjkNfl9F645AqPLFEz5/Fvs2LAWG5K2MYggdunPI2CdhayB7CQmuFs0KJcq02zlxjPoHBEpg3OhdmwsRoeH43dHR7mSXis2VgbZ5zQaObn9WXAwxBWPT0zi5MY4k1iNV8jxO0iplBmZKau2i6r0J+3sEKs34v2QEOhE33WtVu5VF5mTn7v+t5AhJo56d/TGF+92ynC73Ud+4TDGm2R2XHpEpqa7IvGagF5M3riiJUrRY7X7qwNhHxMIW12wrKotiBRoUWhEBO6vDhud6z3Rew0ZiUsq1RPFO8QM59fu7vBw90DTthmrOp5ZhYqWwB1ra1kkJD2iQJwyPh4Xz5xClwbV8MG44amC8yLFS2DKZ9/gwdGv8dXUXubgPDlVu1wxV1loLD0PVCrcUShR37uEXCEXAZmo2J4eFydb837n9EQoFHKW1y4hGj3bVUWl0m5wC7sNz7BbcA67h8Jh12QRlzcGNJZ7yPIqUSFXSDngiUD9g9BQzAgKkgH2/qRK/NODg2U6mwjOk4kAXfwVvBkaKivEznR2lgO3IM78ARsb/ODqAlcHG1SLjZWz6D87OcnHK2o0Jl6AifR3gwEOtmqc2vIuRrzaMMMDtuBgr0GMKbHIXnrE/jT5edrc/bskoryj/8hxsIqLlpOzYrVcSmrF5Rr9GN71G6FC1Wev0one6TsOJK66ikyrZvWzN1ju36021BprfOXuJsesZGLsmuXsjECFUr6f5oTkwm8Xk6p1pzfWCxp11rL16tcpLfdtp1cPRYwth+21aFC3jOzYIcax6KSK8ulxtreRAeLTPFKpoIyPRdOaReHTtAKcov6VRdvEWC8KtrmG3UHdqkWxZcFYrJ4zWu5rF5b+MRszP3ofn01+y/xYP3zUF+VKecLSBIfqEKU3yD3baWvLzA0IkNvBxOT5FbU6MWgPCEg16Z2cyTA0PBxOolK9mxuup9iHL/aef+jmBmsba9n3fYednfwckS3nmZAAh6RgvkhSFsPSWQPx/f96yJ71mRnrBVGkLj0xCgXExjwH+8QMGMo6rqBTnjN0/GRERUVi+7qVcNYHIUGlhtIYC1O8EX2HjELvLLQly24NW7SRz/PvWTOxw9ERNaOjEWVlJdtmuLi44fPZ83NsEqHHgMFYt+hPLHFwwMiIiFQr6cetrLDCYEBkRAQepkllr1ujJCaP8kHvTrVlilZ6RNA2YkBjvP/1Jvja2srUtOTHF8HZD26ucNKq8cvf+zH+43Xy4+6Otni9Tz28NbhZqp7aZYq7oXp5L6w3xKF+YOwTs4Vif7V4HofXTZDtO4zGeOw6fAPrfS8iPFIvU9r7d6+T4cJw2UVMOIg99v/suICwMB1KFneTF2l1axTPVFCbTLQnSZ68SZ5VF8Qj1YyLk0V/RHpbt+hoWXgwrWNJs+5t9HqoQkPlRaEoDCT6rYYrlQizsoJWrUKpYq64Gq6T2RXzAv7rhyqG6oWOjjK1XexLz8q+sQ4tKsne7SI1XuxzS0lcUoh2euWLu6JiGcu7aCIiy82Ym/Txl5j1+XTYhVxDgtpOpryb4vSoVrcBps385bnvuft2bIHBkPhe17tj7WzPtHJ3tcf87wdgyKQlGKRWo26MHtYJCTiltZNB+g//6yELjuYE78pF4OZgg4VxDjJLKWUquug3Lq4BxIpq+5ZVsvT4opXaglXH8bOzM8aHhZkDBnE2RUq2PxS49zAY1Xy+lu/zov1od5/qmDyyFYoVdk71WL0618Tfiw7KYnZiZTily9bWuKlW4+ePe6FXh8QJl/NX/8WyDadx799QuDnZyXTplg3Lyt9fr4618cc3gzD0nfnycxfP/cX8WF3bVMHrPbOn37tYpT918SGW/nMKdx8Ew9nJDt19qqFjy8pZ2tMvJqgVSWN9ep1ThkdEyNozJQwGdE2nhtFBW1toTCa5dUFsYRTb00T2WyGjUT7uYzHJYTKhd+sq+GfLWdlL/ePgYPm54n7xOxLZdN8m7VXPSgvUEkVc5HXbpktxstBf2r8+MSkgrik6t6qc6cem1BigU55jpVJhwvQv0HvgMOzesl4WkxFtxdp27QmvDLQNe1leGToa9Zq2xJbVy3DnyiVY29hgdGsftOncA1qHnNuH6+FVGE3adsA/O7fJFisizT7EaIRI/rouZmPTvPF3aVMD747xkSsLGQkwB3SvjXOXH+GHjWewyckBNaNjZCAoqtVCZYWY6Dh4hPmhXVycHIijoqLw29/7cfzMPSz+aSA06sS3HfG9przRFoMmLZF7rQZGRMjVYvFYG7VaLHNwwLjXG5t7a4pgXbQ7yUzLk+wWEhaN/m8txIUb/qhiNMAzzoCD522wcvNZvNa1Fr6d2jXTF4CiUryDrTU2aLUYHfFkqxPxcaunVHoVxPlK3ibQQq9HPX9/7LW1lSnxorpwuEKB00U94OZmL9PdvgkKSlW5XYTjwyIi5GDunzQ7npX99YP6JF7MiQvCDjqd/N7BSqXcM39co8GcMW2yNIFBRAVXu2690aB5a+za9A/u37kFW1s7mX1W2btWht5PfDckThQLfToltrfKbo1ql8KhtW9j6YbTOHD0FmKM8RhUsyRe71knRztXiJ//8/c6Y9yHq/Gmhwd6RkXJMfSuaM9lby8DQYVoK9oraxlmZUu444fpPTHhk3U4o7VFk8hoOZF+xF6LAChk9vmDc/fQTB8rJ37FyL5ny1nsOXQd6/8cKSeFU3YyWbH+FKYmeGBkSBhqxsbKQF8EnXNdXVCrYmF0a/tf5e8alYrI29MM6dsY/oEReP+rxN72Qomirvj8nY7ZMs6IavFTv9mMxf+cQmGYUFEXgwdqa4w9cE0WZV32yyA5OZMZIvW+daNy2HLomiwAl3YqXGTKia4soi97ekRWpKjXI0Zp8XsWq+4nNRqc12gSe8vHx2O+kxMqlfWE0QQMi4xMVShYnJWmej3uRUbK6yv3TFRuNz+GQoFJo1pj6LvL8IOzs7xu80hIkJNRYtHmL2cnvNa1tsxWoRfDAJ3yrOKly2LwuEmwZKXLV8S4qR+/9O/7wTezMG3cMBzZ7QvfhATEpdkbbK1WY2ifBpg4oi0qlSucqccWAei3H3RDh5aVsWj1cRy96S9TpPrWLY2F607KliIi1UoEiCKoFPvIfSKjsOXMPSz555QcqJO1blwec2b0xdQvN2KMjY3cx6wziWBciTcHNMGU0a1gScZNW40HN/3xQ2AgKialiSWEQbZL+2njGZkVMG5Q00w9pigS99bQFpjx2065AiJapYj0tRClEqvs7eWMunelwthwFbJtX8rVB1NSSlnyAC0GYJH+3inFJMwHHh4oXcJdzviLmfky6ewTF18n9q7/olYjNs5onkTJjI/Gt0e8MQF/rjuJxc5OEMNzgEmkV6rw9aQO6NqGbVeIKGv70Xu+PjTTXyeqeh/eu1MeF/JwRJN6OdPqTBDB2vghzeXtZerWtrqs5/LlLzvxq5OT3LMtqnhbKQCFlRXmff0qihf5b5taZokV7SrlvfDXquM4ePQmTAkmNKtTCrsPXoc+XAc/pRWCbG3lNiuhsS4GN2DCR99twYIf/utSI2qPrJ47DGOmrsQ0hRVsFYkr8XEmoE2Dcvjpk15Pzdx7milvtIdfYDh+/HOXLGY7//vBcHLIntTq35cdldcrb4WFob1OZ87wEynlnyiANz5chVWzM/+afHt4C/Q6fgtfuLthWFi4DLRFCL3Hzg6/uzijRoVCuHDND+fVatRIkwov9qaLSW+xLVAUfhNnq0FsrLwJ4npBZDGIlXFRtydtm99k4uNi4vzCtcfy7yKzRDV6kRky7dvN2G1nBy8FECa2dZiAVzvXknva6cUxQCfKZ2L1emxeswKXz59DcGjq/uVae3u8NnwMBowch1aOvln+HmIWtW2TCvKWbPA7S+W/9fR69E+ayRcDiag0LtLVSxoM+G3BAVloTuxPEilQYpZV9Mts17QifA9ew/1HoXKfuwj+XZ3S3+eeW67c9Mf+k3dk4ZXk4FxQJhVwuaZW44+lhzGqf6Pn9oBPry+7PtaAn+cfwBpHBxnchpoSK+hPf6MNOrWsjK5D52EivNArLFzuYRN7wDY4OJj39W23s5Mr1ymJ9ixnra3xS5962HvsFuyesbAgUueE+Pj0Cw49j7i4mjGlC8YObIqNuy7JfYnFizjLav7JafxERC/Lod2+su2p0KN9rTxTSDSzxr7eVI6j85YdwdFTd2FtrUSLRuXxes+65pahL6JSWS98835X8/+v3HwGa7ecQ0mRlh0aKlfDxQqqKHL2t6MjvAzx2HnkJj77eYcMmJvULY3aVYuhXCkP+C4dixPn7uP0pUdyIr5F/bKyxVlWr0O+n/6KzAL08nCUj+P3+PEL/7xiO928JYfQLqkTSkoVDAa8ERyCGWeAi9ceo1rFzC1w1KlWXLYbnfjxOoxRa2RBNVGDX9Rw6d6mquzLPvK9FfjoDNA1IlJ2cTEqFDIrbrtWC7WVEvOdHPFuSGiqbYGi7sx6Jwe5xSB5gj15TE8r+eNGY9bGeuGVzjXldYkY6+8+CoGTvQ26tKkqU+ApezBAJ8onwkJD8M+SBVj252yEBAWmuq98aU+8M7IdBvVplFQQJOvB+dPSwY6cuI26ej3eCwsz70sSs7xjxH53lQpnNRokBEdhwV/7ZBGRz2Ztl6lQYrZVDCjiAuNZA2ZEdCyslAokJJhkwJdcJOZl2XfsFmxE/1S9Pt37RYGXrWE6XLsVkOlBW1xovDOyFYb0qS8HvMCQKDmzLVL+kveJbfh7pOwb//u+q4hPGmCrlPHAn6NbY/fhG/h5/Wlc1KjRUhcDVdK+9e32WrRtVF6uXouAed0WazkDn17V36O2ogCgywv3LhV7D994PWc6FBARZdTuLRvMxz071MzXJ04ERp+983JWLpdtOAP7hAR8FRQke64jKXMred/0XLGSL6rnLzkkV3K/ngPUrlwEc79+VXYweVbLM7HvOyJKL/8VD621Uz9zv7cYO9s0TdzvHPuUyuKZdeNuEPxCojExnX3ggth7LSa7xTVBZsd6QfQIP7HpHWzbdxU37gZCa6uWdVxKF08s+rfg+wH4bt4eLFl7EqtjEn8mDydbvNe/MYoXdsZbH61BoKcKXSKj4CEK/qrV2OjkABs3B7w3pg2ik75GVHpvlc4quvi46PxXo1Lmn3valP1+3XKvY1J+xwCdKI97cOc2/pg1U+7Ri9WnfjMW+8onj2qHLm1r5OjqwfmrjxEdZ0TvqCgZnIvZ9K12dthmZ4d/k1Z4ZS/O0FDUMBhkWrZY8f1r42k5EM/8sPtT93z/vOAglq47KaufitQ9cTngqNVgUN/6GDewaZZXZ8VKsSg653vgmly9rlK+EF7tUhOuztqnfr74SZ62Nm6elc7iCrTg5qKVQfrTLsB+/+pVBIdG48HjMFlNVaTUiwsUkXJWoYwn5i0+hN2BkeYBfcIrDfDm4GZydVu0U/ny1534xcUFHwQHp9r/dtDGBkdsbPDxK/W5T5yI8jxDXBwO7NxubhnasmHF3H5K+caFK49kMC6CczHqibFD1Eq5qlbLrjViLBT9sMXniPvFPulfr5jw2tj52L74jXSrhovxdcGaE3Ll+r5/Yi0WUfBMYaVE93bV5NaBrK60CxevP8bqLecQFBKNQh4OeKVLTVQonX7R0uQsMlFPJT3iSkr1gmO9WJQQP1d6xCT5tLd85KS9COCVSoUssJqcmSeyC7+ftwffXHyY+FgqJXq0r4EpY1qbU9bFNoSFpxLboYn2a8n+tbLCEmcntGlcnvvELRwDdKI86tyJY1gw+ye5SpCyF6kIxPt0qo13RrVD/ZqlX8pzCQlLnGkWbbtE9VjR6kO0/GoSE4POOp0sViP2aX/h5iZbh5U1GuVeazHQzd14BuOHNn8iNSowOAo9RsxD4ONwdIiKQsW4OPk4m7VaBEbH4vcFB7Fz31Ws+X24TIvPTBX2uUsP46d5exETZ5QBt4MpARu3W+Hbubvx3bTusmJsWrWqFkWUCbiQzt4wJF2kaDUqlC/ljpwkgnhxS0n8zkUrn6F96svWdgkmkzyfKVPtxUTGnBmvYMSUZRihKYzWkVGy7copO1ucsVajbaNS6Ne1Vo4+dyKil+HIvt2yxZogUqDVWairQekHrzFx8ShiNMrge56jI/6xt0fV2FgMioiAQaHAHlH4zckJIgwX265EobJCAYF4Awqs235Bdl9J+5hjp63Clj1X0FSnQz+9XrYBFfubRX2VHdvPY/u+q1jx62DUqpq5QsB7jtzAu19skAXlBFFkLUGpxOwlh2XxvC8md3pi8aJsSTfY21jLleZKKbazJRMFVSNMidcEOUkE6ukVymveoKy8PQ6IQFR0LAp5OsBBm3qh4rvpPdF75J94Q6FAi6houeVQtN/dr7WDl6cDPp/cMUefO704vmMR5SHx8fHYs20TFv72E86dPJbqPls7LXr2H4QBo95Ep0KHXurzKlY4MaXtuloti5uIAnEzg4Lkfq1kr0ZGysD9K1dXWX1U7t3W6bDA2Qmbdl2Se5dT+nTWdoT7hWGWf0Cq9mOiANpHrq5yZf7B3SDM/H0PPp+csdS+OIMRQ99ZKlPT6uv18I6LQ5hSKQuxKRMSUDVKj/Efr5X79tKm4IlKvRVLumFuQjxmBATKQm7Jrllb4x9HB5nupbXLvV7fIu0/OU0uPaIo39aFYzBv+RFs33MF+jgjKpXxxHe96qBBNbeXvm2AiCgnbF+/2nz8Suecqd5eEIkxopCLFjeSipmK4PyNsLBUbcH6RkVhtpMTfnFyQo3YWNk1RASIteNi8U86Afq6HRewac8VTAsJSbWFrF1MDBY6OGCFgwNKR8fg7Y/WYN+q8RnO8vpt0UF88etOlDIYMDgp1fuAjY1s6dYgJgaL156Ep5s9Jg5vmerrxAp/vx51sHjFUTTQ61E5xXWM6Joyx9UFZYu5oGndl7MA8jTJHW7SI65htiwagwWrj2Pl+tPYFxYNLzd7vNmlJnwaFYWHW+Yq0NPLxwCdKA/QRUdjw4rFst/ng7u3n3iTHtizDiaO6ohCHqK82MsNzgWRKlarUmEsv2jEI6VSro6nDM4FkQ43Jjwckz085H702rGxsmq5CO3Do1Lv6w4J12HjzksYHBaRKjgXxNeIfqGi/2cznQ4rN57Gh2+2TTdtLq05Sw7j4PHb+CwkRH7/ZK9FRcmeordU1igRH4/fFh16IkAXFwVzvnoVfcf8jRFWVmgZFS37j1610eCoxga1qhTF1LFtYelEqtzMD7rLWzKxdy87iusQEeW2ihHzsX9bYns1RwcbtG/BDhLZ6bWedTD37/2yjavIbEvbs1tM844MD5dtw7ZotRiR1D7U02DEw/An93UvWHkMtePinqjvIsJw0TddbIcrEmfAoYehOHz6LprUeX5gfPbyIxmci4WBQZGR5ro4faKiZH/4pQ4OaK7TYd6SwxgzoPET1w/vjWmN85cfYcp5USldj8r6WJnBt9vBHjYONlj59WsWX3RQpMKLyYeUExAc6/MOy351ERVwgf6P8fOMj9G+dkV8OXVSquC8WsUi+Pu7Ibi65xOMfb0xXHK56vknkzvhnkaNGKVSzjqnR8xEO8bHyxVnQQx4ohVXqTS9Yu88CIYhPgG1UgTRKYngXxSpEelq0bFGPEras/Ysco/bymNoEx2dKjhPDvrHhYUh3MoKpfSx2HX4ukyFf+L7lvbEjiVjMXRQU5wrVQgrvNwRWrk4PnmnI1b8NkS2TCMiotyzfd8lREQmjkHd29WERvNihS8ptZGvNULhIi64IlainzLWi5Gwtl4v25IJIh3+sp0NSpV8cgvYtdsBqPWUlmDiq8WWsiilEmoFZBHWjJi/+jg8TQl4PUVwnjLoL2w0ylo54bo4HD93/4mvFwH7sl8G49PJnRBeuQRWFnLH6VKFMOT1Jti+eKysbE+Uk7iCTmSBbl27goWzZ2HzmuWy2E1KDVu0xscjqsOneRW5qptdlUtflGgf8v307njro3Vy4EuPCHnFHjXxxiMSxBc5Osh9Vml7ZNsmXVCJdLL0iMcXe9SSO3pnpPJ4YEi0rMw6+ikXFEVFGp7BIL9ngimxcnx67dJEP9epb7SVNyIisizrfc+Zj/syvT3biZov6+YNR8Nu3z91rBf0SqU5yBD70u8prPBVmvT25PFe9Ph+GjEmW5lMMJgyNtYLZ8/fR4NoXbpFXcV3EhMLx2wS923rY5OvJJ4s5CaKtj6tcCtRTuIKOpGFEIXejh3Yi3H9e6JX87r4Z9lCc3CuUqnQ9ZX+WLn7COau3ChT9jK6D+tlESvUxQo5w9XRBv9o06+EftjGRq6wi+qo09zdscvWDl9M6SzbdaRUqawnSng5YutTHkf0WxVD6r/WKlQr54kiz9iLlUz0hhWedkFhSrpP9BcvVcgJNlx1ISLKU0TLz027zstjEcy1bZbYgouyl2jbWbdmSexwsEd6eW4hSiVOaTQoajDgZycnfO/igt4daqBZ/TJPfG6HNlWw2zH9x3loZSXr2mgTEmSbVVFHJSOsrVVyguBpRCcZY4ptX0SWhivoRLnMYDDAd8NaWZH96oX/Zv6T28OMGdACbw1thaKFRJXzC0DshWx/DkdO38X8Vcdw9sJDWFtboXXzirIi+LMKjqW0bvt5fP3rTjwIiDD31B5qbY0poaHmAiui8ukvzs6yVdrvTk6oVs4Lf49uLVuEpSX2do0f3hKTZ2yQ1WJF0RnRZ1WswB+wtZWVY4sZjbik1uD3YS0zNFkh9mOJ77nzcixapLOKLnqJBqhUctZy2isNnnhMUWDu3sNQKK0UMiWfBdWIiCzLxdMnERCU2GpSZJllpDZJQXL3YQj+Xn0cu/ZdhcFgRI2qxTDklQYZ2ted3K5s2jebcSKpxZdIGu9fqBCGRkSgS9Je9CClEl+4usosuc329vByscP7/RrLvd7pjdUjX22I1ZvO4jM3N7wVFiZbsgo3rK3xjYsLHBMScNTODq92rSUz2DKidbOK+PtuIEaHh8trh5TERPwhW1uYlAq0qFcGpYq5PrFYIrqhiJV10XecW9coNzBAJ8ologXM2iXzseT3X+H/76NU95Us5oYJw9pg+GtN4ZDFPt8Z9e3vu/HjX/tRwpSAhlHRctZ5tV8Ylqw9iXlfv2aesRaDVnqD69L1p/DulxvRRB+D8UmF066p1Vhqb4933d1RLTYWMdYq3LBSoXp5L0yf0AFFvJxQsqjLMwNrURE9IDgKM3/fjfX29ihuMCDQygphVlawggn/Wlnj0wkd0Ll1lQz9nOJ7jR3cDGP/txqLHRxk8ZjkZLk7KhW+dXGRaXSN65bG0L6JKW0P/g3F1dsB2LL3Cnbuu4KQyMQ5/qIeDhg5oDGGv9IgRwrFiNWJh35hsLfTPPc8ERFRov2+W82nols7b56WFPYdu4lhk5dBbYxHs6ho2CUk4HhgBF7ZdxVvDW6G999o88yx/uotf/Qe9Rc8dHpMDQtHlbg4OSaLjLnfnJ2xSauFmykB59UaONpr8NPkTqhSvhDKlnRPd7tYsnKlPDD/+wEY/d5yDNNoUNpgQKwCeKSyltl2Yltc9zZV8dk7GevWIogWan8tP4ov3VwxOSTU3HUlQqHATBcX2Q5WTNp/PbWr/Hh4ZAzOX32Mw6duY+uuy7jxIER+3E6jwitda2HK6NZwcsh4O9eMio0z4vb9YIjT/bzzRAULA3Sil8zv0UMsmfcr1iz6G9FRiTP9yarWrI3pI+uiV8daUL2EN+qdh67L4HxIRIRcpU4ekoeHh+MrNzeMfG85urarhh17ryAiJg4lCznj9d71MKRPPbkyoYuJw6c/bkM7nQ4TwsLMXy+qsYpCbBPc3XHTyR5tm1XEVJ/qaNO4fKZ+rreHNkefTt5YuekMTl96BE1QJOp6OKJujeJ4tUtNeLplbDY9Wfd21WQBum9/34MtDvaoGqOXKe1iQkGjVGDcoKaYOLwFHvmF438zt2DPsVvmrxUXM911OtSNjcU+nQ6f/LgdN+4E4uv3u2ZLAC22COw4eA1/rTiKE2fvwyA2wovXRFlPvDO6Ndo3r/TC34OIKD87uHuH+bhTq+q5+lwsieiMMnLKclSP1mFqcIgsjCoMiYzEGq0WPy84gEd+YThx5h4eBkbAWatBz47eGN2/MYoVFt1hgBm/+MJFp8e3/gHmVWm3hAS8HxYGR5NJVmwvXb8MPm5aUY7bjplYXGhWrwxObHpHtlw7eOIO7j0KQUk7DapVLIzeHWugWoXCmfp5ixZyxl/f9cfwd5dhkEYD7xi9vD45o9EgQQG0a14JM97tDCcHG3w4czNWrD+NGEPiyr2YqBfjfM+oKFzUaLB6zQkcO3UXa+cNz9TP9CyXb/jhl4UHsefgdXltJXg622Hoaw0xbmBTZugRA3Sil+XK+TOy8Nv29WtkP/OUWvh0wqA3xqNOo6bwjlv60p7TH8sOo1K8Ea9ERaX6uNgRPjEkBK+r1di85Sy6RkbBIz4el3Q6fP2rLzbvvCirlm/ffw2RMQb0S1MpVRAXAOJxZ1pbo2OLylkOMEU/z7R9Sl/EhGEt0KlVZSz+5xRu3A5EKVtrjGpWCT18qsl95w8fh6HHiD+gDovGxLBwWUFW9ErfrNXKlXyHhARMCguTqwez1p9Grw410LBWqRd6Tqu2nMXXv/jCPzhKPv6gqChUj4uTkweb4+IwbMpyfPdhN7zWtXa2nQciovwkOMAfV86flce1qhVHIU/RxJMEMckdF2fEpJBQc3AuiHG7T3Q0DtvaYv2282gTE4Puou1mhBXWrj6OdVvPYeXsofBwtcfuIzfxVkTkEynjgshI22xnByuVFYa90iBLJ11rp8HrPerKW3YQQf/RfyZg+cYzchtfQoIJb1cvLtvSerg5yE4tA95aiFNn76FPeASaJ1WSF+3hVtnbI87BAZ8HB8uPTwIwe/EhvDcmMcsgq85f/Rcffr0JZy4/ktvpOkdHo5leL7cD7Iu2xcw5u3H1pj9+/awPM+cKOK6gE+VwwZqDu3Zg4eyfcOLQ/lT3aTQqDO7dCBNHtEWlcmJ2+D7wEoNz4cS5B3g9Kjrd+8SMeM3YWDlwDI5MXOnvpNOhe1Q0pgL4eu5ueLk5QAsTCqWZcEhWNmn/+d+rjqFbu2rm9DmR1iUqpOZW6rZol/bpxI7p3vfdH3thCtdhpn8AnJPS4sSeuIphYXCLj8cyBwe01+nkba2TI5b8c+qFAnTRDubDmVvgZTTCUaHArMBAuCd9X9FOrpFej5+cnfG/b7egU6sqMgUuNEIHJ3sbeUFDRETA4X27zKehQ4vE8YYSiVZi1fWx5lTvtEQQKvZ8v50iE05k1X3g5YmxH6zEz5/1kYVUyySN6WmJlXTx2IdO3EaM3mCuti5qt1iJKuxWuVOT2tVZi7EDm8pbWpt3X8ahM/fwZVAQvFN0w+mfNEH+nrs7DtraomVMDNpERWPpupN4d1SrLG9ru3jtsdwi4KCPhUlphenBwaiXouWr+J7VY2Px9c5L6Nu5JprXL4vAkCio1SqZjk8FCwN0ohwQq9dj85oVWDRnFm5fv5rqPndXe4wd2BLjBreEp/vzq4/nJBEfxz8jSE5I501CBI1dIiJlSti0t32gS6rY6prOwP9QlfjVl649lil2vy89gmX/nERQeAy0GhV6dPTG2NebPFGkJbfE6OOwfvsFvBYRaQ7OU+odFSX32+22s5MXL1V0MbhzNyjL3y8yWo8vZu2AT3Q09trZ4bXISHNwnkz8dgZGRGCnnR0GTlyMi1cfQ2+Ih0qpQMeWlTFheAv2ZCWiAu/Qrv/S2zu0TN26kxQytftpxKiT9m4HkwkjQkIxVWkli8sJj1QqeQ2QltjbHalUIt4QL9O3r9zyx9/Lj+Lq3SAoFUCL+mUxZmATNK37ZBX33LJs/SlUNxpSBecpg+UasbHYYWcnA/SqsbHYFB6D6Jg4OGizlub++azt8NLHwsFghCeMqYLzZM31eqxMiMfHP25HRLgOAWGJhffqVC6CcUObc6tbAcIAnSgbhYUEY+X8P7D8rzkIDgxIdV+JMuUwcMxb+KC7Ena2lrHy2bhOaew/FIc+KfafJxNB93mNRu5PT0v0EF2lN6BSGS8oFQqstbfHiDSfJ9bUxcc9jUZEa1ToPvR3+D0OQ9vIKJQ3GPBYpcK29SexaccF/PhxLxw6fRc7dl+GPtaAKhULY3Cf+mjXrCJu3g3ConUncfHqv7CxsYZP80ro09H7idZsz3Ppuh8Onbon+5t7Vy6Klg3LPjETHhKmQ6wx3rzyn5a9yYQi8fGyMI4QaG0Ne4es70nbtOuyzCZoo9Nhh1aLqk/paS96vitNJtw6fx+vREbJ5yculDbtuohuh65j+a9DULtasSw/DyKivMxoNOLQnp3y2NHBBo1qW04gaAma1CmFT/ZfRbBSKVe7UxIr43ttbeEdG/vEdYDY4mUrCrb5R6BUYWesiYtD05gYc4HVZGL7V/LXfjtnFw6euotGsXpM0MXI1qq7j1zDq8du4eMJ7WWq+aqNZxAUHIVCno54tXttvNqllhz7RTr6viM3EZ+QgDreJfB69zooXkR0sMm4gKAo7DhwXRZbFfvnu7Sukm622b+Pw+CtT6+5W6JyBgOOJvVKF9vNxKR4Vtuviv39B07dxTvhEVjk4IBWSen0aYnWb5EJJoTdDYSPTod6ej2ilUr4nhdb3f6V52/ka42y9Bwob2GATpQN7t+5hcVzf8H65YugT/PGW7thEwx+42009+koA0K72CUWc85H9m+M147cxHwHBwyKjERy+bYohQJfubhAYzLJAnBp6ZICW1cXOzSuWwZrT9yWA0uP6GiZDn7d2lpWShf/epkSYG9vg9DHYZjl5y8D3GQ9oqIw0dMTI6Ysh72YZY+MkkHw6ZBIDD1+G7WrFMXpy4/gogBqROsQrbLC9OO38fNf+7D0l8EZ6l8aHBaN8Z9swcnL/tAqANF0J9QElCrshLlfvSaL0CQTVVpFr9V/xcp/OrPb4iMiOHeKj8d9lQpn1Wp865P1QkT/+ofDWQEUSzonAUmBf1q/OjnJ9PofgoLMK/ti9l2k2X/o6YFJn6zFnpVvcc8aERVIF06fQERYqDz2aVZF9sGm/4iibd/9vhvfuLthWmCQXB0XxMgjxuqbarXcb52WmDIWY7tGbYVxQ5phyowN+J+bGwZFRMgWqmI83KDVysn4yrGxuGtvJwPRD0JC0DRFO9Ou0dH40dkZn/2wTY5TTWJiUMdgwO2AUHx00x9/LTuCgJBoxMUaUDtGL6u3/33uPuYuPoTv/9dT1nrJSKHVWQuPY/XWy3JCW+QnhpiA6TO34NPJneQkQEpuLvZ4pH56wC0mwUXavjgH2xwdZMZaVqus/5vUglZMrotrnKeN9Ru1WoRaWeHL4GBUSzFhLwL6Pxwd8elP22VNn+TCfZR/8R2M6AWcPXFUFn7bvWWD3FudTKlUoE+nOnhnVDvUryn6i4YBhmUWd65FEZXp433w6awd2Otgj7rROsQoFDiqtUNcggn9IyPNA3lKO7V2KFXICaWLueKnj3qgfrcfsFWrxQYxi24ywaRQyD3VYl/bHjs7KEOjMCIsPFVwLogwP1ShkK3YPgr5r7Ls65GR2GFrix8vA/ViYjAtNNQ8Y+9vZYVPE9wx8O1FOLDmbbmX/WlEEZihk5bh4U1/fBgSioZ6vfye16ytMSfeiFfHzsf2xW+YBzuxKi/6sm/ae1nOXqcspiNs12oRpVTKgnkfeXqgdFEXWVwuq9xctAhPqhor0unE4CzS6VIO3X5WVjhjY4NJoaFPpN3bmkwYFBqGD6xUco9hg5oln3sBs/fYTdlORq2yQpsm5ZkeT0R53gHfbebjzm2eH8wVNGLyecH3r2PghEUYbF0YDaN1cvw4qbVDIBSoGBcnO6+ktc/WFgYT0LJhObkVTQTMV+8HY7KHh3mst01IQDuxTUurhbO9BtX8Q1MF54IyKStPFEH9JijIPCkt3Fap8F5CggzK5wUGmvfJ68PC8JuzEyZ8sg7lS7mjeqUiz/wZv5m7Bys3X5KTB6L4mjYpEBYTEJM+Xy/blqZsy9q7S018cOkh7qpUKGUU0xD/ERPwJzQaue3sU3d3BFmr8ObgZlk+/27OWnPQ3ywmRtayEb3j025pE9cAIkMhZXAuKJKvixzssXTDadn27XnEVoM9R24izhiPWlWKonl9ZpXkJblTtYEoDxMV2HduXo9BnVtjcJc22LV5vTk4F2lU44e2xs39X2DFb6OSgnPLJtqobF8wGu2618G9aqUQVrMMxg1vKVuZbXRyxAW1WHNOJBK/RXXT/Ta2eGNIM5kR4OXuiD++eQ0KlRVEGRORpi0GGJWVUgbnvTvUgBiDKqYz+O+0tYVeocA7oakrywo+MTGoq9cjxMoqVTqdWKF/LygYjwIjsWXP5Wf+bNv3X8XFmwGYHhiEJnq9DHzFQFfJYMBnAYFI0MXijxVHU33NxBEtEaJRY5qHu/zZxbMKVyplX/e5jo4QH/nRxQWFKhTG8t+GyHZzWdW1TVUolEqZHigmQ25aW2OGiwseJs2ui3OzWps4sIuCfekRKYjiZ7p+J/CZ3+vs5Udo2utHDJq0FH/M24MfZu9EmwGz5USH6AFLRJRX7duxxXzcsSULxKWnnncJ7Fs1Hm+OaCnHeTHet+laGyNebSBbjW60s5Mr6oIY905rNJjn6oJOLSqhdHE3ufK9+KeB8PJylGOpSAFvGBODsvHx8NVq0aBOKblNrGKMXn59Sv9aWeG0jQ2GR0SkCs6FMkajDD7lHvYUHxfXBG+HhsHDlIA/Vx575u8/ODQa81edQP+ICNk9RgTngmd8PCaGhcm2aTPn7Eq1kCKuTcqXdMM0Tw/sERMRSdc44vg9Nze5grnU0RH3PZxkn/aU2XaZVaaEG2pVKox1jg6y5oxjQgKmurnhXNI1hgjTj2k08FepUOspY72YUBHXUaK967OIej/93lyAdgPn4MfZOzFv3h4MmLAYzXr/hIvX/LL8M1A+XEHfvXs3fvzxR9y5cwdFihTBqFGj0Lt372d+zcOHD9GmzZPtDLy9vbFy5cocfLZE6dNFR2PDisUylf3B3dup7vPwKoR+I97Ax686wiVpptSSiX3Ytx8Ey71gYuAVA8+3U7ul+pwhfetj8ITFeM8ElEmIh0ecAddtbWR6+JuDmmJA9zrmz23XtCJ2LRuHBWtPYM/+a4gwxqNyOS+UiTNi/Y4L8nPEjLsYJAdERsoiM5EKBZY6OMhBPu0scjIx0/yDi4tMLU+5g6yE0YjyCfHYe+wWerZ/+mrJBt9LqBhvlAF5WiIzoHVklGwt8/GEDuaPVy1fCMt+HYyJH6/FeypreSEiLhqslQrUqlxEVlZt0aCsvNh50Sr0YgV93OCmshe92Gc+JDwc852ccMTWVqa0i60G4uOCmKhI7zyJyQMxwGttnz5RIAr8vDZuPopGxeCHkFB5/sXPdMjGBrOPA8MnL8dP031e6Gch4lhPubXF7ObVxMnaGnXqw8sjd4uvWpr7/4YiIkqPYoWc4eXuINuWpmxdKrrNGOMTMHv1CaxycULZmFj4aaxxT2GFxt4l8f30HubPFfvBty8ZK/eKr9tyDg/DolG0sDPaOdnJHurxJmCxkxMOJhVSFanZpqRtWoKYKE+PmNSf6+Qkt8U1TBGgivG3eWQU9hy6/syfcdu+q0iIT0CX6Ce70ogRtFtUFKbfD8a12wHmrDE7W7VsIff2x2vx7fHbmOniIj9XjLJlirqgddMKqF2tODq2rAR1NmyZmPqWD/q/tRA/uLliVHg4vnd2xlR3dzjGxyNBoZDZeSI1X6S4P02YSoUydupnXtsNHL8Id64/xtSQENkFxipF1uDACYvx11fdUKhw1icbKJ8E6CdPnsSbb76JPn364IMPPsDRo0fx4YcfQqvVokOH/y6Kn2bSpElo0OC/nori64hepqAAPyz/cy5WLvgD4aGJlUyTVatYBJNH+eC1bvWgyWLxkJdJDMRzlhzG7IUHERKZOFDa21hj6KsN5ICdMl1ctPX4548R2H3kBjb4XkREVCx6F3fFgO61ZZuy9GaIP5nQQd5u3gtCj+F/wDoqBkPCI2QQLvZ1r9dqMcXdHZ8FB8s9a2Ivu+kpLdoEY1Jwml6qjzohAfHG9AP7ZBERMXJi4WnE7HpE9JOz1XWrF8f+1eNl79Qbd4OgtVOjdePyOdLqZPLIVnJf268LDkAXm5hm91ZoKIJVKtglJMiCfOKcbbGzQ4VwkRCf2kqtFlYKyD7qn3y/BRXKemFg73qyME5yEby5Sw/DWm/A5wGB5h624jfdQq+HS2AQ3jcBx849Qq+iRbP956OCgWM95ZYrGz4xHw/wYbHMZLsOX8eMWb64ejdxxVWME51aV8H/xrdHUa//esSLceKLyZ3Rr1ttLN9wRgb09Zzt8GX76nIbXNpiqiJdXmTeiZtoqSYmfw8cuo4OkVGor9fLcV1UP//WxUVuSdMmJMhtWkLqRPIMjvUmk5xAeBYxAWGjSGwP+7SxXn5e0nVPyq46S2YNws27gTh29r78WIOaJVCulAeyW5M6pfH3zP54b8Z6zAhKXHLoFxFhzuwT2XCb7OzkuesVFfVEIb6z1ta4rbSC8dQd1Gj3FTzc7NG3a20M6FHbXFl+56HrOHvtMb4NCkpVdFYsUojxf5SqMFZsuYxa3hWy/eejPBagz549GzVq1MCnn34q/79hw4Z48OABZs2alaEAvWTJkqhZs2ZOP02iJ4gZ+UVzfsbmNcthSLMfqG3Typg82gc+zavkmcJcIrVr7LTV2LLrkmytJgZNsfc5Qg/8Nv8Ajp2+h1Wzh0CVogiK6F0qVsfFLTOmfbsZ2kgdZvr5mwdMMfi01ulkgZnvnJ0RoFKhY3S03LsuUrrTpr0Ju21tZXuTtAOVqER7VWWN155TubxUCTdss7VBfGjiTHxalzVqlCySfrEV8XsVVe7FLSeJ7zNhWAuMeLUhvvjVFwvXnkTrmJhUGQMiZU+sLogVdDFwiyBbvCIXODhgo7097BJMqHX3sdy7dy4kEm+cuYft7aph1se95O9ww/YL8ImINAfnadvJlE6Ix85Dd9CrU/0c/Vkp/+JYT7ll3fYz5uOeHVIXAiuoVm46g4mf/SMDPzF+ir3fIgtry85LOHD0FnYsHZsqSBeqVSiMzydnbmV1wZrjcvvUNwGBqTLVGuv1slr5QkdHuBuNaBQTg2M2NrJafJd0Cs+Kj4tAvHKaay0xYh2x16JO9eLPfB5if3y0CenuJxcuq9XyXDytIrwIyHMiKE9LTPQfXT8JG3ddxLjpa2VHm5QZAzYJCZjk4YEvXF3xRni43NInzsFhjQbfurrKa7aS9/xROs6AewGh+OpeEJatO4nVc4fJgH2970WUjzem2xFGFKdrExmFnQduAlNz/EclS96DHhcXh2PHjj0RiHfq1Am3bt2SaexElhbEHjuwF+P690TvFvXwz7KF5uBcpVJhUO9GOLvtf/BdOhHtW1TNM8G5sOfIDWzedUkGcR8FB2O5nx+W+fvja1GwxWjEyfP3sXDdyRf+PmL2/cDJO3g1LPyJ2WyRmCWqxYvgXKRyDQ0Ph3t8PL5xcZFBdzIRqos935c0GjmApSR+Gz+7usiWa6Iy7bP0715bFsDZlE7mzVVra5lKPqBXPVgCUaBuaN/EADm5tUuybtHRco/6crFXvVAhvFXYC68XKSL3rotKuvP9/PBGRAT6R0Xh64BAmdomsh4WrDkhX9PhuljzCkJa4hXsGWdAZDqZBEQZwbGecotoZ3r09B15XKV8YZQvnZi+XJBF62JltXUxKS3any7z88Nif3/86e8vU8zDovSY+Om6bPlei1afQDNdTLrbyF6JjJQLAUEqFTrpdLJo7AJHR1xKUdcGSXvdxXY3UVg25SSyOFqr1eKW0gpDX234zOchCp66O9pioZNjqn3sgthOt9rJEa0blUNhz9zf/iAmzbu2qYYi7vY4amub6r6yRiOmhYTgolqNoZ6eeKOQF4YWLYwZrq6yq84vgWJ8D8VrUVF4LzQUv/r5I/RRMCZ//o/8+vDw52QNGo2Iinn6/VRAVtDv378Pg8GAMmVSVw4sW7as/Pf27dsoVuzZK2Aff/wxJk6cCGdnZ7knffLkyfI460zyYsJSGAxxqf6l3Dk/RoMBvpvWYcnvv+LaxfOp7rN3dESvAUPwytBRaOu6V34s1pJeQ0nPJe0qf1rf/r5H7nMS6eWlU8wwixXUGcHBGObpKVPfB3R/sRWI67f95b9pq5AmqxIXJ4NzQSSMfxwcLFfVh3p5yZ6fYqZfDNhiUC9Z1Bn/PAJu2WhQVxcjB9o9DvaIUllhzme9oFErn/m7uHrLX1aVnYvEvW2iZZwoPHPExgYbHR1Qs3IRdGxVEWu2nkV4lB4lCjujcZ1ScgDNDeLnbeRdAgvOmVDZP8AcVIsguqVOh80O9vAq5YF6NUsgICgSvgdvYGJo6BMr4830ehyOicGfyw6jf/eaKOHpiCs6nbxISksM1TdsNWjr5fjc11BBldG/sawywfRE/+G8hGP983Gsz5nzs993m7nwV6dW1SxqbM6t96G/Vh2DIT4BoyIiZOvTZIXj4zElNBRBbm44euouAkMi4GifejI4M0QNm7uPw9DtKUXNRBaYWBE/aWMjV8ffDA/HdCsrvOvujiqxsShuNOKOtTWuq9Vwc7LFw/AYjC5SCC0io2VF9yP2drihVGHcwMZoVLvEM3+3F649hpenA46G6zDZ3R09o6JQKKnl6zonR8TY22LKmFbYeeiaXERwsrdBy0ZlZWX33DKgV138MG+v3H8v6vOkvCYrE2/EHXs7NGlfXZ7nFZvPYlBoOEqmyQ4QmYeDQsLw45GbuHHXH8WKOmPHM7MGNSjiYc+xPg+M9TkaoIcn7Zd0dEw9Y5X8/8n3p0etVqNfv35o2rSp/Pxz585hzpw5uHjxIlatWgVr66zt9xUFFAL9HsPSBKfTf5Jy9vxU1y+R+5ZWbDqLv1Ydx+OAyFT3Fy3khGF96+PVLt6Jb+Kxy2CBL50Mn6PbdwLhHRubKjhP5pKQINt77QlSwu/x83/I6BiD3M9ub6d+IotAH5N4HsWKuEjPSitMqZQTBcIpjUb2854TEABfOzu5chxsZSXbiYVbKTDnk444efEx1m67jFV3Q6CxtkKzBqXwSscqKFXM7pnPdeG685iz/JQc+FyNRpywsZFV5QWVAujhUwlOjjZo0ecX6OLiZTqR2OVWyEUM5E3RsGbu7GV8d1QDjPtfEMYovNAyKtp8EbNfa4ciXg74/oO2cHWyxbd/HEZpU/wTreuSiT6z+x6H4+r1u+jUpjz+WBaBHpFRcoY+bVuXEJMCXVtX4PtQLr1Px2uqQ5WHm55yrM84jvXZe352bdlgPq5fo1CGxq/8fo52Hrgi09rbpzMhK8a57tHR+FKjwd6DF1Hf+9l1R2LjjNDHGmGvVcMqzV50wVZthaBnFDUTfdKVSZPiIvD8KjhYHu+ys8Nta2u5KixMGdUYbs62WLnlMraefSiD0qoVPPFGpypyLH7W7/XsFT9M+Hw7CscZ0EMXg7MaDb5ydZX3iVZwdasXQbumZTBk4hI8DtGZx3rx3Af29MbgnjVyJRuya4uSOHyiGD46A9SNi4V3jF5eH+1ysIdercLM99uhVpVC2HvsLpZvStw2kB6RFfGD2CZw6BLaNiyGJf+cllmD4veckpisOGRri7E+lfk+lAfG+kxfEkSK9NSAgOd+XvHiz94v8jyenp5y9TxZ/fr1Ub58eYwePRq+vr4yTT4rxP7aQu6WU71QzBaLF4KbmxusrbPerim/yqnz4/fvQ/w4/wj+XH4YkdGp3/TqVC+Bt4e3Rq8OtVLtx7ZUYqbPfI7SpI6lpEzqTf40YrVWDJXPqu65addl/LHsCM5fT1wlL1fMBUNebYDXutaSvd8FD08vFHHbh00xMagSGvrkY2i1ssBbUaMR8xwdUTY4GK5ib3V0tLyJVmNTPT3Qu5M3KlYoLW8DejXO1DkRVcvnLj8le5iKlHohPjxc9iC9oVLhJ1dX+IfEYvX2q+gRFSVXGURv8xvW1lgc54gpX+/Ewh/6o2GtZ/cVzwmFCgMb54+Se9HXbj6HfWE6eLnb4+2utWQxmOQVD3t7e3NhnfQYku4rUqQQxg0uioPHH+B9kS4fESkLz+kUCvhq7bDH1g7D+tZF2RIuz30NFVQZ/RvLqvAIy3uf4VifvTjWZ//5MRqNOHv8sDx2drRFp7Z188SYndPvQ2qNWqaWi7Zc6UnOzHJ2dX3qeC9WpGcvPATfQzcQn2CCi70Gr3SrjTEDGsoicck6t6kK363n0Ccq6olWqSKV/Z61NZrqdNii1cqK4iJIFxle4iYy4kSLsxJejujVqZ7MXGvdLHM97EUg//Xba1EhJhafBwbKbXSCKE7np1RilrsbYmIT8M3vh1FDr8e74RGoaDAgKKm96e8rTkNja4eJw5ojN/z93etYu/0Clq49iSX3gmBnYy17tYvtbqWLJ04yuLklXsMYnzPWu7q6okXjKhjS51/MXX1SXku10SX2uz+alDVYtbwXerStyLH+KSxprM90gL5t2zZMmzbtuZ+3ZcsWOCW1VRADfUoRERHy3+T7M6pFixaws7PDpUuXshygi2RRsTpvacSAZInPK7+dnysXzmLhbz9h+/o1sp95Sl3b1pCF35rVL5+n9pYnE28mmmecozKlPXAhKrHlSXo/ndjz5Onm8NTH+Gbubvz0937UiYvDO9HRMn3qsF6P/323DecuP8YP03uYz9vEUa3w7pcb4RIfLwuciX3vMQoFNtvZyX3UYjD30enwvrs7Rnp6ytX7wkaj7MUq9mRVr1BYtj571s/zLGu2XoC9Ang1xXuPVVJ7NnE7FqPDoRN30DcyEkNTfI5oP/ZRUDCmeHrgu7l7seGvkcgNhT3VeG9MW3l7mpYNy2HRulNyEBZV8tPaq7VDtXJe8HRLfJ9dNXcYvpq9Cys3nsGypGrxxT0d8cWgpnitqzf8/fye+xoq6HLq/CgsMMGdY33O4Fiffefn0tlTiEzKxPRpXhVau9T7eQvq+1DrRhVw+ORdGaSml8UmCqaJVeS61Uuk+zj7jt3EkHeWwstgxPCISFkn5nKUGouWHcGeg9exZt5wc0eTcYOayto2n7q7YUxomBxfxXc8qdHgB2dnlI+Lk33IP7Oykn2/RcacKPwqVt132WuhtrfB8m/6wc42a6n2B0/exj2/cHwbHm4OzgXxc4tb/7Bw/HBdIQunfRwUbA56PBIS5P58kUo/d/FhjHqtEVxzoUWuRg0M7FlP3p6mcd0yUKuUMgNQXE+lJfq2q5QKNKtfVv4+P3+nM8qW8MCchQexKyjS3K1HVOmfMKwZIsODOdbngbE+0wF637595S0jxF5vkYou9po3a9bM/HHx/0LavelEOUGkYh/avQMLZ8/C8YP7Ut2n0agwqFcjTBrZFpXKWU5mRU6YPKoVXp+4BL62tvCJiUl13zm1WrZB+eT19FeqRZVWEZwPiYhINUDItHhbW3y79Rzat6iEji0ry4/3715Hbh/46rdd2CT2TcOEIJMCepNJBuZiVVsEzLMCA+WKuniMfba2sud357ZV8f207rC1yfqb4+37waigj01VCT0ljSmxIGDPdHqmiufVMyISMy4/kivxojqsJWrbpAJKFXbCd6Z4fBwQZL4QE/8VLexOqDX4+fUm5s8XbVhEK50PxrbFnQchUKutUK6ku2yhk5/3bVLWcKwnS3dot6/5uGOrarn6XCzJ4N718M3sXbLLx+SwsFTVoEOVSqyxt0eDWiXhkk7bUJHSPn76GtTQxeB/QcHmDipN9Xp01Onwrpisn7MbX73XRX5ctFxd9ONAjJm6AmOs1SisMEEPBUJNQDGDAZ+GhEBMm3wSHCzT2rfa2WGFg4Mcp4oUd8PyXwY/UU0+s2O9VVJtm/QUMRjklroeEZHpBjwie261gwM27b6MQRZSMDYtMRnySpdaWL7+lJyMr51iv/p5tRqLnZ3Qs311ucAiiIWSYa80kK8D0fLWYIiXbXBF33cx1kc+fXcxWZAc3fUmZkFFD/Pt27dj8ODBqVbXRaG45xWIS2vPnj3Q6XSoXr16Djxbym8qRszHkn+O4bvffXH5Rur9S24uWowb1ArjBreEp3vuV/V8GcSKa9c2VfDTzku4olajVUwMVCYTDtvaYoNWi7pVi2Jwn/TbbC1ad1IG2b3Tmb0Vj7PJ0QELVh8zB+jCmAFN0LdTTfyz4wIe+YcjOiYOi9edkoXKrFLsfR8YGSlvIUolXi9UCO2bV3qh4FzQajUItX7625vYHy8Kq4m97ukR6ffy88KiLTZAF6mcC34ciNfGzscIhRXqxMbCKT4e5+1s4Q8F3hzcVA7aaWntNKhWMX9PRtHLxbGecsPhPf8F6O2bV+EvIYkIxL75sBsmfPqPXKnunrSFS6ycr7a3R7yDLX6c3jPd87V9/1UEhcfg87DwJ9qbinooXSIisWbzWfzvrXZyLBEa1S6F4xvfwbZ9V3Dh6mOora3w26JDaBURI7PnBPFYHXQ6eRNZfOMKe6FujRIvFJwLoj6QCPbF3m1xPZHWw6R6VaJTTXrE87OHCcGhT07WW5KPJ7THvYchmHYSqBBvRCl9LO7baHDVSoWGNUrgi3c7P/E1YstAxTKeufJ86cXleFmaN954A4MGDZL7yTt27Cjbrm3atAk//CBKGvynSpUq6NGjB2bMmCH//6uvvpKzQKIHuigSd/78ecydOxfVqlVD27ZPT/skCgsJxsr5f2D1Xz/CPzBxO0Wy8qU9MXFEWwzu0wh2trlXvTM3iL+n3z7rgxqVisjUp+2RenPq0+i+9TFpREtYP2X/3pVrj+Ed/V9gnVYtXQx23kjcl552ImR4UnsUgzEevnuvYJNOi0lhYU98rihWZmtthVaNyuFFdW5VBau3nJN74NL2AxVzzzdsNLJn6tNSAEXauGAJLVmeRayA71n5JtZsPS/72wfFxKFV+UIY1KsuvCs/u/gPUXbiWE8vU0hQIC6fS+x/XrNqcRT2epHuPvmPmBx3dbbDlz/74os7gfJjVgqgQ4vK+N94HxQrnP75unorAJ6KxO1g6amt12NpnBEPHoehUtn/Wtpp1Cp0b1dd3oT7/4bBd8d5OamvTmc73V2FFT5pXSVb+orbWFvJTDwx0Z/WEY1GpMvhplr9RIFUIVCpRIQJuZLenhli0WLJTwOx6/ANrNh4Gn5+4Sjp6YjJXWqhXdMK+br2QkGV4wF63bp18fPPP+PHH3/E6tWrUaRIEXz++ecyWE9J7AcWqcjJxAr7smXLsHLlSuj1enh5eaFPnz4YP3687EdNlNaDu7exaO4vWL9sIfRpUrib1isn95eLfeYipbegEj/72IFNMapfI5n6JIq/lC3hBhuN9XNn5COeUak1QqmErc2zH0ME/+OHt8SHM7fANT5eDtwOJhP0CgW22NlhlYMDxvZvlKoATVa1aVwe3hUKYYYCGB8ULPe9id/6A5UKc1yc5fcUlWVFr/UJ4eGpdgWJ+0QKoKj+avuc82IJROr6kD715Y0ot3Csp5fp4K7t5vZqHVpU5clPR5vGFeTt3qMQREbFokghJ/Pe8acR47hYSxZVTayfMtYnf96zjB3YBFt2X8Jn7u4YExqKouIaP2lv+k/ubvAuXwitGr74ZLwoDjiiXyP8tvAg7BISZIaeKIoWoVDIa4qjdnZyLBf91EUf9pSF88TRcgcHOf7b2lh+XCFWxH2aVZQ3yv9eyitS9C8Xt2e5du1alve/UcF29sRRub9895YN5gFbEFXFe3esjXdGtUODWqx3kJKYbU05+/08HVpXwafn7svKp+5p0shENfD99lr0a/P8iySxJ0rsTf9u3h784+iAQiYTAhVibzow9JX6mDKqtbkd4tZ9V7Hsn1N4+CgErq726NXJG306esvJgowMZIt+GoiRU5bjkwuAs8Ike67/a1LA3ckW7WqWxIl9l+Gr1SJaqZT70AoZjbiqVssBW6ysmxQKROvi0t2nR0RP4lhPL8u+HVvNx13aZK7yd0FTsmjGt2m1b15RFhM9YGuL1mkWOsTV1TYHe1Qq5Y4SRVye+ThVyhfC/O8HYOyHqzBSrUYxhQnRSXvTG1Qrhnnf9JPjtHD64kMsWHMc5y48hLW1Fdo0r4SBvepmOP19yujW0Onj8Peq41ji7AR3mBAABUxKJV7pUB0rN5+Tqf6iP7ro7CJ6s/urVFiv1eKgbeKCgMGQ/nY3otxi+VNGROmoqluE9TvOYubvO3DkVGLRwWRiX9TwV5tgwvC2KF3CnecvG/Tt5I3f5u/HRwkeeDcoGKWSUsUeW1nJlmXxGmsMzcAKrkizHz+kOfp1rY2128/j34AIuLto0aNdNRRPGvD1sQYMm7wM+07cRjWjAdX1sXhsbY0PLz7EX8uOYuWcIeZiKM8i0uvX/D4Mx87exYYdZ6DW2KJmlWJyn/ySf05h5/6rGBsaJguqTXH/73UiKsy2iIvDIVcnuLtadtobEVFBExcbi8N7dspjFzd3NKzNCfjsIoq+tW9aAbMPXYdNsAkN9XqZfRatUGCZgwOOqzX4bXiLDHW6aV6/LE5uegebd1/Gxet+Mg2+bdMKqFOtmPnrZ83fj6/n7EYhmFAvKhoxSiX+uhuIv5YfkQF+4zqln/t9RKD/2aROGNqnHpasOwpDvBVKFHVFT5/qCI2IkQF6v8hIHLOxwZdJ/dEFMSkvOr2IonXJLc2ILAUDdMpTdNHR2LBiMVbO+RK37iXuq0pW2NMJ44e2xugBzeFi4fuJ8hqRdr7s1yEY9PYijFVaobQpQRaYu6m0gouDDRZ/288cYGeEh5s9RvdPv2K8mL0/cvI2Pg8OTlWt9L5KhQ9hwlv/W4MVvw3J0PcRFwG1qhZFYVel7Pea3DajV4ca+OLnHbilVmNeQIDsfx6pVKJQfLz8uSYW8kLfrrWem/pPREQv18kjB6CLTixY2rSNj3kllrLHrE96Y/TUFfj8OOCpMMEtPh53rVSy3/b0N9uZ95lnhBhDe3f0lre09hy5IYPz/pGR8pb8W9SFh+MLdzc5UX90/USZxp4RRQs5YVCPGqnGejFR36BGcew+G4+Z/gGyGO1jlQr2CQkoZTDgYw8PlC7iLAvdEVkSBuiUJwQF+GPtor+wcsEfCA8NSXVftYpFMHmUD/p1rw+1mi/pnCKqgR5cNwFb917BwZN3kBCfgDe8S6Bb26ovXHU9WbQuFkvXnZRtzlIG54IoWjMiJBRfn1bg6i3/TKXopyUG/M/f7YzJMzbAz1qFzpFRcl/8QRsbbHRyhGshJ0wa3jIbfiIiIspOu7dsNB+38OkkQjqe4Gxkr9Vg8U8DcfrSQ2zYeQkRkXp0K+6KVzrXRCGP7CucOm/pEVSMN2JAZGSqOjCiw8rk4BAMVquxcvNZWTPnRXwxpQt6jvwD4wt5oUd4BMoaDLIezS9urvhXrcaSaT0ylBFA9DIxmiGLdvv6Vfwxayb2bt0IQ5pq3G2bVpaF33yaV+Gb60siCr11a1tN3nKCSIOLjjXKYi7paazXy0ryR8/ce6EAXejXrbascvvDvD2YkVSBXlSD7dmxBt4f00bOvBMRkeUQxYT3btskj9UaDZq0bgdgfW4/rXxHBKx1qhWXt5xy5MxdDIoSO9OfJFqmVY+NxZHTd184QK9czgsb/hqJr2fvwpwD15CQVKqoRb0y+HlMa9SqmrmWz0QvAwN0sjii0FvI3ulyf/mW3RdT3adSKdGvW31Z+M27Ss4NHJQ7kov8PW0uW5F0S1kM8EWInuvi9vBxmOzTLorSiNUDIiKyPJfOnkagv5889mlaEQ1VDM7zsmetWytM2TfWi731f37TDyFh0QgIjoKbs1ZutSOyVAzQyWIYDAb4bliLBbN/wtUL51Ld52hvgzGvt8BbQ1ujWOGM73WmvKVahcKw06hkZdVS6fQ0PWpjA1GerkHNktn6fZ/WE5aIiCxH8uq50KN9zVx9LvRiGtQsgYPH4tAzWjR2Sy1MqcQFGw2mZvPecNHv3NJ7nhMJDNAp10VGhGPtkvlYOu83+D16mOo+UXhscK86GD+8PTzcMtZyg/IusXrdr3sdLFl1TKa3eafY1vDQygrzXF3QuGYJ2cKFiIgKloO7d5iPO7fOeLEysjwj+jXG4JN3scLeHn2josxF4mIUCnzn6gK1xlrueycqiBigU64RwfiSeb9izaK/ER2VerW0bo2SsvBbl7bVEBQYAEeHjFXxpLxv6ti2uH7LH1NPAd4GA8rFxsqqq6JFSqliLvj5sz7Z9r1E+pwxPkHurSciIssVFOBnzq6rXa0ECnly0j4va9ukAiaPbImZ8/Zip6M96kXpZHB+yF6LBGsV/vq2H1yc7LJtrDcY4+VYz4JwlBcwQKeXznhyBr7/YydWbDwBozEh1X1d2tTAu2N80Kx+efkmGpumMBzlf7Y21lj000DZO3XpPydx8lEoXF20+LRLLfTp6J0te8QvXn+M3xYdwtY9lxFnTEAJL0cM6tsAQ/rUl9+fiIgsy6Hdvubjjq1yplApvVwTh7dE07plMH/1cZy7+ABqaxUGt6iEQb3qZcvWs+DQaMxZchjL159CSKQeDrbW6NO5Ft4Y2ETWnCGyVAzQ6aUQs5eHdu/Agt9+wvGD+1Ldp9GoMLh3I0wc0RaVyhXmb4TkLHcPn+rylhX+QZFYtO4ktu2+hJgYA0qXcMbwfk3QsmF57D16E8PeXQZ3oxH9IqLgEh+PCzE6fP2rL3bsu4KlPw9mkE5EZGEO7koRoLdkgJ5f1PMuIW9ZEaM3YN3281i18Qz8AyPg6myDfr3qo0/HmggOi0avEX8iJDAC7SKjUNpgwMNIFdatOY4NOy5g7e/DUK6UR7b/PETZgQE65ai42FhsXrMcC+f8jNvXrqS6T7SxGjeoFcYNbglP9+zrrUkF25lLDzHgrYWIi4lDk2gd7BMScPrfELx+/B6G9qmHdVvPw1sXg2lBwUheK/eJiUGnqGh8cBH4ecEBTBndOpd/CiIiSmY0GnFk3y557Oxohwa1SvPkFHCh4Tq8Nm4BLt30R924WNSNM+CO2hpTrgVg+T+n4exkC31AOH7xD4BXfLz563pFR+P9BA+8/dFabF4wOld/BqKnYYBOOSIsJBirFvyJZX/ORnBgQKr7ypf2xKQR7TCoT0PY2bKlFWU9K+Pw6bvYf+yW3Edeq2pRNK9fFkMmLUHRiGh8FBgEh6QWLaaICGyys8Ps1SdkW5cxoWHm4DxZZYMBPpFRWLzmOCYOb8F96UREFuLC6ROIDA+Tx+2aVYaKdUMKlFv3g7Bh5yWER8SgZFEXmV333pcbcf+WP2YFBKCsUfR3SXTV2hofitV1AOPDwlMF54JTQgIGh4Th02tWOH/1X9SoVCQXfiKiZ2OATtnqwd3b2PrbW/h75WHoYlLvH29arxwmj/ZB17Y1oFQm1+skyrxH/uEY9s4SXLwZADcFoIYJc0wKOGs1CIuOxdfBIebgXBBBeVedDmsc7GFtAgqnGbCT1Y2NxcYIveyTyv1pRESWgfvPC6Y4g1EG4iu3nINWAbiaTHisUODTn7bDYEzAuLCwVMG5UMlgQPPoaGzXalFXr0/3cevFxsp/L133Y4BOFokBOmWLcyeOyf7lu7dskCubyZRKBXp3rI13RrVDg1pleLbphYk9Z/3GzUfUwxDMCAqWrdhEAH5HpcIsFxdEqVTmdi1pFTcYccvaGqI0YXqfE500cWSj5lsjEZGl2O+71XzcoUXVXH0u9PJ8+O0WrNt6Tgbi7XQ6qEVqu1KJJQ4O2KLVQpXiejMl79hYGaCLMd09IXUxYiEqaazXcKwnC8WrUMqy+Ph47Nm2CQt/+wnnTh5LdZ/WToNhrzTBhOFtUKYki3BQ9tm46xJuPQzF7IBAlEwxc17aaMTnQUEY4uWF9VotRkVEPPG1ZQwGnLKxwWmNRq6WpySGeV97LWpWLCzrIxARUe57/PABrl08L4+reNdCYa8Xr+5NeSNTbvnG0xgRFo7OOp354y4JCRgXHo4AKyussbdHu5gYOUmfUvm4OChNJmy3s0v3WmCHnR1UVgo0r8+FI7JMDNApU2rELkG0LhbzVx3GD3/sxK17ganuL+zphPFDW2P0gOZwcWaQQ9lv865L8DbEpQrOk2lNJjnLvt/WNt1B+bZGA3uNCj+6u+L9wGBUTVp9F71Xlzo44Iy1GvOGNOOvjYjIQq459m/ebf7/19oWz9XnQy/Ptr1XZJDSPkVwnmrbWnQ0PnJzw0OVCsXTXA8cs7UFlApssLdHUaNRPoZ4LLGWfsDGBkucHNGvW224u9q/xJ+IKOMYoFOGBQf443/z1uO3hXsREhad6r5qFYvgnVE+6NetHjQa9pGmnBMVpYer4cngPJlrfDwilEpEKBRwTC4SJ1betVqcUqsx4y0fLF9/GlNMCpQyxcPFEI/rGrUsKPPR+Pbo1KoKf31ERBZi/Y5z5uPuPjVz9bnQyxOli4MdANunpLG7JdWSuW5tnSpAF0Xiljk7oadPddjYWOPX9aex3MUZJWNj8a9aDT8o0Kl5JXwyseNL+1mIMosBOj3XrWtXsGjOz9i0ehkMcakLv4lqqiIw92leBQpF2iQjouxXtownfM/fR3xYGKzSuf+8rQ0USgWGFimMhtE6WbH1lNYWDxRWGN2/EQb1rofXe9bFnqM3sXn3ZUTHxKFFKXf061obxQozdZKIyFKEhkVjz5Fr8rhUcTdUr1Q0t58SvSRlS7ohzATcV6lQIp2MufMajawl852LC/ZptSgeF4c7ajXOqtWoU7kovpjSGQ5aGwzuUx8rNp3B44AIVHPRok8nb9SuWozXrGTRGKBTukShtxOH9mPh7J9wYOf21C8alRKvda0nA/OaVZluRi/X6z3qYMk/p+Q+c9HPNCWxt/yUWoPp430QrYvDtt2XcT8mDuWLO+Pr/k3QokF5+XlWVgq0bVJB3oiIyDJt23cJ8fGJRb56+NRkUFWA+DSrCA8nW/wV54RpQcGpApZgpRLrnBzRqVVltG5SASs3nMbZwAi4Otvgu1710bO9t7kAXNXyhfApV8spj2GATqlUjlqA1VtOY+bcHTh98X6q+5wcbTG6f3O8NbQ1ihV24ZmjXCF6lr4xoDFmLzmMKxoNWul00Iie6DY22GmvRasGZTGsbwPZJ3fi8JaIjYuD3+PHKFS4MH9jRER5yMadicXhhG7tvHP1udDLpbZWYeb0nhg+ZRkmeXmiS0QkPOPjcVWtxiYnB2hc7DF9QgfZEvXVLrVSjfWszk55HQN0kiIjwrF2yXys+v1bPPg3NNVZKVHUVVZjH/FaMzjY2/CMUa778M12KFPCDXMWHcTnDxNfr57Odnj7lQYYN7CJDM6JiCjvMhgM2LrnonmBoGm9crn9lOglE1luq34dgu//2IMfT96VH9OorNCjfXW8O7o1Cns68ndC+RID9ALO79FDLJn3K9Ys+hvRUZGp7qtTvSQmj26HPp3qMOAhiyLqHfTvXkdWYf03IEKmQBbxdOTrlIgon7hw6jjCIhIreHdsWQ3W1rxkLYjq1yyJ5b8MkcWJI6Ji4elmDztb0RGdKP/iu10BdeX8GSycPQvb16+R/cxT6tKmBt4Z1Q4tGlbgfi+y+EBdpLcREVH+cmi3r/lYBOhUsLk6a+WNqCBggF6AVNcvxra9FzFzri92H76a6j6NRoVBvRph4oi2qFyee3WJiIgo9xzcvcN83L5FVf4qiKjAYIBeAMTFxmLzmuXo99tnuHzjcar73Fy0GDuoJcYNagUvD+7lISIiotwVFOCHqxcS+5/XrlaC1ydEVKAwQM/HwkKCsWrBn1j252wEBwakuq98aU9MGtEOg/o0hJ2tJteeIxEREVFKh/fsNB93aMnVcyIqWBig50MP7t7Gorm/YP2yRdDHJBZYSSaqoIr95V3besPKSplrz5GIiIgoPQd2/pfe3qlVdZ4kIipQGKDnI9GHP8XM33dg3bazMJlM5o8rlQr06lAL74zyQcPaZXL1ORIRERE9jdFoxJG9iSvoLk52aFCrNE8WERUoDNDzOFGBfc+2TVj42084d/JYqvtEG4rhrzaVPczLlPTItedIRERElBHnThxFZES4uTicSmXFE0dEBQoD9DwqRqfDhhWLsWjOzzKlPaVCHo4YP7QNRr/enC0piIiIKM/YvXWj+bhza6a3E1HBwwA9jwkO8Meyv+Zi5fx5CA8NSXVf1QpFMHm0D/p1qweNxjrXniMRERFRZonteXu2bpLHKpWKAToRFUgM0POIW9euYPNvE7B43VHExhpT3demSSUZmItUMIVCkWvPkYiIiCirTGe/waP7d+Vxy4bl4eKs5ckkogInxwP0Q4cOYe3atTh37hwePHiAAQMGYPr06Rn62sjISHz55ZfYuXMnDAYDmjVrhmnTpsHT0xMFZSb5xKH9WDj7JxzYuT3VfSqVEv261cekke1Qs2rxXHuOREREHOspO2zwTex9LvTsUIsnlYgKpBwP0A8cOICrV6+iXr16CA9PLPqRURMmTMDNmzfx8ccfQ6PR4Mcff8TIkSOxZs0amfqUX4nJCN8Na7FwzixcOX821X2ODjYY3b85xg9rg2KFXXLtORIRESXjWE/ZYdveS+bjbu28eVKJqEDK8Sh3ypQpeP/99+XxsWOpq4w/y5kzZ3Dw4EH8+eefaNq0qfxY6dKl0alTJ+zYsUP+m99ERUZg7ZL5WPL7r/B79DDVfYWLFce7wxrKquyODra59hyJiIjS4lhPLyo0KBCnLtyXxyIzkIsQRFRQ5XiArlQqs/R1+/fvh6OjI5o0aWL+WJkyZVC5cmV5X34K0AP9HmP33Lfw54pDiIjUp7qvTvWSmDy6HXp3rA1r6/ybNUBERHkXx3p6UScP7zcfs3o7ERVkFhvx3b59W66Ypy16JoJ0cV/WmRAXFwdLcO3ieZnGvmvTesTHpy781qlVNUwY0QbN6peT5yDBlIBYC3neL5Mh6WdO/pd4jvga4t9ZfnsfMsGEglresyCM9YLBEJfqX3ry/Jw6fMD8/+2aVSqQ1zzPwushnh++hgrOWG+xAXpERAQcHBye+LiTkxMuXryY5cc1GuPlinVuFn4Tg9C6xX/j/MnUKf9qtRV6+VTHiFcboFwpd/kxfz+/XHqmliU4ODi3n4LF4zni+eFrKG/+jcVrqiMfl1UpkGP90/B9On3xRiPOHjsij50dbVHcUwO/x5b3+7MEfA3x/PA1lP/H+kxfEojK6gEBAc/9vOLFi0OtVsPSqFRWKORe+KV/37jYWGxbtwqL5/2KO9evprrPwckZ4/rXxxuDW8LL3fGlPzdLJmaxxB+Km5sbrC3w9WQJeI54fvgaytt/Y+ERVrA0HOuzf4XY/Bqy5liW1qkjBxEdFSmP2zWvgqLFimbzbyDv41jP88PXUMEZ6zMdoG/btk22OnueLVu2oGzZssgqsf/cL53VY1EJXsysZ53ipU4chIUEY9WCP7H6zx/gFxiR6r7ypT3x1pBWaNe4JEqXLgENA9CnEn8oPD/PxnPE8/Oi+BrKnfOjsMAEd471OUME55a4eJHbjh/cZz7u3Ko6x/tn4Pv0s/H8PB/PkeWP9ZkO0Pv27StvOU3sPzty5IhMCU+5N+3OnTuoUKECLN2Du7exeO6v+GfZQuhjdKnua1qvHCaP9kHXtjVgMBqZxkVERBaFYz29TIf3+JqPfZpX4cknogItayXWX4LmzZvL1XIRpKcMzi9fvizvs1TnThzDO8MHoGvDGlj+1xxzcK5UKtCnU20c+ed9HFgzBd19ama56i0REVF+kFfHeso+/o//xdUL5+RxrarFUdjLmaeXiAq0HC9L8+jRI1y4cEEex8TE4P79+zJ1TujQoYP586pUqYIePXpgxowZ8v9r1aol+59/8MEHeO+996DRaPDDDz+gYsWK8PHxgSWpqluEDb7nMHPuDhw+dSvVfXa2atm7fMLwNihT0iPXniMREVFOKQhjPeWMgzu3m487tqrG00xEBV6OB+jHjh3D1KlTzf9/4MABeROuXbtm/nh8fDwSEhJSfe2PP/6IL7/8EtOnT4fRaJSDuNj/rrKQcrcxOh02rFiMlXO+xM27qQvnFfJwxPihbTD69eZwddbm2nMkIiLKafl5rKecdWBn4kSO0Kk1A3Qiohwf/Xr16iVvz5NyAE8mWq+IWfbkmXZLERzgj2V/zcWqBX/IInApVa1QRO4v79etHjQa61x7jkRERC9LfhzrKefpoqNxZN8ueezs6oba1YrztBNRgcfp6Uy4de0KFs35GZvXLJdt01Jq06SSDMzbt6iaqqgdERERET3p8N6d0MfEyOMGLVqzNg8R0cvYg57XiSryIXunY+bvO7Bl98VU96lUSrzWtR7eGeWDmlU560tERESUUWc3zTIf92uS8R7BRET5GQP0pzAYDPDdsBYL58zClfNnU93n6GCD0f2bY/ywNihW2OVl/J6IiIiI8g1Rb2DTrvPy2EFrg0a1Sub2UyIisggM0NOIiozA2iXzseT3X+H36GGq+0oUdZXV2EVVdkcH25f5eyIiIiLKN86fPIbQ8MRWtO1bVIFGzUtSIiKB74ZJRDC+9I/fsGbR3zJIT6l2tRJyf3nfznWgUjEFi4iIiOhFHEjRXq1Dq6o8mURESQp8gJ5w+iu5v3zFxhMwGlO3funcuroMzFs0rMDCb0RERETZ5NDuHebjds0qw2SI5rklIiqoAboo/CYGhoWzZ+HYgb2p7tNoVBjYsyEmjWyHyuUL59pzJCIiIsqPAvwe49qlC/K4bo2S8HJ3hN9jBuhERAU2QBdF38b1T92v1c1Fi7GDWmLcoFbw8nDMtedGREREVFBWzzu2qparz4WIyNIUyAC9inct1KzXEGdPHEW5Up6YNLItBvdpBDtbTW4/NSIiIqJ87fCenebjji0ZoBMRFegAXW2KQvXYJfjx3SYIDa+Frm29YWWlzO2nRURERFQg2qsd2bdbHrs42aGedynEJ8Tn9tMiIrIYBS5AT9aiYcXcfgpEREREBcr5U8cRGR4mj9s1qyK748THMUAnIkrGpWMiIiIiein2bd9sPub+cyKiJzFAJyIiIqKXYu/2LYkXoEolurSpzrNORJQGA3QiIiIiynF3b16XN6Fm/UZwd3XgWSciSoMBOhERERHluL0p0ttbtu/EM05ElA4G6ERERESU4/ZsSxGgd+jCM05ElA4G6ERERESUo0KCAnHuxFF5XKZCJZQsU45nnIgoHQzQiYiIiChH7ffdCpPJJI/7timFGrFLeMaJiNLBAJ2IiIiIctR+323m4+4+3jzbRERPwQCdiIiIiHKMwWDAsf175LGbixb1a5bm2SYiegoG6ERERESUY86fPIaoyAh53L5FVVhZ8fKTiOhp+A5JRERERDnm0G5f83GHFlV5pomInoEBOhERERHlmIO7d5iPfZozQCciehYG6ERERESUIwL8HuPaxfPyuE71kvDycOSZJiJ6BgboRERERJQjDqVYPe/UuhrPMhHRczBAJyIiIqIc33/esSUDdCKi52GATkRERETZLi421hygO7m4sr0aEVEGMEAnIiIiomx3/OBe6KKj5HELn45sr0ZElAEM0ImIiIgo253b9IP5eEgbFocjIsoIBuhERERElK3i4+Ox3vecPLbRWMOneRWeYSKiDGCATkRERETZ6tKZU/APjJDH7VtUgdZOwzNMRJQBKuSwQ4cOYe3atTh37hwePHiAAQMGYPr06c/9uocPH6JNmzZPfNzb2xsrV67MoWdLREREmcWxntLa77vVfNy1rTdPEBGRpQToBw4cwNWrV1GvXj2Eh4dn+usnTZqEBg0amP9fq9Vm8zMkIiKiF8Gxnp4VoHduXZ0niIjIUgL0KVOm4P3335fHx44dy/TXlyxZEjVr1syBZ0ZERETZgWM9peT36CGuXbogj+t5l0IhTyeeICIiS9mDrlRymzsREVF+xrGeUjq8Z6f5mKvnRESZY/HR88cff4zKlSujUaNGmDZtGsLCwnL7KREREVE24lifvxza42s+7tiqWq4+FyKivCbHU9yzSq1Wo1+/fmjatCkcHR1lkbk5c+bg4sWLWLVqFaytrbP4yCbExsXBUhiSnkvyv8Tzw9cQ/8b4PmRZcvp92gQTFCiYcnKsj7OgcdVgiEv1b35mNBhwZN8ueezmokW1ioWfe93Fa6Hn4zni+XlRfA3lnbE+0wF6ZGQkAgICnvt5xYsXlwNvVnl6esoZ9WT169dH+fLlMXr0aPj6+qJTp05ZelyjMR5+jx/D0gQHB+f2U7BoPD88R3wN8e8sv74PxWuqQ2Vh0+X5YawP9ONYnxsunTmJ6MhIedykTikEBvhn+Gs51vMcvSi+hniO8sNYn+lLgm3btslU8+fZsmULypYti+zUokUL2NnZ4dKlS1ketFUqKxRyLQxLIWZpxAvBzc0N1i9wkZNf8fzwHPE1xL+z/P4+FB5hBUuTL8Z6dwsa6w0pXkPW+XusX3X2lPm4W/vaKFT4+b8HjvXPx3PE8/Oi+BrKO2N9pgP0vn37ylvepYDGAgNh8UKwxOdlKXh+eI74GuLfWX59H1JYYIJ7fhjrX2RlP6eI4NwSn1d2Orw7cf+5QqFAt7Y1M/U3w7Ge5+hF8TXEc5QfxnqLLxKX0p49e6DT6VC9OvtpEhER5Ucc6/Ouxw8f4ObVy/K4Wq268HBzyO2nRESU5+T4rrdHjx7hwoXEXpgxMTG4f/++TJ0TOnToYP68KlWqoEePHpgxY4b8/6+++krOvooe6KJwzPnz5zF37lxUq1YNbdu2zemnTURERBnEsZ6E/b5bzSeiWbv/rvGIiMiCAvRjx45h6tSp5v8/cOCAvAnXrl0zfzw+Ph4JCQnm/xd72pYtW4aVK1dCr9fDy8sLffr0wfjx46GytGo6REREBRjHehJ2b9lgPhEtfDoCuMgTQ0SUSTke6fbq1UvenidlsJ4/9r8REREVDBzrKSIsFCcPJy7AFCleEhWr1gDiGKATEWVWntqDTkRERESW586W/8FoNMrjV9uXh3fc0tx+SkREeRIDdCIiIiJ6IRt8z5mPe7avxbNJRJRFDNCJiIiIKMsMBgN2HEis3u7qrEXjumV5NomIsogBOhERERFl2bkTRxERqZfH7VtUhZUVLy+JiLKK76BERERElGUHdm03H3duXZ1nkojoBTBAJyIiIqIsO7Rrh/xXoVCgfYsqPJNERC+AAToRERERZYn/439x48oleVzPuyTcXR14JomIXgADdCIiIiLKksN7fM3HHVpU41kkInpBDNCJiIiIKEsO7U4RoLesyrNIRPSCGKATERERUaYZ4uJwZO8ueeziZId63qV4FomIXhADdCIiIiLKtFNHDiIqMkIed2pdHSqVFc8iEdELYoBORERERJm2b8cW83HXNjV4BomIsgEDdCIiIiLKFJPJhH07tspjlUrF/edERNmEAToRERERZcqNyxfx6P5deVynUVM4OdrxDBIRZQMG6ERERESUKbu3bTIft+rYlWePiCibMEAnIiIiokw5tmWh+XhsawPPHhFRNmGATkREREQZZjAYUKGMFxzsbVC7WgkUL+LKs0dElE1U2fVARERERJT/WVtbY9kvIxEXZ8Qjv7DcfjpERPmKwiTKcBYQp0+fhsmUALUyDpbCBBPijfGwUllBAUVuPx2Lw/PDc8TXEP/O8vv7UFyCGgqFErVr1872xy6IEsd6E2BlDUuRH8cytSkq2x4rP56f7MZzxPPD11DBGesL1Aq6QiFOthJQOcBSiGekKlC/hczh+eE54muIf2f5/X1IYTAkjU+ULecz6VxaW1nSOVUAVvltV2H2XUtxrOc54mso5/HvLO+M9QVqBZ2IiIiIiIjIUuW36VwiIiIiIiKiPIkBOhEREREREZEFYIBOREREREREZAEYoBMRERERERFZAAboRERERERERBaAAToRERERERGRBWCATkRERERERGQBGKATERERERERWQAG6EREREREREQWgAE6ERERERERkQVggE5ERERERERkARigExEREREREVkAVW4/AUoUHx+Pv/76C3v37sXNmzdhMplQsWJFvP3226hbty5PU5JDhw5h7dq1OHfuHB48eIABAwZg+vTpBfL83Lp1C59//jnOnDkDrVaL7t27Y8KECVCr1bn91CzCvXv38Oeff8rXyo0bN1CmTBls2rQpt5+Wxdi6dSs2bNiAS5cuISIiAiVLlsTAgQPRu3dvKBQKFHT79u3DvHnz5PtxVFQUvLy80LZtW7z55ptwcHDI7adHeRTH+ozhWP8fjvXPxrH+2TjW583xngG6hdDr9fj999/Rs2dPjBw5EkqlEitXrsSgQYNkkNGoUaPcfooW4cCBA7h69Srq1auH8PBwFFTiZx88eDBKlSqFn3/+Gf7+/vjqq6/k66igTlikJYJy8abr7e2NhIQEOelF/5k/fz6KFi2K999/Hy4uLjh8+DD+97//wc/PTw5KBV1YWBhq1KghJy2cnZ3l60n8rYl/xWQqUVZwrM8YjvWJONY/H8f6Z+NYn0fHexNZBKPRaAoLC3viYx06dDCNHj06156XpYmPjzcft2rVyvTJJ5+YCqI5c+aYatasaQoNDTV/bPny5abKlSub/Pz8cvW5WeJr5b333jN17tw5V5+PpQkODn7iY9OmTTPVrl071bmj/6xYscJUoUIF/o1RlnGszxiO9Yk41mfutcKx/kkc6/PmeM896BbCysoKTk5OT3xMpLkHBATk2vOyNCKzgID9+/fLrAox05esY8eOcqVYpAYSXyvP4+rq+sTHKleuLNO7dDodX0LpSP57MxgMPD+UJRzrM4ZjfSKO9XytvCiO9XlzvGe0Y8GMRqPcPyv2zhKldPv27SdeF46OjvDw8JD3EWXFqVOn5N4re3t7nsAUe4ZjY2PlXv1ff/0VrVu3RrFixXh+KNtwrKen4VhPOYFjveWP99yDbsH++OMPubd4yJAhuf1UyMKIol4iIE9LZGEU5L35lHUnT57Eli1b8N577/E0ptCqVSv5Piw0a9YM3333Hc8PZSuO9fQ0HOspu3GszxvjPQP0HBQZGZmh9PTixYs/UXlbpCmLAgVjx45FtWrVkF+9yDkiouwhCsNNnDgRDRo0kIUp6T+ieGdMTIys7jp79myMGTMGf//9t0xVJhI41j8fx3qi3MexPu+M9wzQc9C2bdswbdq0536eWLUqW7as+f9FasVbb72FLl265Ptqylk9RwWdWD0XFzxpidXztLUMiJ63QiM6R4j9VmJSkHs/U6tUqZL8t1atWqhevbpsZ+jr64sOHTrwhUUSx/rn41ifNRzrKbtwrM9b4z0D9BzUt29fectsP0dxsSxeHKLHdX6XlXNEkPvP0+41FwF7YGAgaxZQplo+jR49Wr52VqxYwf7ezyGKdlpbW+P+/ft8lZEZx/rn41ifNRzrKTtwrM974z2LxFkQkeo9bNgwFC5cGLNmzZIvDKL0NG/eXPatFjOiKVcoxOpnkyZNeNIoQ4WpJkyYICd6xB5YURyOnk0U7RQVXVkkjl4Ex3rKKI719KI41ufN8Z4r6BY0uyVWzkNDQ/Hhhx/ixo0b5vvE3usqVark6vOzFI8ePcKFCxfksdgnIma2RGAqFKSU09deew2LFi3CuHHj5AqoKGrxzTffyI8z0IL59bFv3z7z60a0D0t+rdSvXz/d1iMFySeffII9e/bg/fffl+fm7Nmz5vvE+01Br/kgtheJ+h9iFt3GxgZXr17Fn3/+Kf+/bdu2uf30KI/iWJ8xHOsTcax/Po71z8axPm+O9wrRDD1XvjOl8vDhQ7Rp0ybds1K0aFHs3r2bZwzA2rVrMXXq1HTPxbVr1wrUObp16xY+++wznDlzBlqtVu6VEYW+CnpglZG/qYULF8qCaAWZaB8iLoLTs2vXrgK/SiyKxYjaF2ISUAyT4n24Xbt2GD58ONvQUZZxrM8YjvX/4Vif9b8pjvUc6/PqeM8AnYiIiIiIiMgCcA86ERERERERkQVggE5ERERERERkARigExEREREREVkABuhEREREREREFoABOhEREREREZEFYIBOREREREREZAEYoBMRERERERFZAAboRERERERERBaAAToRERERERGRBWCATkRERERERGQBGKATERERERERIff9H1JGSs2QzrdDAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import make_moons\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "\n",
+    "# Generate Data\n",
+    "X_moon, y_moon = make_moons(n_samples=200, noise=0.2, random_state=42)\n",
+    "\n",
+    "# Train Models\n",
+    "perc = Perceptron(random_state=42).fit(X_moon, y_moon)\n",
+    "mlp_moon = MLPClassifier(hidden_layer_sizes=(10, 10), max_iter=2000, random_state=42).fit(X_moon, y_moon)\n",
+    "\n",
+    "# Visualize\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "bg_cmap = ListedColormap([\"#8ecae6\", \"#ffb703\"])\n",
+    "pt_cmap = ListedColormap([\"#023047\", \"#d00000\"])\n",
+    "\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    perc,\n",
+    "    X_moon,\n",
+    "    ax=ax[0],\n",
+    "    response_method=\"predict\",\n",
+    "    plot_method=\"pcolormesh\",\n",
+    "    cmap=bg_cmap,\n",
+    "    alpha=0.4\n",
+    ")\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    perc,\n",
+    "    X_moon,\n",
+    "    ax=ax[0],\n",
+    "    response_method=\"decision_function\",\n",
+    "    plot_method=\"contour\",\n",
+    "    levels=[0],\n",
+    "    colors=\"black\",\n",
+    "    linewidths=2\n",
+    ")\n",
+    "ax[0].scatter(X_moon[:,0], X_moon[:,1], c=y_moon, cmap=pt_cmap, edgecolors='k')\n",
+    "ax[0].set_title(\"Perceptron (Linear)\")\n",
+    "\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    mlp_moon,\n",
+    "    X_moon,\n",
+    "    ax=ax[1],\n",
+    "    response_method=\"predict\",\n",
+    "    plot_method=\"pcolormesh\",\n",
+    "    cmap=bg_cmap,\n",
+    "    alpha=0.4\n",
+    ")\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    mlp_moon,\n",
+    "    X_moon,\n",
+    "    ax=ax[1],\n",
+    "    response_method=\"predict_proba\",\n",
+    "    plot_method=\"contour\",\n",
+    "    levels=[0.5],\n",
+    "    colors=\"black\",\n",
+    "    linewidths=2\n",
+    ")\n",
+    "ax[1].scatter(X_moon[:,0], X_moon[:,1], c=y_moon, cmap=pt_cmap, edgecolors='k')\n",
+    "ax[1].set_title(\"MLP (Non-Linear)\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "e1c49c09",
@@ -328,6 +448,85 @@
     "-   Does the boundary become smoother or more complex?"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7565eaab",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib.colors import ListedColormap\n",
+    "\n",
+    "mlp_simple = MLPClassifier(hidden_layer_sizes=(2,), max_iter=2000, random_state=42)\n",
+    "mlp_simple.fit(X_moon, y_moon)\n",
+    "\n",
+    "mlp_complex = MLPClassifier(hidden_layer_sizes=(100, 50), max_iter=2000, random_state=42)\n",
+    "mlp_complex.fit(X_moon, y_moon)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "bg_cmap = ListedColormap([\"#8ecae6\", \"#ffb703\"])\n",
+    "pt_cmap = ListedColormap([\"#023047\", \"#d00000\"])\n",
+    "\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    mlp_simple,\n",
+    "    X_moon,\n",
+    "    ax=ax[0],\n",
+    "    response_method=\"predict\",\n",
+    "    plot_method=\"pcolormesh\",\n",
+    "    cmap=bg_cmap,\n",
+    "    alpha=0.4\n",
+    ")\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    mlp_simple,\n",
+    "    X_moon,\n",
+    "    ax=ax[0],\n",
+    "    response_method=\"predict_proba\",\n",
+    "    plot_method=\"contour\",\n",
+    "    levels=[0.5],\n",
+    "    colors=\"black\",\n",
+    "    linewidths=2\n",
+    ")\n",
+    "ax[0].scatter(X_moon[:,0], X_moon[:,1], c=y_moon, cmap=pt_cmap, edgecolors='k')\n",
+    "ax[0].set_title(\"Simple MLP (2)\")\n",
+    "\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    mlp_complex,\n",
+    "    X_moon,\n",
+    "    ax=ax[1],\n",
+    "    response_method=\"predict\",\n",
+    "    plot_method=\"pcolormesh\",\n",
+    "    cmap=bg_cmap,\n",
+    "    alpha=0.4\n",
+    ")\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    mlp_complex,\n",
+    "    X_moon,\n",
+    "    ax=ax[1],\n",
+    "    response_method=\"predict_proba\",\n",
+    "    plot_method=\"contour\",\n",
+    "    levels=[0.5],\n",
+    "    colors=\"black\",\n",
+    "    linewidths=2\n",
+    ")\n",
+    "ax[1].scatter(X_moon[:,0], X_moon[:,1], c=y_moon, cmap=pt_cmap, edgecolors='k')\n",
+    "ax[1].set_title(\"Complex MLP (100, 50)\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "5dc8b504",
diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05C02 The Multilayer Perceptron (MLP).ipynb b/src/S05 Neural Networks I: Foundations and Optimization/S05C02 The Multilayer Perceptron (MLP).ipynb
index adfbb4e..28f72c3 100644
--- a/src/S05 Neural Networks I: Foundations and Optimization/S05C02 The Multilayer Perceptron (MLP).ipynb	
+++ b/src/S05 Neural Networks I: Foundations and Optimization/S05C02 The Multilayer Perceptron (MLP).ipynb	
@@ -87,12 +87,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "e816e6fd",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -145,10 +156,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "d85fbbd7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input Shape: (2, 3)\n",
+      "Hidden Activation Shape: (2, 4)\n",
+      "Output Shape: (2, 1)\n",
+      "Predictions:\n",
+      "[[0.19194084]\n",
+      " [0.37910074]]\n"
+     ]
+    }
+   ],
    "source": [
     "np.random.seed(42)\n",
     "\n",
@@ -209,12 +233,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "85429180",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.datasets import make_moons\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -243,12 +278,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "da6034fb",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Accuracy: 0.98\n",
+      "Test Accuracy:     0.99\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.neural_network import MLPClassifier\n",
     "from sklearn.inspection import DecisionBoundaryDisplay\n",
@@ -277,12 +321,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "7026e80c",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from matplotlib.colors import ListedColormap\n",
     "\n",
@@ -335,6 +390,96 @@
     "-   **Note**: Tanh often creates smoother curves than ReLU (which creates piecewise linear shapes)."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "20eff3e1",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib.colors import ListedColormap\n",
+    "\n",
+    "bg_cmap = ListedColormap([\"#8ecae6\", \"#ffb703\"])\n",
+    "pt_cmap = ListedColormap([\"#023047\", \"#d00000\"])\n",
+    "\n",
+    "# Train Tanh Model\n",
+    "mlp_tanh = MLPClassifier(hidden_layer_sizes=(10, 10), activation='tanh', max_iter=1000, random_state=42).fit(X_train, y_train)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    mlp_tanh,\n",
+    "    X,\n",
+    "    ax=ax,\n",
+    "    response_method=\"predict\",\n",
+    "    plot_method=\"pcolormesh\",\n",
+    "    cmap=bg_cmap,\n",
+    "    alpha=0.4\n",
+    ")\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    mlp_tanh,\n",
+    "    X,\n",
+    "    ax=ax,\n",
+    "    response_method=\"predict_proba\",\n",
+    "    plot_method=\"contour\",\n",
+    "    levels=[0.5],\n",
+    "    colors=\"black\",\n",
+    "    linewidths=2\n",
+    ")\n",
+    "ax.scatter(X[:,0], X[:,1], c=y, cmap=pt_cmap, edgecolors='k')\n",
+    "ax.set_title(\"MLP with Tanh Activation\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Train Sigmoid (Logistic) Model\n",
+    "mlp_sigm = MLPClassifier(hidden_layer_sizes=(10, 10), activation='logistic', max_iter=1000, random_state=42).fit(X_train, y_train)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    mlp_sigm,\n",
+    "    X,\n",
+    "    ax=ax,\n",
+    "    response_method=\"predict\",\n",
+    "    plot_method=\"pcolormesh\",\n",
+    "    cmap=bg_cmap,\n",
+    "    alpha=0.4\n",
+    ")\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    mlp_sigm,\n",
+    "    X,\n",
+    "    ax=ax,\n",
+    "    response_method=\"predict_proba\",\n",
+    "    plot_method=\"contour\",\n",
+    "    levels=[0.5],\n",
+    "    colors=\"black\",\n",
+    "    linewidths=2\n",
+    ")\n",
+    "ax.scatter(X[:,0], X[:,1], c=y, cmap=pt_cmap, edgecolors='k')\n",
+    "ax.set_title(\"MLP with Sigmoid Activation\")\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "d34a2127",
@@ -350,6 +495,31 @@
     "Do they perform differently?"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b39fb8fb",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wide Accuracy: 0.987\n",
+      "Deep Accuracy: 0.980\n"
+     ]
+    }
+   ],
+   "source": [
+    "mlp_wide = MLPClassifier(hidden_layer_sizes=(100,), max_iter=1000, random_state=42).fit(X_train, y_train)\n",
+    "mlp_deep = MLPClassifier(hidden_layer_sizes=(10, 10, 10, 10, 10), max_iter=1000, random_state=42).fit(X_train, y_train)\n",
+    "\n",
+    "print(f\"Wide Accuracy: {mlp_wide.score(X_test, y_test):.3f}\")\n",
+    "print(f\"Deep Accuracy: {mlp_deep.score(X_test, y_test):.3f}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1619639e",
diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05C03 Training Neural Networks.ipynb b/src/S05 Neural Networks I: Foundations and Optimization/S05C03 Training Neural Networks.ipynb
index 24305bb..85dbe0d 100644
--- a/src/S05 Neural Networks I: Foundations and Optimization/S05C03 Training Neural Networks.ipynb	
+++ b/src/S05 Neural Networks I: Foundations and Optimization/S05C03 Training Neural Networks.ipynb	
@@ -104,12 +104,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "815214be",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data Shape: (1437, 64)\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_digits\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -144,12 +152,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "51c044f6",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final Accuracy: 0.98\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.neural_network import MLPClassifier\n",
     "\n",
@@ -179,10 +195,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "4bd4d308",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(8, 5))\n",
     "plt.plot(mlp.loss_curve_, color='blue', linewidth=2)\n",
@@ -211,7 +238,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dd65c04c",
+   "id": "54c97bc3",
    "metadata": {},
    "source": [
     "### The Learning Rate (Goldilocks Zone)\n",
@@ -219,8 +246,61 @@
     "The **Learning Rate** is one of the most critical hyperparameters.\n",
     "\n",
     "-   Train 3 models with rates: `0.0001` (Too Small), `0.01` (Reasonable), `10.0` (Too Large).\n",
-    "-   Plot their loss curves on the same graph.\n",
+    "-   Plot their loss curves on the same graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e5cf93cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/camarocico/Work/Teaching/DataScience/ml-workshop/.venv/lib/python3.13/site-packages/sklearn/neural_network/_multilayer_perceptron.py:785: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rates = [0.0001, 0.01, 10.0]\n",
+    "plt.figure(figsize=(10, 6))\n",
     "\n",
+    "for lr in rates:\n",
+    "      model = MLPClassifier(hidden_layer_sizes=(64,),\n",
+    "                            solver='sgd',\n",
+    "                            learning_rate_init=lr,\n",
+    "                            max_iter=1000,\n",
+    "                            random_state=42)\n",
+    "      model.fit(X_train, y_train)\n",
+    "      plt.plot(model.loss_curve_, label=f\"LR={lr}\")\n",
+    "\n",
+    "plt.title(\"Impact of Learning Rate\")\n",
+    "plt.xlabel(\"Iterations\")\n",
+    "plt.ylabel(\"Loss\")\n",
+    "plt.xlim(0, 100)\n",
+    "#  plt.ylim(0, 5)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd65c04c",
+   "metadata": {},
+   "source": [
     "**Result**:\n",
     "\n",
     "-   Small rate: Loss goes down very slowly.\n",
@@ -240,6 +320,54 @@
     "-   **Hypothesis**: The Sigmoid network will struggle to learn (Vanishing Gradient). The ReLU network will learn quickly."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3f95ec1c",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Deep Architecture: 5 layers of 32 neurons\n",
+    "deep_layers = (32, 32, 32, 32, 32)\n",
+    "\n",
+    "# Sigmoid Model\n",
+    "mlp_sig = MLPClassifier(hidden_layer_sizes=deep_layers,\n",
+    "                        activation='logistic', \n",
+    "                        learning_rate_init=0.01,\n",
+    "                        max_iter=200,\n",
+    "                        random_state=42)\n",
+    "mlp_sig.fit(X_train, y_train)\n",
+    "\n",
+    "# ReLU Model\n",
+    "mlp_relu = MLPClassifier(hidden_layer_sizes=deep_layers,\n",
+    "                         activation='relu', \n",
+    "                         learning_rate_init=0.01,\n",
+    "                         max_iter=200,\n",
+    "                         random_state=42)\n",
+    "mlp_relu.fit(X_train, y_train)\n",
+    "\n",
+    "# Compare Curves\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(mlp_sig.loss_curve_, label=\"Sigmoid (Vanishing Gradient)\", color='red')\n",
+    "plt.plot(mlp_relu.loss_curve_, label=\"ReLU (Healthy)\", color='green')\n",
+    "plt.title(\"Deep Network Training: Sigmoid vs ReLU\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "baa002b2",
diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05C04 Optimizing Neural Networks.ipynb b/src/S05 Neural Networks I: Foundations and Optimization/S05C04 Optimizing Neural Networks.ipynb
index 87b866e..183c008 100644
--- a/src/S05 Neural Networks I: Foundations and Optimization/S05C04 Optimizing Neural Networks.ipynb	
+++ b/src/S05 Neural Networks I: Foundations and Optimization/S05C04 Optimizing Neural Networks.ipynb	
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "6b8a6d8d",
    "metadata": {
     "lines_to_next_cell": 2
@@ -143,12 +143,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "c6ff2b4e",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SGD Accuracy:  0.967\n",
+      "Adam Accuracy: 0.972\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.neural_network import MLPClassifier\n",
     "\n",
@@ -185,12 +194,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "6ad47ade",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(10, 6))\n",
     "plt.plot(mlp_sgd.loss_curve_, label='SGD', color='red', linestyle='--')\n",
@@ -214,12 +234,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "332b0444",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No Early Stopping - Iterations: 149, Test Acc: 0.981\n",
+      "With Early Stopping - Iterations: 34, Test Acc: 0.964\n",
+      "Best Validation Score: 0.972\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Baseline model WITHOUT Early Stopping\n",
     "mlp_no_early = MLPClassifier(hidden_layer_sizes=(100,),\n",
@@ -288,9 +328,84 @@
     "4.  Compare the loss curves and test accuracy."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3717b975",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scaled  - Iterations: 189 | Final Loss: 0.0096 | Test Acc: 0.972\n",
+      "Unscaled- Iterations: 45 | Final Loss: 1.1237 | Test Acc: 0.389\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_wine\n",
+    "\n",
+    "# Wine has features with very different units/scales (e.g., alcohol, color intensity, proline)\n",
+    "X_wine, y_wine = load_wine(return_X_y=True)\n",
+    "X_raw_train, X_raw_test, y_raw_train, y_raw_test = train_test_split(\n",
+    "    X_wine, y_wine, test_size=0.2, random_state=42, stratify=y_wine\n",
+    ")\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "X_scaled_train = scaler.fit_transform(X_raw_train)\n",
+    "X_scaled_test = scaler.transform(X_raw_test)\n",
+    "\n",
+    "common_kwargs = dict(\n",
+    "    hidden_layer_sizes=(32, 16),\n",
+    "    solver='sgd',\n",
+    "    learning_rate_init=0.01,\n",
+    "    max_iter=300,\n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "# Train on scaled features\n",
+    "mlp_scaled = MLPClassifier(**common_kwargs).fit(X_scaled_train, y_raw_train)\n",
+    "\n",
+    "# Train on raw (unscaled) features\n",
+    "mlp_unscaled = MLPClassifier(**common_kwargs).fit(X_raw_train, y_raw_train)\n",
+    "\n",
+    "# Quantitative comparison\n",
+    "print(\"Scaled  - Iterations:\", mlp_scaled.n_iter_,\n",
+    "      \"| Final Loss:\", round(mlp_scaled.loss_, 4),\n",
+    "      \"| Test Acc:\", round(mlp_scaled.score(X_scaled_test, y_raw_test), 3))\n",
+    "print(\"Unscaled- Iterations:\", mlp_unscaled.n_iter_,\n",
+    "      \"| Final Loss:\", round(mlp_unscaled.loss_, 4),\n",
+    "      \"| Test Acc:\", round(mlp_unscaled.score(X_raw_test, y_raw_test), 3))\n",
+    "\n",
+    "# Loss curves (same axis limits for fair visual comparison)\n",
+    "max_len = max(len(mlp_scaled.loss_curve_), len(mlp_unscaled.loss_curve_))\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(mlp_scaled.loss_curve_, label='Scaled Input', color='green')\n",
+    "plt.plot(mlp_unscaled.loss_curve_, label='Unscaled Input', color='orange')\n",
+    "plt.title(\"Effect of Feature Scaling on MLP Training\")\n",
+    "plt.xlabel(\"Iterations\")\n",
+    "plt.ylabel(\"Loss\")\n",
+    "plt.xlim(0, max_len)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "id": "1a2b8fb7",
+   "id": "f29c14a9",
    "metadata": {},
    "source": [
     "### Regularization Strength (Alpha)\n",
@@ -299,8 +414,68 @@
     "\n",
     "-   Train three models with different `alpha` values.\n",
     "-   Compare train/test accuracy and total weight norm.\n",
-    "-   Interpret which model appears underfit vs. better balanced.\n",
+    "-   Interpret which model appears underfit vs. better balanced."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1711f1c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " alpha | train_acc | test_acc | weight_norm\n",
+      "---------------------------------------------\n",
+      " 0.001 |   1.000   |  0.966  |   8.41\n",
+      " 0.003 |   1.000   |  0.966  |   8.40\n",
+      "  0.01 |   1.000   |  0.978  |   8.38\n",
+      "  0.03 |   1.000   |  0.978  |   8.33\n",
+      "   0.1 |   1.000   |  0.978  |   8.16\n",
+      "   0.3 |   1.000   |  0.989  |   7.72\n",
+      "   1.0 |   1.000   |  0.978  |   5.73\n",
+      "   3.0 |   1.000   |  0.966  |   4.01\n",
+      "  10.0 |   1.000   |  0.966  |   3.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_wine\n",
+    "import numpy as np\n",
+    "\n",
+    "# Keep the same dataset flow as the previous exercise\n",
+    "X_wine, y_wine = load_wine(return_X_y=True)\n",
+    "X_train_wine, X_test_wine, y_train_wine, y_test_wine = train_test_split(\n",
+    "    X_wine, y_wine, test_size=0.5, random_state=42, stratify=y_wine\n",
+    ")\n",
     "\n",
+    "scaler = StandardScaler()\n",
+    "X_train_wine = scaler.fit_transform(X_train_wine)\n",
+    "X_test_wine = scaler.transform(X_test_wine)\n",
+    "\n",
+    "alphas = [1e-3, 3e-3, 1e-2, 3e-2, 1e-1, 3e-1, 1e0, 3e0, 1e1]\n",
+    "print(\" alpha | train_acc | test_acc | weight_norm\")\n",
+    "print(\"---------------------------------------------\")\n",
+    "for alpha in alphas:\n",
+    "    model = MLPClassifier(\n",
+    "        hidden_layer_sizes=(32,),\n",
+    "        alpha=alpha,\n",
+    "        max_iter=1000,\n",
+    "        random_state=42\n",
+    "    ).fit(X_train_wine, y_train_wine)\n",
+    "    train_acc = model.score(X_train_wine, y_train_wine)\n",
+    "    test_acc = model.score(X_test_wine, y_test_wine)\n",
+    "    weight_norm = sum(np.linalg.norm(w) for w in model.coefs_)\n",
+    "    print(f\"{alpha:>6} |   {train_acc:.3f}   |  {test_acc:.3f}  |   {weight_norm:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a2b8fb7",
+   "metadata": {},
+   "source": [
     "**Prompt**: Which alpha gives the best trade-off? Look for smaller weights with minimal drop in test accuracy."
    ]
   },
diff --git a/src/S05 Neural Networks I: Foundations and Optimization/S05C05 Building a Network in PyTorch.ipynb b/src/S05 Neural Networks I: Foundations and Optimization/S05C05 Building a Network in PyTorch.ipynb
index 1ae86d9..211c983 100644
--- a/src/S05 Neural Networks I: Foundations and Optimization/S05C05 Building a Network in PyTorch.ipynb	
+++ b/src/S05 Neural Networks I: Foundations and Optimization/S05C05 Building a Network in PyTorch.ipynb	
@@ -28,12 +28,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "d462956c",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PyTorch Tensor: tensor([1, 2, 3])\n",
+      "Math Result: tensor([ 7,  9, 11])\n",
+      "Running on: cuda\n"
+     ]
+    }
+   ],
    "source": [
     "import torch\n",
     "import numpy as np\n",
@@ -72,12 +82,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "644570be",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SimpleMLP(\n",
+      "  (layer1): Linear(in_features=2, out_features=10, bias=True)\n",
+      "  (activation): ReLU()\n",
+      "  (layer2): Linear(in_features=10, out_features=1, bias=True)\n",
+      "  (sigmoid): Sigmoid()\n",
+      ")\n"
+     ]
+    }
+   ],
    "source": [
     "import torch.nn as nn\n",
     "\n",
@@ -143,12 +166,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "aa37b767",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X_train shape: torch.Size([400, 2])\n",
+      "y_train shape: torch.Size([400, 1])\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.datasets import make_moons\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -187,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "7e6200a1",
    "metadata": {
     "lines_to_next_cell": 2
@@ -216,12 +248,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "ee7efdda",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0 | Loss: 0.7536\n",
+      "Epoch 100 | Loss: 0.2539\n",
+      "Epoch 200 | Loss: 0.1323\n",
+      "Epoch 300 | Loss: 0.0759\n",
+      "Epoch 400 | Loss: 0.0591\n",
+      "Epoch 500 | Loss: 0.0516\n",
+      "Epoch 600 | Loss: 0.0469\n",
+      "Epoch 700 | Loss: 0.0440\n",
+      "Epoch 800 | Loss: 0.0418\n",
+      "Epoch 900 | Loss: 0.0399\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "epochs = 1000\n",
     "losses = []\n",
@@ -266,12 +325,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "e791672c",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Accuracy: 0.9900\n"
+     ]
+    }
+   ],
    "source": [
     "model.eval()\n",
     "with torch.no_grad():\n",
@@ -307,6 +374,52 @@
     "-   Train the new model on the same data."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2b1a1153",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Deep Model Loss: 0.0197\n"
+     ]
+    }
+   ],
+   "source": [
+    "class DeepMLP(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        self.net = nn.Sequential(\n",
+    "            nn.Linear(2, 16),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Linear(16, 16), # Second Hidden Layer\n",
+    "            nn.ReLU(),\n",
+    "            nn.Linear(16, 1),\n",
+    "            nn.Sigmoid()\n",
+    "        )\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        return self.net(x)\n",
+    "\n",
+    "# Quick Train Check\n",
+    "deep_model = DeepMLP()\n",
+    "opt = optim.Adam(deep_model.parameters(), lr=0.01)\n",
+    "\n",
+    "for i in range(500):\n",
+    "    pred = deep_model(X_train)\n",
+    "    loss = nn.BCELoss()(pred, y_train)\n",
+    "    opt.zero_grad()\n",
+    "    loss.backward()\n",
+    "    opt.step()\n",
+    "\n",
+    "print(f\"Deep Model Loss: {loss.item():.4f}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "47f0e280",
@@ -321,6 +434,53 @@
     "-   Use `nn.MSELoss()` instead of `BCELoss`."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "20a99e37",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Data\n",
+    "X_reg = torch.linspace(-3, 3, 100).unsqueeze(1)\n",
+    "y_reg = 3 * X_reg + 2 + torch.randn(100, 1) * 0.5\n",
+    "\n",
+    "# Model (Linear Regression is just 1 layer!)\n",
+    "reg_model = nn.Linear(1, 1)\n",
+    "\n",
+    "# Setup\n",
+    "crit_reg = nn.MSELoss()\n",
+    "opt_reg = optim.SGD(reg_model.parameters(), lr=0.01)\n",
+    "\n",
+    "# Train\n",
+    "for i in range(500):\n",
+    "    pred = reg_model(X_reg)\n",
+    "    loss = crit_reg(pred, y_reg)\n",
+    "    opt_reg.zero_grad()\n",
+    "    loss.backward()\n",
+    "    opt_reg.step()\n",
+    "\n",
+    "# Visualize\n",
+    "plt.scatter(X_reg, y_reg, label='Data')\n",
+    "with torch.no_grad():\n",
+    "    plt.plot(X_reg, reg_model(X_reg), color='red', label='Prediction')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "206b382b",

From 92c8b21730a2abf99f71e7e3a948f9abbd3f50df Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Tue, 24 Feb 2026 06:38:45 +0100
Subject: [PATCH 79/81] Session 06: Neural Networks II - DL, Transformers,
 GenAI

---
 ...Deep Learning, Transformers, and GenAI.pdf | Bin 677692 -> 677697 bytes
 ...Deep Learning, Transformers, and GenAI.tex | 126 ++--
 ...Convolutional Neural Networks (CNNs).ipynb | 148 +++-
 .../S06C02 From RNNs to Transformers.ipynb    | 190 +++++-
 .../S06C03 Large Language Models (LLMs).ipynb | 248 ++++++-
 ...enerative AI and Modern Applications.ipynb | 330 ++++++++-
 ...es Forecasting with Feature-Based ML.ipynb | 630 +++++++++++++++++-
 7 files changed, 1529 insertions(+), 143 deletions(-)

diff --git a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06 Neural Networks II: Deep Learning, Transformers, and GenAI.pdf b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06 Neural Networks II: Deep Learning, Transformers, and GenAI.pdf
index da0c28ae7c6953489e7ecd9d919554060c204141..31c345fb590334d50bfe4f4ae3488959d314953f 100644
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diff --git a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06 Neural Networks II: Deep Learning, Transformers, and GenAI.tex b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06 Neural Networks II: Deep Learning, Transformers, and GenAI.tex
index ef73616..a4ddcd2 100644
--- a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06 Neural Networks II: Deep Learning, Transformers, and GenAI.tex	
+++ b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06 Neural Networks II: Deep Learning, Transformers, and GenAI.tex	
@@ -1,4 +1,4 @@
-% Created 2026-02-19 Thu 21:06
+% Created 2026-02-24 Tue 05:41
 % Intended LaTeX compiler: pdflatex
 \documentclass[hyperref={pdfpagelabels=false},aspectratio=169]{beamer}
 \usepackage[utf8]{inputenc}
@@ -201,9 +201,9 @@
 \end{frame}
 
 \section{Convolutional Neural Networks (CNNs)}
-\label{sec:org70519a5}
-\begin{frame}[label={sec:orge37f4f6}]{Why CNNs?}
-\begin{definition}[The Problem with MLPs for Images]\label{sec:org9ac777c}
+\label{sec:org4cf1e3a}
+\begin{frame}[label={sec:orgc51b513}]{Why CNNs?}
+\begin{definition}[The Problem with MLPs for Images]\label{sec:org36ca10d}
 \pause
 Standard MLPs flatten images into vectors, causing two major problems:
 \begin{itemize}[<+->]
@@ -212,8 +212,8 @@ \section{Convolutional Neural Networks (CNNs)}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org8c54d23}]{The Convolution Operation}
-\begin{example}[Filters and Feature Maps]\label{sec:org0b6d308}
+\begin{frame}[label={sec:orgca71214}]{The Convolution Operation}
+\begin{example}[Filters and Feature Maps]\label{sec:orge9b2cbe}
 \pause
 Instead of learning weights for every pixel, CNNs learn small \alert{Filters} (Kernels):
 \begin{itemize}[<+->]
@@ -224,7 +224,7 @@ \section{Convolutional Neural Networks (CNNs)}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org0769352}]{Controlling Dimensions}
+\begin{frame}[label={sec:orge448953}]{Controlling Dimensions}
 \begin{block}{Padding and Stride}
 \pause
 As filters slide, image dimensions change:
@@ -236,8 +236,8 @@ \section{Convolutional Neural Networks (CNNs)}
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:orge0f3f6b}]{Pooling Layers}
-\begin{example}[MaxPool2d]\label{sec:org388c6ac}
+\begin{frame}[label={sec:org8bac696}]{Pooling Layers}
+\begin{example}[MaxPool2d]\label{sec:orgee02a2c}
 \pause
 Pooling reduces spatial size to control overfitting and computation:
 \begin{itemize}[<+->]
@@ -248,7 +248,7 @@ \section{Convolutional Neural Networks (CNNs)}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org43db836}]{Famous Architectures}
+\begin{frame}[label={sec:org93811d5}]{Famous Architectures}
 \begin{block}{VGG and ResNet}
 \pause
 Modern Deep Learning is built on famous architectures:
@@ -261,9 +261,9 @@ \section{Convolutional Neural Networks (CNNs)}
 \end{block}
 \end{frame}
 \section{From RNNs to Transformers}
-\label{sec:orgd6c93a0}
-\begin{frame}[label={sec:org14d23b8}]{Why This Matters}
-\begin{definition}[Sequence Data Is Everywhere]\label{sec:orgeb04f7e}
+\label{sec:orgca6023f}
+\begin{frame}[label={sec:orgddf566d}]{Why This Matters}
+\begin{definition}[Sequence Data Is Everywhere]\label{sec:org9e963e5}
 \pause
 Many research problems are sequential:
 \begin{itemize}[<+->]
@@ -274,8 +274,8 @@ \section{From RNNs to Transformers}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org7576a41}]{Running Example}
-\begin{example}[Clinical Sentence]\label{sec:org0e3f8c8}
+\begin{frame}[label={sec:orgb4059ce}]{Running Example}
+\begin{example}[Clinical Sentence]\label{sec:org798b920}
 \pause
 \emph{"Patient admitted three days ago with acute chest pain, treated with aspirin, now reports reduced discomfort."}
 \pause
@@ -287,7 +287,7 @@ \section{From RNNs to Transformers}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org2772e15}]{RNNs: First Main Solution}
+\begin{frame}[label={sec:org683aa4c}]{RNNs: First Main Solution}
 \begin{block}{Sequential Hidden-State Memory}
 \pause
 RNN update:
@@ -302,8 +302,8 @@ \section{From RNNs to Transformers}
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org6343a19}]{Attention as Weighted Lookup}
-\begin{example}[Query-Specific Relevance Weights]\label{sec:org07ec361}
+\begin{frame}[label={sec:org5b5b33e}]{Attention as Weighted Lookup}
+\begin{example}[Query-Specific Relevance Weights]\label{sec:org02a3726}
 \pause
 Attention replaces "single compressed memory" with dynamic lookup.
 \begin{itemize}[<+->]
@@ -314,8 +314,8 @@ \section{From RNNs to Transformers}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org5cc7223}]{Transformers: Attention-Centered Architecture}
-\begin{definition}["Attention Is All You Need"]\label{sec:orgcb3de4a}
+\begin{frame}[label={sec:orgb8fae7a}]{Transformers: Attention-Centered Architecture}
+\begin{definition}["Attention Is All You Need"]\label{sec:orga3b44d9}
 \pause
 Transformers remove recurrence and rely on attention:
 \begin{center}
@@ -329,7 +329,7 @@ \section{From RNNs to Transformers}
 \end{center}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgc4d3363}]{Core Formula and Transformer Block}
+\begin{frame}[label={sec:orga1b7981}]{Core Formula and Transformer Block}
 \begin{block}{Q, K, V and Scaled Dot Product}
 \pause
 $$\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V$$
@@ -342,8 +342,8 @@ \section{From RNNs to Transformers}
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:orgf47baab}]{Multi-Head and Model Families}
-\begin{example}[Parallel Attention Heads]\label{sec:org1821832}
+\begin{frame}[label={sec:org89621bf}]{Multi-Head and Model Families}
+\begin{example}[Parallel Attention Heads]\label{sec:orgac54c9d}
 \pause
 Multi-head attention runs several Q/K/V projections in parallel.
 \begin{itemize}[<+->]
@@ -355,7 +355,7 @@ \section{From RNNs to Transformers}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:orga77f255}]{Why GPT Is Left-to-Right}
+\begin{frame}[label={sec:org6e06b4e}]{Why GPT Is Left-to-Right}
 \begin{block}{Causal Masking}
 \pause
 For token \(t\), attention is allowed only to tokens \(\le t\).
@@ -367,8 +367,8 @@ \section{From RNNs to Transformers}
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:orgb107b84}]{Practical Research Workflow}
-\begin{example}[Pretrained-First Strategy]\label{sec:orga32f035}
+\begin{frame}[label={sec:org4731803}]{Practical Research Workflow}
+\begin{example}[Pretrained-First Strategy]\label{sec:org65c3640}
 \pause
 Most researchers do not train transformers from scratch:
 \begin{itemize}[<+->]
@@ -381,9 +381,9 @@ \section{From RNNs to Transformers}
 \end{example}
 \end{frame}
 \section{Large Language Models (LLMs)}
-\label{sec:org0717edf}
-\begin{frame}[label={sec:orgd621ae1}]{Evolution of LLMs}
-\begin{definition}[Scale as the Main Driver]\label{sec:org6c1e75b}
+\label{sec:orgf124e33}
+\begin{frame}[label={sec:orgb790a8d}]{Evolution of LLMs}
+\begin{definition}[Scale as the Main Driver]\label{sec:orge3984b5}
 \pause
 LLM progress accelerated with scale (parameters, data, compute):
 \begin{center}
@@ -399,7 +399,7 @@ \section{Large Language Models (LLMs)}
 \end{center}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org2967028}]{Architecture Families}
+\begin{frame}[label={sec:orgce5acae}]{Architecture Families}
 \begin{block}{Encoder, Decoder, Encoder-Decoder}
 \pause
 \begin{itemize}[<+->]
@@ -410,8 +410,8 @@ \section{Large Language Models (LLMs)}
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:orgdb25937}]{The LLM Lifecycle}
-\begin{example}[Pre-training, Fine-tuning, Inference]\label{sec:org4356ec1}
+\begin{frame}[label={sec:org828088b}]{The LLM Lifecycle}
+\begin{example}[Pre-training, Fine-tuning, Inference]\label{sec:org18c19c9}
 \pause
 \begin{itemize}[<+->]
 \item \alert{Pre-training}: The expensive part. Read the internet (TB of text), learn to predict next token. Learns grammar, facts, reasoning.
@@ -424,8 +424,8 @@ \section{Large Language Models (LLMs)}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:orgce35b27}]{Tokenization and Context Windows}
-\begin{example}[BPE and Subword Units]\label{sec:orgb995ac7}
+\begin{frame}[label={sec:org56b0d8a}]{Tokenization and Context Windows}
+\begin{example}[BPE and Subword Units]\label{sec:org937ace5}
 \pause
 LLMs don't see words; they see \alert{Tokens}:
 \begin{itemize}[<+->]
@@ -436,7 +436,7 @@ \section{Large Language Models (LLMs)}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:orgb8769d1}]{Next-Token Prediction Mechanics}
+\begin{frame}[label={sec:org72f5192}]{Next-Token Prediction Mechanics}
 \begin{block}{From Logits to Text}
 \pause
 At each decoding step, the model outputs logits \(z \in \mathbb{R}^{|V|}\) over the vocabulary.
@@ -454,8 +454,8 @@ \section{Large Language Models (LLMs)}
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org42e5ea8}]{Sampling Controls}
-\begin{example}[Temperature, Top-k, Top-p]\label{sec:org55a51a2}
+\begin{frame}[label={sec:org6d5f1d2}]{Sampling Controls}
+\begin{example}[Temperature, Top-k, Top-p]\label{sec:org978955a}
 \pause
 Generation quality/style depends on sampling strategy:
 \begin{center}
@@ -475,8 +475,8 @@ \section{Large Language Models (LLMs)}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org8171ca6}]{Prompt Engineering}
-\begin{example}[Guiding the Predictor]\label{sec:org150d137}
+\begin{frame}[label={sec:org1503a8c}]{Prompt Engineering}
+\begin{example}[Guiding the Predictor]\label{sec:orgc487fff}
 \pause
 LLMs are "next token predictors." They are sensitive to \emph{how} you ask:
 \pause
@@ -488,9 +488,9 @@ \section{Large Language Models (LLMs)}
 \end{example}
 \end{frame}
 \section{Generative AI and Modern Applications}
-\label{sec:org5157b4f}
-\begin{frame}[label={sec:org9ce0a6c}]{Text Generation}
-\begin{definition}[It's Just Statistics]\label{sec:org586c2af}
+\label{sec:org0be19f0}
+\begin{frame}[label={sec:org7e66362}]{Text Generation}
+\begin{definition}[It's Just Statistics]\label{sec:org55e4d87}
 \pause
 At its core, a Generative LLM is a \alert{Next-Token Prediction Engine}:
 \pause
@@ -503,8 +503,8 @@ \section{Generative AI and Modern Applications}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:orgcebf702}]{Retrieval-Augmented Generation (RAG)}
-\begin{example}[Solving LLM Limitations]\label{sec:org97c7202}
+\begin{frame}[label={sec:orgbd6a3bd}]{Retrieval-Augmented Generation (RAG)}
+\begin{example}[Solving LLM Limitations]\label{sec:orgc245b03}
 \pause
 LLMs have two major flaws:
 \begin{itemize}[<+->]
@@ -519,7 +519,7 @@ \section{Generative AI and Modern Applications}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org9907df3}]{Diffusion Models}
+\begin{frame}[label={sec:org7c04a0d}]{Diffusion Models}
 \begin{block}{Image Generation (Stable Diffusion, DALL-E)}
 \pause
 Image generation works differently from LLMs, using \alert{Diffusion}:
@@ -530,8 +530,8 @@ \section{Generative AI and Modern Applications}
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org7f503aa}]{Ethical Considerations}
-\begin{example}[Bias, Hallucination, and Copyright]\label{sec:org87b58a3}
+\begin{frame}[label={sec:org71ab0d3}]{Ethical Considerations}
+\begin{example}[Bias, Hallucination, and Copyright]\label{sec:org34c1e0f}
 \pause
 With great power comes great responsibility:
 \begin{itemize}[<+->]
@@ -543,9 +543,9 @@ \section{Generative AI and Modern Applications}
 \end{example}
 \end{frame}
 \section{Time-Series Forecasting with Feature-Based ML}
-\label{sec:org6311b41}
-\begin{frame}[label={sec:org3470a35}]{Forecasting Problem Setup}
-\begin{definition}[Formal Objective]\label{sec:org6327625}
+\label{sec:org76172cf}
+\begin{frame}[label={sec:orgca58441}]{Forecasting Problem Setup}
+\begin{definition}[Formal Objective]\label{sec:orga4b57bd}
 \pause
 For a univariate series \(\{y_t\}_{t=1}^T\), one-step forecasting is:
 \[
@@ -559,7 +559,7 @@ \section{Time-Series Forecasting with Feature-Based ML}
 \]
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org6898795}]{Why Time Series Is Not i.i.d.}
+\begin{frame}[label={sec:orgf99c1b6}]{Why Time Series Is Not i.i.d.}
 \begin{block}{Temporal Dependence and Structure}
 \pause
 Time series typically combines:
@@ -574,8 +574,8 @@ \section{Time-Series Forecasting with Feature-Based ML}
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:orgdf0cb2f}]{Why Random Splits Fail}
-\begin{example}[Leakage Through Time Mixing]\label{sec:org10432bb}
+\begin{frame}[label={sec:orga63c8e8}]{Why Random Splits Fail}
+\begin{example}[Leakage Through Time Mixing]\label{sec:orgca31eef}
 \pause
 Information sets are ordered:
 \[
@@ -590,8 +590,8 @@ \section{Time-Series Forecasting with Feature-Based ML}
 \end{itemize}
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:orga612268}]{Strong Baselines First}
-\begin{definition}[Naive and Seasonal Naive]\label{sec:org62e79d9}
+\begin{frame}[label={sec:org3be1d32}]{Strong Baselines First}
+\begin{definition}[Naive and Seasonal Naive]\label{sec:org5d6baa0}
 \pause
 \[
 \hat{y}_{t + 1}^{naive}=y_t,\qquad
@@ -607,7 +607,7 @@ \section{Time-Series Forecasting with Feature-Based ML}
 \end{itemize}
 \end{definition}
 \end{frame}
-\begin{frame}[label={sec:org9d7df7d}]{Feature Engineering with Temporal Constraints}
+\begin{frame}[label={sec:org285e0e9}]{Feature Engineering with Temporal Constraints}
 \begin{block}{Building \(\phi_t\) Safely}
 \pause
 Example feature vector:
@@ -625,8 +625,8 @@ \section{Time-Series Forecasting with Feature-Based ML}
 \end{itemize}
 \end{block}
 \end{frame}
-\begin{frame}[label={sec:org56c118b}]{Model Families and Objectives}
-\begin{example}[Ridge vs Boosting]\label{sec:orgfce97c4}
+\begin{frame}[label={sec:orgd5bec38}]{Model Families and Objectives}
+\begin{example}[Ridge vs Boosting]\label{sec:orgf0c59b1}
 \pause
 \begin{itemize}[<+->]
 \item Ridge: stable and interpretable with correlated lags. Ridge objective:
@@ -643,8 +643,8 @@ \section{Time-Series Forecasting with Feature-Based ML}
 \]
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:org7b42126}]{Walk-Forward Backtesting}
-\begin{example}[Estimating Future Performance]\label{sec:org1967bd1}
+\begin{frame}[label={sec:orgdb9d2b9}]{Walk-Forward Backtesting}
+\begin{example}[Estimating Future Performance]\label{sec:orga098d23}
 \pause
 Choose ordered cutoffs \(\tau_1 < \dots < \tau_K\). For fold \(k\):
 \begin{itemize}[<+->]
@@ -660,7 +660,7 @@ \section{Time-Series Forecasting with Feature-Based ML}
 This is usually more reliable than a single holdout split.
 \end{example}
 \end{frame}
-\begin{frame}[label={sec:orgf7650b2}]{Diagnostics After Forecasting}
+\begin{frame}[label={sec:org699b083}]{Diagnostics After Forecasting}
 \begin{block}{Residual Checks}
 \pause
 Even with good MAE/RMSE, verify residual structure.
@@ -673,8 +673,8 @@ \section{Time-Series Forecasting with Feature-Based ML}
 \end{block}
 \end{frame}
 \section{Summary}
-\label{sec:orgd620280}
-\begin{frame}[label={sec:org04a9aa1}]{Key Takeaways}
+\label{sec:org4ac3cf9}
+\begin{frame}[label={sec:org7138bb5}]{Key Takeaways}
 \begin{block}{Neural Networks II}
 \pause
 \begin{enumerate}[<+->]
diff --git a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C01 Convolutional Neural Networks (CNNs).ipynb b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C01 Convolutional Neural Networks (CNNs).ipynb
index 55543d7..5cbbfe5 100644
--- a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C01 Convolutional Neural Networks (CNNs).ipynb	
+++ b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C01 Convolutional Neural Networks (CNNs).ipynb	
@@ -50,10 +50,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "8d1a55c1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -161,10 +172,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "66085083",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input: torch.Size([1, 64, 28, 28]) -> Output: torch.Size([1, 64, 28, 28])\n"
+     ]
+    }
+   ],
    "source": [
     "import torch\n",
     "import torch.nn as nn\n",
@@ -223,12 +242,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "5e01f8ec",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Output Shape: torch.Size([1, 10]) (Should be [1, 10])\n"
+     ]
+    }
+   ],
    "source": [
     "import torch\n",
     "import torch.nn as nn\n",
@@ -282,12 +309,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "78c26775",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1, Loss: 0.4603\n",
+      "Epoch 2, Loss: 0.3092\n",
+      "Epoch 3, Loss: 0.2708\n",
+      "Training Complete!\n"
+     ]
+    }
+   ],
    "source": [
     "from torchvision import datasets, transforms\n",
     "from torch.utils.data import DataLoader\n",
@@ -336,10 +374,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "470024b7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -397,12 +446,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "525fdb77",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Output Layer: Linear(in_features=512, out_features=1000, bias=True)\n",
+      "New Output Layer:      Linear(in_features=512, out_features=2, bias=True)\n"
+     ]
+    }
+   ],
    "source": [
     "from torchvision import models\n",
     "\n",
@@ -440,6 +498,36 @@
     "2.  Apply `MaxPool2d(2)`. What is the new size?"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b11de794",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "After Conv: torch.Size([1, 16, 28, 28])\n",
+      "After Pool: torch.Size([1, 16, 14, 14])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let PyTorch calculate it for you to verify your math\n",
+    "layer_conv = nn.Conv2d(1, 16, kernel_size=5, padding=0)\n",
+    "layer_pool = nn.MaxPool2d(2)\n",
+    "\n",
+    "x = torch.randn(1, 1, 32, 32)\n",
+    "out_conv = layer_conv(x)\n",
+    "out_pool = layer_pool(out_conv)\n",
+    "\n",
+    "print(f\"After Conv: {out_conv.shape}\")\n",
+    "print(f\"After Pool: {out_pool.shape}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "cfe29542",
@@ -450,6 +538,42 @@
     "A \"VGG Block\" consists of: `Conv3x3` -> `ReLU` -> `Conv3x3` -> `ReLU` -> `MaxPool` Implement this as a `nn.Sequential` module."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "225ee295",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sequential(\n",
+      "  (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "  (1): ReLU()\n",
+      "  (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "  (3): ReLU()\n",
+      "  (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "def vgg_block(in_ch, out_ch):\n",
+    "    return nn.Sequential(\n",
+    "        nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),\n",
+    "        nn.ReLU(),\n",
+    "        nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1),\n",
+    "        nn.ReLU(),\n",
+    "        nn.MaxPool2d(2)\n",
+    "    )\n",
+    "\n",
+    "block = vgg_block(3, 64) # RGB Input -> 64 Filters\n",
+    "print(block)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "844a7516",
diff --git a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C02 From RNNs to Transformers.ipynb b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C02 From RNNs to Transformers.ipynb
index 3d2972c..c521a4c 100644
--- a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C02 From RNNs to Transformers.ipynb	
+++ b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C02 From RNNs to Transformers.ipynb	
@@ -79,10 +79,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "52b05736",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step 1:  Patient -> h[:2] = [0.9314628839492798, 0.617828369140625]\n",
+      "Step 2:  reports -> h[:2] = [0.6935661435127258, 0.14799222350120544]\n",
+      "Step 3:    chest -> h[:2] = [0.8815560936927795, 0.5947684645652771]\n",
+      "Step 4:     pain -> h[:2] = [0.7856016755104065, 0.34498998522758484]\n"
+     ]
+    }
+   ],
    "source": [
     "import torch\n",
     "import torch.nn as nn\n",
@@ -135,10 +146,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "63f7f2c2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Attention weights:\n",
+      "   Patient: 0.05\n",
+      "   reports: 0.25\n",
+      "     chest: 0.35\n",
+      "      pain: 0.30\n",
+      "     today: 0.05\n",
+      "       sum: 1.00\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "\n",
@@ -270,10 +295,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "b79b4867",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Row 0: ['1.00', '0.00', '0.00', '0.00', '0.00']\n",
+      "Row 3: ['0.20', '0.06', '0.17', '0.56', '0.00']\n"
+     ]
+    }
+   ],
    "source": [
     "import torch\n",
     "import torch.nn.functional as F\n",
@@ -329,13 +363,62 @@
   },
   {
    "cell_type": "markdown",
-   "id": "820a10c1",
+   "id": "99434d91",
    "metadata": {},
    "source": [
     "### RNN Sensitivity to Early vs Late Tokens\n",
     "\n",
-    "Goal: compare how perturbing early vs late tokens changes final hidden state.\n",
+    "Goal: compare how perturbing early vs late tokens changes final hidden state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "01976b25",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "L2 diff (early change): 0.0001328006328549236\n",
+      "L2 diff (late change):  1.4259880781173706\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "\n",
+    "torch.manual_seed(42)\n",
+    "rnn_cell = nn.RNNCell(input_size=4, hidden_size=4)\n",
+    "seq = [torch.randn(1, 4) for _ in range(10)]\n",
+    "\n",
+    "def run(sequence):\n",
+    "    h = torch.zeros(1, 4)\n",
+    "    for emb in sequence:\n",
+    "        h = rnn_cell(emb, h)\n",
+    "    return h\n",
+    "\n",
+    "h_base = run(seq)\n",
+    "\n",
+    "seq_early = [x.clone() for x in seq]\n",
+    "seq_early[0] = seq_early[0] + 2.0\n",
+    "h_early = run(seq_early)\n",
+    "\n",
+    "seq_late = [x.clone() for x in seq]\n",
+    "seq_late[-1] = seq_late[-1] + 2.0\n",
+    "h_late = run(seq_late)\n",
     "\n",
+    "print(\"L2 diff (early change):\", torch.norm(h_base - h_early).item())\n",
+    "print(\"L2 diff (late change): \", torch.norm(h_base - h_late).item())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "820a10c1",
+   "metadata": {},
+   "source": [
     "**Questions to consider**:\n",
     "\n",
     "-   Which perturbation (early or late) produces a larger difference? Why?\n",
@@ -344,13 +427,49 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0ef6837e",
+   "id": "e61f44f0",
    "metadata": {},
    "source": [
     "### Build Attention Weights\n",
     "\n",
-    "Goal: compute a self-attention matrix and verify row-wise probability behavior.\n",
+    "Goal: compute a self-attention matrix and verify row-wise probability behavior."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "71750207",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Weights shape: torch.Size([1, 5, 5])\n",
+      "Row sums: tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000])\n",
+      "Unmasked row 0: ['0.97', '0.01', '0.00', '0.02', '0.00']\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "torch.manual_seed(42)\n",
+    "x = torch.randn(1, 5, 8)\n",
+    "scores = torch.matmul(x, x.transpose(-2, -1)) / (8 ** 0.5)\n",
+    "weights = F.softmax(scores, dim=-1)\n",
     "\n",
+    "print(\"Weights shape:\", weights.shape)\n",
+    "print(\"Row sums:\", weights[0].sum(dim=-1))\n",
+    "print(\"Unmasked row 0:\", [f\"{v:.2f}\" for v in weights[0, 0].tolist()])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ef6837e",
+   "metadata": {},
+   "source": [
     "**Questions to consider**:\n",
     "\n",
     "-   Each row is one token's attention distribution. Which row attends most uniformly across all tokens? Which is most concentrated on a single token?\n",
@@ -359,13 +478,60 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e92bc45",
+   "id": "3ce5d42a",
    "metadata": {},
    "source": [
     "### Compare Unmasked vs Masked Attention\n",
     "\n",
-    "Goal: see how causal masking changes attention for early and late positions.\n",
+    "Goal: see how causal masking changes attention for early and late positions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e8aaff32",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unmasked row 0: ['0.97', '0.01', '0.00', '0.02', '0.00']\n",
+      "Masked   row 0: ['1.00', '0.00', '0.00', '0.00', '0.00']\n",
+      "Unmasked row 2: ['0.02', '0.03', '0.93', '0.02', '0.01']\n",
+      "Masked   row 2: ['0.02', '0.03', '0.95', '0.00', '0.00']\n",
+      "Unmasked row 4: ['0.00', '0.01', '0.00', '0.02', '0.97']\n",
+      "Masked   row 4: ['0.00', '0.01', '0.00', '0.02', '0.97']\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "torch.manual_seed(42)\n",
+    "x = torch.randn(1, 5, 8)\n",
+    "scores = torch.matmul(x, x.transpose(-2, -1)) / (8 ** 0.5)\n",
+    "\n",
+    "unmasked = F.softmax(scores, dim=-1)\n",
     "\n",
+    "mask = torch.triu(torch.ones(5, 5), diagonal=1)\n",
+    "masked_scores = scores.masked_fill(mask == 1, float('-inf'))\n",
+    "masked = F.softmax(masked_scores, dim=-1)\n",
+    "\n",
+    "print(\"Unmasked row 0:\", [f\"{v:.2f}\" for v in unmasked[0, 0].tolist()])\n",
+    "print(\"Masked   row 0:\", [f\"{v:.2f}\" for v in masked[0, 0].tolist()])\n",
+    "print(\"Unmasked row 2:\", [f\"{v:.2f}\" for v in unmasked[0, 2].tolist()])\n",
+    "print(\"Masked   row 2:\", [f\"{v:.2f}\" for v in masked[0, 2].tolist()])\n",
+    "print(\"Unmasked row 4:\", [f\"{v:.2f}\" for v in unmasked[0, 4].tolist()])\n",
+    "print(\"Masked   row 4:\", [f\"{v:.2f}\" for v in masked[0, 4].tolist()])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e92bc45",
+   "metadata": {},
+   "source": [
     "**Questions to consider**:\n",
     "\n",
     "-   Row 0 in the masked version can only attend to token 0. What does this mean for the first token's representation compared to later tokens?\n",
diff --git a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C03 Large Language Models (LLMs).ipynb b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C03 Large Language Models (LLMs).ipynb
index 46fdcb9..0103d27 100644
--- a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C03 Large Language Models (LLMs).ipynb	
+++ b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C03 Large Language Models (LLMs).ipynb	
@@ -75,12 +75,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "56e35e9d",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Token IDs: [3069, 10128, 389, 13899, 0]\n",
+      "Token Parts: ['LL', 'Ms', ' are', ' fascinating', '!']\n"
+     ]
+    }
+   ],
    "source": [
     "from transformers import AutoTokenizer\n",
     "\n",
@@ -110,10 +119,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "bacba15a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Word -> Token Count -> Subwords\n",
+      "----------------------------------------\n",
+      "'hello' -> 1 tokens: ['hello']\n",
+      "'AI' -> 1 tokens: ['AI']\n",
+      "'Anthropic' -> 3 tokens: ['An', 'throp', 'ic']\n",
+      "'TensorFlow' -> 3 tokens: ['T', 'ensor', 'Flow']\n",
+      "'🤖' -> 3 tokens: ['�', '�', '�']\n"
+     ]
+    }
+   ],
    "source": [
     "# Compare token counts for different words\n",
     "test_words = [\"hello\", \"AI\", \"Anthropic\", \"TensorFlow\", \"🤖\"]\n",
@@ -146,12 +169,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "ab765e90",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "899ebfc385a246cc81c5c65bb11bc53c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/148 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPT2LMHeadModel LOAD REPORT from: gpt2\n",
+      "Key                  | Status     |  | \n",
+      "---------------------+------------+--+-\n",
+      "h.{0...11}.attn.bias | UNEXPECTED |  | \n",
+      "\n",
+      "Notes:\n",
+      "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prompt: The future of AI is\n",
+      "Generated: The future of AI is going to be the most exciting thing for it. There'll be an amazing amount of work to do\n"
+     ]
+    }
+   ],
    "source": [
     "from transformers import AutoModelForCausalLM\n",
     "import torch\n",
@@ -192,10 +251,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "1b35ce99",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prompt: 'The future of AI is'\n",
+      "\n",
+      "Top 5 next-token predictions:\n",
+      "  ' uncertain': 6.1%\n",
+      "  ' in': 5.9%\n",
+      "  ' not': 4.5%\n",
+      "  ' a': 4.0%\n",
+      "  ' still': 3.6%\n"
+     ]
+    }
+   ],
    "source": [
     "import torch.nn.functional as F\n",
     "\n",
@@ -261,6 +335,46 @@
     "-   Generate text with `temperature=1.5` (Chaotic, creative/nonsense)."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "015c9b9f",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Low Temp: Once upon a time in a digital world, the world of digital media was a place where people could buy and sell their own content.\n",
+      "\n",
+      "But now, with the rise of the internet,\n",
+      "\n",
+      "High Temp: Once upon a time in a digital world, all data will reside on its original network where this information should be stored locally on a file or similar thing. It's impossible for one party or others\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = \"Once upon a time in a digital world,\"\n",
+    "input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n",
+    "\n",
+    "# Low Temp\n",
+    "out_low = model.generate(input_ids, max_new_tokens=30, do_sample=True, temperature=0.1, pad_token_id=tokenizer.eos_token_id)\n",
+    "print(f\"Low Temp: {tokenizer.decode(out_low[0])}\\n\")\n",
+    "\n",
+    "# High Temp\n",
+    "out_high = model.generate(input_ids, max_new_tokens=30, do_sample=True, temperature=1.5, pad_token_id=tokenizer.eos_token_id)\n",
+    "print(f\"High Temp: {tokenizer.decode(out_high[0])}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "494a2de3",
@@ -280,6 +394,38 @@
     "-   **Top-p=0.9** (Nucleus Sampling): \"Consider words until their probabilities sum to 90%.\""
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "15df9fc5",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top-k=50: The meaning of life is not always easy, whether you want it or not, but it takes some time for your mind to\n",
+      "\n",
+      "Top-p=0.9: The meaning of life is not the one we've been talking about. Life is the life that we have been talking about.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compare different sampling strategies\n",
+    "prompt = \"The meaning of life is\"\n",
+    "input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n",
+    "\n",
+    "# Top-k sampling\n",
+    "out_topk = model.generate(input_ids, max_new_tokens=20, do_sample=True, top_k=50, pad_token_id=tokenizer.eos_token_id)\n",
+    "print(f\"Top-k=50: {tokenizer.decode(out_topk[0])}\\n\")\n",
+    "\n",
+    "# Top-p (nucleus) sampling\n",
+    "out_topp = model.generate(input_ids, max_new_tokens=20, do_sample=True, top_p=0.9, pad_token_id=tokenizer.eos_token_id)\n",
+    "print(f\"Top-p=0.9: {tokenizer.decode(out_topp[0])}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b27157b7",
@@ -293,9 +439,48 @@
     "-   With examples, it should follow the pattern."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b3d97ca0",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Zero-shot: Capital of France is Paris. Capital of Germany is Frankfurt. Capital of the\n",
+      "Prediction:  Berlin.\n",
+      "Capital of\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Zero-Shot (Likely fails on small models)\n",
+    "zero_shot = \"Capital of France is Paris. Capital of Germany is\"\n",
+    "\n",
+    "# Few-Shot\n",
+    "few_shot = \"\"\"\n",
+    "Capital of Italy is Rome.\n",
+    "Capital of Spain is Madrid.\n",
+    "Capital of France is Paris.\n",
+    "Capital of Germany is\"\"\"\n",
+    "\n",
+    "inputs_zero = tokenizer(zero_shot, return_tensors=\"pt\")\n",
+    "out_zero = model.generate(**inputs_zero, max_new_tokens=5, pad_token_id=tokenizer.eos_token_id)\n",
+    "\n",
+    "inputs_few = tokenizer(few_shot, return_tensors=\"pt\")\n",
+    "out_few = model.generate(**inputs_few, max_new_tokens=5, pad_token_id=tokenizer.eos_token_id)\n",
+    "\n",
+    "print(f\"Zero-shot: {tokenizer.decode(out_zero[0])}\")\n",
+    "print(f\"Prediction: {tokenizer.decode(out_few[0]).split('is')[-1]}\")"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "id": "6b2fad6c",
+   "id": "3f5da692",
    "metadata": {},
    "source": [
     "### Chain of Thought (CoT)\n",
@@ -303,8 +488,51 @@
     "Adding \"Let's think step by step\" can improve performance on some multi-step problems, depending on the model.\n",
     "\n",
     "-   **Without CoT**: \"What is 17 × 24?\" → Model guesses (often wrong).\n",
-    "-   **With CoT**: \"What is 17 × 24? Let's think step by step.\" → Model shows working.\n",
+    "-   **With CoT**: \"What is 17 × 24? Let's think step by step.\" → Model shows working."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fdde0064",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Direct Prompt: Q: What is 17 * 24?\n",
+      "A:\n",
+      "CoT Prompt:    Q: What is 17 * 24? Let's think step by step.\n",
+      "A:\n",
+      "CoT Output:    Q: What is 17 * 24? Let's think step by step.\n",
+      "A: The first thing you need\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Note: GPT-2 is too small to demonstrate CoT well.\n",
+    "# This exercise illustrates the PROMPT PATTERN for larger models.\n",
+    "\n",
+    "# Without CoT\n",
+    "prompt_direct = \"Q: What is 17 * 24?\\nA:\"\n",
     "\n",
+    "# With CoT\n",
+    "prompt_cot = \"Q: What is 17 * 24? Let's think step by step.\\nA:\"\n",
+    "\n",
+    "inputs_cot = tokenizer(prompt_cot, return_tensors=\"pt\")\n",
+    "out_cot = model.generate(**inputs_cot, max_new_tokens=5, pad_token_id=tokenizer.eos_token_id)\n",
+    "\n",
+    "print(f\"Direct Prompt: {prompt_direct}\")\n",
+    "print(f\"CoT Prompt:    {prompt_cot}\")\n",
+    "print(f\"CoT Output:    {tokenizer.decode(out_cot[0])}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b2fad6c",
+   "metadata": {},
+   "source": [
     "**Key Point**: CoT can provide useful intermediate \"scratch space,\" but gains are not guaranteed and depend on model capability and prompt quality."
    ]
   },
diff --git a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C04 Generative AI and Modern Applications.ipynb b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C04 Generative AI and Modern Applications.ipynb
index 70b905b..4ee37b8 100644
--- a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C04 Generative AI and Modern Applications.ipynb	
+++ b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C04 Generative AI and Modern Applications.ipynb	
@@ -80,12 +80,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "b2a8f119",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Indexed 4 documents.\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.feature_extraction.text import TfidfVectorizer, ENGLISH_STOP_WORDS\n",
     "from sklearn.metrics.pairwise import cosine_similarity\n",
@@ -123,12 +131,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "d211882a",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Query: How much does Product A cost?\n",
+      "Best Match Score: 0.554\n",
+      "Retrieved Context: Product A costs $50 and includes a 1-year warranty.\n"
+     ]
+    }
+   ],
    "source": [
     "def retrieve_info(query):\n",
     "    # Vectorize query\n",
@@ -162,10 +180,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "0f318b9d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TF-IDF Retrieval Results:\n",
+      "--------------------------------------------------\n",
+      "Q: How much does Product A cost?\n",
+      "Expected: 0 | Retrieved: 0 | OK\n",
+      "Best score: 0.554\n",
+      "→ Product A costs $50 and includes a 1-year warranty....\n",
+      "\n",
+      "Q: How much is the monthly plan?\n",
+      "Expected: 1 | Retrieved: 0 | MISMATCH\n",
+      "Best score: 0.000\n",
+      "→ Product A costs $50 and includes a 1-year warranty....\n",
+      "\n",
+      "Q: What's the refund policy?\n",
+      "Expected: none | Retrieved: 0 | NO-GOOD-MATCH\n",
+      "Best score: 0.000\n",
+      "→ Product A costs $50 and includes a 1-year warranty....\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "tests = [\n",
     "    (\"How much does Product A cost?\", 0),  # Expected: Product A doc\n",
@@ -213,10 +255,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "6f024163",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f95dcd0aaa384c56855aa0a059dc32f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/148 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPT2LMHeadModel LOAD REPORT from: gpt2\n",
+      "Key                  | Status     |  | \n",
+      "---------------------+------------+--+-\n",
+      "h.{0...11}.attn.bias | UNEXPECTED |  | \n",
+      "\n",
+      "Notes:\n",
+      "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
+      "The following generation flags are not valid and may be ignored: ['temperature']. Set `TRANSFORMERS_VERBOSITY=info` for more details.\n",
+      "Passing `generation_config` together with generation-related arguments=({'do_sample', 'pad_token_id', 'max_new_tokens'}) is deprecated and will be removed in future versions. Please pass either a `generation_config` object OR all generation parameters explicitly, but not both.\n",
+      "Both `max_new_tokens` (=20) and `max_length`(=50) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prompt question: How much does Product A cost?\n",
+      "Retrieved context: Product A costs $50 and includes a 1-year warranty.\n",
+      "Model answer: Product A costs $50 and includes a 1-year warranty.\n"
+     ]
+    }
+   ],
    "source": [
     "from transformers import pipeline\n",
     "import re\n",
@@ -308,10 +397,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "4cb08c6e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x400 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "from skimage import data\n",
@@ -374,12 +474,123 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "7ad82120",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n",
+      "```\n",
+      "pip install accelerate\n",
+      "```\n",
+      ".\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2948b8b6db134290bd1e20d60fc46490",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c76b995d5fb40ecb21150d6dc9254d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/396 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "StableDiffusionSafetyChecker LOAD REPORT from: /home/camarocico/.cache/huggingface/hub/models--runwayml--stable-diffusion-v1-5/snapshots/451f4fe16113bff5a5d2269ed5ad43b0592e9a14/safety_checker\n",
+      "Key                                               | Status     |  | \n",
+      "--------------------------------------------------+------------+--+-\n",
+      "vision_model.vision_model.embeddings.position_ids | UNEXPECTED |  | \n",
+      "\n",
+      "Notes:\n",
+      "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8bea54df9223479f8d82e125db0100c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/196 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "CLIPTextModel LOAD REPORT from: /home/camarocico/.cache/huggingface/hub/models--runwayml--stable-diffusion-v1-5/snapshots/451f4fe16113bff5a5d2269ed5ad43b0592e9a14/text_encoder\n",
+      "Key                                | Status     |  | \n",
+      "-----------------------------------+------------+--+-\n",
+      "text_model.embeddings.position_ids | UNEXPECTED |  | \n",
+      "\n",
+      "Notes:\n",
+      "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "705a518beb5240b3a8a1ed57b21c6622",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/25 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generated and saved: data/diffusion_example.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from pathlib import Path\n",
     "import matplotlib.pyplot as plt\n",
@@ -499,6 +710,59 @@
     "3.  Which queries fail? Why?"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "38563f87",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Q: refund policy\n",
+      "  Score: 0.000 | Doc: Product A costs $50 and includes a 1-yea...\n",
+      "Q: where are you located?\n",
+      "  Score: 0.500 | Doc: Our headquarters is located in San Franc...\n",
+      "Q: Prodct A price\n",
+      "  Score: 0.536 | Doc: Product A costs $50 and includes a 1-yea...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extended knowledge base\n",
+    "documents_extended = documents + [\n",
+    "    \"Returns are accepted within 30 days with receipt.\",\n",
+    "    \"Our headquarters is located in San Francisco.\",\n",
+    "]\n",
+    "\n",
+    "# Re-index (keep settings consistent with the main retrieval demo)\n",
+    "vectorizer_ext = TfidfVectorizer(\n",
+    "    stop_words=custom_stopwords,\n",
+    "    token_pattern=r\"(?u)\\b\\w+\\b\",\n",
+    ")\n",
+    "doc_vectors_ext = vectorizer_ext.fit_transform(documents_extended)\n",
+    "\n",
+    "def retrieve_ext(query):\n",
+    "    query_vec = vectorizer_ext.transform([query])\n",
+    "    sims = cosine_similarity(query_vec, doc_vectors_ext).flatten()\n",
+    "    return documents_extended[np.argmax(sims)], max(sims)\n",
+    "\n",
+    "# Test queries\n",
+    "test_queries = [\n",
+    "    \"refund policy\",           # Synonym for \"returns\"\n",
+    "    \"where are you located?\",  # Paraphrase\n",
+    "    \"Prodct A price\",          # Typo\n",
+    "]\n",
+    "\n",
+    "for q in test_queries:\n",
+    "    doc, score = retrieve_ext(q)\n",
+    "    print(f\"Q: {q}\")\n",
+    "    print(f\"  Score: {score:.3f} | Doc: {doc[:40]}...\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "8d245889",
@@ -513,6 +777,48 @@
     "3.  Real diffusion models use carefully tuned **noise schedules** (linear, cosine, etc.)."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fbae331c",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x900 with 15 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Compare noise schedules\n",
+    "fig, axes = plt.subplots(3, 5, figsize=(15, 9))\n",
+    "noise_levels = [0.1, 0.3, 0.6]\n",
+    "\n",
+    "for row, nl in enumerate(noise_levels):\n",
+    "    current = img.copy()\n",
+    "    axes[row, 0].imshow(img)\n",
+    "    axes[row, 0].set_title(f\"σ={nl}: Clean\")\n",
+    "    axes[row, 0].axis('off')\n",
+    "\n",
+    "    for i in range(1, 5):\n",
+    "        noise = np.random.normal(0, nl, img.shape)\n",
+    "        current = np.clip(current + noise, 0, 1)\n",
+    "        axes[row, i].imshow(current)\n",
+    "        axes[row, i].set_title(f\"Step {i}\")\n",
+    "        axes[row, i].axis('off')\n",
+    "\n",
+    "plt.suptitle(\"Effect of Noise Level on Diffusion Speed\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "d29fcb5c",
diff --git a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C05 Time-Series Forecasting with Feature-Based ML.ipynb b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C05 Time-Series Forecasting with Feature-Based ML.ipynb
index 0c0f182..e800797 100644
--- a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C05 Time-Series Forecasting with Feature-Based ML.ipynb	
+++ b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/S06C05 Time-Series Forecasting with Feature-Based ML.ipynb	
@@ -115,10 +115,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "2f0ebeae",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Range: 1958-03-01 to 2001-12-01 | n=526\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -165,10 +183,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "dd974ab4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lag= 1 autocorr=0.998\n",
+      "lag= 2 autocorr=0.992\n",
+      "lag= 3 autocorr=0.985\n",
+      "lag= 6 autocorr=0.973\n",
+      "lag=12 autocorr=0.999\n",
+      "lag=24 autocorr=0.999\n"
+     ]
+    }
+   ],
    "source": [
     "for lag in [1, 2, 3, 6, 12, 24]:\n",
     "    print(f\"lag={lag:>2} autocorr={y.autocorr(lag=lag):.3f}\")"
@@ -198,10 +229,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "5df259d8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train: 1958-03-01 -> 1989-12-01 (n=382)\n",
+      "Val:   1990-01-01 -> 1995-12-01 (n=72)\n",
+      "Test:  1996-01-01 -> 2001-12-01 (n=72)\n"
+     ]
+    }
+   ],
    "source": [
     "train_end = \"1989-12-01\"\n",
     "val_end = \"1995-12-01\"\n",
@@ -279,12 +320,66 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "ee49a8c8",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>MAE</th>\n",
+       "      <th>RMSE</th>\n",
+       "      <th>MAPE(%)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Naive (t-1)</th>\n",
+       "      <td>1.116</td>\n",
+       "      <td>1.253</td>\n",
+       "      <td>0.304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seasonal Naive (t-12)</th>\n",
+       "      <td>1.670</td>\n",
+       "      <td>1.838</td>\n",
+       "      <td>0.455</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         MAE   RMSE  MAPE(%)\n",
+       "Naive (t-1)            1.116  1.253    0.304\n",
+       "Seasonal Naive (t-12)  1.670  1.838    0.455"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
     "\n",
@@ -355,12 +450,131 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "3de84334",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y</th>\n",
+       "      <th>lag_1</th>\n",
+       "      <th>lag_2</th>\n",
+       "      <th>lag_3</th>\n",
+       "      <th>lag_6</th>\n",
+       "      <th>lag_12</th>\n",
+       "      <th>roll_mean_3</th>\n",
+       "      <th>roll_std_3</th>\n",
+       "      <th>roll_mean_6</th>\n",
+       "      <th>roll_std_6</th>\n",
+       "      <th>roll_mean_12</th>\n",
+       "      <th>roll_std_12</th>\n",
+       "      <th>month_sin</th>\n",
+       "      <th>month_cos</th>\n",
+       "      <th>trend</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1959-03-01</th>\n",
+       "      <td>316.733333</td>\n",
+       "      <td>316.700000</td>\n",
+       "      <td>315.500000</td>\n",
+       "      <td>314.7</td>\n",
+       "      <td>313.5000</td>\n",
+       "      <td>316.100000</td>\n",
+       "      <td>315.633333</td>\n",
+       "      <td>1.006645</td>\n",
+       "      <td>314.547917</td>\n",
+       "      <td>1.348944</td>\n",
+       "      <td>315.427083</td>\n",
+       "      <td>1.440906</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.123234e-17</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1959-04-01</th>\n",
+       "      <td>317.675000</td>\n",
+       "      <td>316.733333</td>\n",
+       "      <td>316.700000</td>\n",
+       "      <td>315.5</td>\n",
+       "      <td>313.4625</td>\n",
+       "      <td>317.200000</td>\n",
+       "      <td>316.311111</td>\n",
+       "      <td>0.702641</td>\n",
+       "      <td>315.086806</td>\n",
+       "      <td>1.485514</td>\n",
+       "      <td>315.479861</td>\n",
+       "      <td>1.478893</td>\n",
+       "      <td>0.866025</td>\n",
+       "      <td>-5.000000e-01</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1959-05-01</th>\n",
+       "      <td>318.325000</td>\n",
+       "      <td>317.675000</td>\n",
+       "      <td>316.733333</td>\n",
+       "      <td>316.7</td>\n",
+       "      <td>313.4250</td>\n",
+       "      <td>317.433333</td>\n",
+       "      <td>317.036111</td>\n",
+       "      <td>0.553545</td>\n",
+       "      <td>315.788889</td>\n",
+       "      <td>1.557987</td>\n",
+       "      <td>315.519444</td>\n",
+       "      <td>1.534433</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>-8.660254e-01</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     y       lag_1       lag_2  lag_3     lag_6      lag_12  \\\n",
+       "1959-03-01  316.733333  316.700000  315.500000  314.7  313.5000  316.100000   \n",
+       "1959-04-01  317.675000  316.733333  316.700000  315.5  313.4625  317.200000   \n",
+       "1959-05-01  318.325000  317.675000  316.733333  316.7  313.4250  317.433333   \n",
+       "\n",
+       "            roll_mean_3  roll_std_3  roll_mean_6  roll_std_6  roll_mean_12  \\\n",
+       "1959-03-01   315.633333    1.006645   314.547917    1.348944    315.427083   \n",
+       "1959-04-01   316.311111    0.702641   315.086806    1.485514    315.479861   \n",
+       "1959-05-01   317.036111    0.553545   315.788889    1.557987    315.519444   \n",
+       "\n",
+       "            roll_std_12  month_sin     month_cos  trend  \n",
+       "1959-03-01     1.440906   1.000000  6.123234e-17   12.0  \n",
+       "1959-04-01     1.478893   0.866025 -5.000000e-01   13.0  \n",
+       "1959-05-01     1.534433   0.500000 -8.660254e-01   14.0  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "def make_feature_table(series, lags=(1, 2, 3, 6, 12), rolling_windows=(3, 6, 12)):\n",
     "    df = pd.DataFrame({\"y\": series})\n",
@@ -421,10 +635,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "84f423a1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature rows -> train: 370 | val: 72 | test: 72\n"
+     ]
+    }
+   ],
    "source": [
     "train_df = feature_table.loc[:train_end]\n",
     "val_df = feature_table.loc[\"1990-01-01\":val_end]\n",
@@ -491,10 +713,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "2625a8e4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>MAE</th>\n",
+       "      <th>RMSE</th>\n",
+       "      <th>MAPE(%)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Model</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Ridge</th>\n",
+       "      <td>0.290</td>\n",
+       "      <td>0.375</td>\n",
+       "      <td>0.081</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>XGBoost</th>\n",
+       "      <td>3.144</td>\n",
+       "      <td>4.037</td>\n",
+       "      <td>0.875</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           MAE   RMSE  MAPE(%)\n",
+       "Model                         \n",
+       "Ridge    0.290  0.375    0.081\n",
+       "XGBoost  3.144  4.037    0.875"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.linear_model import Ridge\n",
     "from sklearn.ensemble import HistGradientBoostingRegressor\n",
@@ -565,12 +848,73 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "19b8718b",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Selected model: Ridge\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>MAE</th>\n",
+       "      <th>RMSE</th>\n",
+       "      <th>MAPE(%)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Seasonal Naive (t-12)</th>\n",
+       "      <td>1.670</td>\n",
+       "      <td>1.838</td>\n",
+       "      <td>0.455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ridge</th>\n",
+       "      <td>0.314</td>\n",
+       "      <td>0.388</td>\n",
+       "      <td>0.086</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         MAE   RMSE  MAPE(%)\n",
+       "Seasonal Naive (t-12)  1.670  1.838    0.455\n",
+       "Ridge                  0.314  0.388    0.086"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "best_model_name = val_results.index[0]\n",
     "print(\"Selected model:\", best_model_name)\n",
@@ -602,12 +946,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "163cbfbb",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top Ridge coefficients (absolute value):\n",
+      "month_sin      1.304\n",
+      "roll_mean_6    0.717\n",
+      "roll_std_6     0.643\n",
+      "lag_1          0.622\n",
+      "roll_std_12   -0.416\n",
+      "lag_3         -0.306\n",
+      "roll_std_3    -0.204\n",
+      "lag_12         0.165\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
    "source": [
     "if best_model_name == \"Ridge\":\n",
     "    coefs = pd.Series(best_model.coef_, index=X_dev.columns).sort_values(key=np.abs, ascending=False)\n",
@@ -630,12 +991,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "b49fe7e7",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(11, 4))\n",
     "plt.plot(y_test_tab.index, y_test_tab.values, label=\"Actual\", lw=2)\n",
@@ -659,10 +1031,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "4e7098da",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Residual mean: 0.0593\n",
+      "Residual std:  0.3838\n"
+     ]
+    }
+   ],
    "source": [
     "residuals = y_test_tab.values - test_pred\n",
     "\n",
@@ -700,10 +1091,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "cbb4a082",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Residual autocorr lag= 1: 0.194\n",
+      "Residual autocorr lag= 2: 0.114\n",
+      "Residual autocorr lag= 3: 0.441\n",
+      "Residual autocorr lag= 6: 0.235\n",
+      "Residual autocorr lag=12: -0.091\n"
+     ]
+    }
+   ],
    "source": [
     "for lag in [1, 2, 3, 6, 12]:\n",
     "    ac = pd.Series(residuals).autocorr(lag=lag)\n",
@@ -754,10 +1157,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "e884138d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Ridge\n",
+      "   Fold    MAE   RMSE  MAPE(%)\n",
+      "0     1  0.689  0.783    0.212\n",
+      "1     2  0.297  0.382    0.090\n",
+      "2     3  0.244  0.301    0.072\n",
+      "3     4  0.293  0.379    0.084\n",
+      "4     5  0.291  0.377    0.082\n",
+      "Mean: {'MAE': 0.363, 'RMSE': 0.444, 'MAPE(%)': 0.108}\n",
+      "\n",
+      "XGBoost\n",
+      "   Fold    MAE   RMSE  MAPE(%)\n",
+      "0     1  2.617  3.280    0.804\n",
+      "1     2  3.163  3.958    0.952\n",
+      "2     3  3.418  4.281    1.002\n",
+      "3     4  3.749  4.800    1.068\n",
+      "4     5  2.888  3.762    0.804\n",
+      "Mean: {'MAE': 3.167, 'RMSE': 4.016, 'MAPE(%)': 0.926}\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.model_selection import TimeSeriesSplit\n",
     "\n",
@@ -841,19 +1269,58 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e120684",
+   "id": "f61ab863",
    "metadata": {},
    "source": [
     "### Leakage trap (random split)\n",
     "\n",
-    "Run a random train/test split and compare to proper time split. Why is random splitting over-optimistic?\n",
+    "Run a random train/test split and compare to proper time split. Why is random splitting over-optimistic?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "c085d7a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Random split metrics: {'MAE': 0.2739402012133601, 'RMSE': np.float64(0.3535751164025241), 'MAPE(%)': np.float64(0.08077989557620198)}\n",
+      "Proper test metrics: {'MAE': 0.31400798693502213, 'RMSE': np.float64(0.388330326893086), 'MAPE(%)': np.float64(0.08566591276636405)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_all = feature_table.drop(columns=\"y\")\n",
+    "y_all = feature_table[\"y\"]\n",
+    "\n",
+    "X_tr_bad, X_te_bad, y_tr_bad, y_te_bad = train_test_split(\n",
+    "    X_all, y_all, test_size=0.2, random_state=42, shuffle=True\n",
+    ")\n",
     "\n",
+    "leak_model = Ridge(alpha=1.0)\n",
+    "leak_model.fit(X_tr_bad, y_tr_bad)\n",
+    "leak_pred = leak_model.predict(X_te_bad)\n",
+    "\n",
+    "print(\"Random split metrics:\", forecast_metrics(y_te_bad, leak_pred))\n",
+    "print(\"Proper test metrics:\", forecast_metrics(y_test_tab, test_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e120684",
+   "metadata": {},
+   "source": [
     "Which assumptions of forecasting are violated by random shuffling?"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8c10dbca",
+   "id": "eae448c5",
    "metadata": {},
    "source": [
     "### Direct vs recursive multi-step forecasting\n",
@@ -861,14 +1328,53 @@
     "Implement a 6-step forecast:\n",
     "\n",
     "-   **Recursive**: one-step model called repeatedly.\n",
-    "-   **Direct**: separate model per horizon.\n",
-    "\n",
+    "-   **Direct**: separate model per horizon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ee76f1fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Recursive 6-step forecast: [360.82 361.56 361.72 362.75 363.25 363.48]\n"
+     ]
+    }
+   ],
+   "source": [
+    "horizon = 6\n",
+    "history = y.loc[:val_end].copy()\n",
+    "\n",
+    "# Recursive strategy with the chosen one-step model\n",
+    "recursive_preds = []\n",
+    "model_recursive = make_models()[best_model_name]\n",
+    "model_recursive.fit(X_dev, y_dev)\n",
+    "\n",
+    "for _ in range(horizon):\n",
+    "    ft = make_feature_table(history).iloc[[-1]].drop(columns=\"y\")\n",
+    "    next_value = model_recursive.predict(ft)[0]\n",
+    "    recursive_preds.append(next_value)\n",
+    "    next_time = history.index[-1] + pd.offsets.MonthBegin(1)\n",
+    "    history.loc[next_time] = next_value\n",
+    "\n",
+    "print(\"Recursive 6-step forecast:\", np.round(recursive_preds, 2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c10dbca",
+   "metadata": {},
+   "source": [
     "Recursive forecasts accumulate error; direct forecasts can reduce accumulation but require more models."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "22625fe8",
+   "id": "a9743f51",
    "metadata": {},
    "source": [
     "### Add domain-specific covariates\n",
@@ -878,8 +1384,64 @@
     "-   original model predictions (without the covariate)\n",
     "-   augmented model predictions (with the covariate)\n",
     "\n",
-    "Keep model family, hyperparameters, and split fixed so the comparison is fair.\n",
-    "\n",
+    "Keep model family, hyperparameters, and split fixed so the comparison is fair."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "4793313e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                           MAE   RMSE  MAPE(%)\n",
+      "Baseline (no covariate)  0.290  0.375    0.081\n",
+      "Augmented (+covariate)   0.290  0.374    0.081\n",
+      "Delta (Aug - Base)      -0.001 -0.001   -0.000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Baseline model (original feature set)\n",
+    "base_model = Ridge(alpha=1.0)\n",
+    "base_model.fit(X_train, y_train_tab)\n",
+    "pred_base = base_model.predict(X_val)\n",
+    "base_metrics = forecast_metrics(y_val_tab, pred_base)\n",
+    "\n",
+    "# Augmented model (one domain-specific covariate)\n",
+    "ft_aug = feature_table.copy()\n",
+    "ft_aug[\"regime_flag\"] = (ft_aug.index >= \"1980-01-01\").astype(int)\n",
+    "\n",
+    "tr_aug = ft_aug.loc[:train_end]\n",
+    "va_aug = ft_aug.loc[\"1990-01-01\":val_end]\n",
+    "\n",
+    "X_tr_aug, y_tr_aug = tr_aug.drop(columns=\"y\"), tr_aug[\"y\"]\n",
+    "X_va_aug, y_va_aug = va_aug.drop(columns=\"y\"), va_aug[\"y\"]\n",
+    "\n",
+    "model_aug = Ridge(alpha=1.0)\n",
+    "model_aug.fit(X_tr_aug, y_tr_aug)\n",
+    "pred_aug = model_aug.predict(X_va_aug)\n",
+    "aug_metrics = forecast_metrics(y_va_aug, pred_aug)\n",
+    "\n",
+    "# Side-by-side comparison\n",
+    "comp = pd.DataFrame({\n",
+    "    \"Baseline (no covariate)\": base_metrics,\n",
+    "    \"Augmented (+covariate)\": aug_metrics,\n",
+    "}).T\n",
+    "comp.loc[\"Delta (Aug - Base)\"] = (\n",
+    "    comp.loc[\"Augmented (+covariate)\"] - comp.loc[\"Baseline (no covariate)\"]\n",
+    ")\n",
+    "print(comp.round(3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22625fe8",
+   "metadata": {},
+   "source": [
     "Do the augmented predictions improve MAE/RMSE/MAPE consistently, or only one metric? If improvements are small or unstable, could the covariate be spurious?"
    ]
   },

From e1874e88c4083a5daf33d00f19b06202be7c248f Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Tue, 24 Feb 2026 07:59:59 +0100
Subject: [PATCH 80/81] Session 07: Ensemble Methods

---
 .../S07 Ensemble Methods.pdf                  |  Bin 0 -> 206826 bytes
 .../S07 Ensemble Methods.tex                  |  331 +
 ...C01 Introduction to Ensemble Methods.ipynb | 3941 +++++++++
 src/S07 Ensemble Methods/S07C02 Bagging.ipynb | 6724 +++++++++++++++
 .../S07C03 Boosting.ipynb                     | 7473 +++++++++++++++++
 .../S07C04 Ensemble of Ensembles.ipynb        | 4152 +++++++++
 6 files changed, 22621 insertions(+)
 create mode 100644 src/S07 Ensemble Methods/S07 Ensemble Methods.pdf
 create mode 100644 src/S07 Ensemble Methods/S07 Ensemble Methods.tex
 create mode 100644 src/S07 Ensemble Methods/S07C01 Introduction to Ensemble Methods.ipynb
 create mode 100644 src/S07 Ensemble Methods/S07C02 Bagging.ipynb
 create mode 100644 src/S07 Ensemble Methods/S07C03 Boosting.ipynb
 create mode 100644 src/S07 Ensemble Methods/S07C04 Ensemble of Ensembles.ipynb

diff --git a/src/S07 Ensemble Methods/S07 Ensemble Methods.pdf b/src/S07 Ensemble Methods/S07 Ensemble Methods.pdf
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diff --git a/src/S07 Ensemble Methods/S07 Ensemble Methods.tex b/src/S07 Ensemble Methods/S07 Ensemble Methods.tex
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+% Created 2026-02-24 Tue 07:39
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+\author{Cristian A. Marocico}
+\date{\today}
+\title{Machine Learning in Python}
+\subtitle{Ensemble Methods}
+\author[Marocico, Tatar]{Cristian A. Marocico, A. Emin Tatar}
+\institute[CIT]{Center for Information Technology\\University of Groningen}
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+\date[Jul 7\textsuperscript{th} 2025]{Monday, July 7\textsuperscript{th} 2025}
+\usepackage{mathtools}
+\newcommand{\intsum}{\mathop{\mathrlap{\raisebox{0.1ex}{\hspace{0.2em}$\textstyle\sum$}}\int}\limits}
+\setbeamercovered{transparent=0}
+\usepackage[timeinterval=60]{tdclock}
+\hypersetup{
+ pdfauthor={Cristian A. Marocico},
+ pdftitle={Machine Learning in Python},
+ pdfkeywords={},
+ pdfsubject={},
+ pdfcreator={Emacs 30.2 (Org mode 9.7.11)}, 
+ pdflang={English}}
+\begin{document}
+
+\maketitle
+\begin{frame}{Outline}
+\setcounter{tocdepth}{1}
+\tableofcontents
+\end{frame}
+
+\section{Introduction to Ensemble Methods}
+\label{sec:orgdd4cc1a}
+\begin{frame}[label={sec:orgd3c9814}]{Introduction to Ensemble Methods}
+\begin{definition}[What are Ensemble Methods?]\label{sec:org4d82927}
+\pause
+\alert{Ensemble methods} combine multiple models to improve predictive performance. They leverage the strengths of individual models while mitigating their weaknesses.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org9d2d1dc}]{Introduction to Ensemble Methods}
+\begin{example}[Why Use Ensemble Methods?]\label{sec:orgdebd7b0}
+\begin{itemize}[<+->]
+\item \alert{Improved Accuracy}: Ensemble methods often outperform individual models by reducing variance and bias.
+\item \alert{Robustness}: Less sensitive to noise and outliers, leading to more stable predictions.
+\item \alert{Reduced Overfitting}: Reduce overfitting by averaging predictions from multiple models.
+\item \alert{Flexibility}: Ensemble methods can be applied to various base models, including decision trees, linear models, and neural networks.
+\item \alert{Interpretability}: Some ensemble methods, like Random Forests, provide insights into feature importance.
+\item \alert{Scalability}: Many ensemble methods can be parallelized, useful for large datasets.
+\item \alert{Combining Different Algorithms}: Ensembles can combine predictions from different types of models, enhancing overall performance.
+\end{itemize}
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:org9aa9704}]{Types of Ensemble Methods}
+\begin{example}[Ensamble Methods]\label{sec:org51be7fb}
+\pause
+Ensemble methods can be broadly categorized into several types, each with its own approach to combining models:
+\begin{itemize}[<+->]
+\item \alert{Bagging (Bootstrap Aggregating)}: Combines predictions from multiple models trained on different subsets of the training data. The most common example is Random Forests.
+\item \alert{Boosting}: Sequentially builds models, where each new model focuses on correcting the errors made by the previous models. Examples include AdaBoost, Gradient Boosting, and XGBoost.
+\item \alert{Stacking}: Combines multiple models by training a meta-model on their predictions. The base models can be of different types, and the meta-model learns how to best combine their outputs.
+\item Other methods include \alert{Pasting}, \alert{Bayesian Model Averaging}, \alert{Blending}, and \alert{Voting}.
+\end{itemize}
+\end{example}
+\end{frame}
+\section{Bagging}
+\label{sec:org3283af6}
+\begin{frame}[label={sec:org0a460c7}]{Bootstrap Aggregating (Bagging) Overview}
+\begin{definition}[Bootstrap Aggregating (Bagging)]\label{sec:orgc54c15b}
+\pause
+\alert{Bagging}, or \alert{Bootstrap Aggregating}, is an ensemble method that combins the predictions of multiple models trained on different subsets of the training data. The key steps in bagging are:
+\pause
+\begin{itemize}[<+->]
+\item \alert{Bootstrap Sampling}: Create multiple bootstrap samples from the training data. Each sample is created by randomly selecting instances with replacement, meaning some instances may appear multiple times while others may not appear at all.
+\item \alert{Model Training}: Train a separate model on each bootstrap sample. These models can be of the same type (e.g., decision trees) or different types.
+\item \alert{Aggregation}: Combine the predictions of the individual models. For regression tasks, this is typically done by averaging the predictions, while for classification tasks, a majority vote is used to determine the final class label.
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgc528114}]{Bagging and Random Forests}
+\begin{example}[Bagging and Random Forests]\label{sec:orgea4fe43}
+\pause
+\alert{Random Forests} is a popular ensemble method that builds multiple decision trees using bagging. It introduces additional randomness by selecting a random subset of features for each tree, which helps to decorrelate the trees and improve overall model performance.
+\end{example}
+\end{frame}
+\begin{frame}[label={sec:orga477a0f}]{Feature Importance in Random Forests}
+\begin{block}{Feature Importance in Random Forests}
+\pause
+\alert{Random Forests} can also provide insights into feature importance, which helps in understanding which features contribute most to the predictions. This is done by measuring the decrease in model performance when a feature is permuted or removed.
+\end{block}
+\end{frame}
+\section{Boosting}
+\label{sec:org8129ea8}
+\begin{frame}[label={sec:orgf9bc8ce}]{Boosting Overview}
+\begin{definition}[Boosting Overview]\label{sec:orgafea492}
+\pause
+\alert{Boosting} is an ensemble method that builds models sequentially, where each new model focuses on correcting the errors made by the previous models. The key steps in boosting are:
+\pause
+\begin{itemize}[<+->]
+\item \alert{Sequential Model Training}: Train a series of models, where each model is trained on the residuals (errors) of the previous model. This allows each new model to learn from the mistakes of its predecessor.
+\item \alert{Weighted Predictions}: Each model's predictions are weighted based on its performance. Models that perform well have a higher weight in the final prediction.
+\item \alert{Final Prediction}: Combine the predictions of all models, typically by summing their weighted predictions. For classification tasks, a threshold is applied to determine the final class label.
+\end{itemize}
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:org38d7850}]{Boosting Algorithms}
+\begin{example}[Boosting Algorithms]\label{sec:orgc70b3ea}
+\pause
+Some popular boosting algorithms include:
+\begin{itemize}[<+->]
+\item \alert{AdaBoost}: Assigns weights to instances based on their classification errors, focusing more on misclassified instances in subsequent iterations.
+\item \alert{Gradient Boosting}: Builds models that minimize a loss function by fitting to the residuals of the previous model. It can handle various loss functions, making it versatile for regression and classification tasks.
+\item \alert{XGBoost}: An optimized implementation of gradient boosting that includes regularization, handling missing values, and parallel processing for faster training.
+\end{itemize}
+\end{example}
+\end{frame}
+\section{Ensembles of Ensemble Methods}
+\label{sec:orge8c199f}
+\begin{frame}[label={sec:orgd10a7b7}]{Ensembles of Ensemble Methods}
+\begin{definition}[Ensembles of Ensemble Methods]\label{sec:org20f3226}
+\pause
+\alert{Ensembles of Ensemble} methods combine multiple ensemble techniques to further enhance predictive performance. This approach can lead to even more robust models by leveraging the strengths of different ensemble methods.
+\end{definition}
+\end{frame}
+\begin{frame}[label={sec:orgad0ecd5}]{Ensembles of Ensemble Methods}
+\begin{block}{Advantages of Ensembles of Ensemble Methods}
+\pause
+Some advantages of ensembles of ensemble methods include:
+\begin{itemize}[<+->]
+\item \alert{Improved Accuracy}: By combining predictions from multiple ensemble methods, the overall accuracy can be significantly improved.
+\item \alert{Robustness}: It reduces the risk of overfitting by averaging out the predictions from different models, leading to better generalization.
+\item \alert{Flexibility}: It allows the use of diverse ensemble methods, which can capture different patterns in the data.
+\item \alert{Diversity}: The combination of different ensemble methods can lead to a more diverse set of predictions, which is beneficial for model performance.
+\item \alert{Enhanced Interpretability}: Some ensemble methods, like stacking, can provide insights into how different models contribute to the final prediction.
+\end{itemize}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org023a6ac}]{Ensambles of Ensemble Methods}
+\begin{block}{Disadvantages of Ensembles of Ensemble Methods}
+\pause
+Some disadvantages of ensembles of ensemble methods include:
+\begin{itemize}[<+->]
+\item \alert{Complexity}: The model can become complex and harder to interpret, as it involves multiple layers of models.
+\item \alert{Computational Cost}: Training multiple ensemble methods can be computationally expensive and time-consuming.
+\item \alert{Risk of Overfitting}: If not managed properly, combining too many models can lead to overfitting, especially if the base models are too complex.
+\item \alert{Hyperparameter Tuning}: Each ensemble method may require its own hyperparameter tuning, adding to the complexity of the model development process.
+\end{itemize}
+\end{block}
+\end{frame}
+\begin{frame}[label={sec:org9848d19}]{Ensembles of Ensemble Methods}
+\begin{block}{Types of Ensembles of Ensembles}
+\pause
+Some common types of ensembles of ensembles include:
+\begin{itemize}[<+->]
+\item \alert{Stacking}: Combines multiple ensemble methods by training a meta-model on their predictions. The base models can be of different types, and the meta-model learns how to best combine their outputs.
+\item \alert{Multi-level EoE}: A more complex version of stacking where multiple layers of models are trained, and predictions from one layer are used as inputs to the next layer. This allows for deeper integration of different ensemble methods and can lead to improved performance.
+\end{itemize}
+\end{block}
+\end{frame}
+\end{document}
diff --git a/src/S07 Ensemble Methods/S07C01 Introduction to Ensemble Methods.ipynb b/src/S07 Ensemble Methods/S07C01 Introduction to Ensemble Methods.ipynb
new file mode 100644
index 0000000..12ed0e2
--- /dev/null
+++ b/src/S07 Ensemble Methods/S07C01 Introduction to Ensemble Methods.ipynb	
@@ -0,0 +1,3941 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0c2007ed",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Introduction to Ensemble Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f177821b",
+   "metadata": {},
+   "source": [
+    "## What are Ensemble Methods\n",
+    "\n",
+    "**Ensemble methods** are a powerful class of machine learning techniques that combine multiple models to improve predictive performance. The main idea is to leverage the strengths of different models while mitigating their weaknesses, leading to more robust and accurate predictions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "156096c7",
+   "metadata": {},
+   "source": [
+    "## Why Use Ensemble Methods?\n",
+    "\n",
+    "Ensemble methods are used for several reasons:\n",
+    "\n",
+    "-   **Improved Accuracy**: They often outperform individual models by reducing variance and bias.\n",
+    "-   **Robustness**: Ensembles are less sensitive to noise and outliers, leading to more stable predictions.\n",
+    "-   **Reduced Overfitting**: By averaging predictions from multiple models, ensembles can reduce overfitting.\n",
+    "-   **Interpretability**: Some ensemble methods, like Random Forests, provide insights into feature importance.\n",
+    "-   **Flexibility**: They can be applied to various base models, including decision trees, linear models, and neural networks.\n",
+    "-   **Scalability**: Many ensemble methods can be parallelized, making them suitable for large datasets.\n",
+    "-   **Combining Different Algorithms**: Ensembles can combine predictions from different types of models, enhancing overall performance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67f0bc3f",
+   "metadata": {},
+   "source": [
+    "## Types of Ensemble Methods\n",
+    "\n",
+    "Ensemble methods can be broadly categorized into two types:\n",
+    "\n",
+    "-   **Bagging (Bootstrap Aggregating)**: Combines predictions from multiple models trained on different subsets of the training data. The most common example is Random Forests, which builds multiple decision trees and averages their predictions.\n",
+    "-   **Pasting**: Similar to bagging, but uses subsets of the training data without replacement.\n",
+    "-   **Boosting**: Sequentially builds models, where each new model focuses on correcting the errors made by the previous models. Examples include AdaBoost, Gradient Boosting, and XGBoost.\n",
+    "-   **Stacking**, **Blending**, **Bayesian Model Averaging**, **Voting**, etc."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f489a43e",
+   "metadata": {},
+   "source": [
+    "## Practical Demonstration\n",
+    "\n",
+    "To illustrate the concepts of ensemble methods, we will use the Iris dataset and apply both Random Forests and Gradient Boosting classifiers. We will compare their performance using accuracy and confusion matrices.\n",
+    "\n",
+    "-   Load the `iris` dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e9b9cfe3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "iris = datasets.load_iris()\n",
+    "X = iris.data\n",
+    "y = iris.target\n",
+    "feature_names = iris.feature_names\n",
+    "class_names = iris.target_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce4761fe",
+   "metadata": {},
+   "source": [
+    "-   Train-test split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cb06387e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5ffb6d8",
+   "metadata": {},
+   "source": [
+    "-   Train a Random Forest Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "72cbacb6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  /* Definition of color scheme for fitted estimators */\n",
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+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
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+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of trees in the forest.<br><br>.. versionchanged:: 0.22<br>   The default value of ``n_estimators`` changed from 10 to 100<br>   in 0.22.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.<br>Note: This parameter is tree-specific.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_features,-%7B%22sqrt%22%2C%20%22log2%22%2C%20None%7D%2C%20int%20or%20float%2C%20default%3D%22sqrt%22\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {\"sqrt\", \"log2\", None}, int or float, default=\"sqrt\"<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. versionchanged:: 1.1<br>    The default of `max_features` changed from `\"auto\"` to `\"sqrt\"`.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=bootstrap,-bool%2C%20default%3DTrue\">\n",
+       "            bootstrap\n",
+       "            <span class=\"param-doc-description\">bootstrap: bool, default=True<br><br>Whether bootstrap samples are used when building trees. If False, the<br>whole dataset is used to build each tree.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('oob_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=oob_score,-bool%20or%20callable%2C%20default%3DFalse\">\n",
+       "            oob_score\n",
+       "            <span class=\"param-doc-description\">oob_score: bool or callable, default=False<br><br>Whether to use out-of-bag samples to estimate the generalization score.<br>By default, :func:`~sklearn.metrics.accuracy_score` is used.<br>Provide a callable with signature `metric(y_true, y_pred)` to use a<br>custom metric. Only available if `bootstrap=True`.<br><br>For an illustration of out-of-bag (OOB) error estimation, see the example<br>:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,<br>:meth:`decision_path` and :meth:`apply` are all parallelized over the<br>trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`<br>context. ``-1`` means using all processors. See :term:`Glossary<br><n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls both the randomness of the bootstrapping of the samples used<br>when building trees (if ``bootstrap=True``) and the sampling of the<br>features to consider when looking for the best split at each node<br>(if ``max_features < n_features``).<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">42</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`Glossary <warm_start>` and<br>:ref:`tree_ensemble_warm_start` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=class_weight,-%7B%22balanced%22%2C%20%22balanced_subsample%22%7D%2C%20dict%20or%20list%20of%20dicts%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: {\"balanced\", \"balanced_subsample\"}, dict or list of dicts,             default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>The \"balanced_subsample\" mode is the same as \"balanced\" except that<br>weights are computed based on the bootstrap sample for every tree<br>grown.<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_samples\n",
+       "            <span class=\"param-doc-description\">max_samples: int or float, default=None<br><br>If bootstrap is True, the number of samples to draw from X<br>to train each base estimator.<br><br>- If None (default), then draw `X.shape[0]` samples.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,<br>  `max_samples` should be in the interval `(0.0, 1.0]`.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
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+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
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+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
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+       "    const paramName = element.parentElement.nextElementSibling\n",
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+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
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+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
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+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
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+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
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+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-1');</script></body>"
+      ],
+      "text/plain": [
+       "RandomForestClassifier(random_state=42)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.metrics import accuracy_score, classification_report, ConfusionMatrixDisplay\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+    "rf_model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ed821ae",
+   "metadata": {},
+   "source": [
+    "-   Evaluate the performance of the RF Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "40cea597",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Random Forest Classifier Accuracy: 0.8888888888888888\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "      setosa       1.00      1.00      1.00        15\n",
+      "  versicolor       0.78      0.93      0.85        15\n",
+      "   virginica       0.92      0.73      0.81        15\n",
+      "\n",
+      "    accuracy                           0.89        45\n",
+      "   macro avg       0.90      0.89      0.89        45\n",
+      "weighted avg       0.90      0.89      0.89        45\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_pred_rf = rf_model.predict(X_test)\n",
+    "\n",
+    "print(\"Random Forest Classifier Accuracy:\", accuracy_score(y_test, y_pred_rf))\n",
+    "print(classification_report(y_test, y_pred_rf, target_names=class_names))\n",
+    "\n",
+    "ConfusionMatrixDisplay.from_estimator(rf_model, X_test, y_test,\n",
+    "                                      display_labels=class_names, cmap='Blues')\n",
+    "plt.title('Random Forest Confusion Matrix')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db19186a",
+   "metadata": {},
+   "source": [
+    "-   Train a Gradient Boosting Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "68642176",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GradientBoostingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('loss',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=loss,-%7B%27log_loss%27%2C%20%27exponential%27%7D%2C%20default%3D%27log_loss%27\">\n",
+       "            loss\n",
+       "            <span class=\"param-doc-description\">loss: {'log_loss', 'exponential'}, default='log_loss'<br><br>The loss function to be optimized. 'log_loss' refers to binomial and<br>multinomial deviance, the same as used in logistic regression.<br>It is a good choice for classification with probabilistic outputs.<br>For loss 'exponential', gradient boosting recovers the AdaBoost algorithm.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;log_loss&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=learning_rate,-float%2C%20default%3D0.1\">\n",
+       "            learning_rate\n",
+       "            <span class=\"param-doc-description\">learning_rate: float, default=0.1<br><br>Learning rate shrinks the contribution of each tree by `learning_rate`.<br>There is a trade-off between learning_rate and n_estimators.<br>Values must be in the range `[0.0, inf)`.<br><br>For an example of the effects of this parameter and its interaction with<br>``subsample``, see<br>:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regularization.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of boosting stages to perform. Gradient boosting<br>is fairly robust to over-fitting so a large number usually<br>results in better performance.<br>Values must be in the range `[1, inf)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('subsample',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=subsample,-float%2C%20default%3D1.0\">\n",
+       "            subsample\n",
+       "            <span class=\"param-doc-description\">subsample: float, default=1.0<br><br>The fraction of samples to be used for fitting the individual base<br>learners. If smaller than 1.0 this results in Stochastic Gradient<br>Boosting. `subsample` interacts with the parameter `n_estimators`.<br>Choosing `subsample < 1.0` leads to a reduction of variance<br>and an increase in bias.<br>Values must be in the range `(0.0, 1.0]`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=criterion,-%7B%27friedman_mse%27%2C%20%27squared_error%27%7D%2C%20default%3D%27friedman_mse%27\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {'friedman_mse', 'squared_error'}, default='friedman_mse'<br><br>The function to measure the quality of a split. Supported criteria are<br>'friedman_mse' for the mean squared error with improvement score by<br>Friedman, 'squared_error' for mean squared error. The default value of<br>'friedman_mse' is generally the best as it can provide a better<br>approximation in some cases.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;friedman_mse&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, values must be in the range `[2, inf)`.<br>- If float, values must be in the range `(0.0, 1.0]` and `min_samples_split`<br>  will be `ceil(min_samples_split * n_samples)`.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, values must be in the range `[1, inf)`.<br>- If float, values must be in the range `(0.0, 1.0)` and `min_samples_leaf`<br>  will be `ceil(min_samples_leaf * n_samples)`.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.<br>Values must be in the range `[0.0, 0.5]`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=max_depth,-int%20or%20None%2C%20default%3D3\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int or None, default=3<br><br>Maximum depth of the individual regression estimators. The maximum<br>depth limits the number of nodes in the tree. Tune this parameter<br>for best performance; the best value depends on the interaction<br>of the input variables. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.<br>If int, values must be in the range `[1, inf)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br>Values must be in the range `[0.0, inf)`.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('init',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=init,-estimator%20or%20%27zero%27%2C%20default%3DNone\">\n",
+       "            init\n",
+       "            <span class=\"param-doc-description\">init: estimator or 'zero', default=None<br><br>An estimator object that is used to compute the initial predictions.<br>``init`` has to provide :term:`fit` and :term:`predict_proba`. If<br>'zero', the initial raw predictions are set to zero. By default, a<br>``DummyEstimator`` predicting the classes priors is used.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the random seed given to each Tree estimator at each<br>boosting iteration.<br>In addition, it controls the random permutation of the features at<br>each split (see Notes for more details).<br>It also controls the random splitting of the training data to obtain a<br>validation set if `n_iter_no_change` is not None.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">42</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=max_features,-%7B%27sqrt%27%2C%20%27log2%27%7D%2C%20int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {'sqrt', 'log2'}, int or float, default=None<br><br>The number of features to consider when looking for the best split:<br><br>- If int, values must be in the range `[1, inf)`.<br>- If float, values must be in the range `(0.0, 1.0]` and the features<br>  considered at each split will be `max(1, int(max_features * n_features_in_))`.<br>- If 'sqrt', then `max_features=sqrt(n_features)`.<br>- If 'log2', then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>Choosing `max_features < n_features` leads to a reduction of variance<br>and an increase in bias.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Enable verbose output. If 1 then it prints progress and performance<br>once in a while (the more trees the lower the frequency). If greater<br>than 1 then it prints progress and performance for every tree.<br>Values must be in the range `[0, inf)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>Values must be in the range `[2, inf)`.<br>If `None`, then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just erase the<br>previous solution. See :term:`the Glossary <warm_start>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validation_fraction',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=validation_fraction,-float%2C%20default%3D0.1\">\n",
+       "            validation_fraction\n",
+       "            <span class=\"param-doc-description\">validation_fraction: float, default=0.1<br><br>The proportion of training data to set aside as validation set for<br>early stopping. Values must be in the range `(0.0, 1.0)`.<br>Only used if ``n_iter_no_change`` is set to an integer.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=n_iter_no_change,-int%2C%20default%3DNone\">\n",
+       "            n_iter_no_change\n",
+       "            <span class=\"param-doc-description\">n_iter_no_change: int, default=None<br><br>``n_iter_no_change`` is used to decide if early stopping will be used<br>to terminate training when validation score is not improving. By<br>default it is set to None to disable early stopping. If set to a<br>number, it will set aside ``validation_fraction`` size of the training<br>data as validation and terminate training when validation score is not<br>improving in all of the previous ``n_iter_no_change`` numbers of<br>iterations. The split is stratified.<br>Values must be in the range `[1, inf)`.<br>See<br>:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_early_stopping.py`.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for the early stopping. When the loss is not improving<br>by at least tol for ``n_iter_no_change`` iterations (if set to a<br>number), the training stops.<br>Values must be in the range `[0.0, inf)`.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed.<br>Values must be in the range `[0.0, inf)`.<br>See :ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-2');</script></body>"
+      ],
+      "text/plain": [
+       "GradientBoostingClassifier(random_state=42)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "\n",
+    "gb_model = GradientBoostingClassifier(n_estimators=100, random_state=42)\n",
+    "gb_model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ad8e6c0",
+   "metadata": {},
+   "source": [
+    "-   Evaluate the performance of the GB Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f2d4c38d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gradient Boosting Classifier Accuracy: 0.9333333333333333\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "      setosa       1.00      1.00      1.00        15\n",
+      "  versicolor       0.88      0.93      0.90        15\n",
+      "   virginica       0.93      0.87      0.90        15\n",
+      "\n",
+      "    accuracy                           0.93        45\n",
+      "   macro avg       0.93      0.93      0.93        45\n",
+      "weighted avg       0.93      0.93      0.93        45\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_pred_gb = gb_model.predict(X_test)\n",
+    "\n",
+    "print(\"Gradient Boosting Classifier Accuracy:\", accuracy_score(y_test, y_pred_gb))\n",
+    "print(classification_report(y_test, y_pred_gb, target_names=class_names))\n",
+    "\n",
+    "ConfusionMatrixDisplay.from_estimator(gb_model, X_test, y_test,\n",
+    "                                      display_labels=class_names, cmap='Blues')\n",
+    "plt.title('Gradient Boosting Confusion Matrix')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2be4b278",
+   "metadata": {},
+   "source": [
+    "-   Feature importances for Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "88965677",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature importances for Random Forest:\n",
+      "petal width (cm): 0.4549\n",
+      "petal length (cm): 0.4002\n",
+      "sepal length (cm): 0.1206\n",
+      "sepal width (cm): 0.0243\n"
+     ]
+    }
+   ],
+   "source": [
+    "importances = rf_model.feature_importances_\n",
+    "indices = importances.argsort()[::-1]\n",
+    "print(\"Feature importances for Random Forest:\")\n",
+    "for i in range(X.shape[1]):\n",
+    "    print(f\"{feature_names[indices[i]]}: {importances[indices[i]]:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2bf0b24",
+   "metadata": {},
+   "source": [
+    "-   Plot feature importances for RF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5fa62b6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(range(X.shape[1]), importances[indices], align='center')\n",
+    "plt.xticks(range(X.shape[1]), [feature_names[i] for i in indices], rotation=45)\n",
+    "plt.title('Feature Importances from Random Forest')\n",
+    "plt.xlabel('Features')\n",
+    "plt.ylabel('Importance')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32524da8",
+   "metadata": {},
+   "source": [
+    "-   Feature importances for Gradient Boosting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f995f1b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature importances for Gradient Boosting:\n",
+      "petal width (cm): 0.6642\n",
+      "petal length (cm): 0.3106\n",
+      "sepal length (cm): 0.0139\n",
+      "sepal width (cm): 0.0113\n"
+     ]
+    }
+   ],
+   "source": [
+    "gb_importances = gb_model.feature_importances_\n",
+    "gb_indices = gb_importances.argsort()[::-1]\n",
+    "print(\"Feature importances for Gradient Boosting:\")\n",
+    "for i in range(X.shape[1]):\n",
+    "    print(f\"{feature_names[gb_indices[i]]}: {gb_importances[gb_indices[i]]:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "621701f6",
+   "metadata": {},
+   "source": [
+    "-   Plot feature importances for GB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7446b0f2",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(range(X.shape[1]), gb_importances[gb_indices], align='center')\n",
+    "plt.xticks(range(X.shape[1]), [feature_names[i] for i in gb_indices], rotation=45)\n",
+    "plt.title('Feature Importances from Gradient Boosting')\n",
+    "plt.xlabel('Features')\n",
+    "plt.ylabel('Importance')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63efc0c6",
+   "metadata": {},
+   "source": [
+    "## Hands-on Exercises\n",
+    "\n",
+    "**Voting Classifier**: Implement a Voting Classifier that combines the predictions of the Random Forest and Gradient Boosting classifiers. Evaluate its performance on the test set.\n",
+    "\n",
+    "-   Import, instantiate, and train a `VotingClassifier` model from `sklearn.ensemble`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b6755c10",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;rf&#x27;, RandomForestClassifier(random_state=42)),\n",
+       "                             (&#x27;gb&#x27;,\n",
+       "                              GradientBoostingClassifier(random_state=42))],\n",
+       "                 voting=&#x27;soft&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>VotingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.VotingClassifier.html\">?<span>Documentation for VotingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.VotingClassifier.html#:~:text=estimators,-list%20of%20%28str%2C%20estimator%29%20tuples\">\n",
+       "            estimators\n",
+       "            <span class=\"param-doc-description\">estimators: list of (str, estimator) tuples<br><br>Invoking the ``fit`` method on the ``VotingClassifier`` will fit clones<br>of those original estimators that will be stored in the class attribute<br>``self.estimators_``. An estimator can be set to ``'drop'`` using<br>:meth:`set_params`.<br><br>.. versionchanged:: 0.21<br>    ``'drop'`` is accepted. Using None was deprecated in 0.22 and<br>    support was removed in 0.24.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">[(&#x27;rf&#x27;, ...), (&#x27;gb&#x27;, ...)]</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('voting',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.VotingClassifier.html#:~:text=voting,-%7B%27hard%27%2C%20%27soft%27%7D%2C%20default%3D%27hard%27\">\n",
+       "            voting\n",
+       "            <span class=\"param-doc-description\">voting: {'hard', 'soft'}, default='hard'<br><br>If 'hard', uses predicted class labels for majority rule voting.<br>Else if 'soft', predicts the class label based on the argmax of<br>the sums of the predicted probabilities, which is recommended for<br>an ensemble of well-calibrated classifiers.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;soft&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.VotingClassifier.html#:~:text=weights,-array-like%20of%20shape%20%28n_classifiers%2C%29%2C%20default%3DNone\">\n",
+       "            weights\n",
+       "            <span class=\"param-doc-description\">weights: array-like of shape (n_classifiers,), default=None<br><br>Sequence of weights (`float` or `int`) to weight the occurrences of<br>predicted class labels (`hard` voting) or class probabilities<br>before averaging (`soft` voting). Uses uniform weights if `None`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.VotingClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel for ``fit``.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('flatten_transform',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.VotingClassifier.html#:~:text=flatten_transform,-bool%2C%20default%3DTrue\">\n",
+       "            flatten_transform\n",
+       "            <span class=\"param-doc-description\">flatten_transform: bool, default=True<br><br>Affects shape of transform output only when voting='soft'<br>If voting='soft' and flatten_transform=True, transform method returns<br>matrix with shape (n_samples, n_classifiers * n_classes). If<br>flatten_transform=False, it returns<br>(n_classifiers, n_samples, n_classes).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.VotingClassifier.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>If True, the time elapsed while fitting will be printed as it<br>is completed.<br><br>.. versionadded:: 0.23</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>rf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"rf__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of trees in the forest.<br><br>.. versionchanged:: 0.22<br>   The default value of ``n_estimators`` changed from 10 to 100<br>   in 0.22.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.<br>Note: This parameter is tree-specific.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_features,-%7B%22sqrt%22%2C%20%22log2%22%2C%20None%7D%2C%20int%20or%20float%2C%20default%3D%22sqrt%22\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {\"sqrt\", \"log2\", None}, int or float, default=\"sqrt\"<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. versionchanged:: 1.1<br>    The default of `max_features` changed from `\"auto\"` to `\"sqrt\"`.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=bootstrap,-bool%2C%20default%3DTrue\">\n",
+       "            bootstrap\n",
+       "            <span class=\"param-doc-description\">bootstrap: bool, default=True<br><br>Whether bootstrap samples are used when building trees. If False, the<br>whole dataset is used to build each tree.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('oob_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=oob_score,-bool%20or%20callable%2C%20default%3DFalse\">\n",
+       "            oob_score\n",
+       "            <span class=\"param-doc-description\">oob_score: bool or callable, default=False<br><br>Whether to use out-of-bag samples to estimate the generalization score.<br>By default, :func:`~sklearn.metrics.accuracy_score` is used.<br>Provide a callable with signature `metric(y_true, y_pred)` to use a<br>custom metric. Only available if `bootstrap=True`.<br><br>For an illustration of out-of-bag (OOB) error estimation, see the example<br>:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,<br>:meth:`decision_path` and :meth:`apply` are all parallelized over the<br>trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`<br>context. ``-1`` means using all processors. See :term:`Glossary<br><n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls both the randomness of the bootstrapping of the samples used<br>when building trees (if ``bootstrap=True``) and the sampling of the<br>features to consider when looking for the best split at each node<br>(if ``max_features < n_features``).<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">42</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`Glossary <warm_start>` and<br>:ref:`tree_ensemble_warm_start` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=class_weight,-%7B%22balanced%22%2C%20%22balanced_subsample%22%7D%2C%20dict%20or%20list%20of%20dicts%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: {\"balanced\", \"balanced_subsample\"}, dict or list of dicts,             default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>The \"balanced_subsample\" mode is the same as \"balanced\" except that<br>weights are computed based on the bootstrap sample for every tree<br>grown.<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_samples\n",
+       "            <span class=\"param-doc-description\">max_samples: int or float, default=None<br><br>If bootstrap is True, the number of samples to draw from X<br>to train each base estimator.<br><br>- If None (default), then draw `X.shape[0]` samples.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,<br>  `max_samples` should be in the interval `(0.0, 1.0]`.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>gb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GradientBoostingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"gb__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('loss',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=loss,-%7B%27log_loss%27%2C%20%27exponential%27%7D%2C%20default%3D%27log_loss%27\">\n",
+       "            loss\n",
+       "            <span class=\"param-doc-description\">loss: {'log_loss', 'exponential'}, default='log_loss'<br><br>The loss function to be optimized. 'log_loss' refers to binomial and<br>multinomial deviance, the same as used in logistic regression.<br>It is a good choice for classification with probabilistic outputs.<br>For loss 'exponential', gradient boosting recovers the AdaBoost algorithm.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;log_loss&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=learning_rate,-float%2C%20default%3D0.1\">\n",
+       "            learning_rate\n",
+       "            <span class=\"param-doc-description\">learning_rate: float, default=0.1<br><br>Learning rate shrinks the contribution of each tree by `learning_rate`.<br>There is a trade-off between learning_rate and n_estimators.<br>Values must be in the range `[0.0, inf)`.<br><br>For an example of the effects of this parameter and its interaction with<br>``subsample``, see<br>:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regularization.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of boosting stages to perform. Gradient boosting<br>is fairly robust to over-fitting so a large number usually<br>results in better performance.<br>Values must be in the range `[1, inf)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('subsample',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=subsample,-float%2C%20default%3D1.0\">\n",
+       "            subsample\n",
+       "            <span class=\"param-doc-description\">subsample: float, default=1.0<br><br>The fraction of samples to be used for fitting the individual base<br>learners. If smaller than 1.0 this results in Stochastic Gradient<br>Boosting. `subsample` interacts with the parameter `n_estimators`.<br>Choosing `subsample < 1.0` leads to a reduction of variance<br>and an increase in bias.<br>Values must be in the range `(0.0, 1.0]`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=criterion,-%7B%27friedman_mse%27%2C%20%27squared_error%27%7D%2C%20default%3D%27friedman_mse%27\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {'friedman_mse', 'squared_error'}, default='friedman_mse'<br><br>The function to measure the quality of a split. Supported criteria are<br>'friedman_mse' for the mean squared error with improvement score by<br>Friedman, 'squared_error' for mean squared error. The default value of<br>'friedman_mse' is generally the best as it can provide a better<br>approximation in some cases.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;friedman_mse&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, values must be in the range `[2, inf)`.<br>- If float, values must be in the range `(0.0, 1.0]` and `min_samples_split`<br>  will be `ceil(min_samples_split * n_samples)`.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, values must be in the range `[1, inf)`.<br>- If float, values must be in the range `(0.0, 1.0)` and `min_samples_leaf`<br>  will be `ceil(min_samples_leaf * n_samples)`.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.<br>Values must be in the range `[0.0, 0.5]`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=max_depth,-int%20or%20None%2C%20default%3D3\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int or None, default=3<br><br>Maximum depth of the individual regression estimators. The maximum<br>depth limits the number of nodes in the tree. Tune this parameter<br>for best performance; the best value depends on the interaction<br>of the input variables. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.<br>If int, values must be in the range `[1, inf)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br>Values must be in the range `[0.0, inf)`.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('init',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=init,-estimator%20or%20%27zero%27%2C%20default%3DNone\">\n",
+       "            init\n",
+       "            <span class=\"param-doc-description\">init: estimator or 'zero', default=None<br><br>An estimator object that is used to compute the initial predictions.<br>``init`` has to provide :term:`fit` and :term:`predict_proba`. If<br>'zero', the initial raw predictions are set to zero. By default, a<br>``DummyEstimator`` predicting the classes priors is used.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the random seed given to each Tree estimator at each<br>boosting iteration.<br>In addition, it controls the random permutation of the features at<br>each split (see Notes for more details).<br>It also controls the random splitting of the training data to obtain a<br>validation set if `n_iter_no_change` is not None.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">42</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=max_features,-%7B%27sqrt%27%2C%20%27log2%27%7D%2C%20int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {'sqrt', 'log2'}, int or float, default=None<br><br>The number of features to consider when looking for the best split:<br><br>- If int, values must be in the range `[1, inf)`.<br>- If float, values must be in the range `(0.0, 1.0]` and the features<br>  considered at each split will be `max(1, int(max_features * n_features_in_))`.<br>- If 'sqrt', then `max_features=sqrt(n_features)`.<br>- If 'log2', then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>Choosing `max_features < n_features` leads to a reduction of variance<br>and an increase in bias.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Enable verbose output. If 1 then it prints progress and performance<br>once in a while (the more trees the lower the frequency). If greater<br>than 1 then it prints progress and performance for every tree.<br>Values must be in the range `[0, inf)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>Values must be in the range `[2, inf)`.<br>If `None`, then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just erase the<br>previous solution. See :term:`the Glossary <warm_start>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validation_fraction',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=validation_fraction,-float%2C%20default%3D0.1\">\n",
+       "            validation_fraction\n",
+       "            <span class=\"param-doc-description\">validation_fraction: float, default=0.1<br><br>The proportion of training data to set aside as validation set for<br>early stopping. Values must be in the range `(0.0, 1.0)`.<br>Only used if ``n_iter_no_change`` is set to an integer.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=n_iter_no_change,-int%2C%20default%3DNone\">\n",
+       "            n_iter_no_change\n",
+       "            <span class=\"param-doc-description\">n_iter_no_change: int, default=None<br><br>``n_iter_no_change`` is used to decide if early stopping will be used<br>to terminate training when validation score is not improving. By<br>default it is set to None to disable early stopping. If set to a<br>number, it will set aside ``validation_fraction`` size of the training<br>data as validation and terminate training when validation score is not<br>improving in all of the previous ``n_iter_no_change`` numbers of<br>iterations. The split is stratified.<br>Values must be in the range `[1, inf)`.<br>See<br>:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_early_stopping.py`.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for the early stopping. When the loss is not improving<br>by at least tol for ``n_iter_no_change`` iterations (if set to a<br>number), the training stops.<br>Values must be in the range `[0.0, inf)`.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed.<br>Values must be in the range `[0.0, inf)`.<br>See :ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+      ],
+      "text/plain": [
+       "VotingClassifier(estimators=[('rf', RandomForestClassifier(random_state=42)),\n",
+       "                             ('gb',\n",
+       "                              GradientBoostingClassifier(random_state=42))],\n",
+       "                 voting='soft')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import VotingClassifier\n",
+    "\n",
+    "voting_model = VotingClassifier(\n",
+    "    estimators=[\n",
+    "        ('rf', rf_model),\n",
+    "        ('gb', gb_model)\n",
+    "    ],\n",
+    "    voting='soft')\n",
+    "voting_model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a234b3df",
+   "metadata": {},
+   "source": [
+    "-   Evaluate the performance of the Voting Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8fa47d0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Voting Classifier Accuracy: 0.9333333333333333\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "      setosa       1.00      1.00      1.00        15\n",
+      "  versicolor       0.88      0.93      0.90        15\n",
+      "   virginica       0.93      0.87      0.90        15\n",
+      "\n",
+      "    accuracy                           0.93        45\n",
+      "   macro avg       0.93      0.93      0.93        45\n",
+      "weighted avg       0.93      0.93      0.93        45\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_pred_voting = voting_model.predict(X_test)\n",
+    "\n",
+    "print(\"Voting Classifier Accuracy:\", accuracy_score(y_test, y_pred_voting))\n",
+    "print(classification_report(y_test, y_pred_voting, target_names=class_names))\n",
+    "\n",
+    "ConfusionMatrixDisplay.from_estimator(voting_model, X_test, y_test,\n",
+    "                                      display_labels=class_names, cmap='Blues')\n",
+    "plt.title('Voting Classifier Confusion Matrix')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "ML Workshop",
+   "language": "python",
+   "name": "ml_workshop"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/S07 Ensemble Methods/S07C02 Bagging.ipynb b/src/S07 Ensemble Methods/S07C02 Bagging.ipynb
new file mode 100644
index 0000000..4ae40da
--- /dev/null
+++ b/src/S07 Ensemble Methods/S07C02 Bagging.ipynb	
@@ -0,0 +1,6724 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "be84baaf",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Bagging\n",
+    "\n",
+    "**Bagging**, or **Bootstrap Aggregating**, is a type of ensemble method that improves the stability and accuracy of machine learning algorithms by training multiple models on different subsets randomly sampled (with replacement) from the training data and then aggregating their predictions.\n",
+    "\n",
+    "The main steps involved in bagging are:\n",
+    "\n",
+    "1.  **Bootstrap Sampling**: Create multiple bootstrap samples from the original training dataset. Each bootstrap sample is created by randomly selecting instances from the original dataset with replacement, meaning some instances may appear multiple times while others may not appear at all.\n",
+    "2.  **Model Training**: Train a separate model on each bootstrap sample. This can be any machine learning algorithm, but decision trees are commonly used due to their high variance and low bias.\n",
+    "3.  **Aggregation**: For regression tasks, the predictions from all models are averaged to produce the final prediction. For classification tasks, a majority vote is taken among the predictions of all models to determine the final class label.\n",
+    "4.  **Final Prediction**: The aggregated prediction is then used as the final output of the bagging ensemble."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "728cd1b4",
+   "metadata": {},
+   "source": [
+    "## Advantages and Disadvantages\n",
+    "\n",
+    "Bagging has several advantages and disadvantages that make it suitable for various machine learning tasks:\n",
+    "\n",
+    "-   **Advantages**:\n",
+    "    -   Can reduce variance and help prevent overfitting by averaging predictions from multiple models.\n",
+    "    -   Can improve accuracy, especially with high-variance models like decision trees.\n",
+    "    -   Robust to noise and outliers, as the aggregation process smooths out individual model errors.\n",
+    "    -   Can be parallelized, as each model can be trained independently on its bootstrap sample, making it efficient for large datasets.\n",
+    "-   **Disadvantages**:\n",
+    "    -   Requires more computational resources due to training multiple models.\n",
+    "    -   May not significantly improve performance if the base model is already stable and has low variance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77bcf703",
+   "metadata": {},
+   "source": [
+    "### Example\n",
+    "\n",
+    "We will illustrate the concept of bagging using a simple example with a synthetic dataset. We will compare the performance of a single `DecisionTreeClassifier` model with a Bagging Classifier that uses the same model as its base estimator.\n",
+    "\n",
+    "-   Generate a synthetic dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "629ce182",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import make_classification\n",
+    "X, y = make_classification(n_samples=1000, n_features=2, n_informative=2,\n",
+    "                           n_redundant=0, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e9a338f",
+   "metadata": {},
+   "source": [
+    "-   Split the dataset into training and testing sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "eb21a9d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = \\\n",
+    "    train_test_split(X, y, test_size=0.3, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5e10c2c",
+   "metadata": {},
+   "source": [
+    "-   Train a single DecisionTreeClassifier model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2ed14b73",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('splitter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=splitter,-%7B%22best%22%2C%20%22random%22%7D%2C%20default%3D%22best%22\">\n",
+       "            splitter\n",
+       "            <span class=\"param-doc-description\">splitter: {\"best\", \"random\"}, default=\"best\"<br><br>The strategy used to choose the split at each node. Supported<br>strategies are \"best\" to choose the best split and \"random\" to choose<br>the best random split.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_features,-int%2C%20float%20or%20%7B%22sqrt%22%2C%20%22log2%22%7D%2C%20default%3DNone\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: int, float or {\"sqrt\", \"log2\"}, default=None<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at<br>  each split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. note::<br><br>    The search for a split does not stop until at least one<br>    valid partition of the node samples is found, even if it requires to<br>    effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the randomness of the estimator. The features are always<br>randomly permuted at each split, even if ``splitter`` is set to<br>``\"best\"``. When ``max_features < n_features``, the algorithm will<br>select ``max_features`` at random at each split before finding the best<br>split among them. But the best found split may vary across different<br>runs, even if ``max_features=n_features``. That is the case, if the<br>improvement of the criterion is identical for several splits and one<br>split has to be selected at random. To obtain a deterministic behaviour<br>during fitting, ``random_state`` has to be fixed to an integer.<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">42</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow a tree with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=class_weight,-dict%2C%20list%20of%20dict%20or%20%22balanced%22%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict, list of dict or \"balanced\", default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If None, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+       "\n",
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+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-1');</script></body>"
+      ],
+      "text/plain": [
+       "DecisionTreeClassifier(random_state=42)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "single_model = DecisionTreeClassifier(random_state=42)\n",
+    "single_model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52f924c2",
+   "metadata": {},
+   "source": [
+    "-   Evaluate the single model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "daf27baa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Single DecisionTreeClassifier Accuracy: 0.90\n"
+     ]
+    }
+   ],
+   "source": [
+    "single_accuracy = single_model.score(X_test, y_test)\n",
+    "print(f\"Single DecisionTreeClassifier Accuracy: {single_accuracy:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35a5b146",
+   "metadata": {},
+   "source": [
+    "-   Train a Bagging model with DecisionTreeClassifier as estimator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8ee79ea9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=100,\n",
+       "                  random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>BaggingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html\">?<span>Documentation for BaggingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=estimator,-object%2C%20default%3DNone\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: object, default=None<br><br>The base estimator to fit on random subsets of the dataset.<br>If None, then the base estimator is a<br>:class:`~sklearn.tree.DecisionTreeClassifier`.<br><br>.. versionadded:: 1.2<br>   `base_estimator` was renamed to `estimator`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">DecisionTreeClassifier()</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=n_estimators,-int%2C%20default%3D10\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=10<br><br>The number of base estimators in the ensemble.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_samples\n",
+       "            <span class=\"param-doc-description\">max_samples: int or float, default=None<br><br>The number of samples to draw from X to train each base estimator (with<br>replacement by default, see `bootstrap` for more details).<br><br>- If None, then draw `X.shape[0]` samples irrespective of `sample_weight`.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max_samples * X.shape[0]` unweighted samples or<br>  `max_samples * sample_weight.sum()` weighted samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=max_features,-int%20or%20float%2C%20default%3D1.0\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: int or float, default=1.0<br><br>The number of features to draw from X to train each base estimator (<br>without replacement by default, see `bootstrap_features` for more<br>details).<br><br>- If int, then draw `max_features` features.<br>- If float, then draw `max(1, int(max_features * n_features_in_))` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=bootstrap,-bool%2C%20default%3DTrue\">\n",
+       "            bootstrap\n",
+       "            <span class=\"param-doc-description\">bootstrap: bool, default=True<br><br>Whether samples are drawn with replacement. If False, sampling without<br>replacement is performed. If fitting with `sample_weight`, it is<br>strongly recommended to choose True, as only drawing with replacement<br>will ensure the expected frequency semantics of `sample_weight`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=bootstrap_features,-bool%2C%20default%3DFalse\">\n",
+       "            bootstrap_features\n",
+       "            <span class=\"param-doc-description\">bootstrap_features: bool, default=False<br><br>Whether features are drawn with replacement.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('oob_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=oob_score,-bool%2C%20default%3DFalse\">\n",
+       "            oob_score\n",
+       "            <span class=\"param-doc-description\">oob_score: bool, default=False<br><br>Whether to use out-of-bag samples to estimate<br>the generalization error. Only available if bootstrap=True.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit<br>a whole new ensemble. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* constructor parameter.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel for both :meth:`fit` and<br>:meth:`predict`. ``None`` means 1 unless in a<br>:obj:`joblib.parallel_backend` context. ``-1`` means using all<br>processors. See :term:`Glossary <n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the random resampling of the original dataset<br>(sample wise and feature wise).<br>If the base estimator accepts a `random_state` attribute, a different<br>seed is generated for each instance in the ensemble.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">42</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: DecisionTreeClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('splitter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=splitter,-%7B%22best%22%2C%20%22random%22%7D%2C%20default%3D%22best%22\">\n",
+       "            splitter\n",
+       "            <span class=\"param-doc-description\">splitter: {\"best\", \"random\"}, default=\"best\"<br><br>The strategy used to choose the split at each node. Supported<br>strategies are \"best\" to choose the best split and \"random\" to choose<br>the best random split.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_features,-int%2C%20float%20or%20%7B%22sqrt%22%2C%20%22log2%22%7D%2C%20default%3DNone\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: int, float or {\"sqrt\", \"log2\"}, default=None<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at<br>  each split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. note::<br><br>    The search for a split does not stop until at least one<br>    valid partition of the node samples is found, even if it requires to<br>    effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the randomness of the estimator. The features are always<br>randomly permuted at each split, even if ``splitter`` is set to<br>``\"best\"``. When ``max_features < n_features``, the algorithm will<br>select ``max_features`` at random at each split before finding the best<br>split among them. But the best found split may vary across different<br>runs, even if ``max_features=n_features``. That is the case, if the<br>improvement of the criterion is identical for several splits and one<br>split has to be selected at random. To obtain a deterministic behaviour<br>during fitting, ``random_state`` has to be fixed to an integer.<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow a tree with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=class_weight,-dict%2C%20list%20of%20dict%20or%20%22balanced%22%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict, list of dict or \"balanced\", default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If None, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
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+       "    navigator.clipboard.writeText(fullParamName)\n",
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+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
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+       "                element.style = originalStyle;\n",
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+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
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+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
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+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
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+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
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+       "        }\n",
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+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-2');</script></body>"
+      ],
+      "text/plain": [
+       "BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=100,\n",
+       "                  random_state=42)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import BaggingClassifier\n",
+    "bagging_model = BaggingClassifier(estimator=DecisionTreeClassifier(),\n",
+    "                                  n_estimators=100, random_state=42)\n",
+    "bagging_model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8e41b21",
+   "metadata": {},
+   "source": [
+    "-   Evaluate the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2b415e1c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bagging Classifier Accuracy: 0.92\n"
+     ]
+    }
+   ],
+   "source": [
+    "accuracy = bagging_model.score(X_test, y_test)\n",
+    "print(f\"Bagging Classifier Accuracy: {accuracy:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9f3e445",
+   "metadata": {},
+   "source": [
+    "The Bagging Classifier typically achieves higher accuracy than the single Decision Tree model due to the reduction in variance and improved generalization."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f63fc2ff",
+   "metadata": {},
+   "source": [
+    "## Random Forests\n",
+    "\n",
+    "**Random Forests** is a popular ensemble method that uses bagging with decision trees as base models. It introduces additional randomness by selecting a random subset of features for each split in the decision tree, which helps to decorrelate the trees and further reduce overfitting.\n",
+    "\n",
+    "This method combines the strengths of bagging and decision trees, resulting in a powerful and flexible model that can handle both classification and regression tasks effectively."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3985144",
+   "metadata": {},
+   "source": [
+    "### Example: Random Forest Classifier on the Titanic Dataset\n",
+    "\n",
+    "To illustrate the use of Random Forest, we will use the Titanic dataset and apply a Random Forest classifier. We will compare its performance with a single decision tree classifier to highlight the benefits of bagging.\n",
+    "\n",
+    "-   Load the Titanic dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9ccb4fcb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "\n",
+    "data = fetch_openml('titanic', version=1, as_frame=True)\n",
+    "df = data.frame\n",
+    "\n",
+    "df_sub = df[['pclass', 'sex', 'age', 'survived']].copy()\n",
+    "\n",
+    "X = df_sub.drop('survived', axis=1)\n",
+    "y = df_sub['survived']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "892827fe",
+   "metadata": {},
+   "source": [
+    "-   Split the dataset into training and testing sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c37905b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set size: 916, Test set size: 393\n",
+      "Features: ['pclass', 'sex', 'age']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = \\\n",
+    "    train_test_split(X, y, test_size=0.3, random_state=42)\n",
+    "print(f\"Training set size: {X_train.shape[0]}, Test set size: {X_test.shape[0]}\")\n",
+    "print(f\"Features: {X_train.columns.tolist()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3767917e",
+   "metadata": {},
+   "source": [
+    "-   Preprocess the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d44cfbd1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "\n",
+    "# Create a preprocessing pipeline\n",
+    "preprocessing_pipeline = ColumnTransformer(\n",
+    "    transformers=[\n",
+    "        ('age', Pipeline(\n",
+    "            steps=[\n",
+    "                ('imputer', SimpleImputer(strategy='mean')),\n",
+    "                ('scaler', StandardScaler())\n",
+    "            ]\n",
+    "        ), ['age']),\n",
+    "        ('sex', Pipeline(steps=[\n",
+    "            ('encoder', OneHotEncoder(drop='first', sparse_output=False))\n",
+    "        ]), ['sex']),\n",
+    "        ('pclass', Pipeline(steps=[\n",
+    "            ('encoder', OneHotEncoder(sparse_output=False))\n",
+    "        ]), ['pclass'])\n",
+    "    ],\n",
+    "    remainder='passthrough'\n",
+    ")\n",
+    "\n",
+    "# Fit and transform the data\n",
+    "X_train_transformed = preprocessing_pipeline.fit_transform(X_train)\n",
+    "X_test_transformed = preprocessing_pipeline.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "491c00ac",
+   "metadata": {},
+   "source": [
+    "-   Train a single Decision Tree classifier with hyperparameter tuning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "61ba5a48",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=5, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('splitter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=splitter,-%7B%22best%22%2C%20%22random%22%7D%2C%20default%3D%22best%22\">\n",
+       "            splitter\n",
+       "            <span class=\"param-doc-description\">splitter: {\"best\", \"random\"}, default=\"best\"<br><br>The strategy used to choose the split at each node. Supported<br>strategies are \"best\" to choose the best split and \"random\" to choose<br>the best random split.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_features,-int%2C%20float%20or%20%7B%22sqrt%22%2C%20%22log2%22%7D%2C%20default%3DNone\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: int, float or {\"sqrt\", \"log2\"}, default=None<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at<br>  each split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. note::<br><br>    The search for a split does not stop until at least one<br>    valid partition of the node samples is found, even if it requires to<br>    effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the randomness of the estimator. The features are always<br>randomly permuted at each split, even if ``splitter`` is set to<br>``\"best\"``. When ``max_features < n_features``, the algorithm will<br>select ``max_features`` at random at each split before finding the best<br>split among them. But the best found split may vary across different<br>runs, even if ``max_features=n_features``. That is the case, if the<br>improvement of the criterion is identical for several splits and one<br>split has to be selected at random. To obtain a deterministic behaviour<br>during fitting, ``random_state`` has to be fixed to an integer.<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">42</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow a tree with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=class_weight,-dict%2C%20list%20of%20dict%20or%20%22balanced%22%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict, list of dict or \"balanced\", default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If None, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
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+       "    navigator.clipboard.writeText(fullParamName)\n",
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+       "\n",
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+       "forceTheme('sk-container-id-3');</script></body>"
+      ],
+      "text/plain": [
+       "DecisionTreeClassifier(max_depth=5, random_state=42)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "# Train a single Decision Tree classifier\n",
+    "single_tree_model = DecisionTreeClassifier(random_state=42)\n",
+    "grid_params = {\n",
+    "    'max_depth': [None, 5, 10, 15],\n",
+    "    'min_samples_split': [2, 5, 10],\n",
+    "    'min_samples_leaf': [1, 2, 4]\n",
+    "}\n",
+    "\n",
+    "# Perform grid search for hyperparameter tuning\n",
+    "grid_search = GridSearchCV(single_tree_model, grid_params, cv=5, n_jobs=-1, verbose=1)\n",
+    "grid_search.fit(X_train_transformed, y_train)\n",
+    "single_tree_model = grid_search.best_estimator_\n",
+    "single_tree_model.fit(X_train_transformed, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "149d57b5",
+   "metadata": {},
+   "source": [
+    "-   Evaluate the single model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "10d72d5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Single Decision Tree Classifier Accuracy: 0.76\n",
+      "Feature Importances from Single Decision Tree:\n",
+      "sex__sex_male       0.580959\n",
+      "pclass__pclass_3    0.226990\n",
+      "age__age            0.147395\n",
+      "pclass__pclass_1    0.042398\n",
+      "pclass__pclass_2    0.002258\n",
+      "dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
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XEaTXhrn9u6C633XhvPPOy79vqxO/78oLTUQV18wCrDkNfgGY9wilAGgQYnWy8qpgP3WiHBUaQ4cOzSd4caJXVt1UrJmFAeVqj+lXtpq+IuHn9jdOAuNkN6pR5iXxXEToULla5PPPP8/NuUN5NbY4eY5KjLKYQhSVTT8XVPzc8xuNl+P7X3311VW2x/Ndbjhfk/dGBAwz27fyzxEVarOy/xGUxdS0uMTzE0FVNJuek1BqevE8xwl9vB4RVpRFM+h4LqpbFa8I5UrFeE/MqfLPEFVPld9zsepgVOb9VAgxu6/Z7IpVLGN1yAjhypVS8V6KVSBj2tlPhYVxu3jtorpsZkHMzMRU2549e+ZLjKloSB6LIpx44om5EnJ6tTUe55by+Itq1Z/al6g+i+c5wv263mcAiqOnFAANQlRKxElPVNnENJDplVfMK1cJTF8VMHDgwGpPDqsLn+L7RDgSvVAqi4qGWRUnmrEvEY5Nvy9xvVw1UBdi6kzl5zBW8YqQoLwyV5wwxtSgiy++uMq+R4gU03C23377Wfo+8fxO/9yGeF6mf06il09N+wOtu+66OfyL13j671f+PhFmxoqAscpfdWFL5RUXp39tosojKnJipbHaVA4Bp39vlqt0ZvV5rqlYFa26sVTudTX9dLGaBlwxJTN6VMV7rCyqsH5ueuHsvGazK6b4Rn+6CEjKq0OWqyzjfVhdT63//e9/uU9SiN5bUd0XUz2nr+77qYqk6d9bMc5iCmDcp7rXojbH49wSK+5FMBWrQZanQ87sd/Nuu+2Wq8MiQJ7Z7QBoWFRKAdAgRFAU4Un09okQ4re//W3+5D2qHaLRb/RkGTRoUL5dVLVEX5w4yYt+MzEFKyoKqjuZCnFSGo8XFRnRhDfClKiIieXu4/9xYh0BVSwJP6viJO3MM8/M1Q9RERInsVElEPtx99135549MS2pLkSFRVSCxAl4VF9E2LbpppumnXbaKX89ntfY7wjUYspjbC/fbr311qvSRPmnxPMbr1k8DxHqRMgQPcF22GGHfDIfVUgbb7xxeu2113JIUbkKZHZEOBDfJ167qFqJx40eWW+//XZuWP7www9XNNGPnzOmZ0WT6Ph+UZX0/PPP5+bTUSETIiSIMCT2Pyqmoll3VHfFEva1ae21187TmCIkjDCte/fuadiwYenvf/97fr9UriyaG84555w0YsSIHKCWK5ai0fT111+ff+6YTjmnIkyJCrPDDjssv/bxnovxcN111+Ux8nNTF2f1NfspMW5vvPHGHOhMnDgx3ydC0AhQIgCM93hZ/H657bbbclP6aGoev1ciuIr3UmyP91L8Poj3c/zeiOmn0dw9nsNo4B8NzJdeeuk0YMCAavclekjF9ON43GgoH/2r4vdWBEvlaq3p1dZ4nFti/MUU1wi1o3l+jL/4vRvhXjyH8Tv5/vvvz7eN36mxLZrxx+sZYy0CwnjfRa+t+DcADUxdL/8HALOivAz6Sy+99JO3i2XJY+nxWD68ZcuWpRVXXLG07777loYPH15xm08++SQv1d6mTZt8uz322KP02WefzbDEeTjjjDNK7du3z8u7V16GPZYmj+XL4/6/+MUvSnvuuWdemn36x4h/x7ZYQr06d955Z2nTTTctLbTQQvmy2mqr5SXq33nnndl+Pnr37p0fY3qxDHx1y8wvt9xype23336Gx3zyySdLBxxwQGmRRRYpLbzwwqV99tmn9MUXX8xw/1hyPva3WbNmpbZt25YOOuig0ldffTVL3zuMHj06f/94/uL7xm3D999/Xzr66KPz0vALLLBAaZNNNik9//zz+evl21Regv7222+v8rjxGsX2+Hkqe+aZZ/Ly8/H94nlaa621SpdcckmV27z//vulXr16ldq1a5d/rnjtd9hhh9Idd9xRcZszzzyztP766+f3T+xfPAdnnXVWacqUKdX+nNPv13nnnVdl+0+9R3744YfSaaedVlp++eXz/nTo0KF04okn5ufop17LsnjceD/Nyn5M79lnn8337dy5c36fx/dfdtll83iK56myOX1tLr744vwztGjRIj+38b27du1a2nbbbX/2vrPyms1MPF75EmM8XtMuXbqUjjjiiNIbb7xR7X3idT7nnHPy+zr2N8ZJ7Gu8ThMmTKhy22uuuSY/Xvl28Rw9+uijM33eLr/88tLmm29eWmyxxfJ94vfXscceW+Vxy+O0/LuoNsZj/O6I539WxXt1+t91M3vNy1555ZXSrrvuWvGzxfeL35tDhw6tcrsxY8bk91281+Nnidd1yy23LF1xxRWzvH8A1B9N4j91HYwBAHUvqlOiiiGqOapb4RCKElPeogIoKoyqmyoHADQMekoBAFBnvv/++xn6LMUUwZiqFdMkAYCGS08pAADqzAsvvJCOOuqotMcee+Sm59E/KJp0d+7cOW8DABouoRQAAHWmY8eOqUOHDnn1uKiOiibqvXr1yk2voxE6ANBw6SkFAAAAQOH0lAIAAACgcEIpAAAAAAo3f2NcYvizzz5Lv/jFL1KTJk3qencAAAAAGpRYWfebb75JSy+9dJpvvpnXQzW6UCoCqWimCQAAAMDc8/HHH6dllllmpl9vdKFUVEiVn5hWrVrV9e4AAAAANCgTJ07MBUHlDGZmGl0oVZ6yF4GUUAoAAABg7vi5tkl13uj80ksvTR07dkwtW7ZMG2ywQRo2bNhP3v7rr79OhxxySFpqqaVSixYt0iqrrJIefPDBwvYXAAAAgDlXp5VSt956a+rXr18aPHhwDqQGDhyYevTokd5555205JJLznD7KVOmpK233jp/7Y477kjt27dPH330UWrTpk2d7D8AAAAANdOkFC3R60gEUeutt14aNGhQxcp4MefwsMMOSyeccMIMt4/w6rzzzktvv/12atasWY3nNbZu3TpNmDDB9D0AAACAWjar2UudTd+LqqcRI0akrbba6v92Zr758vXnn3++2vvcd999aaONNsrT99q2bZs6d+6czj777DR16tSZfp/JkyfnJ6PyBQAAAIC6VWeh1Pjx43OYFOFSZXF99OjR1d7nv//9b562F/eLPlJ//vOf0wUXXJDOPPPMmX6fAQMG5HSufIlKLAAAAADqVp03Op8dMb0v+kldccUVqWvXrqlnz57ppJNOytP6ZubEE0/M5WLly8cff1zoPgMAAAAwDzU6X3zxxVPTpk3TmDFjqmyP6+3atav2PrHiXvSSivuVrb766rmyKqYDNm/efIb7xAp9cQEAAABg3lFnlVIRIEW109ChQ6tUQsX16BtVnU022SS99957+XZl//nPf3JYVV0gBQAAAMC8qU6n7/Xr1y9deeWV6e9//3t666230kEHHZQmTZqU+vTpk7/eq1evPP2uLL7+5ZdfpiOOOCKHUf/85z9zo/NofA4AAABA/VFn0/dC9IQaN25cOuWUU/IUvHXWWScNGTKkovn5qFGj8op8ZdGk/OGHH05HHXVUWmuttVL79u1zQHX88cfX4U8BAAAAwOxqUiqVSqkRmThxYl6FL5qet2rVqq53BwAAAKBRZi/1avU9AAAAABoGoRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFC4+Yv/lg1b12Ovr+tdgFox4rxenkkAAADmGpVSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAADTOUOrSSy9NHTt2TC1btkwbbLBBGjZs2Exve91116UmTZpUucT9AAAAAKg/6jyUuvXWW1O/fv1S//7908svv5zWXnvt1KNHjzR27NiZ3qdVq1bp888/r7h89NFHhe4zAAAAAPU8lLrwwgtT3759U58+fVKnTp3S4MGD04ILLpiuueaamd4nqqPatWtXcWnbtm2h+wwAAABAPQ6lpkyZkkaMGJG22mqr/9uh+ebL159//vmZ3u/bb79Nyy23XOrQoUPaeeed0xtvvDHT206ePDlNnDixygUAAACARhxKjR8/Pk2dOnWGSqe4Pnr06Grvs+qqq+YqqnvvvTfdeOONadq0aWnjjTdOn3zySbW3HzBgQGrdunXFJYIsAAAAABr59L3ZtdFGG6VevXqlddZZJ3Xv3j3dddddaYkllkiXX355tbc/8cQT04QJEyouH3/8ceH7DAAAAEBV86c6tPjii6emTZumMWPGVNke16NX1Kxo1qxZ6tKlS3rvvfeq/XqLFi3yBQAAAIB5R51WSjVv3jx17do1DR06tGJbTMeL61ERNSti+t9rr72Wllpqqbm4pwAAAAA0mEqp0K9fv9S7d+/UrVu3tP7666eBAwemSZMm5dX4QkzVa9++fe4NFU4//fS04YYbppVWWil9/fXX6bzzzksfffRR2n///ev4JwEAAACg3oRSPXv2TOPGjUunnHJKbm4evaKGDBlS0fx81KhReUW+sq+++ir17ds333aRRRbJlVbPPfdc6tSpUx3+FAAAAADMjialUqmUGpGJEyfmVfii6XmrVq1q/fG7Hnt9rT8m1IUR5/XyxAMAADDXspd6t/oeAAAAAPWfUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAAGicodSll16aOnbsmFq2bJk22GCDNGzYsFm63y233JKaNGmSdtlll7m+jwAAAAA0oFDq1ltvTf369Uv9+/dPL7/8clp77bVTjx490tixY3/yfh9++GE65phj0mabbVbYvgIAAADQQEKpCy+8MPXt2zf16dMnderUKQ0ePDgtuOCC6ZprrpnpfaZOnZr22WefdNppp6UVVlih0P0FAAAAoJ6HUlOmTEkjRoxIW2211f/t0Hzz5evPP//8TO93+umnpyWXXDLtt99+P/s9Jk+enCZOnFjlAgAAAEAjDqXGjx+fq57atm1bZXtcHz16dLX3eeaZZ9LVV1+drrzyyln6HgMGDEitW7euuHTo0KFW9h0AAACAejx9b3Z888036fe//30OpBZffPFZus+JJ56YJkyYUHH5+OOP5/p+AgAAAPDT5k91KIKlpk2bpjFjxlTZHtfbtWs3w+3ff//93OB8xx13rNg2bdq0/P/5558/vfPOO2nFFVescp8WLVrkCwAAAADzjjqtlGrevHnq2rVrGjp0aJWQKa5vtNFGM9x+tdVWS6+99loaOXJkxWWnnXZKv/rVr/K/Tc0DAAAAqB/qtFIq9OvXL/Xu3Tt169Ytrb/++mngwIFp0qRJeTW+0KtXr9S+ffvcG6ply5apc+fOVe7fpk2b/P/ptwMAAAAw76rzUKpnz55p3Lhx6ZRTTsnNzddZZ500ZMiQiubno0aNyivyAQAAANBwNCmVSqXUiEycODGvwhdNz1u1alXrj9/12Otr/TGhLow4r5cnHgAAgLmWvShBAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKD+hFI33HBD2mSTTdLSSy+dPvroo7xt4MCB6d57763N/QMAAACgAapRKHXZZZelfv36pV//+tfp66+/TlOnTs3b27Rpk4MpAAAAAKj1UOqSSy5JV155ZTrppJNS06ZNK7Z369YtvfbaazV5SAAAAAAakRqFUh988EHq0qXLDNtbtGiRJk2aVBv7BQAAAEADVqNQavnll08jR46cYfuQIUPS6quvXhv7BQAAAEADNn9N7hT9pA455JD0/fffp1KplIYNG5ZuvvnmNGDAgHTVVVfV/l4CAAAA0KDUKJTaf//90wILLJBOPvnk9N1336W99947r8L317/+Nf32t7+t/b0EAAAAoEGpUSgV9tlnn3yJUOrbb79NSy65ZO3uGQAAAAAN1vw1bXT+448/ppVXXjktuOCC+RLefffd1KxZs9SxY8fa3k8AAAAAGnuj83333Tc999xzM2x/8cUX89cAAAAAoNZDqVdeeSVtsskmM2zfcMMNq12VDwAAAADmOJRq0qRJ+uabb2bYPmHChDR16tSaPCQAAAAAjUiNQqnNN988DRgwoEoAFf+ObZtuumlt7h8AAAAADVCNGp2fc845OZhaddVV02abbZa3Pf3002nixInpscceq+19BAAAAKCBqVGlVKdOndKrr76a9txzzzR27Ng8la9Xr17p7bffTp07d679vQQAAACgQalRpVRYeuml09lnn127ewMAAABAo1DjUOrrr79Ow4YNy5VS06ZNq/K1qJoCAAAAgFoNpe6///60zz77pG+//Ta1atUqr8ZXFv8WSgEAAABQ6z2ljj766PSHP/whh1JRMfXVV19VXL788suaPCQAAAAAjUiNQqlPP/00HX744WnBBRes/T0CAAAAoMGrUSjVo0ePNHz48NrfGwAAAAAahRr1lNp+++3Tsccem95888205pprpmbNmlX5+k477VRb+wcAAABAA1SjUKpv3775/6effvoMX4tG51OnTp3zPQMAAACgwapRKDVt2rTa3xMAAAAAGo0a9ZQCAAAAgMIrpcKkSZPSk08+mUaNGpWmTJlS5WuxMh8AAAAA1Goo9corr6Rf//rX6bvvvsvh1KKLLprGjx+fFlxwwbTkkksKpQAAAACo/el7Rx11VNpxxx3TV199lRZYYIH0wgsvpI8++ih17do1nX/++TV5SAAAAAAakRqFUiNHjkxHH310mm+++VLTpk3T5MmTU4cOHdK5556b/vSnP9X+XgIAAADQoNQolGrWrFkOpEJM14u+UqF169bp448/rt09BAAAAKDBqVFPqS5duqSXXnoprbzyyql79+7plFNOyT2lbrjhhtS5c+fa30sAAAAAGpQaVUqdffbZaamllsr/Puuss9IiiyySDjrooDRu3Lh0+eWX1/Y+AgAAANDA1KhSqlu3bhX/jul7Q4YMqc19AgAAAKCBq1Gl1BZbbJG+/vrrGbZPnDgxfw0AAAAAaj2UeuKJJ9KUKVNm2P7999+np59+uiYPCQAAAEAjMluh1Kuvvpov4c0336y4HpdXXnklXX311al9+/azvROXXnpp6tixY2rZsmXaYIMN0rBhw2Z627vuuitPH2zTpk1aaKGF0jrrrJMbrAMAAADQQHtKRQDUpEmTfKlumt4CCyyQLrnkktnagVtvvTX169cvDR48OAdSAwcOTD169EjvvPNO7lc1vUUXXTSddNJJabXVVkvNmzdPDzzwQOrTp0++bdwPAAAAgHlfk1KpVJrVG3/00Ucpbr7CCivkaqYlllii4msREEUw1LRp09nagQii1ltvvTRo0KB8fdq0aalDhw7psMMOSyeccMIsPca6666btt9++3TGGWf87G2j71Xr1q3ThAkTUqtWrVJt63rs9bX+mFAXRpzXyxMPAADAbJvV7GW2KqWWW2659MMPP6TevXunxRZbLF+fE9GXasSIEenEE0+s2DbffPOlrbbaKj3//PM/e/8IyB577LFcVXXOOefM0b4AAAAAMA83Om/WrFm6++67a+Wbjx8/Pk2dOjW1bdu2yva4Pnr06JneL5K2hRdeOFdnRYVUTBnceuutq73t5MmTc0JX+QIAAABAPVx9b+edd0733HNPqiu/+MUv0siRI9NLL72UzjrrrNyTKlYErM6AAQNyyVj5ElMDAQAAAKhbszV9r2zllVdOp59+enr22WdT165d8yp4lR1++OGz9DiLL7547kE1ZsyYKtvjert27WZ6v5jit9JKK1U0X3/rrbdy+PTLX/5yhtvG1MAIrcqiUkowBQAAAFAPQ6mrr746tWnTJveDiktlsTLfrIZSMf0uQq2hQ4emXXbZpaLReVw/9NBDZ3l/4j4xTa86LVq0yBcAAAAA6nko9cEHH9TaDkQVUzRO79atW1p//fXTwIED06RJk1KfPn3y13v16pXat2+fK6FC/D9uu+KKK+Yg6sEHH0w33HBDuuyyy2ptnwAAAACYB0Op6VfAK1dI1UTPnj3TuHHj0imnnJKbm8d0vCFDhlQ0Px81alSerlcWgdXBBx+cPvnkk7TAAguk1VZbLd144435cQAAAACoH5qUyqnSbLr++uvTeeedl9599918fZVVVknHHnts+v3vf5/mZdFTKhqexwp+rVq1qvXH73rs9bX+mFAXRpzXyxMPAADAXMtealQpdeGFF6Y///nPue/TJptskrc988wz6cADD0zjx49PRx11VE0eFgAAAIBGokah1CWXXJJ7OEW/p7KddtoprbHGGunUU08VSgEAAADwk/6vWdNs+Pzzz9PGG288w/bYFl8DAAAAgFoPpVZaaaV02223zbD91ltvTSuvvHJNHhIAAACARqRG0/dOO+20vNrdU089VdFT6tlnn01Dhw6tNqwCAAAAgDmulNptt93Siy++mBZffPF0zz335Ev8e9iwYek3v/lNTR4SAAAAgEakRpVSoWvXrunGG2+s3b0BAAAAoFGocSg1derUdPfdd6e33norX+/UqVPaeeed0/zz1/ghAQAAAGgkapQgvfHGG2mnnXZKo0ePTquuumreds4556Qlllgi3X///alz5861vZ8AAAAANPaeUvvvv39aY4010ieffJJefvnlfPn444/TWmutlQ444IDa30sAAAAAGpQaVUqNHDkyDR8+PC2yyCIV2+LfZ511VlpvvfVqc/8AAAAAaIBqVCm1yiqrpDFjxsywfezYsWmllVaqjf0CAAAAoAGrUSg1YMCAdPjhh6c77rgjT+GLS/z7yCOPzL2lJk6cWHEBAAAAgFqZvrfDDjvk/++5556pSZMm+d+lUin/f8cdd6y4Hl+LVfoAAAAAYI5Dqccff7wmdwMAAACAmodS3bt3r8ndAAAAAKDmoVT4/vvv06uvvpqbm0+bNq3K13baaaeaPiwAAAAAjUCNQqkhQ4akXr16pfHjx8/wNX2kAAAAAJgrq+8ddthhaY899kiff/55rpKqfNHYHAAAAIC5EkqNGTMm9evXL7Vt27YmdwcAAACgkatRKLX77runJ554ovb3BgAAAIBGoUY9pQYNGpSn7z399NNpzTXXTM2aNavy9cMPP7y29g8AAACABqhGodTNN9+cHnnkkdSyZctcMRXNzcvi30IpAAAAAGo9lDrppJPSaaedlk444YQ033w1mgEIAAAAQCNWo0RpypQpqWfPngIpAAAAAIoLpXr37p1uvfXWmn1HAAAAABq9Gk3fmzp1ajr33HPTww8/nNZaa60ZGp1feOGFjf6JBQAAAKCWQ6nXXnstdenSJf/79ddfr8lDAAAAANCI1SiUevzxx2t/TwAAAABoNGYrlNp1111/9jZNmjRJd95555zsEwAAAAAN3GyFUq1bt557ewIAAABAozFbodS111479/YEAAAAgEZjvrreAQAAAAAaH6EUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQuPmL/5YAta/rsdd7WmkQRpzXq653AQAACqFSCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDGGUpdeumlqWPHjqlly5Zpgw02SMOGDZvpba+88sq02WabpUUWWSRfttpqq5+8PQAAAADznjoPpW699dbUr1+/1L9///Tyyy+ntddeO/Xo0SONHTu22ts/8cQTaa+99kqPP/54ev7551OHDh3SNttskz799NPC9x0AAACAehpKXXjhhalv376pT58+qVOnTmnw4MFpwQUXTNdcc021t//HP/6RDj744LTOOuuk1VZbLV111VVp2rRpaejQoYXvOwAAAAD1MJSaMmVKGjFiRJ6CV7FD882Xr0cV1Kz47rvv0g8//JAWXXTRubinAAAAANSm+VMdGj9+fJo6dWpq27Ztle1x/e23356lxzj++OPT0ksvXSXYqmzy5Mn5UjZx4sQ53GsAAAAA6v30vTnxl7/8Jd1yyy3p7rvvzk3SqzNgwIDUunXrikv0oAIAAACgEYdSiy++eGratGkaM2ZMle1xvV27dj953/PPPz+HUo888khaa621Znq7E088MU2YMKHi8vHHH9fa/gMAAABQD0Op5s2bp65du1ZpUl5uWr7RRhvN9H7nnntuOuOMM9KQIUNSt27dfvJ7tGjRIrVq1arKBQAAAIBG3FMq9OvXL/Xu3TuHS+uvv34aOHBgmjRpUl6NL/Tq1Su1b98+T8ML55xzTjrllFPSTTfdlDp27JhGjx6dty+88ML5AgAAAMC8r85DqZ49e6Zx48bloCkCpnXWWSdXQJWbn48aNSqvyFd22WWX5VX7dt999yqP079//3TqqacWvv8AAAAA1MNQKhx66KH5Up0nnniiyvUPP/ywoL0CAAAAYG6p16vvAQAAAFA/CaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKNz8xX9LAKAh6Xrs9XW9C1ArRpzXyzMJAAVSKQUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAADS+UOrSSy9NHTt2TC1btkwbbLBBGjZs2Exv+8Ybb6Tddtst375JkyZp4MCBhe4rAAAAAA0glLr11ltTv379Uv/+/dPLL7+c1l577dSjR480duzYam//3XffpRVWWCH95S9/Se3atSt8fwEAAABoAKHUhRdemPr27Zv69OmTOnXqlAYPHpwWXHDBdM0111R7+/XWWy+dd9556be//W1q0aJF4fsLAAAAQD0PpaZMmZJGjBiRttpqq//bmfnmy9eff/75utotAAAAAAowf6oj48ePT1OnTk1t27atsj2uv/3227X2fSZPnpwvZRMnTqy1xwYAAACgnjY6n9sGDBiQWrduXXHp0KFDXe8SAAAAQKNXZ6HU4osvnpo2bZrGjBlTZXtcr80m5ieeeGKaMGFCxeXjjz+utccGAAAAoJ6FUs2bN09du3ZNQ4cOrdg2bdq0fH2jjTaqte8TDdFbtWpV5QIAAABAI+0pFfr165d69+6dunXrltZff/00cODANGnSpLwaX+jVq1dq3759noJXbo7+5ptvVvz7008/TSNHjkwLL7xwWmmlleryRwEAAACgvoRSPXv2TOPGjUunnHJKGj16dFpnnXXSkCFDKpqfjxo1Kq/IV/bZZ5+lLl26VFw///zz86V79+7piSeeqJOfAQAAAIB6FkqFQw89NF+qM33Q1LFjx1QqlQraMwAAAADmlga/+h4AAAAA8x6hFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFm7/4bwkAANSGrsde74mkQRhxXq+63gWgDqiUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAAGmcodemll6aOHTumli1bpg022CANGzbsJ29/++23p9VWWy3ffs0110wPPvhgYfsKAAAAQAMIpW699dbUr1+/1L9///Tyyy+ntddeO/Xo0SONHTu22ts/99xzaa+99kr77bdfeuWVV9Iuu+ySL6+//nrh+w4AAABAPQ2lLrzwwtS3b9/Up0+f1KlTpzR48OC04IILpmuuuaba2//1r39N2267bTr22GPT6quvns4444y07rrrpkGDBhW+7wAAAADUzPypDk2ZMiWNGDEinXjiiRXb5ptvvrTVVlul559/vtr7xPaorKosKqvuueeeam8/efLkfCmbMGFC/v/EiRPT3DB18v/myuNC0ebWGJlbjD0aivo29oLxR0Nh/IHxNzs2P/lmbxkahKfO3GuuHVNLpdK8G0qNHz8+TZ06NbVt27bK9rj+9ttvV3uf0aNHV3v72F6dAQMGpNNOO22G7R06dJijfYeGrvUlB9b1LkCjZOyB8QeNkeMfNMzx980336TWrVvPm6FUEaIKq3Jl1bRp09KXX36ZFltssdSkSZM63TdqlrZGoPjxxx+nVq1aeQqhQMYf1B3jD4w9aGwc++q3qJCKQGrppZf+ydvVaSi1+OKLp6ZNm6YxY8ZU2R7X27VrV+19Yvvs3L5Fixb5UlmbNm3meN+pWxFICaXA+IPGxvEPjD1obBz76q+fqpCaJxqdN2/ePHXt2jUNHTq0SiVTXN9oo42qvU9sr3z78Oijj8709gAAAADMe+p8+l5Mrevdu3fq1q1bWn/99dPAgQPTpEmT8mp8oVevXql9+/a5N1Q44ogjUvfu3dMFF1yQtt9++3TLLbek4cOHpyuuuKKOfxIAAAAA6k0o1bNnzzRu3Lh0yimn5Gbl66yzThoyZEhFM/NRo0blFfnKNt5443TTTTelk08+Of3pT39KK6+8cl55r3PnznX4U1CUmIrZv3//GaZkAsYfNGSOf2DsQWPj2Nc4NCn93Pp8AAAAAFDL6rSnFAAAAACNk1AKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpaAGSqWS5w2AestxDOqnadOm1fUuANSqJiV/lcAs/QEw33wyXJgXDB06ND377LNp4sSJaauttkrbbrttXe8S1FvxZ2CTJk3qejeAnzFq1Ki07LLL5n/7uxSKNXz48PTOO+/k4+Waa66ZL9QeoRT8jMoH/ltvvTW9+eabqWnTpmnDDTdM22yzjecPCnTVVVelP/3pT3n8RSj1n//8J9111135OvDzbrzxxvTf//43jRkzJu2///6pS5cunjaYx910003pkEMOSQMGDEgHHnhg3iaYgmJcc801qX///qlt27bpyy+/TEsuuWS68MIL08Ybb+wlqCVKP+DnBsn/H0gdd9xx6dhjj82h1EcffZSrM6699lrPHxTktttuS8cff3z629/+lu677750xRVXpHbt2qlihFkUx7EIdd966600YcKE1LVr13wc++GHHzyHMA9XB8exb5VVVkmDBg1Kl19+ecXfp6bywdx1zz33pGOOOSadf/756emnn84B8WKLLZYee+yx/HWTzmqHUApmYurUqRX/jhPg+CUUJ8W33357RYWUX0RQjC+++CJdffXV+aR69913z9viD/QWLVqkyy67LPXq1StdeeWVXg74iT+s//GPf6R777033XzzzRXVFgsttFBq1qxZ/rdjGsxb/ve//6U777wzfxB6wQUXpK233jpddNFFgikowPjx4/Nx8+CDD049e/ZMCyywQK7M79SpUz6mRihs+nvtEErBdMqfQMUUvR9//DH/+4MPPkibbLJJ/kUUU4ViysPgwYPTH/7whzyF6PXXX/c8wlwUn0qdffbZaaeddqrYtssuu6QPP/wwtWzZMi2yyCLpj3/8YzrvvPO8DlCNsWPHpi222CJP14up6Nttt12uOtxzzz1z1VT88R1/XAumYN4RJ8GHHXZY/jBm0003zVP4IqCaPpgybqH2xdhaY401Uvfu3fP1cmVit27dKm5j7NWO+WvpcaBBiFLMgw46KI0cOTJXX8w///8bIgsuuGD+9y233JL69u2bT3wPOOCA/LVHH300Pfnkk+nUU09Niy66aB3/BNBwxVSjsviE6vvvv8+l1CuttFLe1qpVqzxu99tvvxxS+fQK/k/0wfj888/T/fffn49f5557bkW1VBzbXnzxxXTppZfmk2Bg3hAnvKuvvnq+hDjeRTAVIpgK8YFMhMrxt2i5khiYc3FeF4UIyyyzTL5e/rsyekqVixeaN2+et40YMaLK36nMHqEUVBLJdzSzi54b8YdAVEOFpZdeOr3wwgu5Suovf/lLxR/y3377bZ5StOKKK+aTYKAYUSW15ZZbpl/84hcV2yI4jj/chcPw/zzxxBNpo402ytNco7oiwtxdd901f7ASH8CESZMmpQcffDAttdRSueoQmHdU/nClvFLmyiuvnKcThYEDB+a/RWNsf/bZZ2m33XbzgQzUonIgVXk8fv311/lSDqTi+BoVx88//7znvoZM34NKotIiPmU666yz8gE+Pn0K22+/fdprr71yM9go3XzuuefSsGHD8sE/PnmOT6tMe4BilMunKwdSUTX10ksvpdVWW83LACnlpatjul6sEBRjJqYgRD/EOKGNpeXj6xFa7bHHHnnxjmig7DgG9SOgip6KMa0vphXFIjxxDHz77beNYSio73AEUvH/HXbYIbeSeOqppzz3c0ClFExn4YUXrih/PvHEE/MvnFiG/swzz0zffPNNXk47mi2vt956+aQ4wqmo0IjbRSknUMyKmCGC4vhjoF+/fumTTz7JYXLlT5ShMYpl4ydPnpybmJ988sm5kiI+bDnppJPysSqmnV9yySW5v9Tiiy+eA13HMahfYnn6qMyIKUPx/xjDMZ2o3HoCmHtjLyqLIxQePXp0XtE2Fgwx/mquSUl3Lhq5+AS58klu2VdffZVPcCOYikqpmKZXbnoevTmWWGKJXNIZ9/VLCIoXJ9dDhgzJzZpjwYHoCRd/FAiIaczOOOOMdPHFF6frr78+V0/EQhynnXZaXtI6pp/HMS8Cq9deey116NAh/3HtOAb1S/zdGWN66NCh6eWXX3ZCDAV64IEH8sI78cFOtHcRSM05oRSNWuVAKpbJ/vTTT3N1RSz7GX1pYr7w3XffnU444YT8y6e6JednFmoBs6cm1U3vvfdenrIQK4mVm076lJjGvHz8r3/967xsfPRGLIteibFIx5///Of8QUv0mKrMcQzqTk0re99///3UsWNHxz4oePy9+eab+YOfmEWjQrF2CKVotCr/EorQKZbIjuqn+GM9SjHj06dll102B1NRMRXTHmI53rgdULsqnxTHNLzyWJwdKqRo7GKK+VprrZX23Xff1L9//3ycKxfE/+53v8ur7MUf0ZUDK6D+HfsqH++EylDs+KvMh6G1Q3kHjVY5kIppDjfccEO6/fbbc3+o+GM+Pn3aZJNNcgVGmzZt0m9+85v8R/x3331X0WQZqP0/CsrTjKLHTfzR/XP3KxszZoyebjR60ecwFuX4xz/+kV555ZV8nItLjK/ll18+9ejRI/eYuvbaaxv9cwX19dgXQXM5kBo7dqxqfShw/FX+ehQxqM6vHUIpGrVx48alN954I11wwQWpW7dueY7wUUcdlRvCrrjiivkP+AioWrdunXr37p3uu+++/AtMMAW1p/xHQVQsRn+oWNVy1VVXrRIyTd/+MK6X7xcLERx++OG5Dxw0NtFP7Ysvvqi4HlPNV1pppRw+/fvf/86hVHygEr2lDjjggLwoQKzIN378+BnGFTDvH/vKH6rGsS9W4HPsg+LGX/nrMf6OOOII46+WWJ6BRi3KNPfcc8+8RParr76aT2yjEezBBx+cvxZ/wMcS89G3ZrnllpvhZBioHbEaWFR3PPTQQ2ndddfN5dDxCVSMvTjBbteuXcWnWpX/KL/iiivyHwU33XRTWmSRRbwcNCpxvIoPU+IDlnXWWScNHDgwbbjhhumQQw5Jl112Wdp8883zeCp/mhtVv7FaZTRljQ9brFAJdcuxD4w/hFI00jLNynPxt9xyy4o/DOLkN/puhFgme//9988rE8Uqe2X+iIfabywZ4zOCpwiDo6Lj5ptvzkFTjNMIm+68887c463y/S6//PJ03HHH5TArTrahMYlKqGhgHlPLI3iKCqk4Zp199tl5xdjoLfXkk0/mlbniOBZVwOE///lPHktTpkzJ4RRQHMc+qDvG37xLuQeNQuXqpsGDB6cDDzwwXXTRRemdd96puM1nn32Wnn/++XwSHM1ir7vuuvxJciyvHdt+bo4xMGsigCoHS9EPI65HL5zPP/88rxDWvXv3XNkRjZovueSSNGHChIqxWr5fjOPjjz8+n5TvuuuunnoalX/96195AY5YeOPQQw9NP/zwQ155L5amjqnm0U9q6aWXzh+yxFS9GCsxXS+m7pWbnS+00EJ1/WNAo+LYB8Yf1TN9j0ahfCIbnyCfe+65adttt81N7eJT5P322y/tuOOO+RPmu+66K1dIxRK7EWLdcccdFY9ReY4xMOcVixH4vvXWW7naaeONN86VT1El9cc//jH98pe/zBVSsfrlggsuWKWqKiqo4uQ6FiiIHgDQ2LRs2TJPM99ss83SI488kvbee+88XW+HHXZIq6yySq6iigqqWLAjRM+Z+KAlwqonnngide7cua5/BGhUHPvA+GPmmpR0uaQBm36Z3Aig9tlnn7TFFlvkHlJHHnlkXvozPmmO6Q5RtRGr8MW2WIUvenBYZh5qX1RuXH/99en888/P43GppZaq8vWYWjRp0qRc6REn1E8//XQOhmNMR5XjGmuskcNlaIyiMir6SEVwGx+qbLrppunUU0/NVb4xJX348OG5YqryKntx+zgeLrbYYnW679CYOfaB8ceMhFI0ikDqxRdfTM2bN08XX3xx/gQ5VtYL8anx0UcfXSWYqkwgBbXv4YcfTn369MmrWcaql/HZSEwt+uSTT3Lvm5h2FJWMEUTFymLPPvts7n0Tzc8jKJ6+JwA0VvFBSoS6f/7zn1PPnj3zFL44psXS1rE4hwpfmHc49oHxR/VM36PB95CKaT433nhj/gQ5QqauXbvmACp06dIl99s49thj80lwq1at8nSIMn/QQ+2L5emXX375PM0oKhajiXlMxYugqVOnTrmCKsZhBFDxqXL8vxxIBYEU/D9xzJo8eXKuiIpjXPSY+vLLL/P4inHigxWYdzj2gfFH9VRK0eBUrqJ47bXXUq9evdKgQYNyxUUsHx/NlKMXR2wve+mll/JJcSynXXm6H1A747HyuHzsscfSVlttlacdPffcc7lCMZqbR8XiiSeemE+sY1n7MifWMPNq4FgEIFbei8UCFl100fTPf/4zVxaqKIS649gHxh+zTihFg3X11Venhx56KLVv3z799a9/zduiifJf/vKX9MEHH+QV+H7/+9//bB8qoGYqj6Vvv/02LbDAAjmYim333ntvnlYb1Yq/+tWv8gID0dQ8GpxH9WJMSQJmbYxFtVSMsQilYoxVriwEiuXYB3XH+KufhFI0SNEYOSou7r777lxxESfAZRFMnXPOOWnUqFFpr732yuEUMPf+KIgKxEcffTQ3L49Vv84666y8ol6cSEd1VDRt/v7773NPnKhojFUxTZ2F2R9r1V0HiuPYB3XH+Ku//NVCgxQrEkWj11hpL6YKXXLJJRVfi5Pi6FOz8MILp3//+991up/Q0Hu6RTg8YMCAvCJYTNmL5et32WWX3JA5AqlYYe/ss8/OS9nH6mCPP/54DqRiyh7w8yr3WIvgVyAFdcOxD+qO8Ve/qe2mwVpppZXSYYcdlv996aWX5hPd6CVVDqYiqOrYsWO+rvcGzLlotBx9bconyXfddVdeYS8uG2ywQa5Y/Oijj3Ij5piyFwHUQgstlNZbb718Mh2LDUzf1ByYucrHrqhIHD58eLruuuuMHyiQYx/UHeOvYVApRYMWKxBFEPXrX/86h1CDBw+u+NoKK6yQP1GOUk+recGc2XXXXfMqll988UXFthhb0YA5AqkHHngg/eEPf8g93WIcxiIEO++8c66UivEZU/oiiIoKKYEUzF4gFYt49O/fP48l4weK49gHdcf4a0BK0Ai89957paOPPrrUpk2b0l133VXXuwMNztVXX12ab775Sscff3xp7NixFds//vjj0jfffFPaeOONS2eeeWbeNm7cuFKnTp1KTZo0Ke27775527Rp0+ps36G+qTxeBg8eXGrVqlXpzjvvrNN9gsbIsQ+MP+ac+RHUS7PbyHXFFVdMffv2Tcstt1yu3ABqt2IjqqCiefnee++drx999NFpySWXTMsss0x644038rS9rbfeOt8+mpqvtdZaadCgQal79+55m2pFmPXxVh4vl19+eTruuOPSNddckz8xBorj2Ad1x/hrWIRS1EvlQGrkyJFpzTXXzNd/7qR21VVXzZdgdSKoHZXH0m9/+9vcD6pXr175ekznW3zxxdNSSy2VFx8499xz05FHHplOP/303OMtAqm4b0zZs9oejdUzzzyTV6CMyzbbbDPLgVSEurGQQPSQ2m233QraWyA49kHdMf4aHqEU9Vb0qInV9UaNGpVat249y7+8vvrqq3yCDMyZyuPqhRdeSGuvvXb63e9+l7fF/0NUcSy22GL5/+ecc04OrDp06JD+9a9/VfR0E0jRWEWodPvtt+cFAj799NO02Wab5dUqV1lllZ8MpP72t7+lI444It1yyy0CKSiYYx/UHeOvgaqFKYBQiOl7zgwfPry0wgorlL788stZvt9ll11WOvLII0sTJ06ca/sJjUHlcXXSSSeVunbtWrr00ktLP/74Y95200035Z5RxxxzTGnSpEl5W4y7N954ozR16tR8/YcffqijvYe699e//rW0xBJLlF566aV8/eKLL85j5tlnn52lHlK33357ofsLOPZBXfK3Z8OlUop6ofInxOXl4ldeeeX03Xff5QqN7bbbrtppedOvThSfLP/jH//In0oDNVceV6eddlpeTe+uu+7K02Oj6inG3V577ZW/HtWMMSbLPaY6depUMVatEkZjFr3Wjj/++NStW7d02223pVNOOSVXQG288ca571rLli3z7SpPb63cQ8qUPSieYx/UHeOv4RJKUa9+CcVy8m+++WZabbXV8h/sHTt2zEvKl80skCr/IX/zzTdrBgu1JKYbPfzww+nSSy9Nm2++ecX2OImOwKlyMBWLDBx88MHVjlVobKZMmZJefPHFtO6666bnn38+7bfffum8885LBx54YP7gJQKqjTbaKP3mN7+pCKSuvPJKgRTMAxz7wPijdgmlqFe++OKL3J8mqjIilIo/6qO5cvzhvsACC6TOnTvnfjVbbLFFatu2bUUgFZ9GW50IaldUc0S1R4y9yiKQiirGOJmOYGrRRRdNW265paefRq/c07B58+apT58+uYL39ddfz9WGcT18++236dVXX63S+zACqT/+8Y/pzjvvzMc7oO449oHxR+3yUTXzrJjeM734JPmiiy5KTz31VF6xKCow2rVrl375y1/mP/YjrIo/3pdYYokqFVJXX321qQ4wB6LysPz/8r9DrKz3+eefV9kWnn766dzYPFYU69GjRw6qogIEGqsbb7wxdenSJb3zzjv5ekzbi0A3qqViFdnwySef5OPahAkT8rGrLI5zAikonmMf1B3jr/FQKcU8qXJvqPhDPkql47L//vun5ZdfvqInVExvGDNmTDrkkEPyZfppe6NHj07XXnutKXtQS+Nx8uTJqVmzZrkKasUVV0ybbrppOumkk9Lqq6+eVw6LsRdVUjGlLyo9KveN0kOKxio+MImgKVaL7du3bz4ubbLJJqlfv37p4osvTjvuuGNq06ZNDqmiiipC3Rhj5X5S8XWgWI59UHeMv8alSXQ7r+udgJmJT4pvuOGGtP3226d33303f4ocfWmiYXmc4D7xxBM5cBo5cmRq3759Re+Nyo1hgdoRS9UPGTIkj70IoS688MJ8Ar3nnnumRx55JO2xxx75pDrG45dffpleeeWVHGBVDoqhsTnhhBPyceyYY45J77//fu7DFuPm/vvvTyussEKunIrtcYyLoDcW7ojjV3lRD6BuOfaB8cfcJZRinv5k+cgjj8x/uK+99trpX//6V9pmm22qTGF477338nSIl19+Oa/GB8ydT6nOP//8dMYZZ+RAOKql4iR76aWXzosHxNg766yz0ltvvZWn0cZCBDF1rzxlz4k1jVUszBH91KJ3VLna6YMPPsjTyaPZ+T333JNWWmmlGe7ngxWoO459YPxRLKEU86xo/Proo4/mEOqmm25KBx10UF59L/4fK+7FtL1WrVrlT6Gjd5TKKJg7YipRVEjFUvVRtRiiEipW3IuptLF6WOU+cOUgSyBFYzdixIi09dZbp+eeey6HteWT3WhuHuMnPnC56qqrcoVU5RNhoO459oHxRzH89cM829Q8ekhF0PTSSy/lZbLLgVS45ZZb0t///vc8VSj+oC/33gBq1+OPP55XuPzb3/6WFlxwwbwtKjxiRb0IqmLK0WWXXZa3xxS98kl1TNlTIUVjt9Zaa6WFF144r/4ayuNjmWWWyRWGMcU1pr2Wv6ajAswbHPvA+KM4QinqXOVPh5988sl8khv23nvvvMreBhtskJsmlwOpWIo3pvZFpdRCCy1U8TgqpaD2xcnzXnvtlYOo6BsVoh9OhMDRmDkWHpg4cWLeXrlvlB5SNFYx1Tym5d199925p1r0QYxj2wUXXFBxmxYtWqRVVlklPfDAA2ncuHHpT3/6U95u3MC8wbEPjD+Ko4MmdSo+FS4HUieeeGJuABt/wMcy88suu2xudD5w4MDcqyaanH/44Yfp7LPPzlVU9957b8Vj+EMe5o6o5og+UiH6SMWKejEuIwSOyqlYaa+6SkdojOI4FuNkySWXzMet/fbbL/dAjA9Rolrq2WefzdNg4/gVvdm6deuWOnXqlMaPH1/Xuw5U4tgHdcf4a3yEUtSpcpjUv3//PA3vjjvuSOutt17FNKH4gz5E0+RoFBsr7MVl+PDheWqQZrAw93Xo0CEddthhebxGQ/NYWCDGYYTEUUF17LHHehlo9M4999w8rTyqpNZff/00aNCgdPjhh6f//e9/qVevXnlbTIONxTvatm2beyVG1WFUUy222GL5+fMhC8w7HPvA+KMYGp1T52Ip7N133z33jOrRo0caO3ZsPtmNT5LLjZUnTJiQVzGKP+Q7duyYq6s0UYZijRo1Kl188cXp+uuvz0vZn3LKKenXv/51/pqAmMbss88+y1Pwtttuu9SzZ888xXz//fdPhxxySB4zO+ywQ67yXW655arcLwLdqKyKhspWkIV5k2MfGH/MXSqlqHMxHSgCptdeey21bt06N00eOXJk/gR5wIAB6eqrr059+vRJG220UcV9YrqQJspQrJhSe+ihh+ape9Hk/D//+U9FKGUKLY1ZNP7feeed069+9atcyXv00UenU089NVdKRe+1CJ9i2nkEUFF98e9//zsf26Kn1EMPPSSQgnmYYx8Yf8xdGp1T5+IEt3v37unGG29Mm222WQ6pompq2LBhaccdd0xvv/32DPexbDbUjahUPOCAA3JV45VXXpmn8xmTNHYtW7bM1VARQEWj8zXWWCP17t27oqn57373u7xabEx7DWuvvXbaaaedcgP0Ll261PHeAz/HsQ/qjvHX8Jm+xzwhVh+KRrBRMbXOOutUbI/pe9EkVs8amLdWy4zpSjEdacSIEemf//xnrhSBxqzcD+oPf/hDXkU2xkVU/O6xxx45lIppfeGHH37IfaSA+sOxD4w/5h6hFPOcWM3rv//9bw6iRo8enV566SVT9WAeOekOceIdzc5jfMbCBEsssURd7xrMM1544YW0+eabp1VXXTWvsBdVVDFeTDmH+smxD4w/5i7T95jnPom65ZZb0sknn5z/mI8pfOVV9oC6rwKJS6wqtummm6all15aIAXT2XDDDXMwFT2motl5OZCKSmCgfnHsA+OPuU+lFPOcmBYUTc+33nprq+zBPKDyMvWXX355OvHEE/P/Y1oS8POsFgv1j2MfGH8UQyhFvZnHDxQ/jqb/o/y4445L11xzTdptt928HADM0xz7wPhj3udMn3nuj4eymP4QJ8QCKZjzcVUeR7Ek/dNPP52bLc9uIHXMMccIpACoFxz7wPijfhBKMc+oHEANHjw4r1T0xhtv1PVuQYMZVyeccEKudHr11VfzggKzGkhde+216S9/+Uu67rrrVEgBMM9z7APjj/pj/rreAZjZFKE4Ee7cubMnCOZAeVydd955ucrp4YcfTmuttVZq2rRplXFXWeXtERDHSpg333xz2mGHHbwWAMzzHPvA+KP+UCnFXJl6N6eBlIoMqD1TpkxJw4cPz9PvunTpkj755JN0//33p+222y43LX/qqadmOh6PP/74PB4FUgDUJ459YPxRP6iUotaUpwhdeumlqWPHjmn77bevcRPlXXfd1SsDtdTYNa6///77ecpehw4d0o033pi+//77tPDCC6eHHnooffnll2njjTfOt42l66cfj5qaAzCvc+wD44/6SaUUtSYCpq+//jqdccYZ6dtvv52tQGrQoEF5ipATYKi9P8ojfBo2bFhq2bJlDpneeeedHDStt9566fTTT0/33ntv2n333XPlVEznKwdSMWUvKqSMRwDqA8c+MP6ov1RKUSt/BJT/36ZNm3zC+9577+Wvz0rPmjhZjkDqtttuSzvuuKNXBGqpsWustHf44YenVVZZJXXt2jVP4YvAeOmll863mTp1anrmmWfScsstl8dj3D8WF/jzn/+crr76ahVSAMzzHPvA+KN+a1KK3+Qwh15//fW0xhpr5BPbPn36pC+++CLdd999M9yu/HarbsqeKUJQO2KlvAsuuCA3NV9zzTVTs2bNqnyKHNP4Hn300XTVVVelDz/8ML388sv5NmUx1W/FFVf0cgBQbzj2gfFH/aRSijkW1RgHH3xwat++fWrRokWuuvj8889z5dNmm22Wty266KL5tpWrpgRSUPsmTZqUXnzxxTyNdt11100fffRRrn667LLL0vrrr5+rERdYYIE8tS+m65UDqR9//DGHVnERSAFQnzj2gfFH/aVSitk2/ZS86FMT/WheffXV9NZbb+XLTTfdlCunRo8eXRFU7bvvvqlv374VzdBPPvnkXKmhQgpqd7WhmEK70korpQMOOCD3a5swYUKeWvvSSy+lffbZJ51//vlp1KhRaZlllskhVARS5X5SAFDfOPaB8Uf9JZRitlSeAjR+/Ph8IhvXW7VqVRFWvfDCC2nnnXdOjz/+eJo4cWKeChQ9pk466aR8+2effTbtueee6aKLLsr/B2pnpaGyxx57LO233365f9Qf//jHtM0226TNN988nXLKKWnkyJHprrvuqgihZvYYADAvcuwD44+GxUfj1KiR5JlnnpmeeuqpHDhtuOGGOVyKICpED5vFF188T+Hbcsst89crW3LJJdODDz6Y1l57bc8+1MIf5TEWP/3009zQfNlll01bbLFFeu2119KXX36Zr5dvH4HxqquuWqUqSiAFQH3h2AfGHw2PSilmW6zMFf1prrzyytS8efN04YUXpldeeSU3O49VvWJFr3XWWSftuuuu6bTTTvMMw1ycQhur7N188815LMZU2W7duqUjjzwyj8HwzTff5OrEmMYX/aVirEYoNbOVMQFgXuTYB8YfDZM5G8yW6EPzr3/9Kzcx/81vfpNPbmOZ+XPOOScHUjGnP/pLrbDCCnmFL2Du/VF+3nnn5YblcXn33XfTdtttl+6+++4cHEf4FN58883097//Pd8nmprHmI0eUgIpAOoLxz4w/mi4VEoxW/P2//Of/6Rf/vKX+YR32LBhae+9984nxgceeGD6/vvv88nv7rvvnlf72njjjTVPhlpy8cUXp8MPP7xiXI4ZMyb3i9pjjz3S73//+/TPf/4zj8eYShtjM5qYx9js1KlT7ukWQbGm5gDUJ459YPzRCJRgJqZOnVrx77vuuqv04YcflsaNG1faYostSqeeemqpdevWpcsuu6ziNiNHjiztuuuupSeffLJi248//uj5hTk0fPjwUpMmTUq9e/eusv2pp54qff755/nryyyzTGnQoEF5+wknnFBaaKGFShtuuGHpzTffrHZMA8C8zLEPjD8aB9P3mFlYWVEh9ac//Skddthh6b777ssNzGOp+egVFcvNR4VUmDRpUr5dTNnbdNNNKx4npvIBc6ZLly7pgQceyGOwd+/eFds32GCD1K5du3T//fen9dZbL/Xt2zdvb9u2bVp//fXT1ltvnRubl2lqDkB94dgHxh+Ng9X3qFa538wZZ5yRG5rHanmxsle4/PLLcwh1/fXXp//97395il4sMz9+/PjcsyZOfC0zD7UbEG+77bbphhtuyFP0evXqlcdfNDcPMXX2k08+SZ999lnq2LFjXo0vptEedNBBeSwbjwDUJ459YPzReOgpxUzFcvI9e/ZM++67b9pnn33ykvPRUypW+tp8883Tww8/nFfai2Aq+tZE9VS5iXLlJeeBmqkcJsUiAhFCRcVUjMdddtkl93ALMSYvuuiiHBZHdeIPP/yQXnvtNavsAVDvOPaB8UfjIpRipr766qvUuXPn1KdPn7TNNtukv/3tb+mDDz7Ifyx8/vnn6eSTT87T9yqviBIhlSl7ULt/lA8ePDh9++23aa+99krt27fPTc0jmNpxxx1z9VSIFTFjfMYU2lh9L4Jh4xGA+sSxD4w/Gh+hFD/p6quvTscee2w+uY0AKnrUbLXVVul3v/tdDp/KlRrA3HHcccel6667Lq+kt8UWW6QOHTrkP9ofeuihHEztsMMO6cYbb5zhfgIpAOorxz4w/mg8hFL8rFGjRqXJkyenlVdeOV+PE+KonNpwww3TmWee6RmEueSOO+5IRx11VLrzzjtz4/LpQ6chQ4ak3//+92mTTTbJzc4BoL5z7APjj8bF6nv8rGWXXTYHUjF96Jlnnkk777xzGjt2bDr11FM9ezAXvfXWW3n1vFiBqCxC4RCVittvv3264oorcmhc3g4A9ZljHxh/NC5CKWZJ9I0aPnx4Ouecc3IT5REjRlT0rAHmXHWh0ujRo/N4a9asWR6D5V4bMe5iRcxYfCBW2XvkkUcqVr0EgPrCsQ+MPxBKMUuikflGG22UTj/99HwyHCfJscqepuZQu41d33///fTZZ5/l4Ckamz/99NN5KkOMwfJtvv7663Tttdeml156qcrjlL8OAPM6xz4w/iDoKcUc/yEB1Fzl1StPOOGEdO+996Zx48alNdZYI+26665p0qRJ6YwzzkiXXHJJ+tWvfpWn6h1zzDFpzJgxadiwYYJhAOodxz4w/qBMKAUwD4S7t9xyS25qPnjw4FwJ9eabb6aLL7447b///mmttdZKRx55ZGrVqlVq06ZNWnzxxdNjjz2WKxatsgdAfeLYB8YfVDZ/lWsAFKYcSD3xxBNp6NCheQnsWEggTJgwIS233HLp+OOPT7fffnt644030kcffZSDqFj5Mu4bU2ijtxsA1BeOfWD8QWUqpQDqUDQz33TTTfOKlhFAnXTSSRVf+/LLL9Mf/vCH1KFDhzx9rzJTaAGorxz7wPiDMk2BAOpQu3bt0l133ZWWXHLJ/P9XXnml4muLLrponqoXzc+np6cbAPWVYx8Yf1AmlAKoY9EzKgKp6A81cODANHLkyLz9m2++SW+99VZaZpll6noXAaBWOfZB3TH+mJeYvgcwj4gqqd/97nd52l63bt1S8+bN0wcffJBeeOGF/O/KqxUBQEPg2AfGH42bSimAeUSXLl3SrbfemhZYYIHc6HzrrbdOL7/8cg6kfvjhB4EUAA2OYx8YfzRuQimAeUjnzp3zVL4pU6bkQOq9997L22PVPQBoiBz7wPij8TJ9D2Aenc5w4IEHphVWWCH1798/rbbaanW9SwAwVzn2Qd0x/qgrKqUA5tHpDIMGDUqff/55at26dV3vDgDMdY59UHeMP+qKSimAedj333+fWrZsWde7AQCFceyDumP8UTShFAAAAACFM30PAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAZtO+++6bmjRpMsPlvffem+Pn8rrrrktt2rTxmgAADd78db0DAAD10bbbbpuuvfbaKtuWWGKJNC/54YcfUrNmzep6NwAAqqVSCgCgBlq0aJHatWtX5dK0adN07733pnXXXTe1bNkyrbDCCum0005LP/74Y8X9LrzwwrTmmmumhRZaKHXo0CEdfPDB6dtvv81fe+KJJ1KfPn3ShAkTKqqvTj311Py1+Pc999xTZR+ioioqq8KHH36Yb3Prrbem7t275+//j3/8I3/tqquuSquvvnrettpqq6W//e1vFY8xZcqUdOihh6allloqf3255ZZLAwYM8J4AAOY6lVIAALXk6aefTr169UoXX3xx2myzzdL777+fDjjggPy1/v375//PN998+evLL798+u9//5tDqeOOOy4HRRtvvHEaOHBgOuWUU9I777yTb7/wwgvP1j6ccMIJ6YILLkhdunSpCKbi8QYNGpS3vfLKK6lv3745FOvdu3fel/vuuy/ddtttadlll00ff/xxvgAAzG1CKQCAGnjggQeqBEbbbbdd+uqrr3IoFGFPiEqpM844I4dO5VDqyCOPrLhPx44d05lnnpkOPPDAHEo1b948tW7dOlc8ReVVTcTj77rrrhXX4/tGSFXeFmHYm2++mS6//PK8n6NGjUorr7xy2nTTTfP3jUopAIAiCKUAAGrgV7/6Vbrssssqrkfl0VprrZWeffbZdNZZZ1Vsnzp1avr+++/Td999lxZccMH0r3/9K0+Pe/vtt9PEiRPz1L7KX59T3bp1q/j3pEmTcrXWfvvtl6ujyuJ7RvhVbtq+9dZbp1VXXTX3ydphhx3SNttsM8f7AQDwc4RSAAA1ECHUSiutVGVb9IaKHlKVK5XKYipd9H2K0Oeggw7KwdWiiy6annnmmRwaRW+nnwqlooqpVCrN0Mi8uv2qvD/hyiuvTBtssEGV20X/qxD9rz744IP00EMP5cBszz33TFtttVW64447Zvm5AACoCaEUAEAtiYAnekFNH1aVjRgxIk2bNi1Pp4veUiF6OVUWU/iiump6sbLf559/XnH93XffzdVVP6Vt27Zp6aWXzr2r9tlnn5nerlWrVqlnz575svvuu+eKqS+//DKHZgAAc4tQCgCglkRD8aiEiobhEe5E8PTvf/87vf7667l3VIRVUd10ySWXpB133DFP9Rs8eHCVx4g+U1HhNHTo0LT22mvn6qm4bLHFFrlZ+UYbbZRDq+OPPz41a9bsZ/cpKrcOP/zwPF0vwqbJkyen4cOH5/5X/fr1y6sBxsp70QQ99vf222/P/axiZT8AgLnp/31EBwDAHOvRo0dugP7II4+k9dZbL2244YbpoosuqmgeHiFThEDnnHNO6ty5c14ZL/pLVRYr8EXj86haiuqoc889N2+P6qoOHTrkVf323nvvdMwxx8xSD6r9998/XXXVVenaa69Na665ZurevXu67rrrcsPz8Itf/CJ/j+hFFfscUwwffPDBikouAIC5pUlp+uYEAAAAADCX+QgMAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAABIRfv/ALwiCBZcUME5AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "single_tree_accuracy = single_tree_model.score(X_test_transformed, y_test)\n",
+    "print(f\"Single Decision Tree Classifier Accuracy: {single_tree_accuracy:.2f}\")\n",
+    "\n",
+    "# Feature importances\n",
+    "single_tree_importances = single_tree_model.feature_importances_\n",
+    "st_indices = single_tree_importances.argsort()[::-1]\n",
+    "\n",
+    "feature_names = preprocessing_pipeline.get_feature_names_out()\n",
+    "feature_importances = pd.Series(single_tree_importances, index=feature_names)\n",
+    "print(\"Feature Importances from Single Decision Tree:\")\n",
+    "print(feature_importances.sort_values(ascending=False))\n",
+    "\n",
+    "# Plot feature importances\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.barplot(x=feature_importances.sort_values(ascending=False).index,\n",
+    "            y=feature_importances.sort_values(ascending=False).values)\n",
+    "plt.title('Feature Importances from Single Decision Tree')\n",
+    "plt.xlabel('Features')\n",
+    "plt.ylabel('Importance')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4297ff80",
+   "metadata": {},
+   "source": [
+    "-   Train a Random Forest classifier with hyperparameter tuning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "740448a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 81 candidates, totalling 405 fits\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=5, n_estimators=50, n_jobs=-1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of trees in the forest.<br><br>.. versionchanged:: 0.22<br>   The default value of ``n_estimators`` changed from 10 to 100<br>   in 0.22.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">50</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.<br>Note: This parameter is tree-specific.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_features,-%7B%22sqrt%22%2C%20%22log2%22%2C%20None%7D%2C%20int%20or%20float%2C%20default%3D%22sqrt%22\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {\"sqrt\", \"log2\", None}, int or float, default=\"sqrt\"<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. versionchanged:: 1.1<br>    The default of `max_features` changed from `\"auto\"` to `\"sqrt\"`.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=bootstrap,-bool%2C%20default%3DTrue\">\n",
+       "            bootstrap\n",
+       "            <span class=\"param-doc-description\">bootstrap: bool, default=True<br><br>Whether bootstrap samples are used when building trees. If False, the<br>whole dataset is used to build each tree.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('oob_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=oob_score,-bool%20or%20callable%2C%20default%3DFalse\">\n",
+       "            oob_score\n",
+       "            <span class=\"param-doc-description\">oob_score: bool or callable, default=False<br><br>Whether to use out-of-bag samples to estimate the generalization score.<br>By default, :func:`~sklearn.metrics.accuracy_score` is used.<br>Provide a callable with signature `metric(y_true, y_pred)` to use a<br>custom metric. Only available if `bootstrap=True`.<br><br>For an illustration of out-of-bag (OOB) error estimation, see the example<br>:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,<br>:meth:`decision_path` and :meth:`apply` are all parallelized over the<br>trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`<br>context. ``-1`` means using all processors. See :term:`Glossary<br><n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">-1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls both the randomness of the bootstrapping of the samples used<br>when building trees (if ``bootstrap=True``) and the sampling of the<br>features to consider when looking for the best split at each node<br>(if ``max_features < n_features``).<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">42</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`Glossary <warm_start>` and<br>:ref:`tree_ensemble_warm_start` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=class_weight,-%7B%22balanced%22%2C%20%22balanced_subsample%22%7D%2C%20dict%20or%20list%20of%20dicts%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: {\"balanced\", \"balanced_subsample\"}, dict or list of dicts,             default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>The \"balanced_subsample\" mode is the same as \"balanced\" except that<br>weights are computed based on the bootstrap sample for every tree<br>grown.<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_samples\n",
+       "            <span class=\"param-doc-description\">max_samples: int or float, default=None<br><br>If bootstrap is True, the number of samples to draw from X<br>to train each base estimator.<br><br>- If None (default), then draw `X.shape[0]` samples.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,<br>  `max_samples` should be in the interval `(0.0, 1.0]`.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
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+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-4');</script></body>"
+      ],
+      "text/plain": [
+       "RandomForestClassifier(max_depth=5, n_estimators=50, n_jobs=-1, random_state=42)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "rf_model = RandomForestClassifier(random_state=42, n_jobs=-1)\n",
+    "rf_grid_params = {\n",
+    "    'n_estimators': [50, 100, 200],\n",
+    "    'max_depth': [None, 5, 10],\n",
+    "    'min_samples_split': [2, 5, 10],\n",
+    "    'min_samples_leaf': [1, 2, 4]\n",
+    "}\n",
+    "\n",
+    "# Perform grid search for hyperparameter tuning\n",
+    "rf_grid_search = GridSearchCV(rf_model, rf_grid_params, cv=5, n_jobs=-1, verbose=1)\n",
+    "rf_grid_search.fit(X_train_transformed, y_train)\n",
+    "rf_model = rf_grid_search.best_estimator_\n",
+    "rf_model.fit(X_train_transformed, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ded509cd",
+   "metadata": {},
+   "source": [
+    "-   Evaluate the Random Forest model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "5c2b5938",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Random Forest Classifier Accuracy: 0.77\n",
+      "Feature Importances from Random Forest:\n",
+      "sex__sex_male       0.528668\n",
+      "age__age            0.232155\n",
+      "pclass__pclass_3    0.112150\n",
+      "pclass__pclass_1    0.102198\n",
+      "pclass__pclass_2    0.024828\n",
+      "dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Feature'>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1200x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rf_accuracy = rf_model.score(X_test_transformed, y_test)\n",
+    "print(f\"Random Forest Classifier Accuracy: {rf_accuracy:.2f}\")\n",
+    "\n",
+    "# Feature importances\n",
+    "rf_importances = rf_model.feature_importances_\n",
+    "rf_feature_importances = pd.Series(rf_importances, index=feature_names)\n",
+    "print(\"Feature Importances from Random Forest:\")\n",
+    "print(rf_feature_importances.sort_values(ascending=False))\n",
+    "\n",
+    "# Create a dataframe for feature importances\n",
+    "feature_importances = pd.DataFrame({\n",
+    "    'Feature': feature_names,\n",
+    "    'Decision Tree Importance': single_tree_importances,\n",
+    "    'Random Forest Importance': rf_importances\n",
+    "}).set_index('Feature')\n",
+    "\n",
+    "# Compare feature importances\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Plot feature importances from Random Forest and Single Decision Tree in a stacked bar chart\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "feature_importances.plot(kind='bar', stacked=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c3b1463",
+   "metadata": {},
+   "source": [
+    "## Hands-on Exercises\n",
+    "\n",
+    "**Bagging with Different Base Models**: Implement bagging using different base models (e.g., logistic regression, SVM) and compare their performance on a classification dataset (e.g., `iris`).\n",
+    "\n",
+    "-   Load the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ccfe31dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set size: 105, Test set size: 45\n",
+      "Features: ['sepal length (cm)', 'sepal width (cm)']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "iris = load_iris()\n",
+    "X_iris = iris.data[:,:2]\n",
+    "y_iris = iris.target\n",
+    "\n",
+    "# Split the dataset into training and testing sets\n",
+    "X_train_iris, X_test_iris, y_train_iris, y_test_iris = \\\n",
+    "    train_test_split(X_iris, y_iris, test_size=0.3, random_state=42)\n",
+    "print(f\"Training set size: {X_train_iris.shape[0]}, Test set size: {X_test_iris.shape[0]}\")\n",
+    "print(f\"Features: {iris.feature_names[:2]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9068b991",
+   "metadata": {},
+   "source": [
+    "-   Train a Bagging Classifier with Logistic Regression as the base model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "1246f1dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BaggingClassifier(estimator=LogisticRegression(), n_estimators=100,\n",
+       "                  random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>BaggingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html\">?<span>Documentation for BaggingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=estimator,-object%2C%20default%3DNone\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: object, default=None<br><br>The base estimator to fit on random subsets of the dataset.<br>If None, then the base estimator is a<br>:class:`~sklearn.tree.DecisionTreeClassifier`.<br><br>.. versionadded:: 1.2<br>   `base_estimator` was renamed to `estimator`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">LogisticRegression()</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=n_estimators,-int%2C%20default%3D10\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=10<br><br>The number of base estimators in the ensemble.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_samples\n",
+       "            <span class=\"param-doc-description\">max_samples: int or float, default=None<br><br>The number of samples to draw from X to train each base estimator (with<br>replacement by default, see `bootstrap` for more details).<br><br>- If None, then draw `X.shape[0]` samples irrespective of `sample_weight`.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max_samples * X.shape[0]` unweighted samples or<br>  `max_samples * sample_weight.sum()` weighted samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=max_features,-int%20or%20float%2C%20default%3D1.0\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: int or float, default=1.0<br><br>The number of features to draw from X to train each base estimator (<br>without replacement by default, see `bootstrap_features` for more<br>details).<br><br>- If int, then draw `max_features` features.<br>- If float, then draw `max(1, int(max_features * n_features_in_))` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=bootstrap,-bool%2C%20default%3DTrue\">\n",
+       "            bootstrap\n",
+       "            <span class=\"param-doc-description\">bootstrap: bool, default=True<br><br>Whether samples are drawn with replacement. If False, sampling without<br>replacement is performed. If fitting with `sample_weight`, it is<br>strongly recommended to choose True, as only drawing with replacement<br>will ensure the expected frequency semantics of `sample_weight`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=bootstrap_features,-bool%2C%20default%3DFalse\">\n",
+       "            bootstrap_features\n",
+       "            <span class=\"param-doc-description\">bootstrap_features: bool, default=False<br><br>Whether features are drawn with replacement.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('oob_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=oob_score,-bool%2C%20default%3DFalse\">\n",
+       "            oob_score\n",
+       "            <span class=\"param-doc-description\">oob_score: bool, default=False<br><br>Whether to use out-of-bag samples to estimate<br>the generalization error. Only available if bootstrap=True.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit<br>a whole new ensemble. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* constructor parameter.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel for both :meth:`fit` and<br>:meth:`predict`. ``None`` means 1 unless in a<br>:obj:`joblib.parallel_backend` context. ``-1`` means using all<br>processors. See :term:`Glossary <n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the random resampling of the original dataset<br>(sample wise and feature wise).<br>If the base estimator accepts a `random_state` attribute, a different<br>seed is generated for each instance in the ensemble.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">42</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.BaggingClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: LogisticRegression</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
+       "            penalty\n",
+       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
+       "            l1_ratio\n",
+       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
+       "            dual\n",
+       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
+       "            intercept_scaling\n",
+       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+       "\n",
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+      ],
+      "text/plain": [
+       "BaggingClassifier(estimator=LogisticRegression(), n_estimators=100,\n",
+       "                  random_state=42)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "# Train a Bagging Classifier with Logistic Regression as the base model\n",
+    "bagging_logistic = BaggingClassifier(estimator=LogisticRegression(),\n",
+    "                                     n_estimators=100, random_state=42)\n",
+    "bagging_logistic.fit(X_train_iris, y_train_iris)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbd9a08f",
+   "metadata": {},
+   "source": [
+    "-   Evaluate the Bagging Classifier with Logistic Regression and visualize the results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "3122775f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bagging Classifier with Logistic Regression Accuracy: 0.82\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Evaluate the model\n",
+    "logistic_accuracy = bagging_logistic.score(X_test_iris, y_test_iris)\n",
+    "print(f\"Bagging Classifier with Logistic Regression Accuracy: {logistic_accuracy:.2f}\")\n",
+    "# Confusion Matrix\n",
+    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+    "y_pred_logistic = bagging_logistic.predict(X_test_iris)\n",
+    "ConfusionMatrixDisplay.from_estimator(\n",
+    "    bagging_logistic, X_test_iris, y_test_iris,\n",
+    "    display_labels=iris.target_names, cmap='Blues')\n",
+    "plt.title('Confusion Matrix for Bagging with Logistic Regression')\n",
+    "plt.show()\n",
+    "\n",
+    "# Plot decision boundaries\n",
+    "from sklearn.inspection import DecisionBoundaryDisplay\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    bagging_logistic, X_train_iris, response_method='predict',\n",
+    "    grid_resolution=500, cmap=plt.cm.coolwarm, alpha=0.8,\n",
+    "    xlabel=iris.feature_names[0], ylabel=iris.feature_names[1])\n",
+    "plt.scatter(X_test_iris[:, 0], X_test_iris[:, 1], c=y_test_iris, edgecolor='k', s=20,\n",
+    "            cmap=plt.cm.coolwarm)\n",
+    "plt.title('Decision Boundaries for Bagging with Logistic Regression')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1dcf672c",
+   "metadata": {},
+   "source": [
+    "-   Train a Bagging Classifier with SVM as the base model and hyperparameter tuning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "b870c0e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n",
+      "Best parameters for SVM Bagging Classifier: {'C': 0.1, 'gamma': 'auto', 'kernel': 'rbf'}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-6 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-6 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=0.1, gamma=&#x27;auto&#x27;, probability=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Regularization parameter. The strength of the regularization is<br>inversely proportional to C. Must be strictly positive. The penalty<br>is a squared l2 penalty. For an intuitive visualization of the effects<br>of scaling the regularization parameter C, see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kernel',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=kernel,-%7B%27linear%27%2C%20%27poly%27%2C%20%27rbf%27%2C%20%27sigmoid%27%2C%20%27precomputed%27%7D%20or%20callable%2C%20%20%20%20%20%20%20%20%20%20default%3D%27rbf%27\">\n",
+       "            kernel\n",
+       "            <span class=\"param-doc-description\">kernel: {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable,          default='rbf'<br><br>Specifies the kernel type to be used in the algorithm. If<br>none is given, 'rbf' will be used. If a callable is given it is used to<br>pre-compute the kernel matrix from data matrices; that matrix should be<br>an array of shape ``(n_samples, n_samples)``. For an intuitive<br>visualization of different kernel types see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_kernels.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;rbf&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('degree',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=degree,-int%2C%20default%3D3\">\n",
+       "            degree\n",
+       "            <span class=\"param-doc-description\">degree: int, default=3<br><br>Degree of the polynomial kernel function ('poly').<br>Must be non-negative. Ignored by all other kernels.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('gamma',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=gamma,-%7B%27scale%27%2C%20%27auto%27%7D%20or%20float%2C%20default%3D%27scale%27\">\n",
+       "            gamma\n",
+       "            <span class=\"param-doc-description\">gamma: {'scale', 'auto'} or float, default='scale'<br><br>Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.<br><br>- if ``gamma='scale'`` (default) is passed then it uses<br>  1 / (n_features * X.var()) as value of gamma,<br>- if 'auto', uses 1 / n_features<br>- if float, must be non-negative.<br><br>.. versionchanged:: 0.22<br>   The default value of ``gamma`` changed from 'auto' to 'scale'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('coef0',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=coef0,-float%2C%20default%3D0.0\">\n",
+       "            coef0\n",
+       "            <span class=\"param-doc-description\">coef0: float, default=0.0<br><br>Independent term in kernel function.<br>It is only significant in 'poly' and 'sigmoid'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('shrinking',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=shrinking,-bool%2C%20default%3DTrue\">\n",
+       "            shrinking\n",
+       "            <span class=\"param-doc-description\">shrinking: bool, default=True<br><br>Whether to use the shrinking heuristic.<br>See the :ref:`User Guide <shrinking_svm>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('probability',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=probability,-bool%2C%20default%3DFalse\">\n",
+       "            probability\n",
+       "            <span class=\"param-doc-description\">probability: bool, default=False<br><br>Whether to enable probability estimates. This must be enabled prior<br>to calling `fit`, will slow down that method as it internally uses<br>5-fold cross-validation, and `predict_proba` may be inconsistent with<br>`predict`. Read more in the :ref:`User Guide <scores_probabilities>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=tol,-float%2C%20default%3D1e-3\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-3<br><br>Tolerance for stopping criterion.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cache_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=cache_size,-float%2C%20default%3D200\">\n",
+       "            cache_size\n",
+       "            <span class=\"param-doc-description\">cache_size: float, default=200<br><br>Specify the size of the kernel cache (in MB).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">200</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Set the parameter C of class i to class_weight[i]*C for<br>SVC. If not given, all classes are supposed to have<br>weight one.<br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>Enable verbose output. Note that this setting takes advantage of a<br>per-process runtime setting in libsvm that, if enabled, may not work<br>properly in a multithreaded context.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=max_iter,-int%2C%20default%3D-1\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=-1<br><br>Hard limit on iterations within solver, or -1 for no limit.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">-1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('decision_function_shape',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=decision_function_shape,-%7B%27ovo%27%2C%20%27ovr%27%7D%2C%20default%3D%27ovr%27\">\n",
+       "            decision_function_shape\n",
+       "            <span class=\"param-doc-description\">decision_function_shape: {'ovo', 'ovr'}, default='ovr'<br><br>Whether to return a one-vs-rest ('ovr') decision function of shape<br>(n_samples, n_classes) as all other classifiers, or the original<br>one-vs-one ('ovo') decision function of libsvm which has shape<br>(n_samples, n_classes * (n_classes - 1) / 2). However, note that<br>internally, one-vs-one ('ovo') is always used as a multi-class strategy<br>to train models; an ovr matrix is only constructed from the ovo matrix.<br>The parameter is ignored for binary classification.<br><br>.. versionchanged:: 0.19<br>    decision_function_shape is 'ovr' by default.<br><br>.. versionadded:: 0.17<br>   *decision_function_shape='ovr'* is recommended.<br><br>.. versionchanged:: 0.17<br>   Deprecated *decision_function_shape='ovo' and None*.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('break_ties',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=break_ties,-bool%2C%20default%3DFalse\">\n",
+       "            break_ties\n",
+       "            <span class=\"param-doc-description\">break_ties: bool, default=False<br><br>If true, ``decision_function_shape='ovr'``, and number of classes > 2,<br>:term:`predict` will break ties according to the confidence values of<br>:term:`decision_function`; otherwise the first class among the tied<br>classes is returned. Please note that breaking ties comes at a<br>relatively high computational cost compared to a simple predict. See<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_tie_breaking.py` for an<br>example of its usage with ``decision_function_shape='ovr'``.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the pseudo random number generation for shuffling the data for<br>probability estimates. Ignored when `probability` is False.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+       "forceTheme('sk-container-id-6');</script></body>"
+      ],
+      "text/plain": [
+       "SVC(C=0.1, gamma='auto', probability=True)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "# Train a Bagging Classifier with SVM as the base model\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "svm_model = SVC(probability=True)\n",
+    "svm_grid_params = {\n",
+    "    'C': [0.1, 1, 10],\n",
+    "    'kernel': ['linear', 'rbf'],\n",
+    "    'gamma': ['scale', 'auto']\n",
+    "}\n",
+    "# Perform grid search for hyperparameter tuning\n",
+    "svm_grid_search = GridSearchCV(svm_model, svm_grid_params, cv=5, n_jobs=-1, verbose=1)\n",
+    "svm_grid_search.fit(X_train_iris, y_train_iris)\n",
+    "print(\"Best parameters for SVM Bagging Classifier:\", svm_grid_search.best_params_)\n",
+    "# Use the best estimator from grid search\n",
+    "bagging_svm = svm_grid_search.best_estimator_\n",
+    "bagging_svm.fit(X_train_iris, y_train_iris)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02bba9b9",
+   "metadata": {},
+   "source": [
+    "-   Evaluate the model and visualize the results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "4cb577a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bagging Classifier with SVM Accuracy: 0.82\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "svm_accuracy = bagging_svm.score(X_test_iris, y_test_iris)\n",
+    "print(f\"Bagging Classifier with SVM Accuracy: {svm_accuracy:.2f}\")\n",
+    "\n",
+    "# Confusion Matrix\n",
+    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+    "y_pred_svm = bagging_svm.predict(X_test_iris)\n",
+    "ConfusionMatrixDisplay.from_estimator(\n",
+    "    bagging_svm, X_test_iris, y_test_iris,\n",
+    "    display_labels=iris.target_names, cmap='Blues')\n",
+    "plt.title('Confusion Matrix for Bagging with SVM')\n",
+    "plt.show()\n",
+    "\n",
+    "# Plot decision boundaries\n",
+    "from sklearn.inspection import DecisionBoundaryDisplay\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "DecisionBoundaryDisplay.from_estimator(\n",
+    "    bagging_svm, X_train_iris, response_method='predict',\n",
+    "    grid_resolution=500, cmap=plt.cm.coolwarm, alpha=0.8,\n",
+    "    xlabel=iris.feature_names[0], ylabel=iris.feature_names[1])\n",
+    "plt.scatter(X_test_iris[:, 0], X_test_iris[:, 1], c=y_test_iris, edgecolor='k', s=20,\n",
+    "            cmap=plt.cm.coolwarm)\n",
+    "plt.title('Decision Boundaries for Bagging with Logistic Regression')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "ML Workshop",
+   "language": "python",
+   "name": "ml_workshop"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/S07 Ensemble Methods/S07C03 Boosting.ipynb b/src/S07 Ensemble Methods/S07C03 Boosting.ipynb
new file mode 100644
index 0000000..ebdd71f
--- /dev/null
+++ b/src/S07 Ensemble Methods/S07C03 Boosting.ipynb	
@@ -0,0 +1,7473 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8a2b7893",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Boosting\n",
+    "\n",
+    "Boosting is an ensemble learning technique that combines multiple weak learners to create a strong learner. The main idea is to sequentially train models, where each new model focuses on correcting the errors made by the previous models. This iterative process allows boosting to improve the overall performance of the ensemble.\n",
+    "\n",
+    "Boosting algorithms typically follow these steps:\n",
+    "\n",
+    "1.  **Initialize Weights**: Start with equal weights for all training instances.\n",
+    "2.  **Train Weak Learner**: Train a weak learner (e.g., a decision stump) on the weighted training data.\n",
+    "3.  **Update Weights**: Increase the weights of misclassified instances and decrease the weights of correctly classified instances. This ensures that the next weak learner focuses more on the instances that were previously misclassified.\n",
+    "4.  **Combine Models**: Combine the predictions of all weak learners, often using a weighted sum where the weights are determined by the performance of each learner.\n",
+    "5.  **Repeat**: Repeat steps 2-4 for a specified number of iterations or until a stopping criterion is met (e.g., no significant improvement in performance).\n",
+    "6.  **Final Prediction**: The final prediction is made by combining the predictions of all weak learners, typically using a weighted majority vote for classification or a weighted average for regression."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7fcaa37",
+   "metadata": {},
+   "source": [
+    "## Advantages and Disadvantages\n",
+    "\n",
+    "-   **Advantages**:\n",
+    "    -   Can significantly improve the performance of weak learners, often achieving state-of-the-art results.\n",
+    "    -   Focuses on difficult instances, allowing the model to learn complex patterns in the data.\n",
+    "    -   Can handle a wide range of base learners, including decision trees, linear models, and neural networks.\n",
+    "    -   Robust to overfitting, especially with regularization techniques like shrinkage (learning rate) and early stopping.\n",
+    "    -   Can be used for both classification and regression tasks.\n",
+    "-   **Disadvantages**:\n",
+    "    -   Sensitive to noisy data and outliers, as boosting focuses on correcting errors.\n",
+    "    -   Requires careful tuning of hyperparameters, such as the number of iterations, learning rate, and base learner complexity.\n",
+    "    -   Computationally expensive, especially with large datasets and complex base learners."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "143e2ba9",
+   "metadata": {},
+   "source": [
+    "## Common Boosting Algorithms\n",
+    "\n",
+    "-   **AdaBoost (Adaptive Boosting)**: One of the earliest and most popular boosting algorithms. It adjusts the weights of misclassified instances and combines weak learners in a weighted manner.\n",
+    "-   **Gradient Boosting**: Builds models sequentially, where each new model is trained to minimize the residual errors of the previous models. It can use various loss functions and is highly flexible.\n",
+    "-   **XGBoost (Extreme Gradient Boosting)**: An optimized version of gradient boosting that includes regularization techniques to prevent overfitting and improve performance. It is widely used in machine learning competitions.\n",
+    "-   **LightGBM**: A gradient boosting framework that uses a histogram-based approach to speed up training and reduce memory usage. It is particularly effective for large datasets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f3011e2",
+   "metadata": {},
+   "source": [
+    "## AdaBoost Overview\n",
+    "\n",
+    "AdaBoost, short for Adaptive Boosting, is one of the earliest and most widely used boosting algorithms. It works by combining multiple weak classifiers to create a strong classifier. The key steps in AdaBoost are:\n",
+    "\n",
+    "1.  **Initialize Weights**: Start with equal weights for all training instances.\n",
+    "2.  **Train Weak Learner**: Train a weak learner (e.g., a decision stump) on the weighted training data.\n",
+    "3.  **Update Weights**: Increase the weights of misclassified instances and decrease the weights of correctly classified instances. This ensures that the next weak learner focuses more on the instances that were previously misclassified.\n",
+    "4.  **Combine Models**: Combine the predictions of all weak learners, where each learner's contribution is weighted by its accuracy.\n",
+    "5.  **Repeat**: Repeat steps 2-4 for a specified number of iterations or until a stopping criterion is met (e.g., no significant improvement in performance)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "870c939f",
+   "metadata": {},
+   "source": [
+    "### Example of AdaBoost\n",
+    "\n",
+    "We will demonstrate AdaBoost using the titanic dataset, keeping only the `pclass`, `sex`, and `age` features. We will preprocess the data, train a single Decision Tree, and then use AdaBoost to improve the model's performance.\n",
+    "\n",
+    "-   Load the titanic dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d5c5c4ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature names: ['pclass', 'sex', 'age']\n",
+      "Class labels: ['Not Survived', 'Survived']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "titanic = datasets.fetch_openml('titanic', version=1, as_frame=True)\n",
+    "df = titanic.frame[['pclass', 'sex', 'age', 'survived']]\n",
+    "X = df.drop(columns=['survived'])\n",
+    "y = df['survived'].astype('category').cat.codes\n",
+    "class_names = ['Not Survived', 'Survived']\n",
+    "feature_names = X.columns.tolist()\n",
+    "print(f\"Feature names: {feature_names}\")\n",
+    "print(f\"Class labels: {class_names}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0bd6aafe",
+   "metadata": {},
+   "source": [
+    "-   Train-test split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d1f16a16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = \\\n",
+    "    train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8176051",
+   "metadata": {},
+   "source": [
+    "-   Create a preprocessing pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "eab044d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Transformed feature names: ['age__age' 'sex__sex_male' 'pclass__pclass_1' 'pclass__pclass_2'\n",
+      " 'pclass__pclass_3']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+    "\n",
+    "preprocessing_pipeline = ColumnTransformer(\n",
+    "    transformers=[\n",
+    "        ('age', Pipeline(\n",
+    "            steps=[\n",
+    "                ('imputer', SimpleImputer(strategy='mean')),\n",
+    "                ('scaler', StandardScaler())\n",
+    "            ]\n",
+    "        ), ['age']),\n",
+    "        ('sex', Pipeline(\n",
+    "            steps=[\n",
+    "                ('encoder', OneHotEncoder(drop='first', sparse_output=False))\n",
+    "            ]\n",
+    "        ), ['sex']),\n",
+    "        ('pclass', Pipeline(\n",
+    "            steps=[\n",
+    "                ('enconder', OneHotEncoder(sparse_output=False))\n",
+    "            ]\n",
+    "        ), ['pclass'])\n",
+    "    ],\n",
+    "    remainder='passthrough'\n",
+    ")\n",
+    "X_train_transformed = preprocessing_pipeline.fit_transform(X_train)\n",
+    "X_test_transformed = preprocessing_pipeline.transform(X_test)\n",
+    "\n",
+    "feature_names = preprocessing_pipeline.get_feature_names_out()\n",
+    "print(f\"Transformed feature names: {feature_names}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab7d791c",
+   "metadata": {},
+   "source": [
+    "-   Train a single Decision Tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c7dbf145",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 45 candidates, totalling 225 fits\n",
+      "Best parameters for Decision Tree: {'max_depth': 4, 'min_samples_leaf': 2, 'min_samples_split': 2}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=4, min_samples_leaf=2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('splitter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=splitter,-%7B%22best%22%2C%20%22random%22%7D%2C%20default%3D%22best%22\">\n",
+       "            splitter\n",
+       "            <span class=\"param-doc-description\">splitter: {\"best\", \"random\"}, default=\"best\"<br><br>The strategy used to choose the split at each node. Supported<br>strategies are \"best\" to choose the best split and \"random\" to choose<br>the best random split.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">4</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_features,-int%2C%20float%20or%20%7B%22sqrt%22%2C%20%22log2%22%7D%2C%20default%3DNone\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: int, float or {\"sqrt\", \"log2\"}, default=None<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at<br>  each split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. note::<br><br>    The search for a split does not stop until at least one<br>    valid partition of the node samples is found, even if it requires to<br>    effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the randomness of the estimator. The features are always<br>randomly permuted at each split, even if ``splitter`` is set to<br>``\"best\"``. When ``max_features < n_features``, the algorithm will<br>select ``max_features`` at random at each split before finding the best<br>split among them. But the best found split may vary across different<br>runs, even if ``max_features=n_features``. That is the case, if the<br>improvement of the criterion is identical for several splits and one<br>split has to be selected at random. To obtain a deterministic behaviour<br>during fitting, ``random_state`` has to be fixed to an integer.<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">42</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow a tree with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=class_weight,-dict%2C%20list%20of%20dict%20or%20%22balanced%22%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict, list of dict or \"balanced\", default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If None, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+      ],
+      "text/plain": [
+       "DecisionTreeClassifier(max_depth=4, min_samples_leaf=2, random_state=42)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.metrics import accuracy_score, classification_report, ConfusionMatrixDisplay\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "dt_model = DecisionTreeClassifier(random_state=42)\n",
+    "grid_params = {\n",
+    "    'max_depth': [None, 2, 3, 4, 5],\n",
+    "    'min_samples_split': [2, 5, 10],\n",
+    "    'min_samples_leaf': [1, 2, 4]\n",
+    "}\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "grid_search = GridSearchCV(dt_model, grid_params, cv=5, scoring='accuracy', verbose=1)\n",
+    "grid_search.fit(X_train_transformed, y_train)\n",
+    "print(\"Best parameters for Decision Tree:\", grid_search.best_params_)\n",
+    "\n",
+    "dt_model = grid_search.best_estimator_\n",
+    "dt_model.fit(X_train_transformed, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea073e5d",
+   "metadata": {},
+   "source": [
+    "-   Evaluate the single Decision Tree model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6a90adf0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Single Decision Tree Accuracy: 0.7990\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "Not Survived       0.83      0.85      0.84       243\n",
+      "    Survived       0.74      0.72      0.73       150\n",
+      "\n",
+      "    accuracy                           0.80       393\n",
+      "   macro avg       0.79      0.78      0.79       393\n",
+      "weighted avg       0.80      0.80      0.80       393\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_pred_dt = dt_model.predict(X_test_transformed)\n",
+    "\n",
+    "print(f\"Single Decision Tree Accuracy: {accuracy_score(y_test, y_pred_dt):.4f}\")\n",
+    "print(classification_report(y_test, y_pred_dt, target_names=class_names))\n",
+    "ConfusionMatrixDisplay.from_estimator(dt_model, X_test_transformed, y_test,\n",
+    "                                      display_labels=class_names, cmap='Blues')\n",
+    "plt.title('Single Decision Tree Confusion Matrix')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8760f3b",
+   "metadata": {},
+   "source": [
+    "-   Train an AdaBoost Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b9c9a029",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 540 candidates, totalling 2700 fits\n",
+      "Best parameters for AdaBoost: {'estimator__max_depth': 4, 'estimator__min_samples_leaf': 1, 'estimator__min_samples_split': 10, 'learning_rate': 0.01, 'n_estimators': 200}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=4,\n",
+       "                                                    min_samples_split=10),\n",
+       "                   learning_rate=0.01, n_estimators=200, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>AdaBoostClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.AdaBoostClassifier.html\">?<span>Documentation for AdaBoostClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#:~:text=estimator,-object%2C%20default%3DNone\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: object, default=None<br><br>The base estimator from which the boosted ensemble is built.<br>Support for sample weighting is required, as well as proper<br>``classes_`` and ``n_classes_`` attributes. If ``None``, then<br>the base estimator is :class:`~sklearn.tree.DecisionTreeClassifier`<br>initialized with `max_depth=1`.<br><br>.. versionadded:: 1.2<br>   `base_estimator` was renamed to `estimator`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">DecisionTreeC...ples_split=10)</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#:~:text=n_estimators,-int%2C%20default%3D50\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=50<br><br>The maximum number of estimators at which boosting is terminated.<br>In case of perfect fit, the learning procedure is stopped early.<br>Values must be in the range `[1, inf)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">200</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#:~:text=learning_rate,-float%2C%20default%3D1.0\">\n",
+       "            learning_rate\n",
+       "            <span class=\"param-doc-description\">learning_rate: float, default=1.0<br><br>Weight applied to each classifier at each boosting iteration. A higher<br>learning rate increases the contribution of each classifier. There is<br>a trade-off between the `learning_rate` and `n_estimators` parameters.<br>Values must be in the range `(0.0, inf)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.01</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the random seed given at each `estimator` at each<br>boosting iteration.<br>Thus, it is only used when `estimator` exposes a `random_state`.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">42</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: DecisionTreeClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>DecisionTreeClassifier(max_depth=4, min_samples_split=10)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('splitter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=splitter,-%7B%22best%22%2C%20%22random%22%7D%2C%20default%3D%22best%22\">\n",
+       "            splitter\n",
+       "            <span class=\"param-doc-description\">splitter: {\"best\", \"random\"}, default=\"best\"<br><br>The strategy used to choose the split at each node. Supported<br>strategies are \"best\" to choose the best split and \"random\" to choose<br>the best random split.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">4</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">10</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_features,-int%2C%20float%20or%20%7B%22sqrt%22%2C%20%22log2%22%7D%2C%20default%3DNone\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: int, float or {\"sqrt\", \"log2\"}, default=None<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at<br>  each split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. note::<br><br>    The search for a split does not stop until at least one<br>    valid partition of the node samples is found, even if it requires to<br>    effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the randomness of the estimator. The features are always<br>randomly permuted at each split, even if ``splitter`` is set to<br>``\"best\"``. When ``max_features < n_features``, the algorithm will<br>select ``max_features`` at random at each split before finding the best<br>split among them. But the best found split may vary across different<br>runs, even if ``max_features=n_features``. That is the case, if the<br>improvement of the criterion is identical for several splits and one<br>split has to be selected at random. To obtain a deterministic behaviour<br>during fitting, ``random_state`` has to be fixed to an integer.<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow a tree with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=class_weight,-dict%2C%20list%20of%20dict%20or%20%22balanced%22%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict, list of dict or \"balanced\", default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If None, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+      "text/plain": [
+       "AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=4,\n",
+       "                                                    min_samples_split=10),\n",
+       "                   learning_rate=0.01, n_estimators=200, random_state=42)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import AdaBoostClassifier\n",
+    "grid_params_ab = {\n",
+    "    'n_estimators': [50, 100, 200, 500],\n",
+    "    'learning_rate': [0.01, 0.1, 1.0],\n",
+    "    'estimator__max_depth': [None, 2, 3, 4, 5],\n",
+    "    'estimator__min_samples_split': [2, 5, 10],\n",
+    "    'estimator__min_samples_leaf': [1, 2, 4]\n",
+    "}\n",
+    "grid_search_ab = GridSearchCV(\n",
+    "    AdaBoostClassifier(estimator=DecisionTreeClassifier(), random_state=42),\n",
+    "    grid_params_ab, cv=5, scoring='accuracy', verbose=1, n_jobs=4\n",
+    ")\n",
+    "grid_search_ab.fit(X_train_transformed, y_train)\n",
+    "print(f\"Best parameters for AdaBoost: {grid_search_ab.best_params_}\")\n",
+    "\n",
+    "ab_model = grid_search_ab.best_estimator_\n",
+    "ab_model.fit(X_train_transformed, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cdb12ce",
+   "metadata": {},
+   "source": [
+    "-   Evaluate the AdaBoost model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "878a7232",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AdaBoost Classifier Accuracy: 0.7990\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "Not Survived       0.83      0.86      0.84       243\n",
+      "    Survived       0.75      0.71      0.73       150\n",
+      "\n",
+      "    accuracy                           0.80       393\n",
+      "   macro avg       0.79      0.78      0.78       393\n",
+      "weighted avg       0.80      0.80      0.80       393\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_pred_ab = ab_model.predict(X_test_transformed)\n",
+    "print(f\"AdaBoost Classifier Accuracy: {accuracy_score(y_test, y_pred_ab):.4f}\")\n",
+    "print(classification_report(y_test, y_pred_ab, target_names=class_names))\n",
+    "ConfusionMatrixDisplay.from_estimator(ab_model, X_test_transformed, y_test,\n",
+    "                                      display_labels=class_names, cmap='Blues')\n",
+    "plt.title('AdaBoost Confusion Matrix')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab067804",
+   "metadata": {},
+   "source": [
+    "-   Feature importance for AdaBoost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "45baac79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature importances for AdaBoost:\n",
+      "            Feature  Importance\n",
+      "1     sex__sex_male    0.469367\n",
+      "0          age__age    0.202771\n",
+      "2  pclass__pclass_1    0.163301\n",
+      "4  pclass__pclass_3    0.151265\n",
+      "3  pclass__pclass_2    0.013297\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "importances = ab_model.feature_importances_\n",
+    "indices = importances.argsort()[::-1]\n",
+    "feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': importances})\n",
+    "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
+    "print(\"Feature importances for AdaBoost:\")\n",
+    "print(feature_importance_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35019ac6",
+   "metadata": {},
+   "source": [
+    "-   Plot feature importances"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6e8f7990",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(range(X_train_transformed.shape[1]), importances[indices], align='center')\n",
+    "plt.xticks(range(X_train_transformed.shape[1]), [feature_names[i] for i in indices],\n",
+    "           rotation=45)\n",
+    "plt.title('Feature Importances from AdaBoost')\n",
+    "plt.xlabel('Features')\n",
+    "plt.ylabel('Importance')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1dd81bdb",
+   "metadata": {},
+   "source": [
+    "## Gradient Boosting Overview\n",
+    "\n",
+    "Gradient Boosting is a powerful ensemble learning technique that builds models sequentially, where each new model is trained to minimize the residual errors of the previous models. The key steps in Gradient Boosting are:\n",
+    "\n",
+    "1.  **Initialize Model**: Start with a constant model (e.g., the mean of the target variable for regression).\n",
+    "2.  **Compute Residuals**: Calculate the residuals (errors) of the current model on the training data.\n",
+    "3.  **Train Weak Learner**: Train a weak learner (e.g., a decision tree) on the residuals, focusing on the instances where the current model performs poorly.\n",
+    "4.  **Update Model**: Add the predictions of the weak learner to the current model, scaled by a learning rate (shrinkage).\n",
+    "5.  **Repeat**: Repeat steps 2-4 for a specified number of iterations or until a stopping criterion is met (e.g., no significant improvement in performance)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6842e0d",
+   "metadata": {},
+   "source": [
+    "### Example of Gradient Boosting\n",
+    "\n",
+    "-   Train a Gradien Boosting Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ef43c499",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 324 candidates, totalling 1620 fits\n",
+      "Best parameters for Gradient Boosting: {'learning_rate': 0.1, 'max_depth': 4, 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 50}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(max_depth=4, min_samples_leaf=2, min_samples_split=5,\n",
+       "                           n_estimators=50, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GradientBoostingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('loss',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=loss,-%7B%27log_loss%27%2C%20%27exponential%27%7D%2C%20default%3D%27log_loss%27\">\n",
+       "            loss\n",
+       "            <span class=\"param-doc-description\">loss: {'log_loss', 'exponential'}, default='log_loss'<br><br>The loss function to be optimized. 'log_loss' refers to binomial and<br>multinomial deviance, the same as used in logistic regression.<br>It is a good choice for classification with probabilistic outputs.<br>For loss 'exponential', gradient boosting recovers the AdaBoost algorithm.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;log_loss&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=learning_rate,-float%2C%20default%3D0.1\">\n",
+       "            learning_rate\n",
+       "            <span class=\"param-doc-description\">learning_rate: float, default=0.1<br><br>Learning rate shrinks the contribution of each tree by `learning_rate`.<br>There is a trade-off between learning_rate and n_estimators.<br>Values must be in the range `[0.0, inf)`.<br><br>For an example of the effects of this parameter and its interaction with<br>``subsample``, see<br>:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regularization.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of boosting stages to perform. Gradient boosting<br>is fairly robust to over-fitting so a large number usually<br>results in better performance.<br>Values must be in the range `[1, inf)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">50</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('subsample',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=subsample,-float%2C%20default%3D1.0\">\n",
+       "            subsample\n",
+       "            <span class=\"param-doc-description\">subsample: float, default=1.0<br><br>The fraction of samples to be used for fitting the individual base<br>learners. If smaller than 1.0 this results in Stochastic Gradient<br>Boosting. `subsample` interacts with the parameter `n_estimators`.<br>Choosing `subsample < 1.0` leads to a reduction of variance<br>and an increase in bias.<br>Values must be in the range `(0.0, 1.0]`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=criterion,-%7B%27friedman_mse%27%2C%20%27squared_error%27%7D%2C%20default%3D%27friedman_mse%27\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {'friedman_mse', 'squared_error'}, default='friedman_mse'<br><br>The function to measure the quality of a split. Supported criteria are<br>'friedman_mse' for the mean squared error with improvement score by<br>Friedman, 'squared_error' for mean squared error. The default value of<br>'friedman_mse' is generally the best as it can provide a better<br>approximation in some cases.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;friedman_mse&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, values must be in the range `[2, inf)`.<br>- If float, values must be in the range `(0.0, 1.0]` and `min_samples_split`<br>  will be `ceil(min_samples_split * n_samples)`.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, values must be in the range `[1, inf)`.<br>- If float, values must be in the range `(0.0, 1.0)` and `min_samples_leaf`<br>  will be `ceil(min_samples_leaf * n_samples)`.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.<br>Values must be in the range `[0.0, 0.5]`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=max_depth,-int%20or%20None%2C%20default%3D3\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int or None, default=3<br><br>Maximum depth of the individual regression estimators. The maximum<br>depth limits the number of nodes in the tree. Tune this parameter<br>for best performance; the best value depends on the interaction<br>of the input variables. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.<br>If int, values must be in the range `[1, inf)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">4</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br>Values must be in the range `[0.0, inf)`.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('init',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=init,-estimator%20or%20%27zero%27%2C%20default%3DNone\">\n",
+       "            init\n",
+       "            <span class=\"param-doc-description\">init: estimator or 'zero', default=None<br><br>An estimator object that is used to compute the initial predictions.<br>``init`` has to provide :term:`fit` and :term:`predict_proba`. If<br>'zero', the initial raw predictions are set to zero. By default, a<br>``DummyEstimator`` predicting the classes priors is used.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the random seed given to each Tree estimator at each<br>boosting iteration.<br>In addition, it controls the random permutation of the features at<br>each split (see Notes for more details).<br>It also controls the random splitting of the training data to obtain a<br>validation set if `n_iter_no_change` is not None.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">42</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=max_features,-%7B%27sqrt%27%2C%20%27log2%27%7D%2C%20int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {'sqrt', 'log2'}, int or float, default=None<br><br>The number of features to consider when looking for the best split:<br><br>- If int, values must be in the range `[1, inf)`.<br>- If float, values must be in the range `(0.0, 1.0]` and the features<br>  considered at each split will be `max(1, int(max_features * n_features_in_))`.<br>- If 'sqrt', then `max_features=sqrt(n_features)`.<br>- If 'log2', then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>Choosing `max_features < n_features` leads to a reduction of variance<br>and an increase in bias.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Enable verbose output. If 1 then it prints progress and performance<br>once in a while (the more trees the lower the frequency). If greater<br>than 1 then it prints progress and performance for every tree.<br>Values must be in the range `[0, inf)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>Values must be in the range `[2, inf)`.<br>If `None`, then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just erase the<br>previous solution. See :term:`the Glossary <warm_start>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validation_fraction',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=validation_fraction,-float%2C%20default%3D0.1\">\n",
+       "            validation_fraction\n",
+       "            <span class=\"param-doc-description\">validation_fraction: float, default=0.1<br><br>The proportion of training data to set aside as validation set for<br>early stopping. Values must be in the range `(0.0, 1.0)`.<br>Only used if ``n_iter_no_change`` is set to an integer.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=n_iter_no_change,-int%2C%20default%3DNone\">\n",
+       "            n_iter_no_change\n",
+       "            <span class=\"param-doc-description\">n_iter_no_change: int, default=None<br><br>``n_iter_no_change`` is used to decide if early stopping will be used<br>to terminate training when validation score is not improving. By<br>default it is set to None to disable early stopping. If set to a<br>number, it will set aside ``validation_fraction`` size of the training<br>data as validation and terminate training when validation score is not<br>improving in all of the previous ``n_iter_no_change`` numbers of<br>iterations. The split is stratified.<br>Values must be in the range `[1, inf)`.<br>See<br>:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_early_stopping.py`.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for the early stopping. When the loss is not improving<br>by at least tol for ``n_iter_no_change`` iterations (if set to a<br>number), the training stops.<br>Values must be in the range `[0.0, inf)`.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed.<br>Values must be in the range `[0.0, inf)`.<br>See :ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
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+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
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+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
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+       "\n",
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+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
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+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
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+       "\n",
+       "\n",
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+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
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+      ],
+      "text/plain": [
+       "GradientBoostingClassifier(max_depth=4, min_samples_leaf=2, min_samples_split=5,\n",
+       "                           n_estimators=50, random_state=42)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "\n",
+    "# Gradient Boosting Classifier\n",
+    "grid_params_gb = {\n",
+    "    'n_estimators': [10, 50, 100, 200],\n",
+    "    'learning_rate': [0.01, 0.1, 0.2],\n",
+    "    'max_depth': [3, 4, 5],\n",
+    "    'min_samples_split': [2, 5, 10],\n",
+    "    'min_samples_leaf': [1, 2, 4]\n",
+    "}\n",
+    "gb_model = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, random_state=42)\n",
+    "grid_search_gb = GridSearchCV(gb_model, grid_params_gb, cv=5, n_jobs=4,\n",
+    "                              scoring='accuracy', verbose=1)\n",
+    "grid_search_gb.fit(X_train_transformed, y_train)\n",
+    "gb_model = grid_search_gb.best_estimator_\n",
+    "print(\"Best parameters for Gradient Boosting:\", grid_search_gb.best_params_)\n",
+    "gb_model.fit(X_train_transformed, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84610a18",
+   "metadata": {},
+   "source": [
+    "-   Evaluate the Gradient Boosting Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "6a60a5fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gradient Boosting Classifier Accuracy: 0.8092\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "Not Survived       0.83      0.87      0.85       243\n",
+      "    Survived       0.77      0.71      0.74       150\n",
+      "\n",
+      "    accuracy                           0.81       393\n",
+      "   macro avg       0.80      0.79      0.79       393\n",
+      "weighted avg       0.81      0.81      0.81       393\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_pred_gb = gb_model.predict(X_test_transformed)\n",
+    "print(f\"Gradient Boosting Classifier Accuracy: {accuracy_score(y_test, y_pred_gb):.4f}\")\n",
+    "print(classification_report(y_test, y_pred_gb, target_names=class_names))\n",
+    "ConfusionMatrixDisplay.from_estimator(gb_model, X_test_transformed, y_test,\n",
+    "                                      display_labels=class_names, cmap='Blues')\n",
+    "plt.title('Gradient Boosting Confusion Matrix')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3253b5f",
+   "metadata": {},
+   "source": [
+    "-   Feature importance for Gradient Boosting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f9f39793",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature importances for Gradient Boosting:\n",
+      "            Feature  Importance\n",
+      "1     sex__sex_male    0.574131\n",
+      "0          age__age    0.227912\n",
+      "4  pclass__pclass_3    0.156952\n",
+      "2  pclass__pclass_1    0.035021\n",
+      "3  pclass__pclass_2    0.005983\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "importances = gb_model.feature_importances_\n",
+    "indices = importances.argsort()[::-1]\n",
+    "feature_importance_df = pd.DataFrame({'Feature': feature_names,\n",
+    "                                      'Importance': importances})\n",
+    "feature_importance_df = feature_importance_df.sort_values(by='Importance',\n",
+    "                                                          ascending=False)\n",
+    "print(\"Feature importances for Gradient Boosting:\")\n",
+    "print(feature_importance_df)\n",
+    "\n",
+    "# Plot feature importances\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(range(X_train_transformed.shape[1]), importances[indices], align='center')\n",
+    "plt.xticks(range(X_train_transformed.shape[1]), [feature_names[i] for i in indices],\n",
+    "           rotation=45)\n",
+    "plt.title('Feature Importances from Gradient Boosting')\n",
+    "plt.xlabel('Features')\n",
+    "plt.ylabel('Importance')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f12a4d9a",
+   "metadata": {},
+   "source": [
+    "## XGBoost Overview\n",
+    "\n",
+    "XGBoost, short for Extreme Gradient Boosting, is an optimized version of gradient boosting that includes regularization techniques to prevent overfitting and improve performance. It is widely used in machine learning competitions and real-world applications. The key features of XGBoost include:\n",
+    "\n",
+    "-   **Regularization**: XGBoost includes L1 (Lasso) and L2 (Ridge) regularization to control model complexity and prevent overfitting.\n",
+    "-   **Tree Pruning**: XGBoost uses a depth-first approach to grow trees and prunes them based on a minimum loss reduction criterion, which helps in building more efficient models.\n",
+    "-   **Parallelization**: XGBoost can parallelize the tree construction process, making it faster than traditional gradient boosting methods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c877192d",
+   "metadata": {},
+   "source": [
+    "### Example of XGBoost\n",
+    "\n",
+    "-   Train a XGBoost Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "bc43bc1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 243 candidates, totalling 1215 fits\n",
+      "Best parameters for XGBoost: {'learning_rate': 0.1, 'max_depth': 4, 'min_child_weight': 1, 'n_estimators': 100, 'subsample': 1.0}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+       "              colsample_bylevel=None, colsample_bynode=None,\n",
+       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n",
+       "              feature_types=None, feature_weights=None, gamma=None,\n",
+       "              grow_policy=None, importance_type=None,\n",
+       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
+       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "              max_delta_step=None, max_depth=4, max_leaves=None,\n",
+       "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
+       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
+       "              num_parallel_tree=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier\">?<span>Documentation for XGBClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('objective',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=objective,-typing.Union%5Bstr%2C%20xgboost.sklearn._SklObjWProto%2C%20typing.Callable%5B%5Btyping.Any%2C%20typing.Any%5D%2C%20typing.Tuple%5Bnumpy.ndarray%2C%20numpy.ndarray%5D%5D%2C%20NoneType%5D\">\n",
+       "            objective\n",
+       "            <span class=\"param-doc-description\">objective: typing.Union[str, xgboost.sklearn._SklObjWProto, typing.Callable[[typing.Any, typing.Any], typing.Tuple[numpy.ndarray, numpy.ndarray]], NoneType]<br><br>Specify the learning task and the corresponding learning objective or a custom<br>objective function to be used.<br><br>For custom objective, see :doc:`/tutorials/custom_metric_obj` and<br>:ref:`custom-obj-metric` for more information, along with the end note for<br>function signatures.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;binary:logistic&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('base_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=base_score,-typing.Union%5Bfloat%2C%20typing.List%5Bfloat%5D%2C%20NoneType%5D\">\n",
+       "            base_score\n",
+       "            <span class=\"param-doc-description\">base_score: typing.Union[float, typing.List[float], NoneType]<br><br>The initial prediction score of all instances, global bias.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('booster',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">booster</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('callbacks',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=callbacks,-typing.Optional%5Btyping.List%5Bxgboost.callback.TrainingCallback%5D%5D\">\n",
+       "            callbacks\n",
+       "            <span class=\"param-doc-description\">callbacks: typing.Optional[typing.List[xgboost.callback.TrainingCallback]]<br><br>List of callback functions that are applied at end of each iteration.<br>It is possible to use predefined callbacks by using<br>:ref:`Callback API <callback_api>`.<br><br>.. note::<br><br>   States in callback are not preserved during training, which means callback<br>   objects can not be reused for multiple training sessions without<br>   reinitialization or deepcopy.<br><br>.. code-block:: python<br><br>    for params in parameters_grid:<br>        # be sure to (re)initialize the callbacks before each run<br>        callbacks = [xgb.callback.LearningRateScheduler(custom_rates)]<br>        reg = xgboost.XGBRegressor(**params, callbacks=callbacks)<br>        reg.fit(X, y)</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('colsample_bylevel',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=colsample_bylevel,-typing.Optional%5Bfloat%5D\">\n",
+       "            colsample_bylevel\n",
+       "            <span class=\"param-doc-description\">colsample_bylevel: typing.Optional[float]<br><br>Subsample ratio of columns for each level.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('colsample_bynode',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=colsample_bynode,-typing.Optional%5Bfloat%5D\">\n",
+       "            colsample_bynode\n",
+       "            <span class=\"param-doc-description\">colsample_bynode: typing.Optional[float]<br><br>Subsample ratio of columns for each split.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('colsample_bytree',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=colsample_bytree,-typing.Optional%5Bfloat%5D\">\n",
+       "            colsample_bytree\n",
+       "            <span class=\"param-doc-description\">colsample_bytree: typing.Optional[float]<br><br>Subsample ratio of columns when constructing each tree.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('device',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=device,-typing.Optional%5Bstr%5D\">\n",
+       "            device\n",
+       "            <span class=\"param-doc-description\">device: typing.Optional[str]<br><br>.. versionadded:: 2.0.0<br><br>Device ordinal, available options are `cpu`, `cuda`, and `gpu`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('early_stopping_rounds',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=early_stopping_rounds,-typing.Optional%5Bint%5D\">\n",
+       "            early_stopping_rounds\n",
+       "            <span class=\"param-doc-description\">early_stopping_rounds: typing.Optional[int]<br><br>.. versionadded:: 1.6.0<br><br>- Activates early stopping. Validation metric needs to improve at least once in<br>  every **early_stopping_rounds** round(s) to continue training.  Requires at<br>  least one item in **eval_set** in :py:meth:`fit`.<br><br>- If early stopping occurs, the model will have two additional attributes:<br>  :py:attr:`best_score` and :py:attr:`best_iteration`. These are used by the<br>  :py:meth:`predict` and :py:meth:`apply` methods to determine the optimal<br>  number of trees during inference. If users want to access the full model<br>  (including trees built after early stopping), they can specify the<br>  `iteration_range` in these inference methods. In addition, other utilities<br>  like model plotting can also use the entire model.<br><br>- If you prefer to discard the trees after `best_iteration`, consider using the<br>  callback function :py:class:`xgboost.callback.EarlyStopping`.<br><br>- If there's more than one item in **eval_set**, the last entry will be used for<br>  early stopping.  If there's more than one metric in **eval_metric**, the last<br>  metric will be used for early stopping.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('enable_categorical',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=enable_categorical,-bool\">\n",
+       "            enable_categorical\n",
+       "            <span class=\"param-doc-description\">enable_categorical: bool<br><br>See the same parameter of :py:class:`DMatrix` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('eval_metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=eval_metric,-typing.Union%5Bstr%2C%20typing.List%5Btyping.Union%5Bstr%2C%20typing.Callable%5D%5D%2C%20typing.Callable%2C%20NoneType%5D\">\n",
+       "            eval_metric\n",
+       "            <span class=\"param-doc-description\">eval_metric: typing.Union[str, typing.List[typing.Union[str, typing.Callable]], typing.Callable, NoneType]<br><br>.. versionadded:: 1.6.0<br><br>Metric used for monitoring the training result and early stopping.  It can be a<br>string or list of strings as names of predefined metric in XGBoost (See<br>:doc:`/parameter`), one of the metrics in :py:mod:`sklearn.metrics`, or any<br>other user defined metric that looks like `sklearn.metrics`.<br><br>If custom objective is also provided, then custom metric should implement the<br>corresponding reverse link function.<br><br>Unlike the `scoring` parameter commonly used in scikit-learn, when a callable<br>object is provided, it's assumed to be a cost function and by default XGBoost<br>will minimize the result during early stopping.<br><br>For advanced usage on Early stopping like directly choosing to maximize instead<br>of minimize, see :py:obj:`xgboost.callback.EarlyStopping`.<br><br>See :doc:`/tutorials/custom_metric_obj` and :ref:`custom-obj-metric` for more<br>information.<br><br>.. code-block:: python<br><br>    from sklearn.datasets import load_diabetes<br>    from sklearn.metrics import mean_absolute_error<br>    X, y = load_diabetes(return_X_y=True)<br>    reg = xgb.XGBRegressor(<br>        tree_method=\"hist\",<br>        eval_metric=mean_absolute_error,<br>    )<br>    reg.fit(X, y, eval_set=[(X, y)])</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;logloss&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_types',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=feature_types,-typing.Optional%5Btyping.Sequence%5Bstr%5D%5D\">\n",
+       "            feature_types\n",
+       "            <span class=\"param-doc-description\">feature_types: typing.Optional[typing.Sequence[str]]<br><br>.. versionadded:: 1.7.0<br><br>Used for specifying feature types without constructing a dataframe. See<br>the :py:class:`DMatrix` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=feature_weights,-Optional%5BArrayLike%5D\">\n",
+       "            feature_weights\n",
+       "            <span class=\"param-doc-description\">feature_weights: Optional[ArrayLike]<br><br>Weight for each feature, defines the probability of each feature being selected<br>when colsample is being used.  All values must be greater than 0, otherwise a<br>`ValueError` is thrown.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('gamma',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=gamma,-typing.Optional%5Bfloat%5D\">\n",
+       "            gamma\n",
+       "            <span class=\"param-doc-description\">gamma: typing.Optional[float]<br><br>(min_split_loss) Minimum loss reduction required to make a further partition on<br>a leaf node of the tree.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('grow_policy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=grow_policy,-typing.Optional%5Bstr%5D\">\n",
+       "            grow_policy\n",
+       "            <span class=\"param-doc-description\">grow_policy: typing.Optional[str]<br><br>Tree growing policy.<br><br>- depthwise: Favors splitting at nodes closest to the node,<br>- lossguide: Favors splitting at nodes with highest loss change.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('importance_type',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">importance_type</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('interaction_constraints',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=interaction_constraints,-typing.Union%5Bstr%2C%20typing.List%5Btyping.Tuple%5Bstr%5D%5D%2C%20NoneType%5D\">\n",
+       "            interaction_constraints\n",
+       "            <span class=\"param-doc-description\">interaction_constraints: typing.Union[str, typing.List[typing.Tuple[str]], NoneType]<br><br>Constraints for interaction representing permitted interactions.  The<br>constraints must be specified in the form of a nested list, e.g. ``[[0, 1], [2,<br>3, 4]]``, where each inner list is a group of indices of features that are<br>allowed to interact with each other.  See :doc:`tutorial<br></tutorials/feature_interaction_constraint>` for more information</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=learning_rate,-typing.Optional%5Bfloat%5D\">\n",
+       "            learning_rate\n",
+       "            <span class=\"param-doc-description\">learning_rate: typing.Optional[float]<br><br>Boosting learning rate (xgb's \"eta\")</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_bin',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=max_bin,-typing.Optional%5Bint%5D\">\n",
+       "            max_bin\n",
+       "            <span class=\"param-doc-description\">max_bin: typing.Optional[int]<br><br>If using histogram-based algorithm, maximum number of bins per feature</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_cat_threshold',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=max_cat_threshold,-typing.Optional%5Bint%5D\">\n",
+       "            max_cat_threshold\n",
+       "            <span class=\"param-doc-description\">max_cat_threshold: typing.Optional[int]<br><br>.. versionadded:: 1.7.0<br><br>.. note:: This parameter is experimental<br><br>Maximum number of categories considered for each split. Used only by<br>partition-based splits for preventing over-fitting. Also, `enable_categorical`<br>needs to be set to have categorical feature support. See :doc:`Categorical Data<br></tutorials/categorical>` and :ref:`cat-param` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_cat_to_onehot',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=max_cat_to_onehot,-Optional%5Bint%5D\">\n",
+       "            max_cat_to_onehot\n",
+       "            <span class=\"param-doc-description\">max_cat_to_onehot: Optional[int]<br><br>.. versionadded:: 1.6.0<br><br>.. note:: This parameter is experimental<br><br>A threshold for deciding whether XGBoost should use one-hot encoding based split<br>for categorical data.  When number of categories is lesser than the threshold<br>then one-hot encoding is chosen, otherwise the categories will be partitioned<br>into children nodes. Also, `enable_categorical` needs to be set to have<br>categorical feature support. See :doc:`Categorical Data<br></tutorials/categorical>` and :ref:`cat-param` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_delta_step',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=max_delta_step,-typing.Optional%5Bfloat%5D\">\n",
+       "            max_delta_step\n",
+       "            <span class=\"param-doc-description\">max_delta_step: typing.Optional[float]<br><br>Maximum delta step we allow each tree's weight estimation to be.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=max_depth,-%20typing.Optional%5Bint%5D\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth:  typing.Optional[int]<br><br>Maximum tree depth for base learners.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">4</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaves',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=max_leaves,-typing.Optional%5Bint%5D\">\n",
+       "            max_leaves\n",
+       "            <span class=\"param-doc-description\">max_leaves: typing.Optional[int]<br><br>Maximum number of leaves; 0 indicates no limit.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_child_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=min_child_weight,-typing.Optional%5Bfloat%5D\">\n",
+       "            min_child_weight\n",
+       "            <span class=\"param-doc-description\">min_child_weight: typing.Optional[float]<br><br>Minimum sum of instance weight(hessian) needed in a child.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=missing,-float\">\n",
+       "            missing\n",
+       "            <span class=\"param-doc-description\">missing: float<br><br>Value in the data which needs to be present as a missing value. Default to<br>:py:data:`numpy.nan`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotone_constraints',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=monotone_constraints,-typing.Union%5Btyping.Dict%5Bstr%2C%20int%5D%2C%20str%2C%20NoneType%5D\">\n",
+       "            monotone_constraints\n",
+       "            <span class=\"param-doc-description\">monotone_constraints: typing.Union[typing.Dict[str, int], str, NoneType]<br><br>Constraint of variable monotonicity.  See :doc:`tutorial </tutorials/monotonic>`<br>for more information.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('multi_strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=multi_strategy,-typing.Optional%5Bstr%5D\">\n",
+       "            multi_strategy\n",
+       "            <span class=\"param-doc-description\">multi_strategy: typing.Optional[str]<br><br>.. versionadded:: 2.0.0<br><br>.. note:: This parameter is working-in-progress.<br><br>The strategy used for training multi-target models, including multi-target<br>regression and multi-class classification. See :doc:`/tutorials/multioutput` for<br>more information.<br><br>- ``one_output_per_tree``: One model for each target.<br>- ``multi_output_tree``:  Use multi-target trees.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=n_estimators,-Optional%5Bint%5D\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: Optional[int]<br><br>Number of boosting rounds.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=n_jobs,-typing.Optional%5Bint%5D\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: typing.Optional[int]<br><br>Number of parallel threads used to run xgboost.  When used with other<br>Scikit-Learn algorithms like grid search, you may choose which algorithm to<br>parallelize and balance the threads.  Creating thread contention will<br>significantly slow down both algorithms.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('num_parallel_tree',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">num_parallel_tree</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=random_state,-typing.Union%5Bnumpy.random.mtrand.RandomState%2C%20numpy.random._generator.Generator%2C%20int%2C%20NoneType%5D\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: typing.Union[numpy.random.mtrand.RandomState, numpy.random._generator.Generator, int, NoneType]<br><br>Random number seed.<br><br>.. note::<br><br>   Using gblinear booster with shotgun updater is nondeterministic as<br>   it uses Hogwild algorithm.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">42</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('reg_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=reg_alpha,-typing.Optional%5Bfloat%5D\">\n",
+       "            reg_alpha\n",
+       "            <span class=\"param-doc-description\">reg_alpha: typing.Optional[float]<br><br>L1 regularization term on weights (xgb's alpha).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('reg_lambda',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=reg_lambda,-typing.Optional%5Bfloat%5D\">\n",
+       "            reg_lambda\n",
+       "            <span class=\"param-doc-description\">reg_lambda: typing.Optional[float]<br><br>L2 regularization term on weights (xgb's lambda).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('sampling_method',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=sampling_method,-typing.Optional%5Bstr%5D\">\n",
+       "            sampling_method\n",
+       "            <span class=\"param-doc-description\">sampling_method: typing.Optional[str]<br><br>Sampling method. Used only by the GPU version of ``hist`` tree method.<br><br>- ``uniform``: Select random training instances uniformly.<br>- ``gradient_based``: Select random training instances with higher probability<br>    when the gradient and hessian are larger. (cf. CatBoost)</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('scale_pos_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=scale_pos_weight,-typing.Optional%5Bfloat%5D\">\n",
+       "            scale_pos_weight\n",
+       "            <span class=\"param-doc-description\">scale_pos_weight: typing.Optional[float]<br><br>Balancing of positive and negative weights.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('subsample',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=subsample,-typing.Optional%5Bfloat%5D\">\n",
+       "            subsample\n",
+       "            <span class=\"param-doc-description\">subsample: typing.Optional[float]<br><br>Subsample ratio of the training instance.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tree_method',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=tree_method,-typing.Optional%5Bstr%5D\">\n",
+       "            tree_method\n",
+       "            <span class=\"param-doc-description\">tree_method: typing.Optional[str]<br><br>Specify which tree method to use.  Default to auto.  If this parameter is set to<br>default, XGBoost will choose the most conservative option available.  It's<br>recommended to study this option from the parameters document :doc:`tree method<br></treemethod>`</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate_parameters',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=validate_parameters,-typing.Optional%5Bbool%5D\">\n",
+       "            validate_parameters\n",
+       "            <span class=\"param-doc-description\">validate_parameters: typing.Optional[bool]<br><br>Give warnings for unknown parameter.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbosity',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBClassifier#:~:text=verbosity,-typing.Optional%5Bint%5D\">\n",
+       "            verbosity\n",
+       "            <span class=\"param-doc-description\">verbosity: typing.Optional[int]<br><br>The degree of verbosity. Valid values are 0 (silent) - 3 (debug).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-4');</script></body>"
+      ],
+      "text/plain": [
+       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+       "              colsample_bylevel=None, colsample_bynode=None,\n",
+       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "              enable_categorical=False, eval_metric='logloss',\n",
+       "              feature_types=None, feature_weights=None, gamma=None,\n",
+       "              grow_policy=None, importance_type=None,\n",
+       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
+       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "              max_delta_step=None, max_depth=4, max_leaves=None,\n",
+       "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
+       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
+       "              num_parallel_tree=None, ...)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from xgboost import XGBClassifier\n",
+    "\n",
+    "# XGBoost Classifier\n",
+    "grid_params_xgb = {\n",
+    "    'n_estimators': [50, 100, 200],\n",
+    "    'learning_rate': [0.01, 0.1, 0.2],\n",
+    "    'max_depth': [3, 4, 5],\n",
+    "    'min_child_weight': [1, 2, 3],\n",
+    "    'subsample': [0.8, 0.9, 1.0]\n",
+    "}\n",
+    "\n",
+    "grid_search_xgb = GridSearchCV(\n",
+    "    XGBClassifier(random_state=42, eval_metric='logloss'),\n",
+    "    grid_params_xgb, cv=5, n_jobs=4, scoring='accuracy', verbose=1\n",
+    ")\n",
+    "grid_search_xgb.fit(X_train_transformed, y_train)\n",
+    "print(\"Best parameters for XGBoost:\", grid_search_xgb.best_params_)\n",
+    "xgb_model = grid_search_xgb.best_estimator_\n",
+    "xgb_model.fit(X_train_transformed, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de318eb8",
+   "metadata": {},
+   "source": [
+    "-   Evaluate the XGBoost Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e97dcdd6",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "XGBoost Classifier Accuracy: 0.8168\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "Not Survived       0.84      0.87      0.85       243\n",
+      "    Survived       0.78      0.73      0.75       150\n",
+      "\n",
+      "    accuracy                           0.82       393\n",
+      "   macro avg       0.81      0.80      0.80       393\n",
+      "weighted avg       0.82      0.82      0.82       393\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature importances for XGBoost:\n",
+      "            Feature  Importance\n",
+      "1     sex__sex_male    0.661201\n",
+      "4  pclass__pclass_3    0.226911\n",
+      "2  pclass__pclass_1    0.055751\n",
+      "0          age__age    0.030057\n",
+      "3  pclass__pclass_2    0.026079\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_pred_xgb = xgb_model.predict(X_test_transformed)\n",
+    "print(f\"XGBoost Classifier Accuracy: {accuracy_score(y_test, y_pred_xgb):.4f}\")\n",
+    "print(classification_report(y_test, y_pred_xgb, target_names=class_names))\n",
+    "ConfusionMatrixDisplay.from_estimator(xgb_model, X_test_transformed, y_test,\n",
+    "                                        display_labels=class_names, cmap='Blues')\n",
+    "plt.title('XGBoost Confusion Matrix')\n",
+    "plt.show()\n",
+    "\n",
+    "# Feature importance for XGBoost\n",
+    "import pandas as pd\n",
+    "importances = xgb_model.feature_importances_\n",
+    "indices = importances.argsort()[::-1]\n",
+    "feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': importances})\n",
+    "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
+    "print(\"Feature importances for XGBoost:\")\n",
+    "print(feature_importance_df)\n",
+    "\n",
+    "# Plot feature importances\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(range(X_train_transformed.shape[1]), importances[indices], align='center')\n",
+    "plt.xticks(range(X_train_transformed.shape[1]), [feature_names[i] for i in indices],\n",
+    "           rotation=45)\n",
+    "plt.title('Feature Importances from XGBoost')\n",
+    "plt.xlabel('Features')\n",
+    "plt.ylabel('Importance')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68ea9a26",
+   "metadata": {},
+   "source": [
+    "## Hands-on Exercise\n",
+    "\n",
+    "-   Implement a boosting algorithm of your choice (AdaBoost, Gradient Boosting, or XGBoost) on the `house_prices` dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "4ab7f977",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the house_prices dataset\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "house_prices = fetch_openml('house_prices', version=1, as_frame=True)\n",
+    "df = house_prices.frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "0ad26e9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Missing values in the dataset:\n",
+      "Id                 0\n",
+      "MSSubClass         0\n",
+      "MSZoning           0\n",
+      "LotFrontage      259\n",
+      "LotArea            0\n",
+      "                ... \n",
+      "MoSold             0\n",
+      "YrSold             0\n",
+      "SaleType           0\n",
+      "SaleCondition      0\n",
+      "SalePrice          0\n",
+      "Length: 81, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check whether the dataset has missing values\n",
+    "print(\"Missing values in the dataset:\")\n",
+    "print(df.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "78879cfb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_507440/261620420.py:18: Pandas4Warning: For backward compatibility, 'str' dtypes are included by select_dtypes when 'object' dtype is specified. This behavior is deprecated and will be removed in a future version. Explicitly pass 'str' to `include` to select them, or to `exclude` to remove them and silence this warning.\n",
+      "See https://pandas.pydata.org/docs/user_guide/migration-3-strings.html#string-migration-select-dtypes for details on how to write code that works with pandas 2 and 3.\n",
+      "  ]), X.select_dtypes(include=['object']).columns)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Select relevant features and target variable\n",
+    "X = df.drop(columns=['SalePrice'])\n",
+    "y = df['SalePrice']\n",
+    "\n",
+    "# Train-test split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
+    "\n",
+    "# Preprocessing pipeline\n",
+    "preprocessing_pipeline = ColumnTransformer(\n",
+    "    transformers=[\n",
+    "        ('num', Pipeline([\n",
+    "            ('imputer', SimpleImputer(strategy='mean')),\n",
+    "            ('scaler', StandardScaler())\n",
+    "        ]), X.select_dtypes(include=['int64', 'float64']).columns),\n",
+    "        ('cat', Pipeline([\n",
+    "            ('imputer', SimpleImputer(strategy='most_frequent')),\n",
+    "            ('encoder', OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n",
+    "        ]), X.select_dtypes(include=['object']).columns)\n",
+    "    ],\n",
+    "    remainder='passthrough'\n",
+    "  )\n",
+    "\n",
+    "X_train_transformed = preprocessing_pipeline.fit_transform(X_train)\n",
+    "X_test_transformed = preprocessing_pipeline.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "57628b44",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 18 candidates, totalling 90 fits\n",
+      "Best parameters for Support Vector Regressor: {'C': 10, 'epsilon': 0.01, 'kernel': 'linear'}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR(C=10, epsilon=0.01, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVR</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kernel',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVR.html#:~:text=kernel,-%7B%27linear%27%2C%20%27poly%27%2C%20%27rbf%27%2C%20%27sigmoid%27%2C%20%27precomputed%27%7D%20or%20callable%2C%20%20%20%20%20%20%20%20%20%20default%3D%27rbf%27\">\n",
+       "            kernel\n",
+       "            <span class=\"param-doc-description\">kernel: {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable,          default='rbf'<br><br>Specifies the kernel type to be used in the algorithm.<br>If none is given, 'rbf' will be used. If a callable is given it is<br>used to precompute the kernel matrix.<br>For an intuitive visualization of different kernel types<br>see :ref:`sphx_glr_auto_examples_svm_plot_svm_regression.py`</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;linear&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('degree',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVR.html#:~:text=degree,-int%2C%20default%3D3\">\n",
+       "            degree\n",
+       "            <span class=\"param-doc-description\">degree: int, default=3<br><br>Degree of the polynomial kernel function ('poly').<br>Must be non-negative. Ignored by all other kernels.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('gamma',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVR.html#:~:text=gamma,-%7B%27scale%27%2C%20%27auto%27%7D%20or%20float%2C%20default%3D%27scale%27\">\n",
+       "            gamma\n",
+       "            <span class=\"param-doc-description\">gamma: {'scale', 'auto'} or float, default='scale'<br><br>Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.<br><br>- if ``gamma='scale'`` (default) is passed then it uses<br>  1 / (n_features * X.var()) as value of gamma,<br>- if 'auto', uses 1 / n_features<br>- if float, must be non-negative.<br><br>.. versionchanged:: 0.22<br>   The default value of ``gamma`` changed from 'auto' to 'scale'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;scale&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('coef0',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVR.html#:~:text=coef0,-float%2C%20default%3D0.0\">\n",
+       "            coef0\n",
+       "            <span class=\"param-doc-description\">coef0: float, default=0.0<br><br>Independent term in kernel function.<br>It is only significant in 'poly' and 'sigmoid'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVR.html#:~:text=tol,-float%2C%20default%3D1e-3\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-3<br><br>Tolerance for stopping criterion.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVR.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Regularization parameter. The strength of the regularization is<br>inversely proportional to C. Must be strictly positive.<br>The penalty is a squared l2. For an intuitive visualization of the<br>effects of scaling the regularization parameter C, see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">10</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('epsilon',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVR.html#:~:text=epsilon,-float%2C%20default%3D0.1\">\n",
+       "            epsilon\n",
+       "            <span class=\"param-doc-description\">epsilon: float, default=0.1<br><br>Epsilon in the epsilon-SVR model. It specifies the epsilon-tube<br>within which no penalty is associated in the training loss function<br>with points predicted within a distance epsilon from the actual<br>value. Must be non-negative.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.01</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('shrinking',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVR.html#:~:text=shrinking,-bool%2C%20default%3DTrue\">\n",
+       "            shrinking\n",
+       "            <span class=\"param-doc-description\">shrinking: bool, default=True<br><br>Whether to use the shrinking heuristic.<br>See the :ref:`User Guide <shrinking_svm>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cache_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVR.html#:~:text=cache_size,-float%2C%20default%3D200\">\n",
+       "            cache_size\n",
+       "            <span class=\"param-doc-description\">cache_size: float, default=200<br><br>Specify the size of the kernel cache (in MB).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">200</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVR.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>Enable verbose output. Note that this setting takes advantage of a<br>per-process runtime setting in libsvm that, if enabled, may not work<br>properly in a multithreaded context.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVR.html#:~:text=max_iter,-int%2C%20default%3D-1\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=-1<br><br>Hard limit on iterations within solver, or -1 for no limit.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">-1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-5');</script></body>"
+      ],
+      "text/plain": [
+       "SVR(C=10, epsilon=0.01, kernel='linear')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train a Support Vector Regressor\n",
+    "from sklearn.svm import SVR\n",
+    "grid_params_svr = {\n",
+    "    'kernel': ['linear', 'rbf'],\n",
+    "    'C': [0.1, 1, 10],\n",
+    "    'epsilon': [0.01, 0.1, 0.2]\n",
+    "}\n",
+    "grid_search_svr = GridSearchCV(\n",
+    "    SVR(), grid_params_svr, cv=5, n_jobs=4, scoring='r2', verbose=1\n",
+    ")\n",
+    "grid_search_svr.fit(X_train_transformed, y_train)\n",
+    "print(\"Best parameters for Support Vector Regressor:\", grid_search_svr.best_params_)\n",
+    "svr_model = grid_search_svr.best_estimator_\n",
+    "svr_model.fit(X_train_transformed, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "7537312b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Support Vector Regressor RMSE: 51669.9132\n",
+      "Support Vector Regressor R^2: 0.6174\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate the SVR\n",
+    "import numpy as np\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "y_pred_svr = svr_model.predict(X_test_transformed)\n",
+    "mse_svr = mean_squared_error(y_test, y_pred_svr)\n",
+    "rmse_svr = np.sqrt(mse_svr)\n",
+    "r2_svr = r2_score(y_test, y_pred_svr)\n",
+    "print(f\"Support Vector Regressor RMSE: {rmse_svr:.4f}\")\n",
+    "print(f\"Support Vector Regressor R^2: {r2_svr:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "b2318310",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 243 candidates, totalling 1215 fits\n",
+      "Best parameters for XGBoost: {'learning_rate': 0.2, 'max_depth': 3, 'min_child_weight': 2, 'n_estimators': 200, 'subsample': 1.0}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-6 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-6 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=&#x27;rmse&#x27;, feature_types=None,\n",
+       "             feature_weights=None, gamma=None, grow_policy=None,\n",
+       "             importance_type=None, interaction_constraints=None,\n",
+       "             learning_rate=0.2, max_bin=None, max_cat_threshold=None,\n",
+       "             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,\n",
+       "             max_leaves=None, min_child_weight=2, missing=nan,\n",
+       "             monotone_constraints=None, multi_strategy=None, n_estimators=200,\n",
+       "             n_jobs=None, num_parallel_tree=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor\">?<span>Documentation for XGBRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('objective',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=objective,-typing.Union%5Bstr%2C%20xgboost.sklearn._SklObjWProto%2C%20typing.Callable%5B%5Btyping.Any%2C%20typing.Any%5D%2C%20typing.Tuple%5Bnumpy.ndarray%2C%20numpy.ndarray%5D%5D%2C%20NoneType%5D\">\n",
+       "            objective\n",
+       "            <span class=\"param-doc-description\">objective: typing.Union[str, xgboost.sklearn._SklObjWProto, typing.Callable[[typing.Any, typing.Any], typing.Tuple[numpy.ndarray, numpy.ndarray]], NoneType]<br><br>Specify the learning task and the corresponding learning objective or a custom<br>objective function to be used.<br><br>For custom objective, see :doc:`/tutorials/custom_metric_obj` and<br>:ref:`custom-obj-metric` for more information, along with the end note for<br>function signatures.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;reg:squarederror&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('base_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=base_score,-typing.Union%5Bfloat%2C%20typing.List%5Bfloat%5D%2C%20NoneType%5D\">\n",
+       "            base_score\n",
+       "            <span class=\"param-doc-description\">base_score: typing.Union[float, typing.List[float], NoneType]<br><br>The initial prediction score of all instances, global bias.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('booster',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">booster</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('callbacks',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=callbacks,-typing.Optional%5Btyping.List%5Bxgboost.callback.TrainingCallback%5D%5D\">\n",
+       "            callbacks\n",
+       "            <span class=\"param-doc-description\">callbacks: typing.Optional[typing.List[xgboost.callback.TrainingCallback]]<br><br>List of callback functions that are applied at end of each iteration.<br>It is possible to use predefined callbacks by using<br>:ref:`Callback API <callback_api>`.<br><br>.. note::<br><br>   States in callback are not preserved during training, which means callback<br>   objects can not be reused for multiple training sessions without<br>   reinitialization or deepcopy.<br><br>.. code-block:: python<br><br>    for params in parameters_grid:<br>        # be sure to (re)initialize the callbacks before each run<br>        callbacks = [xgb.callback.LearningRateScheduler(custom_rates)]<br>        reg = xgboost.XGBRegressor(**params, callbacks=callbacks)<br>        reg.fit(X, y)</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('colsample_bylevel',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=colsample_bylevel,-typing.Optional%5Bfloat%5D\">\n",
+       "            colsample_bylevel\n",
+       "            <span class=\"param-doc-description\">colsample_bylevel: typing.Optional[float]<br><br>Subsample ratio of columns for each level.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('colsample_bynode',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=colsample_bynode,-typing.Optional%5Bfloat%5D\">\n",
+       "            colsample_bynode\n",
+       "            <span class=\"param-doc-description\">colsample_bynode: typing.Optional[float]<br><br>Subsample ratio of columns for each split.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('colsample_bytree',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=colsample_bytree,-typing.Optional%5Bfloat%5D\">\n",
+       "            colsample_bytree\n",
+       "            <span class=\"param-doc-description\">colsample_bytree: typing.Optional[float]<br><br>Subsample ratio of columns when constructing each tree.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('device',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=device,-typing.Optional%5Bstr%5D\">\n",
+       "            device\n",
+       "            <span class=\"param-doc-description\">device: typing.Optional[str]<br><br>.. versionadded:: 2.0.0<br><br>Device ordinal, available options are `cpu`, `cuda`, and `gpu`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('early_stopping_rounds',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=early_stopping_rounds,-typing.Optional%5Bint%5D\">\n",
+       "            early_stopping_rounds\n",
+       "            <span class=\"param-doc-description\">early_stopping_rounds: typing.Optional[int]<br><br>.. versionadded:: 1.6.0<br><br>- Activates early stopping. Validation metric needs to improve at least once in<br>  every **early_stopping_rounds** round(s) to continue training.  Requires at<br>  least one item in **eval_set** in :py:meth:`fit`.<br><br>- If early stopping occurs, the model will have two additional attributes:<br>  :py:attr:`best_score` and :py:attr:`best_iteration`. These are used by the<br>  :py:meth:`predict` and :py:meth:`apply` methods to determine the optimal<br>  number of trees during inference. If users want to access the full model<br>  (including trees built after early stopping), they can specify the<br>  `iteration_range` in these inference methods. In addition, other utilities<br>  like model plotting can also use the entire model.<br><br>- If you prefer to discard the trees after `best_iteration`, consider using the<br>  callback function :py:class:`xgboost.callback.EarlyStopping`.<br><br>- If there's more than one item in **eval_set**, the last entry will be used for<br>  early stopping.  If there's more than one metric in **eval_metric**, the last<br>  metric will be used for early stopping.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('enable_categorical',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=enable_categorical,-bool\">\n",
+       "            enable_categorical\n",
+       "            <span class=\"param-doc-description\">enable_categorical: bool<br><br>See the same parameter of :py:class:`DMatrix` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('eval_metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=eval_metric,-typing.Union%5Bstr%2C%20typing.List%5Btyping.Union%5Bstr%2C%20typing.Callable%5D%5D%2C%20typing.Callable%2C%20NoneType%5D\">\n",
+       "            eval_metric\n",
+       "            <span class=\"param-doc-description\">eval_metric: typing.Union[str, typing.List[typing.Union[str, typing.Callable]], typing.Callable, NoneType]<br><br>.. versionadded:: 1.6.0<br><br>Metric used for monitoring the training result and early stopping.  It can be a<br>string or list of strings as names of predefined metric in XGBoost (See<br>:doc:`/parameter`), one of the metrics in :py:mod:`sklearn.metrics`, or any<br>other user defined metric that looks like `sklearn.metrics`.<br><br>If custom objective is also provided, then custom metric should implement the<br>corresponding reverse link function.<br><br>Unlike the `scoring` parameter commonly used in scikit-learn, when a callable<br>object is provided, it's assumed to be a cost function and by default XGBoost<br>will minimize the result during early stopping.<br><br>For advanced usage on Early stopping like directly choosing to maximize instead<br>of minimize, see :py:obj:`xgboost.callback.EarlyStopping`.<br><br>See :doc:`/tutorials/custom_metric_obj` and :ref:`custom-obj-metric` for more<br>information.<br><br>.. code-block:: python<br><br>    from sklearn.datasets import load_diabetes<br>    from sklearn.metrics import mean_absolute_error<br>    X, y = load_diabetes(return_X_y=True)<br>    reg = xgb.XGBRegressor(<br>        tree_method=\"hist\",<br>        eval_metric=mean_absolute_error,<br>    )<br>    reg.fit(X, y, eval_set=[(X, y)])</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;rmse&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_types',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=feature_types,-typing.Optional%5Btyping.Sequence%5Bstr%5D%5D\">\n",
+       "            feature_types\n",
+       "            <span class=\"param-doc-description\">feature_types: typing.Optional[typing.Sequence[str]]<br><br>.. versionadded:: 1.7.0<br><br>Used for specifying feature types without constructing a dataframe. See<br>the :py:class:`DMatrix` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=feature_weights,-Optional%5BArrayLike%5D\">\n",
+       "            feature_weights\n",
+       "            <span class=\"param-doc-description\">feature_weights: Optional[ArrayLike]<br><br>Weight for each feature, defines the probability of each feature being selected<br>when colsample is being used.  All values must be greater than 0, otherwise a<br>`ValueError` is thrown.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('gamma',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=gamma,-typing.Optional%5Bfloat%5D\">\n",
+       "            gamma\n",
+       "            <span class=\"param-doc-description\">gamma: typing.Optional[float]<br><br>(min_split_loss) Minimum loss reduction required to make a further partition on<br>a leaf node of the tree.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('grow_policy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=grow_policy,-typing.Optional%5Bstr%5D\">\n",
+       "            grow_policy\n",
+       "            <span class=\"param-doc-description\">grow_policy: typing.Optional[str]<br><br>Tree growing policy.<br><br>- depthwise: Favors splitting at nodes closest to the node,<br>- lossguide: Favors splitting at nodes with highest loss change.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('importance_type',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">importance_type</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('interaction_constraints',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=interaction_constraints,-typing.Union%5Bstr%2C%20typing.List%5Btyping.Tuple%5Bstr%5D%5D%2C%20NoneType%5D\">\n",
+       "            interaction_constraints\n",
+       "            <span class=\"param-doc-description\">interaction_constraints: typing.Union[str, typing.List[typing.Tuple[str]], NoneType]<br><br>Constraints for interaction representing permitted interactions.  The<br>constraints must be specified in the form of a nested list, e.g. ``[[0, 1], [2,<br>3, 4]]``, where each inner list is a group of indices of features that are<br>allowed to interact with each other.  See :doc:`tutorial<br></tutorials/feature_interaction_constraint>` for more information</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=learning_rate,-typing.Optional%5Bfloat%5D\">\n",
+       "            learning_rate\n",
+       "            <span class=\"param-doc-description\">learning_rate: typing.Optional[float]<br><br>Boosting learning rate (xgb's \"eta\")</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_bin',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=max_bin,-typing.Optional%5Bint%5D\">\n",
+       "            max_bin\n",
+       "            <span class=\"param-doc-description\">max_bin: typing.Optional[int]<br><br>If using histogram-based algorithm, maximum number of bins per feature</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_cat_threshold',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=max_cat_threshold,-typing.Optional%5Bint%5D\">\n",
+       "            max_cat_threshold\n",
+       "            <span class=\"param-doc-description\">max_cat_threshold: typing.Optional[int]<br><br>.. versionadded:: 1.7.0<br><br>.. note:: This parameter is experimental<br><br>Maximum number of categories considered for each split. Used only by<br>partition-based splits for preventing over-fitting. Also, `enable_categorical`<br>needs to be set to have categorical feature support. See :doc:`Categorical Data<br></tutorials/categorical>` and :ref:`cat-param` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_cat_to_onehot',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=max_cat_to_onehot,-Optional%5Bint%5D\">\n",
+       "            max_cat_to_onehot\n",
+       "            <span class=\"param-doc-description\">max_cat_to_onehot: Optional[int]<br><br>.. versionadded:: 1.6.0<br><br>.. note:: This parameter is experimental<br><br>A threshold for deciding whether XGBoost should use one-hot encoding based split<br>for categorical data.  When number of categories is lesser than the threshold<br>then one-hot encoding is chosen, otherwise the categories will be partitioned<br>into children nodes. Also, `enable_categorical` needs to be set to have<br>categorical feature support. See :doc:`Categorical Data<br></tutorials/categorical>` and :ref:`cat-param` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_delta_step',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=max_delta_step,-typing.Optional%5Bfloat%5D\">\n",
+       "            max_delta_step\n",
+       "            <span class=\"param-doc-description\">max_delta_step: typing.Optional[float]<br><br>Maximum delta step we allow each tree's weight estimation to be.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=max_depth,-%20typing.Optional%5Bint%5D\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth:  typing.Optional[int]<br><br>Maximum tree depth for base learners.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaves',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=max_leaves,-typing.Optional%5Bint%5D\">\n",
+       "            max_leaves\n",
+       "            <span class=\"param-doc-description\">max_leaves: typing.Optional[int]<br><br>Maximum number of leaves; 0 indicates no limit.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_child_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=min_child_weight,-typing.Optional%5Bfloat%5D\">\n",
+       "            min_child_weight\n",
+       "            <span class=\"param-doc-description\">min_child_weight: typing.Optional[float]<br><br>Minimum sum of instance weight(hessian) needed in a child.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=missing,-float\">\n",
+       "            missing\n",
+       "            <span class=\"param-doc-description\">missing: float<br><br>Value in the data which needs to be present as a missing value. Default to<br>:py:data:`numpy.nan`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotone_constraints',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=monotone_constraints,-typing.Union%5Btyping.Dict%5Bstr%2C%20int%5D%2C%20str%2C%20NoneType%5D\">\n",
+       "            monotone_constraints\n",
+       "            <span class=\"param-doc-description\">monotone_constraints: typing.Union[typing.Dict[str, int], str, NoneType]<br><br>Constraint of variable monotonicity.  See :doc:`tutorial </tutorials/monotonic>`<br>for more information.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('multi_strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=multi_strategy,-typing.Optional%5Bstr%5D\">\n",
+       "            multi_strategy\n",
+       "            <span class=\"param-doc-description\">multi_strategy: typing.Optional[str]<br><br>.. versionadded:: 2.0.0<br><br>.. note:: This parameter is working-in-progress.<br><br>The strategy used for training multi-target models, including multi-target<br>regression and multi-class classification. See :doc:`/tutorials/multioutput` for<br>more information.<br><br>- ``one_output_per_tree``: One model for each target.<br>- ``multi_output_tree``:  Use multi-target trees.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=n_estimators,-typing.Optional%5Bint%5D\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: typing.Optional[int]<br><br>Number of gradient boosted trees.  Equivalent to number of boosting<br>rounds.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">200</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=n_jobs,-typing.Optional%5Bint%5D\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: typing.Optional[int]<br><br>Number of parallel threads used to run xgboost.  When used with other<br>Scikit-Learn algorithms like grid search, you may choose which algorithm to<br>parallelize and balance the threads.  Creating thread contention will<br>significantly slow down both algorithms.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('num_parallel_tree',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">num_parallel_tree</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=random_state,-typing.Union%5Bnumpy.random.mtrand.RandomState%2C%20numpy.random._generator.Generator%2C%20int%2C%20NoneType%5D\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: typing.Union[numpy.random.mtrand.RandomState, numpy.random._generator.Generator, int, NoneType]<br><br>Random number seed.<br><br>.. note::<br><br>   Using gblinear booster with shotgun updater is nondeterministic as<br>   it uses Hogwild algorithm.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">42</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('reg_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=reg_alpha,-typing.Optional%5Bfloat%5D\">\n",
+       "            reg_alpha\n",
+       "            <span class=\"param-doc-description\">reg_alpha: typing.Optional[float]<br><br>L1 regularization term on weights (xgb's alpha).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('reg_lambda',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=reg_lambda,-typing.Optional%5Bfloat%5D\">\n",
+       "            reg_lambda\n",
+       "            <span class=\"param-doc-description\">reg_lambda: typing.Optional[float]<br><br>L2 regularization term on weights (xgb's lambda).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('sampling_method',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=sampling_method,-typing.Optional%5Bstr%5D\">\n",
+       "            sampling_method\n",
+       "            <span class=\"param-doc-description\">sampling_method: typing.Optional[str]<br><br>Sampling method. Used only by the GPU version of ``hist`` tree method.<br><br>- ``uniform``: Select random training instances uniformly.<br>- ``gradient_based``: Select random training instances with higher probability<br>    when the gradient and hessian are larger. (cf. CatBoost)</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('scale_pos_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=scale_pos_weight,-typing.Optional%5Bfloat%5D\">\n",
+       "            scale_pos_weight\n",
+       "            <span class=\"param-doc-description\">scale_pos_weight: typing.Optional[float]<br><br>Balancing of positive and negative weights.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('subsample',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=subsample,-typing.Optional%5Bfloat%5D\">\n",
+       "            subsample\n",
+       "            <span class=\"param-doc-description\">subsample: typing.Optional[float]<br><br>Subsample ratio of the training instance.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tree_method',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=tree_method,-typing.Optional%5Bstr%5D\">\n",
+       "            tree_method\n",
+       "            <span class=\"param-doc-description\">tree_method: typing.Optional[str]<br><br>Specify which tree method to use.  Default to auto.  If this parameter is set to<br>default, XGBoost will choose the most conservative option available.  It's<br>recommended to study this option from the parameters document :doc:`tree method<br></treemethod>`</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate_parameters',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=validate_parameters,-typing.Optional%5Bbool%5D\">\n",
+       "            validate_parameters\n",
+       "            <span class=\"param-doc-description\">validate_parameters: typing.Optional[bool]<br><br>Give warnings for unknown parameter.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbosity',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor#:~:text=verbosity,-typing.Optional%5Bint%5D\">\n",
+       "            verbosity\n",
+       "            <span class=\"param-doc-description\">verbosity: typing.Optional[int]<br><br>The degree of verbosity. Valid values are 0 (silent) - 3 (debug).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-6');</script></body>"
+      ],
+      "text/plain": [
+       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric='rmse', feature_types=None,\n",
+       "             feature_weights=None, gamma=None, grow_policy=None,\n",
+       "             importance_type=None, interaction_constraints=None,\n",
+       "             learning_rate=0.2, max_bin=None, max_cat_threshold=None,\n",
+       "             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,\n",
+       "             max_leaves=None, min_child_weight=2, missing=nan,\n",
+       "             monotone_constraints=None, multi_strategy=None, n_estimators=200,\n",
+       "             n_jobs=None, num_parallel_tree=None, ...)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from xgboost import XGBRegressor\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "# XGBoost Regressor\n",
+    "grid_params_xgb = {\n",
+    "    'n_estimators': [50, 100, 200],\n",
+    "    'learning_rate': [0.01, 0.1, 0.2],\n",
+    "    'max_depth': [3, 4, 5],\n",
+    "    'min_child_weight': [1, 2, 3],\n",
+    "    'subsample': [0.8, 0.9, 1.0]\n",
+    "}\n",
+    "grid_search_xgb = GridSearchCV(\n",
+    "    XGBRegressor(random_state=42, eval_metric='rmse'),\n",
+    "    grid_params_xgb, cv=5, n_jobs=4, scoring='r2', verbose=1\n",
+    ")\n",
+    "grid_search_xgb.fit(X_train_transformed, y_train)\n",
+    "print(\"Best parameters for XGBoost:\", grid_search_xgb.best_params_)\n",
+    "xgb_model = grid_search_xgb.best_estimator_\n",
+    "xgb_model.fit(X_train_transformed, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "dfa1b7b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "XGBoost Regressor RMSE: 25782.8259\n",
+      "XGBoost Regressor R^2: 0.9047\n",
+      "Feature importances for XGBoost:\n",
+      "                   Feature  Importance\n",
+      "4         num__OverallQual    0.189638\n",
+      "244  cat__GarageFinish_Unf    0.075611\n",
+      "179       cat__BsmtQual_Ex    0.072828\n",
+      "26         num__GarageCars    0.061467\n",
+      "223    cat__KitchenQual_TA    0.057392\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Evaluate the XGBoost Regressor\n",
+    "y_pred_xgb = xgb_model.predict(X_test_transformed)\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "mse = mean_squared_error(y_test, y_pred_xgb)\n",
+    "rmse = np.sqrt(mse)\n",
+    "r2 = r2_score(y_test, y_pred_xgb)\n",
+    "print(f\"XGBoost Regressor RMSE: {rmse:.4f}\")\n",
+    "print(f\"XGBoost Regressor R^2: {r2:.4f}\")\n",
+    "\n",
+    "# Feature importance for XGBoost\n",
+    "importances = xgb_model.feature_importances_\n",
+    "indices = importances.argsort()[::-1]\n",
+    "feature_importance_df = pd.DataFrame({'Feature': preprocessing_pipeline.get_feature_names_out(),\n",
+    "                                      'Importance': importances})\n",
+    "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
+    "print(\"Feature importances for XGBoost:\")\n",
+    "print(feature_importance_df[:5])\n",
+    "\n",
+    "# Plot feature importances\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(range(5), importances[indices[:5]], align='center')\n",
+    "plt.xticks(range(5),\n",
+    "           [preprocessing_pipeline.get_feature_names_out()[i] for i in indices[:5]],\n",
+    "           rotation=45)\n",
+    "plt.title('Feature Importances from XGBoost')\n",
+    "plt.xlabel('Features')\n",
+    "plt.ylabel('Importance')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "ML Workshop",
+   "language": "python",
+   "name": "ml_workshop"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/S07 Ensemble Methods/S07C04 Ensemble of Ensembles.ipynb b/src/S07 Ensemble Methods/S07C04 Ensemble of Ensembles.ipynb
new file mode 100644
index 0000000..83d51e3
--- /dev/null
+++ b/src/S07 Ensemble Methods/S07C04 Ensemble of Ensembles.ipynb	
@@ -0,0 +1,4152 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c38733e1",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Ensemble of Ensembles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8238cdc",
+   "metadata": {},
+   "source": [
+    "## Ensemble of Ensembles\n",
+    "\n",
+    "Ensemble of Ensembles is a meta-ensemble method that combines multiple ensemble methods to improve predictive performance. It leverages the strengths of different ensemble techniques, such as bagging, boosting, and stacking, to create a more robust model. The idea is to use the predictions from various ensemble methods as inputs to a final model, which can be another ensemble method or a simple model like logistic regression."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29668003",
+   "metadata": {},
+   "source": [
+    "## Advantages of Ensemble of Ensembles\n",
+    "\n",
+    "-   **Improved Accuracy**: By combining predictions from multiple ensemble methods, the overall accuracy can be significantly improved.\n",
+    "-   **Robustness**: It reduces the risk of overfitting by averaging out the predictions from different models, leading to better generalization.\n",
+    "-   **Flexibility**: It allows the use of diverse ensemble methods, which can capture different patterns in the data.\n",
+    "-   **Diversity**: The combination of different ensemble methods can lead to a more diverse set of predictions, which is beneficial for model performance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2738f021",
+   "metadata": {},
+   "source": [
+    "## Disadvantages of Ensemble of Ensembles\n",
+    "\n",
+    "-   **Complexity**: The model can become complex and harder to interpret, as it involves multiple layers of models.\n",
+    "-   **Computational Cost**: Training multiple ensemble methods can be computationally expensive and time-consuming.\n",
+    "-   **Risk of Overfitting**: If not managed properly, combining too many models can lead to overfitting, especially if the base models are too complex.\n",
+    "-   **Hyperparameter Tuning**: Each ensemble method may require its own hyperparameter tuning, adding to the complexity of the model development process."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a521cb62",
+   "metadata": {},
+   "source": [
+    "## Types of Ensemble of Ensembles\n",
+    "\n",
+    "Ensemble of Ensembles can be implemented using various ensemble methods, including:\n",
+    "\n",
+    "-   **Stacking**: Combines predictions from multiple base models using a meta-model that learns how to best combine their outputs (e.g., logistic regression, random forest).\n",
+    "-   **Multi-level Stacking**: A more complex version of stacking where multiple layers of models are trained, and predictions from one layer are used as inputs to the next layer."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ebc76c6e",
+   "metadata": {},
+   "source": [
+    "### Example: Stacking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5f029817",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature names: ['pclass', 'sex', 'age']\n",
+      "Class labels: ['Not Survived', 'Survived']\n",
+      "Transformed feature names: ['age__age' 'sex__sex_male' 'pclass__pclass_1' 'pclass__pclass_2'\n",
+      " 'pclass__pclass_3']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(estimators=[(&#x27;rf&#x27;, RandomForestClassifier()),\n",
+       "                               (&#x27;gb&#x27;, GradientBoostingClassifier())],\n",
+       "                   final_estimator=LogisticRegression())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StackingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html\">?<span>Documentation for StackingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=estimators,-list%20of%20%28str%2C%20estimator%29\">\n",
+       "            estimators\n",
+       "            <span class=\"param-doc-description\">estimators: list of (str, estimator)<br><br>Base estimators which will be stacked together. Each element of the<br>list is defined as a tuple of string (i.e. name) and an estimator<br>instance. An estimator can be set to 'drop' using `set_params`.<br><br>The type of estimator is generally expected to be a classifier.<br>However, one can pass a regressor for some use case (e.g. ordinal<br>regression).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">[(&#x27;rf&#x27;, ...), (&#x27;gb&#x27;, ...)]</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('final_estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=final_estimator,-estimator%2C%20default%3DNone\">\n",
+       "            final_estimator\n",
+       "            <span class=\"param-doc-description\">final_estimator: estimator, default=None<br><br>A classifier which will be used to combine the base estimators.<br>The default classifier is a<br>:class:`~sklearn.linear_model.LogisticRegression`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">LogisticRegression()</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cv',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=cv,-int%2C%20cross-validation%20generator%2C%20iterable%2C%20or%20%22prefit%22%2C%20default%3DNone\">\n",
+       "            cv\n",
+       "            <span class=\"param-doc-description\">cv: int, cross-validation generator, iterable, or \"prefit\", default=None<br><br>Determines the cross-validation splitting strategy used in<br>`cross_val_predict` to train `final_estimator`. Possible inputs for<br>cv are:<br><br>* None, to use the default 5-fold cross validation,<br>* integer, to specify the number of folds in a (Stratified) KFold,<br>* An object to be used as a cross-validation generator,<br>* An iterable yielding train, test splits,<br>* `\"prefit\"`, to assume the `estimators` are prefit. In this case, the<br>  estimators will not be refitted.<br><br>For integer/None inputs, if the estimator is a classifier and y is<br>either binary or multiclass,<br>:class:`~sklearn.model_selection.StratifiedKFold` is used.<br>In all other cases, :class:`~sklearn.model_selection.KFold` is used.<br>These splitters are instantiated with `shuffle=False` so the splits<br>will be the same across calls.<br><br>Refer :ref:`User Guide <cross_validation>` for the various<br>cross-validation strategies that can be used here.<br><br>If \"prefit\" is passed, it is assumed that all `estimators` have<br>been fitted already. The `final_estimator_` is trained on the `estimators`<br>predictions on the full training set and are **not** cross validated<br>predictions. Please note that if the models have been trained on the same<br>data to train the stacking model, there is a very high risk of overfitting.<br><br>.. versionadded:: 1.1<br>    The 'prefit' option was added in 1.1<br><br>.. note::<br>   A larger number of split will provide no benefits if the number<br>   of training samples is large enough. Indeed, the training time<br>   will increase. ``cv`` is not used for model evaluation but for<br>   prediction.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('stack_method',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=stack_method,-%7B%27auto%27%2C%20%27predict_proba%27%2C%20%27decision_function%27%2C%20%27predict%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27auto%27\">\n",
+       "            stack_method\n",
+       "            <span class=\"param-doc-description\">stack_method: {'auto', 'predict_proba', 'decision_function', 'predict'},             default='auto'<br><br>Methods called for each base estimator. It can be:<br><br>* if 'auto', it will try to invoke, for each estimator,<br>  `'predict_proba'`, `'decision_function'` or `'predict'` in that<br>  order.<br>* otherwise, one of `'predict_proba'`, `'decision_function'` or<br>  `'predict'`. If the method is not implemented by the estimator, it<br>  will raise an error.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel for `fit` of all `estimators`.<br>`None` means 1 unless in a `joblib.parallel_backend` context. -1 means<br>using all processors. See :term:`Glossary <n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('passthrough',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=passthrough,-bool%2C%20default%3DFalse\">\n",
+       "            passthrough\n",
+       "            <span class=\"param-doc-description\">passthrough: bool, default=False<br><br>When False, only the predictions of estimators will be used as<br>training data for `final_estimator`. When True, the<br>`final_estimator` is trained on the predictions as well as the<br>original training data.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Verbosity level.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>rf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"rf__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of trees in the forest.<br><br>.. versionchanged:: 0.22<br>   The default value of ``n_estimators`` changed from 10 to 100<br>   in 0.22.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.<br>Note: This parameter is tree-specific.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_features,-%7B%22sqrt%22%2C%20%22log2%22%2C%20None%7D%2C%20int%20or%20float%2C%20default%3D%22sqrt%22\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {\"sqrt\", \"log2\", None}, int or float, default=\"sqrt\"<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. versionchanged:: 1.1<br>    The default of `max_features` changed from `\"auto\"` to `\"sqrt\"`.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=bootstrap,-bool%2C%20default%3DTrue\">\n",
+       "            bootstrap\n",
+       "            <span class=\"param-doc-description\">bootstrap: bool, default=True<br><br>Whether bootstrap samples are used when building trees. If False, the<br>whole dataset is used to build each tree.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('oob_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=oob_score,-bool%20or%20callable%2C%20default%3DFalse\">\n",
+       "            oob_score\n",
+       "            <span class=\"param-doc-description\">oob_score: bool or callable, default=False<br><br>Whether to use out-of-bag samples to estimate the generalization score.<br>By default, :func:`~sklearn.metrics.accuracy_score` is used.<br>Provide a callable with signature `metric(y_true, y_pred)` to use a<br>custom metric. Only available if `bootstrap=True`.<br><br>For an illustration of out-of-bag (OOB) error estimation, see the example<br>:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,<br>:meth:`decision_path` and :meth:`apply` are all parallelized over the<br>trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`<br>context. ``-1`` means using all processors. See :term:`Glossary<br><n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls both the randomness of the bootstrapping of the samples used<br>when building trees (if ``bootstrap=True``) and the sampling of the<br>features to consider when looking for the best split at each node<br>(if ``max_features < n_features``).<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`Glossary <warm_start>` and<br>:ref:`tree_ensemble_warm_start` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=class_weight,-%7B%22balanced%22%2C%20%22balanced_subsample%22%7D%2C%20dict%20or%20list%20of%20dicts%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: {\"balanced\", \"balanced_subsample\"}, dict or list of dicts,             default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>The \"balanced_subsample\" mode is the same as \"balanced\" except that<br>weights are computed based on the bootstrap sample for every tree<br>grown.<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_samples\n",
+       "            <span class=\"param-doc-description\">max_samples: int or float, default=None<br><br>If bootstrap is True, the number of samples to draw from X<br>to train each base estimator.<br><br>- If None (default), then draw `X.shape[0]` samples.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,<br>  `max_samples` should be in the interval `(0.0, 1.0]`.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>gb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GradientBoostingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"gb__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('loss',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=loss,-%7B%27log_loss%27%2C%20%27exponential%27%7D%2C%20default%3D%27log_loss%27\">\n",
+       "            loss\n",
+       "            <span class=\"param-doc-description\">loss: {'log_loss', 'exponential'}, default='log_loss'<br><br>The loss function to be optimized. 'log_loss' refers to binomial and<br>multinomial deviance, the same as used in logistic regression.<br>It is a good choice for classification with probabilistic outputs.<br>For loss 'exponential', gradient boosting recovers the AdaBoost algorithm.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;log_loss&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=learning_rate,-float%2C%20default%3D0.1\">\n",
+       "            learning_rate\n",
+       "            <span class=\"param-doc-description\">learning_rate: float, default=0.1<br><br>Learning rate shrinks the contribution of each tree by `learning_rate`.<br>There is a trade-off between learning_rate and n_estimators.<br>Values must be in the range `[0.0, inf)`.<br><br>For an example of the effects of this parameter and its interaction with<br>``subsample``, see<br>:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regularization.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of boosting stages to perform. Gradient boosting<br>is fairly robust to over-fitting so a large number usually<br>results in better performance.<br>Values must be in the range `[1, inf)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('subsample',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=subsample,-float%2C%20default%3D1.0\">\n",
+       "            subsample\n",
+       "            <span class=\"param-doc-description\">subsample: float, default=1.0<br><br>The fraction of samples to be used for fitting the individual base<br>learners. If smaller than 1.0 this results in Stochastic Gradient<br>Boosting. `subsample` interacts with the parameter `n_estimators`.<br>Choosing `subsample < 1.0` leads to a reduction of variance<br>and an increase in bias.<br>Values must be in the range `(0.0, 1.0]`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=criterion,-%7B%27friedman_mse%27%2C%20%27squared_error%27%7D%2C%20default%3D%27friedman_mse%27\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {'friedman_mse', 'squared_error'}, default='friedman_mse'<br><br>The function to measure the quality of a split. Supported criteria are<br>'friedman_mse' for the mean squared error with improvement score by<br>Friedman, 'squared_error' for mean squared error. The default value of<br>'friedman_mse' is generally the best as it can provide a better<br>approximation in some cases.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;friedman_mse&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, values must be in the range `[2, inf)`.<br>- If float, values must be in the range `(0.0, 1.0]` and `min_samples_split`<br>  will be `ceil(min_samples_split * n_samples)`.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, values must be in the range `[1, inf)`.<br>- If float, values must be in the range `(0.0, 1.0)` and `min_samples_leaf`<br>  will be `ceil(min_samples_leaf * n_samples)`.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.<br>Values must be in the range `[0.0, 0.5]`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=max_depth,-int%20or%20None%2C%20default%3D3\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int or None, default=3<br><br>Maximum depth of the individual regression estimators. The maximum<br>depth limits the number of nodes in the tree. Tune this parameter<br>for best performance; the best value depends on the interaction<br>of the input variables. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.<br>If int, values must be in the range `[1, inf)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br>Values must be in the range `[0.0, inf)`.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('init',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=init,-estimator%20or%20%27zero%27%2C%20default%3DNone\">\n",
+       "            init\n",
+       "            <span class=\"param-doc-description\">init: estimator or 'zero', default=None<br><br>An estimator object that is used to compute the initial predictions.<br>``init`` has to provide :term:`fit` and :term:`predict_proba`. If<br>'zero', the initial raw predictions are set to zero. By default, a<br>``DummyEstimator`` predicting the classes priors is used.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the random seed given to each Tree estimator at each<br>boosting iteration.<br>In addition, it controls the random permutation of the features at<br>each split (see Notes for more details).<br>It also controls the random splitting of the training data to obtain a<br>validation set if `n_iter_no_change` is not None.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=max_features,-%7B%27sqrt%27%2C%20%27log2%27%7D%2C%20int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {'sqrt', 'log2'}, int or float, default=None<br><br>The number of features to consider when looking for the best split:<br><br>- If int, values must be in the range `[1, inf)`.<br>- If float, values must be in the range `(0.0, 1.0]` and the features<br>  considered at each split will be `max(1, int(max_features * n_features_in_))`.<br>- If 'sqrt', then `max_features=sqrt(n_features)`.<br>- If 'log2', then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>Choosing `max_features < n_features` leads to a reduction of variance<br>and an increase in bias.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Enable verbose output. If 1 then it prints progress and performance<br>once in a while (the more trees the lower the frequency). If greater<br>than 1 then it prints progress and performance for every tree.<br>Values must be in the range `[0, inf)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>Values must be in the range `[2, inf)`.<br>If `None`, then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just erase the<br>previous solution. See :term:`the Glossary <warm_start>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validation_fraction',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=validation_fraction,-float%2C%20default%3D0.1\">\n",
+       "            validation_fraction\n",
+       "            <span class=\"param-doc-description\">validation_fraction: float, default=0.1<br><br>The proportion of training data to set aside as validation set for<br>early stopping. Values must be in the range `(0.0, 1.0)`.<br>Only used if ``n_iter_no_change`` is set to an integer.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=n_iter_no_change,-int%2C%20default%3DNone\">\n",
+       "            n_iter_no_change\n",
+       "            <span class=\"param-doc-description\">n_iter_no_change: int, default=None<br><br>``n_iter_no_change`` is used to decide if early stopping will be used<br>to terminate training when validation score is not improving. By<br>default it is set to None to disable early stopping. If set to a<br>number, it will set aside ``validation_fraction`` size of the training<br>data as validation and terminate training when validation score is not<br>improving in all of the previous ``n_iter_no_change`` numbers of<br>iterations. The split is stratified.<br>Values must be in the range `[1, inf)`.<br>See<br>:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_early_stopping.py`.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for the early stopping. When the loss is not improving<br>by at least tol for ``n_iter_no_change`` iterations (if set to a<br>number), the training stops.<br>Values must be in the range `[0.0, inf)`.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed.<br>Values must be in the range `[0.0, inf)`.<br>See :ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>final_estimator</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"final_estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
+       "            penalty\n",
+       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
+       "            l1_ratio\n",
+       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
+       "            dual\n",
+       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
+       "            intercept_scaling\n",
+       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
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+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
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+       "    navigator.clipboard.writeText(fullParamName)\n",
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+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
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+       "\n",
+       "/**\n",
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+       "\n",
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+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
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+       "\n",
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+       "            // If the text is very bright we have a dark theme\n",
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+       "\n",
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+       "forceTheme('sk-container-id-1');</script></body>"
+      ],
+      "text/plain": [
+       "StackingClassifier(estimators=[('rf', RandomForestClassifier()),\n",
+       "                               ('gb', GradientBoostingClassifier())],\n",
+       "                   final_estimator=LogisticRegression())"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "titanic = datasets.fetch_openml('titanic', version=1, as_frame=True)\n",
+    "df = titanic.frame[['pclass', 'sex', 'age', 'survived']]\n",
+    "X = df.drop(columns=['survived'])\n",
+    "y = df['survived'].astype('category').cat.codes\n",
+    "class_names = ['Not Survived', 'Survived']\n",
+    "feature_names = X.columns.tolist()\n",
+    "print(f\"Feature names: {feature_names}\")\n",
+    "print(f\"Class labels: {class_names}\")\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = \\\n",
+    "    train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)\n",
+    "\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+    "\n",
+    "preprocessing_pipeline = ColumnTransformer(\n",
+    "    transformers=[\n",
+    "        ('age', Pipeline(\n",
+    "            steps=[\n",
+    "                ('imputer', SimpleImputer(strategy='mean')),\n",
+    "                ('scaler', StandardScaler())\n",
+    "            ]\n",
+    "        ), ['age']),\n",
+    "        ('sex', Pipeline(\n",
+    "            steps=[\n",
+    "                ('encoder', OneHotEncoder(drop='first', sparse_output=False))\n",
+    "            ]\n",
+    "        ), ['sex']),\n",
+    "        ('pclass', Pipeline(\n",
+    "            steps=[\n",
+    "                ('enconder', OneHotEncoder(sparse_output=False))\n",
+    "            ]\n",
+    "        ), ['pclass'])\n",
+    "    ],\n",
+    "    remainder='passthrough'\n",
+    ")\n",
+    "X_train_transformed = preprocessing_pipeline.fit_transform(X_train)\n",
+    "X_test_transformed = preprocessing_pipeline.transform(X_test)\n",
+    "\n",
+    "feature_names = preprocessing_pipeline.get_feature_names_out()\n",
+    "print(f\"Transformed feature names: {feature_names}\")\n",
+    "\n",
+    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, StackingClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "# Define base models\n",
+    "base_models = [\n",
+    "    ('rf', RandomForestClassifier(n_estimators=100)),\n",
+    "    ('gb', GradientBoostingClassifier(n_estimators=100))\n",
+    "]\n",
+    "# Define the final model\n",
+    "final_model = LogisticRegression()\n",
+    "# Create the stacking ensemble\n",
+    "stacking_ensemble = StackingClassifier(estimators=base_models, final_estimator=final_model)\n",
+    "# Fit the stacking ensemble\n",
+    "stacking_ensemble.fit(X_train_transformed, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "50959f54",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stacking Ensemble Accuracy: 0.81\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "Not Survived       0.83      0.86      0.85       243\n",
+      "    Survived       0.76      0.72      0.74       150\n",
+      "\n",
+      "    accuracy                           0.81       393\n",
+      "   macro avg       0.80      0.79      0.79       393\n",
+      "weighted avg       0.81      0.81      0.81       393\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate the stacking ensemble\n",
+    "accuracy = stacking_ensemble.score(X_test_transformed, y_test)\n",
+    "print(f\"Stacking Ensemble Accuracy: {accuracy:.2f}\")\n",
+    "# Predict using the stacking ensemble\n",
+    "y_pred = stacking_ensemble.predict(X_test_transformed)\n",
+    "from sklearn.metrics import classification_report\n",
+    "print(classification_report(y_test, y_pred, target_names=class_names))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3f6df50",
+   "metadata": {},
+   "source": [
+    "### Example: Multi-level Ensemble of Ensembles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ab10d7c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(estimators=[(&#x27;first_level&#x27;,\n",
+       "                                VotingClassifier(estimators=[(&#x27;rf&#x27;,\n",
+       "                                                              RandomForestClassifier()),\n",
+       "                                                             (&#x27;gb&#x27;,\n",
+       "                                                              GradientBoostingClassifier()),\n",
+       "                                                             (&#x27;svc&#x27;,\n",
+       "                                                              SVC(probability=True))],\n",
+       "                                                 voting=&#x27;soft&#x27;)),\n",
+       "                               (&#x27;log_reg&#x27;, LogisticRegression())],\n",
+       "                   final_estimator=LogisticRegression())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StackingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html\">?<span>Documentation for StackingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=estimators,-list%20of%20%28str%2C%20estimator%29\">\n",
+       "            estimators\n",
+       "            <span class=\"param-doc-description\">estimators: list of (str, estimator)<br><br>Base estimators which will be stacked together. Each element of the<br>list is defined as a tuple of string (i.e. name) and an estimator<br>instance. An estimator can be set to 'drop' using `set_params`.<br><br>The type of estimator is generally expected to be a classifier.<br>However, one can pass a regressor for some use case (e.g. ordinal<br>regression).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">[(&#x27;first_level&#x27;, ...), (&#x27;log_reg&#x27;, ...)]</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('final_estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=final_estimator,-estimator%2C%20default%3DNone\">\n",
+       "            final_estimator\n",
+       "            <span class=\"param-doc-description\">final_estimator: estimator, default=None<br><br>A classifier which will be used to combine the base estimators.<br>The default classifier is a<br>:class:`~sklearn.linear_model.LogisticRegression`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">LogisticRegression()</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cv',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=cv,-int%2C%20cross-validation%20generator%2C%20iterable%2C%20or%20%22prefit%22%2C%20default%3DNone\">\n",
+       "            cv\n",
+       "            <span class=\"param-doc-description\">cv: int, cross-validation generator, iterable, or \"prefit\", default=None<br><br>Determines the cross-validation splitting strategy used in<br>`cross_val_predict` to train `final_estimator`. Possible inputs for<br>cv are:<br><br>* None, to use the default 5-fold cross validation,<br>* integer, to specify the number of folds in a (Stratified) KFold,<br>* An object to be used as a cross-validation generator,<br>* An iterable yielding train, test splits,<br>* `\"prefit\"`, to assume the `estimators` are prefit. In this case, the<br>  estimators will not be refitted.<br><br>For integer/None inputs, if the estimator is a classifier and y is<br>either binary or multiclass,<br>:class:`~sklearn.model_selection.StratifiedKFold` is used.<br>In all other cases, :class:`~sklearn.model_selection.KFold` is used.<br>These splitters are instantiated with `shuffle=False` so the splits<br>will be the same across calls.<br><br>Refer :ref:`User Guide <cross_validation>` for the various<br>cross-validation strategies that can be used here.<br><br>If \"prefit\" is passed, it is assumed that all `estimators` have<br>been fitted already. The `final_estimator_` is trained on the `estimators`<br>predictions on the full training set and are **not** cross validated<br>predictions. Please note that if the models have been trained on the same<br>data to train the stacking model, there is a very high risk of overfitting.<br><br>.. versionadded:: 1.1<br>    The 'prefit' option was added in 1.1<br><br>.. note::<br>   A larger number of split will provide no benefits if the number<br>   of training samples is large enough. Indeed, the training time<br>   will increase. ``cv`` is not used for model evaluation but for<br>   prediction.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('stack_method',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=stack_method,-%7B%27auto%27%2C%20%27predict_proba%27%2C%20%27decision_function%27%2C%20%27predict%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27auto%27\">\n",
+       "            stack_method\n",
+       "            <span class=\"param-doc-description\">stack_method: {'auto', 'predict_proba', 'decision_function', 'predict'},             default='auto'<br><br>Methods called for each base estimator. It can be:<br><br>* if 'auto', it will try to invoke, for each estimator,<br>  `'predict_proba'`, `'decision_function'` or `'predict'` in that<br>  order.<br>* otherwise, one of `'predict_proba'`, `'decision_function'` or<br>  `'predict'`. If the method is not implemented by the estimator, it<br>  will raise an error.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel for `fit` of all `estimators`.<br>`None` means 1 unless in a `joblib.parallel_backend` context. -1 means<br>using all processors. See :term:`Glossary <n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('passthrough',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=passthrough,-bool%2C%20default%3DFalse\">\n",
+       "            passthrough\n",
+       "            <span class=\"param-doc-description\">passthrough: bool, default=False<br><br>When False, only the predictions of estimators will be used as<br>training data for `final_estimator`. When True, the<br>`final_estimator` is trained on the predictions as well as the<br>original training data.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Verbosity level.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>first_level</label></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>rf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"first_level__rf__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of trees in the forest.<br><br>.. versionchanged:: 0.22<br>   The default value of ``n_estimators`` changed from 10 to 100<br>   in 0.22.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.<br>Note: This parameter is tree-specific.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_features,-%7B%22sqrt%22%2C%20%22log2%22%2C%20None%7D%2C%20int%20or%20float%2C%20default%3D%22sqrt%22\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {\"sqrt\", \"log2\", None}, int or float, default=\"sqrt\"<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. versionchanged:: 1.1<br>    The default of `max_features` changed from `\"auto\"` to `\"sqrt\"`.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=bootstrap,-bool%2C%20default%3DTrue\">\n",
+       "            bootstrap\n",
+       "            <span class=\"param-doc-description\">bootstrap: bool, default=True<br><br>Whether bootstrap samples are used when building trees. If False, the<br>whole dataset is used to build each tree.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('oob_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=oob_score,-bool%20or%20callable%2C%20default%3DFalse\">\n",
+       "            oob_score\n",
+       "            <span class=\"param-doc-description\">oob_score: bool or callable, default=False<br><br>Whether to use out-of-bag samples to estimate the generalization score.<br>By default, :func:`~sklearn.metrics.accuracy_score` is used.<br>Provide a callable with signature `metric(y_true, y_pred)` to use a<br>custom metric. Only available if `bootstrap=True`.<br><br>For an illustration of out-of-bag (OOB) error estimation, see the example<br>:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,<br>:meth:`decision_path` and :meth:`apply` are all parallelized over the<br>trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`<br>context. ``-1`` means using all processors. See :term:`Glossary<br><n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls both the randomness of the bootstrapping of the samples used<br>when building trees (if ``bootstrap=True``) and the sampling of the<br>features to consider when looking for the best split at each node<br>(if ``max_features < n_features``).<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`Glossary <warm_start>` and<br>:ref:`tree_ensemble_warm_start` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=class_weight,-%7B%22balanced%22%2C%20%22balanced_subsample%22%7D%2C%20dict%20or%20list%20of%20dicts%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: {\"balanced\", \"balanced_subsample\"}, dict or list of dicts,             default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>The \"balanced_subsample\" mode is the same as \"balanced\" except that<br>weights are computed based on the bootstrap sample for every tree<br>grown.<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_samples\n",
+       "            <span class=\"param-doc-description\">max_samples: int or float, default=None<br><br>If bootstrap is True, the number of samples to draw from X<br>to train each base estimator.<br><br>- If None (default), then draw `X.shape[0]` samples.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,<br>  `max_samples` should be in the interval `(0.0, 1.0]`.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>gb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GradientBoostingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"first_level__gb__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('loss',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=loss,-%7B%27log_loss%27%2C%20%27exponential%27%7D%2C%20default%3D%27log_loss%27\">\n",
+       "            loss\n",
+       "            <span class=\"param-doc-description\">loss: {'log_loss', 'exponential'}, default='log_loss'<br><br>The loss function to be optimized. 'log_loss' refers to binomial and<br>multinomial deviance, the same as used in logistic regression.<br>It is a good choice for classification with probabilistic outputs.<br>For loss 'exponential', gradient boosting recovers the AdaBoost algorithm.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;log_loss&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=learning_rate,-float%2C%20default%3D0.1\">\n",
+       "            learning_rate\n",
+       "            <span class=\"param-doc-description\">learning_rate: float, default=0.1<br><br>Learning rate shrinks the contribution of each tree by `learning_rate`.<br>There is a trade-off between learning_rate and n_estimators.<br>Values must be in the range `[0.0, inf)`.<br><br>For an example of the effects of this parameter and its interaction with<br>``subsample``, see<br>:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regularization.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of boosting stages to perform. Gradient boosting<br>is fairly robust to over-fitting so a large number usually<br>results in better performance.<br>Values must be in the range `[1, inf)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('subsample',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=subsample,-float%2C%20default%3D1.0\">\n",
+       "            subsample\n",
+       "            <span class=\"param-doc-description\">subsample: float, default=1.0<br><br>The fraction of samples to be used for fitting the individual base<br>learners. If smaller than 1.0 this results in Stochastic Gradient<br>Boosting. `subsample` interacts with the parameter `n_estimators`.<br>Choosing `subsample < 1.0` leads to a reduction of variance<br>and an increase in bias.<br>Values must be in the range `(0.0, 1.0]`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=criterion,-%7B%27friedman_mse%27%2C%20%27squared_error%27%7D%2C%20default%3D%27friedman_mse%27\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {'friedman_mse', 'squared_error'}, default='friedman_mse'<br><br>The function to measure the quality of a split. Supported criteria are<br>'friedman_mse' for the mean squared error with improvement score by<br>Friedman, 'squared_error' for mean squared error. The default value of<br>'friedman_mse' is generally the best as it can provide a better<br>approximation in some cases.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;friedman_mse&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, values must be in the range `[2, inf)`.<br>- If float, values must be in the range `(0.0, 1.0]` and `min_samples_split`<br>  will be `ceil(min_samples_split * n_samples)`.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, values must be in the range `[1, inf)`.<br>- If float, values must be in the range `(0.0, 1.0)` and `min_samples_leaf`<br>  will be `ceil(min_samples_leaf * n_samples)`.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.<br>Values must be in the range `[0.0, 0.5]`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=max_depth,-int%20or%20None%2C%20default%3D3\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int or None, default=3<br><br>Maximum depth of the individual regression estimators. The maximum<br>depth limits the number of nodes in the tree. Tune this parameter<br>for best performance; the best value depends on the interaction<br>of the input variables. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.<br>If int, values must be in the range `[1, inf)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br>Values must be in the range `[0.0, inf)`.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('init',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=init,-estimator%20or%20%27zero%27%2C%20default%3DNone\">\n",
+       "            init\n",
+       "            <span class=\"param-doc-description\">init: estimator or 'zero', default=None<br><br>An estimator object that is used to compute the initial predictions.<br>``init`` has to provide :term:`fit` and :term:`predict_proba`. If<br>'zero', the initial raw predictions are set to zero. By default, a<br>``DummyEstimator`` predicting the classes priors is used.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the random seed given to each Tree estimator at each<br>boosting iteration.<br>In addition, it controls the random permutation of the features at<br>each split (see Notes for more details).<br>It also controls the random splitting of the training data to obtain a<br>validation set if `n_iter_no_change` is not None.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=max_features,-%7B%27sqrt%27%2C%20%27log2%27%7D%2C%20int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {'sqrt', 'log2'}, int or float, default=None<br><br>The number of features to consider when looking for the best split:<br><br>- If int, values must be in the range `[1, inf)`.<br>- If float, values must be in the range `(0.0, 1.0]` and the features<br>  considered at each split will be `max(1, int(max_features * n_features_in_))`.<br>- If 'sqrt', then `max_features=sqrt(n_features)`.<br>- If 'log2', then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>Choosing `max_features < n_features` leads to a reduction of variance<br>and an increase in bias.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Enable verbose output. If 1 then it prints progress and performance<br>once in a while (the more trees the lower the frequency). If greater<br>than 1 then it prints progress and performance for every tree.<br>Values must be in the range `[0, inf)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>Values must be in the range `[2, inf)`.<br>If `None`, then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just erase the<br>previous solution. See :term:`the Glossary <warm_start>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validation_fraction',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=validation_fraction,-float%2C%20default%3D0.1\">\n",
+       "            validation_fraction\n",
+       "            <span class=\"param-doc-description\">validation_fraction: float, default=0.1<br><br>The proportion of training data to set aside as validation set for<br>early stopping. Values must be in the range `(0.0, 1.0)`.<br>Only used if ``n_iter_no_change`` is set to an integer.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=n_iter_no_change,-int%2C%20default%3DNone\">\n",
+       "            n_iter_no_change\n",
+       "            <span class=\"param-doc-description\">n_iter_no_change: int, default=None<br><br>``n_iter_no_change`` is used to decide if early stopping will be used<br>to terminate training when validation score is not improving. By<br>default it is set to None to disable early stopping. If set to a<br>number, it will set aside ``validation_fraction`` size of the training<br>data as validation and terminate training when validation score is not<br>improving in all of the previous ``n_iter_no_change`` numbers of<br>iterations. The split is stratified.<br>Values must be in the range `[1, inf)`.<br>See<br>:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_early_stopping.py`.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for the early stopping. When the loss is not improving<br>by at least tol for ``n_iter_no_change`` iterations (if set to a<br>number), the training stops.<br>Values must be in the range `[0.0, inf)`.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed.<br>Values must be in the range `[0.0, inf)`.<br>See :ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>svc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"first_level__svc__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Regularization parameter. The strength of the regularization is<br>inversely proportional to C. Must be strictly positive. The penalty<br>is a squared l2 penalty. For an intuitive visualization of the effects<br>of scaling the regularization parameter C, see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kernel',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=kernel,-%7B%27linear%27%2C%20%27poly%27%2C%20%27rbf%27%2C%20%27sigmoid%27%2C%20%27precomputed%27%7D%20or%20callable%2C%20%20%20%20%20%20%20%20%20%20default%3D%27rbf%27\">\n",
+       "            kernel\n",
+       "            <span class=\"param-doc-description\">kernel: {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable,          default='rbf'<br><br>Specifies the kernel type to be used in the algorithm. If<br>none is given, 'rbf' will be used. If a callable is given it is used to<br>pre-compute the kernel matrix from data matrices; that matrix should be<br>an array of shape ``(n_samples, n_samples)``. For an intuitive<br>visualization of different kernel types see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_kernels.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;rbf&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('degree',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=degree,-int%2C%20default%3D3\">\n",
+       "            degree\n",
+       "            <span class=\"param-doc-description\">degree: int, default=3<br><br>Degree of the polynomial kernel function ('poly').<br>Must be non-negative. Ignored by all other kernels.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('gamma',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=gamma,-%7B%27scale%27%2C%20%27auto%27%7D%20or%20float%2C%20default%3D%27scale%27\">\n",
+       "            gamma\n",
+       "            <span class=\"param-doc-description\">gamma: {'scale', 'auto'} or float, default='scale'<br><br>Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.<br><br>- if ``gamma='scale'`` (default) is passed then it uses<br>  1 / (n_features * X.var()) as value of gamma,<br>- if 'auto', uses 1 / n_features<br>- if float, must be non-negative.<br><br>.. versionchanged:: 0.22<br>   The default value of ``gamma`` changed from 'auto' to 'scale'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;scale&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('coef0',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=coef0,-float%2C%20default%3D0.0\">\n",
+       "            coef0\n",
+       "            <span class=\"param-doc-description\">coef0: float, default=0.0<br><br>Independent term in kernel function.<br>It is only significant in 'poly' and 'sigmoid'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('shrinking',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=shrinking,-bool%2C%20default%3DTrue\">\n",
+       "            shrinking\n",
+       "            <span class=\"param-doc-description\">shrinking: bool, default=True<br><br>Whether to use the shrinking heuristic.<br>See the :ref:`User Guide <shrinking_svm>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('probability',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=probability,-bool%2C%20default%3DFalse\">\n",
+       "            probability\n",
+       "            <span class=\"param-doc-description\">probability: bool, default=False<br><br>Whether to enable probability estimates. This must be enabled prior<br>to calling `fit`, will slow down that method as it internally uses<br>5-fold cross-validation, and `predict_proba` may be inconsistent with<br>`predict`. Read more in the :ref:`User Guide <scores_probabilities>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=tol,-float%2C%20default%3D1e-3\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-3<br><br>Tolerance for stopping criterion.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cache_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=cache_size,-float%2C%20default%3D200\">\n",
+       "            cache_size\n",
+       "            <span class=\"param-doc-description\">cache_size: float, default=200<br><br>Specify the size of the kernel cache (in MB).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">200</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Set the parameter C of class i to class_weight[i]*C for<br>SVC. If not given, all classes are supposed to have<br>weight one.<br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>Enable verbose output. Note that this setting takes advantage of a<br>per-process runtime setting in libsvm that, if enabled, may not work<br>properly in a multithreaded context.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=max_iter,-int%2C%20default%3D-1\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=-1<br><br>Hard limit on iterations within solver, or -1 for no limit.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">-1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('decision_function_shape',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=decision_function_shape,-%7B%27ovo%27%2C%20%27ovr%27%7D%2C%20default%3D%27ovr%27\">\n",
+       "            decision_function_shape\n",
+       "            <span class=\"param-doc-description\">decision_function_shape: {'ovo', 'ovr'}, default='ovr'<br><br>Whether to return a one-vs-rest ('ovr') decision function of shape<br>(n_samples, n_classes) as all other classifiers, or the original<br>one-vs-one ('ovo') decision function of libsvm which has shape<br>(n_samples, n_classes * (n_classes - 1) / 2). However, note that<br>internally, one-vs-one ('ovo') is always used as a multi-class strategy<br>to train models; an ovr matrix is only constructed from the ovo matrix.<br>The parameter is ignored for binary classification.<br><br>.. versionchanged:: 0.19<br>    decision_function_shape is 'ovr' by default.<br><br>.. versionadded:: 0.17<br>   *decision_function_shape='ovr'* is recommended.<br><br>.. versionchanged:: 0.17<br>   Deprecated *decision_function_shape='ovo' and None*.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('break_ties',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=break_ties,-bool%2C%20default%3DFalse\">\n",
+       "            break_ties\n",
+       "            <span class=\"param-doc-description\">break_ties: bool, default=False<br><br>If true, ``decision_function_shape='ovr'``, and number of classes > 2,<br>:term:`predict` will break ties according to the confidence values of<br>:term:`decision_function`; otherwise the first class among the tied<br>classes is returned. Please note that breaking ties comes at a<br>relatively high computational cost compared to a simple predict. See<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_tie_breaking.py` for an<br>example of its usage with ``decision_function_shape='ovr'``.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the pseudo random number generation for shuffling the data for<br>probability estimates. Ignored when `probability` is False.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>log_reg</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"log_reg__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
+       "            penalty\n",
+       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
+       "            l1_ratio\n",
+       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
+       "            dual\n",
+       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
+       "            intercept_scaling\n",
+       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>final_estimator</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"final_estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
+       "            penalty\n",
+       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
+       "            l1_ratio\n",
+       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
+       "            dual\n",
+       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
+       "            intercept_scaling\n",
+       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
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+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
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+       "\n",
+       "/**\n",
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+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
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+      "text/plain": [
+       "StackingClassifier(estimators=[('first_level',\n",
+       "                                VotingClassifier(estimators=[('rf',\n",
+       "                                                              RandomForestClassifier()),\n",
+       "                                                             ('gb',\n",
+       "                                                              GradientBoostingClassifier()),\n",
+       "                                                             ('svc',\n",
+       "                                                              SVC(probability=True))],\n",
+       "                                                 voting='soft')),\n",
+       "                               ('log_reg', LogisticRegression())],\n",
+       "                   final_estimator=LogisticRegression())"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import VotingClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "# Define base models for the first level\n",
+    "base_models_level_1 = [\n",
+    "    ('rf', RandomForestClassifier(n_estimators=100)),\n",
+    "    ('gb', GradientBoostingClassifier(n_estimators=100)),\n",
+    "    ('svc', SVC(probability=True))\n",
+    "]\n",
+    "# Create the first level ensemble using voting\n",
+    "first_level_ensemble = VotingClassifier(estimators=base_models_level_1, voting='soft')\n",
+    "# Fit the first level ensemble\n",
+    "first_level_ensemble.fit(X_train_transformed, y_train)\n",
+    "\n",
+    "# Define base models for the second level\n",
+    "base_models_level_2 = [\n",
+    "    ('first_level', first_level_ensemble),\n",
+    "    ('log_reg', LogisticRegression())\n",
+    "]\n",
+    "# Create the second level ensemble using stacking\n",
+    "second_level_ensemble = StackingClassifier(estimators=base_models_level_2, final_estimator=LogisticRegression())\n",
+    "# Fit the second level ensemble\n",
+    "second_level_ensemble.fit(X_train_transformed, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "58bd9117",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Multi-level Ensemble of Ensembles Accuracy: 0.81\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "Not Survived       0.84      0.86      0.85       243\n",
+      "    Survived       0.77      0.73      0.75       150\n",
+      "\n",
+      "    accuracy                           0.81       393\n",
+      "   macro avg       0.80      0.80      0.80       393\n",
+      "weighted avg       0.81      0.81      0.81       393\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate the second level ensemble\n",
+    "accuracy_level_2 = second_level_ensemble.score(X_test_transformed, y_test)\n",
+    "print(f\"Multi-level Ensemble of Ensembles Accuracy: {accuracy_level_2:.2f}\")\n",
+    "# Predict using the second level ensemble\n",
+    "y_pred_level_2 = second_level_ensemble.predict(X_test_transformed)\n",
+    "print(classification_report(y_test, y_pred_level_2, target_names=class_names))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ca503e0",
+   "metadata": {},
+   "source": [
+    "In this example, we first create a first-level ensemble using a voting classifier that combines predictions from a random forest, gradient boosting, and support vector machine. Then, we use the predictions from this first-level ensemble as input to a second-level stacking ensemble that combines it with logistic regression. This multi-level approach allows us to leverage the strengths of different ensemble methods at multiple levels, potentially improving predictive performance."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,md"
+  },
+  "kernelspec": {
+   "display_name": "ML Workshop",
+   "language": "python",
+   "name": "ml_workshop"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 3529eef97b493be9ce4ce098b87fffb1339fc654 Mon Sep 17 00:00:00 2001
From: "Cristian A. Marocico" <c.a.marocico@gmail.com>
Date: Tue, 24 Feb 2026 08:01:29 +0100
Subject: [PATCH 81/81] Updated Session 06

---
 .../data/.placeholder                                             | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/data/.placeholder

diff --git a/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/data/.placeholder b/src/S06 Neural Networks II: Deep Learning, Transformers, and GenAI/data/.placeholder
new file mode 100644
index 0000000..e69de29